Data collection will help India solve its food inflation problem

The recent rise in Indian inflation is a reminder of how volatile food prices are. From climate and farming practices to consumer tastes and revenues, drivers of price fluctuations — and their interlinkages — are unstoppable. But an important factor behind these fluctuations is the uninterrupted transition from high supply to under supply. Especially when there are not enough cold-storage facilities, small changes in supply can have a big impact on perishable prices.

Why are there such fluctuations in supply? And can anything be done to reduce them?

Irregular weather drives most of the supply fluctuations. However, there is another problem described by the KobeWeb model, which was first proposed by the agronomist Mordecai Ezekiel in 1938. Its main argument is that farmers miscalculated expectations. That is, farmers base their future price estimates on what they see in the spot markets at the time of sowing. For example, when farmers see high prices for onions, they decide to grow more of these, thus creating a platform for higher production. It also works the other way around. The result is “coordination failure”.

Rational Assumptions Junkie may ask, “Do people really have false assumptions?” Their model in Ethiopia forms crop-time price estimates based on current prices. Similar examples exist elsewhere. Price estimates drive farmers’ production decisions. Appropriate intervention, on an abstract level, will then be clear — providing good guidance to farmers.

But, can the government really do this? Yes, how? This part is an attempt towards a specific proposition.

In this age of technology and smartphones, governments can develop the infrastructure to collect real-time data, with crop harvest dates, on the area under cultivation for certain important crops. One way to do this is to develop a smartphone app for farmers to enter this information after sowing and get modest monetary incentives to do so. The mechanism is relatively straightforward. Since not all farmers make seed decisions at the same time, if the app shows that many farmers are sowing onions, its price is likely to be lower due to the high supply. Therefore, this app can advise farmers who are making seed decisions, albeit a little late, in favor of another crop for a better price. It also insures early transportation, as farmers who make later seed decisions are not encouraged to grow onions. We would like to emphasize that we are not referring to the socialist-era remnant of centralized production planning. Instead, price estimates can be useful for this purpose.

We were not the first to suggest the idea of ​​providing such pricing information to farmers. There is extensive literature in economics on this. Several studies have been conducted. The evidence is mixed. The main difference in our proposal is that we suggested intervention in the production phase, but most field experiments are aware of the interventions studied in the final phase, i.e. around harvest time.

The whole idea is called distance for three reasons. First, can farmers actually use smartphones to provide such data to the government? Second, can farmers diversify so easily? And third, do we have algorithms to predict future prices? These are valid concerns.

First, there is a somewhat nutritional tendency to question the ability of farmers to use smartphones. With cheap data, these devices have already penetrated into remote areas. Maybe we need to develop applications based on local languages. But, if such an application is developed, and the government can provide modest monetary incentives for people to honestly report what they have sown, it is difficult to see why farmers do not comply. “What if people cheated and misreported?” Naysayers ask. This is the most crucial aspect of the design. With mechanisms such as random audit and ultimately sales monitoring, it is possible to prevent abuses.

The ability of farmers to diversify their crops has been a long-standing challenge. But having a reliable future ahead of time prompts them to diversify. Finally, the above two questions need to be studied through field experimentation.

There has been considerable progress in using techniques such as the ability to provide final anxiety-reliable indications — machine learning. Recent advances in time series estimation and machine learning have led to vast improvements in price estimating algorithms. In fact, some even have mechanisms for detecting anomalies, such as hoardings (e.g. Lowish Madan and co-authors’ paper published in ACM Compass). Truth be told, we do not know about the algorithms that will offer far-flung areas in the future, but they also have no information on the area under cultivation, which we believe is now possible to obtain as mentioned earlier.

Creating a system as we described above plays a meaningful role in reducing the price volatility of agricultural products and provides a policy option to reduce losses beyond the traditional focus on support prices or forward markets or contract farming. It helps governments maintain a delicate balance between protecting the interests of food producers and consumers, which is a major challenge of the Indian political economy.

Aditya Kuwalekar & Niranjan Rajyadhya are Lecturers in Economics at the University of Essex respectively and a member of the Academic Advisory Board of the Meghnad Desai Academy of Economics.

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