This study examines labor and employment practices in the residential construction industry in Massachusetts. Our research is based on more than 60 in-depth interviews with documented and undocumented workers, union and non-union contractors, union and public officials, and community activists involved in residential construction. The results of these interviews are complemented by a comprehensive quantitative analysis of data from the Massachusetts Department of Revenue, Department of Unemployment Assistance and Department of Industrial Accidents that provides statistical projections of the extent and economic costs of worker misclassification in Massachusetts construction.

The rapid development of information technology has opened up the era of big data. A large number of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy preserving data publishing (PPDP) has gained significant attention in recent years as a promising approach for information sharing while preserving data privacy [1]. Differential privacy has gradually become the de facto standard privacy definition and provides a strong privacy guarantee. In this dissertation, in my efforts to fill the gap between the differential privacy theory and its applications, we target to design multiple differentially private data publishing mechanisms addressing the challenges for high-dimensional, high-volume and high-variety data. To be specific, we study this problem from the following three aspects. First, we provide intuitive interpretations and illustrations on the important ideas in differential privacy, especially noise calibration to global sensitivity and smooth sensitivity, and composition properties. Taking social networking as an example to study how to adapt. differential privacy from tabular data to social network data, we explore the interplay between differential privacy and social network analysis by systematically introducing four models of differential privacy definitions, then we review existing differentially private methods for three most widely-used graph analysis techniques, and put forward a research agenda that involves four open challenges in differentially private data publishing. Second, differentially private publishing of high dimensional data remains a challenging problem – it suffers from the “Curse of High-Dimensionality” [2]. Most existing approaches generally ignore the different roles a dimension may play for a specific query-one dimension may be more important than another for a particular query. Additionally, one dimension may release more information than another if the same amount of noise is added; thus evenly allocating the total privacy budget to each dimension degrades the performance. In order to address these challenges, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases of a Markov-blanket-based cluster learning and an invariant post randomization (PRAM). We also extend our DP2-Pub mechanism to the distributed scenario with an untrustworthy central server. Third, conventional private data publication mechanisms aim to retain as much data utility as possible while ensuring sufficient privacy protection on sensitive data. Such data publication schemes implicitly that all data analysts and users have the same data access privileges levels. However, it is not applicable for the scenario that data users often have different levels of access to the same data, or different requirements of data utility. The multi-level privacy requirements for different authorization levels pose new challenges for private data publication. Traditional PPDP mechanisms only publish one perturbed and private data copy satisfying some privacy guarantee to provide relatively accurate analysis results. To find a good tradeoff between privacy preservation level and data utility itself is a hard problem, let alone achieving multi-level data utility on this basis. In this work, we address this challenge in proposing a novel framework of data publication with compressive sensing supporting multi-level utility-privacy tradeoffs, which provides differential privacy.

This paper analyses the effect of California's, New Jersey's, Rhode Island's, and New York's paid family leave (PFL) programs on mothers' leave-taking, and short-term labor market outcomes following childbirth. This analysis uses March Current Population Survey data from 1999-2020 and a difference-indifference approach to evaluate leave-taking, as well as a difference-indifference in difference approach to evaluate the short-term labor market outcomes. I obtain evidence that PFL increases Part-time workers' leave-taking by 3 percent, while Job-Protected PFL leads to a 17.2 percent increase in mothers being employed and at work the year of childbirth. I also provide evidence of PFL increasing part-time workers'usual weekly hours worked by 6 hours, following childbirth.

