The problem of joblessness for Black men is on average three times worse than what is generally assumed. We typically assess joblessness based on the unemployment rate, but prime-age (ages 25 to 54) Black men’s employment-to-population ratio (EPOP) lags the EPOPs of prime-age white, Latinx, and Asian men by over ten percentage points. Among prime-age men, Black men’s EPOP is an outlier. The white-Black EPOP jobs gap is about three times the unemployment rate jobs gap during a period of moderately high unemployment. When we use the unemployment rate to understand joblessness for Black men, we grossly underestimate the problem, the harm it causes to Black communities, and the need for bold policy interventions. If we could close the white-Black EPOP jobs gap, we could add about $30 billion annually to Black communities and make a significant reduction in Black poverty. This report calculates the white-Black unemployment rate and EPOP jobs gaps during periods of “low,” “moderate,” and “high” Black unemployment. Using 2014 as the year for “moderate” unemployment, the analysis finds that for Black men to have a similar EPOP to white, Latinx, and Asian men would have required 947,000 jobs, 2.8 times the number to close the unemployment rate gap. A problem with the official labor market statistics is that they do not include the Black men who are incarcerated or allow us to evaluate the economic impact of the higher mortality rate of Black men. Prime-age Black men who are incarcerated or deceased still have children, family members, and partners who, under different circumstances, could benefit from their financial support. When one takes into account the incarceration and mortality rates of Black men, the EPOP jobs gap jumps to four times the unemployment rate jobs gap, and the income deficit approaches $50 billion a year. Addressing the prime-age men’s white-Black EPOP jobs gap is one important step in building up the economic health of Black communities. Among the other steps are reducing the high incarceration and mortality rates of Black men.

With roots dating back centuries, son preference has been present in India and continued to the current day with implications such as millions of girls missing from the population. The prevalence of son preference stems from patriarchal practices such as a patrilineal inheritance an aging parent's dependency on their sons. Daughters can be seen as a burden, for reasons such as the practice of dowry payments when women get married and some women not bringing an economic contribution to their family. This can differ by region, as in rural Northern areas, women do not participate in agricultural production, but in rural Southern areas where rice-growing is practiced, women's involvement is crucial. As India continues to grow in population, with predictions that it will be the most populous country by 2050, it is increasingly important to seek the end of son preference. Tools such as sex-selective abortions have grown in popularity in order to limit the number of daughters, and many daughters that are born into larger families get less resources than their brothers. There are many studies that look into son preference in India, often by analyzing parity progression, the likelihood that families will try for more children based on the sex composition of their existing children. This study is different than many of the other sex preference studies in India. Instead of looking at parities, this study compares a woman's expressed ideal sex ratio with the actual sex ratio of her children. By looking at attainment of sex preference, we can see what variables might influence hitting the ratio or not. One finding that might come from looking at this type of analysis, is seeing if higher levels of wealth are correlated with hitting the desired ratio, which could give hint to sex-selective abortions taking place.

Research has made strides in disaggregating health data among racial/ethnic minorities, but less is known about the extent of diversity among Whites. Using logistic regression modeling applied to data on respondents aged 40+ from the 2008 to 2016 American Community Survey, we disaggregated the non-Hispanic White population by ancestry and other racial/ethnic groups (non-Hispanic Black, non-Hispanic Asian, and Hispanic) by common subgroupings and examined heterogeneity in disability. Using logistic regression models predicting six health outcome measures, we compared the spread of coefficients for each of the large racial/ethnic groups and all subgroupings within these large categories. The results revealed that health disparities within the White population are almost as large as disparities within other racial groups. In fact, when Whites were disaggregated by ancestry, mean health appeared to be more varied among Whites than between Whites and members of other racial/ethnic groups in many cases. Compositional changes in the ancestry of Whites, particularly declines in Whites of western European ancestry and increases in Whites of eastern European and Middle Eastern ancestry, contribute to this diversity. Together, these findings challenge the oft-assumed notion that Whites are a homogeneous group and indicate that the aggregate White category obscures substantial intra-ethnic heterogeneity in health.

