economics article review

Weltwirtschaftliches Archiv

Gravity and trade in video on demand services

• Annette Broocks
• Zuzanna Studnicka

Patterns of global and regional integration in the East African Community

• Sebastian Krantz

The evolution of manufacturing comparative advantage along global value chains: the amplifying role of logistics performance

• Qianli Dong

Do investors reward sovereign catastrophe bond issuance? Evidence from a panel of 26 disaster-prone countries

• Raluca Maran

Trade, productivity, and services input intensity

• Bernard Hoekman
• Marco Sanfilippo
• Rohit Ticku

How does consumer quality misperception change European Union antidumping actions?

• Cassagnard Patrice
• Thiam Mamadou

Curse or blessing? multinational corporations and labor market outcomes in Africa

• Mariapia Mendola
• Giovanni Prarolo
• Tommaso Sonno

Do former employees of foreign MNEs boost incumbent workers’ wages in domestic firms?

• Eoin T. Flaherty

“When you need it quick, let us ship it right”: on the importance of port efficiency and service quality to comply with food trade standards in Ghana

• Lukas Kornher
• Daniel Sakyi
• Linus Linnaeus Tannor

The impact of natural disasters on US business credit markets: a comparative analysis of short-term and long-duration events

• Capucine Nobletz

On deep trade agreements, institutions, and global value chains: evidence from Egypt

• Chahir Zaki

Global value chains and aggregate productivity growth in developing countries: the role of intra-sectoral allocation and structural change

• Solomon Owusu

Environmental preferences and sector valuations

• Tristan Jourde
• Arthur Stalla-Bourdillon

An analytical framework for assessing climate transition risks: an application to France

• Thomas Allen
• Stéphane Dées
• Marie Rabaté

Functional shocks to inflation expectations and real interest rates and their macroeconomic effects

• Christina Anderl
• Guglielmo Maria Caporale

Managers’ country-specific experience and outward foreign direct investment: China and cross-countries’ evidence

• Hongzhong Fan

Energy abundance, the geographical distribution of manufacturing, and international trade

• Robert J. R. Elliott

Appropriation and comparative advantage

Assessing climate-related disclosures of european banks through text mining.

• Angel-Ivan Moreno
• Teresa Caminero

Sectoral credit sensitivity to carbon price with value chain effects

• Edouard Pineau
• Elizabeth Zuñiga

Uncertainty and the uncovered interest parity condition: How are they related?

• Nelson R. Ramírez-Rondán
• Marco E. Terrones

Will you take my (s)crap? Waste havens in the global plastic waste trade

• Pukitta Chunsuttiwat
• Ian Coxhead

How sustainable finance creates impact: transmission mechanisms to the real economy

• Ben Caldecott
• Felicia Liu

The market for carbon offsets: insights from US stock exchanges

• Stephen F. Diamond
• Jennifer W. Kuan

Environmental regulation, pollution emissions and the current account

• Shuang Zheng
• Xiaohui Liu

Fiscal performance under inflation and inflation surprises: evidence from fiscal reaction functions for the euro area

• Karsten Staehr
• Oļegs Tkačevs

The complex regional effects of macro-institutional change: evidence from EU enlargement over three decades

• Philipp Breidenbach

Necessary evil: water treaties and international trade

• Tchapo Gbandi

International firms and COVID-19: evidence from a global survey

• Floriana Borino
• Eric Carlson
• Olga Solleder

External relations, regional productivity, and exogenous shocks: lessons from the Italian experience

• Luca Bettarelli
• Laura Resmini

Environmental migration? A systematic review and meta-analysis of the literature

• Maria Cipollina
• Luca De Benedictis
• Elisa Scibè

How do social capabilities shape a country’s comparative advantages? Unpacking industries’ relatedness

• Gonzalo Castañeda
• Luis Castro Peñarrieta
• Florian Chávez-Juárez

Putting the news in New York and New Orleans: the impact of information frictions on trade

• James M. Harrison

Did tax treaties restrain the profit shifting of Chinese multinationals?

• Mantian Xue
• Yuwei Zhang

Real exchange rates and manufacturing exports in emerging economies: the role of sectoral heterogeneity and product complexity

• Thomas Goda
• Alejandro Torres García
• Cristhian Larrahondo

Patterns of variability in the structure of global value chains: a network analysis

• Carlo Piccardi
• Lucia Tajoli
• Riccardo Vitali

Product differentiation, interdependence, and the formation of PTAs

Agricultural fluctuations and global economic conditions

• William Ginn

On the heterogeneous trade and welfare effects of GATT/WTO membership

• Gabriel Felbermayr
• Mario Larch
• Yoto V. Yotov

Immigrant employment and the contract enforcement costs of offshoring

• Andreas Hatzigeorgiou
• Patrik Karpaty
• Magnus Lodefalk

Monetary policy frameworks since Bretton Woods, across the world and its regions

• David Cobham

Regulatory harmonization with the European Union: opportunity or threat to Moroccan firms?

• Patricia Augier
• Olivier Cadot
• Marion Dovis

What ‘special purposes’ explain cross-border debt funding by banks? Evidence from Ireland

• Brian Golden
• Eduardo Maqui

Technical barriers to trade, product quality and trade margins: firm-level evidence

• Ha Thi Thanh Doan
• Hongyong Zhang

Export quality and wage premium

• Francesco Guerra

Time stationarity, shape and ordinal ranking bias of RCA indexes: a new set of measures

• Rémi Stellian
• Jair N. Ojeda-Joya
• Jenny P. Danna-Buitrago

World commodity prices and partial default in emerging markets: an empirical analysis

• Manoj Atolia
• Shuang Feng

Aid modality and growth under post-conflict conditions

• Yoon S. Hur

The effect of IMF communication on government bond markets: insights from sentiment analysis

• Hamza Bennani
• Cécile Couharde
• Yoan Wallois

New perspectives on the rise and fall of global imbalances: evidence from large emerging market economies

• Krittika Banerjee
• Ashima Goyal

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Which topics in the Economics collection generated the most attention from readers in 2020?

The following list represents highly downloaded articles from the 3 Annual Reviews Economics titles during the year.

Return to Most Read Articles in 2020 Collection

Environmental, Economic, and Social Consequences of the Oil Palm Boom

Matin Qaim , Kibrom T. Sibhatu , Hermanto Siregar , and Ingo Grass , Annual Review of Resource Economics

Behavioral Implications of Causal Misperceptions

Ran Spiegler , Annual Review of Economics

Political Effects of the Internet and Social Media

Ekaterina Zhuravskaya , Maria Petrova , and Ruben Enikolopov , Annual Review of Economics

On Measuring Global Poverty

Martin Ravallion , Annual Review of Economics

Social Identity and Economic Policy

Moses Shayo , Annual Review of Economics

The Theory and Empirics of the Marriage Market

Pierre-André Chiappori , Annual Review of Economics

A Conversation with Angus Deaton

Angus Deaton , Gordon Rausser , and David Zilberman , Annual Review of Resource Economics

Social Identity, Group Behavior, and Teams

Gary Charness and Yan Chen , Annual Review of Economics

Informality: Causes and Consequences for Development

Gabriel Ulyssea , Annual Review of Economics

Peer Effects in Networks: A Survey

Yann Bramoullé , Habiba Djebbari , and Bernard Fortin , Annual Review of Economics

Annual Reviews is a nonprofit publisher with a mission to synthesize and integrate knowledge for the progress of science and the benefit of society. We currently publish 51 highly cited journals in the Biomedical, Life, Physical, and Social Sciences, including Economics.

