In The Knowledge Tax, I argued that federal taxes and subsidies in aggregate likely disadvantage investments in higher education relative to other investments.I When it comes to investments in higher education, the tax rates are higher and the tax base is larger.2 The purpose of The Knowledge Tax is not to assert that the only explanation for underinvestment in higher education is differences in tax treatment and subsidies. Rather, The Knowledge Tax highlights that a simple neoclassical model can explain much of the observed data, and that a simple and underexplored explanation is credibly at least one important driver.3 An economic model can remove higher education policy from the realm of anecdotes and narrow interest group politics, and situate higher education in broader conversations about efficiency (relative to alternatives), investment, and economic growth.4 As Professor Shu-Yi Oei's response highlights, even demonstrating that higher education is at a disadvantage relative to other investments would be a substantial contribution to the scholarly literature Demonstrating such a disadvantage would . . .

This dissertation examines how descriptive information inhibits or facilitates data sharing and reuse. DataONE serves as the test environment. The objective is to identify descriptive information made discoverable through DataONE and subsequently determine what of this descriptive information is helpful for scientists to determine data reusability. This study uses a mixed method approach, which includes a data profiling assessment in the form of a quantitative and qualitative content analysis and a quasi-experiment think-aloud. A quantitative and qualitative content analysis was conducted on a stratified sample of data extracted from DataONE to examine types of descriptive information made available through the shared data. Participants searched a quasi-experiment interface and thought-aloud about what information inhibited or facilitated them to determine data reusability. Additionally, participants completed a post result usefulness survey, post search rank order survey, and a post search factors survey. The quantitative and qualitative content analysis shows that the shared data contains 30 unique pieces of descriptive information found in the records. The . . .

Despite a large literature investigating how spouses earnings and division of labor relate to their risk of divorce, findings remain mixed and conclusions elusive. Core unresolved questions are (1) whether marital stability is primarily associated with the economic gains to marriage or with the gendered lens through which spouses earnings and employment are interpreted and (2) whether the determinants of marital stability have changed over time. Using data from the 1968 to 2013 waves of the Panel Study of Income Dynamics, I consider how spouses division of labor, their overall financial resources, and a wifes ability to support herself in the event of divorce are associated with the risk of divorce, and how these associations have changed between couples married before and after 1975. Financial considerationswives economic independence and total household incomeare not predictive of divorce in either cohort. Time use, however, is associated with divorce risk in both cohorts. For marriages formed after 1975, husbands lack of full-time employment is associated with higher risk of divorce, but neither wives full-time employment nor wives share of household labor is associated with divorce risk. Expectations of wives homemaking may have eroded, but the husband breadwinner norm persists.

The early years of the twentieth century were a formative time in the long history of struggle for black representation. More than any other medium, movies reflected the tremendous changes occurring in American society. Unfortunately, since they drew heavily on the nineteenth-century theatrical conventions of blackface minstrelsy and the "Uncle Tom Show" traditions, early pictures persisted in casting blacks in demeaning and outrageous caricatures that marginalized and burlesqued them and emphasized their comic or servile behavior. By contrast, race filmsthat is, movies that were black-cast, black-oriented, and viewed primarily by black audiences in segregated theatersattempted to counter the crude stereotyping and regressive representations by presenting more authentic racial portrayals. This volume examines race filmmaking from numerous perspectives. By reanimating a critical but neglected period of early cinemathe years between the turn-of-the-century and 1930, the end of the silent film erait provides a fascinating look at the efforts of early race film pioneers and offers a vibrant portrait of race and racial representation in American film and culture.

Computational social scientists often harness the Web as a "societal observatory" where data about human social behavior is collected. This data enables novel investigations of psychological, anthropological and sociological research questions. However, in the absence of demographic information, such as gender, many relevant research questions cannot be addressed. To tackle this problem, researchers often rely on automated methods to infer gender from name information provided on the web. However, little is known about the accuracy of existing gender-detection methods and how biased they are against certain sub-populations. In this paper, we address this question by systematically comparing several gender detection methods on a random sample of scientists for whom we know their full name, their gender and the country of their workplace. We further suggest a novel method that employs web-based image retrieval and gender recognition in facial images in order to augment name-based approaches. Our findings show that the performance of name-based gender detection approaches can be biased towards countries of origin and such biases can be reduced by combining name-based an image-based gender detection methods.

