ABSTRACT. Local policymakers use economic development incentives and other policies to attract business, often bringing large new plants or major expansions of existing operations to localities who make these (often high-profile) deals. When one of these deals is unexpected or a surprise, a local economy may experience a shock that spills over into other sectors, like the housing market. For instance, expectations of a sudden influx of new jobs may drive up demand for housing; yet, on the other hand, higher local taxes or diverted public revenues to accommodate business incentives (e.g., tax abatements, subsidies, etc.) may cool local demand for homeownership. To resolve this ambiguity and to quantify this potential spillover empirically, we therefore examine what effect these deals have on home prices in local markets. Rather than investigate a single deal (or narrow set of deals) on a single market like prior literature, we provide the first evidence answering this question on a broader, national scale. We do this by exploiting a national sample consisting of millions of transactions from Zillow’s ZTRAX data, a large dataset containing detailed property-level information from across the United States, to answer this question for 114 distinct events from 2005-2016. Using a regression discontinuity in time (RDiT) approach, overall we find a significant increase in prices for nearby residential homes (within a 15-minute drive) directly following announcements of new investments or expansions of existing businesses. This effect is larger when relatively more jobs are promised and when housing supply is relatively inelastic.

ABSTRACT. Taxes create incentives; yet, the potency of these incentives may depend on the salience and households’ perceptions of the tax itself. We investigate this issue in the context of property taxes, exploring how accurately households perceive their property tax liabilities and what factors determine misperception. Leveraging a unique national dataset, created by linking Zillow’s ZTRAX data to internal data from the American Community Survey, we first compare survey responses for how much households think they pay in property taxes to how much they actually pay based on municipal administrative records from ZTRAX. While homeowners’ tax perceptions are not substantially biased on average, we observe significant inaccuracy and systematic bias across different subpopulations (e.g., race, income, home value, mortgage escrow status, and age group). Further, we document substantial variation in property tax misperceptions across states. Given that the vast majority of studies in the property tax capitalization literature use data concentrated in one state or locality, we also explore whether variation in tax misperceptions across states can help explain the heterogeneity in property tax effects on home prices. Results from a meta-analysis show that studies conducted in states with higher property tax misperceptions are significantly less likely to find property tax policy changes are fully capitalized into home prices.

ABSTRACT. In this paper we deliver first causal evidence on the relationship between immigrant host country language proficiency and homeownership. Using an instrumental variable strategy, we find a substantial positive impact of language skills on the propensity to own a home and the quality of housing. While this effect is mediated by education and household income, our estimates also speak in favor of a direct effect. Our results highlight the importance of host country specific human capital and, in particular, language proficiency for socio-economic assimilation.

ABSTRACT. Housing is a basic need and is intricately connected to a household’s health and wellness. The current pandemic has exposed the housing vulnerability for certain subgroups of the population and further jeopardized these household’s health and stability. Using the Household Pulse Survey launched by the US Census Bureau since April 2020, we examine the correlates of housing vulner- ability during the pandemic. We explore both subjective and objective measures of vulnerability. In addition, we explore heterogeneity in the evolution of housing vulnerability along demographic characteristics such as ethnicity and housing type (renter vs owner) during the pandemic. Our results suggest that individuals perception on their housing vulnerability in the immediate future is on average higher than the objective evaluation of their current vulnerability. In addition, not being employed, lower levels of education and household size all increase home vulnerability. We also find significant heterogeneity across race in the evolution of vulnerability during the pandemic (2000-2022) with a “chilling effect” on Asians.

ABSTRACT. We estimate the impact of pandemic unemployment insurance (PUI) on unemployment-to-employment (U-E) transitions. Exploiting cross-state variation in the timing of the reduction in PUI benefits during summer 2021, using longitudinally linked Current Population Survey data, and controlling for other individual and state characteristics, we find the U-E rate rose 10 percentage points, 37 percent of the unconditional mean U-E rate for experienced unemployed in states ending the program early versus states continuing the additional benefits. We estimate an elasticity of 0.3 for U-E with respect to the PUI replacement rate. Hazard function estimates imply qualitatively similar patterns. The increase is similar for job losers, who are likely PUI recipients, but smaller for job leavers and re-entrants. The impact is negligible for placebo groups - nonparticipants and new entrants to the labor force - and it arises only during the treatment period, disappearing immediately after. The increase is slightly higher for women and much higher for Blacks, both of which experienced relatively slow recoveries in employment from the pandemic recession. Covid-related deaths also rose in the states ending PUI early, with the covid death rate estimated to triple over the three subsequent months, and additional deaths estimated at about 18,000.

ABSTRACT. Amid a recent spark in the global debate on parental leave policies for better worker benefits, following suit of several developed nations, India too decided to extend its existing paid maternity leave policy. But in this seemingly progressive step to provide better financial relief to the working mothers, can there be unintended harmful consequences on female employment? Previous literature finds widely contrasting results to the impact of maternal leave policies on labor outcomes, primarily on account of varying policy structure, underlying labor market dynamics, availability of child-care and social norms defining gender-roles in the country. In order to understand how gendered regulations can also fuel gender differences when implemented in the context of existing low and declining female labor-force participation, this paper will study impact of India’s maternity leave legislation of 2017 on gendered labor market outcomes like employment and income. The empirical analysis will utilize two-fold analysis to determine the effect of demand side factors by analyzing firm-level employment data to capture any disincentives in hiring and retention of female employees, along with supply side factors from individual-level data to measure substitution to care work and extent of informalization of the female workers as a result of the policy.

ABSTRACT. I propose a model in which agents decide on job search intensity for each possible wage, unlike the usual setup of constant search intensity over wage draws. The proposed framework entails efficiency gains in that agents do not waste effort to searching for low paying unacceptable jobs or less offered high paying jobs. The proposed framework generates accepted wages distributions that differ substantially from the truncated distributions stemming from the usual setup. These different empirical implications are exploited for building two nonparametric tests, which reject constant search intensity over wages, using NLSY97 data. I further estimate the identifiable structural parameters of the two models resulting in better fit for the wage-specific setup. I quantify the increased effectiveness of wage-specific search in more total search intensity, faster transitions to the upper tail of the wage distribution, and higher wages, in particular, a 20% increase in accepted wages after unemployment.

