In this paper, we develop and make available measures of public ideology in 2010 for the 50 Ameri-can states, 435 congressional districts, and state legislative districts. We do this using the geospatial statistical technique of Bayesian kriging, which uses the locations of survey respondents, as well as population covariate values, to predict ideology for simulated citizens in districts across the country. In doing this, we improve on past research that uses the kriging technique for forecasting public opinion by incorporating Alaska and Hawaii, making the important distinction between ZIP codes and ZIP code tabulation areas, and introducing more precise data from the 2010 Census. We show that our estimates of ideology at the state, congressional district, and state legislative district levels appropriately predict the ideology of legislators elected from these districts, serving as an external validity check.

Productivity has again moved to center stage in two critical academic and policy debates: the slowing of global growth amid spectacular technological advances, and developing countries’ frustratingly slow progress in catching up to the technological frontier. Productivity Revisited brings together the new conceptual advances of 'second-wave' productivity analysis that have revolutionized the study of productivity, calling much previous analysis into question while providing a new set of tools for approaching these debates. The book extends this analysis and, using unique data sets from multiple developing countries, grounds it in the developing-country context. It calls for rebalancing away from an exclusive focus on misallocation toward a greater focus on upgrading firms and facilitating the emergence of productive new establishments. Such an approach requires a supportive environment and various types of human capital--managerial, technical, and actuarial--necessary to cultivate new transformational firms. The book is the second volume of the World Bank Productivity Project, which seeks to bring frontier thinking on the measurement and determinants of productivity . . .

A novel hypothesis posits that levels of genetic diversity in a population may partially explain variation in the development and success of countries. Our paper extends evidence on this question by subjecting the hypothesis to an alternative context that eliminates many competing hypotheses. We do this by aggregating representative individual-level data for high schools from a single US state (Wisconsin) in 1957, when the population was composed nearly entirely of individuals of European ancestry. Using this sample of high school aggregations, we too find a strong association between school-level genetic diversity and a range of student socioeconomic outcomes. Our use of survey data also allows for a greater exploration into the potential mechanisms of genetic diversity. In doing so, we find positive associations between genetic diversity and indexes for openness to experience and extraversion, two personality traits tied to creativity and divergent thinking.

This article develops a politico-economic model of native preferences over illegal immigrants. In a referendum like scenario, native agents who may be high or low-skilled and belong to three generations vote on whether to grant amnesty to illegal immigrants, or support no change in their immigration status. Individual choices are aggregated to form the collective policy response, using majority-rule. In doing so, the article shows that economic incentives are driving the political impasse on a policy on illegal immigrants. If there were a vote on illegal immigrants, all generations of high-skilled agents vote against amnesty on account of the increased tax burden which are determined by a Utilitarian government. Low-skilled workers prefer amnesty as it increases the transfers received by them. The gains from additional transfers more than enough compensate for the loss in wages for the low-skilled. Finally, the article shows that an increase in the consumption tax rate can generate welfare gains for a majority of agents in the amnesty steady state and thus break the policy impasse on illegal immigration. JEL Classication Code: D580, J18, J610

The tax legislation enacted in December 2017 repealed the tax penalty associated with the individual mandate—the Affordable Care Act (ACA) requirement that people who do not qualify for an exemption obtain health insurance coverage—thereby effectively repealing the mandate itself. 1 Repeal of the individual mandate will take effect in 2019, so understanding how the mandate has affected insurance coverage is important for predicting how insurance coverage and insurance markets, particularly the individual health insurance market, are likely to evolve in the coming years. How the individual mandate has affected insurance coverage is controversial. Most formal analyses, including those produced by the Congressional Budget Office (CBO), conclude that the individual mandate substantially increased insurance coverage and, correspondingly, that the mandate’s repeal will substantially reduce coverage (Blumberg et al. 2018; CBO 2017; CBO 2018). But some, including the Trump Administration and Congressional Republicans, have argued that the mandate has had . . .

