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Internal Remittances, Household Welfare, Spending Patterns and Labour Supply: A Study from Rural Areas of Hailakhandi District of South Assam

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Abstract

This paper uses primary data collected from 325 rural households in one of the remote but densely populated districts of Assam, India, to evaluate the impact of internally generated remittances on household welfare, spending patterns and labour supply decisions of left-behind adult family members. Using selectivity-corrected covariate balancing propensity score matching method and also endogeneity-corrected instrumental variable analysis, the study finds that remittances from kith and kin residing elsewhere in the country serve to increase the monthly per-capita consumption expenditure of rural households and help to lower the level, depth and severity of poverty. Remittances have also been observed to influence household spending patterns with higher proportion of annual expenditure being devoted to food and education by recipient households. In the labour market, remittances are found to give rise to a ‘dependency syndrome’ as adult members belonging to remittance-receiving households were less likely to enter the labour market. However, no significant adverse impact of remittances on labour intensity by employed workers was observed. Remittances were also found to be lowering the probability of workers being engaged as casual daily wage labourers while enhancing the likelihood of salaried employment and agricultural and non-agricultural businesses.

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Fig. 1

Source: District census handbook, Hailakhandi, population census

Fig. 2

Source: Authors

Fig. 3

Source: Based on authors’ computation from secondary data

Fig. 4

Source: Based on authors’ calculation from primary data

Fig. 5

Source: Based on authors’ calculation from primary data

Fig. 6

Source: Based on authors’ calculation from primary data

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Notes

  1. The Net Migration Rate of Workers (NMR) is calculated using the CMM as follows.

    \({\text{NMR}} = 100*\left[ {\frac{{P_{t} - P_{t - 1} - {\text{CM}}}}{{P_{t - 1} }}} \right]\)

    where Pt = Population in (20–29) age cohort in Census Year (t) Pt−1 = Population in (10–19) age cohort in Census year (t − 10), CM (Cohort Mortality) = 10 ∗ ASMR ∗ Pt − 1 Where, ASMR = Age-specific Mortality Rate per year in the age cohort (10–19). For details of the method, readers may refer to Dey et al. (2020).

  2. The ADSM (dx) for each covariate is calculated as \(d_{x} = \frac{{{\text{modulus}}\left( {M_{xt} - M_{xc} } \right)}}{{S_{x} }}\) where Mxt = Mean of variable X for treated group, Mxc = Mean of Variable X for control group and Sx = Pooled standard deviation of the groups.

  3. The Poverty Head Count (H), Poverty Gap Index (PGI) and Squared Poverty Gap Index (SPGI) are as follows: \(H = \frac{q}{n}\); \({\text{PGI}} = \frac{1}{n}\sum \frac{{\left( {z - y_{i} } \right)}}{z}, \;y_{i} < z\); \({\text{SPGI}} = \frac{1}{n}\sum \left\{ {\frac{{(z - y_{i} }}{z}} \right\}^{2}\), yi < z where q = number of poor households, n = total number of households in the sample, z = poverty line and yi = monthly per capita consumption expenditure of ith household = total number of individuals in the sample, z = poverty line and yi = monthly consumption expenditure of ith individual.

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  1. Department of Economics, Assam University, Silchar, 788011, India

    Sagarika Dey & Hussain Ahmed Laskar

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  1. Sagarika Dey
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  2. Hussain Ahmed Laskar
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Correspondence to Sagarika Dey.

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Dey, S., Laskar, H.A. Internal Remittances, Household Welfare, Spending Patterns and Labour Supply: A Study from Rural Areas of Hailakhandi District of South Assam. Ind. J. Labour Econ. 65, 161–184 (2022). https://doi.org/10.1007/s41027-022-00361-1

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