GROWTH AND NON-FARM EMPLOYMENT: THE CASE OF GUJARAT

Transcription

1 The Indian Journal of Labour Economics, Vol. 52, No. 3, 2009 GROWTH AND NON-FARM EMPLOYMENT: THE CASE OF GUJARAT Anita K. Dixit* This paper analyses employment in the primary and other sectors in Gujarat, one of the fastest growing states in India, by using NSS data and the results of a survey of four villages. It has been found that state level employment in the agricultural sector has stagnated, as opposed to a secular decline at the all- India level. The survey data indicate that employment decisions at the household level are income-dependent and cannot be generalised. While ownership of nonfarm assets and higher education determine diversification for the high-income households, poor households diversify due to inadequate means of agricultural production. At the policy level, active diversification into non-farm employment must be promoted, in a regionally diversified and employment-focussed manner, through initial public investment, especially in infrastructure. Economic theory states that an economy in the process of growth undergoes structural change, both at the level of the share of national income and employment. This paper analyses trends in employment in the primary sector and other sectors in Gujarat, one of the fastest growing states in India. The paper starts with an overview of the theoretical and empirical literature on economic growth and employment in the primary and other sectors. In Section II, which deals with growth and employment at the all-india and the state levels, it can be seen that state level employment in the agricultural sector has stagnated, as opposed to a secular decline at the all-india level. In Section III, data from a survey of two districts of Gujarat is used to indicate that employment decisions at the household level depend on the level of income/asset ownership; and, therefore, it is not possible to draw any generalised conclusions regarding the nature of relationship between growth and rural non-farm employment in Gujarat. Section IV concludes the paper. I. Brief overview of LITERATURE ON GROWTH AND EMPLOYMENT According to Kuznets (1966; 1973), as well as Chenery and Syrquin (1975), the share of the primary sector in national income declines as an economy grows. The share of the primary sector in employment is also theorised to decline, albeit at a later time. These changes in output and employment have been attributed to changes in the structure of demand with economic growth. With increased income levels, the demand for industrial products and services increases in relation to that for agricultural goods. Mellor (1976) opined that agricultural growth would act as a stimulant for increasing demand through a multiplier effect, encompassing both the farm and the non-farm, the rural * Research Scholar, Jawaharlal Nehru University, New Delhi, and Visiting Student Fellow, Queen Elizabeth House, University of Oxford.

2 520 The Indian Journal of Labour Economics and the urban sectors. The non-farm sector is promoted through an increased demand for both consumer goods and agricultural inputs, and the farm sector is stimulated due to a demand for food crops as well as inputs for agro-based industries. While industrial development is envisaged to take place in the urban sector, its benefits would also spill over to the rural sector. In fact, the process would enable greater inter-linkage of rural and urban areas. Mellor also envisaged that an increase in incomes of the low-income rural classes would give rise to a demand for non-agricultural goods of a labour-intensive type, which would be produced in the rural sector itself. A higher level of employment would be generated in the non-farm sector, the wage rate in this sector would increase, and labour would move from the farm to the non-farm sectors. Therefore, non-farm employment would be generated through a demand-pull process. The above theory is based on the understanding that a considerable surplus is generated in the farm sector and that there is an adequate demand from the rural areas for industrial output, while the rural sector itself diversifies to generate the non-farm industry. However, Hymer and Resnick (1969) take the view that labour-intensive non-industrial products of the rural sector are intrinsically inferior and, therefore, the demand for these goods would decline with an increase in income. This would thus leave the possibility of occupational diversification only through rural-urban migration, since non-farm employment would cease to be generated in the rural areas. The third theoretical understanding of non-farm employment was propounded by Vaidyanathan (1986). He asserted that far from generating a means of lucrative employment, the non-farm sector becomes a residual or sink wherein excess labour, unable to find productive employment in the agricultural sector, is transferred. Labour does not move out of the agricultural sector due to higher wages, but is pushed out to low-wage non-agricultural employment when it is impossible to find agricultural employment. Much has been written about the role of the non-farm sector in India, supporting the growth-led as well as the distress-driven theses. Among those who have applied Mellor s (1976) growth-linkage theory to India, one of the important studies is that of Hazell and Haggblade (1991), who stress the importance of both consumption and production linkages between agriculture and non-agricultural growth. Among others who posit that agricultural growth is the most important factor for growth in the non-agricultural sector are Nachane, et al. (1989), and Shukla (1992). However, other scholars have emphasised the importance of different factors (apart from agricultural growth) for the growth of the non-agricultural sector. Harriss (1987), in a field survey of North Arcot district in Tamil Nadu, concluded that the growth of non-local markets, regional integration, and the growth of banking and commercial activity, among other factors, were responsible for the growth of the non-agricultural sector. 1 Similarly, Bhalla (1993; 1997) and Papola (1992) argue that proximity to urban centres is an important factor for the generation of livelihoods in the non-agricultural sector, while Sen (1997) has stressed the importance of government intervention in generating non-agricultural employment.

