NBER工作论文2021年5月3日

This paper examines the association between episodes of large fiscal impulses (expansions and adjustments) and sustainable development indicators (prosperity, resilience, and inclusivity). We provide country studies of Chile, Poland, South Africa, and Thailand, examining the components of government expenses and tax revenues, and reporting four stylized patterns from the analysis. (i) Fiscal expansions led to higher growth rates and reduced negative trade-offs, e.g., pollution and poor-health mortalities associated with economic growth. (ii) Fiscal adjustments led to a more inclusive economy, lowered poverty headcounts, improved sanitation, and cleaner technology access. (iii) Fiscal expansions followed an increase in direct taxes (especially corporate taxes) and a decline in social contributions, and preceded a decline in other direct taxes and an increase in wage bills. (iv) Fiscal adjustments followed a decline in other direct taxes and social contributions, an increase i! n wage bills, and preceded a decline in government consumption expenditure and transfers. In light of these findings, the domestic resource mobilization should consider the time paths of the taxes and expenditure components to understand their empirical linkages with the sustainable development outcomes in the respective countries.

Previous research has shown that women in the treatment group of the CeMENT randomized controlled trial increased their publications and the likelihood that they were tenured in top 50 economics departments. This paper examines one potential mechanism, namely, that CeMENT expanded the collaboration networks of the participants. Our analysis finds that women who received the mentoring treatment had three additional pre-tenure coauthors, 1.6 more pre-tenure publications and 43 additional citations to those publications. After controlling for additional coauthors, the CeMENT program increased publications, and top-tier publications. These results suggest that the information conveyed at the workshop facilitated participants’ career success.

One of the biggest challenges that countries face today is the very unequal distributions of opportunities, resources, income and wealth across people. Inclusive prosperity – whereby many people from different backgrounds can benefit from economic growth, new technologies, and the fruits of globalization – remains elusive. To address these issues, societies face choices among many different policies and institutional arrangements to try to ensure a proper supply of productive jobs and activities, as well as access to education, financial means, and other endowments that prepare individuals for their participation in the economy. In this paper we offer a simple, organizing framework to think about policies for inclusive prosperity. We provide a comprehensive taxonomy of policies, distinguishing among the types of inequality they address and the stages of the economy where the intervention takes place. The taxonomy clarifies the differences among contending approaches to e! quity and inclusion and can help analysts assess the impacts and implications of different policies and identify potential gaps.

Knowledge of how science is consumed in public domains is essential for a deeper understanding of the role of science in human society. While science is heavily supported by public funding, common depictions suggest that scientific research remains an isolated or ‘ivory tower’ activity, with weak connectivity to public use, little relationship between the quality of research and its public use, and little correspondence between the funding of science and its public use. This paper introduces a measurement framework to examine public good features of science, allowing us to study public uses of science, the public funding of science, and how use and funding relate. Specifically, we integrate five large-scale datasets that link scientific publications from all scientific fields to their upstream funding support and downstream public uses across three public domains – government documents, the news media, and marketplace invention. We find that the public uses of science ! are extremely diverse, with different public domains drawing distinctively across scientific fields. Yet amidst these differences, we find key forms of alignment in the interface between science and society. First, despite concerns that the public does not engage high-quality science, we find universal alignment, in each scientific field and public domain, between what the public consumes and what is highly impactful within science. Second, despite myriad factors underpinning the public funding of science, the resulting allocation across fields presents a striking alignment with the field’s collective public use. Overall, public uses of science present a rich landscape of specialized consumption, yet collectively science and society interface with remarkable, quantifiable alignment between scientific use, public use, and funding.

This paper examines how government funding programs geared towards early-stage companies interact with private capital markets. Using hand-collected data on 755 government programs worldwide, we find that governments’ allocations to such funding programs have been comparable to global venture capital disbursements in the past decade. Government programs were more frequent in periods with more private venture activity, a relationship that was stronger in nations with better public governance. The programs’ structures often relied on the local private sector. The private sector’s involvement was greater when government programs targeted earlier-stage companies and when rankings of government effectiveness were higher. We find that such government funding programs increased local innovation, particularly when the programs focused on early-stage ventures or collaborated with the private sector. These findings are most consistent with the explanation that the reliance on pr! ivate capital markets enabled governments to mitigate investment frictions and improve capital allocation.

