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Alternative Approaches to Full Employment in Bangladesh: Role of the Rural Non-farm Sector

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Quest for Inclusive Growth in Bangladesh

Part of the book series: South Asia Economic and Policy Studies ((SAEP))

Abstract

The slow growth of employment in the modern manufacturing sector has raised scepticism on how far and how quickly Bangladesh economy could productively absorb its large pool of surplus labour and attain full employment. Following (Ranis and Stewart, J. Dev Econ 40:75–101, Ranis and Stewart, Journal of Development Economics 40:75–101, 1993), the present chapter reengages in and examines the potential role of rural non-farm activity (RNA) in wiping out rural unemployment and underemployment, as it did in the case of some East Asian countries. The chapter explores the extent and pattern of RNA growth in Bangladesh, its impact on jobs and incomes, as well as on labour market formations. It further contends that changes in rural labour market are often not reflected as structural change due to the presence of informality within the three traditional sectors, viz. agriculture, industry and services. It explores pathways in enhancing rural non-farm (RNF) employment and assesses the prospects of reaching rural full employment. Finally, the study stresses the need for a comprehensive policy framework and coordinated strategy for a vigorous growth of RNF enterprises, in order to increase the potential for higher productivity jobs and household incomes, and to enhance aggregate demand.

This chapter draws from a working paper by the author, Muqtada (2018), published by the CPD; content re-used with permission.

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Notes

  1. 1.

    The dual model (Lewis 1954) of economic development; the investment criteria model (Chenery 1953); the thesis of critical minimum effort (Leibenstein 1957); the theory of Big Push (Rosenstein-Rodan 1943); and the tunnel effect (Hirschman and Rothschild 1973) are major examples of such efforts.

  2. 2.

    Nearly a century ago, in 1918, B. R. Ambedkar recognised the seriousness of the problem of disguised labour in agriculture, and stressed the need for industrialisation to absorb surplus labour (Krishnamurty 2008).

  3. 3.

    Citing analysis of the role of RNA in Taiwan, and contrasting the experience of the Philippines, Ranis and Stewart (1990, p. 38) demonstrates how growth of Z-goods sector in rural areas (i.e. rural non-agricultural goods such as textile, garments, processed food, etc.), along with a rapid growth food agriculture, can lead to a dynamic expansion of employment and incomes, “with a more egalitarian distribution of income and the elimination of rural underemployment.” Also see Ranis and Stewart (1993).

  4. 4.

    Also see details in Chap. 3 in this volume.

  5. 5.

    See Islam (2014); also, discussion on unemployment in Chap. 3 of the present volume.

  6. 6.

    See Chap. 5, on the overwhelmingly dominant share of readymade garments (RMG) in manufacturing output, employment and exports.

  7. 7.

    For example, Adelman (1984) had contested the industry-first orthodoxy and advocated an agriculture-demand-led industrialisation.

  8. 8.

    Among others, see Rosegrant and Hazell (2000), Haggblade et al. (2007).

  9. 9.

    Evidently, the Hymer and Resnick model lacked empirical validation, and was also weak on the assumptions that were used. In particular, by assuming labour as the only input (for which the food and cash crops sectors would compete), the possibility of technology and coexistence of multiple activities, including RNA, is ignored.

  10. 10.

    The existence and growth of rural manufacturing goods in the informal sector was recognised by the ILO’s Kenya Mission, 1971.

  11. 11.

    Ranis and Stewart (1990) further argues that a dynamic Z-good sector would lead to a more egalitarian income distribution and the gradual elimination of rural underemployment.

  12. 12.

    In this context, Ranis and Stewart (1990) specifically stressed the spatial dimension to economic growth.

  13. 13.

    See World Bank (2012); also, Gimenez et al. (2014).

  14. 14.

    See BBS (2018) on recent trends in the growth of employment in the informal sector.

  15. 15.

    Grilli and Zanalda (1999).

  16. 16.

    Also see subsequent section on informal sector and structural change.

  17. 17.

    This is from the Preliminary LFS 2016–17.

  18. 18.

    These shares increase significantly if the semi-urban areas within rural areas are included (Mahmud 1996).

  19. 19.

