It is widely believed that the antebellum puzzle—a simultaneous decline in average stature and rise in GDP per capita, coupled with lower average stature in richer regions, in the antebellum United States— reveals a welfare cost of economic development that is hidden by conventional indicators; but it has been argued that the puzzle may be an artifact of sample-selection bias, stemming from a reliance on data from military volunteers to construct height samples. In this paper I provide the first empirical test of whether sample-selection bias explains the antebellum puzzle by using a two-step semi-parametric sample-selection model to estimate trends and patterns in average stature in the antebellum United States that are corrected for selection into military service on the basis of both observable and unobservable characteristics. This estimation is based on a new data set of my construction, consisting of military data from US Army enlistment records—including stature—for the birth cohorts of 1832–1860, linked to US census data and combined with similar data from the Union Army project. Identification is based on the incorporation of voting data from the presidential elections of 1856 and 1860, which measure political motives for military enlistment, and on differences in the nature of the military enlistment decision depending on whether an individual was old enough to serve in the Civil War. I find that the antebellum puzzle is robust to these corrections, and therefore is not an artifact of sample-selection bias. A net decrease in average stature of approximately 0.6 to 0.7 inches between 1832 and 1860 is present despite the correction, as is a gap of about 0.5 to 0.6 inches in favor of Southerners relative to Northeasterners. These results, however, do not imply that sample-selection bias can be disregarded in studies of historical heights. On the contrary, the degree of sample-selection bias is shown to vary over birth cohorts and across regions and sectors, and accounting for sample selection meaningfully and statistically significantly alters patterns in average stature across birth cohorts, regions, and sectors.

The thesis proposes a novel solution to the information integration problem. The proposed solution enables information integration in the Semantic Web by means of rewriting SPARQL queries. The work begins by introducing the notion of computer systems integration and describing the issue of heterogeneous data as well as known solutions dealing with such data. The author also briefly describes technologies and standards created for the Semantic Web. Following the description of existing research work, a novel approach to information integration in the Semantic Web is proposed. Both the theoretical aspect of the solution and the implementation details are described. The thesis concludes with the assessment of the proposed solution and indication of directions for further research.

More than 1,000 tornadoes strike the United States each year, and no population segment has been impacted to a greater degree than those who live in mobile homes. Despite being only about 7% of the total population, mobile home residents have comprised 40% to 50% of all tornado deaths over the past fifty years. Inhabitants of mobile homes must therefore act quickly to protect themselves when they are threatened by tornadoes. Warning messages instruct mobile home occupants to move to a sturdier building until the storm passes, but what are the residents perspectives on this recommended behavior? It is unknown whether mobile home residents plan ahead to protect themselves, how they develop these plans, where they plan to go, and what are some of the difficulties or obstacles that might discourage evacuation behavior. This research provides new insights to better understand mobile home residents perspectives about tornado preparedness and protective actions, and reveals which factors are most influential in their evacuation planning and intentions. The study area is central South Carolina, where mobile homes are integral to the housing stock and casualties from tornadoes pose an appreciable risk. A wide range of data is utilized, from both qualitative interviews and mailed questionnaires, to develop a typology of tornado preparedness and response perspectives. Three types are identified: those who are relatively unconcerned about tornadoes, those who are concerned and informed about tornadoes, and those who anticipate warnings and take protective action. The three types are significantly associated with demographic and household variables such as gender, race, the presence of children in the home, income, housing tenure, and home size and quality, as well as differing according to previous experience, places of residence, and urban/rural context. Participants responses to several hypothetical evacuation scenarios are also presented and the most important factors for predicting tornado evacuation intentions of mobile home residents are identified. Respondents are more likely to evacuate if given fifteen minutes warning than if given five minutes warning, but are not more likely to evacuate if given forty-five minutes compared to the fifteen minute scenario. Evacuation intentions are significantly influenced by several factors, including the evacuation destination, travel time to the destination, previous evacuation experience, having an evacuation plan, race, gender, age, housing tenure, presence of children and pets in the home, expectation of harm, and the urban/rural context. The theoretical and practical implications of the research findings are discussed, along with the study limitations and potential directions for future research.

This paper investigates spouses shared time across countries to understand how social conditions and policy are related to married couples time allocation. To do this, we use time use surveys from four countries with varying social conditions and welfare regimes. We use information about who is present during activities to analyze total time with spouse, time with spouse only and time with spouse and children. We use two surveys from each country to estimate changes over time and compare shared time during periods of economic expansion (around 2000) and periods of recession (around 2010). To our knowledge, this is the first study analyzing couples time spent together in a cross national context. Our preliminary results comparing USA and Spain show American couples spending less time with a spouse than Spanish couples.