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Economics →

• 03 Sep 2024
• Cold Call Podcast

How the US Government Is Innovating in Its Efforts to Fund Semiconductor Manufacturing

In February 2023, US Commerce Secretary Gina Raimondo was deciding whether or not to sign off on a Notice of Funding Opportunity (NOFO) for $39 billion in direct semiconductor manufacturing incentives. But this NOFO had several unconventional provisions: a pre-application (pre-app) to the actual application, upside sharing provisions to align incentives, and funding milestones so that only awardees making progress would receive additional funds. The funding had been made available through the US Department of Commerce by the CHIPS (Creating Helpful Incentives to Produce Semiconductors) and Science Act passed a few months earlier. Raimondo’s team had proposed additional measures that would help the US regain technological leadership while protecting taxpayer funds. Should Raimondo move forward with the “innovative” NOFO, despite the risks? Harvard Business School professor Mitch Weiss explores the issue of risk-taking and innovation in government in his case, “The CHIPs Program Office.”

• 29 Aug 2024
• Research & Ideas

Shoot for the Stars: What to Know About the Space Economy

Outer space has come a long way since the 1960s. Matthew Weinzierl explains the current state of the space economy, highlighting the various opportunities for businesses hidden among the stars.

• 22 Aug 2024

Reading the Financial Crisis Warning Signs: Credit Markets and the 'Red-Zone'

While fears about slowing economic growth have roiled stock markets in recent weeks, credit markets remain stable and bullish, and a recession hasn't materialized as some analysts predicted. Robin Greenwood discusses the market conditions that are buoying the economy—and risk signals to watch.

• 05 Aug 2024

Watching for the Next Economic Downturn? Follow Corporate Debt

Rising household debt alone isn't enough to predict looming economic crises. Research by Victoria Ivashina examines the role of corporate debt in fiscal crashes since 1940.

• 23 Jul 2024

Forgiving Medical Debt Won't Make Everyone Happier

Medical debt not only hurts credit access, it can also harm one's mental health. But a study by Raymond Kluender finds that forgiving people's bills—even $170 million of debt—doesn't necessarily reduce stress, financial or otherwise.

• In Practice

The New Rules of Trade with China: Navigating Tariffs, Turmoil, and Opportunities

Trade tensions between the US and China have continued well beyond the Trump Administration's tariffs. Harvard Business School faculty offer insights for leaders managing the complexities of doing business with the world's second-largest economy.

• 18 Jun 2024

Central Banks Missed Inflation Red Flags. This Pricing Model Could Help.

The steep inflation that plagued the economy after the COVID-19 pandemic took many economists by surprise. But research by Alberto Cavallo suggests that a different method of tracking prices—a real-time model—could predict future surges better.

• 28 May 2024

Job Search Advice for a Tough Market: Think Broadly and Stay Flexible

Some employers have pared staff and reduced hiring amid mixed economic signals. What does it mean for job seekers? Paul Gompers, Letian Zhang, and David Fubini offer advice for overcoming search challenges to score that all-important offer.

• 21 May 2024

What the Rise of Far-Right Politics Says About the Economy in an Election Year

With voters taking to the polls in dozens of countries this year, could election outcomes lean conservative? Paula Rettl says a lack of social mobility and a sense of economic insecurity are some of the factors fueling far-right movements around the world.

• 11 Apr 2024

Why Progress on Immigration Might Soften Labor Pains

Long-term labor shortages continue to stoke debates about immigration policy in the United States. We asked Harvard Business School faculty members to discuss what's at stake for companies facing talent needs, and the potential scenarios on the horizon.

• 01 Apr 2024

Navigating the Mood of Customers Weary of Price Hikes

Price increases might be tempering after historic surges, but companies continue to wrestle with pinched consumers. Alexander MacKay, Chiara Farronato, and Emily Williams make sense of the economic whiplash of inflation and offer insights for business leaders trying to find equilibrium.

• 29 Jan 2024

Do Disasters Rally Support for Climate Action? It's Complicated.

Reactions to devastating wildfires in the Amazon show the contrasting realities for people living in areas vulnerable to climate change. Research by Paula Rettl illustrates the political ramifications that arise as people weigh the economic tradeoffs of natural disasters.

• 10 Jan 2024

Technology and COVID Upended Tipping Norms. Will Consumers Keep Paying?

When COVID pushed service-based businesses to the brink, tipping became a way for customers to show their appreciation. Now that the pandemic is over, new technologies have enabled companies to maintain and expand the use of digital payment nudges, says Jill Avery.

• 17 Aug 2023

‘Not a Bunch of Weirdos’: Why Mainstream Investors Buy Crypto

Bitcoin might seem like the preferred tender of conspiracy theorists and criminals, but everyday investors are increasingly embracing crypto. A study of 59 million consumers by Marco Di Maggio and colleagues paints a shockingly ordinary picture of today's cryptocurrency buyer. What do they stand to gain?

• 15 Aug 2023

Why Giving to Others Makes Us Happy

Giving to others is also good for the giver. A research paper by Ashley Whillans and colleagues identifies three circumstances in which spending money on other people can boost happiness.

• 13 Mar 2023

What Would It Take to Unlock Microfinance's Full Potential?

Microfinance has been seen as a vehicle for economic mobility in developing countries, but the results have been mixed. Research by Natalia Rigol and Ben Roth probes how different lending approaches might serve entrepreneurs better.

• 23 Jan 2023

After High-Profile Failures, Can Investors Still Trust Credit Ratings?

Rating agencies, such as Standard & Poor’s and Moody's, have been criticized for not warning investors of risks that led to major financial catastrophes. But an analysis of thousands of ratings by Anywhere Sikochi and colleagues suggests that agencies have learned from past mistakes.

• 29 Nov 2022

How Much More Would Holiday Shoppers Pay to Wear Something Rare?

Economic worries will make pricing strategy even more critical this holiday season. Research by Chiara Farronato reveals the value that hip consumers see in hard-to-find products. Are companies simply making too many goods?

• 21 Nov 2022

Buy Now, Pay Later: How Retail's Hot Feature Hurts Low-Income Shoppers

More consumers may opt to "buy now, pay later" this holiday season, but what happens if they can't make that last payment? Research by Marco Di Maggio and Emily Williams highlights the risks of these financing services, especially for lower-income shoppers.

• 01 Sep 2022
• What Do You Think?

Is It Time to Consider Lifting Tariffs on Chinese Imports?

Many of the tariffs levied by the Trump administration on Chinese goods remain in place. James Heskett weighs whether the US should prioritize renegotiating trade agreements with China, and what it would take to move on from the trade war. Open for comment; 0 Comments.

Food From the East: Fertilizer Dependence and its Effect on America-China Relations

How Piracy Websites Could Incentivize Innovation in the Entertainment Industry

• Oct 26, 2023

The Tradeoffs of Shopping: Ethics vs. Sustainability

• Oct 23, 2023

The Commercialization of Girl Scout Cookies

• Feb 21, 2023

How Labor Informality in Mexico Hinders Socioeconomic Progress

• Jan 2, 2023

Dark Pools: Are Markets Unfair?

