Social Networks of Migrant Workers in Construction Industry: Evidence from Goa Denzil Fernandes, Research Scholar, Tata Institute of Social Sciences Bino Paul G D, Associate Professor, Tata Institute of Social Sciences Abstract Goa is a small prosperous enclave on the west coast of India, which has been attracting migrants from all over the country. A large proportion of migrants to Goa are from backward districts of the country, who come in search of employment. The high demand for labour and the high wage rates of labour in Goa have resulted in a large influx of migrants in the casual labour market. One of the sectors that have absorbed a significant number of migrants in its work force is the construction industry. This paper provides, using Census 2001 and NSSO 62nd Round data, an overview of the labour market in Goa using variables such as demographic characteristics, work participation ratio (WPR), rate of unemployment and status of employment. It also examines the role of the construction sector in the Goan economy, both in the growth of the Net Domestic State Product (NSDP) and employment. Further, using census data it captures the trend of increase in migration to Goa. Moreover, using primary data collected in May-June, 2008, from seven construction sites spread all over Goa, it presents a socio-economic profile of migrant construction workers, which include their place of origin, age distribution, social category, education level, occupation status, type of skill, employment status and daily wage rate. Finally, it traces the social networks among migrant construction workers showing different phases of the labour market, including the flow of information regarding the labour market, entry into the labour force, allocation of work at the construction sites, friendly relations among them and the flow of credit among the migrant workers in order to meet their financial requirements. Key words: Construction workers, migration, labour market, social network. JEL codes: R23, J40, Z13
1. Introduction For 451 years (1510 – 1961), Goa remained a colonial enclave of Portugal with hardly any interaction with the rest of India. However, after the liberation of Goa in 1961, the economic growth resulted in the influx of migrants from the rest of India. The migration process has an underlying explanatory frame, including conventional labour market related variables and social network of migrants. This paper, based on the data collected from seven construction sites in Goa during May-June 2008, examines the nature of labour market in construction industry and social networks of migrant labour. The 1990s has seen a rapid pace of economic growth in Goa which has been the result of four major factors. First of all, there has been a significant increase in the development expenditure resulting in the implementation of several development projects. Secondly, rapid industrialization has resulted in a large increase in the number of factories as well as small scale industries. The third factor that has stimulated growth is the tourism sector, where large inflows of domestic and foreign tourists has necessitated the building the necessary infrastructure to accommodate as well as entertain them. Finally, the real estate boom experienced in Goa is a result of large scale land conversion and change in land use pattern, which has made a lot of land available for real estate development. These four factors have given a big boost to the construction industry. Further, the growth in construction sector generated a great demand for unskilled as well as skilled labour. Since the domestic supply of labour falls short of the demand for labour, excess demand is met by migrant labour force. The migrant worker enters the construction labour market due to various reasons. First of all, the migrant labourer is “pushed” out of his place of origin due to unemployment or under-employment in the agricultural sector, low wage rate, poor working conditions for labourers, drought or famine, conflicts, high cost of living, hardly any opportunity for business, less opportunity to enhance human capital and other factors. At the same time, the migrant labourer is “pulled” into the construction labour market in Goa due to greater demand for labour and the assurance of employment, higher wage rate, better working conditions, proximity or easy communication to place of origin, better opportunity to enhance human capital, better opportunity for business, and similar other factors. After the migrant worker decides to move out of his/her place of origin he/she enters the labour market with the help of social networks of migrant labour force. These networks consist of contractors, agents, former migrants, friends, relatives and acquaintances who have already been working in Goa. Finally, the migrant workers entry into the construction labour market is assisted by the phenomenon of cumulative causation1. Cumulative causation is a result of two processes. In the first process, entry of migrant labour into the construction sector makes the native workers label the job as “migrant” job. Natives look down on this job and feel that it is culturally unacceptable to join the construction labour force. The withdrawal of native labourers only increases the demand for migrant labour in the construction labour market. Yet another process of cumulative causation is that the availability of surplus migrant labour brings down the 1
Cumulative causation refers to a sequence of processes, where one processes leads to another process. Chains of this nature exist in the labour market (Massey et al, 1993).
wage rate. The native labourers are not ready to work for a low wage rate and they move out of the construction labour force. The absence of any competition in the labour market from native labour opens the construction labour market entirely for migrant labour. The flow of migrant labour into the construction labour market is assisted by migrant networks. Once the migrant worker enters into the construction labour market, his continuance in Goa or his duration of residence depends not only on migrant networks, but also networks with locals or earlier migrants, who share information, develop friendships and form a social support system for the migrants in the place of destination. Moreover, the migrant worker maintains links with friends and family members in their places of origin through communication or home visits or by sending remittances. Analysis of the construction labour market through “push-pull” model or “demand-supply” model is inadequate without an analysis of migrant networks that facilitate a constant flow of migrant labour. Migrant workers not only need networks to enter the labour market, but they also require networks with locals or earlier migrants in order to have a social support system in the place of destination, in addition to the links they maintain with friends and family in their places of origin. This analysis of migrant networks will provide crucial insights into nature of the highly unorganized construction labour market. The paper is organised into six sections. Section two provides a review of literature. Section three gives a factual profile of labour market, migration and construction sector in Goa. Section four deals with the methodology. Findings are discussed in section five. Section six gives concluding remarks. 2. Review of Literature Migration is the movement of human beings from one geographical point to another geographical point. People who moved from their native place to another place are called migrants. They are more specifically called immigrants, emigrants, refugees, etc. according to the historical setting, context and perspective. The Census of India has defined migrants in two ways, namely ‘by place of birth’ and ‘by place of last residence’. In the first definition, “migrants by place of birth are those who are enumerated at a village/town at the time of the census other than their place of birth.” And in the second definition, “a person is considered as migrant by place of last residence, if the place in which he is enumerated during the census is other than his place of immediate last residence. (Census of India, 2001) 2.1 Scholarly lineages Classical Economists view migration as a result of fluctuations in the demand and supply of factors of production. According to Malthus, the population increases in geometric progression while food production increases by arithmetic progression, and therefore, a rapid increase in population creates a situation of distress. At the same time, a fast growing population results in surplus labour, which Malthus calls redundant population. This ‘redundant population needs to move to ‘uncultivated’ areas where there is greater demand for labour. (Malthus: 1967) This voluntarist perspective was later developed into the push-pull model. On the other hand, the seeds of the structural perspective of migration was sown in the Marxist theory which refers to the cycles of expansion and contraction of the capitalist economy creating
