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1: author: Varela,Gonzalo J.
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docna: 0: docna: Integration and Price Transmission in Key
            Food Commodity Markets in India
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Transport Services,Trade and Services,Hydrology,Food Security
teratopic: Transport,International Economics and Trade,Water Resources,Agriculture
abstracts: cdata!: This paper examines patterns of market
            integration for food commodities in India. First, it tests
            the extent of domestic spatial market integration for retail
            and wholesale markets in 2006–14 and 2008–15, respectively,
            and looks at patterns of price transmission of shocks from
            international sources. Second, it measures vertical
            integration from wholesale to retail markets and tests for
            asymmetric speed of adjustment to shocks. Third, it examines
            the determinants of spatial integration. The results reveal
            that in India, food markets are imperfectly integrated
            across space, with the law of one price being systematically
            rejected, with heterogeneities across states and products.
            There is substantial co-movement between wholesale and
            retail prices, although integration is still imperfect in
            all commodities but one: rice, for which perfect vertical
            integration cannot be rejected. Retail prices adjust faster
            when wholesale prices rise than when wholesale prices fall.
            The analysis of the determinants of spatial integration
            reveals that prior to implementation of the Goods and
            Services Tax, the mere act of crossing a state border
            increased prices; unexploited gains from arbitrage persisted
            after considering the effects of transport costs; and
            information frictions and menu costs reduced market integration.
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repnme: repnme: Integration and Price Transmission in Key
            Food Commodity Markets in India
display_title: Integration and Price Transmission
            in Key Food Commodity Markets in India
listing_relative_url: /research/2019/02/30858019/integration-price-transmission-key-food-commodity-markets-india
topicv3: Agriculture and Food Security m1326205 435,Water m1326522 137,Transport m1326661 135,Trade m1326924 698,Trade in Services m1326928 780
publishtoextweb_dt: 2019-02-25T00:00:00Z
historic_topic: Transport,International Economics and Trade,Water Resources,Agriculture
no_of_pages: 64
author_keys: 0: author_key: 1142608
1: author_key: 989943
model_extractions: Policy Research Working Paper 8755 Integration and Price Transmission in Key Food Commodity Markets in India _Mauro Boffa Gonzalo J. Varela_ Macroeconomics, Trade and Investment Global Practice February 2019
Policy Research Working Paper 8755

# **Abstract**

This paper examines patterns of market integration for food commodities in India. First, it tests the extent of domestic spatial market integration for retail and wholesale markets in 2006–14 and 2008–15, respectively, and looks at patterns of price transmission of shocks from international sources. Second, it measures vertical integration from wholesale to retail markets and tests for asymmetric speed of adjustment to shocks. Third, it examines the determinants of spatial integration. The results reveal that in India, food markets are imperfectly integrated across space, with the law of one price being systematically rejected, with heterogeneities across states and products. There is substantial co-movement between wholesale and retail prices, although integration is still imperfect in all commodities but one: rice, for which perfect vertical integration cannot be rejected. Retail prices adjust faster when wholesale prices rise than when wholesale prices fall. The analysis of the determinants of spatial integration reveals that prior to implementation of the Goods and Services Tax, the mere act of crossing a state border increased prices; unexploited gains from arbitrage persisted after considering the effects of transport costs; and information frictions and menu costs reduced market integration.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at gvarela@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Integration and Price Transmission in Key Food Commodity Markets in India**<sup>*****</sup>

## Mauro Boffa

_Universal Postal Union_ Gonzalo J. Varela _World Bank_

**Keywords:** Law of one price, pass through, speed of adjustment, asymmetric price transmission, determinants of market integration, border effects, panel cointegration

**JEL:** C33, D40, F15, N75

> * Mauro Boffa is an Economist with the Research and Strategy Program of the Universal Postal Unit, Gonzalo J. Varela is a Senior Economist with the Macroeconomics, Trade and Investment Global Practice of the World Bank Group. Corresponding author: Gonzalo Varela, <u>gvarela@worldbank.org. The authors are indebted to David Atkin for insightful conversations at the onset of the</u> research, Arvind Subramanian for guidance on relevant research questions, and to Sebastian Saez, Alvaro Gonzalez, and Frederico Gil Sander for valuable comments on this draft. We also are grateful to Aldo Pazzini for excellent research assistance.
# Introduction

On December 31, 2015, a consumer in the city of Akluj in Maharashtra paid 19 rupees for a kilo of red onions in the local market. Meanwhile, less than one hour driving away, in Pandharpur, another consumer paid only 13 rupees for the same quantity of the same product. Are these price differences an exception, or a good representation of those that prevail among all cities in India for onions and other food products? What prevents Akluj’s consumers to drive up to Pandharpur to buy red onions there and sell them back home until prices equalize? Under these specific questions lies a more relevant one: are price signals in India transmitted spatially (across regions), and vertically (along the supply chain), facilitating an efficient allocation of resources? And if not, why not?

This paper looks at these issues by examining the extent to which food markets in India are integrated and at the drivers of integration. It defines integration as a situation in which spatially separated markets of a homogenous commodity are considered to be integrated when they are connected through trade in such a way that the forces of arbitrage make the long‐run price differences that may remain among them reflect transportation costs only. This implies that shocks arising in one location (or segment of the marketing chain) are transmitted to another one. To make this definition operational, it measures price differences across locations or segments of the marketing chain, and tests for price co‐movements.

Efficient markets in which price signals are transmitted speedily are essential for optimal allocation of resources. When goods are more highly valued on the margin in one market than another, a price differential arises that induces suppliers to reallocate goods towards that market, or producers nearby to devote more resources to the production of the highly priced good, reducing the price differential – such as that observed in Akluj and Pandharpur for red onions ‐ and increasing total welfare in the process. The well‐functioning of output markets is crucial in determining the incomes of the significant fraction of households involved in agriculture production in developing countries. Indeed, for most of the world’s poor, living standards are determined largely by how much they get for their output.

Many of the gains associated with liberalization of trade, reductions of distortive regulations or improvements in connectivity only materialize if prices are transmitted spatially and vertically. Take trade reforms. If price changes are an important pathway through which liberalization affects the poor, then understanding how much of any price change gets passed through to poor households is important (Winters et al, 2004).

Integration also allows, through scale, for the spreading of fixed costs and improvements of total factor productivity, through specialization and through the facilitation of technology adoption. Conversely, without good access to distant markets that can absorb excess local supply, the adoption of more productive agricultural technologies will lead to a reduction in farm gate product prices, erasing the gains from technological change, and reducing the incentives for technology adoption. Furthermore, better integrated markets contribute to development by helping to stabilize prices, as local disturbances on prices are mitigated through induced trade between surplus and deficit areas. Timmer (1986), for example, argues that when markets are integrated, price intervention as a tool of public policy can be powerful, since, by operating on small amounts of easily controlled trade flows, governments or parastatal agencies could affect price structures for the commodity produced and consumed within the country through arbitrage, as low‐priced commodities find their way to high‐ priced markets (Varela et al 2012).

In addition to understanding the spatial dimension of market integration and price transmission, vertical integration along the different stages of the value chain is also of policy interest. Vertical transmission of shocks among different levels of the market is an important feature describing the overall operation of a market. Prices are the primary mechanism by which various levels of the market are linked. The extent of adjustment, the speed with which shocks are transmitted among producers, 2
wholesalers and retailers, as well as whether this transmission is symmetric for upward and downward shocks are all important elements reflecting the actions of market participants at alternative market levels, with important implications on marketing margins, spreads and pricing strategies. Also, and in contrast to what is observed at the spatial dimension, the differences in prices along different stages of the value chain respond to the dynamics at play among the several layers of intermediary networks that exist between each stage, and that are likely to change over time due to technological change and consumer preferences. If what we observe is a _value_ chain, rather than a pure _supply_ chain at work, a full understanding of the evolution of value creation at each stage is needed to make sense of the price wedges observed.<sup>2</sup> As important as measuring the extent of integration of markets is to understand the drivers of integration. The recent implementation of the GST in July 2017, and the preliminary evidence on its effects on market integration in India suggests policies do play a role, and that role needs to be better understood if evidence‐based reforms are to be implemented. The 600‐rupee per quintal difference in red onion prices between Akluj and Pandharpur, for example, may reveal untapped gains from arbitrage or poor connectivity that prevents any arbitrage from happening. In the latter scenario, improvements in transport infrastructure may help to bring markets together. In the former, reducing the costs of accessing information will help maximize welfare through better consumers’ and producers’ decisions.

The paper contributes to the literature by addressing three related, but distinct questions. First, what is the evidence for spatial market integration, and what are the main features of price transmission, in terms of pass‐through, speed of adjustment to shocks, and symmetry of adjustment upward and downward, in retail and wholesale markets in India? Second, what is the evidence for vertical price transmission along the different stages of the marketing chain of these key food products (i.e., farm‐ gate, wholesale, retail)? Third, what determines spatial market integration or the lack thereof in India?

The main messages that emerge are as follows. In terms of spatial integration, the analysis reveals that in India, food markets are imperfectly integrated across space. Price signals are geographically transmitted both in retail and wholesale markets, with substantial heterogeneities by states and by products. Moreover, substantial price differences prevail. The case of red onions in Akluj and Pandharpur is not an exception: Substantial price differences exist for narrowly defined products in India. In 90 percent of the cases, the price difference paid in any given city with respect to Delhi for a given product ranges from ‐28 percent to +28 percent. And there is substantial heterogeneity across states and cities, and across products. Bananas in retail markets in Shillong, for example, can trade for up to three times the price in Delhi. Pulses, at the other extreme, show substantially lower price dispersion across India. In wholesale markets, their price dispersion is between 15 and 19 percentage points lower than for bananas. Heterogeneity across states, cities and districts is also marked. For example, potato prices in Panipat, Haryana, just 90 kilometers north of Delhi have registered prices up to 88.5 percent cheaper than Delhi.

Despite substantial, and in some cases increasing price differences, prices of narrowly defined products in spatially separated locations tend to systematically co‐move both for retail and wholesale. To be sure, the hypothesis of integration, implying that the law of one price holds, is almost systematically rejected. Transmission is found to be incomplete, heterogeneous by region and product, although symmetric upwards and downwards, with strong co‐movement of prices within

> 2 For example, for products in which the cold chain is important, technical change has reduced its perishability. This means that more value is added at the intermediary stage between the farm‐gate and the retailer, increasing the wedge between the prices observed at one level and another. Product differentiation that may happen at the retail level following marketing strategies to maximize appeal to consumers is another form of value addition that will naturally increase the wedge between prices at different stages of the value chain.

3
India and between India and the world. Within India, the average speed of adjustment is in the range of 16 to 17 percent for retail, and 24 to 25 percent for wholesale.

Examining patterns of integration by commodity in light of the extent of government intervention in each market reveals some patterns. For example, for wheat and rice, whose markets are subject to substantive intervention such as minimum support prices, price shocks transmit in the long run but at low speeds. In retail markets, less than 10 percent of the disequilibrium that results after a rice price change in a center is corrected within one month, compared to about 50 percent in onion markets, or 20 percent in potato markets. In wholesale, 11 percent of the disequilibrium is corrected within a month, compared to 42 percent for onions, or 22 percent for potatoes. Integration between Indian and world rice markets is also weaker than for edible oils, for example, that face very low tariffs at the border as compared to rice. This result is in line with what has been previously documented in the literature.

In terms of vertical integration, the main message that emerges from this section of the analysis is that there is substantial co‐movement between wholesale and retail prices, but integration is still imperfect, with one exception: rice. In addition, retail prices adjust faster when wholesale prices rise than when they fall.

Differences in prices at different levels of the marketing chain (price wedges) are substantial, and heterogeneous across states and commodities. When comparing farm gate to wholesale, with wholesale to retail price wedges, we observe that: (i) the distribution of price wedges in the two cases is different, (ii) wholesale to retail wedges are on average higher than farm gate to wholesale ones, and (iii) at both levels, heterogeneity is marked across commodities and states. Over time, the wedges for wholesale/retail have declined at a rate of two‐thirds of a percentage point per year.

In line with the findings on spatial market integration, the hypothesis of full vertical price transmission is rejected for all but one commodity. Yet, the price co‐movement is substantial. Shocks to wholesale prices are not fully transmitted to retail prices ‐ the pass‐through coefficient is less than one for all commodities but for rice. Though incomplete, price co‐movements are sizable and systematic: In all cases the pass‐through is highly statistically significant and ranges from 52 to 97 percent.