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What We Learned About the Economy in 2021

For once, the government tried overheating the economy. For better and worse, it succeeded.

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By Neil Irwin

For people who study the vicissitudes of the economy, 2021 has been the most interesting year of the 2000s.

It hasn’t been the most dramatic (that would be 2008 or 2020), and neither the best (2000 or 2019) nor the worst (2009). Rather, it has been a year in which economic dynamics that had seemed entrenched for decades came apart, or changed in fundamental ways. Workers attained the upper hand over employers; supply chains broke; inflation surged; and the economy rebuilt itself from its depressed pandemic levels with astounding speed.

In contrast to the last economic cycle, the government tried overheating the economy for once. For better and worse, it succeeded.

The unemployment rate, 6.7 percent in December 2020, fell to 4.2 percent 11 months later. That same shift took three and a half years in the last expansion, from March 2014 to September 2017.

But the flip side has been soaring prices and many goods in short supply. Inflation has reached its highest levels in four decades. In surveys, Americans are remarkably unsatisfied with economic conditions. The growth numbers have been good. The vibes have been bad.

These are the most important things to learn from a year in which the economic ground beneath our feet shifted.

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• Trumponomics would not be as bad as most expect

Opposition would come from all angles

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I n markets it is known as the “Trump trade”, a bet that Donald Trump’s return to the White House would herald more inflation and higher interest rates. Many of Mr Trump’s core policies push in this direction: tariffs would add to import costs, deportations of immigrants could push up wages and deficit-financed tax cuts would juice the economy. Amid mounting inflation, the Federal Reserve would have little choice but to opt for higher rates.

In the wake of Joe Biden’s calamitous debate on June 27th, a preview of the trade played out. As investors grappled with the likelihood that Mr Trump would romp to the presidency, they sold off Treasuries, which led to a brief surge in yields. The big fear is that much worse would come to pass. If Mr Trump fought the Fed on rates, he might sow doubts about the central bank’s independence, undermining confidence in America’s markets and the dollar. That is the economic nightmare scenario for a second Trump administration.

But as with any nightmare, the bogeyman of Trumponomics may be more terrible than its reality. Mr Trump and his advisers have many rotten ideas. They also have some decent ones. And their ability to implement damaging policies will be constrained, with Congress, America’s institutions and markets all serving as checks.

Mr Trump has honed his agenda in speeches and interviews, and on July 8th it was enshrined by Republicans as the party’s election platform. Three elements stand out. The first is deregulation, a staple for Republicans. In contrast to 2017, when he and his advisers were ill prepared for the presidency, this time they have lined up personnel and policies. Mr Trump will waste little time in rescinding many of the Biden administration’s environmental rules, easing drilling restrictions for oil companies and putting pressure on federal agencies to cut spending. He has promised, as in his first presidency, to eliminate two regulations for each one issued.

But much of this is marketing buzz. The number of restrictions in the Code of Federal Regulations, a proxy for the intensity of regulation in America, was basically unchanged under Mr Trump. What is more, his administration was stymied by the courts. It was unsuccessful in nearly 80% of litigation over its use of federal agencies, according to the Institute for Policy Integrity, a research group. Goldman Sachs, a bank, reckons that the impact of all Mr Trump’s deregulation was ultimately insignificant for the wider economy—a result likely to be repeated.

On tax, Mr Trump can, in some sense, be seen as a continuity candidate. Action will focus on the looming expiration of much of the Tax Cuts and Jobs Act, Mr Trump’s package from 2017. The TCJA ’s reduction in corporate taxes was permanent, but much of the rest of the law, including cuts to personal income taxes, will expire at the end of 2025. Mr Trump’s main objective is to make these cuts permanent.

That will not be simple because to get the bill through Congress, Republicans will need to pay the cost of extending the cuts, about $4.5trn over the next decade. But Mr Trump has options. One slug of revenue may come from tariffs, which could bring in $3trn over the decade. There is also money in reversing some of Mr Biden’s policies. The cost of the Inflation Reduction Act, Mr Biden’s climate-subsidy package, is expected to reach about $1trn. Republicans can eliminate some tax credits, starting with discounts for electric vehicles. Mr Trump has also suggested he may unwind Mr Biden’s student-debt cancellations, on track to cost $1trn.

Mr Trump’s other tax ideas are more modest. He has talked about shaving a percentage point off the corporate tax rate, to cut it to 20% (he likes a round number). His zaniest proposal is to make tipping exempt from taxes. Legislators would have to craft that exemption carefully, otherwise everyone might demand pay as gratuities. Without offsetting revenue or spending cuts, every tax cut will make America’s deficit worse, a risk under Mr Trump. But it is not as if Mr Biden has been a paragon of fiscal rectitude: the federal deficit is on track to hit a hefty 7% of GDP this year.

Tariff man, part two

The economic strategy for which Mr Trump is most infamous, especially outside America, is his protectionism. He has been clear about where he wants to go next, aiming for a 10% universal tariff on all imports into America and a 60% levy on Chinese-made goods. He also wants a more concerted decoupling from China.

There is no doubting the global fallout if Mr Trump were to deliver on his panoply of America-first trade policies. Would he be able to do so? In the traditional wing of the Republican Party, there is still resistance to tariffs. If Mr Trump decides to cut Congress out of the picture, he may declare a national-security emergency, which would give him special powers. That, however, may be struck down in court.

Higher tariffs on China would be more achievable, since the White House could piggyback them on existing measures. It could, for instance, conclude that China has not lived up to a deal signed with Mr Trump in 2020, which would be easy to demonstrate. Cracking down on the rerouting of Chinese exports via other countries would be harder without co-operation from foreign governments, which Mr Trump has struggled to elicit in the past.

Moreover, even within Mr Trump’s White House, there may well be opposition to his most aggressive trade policies. Hawks such as Peter Navarro, an economic adviser, have been the most voluble, but Mr Trump likes to assemble a team of rivals, letting him adjudicate between contrasting opinions. He may once again appoint a Wall Street veteran as his treasury secretary and such a figure would be a counterweight to fire-breathing protectionists.

Mr Trump’s agenda will face other hurdles. He wants to hit the ground running but his first year in office features a dense legislative calendar. The debt ceiling will be reinstated on January 2nd, forcing the White House to enter talks with Congress. Another deadline looms at the end of April when Congress will have to make swingeing cuts if it has not worked out a new budget. All the while, the clock will be ticking on Mr Trump’s tax cuts. If the Democrats manage to win the House, all of these negotiations will be that much thornier.

Mr Trump will make even less headway on reshaping the Fed. Investors worry that he wants to influence the central bank’s rate decisions. Putting that desire into practice, though, is difficult. His first chance to appoint a new governor will come in 2026, after which he can also nominate a chair to replace Jerome Powell. But the Fed’s board is seven-strong and all nominations must go through the Senate, which previously blocked two of Mr Trump’s four nominees. If Mr Trump tried to fire Mr Powell, insiders at the Fed think that he would have another unwinnable legal fight on his hands.

Perhaps the biggest short-term damage that Mr Trump can inflict on America’s economy is through his immigration policy. Stopping “the invasion”, as he calls it, will consume his administration. The millions who have entered the country in the past few years have been vital to sustaining economic growth while taming inflation. A halt to migration would be a shock to the labour market. Nevertheless, as with other Trumpian policies, there will be resistance every step of the way, with courts striking down deportation orders, Democrat states refusing to co-operate and businesses lobbying for a lighter touch.

