Purpose – Many European countries suspended mandatory conscription after the Cold War, and especially between 2000 and 2010. However, with the changing security situation in Europe, more and more countries are considering the re-introduction of the draft. That is why, it is important to evaluate the impact of conscription on draftees, including its effect on fertility outcomes. Additionally, fertility is of particular interest because birth rates have been below replacement levels in most European countries at least in the last two decades. This, combined with the increase in life expectancy, has contributed to aging population and raises concerns about the future economic prospects and sustainability of the continent. Military service could be related to fertility in several ways. Compulsory service for men would affect the marriage market and subsequently child-bearing outcomes. For example, men who serve in the military would have to delay higher education at least by a year, given that they plan to continue their education after high school. One possibility is that this leads to older men meeting younger women if partners meet at college. Alternatively, in case the partners know each other prior to the draft, service could delay marriage by up to a year due to the conscription, postponing planning and having children, and potentially having fewer children as women might be less able or less willing to have a child after a certain age. Finally, some men who plan and would otherwise continue their education might choose to not do so or to further postpone it once they disattach from studying during their service. For some men, this might influence their marital and subsequent fertility outcomes. In either of these scenarios, a draft or its suspension is likely to be connected to fertility. Design/methodology/approach – This study examines the effect of the suspension of the draft in Spain in December 2001 on three fertility outcomes of men that would have been drafted in the absence of the suspension. The author performs the analysis in a difference-in-differences framework. Potential concerns and policy implications are also discussed. Findings – The findings suggest that after the suspension of the draft, individuals started to have their first child earlier given that they decide to have children. Consistent with the overall time trend, they became less likely to have a child and started to have fewer children. However, the age at birth of the first child decreased while the number of children and the likelihood of having a child increased for men relative to women, after compared to before the suspension of the mandatory draft. Originality/value – The author extends prior literature by investigating the effect of the abolition of compulsory military service in Spain in December 2001 on fertility. This is novel is several ways. First, to the best of the author’s knowledge, previous literature has examined the effect of this Spanish reform only on labor market outcomes prior to men’s conscription. Second, even for other countries that terminated the compulsory draft, fertility has been under-studied, providing an opportunity for further exploration. Third, this analysis is based on rich Census data, representative of the population in Spain. Finally, given the inconclusive findings of previous studies for other countries and the proposed re-introduction of the draft in some parts of Europe, additional evidence of the effect of the conscription has important policy implications necessary for the evaluation of future military service policy decisions.

In this paper I show that Okun's Law, the relationship between changes in the unemployment rate and real GDP, is state dependent: the relationship is stronger during recessions. I hypothesize that this state dependency arises from firms engaging in selective labor hoarding. If firms hoard high-skilled workers outside of recessions to economize on training costs, the Okun relationship will be relatively flat in those times. Such labor hoarding becomes untenable during recessions, which produces a nonlinear response of unemployment. I build a dynamic model of directed search with heterogeneous firms, endogenous exit, and training costs that generates the nonlinear response of unemployment to changes in real GDP.

In the 1920s, the United States substantially reduced immigration by imposing country-specific entry quotas. We compare local labor markets differentially exposed to the quotas due to variation in the national-origin mix of their immigrant population. US-born workers in areas losing immigrants did not benefit relative to workers in less exposed areas. Instead, in urban areas, European immigrants were replaced with internal migrants and immigrants from Mexico and Canada. By contrast, farmers shifted toward capital-intensive agriculture and the immigrant-intensive mining industry contracted. These differences highlight the uneven effects of the quota system at the local level.

Despite concerted efforts by health authorities worldwide to contain COVID-19, the SARS-CoV-2 virus has continued to spread and mutate into new variants with uncertain transmission characteristics. Therefore, there is a need for new data-driven models for determining optimal vaccination strategies that adapt to the new variants with their uncertain transmission characteristics. Motivated by this challenge, we derive an integrated chance constraints stochastic programming (ICC-SP) approach for finding vaccination strategies for epidemics that incorporates population demographics for any region of the world, uncertain disease transmission and vaccine efficacy. An optimal vaccination strategy specifies the proportion of individuals in a given household-type to vaccinate to bring the reproduction number to below one. The ICC-SP approach provides a quantitative method that allows to bound the expected excess of the reproduction number above one by an acceptable amount according to the decision-maker's level of risk. This new methodology involves a multi-community household based epidemiology model that uses census demographics data, vaccination status, age-related heterogeneity in disease susceptibility and infectivity, virus variants, and vaccine efficacy. The new methodology was tested on real data for seven neighboring counties in the United States state of Texas. The results are promising and show, among other findings, that vaccination strategies for controlling an outbreak should prioritize vaccinating certain household sizes as well as age groups with relatively high combined susceptibility and infectivity.

