Publications

Using dyadic trade data and bilateral aid for trade (AfT) figures from the OECD Creditor Reporting System (CRS) for a sample of 155 countries over the period 2002-2019, we implement a gravity analysis, to assess whether bilateral aggregate AfT and AfT for trade policy and regulation flows have managed to bolster exports of AfT-recipient countries to donors. We also examine if the institutional distance between recipients and donor countries affected the efficacy of AfT in promoting the former’s bilateral exports. When accounting for the large heterogeneity amongst recipient countries, in terms of geographical location and income level, our investigation yields the following results: i) AfT flows tend to foster exports of recipients, both at the extensive margin (AfT augments the recipients’ likelihood of exporting) and intensive margin (AfT stimulates the recipients’ exports); ii) the trade-stimulating effect of AfT for trade policy and regulation is more pronounced than that of aggregate AfT flows; iii) institutional disparities between trading partners dampens the effectiveness of AfT in promoting exports at both margins of trade; and iv) the stimulating effect of AfT on trade in recipient countries varies with their geographical location and income level. Our findings suggest that i) allocating aid flows, notably the ones targeting trade policy reforms, are likely to speed-up the insertion of developing countries into world markets; and ii) strengthening governance in AfT-beneficiary countries and closing their institutional gap with donors would enhance the effect of AfT on their exports.

Factors detrimental to financial inclusion (account holding and borrowing) regarding female entrepreneurs arise both from the demand side of businesses, such as the absence of funding need versus self-selection despite account holding, and from the supply side of financial institutions, such as deficient financial infrastructure and discrimination towards loan applicants. A sequential model takes care of both the demand and the supply sides, including descriptive statistics prior to and during the COVID-19 pandemic, upon four MENA countries, namely Egypt, Jordan, Morocco and Tunisia.

Probit regressions (marginal effects) analyse financial inclusion from the demand side, using two different but somehow comparable sub-samples of micro enterprises from the 2020 World Bank Enterprise Survey (WBES) and the Economic Research Forum (ERF) COVID-19 Monitor in 2021.

We address (female) micro-entrepreneur self-selection (i) prior to and (ii) during the COVID-19 pandemic, as well as discrimination (iii) prior to and (iv) during the pandemic.

Prior the pandemic, microenterprises are prone to self-selection vis-à-vis loan application in Tunisia (ERF) and in all countries from North Africa (WBES). During the pandemic, there is no self-selection vis-à-vis government support affecting either gender or micro-entrepreneurs. Prior to the pandemic, females or micro-entrepreneurs do not face discrimination in loan applications (WBES). During the pandemic, females are not subject to discrimination as a result of government support, whereas Moroccan micro-entrepreneurs do face discrimination (ERF).

Prior to the pandemic, financial inclusion runs opposite to both self-selection and discrimination (WBES), but not for self-selection (ERF), whereas it proves insignificant during the pandemic with respect to self-selection or discrimination, whatever the sub-sample. Hence, financial inclusion may not preclude self-selection or discrimination, which remain obstacles to the business growth of micro-entrepreneurs, that policies enhancing funding should help overcome.

This article is based on the explanation of the pay differences between women and men in the Tunisian labour market. The theoretical framework of our research is articulated around the economic theory of gender discrimination in the labour market, by focusing on the main typologies highlighted in the work of Becker (1972), Phelps (1972), and Arrow (1973). Based on the Oaxaca-Blinder model (1973), the use of micro-econometric data from the 2015 national population and employment survey shows a pay gap between men and women of 10.4%. The unexplained part of this gap, which can be attributed to discrimination, is 14%. This work has also made it possible to identify the various socio-economic factors that can, directly and indirectly, impact wage inequalities between women and men.