Although extensive previous research has explored the relationship between income and happiness, no large-scale research has ever examined the relationship between income and sadness. Yet, happiness and sadness are distinct emotional states, rather than diametric opposites, and past research points to the possibility that wealth may have a greater impact on sadness than happiness. Using data from a diverse cross section of the U.S. population (N 12,291), we show that higher income is associated with experiencing less daily sadness, but has no bearing on daily happiness. This pattern of findings could not be explained by relevant demographics, stress, and peoples daily time use. Although causality cannot be inferred from this correlational data set, the present findings point to the possibility that money may be a more effective tool for reducing sadness than enhancing happiness.

We examine the extent to which assimilation and residential ethnic enclaves are associated with immigrant access to smoking-related fringe benefits. In particular, we consider access to office smoking bans and employer-sponsored smoking cessation programs. These worksite characteristics are important and understudied fringe benefits. They are critical because they can protect immigrants from exposure to environmental tobacco smoke in the workplace and can help immigrant smokers quit smoking. We first document that immigrants have lower access to these benefits than natives. Second, we show that assimilation is positively associated with smoking-related fringe benefit access while enclave residence does not predict access.

This Paper studies the effect of home-owners' migration costs on migration and unemployment in an economy where workers move both for work- and non-work-related reasons. To this end, a search model with heterogeneous locations is developed and calibrated to the U.S. economy. Consistent with the empirical evidence, the model predicts that home-owners have a lower unemployment rate than renters despite their higher migration costs. The result is due to home-owners' higher transition rate to employment and lower transition rate to unemployment. In addition, the model generates lower inequality in home-owners' local unemployment rates than in renters'. In line with this result, it is documented that, for the period 1996-2013, hoome-owners had less unemployment dispersion across metropolitan areas than renters.

This study aims to determine some important determinants of the wage differential between native and immigrant physicians by applying a human capital framework and Ordinary Least Squares (OLS) regression analysis. Section II of the paper details the human capital theoretical framework that is central to this study and that is used in the most important previous literature. Then, this literature is discussed in the context of the research problem that immigrant physicians earn substantially less than native physicians in the U.S. The principle hypotheses are also developed following the theory and literature review. Section III describes the data set and the empirical model that is employed to test the hypotheses. Section IV presents descriptive statistics as well as the results obtained from the regression models and discusses important findings. Section V concludes the paper with policy implications and avenues for future research.

The Supplemental Nutrition Assistance Program (SNAP), previously known as the Food Stamp Program, provides a crucial resource for impoverished households in increasing purchasing power and food security. Fraud presents a large cost concern to the program, and the federal government mandates that the states must maintain a system that prevents duplicate or ineligible participants from receiving benefits. To satisfy this requirement, four states (Arizona, California, New York, and Texas) required applicants to submit to a finger-imaging requirement to receive benefits at different times in the last two decades. However, a large opposition to finger-imaging appeared, claiming it decreased SNAP participation by increasing the costs in time, money, stigma, and inconvenience of applying for benefits, and every state save Arizona repealed the requirement. I exploit the variation in timing by state with a difference in difference framework to examine the impact of this eligibility requirement on SNAP participation and food insecurity. I find the requirement decreases SNAP participation by 1.3 and 1.5 percentage points for individuals at or below 200% and 130% of the poverty threshold, respectively, and the effect intensifies for whites (non-Hispanic), children, and single mothers. Food insecurity increases for white mothers below 185% of the poverty line and white children of all incomes and below 185% of the poverty line. However, a finger-imaging requirement improves SNAP participation and food security for Hispanics and has no effect for blacks (non-Hispanic), suggesting that the increased costs associated with finger-imaging are equaled or exceeded by the benefit of reducing discrimination from SNAP officers by confirming one's legal US residence and freedom from felony drug convictions. Pietrini 3

This dissertation presents three essays on the topic of the economics of education. In the first essay, I examine the importance of family shocks for student learning. In the second, I describe how the evolution of the Hispanic-white test score gap varies by immigrant generation. The last essay explores the racial divide in education and the labor market through evidence from interracial families.