The model of compensating wage differentials is among the cornerstone models of equilibrium wage determination in labor economics. However, empirical estimates of compensating differentials have faced persistent credibility challenges. This article summarizes the Rosen model of compensating differentials and chronicles the advances, setbacks, and lessons learned from empirical studies. The progression from cross-sectional to panel models alleviated biases caused by unobserved human capital but yielded new insights into the importance of other biases, including those caused by labor market frictions and endogenous job mobility. I discuss recent approaches that use matched employer-employee data and quasi-random variation in job amenities to address some of these challenges. I then present two examples of applications of compensating differentials: the evaluation public health and safety policies that rely on the value of statistical life, and the measurement and interpretation of earnings inequality.

Deep learning has proven to be useful for predicting economic variables at fine spatial scales using satellite imagery and can be used to predict data that is challenging to obtain, coarse, or released infrequently. However, training and tuning these models have substantial computational and time requirements. If transfer learning could be effectively applied to these deep learning methods, this would allow various stakeholders to obtain accurate predictions without the need for training models themselves. This paper explores this possibility, where predictions of income per capita and population levels at fine spatial scales of England are made using satellite images and a convolutional neural network trained on US data. The predictions achieve negative R 2 values, however, this paper further employs a robust model stacking approach using an ordinary least squares regression, which results in R 2 values of 0.051 and 0.460 for income per capita and population levels respectively. Furthermore, this paper finds that the predictions are heteroskedastic, where the residuals increase as the actual values increase. The implications of these results are that transfer learning has the potential to be successfully applied to deep learning methods that predict economic variables, however , further research is required in finding improved model stacking approaches and more adaptable deep learning methods for predicting economic variables in different geographic contexts.

How do most Americans wind down before going to sleep? They watch television. That’s been true since at least the 1970s, when the boob tube overtook the toothpaste tube (and other personal grooming) to become our most common pre-bed activity, according to our analysis of time diaries collected by the Bureau of Labor Statistics’ American Time Use Survey and, for the longer-run statistics, the American Heritage Time Use Study from IPUMS and the Center for Time Use Research. And the TV screen is still the last thing 2 in 5 of us see before we close our eyes. Personal grooming and washing takes second place: It’s the final activity for about 1 out of 5 of us — and has actually regained ground recently, according to our analysis of the diaries, in which thousands of Americans report everything they did in every minute of a given day, from “fundraising” to “watching billiards” to “tobacco and drug use.” The diaries offer a host of insights into American behavior — including what each person was doing the minute before the sandman entered. After television and grooming, things get complicated. Reading has long held third place, with 6 to 7 percent of us reading ourselves to sleep nightly in any given year. But in 2020 and 2021, reading ran neck and neck with using the computer and/or playing games before bed, a combo that has more than doubled in popularity since 2010.

Over the past several decades, there has been intense scrutiny on the effect of pollution on human health. This literature typically examines health consequences at the individual level. In this paper, we document the impact of prenatal exposure to pollution on CEOs, individuals who are likely to make consequential real decisions that affect large sections of society. Specifically, we draw on the extensive medical literature documenting the harm caused by developmental pollutants released by the most hazardous plants in the U.S. These effects were plausibly unknown when the CEO was born. We find that the CEOs with greater prenatal exposure to Superfund sites take more risks, but the risks do not pay off, adversely affecting the firm's value, and the CEOs experience higher forced turnover. Our results point to an indirect effect of pollution beyond the immediate health effects. They also demonstrate the role that prenatal exposure to pollution plays in affecting CEO managerial styles.