This paper studies how demand for labor reacts to financial technology (fintech) shocks based on comprehensive databases of fintech patents and firm job postings in the U.S. during the past decade. We first develop a measure of fintech exposure at the occupation level by intersecting the textual information in job task descriptions and fintech patents. We then document a significant decline of job postings in the most exposed occupations, and an increase in industry as well as geographical concentration of these occupations. Firms resort to an upskilling strategy in face of the fintech disruption, requiring “combo” (finance and software) skills, higher education attainments, and longer work experiences in the hiring of fintech-exposed jobs. Financial firms and those with high innovation outputs are able to offset the disruptive effect from the fintech shock. Among innovating firms, however, only inventors (but not acquisition-driven innovators) experience growth in hiri! ng, sales, investment, and enjoy better returns on assets.

How do you measure the value of a commodity that transacts at a price of zero from an economic standpoint? This study examines the potential for and extent of omission and misattribution in standard approaches to economic accounting with regards to open source software, an unpriced commodity in the digital economy. The study is the first to follow usage and upgrading of unpriced software over a long period of time. It finds evidence that software updates mislead analyses of sources of firm productivity and identifies several mechanisms that create issues for mismeasurement. To illustrate these mechanisms, this study closely examines one asset that plays a critical role in the digital economic activity, web server software. We analyze the largest dataset ever compiled on web server use in the United States and link it to disaggregated information on over 200,000 medium to large organizations in the United States between 2001 and 2018. In our sample, we find that the omission ! of economic value created by web server software is substantial and that this omission indicates there is over $4.5 billion dollars of mismeasurement of server software across organizations in the United States. This mismeasurement varies by organization age, geography, industry and size. We also find that dynamic behavior, such as improvements of server technology and entry of new products, further exacerbates economic mismeasurement.

This study uses data from Academic Analytics to examine gender differences in promotion to associate professor in economics. We found that women in economics were 15% less likely to be promoted to associate professor after controlling for cumulative publications, citations, grants and grant dollars. In contrast, we found no significant gender differences in promotion in other fields including biomedical science, physical science, political science, mathematics and statistics, and engineering. We separated the sample by the research intensity of institutions and found suggestive evidence that these results were being driven by less research-intensive institutions.

This paper uses new data to reexamine trends in concentration in U.S. markets from 1994 to 2019. The paper's main contribution is to construct concentration measures that reflect narrowly defined consumption-based product markets, as would be defined in an antitrust setting, while accounting for cross-brand ownership, and to do so over a broad range of consumer goods and services. Our findings differ substantially from well established results using production data. We find that 42.2% of the industries in our sample are “highly concentrated” as defined by the U.S. Horizontal Merger Guidelines, which is much higher than previous results. Also in contrast with the previous literature, we find that product market concentration has been decreasing since 1994. This finding holds at the national level and also when product markets are defined locally in 29 state groups. We find increasing concentration once markets are aggregated to a broader sector level. We argue that these ! two diverging trends are best explained by a simple theoretical model based on Melitz and Ottaviano (2008), in which the costs of a firm supplying adjacent geographic or product markets falls over time, and efficient firms enter each others' home product markets.

Governments often prohibit resale of the benefits-in-kind provided by antipoverty programs. Yet the personal gains from those benefits are likely to vary and to be known privately, so there can be gains to poor people from trading their assignments. We know very little about those gains. To help address this knowledge gap, the paper models a competitive market for assignments, and simulates the market using an unusual survey of workers on a rural public-works scheme in a poor state of India. The results indicate large gains from tradeable assignments after first randomizing. The gains exceed those from poverty targeting without trade and are not lower for poorer households or female workers. Fully realizing the gains from trade in practice may require complementary policies to help people access the market and to support its administration and regulation.