Since the literature on the effect of the unemployment rate as reflection of economic fluctuations on crime shows an empirically ambiguous effect, the purpose of this paper is to argue that a new way of modeling the dynamics of unemployment and crime by focussing on the transitory and persistent effect of unemployment on crime helps resolve this ambiguity.,Panel data for US states from 1965 to 2006 are examined using the Mundlak (1978) methodology to incorporate the dynamic interactions between crime and unemployment into the estimation.,After decomposing the unemployment effect on crime into a transitory and persistent effect, evidence of a strong positive correlation between unemployment and almost all types of crime rates is unearthed. This evidence is robust to endogeneity and the controlling for cross-panel correlation and indicators for state imprisonment.,The paper is the first to examine the dynamics of the interaction of crime and economic fluctuations using the temporary and persistent effects framework of Mundlak (1978). In one set of estimates, one can evaluation both the short- and long-run effects of changes of unemployment on crime.

Objective Whether using provider-attributed end-stage renal disease (ESRD) cause of systemic lupus erythematosus (SLE) in national surveillance data captures the entire population of patients with SLE and ESRD remains uncertain. Our goal was to examine attributed cause of ESRD in US surveillance data among patients with SLE who have developed ESRD. Methods Data from a national registry of treated ESRD (United States Renal Data System (USRDS)) were linked to the population-based Georgia Lupus Registry (GLR). The provider-attributed cause of ESRD was extracted from the USRDS for each validated patient with SLE in the GLR (diagnosed through 2004) who initiated treatment for ESRD through 2012. The percentage of these patients with SLE whose ESRD was subsequently attributed to SLE in the USRDS was calculated, overall and by patient characteristics. Results Among 251 patients with SLE who progressed to ESRD, 78.9% had SLE as their attributed cause of ESRD. Of the remaining 53 patients, 43.4%, 18.9% and 15.6% had ESRD attributed to hypertension, diabetes mellitus type II and non-SLE-related glomerulonephritis, respectively. Attribution of ESRD to SLE was higher among patients aged ≤30 (87.9–93.9%) vs >30 (52.6%; p<0.001) but did not differ by sex or race. Having Medicaid (86.2%) or no insurance (93.5%) was associated with greater attribution of ESRD to SLE than having private insurance (72.5%; p=0.02), as was having two or more providers state a diagnosis of SLE (89.0% vs 73.5% with a rheumatologist diagnosis alone; p=0.008). Conclusions These estimates indicate that USRDS-based studies may underreport ESRD among US patients with SLE. However, observed patterns of differential attribution of ESRD cause, particularly by age, suggest that providers may be correctly attributing ESRD to causes other than SLE among some patients with SLE.

Demographics determine the direction of your business. Demographic trends can be overwhelming, misleading, confusing, conflicting, and difficult to predict. Not anymore.

Prior research on parenthood effects has typically used single-sex models and estimated average effects. By contrast, we estimate population-level variability in partners' changes in housework hours, paid work hours, occupation traits, and wages after becoming parents, and we explore whether one partner's adjustment offsets or supplements the other's. We find tradeoffs between spouses on paid work adjustments to parenthood, but complementarity in adjustments to housework hours, occupation traits, and wages. The effect of parenthood on wives' behaviors is larger and more variable than on husbands' behaviors in every domain. The modest variation between husbands in work responses to parenthood explains little of the variation in the motherhood penalty, while variation in wives' own behaviors plays a larger role. We refer to this pattern as tethered autonomy: variation across American couples in work responses to parenthood is shaped primarily by variation in wives' adjustments, while husbands' work acts largely as a fixed point.

