India’s labor-market picture over roughly a decade is mixed: measured unemployment fell from its 2017–18 level and later pandemic peak, but that rate alone cannot show whether work is secure or pays enough. Scroll.in’s March 21, 2024 retrospective also reported a rising self-employment share, a falling salaried share and weak inflation-adjusted wage growth. Those are descriptive trends—not proof that a particular government policy caused them.
Did unemployment rise or fall?
It depends on the period and measure. Scroll.in’s March 2024 article reported Periodic Labour Force Survey (PLFS) unemployment of 6.1% in 2017–18, 20.8% in April–June 2020 and 3.2% in 2022–23. The 20.8% figure covers a pandemic quarter; it is not an annual rate directly comparable with the other figures.
The latest annual figure in the official context considered here is also 3.2%: the Ministry of Statistics and Programme Implementation (MoSPI) reported that rate for people aged 15 and above in usual status in PLFS 2023–24. That survey year runs from July 2023 through June 2024, not from January to December 2023.
| Measure and period | Reported figure | Source and qualification |
|---|---|---|
| Unemployment, 2017–18 | 6.1% | PLFS figure as reported by Scroll.in in March 2024. |
| Unemployment, April–June 2020 | 20.8% | PLFS figure as reported by Scroll.in; this is a pandemic-quarter observation, not an annual rate. |
| Unemployment, 2022–23 | 3.2% | PLFS figure as reported by Scroll.in in March 2024. |
| Unemployment, July 2023–June 2024 | 3.2% | MoSPI’s PLFS 2023–24 usual-status rate for people aged 15 and above. |
The government’s 3.2% measure is “usual status (principal plus subsidiary status).” It classifies a person by their principal activity over the survey’s usual reference period, while also counting subsidiary work. PLFS also publishes a current weekly status measure based on the preceding seven days. MoSPI defines it this way: “The activity status determined on the basis of a reference period of last 7 days preceding the date of survey is known as the current weekly status (CWS) of the person.” Rates from these measures should not be treated as interchangeable.
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What do the other headline labor-market measures show?
Unemployment is the share of people in the labor force who are unemployed; it does not count everyone who is outside the labor force. Two other measures help put it in context: the labor-force participation rate (LFPR), the share of the population working or seeking work, and the worker-population ratio (WPR), the share classified as working.
For people aged 15 and above, MoSPI’s PLFS 2023–24 reported an LFPR of 60.1%, a WPR of 58.2% and usual-status unemployment of 3.2% for July 2023–June 2024. The higher participation and worker-population ratios than in the previous annual period provide important context, but they do not by themselves establish how well jobs pay or how stable they are.
Did workers move into more secure or better-paid jobs?
Not necessarily. Scroll.in reported that self-employment grew as a share of workers while the salaried share declined between 2013–14 and 2022–23. “Self-employed” is a broad category: it includes unpaid helpers in household enterprises, so a larger share does not automatically mean more independent businesses, paid work or secure livelihoods.
| Employment status | Earlier share | Later share | Source and period |
|---|---|---|---|
| Self-employed | 49.5% in 2013–14 | 57.3% in 2022–23 | Shares of workers reported by Scroll.in in March 2024; self-employment includes unpaid helpers in household enterprises. |
| Salaried | 23.1% in 2013–14 | 20.9% in 2022–23 | Shares of workers reported by Scroll.in in March 2024. |
The official PLFS breakdown for 2023–24 also shows why a single national label such as “good jobs” or “poor-quality jobs” can hide differences by place and gender. Among rural men, 59.4% of workers were self-employed and 15.8% were regular wage or salaried workers; among rural women, the corresponding shares were 73.5% and 7.8%, according to MoSPI. These categories describe employment status, not pay, benefits or job security in every individual case.
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What does the evidence say about wages?
Scroll.in reported that inflation-adjusted wages rose by less than 1% from 2014–15 to 2021–22. It gave reported real-wage growth of 0.9% in agriculture and 0.2% in construction over that period. Because these are inflation-adjusted figures, they refer to purchasing power rather than simply the number of rupees paid. The original wage series and deflator behind the article’s figures are not established here, so they should be read as figures reported by Scroll.in, not as independently verified estimates.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which sectors gained or lost workforce share?
Scroll.in’s account describes a shift toward construction and a partial return to agriculture’s share of workers after a long decline. These are workforce-share figures, not counts of jobs; a change in share does not by itself establish that the absolute number of workers rose or fell.
- Construction: Scroll.in reported that construction’s share of workers increased from 10.6% in 2011–12 to 13% in 2022–23. It also reported that more than 83% of construction workers were casual laborers and another 11% were self-employed.
- Agriculture: Scroll.in reported a decline in agriculture’s workforce share from 58.5% in 2004–05 to 48.9% in 2011–12 and 42.5% in 2018–19, followed by an increase to 45.5% in 2021–22.
- Manufacturing: Scroll.in said manufacturing employment halved between 2016 and 2021, despite the launch of Make in India in 2014 and Atmanirbhar Bharat in 2020 to boost manufacturing. The underlying sectoral tables and studies were not examined here, so this should remain attributed to Scroll.in.
Do these figures prove that government policy caused the changes?
No. The PLFS figures describe labor-market outcomes; they do not isolate the effect of a specific policy. Scroll.in’s retrospective places trends alongside government initiatives and reports findings from other sources, but that does not establish causation. A causal claim would require evidence designed to separate policy effects from other forces affecting jobs, wages and the economy.
Source context matters, too. Scroll.in noted that private Centre for Monitoring Indian Economy (CMIE) unemployment estimates have consistently exceeded government estimates and that researchers use CMIE data because of gaps in government employment data. CMIE and PLFS results come from different sources and methods; their rates should not be combined as if they were one continuous series.
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