Through all of this, the financial world would also rein in Mr Trump. He is sensitive to the stockmarket, even ascribing its good run earlier this year to expectations of his victory. Were equities to fall or yields to soar when Mr Trump attacks his latest target—whether the Fed, migrants or foreign trade—it would catch his attention.

This is not to be sanguine about Mr Trump’s hold over American politics. There is a risk that his second term would spiral out of control. Checks on his excesses are not automatic, and would need people to go against him in the Republican Party, the courts and society at large. But that ought to happen, which would keep the worst of Trumponomics at bay. ■

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This article appeared in the Finance & economics section of the print edition under the headline “Halting his charge”

Finance & economics July 13th 2024

• Betting markets are useful when politics is chaotic
• Europe prepares for a mighty trade war
• The dangerous rise of pension nationalism
• How strongmen abuse tools for fighting financial crime
• Xi Jinping really is unshakeably committed to the private sector

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Peer-reviewed

Research Article

The impact mechanism and empirical analysis of financial efficiency of science and technology empowering regional real economy growth

Roles Conceptualization, Writing – original draft

Affiliation School of Economics, Hebei GEO University, Shijiazhuang, China

Roles Conceptualization, Formal analysis, Writing – review & editing

* E-mail: [email protected]

• Tao Zhang, 

• Published: September 13, 2024
• https://doi.org/10.1371/journal.pone.0307497
• Reader Comments

With the aim of exploring the impact mechanism of scientific and technological financial efficiency on regional real economy growth in the context of ecological civilization construction, this study introduces environmental regulation as a mediating factor. By analyzing changes in science and financial efficiency of science and technology, we provide an effective basis for regional real economy development. To achieve this goal, we define concepts such as science and financial efficiency of science and technology and regional real economy, measure data from 2012 to 2021, analyze the impact of science and financial efficiency of science and technology on economic growth using intermediary models, test mediation effects with bootstrap methods, and identify significant differences between regions. It indicates that enhancing science and financial efficiency of sci-tech benefits China’s regional real economy growth, but there’s unbalanced development across regions. Additionally, environmental regulation serves as a crucial intermediary in the relationship between sci-tech finance and economic growth. There exist regional disparities in the mediation effects of environmental regulation, with eastern regions demonstrating stronger effects compared to central and western regions.

Citation: Zhang T, Tian J (2024) The impact mechanism and empirical analysis of financial efficiency of science and technology empowering regional real economy growth. PLoS ONE 19(9): e0307497. https://doi.org/10.1371/journal.pone.0307497

Editor: Yuantao Xie, University of International Business & Economics, CHINA

Received: November 30, 2023; Accepted: July 6, 2024; Published: September 13, 2024

Copyright: © 2024 Zhang, Tian. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In terms of the new normal of China’s economic development, it is imperative to comprehensively grasp the scientific connotation of high-quality development in order to facilitate the transformation towards enhanced quality, efficiency, and power. This can be achieved through robust power reforms that drive changes in economic development quality and efficiency, as well as by fostering scientific and technological innovation to enhance output quality and efficiency. Scientific and technological innovation takes precedence in promoting economic development and facilitating high-quality industrial growth. It serves as the fundamental driving force behind achieving high-quality development while simultaneously bolstering overall innovative capabilities. The significance of scientific and technological innovation has garnered increased attention with more profound discussions taking place on this subject matter; it has become a topic of great interest for society at large. The latest Chinese government work report explicitly emphasizes the need to strengthen support for scientific and technological innovation—an aspect particularly crucial within China’s context where such innovation plays an irreplaceable role in advancing industrial economy, national economy, and regional real economy.

In the context of scientific and technological innovation, the deep integration of science, technology, and finance has facilitated the emergence of "scientific and technological finance". Scientific and technological finance serves as a fundamental component within both national innovation systems and financial systems. The government’s financial sector takes the lead in guiding numerous financial institutions and intermediary service agencies to continuously innovate financial products, establish corresponding service platforms, optimize service models, thereby promoting the development of the real economy. The efficiency of technological financing operations has always been an urgent issue that needs to be addressed–specifically how to maximize output from investments in scientific and technological achievements. This disparity between regions can impact the efficiency of science and technology finance output while diminishing its substantive promotion effect on regional real economies. Taking Shanghai as an example, the city has established 17 scientific and technological financial service stations, serving enterprises more than 2,000 times a year, effectively promoting the docking between the government, financial institutions, and scientific and technological innovation enterprises.

Consequently, it becomes evident that enabling regional entity growth through efficient sci-tech finance is crucial. The higher the input-output efficiency of sci-tech finance, the more significant its positive impact on regional economic growth. This study aims to investigate this positive relationship by examining existing mechanisms influencing it as well as exploring how science and technology finance empowers regional economic growth. Additionally, we introduce environmental regulation’s role in enabling such growth while laying a solid foundation for future developments within regional economies.

Literature review

The research on technology finance can be traced back to the mid to late 20th century. Schumpeter clarified the advantages of the integration of finance and technology in his book “Economic Development Theory” and explained the supporting role of credit capital for technological innovation. Ang believes that technological innovation cannot do without the liberalization of financial development [ 1 ]. Peter also pointed out in his book “Technology Revolution and Financial Capital” that the new paradigm formed by the integration of technology and finance is more conducive to promoting the development of regional economy [ 2 ]. Guarnieri points out from the enterprise level that the integration between public financial investment and technological innovation promotes the development of technological innovation [ 3 ]. Similarly, Sasidharan also believes that the development of technology finance has played an important economic support role in enterprise product innovation [ 4 ]. Zetsche believes that the advantages of technology finance lie in improving business quality, improving risk management level, reducing transaction costs, and having good financial inclusivity, which can provide more credit support for the development of small and medium-sized enterprises and consumers in the regional real economy [ 5 ]. Guariglia’s research found that high-tech industries dominated by technological innovation are more susceptible to financial influences in the early stages of development, indicating that technology finance has a significant impact on local high-tech industries to a large extent [ 6 ]. Lenong’s discussion on the economic value and impact of technology finance indicates that technology finance is based on the integration of technology, innovation, and capital [ 7 ]. The vast majority will view “technology finance” as a result of the connection between technology and finance, which can have a positive impact on regional economy, industrial development, and personal credit. As Seoh believes, technology finance can promote national economic development on the one hand, and effectively improve the level of technological innovation on the other hand, thereby enhancing the competitiveness of national technology research and development [ 8 ]. At present, the academic community has conducted a detailed and in-depth analysis of technology finance, but has not provided a clear definition of it. Moreover, the mechanism of technology finance also needs further optimization.

Regarding the efficiency of technology finance, it generally refers to the efficiency of resource allocation in technology finance, especially under the role of innovation driven development strategies, the development scale of technology finance is increasingly expanding, and the efficiency of resource allocation in technology finance is more crucial due to limited financial resources [ 9 ]. The measurement and analysis of the efficiency of technology finance generally adopts frontier analysis, such as stochastic frontier production function, data envelopment analysis method, Malmquist index method and its improved model. After Charnes and Cooper proposed the DEA method, Banker made improvements to the DEA method and proposed the BBC model method [ 10 ]. Kundi used DEA models with constant and variable returns to scale to measure the efficiency of financial support. Huang used the DEA Malmquist index method to calculate the efficiency of technology finance, in order to analyze the differences in financial efficiency of science and technology between different regions [ 11 ]. Similarly, Yi reached similar conclusions using the DEA-BCC model and Gini coefficient analysis method [ 12 ].

