📈 economics

Procurement Certainty, Coordination Amplification, and Acreage Lock-in: Graduated Intensity Identification and Panel Quantile Evidence from Maharashtra, India

This paper demonstrates that cooperative sugarcane mill density in Maharashtra creates a self-reinforcing coordination equilibrium that locks in acreage and amplifies persistence across districts, a structural inertia that digital information interventions like the AgriStack Farmers' Registry fail to disrupt.

Original authors: Prakash Gurumurti Vhankade

Published 2026-07-21

📖 6 min read🧠 Deep dive

CC BY 4.0

Original authors: Prakash Gurumurti Vhankade

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to decide what to plant in your garden. In a perfect world, you would look at the weather, check the market prices for tomatoes versus potatoes, and pick whichever one promises the most money. This is how most economists think farmers work: they are rational calculators who always choose the most profitable option. But sometimes, reality is messier. Imagine a scenario where everyone in your town is growing pumpkins. Even if you hear that pumpkins are crashing in price and carrots are skyrocketing, you might still plant pumpkins. Why? Because the only truck that comes to buy your harvest is a pumpkin truck, and it only shows up if enough people are growing pumpkins to fill its trailer. If you switch to carrots alone, the truck won't come, and you lose everything. This is a "coordination game." It's a situation where your best choice depends entirely on what everyone else is doing, creating a kind of group lock-in that is hard to break, even when better options exist.

This paper dives into a real-life version of this puzzle in the Indian state of Maharashtra. For years, farmers in certain districts have been growing massive amounts of sugarcane, even though other crops like cotton or soybeans might be more profitable and require less water. The big question was: Why won't they switch? Is it because they don't know about the better prices? Is it because they can't get water for other crops? Or is it something else entirely? The researchers wanted to see if a digital app designed to give farmers market information could break this lock-in, or if the farmers were stuck in a coordination trap where the only way to win is to keep doing what everyone else is doing.

The Story of the Sugar Trap

The researchers looked at data from 34 agricultural districts in Maharashtra over seven years (from 2016–17 to 2022–23). They were investigating a strange pattern: in some districts, farmers grow sugarcane on about half of their land, while in neighboring districts, they barely grow any at all. The paper argues that this isn't because of the weather or the soil, but because of a "coordination game" involving sugar mills.

Think of a cooperative sugar mill like a giant, hungry monster that only eats sugarcane. This monster is owned by the farmers themselves. It promises to pay a guaranteed price for the cane, but it has a catch: it can only survive if enough farmers bring it enough cane to fill its belly. If too many farmers decide to stop growing cane and switch to something else, the monster starves, shuts down, and the guaranteed price disappears.

The paper finds that this creates a powerful "lock-in" effect. The more sugar mills a district has, the harder it is for any single farmer to switch crops. It's like a game of musical chairs where the music stops, but the chairs are the sugar mills. If you try to leave the circle to dance to a different song (grow a different crop), you get left standing with no chair and no guaranteed price.

The Three Big Discoveries

The study used four different statistical methods to test this idea, and the results were surprisingly consistent.

First, they found a dose-response relationship. This is a fancy way of saying that the more sugar mills you have, the more sugarcane you grow. It's not just a simple "yes or no" switch. The data showed that for every time the number of mills in a district doubled, the share of land dedicated to sugarcane went up by 3.2 percentage points. It's a smooth, steady climb: more mills mean a stronger pull toward sugarcane.

Second, they discovered that these mills make the habit of growing sugarcane self-amplifying. In districts with no mills, farmers tend to stick with their previous crop choices about 34.4% of the time. But in districts with the highest number of mills (19 in the district of Kolhapur), that stickiness jumps to 101.1%. This means that in these high-mill areas, the tendency to keep growing sugarcane actually gets stronger every single year, regardless of what the weather is like or what the prices of other crops are doing. The more farmers grow cane, the more likely their neighbors are to keep growing it, creating a feedback loop that is almost impossible to break.

Third, the researchers tested a popular idea: that if you just give farmers better information, they will make smarter choices. The Indian government launched a digital program called AgriStack, which gives farmers access to land records and real-time market prices for different crops. The researchers treated the rollout of this app as a natural experiment. They expected that once farmers knew the prices for cotton or soybeans, they would start switching.

But the data said no. The study found that the AgriStack program had no detectable positive effect on getting farmers to switch away from sugarcane. The statistical results were essentially zero. This suggests that the problem isn't that farmers are ignorant; they know the prices. The problem is that the "coordination trap" is so strong that knowing the price of carrots doesn't matter if the carrot-buying truck won't show up. The information didn't change the rules of the game, so the farmers kept playing the same game.

Who Gets Stuck the Most?

The paper also looked at who is most affected by this trap. They found that districts with a higher share of small farmers (those with less land and fewer resources) were even more likely to be stuck in the sugarcane cycle when mill density was high. It turns out that small farmers rely heavily on the sugar mills for things like credit and guaranteed sales. Because they have fewer resources to take risks, they are the most likely to stay in the "safe" zone of sugarcane, even if it's bad for the water supply or their long-term profits.

What This Means

The paper concludes that you can't solve this problem just by giving farmers a smartphone app or telling them about better prices. The issue is structural. As long as the sugar mills depend on a critical mass of farmers to survive, and as long as that guarantee is the only thing keeping the farmers safe, they will keep growing sugarcane.

To break the cycle, the researchers suggest that you would need to create a similar "safety net" for other crops. If the government could guarantee a price and a buyer for crops like soybeans or tur (pigeon peas) with the same legal force as the sugar mills, farmers might finally feel safe enough to switch. Until then, the coordination game continues, and the sugarcane fields keep growing, one doubling of mills at a time.