3 RESEARCH NOTES AND COMMUNICATIONS 521 Following Vaidyanathan (1986), a number of studies also examined the possibility of distress-driven non-agricultural growth in the country. Verma and Verma (1995) and Singh (1994) identified agricultural distress as an important determinant of employment diversification in eastern India and in Uttar Pradesh, respectively. A similar position is taken by Bhalla (1990) (cited in Basu and Kashyap, 1992), Kundu, et al. (2003), and Abraham (2009). The last, that is, Abraham (2009), in particular, claims that the growth of rural non-farm employment between the years and is distress-driven, a fact especially indicated by the increased participation of women and the elderly in the labour force. However, Unni (1991) finds no correlation of rural poverty or landlessness with nonfarm growth. Basant and Kumar (1989) have noted the increase in the share of the rural non-agricultural sector in the rural labour force, with the trend being more clearly observed for male than for female workers. They suggest the presence of both pull and push factors in non-agricultural employment. Basu and Kashyap (1992) take a similar position. They study the relation of the agricultural sector and non-agricultural employment, disaggregated by agro-ecological zones, to conclude that it is difficult to theorise unequivocally regarding the pull or push nature of the non-agricultural sector. In the light of these conflicting findings, the need for disaggregation in studying nonagricultural employment is apparent. Various studies have been conducted on the issue at the level of different states. Basant (1994) concludes that there is no evidence of rural non-farm employment in Gujarat being driven by distress-diversification. Dev s (1990) analysis, disaggregated by regions, indicates both growth-induced and distress-driven nonfarm employment growth. Unni (1998) has emphasised the importance of disaggregation at another level by the type of non-agricultural activity. The non-farm sector consists of low-productivity and high-productivity enterprise segments, as well as wage/salaried workers and self-employed workers. According to her, regional studies must be carried out in conjunction with this categorisation, since factors that facilitate or retard the growth of the non-farm sector affect the different segments differently. She also emphasises the need for micro-level (household/individual level) studies to assess the factors affecting rural nonfarm employment. In a related vein, Lanjouw and Shariff (2002) have argued that the direct contribution of the non-farm sector to poverty reduction may be muted when the poor lack assets. This opens up the need for disaggregation at another level the level of income. As the preceding survey indicates, there is no consensus in the literature regarding the determinants of rural employment diversification. This highlights the need for disaggregated studies, both at the regional level and at the level of household decision-making. The contribution of this paper is in providing such a disaggregated regional study for Gujarat. A combination of secondary and survey data is used here to present a nuanced picture of trends in sectoral employment at the macro level, in consonance with survey data to analyse the factors affecting employment decisions at the household level. A disaggregated analysis of households by income is thus presented here in order to study the factors affecting diversification decisions for different income categories.

4 522 The Indian Journal of Labour Economics II. GROWTH AND EMPLOYMENT IN GUJARAT AND INDIA Gujarat is one of the fastest growing states in India. It has always reflected a better performance than the all-india level in terms of economic growth. In , the Net State Domestic Product (NSDP) of Gujarat at constant ( ) prices was Rs. 90,783 crore, while the corresponding all-india figure was Rs. 13,64,259 crore, so that the state had a share of 6.65 per cent. Gujarat accounted for 4.91 per cent of India s total population in Thus, the state s share of the national product is somewhat higher as compared to its share of the country s population. The estimated real (at prices) per capita NSDP for in Gujarat was Rs. 16,878 for the state as compared to an all-india level of Rs. 12,416. Although Gujarat has improved its rank in NSDP and per capita NSDP during the post-liberalisation years, it had registered a growth of over 5 per cent even during the earlier years. Table 1 Annual Average Growth of NSDP for Major States (%) State Ranking Ranking Ranking Andhra Pradesh Assam Bihar Gujarat Haryana Himachal Pradesh Karnataka Kerala Madhya Pradesh Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal Source: Calculated from Growth in the state, as detailed in Dixit (forthcoming), is driven by the secondary sector. Between and , the NSDP in the state increased by more than 10 times, but the growth in the primary sector was only about 4 times, as compared to 17 times in the secondary and 15 times in the tertiary sector. The secondary sector has grown the fastest, at an average rate of 7 per cent during the entire period, followed by the tertiary sector at 6 per cent, while the primary sector has grown at an average rate of 3 per cent. Similarly, Dholakia (2007) notes that growth in agriculture as a sub-sector of the primary sector is so small as to be statistically insignificant, both during the 1980s as well as the 1990s up to Declines in other sub-sectors such as mining, and some of the services, have been offset by rapid increases in manufacturing, construction, trade and transport. The state has thus definitely undergone a transformation in its income structure.