the availability of cheap and dispensable labour, which he called ‘reserve army’. These people are either the floating surplus population, workers laid-off in times of recession or marginalized workers. This ‘reserve army’ offer cheap labour wherever there is demand for labour resulting in migration of workers in search of employment. (Papastergiadidis, 2000) Neo-classical economic theories view migration as an individual decision for income-maximization. They focus on differentials in wage and employment conditions between countries. The neo classical economic theories may be further classified into Macro Theory and Micro Theory. Macro Theory The oldest and the best-known theory of international migration was developed to explain labour migration in the process of development. According to them, international migration is caused by geographical differences in the supply and demand of labour (Todaro, 1976; Harris and Todaro, 1970). This theory developed into the “push-pull” model. According to this model, which has a voluntarist perspective, migration is caused by twin and counterbalancing forces: people were ‘pushed’ out of stagnant rural peasant economies, and ‘pulled’ up towards industrial urban centers. This model tended to see migration as being caused by the individual calculation of economic opportunity (Papastergiadidis, 2000). Migration has also been explained using economic models such as the Dual Economy Model of Arther Lewis (1954) who focused on agriculture-industry relationship as a two-sector model. According to him, sustained economic growth fuelled by industrial expansion will result in a large number of underemployed and virtually unemployed workers living off the family farms, working at low marginal productivity, moving at some point to the industrial sector. (Bardhan: 1999). Another perspective of growth from the point of view of human capital looks at persistent growth as a result of increased investment of human capital, such as education and training of the labour force. (Becker: 1993) Micro Theory In the micro economic model of individual choice, individual rational actors decide to migrate because a cost-benefit calculation leads them to expect a positive net return, usually monetary, from movement. Here, migration is conceptualized as a form of investment in human capital (Massey, 1993). Potential migrants estimate the costs and benefits of moving to alternative international locations and migrate to where the expected discounted net returns are the greatest over some time horizon (Borjas, 1990; Borjas, 1994). According to Stark and Bloom (1985), exponents of the new economics of migration, migration decisions are not made by isolated individual actors, but by larger units of related people – typically families or households – in which people act collectively not only to maximize expected income, but also to minimize risks and to loosen constraints associated with a variety of market failures, apart from those in the labour market, such as crop insurance markets, futures markets, unemployment insurance and capital markets. It is important to note, migration is not just seen as an economic phenomenon but is viewed as a social one, involving collective processes and networks. Migration could also be a result of cumulative causation, which is a process by which each act of migration alters the social context within which subsequent migration decisions are
made, typically in ways that make additional migration more likely. For example, migration changes the social definition of work. Once migrants are recruited into particular occupations in significant numbers, these jobs become culturally labeled as “migrant” jobs and native workers are reluctant to fill them, reinforcing a structural demand for migrants for these jobs (Piore, 1979). Lately migration systems theorists have vigorously applied social network theory. A network is defined as a set of individual or collective actors – ranging from individuals, families, firms, and nationstates – and the relations that couple them. Networks consist of more or less homogenous sets of ties among three or more positions. Social networks encompass ties linking nodes in a social system – ties that connect persons, groups, organizations, or clusters of ties, as well as people. Network patterns of ties comprise social, economic, political networks of interaction, as well as collectives such as groups – kinship groups or communities – and private or public associations. Network is a concept or strategy to study how resources, goods, and ideas flow through particular configurations of social and symbolic ties. Analysis of networks allows statements about the possibility of people to interact. Indicators are size, density or connectedness, degree, centrality, and clustering of positions. An added benefit of network analysis is that positions can be included which are not part of formal and tightly bound groups. Descriptions and explanations based solely on bound groups sometimes overlook members’ cross-cutting memberships in various circles (Simmel 1995, in ‘Dominant Theories of Migration’, p. 51-522). Migrant networks are sets of interpersonal ties that connect migrants, former migrants, and non-migrants in origin and destination areas through ties of kinship, friendship, and shared community origin. They increase the likelihood of migration because they lower the costs and risks of movement and increase the expected net returns to migration. Network connections constitute a form of social capital. The potential costs of migration are substantially lowered for friends and relatives as they use these kinship and friendship ties to gain access to employment and assistance at the point of destination. Once the number of network connections in an origin area reaches a critical threshold, migration becomes self-perpetuating because each act of migration itself creates a social structure needed to sustain it. Networks are also a strategy for risk diversification. Self-sustaining growth of networks can be explained theoretically by the progressive reduction of risks, eventually making it risk-free and costless to diversify household labour allocations through migration. Thus, the expansion of networks reduces the costs and risks of movement, which causes the probability of migration to rise resulting in additional movement, which further expands the networks, and so on. (Massey, 1993) Network scholars have demonstrated that many migrants continued to maintain ties to their ancestral villages to form new urban ties. The complex networks of migrants are often made up of both, rural and urban ties, that help them to obtain resources from their native village as well as their place of destination in order to meet their financial needs for a decent living (Mitchell 1969, in Dominant Theories of Migration, p. 55).
