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Bronze Age Europeans exhibited modern economic behaviour

The foundations of modern economic systems are rooted in the economic behaviour of contemporary humans, and ‘primitive’ societies have been assumed not to fit standard economic theory. But an analysis of metal fragments — effectively, money — shows that modern-style economic behaviour can be identified at least as far back as 3,500 years ago.

Carbon pricing reduces emissions

A meta-analysis of 21 carbon-pricing schemes suggests that the strategy reduces greenhouse-gas emissions. Deciding how high to set prices is the next step — and one that might benefit from the insights of less-aggregated studies in various sectors.

• Thomas Sterner

A mix of reforestation methods offers more cost-effective climate mitigation

The greenhouse gas abatement costs for two forest restoration methods — natural regeneration and plantations — are estimated by integrating observations on the costs of reforestation projects with other biophysical and economic data. This analysis reveals that a mix of reforestation methods offers greater potential to mitigate climate change at low cost than previously estimated.

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Assessing the Belt and Road Initiative’s environmental footprint: an impact evaluation analysis of African member countries

• Mostafa E. AboElsoud

ESG and customer stability: a perspective based on external and internal supervision and reputation mechanisms

Income, environmental quality and willingness to pay for organic food: a regional analysis in South Korea

• Kwansoo Kim

Consumption patterns in prehistoric Europe are consistent with modern economic behaviour

Prehistoric economic behaviour in Europe fits well with modern economic theory, based on a large database of metal objects from across Europe.

• Nicola Ialongo
• Giancarlo Lago

The impact of unconditional cash transfers on enhancing household wellbeing in Pakistan: evidence from a quasi-experimental design

• Abdul Hameed
• Tariq Mahmood Ali
• Muhammad Omar Najam

The great recession, neurotic personality, and subjective sleep quality among patients with mood disorders

• I-Ming Chen
• Hsi-Chung Chen
• Po-Hsiu Kuo

News and Comment

Connecting ecosystem services research and human rights to revamp the application of the precautionary principle

With ecosystem services (ES) vital for human wellbeing 1 , the protection of nature is a human rights matter. We outline how recent advances in international human rights law should inform a revamp of how precaution is applied within environmental decision-making. Critically, precautionary decision-making must evolve to make use of best-available evidence, including novel ES research approaches, to assess ‘foreseeable’ harms to all aspects of human wellbeing that are protected as human rights.

• Holly J. Niner
• Elisa Morgera
• Siân E. Rees

Community solar reaches adopters underserved by rooftop solar

Community solar, a business model where multiple customers buy output from shared solar systems, has expanded solar access among multifamily housing occupants, renters, and low-income households. Policies to enable community solar could be expanded and benefits of access augmented through targeted measures to support community solar adoption in underserved communities.

• Eric O’Shaughnessy
• Galen Barbose
• Jenny Sumner

Rapid rise in corporate climate-tech investments complements support from public grants

Investment in climate and energy (climate-tech) startups is growing in the US and worldwide, with public grants backing high-risk sectors and publicly funded startups exiting at higher rates with corporate investment. Public policies to incentivize corporate investment in these startups can therefore be an important, yet sometimes underestimated, part of meeting net-zero goals.

• Kathleen M. Kennedy
• Morgan R. Edwards
• Kavita Surana

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Gender inequality as a barrier to economic growth: a review of the theoretical literature

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• Published: 15 January 2021
• Volume 19 , pages 581–614, ( 2021 )

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In this article, we survey the theoretical literature investigating the role of gender inequality in economic development. The vast majority of theories reviewed argue that gender inequality is a barrier to development, particularly over the long run. Among the many plausible mechanisms through which inequality between men and women affects the aggregate economy, the role of women for fertility decisions and human capital investments is particularly emphasized in the literature. Yet, we believe the body of theories could be expanded in several directions.

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1 Introduction

Theories of long-run economic development have increasingly relied on two central forces: population growth and human capital accumulation. Both forces depend on decisions made primarily within households: population growth is partially determined by households’ fertility choices (e.g., Becker & Barro 1988 ), while human capital accumulation is partially dependent on parental investments in child education and health (e.g., Lucas 1988 ).

In an earlier survey of the literature linking family decisions to economic growth, Grimm ( 2003 ) laments that “[m]ost models ignore the two-sex issue. Parents are modeled as a fictive asexual human being” (p. 154). Footnote 1 Since then, however, economists are increasingly recognizing that gender plays a fundamental role in how households reproduce and care for their children. As a result, many models of economic growth are now populated with men and women. The “fictive asexual human being” is a dying species. In this article, we survey this rich new landscape in theoretical macroeconomics, reviewing, in particular, micro-founded theories where gender inequality affects economic development.

For the purpose of this survey, gender inequality is defined as any exogenously imposed difference between male and female economic agents that, by shaping their behavior, has implications for aggregate economic growth. In practice, gender inequality is typically modeled as differences between men and women in endowments, constraints, or preferences.

Many articles review the literature on gender inequality and economic growth. Footnote 2 Typically, both the theoretical and empirical literature are discussed, but, in almost all cases, the vast empirical literature receives most of the attention. In addition, some of the surveys examine both sides of the two-way relationship between gender inequality and economic growth: gender equality as a cause of economic growth and economic growth as a cause of gender equality. As a result, most surveys end up only scratching the surface of each of these distinct strands of literature.

There is, by now, a large and insightful body of micro-founded theories exploring how gender equality affects economic growth. In our view, these theories merit a separate review. Moreover, they have not received sufficient attention in empirical work, which has largely developed independently (see also Cuberes & Teignier 2014 ). By reviewing the theoretical literature, we hope to motivate empirical researchers in finding new ways of putting these theories to test. In doing so, our work complements several existing surveys. Doepke & Tertilt ( 2016 ) review the theoretical literature that incorporates families in macroeconomic models, without focusing exclusively on models that include gender inequality, as we do. Greenwood, Guner and Vandenbroucke ( 2017 ), in turn, review the theoretical literature from the opposite direction; they study how macroeconomic models can explain changes in family outcomes. Doepke, Tertilt and Voena ( 2012 ) survey the political economy of women’s rights, but without focusing explicitly on their impact on economic development.

To be precise, the scope of this survey consists of micro-founded macroeconomic models where gender inequality (in endowments, constraints, preferences) affects economic growth—either by influencing the economy’s growth rate or shaping the transition paths between multiple income equilibria. As a result, this survey does not cover several upstream fields of partial-equilibrium micro models, where gender inequality affects several intermediate growth-related outcomes, such as labor supply, education, health. Additionally, by focusing on micro-founded macro models, we do not review studies in heterodox macroeconomics, including the feminist economics tradition using structuralist, demand-driven models. For recent overviews of this literature, see Kabeer ( 2016 ) and Seguino ( 2013 , 2020 ). Overall, we find very little dialogue between the neoclassical and feminist heterodox literatures. In this review, we will show that actually these two traditions have several points of contact and reach similar conclusions in many areas, albeit following distinct intellectual routes.

Although the incorporation of gender in macroeconomic models of economic growth is a recent development, the main gendered ingredients of those models are not new. They were developed in at least two strands of literature. First, since the 1960s, “new home economics” has applied the analytical toolbox of rational choice theory to decisions being made within the boundaries of the family (see, e.g., Becker 1960 , 1981 ). Footnote 3 A second literature strand, mostly based on empirical work at the micro level in developing countries, described clear patterns of gender-specific behavior within households that differed across regions of the developing world (see, e.g., Boserup 1970 ). Footnote 4 As we shall see, most of the (micro-founded) macroeconomic models reviewed in this article use several analytical mechanisms from "new home economics”; these mechanisms can typically rationalize several of the gender-specific regularities observed in early studies of developing countries. The growth theorist is then left to explore the aggregate implications for economic development.

The first models we present focus on gender discrimination in (or on access to) the labor market as a distortionary tax on talent. If talent is randomly distributed in the population, men and women are imperfect substitutes in aggregate production, and, as a consequence, gender inequality (as long as determined by non-market processes) will misallocate talent and lower incentives for female human capital formation. These theories do not rely on typical household functions such as reproduction and childrearing. Therefore, in these models, individuals are not organized into households. We review this literature in section 2 .

From there, we proceed to theories where the household is the unit of analysis. In sections 3 and 4 , we cover models that take the household as given and avoid marriage markets or other household formation institutions. This is a world where marriage (or cohabitation) is universal, consensual, and monogamous; families are nuclear, and spouses are matched randomly. The first articles in this tradition model the household as a unitary entity with joint preferences and interests, and with an efficient and centralized decision making process. Footnote 5 These theories posit how men and women specialize into different activities and how parents interact with their children. Section 3 reviews these theories. Over time, the literature has incorporated intra-household dynamics. Now, family members are allowed to have different preferences and interests; they bargain, either cooperatively or not, over family decisions. Now, the theorist recognizes power asymmetries between family members and analyzes how spouses bargain over decisions. Footnote 6 These articles are surveyed in section 4 .

The final set of articles we survey take into account how households are formed. These theories show how gender inequality can influence economic growth and long-run development through marriage market institutions and family formation patterns. Among other topics, this literature has studied ages at first marriage, relative supply of potential partners, monogamy and polygyny, arranged and consensual marriages, and divorce risk. Upon marriage, these models assume different bargaining processes between the spouses, or even unitary households, but they all recognize, in one way or another, that marriage, labor supply, consumption, and investment decisions are interdependent. We review these theories in section 5 .

Table 1 offers a schematic overview of the literature. To improve readability, the table only includes studies that we review in detail, with articles listed in order of appearance in the text. The table also abstracts from models’ extensions and sensitivity checks, and focuses exclusively on the causal pathways leading from gender inequality to economic growth.

The vast majority of theories reviewed argue that gender inequality is a barrier to economic development, particularly over the long run. The focus on long-run supply-side models reflects a recent effort by growth theorists to incorporate two stylized facts of economic development in the last two centuries: (i) a strong positive association between gender equality and income per capita (Fig. 1 ), and (ii) a strong association between the timing of the fertility transition and income per capita (Fig. 2 ). Footnote 7 Models that endogenize a fertility transition are able to generate a transition from a Malthusian regime of stagnation to a modern regime of sustained economic growth, thus replicating the development experience of human societies in the very long run (e.g., Galor 2005a , b ; Guinnane 2011 ). In contrast, demand-driven models in the heterodox and feminist traditions have often argued that gender wage discrimination and gendered sectoral and occupational segregation can be conducive to economic growth in semi-industrialized export-oriented economies. Footnote 8 In these settings—that fit well the experience of East and Southeast Asian economies—gender wage discrimination in female-intensive export industries reduces production costs and boosts exports, profits, and investment (Blecker & Seguino 2002 ; Seguino 2010 ).

Income level and gender equality. Income is the natural log of per capita GDP (PPP-adjusted). The Gender Development Index is the ratio of gender-specific Human Development Indexes: female HDI/male HDI. Data are for the year 2000. Sources: UNDP

Income level and timing of the fertility transition. Income is the natural log of per capita GDP (PPP-adjusted) in 2000. Years since fertility transition are the number of years between 2000 and the onset year of the fertility decline. See Reher ( 2004 ) for details. Sources: UNDP and Reher ( 2004 )

In most long-run, supply-side models reviewed here, irrespectively of the underlying source of gender differences (e.g., biology, socialization, discrimination), the opportunity cost of women’s time in foregone labor market earnings is lower than that of men. This gender gap in the value of time affects economic growth through two main mechanisms. First, when the labor market value of women’s time is relatively low, women will be in charge of childrearing and domestic work in the family. A low value of female time means that children are cheap. Fertility will be high, and economic growth will be low, both because population growth has a direct negative impact on long-run economic performance and because human capital accumulates at a slower pace (through the quantity-quality trade-off). Second, if parents expect relatively low returns to female education, due to women specializing in domestic activities, they will invest relatively less in the education of girls. In the words of Harriet Martineau, one of the first to describe this mechanism, “as women have none of the objects in life for which an enlarged education is considered requisite, the education is not given” (Martineau 1837 , p. 107). In the long run, lower human capital investments (on girls) lead to slower economic development.

Overall, gender inequality can be conceptualized as a source of inefficiency, to the extent that it results in the misallocation of productive factors, such as talent or labor, and as a source of negative externalities, when it leads to higher fertility, skewed sex ratios, or lower human capital accumulation.

We conclude, in section 6 , by examining the limitations of the current literature and pointing ways forward. Among them, we suggest deeper investigations of the role of (endogenous) technological change on gender inequality, as well as greater attention to the role and interests of men in affecting gender inequality and its impact on growth.

2 Gender discrimination and misallocation of talent

Perhaps the single most intuitive argument for why gender discrimination leads to aggregate inefficiency and hampers economic growth concerns the allocation of talent. Assume that talent is randomly distributed in the population. Then, an economy that curbs women’s access to education, market employment, or certain occupations draws talent from a smaller pool than an economy without such restrictions. Gender inequality can thus be viewed as a distortionary tax on talent. Indeed, occupational choice models with heterogeneous talent (as in Roy 1951 ) show that exogenous barriers to women’s participation in the labor market or access to certain occupations reduce aggregate productivity and per capita output (Cuberes & Teignier 2016 , 2017 ; Esteve-Volart 2009 ; Hsieh, Hurst, Jones and Klenow 2019 ).

Hsieh et al. ( 2019 ) represent the US economy with a model where individuals sort into occupations based on innate ability. Footnote 9 Gender and race identity, however, are a source of discrimination, with three forces preventing women and black men from choosing the occupations best fitting their comparative advantage. First, these groups face labor market discrimination, which is modeled as a tax on wages and can vary by occupation. Second, there is discrimination in human capital formation, with the costs of occupation-specific human capital being higher for certain groups. This cost penalty is a composite term encompassing discrimination or quality differentials in private or public inputs into children’s human capital. The third force are group-specific social norms that generate utility premia or penalties across occupations. Footnote 10

Assuming that the distribution of innate ability across race and gender is constant over time, Hsieh et al. ( 2019 ) investigate and quantify how declines in labor market discrimination, barriers to human capital formation, and changing social norms affect aggregate output and productivity in the United States, between 1960 and 2010. Over that period, their general equilibrium model suggests that around 40 percent of growth in per capita GDP and 90 percent of growth in labor force participation can be attributed to reductions in the misallocation of talent across occupations. Declining in barriers to human capital formation account for most of these effects, followed by declining labor market discrimination. Changing social norms, on the other hand, explain only a residual share of aggregate changes.

Two main mechanisms drive these results. First, falling discrimination improves efficiency through a better match between individual ability and occupation. Second, because discrimination is higher in high-skill occupations, when discrimination decreases, high-ability women and black men invest more in human capital and supply more labor to the market. Overall, better allocation of talent, rising labor supply, and faster human capital accumulation raise aggregate growth and productivity.

Other occupational choice models assuming gender inequality in access to the labor market or certain occupations reach similar conclusions. In addition to the mechanisms in Hsieh et al. ( 2019 ), barriers to women’s work in managerial or entrepreneurial occupations reduce average talent in these positions, resulting in aggregate losses in innovation, technology adoption, and productivity (Cuberes & Teignier 2016 , 2017 ; Esteve-Volart 2009 ). The argument can be readily applied to talent misallocation across sectors (Lee 2020 ). In Lee’s model, female workers face discrimination in the non-agricultural sector. As a result, talented women end up sorting into ill-suited agricultural activities. This distortion reduces aggregate productivity in agriculture. Footnote 11

To sum up, when talent is randomly distributed in the population, barriers to women’s education, employment, or occupational choice effectively reduce the pool of talent in the economy. According to these models, dismantling these gendered barriers can have an immediate positive effect on economic growth.

3 Unitary households: parents and children

In this section, we review models built upon unitary households. A unitary household maximizes a joint utility function subject to pooled household resources. Intra-household decision making is assumed away; the household is effectively a black-box. In this class of models, gender inequality stems from a variety of sources. It is rooted in differences in physical strength (Galor & Weil 1996 ; Hiller 2014 ; Kimura & Yasui 2010 ) or health (Bloom et al. 2015 ); it is embedded in social norms (Hiller 2014 ; Lagerlöf 2003 ), labor market discrimination (Cavalcanti & Tavares 2016 ), or son preference (Zhang, Zhang and Li 1999 ). In all these models, gender inequality is a barrier to long-run economic development.

Galor & Weil ( 1996 ) model an economy with three factors of production: capital, physical labor (“brawn”), and mental labor (“brain”). Men and women are equally endowed with brains, but men have more brawn. In economies starting with very low levels of capital per worker, women fully specialize in childrearing because their opportunity cost in terms of foregone market earnings is lower than men’s. Over time, the stock of capital per worker builds up due to exogenous technological progress. The degree of complementarity between capital and mental labor is higher than that between capital and physical labor; as the economy accumulates capital per worker, the returns to brain rise relative to the returns to brawn. As a result, the relative wages of women rise, increasing the opportunity cost of childrearing. This negative substitution effect dominates the positive income effect on the demand for children and fertility falls. Footnote 12 As fertility falls, capital per worker accumulates faster creating a positive feedback loop that generates a fertility transition and kick starts a process of sustained economic growth.

The model has multiple stable equilibria. An economy starting from a low level of capital per worker is caught in a Malthusian poverty trap of high fertility, low income per capita, and low relative wages for women. In contrast, an economy starting from a sufficiently high level of capital per worker will converge to a virtuous equilibrium of low fertility, high income per capita, and high relative wages for women. Through exogenous technological progress, the economy can move from the low to the high equilibrium.

Gender inequality in labor market access or returns to brain can slow down or even prevent the escape from the Malthusian equilibrium. Wage discrimination or barriers to employment would work against the rise of relative female wages and, therefore, slow down the takeoff to modern economic growth.

The Galor and Weil model predicts how female labor supply and fertility evolve in the course of development. First, (married) women start participating in market work and only afterwards does fertility start declining. Historically, however, in the US and Western Europe, the decline in fertility occurred before women’s participation rates in the labor market started their dramatic increase. In addition, these regions experienced a mid-twentieth century baby boom which seems at odds with Galor and Weil’s theory.

Both these stylized facts can be addressed by adding home production to the modeling, as do Kimura & Yasui ( 2010 ). In their article, as capital per worker accumulates, the market wage for brains rises and the economy moves through four stages of development. In the first stage, with a sufficiently low market wage, both husband and wife are fully dedicated to home production and childrearing. The household does not supply labor to the market; fertility is high and constant. In the second stage, as the wage rate increases, men enter the labor market (supplying both brawn and brain), whereas women remain fully engaged in home production and childrearing. But as men partially withdraw from home production, women have to replace them. As a result, their time cost of childrearing goes up. At this stage of development, the negative substitution effect of rising wages on fertility dominates the positive income effect. Fertility starts declining, even though women have not yet entered the labor market. The third stage arrives when men stop working in home production. There is complete specialization of labor by gender; men only do market work, and women only do home production and childrearing. As the market wage rises for men, the positive income effect becomes dominant and fertility increases; this mimics the baby-boom period of the mid-twentieth century. In the fourth and final stage, once sufficient capital is accumulated, women enter the market sector as wage-earners. The negative substitution effect of rising female opportunity costs dominates once again, and fertility declines. The economy moves from a “breadwinner model” to a “dual-earnings model”.

Another important form of gender inequality is discrimination against women in the form of lower wages, holding male and female productivity constant. Cavalcanti & Tavares ( 2016 ) estimate the aggregate effects of wage discrimination using a model-based general equilibrium representation of the US economy. In their model, women are assumed to be more productive in childrearing than men, so they pay the full time cost of this activity. In the labor market, even though men and women are equally productive, women receive only a fraction of the male wage rate—this is the wage discrimination assumption. Wage discrimination works as a tax on female labor supply. Because women work less than they would without discrimination, there is a negative level effect on per capita output. In addition, there is a second negative effect of wage discrimination operating through endogenous fertility. Since lower wages reduce women’s opportunity costs of childrearing, fertility is relatively high, and output per capita is relatively low. The authors calibrate the model to US steady state parameters and estimate large negative output costs of the gender wage gap. Reducing wage discrimination against women by 50 percent would raise per capita income by 35 percent, in the long run.

Human capital accumulation plays no role in Galor & Weil ( 1996 ), Kimura & Yasui ( 2010 ), and Cavalcanti & Tavares ( 2016 ). Each person is exogenously endowed with a unit of brains. The fundamental trade-off in the these models is between the income and substitution effects of rising wages on the demand for children. When Lagerlöf ( 2003 ) adds education investments to a gender-based model, an additional trade-off emerges: that between the quantity and the quality of children.

Lagerlöf ( 2003 ) models gender inequality as a social norm: on average, men have higher human capital than women. Confronted with this fact, parents play a coordination game in which it is optimal for them to reproduce the inequality in the next generation. The reason is that parents expect the future husbands of their daughters to be, on average, relatively more educated than the future wives of their sons. Because, in the model, parents care for the total income of their children’s future households, they respond by investing relatively less in daughters’ human capital. Here, gender inequality does not arise from some intrinsic difference between men and women. It is instead the result of a coordination failure: “[i]f everyone else behaves in a discriminatory manner, it is optimal for the atomistic player to do the same” (Lagerlöf 2003 , p. 404).

With lower human capital, women earn lower wages than men and are therefore solely responsible for the time cost of childrearing. But if, exogenously, the social norm becomes more gender egalitarian over time, the gender gap in parental educational investment decreases. As better educated girls grow up and become mothers, their opportunity costs of childrearing are higher. Parents trade-off the quantity of children by their quality; fertility falls and human capital accumulates. However, rising wages have an offsetting positive income effect on fertility because parents pay a (fixed) “goods cost” per child. The goods cost is proportionally more important in poor societies than in richer ones. As a result, in poor economies, growth takes off slowly because the positive income effect offsets a large chunk of the negative substitution effect. As economies grow richer, the positive income effect vanishes (as a share of total income), and fertility declines faster. That is, growth accelerates over time even if gender equality increases only linearly.

The natural next step is to model how the social norm on gender roles evolves endogenously during the course of development. Hiller ( 2014 ) develops such a model by combining two main ingredients: a gender gap in the endowments of brawn (as in Galor & Weil 1996 ) generates a social norm, which each parental couple takes as given (as in Lagerlöf 2003 ). The social norm evolves endogenously, but slowly; it tracks the gender ratio of labor supply in the market, but with a small elasticity. When the male-female ratio in labor supply decreases, stereotypes adjust and the norm becomes less discriminatory against women.

The model generates a U-shaped relationship between economic development and female labor force participation. Footnote 13 In the preindustrial stage, there is no education and all labor activities are unskilled, i.e., produced with brawn. Because men have a comparative advantage in brawn, they supply more labor to the market than women, who specialize in home production. This gender gap in labor supply creates a social norm that favors boys over girls. Over time, exogenous skill-biased technological progress raises the relative returns to brains, inducing parents to invest in their children’s education. At the beginning, however, because of the social norm, only boys become educated. The economy accumulates human capital and grows, generating a positive income effect that, in isolation, would eventually drive up parental investments in girls’ education. Footnote 14 But endogenous social norms move in the opposite direction. When only boys receive education, the gender gap in returns to market work increases, and women withdraw to home production. As female relative labor supply in the market drops, the social norm becomes more discriminatory against women. As a result, parents want to invest relatively less in their daughters’ education.

In the end, initial conditions determine which of the forces dominates, thereby shaping long-term outcomes. If, initially, the social norm is very discriminatory, its effect is stronger than the income effect; the economy becomes trapped in an equilibrium with high gender inequality and low per capita income. If, on the other hand, social norms are relatively egalitarian to begin with, then the income effect dominates, and the economy converges to an equilibrium with gender equality and high income per capita.

In the models reviewed so far, human capital or brain endowments can be understood as combining both education and health. Bloom et al. ( 2015 ) explicitly distinguish these two dimensions. Health affects labor market earnings because sick people are out of work more often (participation effect) and are less productive per hour of work (productivity effect). Female health is assumed to be worse than male health, implying that women’s effective wages are lower than men’s. As a result, women are solely responsible for childrearing. Footnote 15

The model produces two growth regimes: a Malthusian trap with high fertility and no educational investments; and a regime of sustained growth, declining fertility, and rising educational investments. Once wages reach a certain threshold, the economy goes through a fertility transition and education expansion, taking off from the Malthusian regime to the sustained growth regime.

