Household wealth data at the local level are generally not widely available, especially statistics disaggregated by race and ethnicity. Using machine learning, we estimate net worth and emergency savings data at the local, city, state, and national levels. We also disaggregate our estimates by racial and ethnic groups at the city, state, and national levels. We use machine-learning models trained on the Survey of Income and Program Participation (SIPP), a survey with detailed wealth information but too few observations for local estimates, to estimate emergency savings and net worth using the American Community Survey (ACS), which allows for state and local estimation. The imputations allow us to estimate the proportion of households with more than $2,000 in emergency savings and median net worth at the household level. We then aggregate household-level data to different geographic levels. Household wealth is a safety net. It protects families from unexpected expenses such as replacing a water heater and income shocks such as a jobless spell. Household wealth is also a springboard. It gives families the opportunities to invest in wealth-building opportunities like starting a small business or moving to an area with more opportunity. Understanding wealth is necessary for uncovering the barriers to wealth generation and designing policies that unlock opportunity for everyone. Two datasets, the Federal Reserve Board’s Survey of Consumer Finances and the US Census Bureau’s Survey of Income and Program Participation (SIPP), have detailed information about wealth and are typically used for national or state estimates (Bhutta et al. 2020). Unfortunately, little information is widely available about wealth at the local level, especially disaggregated by different racial and ethnic groups. This is because household surveys with adequate sample sizes for local estimation, such as the American Community Survey (ACS), do not include detailed information about wealth. We employ machine learning to leverage a smaller nationally representative survey with detailed questions about household wealth to impute household wealth onto the ACS, which allows for small area estimates. We released this novel dataset with the Urban Institute’s “Financial Health and Wealth Dashboard” in 2022. In particular, we use machine learning to estimate median net worth and the proportion of households with at least $2,000 emergency savings at the local level defined as Public Use Microdata Areas (PUMAs). We provide financial measures not only for a city, but also for subregions (Public Use Microdata Areas, or PUMAS) within many cities and for different racial and ethnic groups. City leaders and policymakers are able to use the dashboard to identify wealth disparities within cities. Because household wealth measures for subregions within cities are not typically available in public survey data, we use a machine-learning method to fill the gap in the existing survey data. We use household measures including net worth and emergency savings to better understand households’ wealth. Liquid assets—the sum of assets in checking accounts, stocks, bonds, and other liquid savings accounts—can indicate households’ resilience and ability to bounce back from financial shocks. Net worth—total assets minus total debt—can provide an overview of households’ economic well-being and their ability to pursue new opportunities. Overall, the 2022 financial health dashboard provides firsthand data and analysis to city leaders and policymakers for a better understanding of financial health, financial resilience, wealth, and debt within local regions in their cities and counties. They will also be able to compare their cities and counties with neighboring cities and counties. This works builds on previous asset and wealth imputation research. Using 2014 data, the Prosperity Now Asset Scorecard estimated the asset poverty rate, liquid asset poverty rate, and share of households with liquid assets for many cities in the US. The Federal Reserve Bank of “St. Louis’s Real State of Family Wealth”2 provides national-level average wealth for various demographic groups overtime. Using 2013 data, Ratcliffe and colleagues (2017) estimated the shares of unbanked and underbanked populations for different PUMAs in New York City. This project advances this literature by (1) looking at a broader set of asset outcomes; (2) estimating assets at the PUMA level throughout the country; (3) using more recent data than the literature; and (4) disaggregating the estimates by race and ethnicity when possible. Because PUMA boundaries are often smaller than city boundaries, our analysis sheds light on wealth disparities within cities.

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.