During the first year of the COVID-19 pandemic, federal spending on government safety net programs in the United States increased dramatically. Despite this unparalleled spending, government safety nets were widely critiqued for failing to fully meet many households' needs. Disaster research suggests that informal modes of social support often emerge during times of disruption, such as the first year of the pandemic. However, use of formal government programs and informal support are rarely examined relative to each other, resulting in an incomplete picture of how households navigate disaster impacts and financial shocks. This study compares estimates of informal social support to formal government program use in the rural U.S. West, drawing on data from a rapid response survey fielded during the summer of 2020 and the 2021 Annual Social and Economic Supplement of the Current Population Survey (CPS-ASEC). We find that informal social support systems were, on aggregate, used almost as extensively as long-standing government programs. Our findings highlight the critical role of person-to-person assistance, such as sharing financial resources, among rural households during a disruptive disaster period. Routine and standardized data collection on these informal support behaviors could improve future disaster research and policy responses, especially among rural populations.

The inexorable rise in personal travel in the 20th century has given way to stagnation in the 21st, a phenomenon some call “peak travel.” We use 2003–2019 data from the American Time Use Survey to explore whether and why personal travel per capita has stopped growing. We show that time spent on personal travel has been dropping consistently over these years, and suggest that one important cause is likely a dramatic and ongoing decline in the time Americans spend on out-of-home activities. We find significant changes in time spent on many of the 34 activities conducted inside and outside of the home that we examine. Many of these changes appear related to advances in information and communications technology (ICT), as this period saw the quality of in-home ICT continually rising and its real cost falling, resulting in ever-improving gaming, surfing, watching, and streaming options. For example, our data suggest that out-of-home work and shopping time fell significantly during our study period, while in-home time spent on work and education rose. Game playing (presumably mostly computer games) and TV watching in the home both increased dramatically, while attendance at live entertainment, arts, and sports activities fell. Reading and writing fell substantially both inside and outside the home, perhaps replaced by electronic communication. Our findings suggest that increased in-home ICT use may have been associated with 25–30% of the reduction of out-of-home time. We also find a significant increase in sleeping, and a decrease in time spent eating and drinking both inside and outside of the home. Although we deliberately chose to examine time use and travel prior to the COVID-19 pandemic, we suspect that, even as the pandemic fades, the trend toward more time at home and less time spent traveling may well increase further.

Racial Landscape (RL) is an innovative methodology for studying racial geography that offers several advantages over current approaches. This paper aims to highlight two key features of RL that have the potential to significantly impact the discipline. Firstly, the RL approach introduces a fundamentally different method for assessing segregation compared to existing methods. We delve into this distinction and emphasize its benefits, such as the ability to calculate segregation for any arbitrary area without the need for subdivisions, diversity measures, reference regions, or reliance on census geography. Importantly, by utilizing data from 51 Metropolitan Statistical Areas (MSAs) across the US, we demonstrate that the RL's segregation metric produces comparable rankings of segregation among MSAs when compared to existing segregation indices. Thus, while the RL expands the scope of problems where segregation can be quantified, it remains compatible with current segregation assessment practices. Secondly, we utilize data from the core parts of four selected MSAs in 1990 and 2020 to showcase how high-resolution RL-based racial maps can be employed for spatially explicit visual analyses of racial change. We discuss the potential impact of RL on the field, particularly in relation to segregation assessment and the evaluation of spatially explicit models of racial dynamics. While initially developed for analyzing racial geography in the United States, the RL framework can be extended to other countries as well.

Background: Childhood chronic illness imposes financial burdens that may affect the entire family. Objective: The aim was to assess whether adults living with children with 2 childhood chronic illnesses - asthma and diabetes - are more likely to forego their own medical care, and experience financial strain, relative to those living with children without these illnesses. Research Design: 2009-2018 National Health Interview Survey. Subjects: Adult-child dyads, consisting of one randomly sampled child and adult in each family. Measures: The main exposure was a diagnosis of asthma or diabetes in the child. The outcomes were delayed/foregone medical care for the adult as well as family financial strain; the authors evaluated their association with the child's illness using multivariable logistic regressions adjusted for potential confounders. Results: The authors identified 93,264 adult-child dyads; 8499 included a child with asthma, and 179 a child with diabetes. Families with children with either illness had more medical bill problems, food insecurity, and medical expenses. Adults living with children with each illness reported more health care access problems. For instance, relative to other adults, those living with a child with asthma were more likely to forego/delay care (14.7% vs. 10.2%, adjusted odds ratio: 1.27; 95% CI: 1.16-1.39) and were more likely to forego medications, specialist, mental health, and dental care. Adults living with a child with diabetes were also more likely to forego/delay care (adjusted odds ratio: 1.76; 95% CI: 1.18-2.64). Conclusions: Adults living with children with chronic illnesses may sacrifice their own care because of cost concerns. Reducing out-of-pocket health care costs, improving health coverage, and expanding social supports for families with children with chronic conditions might mitigate such impacts.