A new form of lending using digital collateral has recently emerged, most prominently in low and middle income countries. Digital collateral (DC) relies on “lockout” technology, which allows the lender to temporarily disable the low value of the collateral to the borrower without physically repossessing it. We explore the effect of this new form of credit both in a model and in a field experiment using school-fee loans digitally collateralized with a solar home system. We find that securing a loan with DC drastically reduces default rates (by 19 pp) and increases the lender's rate of return (by 38 pp). Employing a variant of the Karlan and Zinman (2009) methodology, we decompose the total effect and that roughly one-third of the total effect is attributable to (ex-ante) adverse selection and two-thirds of the effect is attributable to (interim or ex-post) moral hazard. Access to a school-fee loan significantly increases school enrollment and school-related expenditures w! ithout detrimental effects to household balance sheet.

I study knowledge spillovers, positive externalities that augment the information set of an economic agent, and reviews the evidence on such spillovers in the context of international economic transactions. Even though spillovers are by their very nature difficult to identify, over recent decades a number of advances-conceptual, empirical, as well as in form of new data–have produced robust evidence that both trade and foreign direct investment lead to sizable knowledge spillovers.

A policy debate centers around the question how news aggregators such as Google News affect traffic to online news sites. Many publishers view aggregators as substitutes for traditional news consumption while aggregators view themselves as complements because they make news discovery easier. We use Spain as a natural experiment because Google News shut down altogether in response to a copyright reform enacted in December 2014. We compare the news consumption of a large number of Google News users with a synthetic control group of similar non-Google News users. We find that the shutdown of Google News reduces overall news consumption by about 20% for treatment users, and reduces page views on publishers other than Google News by 10%. This decrease is concentrated around small publishers. We further find that users are able to replace some but not all of the types of news they previously read. Post-shutdown, they read less breaking news, hard news, and news that is not well co! vered on their favorite news publishers. These news categories explain most of the overall reduction in news consumption, and shed light on the mechanisms through which aggregators interact with traditional publishers.

Regulators often rely on self-reported data to determine compliance. Tolerance for missingness in self-monitoring data may create incentives for local agents to strategically decide when (not) to monitor regulated activities. This paper builds a framework to detect whether local governments skip air pollution monitoring when they expect air quality to deteriorate. We infer this expectation from air quality alerts – public advisories based on local governments’ own pollution forecasts – and test whether monitors’ sampling rates fall when these alerts occur. We first use this method to test an individual pollution monitor in Jersey City, NJ, suspected of a deliberate shutdown during the 2013 “Bridgegate” traffic jam. Consistent with strategic shutdowns, this monitor’s sampling rate drops by 33% on days that Jersey City issues pollution alerts. Building on large-scale inference tools, we then apply the method to test over 1,300 monitors across the U.S., finding at! least 14 metro areas with clusters of monitors showing similar strategic behavior. We discuss imputation methods and policy responses that may help deter future strategic monitoring.

This paper revisits capital-skill complementarity and inequality, as in Krusell, Ohanian, Rios-Rull and Violante (KORV, 2000). Using their methodology, we study how well the KORV model accounts for more recent data, including the large changes in labor’s share of income that were not present in KORV. We study both labor share of gross income (as in KORV), and income net of depreciation. We also use non-farm business sector output as an alternative measure of production to real GDP. We find strong evidence for continued capital-skill complementarity in the most recent data, and that the model continues to closely account for the skill premium. The model captures the average level of labor share, though it overpredicts its level by 2-4 percentage points at the end of the period.

Previous research has documented that Social Security Disability Insurance (SSDI) applications and awards increase during economic downturns and that expanded access to SSDI leads to a reduction in employment. We build on these insights and investigate to what extent differential access to SSDI during economic downturns leads to differential changes in SSDI enrollment and employment during the subsequent recovery. We exploit plausibly exogenous variation in SSDI appeals processing time (a measure of hassle or access) facing individuals living in ZIP codes that straddle Social Security Administration hearing office catchment borders. During the Great Recession, ZIP codes assigned to hearing offices with faster appellate processes saw a larger increase in SSDI enrollment than their cross-border neighbors. These enrollment effects are concentrated among ZIP code pairs that experienced more severe labor market downturns, and they persist as late as 2015. In the full sample, ther! e is no clear effect of longer processing times on subsequent employment rates. However, we find some limited evidence that faster appellate processes may have weighed on the employment recovery in hard-hit ZIP codes that had high pre-recession rates of SSDI enrollment. Our findings highlight the importance of considering interaction effects between economic shocks and ease of access to the safety net.

