The use of the National Transfer Accounts (NTA) methodology has opened up the possibility of examining gender differences in income and consumption throughout the life cycle. This article presents the results of the study of the gender profile of income and consumption based on the NTA of Moldova for 2019. Moldova is characterized by a low level of employment of the population, low incomes and a high involvement of the population in international labor migration. Women's labor incomes are lower than men's throughout the life cycle, and the life cycle surplus is entirely formed by men, who are net donors to cover the life cycle deficit of other age groups during the working period. More than two-thirds of the economic life cycle deficit is held by women, and the gender gap in economic dependence was 22.7% - an additional share of the total labor income needed to finance women's economic dependence compared to the total labor income needed to finance men's economic dependence. Differences in the age profile of working income are due to the fact that women enter the labor market a little later than men, due to much higher enrollment rates in higher education institutions, as well as due to low participation in the labor market during the period associated with the birth and upbringing of children. The economic dependence of women is a reflection of the “gender contract” characteristic of Moldova, according to which a man and a woman participate in the labor market, but the woman still has most of the household chores.” It can be assumed that the problem of underestimation of income by the population in the household survey could cause an underestimation of the age profile of labor income, which, in turn, influenced the estimation of the size of the economic life cycle deficit.

The author employs a Computable General Equilibrium (CGE) model calibrated on a SocialAccounting Matrix for the Moldovan economy and enhanced with demographic details to answer three questions: 1) what has been the short-term socioeconomic impact of COVID-19, including the distributional ones from the gender and age perspective? 2) how likely were the 2020 policy measures to provide an adequate immediate response to the crisis? and 3) would there exist an alternative, more optimal policy? According to the CGE-based simulation results, cumulative effect of the COVID-19 economic shocks represents around 11% of the Moldovan GDP. All economic sectors are predicted to decline, with transport, HORECA and services to population sectors suffering the heaviest contractions. Transport sector employs predominantly mid-aged men, while the latter two typically employ women. Age-and sex-structure of employment by sectors explain why men aged 25-34 and women aged 15-24 suffer the largest reduction of their wage income (around 10%). Reflecting the income contraction of the breadwinning age categories and reduction in intra-household transfers, children' consumption declines accordingly. The older generations relying on public pensions are relatively better sheltered against the COVID-19 socioeconomic effects, as pensions remain rather stable. The analysis suggests that the package of measures adopted by Moldovan government has had minor impact, with VAT reduction to HORECA sector having smaller compensatory effect compared to direct payments to infected doctors and labor-related subsidies. A combination of fiscal and structural measures would have provided a socially fairer and economically more efficient response to the crisis.

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The Republic of Moldova lacks an updated Input-Output Table (IOT), which is necessary for compiling a Social Accounting Matrix and calibrating a Computable General Equilibrium model. The author’s goal is to estimate by economic, mathematical and statistical methods the IOT for 2019 using a IOT for 2014 as a reference. The author first defines an optimal classification of sectors present in IOT-2019 to ensure comparability with IOT-2014. Subsequently, the author reconciles data from National Accounts Statistics, Balance of Payments and budget execution reports to compile the IOT quadrant 1. For quadrants 2 and 3, the columns for which there are statistical data in the aforementioned sources are first determined, after which the missing parts are compiled formulating and solving a mathematical optimization problem, in which the objective function is minimization of structural differences between IOT-2019 and IOT- 2014.