Female health promotes growth in both regimes, and it affects the timing of the takeoff. The healthier women are, the earlier the economy takes off. The reason is that a healthier woman earns a higher effective wage and, consequently, faces higher opportunity costs of raising children. When female health improves, the rising opportunity costs of children reduce the wage threshold at which educational investments become attractive; the fertility transition and mass education periods occur earlier.

In contrast, improved male health slows down economic growth and delays the fertility transition. When men become healthier, there is only a income effect on the demand for children, without the negative substitution effect (because male childrearing time is already zero). The policy conclusion would be to redistribute health from men to women. However, the policy would impose a static utility cost on the household. Because women’s time allocation to market work is constrained by childrearing responsibilities (whereas men work full-time), the marginal effect of health on household income is larger for men than for women. From the household’s point of view, reducing the gender gap in health produces a trade-off between short-term income maximization and long-term economic development.

In an extension of the model, the authors endogeneize health investments, while keeping the assumption that women pay the full time cost of childrearing. Because women participate less in the labor market (due to childrearing duties), it is optimal for households to invest more in male health. A health gender gap emerges from rational household behavior that takes into account how time-constraints differ by gender; assuming taste-based discrimination against girls or gender-specific preferences is not necessary.

In the models reviewed so far, parents invest in their children’s human capital for purely altruistic reasons. This is captured in the models by assuming that parents derive utility directly from the quantity and quality of children. This is the classical representation of children as durable consumption goods (e.g., Becker 1960 ). In reality, of course, parents may also have egoistic motivations for investing in child quantity and quality. A typical example is that, when parents get old and retire, they receive support from their children. The quantity and quality of children will affect the size of old-age transfers and parents internalize this in their fertility and childcare behavior. According to this view, children are best understood as investment goods.

Zhang et al. ( 1999 ) build an endogenous growth model that incorporates the old-age support mechanism in parental decisions. Another innovative element of their model is that parents can choose the gender of their children. The implicit assumption is that sex selection technologies are freely available to all parents.

At birth, there is a gender gap in human capital endowment, favoring boys over girls. Footnote 16 In adulthood, a child’s human capital depends on the initial endowment and on the parents’ human capital. In addition, the probability that a child survives to adulthood is exogenous and can differ by gender.

Parents receive old-age support from children that survive until adulthood. The more human capital children have, the more old-age support they provide to their parents. Beyond this egoistic motive, parents also enjoy the quantity and the quality of children (altruistic motive). Son preference is modeled by boys having a higher relative weight in the altruistic-component of the parental utility function. In other words, in their enjoyment of children as consumer goods, parents enjoy “consuming” a son more than “consuming” a girl. Parents who prefer sons want more boys than girls. A larger preference for sons, a higher relative survival probability of boys, and a higher human capital endowment of boys positively affect the sex ratio at birth, because, in the parents’ perspective, all these forces increase the marginal utility of boys relative to girls.

Zhang et al. ( 1999 ) show that, if human capital transmission from parents to children is efficient enough, the economy grows endogenously. When boys have a higher human capital endowment than girls, and the survival probability of sons is not smaller than the survival probability of daughters, then only sons provide old-age support. Anticipating this, parents invest more in the human capital of their sons than on the human capital of their daughters. As a result, the gender gap in human capital at birth widens endogenously.

When only boys provide old-age support, an exogenous increase in son preference harms long-run economic growth. The reason is that, when son preference increases, parents enjoy each son relatively more and demand less old-age support from him. Other things equal, parents want to “consume” more sons now and less old-age support later. Because parents want more sons, the sex ratio at birth increases; but because each son provides less old-age support, human capital investments per son decrease (such that the gender gap in human capital narrows). At the aggregate level, the pace of human capital accumulation slows down and, in the long run, economic growth is lower. Thus, an exogenous increase in son preference increases the sex ratio at birth, and reduces human capital accumulation and long-run growth (although it narrows the gender gap in education).

In summary, in growth models with unitary households, gender inequality is closely linked to the division of labor between family members. If women earn relatively less in market activities, they specialize in childrearing and home production, while men specialize in market work. And precisely due to this division of labor, the returns to female educational investments are relatively low. These household behaviors translate into higher fertility and lower human capital and thus pose a barrier to long-run development.

4 Intra-household bargaining: husbands and wives

In this section, we review models populated with non-unitary households, where decisions are the result of bargaining between the spouses. There are two broad types of bargaining processes: non-cooperative, where spouses act independently or interact in a non-cooperative game that often leads to inefficient outcomes (e.g., Doepke & Tertilt 2019 , Heath & Tan 2020 ); and cooperative, where the spouses are assumed to achieve an efficient outcome (e.g., De la Croix & Vander Donckt 2010 ; Diebolt & Perrin 2013 ). As in the previous section, all of these non-unitary models take the household as given, thereby abstracting from marriage markets or other household formation institutions, which will be discussed separately in section 5 . When preferences differ by gender, bargaining between the spouses matters for economic growth. If women care more about child quality than men do and human capital accumulation is the main engine of growth, then empowering women leads to faster economic growth (Prettner & Strulik 2017 ). If, however, men and women have similar preferences but are imperfect substitutes in the production of household public goods, then empowering women has an ambiguous effect on economic growth (Doepke & Tertilt 2019 ).

A separate channel concerns the intergenerational transmission of human capital and woman’s role as the main caregiver of children. If the education of the mother matters more than the education of the father in the production of children’s human capital, then empowering women will be conducive to growth (Agénor 2017 ; Diebolt & Perrin 2013 ), with the returns to education playing a crucial role in the political economy of female empowerment (Doepke & Tertilt 2009 ).

However, different dimensions of gender inequality have different growth impacts along the development process (De la Croix & Vander Donckt 2010 ). Policies that improve gender equality across many dimensions can be particularly effective for economic growth by reaping complementarities and positive externalities (Agénor 2017 ).

The idea that women might have stronger preferences for child-related expenditures than men can be easily incorporated in a Beckerian model of fertility. The necessary assumption is that women place a higher weight on child quality (relative to child quantity) than men do. Prettner & Strulik ( 2017 ) build a unified growth theory model with collective households. Men and women have different preferences, but they achieve efficient cooperation based on (reduced-form) bargaining parameters. The authors study the effect of two types of preferences: (i) women are assumed to have a relative preference for child quality, while men have a relative preference for child quantity; and (ii) parents are assumed to have a relative preference for the education of sons over the education of daughters. In addition, it is assumed that the time cost of childcare borne by men cannot be above that borne by women (but it could be the same).

When women have a relative preference for child quality, increasing female empowerment speeds up the economy’s escape from a Malthusian trap of high fertility, low education, and low income per capita. When female empowerment increases (exogenously), a woman’s relative preference for child quality has a higher impact on household’s decisions. As a consequence, fertility falls, human capital accumulates, and the economy starts growing. The model also predicts that the more preferences for child quality differ between husband and wife, the more effective is female empowerment in raising long-run per capita income, because the sooner the economy escapes the Malthusian trap. This effect is not affected by whether parents have a preference for the education of boys relative to that of girls. If, however, men and women have similar preferences with respect to the quantity and quality of their children, then female empowerment does not affect the timing of the transition to the sustained growth regime.

Strulik ( 2019 ) goes one step further and endogeneizes why men seem to prefer having more children than women. The reason is a different preference for sexual activity: other things equal, men enjoy having sex more than women. Footnote 17 When cheap and effective contraception is not available, a higher male desire for sexual activity explains why men also prefer to have more children than women. In a traditional economy, where no contraception is available, fertility is high, while human capital and economic growth are low. When female bargaining power increases, couples reduce their sexual activity, fertility declines, and human capital accumulates faster. Faster human capital accumulation increases household income and, as a consequence, the demand for contraception goes up. As contraception use increases, fertility declines further. Eventually, the economy undergoes a fertility transition and moves to a modern regime with low fertility, widespread use of contraception, high human capital, and high economic growth. In the modern regime, because contraception is widely used, men’s desire for sex is decoupled from fertility. Both sex and children cost time and money. When the two are decoupled, men prefer to have more sex at the expense of the number of children. There is a reversal in the gender gap in desired fertility. When contraceptives are not available, men desire more children than women; once contraceptives are widely used, men desire fewer children than women. If women are more empowered, the transition from the traditional equilibrium to the modern equilibrium occurs faster.

Both Prettner & Strulik ( 2017 ) and Strulik ( 2019 ) rely on gender-specific preferences. In contrast, Doepke & Tertilt ( 2019 ) are able to explain gender-specific expenditure patterns without having to assume that men and women have different preferences. They set up a non-cooperative model of household decision making and ask whether more female control of household resources leads to higher child expenditures and, thus, to economic development. Footnote 18

In their model, household public goods are produced with two inputs: time and goods. Instead of a single home-produced good (as in most models), there is a continuum of household public goods whose production technologies differ. Some public goods are more time-intensive to produce, while others are more goods-intensive. Each specific public good can only be produced by one spouse—i.e., time and good inputs are not separable. Women face wage discrimination in the labor market, so their opportunity cost of time is lower than men’s. As a result, women specialize in the production of the most time-intensive household public goods (e.g., childrearing activities), while men specialize in the production of goods-intensive household public goods (e.g., housing infrastructure). Notice that, because the household is non-cooperative, there is not only a division of labor between husband and wife, but also a division of decision making, since ultimately each spouse decides how much to provide of his or her public goods.

When household resources are redistributed from men to women (i.e., from the high-wage spouse to the low-wage spouse), women provide more public goods, in relative terms. It is ambiguous, however, whether the total provision of public goods increases with the re-distributive transfer. In a classic model of gender-specific preferences, a wife increases child expenditures and her own private consumption at the expense of the husband’s private consumption. In Doepke & Tertilt ( 2019 ), however, the rise in child expenditures (and time-intensive public goods in general) comes at the expense of male consumption and male-provided public goods.

Parents contribute to the welfare of the next generation in two ways: via human capital investments (time-intensive, typically done by the mother) and bequests of physical capital (goods-intensive, typically done by the father). Transferring resources to women increases human capital, but reduces the stock of physical capital. The effect of such transfers on economic growth depends on whether the aggregate production function is relatively intensive in human capital or in physical capital. If aggregate production is relatively human capital intensive, then transfers to women boost economic growth; if it is relatively intensive in physical capital, then transfers to women may reduce economic growth.

There is an interesting paradox here. On the one hand, transfers to women will be growth-enhancing in economies where production is intensive in human capital. These would be more developed, knowledge intensive, service economies. On the other hand, the positive growth effect of transfers to women increases with the size of the gender wage gap, that is, decreases with female empowerment. But the more advanced, human capital intensive economies are also the ones with more female empowerment (i.e., lower gender wage gaps). In other words, in settings where human capital investments are relatively beneficial, the contribution of female empowerment to human capital accumulation is reduced. Overall, Doepke and Tertilt’s ( 2019 ) model predicts that female empowerment has at best a limited positive effect and at worst a negative effect on economic growth.

Heath & Tan ( 2020 ) argue that, in a non-cooperative household model, income transfers to women may increase female labor supply. Footnote 19 This result may appear counter-intuitive at first, because in collective household models unearned income unambiguously reduces labor supply through a negative income effect. In Heath and Tan’s model, husband and wife derive utility from leisure, consuming private goods, and consuming a household public good. The spouses decide separately on labor supply and monetary contributions to the household public good. Men and women are identical in preferences and behavior, but women have limited control over resources, with a share of their income being captured by the husband. Female control over resources (i.e., autonomy) depends positively on the wife’s relative contribution to household income. Thus, an income transfer to the wife, keeping husband unearned income constant, raises the fraction of her own income that she privately controls. This autonomy effect unambiguously increases women’s labor supply, because the wife can now reap an additional share of her wage bill. Whenever the autonomy effect dominates the (negative) income effect, female labor supply increases. The net effect will be heterogeneous over the wage distribution, but the authors show that aggregate female labor supply is always weakly larger after the income transfer.

Diebolt & Perrin ( 2013 ) assume cooperative bargaining between husband and wife, but do not rely on sex-specific preferences or differences in ability. Men and women are only distinguished by different uses of their time endowments, with females in charge of all childrearing activities. In line with this labor division, the authors further assume that only the mother’s human capital is inherited by the child at birth. On top of the inherited maternal endowment, individuals can accumulate human capital during adulthood, through schooling. The higher the initial human capital endowment, the more effective is the accumulation of human capital via schooling.

A woman’s bargaining power in marriage determines her share in total household consumption and is a function of the relative female human capital of the previous generation. An increase in the human capital of mothers relative to that of fathers has two effects. First, it raises the incentives for human capital accumulation of the next generation, because inherited maternal human capital makes schooling more effective. Second, it raises the bargaining power of the next generation of women and, because women’s consumption share increases, boosts the returns on women’s education. The second effect is not internalized in women’s time allocation decisions; it is an intergenerational externality. Thus, an exogenous increase in women’s bargaining power would promote economic growth by speeding up the accumulation of human capital across overlapping generations.

De la Croix & Vander Donckt ( 2010 ) contribute to the literature by clearly distinguishing between different gender gaps: a gap in the probability of survival, a wage gap, a social and institutional gap, and a gender education gap. The first three are exogenously given, while the fourth is determined within the model.

By assumption, men and women have identical preferences and ability, but women pay the full time cost of childrearing. As in a typical collective household model, bargaining power is partially determined by the spouses’ earnings potential (i.e., their levels of human capital and their wage rates). But there is also a component of bargaining power that is exogenous and captures social norms that discriminate against women—this is the social and institutional gender gap.

Husbands and wives bargain over fertility and human capital investments for their children. A standard Beckerian result emerges: parents invest relatively less in the education of girls, because girls will be more time-constrained than boys and, therefore, the female returns to education are lower in relative terms.

There are at least two regimes in the economy: a corner regime and an interior regime. The corner regime consists of maximum fertility, full gender specialization (no women in the labor market), and large gender gaps in education (no education for girls). Reducing the wage gap or the social and institutional gap does not help the economy escaping this regime. Women are not in labor force, so the wage gap is meaningless. The social and institutional gap will determine women’s share in household consumption, but does not affect fertility and growth. At this stage, the only effective instruments for escaping the corner regime are reducing the gender survival gap or reducing child mortality. Reducing the gender survival gap increases women’s lifespan, which increases their time budget and attracts them to the labor market. Reducing child mortality decreases the time costs of kids, therefore drawing women into the labor market. In both cases, fertility decreases.

In the interior regime, fertility is below the maximum, women’s labor supply is above zero, and both boys and girls receive education. In this regime, with endogenous bargaining power, reducing all gender gaps will boost economic growth. Footnote 20 Thus, depending on the growth regime, some gender gaps affect economic growth, while others do not. Accordingly, the policy-maker should tackle different dimensions of gender inequality at different stages of the development process.

Agénor ( 2017 ) presents a computable general equilibrium that includes many of the elements of gender inequality reviewed so far. An important contribution of the model is to explicitly add the government as an agent whose policies interact with family decisions and, therefore, will impact women’s time allocation. Workers produce a market good and a home good and are organized in collective households. Bargaining power depends on the spouses’ relative human capital levels. By assumption, there is gender discrimination in market wages against women. On top, mothers are exclusively responsible for home production and childrearing, which takes the form of time spent improving children’s health and education. But public investments in education and health also improve these outcomes during childhood. Likewise, public investment in public infrastructure contributes positively to home production. In particular, the ratio of public infrastructure capital stock to private capital stock is a substitute for women’s time in home production. The underlying idea is that improving sanitation, transportation, and other infrastructure reduces time spent in home production. Health status in adulthood depends on health status in childhood, which, in turn, relates positively to mother’s health, her time inputs into childrearing, and government spending. Children’s human capital depends on similar factors, except that mother’s human capital replaces her health as an input. Additionally, women are assumed to derive less utility from current consumption and more utility from children’s health relative to men. Wives are also assumed to live longer than their husbands, which further down-weights female’s emphasis on current consumption. The final gendered assumption is that mother’s time use is biased towards boys. This bias alone creates a gender gap in education and health. As adults, women’s relative lower health and human capital are translated into relative lower bargaining power in household decisions.

Agénor ( 2017 ) calibrates this rich setup for Benin, a low income country, and runs a series of policy experiments on different dimensions of gender inequality: a fall in childrearing costs, a fall in gender pay discrimination, a fall in son bias in mother’s time allocation, and an exogenous increase in female bargaining power. Footnote 21 Interestingly, despite all policies improving gender equality in separate dimensions, not all unambiguously stimulate economic growth. For example, falling childrearing costs raise savings and private investments, which are growth-enhancing, but increase fertility (as children become ‘cheaper’) and reduce maternal time investment per child, thus reducing growth. In contrast, a fall in gender pay discrimination always leads to higher growth, through higher household income that, in turn, boosts savings, tax revenues, and public spending. Higher public spending further contributes to improved health and education of the next generation. Lastly, Agénor ( 2017 ) simulates the effect of a combined policy that improves gender equality in all domains simultaneously. Due to complementarities and positive externalities across dimensions, the combined policy generates more economic growth than the sum of the individual policies. Footnote 22

In the models reviewed so far, men are passive observers of women’s empowerment. Doepke & Tertilt ( 2009 ) set up an interesting political economy model of women’s rights, where men make the decisive choice. Their model is motivated by the fact that, historically, the economic rights of women were expanded before their political rights. Because the granting of economic rights empowers women in the household, and this was done before women were allowed to participate in the political process, the relevant question is why did men willingly share their power with their wives?

Doepke & Tertilt ( 2009 ) answer this question by arguing that men face a fundamental trade-off. On the one hand, husbands would vote for their wives to have no rights whatsoever, because husbands prefer as much intra-household bargaining power as possible. But, on the other hand, fathers would vote for their daughters to have economic rights in their future households. In addition, fathers want their children to marry highly educated spouses, and grandfathers want their grandchildren to be highly educated. By assumption, men and women have different preferences, with women having a relative preference for child quality over quantity. Accordingly, men internalize that, when women become empowered, human capital investments increase, making their children and grandchildren better-off.

Skill-biased (exogenous) technological progress that raises the returns to education over time can shift male incentives along this trade-off. When the returns to education are low, men prefer to make all decisions on their own and deny all rights to women. But once the returns to education are sufficiently high, men voluntarily share their power with women by granting them economic rights. As a result, human capital investments increase and the economy grows faster.

In summary, gender inequality in labor market earnings often implies power asymmetries within the household, with men having more bargaining power than women. If preferences differ by gender and female preferences are more conducive to development, then empowering women is beneficial for growth. When preferences are the same and the bargaining process is non-cooperative, the implications are less clear-cut, and more context-specific. If, in addition, women’s empowerment is curtailed by law (e.g., restrictions on women’s economic rights), then it is important to understand the political economy of women’s rights, in which men are crucial actors.

5 Marriage markets and household formation

Two-sex models of economic growth have largely ignored how households are formed. The marriage market is not explicitly modeled: spouses are matched randomly, marriage is universal and monogamous, and families are nuclear. In reality, however, household formation patterns vary substantially across societies, with some of these differences extending far back in history. For example, Hajnal ( 1965 , 1982 ) described a distinct household formation pattern in preindustrial Northwestern Europe (often referred to as the “European Marriage Pattern”) characterized by: (i) late ages at first marriage for women, (ii) most marriages done under individual consent, and (iii) neolocality (i.e., upon marriage, the bride and the groom leave their parental households to form a new household). In contrast, marriage systems in China and India consisted of: (i) very early female ages at first marriage, (ii) arranged marriages, and (iii) patrilocality (i.e., the bride joins the parental household of the groom).

Economic historians argue that the “European Marriage Pattern” empowered women, encouraging their participation in market activities and reducing fertility levels. While some view this as one of the deep-rooted factors explaining Northwestern Europe’s earlier takeoff to sustained economic growth (e.g., Carmichael, de Pleijt, van Zanden and De Moor 2016 ; De Moor & Van Zanden 2010 ; Hartman 2004 ), others have downplayed the long-run significance of this marriage pattern (e.g., Dennison & Ogilvie 2014 ; Ruggles 2009 ). Despite this lively debate, the topic has been largely ignored by growth theorists. The few exceptions are Voigtländer and Voth ( 2013 ), Edlund and Lagerlöf ( 2006 ), and Tertilt ( 2005 , 2006 ).

After exploring different marriage institutions, we zoom in on contemporary monogamous and consensual marriage and review models where gender inequality affects economic growth through marriage markets that facilitate household formation (Du & Wei 2013 ; Grossbard & Pereira 2015 ; Grossbard-Shechtman 1984 ; Guvenen & Rendall 2015 ). In contrast with the previous two sections, where the household is the starting point of the analysis, the literature on marriage markets and household formation recognizes that marriage, labor supply, and investment decisions are interlinked. The analysis of these interlinkages is sometimes done with unitary households (upon marriage) (Du & Wei 2013 ; Guvenen & Rendall 2015 ), or with non-cooperative models of individual decision-making within households (Grossbard & Pereira 2015 ; Grossbard-Shechtman 1984 ).

Voigtländer and Voth ( 2013 ) argue that the emergence of the “European Marriage Pattern” is a direct consequence of the mid-fourteen century Black Death. They set up a two-sector agricultural economy consisting of physically demanding cereal farming, and less physically demanding pastoral production. The economy is populated by many male and female peasants and by a class of idle, rent-maximizing landlords. Female peasants are heterogeneous with respect to physical strength, but, on average, are assumed to have less brawn relative to male peasants and, thus, have a comparative advantage in the pastoral sector. Both sectors use land as a production input, although the pastoral sector is more land-intensive than cereal production. All land is owned by the landlords, who can rent it out for peasant cereal farming, or use it for large-scale livestock farming, for which they hire female workers. Crucially, women can only work and earn wages in the pastoral sector as long as they are unmarried. Footnote 23 Peasant women decide when to marry and, upon marriage, a peasant couple forms a new household, where husband and wife both work on cereal farming, and have children at a given time frequency. Thus, the only contraceptive method available is delaying marriage. Because women derive utility from consumption and children, they face a trade-off between earned income and marriage.

Initially, the economy rests in a Malthusian regime, where land-labor ratios are relatively low, making the land-intensive pastoral sector unattractive and depressing relative female wages. As a result, women marry early and fertility is high. The initial regime ends in 1348–1350, when the Black Death kills between one third and half of Europe’s population, exogenously generating land abundance and, therefore, raising the relative wages of female labor in pastoral production. Women postpone marriage to reap higher wages, and fertility decreases—moving the economy to a regime of late marriages and low fertility.

In addition to late marital ages and reduced fertility, another important feature of the “European Marriage Pattern” was individual consent for marriage. Edlund and Lagerlöf ( 2006 ) study how rules of consent for marriage influence long-run economic development. In their model, marriages can be formed according to two types of consent rules: individual consent or parental consent. Under individual consent, young people are free to marry whomever they wish, while, under parental consent, their parents are in charge of arranging the marriage. Depending on the prevailing rule, the recipient of the bride-price differs. Under individual consent, a woman receives the bride-price from her husband, whereas, under parental consent, her father receives the bride-price from the father of the groom. Footnote 24 In both situations, the father of the groom owns the labor income of his son and, therefore, pays the bride-price, either directly, under parental consent, or indirectly, under individual consent. Under individual consent, the father needs to transfer resources to his son to nudge him into marrying. Thus, individual consent implies a transfer of resources from the old to the young and from men to women, relative to the rule of parental consent. Redistributing resources from the old to the young boosts long-run economic growth. Because the young have a longer timespan to extract income from their children’s labor, they invest relatively more in the human capital of the next generation. In addition, under individual consent, the reallocation of resources from men to women can have additional positive effects on growth, by increasing women’s bargaining power (see section 4 ), although this channel is not explicitly modeled in Edlund and Lagerlöf ( 2006 ).

