Alternate methods of benchmarking consumption expenditure

Section 1: Introduction

The recently released fact sheet of Household Consumption Expenditure Survey (HCES) 2022-23 (Source: S1) has created quite a stir with one set of academics/ economists saying it is not comparable to the earlier surveys due to change in the methodology and other reasons while there is another set which argued the opposite. Though not comprehensive, some of the concerns have been addressed in an earlier article titled “Debunking the K-shaped Recovery Theory” (Source: S2). However, in this article I shall not directly address the concerns raised on the issue of comparability with the earlier surveys but rather look at alternate indicators which may be used to benchmark the growth in consumption expenditure from 2011-12 to 2022-23.
Considering the HCES is a relatively rare occurrence (there is one more going on for the year 2023-24 but it may not happen post that immediately), two benchmark indicators have been used for measuring consumption expenditure using the Periodic Labour Force Survey (PLFS) data and the National Accounts Statistics (NAS) which is basically the detailed schedules of our GDP calculations. These data points are typically available on an annual basis with a lag of around a year from the end of the fiscal year.
These benchmark indicators confirm that the growth in consumption expenditure from 2011-12 to 2022-23 is reasonable.
In this article, the year-end period is mostly tallied with the agricultural year. Agricultural years shall be represented using the starting and ending years (e.g., 2018-19 or FY19 represents year ended 30 June 2019 since the HCES and PLFS follow this cycle).
Further, the HCES 2022-23 shows consumption expenditure under two-scenarios (i) considering imputed values of items received free of cost through various social welfare programs, (ii) without imputation. I have compared the first scenario (with imputed values) with the earlier period surveys for the sake of this article. It should be noted that the difference in average consumption expenditures between both scenarios is only ~2%.

Section 2: A quick peek into India’s workforce over the years

As per the PLFS, there are different classes of workers (Source: S3), which can be summarized as below:

Self-employed: Persons who operated their own farm or non-farm enterprises or were engaged independently in a profession or trade on own-account or with one or a few partners were deemed to be self-employed in household enterprises. These are further categorized into own-account workers i.e., those who run a business on their own (without any permanent paid staff), employers i.e., those who run a business on their own (with one or more permanent paid staff), unpaid family members who do not earn any income from their work.
Regular wage/salaried employee: These were persons who worked in others’ farm or nonfarm enterprises (both household and non-household) and, in return, received salary or wages on a regular basis (i.e., not based on daily or periodic renewal of work contract).
Casual labour: A person who was casually engaged in others’ farm or non-farm enterprises (both household and non-household) and, in return, received wages according to the terms of the daily or periodic work contract, was considered as a casual labour.

There are different measures of employment as well like Usual Principal Status (UPS), Usual Principal and Subsidiary status (UPSS), Current Weekly status (CWS) etc. Out of this, for the purposes of this article, UPSS shall be used. It basically means any person who has carried out some economic activity for 30 days in a year will be considered as employed.
The movement in the workforce participation rates (WPR) (the total workers as a percentage of total population) since 1993-94 is shown in the chart below. WPR shown below excludes the unpaid family workers.

Chart 1

Source: S3, S4, S5 and author’s calculations

As can be observed, India’s WPR has remained stable across the years at ~32% with a slight bump from FY12 to FY23 from 31.7% to 33.6%. Among the distinct types of workers, the Regular workers earn the most while Casual workers earn the least (Source: S3). Chart 2 below shows the percentage of regular workers (among the workers who are not self-employed and as a percentage of population) has been increasing consistently since 1993-94. Further, they have been earning more than two times of what a casual worker earns in 2022-23 down from 3.9x in 1993-94. Thus, there is an improvement in the quality of the workforce (though not to the extent desired) over the years.

Chart 2

Source: S3, S5 and author’s calculations

These trends suggest a consistent improvement in the incomes and expenditure of households over the past 30 years. But critics would say: Not so fast. So, we go to the next section.

