Four alternative but related approaches to empirical evaluation of policy interventions are studied: social experiments, natural experiments, matching methods, and instrumental variables. In each case the necessary assumptions and the data requirements are considered for estimation of a number of key parameters of interest. These key parameters include the average treatment effect, the treatment on the treated and the local average treatment effect. Some issues of implementation and interpretation are discussed drawing on the labour market programme evaluation literature.

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Alternative Approaches to Evaluation in Empirical Microeconomics

This paper empirically assesses the wage effects of the Job Corps program, one of the largest federally funded job training programs in the US Even with the aid of a randomized experiment, the impact of a training program on wages is difficult to study because of sample selection, a pervasive problem in applied microeconometric research Wage rates are only observed for those who are employed, and employment status itself may be affected by the training program This paper develops an intuitive trimming procedure for bounding average treatment effects in the presence of sample selection In contrast to existing methods, the procedure requires neither exclusion restrictions nor a bounded support for the outcome of interest Identification results, estimators, and their asymptotic distribution are presented The bounds suggest that the program raised wages, consistent with the notion that the Job Corps raises earnings by increasing human capital, rather than solely through encouraging work The estimator is generally applicable to typical treatment evaluation problems in which there is nonrandom sample selection/attrition Copyright , Wiley-Blackwell

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Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects

Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; semi-nonparametric models are more flexible and robust, but lead to other complications such as introducing infinite-dimensional parameter spaces that may not be compact and the optimization problem may no longer be well-posed. The method of sieves provides one way to tackle such difficulties by optimizing an empirical criterion over a sequence of approximating parameter spaces (i.e., sieves); the sieves are less complex but are dense in the original space and the resulting optimization problem becomes well-posed. With different choices of criteria and sieves, the method of sieves is very flexible in estimating complicated semi-nonparametric models with (or without) endogeneity and latent heterogeneity. It can easily incorporate prior information and constraints, often derived from economic theory, such as monotonicity, convexity, additivity, multiplicity, exclusion and nonnegativity. It can simultaneously estimate the parametric and nonparametric parts in semi-nonparametric models, typically with optimal convergence rates for both parts. This chapter describes estimation of semi-nonparametric econometric models via the method of sieves. We present some general results on the large sample properties of the sieve estimates, including consistency of the sieve extremum estimates, convergence rates of the sieve M-estimates, pointwise normality of series estimates of regression functions, root-n asymptotic normality and efficiency of sieve estimates of smooth functionals of infinite-dimensional parameters. Examples are used to illustrate the general results.

Large Sample Sieve Estimation of Semi-Nonparametric Models

Innovation in SMEs exhibits some peculiar features that most traditional indicators of innovation activity do not capture Therefore, in this paper, we develop a structural model of innovation which incorporates information on innovation success from firm surveys along with the usual R&D expenditures and productivity measures We then apply the model to data on Italian SMEs from the “Survey on Manufacturing Firms” conducted by Mediocredito-Capitalia covering the period 1995-2003 The model is estimated in steps, following the logic of firms’ decisions and outcomes We find that international competition fosters R&D intensity, especially for high-tech firms Firm size, R&D intensity, along with investment in equipment enhances the likelihood of having both process and product innovation Both these kinds of innovation have a positive impact on firm’s productivity, especially process innovation Among SMEs, larger and older firms seem to be less productive

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Innovation and Productivity in SMEs - Empirical Evidence for Italy

Innovation in SMEs exhibits some peculiar features that most traditional indicators of innovation activity do not capture Therefore, in this paper, we develop a structural model of innovation that incorporates information on innovation success from firm surveys along with the usual R&D expenditures and productivity measures We then apply the model to data on Italian SMEs from the “Survey on Manufacturing Firms” conducted by Mediocredito-Capitalia covering the period 1995–2003 The model is estimated in steps, following the logic of firms’ decisions and outcomes We find that international competition fosters R&D intensity, especially for high-tech firms Firm size and R&D intensity, along with investment in equipment, enhances the likelihood of having both process and product innovation Both these kinds of innovation have a positive impact on firm’s productivity, especially process innovation Among SMEs, larger and older firms seem to be less productive

/pdf/innovation-and-productivity-in-smes-empirical-evidence-for-3kwfj88pbf.pdf

Innovation and productivity in SMEs: empirical evidence for Italy

Sample selection models provide an important way of accounting for economic decisions that combine discrete and continuous choices and of correcting for nonrandom sampling. Nonparametric estimators for these models are developed in this paper. These can be used for estimating shapes and important economic quantities, as in standard nonparametnc regression. Endogeneity of regressors of interest is allowed for. Series estimators for these models are developed, which are useful for imposing additivity restnctions that arise from selection corrections. Convergence rates and asymptotic normality results are derived. An application to returns to schooling among Australian young females is given.

Francis Vella

Papers

Nonparametric Estimation of Sample Selection Models