Only the principal investigator (with university or institution affiliation) and project title are listed. Available project abstracts are nested. A * after the principal investigator's name indicates that person is a graduate student. 

APPROVED PROJECTS

This project will demonstrate the value of combining data collected and/or organized by the U.S. Census Bureau with recently developed methodology for estimating two-sided assignment models for producing forecasts or simulations about a range of labor market phenomena of interest to the Census Bureau. In particular, we propose to use matched employer-employee data from the LEHD from 1990 to the present linked with demographic surveys (ACS, CPS, SIPP, and Decennial Census). The linked LEHD-survey data feature two key properties that make them well-suited to this class of models: 1) they contain a very large random sample of all matches (worker-firm job matches or husband-wife marriage matches) within the LEHD sampling frame, which approaches the universe of matches within the states providing data to external researchers, and 2) they provide a very rich set of characteristics that describe agents or units on both sides of the market (workers and firms, or men and women). In order for two-sided assignment models to generate accurate and useful forecasts, one needs to be able to observe the key characteristics that capture the heterogeneity on both sides of the market that leads certain agents on one side to be disproportionately likely to match with certain agents or units on the opposite side. In this project, we will use the data and model to produce forecasts about 1) which workers in which locations would be most affected by alternative forms of local labor demand shocks (plant relocations, stimulus packages, natural disasters), 2) the degree to which differential access to jobs with strong career paths, differential promotions, and differential frequency and quality of outside offers (conditional on job type) contribute to gender and racial income disparities at different points in the life cycle, 3) how the earnings distributions by race and gender are likely to evolve in the next decade, given the differences in the racial, gender, and educational attainment composition of entering vs. exiting cohorts in the U.S labor market, and 4) how assortative mating patterns along various dimensions might change as the occupational and industry composition of the labor demand changes, given the degree to which occupation and industry affect search costs in the marriage market. In the process of producing the papers that comprise this project, we will also shed light on the incidence of firm UI non-reporting across demographic groups defined by age, race, gender, education, or occupation and identify state-year combinations in which disproportionate representation by these groups suggests previously undetected anomalies in the data.

Much recent research has attempted to determine the net effects of international trade on the U.S. economy. We aim to determine which types of jobs and industries are most impacted by international trade, and in particular how China’s ascension to the World Trade Organization has impacted U.S. earnings and employment among firms that engage with international companies in a variety of ways. By combining data from the Longitudinal Employer Household Dynamics (LEHD), Economic Censuses, and the Longitudinal Firm Trade Transactions Database (LFTTD), we observe job transitions among workers with skills and firms that are heterogeneous in their susceptible to trade shocks. Using a two-sided matching model, we are able to estimate how job matches change in response to these shocks, both directly as a result of foreign competition and indirectly in response to effects on other industries. Results thus generate a comprehensive account of “winners” and “losers” from foreign trade.

This project investigates the welfare implications of corporate actions by examining the effects of leverage, mergers, and acquisition on other nearby firms and local labor markets. In addition to leveraging well-established estimates of local commuting zones to assess spillover effects of these actions, we use Longitudinal Employer Household Dynamics (LEHD) and Longitudinal Business Database (LBD) data to compute granular overlapping zones and a new measure of local labor and firm competition that we call Regions of Spatial Competition, which should be more accurate for areas on the borders of existing commuting zones. We then compare the mobility and wages of workers in these markets who do and do not experience corporate shocks. If differences are found, results suggest that existing corporate finance theories focusing on privately optimal frameworks fail to capture ways that spillover impacts local markets, more widely.