Introduction: Vaccination rates may be improved through culturally tailored messages, but little is known about them among disaggregated Asian American subgroups. We assessed the vaccination rates for key vaccines among these subgroups. Methods: Using the National Health Interview Survey, we analyzed recent vaccination rates (2015–2018, n=188,250) and trends (2006–2018) among Asians (Chinese [n=3,165], Asian Indian [n=3,525], Filipino [n=3,656], other Asian [n=5,819]) and non-Hispanic White adults (n=172,085) for 6 vaccines (the human papillomavirus, hepatitis B, pneumococcal, influenza, tetanus-diphtheria [tetanus], and shingles vaccines). We controlled demographic, socioeconomic, and health-related variables in multivariable logistic regression and predicted marginal modeling analyses. We also computed vaccination rates among Asian American subgroups on the 2015–2018 National Health Interview Survey data stratified by foreign-born and U.S.-born status. We used Joinpoint regression to analyze trends in vaccination rates. All analyses were conducted in 2021 and 2022. Results: Among Asians, shingles (29.2%; 95% CI=26.6, 32.0), tetanus (53.7%; 95% CI=51.8, 55.6), and pneumococcal (53.8%; 95% CI=50.1, 57.4) vaccination rates were lower than among non-Hispanic Whites. Influenza (47.9%; 95% CI=46.2, 49.6) and hepatitis B (40.5%; 95% CI=39.0, 42.7) vaccination rates were similar or higher than among non-Hispanic Whites (48.4%; 95% CI=47.9, 48.9 and 30.7%; 95% CI=30.1, 31.3, respectively). Among Asians, we found substantial variations in vaccination rates and trends. For example, Asian Indian women had lower human papillomavirus vaccination rates (12.9%; 95% CI=9.1, 18.0) than all other Asian subgroups (Chinese: 37.9%; 95% CI=31.1, 45.2; Filipinos: 38.7%; 95% CI=29.9, 48.3; other Asians: 30.4%; 95% CI=24.8, 36.7) and non-Hispanic Whites (36.1%; 95% CI=34.8, 37.5). Being male, having lower educational attainment and income, having no health insurance or covered by public health insurance only, and lower frequency of doctor visits were generally associated with lower vaccine uptakes. Foreign-born Asian aggregate had lower vaccination rates than U.S.-born Asian aggregate for all vaccines except for influenza. We also found subgroup-level differences in vaccination rates between foreign-born and U.S.-born Asians. We found that (1) foreign-born Chinese, Asian Indians, and other Asians had lower human papillomavirus and hepatitis B vaccination rates; (2) foreign-born Chinese and Filipinos had lower pneumococcal vaccination rates; (3) foreign-born Chinese and Asian Indians had lower influenza vaccination rates; and (4) all foreign-born Asian subgroups had lower tetanus vaccination rates. Conclusions: Vaccination rates and trends differed among Asian American subgroups. Culturally tailored messaging and interventions may improve vaccine uptakes.

We measure the importance of increasing returns to scale and trade in medical services. Using Medicare claims data, we document that "imported" medical care-services produced by a medical provider in a different region-constitute about one-fifth of US healthcare consumption. Larger regions specialize in producing less common procedures, which are traded more. These patterns reflect economies of scale: larger regions produce higher-quality services because they serve more patients. Because of increasing returns and trade costs, policies to improve access to care face a proximity-concentration tradeoff. Production subsidies and travel subsidies can impose contrasting spillovers on neighboring regions.

We examine disparities in Child Tax Credit (CTC) eligibility and anti-poverty effects since 1998 by family type. Initially, single mothers were least likely to be eligible and were underrepresented among those lifted from poverty by the CTC, because the credit was virtually nonrefundable. By 2017, disparities by family type mostly disappear, as eligibility and anti-poverty effectiveness of the CTC among single mothers increases dramatically, because of reforms increasing CTC refundability. When the credit doubles in 2018, disparities revert toward initial levels, as eligibility and the anti-poverty effectiveness of single mothers rises least, because of a phaseout threshold expansion and partial refundability.