Section 3: Wage data (Labour Bureau)

The Labour Bureau’s data on wages (Source: S6) is a high frequency indicator available monthly and is typically available with just a lag of a few months. This gives data on wages for various agricultural and non-agricultural occupations in rural areas.
The long-term trends, as per an RBI Working Paper dated April 2018 (Source: S7), the rural wages for men followed three distinct stages:

Phase I (from January 2002 to September 2007): Average real rural wages were slightly negative.
Phase II (October 2007 to October 2013): Average real rural wages turned positive and were growing at 5-7% per year in real terms.
Phase III (November 2014 onwards): Average real rural wages were slightly positive which basically became slightly negative since the onset of the pandemic.

The chart below shows the average growth in wages for various occupations for rural men and it is compared with the growth in consumption expenditure during the years in which HCES was conducted. This confirms the various stages of wage growth as per the RBI study.
Most of the growth between 2011-12 to 2022-23 occurred before the Modi Government came to power and till the pre-COVID period there was marginal growth while post COVID it has turned marginally negative.
So, how can consumption grow by 3.3% in rural areas when real wages have increased only by ~1% over the same period. However, it can be observed in Chart 3 that (except for the period from 2009-10 to 2011-12), the real rural wage growth has been lower than the growth in consumption expenditure consistently for all the other periods.

Chart 3

Source: S3, S8 and author’s calculations

The main limitation with rural wage growth data is that it mostly concerns casual labourers in rural areas which would constitute only ~20% of our workforce. Plus, possible reasons for consumption expenditure growing almost consistently faster than rural wage growth could be:

Due to movement of the same person from unskilled labour to skilled labour leading to higher wages for the same person over time. E.g., In the IT industry, the salary of an entry level employee would be INR 4 lakhs per annum, and it would have remained at a similar level for the past 10 years or so. So, the real wage growth for an entry level IT position would be negative. However, for a specific employee, over a 10-year span, that person would have received promotions and his/ her salary growth would have been higher than inflation rate.
Due to higher proportion of workers in the sectors paying more wages. E.g., As per PLFS 2022-23 (Source: S3), construction workers in rural areas get paid almost 1.4x as compared to agricultural workers.

Do we have evidence of this movement which can justify a substantially higher consumption growth from 2011-12 to 2022-23 as compared to wage growth? Turns out there is. Refer the charts below:

Chart 4

Source: S3, S5

Chart 5

Source: S3, S5, S9

As one can observe from Chart 4, the proportion of skilled workers (for both males and females) increased from 2011-12 to 2021-22. Further, as per Chart 5, from 2004-05 till 2022-23, there has been a substantial increase in the proportion of casual workers engaged in non-agricultural occupations (primarily construction) where the wages are higher (as mentioned above). One can see in Chart 6 that the wage increases for Casual Labourers in Rural areas has been consistently higher than the growth in Rural Wage growth as per Labour Bureau.

Chart 6

Source: S3, S5, S6 and author’s calculations

Being a high frequency indicator, the Labour Bureau data on wages is frequently used. However, considering all these factors, it would be prudent to look at the wage growth data as per PLFS (and its earlier avatar, Employment Unemployment Surveys (EUS)) since it would factor the impact of higher proportion of skilled workers and non-agricultural workers.

Section 4: Approach 1: Using PLFS wage growth as per EUS-PLFS to estimate consumption growth

Out of the three classes of workers mentioned in Section 2, the EUS (earlier version of the employment survey) did not collect incomes of Self-employed workers and it included incomes of Regular workers and Casual workers. The PLFS carried out from 2017-18 included the incomes of Self-employed category as well. Since our objective is to assess the growth in incomes over a longer time-period, we shall investigate the income growth of Regular and Casual workers.
Chart 7 below shows the growth in Casual workers, regular workers, and weighted average growth in incomes per worker in real terms (adjusted for inflation). The weighted average income growth per worker assumes the proportion of casual and regular workers in the non-self-employed worker category as shown in Chart 2 above.
The growth in incomes per worker from 2011-12 to 2022-23 has been the second slowest since the period from 1999-00 to 2004-05. The key reason being incomes of regular workers showing negative growth from 2011-12 to 2022-23. The same was the case between 1999-00 to 2004-05. However, for Casual workers who are the poorest section among the various classes of workers, the period between 1990-00 to 2004-05 showed marginal growth in incomes while the period from 2011-12 to 2022-23 showed relatively better growth in incomes and it is quite comparable to the growth in other periods as well (albeit slightly lower).

