Unconditional Quantile Treatment Effects Research Articles

Farmland Transfer and Income Distribution Effect of Heterogeneous Farmers with Livelihood Capital: Evidence from CFPS

Farmland transfer is a crucial aspect of modernizing China’s agriculture, which has a significant impact on farmers’ income distribution. With rapid urbanization, farmers’ livelihood has changed significantly, and the income effects of farmland transfer will differ among heterogeneous farmers. Based on the China Family Panel Studies (CFPS) data, this study examines the impact of farmland transfer on farmers’ income growth and income disparity, as well as the income differences in farmland transfer among farmers with different types of livelihood capital. An endogenous switching regression model and unconditional quantile treatment effects are used to correct the selection bias and farmer heterogeneity. The results show that (1) farmland transfer increases farmers’ income. Specifically, farmland transfer-in increases farmers’ income by 21.15%, while transfer-out increases it by 43.33%. (2) The impact of farmland transfer on farmers’ income has a “Matthew effect” and will widen the income gap between farmers. (3) Moreover, farmland transfer exhibits diverse income effects on heterogeneous farmers with livelihood capital. Capital-rich farmers experience the largest income-growth effect from farmland transfer-in, while capital-balanced farmers experience the largest income-growth effect from farmland transfer-out. The policy implications are to further improve the farmland transfer market and enable low-income farmers to participate by improving their qualifications and abilities.

Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations

Datasets from field experiments with covariate-adaptive randomizations (CARs) usually contain extra covariates in addition to the strata indicators. We propose to incorporate these additional covariates via auxiliary regressions in the estimation and inference of unconditional quantile treatment effects (QTEs) under CARs. We establish the consistency and limit distribution of the regression-adjusted QTE estimator and prove that the use of multiplier bootstrap inference is non-conservative under CARs. The auxiliary regression may be estimated parametrically, nonparametrically, or via regularization when the data are high-dimensional. Even when the auxiliary regression is misspecified, the proposed bootstrap inferential procedure still achieves the nominal rejection probability in the limit under the null. When the auxiliary regression is correctly specified, the regression-adjusted estimator achieves the minimum asymptotic variance. We also discuss forms of adjustments that can improve the efficiency of the QTE estimators. The finite sample performance of the new estimation and inferential methods is studied in simulations, and an empirical application to a well-known dataset concerned with expanding access to basic bank accounts on savings is reported.

Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations

Datasets from field experiments with covariate-adaptive randomizations (CARs) usually contain extra covariates in addition to the strata indicators. We propose to incorporate these additional covariates via auxiliary regressions in the estimation and inference of unconditional quantile treatment effects (QTEs) under CARs. We establish the consistency and limit distribution of the regression-adjusted QTE estimator and prove that the use of multiplier bootstrap inference is non-conservative under CARs. The auxiliary regression may be estimated parametrically, nonparametrically, or via regularization when the data are high-dimensional. Even when the auxiliary regression is misspecified, the proposed bootstrap inferential procedure still achieves the nominal rejection probability in the limit under the null. When the auxiliary regression is correctly specified, the regression-adjusted estimator achieves the minimum asymptotic variance. We also discuss forms of adjustments that can improve the efficiency of the QTE estimators. The finite sample performance of the new estimation and inferential methods is studied in simulations and an empirical application to a well-known dataset concerned with expanding access to basic bank accounts on savings is reported.

The selection and quantile treatment effects on the economic returns of green buildings

This paper adds a fresh perspective on the economic returns of green buildings using Singapore housing data. It estimates both the conditional and unconditional quantile treatment effects, by applying a recent econometric theory that explicitly recognizes and addresses the fundamental difference between the two types of quantile effects. The theory exploits selection on observables, which is widely accepted and commonly handled through one of two propensity score methods in the green building literature. The research finds that inverse probability weighting outperforms the other and adopts it to estimate the quantile effects, after evaluations including a comparison between the conditional and unconditional mean effects. Green buildings' premium is significant but varies notably across housing price quantiles. The treatment effect estimates for the conditional price distribution exhibit a hump shape, but the estimates for the marginal distribution display an upward trend. The conditional and unconditional quantile treatment effects both provide useful information for research, policy, and practice. The results indicate that the incentive for going green and the need for government intervention are both non-uniform.

The Selection and Quantile Treatment Effects on the Economic Returns of Green Buildings

Global warming changes societal values and political climate. The movement toward environmental sustainability and initiatives of green-building certification have created an implicit mark for sustainable buildings. Whether the economic return adequately incentivizes developers is an important question for research, because the building sector is a primary source of greenhouse gas emissions and many countries have ratified international treaties and committed to reduce emissions. This paper uses Singapore housing data and examines the green building premium. It carefully handles the issue of selection to treatment. Specifically, propensity score methods, which include the propensity score matching and inverse probability weighting, address selection on observables and remove confounding. The quality of these remedial methods are assessed through comparison between unconditional and conditional average treatment effects. Inverse probability weighting performs better than matching in this research. The paper further extends the literature of green building from the examination of the average treatment effect to the study of the quantile treatment effects. Both the conditional and unconditional quantile treatment effects are estimated and analyzed. Economic returns are significant but vary considerably along the conditional and unconditional distributions of housing prices. Policy implications are discussed.