5 RESEARCH NOTES AND COMMUNICATIONS 523 Table 2 Annual Average Growth of per Capita NSDP for Major States (per cent) State Ranking Ranking Ranking Andhra Pradesh Assam Bihar Gujarat Haryana Himachal Pradesh Karnataka Kerala Madhya Pradesh Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal Source: Calculated from At the level of employment, however, there is little change in the state. The NSS data used in Table 3 indicates the share of the various sectors in employment. At both the all- India as well as the state levels, the major proportion of the rural population is engaged in agriculture, considering employment by principal and subsidiary status taken together. While this proportion has declined at both the national and the state levels, the decline has been negligible in the case of Gujarat. The proportion of the rural population engaged in agriculture was almost the same at the national and state level in per cent for Gujarat, and 78.4 per cent for all-india. However, while the all-india share of agricultural workers declined to 62.9 per cent in , it remained almost stagnant at 77.3 per cent for the state. Not only this, the same trend is seen in the urban sector as well, though the proportion of the urban workers engaged in agriculture is small. Over the 12-year period from to , there has been a 57 per cent decline in the population engaged in agriculture in urban India, while at the state level, this proportion is about 23 per cent. In terms of aggregate rural employment figures, Gujarat has done better than India. However, while rural employment in Gujarat has been rising, growth rates in the rural male usual status employment have steadily declined (that is, employment has increased at a decreasing rate) in Gujarat after After , the rural male usual status employment has actually declined. In the rural sector, self-employment constitutes the largest proportion of the usually employed (males), while regular wage employment constitutes the smallest proportion. However, the share of casual labour in the rural usual status male employment has shown an increasing trend.

6 524 The Indian Journal of Labour Economics Table 3 Percentage Distribution of Usually Working Persons in the Principal and Subsidiary Status Together by Industry, Gujarat and India Survey Round Agri. Ming. Mfg. Elec. Constr. Whole sale/ Trans, All All and quarrg. Retail Trade Storage, etc. services Gujarat Rural Gujarat Urban India Rural India Urban Source: NSS data, Various Rounds. III. WOMEN IN THE NON-FARM SECTOR Table 4 shows a comparative picture of the share of the agricultural and non-agricultural sectors in the female labour force at the Gujarat and all-india levels. The most obvious fact is that the proportion of women in agriculture is comparatively larger than their male counterparts. This is true at the all-india as well as at the state levels, and for the rural as well as the urban sectors. Moreover, in rural Gujarat, the proportion of women involved in agriculture has remained largely stagnant, as compared to the same at the all-india level, which has declined, albeit slightly. Within the non-agricultural sector, the largest source of employment for rural women both at the state and all-india levels is the manufacturing sector, followed by the services sector. The share of these sectors in rural female employment is proportionately larger (because the share of agriculture is smaller) at the all-india than at the Gujarat level. A small proportion of women are involved in trade; again this percentage is smaller for Gujarat than for all-india. On the other hand, a slightly larger proportion of rural women in Gujarat work in construction than those at the all-india level. In the urban sector, the story is quite different. Between and , the share of the manufacturing sector in urban female employment outstripped the agricultural sector both in Gujarat and at the all-india level. However, the proportion of women employed in manufacturing was somewhat larger in Gujarat than at the all-india level in Apart from the manufacturing sector, the contribution of the construction sector to female employment is larger in Gujarat than it is at the all-india level.

7 RESEARCH NOTES AND COMMUNICATIONS 525 Table 4 Percentage Distribution of Usually Working Females in the Principal and Subsidiary Status together by Industry, Gujarat and India Survey Round Agri. Ming. Mfg. Elec. Constr. Wholesale/ Trans., All All and quarrg. retail trade storage, etc services Gujarat Rural Gujarat Urban India Rural India Urban Source: NSS data, Various Rounds. A feature specific to Gujarat is that between and , the profile of female employment in the non-agricultural sector has changed. While in , about 20 per cent of the urban female workers were employed in manufacturing and about 42 per cent in services, manufacturing took the centre-stage in , with 33 per cent of urban women workers employed in it. However, this phenomenon has not been so much due to a shift in the urban female workforce out of the agricultural sector, as it has been due to a shift of this workforce out of the service sector. This is not very different from the patterns for all workers (male and female) observed in Table 3. In general, therefore, the pattern of female employment in the state is not very different from the overall employment patterns at the state level. Of course, in a comparison of Tables 3 and 4, it can be seen that the agricultural sector in the urban sector contributes a smaller proportion when all urban persons are taken into account than when only women are taken into account. This implies that a larger proportion of the female labour force than the male labour force is still involved in agriculture, even in the urban sector. The general picture from an analysis of the macro-level secondary data is that both at the Gujarat and at the all-india levels, the agricultural sector is still the mainstay of female employment. In the next section, data from a field survey has been used to draw inferences regarding employment decisions at the household level. The data is not disaggregated by sex, therefore, it is not possible to present a separate picture regarding employment decisions for females. However, discussions during the course of fieldwork did indicate that it was quite