Chart 7

Source: S3, S5 and author’s calculations

To arrive at a proxy for per capita consumption growth using this data would require 2 further adjustments:

Arriving at a per capita income growth
Adjusting for savings rates to arrive at consumption growth

So, we can carry out the following:

Step 1: As per Chart 2 above, India’s WPR (adjusted for unpaid workers) has remained stable around 32% from 1993-94 to 2011-12 while there has been a slight increase from 2011-12 to 2022-23 from 31.7% to 33.6%. Apply the WPR for each year to the per worker incomes to arrive at per capita income levels.
Step 2: Apply the savings rates for households (as a % of GDP) as available in the National Accounts Statistics (Source: S10 to S13) to the per capita income arrived at in the above step. This would help us calculate the growth in per capita consumption expenditure.

Refer the following chart.

Chart 8

Source: S3, S5, S10 to S13 and author’s calculations

As per this method, the growth in consumption expenditure from FY11-12 to FY22-23 should be close to 2.3% compared to the 3.1% growth as per the HCES. Though there are some differences, it nevertheless suggests that the consumption expenditure growth is largely supportable. But we are not done yet. The increase in per capital consumption compared to per capita worker growth from 2011-12 to 2022-23 is mainly due to higher WPR and household lower savings rates as shown in the chart below.

Chart 9

Source: S3, S5, S10 to S13 and author’s calculations

Some limitations of this approach

This approach does not include the incomes of the Self-Employed category. However, it is still representative of more than 50% of the population. Plus, since we have incomes of the Self-employed workers from 2017-18, this approach can be used to gauge the growth of consumption and incomes since 2017-18. A summary of this analysis and a comparison with the GDP per capita and PFCE per capita growth is shown below. Over a 5-year period from 2017-18 to 2022-23, it appears that the growth in incomes and consumption per capita using PLFS data broadly mirror the growth in income and consumption per capita using National Accounts data (where nominal GDP and PFCE is adjusted using CPI and population growth).
The wage data as per EUS-PLFS is on a Current Weekly status basis while I have used the UPSS for the number of workers and for workforce participation calculations. However, since we are more concerned with the growth rates rather than absolute levels of per capita incomes, the differences will be marginal.
I have assumed that Casual workers work for 300 days a year (25 days per month) from 1993-94 to 2011-12 while for 2022-23 it is assumed to be 280 days based on the data available from PLFS. As a sensitivity, if I assume that a Casual worker worked for 300 days in 2022-23, the overall per worker incomes will increase by 0.3% from 1.2% (as shown in Chart 8) to 1.5% and the per capita consumption growth will be 2.6%.
Applying Savings rates available from the National Accounts to the Incomes as per PLFS may not be a like to like adjustment, but this is the best adjustment I could do to arrive at the Consumption expenditure growth rates.

Chart 10

Source: S3, S14 and author’s calculations

Section 5: Missing the rich

In an earlier article titled, “Missing the Rich - Analysis of NSSO Data” (Source: S15), I had shown how the PLFS undercounts the richer segment of the population and as we moved from EUS 2011-12 to PLFS 2017-18, this problem exacerbated substantially. This is the primarily why the earnings of Regular wage workers declined from 2011-12 to 2022-23 (refer Chart 7) and hence, the growth in per capita consumption expenditure estimated in Chart 8 is also most likely an underestimate. Some red flags are as follows:

As per an analysis in a prominent financial daily newspaper (Source: S16), the number of Regular wage earners earning more than INR 6 lakhs per year in 2017-18 was 3.6%. As per PLFS, the number of regular wage earners was close to 10.6 cr in 2017-18 which would put the number of persons earning above INR 6 lakhs per year at ~40 lakhs. However, as per Income Tax returns data (Source: S17), the number of salaried people earning more than INR 6 lakhs was more than 1 crore and around 40 lakh people got salaries more than INR 10 lakhs per year.
The National Accounts data (Sources: S10 to S13) provide data on Compensation of Employees. As per the definition of COE (Source: S18), Compensation of employees is the total remuneration in cash or in-kind payable by employers to employees for the work done. Direct social transfers from employers to their employees or retired employees and their family, such as payments for sickness, educational grants and pensions that do not set up an independent fund, are also imputed to compensation of employees. While this includes pension payments, it can broadly track the growth in total incomes earned by workers (non-self-employed) in both formal and informal sectors. One can compare the Total incomes generated by Regular and casual labourers as per the EUS-PLFS surveys and compare it with the COE for the respective year. Refer the following chart.