http://fds.oup.com/www.oup.co.uk/pdf/0-19-829391-7.pdf "Dominant Theories of Migration"
2.2 Migration and Indian Labour Market The bulk of the research on migration in India is concerned with the description and analysis of patterns of internal migration in terms of streams of migration, spatial patterns, characteristics of the migrants and reasons for migration. Of the four streams of migration, the bulk of the research on migration has been on rural-urban migration in India. There are 6 broad areas of research on migration in India. They are: (1) Rural-urban migration and urban growth (2) Migration streams, (3) Regional patterns of migration, (4) Characteristics of migrants, (5) Determinants of Rural-urban migration and (6) Consequences of rural-urban migration. There are also studies done on female migration in India, which highlights not only survival strategies but also the cultural life of the people (Sundari, 2005). An ethnographic study of the journey of migrants highlights the interplay of history, social structure and culture during migration (Chopra, 1995). Census of India and National Sample Survey Organization (NSSO) provide a great deal of data on migrants in India. Therefore, several studies have focused on the demographic aspects of migration to the cities, relying heavily on the decennial censuses of population. A lot of studies cover spatial patterns of migration, such as rural-rural, rural-urban, urban-rural and urban-urban. A lot of attention, however, has been given to rural-urban migration to find out the effects of neo-liberal economic policies on patterns of migration. The content and methodology of these studies range from descriptive to those aiming to develop explanatory models, or evaluate applicability of existing models to India. However, most of the models of migration have emerged in the context of international migration, which do not take into consideration the peculiarities of internal migration in the Indian context. For example, according to the theory of dualism, migration from rural areas is considered as a prime indicator of development as it will lead to a new labour that is more skilled and receive higher wages resulting in upward social mobility. This phenomenon does not exist in the Indian situation as the rural migrants do not enter the modern working class but are absorbed in the informal sector and are called the “urban poor’ (Vijay, 2005). Their entry into the causal labour market, one of the segments of unorganized sector, is characterized by long working hours, poor living conditions, social isolation and inadequate access to basic amenities. Since they are ready to accept any distress wage, they undercut prospects of local labour and bring down the wage rate (NCEUS, 2007: 97) Construction Workers Construction sector is an integral part of the nation’s development process. The development of a country’s infrastructure and industry is intimately linked to the construction industry. Construction activities include building schools, hospitals, houses, offices, townships, highways, roads, ports, airports, railways, power projects, irrigation projects, and so on. Besides being the basic input for economic and industrial development of the country, the construction sector provides a lot of employment opportunities to the poor people especially from the rural areas as unskilled labourers as well as skilled labourers. It also generates seasonal employment for women as well as poor peasants, who often work at construction sites to augment their farm income. Construction sector has been growing at a very rapid rate and has registered the highest growth rate in generation of jobs in the last two decades.
The studies conducted on construction workers in India have been basically to study their profile and working conditions. These studies have found that construction workers are invariably migrant in nature. They belong to marginalized sections of society and are often underpaid and exploited. They live in unhygienic conditions at construction sites and are often denied any rights. Most of the construction workers are employed through contractors, who play a major role in bringing the workers into the construction labour market. There have been very little studies done on migration in Goa. Since migrants are viewed negatively in Goa, most of the studies focus on their health, prevalence of HIV/AIDS and their involvement in sex work, sex tourism and human trafficking. Studies on construction workers identified their unhygienic lifestyle as the main reason for the spread of the malaria epidemic in the 1990s (Mukhopadyay and De Souza, 1997). Using the push-pull model for analysis, one study gave a description of the profile of 100 construction workers in Goa and concluded that construction workers are almost entirely migrant in nature. According to this study on construction workers in Goa conducted in 1998, the high demand for casual labour in Goa had been attracting migrants as they were assured of getting some employment almost round the year. The lack of competition from locals for jobs in the construction sector had also made the jobs in this sector the preserve of migrants. This study claimed that the working conditions of migrant construction workers in Goa were better than what they got in the states of their origin. Besides, the daily wage they got in Goa was higher than their native states. The minimum wage fixed by the Government of Goa in the 1990s was Rs. 58. The daily wage of skilled male workers was Rs. 100 - 150 and unskilled labourers got Rs. 50 –100. However, the wage rate of women casual labour was less than Rs. 50, which was below the minimum wage (Noronha, 1998).
3. Labour Market, Construction sector and Migration in Goa In this section, we examine, using the data obtained from sources such as Census reports, National Sample Survey 62nd Round and Economic Survey of Goa, the characteristics of labour market, salient features of the construction sector, and migration. Characteristics of labour market The labour market is described by the variables such as population, work participation rate (WPR), rate of unemployment and status of employment. Table 1 gives salient features of population in Goa during 1961-2001. While decadeal growth rate is on decline, except an increase in 1961-713, population density shows a steady increase, that is an increase from 164 in 1961 to 363 in 2001. Moreover, proportion of urban population and literacy report a steady increase during this period. However, the sex ratio has been declining from 1066 in 1961 to 960 in 2001. This change is rather due to migration than fertility preferences.4 3
Similar trend exist for exponential growth rate as well. According to census 2001, males account for 53.57% of migrants by place of birth in Goa. This is consistent with the general trend of migration throughout India. For example, in South Goa district the sex ratio of non-migrants is as high as 1002. However, due to pre-dominantly male migration, the sex ratio of migrants is 866, thus reducing the overall sex ratio of the district to 972. 4
Table 1: Population Characteristics of Goa Characteristics Total Population Population Density Decadal Rate of Change Exponential Growth Rate Sex Ratio Urban population as a proportion of total (%) Literacy rate (%)
1961 589,997 164 7.77
1971 795,120 225 34.77
1981 1,007,749 284 26.74
1991 1,169,793 316 16.08
2001 1,347,668 363 15.2
Source: Census Reports 1961, 1971, 1981, 1991 and 2001
The work participation rate (WPR), which is the proportion of workers to the population of Goa, is less than that of the country in every status, namely the usual principal status, principal and subsidiary status, current weekly status as well as current daily status. The WPR of Goa ranges from 35% according to ‘usual status’ (principal status and subsidiary status) to 33% according to current daily status (See Table 2). This is a slight decline from the census data of 2001 which showed that the WPR in Goa was 38.8%. Similarly, the WPR among males stood at 52% and that of females was at 18.4% according to ‘usual status’ (principal status and subsidiary status). This is marginally lower than the Census 2001 figures which showed that WPR for males was 54.6% and that of females was 22.4%.
