The missing learning curve
We need to build institutional memory, improve decision-making. Let’s start with data
If a debate starts, for example, with whether GDP statistics are right, if inflation data are representative, or the number of (formal and informal) jobs being created every year is high or low, drawing policy lessons would be difficult.
Humans have incredible powers of learning and adaptation. Every few years, looking back, one feels the younger version of oneself was so unwise. But as human life is finite, the lessons of a lifetime often get lost with the individual. Societies, on the other hand, (can) last thousands of years, and much has been written about the accelerating learning curve of the human species. But can the collective brain also refuse to learn some lessons, and occasionally even unlearn?
Primitive, pre-historic isolated tribes have been known to have culturally regressed, like the aborigines of Tasmania that forgot how to fish and to make winter clothes. While it may be difficult to get that level of isolation today, can some necessary but sophisticated lessons be ignored or unlearned? They can, indeed, as two examples demonstrate.
Anupam Mishra in his book “Aaj bhi khare hain talab” lamented the loss of skills to make and maintain ponds that were an essential part of Indian life till only a century back. These skills degenerated as digging of new ponds and maintenance of old ones took a back seat, either due to rapid population growth, a breakdown of social mores, changing economic incentives or all of these. The recent investment surge in rainwater harvesting structures by state governments, triggered by droughts and depletion of groundwater reserves, is an attempt to re-learn what we already had before.
Or take the policy efforts to reduce gold imports: Large imports of gold are a costly drain on domestic savings, and aid black money hoarders in escaping the tax net. In nearly every decade since Independence the government has launched gold monetisation schemes sometimes with and sometimes without tax amnesty, but with limited success: Insignificant amounts of gold were deposited. And yet, such schemes continue to be launched.
While all three arms of government (legislature, executive and judiciary) may be progressing, one wonders if the pace is acceptable. The absence of a learning curve on several fronts could be an important reason why we seem to know most of our problems reasonably well but have struggled to solve them.
Like the problem of under-employment in agriculture (see ‘Beyond the farm’, IE, March 30) that we have known about since 1880, or the repeated bouts of currency crises brought on by complacency following periods of stability. It is as if every individual taking decisions is learning on the job and through his/her own experiences: The system therefore progresses slowly.
This may not be a uniquely Indian frailty: One can observe policies swinging between extremes even in the developed economies. However, those swings are smaller in magnitude, and a learning curve is visible. The failure to foresee the global financial crisis (GFC) of 2008 may have been an example of inter-generational loss of caution (the Great Depression, after all, was nearly four generations back). However, the coordinated response to the GFC by central banks showed that lessons had been learnt — in the 1930s a lack of coordination among major economies had exacerbated the slowdown.
So how does one build institutional memory and improve decision-making? Policy-making should not suffer while the policy-maker is on a learning curve. Here, we focus on economic issues.
Let’s start with data. An objective evaluation of policies, needed to draw corrective lessons, is difficult without consistent, accurate and accessible data. As economic analysis often has political implications, making biases unavoidable, data can be the starting point that all sides to an argument can agree on. If a debate starts, for example, with whether GDP statistics are right, if inflation data are representative, or the number of (formal and informal) jobs being created every year is high or low, drawing policy lessons would be difficult.
Similarly, economic history in India lacks depth, and explanations of even basic questions lack rigour. For example, what explains the significant economic disparity between the Hindi-speaking heartland states and the more prosperous western and southern states — was it because they were colonised the earliest, because caste reforms took place much later or a combination of other factors?
What was the economic impact of Partition (in 1947) on border-states as supply chains suddenly shut down as counterparties were in Pakistan or Bangladesh? How and why did specialised industrial clusters for disparate items like surgical cotton products (Chatrapatti) and transportation (Sankagiri) develop in Tamil Nadu, but not in other states?
Why does nearly a fifth of vegetable production come from West Bengal, and did the land reforms of the early-1980s play a role? The lack of technology research in Indian universities is much lamented, but the lack of quality research on economic history could be as damaging if not more. If one does not understand the disease, how can one attempt a cure?
Data and interpretation are, however, just the first steps. Decision-makers are individuals or groups of individuals, and no one person or group of persons can learn it all before they start drafting policy. This is why an institutional framework is needed: Empowered institutions supported by a body of researchers in academia and industry to make sure past lessons are not forgotten, and mistakes not repeated. But there are glaring gaps. For example, several Indian states have annual output of hundreds of billions of dollars, but lack chief economic advisors when even small business groups have chief economists.
Some recent developments on each of these fronts are encouraging: For example, some state governments making available detailed Economic Surveys, or the release of income tax statistics (this may have helped draw attention to poor tax compliance). New institutions like the Monetary Policy Committee should hopefully drive an improvement in data quality: For example, the focus on “core inflation” has raised important questions on how inflation data on rents and various services is collected. The Fiscal Council recommended by the FRBM review committee to independently assess the government’s fiscal performance and compliance could do the same for fiscal data.
However, a lot more needs to be done on all these fronts, most importantly with data, which needs manifold improvement in quality, depth and accessibility. It may also be the easiest to rectify.
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