BACKGROUND: Smoking is a leading cause of premature death across contemporary developed nations, but few longitudinal individual-level studies have examined the long-term health consequences of exposure to smoking. OBJECTIVE: We examine the effect of fetal and infant exposure to exogenous variation in smoking, brought about by state-level cigarette taxation, on adulthood and old-age mortality (ages 55‒73) among cohorts of boys born in the United States during the 1920s and 1930s. METHODS: We use state-of-the-art methods of record linkage to match 1930 and 1940 US full-count census records to death records, identifying early life exposure to the implementation of state-level cigarette taxes through contemporary sources. We examine a population of 2.4 million boys, estimating age at death by means of OLS regression, with post-stratification weights to account for linking selectivity. RESULTS: Fetal or infant exposure to the implementation of state cigarette taxation delayed mortality by about two months. Analyses further indicate heterogeneous effects that are consistent with theoretical expectations; the largest benefits are enjoyed by individuals with parents who would have been affected most by the tax implementation. CONCLUSIONS: Despite living in an era of continuously increasing cigarette consumption, cohorts exposed to a reduction in cigarette smoking during early life enjoyed a later age at death. While it is not possible to comprehensively assess the treatment effect on the treated, the magnitude of the effect should not be underestimated, as it is larger than the difference between having parents belonging to the highest and lowest socioeconomic groups. CONTRIBUTION: The study provides the first estimates of long-run health effects from early life exposure to cigarette smoking.

Purpose: Self-rated physical health (SRPH) and self-rated mental health (SRMH) are both linked to excess morbidity and premature mortality and can vary across rural and urban contexts. This can be particularly problematic for rural residents who have less access to important health care infrastructure. In this paper, we assess the prevalence of and rural-urban disparities at the intersection of SRPH and SRMH, specifically self-rated physical/mental multimorbidity (SRPMM) overall and across rural-urban contexts. Methods: Using a cross-sectional demographically representative national dataset of over 4000 working age adults in the U.S., we expose rural-urban differences in the prevalence of SRPMM and explore individual-level factors that may explain this disparity. Results: Approximately 15 percent of working age adults reported SRPMM, but rural adults were at higher risk than their urban counterparts. However, this disadvantage disappeared for remote rural working-age adults and was attenuated for metro-adjacent rural working-age adults when we controlled for the fact that rural adults had lower household incomes. Conclusion: Findings reveal a higher risk of SRPMM among rural adults, in part because of lower incomes among this group. This work acts as the foundation for facilitating research on and addressing rural-urban disparities in SRPMM.

Objectives The prevalence of long working hours has been accompanied by a corresponding rise in sleep disorders. Sedative-hypnotic agents (SHAs), have been reported as the second most commonly misused drug class in the U.S. The key objective of this study was to examine the relationship between working hours on the use of sleep aids and medications with sedative properties. Methods The 2010-2019 Medical Expenditure Panel Survey data was utilized. SHAs and medications with sedative related properties (MSRPs) were identified. Furthermore, we employed different regression models ranging from multivariable linear regression, Tobit regression, Heckman regression, and multivariable logistic regression, to ensure consistency, robustness, and reliability of associations. Results Overall, a sample of 81,518 observations of full-time workers was analyzed. Working 56hours or more per week was significantly associated (p < 0.05) with an increased odds of using SHAs and MSRPs by 13% (Adjusted Odds Ratio, aOR =1.13, 95% Confidence Interval, CI=1.01:1.26) and 9% (aOR=1.09, 95% CI=1.03:1.16), respectively more than that among those who worked fewer hours. Females in our study had a higher likelihood (aOR=1.11, 95% CI=1.05:1.19) of using SHAs when compared to males. Also, professional services had the highest likelihood (aOR=1.31, 95% CI=1.14:1.50) of using SHAs. Conclusion We found that long working hours were significantly associated with an elevated use of SHAs and MSRPs among U.S. workers. Specifically, female workers and individuals working in professional services had the highest likelihood of using sleep medications.