Purpose: Previous literature has shown decreases in pediatric trauma during the COVID-19 outbreak, but few have analyzed beyond the peak of the pandemic. This study assesses the epidemiology of pediatric trauma cases in a high-volume teaching hospital in New York City before, during, and after the height of the COVID-19 pandemic. Methods: Institutional data on pediatric trauma orthopedic cases from January 1, 2018 to November 30, 2021 were extracted. The following time frames were studied: (1) April 1–June 22 in 2018 and 2019 (pre-pandemic), (2) April 1–June 22, 2020 (peak pandemic), and (3) April 1–June 22, 2021 (post-peak pandemic). Inferential statistics were used to compare patient and trauma characteristics. Results: Compared to the pre-pandemic cohort (n = 6770), the peak pandemic cohort (n = 828) had a greater proportion of fractures (p < 0.01) and had a significantly decreased overall traumas per week rate (p < 0.01) and fractures per week rate (p < 0.01). These decreased trauma (p < 0.01) and fracture rates (p < 0.01) persisted for the post-peak pandemic cohort (n = 2509). Spatial analysis identified zip code clusters throughout New York City with higher rates of emergency department presentation during the peak pandemic compared to pre-pandemic, and these areas aligned with lower-income neighborhoods. Conclusion: During the peak of the pandemic, overall trauma and fracture volumes decreased, the types of prevalent injuries changed, and neighborhoods of different economic resources were variably impacted. These trends have mostly persisted for 12 months post-peak pandemic. This longitudinal analysis helps inform and improve long-term critical care and public health resource allocation for the future. Level of evidence: Level III

Despite extensive research on the impact of smoking on health, there remains an inadequate understanding of the temporal relationship between smoking, education levels, and racial/ethnic groups. This study aims to clarify the influence of smoking initiation and cessation on the changing trends of educational disparities in smoking prevalence among women from diverse racial/ethnic backgrounds across varying birth cohorts. Using a sample of 336,732 women from the U.S. National Health Interview Survey (1997–2018), we employ Cox proportional hazards models to analyze the relative risks of smoking initiation and cessation across birth cohorts by race/ethnicity and education. Our investigation reveals that the education gap in smoking prevalence has experienced the most notable increase across cohorts among White women, followed by Black and Hispanic women. Particularly pronounced is the widening of the education gap in smoking initiation rates among White women in successive cohorts. This is primarily driven by a decline in smoking initiation among college-educated women and a marked surge in smoking initiation among those without a college degree. Conversely, among Black and Hispanic women, the rate of smoking initiation has declined in recent cohorts, with a more substantial decrease among those with a college degree. In contrast, smoking cessation rates have consistently risen across cohorts within each racial group, regardless of their education level. Hence, the expanding education gap in smoking prevalence across birth cohorts predominantly arises from the increasing disparity in smoking initiation rates. Given that smoking initiation typically occurs before college education is completed, this may underscore a growing trend of smokers being selected into cohorts without a college degree. We discuss the implications of this phenomenon within the context of selection based on family socioeconomic status and individual traits.

We show that the first nationwide mass vaccination campaign against measles increased educational attainment in the United States. Our empirical strategy exploits variation in exposure to the childhood disease across states right before the Measles Eradication Campaign of 1967–68, which reduced reported measles incidence by 90 percent within two years. Our results suggest that mass vaccination against measles increased the years of education on average by about 0.1 years in the affected cohorts. We also find tentative evidence that the college graduation rate of men increased.

