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ABSTRACT: This paper develops a threshold panel data nonlinearity test for poverty traps. This test is applied to describe the relationship between GDP per capita and capital stock per capita. The new testing strategy extends the work on nonlinearity tests for panel data by considering threshold nonlinearities in the fixed-effects components. Monte Carlo simulations are conducted to evaluate the finite-sample performance of these tests. Our application to a panel of countries for the period 1973-2007 uncovers the presence of two regimes determined by the level of capital stock per capita. The conclusions from our test also support the existence of a poverty trap determined by a capital stock per capita level at the 11% quantile of its pooled worldwide distribution.
ERN: Panel Data Models (Single) (Topic). 10/2010;
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ABSTRACT: This paper studies the effects of increasing formality via tax reduction and simplification schemes on micro-firm performance. We develop a simple theoretical model that yields two intuitive results. First, low- and high- ability entrepreneurs are unlikely to be affected by a tax reduction policy reform and therefore, this policy has an impact only on a segment of the micro-firm population. Second, the benefits to such reform, as measured by profits and revenues, are increasing in the entrepreneur's ability. Empirically, we estimate the effect of formality on the entire conditional distribution (quantiles) of performance using the 1997 Brazilian SIMPLES program and a rich survey of formal and informal micro-firms. The econometric approach employed compares eligible and non-eligible firms, born before and after SIMPLES in a local interval about the introduction of SIMPLES. Moreover, to carry the estimations, we use an estimator that combines both quantile regression and the regression discontinuity strategies. The empirical results corroborate the positive effect of formality on micro-fims' performance and produce a clear characterization of who benefits from these programs.
ERN: Allocative Efficiency; Cost-Benefit Analysis; Externalities (Topic). 10/2010;
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ABSTRACT: This paper studies the connections among quantile regression, the asymmetric Laplace distribution, maximum likelihood and maximum entropy. We show that the maximum likelihood problem is equivalent to the solution of a maximum entropy problem where we impose moment constraints given by the joint consideration of the mean and median. Using the resulting score functions we propose an estimator based on the joint estimating equations. This approach delivers estimates for the slope parameters together with the associated "most probable" quantile. Similarly, this method can be seen as a penalized quantile regression estimator, where the penalty is given by deviations from the median regression. We derive the asymptotic properties of this estimator by showing consistency and asymptotic normality under certain regularity conditions. Finally, we illustrate the use of the estimator with a simple application to the U.S. wage data to evaluate the effect of training on wages.
ERN: Estimation (Topic). 10/2010;
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Journal of Time Series Analysis 32(3):253-267. · 0.76 Impact Factor