Entrepreneurs appear to borrow largely against their near-term revenues, even when their investment has a longer horizon. In this paper, we develop a model of credit horizons. A question of particular concern to us is whether persistently low interest rates can stifle economic activity. With this in mind, our model is of a small open economy where the world interest rate is taken to be exogenous. We show that a permanent fall in the interest rate can reduce aggregate investment and growth, and even lead to a drop in the welfare of everyone in the domestic economy. We use our framework to examine how credit horizons interact with plant dynamics and the evolution of productivity. Finally, we speculate that the measurement of total investment may camouflage the true level of productive investment in plant and human capital, and give too rosy a picture of property-fuelled booms sparked by low interest rates.

COVID-19 drove a mass social experiment in working from home (WFH). We survey more than 30,000 Americans over multiple waves to investigate whether WFH will stick, and why. Our data say that 20 percent of full workdays will be supplied from home after the pandemic ends, compared with just 5 percent before. We develop evidence on five reasons for this large shift: better-than-expected WFH experiences, new investments in physical and human capital that enable WFH, greatly diminished stigma associated with WFH, lingering concerns about crowds and contagion risks, and a pandemic-driven surge in technological innovations that support WFH. We also use our survey data to project three consequences: First, employees will enjoy large benefits from greater remote work, especially those with higher earnings. Second, the shift to WFH will directly reduce spending in major city centers by at least 5-10 percent relative to the pre-pandemic situation. Third, our data on employer plans and ! the relative productivity of WFH imply a 5 percent productivity boost in the post-pandemic economy due to re-optimized working arrangements. Only one-fifth of this productivity gain will show up in conventional productivity measures, because they do not capture the time savings from less commuting.

The literature on the optimal harvest of fisheries has concentrated on a single fishing area with biomass uncertainty and to a lesser degree also with price uncertainty. We develop and implement a stochastic optimal control approach to determine the harvest that maximizes the value of a fishery participating in a global market, where all the considered harvesting zones sell their production. This market is characterized by an inverse demand function, which combines an exogenous demand shock and the aggregate harvesting of all zones. Accordingly, a fishery's harvest will be affected by the global demand shocks and the harvesting in all the competing zones through the global selling price. In addition, we decompose the biomass uncertainty into local and global biomass shocks. Through global biomass shocks, the model provides enough flexibility to acknowledge for correlation in the biomass shocks faced by the multiple perhaps adjacent areas. When we compare our global framework! with an alternative where the individual zones are aggregated into a single optimizing fishery we find that competition will increase the global harvest and consequently reduced the resource price.

Were workers more likely to be infected by COVID-19 in their workplace, or outside it? While both economic models of the pandemic and public health policy recommendations often presume that the workplace is less safe, this paper seeks an answer both in micro data and economic theory. The available data from schools, hospitals, nursing homes, food processing plants, hair stylists, and airlines show employers adopting mitigation protocols in the spring of 2020. Coincident with the adoption, infection rates in workplaces typically dropped from well above household rates to well below. When this occurs, the sign of the disease externality from participating in large organizations changes from negative to positive, even while individuals continue to have an incentive to avoid large organizations due to the prevention costs they impose on members. Rational cooperative prevention sometimes results in infectious-disease patterns that are opposite of predictions from classical e! pidemiology.

Machines increasingly replace people in routine job tasks. The remaining tasks require workers to make open-ended decisions and to have “soft” skills such as problem-solving, critical thinking and adaptability. This paper documents growing demand for decision-making and explores the consequences for life-cycle earnings. Career earnings growth in the U.S. more than doubled between 1960 and 2017, and the age of peak earnings increased from the late 30s to the mid-50s. I show that a substantial share of this shift is explained by increased employment in decision-intensive occupations, which have longer and more gradual periods of earnings growth. To understand these patterns, I develop a model that nests decision-making in a standard human capital framework. Workers predict the output of uncertain, context-dependent actions. Experience reduces prediction error, improving a worker’s ability to adapt using data from similar decisions they have made in the past. Experience t! akes longer to accumulate in high variance, non-routine jobs. I test the predictions of the model using data from the three waves of the NLS. Life-cycle wage growth in decision-intensive occupations has increased over time, and it has increased relatively more for highly-skilled workers.