Table 2: Work Participation Rate (WPR) UPS PS+SS CWS CDS R+U Male Goa 51.4 52.0 51.1 49.7 India 53.6 54.7 52.5 49.7 R+U Female Goa 16.5 18.4 17.8 16.7 India 19.9 27.0 22.7 18.2 R+U Person Goa 33.7 35.0 34.2 33.0 India 37.3 41.3 38.1 34.4 R + U = Rural + Urban UPS = Usual Principal Status, PS + SS = Principal + Subsidiary Status, CWS = Current Weekly Status, CDS = Current Daily Status Source: NSSO 62nd Round Report No 522, 2005-2006
Interestingly, the rate of unemployment in Goa continues to be much higher than that of the rest of the country. The 62nd round of NSSO shows that the rate of unemployment in Goa stood at 4.5% according to the ‘usual status’ (ps+ss) (See Table 3). In addition, the total number of job seekers at the employment exchange in 2006 was 1,01,847. Though this figure may not be indicative of the exact number of
unemployed in Goa, it clearly underlines the fact of a comparatively high level of unemployment in the State. Therefore, the employment scenario in Goa throws up a paradoxical situation where increased economic growth co-exists with high unemployment5. Perhaps, such a scenario can be explained by considering the fact that Goa is a state with high educational attainment. As shown by NSS 62nd round, there is a positive relation between rate of unemployment and educational attainment. Table 3: Unemployment Rate (%) UPS PS+SS CWS R+U Male 4.9 4.9 5.6 Goa 1.5 2.6 India 1.7 R+U Female Goa 4.4 4.2 4.2 0.5 0.9 India 0.6 R+U Person Goa 4.6 4.5 4.9 1.0 1.8 India 1.2 Source: NSSO 62nd Round Report No 522, 2005-2006
CDS 6.4 4.4 4.2 1.6 5.3 3.0
According to the 62nd round of NSSO, 35% population is employed, 4.5% of the population is unemployed and 60.5% of the people are not in the labour force (Table 4). Among the working classes, men and women employed with a regular wage constitute the largest proportion. While nearly 50% of the employed enjoy a regular wage, only 16.57% are engaged in casual labour. Apparently, higher literacy generates a higher preference for employment with a regular wage6. This results in a change in the composition of the labour force with a greater movement of people from casual labour to self employment or regular wage employment. Such a scenario creates a vacuum in the casual labour market which is addressed by large scale influx of migrant labour. Table 4: Broad Usual Activity (Principal + Subsidiary) Status for Goa (%) Working Casual Total Selfsalaried/ Unemployed Labour Employed employed regular wage 1 2 3 1+2+3=4 5 18.9 23.9 9.3 52.0 4.9 R+U Male 5 10.9 2.5 18.4 4.2 R+U Female 11.9 17.3 5.9 35.0 4.5 R+U Person NSSO 62nd Round Report No 522, 2005-2006
Not in Labour Force
Salient features of the construction sector The contribution of the construction sector to the State GDP has been steadily increasing during the last decade. The trend growth rate of the construction in the Net State Domestic Product (NSDP) has been 2.67% (See Table 5). However, due to the larger increase in the trend growth rate of 5.81% of NSDP, the share of the 5
It is important to note that Net Domestic State Product at constant prices grew at 5.81% during 200001 to 2005-06. This is the trend growth rate we estimated from the Economic Survey 2007-08, published by Government of Goa. 6 As revealed by NSSO 62nd round, there is a positive relation between education attainment of proportion of employed with salary and regular wage.
construction sector in the NSDP has marginally declined from 6.57% to 5.48% between 2000 and 2006. Nevertheless, the construction sector continues to be one of the vibrant sectors in the Goan economy. Table 5: Net State Domestic Product at Factor Cost From Period 2000-01 to 2005-2006 (Q) at Constant Prices (in Lakhs) (Base Year 1999-2000) Year NDSP by NDSP Share of Construction Construction Sector 34721 528685 6.57 2000-2001 35215 549169 6.41 2001-2002 36194 582633 6.21 2002-2003 37549 622542 6.03 2003-2004 38967 689075 5.65 2004-2005 40669 742060 5.48 2005-2006 Trend Growth7 2.67 5.81 Source: Economic Survey 2007-2008, Govt. of Goa.
The growth of the construction sector in the Goan economy in the first few years of the 21st century are analysed by using the data provided by Census 2001 and the 62nd round of NSSO, 2005-06. The estimates of the employment scenario in Goa using the 62nd Round of NSSO indicate that though there is an increase in the population by 1.03 lakhs, there is a decline in the working population by 15,000 (Table 6). The estimated decline in the working population is due to the variation in the work participation rate in Census 2001 and 62nd round NSSO data. According to Census 2001, the WPR stood at 38.8 while the data collected in the 62nd Round of NSSO show that the WPR based on ‘usual status’ (principal status + subsidiary status) was 35%. However, what is more noteworthy is that in spite of the estimated decline in the working population, the NSSO data for 2005-06 indicate that there is not only an increase in the construction labour force by nearly 14,000 workers between 2001 and 2006, but the proportion of workers in the construction sector has also swelled up by 3% during the corresponding period. The annual growth rate of the construction labour force is estimated to be 4.31%. It appears an increase in NDSP from the construction sector results in more than proportionate increase in employment. The employment elasticity, for the construction sector, is 1.61. Obviously, the construction is a major source of employment in Goa.
Trend growth is found by regressing natural logarithm of NDSP on time. The reported growth rates are statistically significant at 1%, with R square values above 90%.
Table 6: Workforce estimates in 2001 and 2006 2001 2006** Population of Goa 1347668 1450889* Working Population 522855 507811 WPR 38.8 35.0 Construction Labour Force 47977 61796 % of Construction Labour Force 9.17 12.17 Note: Growth rate of construction labour force = 4.31 Employment Elasticity in Construction Sector8 = 1.61 Source: based on Census 2001, * projected mid-year population from 2001 to 2050 (Annexure 14: Economic Survey 2007-08, Government of Goa), ** based on usual status (ps+ss) estimates in 62nd Round NSSO Report No 522 (2005-06).
The construction labour force in India is dominated by casual labour. According to 62nd round of NSSO, there are 18.25 million casual labourers out of a total of 22.68 million people employed in the construction industry (Table 7). This accounts for 80.5% of the total labour force in the construction sector in India. In Goa, though casual labour accounts for the majority of the construction labour force (i.e. 54.1%), there is a substantial proportion (i.e. 26.7%) of the labour force who enjoy a regular salary or wage. The remaining 19.2% of the construction work force in Goa are self-employed, either as employers or as own account workers. Table 7: Household Status by Usual Principal Status in Construction Industry Goa India Workers Percentage Workers Percentage Self employed own account worker 5,939 13.4 2,797,544 12.3 Self employed employer 2,555 5.8 172,848 0.8 Unpaid family worker 170,082 0.7 Regular salaried or wage employee 11,777 26.7 1,291,908 5.7 Casual wage labour public works 470,358 2.1 Casual wage other types of work 23,911 54.1 17,780,647 78.4 Total 44,182 100 22,683,387 100 Source: 62nd Round NSSO Data, 2005-2006
Migration in Goa The decennial census of India provides a great deal of insights into the patterns of migration in the country. The last census conducted in 2001 provides vital information with regard to the characteristics of migration in Goa. Over 50% of migrants come from Karnataka and 24% of the migrants are from Maharashtra. These two states account for 74% of the migrants in Goa suggesting that there exists a neighbourhood effect on trends of migration. Proximity to the home state is one of the reasons for migration. The only study done on migrant construction workers in Goa found as many as 77% of them from Karnataka and another 10% from Maharashtra, thus reinforcing the view that distance and neighbourhood plays an important role in 8
Employment Elasticity in Construction Sector is defined as the ratio of compound annual growth rate (CAGR) of labour force in construction to CAGR of net domestic product (NDP) at constant prices from the construction sector. The absolute value of this ratio varies from zero to infinity. When the value is one, the ratio indicates an extra unit of increase in proportionate change in NDP results in a change of same magnitude in employment.
