Anonymized data publication has received considerable attention from the research community in recent years. For numerical sensitive attributes, most of the existing privacy-preserving data publishing techniques concentrate on microdata with multiple categorical sensitive attributes or only one numerical sensitive attribute. However, many real-world applications can contain multiple numerical sensitive attributes. Directly applying the existing privacy-preserving techniques for single-numerical-sensitive-attribute and multiple-categorical-sensitiveattributes often causes unexpected disclosure of private information. These techniques are particularly prone to the proximity breach, which is a privacy threat specific to numerical sensitive attributes in data publication. In this paper, we propose a privacy-preserving data publishing method, namely MNSACM, which uses the ideas of clustering and Multi-Sensitive Bucketization (MSB) to publish microdata with multiple numerical sensitive attributes. We use an example to show the effectiveness of this method in privacy protection when using multiple numerical sensitive attributes.

Recent attention has focused on whether the decisions of parents play an important role in the persistence of racial and economic segregation. This analysis compares PSID families from 1968 through 2009 at different stages of child-rearing as they were exposed to local, mandated school desegregation plans. Families were linked by Census tract to 1970 school district boundaries, which were then merged to desegregation court case data. Preliminary analyses demonstrate that mobile white families with children avoided destination districts in desegregating districts to a higher degree than young couples without children, and to a much higher degree than black families. This highlights the unique contribution of parental behavior in shaping the segregated residential landscape.

In 1980, housing prices in the main US cities rose with distance to the city center. By 2010, that relationship had reversed. We propose that this development can be traced to greater labor supply of high-income households through reduced tolerance for commuting. In a tract-level data set covering the 27 largest US cities, years 1980-2010, we employ a city-level Bartik demand shifter for skilled labor and find support for our hypothesis: full-time skilled workers favor proximity to the city center and their increased presence can account for the observed price changes, notably the rising price premium commanded by centrality.

Praised for their ability to overcome transaction costs and reduce externalities of water distribution, irrigation districts formed rapidly throughout the United States in the 20th century. To better understand and quantify the gains, I compare and contrast the smaller acequia organization with the large centralized irrigation districts in New Mexico. Utilizing the Social-Ecological System framework, I highlight the distinction between irrigation districts and acequias. Next, I conduct a hedonic difference-indifference analysis comparing counties that formed irrigation districts to those that continue irrigating under decentralized acequias from 1910 to 1978. I find the central districts increase agriculture land values by nearly 12 percent.

Health and health care are markedly impacted by demographic factors including rates of population growth, aging and racial/ethnic diversification. The number of incidences of disease/disorders will increase by 48.6%, although the population is projected to increase by only 36.1% from 2010 to 2060. The nonHispanic White population is projected to show a 3.7% increase in the number of health incidences from 2010 to 2060, nonHispanic Black population increasing by 77.3%, the Hispanic population by 239.3 and nonHispanic Asians and Others by 193.3%. Projections indicate future shortages in the number of health care personnel with the number of personnel increasing by 7.4% while disease incidences increase by 48.6%. From 2010 to 2060 physician contacts are projected to increase by 54.1%, hospital days by 76.0%, physician costs by 56.8%, and hospital costs by 55.1% (in 2010 constant dollars). The reduced financial resources projected for the population and the aging of the population will increase Medicaid enrollment by 58.5% and Medicare enrollment by 102.7%. Population aging will increase Medicare costs to one trillion dollars per year, persons in nursing homes to 3.8 million and nursing home costs to $12.1 billion per month by 2060. Clearly understanding the characteristics of the population of the United States is essential to understanding its future health care requirements and costs.

