Oregon has a refundable earned income tax credit (OEIC) that is equal to 8 percent of the Federal Earned Income Tax Credit (EITC). In 2017, Oregon introduced a unique supplement to the OEIC that provided an additional 3% of the Federal EITC to families with children under age 4. To date, there has been no research examining the impact of the OEIC on child poverty. Using data from the Current Population Survey, we simulate the static effects of this unique state OEIC on overall poverty, child poverty, and early child poverty rates in Oregon. We find that the OEIC does not yield a change in the estimated headcount poverty rate for either children or young children. However, focusing exclusively on changes in poverty rates underestimates the impact of the OEIC. The estimated impact on the poverty gap and poverty severity is greater – about 2 to 4 percent. Children and young children in families closer to the poverty threshold experienced reductions in the poverty gap and poverty severity by about 7 to 8 percent. We tested three policy simulations and found that a simulated OEIC set at 11% of EITC for children and 29% for young children would significantly decrease the child and young child poverty rates by 4 percent and 6 percent, respectively. To reduce more poverty via the OEIC would require substantially more resources which may not be feasible.

There has been a persistent gap in the poverty rate between urban and rural areas of the United States. Much of this gap has be attributed to industrial composition, however employment composition also likely plays a key role. Underemployment and labor force non-participation have been become significant issues in rural areas. This study uses data from the Current Population Survey for 1970 to 2018 to understand how poverty rates among 6 employment groups —(1) not in the labor force, (2) discouraged workers, (3) unemployed workers, (4) low hours workers, (5) low income workers, and (6) adequately employed workers— can explain the persistent gap in poverty between urban and rural areas. Demographic standardization and decomposition techniques reveal that majority of the poverty gap is explained by differences in poverty rates for the employment groups. Rural individuals in all employment group have higher poverty rates than urban individuals in the same group. Analysis also shows that if rural America had either the employment structure or the employment specific poverty rates of urban America than poverty rates would be much lower in rural areas.

The decennial census is foundational to our democratic society. Census population counts guide appropriations and federal funding allocations, congressional redistricting, state and local budgets, and data-driven business and research decisions. Yet despite its importance, the 2020 Census faces unprecedented threats to its accuracy. Since 2010, decennial census preparations have faced (1) underfunding leading to scaled-back testing and outreach operations, (2) innovations that promise efficient implementation but remain undertested and underdeveloped, and (3) the last-minute introduction of a citizenship question. For this reason and others, the US Government Accountability Office (GAO) has added the 2020 Census to its “high risk” list of government activities in jeopardy in the coming years

Purpose – The purpose of this paper is to expand the empirical literature on state tax and expenditure policies and entrepreneurial activity in several meaningful ways. Design/methodology/approach – The authors update the panel data to include several more recent years and also consider other elements of state policy. Findings – The most important takeaway is that even after dealing with some of the known shortcomings of dynamic panel analysis, we are still not able to find economically meaningful impacts of state tax and expenditure policies (generally defined) on entrepreneurial performance. Research limitations/implications – Earlier studies that have found statistical significance have generally been limited to extensive-margin impacts on such things as self-employment rates or counts of new or small firms. When the authors examine what policy makers actually care about – things like income and employment among entrepreneurial ventures – the authors do not find much in the way of useful policy impacts. Practical implications – To be sure, the authors find entrepreneurial performance to be statistically significantly related to certain tax rates and expenditure amounts, but the magnitudes of our estimated results cast serious doubts on the usefulness of these particular policy levers for generating meaningful improvements in entrepreneurial success. Originality/value – The authors’ primary contribution is to improve the empirical consideration of the time series properties of the data. The authors provide a battery of more general and robust analyses to more completely surround the question.

For many years, the nation has been on a positive trajectory reducing the number and rate of uninsured children. Having health insurance is important for children as they are more likely to receive needed services, have better educational outcomes, and their family is protected from the financial risks associated with being uninsured—even for a short period of time. Recently released data show that this progress is now in jeopardy. For the second year in a row, the uninsured rate and number of uninsured children moved in the wrong direction.1 This is unprecedented since comparable data began to be collected in 2008. The number of uninsured children now exceeds 4 million— wiping out a sizable share of the gains in coverage made following the implementation of the Affordable Care Act (ACA) in 2014 (see Figure 1). Coverage improvements for children began many years before the ACA was enacted through expansions of Medicaid and the creation of the Children’s Health Insurance Program (CHIP). The ACA primarily improved children’s coverage rates by increasing the likelihood that eligible children would be enrolled in Medicaid/CHIP when their parents obtained coverage, simplifying eligibility and enrollment procedures, funding new outreach and enrollment efforts, and establishing the individual mandate. Some children benefited from newly available subsidized coverage in the ACA Marketplaces as well...