Background: In Burkina Faso, gender inequality prevents women from meeting their reproductive needs, leading to high rates of unintended pregnancies, abortions and deaths. Evidence shows that empowering women may increase the proportion of demand for family planning satisfied using modern methods (mDFPS), but few studies have measured this process in multiple spheres of life. We investigated how empowerment influences the mDFPS among married women of reproductive age (MWRA) in Burkina Faso. Methods: We analyzed data from the 2010 Burkina Faso Demographic and Health Survey (DHS) on 4714 MWRA with reproductive needs living in 573 communities. We used principal component analysis (PCA) and Cronbach’s alpha test to explore and assess specific and consistently relevant components of women’s agency in marital relationships. Aggregated measures at the cluster level were used to assess gender norms and relationships in communities. Descriptive statistics were performed and multilevel logistic regression models were carried out to concurrently gauge the effects of women’s agency and community-level of gender equality on mDFPS, controlling for socioeconomic factors. Results: Overall, less than one-third (30.8%) of the demand for family planning among MWRA were satisfied with modern methods. Participation in household decision-making, freedom in accessing healthcare, and opposition to domestic violence were underlying components of women’s agency in marital relationships. In the full model adjusted for socioeconomic status, freedom in accessing healthcare was significantly (aOR 1.27, CI 1.06–1.51) associated with mDFPS. For community-level variables, women’s greater access to assets (aOR 1.72, 95% CI 1.13–2.61) and family planning messages (aOR 2.68, 95% CI 1.64–4.36) increased mDFPS, while higher fertility expectations (aOR 0.75, 95% CI 0.64–0.87) reduced it. Unexpectedly, women in communities with higher rates of female genital mutilation were more likely (aOR 2.46, 95% CI 1.52–3.99) to have mDFPS. Conclusions: Empowering women has the potential to reduce gender inequality, raise women’s agency and increase mDFPS. This influence may occur through both balanced marital relationships and fair community gender norms and relationships. Progress toward universal access to reproductive services should integrate the promotion of women’s rights. Trial registration: No clinical trial has been performed in this study.

Over the past two decades, population researchers have engaged in a far-reaching and productive program of research on demographic responses to changes in the natural environment. This essay “looks back” to the origins of these developments, identifying pivotal agenda-setting moments in the 1990s and tracing the impact on contemporary research. The essay also “looks forward” to identify critical gaps and challenges that remain to be addressed and to set an agenda for future research on population responses to environmental change. It recommends that the multidimensionality of environmental contexts and change be fully embraced, long run as well as short term effects be investigated, variability in the effects of environmental change in relation to social institutions, policy implementation, and environmental context be examined, movement between contexts as well as change in situ as sources of environmental change be considered, and interconnections among demographic processes in response to environmental change be explored. Taking these steps will position demographers to contribute significantly to a larger and deeper understanding of environmental change and its consequences, locally, regionally, and globally.

Scholars have long examined how generations or, more technically, cohorts produce social change. According to theory, people’s lives are shaped by the years in which they were born because they experience, along with their peers, particular historical events at the same points in the life course. Despite the importance of history, however, many scholars have evaluated cohorts not defined by clear start and end dates, but rather by arbitrary cut points, such as five-year intervals. In contrast, this article uses defined changes in military service in the United States stemming from shifts between war and peace, and from draft to volunteer service to assess how cohorts have contributed to change in socioeconomic attainment. It uses the Current Population Survey from 1971 to 2017, which has not previously been used to evaluate how veteran status may have produced shifting outcomes across cohorts. It finds evidence that cohorts had different average income overall and between groups, with veterans earning more money than nonveterans who were eligible to serve during the draft era before the Vietnam War. These gaps are partially explained by racial and educational differences. The findings provide a model for analyses of changes in the relative status of other groups, as well as information about how the role of military service in social mobility changed historically.

This paper seeks to document how immigrant earnings for a given country evolve over timeand identify evidence from immigration literature that could explain any observable trends. By analyzing birth cohorts of immigrants from IPUMS Microsamples and the American Community Survey, I find that immigrant earnings have been increasing since 1950 with each cohort, earning 0.95% higher wages per year relative to natives. I also attempt to rationalize the presence this cohort trend through the use of the human capital proxies of educational attainment and literacy rates. They do not seem to account for the presence of the cohort effect. Moreover, I observe that as low income countries grow in per capita income, the relative earnings of their immigrants rise. Finally, I find that exposure to their source country contributes to the presence of stronger cohort effects by studying the children of immigrants.