This paper reassesses whether the optimal income tax program features an Earned Income Tax Credit (EITC) or a Negative Income Tax (NIT) at the bottom of the income distribution, in the presence of unemployment and wage responses to taxation. The paper makes two key contributions. First, it derives a sufficient statistics optimal tax formula in a general model that incorporates unemployment and endogenous wages. This formula nests a broad variety of structures of the labor market, such as competitive models with fixed or flexible wages and models with matching frictions. Our results show that the sufficient statistics to be estimated are: the macro employment response with respect to taxation and the micro and macro participation responses with respect to taxation. We show that an EITC-like policy is optimal provided that the welfare weight on the working poor is larger than the ratio of the micro participation elasticity to the macro participation elasticity. The second contribution is to estimate the sufficient statistics that are inputs to the optimal tax formula using a standard quasi-experimental research design. We estimate these reduced-form parameters using policy variation in tax liabilities stemming from the U.S. tax and transfer system for over 20 years. Using our empirical estimates, we implement our sufficient statistics formula and show that the optimal tax at the bottom more closely resembles an NIT relative to the case where unemployment and wage responses are not taken into account.

Roughly 40% of the nearly 3 million registered nurses (RNs) in the United States have an associate's degree (ADN) as their highest level of nursing education. Yet even before the recent Institute of Medicine report on The Future of Nursing, employers of RNs have increasingly preferred baccalaureate-prepared RNs (BSNs), at least anecdotally. Data from the American Community Survey (20032013) were analyzed with respect to employment setting, earnings, and employment outcomes of ADN and BSN-prepared RNs. The data reveal a divergence in employment setting: the percentage of ADN-prepared RNs employed in hospitals dropped from 65% to 60% while the percentage of BSN-prepared RNs employed in hospitals grew from 67% to 72% over this period. Many ADNs who would have otherwise been employed in hospitals seem to have shifted to long-term care settings.

Race-specific given names have been linked to a range of negative outcomes in contemporary studies, but little is known about their long term consequences. Building on recent research which documents the existence of a national naming pattern for African American males in the late nineteenth and early twentieth centuries (Cook, Logan and Parman 2014), we analyze long-term consequences of distinctively racialized names. Using over three million death certificates from Alabama, Illinois, Missouri and North Carolina from 1802 to 1970, we find a robust within-race mortality difference for African American men who had distinctively black names. Having an African American name added more than one year of life relative to other African American males. The result is robust to controlling for the age pattern of mortality over time and environmental factors which could drive the mortality relationship. The result is not consistently present for infant and child mortality, however. As much as 10% of the historical between-race mortality gap would have been closed if every black man were given a black name. Suggestive evidence implies that cultural factors not captured by socioeconomic or human capital measures may be related to the mortality differential.

This article estimates the effect of crime on migration rates for counties in U.S. metropolitan areas and makes three contributions to the literature. First, I use administrative data on migration flows between counties, which gives me more precise estimates of population changes than data used in previous studies. Second, I am able to decompose net population changes into gross migration flows in order to identify how individuals respond to crime rate changes. Finally, I include county-level trends so that my identification comes from shocks away from the trend. I find effects that are one-fiftieth the size of the most prominent estimate in the literature; and although the long-run effects are somewhat larger, they are still only approximately one-twentieth as large. I also find that responses to crime rates differ by subgroups, and that increases in crime cause white households to leave the county, with effects almost 10 times as large as for black households.

Patients are increasingly using the Internet and other technologies to engage in their own healthcare, but little research has focused on the determinants of consumer eHealth behaviors related to Internet use. This study uses data from 115,089 respondents to four years of the National Health Interview Series to identify the associations between one consumer eHealth behavior (information seeking) and demographics, health measures, and Personal Health Information Management (PHIM) (messaging, scheduling, refills, and chat). Individuals who use PHIM are 7.5 times more likely to search the internet for health related information. Just as health has social determinants, the results of this study indicate there are potential social determinants of consumer eHealth behaviors including personal demographics, health status, and healthcare access.

