Monotonicity Conditions and Inequality Imputation for Sample Selection and Non-Response Problems

Under a sample selection or non-response problem where a response variable y is observed only when a condition δ=1 is met, the identified mean E(y|δ=1) is not equal to the desired mean E(y). But the monotonicity condition E(y|δ=1)≤E(y|δ=0) yields an informative bound E(y|δ=1)≤E(y), which is enough for certain inferences. For example, in a majority voting with δ being vote-turnout, it is enough to know if E(y)>0.5 or not, for which E(y|δ=1)>0.5 is sufficient under the monotonicity. The main question is then whether the monotonicity condition is testable, and if not, when it is plausible. Answering to these queries, when there is a "proxy" variable z related to y but fully observed, we provide a test for the monotonicity; when z is not available, we provide primitive conditions and plausible models for the monotonicity. Going further, when both y and z are binary, bivariate monotonicities of the type P(y,z|δ=1)≤P(y,z|δ=0) are considered, which can lead to sharper bounds for P(y). As an empirical example, a data set on the 1996 US presidential election is analyzed to see if the Republican candidate could have won had everybody voted, i.e., to see if P(y)>0.5 where y=1 is voting for the Republican candidatesample selection, non-response, monotonicity, imputation, orthant dependence

Nonparametric Derivative Estimation for Related-Effect Panel Data

In a "fixed-effect" panel data model with a nonparametric regression function \rho(x_{it}), the usual first-differencing yields a nonparametric regression function \mu(x_{it},x_{i,t+1}) with the restriction \mu(x_{it},x_{i,t+1}) = \rho(x_{i,t+1}) - \rho(x_{it}). Although \mu(x_{it},x_{i,t+1}) can be easily estimated nonparametrically with a kernel method, it is not clear that how to identify and estimate \partial\rho(x_{it})/\partial x_{it} (and \rho(x_{it})) using a kernel method, and this task becomes more difficult when a time-invariant variable c_i enters \rho(x_{it}). In this paper, we propose a kernel estimator that is a linear combination of partial derivative estimators for \partial\mu(x_{it},x_{i,t+1},c_i)/\partial x_{i,t+1} and \partial\mu(x_{it},x_{i,t+1},c_i)/\partial x_{i,t}, prove its consistency for \partial\rho(x_{it})/\partial x_{it} and derive the asymptotic distribution. An extensive Monte Carlo study is presented. Also multiple periods longer than two and mixed continuous/discrete regressor cases are considered to enhance the applicability.nonparametrics, partial derivatives, panel data, related-effect.

Dynamic Treatment Effect Analysis of TV Effects on Child Cognitive Development

We investigate whether TV watching at ages 6-7 and 8-9 affects cognitive development measured by math and reading scores at ages 8-9 using a rich childhood longitudinal sample from NLSY79. Dynamic panel data models are estimated to handle the unobserved child-specific factor, endogeneity of TV watching, and dynamic nature of the causal relation. A special emphasis is put on the last aspect where TV watching affects cognitive development which in turn affects the future TV watching. When this feedback occurs, it is not straightforward to identify and estimate the TV effect. We adopt estimation methods available in the biostatistics literature which can deal with the feedback feature; we also apply the standard econometric panel data IV approaches. Overall, for math score at ages 8-9, we find that watching TV for more than two hours per day during ages 6-9 has a negative total effect mostly due to a large negative effect of TV watching at the younger ages 6-7. For reading score, there are evidences that TV watching between 2-4 hours per day has a positive effect whereas the effect is negative outside this range. In both cases, however, the effect magnitudes are economically small.TV watching, treatment effect, panel data, dynamic model, Granger Causality

Dynamic Treatment Effect Analysis of TV Effects on Child Cognitive Development

We investigate whether TV watching at ages 6-7 and 8-9 affects cognitive development measured by math and reading scores at ages 8-9 using a rich childhood longitudinal sample from SY79. Dynamic panel data models are estimated to handle the unobserved child-specific factor, endogeneity of TV watching, and dynamic nature of the causal relation. A special emphasis is put on the last aspect where TV watching affects cognitive development which in turn affects the future TV watching. When this feedback occurs, it is not straightforward to identify and estimate the TV effect. We adopt estimation methods available in the biostatistics literature which can deal with the feedback feature; we also apply the “standard” econometric panel data IV approaches. Overall, for math score at ages 8-9, we find that watching TV for more than two hours per day during ages 6-9 has a negative total effect mostly due to a large negative effect of TV watching at the younger ages 6-7. For reading score, there are evidences t at TV watching between 2-4 hours per day has a positive effect whereas the effect is negative outside this range. In both cases, however, the effect magnitudes are economically small.TV watching, treatment effect, panel data, dynamic model, Granger causality

