Migration has quietly become one of the most powerful demographic forces shaping the twenty-first century of Rajasthan. Every working day, nearly 2,300 Rajasthani residents cross a district or state border; annually, the state records a total migration incidence of 24.7 % of its population, according to the Periodic Labour Force Survey (PLFS 2023). The resulting currents are anything but uniform: they connect drought-stricken hamlets of Barmer to the construction sites of Surat, funnel newly married women from Hadoti villages into Jaipur’s peri-urban wards, and carry skilled diploma holders from Ajmer engineering colleges to the information‑technology campuses of Toronto and Dallas. This report responds to a growing recognition that Rajasthan’s development outlook is inseparable from understanding these diverse mobilities—who moves, where, when, and why. By integrating high-quality datasets and a 2024 field survey, we trace the state’s migration story in unprecedented detail, revealing structural patterns that conventional statistics obscure.

A state on the move: headline metrics

Rural‑to‑urban drift within Rajasthan

• Net rural‑to‑urban migration climbed from 7 migrants per 1,000 rural residents in 2008 (NSS 64ᵗʰ Round) to 12 per 1,000 in 2023 (PLFS). In that same year, Jaipur (40 migrants per 1,000), Kota (33 / 1,000), and Ajmer (28 / 1,000) posted the highest rates, together absorbing more than half of all intra-state rural‑to‑urban movers.

Gendered pathways

• Female migration is dominated by marriage: 60 % of female intra-state movers cite post-marital relocation; only 15 % move for employment.
• Male migration remains job-centred: 46 % of male intra-state migrants and 68 % of inter-state migrants relocate for work, primarily in construction, mining and service sectors.

Duration of movement

• Short-term (≤ 6 months) moves constitute 22 % of intra-state migrants and 35 % of male migrants from drought belts.
• Long-term (≥ 3 years) residence characterises 45 % of intra-state migrants, primarily driven by female settlement after marriage.

Destination corridors

• Gujarat leads with 27 % of all inter‑state out‑migrants, followed by Maharashtra (16 %) and Delhi‑NCR (11 %). Migration flows are led by Uttar Pradesh (31 %) and Madhya Pradesh (18 %), feeding Jaipur’s wholesale trade and Kota’s coaching economy.

Sectoral absorption outside the state:

• Construction (38 %) is the single largest absorber of Rajasthan labour, followed by services/hospitality (20 %)and textiles/diamond polishing (17 %).

Distress migration trigger points:

• In the Thar districts of Barmer and Jaisalmer, 22 % and 19 % of households, respectively, report migration due to drought or agrarian crisis, mapping closely to years with Standardised Precipitation‑Evapotranspiration Index (SPEI) scores ≤ –1.

International footprint

• Only 0.74 % of Rajasthan’s residents live abroad. Yet, half of these migrants concentrate in the Gulf Cooperation Council (GCC) countries, and their median annual remittance is ₹ 54,000, which covers 38 % of recipient households’ yearly consumption spending.

Social and household consequences

Migration reshapes the social fabric long after the traveller has departed. Households with a male out‑migrant exhibit a higher dependency ratio (0.73 vs 0.59) and a 10‑percentage‑point increase in female labour‑force participation as women compensate for absent male earners. Remittances moderate consumption volatility but are channelled heterogeneously: 38 % of cash flows finance daily necessities, 22 % support children’s education, and a valuable 18 % are invested in livestock, machinery, or micro‑enterprises—evidence that mobility can translate into productive asset formation when conditions allow.

Yet mobility can also compound vulnerability. Return migrants, particularly those from the Gulf, report a 42 % employment mismatch rate and a 28 % incidence of unrecognised foreign certifications, squandering human capital accumulated abroad. Documentation hurdles—reported by 18 % of returnees—delay access to welfare schemes, while 7 % cite social stigma on re-entry, underscoring reintegration’s emotional and economic dimensions.

Climate, caste and community filters

Migration in Rajasthan is not randomly distributed across the population; it is filtered through climate exposure and community networks:

Climate stress

The western drought belt (Barmer‑, Jaisalmer‑, Nagaur) posts the highest short-term migration incidence in drought years. SPEI anomalies and migration spikes are moving near‑lockstep, highlighting how climate volatility directly shapes human movement.

Caste networks

• Scheduled Tribes (Bhil and Garasia) channel nearly half (48 %) of their out‑migrants to Gujarat, leveraging kin-based ties in Surat’s construction and diamond hubs.
• OBC communities diversify towards Delhi‑NCR (21 %) and Gujarat (22 %), reflecting wider business networks, while SC migrants follow contractor chains to Mumbai–Thane construction.

Skill stratification:

International migrants are modestly more skilled—29 % hold high‑skill positions versus 18 % of inter‑state migrants, suggesting early signs of “brain exchange” alongside the dominant low‑skill flow.

About this evidence base

The analysis rests on the following data architecture:

Dataset & year span	Sample size (Rajasthan observations)	Key variables utilised
PLFS Microdata (2017–24)	11,246 persons; 3,420 migrants	Usual residence, employment, duration, reason, remittances
NSS 64ᵗʰ Round – Migration (2007–08)	5,803 persons	Last residence, duration, marital status
NFHS‑5 (2019–21)	22,674 women	Place of residence at age 12 and survey, marital migration
IHDS‑II (2011–12)	1,672 households	Circular migration, remittances, and welfare use
CHIRPS‑SPEI (2000–24)	33 district grids	12-month drought intensity
SDEI & RS Parliamentary Queries (2015–24)	147 events	Industrial closures, labour distress signals
Return‑Migrant Rapid Survey (2024)	460 returnees	Reintegration barriers, skill recognition

Design weights were applied to all sample surveys, and drought overlays used district-level spatial joins. Visualisations were generated on a standard 12 × 8-inch canvas for consistency.

In sum, Rajasthan’s migration system is complex but decipherable: it is simultaneously a safety valve for drought-affected districts, a driver of urban growth, a conduit for global remittances, and a ladder or sometimes a trap for household welfare. The following pages unpack each dimension in depth, providing the empirical foundation for targeted legislative and administrative action.

Data Sources: Scope, Rationale, and Unique 

Below is an expanded account of every data stream that feeds into the migration diagnostics. Each entry explains why the dataset was selected, its granularity for Rajasthan, and the specific variables extracted. Where relevant, we note design peculiarities (strata, replicate weights) that determine how confidence intervals were later computed.

	DATASET	TEMPORAL SPAN	GEOGRAPHIC RESOLUTION	SAMPLE FOR RAJASTHAN	SALIENT MIGRATION FIELDS	WHY IT MATTERS
1	Periodic Labour Force Survey (PLFS) Annual & quarterly micro files, Ministry of Statistics (MoSPI)	2017–18 ⇢2023–24 (7 annual rounds + 28 quarter waves)	District code; rural–urban; PSU	11,246 persons(≈ 3,420 migrants after design weights)	Last usual residence, reason, duration, remittance amount, sector of employment, replicate jackknife weights (BJK).	Flagship, high-frequency labour dataset—the only source that measures migration andcontemporaneous employment every quarter. Enables trend lines rather than static snapshots.
2	NSS 64ᵗʰ Round – Schedule 10.2 “Migration in India”	July 2007 – June 2008	District (2001 map); PSU strata	5,803 persons	Place of last residence, months since move, marital status at move, occupation before & after migration.	The baseline for the pre-PLFS era lets us quantify a 15-year change trajectory.
3	National Family Health Survey‑5 (NFHS‑5) – IR & KR files	2019–21	District (2011 map), urban/rural	22,674 women(15,49)	Place of residence at age 12, age at marriage, current residence, spousal co-residence, and maternal migration.	It captures the marriage–migration nexus absent from labour surveys and is also highly reliable for district-level disaggregation.
4	India Human Development Survey‑II (IHDS‑II)	2011–12	Village/ward; PSU	1,672 households	Circular migration (≤ 6 m), purpose, remittance channel & value, expenditure modules.	Only national panel that explicitly asks about short‑duration and circular migration, thus correcting undercounts in PLFS.
5A	CHIRPS v2.0 Monthly Rainfall	Jan 2000 – Dec 2024	0.05° grid (~5 km)	33 district polygons (area‑weighted)	Rainfall anomalies (mm)	Enables climate overlay to isolate distress‑migration peaks.
5B	Standardised Precipitation‑Evapotranspiration Index (SPEI 12‑month)	Same	Same	Same	12‑m Z-score drought index	Flags multi-year drought sequences—key for Barmer/Jaisalmer exodus analysis.
6	State Disaster Event Index (SDEI)+ Rajya Sabha Unstarred & Starred Questions	2015–2024	District–event lines	147 events	Industrial shutdowns, layoffs, drought declarations, relief allocations.	Real-time signal of shock-triggeredmobility; triangulates official survey lags.
7	Return‑Migrant Rapid Survey 2024(Department of Labour, GoR)	Feb‑Mar 2024	6 high-return districts	460 returnees	Skill utilisation, documentation hurdles, wage comparison, psychosocial adaptation scales.	It fills the data void on post-return reintegration and is not covered by any standard survey.
8	UNDESA International Migrant Stock Grid 2024	2020 reference date	Country × sex	Rajasthan aggregated	Stock abroad by destination & origin	Global lens: who leaves India from Rajasthan, where they go, and the sex distribution.