The speed of adjustment is highly significant, both statistically and economically for all commodities when looking at vertical transmission of shocks. To reiterate, the speed of adjustment measures the percent adjustment back towards equilibrium from a shock in a foreign market, in this case the wholesale market. The speed of adjustment is significant for all commodities but the percent of adjustment per period differs by commodity. For example, prices in the onion retail market adjust back to equilibrium around 30 percent of the disequilibrium per month caused by a price shock in the onion wholesale market, while for rice, the adjustment is the slowest, at about 17 percent per month.

Retail prices increase like rockets and fall like feathers. In contrast to the findings for spatial integration, in the vertical price transmission patterns, there are substantial departures from symmetry, mostly implying that prices increase faster than they fall. Conventional wisdom has it that prices tend to rise faster than they fall. Evidence for patterns of vertical transmission of price changes in India give some kernel of truth to that. For the eight food products considered for the analysis of vertical transmission, the hypothesis of symmetry of transmission is rejected. Adjustments upwards and downwards do not happen at the same speed. For six of the eight products, the upward adjustment of prices happens faster than the downward adjustment. Only for onions and tur arhar retail prices seem to fall faster than they rise. For rice and moong, the differences in the speed of adjustment for price rises or falls exceed a factor of three.

The main message that emerges from the analysis of determinants of spatial integration is that unexploited gains from arbitrage persist after considering the effects of transport costs. Part of these unexploited gains is related to information frictions and menu costs, while another portion remains 4
unexplained. Moreover, there is evidence that crossing state borders adds to trade costs the equivalent of having to travel an additional 164 kilometers, on average. After controlling for transport costs, information frictions and menu costs, the law of one price still does not hold in India – with some exceptions (tur arhar and moong in retail, wheat and mustard oil in wholesale). Therefore, India’s markets are at least partially segmented.

Spatial frictions are the main predictor for departures from the law of one price. In retail markets, distance is the most important variable that we find to be significant. However, the effect of distance is even greater in wholesale markets. While in wholesale markets a price gap of 10% if explained by 1,000km, in retail markets the marginal effect is less than half that. Similar results are obtained when looking at the role of travel times between districts. Travel time between districts could be considered a better proxy for transport costs, as they also include factors associated with inefficiencies in the supply chain, as well as costs associated with border controls, etc. These results point to logistics efficiency enhancements as an important vehicle to reduce food price differences across India. Note that improving the efficiency of the supply chain requires, on the one hand, improvements in hard infrastructure (roads, railways), and on the other, promoting competition along the different players of the supply chain – including for example, competition in trucking and distribution segments – as well as streamlining regulations that add costs to inter‐state trade. Interestingly, while spatial frictions are one of the most important determinants, they do not explain all the variability in our sample, pointing to persistent arbitrage opportunities.

Information frictions impede integration of wholesale markets in India: a 1 percent increase in wireless phone penetration displays the same marginal effect as a reduction in distance of 230 km. Wireless phone penetration has an even stronger marginal effect than distance. A 1‐percentage point increase in cellular penetration is associated with an effect between 3 and 11 percentage points in price dispersion, depending on the specification. The strong effect of information technologies is consistent with previous findings in the literature that point to information frictions as one of the most important barriers for price integration. These results suggest that increasing the network of cell phone coverage could help reduce informational asymmetries and allow market players to make better informed decisions about production and trade, thus exhausting arbitrage opportunities. Although not formally tested, it is likely that internet penetration plays a similar role as it is a powerful vehicle for information diffusion.

The presence of price stickiness indicates high menu costs in wholesale markets. Future research should focus on the factors that give rise to such nominal rigidities, especially since prices in India are more volatile than in developed economies. One would think that in the presence of more flexible prices, there it would be less room for nominal rigidities. Instead, we observe that because of price stickiness, there are persistent differences in relative prices.

There are strong border effects in wholesale markets in India. During the period analyzed in this study, the internal movement of goods in India was subject to several barriers, including taxes on inter‐state movements of goods, significant cross‐state differences in tax structures, and cumbersome inspections at state borders that led to large delays in the movement of goods. The analysis shows that the existence of these barriers to inter‐state trade impeded domestic arbitrage. The analysis reveals that the border effect was significant in India during the period of analysis; stable through time and heterogeneous across commodities. The average effect is equivalent to adding about 164 km of distance to the transport bill, with no visible time trend, going from 149 km in 2008 to 101 km in 2015. The recent implementation of the Goods and Services Tax (GST) in July 2017 is likely to have at least reduced this border effect. From a policy perspective, the GST unified the country into a common economic market, by simplifying the multilayered federal, state, and local indirect tax structure. Recent evidence suggests that after the implementation of the GST, the average driving speed increased at the border, and that overall, it increased by 1% (Krishna and Van Leemput, 2018a). This is an indicator of a reduction in internal trade costs likely associated with a reduction of the price 5
border effects estimated here. Unfortunately, lack of disaggregated price data post‐GST implementation prevents us from testing the stability of the border effect post‐GST. This is an area for future research.

There are two caveats that need to be noted in the analysis. First, ideally, the analysis of price dispersion and market integration should be done with focus on homogeneous products. One example could be the comparison of the price of a specific brand of bottled water that has the same characteristics regardless of where in India it is purchased. Unfortunately, data were not available for such type of products. In addition, the choice of products aimed at identifying relevant ones for household consumption bundles. The reader should notice, therefore, that observed price differences between, for example, bananas in Shillong and in Delhi may be driven by quality differences, on top of other elements related to departures from the law of one price. This problem should be minimized when looking at patterns of price transmission over time, which accounts for most of the focus of this paper. Second, the definition of what is understood for a ‘market’ deserves clarification. In this paper, markets are geographically defined (equated with a city, a district or a state). Alternative definitions, based, for example, on differences in demand elasticities, would require additional data that were not available.

The rest of the paper is structured as follows. Section 1 discusses the choice of products to be analyzed. Section 2 addresses the question of the extent of spatial market integration within India and with world markets for retail and wholesale prices, it presents the empirical methodology and the discusses results in the context of the literature. Section 3 analyzes vertical price transmission in India. The section presents the methodology and discusses results in the context of the literature. Section 4 focuses on the determinants of market integration, also presenting the empirical methodology and discussing results in the context of the literature. Section 5 concludes.

# Section 1: Choice of products

The analysis in this paper focuses on select food commodities. The choice of these commodities was done in consultation with relevant stakeholders at the Government of India. It has, as a primary objective, being representative of a significant consumption share, particularly among poor households (rice, pulses, wheat, see Table 8 in the Appendix for consumption shares for the poor and non‐poor households). As a second objective, it aims to provide some variation in terms of market institutions that govern their trade, both with the rest of the world, and within India (some commodities are subject to substantial trade restrictions and other interventions, such as rice and wheat, while others face lower or no restrictions, such as edible oils, pulses and grams). Another consideration was given to products that maintain, to some extent, the same characteristics along different stages of the supply chain (e.g. onions, potatoes, tomatoes and bananas). Finally, data availability constrains product choice, and prevents full symmetry in coverage across retail and wholesale. Not all commodities considered for the wholesale analysis can be considered for the retail analysis; second, that only the overlapping set of commodities is used for the analysis of transmission of prices from wholesale to retail levels; third, that the analysis of integration with world markets is performed for those products in which an international reference price is available.

# Section 2: Spatial Market Integration in Select Food Markets in India

This section describes spatial market integration patterns for key food commodities for retail and wholesale. It examines four related but distinct elements. These are: commodity price dispersion across space, over time, the links between the observed spatial price dispersion and the patterns of price transmission and market integration within India, and between Indian and world markets.

Understanding how integrated Indian markets are matters for policy making. Two markets are integrated when shocks arising in one market are transmitted to the other market. This implies a co‐ movement in prices in the long run. Yet, this does not imply that prices in both markets are always 6
equal. It is possible that price differences exist between integrated markets, as long as these differences are stable over time and explained by transport and distribution costs. Indeed, when these costs are substantial, high price differences between markets can exist without implying a failure in how price signals flow to guide consumers’ and producers’ decisions. This section starts exploring the patterns of these price differences, and then, it formally tests for market integration within India, and between India and world markets.<sup>3</sup>

# Patterns of price dispersion

## Overall dispersion across states and products

Retail and wholesale prices vary substantially across cities and districts in India.

For bananas, in retail markets in the northeast city of Shillong, consumers could pay prices up to three times higher than those paid by Delhi consumers. While this is an extreme example, on average, retail price differences for bananas are at 49.6 percent across Indian cities, high among the products analyzed, likely related to its perishability. This is affected by some atypical differences such as that reported for Shillong. Indeed, pooling all products, in 90 percent of the cases, the price difference paid in any given city with respect to Delhi for a given product ranges from ‐28 percent to +28.6 percent (the area delimited by red bars in Figure 1). In absolute terms, the median price difference with respect to Delhi is 13.3 percent, with 90 percent of differences between 1.3 percent and 40 percent (the area delimited by red bars in Figure 2). And these differences are not randomly distributed. For 55 percent of the state‐city/product combinations in retail, prices tend to be higher in Delhi.

Figure 1: Distribution of retail price differences (with respect to Delhi) across cities Figure 2: Distribution of retail absolute price differences (with respect to Delhi) across districts <!-- Start of picture text -->
-2 0 2 4 6 0 1 2 3 4 5<br>Proportional Retail Price Differences with Respect to Delhi Proportional Retail Price Absolute Differences with Respect to Delhi<br>Source: Authors’ calculations.  Source: Authors’ calculations.<br>2.5 4<br>2<br>3<br>1.5<br>2<br>1<br>Density Simple Differences Density Absolute Differences 1<br>.5<br>0 0<br><!-- End of picture text -->

The distribution of price differences is more spread out at the wholesale level. In this case, about 60 percent of district/product combinations display higher prices in Delhi than elsewhere in the country, with 90 percent of differences with respect to Delhi lying in the range of ‐44.4 percent to +41.6 percent (Figure 3). In absolute terms, the median price difference (with respect to Delhi) is greater than in retail at 17.2 percent, with 90 percent of the differences lying between 2.8 and 58 percent (Figure 4).

Figure 3: Distribution of wholesale price differences (with respect to Delhi) across districts Figure 4: Distribution of wholesale absolute price differences (with respect to Delhi) across districts

> 3 To measure price differences and to test for price transmission, it takes Delhi as a reference point. Apart from being the nation’s capital, and being economically crucial, Delhi’s geographical location, at the center of the northern landlocked land mass makes it a major redistribution hub for consumer products.

7
<!-- Start of picture text -->
-2 0 2 4 6 0 1 2 3 4 5<br>Proportional Wholesale Price Differences wtih Respect to Delhi Proportial Wholesale Price Absolute Differences with Respect to Delhi<br>Source: Authors’ calculations.  Source: Authors’ calculations.<br>2 4<br>1.5 3<br>1 2<br>Density Simple Differences .5 Density Absolute Differences 1<br>0 0<br><!-- End of picture text -->

All prices are disperse, but some are more disperse than others.

Aggregate indicators of retail price dispersion mask substantial heterogeneity across products. Within states and for a given year, prices of pulses tend to show the lowest dispersion levels (differences with respect to Delhi) among all products considered. For example, tur arhar, moong, masur or urad show dispersion levels between 15 and 19 percentage points lower than bananas – the product with the highest level of dispersion after potatoes (Figure 5).

Figure 5: Differences in price dispersion by product relative to bananas (percentage points) <!-- Start of picture text --> 5%<br>0%<br>‐5%<br>‐10%<br>‐15%<br>‐20%<br>‐25%<br>Note: The columns represent regression coefficients of<br>commodity dummies on absolute price differences,<br>controlling for year and state effects. Lines show the<br>confidence interval for the coefficients. The omitted<br>commodity is bananas. Source: Authors’ calculations .<br>Tur Arhar Moong Masur Urad Mustard Oil Rice Gram Groundnut Oil Coconut Oil Onion Potato<br><!-- End of picture text --> Dispersion also varies across states. For a given product in a given year, prices in the northeast state of Nagaland show the highest differences with respect to Delhi (about 13 percentage points higher than those of the baseline ‐ Andhra Pradesh). The lowest differences are observed in cities in Madhya Pradesh, at about 4 percentage points below the differences of the baseline (Figure 6).

Figure 6: Differences in price dispersion by state relative to Andhra Pradesh (percentage points) <!-- Start of picture text --> 20%<br>15%<br>10%<br>5%<br>0%<br>‐5%<br>‐10%<br><!-- End of picture text --> Note: the columns represent regression coefficients of state dummies on absolute price differences, controlling for year and commodity effects. Lines show the confidence interval for the coefficients. The omitted state is Andhra Pradesh. Source: Authors’ calculations.