8 526 The Indian Journal of Labour Economics unusual for females to move out of the village for non-farm employment. The employment options open to women in the non-farm sector were either in family-owned small businesses/ shops, or domestic service within the village. Women migrated as construction labourers only as part of a family unit, unlike their male counterparts. IV. EMPLOYMENT IN THE RURAL SECTOR: RESULTS OF field SURVEY The data analysed above provide a picture of the employment situation in Gujarat at a macro level. As has been observed that rural employment is mainly concentrated in agriculture, and predominantly takes the form of self-employment. The period after shows a downturn in employment growth. In the following sections, the results of a survey of 200 rural households in two districts of Gujarat are presented, wherein an attempt has been made to analyse the factors determining non-agricultural employment. The idea was to study the determinants of rural non-farm employment 2 in regions with predominantly agricultural populations. The districts selected for the study were Banaskantha and Dangs. The Below the Poverty Line (BPL) Census (2000) conducted by the Government of Gujarat provides the percentage of rural families living below the poverty line in the various districts of the state. It estimates per cent of rural families in Gujarat to be below the poverty line. Of the total rural families in Dangs, per cent live below the poverty line, while Banaskantha district is backward in parts and overall has per cent of rural families below the poverty line. Both Banaskantha and Dangs districts have predominantly rural and agricultural populations. The percentage of the rural to the total population in Banaskantha is 89.81, while for Dangs, the corresponding figure is Both these figures are higher than the state average of per cent (as per the 2001 Census). The percentage of cultivators among the total workers in Banaskantha is 44.2, while in Dangs it is Both these figures are higher than the state average (27.3 per cent) and among the highest in all the districts of the state. The percentage of total workers in the agricultural sector is per cent in Banaskantha and per cent in Dangs. The state average, on the other hand, is per cent. These two districts rate among the five districts with the highest percentage of agricultural workers. This indicates that the proportion of population in the state that is dependent on agriculture is high. The districts of Banaskantha and Dangs have, therefore, been selected purposively from among the districts of Gujarat for the field survey. Within each district, two villages were selected on the basis of economic development. In Banaskantha, one village from a prosperous taluka and one from a poor taluka were selected. The distance from the district headquarters was treated as an indicator of development. Dangs district consists of a single taluka; therefore, two villages were selected on the basis of the distances from the district headquarters. The average population per village in Gujarat is 1,709 (as per the 2001 Census). Therefore, villages with populations ranging between 1000 and 2000 persons were selected. A stratified random sample of 50 households was selected from each village on the basis of land ownership, including both landless and land-owning households.

9 RESEARCH NOTES AND COMMUNICATIONS 527 The following variables were identified and considered for testing: Dependent variables: 1. Non-farm workers as a percentage of workers in the household (NFPW) 3, and 2. Labour days in non-farm work as a percentage of the total household labour days (PLDNF). Independent variables: 1. Amount of land owned (LO), 2. Amount of irrigated land owned (LI), 3. Value of agricultural assets 4 (AA), 4. Value of agro-based industry and non-farm business assets (NFA), 5. Percentage of household adults illiterate or educated up to the primary school level (UNED), 6. Percentage of household adults educated beyond the school level (COLLED), 7. Ratio of dependents per worker (DPW), 8. Ratio of non-farm to primary sector daily wage rate (WRATIO), 9. Total primary sector income (TFI), 10. Primary sector income per capita (PCFI), 11. Average daily wage rate for non-farm work (DWNF), and 12. Income per capita 5 (PCI). While it is accepted that other aggregate-level (village/taluka level) variables play a role in determining the decision to engage in non-agricultural employment, it was not possible to include these variables in the household-level model. Preliminary correlation analysis was carried out in two stages, using each of the dependent variables. The results, presented in Table 4, revealed significant correlation between: (a) NFPW and all the other variables except DPW, and (b) PCLDNF and NFA, UNED, COLLED, WRATIO, DWNF and PCI. The results of the correlation analysis are described in Table 5. Following this, log-linear OLS regression models were defined by using these variables, with individual variables being removed in stages through the step-wise (backward) process. ln (NFPW) = ln(LI) ln(NFA) 0.15ln(UNED) (1) (0.53) (0.29) (0.04) (0.09) ln (DWNF) 0.23ln(PCFI) (0.06) (0.06) R 2 (adj.) = ln(pcldnf) = ln(AA) + 0.6ln(NFA) 0.03ln(UNED) (2) (0.41) (0.04) (0.03) (0.07) ln(COLLED) ln(DWNF) 0.13ln(PCFI) (0.06) (0.04) (0.04) R 2 (adj.) = The figures in parentheses are S.E. values.