This project aims to provide a more comprehensive understanding of the change in U.S. income inequality and labor market dynamics by characterizing the nation’s income distribution using an innovative set of distributional forms, namely a mixture model with a finite number of components. We use data from the Census Bureau’s Current Population Survey Annual Social and Economic Supplement (CPS ASEC) to construct fine-grained, highly populated frequency histograms that allow us to estimate a multi-component statistical model of the income distribution, which better capture the three generative processes that prior researchers have speculated shape the income distribution. Findings will identify the crucial characteristics of each of these processes and inform empirically-based theorizations of different modalities of income appropriation. This will ultimately cast light on the occurrence and consequences of unemployment or partial engagement with labor markets, segmentation and stratification in labor markets, and the relationship between functional and individual income.

This project will explore the role that barriers to moving in response to environmental harms may play in shaping the patterns found by the EJ literature. In other words, this paper addresses the question: To what extent do moving costs prohibit households from moving to cleaner locations? A recent finding in Lee (2017) suggests that lower income households, especially those with children, face higher moving costs than their wealthier/childless counterparts. Using counterfactual experiments, Lee verifies that lowering moving costs encourage households to move to locations with better air quality. This study proposes to exploit the exogenous variation in moving costs generated by a series of legislative acts in California. Proposition 13 was passed in 1978 to replace old property tax rates in California. Under Proposition 13, tax rates are based on the price at the time of purchase rather than the current value of the property. This created an incentive for people to stay in current housing for a long time to enjoy implicit tax breaks. Then Proposition 60 (1986) and Proposition 90 (1988) provided a substantial variation in the tax break across different age groups. Under the two subsequent amendments, homeowners aged 55 and older can keep their tax base value when they sell their current house and buy a new one at the same value or less. This indicates that an exogenous shock (i.e., Proposition 13, 60, and 90) reduced migration costs of homeowners aged 55 and older, compared to those of younger homeowners. Ferreira (2010) finds that the moving rate is 1.2-1.5 percentage points higher for 55-year olds compared to 54-year olds in 1990 in San Francisco Bay Area. for 55-year olds compared to 54-year olds in 1990 in San Francisco Bay Area.
Note that the propositions in California do not provide a direct measure of moving costs. We know, however, that the moving costs of the “treatment group” (i.e., those who are eligible for Proposition 60 and 90) are smaller than the moving costs of the control group, which is the main identification assumption. There is no reason to believe that people aged 55 are systematically different from people aged 54, other than the treatment. Thus, if those two age groups behave differently in terms of residential choice decision, then we can plausibly conclude that the difference in cost of moving caused contrasting residential choice decisions of the two groups. In particular, we will analyze if those who have lower moving costs due to the legislation moved to locations with higher air quality. The residential choice decision of households will be provided by the 1990 Decennial Census Long Form Data (15% sample).

Economists are increasingly applying spatial analyses to economic questions, with most researchers applying these methods to publicly-available, aggregate data. However, there is strong evidence to believe that these analyses produce biased results when the unit of aggregation does not match the spatial scale of the phenomenon under study. We seek to document how the use of spatially aggregated data can bias conclusions in economic impact studies of energy development by combining disaggregated Census data containing detailed location and production information with external wind, oil, and gas industry data. From these linked data, we hope to produce new, unbiased estimates of the economic impact of new energy development investments on local economies, for different types of industries.

Employer-sponsored health insurance (ESI) is the backbone of coverage and health care in the U.S. Yet, there has been a substantial decline in employers offering health insurance, especially among small businesses, over the past 20 years. These small businesses occupy an especially important position in the employment of racial/ethnic minorities, and thus lower rates of ESI provision for small businesses may help explain the lower rates of health coverage among disadvantaged groups. However, not all small businesses fail to provide ESI, and researchers have failed to examine the potential impacts of business owner and community demographics, which may play an important role in explaining ESI coverage. By combining data from the Survey of Business Owners (SBO), Annual Survey of Entrepreneurs (ASE) and Annual Business Survey (ABS), we are able to examine ESI provision among small businesses by employer race/ethnicity, employer nativity, and the race/ethnic composition of the surrounding geographic area.