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At the wholesale level, price differences also exist, even within narrowly defined geographical areas. Take the case of potatoes, for example. Price records show that 90km north of Delhi, in Panipat, Haryana, potatoes have sold up to 88.5 percent cheaper than in Delhi. On average, that district shows potato prices being 38 percent cheaper (Table 12 in the Appendix). Also, on average, focusing on absolute differences for all products considered for the wholesale analysis, Punjab and Kerala have the lowest and highest recorded price differences with respect to Delhi, respectively. In terms of products, tomatoes have the highest recorded differences, while Bengal grams have the lowest (see Appendix to this section for descriptive statistics).

_The GST as an integrating force_ Until the summer of 2016, Indian legislation allowed for a complex, and multilayered federal, state, and local indirect tax structure. During that summer, the Indian Parliament approved the Goods and Services Tax (GST) legislation that will help simplifying the current structure and will facilitate India becoming a single, unified market, by removing tax and non‐tax barriers that increase the costs of inter‐state and international trade. Under the existing structure (expected to be gradually replaced during 2017), at each point of sale, additional taxes are applied to the after‐tax value of each good and service. Many of these taxes vary by state. A central objective of the GST is to eliminate the compounding effect by fixing the final tax rate, with goods falling into one of four rate categories (5, 12, 18, and 28 percent).

Before GST, inter‐state trade has been subject to tax and non‐tax barriers. Tax barriers include the Central Sales Tax (CST) on goods, a tax levied on all cross‐state trade that is not destined for, nor originates from abroad (2 percent), entry taxes – levied on the entry of goods into a state for the consumption, use or sale therein (varying from 0‐12.5 percent depending on the state), and the restriction of input tax credit on inter‐state sales. Non‐tax barriers include the transaction costs of dealing with a different tax regime in each state, comprising of multiple indirect taxes. Take for example a manufactured good that is produced in Andhra Pradesh (AP). If sold in AP, the total tax would amount to 29 percent (compounding the Central Value Added Tax – CENVAT – of 12.36 percent and the AP VAT of 14.5 percent). If instead the good was to be sold in Maharashtra, the total tax would be of 48 percent, including CENVAT, AP VAT, an additional 2 percent of CST, and an entry tax of 12.5 percent in Maharashtra.<sup>4</sup> This system discourages across‐state specialization and market integration, encouraging instead, state autarky, increasing price dispersion among the products that are subject to these taxes. The new GST merges many of the central and state taxes into a four‐tier schedule going from 5 to 28 percent (necessity goods taxed at 5 percent, and luxury and consumer durables at 28 percent, with most goods and all services being at standard rates of 12 or 18 percent. This implies that goods produced in AP will be subject to the same tax regardless of where in India they are sold. In summary, the GST reduces the average tax rate on manufacturing goods and makes them uniform across states (see Van Leemput and Wiencek (2017) for a thorough discussion).

By merging central and state taxes, and increasing incentives to trade across state‐borders, the GST is expected to be welfare‐improving for all states in India. Some effects have already started to materialize. Van Leemput and Wiencek report an expected estimated improvement on real GDP of between 3.1 and 4.2 percent (under alternative product allocation assumptions among the different proposed GST rate tiers). The intuition is that a reduction of domestic and international trade barriers increases welfare through consumers’ access to cheaper products. Additional sources of welfare gains could result from cheaper sourcing of intermediate inputs – the creation of Indian‐level rather than

> 4 See more examples in Van Leemput and Wiencek (2017).

9
state‐level value chains, which increases the competitiveness (reduces the price) of the final good. A more recent study using detailed GPS data for Indian trucks before and after the implementation of the GST shows that internal borders in India reduce the driving speed of trucks by around 3km per hour. The implementation of the GST is shown to have increased driving speed at the border by 1% (Krishna and Van Leemput, 2018a).

## Dispersion over time

Spatial price dispersion has fallen over time in wholesale markets, but not in retail markets. Coefficients of variation for retail prices across spatially separated locations have _not_ been decreasing over time, for the period analyzed.<sup>5</sup> On the contrary, when pooling all commodities, price dispersion has increased at a low rate of half a percent from 2006 to 2014. The coefficient of variation varies by commodity. Increases in dispersion are significant for bananas, coconut oil, split and whole grams and rice. It is only for mustard oil where price dispersion has fallen. For the rest, the trend is not significant (see Appendix, Figure 26).

At the wholesale level, price dispersion has been decreasing at a rate of a third of a percentage point per year. Dispersion in potato and wheat wholesale prices has been on the rise, while for all other products, dispersion has fallen – in the case of onions and paddy, dispersion has fallen at a rate of 1 percent per year (see Appendix, Figure 27).<sup>6</sup> To what extent are these patterns of price dispersion associated with the way price shocks are transmitted across Indian districts or cities? To what extent are they associated with the way international price shocks are transmitted? The following subsection focuses on these questions.

# Patterns of price transmission and market integration

Price differences across spatially separated locations are in part related to different patterns of price transmission. To examine patterns of price transmission we focus on three characteristics: the completeness of transmission of shocks in the long run, measured by the pass‐through; the speed with which shocks transmit, measured by the speed of adjustment parameter; and the extent to which transmission is symmetric when comparing shocks that imply price increases and those that imply price reductions. For the analyzed products, transmission of price shocks is found to be incomplete (the law of one price is generally rejected), heterogeneous by region and product both in terms of speed and in terms of completeness, although symmetric upwards and downwards, with strong co‐ movement of prices both within India and between India and international markets.

## Methodology

The first requirement to assess market integration is to have an operative definition of integration. In this paper, spatially separated markets are considered to be integrated when they are connected by trade in such a way that the forces of arbitrage make the long‐run price differences that remain among them reflect transport costs. This means that shocks arising in one region are transmitted to another region (Fackler and Goodwin, 2001). Thus, the underlying framework is that of the law of one price (LOP), and the tests of integration to be performed are tests of the relative version of this law.

To quantify patterns of price co‐movements, we use two complementary approaches. The first one focuses on transmission of large price shocks and relies on a non‐parametric framework. The second one focuses on transmission of shocks along the whole distribution and relies on a parametric framework.

> 5 The coefficient of variation is an indicator of dispersion of prices. It measures, at every point in time and for each commodity, the standard deviation of prices relative to their mean.

> 6 For mustard, the reduction in dispersion of prices is only significant at 10 percent.

10
The first approach consists of examining the co‐movement of prices for homogeneous products (both the co‐movement among spatially separated markets within India, and between Indian markets and international markets), purging the price series from their irregular components. Thus, this analysis focuses on co‐movements of substantial price changes. Since the raw series for prices are highly volatile, a smoother is applied that preserves cycles but reduces the noise in the series. For each city‐ commodity, the Christiano and Fitzgerald (2003) filter is applied to isolate cycle‐components. This filter has the property of approximating the optimal linear smoother, which can only be applied to an infinite time series. The filter minimizes the mean squared error between the filtered series and the series filtered by an ideal band‐pass filter. We select periodicities between 12 and 36 months.<sup>7</sup> The choice of these periodicities was driven by visual inspection of the peak‐to‐peak analysis of price data.

This approach allows visualizing the extent of transmission of substantial price changes, as well as other patterns, such as the symmetry of transmissions from peak‐to‐peak and from bust‐to‐bust.

The second approach directly tests the relative version of the LOP. Relative LOP establishes that the growth rate of domestic prices reflects the growth rate of international prices and of the nominal exchange rate. The absolute LOP postulates that in the long‐run there are no arbitrage opportunities, because the price of a homogenous good in spatially separated markets needs to be equal. Given the price in market i, Pi , the price in market j, Pj, the absolute LOP postulates that:

# Pi�Pj

In growth rates (denoting log differences in minuscule), the relative PPP can be defined as: Empirically, to assess the extent of spatial integration we analyze how the prices in i and j co‐move.

A natural way of testing the relationship for India is to estimate a vector error correction model for all goods in all states. Following Pesaran 2000, the VEC model for panel data is specified as follows: Where _p_ict_ is the domestic price in city _i_ of commodity _c_ at month _t_ . The first term between parentheses is the equation for the long‐run relationship of interest. The gamma coefficient provides an indicator of the pass through from price shocks in _j_ to prices in _i_ . If the gamma coefficient equals 1, the LOP holds in the long‐run. That is, market _i_ is found to be integrated with market _j_ for commodity _c_ . The coefficient φ provides an indicator of the speed at which domestic prices in _i_ adjust after shocks in _j_ bring the long‐run relationship of prices between the two prices away from long‐run equilibrium (the speed of adjustment). Pesaran’s approach allows one to estimate different short‐run coefficients for each state commodity and control for unobserved city‐commodity fixed effects. In addition, the speed of adjustment parameter is allowed to vary through city and commodity.

To analyze patterns of transmission with world prices, _pj_ is substituted with _p*_ , the international price, and an exchange rate variable is added into the regression to compare international and domestic prices measured in the same currency.

> 7 The Christiano and Fitzgerald filter is a band‐pass filter. It keeps periodicities within the chosen band‐pass and attenuates the ones outside the band.

11
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<!-- Start of picture text --> Filtered vs Normal Price Series: Growth RatesKerala, Trivandrum: Coconut Oil Filtered vs Normal Price Series: Growth RatesKarnataka, Bangalore: Rice<br>Domestic Domestic<br>International International<br>Note: Smoothed series are estimated using Christiano‐Fitzgerald band‐pass filter<br>Source: Author’s calculations<br>1 1<br>.5 .5<br>0 0<br>-.5 -.5<br>1 1<br>.5 .5<br>0 0<br>-.5 -.5<br>2006m1 2007m1 2008m1 2009m1 2010m1 2011m1 2012m1 2013m1 2014m1 2015m1 2006m1 2007m1 2008m1 2009m1 2010m1 2011m1 2012m1 2013m1 2014m1 2015m1<br><!-- End of picture text --> The timing with which these “boom” and “bust” international price cycles are transmitted to domestic markets matter since the commodities in question are an important element in the consumption basket of poor Indian households. To identify the average transmission time that “busts” (troughs) take to transmit to domestic retail prices we use standard time series filters. Figure 10 plots these results for the four commodities for which domestic retail prices can be mapped to international prices (groundnut oil, rice, bananas and coconut oil).<sup>9</sup> These charts plot the extent of transmission in the vertical axis (the portion of the shock that is actually transmitted), and the time it takes for the trough to transmit (international to domestic trough distance in months). Results show that on average it takes five to fourteen months for international troughs to transmit, with substantial heterogeneity by city. Interestingly, the time it takes for shocks to transmit is independent of the portion of the shock that is passed through to domestic prices.

Figure 10: Time it takes for international troughs to transmit to domestic markets <!-- Start of picture text --> Groundnut Oil Rice<br>Raja Karn<br>Raja<br>KeraAPraTNadMaha RajaPunj UPraDelhiKarnRajaAPraRaja APra PunjAPraTNadTNadKeraUTMPraKarnGu Bihar MeghWBenKerajaTNadUPra HaryAPraRaja TNadRajaAPra UPra MeghAPra NagaMPra HaryRaja Jhar UTKarnGujaMizoMaha OrisHary UPraWBenHPra APraKarn Bihar Punj DelhiKeraUPraTpurAPraGoa APra KeraPunjJKasTNadTNad WBen TNad Kera OrisUPra<br>0 5 10 15 20 0 5 10 15 20<br>Average International to Domestic Trough Distance in Months Average International to Domestic Trough Distance in Months<br>Bananas Coconut Oil<br>Megh<br>Raja<br>MizoKeraTNadWBenAPraMPraTNadUPraAPraRajaRajaTNadUPra JharRaja JKasHaryOrisDelhiPunjHPra UPraHaryKeraNaga APra Raja APraKeraAPraPunjTpurUT MahaGoa Bihar Delhi Karn APra Raja BiharHaryMahaAPraTpur KeraKera Goa Guja TNadAPraPunjPunjWBenKeraKarn APra UPraTNadTNad Oris UT UPra MPra<br>TNad<br>0 5 10 15 20 0 5 10 15 20<br>Average International to Domestic Trough Distance in Months Average International to Domestic Trough Distance in Months<br>Source: Authors’ calculations<br>.25 .25<br>.2 .2<br>.15 .15<br>.1 .1<br>.05 .05<br>0 0<br>.25 .25<br>.2 .2<br>.15 .15<br>.1 .1<br>.05 .05<br>0 0<br><!-- End of picture text --> 9 These four products matter in the consumption basket of Indian households. For example, rice accounts for 6.6 percent of the consumption of non‐poor households and 9.2 percent of that of poor households. Bananas, for about half of a percent for both types of households, and groundnut and coconut oil combined, for 4.95 percent among non‐poor households and 5.5 percent for poor households.

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International price booms and busts are transmitted symmetrically to domestic retail prices.

