ممیزی راهبردی نیروی کار ایران در نواحی کوچک

نوع مقاله : علمی

نویسندگان

1 استادیار، گروه پژوهشی پردازش داده‌ها و اطلاع رسانی پژوهشکده آمار ، تهران، ایران

2 استادیار ، دانشکده علوم ریاضی،گروه آمار ، دانشگاه تربیت مدرس، تهران، ایران

3 استادیار، گروه اقتصاد امور عمومی- دانشکده اقتصاد-دانشگاه خوارزمی- تهرلن-ایران

چکیده

مدیریت نیروی کار کشور موضوعی راهبردی است که برای مدیریت آن نیاز به تصمیم‌گیری راهبردی،تحلیل راهبردی محیطی شرایط و عملکرد موجود است که البته داشتن آمار و اطلاعات پیش‌نیاز این تحلیل است. در حال حاضر با توجه به شرایط اقتصادی کشور نیاز است تا وضعیت نیروی کار علاوه بر سطوح ملی واستانی، در سطح شهرستان نیز ممیزی شده تا امکان بهبود وضعیت نیروی کار کشور در یک نظام مدیریت یکپارچه صورت پذیرد. با این وجود در حال حاضر آمارگیری‌های رسمی نیروی کار ایران تنها در سطح ملی و استانی بهینه بوده و در سطح نواحی کوچک یعنی شهرستان قابل اعتماد نیست. هدف اصلی این مقاله این است که نرخ بیکاری استان‌ها و شهرستان‌های کشور به عنوان نواحی کوچک برآورد شوند تا امکان تحلیل شکاف و به تبع آن ممیزی و برنامه‌ریزی راهبردی برای وضعیت نیروی کار ایران و کاهش نرخ بیکاری از سطح شهرستان‌ها به عنوان کوچکترین نواحی این حوزه فراهم شود. در این مقاله به عنوان نمونه از داده‌های آمارگیری نیروی کار مرکز آمار ایران سال ۱۳۹۷ استفاده شده است. شایان گفتن است در آمارگیری‌های نمونه‌ای که در آن‌ها واحدهای نمونه وزن‌دهی ‌شده‌اند، برآورد به روش‌های مدل‌بندی معمول و بدون در نظر گرفتن وزن‌های آمارگیری منجر به برآوردهای اریب ‌می‌شود. بنابراین در این مطالعه، برای برآورد نرخ بیکاری شهرستان‌ها، از روش‌های بیزی با در نظر گرفتن وزن‌های نمونه‌گیری استفاده شده است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Strategic Audit of Iranian Labor Force in Small Areas

نویسندگان [English]

  • Ashkan Shabbak 1
  • taban baghfalaki 2
  • marzieh khakestari 3
1 Academic member, Data processing and Dissemination Department, SRTC, Tehran, Iran
2 Academic mamber, Faculty of Mathematical Sciences, Department of Statistics, Tarbiat Modares University, Tehran, Iran
3 Academic memeber, Department of Public Economics, Faculty of Economics, Kharazmi University, Tehran, Iran
چکیده [English]

The management of the labor force is a strategic issue that requires strategic decision-making, strategic environmental analysis, which is clear that having statistics and information is a prerequisite for this. According to the current economic conditions of the country, it is necessary to audit the situation of the labor force not only in the national and provincial levels, but also at the city. However, at present, the official statistics of the Iranian labor force are only optimal at the national and provincial levels and are not reliable at the level of small areas. This article tries to estimate the unemployment rate of the provinces and cities of the country as small areas to enable the analysis of the gap and consequently audit and strategic planning for the situation of Iran's labor for. In this article, results of the labor force survey, which has been done by the Statistics Center of Iran in 1397, have been used. It is worth noting that in sampling surveys in which sample units are weighted, estimations by conventional modeling methods without considering the weights, leads to bias estimates. Therefore, in this study, weighted Bayesian methods have been used to estimate the unemployment rate of small areas.

کلیدواژه‌ها [English]