The purpose of this research is to examine the effects of race/ethnicity, household characteristics and environment, and census tract, county, and state-level social and economic characteristics on voluntary and involuntary migration. The researchwill increase the utility of Census Bureau data by helping to clarify the importance of economic and social conditions for the residential stability of people from different racial and ethnic backgrounds (Criterion 3), as well as providing an examination of the correlates of migration variable nonresponse (Criterion 5). We will also prepare estimates of the total U.S. population, stratified by race and ethnicity, that migrates over various distances and provide an analysis of the factors influencing this migration (Criterion 11). In attempting to explain the rise of intrastate migration and reduced rate of interstate migration, researchers have highlighted the importance of both economic and non-economic factors. However, many of these factors are theorized to operate at more localized geographical levels than are available in public data. This has led to a dearth of research capable of examining the relative importance of economic and social factors on internal migration among major racial and ethnic groups. To analyze the neighborhood-level dynamics that theoretically drive differences in migration patterns, researchers must be able to identify the neighborhood of residence of individuals and households, as well as the aggregate characteristics of these places. For our purposes, Census tracts are sufficient proxies for neighborhoods, but public use census microdata do not identify census tracts for respondents; therefore, publicly-available data are not sufficient for answering our questions. Restricted ACS and AHS data, starting in 2005 and extending to 2022 (as available) will allow us the opportunity to examine both inter- and intra-state migration dynamics at an appropriate unit of analysis.

The primary goal of this project is to benefit the Census by enhancing the data collected by the Census and providing the Census with population estimates. We will enhance the data provided by the Commodity Flows survey using an economic geography model and a researcher provided data set that maps firm-to-firm transactions for the universe of Compustat firms, which allows us to estimate destination parent firm characteristics for all transactions recorded in the CFS. We will then enhance the Longitudinal Employer-Household Dynamics (LEHD) by linking our estimates of parent firm characteristics (and the existing CFS) with the LEHD. This will increase the Census Bureau’s ability to understand and provide estimates of labor market statistics and their dependence on domestic trading networks. Furthermore, this linkage will provide future researchers with a dataset that includes a firm production network and employment characteristics of each firm. Lastly, this project will benefit the Census by providing population estimates of within-sector, within-worker skill-level estimates of wage inequality, as well as quantifying the extent to which the distribution of wages shifted in response to China joining the World Trade Organization.

Although U.S. early life mortality rates are magnitudes lower than later life mortality rates and have continued to decline (Murphy et al. 2013), they are unacceptably high, particularly for some population subgroups. Nonetheless, social demographic and epidemiological research on early life mortality, especially beyond infancy, has been scarce over the past several decades. This is most likely because research attention has focused on other stages of the life course given that deaths are highly concentrated at older ages, and because there are very few large, nationally representative U.S. data sets that facilitate research on early life mortality. However, U.S. infants, children, adolescents, and young adults are growing up in a context of widening socioeconomic inequality and rapidly changing family structures. Overall, such social and economic changes may differentially affect early life mortality risks, with particularly harmful consequences for the most vulnerable population subgroups. But very little recent research has examined early life mortality disparities and trends in the context of these broad social and economic changes. We plan to use the recently-released National Health Interview Survey Linked Mortality Files together with: multivariate linear regression, multivariate logistic regression, multivariate hazard models, and multivariate Poisson analyses to examine patterns and trends in early life mortality within the United States.

SNAP comprises 70% of total U.S. expenditures on food and nutrition programs, 20% of all U.S. spending on safety net programs, and provides benefits to 12% of the U.S. population making it the key safety net program today.2 However, because there has typically not been variation in benefit amounts or eligibility, which is typically used to estimate the causal effects of safety net programs, very little is known about the effects of SNAP. I plan to take advantage of the only recent variation of this type--the loss of SNAP eligibility for immigrant families as a result of welfare reform and then the haphazard restoration of eligibility across states and over time in the following years--to estimate the effects of SNAP on children's health. In order to analyze how these eligibility changes affected children's health I need a large data set that allows me to observe state of residence and birth, country of birth (foreign or not), and the number of years foreign-born have been in the U.S., because the eligibility rules depend on whether the family is a recent immigrant or not. The NHIS has a large sample size, this necessary demographic information and detailed health and health care utilization information. The NHIS will allow me to have more power and examine more health outcomes than I can with existing public use data. Unfortunately the public use NHIS data do not have state of residence, state of birth, or a detailed measure of number of years immigrants have lived in the U.S., which is the information necessary to take advantage of the policy variation, so I need access to the restricted use version of the data.