Do price changes that imply domestic price increases transmit as fast as those that imply price decreases? The literature tends to show that prices rise like rockets but fall like feathers – that is, increases tend to transmit faster than price decreases.<sup>10</sup> For India, when analyzing large price shocks originated in world markets, this pattern of asymmetry does not seem to hold.

Figure 11 shows for groundnut oil, rice, bananas and coconut oil, the time it takes for troughs to transmit (horizontal axis) against the time it takes for booms to transmit (vertical axis). Under perfectly symmetrical transmission, we would observe that all states would be on a 45‐degree line (the time it took for busts to transmit would be identical to the time it took for troughs). While results show a departure from perfect symmetry, the trend shows an approximate alignment in the speed with which the adjustment materializes. Most states are in the bottom left or top right quadrant, implying fast adjustment upwards and downwards (bottom left) or slow adjustment upwards and downwards (top right). Only a few are in the top left or bottom right quadrants (that would imply slow adjustment upwards and slow downwards or fast upwards and slow downwards respectively). The case of groundnut oil shows minor departures from the symmetric pattern with faster trough transmission relative to boom transmission.

Figure 11: Comparison of time it takes for booms and busts to transmit (4 commodities, retail) <!-- Start of picture text --> Groundnut Oil Rice<br>TNad<br>Kera<br>TNad<br>Kera APra TNadMahaRajaUPraPunj Karn RajaAPraRajaAPraPunjAPraTNadTNadKeraMPraUTKarnGujaBihar Megh WBenKeraTNadUPra HaryAPraRaja TNadRajaAPraUPraMeghAPraNagaMPraHaryRajaJharUTKarnRajaGujaMizoMahaOrisHaryUPraWBen HPra APraKarnBihar Punj DelhiRajaKeraUPraTpurAPraGoaAPraKeraJKasPun TNad WBenj Karn OrisUPra<br>Delhi<br>0 5 10 15 20 0 5 10 15 20<br>Average International to Domestic Bust Distance in Months Average International to Domestic Bust Distance in Months<br>Bananas Coconut Oil<br>UT<br>RajaJKasUPraAPraKeraMahaGoaBihar GoaAPra UPra<br>MizoKeraTNadWBen APra MPra TNad UPraAPraRajaTNadUPraTNadJharRajaHary Oris Delhi Punj HPraMeghHary Naga KeraRajaAPraAPra Punj TpurUT Delhi Karn APraRajaBiharHary MahaAPra TpurKeraKeraGujaTNadAPraWBen Punj PunjKarnKeraUPraTNadTNad Raja OrisMPra<br>0 5 10 15 20 0 5 10 15 20<br>Average International to Domestic Bust Distance in Months Average International to Domestic Bust Distance in Months<br>20 20<br>15 15<br>10 10<br>5 5<br>Boom Distance in Months Peak Distance in Months<br>Average International to Domestic Average International to Domestic<br>0 0<br>20 20<br>15 15<br>10 10<br>5 5<br>Boom Distance in Months Boom Distance in Months<br>Average International to Domestic Average International to Domestic<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> Source: Authors’ calculations.<br><!-- End of picture text --> Markets operate relatively efficiently transmitting signals when they are shocked with sizable international price changes. Booms and busts of food prices, either originating domestically or abroad are transmitted to different Indian markets and in a relatively symmetric manner, even if to different 10 According to Peltzman’s comprehensive study of 165 producer goods and 77 consumer goods “in two out of three markets, output prices rise faster than they fall” (Peltzman, 2000; p. 480). Bacon (1991) was the first to use the term rockets and feathers to describe the patterns of retail gasoline prices in the UK. Varela and Taniguchi (2013) report a similar finding for wheat and wheat flour markets in Indonesia.

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||@)||2)|@)||@)||
|---|---|---|---|---|---|---|---|
|Retail|LR|SoA|LR<br>SoA|LR|SoA|LR|SoA|
|Speed ofAdj (Avg)||-0.170***<br>(0.0059)|-0.160***<br>(0.00677)||-0.157***<br>(0.00663)||-0.159***<br>(0.00713)|
|Log Price in Delhi<br>|0.787***<br>||0.770***<br>|0.791***<br>||0.805***<br>||
|Constant<br>|(0.00731)<br>|0.120***<br><br>|(0.00795)<br>0.128***<br><br><br>|(0.00816)<br><br>|0.415***<br><br>|(0.00790)<br>|0.108***<br><br>|
|Observations|50017|(**0**.00418)<br>5 917|(**0**.00455<br>50423<br>5 423|49035|(**0**.00409)<br>4 035|49447|(**0**.00414)<br>4 447|
|Wholesale||(1)<br>LR|SoA|(2)<br>LR|SoA|(3)<br>LR|SoA|
|Speed of Adj (Avg)|||-0.251***<br>(0.00513)||-0.245***<br>(0.00535)||-0.251***<br>(0.00567)|
|Log Price in Delhi<br>||0.<br>|651***<br>|0.641***<br>||0.650***<br>||
|Constant||(0|00504)<br>0.221***|(0.00558)|0.223***|(0.00571)|0.223***|
|Observations<br>Standard errors in p<br>*#*pb<0.01,**p|arentheses<br><0.05,*p|12<br><0.1|3,234<br>123,234|121,923|121,923|120,612|120,612|
Heterogeneity is pervasive. For Bengal grams, at the wholesale level, the law of one price holds. This implies that a shock originating, for example, in Delhi, is fully transmitted to other markets within India in the long run, with the speed of adjustment to short‐term disequilibria being relatively high, in the range of 31‐35 percent (see Appendix). In retail, also for pulses (but slightly different products: grams whole and split, masur, moong, tur arhar and urad), while the pass‐through coefficient is significantly different from one, it is well above 80 percent, suggesting almost complete transmission of shocks in the long run, but at relatively slower speed, in the range of 10‐20 percent of adjustment per period ‐ more in line with what is found for other products (see Appendix to this section).

Prices of onions and potatoes ‐ both for retail and wholesale ‐ show a fast speed of adjustment to long‐run equilibrium levels (tomato wholesale prices show high speed of adjustment well above 50 percent per period). Conversely, rice and paddy show slow adjustments to shocks, at 10 percent per period both for retail and wholesale (Figure 12, Figure 13).

Also, for a given commodity, heterogeneity is high across states. For example, in Uttar Pradesh banana prices adjust at a rate of 32 percent per period after a price shock originating in Delhi, while in Meghalaya prices adjust at a rate of only 8 percent per period. For onion prices, the variation is lower, and the adjustment is faster, with Bihar’s prices adjusting at 78 percent per period, while in Punjab prices adjust at a rate of 53 percent per period, after a shock is observed in Delhi’s prices (see patterns of speed of adjustment by state for retail and wholesale prices in Figure 14).

Figure 12: Market Integration within India – Speed of Adjustment, Retail by Commodity Figure 13: Market Integration within India – Speed of Adjustment, Wholesale by Commodity <!-- Start of picture text --> 0%<br>0%<br>‐10%<br>‐10%<br>‐20%<br>‐20%<br>‐30%<br>‐30%<br>‐40%<br>‐40%<br>‐50% ‐50%<br>‐60% ‐60%<br>Source: Authors’ calculations. Source: Authors’ calculations.<br>Onion Potato Mustard Oil Banana Masur: Split Gram: Split Tur Arhar: Split Urad: Split Gram: Whole Moong: Split Rice:... Groundnut Oil Coconut Oil<br><!-- End of picture text --> Figure 14: Market Integration within India – Speed of Adjustment, Wholesale and Retail by State <!-- Start of picture text --> 0%<br>‐10%<br>‐20%<br>‐30%<br>‐40%<br>‐50%<br>‐60%<br><!-- End of picture text --> <!-- Start of picture text --> Wholesale Retail<br><!-- End of picture text --> Notes: Spearman’s rank correlation coefficient between retail and wholesale speed of adjustment equals 0.1429 Source: Authors’ calculations.

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# Integration with world markets

The hypothesis of the law of one price between Indian and world markets is clearly rejected both for retail and wholesale prices. However, results show that the pass‐through from international to domestic prices is highly significant, in retail in the range of 62‐64 percent, while in wholesale in the range of 10‐11 percent. The results are robust across alternative specifications allowing for different lag structures (Table 2 for retail, Table 3 for wholesale), and they are aligned with the findings reported earlier when focusing on transmission of large price changes. Interestingly, exchange rate changes are fully transmitted to domestic wholesale prices in the long run, while domestic prices seem to react more proportionally to exchange rates, with a pass‐through coefficient above 1.4. For the speed of adjustment, the pattern reverses: it is greater for wholesale (more than double, in the range of 22 to 24 percent) than for retail (in the range of 11 to 12 percent). Notice however, that the reported speed of adjustment coefficients are averages across products and cities/districts, masking some heterogeneity.

Table 2: Results of error correction model, for retail, Table 3: Results of error correction model, for all commodities, all cities wholesale, all commodities, all districts

|Retail|(1)|(2)|(3)|(4)|Wholesale|(1)|(2)|(3)|(4)|
|---|---|---|---|---|---|---|---|---|---|
|_Speed of Adj (Avg)_|‐0.11***|‐0.11***|‐0.11***|‐0.12***|_Speed_<br>_of_<br>_Adj_<br>_(Avg)_|‐0.240***|‐0.22***|‐0.22***|‐0.22***|
||(0.008)|(0.008)|(0.009)|(0.009)||(0.0095)|(0.0081)|(0.008)|(0.008)|
|_Log International_|||||_Log International_|||||
|_Price_|0.62***|0.63***|0.64***|0.63***|_Price_|0.105***|0.11***|0.11***|0.11***|
||(0.0217)|(0.0213)|(0.0210)|(0.0212)||(0.007)|(0.008)|(0.008)|(0.008)|
|_Log NER_|1.40***|1.41***|1.44***|1.51***|_Log NER_|1.004***|1.02***|1.02***|1.04***|
||(0.0414)|(0.0399)|(0.0382)|(0.0378)||(0.0104)|(0.0108)|(0.0105)|(0.0103)|
|_Constant_|‐0.43***|‐0.45***|‐0.48***|‐0.53***|_Constant_|‐0.282***|‐0.27***|‐0.27***|‐0.29***|
||(0.026)|(0.029)|(0.031)|(0.035)||(0.0115)|(0.0106)|(0.0106)|(0.0110)|
|_Observations_|14,846|14,703|14,560|14,417|_Observations_|28,952|28,644|28,636|28,028|

Source: Authors’ calculations Source: Authors’ calculations The speed with which food prices adjust after a world price shock varies substantially by product, less so by state. Table 4 shows the speed of adjustment for each of the four products considered in this part of the analysis, averaged across states. For example, while in Uttar Pradesh coconut oil prices may only adjust at a rate of 3 percent per period, in Maharashtra prices adjust 14 percent of the disequilibrium per period. The heterogeneity is more marked across products than across city/states. For bananas, the speed of adjustment is, on average, 19 percent, while for rice it is less than a third, at 5.8 percent.

Table 4: Speed of adjustment of domestic to world price shocks by commodity and state (retail)

|**_State_**|**_Bananas_**|**_Coconut Oil_**|**_Groundnut Oil_**|**_Rice_**|
|---|---|---|---|---|
|Andhra Pradesh|‐0.1779|‐0.1554|‐0.0442|‐0.0967|
|Bihar|‐0.2111|‐0.0343|‐0.0381|‐0.0353|
|Goa|‐0.1032|‐0.0795||‐0.0814|
|Gujarat||‐0.0683|‐0.0755|‐0.0274|
|Haryana|‐0.2244|‐0.0568|‐0.0642|‐0.0570|
|Himachal Pradesh|‐0.1136|||‐0.0310|
|Jammu And Kashmir|‐0.3419|||‐0.0600|
|Jharkhand|‐0.4279|||‐0.0284|
|Karnataka||‐0.0764|‐0.1561|‐0.2299|
|Kerala|‐0.3828|‐0.0956|‐0.0816|‐0.0546|
|Madhya Pradesh|‐0.1685|‐0.0845|‐0.0315|‐0.1023|

17
|Maharashtra|‐0.1352|‐0.1381|‐0.1224|‐0.1147|
|---|---|---|---|---|
|Meghalaya|‐0.0991||‐0.1274|‐0.0126|
|Mizoram|‐0.0641|||‐0.0181|
|Nagaland|‐0.0028|||‐0.0123|
|Nct Of Delhi|‐0.2535|‐0.0933|‐0.2088|‐0.0804|
|Orissa|‐0.2714|‐0.0904||0.0075|
|Pondicherry|||||
|Punjab|‐0.1183|‐0.0270|‐0.0252|‐0.0533|
|Rajasthan|‐0.2127|‐0.0782|‐0.1783|‐0.0657|
|Sikkim|||||
|Tamil Nadu|‐0.1319|‐0.1084|‐0.0826|‐0.0559|
|Tripura|‐0.0947|‐0.0024||0.0000|
|Uttar Pradesh|‐0.3271|‐0.0032|‐0.0516|‐0.0911|
|West Bengal|‐0.0889|‐0.1007|‐0.0343|‐0.0285|

Source: Authors’ calculations The speed of adjustment for wholesale prices in India with respect to the world is also heterogeneous by product and state. For maize, it stands, on average at 28 percent per period, while for rice it stands at 17 percent. From a state angle, while markets in West Bengal adjust slowly (with the SoA coefficient being at 5 percent), adjustment is the highest, on average in Karnataka, at 32 percent per period.

