Changing American Congregations: Findings from the Third Wave of the National Congregations Study

The third wave of the National Congregations Study (NCS-III) was conducted in 2012. The 2012 General Social Survey asked respondents who attend religious services to name their religious congregation, producing a nationally representative cross-section of congregations from across the religious spectrum. Data about these congregations was collected via a 50-minute interview with one key informant from 1,331 congregations. Information was gathered about multiple aspects of congregations’ social composition, structure, activities, and programming. Approximately two-thirds of the NCS-III questionnaire replicates items from 1998 or 2006-07 NCS waves. Each congregation was geocoded, and selected data from the 2010 United States census or American Community Survey have been appended. We describe NCS-III methodology and use the cumulative NCS dataset (containing 4,071 cases) to describe five trends: more ethnic diversity, greater acceptance of gays and lesbians, increasingly informal worship styles, declining size (but not from the perspective of the average attendee), and declining denominational affiliation.

Association between attention-deficit/hyperactivity disorder symptoms and obesity and hypertension in early adulthood: a population-based study.

OBJECTIVE: To examine the associations between attention-deficit/hyperactivity disorder (ADHD) symptoms, obesity and hypertension in young adults in a large population-based cohort. DESIGN, SETTING AND PARTICIPANTS: The study population consisted of 15,197 respondents from the National Longitudinal Study of Adolescent Health, a nationally representative sample of adolescents followed from 1995 to 2009 in the United States. Multinomial logistic and logistic models examined the odds of overweight, obesity and hypertension in adulthood in relation to retrospectively reported ADHD symptoms. Latent curve modeling was used to assess the association between symptoms and naturally occurring changes in body mass index (BMI) from adolescence to adulthood. RESULTS: Linear association was identified between the number of inattentive (IN) and hyperactive/impulsive (HI) symptoms and waist circumference, BMI, diastolic blood pressure and systolic blood pressure (all P-values for trend <0.05). Controlling for demographic variables, physical activity, alcohol use, smoking and depressive symptoms, those with three or more HI or IN symptoms had the highest odds of obesity (HI 3+, odds ratio (OR)=1.50, 95% confidence interval (CI) = 1.22-2.83; IN 3+, OR = 1.21, 95% CI = 1.02-1.44) compared with those with no HI or IN symptoms. HI symptoms at the 3+ level were significantly associated with a higher OR of hypertension (HI 3+, OR = 1.24, 95% CI = 1.01-1.51; HI continuous, OR = 1.04, 95% CI = 1.00-1.09), but associations were nonsignificant when models were adjusted for BMI. Latent growth modeling results indicated that compared with those reporting no HI or IN symptoms, those reporting 3 or more symptoms had higher initial levels of BMI during adolescence. Only HI symptoms were associated with change in BMI. CONCLUSION: Self-reported ADHD symptoms were associated with adult BMI and change in BMI from adolescence to adulthood, providing further evidence of a link between ADHD symptoms and obesity.

Essays on Microeconometrics

My dissertation has three chapters which develop and apply microeconometric tech- niques to empirically relevant problems. All the chapters examines the robustness issues (e.g., measurement error and model misspecification) in the econometric anal- ysis. The first chapter studies the identifying power of an instrumental variable in the nonparametric heterogeneous treatment effect framework when a binary treat- ment variable is mismeasured and endogenous. I characterize the sharp identified set for the local average treatment effect under the following two assumptions: (1) the exclusion restriction of an instrument and (2) deterministic monotonicity of the true treatment variable in the instrument. The identification strategy allows for general measurement error. Notably, (i) the measurement error is nonclassical, (ii) it can be endogenous, and (iii) no assumptions are imposed on the marginal distribution of the measurement error, so that I do not need to assume the accuracy of the measure- ment. Based on the partial identification result, I provide a consistent confidence interval for the local average treatment effect with uniformly valid size control. I also show that the identification strategy can incorporate repeated measurements to narrow the identified set, even if the repeated measurements themselves are endoge- nous. Using the the National Longitudinal Study of the High School Class of 1972, I demonstrate that my new methodology can produce nontrivial bounds for the return to college attendance when attendance is mismeasured and endogenous.

