Gender income gap in rural informal micro-enterprises: an unconditional quantile decomposition approach in the handloom industry

Abstract

Based on primary data, the present study analyzes the gender income gap and its compositions throughout the income distribution of the handloom micro-entrepreneurs in Assam. The unconditional quantile decomposition reveals the existence of substantial gender income gaps along the income distribution. The differences in the productive characteristics explain much of the gap at the median and beyond. The endowment effects of education, financial literacy, risk attitude, SHGs membership, and technology adoption are found in favor of the male micro-entrepreneurs. The results suggest that the extent of risk aversion towards producing high-valued dress materials and poor management of entrepreneurial activities of the females have widened gender gap, particularly at the upper quantiles of the income distribution.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Notes

  1. 1.

    Of the total female owned enterprises in India, about 48.02% are in textile and wearing apparel sector (Deshpande 2013).

  2. 2.

    Data for the stratification was used from Statistical Handbook of Assam, 2010 (Directorate of Economics and Statistics 2011).

  3. 3.

    An enterprise that does not exceed INR 2.5 million in terms of investment in plant and machinery is categorized as a micro-enterprise in Indian formal sector whereas the informal entrepreneurship covers all the enterprises which are not registered and are within the definitions for the formal sector (MoMSME 2017). As the present study concerns with informal sector and a low capital intensive industry, the operational definition of handloom micro-enterprise is derived based on a few earlier studies (Honig 1998; Hazarika et al. 2016).

  4. 4.

    A robust tool of statistical or econometric estimation that represents the influence of an individual observation on a distributional statistic such as quantile (Firpo et al. 2009; Fortin et al. 2011).

  5. 5.

    For technical details, see Firpo et al. (2009) and Fortin et al. (2011).

  6. 6.

    For a review of these methods, please see Fortin et al. (2011).

  7. 7.

    Often technology adoption appears to be endogenous variable in income model. Following the literature, the endogeneity of the access to technology was examined considering ‘technological awareness’ and ‘access to extension services’ as instruments for weaving machinery adoption. However, no evidence of endogeneity of technology adoption is observed for the present sample (results are not presented, available upon request).

  8. 8.

    ARTFED and BRAWFED are the two apex cooperative societies looking after the handloom cooperative activities in the State which are currently facing some structural challenges including shortage of working capital.

  9. 9.

    The two-sample Kolmogorov–Smirnov test compares the observed cumulative distribution function for a variable with a specified theoretical distribution, which may be normal, uniform, Poisson, or exponential. The Kolmogorov–Smirnov Z-statistic is computed from the largest difference (in absolute value) between the empirical and theoretical cumulative distribution functions. It gives the goodness-of-fit test about the observations for a specified distribution.

  10. 10.

    It should be noted that though the recommendations of Gardeazable and Ugidos (2004) are followed in the present study to tackle the omitted category problem for categorical variables, the discriminatory coefficients of the determinants other than the continuous are somewhat arbitrary (Jann 2008; Magnani and Zhu 2012).

  11. 11.

    In order to overcome the problem of omitted category in detailed decomposition for categorical variables, the present study follows the procedure presented in Jann (2008).

References

  1. Ahmed, S., & Maitra, P. (2015). A distributional analysis of the gender wage gap in Bangladesh. The Journal of Development Studies,51(11), 1444–1458.

    Google Scholar 

  2. Ahmed, S., & McGillivray, M. (2015). Human capital, discrimination, and the gender wage gap in Bangladesh. World Development,67, 506–524.

    Google Scholar 

  3. Álvarez G, Gradín C, Otero MS (2009). Self-employment in Spain: transition and earnings differential. Universidade de Vigo, Departamento Economía Aplicada Documento de Traballo 0907, Vigo.

  4. Arellano, M., & Bonhomme, S. (2017). Sample selection in quantile regression: a survey. In Koenker et al. (Eds.), Handbook of quantile regression (pp. 209–224). New York: Taylor and Francis Group.