At the wholesale level, integration of pulses with the world is weak, and in fact for Bengal grams, results reveal no long run relationship between international and domestic prices. At the retail level, while integration is imperfect, the pass‐through coefficient stands in the range of 40‐79 percent depending on the type of pulses being analyzed, again with heterogeneous speeds of adjustments.

# Our results in perspective: The literature on spatial market integration for India

While the approach in this paper is unique because of its methodology, scope of products covered, and type of analysis of integration both within India and with the world for both wholesale and retail markets, it is important to put the findings in perspective. To do so, this subsection compares the results reported in here with those available in the literature on the subject.

Two common findings characterize much of the literature on food market integration in India: that the extent of integration has been increasing over time, and that there seems to be an association between that extent of integration and government interventions in different forms.

The work of Jha et al (2005) offers a look at integration patterns over 1970‐1999, while focusing on only one product, rice, in 55 wholesale markets. They find that, during that period, market integration was far from complete, and they link that finding with what they call ‘excessive interference in rice markets by government agencies. They argue, but do not formally test it, that government intervention makes it hard for scarcity conditions in isolated markets to be picked up by markets with abundance in supply, reducing the scope for market integration. Specifically, they point to restrictions on inter‐state trade, regulatory burdens associated with multiple licenses needed for trading, different authorities issuing licenses for different goods, and government intervention programs for rice (and wheat) operated through Food Corporation of India in the form of quantitative interventions, price distortions at the three levels of the marketing chain (farm, wholesale, retail), and heavy subsidies.

For rice and wheat, and covering more recent periods, Ghosh (2003 and 2010) finds instead that, prices, while geographically dispersed across locations, within and across states, show long‐run spatial linkages, suggesting that markets _are_ integrated, and price signals _are_ being transmitted. Then, Mittal 18
and Virmani (2006), focusing only on price dispersion patterns for several commodities, find a decline in dispersion when comparing 1994 with 2004, attributing the decline to increased market integration.

# The role of reforms & the role of government interventions

Reforms affecting food markets were implemented in India since the early 1990s. Some of these affected internal trade, some external trade. Examples of the former are the relaxation of restrictions on inter‐regional movement of farm produce, the abolishment of licensing requirements and stocking limits for wholesale and retail trade, and the allowance of entrance to the corporate sector in agricultural markets through contract farming, among others. Examples of the latter are reductions in tariffs on agricultural imports, liberalization of export policies, including relaxation of export quotas, removal of restrictions on licensing, minimum export prices and increased availability of credit.<sup>11</sup> Ghosh (2010) addresses whether these reforms played a role in food market integration by breaking the period of analysis into pre and post reform. The author takes July 1991 as a cut‐off, finding that integration of food grain markets in India was substantially greater in the post‐reform period than in the pre‐reform period, leading the author to conclude that reforms associated with less government intervention in food markets help market integration increase. Apart from the usual attribution challenge (many other things happened concomitantly that could have helped markets integrate further), probably the major drawback of the study is the categorization of these two sub‐periods. While structural reforms did start as early as 1991, Indian agriculture remained virtually insulated until 1994, when edible oil imports were liberalized.<sup>12</sup> An alternative approach to link policies to the extent of integration of markets is that of Sekhar (2012). The author exploits the cross‐section variation in policies for trade in food commodities to hint at their effects. The paper examines market integration in India for rice, edible oils and pulses in wholesale markets, over the period 1986/87‐2006/07. The author finds that the extent of integration is lower for rice markets as compared to other crops, such as grams or edible oils (both groundnut and mustard oils) that appear completely integrated both regionally and nationally. While not formally tested, the author links the findings with the importance of trade policy in ensuring market efficiency. He argues that commodities with liberal border and domestic trade policies such as gram, and commodities with border regulations but open domestic trade such as edible oils, are substantially more integrated than commodities subject to strict regulations on domestic as well as external trade such as rice.

The evidence unveiled in this paper complements these results. For example, for wheat and rice, whose markets are subject to substantive intervention, such as minimum support prices, price shocks transmit in the long run but at low speeds. For rice in retail markets, less than 10 percent of the disequilibrium that results after a price change in a center is corrected within one month, compared to about 50 percent in onion markets, or 20 percent in potato markets. In wholesale, 11 percent of the disequilibrium is corrected within a month, compared to 42 percent for onions, or 22 percent for potatoes (Figure 12 and Figure 13). Integration between Indian rice markets and the world is also substantially weaker than for edible oils, for example, that face very low tariffs at the border as compared to rice.

# Benchmarking Indian Price Dispersion

Results reported in the first part of this section suggest that, pooling all products and focusing on retail markets, in 90 percent of the cases, the price difference paid in any given city with respect to Delhi for a given product ranges from ‐28 percent to +28.6 percent. Is this high or low? For the reader to decide, it is necessary to benchmark the dispersion of prices in India with what is observed in a reference country.

> 11 For more on reforms see Athukorala (2005), Bathla (2006) and Chadha et al (2008).

> 12 See Sekhar (2012). Similar analysis, methodology and findings are reported by Wilson (2001).

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Figure 16 shows the average price difference between farm‐gate and wholesale markets by state and for all products, which are wheat, tur arhar, moong, gram, and masur, within each state. The distribution of these price differences for each state is shown by the bands around each bar, representing the 25<sup>th</sup> and 75<sup>th</sup> percentile of the distribution. The average farm‐gate to wholesale market price difference for most states is around 20 percent; however, the distribution of the differences varies by state. Maharashtra, Rajasthan and Andhra Pradesh exhibit smaller distributions in price differences, while Gujarat and Uttar Pradesh have larger variations in price differences.<sup>13</sup> These results reveal the different nature of interactions and market institutions between farmers and wholesalers in each state. Interestingly, the wide variation in the distribution of price differences across states does not seem to be correlated with the average state price difference. In other words, there is no clear correlation between having a large distribution of price differences and a higher average state price difference.

Figure 16: Average price difference between farm‐gate and wholesale markets <!-- Start of picture text --> 2.00<br>1.60<br>1.20<br>0.80<br>0.40<br>0.00<br>‐0.40<br>Punjab Haryana Gujarat Uttar Pradesh Maharashtra Rajasthan Jharkhand Madhya Pradesh Tamil Nadu Andhra Pradesh Karnataka West Bengal<br><!-- End of picture text --> Note: The band around each bar represents the distribution of price differences. Each band represents the 25th and 75th percentile of the distribution of price differences for each state Source: Author’s calculations Analogously, there are substantial variations in the average price difference by product across India (see Appendix to this section, Figure 29). For example, price differences exceed 80 percent for masur while differences are below 10 percent for tur arhar. Furthermore, price differences within product also vary. For example, price differences in Uttar Pradesh for masur are 35 percent while in West Bengal they are 131 percent. (See Appendix to this section, Table 18.) There are three salient features of price differences between wholesale and retail markets. First, the distribution of price differences between wholesale and retail is different from farm‐gate to wholesale **.** Figure 17 shows the average price difference between wholesale and retail markets by state and for all products, which are rice, masur, wheat, moong, gram, onion, potato, and tur arhar, within each state. Two patterns emerge when comparing price differences between wholesale to retail markets and farm‐gate to wholesale markets. First, the distribution of the average price differences by state across products between wholesale and retail markets seems to show a continuum of values, rather than a bi‐modal distribution. Second, unlike farm‐gate‐to‐wholesale, the distribution of price differences wholesale‐to‐retail differences appears to be associated with the average price difference by state, across all products considered, suggesting state‐specific factors play a role in the price gaps between the wholesale and retail stage of the value chain.

> 13 Nyman (2017) reports, for the case of onions in Maharashtra, that retailers and wholesalers have similar price‐cost margins, at around 12 percent over wholesale price and 10 percent over farm‐gate prices respectively, according to a static analysis of margins along the value chain.

21
Second, price differences between wholesale and retail markets are higher than from farm‐gate to wholesale, likely due to a more substantial process of value addition in terms of transport, distribution, packaging, and marketing. Figure 31 in the Appendix shows the average difference across products for all of India. At first glance, the average price difference between wholesale and retail is larger than the average difference between farm‐gate and wholesale, despite the heterogeneity. However, price differences vary by state, so not all states have observed price differences that are larger between wholesale and retail, but most do. The average state level price difference varies between 10 percent in Andhra Pradesh to 60 percent in Haryana (See Figure 17).

Third, price wedges are _also_ heterogeneous across commodities, and within states **.** This is a common feature with the wedges observed for farm gate to wholesale. While for masur and wheat, wedges stand in the range 10‐20 percent, for tur arhar they stand at 80 percent, which, incidentally, displayed the lowest wedge at the farm‐gate/wholesale stage. This can also be seen by examining the dispersion of price wedges around the 10<sup>th</sup> and 90<sup>th</sup> percentile for a given state‐commodity pair. For example, for potatoes, price wedges in Punjab stand at 36 percent at the 10<sup>th</sup> percentile and at 133 percent at the 90<sup>th</sup> percentile; for onions, price wedges in Rajasthan stand at 35 percent at the 10th percentile and at 107 percent at the 90th percentile (Table 19).

Figure 17: Average price difference between wholesale and retail markets <!-- Start of picture text -->
1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>‐0.20<br>‐0.40<br>‐0.60<br>Meghalaya Orissa Madhya Pradesh Jharkhand Andhra Pradesh Uttar Pradesh Kerala Karnataka Jammu And... Gujarat West Bengal Maharashtra Rajasthan Himachal Pradesh Punjab NCT of Delhi Haryana<br><!-- End of picture text -->

Note: The band around each bar represents the distribution of price differences. Each band represents the 25th and 75th percentile of the distribution of price differences for each state Source: Author’s calculations

# Our results in perspective: The literature on vertical integration in India

The Government of India conducted some analysis similar to the one in this paper in its 2015‐16 Economic Survey Volume I. Studying market segmentation and using similar data, the survey had three major findings. First, prices in India are widely dispersed and substantially more so than in comparator countries, including the United States. The study shows that the ratio between the highest and lowest price received by farmers is not equal to one. A ratio of one would signify that there is no price dispersion. A comparison of these ratios to US markets in both 1960 and 2012, shows that in India price dispersion is greater by about 45 percent to 100 percent. Second, products that are typically thought of as more perishable tend to have higher price wedges at all levels of the marketing chain. A comparison of prices between farm and wholesale shows that products such as potatoes, onions, and groundnuts had higher price wedges than pulses, which are relatively less perishable. Similarly, the analysis at the wholesale and retail level showed higher margins for more perishable products such as onions. Third, variation in price wedges is substantial across both states and commodities. The report 22
suggests that states with higher markups could be the result of state specific effects such as rural infrastructure, storage capabilities, and rural political economy.<sup>14</sup>

# Patterns of price differences over time & overall dispersion

In aggregate, price differences between wholesale and retail markets have declined.<sup>15</sup> An assessment of price wedges, which is taken at both the state and commodity levels, between wholesale and retail markets reveals that a negative and significant pattern exists over time. The negative effect is around two‐ thirds of a percent, accumulating to a 4.9 percent decrease over the period of analysis: 2007‐2014 (Table 5).

Table 5: Regression on price wedge and year, controlling for commodities and states

|(1)<br>Price Wedge|
|---|
|Year<br>‐0.00683***<br>(0.00166)|
|Constant<br>14.05***<br>(3.348)|
|Observations<br>3,628|
|R‐squared<br>0.626|
|Standard errors in parentheses|
|*** p<0.01, ** p<0.05, * p<0.1|
|Note: Commodity and state fixed effects are included, but<br>not reported.|

# Measures of Vertical Market Integration and Price Transmission

To examine how price shocks transmit vertically from wholesale to retail, we rely on the same methodological approach used to analyze spatial integration in Section 2 (see methodological section for details). In this case, the focus of analysis would be in the parameters that describe how price changes originating in wholesale markets are transmitted to retail markets.