Economists often evaluate public policies by measuring the effects of those policies on participants' wage income. I use two historical settings to argue that this focus on wage income biases estimates of the returns to education, the returns to local investment, and the measurement of inequality. I investigate a rich set of non-wage outcomes that are available in historical decennial Census data, and I show that by augmenting standard economic and econometric analyses with these new outcomes, economists and policymakers can develop a more complete understanding of long-run trends in inequality and the causal effects of public policies on children. In the first chapter of my dissertation, I measure the causal effects of public libraries on children. Between 1890 and 1921, Andrew Carnegie funded the construction of 1,618 public libraries in cities and towns across the United States. I link these library construction grants to census data and measure the effect of childhood public library access on adult outcomes. Library construction grants increased children's educational attainment by 0.10 years, did not affect wage income, and increased non-wage income by 4%. These income effects are driven by occupational choice. Access to a public library caused children to shift away from occupations like manual labor, factory-work, and mining into safer and more prestigious occupations like farm-ownership, clerical, and technical jobs. I show that compulsory schooling laws had parallel effects on children, increasing educational attainment, non-wage income and occupational prestige without affecting wage income. Economists often rely solely on wage income to measure the returns to education. But public libraries and compulsory schooling laws in the early 1900s increased educational attainment and had positive effects on children's adult labor market outcomes without affecting wage income. In the second chapter of my dissertation, I embed income and mortality in a unified measure of black-white inequality. In the 1940 census, white males earned twice as much as black males. But white males born in 1900 had lifetime earnings 3.4 times as large as black males born in 1900. The black-white lifetime earnings gap is so much larger than the cross-sectional earnings gap because of the large black-white gap in life expectancy. 48% of black males born in 1900 died before the age of 30 as compared to just 26% of white males. Economists often use cross-sectional earnings gaps to measure inequality between groups, but a more complete measure of inequality combines income profiles and mortality risk. I develop a model of optimal consumption in a world with mortality and I calibrate the model separately for each cohort of black and white males born between 1900 and 1970. Using this model, I find that the black-white welfare gap shrank by 48% from the 1900 to the 1920 birth cohorts as black and white mortality rates converged, but the black-white welfare gap declined only modestly from the 1920 to 1970 birth cohorts.

A perceived driver shortage causing difficulty in hiring and retaining qualified drivers threatens carrier productivity and profitability. Logistics literature concerning driver turnover has focused on three primary groups of factors contributing to the driver turnover: driver demographics, manager quality, and work environment. A key consideration, however, is that drivers have an outside option to work in other industry-occupations. We leverage the Roy model of occupational choice to develop hypotheses regarding the effects of wages and tenure on workers’ occupational choice. To test our theory, we conduct a series of econometric studies on a panel data set we assemble from publicly available data sets. Our results show that relative wages strongly influence the choice of whether to drive a truck for a living. A particularly important finding is that the effect of relative wages varies significantly across industry-occupations. In fact, contravening industry wisdom that wages are insufficient to attract qualified workers, wage elasticities are greater in trucking than in competing industry-occupations, such as construction or retail. These results persist after accounting for a wage–tenure interaction. This indicates that the persistent “driver shortage” faced by the industry is the result of wages failing to keep pace with wage increases in competing industry-occupations.

Objective: This study empirically investigates the impact of economic inequality on the cost of living in U.S. metropolitan areas. Methods: Using a special Census tabulation, a standard cost-of-living model is sequentially augmented with measures of poverty and income inequality in a cross-sectional regression analysis of 90 metropolitan areas; interaction and regional effects are also explored. Results: Higher costs of living are associated with increasing economic inequality, especially in the distribution of metropolitan income. The effects of household poverty are significant but less consistent. Conclusions: Reducing economic inequality would produce benefits enjoyed by all metropolitan residents via lower living costs. The benefits are likely to be greater in large, fast growing areas where income disparities are pronounced.