Natural disasters are projected to increase in number and severity, in part due to climate change. At the same time a growing number of disaster risk reduction (DRR) and climate change adaptation measures are being implemented by governmental and non-governmental organizations, and substantial post-disaster donations are frequently pledged. At the same time there has been increasing demand for transparency and accountability, and thus evidence of those measures having a positive effect. We hypothesized that resilience-enhancing interventions should result in less damage during a hazard event, or at least quicker recovery. In this study we assessed recovery over a 3 year period of seven municipalities in the central Philippines devastated by Typhoon Haiyan in 2013. We used very high resolution optical images (<1 m), and created detailed land cover and land use maps for four epochs before and after the event, using a machine learning approach with extreme gradient boosting. The spatially and temporally highly variable recovery maps were then statistically related to detailed questionnaire data acquired by DEval in 2012 and 2016, whose principal aim was to assess the impact of a 10 year land-planning intervention program by the German agency for technical cooperation (GIZ). The survey data allowed very detailed insights into DRR-related perspectives, motivations and drivers of the affected population. To some extent they also helped to overcome the principal limitation of remote sensing, which can effectively describe but not explain the reasons for differential recovery. However, while a number of causal links between intervention parameters and reconstruction was found, the common notion that a resilient community should recover better and more quickly could not be confirmed. The study also revealed a number of methodological limitations, such as the high cost for commercial image data not matching the spatially extensive but also detailed scale of field evaluations, the remote sensing analysis likely overestimating damage and thus providing incorrect recovery metrics, and image data catalogues especially for more remote communities often being incomplete. Nevertheless, the study provides a valuable proof of concept for the synergies resulting from an integration of socio-economic survey data and remote sensing imagery for recovery assessment.

As individuals specialize in specific knowledge areas, a society’s know-how becomes distributed across different workers. To use this distributed know-how, workers must be coordinated into teams that, collectively, can cover a wide range of expertise. This paper studies the interdependencies among co-workers that result from this process in a population-wide dataset covering educational specializations of millions of workers and their co-workers in Sweden over a 10-year period. The analysis shows that the value of what a person knows depends on whom that person works with. Whereas having co-workers with qualifications similar to one’s own is costly, having co-workers with complementary qualifications is beneficial. This co-worker complementarity increases over a worker’s career and offers a unifying framework to explain seemingly disparate observations, answering questions such as “Why do returns to education differ so widely?” “Why do workers earn higher wages in large establishments?” “Why are wages so high in large cities?”

Introduction Immigrants in the United States are less likely to smoke than those born in the U.S, but studies have not fully described the diversity of their smoking patterns. We investigate smoking by world region of birth and duration of residence in the United States, with a comprehensive approach covering current prevalence levels, education gradients, and male-to-female ratios. Methods The data originate from the National Health Interview Surveys, 2000-2015, and the sample of 365,404 includes both U.S.-born and foreign-born respondents ages 25-70. World region of birth and duration of residence in the United States measure immigrant characteristics. Current cigarette . . .

Equal Pay Day in 2019 was on Tuesday, April 2nd, representing the extra three months the average woman has to work into the new year just to make what the average white man earned at the end of 2018. To celebrate, we decided to investigate the gender pay gap. The pay gap persists, despite more women earning college degrees and entering the workforce at higher pay rates, because of several factors, one of the largest being sex segregation, which accounts for 23% of the pay gap. In 2018, the percentages of women working in the 10 lowest paid occupations in America were higher on average than the percentages of women working in the 10 highest paid occupations. Additionally, the gender pay gap is not the same for all women. It is worse for some minorities, especially black and Hispanic women.

Science, Technology, Engineering, and Math (STEM) jobs are a key contributor to economic growth and national competitiveness. Yet STEM workers are perceived to be in short supply. This paper shows that the “STEM shortage” phenomenon is explained by technological change, which introduces new job skills and makes old ones obsolete. We find that the initially high economic return to applied STEM degrees declines by more than 50 percent in the first decade of working life. This coincides with a rapid exit of college graduates from STEM occupations. Using detailed job vacancy data, we show that STEM jobs change especially quickly over time, leading to flatter age-earnings profiles as the skills of older cohorts became obsolete. Our findings highlight the importance of technology-specific skills in explaining life-cycle returns to education, and show that STEM jobs are the leading edge of technology diffusion in the labor market.

Evidence suggests that fathers have stronger ties to sons than daughters, which may result in differential investments in their children. This paper investigated whether girls’ gender restricts their access to fathers’ contributions if they do not live together. The data used were the 1994–2008 March/April Match Current Population Survey Child Support Supplements, a large, nationally representative sample which identifies child support eligible mothers of all marital statuses and collects information on nonresident fathers’ financial and social investments in their children. Results for court-mediated outcomes such as the existence and amounts of child support orders showed that courts do not allocate child support differentially by child gender. Small but suggestive effects of child gender were found on fathers’ post-dissolution investments, but these effects disadvantaged boys rather than girls.