Data‑access governance: PLFS and NSS micro files were obtained through MoSPI’s Data Archive with formal approval (License 340/2023). NFHS and IHDS are open‑access, while the Rapid Survey employed anonymised respondent IDs under an MoU with the Department of Labour, Government of Rajasthan. Climate rasters follow the Creative Commons license of ClimateSERV/UC‑Santa Barbara.

Methodology: From Raw Micro‑Files to Policy-Ready Indicators

The analytical pipeline comprised seven sequential modules. Each step was scripted in Python 3.10 (pandas, xarray, geopandas, statsmodels) with replication logs archived in a Git repository (commit hash 0f3a7f).

1. Universe Definition & Weight Calibration

1.Migration flag harmonisation:

df[‘migrant’] = np.where(df[‘CURRENT_USUAL_RES’] != df[‘LAST_USUAL_RES’], 1, 0)

Duration filters—short (≤ 6 m), medium (6‑36 m), long (≥ 3 y)—were then applied uniformly.

2. Design weights:

• PLFS & NSS: primary sampling weights plus replicate jackknife series ^[1](BJK, BRR) to form 95 % CIs.

• NFHS & IHDS: DHS-style person weights were rescaled to Rajasthan population totals using SRS mid-year estimates.

2. Temporal Harmonisation & Index Construction

• Occupation codes (NCO‑2004 vs NCO‑2015) were cross-walked to five aggregated sectors: Agriculture, Construction, Manufacturing, Services & Hospitality, Transport/Other.
• Earnings and remittances were deflated to 2023‑24 constant rupees via CPI‑IW (industrial workers) and CPI‑AL (agricultural labourers) indexes.
• A net‑migration rate (inflows – outflows / mid-year pop × 1,000) was computed for each district‑year pair 2007–2024, granting a fifteen-year trajectory.

3. Geocoding & Climate Overlay

• District centroids were buffered to underlying 5 km CHIRPS rasters, yielding monthly rainfall totals.
• The 12-month SPEI index was interpolated to district polygons and tagged with a boolean Drought=1 when SPEI ≤ -1 for three or more consecutive months.
• A difference‑in‑difference specification estimated the excess migration probability attributable to drought,controlling for district and year fixed effects.

4. Sectoral & Gender Decomposition

• Blinder–Oaxaca decompositions^[2] separated the change in female migration share (2008 → 2023) into ‘composition’ (education, marital age) and ‘returns’ (propensity to migrate at given traits).
• Sankey diagrams (Python plotly) traced farm‑to‑non-farm occupation switches for intra-district movers, feeding into the earlier bar charts.

5. Inter‑State Corridor Matrix

1. Created an Origin–Destination (O–D) matrix using PLFS person weights.
2. Normalised rows to derive corridor shares (e.g., Banswara → Surat corridor = 14 % of all out‑migrants).
3. Edge-weighted graph analysis produced degree centrality scores. Gujarat ranked highest (0.27), Delhi‑NCR second (0.18).

6. Return‑Migrant Reintegration Indices

• Skill‑recognition score: proportion of overseas certificates accepted by domestic employers.
• Employment match index: ratio of current wage to expected wage from skill profile; < 0.8 flagged as mismatch.
• Identity adaptation scale: five-item Likert (Cronbach α = 0.81) on feelings of acceptance, used to compute stigma prevalence.

7. Visualisation & QC

• Axis ticks are rounded to the nearest 5 % or ₹ 5,000 to avoid spurious precision.
• Dynamic checks: Each plot’s data frame is automatically compared against the master aggregation table; discrepancies halt rendering and log an error

Robustness & Limitations

• Short-term migrants undercaptured: PLFS interviews households at their usual residence; migrants absent on the survey day may be missed. Using IHDS circularity weights, we ‑adjusted the short-term estimate by 1.2 × for tribal districts.
• Spatial boundary changes: Post‑2011 district bifurcations were retro‑fitted to the 2011 geometry to preserve time comparability; this may blur micro‑zones (e.g., Pratapgarh).
• International stocks extrapolated: UNDESA relies on 2010‑20 intercensal growth assumptions; fundamental‑time shifts (e.g., post-COVID GCC lay-offs) might deviate.
• Return‑Migrant Survey is purposive, covering six districts with known high return volumes; findings illuminate patterns but are not statewide incidence measures.

Despite these caveats, triangulation across six independent sources, climate overlays, and field verification interviews provides strong internal validity. Every major pattern—rising rural‑urban drift, Gujarat-bound tribal corridors, female marriage concentration—is replicated in at least two datasets, lending confidence that the trends are not artefacts of any single survey instrument. By blending official, high-frequency labour micro‑files with climate data, administrative event logs, and bespoke field surveys, the methodology captures the multidimensional nature of migration—economic, climatic, demographic and social—delivering a foundation sturdy enough to inform strategic planning and day-to-day programme design.

Research Objectives

The study sets out to deliver a system-wide, evidence-rich appraisal of migration affecting Rajasthan, structured around six inter‑locking objectives. Together, they capture the demographic, economic, spatial, social and climatic dimensions that a state-level decision‑maker must track to manage mobility as both opportunity and risk.

1. Chart the Scale and Geography of Population Mobility

Objective 1.1 – Quantify Incidence

Objective 1.2 – Map Spatial Polarity

2. Diagnose the Economic Drivers and Occupational Shifts

Objective 2.1 – Sectoral Absorption

• Measure the share of migrants employed in construction, services/hospitality, textiles/diamond, manufacturing and agriculture within Rajasthan and destination states.

Objective 2.2 – Labour‑Force Impact at Origin and Destination

• Track changes in labour‑force participation, sector composition and informal‑sector intensity attributable to migrant inflows (urban) and out‑flows (rural).

3. Unpack the Social and Gender Dimensions of Mobility

Objective 3.1 – Marriage-Linked Female Migration

• Quantify marital relocation as a share of female migration; chart settlement patterns of married women in peri-urban wards.

Objective 3.2 – Household Reconfiguration

• Examine how male out-migration alters household dependency ratios, female labour participation and care burdens in origin villages.

4. Assess Climate-Induced and Distress Migration

Objective 4.1 – Drought‑Migration Coupling

• Overlay SPEI 12-month drought anomalies with migration spikes to estimate excess mobility attributable to climatic stress in western arid districts.

Objective 4.2 – Push‑Factor Decomposition

• UNK Through multivariate logistic modelling, 
• Isolate economic versus climatic versus debt-related motives in Barmer, Jaisalmer, Banswara and Dungarpur.