10 528 The Indian Journal of Labour Economics Table 5 Correlation Results from Survey Data Variable NFPW PLDNF LO Sig 5% Not sig LR Sig 5% Not sig AA Not sig Not sig NFA Sig 5% Sig 1% UNED Sig 1% Sig 1% COLLED Sig 1% Sig 1% DWNF Sig 1% Sig 1% WRATIO Sig 1% Sig 1% PCFI Sig 5% Not sig DPW Not sig Not sig PCI Sig 1% Sig 1% Source: Survey Data. As is seen above, the model (a) is found not to be a good fit, as the R 2 value is extremely low. It appears that no single model can be defined to determine household-level determinants of the percentage of workers in non-farm employment. It is not possible to state unequivocally whether rural non-farm employment for the entire sample is based on demand-pull or distressdiversification factors. Equation (b), indicating the percentage of the total household labourdays in non-farm employment, gives a good fit. The factors determining this variable are the value of agricultural and non-agricultural assets, the percentage of household members educated up to the primary level, the percentage of household members educated above the school level, and the average daily wage earned in farm and non-farm activities, respectively. The signs on these variables are as expected. There is mention in the literature of the parallel presence of both demand-pull and distress-push factors being responsible for diversification into non-farm activities. Ranjan (2006) mentions a bimodal distribution wherein the well-off households as well as the poor households would undertake non-farm occupations, but for different reasons in each case. In the case of economically better-off households, diversification takes place because of more lucrative opportunities in the non-farm sector, while in the case of the poorer households, it takes place as a result of distress-push factors, that is, low productivity or disguised unemployment in the farm sector. It is the contention of this paper that it is not possible to attribute demand-pull or distress-push reasons to diversification on the basis of a household s level of living, unless the underlying variables are first analysed. Without drawing any a priori connection between the standard of living and the causes of diversification, however, it is recognised that the factors responsible for diversification for the well-off and poor households may be different and need to be separately examined. Since model (a) above does not indicate a good fit, it shall be tested separately for households with different levels of living. As a matter of interest, model (b) is also tested in the same way, though it is a good fit. Defining well-off and poor households in the rural context is not simple. A predominant factor in determining the levels of living is the size of the landholding. However, a number

11 RESEARCH NOTES AND COMMUNICATIONS 529 of households in the present sample hold little land, but sustain themselves through salaried service-sector employment. These are previously farm-based households, which have now diversified into non-farm means of income. Therefore, such households have been included in the analysis here, and the levels of living have been defined by the aggregate level of household income (from all sources) during the year preceding the period of the survey, while recognising that it is difficult to estimate agricultural income accurately. The households have been classified into three income categories: (1) those with an annual income up to the median level of income (income <= Rs. 19,750); (2) those with an annual income greater than the median income and up to the mean level of income (Rs. 19,750 < income <= Rs. 46,000), and (3) those with an annual income greater than the mean level (income > Rs. 46,000). The following regression equations indicate causality for each of the income groups: ln(nfpw) = ln(LI) n(NFA) 0.22ln(UNED) 0.42ln(PCFI) (0.74) (0.55) (0.58) (0.14) (0.09)...(1A) R 2 (adj.) = ln(nfpw) = ln(LI) ln(NFA) ln(DWNF) 0.32ln(PCFI) (1.27) (0.59) (0.09) (0.16) (0.15)...(1B) R 2 (adj.) = ln(nfpw) = ln(DWNF) 0.42ln(PCFI) (0.82) (0.08) (0.09)...(1C) R 2 (adj.) = ln(pcdnf) = ln(AA)- 0.09ln(UNED)+ 0.64ln(DWNF) 0.25ln(PCFI) (0.52) (0.05) (0.12) (0.07) (0.06)...(1D) R 2 (adj.) = ln(pcdnf) = ln(COLLED) ln(DWNF) (0.13) (0.09) (0.06)...(1E) R 2 (adj.) = ln(pcnf) = ln(NFA) ln(DWNF) 0.29ln(PCFI) (0.56) (0.04) (0.05) (0.06)...(1F) R 2 (adj.) = The figures in parentheses are the S.E. values. The first three equations (1.A. 1.C.) have the percentage of household workers employed in non-farm occupations as the dependent variable, while the last three (2.A. 2.C.) have the percentage of labour days occupied in non-farm work as the dependent variable. The codes A, B and C indicate the income categories as mentioned above. 1. Percentage of Household Workers in Non-farm Occupations For households with an annual income category A (up to Rs. 19,750), the decision for workers to diversify into non-agricultural occupations is dependent on a number of factors. The model is still not a good fit, which indicates that exogenous variables (village-level or other non-household factors) play an important role. Within the household, the factors responsible are the amount of irrigated land owned, the value of non-farm assets owned by

