Gabriel Montes-Rojas

Releasing Constraints to Growth or Pushing on a String? Policies and Performance of Mexican Micro-Firms

Abstract Using firm-level data from Mexico, this paper investigates the firm characteristics associated with participation in credit markets, access to training, tax payments and membership in business associations. We find that firms which participate in these institutions exhibit significantly higher profits. Moreover, firms that borrow from formal or informal sources and those that pay taxes are significantly more likely to stay in business but firms that received credit exhibit lower rates of income growth. These results persist when firm characteristics that are arguably correlated with unobserved entrepreneurial ability are controlled for. Our findings suggest that the significant within-country differences in firm productivity observed in developing economies are due in part to market and government failures that limit the ability of micro-firms to reach their optimal sizes.

Threshold Quantile Autoregressive Models

This article studies estimation and asymptotic properties of Threshold Quantile Autoregressive processes. In particular, we show the consistency of the threshold and slope parameter estimators for each quantile and regime, and derive the asymptotic normality of the slope parameter estimators. A Monte Carlo experiment shows that the standard ordinary least squares estimation method is not able to detect important nonlinearities produced in the quantile process. The empirical study concentrates on modelling the dynamics of the conditional distribution of unemployment growth after the second world war. The results show evidence of important heterogeneity associated with unemployment and strong asymmetric persistence of unemployment growth in the higher quantiles.

From Privatization to Re-nationalization: What went Wrong with Privatizations in Argentina?

The privatization process in Argentina is analysed. Beginning with a very ambitious programme, a weak regulatory environment was created because of lack of experience and as a result of the lobbying power of the newly created enterprises. Numerous exclusive privileges were assigned to these sectors, making them the most profitable industries during the 1990s. As the economy crashed in 2001 and the local currency was devalued, a new government took office, which renegotiated all contracts. This resulted in re-privatization and re-nationalization of many services. The pro- and anti-privatization arguments are reviewed, using empirical evidence from Argentina. An in-depth study is also provided of the three sectors where this negotiation was most intense: the railways, water and sewerage and postal services.

Quantile autoregressive distributed lag model with an application to house price returns

This article studies quantile regression in an autoregressive dynamic framework with exogenous stationary covariates. We demonstrate the potential of the quantile autoregressive distributed lag model with an application to house price returns in the United Kingdom. The results show that house price returns present a heterogeneous autoregressive behaviour across the quantiles. Real GDP growth and interest rates also have an asymmetric impact on house prices variations.

Trade Reform and Inequality: The Case of Mexico and Argentina in the 1990s

This paper provides empirical assessments of one of the leading explanations for the increase in skill premium in Mexico and Argentina during the 1990s: trade liberalisation. We present evidence showing that imports increase skill premium in Mexico, while exports reduce it. In Argentina, trade increased skill premium in the early 1990s (the beginning of trade reforms), although it reduced it later in the decade. These results are helpful for a comparison between South-South integration, FTAA or bilateral FTAs with Northern economies as alternative trade policy options for Latin American countries. Copyright 2008 The Authors. Journal compilation 2008 Blackwell Publishing Ltd.

Robust Misspecification Tests for the Heckman's Two-Step Estimator

This article constructs and evaluates Lagrange multiplier (LM) and Neymans C(α) tests based on bivariate Edgeworth series expansions for the consistency of the Heckmans two-step estimator in sample selection models, that is, for marginal normality and linearity of the conditional expectation of the error terms. The proposed tests are robust to local misspecification in nuisance distributional parameters. Monte Carlo results show that testing marginal normality and linearity of the conditional expectations separately have a better size performance than testing bivariate normality. Moreover, the robust variants of the tests have better empirical size than nonrobust tests, which determines that these tests can be successfully applied to detect specific departures from the null model of bivariate normality. Finally, the tests are applied to womens labor supply data.

GENERAL SPECIFICATION TESTING WITH LOCALLY MISSPECIFIED MODELS

A well known result is that many of the tests used in econometrics, such as the Rao score (RS) test, may not be robust to misspecified alternatives, that is, when the alternative model does not correspond to the underlying data generating process. Under this scenario, these tests spuriously reject the null hypothesis too often. We generalize this result to generalized method of moments–based (GMM-based) tests. We also extend the method proposed in Bera and Yoon (1993, Econometric Theory 9, 649–658) for constructing RS tests that are robust to local misspecification to GMM-based tests. Finally, a further generalization for general estimating and testing functions is developed. This framework encompasses both likelihood and GMM-based results.

Tests for skewness and kurtosis in the one-way error component model

This paper derives tests for skewness and kurtosis for the panel data one-way error component model. The test statistics are based on the between and within transformations of the pooled OLS residuals, and are derived in a moment conditions framework. We establish the limiting distribution of the test statistics for panels with large cross-section and fixed time-series dimension. The tests are implemented in practice using the bootstrap. The proposed methods are able to detect departures away from normality in the form of skewness and kurtosis, and to identify whether these occur at the individual, remainder, or both error components. The finite sample properties of the tests are studied through extensive Monte Carlo simulations, and the results show evidence of good finite sample performance.

Informal Jobs and Trade Liberalisation in Argentina

Abstract Rapid trade liberalisation can exert profound effects on labour markets. Domestic firms, to sustain competitiveness for survival, could react by cutting labour benefits to achieve cost reductions. Alternatively, trade liberalisation may alter the industry composition of firms, changing the aggregate formality rates. This paper studies the relationship between trade liberalisation and informality in Argentina. Using manufacturing industry-level data for 1992-2003, the results confirm the hypothesis that trade increases informality in industries that experience sudden foreign competition. This explains about a third of the increase in informality. Sectors with higher investment ratios are able to neutralise and reverse this effect.

Which quantile is the most informative? Maximum likelihood, maximum entropy and quantile regression

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.