According to the theory of effectively maintained inequality, families advantaged by income or race/ethnicity attend colleges and complete their degrees at higher rates due to both quantitative and qualitative distinctiveness from other families. This study extends this line of research by investigating whether the distribution and payoffs of accelerated credits from Advanced Placement, International Baccalaureate, and dual enrollment programs likewise follow a pattern of perpetuating racial/ethnic gaps in college completion. We hypothesize that racial inequality in college outcomes will be maintained by the concentration of minority students in lesser-rewarding types of accelerated credit and by racial differences in the payoff of specific types of accelerated credit. Using institutional data from a large public four-year university in Florida, we find notable racial/ethnic differences in amount and type of accelerated credit. Event history analyses suggest that these differences account for a relatively small portion of the Black/White difference in college completion. Overall, the results provide little support for theories of maintained inequality, and we conclude accelerated credit programs do not meaningfully contribute to the racial stratification of higher education among college matriculants.

Prior research substantially overstates the cost of retention under test-based promotion policies to both taxpayers and students who delay labor market entry because it omits two important factors. First, there is a delay between the intervention and the taxpayer’s expenditure. Second, on average, the treatment leads to less than a full year of additional schooling. I provide formulas for calculating the cost of grade retention within a test-based promotion policy and illustrate using data from Florida. Retaining a third-grade student under Florida’s policy was about 45% less costly to taxpayers and about 37% less costly to retained students than would be suggested by prior authors.

The COVID-19 pandemic has hit the world and made significant impacts on all parts of human settlement areas. Passenger journeys on public transportation have dropped significantly. This study looks at the effects of the COVID-19 on the change of bike usage-subway ridership integration between 2019 and 2020 in New York City (NYC), USA. To investigate the effect, this study uses various data sources including bike sharing data from Citi Bike, subway ridership data from Metropolitan Transportation Authority, Census data from IPUMS, land use data from Department of City Planning (DCP) and transportation-related data from U.S. Department of Transportation (DOT). The Geographically Weighted Regression was employed to examine the spatiotemporal varying effects of bike-subway integration for casual users and subscribers in the shared bike system. The results show that the pandemic impacted the usage of bike-subway integration spatially and temporally. The bike-transit integration impact is largely positive and tends to be stronger when the subway stations are located farther away from CBD areas in 2019, while the bike-subway integration tend to be insignificant for a large number of stations in 2020. It also confirms that the impact of the shared bike usage on subway ridership during workdays present a larger magnitude of the coefficients than the ones on non-workdays in 2019. In contrast, the 2020 model shows that the impacts do not differ between workdays and non-workdays. These findings are rarely discussed in earlier studies. This study also used an 800-meter boundary captures the spatial impact of shared bike usage on subway ridership in NYC. However, it is barely discussed what network typologies determines such a spatial boundary of the shared bike impact area. This will be further discussed in future research.

We document large differences among regions of the United States in the trends of the black/white earnings gap among full-time workers from 1976 to 2017. Outside the South, the earnings of black workers grew more slowly than those of non-Hispanic white workers. These relative decreases were much larger for women than for men. However, in the South, the racial earnings gap shrank for men, and changed little for women. Thus, we find a substantial convergence across regions in the black/white earnings gap. The most important explanations of the racial earnings gaps are persistent racial differences in educational attainment and persistent occupational segregation. However, differences in observables leave much of the racial earnings gaps unexplained. The regional convergence in the racial earnings gap is evident in both explained and unexplained gaps for both women and men.

We develop an expectations-based measure of gentrification. Property values today incorporate market participants’ expectations of the neighbourhood’s future. We contrast this with present-oriented variables like demographics. To operationalise the signal implicit in property values, we contrast the percentile rank of a neighbourhood’s average house price to that of its average income, relative to its metropolitan area. We take as our signal of gentrification the rise of a neighbourhood’s house value percentile above its income percentile. We show that a gap between the house value and income percentiles predicts future income growth. We further validate our metric against existing approaches to identify gentrification, finding that it aligns meaningfully with qualitative analyses built on local insight. Compared to existing quantitative approaches, we obtain similar results but usually observe them in earlier years and with more parsimonious data. Our approach has several advantages: conceptual simplicity, communicative flexibility with graphical and map forms and availability for small geographies on an annual basis with minimal lag.