Socio-economic Determinants of Temporary Labour Migration in Western Jharkhand,India

Abstract

This article examines the socio-economic determinant of temporary labour migration in Jharkhand. The study uses primary data collected from 12 villages in the western region of Jharkhand. We have used logistic regression model to find out the socio-economic determinant of migration by setting, individual (n = 4,241) and households (n = 781). The regression results show that temporary migration is basically from poor households, and migrants are basically male, young and form Scheduled Caste community. Here, landholding and Monthly Per capita Consumption Expenditure (MPCE) have been taken as the indicator of poverty. We find that there is significant and negative relationship between land owned and migration, that is, the more the land owned, the less the household is likely to migrate. Further, we find that higher the MPCE, lesser the induvial is likely to migrate. Poverty, lack of sufficient means of subsistence, unequal distribution of landholding to meet household expenditure, availability of employment opportunities and loan are the main causes of temporary migration. Since temporary labour migration is very large, it needs to be given high priority with specific policy interventions.

Keywords

Labour migration MPCE poverty Jharkhand

I. Introduction

Migration of labourers for manual work has become one of the most important components of the livelihood strategies of people living in rural areas. It is one of the most common coping strategies adopted by poor households to stabilize their livelihoods and to adapt to the climate, political and economic changes. It is also one of the means for poor rural farm households to overcome shortfalls of seasonal agricultural income and employment. Despite the problem of unavailability of temporary labour migration data and insufficient data, there is an increasing number of micro-studies, which have proven that temporary migration for employment continues to grow in absolute numbers as well as in relation to the size of the population (Breman, 1985, 1996; Deshingkar, 2006; Deshingkar & Start, 2003; Dodd et al. 2017; Korra, 2010; Rao, 1994; Rogaly et al., 2001). According to National Sample Survey Office (NSSO, 2010) estimates, short-term migrants varied from 15 million to 100 million. The National Commission for Rural Labour [NCRL] (2011) puts the number of circular migrants in rural areas alone at around 10 million (including roughly 4.5 million inter-State migrants and 6 million intra-State migrants). According to NSSO (2010) data, the temporary migration rate is seven times higher than the permanent migration rate. Short-term migrants mainly comprise the ones who are from the socially and economically deprived backgrounds. The socio-economic profile of temporary migrants is very different from that of the other migrants. These migrants are much more likely to belong to the socially deprived and poorer groups, and are likely to have low level of education and are more likely to engage in casual work (Srivastava, 2011). There is a macro–micro paradox. On the one hand, national-level or macro-level data highlight that migration is selective with opportunities biased against the poor, a process that might be reinforced with technological change. On the other hand, micro-studies often show very high rates of migration among poorest and socially marginalized groups, and over-representation of migrants including bonded and child labour, with Adivasis and Dalits over-represented among the bottom layer of the working class (National Commission for Enterprises in the Unorganised Sector [NCEUIS], 2009). Temporary migration tends to be less visible at macro-level because it is inadequately captured by the National Sample Survey surveys and in the Census. The lack of a detailed data renders much of the temporary migrants go unrecorded that adds to their vulnerability. Study on temporary migration is essential because it has greater potential for poverty reduction and development. This is because of three reasons: first, temporary migration remittances have a greater potential to contribute to poverty reduction. Although the individual quantities are smaller, the total volume of temporary migration remittance is likely to be enormous because of the number of people involved. Second, temporary migrations continue to increase at a faster rate. Third, it involves poorer people from poorer regions.

Among all the states, Jharkhand presents a very gloomy picture about temporary migration (Keshri & Bhagat, 2012; Rao & Mitra, 2013). Dayal and Karan (2003) studied 12 villages in the tribal forested state of Jharkhand and found that one-third of the households had at least one member migrating to states like Bihar, Jharkhand, Gujarat and Madhya Pradesh. The temporary migration rate (30–50) migrants per 1,000 population, which is very high. Among the major states, Jharkhand (36), Gujarat (34), Madhya Pradesh (33), West Bengal (30) and Rajasthan (21) also had a high intensity of temporary labour migration above the national average (20) (Keshri & Bhagat, 2012). Jharkhand has 539,600 temporary migrants. Temporary migration provides an important livelihood strategy during the agricultural lean season with people moving to big cities in the same state or to the other states (Breman, 1994; Deshingkar & Farrington, 2009; Jayaraman, 1979; Rogaly et al., 2001). Jharkhand lost close to 5 million of its working-age population between 2001 and 2011 due to migration (Government of India, 2017). Lack of employment opportunity, deprivation and loss of traditional livelihood resulted in migration of more than 5 per cent of the population annually. This also constitutes to be the highest net outflow rate in the country. Unemployment rate has remained a major challenge for Jharkhand. According to the NSSO’s 2015–2016 data on employment and unemployment situation in India, unemployment rate of Jharkhand is at 7.4 per cent compared to 5 per cent at the all-India level (Jharkhand Economic Survey, 2018).

Natural resources are available abundantly in Jharkhand; despite this, the region is ranked the lowest in development. The population of Jharkhand suffers from chronic hunger and seasonal food insecurity. Jharkhand is the second highest food-insecure state of India after Bihar. According to the Planning Commission Report (2013), Jharkhand has the second highest percentage (36.96%) of population below the poverty line in comparison to other states, which is much higher than the national average (21.92%). The Global Multidimensional Poverty Index (MPI) Report (2015–2016), released by Oxford Poverty and Human development Initiative (OPHDI) and United Nations Development Programme (UNDP) reveals that about 46 per cent of the population of Jharkhand are multi-dimensionally poor, which is next to only Bihar (Jharkhand Economic Survey, 2018). The condition of poverty in Jharkhand manifests in myriad ways. Malnutrition, ill health and illiteracy at the household level are more acute with respect to women and children (particularly girl children) and, among the various social groups, the Scheduled Tribes (STs), SCs and Muslims (Report of the task force on poverty elimination in Jharkhand, 2015). According to National Family Health Survey (NFHS-4), conducted in 2015–2016, the nutritional status of children under 5 years is critical in Jharkhand. Jharkhand has 45.3 per cent undernourished children. Jharkhand stands second after Bihar in the population of undernourished children under 5 years. The undesirable impacts of malnutrition are significantly seen in adults, too. For example, th Body Mass Index (BMI or the ratio of weight for height) of a sizeable proportion of women (31.5%) and men (23.5%) in the age group of 15–49 years is found to be falling below the norm, which is much greater than the national average. Jharkhand has the highest number of women below normal BMI in comparison to other states. Further, men (29.9%) and women (65.2%) between the age group of 15 and 49 years are found to be anaemic, which manifests the poor health condition and lack of iron and nutrition among the population of the state. About 70 per cent of children aged between 6 and 59 months are anaemic.

