@TechReport{dp-503,
  author        = {Schwiebert, Jörg},
  astring       = {Jörg Schwiebert},
  title         = {Analyzing the Composition of the Female Workforce - A
                  Semiparametric Copula Approach},
  month         = {October},
  year          = {2012},
  pages         = {30},
  size          = {294},
  number        = {503},
  language      = {en},
  keywords      = {Sample selection model, semiparametric estimation, copula
                  approach, composition of the female workforce, female labor
                  force participation},
  jelclass      = {C21, C24, J21, J31},
  abstract      = {We provide a semiparametric copula approach for estimating
                  a "classical" sample selection model. We impose that the
                  joint distribution function of unobservables can be
                  characterized by a specifc copula, but the marginal
                  distribution functions are estimated semiparametrically. In
                  contrast to existing semiparametric estimators for sample
                  selection models, our approach provides a measure of
                  dependence between unobservables in main and selection
                  equation which can be used to analyze the composition of,
                  say, the female workforce. We apply our estimation
                  procedure to a female labor supply data set and show that
                  those women with the best skills participate in the labor
                  market; moreover, we find evidence for the existence of an
                  ability threshold which involves that women with high
                  ability are to some extent advantaged and, therefore, have
                  also obtained the best skills.}
}