We use the universe of commercial property tax records to estimate the aggregate effects of commercial land use regulations on output and welfare. We introduce a method for identifying distortions at the address level, and then aggregating the distorted address-level problems into a city-level builder’s problem. We apply this method to commercial buildings using property tax records compiled by CoreLogic. We then incorporate the builder’s problem into a spatial general equilibrium model, and use it to assess the extent and impact of commercial zoning regulations. We find that even partial deregulations can produce significant gains.

How does an interview interruption affect the probability women will report their experience with intimate partner violence (IPV)?

The 1944 Servicemen’s Readjustment Act (the “G.I. Bill”) provided returning WWII veterans with educational benefits sufficient to cover tuition, fees, and living expenses at almost any U.S. university or college. While several studies examine subsequent educational attainment and earnings for male veterans, little is known about how the G.I. Bill affected the 330,000 American females who served in WWII. Using data from the 1980 5 percent Census Public-use Microdata Sample, I find that female WWII veteran status is associated with a 19 percentage point increase in the proportion who report any college attendance, a 7.8 percentage point increase in college completion, and earnings that are 19.8 percent greater relative to comparable females who are not veterans. Because service was entirely voluntary for females, I use service eligibility requirements, enlistment records, 1940 Census data, and the G.I. Bill’s retroactive nature to establish a causal relationship among veteran status, educational attainment via the G.I. Bill, and increased earnings. To help separate the effect of the G.I. Bill from the effect of military service itself, and because benefits increased with longer service, I instrument for female veterans’ educational attainment using age at the timer of the G.I. Bill’s announcement. My instrumental variables estimates imply that female veterans’ earnings increase by $1,350 (11.6 percent) per year of G.I. Bill-induced education, explaining 73 percent of the overall difference between veteran and non-veteran females’ earnings in 1980.

We use information on the occupation choices and earnings of immigrants to measure differences in specific skills between workers from rich and poor countries. We have several findings. First, the skills which rich country workers specialize in mirror the skills which high-income individuals specialize in. Second, rich country workers have the greatest advantage in skills related to the ability to generate ideas (like creativity and critical thinking) rather than scientific or technical knowledge. Third, the skills in which rich country workers have the greatest advantage align closely with the skills used in management occupations. Fourth, workers from rich countries are more varied in their skills (e.g., what one Canadian is good at is different from what another Canadian is). These findings do not appear to be accounted for by the non-randomness of immigration or mismeasurement of skills. Overall, our results suggest that rich country workers have skills particularly well-adapted to production processes involving the coordinated efforts of large groups of people.

The United States’ two chief disability programs, Social Security Disability Insurance (SSDI) and Supplemental Security Income (SSI), have been subject to two widespread sets of policy concerns: the potential effect of these programs in suppressing recipients’ labor supply and the insufficiency of these programs in providing economic stability and dignity. The Alaska Permanent Fund Dividend (PFD) – an unconditional cash transfer program paid to all Alaska residents – may provide a model for increasing economic security for working aged (18 to 62 year old) SSI and SSDI recipients without imposing distortionary labor supply effects. By leveraging the stochastic fluctuations in the size of the PFD, this paper utilizes a plausible source of exogenous variation in the PFD transfer income to estimate the effect of this unconditional cash transfer on employment among adult SSI and SSDI recipients. This paper estimates the effect of this program through the construction of a synthetic control variable and the application of a difference-in-difference estimator to the American Community Survey (ACS). After establishing that the Alaska PFD has a small and statistically insignificant effect on employment, this paper asks what effect this program may have on poverty if it were implemented nationwide. Via a microsimulation approach, this paper estimates that if a transfer comparable to the Alaska PFD had been scaled nationally for adult SSI and SSDI recipients poverty rates would have been reduced by as much as 10 percentage points and would have offset the entirety of the rise in poverty among adult SSI and SSDI recipients since 2000.

