The impact coefficients of all commodities with the exception of beans are around -11%, while in the case of beans the impact tends to be slightly stronger (see supplemental appendix). We assume that this should be associated with the higher perishability of beans and the related reduced scope for intertemporal arbitrage (relative to storable commodities). Other studies also confirm a higher impact in case of perishable crops (see, for example, Jensen <u>2007,</u> Muto and Yamano <u>2009,</u> Aker and Fafchamps 2014).

Table 1. Impact (ATE) of Rail Transport Services on Price Dispersion: Selected Output

|Dependent variable:ln|pjt-pkt|<br>Crop / Commodity|(1)<br>Maize|(2)<br>Rice|(3)<br>Groundnuts|(4)<br>Beans|
|---|---|---|---|---|
|Connected by rail|-0.119<sup>***</sup><br>(0.0395)|-0.109<sup>***</sup><br>(0.0416)|-0.094<sup>**</sup><br>(0.0374)|-0.116<sup>***</sup><br>(0.0346)|
|Lagged dependent variable (t-1)|0.183<sup>***</sup>|0.182<sup>***</sup>|0.245<sup>***</sup>|0.193<sup>***</sup>|
||(0.0329)|(0.0236)|(0.0210)|(0.0552)|
|Lagged dependent variable (t-2)||0.096<sup>***</sup><br>(0.0214)|0.056<sup>***</sup><br>(0.0223)|0.095<sup>**</sup><br>(0.0441)|
|Lagged dependent variable (t-3)|||0.053<sup>**</sup><br>(0.0262)||
|Season x market dummies|Yes|yes|yes|yes|
|Time trend x market dummies|Yes|yes|yes|yes|
|Market-pair dummies|Yes|yes|yes|yes|
|Month dummies|Yes|yes|yes|yes|
|Covariates|No|no|no|no|
|R<sup>2</sup>|0.3898|0.4234|0.4202|0.4863|
|Max trading distance (km)|110|160|250|110|
|Sample period|1/99-4/05|1/99-4/05|1/99-4/05|1/00-4/05|
|Months before 1/03 and after 12/02|48; 28|48; 28|48; 28|36; 28|
|Number of observations|2062|2748|2930|1534|
|no. of intervention pairs|251|366|365|157|
|no. of control pairs connected by rail|161|190|210|107|
|no. of other controls (not connected)|1650|2192|2355|1270|
|Longterm impact|-0.146|-0.150|-0.146|-0.164|

_Note_ : The dependent variable: ln(abs(pjt-pkt)) is the natural logarithm of the price difference between locations j and k in month t. _Connected by rail_ has the value ln(distance) if both markets are less than 20 km away from a railway station while the railway was operational, and zero elsewhere (in case of groundnuts less than 10 km). Prices are deflated with the rural consumer price index (source: National Statistical Office, Zomba, Malawi). Robust standard errors in parentheses below the coefficient are clustered by market pairs. The long term effect is calculated as ��/(1 −∑�� ��) (see regression model above). Estimation results with two-way clustered standard errors (by markets of each market pair) are shown in the supplemental appendix. ∗ _p_ < 0.10. ∗∗ _p_ < 0.05. ∗∗∗ _p_ < 0.01.

Table 1 shows a selection of the estimation results that allow for different maximum trading distances and sample periods across commodities and summarizes the key results of this study. The estimations confirm a statistically significant reduction in the dispersion of agricultural commodity prices across markets of around 9–12%. The size of the reduction is remarkably similar across commodities. The inclusion of the lagged dependent variable allows the distinction of short- and longrun impact, where the long-run impact is calculated as ��/(1 −∑�� ��), using the notation from the regression model. Long-run impacts range from a reduction of 14% to a reduction of 17%. These results point at substantial welfare effects from the enhanced efficiency of markets for agricultural commodities. The reduction in price dispersion is also likely to affect growth since lower food prices in subsistence economies constitute an important transmission mechanism to higher productivity (see De Janvry and Sadoulet 2010).

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