5. Trace Inter‑State and International Corridors

Objective 5.1 – Corridor Network Analysis

• Build an origin–destination matrix to rank top inter‑state corridors (e.g., Banswara → Surat) and compute corridor centrality scores.

Objective 5.2 – Global Footprints and Remittances

• Profile international migrants by destination country, age, skill tier; estimate remittance volumes and household utilisation patterns (consumption, education, assets, debt).

6. Examine Return Migration and Reintegration Barriers

Objective 6.1 – Skill Recognition & Employment Match

• Measure mismatch rates between skills acquired abroad and jobs secured on return; document certificate non-recognition.

Objective 6.2 – Administrative, Social and Welfare Hurdles

• Quantify the prevalence of documentation delays, scheme access gaps, and perceived social stigma among returnees, using the 2024 Rapid Survey.

Cross-Cutting Objective:  Data Integration & Method Transparency

• Harmonise six national datasets, climate rasters and field survey inputs into a reproducible codebase; publish metadata, weighting schemes and confidence intervals for public scrutiny.

These objectives collectively furnish the empirical backbone required for Rajasthan to transition from reactive, anecdote-driven migration management to anticipatory, data-driven governance, ensuring that mobility strengthens—not strains—the state’s social and economic fabric.

Research Questions: 

Intra-State Migration (Within Rajasthan)

1. What is the distribution of rural-to-urban migration within Rajasthan across districts and ecological zones?
2. Which districts in Rajasthan serve as net senders or receivers of migrants, and how have these patterns evolved across NSSO and PLFS rounds?
3. What are the primary reasons for intra-state migration in Rajasthan, such as marriage, employment, education, and family movement?
4. How do intra-state migration patterns vary by sex, explicitly focusing on gendered migration linked to social customs like post-marital relocation?
5. What proportion of intra-state migrants are short-term (≤6 months), medium-term (6–36 months), and long-term (≥3 years) in Rajasthan?
6. How does intra-district migration contribute to changes in labour force participation and occupational engagement, particularly in informal urban economies?
7. What is the prevalence of distress-induced migration (e.g., due to droughts or agrarian crisis) within western Rajasthan, especially in Barmer, Jaisalmer, and Nagaur?

Inter-State Migration (Between Rajasthan and Other Indian States)

8. Which Indian states are the major destinations for out-migrants from Rajasthan (e.g., Maharashtra, Gujarat, Delhi), and how have these trends shifted over time?
9. What is the volume and pattern of in-migration to Rajasthan from other states, and which states (e.g., Uttar Pradesh, Madhya Pradesh) are key contributors?
10. How does interstate migration differ by gender, with men migrating for employment and women for marriage or household relocation?
11. What share of Rajasthan’s population are inter-state migrants, and what is the socio-demographic profile (age, education, caste) of these migrants?
12. What occupational transitions occur for Rajasthan-origin migrants moving to other states, particularly from agricultural to non-agricultural sectors (e.g., construction, services)?
13. How do remittance flows vary among households with interstate migrants, and what are their implications for rural household consumption and savings in Rajasthan?

In-Migration to Rajasthan (From Other States)

14. What are the leading causes of in-migration to Rajasthan from other Indian states, such as employment in mining, services, or trade sectors?
15. Which urban centres in Rajasthan (e.g., Jaipur, Kota, Udaipur) attract the most immigrants and for what economic opportunities?
16. What is the spatial concentration of in-migrants in urban vs. rural Rajasthan, and how does it affect infrastructure, housing, and service delivery?
17. What is the gender distribution of in-migrants to Rajasthan, and what roles do marital migration or household movement play in female mobility?
18. How does the socioeconomic status of in-migrants compare with that of native populations regarding income, education, and access to social protection?

Out-Migration from Rajasthan (To Other States)

19. What are Rajasthan’s most prominent out-migration corridors, and how do they vary by district (e.g., outflows from southern Rajasthan to Gujarat)?
20. What push factors lead to large-scale out-migration from drought-prone and economically underdeveloped districts like Barmer, Dungarpur, and Banswara?
21. How does male-dominated labour migration from Rajasthan affect household structures, dependency ratios, and female labour participation at the origin?
22. What is the extent and nature of seasonal/circular migration from Rajasthan, particularly in tribal and marginalised regions?
23. Which sectors absorb most of Rajasthan’s labour migrants outside the state—agriculture, construction, textiles, hospitality, etc.?
24. How do social networks and caste/community affiliations shape out-migration patterns and destination choice?

International and Return Migration (Rajasthan-Origin)

25. What proportion of Rajasthan’s population has experienced international migration as labour migrants, students, or expatriates?
26. What are the key destination countries for international migrants from Rajasthan (e.g., Gulf countries, Southeast Asia), and what sectors do they engage in (e.g., construction, domestic work)?
27. What are the socioeconomic drivers of international migration from Rajasthan—lack of local employment, debt, education, or family sponsorship?
28. What is the age, gender, and skill profile of international migrants from Rajasthan, and how do they differ from inter-state migrants?
29. What are the reintegration challenges faced by return migrants to Rajasthan, in terms of employment, identity, social acceptance, and access to schemes?
30. How do remittances from international migrants affect household welfare, investment in education, and asset creation in Rajasthan?

1.High Migration Rates in Major Urban Hubs:

• Jaipur is the most significant urban destination, with a 40 per 1,000 population migration rate. This indicates a high rate of rural-to-urban movement, reflecting the city’s strong economic pull in sectors like trade, services, and education.
• Kota follows closely at 33 per 1,000, driven by its prominence as an educational hub, particularly for students from rural areas seeking coaching for competitive exams.

2. Moderate Migration Rates in Other Cities:

• Ajmer (28) and Udaipur (18) also experience substantial rural-to-urban migration, suggesting a balanced distribution of population growth in other urban centres. These cities benefit from diverse economies, including tourism, education, and trade, but on a smaller scale than Jaipur and Kota.
• Bikaner (15) and Jodhpur (14) show notable migration rates, indicating that urbanisation spreads across the state’s larger cities, even outside the more central areas like Jaipur.

3. Lower Migration Rates in Remote and Less Developed Districts

• Barmer (10), Jaisalmer (9), and Hanumangarh (10) report low migration rates, pointing to limited urbanisation in more remote, arid, or agrarian districts. These areas face challenges such as low industrial development and fewer job opportunities, which could be contributing factors to lower migration.
• Pratapgarh and Jhunjhunun, with migration rates of 7-9 per 1,000, also fall in the lower range, suggesting relatively more stable populations with less urban pull.

4. Overall Trends in Urbanisation

• The chart highlights that urban migration is concentrated in a few key areas (mainly Jaipur and Kota). Still, moderate urbanisation is present in districts like Ajmer, Udaipur, and Bikaner, which are emerging as secondary urban hubs.
• The lower migration rates in more rural, less industrialised districts could suggest either stagnation or delayed urbanisation in these regions, possibly influenced by limited employment prospects, infrastructural deficiencies, and the prevalence of agriculture.

Persistent receivers:

• Jaipur moved from +20% → +29%, confirming its position as the state’s primary migration hub.
• Kota and Ajmer roughly doubled their net gains, mirroring the rise of services (ed‑tech/coaching in Kota) and tourism/IT spin-offs in Ajmer.

Newly positive:

• Udaipur swung from -5% to +3%, thanks to marble‑processing clusters and tribal‑focused urban schemes.
• Sikar climbed to +8% on agro‑logistics and proximity to NH 52.

Deepening senders:

• Barmer (–18 → –38‰) and Jaisalmer (–20 → –24‰) intensified outflows, correlating with repeated drought episodes and limited urban job absorption.

• Banswara and Dungarpur (not shown for clarity) also slipped further negative, signalling continued tribal distress migration.

Convergence in the east: 

• Alwar improved slightly, reflecting spill-over industrialisation from NCR, but still shows a minor net loss due to commuting rather than permanent relocation.

Overall, the map of migration polarity is widening: western arid districts lose population at accelerating rates, while the central‑eastern urban corridor consolidates its pull, indicating the need for balanced regional planning and climate-resilient livelihoods.

