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On Wedges and Spatial Price Gaps

Shoumitro Chatterjee
July 30, 2018

Indian farmers realize extremely low revenues. Revenues can be low either because farmers are unproductive and/or because they receive low prices for their output. While productivity relates mostly with technical aspects of farming, price realization depends on the state of the agricultural economy and can potentially be addressed by economic policy. In this article, I will discuss two dimensions of prices—wedges and dispersion—and shed light on some common misconceptions.

Are Intermediaries Bad?

Intermediaries are often blamed for driving a big wedge between prices that consumers pay and prices that farmers receive, according to a 2012 Economic & Political Weekly article by Ramesh Chand titled “Development Policies and Agricultural Markets.” The two common accusations levied are that several layers of intermediation before the output reaches the consumer increases the wedges and that intermediaries earn rent without providing any value added. Though there might be some truth to this, without serious investigation, it is hard to know.

Economic theory views intermediation as “greasing the wheels of the economy” rather than necessarily causing inefficiency. In recent work, Matthew Grant and Meredith Startz show that under certain conditions in long supply chains, multiple intermediaries increase consumer welfare by inducing competition. A farmer has finite time to spend on cultivation and on other activities. As I learned from my field visits to Punjab and Bihar, time is a major constraint which prevents farmers from marketing their own crops, giving rise to intermediaries.

Furthermore, transportation and marketing require specialized skills that farmers may not have. Lack of storage and perishability increases the risk that agents downstream the supply chain face. Missing credit markets implies that not only do farmers borrow from intermediaries, but also intermediaries borrow from one another. These factors indeed contribute to increasing the retail-farmgate wedge.

In general, price wedges comprise of transport costs, processing costs, and rents due to inefficiencies. In the current Indian context, it is unclear what fraction of the observed wedges are pure rents and understanding this requires more research. Separating out the rents from legitimate costs requires high-frequency price data at various points along the supply chain—the subject of my ongoing work.

Viewed from the lens of division of labor, intermediaries may not be the source of the problem. They could simply be earning the marginal value for their services and the risk they bear. Therefore, forcing them out could potentially disrupt the agricultural supply chain as happened in Bangladesh.

Should Farmers in Different Regions of the Country Get the Same Price?

While wedges are computed along vertical supply chains, the other oft-cited statistic is about spatial price dispersion. It is a common misconception that with well-functioning markets, farmers of the same crop across the country would get the same price for their output.

A price rise of a commodity in one region signals excess demand. Suppliers would then move goods into this region, mitigating the price rise. However, transporting goods is costly and to that extent, even with well-functioning markets, there will always remain some differences in regional prices to cover for costs of transportation. In India, the ratio of the highest price of a commodity to its lowest price—a measure of price dispersion as reported in the Economic Survey of India 2015-16—is almost thrice that observed in the US. But even in the US, with efficient markets, this ratio is close to 1.5 but not equal to 1.

While interpreting Indian numbers, we must acknowledge and be cautious about data quality which is often ignored in newspaper articles. Unlike the US, Indian price data provide little information on the variety and quality of commodities. Moreover, there is greater variety and quality heterogeneity in Indian commodities than those in the US.

Closely examining mandi price data reveals that for many commodities, much data is clubbed under the variety “others.” Rice variety “others” is totally different in UP than in West Bengal. Added to this are differences in pest infestation, moisture content, and length or shine of the grains not captured in the data. This variation is often falsely attributed to marketing and intermediation inefficiencies.

What Do We Know?

The take away here is not that Indian markets are fully efficient or that intermediaries everywhere are not earning rents. Rather, it is that these issues are complex and understanding them requires careful research and data collection.

Spatial variation in prices is indeed indicative of a range of factors—costs of transportation, regulatory barriers, and local market power of intermediaries or retailers—which prevent free movement of goods. As these factors prevent farmers from realizing higher prices and consumers from buying food at lower prices, they lower overall welfare. The challenge is to assess the extent to which these factors drive unfavorable outcomes for farmers.

In “Six Puzzles in Indian Agriculture,” a 2017 India Policy Forum article I co-authored with Devesh Kapur, we note the following: first, the spatial price variation in India is not only high but also has been very stable over the last decade. Over these ten years, massive investments in building new roads have brought down transportation costs. Increased cell phone penetration has also increased the information that farmers have about prices. The fact that price dispersion still remains high implies that information and transport costs are contributing little to spatial price differences.

Second, we found that 37 percent of the overall variation in prices is due to time-invariant region-specific factors such as local quality and variety of crops, local market power, and soil quality. 20 percent of the spatial variation is due to aggregate shocks like fluctuations in global demand and 4 percent is due to regional differences in rainfall. More work is required to understand the relative contribution of each micro-component.

In my own research, I highlight the spatial heterogeneity in the market power of mandis in India. I find that farmers in regions with more mandis receive higher prices on average. More mandis increase the set of potential buyers of farm output, increasing local competition and, therefore, prices. I also model lowering transport costs—e.g. by building better roads—and find that this by itself does not increase the prices that farmers receive in regions where intermediaries exert substantial market power—e.g. in Haryana, Odisha, and parts of MP. Instead, much of the benefits of road construction accrue to intermediaries and the spatial variation in farm-gate prices remains unaltered.

Understanding the underlying reasons for wedges and regional price differences is key for policymaking. In regions where intermediaries have market power, road construction will provide little solace unless complemented with pro-competitive policies. In other regions, intermediaries may not be a problem and infrastructural deficiencies such as lack of storage and inefficient mills could be increasing price wedges.

A central challenge in studying Indian agricultural markets is the absence of good quality price data of a crop of the same variety and quality across different locations and at different points along the supply chain. As such, the inferences we draw from wedges and spatial price gaps become muddied. Over the next year, Marshall Bouton, Devesh Kapur, Mekhala Krishnamurthy, and I will set out to create such a data set across multiple villages in the states of Punjab, Bihar, and Odisha. The goal is to better identify the factors that are (or are not) keeping prices low for Indian farmers.

Shoumitro Chatterjee is a CASI Non-Resident Visiting Scholar, and an INET-Post Doctoral Research Associate in the Faculty of Economics, University of Cambridge, UK.


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