This section looks at the fall in earnings between September-October 2019, and September-
October 2020 for workers in different employment arrangements, as well as the fall in earnings that accompanies transition across employment types.
For salaried and wage workers, earnings are an individual’s monthly income from wages and salaries, while for self-employed workers, both
labour income and business income are considered.8 The other months of the wave, i.e. November and December are not included since income data for these months are not available at time of writing.9 Note that earnings per worker observed in CMIE- CPHS data are higher than those observed in PLFS by around 20 per cent on average. We discuss this issue further in Box 4.2.
Between September-October 2019 and September- October 2020, real average earnings per worker fell by 17 per cent. This drop in earnings is seen across all employment arrangements (Table 4.4).
Self-employed workers saw the highest fall, with earnings declining by nearly 18 per cent from approximately I15,000 in 2019 to I13,000 in 2020.
Mean earnings of of daily wage workers also saw a fall of about 13 per cent. Given that a large share of the workforce is in self-employment and daily wage work, this sharp fall in earnings has significant welfare implications.
Employment
Casual/Daily wage worker Self-employed
Temporary salaried Permanent salaried
2019
I9,135 I15,831 I11,422 I29,226
2020
I7,965 I12,955 I9,441 I27,697 Table 4.4 :
Monthly earnings fell for all employment categories during the pandemic
Sources and notes: Authors’ calculations based on CMIE-CPHS. Data are for the months of September- October of 2019 and 2020. Earnings includes income from wages and salaries and income from business for the self employed. Earnings refer to real average earnings. See Appendix Section 2 for details.
Figure 4.7 : Transition across employment types
accompanied by a fall in earnings
D- daily wage, S- self employed, T- temporary salaried and P- permanent salaried.
The size of the bubble indicates share of that transition in total workforce.
Sources and notes: Authors’ calculations based on CMIE-CPHS. Data is for the months of
September,October of 2019 and 2020. Earnings includes individual’s income from wages and salaries and income from business. See Appendix Section 2 for details.
The aggregate impact on labour incomes is further exacerbated by the fact that there were frequent movements into informal work arrangements as
Figure 4.7 shows the median earnings pre and post Covid on the axes. The size of the bubble represents the share experiencing the transition.
Change in earnings (%)
-13 -18 -17 -5
4. Informalisation and earnings losses
Box 4.2 : Labour earnings prior to the pandemic - A comparison of CMIE-CPHS and PLFS data
Historically, nationally representative, large-scale surveys in India such as those carried out by the NSSO have collected data on consumption rather than income. The reason is that consumption data can be collected at a disaggregated level and are therefore more reliable. Collecting earnings of household members and total household incomes is difficult due to variability of incomes, difficulty in recall and time required to ask detailed questions to ascertain incomes. Survey respondents (typically one per household) may also have much more imperfect knowledge of other members’ income than they do of household consumption. Finally, in an highly informal economy like India, collecting information on incomes is even more difficult as most micro and small businesses lack proper accounting and are wary of disclosing information to surveyors.
The NSSO Employment-Unemployment surveys (conducted till 2011-12) did collect data on salaries and wages, but not on earnings from self-
employment. This left out more than half of the workforce. In addition non-labour incomes were not collected at all. Until the 2004-05 and 2011-12 India Human Development Survey (IHDS) rounds,
individual and household incomes from labour and non-labour sources were not available at the national level. Since 2011-12 (the last IHDS round) labour earnings for salaried and wage workers as well as for the self-employed are available from the two PLFS Employment Unemployment surveys (2017-18 and 2018-19). In the interim (2015-2016), the Labour Bureau Employment-Unemployment survey reported incomes in categories (Azim Premji University 2018) for an analysis of these data.
Lastly, the CMIE-CPHS has been reporting income data at the household and individual level since 2014.
Here we compare incomes as captured in CMIE- CPHS and PLFS 2018-19 to provide context to our income data analysis reported in Chapter Four and Chapter Five.12 On average, we find that CMIE earnings levels are substantially higher than those collected by PLFS for the 2018-19 period for the individuals who are employed. As we saw in Table 2.6, CMIE-CPHS data also show higher earnings for all four employment arrangements. The distribution of CMIE labor earnings in rural and urban areas is shown in the Figures below.
Frequency distribution of labour earnings in PLFS and CMIE-CPHS compared (Rural)
Average monthly earnings across all employment types in rural India in 2018-19, were I12,286 as reported by CMIE-CPHS and Rs. 8413 as reported by PLFS. In urban India, average monthly labour earnings were I19,207 in CMIE-CPHS and I17,021 in PLFS. The corresponding median values are shown in table below. More details on the distribution as well as the Gini are given in Appendix Table 13.
CMIE-CPHS also reports a significant number of zero incomes within the employed sample.