This study estimates the prevalence of prescription opioid use (POU) in the United States (US) in 2019–2020, both in the general population and specifically among adults with pain. It also identifies key geographic, demographic, and socioeconomic correlates of POU. Data were from the nationally-representative National Health Interview Survey 2019 and 2020 (N = 52,617). We estimated POU prevalence in the prior 12 months among all adults (18+), adults with chronic pain (CP), and adults with high-impact chronic pain (HICP). Modified Poisson regression models estimated POU patterns across covariates. We found POU prevalence of 11.9% (95% CI 11.5, 12.3) in the general population, 29.3% (95% CI 28.2, 30.4) among those with CP, and 41.2% (95% CI 39.2, 43.2) among those with HICP. Findings from fully-adjusted models include the following: In the general population, POU prevalence declined about 9% from 2019 to 2020 (PR = 0.91, 95% CI 0.85, 0.96). POU varied substantially across US geographic regions: It was significantly more common in the Midwest, West, and especially the South, where adults had 40% higher POU (PR = 1.40, 95% CI 1.26, 1.55) than in the Northeast. In contrast, there were no differences by rural/urban residence. In terms of individual characteristics, POU was lowest among immigrants and among the uninsured, and was highest among adults who were food insecure and/or not employed. These findings suggest that prescription opioid use remains high among American adults, especially those with pain. Geographic patterns suggest systemic differences in therapeutic regimes across regions but not rurality, while patterns across social characteristics highlight the complex, opposing effects of limited access to care and socioeconomic precarity. Against the backdrop of continuing debates about benefits and risks of opioid analgesics, this study identifies and invites further research about geographic regions and social groups with particularly high or low prescription opioid use.

Objective: Parental leave and breastfeeding breaks influence the ability to initiate and continue breastfeeding. We investigated how eligibility criteria in the Family and Medical Leave Act (FMLA) and Affordable Care Act (ACA) affect access to unpaid parental leave and breastfeeding breaks and assessed affordability and alternative policy models. Methods: We used family income data to assess the affordability of unpaid leave by race and ethnicity. We used 2017-2018 US Current Population Survey data to determine the percentage of private sector workers aged 18-44 years who met the minimum hour (1250 hours of work during a 12-month period), tenure (12 months), and firm size (≥50 employees) requirements of FMLA and ACA. We analyzed eligibility by gender, race and ethnicity, and age. We also examined parental leave and breastfeeding break policies in 193 countries. Results: Most Latinx (66.9%), Black (60.2%), and White (55.3%) workers were ineligible and/or unlikely to be able to afford to take unpaid FMLA leave. Of 69 534 workers, more women (16.9%) than men (10.3%) did not meet the minimum hour requirement. Minimum tenure excluded 23.7% of all workers and 42.2% of women aged 18-24 years. Minimum firm size excluded 30.3% of all workers and 37.7% of Latinx workers. Of 27 520 women, 28.8% (including 32.9% of Latina women) were excluded from ACA breastfeeding breaks because of firm size. Nearly all other countries guaranteed mothers paid leave regardless of firm size or minimum hours and guaranteed ≥6 months of paid leave or breastfeeding breaks. Conclusions: Adopting a comprehensive, inclusive paid parental leave policy and closing gaps in breastfeeding break legislation would remove work-related barriers to breastfeeding; reduce racial, ethnic, and gender inequities; and align US national policies with global norms.

We study differences in economic outcomes by perceived skin tone among African Americans using full-count U.S. decennial census data from the late-19 th and early-20 th centuries. Comparing children coded as "Black" or "Mulatto" by census enumerators and linking these children across population censuses, we first document large gaps in educational attainment and income among African Americans with darker and lighter perceived skin tones. To disentangle the drivers of these gaps, we identify all 36, 329 families in which enumerators assigned same-gender siblings different Black/Mulatto classifications. Relative to sisters coded as Mulatto, sisters coded as Black had lower educational attainment, were less likely to marry, and had lower-earning, less-educated husbands. These patterns are consistent with more severe contemporaneous discrimination against African-American women with darker perceived skin tones. In contrast, we find similar educational attainment, marital outcomes, and incomes among differently-classified brothers. Men perceived as African Americans of any skin tone faced similar contem-poraneous discrimination, consistent with the "one-drop" racial classification rule that grouped together individuals with any known Black ancestry. Lower incomes for African-American men perceived as having darker skin tone in the general population were driven by differences in opportunities and resources that varied across families, likely reflecting the impacts of historical or family-level discrimination.