I investigate the impact of the housing dynamics on local employment. This can occur through at least two channels – the price channel and the quantity channel. I use changes in house prices to capture the first channel, and changes in housing supply to account for the second. To evaluate the two effects, I build an econometric model using a panel of commuting zone level employment, housing price and building permit data from 1991 to 2015. I incorporate lagged housing variables in the model to allow that both housing effects could take time to play out fully, and the lagged dependent variable to account for partial adjustment in the labor market. To identify the causal effects, I use the housing supply elasticity and the national mortgage interest rate as instruments for the endogenous housing variables. I find evidence that 1) both price and quantity effects have significant impacts on employment changes; 2) the quantity effect is more persistent, widespread and economically significant than the price effect. Moreover, I exploit home refinancing data to show that the price effect primarily works through the collateral borrowing channel rather than the direct wealth channel. Finally, my case study indicates that the price and quantity effects account for 45% of the employment growth drop . . .

Donald J. Trump’s extreme nationalist and anti-immigrant platform won him the presidency and the support of the majority of white Americans. To investigate a potential reason for the high levels of support by whites, this research calculated the white index of isolation to explore the relationship between white residential seclusion and supporting Trump in North Carolina. The results show a strong positive correlation between high levels of white residential isolation and high percentages of a community voting for Trump. Or contrarily, communities with greater racial integration voted for Hillary Clinton at higher rates. One potential reason for this finding is the contact hypothesis: in heterogeneous precincts, inter-racial contact led to greater understanding and white Americans rejecting Trump’s polarizing platform. In homogenous communities, whites lacked this cross-cultural communication and supported Donald Trump. Based on these conclusions, I claim that the election of Trump can be viewed as a modern day manifestation of the governmental policies of the 20th centuries that promoted the isolation of white communities from minorities

Generating synthetic data is an attractive method for conducting private analysis of a sensitive dataset. It allows analysts to run their own non-private algorithms on the synthetic dataset without having to pre-specify the analyses they wish to perform. Further, both the dataset and any statistical results can be freely disseminated without incurring additional privacy loss. The goal of synthetic data generation is create data that will perform similarly to the original dataset for many analysis tasks. In this working paper, we propose using a Differentially Private Generative Adversarial Network (DP-GAN) to generate private synthetic data. DP-GANs are a variant of Generative Adversarial Networks that are trained privately. GANs were first proposed by Goodfellow et al. [4], and there has since been a tremendous amount of research employing GANs to generate synthetic data. DP-GANs have recently been used for privately generating clinical trial data [2] and image datasets [8, 9]. We build off of previous work on DP-GANs and add further optimizations to enhance performance on wide variety of data types and analysis tasks. We also propose empirical validation of our algorithm’s performance as future work.

Childhood poverty has long-lasting effects, but much of the literature on human capital formation suggests that different kinds of poverty have different effects. We classify children's poverty spells according to four distinct features-duration, intensity , timing, and concentration-which allows us to differentiate between childhood poverty profiles, to assign roles to each feature, and to examine interactions between them. We find that the duration of childhood poverty has a compounding nonlinear effect on adulthood rank, and that the other features matter much less. These findings suggest that policy interventions aimed at children who have experienced a short to moderate spell of poverty, and are at risk of experiencing more, will be most efficient.

The application of information technology to fi nance, or “fi ntech,” is expected to revolutionize many aspects of borrowing and lending in the future, but technology has been reshaping consumer and mortgage lending for many years. During the 1990s computerization allowed mortgage lenders to reduce loanprocessing times and largely replace human-based assessment of credit risk with default predictions generated by sophisticated empirical models. Debt-to-income ratios at origination add little to the predictive power of these models, so the new automated underwriting systems allowed higher debt-to-income ratios than previous underwriting guidelines would have typically accepted. In this way, technology brought about an exogenous change in lending standards, which helped raise the homeownership rate and encourage the conversion of rental properties to owner-occupied ones, but did not have large eff ects on housing prices. Technological innovation in mortgage underwriting may have allowed the 2000s housing boom to grow, however, because it enhanced the ability of both borrowers and lenders to act on optimistic beliefs about future house-price growth

