Using a state-level panel data set on the incidence of hate crimes in India, this paper implements difference in difference (DID) and triple difference in difference (DDD) research designs to estimate the causal impact of the right-wing BJP's win in the 2014 parliamentary elections on hate...

Kalyal Sanayal's work on postcolonial capitalism has been influential in many strands of critical social theory. In this brief note, I investigate three key components of his argument and find them wanting. In particular, I show that the evolution of land ownership in India does not support the...

This book presents the main economic argument developed by Marx in the three volumes of Capital in a coherent and comprehensive manner. The first part presents the main economic argument contained in the Capital in a coherent and comprehensive manner. It also delves into three long-standing...

In economics, it is common to use dimensioned variables, e.g. earnings (measured in dollars per year), as arguments in the logarithmic function. This is conceptually problematic because a logarithmic function can only take dimensionless quantities as its argument. One way to avoid this...

Building on a recently developed methodology for sensitivity analysis that parametrizes omitted variable bias in terms of partial R2 measures, I propose a simple statistic to capture the severity of omitted variable bias in any observational study: the probability of omitted variable bias...

Covariate benchmarking is an important part of sensitivity analysis about omitted variable bias and can be used to bound the strength of the unobserved confounder using information and judgments about observed covariates. It is common to carry out formal covariate benchmarking after...

In this paper, I discuss three aspects of the Frisch-Waugh-Lovell theorem. First, I show that the theorem holds for linear instrumental variables estimation of a multiple regression model that is either exactly or overidentified. I show that with linear instrumental variables estimation: (a)...

Covariate benchmarking is an important part of sensitivity analysis about omitted variable bias and can be used to bound the strength of the unobserved confounder using information and judgments about observed covariates. It is common to carry out formal covariate benchmarking under the...