increases crop production and the availability of agricultural commodities after harvest, and increased supply will reduce prices. Hence, we expect current crop season rainfall to have a negative impact on the following year price. In terms of price dispersion, we expect that a large difference in rainfall between locations increases price dispersion. Rainfall data are from Department of Climate Change and Meteorological Services, Ministry of Natural Resources, Energy and Environment in Blantyre.

Population density is the number of people per square kilometer, by Extension Planning Area (EPA) or district/Rural Development Project (RDP). Population size varies between districts but moves only gradually over time. Higher population densities are associated with more trade and more efficient trade (see Fafchamps et al. 2005). Hence, we expect price dispersion between locations to decrease the larger the population density in both locations. Population data are from the National Statistical Office in Zomba, and district area is taken from www.geohive.com.

Per capita income is an annual district variable, constructed as the sum of agricultural income and imputed urban income. Gross income from agriculture is calculated by multiplying agricultural production with average retail market prices, both by crop season and district and by summing over crops and livestock products.<sup>14</sup> All prices are deflated with the consumer price index for rural areas (source: National Statistical Office, Zomba, Malawi). We then exploit data on rural and urban population by district: we first calculate the per capita agricultural income by using rural population data by district. Next, we assume that the highest per capita agricultural income in the region is related to per capita income of the urban population: we impute n times the region highest per capita agricultural income to the urban population, where n reflects the productivity differences between urban and rural workers.<sup>15,16</sup> Per capita income accounts for demand, and we expect that a higher per capita income increases demand and pushes up prices of agricultural commodities. Large differences in income between locations will, _ceteris paribus_ , increase price dispersion. All covariates are expressed in terms of the natural logarithm of the absolute value of the difference of between markets (ln|xk-xj|).

# _Theoretical Considerations_

Costs of railway transport are relatively low compared to transport costs by road, which is the standard mode of transport in Malawi. Farmers and traders in Malawi, based in areas near to a railway station, potentially benefit from these cheap transport services. The lower transport costs enhance trade in agricultural commodities markets along the railway line, increasing flows of goods from surplus to deficit areas and thereby raising low prices in surplus areas and reducing high prices in deficit areas. Hence, availability of railway transport services should reduce price dispersion across markets along the railway line. The key mechanism that drives this process is standard profit maximizing producer behavior with transaction costs.

# _Empirical Specification_

> 14. We distinguish the crops maize (local, composite, and hybrid), rice, millet, sorghum, cassava, sweet potatoes, groundnuts, pulses, cotton, tobacco, tea, and sugar, and the livestock products steak, pork, mutton, and goat meat. 15. Urban population only refers to a fraction of the population of the districts of Lilongwe, Blantyre, Zomba, and Mzimba. We use n = 1.5; However, a range of values varying between one and three did not fundamentally change the estimation results. We cannot calibrate the value of n with GDP data because of the subsistence character of the Malawi economy: home-consumed production is included in our per capita income concept, but it does not show up in per capita GDP.

> 16. There is a multitude of explicit and implicit assumptions in this per capita income calculation with many arbitrary elements, which many researchers will label as “heroic.” Nevertheless, the constructed data should give a sensible order of magnitude approximation for per capita income.

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