Essays on econometric methods

This thesis consists of three chapters on econometric methods. In Chapter 1, I investigate the consequences of the simultaneous presence of small sample size, leveraged data, and heteroskedastic disturbances on the validity of the statistical inference in linear panel data models. I formalise the panel versions of two jackknife-type estimators and propose a new hybrid estimator. I derive their asymptotic distributions and analyse their finite sample properties with Monte Carlo simulations. I find that test statistics obtained with conventional robust standard errors are over-sized, upward biased, and with less power under heteroskedasticity and with good leveraged data and in small samples. In Chapter 2, I develop diagnostic methods for panel data to detect three types of anomalous units. I formalise statistical measures for quantifying the degree of leverage and outlyingness of units, and develop a method to visually detect the type of anomaly and its effect on other units. I use network analysis tools to show the total and bilateral influence. I then apply my method to four cross-country data sets used in published articles. Chapter 3 investigates the effect of gender sectoral segregation on employment contracts (part-time, permanent, remote work, number of weekly working hours) and hourly wages for both men and women. We use propensity score matching, the Kitagawa-Blinder-Oaxaca decomposition and Mincerian wage regressions to analyse the contribution of observable and unobservable factors on labour outcomes. We find that contractual features systematically chosen by a specific gender are more common in sectors dominated by that group and for both genders. Workers employed in female-dominated sectors are on average paid less but most of the gap is explained by the coefficient effect rather than differences in endowments in both gender dominated sectors. Women self-select into low-paid jobs where their skills are valued less, especially in female-dominated sectors.