ABSTRACT. This paper examines the labor market returns to earning industry-certified credentials in the manufacturing sector. We are interested in estimating the impact of a manufacturing credential on wages, probability of employment, and probability of employment in the manufacturing sector post credential attainment. We link students who earned manufacturing credentials to their enrollment and completion records, and then further link them to their IRS tax records for earnings and employment (Form W2 and 1040) and to the American Community Survey and decennial census for demographic information. We present earnings trajectories for workers with credentials by type of credential, industry of employment, age, race and ethnicity, gender, and state. To obtain a more causal estimate of the impact of a credential on earnings, we implement a coarsened exact matching strategy to compare outcomes between otherwise similar people with and without a manufacturing credential. We find that the attainment of a manufacturing industry credential is associated with higher earnings and a higher likelihood of labor market participation when we compare attainers to a group of non-attainers who are otherwise similar.

ABSTRACT. The Internet has magnified the importance of consumer voice for policymakers by making it easy for consumer to complains about fraudulent firm practices. Federal agencies such as the Federal Trade Commission (FTC) now receive millions of complaints every year from consumers.

However, most fraud victims do not complain. Because complaints generate positive externalities such as improved law enforcement and increased consumer awareness of problems in the marketplace, they are likely underreported. While policymakers can make it easier to complain, it remains unclear whether the marginal complaint differs from existing complaints.

In this paper, we examine how a website redesign affected the quantity, quality, and characteristics of consumer complaints. In October 2020, the FTC suddenly released a redesigned version of its online interface for logging consumer complaints, which made the site much easier to use. We employ a regression discontinuity in time design, leveraging the fact that, while the FTC’s online and mobile complaint interfaces abruptly changed, the FTC’s phone line and other sources such as the Better Business Bureaus (BBBs) and Consumer Financial Protection Bureau (CFPB) did not.

We find that the FTC’s change resulted in a 30-40 percent increase in complaints across mobile and online platforms. The jump comes exclusively from the completion margin. Complaint quality also improved, with large increases in complaints with information on consumer geography, demographics, and product characteristics. The largest increases were for consumers above age 65 and non-White consumers.

In ongoing work, we use machine learning techniques to study how the content of the complaints changed based on the open-ended text of the complaints. We find that the readability and length of complaint texts declined, suggesting that the redesign brought in consumers who may have lower education levels.

Beyond an evaluation of the change itself, our work contributes to a broader literature on how the design of government programs affect program users.

ABSTRACT. Scholars and policymakers have long debated the impact of campaign finance on democratic processes. Using detailed donation data from Texas, we show that defense attorneys who contribute to a district judge's electoral campaign are preferentially awarded assignments by that judge to indigent defense cases, i.e., private contracts to provide state-funded legal counsel to poor defendants. We estimate that attorney donors receive twice as many cases as non-donors during the month of their campaign contribution. Nearly two-thirds of this increase is explained by the contribution itself, with the remainder attributable to shared preferences within attorney-judge pairs, such as professional, ideological, political, or personal ties. Our results provide some of the strongest causal evidence to date on the corrosive potential of campaign donations, including their impact on the right to counsel as guaranteed by the U.S. Constitution.

ABSTRACT. Deaths from zoonotic diseases—animal to human transmitted diseases—have been of increasing concern in the United States. This study examines zoonotic disease mortality, in adults 20 and older, by type of zoonoses, age group, rurality, and state in 2020 using the CDC Multiple Cause Mortality data files. We find that nearly 2% of all deaths in 2020, or 58,920 deaths, were caused by zoonotic disease, mainly from zoonotic septicemia, influenza, bacterial intestinal infection, and viral hepatitis. Those over 65 years of age contributed to 79% of these deaths. Texas, California, Florida, Pennsylvania, and New York account for 31% of all deaths from zoonoses.

ABSTRACT. Adolescence is an important developmental period when teens begin spending less time with their parents and more time with friends and others outside their household as they transition into adulthood. Using the 2017–2021 American Time Use Survey, we examine how the time teens spent alone and with parents, friends, and others changed during the pandemic, shedding light on how the social isolation of the pandemic disrupted this important developmental period. To explore other changes in teens’ time use due to the pandemic, we also examine time spent on particular activities. Our results show that teens spent more time alone during the pandemic than before. Boys spent less time with friends, less time on schooling and work, and more time gaming and using computers for leisure. Teen girls spent less time with parents and more time using computers for leisure.

ABSTRACT. We examine how facilitating a standard-essential patent (SEP) holder's ability to obtain an injunction against an alleged infringer affects the parties' license negotiations and equilibrium royalty rates.

Injunctions are a powerful tool for SEP holders to enforce their rights by preventing others from using their technology, making adopting a standard virtually impossible. This gives the holders of SEPs a powerful bargaining chip in licensing negotiations. To maintain the balance of power between SEP holders and those seeking licenses, SEP holders must usually commit to licensing their patents on fair, reasonable, and non-discriminatory (FRAND) terms. In its 2015 Huawei v ZTE decision, the European Court of Justice (ECJ) provided further guidance on the steps a SEP holder must take when applying for an injunction to avoid triggering an antitrust defense by the alleged patent infringer. The decision stipulates a framework that requires the SEP holder to make a FRAND license offer and, if refused, the prospective licensee to make a non-FRAND counter offer. Recent court decisions in Germany have lowered the bar for injunctive relief by holding that a non-FRAND counteroffer by an alleged infringer (the adopter of a standard) is sufficient for an injunction, regardless of the SEP holder's initial offer. Under this amended framework, a SEP holder can now initiate negotiations with a non-FRAND offer and not necessarily jeopardize an injunction.

With more accessible injunctions we would expect higher, more aggressive license offers by patent holders, as non-FRAND offers do not necessarily trigger the denial of an injunction. We should also expect more generous (that means higher) offers by prospective licensees because aggressively low non-FRAND offers can now trigger an injunction even when the SEP holder's own offer is non-FRAND. Overall, equilibrium royalty rates should increase in a litigation framework with easier access to injunctions.

We show that this initial intuition is correct only when there is sufficient uncertainty about the court's assessment of FRAND. We propose a simple model in which a SEP holder and a standard adopter negotiate over the license of a patent in the shadow of litigation (determining an injunction and damages). We consider heterogeneous courts that differ (i) in their prior view of what FRAND ought to be and (ii) in their tolerance for deviations from that view. Parties know the width of the band of offers/counteroffers that courts consider FRAND, but they do not know the band's location. This means, whether a court takes a more SEP holder-friendly view or a standard adopter-friendly view is random.