Research in the social sciences is increasingly based on large and complex data collections, where individual data sets from different domains are linked and integrated to allow advanced analytics. A popular type of data used in such a context are historical censuses, as well as birth, death, and marriage certificates. Individually, such data sets however limit the types of studies that can be conducted. Specifically, it is impossible to track individuals, families, or households over time. Once such data sets are linked and family trees spanning several decades are available it is possible to, for example, investigate how education, health, mobility, employment, and social status influence each other and the lives of people over two or even more generations. A major challenge is however the accurate linkage of historical data sets which is due to data quality and commonly also the lack of ground truth data being available. Unsupervised techniques need to be employed, which can be based on similarity graphs generated by comparing individual records. In this paper we present initial results from clustering birth records from Scotland where we aim to identify all births of the same mother and group siblings into clusters. We extend an existing clustering technique for record linkage by incorporating temporal constraints that must hold between births by the same mother, and propose a novel greedy temporal clustering technique. Experimental results show improvements over non-temporary approaches, however further work is needed to obtain links of high quality.

This study uses logit regressions on a pooled sample of children from the 2012, 2013, and 2014 American Community Survey to perform a nationally representative analysis of school progress for a large sample of 4,430 children who reside with same-sex couples. Odds ratios from regressions that compare children between different-sex married couples and same-sex couples fail to show significant differences in normal school progress between households across a variety of sample compositions. Likewise, marginal effects from regressions that compare children with similar family dynamics between different-sex married couples and same-sex couples fail to predict significantly higher probabilities of grade retention for children of same-sex couples. Significantly lower grade retention rates are sometimes predicted for children of same-sex couples than for different-sex married couples, but these differences are sensitive to sample exclusions and do not indicate causal benefits to same-sex parenting.

The Evolving Role of ICT Since the ICT-related surge in the 1990s and early 2000s there has been a major, persistent slowdown in productivity growth across a range of economies. The most likely explanation for this is that the slowdown is part of a cyclical process underpinned by technology, in particular ‘waves’ that relate to the development of General Purpose Technologies (GPTs). The other major explanation of the slowdown relates to mismeasurement, specifically the idea that a large fraction of the consumer surplus generated by internet-related goods and services is not being captured effectively in the national accounts. However, rigorous estimates of internet-related consumer surplus struggle to account for the gap implied by the productivity slowdown. In this report we put forward some original research on: (1) the role of ICT-related research in generating ideas via knowledge spillovers, (2) a study of the history and economic consequences of UK broadband since 2000, and (3) an analysis of recent US employment data in relation to automation. As we outline below, the two main policy implications of our research are that (1) the high knowledge spillovers that occur with respect to ICT-related innovations suggests that there is a window for the government to produce social returns through support for R&D in this area, and (2) the automation challenge will need to be met by a comprehensive skills policy and that the tax treatment of skills investment (for example, via a Skills . . .

We study the effect of the minimum wage on the employment outcomes and Social Security claiming of older US workers from 1983 to 2016. The probability of work at or near the minimum wage increases substantially near retirement, and previous researchers and policies suggest that older workers may be particularly vulnerable to any disemployment effects of the minimum wage. We find no evidence that the minimum wage causes earlier retirements. Instead, our estimates suggest that higher minimum wages increase earnings and may have small positive effects on the labor supply of workers in the key ages of 62 to 70. Consistent with increased earnings and delayed retirement, higher minimum wages decrease the number of Social Security beneficiaries and amount of benefits disbursed. The minimum wage appears to increase financial resources for workers near retirement.

At the request of the President’s Office at the University of Utah and in support of the Alliance for the American Dream, the Kem C. Gardner Policy Institute reviewed leading definitions of the middle class, with an emphasis on income-based measures. We estimated middle class income levels and populations for Utah and the U.S. for selected years since 1980. We expect these definitions and results will support efforts to create pathways to Utah’s middle class and improve stability for people already there. “Middle class” is a malleable, multifaceted identity tied to opportunity, quality of life, and financial stability. There is no standard definition for the U.S. or Utah. Researchers measure this fluid concept with criteria such as income, wealth, occupation, educational attainment, and self-perception.