This study evaluates the auditing profession’s diversity initiatives by comparing diversity in the university to job pipeline for auditing and other disciplines. In tests using data describing millions of Americans, my most consistent finding is that Black representation in the audit labor pipeline is anomalous relative to comparable occupations. Specifically, I find that Black people are overrepresented among college freshmen planning to major in accounting but are underrepresented among recipients of accounting bachelor’s and master’s degrees, that audit firm hiring is concentrated at universities with few Black graduates, and that Black people are underrepresented among entry-level auditors compared to other similar professions. Regarding prominent theories about Black underrepresentation in auditing, my findings conform best with unintentionally biased recruiting and do not conform well with lower interest in accounting among Black students, explicit racism in audit firm recruiting, or lower interest among Black accounting graduates in pursuing careers in audit firms

The COVID-19 pandemic and its effect on public policy has the potential to provide new causal evidence on the relationship between the social determinants of health (SDOH) and health disparities in the US. Pre-pandemic data is suggestive that SDOH were the primary reason why Black and Hispanic New Yorkers were disproportionately affected by COVID-19 but rigorous causal inference statistical methods and high quality data are needed to fully understand the relationship. In this chapter I discuss the complex relationship between SDOH and health disparities and the role COVID-19 can play in understanding that causal relationship. I discuss several statistical and econometric methods and how they can be employed in COVID-19 related causal inference including panel data methods, instrumental variables, and regression discontinuity designs. I also discuss the importance of leveraging new and existing sources of high quality data.

U.S. housing advocates have called for a fundamental rethinking of the nation’s federal housing policies. In this study, I examined the geographic and household-level consequences of a plan that redistributes federal income tax expenditures from homeowners to very-low-income households. I propose, defend, and illustrate the effects of the SHELTER plan, a redistributive policy that a) is revenue neutral, b) is tenure neutral, c) is progressive, d) prioritizes local redistribution, and e) targets excess revenues to affordable housing production subsidies in communities where an increase in cash-based housing assistance is most likely to inflate housing rents. I demonstrate that the savings from the elimination of four homeownership tax expenditures would provide more than enough revenue to fund a universal housing allowance for all very-low-income households. Ignoring housing market adjustments, the plan would reduce housing cost burdens for the nation’s very-low-income renters by 36% and reduce very-low-income homeowners’ cost burdens by 28%.

A longstanding literature asserts that many bachelor's graduates in the US are underemployed, working in jobs that do not require a degree, earning little more than high school graduates. Our analyses of American Community Survey data paint a different picture. The most common form of underemployment finds college graduates working alongside individuals who have attended college but who have not completed a baccalaureate, rather than working in occupations dominated by employees with high school diplomas or less. Underemployed college graduates retain an earnings advantage compared to their non-graduate occupational workmates. However, selection into underemployment reflects large inequalities along socio-demographic dimensions. We document that women, racial-ethnic minorities, and foreign-born individuals are much more likely to be underemployed, after controlling for their college majors, and less likely to be employed in the most sought-after graduate occupations. We describe this pattern as occupational marginalization: a sorting of graduates on social attributes into less lucrative jobs and show that this social segregation across occupations is substantial even among bachelor's graduates after controlling for their college major. We consider these findings in the light of Human Capital Theory, Credentialism, and theories of Social Exclusion.

How accurately can age of death be predicted using basic sociodemographic characteristics? We test this question using a large-scale administrative dataset combining the complete count 1940 Census with Social Security death records. We fit eight machine learning algorithms using 35 sociodemographic predictors to generate individual-level predictions of age of death for birth cohorts born at the beginning of the 20th century. We find that none of these algorithms are able to explain more than 1.5% of the variation in age of death. Our results suggest mortality is inherently unpredictable and underscore the challenges of using algorithms to predict major life outcomes.

This study is the first to examine the effect of the Earned Income Tax Credit (EITC) on informal caregiving for adults, which could operate through the credit’s effects on labor supply and household income. Using a sample of unmarried mothers aged 25-59 and data from the American Time Use Survey (ATUS), I find that policy-induced increases in the generosity of average EITC benefits have differential effects on informal caregiving by recipient age. No effect is estimated for the youngest group of unmarried mothers (ages 25-34), who increase employment without reducing informal care for adults. In contrast, the oldest group of unmarried mothers (ages 45-59) responds to increases in EITC generosity by increasing their time spent and probability of informally caring for their adult family members while maintaining their labor supply. Unmarried mothers aged 35-44 also respond by increasing time spent on informal caregiving for adults, alongside intensive margin increases in labor supply. Results for other types of time use (home production, leisure, informal childcare), as well as probability of multigenerational co-residence, help explain these shifts. By going beyond outcomes for EITC recipients and their children, these results demonstrate that the effects of the EITC span generations and reach across households.