In a world without court noise (where the court's FRAND view is known), both parties make offers and counteroffers at the respective upper and lower bounds of the FRAND band in both litigation frameworks. With court noise, however, the amended framework puts upward pressure on the licensee's counteroffer. Only in the amended framework can too low an offer that is found non-FRAND trigger an injunction (costly for the alleged infringer) when she responds to a SEP holder's high (and likely non-FRAND) initial offer.

ABSTRACT. The slow economic growth combines disappointing productivity growth in the past two decades with a continual rise of inequality in the past four decades. In the U.S., a key characteristic of the evolution of inequality has been employment and wage polarization. As employment grows, wage grows in a U-shaped form in relation to skill level. The highest gains are in the upper tail, modest gains in the lower tail, and significantly smaller gains in the median. The turning of the lower tail of the wages and employment distributions is largely defined by growing wages and employment in only “service occupations” (Autor and Acemoglu 2013). The U-shaped evolution of labor demands and the future demographics of the U.S. imply that if there is continued labor demand growth at the left side of the U, there will not be enough native-born workers willing and able to do the low-education work. Innovation is considered to have a low impact since Total Factor Productivity growth is relatively slow. Hence, automation is not likely to solve the lack of low-education labor any time soon. The technology required to replace workers in low-education service occupations is far more advanced than the current state of technology advances. Labor mobility can solve the problems by having adequate workers in the U.S. filling low-education jobs and providing jobs for the youth of low-income countries. Regardless of the effects of labor mobility on economic growth, it would address those fundamental causes and create a more equitable and better-educated society, with higher labor participation rates for women and new sources of tax revenues to address the fiscal headwind and pay for high-priority government programs.

ABSTRACT. Asthma, a common chronic respiratory illness characterized by inflammation and narrowing oft he airways, is a highly burdensome disease with severe and debilitating consequences, impacting 262 million people in 2019 and causing approximately 455,000 deaths worldwide. In the United States, in 2020, 21 million adults had asthma, causing around 4,200 deaths. Studieshave shown the risk for asthma varies for each individual, accounting for differences in healthcare quality, access and utilization, and exposure to environmental and behavioral factors. Little is known about the variations in social determinants of health and barriers to accessing healthcare for adults with asthma, who experience higher mortality risks, as the focus has primarily been on children. We utilize the National Health Interview Survey data from 2010 to 2018 in this study to compare adults with and without asthma. Using means and adjusted odds ratios, we examine the differences between adults with and without asthma and locate thebarriers and facilitators they face in accessing healthcare from Levesque’s conceptual framework. In addition, using a probit model, we examine how risky behaviors and other social determinants of health may increase the likelihood of emergency room visits for adults with asthma compared to adults without asthma.

ABSTRACT. Remote work surged during the pandemic. Industries that could conduct much of their business from home were relatively shielded from the negative effects of the physical distancing measures enacted to prevent the spread of COVID-19. We examine the relationship between remote work and total factor productivity (TFP) growth among 61 detailed industries. Using the 2021 American Community Survey, we classify remote work industries as those in which at least 20 percent of workers reported that they usually worked from home. We find a small, positive relationship between remote work and TFP. In the top remote industries, there were large increases in sectoral output but little growth in hours worked. Our findings cannot be explained by pre-pandemic TFP growth. Apart from the information sector, we find little change in labor composition between 2019 and 2021 in the top remote industries. These results provide suggestive evidence that workers in industries that were remote ready may have been more productive when working from home than they had previously been working on-site.

ABSTRACT. BEA publishes Personal Consumption Expenditures (PCE) at the national-level on an annual, quarterly, and monthly basis. However, PCE by state is only published at the annual level. This presentation is an attempt to discuss the development of quarterly PCE by state from 2012Q1 to 2022Q3. The data sources used to develop this new quarterly measure of household expenditures are coming from various statistical government agencies as well as private sources e.g. card transaction data (Fiserv).

There are several ways that this new statistics are important. First, they complement quarterly state personal income and GDP statistics. Second, they provide more timely data for policy making, economic analysis, and business decision. Third, it satisfies increasing need for timely spending data at a more granular level.

ABSTRACT. Although unincorporated self-employed workers comprise less than six percent of the total workforce, high volatility in the measurement of their hours sometimes has a disproportionate effect on major sector quarterly labor productivity growth published by the Bureau of Labor Statistics. Self-employment can be volatile for two broad reasons. First, self-employment is genuinely volatile. For example, people start businesses and shut them down because they often use self-employment as a bridge between spells of employment. And second, because the self-employed are such a small share of the labor force, it is harder to measure their size using the Current Population Survey (CPS), which is not designed to track this class of workers. We perform both cross-sectional and panel analyses to examine how the CPS design—the sample rotation framework, sample weighting, imputation of missing data, and proxy respondents answering on behalf of other household members—can lead to volatility in self-employment that affects the labor input used in productivity measurement. Results indicate that the sample rotation framework plays an outsized role in the volatility, but changes in class of worker status when there is a change from proxy to self-response, or vice versa, also contribute to increased volatility. In addition, the imputed data are more volatile than the non-imputed data. And although relatively few responses are imputed, the number has been rising steadily. We propose several solutions to smooth the series.

ABSTRACT. The topic of the measuring the growth and size of government has evolved from relatively brief references in the early writings economists in the 18th and 19th centuries to that on which there is now a robust literature and policy debate. This paper will briefly review the history of thought on the topic of measuring the size of “government”, and then proceed to examine the manner in which growth and size can be measured in United States. In order to accomplish this, the trends in four key post WWII measures are defined and documented. Two especially important features are revealed. The first is the increase in the role of the subnational government sector. The second is that in both countries the public sector is trending away from spending on (and taxing for) the public’s physical infrastructure and toward consumption, particularly in the form of health and income security programs.