Quantile Treatment Effects in the Presence of Covariates

This paper proposes a method to estimate unconditional quantile treatment effects (QTEs) given one or more treatment variables, which may be discrete or continuous, even when it is necessary to condition on covariates. The estimator, generalized quantile regression (GQR), is developed in an instrumental variable framework for generality to permit estimation of unconditional QTEs for endogenous policy variables, but it is also applicable in the conditionally exogenous case. The framework includes simultaneous equations models with nonadditive disturbances, which are functions of both unobserved and observed factors. Quantile regression and instrumental variable quantile regression are special cases of GQR and available in this framework.

Are there any distributive effects of land title on labor supply? evidence from Brazil

This paper investigates the effects of land titling on labor supply for households heads. The effect of legal ownership security on the adult labor supply is identified by comparing the impact of being part of, or excluded from, a land title program in a unique public intervention in two neighboring communities in the Brazilian city of Osasco. We employ a quasi-experimental technique to identify the average treatment effect on treated by using diff-in-diff and ANCOVA methods. We also estimate distributive impact on the hours by adults by estimating unconditional quantile treatment effects. The results suggest that the impact of land titling on labor is positive and greater at the median of the weekly hours worked distribution. numbers; J18; J22; K11 and R38

Returns to education and urban-migrant wage differentials in China: IV quantile treatment effects

This article presents new evidence on urban–rural migrant wage differentials of workers in full-time employment in China. It utilises a nationally representative data set, recent matching techniques, and IV estimation methods to evaluate conditional and unconditional quantile treatment effects for education and for migrant status. Particular attention is paid to rural-migrant wage differentials and the migration effect on earnings. The evidence indicates that (1) the returns to education are significant, especially when we account for endogeneity; (2) long-term migrants seem disadvantaged, mainly due to endowment effects, and (3) the urban–rural migrant wage gap completely disappears when matching or IV methods are employed to account for endogeneity in education.

Unconditional Quantile Regression for Panel Data with Exogenous or Endogenous Regressors

Unconditional quantile treatment effects are difficult to estimate in the presence of fixed effects. Panel data are frequently used because fixed effects or differences are necessary to identify the parameters of interest. The inclusion of fixed effects or differencing of data, however, redefines the quantiles. This paper introduces a quantile estimator for panel data which conditions on fixed effects for identification but allows the parameters of interest to be interpreted in the same manner as cross-sectional quantile estimates. The quantile treatment effects are unconditional in the fixed effect but identification originates from differences in the covariates or instruments. The fixed effects are never estimated and the estimator is consistent for small T.

A Model of Unconditional Quantile Treatment Effects in the Presence of Covariates

Many economic applications have found quantile models useful when the explanatory variables may have varying impacts throughout the distribution of the outcome variable. Traditional quantile estimators provide conditional quantile treatment effects. Typically, we are interested in unconditional quantiles, characterizing the distribution of the outcome variable for different values of the treatment variables. Conditioning on additional covariates, however, may be necessary for identification of these treatment effects. With conditional quantile models, the inclusion of additional covariates changes the interpretation of the estimates. This paper discusses identification of unconditional quantile treatment effects when it is necessary or simply desirable to condition on covariates. It discusses identification for both exogenous and endogenous treatment variables, which can be discrete or continuous, without functional form assumptions.

Unconditional Quantile Regression for Panel Data with Exogenous or Endogenous Regressors

Unconditional quantile treatment effects are difficult to estimate in the presence of fixed effects. Panel data are frequently used because fixed effects or differences are necessary to identify the parameters of interest. The inclusion of fixed effects or differencing of data, however, redefines the quantiles. This paper introduces a quantile estimator for panel data which conditions on fixed effects for identification but allows the parameters of interest to be interpreted in the same manner as cross-sectional quantile estimates. The quantile treatment effects are unconditional in the fixed effect but identification originates from differences in the covariates or instruments. The fixed effects are never estimated and the estimator is consistent for small T.

Unconditional Quantile Treatment Effects Under Endogeneity

This paper develops IV estimators for unconditional quantile treatment effects (QTE) when the treatment selection is endogenous. In contrast to conditional QTE, i.e. the effects conditional on a large number of covariates X, the unconditional QTE summarize the effects of a treatment for the entire population. They are usually of most interest in policy evaluations because the results can easily be conveyed and summarized. Last but not least, unconditional QTE can be estimated at n rate without any parametric assumption, which is obviously impossible for conditional QTE (unless all X are discrete). In this paper we extend the identification of unconditional QTE to endogenous treatments. Identification is based on a monotonicity assumption in the treatment choice equation and is achieved without any functional form restriction. Several types of estimators are proposed: regression, propensity score and weighting estimators. Root n consistency, asymptotic normality and attainment of the semiparametric efficiency bound are shown for our weighting estimator, which is extremely simple to implement. We also show that including covariates in the estimation is not only necessary for consistency when the instrumental variable is itself confounded but also for efficiency when the instrument is valid unconditionally. Monte Carlo simulations and two empirical applications illustrate the use of the proposed estimators.

Unconditional Quantile Treatment Effects Under Endogeneity

This article develops estimators for unconditional quantile treatment effects when the treatment selection is endogenous. We use an instrumental variable (IV) to solve for the endogeneity of the binary treatment variable. Identification is based on a monotonicity assumption in the treatment choice equation and is achieved without any functional form restriction. We propose a weighting estimator that is extremely simple to implement. This estimator is root n consistent, asymptotically normally distributed, and its variance attains the semiparametric efficiency bound. We also show that including covariates in the estimation is not only necessary for consistency when the IV is itself confounded but also for efficiency when the instrument is valid unconditionally. An application of the suggested methods to the effects of fertility on the family income distribution illustrates their usefulness. Supplementary materials for this article are available online.