This project estimates the health returns to medical spending and examines whether these returns vary with age, gender, and pre-existing health conditions. We start by studying how medical spending affects an individual’s mortality rate, physical health, and mental health by running instrumental variable regressions of health outcomes on medical spending. This enables us to quantify the average marginal health returns to medical spending in the population. Next, we test for age-specific marginal returns to medical spending by estimating the same relationships on different age groups in the data, and test for gender-specific returns by estimating the relationships on men and women separately. Lastly, we examine whether the returns to medical spending vary with pre-existing health conditions by comparing the marginal returns to medical spending for people that have been diagnosed with angina, arthritis, asthma, cancer, coronary heart disease, high cholesterol, diabetes, emphysema, high blood pressure, heart attack, other heart diseases, or stroke.

This analysis will provide needed public health surveillance on mortality involving stimulant drug substances. Little mortality data are provided in the literature for specific drug substances beyond the level of International Statistical Classification of Diseases and Related Health Problems, 10th Edition (ICD-10) coding. This analysis will calculate age-adjusted rates and trends over time for several prescription stimulant substances. A set of illicit stimulants, opioids, and nonsteroidal anti-inflammatory drugs (NSAIDs) are also examined to provide benchmark comparisons for substances expected to be commonly and uncommonly involved in death. Finally, polysubstance mortality will also be examined.

The economic and social consequences of violent victimization for individuals are not well understood. As a major and deleterious life event often effecting one's mental, physical and emotional well-being, it is likely that victimization either directly (i.e. through missed work due to health complications) or indirectly (i.e. resulting from psychological trauma that negatively impacts living conditions) disrupts economic stability. We propose to exploit the panel nature of the geocoded NCVS data to create victimization histories for respondents over a maximum 3 year window, which we can then use to assess how changes in employment, earnings, housing, and marital status are related to instances of criminal victimization, after adjusting for demographic and economic characteristics of their communities. Results will shed light on the previously-ignored socioeconomic burden of victimization for individuals, as well as providing new estimates of the relationship between local socioeconomic contexts, policing expenditures, and victimization.

The proposed study uses the National Crime Victimization Surveys (NCVS) to investigate intimate partner violence victims' use of healthcare facilities and state-specific correlates associated with intimate partner violence (IPV) rates. Some argue that mandatory reporting policies are beneficial in that they helps law enforcement officers identify abusers, while others say mandatory reporting discourages victims from seeking medical help and robs them of their autonomy for safely reporting abuse. Despite this debate, none have quantitatively examined the impacts of mandatory physician reporting laws on healthcare utilization rates of IPV victims using a quasi-experimental framework. Through multilevel modeling of state differences in reporting laws over time, we seek to determine impacts on individual and state-level IPV outcomes. These results will provide much-needed evidence in the debate over mandatory reporting of IPV.

A growing literature highlights the huge dispersion in productivity across establishments in the US. New research demonstrates that resource reallocation is a key factor in growth and explains much of the variation in productivity. Despite a mounting recognition of the importance of asset allocation, existing measures of asset allocation rely on aggregate estimates that do not track how individual assets change hands and are ultimately reutilized. In this project, we construct new measures of the real estate assets used by firms from purchased US Postal Service data linked with restricted data from the LBD, SSEL, and economic surveys/censuses. We then use these measures to evaluate asset utilization and reallocation in the economy to determine how assets are used or redeployed following a variety of economic shocks, and how reallocation activity affects other measures of performance and economic activity with fixed effects models.