By measuring the efficiency of technology finance, we have further analyzed the relationship between technology finance and regional real economic growth. As Rjoja and Valev pointed out in their research, the development of the financial industry has a strong stimulating effect on the real economy, and there are significant regional differences [ 13 ]. Adusei validated the relationship between financial development and economic growth in his research on economic and financial development in South Africa, indicating that the financial industry has a certain one-way promoting effect on economic development [ 14 ]. Peng analyzed the impact of environmental regulations on the efficiency of technology finance from empirical evidence from provincial-level administrative regions in China, and constructed a model to measure the efficiency of technology finance. Based on the CCR model, the BCC model was improved to achieve the measurement of financial efficiency of science and technology [ 15 ]. Based on the measurement of the efficiency of technology finance, the impact mechanism of technology finance on regional real economic growth can be effectively analyzed. For example, Adusei analyzed the economic and financial development of South Africa, and based on the Granger causality test method, verified the impact relationship between financial development and economic growth. The results showed that the development of the financial industry can promote regional economic growth [ 14 ]. Peng pointed out that the impact mechanism between technology finance and real economic growth is largely influenced by environmental regulations, which is closely related to the impact of environmental regulations on the efficiency of technology finance [ 16 ]. Wasi uses DEA method to measure energy efficiency, which can also be applied to measure the financial efficiency of science and technology [ 17 – 19 ]. In addition, the inline study used data envelopment analysis (DEA-SBM) to measure the energy efficiency of Chinese provinces from 2004 to 2017. Mann-Whitney U test was used to explore whether there are significant differences in energy efficiency levels between China’s energy security policy and energy conservation and emission reduction policy. Meta-frontier analysis was used to further investigate the regional heterogeneity of production technology gap in east, middle and west China, in which DEA-SBM was used to measure efficiency [ 20 – 25 ]. In general, there are abundant researches on the evaluation of financial efficiency of science and technology. However, the current researches still face some challenges and limitations. First of all, although there are various models and methods used to measure the financial efficiency of science and technology, each method has its specific applicable conditions and limitations, so it needs to be selected and adjusted according to the specific situation in practical application. The evaluation of financial efficiency of science and technology is a complex and important research field, which needs constant exploration and innovation. Future research should focus on the improvement of influencing factors, mechanism and evaluation methods of S&T finance efficiency, so as to provide strong support for promoting the healthy development of S&T finance in our country.

To sum up, the existing research has made some progress in exploring the mechanism of technological finance’s influence on regional economic growth, but there are still some deficiencies and gaps. First of all, although the measurement model of technical finance efficiency has been established, most studies are still focused on the macro level, and there are few discussions on the micro level of technical finance efficiency. Secondly, the existing research on the relationship between technical finance and regional economic growth often ignores the differences between regions. Although studies have mentioned the impact of environmental regulations on the relationship between technological finance and real economic growth, research in this area is still weak. This study presents a comprehensive overview of the implications of technology finance and enhances its theoretical framework from a deeper perspective, aiming to further enhance and optimize China’s technology finance service system, thereby contributing to the development of the regional real economy. The interconnection between the technology finance system and the regional real economy is highly significant. This study is based on empirical evidence from various regions in China, utilizing statistical data analysis to conduct a thorough examination of how technology finance influences the development of the regional real economy. It explores the impact of technological advancements and financial efficiency on real economic growth within an environmental regulatory context. Moreover, it proposes corresponding solutions and suggestions for addressing challenges encountered during this developmental process, with an ultimate goal to elevate the level of regional real economic development while providing valuable insights for research conducted in other provinces and cities.

Empirical analysis

Measurement of variables, efficiency of technology finance..

The financial efficiency of science and technology can be measured using the DEA model, which constructs a production frontier to evaluate the relative distance between each DMU and the frontier, thereby obtaining an efficiency value. The DEA model demonstrates robustness and flexibility, making it adaptable to different scales and types of datasets. Given the challenges in data collection and processing within the field of science and technology finance, selecting a DEA model that is more adaptable with relatively low data requirements is crucial for accurately evaluating financial efficiency of science and technology. To begin with, constructing an efficient index system for science and technology finance is necessary. This index system focuses on the input and output aspects of science and technology finance. Specifically, investments in science and technology finance encompass bank credit investment in science and technology, VC/PE capital investment, investment in science and technology capital markets, government financial investments in science and technology, as well as R&D personnel investments. As for outputs related to science and technology finance, they include scientific research papers published, valid patent numbers obtained, as well as industrial added value generated by science-technology enterprises.

How to choose the above indicators is because as an important means to promote the development of science and technology and economic growth, the measurement of its efficiency is very important. Through the application of DEA model, the efficiency of science and technology finance can be quantitatively analyzed, so as to better understand its operating mechanism and existing problems, further reveal the inherent laws and potential problems of science and financial efficiency of science and technology, and provide a scientific basis for optimizing the allocation of science and technology financial resources and improving the efficiency of science and technology finance. As shown in Table 1 , it is the efficiency index system of science and technology finance.

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https://doi.org/10.1371/journal.pone.0307497.t001

As illustrated in Table 1 above, venture capital and private equity fund investments are pivotal concepts within the contemporary financial market, playing a crucial role in fostering scientific and technological innovation, facilitating the growth of small and medium-sized enterprises, and driving economic expansion [ 26 ]. Venture capital represents a specialized form of capital operation that focuses on investing in nascent or expanding stage enterprises with the aim of attaining substantial returns. Typically targeting innovative small to medium-sized enterprises exhibiting high growth potential, venture capital investments often involve significant technical barriers and possess considerable market prospects [ 27 ]. Private equity encompasses an unpublicized approach for raising funds from a limited number of investors to invest in non-publicly listed companies through equity investment, aiming to capitalize on enterprise growth-induced appreciation.

There is a scarcity of data on technology credit in the relevant literature pertaining to indicators for technology finance investment. The financing of Chinese technology-based enterprises predominantly relies on indirect funding. Therefore, the year-end debt of innovative technology industries in each province is selected as the primary measure for assessing technology credit. The quantification methods and data sources for other indicators are presented in Table 2 .

https://doi.org/10.1371/journal.pone.0307497.t002

To address the efficiency issue of technology finance, it is imperative to establish a robust evaluation index system and methodologies for assessing the financial efficiency of science and technology. Therefore, this study has opted to employ the Data Envelopment Analysis (DEA) method for implementation. DEA is a mathematical programming approach that enables an assessment of input-output indicators to evaluate the performance of similar departments, units, or types [ 28 ]. This method can further evaluate multiple input and output decision units of the same department, unit, and type [ 29 ]. Simultaneously, the DEA method is a non-parametric statistical approach that treats each decision-making unit as an evaluated entity. It identifies relevant issues by evaluating other decision-making units and constructs appropriate data models to analyze relative efficiency, determine potential input-output combinations within the production frontier, measure the distance between each decision-making unit and the production frontier, assess the effectiveness of DEA for each unit, and ultimately derive an evaluation ranking. Generally speaking, within the model-defined production possibility set, it is required to either maintain inputs while increasing outputs or maintain outputs while reducing inputs. Previous research has extensively employed DEA models in various industries primarily for assessing relative effectiveness of multiple inputs and outputs in social and economic domains; particularly suitable for analyzing benefits and efficiency in cultural industries and government sectors.