Consequently, we do the analysis both including zeroes, as well as excluding them. These data are available in Appendix Table 13 and 14.
One reason for greater disagreement in measuring rural incomes (46 per cent) compared to urban (13 per cent) could be that agricultural incomes are more difficult to ascertain. Indeed, farm incomes estimated by CMIE are 75 per cent higher than those estimated by PLFS, while the divergence between non-farm incomes is only 20 per cent.
Frequency distribution of labour earnings in PLFS and CMIE-CPHS compared (Urban)
Sources and notes: PLFS 2018-19 and CMIE-CPHS Wave 3 of 2018, Waves 1 and 2 of 2019. X axis scale is on log base 2. See Appendix Section 2 for details.
A comparison of monthly earnings in PLFS and CMIE-CPHS for rural and urban areas
Rural
CMIE-CPHS PLFS
Difference between CMIE- CPHS and PLFS (%)
Mean income
12,286 8,413
46%
Median income
8,971 6,986 28%
Gini
0.53 0.38 39%
% Share of Zero Incomes
16.37 0.84
Urban CMIE-CPHS
Mean income 19,207
Median income
14,562
Gini 0.45
% Share of Zero Incomes
9.59
4. Informalisation and earnings losses
A comparison of monthly farm and non-farm earnings in PLFS and CMIE-CPHS
Non-Farm earnings
CMIE-CPHS PLFS
Difference between CMIE- CPHS and PLFS (%)
Mean income
14,464 12,099 20%
Median income
10,593 8,383
26%
Gini
0.46 0.46 0%
% Share of Zero Incomes
10.23 0.73
Farm earnings
CMIE-CPHS PLFS
Difference between CMIE- CPHS and PLFS (%)
Mean income
14,711 8,384 75%
Median income
8,673 7,033 23%
Gini
0.64 0.35 83%
% Share of Zero Incomes
24.93 0.71
Sources and notes: PLFS 2018-19 and CMIE-CPHS Wave 3 of 2018, Waves 1 and 2 of 2019. See Appendix Section 2 for details.
Other reasons for the divergence include differences in sample composition, method of asking questions, under-representation of women workers (who are usually paid less) in CMIE-CPHS, and selective attrition in the CMIE-CPHS panel.
Without further analysis it is difficult to say more on which estimate might be closer to the actual values. Further, as noted at the beginning of this box, incomes are intrinsically harder to measure, particularly when the majority of the workforce is informal and incomes fluctuate on a daily basis.
Hence, we believe that the two estimates (PLFS and CMIE-CPHS) should be used to define a range within which actual incomes likely lie wherever possible. Secondly, and importantly for our present purposes, the impact of Covid is mostly measured by changes in levels and not in levels themselves.
Thus, even if CMIE-CPHS level estimates are higher than actual incomes, we may still be able to get a good idea of the extent of fall in incomes due to the pandemic.
The highest fall in earnings is experienced, not surprisingly, by individuals moving from permanent salaried work into self-employment.10 Earnings for this group of workers fell by 40 per cent from I30,000 in 2019 to I16,000 in 2020. They accounted for 4.5 per cent of the workforce.
Transitions from permanent salaried work into daily wage work or temporary work was also accompanied by a similar fall in income by 40 per cent, although the share of such workers is smaller, together accounting for 2 per cent. In fact,
any movement into daily wage work, as would be expected, is accompanied by large loss in earnings.
Self-employed and temporary workers moving into daily wage faced an income loss of nearly 10 per cent, and together accounted for 10 per cent of the workforce. And since daily wage work and self employment absorbed large shares of the displaced workforce, this also meant a fall in earnings for these workers from anywhere between 40 per cent to 10 per cent of pre-Covid earnings.
Even earnings of workers who remain in the same kind of employment were affected. Thirty-five per cent of the workforce were self-employed in both time periods and experienced a fall in median earnings by 15 per cent. Similarly, for daily wage workers who remain as daily wage workers and account for 18 per cent of the workforce, there was a similar fall in median earnings by 11 per cent.
Tahir, a fellow with the Stranded Workers Action Networks (SWAN) notes,
People are finding it difficult to find jobs, we have work on one day…then no work for three days. It is not like before where we had confirmed work. We have to go and live with a friend, look for work and do whatever we get.
Similarly, Raunaq Parveen (also a SWAN fellow) notes,
We get between I200-300 for the pant suits we stitch. Before Corona we used to get at least 2 pant shirts a day (I600), now there is hardly any demand.11
For a small share of workers, about 5 per cent, there is an increase in earnings, which has come from moving into irregular employment to permanent salaried work.
Notably, such high volume of transitions associated with a fall in median earnings, is specifically a characteristic of the lockdown year. In the baseline, barring a few exceptions, almost all transitions were associated with an increase in median earnings as depicted by earnings ratio greater than one (Appendix 1 Table 7).