This study evaluates the impacts of the Louisville Enterprise Zone (EZ) program on the growth of different industries, covering a 20-year (1980-2000) time span. The research compares the preprogram (1980-1990) and postprogram (1990-2000) growth. A shift-share analysis first compares the regional competitiveness of EZ and non-EZ areas in 1990 and 2000, and suggests that most industries in EZ areas have been gaining strength. Five regression equations are estimated to quantify the effects of the EZ program on different industries. An instrumental variable approach is developed to deal with the possible issue of endogeneity during the EZ designation process. Results suggest that the Louisville EZ program has significantly increased the growth of manufacturing and service activities, and the program achieved its expected goals in the long run.

Mexican migration in the early twentieth century was characterized by relatively unimpeded entry in the first part of the century, repatriation policies in the late 1920s and 1930s, and guest worker migration under the Bracero Program from 1942 to 1964. I utilize newly- collected, archival datasets and novel empirical techniques to better understand these episodes. In the first paper we estimate the self-selection of Mexican migrants into and out of the United States in the 1920s. We use height to proxy migrant quality and to measure self-selection into migration in 1920. Migrants were positively selected on height compared to the Mexican population. We then link these migrants to the 1930 U.S. and Mexican censuses to obtain samples of permanent and return migrants and estimate the selection into return migration. Return migrants were not differentially self-selected on height relative to permanent migrants. In the second paper I examine the development impacts of the Bracero Program. Exploiting a natural experiment in the institutional structure of the program, I use a state’s proximity to the nearest recruitment center as an instrument for bracero out-migration to estimate the causal effect of bracero migration on human capital investments in sending states. IV estimates show that bracero migration caused increases in primary school enrollments and education spending. Analysis of heterogeneous effects reveals that the effect occurred for the marginal years of education and that the effect was relatively bigger for female children than for male children. These results suggest that guest worker programs can serve as good development policy. In the third paper, I examine the impact of the Bracero Program on entrepreneurial investments. Exploiting microdata and within person variation in migration choices, I estimate both an individual fixed effects model and a hazard model to ascertain the effect of bracero migration on the decision to start a new business. Results indicate that bracero migration caused an increase in the propensity to start new businesses, and that bracero trips were more likely to result in business investment than were illegal trips. This chapter provides further evidence of guest worker programs as good development policy.

This paper considers the impact of skill-based immigration restrictions, using the Chinese Exclusion Act as a natural experiment. I find that restrictions reduced the average occupational standing of Chinese immigrants, suggesting substitution between observed and unobserved skills. Conversely, children of restricted immigrants have greater human capital than those of unrestricted immigrants, despite restricted immigrants themselves having lower skill. This suggests particularly strong inter-generational transmission of skill among Chinese immigrants of the exclusion era. More generally, the findings indicate that the effects of skill-based restrictions are not always straightforward and may be heterogeneous across groups.

Income inequality remains a hot topic, and many wonder how efforts to combat it might impact the economy and markets. Weve written occasionally on why such efforts would be a solution in search of a problem: Most studies on income distribution fail to account for demographics, taxes and other mitigating factors. Our guest columnist, Monte Stern, has conducted significant research on this topic, and we think his insightful findings present an unusual and fascinating way to think about income distributiona view you typically wont get from most major outlets, which weve found forsake nuance for sensationalism and soundbites. For all the bluster in Washington and on the campaign trail, as youll see from Mr. Sterns analysis, very little needs to be done about income distribution from an economic perspective. Hence, that our gridlocked government is unlikely to advance legislation targeting this issue any time soon is actually quite ok for markets. Yet governments of all political stripes have a long history of advancing popular legislation, regardless of the actual economics underpinning it. (See the Tariff Act of 1930, for just one famous example.) If every politician had access to universally correct economic thinking, it still wouldnt mean they would act on itan important reason why we believe politics can be a key driver for markets. As always, MarketMinder endorses no political views and favors no political party. We dont believe you can analyze markets and economics through a biased lens. The views below are the opinion of the author, not Fisher Investments or the MarketMinder editorial staff. We simply enjoyed the analysis and thought you would, too.

Focusing on North America and the United Kingdom, this critical literature review underscores the ways in which sharing, accessing, and reusing science data allow researchers and other stakeholders to address new imperatives in scientific research. Science data stakeholders should harness the principles and practices of digital curation, an overarching concept that encompasses data curation and that centers on adding value to digital data assets. This review first probes data sharing, access, and reuse in specific intellectual and institutional contexts. Next, it examines the ways in which science data sharing, access, and reuse benefits scholarship, primarily by encouraging new research questions and by allowing the reproduction of previous findings. Third, it addresses the infrastructure of science data curation, particularly the roles of cyberinfrastructure, research communities, collaboration, planning, policy, and standards and best practices. Fourth, it turns to the role of institutions—archives, research libraries, institutional repositories, and centers—in curating science data. Archival principles such as provenance, selection and appraisal, authenticity, metadata, risk management, and trust play a pivotal role in digital curation. Finally, it delineates avenues for further research such as sustainability, costing, planning and policy, training and education, researcher practices, and raising awareness.

