looks at the drivers of those departures from market integration and complete price transmission in India focusing on the spatial dimension.

This section explores the importance of six potential drivers of integration. These are transport costs, price adjustment dynamics (measured as the time it takes for shocks to be corrected), information frictions, the regulatory framework under which trade happens, commodity specific characteristics, state level production and regional consumption preferences.<sup>16</sup> To conclude, the costs of crossing state borders in terms of price differentials are explored. With India being a federal country, its states behave in several ways like separate countries, with state border authority checks on both parts of the border levying taxes, conducting road transport checks and imposing commodity specific taxes. To understand the impact of these barriers to trade across states on food prices, we examine whether, for a given distance between a pair of districts, deviations from market integration are larger when there is a state border between them. For these purposes we build on the “border effect” literature to estimate the cost of crossing borders in India. The section exploits the district level information from the wholesale price database. The specific question posed is if, for a given distance, the average price‐difference between any two pairs of districts is lower in the case that they belong to a common state.

The section is structured in three sections. The first section presents the details of the analysis as well as the database. The second section looks at the economic determinants behind the deviations from the law of one price in retail markets. The third section expands the analysis to wholesale markets and tests for the border effect.

# The Analysis

In this section we present the two databases that we use for the analysis. The database covers retail and wholesale prices utilized in the previous two sections. Following the methodology outlined in the next subsection, we construct a database that measures deviations from the law of one price. The variable of interest for analyzing deviations from the law of one price – the main dependent variable ‐ is the average absolute price difference, in logs, between commodity prices in a given state and Delhi.

We compute the average of each variable for all commodities and states between 2005 and 2014. Through averaging, we distinguish signal from noise.<sup>17</sup> For retail, the sample is a cross section consisting of 23 states and thirteen of commodities. Table 6 describes the variables in our database. For wholesale, it covers 5 commodities in 21 states, averaged over the period 2008‐2015. The smaller set of commodities for the analysis of drivers of departures from the law of one price in wholesale markets is due to the lower availability of data points in the city of reference. The dependent variable is constructed as the log‐difference between prices in a district and prices in Delhi.

Table 6: Sample Overview

|Variable Name|Variable Description|
|---|---|
|Commodities in Retail|Includes: Bananas, Coconut Oil,|
|Database|Gram, Groundnut Oil, Masur,<br>Moong, Mustard Oil, Onion,<br>Potatoes, Rice, Tur Arhar, Urad,<br>Wheat.|
|Commodities in|Includes: Mustard, Onion, Paddy,|
|Wholesale Database|Potatoes and Wheat|

> 16 These potential drivers are aligned with those identified in the literature.

> 17 Averaging also implies that the data set is transformed from a panel into a cross‐section. Given that market integration is a long‐run concept, and that many of the determinants are slow to adjust (in some cases time‐invariant), analyzing this in cross‐section is reasonable.

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