ABSTRACT. Twenty-first Century economic challenges demand careful, evidence-driven understanding of the direct and transitional impacts of climate change and biodiversity loss, tradeoffs in the allocation of scarce natural resources and investments in innovation, and the potential impacts of environmental change on financial stability. President Biden’s April 2022 Earth Day Executive Order 14072 directed federal agencies to establish a system of accounts to track the status and value of nature and the environment. This new system of natural capital accounts will put nature on the Nation’s balance sheet. The National Strategy to Develop Statistics for Environmental-Economic Decisions (SEED) was released in January 2023, and it will use legal authorities such as the Paperwork Reduction Act and the Evidence Act to coordinate the development and future updating of natural capital accounts. Other legal authorities such as the National Environmental Policy Act may further bolster this endeavor. The Department of the Interior (DOI) has a critical role to play given its responsibility for an extensive portfolio of natural capital, the scientific expertise of the Department’s bureaus and offices, and the value NCA can provide to decision making for resource management across DOI. The natural capital that comprises the vast area of DOI lands and waters provides valuable ecosystem services, supports socioeconomic well-being, and contributes to our Nation’s economy in a range of ways that can be measured and quantified. Information related to the values of ecosystem services (ES) and natural capital accounting (NCA) can play an important role in providing information to DOI decision makers that can affect resource management decisions. The information generated via the ES and NCA frameworks can help make tradeoffs more explicit, rigorous, and comparable over time. However, developing information that is consistent with those frameworks can be challenging because data are often limited and there is not a consistent understanding of the meaning of the concepts within DOI or across the federal government. In addition, the advantages of developing statistics that could inform decisions using the ES and NCA frameworks may not be readily apparent to decision makers. This presentation will outline the efforts by the DOI to implement ES and NCA and discuss how those frameworks can better inform DOI decision-making beyond approaches that do not rely on ES or NCA. The presentation will discuss data gaps that limit the application of ES and NCA frameworks; challenges associated with development and implementation of consistent data standards; how data collection might be undertaken; and DOI’s role in the implementation of the National Strategy to Develop SEED.

ABSTRACT. Do gender differences in job mobility explain gender differences in pay? Various theories have been proposed to explain the differences in pay by gender who appear to have identical productivity characteristics. These include theories based on prejudice (e.g., Becker, 1957; 1971) and statistical discrimination (e.g., Arrow, 1973; Phelps, 1972). Labor monopsony was formulated by Robinson (1933) who posited that a monopsony employer lacks competition from other firms. The measure of monopsony power is related to the wage elasticity of labor supply or the likelihood that workers respond to a lower wage by leaving the market. A firm faces a labor supply with infinite wage elasticity in competitive labor markets. If the firm decreases wages below the market, workers will quit and look for a job at a higher-paying firm. The approach by Robinson (1933) was at odds with the standard assumption of competitive labor markets. Consequently, the monopsonistic model largely remained dormant until recent years. Some recent inquiries reinvigorated the application of monopsony to provide reasons why larger firms pay more than smaller firms, why similar-ability workers are paid different wages, and why race and gender are correlated with compensation. Manning (2003) established an analytical framework for estimating the wage elasticity of labor supply. He combined methods for analyzing worker transitions to and from jobs over time with the monopsony rate of exploitation introduced by Robinson (1933). Delving into job mobility is relevant to the gender earnings differences because female workers are paid less than male workers in many countries worldwide (Nopo et al., 2011). More importantly, female workers may face greater market frictions that could prevent workers from obtaining new jobs. Also, female workers may have greater familial and house responsibilities, so they may devote less time to conducting job searches or have concerns about obtaining a new job (e.g., being more risk-averse). Moreover, many female workers face gendered cultural constraints and often select occupations that offer greater flexibility. In contrast, male workers often have fewer cultural constraints and would earn more for sacrificing such flexibility (Goldin, 2014). Webber (2016) found a similar finding using US data because female workers are more likely to work in firms with lower labor supply elasticities. Hirsch et al. (2010) used Manning’s empirical framework and linked employer-employee data in Germany to estimate labor supply elasticity and found a lower elasticity for women than men. Barth and Dale-Olsen (2009) showed that gender elasticity differences accounted for 70 to 90 percent of the gender wage gap for lower-educated workers. Webber (2015) estimated the individual labor supply elasticities facing every US firm and found that a one-unit increase in elasticity was associated with wage gains of 5 to 16 percent, with significant variability across firms. Sokolova and Sorensen (2020) estimated via a meta-analysis that the average estimated elasticity is lower for women than for men. Through this survey of the literature this paper identifies and outlines the breadth of explanations for the gender gap that occurs with monopsonies.

ABSTRACT. Long gone are those days when the concern of forest resource was limited to a region or a nation – it is global now. This study aims to reflect this concern by revolving around the topic of the causes of deforestation. To some extent, deforestation might be necessary for the growth of an economy. However, not all deforestation is good. This paper focuses on the immoral reasons of deforestation. The research question of the study is to find out the impact of corruption on deforestation. The report by Harwel (2009) summarizes the loss of around $2 billion by Indonesia, country with world’s highest deforestation rates, in 2006 was due to illegal logging, corruption, and mismanagement. Exports from its flourishing timber sector were worth $US6.6 billion in 2007, second only to Brazil. Koyuncu (2009) found a positive correlation between corruption and deforestation which is statistically significant. According to Scarrow, R. (2017), corruption might not have a large impact on deforestation, but it is certainly more significant than when taking a debt from IMF to engage in heavy logging activities. Since deforestation is a global problem, this study targets all the countries around the world. To conduct this study, I have taken data for 22 years, which is a significant time to observe change in deforestation patterns. Using this panel data, I will be performing regression analysis. The models used in this study are entity fixed effects and time fixed effects. To define corruption, the study uses two corruption datasets, namely Corruption Perception Index (CPI), and Control of Corruption. Further, I have included covariates like countries with larger forest areas, GDP, government effectiveness, and some others to account for omitted variable bias. The importance of this study is to provide a quantitative basis for strategy builders and policy makers who come across the question of corruption being another cause for deforestation. The results found in the study show a highly statistically significant decrease in deforestation activities with 1 unit decrease in corrupt practices in a country.

