For estimating impact we apply a panel fixed effect strategy and use the following regression model: where _Yjk,t_ is the dispersion of prices across markets _j_ and _k_ , at time _t_ , _‘connected by rail’_ is a variable with a value ln( _distance_ ) if both markets are less than 20 km away from a railway station and zero otherwise, where _distance_ measures the distance in kilometres between the markets, _Xn,jk,t_ is a vector of n market-pair variables at time _t_ , affecting the dispersion of prices across markets _j_ and _k_ , _time_ is a time trend, _season_ is seasonal dummy, _ωl_ , _φt_ and _ηjk_ are market, time, and market-pair fixed effects and _ζjk,t_ is a cluster robust error term.

There are several ways to measure dispersion of prices across markets ( _Yjk,t_ ), like, for example, the coefficient of variation or the maximum minus the minimum. We follow Aker (2010) and use (the natural logarithm of) the absolute price difference across markets (ln|pj-pk|). In the estimated specification we have included the lagged dependent variable as explanatory variable in order to filter out lagged responses. The vector of ���,� variables are determinants of price dispersion between markets j and k, associated with either transaction costs (like transport costs, gasoline prices, and economies of scale), or local supply and demand balances. In the estimated equations we further include time trends and seasonality by markets.<sup>17</sup> Crop and market-specific seasonality in prices is supported by the data (see supplemental appendix). Monthly fixed effects are included to control for countrywide variations in agricultural production between years (for example, caused by bumper crops and droughts).

# _Identification Strategy_

The exogenous collapse of railway transport services due to the disruption of the railway bridge at Rivirivi, documented in the previous section, creates a quasi-experimental design that offers an opportunity to identify the impact of railway services on the dispersion of agricultural commodity prices. The regression equation represents a panel fixed effect model with markets connected by rail when the railway was operational, as intervention observations. The coefficient of interest in the regression equation is ��: this coefficient reflects the impact of the availability of railway transport services on the dispersion of agricultural commodity prices. We expect price dispersion to be lower in locations that have access to railway transport services. Hence, we expect �� to be negative.

# _Intervention Locations_

In this study, interventions are the market pairs that are connected with each other by rail when the railway is operational. Moreover, we have assumed that, after the bridge collapse, all segments of the network were affected, and, hence, all market pairs formerly connected were no more connected after the bridge collapse. This assumption is supported by communication with CEAR staff and freight data on domestic trade (see paragraph on rail operations). We assume that markets potentially have

> 17. We discarded the option to interact a time trend and seasonality with market pairs since this nearly exhausts the degrees of freedom in estimation. Moreover, we do have empirical support for trends and seasonality in prices (which explains part of the trend and seasonality is price dispersion), but trends and seasonality in prices dispersion are less evident from the data.

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