Tertilt ( 2005 ) explores the effects of polygyny on long-run development through its impact on savings and fertility. In her model, parental consent applies to women, while individual consent applies to men. There is a competitive marriage market where fathers sell their daughters and men buy their wives. As each man is allowed (and wants) to marry several wives, a positive bride-price emerges in equilibrium. Footnote 25 Upon marriage, the reproductive rights of the bride are transferred from her father to her husband, who makes all fertility decisions on his own and, in turn, owns the reproductive rights of his daughters. From a father’s perspective, daughters are investments goods; they can be sold in the marriage market, at any time. This feature generates additional demand for daughters, which increases overall fertility, and reduces the incentives to save, which decreases the stock of physical capital. Under monogamy, in contrast, the equilibrium bride-price is negative (i.e., a dowry). The reason is that maintaining unmarried daughters is costly for their fathers, so they are better-off paying a (small enough) dowry to their future husbands. In this setting, the economic returns to daughters are lower and, consequently, so is the demand for children. Fertility decreases and savings increase. Thus, moving from polygny to monogamy lowers population growth and raises the capital stock in the long run, which translates into higher output per capita in the steady state.

Instead of enforcing monogamy in a traditionally polygynous setting, an alternative policy is to transfer marriage consent from fathers to daughters. Tertilt ( 2006 ) shows that when individual consent is extended to daughters, such that fathers do not receive the bride-price anymore, the consequences are qualitatively similar to a ban on polygyny. If fathers stop receiving the bride-price, they save more physical capital. In the long run, per capita output is higher when consent is transferred to daughters.

Grossbard-Shechtman ( 1984 ) develops the first non-cooperative model where (monogamous) marriage, home production, and labor supply decisions are interdependent. Footnote 26 Spouses are modeled as separate agents deciding over production and consumption. Marriage becomes an implicit contract for ‘work-in-household’ (WiHo), defined as “an activity that benefits another household member [typically a spouse] who could potentially compensate the individual for these efforts” (Grossbard 2015 , p. 21). Footnote 27 In particular, each spouse decides how much labor to supply to market work and WiHo, and how much labor to demand from the other spouse for WiHo. Through this lens, spousal decisions over the intra-marriage distribution of consumption and WiHo are akin to well-known principal-agent problems faced between firms and workers. In the marriage market equilibrium, a spouse benefiting from WiHo (the principal) must compensate the spouse producing it (the agent) via intra-household transfers (of goods or leisure). Footnote 28 Grossbard-Shechtman ( 1984 ) and Grossbard ( 2015 ) show that, under these conditions, the ratio of men to women (i.e., the sex ratio) in the marriage market is inversely related to female labor supply to the market. The reason is that, as the pool of potential wives shrinks, prospective husbands have to increase compensation for female WiHo. From the potential wife’s point of view, as the equilibrium price for her WiHo increases, market work becomes less attractive. Conversely, when sex ratios are lower, female labor supply outside the home increases. Although the model does not explicit derive growth implications, the relative increase in female labor supply is expected to be beneficial for economic growth, as argued by many of the theories reviewed so far.

In an extension of this framework, Grossbard & Pereira ( 2015 ) analyze how sex ratios affect gendered savings over the marital life-cycle. Assuming that women supply a disproportionate amount of labor for WiHo (due, for example, to traditional gender norms), the authors show that men and women will have very distinct saving trajectories. A higher sex ratio increases savings by single men, who anticipate higher compensation transfers for their wives’ WiHo, whereas it decreases savings by single women, who anticipate receiving those transfers upon marriage. But the pattern flips after marriage: precautionary savings raise among married women, because the possibility of marriage dissolution entails a loss of income from WiHo. The opposite effect happens for married men: marriage dissolution would imply less expenditures in the future. The higher the sex ratio, the higher will be the equilibrium compensation paid by husbands for their wives’ WiHo. Therefore, the sex ratio will positively affect savings among single men and married women, but negatively affect savings among single women and married men. The net effect on the aggregate savings rate and on economic growth will depend on the relative size of these demographic groups.

In a related article, Du & Wei ( 2013 ) propose a model where higher sex ratios worsen marriage markets prospects for young men and their families, who react by increasing savings. Women in turn reduce savings. However, because sex ratios shift the composition of the population in favor of men (high saving type) relative to women (low saving type) and men save additionally to compensate for women’s dis-saving, aggregate savings increase unambiguously with sex ratios.

In Guvenen & Rendall ( 2015 ), female education is, in part, demanded as insurance against divorce risk. The reason is that divorce laws often protect spouses’ future labor market earnings (i.e., returns to human capital), but force them to share their physical assets. Because, in the model, women are more likely to gain custody of their children after divorce, they face higher costs from divorce relative to their husbands. Therefore, the higher the risk of divorce, the more women invest in human capital, as insurance against a future vulnerable economic position. Guvenen & Rendall ( 2015 ) shows that, over time, divorce risk has increased (for example, consensual divorce became replaced by unilateral divorce in most US states in the 1970s). In the aggregate, higher divorce risk boosted female education and female labor supply.

In summary, the rules regulating marriage and household formation carry relevant theoretical consequences for economic development. While the few studies on this topic have focused on age at marriage, consent rules and polygyny, and the interaction between sex ratios, marriage, and labor supply, other features of the marriage market remain largely unexplored (Borella, De Nardi and Yang 2018 ). Growth theorists would benefit from further incorporating theories of household formation in gendered macro models. Footnote 29

6 Conclusion

In this article, we surveyed micro-founded theories linking gender inequality to economic development. This literature offers many plausible mechanisms through which inequality between men and women affects the aggregate economy (see Table 1 ). Yet, we believe the body of theories could be expanded in several directions. We discuss them below and highlight lessons for policy.

The first direction for future research concerns control over fertility. In models where fertility is endogenous, households are always able to achieve their preferred number of children (see Strulik 2019 , for an exception). The implicit assumption is that there is a free and infallible method of fertility control available for all households—a view rejected by most demographers. The gap between desired fertility and achieved fertility can be endogeneized at three levels. First, at the societal level, the diffusion of particular contraceptive methods may be influenced by cultural and religious norms. Second, at the household level, fertility control may be object of non-cooperative bargaining between the spouses, in particular, for contraceptive methods that only women perfectly observe (Ashraf, Field and Lee 2014 ; Doepke & Kindermann 2019 ). More generally, the role of asymmetric information within the household is not yet explored (Walther 2017 ). Third, if parents have preferences over the gender composition of their offspring, fertility is better modeled as a sequential and uncertain process, where household size is likely endogenous to the sex of the last born child (Hazan & Zoabi 2015 ).

A second direction worth exploring concerns gender inequality in a historical perspective. In models with multiple equilibria, an economy’s path is often determined by its initial level of gender equality. Therefore, it would be useful to develop theories explaining why initial conditions varied across societies. In particular, there is a large literature on economic and demographic history documenting how systems of marriage and household formation differed substantially across preindustrial societies (e.g., De Moor & Van Zanden 2010 ; Hajnal 1965 , 1982 ; Hartman 2004 ; Ruggles 2009 ). In our view, more theoretical work is needed to explain both the origins and the consequences of these historical systems.

A third avenue for future research concerns the role of technological change. In several models, technological change is the exogenous force that ultimately erodes gender gaps in education or labor supply (e.g., Bloom et al. 2015 ; Doepke & Tertilt 2009 ; Galor & Weil 1996 ). For that to happen, technological progress is assumed to be skill-biased, thus raising the returns to education—or, in other words, favoring brain over brawn. As such, new technologies make male advantage in physical strength ever more irrelevant, while making female time spent on childrearing and housework ever more expensive. Moreover, recent technological progress increased the efficiency of domestic activities, thereby relaxing women’s time constraints (e.g., Cavalcanti & Tavares 2008 ; Greenwood, Seshadri and Yorukoglu 2005 ). These mechanisms are plausible, but other aspects of technological change need not be equally favorable for women. In many countries, for example, the booming science, technology, and engineering sectors tend to be particularly male-intensive. And Tejani & Milberg ( 2016 ) provide evidence for developing countries that as manufacturing industries become more capital intensive, their female employment share decreases.

Even if current technological progress is assumed to weaken gender gaps, historically, technology may have played exactly the opposite role. If technology today is more complementary to brain, in the past it could have been more complementary to brawn. An example is the plow that, relative to alternative technologies for field preparation (e.g., hoe, digging stick), requires upper body strength, on which men have a comparative advantage over women (Alesina, Giuliano and Nunn 2013 ; Boserup 1970 ). Another, even more striking example, is the invention of agriculture itself—the Neolithic Revolution. The transition from a hunter-gatherer lifestyle to sedentary agriculture involved a relative loss of status for women (Dyble et al. 2015 ; Hansen, Jensen and Skovsgaard 2015 ). One explanation is that property rights on land were captured by men, who had an advantage on physical strength and, consequently, on physical violence. Thus, in the long view of human history, technological change appears to have shifted from being male-biased towards being female-biased. Endogeneizing technological progress and its interaction with gender inequality is a promising avenue for future research.

Fourth, open economy issues are still almost entirely absent. An exception is Rees & Riezman ( 2012 ), who model the effect of globalization on economic growth. Whether global capital flows generate jobs primarily in female or male intensive sectors matters for long-run growth. If globalization creates job opportunities for women, their bargaining power increases and households trade off child quantity by child quality. Fertility falls, human capital accumulates, and long-run per capita output is high. If, on the other hand, globalization creates jobs for men, their intra-household power increases; fertility increases, human capital decreases, and steady-state income per capita is low. The literature would benefit from engaging with open economy demand-driven models of the feminist tradition, such as Blecker & Seguino ( 2002 ), Seguino ( 2010 ). Other fruitful avenues for future research on open economy macro concern gender analysis of global value chains (Barrientos 2019 ), gendered patterns of international migration (Cortes 2015 ; Cortes & Tessada 2011 ), and the diffusion of gender norms through globalization (Beine, Docquier and Schiff 2013 ; Klasen 2020 ; Tuccio & Wahba 2018 ).

A final point concerns the role of men in this literature. In most theoretical models, gender inequality is not the result of an active male project that seeks the domination of women. Instead, inequality emerges as a rational best response to some underlying gender gap in endowments or constraints. Then, as the underlying gap becomes less relevant—for example, due to skill-biased technological change—, men passively relinquish their power (see Doepke & Tertilt 2009 , for an exception). There is never a male backlash against the short-term power loss that necessarily comes with female empowerment. In reality, it is more likely that men actively oppose losing power and resources towards women (Folbre 2020 ; Kabeer 2016 ; Klasen 2020 ). This possibility has not yet been explored in formal models, even though it could threaten the typical virtuous cycle between gender equality and growth. If men are forward-looking, and the short-run losses outweigh the dynamic gains from higher growth, they might ensure that women never get empowered to begin with. Power asymmetries tend to be sticky, because “any group that is able to claim a disproportionate share of the gains from cooperation can develop social institutions to fortify their position” (Folbre 2020 , p. 199). For example, Eswaran & Malhotra ( 2011 ) set up a household decision model where men use domestic violence against their wives as a tool to enhance male bargaining power. Thus, future theories should recognize more often that men have a vested interest on the process of female empowerment.

More generally, policymakers should pay attention to the possibility of a male backlash as an unintended consequence of female empowerment policies (Erten & Keskin 2018 ; Eswaran & Malhotra 2011 ). Likewise, whereas most theories reviewed here link lower fertility to higher economic growth, the relationship is non-monotonic. Fertility levels below the replacement rate will eventually generate aggregate social costs in the form of smaller future workforces, rapidly ageing societies, and increased pressure on welfare systems, to name a few.

Many theories presented in this survey make another important practical point: public policies should recognize that gender gaps in separate dimensions complement and reinforce one another and, therefore, have to be dealt with simultaneously. A naïve policy targeting a single gap in isolation is unlikely to have substantial growth effects in the short run. Typically, inequalities in separate dimensions are not independent from each other (Agénor 2017 ; Bandiera & Does 2013 ; Duflo 2012 ; Kabeer 2016 ). For example, if credit-constrained women face weak property rights, are unable to access certain markets, and have mobility and time constraints, then the marginal return to capital may nevertheless be larger for men. Similarly, the return to male education may well be above the female return if demand for female labor is low or concentrated in sectors with low productivity. In sum, “the fact that women face multiple constraints means that relaxing just one may not improve outcomes” (Duflo 2012 , p. 1076).

Promising policy directions that would benefit from further macroeconomic research are the role of public investments in physical infrastructure and care provision (Agénor 2017 ; Braunstein, Bouhia and Seguino 2020 ), gender-based taxation (Guner, Kaygusuz and Ventura 2012 ; Meier & Rainer 2015 ), and linkages between gender equality and pro-environmental agendas (Matsumoto 2014 ).

See Echevarria & Moe ( 2000 ) for a similar complaint that “theories of economic growth and development have consistently neglected to include gender as a variable” (p. 77).

A non-exhaustive list includes Bandiera & Does ( 2013 ), Braunstein ( 2013 ), Cuberes & Teignier ( 2014 ), Duflo ( 2012 ), Kabeer ( 2016 ), Kabeer & Natali ( 2013 ), Klasen ( 2018 ), Seguino ( 2013 , 2020 ), Sinha et al. ( 2007 ), Stotsky ( 2006 ), World Bank ( 2001 , 2011 ).

For an in-depth history of “new home economics” see Grossbard-Shechtman ( 2001 ) and Grossbard ( 2010 , 2011 ).

For recent empirical reviews see Duflo ( 2012 ) and Doss ( 2013 ).

Although the unitary approach has being rejected on theoretical (e.g., Echevarria & Moe 2000 ; Folbre 1986 ; Knowles 2013 ; Sen 1989 ) and empirical grounds (e.g., Doss 2013 ; Duflo 2003 ; Lundberg et al. 1997 ), these early models are foundational to the subsequent literature. As it turns out, some of the key mechanisms survive in non-unitary theories of the household.

For nice conceptual perspectives on conflict and cooperation in households see Sen ( 1989 ), Grossbard ( 2011 ), and Folbre ( 2020 ).

The relationship depicted in Fig. 1 is robust to using other composite measures of gender equality (e.g., UNDP’s Gender Inequality Index or OECD’s Social Institutions and Gender Index (SIGI) (see Branisa, Klasen and Ziegler 2013 )), and other years besides 2000. In Fig. 2 , the linear prediction explains 56 percent of the cross-country variation in per capita income.

See Seguino ( 2013 , 2020 ) for a review of this literature.

The model allows for sorting on ability (“some people are better teachers”) or sorting on occupation-specific preferences (“others derive more utility from working as a teacher”) (Hsieh et al. 2019 , p. 1441). Here, we restrict our presentation to the case where sorting occurs primarily on ability. The authors find little empirical support for sorting on preferences.

Because the home sector is treated as any other occupation, the model can capture, in a reduced-form fashion, social norms on women’s labor force participation. For example, a social norm on traditional gender roles can be represented as a utility premium obtained by all women working on the home sector.

Note, however, that discrimination against women raises productivity in the non-agricultural sector. The reason is that the few women who end up working outside agriculture are positively selected on talent. Lee ( 2020 ) shows that this countervailing effect is modest and dominated by the loss of productivity in agriculture.

This is not the classic Beckerian quantity-quality trade-off because parents cannot invest in the quality of their children. Instead, the mechanism is built by assumption in the household’s utility function. When women’s wages increase relative to male wages, the substitution effect dominates the income effect.

The hypothesis that female labor force participation and economic development have a U-shaped relationship—known as the feminization-U hypothesis—goes back to Boserup ( 1970 ). See also Goldin ( 1995 ). Recently, Gaddis & Klasen ( 2014 ) find only limited empirical support for the feminization-U.

The model does not consider fertility decisions. Parents derive utility from their children’s human capital (social status utility). When household income increases, parents want to “consume” more social status by investing in their children’s education—this is the positive income effect.

Bloom et al. ( 2015 ) build their main model with unitary households, but show that the key conclusions are robust to a collective representation of the household.

This assumption does not necessarily mean that boys are more talented than girls. It can be also interpreted as a reduced-form way of capturing labor market discrimination against women.

Many empirical studies are in line with this assumption, which is rooted in evolutionary psychology. See Strulik ( 2019 ) for references. There are several other evolutionary arguments for men wanting more children (including with different women). See, among others, Mulder & Rauch ( 2009 ), Penn & Smith ( 2007 ), von Rueden & Jaeggi ( 2016 ). However, for a different view, see Fine ( 2017 ).

They do not model fertility decisions. So there is no quantity-quality trade-off.

In their empirical application, Heath & Tan ( 2020 ) study the Hindu Succession Act, which, through improved female inheritance rights, increased the lifetime unearned income of Indian women. Other policies consistent with the model are, for example, unconditional cash transfers to women.

De la Croix & Vander Donckt ( 2010 ) show this with numerical simulations, because the interior regime becomes analytically intractable.

We focus on gender-related policies in our presentation, but the article simulates additional public policies.

Agénor and Agénor ( 2014 ) develop a similar model, but with unitary households, and Agénor and Canuto ( 2015 ) have a similar model of collective households for Brazil, where adult women can also invest time in human capital formation. Since public infrastructure substitutes for women’s time in home production, more (or better) infrastructure can free up time for female human capital accumulation and, thus, endogenously increase wives’ bargaining power.

Voigtländer and Voth ( 2013 ) justify this assumption arguing that, in England, employment contracts for farm servants working in animal husbandry were conditional on celibacy. However, see Edwards & Ogilvie ( 2018 ) for a critique of this assumption.

The bride-price under individual consent need not be paid explicitly as a lump-sum transfer. It could, instead, be paid to the bride implicitly in the form of higher lifetime consumption.

In Tertilt ( 2005 ), all men are similar (except in age). Widespread polygyny is possible because older men marry younger women and population growth is high. This setup reflects stylized facts for Sub-Saharan Africa. It differs from models that assume male heterogeneity in endowments, where polygyny emerges because a rich male elite owns several wives, while poor men remain single (e.g., Gould, Moav and Simhon 2008 ; Lagerlöf 2005 , 2010 ).

See Grossbard ( 2015 ) for more details and extensions of this model and Grossbard ( 2018 ) for a non-technical overview of the related literature. For an earlier application, see Grossbard ( 1976 ).

The concept of WiHo is closely related but not equivalent to the ‘black-box’ term home production used by much of the literature. It also relates to feminist perspectives on care and social reproduction labor (c.f. Folbre 1994 ).

In the general setup, the model need not lead to a corner solution where only one spouse specializes in WiHo.

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Acknowledgements

We thank the Editor, Shoshana Grossbard, and three anonymous reviewers for helpful comments. We gratefully acknowledge funding from the Growth and Economic Opportunities for Women (GrOW) initiative, a multi-funder partnership between the UK’s Department for International Development, the Hewlett Foundation and the International Development Research Centre. All views expressed here and remaining errors are our own. Manuel dedicates this article to Stephan Klasen, in loving memory.

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Santos Silva, M., Klasen, S. Gender inequality as a barrier to economic growth: a review of the theoretical literature. Rev Econ Household 19 , 581–614 (2021). https://doi.org/10.1007/s11150-020-09535-6

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Reviewing Recent Evidence of the Effect of Taxes on Economic Growth

With the Biden administration proposing a variety of new taxes, it is worth revisiting the literature on how taxes impact economic growth. In 2012, we published a review of the evidence , noting that most studies find negative impacts. However, many papers have been written since, some using more sophisticated empirical methods to identify a causal impact of tax A tax is a mandatory payment or charge collected by local, state, and national governments from individuals or businesses to cover the costs of general government services, goods, and activities. es on economic growth. Below we review this new evidence, again confirming our original findings: Taxes, particularly on corporate and individual income, harm economic growth.

The economic impacts of tax changes on economic growth, measured as a change in real GDP or the components of GDP such as consumption and investment, are difficult to measure. Some tax changes occur as a response to economic growth, and looking at a tax cut at a certain point in time could lead to the mistaken conclusion that tax cuts are bad for growth, since tax cuts are often enacted during economic downturns. For this reason, most of the literature in recent years, and reviewed below, has followed the methodology developed in Romer and Romer (2010) : Looking at unanticipated changes in tax policy, which economists refer to as “exogenous shocks.”

There are other methodological challenges as well. Failure to control for other factors that impact economic growth, such as government spending and monetary policy, could understate or overstate the impact of taxes on growth. Some tax changes in particular may have stronger long-run impacts relative to the short run, such as corporate tax changes, and a study with only a limited time series would miss this effect. Finally, tax reforms involve many moving parts: Certain taxes may go up, while others may drop. This can make it difficult to characterize certain reforms as net tax increases or decreases, leading to mistaken interpretations of how taxes impact growth.

We investigate papers in top economics journals and National Bureau of Economic Research (NBER) working papers over the past few years, considering both U.S. and international evidence. This research covers a wide variety of taxes, including income, consumption, and corporate taxation. All seven papers reviewed here find that tax cuts have positive effects on growth, although some papers note that the strength of this effect depends on which taxes are cut, for whom, and when.

Mertens and Olea (2018) used time series data from 1946 to 2012 to estimate the impacts of marginal tax rates on individual income. They found that marginal rate cuts led to both increases in real GDP and declines in unemployment. A 1 percentage-point decrease in the tax rate increases real GDP by 0.78 percent by the third year after the tax change. Importantly, they find that changes in income following a tax change are responsive to the marginal rate change regardless of the change in the average tax rate The average tax rate is the total tax paid divided by taxable income . While marginal tax rates show the amount of tax paid on the next dollar earned, average tax rates show the overall share of income paid in taxes. . This illustrates that the positive GDP changes the authors find are the response to changes in the incentives, rather than due to an increase aggregate demand through the consumption channel. Cuts in tax rates for the top 1 percent also have positive impacts on other income groups, consistent with a supply-side narrative of how reductions in top marginal rates can increase incomes for other groups over time. However, tax cuts for the top 1 percent do increase inequality.

Zidar (2019) examines the impact of federal tax burdens on economic growth and labor supply across different income groups and states from 1950-2011. He finds positive impacts of tax cuts on economic growth following two years after the change in policy but finds that tax cuts for low- and moderate-income taxpayers affect growth more than tax cuts for high-income taxpayers. The paper finds that a 1 percent of state GDP tax decrease for the bottom 90 percent of earners increases state GDP by 6.6 percent. Looking at labor supply effects in particular, he finds that a 1 percent of state GDP tax decrease increases labor force participation for the bottom 90 percent of earners by 3.5 percentage points and hours worked by 2 percent. He does not find any significant impact on labor force participation rates, hours worked, or GDP growth for the top 10 percent of earners from a similarly sized tax change, somewhat in contrast to the results found in Mertens and Olea (2018) for top earners.

This result may lead some to assume that Zidar is identifying “Keynesian” effects of tax changes, or aggregate demand effects. However, the paper finds strong effects of tax cuts on real wages as well. As Zidar notes, “the increase in real wages suggests that supply-side responses are important and may exceed demand-side responses to tax changes for the bottom 90%.” Additionally, some may go further and argue that this paper shows that tax cuts for top earners have no impact on growth. However, this paper only looks at short-run impacts of tax changes on GDP and does not consider the broader implication of tax policy on long-run growth, human capital, or innovation. Nonetheless, the paper provides compelling evidence of tax cuts impacting growth through the supply side, consistent with neoclassical economic theory.

Ljungvist and Smolyansky (2018) look at 250 state corporate tax changes from 1970-2010 to assess their impact on employment and income. By comparing nearby counties across states, this allows the authors to isolate the impacts of corporate tax changes relative to other policies that might affect economic growth. They find that a 1 percentage-point cut in statutory corporate tax rates leads to a 0.2 percent increase in employment and a 0.3 percent increase in wages. They find that tax increases are almost uniformly harmful, while tax cuts seem to have their strongest positive impact during recessionary environments. As with some of the other studies discussed here, the paper mainly examines short-runs effects, and it is possible that these positive effects could grow over a longer time horizon.