In an under-developed district of Garhwa, temporary migration has become a vital component of struggle for survival by the poor. Garhwa is a backward district of Jharkhand in comparison to other districts. As per the Global MPI Report (2015–2016), released by OPHDI and UNDP, Garhwa district comes under the very high incidence of MPI—about 56 per cent of people come under MPI. BMI of a sizeable proportion of women (31.8%) and men (28.3%) in the age group of 15–49 years is found to be below the norm. About 65.4 per cent of children aged 6–59 months are anaemic. Garhwa ranks high in the socio-economic vulnerability index (Jharkhand Economic Survey, 2015–2016). Low per capita income with smaller number of banks and poor credit deposit ratio show that the district is economically vulnerable. According to the task force report on poverty elimination in Jharkhand, the population of Garhwa is large with low sex ratio, and the population of SCs in the region is as high as 24.19 per cent. The region is subjected to deprivation, which has been deprived of social and economic development. Food security is one of the most crucial and pressing issues for the district. The district comes under what is called as the rain shadow area. In addition, the district receives low rainfall with an irrigated land of only 25.9 per cent of the total cultivable area (Census of India, 2011). With regard to human development indicators, Garhwa district as a part of Jharkhand state is ranked at 19 among 23 states taken for comparison (UNDP, 2011). The literacy rate of any country is one of the key indicators to determine its socio-economic progress, and the Indian literacy rate stands at 71.04 per cent, whereas Garhwa’s literacy rate stands almost 10 points below at 60.33 per cent (Census of India, 2011). The present study has been conducted in Garhwa district of Jharkhand. The choice of Garhwa as the focus of the empirical component of this research is made due to several reasons. Justification lies primarily in three different aspects: the socio-economic characteristics of the place; the relevance of its migration dynamics; and the fact that relatively little research has been carried out in this district.

Therefore, this article explores the socio-economic determinants of temporary labour migration in the western region of Jharkhand. The rest of the article is classified into six sections, including the present one. The second section presents theoretical framework of labour migration. The third section reviews earlier literature on temporary migration. The fourth section presents data source and study area. The fifth section explains the empirical results. Finally, the sixth section presents the conclusion with some policy implications.

II. Theoretical Framework

Lee (1966) analyses that migration is determined by the marital status, health, education and family responsibility. Lee also stated that migration is selective with respect to the individual characteristics of migrants. Lee rests on the individualistic interpretation of factor; as such, his approach is micro-level. Lee was the first to conceptualize migration as the play of negative and positive forces that, respectively, push a migrant from the place of origin to migrate and pull him to the place of destination.

Neoclassical economics focuses on differentials in wages and employment condition between origin and destination, and on migration costs. The first comprehensive model of rural migration (labour transfer) could be seen in the Lewis model (1952), which was later modified and extended by Ranis and Fei (1961) and known as the L–F–R model. The model is based on the concept of a dual economy, comprising a subsistence agricultural sector characterized by surplus labour with low productivity, and the other one is the industrial sector, which is characterized by full employment—the capitalists reinvest the full amount of their profit. Labour migration takes place from rural to urban areas (Lewis, 1952). The Harris–Todaro (1970) theory locates the decision of migration at the household level and argues that such decisions are based on opportunities and the constraints faced by the household. The neoclassical theorists propagated human capital theory (Sjaastad, 1962; Todaro, 1969, 1980). This construct argued that the inclination to migrate is determined by wage difference between sources and destination of migration, and it may result in equating expected income. Given their skills, their decisions about where to live are based on where the individuals can optimize the present value of their discounted stream of expected future earnings. It generally conceives movement as an individual decision for income maximization (Borjas, 1989; Lewis, 1952; Todaro, 1969). According to Saxena (1977), migration may be motivated by a desire to seek skill and that leads to development, urbanization and socio-economic transformation. In the same vain, Stark (1980) identifies transaction cost, imperfect information and imperfect credit, land and labour markets as the main determinants of migration. Stark and his colleagues have offered a ‘new economics of migration’ (e.g., Stark & Bloom, 1985). New economics of migration takes into consideration not only the labour market but, instead, does a comparative study of the conditions in other markets—those being the capital market or the unemployment insurance market. It views migration as a household strategy that focuses on minimizing the income risk and helps in coping up with capital constraints by ensuring regularity and stable inflow of income (Stark, 1991). The New Economics of Labour Migration (NELM) framework of analysis (Taylor, 1991) bases its study on multiple factors that eventually are seen as deciding factors for migration to take place and also studies the impact or effect of such migration on the economy both on the origin of migrant and at the destination of the migrant.

According to Marx, value transfer via migration is a form of super-exploitation by which monopoly capital obtains extra profits. Imperialist capital draws profit by paying the migrant workers below the value of their labour force in several ways. The capitalists can often exploit the migrants with limited or no costs for their education since the migrants are often educated in their home country. The capitalists often have to pay either no or only reduced costs for the pension and social security of the migrants since they have limited access to social services, and when they get old, they often go back to their home country. Migrant women and youth experience an additional suppression. Migrant women are—even more than their male colleagues—employed as a very low-paid unskilled workforce. Migrant youth are also oppressed in the patriarchal family and, owing to the social and language discrimination, their education level is significantly lower than that of their domestic colleagues (Rubin, 1973).

Researches driven by Marxist ideology (Breman, 1985; Olsen & Murthy, 2000) identified the structural constraints of the capitalist system as the main source of exploitation of migrant labourers. In the absence of alternatives, in extreme cases, monopoly creditor also becomes a monopsony buyer of migrant’s labour (Olsen & Murthy, 2000). Breman (1985) explained how migration is viewed as a coping mechanism that provides means for debt-servicing, for the well-endowed it adds to their income. Breaking away from the neoclassical interpretations of determinants of migration, the National Commission on Rural Labour (1991) is focused on temporary migration, and it concluded that uneven development is the main cause of temporary migration.