This article shows that parents reveal information about their fertility behavior through how they name their children. I arrive at this finding from detailed examination of the net fertility of 130,000 married couples in Ireland circa 1910, a country known for its historically high fertility rate. After stringently accounting for the occupation, religion and location of couples, I find higher fertility rates among couples who chose distinctly Catholic names and traditional names for their children, with the latter being particularly important. Exposure to towns and cities lowered net fertility and weakened preferences for traditional and Catholic names. Cumulatively, these findings highlight the role of traditional rural norms over explicitly religious influences in driving high fertility rates in Ireland. The impact of towns and cities in reducing net fertility suggests that Ireland’s sluggish urbanization was a key factor in its high historical fertility rate.

Urban scaling theory explains the increasing returns to scale of urban wealth indicators by the per capita increase of human interactions within cities. This explanation implicitly assumes urban areas as isolated entities and ignores their interactions. Here we investigate the effects of commuting networks on the gross domestic product (GDP) of urban areas in the US and Brazil. We describe the urban GDP as the output of a production process where population, incoming commuters, and interactions between these quantities are the input variables. This approach significantly refines the description of urban GDP and shows that incoming commuters contribute to wealth creation in urban areas. Our research indicates that changes in urban GDP related to proportionate changes in population and incoming commuters depend on the initial values of these quantities, such that increasing returns to scale are only possible when the product between population and incoming commuters exceeds a well-defined threshold.

Hearing loss is one of the most common health problems in later life (Lin et al. 2011b; Whitson et al. 2018), affecting 27.3% of individuals aged 65–74 and 45.1% of individuals aged 75 and older in the United States (CDC 2017). Globally, the World Health Organization (WHO) (2018) estimates that one-third of individuals over the age of 65 have a disabling hearing loss. As the global population continues to age, effectively screening for and diagnosing hearing loss will be paramount to improve population health.

Although models of social vulnerability to environmental hazards are commonly developed to support policy interventions in emergencies and disasters, their utility is hindered by a lack of contextual information on individuals exposed to and affected by hazards. We develop a novel approach to model social vulnerability that couples individuals and their varying forms of protective capacity with the social fabric of the communities in which they reside. The backbone of our model is the Public-Use Microdata Sample (PUMS), a product of the U.S. Census Bureau that preserves a representative sample of completed responses to the American Community Survey (ACS). The PUMS enables us to understand the full range of individual protective capacities against a hazard in an exposed area, which we term individual vulnerability profiles (IVPs). In this case, we examine IVPs in the Coney Island–Brighton Beach section of New York City, which suffered severe impacts during Hurricane Sandy in 2012. To manage the large number of unique IVPs in Coney Island–Brighton Beach, we perform a segmentation analysis to generalize them into thematic cohort vulnerability profiles (CVPs) representing a typology of vulnerable people in Coney Island–Brighton Beach during Sandy. From synthetic populations of CVPs, we then estimate how individuals in varying housing types were coexposed to Sandy at the census tract level by classifying these areas into community social vulnerability profiles (SVPs). Our results provide a topology of social vulnerability that simultaneously links individual, community, and population-wide concerns, enabling a more holistic understanding of resources and interventions beneficial to human security during events like Sandy than is attainable with area-level metrics.

How does a mother’s agency and access to health systems affect their child’s nutritional health?

Does a child’s birth date affect his or her probability of attending a private school? In the United States, most children must be five years old by September to start public kindergarten. An alternative option is to attend private schools, which are not obliged to comply with states’ cutoffs. To explore this, I look at the effect of children’s quarter of birth on their probability of attending private school by grade (pre-kindergarten through 12th grade). Using the American Community Survey, I find that children born in July–September and October–December are more likely to attend private kindergarten than children born between April and June. The effect does not persist at higher grades. These findings indicate that, when limited by the entrance age cutoff, parents use private schools to bypass the restriction, giving their children a head start on schooling, and later transfer them to public school as they progress through K–12.