Demographic datasets are aggregated over areas to protect privacy. To study micro-scale demographic processes, those datasets have to be collected over temporally consistent small areas. However, the availability of such data is limited. That is, the demographic data is either aggregated over large geographical areas (i.e., counties), or collected over small population-derived census units that are temporally inconsistent (i.e., census tracts). Areal interpolation methods transfer the variable of interest from source zones to target zones. The methods can be used in temporal demographic applications to create temporally consistent population estimates over small areas by transferring population values from the areal units of one census year (i.e., source zones) to the units of another census year (i.e., target zones). In this research, spatial refinement is incorporated into areal interpolation methods to enhance their population interpolation accuracy. Moreover, one method called Enhanced Expectation Maximization (EEM) is introduced. Areal interpolation methods -- with and without spatial refinement are used to estimate total population values from census tracts in 1990 to census tract boundaries in 2010 in Mecklenburg County, North Carolina. Based on validation results, EEM is the most accurate method to create temporally consistent population estimates for the 1990-2010 period in the study area.

Rashda:The Birth and Growth of an Egyptian Oasis Village offers an detailed analysis and description on the history and life under the uncertainty of water supply of an Egyptian oasis village based on various kinds of data and information.

Just about everyone is familiar with the balancing act—location of home and location of work usually means commuting. Either the location of our home determines the work available near to us, or the place of work affects where we live. This article takes a close look at commuting within, to and from Indiana to see how Hoosiers balance their work and home locations.

Does the ethnoracial composition of local labor markets influence informal regulation of employment opportunities? To address this question, we link Census data on racial composition with survey data on unsolicited job leads in the 23 largest U.S. metro areas. The aim is twofold: (1) to operationalize three distinct conceptualizations of ethnoracial composition (general diversity, co-ethnic presence, and particularistic representation), and (2) to examine the influence of each at two distinct levels of local labor markets (the metropolis as a whole and occupational segments within each respective metropolis). Logistic regression results reveal that the odds of receiving unsolicited job leads do not vary by metro-level composition, but they do increase significantly with shares of white workers in local occupational segments. These results suggest that racial preference and privilege scale up to influence how employment opportunities are socially regulated in and across local occupational fields.

Background. To examine whether the US EPA (2010) lung cancer risk estimate derived from the high arsenic exposures (10-934 µg/L) in southwest Taiwan accurately predicts the US experience from low arsenic exposures (3-59 µg/L). Methods. Analyses have been limited to US counties solely dependent on underground sources for their drinking water supply with median arsenic levels of ≥3 µg/L. Results. Cancer risks (slopes) were found to be indistinguishable from zero for males and females. The addition of arsenic level did not significantly increase the explanatory power of the models. Stratified, or categorical, analysis yielded relative risks that hover about 1.00. The unit risk estimates were nonpositive and not significantly different from zero, and the maximum (95% UCL) unit risk estimates for lung cancer were lower than those in US EPA (2010). Conclusions. These data do not demonstrate an increased risk of lung cancer associated with median drinking water arsenic levels in the range of 3-59 µg/L. The upper-bound estimates of the risks are lower than the risks predicted from the SW Taiwan data and do not support those predictions. These results are consistent with a recent metaregression that indicated no increased lung cancer risk for arsenic exposures below 100-150 µg/L.

This thesis studies the implications of tax and transfer policy on income and employment, with emphasis on the low end of the income distribution. It also compares the labor market outcomes of recent veterans to those of veterans who served prior to 2001, when military utilization rates were much lower. The first chapter observes that many overlapping income support programs exist in the United States, each with the goal of transferring resources to low income individuals with minimal employment disincentives. Each of the programs considered addresses this tension in a different way, potentially creating differences in the degree to which labor supply adjusts in response to program changes. I separately and simultaneously estimate labor supply elasticities associated with the income support programs in the context of a discrete choice model. The differences in elasticities I document across programs can inform both policy and optimal taxation theory. In the second chapter I reassess whether the optimal income tax program has features akin to an Earned Income Tax Credit or a Negative Income Tax shape at the low end of the income distribution, in the presence of unemployment and wage responses to taxation. I derive a sufficient statistics optimal tax formula in a general model incorporating unemployment and endogenous wages. I then estimate the parameters using policy variation in tax liabilities stemming from the U.S. tax and transfer system. Using the empirical estimates, I implement the sufficient statistics formula and show that the optimal tax at the bottom has features that resemble those of a a Negative Income Tax relative to the case where unemployment and wage responses are not taken into account. In the third chapter, I compare labor market outcomes of veterans with post-2001 service time to those of similar veterans whose service did not extend past 2001. Veterans who served post-2001 are at a higher risk of long tours of duty, many of whom return with mental or physical disability. I find that veterans with post-2001 service are underemployed; conditional on employment however, veterans with post-2001 service earn at least as much, relative to veterans without post-2001 service.

