Social Indicators Research

, Volume 147, Issue 1, pp 95–109 | Cite as

A Child Labour Estimator: A Case of Bahawalpur Division

  • Muhammad Salman ShabbirEmail author
  • Ahmed F. Siddiqi
  • Normalini Md Kassim
  • Faisal Mustafa
  • Rabia Salman


Child labor is a distressing issue. There have been many attempts to estimate its magnitude. It is attempted here to develop an estimator to assess the magnitude of this issue using a (Horvitz and Thompson in J Am Stat Assoc 47(260):663–685, 1952) type of estimator where weights are calculated on the basis of poverty and illiteracy to increase the sampling efficiency. The estimator is used to assess the magnitude of child labor in Bahawalpur division. Subsequent different statistical properties of this estimator, like its unbiasedness, variance, probability distribution, confidence intervals are also developed for its study from different angles.


Child labor Horvitz and Thompson estimator Illiteracy Poverty 



  1. Akabayashi, H., & Psacharopoulos, G. (1999). The trade-off between child labour and human capital formation: A Tanzanian case study. The Journal of Development Studies,35(5), 120–140.Google Scholar
  2. Kazmi, R., Amjad, S., & Khan, D. (2008). Occupational stress and its effect on job performance. A case study of medical house officers of district Abbottabad. J Ayub Med Coll Abbottabad, 20(3), 135–139.Google Scholar
  3. Anker, R. (2000). The economics of child labour: A framework for measurement. International Labour Review,139(3), 257–280.Google Scholar
  4. Basu, K. (1999). Child labor: Cause, consequence, and cure, with remarks on international labor standards. Journal of Economic Literature,37(3), 1083–1119.Google Scholar
  5. Basu, A. K., & Chau, N. H. (2003). Targeting child labor in debt bondage: Evidence, theory, and policy implications. The World Bank Economic Review,17(2), 255–281.Google Scholar
  6. Bequele, A., & Boyden, J. (1988a). Combating child labour. Geneva: International Labour Organization.Google Scholar
  7. Bequele, A., & Boyden, J. (1988b). Working children: Current trends and policy responses. The International Labour Review,127, 153.Google Scholar
  8. Biggeri, M., Libanora, R., Mariani, S., & Menchini, L. (2006). Children conceptualizing their capabilities: Results of a survey conducted during the first children’s world congress on child labour. Journal of Human Development,7(1), 59–83.Google Scholar
  9. Brewer, K. (1995). Combining design-based and model-based inference. In Business survey methods (pp. 589–606).Google Scholar
  10. Brus, D., & De Gruijter, J. (1997). Random sampling or geostatistical modelling? Choosing between design-based and model-based sampling strategies for soil. Geoderma,80(1–2), 1–44. (with discussion).Google Scholar
  11. Cochran, W. G. (1977). Labor and communism: the conflict that shaped American unions (p. 166). Princeton: Princeton University Press.Google Scholar
  12. Cockburn, J. (2001). Child work and poverty in developing countries. PhD thesis, University of Oxford.Google Scholar
  13. Davis, J. A. (1989). Survey research in the united states: Roots and emergence, 1890–1960. JSTOR,53, 136–138.Google Scholar
  14. Deville, J.-C., & Tille, Y. (1998). Unequal probability sampling without replacement through a splitting method. Biometrika,85(1), 89–101.Google Scholar
  15. Edmonds, E., & Pavcnik, N. (2002). Does globalization increase child labor? Evidence from Vietnam. New York City: National Bureau of Economic Research.Google Scholar
  16. Faridi, M. Z., Chaudhry, I. S., & Anwar, M. (2009). The socio-economic and demographic determinants of women work participation in pakistan: Evidence from bahawalpur district.Google Scholar
  17. FBS (1996). Summary results of child labour survey in pakistan. In Federal bureau of statistics (FBS), statistics division, ministry of labour, manpower and overseas Pakistanis, international labour organization (ILO) and international programme on the elimination of child labour (IPEC).Google Scholar
  18. Gabler, S. (1981). A comparison of sampford’s sampling procedure versus unequal probability sampling with replacement. Biometrika,68, 725–727.Google Scholar
  19. Grootaert, C., & Kanbur, R. (1995a). Child labour: An economic perspective. The International Labour Review,134, 187.Google Scholar
  20. Grootaert, C., & Kanbur, R. (1995b). The lucky few amidst economic decline: Distributional change in côte d’Ivoire as seen through panel data sets, 1985–88. The Journal of Development Studies,31(4), 603–619.Google Scholar
  21. Gupta, V., Nigam, A., & Kumar, P. (1982). On a family of sampling schemes with inclusion probability proportional to size. Biometrika,69(1), 191–196.Google Scholar
