|Title:||Still ‘few, slow and low’? On the female dimension of technology, labour markets and economic activity: Evidence for the period of 1990-2017|
Vol. 12, No 1, 2019
Published date: 03-2019 (print) / 03-2019 (online)
Economics & Sociology
ISSN: 2071-789X, eISSN: 2306-3459
Gdansk University of Technology
|Keywords:||ICT, female labour, women, developing countries|
|JEL classification:||O30, O40|
|This research is part of Project no.2015/19/B/HS4/03220 financed by the National Science Centre, Poland.|
The known in empirical economics question ‘Why so Few? Why so Slow? Why so Low?’ refers here to the persistently small number of women involved in innovative activities, the slowness of change in the inequalities between women and men in these fields, and women’s continuing lower rank in business and academic positions. In developing countries, women`s labour and entrepreneurial activity remains an ‘untapped resource’ for economic growth. In recent years, the rising proportion of women participating in the labour market has drawn the attention of many scholars. This positive change towards mobilising previously unused human resources is perceived as one of the positive externalities enhanced by the seemingly boundless flow of information and communication technology. This research examines, from a macroperspective, the association between economic deployment of ICT, women`s labour market participation, and economic growth in 64 developing countries between 1990 and 2017. We rely on the macrodata extracted from the World Bank Development Indicators (2018), the World Bank Enterprise Survey, the World Development Reports and the World Telecommunication/ICT Indicators Database (2018). Our methodological framework, in addition to standard descriptive statistics, combines time trends, graphical non-parametric analysis and panel vector-autoregressive models.
1. Abrigo, M. R. & Love, I. (2016). Estimation of panel vector autoregression in Stata. Stata Journal, 16(3), 778-804.
2. Abrigo, M. R., & Love, I. (2016a). Estimation of panel vector autoregression in stata: A package of programs. University of Hawaii. Working paper, (16-2).
3. Afrah, S. H. & Fabiha, S. T. (2017). Empowering Women Entrepreneurs through Information and Communication Technology (ICT): A Case Study of Bangladesh. Management, 7(1), 1-6.
4. Afshar, H. (Ed.). (2016). Women and empowerment: Illustrations from the Third World. Springer.
5. Akaike, H. (1969). Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics, 21(1), 243-247.
6. Akaike, H. (1977). On entropy maximization principle, in P. R. Krishnaiah, (ed.) Applications of Statistics. Amsterdam: North-Holland, pp. 27-41.
7. Aknouche, A. (2007). Causality conditions and autocovariance calculations in PVAR models. Journal of Statistical Computation and Simulation, 77(9), 769-780.
8. Allen, S. & Truman, C. (2016). Women in business: Perspectives on women entrepreneurs. Routledge.
9. Amisano, G., & Geweke, J. (2017). Prediction using several macroeconomic models. Review of Economics and Statistics, 99(5), 912-925.
10. Anderson, T. W. & Hsiao, C. (1982). Formulation and estimation of dynamic models using panel data. Journal of Econometrics, 18(1), 47-82.
11. Andersson, A. & Hatakka, M. (2017). Victim, Mother, or Untapped Resource? Discourse Analysis of the Construction of Women in ICT Policies. Information Technologies & International Development, 13, 15.
12. Andrews, D. W. & Lu, B. (2001). Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. Journal of Econometrics, 101(1), 123-164.
13. Bagliano, F. C., & Morana, C. (2009). International macroeconomic dynamics: A factor vector autoregressive approach. Economic Modelling, 26(2), 432-444.
14. Bandiera, O., Buehren, N., Burgess, R., Goldstein, M., Gulesci, S., Rasul, I. & Sulaiman, M. (2017). Women”s empowerment in action: Evidence from a randomized control trial in Africa. World Bank.
15. Becketti, S. (2013). Introduction to time series using Stata (pp. 176-182). College Station, TX: Stata Press.
16. Benería, L., Berik, G. & Floro, M. (2015). Gender, development and globalization: economics as if all people mattered. Routledge.