The second chapter, which is a part of a coauthored project with Federico Bugni, considers the problem of inference in dynamic discrete choice problems when the structural model is locally misspecified. We consider two popular classes of estimators for dynamic discrete choice models: K-step maximum likelihood estimators (K-ML) and K-step minimum distance estimators (K-MD), where K denotes the number of policy iterations employed in the estimation problem. These estimator classes include popular estimators such as Rust (1987)’s nested fixed point estimator, Hotz and Miller (1993)’s conditional choice probability estimator, Aguirregabiria and Mira (2002)’s nested algorithm estimator, and Pesendorfer and Schmidt-Dengler (2008)’s least squares estimator. We derive and compare the asymptotic distributions of K- ML and K-MD estimators when the model is arbitrarily locally misspecified and we obtain three main results. In the absence of misspecification, Aguirregabiria and Mira (2002) show that all K-ML estimators are asymptotically equivalent regardless of the choice of K. Our first result shows that this finding extends to a locally misspecified model, regardless of the degree of local misspecification. As a second result, we show that an analogous result holds for all K-MD estimators, i.e., all K- MD estimator are asymptotically equivalent regardless of the choice of K. Our third and final result is to compare K-MD and K-ML estimators in terms of asymptotic mean squared error. Under local misspecification, the optimally weighted K-MD estimator depends on the unknown asymptotic bias and is no longer feasible. In turn, feasible K-MD estimators could have an asymptotic mean squared error that is higher or lower than that of the K-ML estimators. To demonstrate the relevance of our asymptotic analysis, we illustrate our findings using in a simulation exercise based on a misspecified version of Rust (1987) bus engine problem.

The last chapter investigates the causal effect of the Omnibus Budget Reconcil- iation Act of 1993, which caused the biggest change to the EITC in its history, on unemployment and labor force participation among single mothers. Unemployment and labor force participation are difficult to define for a few reasons, for example, be- cause of marginally attached workers. Instead of searching for the unique definition for each of these two concepts, this chapter bounds unemployment and labor force participation by observable variables and, as a result, considers various competing definitions of these two concepts simultaneously. This bounding strategy leads to partial identification of the treatment effect. The inference results depend on the construction of the bounds, but they imply positive effect on labor force participa- tion and negligible effect on unemployment. The results imply that the difference- in-difference result based on the BLS definition of unemployment can be misleading

due to misclassification of unemployment.

Editor's choice Regional trade agreements and export competitiveness: the uncertain path of Nicaragua’s apparel exports under CAFTA

The Central American Free Trade Agreement (CAFTA) has been a mixed blessing for economic development. While exports to the US economy have increased, dependency may hinder economic growth if countries do not diversify or upgrade before temporary provisions expire. This article evaluates the impact of the temporary Tariff Preference Levels (TPLs) granted to Nicaragua under CAFTA and the consequences of TPL expiration. Using trade statistics, country- and firm-level data from Nicaragua’s National Free Zones Commission (CNZF) and data from field research, we estimate Nicaragua’s apparel sector will contract as much as 30–40% after TPLs expire. Our analysis underscores how rules of origin and firm nationality affect where and how companies do business, and in so doing, often constrain sustainable export growth.

Using electronic health record data for substance use Screening, Brief Intervention, and Referral to Treatment among adults with type 2 diabetes: Design of a National Drug Abuse Treatment Clinical Trials Network study.

BACKGROUND: The Affordable Care Act encourages healthcare systems to integrate behavioral and medical healthcare, as well as to employ electronic health records (EHRs) for health information exchange and quality improvement. Pragmatic research paradigms that employ EHRs in research are needed to produce clinical evidence in real-world medical settings for informing learning healthcare systems. Adults with comorbid diabetes and substance use disorders (SUDs) tend to use costly inpatient treatments; however, there is a lack of empirical data on implementing behavioral healthcare to reduce health risk in adults with high-risk diabetes. Given the complexity of high-risk patients' medical problems and the cost of conducting randomized trials, a feasibility project is warranted to guide practical study designs. METHODS: We describe the study design, which explores the feasibility of implementing substance use Screening, Brief Intervention, and Referral to Treatment (SBIRT) among adults with high-risk type 2 diabetes mellitus (T2DM) within a home-based primary care setting. Our study includes the development of an integrated EHR datamart to identify eligible patients and collect diabetes healthcare data, and the use of a geographic health information system to understand the social context in patients' communities. Analysis will examine recruitment, proportion of patients receiving brief intervention and/or referrals, substance use, SUD treatment use, diabetes outcomes, and retention. DISCUSSION: By capitalizing on an existing T2DM project that uses home-based primary care, our study results will provide timely clinical information to inform the designs and implementation of future SBIRT studies among adults with multiple medical conditions.