Those who choose to serve in the armed forces often do so out of a powerful sense of civic duty. But for generations, military service has also been packaged by recruiters, veterans, and popular culture as a clear path to upward social mobility. This report explores the extent to which that aphorism has remained true for the New York metropolitan area’s 9/11 era veterans over the last decade.1 This study examines key socioeconomic and demographic trends among non-active duty veterans in the New York metropolitan area who served in the U.S. armed forces during the post-9/11 era. To achieve a richer understanding of the conditions former servicemen and servicewomen face as they transition into civilian life, this report looks at topics such as sex, race/ethnicity, age, employment status, income, poverty rates, and educational attainment between 2007 and 2017. 2 Overall, the news is good. 9/11 era veterans in the New York metropolitan area performed well above their non-veteran counterparts in most socio-economic categories. The data indicate that between 2007 and 2017 employment, income, and educational attainment rates were consistently higher, and poverty rates consistently lower, than those of the metro area’s general population. These trends held relatively firm during the financial crisis of 2008 and as the veteran population continued to grow into the 2010s. In short, there is considerable evidence within this report to affirm that serving in the armed forces continues to have a direct correlation with greater socio-economic success. This correlation is particularly stark among Latinos and non-Hispanic blacks, where the variances between their nonveteran counterparts are prevalent in income, employment, poverty rates, and educational attainment. Perhaps most striking among these findings is the rapid growth of the 9/11 era veteran population. Over the ten-year period studied here, the number of former servicemen and servicewomen in the New York metro area almost doubled while the general population grew at a much smaller rate. Along race/ethnic lines, the composition of the veteran population became significantly more Latino over the ten-year period -- so much so that Latinos eventually eclipsed non-Hispanic blacks as the second largest race/ethnic group after non-Hispanic whites.

This thesis contains four independent essays in heterogeneous agent macroeconomics. They explore the sources of income inequality and income risk and study the optimal design of public redistribution and insurance. The first chapter, joint with Filip Rozsypal, studies the origins of idiosyncratic earnings risk in frictional labor markets, with a particular focus on the role of firms for worker earnings risk. First, using administrative matched employer-employee data from Denmark, we document key properties of the worker earnings growth distribution, the firm revenue growth distribution, and their joint distribution. The worker earnings and firm revenue growth distributions exhibit strong deviations from normality, in particular excess kurtosis, with many workers and firms experiencing very small changes to their earnings/revenues, but a significant minority experiencing very large changes. Large earnings losses are more likely for workers in firms with negative revenue growth, driven both by separations to unemployment and earnings losses on the job. Second, we develop a model framework consistent with the data, with four key features: i) frictional labor markets and on the job search to capture unemployment risk and wage growth through a job ladder, ii) multi-worker firms to capture gross and net worker flows, iii) risk averse workers such that earnings risk matters, and iv) contracting with two-sided limited commitment because earnings of job stayers are changing infrequently in the data. Third, we use the model to explore policies designed to mitigate earnings fluctuations. The second chapter, joint with Annika Bacher and Lukas Nord, studies one particular private insurance margin against individual income risk only available to couples, which is the so called added worker effect. Specifically, we study how this intra-household insurance against individual job loss through increased spousal labor market participation varies over the life cycle. We show in U.S. data that the added worker effect is much stronger for young than for old households. A stochastic life cycle model of two-member households with job search in a frictional labor market is capable of replicating this finding. The model suggests that a lower added worker effect for the old is driven primarily by better insurance through asset holdings. Human capital differences between employed young and old contribute to the difference but are quantitatively less important, while differences in job arrival rates play a limited role. In the third chapter, joint with Axelle Ferriere, Gaston Navarro, and Oliko Vardishvili, we study optimal redistribution, taking into account not just the large income and wealth inequality in the data, but also the distribution of income risk that is key in the first two chapters. The U.S. fiscal system redistributes through a rich set of taxes and transfers, the latter accounting for a large part of the income of the poor. Motivated by this, we study the optimal joint design of transfers and income taxes. Within a simple heterogeneous-household framework, we derive analytical results on the optimal relationship between transfers and tax progressivity. Higher transfers are associated with lower optimal income tax progressivity. Redistribution is achieved with generous transfers while efficiency is preserved via a lower progressivity of income taxes. As such, the optimal tax-and-transfer system features larger progressivity of average than of marginal tax rates. We then quantify the optimal tax-and-transfer system in a rich incomplete-market model with realistic distributions of income, wealth, and income risk. The model features a novel flexible functional form for progressive income taxes and means-tested transfers. Relative to the current U.S. fiscal system, the optimal policy consists of more generous means-tested transfers, which phase-out at a slower rate. These larger transfers are financed with higher tax rates, but the taxes are not more progressive than the current system. The fourth chapter, joint with Axelle Ferriere and Dominik Sachs, also studies optimal redistribution, but instead of considering a stationary environment it analyzes the dynamics of the equity-efficiency trade-off along the growth path. To do so, we incorporate the optimal income taxation problem into a state-of-the-art multi-sector structural change general equilibrium model with non-homothetic preferences. We identify two key opposing forces. First, long-run productivity growth allows households to shift their consumption expenditures away from necessities. This implies a reduction in the dispersion of marginal utilities, and therefore calls for a welfare state that declines along the growth path. Yet, economic growth is also systematically associated with an increase in the skill premium, which raises inequality and the desire to redistribute. We quantitatively analyze these opposing forces for two countries: the U.S. from 1950 to 2010, and China from 1989 to 2009. Optimal redistribution decreases at early stages of development, as the role of non-homotheticities prevails. At later stages of development the rising income inequality dominates and the welfare state should become more generous.