We study the impact of recessions on the real wages of undocumented immigrants in the US using data from the Mexican Migration Project. Empirical evidence shows that undocumented immigrants experience larger wage drops during recessions than natives, suggesting that the frequent renegotiation of contracts leads to greater wage flexibility. Because migration decisions also adjust to these wage changes, the ob- served equilibrium wages are capturing both lowered aggregate productivity and a smaller supply of migrant workers. To separate these effects, we analyze an equi- librium migration model where native wages are rigid while immigrant wages are flexible. In a counterfactual experiment with a fixed supply of immigrant workers, we see a stronger relationship between aggregate negative productivity shocks and immigrant wages. We also find that the flexibility of immigrant wages reduces the volatility of native employment over the business cycle.

The sparse vector technique is a powerful differentially private primitive that allows an analyst to check whether queries in a stream are greater or lesser than a threshold. This technique has a unique property -- the algorithm works by adding noise with a finite variance to the queries and the threshold, and guarantees privacy that only degrades with (a) the maximum sensitivity of any one query in stream, and (b) the number of positive answers output by the algorithm. Recent work has developed variants of this algorithm, which we call {\em generalized private threshold testing}, and are claimed to have privacy guarantees that do not depend on the number of positive or negative answers output by the algorithm. These algorithms result in a significant improvement in utility over the sparse vector technique for a given privacy budget, and have found applications in frequent itemset mining, feature selection in machine learning and generating synthetic data. In this paper we critically analyze the privacy properties of generalized private threshold testing. We show that generalized private threshold testing does not satisfy \epsilon-differential privacy for any finite \epsilon. We identify a subtle error in the privacy analysis of this technique in prior work. Moreover, we show an adversary can use generalized private threshold testing to recover counts from the datasets (especially small counts) exactly with high accuracy, and thus can result in individuals being reidentified. We demonstrate our attacks empirically on real datasets.

From 1980 to 2009, the African-born population in United States grew from just under 200,000 to almost 1.5 million (McCabe, 2011). According to the New York Times, the number of black African immigrants alone doubled in the 2000's (2014). Clearly, there has been a significant increase in the population of Africans in the U.S. A logical question to ask is what has been attracting Africans? If we agree with Barry Chiswick that "economic migrants tend on average to be more able, ambitious, aggressive, entrepreneurial, or otherwise more favorably selected than similar individuals who choose to remain in their place of origin" (1999), we may conclude that the economic returns to all these qualities must be increasing. Therefore, another question to ask, and the focus of this research, is what is the labor market experience of the workers among African immigrants, and how does it change over time in comparison to U.S. natives? If the population of Africans in the U.S is increasing significantly, it is important to conduct an empirical study of their labor market performance. The purpose of this research, therefore, is to determine the wage differential between African immigrants and U.S. natives, and study how this differential varies with time as a way of testing if African immigrants assimilate with natives. I hypothesize that ceteris . . .

This study examines the relationship between baseflow and urbanization over time with the help of spatial analysis using Geographic Information Systems. The urbanization parameters used were population and urban land use. Five urban and three non-urban streams were chosen for analysis in the state of Georgia. Four percentile baseflows for each stream were identified and analyzed for trends over time. A correlation analysis was also run to determine how baseflow varies as a function of urbani- zation. According to the trend analysis, the baseflows over time were considered stable or had no sta- tistically significant trend. The correlation analysis between baseflow and urbanization revealed some scattered relationships though a general conclusion cannot be drawn. The simplicity of the study may have contributed to not capturing all of the baseflow changes with the urbanization parameters.

or the youngest cohorts whose entire working life can be observed, hours start falling much earlier than wages. Wages do not fall (if they fall at all) until one׳s late 60s. The data suggest that many workers start a smooth transition into retirement by working progressively fewer hours while still facing an upward-sloping wage profile. This pattern is not an artifact of staggered abrupt retirement or selection. This evidence imposes restrictions on dynamic models of the aggregate economy, and provide updated numerical profiles that can be readily used in quantitative macroeconomic analysis to incorporate this new pattern into aggregate models.