This study is based on new economics of migration, apart from root causes of the intervening factors taken into account. The root cause is mainly of an economic nature like causes are associated with more underlying longer-term factors, such as poverty or employment opportunities and wages. The intervening factors like multiple causes, such as transaction cost, imperfect information and imperfect credit, land and labour markets as the main determinants of migration, have also been taken into account. This framework structures the determinants of migration according to three different levels: the macro-, meso- and micro-levels. The importance of a macro-level factor—such as economic opportunities, poverty and development—is likely to be sensitive to meso-level factors, which are networks (that can facilitate jobs) and infrastructure or to micro-level factors, such as age, gender and educational level (affecting employability). Apart from this, household capability has been taken into consideration.

III. Review of Empirical Studies

Temporary migration during the lean period seems a necessary tool for poor families, as external help or their own resources are inadequate for dealing with the seasonality of income and consumption. The pattern of migration shows that the rate of migration among the youth is higher than that of the old as the young are more capable to be working as labourers than the old. Generally, young males were most likely to temporarily migrate for work (Dodd et al. 2016). However, earlier studies by Hogan and Steinnes (1998) concluded that seasonal migration may be an integral part of a life cycle of elderly migration. Seasonal migration increases initially as the household reaches retirement and then declines at older ages due to the increasing likelihood of poor health or other problems that would limit travel. Similarly, Mosse et al. (2002) argued that migration is influenced by social structure. Collision et al. (2003) argued that owners of large tracts of land or the non-poor migrate to enhance their wealth and to accumulate more wealth; on the other hand, the poor migrate because of the insufficient income from farming where migration acts as a coping mechanism to survive during lean years, resulting in seasonal migration (Abril and Rogaly, 2001). Khandker, Khalily, and Samad (2012) showed that the probability of seasonal migration is high for households with a high dependency ratio and in villages with high unemployment, but low in villages with microcredit access. He finds seasonal migration helps households to smooth consumption, but the cost of migration and lack of networking are potential barriers. According to Korra (2010), the primary survey of Mahabubnagar district of Andhra Pradesh shows that migration takes place mainly for survival and repayment of debts, and other reasons are inadequate yield of food grains from cultivation, lack of employment, earning for children’s marriage and investment in agriculture. Several earlier studies have examined the association between temporary migration and its determining factors and found negative association between economic status and temporary migration. Shortage of farmland, low income from agriculture and low livestock-intensive households are the major cause of migration (Asfaw et al., 2010; Deshingkar & Start, 2003; Dodd et al., 2016; Haberfeld et al., 1999; Wondimagegnhu & Zeleke, 2017). In general, socio-economically deprived groups such as STs and SCs have a greater propensity to migrate seasonally, which also reflects its distress-driven nature (Agrawal & Chandrasekhar, 2015; Deshingkar & Start, 2003; Dodd et al., 2016; Keshri & Bhagat, 2012). Temporary and short-duration migrations are guided by employment-related factors. The empirical result supports the theoretical argument that higher wages and the cost of separation shape seasonal migration to a significant degree in India (Parida & Madheswaran, 2012). The patterns of migration or movement are determined by social network (De Haan et al., 2002). De Brauw and Harigaya (2007) stated networking and knowledge about jobs outside local markets, which can only influence the decision to migrate, but not directly affect the outcome of migration. Indeed, networking ability may enable an individual to more easily secure economic opportunities through migration.

Using nationally representative NSSO (2010) data for India, Chandrasekhar, Das, and Sharma (2015) find households with a short-term migrant having lower monthly per capita consumption expenditure and monthly per capita food expenditure, compared to households without short-term migrants. Shah (2006) draws on fieldwork in a village in Jharkhand and a brick kiln in West Bengal to argue that migrants do not understand their movement in economic terms alone. Many see the brick kilns as a temporary space of freedom to escape problems back home, explore a new country, gain independence from parents or live out prohibited amorous relationships. Gubhaju and De Jong (2009) explained that new household economic theoretical assumptions on migration decision-making rules are segmented by gender, marital status and time frame of intention to migrate. Neoclassical micro-economic theory proposition is more applicable for men and women who are not married, the ‘maximizing household income’ proposition for married men with short-term migration intentions and the ‘reduced household risk’ proposition for longer time horizon migration intentions of married men and women. Temporary out-migration received considerable attention at international and national domain, but there is no common view on temporary labour migration. Some predictive power remains inadequate for understanding the complexity of migration patterns. In an under-developed state like Jharkhand, where seasonal migration is very high, very few studies have been conducted on this issue. Therefore, an attempt has been made to find out various causes of temporary labour migration in Jharkhand.

IV. Data Source and Study Area

The present study is based on data collected through a 2018–2019 rural household survey of 781 households living in the western region of Jharkhand. These primary research findings are based on my own field survey along with two field investigators. In this research, multi-stage stratified random sampling and purposive techniques were used to select households and study locations. The reason for using stratified sampling technique is to include and represent specific groups of the research interest in different meaningful strata and with certain purposes of the research. The specific categorization includes non-migrant sending; temporary labour migrant sending households; income status (poorest, poor and better-offs); social group (SC, ST, OBC and GEN), religion (Hindus, Muslims and Christians). Purposive technique was used to select the research region, whereas multi-stage stratified random sampling technique was used to select blocks/villages and households taking to account certain strata. Garhwa district in Jharkhand is chosen as the study area on the basis of socio-economic condition of temporary labour migration. Garhwa district is situated on the north-western most boundary of Jharkhand state (Garhwa District Website). Garhwa district and Jharkhand state share their borders with numerous other districts and states (see Figure 1). All these bordering states and districts have one thing in common—‘the red corridor’. It is considered as an extremely affected backward district (Rashtriya Sam Vikas Yojana Planning Commission, 2010). Here, temporary labour migration is captured by asking the question to each household, whether any member had gone away for employment purposes for more than 1 month but less than 6 months during the previous year. The place holds relevance due to the dynamic migration pattern, and it also continues to be an area which has not been studied. Hence, there is immense scope to study and carry out a research on migration. In this study, we have basically considered Dandai, Meral, Sagma and Ramna block of Garhwa district as the study area. From each block, three villages have been chosen for the study. Six on the roadside and six off the road, villages have been taken for the study. Jarde, Doni, Alias Lawadoni and Kadailia villages of Dandai block have been chosen for the study. Dandai block is one of the poorest blocks of Garhwa with only 2.63 per cent of households having a monthly income, and the highest earning household member earning ₹10,000 or more (Socio-Economic Caste Census [SECC], 2011).