Objective: The Asian American population in the U.S. comprises various, ethnically diverse subgroups. Traditionally, this population has been studied as a single, aggregated group, potentially masking differences in risk among subgroups. Analyses using disaggregated data can help better characterize the health needs of different Asian subpopulations and inform targeted, effective public health interventions. We assessed the prevalence of cardiovascular disease (CVD) risk factors and atherosclerotic CVD (ASCVD) and their associations with socioeconomic factors among Chinese, Asian Indian, Filipino and Other Asian subjects, compared with non-Hispanic White (NHW) subjects in the U.S. Methods: Cross-sectional study using data from 298,286 adults from the National Health Interview Survey (NHIS) from 2007 to 2018. We utilized chi-squared tests to compare characteristics across subgroups. Weighted proportions and unadjusted and adjusted logistic regression models were utilized to examine the associations between Asian subgroups, self-reported CVD risk factors and self-reported ASCVD, as well as between socioeconomic factors within each Asian subgroup. Results: Asian Indian subjects had the highest prevalence of diabetes (12.5%), while Filipino subjects had the highest prevalence of hyperlipidemia (27.7%), hypertension (29.8%) and obesity (19.8%). Despite this, the prevalence of self-reported ASCVD was lower in all Asian groups compared with NHWs. Chinese subjects had the lowest odds of having each of the CVD risk factors assessed. Conclusion: We found considerable heterogeneity in the distribution of risk factors as well as ASCVD among Asian subgroups in the US. Compared with health system or community-based reports, the prevalence of risk factors and ASCVD may be underestimated in some Asian NHIS subgroups. There is an urgent need for efforts to improve recruitment of Asian participants of heterogeneous socioeconomic backgrounds in national surveys, as well as to perform a thorough assessment of risk factors and disease in this population, not relying solely on self-report.

Adopted in 2017, the Buffalo Green Code was lauded as a comprehensive urban vision for Buffalo, New York. This form-based code would simplify Buffalo’s land use regulations by prescribing what could be built and where, and provide a unified framework for its future. Yet, despite the claims of comprehensiveness, the code left two pressing challenges of the city unaddressed: urban integration of vacant lots and lack of affordable housing. The paper argues that, given these omissions, the code fails to provide a grounded vision, while universalizing reductive fabric-making notions unsuitable for much of the city.

