What the data hides and shows: Indian Express 1st August 2023

 High-frequency economic data tell us whether things are looking good or not. These indicators help us formulate opinions about what is happening in the economy. However, often, we tend to extrapolate such data to draw conclusions which may not materialise. This raises scepticism about the accuracy of these variables.

In this context, the purchasing managers indices are relevant. They tell us about the state of industry and services on a monthly basis. Such information is provided for several countries and hence is seen as adding value to economic understanding. The fact that this information is available on the first of every month is valuable as we get to know about how the sector has performed immediately. In comparison, official data on the index of industrial production is available with a lag of around 40-45 days. That’s why PMI is popular and taken to be a leading economic indicator. But, in India, these leading indicators have tended to be misleading.

The PMI manufacturing averaged above 55 last year. This was interpreted as a sign of a turnaround in the manufacturing sector. In fact, not just a turnaround but a sharp revival. But, manufacturing output as per the GDP estimates grew by just 1.3 per cent — which is very different from what the PMIs were indicating. Is there something wrong somewhere?

The National Statistical Office, which computes these numbers, provides information for May in July. Here, too, revisions are made and critics maintain that the coverage is not optimal as the unorganised sector is largely excluded. In such a situation where a large part of the output comes from the unorganised sector, it does seem a stretch to accept PMIs where the sample size is only 400. Moreover, information is collected on only five indicators. While there is value in any information — it is better than not having any data — interpreting these indices is fraught with the risk of reaching erroneous conclusions.

A similar issue crops up in GST collections data. It has been observed that over the years, tax collections have risen, implying a revival of consumption. But the same does not reflect in other data with companies lamenting about stagnant demand. Collections appear to have risen more due to higher inflation, as well as better compliance. With the growing formalisation of the economy, GST collections will increase as a larger part of the SME world will get integrated into the system. But the usefulness of this indicator ends here. Using it to assess growth may not be correct.

There are also similar issues with the index of industrial production numbers. These numbers are known for extreme volatility with a standard deviation of 3-3.5 in the pre and post-pandemic periods. This means that growth numbers are volatile.

A similar problem is encountered when single-month export data is extrapolated into the future. High commodity prices often contribute to a higher value in trade and hence, whenever exports have increased at a high rate, the same holds for imports too, resulting in a higher deficit. The timing of exports also plays a role — spikes in trade could be due to logistical issues. Single-month numbers inherently carry such biases. This also holds for investment flows. One should eschew falling into the trap of the “highest ever monthly” flows as the bunching effect is quite common.

There has also been a tendency of late to get carried away by investment announcements made by companies as it perhaps supports the belief that the capital cycle has turned. This gets magnified when one looks at the state government-sponsored investment summits, which have a large number of MoUs signed. However, these seldom materialise. Here too, the indicators are misleading.

The flow of economic data in India has definitely improved over time. Various agencies are trying to release as close to possible real-time data to provide insights on developments taking place. While there is value in these, there are deviations when the final picture emerges. This can create problems at the policy end. Therein lies the rub.