Chart 11

Source: S3, S5, S10 to S13 and author’s calculations

From 1993-94 to 1999-00 the fall was from 86% to 80% (by almost 6%), the fall from 1999-00 to 2004-05 is a substantial fall of almost 11% which calls into suspect the Wage growth data as per EUS from 1999-00 to 2004-05 (Chart 7). By 2011-12, it remained at the same levels as 2004-05. However, in 2017-18 the fall was almost 16% which is huge (even more than the fall in 2004-05). Till 2021-22 (the latest period for which we have the data), it has remained in a similar range.
As per National Accounts Statistics (Source: S13), almost 2/3rds of the COE data comes from the organized sector. Out of that almost 50-60% is from the Public sector and the rest from the Private corporate sector. Thus, a substantial portion of this data is reliable. Only 1/3^rd of the COE number, being from the unorganized sector may not be reliable. So, growth in COE plus adjustments as done in Section 4 above would be a very good indicator to gauge the growth in consumption expenditure.

While scholars are generally aware that the survey data like PLFS (and across the world) understate the incomes of the rich / under sample the rich (Source: S19), it is rarely acknowledged that the level of underestimation substantially increased in the employment surveys from 2011-12 to 2017-18.

Section 6: Approach 2: Using Compensation of Employees from National Accounts

As in Approach 1 above, we can carry out the following:

Step 1: Take the COE data for the required years as per NAS (Sources: 10 to 13) and adjust for the CPI inflation factors.
Step 2: Divide the number in Step 1 by the total number of Regular and Casual workers to arrive at Earnings per worker.
Step 3: Apply the WPR for each year to the per worker incomes to arrive at per capita income levels.
Step 4: Apply the savings rates for households (as a % of GDP) as available in the NAS (Source: S10 to S13) to the per capita income arrived at in the above step. This would help us calculate the growth in per capita consumption expenditure.

As per the chart below, the growth in Consumption from 2011-12 to 2022-23 is the highest growth observed over a longer timeframe (5 years or more).

Some limitations of this approach

Applying Savings rates available from the National Accounts to COE as per PLFS may not be a like to like adjustment since COE does not include the Self-Employed, but this is the best adjustment I could do to arrive at the Consumption expenditures.
This approach does not include the incomes of the Self-Employed category. However, it is still representative of more than 50% of the population.
Pensions have not been adjusted. However, the impact on growth due to the same would not be significant.

Chart 12

Source: S3, S5, S10 to S13 and author’s calculations

Section 7: What about the Self-Employed Category?

From 2004-5 to 2022-23, more than 50% of the Self-Employed persons (excluding unpaid workers) are in the Agricultural Sector. As per Situation Assessment Surveys (S20 to S22), around 55 to 60% of the households in Rural areas were covered to understand the Agricultural and other incomes earned by them. The chart below shows the growth in Agricultural incomes and Total Incomes of Agricultural households. Incomes of households that fall under Small, Marginal farmers (including landless labourers) have been shown separately.
In terms of Total income growth from 2002-03 to 2012-13, due to a drought year in 2002-03, the growth is overstated. Adjusted for the drought factor, the growth during 2002-03 to 2012-13 would have been like growth from 2012-13 to 2018-19. For Small and marginal farmers, the growth in overall incomes was more in the 2012-13 to 2018-19 period compared to 2002-23 to 2012-13.
Coming to Agricultural incomes (Crop production + farming of animals), the growth for Small and Marginal farmer households is close to 1.6% from 2012-13 to 2018-19 while it was 3.6% in the earlier period. For all households, the growth for the aforesaid periods were 0.6% and 3.3% respectively. Whether this survey has the same under sampling problem as the PLFS since Small and Marginal farmers (constituting 90% of households) show robust growth in Agricultural incomes while the relatively richer households don’t show much lower growth.