• Marriage: ~60 % of female migrants cite marriage versus ~16 % of males, reinforcing the dominant role of post-marital relocation for women.
• Employment: Nearly half of male migrants (≈46 %) move for work, compared with only 15 % of females, reflecting persistent labour‑market segmentation.
• Education & Family movement: Secondary drivers remain modest but show narrowing gaps—evidence of gradual improvement in female educational mobility.
• Other: Both sexes report <10 % for health or “other” reasons.

These patterns suggest policy levers should differentiate between economic empowerment for men (skill-linked migration) and social‑support services for women (safe housing, urban amenities, and spousal job facilitation).

Duration	Male %	Female %	Interpretation
Short‑term (≤ 6 m)	35 %	18 %	Male migrants dominate seasonal/circular moves tied to harvest and construction peaks.
Medium (6–36 m)	40 %	22 %	Men continue to take mid-cycle contracts, often renewing based on labour demand in Gujarat and Maharashtra.
Long‑term (≥ three y)	25 %	60 %	Women are six in ten long-term movers, mainly following marriage into urban households where they settle permanently.

Professional insight

The contrast underscores two parallel streams of mobility:

1. Male circular labour migration—policy levers: portable welfare, dormitory-style housing near worksites to reduce vulnerability.
2. Female permanent relocation—policy levers: integrating spousal employment schemes and urban inclusion services (health, ration cards) to enhance social protection for married women.

Duration	Rajasthan %	India %	Policy implication
Short‑term (≤ 6 m)	22	27	Lower incidence of purely seasonal moves within the state indicates limited short-cycle job absorption in Rajasthani towns.
Medium (6–36 m)	33	39	Mid-term mobility also trails the national mean; it suggests male workers often skip local circuits and head directly out‑of‑state.
Long‑term (≥ three y)	45	34	Rajasthan has a higher share of long-term stayers, primarily driven by female marital migration into urban centres.

Professional Insight

The tilt toward long-term moves reveals that once migrants relocate within Rajasthan, they tend to settle, creating stable yet gender‑skewed urban growth. In contrast, the lower short/medium shares indicate a gap in intra-state labour‑market dynamism.

Interventions could focus on:

• Stimulating rural-urban commuter jobs (MSME clusters, agri‑processing) to create short/medium‑cycle opportunities.
• Strengthening housing and social infrastructure in receiving cities to absorb long-term female migrants sustainably.

Sector	Origin %	Destination %	Δ (ppt)
Agriculture	65	10	–55
Informal trade	12	10	–2
Construction	10	28	+18
Manufacturing	5	25	+20
Services	8	27	+19

• Labour‑force participation (LFP) rises from 77 % at origin to 84 % at the destination, reflecting improved job uptake (annotation on the right side).

Professional insight:

Intra-district migrants abandon agriculture and diversify into low-skill urban informal sectors, such as construction, small manufacturing workshops, and petty services. The sharp +20 ppt manufacturing jump (often in marble, textile or food processing units) indicates cluster-based MSMEs’ importance in absorbing rural youth.

District	Distress‑migration %	Relative to the state mean (8 %)
Barmer	22 %	2.8 × higher
Jaisalmer	19 %	2.4 × higher
Nagaur	17 %	2.1 × higher
Rajasthan (avg.)	8 %	—

Professional insight:

• The Thar drought corridor (Barmer‑Jaisalmer) shows the highest distress outflows, aligning with persistent SPEI ≤ –1 episodes (2002, 2015, 2023).
• Nagaur’s mixed farming belt now approaches 17 %, indicating an eastward spillover of climatic and debt pressures.
• Statewide, only 8 % of households report distress-led migration, highlighting the geographical concentration of vulnerability.

Rank	Destination state	Share %	Key pull factors
1	Gujarat	27	Construction, diamond‑textile clusters in Surat/Ahmedabad; wage differential ≈ +24 %.
2	Maharashtra	16	Mumbai‑Thane construction & services; Nasik agro‑processing.
3	Delhi‑NCR	11	Logistics, retail, security services; easy rail connectivity via the Jaipur‑Rewari line.
4	Haryana	7	Gurugram‑Manesar auto ancillaries and warehousing.
5	Uttar Pradesh	6	Noida‑Ghaziabad construction; agrarian ties in border districts.

Professional insight

• The Gujarat corridor now absorbs over one‑quarter of all out‑migrants, driven by robust construction and MSME growth.
• Delhi‑NCR and Haryana jointly draw nearly one-fifth, underscoring Rajasthan’s role as a feeder of labour to the NCR logistic‑industrial belt.
• Maharashtra maintains a strong second position, but growth has plateaued, suggesting saturation in Mumbai construction demand.

Rank	Origin state	Share %	Typical skills & sectors
1	Uttar Pradesh	31	Construction labour, informal retail, and security services in Jaipur/Kota.
2	Madhya Pradesh	18	Mining & stone‑crushing (Chittorgarh), marble sector (Udaipur), farm labour in Hadoti.
3	Bihar	12	Hospitality and dhaba work on the NH‑48 corridor; gig deliveries in Jaipur.
4	Haryana	9	Seasonal agricultural hands in eastern Rajasthan include some skilled mechanics.
5	West Bengal	4	Handicraft and textile artisans, especially in Jaipur’s zari clusters.

Professional insight:

• Proximity matters: The top three are contiguous or well-connected states, benefiting from rail/highway corridors (NH‑21, NH‑48, and Kota rail loop).
• Skill mix: In‑migrants, it complements local labour shortages in the construction, mining, and service sectors, helping sustain Rajasthan’s urban growth.

Reason	Men %	Women %	Insight
Employment	68	15	Male mobility is overwhelmingly job-driven, targeting higher wages in Gujarat, Maharashtra and NCR.
Education	7	15	While still modest, female educational migration is increasing, particularly to Jaipur/Kota coaching hubs.
Marriage	12	54	Over half of female inter‑state movers relocate upon marriage, often into Rajasthan from UP/MP.
Family relocation	8	10	Joint moves remain a secondary factor.
Others	5	6	Health, pilgrimage, etc.

Professional insight:

• Male employment corridors need robust job‑readiness and safety nets.
• Female marriage-driven inflows underscore the need for inclusive urban planning (gender‑responsive housing, health, and integration services).
• The emerging female education share (15 %) suggests rising aspirations but calls for safe hostel infrastructure and scholarships.

Age profile:  Key points

• Youth tilt: 15‑29 age group forms 46 % of migrants vs. 32 % of the state population.
• Under‑representation of 45+ cohort among migrants (16 % vs. 34 %).

Interpretation: Interstate migration is a young person’s strategy, reflecting labour‑market and marital mobility; older adults are far less likely (financial, social ties).

Education profile:  Key points

Level	Migrants %	Rajasthan %	Gap
Illiterate	18	26	–8 ppt
Primary	34	32	+2 ppt
Secondary+	30	23	+7 ppt

Interpretation: Interstate migrants are better educated on average, with a higher share holding at least primary or secondary schooling. Better schooling likely lowers information barriers and raises migration readiness.

Caste profile:  Key points

Caste group	Migrants %	Rajasthan %	Interpretation
SC/ST	25 %	29 %	Slight under‑representation; financial and network constraints may limit long-distance mobility.
OBC	48 %	44 %	Over‑representation; OBC communities often maintain labour networks in Gujarat/Maharashtra.
Others	27 %	27 %	Near parity, reflecting mixed socio-economic standing.

Overall, interstate migrants skew toward OBC groups with moderate resources to finance relocation, whereas SC/ST households face hurdles, warranting targeted support (migration loans, network facilitation).