Is it feasible to include reproducible research methods in undergraduate training in quantitative data analysis? There are reasons to believe the answer to that question is ‘no’—that reproducibility is an advanced topic best left to graduate school or early career training. Professional standards such as the AEA Data Editor’s (2023) guidelines and the World Bank Development Impact Evaluation (DIME) manual (Bjarkefur et al., 2021) may appear too technical and complex to introduce to undergraduates. Even the TIER Protocol (Project TIER, 2023a), which was designed to be accessible to students at all levels, is elaborated with a degree of specificity and detail that could give instructors the impression that incorporating reproducibility into undergraduate classes and research supervision would be a costly and disruptive undertaking. This essay argues that, on the contrary, integrating reproducibility into the undergraduate curriculum is eminently feasible. To support this claim, we present a simple exercise of the kind that might be assigned in an introductory quantitative methods class, and then develop four versions of the exercise: a baseline in which the issue of reproducibility is entirely neglected, and three subsequent versions that incrementally introduce essential elements of reproducibility. The additional skills students must acquire for each version of the exercise are modest, but cumulatively they prepare students in computational methods that achieve state-of-the-art standards of reproducibility. These exercises demonstrate the feasibility of teaching reproducibility to undergraduates, and provide instructors with concrete examples of small, practical steps they can take to achieve that goal.

Background: Women in low- and middle-income countries (LMICs) are primary producers of subsistence food and significant contributors to the agricultural economy. Gender-Based Violence (GBV) adversely impacts their capacity to contribute and sustain their families and undermines social, economic, and human capital. Addressing GBV, therefore, is critical to creating safe and inclusive environments for women as primary producers to participate fully in rural communities. The aim of this scoping review is to explore the existing evidence on GBV in the context of women primary producers in LMICs to inform research gaps and priorities. Methods: A scoping review was conducted using PubMed, Web of Science, Ebscohost, and Google Scholar using keywords related to GBV and women producers in LMICs. Peer-reviewed journal articles published between January 2012 and June 2022 were included in the review. Duplicates were removed, titles and abstracts were screened, and characteristics and main results of included studies were recorded in a data charting form. A total of 579 records were identified, of which 49 studies were eligible for inclusion in this study. Results: Five major themes were identified from our analysis: (1) extent and nature of GBV, (2) the impact of GBV on agricultural/primary production livelihood activities, (3) sociocultural beliefs, practices, and attitudes, (4) aggravating or protective factors, and (5) GBV interventions. Addressing GBV in agriculture requires inclusive research approaches and targeted interventions to empower women producers, promote gender equality, enhance agricultural productivity, and contribute to broader societal development. Despite attempts by researchers to delve into this issue, the pervasive under-reporting of GBV remains a challenge. The true extent and nature of GBV perpetrated against women is far from fully understood in this context. Conclusion: Despite the significant challenges posed by GBV to the health, economy, and livelihoods of women primary producers in LMICs, there is a paucity in the current state of knowledge. To make meaningful progress, more research is required to understand the relationship between GBV and agricultural settings and to gain nuanced insight into the nature and impact of GBV on women primary producers in different regions and contexts.

Tall buildings are central to facilitating sustainable urbanization and growth in cities worldwide. We estimate average elasticities of city population and built area to aggregate city building heights of 0.12 and-0.17, respectively, indicating that the largest global cities in developing economies would be at least one-third smaller on average without their tall buildings. Land saved from urban development by post-1975 tall building construction is over 80% covered in vegetation. To isolate the effects of technology-induced reductions in the cost of height from correlated demand shocks, we use interactions between static demand factors and the geography of bedrock as instruments for observed 1975-2015 tall building construction in 12,877 cities worldwide, a triple difference identification strategy. Quantification using a canonical urban model suggests that the technology to build tall generates a potential global welfare gain of 4.8%, of which only about one-quarter has been realized. Estimated welfare gains from relaxing existing height constraints are 5.9%in the developed world and 3.1% in developing economies.