Figure 1.

Source: https://www.jharkhand.gov.in/

According to the SECC, 2011, data, Jardey village has 27 per cent SC households, 3.84 per cent kutcha households and 60.90 per cent pucca households. Jardey village comprises the (a) Bhuyian, (b) Lohar, and Bishwakarma, and the (c) Kamar who belong to the SC, ST and OBC categories, respectively. In the Doni, Alias Lawadoni village, 97.43 per cent of households are ST and SC households (SECC, 2011). Lawadoni comprises of the SC, ST and OBC households. SC household comprises the Dhobi and Chamar communities. STs include Kharwar, and OBC comprises the teli and dhunia. Dhunia are the people belonging to the Muslim community. According to the Socio-Economic Caste Census, Jharkhand 2011 (SECC Jharkhand, 2011) data, Kadailia village has 79.62 per cent pucca households. A total of 64 per cent of the population of this village are SCs (Village Census, 2011). There are two social groups (castes) in Kadalia village, out of which the Dusad belong to the SCs and Mallah belong to Other Backward Classes (OBCs). Kotam, Bouraha and Kumbhi villages of Meral block have been taken for the study. In the Meral block, 3.38 per cent of households have monthly incomes of ₹10,000 or more (SECC Jharkhand, 2011). In the Kotam village, 28.57 per cent households are SC and ST households. A total of 78 per cent of households of this village are kutcha household. There are two social groups (castes) in the village, out of which the Momin belong to the OBCs. Momin are the Muslim community of Jharkhand. Parhaiya belong to the STs and are the particularly vulnerable tribal group of Jharkhand. In the Bourah village, 47 per cent of households are SC and ST household. A total of 92 per cent of household of this village are kutcha household (SECC, 2011). There are three social groups (castes) in the Bourah village, out of which the Lambadis and Oraon belong to the STs. Among Oraon, Kisppota, Toppo and Ekka sub-castes have been covered. Chamber and Bhuinya belong to the SCs and Dusad, Lohar and Kuswaha belong to OBCs. In Kumbhi village, near to 48 per cent of households are SC and ST households. Near to 89 per cent of households of this village are kutcha households (SECC, 2011). There are two social groups (castes) in the Kumbhi village out of which Bhuinya and Dusad belong to the SCs. Lohar, Kuswaha, Kumbar, Biswakarma and Kurmi (Mahto) belong to OBCs. Lolki, Chainpur and Jhunka villages from Sagma block have been taken for the study. In the Sagma block, 2.47 per cent of households have monthly incomes of at ₹10,000 or more (SECC, 2011). A total of 60 per cent of the Lolki village households are SC and ST households. Overall, 97 per cent of the households of this village are kutcha household (SECC, 2011). There are two social groups in the village, out of which Pahariya belong to the particularly vulnerable tribal group; Momin and Ansari are the Muslim communities and belong to the OBC; and the Yadav community belongs to the OBC. In Chainpur village, 51 per cent of the households are SC and ST households. A total of 98 households of this village are kutcha households (SECC, 2011). There are three social groups (castes) in the Chainpur village, out of which the Bhuinya and Dusad belong to the SCs, Lohar, Ahir, and Koiri belong to OBC, and Gond belong ST category. A total of 51 per cent in Junka village are SC and ST households. A total of 97 per cent of households of this village are kutcha households (SECC, 2011). There are two social groups in the village, out of which Bhuinya, Chamar and Dusad belong to the SCs and Ahir and Kurmi belong to the OBC. Siria Tonger, Karcha and Garda villages of Ramna block have been taken. It is 4.95 per cent of households have monthly income of ₹10,000 or more, which is greater than district average of 4.4 per cent (SECC, 2011). In the Siria Tonger village, 21 per cent of households are SC and ST households. A total of 81 per cent of the households of this village are kutcha households (SECC, 2011). In Siria Tonger village, there are two social groups, out of which the Bhuinya belong to the SCs and Ahir and Momin belong to the OBC. In the Karcha village, 52 per cent of the households are SC and ST households. A total of 74 per cent of households of this village are kutcha households (SECC, 2011). There are two social groups in Karcha village, Dhobi and Chamar belong to SCs and Koiri, Biar, Lohar, Khangar and Lohar belong to OBC. In the Garda village, 78 per cent of households are SC and ST households. A total of 97 per cent of households are kutcha households (SECC, 2011). There are two social groups in the village—ST and OBC. Parhaiya belong to the STs, they are the particularly vulnerable tribal group, and Saha belongs to the OBC group.

Socio-economic Determinates of the Study Area

In the micro-level determinant of migration, it is important to take individual characteristics into account. They should, in general, not be regarded as primary drivers, but as factors that, nevertheless, have a significant influence on migration decisions and lead to the self-selection of migrants. Only a selected set of individual characteristics is discussed below as follows:

Individual Level Factors Associated with Temporary Labour Migration

The basic objective of this section is to analyse who is migrating on the basis of individual and household characteristics. The characteristics of the individual, that is, age, marital status, years of schooling and occupation of the respondents have been considered to understand the selectivity of the migration process. A total of 4,241 individuals were chosen, out of which 739 are migrants (see Table 1). The demographic profile of the temporary labour migrant has been analysed in Table 2. From Table 2, we can find that out of the 739 migrants, invariably, 638 of them are male migrants. Labour migration is a highly gendered activity. Gender is a crucial factor of the causes and consequences of internal migration. Women are less likely than men to migrate, and that the gender migration gap is larger and temporary compared to permanent migration. Due to social and cultural norms and low wage rate at destination in comparison to males, migration among females is lower than males. Though the opportunity cost of migration is high for females, they stay at home and share household responsibility. In the study area, migration is dominated by married men.