Sector	Origin %	Destination %	Δ (ppt)
Agriculture	60	22	–38
Informal trade	15	8	–7
Construction	15	28	+13
Services	8	25	+17
Manufacturing	0	10	+10
Other	2	5	+3

• 55 % of migrants exit agriculture altogether, the majority shifting into construction (28 %) and services (25 %)—chiefly logistics, retail and hospitality in Gujarat, Maharashtra and NCR.
• Manufacturing (10 %) includes diamond cutting (Surat) and garment units (Gurugram).
• Informal petty trade shrinks—migrants prefer wage employment over self-employment in unfamiliar markets.

Professional insight

• Skill‑demand alignment: Construction and service sectors remain primary absorbers—policy should focus on construction safety training, plumbing/electrician courses, and customer‑service skills.
• Upward mobility: Migrants with secondary education fare better in services/manufacturing, highlighting the value of basic schooling for non-farm transitions.
• Risk mitigation: Construction work is high risk; enforcing inter‑state labour standards (ESI, accident insurance) is critical.

Metric	Rajasthan	India
Median	₹ 54k	₹ 42k
IQR (25‑75 %)	₹36 k – ₹88 k	₹28 k – ₹65 k
90th pctl	₹1.4 L	₹1.0 L

Insights

• Higher median (+₹12k) indicates Rajasthan migrants send more per household, linked to longer migration spells and concentration in better-paid construction/service work in Gujarat & NCR.
• Wider upper tail (90th pctl) reflects a subset of high-earning migrants in skilled trades (diamond polishing, skilled masonry).
• Remittances account for ≈18 % of household cash income in sender districts, improving food security and education spending and increasing dependence on external labour markets.

Cause	Share %	Typical locations
Employment: Mining	35	Marble & granite belts (Udaipur, Rajsamand), zinc‑lead mines (Zawar, Rampura Agucha).
Construction	22	Urban expansion of Jaipur, Kota, Ajmer, and the NH‑48 industrial parks.
Services (hospitality, retail, transport)	18	Jaipur tourism hub, Udaipur hotels, Kota coaching economy.
Trade & business	12	Textile & gemstones are in Jaipur, and handicrafts are in Jodhpur.
Family relocation	8	Dependents accompanying the worker spouse.
Education	5	Students are to attend Kota coaching institutes & Jaipur universities.

Professional insight

• Resource-based employment (mining) remains the most significant pull, distinguishing Rajasthan from states where construction dominates migration.
• The rising share of service‑sector migrants (18 %) signals diversification of the state’s urban economy, necessitating hospitality and retail management training.
• Education-led ‑migration, though small, underlines Rajasthan’s emerging role as a national coaching/university hub.

Urban centre	Immigrant share %	Main economic magnets
Jaipur	34	Tourism, wholesale trade, IT services, and gemstones.
Kota	18	Coaching/ed‑tech industry, cement, power plants.
Udaipur	16	Tourism, marble & granite processing, zinc mining.
Ajmer	12	Wholesale trade, rail logistics, pilgrimage tourism.
Jodhpur	10	Handicrafts, furniture export, and defence supplies.
Others (Alwar, Bhilwara, etc.)	5	Auto ancillaries, textiles.

Professional insight

• Jaipur dominates immigrant inflows, driven by its diversified services and tourism ecosystem.
• Kota’s rise to 18 % stems from its booming coaching/ed‑tech economy, drawing support staff and student families.
• Udaipur balances tourism with mining-linked jobs, attracting skilled and semi-skilled workers.

Spatial distribution of migrants (PLFS 2023):

Location	Share %	Implications
Urban areas	78	Concentrated in Jaipur, Kota, Udaipur → drives housing demand (+7 % rental inflation), strains water supply and transit networks.
Rural areas	22	Mining villages (Rajsamand) and agri belts (Hadoti) absorb a minority, easing local farm labour shortages but adding pressure on groundwater use.

Infrastructure effects

• Housing: Rental stock deficit of ~0.18 million units across the top five cities.
• Water & sanitation: Jaipur’s per‑capita water availability dips below the CPHEEO norm (<135 lpcd) during peak influx months.
• Transport: Urban bus ridership up 11 % YoY; need fleet expansion & last‑mile e‑mobility.
• Social services: Primary health centres in Jaipur Urban register a 14 % outpatient uptick, indicating migrant dependence on public facilities.

Top out-migration corridors (PLFS 2023 weighted, corridors share of all Rajasthan out-migrants):

Corridor	Share %	Notes
Banswara–Dungarpur → Gujarat (Surat, Ahmedabad)	14	Tribal youth to construction & diamond units; proximity, social networks.
Sikar–Jhunjhunu → NCR (Delhi/Gurugram)	11	Logistics, security services, and auto ancillaries.
Barmer–Jaisalmer → Maharashtra (Mumbai–Thane)	9	Construction labourers, some retail helpers.
Ajmer–Kota → Delhi	7	Service jobs, hospitality and coaching-related support roles.
Alwar–Bharatpur → Haryana (Faridabad/Palwal)	5	Auto parts, warehousing, and commuting advantage via NH-19.

Key insights

• Geography & connectivity shape flows, southern tribes cross into nearby Gujarat; the Shekhawati belt uses the Delhi rail corridor.
• Sector specificity: diamond cutting in Surat, auto ancillaries in NCR/Haryana, metro construction in Mumbai.

Push factor composition driving out‑migration from drought-prone districts (PLFS 2023 + CHIRPS/SPEI overlay, shares by reason)

District	Drought %	Lack of local jobs %	Poor wages %	Debt %	Key message
Barmer	55	20	15	10	Acute rainfall deficit + saline soils trigger distress moves.
Dungarpur	30	25	35	10	Limited non-farm work + low wage rates push tribal labourers out.
Banswara	25	10	50	15	Chronic under‑payment in agriculture; high informal debt drives exits.

Insights

• Drought is the dominant driver in Barmer, aligning with SPEI ≤ –1 anomalies and chronic water scarcity.
• Wage depression & job scarcity combine in Dungarpur and Banswara, indicating structural underdevelopment beyond climatic stress.
• Debt (10‑15 %) often compounds other push factors—micro‑finance and money‑lender liabilities, forcing migration.

Impact of male-dominated out-migration on origin households (IHDS‑II 2011‑12 matched pairs)

Metric	Households with male migrants	Households without migrants	Effect
Household dependency ratio (dependents : workers)[3]	0.73	0.59	↑ +0.14 — more children/elderly per working adult.
Female labour‑force participation rate	28 %	18 %	↑ +10 ppt — women backfill absent male roles.

Implications

• Male out-migration raises the dependency burden on remaining workers, pressuring household resources.
• Women step into the labour market, mainly in agriculture and informal home-based work, partially offsetting lost male income but often with low pay and poor protections.

Migration pattern	Share of migrant households %	Timing & destinations
Seasonal (Nov – Feb)	53	Post-harvest lean months → construction sites in Surat/Mumbai.
Seasonal (May – Jul)	20	Pre-monsoon agricultural slack → brick kilns in Gujarat.
Circular (> one cycle per year)	27	Tribal labourers are alternating between home farms and urban jobs.

Insights

• Over half of migrant households participate in a single Nov-Feb seasonal cycle, coinciding with post-kharif slack and peak construction demand.
• A significant 27 % engage in circular migration, reflecting entrenched livelihood strategies in tribal belts (Banswara, Dungarpur).
• The May–July secondary peak aligns with the‑pre-monsoon sowing lull and the brick‑kiln season.

Sector	Share of out‑state migrants %	Typical destinations
Construction	38	Metro projects in Gujarat, Maharashtra, and NCR.
Services/hospitality	20	Hotels, restaurants, and logistics in Delhi, Mumbai, and Goa.
Textiles (garment / diamond)	17	Surat diamond & textile units, Gurugram apparel.
Agriculture (seasonal)	10	Sugarcane harvesting in Maharashtra and Punjab wheat.
Manufacturing – other	8	Auto ancillaries (Manesar), ceramics (Morbi).
Others	7	Security, gig delivery, retail.

Insights

• Construction dominates, driven by ongoing infra‑booms; safety and skill certification should be central policy foci.
• Service and textile clusters collectively absorb over one-third, highlighting the importance of soft‑skills and machine‑operation training.
• Agriculture still draws 10 % seasonally, highlighting the need for portable welfare during farm peak seasons.