12 530 The Indian Journal of Labour Economics the household, the percentage of uneducated 6 adults in the household, and the per capita income that the household availed of during the preceding year from farm employment. When one moves to the next income category, the factors change to some extent. Again, since the model is not a good fit, it can be assumed that non-household and village level factors play an important role. It may be noted that for households in income category (B), the percentage of uneducated members ceases to be an obstacle to non-farm employment. On the other hand, the average daily wage earned from non-farm sources becomes an additional determinant. The third equation, for households in category (C), is a better fit, explaining more than half of the dependent variable. The independent variables in this equation indicate that for the high-income households in the sample, the percentage of workers taking up nonfarm employment is determined purely by consideration of the relative returns the wage in the non-farm sector and the per capita income received from the farm sector. However, other non-household factors continue to play a role in the phenomenon of workers shifting to non-farm activities. 2. Percentage of Labour Days in Non-farm Occupations As explained earlier, this variable reflects the situation of individual members being engaged in both farm and non-farm occupations. Rather than the percentage of non-farm workers in total workers in the household, it indicates the proportion of the household s labour time devoted to non-farm activity. This model is a better fit than model (1), indicating that multiple occupations, rather than a single primary occupation assigned to a single individual, better reflect the reality. For households in category (A), the factors determining the proportion of non-farm labour time are multiple, and include the value of agricultural assets held by the household, the proportion of uneducated workers, along with considerations of wages earned in the non-farm and farm sectors. As one moves on to the next income category, however, lack of education or the value of agricultural assets ceases to negatively influence labour days in non-farm occupations. Instead, higher education emerges as a strong positive factor. Again, the level of farm sector wages ceases to be a deterrent to non-farm labour time, but the non-farm wage level continues to be a determining factor. Clearly, for the bottom 50 per cent of the households, the preference is farm-based occupation. Given adequate agricultural assets, they would choose to remain in the farm sector, while for higher income households, ownership of adequate agricultural assets does not deter them from moving into non-farm occupations. In the case of the third (highest) income category, (c) the ownership of nonfarm and business assets becomes a determinant of diversification, along with considerations of farm and non-farm wages. Presumably, such high-income households own a reasonable amount of agricultural assets. This, therefore, does not remain a significant deterrent to diversification. For these households, the size of their non-farm or business assets determines whether they will remain predominantly farm-based or non-farm based. It is clear that the classification of households by income gives a clearer picture of the factors determining diversification into non-farm occupations. Comparing equations (1.A.)

13 RESEARCH NOTES AND COMMUNICATIONS 531 to (2.C.) with the earlier equations (1) and (2) shows the difference. In the case of equation (1), the goodness of fit is not substantially improved by income classification; however, a better fit is achieved for the high income (category C) households. For the other two categories, the goodness of fit achieved is more or less similar to that in equation (1), with a smaller number of variables. In the case of equation (2), categorisation by income levels gives a better fit for households in categories (B) and (C). In the case of category (A), while the equation fit is poorer, it still explains more than 60 per cent of the variation in non-farm labour time. Most importantly, it facilitates sharper identification of a smaller number of variables responsible for occupational diversification. It reveals that the factors determining occupational diversification in the rural areas differ for different income groups. Firstly, it may be noted that diversification of labour time rather than diversification of the individual worker is the rule in the sample villages. The model with worker diversification is a poor fit for all except the high-income households. For high-income households (the highest 55 households in the sample), household-level factors relative wages in the farm and non-farm sectors during the preceding year explain about half of the shift of workers to non-farm occupations as their primary occupation. In the case of incomes that are lower than this level, such a total shift to a non-farm occupation is just not defined. What is defined for poorer households is a shift in labour time from farm to non-farm work. Secondly, even in terms of the diversification of labour time, there are multiple factors determining such a shift. For poor households, this shift is the result of a larger number of factors. The lack of adequate agricultural assets and of education form important reasons as to why these households diversify their time into non-farm occupations. These reasons are specific to poor households. This could be taken as an indicator of labour being pushed out of the farm sector because of inadequate means of production. Of the entire sample, about 20 per cent of the households have one or more members primarily involved in non-farm occupations (see Table 6). About 18 per cent of the total labour days are spent in non-farm work, and the households earn approximately 20 per cent of their income from it. Thus, the earning from non-farm work is approximately proportionate to the workers. However, there are differences in terms of both income levels and districts. A much lower proportion of the poor households (both categories A and B constituting about 16 per cent each) are involved in non-farm work than the high-income households (32 per cent workers, 30 per cent labour days in category C households). District-wise, while the proportion of workers in non-farm work is approximately the same (20-21 per cent), non-farm workers in the Dangs district get returns from non-farm work, which is disproportionately higher than the workers and the labour time involved. This is especially true for the poorest households, wherein 9 per cent of the workers and 8 per cent of the labour days are invested in non-farm work, but these households earn 15 per cent of their total income from non-farm sources. At the highest income category, as many as half of the Dangs households in the sample have diversified into the non-farm sector, and half of all the income is non-farm income. In Banaskantha, the situation is the reverse the largest proportion of non-farm workers are in the poorest households, but they engage in both farm and non-farm occupations (while

