The search for a reliable data-set of entrepreneurial firms is ongoing. We analyze and assess longitudinal data on startups from two data sources–the National Establishment Time-Series (NETS) database and the Secretary of State (SOS) business registry data. Our primary purposes in this paper are to assess the usefulness and reliability of these databases in measuring startup activity along several quality indicators and to explore the possibility of integrating these large databases using both automated and manual processes. The NETS identifies a firm’s employment, sales, and industry but is expensive and suffers from a temporal lag. The SOS data provide up-to-date startup counts but offer limited variables. We conclude that policymakers and researchers will benefit from combing both the SOS and adjusted NETS since they provide complementary information on startups. We carefully document our methodology and make suggestions for use of the data for future research.

This research paper examines factors that influence young adult voter behavior during the 2016 presidential election. Data was derived from the Current Population Survey, controlled for individuals ages 18 to 24. The cross-sectional data from the random sample of 8,433 people were then used to estimate the marginal probit regression model that tested certain voting factors’ impact on the probability to vote. Gender, education, race, and age were control variables in the model. The study focuses on different methods of registration, household income, and the duration of residence. The results found that registering to vote via the internet, registration drives, and at school are statistically significant and increase the probability of voting among young people more than other methods. Additionally, I introduce behavioral economic concepts, such as framing, anchoring, herding, etc., that could be applied to certain significant factors to increase young voter turnout. This allows further research to be conducted on the impact behavioral economics could have when targeting significant voting behavior factors.

Regulatory constraints on housing production have shut millions of Americans out of the country’s most productive labor markets. Historically, Americans have moved to the parts of the country that offered the highest wages and most economic opportunity. This tendency for Americans to move has changed in recent decades, as changes in legal land-use restrictions have limited housing construction in America’s richest locations. These restrictions have created limits on housing supply and have led to rapidly rising prices that make high-wage places unaffordable to less-educated workers. As a result, workers without a college education are now moving away from the places that offer them the highest wages and their children the best later-life outcomes. In this proposal, I discuss strategies that policymakers at various levels of government can use to combat this relatively new problem, including case studies of cities that have successfully expanded access at the local level. This challenge differs from the more-traditional problem of making housing affordable for low-income households. Combating it requires new political coalitions and a sharper focus on the barriers, both political and legal, to development.

Coasts contain a disproportionate share of the world's population, reflecting historical advantages, but environmental change threatens a reversal of coastal fortune in the coming decades as natural disasters intensify and sea levels rise. This thesis considers whether large infrastructure investments should continue to favour coastal areas. I use a dynamic spatial equilibrium framework and detailed georeferenced data from Vietnam to examine this issue and find evidence that coastal favouritism has significant costs. Road investments concentrated in coastal regions between 2000 and 2010 had positive returns but would have been outperformed by allocations concentrated further inland even in the absence of sea level rise. Future inundation renders the status quo significantly less efficient. Under a central sea level rise scenario, welfare gains 72% higher could have been achieved by a foresighted allocation avoiding the most vulnerable regions. The results highlight the importance of accounting for the dynamic effects of environmental change in deciding where to allocate infrastructure today.

This article uses a comprehensive descriptive analysis to examine the determinants of racial intermarriage for native-born men and women using the 2012 American Community Survey. A comparison between military and civilian samples is the main focus of the article. We improve upon the existing literature by identifying the proximity of the respondent’s current residence to a military base and including an analysis of anti-miscegenation laws by state. Further, we provide a cohort analysis to parse out generational differences. We find that military members are more likely to intermarry, regardless of cohort, and that non-White military members have higher rates of education than their civilian counterparts. Black females in the military are more educated and have a significantly higher rate of intermarriage than their civilian counterparts. Additionally, the difference in intermarriage rates between civilian and military members is 31 percentage points higher for Black women than Black men.