Destination preferences by caste/community (PLFS 2023 cross‑tab, % share within each caste’s out‑migrants):

Caste/community	Gujarat	Delhi	Maharashtra	Key network drivers
Scheduled Tribes (ST)	48	15	5	Kin-based labour lines to Surat diamond & construction.
OBC	22	9	6	Extended clan networks in Gujarat MSMEs; transport to NCR.
Scheduled Castes (SC)	13	8	12	Intermediaries link SC workers to the Mumbai–Thane construction.
‘Upper’ castes	12	7	5	Smaller share; often skilled jobs in NCR.

Insights

• Social networks strongly channel ST migrants to Gujarat (≈ half of their out‑flows), reflecting longstanding Bhil community chains in Surat.
• OBC migrants diversify across Gujarat and NCR, leveraging caste‑clan ties in truck transport and textiles.
• SC migrants follow contractor networks to Maharashtra’s construction sites.

Destination regions for Rajasthan‑origin international migrants (UNDESA 2024 stocks, % share):

Region	Share %	Dominant sectors
Gulf (UAE, Saudi Arabia, Qatar)	55	Construction, driving, and domestic work.
Southeast Asia (Malaysia, Singapore)	34	Plantations, security, and F&B services.
North America	25	IT & hospitality: small but growing student cohort.
Europe	22	Skilled healthcare (UK), logistics.
Other	18	Africa trade links, Australia students/skilled visas.

Insights

• The Gulf remains the overwhelming magnet (> half) for Rajasthan’s overseas workforce, necessitating robust pre-departure training on labour rights and safety.
• Rising shares in North America and Europe reflect new skilled & student pathways, suggesting a need for credential recognition support and diaspora engagement policies.

Top destination countries & typical employment sectors (% share within all international migrants):

Country	Share %	Main sectors
United Arab Emirates	30	Construction, retail services.
Saudi Arabia	20	Construction, domestic work.
Qatar	15	Construction, driving.
Malaysia	12	Plantations, security.
United States	10	IT, hospitality (student-to-work pathways).

• Gulf countries together absorb 65 % of Rajasthan’s overseas workers, consistent with low‑skill labour demand.
• Malaysia offers plantation/security roles, often under less regulated conditions.
• A growing share targets higher‑skill or student routes to North America.

Driver	Share %	Explanation
Lack of local employment	40	Limited non-farm jobs in semi-arid districts push youth abroad, often via contractor networks.
Family sponsorship	22	Relatives already abroad facilitate the visa and initial settlement.
Debt repayment	15	Households leverage overseas earnings to clear high-interest informal loans.
Education/professional advancement	13	Rising student & skilled migration to North America and Europe.
Other (marriage, adventure, etc.)	10	Miscellaneous motives.

Insights

• Economic compulsion remains the prime catalyst—4 in 10 migrants cite job scarcity at home.
• Family sponsorship plays a notable enabling role, indicating strong diaspora ties.
• Debt-driven migration underscores financial vulnerability; safe‑migration loans and pre-departure counselling are essential.

Age

Age group	International %	Inter‑state %
18‑29	46	33
30‑44	38	38
45+	16	29

• International migrants skew younger (46 % 18‑29) than inter-state flows.
• Inter‑state migration retains a larger 45+ cohort, reflecting circular work among mid-life males.

Skill level

Skill tier	International %	Inter‑state %
Low/medium	71	82
High skill	29	18

• Low/medium skills dominate both streams, but international migration has a higher share of skilled workers (29 %), including IT professionals, nurses, and students.
• Inter‑state migrants remain largely low‑skill (construction, textiles).

Implications

• Skill-selective visas are emerging as a pathway abroad; promoting credential equivalence can widen skilled routes.
• Domestic upskilling remains critical—inter‑state migrants need vocational training to break low-wage traps.

Allocation category	Share % of remittance use	Welfare impact
Daily consumption	38	Smooths food & healthcare spending; cushions shocks.
Education	22	Pays school fees, coaching, and increasing human‑capital investment.
Productive assets / small business	18	Livestock and equipment boost long-term income.
Housing improvement	12	Better roofing and sanitation improve living conditions.
Debt repayment	10	Reduces informal interest burden, freeing future income.

Key findings

• Nearly one-fifth invested in productive assets, indicating remittances are not purely consumptive but catalyse asset creation.
• A sizeable 22 % devoted to education suggests remittances raise the next generation’s prospects, potentially breaking inter‑generational poverty cycles.
• Debt repayment remains non-trivial (10 %), underscoring the role of remittances in financial stabilisation

Discussion: 

The composite evidence drawn from six national surveys, high-resolution climate rasters and a 2024 rapid assessment paints a nuanced portrait of migration in Rajasthan—one that departs from the stereotypical image of an overwhelmingly outward-bound, low‑skill exodus. Three overarching themes emerge.

1. Mobility as an Adaptive, Multi-Scalar Strategy

The data show that no single migration stream dominates: rural‑to‑urban shifts within the state, inter‑state corridors to Gujarat and Maharashtra, and modest but rising overseas flows all coexist, each serving a distinct adaptive function.

• Intra-state migration (net +12% ‰ in 2023) mitigates agrarian under‑employment and channels labour into Jaipur’s diversified service economy, Kota’s education-driven boom and Udaipur’s tourism–marble nexus.
• Inter‑state migration fills wage gaps left by Rajasthan’s limited industrial base; Gujarat absorbs tribal youth into construction and diamond polishing, while Delhi‑NCR attracts semi-skilled workers for logistics and security services.
• International migration, though small in head‑count, plays an outsized role in rural livelihoods, with remittances financing 38 % of recipient household consumption and one-fifth of their investment in productive assets.

2. Climate Stress and the Geography of Distress Migration

The drought overlay confirms a direct coupling between climatic shocks and mobility in Rajasthan’s western flank. Districts with consecutive SPEI ≤ -1 months recorded a 2.8× higher probability of short-term household migration (e.g., Barmer 22 %, Jaisalmer 19 %), underlining mobility’s role as a survival buffer. Yet the climatic trigger interacts with structural deficits: Dungarpur and Banswara, less arid but economically lagging, exhibit high outflows driven by poor wages and job scarcity, not drought. Migration is therefore best viewed as a compound response to intertwined climatic and economic stressors.

3. Gendered and Socially Mediated Pathways

The report’s disaggregation reveals sharp gender asymmetries:

• Marriage remains the single most significant driver of female mobility (60 % intra-state; 54 % inter-state), producing a female-skewed inflow to urban peripheries.
• Male migration is predominantly employment‑seeking; however, it indirectly elevates female labour‑force participation at origin by 10 percentage points as women substitute for absent male earners, particularly in agriculture and home-based craft.
• Caste networks filter destinations: Bhil and Garasia tribal migrants rely heavily on kinship chains to Surat, whereas OBC and SC migrants access a broader set of corridors via contractor networks. These social channels determine both risk exposure and opportunity structure.

4. Occupational Reallocation and Skill Dynamics

Occupational tracking shows that over half of migrants exit agriculture, with pronounced shifts to construction (38 % outside the state) and services/hospitality (20 %). Whereas international migrants exhibit a higher skilled share (29 %), the bulk of inter‑state and intra‑state flows remain low/medium‑skill. The long-term development pay‑off may thus hinge on whether emerging skilled segments, students and IT professionals headed to North America, scale up relative to the low‑skill majority.

5. Reintegration: The Missing Link in the Migration Cycle

Often ignored in policy analytics, return migration surfaces as a critical bottleneck. The rapid survey finds a 42 % employment‑mismatch rate and a 28 % incidence of non-recognition of overseas qualifications. Administrative hurdles (18 % report documentation delays) further impede reintegration, suggesting that without deliberate mechanisms to valorise acquired skills and simplify re-entry, the developmental dividends of international mobility risk being lost.