14 532 The Indian Journal of Labour Economics 27 per cent are primarily non-farm workers, only 19 per cent of total labour time is spent on non-farm occupations). In the next income category, the households prefer to stay within the agricultural sector, with only 10 per cent of the workers engaged in non-farm work and only 4 per cent or total labour time invested therein. At the highest level of income in Banaskantha, a somewhat larger proportion of all workers are involved in non-agricultural work, investing almost all their time in it (with about 19 per cent of the workers and about 17 per cent of the labour time invested in non-farm work). Table 6 Occupational Diversification by Income Categories and Districts Category of annual household income District NF workers as % of workers % of labour days in NF occupations (A) <=median (Rs ) NA income as % of total income Banaskantha Dangs Total (B) Rs to Rs Banaskantha Dangs Total (C) > mean (Rs ) Banaskantha Dangs Total Total Banaskantha Dangs Total Source: Survey Data. The income from non-farm sources is proportional to the labour time invested in non-farm work within the two higher income categories (B and C). However, for the lowest income category, non-farm sources provide an income that is somewhat higher than the proportion of time invested. As pointed out earlier, this is especially true in the Dangs district. Table 7 reveals that workers from the sample households in Dangs have moved into construction and small business much more than those in Banaskantha. Work in the service sector is also available to a larger extent in Dangs than in Banaskantha. Table 7 Distribution of Workers who Moved to Non-farm Occupation Non-farm occupation Banaskantha Dangs Total Construction Shop-owner Schoolteacher Other govt. employee NGO worker Diamond polishing Domestic work Professionals Private service Source: Survey Data.

15 RESEARCH NOTES AND COMMUNICATIONS 533 Data from the Banaskantha and Dangs collectorates indicates that there are no large industrial units in Dangs. On the other hand, Banaskantha district has 13 large and 5,606 small industrial units. However, these do not seem to generate adequate employment all over the district. The main source of non-farm employment for the poor of Dangs district is the rapid growth in construction in the nearby industrialised districts like Surat, among others. Moreover, the development of employment in tribal schools set up by the government and private institutions 7 in the district has resulted in the creation of an educated upper class, and thus also a demand for non-farm consumption goods. This has, in turn, facilitated the establishment of small shops and businesses within the villages in Dangs. Non-farm employment has been generated not by increased agricultural prosperity but by exogenous investment and industrial development. V. CONCLUSION It is theorised that economic growth would lead to a shift in both income and employment from the primary sector to the secondary and tertiary sectors. While the Gujarat economy has certainly undergone a structural change in terms of the share of the sectors in state income, such a shift has not taken place in employment 8. Growth has been largely based on the secondary, and to some extent, the tertiary sector, while the primary sector has stagnated. Therefore, it is not possible in the context of Gujarat to test the hypothesis that agricultural growth could act as a stimulant to increasing demand. Where diversification has occurred, the sources have been non-agricultural industrial and real estate development in the nearby urban areas, and government employment in schools and government offices. It is also not possible to draw any generalised conclusions about the nature of rural employment diversification. As the micro-level data shows, the motivations for diversification are quite different in the case of the high-income and low-income households. While relative income considerations and the ownership of non-farm assets and higher education determine diversification for the high-income households, the reverse situation holds true for poor households. Their occupational diversification decisions are based on the lack of agricultural assets. For poor households, therefore, there is evidence that inadequacy of means of production forms one of the reasons for diversification. Gujarat is regionally a highly unequal state, in terms of both the overall income levels as well as agricultural and industrial development. The districts with a high proportion of population in the agricultural sector have been chosen in order to facilitate a study of the factors responsible for the limited diversification. In the other districts of Gujarat, agriculture has, no doubt, led to prosperity in small pockets. But as the overall state-level data shows, there has been hardly any shift of the rural population out of the farm sector. Dixit (forthcoming) finds that incomes per worker in the agricultural sector have been stagnating. Calculations from the Census data reveal that real incomes (at prices) in the primary sector have actually declined from Rs in to Rs in , that is, about 19 per cent over twenty years. As compared to this, the incomes in the secondary and tertiary sectors in the state have increased by 172 per cent and 146 per cent, respectively. This has created low levels of demand from within the agricultural sector.

