muslimdata.in

Last updated 29 July 2026

Muslim unemployment rate in India

Of people in the labour force (working or looking for work), what share cannot find work. A low rate is not automatically good news: people who cannot afford to stay unemployed take any informal work, so read it beside the salaried-jobs card.

3.2%of the labour force, age 15+(2023)

lower is better

vs all communities: +0.0pp even

Explore the interactive chart, with breakdowns by sex and urban vs rural →

Latest figures by community (2023)

CommunityLatest value
Muslim3.2%
Hindu3.1%
Sikh5.8%
Christian4.8%
All communities3.2%

By sex2023

CommunityMaleFemale
Muslim3.1%3.6%
Hindu3.1%2.9%
Christian4.3%5.5%
Sikh5.0%7.9%
All communities3.2%3.2%

From microdata Reproduce this view. This figure is recomputed from the unit-level survey microdata, the raw NSS or PLFS records behind the linked source. Source: PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · EUS microdata 2004-12 (NSS 61st/66th/68th) · EUS microdata 2004-12 (NSS 61st/66th/68th) · EUS microdata 2004-12 (NSS 61st/66th/68th). Each value is computed from the data file · transform code; every row records its own source and method.

Urban vs rural2023

CommunityUrbanRural
Muslim4.5%2.6%
Hindu5.1%2.4%
Christian6.6%3.9%
Sikh6.2%5.7%
All communities5.1%2.5%

From microdata Reproduce this view. This figure is recomputed from the unit-level survey microdata, the raw NSS or PLFS records behind the linked source. Source: PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · EUS microdata 2004-12 (NSS 61st/66th/68th) · EUS microdata 2004-12 (NSS 61st/66th/68th) · EUS microdata 2004-12 (NSS 61st/66th/68th). Each value is computed from the data file · transform code; every row records its own source and method.

Bottom line

At 3.2%, measured Muslim unemployment in 2023 was barely above the Hindu rate of 3.1%, and well below its late-2010s peak. But a low unemployment rate is not the good news it looks like: people who cannot afford to sit idle take whatever informal work they can find, so distress, not comfort, often keeps the number down.

How to read the chart

The chart tracks the share of the labour force that is unemployed; the 2017 dashed break marks the switch to the PLFS survey. Read it alongside the salaried-jobs and consumption cards, because a low rate here sits next to low-paid, insecure work. The By sex and Urban vs rural tabs show where joblessness concentrates: among the educated, the urban and the young.

Why it matters

Open unemployment counts only those who can hold out for a job. For a poorer community it understates the real problem, which is not idleness but the scarcity of secure, decently paid work.

Status

At paritygap narrowing

Muslim 3.2% vs Hindu 3.1% in 2023, about level. Muslim unemployment ran clearly above the Hindu rate from 2017 to 2019 and has since converged; the gap that matters now is in the quality of work, not the headline rate.

Deeper analysis

Potential drivers

  • Educated joblessness · As in every community, unemployment is highest among the better-educated young, who can wait for a suitable job; for Muslims this overlaps with the urban salaried gap ↗ Sachar Committee Report, 2006.
  • No cushion · Poorer households cannot afford open unemployment, so members take informal, casual work, which holds the measured rate down ↗ Sachar Committee Report, 2006.
  • Discrimination · Audit studies that sent matched job applications found CVs with Muslim names called back less often, one barrier to the formal jobs people queue for ↗ Thorat and Attewell, 2007.

Key levers

  • Better jobs, not just any jobs · The goal is secure, formal employment, not a lower headline rate.
  • Skilling and placement · Training paired with active placement links job-seekers to formal work.
  • Fair hiring · Anonymised or audited recruitment counters the name-based bias the studies record ↗ Thorat and Attewell, 2007.
Key stakeholders

Badges link to an independent or registered credential where the organisation publishes one (Credibility Alliance, GiveIndia, 80G, FCRA). Many smaller NGOs do not take part in these registries, so a missing badge is not a mark against an organisation.

About this measurement

Definition. Of the labour force aged 15 and older (everyone working or looking for work), the share that cannot find work. A low rate is not automatically good news here: people who cannot afford to be unemployed take any informal work, so read this beside the salaried-jobs card.

Methodology. The 2017-18 to 2023-24 points are computed from each round's PLFS unit-level microdata, pulled via the MoSPI NADA API: the published PLFS annual reports never break the unemployment rate down by religion, so the microdata is the only source for that stretch. The 2004/2009/2011 points come from the three quinquennial EUS rounds (NSS 61st/66th/68th) the same way, and there the gate is direct: the computation reproduces the published by-religion unemployment rates of NSS Reports 568/552. The EUS era shows the same pattern in miniature: Muslim unemployment (2.3-3.0%) sat above the all-India rate (2.0-2.3%) in all three rounds, before both spiked in 2017-18.

EUS and PLFS designs are not strictly comparable; the line is dashed at the 2017 break. Unemployed = usual principal status 81 (seeking/available for work); labour force = workers (ps+ss) plus unemployed; weights per the official estimation rule. The computation reproduces each round's published all-India unemployment rate to 0.1 points.

Muslim unemployment fell from 7.2% (2017-18, a point above the national 6.0%) to 3.2% (2023-24, at par with the national average), but over the same rounds the salaried share of Muslim workers also fell, so part of the convergence is absorption into self-employment and casual work rather than formal hiring. Religion is self-reported (NSO unit-data rider: indicative, no sub-state estimates). Year=Y is the start of the Jul-Y to Jun-(Y+1) reference period.

Sources. PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · PLFS microdata 2017-24 · EUS microdata 2004-12 (NSS 61st/66th/68th) · EUS microdata 2004-12 (NSS 61st/66th/68th) · EUS microdata 2004-12 (NSS 61st/66th/68th). Download CSV: unemployment-rate-15plus.csv.

From microdata How to reproduce. This figure is recomputed from the unit-level survey microdata, the raw NSS or PLFS records behind the linked source.

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