Gunter et al. (2019) use a data set of 51 countries from 1970-2014 to examine the impacts of value-added taxes (VAT) on economic growth. They find that the effect of taxes on growth are highly non-linear: At low rates with small changes, the effects are essentially zero, but the economic damage grows with a higher initial tax rate and larger rate changes. For this reason, increases in the VAT in countries with high VAT rates, such as much of industrialized Europe, will have more significant impacts on GDP than increases in countries with low VAT rates. These non-linearities imply strong Laffer curve effects: At certain tax rates, further increases beyond that point will actually reduce federal tax revenues. For European industrialized countries, the authors estimate a tax multiplier of -3.6 for two years after a tax change, suggesting that tax cuts strongly stimulate economic activity in these countries.

Nguyen et al. (2021) examine the effects of individual income, corporate, and consumption taxes in the United Kingdom from 1973-2009. They find that income tax cuts, defined in their paper as an aggregate of individual and corporate income, have large effects on GDP, private consumption, and investment. A percentage-point cut in the average income tax rate raises GDP by 0.78 percent. The effects of consumption tax A consumption tax is typically levied on the purchase of goods or services and is paid directly or indirectly by the consumer in the form of retail sales taxes , excise taxes , tariffs , value-added taxes (VAT) , or an income tax where all savings is tax- deductible . cuts are comparatively smaller and did not produce statistically significant effects, but the paper finds that switching from an income to a consumption tax base The tax base is the total amount of income, property, assets, consumption, transactions, or other economic activity subject to taxation by a tax authority. A narrow tax base is non-neutral and inefficient. A broad tax base reduces tax administration costs and allows more revenue to be raised at lower rates. has positive effects on growth. Consumption taxes are generally viewed as less distortionary than other forms of taxation, as they do not significantly impact incentives to work and invest that are essential for ensuring long-run economic growth.

Cloyne et al. (2018) study the interwar period of the UK, 1918-1939, a period of high debt and low interest rates, to understand the impact of taxes on economic growth. The British tax system at this time consisted largely of excise tax An excise tax is a tax imposed on a specific good or activity. Excise taxes are commonly levied on cigarettes, alcoholic beverages, soda , gasoline , insurance premiums, amusement activities, and betting, and typically make up a relatively small and volatile portion of state and local and, to a lesser extent, federal tax collections. es on alcohol, tobacco, and motor vehicles, and to a lesser degree taxes on income and corporate profits. As this time period predates the development of Keynesian macroeconomic theory, tax changes were generally not designed to be countercyclical, but rather focused on balancing the budget, inequality, or enhancing productivity. The authors find that a 1 percentage-point reduction in taxes as a share of GDP increased GDP between 0.5 to 1 percent, rising to 2 percent after one year. While the British economy of a century ago vastly differs from modern economies, this paper does provide compelling evidence of how taxes impact growth in high debt and low interest rate environments.

Alinaghi and Reed (2021) conduct a meta-analysis on the effects of taxes on growth for OECD countries. Their sample includes 979 estimates from 49 studies. Unlike other papers discussed in this review, this paper considers both the effects of taxes and spending on growth. The authors disaggregate policy changes into three categories: tax negative fiscal policies, tax positive fiscal policies, and tax ambiguous fiscal policies. Tax negative fiscal policies include increases to fund unproductive investments, or increases in distortionary taxes combined with a decrease in non-distortionary taxes. Tax positive fiscal policies include tax increases to fund productive investment, decreases in distortionary taxation combined with increases in non-distortionary taxation, or tax increases to reduce the deficit. Tax ambiguous fiscal policies are those where the overall economic effect is unclear. Using these classifications, the authors find a 10 percent decrease in taxes of a tax negative fiscal package increases GDP growth by 0.2 percent. The same sized tax decrease for tax positive fiscal policies reduces GDP growth by 0.2 percent.

Localizing the economic impact of research and development

Fifty policy proposals for the trump administration and congress, stephen ezell and stephen ezell vice president, global innovation policy - the information technology and innovation foundation scott andes scott andes former fellow - brookings centennial scholar initiative.

December 7, 2016

• 109 min read

The following paper is the product of a joint research effort between the Brookings Institution’s Anne T. and Robert M. Bass Initiative on Innovation and Placemaking and the Information Technology and Innovation Foundation .

The investments government and businesses make in basic and applied research and development (R&D) plant the seeds for the technologies, products, firms, and industries of tomorrow. They contribute substantially to the fact that at least one-half of America’s economic growth can be attributed to scientific and technological innovation. 1 But the increased complexity of technological innovation as well as the growing strength of America’s economic competitors mean that it’s no longer enough to simply fund scientific and engineering research and hope it gets translated into commercial results. The U.S. government needs to expand federal support for research and, just as important, it needs to improve the efficiency of the process by which federally funded knowledge creation leads to U.S. innovation and jobs. 2

This report provides 50 policy actions the Trump administration and Congress can take to bolster America’s technology transfer, commercialization, and innovation capacity, from the local to the national level. These recommendations include:

• Prioritize innovation districts within federal R&D outlays
• Task federal laboratories with a local economic development mission
• Create off-campus “microlabs” to provide a front door to labs
• Support technology clusters by assessing and managing local-level federal R&D investments
• Assess federal real estate holdings and reallocate physical research assets to innovation districts
• Allow labs to repurpose a small portion of existing funds for timely local collaboration
• Standardize research partnership contracts within cities
• Create NIH regional pre-competitive consortia to address national health concerns
• Allow DOE labs to engage in non-federal funding partnerships that do not require DOE approval
• Dismantle funding silos to support regional collaboration
• Incentivize cross-purpose funding based on the economic strength of cities
• Expand the national Regional Innovation Program
• Support the innovation potential of rural areas
• Facilitate regional makerspaces
• Introduce an “Open Commercialization Infrastructure Act”

Bolster institutions supporting tech transfer, commercialization, and innovation

• Establish a core of 20 “manufacturing universities”
• Complete the buildout of Manufacturing USA to 45 Institutes of Manufacturing Innovation (IMIs)
• Create a National Engineering and Innovation Foundation
• Create an Office of Innovation Review within the Office of Management and Budget
• Create a network of acquisition-oriented DoD labs based in regional technology clusters
• Establish manufacturing development facilities
• Establish a foundation for the national energy laboratories

Expand technology transfer and commercialization-related programs and investments

• Increase the importance of commercialization activities at federal labs/research institutes
• Allocate a share of federal funding to promote technology transfer and commercialization
• Develop a proof-of-concept, or “Phase Zero,” individual and institutional grant award program within major federal research agencies
• Fund pilot programs supporting experimental approaches to technology transfer and commercialization
• Support university-based technology accelerators/incubators to commercialize faculty and student research
• Allow a share of SBIR/STTR awards to be used for commercialization activities
• Increase the allocation of federal agencies’ SBIR project budgets to commercialization activities
• Modify the criteria and composition of SBIR review panels to make commercialization potential a more prominent factor in funding decisions
• Encourage engagement of intermediary organizations in supporting the development of startups
• Expand the NSF I-Corps program to additional federal agencies
• Authorize and extend the Lab-Corps program
• Provide federal matching funds for state and regional technology transfer and commercialization efforts
• Incentivize universities to focus more on commercialization activities
• Establish stronger university entrepreneurship metrics
• Expand the collaborative R&D tax credit to spur research collaboration between industry and universities and labs
• Increase funding for cooperative industry/university research programs at universities
• Establish an International Patent Consortium

Promote high-growth, tech-based entrepreneurship

• Encourage student entrepreneurship
• Help nascent high-growth startups secure needed capital
• Establish an entrepreneur-in-residence program with NIH
• Implement immigration policies that advantage high-skill talent
• Implement a research investor’s visa

Stimulate private-sector innovation

• Implement innovation vouchers
• Incentivize “megafunds” around high-risk research and development
• Increase R&D tax credit generosity
• Ensure that small and medium-sized enterprises are familiar with available R&D tax credits
• Implement an innovation box to spur enterprises’ efforts to commercialize technologies
• Revise the tax code to support innovation by research-intensive, pre-revenue companies

Introduction

Innovation is key to increasing economic growth and wages in the moderate to long run. Yet innovation does not fall like “manna from heaven,” as economists once suggested. It is the product of intentional human action, and, to have more of it, we must enact public policies that connect research and development investments to firms and inventors in the communities where they are located.

After seven years of growth following the end of the Great Recession and after over 70 straight months of employment growth, there is a case to be made that the country has rebounded and the main thrust of economic policy should focus on those who have been left behind. But the reason so many Americans aren’t seeing their wages rise fast enough isn’t just because they’ve been left behind, it’s because the country as a whole isn’t moving ahead fast enough.

It’s certainly true the labor market has begun to inch closer to full employment (in fact, in December 2016 the unemployment rate dropped to 4.6 percent), but that’s far from a leading indicator of the health of the U.S. economy. For the reality is the economy still has a long way to go to return to its full potential. Employment growth in the 36 months following the trough of the recession was the slowest of the 11 post-World War II recoveries, and average productivity growth was twice as high in the four decades following World War II as it has been since the end of the Great Recession. 3 Brookings economists Martin Baily and Nicholas Montalbano describe the country’s productivity growth as “weak since 2004 and dismal since 2010.” 4 And as the Information Technology and Innovation Foundation (ITIF) reports, U.S. productivity growth over the last decade is the lowest since the government started recording the data in the late 1940s. 5 Yet if the United States could boost its productivity levels by even just one percentage point, it could make the economy $2.3 trillion bigger than it is otherwise projected to be in 10 years while shrinking the federal budget deficit by more than $400 billion. 6

America’s innovation economy exists at three levels: technological, industrial, and spatial.

Meanwhile, other countries are increasing their technological sophistication, capturing crowded international markets and pushing U.S. firms—and, by extension, U.S. workers—behind. And whereas once America’s leading technology competitors were largely isolated to Western Europe and Japan, today many developing nations are crafting innovation strategies designed to wrest leadership in advanced technology categories such as life sciences, clean energy, new materials, flexible electronics, computing and the internet, and advanced manufacturing. As evidence of these trends, the United States has run a trade deficit in advanced technology products every year since 2002; the cumulative deficit since 2010 is $580 billion. 7 Improving America’s capacity to innovate is a key step toward confronting these challenges.

America’s innovation economy exists at three levels: technological, industrial, and spatial. Much innovation occurs in particular technology areas, for example life science innovation funded by the National Institutes of Health (NIH), additive manufacturing supported by America Makes, and composite and lightweight materials supported by the Institute for Advanced Composites Manufacturing Innovation (IACMI) and the Lightweight Innovations for Tomorrow (LIFT) Institutes for Manufacturing Innovation, respectively. Innovation also occurs across firms in the same industries that collaborate to drive technology advancements (e.g., aerospace and automotive). For this reason, sector- and technology-based innovation policies and programs like Manufacturing USA’s Institutes of Manufacturing Innovation and the Advanced Research Projects Agency-Energy do an effective job targeting R&D dollars.

The spatial level of innovation includes not just hot spots like Silicon Valley; Austin, Texas; or Boston, but also scores of communities throughout the country in places like Chattanooga, Tenn.; Denver; Minneapolis; Mobile, Ala.; and Pittsburgh, Pa. which are intensively developing their innovation ecosystems at the regional level. Indeed, as ITIF has shown, innovation occurs in all of America’s 435 congressional districts. 8

This dispersion matters because regional technology clusters engender concentrated knowledge flows and spillovers, workers with specialized skills, and dense supply chains that improve firm productivity. Many R&D-intensive firms benefit from proximity to innovation resources such as universities and federal laboratories, and this closeness produces myriad “ecosystem” benefits. 9

This is particularly the case for knowledge spillovers—the ability of workers and firms to learn from one another without incurring costs. Recent research shows that the value of proximity for firms and workers to share ideas attenuates extremely quickly with distance. For example, Rosenthal and Strange find that, for software companies, the spillover benefits are 10 times greater when firms are within one mile of each other than when they are two and five miles apart, and by 10 miles there are no more within-city localization benefits. 10

In other words, to be effective, technology policy needs to focus not just on the first two levels, technology and industry, but also on the spatial—the regional. Thus, if America’s innovation economy is to function maximally, Washington needs to promulgate smart policies and initiatives that effectively work in concert at the city, regional, state, and national levels.

The central component of an effective national technology policy system is robust government funding of scientific and engineering research. But in that respect, the United States is failing. If the federal government invested as much in R&D today as a share of GDP as it did in 1983, we would be investing over $65 billion more per year. 11 Unfortunately, given budget and political constraints, the Trump administration and the forthcoming 115th Congress may find it difficult to significantly increase overall federal investment in science and technology. This despite the fact that doing so would be a wise investment, as economists estimate that a 1 percent increase in the U.S. R&D capital stock improves GDP by 0.13 percent. 12 But regardless, one thing on which America should be able to achieve bipartisan consensus is the need to find ways to increase the return on investment from existing resources and programs.

What follows are 50 policy recommendations President Trump and Congress can enact to improve the economic impact of existing resources (with some modest additional investments). Many of these recommendations could be added to the COMPETES-related reauthorization legislation currently being considered in both the House and Senate. The recommendations are divided into five categories: strengthening innovation districts and regional technology clusters; launching or extending institutions supporting America’s innovation economy; facilitating technology transfer and commercialization activities; promoting the formation of high-growth firms; and stimulating private-sector innovation. These recommendations are the output of a joint research effort between the Brookings Institution and ITIF.

Why and how federal R&D policy impacts local economies

The federal government invests $146 billion a year in R&D, and whether these dollars are directed to military bases, federal laboratories, universities, or small technology firms, they come to ground in communities and play a critical role in local technological capacity. Federal investments often drive high-skilled employment, fund local universities and hospitals, support high-tech entrepreneurs, and lead to exports from large companies—all of which bring outside dollars and jobs into a region.

To maximize and capture the benefits of R&D within regional economies, mayors, regional economic developers, and philanthropic and private-sector leaders should understand their federal research portfolio. Indeed, regions should take stock of their portfolios as they would any other asset class. To do so, regional leaders need to understand how the federal government funds research.

The government allocates R&D through federal agencies. While most agencies have some level of R&D budget, 84 percent of funding flow from the Department of Defense (DoD), the Department of Health and Human Services (DHHS), the Department of Energy (DoE), and the National Science Foundation (NSF). These agencies have different areas of investment and different funding vehicles that impact local economies.

The Department of Defense: With 49 percent of all federal R&D, DoD represents the largest federal investor in research. But DoD’s size is not the only reason the department matters for local communities. No other federal agency has such a quasi-fiduciary relationship with the commercial outcomes of its own R&D funding. DoD pursues basic and applied research through its dozens of labs located in 22 states and then transfers that research to firms that create products and services for the military. For regions, DoD funding often implies near-to-market engineering, computer science, and material research that local firms can utilize to meet defense and civilian needs. Yet research partnerships are conducted predominantly through large defense contractors and less often with small and medium-sized firms. 13

The Department of Health and Human Services : DHHS invests over $32 billion every year in research, the vast majority of which is conducted by and through the National Institutes of Health. The primary vehicle for NIH R&D is competitive grants: currently more than 80 percent of NIH funding is awarded through 50,000 grants to more than 300,000 researchers at universities, medical schools, and other research institutions. NIH research dollars touch every state and almost every city, and so the agency is ideally situated to play an important role in improving the return on investment of federal R&D at the local level. Also, because the lion’s share of investment comes from NIH’s grants to research universities and medical schools, as opposed to being spent at its own labs, NIH is in a unique position to incentivize commercialization across the U.S. university system. Finally, through its investments in teaching hospitals, NIH represents a critical employment driver for local communities.

The Department of Energy: DoE invests heavily in its 17 federal laboratories across the country. Though the labs are not located in dense regional technology clusters, they exist at the frontiers of science and often partner with universities, firms, and other research institutions to improve product development in industries such as aerospace, automobiles, battery storage, and information technology. Regions with companies and institutions that have DoE partnerships are often at the cutting edge of technology and are ideally situated for high-value technology exports.

The National Science Foundation: NSF is an independent federal agency that invests specifically in basic science and engineering and scientific education. Unlike other agencies that focus on specific missions (e.g., defense, health, energy), NSF has a broad mandate to fund discovery, learning, and the research infrastructure across scientific domains. Like NIH, the primary funding vehicle for NSF is its competitive grants that are distributed across the nation’s educational, training, and research institutions. NSF represents roughly one-quarter of federal investments in basic science at U.S. universities and colleges

By understanding what government funding flows to their respective regions and then how to leverage agencies’ distinct funding vehicles, leaders can better maximize the local influence of R&D.

Strengthen innovation districts and regional technology clusters

Regional technology clusters are a key driver of economic growth and should be viewed by the incoming administration and Congress as a critical component of innovation policy. Large-scale manufacturing clusters can be found in suburban research parks and key agriculture technology clusters in many rural areas throughout the United States.

In many technology sectors—particularly life sciences, software and digital design, and robotics—the geography of innovation is changing. Firms in these industries are now beginning to relocate research activities into employment-dense areas of cities (generally the downtowns and midtowns) to be in greater proximity to other firms, universities, and research labs. 14 Companies are also realizing that attracting and retaining talented workers increasingly means situating themselves in amenity-rich places where their workers want to live. The result has been a rise of “innovation districts,” defined by the Brookings Institution as “geographic areas where leading anchor institutions and companies cluster and connect with entrepreneurs. They are physically compact, transit- and broadband-accessible, and offer mixed-use housing, office, and retail. 15

Innovation districts are critical to the nation’s innovation capacity because they are home to some of the country’s leading universities, research labs, and high-value companies and they generate outsized economic output. For example, research universities located within employment-dense areas of cities outperform their rural and suburban peers in terms of number of patents, invention disclosures, licensing revenue, and startups per student. 16  But federal laboratories built in the shadow of World War II are often located far from firms and cities and have difficulty impacting regional economies. And too often cluster policy receives lip service from Washington, with little actual attention paid to how the federal government can accelerate the economic capacity of regional economies. Reconfiguring the federal government’s $146 billion annual R&D investment portfolio to achieve greater economic outcomes should therefore be a prime objective of national policy.

In order to strengthen innovation districts and other regional technology clusters, the next administration should work with Congress on the following goals:

1. Prioritize innovation districts within federal R&D outlays

Federal agencies that fund R&D should prioritize innovation districts because the density of corporate research centers and entrepreneurs increases the likelihood that research will lead to commercial outcomes. Moreover, Federally Funded R&D Centers (FFRDCs) and University Affiliated Research Centers (UARCs) should be assessed in part based on their proximity to corporate research and employment density, and federal grants in engineering, computer science, life sciences, and other similar fields should prioritize academic institutions located within innovation districts. Of course, the geographic location of research assets is not the ultimate determinant of economic impact, but co-location and density are important and should be a consideration for all funding agencies.

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2. Task federal laboratories with a local economic development mission

Federal agencies such as DoD, DoE, DHHS, and the NSF that own and fund federal laboratories and FFRDCs should adopt an explicit mission to support the regional economies in which they are located. Many lab managers and agencies approach regional economic development as mutually exclusive from their core missions; this is especially true for weapons labs located within the Departments of Defense and Energy. But defense and weapons labs like Sandia and Los Alamos in New Mexico have successfully integrated regional economic development programs within their broader research objectives.

For example, both labs have partnered with the state of New Mexico on the New Mexico Small Business Assistance Program, which connects small businesses seeking technical assistance with lab researchers. 17 Every federal agency and federal lab should view regional economic development as part of its overarching mission. Moreover, increasing the technical capacity of the regions in which labs are located is mutually beneficial for the labs and the local economy. Moreover, given the mobility of the scientific workforce, creating homegrown talent helps labs address attrition.

3. Create off-campus “microlabs” to provide a front door to labs

Federal funding agencies, state governments, and regional consortia that utilize the lab system should work together to create and co-fund a number of off-campus, small-scale “microlabs”—co-located within or near universities or private-sector clusters—that would cultivate strategic alliances with regional innovation clusters. Microlabs would help overcome the problems that most labs are located outside of technology clusters and that most lab research occurs behind the walls of main campuses. These microlabs could take the form of additional joint research institutes or new facilities that allow access to lab expertise for untapped regional economic clusters. Accessible, off-campus lab space would also help labs engage with small to medium-sized enterprises (SMEs). The next administration should work to create microlabs and require state buy-in, or state governments or regional consortia could create voucher programs in concert with DoE and particular labs.

Several federal labs are already creating microlabs in cities; for example, Argonne National Laboratory has created office space in the Chicago Innovation Exchange, located on the University of Chicago’s Hyde Park campus. Another example is Cyclotron Road, a program of Lawrence Berkeley National Laboratory funded by the DOE EERE Advanced Manufacturing Office, which provides assistance to entrepreneurial researchers to advance technologies until they can succeed beyond the research lab. Cyclotron Road plays a pivotal role in providing entrepreneurs with technology development support (often leveraging technologies coming directly out of the Lawrence Berkeley laboratory) and helps them with identifying the most suitable business models, partners, and financing mechanisms for long-term impact. 18 Beyond external offices, microlabs can serve as funding gateways to align multiple public and private research dollars to meet industry needs.

4. Support technology clusters by assessing and managing local-level federal R&D investments

The $146 billion invested by the federal government in R&D takes place within specific institutions within communities, and these resources often dwarf the research investments and research-driven employment of non-federal companies and institutions. But federal research dollars do not necessarily pass through local political, civic, or private-sector leadership. As such, mayors, chambers of commerce, and philanthropies are often unaware of the innovation portfolio of their regions. The issue is most pronounced in large cities that can have over a billion dollars flowing annually from Washington. Without understanding their regional innovation portfolios, regions cannot coordinate and maximize federal investment for local economic growth.

To address this knowledge barrier, the federal government should help regions understand their research inflows by packaging their federal dollars by institution, areas of science, connections to global markets, and other data points. However, the federal government will never be able to whole cloth catalog what regions need to know about their innovation assets. Therefore, the government should also fund and advise regional innovation asset inventory and management assessments that are tailored to the specific economic development goals of individual communities.

5. Assess federal real estate holdings and reallocate physical research assets to innovation districts

The federal government owns billions of dollars’ worth of real estate that houses operations from post offices to federal laboratories. There is no national registry of these holdings and little information regarding their commercial value. Many of these physical operations were created before innovation districts and other technology clusters came into existence and are poorly placed to take advantage of the agglomeration benefits of cities.

The Trump administration should task the General Services Administration with identifying federally owned real estate parcels and strategically move research-intensive activities into existing federal buildings in cities. Agencies should also be able to register unused space within their own research institutions to identify and allocate vacant space for regional entrepreneurship and private-sector use. Congressional appropriation committees have traditionally been skeptical of allowing federal labs discretion on the use of space, but allowing lab managers to contract out unused space would increase the flexibility and regional responsiveness of the lab system. For example, Amtrak operates an office building in the heart of the Philadelphia innovation district, just a few blocks from Drexel University and the University of Pennsylvania. Amtrak does no research and extracts little benefit from being near major research universities; on the other hand, NIH, DoD, and NSF operate or fund numerous facilities that would greatly benefit from such a location. One mechanism for better allocating physical assets would be to create an intra-governmental auction whereby agencies could identify strategically located federal buildings and bid on these parcels. Agencies like Amtrak that don’t value their legacy locations in cities could sell such buildings to agencies that would benefit, creating a market dynamic within the federal government.

6. Allow labs to repurpose a small portion of existing funds for timely local collaboration

Increasing collaboration between regional universities and tech-based entrepreneurs and corporate partners requires greater flexibility in funding contracts. The next administration should allow federal labs to set aside a small amount—perhaps 5 percent—of fiscal year funding for unexpected research partnerships that may emerge throughout the year and that clearly align with lab mission and research goals. Labs would not be required to reserve these funds, nor be required to invest in regional partnerships, but interested labs would have the option. Similar repurposing rules should be encouraged for all federal funding opportunity announcements (FOAs) intended for federal labs.

7. Standardize research partnership contracts within cities

Virtually all innovation districts cluster numerous research institutions, but each one has its own rules relating to the commercialization of research. Cities should work to develop standardized partnership contracts that all research facilities can adopt to help researchers access the full spectrum of activity within a city. For example, in Philadelphia, the Wistar Institute—a National Cancer Institute-designated Cancer Center—has created a simple, standard contract for research partnerships that has been adopted by a number of medical schools in the city. The federal government should incentivize cities with multiple academic medical centers, federal labs, universities, and research institutions to develop standardized, simple research partnership agreements. Their development could either occur through pilot grants from the Economic Development Agency or directly through federal R&D funding agencies, such as NIH. The latter may be particularly effective given that in many cities research institutions with similar areas of expertise receive federal funding from the same federal agencies.