This project proposes to link firm-level data from the U.S. Census Bureau with detailed trade data to understand the role of increased trade exposure in accounting for this decline in entrepreneurship and the consequences for aggregate employment and productivity growth. The proposal is motivated by the burgeoning recent literature documenting that the rise in import competition from China and other low wage countries in the early 2000s has exerted important negative effects on employment (e.g. Autor et al., 2013; Pierce and Schott, 2015) while simultaneously leading to increased technical change within firms and reallocation of employment towards more productive firms (e.g. Bloom et al., 2015). This project will address a set of important questions that have so far been left unexplored. What are the effects of increased trade exposure on startup rates and the post-entry dynamics of firms in terms of survival and employment growth? What is the role of offshoring in explaining these employment effects? How do firms react to increased trade exposure in terms of capital intensity, technical change, organization and management practices? To what extent can increased trade exposure, through its impact on firm dynamics, account for the slowdown in aggregate employment and productivity growth observed in U.S. data? The predominant purpose of this project is to benefit Title 13 Chapter 5 programs of the Census Bureau under three particular criteria. First, we will link establishment- and firm-level data from the Longitudinal Business Database (LBD), the Standard Statistical Establishment List (SSEL), and different industry censuses and annual surveys. We will then relate this data to trade flows from the Longitudinal Firm Trade Transactions Database (LFTTD) and create a bridge with external data on trade exposure from Comtrade and other sources as well as firm survey data on investment, research and development, organization, and management practices from Bureau Van Dijk's ORBIS dataset, the Harte Hanks IT dataset, and Bloom and Van Reenen’s management survey. These links will increase the utility of Census data as well as provide information on potential improvements to future data collection (Criterion 7). Second, we will improve the Census Bureau’s understanding of the quality of Title 13 Chapter 5 data (Criterion 5) in four specific dimensions: (a) we will compare employment tabulations by industry and firm age that have recently been made available in the public domain as part of the Quarterly Workforce Indicators (QWI) to researcher-generated equivalents from the LBD; (b) we will construct establishment-level productivity measures from the Census of Manufacturers (CMF) and the Annual Survey of Manufactures (ASM) and aggregate them to the NAICS-6 industry level in order to compare these measures to publicly available productivity estimates from the NBER-CES Manufacturing Industry database and other publicly available indicators of productivity; (c) we will compare estimates of sales, capital stocks, investment and R&D expenditures from the ASM and CMF, the Annual Capital Expenditures Survey (ACES), and the Survey of Industrial Research and Development (SIRD and BRDIS) to the information available in the ORBIS dataset; (d) we will compare estimates from the Information and Communications Technology (ICT) supplement of the ACES to firm survey data on hardware and software utilization from the HH dataset. Third, we will generate population estimates characterizing firm dynamics and their relation with different measures of trade exposure as described in detail below and as authorized under Title 13, Chapter 5 (Criterion 11). This project will use eleven Census provided datasets: Longitudinal Business Database (LBD), 1976 2020, Firm Trade Transactions Data (LFTTD), 1992-2020, Census of Manufactures (CMF), 1976-2017, Annual Survey of Manufactures Data (ASM), 1973-2020, Census of Wholesale Trade (CWT), 1976-2017, Annual Wholesale Trade Survey (AWTS), 1976-2020, Standard Statistical Establishment List (SSEL-NA & SSEL-XN), 1976-2020, Annual Capital Expenditures Survey (ACES), 1993-2014, Survey of Plant Capacity Utilization (PCU and QPC), 1974-2014, Survey of Industrial Research and Development (SIRD and BRDIS), 1972-2020, and the Compustat-SSEL Bridge.