16 534 The Indian Journal of Labour Economics Agricultural policy in the state has focused on the production of agro-industrial inputs; and the years of the present decade have witnessed explosive, albeit fluctuating, growth in agriculture, especially commercial crops. On the other hand, the fact that incomes in the agricultural sector have stagnated indicates that the benefits of commercial agricultural production have accrued to a selective few. In the sample villages, agriculture has neither been a source of increased prosperity nor of employment generation. The poorer households prefer the farm sector, but lack of an income/asset base leading to unproductive farming pushes them into the non-farm sector. At the policy level, the implications are two-fold. Firstly, the situation of agriculture and of the majority of the agricultural population (which forms the majority of the rural population in the state) is not conducive to private investment. If agriculture has to be developed as an engine of growth or employment, large doses of public investment, in terms of infrastructure and targeted subsidised credit, are needed. On the other hand, active diversification into non-farm sources of employment must be promoted all over the state, also through initial public expenditure. The development of industries in a regionally diversified and employment-focussed manner, roads and other infrastructural development are urgently required. In addition, the provision of schools, hospitals, etc., and sources of service sector employment is essential in a regionally diversified manner, and also to ensure a healthy and qualified labour force. Notes 1. She also points out that the non-farm consumer goods industries in her study area were non-local and non-labour-intensive. This could be taken to mean that the growth of a non-agricultural sector does not automatically translate into the generation of non-agricultural employment. 2. The term non-farm rather than non-agricultural employment has been purposefully used. In the term farm employment, all economic activities dealing with cultivation and livestock rearing/maintenance, whether as self-employment or as wage labour, are included. Non-farm employment, therefore, includes all activities except these. 3. It was recognised that members of rural households engage in more than one occupation. While defining the percentage of members engaged in non-farm occupations, each individual s primary occupation as defined by the individual herself/himself has been used. The issue of multiple occupations was dealt with separately by defining a model with percentage of labour days in non-farm occupations as the dependent variable. 4. Although the term agricultural has been used, this includes physical (farm implements and machinery) assets and livestock, used both in agricultural operations and in tending to livestock. 5. Includes income from all (farm and non-farm) sources. 6. The term uneducated should be understood from here onwards to mean illiterate or educated up to primary school. 7. A proportionately larger number of schools one secondary school per 7600 population in Dangs as against per 12,000 population in Banaskantha has also resulted in a larger proportion of the educated population 50 per cent literacy rate in Banaskantha and about 60 per cent in the Dangs. 8. The general argument is that of increased non-agricultural employment through massive rural-urban migration. Such migration, if it had occurred, would have resulted in a large increase in the percentage of urban population in non-farm employment. However, as Table 3 indicates, this has not happened.

17 RESEARCH NOTES AND COMMUNICATIONS 535 Moreover, Census data shows that urban population growth during in Gujarat (2.87 percentage points) is not significantly different from that at the all-india level (2.1 percentage points). This rules out the migration argument and strengthens the case made here that structural change in state income has not resulted in a similar structural change in employment. References Abraham, Vinoj (2009), Employment Growth in Rural India: Distress-Driven?, Economic and Political Weekly, Vol. 44, No. 6, April 18, pp Basant, Rakesh (1994), Economic Diversification in Rural Areas: A Review of Processes with Special Reference to Gujarat, Economic and Political Weekly, Vol. 29, No. 39, September 24, pp. A-107-A116. and Kumar, B.L. (1989), Rural Non-agricultural Activities: A Review of Available Evidence, Social Scientist, Vol. 17, No. 1/2 (January-February), pp Basu D.N. and Kashyap, S.P. (1992), Rural Non-agricultural Employment in India Role of Development Process and Rural-Urban Employment Linkages, Economic and Political Weekly, Vol. 27, Nos , pp. A-178-A189. Bhalla, Sheila (1993), Patterns of Employment Generation, The Indian Journal of Labour Economics, Vol. 36, No.4, pp (1997), The Rise and Fall of Workforce Diversification Process in Rural India, in G.K. Chadha and Alakh N. Sharma (eds.), Growth, Employment and Poverty: Change and Continuity in Rural India, Vikas Publishing House, New Delhi. Chenery. H.B. and Syrquin, M. (1975), Patterns of Development, , Oxford University Press, London. Dev, S.M. (1990), Non-agricultural Employment In Rural India Evidence at a Disaggregate Level, Economic and Political Weekly, Vol. 25, No. 28, pp Dholakia, Ravindra H. (2007), Sources of Economic Growth and Acceleration in Gujarat, Economic and Political Weekly, March 3, pp Dixit, Anita (forthcoming), Agriculture in a High Growth State: The Case of Gujarat , Economic and Political Weekly. Government of India, (1996), Key Results on Employment and Unemployment, Report No. 406, 50th Round , National Sample Survey, Ministry of Statistics and Programme Implementation. (2000), Key Results on Employment and Unemployment in India, Report No. 455, 55th Round , National Sample Survey, Ministry of Statistics and Programme Implementation (2006), Employment-Unemployment Situation in India , Report No. 515, 61st Round , National Sample Survey, Ministry of Statistics and Programme Implementation. Harriss, Barbara (1987), Regional Growth Linkages from Agriculture and Resource Flows in Non-farm Economy, Economic and Political Weekly, Vol. 22 Nos.1, 2, pp Hazell, P.B.R. and Haggblade, S. (1991), Rural Growth Linkages in India, Indian Journal of Agricultural Economics, Vol. 46, No. 4, pp Hymer, E. and Resnick, S. (1969), A Model of an Agricultural Economy with Non-agricultural Activities, The American Economic Review, Vol. 59, No. 4, pp Kundu, A. (1991), Growth of Non-agricultural Employment A Hypothesis on Rural-Urban Linkages, IASSI Quarterly, Vol. 10, No. 2, pp ; Sarangi, Niranjan and Das, Bal Paritosh (2003), Rural Non-farm Employment: An Analysis of Rural- Urban Interdependencies, Working Paper No. 196, Overseas Development Institute, London. Kuznets, Simon S. (1966), Modern Economic Growth: Rate, Structure, and Spread, Yale University Press, New Haven.