  5. Åstebro, T., & Chen, J. (2014). The entrepreneurial earnings puzzle: mismeasurement or real? Journal Business Venturing,29, 88–105.

    Google Scholar 

  6. Becolod, M. (2016). Skills, the gender wage gap, and cities. Journal of Regional Science,57, 290–318.

    Google Scholar 

  7. Bhagavatula, S., Elfring, T., Tilburg, A. V., & Bunt, G. G. V. (2010). How social and human capital influence opportunity recognition and resource mobilization in India’s handloom industry. Journal Business Venturing,25, 245–260.

    Google Scholar 

  8. Blinder, A. S. (1973). Wage discrimination: reduced form and structural estimates. Journal of Human Resource,8, 436–455.

    Google Scholar 

  9. Bortamuly, A. B., & Goswami, K. (2012). Factors influencing wage structure of the handloom workers in Assam: An assessment from gender perspective. Journal of Rural Development,31(139–150), 2012.

    Google Scholar 

  10. Bortamuly, A. B., Goswami, K., & Hazarika, B. (2013). Determinants of occupational choice of workers in handloom industry in Assam. International Journal Social Economics,40, 1041–1057.

    Google Scholar 

  11. Bortamuly, A. B., Goswami, K., Hazarika, B., & Handique, K. (2014). Do different determinants affect differently across gender and location in handloom entrepreneurship development? Journal of Small Business & Entrepreneurship,27, 427–449.

    Google Scholar 

  12. Buchinsky, M. (2001). Quantile regression with sample selection: estimating women’s return to education in the US. Empirical Economics,26, 87–113.

    Google Scholar 

  13. Carter, S. (2011). The rewards of entrepreneurship: exploring the incomes, wealth, and economic well-being of entrepreneurial households. Entrepreneurship Theory and Practice,35(39–55), 2011.

    Google Scholar 

  14. Chi, W., & Li, B. (2014). Trends in China’s gender employment and pay gap: estimating gender pay gaps with employment selection. Journal of Comparative Economics,42, 708–725.

    Google Scholar 

  15. Chzhen, Y., & Mumford, K. (2011). Gender gaps across the earnings distribution for full-time employees in Britain: Allowing for sample selection. Labour Economics,18, 837–844.

    Google Scholar 

  16. Cressy, R. (2006). Why do most firms die young? Small Business Economics,26(2), 103–116.

    Google Scholar 

  17. Das, P. (2012). Wage inequality in India: Decomposition by sector, gender and activity status. Economic and Political Weekly,47(50), 58–64.

    Google Scholar 

  18. Deininger, K., Jin, S., & Nagarajan, H. (2013). Wage Discrimination in India’s informal labor markets: Exploring the impact of caste and gender. Review of Development Economics,17(1), 130–147.

    Google Scholar 

  19. Deshpande, A. (2013). Entrepreneurship or survival? Caste and gender of small business in India. Economic and Political Weekly,118(80), 38–49.

    Google Scholar 

  20. Deshpande, A., & Sharma, S. (2016). Disadvantage and discrimination in self-employment: caste gaps in earnings in Indian small businesses. Small Business Economics,46, 325–346.

    Google Scholar 

  21. Directorate of Economics and Statistics. (2011). Statistical Handbook of Assam 2010. Guwahati: Government of Assam.

    Google Scholar 

  22. Fairlie, R. W., & Robb, A. M. (2009). Gender differences in business performance: evidence from the characteristics of business owners survey. Small Business Economics,33, 375–395.

    Google Scholar 

  23. Firpo, S., Fortin, N., & Lemieux, T. (2009). Unconditional quantile regressions. Econometrica,77, 953–973.

    Google Scholar 

  24. Fortin, N. (2008). The gender wage gap among young adults in the United States: The importance of money versus people. Journal of Human Resources,43, 886–929.