Technical Summary: Procurement Certainty, Coordination Amplification, and Acreage Lock-in

Problem Statement
This paper investigates a persistent anomaly in Maharashtra, India's agricultural sector: despite rational profit-maximization signals favoring alternative crops (cotton, soybean) and the availability of digital market information (AgriStack), sugarcane cultivation remains heavily concentrated in districts with cooperative sugar mills. In the seven districts with the highest cooperative mill density, farmers allocate a mean of 51.0% of their kharif cropped area to sugarcane, compared to only 5.8% in the remaining 27 districts. Standard agricultural economics, which predicts acreage reallocation based on price signals and risk mitigation (e.g., PMFBY insurance), fails to explain this rigidity. The paper posits that this is not a structural constraint (like irrigation deficits) but a coordination game problem. Specifically, the viability of cooperative mills depends on aggregate cane supply ( $N_j \ge N^*$ ). The Fair and Remunerative Price (FRP) guarantee is conditional on this viability. Consequently, individual farmers face a "stag hunt" scenario where deviating from sugarcane to an alternative crop is individually irrational because it does not shift the mill's viability threshold but imposes full switching costs on the deviator.

Methodology
The study utilizes a balanced district-year panel of 238 observations across 34 agricultural districts in Maharashtra from the 2016–17 to 2022–23 seasons. The analysis employs four distinct estimation strategies to test three specific predictions derived from the coordination game framework:

Panel Fixed Effects with Continuous Dose-Response: Instead of a binary "cooperative belt" indicator, the primary identification strategy uses the natural log of cooperative mill counts ( $\log(1 + \text{mills})$ ) as a continuous variable. This exploits the variation in mill density ranging from 0 to 19 mills across districts to estimate a dose-response gradient.
Coordination Amplification Interaction: The model tests whether mill density amplifies year-on-year persistence by interacting log mill density with the lagged sugarcane acreage share ( $\log(\text{mills}) \times s_{t-1}$ ).
Panel Quantile Regression (Canay, 2011): A two-step estimator is applied at quantiles $\tau \in \{0.25, 0.50, 0.75, 0.90\}$ to determine if persistence dynamics differ across the distribution of sugarcane concentration, specifically testing for self-amplifying dynamics in the upper tail.
Causal Identification (TWFE DiD and Generalized Synthetic Control): To test the third prediction—that information provision cannot dissolve coordination equilibria—the paper evaluates the staggered rollout of the AgriStack Farmers' Registry (2020–2022) across 14 treatment districts against 20 never-treated controls. This uses both Two-Way Fixed Effects (TWFE) and the Generalized Synthetic Control (GSC) method (Xu, 2017) to estimate the Average Treatment Effect on the Treated (ATT).

Key Results

Dose-Response Relationship: Cooperative mill density exhibits a monotonic, positive relationship with sugarcane acreage share. Each doubling of the cooperative mill count is associated with a 3.2 percentage point increase in sugarcane share ( $\beta = 0.047$ , $p=0.005$ ).
Coordination Amplification: Mill density significantly amplifies the persistence of sugarcane allocation. The interaction coefficient is 0.223 ( $p < 0.001$ ). Implied year-on-year persistence rises continuously from 34.4% in districts with zero mills to 101.1% in districts with 19 mills (Kolhapur). At the highest density, the equilibrium is not merely self-sustaining but self-amplifying.
Quantile Heterogeneity: Panel quantile regression reveals that persistence exceeds unity (1.073) at the 90th percentile of the sugarcane distribution. This indicates that the most institutionally embedded districts experience a positive feedback loop where concentration grows year-over-year independent of contemporaneous price signals.
Null Effect of Digital Information: The AgriStack intervention produced no detectable positive reallocation away from sugarcane. The TWFE ATT was −0.037 ( $p=0.088$ ) and the GSC ATT was −0.005. These null results confirm that providing digital information on alternative crop prices does not alter the coordination payoff structure or the mill viability threshold.
Smallholder Sensitivity: Districts with higher smallholder shares exhibit greater sugarcane concentration when mill density amplifies persistence ( $\beta = 1.738$ , $p=0.039$ ), suggesting the coordination trap disproportionately binds resource-constrained farmers who rely on cooperative input credit and guaranteed procurement.

Significance and Contributions
The paper claims three primary contributions to the literature:

Agricultural Coordination Games: It provides the first continuous dose-response identification of a cooperative procurement coordination mechanism using administrative panel data, moving beyond binary classifications to show how institutional density continuously strengthens equilibrium concentration.
Digital Agriculture Evaluation: It offers a multi-estimator causal test demonstrating that digital information interventions (AgriStack) fail to shift crop allocation in settings where the binding constraint is a coordination payoff structure rather than an information deficit. This challenges the assumption that information provision alone can drive diversification.
Institutional Economics in India: It identifies cooperative procurement infrastructure, rather than agro-climatic suitability or irrigation access, as the primary predictor of sugarcane concentration in Maharashtra. It highlights that the "certainty premium" of the legally enforceable FRP creates a coordination equilibrium that standard price signals cannot break.

Conclusion
The study concludes that the persistence of sugarcane in Maharashtra is driven by a coordination trap where mill viability depends on aggregate supply. Individual deviation is irrational because it does not affect the collective threshold but imposes full switching costs. Consequently, policies relying on price signals or digital information are insufficient. The paper suggests that breaking this equilibrium requires addressing the procurement certainty asymmetry, potentially by extending legally enforceable procurement guarantees to alternative crops to create competing coordination devices.

Technical Summary: Procurement Certainty, Coordination Amplification, and Acreage Lock-in

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