Incomplete data, or the absence of data itself, paves the way for economists to bring out their macroeconomic models to estimate sensational and eye-catching outcomes, whether it’s Covid-19-related deaths or the impact of the pandemic on the vulnerable population. Some economists have argued that the pandemic has had a direct impact on informal workers and those employed in small establishments by pushing them into poverty. Estimating poverty through the consumption approach has its challenges. Some researchers have attempted to arrive at poverty estimates based on auxiliary data available from government or other small-sample studies.
Recently, IMF and World Bank policy advisers and researchers have also attempted to estimate staffing ratios under various assumptions. The huge differences in headcount ratios between the two studies only add to the confusion in the already complicated problems of measuring poverty in India.
The IMF carried out the exercise using adjustments for private final consumption expenditure from national accounts statistics and also using expenditure incurred by the government under the public distribution system. The World Bank attempted to estimate headcount ratios using CMIE data from the Household Consumer Pyramids Survey from 2015 to 2019, linking it to NSS consumer expenditure data from 2011 and to data from other sources such as the National Family Health Survey, the Periodic Labor Force Survey. , the Farm Household Situation Assessment and the All India Debt and Investment Survey. This exercise does not seem to follow the basic principles of statistics.
In fact, many econometric adjustments were likely made in an effort to relate the divergent NSS datasets to the CMIE. It can be noted that India uses the NSS Consumer Expenditure Survey for the measurement of poverty and the results of it, conducted in 2017, are not available due to quality issues in the data. collected.
The World Bank document appears to be based on unrealistic and unsustainable assumptions. First, the sample design of the NSS and CMIE surveys is different. The NSS adopts multistage stratified sampling while the CMIE uses rotational sampling. In addition, CPHS households have unequal sampling probabilities because main street households have a higher probability of selection. Even the basic definition of household is different in the two surveys. Unlike the NSS, the CPHS does not perform a listing exercise and instead uses projections of households and population growth to construct sampling weights. It has been stated by various researchers that the NSS adequately captures information from households at the lower end of the consumption distribution, but inadequately for those at the higher end. However, many doubts have been expressed about the CMIE data in terms of representativeness.
The best of Express Premium
Prime
Prime
PrimeSecond, NSS collects information on more than 345 unique items to arrive at consumer spending estimates, while CMIE does so through 114 items. While NSS spending is based on a 30-day recall period for food items and others on 365 days, CPHS consumer spending is based on a recall period of the past four months. Attempting to make a comparison using expenditure sub-groups like food, non-food items and durable goods may also not be of much help since errors in collecting data from the two sources will not necessarily cancel each other out, but may add up due to different sets. of items.
Third, there is a time lag between the data used from the NSS survey, which covers the year 2011, and the CMIE survey from 2015 to 2019. This lack of comparable years for the development of the model introduced a another error.
Fourth, changing weights in the CMIE household-level survey using the NFHS and other surveys may not correctly reflect the weighting scheme, since changes in consumption expenditures over a long period may show necessary demographic and other changes.
Indeed, measuring poverty at the national level serves no political purpose. It is necessary to go down to the state, district, block and village level to identify the pockets of poverty in order to develop and implement the special programs needed in each case. India already has a multidimensional poverty measure that helps to better understand deprivation. Another initiative is the Ambitious Districts program, which is extended to the block level and provides the direction and location where specific interventions are needed.
If the World Bank wants to estimate poverty in India, it should only use the CMIE data available from 2015 and measure the evolution of poverty ratios, given the structural limitations of the survey. Alternatively, one can wait for the results of the survey to be conducted by the National Statistics Office between July 2022 and June 2023. The results will probably be available around a year after the survey is completed.
Kumar is NITI Aayog Senior Fellow, Verma is former MOSPI DG and Srivastava is former MOSPI Secretary
Comments are closed.