    Google Scholar 

  25. Fortin, N., Lemieux, T., & Firpo, S. (2011). Decomposition methods in economics. In O. Ashenfelter & D. Card (Eds.), Handbook of labor economics (4A) (pp. 1–102). Amsterdam: Elsevier.

    Google Scholar 

  26. Gardeazabal, J., & Ugidos, A. (2004). More on identification in detailed wage decompositions. Review of Economics and Statistics,86, 1034–1036.

    Google Scholar 

  27. Gardner, D. G., Cummings, L. L., Dunham, R. B., & Pierce, J. L. (1998). Single-item versus multiple-item measurement scales: An empirical comparison. Educational and Psychological Measurement, 58(6), 898–915.

  28. Goswami, K., Hazarika, B., & Handique, K. (2019). Entrepreneurial motivations of socio-cultural relevance: an exploratory analysis in the handloom industry in Assam. Asian Journal of Women’s Studies, 25(3), 317–351.

    Google Scholar 

  29. Hazarika, B., Bezbaruah, M. P., & Goswami, K. (2016). Adoption of modern weaving technology in the handloom micro-enterprises in Assam: A double hurdle approach. Technological Forecasting and Social Change,102, 344–356.

    Google Scholar 

  30. Hazarika, B., & Goswami, K. (2014). Rural non-farm micro-entrepreneurship or not: gender issue in decision making. Paper presented at the 6th Bolivian Conference on Development Economics, Cochabamba.

  31. Hazarika, B., & Goswami, K. (2016). Do home-based micro-entrepreneurial earnings empower rural women? Evidence from the handloom sector in Assam. Asian Journal of Women’s Studies,22, 289–317.

    Google Scholar 

  32. Heckman, J. (1979). sample selection bias as a specification error. Econometrica,47, 153–163.

    Google Scholar 

  33. Honig, B. (1998). What determines success? Examining the human, financial, and social capital of Jamaican microentrepreneurs. Journal of Business Venturing,13, 371–394.

    Google Scholar 

  34. Hundley, G. (2001). Why women earn less than men in self-employment. Journal of Labor Research,22, 817–829.

    Google Scholar 

  35. Iyer, L., Khanna, T., & Varshney, A. (2013). Caste and entrepreneurship in India. Economic and Political Weekly,48, 52–60.

    Google Scholar 

  36. Jann, B. (2008). A Stata implementation of the Blinder–Oaxaca decomposition. Stata Journal,8, 453–479.

    Google Scholar 

  37. Jimenez, G., Ongena, S., Peydro, J. L., & Saurina, J. (2014). Hazardous times for monetary policy: What do 23 million bank loans say about the effects of monetary policy on credit risk-taking? Econometrica,82(2), 463–505.

    Google Scholar 

  38. Jodhka, S. S. (2010). Dalits in business: Self-employed scheduled castes in Northwest India. Economic and Political Weekly,55, 41–48.

    Google Scholar 

  39. Khanna, S. (2012). Gender wage discrimination in India: Glass ceiling or sticky floor? Working Paper No. 214. Centre for Development Economics, Delhi School of Economics, New Delhi.

  40. Kijima, Y. (2006). Why did wage inequality increase? Evidence from Urban India 1983–1999. Journal of Development Economics,81, 97–117.

    Google Scholar 

  41. Koellinger, P., Minniti, M., & Schade, C. (2013). Gender differences in entrepreneurial propensity. Oxford Bulletin of Economics and Statistics,75, 213–234.

    Google Scholar 

  42. Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica,46, 33–50.

    Google Scholar 

  43. Koenker, R., & Hallock, K. (2001). Quantile regression: An introduction. Journal of Economic Perspectives,15(43–56), 2001.

    Google Scholar 

  44. Langowitz, N., & Minniti, M. (2007). The entrepreneurial propensity of women. Entrepreneurship Theory and Practice,31(3), 341–364.

