Is unemployment data such an inconsequential macro-economic indicator that Indian policymakers require it only once every five years? Or, is it because the cost of tracking this variable is prohibitively high in a billion-strong nation?

Financial constraints cannot be a possible or a comprehensive explanation for a country that could afford to spend Rs 28,000 crore on the Commonwealth Games.

For most advanced countries, unemployment is an important macro-economic indicator for policymaking and is tracked frequently. It is puzzling as to how India frames its monetary and fiscal policies using episodic and archaic unemployment data — assuming the country uses this variable in policymaking.

To understand how unemployment data is a vital ingredient for structuring monetary or fiscal policy, let us look at how both policies function independently. For simplicity, let us assume that the nature of both policies is expansionary in nature — to boost or expand economic activity.

trigger button

To boost economic activity, in the short run, government can increase its spending (fiscal spending) on goods and services, say, through building roads. Government hires people for this, which creates employment and generates income.

With this income, the newly employed buy goods and services, which in turn creates income for the sellers, and thus boosts economic activity. In essence, the trigger button of expansionary fiscal policy is increasing employment or alternatively, decreasing unemployment.

To what extent can unemployment statistics that is available every five years impact the effectiveness of such a policy? Assume that in the last five years the number of casual labourers in the total unemployed force has come down to zero. Would expansionary fiscal policy have the desired effect? Sure enough, fiscal (and monetary) policy affects other variables too, which can affect economic activity. However, the effectiveness of an expansionary fiscal policy is dampened to the effect that up-to-date unemployment figures are ignored.

data for policy making

The aim of an expansionary monetary policy is to increase the flow of money in the economy. Assume that the Reserve Bank of India reduces interest rates from 15 per cent to 0.5 per cent (exaggerated figures help to understand the underlying mechanism better). At such a reduced interest rate, the incentive to save is low.

People either save or spend money; so reduced savings typically implies increased spending. This increase in spending translates to higher sale of goods and services. With increased sales and production, producers hire more people. More hiring (or reduction in unemployment) leads to increased economic activity, as explained earlier.

The other effect of such monetary policy is that at a lower interest rate, the cost of borrowing is lower, which incentivises higher private investment. These investors hire workers (which reduces unemployment) to produce goods and services. Again, this increase in employment leads to more spending and, thus, boosts economic activity.

Let us assume that in the last five years the mix of labour and physical capital (machinery) underwent substantial change. How accurately would monetary policy achieve its desired target? The analysis above is not to suggest that fiscal or monetary policy is a panacea for unemployment; rather, it plainly outlines the importance of having frequent unemployment data for policymaking.

ineffectual in isolation

Some may argue that the RBI's monetary rules are intended to affect aggregate demand, and thus, can choose to ignore labour market conditions that are an aggregate supply phenomenon. But this argument is unscientific and hardly a justification for ignoring up-to-date unemployment statistics, which reflect capacity constraints. If the aggregate supply details are not (or only partially) known, how can policies that alter aggregate demand be targeted? In other words, if the position or slope (or both) of aggregate supply curve is not completely known, how can a change in aggregate demand curve achieve its very purpose?

success metric

Besides serving as a policy input, unemployment figures also serve as a relevant and comprehensible metric for people to measure the effectiveness of policy decisions. A statement such as “Two months after the policy was introduced unemployment decreased from x per cent to y per cent” is more grounded than a statement that goes as “Two months after the policy was introduced the external commercial borrowings decreased by 7 per cent.”

It is possible to argue that many indicators serve as a proxy for unemployment. But such proxies can be imperfect substitutes at best. It is hard to think of an alternative for unemployment data, which is perhaps the all-encompassing indicator of labour market strength.

Moreover, India has been frequently tweaking its monetary and fiscal policies. The country can show the same enthusiasm and regularity in collecting unemployment data. Disregarding the importance of up-to-date data as an input and focusing on the output is not only over-ambitious, but slightly illogical, too.

Lessons from the us

As we aim to become a global superpower alongside countries such as the US, we should straighten out basic processes in data collection that can help effective policymaking.

In this regard, we can learn from those countries on how they collect quarterly data on unemployment. It is another issue that many economists criticise US unemployment data on the grounds that the figures do not completely reflect the labour market conditions.

Regardless, we need to take the first few steps to gather frequent data — and then possibly work towards refining the quality of data.

Using a few years' data for an input for policymaking is no different from providing food to people who were hungry a few months back!

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