There are many possible pathways between parental education, income, and health, and between child health and education, but only some of them have been explored in the literature. This essay focuses on links between parental socioeconomic status (as measured by education, income, occupation, or in some cases area of residence) and child health, and between child health and adult education or income. Specifically, I ask two questions: What is the evidence regarding whether parental socioeconomic status affects child health? And, what is the evidence relating child health to future educational and labor market outcomes? I show that there is now strong evidence of both links, suggesting that health could play a role in the intergenerational transmission of economic status.

/pdf/healthy-wealthy-and-wise-socioeconomic-status-poor-health-in-1x6ojp6z8r.pdf

Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development

In the epidemiological literature, the fetal origins hypothesis associated with David J. Barker posits that chronic, degenerative conditions of adult health, including heart disease and type 2 diabetes, may be triggered by circumstance decades earlier, in utero nutrition in particular. Economists have expanded on this hypothesis, investigating a broader range of fetal shocks and circumstances and have found a wealth of later-life impacts on outcomes including test scores, educational attainment, and income, along with health. In the process, they have provided some of the most credible observational evidence in support of the hypothesis. The magnitude of the impacts is generally large. Thus, the fetal origins hypothesis has not only survived contact with economics, but has flourished.

/pdf/killing-me-softly-the-fetal-origins-hypothesis-3b7mkniv72.pdf

Killing Me Softly: The Fetal Origins Hypothesis.

The ability of quantile regression models to characterize the heterogeneous impact of variables on different points of an outcome distribution makes them appealing in many economic applications. However, in observational studies, the variables of interest (e.g., education, prices) are often endogenous, making conventional quantile regression inconsistent and hence inappropriate for recovering the causal effects of these variables on the quantiles of economic outcomes. In order to address this problem, we develop a model of quantile treatment effects (QTE) in the presence of endogeneity and obtain conditions for identification of the QTE without functional form assumptions. The principal feature of the model is the imposition of conditions that restrict the evolution of ranks across treatment states. This feature allows us to overcome the endogeneity problem and recover the true QTE through the use of instrumental variables. The proposed model can also be equivalently viewed as a structural simultaneous equation model with nonadditive errors, where QTE can be interpreted as the structural quantile effects (SQE).

/pdf/an-iv-model-of-quantile-treatment-effects-2j8id40hla.pdf

An IV Model of Quantile Treatment Effects

This paper develops a framework for performing estimation and inference in econometric models with partial identification, focusing particularly on models characterized by moment inequalities and equalities. Applications of this framework include the analysis of game-theoretic models, revealed preference restrictions, regressions with missing and corrupted data, auction models, structural quantile regressions, and asset pricing models. Specifically, we provide estimators and confidence regions for the set of minimizers Θ I of an econometric criterion function Q(θ). In applications, the criterion function embodies testable restrictions on economic models. A parameter value θ that describes an economic model satisfies these restrictions if Q(θ) attains its minimum at this value. Interest therefore focuses on the set of minimizers, called the identified set. We use the inversion of the sample analog, Q n (θ), of the population criterion, Q(θ), to construct estimators and confidence regions for the identified set, and develop consistency, rates of convergence, and inference results for these estimators and regions. To derive these results, we develop methods for analyzing the asymptotic properties of sample criterion functions under set identification.

