THE CONTEXT: The 2022-23 Household Consumption Expenditure Survey (HCES) is not comparable to previous surveys due to major methodology changes. With limited factsheet data released so far, it does not provide clear insights into changing consumption patterns or poverty trends. Complete findings are awaited to understand the current ground reality.
THE ISSUES:
- Rural-Urban Inequality: Some HCES data indicate a fall in rural-urban inequality, with the urban average monthly per capita consumption expenditure (MPCE) being 72 percent more than the rural areas in 2022-23 compared to 84 percent in 2011-12. Nevertheless, this neglects rural-urban price differentials, and historical evidence shows that the rural-urban gap is a highly variable empirical phenomenon.
- Changes in Consumption Patterns: The share of food expenditure has significantly decreased, with cereals’ share per average MPCE falling substantially in rural and urban areas. This comes with rising trends in the proportion of processed items, beverages, and other non-food products such as medical charges, conveyance, the cost of living, consumer services, and durable commodities.
- Imputed Values for Social Welfare Items: The HCES now provides imputed values for items received or consumed free of cost under the social welfare programs. Nevertheless, the imputed values are only verifiable once unit-level price and quantity data become available.
- Changes in Survey Design: The HCES design evolved significantly, bringing in three separate surveys for different groups of goods and several household visits and improving data collection methodologies, ensuring comparability with previous rounds and higher estimates because of these changes.
- Inter-Caste Differences: The HCES data indicates that the midpoint of MPCE for the inter-caste gap has mostly stayed the same in the rural sector. The ratio of the average rural SC MPCE to higher-caste MPCE about others remained constant at 0.7. Nevertheless, urban statistics indicate reduced inter-group MPCE gaps among SCs, Scheduled Tribes (STs), and Other Backward Classes (OBCs).
THE WAY FORWARD:
- Developing Concordance Tables: Concordance tables can be developed whenever there are changes in item coverage between the two surveys, i.e., 2011-12 and 2022-23, that map items from the 2011-12 survey to those in the 2022-23 survey, and this will help to make better comparison over time. This involves thoroughly documenting changes in the item definition and categories. For instance, the United Nations Statistics Division provides guidelines for linking household survey data over time, even when methodologies change.
- Transparent Reporting of Imputation Methods: The survey should provide detailed documentation on the methods used for imputing values for items received free of cost through social welfare programs. This includes the assumptions made, the source of unit-level price and quantity data, and the imputation process.
- Longitudinal Panel Surveys: To capture changes in consumption patterns over time with minimal modifications in survey instruments, India may think of creating a longitudinal panel survey of households. This approach makes it possible to follow the same households for long periods, enabling the researchers to learn about the changes in consumption, income, and poverty.
- Addressing Rural-Urban and Intra-Household Inequality: Further analysis of the HCES data should focus on understanding the drivers of rural-urban inequality and intra-household consumption differences. This may involve more detailed surveys or qualitative studies that explore the underlying factors contributing to observed disparities.
- Engaging with Global Best Practices: Active interaction with international bodies such as the International Statistical Institute, ISI, or the International Household Survey Network, IHSN, allows drawing attention to the best survey re-designers practices and the measures improving data comparability. The lessons from the experience of the countries that have successfully handled the changes in the household surveys, for instance, the Netherlands or Sweden, can be instrumental.
- Utilizing Technology and Big Data: Leveraging big data sources and advanced analytical techniques, including machine learning and AI, can augment traditional survey methods for a nuanced understanding of consumption patterns. For instance, transaction data from digital financial services can provide real-time insights into consumption behaviors. The World Bank’s LSMS+ initiative explores integrating big data with traditional surveys to enhance data quality and timeliness.
THE CONCLUSION:
While the methodological upgrades in the HCES signify progress toward capturing nuanced consumption data, they also underscore the necessity for comparability with historical data. Adopting concordance measures, transparency, and international best practices is essential to navigate these challenges. Doing so will ensure the survey’s ongoing relevance and utility in shaping effective policy and understanding economic dynamics.
UPSC PAST YEAR QUESTIONS:
Q.1 Explain the difference between the computing methodology of India’s gross domestic product (GDP) before the year 2015 and after 2015. (2021)
Q.2 What is the meaning of the term tax-expenditure? Taking the housing sector as an example, discuss how it influences the government’s budgetary policies. (2013)
MAINS PRACTICE QUESTION:
Q.1 Examine the challenges posed by the methodological changes in the Household Consumption Expenditure Survey (HCES) of 2022-23 for long-term trend analysis in India. Discuss the potential solutions to address these challenges and ensure the utility of such surveys for policy formulation and socio-economic analysis. Illustrate your answer with examples.
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