Conclusion:

Rajasthan’s migration landscape is diverse, dynamic and deeply embedded in the state’s economic and climatic fabric. Far from a unidirectional out‑flow narrative, the evidence depicts a circulation of people, skills and remittances that—if harnessed—can accelerate inclusive growth. Yet the same currents also expose vulnerabilities: drought-induced distress in the west, gendered settlement pressures in expanding cities, and systemic hurdles that returning migrants face.

Three takeaways crystallise from the analysis:

1. Multiplicity of streams: Policy must grapple with migration at multiple scales—within‑district, inter‑state and international—each with distinct drivers and consequences.
2. Dual face of mobility: Migration simultaneously alleviates rural distress and strains urban infrastructure; it raises household incomes yet can magnify social protection gaps.
3. Need for cyclical vision: Maximising gains requires viewing migration not as departure alone but as a cycle that includes origin‑area preparedness, destination conditions and returnee reintegration.

By grounding these insights in harmonised, design-weighted micro‑data and climate overlays, the report equips state leadership with a factual baseline against which to craft targeted, evidence-backed interventions, ensuring that Rajasthan’s age-old tradition of mobility evolves into a modern engine of shared prosperity.

Policy Recommendations:

We propose interlinked policy measures based on the comprehensive, multi-source analysis of migration dynamics across Rajasthan. These recommendations are designed to harness the positive potential of mobility—enhancing skills, channelling remittances into growth, and protecting vulnerable households, while mitigating its strains on urban systems, rural communities, and returning migrants.

1. Strengthen Skill Development and Employment Linkages

1. Targeted Vocational Training

• Construction & Allied Trades: Establish certification–placement cells in central sending districts (Barmer, Dungarpur, Banswara) to deliver short‑cycle courses (masonry, plumbing, electrical) aligned with demand in Gujarat, Maharashtra and Delhi‑NCR.
• Diamond‑Textile Clusters: Partner with Surat and Ahmedabad industry bodies to institute mobile training units for tribal youth, covering diamond polishing, garment finishing and quality control.
• Hospitality & Retail Services: Scale up hospitality management modules at Kota and Jaipur polytechnics, tapping into the 20 % of migrants absorbed by hotels, restaurants and logistics.

2. Inter‑State Placement Facilitation

• Forge formal MoUs with key destination states (Gujarat, Maharashtra, Haryana) to deploy migrant career kiosks at major bus/rail terminals, offering pre-departure orientation, rights awareness and job‑matching services.

2. Expand Portable Social Protection and Urban Services

1. One Nation–One Ration Card Implementation

• Fast-track end-to-end portability for Public Distribution System (PDS) cards, ensuring the nine‑per cent coverage gap for migrants is closed; deploy migrant-centric enrollment drives in Jaipur, Kota, and Udaipur.

2. E‑Shram & Social Security Portability

• Integrate Rajasthan’s e-Shram registry with Gujarat, Maharashtra and NCR labour departments to guarantee continuity of health insurance (ESI) and pension contributions for circular and inter-state migrants.

3. Affordable Rental Housing and Urban Infrastructure

• Incentivise public-private partnerships to develop low-cost rental housing near employment hubs; mandate that 25 % of city-sponsored complexes be reserved for migrants at ≤ ₹ 3,000/month rent.
• Bolster water supply and sanitation in high-inflow wards through ring‑central augmentation, rainwater‑harvesting subsidies for multi-story tenements, and last-mile e-mobility fleets to decongest bus routes.

3. Mitigate Distress Migration through Rural Livelihoods and Climate Resilience

1.Drought‑Contingent Employment Schemes

• Expand MGNREGA person‑days by 30 % in Barmer, Jaisalmer, Nagaur during SPEI ≤ –1 years; link payouts to watershed restoration, agro‑forestry and solar‑park maintenance.

2. Climate-Proof Agriculture and Non-Farm Enterprises

• Subsidise drought-tolerant crop varieties and drip‑irrigation kits; launch micro-processing units for solar salt, spices and medicinal plants to create localised non-farm work.
• Introduce crop‑and‑income insurance bundles calibrated to district-level SPEI trends, with digital claim settlement and advance payments during peak heat months.

4. Advance Women’s Urban Integration and Household Resilience

1. Safe Housing & Urban Amenities for Female Migrants

• Establish women-only hostel complexes in Jaipur peri-urban wards and Kota’s coaching clusters, offering on-site childcare, healthcare, and vocational upskilling.
• Deploy gender-sensitive urban safety audits and rapid‑response helplines in destination municipalities to address the needs of 60 % of intra-state female migrants moving for marriage.

2. Empower Left-Behind Women

• Expand self-help group federations in high out-migration blocks, linking them to micro‑credit, market access and digital finance training.
• Provide community-based childcare centres under the Integrated Child Development Services (ICDS) at the gram‑panchayat level, reducing care burdens and enabling sustained female labour‑force participation (↑ 10 ppt).

5. Facilitate Return‑Migrant Reintegration and Skill Recognition

1. Skill‑Passport and Certification Equivalence

• Institute a “Rajasthan Skill Passport” that pre-validates common Gulf and ASEAN vocational certificates (e.g., welding, AC repair), enabling employers to match returnees to vacancies instantly.
• Collaborate with the National Skill Development Corporation (NSDC) to offer bridge courses for any skill gaps identified in rapid‑survey assessments (42 % mismatch).

2. Single‑Window Reintegration Desks

• Set up migrant help‑desks at district labour offices, one‑stop for Aadhaar/PAN updates, ESI/PF transfers, and scheme enrolment (PMAY, Stand‑Up India).
• Embed counselling units within these desks to address social stigma and psychosocial stress, as reported by 7 % of returnees.

6. Strengthen Data-Driven Governance and Ongoing Monitoring

1. State Migration Observatory

• Launch a live dashboard, updated quarterly with PLFS and administrative data, tracking net‑migration rates, corridor flows, remittance volumes (₹ 54k median) and distress signals (CHIRPS/SPEI triggers).

2. Annual Migration Review Summit

• Convene a multi-stakeholder forum—including departmental secretaries, industry chambers, destination‑state representatives and civil‑society groups to review the latest data, assess policy impact and refine interventions for the coming year.

By weaving together skill‑building, social‑protection portability, climate‑resilient rural livelihoods, gender‑sensitive urban integration and streamlined return‑migration pathways, Rajasthan can transform mobility into a sustainable engine of shared prosperity—one that safeguards vulnerable communities, addresses urban pressures, and capitalises on the full potential of its migrant population.

Explanations

Sectoral & Gender Decomposition of Migration in Rajasthan:

The sectoral and gender decomposition approach was employed to analyse the complex migration patterns in Rajasthan, focusing on the factors influencing female migration and the sectoral shifts observed among intra-district movers. This method incorporates both economic and social dimensions of migration and helps understand how migration affects gender dynamics and occupational transitions.

Blinder-Oaxaca Decomposition: A Tool for Understanding Gendered Migration Patterns

Background:

• The Blinder–Oaxaca decomposition is a widely used statistical technique, initially developed by Blinder (1973)and Oaxaca (1973), to decompose differences in means (or shares) between two groups. In this case, it was applied to gendered migration patterns, explicitly focusing on the change in the female migration share between 2008 and 2023.

Who started it and when?

• The Blinder–Oaxaca decomposition method was introduced in the early 1970s. It has since become a core tool in labour economics, especially for studying wage disparities and migration patterns. Migration research has been applied to understand how the characteristics of different populations, like education level and marital status, explain gendered differences in migration.

How we used it in our analysis:

In our study, we employed the Blinder–Oaxaca decomposition to break down the change in female migration share between 2008 and 2023. This method allowed us to split the change into two parts:

1. Composition Effect: This measures how changes in factors like education levels and marital age among women contribute to the overall increase in female migration. If, for example, more women today have secondary education than in 2008, it could explain a portion of the rise in female migration.
2. Returns Effect: This part captures the propensity to migrate among women with similar traits (e.g., education, marital status). If women with the same educational qualifications are now more likely to migrate than in 2008, the returns effect helps explain this change in behaviour.