/pdf/estimation-and-confidence-regions-for-parameter-sets-in-334kpbnbt3.pdf

Estimation and Confidence Regions for Parameter Sets in Econometric Models

This chapter focuses on two of the developments in panel data econometrics since the Handbook chapter by Chamberlain (1984). The first objective of this chapter is to provide a review of linear panel data models with predetermined variables. We discuss the implications of assuming that explanatory variables are predetermined as opposed to strictly exogenous in dynamic structural equations with unobserved heterogeneity. We compare the identification from moment conditions in each case, and the implications of alternative feedback schemes for the time series properties of the errors. We next consider autoregressive error component models under various auxiliary assumptions. There is a trade-off between robustness and efficiency since assumptions of stationary initial conditions or time series homoskedasticity can be very informative, but estimators are not robust to their violation. We also discuss the identification problems that arise in models with predetermined variables and multiple effects. Concerning inference in linear models with predetermined variables, we discuss the form of optimal instruments, and the sampling properties of GMM and LIML-analogue estimators drawing on Monte Carlo results and asymptotic approximations. A number of identification results for limited dependent variable models with fixed effects and strictly exogenous variables are available in the literature, as well as some results on consistent and asymptotically normal estimation of such models. There are also some results available for models of this type including lags of the dependent variable, although even less is known for nonlinear dynamic models. Reviewing the recent work on discrete choice and selectivity models with fixed effects is the second objective of this chapter. A feature of parametric limited dependent variable models is their fragility to auxiliary distributional assumptions. This situation prompted the development of a large literature dealing with semiparametric alternatives (reviewed in Powell, 1994’s chapter). The work that we review in the second part of the chapter is thus at the intersection of the panel data literature and that on cross-sectional semiparametric limited dependent variable models.

/pdf/panel-data-models-some-recent-developments-5aaco1mthe.pdf

Panel data models: some recent developments*

The Effect of the Affordable Care Act Medicaid Expansions on Financial Wellbeing.

Estimation of cross sectional and panel data censored regression models with endogeneity

PANEL DATA MODELS have a long history in econometrics and have become increasingly popular in empirical economics over the past two decades. Panel data expand our opportunities to study more complex economic relationships by, for example, allowing for individual heterogeneity and dynamic feedback. These goals are often achieved by including in the model individual specific effects and lagged dependent variables. These two features of the dynamic panel data model, however, often create difficulties in estimation. Although much progress has been made in the linear panel data model (see, among others, Hsiao (1986), Baltagi (1995), and Arellano and Honore (2001) for review), our knowledge of general nonlinear dynamic panel data models is very limited. In most nonlinear models, strict exogeneity of the explanatory variables is still the key assumption. The main difficulty is that with nonlinearity, it is not obvious how to "difference away" the individual specific effects and how to use instrumental variable type techniques.2 Despite these difficulties, some developments have been made on estimating certain nonlinear dynamic models using the "fixed-effects" approach, for example, the censored regression models (Honore (1993), Honore and Hu (2001)), the sample selection models (Kyriazidou (2001)), the discrete choice models (Honor6 and Kyriazidou (2000)), and the models with multiplicative individual effects (Chamberlain (1992), Wooldridge (1997)).

https://www.jstor.org/stable/pdfplus/3081995.pdf

Estimation of a Censored Dynamic Panel Data Model

This paper investigates differences between large and small firms in new hires' ages and in compensation structures. An analysis of data from the Benefits Supplement to the Current Population Surve...

The Hiring Decisions and Compensation Structures of Large Firms

We examine the effect of the Medicaid expansions under the 2010 Patient Protection and Affordable Care Act (ACA) on financial outcomes using credit report data for a large sample of individuals. We employ the synthetic control method (Abadie et al., 2010) to compare individuals living in states that expanded Medicaid to those that did not. We find that the Medicaid expansions significantly reduced the number of unpaid bills and the amount of debt sent to third-party collection agencies among those residing in zip codes with the highest share of low income, uninsured individuals. Our estimates imply a reduction in collection balances of around $600 to $1,000 among those who gain Medicaid coverage due to the ACA. Our findings suggest that the ACA Medicaid expansions had important financial impacts beyond health care use.

https://papers.ssrn.com/sol3/Delivery.cfm/nber_w22170.pdf?abstractid=2766155

Luojia Hu

Papers

The Effect of the Affordable Care Act Medicaid Expansions on Financial Wellbeing.

Estimation of cross sectional and panel data censored regression models with endogeneity

Estimation of a Censored Dynamic Panel Data Model

The Hiring Decisions and Compensation Structures of Large Firms

The Effect of the Patient Protection and Affordable Care Act Medicaid Expansions on Financial Wellbeing