equations test for estimators and endogeneity. The last six equations test for different sets of regressors.

Equations (1) and (2) test for alternative estimators. Equations (1) uses an OLS-IV model with one lag of the dependent variable while Equation (2) uses an AB-GMM estimator. Results are very close between the two equations, there are no changes in either sign or significance in any of the regressors and the coefficients are all very close.

Equations (3) and (4) test for lags and endogeneity. Equations always use quarter fixed effects to control for seasonal effects. However, this may not eliminate seasonal effects completely particularly because these effects may be different across regions. Equations (3) tests therefore with five lags so as to reach the same quarter of the previous year and the quarter before that (L4 and L5). We find that all quarter except L3 are significant and that the highest coefficient is in L4 (the same quarter in the previous year). This confirms that using only the first lag may not be optimal. However, Equation (3) also shows that L1 is the second most significant lagged value and also that the Sargan test is only slightly better than the one in Equation (2). Instead, if we treat the log of GDP per capita as endogenous (this is an option of the AB-GMM estimator), the Sargan test cannot reject the null hypothesis of exogeneity.<sup>5</sup> Therefore, we keep Equation (2) as the best compromise in terms of simplicity and endogeneity and test this model further with different specifications of regressors (Equations 5-10).

Results show that GDP per capita is non-significant across all specifications of the model except (3) where the effect is weakly significant and negative. This complies with what found for Morocco by Verme (2014) in the MENA countries study that used the ILO-EAPEP data. A middle level diploma as we defined it (see data section) reduces the probability of participation and this result is significant across all specifications of the model. We will see that this result is confirmed by the individual panel short-term models and has some clear explanations discussed further below. Instead, having a higher education increases the probability of participation as one should expect. It is noticeable that both the third and fourth quarter of the year significantly reduce the probability of participation. This is related to the agricultural season which is labor intensive during the second quarter of the year and by the fact that most active women are in agriculture.

Equations (5) and (7) include the share of the secondary and tertiary sector in total GDP while Equations (8), (9) and (10) include the log of value added for the primary, secondary and tertiary sectors separately. Equation (6) also tests for linearity of GDP per capita rather than for the U- shape hypothesis. The rationale of these equations is to see whether FLP is sensitive to structural changes of the economy or to changes in the output of particular sectors rather than simply GDP per capita. Gaddis and Klasen (2013), for example, find sectors values added to be more relevant than GDP per capita. We also saw that manufacturing and services are key sectors for female participation while GDP growth _per se_ has done nothing to increase FLP in Morocco. However,

> 5 The null hypothesis of the Sargan test is that the overidentifying restrictions are valid or, in other words, that the instruments taken together are exogenous. Therefore rejecting the null hypothesis is what validates our model in respect to endogeneity of the instruments.

10