Women who migrate to the United States often face structural and cultural obstacles when joining the workforce. The US-born daughters of these women show considerable upward mobility, yet recent scholarship finds substantial variation in the employment of second-generation women by parental country of origin. This study assesses whether gender traditionalism in the parental country of origin has a persistent effect on the labor force participation of partnered second-generation women in the United States. An analysis of 1995–2015 Current Population Survey data supplemented with parental origin country characteristics finds that gender-traditional behaviors, religions, institutions, and attitudes are each associated with a lower likelihood of female labor force participation (FLFP). We propose that the successful intergenerational transmission of conservative cultural repertoires from the first to the second-generation accounts for these relationships. Conservative religious context is the best overall predictor of lowered second-generation FLFP. However, patriarchal attitudes and institutions in the parental birthplace best account for the participation of women with parental origins in Latin America and the Caribbean, while the effect of religious context is strongest among women with parental origins in Asia and Europe.

This paper provides a quantitative framework for estimating the effects on house prices and household welfare of building different types of housing within a city or metropolitan area. According to our estimates, low-income households without a college degree benefit more from the construction of low-quality rather than high-quality housing, but low-quality construction makes many other households worse off. These conclusions depend on household mobility across cities, the strength of urban spillovers, the indivisibility of housing, and the differential preferences of households with and without a college degree.. First draft: November 15, 2018. We thank Therese McGuire for helpful comments and Anthony A. DeFusco for conversations that inspired this paper.

We study the allocation of skills across space and time in the U.S. Employment polarization is stronger in larger cities and mainly driven by number of workers. Using a theory-based measure of skills obtained within a spatial general equilibrium model, we investigate how the skill distribution changes across space and time. Consistent with employment polarization by city size, during the 1980- 2008 larger cities display a higher increase in the fraction of high- and low-skilled workers, while smaller cities display a higher increase in the fraction of medium-skilled. These patterns are largely determined by the emergence of skill-biased technological change.

We experimentally varied information mailed to 87,000 households in California's health insurance marketplace to study the role of frictions in insurance take-up. Reminders about the enrollment deadline raised enrollment by 1.3 pp (16 percent), in this typically low take-up population. Heterogeneous effects of personalized subsidy information indicate systematic misperceptions about program benefits. Consistent with an adverse selection model with frictional enrollment costs, the intervention lowered average spending risk by 5.1 percent, implying that marginal respondents were 37 percent less costly than inframarginal consumers. We observe the largest positive selection among low income consumers, who exhibit the largest frictions in enrollment. Finally, the intervention raised average consumer WTP for insurance by $25 to $54 per month. These results suggest that frictions may partially explain low measured WTP for marketplace insurance, and that interventions reducing them can improve enrollment and market risk in exchanges.

I analyze how allowing undocumented immigrants to legally obtain driver’s licenses shifts commuting patterns, increases job accessibility, and improves labor market outcomes. Using state- and nativity-level variation in reforms, I show that granting driving privileges to undocumented immigrants increases vehicle ownership and the probability of car commute by 2.5 percentage points. This improvement in accessibility leads to a 0.8 percentage point increase in the employment rate for undocumented immigrants. The effects of license reforms on the undocumented are larger in low-accessibility localities, which are more rural and entail longer commuting times, particularly for undocumented workers. Undocumented immigrants exhibit stronger positive employments effects in more car-dependent occupations, shifting away from less car-dependent occupations. These findings highlight the quantitative importance of transportation barriers in determining the labor market outcomes of minority workers.

