Factories are running at below 75 per cent of their capacity for at least three years, leading some policymakers to argue for a sharp rate cut by the Reserve Bank of India. A rate cut would lead to cheaper cost of funds and will drive demand, which, in turn, will lead to improved capacity utilisation, goes the underlying theory. A lower interest rate would also help companies increase their capital expenditure, leading to more investment, which, in turn, would lead to higher economic growth. However, an analysis of savings and investment data for the past few years shows that while corporate investments have indeed slowed down in line with nominal GDP growth, household investments have decelerated much more sharply. Households are not investing as much in physical savings.
About 89 per cent of the physical household savings are in the form of property, and the remaining in assets, such as cars, machinery and equipment. A sharp downturn in physical savings means slowdown in construction sector activities, which then feeds into slowdown in various allied sectors, such as steel and cement. This is essentially what the fall in capacity utilisation captures. But, what has led to the slowdown in physical household savings? It is the sharp cut in salaries and job losses after the credit crisis of 2008-09, which essentially squeezed out discretionary and real income in the hands of households, while high inflation also played a part. The Sixth Pay Commission and other stimulus measures by the government in fiscal year 2009-10 did mend the savings for government employees for some time, but a sharp downturn in housing prices meant that households did not find any merit in saving on physical assets. The charts below show how it is the households and not corporates that led to the current slowdown, even as the initial push came from the corporate side. Most of the data used in the charts below end in 2015-16, as disaggregated data are the last available for this year only.
Note: Axis Bank Chief Economist Saugata Bhattacharya helped interpret the data for this analysis