Table 3. Common Trends in Price Dispersion Outside the Intervention Period*

|**Market-pair data**|Differences in trends<br>F-statistic (n,m)|_p_-value|
|---|---|---|
|Maize price: ln|pj-pk||F(2, 67): 0.52|0.599|
|Rice price: ln|pj-pk||F(2, 103): 0.16|0.856|
|Groundnuts price: ln|pj-pk||F(2,180): 5.11|0.007***|
|Beans price: ln|pj-pk||F(2, 67): 0.82|0.444|
|Maize price: |pj-pk||F(2, 67): 2.32|0.106|
|Rice price: |pj-pk||F(2, 103): 0.25|0.776|
|Groundnuts price: |pj-pk||F(2,180): 9.02|0.000***|
|Beansprice:|pj-pk||F(2,67): 1.09|0.341|

Next, we have run the estimations with two subsets of controls as a robustness check. One may argue that using remote market pairs as controls is merely measuring the difference between remote market pairs and market pairs along the rail line. Therefore, we have re-estimated impact by comparing market pairs (both) connected by rail with market pairs of which one market is located along the rail line (connected by rail) and the other market is not. Market pairs near to the rail line but not connected are more likely to be similar to market pairs that are both connected to the rail line. In this way, we avoid comparing remote, isolated, and exclusively rural market pairs with exclusively urban market pairs, market pairs that are both located in a relatively densely populated area.

Conversely, one may argue that market pairs of which only one market is connected with the rail line are to some extent also benefitting from rail connection. Since this will blur the result, estimation with non-connected market pairs as controls should be preferred. Therefore, we have re-estimated impact by comparing market pairs (both) connected by rail with market pairs of which both markets are not connected by rail. The results (see appendix tables A2 and A3) are different but to a large extent confirm previous results: coefficients of the impact variable and lagged dependent variables have the right sign and statistical significance is acceptable to good in most estimations. The assertion that impacts reflect a selective choice of control market pairs is not supported by these robustness checks.

As a final robustness check, we have re-run the estimations with a smaller/larger number of intervention locations (see previous section). As a starting point, we have used 20 km as cutoff to define locations to be intervention locations. Alternatively, we used as cutoff less than 10 km, leading to eight intervention locations and 28 intervention market pairs, and less than 30 km, leading to 13 intervention locations and 78 intervention market pairs. The outcome of this exercise (not shown) indicates that the estimated impact remains more or less the same with a smaller number of intervention points but deteriorates substantially with a larger number of intervention points.

# _Alternative Explanations and Potential Threats_

One may question the estimated impact of transport services by rail on agricultural commodity prices on several grounds. We consider three concerns in some detail. The first concern is about the bias caused by large cities. One may argue that the estimated impact is exclusively due to trade with the two main cities, Lilongwe and Blantyre. In terms of population these cities are much larger than all other cities and towns (Lilongwe and Blantyre are similar in population size, and five to six times larger than the third largest city, Mzuzu). Consequently, proportionally more trade will take place towards these cities and results may be driven by these cities. Omitting observations to a certain degree confirms this, since the results deteriorate (not shown). However, we do not consider this a major problem since domestic trade in food is naturally directed from excess supply rural areas towards high demand urban markets.

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