My dissertation studies the interaction between business cycles in the time series and inequality in cross-section, of which racial inequality is one of the most salient dimensions. The first chapter of my dissertation explores how heterogeneous behaviors over business cycles affect wealth inequality. Wealth is distributed more unevenly than income, and one contributing factor might be that richer households earn higher portfolio returns. I uncover one channel that causes portfolio returns to be increasing in wealth: Poorer households consistently buy risky assets in booms—when expected returns are low—and sell after a bust—when expected returns are high. Although time-varying expected returns are a robust empirical fact, theories are ambiguous on whether poorer or richer households engage in such cyclical trading patterns. I estimate the trading patterns for households across wealth levels, in the US housing market for 1988-2013. I interact housing ownership patterns from deeds records with household-level wealth, which I infer from merging owners' surnames with their name-based income in the 1940 full Census. The estimated dispersion in expected returns from this “buy-high-sell-low” channel is large: The interquartile-range difference is 60 basis points per year. The channel predicts that geographies with historically higher volatility will feature more wealth inequality than income inequality: I verify this implication in the data. These results suggest that a government policy intended to boost poorer households' wealth via homeownership can backfire if it ignores the status of house prices. The second chapter of my dissertation finds that racial prejudice, and hence racial discrimination, is countercyclical and may partially account for the higher incidence of business cycles on racial minorities. It starts with the question: Does deteriorating economic condition cause racial prejudice to rise? Despite psychological/sociological microfoundations and multiple economic implications of the inferiority of racial prejudice, empirical evidence has been inconclusive. This paper constructs better-powered measures of local areas' racial prejudice using Google searches for racial slur and "KKK," white-on-black non-pecuniary crime, survey responses and corporal punishment at school. Across these measures, racial prejudice correlates negatively with the local economic condition. Using predictors of local economic condition in the 2000s, I show that the relationship is causal: lower income causes higher racial prejudice in the area. Within the context of this dissertation, the third chapter of my dissertation studies the role of racial prejudice and discrimination in determining racial economic inequality, using a natural experiment that shifts local areas' racial bias against black Americans. Following the Obama presidency, pundits and researchers have asked how having a black leader affects white Americans’ attitude toward black Americans. Given theoretical ambiguity, I test for causal impact of a black leader on racial attitudes using local elections of black politicians at the municipal level. Using Race Implicit Attitude Test (IAT) scores as a measure of racial prejudice and close election regression discontinuity design for causal inference, I find that electoral victory of a black leader leads to a rise in racial prejudice among white Americans against black Americans. Following a close electoral victory, the IAT score rises by about 0.03, or 7% of the average black-white difference. Simultaneously, using the same discontinuity design, black politicians’ electoral victory causes lower employment and higher mortgage denial for black Americans relative to white Americans. By ruling out other channels by which electoral victory could adversely affect black Americans’ relative economic outcome, I argue that the rise in prejudice caused black-white economic inequality to widen.

A large literature emphasizes that elite capture of political institutions hampered the spread of mass schooling in the nineteenth and twentieth century. We collect new data on investments in elementary education and the distribution of voting rights for more than 2,000 local governments in nineteenth‐century Sweden and document that educational expenditure was higher where the distribution of political power was more unequal. In particular, areas governed by local landed elites—even those where a single landowner had de jure dictatorial powers—invested substantially more in mass schooling relative to areas where political power was more widely shared, or where it lay in the hands of capitalist elites. Our findings lend quantitative support to an earlier literature produced by economic and social historians which argues that landed elites advanced mass schooling as part of their historical role as patrons of the local community and as a response to the increasing proletarianization of the rural population, while also furthering our understanding of how Sweden maintained a high level of human capital despite its low level of economic development and restricted franchise in the nineteenth century.

Preserving privacy while maintaining high utility during sequential publication for data providers and data users in mathematical statistics, scientific researching, and organizations making decisions play an important role recently. The τ -safety model is the state-of-the-art model in sequential publication. However, it is based on the generalization technique, which has some drawbacks such as heavy information loss and difficulty of supporting marginal publication. Besides, the privacy of individuals is the major aspect that needs to be protected in privacy preserving data publishing. In this paper, to protect the privacy of individuals in sequential publication, we develop a new τ -safe (l, k)-diversity privacy model based on generalization and segmentation by record anonymity satisfying l-diversity and individual anonymity satisfying k-anonymity. This privacy model ensures that each record’s signatures keep consistency or have no intersection in all releases. It can get high data utility while resisting the linking attacks due to arbitrary updates. In addition, it can also be applied to a dataset where individual has multiple records and arbitrary marginal publication. The results of our experiments show that the proposed privacy model achieves better anonymization quality and query accuracy in comparison with the m-invariance and τ -safety model in the sequential publication with arbitrary updates.