We investigate heterogeneous effects of new digital technologies on the individual-level employment- and wage dynamics in the U.S. labor market in the period from 2011- 2018. We employ three measures that reflect different aspects of impacts of new digital technologies on occupations. The first measure, as developed by Frey and Osborne (2017), assesses the computerization risk of occupations, the second measure, developed by Felten et al. (2018), provides an estimate of recent advances in artificial intelligence (AI), and the third measure assesses the suitability of occupations for machine learning (Brynjolfsson et al., 2018), which is a subfield of AI. Our empirical analysis is based on large representative panel data, the matched monthly Current Population Survey (CPS) and its Annual Social and Economic Supplement (ASEC). The results suggest that the effects of new digital technologies on employment stability and wage growth are already observable at the individual level. High computerization risk is associated with a high likelihood of switching one’s occupation or becoming non-employed, as well as a decrease in wage growth. However, advances in AI are likely to improve an individual’s job stability and wage growth. We further document that the effects are heterogeneous. In particular, individuals with high levels of formal education and older workers are most affected by new digital technologies.

This study documents an important role for Social Security income in workers' retirement timing. About 40% of public school teachers are not covered by Social Security. This provides an opportunity to analyze the causal impact of Social Security on retirement timing by comparing covered and non-covered teachers. Using individual-level data from the American Community Survey, we find robust evidence of higher rates of retirement among covered teachers at Social Security eligibility ages. This pattern is confirmed using an alternative regression model of participation in the teacher labor force. These estimates suggest that, should the federal government mandate full inclusion in Social Security for all public sector workers, the retirement timing patterns of newly covered teachers and other public sector workers would likely change.

Many data owners are required to release the data in a variety of real world application, since it is of vital importance to discovery valuable information stay behind the data. However, existing re-identification attacks on the AOL and ADULTS datasets have shown that publish such data directly may cause tremendous threads to the individual privacy. Thus, it is urgent to resolve all kinds of re-identification risks by recommending effective de-identification policies to guarantee both privacy and utility of the data. De-identification policies is one of the models that can be used to achieve such requirements, however, the number of de-identification policies is exponentially large due to the broad domain of quasi-identifier attributes. To better control the trade off between data utility and data privacy, skyline computation can be used to select such policies, but it is yet challenging for efficient skyline processing over large number of policies. In this paper, we propose one parallel algorithm called SKY-FILTER-MR, which is based on MapReduce to overcome this challenge by computing skylines over large scale de-identification policies that is represented by bit-strings. To further improve the performance, a novel approximate skyline computation scheme was proposed to prune unqualified policies using the approximately domination relationship. With approximate skyline, the power of filtering in the policy space generation stage was greatly strengthened to effectively decrease the cost of skyline computation over alternative policies. Extensive experiments over both real life and synthetic datasets demonstrate that our proposed SKY-FILTER-MR algorithm substantially outperforms the baseline approach by up to four times faster in the optimal case, which indicates good scalability over large policy sets.

The oversaturation of alcohol outlets can have disastrous public health consequences. The goal of this study was to evaluate the potential impact of new zoning legislation, TransForm Baltimore on locations of alcohol outlets. More specifically, the study sought to determine the effect of the new zoning code on the potential redistribution of alcohol outlets and also provide empirical support for the need to actively monitor redistribution of outlets to avoid further inequitable oversaturation in disadvantaged neighborhoods. Data on off-premise alcohol outlets (e.g., packaged goods stores) were obtained from the Board of Liquor License Commissioners for Baltimore City. The alcohol outlets were geocoded and assigned to zoning parcels. Churches and schools were also geocoded. The alcohol outlets were also assigned to census tracts to calculate socioeconomic statuses. One hundred seventy-two of the 263 off-premise packaged goods stores (PGS) were in violation of the new zoning law. TransForm will reduce the land parcels available to alcohol outlets by 27.2%. Areas containing non-conforming PGS were more likely to have a higher percentage of Black residents, single parent-families, unemployment, household poverty, and vacancy compared to Baltimore City averages and areas without non-conforming PGS. Planning enforcement efforts need to accompany related laws to prevent/reduce overconcentration of PGS in disadvantaged neighborhoods.

