Cost and affordability of recommended diets in Rwanda using [near] real-time market data
利用卢旺达政府2019年4月至2024年3月的月度市场价格数据，估算该国推荐膳食的成本，发现城乡成本差异大、季节性波动明显，70%的就业人口难以负担，呼吁高频次、次国家级的价格监测以优化粮食政策。
Countries are increasingly benchmarking food assistance and labour laws on the cost of nationally recommended diets. Benchmarking is made against national annual estimates, which fail to account for sub-national and intra-annual variation in cost, blunting the impact of policies. Using monthly market price data collected by the Government of Rwanda (April 2019-March 2024), we estimate the cost of the country’s proposed food-based dietary guidelines, using a standardised diet costing methodology. We found rural areas experienced greater inflation in diet cost over the study period than urban areas (41% vs 28%), yet the recommended diet was 12.7% higher in urban locations. Diet costs were approximately 6% lower in districts with international border crossings to Tanzania, but 7% more in those with borders with the Democratic Republic of Congo. Fruits and vegetables (110% and 71%) and starchy staples (86% and 83%) contributed most to cost increases in rural and urban locations respectively. Seasonal diet cost fluctuations were also evident with a seasonal amplitude of 5.6% and 6.9% in rural and urban locations, synchronised to Rwanda’s agricultural calendar. 70% of employed Rwandans would find the recommended diet unaffordable, if spending 52% of wages on food. Diet costs varied 4.2-fold across all districts throughout the study period, meaning that uniform national policies to address costs and affordability would be systematically inadequate in high-cost settings and wasteful in low-cost ones. That such spatial–temporal variation exists in a small, relatively market integrated country like Rwanda suggests variation would be at least equally consequential in other low and lower-middle-income countries. High-frequency and sub-national monitoring of prices, diet costs, and affordability provides essential intelligence for policymakers to enable spatially and seasonally targeted interventions, improving both the adequacy and efficiency of food policy.