Why did we use it?

• Using the Blinder–Oaxaca decomposition, we could better understand why female migration increased over the 15 years and whether it was driven more by changing social structures (education, marital age) or a shift in migration behaviour among women with similar traits. This decomposition also gave us a deeper insight into how structural factors like education and marriage patterns influenced female migration in Rajasthan.

Sankey Diagrams: Tracing Occupational Shifts Among Migrants

Background

• Sankey diagrams are flow diagrams that visually represent the movement of data between categories. They are beneficial for illustrating the flow of people or resources from one state to another. Sankey diagrams were first used in the early 19th century by Matthew Sankey to visualise energy flows. Still, they have since been applied across many fields, including economics, migration studies, and policy analysis.

Who started it and when?

• Matthew Sankey invented the Sankey diagram in 1898 and has since become a popular method of visualising flow data in various fields. Migration studies have increasingly used it to show how people transition between sectors or locations. In our context, Sankey diagrams are a powerful tool for visualising the shift from farm-based to non-farm occupations among intra-district migrants in Rajasthan.

How we used it in our analysis:

In our analysis, we used Sankey diagrams to track the migration of people within districts, particularly their transition from farm-based jobs to non-farm occupations. The Sankey diagrams visually represented how migrants shifted from agricultural work (e.g., farming, animal husbandry) to urban non-farm sectors like construction, retail, and services.

These diagrams helped us understand:

• Where the migrants were coming from (rural farm jobs).
• Where they were going (urban non-farm sectors).
• How migration reshaped labour dynamics within Rajasthan’s districts, particularly in urbanising areas like Jaipur and Kota.

Why did we use it?

• We used Sankey diagrams because they provide an intuitive and straightforward way of showing the flow of people between different sectors. In this case, they allowed us to trace occupational transitions among intra-district migrants, making it easier to visualise how migration influences occupational shifts from agriculture to non-agricultural sectors. These diagrams supported the bar charts, providing a more granular understanding of migration’s impact on labour force transitions in Rajasthan.

Design Weights and Statistical Methodology for Population Representation:

We applied design weights to the datasets we used in the study to ensure that our analysis of migration trends is statistically robust and representative. Design weights are crucial in survey analysis, adjusting for the unequal selection probabilities, non-response, and sample design.

Below, we describe how we applied these weights for the different datasets.

PLFS & NSS: Use of Primary Sampling Weights with Replicate Jackknife Series

Primary Sampling Weights:

• For the PLFS (Periodic Labour Force Survey) and NSS (National Sample Survey), primary sampling weightswere used to ensure that the survey results represent the broader population of Rajasthan. These weights account for the probability that each individual or household was selected, which can vary depending on factors like geographic location and socio-economic strata.

Replicate Jackknife Series (BJK, BRR):

To compute the 95% confidence intervals (CIs), we employed replicate weights through the jackknife and balanced repeated replication (BRR) methods. These methods allow for robust estimation of variability and standard errors:

• Jackknife: A resampling technique that systematically leaves out one observation at a time to assess the impact on the estimator.

• BRR: A method where multiple resampled datasets are created by repeatedly splitting the data and recalculating estimates for each resample.

Using these methods, we obtained reliable confidence intervals for key migration metrics, ensuring that the findings are statistically valid and not just due to sampling error.

NFHS & IHDS: Rescaling with DHS-Style Person Weights

DHS-Style Person Weights:

The NFHS (National Family Health Survey) and IHDS (India Human Development Survey) datasets are typically designed using person-level weights that account for the different probabilities of selection based on age, gender, region, and household composition. These weights allow for proper generalisation to the broader population.

Rescaling to Rajasthan Population Totals:

To ensure that the NFHS and IHDS data accurately reflected the population of Rajasthan, we rescaled the person weights to match the state’s population totals using SRS (Sample Registration System) mid-year estimates. This process aligns the sample with the actual population distribution of Rajasthan, ensuring that the survey results represent the state’s actual demographic structure.

By rescaling, we ensured that the data from these two surveys were consistent with the state’s population figures, adjusting for underrepresented or overrepresented groups, particularly in rural versus urban regions.

Why Are Design Weights Important?

1. Ensuring Representativeness:
   Design weights ensure that the survey samples represent the entire population, especially when certain groups (such as rural or marginalised communities) are underrepresented in the sample.
2. Estimating Variability and Confidence Intervals:
   We can estimate the uncertainty around our migration estimates using replicate weights (like BJK and BRR), providing more accurate and reliable conclusions.
3. Adjusting for Non-Response and Sampling Bias:
   Surveys often encounter non-response or sampling bias. Design weights help correct these biases by adjusting the results to reflect the proper proportions in the population.
4. Refining Data for Subgroups:
   When examining specific subgroups, such as female migration or rural-to-urban shifts, design weights help adjust for imbalances in subgroup representation, ensuring that the findings are not skewed by underrepresentation.

By applying these statistical techniques, we can confidently interpret the data from the PLFS, NSS, NFHS, and IHDS as representative of Rajasthan’s overall demographic and migration trends. This ensures the validity of our findings and allows for sound policy recommendations based on robust statistical analysis.

Return-Migrant Reintegration Indices: –

To evaluate the reintegration challenges return migrants face in Rajasthan, we used indices assessing skill recognition, employment match, and social integration. These indices provide a comprehensive understanding of the difficulties returnees experience when transitioning back into the local economy and society.

Skill-Recognition Score

Definition

The skill-recognition score measures how overseas qualifications and certifications are accepted and valued by domestic employers in Rajasthan. Many return migrants have gained skills abroad, particularly in construction, driving, hospitality, and healthcare. However, when they return, they often lack recognition of these skills in the local job market.

Calculation

The skill-recognition score is computed as the proportion of overseas certifications acknowledged and utilised by domestic employers. This index highlights returnees’ challenges in validating their foreign training and experience. A low score would suggest that returnees face difficulties finding jobs matching their qualifications, leading to underemployment or employment in lower-wage sectors.

Why It Matters:

This index is crucial for understanding how well the local labour market integrates returnees who possess foreign skills. It also identifies whether the recognition of international qualifications is a barrier to their full reintegration into the workforce.

Employment Match Index

Definition

The employment match index compares the current wage of a return migrant to the expected wage based on their skill profile. This ratio helps us assess whether returnees are employed at levels commensurate with their qualifications or if they are underemployed.

Calculation

The index is calculated as:

Employment Match Index = Current Wage/Expected Wage from Skill Profile

An index below 0.8 indicates a wage mismatch, suggesting that the returnee earns less than expected based on their skills and experience. This underemployment could reflect a lack of recognition of their foreign qualifications, the unavailability of suitable jobs in the local labour market, or an overqualification for the available positions.

Why It Matters

The employment match index is a key measure of economic reintegration. A low value points to the inadequate absorption of return migrants into the formal labour market, which can adversely affect their financial stability and well-being.

Identity Adaptation Scale

Definition

The identity adaptation scale measures how return migrants perceive their acceptance and integration into their home communities. It assesses returnees’ social and emotional reintegration, particularly regarding how family, peers, and the broader community accept them.

Methodology:

This scale uses a five-item Likert scale (with responses ranging from strongly disagree to agree strongly) to evaluate factors such as:

• Feelings of social acceptance from peers and family.
• Perceptions of being seen as an outsider or an ‘other’.
• The degree to which returnees feel socially and culturally comfortable in their home community.

The Cronbach alpha (α = 0.81) indicates good internal consistency, meaning that the items in the scale reliably measure the concept of identity adaptation.

Why It Matters:

This index provides insight into the psychosocial challenges return migrants face upon returning. Feelings of stigma or rejection can make it harder for them to reintegrate emotionally and socially into their communities, potentially leading to mental health issues or social isolation. Understanding these dynamics is essential for creating supportive interventions and services for returnees.

References:

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