This study considers different provinces in the technology and finance industry as different types of decision-making units. For each decision-making unit, there are m types of inputs and p types of outputs. Therefore, the input vector can be represented by Formula ( 1 ), and the output vector can be represented by Formula ( 2 ).

https://doi.org/10.1371/journal.pone.0307497.t003

Measurement of regional real economy indicators.

Currently, there is no universally accepted definition of the real economy; however, numerous studies have been conducted in this area. This study proposes that the real economy encompasses economic activities facilitated through social capital. Specifically, it includes direct economic activities associated with material and financial production as well as cultural service consumption. Considering the volatility of China’s real estate market, the regional real economy discussed in this study refers to the residual portion obtained after subtracting the added value contributed by both the real estate industry and financial sector. The growth of regional real economy is defined as actual GDP minus the added value from both real estate and financial industries, with this remaining portion serving as a dependent variable. Regional indicators for real economic growth are calculated based on actual regional GDP and nominal GDP.

Measurement of environmental regulation.

The efficiency of technology finance in relation to capital flows may be influenced by environmental regulations, which can vary depending on market volatility and subsequently impact regional real economic growth. Therefore, this study considers environmental regulation as a key explanatory variable, specifically focusing on the intensity of such regulation. The intensity is measured by the ratio of regional pollution control investment to regional GDP.

Analysis of the impact of technology and financial efficiency on regional real economic growth

Construction of mediation effect model..

Hierarchical regression analysis is a widely employed approach for examining mediating effects. By assessing the significance of regression coefficients from independent variables to dependent variables, independent variables to intermediate variables, and intermediate variables to dependent variables, progression to the next step is contingent upon each test being statistically significant. The presence of statistical significance across all steps indicates a significant mediating effect. In cases where non-significant findings arise during testing, subsequent evaluation using Bootstrap or Sobel tests is conducted to further ascertain the significance of the mediating effect [ 30 ]. The specific method steps are shown in Fig 1 .

https://doi.org/10.1371/journal.pone.0307497.g001

In the above figure, the first step, verify a × b significant (Bootstrap method), if a × b is significant, and the intermediate test result does not include 0 at the 95% confidence interval. If the intermediate path exists, perform the first step of the test. Conversely. If a × b is not significant, then the intermediate test result contains 0 at the 95% confidence interval, and the intermediate path does not exist, skip to the third step;

The second step is to test whether c’ is significant. If c’ is not significant, it is a complete mediating effect. If c’ is significant, it is a partial mediating effect;

Step 3, if a × b is not significant, then the mediation is not established. Continue to test whether c’ is significant. If c’ is significant, then there is only a direct effect. If c’ is not significant, then it has no effect.

In this study, the direct and intermediate effects were tested using the hierarchical regression method, and the regression equation constructed is shown in Formulas ( 3 )–( 5 ).

Among them, coefficient c refers to the total effect of the independent variable on the dependent variable, that is, the total effect of the efficiency of technology and finance on the growth of regional real economy. The coefficient a refers to the effect of the independent variable on the environmental regulation of the intermediary variable; The coefficient b refers to the effect of the mediating variable environmental regulation on the regional real economic growth of the dependent variable after controlling for the influence of the independent variable; The coefficient c’ refers to the direct effect of technology and finance efficiency on the growth of regional real economy after controlling for the influence of intermediary variables. e1—e 3 refers to the regression residual. If there is a mediating effect in the model, then the mediating effect is a × b, so the total effect is c = c’ + a × b.

In the mediation effect test, considering the impact of factors such as urbanization level (Urban), degree of openness to the outside world (Open), and fiscal expenditure (Fis) on environmental regulation intensity and regional real economic growth in the survey sample, the above variables are used as control variables. Among them, Urban refers to the proportion of urban population in total labor force; Open refers to the proportion of the total import and export volume of each region to GDP; Fis refers to the proportion of the total fiscal expenditure of each province to GDP. The data source is the “China Statistical Yearbook” and “Provincial Statistical Yearbook” from 2012 to 2021, and relevant adjustments are made based on local price levels.

Robustness test.

As for the robustness of this model analysis, the Tobit model is selected for testing in this study. Its principle and hypothesis are shown in the following Eqs ( 6 ) and ( 7 ).

Multiple regression model was used to replace the Tobit model and the results were shown in Table 4 .

https://doi.org/10.1371/journal.pone.0307497.t004

As can be seen from Table 4 , after replacing the model, the contribution degree and influence direction of the above five independent variables to the dependent variables did not change significantly. Among the control variables, the influence of Fis is very significant. Even after replacing the model, the influence of the core explanatory variables on the explained variables in this study did not change significantly, and only the correlation coefficient changed. Therefore, the model estimation adopted in this study was robust.

Analysis of empirical results.

Based on the financial efficiency of science and technology index TE, environmental regulation intensity ERI, and real economy development index Y measured in the previous text, the mediation effect model is used to analyze the mediation effect between the three variables. This study analyzed the mediating effect of optimizing environmental regulations on the efficiency of technology finance in 30 provinces of China on regional real economic growth, and tested the experimental results. Considering the potential regional heterogeneity in technology finance, environmental regulation, and real economy development, this study divides 30 provinces into three regions: Eastern, central, and western. Conduct an empirical analysis on the mediating effects of technology and finance efficiency, environmental regulation intensity, and regional actual economic growth in three regions.

Firstly, conduct a regression analysis on the efficiency of technology finance and regional real economic growth, and obtain a regression coefficient c to test the significance between the two; Secondly, regression analysis is conducted on the impact of technology and finance efficiency on the regional real economic growth as an intermediary variable, and a regression coefficient a is obtained to test the significance between the two; Finally, a hierarchical regression analysis is conducted on the effects of technological and financial efficiency and environmental regulation as mediators on regional real economic growth, from which the regression coefficients are obtained b and c’ . To test the significance between the three variables. The results are shown in Tables 5 – 7 .

https://doi.org/10.1371/journal.pone.0307497.t005

https://doi.org/10.1371/journal.pone.0307497.t006

https://doi.org/10.1371/journal.pone.0307497.t007

From the data in the eastern part of Table 5 , it can be seen that the efficiency of technology finance has a significant positive predictive effect on regional real economic growth (c = 0.325); The efficiency of technology finance also has a significant positive predictive effect on environmental regulation (a = 0.168); The mediating variable environmental regulation also has a significant positive predictive effect on regional real economic growth (b = 0.222); When environmental regulations are included as mediating variables in the model, the positive predictive effect of financial efficiency of science and technology on regional real economic growth becomes less significant (c’ = 0.213).

Similarly, in the central data of Table 6 , it can be seen that the efficiency of technology finance has a significant positive predictive effect on regional real economic growth (c = 0.313); The efficiency of technology finance also has a significant positive predictive effect on environmental regulation (a = 0.172); The mediating variable environmental regulation also has a significant positive predictive effect on regional real economic growth (b = 0.322); When environmental regulations are included as mediating variables in the model, the positive predictive effect of financial efficiency of science and technology on regional real economic growth becomes less significant (c’ = 0.257).