8. Create NIH regional pre-competitive consortia to address national health concerns

Given that over 80 percent of NIH R&D funding is allocated through its more than 50,000 grants across the country, the agency is ideally situated to support regional technology development. However, most NIH research grants don’t directly incentivize partnerships that lead to collaboration—particularly at the institutional-leadership level (e.g., for universities, the provost of research or president level). Rather, most collaboration around NIH grants occurs at the principal investigator level. While partnerships between researchers are important, more can be done to stimulate research-based partnerships between the public, civic, and private sectors.

To improve the commercial impact of research grants, the next administration should support regional pre-competitive consortia to address national health concerns. When applying for NIH grants, research institutions should be incentivized to coordinate with peers in their region. Making the consortia pre-competitive (i.e., uninvolved in patent development) will help to avoid intellectual property disputes and allow the efforts of its members to dovetail more closely with the academic missions of NIH research grants. One way to further incentivize partnerships would be to give grant proposals extra weight if multiple technology transfer offices, private-sector actors, and others within a city are designated as principal investigators. NIH already supports some pre-competitive consortia at the national level, such as the Accelerating Medicines Partnership and within its Clinical and Translational Science Awards, but doing so even more within technology clusters at the local level would enable research institutions to take advantage of proximity to form more long-lasting partnerships. 19

9. Allow DOE labs to engage in non-federal funding partnerships that do not require DOE approval

Currently, DoE must approve all non-DoE lab funding; this model is out of date, given that external funding is not trivial. For example, Oak Ridge National laboratory (ORNL) and Pacific Northwest National laboratory (PNNL) already receive 50 percent and 80 percent of their respective budgets from outside their DoE offices (though the majority of funding still comes from the federal government from agencies such as DoD). DoE should acknowledge that today’s multidisciplinary lab work requires varied funding sources. As labs increase their relevance to regional technology clusters, DoE should allow non-federal funding partnerships at lab managers’ discretion. Initially, DoE could specify a minimum amount of regional funding to be drawn from non-federal sources without its approval, and then gradually expand the minimum. 20

10. Dismantle funding silos to support regional collaboration

Stove-piped appropriations keep lab research projects unnecessarily compartmentalized and hinder lab managers from responding to regional demands. Labs should be funded to encourage broad, flexible engagements with numerous public- and private-sector actors. To this end, Congress and DoE should reorganize lab funding to mimic the financial design of Manufacturing USA (formerly known as the National Network for Manufacturing Innovation) or DoE’s energy hubs, institutions through which large, unencumbered appropriations are directed to complex, multidisciplinary regional technology and economic issues.

11. Incentivize cross-purpose funding based on the economic strength of cities

Like countries, cities and states specialize in technologies and industries. However, federal R&D funding agencies often ignore the potential interplay between seemingly discrete technologies, and doing so dampens the innovative potential of innovation districts. For example, Houston is an epicenter of the oil and gas and the health care industry, but little of the $160 million DHHS invests annually in the University of Texas MD Anderson Cancer Center considers what the health care field can learn from oil and gas. On the ground, researchers, medical professionals, and industry leaders in Houston recognized the potential for cross-pollination between these two areas of specialization and created “Pumps & Pipes,” an association of medical, energy, aerospace, and academic professions with the stated goal of problem solving through “using the other guy’s toolkit.” 21

Federal agencies should map the research and industrial comparative advantages of cities and create cross-agency funding opportunities in those areas. They should seek similar synergies with state-based technology-based economic development organizations, through which individual states focus on a few core technologies for economic development advantage.

12. Expand the national Regional Innovation Program

Regional innovation programs have proven a highly successful form of economic development for communities across the United States. 22 Programs such as the i6 Challenge and the Jobs and Innovation Accelerator Challenge have helped local, regional, and state entities leverage existing resources, spur regional collaboration, and support economic recovery and job creation in high-growth industries. The Regional Innovation Program operated by the Economic Development Administration identifies and supports regional innovation clusters, convenes relevant stakeholders, creates a cluster support framework, disseminates information, and provides targeted capital investments to spur economic growth. 23 There is great demand for this program from regions all around the nation, but in 2015 just $15 million in grants were awarded. More funding is needed, and more needs to be done to support regional innovation programs in the United States. Accordingly, the next administration and Congress should expand funding for the Regional Innovation Program to as much as $75 million. 24

13. Support the innovation potential of rural areas

While the vast majority of technology development, commercialization, and innovation occurs in cities and metropolitan regions, the innovation potential of more rural areas should not be neglected, both for these areas’ own economic growth prospects and for the contributions they can make to America’s innovation system. For example, consider the Natural Resources Research Institute (NRRI) located at the University of Minnesota Duluth. NRRI is a non-profit applied research organization, chartered by the Minnesota legislature, that works to develop and deliver the understanding and tools needed to better utilize Minnesota’s mineral, forest, energy, and water resources in a way that expands value-added and jobs in rural communities. 25 Other programs that support rural technology entrepreneurship and manufacturing include the Ben Franklin Technology Partners of Central and Northern Pennsylvania, which funds young companies and provides professional assistance in areas like prototype development and customer site visits. 26

But the next administration could support a network of institutes such as NRRI nationwide across more sectors, including aquaculture, agriculture, wind and water energy, and mining. One idea would be to have the U.S. Department of Agriculture (USDA) lead a major technology initiative around getting more value-added out of rural communities, whether from fish, fiber, food, wind, water, etc. Such a program, perhaps in coordination with the U.S. Department of Commerce’s Manufacturing Extension Partnership (MEP), could also build on and support existing rural manufacturing clusters, such as snowmobiles in northern Minnesota, wine in Western New York, or shipbuilding in Michigan. One aspect of this could be supporting rural Internet of Things projects, such as pilot programs for farms and vineyards. 27

14. Facilitate regional makerspaces

Makerspaces are community centers that combine manufacturing equipment and education for the purposes of enabling community members to design, prototype, and create manufactured works that couldn’t be created with the resources available to individuals working alone. 28 But well-staffed and programmed makerspaces are located disproportionately in large cities.

To more fully realize regional innovation potential, especially in manufacturing, the federal government should support a Public Library Makerspace grant program that enables the use of libraries not only for public education but also for economic development. Such a program would democratize the maker movement into communities that are traditional laggards in technology infrastructure, like broadband. This approach would make more widely available so-called lower-level innovation infrastructure (e.g., 3-D printing capability) that could seed innovations that ultimately feed into universities or federal labs. Another proposal to expand access to makerspaces is proposed legislation (in the House, H.R. 1622, in the Senate, S. 1705) that calls for a federal charter to launch a non-profit “National Fab Lab Network” (NFLN). 29 NFLN would act as a public-private partnership whose purpose is to facilitate the creation of a national network of fabrication labs and serve as a resource to assist stakeholders with their operations. The network would be comprised of local digital fabrication facilities providing community access to advanced manufacturing tools for learning skills, developing inventions, creating businesses, and producing personalized products. 30

15. Introduce an “Open Commercialization Infrastructure Act”

Another way to increase the use of America’s national R&D infrastructure would be through an Open Innovation Infrastructure Act, which would permit the private use of public-funded equipment and facilities—including universities, federal labs, and public libraries—for certain activities related to entrepreneurial education and training as well as for economic development and job creation. At present, buildings financed through tax-exempt bonds are not permitted to develop private programming within the facility, even though many private operations—such as incubators, accelerators, and training programs—that benefit entrepreneurs and others are important for broader economic development. For example, a small business that would like to use a 3-D printer in a makerspace at a public library to develop a commercial product is restricted from doing so. Such an Open Innovation Infrastructure Act would remove many such barriers.

Some worry the concept of innovation districts is just the latest urban fad, but there is nothing new about the economics of clusters and agglomeration; they have been studied by economists for over a century. Just as research parks defined much of the geography of innovation over the last half of the 20th century, innovation districts and other technology clusters are becoming emblematic of this century’s spatial science and technology research. The next administration should consider innovation districts and other regional clusters of technology generation (rural, suburban, and urban)—as strategic assets in the same vein as federal laboratories, military research facilities, and the university system. These institutions would not exist as they do without longstanding, substantial support from the federal government. The new president should add innovation districts to the list of national treasures that are supported and nurtured by the federal government, in partnership both with cities and with state technology-based economic development organizations.

In the private sector, firms need to innovate to respond to competition. Likewise, the competition for innovation leadership among nations has only grown fiercer. 31 Throughout its history, the United States has responded to international economic competition by chartering new institutions to bolster its innovation economy. For instance, the Morrill Act of 1862 chartered new universities in the agricultural and mechanical arts. 32 In the 1980s, the United States launched Sematech (a semiconductor research consortium) and the Manufacturing Extension Partnership in part as a response to heighted German and Japanese economic competition. The Obama administration launched Manufacturing USA in part to address the erosion of America’s industrial commons. Meanwhile, America’s global competitors have launched new institutions of their own, as documented in ITIF’s report, The Global Flourishing of National Innovation Foundations, which catalogued the efforts of almost 50 nations in chartering national innovation foundations and articulating national innovation strategies. 33 Yet the United States still lacks a national innovation foundation. Addressing that need and other proposals to expand the institutions underpinning America’s innovation economy are considered below.

16. Establish a core of 20 “manufacturing universities”

Across many American universities, the focus on engineering as a science has increasingly moved university engineering education away from a focus on real-world problem solving toward more abstract engineering questions, leaving university engineering departments more concerned with producing pure knowledge than working with industry to help it solve problems. To address this, the United Sates should designate a core of at least 20 “manufacturing universities” that revamp their engineering programs to focus more on manufacturing engineering and on work that is relevant to industry. 34 This effort would include more joint industry-university research projects, more student training that incorporates manufacturing experiences through co-ops or other programs, and a Ph.D. program focused on turning out more engineering graduates who work in industry.

At these manufacturing universities, criteria for faculty tenure would consider professors’ work with or in industry as much as their number of scholarly publications. In addition, these universities’ business schools would integrate closely with engineering and focus on manufacturing issues, including management of production. The schools would also appoint a chief manufacturing officer, as Georgia Tech has done, to oversee universities’ interdisciplinary manufacturing programs and ascertain how they can maximize their impact on regional economic development. A good model for these manufacturing universities is the Olin College of Engineering in Massachusetts, which reimagined engineering education and curricula to prepare students “to become exemplary engineering innovators who recognize needs, design solutions, and engage in creative enterprises for the good of the world.” Olin’s students now launch more startups per graduate than even MIT.

The Manufacturing Universities Act seeks to establish a competitive grant program for universities that propose to revamp their engineering programs and to focus much more on manufacturing engineering and in particular work that is more relevant to industry. Academic institutions receiving a manufacturing university designation would be eligible for an annual award of up to $5 million for up to four years. 35 The Manufacturing Universities Act of 2015 was incorporated into the 2017 National Defense Authorization Act (NDAA) passed by the Senate in June 2016, but it was not included in the House’s version of the NDAA. Ideally, the conference version of the NDAA that comes out of committee would include the manufacturing universities legislative text. The next administration should make implementation of the manufacturing universities legislation a top priority, directing relevant agencies (notably NSF and the National Institute of Standards and Technology) to implement it swiftly and effectively.

17. Complete the buildout of Manufacturing USA to 45 Institutes of Manufacturing Innovation (IMIs)

Manufacturing USA, launched in 2013 as the National Network for Manufacturing Innovation by the Obama administration and endorsed on a bipartisan basis by Congress through the Revitalizing American Manufacturing Innovation Act, has played a pivotal role in revitalizing America’s industrial commons and helping ensure U.S. leadership across a range of advanced manufacturing process and product technologies. 36 Thus far, nine Institutes of Manufacturing Innovation have been launched, focused on additive manufacturing, digital manufacturing and design innovation, lightweight and modern metals, power electronics, advanced composites, integrated photonics, flexible hybrid electronics, clean energy smart manufacturing, and revolutionary fibers and textiles.

As of December 2016, six more IMIs are under development, including two in a competition to be overseen by DoE (focused on Chemical Process Intensification and Sustainable Manufacturing), two expected to be led by the Department of Defense (focused on Regenerative Medicine and Assistive and Soft Robotics), and two more open topic competitions to be spearheaded by the Department of Commerce. The Obama administration has articulated a vision for a total of 45 IMIs. The Trump administration should collaborate with Congress to provide funding and authorization to build out the 45-institute network of industry-led Manufacturing USA institutes.

18. Create a National Engineering and Innovation Foundation

Science-based discoveries without a commercialization component mute the potential impact of R&D. Connecting discovery with production requires engineering-based innovation, an appropriable activity through which U.S. establishments can add and capture value. 37 And this requires the United States getting better at generating pathways that turn science into U.S.-made high-technology products. Engineering is not science; the two have distinctly different purposes. As Sridhar Kota, formerly assistant director for advanced manufacturing at the Office of Science and Technology Policy, writes, “Science is about analysis and discovery and dissemination of knowledge. Engineering is about synthesis and invention and turning ideas into reality through a process called innovation and through translational research and entrepreneurship.” 38 Both science and engineering are instrumental if American firms are to introduce successful innovations over the long term.

Yet the United States invests significantly more in scientific research than it does in engineering. For example, of the total federal research investments in science and engineering in 2008, approximately 14 percent were allocated to engineering development and the remainder to other scientific fields. 39 NSF invests roughly one-tenth on engineering education as it does on science and mathematics education.

Accordingly, it’s time to raise the profile of engineering within our national innovation system. While NSF supports phenomenal work, its primary mission is funding scientific research while its engineering support programs get short shrift. Therefore, the next administration should work with Congress to create a National Engineering and Innovation Foundation as a separate entity operating alongside the National Science Foundation. 40 The new National Engineering and Innovation Foundation would consolidate the current Engineering Directorate within NSF including the ERC and I/UCRC programs, the tech commercialization parts of the National Institute of Standards and Technology (e.g., including MEP and the Advanced Manufacturing Technology Consortia (AMTech) program), DoD’s Manufacturing Technology (ManTech) program, and DoE’s Advanced Manufacturing office into a single entity with an engineering and innovation focus.

19. Create an Office of Innovation Review within the Office of Management and Budget

Because federal agencies often propose regulations with little consideration given to their effect on innovation, Congress should task the Office of Management and Budget’s Office of Information and Regulatory Affairs with creating an Office of Innovation Review (OIR) to review proposed regulations to determine their effect not just on costs in the short term but also on innovation over the long term. OIR would have the specific mission of being the “innovation champion” within agency rule-making processes. 41 It would have authority to push agencies to either affirmatively promote innovation or to achieve a particular regula¨tory objective in a manner least damaging to innova¨tion. OIR would be authorized to propose new agency actions and to respond to existing ones, and could incorporate a “competitiveness screen” in its review of federal regulations that affect globally traded industries.

20. Create a network of acquisition-oriented DoD labs based in regional technology clusters

The Department of Defense is uniquely positioned to commercialize research from its over $70 billion of R&D investments annually because it invests with the intent of deploying R&D outcomes throughout its own operations. According to its own accounting, between 2000 and 2014 DoD paid private companies that had licensing arrangements with its labs $3.4 billion for military technology; during the same period, companies that licensed technology from DoD labs generated $20 billion in sales outside of DoD. 42 This is a positive outcome, because it suggests that even the licensing arrangements companies have with DoD that don’t end in procurement still generate broader economic impact. In other words, companies pay to use technology generated by DoD and then develop products and services around the technological discovery to meet defense as well as market needs.

This continuous cycle of development well positions the department’s R&D to impact the broader economy in general and regional clusters in particular. But the same report finds that the majority of licensing agreements are signed with a few large defense contractors, leaving many regions without such firms out of the game. 43 Moreover, as DoD seeks to acquire technologies beyond munitions, moving into areas such as software, material science, autonomous systems and vehicles, energy, and medical devices, it will need a broader scope of suppliers.

To increase the breadth of R&D-based procurement, the Trump administration should create a network of applied defense R&D facilities around regional technology clusters. 44 The network would be similar to Manufacturing USA but with numerous smaller centers that are highly focused around the virtuous cycle of firms working with DoD labs and creating products and services that meet military needs. DoD is already moving in this direction, in accordance with Secretary of Defense Ash Carter’s Third Offset strategy, which seeks to counter declining force sizes with the development of novel capabilities and concepts. 45 For example, the Defense Innovation Unit Experimental (DIUx) seeks to create bridges between the Pentagon and the commercial technology sector. It currently has locations in Silicon Valley, Boston, and Austin, Texas; last year it awarded 12 contracts worth $36.3 million. While DIUx is a good start, its budget is tiny compared to the changing demands for new technologies within the military. Accordingly, DoD should invest $500 million to develop 50 similar centers as technology platforms across the country. Given that DoD already operates dozens of laboratories across 22 states, in many cases existing labs could shift their research and commercialization strategies to better align with adjacent technology clusters. In other regions, the department would need to develop new assets.

21. Establish manufacturing development facilities

Oak Ridge National Laboratory in Tennessee operates the Department of Energy’s first manufacturing development facility (MDF), which focuses on assisting industry’s adoption of new manufacturing technologies that can lower production costs, speed time to market, and reduce energy consumption in manufacturing processes. The facility focuses on additive manufacturing (3D printing), carbon fiber and other composites, and new battery technologies and is also the location of the Institute for Advanced Composites Manufacturing Innovation, part of Manufacturing USA. 46 The MDF helps bridge basic research at Oak Ridge and the real-time commercial needs of industry. Also, because East Tennessee has historical technical strengths in composites and advanced manufacturing, the MDF is strategically positioned to amplify the region’s economy.

The next administration should create 20 additional manufacturing development facilities to bring to market the fruits of scientific and technical research discoveries made by federal laboratories run by DoD, DHHS, DoE, and other federal agencies. It is important to note that MDFs are not the same thing as manufacturing institutes; rather, they are specific lab departments, offices, or facilities that are either currently located behind the fence or new facilities that would traditionally be developed behind the fence. Therefore, relocating these assets would require less funding than developing new manufacturing institutes (which are also intended to meet different needs).

22. Establish a foundation for the national energy laboratories

A number of agencies—including USDA, the Department of Veterans Affairs, the Department of the Interior, NIH, the Food and Drug Administration, and DoD—have established foundations to provide them with more flexibility to accomplish their missions. These foundations are legally chartered to accept donations from alumni inventors and scientists, philanthropists, and high-wealth individuals to support research efforts in ways that federal and private funding alone cannot. Foundations are often highly capitalized, for example the foundation for the National Institutes of Health has a $100 million endowment and a $500,000 operating budget. Based off of the success of existing research foundations, the next administration should create a foundation for the national energy laboratories. Because many philanthropies are forbidden by their charters to fund overhead, and the federal lab system is congressionally mandated to charge overhead from donations, a foundation for the national energy labs could serve as a funding intermediary between the civic sector and federal labs. The foundation could also endow research chairs around areas of national interest, help support moving translational research to market, and even fund and take equity in startups.

If the United States wishes to keep pace in the increasingly intense competition for global innovation leadership, it will need to evaluate its existing base of institutions underpinning America’s innovation system and consider new ones that can play important roles in bolstering the country’s levels of technology transfer, commercialization, and innovation. In launching the Manufacturing USA network of Institutes of Manufacturing Innovation, the United States has shown a commendable ability to do so, but it alone is not enough and continued institutional innovation will be needed going forward.

Publicly funded research institutions—federal laboratories, universities, academic hospitals, military and space laboratories, and non-profit research centers—represent core assets in the U.S. innovation system. Not only do these institutions push the frontiers of science, they are anchors of regional economic growth. While the charters of many of these facilities are related to mission-oriented, non-economic public priorities, their activities are deeply tied to the future of the American economy. Strong R&D in defense supports aerospace and materials science industries, clean energy research promotes clean technologies such as wind turbines and new batteries, and scientific advances in public health lead to drug discoveries and health information technology platforms, to name but a few examples. These institutions also train and employ current and future generations of scientists and engineers. However, realizing the economic potential of R&D activities is no sure thing. In order for university and lab research to reach the market, these institutions must be supported by strong policies, incentives, and funding streams that collectively make commercialization a priority.

To date, the efficacy of technology transfer mechanisms at federal laboratories and federally supported universities is mixed. 47 Some labs and universities have elevated the importance of technology transfer and put in place creative and impactful policies to promote commercialization in their economic regions. For example, in 2015 the Oak Ridge National Laboratory established an innovation voucher program to enable technical assistance to small and medium-sized manufacturers in the state. And universities such as MIT, Pepperdine, and Carnegie Mellon have strong track records of implementing flexible, business-friendly technology transfer agreements. Unfortunately, as the report Innovation U 2.0: Reinventing University Roles in a Knowledge Economy documents, there is little consistency and insufficient adoption of best practices across universities, federal laboratories, and funding agencies. 48

As the largest funder of federal laboratory and university research, the executive branch has an enormous opportunity to incentivize the commercialization of research. President Obama’s Lab-to-Market Initiative was a step in the right direction, but there is more to be done. In order to unleash the full economic power of federally funded universities and laboratories, the incoming administration should work with Congress in the following areas:

23. Increase the importance of commercialization activities at federal labs/research institutes

America’s federal laboratories are insufficiently incentivized to invest time, energy, and resources in facilitating technology transfer, in large part because technology transfer is not even one of the eight main criteria in the Performance Evaluation and Management Plan (PEMP), a kind of annual report card for the federal labs. 49 Rather, PEMP treats successful transfers of technology to market as an afterthought. Elevating this important function to its own category would have significant impacts on the management of the labs and help to reverse the buildup of decades of skepticism and intransigence toward commercialization. Adding a ninth category to the PEMP for “Technology Impact” would create a mechanism to evaluate the economic impact of lab-developed technology, creating a stronger incentive for lab managers to focus on market implementation of valuable government intellectual property assets and technical capabilities. 50

24. Allocate a share of federal funding to promote technology transfer and commercialization

The current federal system for funding research pays little attention to the commercialization of technology, and is based instead on the linear model of research that assumes that basic research gets easily translated into commercial activity. Yet the reality is that the innovation process is choked with barriers, including institutional inertia, coordination and communication challenges, and lack of funding for proof of concept research and other “valley of death” activities. Accordingly, federal policy should explicitly address this challenge and allocate more funding toward commercialization activities.

The incoming administration should work with Congress to establish an automatic set-aside program that takes a modest percentage of federal research budgets and allocates this money to technology commercialization activities. 51 For instance, the Information Technology and Innovation Foundation has suggested that Congress allocate 0.15 percent of agency research budgets (about $110 million per year) to fund university, federal laboratory, and state government technology commercialization and innovation efforts. 52

Such funds could be used to provide “commercialization capacity-building grants” to organizations pursuing specific innovative initiatives to improve an institution’s capacity to commercialize faculty research as well as “commercialization accelerator grants” to support institutions of higher education pursuing initiatives that allow faculty to directly commercialize research. 53 These funds could also support a variety of different initiatives, including mentoring programs for researcher entrepreneurs, student entrepreneurship clubs and entrepreneurship curricula, industry outreach programs, and seed grants for researchers to develop commercialization plans.

In addition, the incoming administration should broaden beyond universities the number of institutions that are eligible for commercialization funds. At the state and regional levels many organizations outside the university play a critical role in assisting faculty and students in the commercialization of research. Institutions like BioCrossroads in Indiana and TEDCO in Maryland offer mentorship, funding, and access to customers for research entrepreneurs. These organizations should be eligible for federal research dollars specifically aimed at technology transfer.

25. Develop a proof-of-concept, or “Phase Zero,” individual and institutional grant award program within major federal research agencies

The Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs support innovation, but both SBIR and STTR approval are a high bar for early-stage companies. There is often insufficient funding available at universities (or from other sources) to push nascent technologies to the point where these companies are positioned to receive an SBIR or STTR grant. The problem is essentially that researchers and universities do not have the resources available to support the proof-of-concept work, market analysis, and mentoring needed to translate ideas and nascent technologies from the university laboratory into a commercial product.