This dissertation comprises three chapters in applied labour economics. The first chapter studies the extent to which occupation flexibility explains the evolution of the UK graduate gender wage gap. It documents that the share of graduate women working in flexible occupations increased both over the life cycle and over time, whereas men increased work in inflexible occupations at older ages. The wage penalty associated with flexibility increased over time and over the life cycle. The graduate gender wage gap is small at labour market entry and widens over the life cycle. Quantile decomposition analysis shows that sorting into flexible occupations explained between 15% to two-thirds of the life cycle increase in the gender wage gap. The reduction in the gap would have been up to 150% larger across cohorts if sorting into flexible occupations had not increased over time. The second chap- ter estimates an equilibrium model to investigate how changes in labour demand and supply explained patterns in flexibility and the gender wage gap. Higher rel- ative demand for male labour at older ages, and in inflexible occupations, largely explained the life cycle increases in the gender wage gap, whereas women’s higher preferences for working in flexible occupations drove the increases in sorting into flexible occupations over time. The third chapter uses a difference-in-differences strategy to evaluate the effect of declines in child malaria mortality on fertility and female labour force participation in Tanzania. Exposure to the decline in child mor- tality led to increases in extensive margin fertility for women aged 15–25 in areas where malaria was not endemic, in line with reductions in malaria risk during first pregnancy, especially among adults with low levels of acquired immunity. Labour force participation fell for mothers aged 26–40, particularly those with children un- der five in the household

Italians were the largest contributors to the rise in southern European immigration that took place in the US at the turn of the 20th century. This rise fueled anti-immigrant sentiments which concluded with the US abandoning its open-door policy for European immigrants. I study the selection and economic outcomes of Italians in Argentina and the US, the two largest destinations for Italians in this period. Prior cross-sectional work shows that Italians had faster assimilation in Argentina, but is inconclusive on whether this was due to differences in selection or in host-country conditions. I assemble data following Italians from passenger lists to population censuses, enabling me to compare migrants with similar regional origins and pre-migration characteristics. First-and second-generation Italians had better economic outcomes in Argentina. Observable pre-migration characteristics cannot explain these differences. Path dependence in migration flows can rationalize these differences in an era of open borders.

The American Time Use Survey (ATUS) accurately measures hours worked on a single day. We propose several estimators of elasticities of weekly labor supply in a linear regression model, despite certain impossibility results due to the time specific feature of the ATUS. We recommend the impute estimator, a simple modification of the standard two stage least squares estimator, that imputes the dependent variable using daily subsamples, based on our careful investigation of asymptotic and finite sample properties of the estimators under the potential outcome framework. We apply the impute estimator to the ATUS and find substantially different elasticity estimates from the Current Population Survey, especially for married women.

Living alone is increasingly common across the U.S. It is also associated with poorer health outcomes and greater risks of loneliness and social isolation. However, less is known about how the demographic and health characteristics of adults living alone varies by rural/urban location. We address this gap by examining how the types of people who live alone differ between urban (metro) and rural (non-metro) areas, focusing on differences in age and disability status.

This paper disaggregates the GTAP U.S. labor data into 20 worker types, by gender, education, and broad occupation category, to understand how different workers in the U.S. are exposed to hypothetical changes in tariff rates. First, a methodology is provided to disaggregate the U.S. labor input in GTAP into twenty types. A database of wage bill shares and mean wages by GTAP sector are calculated using 2017 data from the Current Population Survey (CPS) Annual Social and Economic Supplements (ASEC). The wage bill share matrix can be used as a tool to split wage payments in GTAP to each labor type. Second, the paper provides illustrative policy simulations on heavy manufacturing, agriculture, and services trade, demonstrating differential impacts on worker types for each of the scenarios considered.

The recent growing Hispanic population in the Bronx, New York has made this borough highly diverse. The reasons for the boom of Latinos are attributed to the ease of immigration acts, but the changes in immigration policies do not explain this population's concentration in eight community districts. In this paper, we explore the factors that cluster this growing community in one region of the borough and the possibilities of dwindling population in the future. We particularly examine how networking and urban accessibility impact Hispanic’s decision-making on internal migration into the Bronx, New York.