17. Bresnahan, T. F. & Trajtenberg, M. (1995). General purpose technologies “Engines of growth”?. Journal of econometrics, 65(1), 83-108.
18. Buvinic, M. & Furst-Nichols, R. (2014). Promoting women”s economic empowerment: what works? The World Bank.
19. Canova, F. & Ciccarelli, M. (2009). Estimating multicountry VAR models. International Economic Review, 50(3), 929-959.
20. Canova, F., & Ciccarelli, M. (2013). Panel Vector Autoregressive Models: A Survey☆ The views expressed in this article are those of the authors and do not necessarily reflect those of the ECB or the Eurosystem. In VAR Models in Macroeconomics–New Developm
21. Castells, M., Fernandez-Ardevol, M., Qiu, J. L. & Sey, A. (2009). Mobile communication and society: A global perspective. MIT Press.
22. Clark, G. (2008). A farewell to alms: a brief economic history of the world. Princeton University Press.
23. Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American statistical association, 74(368), 829-836.
24. Cornwall, A. (2016). Women”s empowerment: What works? Journal of International Development, 28(3), 342-359.
25. Dées, S. & Guntner, J. (2014). Analysing and forecasting price dynamics across euro area countries and sectors: A panel VAR approach. European Central Bank Working Paper Series, 1724.
26. Drucker, P. (2017). The age of discontinuity: Guidelines to our changing society. Routledge.
27. Fielden, S. L., Davidson, M. J., Gale, A. W. & Davey, C. L. (2000). Women in construction: the untapped resource. Construction Management & Economics, 18(1), 113-121.
28. Gaddis, I. & Klasen, S. (2014). Economic development, structural change, and women”s labor force participation. Journal of Population Economics, 27(3), 639-681.
29. Gnimassoun, B., & Mignon, V. (2016). How do macroeconomic imbalances interact? Evidence from a panel VAR analysis. Macroeconomic Dynamics, 20(7), 1717-1741.
30. Granger, C. W. (1980). Testing for causality: a personal viewpoint. Journal of Economic Dynamics and control, 2, 329-352.
31. Hafkin, N. J. & Huyer, S. (2006). Cinderella or cyberella?: Empowering women in the knowledge society. Kumarian Press, Incorporated.
32. Hayakawa, K. (2016). Improved GMM estimation of panel VAR models. Computational Statistics & Data Analysis, 100, 240-264.
33. Hannan, E. J. & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society. Series B (Methodological), 41(2), 190-195.
34. Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica: Journal of the Econometric Society, 50(4), 1029-1054.
35. Hanna, N. K., Guy, K. & Arnold, E. (1995). Information technology diffusion: Experience of industrial countries and lessons for developing countries. World Bank Staff Working Paper (1995). Washington DC The World Bank.
36. Helpman, E. & Trajtenberg, M. (1996). Diffusion of general purpose technologies (No. w5773). National bureau of economic research.
37. ILO (2013). Guide to the Millennium Development Goals Employment Indicators. International Labour Office.
38. Islam, M. S. (2015). Impact of ICT on women empowerment in South Asia. Journal of Economic & Financial Studies, 3(03), 80-90.
39. ITU World Telecommunication/ICT Indicators database 2017.
40. Jovanovic, B. & Rousseau, P. L. (2005). General purpose technologies. Handbook of economic growth, 1, 1181-1224.
41. Kabeer, N. (2017). Economic pathways to women”s empowerment and active citizenship: what does the evidence from Bangladesh tell us? The Journal of Development Studies, 53(5), 649-663.
42. Kabir, N. (2016). Women”s economic empowerment and inclusive growth: labour markets and enterprise development. School of Oriental and African Studies, UK.
43. Kaur, H., Lechman, E. & Marszk, A. (Eds.). (2017). Catalyzing development through ICT adoption: the developing world experience. Springer.