Three questions are addressed. First, do wholesale price shocks fully transmit to retail prices in the long run? This would imply a pass‐through coefficient from wholesale to retail prices equal to 1. Second, do retail prices adjust in the short run, to gradually return to the long‐run equilibrium relation with wholesale prices, after a shock that affects the latter? This would imply a speed of adjustment coefficient that is negative (to gradually correct for the disequilibrium). Third, is the short‐run adjustment to return to the long‐run equilibrium after a shock symmetric upward and downward? This would imply that there is no difference between the speed of adjustment that implies an increase of retail prices, and that implying a decrease of retail prices after a shock in wholesale prices.

The hypothesis of full price transmission is rejected for all but one commodity. Yet, the price co‐ movement is substantial. Shocks to wholesale prices are not fully transmitted to retail prices, because the pass‐through coefficient is less than one for all but one commodity. However, the pass‐through is highly significant and ranges from 52 to 97 percent. For example, 89 percent of a price shock at the wholesale market is transmitted, in the long run, to the retail market for gram. In the case of rice, it is not possible to reject the hypothesis that the pass‐through equals one (Figure 18).

The speed of adjustment is both statistically and economically significant for all commodities, and heterogeneous. Prices in the onion retail market adjust back to equilibrium for around 30 percent of the disequilibrium per period caused by a price shock in the onion wholesale market (Figure 19).

> 14 See Government of India “Economic Survey 2015‐16, Volume I” pgs. 79‐83.

> 15 Due to the lack of availability of time series farm‐gate data, this analysis had to focus on the trends in price wedges between wholesale and retail.

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Figure 18: Pass through across commodities Figure 19: Speed of adjustment across commodities <!-- Start of picture text --> 100% 80%<br>90% 70%<br>80%<br>60%<br>70%<br>50%<br>60%<br>50% 40%<br>40% 30%<br>30%<br>20%<br>20%<br>10%<br>10%<br>0% 0%<br>Rice Wheat Gram Onion PotatoMasur<br><!-- End of picture text --> Source: Author’s calculations Source: Author’s calculations Note: Speed of adjustments are plotted in absolute value Commodities with the largest pass‐through coefficient do not necessarily exhibit the fastest speeds of adjustment. In the long run, price shocks originating at the wholesale market for rice, gram, and wheat have the largest pass‐through; however, these same commodities have the slowest speeds of adjustment. For example, 96.8 percent of a price shock in wholesale rice markets is transmitted to retail rice markets in the long run, but the speed of this transmission is relatively slow compared to other commodities, at 16.3 percent. On the other hand, potatoes, which have the smallest wholesale to retail pass‐through of 52.3 percent, have one of the fastest speeds of adjustment at 30 percent (Figure 18 and Figure 19). Adjustment in retail prices to shocks in wholesale prices is slower for products in which there is substantial government intervention (rice and wheat), just as it was reported when looking at horizontal transmission in the previous section, and as reported by Sekhar (2012), also for horizontal transmission of shocks.

The speed of adjustment is highly significant for most of the state‐district‐commodity pairs, but its size varies. Much like the speed of adjustment at the commodity level, the percent of adjustment per period differs by state. For example, prices for all commodities in the Haryana retail market adjust back to equilibrium around 45 percent of the disequilibrium per period caused by a price shock in the Haryana wholesale commodity market. Similarly, price in the Madhya Pradesh retail commodity market adjust back to equilibrium around 10 percent of the disequilibrium per period caused by a price shock in the Madhya Pradesh wholesale commodity market (Figure 20).

Figure 20: Speed of adjustment across states <!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Note: Speed of adjustments are plotted in absolute value<br>Source: Author’s calculations<br>Speed of Adjustment<br><!-- End of picture text --> 24
# Symmetry of Adjustment to Shocks

Are price shocks that imply an increase in retail prices transmitted as fast as those that imply a decrease in retail prices? This is a key feature of the patterns of transmission of shocks. Under asymmetric transmission, the size and timing of welfare changes associated with price changes are altered, and the distribution of welfare will be different than it would be under symmetric transmission. For instance, some groups may not benefit as much from a price reduction (e.g.: buyers) or a price increase (e.g.: sellers) under asymmetric transmission, as if transmission were symmetric.

_Of rockets and feathers:_ there are substantial departures from symmetry, mostly implying that prices increase faster than they fall. The test of asymmetric price transmission is a test on whether the speed of adjustment to shocks that reduce the price difference between wholesale and retail markets, and therefore require an upward change of retail prices to bring these differences back to equilibrium, equals the speed of adjustment to shocks that increase price differences, and therefore require a downward change in retail prices. Conventional wisdom has it that prices tend to rise faster than they fall. Evidence for patterns of vertical transmission of price changes in India give some kernel of truth to that (for potential drivers of this asymmetry, see next subsection). For all eight food products considered for the analysis of vertical transmission, the hypothesis of symmetry of transmission is rejected. Adjustments upwards and downwards do not happen at the same speed. For six out of the eight products, the upward adjustment of prices happens faster than the downward adjustment. Only for onions and tur arhar retail prices seem to fall faster than they rise. In the case of rice and moong, the differences in the speed of adjustment for price increases or reductions exceed a factor of three (Figure 21). Interestingly, for the case of onions in Maharashtra, Nyman (2017) examines daily prices across specific markets revealing the opposite type of asymmetric pass‐through from the wholesale to the retail level so that when wholesale prices fall, retailer margins tend to increase. At the same time, the author argues that competition dynamics (one potential driver of asymmetric pass‐through), tend to be affected by regulations, policies and other government interventions that restrict the ability of players to compete in food markets in India (these include, for example, the creation of buffer stocks, official minimum prices, regulations of fees paid to intermediaries, such as commission agents, etc.) (see Nyman (2017), pp 7‐10).

Figure 21: Speed of adjustment for upward and downward changes ‐ test of asymmetry <!-- Start of picture text --> Gram*** Masur*** Moong** Onion*** Potatoes*** Rice*** Tur Arhar** Wheat***<br>0%<br>‐20%<br>‐40%<br>‐60%<br>‐80%<br>Adjustment Upwards Adjustment Downwards<br><!-- End of picture text --> Note: *** denotes rejection of symmetry at 1% confidence, ** denotes rejection at 5%, * denotes rejection at 10%. Source: Authors’ elaboration.

## _Literature review on drivers of asymmetric price transmission_

Six factors have been identified in the literature as potential drivers of asymmetric price transmission: market power, search costs, asymmetric information, adjustment costs, government intervention, and the nature of products.

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The typical explanation for asymmetric price transmission lies on the existence of some sort of market failure. In the case of a monopolistic market structure, input price increases are transmitted to output prices faster ‐to avoid profit losses ‐ than input price reductions that can increase profit margins. A positive asymmetry in price transmission arises. However, market power can also result in a negative asymmetry (Ward, 1982). If the oligopolist cares about market shares, it will be reluctant to increase output prices when input price increases, although leaning to reduce them when input prices fall. Informal collusion among firms with high reputational costs can also lead to asymmetric price transmission. After the input price increases, all firms will adjust output prices upwards to ensure competitors that they are willing to collude, while when the input price falls, they will avoid signaling the break of the implicit agreement (Balke et al 1998 and Brown and Yucel 2000).

It is often the case that retail firms enjoy local market power due to the lack of similar firms in the neighborhood. Because of search cost, customers of these firms may not be able to acquire complete information about prices offered by other firms, although they face a finite number of choices. Customers may observe a price increase at a retail shop, but they may be uncertain as to whether prices elsewhere have also increased. Thus, firms can quickly raise prices as producer prices increase and can slowly reduce prices as producer prices fall.

Asymmetric information also may matter. The old output price naturally serves as the focal point when information about other firms’ pricing decisions is lacking. For instance, increases in input prices lead to faster output price adjustments, on the one hand, as profit margins fall. On the other hand, input price reductions result in output reductions only when sales fall below a threshold.

Asymmetric price transmission can arise when it is costly to adjust input or output prices or quantities and if these costs change differently with respect to increases or decreases in prices or quantities. For example, when a reduction in input price requires increasing quantity of inputs or outputs, which entails greater costs (e.g.: search costs) than when reducing inputs or output (Peltzman 2000). In this case, the asymmetric transmission arises because the former leads to search cost and price premium in increasing phases. A nominal input price increase is more likely to lead to output price changes than an input price decrease. This is because with inflation, a portion of the necessary adjustment associated with an input price reduction is carried out by inflation, reducing the real value of the margin (Ball and Mankiw, 1994).

Asymmetry could be a by‐product of government intervention. Policies that support producer prices, for example, may lead processors to expect that decreases in producer prices will be countered by government support, and thus, will be more short‐lived relative to increases. With menu costs, retailers may adopt a wait‐and‐see approach to input price reductions but adjust output prices more rapidly in response to input price increases (Kinnucan and Forker, 1987).

Finally, the nature of products may help understand asymmetric transmission. Firms in possession of perishable goods may resist the temptation to increase prices for fear of being left with spoilt products (Ward, 1982).

# Section 4: Determinants of Spatial Market Integration in India

The previous two sections documented significant departures from the hypothesis of perfect integration in food markets in India. While on average the co‐movement of food prices across different districts and states in India, as well as the co‐movements of retail with wholesale prices is substantial, transmission is still imperfect. Price shocks originating in an economic center are only partially transmitted in both wholesale and retail markets. Moreover, patterns of transmission are heterogeneous in terms of the speed of adjustment both across commodities and states. This section 26
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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a0) 0) =In Pie @) — In Poemit© jtO iO = ~~1-~~ ) 40 t ieO itD = pdjt-1/ + wij + €xjt
1986, Goodwin and Piggott, 2001, Rashid, 2004 and Van Campenhout, 2007). For Indonesia, Varela et al (2012), and for Bangladesh, Goletti et al (1995) test the role of transport costs, and distance respectively, explaining a significant portion of the variation of market integration and price differences.

Transport costs, integration and welfare. Going one step further, and assuming a direct link between transport costs and market integration, Donaldson (2010) estimates the welfare benefits of improved connectivity (associated with increased integration). Using colonial data on India, he finds that the benefits of building the railroads in India were threefold. First, because of better infrastructure, price gaps between regions have decreased. Second, trade has been promoted (nationally and internationally). Third, real income has increased because of lower prices. In a recent contribution Allen and Atkin (2016), using data on Indian highways, show that transport improvements result in farmers being able to allocate their land to less volatile crops, avoiding the increased volatility in earnings that they would have otherwise faced, thus amplifying the gains from trade.

Information and the digital dividend. Jensen (2007) argues that, when information is limited or costly, agents are unable to engage in arbitrage, and excess price dispersion arises. The author uses micro‐ level survey data on prices obtained by south Indian fishermen, combined with data on the fishermen and wholesalers’ adoption of mobile phone technologies. He finds that mobile phone adoption is associated with the elimination of waste and near‐perfect adherence to the law of one price. As a result, both consumer and producer welfare increased.

Nominal rigidities. Crucini et al (2010) look at the role of price stickiness in explaining persistent deviations of the law of one price. To that effect, they build a model with nominal rigidities and transport costs that explains how pricing to market, transport costs and price stickiness lead to failures of the law of one price. In their model, deviation from the law of one price is negatively related with price stickiness and positively related with transport costs. This happens because productivity shocks are not transmitted in the case of full price stickiness. Similarly, Hickey & Jacks (2011) look at nominal rigidities in Canada. They find that price dispersion tends to be higher whenever inflation is persistent because it puts pressure on menu costs and stimulates lower price stickiness.

Government interventions. Ismet et al (1998) focus on the effects of government intervention on rice market integration in Indonesia. They find that procurement policies by Bulog, Indonesia’s rice policy agency contributed to market integration. For the case of India, Sekhar (2012) examines spatial integration for selected commodity markets in India, revealing an interesting pattern: commodity markets that do not face restrictions to trade, either inter‐state or inter‐region (gram and to a lower extent edible oils), appear well‐integrated, while those in which inter‐state movement restrictions prevailed (such as rice) do not show integration at a national level.

# _Our approach_

We focus on six factors that may explain departures from long run market integration and its heterogeneous patterns across commodities and states. These include transport costs; price adjustment dynamics (measured as the time it takes for shocks to be corrected), information frictions, the regulatory framework, commodity specific characteristics, state level production and regional consumption preferences. These factors have been identified in the empirical literature as potential drivers of market integration and price transmission as outlined in the previous subsection.

We take the case of wheat flour to illustrate bivariate correlations for all our explanatory variables. We choose wheat flour because it is a commodity that is present in both the wholesale (as wheat) and the retail database (as wheat flour). By fixing one commodity, we are able to visualize the data and give a first grasp of the correlation of each of the covariates and the dependent variable.

