A New York Times bestseller and “a passionate, urgent” (The New Yorker) examination of the growing inequality gap from the bestselling author of Bowling Alone: why fewer Americans today have the opportunity for upward mobility. Central to the very idea of America is the principle that we are a nation of opportunity. But over the last quarter century we have seen a disturbing “opportunity gap” emerge. We Americans have always believed that those who have talent and try hard will succeed, but this central tenet of the American Dream seems no longer true or at the least, much less true than it was. In Our Kids, Robert Putnam offers a personal and authoritative look at this new American crisis, beginning with the example of his high school class of 1959 in Port Clinton, Ohio. The vast majority of those students went on to lives better than those of their parents. But their children and grandchildren have faced diminishing prospects. Putnam tells the tale of lessening opportunity through poignant life stories of rich, middle class, and poor kids from cities and suburbs across the country, brilliantly blended with the latest social-science research. “A truly masterful volume” (Financial Times), Our Kids provides a disturbing account of the American dream that is “thoughtful and persuasive” (The Economist). Our Kids offers a rare combination of individual testimony and rigorous evidence: “No one can finish this book and feel complacent about equal opportunity” (The New York Times Book Review).

This comprehensive report examines the magnitude of the crisis of opportunity in America and its long-term implications. Interactions among powerful global economic forces, government policies and business practices have generated a self-sustaining set of dynamics that have led to increasing economic, social and political polarization, with the accumulation of advantage or disadvantage experienced by one generation passed along to the next. "Choosing Our Future" argues that ignoring these forces will lead to growing disparities among our citizens and place an unsustainable strain on the nation's social fabric and the character of our democracy.

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The largest cities in the world today lie mainly in poor countries, which is a departure from historical experience, when they were typically found in the richest places. We use novel historical data on city-level demographics to establish that poor mega-cities are distinct because of the exceptionally low mortality rates they experienced after the post-war mortality transition. To quantify the importance of this mortality shock on the origin of poor mega-citites, we develop a general equilibrium model of location choice with endogenous population growth, a rural sector, and distinct differences between informal urban areas (i.e. slums) and formal urban areas. The model shows that the equilibrium allocation of population across locations depends on aggregate population growth, and an increase in this pushes population into informal urban areas, creating poor mega-cities. We then calibrate the model to data from a sample of developing countries, and use this to calculate that the mortality transition accounted for roughly one-third of the increase in the urbanization rate, and over half of the increase in the size of slums, from 19602005. We also use the calibrated model to evaluate several policies aimed at slowing the growth of poor mega-cities and their slums, and find that family planning programs, and not migration restriction, are the most effective and show the greatest welfare gains.

Despite being the largest migratory movement between two states in modern history, the origins and operation of Mexican migration to the United States has not been a major research topic. We lack a comprehensive view of Mexican migration as it was established in early twentieth century and reproduced throughout the century as a system that reached from Texas borderlands to California and to western agricultural regions and beyond to Midwestern farming and industrial areas, a system that continued to be circular in nature even as permanent settlement increased, and which was in constant interaction with families, villages, and towns throughout Mexico. This interdisciplinary, bilingual, and transnational project is one of the first histories of the creation of migrant networks narrated from multiple geographic and institutional sites, analyzing the relationship between state agents, civic organizations, and migrants on both sides of the border. My project utilizes a statistical analysis of migration trends combined with qualitative research in order to show how migration arose as a mass phenomenon in Mexico and extended into the United States. This dissertation argues that large scale Mexican migration was created and operated through an interconnected transnational migrant economy made up of self-reinforcing local economic logics, information diffusion, and locally based social networks. I demonstrate that town-based interpersonal networks formed the engine that propelled and sustained large scale migration. Migrants needed transportation, capital, and information to travel north. Town-based networks provided all of these things. I follow the spread of migrant routes, explaining the creation of Mexican communities in the US Showing why communities were located where they are and their links to the larger economy of migrant labor before turning to . . .

We provide theory and evidence on changes in task inputs in the United States from 1880-2000. We combine a Roy model of worker selection across occupations with a new methodology for measuring individual production tasks performed by workers within occupations. We show that the recentlydocumented rise in non-routine tasks and decline in manual tasks extends much further back than hitherto thought to the late-nineteenth century. We reveal substantial heterogeneity within these broad categories of tasks, with those involving the formation of ideas increasing by up to twice the growth for non-routine tasks as a whole, and those involving the manipulation of inorganic matter decreasing by nearly twice the overall decline for manual tasks. We establish that these changes in task inputs are explained by new technologies (in particular oce and computing machinery) and are larger in urban than in rural areas (implying a transformation in the nature of agglomeration). We show that changes in the wage premia for tasks can account for a substantial proportion of the decline in wage inequality from 1880-1940, the rise in wage inequality from 1940-2000, and the larger rise in wage inequality in urban areas than in rural areas, even after controlling for observed worker characteristics.