ABSTRACT. Immigration law uses a variety of admission categories to satisfy different policy objectives. Some policy makers assume that only immigrants admitted via employment visas bring economic benefits. However, there is much evidence that family-based admissions lead to greater human capital investment by immigrants and that their efforts to find niches for themselves in U.S. labor markets adds to the dynamism of the American economy. This paper presents evidence on both human capital investment and earnings growth, while comparing the U.S. system to those in Canada and other countries. The paper finds that family-based immigrants usually do not enjoy the immediate high demand for their skills that employment-based immigrants do, but they experience much higher rates of earnings growth. Their entire earnings path is a better indicator of the value of their migration, both to the immigrant and to the host country. Their high rates of investment in new skills help to make their host country’s labor market more flexible to changing needs, and lead to entrepreneurial creation of new goods and services. Thus family-based immigration is a valuable component of any national immigration strategy, offering different types of benefits to the host economy. The paper points out that, in the literature, the debate about immigrant admission policies has been presented as a dichotomy: either (1) admit highly educated immigrants or immigrants with specific skills, or (2) admit immigrants with kinship ties. Yet, this ignores the basic fact that highly educated immigrants have families too. These educated immigrants will also be more likely to choose a country where their siblings, parents, and adult children are also welcome, or where only certain family members can follow. When immigrant scientists and engineers are asked why they moved to the United States, for instance, family reasons often dominate their answers. A family-friendly policy may thus help attract highly educated immigrants as well. The paper concludes that the strong inverse relationship between immigrant entry earnings and earnings growth in the US suggests that policymakers should not be overly concerned about low initial earnings among immigrants with otherwise similar schooling levels. It recognizes that those who immigrate to fill specific jobs, and are paid accordingly, have less of an incentive to invest in new human capital than immigrants lacking immediately transferable skills. As such, an immigrant admission policy designed to fill specific labor market needs may be less likely to promote a flexible labor force than a family-based or human-capital-based policy. It further suggests that, in countries with flexible labor markets and a societal openness to learning throughout life, immigrant economic adjustment confers broad economic benefits.

ABSTRACT. According to the World Bank, about 650 million people in the world live below the extreme poverty line defined as earning less than $2.15 per day. In the United States and other industrialized countries, living on less than $2.15 a day (absolutely, without additional social assistance) would surely make a person homeless and starving. However, is that also the situation for those poorest 650 million in developing countries who are said to be living below this poverty line? In Poor Economics, Banerjee and Duflo (2012) mentioned that, by purchasing power parity, one U.S. dollar (in 2010) was worth about 20 Indian rupees. They said that, for only one rupee, worth $0.05 by PPP, one could purchase a dosa (Indian pancake filled with lentils or other ingredients) for breakfast on the street in India. So, for $0.75 one could buy 15 of them in a day, and get 2,000 calories of relatively nutritious food. But, in the United States, if one orders just one dosa to take-out from an Indian restaurant, it can cost $10.00. In The Gambia, the average income, per person, per day is about $2.00. Yet, according to a recent study by the United Nations, 75 percent of Gambians own smartphones, and most live in permanent homes that have Internet service and televisions. So, how poor are the poorest people in the world, really? Are we measuring extreme poverty and income correctly? In Angus Deaton’s 2010 Presidential Address to the American Economic Association, he describes the source of this enormous inconsistency between what the poor in developing countries can truly afford in their own country, and the purchasing power parity that the World Bank uses to measure their income in dollars. As Deaton and many others have demonstrated, the “devil is in the details” of how purchasing power parity estimates may not accurately reflect the true purchasing power of the poorest people on our planet. This raises not only the question of how these estimates of true purchasing power can be corrected, but whether there have been reasons for our reluctance to correct them even when the solutions to the problem are not difficult to find. Is the discrepancy merely an analytical oversight, or has it been influenced by the fact that lower income estimates strengthen the appeal for greater assistance? Furthermore, to the extent that true purchasing power may be inaccurate, could this lead to assistance not being directed to where it is most needed? This paper answers these questions, drawing from the knowledge and literature of leading economists on the topic. It argues that the prices actually paid by the poorest people can, in fact, be acquired and used by economists to derive more accurate measures of their actual level of well-being. In this way, the world can have a better understanding of extreme poverty in its truest sense, and can direct assistance to where it is genuinely needed the most.

ABSTRACT. Individual credit files are very different from the complex digital data that has been studied by previous national accounting papers (Statistics Canada 2019) (Coyle 2022) (Calderon and Rassier 2022). Individual credit files are a simple record of loans and payments that can be stored on paper (Brenton 1964) and managed by customer service workers with only a high school degree (Bureau of Labor Statistics 2022a). Despite their simplicity, individual credit files are extremely valuable data. This paper uses recent research (Herkenhoff et al. 2021) (Dobbie et al. 2020) (Jansen et al. 2022) (Friedberg et al. 2021) to calculate that individual credit files yielded $1.1 trillion of services in 2017. Capitalizing individual credit files changes measured growth in the 1970s noticeably, but has little impact on measured growth in other decades. This difference is due to the Fair Credit Reporting Act of 1970, which reduced the lifespan of credit file data from decades to only seven years. This paper treats the shorter lifespan as a doubling of quality-adjusted data prices and calculates that the sudden doubling in data prices reduced real GDP growth in 1971 by 2.7 percentage point. This decline in real output growth is more than matched by a decline in the real input growth due to the sudden disappearance of all credit file data older than seven years. So, capitalizing individual credit files raises productivity growth in the 1970’s by 0.2 percentage point per year.

ABSTRACT. States and the federal government have imposed caps on the annual percentage rates (APRs) of some credit products to protect consumers from high-cost lending. While most policy initiatives have been restricted to closed-end credit products—such as payday loans—there is increasing interest in expanding such caps to open-end products—such as credit cards. Even though APR capping might reduce the cost of credit for some borrowers, there may be unintended consequences for consumers whose credit histories disqualify them from cheaper loan products. We evaluate the effect of a 36% APR cap on open-end credit products for members of the military community implemented during the 2015 expansion of the Military Lending Act (MLA). We do not find evidence that subprime residents of military communities improved their credit health outcomes after the MLA expansion. Using secondary data analysis, we show that a 36% cap on open-end credit products does not have a positive impact on most subprime borrowers. We conclude that the currently proposed APR caps of 36% on open-end loans are an ineffective way to improve subprime consumers’ financial well-being.

ABSTRACT. Participation in government programs to promote conservation practices by farmers has grown significantly over the last two decades. Farms investing in and participating in the three top USDA NRCS conservation programs grew from $1.4 billion in 2002 to $4.8 billion in 2020 (1). To provide farmers with an additional source of capital to finance conservation projects conservation loans were first introduced in the 2008 Farm Bill. Surprisingly, these conservation loan programs have been largely underutilized.