the decision-making process of migrants (Noronha, 1998). Slightly more than half of these migrants come from rural areas. This is consistent with the all-India trend of large-scale migration of people from rural areas to other states. Goa also exhibits a clear trend towards migration to urban areas. 70% of the migrants from other states migrate to urban areas. This is partly the reason for the sharp increase in the ratio of urban population to total population from 14.8% in 1961 to 49.76% in 2001 (Table 1). There is also a clear trend of migration for employment. Nearly 55% of males and 9% of females migrate in search of a job. Consequently, 77% of the migrants are in the working age group of 15-60 compared to 67% for the total population of Goa. The main reasons for migration for women were found to be marriage and moving with household, which accounts for 71% of the female migrants. Other reasons for migration mentioned in the census includes Business (2.01%), Education (0.82%) and those who moved after birth (8.67%). There has been a significant increase in the number of migrants entering Goa in the 1990s. Census figures show that between 1991 and 2001, 120,824 people from other states in India migrated to Goa. This amounts to more than 50% of the total migrants in Goa by place of last residence. An analysis of the duration of residence reveals a sharp increase in the number of migrants in the late 1990s. This is reflected in the 2001 census data, which show that while 16.51% of all migrants entered Goa between 1992-1996, there was a huge influx of people from other Indian states into Goa from 1996 to 2001 which amounts to 36.27% of all migrant population in Goa recorded in 2001. This trend is confirmed by the comparative growth rates of migrant and native population. It has been found that after an initial surge in the influx of migrants after the liberation of Goa in 1961, the annual migrant population growth rate as well as the natural increase in the population was declining until 1991. The trend in the period 1991-2001 was however very different. During this period, while the natural increase in the population decreased from 1.35 to 0.7, the CAGR of migrants increased sharply from 0.17 to 0.77, thus exceeding the rate of natural increase for the first time (Table 7). Table 7: Growth of Migrant Population in Goa Year Migrants % of Migrants to Migrant Total Population Population CAGR 2.52* 1961 14,875* 12.45* 1.34* 1971 99,027* 15.14 0.65 1981 152,565 14.51 0.17 1991 169,789 19.56 0.77 2001 263,653 * Goa, Daman & Diu
Natural Population CAGR
CAGR of Total Population
1.88* 1.84 1.35 0.70
3.03* 2.40 1.50 1.43
Source: Computed from Census 2001
4. Methodology Sampling According to Census 2001, there are 47,977 construction workers in Goa. Though the proportion of migrants in the construction labour force is not known, a study claimed that migrants constituted a significant proportion of the construction labour force. The migrant construction workers have been considered as the universe of the study. The sampling technique adopted will be purposive sampling. A sample 12
of construction sites was selected from different parts of Goa according to its theoretical relevance. Since a lot of construction activity is concentrated in coastal talukas of Goa, construction sites from Tiswadi and Bardez in North Goa as well as Salcette in South Goa were chosen. From each construction site, construction workers with different skills were chosen to be interviewed and this constituted the sample for the study of construction workers. The unit of observation is the migrant construction worker. Tools of Data Collection The main tool of collection of primary data was a well-designed questionnaire. The questionnaire was designed to collect relational data such as friendship ties, transactions of material resources like lending and borrowing, transfer of non-material resources like communication and exchanging information, interactions in workplace and out of workplace, formal roles and kinship ties. Besides relational information, characteristics of migrant construction workers and their attributes such as age, gender, caste, religion, daily wage/salary, place of origin, place of origin, present residence, educational status, social category, type of skill, duration of stay in Goa, etc. was sought from the workers. In addition, the interactions among construction workers at the site were also keenly observed. The secondary data sources include census data, NSSO data, news clippings, relevant data available at the Department of Planning, Statistics and Evaluation, and other departments of the Government of Goa as well as data available on websites. Field survey The field survey was conducted from 15th May to 12th June, 2008. A welldesigned interview schedule was prepared and administered to construction workers at seven sites spread over both the districts of Goa, namely North Goa and South Goa. At the end of the survey, 122 construction workers were interviewed and the data that had been collected was analysed using software packages such as SPSS and UCINET. 5. Findings The sample of 122 migrant construction workers provides an insight into their composition and characteristics. Most of the migrants have joined the construction labour market very recently. A majority of migrant construction workers (56.56%) joined the labour force in Goa less than two years ago (i.e. 2006-08). 21.31% of migrant workers entered Goa in 2001-2005 and yet another 10.66% of them joined the construction labour market in 1996-20009. This suggests that there has been a huge influx of migrant workers that entered the construction labour force during the last decade. The place of origin of the migrants throws up an interesting scenario. Migrants from Karnataka constitute the largest chunk of the migrant work force (Table 8). 30.3% of the construction workers were migrants from Karnataka, followed by Uttar Pradesh (23%), West Bengal (17.2%) and Bihar (14.8%). An earlier study on construction workers in Goa conducted in 1998 claimed that 77% of the workers in 9 Proportion of migrant workers that joined the construction labour force in Goa in year intervals: 1973-1980 (1.64%); 1981-1985 (1.64%); 1986-1990 (4.92%); 1991-1995 (3.28%); 1996-2000 (10.66%); 2001-2005 (21.31%); 2006-2008 (56.56%).