The Spatial Sciences Census Research Node (SSCRN) will foster a connection between the spatial and the survey sciences. This bridge will yield both immediate and long‐term benefits for the estimation, dissemination, and usability of the small area statistics produced by the Census Bureau. Small area statistics describe the character of the population within small geographic zones, such as census tracts. Small area estimates from the American Community Survey (ACS) are imprecise. Historically, very little attention has been paid to the geographic distribution of populations within these small areas. The basic research of SSCRN will increase knowledge about the organization of the American population within small geographic areas for the purpose of improving small area estimates. Apart from the impractical solution of increasing the sample size, the only way to reduce the uncertainty of survey estimates is to utilize ancillary information about the population. SSCRN will exploit new forms of geographic information and recent advances in spatial statistics to make small area estimates more accurate. In addition to improving small area population estimates, an improved understanding of the geographic micro‐structure of the American population is of broad scientific interest and may expand knowledge about socio‐spatial processes like segregation and neighborhood effects. SSCRN will bridge the spatial and the survey sciences through basic research that addresses the current needs of the Census Bureau and yields tangible products for researchers. SSCRN will develop and disseminate training materials, software, and research for a broad community of Census data users and producers. SSCRN will convene a series of interdisciplinary meetings designed to identify research frontiers at the intersection of the spatial and the survey sciences. The meetings will be used to design and refine a novel curriculum and workshop series aimed at developing a cohort of scientists that are capable of establishing and elucidating links between the spatial and survey sciences. SSCRN will develop software tools that will enhance the usability of ACS small area estimates by allowing users to intelligently combine tracts to reduce uncertainty in variables of interest. SSCRN will educate and train a large group of students, post docs, and scientists through meetings, structured mentoring, and workshops. The impact of these activities will be multiplied through design and dissemination of a broadly accessible model curriculum and training workshop. Finally, SSCRN will have a broad impact by releasing a novel form of spatial microdata that will improve small area estimation and enable researchers to explore the spatial structure of the American population.

This analysis will provide needed public health surveillance on mortality involving stimulant drug substances. Little mortality data are provided in the literature for specific drug substances beyond the level of International Statistical Classification of Diseases and Related Health Problems, 10th Edition (ICD-10) coding. This analysis will calculate age-adjusted rates and trends over time for several prescription stimulant substances. A set of illicit stimulants, opioids, and nonsteroidal anti-inflammatory drugs (NSAIDs) are also examined to provide benchmark comparisons for substances expected to be commonly and uncommonly involved in death. Finally, polysubstance mortality will also be examined.

The immigrant health advantage suggests that, despite significant socioeconomic disadvantage, immigrants report better health than U.S.-born counterparts. The immigrant health advantage has been shown in Hispanic-origin immigrant populations in the United States, with little focus on other racial/ethnic groups. In a previous study, I examined this immigrant health advantage as it pertained to overweight, obesity, hypertension, and diabetes in African-origin black immigrants relative to U.S.-born non-Hispanic blacks. Additionally, to investigate health deterioration associated with duration, I compared the health of African-origin black immigrants to non-Hispanic white and Mexican immigrants. I analyzed data on adults aged 18-85+ in the 2002-2017 National Health Interview Survey using multinomial (overweight; obesity) and binomial (hypertension; diabetes) logistic regressions. Findings provide support for the immigrant health advantage and suggest that, relative to U.S.-born non-Hispanic blacks, African-origin black immigrants are at lower odds for obesity (p<0.001), hypertension (p<0.001), and diabetes (p<0.001), regardless of duration. Further, I find that African-origin black immigrants report similar health status and health deterioration when compared to non-Hispanic white and Mexican immigrants. These findings provide new insights into the health of African-origin black immigrants, a rapidly growing proportion of the immigrant population of the United States. This study only answers a portion of questions. Africans, and black immigrants as a whole, represent a largely heterogeneous population. As such, it is essential that researchers disaggregate by country of origin to better understand differential health outcomes. I seek to address this by identifying country of origin groups using NHIS and NHANES. Additionally, to better understand the influence of period and cohort effects, I seek to analyze year of arrival data in the same two datasets. Country of origin and year of arrival are restricted and only available through the Research Data Center.

