Agricultural Marketing Laws Need Reforms | India’s Transition to a High Growth Regime

Ideas and Institutions Issue #4

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1. Analysis
2. Review

Analysis

Agricultural Marketing Laws Prevent Agricultural Markets from Developing

The Indian Supreme Court constituted a committee to examine the controversial farm laws that were drafted with the aim of liberalizing agricultural markets. This committee recently published a report, which states that a significant majority of the stakeholders it consulted were in favor of the farm laws and believed that the laws would benefit them. The reasons for this perception are not hard to understand. Agricultural markets, unlike markets for most other products in India, are highly regulated and restrictive. In this essay, I first put forward a hypothetical set of conditions under which farmers would benefit from linkages with markets. I then juxtapose the conditions with the regulatory and structural restrictions that prevent Indian farmers from developing lasting and stable links with markets.

In an ideal agricultural market, a farmer would have the following benefits from regular interactions with markets:

Before harvesting, a farmer should be able to make the best judgement about which crop to grow, decide the lock-in a price for the crop, and be able to invest the requisite labor and capital into the crop. In addition, mechanisms to hedge against price uncertainties and natural calamities should also be available. This translates into the ability to
 

1. enter into a contract for direct sale at a future date. An early contract provides certainty to the farmer and reduces entrepreneurial risk.
2. hedge against price volatility, which reduces uncertainty and the risk of loss for the farmer.
3. anticipate prices for produce. In the absence of a pre-harvest contract, information systems that enable farmers to anticipate prices for their produce are the next best alternative. Being able to anticipate the price of produce would help farmers make crop-selection decisions, as well as decisions about which markets to sell in.
4. access capital/ credit for investing into inputs.
5. guard against loss. Financial indemnity or insurance is essential in the absence of contracts that hedge against price uncertainty and natural calamities.

1. sell produce anywhere within and outside the country.
2. discover the best price for produce. Information systems are necessary infrastructure that enable farmers to make decisions about which markets to sell in. Price information from different spot markets, government procurement prices, international market prices, and prices in commodity exchanges should be readily available with negligible transaction costs.
3. sell at the best price. Transaction costs and regulatory bottlenecks in reaching the best market should be minimal. This also requires that all crops be properly assessed for quality and weighed and labelled as such. The provision of infrastructure for this is a pre-requisite for developing agricultural markets.
4. store produce until the appropriate time. Farmers should have warehousing and storage infrastructure that enables them to maintain the quality of their crop and avoid distress sales.
5. access capital/ credit for costs incurred.

1. The choice of markets is restricted due to Agricultural Produce Marketing Committee (APMC) laws, which, in many states, penalize farmers if they sell outside an APMC mandi (a wholesale market for agricultural produce, established under the state APMC law, where all farm produce of government notified crops is weighed, assayed, and graded, before being sold to licensed traders). However, APMC mandis are hard to access. The extent of the distance from APMC markets can be gauged from one study that found that farmers in Bihar were unaware that their state APMC law had been repealed. They continued to sell their produce to the same local aggregators. On an average, there is one APMC mandi every 496 square kilometers in India, with Punjab, Haryana, and Chandigarh having the highest market density. This distance to the market increases transportation costs significantly and incentivizes farmers to sell at lower prices.
2. In addition to being distant from farm-gates, APMC mandis provide poor infrastructure for weighing, assessing, and grading agricultural commodities. As per a 2019 report of the Parliament’s Standing Committee on agriculture,
   1. only 22% mandis have grading facilities
   2. only 50% have weighing facilities
   3. only 30% have drying facilities
   4. only 7% have banking facilities
   5. only 15% of the produce goes into cold storage facilities.
3. APMC laws also prevent private, competing market infrastructure from developing. This restricts market access for farmers. Some states like Madhya Pradesh and Rajasthan have amended APMC laws to introduce private mandis, but such efforts are exceptions.
4. APMC laws also restrict negotiating power. By confining access to local markets, APMC laws reduce incentives for price discovery. Significant bargaining power is needed to insist that prices for agri-produce at the mandi be benchmarked against Minimum Support Price (MSP), commodity-exchange prices, or other markets. Traders therefore have significant bargaining power in price negotiations since access to other markets is restricted. Government procurement at MSP alleviates some part of this problem, but is extremely distortionary. In addition, government procurement at MSP is mostly confined to a few states, and available only for a few crops.
5. Contract farming is permitted to varying degrees in some states. The lack of opportunity to enter into contract farming agreements (a) deprives farmers of a natural linkage to markets at the farm-gate, and in the process, it avoids costs involved in reaching spot markets; (b) It reduces one avenue of developing alternative market linkages, as compared to the avenues available in spot markets; (c) it deprives farmers of one avenue for hedging against price uncertainty; (d) it deprives farmers of the ability to access capital in the form of advances in contracts.
6. The lack of contract farming keeps farm-gate prices low and increases the farmer’s risk of loss. The lack of proper warehousing and storage facilities further inhibits the farmer’s ability to avoid distress sales.
7. Export controls and the Essential Commodities Act (ECA), 1955 further destroy opportunities for creating market linkages. Policy capriciousness in enforcing export controls and the ECA makes it hard to deliver on contracts and create a niche in world markets. In September 2020, the government invoked the Foreign Trade (Development and Regulation) Act, 1992 to ban export of onions to reduce their market prices in the domestic market. While consumers were happy, farmers protested.