The WTO Trade Effect

This paper reexamines the GATT/WTO membership effect on bilateral trade flows, using nonparametric methods including pair-matching, permutation tests, and a Rosenbaum (2002) sensitivity analysis. Together, these methods provide an estimation framework that is robust to misspecification biases, allows general forms of heterogeneous treatment effects, and addresses potential hidden selection biases. This is in contrast to most conventional parametric studies on this issue. Our results suggest large GATT/WTO trade-promoting e®ects, robust to various restricted matching criteria, alternative indicators for GATT/WTO involvement, different matching methodologies, non-random incidence of positive trade flows, and inclusion of multilateral resistance terms.Trade flow,Treatment effect,Matching,Permutation test,Signed-rank test,Sensitivity analysis

Protection for Sale Under Monopolistic Competition: An Empirical Investigation

This paper proposes a general empirical framework to estimate the protection-for-sale model, where the protection regime shifts according to a sector's market structure (perfectly or monop-olistically competitive). We base the protection structure on Grossman and Helpman (1994) for the subset of perfectly competitive sectors and on Chang (2005) for the subset of monop- olistically competitive sectors. The two protection regimes are simultaneously estimated with joint constraints. The results of the J-test consistently reject the homogeneous (perfect compe- tition) protection-for-sale model often adopted in previous literature and suggest a direction of improvement toward the proposed heterogeneous protection structure model.endogenous trade policy; campaign contribution; monopolistic competition; intrain- dustry trade; import penetration

Protection for Sale Under Monopolistic Competition : An Empirical Investigation

This paper proposes a general empirical framework to estimate the protection-for-sale model, where the protection regime shifts according to a sector's market structure (perfectly or monopolistically competitive). We base the protection structure on Grossman and Helpman (1994) for the subset of perfectly competitive sectors and on Chang (2005) for the subset of monopolistically competitive sectors. The two protection regimes are simultaneously estimated with joint constraints. The results of the J-test consistently reject the homogeneous (perfect competition) protection-for-sale model often adopted in previous literature and suggest a direction of improvement toward the proposed heterogeneous protection structure model.endogenous trade policy, campaign contribution, monopolistic competition, intraindustry trade, import penetration

Non-market Leadership Experience and Labor Market Success : Evidence From Military Rank

There has been much recent interest in the effects of pre and non-market skills on future labor market outcomes. This paper examines one such effect : the effect on future wages of military leadership experience among "Vietnam generation" American men. We study rank, not just veteran status. We argue that rank is a good measure of pre-market leadership skills because of the clear military hierarchy and the primarily youth experience of Vietnam service. Two sources of selection bias are accounted for : non-random military entry and eventual rank attained. We apply a modified 2-stage parametric sample selection method. The rank premia on future wages are estimated using the parametric selection corrections and a propensity score matching with two indices. We find evidence of a leadership premium, but not a veterans' premium. It is the rank that matters. If one joins the military believing that military service commands a future wage premium, he had better become an NCO or an officer.non-market skills, military, future wages, parametric sample selection

The WTO Trade Effect

Rose (2004) showed that the WTO or its predecessor, the GATT, did not promote trade, based on conventional econometric analysis of gravity-type equations of trade. We argue that conclusions regarding the GATT/WTO trade effect based on gravity-type equations are arbitrary and subject to parametric misspecifications. We propose using nonparametric matching methods to estimate the `treatment effect' of GATT/WTO membership, and permutation-based inferential procedures for assessing statistical significance of the estimated effects. A sensitivity analysis following Rosenbaum (2002) is then used to evaluate the sensitivity of our estimation results to potential selection biases. Contrary to Rose (2004), we find the effect of GATT/WTO membership economically and statistically significant, and far greater than that of the Generalized System of Preferences (GSP).GATT/WTO, GSP, treatment effect, matching, permutation test, signed-rank test, sensitivity analysis