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Abstract

In this paper, we reassess the impact of inequality on growth, The majority of previous papers have employed (system) GMM estimation. However, recent simulation studies indicate that the problems of GMM when using non-stationary data such as GDP have been grossly underestimated in applied research. Concerning predetermined regressors such as inequality, GMM is outperformed by a simple least square dummy variable estimator. Additionally, new data have recently become available that not only double the sample size compared to most previous studies but that also address the substantial measurement issues that have plagued past research. Using these new data and an LSDV estimator, we provide an analysis that both accounts for the conditions where inequality is beneficial or detrimental to growth and distinguishes between market-driven inequality and redistribution. We show that there are situations where market inequality affects growth positively while redistribution is simultaneously beneficial.

Abstract

In this paper, we propose a simple approach to estimate impulse response function through smoothed local projections, thereby utilizing the flexibility of local projections, while creating smooth and economically plausible impulse response functions as provided by VARs. The approach allows to determine the appropriate degree of smoothing endogenously through a standard information criterion. This also avoids oversmoothing and provides an estimator that is generally more efficient than standard local projections.