Contraceptive method supply shares reflect the contributions of the public and private sectors to the distribution of a given method each year. Quantifying these public/private-sector supply shares of contraceptive methods within countries is vital for effective and sustainable family planning delivery. They are useful to Family Planning officials as they show where contraceptive users have obtained their most recent supplies. Unfortunately, due to the cost, many low- and middle-income countries (LMIC) are not in a position to carry out the national-scale surveys necessary to collect this data regularly. Therefore, they evaluate the contraceptive supply market using out-of-date data. Using out-of-date supply share estimates for family planning monitoring has significant knock-on effects. They may lead to inaccurate conclusions on the stability of the contraceptive supply market. In addition, the estimation of other family planning indicators that depend on method supply shares will also be inaccurate and distorted. To date, neither Bayesian nor frequentist methods have been used to estimate this important family planning indicator. In this thesis, a methodology using Bayesian hierarchical penalised spline models for estimating modern contraceptive method supply shares, at national and subnational administration levels, for LMICs participating in the global Family Planning 2030 (FP2030) initiative is proposed. A series of Bayesian models that evaluate method supply shares using both large multi-country datasets and computationally efficient single-country datasets are described. Lastly, the impact of using national-level annual contraceptive method supply shares with uncertainty in the calculation of another key family planning indicator, estimated modern use (EMU), is evaluated. To begin, an approach for estimating the proportion of modern contraceptive methods supplied by the public and private sectors at the national administration level using a multi-country dataset is described and evaluated. The proposed approach utilises Bayesian hierarchical modelling techniques, taking advantage of the geographic nature of the data, in combination with penalised splines, capturing the complex shape of the data over time, to produce annual estimates with uncertainty for these supply-share proportions. Global-level correlations between rates of change in method supply shares are estimated and incorporated into the modelling approach to promote more precise estimation, even in the absence of data. This modelling approach is compared and validated against simpler modelling alternatives. Next, the mcmsupply R package is presented. This R package combines 4 variations of the multi-country national model. The national modelling approach is extended in two directions. Firstly, the model is extended to estimate method supply shares at the sub-national administration level using a multi-country subnational dataset. The motivation for this model extension is an ever-growing interest in subnational family planning indicators due to the decentralisation of family planning services in countries participating in FP2030. Utilising the knowledge gained from modelling the national-level estimates, hierarchical modelling structures of the multi-country national model are incorporated into the multi-country subnational model. Secondly, the model is extended again to estimate the method supply shares (at either national or subnational administration levels) using only the data for a single country. Similarly, informative priors informed by the multi-country model parameter estimates are used in the single-country modelling approach. This reduces the uncertainty of survey estimates, making the estimates more precise and reliable. The single-country modelling approach is computationally efficient, without a loss of model accuracy. Lastly, an alternative application of annual method supply share estimates in the calculation of another family planning indicator, estimated modern use (EMU) derived from family planning service statistics, is demonstrated. Presently, EMUs are a stand-alone estimate without any associated uncertainty. This chapter considers the calculation process for EMUs. An updated methodology is proposed that calculates EMUs using annual method supply shares with uncertainty, rather than the existing approach that uses the method supply shares observed in the most recent DHS survey for a given country. The impact of this potential update is considered and compared against the existing approach. The benefits and strengths of the proposed update are illustrated using case studies in Country A and Country B.

Most US workers have health insurance plans sponsored and subsidized by their employers. The US Affordable Care Act (ACA) improved and expanded the availability of non-employer-based health insurance, with protections for pre-existing conditions, guaranteed issue, and community rating in non-group markets. Using National Health Interview Survey data for 2009 to 2018 and a difference-in-differences modeling approach, this study finds that the ACA increased self-employment in 2015 and 2016 among US adults with higher demand for health insurance. The probability of self-employment increased by 1.4 to 1.8 percentage points among adults ages 30 to 64 with at least one pre-ACA declinable condition and no alternative source of health insurance through a spouse’s employer or public programs. However, these effects were short-lived. As uncertainty about the long-term viability of the ACA’s health insurance exchanges increased in 2017 and 2018, the probability of self-employment among individuals with high demand for insurance fell to pre-ACA levels.

The Upper Valley of New Hampshire (Grafton and Sullivan Counties) and Vermont (Orange and Windsor Counties) is a cohesive region in many ways: for instance, workers flow across state borders and the demographic makeup of the area is similar across states. Yet the early childhood context of the Upper Valley has some sharp distinctions across state borders, many of which are tied to state policy decisions over time. This brief explores how state-level decisions manifest in the child care sector, contextualizing findings within the specific context of the Upper Valley as an interstate region. Perhaps the most immediately visible difference between New Hampshire and Vermont’s early childhood landscape is the existence of public pre-kindergarten. New Hampshire remains one of only six states that does not have a state pre-kindergarten program, whereas Vermont enacted public pre-kindergarten effective July 2015. In 2021, the National Institute for Early Education Research ranked Vermont fifth in the nation for access to pre-kindergarten for four-year-olds—one of only seven states nationwide to enroll at least half of four-year-olds in state pre-kindergarten. Beyond just pre-kindergarten, however, New Hampshire and Vermont consistently make different choices within their early childhood serving systems from different staff-to-child ratios for certain age groups to different requirements for teacher qualifications.