Review

When Did India Transition to a High Growth Regime?

It was considered unusual when India’s economy slowed down between 2016-2017 and 2019-2020. Decades of high growth, punctuated only by brief downturns, have created great expectations. Since countries like China, Japan, South Korea, and others defied regression to the mean for a long time, perhaps it was assumed that India would also do the same. When did high growth become the norm in India? India’s GDP per capita was stagnant between 1900 and 1950. Growth then accelerated but remained low to moderate for many years. The economy then transitioned to a high growth regime. There is much debate about when this transition was achieved. The answers range from the mid-1970s to the early-1990s.

This essay gives a brief overview of this debate. Growth transitions are outcomes of complex, multi-causal processes that involve changes in political economy, technology, institutions, and/or policies domestically and globally. This lends plausibility to multiple, often conflicting and ideologically coloured, explanatory narratives about economic growth, each privileging certain causal pathways while neglecting others. Rigorous identification of moments of transition could help us in understanding which changes mattered.

Some of the studies filter the transitions through criteria that may pertain to the scale of acceleration, threshold for growth rate in the post-transition period, and a minimum time period for the growth regime after each transition.

Nayar (2006) advocates for 1975-1976 as the transition year, because the economy grew at 9 percent in that year and the average growth rate between 1975-1976 and 1978-1979 was 5.8 percent—much higher than that during the preceding years. GDP declined in 1979-1980 on account of shocks like the increase in oil prices and deficient rainfall. In addition to thresholds for the scale of acceleration and the post-acceleration growth rate, the argument implies a minimum four-year period for the growth regime. A longer minimum period does not yield 1975-1976 as the transition year. With such a short period, the finding can be easily affected by shocks and cyclical changes.

Growth had decelerated sharply in the four-year period between 1971-1972 and 1974-1975, from 5.7 percent average GDP growth in the preceding four years to 1.4 percent during this period. This was, inter alia, due to deficient rainfall in 1972-1973 and 1974-1975. It was, by far, the worst four-year period since 1950. The GDP per capita in 1974-1975 was less than what it had been in 1969-1970. Economic growth is non-linear. Acceleration is easier after a period of deceleration. Between 1975-76 and 1978-79, helped by normal rains, the economy was recovering from crisis. Another issue with this filter is that it also qualifies 1967-1968 as a transition year. In 1967-1968, GDP growth was 7.8 percent, and average growth between 1967-1968 and 1970-1971 was 5.7 percent—3 percentage points higher than that in the preceding four years.

This highlights the importance of choosing the right filter. Other studies have defined and applied the filters more rigorously. Looking for growth accelerations, Hausmann et al (2005) consider at least 2 percentage points increase in per capita income across growth regimes, at least 3.5 percent growth in the post-acceleration regime, and a minimum 8-year period for the growth regime. They identify 1982 as the transition year for India. Interestingly, the growth in that year was low. Since the objective is to identify a transition to a multi-year growth regime, the transition year itself need not have a high growth rate, as long as growth acceleration and the average growth during the post-transition period are above the respective thresholds.

Aizenman and Spiegel (2007) also apply a filter, but with a narrower focus. They look for growth take-offs—which are transitions from stagnations, defined as five-year periods with average real per capita GDP growth below 1 percent—to periods of robust growth, defined as real per capita GDP growth exceeding 3 percent over a minimum of 5 years, within 10 years of the stagnation period. For India, they identify 1980 as the start of take-off from the stagnation period ending in 1974. A five-year minimum period for the growth regime runs the risk of being affected by shocks and cyclical changes, but their filter is, in any case, meant to identify robust recoveries, following periods of stagnation, and not any growth acceleration. Please note that we have referred to an earlier version of this paper, as the journal publication version did not specifically list the countries that experienced take-offs.

Some of the studies conduct statistical tests with the time series data to identify structural breaks that were followed by a statistically significant change in growth rates. Wallack (2003) identified the transition year (highest F-statistic) as 1980 based on GDP data, and 1987 based on GNP data. Rodrick and Subramanian (2004) used the procedure described in Bai and Perron (1998) to compute the breaks for the growth rate of four series: per capita GDP computed at constant dollars and at PPP prices, GDP per worker, and total factor productivity. They identify 1979 as the transition year. Using a modified version of this procedure, Kerekes (2007) identified 1993 as the year of up-break for India.

Kar et al (2013) try to overcome the limitations of the filter-based and statistical methods by combining their strengths. Their method involves using the procedure from Bai and Perron (1998) to estimate the best potential breaks, and then applying a filter to confirm the genuine breaks. Their filter recognizes the non-linearity in growth. For the first break, since it is not known whether it follows an acceleration or deceleration, any change of more than 2 percentage points in GDP per capita growth is counted as a genuine break. After that, the threshold depends on the previous history. If an acceleration follows a previous deceleration, or if a deceleration follows a previous acceleration, to qualify as a genuine break, the growth difference has to be 3 percentage points. If an acceleration follows a previous acceleration, or a deceleration follows a previous deceleration, then a change of only 1 percentage point qualifies. Their filter includes a minimum period of 8 years for a growth regime. Using data from 1951 to 2010, they identify 1993 as the year of transition to high growth, and 2002 as marking a transition to even higher growth.

Here are three questions for you, dear reader: