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Incentivising Agri-Financing

Data and digitisation are creating conditions conducive to agricultural financing, writes Ammar H. Khan. 
Updated 06 May, 2024 05:25pm

As other economies have enhanced yields and increased production, consequently improving household incomes, agriculture in Pakistan is plagued by low productivity and a dismal capital-output ratio, resulting in mediocre yields and a largely flat output over the years. Despite making up 23% of the annual output, financing extended to agriculture makes up less than three percent of the total asset base of banks in the country.

Financial institutions deem agriculture to be a high-risk segment given the extended repayment cycles (heavily dependent on crop cycles), susceptibility to external factors (climate, diseases, etc.), and a largely informal cash-oriented economy. Furthermore, intervention by the government in setting prices of key crops further distorts the market, resulting in an inefficient allocation of land and capital. Due to such inefficient allocation, there is little incentive for farmers and investors alike to invest in crops other than wheat and sugarcane. In the absence of such incentives, financial institutions also stay wary of financing other crops and do not take any serious interest in developing the necessary storage and logistics infrastructure required to support movement and storage. Myriad factors affect agricultural productivity, and there is a lack of financing for the same, but technology is bringing about some necessary changes.

The biggest problem with agricultural finance has been the availability of data and a network to carry out due diligence and credit assessments. However, there are now datasets available that utilise the geographic information system (GIS) to accurately identify what area is suitable for a particular crop, how yields have changed in a particular area, and how much output can be generated from a particular area. The same information is available at a granular level. If the same datasets are used, it can be easily inferred what the relatively safer agricultural areas are that can be targeted by financial institutions for financing in a particular season or in a crop cycle. Similarly, by collating high-frequency data on market prices (now available through various sources), it is also possible to assess, with a high degree of accuracy, potential crop yields and outputs in a particular area. Similar datasets are available for water availability, which, when collated with land utilisation data, can provide insights into potential yield changes and outputs based on historical data.

Using GIS data, it is possible to map out credit risk for various agricultural areas. The data can be used to create customised agriculture-specific credit scorecards based on what type of agri-finance can be done in a more granular and data-driven manner.

Another constraint for agricultural finance was access to bank branch networks and basic financial literacy, even if farmers had been dealing with informal lenders for centuries in the region.

The financial landscape in rural areas has significantly changed. The number of 3G and 4G connections has exploded, as has the utilisation of mobile wallets; effectively, a large percentage of the rural population can be deemed as banked due to their access to mobile wallets, although more needs to be done to convert mobile wallets into more accessible banking accounts and eventually move towards financing. The first step has been taken, but the second step requires a more granular assessment of individual and land data to understand which areas can be more credit-worthy, eventually leading to a transition to greater financing in that area.

Before any financing can be done, it is essential to understand the flow of money in a particular commodity or sub-economy. As most transactions are done on a cash basis, it is difficult to understand how cash moves, thereby making it difficult to validate information. The digitisation of the agricultural value chain remains crucial, and financing cannot take off without the digitisation of the payment value chain. A key role the government can play here is transitioning from providing subsidies to fertiliser producers to providing direct cash transfers to farmers for the procurement of fertilisers. This will effectively redistribute capital in a way that benefits the farmer directly. The transmission of such subsidies would be conducted through formal banking channels, similar to the Benazir Income Support Programme. The same transmission mechanism can be used to understand payments that are received by a farmer and to incentivise more purchases through the same account rather than cash. As more data is collected, it will be possible to create farmer profiles and clusters, which, when combined with GIS-driven land and water data, will make it possible to identify eligible borrowers through a straightforward algorithm.

Another dataset that can be used is the Relative Wealth Index, updated by Meta. This dataset takes into consideration the relative wealth of a particular area, with granularity as low as two miles. It takes into consideration access to network connectivity, the nature of mobile devices, usage of mobile devices, etc., to ascertain the relative wealth of an area. The same dataset can be used to supplement farmer profiles, which, when reviewed in conjunction with land and water data, can yield unknown insights and make credit scoring a more data-driven process. As credit scoring becomes more data-driven and granular, it will be possible to de-risk agricultural financing, eventually leading to more widespread utilisation of agricultural finance to finance inputs or bridge receivables, resulting in better outcomes.

There exists a strong case to scale up agricultural finance in Pakistan by utilising publicly available datasets and existing digital infrastructure, but the incentive to do this is missing, as agricultural financing is often deemed something that financial institutions have to do to appease the regulator rather than something that generates economic returns. Through the application of advanced credit scoring models, it is possible to generate positive economic returns as well. This is a scenario whereby the financial institution takes the first leap forward and creates a market, which will eventually drive greater adoption and availability of financing in unbanked and unfinanced areas.

Ammar H. Khan is a macroeconomist.