Roughly one in four children in the United States today is Hispanic. As such, Hispanics represent the largest and fastest-growing racial/ethnic minority group among the nations children. It is also a group that is disproportionately poor:Approximately one-third of Hispanic children live in poverty and two-thirds live in low-income households. Given the size and growth of the Hispanic child population, as well as Hispanic childrens high poverty rates, many policies and programs are attempting to better reach Hispanic children in need. A key to realizing these efforts is understanding the nature of the households in which low-income Hispanic children live. A large body of research finds that children who grow up in stable, low-conflict, two-parent households generally fare better than children in other types of households. Low-income children are less likely than are other children to live in stable households or with their biological father. However, focusing on the immediate family structure of children (i.e., the relationship status of the parents) often overlooks the presence of other adults in householdsboth related and unrelatedwho may also support the healthy development of children. For example, employment, child care, and housework by other household members can help provide needed resources to children and parents. At the same time, adult employment in the households of low-income children is more precarious than it is in other households, and the presence of additional household members can strain limited resources or become a source of stress and conflict. This research brief examines the household composition of low-income Hispanic children, based on our analyses of recent nationally-representative data. We report on the size and structure of low-income childrens households and the employment status of adult household members. We also explore variation in these patterns by whether the parents were born in the United States or outside it, in light of the unique challenges that immigrant households may face.15, Roughly two-thirds of low-income Hispanic children live in households with at least one foreign-born parent; one in three low-income Hispanic children lives in a household with only U.S-born parents.ii In addition, we compare the households of low-income Hispanic children with the households of low-income non-Hispanic white and non-Hispanic black children (hereafter referred to as white and black).

This paper reassesses whether the optimal income tax program features an Earned Income Tax Credit (EITC) or a Negative Income Tax (NIT) at the bottom of the income distribution, in the presence of unemployment and wage responses to taxation. The paper makes two key contributions. First, it derives a sufficient statistics optimal tax formula in a general model that incorporates unemployment and endogenous wages. This formula nests a broad variety of structures of the labor market, such as competitive models with fixed or flexible wages and models with matching frictions. Our results show that the sufficient statistics to be estimated are: the macro employment response with respect to taxation and the micro and macro participation responses with respect to taxation. We show that an EITC-like policy is optimal provided that the welfare weight on the working poor is larger than the ratio of the micro participation elasticity to the macro participation elasticity. The second contribution is to estimate the sufficient statistics that are inputs to the optimal tax formula using a standard quasi-experimental research design. We estimate these reduced-form parameters using policy variation in tax liabilities stemming from the U.S. tax and transfer system for over 20 years. Using our empirical estimates, we implement our sufficient statistics formula and show that the optimal tax at the bottom more closely resembles an NIT relative to the case where unemployment and wage responses are not taken into account.

This dissertation adds to ongoing research efforts that seek a better understanding of the complex relationships between human beings and their environment. Specifically, the impact of natural disasters on coastal communities from the sudden occurrences of such disasters and their potentially devastating consequences - e g., earthquakes and tsunamis - requires communities to be prepared, by planning specific land-use polices and by developing evacuation plans. Through identifying the locations of populations at risk of a tsunami, determining their behavioral responses during an evacuation, and utilizing a GIS evacuation tool - Capacity-Aware Shortest Path Evacuation Routing (CASPER), the research reported in this dissertation proposes a means to predict the number of evacuating vehicles from various tsunami scenarios and calculate evacuation clearance time for these scenarios. Motivated by the need to provide new ways to use the results of the behavioral analysis in evacuation modeling, this study developed a methodology to predict the number of evacuating vehicles based on the result of the behavioral analysis to estimate the evacuation clearance time using evacuation modeling. In order to estimate the evacuation clearance time, a framework was utilized and is described in four analytical chapters of this dissertation - chapters 4 - 7: Chapter 4 identifies the population at risk of tsunami. Chapter 5 explores the characteristics of the sample population; Chapter 6 offers predictions on the number of evacuating vehicles, and, Chapter 7 models the evacuation process to estimate the evacuation clearance time with and without congestion. Each of these chapters interconnects with the succeeding chapter as they together complete the framework used to model evacuation clearance time. The ability to assess the preparedness for a natural hazard will broaden the understanding of the relationship between behavioral responses and evacuation transportation options, which in turn will provide planners, government officials, and geographers the required knowledge to address related planning issues such as evacuation planning and sustainable development.

