The dummy variables for each RO category are included to control for RO category specific fixed effects and for the endogenous correlation of employment and minimum wages across RO categories. Value added for each broad industry category is included to control for changes in demand over time. To control for endogenous changes in yearly average minimum wages as well as other year-specific factors, a dummy variable is included for each year.

We estimate equation (1) with OLS and test for an employment effect of legal minimum wages in the covered sector. We then proceed to use equation (1) to consider the effect of minimum wages on the number of hours worked per week in the covered and uncovered sectors by substituting the log of hours worked for the EMP variable.

Further, to test the exogeneity of the minimum wage variable, we use one-year lagged minimum wages as an instrumental variable and obtain two-stage least squares estimates for the employment and hours worked regressions.

# **4.2 Challenges and limitations**

One of limitations in our estimation strategy is that we are unable, due to data limitations, to assign the exact minimum wage schedule, within the broader RO category to each worker in the sample. We have thus taken the minimum wage for each RO category to be the lowest stipulated wage in each RO for that category of RO workers. We have not assigned specific minimum wages for specific job titles and years of experience.<sup>11</sup> Further, we do need to be partially concerned with the problem of endogeneity with respect to the minimum wage variable in our analysis. The variation in minimum wage shifts over time in our sample is not as significant and we also do not observe structural changes in the minimum wage system for Mauritius. This may mean that endogeneity may remain a problem. We take account of this however, by obtaining two-stage least squares estimates which use lagged minimum wages as an instrument for minimum wages in addition to our OLS estimates.

With respect to the unemployed workers included as part of the uncovered sector in our analysis, we are limited in that we do not have complete occupation data for all years in the period. We have the required industry data for all individuals used in the analysis. However, for the unemployed who have worked before, we have occupational data about their last job for only eight of the eleven years we consider.<sup>12</sup> These workers however do not constitute a large number of individuals in the sample and are more likely than not to be workers in the occupational classes covered by ROs. The implication of this minor data limitation is thus that, in the years 2004, 2006 and 2007, for which we do not have occupational data for the

> 11 See Table 1 above which shows the number of wage rates there were in 2016. Ideally, we would want to assign each worker their very specific wage rate to fully exploit the variation in minimum wages in the complex remuneration orders system to obtain estimates that do not raise any endogeneity concerns with regard to the minimum wage variable.

> 12 In Appendix Two we explain how we have used occupational and industry codes to assign workers and unemployed workers who have worked before to remuneration order categories.

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