The Centre for Monitoring Indian Economy (CMIE) a leading business information company which uses specialised analytical tools to produce economic and business databases for research released its latest reports on February 5 last. The figures show Meghalaya’s unemployment ration to be the third lowest among all 28 states at 1.5%. Telangana has the lowest unemployment ratio at 0.7 % while Gujarat is second at 1.2%. Among the North Eastern states, Tripura has the highest unemployment ratio at 17.1% while in the country, Haryana has the highest unemployment ratio at 23.4%. The CMIE website while explaining the methodology of its unemployment data says that individuals surveyed are members of a panel of households included in CMIE’s Consumer Pyramid Household Survey (CPHS). This would mean that any shortcomings with the CPHS data will impact the unemployment data as well.
During the Covid-19 crisis of 2020 there was a significant job and income loss. Hence researchers and policymakers turned to unemployment data compiled by the CMIE to understand the extent of job losses especially in the in formal sector. But Jean Dreze a Development Economist based in India had recently pointed out that that CMIE’s CPHS was biased towards better-off households and the bias was rising over time. Dreze wondered if sampling is too focused on ‘main streets’ within towns and villages where the better-off may reside, while under-representing those that may live in the interior areas. This is the problem with data gathering as far as India is concerned. As expected CMIE has stoutly defended the data and explained the methodology. It says the individuals surveyed are members of a panel of households included in CMIE’s CPHS. But any shortcomings with the CPHS data will impact the unemployment data as well. Economists have therefore cautioned that CMIE’s unemployment rates should be interpreted with caution.
John Dreze’s argument is that there is a bias towards higher income groups whereas during the pandemic, it was the lowest income earners that were worst affected. If during the sampling, the worst impacted are left out and these figures are then extrapolated for the country then there is a bias and policy-making will be impacted adversely. The CMIE survey sampling includes mostly those in the formal sector whereas in India about 85% of employees are in the informal sector. Besides if the data sampling leaves out unreached villages then the survey will not be accurate. Those that were laid off during the lockdown are basically from the informal sector. Development economists therefore suggest that getting the employment data would give a more reasonable sense of what is happening in the informal sector. Meghalaya had therefore take a second look at the unemployment data because it appears skewed and unreal.