A national “Phase Zero” proof-of-concept program would not only help more projects cross the “valley of death,” but would also help enhance the infrastructure (e.g., expertise, personnel, support, small business, and venture capital engagement) and facilitate the cultural change necessary for universities, federal laboratories, and other non-profit research organizations to support commercialization activities.

America’s competitors have recognized the need for such an instrument. For instance, the European Research Council (ERC) has announced a new proof-of-concept funding initiative to help bridge the gap between ERC-funded research and the earliest stage of marketable innovations. 54 These awards can be as high as $215,000 for individual researchers, in total, equivalent to about 1 percent of ERC’s budget. 55 Here in the United States, the Wallace H. Coulter Foundation has established Translational Research (for individual researchers) and Translational Partnership (for institutions) Awards for proof-of-concept research in biomedical engineering. 56 The Translational Research Awards are made in amounts of approximately $100,000 per year, while the university grants have a duration of five years at over $500,000 per year.

Similarly, NIH’s Research Evaluation and Commercialization Hub (REACH) program fosters the development of therapeutics, preventatives, diagnostics, devices, and tools that address diseases within NIH’s mission in a manner consistent with business case development. The work supported by the REACH program may include technical validation, market research, clarification of intellectual property position and strategy, and investigation of commercial or business opportunities. 57 Finally, a number of states, such as Kentucky and Louisiana, have developed Phase Zero grants to help firms apply for SBIR grants and support early proof-of-concept research. One way for the federal government to implement such a proof-of-concept-program would be through a grant program for states that agree to match the funds dollar-for-dollar.

26. Fund pilot programs supporting experimental approaches to technology transfer and commercialization

A number of organizations throughout the United States are experimenting with novel approaches to bolster technology transfer from universities and federal laboratories to industry and to accelerate the commercialization of university-developed technologies. For example, the Applied Physics Laboratory (APL) at Johns Hopkins University is considering an Innovation Launch Program that would leverage a $110,000 investment to support 10 entrepreneurial student teams in commercializing intellectual property developed at APL.

Congress could support these types of novel approaches by providing $5 million annually to fund experimental programs exploring new approaches to university and federal laboratory technology transfer programs. This effort could be funded either through one central agency or through the respective R&D mission agencies and managed by the Department of Commerce’s Office of Innovation and Entrepreneurship. Organizations would apply for the grants, and winning proposals would be selected on criteria such as innovative approach to demonstrating a new model, recent documented success of the program, and willingness to publicly disclose best practices learned from the programs. The effort could be thought of as a “Commercialization Experiments Program.”

27. Support university-based technology accelerators/incubators to commercialize faculty and student research

As universities try to develop new pathways to commercialize research, the federal government can do more to support university efforts to promote research-based entrepreneurs. For example, Stanford has created StartX, Johns Hopkins has created Fast Forward, and MIT has created the Deshpande Center as technology accelerators and incubators that assist university students and faculty in establishing entrepreneurial ventures that seek to move university-developed discoveries and inventions into the commercial sector. These programs and co-working spaces provide a range of support services that may include physical space, legal advice on incorporation and preferred treatment of intellectual property, connections to sources of capital, and a range of business, technical, and potential customer contacts important to launching a new business. While these types of accelerators are increasingly proliferating throughout the U.S. university system, additional funding could support development into a wider set of universities and colleges, particularly those that don’t have large endowments or wealthy alumni to self-fund such programs.

28. Allow a share of SBIR/STTR awards to be used for commercialization activities

Billed as “America’s Seed Fund,” the Small Business Innovation Research and Small Business Technology Transfer programs provide over $2 billion per year to qualified small businesses to fund R&D activities through multiple federal agencies. While SBIR accounts for only 3.4 percent of federal extramural research funding, the program punches well above its weight, with as much as 22 percent of America’s top innovations (as reflected by studies of previous winners of R&D Magazine’s R&D 100 innovation awards) coming from companies that received SBIR grants at some point in their history. 58

Yet SBIR’s impact could be even greater, particularly if some facets of the program were geared slightly more strongly toward commercialization. In particular, awardees are currently prohibited from utilizing grant money to fund critical commercialization activities related to building product or service prototypes, acquiring commercial customers, attracting private capital, or accelerating market entry. These activities cover the gamut of important commercial activities, including intellectual property development and prosecution, marketing and market development, and the recruitment of key team members associated with customer acquisition (e.g., marketing and sales)—all critical to commercialization. 59

SBIR awardees should be permitted to expend up to 5 percent of their award funds for commercialization-oriented activities. For Phase 1 awardees this expansion would include a narrow set of allowable activities (such as market validation), while for Phase 2 awardees, who are closer to market, a broader set of allowable activities would include market validation, intellectual property protection, business model development, and market research. The Support Startup Businesses Act (S. 2751) has a similar goal; it would allow SBIR grantees to devote up to $30,000 for commercialization expenses. 60

29. Increase the allocation of federal agencies’ SBIR project budgets to commercialization activities

In addition to permitting SBIR awardees to increase the share of funds they can allocate to commercialization-oriented activities, the federal agencies making SBIR awards should do the same. Though some participating agencies offer SBIR/STTR award “supplements” to awardees to select their own vendors (or offer commercialization programs organized by outside vendors), these are capped at $5,000 per year per awardee for commercialization activities and cannot be used to fund company employees specifically devoted to these activities.

Accordingly, SBIR/STTR-sponsoring federal agencies should increase the share of SBIR project funds that can be allocated toward commercialization. Agencies should be encouraged or required to evaluate the performance of outside vendors in order to ensure quality, and to match outside vendors to SBIR awardees in order to ensure an appropriate fit with respect to sector, stage, region, and other applicable factors. 61 Additionally, agencies should implement their current authority to allow each individual SBIR awardee to choose outside vendors that provide such services to that awardee. This proposal has been incorporated into the SBIR and STTR Reauthorization and Improvement Act of 2016.

30. Modify the criteria and composition of SBIR review panels to make commercialization potential a more prominent factor in funding decisions

All participating agencies consider commercialization potential and plans in their grant funding decisions. However, agencies differ in the weight or emphasis they place on commercialization. In particular, some agencies, such as NASA and DoD, intend to use the commercial products that flow from their own R&D. In agencies where the intended customers are external, a greater portion of the merit review evaluation criteria and scoring should include commercialization factors, such as the company’s understanding of market opportunity, product development timelines, and needed resources. 62 Further, to evaluate these important criteria, the composition of SBIR/STIR review panels at these agencies should include industry experts, investors with relevant industry or technology expertise, and/or representatives from commercialization intermediary organizations or venture development organizations.

31. Encourage engagement of intermediary organizations in supporting the development of startups

While agencies have expanded their commercialization programs through funding services offered by third-party organizations, federal R&D funding agencies should fund and encourage the engagement of science- and technology-oriented intermediary organizations that have been effective in translating science-based plans into commercial opportunities in regions around the country. As a key pillar of economic development, these organizations could more effectively leverage federal funding, engage local resources in various functions, and generate local interest amongst awardees. Therefore, funding agencies should systematically map intermediary organizations within technology clusters and support startup grant awardees in connecting with these institutions. Moreover, these organizations should be eligible for federal R&D funding that relates to technology commercialization.

SBIR/STTR investments that are coupled with guidance from regional intermediaries experienced in helping innovators have greater likelihood for success and long-term stability. 63 Currently, ad hoc consultations occur across the board, but this proposal would help fund and create formal pathways linking the many efforts that have grown in the past few years to the program itself and add a level of higher-touch support to companies than federal agencies are able to provide.

32. Expand the NSF I-Corps program to additional federal agencies

The National Science Foundation’s I-Corps program has successfully helped scientists and researchers translate federally funded technologies into marketable products and services. I-Corps has three distinct components: teams, nodes, and sites. Teams are composed of the principal investigator(s), an entrepreneurial lead, and a mentor. Nodes serve as hubs for education, infrastructure, and research that engage academic scientists and engineers in innovation. Sites are academic institutions that catalyze the engagement of multiple local teams in technology transition and strengthen local innovation. 64

NIH and DoE have created similar programs, but current funding levels are too low to truly impact startup activity across the vast panoply of federal funding agencies. The scale of NSF’s I-Corps program should be increased across the federal government so that it can be made available to scientists and engineers at all federal agencies. For example, the American Innovators and Entrepreneurs Act would provide additional funding for the I-Corps program and encourage collaboration between the NSF I-Corps program and other federal agencies, including the Small Business Administration. The bill would also ensure accountability regarding the I-Corps program by requiring NSF to submit to Congress biennial reports regarding the program’s effectiveness.

The I-Corps program gets paid out of 3 percent administrative funds generated as part of general SBIR program funding, but the current version of the SBIR/STTR Reauthorization of Act of 2016 failed to include a five-year reauthorization of that element of the program, meaning that in theory funding for the SBIR program could lapse in August 2017 (before the following fiscal year begins in October 2017). Congress should reinsert allowance for the 3 percent administrative funding for I-Corps into the SBIR/STTR Reauthorization of Act of 2016, or if necessary provide a fix in subsequent COMPETES or National Defense Authorization Act (NDAA) legislation. Further, ideally, the final SBIR/STTR Reauthorization of Act of 2016 would contain language affirming the permanency of the commercialization pilot program for civilian agencies by omitting the words “pilot program” from current Small Business Act legislation (15 U.S.C. 638(gg)(7) and inserting the words “commercialization development program” instead.

33. Authorize and extend the Lab-Corps program

The Department of Energy created the Lab-Corps pilot program (modeled after NSF’s I-Corps program) for the national labs to support investments in technology maturation, entrepreneurs, mentors, scientists, and engineers. The program has not been formally authorized by Congress, but the Accelerating Technology Transfer to Advance Innovation for the Nation (ATTAIN) Act would authorize the program and expand it to engage all national laboratories as well as entrepreneurs and innovators who are competitively selected through an open solicitation.

34. Provide federal matching funds for state and regional technology transfer and commercialization efforts

Many states and regions fund technology transfer and commercialization efforts between their universities and the private sector; examples include TEDCO in Maryland and the Georgia Research Alliance. These programs have strong track records and are strategically tied to regional technical capabilities. But states underfund these efforts, in part because the benefits can spill over beyond their borders. Federal funds should match these state efforts at some percentage level to bolster their impact.

One example is Senate bill S. 4047, which would create a Federal Acceleration of State Technologies Deployment Program, or “FAST,” a federal funding strategy for accelerating the local commercialization of newly developed technologies by matching cash-poor state programs. 65 The matching federal funds would be available concomitant with a state’s level of investment (pro-rated against state population with a maximum cap) in its technology commercialization programs. States would use the money for direct, merit-based project grants to existing SMEs or to startup companies looking to commercialize new products or technologies (with the expectation that a major source for those technologies would be ones currently untapped at local colleges and universities).

35. Incentivize universities to focus more on commercialization activities

A number of countries have sought to increase their R&D efficiency by using existing funding for scientific research to incentivize universities to focus more on technology commercialization. 66 For example, in Sweden, 10 percent of regular research funds allocated by the national government to universities are now distributed using performance indicators. Finland allocates 25 percent of the research budgets of Finnish universities based on “quality and efficacy,” including the quality of scientific and international publications and the university’s ability to attract research investment from businesses. In other words, without increasing government budgets, these nations are using existing funds to provide an incentive for universities to become greater engines of national innovation. 67

In the United States, federal research funding agencies, particularly the National Science Foundation, should consider allocating a small share (e.g., 5 percent) of university R&D funding based on indicators of universities’ effectiveness in attracting industry funding for university research as well as success at commercialization-oriented activities (e.g., number of faculty and student spinoffs or startups, extent of technology licensing, etc.). As in Sweden, the amount of industry-funded university research should be the first variable used to make such allocation decisions. This goal could be achieved by making a share of NSF institutional support grants (which support infrastructure, research, teaching, etc.) contingent on industry collaboration and commercialization performance.

36. Establish stronger university entrepreneurship metrics

The United States should collect better data regarding new business startups coming out of U.S. universities. For example, Congress could direct the National Science Foundation to develop a metric by which universities report such information annually. Funding agencies could use this data to reward universities—for example, by giving bonus points on research grant proposals. In addition, the Department of Commerce could use data available through the ES-202 form (unemployment insurance tax records), which tracks how many employees an establishment has every quarter. If the form noted the university that the founder of the organization attended, it could reveal which colleges and universities have graduates who are founding and running high-growth businesses.

37. Expand the collaborative R&D tax credit to spur research collaboration between industry and universities and labs

Over the last two decades, firms have increased their collaborations with institutions, particularly universities, in order to lower the cost of research and increase effectiveness by maximizing idea flow and creativity. Recognizing this, at least a dozen nations have established collaborative R&D tax credits designed to incentivize industry investment in collaborative research, often including universities, and enrolling multiple partners to do so. 68 The United States has a collaborative R&D credit, but only for the energy sector: as part of the Energy Policy Act of 2005, Congress created an energy research credit that allowed companies to claim a credit equal to 20 percent of the payments to qualified research consortia for energy research.

The next administration and Congress should allow firms to take a flat credit of 20 percent for collaborative research undertaken in conjunction with universities, research institutes, federal laboratories, or multi-firm consortia. 69 This has been suggested before: in 2006, several bills were proposed which would have allowed all research consortia, not just energy-related ones, to become eligible for a 20 percent credit. 70

38. Increase funding for cooperative industry/university research programs at universities

Industry-university partnerships spur greater levels of commercialization and innovation. In the United States, NSF’s Engineering Directorate operates two kinds of industry-university partnerships: Engineering Research Centers (ERCs) and Industry/University Cooperative Research Centers (I/UCRCs). The ERCs are a group of 19 interdisciplinary centers located at universities, where academia and industry collaborate in pursuing strategic advances in complex engineered systems and systems-level technologies that have the potential to spawn whole new industries or to radically transform the product lines, processing technologies, or service-delivery methodologies of current industries. 71 The 75 I/UCRC programs forge partnerships between universities and industry, featuring industrially relevant fundamental research, industrial support of and collaboration in research and education, and direct transfer of university-developed ideas, research results, and technology to U.S. industry to improve its competitive posture in global markets. 72 In other words, the ERCs are focused on collaborative research among universities in advanced engineering systems, whereas the I/UCRCs bring in the industry component of advanced engineering systems research in collaboration with universities.

The Trump administration should work with Congress to increase I/UCRC funding to at least $50 million annually (a considerable increase from the $8 million budgeted in 2016). 73 The National Science Foundation has requested $61 million to fund 18 ERCs in FY 2017, but by 2020 Congress and the administration should look to grow the network of ERCs to 30 with appropriations of $100 million. 74 There is good reason to do so, for the ERC and I/UCRC programs represent some of the most impactful initiatives in the federal government. For instance, each dollar invested by I/UCRC generates an estimated $64.70 in economic impact. 75 While the increased funding being called for here for the two programs is relatively minor (about $80 million), even this need not increase spending, since funds can be reallocated in a budget-neutral manner from other activities. Again, the goal is to prioritize those federal programs and initiatives that have demonstrated the most powerful impacts.

39. Establish an International Patent Consortium

U.S. government and university technology transfer offices cannot afford to file and prosecute foreign patent applications on all their technology inventions. Accordingly, foreign rights to technologies invented at U.S. federal laboratories or universities often go wanting, and so commercialization opportunities are missed in foreign markets.

One solution would be to create an International Patent Consortium, comprising country-specific (or regional) groups of international industry, financial, government, economic development, and technology transfer professionals who would collectively pay the patent expenses for at least two inventions per year from a U.S. technology transfer office in exchange for the exclusive marketing rights to those inventions (within a foreign country or region), with such rights then locally sublicensed by the consortium.

This process could help ameliorate the current practice of filing foreign patents in only a handful of countries. The consortium concept could increase the breadth and value of the intellectual property portfolio of U.S. government labs and provide their U.S. licensees (particularly small companies) with international marketing and distribution partners who could also provide complementary technology, equity, and international business experience.

Given mounting fiscal pressures, both the incoming Trump administration and Congress need to focus on improving the economic return on investment from existing infrastructure and resources. It is clearly time to elevate the importance attached to commercialization-oriented activities associated with federal R&D funding programs as well as raise commercialization’s profile in the missions of federal laboratories and federally funded universities.

One key step the federal government can take to boost the economy is to better support high-growth, tech-based startups because these firms play an important role in job creation and innovation. According to research by MIT economist Scott Stern, 75 percent of employment generated by startups can be attributed to just 5 percent of entrepreneurs. 76

Moreover, the relationship between young firms and larger companies is an essential ingredient for innovation. 77 Large companies house much of the industry knowledge needed for finding new solutions, but they often have tightly controlled product lines and corporate governance structures that can make radical innovation difficult. At the same time, young firms lack the market intelligence to know exactly what solutions can be monetized, but they represent a disproportionate share of radical innovation and are often acquired by large companies better suited to market new ideas. Dense, regional clusters are important to the interplay between young and large firms because economic research shows that entrepreneurs and larger firms collaborate most when they are geographically close. 78

Unfortunately, the job-creating capacity of high-growth entrepreneurial firms has declined over the last 15 years. Decker et al. find that before 2000 the fastest-growing young firms (those in the 90th percentile of all young firms) grew employment at a steady rate of just under 70 percent a year, but by 2012 that rate had declined to 55 percent. 79 The authors also find that the portion of young, high-growth technology firms has declined since 2000, as Figure 2 shows. 80 Figure 2: High-growth firms by firm age and annual employment growth rates, 1980-2012

While startups once represented a wellspring of employment opportunities in new technology industries, today the flow is smaller. Therefore, supporting high-growth entrepreneurship should be a key pillar of the next administration’s innovation policy priorities.

40. Encourage student entrepreneurship

The next administration should encourage universities to define an entrepreneurial leave policy for undergraduate and graduate students in which students could retain full-time student status for one to two years while launching their own companies. In the United States, for example, federal agencies supporting university research in science, technology, engineering, and mathematics (STEM) education should adopt a policy whereby any graduate or post-doctoral students on an assistantship, fellowship, or other form of federal support can petition for a no-cost one- to two-year extension of their status as they take “entrepreneurial leave.” Another option would be to provide graduates an entrepreneurial student loan deferment when they are attempting to start a business. The deferment could be extended if certain metrics were being met, such as jobs created or venture capital raised.

41. Help nascent high-growth startups secure needed capital

In 1995, Silicon Valley accounted for 22.6 percent of U.S. venture capital, Los Angeles/Orange County 12.5 percent, Boston 9.9 percent, New York 6.4 percent, and all other areas of the United States 48.6 percent. Twenty years later, in 2015, Silicon Valley had more than doubled its share, to 46.4 percent, New York’s share rose to 12.4 percent, Boston moved to 10.2 percent, and Los Angeles to 8.7 percent, while the share for the rest of the United States fell to 22.2 percent. 81 In other words, today just four regions of the United States account for 78 percent of all U.S. venture capital investment, while the remainder of the country fights over the remaining one-fifth.

Thus, a substantial number of promising young businesses scattered throughout all regions of the United States likely have difficulty securing capital, particularly venture capital, because most venture capital investment is concentrated on America’s coasts. The Small Business Jobs Act of 2010 helped to address this problem; it created the State Small Business Credit Initiative (SSBCI), a $1.5 billion fund designed to strengthen state programs that support lending to small businesses and small manufacturers. 82 The SSBCI gave states significant flexibility to design programs to meet local market conditions, with SSBCI supporting 152 small business programs from 2011 to 2015. Approximately 69 percent of the funding supported lending or credit support programs and 31 percent supported venture capital programs. From 2011 to 2015, SSBCI programs supported nearly $8.4 billion in new capital in small business loans and investments. 83

In effect, SSBCI provides an opportunity for states to supplement existing venture capital programs, revitalize programs lacking sufficient state support, and create new programs where state managers perceive unmet needs in evolving entrepreneurial ecosystems. The SSBCI has made a positive impact in expanding high-potential businesses’ access to credit, and so the next administration should reauthorize it and double its funding.

42. Establish an entrepreneur-in-residence program with NIH

While all federal funding agencies should support greater research-driven entrepreneurs, NIH is unique in that health care and life science startups are particularly difficult to grow—but often represent significant economic value when they do. Moreover, among all agencies, NIH distributes the largest share of federal funding to universities, many of which have only recently begun to seriously think about technology transfer through faculty and student-generated businesses. Universities and academic medical centers that receive funding from NIH often follow the narrow and traditional path to commercializing research that revolves around patenting and licensing. In the “classic” model of technology transfer, researchers at universities and medical centers apply for NIH and other federal funds to pursue basic science and patent their discoveries. The technology transfer office at the university/medical center then takes these patents and licenses their use to biotechnology and pharmaceutical firms for the development of products.

While the classic model can be an appropriate vehicle for commercialization, it often lacks strong connections between firms and research organizations. Successfully scaling a life-sciences startup requires social and capital networks, mentorship, public-private partnerships, and access to both scientific and managerial talent. Developing, recruiting, and coordinating these disparate pieces of the medical entrepreneurial ecosystem is difficult but once achieved can spur new economic clusters, firms, and employment.

For years venture capital firms have run entrepreneur-in-residence (EIR) programs, where the firm hires proven entrepreneurs to review its patent portfolio and work with other star entrepreneurs to help them grow. By establishing an entrepreneur-in-residence program at universities that receive NIH research funding, including basic and translational (DHHS already has an EIR program that serves a different purpose), the agency can help universities identify, support, and grow the research efforts best positioned to become high-growth companies. 84

43. Implement immigration policies that advantage high-skill talent

Talent has become the world’s most sought-after commodity. Immigration plays an important role in contributing to a country’s knowledge pool and creative potential by bringing in new perspectives and needed skills. As the report Not Coming to America: Why the U.S. Is Falling Behind in the Global Race for Talent finds, at least nine nations—Australia, Canada, Chile, China, Germany, Ireland, Israel, Singapore, and the United Kingdom—have implemented innovative policies to attract foreign entrepreneurs and investors to their countries as part of a concerted effort to drive economic and employment growth. These countries “see immigration as an integral part of their national economic strategy—a factor in their prosperity as significant as education and infrastructure.” 85 America’s immigration policies should adopt a more open approach toward high-skill talent. One simple way to accomplish this is to grant more work visas to foreign students in American universities after they graduate. In the 2014-2015 school year approximately 975,000 foreign nationals were attending U.S. universities; 57 percent of the students were in STEM fields. 86 Extending a green card to foreign-born students graduating in STEM fields would provide a boost to the U.S. innovation economy. Accordingly, the United States should make it easier for talented individuals from foreign nations who receive a graduate degree in STEM fields to stay in the United States after graduation by making them eligible for permanent residency.

44. Implement a research investor’s visa

The United States should create a research investors’ visa for foreign individuals investing substantially in ongoing federally funded R&D activities at U.S. universities or federal laboratories. 87 Such a visa could make important contributions to U.S. economic and employment growth.

One reason a research investor’s visa could have a particularly powerful economic effect is that it would specifically support the most R&D-intensive sectors of the U.S. economy that are best positioned to compete globally. A potential weakness of the immigrant entrepreneurs’ visa is that it is impossible to know which entrepreneurial activities will grow to global scale and become a source of employment. By specifically focusing on high-value, scientifically focused startups, the new visa would better capture growth-oriented firms. For example, the Kauffman Foundation finds that a general startup visa program would create significantly fewer jobs, perhaps only one-third as many, as a program focused on high-technology or engineering startups. 88

Political economists Peter Hall and David Soskice argue that the United States’ entrepreneurship ecosystem is central to the country’s ability to produce innovations that lead to new industries—automobiles, planes, electronics, software, etc. 89 While other countries such as Germany have strong industrial policies that allow legacy industries to remain competitive through technology adoption, radical innovation through new firms is a unique American strength. To continue to build on this, the next administration will need to create policies that better support high-growth tech-based startups and attract foreign tech-based entrepreneurs while also incentivizing universities, federal labs, and other federally funded institutions to encourage entrepreneurship.