Background and Objectives Extreme heat is an environmental health equity concern disproportionately impacting low-income older adults and people of color. Exposure factors, such as living in rental housing and lack of air conditioning, and sensitivity factors, such as chronic disease and social isolation, increase mortality risk among older adults. Older persons face multiple barriers to adaptive heat mitigation, particularly for those living in historically temperate climates. This study measures two heat vulnerability indices to identify areas and individuals most vulnerable to extreme heat and discusses opportunities to mitigate vulnerability among older adults. Research Design and Methods We constructed two heat vulnerability indices for the Portland, Oregon metropolitan area: one using area scale proxy measures extracted from existing regional data and another at the individual scale using survey data collected following the 2021 Pacific Northwest Heat Dome event. These indices were analyzed using principal component analysis (PCA) and Geographic Information Systems (GIS). Results Results indicate that the spatial distribution of areas and individuals vulnerable to extreme heat are quite different. The only area found among the most vulnerable on both indices has the largest agglomeration of age- and income-restricted rental housing in the metropolitan area. Discussion and Implications Due to spatial variations in heat-related risk at the individual and area scales, measures addressing heat risk should not be spatially uniform. By focusing resources on older adult individuals and areas in particular need of assistance, heat risk management policies can be both highly efficient and cost-effective.

For at least half a century, much of the literature on residential segregation has primarily focused on large metropolitan areas, where most of the population resides in one or more high-density urban cores and medium-density, outlying suburban environments. Many influential landmark segregation studies have focused on small samples featuring primarily the largest 50–60 metropolitan areas in the country. In contrast, our knowledge of residential segregation outside of metropolitan contexts is very limited, even as nonmetropolitan communities are becoming more racially and ethnically diverse. This is due in large part to the challenges with measuring residential segregation in nonmetropolitan contexts, which we address in this book. By using segregation measures that are corrected for index bias, measuring segregation of households rather than persons, and relying on the often overlooked separation index, we draw substantive conclusions about patterns and trends of White-Black, White-Asian, and White-Latino residential segregation in nonmetropolitan communities from 1990 to 2010. We also further demonstrate the superiority of our methodological choices by comparing our findings to those that would be produced through conventional approaches and by reviewing case studies of selected nonmetropolitan areas. Substantively, we find that segregation in nonmetropolitan communities is often not as high as what is observed in metropolitan areas, especially for Asian and Latino households. However, segregation is certainly capable of being high in nonmetropolitan communities, even when the minority proportion is very small. White-Latino segregation is typically quite low and has remained stable in micropolitan areas while slightly declining in noncore counties. This is more so true for White-Asian segregation in nonmetropolitan communities where segregation is very low and has remained low since at least 1990. Methodologically, we conclude that our innovations to segregation measurement expand opportunities to broaden and deepen the literature to understand the nature of residential segregation in nonmetropolitan communities.

This paper studies how aggregate labor market conditions affect the intra-generational assimilation of immigrants in the hosting country. Using data from the American Community Survey, we leverage variation in the national unemployment rates in the U.S. at the time of arrival of different cohorts of immigrants to identify short-and long-run effects of recessions on their careers. We document that immigrants who enter the U.S. when the labor market is slack face large and persistent earnings reductions: a 1 p.p. rise in the unemployment rate at the time of migration reduces annual earnings by 4.9 percent on impact and 0.7 percent after 12 years since migration, relative to the average U.S. native. Change in the employment composition across occupations with different skill contents is the key driver: were occupational attainment during periods of high unemployment unchanged for immigrants, assimilation in annual earnings would slow down on average by only 3 years, instead of 12. Slower assimilation costs between 1.7 and 2.4 percent of lifetime earnings to immigrants entering the U.S. labor market when unemployment is high.

The tax systems of different jurisdictions reflect different social preferences about the relative value, or weight, placed on taxes or transfers to each income group. I apply the inverse-optimum income tax method to quantify these preferences by calculating the implied weights for every US state. To capture major features of US state tax systems, I extend the existing theory underlying the inverse-optimum method to include sales taxes, property taxes, and both national and state income taxes. Using Internal Revenue Service data, I calculate effective marginal income tax and commodity tax rates for each state and income level, and use these to find the implied weights for both single and joint filers in each income group in every US state. I observe substantial differences in social preferences across states, and I find weights that decline little above $100,000 and that do not decrease monotonically as might be expected from most theories of social welfare.