Background There is conflicting evidence regarding whether men and women are equally likely to quit smoking. We assessed whether gender differences in smoking cessation varied between different sociodemographic groups and across e-cigarette use. Methods The 2014–15 cross-section of the Current Population Survey Tobacco Use Supplement was weighted to represent the US adult population of current/former smokers (N = 16 040). Log binomial models tested whether gender modified the relationships between race/ethnicity, education, income or e-cigarette use and 90-day smoking cessation in the past year. Results Gender was not associated with cessation in adjusted models (RR = 0.97, CI: 0.85, 1.11). There were no statistically significant interactions between gender and sociodemographic covariates. Current e-cigarette use was associated with higher cessation (RR = 1.53, CI: 1.30, 1.81), and the association varied by gender (Interaction P = 0.013). While male e-cigarette users had a 15% predicted cessation in the past year (CI: 12, 18%), female users had a 9% predicted cessation (95% CI: 7, 11%). Probability of cessation for female e-cigarette users was not different from non-users. Conclusions These findings suggest that there are no gender differences in smoking cessation in the USA overall, or by sociodemographic groups. Current e-cigarette use is associated with higher likelihood of recent successful smoking cessation, particularly for men.

Despite interventions intended to reduce impacts of coastal hazards, the risk of damage along the US Atlantic coast continues to rise. This reflects a long-standing paradox in disaster science: even as physical and social insights into disaster events improve, the economic costs of disasters keep growing. Risk can be expressed as a function of three components: hazard, exposure, and vulnerability. Risk may be driven up by coastal hazards intensifying with climate change, or by increased exposure of people and infrastructure in hazard zones. But risk may also increase because of interactions, or feedbacks, between hazard, exposure, and vulnerability. Using empirical records of shoreline change, valuation of owner-occupied housing, and beach-nourishment projects to represent hazard, exposure, and vulnerability, here we present a data-driven model that describes trajectories of risk at the county scale along the US Atlantic coast over the past 5 decades. We also investigate quantitative relationships between risk components that help explain these trajectories. We find higher property exposure in counties where hazard from shoreline change has appeared to reverse from high historical rates of shoreline erosion to low rates in recent decades. Moreover, exposure has increased more in counties that have practised beach nourishment intensively. The spatio-temporal relationships that we show between exposure and hazard, and between exposure and vulnerability, indicate a feedback between coastal development and beach nourishment that exemplifies the “safe development paradox”, in which hazard protections encourage further development in places prone to hazard impacts. Our findings suggest that spatially explicit modelling efforts to predict future coastal risk need to address feedbacks between hazard, exposure, and vulnerability to capture emergent patterns of risk in space and time.

Objective: To examine reported racial discrimination and harassment against Native Americans, which broadly contribute to poor health outcomes. Data Source and Study Design: Data come from a nationally representative, probability-based telephone survey including 342 Native American and 902 white US adults, conducted January-April 2017. Methods: We calculated the percent of Native Americans reporting discrimination in several domains, including health care. We used logistic regression to compare the Native American-white difference in odds of discrimination and conducted exploratory analyses among Native Americans only to examine variation by socioeconomic and geographic/neighborhood characteristics. Principal Findings: More than one in five Native Americans (23 percent) reported experiencing discrimination in clinical encounters, while 15 percent avoided seeking health care for themselves or family members due to anticipated discrimination. A notable share of Native Americans also reported they or family members have experienced violence (38 percent) or have been threatened or harassed (34 percent). In adjusted models, Native Americans had higher odds than whites of reporting discrimination across several domains, including health care and interactions with the police/ courts. In exploratory analyses, the association between geographic/neighborhood characteristics and discrimination among Native Americans was mixed. Conclusions: Discrimination and harassment are widely reported by Native Americans across multiple domains of their lives, regardless of geographic or neighborhood context. Native Americans report major disparities compared to whites in fair treatment by institutions, particularly with health care and police/courts. Results suggest modern forms of discrimination and harassment against Native Americans are systemic and untreated problems.

If someone lives in an economically depressed place, they were probably born there. The presence of people with local ties – a preference to live in their birthplace – leads to smaller migration responses. Smaller migration responses to wage declines lead to lower real incomes and make real incomes more sensitive to subsequent demand shocks, a form of hysteresis. Local ties can persist for generations. Place-based policies, like tax subsidies, targeting depressed places cause smaller distortions since few people want to move to depressed places. Place-based policies targeting productive places increase aggregate productivity, since they lead to more migration.

Low-income residents in Detroit face a housing crisis. This crisis manifests in the cost of rent (Seymour and Akers, 2019), deteriorating structures (Dewar et al., 2015), speculative ownership practices (Akers and Seymour, 2018), and toxic housing (City of Detroit Health Department Task Force on Demolitions and Health, 2017). For those living in or near poverty these conditions are consistent throughout the city. Despite programs to maintain affordable housing and assist people facing foreclosure and eviction, the underlying conditions within these houses remain a threat to the most vulnerable populations in Detroit, particularly children. The age and decline in the city’s housing stock leaves tenants with few options but to choose between toxic structures. The rise in bulk ownership and speculation in the city following the financial crisis makes it more likely these structures are under-maintained if maintained at all.