Accelerations in population growth and urban expansion are transforming landscapes worldwide and represent a major sustainability challenge. In the United States, land conversion to impervious surfaces has outpaced population increases, yet there are few spatial metrics of urbanization and per capita land change available nationwide for assessing local to regional trends in human footprint. We quantified changes (2000–2010) in housing density, imperviousness, per capita land consumption, and land-use efficiency for block groups of the contiguous U.S. and examined national patterns and variation in these metrics along the urban–rural gradient and by megaregion. Growth in housing (+13.6%) and impervious development (+10.7%) resulted in losses of rural lands, primarily due to exurbanization and suburbanization. Mean per capita consumption increased in all density classes but was over 8.5 times greater in rural lands than in exurban, suburban, and urban areas. Urban and suburban areas had significantly lower mean consumption, yet change was unsustainable in 60% of these areas. Megaregions across the sprawling Sun Belt, spanning from Arizona to North Carolina, grew most unsustainably, especially compared to regions in the Pacific Northwest and Front Range. This work establishes 21st-century benchmarks that decision-makers can use to track local and regional per capita land change and sustainable growth in the U.S.; however, these metrics of the form, extent, rate, and efficiency of urbanization can be applied anywhere concurrent built-up area and population data are available over time. Our web mapping application allows anyone to explore spatial and temporal trends in human footprint and download metrics, and it is designed to be easily updatable with future releases of validated developed land cover, protected areas, and decennial Census data.

The goal of this article is to consider data solutions to investigate the differential pathways that connect climate/weather variability to child health outcomes. We apply several measures capturing different aspects of climate/weather variability to different time periods of in utero exposure. The measures are designed to capture the complexities of climate-related risks and isolate their impacts based on the timing and duration of exposure. Specifically, we focus on infant birth weight in Mali and consider local weather and environmental conditions associated with the three most frequently posited potential drivers of adverse health outcomes: disease (malaria), heat stress, and food insecurity. We focus this study on Mali, where seasonal trends facilitate the use of measures specifically designed to capture distinct aspects of climate/weather conditions relevant to the potential drivers. Results indicate that attention to the timing of exposures and employing measures designed to capture nuances in each of the drivers provides important insight into climate and birth weight outcomes, especially in the case of factors impacted by precipitation. Results also indicate that high temperatures and low levels of agricultural production are consistently associated with lower birth weights, and exposure to malarious conditions may increase likelihood of nonlive birth outcomes.

We study the effects of the 1930s-era HOLC “redlining” maps on the long-run trajectories of neighborhoods. Using a boundary design and propensity score methods, we find that the maps led to reduced home ownership rates, house values, and rents and increased racial segregation in later decades. We also compare cities on either side of a population cutoff that determined whether maps were drawn and find broadly similar results. We conclude that the HOLC maps had meaningful and lasting effects on the development of urban neighborhoods through reduced credit access and subsequent disinvestment.

This study discusses the effectiveness of a science, technology, engineering, and math (STEM) program spanning grades six through eight in a traditional, urban school district located in Northeastern Ohio. The history and expressed need for STEM education within post-steel producing and economically depressed regions are discussed. Important factors describing STEM programming such as curriculum, standards, content delivery, integration, and aspects of implementation are described. This longitudinal study reports Ohio State Test (OST) scores along with the demographic factors of gender, socioeconomic status, student race, and attendance rate to determine the impact of STEM programming. The use of multilevel, statistical analyses through hierarchical linear modeling (HLM) determined integrated STEM had a significant, positive effect on student achievement in both math and science and an even stronger impact isolating science achievement by itself. The predictive results of HLM analysis determined STEM students scored significantly higher on the OST in science and math combined scoring 31.8 points higher on average and 38.2 points higher in science compared to their general education peers. No interaction effects were determined between STEM participation and gender, socioeconomic status, student race, and attendance rate. This research has powerful implications for educational leaders as they need to be aware of the impact integrated STEM programming and project-based learning (PBL) has on student achievement. The results indicate that integrated STEM programming in middle school has a positive effect on student achievement indicating it is indeed a pathway to success.