We evaluate a large-scale set of interventions to increase demand for immunization in Haryana, India. The policies under consideration include the two most frequently discussed tools—reminders and incentives—as well as an intervention inspired by the networks literature. We cross-randomize whether (a) individuals receive SMS reminders about upcoming vaccination drives; (b) individuals receive incentives for vaccinating their children; (c) influential individuals (information hubs, trusted individuals, or both) are asked to act as “ambassadors” receiving regular reminders to spread the word about immunization in their community. By taking into account different versions (or “dosages”) of each intervention, we obtain 75 unique policy combinations. We develop a new statistical technique—a smart pooling and pruning procedure—for finding a best policy from a large set, which also determines which policies are effective and the effect of the best policy. We proceed! in two steps. First, we use a LASSO technique to collapse the data: we pool dosages of the same treatment if the data cannot reject that they had the same impact, and prune policies deemed ineffective. Second, using the remaining (pooled) policies, we estimate the effect of the best policy, accounting for the winner’s curse. The key outcomes are (i) the number of measles immunizations and (ii) the number of immunizations per dollar spent. The policy that has the largest impact (information hubs, SMS reminders, incentives that increase with each immunization) increases the number of immunizations by 44 % relative to the status quo. The most cost-effective policy (information hubs, SMS reminders, no incentives) increases the number of immunizations per dollar by 9.1%.

We study the determinants of asset market fragmentation in a model with strategic investors that disagree about the value of an asset. Investors' choices determine the market structure. Fragmented markets are supported in equilibrium when disagreement between investors is low. In this case, investors take the same side of the market and are willing to trade in smaller markets with a higher price impact to face less competition when trading against a dealer. The maximum degree of market fragmentation increases as investors' priors are more correlated. Dealers can benefit from fragmentation, but investors are always better off in centralized markets.

A randomized-trial of community-level mask promotion in rural Bangladesh during COVID-19 shows that the intervention tripled mask usage and is a cost-effective means of promoting public health.

Women are less financially literate than men. It is unclear whether this gap reflects a lack of knowledge or, rather, a lack of confidence. Our survey experiment shows that women tend to disproportionately respond “do not know” to questions measuring financial knowledge, but when this response option is unavailable, they often choose the correct answer. We estimate a latent class model and predict the probability that respondents truly know the correct answers. We find that about one-third of the financial literacy gender gap can be explained by women’s lower confidence levels. Both financial knowledge and confidence explain stock market participation.

Whether, and to what extent, behavioral anomalies uncovered in the lab manifest themselves in the field remains of first order importance in finance and economics. We begin by examining behavior of retail traders/investors making investment decisions in constructed laboratory markets. Our results show that the behaviors of the traders are consistent with myopic loss aversion. We combine the lab results with a unique individual-level matched dataset on daily stock market transactions and portfolio positions over a two year period. We find that lab behaviors help to predict, but do not fully capture, the essential real-world trading analogs of retail traders.

We measure the individual and collective viewpoints of US Congress members on various economic policies by scraping their Twitter accounts. Tweets that criticize (support) a particular company are associated with a significant negative (positive) stock price reaction in a narrow time window around the tweet. A sharp partisan divide emerges, with Republicans and Democrats coordinated in both their support and opposition for different industries emanating from disparate legislative agendas. Members of congress coordinate within parties to push legislation through their social media accounts. As an illustrative and relevant example, we analyze the Tax Cuts and Jobs Act of 2017 and document significant aggregate stock market responses to the real-time evolution of partisan viewpoints about the bill.

We analyze data from a survey we administered during the COVID-19 pandemic to investigate the relationship between people's subjective risk beliefs and their protective behaviors. We report three main findings. First, on average, people substantially overestimate the absolute level of risk associated with economic activity, but have correct signals about their relative risk. Second, people who believe that they face a higher risk of infection are more likely to report avoiding economic activities. Third, government mandates restricting economic behavior attenuate the relationship between subjective risk beliefs and protective behaviors.