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Identifying geographic areas with increased incidence of disease may elucidate community-level risk factors for intervention development. Lower respiratory illnesses (LRIs) are the leading cause of death in children and are associated with other morbidities. We assessed geographic clustering of LRIs and evaluated if these spatial patterns and associated risk factors differed by phenotype. Participants enrolled at birth in the Tucson Childrens Respiratory Study were followed through age three for physician diagnosed LRIs. Spatial clustering analysis, based upon each participants birth address, was performed for four LRI phenotypes. We conducted principal component analysis at the census tract level to generate indices for lower socioeconomic status (SES), poorer housing conditions, and increased air pollution. Enrollment addresses were mapped for 812 subjects, of whom 58.4%, 33.5%, 34.2%, and 23.4% had any LRI, a wheezing LRI, a viral LRI, and a respiratory syncytial virus (RSV) LRI, respectively. Patterns of spatial clustering and associated risk factors differed by LRI phenotype. Multivariable regression analyses showed that wheezing LRI clusters were associated with increased air pollution (OR - 1.18, P - 0.01). Being in a viral cluster was associated with poorer housing conditions (OR - 1.28, P - 0.01), while being in a RSV cluster was associated with increased air pollution (OR - 1.14, P - 0.006), poorer housing conditions (OR - 1.54, P - 0.003), and higher SES (OR - 0.77, P - 0.001). Our use of social and environmental indices allowed us to identify broad contextual factors that may contribute to increased incidence of LRIs in specic geographic regions. To reduce LRI incidence, multifaceted interventions should be developed at the community level.

We examine immigrant entrepreneurship and the survival and growth of immigrant-founded businesses over time relative to native-founded companies. Our work quantifies immigrant contributions to new firm creation in a wide variety of fields and using multiple definitions. While significant research effort has gone into understanding the economic impact of immigration into the United States, comprehensive data for quantifying immigrant entrepreneurship are difficult to assemble. We combine several restricted-access U.S. Census Bureau data sets to create a unique longitudinal data platform that covers 1992-2008 and many states. We describe differences in the types of businesses initially formed by immigrants and their medium-term growth patterns. We also consider the relationship of these outcomes to the immigrants' age at arrival to the United States.

This paper assesses regional inequality in contemporary urban China by predicting earningss for individual workers in multiple provinces, comparing the province of maximum predicted earnings to the province of residence and assessing the predicted gains from relocation. The paper performs the same comparison for the U.S. in 1940 to provide an informal baseline comparison. Workers predicted relatively similar earningss in each of the nine U.S. Census divisions. Fewer than 10% of them predicted maximum earnings in divisions other than their home division that exceeded their predicted home division earnings by more than 20%. In contrast, 45% of Chinese urban workers in 1988 predicted maximum earnings in provinces outside their home province that exceeded their predicted home province earningss by more than 50%. The same was true of 54% of Chinese urban workers in 1995, 74% in 2002 and 57% in 2008. If all Chinese urban workers received the maximum of their predicted earningss across all provinces, rather than their predicted earnings in their home province, average earningss would approximately double, interpersonal inequality would decline by 40-50% and inter-provincial inequality would vanish. In all years, predicted earnings in Guangdong province have generally been greater than in any other province. The implicit value of the right to live in Guangdong was at least 26% of earnings in 1988 and 41% in 1995. It declined to about 7% of earnings in 2002 and 2008, but only because predicted earnings in Beijing and Shanghai had risen. The gaps between predicted earnings in Guangdong and other provinces in those years was similar to those in earlier years.

Governor's task force involved in identifying those youth that need assistance to be connected to school or work.

Recent contributions provide researchers with a useful toolbox to estimate counterfactual distributions of scalar random variables. These techniques have been widely applied in the literature. Typically, the dependent variable of interest has been a scalar and little consideration has been given to spatial factors. In this paper we propose a simple method to construct the counterfactual distribution of the location of a variable across space. We apply the spatial counterfactual technique to assess how much changes in individual characteristics of Hispanics in the Washington DC area account for changes in the distribution of their residential location choices.

