Economics & Corporate Law

Fixing the Labour Market: India’s Need For Better Data

Dr. Aparna Mathur

For a country and a government that is promising economic change and improved fortunes for its citizens, the lack of data on the labour market and employment trends is shocking

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The Indian economy grew at a rate of 8.2 percent in the second quarter of 2018, an increase in pace since 2016, and one that beat market expectations. However, in order to judge how well an economy is doing, it’s not enough to look at GDP growth rates because that tells us little about how those incomes are distributed across the country and how well off people actually feel. A more relevant indicator is the strength of the labor market and statistics on employment, unemployment, wages and labor force participation. A further breakdown of such data by demographics such as age, sex, and incomes, is critical in evaluating not only the welfare of households but also how specific economic policies are impacting the average Indian. Unfortunately, for India, reliable and comprehensive data on these metrics are tough to come by.

A 2017 report from the NITI Aayog highlighted the inadequacy of existing data sources and proposed suggestions for reforms. As pointed out by the task force, one of the main sources of employment data in India is the household and establishment survey done by the Labour Bureau. The Labour Bureau instituted the Annual Household Labour Force Survey in 2010, but the annual data was last updated in 2016. How do we judge the government’s policies if our data lags by two years? To improve the frequency of reporting, the Labour Bureau has also undertaken the production of quarterly establishment surveys. The most recent report is from March 2018, which covers the period October 2017, so there is still quite a lag in the data. Moreover, data coverage is very limited because it only covers establishments with 10 or more workers. As per the report, these establishments comprise less than 2 percent of all establishments and about 15 percent of the entire workforce. Therefore, a majority of the Indian workforce is excluded from the data. Moreover, the sectors and states covered have changed over time, making comparisons with prior surveys difficult.

Compare this to the employment data coming out of the US Bureau of Labor Statistics (BLS). These data come from both a representative household survey as well as an establishment survey, and cover between 20 to 35 percent of all households and non-farm payroll employees, respectively. The surveys are done every month, and the jobs report is released the following month. While one can question whether it makes sense to try to read too much into changes in this report from month to month, there is no ambiguity that having access to such monthly data is highly informative about long-term trends in the economy, about workers and how families perceive their economic situation changing in response to policy changes.

Problems have been highlighted in other data sources in India as well, such as the National Sample Survey data. Until recently, the Employment-Unemployment Survey (EUS) conducted by the NSSO was only undertaken every 5 years, with the last collection in 2011-2012 – so that data is not very useful in gauging current labor market trends. In 2017, the taskforce recommended that the NSSO surveys be scrapped and provided a comprehensive list of ways to improve India’s labor statistics database, including making these data annual. More recently, the BSE-CMIE has started to produce data on unemployment rates which is often in contrast with other series, such as the new Employee Provident Fund, ESIC, and Pension Fund Regulatory and Development Authority payroll data.

In short, India’s employment data sources suffer from inconsistent coverage, lack of representativeness, outdated sample frames, time lags in data availability, infrequent data collection, and lack of comparability across years. The Task Force’s three major suggestions include conducting household surveys on an annual basis, introducing a time use survey, and progressively introducing the use of technology that can speed up the data collection and processing time. All of these are worth undertaking.

Often a problem that is highlighted in collecting such labor data is that about 80 percent of the workforce is in the informal sector. So what does employment mean for these workers? Are they in gainful work or should we classify them as disguised under-employed? I think a useful parallel can be drawn here to the U-6 rate also produced by the BLS for the U.S. economy that produces data on “alternative measures of labor under-utilization”. The U-6 rate captures all unemployed workers but also includes workers who are in involuntary part-time work but who want and are available for full-time work. It further includes discouraged workers who have stopped looking for work but would like to get a full-time job if employment conditions improve. And finally, it includes workers who have not looked for work recently but who have looked for a job sometime in the previous 12 months. Doing a similar analysis for Indian workers, through a household survey that tracks their hours worked, the nature of their work, their primary occupation and whether they would prefer different work that might be full-time in the organized sector, whether they feel marginalized and discouraged from applying for jobs in the organized sector, would be a meaningful way to get at the issue of under-utilized or under-employed workers. In addition, such surveys should be done quarterly, with the goal to moving towards a monthly frequency.

For an economy that views itself as a powerful engine of growth, we need to tackle the tough questions of how that growth has been distributed. That involves analysis of the labour market and particularly data on employment trends. As per the World Bank, in India, labour force participation rates have declined significantly from 60.6 percent in 2005 to 53.8 percent in 2017. Female labour force participation rates are not only low, but declining further, from 36.8 percent in 2005 to 27.2 percent in 2017. What does that mean for income and wage inequality? And what is the true unemployment rate or job creation rate when numbers often differ between the NSSO surveys and the Labour Bureau surveys? How do we assess the labour market impacts of policies like demonetization and the new goods and services tax if we cannot agree on the jobs numbers? For answers to these and other pertinent questions, the Modi government has to make comprehensive, frequent and consistent labour market data collection a priority.

Dr. Aparna Mathur (@aparnamath) is a Resident Scholar in Economic Policy Studies at the American Enterprise Institute. In Politico Magazine’s 50 Ideas Blowing Up American Politics, her work on federal paid parental leave was ranked 32nd. 

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