Labor costs, which account for the largest portion of retailers’ total spending, have increased as a number of U.S. states have raised minimum wages in recent years. There is an ongoing debate about whether minimum wage increases improve or harm the welfare of retail workers. Despite the relevance of this issue, evidence from the literature is mixed. Further, it is not clear if the impact of minimum wage increases affects full-time and part-time retail employees differentially. In this project, the researchers use state-level monthly data from the Current Population Survey to assess the impact of changes in minimum wages on full- and part-time retail workers. They find that minimum wage hikes negatively affect employment of retail workers and their hours of work. Another finding is that these job losses occur mostly among full-time workers, which may have further adverse effects on the accumulation of human capital in the retail sector.

Differential privacy, which is due to its rigorous mathematical proof and strong privacy guarantee, has become a standard for the release of statistics with privacy protection. Recently, a lot of dynamic data publishing algorithms based on differential privacy have been proposed, but most of the algorithms use a native method to allocate the privacy budget. That is, the limited privacy budget is allocated to each time point uniformly, which may result in the privacy budget being unreasonably utilized and reducing the utility of data. In order to make full use of the limited privacy budget in the dynamic data publishing and improve the utility of data publishing, we propose a dynamic data publishing algorithm based on reinforcement learning in this paper. The algorithm consists of two parts: privacy budget allocation and data release. In the privacy budget allocation phase, we combine the idea of reinforcement learning and the changing characteristics of dynamic data, and establish a reinforcement learning model for the allocation of privacy budget. Finally, the algorithm finds a reasonable privacy budget allocation scheme to publish dynamic data. In the data release phase, we also propose a new dynamic data publishing strategy to publish data after the privacy budget is exhausted. Extensive experiments on real datasets demonstrate that our algorithm can allocate the privacy budget reasonably and improve the utility of dynamic data publishing.

Background There are a growing number of cancer survivors in the United States who are at risk for chronic pain due to cancer disease and treatments. The prevalence of chronic pain among cancer survivors has not been comprehensively reported. Methods This study used data from the National Health Interview Survey (2010‐2017) to compare the prevalence of chronic pain between participants with a cancer diagnosis and participants without one. Adjusted odds ratios (AORs) of having chronic pain were assessed by multivariable logistic regression, which included an age (less than the median age vs greater than or equal to the median age) × cancer diagnosis (yes vs no) interaction term. Among cancer survivors, multivariable logistic regression defined the odds of feeling depressed, feeling worried/nervous/anxious, being unable to work, and needing assistance for activities of daily living (ADLs) and instrumental activities of daily living (IADLs). Results Among 115,091 participants, a cancer diagnosis was associated with an increased AOR of chronic pain in comparison with the general population (30.8% vs 15.7%; AOR, 1.48; 95% confidence interval, 1.38‐1.59). Older age was associated with higher odds of chronic pain (P < .001 across all increasing age categories); however, the positive association between older age and chronic pain was seen only in participants without cancer and was not seen in those with a cancer diagnosis (Page×cancer < .001). Among patients reporting a cancer diagnosis, chronic pain was associated with greater odds of feeling depressed, feeling worried/nervous/anxious, being unable to work, and needing assistance with ADLs or IADLs (P < .001 for all). Conclusions Cancer survivors appear to have a high prevalence of chronic pain, which is associated with worse mental, functional, and employment outcomes. Screening and management of chronic pain should be addressed by policymakers to improve cancer survivorship care.