How do societies respond to adversity? After a negative shock, separate strands of research document either an increase in religiosity or a boost in innovation efforts. In this paper, we show that both reactions can occur at the same time, driven by different individuals within society. The setting of our study is the 1918-1919 influenza pandemic in the United States. To measure religiosity, we construct a novel indicator based on naming patterns of newborns. We measure innovation through the universe of granted patents. Exploiting plausibly exogenous county-level variation in exposure to the pandemic, we provide evidence that more-affected counties become both more religious and more innovative. Looking within counties, we uncover heterogeneous responses: individuals from more religious backgrounds further embrace religion, while those from less religious backgrounds become more likely to choose a scientific occupation. Facing adversity widens the distance in religiosity between science-oriented individuals and the rest of the population, and it increases the polarization of religious beliefs.

This paper proposes a new method for testing the spatial equilibrium model that is widely used to describe the spatial economy of the United States. To do this, I examine the relationship between workers' wages and the population density they face, namely the urban wage premium (UWP). Using U.S. data from 2000 to 2019, I find that unskilled workers in non-tradable service occupations consistently have a larger UWP compared with other unskilled workers, and the difference has been increasing over time, which I take as evidence against the spatial equilibrium model being a suitable approximation for unskilled workers' behavior. In contrast, skilled workers in both service and non-service occupations have a similar UWP that has remained fairly stable over the same period. My findings also suggest that the significant decrease in unskilled workers' UWP in recent decades was driven not only by the movement of unskilled workers from middle-wage tradable jobs to low-wage non-tradable service jobs, which was particularly pronounced in big cities, but also by the decrease in UWP within both tradable and non-tradable occupations.

Previous literature suggests that empowering women is associated with children's improved outcomes. However, little is known about its effects on children's later-life crime and incarceration. We argue that women empowerment through suffrage law changes during the late nineteenth and early twentieth centuries in the US generated incentives for women to invest in their children's human capital. The accumulated human capital then has the potential to reduce future incarceration of children. We use full-count censuses 1920– 1930, implement a difference-in-difference framework, and empirically show that childhood exposure to suffrage laws is associated with considerable reductions in incarceration. The effects appear to be primarily driven by decreases in male and Black incarceration. The balancing tests rule out the concern that the effects are driven by demographic compositional changes or endogenous changes in other state-level characteristics. Furthermore, an event-study analysis rejects the concern that the effects are driven by preexisting trends in incarceration among exposed cohorts. The findings of this research note offer informative implications for overlooked externalities of women empowerment in a historical setting.