construction sites came from Karnataka (Noronha, 1998). But the present finding shows that the proportion of migrant construction workers from Karnataka has dropped significantly during the last decade. This is probably on account of improvement in road and rail connectivity to Goa in the last few years that brought labourers from different parts of the country, particularly from Uttar Pradesh, Bihar and West Bengal. Karnataka, being a neighbouring State, has been the source a large number of migrants to Goa on account of its relative prosperity and its proximity to the State. This only confirms the neighbourhood effect of trends in migration. However, the neighbourhood effect does not apply if one takes into account the Districts from which the migrant workers have come. The largest number of workers have come from distant Haveri and Koppal Districts rather than from the nearby Belgaum or Uttara Kannada Districts. This phenomenon can be explained by taking a look at the HDI of the different districts of Karnataka. The Human Development Report (HDR) 2005 of Karnataka have revealed that out of 27 districts, the HDI rank of Haveri is 20 and that of Koppal is 24. Low HDI indicates low literacy levels and higher poverty levels. Therefore, the migrant workers entering the construction sector come from relatively backward districts of Karnataka. Similar trends exist with migrant workers from other States. Table 8: Native State of Migrant Construction Workers Native State Workers Percentage of Workers Bihar 18 14.8 Orissa 6 4.9 Uttar Pradesh 28 23.0 Karnataka 37 30.3 West Bengal 21 17.2 Rajasthan 9 7.4 Jharkhand 2 1.6 Madhya Pradesh 1 0.8 Total 122 100
The age profile of the construction work force indicates a dominance of youth. Our findings show that 86.06% of workers are between the age of 15 and 34. The average age of a construction worker is 25.7 years. This indicates that the construction labour force demands a great deal of hard manual labour which can be largely performed by a youthful work force. A majority of them (i.e. 55.74%) are unmarried, as they teenagers or in their early 20s. Most of the construction work force constitutes workers who have come alone leaving the rest of their family members behind. Such workers constitute 72.95% of the construction labour force. Interestingly, 26.23% of the construction labour force constitutes those who have brought their family along with them. The social profile of the construction labour force indicates that the construction sector is an attractive employment prospect for the poorest sections of society in India. Our findings show that the largest proportion of 40.98% of the migrant construction workers is from those classified as the backward classes. Muslims account for 23.77% of the construction labour force, while scheduled castes constitute 16.39% of the workers (Table 9).
Table 9: Social Category of Migrant workers Social Category Workers Percentage of Workers Higher castes 9 7.38 General castes 13 10.66 Other Backward Castes 50 40.98 Scheduled Castes 20 16.39 Scheduled Tribes 1 0.82 Minorities 29 23.77 Total 122 100
The education status of migrant workers in the construction labour force is low. The largest proportion of 26.23% of them is illiterate. The overall picture of the level of educational attainment of construction workers show that 87.56% of the labour in the construction sector have either not gone to school or are dropouts not being able to complete their schooling. This also indicates that the construction sector attracts a large proportion of people who need not be skilled or have some minimum educational background. Table 10: Type of Occupation in construction NCO Occupation Migrant 68 Workers (Sample)
Workers (Sample) (%)
2008 Construction 48 Worker 951 Mason 25 241 Contractor 8 811 Building Carpenter 10 955 Plasterer 6 931 Painter 2 950 Supervisor 2 851 Electrician 2 Others 19 Total 122 *Source: Estimated from 62nd Round NSSO unit level data. 959
Workers GOA (%)*
Workers INDIA (%)* 2005-06 19.7 35.9
20.5 6.6 8.2 4.9 1.6 1.6 1.6 15.7 100.0
24.6 6.3 2.2 6.8 1.6 3.0 6.9 28.9 100.0
17.0 1.9 0.8 1.2 3.6 1.4 1.3 36.9 100.0
The occupation profile of migrant workers in the construction labour force reveals that construction workers (39.3%) and masons (20.5%) constitute the largest proportion of the construction workers (Table 10). This finding resembles the all India occupational profile of the construction labour force provided by 62nd Round of NSSO, which indicates that construction workers and masons are dominant occupations this sector constituting 35.9% and 17% of the total construction labour force respectively. However, this finding slightly differs from the NSSO data on the occupational profile of the construction industry in Goa, which show that 24.6% of construction labour force are masons and 19.7% are construction workers. But, unlike the NSSO data, our field study has shown that building carpenters also form a significant proportion of the construction labour in Goa with 8.2% of the workers. Other workers found at the construction sites were contractors, supervisors, glass fitters, painters, plasterers, welders, electricians, machine operators and security staff. During the survey of workers at the different construction sites, it was found that over 40% of the workers were unskilled and the remaining had some skill, such 15
as electrician, masonry, welder, carpenter, plasterer, etc.10 The large number of unskilled workers reflect the low educational attainment of the workers and the failure of the education system in the country to provide the necessary skills to the youth. When the workers were asked where they got the necessary training in their skill, only 4.1% of the workers said that they were trained before coming to Goa. A majority of the workers (i.e. 54.10%) have learnt their skill at the construction site. In addition to this 18.85% of the workers, who are unskilled are learning various skills at the various construction sites. This information gives an insight into the skill formation of the workers. A vast majority of the workers have acquired the necessary skills needed to remain in the construction work force through on-the-job training as they had no skill prior to their entrance into the work force. However, on-the job training is normally done under some supervision. Thus, the workers were asked under whose supervision they got their training. The responses surprisingly indicated that family networks, friendship networks as well as village networks played an important role in the skill formation of the workers. Slightly above one third of the workers revealed that it was under the supervision of family members and relatives they got on-the-job training and acquired or are acquiring the skills necessary in the construction sector. Others acquired the necessary skills under the supervision of a fellow villager, contractor, friend, etc.11 A large proportion of the migrant work force in the construction sector is casual labour. In our findings casual labour accounts for 82.79% of the construction labour force confirm the fact that the construction sector attracts the largest number of casual labour in Goa. Self-employed accounts for 11.48% of the construction labour force, while the category of those employed with a regular wage constitutes 5.74%. These figures correspond to the all-India NSSO data on the household type by usual principal status in the construction industry (Table 7). Since this study focuses only on migrants in the construction labour force, the findings are not proportionate to the same NSSO data on the construction industry in Goa. The booming construction sector in Goa has resulted in an ever increasing demand for casual labourers. The high demand for casual labour has caused a surge in the wages of casual labour. Goa has the highest average wage rate in India of casual labour in urban areas at Rs. 140.91, while in rural areas, with an average wage rate of Rs. 101.41 (Table 11). The average wage rate for male workers is Rs. 192.38 in urban areas, which is highest in India. The average wage rate for male workers in rural areas is Rs. 102.80. The high wage rate in the casual labour market in Goa has been attracting skilled as well as unskilled workers from all parts of the country as the wage rates in almost all the other states in the country is not as high as that of Goa.