Statistical agencies are increasingly adopting synthetic data methods for disseminating microdata without compromising the privacy of respondents. Synthesis models for multivariate categorical data need to preserve complex multivariate structures, including impossible combinations of responses, also known as structural zeros. Here we propose the use of a Bayesian non parametric method for generating discrete multivariate synthetic data subject to structural zeros. This method can preserve complex multivariate relationships between variables; can be applied to high dimensional datasets with massive collections of structural zeros; requires minimal tuning from the user; and is computationally efficient. We demonstrate our approach by synthesizing an extract of 17 variables from the 2000 U.S. Census. Our method produces synthetic samples with high analytic utility and low disclosure risk.

Education usually shows a relationship with self-rated health such that those with highest education have the best health and those with lowest education have the worst health. We examine these educational gradients among Asian immigrants and whether they differ by country of origin, duration in the United States, and generational status. Migration theories suggest that recent immigrants from poorer countries should show a weaker relationship between education and health than US-born Whites. Acculturation theory further suggests that differences in gradients across country of origin should diminish for longer-term immigrants and the US-born and that these groups should display gradients similar to US-born Whites. We use the March Current Population Survey (2000 2010) to examine educational gradients in self-rated health among recent immigrants ( 15 years duration), longer-term immigrants (> 15 years duration), and second generation US-born Asians from China (n = 4473), India (n = 4,307), the Philippines (n = 5746), South Korea (n = 2760), and Japan (n = 1265). We find weak or non-significant educational gradients among recent Asian immigrants across the five countries of origin. There is no indication that longer-term immigrants display significant differences across educational status. Only second generation Chinese and Filipinos show significant differences by educational status. Overall, Asians show an attenuated relationship between education and self-rated health compared to US-Whites that persists over duration in the US and generational status. Our findings show shortcomings in migration and acculturation theories to explain these gradient patterns. Future research could use binational data or explore psychosocial factors to identify potential suppressors of educational gradients.

In April 2016, California passed legislation to increase the state minimum wage annually until it reaches $15 an hour in 2023 for all businesses. As a result, child care centers and licensed in-home providers will be required to increase the wages of their employees who currently earn less than the new minimum wage. Because a large proportion of workers in the child care industry is low-wage, this could have a significant impact on providers. Providers with private clients may respond by raising their prices to cover the cost of the wage increase. However, the amount providers receive for caring for children covered by state child care subsidy programs is determined by state and county reimbursement rates. Without the ability to change the amount charged for caring for subsidized children, child care centers or licensed in-home facilities may not be able to cover the cost of raising workers wages to the new minimum wage. In this memo, we describe a methodology we have developed for estimating the additional child care subsidy funding needed to cover the cost of a state minimum wage increase for programs administered by the California Department of Education (CDE) and the Department of Social Services through the CalWORKs 1 (Welfare to Work) program. The challenge is that standard government datasets are not able to provide accurate estimates of all of the components needed for this estimation. Therefore, the logic of our method is to (a) use administrative data to estimate the number of child care workers in California who care for subsidized children, and (b) use worker survey data to estimate the wage increases that child care workers will receive as a result of the state minimum wage increase. We then combine these estimates to calculate the total subsidy share of the cost of the mandated wage increases.

This thesis consists of three essays on urban structure and dynamics. These essays use empirical tools from empirical industrial organization and applied microeconomics to examine how cities grow and change. Access to high-quality local services constitutes an important amenity in residents valuation of cities. In Industry dynamics and the value of variety in nightlife: evidence from Chicago, I examine consumer preferences for variety in nightlife to understand these preferences and their impact on nightlife industry dynamics. I develop a structural dynamic model for venue entry and exit in the nightlife industry and estimate the model using a panel of liquor license data from Chicago. I find strong preferences for variety. My results suggest that in equilibrium a new entrant can increase profits for incumbent venues in some cases due to increased demand. However, potential entrants face high barriers to entry. In Land value gradients and the level and growth of housing prices, coauthor Tom Davidoff and I ask whether urban land rent gradients affect the level and growth of housing rents and prices. We use residential rents and the location of Starbucks stores to proxy for land prices, and calculate a gradient measure that allows for multiple peaks of land rent within a metropolitan area. Our measures of land rent gradients are significantly associated with high and rising prices, and explain some of the cross-sectional variation in prices. However, our measure does not explain the abnormally high rent and prices in Pacific and Northeastern coastal Superstar Cities. Bartik shocks are widely used as an instrument for local labour demand. A potential concern with this instrument is potential endogeneity in the presence of correlation between city-level industrial composition and the outcome variable of interest. In A control function approach to the correlated components of Bartik shocks, I formalize this endogeneity concern and introduce a control function correction that, given additional assumptions, addresses the potential endogeneity. I demonstrate the application of this novel approach by estimating a housing supply function.