This paper explores whether occupations involving routine tasks are more susceptible to replacement by automation. Using a recent dataset on industrial robots and occupational task profiles, I examine how increases in robot intensities within an industry affect workers in routine occupations. I find that routine production workers, along with routine workers with low and medium education experience significantly decreased wages and employment with an increase in robot intensity. For the most routine occupations, a unit increase in robots per thousand workers results in over 0.2 percentage points decline in real hourly wage. Surprisingly, I find no employment or wage gains in workers with high education. The results suggest that robot automation impact may be localized around certain occupational and educational groups, with other workers insulated from robot impact.

The Age of Mass Migration came to an end in the interwar period with new American immigration restrictions, but did this end affect some potential migrants more than others? I use previously unanalysed data from passenger lists of ships leaving Bremen, one of the major European ports of emigration, between 1920 and 1933, to identify occupations and skill levels of individual migrants. The main focus of the paper is on the role that policy played in influencing the selection of migrants. I study the American quota laws of 1921, 1924, and 1929, and find that increasingly strict quotas led to an increase in the skill level of migrants as well as a shift from agricultural to manufacturing workers first, and from manufacturing to professional workers later.

This article analyzes education demand, competencies and credentials, earnings, and demographic distribution of the nursing workforce in the United States. It finds that nursing remains an oasis of opportunity for women, paying wages above the national average and offering well-developed career pathways for those who acquire the credentials necessary to practice and advance in the nursing career. Despite the fairly competitive wages in nursing compared to other occupations for workers with a baccalaureate degree, nursing remains highly segregated by sex, largely for social reasons. Furthermore, notwithstanding great progress in equity and racial and ethnic inclusion in the nursing professions, LPNs/LVNs (licensed practical and licensed vocational nurses) are still disproportionately minority women, while RNs (registered nurses) and APRNs (advance practice registered nurses) are disproportionately white women. Transitioning from the LVN/LPN to RN also proves to be much more difficult in practice than those starting off with the BSN degree, as evidence by length of time to graduation. Continuous up-skilling to the BSN (bachelor of science in nursing) as the new entry-level credential in the field, presents additional challenges, as low-income nurses and those from racial and ethnic minority groups face additional new educational barriers to upward economic mobility.

Michicare is a plan for Medicare for All for the state of Michigan, providing publicly financed health insurance to all Michigan residents under age 65. Michicare would cover a comprehensive set of benefits based on the essential health benefits outlined by the Affordable Care Act, and every Michigander would choose a primary care provider to help direct their care. Michicare would eliminate co-pays and deductibles for medically necessary services, so you would not pay out-of-pocket fees when you are seeking needed care. Michicare would decrease overall healthcare costs in Michigan by moving to a streamlined system with lower administrative costs and fairer prices. Instead of deductibles, out-of-pocket costs, and premiums paid to private insurers, Michicare would be publicly financed through a combination of a payroll tax and a business tax. Private insurance companies would still be permitted to offer supplemental health insurance, but Michicare would provide comprehensive benefits to all Michiganders, diminishing the need for private coverage. Michicare is an ambitious and realistic plan to secure truly universal healthcare in Michigan.

In the United States, immigration based on family ties has long been the main criterion for admitting new immigrants. Under the provisions of current immigration law, the family-based immigration category allows U.S. citizens and lawful permanent residents, or “green card” holders, to bring certain family members to the United States. Much has been debated about family-based immigration and the critical role families play in the adaptation, integration, and wellbeing of newcomers. But some confusion exists regarding the economic aspects of family-based immigration. To help unpack those aspects, this report focuses on one of them— namely, the earnings of family-based immigrants. A defining feature of immigrants coming to the United States via the family-based system is their upward economic mobility. Since 1965, when family-based immigration became the dominant means of migrating to the United States, the earnings of immigrants in general have increased dramatically during their first decade in the country. This trend is completely missed when economists focus only on the initial earnings of immigrants upon their entry into the U.S. labor market.