How did the border closure affect price volatility across the country? Similarly to the analysis of the impact of the border closure on price differentials, we also examine the patterns of price volatility pre and post closure by geographic region. For this purpose, we construct a rolling measure of price volatility, that consists of the standard deviation of monthly price changes for a given commodity in a given city, over a window of five months (the graphs above, instead, show the standard deviation of monthly price changes for a given commodity in a given city over the whole pre-border closure period, and post-border closure period). Results from tabulating that measure of price volatility across the pre and post border closure periods and by region are presented in Table 3. They confirm that for sorghum, price volatility increased across the board after the border closure. In addition, they show that volatility was on average higher for northern states, although the post-border closure did not affect northern states any differently than it affected the rest of the country. For groundnuts and maize, for example, price volatility increased in northern states after the border closure, but it fell in the rest of the country.<sup>16</sup> For sesame, price volatility increased in northern states but remain unchanged in the rest of the country after the border closure, while for millet, volatility remained relatively stable in northern states and fell in the rest of the country after the border closure. For wheat flour, instead, volatility decreased in northern states, but remained stable in the rest of the country.

Table 3: Price Volatility: Before and After the Border Closure, For Northern and Southern <u>States</u>

|Volatility|Broad<br>Beans|Groundnuts|Maize|Millet|Sesame|Sorghum|Wheat<br>Flour|
|---|---|---|---|---|---|---|---|
|_Border Closure_|0.0484***|-0.0624**|-0.0288|-0.084***|-0.0231|0.0605***|-0.00092|
||(0.02)|(0.03)|(0.03)|(0.03)|(0.02)|(0.01)|(0.02)|
|_Northern States_|0.0732***|-0.0163|-0.053**|-0.128***|-0.071***|0.0467***|0.0271|
||(0.02)|(0.03)|(0.02)|(0.02)|(0.02)|(0.01)|(0.02)|
|_Border Closure X_<br>_Northern States_|-0.079***|0.199***|0.122***|0.0647*|0.0592**|0.00279|-0.083***|
||(0.02)|(0.04)|(0.04)|(0.03)|(0.03)|(0.02)|(0.03)|
|_Constant_|0.0719***|0.186***|0.173***|0.233***|0.167***|0.0736***|0.183***|
||(0.01)|(0.02)|(0.02)|(0.02)|(0.02)|(0.01)|(0.02)|
|_Observations_|251|378|289|303|275|452|289|
|_R-squared_|0.073|0.128|0.045|0.121|0.05|0.157|0.067|

Source: Authors’ calculations

# **3.4. Market Integration**

The presence of high price differences for a given product in two different cities does not necessarily reflect lack of integration between these two markets. In fact, the notion of market

> 16 Formally, we test these statements by jointly testing the significance of the border closure effect and the interaction term of border closure and northern states. For groundnuts we reject the null of the sum of the border closure coefficient and the interaction being equal to zero at any conventional level of significance (the test statistic is distributed as F (1, 374), and it takes value 28.41, p-value=0.000). The same results are obtained for maize (F(1, 255)=11.99, p-value=0.0006)).

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