The logical question is Why has the demand for conservation loans remained static or fell while the demand for conservation services continues to grow? To address this paradox, we merged data on the Natural Resources Conservation Service (NRCS) conservation program participation rates and funding levels with data on Farm Service Agency (FSA) FLP borrower participation. We first looked at patterns on current conservation participation, funding levels, and borrower characteristics for a subset of producers participating in both programs. We next used these results to model how changes to the current direct and guaranteed loan program parameters might increase future usage of these programs. Finally, we broadened this analysis to ask how these changes might impact future USDA conservation program spending across all FSA and NCRS programs.

Our initial findings indicate that without changes to the NRCS program requirements or process, the likelihood that a producer would obtain a conservation loan is small. This is mainly due to the small cost-share portion incurred by the vast majority of producers (2). We suggest five possible changes to the conservation loan program to increase usage while addressing this finding. We also outline possible future research avenues using this data, including incorporating USDA ARMS survey data on all U.S. farmer conservation spending and practices.

This is the first time anyone has attempted to merge the USDA NRCS conservation program data with the FSA direct and guaranteed loan data by borrower. As a result, our methodology and findings are unique. They illustrate the process and difficulties of combining data across programs and branches within the same federal agency. Our findings are timely given the upcoming 2023 Farm Bill negotiations and the federal government’s promotion of greater implementation of conservation practices across industry groups. The ability of agriculture to contribute, through on-farm implementation of conservation practices, will be essential in reaching the administration’s conservation goals.

footnotes: (1) These programs are EQIP, CSP, and CTA program. Expenditures for these main three programs comprised 78% of all expenditures in NRCS programs in 2020. https://www.nrcs.usda.gov/Internet/NRCS_RCA/reports/cp_nat.html

(2) USDA Cost-share conservation programs pay a fixed portion of the estimated cost of an approved conservation practice implementation plan. The producer is responsible for the remaining cost-share portion of the estimated expense (as well as any further incurred expenses).

ABSTRACT. Central bank digital currencies (CBDCs) are being actively developed in several jurisdictions and have already been introduced, fully or as pilot projects, in 28 countries. At the same time, the use of cash is dwindling at global level. Could CBDCs eventually lead to a “cashless society” and would this outcome be desirable? This study argues that physical cash plays an important role in the payments system, providing privacy, inclusion, resilience, and a safeguard against negligence or abuse in the management of other payments instruments. Physical cash thus exhibits strong complementarities with the other payments instruments. Since these effects include a large component of externalities, cash can be considered as a public good, that would be undersupplied by the market in equilibrium, providing a justification for policy interventions in its favor. The urgency of such policies is underscored by the risk that, in their absence, cash use could reach a “tipping point’ beyond which cash would become extinct.

DISCLAIMER: The views expressed herein are those of the author and should not be attributed to the IMF, its Executive Board, or IMF management.

ABSTRACT. Independent inventors, those inventors who are not affiliated with a formal organization or institution when filing for a patent, have made outstanding contributions to economic growth and prosperity in the United States. While a key source of invention, independent inventors face several challenges related to successful commercialization. This paper extends the literature on the potential benefits of patent protection to independent inventors. We find that patents have significant positive effects on the use of legal representation on subsequent patent applications, participation in markets for technology through patent sales, and in certain cases, becoming connected to first-time and established firms. These results demonstrate that patenting lowers the barriers to successful innovation and commercialization for independent inventors.

ABSTRACT. One of the primary goals of the patent system is to incentivize disclosure of new innovations. But do inventors actually disclose all relevant information about their innovations when they write patents (as nominally required in patent law), or do they strategically withhold information? How can courts and policymakers increase the amount of disclosure in the face of strategic information withholding?

To investigate these questions, we build a duopoly model in which an innovator chooses how much information about their invention to disclose before competing against a follower firm. More disclosure allows the follower to copy more information from the patent, while also signaling to the follower that the innovator is a strong competitor. Novel to our model, we also introduce a legal role of disclosure: it increases the probability that the innovator can win an infringement suit against a follower that copies. The inclusion of the legal channel allows us to investigate how policy choices affect patent disclosure. In particular, and in contrast to much of the economics of crime literature, we find that policies that change the probability that an innovator wins an infringement suit affect disclosure differently than policies that change the penalty that the innovator receives conditional on winning the suit. The intuition is that a larger penalty always increases the marginal value of disclosure to the innovator, and hence always increases disclosure. A higher probability of winning, however, lowers the marginal benefit of disclosure, and hence whether disclosure increases or decreases depends on the underlying quality of the invention. Specifically, high-quality innovators disclose less and low-quality innovators disclose more when the probability of winning increases.

To test this model, we investigate recent patent law cases that change either the probability that an innovator wins an infringement suit or the penalty the innovator receives conditional on winning, but not both. The 2011 Microsoft v. i4i decision made it easier for innovators to win infringement suits. The 2016 Halo Electronics v. Pulse Electronics decision made it easier for innovators to prove willful infringement and so increased innovator’s rewards conditional on winning a suit without changing the probability of winning. Using several novel proxies for patent disclosure and quantile regression methods, we find, consistent with the predictions of our model, that the Microsoft v. i4i decision reduced patent disclosure for the highest quality patents and increased disclosure for the lowest quality patents, while the Halo v. Pulse decision increased disclosure for the entire distribution of patents.

These results have important implications for innovation policy. In particular, not all “pro-patent” policy changes affect the disclosure of innovations in the same way. Increasing innovators’ protection through a greater penalty increases the disclosure by everyone. Increasing the protection by increasing the probability of winning, however, decreases the disclosure about high-quality inventions, which can be counterproductive to the policy goal of creating positive spillovers and driving economic growth. Thus, we show that the specific manner in which a regulation increases patent protection matters as much as the extent of the protection.

ABSTRACT. In the United States and many other jurisdictions, pending patent applications are typically published 18 months after the initial filing. This practice has been criticized because it puts inventors’ intellectual property into the public realm before a final grant decision on the patent has been made, affording competitors more time to engineer around the invention or depriving the inventors of using other means of intellectual property protection (e.g., trade secrets). Recent empirical evidence, however, indicates that inventors generate little value from the secrecy of their patent applications and attach more strategic importance to the duration of pendency after the publication of the application. Unless the inventors mark their products (“pending patent” or “patent applied for”), the existence of a patent application before its publication is private information. Disclosing the fact that a technology or invention exists and a patent has been applied for (without disclosing technical details) does not necessarily generate the same effects as the publication of the application (including the technical details). Without the technical details, competitors will not be able to engineer around, nor do inventors forego the opportunity to seek patent protection elsewhere. However, announcing a pending patent can affect rivals and competition in other ways not previously studied.

