India’s grain markets grew closer together. Railways explain only part of the story.
Railways carried an appealing promise: cheaper transport would connect distant markets and bring their prices closer together. In nineteenth-century India, the expanding railway network coincided with narrowing differences in grain prices. It is tempting to credit the tracks with creating a national market. Research by Tahir Andrabi and Michael Kuehlwein suggests that much of the convergence needs another explanation.
The first chart shows why the conventional account looks convincing. Across the five-year observations shown, the number of districts with railways rises sharply between 1860 and 1915, while the dispersion of wheat and rice prices generally falls. Dispersion is measured using the coefficient of variation: a lower value means prices vary less relative to their average. These series reveal a striking association, but cannot tell us how much convergence the railways caused.
To investigate that question, the researchers examine annual retail grain prices across more than 150 districts during 1860–1920. Their regressions compare price gaps between district pairs, accounting for persistent differences between those pairs. Without controls for changes common to each year, the estimated railway contribution amounts to roughly 43% of the observed decline in wheat-price dispersion and 50% for rice.
Adding year controls changes the picture. The estimated contribution falls to about 9% for wheat and 7% for rice, as the second chart shows. Prices were already drawing closer together before districts acquired railway connections. Attributing that broader movement to railway expansion makes the railway effect look much larger.
Yet a district did not need its own railway to benefit. Traders could move grain through neighbouring districts connected to the network. Once the researchers distinguish places near railways from those farther away, the estimated direct-link contribution rises to around 18% for wheat and 17% for rice. These comparisons support the authors’ broader conclusion that railways explain approximately one-fifth of convergence, although the estimates depend on the model and comparison group.
The map offers another clue. The Delhi–Calcutta corridor already benefited from the Ganges and the Grand Trunk Road. The Bombay–Allahabad route crossed less-established commercial territory. Among districts within 50 miles of the respective straight-line corridors, the estimated railway effect on wheat-price gaps was about 7.5 times larger along Bombay–Allahabad. New tracks made a greater difference where alternative connections were weaker.
Railways therefore helped integrate Indian grain markets, but inherited an economy that was already partly connected. Their modest contribution to price convergence does not measure their total economic value. It does show why an impressive transport network, alongside falling price gaps, is insufficient evidence that one principally caused the other.
Sources and data notes
Andrabi, T., and M. Kuehlwein (2010). “Railways and Price Convergence in British India.” The Journal of Economic History, 70(2), 351–377. Figure 3; Figure 1; Tables 2–4; discussion pp. 362–369. Historical series and regression estimates are reproduced from this paper, not independently re-estimated. Its underlying sources include Prices and Wages in India (1896, 1922) and History of Indian Railways (1947).
Runfola, D., et al. (2020). “geoBoundaries: A global database of political administrative boundaries.” PLOS ONE 15(4): e0231866. https://doi.org/10.1371/journal.pone.0231866. Boundary data supplied by Person 3; metadata identify DataMeet India community / Election Commission of India, CC BY 2.5 IN, representative year 2011, built December 2023. See included metadata and citation file.
Chart categories: change over time (chart 1), magnitude (chart 2), spatial (chart 3). Map lines show connections, not measured traffic flows. Approximate city-centre coordinates were manually supplied during integration and are included in stations.csv. All data transformations are documented in the R code. Approximate digitisation and rounded denominators limit numerical precision. The descriptive CV series and regression log-price-gap outcome are different measures.