13 March 2024 – The Indian Express

Household Consumption Expenditure Survey

Which Recent HCES Highlights Are the Most Important?

• The National Statistical Office (NSO) typically conducts the HCES every five years.
• Its purpose is to gather data on household consumption of goods and services.
• In addition, the information gathered in HCES is used to calculate a number of other macroeconomic indicators, including the GDP, CPI, and rates of poverty.
• According to NITI Aayog, the country’s poverty rate has decreased to 5% based on the results of the most recent consumer spending survey.
• The results of the most recent HCES, which was carried out in 2017–18, were withheld by the government due to concerns over “data quality.”
• Gives data on average spending on both services and commodities (food and non-food items combined).
• Helps with the analysis of the distribution of homes and individuals across various MPCE categories as well as the computation of estimates for household Monthly Per Capita Consumer Expenditure (MPCE).
• Highlights of the Most Recent Survey: The average monthly consumption spending estimates were produced without factoring in the value of the free goods that households received under various social welfare programmes, including the Pradhan Mantri Garib Kalyan Yojana.

An increase in MPCE:

• It shows that during 2011–12, urban households’ MPCE climbed by 33.5% to ₹3,510, while MPCE in rural India increased by 40.42% to ₹2,008.
• Food accounted for 46% of household spending in rural areas and 39% of household spending in urban areas in 2022–2023.

The MPCE distribution by population percentiles:  

• According to MPCE, the lowest 5% of India’s rural population has an average MPCE of Rs. 1,373, compared to Rs. 2,001 for the same demographic in urban areas.
• According to MPCE, the top 5% of Indians are divided into rural and urban populations, with average MPCEs of Rs. 10,501 and Rs. 20,824, respectively.

State-specific MPCE Differences:  

• With ₹7,731 in rural and ₹12,105 in urban areas, Sikkim has the highest MPCE, while Chhattisgarh has the lowest, with ₹2,466 in rural and ₹4,483 in urban households.
• Meghalaya (83%) and Chhattisgarh (82%), the two states with the largest differences in average MPCE between rural and urban areas, are the highest.

UT-specific MPCE Differences:  

• Of all the UTs, Chandigarh has the greatest MPCE (Rs. 7,467 for rural regions and Rs. 12,575) for urban areas, while Ladakh and Lakshadweep have the lowest MPCE (Rs. 4,035) and Rs. 5,475) for rural and urban areas, respectively.

Trends in Food Spending:  

• For both urban and rural households, the percentage of spending on non-food products has climbed and the percentage of spending on food has gradually decreased since the 1999–2000 study.
• It is generally accepted that a decrease in food costs corresponds to an increase in income, which frees up funds for other expenses such as entertainment, durable goods, medical care, clothing, education, and transportation.
• The results of a recent survey indicated that, in both urban and rural families, the percentage of total food consumption expenditure that goes towards cereals and pulses has been declining.
• The percentage of spending on transport was the highest of all non-food goods.
• Among non-food commodities, fuel and light were the most expensive to consume until 2022–2023.

Modifications to the Approach Since the Previous Survey:  

• In comparison to earlier surveys, the HCES 2022–2023 has experienced certain modifications in terms of consumption spending.

These are the following:  

• item coverage
• modifications to the survey;
• Compared to pen and paper interviews, multiple visits were made for data collecting and calculated assisted personal interviews.

Which Three Problems Are Associated with the Found Trends in Poverty Estimation?  

Modifications to Consumption Patterns as Given by NAS and NSSO:

• The first task is to use the updated data on consumption spending to assess how poverty has changed:
• According to the Expert Group (Tendulkar) Methodology, the poverty limits for 2011–12 were Rs. 816 and Rs. 1,000 per capita per month, respectively, for rural and urban areas.
• An SBI research updated poverty lines to anticipate 2022–2023 poverty ratios. In rural regions, the revised poverty line is Rs 1,622, and in urban areas, it is Rs 1,929.
• This SBI research states that while poverty in urban areas decreased from 13.7% to 4.6%, it decreased in rural regions from 25.7% in 2011–12 to 7.2% in 2022–23. The Tendulkar committee methodology yields a total poverty ratio of 6.3% when using the shares of rural and urban people.
• According to the Expert Group (Rangarajan) Methodology, the poverty level for 2011–12 was Rs 972 and Rs 1,407 per capita per month, respectively, for rural and urban areas.
• The CPI was used to revise the poverty line, which for 2022–2023 is Rs 2,603 for urban areas and Rs 1,837 for rural regions. In rural areas, the poverty rate decreased from 30.9% in 2011–12 to 12.3% in 2022–2023 years.
• In urban regions, it decreased to 8% in 2022–2023 from 26.4% in 2011–2012. The poverty ratio obtained using the Expert Group (Rangarajan) approach is, it should be emphasised, 71% higher in rural and 74% higher in urban regions than the one obtained using the Expert Group (Tendulkar) method.
• According to the Rangarajan methodology, the overall poverty rate for 2022–2023 will be 10.8%. In rural areas, the order of the percentage point reduction between the two periods under both approaches is the same, despite the fact that the poverty ratio is larger under this methodology. However, there has been a greater reduction in this practice in metropolitan regions.
• But as was already indicated, there are some differences between the 2022–2023 consumption spending figures and previous polls. Under the Rangarajan Committee methodology, the similar data would have most likely shown substantially higher statistics for poverty, especially for metropolitan regions.
• Disparity Between the NAS and NSSO’s Aggregate Private Consumption Expenditure:

The second concern is the concerning discrepancy between the amount provided by the NAS and the total private consumption expenditure provided by the NSSO:  

• What is troubling is that the NSSO share increased very little in 2022–2023 despite significant methodological adjustments to account for private spending.
• Nevertheless, no nation—including India—has consumption estimates (NSSO and NAS) that are comparable.
• The confusing thing is that, in India, the gap between NSS and NAS consumption is growing with time. In 2011–12, the disparity increased to 53% from less than 10% in the late 1970s.
• Only a small decrease in this gap occurred, reaching 52% in 2022–2023. But since the discrepancies of over 50% have persisted, it is now necessary to conduct a more thorough examination of the elements that influence the difference. There are consequences for calculating the poverty ratio due to such a big discrepancy.

The HCES 2022–2023’s effects on the Consumer Price Index (CPI):

• The third concern is how the HCES 2022–2023 may affect the CPI. The most recent data show that between 2011–12 and 2022–2023 there were some changes in consumption habits.
• The percentage of food expenditure in monthly per capita expenditure (MPCE) in rural regions decreased by 6.5 percentage points in 11 years, from 52.9% in 2011–12 to 46.4% in 2022–23.
• During the same period, the share of food consumed overall in urban areas fell from 42.6% to 39.2%, a loss of 3.5 percentage points in 11 years.
• In rural areas, the percentage of grains in the average MPCE has significantly decreased, going from 10.8% in 2011–12 to 4.9% in 2022–23, whereas in urban areas, it has increased from 6.7% to 3.6%.
• Fruits, drinks, and processed foods increased in percentage of total food items in both rural and urban areas. The amount of vegetables is somewhat decreased.
• Both in rural and urban areas, the shares of non-food products such as durable goods, home items, and transportation have climbed dramatically.

Consequences for the CPI and Inflation Weights of Food and Non-Food Items:  

• The weights in the CPI basket, which are now based on 2011–12 weights, may be adjusted with the use of this new data. Given that the prices of food are often much higher than those of non-food items and can fluctuate greatly, the fall in food item weights is encouraging.
• The concern is whether the current decrease in the food share is sufficient to significantly affect inflation rates.
• In both rural and urban areas, the food share is still high, at 46% and 39%, respectively. Cereals, vegetables, and edible oils have seen a decrease in share, whilst fruits have seen an increase, and the shares of eggs, fish, and meat have remained constant.
• A decrease in the food portion will, nevertheless, have some effect on inflation. A new price index will need to be used by the monetary policy committee.

What Improvements Are Needed to Increase the Robustness and Indicativeness of HCES Data?  

Guaranteeing Equitable Representation of Every Group:  

• The sample mechanism, including the stratum and second-stage stratum, has changed significantly for HCES 2022–2023. For HCES 2022–2023, there are only two strata that are classified as “rural.” The first stratum includes all villages that are located less than five kilometres from the district headquarters; the remaining villages are included in the other strata. The “urban” strata are categorised according to both “population” and “affluence” levels.
• As there appears to be a higher representation of the wealthy groups in the HCES 2022–23 sampling approach, higher consumption expenditure and the elimination of ground reality, sampling should be done to ensure proper representation of households of different economic categories.

Aligning Actualities With Conditions of the Labour Market:  

• The NITI Aayog’s assertion that growth is inclusive and broad-based and that inequality is declining while ignoring the economic hardship of rural areas must also be considered in light of the results of the labour market. Before assuming that India has managed to eradicate poverty, it is important to examine the country’s labour market conditions in light of the growing number of working poor people and the real wages falling.

Dividing the Survey’s Debts and Savings:  

• Bank loans, Kisan Credit Cards, or equal monthly payments (EMIs) for durable or non-durable items would eventually contribute to household debt while also being consumed.
• The National Academy of Statistics (NAS) unequivocally notes that during the current administration, household debt has increased while household expenditure has decreased as a percentage of GDP. As a result, the amount of debt taken on shouldn’t be taken into account when calculating family consumption statistics.

Steer Clear of Dependency on Food Consumption Spending Information:  

• Just 6.4% of all consumer spending goes towards food in the USA (2018), 6.9% goes towards Singapore (2018), 7.9% goes towards the UK (2019), and 8.9% goes towards Switzerland (2019).
• We still spend a lot of money on food when compared to other wealthy nations, and we still have a long way to go before we consume as much as the United States and Switzerland do. It is being read as though the HCES 2022–23 signifies something that neither exists nor can be deduced from it.
• A number of important concerns have received attention since the NSSO released the HCES, 2022–2023. In the first place, the study shows notable drops in poverty rates, particularly in rural regions, in comparison to previous estimates made using various approaches. Second, a closer examination is necessary to reconcile the growing discrepancy between the estimates of private consumption expenditures from the NSSO and NAS. Lastly, the shifting patterns of consumption, which include a discernible move away from food products, have an impact on the CPI and inflation computations and call for modifications to the CPI basket. All things considered, these results highlight how crucial it is to reliably record consumption statistics and the consequences this has for economic analysis and policy formation.