Polysubstance use has higher risks of mortality, and multiple substances are frequently reported on death certificates. Due to the huge plethora of drugs that could be involved in mortality, polysubstance analysis can be overwhelming. However, statistical strategies are available for such high dimensional problems. Multivariate analyses can be useful in reducing the dimensionality and can provide insight into how variables cluster together according to latent variables. MCA has been used to investigate facial injuries in violent crime, trauma patterns in blast injuries, and malaria infection patterns. This proposal will analyze polysubstance drug mortality from the perspective of a multidimensional analysis. The primary strength of this approach is that many more drug substances can be analyzed concurrently than in traditional polysubstance analyses, which typically rely on ICD-10 codes and univariate proportions. The purpose of the analysis is to determine what patterns (if any) exist in which drugs are concomitantly present in fatal overdoses. Further, the analysis will show what characteristics are associated with the different dimensions of polysubstance overdose, which could be valuable to broader drug safety initiatives. A multivariate analysis is proposed to provide needed public health information on mortality involving multiple drug substances. Mortality data are incomplete in the literature for if and how polysubstance use of drug substances contributes to mortality. Multiple correspondence analysis (MCA) will examine how drug substances cluster together along latent variables; clustering of drugs within drug classes will also be examined. This analysis will also examine associations between demographic and other characteristics among drug substance clusters in mortality data. The primary objective of this analysis is to provide public health surveillance on mortality involving the clustering of drug substances. This study is descriptive, and no formal hypotheses are pre-defined. Death certificate information from Drug Mentions with Involvement (DMI) database will be analyzed to characterize how specific drug substances are involved at the same time.

Economic theory suggests that trading firms should be particularly dependent on external and internal financing due to issues such as long shipment lags, currency and regulatory risks, and high initial fixed costs to set up a distribution network abroad. This implies that firms with tighter credit constraints should be significantly less likely to export to a given destination (extensive margin) and have significantly lower export shares (intensive margin) than comparable, but financially unconstrained, firms. However, existing strategies fail to document how financial constraints determine trade shares because they lack firm-level microdata. By making use of linked Economic Census, Quarterly Financial Report, LFTTD, and Compustat data, we can more directly measure firm-level constraints and determine how biased standard techniques for assessing this relationship at the industry-level have been. Results will better characterize the true contribution of financing constraints on trade shares in the US economy.

Existing research demonstrates clear seasonal fluctuations in housing units' demands for electricity and gas resources, where characteristics of dwellings and their outside environment determine the degree of fluctuation in demands. Despite this significant and well-documented variation, Census only currently produces tabulations of "average annual [energy] expenditures" that are extrapolated from monthly responses in the ACS data instead of imputed using other meaningful characteristics that account for significant uncertainty. We improve these measures by imputing monthly energy expenditures through weather normalization with external data, linked to ACS geographies and survey month, and provide external checks on the validity and reliability of estimates with energy and natural gas utility revenues reported by the Energy Information Association. The resulting energy expenditures may be very important for accurately capturing the costs of living and working in places with high seasonal variation in energy demands.

In this project, we estimate the association between residential coethnic concentration, socioeconomic status, and neighborhood turnout, and individual level adult health outcomes and risk factors according to race/ethnicity/nativity, focusing on four main groups: foreign-born Mexican immigrants, US-born Mexican-Americans, US-born Non-Hispanic Blacks, and US-born Non-Hispanic Whites. Prior work on residential area “effects” has only examined particular cities with high concentrations of (especially Mexican Americans). We use restricted-use data from the continuous National Health and Nutrition Examination Survey (NHANES) 1999-2010 survey cycles linked to 1970, 1980, 1990, 2000, and 2010 Census as well as to 2005-2009 ACS data, at Census Tract levels using the Longitudinal Tract Database put together by the Spatial Structures in the Social Sciences Center at Brown University. We also use more limited Block Group data from the 2000 Census and 2005-2009 ACS, downloaded directly from American Fact Finder. These data have already been merged and used at the Hyattsville, MD NCHS RDC. We are requesting approval for Census-RDC use to transfer the data when the Rocky Mountain RDC open in Boulder, CO, in early 2017.