In this dissertation, I explore how economic decisions diverge for different age groups. Two essays address the location decisions of older households while the third examines why different age cohorts donate to charities.The first essay estimates how the age distribution of the population across cities will change as the number of older adults rises. I use a residential sorting model to estimate the location preference heterogeneity between younger and older households. I then simulate where the two household types will live in 2030. All MSAs end up with a higher proportion of older households in 2030, and only eight of 243 MSAs experience a decline in the number of older households. The results suggest that MSAs in upstate New York and on the west coast, particularly in California, will have the largest number of older households in 2030. Florida will remain a popular place for older households, but its relative importance may diminish in the future.The second essay explores whether the basic motivations for charitable giving differ by age cohort. Using the results from a randomized field experiment, I test whether benefits to self or benefits to others drives the charitable giving decision for each age cohort. I find limited heterogeneity for benefits to self. Individuals between the ages of 50 and 64 increase average donations more than any other age cohort in response to emphasizing warm glow, and this heterogeneity is exclusively driven by larger conditional gifts. The third essay is preliminary joint work with H. Spencer Banzhaf and Carlianne Patrick. We build a unique data set of local homestead exemptions, which vary by generosity and eligibility requirements, for tax jurisdictions in Georgia. Using school-district-level Census data since 1970 along with the history of such exemptions, we will explore the impact of these exemptions, particularly exemptions targeting older households, on the demographic makeup of each jurisdiction and consider the impact of these laws on the relative levels of housing capital consumed by older and younger households.

Are longer trips more strenuous or unpleasant than shorter ones? This paper examines this question using data from the American Time Use Surveys well-being module, which queried individuals about the extent to which they felt happiness, pain, sadness, stress, and fatigue during three randomly selected daily activities. Over 22,000 instances of individuals traveling are observed, including their trip duration, mode, purpose, and demographic and geographic information. Trip duration is regressed on each emotion, plus a constructed, composite mood variable. Overall, the relationship between trip duration and traveler mood is not strong, which is unsurprising given prior findings on the limited impact of activities on mood. However, there is a statistically significant and negative association between trip duration and mood, primarily because of rising stress, fatigue and sadness on long trips. This is particularly true for drivers, while negative emotions do not rise with increasing trip duration for auto passengers. This suggests strain rises as the result of operating the vehicle for long periods, not traveling in an auto per se. Long bicycle trips are more painful than shorter ones, probably due to the physical demands of the mode, and long train trips are associated with less sadness. For commutes, long trips significantly degrade the mood of both drivers and bus riders, in the latter case probably due to vehicle crowding and standing. The findings imply that reducing the duration of trips, for example through land use policies that reduce trip distances, or congestion reduction, would have emotional benefits. Policies to promote ridesharing instead of solo driving for long trips may increase traveler mood in the aggregate. Improving bus service or substituting rail for bus for long commuting trips may also improve traveler mood.

American trypanosomiasis or Chagas disease caused by Trypanosoma cruzi affects many mammals, including humans and dogs, in all Latin American countries outside the Caribbean and increasingly also in the southern United States. Dogs are considered as reliable sentinels and have been identified as an important risk factor for the disease in humans in endemic countries. Factors that determine American trypanosomiasis in dogs may therefore have public health relevance. Associations of different environmental, locational, and pet owner socioeconomic conditions were evaluated retrospectively as potential risk factors for American trypanosomiasis status in dogs in a casecontrol study. Laboratory-confirmed cases received at the Texas A&M University Veterinary Medical Teaching Hospital between the years 2000 and 2012 and candidate risk factor variables extracted from publicly available environmental data and 2010 US Census Bureau were used. The sample included 42 dogs serologically positive and 82 dogs serologically negative determined by indirect immunofluorescent assay. The diagnostic titer was 1:160 (case). Univariate logistic regressions followed by stepwise multivariate logistic modeling were used for variable screening and to determine the strengths of variable associations with case status. Total Edge Contrast Index (odds ratio [OR]=3.35, 95% confidence interval [CI] 3.10, 3.62), residing in homes that had rural addresses (OR=2.48, 95% CI 2.43, 2.53), total number of owner occupied housing units in a neighborhood with a householder who is Hispanic or Latino (OR=1.66, 95% CI 1.04, 2.66), and the total number of housing units in a neighborhood that were built on or prior to year 1980 (OR=2.22, 95% CI 1.94, 2.55) were identified as risk factors. Suitable awareness campaigns and future research that considers pet owner housing and socioeconomic circumstances are necessary for effective prevention and control of this disease among dogs.