Individuals born outside of the United States represent a significant and growing share of the U.S. population. This is also true of Pennsylvania, in general, and in rural Pennsylvania, in particular. Immigrants and their children are driving increases in racial and ethnic diversity in Pennsylvania, and the degree to which they can integrate socially and economically will have a range of social, economic, and political implications across the Commonwealth. Foreign-born Pennsylvanians represent an important and growing set of workers, business owners, taxpayers, and voters. This research analyzed records from the U.S. Census Bureau and Bureau of Labor Statistics to develop a descriptive profile of the socioeconomic characteristics of the foreign-born workforce in rural Pennsylvania, draw comparisons with the native-born and urban Pennsylvania workforce, and identify trends in these population characteristics that have occurred since the 2000 Census. Specifically, this research produced estimates of the foreign-born share of rural Pennsylvania’s workforce (defined as individuals aged 16-64 years) overall and across different regions within the Commonwealth, described their social and economic characteristics, identified their levels and sources of income, and tracked changes in these outcomes over time.

Information technology (IT) professionals play an important role in the U.S. economy by facilitating IT investments, development, and innovation. The use of temporary work visas and related immigration policies that import foreign IT professionals has attracted significant policy debates. On the one hand, foreign IT professionals may complement domestic IT professionals by facilitating local innovation and expanding local employment. On the other hand, foreign professionals may substitute the domestic counterparts by intensifying labor-market competition and decreasing wage. This study focuses on Optional Practical Training (OPT) extension program for graduates in science, technology, engineering, and mathematics (STEM) from U.S. institutions, a policy change which greatly increases the supply of foreign IT professionals in local labor markets. Specifically, we explore (1) the effects of the OPT extension on the number and wage of domestic workers in STEM occupations and (2) how the effects may differ between IT and non-IT STEM occupations. We test the effects using a novel dataset that is merged from several sources and use a difference-in-differences model to bring identification. Our results demonstrate that an increase in the supply of foreign IT professionals as a result of the OPT extension boosts employment for domestic IT professionals. The unique characteristics of IT human capital differentiate the impacts on IT occupations from non-IT STEM occupations. This study contributes to the information systems, labor economics, and public policy literature by quantifying the impacts of a policy change on the supply and return of IT professionals and provides rich implications for policymakers.

To advance healthy transportation via increased bicycling, cities combat one of the primary barriers to such cycling – traffic safety concerns – through the provision of various bicycle treatments. Shared lane markings (more commonly known as “sharrows”) are an increasingly common treatment utilized to improve bicyclist safety. While past research confirms that sharrows may effectively influence spacing and other operational measures, the impact on actual safety outcomes remains unsubstantiated due to a lack of dooring-related bicycle crash data. Fortunately, the city of Chicago instituted a program to collect dooring crash data in 2010. Thus, the purpose of this research is to longitudinally examine the association between sharrows and bicyclist injuries by combining traditional crash data with dooring-related crash data. To perform this examination, we divide Census block groups in Chicago into three categories based on what bicycle treatment was installed between the years 2011 and 2014: (i) those block groups with no bicycle facilities installed; (ii) those with only sharrows installed; or (iii) those with only bicycle lanes (standard, buffered, and/or protected) installed. Negative binomial regressions and Kruskal–Wallis tests suggest that block groups with only sharrows installed experienced the largest increase in bicyclist injury rates, with exposure being accounted for through levels of bicycle commuter activity. This relationship held true for overall crashes as well as for dooring-related crashes. These findings raise concerns regarding the safety effectiveness of sharrows as used by the City of Chicago during the study period and should be a call for more research on the subject in a variety of different contexts using various exposure metrics.

This paper examines the extent to which children enter into occupations that are different from their father’s occupation, but require similar skills, which we call task following. We consider the possibility that fathers are able to transfer task-specific human capital either through investments or genetic endowments to their children. We show that there is indeed substantial task following, beyond occupational following and that task following is associated with a wage premium of around 5% over otherwise identical workers employed in a job with the same primary task. The size of the premium is similar in magnitude to the size of the premium associated with occupational following. The wage premium is robust to controls for industry, occupation categories and occupation characteristics.