authors found the U-shaped relation to hold for the period 1970-1985 (although the turning point of the U curve lowers to $1,600).

Only one recent paper has questioned the U-shaped hypothesis (Gaddis and Klasen, 2013). This paper used cross-country data for the period 1980-2005 and found that results related to the U-shape hypothesis are very sensitive to the data source used and that they tend to disappear with the use of panel data. The paper also finds a great heterogeneity of results across countries, little support for the structural change hypothesis from agriculture to industry as an important factor in driving declining FLP rates and a more relevant role for sector specific growth rates as opposed to GDP growth. It recognizes, however, the role of fertility and education in explaining rising FLP on the positive side of the U curve.

While the declining portion of the U curve is mainly explained in terms of structural transformation of the economy from agriculture to industry, the explanation of the rising part of the curve relies on a more complex combination of factors. Fertility has been among the first factors to be identified (Golding, 1995) a factor that found consistent evidence in subsequent studies (Angrist and Evans, 1998; Bloom et Al., 2009). Similarly, female education was detected early on as a major factor of rising FLP rates (Goldin, 1995) and found matching evidence over the years across countries. These two factors are also related to each other (female education delays marriage and pregnancies) so that identifying the specific contribution of each factor on FLP is not straightforward but the evidence is rather solid in determining a correlation between fertility and education and FLP cross-country and longitudinally.

A more controversial set of factors relates to culture, gender norms and identity. Akerlof and Kranton (2000) made a convincing case of identity as being a major drive in female labor participation: _“Similarly women’s assumed lower desire for labor force participation (...) can be understood as the result of their identity as homemakers.”_ (p. 732). Goldin (2006) indicated two major factors that could explain the gains in female labor participation in the US during the 1970s and 1980s described as “expanded horizons” (an increase in women’s expectations that leads to a change in educational choices) and “altered identities” (an increase in women’s decisional power in relation to labor market choices within the family). These changes were largely the product of a cultural revolution that revolutionized female marriage age, college graduation rates and professional school enrollments, all factors occurring in the 1970s. US data clearly show a turning point around 1970 followed by the fast increase in FLP rates between 1970 and 2000. The factors that led to the cultural revolution of the 1960s may be debatable but the effect of this revolution on FLP rates is clearly evident in the US data.

A recent World Bank report on gender equality in the Middle East and North Africa (World Bank, 2013) found FLP to be low in the region and the gap between male and female labor force participation to be the highest by world standards. The report did not test the U-shape hypothesis but finds some of its drivers to have developed in the expected direction during the past few decades. The performance of the MENA countries in terms of education and health has been outstanding with major achievements in terms of declining fertility and mortality rates and in terms of steep improvements in female education. This makes low FPR in the MENA region the most puzzling unless these countries find themselves at the turning point of the U curve.

This paper returns to the U-shape hypothesis by focusing on MENA countries. In our knowledge, the only paper that tested the U-shape hypothesis in the MENA region is Tsani et al. (2013). This paper uses a pooled regression on 160 countries with the South-Mediterranean countries as control and then uses the coefficient for these countries to carry out CGE simulations of alternative scenarios. In this paper, we follow a different approach using cross-country panel equations and within countries time-series 4