44. Klasen, S., Lechtenfeld, T. & Povel, F. (2015). A feminization of vulnerability? Female headship, poverty, and vulnerability in Thailand and Vietnam. World Development, 71, 36-53.
45. Klasen, S. (2018). What explains uneven female labor force participation levels and trends in developing countries? (No. 246). Courant Research Centre: Poverty, Equity and Growth-Discussion Papers.
46. Klasen, S. (2018a). The Impact of Gender Inequality on Economic Performance in Developing Countries. Annual Review of Resource Economics, 10, 279-298.
47. Koop, G. (2017). Bayesian methods for empirical macroeconomics with big data. Review of Economic Analysis, 9(1), 33-56.
48. Koop, G. & Korobilis, D. (2016). Model uncertainty in panel vector autoregressive models. European Economic Review, 81, 115-131.
49. Lechman, E. (2015). ICT Diffusion in Developing Countries. Springer International.
50. Lindio-McGovern, L., & Wallimann, I. (2016). Globalization and third world women: Exploitation, coping and resistance. Routledge.
51. Maddala, G. S. & Lahiri, K. (2009). Introduction to econometrics. Wiley.
52. McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (Second Edition). Chapman and Hall/CRC.
53. Ng, S. & Perron, P. (2001). Lag length selection and the construction of unit root tests with good size and power. Econometrica: Journal of the Econometric Society, 69(6), 1519-1554.
54. Ocampo, S. & Rodríguez, N. (2012). An introductory review of a structural VAR-X estimation and applications. Revista Colombiana de Estadística, 35(3), 479-508.
55. Ortiz Rodríguez, J., & Pillai, V. K. (2019). Advancing support for gender equality among women in Mexico: Significance of labor force participation. International Social Work, 62(1), 172-184.
56. Perez, C. & Soete, L. (1988). Catching up in technology: entry barriers and windows of opportunity. Technical change and economic theory, 458-479.
57. Pryer, J. A. (2017). Poverty and vulnerability in Dhaka slums: the urban livelihoods study. Routledge.
58. Ramey, V. A. & Shapiro, M. D. (1998). Costly capital reallocation and the effects of government spending. Carnegie-Rochester Conference Series on Public Policy, 48, 145-194.
59. Ranga, M. & Etzkowitz, H. (2010). Athena in the world of techne: The gender dimension of technology, innovation and entrepreneurship. Journal of technology management & innovation, 5(1), 1-12.
60. Sachs, C. E. (2018). Gendered fields: Rural women, agriculture, and environment. Routledge.
61. Shapiro, C., Carl, S., & Varian, H. R. (1998). Information rules: a strategic guide to the network economy. Harvard Business Press.
62. Sigmund, M., Gunter, U., & Krenn, G. (2017). How Do Macroeconomic and Bank‐specific Variables Influence Profitability in the Austrian Banking Sector? Evidence from a Panel Vector Autoregression Analysis. Economic Notes: Review of Banking, Finance and Mone
63. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464.
64. Thach, N. N., & Oanh, T. T. K. (2018, January). Effect of Macroeconomic Factors on Capital Structure of the Firms in Vietnam: Panel Vector Auto-regression Approach (PVAR). In International Conference of the Thailand Econometrics Society (pp. 502-516). Spr
65. Tsani, S., Paroussos, L., Fragiadakis, C., Charalambidis, I. & Capros, P. (2015). Female labor force participation and economic development. In Economic and Social Development of the Southern and Eastern Mediterranean Countries (pp. 303-318). Springer.
66. Valente, T. W. (1996). Social network thresholds in the diffusion of innovations. Social networks, 18(1), 69-89.
67. Verick, S. (2014). Female labor force participation in developing countries. IZA World of Labor.
68. Verick, S. (2018). Female labor force participation and development. IZA World of Labor.