Nicole Fortin

To explore the consequences of the under-representation of women among top-earners for the overall gender pay gap, Nicole Fortin, Brian Bell and Michael Boehm employ administrative annual earnings data from three different countries, namely Canada, Sweden, and the United Kingdom. They apply the approach used when analysing the earnings inequality among top incomes, and reweighting techniques, to the exploration of the gender pay gap. They argue that substantial "swimming upstream" effects were the result of the recent rise in top incomes, therefore accounting for differential progress in the gender gap in wages across time and an expanding gap that could not be explained by traditional factors.[4]

Nicole Fortin, Thomas Lemieux and Javier Torres use new information regarding the location of the immigrant study available in the 2006 Canadian Census to estimate the returns to Canadian and foreign human capital. They find that controlling the source of human capital (Canadian vs. foreign) explains a large part of the wage gap between immigrants and natives. They also demonstrate that the commonly-used imputation procedures (e.g. Friedberg, 2000)[5] that allocate national and foreign education according to age at arrival is more likely to overestimate the returns to foreign education and underestimate the returns to foreign work experience. They also argue that the wage gap between immigrants and natives is very heterogeneous across all places of birth, even after including the location fixed effects, although this inclusion significantly reduces the negative effects of the country of origin in countries such as China, Pakistan and India. The authors also observe a substantial heterogeneity in the mobility of human capital.[6]

John DiNardo, Thomas Lemieux and Nicole Fortin employ the semiparametric regression approach to analyse the impacts of various institutional changes and labour market factors on wage distribution. The procedure provides a visually clear representation of where in the density of wages these various factors exert the greatest impact. Using data from the Current Population Survey (CPS), they find similar results as in previous research. De-unionization and supply and demand shocks were key factors to account for the increasing wage inequality from 1979 to 1988. Moreover, they find that a decline in the real value of the minimum wage during the 1980s contributes to a massive proportion of a wider gender pay gap despite a nominal rise in the minimum wage, where the effect is found to be more significant among women.[7]

Fortin co-authored this research with Yann Algan. They employ the Programme for International Student Assessment (PISA) surveys to determine whether the gender gap in math test scores correlates with computer (digital devices) gaming. Using a decomposition based on a pooled hybrid specification, they find that 13% to 29% of the gender math gap exists due to gender differences in the incidence and returns to intense gaming. They argue that there is a potential role for gaming network effects as they compare and contrast the negative and positive girl-specific effects found for collaborative games and single-player games.[8]

Fortin, Philip Oreopoulos, and Shelley Phipps employ three decades of data from the "Monitoring the Future" cross-sectional surveys to show that the mode of girls' high school GPA distribution has shifted from "B" to "A" from the 1980s to the 2000s. This essentially leave boys behind as the mode of boys' GPA distribution remains the same at "B". In a reweighted OB decomposition of achievement at each GPA level, they find that gender differences in tertiary education expectations, controlling for school ability, and as early as 8th grade are the most crucial factor to account for this trend. They find that the growing population of girls aiming for a postgraduate degree are adequate to explain the rise in the proportion of girls obtaining "A's" over time. The relatively greater share of boys receiving "C" and "C+" can be accounted for by a more frequent misbehaviour at school, as well as a greater portion of boys only aiming to finish a two-year college degree.[9]

Nicole Fortin co-authored with Sergio Firpo and Thomas Lemieux to explain an extension of the Oaxaca-Blinder decomposition method that can be applied to various distributional measures. First of all, it requires the division of distributional changes into a wage structure effect and a composition effect using a reweighting method. Second of all, it requires the further division of the two components into the contribution of each explanatory variable using recentered influence function (RIF) regressions. They demonstrate the practical aspects of the procedure by exploring how factors, such as de-unionization, education, occupations, and industry changes, impacted the polarization of U.S. male wages from the late 1980s to the mid 2010s.[10]

This chapter, authored by Thomas Lemieux, Sergio Firpo and Nicole Fortin, provides an overview of decomposition methods that have been developed after the seminal work of Oaxaca and Blinder in the early 1970s. These methods could be applied to decompose the divergence in a distributional statistic between two groups, or its change over time, into various explanatory factors. While the original work of Oaxaca and Blinder considered the case of the mean, their main focus is placed on other distributional statistics aside the mean (e.g. quantiles, the Gini coefficient or the variance). They also discuss the assumptions required in order to identify the various decomposition elements, different estimation methods are also proposed in the chapter. The authors also demonstrate how these methods work practically by discussing existing applications and working through a set of empirical examples.[11]