The primary objective of this analysis is to provide public health surveillance of buprenorphine overdose mortality. This study is descriptive, and no formal hypotheses are pre-defined. Death certificate information from the Research Data Center (RDC) will be analyzed to calculate counts and proportions of deaths associated with buprenorphine and selected comparator opioid analgesics. Age-specific, geography-specific, and polysubstance fatalities will be characterized (with counts and proportions). Additionally, sensitivity analyses will be conducted to assess the impact the drug overdose definition and reporting specificity on rates. Counts and proportions will be obtained from the RDC. After data are obtained from the RDC, counts will be used to calculate population-adjusted and drug utilization-adjusted rates of mortality for buprenorphine and comparator substances. Calculations of rates will be at the national and state level, and therefore individual-level linkages to denominator data (population and drug utilization) within the RDC are not necessary.

The main objective of this analysis is to provide public health surveillance of prescription opioid overdose mortality. This study is descriptive, and no formal hypotheses are pre-defined. Death certificate information from the Research Data Center (RDC) will be analyzed to calculate counts and proportions of deaths associated with specific opioid analgesics, a combined group of all opioids investigated, and selected comparator groups at the annual level. Polysubstance fatalities will be characterized (with counts and proportions). Results presented to FDA will be slightly different than the data obtained from the RDC. After data are obtained from the RDC, counts will be used to calculate population-adjusted and utilization-adjusted rates of mortality for opioids and other comparator substances. Calculations of rates are at the national level, and therefore individual-level linkages to denominator data (population and drug utilization) within the RDC are not necessary.

Katie Genadek (U.S. Census Bureau, University of Colorado Boulder) The Relationship between Childbearing and Life-Cycle Employment and Wages

Katie Genadek (U.S. Census Bureau, University of Colorado Boulder) Assessing life course impacts of exposure to long-acting reversible methods of contraception

The past several decades have seen an increase in immigration policy and immigration enforcement. These policies have been increasingly conducted by states and localities, rather than the federal government, as well through piecemeal federal immigration policy changes. These changes have resulted in a wide variety of policies being implemented across the country that affect immigrants, and in this project we will examine the consequences of these different policies on immigrants’ health and the spillover effects of this onto natives’ health. In order to analyze how these policy changes affected immigrants’ and natives’ health, we need a large data set that allows us to observe state of residence, county of residence, and country of birth, as well as the number of years foreign-born immigrants have been in the U.S., because the policies will likely have heterogeneous effects depending on whether individuals are recent immigrants or not. The NHIS has a large sample size, the necessary demographic information, and detailed health, health insurance, and health care utilization information. The NHIS will allow us to have more power and examine more relevant outcomes than with public use data. Unfortunately, the public use NHIS data do not have state of residence or county of residence, which is the information necessary to take advantage of the policy variation, so we need access to the restricted use version of the data.

SUBMITTED PROJECTS

Ethan Dahl  (University of Wyoming) Supporting Children of the Opioid Epidemic

Kim Truong-Vu* (University of Colorado Boulder) HPV, Gender, and Racial/Ethnic Differences in the Timing of Initiating the HPV Vaccine Vaccination

Joe Chestnut* (University of Denver) Variations in Commuting Accessibility by Income and Departure Time

Kevin Duncan (Colorado State University-Pueblo) Effects of Subcontracting and other Contractor Characteristics on Injuries and Fatalities in the Construction Industry

Peter Maniloff (Colorado School of Mines) Oil and Gas Siting, Housinng Choices, and Environmental Justice

Jared Carbone (Colorado School of Mines) Impacts of Changes in Air Quality on Consumer Expenditure Demands