Leveraging federal R&D alone won’t be enough to re-establish U.S. leadership in advanced manufacturing and technology sectors. Because over two-thirds of R&D is performed by the private sector, the administration must also incentivize and support private-sector R&D and create stronger linkages between public and private R&D. Supporting such R&D is crucial because it is a critical input to the private-sector innovations that drive long-term U.S. economic growth.

There are at least four reasons why the government should support private-sector innovation. First, without government incentives for R&D, worker training, and investments in new capital equipment, the private sector would underinvest in innovation because new technologies are often easily replicated and transferred between firms. This is particularly true as technology imitation occurs far more quickly today than in the past, due in part to the global base of technology competitors and the speed of reverse engineering. Consider the iPad, first released in March 2010. At the 2011 Consumer Electronics Show, close to a dozen competing tablets were on display. 90 Effects like these are why the economist Lorin Hitt finds that spillovers to other firms from firms’ investments in information technology are “almost as large in size as the effect of their own investments.” 91 This is good for the economy but bad for the innovative company that cannot reap the full market benefits of its technology.

Second, the gulf between federal and private-sector R&D is widening. Over the last half century, firms have moved away from investing in basic research and toward market-oriented development research; at the same time, the federal government has shifted its R&D portfolio toward basic science. Between 1965 and 2015, the share of federal R&D going to basic research increased from less than 10 percent to 25 percent. 92 Figure 3 shows that federal investment in development-oriented activities (i.e., the “D” in “R&D”) as a share of GDP has trailed off significantly since the mid-1980s. The impact of these trends is that now federal research outcomes leave off far too early for corporate research centers to commercialize. To fix the problem, greater linking mechanisms are needed.

Third, economic research clearly shows that innovation-oriented tax credits work. Bloom, Griffith, and Van Reenen find that R&D tax credits stimulate $1.10 for every dollar lost in tax revenue. 93 Coopers and Lybrand find higher benefits, of between $1.30 and $2.90. 94 Similarly, Klassen, Pittman, and Reed find that, for every one dollar of tax revenue lost, the R&D credit induces $2.96 in private-sector R&D. 95

Finally, the United States now lags far behind many other countries in innovation-incentivizing tax policy. The United States invented the R&D tax credit in the early 1980s, and as late as 1992 ranked first globally in R&D tax incentive generosity. But today the United States ranks 27th. 96 While in 2015 Congress laudably made the R&D tax credit permanent, other countries have raced ahead, creating robust investment tax credits, bridging public and private R&D, and incentivizing workforce training and technology investments by the private sector.

To stimulate private sector innovation, the incoming administration should work with Congress on the following policies.

45. Implement innovation vouchers

Innovation vouchers are low-cost tools for connecting startups with public research institutes or universities to incentivize R&D among young, innovative firms. The main goals of an innovation voucher are to enable knowledge transfers between startups and research institutes, support sectoral innovation in manufacturing, support innovation management and advisory services, speed commercialization of startup ideas, and focus research institutions on the commercial applications of their research. Several countries, including Austria, Belgium, Canada, Denmark, Germany, the Netherlands, Ireland, and Sweden, have begun using innovation vouchers to support R&D, innovation, and new product development in small businesses.

With traditional voucher programs SMEs can typically receive a $5,000-$10,000 voucher for a cooperation project with a university, community college, or research institution for R&D assistance, technology feasibility studies, analysis of technology transfer, or analysis of the innovation potential of a new technology. The voucher creates an incentive to bring SMEs and academia closer together and also empowers innovation at SMEs.

Several U.S. states, including New Mexico, Rhode Island, and Tennessee, are experimenting with innovation vouchers. For example, in 2015, Oak Ridge National lab established an innovation voucher program to enable technical assistance to SME manufacturers in Tennessee. Los Alamos and Sandia national laboratories in New Mexico operate a similar program. 97 And the Energy Efficiency and Renewable Energy office within DoE has created a pilot innovation voucher for its national laboratories. Congress should extend vouchers to entire federal lab system by authorizing $50 million to the National Institute of Standards and Technology to fund a program operated by select states that agree to match the funding dollar for dollar (perhaps through tax credits to national labs within their borders). As a potential source of funds to keep the initiative revenue-neutral, one option would be to reallocate 0.5 percent of the laboratories’ current budgets to fund the vouchers. 98

46. Incentivize “megafunds” around high-risk research and development

In 1960, private-sector R&D was split one-third to research and two-thirds to development. Today, only one-fifth of firm R&D goes to research. One reason companies are moving away from basic and applied research is because of the risk involved in financing. In drug development, for example, it often takes years or decades and hundreds of millions of dollars to produce a profitable product. Individual companies and even venture capitalists often lack the appetite for such long-term, high-risk investments.

This risk could be mitigated through large portfolios that aggregate and manage risk. Mutual funds, pension funds, and 401(k) retirement accounts work this way, and MIT economist Andrew Lo has proposed extending this idea by establishing “megafunds” that utilize financial engineering techniques to fund R&D in long-term, high-risk, high-payoff areas such as drug discovery for cancer or orphan diseases. 99 However, to date, no such megafunds have been created by the market. The government incentives required for the creation of these funds could include one or more approaches from four broad categories: research and investment data streams; clear rules for private foundation program-related megafund investments; federal credit support; and tax incentives for funds investing in technologies with high societal impact (for example through the establishment of schedules and values of basis point step-ups and penalties).

To promote the creation of R&D megafunds, the Trump administration should establish an office within the Department of Commerce to develop and implement the needed incentives and oversight. The office would be tasked with establishing the rules for the funds and coordinating with federal agencies and the private sector to identify the technical areas of national interest where private-sector engagement is needed and the incentives required. The office should work with researchers, industry, and regulators to develop data-reporting and transparency standards that promote the translation of research to the market, provide better understanding of the societal benefits of research and an efficient data stream for regulation, and coordinate with federal funding agencies to enforce the provision and collection of such data.

47. Increase R&D tax credit generosity

R&D tax incentives are one of the most effective policy instruments in spurring a nation’s private-sector R&D investment. Almost all scholarly studies conducted since the early 1990s find R&D tax incentives to be both effective and efficient. Studies of the U.S. credit find even greater benefits, with the research-investment-to-tax-cost ratio falling between 1.3 and 2.9. 100 Yet France and Spain offer R&D tax credits over five times more generous than those of the United States, and even Brazil, China, and India have exceeded the United States in R&D tax credit generosity. Ideally, the United States should increase the rate of the Alternative Simplified Credit from 14 to 24 percent. ITIF has calculated that expanding the R&D tax credit would pay for itself in added revenues from growth after 15 years. 101

48. Ensure that small and medium-sized enterprises are familiar with available R&D tax credits

It is important that America’s SMEs take full advantage of tax incentives, whether for R&D or investment in new machinery and equipment. Congress passed the PATH Act in December 2015 to expand small businesses’ access to the R&D credit by permitting them to claim the credit against their employment taxes or against their alternative minimum tax. But not enough small businesses are aware that this legislation greatly expands their access to the credit. Accordingly, Congress should pass the Support Small Business R&D Act, which would require the Small Business Administration and the Internal Revenue Service to expand knowledge sharing and training on these instruments and provide a report to Congress on their progress.

49. Implement an innovation box to spur enterprises’ efforts to commercialize technologies

A growing number of nations have put in place tax incentives to spur the commercialization of R&D, not just the conduct of R&D. These patent box —also called “innovation box”—incentives allow corporate income from the sale of patented products (or in some countries from innovation-based products) to be taxed at a significantly lower rate than other income. 102 A number of nations—including Belgium, China, France, Ireland, Luxembourg, the Netherlands, Spain, Switzerland, and the United Kingdom—have established patent boxes. The United Kingdom implemented its policy in 2013 with a tax rate of 10 percent on income generated from patented products, compared to the standard rate of 28 percent. France’s patent box reduces corporate income tax from 34 percent to 15 percent on qualifying income.

A patent box that reduces the corporate tax rate on revenue from qualifying intellectual property, coupled with an incentive for corresponding R&D and production to be located in the United States, would provide firms with a much stronger incentive to innovate and to produce in the United States. The Innovation Promotion Act of 2015 calls for creating an innovation box that allows companies to claim an effective 10.15 percent tax rate for income derived from a wide range of qualifying intellectual property, including patents, inventions, formulas, processes, and designs and patterns, as well as other types of intellectual property, such as copyrighted computer software. Innovation boxes have received bipartisan support in the Senate. 103 The incoming administration should work with Congress to develop legislation to implement an innovation box for the United States.

50. Revise the tax code to support innovation by research-intensive, pre-revenue companies

The primary mechanism in the tax code to facilitate innovation is the R&D tax credit, but the credit is less useful for pre-revenue companies because it requires tax liability, which requires income. In other words, the tax credit is designed more for established innovators, not so much for research-intensive, pre-revenue companies that are trying to develop new technologies such as medical devices or biopharmaceutical drugs. These are extremely R&D-intensive companies, which tend to invest 75 percent or more of their expenditures in R&D.

Firms in this position often find it difficult to raise the capital needed to get them through the long development phase until they are near enough to profitability to conduct an initial public offering or be attractive to a prospective buyer. The PATH Act (Protecting Americans From Tax Hikes) of 2015 made the R&D tax credit refundable for small businesses (i.e., it allowed small businesses to take the credit against their payroll taxes). But two additional tax reform proposals could further address these challenges.

The first proposal would amend Section 469 of the tax code to permit passive investors to take advantage of the net operating losses and research tax credits of companies in which they invest. 104 (The Tax Reform Act of 1986 severely limited this ability because it was seen as a way for high-income individuals to reduce their taxes by investing in operations that were never meant to be profitable.) Under this reform, investors could immediately use their share of net operating losses, as well as any credits for research and development. The percentage of losses or credits that could be passed through would be limited to the portion of investment that was specifically targeted for qualified research activities as determined for purposes of the research and development tax credit. In order to qualify, a company would have to devote at least half of its expenses to research and development. The company would also have to have fewer than 250 employees and less than $150 million in assets. A recent study by Ernst & Young estimates that this change would increase investment in such companies by $9.2 billion, allowing them to create 47,000 jobs. 105 The proposal is currently contained in both the Start-Up Jobs and Innovation Act (S. 341) and the COMPETE Act (S. 537).

The second change would make it easier for small companies to carry net operating losses forward even as they continue to attract new investors. Small, research-intensive companies often go through several rounds of financing as they rack up expenses while getting nearer to their goal of profitability. Unfortunately, Section 382 of the tax code prevents companies from carrying net operating losses forward if they undergo an ownership change. This rule eliminates an attraction to investors. It also means that the company will start paying taxes on its revenue long before its total revenues exceed it total expenses. Under the proposed change, Section 382 would not apply to net operating losses generated by qualifying research and development activities conducted by a small business. The Ernst & Young analysis estimated that this change would increase direct investment in these companies by $4.9 billion and boost their employment by 25,000 jobs. 106

Coming out of World War II the United States was the first country to make research and development a national priority. At the time the federal government accounted for over 50 percent of global R&D, public and private. Today, the federal government accounts for 8 percent of global R&D investment. While robust, U.S. federal investments in science represent a shrinking portion of technology development. In order for the United States to remain competitive, firms must find a country to be an attractive location to innovate. The incoming administration should use the tax system and other policy levers to ensure the United States remains the top destination of enterprise R&D.

Conclusion: The American economy in 2025 and beyond

There will be no shortage of pressing issues for the Trump administration to focus on in its first 100 days. But none will affect as many Americans for as long a period as stagnant economic growth. Indeed, the trajectory of the American economy in 2025 and beyond begins on January 1, 2017. Without a multi-decade turnaround of the U.S. economy, neither party will be able to achieve its other economic priorities. In the absence of consistent economic success, those on the left will find the social safety net overburdened and underfunded, while those on the right will find public coffers too diminished to lower taxes. At the same time, American families will continue to be squeezed.

The first step toward fixing America’s economy is correctly diagnosing the problem. It is not automation or globalization. Rather, the United States has a productivity and innovation problem. Both are lacking, and that’s problematic when productivity growth is the fundamental source of economic growth and when innovation drives productivity. Upon entering the White House, President Obama was faced with the 2008 financial crisis and was able to leverage the moment to pass the American Recovery and Reinvestment Act, investing $787 billion in the economy. Bold action will likewise be needed from the incoming Trump administration, and the policy proposals outlined here provide a template to maximize the levels of technology transfer, commercialization, and innovation that will drive America’s economy robustly forward into the future.

• Executive Office of the President National Science and Technology Council Advanced Manufacturing National Program Office, National Network for Manufacturing Innovation Program: Annual Report (Executive Office of the President, February 2016), https://www.manufacturing.gov/files/2016/02/2015-NNMI-Annual-Report.pdf .
• Robert D. Atkinson, “Leveraging the U.S. Science and Technology Enterprise,” written testimony to the U.S. Senate Committee on Commerce, Science, and Transportation, 2016, p. 1, http://www2.itif.org/2016-senate-competes-act-testimony.pdf .
• Gregory Tassey, “Why the U.S. Needs a New, Tech-Driven Growth Strategy” (Washington: Information Technology and Innovation Foundation, February 2016), https://itif.org/publications/2016/02/01/why-us-needs-new-tech-driven-growth-strategy .
• Martin Neil Baily and Nicholas Montalban, “Why Is US Productivity Growth So Slow? Possible Explanations and Policy Responses,” Working Paper # 22 (Washington: Brookings Institution Hutchins Center on Fiscal and Monetary Policy, 2016), https://www.brookings.edu/wp-content/uploads/2016/09/wp22_baily-montalbano_final3.pdf .
• Robert D. Atkinson. “Think Like an Enterprise: Why Nations Need Comprehensive Productivity Strategies,” (Washington: Information Technology & Innovation Foundation, 2016), http://www2.itif.org/2016-think-like-an-enterprise.pdf?_ga=1.167003194.568129823.1475259628 .
• Information Technology and Innovation Foundation, “As Productivity Continues to Lag, ITIF Reiterates Call for Wholesale Shift in Economic Policy Focus,” news release, August 9, 2016, https://itif.org/publications/2016/08/09/productivity-continues-lag-itif-reiterates-call-wholesale-shift-economic .
• U.S. Census Bureau, Foreign Trade Division, “Trade in Goods With Advance Technology Products” (1989-2016), https://www.census.gov/foreign-trade/balance/c0007.html .
• John Wu, Adams Nager, Joseph Chuzhin, High-Tech Nation: How Technological Innovation Shapes America’s 435 Congressional Districts (Information Technology and Innovation Foundation, November 2016), http://www2.itif.org/technation-2016-report.pdf?_ga=1.139274675.1806060799.1471894729 .
• See, for example, Mark Muro and Bruce Katz, “The New Cluster Moment: How Regional Innovation Clusters Can Foster the Next Economy” (Washington: Brookings Institution, 2010). See also S. Rosenthal and W. Strange, “Evidence on the Nature and Sources of Agglomeration Economies,” in J.V. Henderson and J.F. Thisse, eds., Handbook of Regional and Urban Economics, Vol. 4 (Amsterdam, North-Holland: 2004); MaryAnn Feldman and David Audretsch, “Innovation in Cities: Science-Based Diversity, Specialization, and Localized Competition,” European Economic Review 43 (1999): 409–29; and Gregory Tassey, “Competing in Advanced Manufacturing: The Need for Improved Growth Models and Policies” Journal of Economic Perspectives 28 , No. 1 (Winter 2014): 27-48, http://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.28.1.27 .
• S. Rosenthal and W. Strange, “Geography, Industrial Organization, and Agglomeration,” Review of Economics and Statistics , 85, no. 2 (2003): 377-93. Similarly, Arzaghi and Henderson study ad agencies in Manhattan and show knowledge spillovers and the value of networking with nearby firms are substantial but the benefits dissipate extremely rapidly. The strongest effects are when firms are within 0-250 meters and decline by 80 percent when two firms are 500 meters apart. See: Mohammad Arzaghi and J. Vernon Henderson, “Networking off Madison Avenue” Review of Economic Studies 75 , No. 4 (2008): 1011-1038, https://ideas.repec.org/a/oup/restud/v75y2008i4p1011-1038.html .
• Atkinson, Leveraging the U.S. Science and Technology Enterprise , p. 2.
• Bronwyn H. Hall, Jacques Mairesse, and Pierre Mohnen, “Measuring the Returns to R&D,” Working Paper No. 15622 (Cambridge, Mass.: National Bureau of Economic Research, 2009), http://www.nber.org/papers/w15622 .
• U.S. Department of Defense, “National Economic Impacts from DoD License Agreements With U.S. Industry: 2000-2014,” (2016).
• Smart Growth America, “Core Values: Why American Companies Are Moving Downtown” (Washington, 2015), https://www.smartgrowthamerica.org/app/legacy/documents/core-values.pdf .
• Bruce Katz and Julie Wagner, “The Rise of Innovation Districts: A New Geography of Innovation in America” (Washington: Brookings Institution, 2014), https://www.brookings.edu/essay/rise-of-innovation-districts/ .
• Scott Andes, “Hidden in Plain Sight: The Oversized Impact of Downtown Universities” (Washington: Brookings, 2016, forthcoming).
• New Mexico Small Business Assistance Program, http://www.nmsbaprogram.org/ .
• Cyclotron Road, “About Us,” http://www.cyclotronroad.org/ ; Joseff Kolman, “Summary of Federal, State, University, and Private Programs for Supporting Emerging Technology” (Washington, DC: Massachusetts Institute of Technology Washington, DC Office, July 2015), http://dc.mit.edu/sites/default/files/doc/MIT%20Innov%20Orchard%20Summary%20of%20Federal,%20State,%20University,%20and%20Private%20Programs%20for%20Emerging%20Technologies%207.10.2015.docx .
• NIH’s Clinical and Translational Science awards are geared towards cross-institution collaboration and have broadly been successful and offer a good example how NIH can extend pre-competitive, collaborative opportunities across its programs.
• A significant amount of funding for the federal labs already comes from outside of DoE. At the federal level in FY 2011, the labs received just under $3 billion from the Department of Homeland Security, the National Institute of Standards and Technology, the Centers for Disease Control and Prevention, the intelligence community, the Department of Defense, and NASA. On the other hand, some labs—such as NREL and SLAC—receive over 90 percent of their funding from their funding steward. See National Academy of Public Administration, “Positioning DOE’s Labs for the Future.”
• Pumps & Pipes, http://www.pumpsandpipes.com/index.html#rj-who-we-are .
• Stephen J. Ezell and Robert D. Atkinson, “25 Recommendations for the 2013 America COMPETES Act Reauthorization” (Washington: Information Technology and Innovation Foundation, 2013), p. 17, http://www2.itif.org/2013-twenty-five-policy-recs-competes-act.pdf .
• U.S. Economic Development Administration, “Regional Innovation Clusters Initiative Overview” (2010), http://www.eda.gov/AboutEDA/RIC/ .
• Atkinson, Leveraging the U.S. Science and Technology Enterprise , p. 4.
• University of Minnesota Duluth Natural Resources Research Institute, “History,” http://www.nrri.umn.edu/about/discover-nrri/history .
• Ben Franklin Technology Partners of Central & Northern Pennsylvania, cnp.benfranklin.org.
• Joshua New and Daniel Castro, “Why Countries Need National Strategies for the Internet of Things” (Washington: Center for Data Innovation, 2015), p. 14, http://www2.datainnovation.org/2015-national-iot-strategies.pdf .
• Makerspace, “What’s a Makerspace?” http://spaces.makerspace.com/ .
• “National Fab Lab Network Act of 2015,” H.R.1622, 114 th Cong. (2015-2016), https://www.congress.gov/bill/114th-congress/house-bill/1622/actions?q=%7B%22search%22%3A%5B%22hr+1622%22%5D%7D&resultIndex=1 .
• Stephen Ezell, “’Fab Lab’ Bill Would Stimulate Manufacturing Innovation,” The Innovation Files , April 29, 2013, http://www.innovationfiles.org/fab-lab-bill-would-stimulate-manufacturing-innovation/ .
• Robert D. Atkinson and Stephen J. Ezell, “Innovation Economics: The Race for Global Advantage” (New Haven, Conn.: Yale University Press, 2012).
• Robert D. Atkinson, “The Morrill Act at 150 Years: We Need a New Morrill Act for the 21st Century,” The Innovation Files , July 12, 2012, http://www.innovationfiles.org/the-morrill-act-at-150-years-we-need-a-new-morrill-act-for-the-21st-century/ .
• Stephen J. Ezell, Frank Spring, and Katarzyna Bitka, “The Global Flourishing of National Innovation Foundations” (Washington: Information Technology and Innovation Foundation, 2015), http://www2.itif.org/2015-flourishing-national-innovation.pdf .
• Robert D. Atkinson and Stephen J. Ezell, “Cut to Invest: Support the Designation of 20 U.S. Manufacturing Universities” (Washington: Brookings Institution and Information Technology and Innovation Foundation, 2013), https://www.brookings.edu/research/papers/2013/01/14-federalism-series-manufacturing-universities .
• Sponsored in the U.S. Senate by Senator Coons (D-DE) along with Senators Ayotte (R-NH), Gillibrand (D-NY), Graham (R-SC), and Baldwin (D-WI), and mirrored by House legislation introduced by Representatives Etsy (D-CT) and Collins (R-NY).
• David M. Hart, Stephen J. Ezell, and Robert D. Atkinson, “Why America Needs a National Network for Manufacturing Innovation” (Washington: Information Technology and Innovation Foundation, 2012), https://itif.org/publications/2012/12/11/why-america-needs-national-network-manufacturing-innovation .
• Ezell and Atkinson, “25 Recommendations for the 2013 America COMPETES Act Reauthorization,” p. 22.
• Justin Talbot Zorn and Sridhar Kota, “Engineering an Economic Recovery,” The Huffington Post (blog), January 11, 2013, http://www.huffingtonpost.com/justin-zorn/manufacturing-economic-recovery_b_2662720.html .
• Robert D. Atkinson and Howard Wial, “Boosting Productivity, Innovation, and Growth Through a National Innovation Foundation” (Washington: Information Technology and Innovation Foundation, 2008), http://www.itif.org/files/NIF.pdf .
• Stuart Benjamin and Arti Rae, “Structuring U.S. Innovation Policy: Creating a White House Office of Innovation Policy” (Washington: Information Technology and Innovation Foundation, 2009), http://www.itif.org/files/WhiteHouse_Innovation.pdf .
• Department of Defense, “National Economic Impacts From DoD License Agreements With U.S. Industry: 2000-2014, (2016).
• Scott Andes, “Maximizing the Local Economic Impact of Federal R&D” (Washington: Brookings Institution, 2016).
• Timothy A. Walton, “Securing the Third Offset Strategy: Priorities for Next US Secretary of Defense” (Washington: Center for Strategic and Budgetary Assessments, 2016), http://csbaonline.org/about/news/securing-the-third-offset-strategy-priorities-for-next-us-secretary-of-defe .
• Oak Ridge National Laboratory, “AMO Announces Funding Opportunity for Low-Cost, Energy-Efficient Manufacturing and Recycling of Advanced Fiber-Reinforced Composites,” Innovations in Manufacturing , February 26, 2014, http://web.ornl.gov/sci/manufacturing/nnmi/ .
• Scott Andes, Mark Muro, and Matthew Stepp, “Going Local: Connecting the National Labs to their Regions to Maximize Innovation and Growth” (Brookings and Information Technology and Innovation Foundation, September 2014), https://www.brookings.edu/wp-content/uploads/2016/06/BMPP_DOE_Brief.pdf .
• Louis G. Tornatzkyand Elaine C. Rideout, “Innovation U 2.0: Reinventing University Roles in a Knowledge Economy” (2014), http://ssti.org/report-archive/innovationu20.pdf .
• Matthew Stepp, Sean Pool, Nick Loris, and Jack Spencer, “Turning the Page: Reimagining the Federal Labs in the 21 st Century Innovation Economy” (Washington: Information Technology and Innovation Foundation, Center for American Progress, and The Heritage Foundation, 2013): pp. 23, 45, 53, http://www2.itif.org/2013-turning-the-page.pdf?_ga=1.172902691.1806060799.1471894729 .
• Ibid., p. 54.
• Ezell and Atkinson, “25 Recommendations for the 2013 America COMPETES Act Reauthorization,” p. 14.
• Similar legislation is proposed in Section 8 of the Startup Act 3.0 titled “Accelerating Commercialization of Taxpayer Funded Research.” See Representative Michael Grimm, “H.R.714–Startup Act 3.0,” Congress.gov, https://www.congress.gov/bill/113th-congress/house-bill/714/text#toc-HFE43E635A9674068882957133E8E662C .
• European Research Council, “Proof of Concept Grants,” https://erc.europa.eu/proof-concept.
• Gretchen Vogel, “Europe Nudges Top Scientists to Market,” Science , March 25, 2011, http://www.sciencemag.org/news/2011/03/europe-nudges-top-scientists-market .
• Wallace H. Coulter Foundation, “Translational Research” (Miami, Fla., 2016), www.whcf.org/partnership-award/overview .
• Department of Health and Human Services, “National Institute of Health Evaluation and Commercialization Hub (REACH) Awards” (2014), http://grants.nih.gov/grants/guide/rfa-files/RFA-OD-14-005.html .
• Fred Block and Matthew Keller, “Where Do Innovations Come From? Transformations in the U.S. National Innovation System, 1970-2006” (Washington: Information Technology and Innovation Foundation, 2008), http://www.itif.org/files/Where_do_innovations_come_from.pdf .
• National Advisory Council on Entrepreneurship (NACIE), “Letter to The Honorable Penny Pritzker Offering Recommendations to Improve the Outcomes of the SBIR/STTR Programs” (March 4, 2016).
• “Support Startup Businesses Act of 2016,” S.2751, 114 th Cong. (2015-2016), https://www.congress.gov/bill/114th-congress/senate-bill/2751/text?format=txt .
• NACIE, “Letter to Pritzker on Improving SBIR/STTR Outcomes,” https://www.eda.gov/oie/files/nacie/meetings/20160303-SBIR-STTR-Recommendations-NACIE.pdf .
• “FAST Deployment Act of 2010,” S. 4047, 111th Cong. (2010), http://www.gpo.gov/fdsys/pkg/BILLS-111s4047is/pdf/BILLS-111s4047is.pdf .
• Ezell and Atkinson, “25 Recommendations for the 2013 America COMPETES Act Reauthorization,” p. 23.
• Jukka Haapamäki and Ulla Mäkeläinen, “Universities 2006” (Helsinki: Finnish Ministry of Education, 2007), pp. 23-24, http://www.minedu.fi/export/sites/default/OPM/Julkaisut/2007/liitteet/opm19.pdf .
• Matthew Stepp and Robert D. Atkinson, “Creating a Collaborative R&D Tax Credit” (Washington: Information Technology and Innovation Foundation, 2011), http://www.itif.org/files/2011-creating-r&d-credit.pdf .
• Paul R. Sanberg et al., “Changing the Academic Culture: Valuing Patents and Commercialization Toward Tenure and Career Advancement” (Cambridge, Mass.: Proceedings of the National Academy of Sciences, 2014), http://www.pnas.org/content/111/18/6542.long .
• The 109th Senate considered versions of HR.4297 (Thomas, [R-CA]), S.14 (Stabenow [D-MI]), S.2199 (Domenici [R-NM]), and S.2357 (Kennedy [D-MA]). S.2357 would institute a flat credit for payments to qualified research consortia.
• Engineering Research Centers Association, “About the ERCs,” http://www.erc-assoc.org/ .
• National Science Foundation, “I/UCRC Model Partnerships,” http://www.nsf.gov/eng/iip/iucrc/program.jsp .
• National Science Foundation, “NSF FY 2017 Budget Request: Directorate for Computer and Information Science and Engineering (CISE),” p. 22, https://www.nsf.gov/about/budget/fy2017/pdf/18_fy2017.pdf .
• National Science Foundation, “NSF FY 2017 Budget Request: National Science Foundation Centers,” https://www.nsf.gov/about/budget/fy2017/pdf/46_fy2017.pdf .
• Denis O. Gray, Drew Rivers, and George Vermont, “Measuring the Economic Impact of the NSF Industry/University Cooperative Research Center Program: A Feasibility Study” (Arlington, Va.: I/UCRC, 2011), p. 28, http://www.min.uc.edu/me/news_folder/files/EconImpact_IUCRCMtg_June9.2011(final).pdf .
• Jorge Guzman and Scott Stern, “Nowcasting and Placecasting Entrepreneurial Quality and Performance,” Working Paper No. 20954 (Cambridge, Mass.: National Bureau of Economic Research, 2015), http://www.nber.org/papers/w20954 .
• John Hagedoorn and Nadine Roijakkers, “Small Entrepreneurial Firms and Large Companies in Inter-Firm R&D Networks: The International Biotechnology Industry,” in M.A. Hitt et al., eds., Strategic Entrepreneurship (Cambridge, Mass.: Blackwell, 2002).
• Gerald Carlino and William Kerr, “Agglomeration and Innovation,” Harvard Business School Entrepreneurial Management Working Paper, No. 15-007 (Cambridge, Mass., 2014).
• Ryan Decker, John Haltiwanger, Ron Jarmin, and Javier Miranda, “Where Has All the Skewness Gone? The Decline in High-Growth (Young) Firms in the U.S.” Working Paper No. 21776 (Cambridge, Mass.: National Bureau of Economic Research, 2016), http://www.nber.org/papers/w21776 .
• Decker et al.’s identified industry groups draw heavily from the information sector but also from the information technology industries in the manufacturing sector and from scientific industries in the services sector.
• Center for Regional Economic Competitiveness and Cromwell Schmisseur, “Program Evaluation of the US Department of Treasury State Small Business Credit Initiative” (2016), p. 62, https://www.treasury.gov/resource-center/sb-programs/Documents/SSBCI_pe2016_Full_Report.pdf .
• U.S. Department of the Treasury, “State Small Business Credit Initiative (SSBCI),” https://www.treasury.gov/resource-center/sb-programs/Pages/ssbci.aspx .
• Center for Regional Economic Competitiveness and Cromwell Schmisseur, “Program Evaluation,” p. 1.
• Partnership for a New American Economy and Partnership for New York City, “Not Coming to America: Why the U.S Is Falling Behind in the Global Race for Talent,” (2012), http://www.renewoureconomy.org/sites/all/themes/pnae/not-coming-to-america.pdf .
• Allie Bidwell, “Foreign Brain Drain a Call for Immigration Reform, Some Say,” U.S. News and World Report , May 7, 2014, http://www.usnews.com/news/articles/2014/05/07/report-33-percent-of-international-students-in-stem-fields .
• Stephen Ezell, “A Research Investor’s Visa Would Spur U.S. Economic and Employment Growth,” The Innovation Files (blog), April 30, 2013, http://www.innovationfiles.org/a-research-investors-visa-would-spur-u-s-economic-and-employment-growth/#sthash.LODnPuEu.dpuf .
• Dane Stangler and Jared Konczal, “Give Me Your Entrepreneurs, Your Innovators: Estimating the Employment Impact of a Startup Visa” (Kansas City, Mo.: Ewing Marion Kauffman Foundation, 2013), http://www.kauffman.org/~/media/kauffman_org/research%20reports%20and%20covers/2013/02/startup_visa_impact_final.pdf .
• Peter Hall and David Soskice, Varieties of Capitalism: The Institutional Foundations of Comparative Advantage , (Oxford: Oxford University Press, 2001).
• Bianca Bosker, “Best Products of CES 2011: The Coolest Gadgets from The Consumer Electronics Show,” Huffington Post (blog), May 25, 2011.
• Lorin Hitt and Prasanna Tambe, “Measuring Spillovers From Information Technology Investments,” Proceedings of the 27 th International Conference on Information Systems, Milwaukee, Wis., 2006, p. 1793.
• The National Science Foundation, Science and Engineering Indicators, 2016.
• Nicholas Bloom and Rachel Griffith, “The Internationalization of R&D,” Fiscal Studies 22, no. 3 (2001), 337–55.
• Coopers & Lybrand, “Economic Benefits of the R&D Tax Credit” (New York, 1998).
• Kenneth J. Klassen, Jeffery A. Pittman, Margaret P. Reed, and Steve Fortin, “A Cross-National Comparison of R&D Expenditure Decisions: Tax Incentives and Financial Constraints,” Contemporary Accounting Research 21, no. 3 (2004), 639–80.
• Luke Stewart, Jacek Warda, and Robert Atkinson, “We’re #27!: The United States Lags Far Behind in R&D Tax Incentive Generosity” (Washington: Information Technology and Innovation Foundation, 2012).
• New Mexico Small Business Assistance Program, http://www.nmsbaprogram.org/.
• Stephen J. Ezell and Robert D. Atkinson, “Fifty Ways to Leave Your Competitiveness Woes Behind: A National Traded Sector Competiveness Strategy” (Washington: Information Technology and Innovation Foundation, 2011): 20-21, http://www2.itif.org/2012-fifty-ways-competitiveness-woes-behind.pdf .
• Jose-Maria Fernandez, Roger Stein, and Andrew Lo, “Commercialization Biomedical Research Through Securitization Techniques,” Nature Biotechnology 30 (2012).
• The U.S. tax credit has been heavily studied. For example, the former U.S. Congressional Office of Technology Assessment concluded that, “For every dollar lost in tax revenue, the R&D tax credit produces a dollar increase in reported R&D spending, on the margin.” See Bronwyn Hall, “The Effectiveness of Research and Experimental Tax Credits: Critical Literature Review and Research Design” (Washington: Office of Technology Assessment, 1995), http://emlab.berkeley.edu/~bhhall/papers/BHH95%20OTArtax.pdf . See also Coopers & Lybrand, Economic Benefits of the R&D Tax Credit (New York, 1998).
• Information Technology and Innovation Foundation, “Winning the Race Memo: Corporate Taxes” (2012), http://www2.itif.org/2012-wtr-taxes.pdf?_ga=1.202252433.1806060799.1471894729 .
• Robert D. Atkinson and Scott Andes, “Patent Boxes: Innovation in Tax Policy and Tax Policy for Innovation,” (Washington: Information Technology and Innovation Foundation, 2011), http://www.itif.org/files/2011-patent-box-final.pdf .
• See Stephen J. Ezell, “‘Innovation Box’ Proposal Would Stimulate U.S. R&D and Innovation,” The Innovation Files , July 31, 2015, http://www.innovationfiles.org/innovation-box-proposal-would-stimulate-u-s-rd-and-innovation/ , and United States Senate Committee on Finance. “The International Tax Bipartisan Tax Working Group: Final Report,” July 7, 2015, http://www.finance.senate.gov/imo/media/doc/The%20International%20Tax%20Bipartisan%20Tax%20Working%20Group%20Report.pdf .
• Joe Kennedy, “Tax Proposals Attempt to Bridge the “Valley of Death” for Small Research Firms,” The Innovation Files , March 24, 2015, http://www.innovationfiles.org/tax-proposals-attempt-to-bridge-the-valley-of-death-for-small-research-firms/ .
• Ernst & Young, Economic Impact of Tax Proposals Affecting Research-Intensive Start-Up Businesses and Qualified Small Business Companies (Washington: Ernst & Young, July 2013), http://smallbusinessinnovators.org/userfiles/ey%20csbi%20report%20economic%20impact%20of%20tax%20proposals%20for%20start-ups.pdf .