We propose a model in which the applicant balances a negative effect of disclosure on its informational advantage in the short run (value of secrecy) with a positive long-run effect stemming from potential deterrence of a rival’s R&D (value of deterring innovation). We give conditions under which announcing the pending patent deters a rival’s innovation. We show that, in equilibrium, the applicant’s decision to announce and the rival’s decision to innovate are non-monotonic in the strength of the application and the strength of the patent. Furthermore, we predict that announcements of pending patent applications are much more common in industries where competition is in strategic substitutes. We present evidence supporting this claim by identifying press releases, one channel for disclosing business information, that announce nothing but the recent filing of patent applications. Using a technique suggested in the finance literature, we estimate a measure of the nature of competition for all major NAICS codes. The firms announcing patent applications are predominantly from industries with competition in substitutes.

ABSTRACT. Much of the research within economics is interested in understanding the effects behaviors and policies have on utility and rely on the relationship between utility and consumption or utility and income for their analysis. Because income is more easily collected and available for much larger samples, it is a more common proxy for well-being than consumption. Additionally, researchers might prefer income as a measure of well-being because it reflects access to resources than can be used for consumption, whereas well-being measured by consumption could be artificially low due to preferences. However, income is more sensitive to short-run fluctuations whereas consumption better reflects long-run resources and is more likely to capture disparities that result from differences across families in the accumulation of assets or access to credit. There is also evidence that components of consumption that are particularly important for the poor are fairly well-captured in surveys, while many components of income important to the poor may not be as well captured. Furthermore, consumption has been shown to be more strongly correlated with other indicators of economic well-being than income. Many researchers use expenditures as a proxy for consumption, but consumption and expenditures will not be equal for durable goods that are consumed over multiple periods and expenditures will not include in-kind benefits received. In this research, we describe the data and methods used to create a Bureau of Labor Statistics consumption measure. The consumption measure is based on expenditures reported in the Consumer Expenditure Survey (CE) with a flow of services value used for housing and vehicles. We also impute the value of in-kind benefits. As examples of how the consumption measure can be used, we produce means, and poverty and inequality results for 2019-2021. We produce results for our comprehensive consumption measure with and without a value for health insurance, expenditures, and pre-tax income. For all years, consumption is more equally distributed than total expenditures or income. The timing of our study allows us to examine the impact of the pandemic and recovery. For example, we find that consumption without health insurance poverty fell in 2020 from the 2019 rate with the onset of the pandemic; and inequality fell for consumption with and without health insurance in 2020. For consumption with and without health insurance, distributions became more equal in 2020 and 2021 relative to 2019.

ABSTRACT. We examine the effect of eviction moratoria enacted in 2020 in the United States. We leverage the staggered implementation of strict state-level policies to estimate the effects of the moratoria using a difference-in-differences approach. We use detailed criminal incident data to estimate the effect of moratoria on intimate partner violence, and we find evidence that eviction moratoria decreased reported intimate partner violence by 39% relative to the pre-treatment average, driven by a 42% reduction in reports of simple assault. The results are consistent for both male and female victims.

ABSTRACT. The Census Bureau produces the Supplemental Poverty Measure annually. This measure of poverty incorporates money income and non-cash benefits (such as nutritional assistance programs, housing subsidies, tax credits, and stimulus payments) while subtracting necessary expenses such as income and payroll taxes and work and medical expenses. This paper examines the impact of the expanded Child Tax Credit on child poverty. We find that the Child Tax Credit lifted 2.9 million children out of poverty. Additionally, we find that the 2021 expansion of the Child Tax Credit accounted for 2.1 million of these 2.9 million children lifted above the poverty line.

ABSTRACT. We introduce a new Python package PyPE for developing and conducting partial equilibrium (PE) analysis of trade policies. PyPE offers practitioners a flexible framework to select and combine different trade model features to determine the impact of select policies on a particular market or industry. With a user-friendly interface, PyPE allows users to run model simulations and report results with only a few lines of code. Interested users are also encouraged to assist with PyPE development by proposing or constructing new model features that can be incorporated within the package.

ABSTRACT. A first order approximation of the effects of proposed mergers can be obtained with a relatively limited set of information: diversion ratios and margins. However, appropriate data to estimate credible diversion ratios is not always readily available. A common approach in such cases is to assume that diversion is proportional to market shares, which is consistent with standard Logit demand. In this paper, I consider instead the nested Logit demand model for the purposes of calibrating diversion ratios. A key advantage of this approach is that the nested Logit is a more flexible model that includes the standard Logit as a special case. In lieu of making explicit assumptions about the nesting structure of demand (as would be required by simulation), I treat the nested Logit as a partially identified model and present diversion ratio bounds that account for uncertainty about the true nesting structure, as well as uncertainty about market size. In many instances, the standard Logit assumption of diversion proportional to share can lead to poor approximations of true diversion in the more general Logit model. Furthermore, the nested Logit model also highlights that relative margins are predictive of diversion, even after accounting for observed market shares. Diversion ratios based on the standard Logit fail to leverage the additional information provided by relative margins.

ABSTRACT. This paper examines the link between industrial production and the sentiment expressed in text survey responses from U.S. manufacturing firms. We compare several natural language processing (NLP) techniques for classifying sentiment on our manufacturing-specific corpus, ranging from dictionary-based to deep learning methods. We find that deep learning models—partially trained on our data—achieve the highest sentiment classification performance on a manually-labeled sample. We assess the extent to which each sentiment measure, aggregated to monthly time series, can forecast industrial production. Our results suggest that sentiment conveyed in text responses provides new information beyond traditional numerical data and improves out-of-sample forecasting.

ABSTRACT. The number of patents each year related to aeronautics and aircraft increased sharply worldwide from 1905 to 1910. This five-year spike appears to be steeper than any other field-specific growth in patents in history. We examine the proximate causes of the increase, based on a database of 15,000 aeronautics-related patents from before 1920 and a small comparison sample from the same period.

Public demonstrations in 1906 made it suddenly common knowledge that the long-sought capacity to make a controllable airplane had been achieved. Experimenters responded. Firms appeared, but little of the patent increase is attributable to their R&D, based on the affiliations that can be observed. Some of the increase is due to duplicative patents, some of which we can measure by examining the patent specifications: foreign filings of essentially the same patent in multiple countries.