    The study, using the Rural Industry Study Project conducted by Bangladesh Institute of Development Studies (BIDS) during 1978–1981, finds that the average productivity in rural cottage and small industries was about 57% higher than agricultural wage rate.

  20. 20.

    Hossain (2004).

  21. 21.

    See GED (2015), Khan (2015).

  22. 22.

    BBS (2015a).

  23. 23.

    Various other statistical sources appear to confirm the trend increase in non-farm employment. The HIES also shows the share of employment in 2015–16 to be around 57% of total rural employment increasing from 37% in 2000.

  24. 24.

    Hossain (2004).

  25. 25.

    See Chap. 4 in this volume for insights on the growth and influence of FIs, especially on the rural agriculture and rural self-employment initiatives.

  26. 26.

    These are important initiatives, and are likely to generate an ethos of expanding credit extension, not only through corporate social responsibility (CSR)-induced programmes, but through normal banking channels as well. Some banks are already experimenting in mobile financial services (MFS). BRAC Bank launched (in 2011) its subsidiary, bKash, which is using the mobile banking platform to reach MFS to millions of unbanked populations.

  27. 27.

    Such structural changes could be, for example, “from farm to nonfarm, from informal to formal, and from low to high skilled economic activities” (Shilpi and Emran 2016, p. 2). Also see Ranis and Stewart (1990).

  28. 28.

    Apart from the size of landownership there are several other factors that tend to obscure the debate on household behaviour; e.g. whether the survey analysis refers to workers or labour time, to head of household or total members in household, to issues of principal or subsidiary occupations, varying extent of ownership of physical and human capital, or to whether households belong to agriculturally backward or progressive regions, etc. (Hossain 2004).

  29. 29.

    This is a BIDS-IRRI (International Rice Research Institute) survey of 1245 rural households from 62 villages in 57 districts, conducted in FY1987–88. This benchmark survey was followed by an IRRI survey of 1880 households from the same villages, and covered the households in the former survey. See Hossain (2004).

  30. 30.

    See Pramanik et al. (2014).

  31. 31.

    Such a process is historically observed in various other countries, see for example, Ranis and Stewart (1990) for Taiwan-China; Oshima (1988) for Taiwan-China and Japan; and Shilpi and Emran (2016) for Bangladesh.

  32. 32.

    Note, there have been definitional changes of informal employment in the LFS; LFS 2010 included not only those in informal unregistered enterprises, but also those informally employed in formal enterprises, as well as unpaid family, domestic work that were excluded previously; LFS 2013 additionally included enterprise criteria of no pension or no contribution to retirement fund (see Rahman et al., 2018).

  33. 33.

    Various issues of LFS.

  34. 34.

    For example, out of all registered enterprises, not all are registered for value added tax (VAT); and from among the latter, not all pay VAT.

  35. 35.

    These included factors such as technology, financial tools, record keeping, legal dimensions, etc. See McKenzie (2010).

  36. 36.

    See Ghose (2017), who challenges the traditional formal–informal dichotomy, maintaining that such a view is an import from Western analysts on dualism in post-colonial developing countries. Also see Chen (2003).

  37. 37.

    See Chandrasekhar and Ghosh (2018) on constraints to formalisation in the context of India.

  38. 38.

    Lewis (1954), while proposing the primacy of the growth of the modern industrial sector also acknowledged the significance of improved labour productivity in agriculture in the process of structural change; a potential decline in food/wage goods production would cause a rise in prices and wages.

  39. 39.

    Note, the text follows a qualified view of full employment.

  40. 40.

    One may note that in South Korea during the early stages of industrialisation (1970s), nearly three quarters of the rural labour force that migrated to urban areas were in fact absorbed by the manufacturing sector. See Edgren and Muqtada (1990).

  41. 41.

    Oshima (1988) further contended that reaching a state of rural full employment would facilitate capital labour substitution and help accelerate growth in the overall economy.

  42. 42.

    See Oshima (1988).

  43. 43.

    See Ranis and Stewart (1990), Rosegrant and Hazell (2000), Shilpi and Emran (2016).

  44. 44.

    This was achieved through widespread introduction of high-yielding variety (HYV) technology, input subsidies, extension services, irrigation and mechanisation, etc.

  45. 45.

    See Haggblade et al. (2007), Hossain (2004), Shilpi and Emran (2016).

  46. 46.