Chart 13

Source: S20 to S22 and author’s calculations

Since we have covered agriculture above, one can look at IT Returns data and calculate the average income of individuals who filed business income from FY13 to FY20. This was carried out using the following steps.

Step 1: Take IT returns data and extract the business incomes filed by individuals.
Step 2: Divide the business income by number of persons with non-0 business incomes. The average business incomes of individuals were INR 2.63 lakhs per person in FY12 to INR 4.65 lakhs per person in FY20. The average Self-employed person as per PLFS earned ~INR 2 lakhs per year in urban areas in 2019-20 so the figures are quite comparable (rural areas not used since it is largely agriculture dependent where income taxes payments are not required).
Step 3: One can deflate the nominal per person incomes by either the Consumer price index or GDP Deflator. I have done both, but I favour using GDP deflator (which gives weightage to both consumer price index and wholesale price index) since the business incomes by individuals may be generated from B2B sectors also. This gives real income CAGR of 1.5% (from FY12 to FY20) if we use CPI and 3.1% CAGR if one uses national GDP deflator.
Step 4: One can bring in the argument that the increase in business incomes in IT returns may have been due to much better compliance/ formalization post demonetization and GST. But it may have fallen due to corporates taking market share. So, we do the following:

Step 1: Take total individual business incomes as per IT returns.
Step 2: Take household GVAs from NAS and adjust the private sector for quasi corporates (Source: S24) and add that to household (HH) GVA available in NAS.
Step 3: Reduce agricultural GVA since taxes are not paid on agricultural incomes to arrive at an adjusted non-agricultural HH GVA
Step 4: Do the same adjustments as in Step 2 above to Consumption of fixed capital and COE in the unorganized sector to arrive at a rough (not exact) number for operating surplus (OS) of unorganized sector. This will broadly be comparable with the Gross-total incomes in IT returns on which the relevant tax rates will be applied.
Step 5: Divide the individual business incomes with the OS as arrived at in Step 4. In FY12, it was 23.1% which increased to 26.9% by FY15 and which further increased to 30.9% by FY20.
Step 6: Create an index with FY12’s individual business (as % of OS) at 100

Chart 14

Source: S10, S23, S24 and author’s calculations

Household sector has grown (in real terms) in line with the overall GDP growth (household sector contributes ~45-50% to the GDP). If business incomes as per IT returns grew faster, even assuming improved compliance, it means that individual businesses have grown at least in line with the household sector growth (as per NAS). This implies that the self-employed individuals have also registered growth (in real terms). It can be observed that Business incomes (as % of HH sector’s operating surplus) reduced in FY17 and FY18 (compared to FY16) which would have been due to a temporary setback on account on demo and GST, but it surpassed the FY16 levels by FY19 and improved further in FY20 suggesting that the impact of these policies were temporary.

Section 8: Comparing the outcomes of both approaches with HCES

The growth in Consumption expenditure as per HCES has been typically lower than that estimated via Approach 2. Even for the period from 2011-12 to 2022-23, this trend holds steady.

Chart 15

Source: S1, S8, earlier charts and author’s calculations

Using Approach 1, the growth in consumption expenditure has been similar during 1999-00 to 2004-05 and lower in other periods (except 2011-12 to 2022-23). The growth in incomes as per Employment surveys for the periods 1999-00 to 2004-05 and 2011-12 to 2022-23 may be an underestimate for reasons mentioned above. Even the growth in consumption as per HCES from 2004-05 to 2009-10 may be an underestimate since 2009-10 was a drought year which would have supressed consumption temporarily. Considering these factors, one can conclude that the growth in consumption as per HCES 2022-23 is reasonable and by implication, HCES 2011-12 and 2022-23 are comparable.

