Fontana and Wood (2000) were the first to extend a gendered CGE model to household production, i.e., going beyond GDP production. This adds to data requirements but has the important advantage of transcending the artificial boundary between time spent on GDP production and (often larger amounts of) time spent on production of household services for own consumption and leisure. As a result, it becomes possible to consider the impact (including gender aspects) of changes in market work on time spent on leisure and household work, all of which in different ways contribute to household and individual well-being, including various trade-offs. The terminology for and extent of disaggregation of household work have varied but reference is often made to social reproduction, an activity that may be further disaggregated into activities like different types of care, cooking, cleaning, washing, and shopping. Both the initial contribution by Fontana and Wood and subsequent contributions have focused on trade-related policy simulations. This narrow focus suggests that studies on other issues, like care policies in this paper, could yield new insights at the same time as they may lead to enriched model formulations and impose new data requirements. In an analysis of Ethiopia, Ruggeri Laderchi et al. (2010) developed a gendered version of MAMS (Maquette for Millennium Development Goal Simulations), a recursive dynamic CGE model designed for medium- and long-run policy analysis that covers indicators related to the Millennium Development Goals (MDGs). The model applies the Fontana and Wood approach to household production and gender. Its innovative aspects lie in its treatment of the educational system: it is split into three levels with endogenous and gendered entry, graduation, dropout, and repetition. Students exiting from the educational system enter the segment of the labor market that corresponds to their educational attainment in shares that reflect data on labor force participation. Those who leave school early wait until they reach labor force age. Model simulations analyzed the impact of rapid expansion in the educated female labor force on wages, employment and household services and how these impacts are conditioned by labor market segmentation and productivity growth within household services. 

Future research will likely generate methodological advances that help analysis based on gendered CGE models contribute to emerging policy debates. One broad area is related to policymaking in the context of a global rise in female labor force participation – what impact may different policies have on wages, household production, welfare, and inequality, including both gender-specific and more aggregate indicators? The analysis in the rest of this paper is an example of this: East Asia faces important gender-related policy challenges in the context of little (or no) growth for the working-age population, low rates of female labor force participation, rapid growth in an elderly population that needs care, and gender inequalities both in the household and market spheres. As female participation in GDP work increases, it is important to try to better understand the mechanisms that generate lower wages for women, the impacts of wage gaps, and how they may be overcome. This is important in the context of South Korea: In 2010, its gender pay gap was almost 40 percent, the largest among OECD countries with data (Cahuc et al. 2014, pp. 481-482). Lower wages are related to the concentration of female employment in occupations and sectors in which wages are relatively low. However, also within sectors, women tend to earn lower wages; this may be due to differences in productivity (which in their turn may be due to differences in experience and education, both related to differences in work tasks) or wage discrimination (wage differences that are not associated with productivity differences). While wage discrimination seems to be common, it is difficult to come up with exact measures since it is hard to measure productivity. Another broad and challenging area revolves around the impact of different types of consumption and investment on the accumulation of human capital and growth. The education analysis in the Ethiopia MAMS application touches on this aspect. However, this analysis could be extended to consider the links between, on one hand, growth and human capital accumulation and, on the other hand, the consumption of prepared food, care, and education services, supplied by the market and households.