    Google Scholar 

  45. Lechmann, D. S. J., & Schnabel, C. (2012). Why is there a gender earnings gap in self-employment? A decomposition analysis with German data. IZA Journal of European Labor Studies,1, 1–25.

    Google Scholar 

  46. Leung, D. (2006). The male/female earnings gap and female self-employment. Journal of Socio Economics,35, 759–779.

    Google Scholar 

  47. Loscocco, K., & Bird, S. R. (2012). Gendered paths: Why women lag behind men in small business success. Work and occupations,39(2), 183–219.

    Google Scholar 

  48. Machada, J. A. F., & Mata, J. (2005). Counterfactual decomposition of changes in wage distributions using quantile regression. Journal of Applied Economics,20, 445–465.

    Google Scholar 

  49. Magnani, E., & Zhu, R. (2012). Gender wage differentials among rural–urban migrants in China. Regional Science and Urban Economics,42, 779–793.

    Google Scholar 

  50. Matano, A., & Naticchioni, P. (2016). What drives the urban wage premium? Evidence along the wage distribution. Journal of Regional Science,56, 191–209.

    Google Scholar 

  51. Melly, B. (2005). Decomposition of differences in distribution using quantile regression. Labour Economics,12, 577–590.

    Google Scholar 

  52. Mincer, J. (1958). Investment in human capital and personal income distribution. Journal of Political Economy,66, 281–302.

    Google Scholar 

  53. Ministry of Textile. (2018). Annual report 2017–18. New Delhi: Ministry of Textile, Government of India.

    Google Scholar 

  54. MoMSMEs. (2017). Annual report 2016–17. New Delhi: Ministry of Micro, Small and Medium Enterprises, Government of India.

    Google Scholar 

  55. Mulligan, C., & Rubinstein, Y. (2008). Selection, investment, and women’s relative wages over time. Quarterly Journal of Economics,123(3), 1061–1110.

    Google Scholar 

  56. NCAER. (2004). Joint census of handloom & powerloom 1995–1996: handloom sector. New Delhi: National Council of Applied Economic Research.

    Google Scholar 

  57. NCAER. (2010). Handloom census 2009–2010. New Delhi: Development Commissioner for Handlooms, Government of India.

    Google Scholar 

  58. Nordman, C. J., Robilliard, A., & Roubaud, F. (2011). Gender and ethnic earnings gaps in seven West African cities. Labour Economics,18, S132–S145.

    Google Scholar 

  59. Oaxaca, R. (1973). Male–female wage differentials in urban labor markets. International Economic Review,14, 693–709.

    Google Scholar 

  60. Orser, B. J., Riding, A. L., & Manley, K. (2006). Women entrepreneurs and financial capital. Entrepreneurship Theory and Practice,30, 643–665.

    Google Scholar 

  61. Shariff, A., & Azam, M. (2011). Income inequality in rural India: Decomposing the Gini by income sources. Economics Bulletin,31, 739–748.

    Google Scholar 

  62. Simon, J. K., & Way, M. M. (2016). Why the gap? Determinants of self-employment earnings differentials for male and female millennials in the US. Journal of Family and Economic Issues,37(2), 297–312.

    Google Scholar 

  63. Verrest, H. (2013). Rethinking microentrepreneurship and business development programs: Vulnerability and ambition in low-income urban Caribbean households. World Development,47, 58–70.

    Google Scholar 

  64. Yun, M. (2006). Earnings inequality in USA, 1969–1999: Comparing inequality using earnings equations. Review of Income and Wealth,52(1), 127–144.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Bhabesh Hazarika.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hazarika, B. Gender income gap in rural informal micro-enterprises: an unconditional quantile decomposition approach in the handloom industry. Eurasian Bus Rev 10, 441–473 (2020). https://doi.org/10.1007/s40821-019-00139-4

Download citation

Keywords

  • Micro-entrepreneurs
  • Handloom
  • Gender
  • Income gap
  • Unconditional quantile regression

JEL Classification

  • L26
  • L67
  • D13
  • D33
  • D63