Most economies in the Middle East and North Africa region suffer from high unemployment rates, particularly among the educated youth, and with substantially higher rates for women compared to men. At the same time, female labor force participation in the region is also quite low. These phenomena seem to be not just signs of inequity, but also indicators of conditions that lead to resource waste and low growth. In this sense, it is important to understand the underlying causes of these phenomena so that more lights can be shed on the factors that hamper inclusive growth in the region. The high unemployment and low participation problems have been blamed on a host of factors ranging from dysfunctional educational systems to the adverse incentives created by the distribution of resource rents, extensive public sector employment, and rigid labor regulations. However, there are also scholars who question such attributions. For example, some contend that, depending on the circumstances, embracing the free market might adversely affect parts of the labor market, particularly women’s employment conditions. The argument is that although deregulation and structural adjustment packages have led to flexible labor markets, a common consequence for women is a shift towards low-pay jobs with limited prospects for productivity growth. In this context, it is important to measure the correct effects of each factor on various types of labor in order to identify the main sources of the problem and to design appropriate policy responses. In this paper, we take advantage of the cross-country micro dataset of Global Entrepreneurship Monitor to take a step towards addressing this concern. The dataset allows us to control for some key personal characteristics and, thus, isolate them from the relationship of country policies and institutions with the pattern of labor allocation by men and women. Based on the information available in the dataset, we construct a labor allocation indicator with nine possible outcomes (e.g., homemaking, self-employment, full-time employment, etc.) for each individual. We use a statistical model to relate the labor allocation of each individual to his/her age, education, and country of residence, separately for men and women. We then calculate the marginal probability effects of the country of residence on the outcomes and regress them on country institutions and policies. For measuring labor-market related country institutions and policies, we use the subcomponents of World Bank’s datasets on Employing Workers, Doing Business, and Women, Business and the Law.