This paper empirically investigates the link between the level of government revenue per capita and six indicators of quality of governance in an unbalanced panel data set consisting of all countries in the world (217) using data from 1980 to 2020. It uses single-equation GMM techniques and a...

Can large-scale social safety nets be nutrition sensitive even if they do not explicitly incorporate health and nutrition as programmatic goals? This paper focuses on the consequences of a countrywide guaranteed workfare programme (MGNREGA) and subsidised food distribution scheme (PDS) in India...

Can large-scale social safety nets be nutrition sensitive even if they do not explicitly incorporate health and nutrition as programmatic goals? This paper focuses on the consequences of a countrywide guaranteed workfare programme (MGNREGA) and subsidised food distribution scheme (PDS) in India...

This paper empirically investigates the link between the level of government revenue per capita and six indicators of quality of governance in an unbalanced panel data set consisting of all countries in the world (217) using data from 1980 to 2020. It uses single-equation GMM techniques and a...

This paper considers a class of GMM estimators for general dynamic panel models, allowing for cross sectional dependence due to spatial lags and due to unspecified common shocks. We significantly expand the scope of the existing literature by allowing for endogenous spatial weight matrices,...

A new and rapidly growing econometric literature is making advances in the problem of using machine learning (ML) methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential...

This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A Generalized Method of...

The econometrics literature proposed several new causal machine learning methods (CML) in the past few years. These methods harness the strength of machine learning methods to flexibly model the relationship between the treatment, outcome and confounders, while providing valid inferential...

This paper studies semiparametric versions of the classical sample selection model (Heckman (1976, 1979)) without exclusion restrictions. We extend the analysis in Honor'e and Hu (2020) by allowing for parameter heterogeneity and derive implications of this model. We also consider models that...

There is a large theoretical literature on methods for estimating causal effects under unconfoundedness, exogeneity, or selection-on-observables type assumptions using matching or propensity score methods. Much of this literature is highly technical and has not made inroads into empirical...