Section 9: Comparability issues between HCES 2011-12 and 2022-23

While the comparability issues have been indirectly addressed in the above sections, here are some additional thoughts. I shall look at some of the criticisms made by some prominent economists in an online news portal (Source: S25).
Quote: There seems to be a higher representation of the well-off groups in the HCES 2022-23 sampling approach, thereby resulting in higher consumption expenditure.
If we had used a similar sampling approach as HCES 2011-12, we may have gotten results like we got in HCES 2017-18 (which got junked due to bad data quality) (Source: S8 and S26). To quote a few anomalies in HCES 2017-18 (compared to HCES 2011-12 in real terms):

The consumption expenditure fell by almost 12% for the top 10%.
Consumption of cereals, pulses fell by 15-20% per capita.
Medical expenses fell by 26% and 44% in urban and rural areas respectively.
Expenditure on sugar, salt and spices fell by ~15%.

Quote: Moreover, 190 million workers (2021-22) in India are earning just up to Rs. 100 per day (in real terms at 2010 prices) which can be categorised as absolute poor, as compared to just 106.1 million workers in 2011-12. There has been a massive surge in the number of poor workers in recent times. There are 144.0 million workers (2021-22) that are earning between Rs 100 and Rs 200 per day which can be categorised as poor and vulnerable. Additionally, there are still 127.5 million workers (2021-22) who earn between Rs 200 and Rs 300 per day, which can be categorised as non-poor but definitely vulnerable.
This is a sensational claim. To get more clues into this, I referred to one of the debates on a prominent news channel (Source: S27) wherein one of the co-authors of the aforesaid article mentions that in 2018-19, as per PLFS, the number of workers who earned less than INR 100 a day (I assume he meant in Real prices with 2010 year as base) is at 15.2 cr. To corroborate these, I was able to find a CSE working paper from Azim Premji University (Source: S28) whose Appendix gave important insights on PLFS 2018-19 data.
Anyway, after factoring inflation, INR 5,000 per month would roughly translate to INR 100 per day real wages in 2010 prices. So, as per the CSE working paper and author's simple extrapolations, around 11% of regular wage earners, 24% of the Self-Employed and 42% of casual wage earners earn less than INR 100 per day (in 2010 prices). Considering Current Weekly Status based employment measurement, almost 25% or roughly 10 crore paid workers earn less than INR 100 per day (in 2010 prices). Where did the rest 5 crore go (since the claim made by the prominent economist is ~15 cr)? Well, they are unpaid family workers who are not included in CSE working paper study as they don't earn. So, we have broadly matched the 2018-19 numbers.
Now, coming to 2011-12 earnings, as mentioned earlier earnings of self-employed were not available in the Employment unemployment surveys of 2011-12 and earlier. Plus, it seems like the article (my inference) did not add the crores of unpaid family workers in its calculation of 10.4 crores in 2011-12. So, the 10.4 crore number for 2011-12 would only include Regular plus Casual workers and does not include Self-Employed and unpaid family workers. On the other hand, the number for 2018-19 includes all workers (including unpaid workers). So, the comparison is between apples and oranges.
Nevertheless, since the wages of regular employees in the informal sector have remained stagnant since 2011-12 to 2018-19 as per an ILO Study (Source: S29), one can assume that the proportion of regular wage earners earning below INR 100 per day (in 2010 prices) has remained the same at about 11%. However, basis the methodology used by CSE working paper for calculating wages of casual labourers, one can say that more than 50% earned less than INR 100 per day (in 2010 prices) in 2011-12. Further, between 2011-12 and 2018-19, the number of casual workers reduced, and regular workers increased. So, a rough calculation after combining regular and casual workers suggests that around 36% earned less than INR 100 per day in 2011-12 which reduced to 27% in 2018-19.
Quote: In fact, the National Survey Organisation (NSO), which conducts these surveys, has personally informed one of the authors that the HCES is not comparable with the earlier CES…. Additionally, it would be curious to argue that consumption expenditure is rising when real wages have been stagnating in recent years.
So, here the author is suggesting that poverty has increased from 2011-12 to 2022-23. Well, it means the real per capita consumption expenditure increase of ~40% from 2011-12 to 2022-23 is explained by the change in methodology alone and the actual increase is either 0 or negative.
Here’s some evidence to the contrary:

During 2011-12, Employment-Unemployment survey (EUS) also had a shorter questionnaire of ~40 questions on consumption expenditure vs the HCES 2011-12 of 400+ questions on consumption. Similar, was the case with EUS 2004-05 and HCES 2004-05 where the difference was around 5%. EUS 2004-05 also states: “The abridged worksheet that was used to reduce the respondent fatigue is known to understate the level of consumer expenditure in comparison with the detailed schedule.” Any NSO statistician would have also said that the consumption expenditure as per EUS and HCES are not comparable even though the difference is less than 10%. So, even with 10x increase in number of questions the consumption expenditure was higher by just 5-7% in HCES (Source: S30). This clearly suggests that the change in survey methodology / slightly increasing the questionnaire length / splitting the questionnaire would not lead to more than 40% increase in consumption expenditure.
India Employment Report, 2024 by the International Labour Organization (Source: 32) calculates the poverty rate in 2021-22 using PLFS data on consumption (only 5 questions on consumption). This consumption data is marked-up by 12-15% to make it comparable to an HCES survey (which has 400 odd questions). So, the ILO believes that marking up by 12-15% is sufficient to cover the distance between 5 questions to 400 odd questions.
Even the PLFS is using a different sampling methodology compared to the earlier Employment-Unemployment surveys. This was highlighted by NSO in the PLFS report just like it has been done in HCES 2022-23. But academics (critics and supporters alike) don't have many issues in comparing PLFS with the earlier Employment-Unemployment surveys.
Some prominent economists had even used consumption expenditure in the PLFS (with a single question on consumption vs 400+ in HCES) to estimate poverty rates with suitable adjustments (Source: S31) even though the PLFS report clearly says "Information on household Usual Monthly Consumer Expenditure (UMPCE) was collected in PLFS only to classify the households in different UMPCE classes and it cannot be used to estimate the household consumer expenditure which is generally estimated based on detailed survey". Here, PLFS makes it categorically clear that the consumption expenditure as per PLFS is not comparable with HCES.
HCES 2022-23 factsheet lists out the changes in the methodology and finally says "These are required to be noted while comparing the results of HCES:2022-23 with those of the previous surveys." It does not say they are not comparable with the earlier rounds.

HCES 2022-23 factsheet lists out the changes in the methodology and finally says "These are required to be noted while comparing the results of HCES:2022-23 with those of the previous surveys." It does not say they are not comparable with the earlier rounds.

Section 10: Conclusions

This article does not attempt to match the consumption expenditure growth using other sources but rather to see whether the consumption growth from 2011-12 to 2022-23 is reasonable.
Considering the criticisms regarding changes in methodology from HCES 2011-12 to 2022-23, adequate investigation has not been carried out whether the growth in consumption expenditure from 2011-12 to 2022-23 is justified using other data- sources. Many experts don’t prefer to use PFCE growth as per National accounts data and hence, alternate methods had to be used for benchmarking. Although the Compensation of employees are from the National Accounts data, considering around 2/3rds of it is coming from the Organized sector itself, it may be robust.
Using just rural wage data to benchmark consumption expenditure is partial at best and one is reminded of the parable of six blind men and the elephant. Rural wage data gives wages for a particular occupation, say for unskilled agricultural laborer. However, it does not factor, (i) increase in the proportion of skilled agricultural laborers in the overall workforce, (ii) increase in proportion of construction workers vis-à-vis agricultural workers. Due to these factors Labour Bureau data on wages constantly understates the actual wage growth taking place in the economy.
As shown in Chart 15, the growth in consumption expenditure from 2011-12 to 2022-23 is the second highest after the period from 2004-05 to 2011-12 (though quite close to it). This is not surprising considering Real PFCE per capita growth has been the second fastest as per the National Accounts Data as shown in the chart below (Source: S14). One of the reasons why the PFCE growth as per NAS has been faster could be due to higher income deciles being increasingly under sampled in HCES across the years. Does this indicate rising inequality as Thomas Piketty et al. say (Source: S19)? More work needs to be done to conclude on it.
Since the growth in consumption expenditure has been corroborated using other sources of data and the issues of comparability largely addressed, it seems that HCES 2011-12 can be directly compared with HCES 2022-23. It is just that the methodology had to be changed due to changing circumstances.

Chart 16