component, suggesting either changes in unobservable characteristics or increases in returns to endowments.

**Figure 15 - Oaxaca-Blinder Decomposition of Change in Average Wages (2007-2012)**

<!-- Start of picture text --> Total gap 0.57<br>Explained<br>Unexplained<br>Decomposition -0.02 0.59<br>-0.10 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70<br><!-- End of picture text --> Source: EEEI (2007) and ECVMAS (2012) – authors’ calculations. Note: Total gap refers to the total difference in mean real log wages between 2007 and 2012. The decomposition bar presents the results of a two-fold decomposition using a pooled model for the reference coefficients. The estimated model includes explanatory variables dummies for region of residence, type of work (agriculture, public sector and NGO, private informal, and private formal), household size, gender, age group, level of education, and 10 economic sectors of employment.

Understanding how the increase in labor income played out across the income distribution requires looking beyond the mean. Using the simple OB decomposition allows one to decompose the gap between two groups on a specific parameter of the distribution, i.e. the mean. This imposes a slight limitation to the interpretation of results since one cannot make any inference about quantiles or other distributional parameters.<sup>13</sup> There are several reasons why going beyond the mean may help the analysis. The main one is that there may be significant differences in the composition of the wage increase between the rich and poor. This is an important consideration because these differences may lead to different policy decisions.

Based on the recentered influence function (RIF) methodology proposed by Firpo et al. (2009) and Fortin et al. (2011), RIF regressions are used to extend the analysis beyond the mean. This methodology allows measurement of the contribution of each factor to changes in labor income, and also decomposition of whether the effect of each factor is due to changes in endowments (“composition effect”) or changes in returns (“pay structure effect”). Furthermore, one can analyze changes for each quantile of the labor income distribution. The changes in hourly labor income between 2007 and 2012 are thus decomposed along three dimensions: factors, endowment versus returns, and quantiles.<sup>14</sup> Looking across the distribution shows that hourly wages grew faster for lower income individuals, with returns to observable characteristics explaining almost all of this wage increase between 2007 and 2012. Quantile decomposition of change in hourly income shown in Figure 16 shows the predominant role played by returns to observable (i.e. endowment or explained effects) and unobservable characteristics of workers. Changes in observable characteristics did play a small part (10 percent) for individuals at the lower end of

> 13 As detailed in Firpo et al. (2010), going beyond the mean for the detailed decomposition is not as straightforward: while the mean can be decomposed using standard regressions, one cannot decompose quantiles using simple quantile regressions. See Annex 4 for further details on the RIF methodology.

> 14 When reading the results of RIF decompositions, it is important to keep in mind that changes in coefficient interpreted as _returns_ may also be picking up changes in omitted variables rather than solely changes in the returns to endowments.

17