  • Strategic decision making
  • Strategic Audit
  • small area estimation
  • ‌Bayesian Weighted Estimator
  • Bayesian Pseudo Posterior Estimator
  1. Abbaszadeh, F., & Nawabpour, H.R. (2013). Small area estimates of the unemployment rate. Iranian Journal of Official Statistics. 24(2), 225-205.
  2. Akhbari, M., & Mohagheghnia, M. J. (2015). Estimating unemployment rate with non-accelerated inflation in Iran's economy and its application in economic policy making. Journal of Quantitative Economics, 11(4), 134-113.
  3. Arabi, M., & Nasabi, N. (2016). Application of strategic audit model in formulating human resource strategies. Journal of Strategic Management Studies, 7(25).
  4. Aristotle T. (2010). Creativity at the core of strategic decision making, Journal of Strategic Management Studies, 1(4).
  5. Beyrouthy, S. (2017). The adoption of corporate social networks: a technology to support strategic scanning? (Doctoral dissertation, Université Grenoble Alpes).
  6. Chouk, S. K., Hammami, M., Dhraou, S. B., & Ayari, Z. (2020, February). Overview of the research on strategic environmental scanning and competitive intelligence. In 2020 International Multi-Conference on: Organization of Knowledge and Advanced Technologies (OCTA) (1-16). IEEE.
  7. Data, G. S., Lahiri, P., Maiti, T., & Lu, K. L. (1999). Hierarchical Bayes estimation of unemployment rates for the states of the U.S. Journal of the American statistical association, 94(448).
  8. Fabrizi, E. (2002). Hierarchical Bayesian models for the estimation of unemployment rates in small domains of the Italian labour force survey. Statistica, anno LXII,n.4.
  9. Ganjali, M., & Baghfaleki, T. (2017). Basics and Bayesian modeling of data using BUGS programming and R. software Shahid Beheshti University Press. Tehran. Iran.
  10. Garnett, K., Lickorish, F. A., Rocks, S. A., Prpich, G., Rathe, A. A., & Pollard, S. J. (2016). Integrating horizon scanning and strategic risk prioritisation using a weight of evidence framework to inform policy decisions. Science of the Total Environment, 560, 82-91.
  11. Gelman, A. (2007). Struggles with survey weighting and regression modeling. Statistical Science, 22(2), 153-164.
  12. Ghobadi, S., Samati, M., & Samadi, S. (2004). Estimating the optimal unemployment rate and comparing it with the natural rate (with emphasis on the variables of the third socio-economic development plan). Journal of Economic Research. 67.
  13. Gunawan, D., Panagiotelis, A., Griffiths, W., & Chotikapanich, D. (2020). Bayesian weighted inference from surveys. Australian & New Zealand Journal of Statistics, 62(1), 71-94.
  14. Hamidi Zadeh, M. (2014). Strategic and long-term planning. Eighth edition. Organization for the Study and Compilation of Humanities Books of Universities, Position, Center for Research and Development of Humanities.
  15. Hidiroglou, M. A., & You, Y. (2016). Comparison of unit level and area level small area estimators. Survey Methodology, 42(42), 41-61.
  16. 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.
  17. Hosseini Nasab, M.I., Ahmadloo, R. (2014). Estimation and small prediction of the average areas of duration of unemployment in Iran and the effect of the province on it using three-level models. Journal of Advanced Mathematical Modeling, 3(1).
  18. Ikebujo, P. U. (2020). Environmental scanning as a process of strategic decision-making–a review. Journal of global social sciences, 1(2), 43-62.
  19. Johanson, J. E. (2019). Internal strategic scanning. In strategy formation and policy making in government (121-142). Palgrave Macmillan, Cham.
  20. Kazemizadeh, R. (1999). Comparative comparison of Phillips curve and determination of unemployment rate in Iran. Journal of Applied Theories of Economics, 6(1).
  21. Korinek, A., Mistiaen, J. A., & Ravallion, M. (2007). An econometric method of correcting for unit nonresponse bias in surveys. The World Bank.
  22. Liu, B., & Lahiri, P. (2017). Adaptive hierarchical Bayes estimation of small area proportions. Calcutta Statistical Association Bulletin, 69(2), 150-164.
  23. Nandram, B., & Choi, J. W. (2002). Hierarchical Bayesian nonresponse models for binary data from small areas with uncertainty about ignorability. Journal of the American Statistical Association, 97(458), 381-388.
  24. Nematollahi, N., Nawabpour, H.R., Rahimi, A., Rihani, M.,Abasi, & A., Yousefi, N. (2013). Small area estimates in the labor force survey plan. Statistics Research Institute. Tehran. Iran.
  25. Pereira, L., Mendes, N., Jorge, M., & Coelho, P. S. (2011). Estimation of unemployment rates in small areas of Portugal: a best linear unbiased prediction approach versus a hierarchical Bayses approach. 17th European 17th European Young Statisticians Meeting. Lisbon, Portugal.
  26. Raghunathan, T. E., Xie, D., Schenker, N., Parsons, V. L., Davis, W. W., Dodd, K. W., & Feuer, E. J. (2007). Combining information from two surveys to estimate county-level prevalence rates of cancer risk factors and screening. Journal of the American Statistical Association, 102(478), 474-486.
  27. Rao, J. N. (2014). Small‐Area Estimation. Wiley StatsRef: Statistics Reference Online, 1-8.
  28. Rao, J.N.K., & Molina, I. (2015) Small area estimation, 2nd edn, John Wiley & Sons, Inc, Hoboken.
  29. Shabbak, A., & Baghfaleki, T. (2018). Using Bayesian inference and prediction methods to predict the unemployment rate in 2016 in Iran. Statistics Research Institute, Tehran. Iran.
  30. Shabbak, A., Kiani, K., Moradi, A., & Hakimipour, N.  (2019). Feasibility study of producing a simulated population for the labor force plan of the Statistics Center of Iran by the method of coexistence. Statistics Research Institute, Tehran. Iran.
  31. Simionescu, M. (2017). Prediction intervals for inflation and unemployment rate in Romania. A Bayes approaches. GLO Discussion paper, No.82.
  32. Statistics Center of Iran. (2019). Labor force survey results, Teran. Iran.
  33. Strobel, M., Tumasjan, A., Spoerrle, M., & Welpe, I. M. (2017). Fostering employees' proactive strategic engagement: Individual and contextual antecedents. Human Resource Management Journal, 27(1), 113-132.
  34. Taybi Abolhassani, A.H., & Rouhani Rad, S. (2018). Analysis of the structure and trend of thematic networks of strategic management in Iran. Journal of Strategic Management Studies, 9(36).
  35. Wheelen, T. L., Hunger, J. D., Hoffman, A. N., & Bamford, C. E., (2018). Concepts in Strategic Management and Business Policy, Globalization, Innovation and Sustainability, © Pearson Education Limited.
  36. Wooldridge, J. M. (2007). Inverse probability weighted estimation for general missing data problems. Journal of econometrics, 141(2), 1281-1301.
  37. You, Y., & Chapman, B. (2006). Small area estimation using area level models and estimated sampling variances. Survey Methodology, 32(1), 97.