Table 1.

Profile of Study Area Villages

Study Village	Total No of HHs	No. of HHs Covered	No. of Migrant HHs Covered	No. of Non-migrant HHs Covered	Total No. of SC HH	No. of SC HHs Covered	Total No. of ST HHs	No. of ST HHs Covered	Total No. of OBC HHs	No. of OBC HHs Covered	Total No. of General HHs	No. of General HHs Covered	Quality of Road to and from Village	Access to Public Transportation
Jarde	189	66 (33%)	53	13	64	16	0	0	125	50	0	0	Good	Good
Doni Alias Lawadoni	106	36 (36%)	24	12	9	6	92	26	5	4	0	0	Poor	Poor
Kadailia	143	49 (34%)	37	12	102	35	0	0	41	14	0	0	Poor	Poor
Kotam	105	36 (34%)	25	9	0	0	28	8	77	28	0	0	Good	Good
Bouraha	250	82 (35%)	70	12	93	40	27	7	130	35	0	0	Poor	Poor
Kumbhi	211	76 (34%)	50	26	60	24	35	19	116	33	0	0	Good	Good
Lolki	105	36 (34.28%)	27	9	0	0	60	15	45	12	0	0	Good	Good
Chainpur	250	81 (32.40%)	54	27	106	33	8	4	136	44	0	0	Poor	Poor
Jhunka	266	93 (34.96%)	66	27	115	37	9	6	142	23	0	0	Poor	Poor
Siria Tonger	290	100 (34.48%)	73	27	50	10	0	0	240	62	0	0	Good	Good
Karcha	191	91 (47.64%)	57	34	110	35	3	1	28	9	50	16	Good	Good
Garda	95	35 (36.84%)	28	7	0	0	76	23	19	5	0	0	Poor	Poor
Total	2201	781 (35%)	564 (72%)	217 (27%)	707 (32%)	236 (30%)	341 (15%)	103 (13%)	1044 (47%)	315 (4%)	50 (2%)	16 (2%)

Source: Field survey (2018).

Note: The value in the parentheses represents the percentage of households covered in total households.

Age is the major determinant of migration from the employment opportunity point of view. The migration tendency is high for the early age group. The mean age of the migrant (at the time of survey) is 30.52 years old. It is quite clear that in Garhwa, young people tend to experience rural–urban migration more than the older people. Out of the total migrant members, 48 per cent were between the early age group of 15 and 29 followed by 38 per cent of prime age group (30–44), while it is less than 2 per cent for the 60–74 years age group.

Migration and education are decisions that are, indeed, intertwined in many dimensions. Education and resultant skill acquisition influence the decision to migrate. The differential returns to skills between origin and destination country have been identified as the main drivers for migration. The study has found positive effects of educational attainment on the propensity to migrate. People who have a low level of education tend to be more mobile or migrate more. We find that 30 per cent of migrants are illiterate, but near to 60per cent are literate without formal education—below primary, primary school and upper primary. People with higher education like secondary school, higher secondary and graduation constitute only 11 per cent of migrants (see Table 2). In conclusion, the general trend is that people who have low level of education tend to be more mobile and end up getting jobs in the informal sector. A positive relationship is hypothesized between marital status and migration because previous research (Happel et al., 1988; Hogan, 1987; Longino & Marshall, 1990; Martin et al., 1987) has identified that a high proportion of seasonal migrants are married. One explanation is that seasonal migration is often tied to social activities. Further, we find that married people are migrating more for their family responsibility than unmarried people. Migration among married people is near to 80 per cent, but in case of unmarried migrants, the movement is less than 20 per cent (see Table 2).

Table 2.

Demographic Profile of Temporary Labour Migrants

Variable	Number of Migrants	Percentage in Total
Sex
Female	101	13.67
Male	638	86.63
Age
0–14 (child labour)	25	3.38
15–29	356	48.17
30–44	279	37.75
45–59	66	8.93
60–74	11	1.48
75 Above	2	0.27
Mean age	30.52
Education
Not Literate	222	30.04
Literate without formal education	75	10.14
Below primary	125	16.91
Primary school	143	19.35
Upper primary	94	12.71
Secondary school	47	6.35
Higher secondary	32	4.33
Graduate	1	0.13
Mean year of education	5.4
Marital status
Married	584	79.02
Widowed	3	0.4
Single/never married	152	20.56

Source: Field survey (2018).

Table 3 shows that the seasonal out-migrants are mostly interstate (96%). Inter-district migrants accounted for 4.47 per cent, and there are 2.3 per cent intra-district migrants. Only 0.95 per cent is international migrants. About 96 per cent from the study area migrate to other states. It is important to note that a desire to migrate does not necessitate a decision to migrate or an actual attempt to migrate. The latter is constrained by the individual or household capability, including financial or social capital.

Table 3.

Distribution of Temporary Labour Migrants by Destination

Migrants’ Destination	Number of Migrants	Percentage in Total
Intra district (within district)	17	2.3
Inter district (outside district)	33	4.47
Interstate (outside state)	682	92.29
International (outside country)	7	0.95

Source: Field survey (2018).

Household Factors Associated with Temporary Labour Migration

In this study, 565 migrants and 216 non-migrant households have been covered. The distribution of temporary labour migrant and non-migrant households by socio-economic dimensions has been analysed in Table 4.The study found that people are migrating more from marginal landholding households. We find the mean landholding of migrant households is 0.23 ha, whereas the mean landholding of non-migrant households is 0.33 ha (see Table 4).

Table 4.