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Transport costs are considered relevant in assessing deviations from the law of one price. Arbitrage opportunities decrease as the cost of moving goods from one market to the other increases. We measure transport costs using two variables. As a proxy of transport costs two variables are considered: distance (in km) and travel times (in driving hours). Travel times are likely to capture relevant information beyond distance. For example, it would include time‐spent at border controls, which, in fact, can be a substantial amount of travel time.<sup>18</sup> Figure 34 and Figure 35 plot the relationship between both distance and travel times in retail markets for wheat. Figure 40 Figure 41 show the same relationship for wholesale.

Two indicators of nominal rigidities are considered: the speed of adjustment to shocks and a measure of price stickiness, proxying for menu costs (see next subsection for details on this variable). The speed of adjustment reflects how fast domestic shocks in Delhi are transmitted to other cities. If prices between a given state and Delhi do not respond to shocks at the same speed, we can expect higher differences in the law of one price. Price stickiness is measured as the probability that prices remain unchanged in consecutive months. We compute price stickiness for all commodities and states as the number of months where price differences were lower than 1 percent over the total number of months. High price stickiness indicates that prices are unlikely to change, due to, for example, high menu costs. In retail markets, price stickiness is on average 42 percent, which means that in every period there is a probability of 58 percent that prices change. We plot both measures against deviations from the law of one price for wheat in Figure 36 and Figure 37 for retail, and in Figure 42 and Figure 43 for wholesale.

Wireless phone penetration proxies for information frictions. Information frictions may also lead to failure of the law of one price. When pricing information is not shared across markets or is only available to a few agents in the market, agents might miss out on arbitrage opportunities. In recent years, both the private and public sectors have introduced several initiatives to increase access to information for different players in the market. Many of these initiatives consist of provision of information through, for example, mobile phones or online platforms. Therefore, state‐level data on the penetration of wireless cellular networks act as a good proxy for how well information flows. Lower cellular coverage is expected to be associated with larger deviations from the law of one price. Figure 38 and Figure 44 plot the correlation between the measure of information frictions and deviation from the law of one price for retail and wholesale, respectively.

A key feature of the India’s agricultural market is the APMC act. A dummy variable was constructed that takes the value of 1 if the act has been repealed or reformed and 0 otherwise (See Data Appendix for sources of information to construct this variable). On average, we do not observe that the presence of the APMC act is correlated with larger departures from the law of one price (Table 24 and Table 25 in the Appendix). One possible explanation is that the variable may be capturing state‐specific characteristics that cannot be fully controlled for in a cross‐sectional setting. States reformed the APMC act at different times – for example, some may have reformed toward the end of the period considered, but deviations from the law of one price are calculated on average for a relatively long period of time.<sup>19</sup>

> 18 World Bank (2016), for example, reports a study commissioned by the Ministry of Transport and Highways in the context of the Logistics Efficiency Enhancement Program (LEEP) logistics efficiency enhancement showing that 5 to 10 percent of travel time in India is spent on document verification.

> 19 Even though no clear relationship can be assessed between adherence to the APMC act and the extent of departures from the law of one price, it is worth mentioning that some of APMC marketing rules have a number of implications for market outcomes and the risk of anticompetitive behavior. Nyman (2017) argues that, first, restrictive licensing requirements for commission agents (CAs), for example, and the previous monopoly of the APMC marketing channel increase the risk of anticompetitive outcomes for farm‐gate prices. Restricting entry among CAs at the village level compounds issues raised by the reliance of farmers on CAs for information on prevailing wholesale prices and the importance of long‐term relationships between CAs and farmers / wholesalers in order to ensure reliability in the face of incomplete contracts. Several factors

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# _Analysis of Price Stickiness_

An important concept in measuring deviations from the Law of One price is the rigidity or “stickiness” of prices over time. This is a concept examined in depth by Crucini _et al._ (2010). Price stickiness measures the probability that prices will change by more than 1 percent in a given period. Using Indian retail and wholesale data, the measure is calculated by first generating the change in commodity prices between the current and previous month at the state, district, commodity, variety, and period level.

A dummy is then created equaling 1 if the price change is either greater or lower than 0. The dummy equals 0 if the price change is 0. In other words, if prices in a month either increased or decreased the dummy will show a 1 but if prices remained the same then the dummy equals 0.

This dummy is then summed at the state, district, commodity, variety, and period level to get a count of all month in which a change in prices occurred. This is then divided by the total number of months.

Where, s represents the state, d the district, c the commodity, v the variety, and p the period.

Figure 22 displays information on the median, minimum, maximum, first quartile and third quartile for price stickiness by commodity **.** These box‐and‐whisker plots and are a good way of seeing the dispersion in the data. The plots in Figure 22 show these values for price stickiness at the commodity level for all states, district, varieties and periods under inspection. This allows for easier comparison of the distribution of price stickiness across commodities.

Price stickiness varies by commodity at both the retail and wholesale level. The median price stickiness varies by commodity. Onions have a median price stickiness around 25 percent, which may be interpreted as a 25 percent probability that onion prices will change by more than 1 percent in a given month. Within onions, price stickiness varies less than in other products, ranging from 5 to 50 percent. In contrast, groundnut oil, and all oil in general, has a median price stickiness of 40 percent and ranges from 10 to 95 percent. In other words, the range of price stickiness suggests that there is between a

> appear to raise the risk of collusive outcomes among CAs, wholesalers, and retailers. For example: the physical set‐up of APMC markets where onion trade is carried out near other traders, APMC rules on price transparency, the presence of trade associations, and low seller power on the part of the individual farmer. The prevalence of vertical integration of CAs as well as dominant players ‐ can also raise the risk of abuse of dominant positions: for example, through foreclosure of non‐ integrated wholesalers by integrated CA‐wholesalers.

31
10 percent to nearly 100 percent probability that groundnut oil prices will change more than 1 percent in a given month.

Figure 22: Retail price stickiness by commodity <!-- Start of picture text --> excludes outside values<br>100<br>80<br>60<br>40<br>20<br>0<br>BananaCoconut Oil GramGroundnut Oil Masur MoongMustard Oil Onion Potato RiceTur Arhar Urad Wheat<br>Price Stickiness (%)<br><!-- End of picture text --> Source: Author’s calculations Price stickiness is lower for wholesale than retail. In relation to retail, wholesale prices appear to be less “sticky”. For example, onion prices have a median wholesale price stickiness of 5 percent compared to 25 percent in retail. The range is much lower from nearly 0 to 10 percent. This suggests that wholesale prices in a given month have a lower probability of changing more than 1 percent compared to retail prices.

# Methodology

There are multiple factors that play a role in determining market integration. Even though looking at correlations is insightful, economic linkages are far more complex, and a market integration outcome is likely multi‐causal, which means a multivariate approach needs to be considered. To address the multivariate process, we estimate a regression model using all the previously discussed frictions. Formally, we regress the average deviation from the law of one price on all the covariates. Namely, transport costs, information frictions, price stickiness and regulatory variables. In all our specifications we control for commodity specific effects.

A common problem with reduced‐form estimations is the selection of variables. To select the most relevant explanatory variables we perform a stepwise backward elimination algorithm (see next subsection for the methodology). The algorithm picks the best model by successively dropping the explanatory variables that are not statistically significant. The main results are presented first for retail markets and then for wholesale.

We use a multivariate regression model to determine the importance of each or the potential drivers of integration **.** We start by fitting a regression model containing all potential explanatory variables. The model is of the type: 32
Where q�� is the deviation from the law of one price in state _i_ with respect to Delhi for commodity _j_ . The vector of explanatory variables, X�� , contains all the potential drivers of integration and ε�� is a heteroskedastic error term.

After fitting the full model, we follow a backward elimination algorithm to keep the model whose fit explains the best the variability of the dependent variable. The algorithm allows us to pick the best model by successively dropping the explanatory variables that are not statistically significant and that explain that contribute the least to explaining the total variance of the dependent variable. The algorithm is iterative; its starting point is a full regression model that contains all the covariates. At each step it drops, among the non‐statistically significant variables, the variable with the highest p‐ value. Afterwards it re‐estimates the model without the dropped variable until all the remaining variables are significant at the 10 % level.

We perform the variable selection algorithm for specifications containing either distance or travel times because of the high correlation between the two variables. In addition, for a subset of our estimation sample we control for local supply patters and regional consumer preferences. We present the best models in Table 20 and Table 21 in the appendix.

# Retail

Two main messages emerge from the analysis for retail markets. First, the most important impediment to integration is related to transport costs. Second, there are persistent departures from the hypothesis of integration after controlling for all potential drivers of integration considered.

Commodity specific effects, distance, and travel times remain the main drivers of departures from the law of one price. For retail markets, the preferred specifications are in Table 20 in the Appendix. We estimate the results both with distance, column (1), and travel times, column (2). We find that distance and commodity fixed‐effects are the main covariates that explain long‐run price differences in retail markets. The distance coefficient in Table 20 indicates that an increase in distance of 1,000 km, raises the deviation from the law of one price by 4 percentage points. For travel times, located in column (2), each additional 10 hours of driving increases the deviation from the law of one price by 1.7 percentage points. These results indicate that costs associated to transporting goods matter for deviations from the law of one price.

This result points to logistics efficiency enhancements as an important vehicle to reduce food price differences across India. Note that improving the efficiency of the supply chain requires, on the one hand, improvements in hard infrastructure (roads, railways), and on the other, promoting competition along the different players of the supply chain – including for example, competition in trucking and distribution segments – as well as streamlining regulations that add costs to inter‐state trade. Domestic trade frictions make up a substantial part of total trade costs. A recent study by Van Leemput (2014) argues that internal trade barriers make up 30 percent of the total trade costs on average – although varying substantially by state, depending on distance to the closest port. One example of such frictions is the added burden associated with obtaining national level permits, or inter‐state permits (for two states) for trucking companies, when compared to single‐state permits.

The law of one price does not hold even after controlling for transport costs. In Table 22 we test the hypothesis that the law of one price holds for all commodities. We find strong statistical evidence against this claim. When testing the law of one price, we also look at each commodity individually. In the case of moong and urad we do not reject the hypothesis that the law of one price holds. The consequence for these two results is that there is no price equalization in the long run in India. The commodities that exhibit the higher deviations from the law of one price are bananas, wheat, potatoes and groundnut oil.

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Departures from the hypothesis of integration persist for all products but tur arhar and moong, once controlling for the considered determinants. This points to unobserved factors that matter for integration. For the cases of tur arhar and moong, we cannot reject the hypothesis that the law of one price holds after controlling for transport costs. From the estimates in Table 20 we device a statistical test for the law of one price. The null hypothesis is that the law of one price holds. Therefore, after controlling for the determinants, we expect the fixed effects plus the constant term to be statistically equal to zero. We report the test statistics in Table 22. We test all commodities together and each commodity individually. We adjust the individual using the Bonferroni method that corrects for the fact that when testing individual hypothesis there is a random probability of rejection because of the number of tests. When testing all the commodities together we reject the hypothesis of the law of one price holding for every good. When testing this hypothesis by commodity, we find that, for tur arhar and moong, price deviations are equal to zero once controlled for transport costs.

## _The role of consumers’ preferences and local production_

For a subset of the sample we measure local production and consumption preferences **.** We rely on household surveys and local production statistics to test for the role of consumption and production on the deviations from the law of one price. Because data on consumption and production patterns are not available across all district/states and commodities, these variables are included in an alternative specification (reported in the last two columns in Table 20).

Consumer preferences play an important role in retail markets. The marginal effect of increasing the consumption share of a given commodity by 1 percentage point is correlated with a reduction of deviations from the law of one price of 1.7 percentage points. The production variable is not statistically significant, and it is not retained in the final model.

Commodities for which there is strong demand have lower deviations from the law of one price. In Table 20, we report the effect of production and consumption on departures from the law of one price. While production plays no significant role, consumption shares are negatively correlated with deviations from the law of one price.

In conclusion, spatial frictions dominate the failures of the law of one price in retail markets. The most important determinant of departures from the law of one price is transport costs. Consumer preferences and commodity fixed effects explain the rest of the variation.

# Wholesale Markets

In this section we present the results on the determination of price integration for wholesale markets. The four factors under scrutiny are transport costs, information frictions, nominal rigidities and intervention.

## _Multivariate Analysis of Drivers of Price Integration in Wholesale Markets_

Two main messages emerge from the analysis of wholesale markets. First, three factors explain departures from the integration hypothesis for the case of wholesale markets: transport costs, information frictions, and price stickiness. Second, departures of the law of one price remain, after controlling for these determinants, except for wheat and mustard oil. In addition to commodity fixed effects, three variables play a role explaining departures from integration. These are distance (proxy for transport costs, wireless phone penetration (proxy for information availability) and price stickiness. The fit of this model is almost twice as good as for that fitted to retail, with the model explaining 70 percent of the variance. Results are presented in Table 21.