A key objective of the military compensation system is to enable the military to attract and retain personnel in sufficient numbers and quality to meet its manpower requirements, and to do so efficiently. With respect to the quality of military enlisted recruits, the U.S. Department of Defense’s (DoD’s) stated requirement is that 90 percent of recruits each year and in each service must be at least high school graduates and that 60 percent must be above average in terms of aptitude. To meet these requirements, past commissions, including the work of the Ninth Quadrennial Review of Military Compensation (9th QRMC) in 2002, found that military pay should be set at around the 70th percentile of the earnings for similar civilians. The 9th QRMC found that pay must be higher than average civilian pay because of the unusual demands and arduous nature of military service. The specific benchmark of the 70th percentile was chosen based on research from the 1990s that used data from the late 1980s and mid-1990s. Setting military pay involves not just setting the level but also determining the annual military pay adjustment. By law, the annual increase in military basic pay is guided by changes in the Employment Cost Index (ECI), a measure of the growth in private-sector employment costs computed by the Bureau of Labor Statistics within the U.S. Department of Labor. The ECI is also used to guide changes in the pay of federal General Schedule civil service employees, and, since 1967, the annual pay adjustment for military pay has been tied to the adjustment for General Schedule employees. More recent research, including the work of the 11th QRMC in 2012, has found that military pay, as measured by regular military compensation (RMC), far exceeds the 70th percentile and has done so for quite some time.1 This finding leads to the question of whether the level of military pay is being set efficiently, and, in particular, whether RMC is too high relative to the earnings of similar civilians. Or, put differently, it raises the question of whether the 70th percentile is still relevant as the benchmark, and, if not, what the new benchmark for setting the level of military pay should be. Regarding the ECI, research from the early 1990s (Hosek et al., 1992) found that the ECI does not accurately measure the opportunity wages of active duty personnel or perform well in terms of tracking recruiting and retention outcomes. The use of the ECI led to a “pay paradox” in the 1990s, when growth in basic pay fell well short of changes in the ECI, even though the services were meeting their recruiting and retention objectives. The research recommended the use of an alternative index, the Defense Employment Cost Index (DECI), that better reflected the demographics of military personnel and so more accurately measured the opportunity wages of active duty personnel. Not surprisingly, the DECI performed better than the ECI in terms of tracking recruiting and retention outcomes. The 7th QRMC in 1992 chose not to recommend that the DECI replace the ECI but recognized its advantages and recommended that its continued development be supported by DoD. No such development has occurred since then. The 13th QRMC requested that the RAND National Defense Research Institute assess the continued relevance of the 70th percentile as a benchmark for setting the level of military pay and the advantages and disadvantages of the DECI. It also requested that RAND provide computer code for computing the RMC percentile and for computing the DECI, for DoD’s future use. This report summarizes our analysis and findings and includes, in appendixes, the relevant computer code. Our approach involved using military personnel and pay data provided by the Defense Manpower Data Center (DMDC), active duty personnel survey data provided by DoD’s Office of People Analytics, and Current Population Survey data from the Bureau of Labor Statistics. Our analysis builds on and extends previous studies that have computed RMC percentiles (Hosek, Asch, and Mattock, 2012; Hosek et al., 2018; Smith, Asch, and Mattock, 2020) and the Hosek, Asch, and Mattock (2012) study that computed the DECI and compared it with the ECI.

Investments in the green economy are used for both environmental policy and fiscal stimulus. The success of these investments depends, at least in part, on whether they create new jobs and whether such jobs are available to workers negatively impacted by a green transition. We evaluate the employment effect of green investments from the American Recovery and Reinvestment Act (ARRA). Most job creation from green ARRA investments emerged in the postARRA period (2013-2017) and mostly benefited areas with a greater prevalence of pre-existing green skills. On average, each $1 million of green ARRA created approximately 10 long-run jobs, but the job creation effect doubled in regions in the last quartile of green skills distribution. New jobs are primarily in construction and in occupations performing green tasks that have, on average, a higher training requirement than comparable occupations. Manual workers are the main winners in terms of employability, but not of wage gains. Descriptive evidence suggests that potentially displaced workers in fossil fuel sectors have skills necessary to benefit from green investments, but that communities with high levels of green skills are often wealthier. Thus, using green stimuli as part of a green energy transition may exacerbate regional inequities.