Distribution of Temporary Labour Migrant and Non-migrant Households by Socio-economic Dimensions

Household Variable	Number of Migrant HHs	Percentage in Total	Number of Non-Migrant HHs	Percentage in Total
Caste
Scheduled Caste	203	35.92	67	31.01
Scheduled Tribe	86	15.22	31	14.35
Other Backward Caste	261	46.19	106	49.07
General (Other)	15	2.65	12	5.55
Religion
Hindu	474	83.89	180	83.33
Muslim	91	16.10	36	16.66
Type of household
Kutcha	434	76.81	163	75.46
Semi-Pucca	47	8.31	26	12.03
Pacca	84	14.86	27	12.5
Ration card
Priority household	405	71.68	151	69.90
Antyodaya Anna Yojana (AAY)	80	14.15	29	13.42
Non-priority household	74	13.09	33	15.27
No ration card	6	1.06	3	1.38
Monthly per capita consumption expenditure (MPCE)
Below poverty line (BPL) (less than ₹500)	8	1.41	3	1.38
Lower income group (LIG) (₹500–4,999)	509	90.08	191	88.42
Medium income group (MIG) (₹5,000–9,999)	39	6.90	16	7.40
High income group (HIG) (₹10,000 above)	9	1.59	6	2.77
Average monthly per-capita expenditure (MPCE)	2,181.75		2683.5
Land holding (in ha)
Landless	22	3.89	2	0.92
Marginal	527	93.27	207	95.83
Small	13	2.30	5	2.31
Semi-medium	3	0.53	0	0
Medium	0	0	2	0.92
Average	0.23		0.33
Average HH size	5.66		4.79

Source: Field survey (2018).

Note: Marginal land holding: less than 1 ha of land; Small landholding: 1–2 ha of lands; semi-medium: 2–4 ha; medium: 4–10 ha; and large: more than 10 ha.

The migrant household mean monthly consumption expenditure is ₹2,181.75, whereas the mean monthly consumption expenditure of the non-migrant household is ₹2,683.5 (see Table 4). In the study, we found that migrants are more from lower-income-group households. Migrants are mostly from kutcha households—they are mostly from poor households. These ﬁndings indicate that poor households are involved in temporary labour migration; however, some minimal threshold of resources may be required to participate in and leverage the beneﬁts from these labour movements. We find the mean household size of migrants is 5.66, whereas mean household size of non-migrants is 4.79. It means that larger the household size, more the people are migrating for livelihood. It is important to note that the highest migration is generally from OBC, which account for 46 per cent, followed by the SCs, which accounts for 36 per cent. The STs account for 15.22 per cent (See Table 4).

There are three major forms of migration through which migration is taking place from the origin: single-member-migrating households, more than one member migrating households and whole family member is migrating. We find there are 453 households with single migrants, 103 households with more than one family member migrant and 9 households with whole family members migrating. Further, we found around 80 per cent of households are single-migrant households (see Table 5).

Table 5.

Distribution of Migrant Households by Form of Migration

Form of Migration	Number of HHs	Percentage in Total
Household with single member migrant	453	80.17
Household with more than one family member migrant	103	18.23
Household with whole family member migrant	9	1.59

Source: Field survey (2018).

Near to 2 per cent households are migrating with all the family members seasonally. Children accompanying their migrant parents for seasonal employment are the most ‘at-risk’ group of all in terms of educational vulnerability and capability formation. They are deprived of basic education and therefore become bonded to the low-skill–low-wage trap that their parents are currently in. Labour migrants are employed in a few key subsectors, including construction, brick manufacturing, transportation, domestic work, textile mining, quarrying and agriculture.

Table 6 analyses the reasons for temporary labour migration. We find that people are mostly migrating due to lack of sufficient means of subsistence, availability of employment opportunities at destination, to meet household expenditure, due to indebtedness and land shortage. One of the factors that attract people to migrate elsewhere in search of wage employment is the availability of job opportunities. The increase in the amount of construction work in major towns and increasing daily wages have attracted many rural people to towns. We find that the need to repay debt is one of the major reasons for migration. People in the study area were found to migrate temporarily because they were unable to repay debts and avoid incurring further debts. Informants stated that they often repaid credit from micro-finance institutions and covered their debt from the cash they obtained from off-farm wage employment. The empirical findings suggest that poverty, lack of sufficient means of subsistence, unequal distribution of landholding to meet household expenditure, lack of employment opportunities and loan are the main causes of temporary migration.

Table 6.

Distribution of Temporary Labour Migrants by Reasons of Migration

Factors	Rank 1	Rank 2	Rank 3	Rank 4
Lack of sufficient means of subsistence	578	0	0	0
To earn additional income	94	128	0	0
Land shortage	12	210	18	0
Availability of employment opportunities at origin	11	294	253	4
Indebtedness	33	21	135	70
To buy modern clothes	0		2	2
To cover festival expenditure	0	2		2
To cover children’s educational expenses	0	1	4	3
To Meet household expenditure	562	0	0	0
Increased household size	529	0	27	154
Construction purchase of house	1	0	0	0
Marriage of sibling/children	0	0	0	0

Source: Field survey (2018).

Note: This table is multiple response table in which ranks 5, 6 and 7 is zero for all the reason for migration except for the reason of increased household size, the ranks are 261, 81 and 3, respectively.

Box 1.

Case of Migrants from Kumbhi

I am 65 years old Ramgat Ravi. I have no land to till. I am from the Bhuinya caste. I migrate temporarily to Sultanpur to work in the brick kilns along with all my family members. Since the last 30 years, I am working in brick kilns, and now my family members are also involved in this work. It has become a family occupation. We migrate through agents (Thikadar). The Thikadar is familiar with both the source and the destination. He visits our village and nearby villages annually to recruit labourers. We usually migrate for 8 months from October to May to work in brick kilns. During the rainy season, we return and stay at our village. Landlessness and Indebtedness are the main causes of migration. I usually borrow from the agent (Thikadar) for a day’s expenditure and repay the loan through migration work. Due to the migration, my daughter-in-law, Pinki Kumari, and Rakesh Kumar’s education get adversely affected. At the destination, the children do not have accessibility to education. When they come back home, they have a separate school run by the government for their children. At the destination, the household members have to share the dwelling place with other household members and usually do not have an electricity connection. At the destination, payment is made according to the piece rates of bricks.

Social Factors

Social networks, information flow and personal networks like friendship and kinship ties are among the important determinants of migration. In Table 7, we analyse various channels of migration. We find almost all of migrants had obtained information about the destination area prior to migration. Their sources of information are friends, own brother, relatives and their own earlier experience. Seasonal migrants were also asked if someone had arranged employment for them before migration to the destination.