Our findings have shown that 40% of the migrant workers were engaged in unskilled occupations, while 60% were engaged in skilled occupations. Skilled occupations include masons (21.31%), fitter and bar benders (15.57%), carpenters (8.20%), plasterers (3.28%), machine operators (3.28%), glass fitter (1.64%), stone dresser (1.64%), painter (1.64%), welder (0.82%), tile-fitter (0.82%), electrician (0.82%) and supervisor (0.82%). It is important to note that all workers under the category of skilled occupation have acquired skills through informal training but not through formal vocational training system. 11 The skills acquired by these migrant workers were under the supervision of family members (9.84%), relatives (27.05%), fellow villager (13.93%), contractor (10.66%), friend (9.02%), fellow worker (0.82%) and Company officials (4.92%). One worker (0.82%) did not reveal his supervisor and this question was not applicable to 22.95% of the workers as they were unskilled.
Table 11: Average wage/salary earnings per day received by casual labourers of age 15-59: Comparison between Goa and native States of migrants Sr. No. State Rural/Urban Male Female Person 1 Bihar Rural 52.58 45.85 51.34 Urban 61.29 40.45 57.80 2 Goa Rural 102.80 69.69 101.41 Urban 192.38 52.16 140.91 3 Jharkhand Rural 54.31 45.78 52.52 Urban 79.34 32.75 73.83 4 Karnataka Rural 55.26 33.85 46.66 Urban 84.25 38.83 74.20 5 Madhya Pradesh Rural 43.30 34.94 40.48 Urban 62.27 39.28 58.92 6 Maharashtra Rural 51.21 30.92 42.66 Urban 75.46 37.61 66.89 7 Orissa Rural 45.55 31.06 41.80 Urban 56.57 38.74 52.72 8 Rajasthan Rural 64.52 52.93 62.12 Urban 80.59 53.03 78.19 9 Uttar Pradesh Rural 56.94 44.22 55.00 Urban 66.04 34.68 64.05 10 West Bengal Rural 52.44 40.81 50.99 Urban 78.79 54.94 76.33 11 INDIA Rural 59.29 37.97 52.91 Urban 80.70 44.57 74.37 Source: 62nd Round, NSSO Report No 522, 2005-2006.
Social Networks among Migrant Construction Workers Our findings show that the average wage rate for male workers in the construction sector was Rs. 148.19, which is lower than the average wage rate for male workers working in urban areas in Goa. This is probably due to the general trend of the wage rate of migrant workers being less than that of the native workers. On the other hand, the average wage rate of female workers is not as high as their male counterparts. It is Rs. 69.69 in rural areas and Rs. 52.16 in urban areas. The average wage rate of female construction workers was found to be marginally higher at Rs. 75, which is less than the minimum wage of Rs. 87 fixed by the Government of Goa in 2007. Consequently, the casual labour market has few female workers, who are mainly wives and daughters of male casual labourers. Quite interestingly, average wage rate in Goa for casual labour market is significantly higher than states from where migrants came from (Table 11). Quite importantly, relationship is quite pivotal in shaping the labour market for migrant labour in construction sector, covering different phases of an evolving labour market such as information about the labour market, entry to the labour market, link between labour and organisation through work allocation, building friendship with fellow workers, and availing the help of relations for financial needs. These five phases have been converted into information network, job network, work allocation network, friendship network and credit network respectively. In order to find out the source of information among migrant workers regarding the labour market in Goa, the respondents, called nodes12, in this paper, 12
Nodes are alternatively called points, actors and so on.
were asked an open-ended question to reveal the person who initially gave them information regarding the labour market before coming to Goa. While the responses included persons not working in that construction site, we restricted our analysis only to those persons who are working at the construction site. The responses that emerged helped in arriving at the information network. Job network is a result of the responses to the open-ended query about the person through whom the migrant construction worker entered the construction labour market in Goa. Similarly, an open-ended question was asked regarding the organisation of labour at the construction site through allocation of work. The responses given traced the chain of command in the hierarchical organizational structure of the construction company. From this, we drew the work allocation network. The migrant construction workers were asked to give a list of their friends with whom they share a very close relationship. The network that emerged from the responses formed the friendship network. Finally, in order to trace the flow of credit among the migrant construction workers, questions were asked regarding the persons who lend money to workers and those who seek financial assistance to meet their needs. The responses that emerged resulted in the credit network. All these networks were drawn with the help of Ucinet (Borgatti et al, 2002), a software for social network analysis. In order to understand these social networks, construction workers from three nearby construction sites of a single builder at the heart of the city of Panaji were chosen. There were 53 migrant workers in addition to contractors, supervisors and the builder working at these three sites. Social networks used in this research are of two types: network of undirected tie and network of directed tie. While the first category implies a tie which has neither source nor end, the latter has a source and an end. Here, friendship network contains only undirected ties whereas other networks consist of only directed ties. We assess three important structural aspects of networks: inclusiveness, cohesion and centrality. The inclusiveness measures the degree of connectedness, expressed connected nodes as the proportion of total number of nodes in the network. A higher value denotes higher degree of connectedness and vice versa. The objective of measuring cohesion is to convey the degree of completeness of a network. For this we use density, a popular measure of cohesion. Scott (2000) defines density of a network as “the number of ties as a proportion of the maximum possible number of ties.” High density implies that the network is relatively complete and vice versa. The second structural aspect, the centrality, indicates to what extent the network is organised. For assessing the centrality, the frequency of ties for each member in the network is counted; the measure is called degree. This measure is decomposed into in-degree and out-degree for the network of directed ties, implying number of ties reaching the target and those originating from the source respectively. According to Scott (2000), degree is considered as a measure of local centrality. These measures are given in Table 12 and Table 13.