Air pollution is a pervasive environmental issue that has significant impacts on human health. Urban areas are particularly susceptible to high levels of air pollution due to concentrated emissions and populations. Urban air pollution has been linked to a range of adverse health effects, including respiratory diseases, cardiovascular diseases, and increased mortality rates. Pollution arises from the release of chemicals into the atmosphere, largely stemming from industrial activities, transportation, and energy generation. Understanding the sources, transport, and effects of air pollutants is essential, though limitations in computational capability and data availability has long hindered the identification of high- impact hotspots at health relevant scales. Recent developments into chemical transport models and non- regulatory monitoring techniques now play a crucial role, as these methods contribute to the identification of pollutant levels, the identification of vulnerable populations, and the support of evidence-based decision-making for effective pollution control strategies. This dissertation investigates air quality over the Midwestern United states, centered over Lake Michigan-Chicago, using the latest developments in air quality modeling and observational tools. This research employs novel geospatial statistics and analytical methods to investigate concentrations of health-hazardous pollutants, namely nitrogen dioxide (NO2), ozone (O3), and fine particulate matter (PM2.5). Chapter 2 presents a high-resolution simulation of air pollution over the Southern Lake Michigan- Chicago region. Chemical transport models (CTMs) allow researchers to simulate how pollutants disperse and transform in the atmosphere. High-resolution simulations may enhance the accuracy of the model and increase its usefulness for public health assessments and policymaking. In this chapter, I examine the performance of a 1.3 km and 4 km simulation of pollutant concentrations over a four-month period (August 2018, October 2018, January 2019, and April 2019) in the Southern Lake Michigan-Chicago region. The 1.3 km simulation exhibits slightly better performance compared to the 4 km simulation. The study reveals distinct urban and rural patterns of pollution, with urban areas experiencing significantly higher concentrations of NO2 and PM2.5 (20% – 60% higher than rural areas), while O3 is simulated to be lower in urban areas (-6% compared to rural areas). Furthermore, the simulation highlights significant disparities in pollutant concentrations across neighborhoods in Chicago, with features such as highways contributing to substantial variations in pollution levels. This simulation provides valuable insights into the O3 chemistry regime in Chicago, finding that the O3 regime is transitional and VOC-limited, depending on the month of study. Overall, this research contributes to a better understanding of air pollution dynamics in the Southern Lake Michigan-Chicago region, shedding light on the spatial distribution of pollutants and their underlying chemistry. Chapter 3 presents a hotspot analysis of Chicago air quality by using three novel air quality products: a low-cost sensor network, observations from a satellite instrument, and a chemical transport model. The study addresses the lack of intraurban data validation by assessing the spatial agreement of air pollution patterns across multiple high-resolution datasets and applies the hotspot analysis to make recommendations for researchers and policymakers. I apply a hotspot clustering algorithm, Getis Ord Gi*, to identify areas of agreement and disagreement among the data products. The analysis reveals a Consensus hotspot on the West side of Chicago, indicating elevated pollution levels across different data products, wind directions, and seasons. This hotspot, predominantly inhabited by Hispanic and Latino people, requires urgent intervention as an environmental justice priority. Additionally, a medium- agreement hotspot identified by the low-cost sensors and satellite (i.e., Observational hotspot) highlights the need for additional regulatory monitoring in the affected community. Furthermore, a highway hotspot shows variations in NO2 concentrations near recessed and elevated highways, a feature not captured in model simulations. These findings provide insights into areas of high pollution exposure, underscores the importance of targeted interventions, and recommends additional development of monitoring tools for improved air quality management in Chicago. By integrating this hotspot approach into air quality management frameworks, policymakers can develop targeted interventions and implement sustainable practices to mitigate the effects of air pollution on vulnerable populations. In Chapter 4, the WRF-CMAQ simulation is used to investigate air pollution from an environmental justice perspective in Chicago. By integrating socioeconomic characteristics with air quality data, the study aims to uncover disparities in exposure and the contributing factors to environmental injustice. This research is the first to examine intraurban air pollution using a high- resolution CTM, addressing the limitations of coarser models that overlook significant pollution sources like highways within cities. This study is also the first comprehensive analysis to consider multiple pollutants, health outcomes, and their connection to environmental justice issues in Chicago. Findings reveal that pollutants and demographics exhibit high spatial variability, with no strong linear relationship between pollutants and racial, ethnic, or economic demographics. Although average exposure disparities in race and ethnicity are relatively small compared to baseline health rates, the Black population consistently experiences significantly higher rates of mortality, asthma, and pediatric asthma hospitalizations related to pollution. Racial and ethnic exposure disparities persist across income levels, indicating that income does not alter the relationship between pollution exposure or health outcomes. To address these inequalities, policies should consider both impact and exposure, as areas of health impact may not align with areas of high exposure. The study highlights the importance of incorporating health and exposure information in addressing air pollution injustices and advocates for equitable solutions. In summary, this PhD research is the first to apply a 1.3 km high-resolution CTM to study Chicago air pollution. By applying this high-resolution data, this research also presents frameworks to isolate areas of outsized exposure and analyze dataset disagreement through hotspot analysis. Further, the data generated in this research is applied to analyze the link of exposure and public health at urban scales. By advancing CTM research, developing new methods of evaluation, and pioneering methods to investigate pollution inequalities at intraurban scales, this research ultimately supports the development of evidence-based pollution analysis and effective controls.

