FY21 was a bad year for the Indian economy due to the ongoing Covid pandemic. Overall GDP is estimated to have fallen by 7.3% YoY, the worst outcome, since data has become available post-Independence. Most other indicators saw a decline on a YoY basis. But how did the national aggregates play out across the states - did some states see lower impact than the others? And if so, is there a common thread that explains the divergence in the impact?
We unfortunately do not have reliable state-level GDP data. And in any case, very few states have reported data for FY21. Consequently, we must use indicators that are proxies for growth. We choose two for this piece – the choice is largely driven by their availability and robustness. The two indicators being GST collections in the state and Electricity demand. For this exercise, we exclude IGST collections since they pertain to inter-state trade and thus capture the dynamics across two states. We thus only include CGST, SGST and Cess – all three being charged on sales within the state and thus are proxies for end demand/consumption within a state.
Both GST and Power demand are quite different indicators – one is a nominal indicator while the other is a real or volume indicator. GST captures end transactions while Power demand captures just that. And both have limitations of their own. For example, GST is generally not levied on food products, especially unbranded. While the rate of GST is relatively higher on items of discretionary consumption. Thus, if discretionary consumption has got impacted, then GST would overstate the decline in demand as discretionary consumption contributes disproportionately to the GST pie (relative to non-Discretionary consumption). Similarly, if Industries have had to run at lower capacity but lockdown forced people to sit at home and run their ACs for a full day, Power demand data will understate the extent of the impact on output/economy.
Source: GSTN, CEA. Note: GST Collections exclude IGST
At an All-India level, GST collections declined by 10% YoY in FY21 while power demand declined by just 1% YoY. But as the chart below shows, there is very stark divergence across states. While with GST all large states barring Odisha saw a decline in Power demand several states saw a YoY growth. A state like Jharkhand saw a 10% decline in GST collections but an 11% growth in power demand. On the other hand, Delhi saw a 24% decline in GST collections and an 11% decline in power demand.
So, there is a huge divergence across states. The question is, is there a common thread running through this that can explain this? And it turns out, there is a simple and intuitive thread that connects all these states. And that is Services employment – the proportion of people in that state employed in the Services sector as per the Periodic Labour Force Survey (PLFS) for the year 2018-19. GST collections growth has had a negative 85% correlation with the level of Services employment in the state – implying that the higher the share of services employment, the higher the decline in GST collections. And Power Demand growth has had a negative 68% correlation with the level of Services employment in the state – implying that the higher the share of services employment, the higher the decline in Power demand.
Source: CEA, CSO
Source: GSTN, CSO. Note: GST Collections exclude IGST
Not surprisingly, the level of Services employment is largely explained by the level of Urbanisation – the more urbanised a state, the higher the level of services employment. There is a positive 87% correlation between the level of Services employment in a state and its level of urbanisation. it seems that the first wave of Covid (since the data here is till March-2021) largely impacted urban/services sector dominated areas. This is also the generally accepted consensus view also. the data backs this popular perception.
Source: CSO, National Commission on Population
As explained above, GST Collections and Power Demand are quite different indicators. But the fact that both indicators suggest the same thing – Services sector/ Urbanisation as being the factor that explains the divergent impact of Covid across states – means that our confidence in this conjecture is higher. As more indicators become available, we will update this analysis and see if this finding holds.
Further, given that this data is until March-2021, this covers the first wave of the Covid pandemic only. The general perception is that the second wave of the pandemic impacted rural areas much more than the first wave. We will update this analysis in the next few months when more data for the current year becomes available to see if data backs up this perception.