Most of them responded that the contractor had arranged for their employment before their migration to the destination. Seasonal migration is managed in many cases by private contractors (73%) and fuelled by social network. Migration flows are mediated by an elaborate chain of contractors and middlemen who perform the critical function of sourcing and recruiting workers. The lowest links in this chain are most often older migrants or friends and relatives who are part of the same regional or caste-based social network in rural areas. The chain then progresses towards destination-based contractors who aggregate workers from different geographies and link them finally with the principal employers (see Table 7).

Table 7.

Distribution of Temporary Labour Migrants by Channels of Migration

Channel of Migration	Number of Migrants	Percentage in Total
Non-government organization agency	14	1.89
Licenced recruit agency	1	0.13
Relative	2	0.27
Friends	147	19.89
Contractor	541	73.2
Self	34	4.60

Source: Field survey (2018).

Table 8 analyses the distribution of temporary labour migrant by occupation of migrants at the destination. We find that near to 80 per cent migrant occupation is in the construction sector. The employment of migrants in other sectors like agriculture and services is very low compared to the construction sector.

Table 8.

Distribution of Temporary Labour Migrants by Occupation of Migrant at Destination

Occupation of Migrants at Destination	Number of Migrants	Percentage in Total
Causal labour in agriculture	28	3.78
Causal labour in non-agriculture (mining and quarrying)	3	0.40
Causal labour in non-agriculture (manufacturing)	38	5.14
Causal labour in non-agriculture (electricity)	2	0.27
Causal labour in non-agriculture (construction)	587	79.43
Causal labour in non-agriculture (brick maker)	58	7.84
Causal labour in non-agriculture trade, hotel and restaurant	9	1.21
Causal labour in non-agriculture transport	6	0.81
Causal labour in other services	8	1.08

Source: Field survey (2018).

Table 9 analyses the month of migration and the month of return of temporary labour migrant. We find that the month of migration and return is mostly not specified. Usually, during the month of October and November, there is a high rate of migration from Garhwa. These families or individuals return home by June and July (see Table 9).

Table 9.

Distribution of Temporary Labour Migrants by Month of Migration and Month of Return

Month of Migration	Number of Migrants	Month of Return	Number of Migrants
January	17	January	6
February	5	February	6
March	15	March	37
April	7	April	6
May	8	May	21
June	13	June	193
July	9	July	34
August	36	August	104
September	17	Sept	6
October	255	October	12
November	105	November	8
December	63	December	7
Not in specific month (but in non-agricultural season)	190	Not in specific month (depends on the duration of work assigned)	299

Source: Field survey (2018).

V. Empirical Results and Analysis

In order to find out who migrate and why, here, logistic regression has been used to find out the socio-economic determinant of seasonal migration. In logistic regression, the dependent variable is binary or dichotomous, that is, it only contains data coded as 1 (TRUE, success, migrant, etc.) or 0 (FALSE, failure, non-migrant, etc.). The goal of logistic regression is to find the best fitting (yet biologically reasonable) model to describe the relationship between the dichotomous characteristic of interest (dependent variable = response or outcome variable) and a set of independent (predictor or explanatory) variables. Logistic regression generates the coefficients (and its standard errors and significance levels) of a formula to predict a logit transformation of the probability of presence of the characteristic of interest:

The logit model is defined as:

\begin{matrix} P_{i} = E (Y = \frac{1}{X_{1}}, X_{2}, \dots, X_{k}) \\ = β_{0} + β_{1} X_{1} + β_{2} X_{2} + β_{3} X_{3} + β_{4} X_{4} + β_{5} X_{5} \\ + β_{6} X_{6} + β_{7} X_{7} + β_{8} X_{8} + β_{9} X_{9} + β_{10} X_{10} \\ + β_{11} X_{11} + β_{12} X_{12} + β_{12} X_{13} \end{matrix}

(1)

Y = 1, if at least one member of the household migrates during the year, and 0 otherwise

P_{i} = E (Y = \frac{1}{X_{1}}, X_{2}, \dots, X_{13}) = \frac{1}{1 + e^{- (β_{0} + β_{1} X_{1} + β_{2} X_{2}, \dots, β_{12} X_{12})}}

(2)

For ease of exposition, we can write the Equation (2) as

P_{i} = \frac{1}{1 + e^{- Z_{i}}} = \frac{e^{z}}{1 + e^{z}}

(3)

where

\begin{matrix} Z_{i} = β_{0} + β_{1} X_{1} + β_{2} X_{2} + β_{3} X_{3} + β_{4} X_{4} \\ + β_{5} X_{5} + β_{6} X_{6} + β_{7} X_{7} + β_{8} X_{8} + β_{9} X_{9} \\ + β_{10} X_{10} + β_{11} X_{11} + β_{12} X_{12} + β_{12} X_{13} \end{matrix}

Equation (3) represents the cumulative logistic distribution function. Here, our explanatory variable (X) is a vector of household and individual character. In the individual character, age, year of education, marital status and sex have been taken as the explanatory variable. Among the household character, we include the (log) value of per capita land passed (in hectares), log per capita monthly consumption expenditure, size of the household, SC, ST, OBC and, lastly, the dependency ratio of the household (ratio of non-working members to total member of household). We estimated the regression using the logit method. We expect that households with more land and monthly per capita expenditure will be less likely to migrate, and that larger households with a lower dependency ratio will be more likely to migrate. Here male, Hindu, SC, ST and OBC have been taken as one, otherwise zero. The logistic regression result shows at the individual level, both sex, age and year of education are significantly associated with temporary labour migration. Largely, temporary labour migration is considered to be a male-dominated activity; younger people are more likely to migrate than older ones simply because they are able to work harder. In the study area, education is associated with migration in analysis, as migrants had signiﬁcantly higher levels of education than non-migrants.

From Table 10, we find that there is a statistically signiﬁcant relationship between years of formal education and migration. Other things remaining constant, the likelihood odd ratio discloses that there is significant positive association between age and migration, whereas there is a negative association between age square and migration. An individual with younger age is 1.28 times more likely to migrate out than with the older age group. Similarly, a male is 10.78 times more likely to migrate out than with females. Married individuals are 2.267 times more likely to migrate than others. In household character per capita landholding, monthly per capita consumption expenditure, dependency ratio and Scheduled Castes show a significant association with migration.

Table 10.