Francis Annan

July 31, 2024

Mark MacCarthy

Anton Korinek

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U.S. Economic Growth Accelerates, Outpacing Forecasts

Gross domestic product rose at a 2.8 percent annual rate in the second quarter, new evidence of the economy’s resilience despite high interest rates.

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Economic growth picked up more than expected in the spring, as cooling inflation and a strong labor market allowed consumers to keep spending even as high interest rates weighed on their finances.

Gross domestic product, adjusted for inflation, increased at a 2.8 percent annual rate in the second quarter, the Commerce Department said on Thursday. That was faster than the 1.4 percent rate recorded in the first quarter, but shy of the unexpectedly strong growth in the second half of last year.

Consumer spending, the backbone of the U.S. economy, rose at a 2.3 percent annual rate in the second quarter — a solid pace, albeit much slower than in 2021, when businesses were reopening after pandemic-induced closings. Business investment in equipment rose at its fastest pace in more than two years. Inflation, which picked up unexpectedly at the start of the year, eased in the quarter.

The data is preliminary and will be revised at least twice.

Taken together, the findings suggest that the economy remains on track for a rare “soft landing,” in which inflation eases without triggering a recession. That is something few forecasters considered likely when the Federal Reserve began raising interest rates two years ago to combat inflation.

“It’s the perfect landing,” said Sam Coffin, an economist at Morgan Stanley.

Recession fears re-emerged in recent months, first when inflation briefly surged and then when the previously rock-solid job market showed signs of cracking in the spring. But recent data, including the surprisingly strong second-quarter growth figures, indicate that the expansion is on firm footing.

“The economy is in a transition, but it’s in a good place,” said Ryan Sweet, chief U.S. economist at Oxford Economics. “The economy is slowing from very strong growth in the second half of last year. We’re just settling down into something that’s a little more sustainable.”

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Tax Incentives for Charitable Giving: New Findings from the TCJA

The Tax Cuts and Jobs Act eliminated federal charitable giving incentives for roughly 20 percent of US income-tax payers. We study the impact of this on giving. Basic theory and our empirical results suggest heterogeneous effects for taxpayers with different amounts of itemizable expenses. Overall, the reform decreased charitable giving by about $20 billion annually. Using a new method to adjust estimates for retimed giving, we find evidence of moderate intertemporal shifts from pre-announcement of the law. The permanent price elasticity of giving estimates range from .6 for the average donor to over 2 for those predicted to be most responsive to the reform.

The authors received no funding to conduct this study. The collection of the PSID data used by the authors was partly supported by the National Institutes of Health [R01 HD069609, R01 AG040213]; and the National Science Foundation [SES 1157698, 1623684]. Collection of the Philanthropy Panel Study data within the PSID was begun in 2001 with funding from the Atlantic Philanthropies, with continuing waves funded by partnering donors; recent institutional donors include the Bill & Melinda Gates Foundation, Charles Stewart Mott Foundation, Fidelity Charitable Catalyst Fund, Google.org Charitable Giving Fund, and The John Templeton Foundation. We are grateful for helpful comments and suggestions from participants at the 9th Annual Conference of the Science of Philanthropy Initiative, the University of Colorado Denver, and the 51st ARNOVA Annual Conference. We are also grateful to Dan Feenberg for his creation of, and providing help with, NBER’s TAXSIM. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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• Midterm Voting Intentions Are Divided, Economic Gloom Persists

2. Views of the economy and economic concerns

Table of contents.

• Republicans have a modest engagement edge
• Top midterm issues: the economy, future of democracy
• Wide partisan divides in voters’ attitudes, beliefs
• Biden’s job rating more negative than positive in most demographic groups
• Views of the economy and midterm voting
• Acknowledgments
• Validated voters

Americans’ views of the nation’s economy remain overwhelmingly negative, with roughly eight-in-ten adults (82%) saying that economic conditions today are poor (36%) or only fair (46%). Fewer than two-in-ten (17%) say that conditions are excellent (2%) or good (16%).

Ratings of the economy have improved since July, when 49% described conditions as poor and 13% described them as excellent or good. Yet ratings remain lower than they were in January of this year, when 28% rated economic conditions as excellent or good.

Both Republicans and Democrats express more negative views of the economy than they did in January.

Today, just 9% of Republicans and Republican-leaning independents rate conditions as excellent or good, compared with 20% who said this at the beginning of the year. And about a quarter of Democrats and Democratic leaners (26%) currently rate conditions positively, compared with 36% in January.

Americans continue to be more likely to express pessimistic views of the economy than optimistic views. About four-in-ten (41%) say they expect economic conditions to be worse a year from now than they are today, compared with 23% who expect conditions to be better in a year and 35% who expect them to be about the same.

As with views of current economic conditions, the public’s expectations for the economy are more positive than they were in July, but more negative than in January.

Republicans account for most of the change in expectations. While a majority of Republicans (55%) expect economic conditions to worsen over the next year, this share is down 13 percentage points since July – and almost identical to the 54% of Republicans who expressed pessimistic views of the economy at the beginning of the year. The share of Republicans who say economic conditions will be better a year from now is roughly double the share who said this three months ago (15% now vs. 8% in July).

Democrats remain more optimistic than Republicans about future economic conditions. A third expect economic conditions to improve over the next year, which has changed only modestly since January (38% then vs. 33% now).

The public’s concerns about the economy – like views about current and future economic conditions – reflect partisan differences. Republicans express more concern than Democrats about five of the seven economic issues included in the survey.

While majorities in both parties say they are very concerned about the price of food and consumer goods and the price of gasoline and energy, Republicans are much more likely to say they are very concerned: Republicans are 22 points more likely than Democrats to say they are very concerned about the price of gas and energy and 15 points more likely to say this about the price of food and consumer goods.

Republicans are also more likely than Democrats to be very concerned about the limited availability of some consumer products (43% vs. 31%), employers being unable to find workers for hire (41% vs. 27%) and how the stock market is performing (43% vs. 24%).

By contrast, Democrats are somewhat more likely than Republicans to be very concerned about the cost of housing (62% vs. 57%) and people who want to work being unable to find jobs (33% vs. 25%).

Voters’ views of current economic conditions are closely related to their candidate preferences: those who rate the economy as poor are roughly four times as likely to favor a Republican candidate for the U.S. House of Representatives as those who rate the economy as excellent or good (61% vs. 13%).

The large gap in partisans’ assessments of the economy – with Democrats rating conditions more positively than Republicans – explains a substantial portion of this relationship, though there are differences within each party.

More than eight-in-ten Democrats who rate economic conditions as excellent or good (87%) or only fair (84%) also say they support a Democratic candidate for the House. Among the 22% of Democrats who rate economic conditions as poor, a smaller majority (67%) support a Democratic candidate.

Even among Democrats with very negative evaluations of the economy, hardly any (just 3%) say they support a Republican candidate for the House. But 30% of Democrats who say conditions are poor also say they will vote for a candidate from another party or that they aren’t sure who they will vote for, compared with 14% of those who rate the economy as only fair and 10% of those who rate it excellent or good.

Among Republicans, those who rate the economy as poor are 9 percentage points more likely to say they support a Republican candidate – and 7 points less likely to favor a third-party candidate or say they aren’t sure who they will vote for – than those who rate the economy as only fair. Very few Republicans (6%) have positive views of the economy.

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Republican Gains in 2022 Midterms Driven Mostly by Turnout Advantage

How the gop won the turnout battle and a narrow victory in last year’s midterms, turnout in 2022 house midterms declined from 2018 high, final official returns show, in 2022 midterms, nearly all senate election results again matched states’ presidential votes, public has modest expectations for washington’s return to divided government, most popular, report materials.

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Implicit Land Taxes and Their Effect on the Real Economy

Today’s population health challenges are complex. Addressing these challenges and building environments that value and promote health for everyone requires expertise from multiple scientific disciplines and public and private partners.

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Property Tax Web Series

Daniel Murphy and Nathan Seegert

February 2024

We show that land taxes are associated with higher density, neighborhood diversity, business formation, and other indicators of economic performance. To demonstrate as much, we first estimate implicit land taxes (or subsidies) for over 2,000 counties in the U.S. These implicit land taxes arise due to differences between tax assessors and market valuations of land and, therefore, are likely idiosyncratic. We find substantial dispersion in these implicit land taxes across U.S. counties and within metropolitan areas, consistent with them being idiosyncratic. Finally, we develop a model of land taxes and endogenous population to rationalize our results.

This paper was presented by Nathan Seegert (University of Utah) on February 2, 2024 as part of the 2023-2024 Syracuse-Chicago Webinar Series on Property Tax Administration and Design. John Anderson (University of Nebraska-Lincoln) was the discussant for this presentation. Anderson comments on Seegert's "Implicit Land Taxes and Their Effect on the Real Economy."

This Syracuse-Chicago Webinar Series on Property Tax Administration and Design aims to gather insight and scholarship through domestic and international comparative studies with common threads to help reform and improve property tax administration and design in the U.S. and other countries facing similar problems.

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DARPA’s Ensuring Consistency of Systemic Information (ECoSystemic) program aims to develop innovative techniques for the robust recovery of financial information systems. Federated information systems are large, complex, and distributed computer and data systems, exactly the systems on which today’s financial system relies. With improved backups and processes for recovering data in the event of disruption or corruption, information systems can promptly return to a functional and mutually consistent restored state. ECoSystemic has engaged teams, each exploring a different facet of the financial system, and developing distinct analytical approaches and tools. Each team produces an executive summary of its work and results. The techniques developed for maintaining resilient datasets have applications in the military and commercial arenas as well.

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Resilient Knowledge Graph Representations for Federated Financial Data

This paper presents a new data paradigm that can facilitate analyses of critical financial issues. Specifically, the paper examines how the widespread use of resilient data structures could enhance the efficiency and stability of financial markets by allowing regulators and market participants to understand and better identify systemic risks. The ability to obtain value from data depends on how easily the data can be accessed for their intended use. A knowledge graph organizes federated data that lends itself to understanding relationships among entities such as market participants, exchanges, or instruments. Compared to other data structures, such as flat files or relational databases, knowledge graphs are more extensible, have lower barriers to access, and are uniquely suited to identifying relations within networks for visualization and analysis. The research shows that knowledge graphs also can be made resilient to attacks by malicious actors and physical failures. The paper demonstrates through examples how knowledge graphs can be leveraged to derive resilient meta statistics that financial regulators can use to identify abnormal behaviors and unusual variations in financial database characteristics over time.

Improving the resilience of machine learning in financial systems through synthetic data

The stability of a financial system requires the ability to recognize and recover from catastrophic events quickly. It requires that data backups and their connected systems are consistent. This inference problem requires a model of why acceptable differences exist to detect when inconsistencies arise. Synthetic data that systematically generate acceptable and unacceptable inconsistencies can significantly improve the financial system’s resilience. This paper outlines an interpretable procedure using Bayesian probabilistic models to create synthetic data. The approach allows the development of machine learning tools to detect inconsistencies in federated backups. The paper shows how this synthetic-data approach can reveal the conditions under which a machine-learning tool may fail and how that information can be used to build a more robust tool for detecting potential operation outages or cybersecurity threats.

Protecting Distributed Financial Networks

Distributed financial networks are a feature of the international financial system of payments, but they are also increasingly vulnerable to disruption as new technologies create unexpected opportunities for surprises, threats, and shocks. These vulnerabilities arise due to current economic and technical trends, including the increasing velocity and digitalization of individual economic activity, as well as the growing interconnectedness of the global economy. This brief discusses these financial system challenges through the lens of a credit card payment system. It present a range of integrated tools and procedures tailored to meet the needs of the financial firm, network, and system as no single “silver bullet” solution exists. Instead, protecting networks requires multiple, integrated solutions that work together to reduce system fraud and errors.

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ADP: Hiring slowed, economy created 122,000 jobs in July

July 31 (UPI) -- Private nonfarm payrolls increased by 122,000 jobs in July and saw a contraction in small business positions, according to the newest National Employment Report released by the ADP Research Institute on Wednesday.

The number was off the 150,000 positions gained in June while reporting that annual pay for workers rose 4.8% from the same time in 2023. Advertisement

Nela Richardson, the chief economist with ADP, said the slowing growth of jobs will likely play a role in the Federal Reserve Board's decision on whether to cut interest rates.

"With wage growth abating, the labor market is playing along with the Federal Reserve's effort to slow inflation," Richardson said in a statement . "If inflation goes back up, it won't be because of labor."

The service-providing sector drove the bulk of the hiring in July by creating 81,000 jobs. Within that sector, trade/transportation/utilities produced 61,000 positions. Professional and business services lost 37,000 people.

The goods-producing sector offset those losses by growing its hiring by 37,000. Construction made up for 39,000 hires in July, to make up for the 18,000 in losses in information employment.

Small businesses took the brunt of the losses in July, losing 7,000 people. Employers with 20-49 employees lost 22,000 positions in this month, ADP reported. Advertisement

Large companies, businesses with more than 500 employees, picked up the slack with 62,000 more hires this month.

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