_The method used to calculate changes in trends is illustrated using data from Mexico._ Panel A of Table 2 gives the employment ratios for low educated male youth in urban areas, for each quarter between 2007 and 2009. We then calculate the change of employment ratio relative to the same quarter in the previous year. The employment ratio changed from 57.3 percent in the first quarter of 2007 to 57.8 percent in the same quarter in 2008, yielding a slight increase by 0.5 percentage points. For each cell, we then take the average of these changes across all pre-crisis periods (up until 4<sup>th</sup> quarter of 2008) and all post-crisis periods (from 1<sup>st</sup> quarter of 2009 and onwards).In this cell, the average year on year change in the employment rate after the crisis trend in employment ratio is -3.6 percentage points, and the average pre crisis trend is -0.3 percentage points, indicating that for this cell, growth in the employment ratio slowed by 3.3 percentage points. Taking a weighted average over all 16 cells, with weights equal to the cell population in this case, gives the overall change in the employment ratio. Among all Mexican workers, the employment ratio slowed by 1.7 percentage points (Panel B of Table 2). This difference, between the pre-crisis and post-crisis averages of year-on-year changes of the indicator, is used to measure workers’ labor market adjustment throughout this paper.

# **_Group Comparison_**

_The analysis examines differences in labor market adjustment by group, controlling for selected observable characteristics_ . In particular, we regress the change in the trend, described above, for each cell in each country on dummy variables for the four characteristics, separately for each outcome and country.<sup>18</sup> There are therefore 16 observations in each regression. Using regression coefficients rather than simple tabulations isolates changes in the returns to a single characteristic, while holding the other three constant; for example, a rise in the coefficient on youth unemployment cannot be attributed to higher education levels among youth. That is, we estimate the following equation (See Appendix B for more details regarding the specification): (1) where<sup>_Y_~</sup> _i_<sup>is the difference in the trend for each cell</sup><sup>_i_in each country, and</sup><sup>_Men_,</sup><sup>_Young_,</sup><sup>_LowEduc_, and</sup> _Urban_ represent dummy variables for each group.

_We present the coefficients from the linear regressions to capture the difference between groups._ The sign and magnitude of each coefficient indicate each group’s relative vulnerability to the crisis compared to their counterparts. For example, a negative coefficient on employment ratio suggests that men’s employment deteriorated more rapidly than women’s. Meanwhile, the relative magnitudes of the coefficients indicate which groups were most exposed to the shock. For example, a value of that is greater in absolute value than would indicate that age disparities were larger than education disparities in the employment adjustment.

> 18 Observations are weighted both by the population weight for that cell, and by the number of unweighted observations used to generate the average, to adjust for heteroscedasticity in the cell mean outcomes.

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