From the western data in Table 7 , it can be seen that the efficiency of technology finance has a significant positive predictive effect on regional real economic growth (c = 0.209); The efficiency of technology finance also has a significant positive predictive effect on environmental regulation (a = 0.211); The mediating variable environmental regulation also has a significant positive predictive effect on regional real economic growth (b = 0.222); When environmental regulations are included as mediating variables in the model, the positive predictive effect of financial efficiency of science and technology on regional real economic growth becomes less significant (c’ = 0.213).

On the previous text, c represents the regression coefficient between X and Y (when there is no intermediate variable M in the model), which is the total effect; A represents the regression coefficient between X and M, b represents the regression coefficient between M and Y, and a × b is the product of a and b, which is the mediating effect; 95% Boot CI represents the 95% confidence interval obtained from Bootstrap sampling calculation. If the interval does not include 0, it indicates significance; C’ represents the regression coefficient between X and Y (when there is an intermediate variable M in the model), which is the direct effect; If a and b are significant, and c ‘is not significant, then it is a complete mediator; If a and b are significant, and c ‘is significant, and a × b is the same sign as c’, then it is a partial mediating effect;

If at least one of a and b is not significant, and the 95% Boot CI of a × b includes the number 0 (not significant), then the mediating effect is not significant. If at least one of a and b is not significant, and the 95% Boot CI of a × b does not include the number 0 (significant), and c ‘is not significant, then it is a complete mediator; If at least one of a and b is not significant, and the 95% Boot CI of a × b does not include the number 0 (significant), and c’ is significant, and a * b is signed with c’, then it is a partial mediating effect.

Based on this, the mediating effect of this study was tested, and the results are shown in Table 8 .

https://doi.org/10.1371/journal.pone.0307497.t008

From the above table, it can be seen that the efficiency of technology and finance in the eastern, central, and western regions has a significant and documented positive impact on regional real economic growth. The efficiency of technology and finance has a significant positive impact on environmental regulation, and environmental regulation also has a significant positive impact on regional real economic growth. This indicates that the mediating effect of technology finance on regional real economy growth through environmental regulation in various regions is significant, and the mediating effect in the eastern region is greater than that in the western region. There are regional differences in the mediating effect of technology finance on regional real economy growth through environmental regulation.

Similarly, the mediating effect of this study was tested, and the results are shown in Table 9 .

https://doi.org/10.1371/journal.pone.0307497.t009

In summary, it is not difficult to see that environmental regulations play a partial intermediary role between the efficiency of technology finance and the growth of regional real economy. That is to say, the efficiency of technology finance has a significant positive effect on environmental regulation, and environmental regulation has a significant positive effect on regional real economic growth. Therefore, environmental regulation has a mediating effect between financial efficiency of science and technology and regional real economic growth. However, the efficiency of technology finance also plays a significant positive role in the growth of regional real economy, which makes the mediating role played by environmental regulations not entirely mediating, but partially mediating. The relationship between the efficiency of technology finance and the growth of regional real economy is not only influenced by environmental regulations, but also by many other factors. For example, it is influenced by factors such as Uran, Open, Fi, and industrial structure [ 18 ]. This to some extent weakens the chain effect of financial efficiency of science and technology and environmental regulation on regional real economic growth, resulting in a direct relationship between financial efficiency of science and technology and regional real economic growth, with environmental regulation playing a partial intermediary role between the two.

Based on the current situation of science and technology finance, environmental regulation, and regional real economy development in China, this paper analyzes the theoretical relationship among these three factors. It then utilizes data from 2012 to 2021 based on 2011 to construct an index system for each factor, measure their respective indices, and build an intermediary effect model. This study examines the mediating effect of environmental regulation intensity on the influence of science and technology financial efficiency on real economic growth. Additionally, it investigates the mediating effect of industrial structure optimization on the impact of science and technology financial efficiency on real economic growth in three regions. The research findings are as follows: (1) The enhancement of science and technology financial efficiency has a significant positive impact on China’s regional real economy growth. Over the past decade, there has been a general increase in science and technology financial efficiency across China’s 30 provinces and autonomous regions; however, there exists an imbalance in its development between central/western regions compared to other areas. (2) Science and technology financial efficiency significantly affects environmental regulation intensity, with environmental regulation playing a crucial intermediary role in linking science and financial efficiency of science and technology to real economic growth. (3) There are regional disparities regarding the intermediary effect of science and technology finance on real economic growth through increased environmental regulation intensity; specifically, this effect is more pronounced in eastern regions compared to central or western ones. Therefore, enhancing both the development and efficacy of sci-tech finance is essential for promoting real economic growth.

In order to further promote science and technology finance to empower regional real economic growth, policy recommendations that can be adopted:

• We will strengthen financial support for local governments. Chinese government departments should be able to formulate highly targeted legal provisions to effectively implement fiscal and tax policies into specific green spending practices such as energy conservation, pollution reduction, and carbon reduction.
• Increase the intensity of fiscal investment in science and technology, and establish a mechanism for the growth of fiscal green spending on science and technology. Strengthen the role of government green guidance funds, vigorously develop government guidance funds, and increase the amount of venture capital for regional real economy enterprises.
• Optimize the government-guided venture capital mechanism. Through the establishment of special channels, improve the level of internal management, and increase the proportion of total government investment.
• Vigorously support and build a science and technology financial service platform, and formulate a coordinated development strategy for the coupling of environmental regulation and science and technology finance according to different local economic development levels and ecological civilization construction requirements.
• The central and western regions should give priority to the development of science and technology finance, and strengthen the research and development institutions within leading financial institutions and large industrial enterprises.
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Volume 91, Issue 5, October 2024

Fixed effects and the generalized mundlak estimator.

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Informality, Consumption Taxes, and Redistribution

Repayment flexibility and risk taking: experimental evidence from credit contracts, exploiting growth opportunities: the role of internal labour markets, evaluating the accuracy of counterfactuals: heterogeneous survival expectations in a life cycle model, multi-dimensional screening: buyer-optimal learning and informational robustness, bargaining as a struggle between competing attempts at commitment, contingent thinking and the sure-thing principle: revisiting classic anomalies in the laboratory, how credit constraints impact job finding rates, sorting, and aggregate output, dollar safety and the global financial cycle, path dependency in physician decisions, competition and career advancement, single-crossing differences in convex environments, incorporating diagnostic expectations into the new keynesian framework, capital regulation and shadow finance: a quantitative analysis, endogenous uncertainty and credit crunches, correction to: how credit constraints impact job finding rates, sorting, and aggregate output, email alerts.

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Breaking news, kamala harris once again touts goldman sachs’ review of her economic plan – after firm’s ceo dismissed it.

WILKES-BARRE, Pa. — Vice President Kamala Harris played up a Goldman Sachs review of her economic plan on Friday after the firm’s CEO noted that the report actually showed her policies would have minimal impact on the economy. 

“Independent economists like Goldman Sachs have said my plan would grow our economy and [former President Donald Trump’s] plan would shrink the economy, reignite inflation and send us into a recession by the middle of next year,” Harris claimed at a rally in Wilkes-Barre, Pa. 