Differences in patent law and practice affect the levels of patent filings and grants but not these trends. After 1911, the numbers of aero patents declined, and continued to decline in World War I despite a sharp increase in corporate and military research in development, because of the need to keep military technologies secret. Distinctive laws made it possible for the national governments to restrict publication of granted patents if they had military applications, but we see only a few examples where this was applied.

ABSTRACT. What is the role of startups within the innovation ecosystem? Since 2000, startups have grown in their share of commercializing research from top U.S. universities; however, prior work has little to say on the particular advantages of startup ventures in the innovation process relative to more traditional alternatives such as academia and established private-sector incumbents. Recent startup-driven advances in the fields of health, energy, and manufacturing suggest that startups are often involved with transformative technologies that are likely to disrupt traditional approaches and business models. Building on the insights from these examples, we develop a simple model of startup advantage from the perspective of the initial inventor of a multi-stage research line, and generate predictions related to the value and impact of startup innovation. Our model highlights two key sources of startup advantage: "vision," which refers to the initial inventor’s private information about the probability of success for the research line, and "creative destruction," which refers to the possibility for the research line’s output to be more valuable to a new entrant relative to an established incumbent. We then explore these predictions using granted patent applications within the regional ecosystems of top-25 U.S. research universities from 1990 through 2018. We use the 2013 introduction of new startup-focused patent policies from the America Invents Act (AIA), in combination with location-specific historical startup rates, we implement a difference-in-differences estimator. We find that the AIA’s impact increased startup patenting in regions that experienced higher historical startup patent rates, and proceed to use this variation as an instrument to identify the causal impact of startups on innovation. Our results show a significant startup advantage in terms of average forward citations and most-cited-patent rates, with even larger magnitudes than those observed in correlational analyses. Moreover, startups that survive to become “scale-ups” tend to maintain their initial advantage, and quickly grow to dominate their regional innovation ecosystems. Our findings have important implications for innovation policy, including the importance of technology transfer and commercialization training for early-stage researchers and inventors, and the value of reducing entry barriers and anti-competitive behavior in concentrated industries.

ABSTRACT. We study how an interruption in the flow of research funding to a major NIH grant — the R01 — affects the career outcomes of research personnel using comprehensive wage and tax records that have been linked to university transaction data. Using a difference-in-differences design, we find that for employees who work for labs with fewer grants, an interruption of more than 30 days has a substantial effect on job placement, including a 2.5 pp increase in the probability of no longer working in the US. The effects are strongest among postdocs and graduate students. We also find that interrupted employees who stay in the US labor force earn 20% less than uninterrupted employees who also remain in the US.

ABSTRACT. This paper provides estimates from 2014 to 2021 using the Health Inclusive Poverty Measure (HIPM) (Korenman and Remler 2016), which adapts the Supplemental Poverty Measure (SPM) to incorporate health insurance values into poverty measurement. The HIPM poverty rate in 2021 was 9.5 percent, 1.7 percentage points higher than the SPM poverty rate of 7.8 percent, and 2.1 percentage points lower than the official poverty rate of 11.6 percent. This represents a decline in HIPM rates of 7.4 percentage points since 2014, not statistically different from the SPM, which declined 7.8 percentage points in the same period. In 2021, differences between HIPM and SPM were notable for Hispanics, reflecting higher uninsured rates for this group. The impact of public assistance is considered too, with Medicare and Medicaid having a 4.6 and 4.2 percentage point impact on overall HIPM rates respectively. Sensitivity tests are performed to determine the impact of methodological changes compared to previous estimates. Overall, the estimates support HIPM implementation at the Census Bureau under the current income, poverty, and health insurance release timelines.

ABSTRACT. Narrow network insurance plans comprise a growing share of the health insurance market. While recent work has examined the effects of these plans on insurance premiums and utilization, little is known about the extent to which the use of these narrow net-work plans alters the nature of provider competition within these markets. This study analyzes competition in a narrow network hospital insurance market. Accounting for the existence of narrow network insurance plans can imply larger merger-induced price effects than would be predicted using models that assume insurers contract with all providers in the market. Additionally, our framework can theoretically ground the growing empirical literature surrounding cross-market" hospital merger effects.

ABSTRACT. Military policies may signal women’s work abilities to civilians. To test this, I construct a policy experiment under the hypothesis that the 1972 gender desegregation of the U.S. Army updated public knowledge of women’s abilities. During this period, the Army sought to replace conscripted men with women by expanding the use of women in blue-collar occupations. Thus, discrimination decreased against women workers seeking entry into civilian blue-collar male-dominated occupations. I find that living in an area with one percentage point higher in veterans and military increases the probability that a woman works in a male-dominated occupation by 0.535 percentage points.

This paper’s findings are consistent with the hypothesis that prominent military policy change regarding women signal a shift in women’s ability to work in male-dominated civilian occupations. After the desegregation of the Army, the treatment effect of living in an area with one percentage point higher in active duty and Vietnam veterans increases the probability that a woman works in a male-dominated occupation by 0.535 percentage points. This result is statistically significant even after controlling for race, number of children, educational attainment, state unemployment, state female labor force participation, and MSA fixed effects. I conclude that the military contributes to civilian occupational desegregation by signalling the expanding role of women. This finding plays a nontrivial role in the 3.22 percentage point increase in women’s entry into male-dominated occupations. This effect contributes to the decrease in occupational sorting occurring during the period when measures of occupational sorting decreased by 6.1 (4.3) percentage points in the 1970s (1980s) (Blau, Brummund and Liu, 2013).

While civilian occupational desegregation between 1970-1999 made great progress, it has stalled in recent decades. Recognizing the connections between the military and civilian labor forces enables us to examine the extent to which military policies drive civilian gender inequality. This research has important implications as the U.S. Army modifies existing physical fitness testing standards and faces consistent recruiting shortfalls. The process for validating occupational standards is currently under review for gender neutrality. Because the validation of these tests were only performed on men, they may only accurately predict men’s performance (Kamarck, 2016) and validate existing social norms that justify men’s resistance of women’s entry into male-dominated civilian work. My paper suggests that the resulting gender-neutral standards may update the public’s signal regarding women’s ability to perform in male-dominated civilian occupations.