This appendix describes the data, methods, and statistical programs used to construct the historical occupation-level employment and unemployment series in Foote and Ryan (forthcoming). We also describe the occupational codes that are applied to 19762013 microdata from the Current Population Survey (CPS). Finally, we outline some standard statistical adjustments made to the occupation-level wage averages and worker flows generated from the CPS.

Using measures of Compulsory Schooling Laws as instruments for state average schooling, we find that one more year of average schooling leads to a 6-8% increase in individual wages. The effect is statistically significant and robust to different specifications. We also find the effect to be larger for less-educated workers. The key difference from previous strategies is that, the exogenous variation in average schooling induced by our instruments comes mainly from workers not used directly in the wage regression. We construct a model of human capital accumulation where the average human capital of an economy is allowed to affect the productivity of a typical firm in the economy. The calibration of the model suggests that the elasticity of a firms productivity with respect to the average human capital of the economy is about 0.12.

Coming out of neo-classical growth theory, convergence theory suggests that regional differences in income will decrease over time. Studied in a variety of frameworks, there is evidence that convergence occurs, though conditioned upon regional economic capacities and participation in the larger neo-classical economy. Within the United States, convergence evidence is present at the national and sub-national levels. Of particular interest in this process is Appalachia, a region understood to have taken a peripheral role to the larger neo-classical economy, and not subject to convergence. Recent work suggests the possibility of convergence and movement towards a neo-classical growth process, though the evidence is mixed and indirect. This paper tests for convergence in Appalachia and the influence of neo-classical and Appalachian-specific growth factors. Results indicate convergence and a movement of the Appalachian economy away from a core-periphery structure to a more neo-classical process largely driven by industrial structure and human capital.

The two essays included in this dissertation investigate issues related to the crowdout of private health insurance resulting from Medicaid expansions among the working age disabled. The first essay estimates the take-up and crowd-out rates that resulted from Medicaid expansions that began in the late 1990s among working age individuals with disabilities. Disabled individuals under 65 years old account for 15% of Medicaid recipients but half of all Medicaid spending. Despite their large cost, few studies have investigated the effects of Medicaid expansions for disabled individuals on insurance coverage and crowd-out of private insurance. Using an eligibility expansion that allowed states to provide Medicaid to disabled individuals with incomes less than 100% of the federal poverty level, I address these issues. Crowd-out estimates range from 49% using an ordinary least squares procedure to 100% using two-stage least-squares analysis. The second essay evaluates how hospitals set their prices in response to a potential revenue reduction that results from the crowd-out measured in Essay 1. Medicaid reimburses health care providers for medical services at a lower rate than any other type of insurance coverage. To make up for the burden of treating Medicaid patients, providers claim that they must raise the rates of individuals covered by private insurance a phenomenon referred to as cost-shifting. The current literature attempting to identify and measure the degree of cost shifting in the US health care market has produced mixed results. In Essay 2, I examine cost-shifting with a new identification strategy where I exploit the Medicaid expansion for the disabled from Essay 1 where for every new Medicaid enrollee another individual dropped private coverage, creating an opportunity for significant costshifting. Using the Healthcare Cost and Utilization Projects Nationwide Inpatient Sample, I first replicate previous findings from the March Current Population Survey and the Survey of Income and Program Participation and demonstrate that there is 100 percent crowd-out from these particular Medicaid expansions. Next, I find that hospitals reduce the charge rates of the privately insured, a result that suggests that hospitals are not employing cost-shifting strategies as they claim.