The vice president leaned on the investment bank’s report during her Tuesday debate against Trump as well, prompting Goldman Sachs CEO David Solomon to call out Harris for blowing the analysis out of proportion. 

“I think this blew up into something that’s bigger than what it was intended to be,” Solomon told CNBC on Wednesday.

The Wall Street heavyweight also noted that the report “came from an independent analyst” – not someone employed at the firm – and that Harris left out key details – including that the difference between her plan and Trump’s was “about two-tenths of 1%.”

“What the report did is it looked at a handful of policy issues that have been put out by both sides, and it tried to model their impact on GDP growth,” Solomon explained. “The reason I say a bigger deal has been made of it is what it showed is the difference between the sets of policies that they’ve put forward is about two-tenths of 1%.”

The Trump campaign accused Harris of “lying” about the report’s findings. 

At the campaign event, Harris also misrepresented Trump’s positions on several policy issues, claiming the GOP nominee for president “intends to cut Social Security and Medicare” and will use the Heritage Foundation’s Project 2025 as the blueprint for his administration. 

The Trump campaign’s official platform states that the 45th president would “fight for and protect Social Security and Medicare with no cuts, including no changes to the retirement age.”

Trump, 78, has also repeatedly disavowed Project 2025 stating that he has no intention of even reading about the think-tank’s policy suggestions.

In a new campaign promise, Harris pledged to remove “unnecessary degree requirements” for federal jobs, which is already an ongoing government initiative. 

“As president, I will get rid of the unnecessary degree requirements for federal jobs to increase jobs for folks without a four-year degree,” she told her Keystone State supporters. “Understanding that requiring a certain degree does not necessarily talk about one’s skills.” 

“And I will challenge the private sector to do the same,” she vowed. 

Catch up on The Post’s debate coverage

• Kamala Harris’ dismissive laugh on full display in first presidential debate with Trump
• Taylor Swift endorses Kamala Harris minutes after presidential debate: ‘I’ve made my choice’
• Read Trump’s and Harris’ closing statements from their first presidential debate
• Harris campaign calls for second debate after Trump’s performance is panned by critics
• Trump refers to Kamala Harris’ 2020 viral ‘I’m speaking’ moment in presidential debate: ‘Does that sound familiar?’
• Harris tries to pin Project 2025 on Trump during first minutes of presidential debate
• Trump claims Harris supports abortions in the ‘ninth month — and she doesn’t deny it’ during presidential debate
• Everything to know about the Donald Trump-Kamala Harris presidential debate

The US Office of Personnel Management’s federal jobs portal states that “except for certain professional and scientific positions, a college education may not be necessary” to apply for a slew of government positions.  

“You can qualify for many federal jobs based on job-related work experience,” the website notes.

The White House also announced in April that federal information technology jobs would move to a skills-based hiring process, removing educational requirements for certain tech and cybersecurity positions. 

The Harris campaign did not respond to The Post’s request for comment. 

The vice president’s speech was interrupted by multiple anti-Israel protester, at least one of whom was removed from the event. 

“I respect your voice. But right now, I am speaking,” Harris said during one boisterous disruption. 

Mail-in voting for the presidential election starts on Monday in Pennsylvania, earlier than in any other state. The battleground state has 19 Electoral College votes up for grabs in November. 

“It’s great,”  Bridget Kosierowski, a 53-year-old, Democratic state representative from Scranton told The Post about the Keystone State’s voting process. “The earlier we [allow] people to get out and vote, give them time and access to such, is very important.”

“I think it’s a fair, legal process,” she added, noting that she intends to vote on Election Day but her children will vote by mail. 

Kamala Harris is not in the swing of things…

Swing . . . and a miss

In the topics that matter most in three key swing states, Kamala Harris showed that she was out of touch in Thursday’s interview :

Top issue: Immigration

58% of Arizonans, of either party, think that the United States does not have control over its border — a reality they see every day as a border state, according to a Redfield and Wilton Strategies poll.

Kam’s response:  CNN’s Dana Bash claimed Harris was put in charge of “root causes” — avoiding the term used at the time, “border czar” — and even then Harris corrected her, saying she was only tasked with dealing with “Northern Central America.” So she dodged all responsibility on the flood of migrants from Venezuela and other South America nations (and maybe Nicaragua? What counts as “Northern?”) Harris insisted the biggest problem was that a recent border bill didn’t pass, while she has been in office for three-and-a-half years without any action.

Top issue: The auto industry

Just 20% of Michiganders, home of much of America’s auto industry, back an electric vehicle mandate , the lowest of any state surveyed, according to Morning Consult.

Kam’s response:  “You mentioned the Green New Deal. I have always believed, and I have worked on it, that the climate crisis is real, that it is an urgent matter to which we should apply metrics that include holding ourselves to deadlines around time.” Harris has previously said those deadlines include getting rid of gas cars.

PENNSYLVANIA

Top issue: Energy and fracking

83% of Pennsylvanians believe drilling for more for gas and oil in the US would lower costs, 86% say it would improve national security, according to Morning Consult.

Kam’s response:  “There is no question I’m in favor of banning fracking,” she said in 2019. In the interview, she claimed she no longer wanted to ban fracking, but insisted, “My values have not changed.” Harris dubiously said she still favored the Green New Deal but would make an exception for fracking.

Kosierowski said she was “excited” about Harris’ “enthusiasm” and “empathy” and “her focus on women’s health, reproductive rights, child care, the workforce, health insurance, protecting the Affordable Care Act, protecting access to care.”

“She gets what people are thinking about,” the state rep said. 

James Ayrton, a 42-year-old higher education administrator from Blandon, Pa., told The Post that “policy-wise” he likes that Harris has strongly supported keeping the Affordable Care Act in place and her stance on the Israel-Hamas war.   

“I like holding on to Obamacare. I like her ideas in the Middle East, finding solutions to problems. And economically, having a fair tax system,” he said. 

When asked about Harris’ debate performance, Ayrton said he didn’t believe it helped the VP all that much.

“Unfortunately, I don’t know that it helps a lot,” he said. “Maybe Trump’s bad performance actually helps more than her good performance.”

The latest RealClearPolitics average of polls shows Harris with a razor-thin 0.1 percentage point advantage over Trump in Pennsylvania. 

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• Published: 13 September 2024

Challenges and strategies for addressing antibacterial drug resistance in LMICs

• Ursula Theuretzbacher   ORCID: orcid.org/0000-0003-2116-7128 1  

Nature Reviews Microbiology volume  22 ,  pages 591–592 ( 2024 ) Cite this article

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• Antibiotics
• Antimicrobial resistance

Antibacterial drug resistance is a critical global health issue that affects countries across all economic levels, though it disproportionately affects populations in low- and middle-income countries. Infection and resistance rates vary considerably, necessitating tailored interventions to meet the specific demands of each area. This underscores the need for global solidarity and national accountability in effectively addressing antibacterial drug resistance.

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Ursula Theuretzbacher

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Theuretzbacher, U. Challenges and strategies for addressing antibacterial drug resistance in LMICs. Nat Rev Microbiol 22 , 591–592 (2024). https://doi.org/10.1038/s41579-024-01080-8

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Published : 13 September 2024

Issue Date : October 2024

DOI : https://doi.org/10.1038/s41579-024-01080-8

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