Transport costs play, here again, a crucial role in explaining departures from integration, in this case with a stronger effect than in retail markets. Results suggest that adding 1,000 km of distance between two districts adds a 10% to the price difference. Using travel times as an alternative proxy for transport costs yields a similarly economically significant result: a 10 % difference in prices is observed for districts that are 24‐hour away from each other, in terms of the time it takes to travel. Notice that the 34
marginal effect is stronger than for retail markets. The result hints that improving connectivity could imply a more drastic reduction in price dispersion than in retail markets. An alternative reading of these results suggests that a reduction of 1 hour driving lowers price dispersion by 0.42 percentage points. As mentioned above, travel time reductions could be achieved both through the improvement of the road and railway networks, and through the reduction of other costs associated with traveling within India, including those related to inter‐state border controls, or those arising due to imperfectly competitive transport and distribution sectors (see World Bank, 2016 “Improving Supply Chain and Domestic Connectivity in India”, for an assessment of the constraints to efficient logistics in the country).

Information frictions impede integration of wholesale markets in India: a 1 percent increase in wireless phone penetration displays the same marginal effect as a reduction in distance of 230 km. Wireless phone penetration has an even stronger marginal effect than distance. A 1‐percentage point increase in cellular penetration, is associated with an effect between 3 and 11 percentage points in price dispersion, depending on the specification. The strong effect of information technologies is consistent with previous findings in the literature that point as information frictions as one of the most important barriers for price integration. These results suggest that increasing the network of cell phone coverage could help reduce informational asymmetries and allow market players to make better informed decisions about production and trade, thus exhausting arbitrage opportunities. Although not formally tested, it is likely that internet penetration plays a similar role as it is a powerful vehicle for information diffusion.

Nominal frictions, specifically, price stickiness, play a role in explaining departures from the law of one price. Increasing price stickiness by 1 percentage point is associated with a 0.3 percentage points increase in price differences with respect to Delhi. This result implies that menu costs are in partly responsible of price differences in India. The result goes in line with the study of Crucini (2010) in Japan, which shows that price stickiness is one of the factors explaining the departures from the law of one price between Japanese cities.

The best model explains 70% of the total variance, which is more than twice of the explanatory power achieved in the retail analysis. The higher fit of the model suggests that wholesale markets are more sensitive to changes in fundamentals. The lack of explanatory power of the retail model may be explained by the fact that the lack of integration in wholesale markets directly affects integration in retail markets (as the two markets are strongly linked, as discussed in Section 3).

The law of one price does not hold in wholesale markets for all commodities. However, when testing product by product, for mustard oil and wheat the law of one price hypothesis is upheld. Just like in the case of departures from the law of one price in retail, results for wholesale suggest that there are unobservable factors that impede integration.

# Border effect

How costly is it to cross state borders in India pre‐GST? What do these borders imply in terms of deviations from the law of one price? Typically, the question of crossing borders has been studied in the case of international borders rather than intra‐national ones. However, India is a federal country, and during the period of analysis (i.e.: prior to the implementation of GST in July 2017), states often behaved like separate countries, exporting and importing from each other in a similar manner to international trade. At state borders, this resulted in state border authority checks on both parts of the border levying taxes, conducting road transport checks and commodity specific taxes.

Recent studies have pointed to how disruptive state‐border controls were in India. A recent World Bank survey conducted among 70 manufacturing companies revealed that one of the major reasons for freight delays is related with state border check‐post clearances, in addition to infrastructure 35
deficiencies.<sup>20</sup> A study commissioned by the Ministry of Transport and Highways, under the Logistics Efficiency Enhancement Program, revealed that 5‐10 percent of time in transit is spent on document verification at state border check points. Also, Van Leemput (2014) argues that the largest barrier within India is not the rural‐urban trade barrier, but Indian states not being well connected (p.1), going further to say, that if domestic integration was brought to US levels, welfare increases could reach 15 percent, compared to increases of 7 percent associated with fully eliminating barriers to international trade. Finally, differences in GST across states also imply an additional burden of trading across states, with efficiency implications as they affect otherwise optimal firms’ location decisions. Indeed, studies conducted after the implementation of GST show reductions in transport costs – specifically increases in driving speeds at state borders (Krishna and Van Leemput, 2018a), while anecdotal evidence gathered from interviews with truckers suggests substantial costs reductions associated with fewer checkpoints at state borders.

These elements point to the importance of understanding the magnitude of the state border effects on price differences. The estimates provide useful information on the costs associated with existing intra‐national, inter‐state trade barriers, thus facilitating the evaluation of evidence‐based reforms not only for India, but also for other developing countries with similar restrictions to internal trade.

The literature on the border effect estimates the additional cost in terms of distance of crossing national borders. There are two methods for calculating the border effect based on price data. The first was introduced by Engel & Rogers (1996) and consists in estimating the border effect as the number of kilometers (miles) that would yield a marginal effect equivalent to the coefficient of a binary border variable. Parsley & Wei (2001) developed a second method. It is similar in nature, but it approaches the question in a different manner. They argue that the border effect is an average effect. Therefore, it should be calculated as the marginal effect for the average distance in the sample. They define their border effect as the additional distance, from the average, that is required to match the coefficient of a binary border variable. Traditionally, the border effect has been found to be highly significant using both methods, although the scales are rather different.

Both methods for estimating the border effect use the same baseline regression. Engel & Rogers (1995) regress the standard deviation of log‐price differences, q��� , for any two pairs of cities, _i_ and _j, and_ commodity _c_ in Canada and the United States. They do that because their model is based on price indices, they do not observe the actual transaction prices. In our case, we observe exact transaction prices and we take as our dependent variable the of log‐price differences. Formally, the model contains two covariates, two sets of state fixed‐effects, α� and γ� , and one set of commodity‐fixed effects, η� .

q��� �β� �β� ∗ln�distance����β� ∗BorderDummy�� �α� �γ� �η� �ε��� Where ln �distance��� is the log‐distance between city _i_ and _city j_ , and BorderDummy�� is a binary variable taking value 0 if the two cities belong to the same country and 1 otherwise. The border effect is then defined by the following equation:

# β� ∗ln�BorderEffect��β� ↔BorderEffect�e<sup>��/��</sup>

Parsley & Wei (2001) use the same baseline regression to estimate the border effect. However, they criticize the Engel and Rogers approach because it is scale invariant. More specifically, the border effect would be the same if the variable distance�� were to be measured in miles or kilometers; it is not linear in the distance variable. They propose a correction that estimates the border effect for the average distance in the estimation sample. Their effect is given by the following equation:

> 20 Kamphuis and Jordan (2014): “Supply Chain Delays and Uncertainty in India: The Hidden Constraint on Manufacturing Growth”.

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1 * In(AverageDistance + BorderEf fect) = Bz + B, * In(AverageDistance) ~~Bo~~ © BorderEf fect = AverageDistance * (ef: — 1)
# Section 5: Conclusions

This paper examined patterns of market integration for food commodities in India. First, it tested the extent of domestic spatial market integration for retail and wholesale markets for the periods 2006‐ 2014 and 2008‐2015, respectively, and looked at patterns of price transmission of shocks from international sources. Second, it measured vertical integration from wholesale to retail markets, and tested for asymmetric speed of adjustment to shocks. Third, it examined the determinants of spatial integration.

The results reveal that food markets in India are imperfectly integrated across space, with the law of one price being systematically rejected. To be sure, price signals are geographically transmitted both in retail and wholesale markets, but substantial heterogeneities by states and by products prevail. The case of red onions in Akluj and Pandharpur is not an exception, but rather a rule: Substantial price differences exist for narrowly defined products. In 90 percent of the cases, the price difference paid in any given city with respect to Delhi for a given product ranges from ‐28 percent to +28 percent. with substantial heterogeneities by states and by products.

There is substantial co‐movement between wholesale and retail prices, although integration is still imperfect in all commodities but one: rice, for which perfect vertical integration cannot be rejected. Also, retail prices adjust faster when wholesale prices rise than when wholesale prices fall. The analysis of determinants of spatial integration reveals that prior to GST implementation, the mere act of crossing a state border increased prices, that unexploited gains from arbitrage persisted after considering the effects of transport costs, and that information frictions and menu costs reduced market integration.

The results this paper unveils present some policy implications. First, distance and travel times explain part of the incompleteness of market integration documented in this paper, adding to the price premium that consumers in remote regions pay for food products. This suggests that investing in hard infrastructure will reduce spatial price differences. Indeed, the results reported here suggest that 10 percent price differences in wholesale markets are associated with travel times of about 24 hours that are roughly equivalent to 1,000km. Relatedly, enhancing logistics efficiency through increased competition will also help. While part of the effect captured through the travel times and distance variables relate to hard infrastructure, another part relates to how efficient logistics services are in India. Travel times could also increase, for example, because of inadequate maintenance of vehicles, or due to coordination problems at different stages of the logistics supply chain, or de jure and de facto requirements that arbitrarily increase travel times.<sup>23</sup> Encouraging competition in the transport and logistics sectors through the reduction of barriers to entry, for example, by issuing national‐level only trucking permits, could help reduce transport and logistics costs and stimulate quality upgrading in the sector, thus reducing the economic costs of distance.

Second, information frictions also play a role in spatial price dispersion, after controlling for distance. Indeed, estimates suggest that in wholesale markets, information frictions explain more of the departures from the law of one price than the transport costs related variables, with a 1‐percentage point increase in cellular penetration being associated with up to an 11‐percentage point reduction in

> 23 An example of de facto requirements that arbitrarily increase travel times is cited in World Bank (2016). The report argues that local trade unions may, for example, require cargo owners to have their goods transited to a local warehouse before dispatch to the destination market, reducing the efficiency of the domestic supply chain. An example of de jure requirements that reduce logistics efficiency is the excess burden of obtaining a national or multi‐state trucking permit, compared to a single state one.

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price dispersion. Two actions could help reduce information frictions. Strengthen information platforms: in particular cell phone and internet coverage. While results reported here focus on cell phone coverage (due to data availability), it is likely that internet coverage also plays a role in providing information that reduces arbitrage opportunities. In addition, encouraging information provision initiatives will help. In this area, both the private sector and the Government of India have been implementing initiatives towards the provision of information on market conditions (as well as technical assistance) through mobile phone and online platforms. These efforts seem to be helpful, particularly in remote locations, where physical connectivity is a challenge.

Third, evidence unveiled here reveals a state‐border effect on prices that adds to the transportation bill the equivalent of having to drive an extra 164 kilometers. This result suggests that crossing state borders is costly, sizably impacting food prices. Reconsidering state border crossing barriers, streamlining procedures, and moving controls to the source and destination rather than at the border could help increase integration. In this respect, evidence reported by World Bank (2016) suggests that were all state border crossing procedures and documentation to be eliminated, logistics costs and transit time would fall by around 10‐15 percent, enabling at least another inventory turnover for the logistics supply chain. Other indirect gains are also stressed, related to the reduction of the fragmentation of the logistics markets in India. Results by Van Leemput (2014) also point in this direction. The author estimates that domestic trade frictions make up 30 percent of the total trade costs on average, and their removal would increase welfare by about double as much as the removal of barriers to international trade. The recent implementation of the GST that aims at reducing internal trade frictions is already showing positive effects on reduced transport costs and increased domestic trade (Krishna and Van Leemput 2018a; Krishna and Van Leemput 2018b).

Fourth, results reported here on substantial (although imperfect) food price co‐movements spatially, and vertically – from wholesale to retail, call for re‐evaluating government intervention in internal and external trade in agricultural commodities, while maintaining its role of price stabilization. If agricultural commodity markets are spatially integrated, then producers and consumers will realize the gains from this liberalization, since price signals will be transmitted through the marketing channels. In fact, for the commodities in which there is substantially more intervention – wheat and rice – our findings suggest substantially slower transmission of price signals than for those with relatively more liberal trading schemes – e.g., grams or edible oils (similar conclusions have been reached by Ghosh (2010) or Sekhar (2012), their contributions are discussed in Section 2 of this paper). By limiting interventions, the government could reduce costs without substantial effects to be expected on market performance. These limited interventions could mean, for example, managing relatively small quantities of buffer stocks in certain key locations to ensure food security. Government interventions could re‐focus on reducing transport and logistics costs, facilitating the provision of market information, and reducing barriers to domestic trade, as suggested above. To be sure, this last suggestion is based on a qualitative reading of the results presented here, as well as of the existing literature on the matter. However, in this paper no link has been identified formally between government intervention and departures from market integration.

Further research into the effects of the implementation of GST on market integration and price dispersion in India would be useful to better understand the benefits of policies aiming at integrating markets.

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