Rwanda

Cost and affordability of recommended diets in Rwanda using [near] real-time market data

Gender, youth, and growth: Unpacking the productivity–transformation nexus in Rwanda

The relationship between labor productivity and economic transformation, and their combined impact on labor market dynamics, remains insufficiently understood and highly country specific. This study applies an economywide analytical framework, using Rwanda as a case study, to examine how youth and women’s productivity influence economic growth and structural transformation, and how this transformation process, in turn, affects these groups. The results indicate that labor productivity gains—whether economywide or concentrated among youth—shift growth toward the industrial and service sectors, while growth in the agricultural sector is minimal. Increases in productivity of women’ labor generate more balanced growth across sectors and substantially enhance women’s industrial participation. Productivity gains of youth labor induce stronger structural shifts, as young workers move from agriculture to expanding industrial and service sectors, though this transition partially displaces adult workers. In general, labor income in these simulations rises broadly in line with GDP, with youth and women benefiting most under targeted scenarios. Sector-specific growth strategies yield distinct distributional effects, however: industry-led growth benefits women and adults, while service-led growth favors the versatile youth. Overall, productivity-driven structural transformation in Rwanda fosters welfare gains, although potential trade-offs between inclusiveness across gender and age groups and aggregate economic performance warrant further investigation. In conclusion, policy design in Rwanda should ensure that gains in aggregate economic growth are balanced with inclusive outcomes for women, youth, and adults.

Systematic risk profiling: Assessing compounding economic risks in developing countries

This paper presents a systematic risk profiling (SRP) framework to identify the most critical economic risks facing developing countries. Integrating computable general equilibrium (CGE) models with historical shock data and machine-learning tools, we examine how compound shocks affect development outcomes. We apply this method to Kenya, Rwanda, and Malawi, simulating thousands of plausible combinations of world price, capital flow, and productivity exogenous shocks and their impacts on countries’ GDP, household consumption, poverty, and undernourishment. The results reveal distinct risk profiles driven by structural differences: Kenya’s primary vulnerability is the volatility in global beverage crop prices, whereas Rwanda and Malawi face the highest risks from domestic root crop and cereal yields, respectively. These findings underscore that vulnerability is not just a function of shock magnitude, but of the specific structure of each economy. Specifically, the high economic volatility in Malawi and Rwanda is driven by the larger role of subsistence agriculture and more volatile domestic yields, whereas Kenya’s agricultural sector is more export-oriented. Unlike standard ad hoc scenario analysis, SRP quantifies both the likelihood of compound events and the relative importance of their drivers. This transparent, scalable framework provides policymakers a new tool to move beyond reactive measures and design targeted, country-specific resilience strategies for an increasingly volatile world.