Housing shortages following the global financial crisis have been accompanied by a new, sharp downturn in rates of residential mobility, largely among renters. The Great Recession precipitated major, lingering housing disruptions, with local mobility declining by one-third in the US from 2010 to 2019. Slow construction despite employment recovery and burgeoning numbers of young Millennials led to intensified competition for vacancies. That ‘friction of competition’ is posited to delay moves and reduce overall mobility rates. Questions investigated are how urban area declines in renter mobility are related to slower housing construction than job growth, fewer rental vacancy chains released by home buyers, concentrations of young adults, and affordability. Analysis is with the American Community Survey for the 100 largest metropolitan areas in the US Mobility constriction is a new indicator of declining housing opportunity. Similar outcomes bear investigation in other cities and nations impacted by housing shortages and the shift to renting.

Prior literature analyzing gender differences in commuting has reported that men commute longer distance/time than do women, and one explanation for this gender gap is based on household responsibilities falling on women. But most of the literature examining gender differences in commuting has not considered the interdependence that exists between the members of couples. We analyze gender differences in commuting time, and we take into account the inter-relatedness of decisions, estimating Seemingly Unrelated Regressions (SUR) on commuting time and mode of transport (private, public, and active transport) for dual-earner couples in Spain, Italy, Korea, and the United Kingdom. Our results indicate that the number of children is signicantly related to shorter female commuting times in Spain, Italy, and the UK, with no associations found for men, while in Korea there is a reduction in both male and female commuting times. Additionally, the presence of children signicantly increases (decreases) the proportion of commuting time done by private (public) transport, but results differ signicantly by gender, number of children, and country. Our evidence indicates that couples' decisions on commuting are complementary, which may shed light on the relationship in commuting that theoretical models should address.

Workhorse search and matching models assume constant bargaining weights, while recent evidence indicates that weights vary across time and in cross section. We endogenize bargaining weights in a life-cycle search and matching model by replacing a standard Cobb-Douglas (CD) matching function with a general constant elasticity of substitution (CES) matching function and study the implications for the long-term labor share and bargaining power in the U.S. The CES model explains 64 percent of the reported decline in the labor share since 1980, while the CD model explains only 28 percent of the decline. We then use the model to recover changes in bargaining power and find that workers’ bargaining power has declined 11 percent between 1980 and 2007 because of a decline in tightness.

Due to the shifting climate, extreme events are being observed more frequently globally. Drought is one of the most common natural hazards that severely impacts communities in terms of economic losses and agricultural production disruption. Considering global trade, drought in an agricultural region affects the food security in other regions because of disrupted supply. Decisionmakers often consult susceptibility maps when preparing mitigation plans so that the adverse impacts of a drought event can be reduced. Creating drought susceptibility maps can be demanding, requiring a lot of data (i.e., hydrological and land use), expertise, and thorough assessment to accurately picture a vulnerable region’s condition. The process also relies on complex hydrological and hydrometeorological models. The objective of this investigation is to examine the vulnerability and impact of drought and formulate maps of drought susceptibility, exposure, and risk by considering a multitude of atmospheric, physical and social indicators. Subsequent to this notion, a fuzzy logic algorithm has been devised by assigning a comprehensive array of weights to each parameter derived from an exhaustive literature review and used for a preliminary investigation for the state of Iowa. This state is located in the Corn Belt region, and its primary economic activity is agriculture. Drought susceptibility maps for the state of Iowa have been generated for the period spanning from 2015 to 2021 and validated using the Kappa coefficient. The produced drought susceptibility maps can support drought mitigation plans and decisions for communities in Iowa.