This study investigates the gender difference in housework activities among never-married American women and men living solo (one-person households), using 2005-2019 nationally representative data from the American Time Use Survey (ATUS). We find that women in solo households spend more time on housework and old women and men spend more time on it, compared to their younger counterparts. Results also show that education only plays a modest role in time use in solo households, contrary to the negative association of education among partnered women and men. Our findings that influences of economic resources on housework are gender-neutral in solo households suggest dyadic interactions and processes gender resources – more than women’s resources being discounted when they enter a relationship. And gender socialization – and engagement in behaviors coded as feminine – may be more acceptable for younger adults living alone.

In the first half of the twentieth century, deaths from infectious disease, especially among the very young, fell dramatically in American cities. However, as infant mortality fell and life expectancy rose, racial inequality in urban infectious disease mortality grew. In this paper, we show that the fall in mortality and the rise in racial inequality in mortality reflected two countervailing processes. The dramatic decline in infant mortality from waterborne diseases drastically reduced the total urban infectious disease mortality rate of both Black and white Americans while having a comparatively small effect on the total racial disparity in urban infectious disease mortality. In contrast, the unequal fall in tuberculosis mortality, particularly in the prime of life, widened racial inequality in infectious disease mortality in US cities.

Researchers and policymakers often attempt to forecast trends in automobile ownership. But to understand recent changes in demand for cars, researchers must account for behaviors specific to different generations, while simultaneously controlling for the influence of lifecycle and historical effects. To overcome the analytical challenges of cross-sectional data in Age-Period-Cohort (APC) analysis, we apply three different approaches largely used by biostatisticians to isolate how cohort effects influence the likelihood that a U.S. adult lives in a zero-vehicle household. Our analyses draw on data from the U.S. Census Public Use Microdata Samples (PUMS) from 1970 to 2019. To test for cohort effects, we use constraint-based binary logistic regression, a nonlinear parametric approach to log-linear models, and median polish analysis. We find that people born from 1935 to 1944 experienced the strongest negative cohort effect of all groups, and thus were least likely to live in zero-vehicle households (after accounting for age and period effects). Compared to this cohort, persons born before 1924 and after 1955 saw higher likelihoods of living in zero-vehicle households, all else equal. The peak cohort effect of people born in the 1930s to 1940s may please those interested in reducing automobile use. But because automobiles offer access benefits, more recent cohorts may experience transportation challenges. Negative effects may be especially salient for Millennials, a group faring worse economically than previous generations. Further, recent changes in the transportation landscape – including the growth of services like carshare and ride-hail and behavioral changes emerging from the COVID-19 pandemic – complicate efforts to forecast demand for automobiles.

In this thesis, we examine the influence of extended family structures, specifically the presence of parents or in-laws in the household, on women's employment decisions in the United States between 1850 and 1950. Our analysis utilizes the IPUMS USA census dataset to explore married women's labor supply in nuclear and vertically extended families, aiming to understand the historical role of families in shaping women's work and its potential implications for contemporary female wage labor. I find a positive association between co-residing with parents or parents-in-law and female employment, supporting the "grandparental childcare" hypothesis. Co-residing increased married women's probability of employment by 2 to 3 percentage points, particularly benefiting young women with children below the age of 5. Our instrumental variable approach underlines these findings, and most instruments suggest that the effect is between 4 and 15 percentage points. Additionally, our results indicate that co-residing women were more likely to pursue higher-paying occupations, suggesting that extended family support acted as a financial and emotional safety net, enabling women to invest in education and their careers. However, caution is advised when extrapolating historical findings to the present, as we observe a structural break in the relationship between co-residence and female employment in the 1960s. Nonetheless, this research underscores the significance of family dynamics in shaping women's employment decisions in the 19th and early 20th century.

We study the effects of state hospital regulations intended to increase breastfeeding by requiring certain standards of care during the immediate postpartum hospital stay. We find that these regulations significantly increased breastfeeding initiation by 3.8 percentage points (5.1 percent) and the probability of breastfeeding at 3 and 6 months postpartum by approximately 7 percent. We also provide evidence that these breastfeeding-promoting policies significantly increased maternal time spent on child care, crowding out time spent on formal work. Observed reductions in employment are concentrated among mothers with infants between zero and three months of age.