Logistic Regression Result

Variables	Model 1			Model 2
Variables	Odds Ratio	Std. Err.	Z	Odds Ratio	Std. Err.	Z
Age	1.274***	0.025	12.44	1.287***	0.026	12.61
Age square	0.996***	0	−12.99	0.996***	0	−13.12
Male	10.659***	1.308	19.29	10.782***	1.338	19.16
Year_Edn.	1.020*	0.012	1.76	1.032**	0.012	2.70
Married	2.425***	0.383	5.61	2.267***	0.361	5.14
lnMPCE				0.856*	0.074	−1.79
ln_pland				0.012***	0.015	−3.67
DR				0.987***	0.002	−5.32
Hindu				1.03	0.171	0.18
SC_dum				1.761**	0.505	1.97
ST_dum				1.185	0.365	0.55
OBC_dum				1.382	0.404	1.11
HH_size				1.015	0.022	0.69
Constant	0.00089	0.00025	−25.03	0.003	0.0026	−7.26
Number of observations = 4241				Pseudo R² = 0.3253
Log likelihood = −1323.6				Prob > χ² = 00000
				LR χ² (12) = 1276.26

Source: Author’s calculation from field survey data (2018).

Notes: (1) ***p < 0.01, **p < 0.05 and *p < 0.1.

(2) Year_Edn = Year of education, ln_MPCE = log of MPCE, ln_pland = log per capita land, DR = dependency ratio, SC_dum = scheduled caste dummy, ST_dum = scheduled tribe dummy, OBC_dum = OBC dummy, HH Size = household size.

Land is a basic asset of people’s livelihoods in rural areas and particularly in the study area. Land ownership, in particular, is not only the basis of relative wealth comparisons between rural households but also a source of rural employment, making this asset of particular interest to the study of the determinants of temporary labour migration in this and other contexts. The study shows that the highest rates of labour migration are from the households having marginal landholdings with low agricultural potential. The logistic regression analysis shows that there is a significant negative relationship between land owned and migration, that is, the more the land owned, the less the household is likely to migrate. Scarcity of farmland is an important factor of out-migration of rural people seeking for wage and related employment opportunities. About 93 per cent of migrant households have marginal landholding. Households having more land are 0.012 times less likely to migrate. For instance, an increase in (log) land by one unit decreases the probability of migration by a factor of 0.012 (see Table10).

Households having more the per capita monthly consumption expenditure is less likely to migrate. Odds ratio shows that, other things remaining constant, an increase in (log) monthly per capita consumption expenditure by one unit decreases the probability of migration by a factor of 0.85. Migration is significantly associated with dependency ratio. Migrations are from the lower dependant households. Other things remaining constant, higher the dependency ratio, the individual is 0.98 times less likely to migrate. An increase in the ratio of non-working to total members in the household also decreases the relative likelihood of migration by nearly 0.98 times (see Table 11).

Table 11.

Descriptive Statistics of Variable Used in Logistic Regression Model

Variables	Obs.	Mean	St. Dev.	Min.	Max.
Age	4241	24.32	17.76	0	95
Age square	4241	906.98	1208.22	0	9025
Year_edn	4241	4.04	4.27	0	17
ln_MPCE	4241	7.33	0.69	5.92	10.95
ln_pland	4241	0.05	0.07	0	1.37
hhsize	4241	6.28	2.53	1	17
DR	4241	58.37	58.37	0	100

Source: Author’s calculation from field survey data (2018).

Note: Year_Edn = Year of education, ln_MPCE = log of MPCE, ln_pland = log per capita land, DR = dependency ratio.

As hypothesized, larger-sized households have positive effect on raising the migration. Since labour is the main input in crop production, larger households face fewer labour bottlenecks at critical points in the farming cycle like land preparation and harvest time. Thus, household size is hypothesized to determine migration positively in one or other ways. Results show that there is positive association between migrations of household size. It is hypothesized that larger-sized households have positive effect on raising the flow of migration. Since labour is the main input in crop production, larger households face fewer labour bottlenecks at critical points in the farming cycle such as land preparation and harvest time. Thus, family size is hypothesized to determine migration positively in one or other ways. Logistic regression results show that there is no significant association between migrations of household size. Since Garhwa is a rain shadow area and average size of land holding is low, migration is independent of household size.

VI. Conclusion

Agriculture is confronted with high price volatility, climate risks and indebtedness. Since the majority of farmers are marginal with declining and fragmenting landholdings, these uncertainties make them even more vulnerable and risk prone. Improving soil health promotes agro-processing, and cover production risk is essential. Agriculture is becoming crowded and does not provide regular employment opportunities. In the absence of regular employment in rural areas, the rural population, especially youth, is migrating to urban areas to explore better opportunities and income. Owing to the selective nature of temporary migration of labour in the study area is undertaken by young men predominantly aged between 15 and 29 years old, with 48 per cent within this range. The mean age of migrants was calculated to be 30.52 years. The migrants are predominantly young men as they are often marginal farmers or landless, a situation that partly compels them to migrate looking for wage employment to earn additional income. Temporary migrants mainly comprise the ones who are from the socially and economically deprived backgrounds. The research found that married men are predominantly involved in migration, while the participation of women is negligible. The empirical findings suggest that poverty, lack of sufficient means of subsistence, unequal distribution of land holding, to meet household expenditure, availability of employment opportunities at destination and loan are the major reasons for temporary migration of labour. These ﬁndings indicate that poor households are involved in temporary labour migration; however, some minimal threshold of resources may be required to participate in and leverage the beneﬁts from these labour movements. Children accompanying their migrant parents for seasonal employment are the most ‘at-risk’ group of all in terms of educational vulnerability and capability formation. They are deprived of basic education and therefore become bonded to the low-skill–low-wage trap that their parents are currently in. Proper understanding of the magnitude and severity of the problem and suggesting innovative policies for breaking this vicious cycle are of utmost importance. Since temporary labour migration is very large, it needs to be given high priority with specific policy interventions. Governments and policy makers can play a vital role in ensuring that migrant workers undertake safe migration.

Footnotes

Acknowledgements

The author is grateful to the editors of the journal, particularly Prof. Sukhpal Singh and anonymous referees for their extremely useful and insightful suggestions and comments for the improvement of the article. Usual disclaimers apply.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

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