Attending high school can alter student life trajectories by affecting labor market prospects and exposure to ideas and networks. However, schoolings influence competes with powerful early socialization forces, and may be confounded by well-established selection biases. Consequently, little is known about whether or how high school education shapes downstream political preferences and voting behavior. Using a difference-in-differences design exploiting variation in U.S. state dropout laws across cohorts, I find that raising the school dropout age substantially increases Republican partisan identification and voting later in life. Instrumental variables estimates, which reflect dropout laws principally impacting minorities, show that each completed grade of high school increases Republican support by around 10 percentage points. High schools effects operate by increasing income, which increases support for conservative economic policies and ultimately the Republican party. These effects influence future state legislature composition, suggesting that recent Democrat attempts to raise state dropout ages are strategically misguided.

The present study examines the relationship between neighborhood quality and parental monitoring of youth aged 10 to 18 (N = 1,630) from the Child Development Supplement of the Panel Study of Income Dynamics. Multiple measures of the neighborhood, including parents perceptions of quality, structure (i.e., poverty and affluence), and social organization (i.e., collective efficacy), are examined to gain a deeper understanding of how neighborhoods influence parenting. Parental monitoring is assessed through two separate measures: parents knowledge of their youths friends and whereabouts and having rules to regulate after-school activities. Bivariate models show that parents perceptions of neighborhood quality are differentially related to each aspect of parental monitoring, but these relationships are accounted for by child/family characteristics. Collective efficacy, however, remains positively tied to both aspects of parental monitoring. Social organization is also more strongly associated with parental monitoring than neighborhood structure. The policy implications of these findings for youth are discussed.

The unemployment rate, a leading indicator of the nation’s economic health, has fallen steadily in the wake of the Great Recession of 2007–09. However, other indicators of labor force strength paint a more complex picture of how workers are faring economically. In this article, we use 1971–2014 data from the Current Population Survey to examine temporal changes in involuntary part-time work—an increasingly common type of underemployment. Our analysis identifies several shifts in involuntary part-time work, including high rates of such work among Hispanic workers since the late 1980s. While this form of underemployment grew substantially among all racial/ ethnic groups during the Great Recession, it was especially prevalent among foreign-born Hispanics, in particular those without citizenship. Although our analyses of 2014 data suggest that educational attainment accounts for much of these racial/ethnic and nativity gaps, other factors—namely, job skill, industry of employment,and occupational composition—also help explain the observed differential rates of involuntary part-time work.

Silicon Valley, an area famous for technological innovation and affluence, ranks eighth in the nation in gross domestic product growth and third in per capita personal income.1 The area is also marked by severe disparities. Over one-third of workers earn low wages (defined as two-thirds of the county minimum wage).2 The average annual pay for a direct technology employee is five times the salary of a blue-collar contract industry worker, who earns less than the median rent in Santa Clara County.3 Black and Latino communities also face pronounced hiring barriers for higher paying, direct technology jobs.4 The immigrant community in Silicon Valley experiences low wages, a lack of access to affordable housing, limited access to health care, and a frequently discriminatory social and political environment.5 And for undocumented immigrant communities, these challenges are amplified. Eight percent of the 2.5 million residents in Silicon Valley are undocumented.6 Presently, 14% of the half a million young people (18-32) in Silicon Valley are undocumented. They are a core part of the daily economic life of the valley but encounter a singular set of economic and social challenges. The Deferred Action for Childhood Arrivals (DACA) program, which provided two-year work permits and relief from deportations for youth who entered the country before their sixteenth birthdays and before June 2007, reduced the challenges for some of these young people. But many, particularly those who are not DACA-eligible or may not have the financial and social capital to fully utilize its benefits, are still highly and persistently vulnerable.7 This report intends to understand the experience of undocumented immigrant youth (including DACA recipients) in the Silicon Valley, based on census data and interviews with undocumented youth. Additionally, the report suggests tangible recommendations by which the Silicon Valley can enhance its support of undocumented immigrant youth.