  22. Hanif, M., & Ahmad, M. (2010). Design and model based sampling inference. Riga: LAP LAMBERT Academic Publishing.Google Scholar
  23. Hansen, M., & Hurwitz, W. (1943). On the theory of sampling from finite populations. The Annals of Mathematical Statistics,14(4), 333–362.Google Scholar
  24. Hansen, M. H., Hurwitz, W. N., & Madow, W. G. (1953). Sample survey methods and theory (Vol. 1). New York: Wiley.Google Scholar
  25. Hilowitz, J., Kooijmans, J., Matz, P., Dorman, P., de Kock, M., & Alectus, M. (2004). Child labour: A textbook for university students. Geneva: International Labour Office.Google Scholar
  26. Hindman, H. D. (2009). The world of child labor: An historical and regional survey. New York: M. E. Sharpe.Google Scholar
  27. Horvitz, D. G., & Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association,47(260), 663–685.Google Scholar
  28. Jensen, P., & Nielsen, H. S. (1997). Child labour or school attendance? Evidence from Zambia. Journal of Population Economics,10(4), 407–424.Google Scholar
  29. Lehmann, E. L., & Romano, J. P. (2006). Testing statistical hypotheses. Berlin: Springer.Google Scholar
  30. Lohr, S. L. (1999). Sampling: Design and analysis. Seattle: Brooks.Google Scholar
  31. Lohr, S. L. (2009). Sampling: Design and analysis. Scarborough: Nelson Education.Google Scholar
  32. Mehran, F. (2000). ILO labour force participation rates for 10–14 years old versus unesco school enrolment ratios. ILO Bulletin of Labour Statistics, 3.Google Scholar
  33. Mohammad, S. M. (1999). Rao-blackwell versions of the horvitz-thompson and hansen-hurwitz in adaptive cluster sampling. Environmental and Ecological Statistics,6(2), 183–195.Google Scholar
  34. Nielsen, A. (2004). Retail census. Santiago. Google Scholar
  35. PCO. (1998). Population size & growth of major cities. Pakistan population. New York: JSTOR.Google Scholar
  36. Poisson, S. D. (1837). Recherches sur la probabilité des jugements en matière criminelle et en matière civile precédées des règles générales du calcul des probabilités par sd poisson. Bachelier.Google Scholar
  37. Raj, D. (1966). Some remarks on a simple procedure of sampling without replacement. Journal of the American Statistical Association,61(314), 391–396.Google Scholar
  38. Ravallion, M., & Wodon, Q. (2000). Does child labour displace schooling? Evidence on behavioural responses to an enrollment subsidy. Economic Journal: The Journal of the Royal Economic Society,110(462), C158–C175.Google Scholar
  39. Ray, R. (2000). Analysis of child labour in peru and pakistan: A comparative study. Journal of Population Economics,13(1), 3–19.Google Scholar
  40. Ray, R. (2004). Child labour: A survey of selected asian countries. Asian-Pacific Economic Literature,18(2), 1–18.Google Scholar
  41. Reardon, T., & Berdegué, J. A. (2002). The rapid rise of supermarkets in latin america: Challenges and opportunities for development. Development Policy Review,20(4), 371–388.Google Scholar
  42. Ross, S. (2002). A first course in probability. Upper Saddle River: Prentice Hall.Google Scholar
  43. Särndal, C.-E., Thomsen, I., Hoem, J. M., Lindley, D., Barndorff-Nielsen, O., & Dalenius, T. (1978). Design-based and model-based inference in survey sampling. Scandinavian Journal of Statistics,5, 27–52. (with discussion and reply).Google Scholar
  44. Siddiqi, A. F. (2005). Child labor; regression analytics investigating the role of literacy in alleviating the menace of child labor. European Journal of Scientific Research,4(4), 1–11.Google Scholar
  45. Siddiqi, A. F. (2008). A child labour estimator for lahore based on literacy and poverty variables. Korean Journal of Applied Statistics,21(5), 889–900.Google Scholar
  46. Siddiqi, A. F. (2009). Child labor dynamics in Punjab. Journal of Third World Studies,26(2), 255.Google Scholar
  47. Siddiqi, A. F. (2013). Important determinants of child labor: A case study for lahore. American Journal of Economics and Sociology,72(1), 199–221.Google Scholar
  48. SPARC. (2005). The state of pakistan’ children 2004. Islamabad: SPARC.Google Scholar
  49. Sudman, S. (1997). Where have we been; survey research 1967–1997. Survey Research,28(3), 27–52.Google Scholar
  50. Walther, B. A., & Moore, J. L. (2005). The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography,28(6), 815–829.Google Scholar
  51. Yates, F. (1953). Sampling methods for censuses and surveys. In Sampling methods for censuses and surveys (2nd ed.).Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of ManagementUniversiti Sains Malaysia (USM)PenangMalaysia
  2. 2.UCP Business SchoolUniversity of Central PunjabLahorePakistan
  3. 3.School of Business ManagementUniversity Utara MalaysiaSintokMalaysia

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