    There are empirical studies to show that, assuming fixed input coefficients per unit of output, with increases in yield, there would be an implicit increase in the average productivity of labour, and a corresponding increase in the real agricultural wage rate (Bannerjee 2007).

  47. 47.

    See Oshima (1988).

  48. 48.

    “It is necessary to pursue the supplementary strategy of increasing the supply price of labour directly in the subsistence sector by increasing land productivity” (Mazumdar 1999, p. 6).

  49. 49.

    See among others, Haggblade et al. (2007).

  50. 50.

    Also see Khan (2015).

  51. 51.

    For example, see Hossain (2004), Osmani et al. (2015).

  52. 52.

    Table 6.12, which refers to salaried employees in various sectors.

  53. 53.

    Hossain (2004, p. 23), based on HIES (2000), through his assessment of income elasticities of demand by rural households, argues that since “the non-farm income is now a major component of the rural income, and the rural income has been growing in spite of the sluggish performance of agriculture, the respectable growth in non-farm income itself with generate demand for non-farm goods and services.”

  54. 54.

    See Rosegrant and Hazell (2000).

  55. 55.

    See Osmani et al. (2015). The study is based on a survey of 6300 households.

  56. 56.

    Unit-level data were not available for the more recent HIES 2016–17.

  57. 57.

    The expenditure pattern has been estimated by fitting the following log–log, double-log or log-linear models: \(\text{ln}\,E_{i} = \alpha + \beta\,\text{ln}\,Y + u_{i}\), where \({E}_{i}\) is the household expenditure on the ith consumption item, Y is the total household income, \({u}_{i}\) is the disturbance term and \(\alpha\) and \(\beta\) are the parameters of the function. This model is linear in the parameters \(\alpha\) and \(\beta\), linear in the logarithms of the variables \({E}_{i}\) and Y, and can be estimated by simple Ordinary Least Squares (OLS) regression. In this exercise, the main target is to estimate the slope coefficient \(\beta\) which measures the elasticity of \({E}_{i}\) with respect to Y, that is, the percentage change in \({E}_{i}\) for one percentage change in Y. Following Hazell and Roell (1983), this exercise used total expenditure for each household as a proxy for income.

  58. 58.

    While the income elasticity of demand for non-farm products is observed to be high, it would be difficult to ascertain the relative weights of consumption of RNF versus the urban and imported products (and hence, possible leakages). Nevertheless, there have been perceptible changes in labour markets and productivity gains in RNA to endorse tangible growth in the sector as a whole.

  59. 59.

    See World Bank (2013). According to HIES (2010), annual remittances received per household, on average, was BDT 152 thousand.

  60. 60.

    One must note, and as pointed out by several studies (e.g. Khan 2015), remittances have arguably been a disequalising force, at the household and regional levels. For instance, total migration during 2005–2018, from Cumilla and Chattogram had been 873 and 680 thousand, compared to paltry 36 and 28 thousand in Khulna and Rangpur, respectively.

  61. 61.

    As noted earlier, the growth rate of real wages had been stagnant in the recent period (between 2012 and 2017).

  62. 62.

    While withdrawing surplus labour from agriculture would release some amount of wage-goods that could enhance wage employment in the RNA sub-sectors, it is unclear how far this will be sufficient to provide employment to all the potentially available labour force (Oshima 1986).

  63. 63.

    Most of these problems were identified in late 1950s when the First Five Year Plan of Pakistan (1955–60) was formulated, which were repeated almost religiously in successive development plans (Hossain et al. 1994).

  64. 64.

    The utter neglect of the non-farm sector has been noted in various studies. Cf. Rosegrant and Hazell (2000).

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  1. Development Economist; Formerly a Director in the Employment Sector of the International Labour Organization (ILO) Geneva and a Visiting Fellow, Centre for Policy Dialogue, Dhaka, Bangladesh

    Muhammed Muqtada

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    Muhammed Muqtada

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Muqtada, M. (2020). Alternative Approaches to Full Employment in Bangladesh: Role of the Rural Non-farm Sector. In: Muqtada, M. (eds) Quest for Inclusive Growth in Bangladesh . South Asia Economic and Policy Studies. Springer, Singapore. https://doi.org/10.1007/978-981-15-7614-0_6

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