Table 12: Networks in Three sites of One Builder Measures Information Job Credit Network Network Network Number of nodes Number of Connected Nodes Inclusiveness Nature of Network Number of Lines Maximum possible lines Density
Work Allocation Network 63 63
0.74 Directed 32 2862
0.74 Directed 32 2862
0.60 Directed 28 2970
0.95 Undirected 75 1485
1 Directed 65 3906
Table 13: Frequency Distribution of Degree/In-Degree/Out-Degree Information Job Network Work Allocation Degree/ Network Network In-Degree/ Out-Degree InOutInOutInOutdegree degree degree degree degree degree 0 22 41 22 44 1 48 1 32 5 32 3 59 2 2 0 4 0 3 3 4 3 0 1 0 1 0 0 4 0 0 0 0 0 3 5 0 2 0 2 0 2 6 0 1 0 0 0 1 7 0 0 0 0 0 0 8 0 0 0 0 0 1 9 0 0 0 0 0 1 10 0 0 0 1 0 1
Friendship Network Degree 3 17 6 10 6 12 0 1 0 0 0
Credit Network Indegree 28 26 1 0 0 0 0 0 0 0 0
Outdegree 48 2 0 0 2 2 0 0 1 0 0
While all the networks report relatively higher degrees of inclusiveness, the density, across networks, is low. Work allocation network has the highest inclusiveness, indicating that the work is organised by a communication system consisting of a well-orchestrated chain of commands, which flows in a hierarchical manner (Figure 1). On the other hand, credit network reports the lowest degree of inclusiveness. Perhaps the credit flow requires more coherent social structure, requiring a high degree of trust among nodes.
Work Allocation Network
Figure 1: Networks in construction site
Information Network The information network analyses the flow of information among the workers regarding the labour market in the construction industry in Goa. The sociogram of the information network reveal the important role played by contractors in providing the information regarding work in Goa. It is evident that most of the workers who have entered the construction labour market have been brought by contractors. Consequently, contractors have a much higher out-degree than the rest of the workers. But it was also found that there are some migrant workers who have become the source of information regarding labour market in Goa. It is most likely that these nodes with an out-degree of more than one may emerge later as future labour contractors. Job Network The job network is the network of migrant workers through which they enter the construction labour market by getting employment at some construction site. The sociogram on job network makes it evident that the migrant workers make an entry into the construction labour market largely with the help of contractors. Therefore, there are several ego-networks centred on contractors, who are represented in the graph as nodes with a relatively high degree. There are a few exceptions, where some workers have helped other workers enter the labour market. This indicates either that such workers are deputed by the contractor to bring workers into the construction industry or that the worker is emerging as a future contractor. Work Allocation Network The work allocation network presents the organisation of work at the three sites of the builder in Panaji. It traces the chain of command from the builder to the construction worker. The sociogram on the organization of the work in the three construction sites of a Goa-based builder show a hierarchical command structure. The path length of the command structure range from two to four, which includes the sitein-charge, contractors or sub-contractors and the skilled or unskilled worker. The sociogram reveals that the migrant construction workers are on the periphery of the organisational structure, while the natives or locals are in the highest positions in the command structure. It is apparent that those higher up in the hierarchy of the organisational structure have a higher out-degree than the rest of the workers lower down in the command chain. Therefore, it is obvious that the normal trend is that a migrant enters the construction labour market through the periphery by being an ordinary worker, but he gradually may move up higher by becoming a contractor later. Friendship Network The friendship network traces the friendship relations that exist among the migrant workers at the three construction sites of a Panaji builder. The friendship network among the migrant construction workers is a complex graph of highly dense sub-graphs to very loosely knit group of workers, normally centred on contractors. However, there are several friendship relations that do not involve contractors. Therefore, the out-degree is high among several workers. Friendship relations often involve family relations or relations between workers coming from the same village or State.
Credit Network Credit network traces the flow of credit among migrant construction workers to sustain themselves in vulnerable situations. The sociogram on the credit network clearly show the power wielded by the contractors over the workers. Since the daily wage of the workers is almost at subsistence level, these migrant workers are totally dependent on the contractors for any financial assistance in time of need. Only contractors, therefore, have out-degree of more than one in the credit network graph. It is also evident that there is very little borrowing among the workers as they have hardly any money to lend. There is only one exception, which can be understood because that worker who lent to another worker is the brother of the contractor. 6. Conclusion The construction sector in Goa has been one of the most vibrant industries in the Goan economy since its liberation in 1961. In particular, the last decade has witnessed rapid growth in the construction industry. This study has shown that the construction labour force constitutes 12.17% of the total labour force in Goa with an annual growth rate of 4.31% and with employment elasticity at 1.6. The Net Domestic State Product (NSDP) of the construction industry grew at the rate of 2.67% though the share of the construction sector in the NSDP declined from 6.57% in 2000-01 to 5.48% in 2005-06. This created a huge demand in the construction labour market, which has resulted in a significant increase in the influx of migrant labour to augment the construction labour force in Goa. This fact has been vindicated by this study, which revealed that more than half of the migrants in the present construction labour force have come after 2005. Due to the neighbourhood effect, migrants from Karnataka constitute the largest proportion of construction labour force, though their share in the labour market is dwindling. The migrants from Uttar Pradesh, West Bengal and Bihar were found to have made significant inroads in the construction labour force. The migrants who have joined the construction labour force in Goa is found to be largely in the age group 15-34, which implies that they are in young people coming from backward regions of the country. They belong to socially backward sections of society such as backward classes, scheduled castes and muslims. Their educational attainment is low as a majority of them are either illiterate or school drop-outs. Consequently, over 80% of them are casual labourers. Over 40% of the workers are engaged in unskilled occupations and those involved in skilled occupations have acquired their skills through on-the-job training under family members, relatives, fellow villagers, or friends and not through the formal vocational training system. The high demand in the casual labour market has consistently kept the wage rate high in Goa. This has attracted migrants from all over the country into various sectors in the Goan economy, including the construction industry. However, the flow of migrants has been facilitated through networks involving family members, relatives, friends, fellow villagers and contractors. Networks among migrant construction workers show that contractors provide migrants with relevant information related to the labour market in Goa and bring workers to Goa. There are also some workers who go back to their native place and provide labour market related information to family members, relatives, friends and fellow villagers. They also bring along more workers for employment in Goa and in due course of time they are likely to be contractors in the future. Similar trends are seen in the networks for entry into the labour market in Goa. After entry into the
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