|ECS|40-44|0.926***|-6.436**|0.372**|33.247***|1,120|U|
|---|---|---|---|---|---|---|---|
|LCN|40-44|0.921***|-2.089|0.238|5.369|662||
|MEA|40-44|0.915***|3.273|-0.172|-12.462|410||
|NAC|40-44|0.760***|315.680***|-15.050***|-1,635.717***|62|IU|
|SAS|40-44|0.943***|-3.766|0.273|15.969|167||
|SSF|40-44|0.944***|-1.755***|0.110***|11.357***|1,000|U|
|ALL|50-54|0.950***|-5.732***|0.374***|24.338***|4,028|U|
|EAS|50-54|0.917***|-4.939***|0.311***|24.287***|595|U|
|ECS|50-54|0.920***|-17.879***|1.082***|77.042***|1,120|U|
|LCN|50-54|0.920***|6.170|-0.163|-36.930|663||
|MEA|50-54|0.928***|-1.444|0.095|6.832|419||
|NAC|50-54|0.812***|339.896***|-16.008***|-1,789.375***|62|IU|
|SAS|50-54|0.920***|-6.196|0.487|22.862|168||
|SSF|50-54|0.945***|-2.730***|0.164***|15.189***|1,001|U|

Source: ILO-EAPEP database (Female LFP) and World Bank, World Development Indicators (GDP per capita in PPP values). Significance level: ***=.01; **= .05; *=.1 - *; Standard errors omitted. EAS: East Asia and the Pacific; ECS: Europe and Central Asia; LCN: Latin America and the Caribbean; MEA: Middle East and North Africa; NAC: North America; SAS: South Asia; SSF: Sub-Saharan Africa.

In addition to the share of agriculture in output, the literature on FLP has highlighted two other factors that help explain the U shape hypothesis in the long-run. The first is the increased level of female education, especially at the secondary level. Secondary education allows women to take up nonagricultural jobs in the modern sectors and increases the bargaining capacity within the household. The second is the steady decline in fertility that frees some of the time used for household chores. It is therefore instructive to include into the U-shape hypothesis equations fertility and female secondary education and try to see whether the disappointing performance of MENA countries in terms of FLP gains can be explained at least in part by non-increasing female secondary education or fertility rates that are not declining.

However, there are two problems with including both secondary education and fertility rates in the same FLP equation. The first is that female secondary education and fertility rates are strongly inversely correlated, creating a collinearity problem in the FLP equations. This collinearity, in turn, can artificially alter the sign and significance of the GDP coefficients. The second is that, somehow surprisingly, female secondary education rates are much less available worldwide than fertility rates, FLP rates or GDP per capita. The inclusion of female secondary education would make our countries’ longitudinal equations too short to run. Forcibly, in what follows, we only use fertility rates on the assumption (widely supported by the literature) that female secondary education and fertility rates are highly inversely correlated.

Following from the above and given the results on the main pillars of the U-shape hypothesis presented in the nonparametric section, in Table 3 we re-run FPR equations using fertility rates, the share of agriculture in value added and the log of value added in services and manufacturing (constant prices, local currency) as independent variables. Here we want to test the contribution of each factor to FLP. Note that we are taking separately the share of agricultural value added as it is standard for testing the U-shape hypothesis and the value added of manufacturing and services as indicated by Gaddis and Klasen (2013) in their critique.

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