Essays in labor economics and econometrics applications of the copula method

This dissertation mainly consists of three essays of original research. One element that these essays have in common is a copula method that generates joint distributions in flexible ways. Therefore, Chapter 1 describes the copula method as an introduction. Two essays, Chapter 2 and Chapter 3, are empirical research in the field of labor economics, in which the copula method is applied to construct econometrics models. One essay, Chapter 4, uses copulas in order to develop a new econometric technique.;Chapter 2 empirically investigates the difference in wage structures of permanent workers and temporary workers in the Netherlands. The findings are that starting wages of permanent workers are slightly lower than starting wages of temporary workers and that wages of permanent workers grow more rapidly than wages of temporary workers. These findings derive from an econometric model that is built on a distributional assumption using the copula method that relaxes the traditional model.;Chapter 3 empirically investigates the structure of adjustment costs of factors of production with a plant-level panel dataset from the Indonesian manufacturing sector. The copula method is applied in order to estimate the adjustment costs of labor and capital simultaneously and to differentiate the distribution assumption from a more standard approach used in previous studies. The estimates provide evidence of nonconvex and asymmetric adjustment costs of both labor and capital.;Chapter 4 proposes a new approach to estimating sample selection models that combines Generalized Tukey Lambda (GTL) distributions with the copula method. The GTL distribution is a versatile univariate distribution that permits a wide range of skewness and thick- or thin-tailed behavior in the data that it represents. The versatility arising from inserting GTL marginal distributions into copula-constructed bivariate distributions reduces the dependence of estimated parameters on distributional assumptions in applied research. A thorough Monte Carlo study illustrates that our proposed estimator performs well under various data generating processes. Six applications illustrate the value of the proposed GTL-copula estimator.