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Applications of Machine Learning Models in Regional and Demographic Economic Analysis: A Literature Survey

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Labor Markets, Migration, and Mobility

Part of the book series: New Frontiers in Regional Science: Asian Perspectives ((NFRSASIPER,volume 45))

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Abstract

Big data is becoming increasingly available in regional and demographic economic analysis. Old challenges related to data unavailability are being replaced with difficulties dealing with more complex data sets that are often updated almost in real-time. Model-based, causality-oriented approaches are being substituted with black-box prediction-oriented techniques—primarily in nonacademic fields for expediting policy and management decisions. Big data sets allow for highly flexible relationships between variables, and machine learning (ML) techniques can enable researchers to discover highly complex relationships (Varian 2014). Complexity in this regard is related to “the unpredictable nature of non-linear and dynamic systems” (Nijkamp et al. 2001). Alpaydin (2016) defines ML as a way to achieve artificial intelligence (AI) and states that ML, which is grounded in statistical theory, is the driving force and a requirement for AI. The latter, in turn, can be defined as “The automation of activities that we associate with human thinking, activities such as decision-making, problem solving, and learning” (Bellman 1978). Regarding the ability of ML to discover complex relationships, Harding and Hersh (2018) state:

I am completely operational, and all my circuits are functioning perfectly. - Quote by HAL 9000, in 2001 A Space Odyssey.

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Notes

  1. 1.

    Athey and Imbens (2019) present a detailed overview on the overlaps between the two fields. The textbooks by Friedman et al. (2001) and James et al. (2013) can also serve as valuable sources to observe these similarities.

  2. 2.

    This dissertation is not yet available and the presented information relies on the available abstract of the study at: https://ir.uiowa.edu/etd/6977/.

  3. 3.

    Using ML methods as intermediary steps for creating variables is an approach recommended by Athey (2018).

References

  • Alpaydin E (2016) Machine learning: the new AI. MIT Press, Cambridge

    Google Scholar 

  • Arribas-Bel D, Garcia-L’opez M-A, Viladecans-Marsal E (2019) Building (s and) cities: delineating urban areas with a machine learning algorithm. J Urban Econ 2019:103217

    Google Scholar 

  • Arribas-Bel D, Kourtit K, Nijkamp P, Steenbruggen J (2015) Cyber cities: social media as a tool for understanding cities. Appl Spat Anal Policy 8(3):231–247

    Article  Google Scholar 

  • Arribas-Bel D, Patino JE, Duque JC (2017) Remote sensing-based measurement of living environment deprivation: improving classical approaches with machine learning. PLoS One 12(5):e0176684

    Article  Google Scholar 

  • Athey S (2018) The impact of machine learning on economics. In: The economics of artificial intelligence: an agenda. University of Chicago Press, Chicago, pp 507–547

    Google Scholar 

  • Athey S, Imbens G (2016) Recursive partitioning for heterogeneous causal effects. Proc Natl Acad Sci 113(27):7353–7360

    Article  Google Scholar 

  • Athey S, Imbens GW (2019) Machine learning methods that economists should know about. Ann Rev Econ 11:685–725

    Article  Google Scholar 

  • Atkinson EJ, Therneau TM (2000) An introduction to recursive partitioning using the rpart routines. Mayo Foundation, Rochester

    Google Scholar 

  • Bajari P, Nekipelov D, Ryan SP, Yang M (2015) Machine learning methods for demand estimation. Am Econ Rev 105(5):481–485

    Article  Google Scholar 

  • Bang J, Sen T, Basuchoudhary A (2017) New tools for predicting economic growth using machine learning: a guide for theory and policy. In: Southern Economic Association Conference

    Google Scholar 

  • Bellman R (1978) An introduction to artificial intelligence: can computers think? Boyd & Fraser, San Francisco

    Google Scholar 

  • Boselli R, Cesarini M, Mercorio F, Mezzanzanica M (2017) Using machine learning for labour market intelligence. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, pp 330–342

    Chapter  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Chang H-J, Shih Y-S, Su T-J (2014) Split selection methods for regression tree on detecting regional economic convergence. J Chin Statist Assoc 52(2):180–208

    Google Scholar 

  • Chen T, He T, Benesty M, Khotilovich V, Tang Y (2015) Xgboost: extreme gradient boosting. R Package Version 0.4-2. pp. 1–4

    Google Scholar 

  • Cogoljević D, Alizamir M, Piljan I, Piljan T, Prljić K, Zimonjić S (2018) A machine learning approach for predicting the relationship between energy resources and economic development. Phys A Statistic Mech Appl 495:211–214

    Article  Google Scholar 

  • Colace F, De Santo M, Lombardi M, Mercorio F, Mezzanzanica M, Pascale F (2019) Towards labour market intelligence through topic modelling. In: Proceedings of the 52nd Hawaii international conference on system sciences

    Google Scholar 

  • Comber S (2019) Demonstrating the utility of machine learning innovations in address matching to spatial socio-economic applications. Region 6(3):17–37

    Article  Google Scholar 

  • Comber S, Arribas-Bel D (2019) Machine learning innovations in address matching: a practical comparison of word2vec and crfs. Trans GIS 23(2):334–348

    Article  Google Scholar 

  • Cook T, Hall AS (2017) Macroeconomic indicator forecasting with deep neural networks. In: Federal Reserve Bank of Kansas City, Research Working Paper, pp 17–11

    Google Scholar 

  • Einav L, Levin J (2014) Economics in the age of big data. Science 346(6210):1243089

    Article  Google Scholar 

  • Fazenbaker C (2009) Exploring impact of educational and economic factors on national intellectual productivity using machine learning methods. Virginia Commonwealth University, Richmond

    Google Scholar 

  • Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning, vol 1. Springer, New York

    Google Scholar 

  • Glaeser EL, Kominers SD, Luca M, Naik N (2018) Big data and big cities: the promises and limitations of improved measures of urban life. Econ Inq 56(1):114–137

    Article  Google Scholar 

  • Grimmer J (2015) We are all social scientists now: how big data, machine learning, and causal inference work together. Political Sci Politics 48(1):80–83

    Article  Google Scholar 

  • Gründler K, Krieger T (2018) Machine learning indices, political institutions, and economic development. KOPS Universität Konstanz, Konstanz

    Book  Google Scholar 

  • Günther F, Fritsch S (2010) Neuralnet: training of neural networks. R J 2(1):30–38

    Article  Google Scholar 

  • Harding M, Hersh J (2018) Big data in economics. IZA World of Labor, Bonn, p 451

    Google Scholar 

  • Iman HS, Tarasyev A (2018) Machine learning methods in individual migration behavior. In: Russian regions in the focus of changes: conference proceedings.—Ekaterinburg, 2018. LLC Publishing office EMC UPI, Saint Paul, pp 72–81

    Google Scholar 

  • James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning, vol 112. Springer, New York

    Book  Google Scholar 

  • Kaiser M (2018) Segmentation in urban labor markets: a machine learning application and a contracting perspective

    Google Scholar 

  • Karatzoglou A, Meyer D, Hornik K (2006) Support vector machines in r. J Stat Softw 15(9):1–28

    Article  Google Scholar 

  • Kreiner A, Duca JV (2019) Can machine learning on economic data better forecast the unemployment rate? Appl Econ Lett 27:1–4

    Google Scholar 

  • Liaw A, Wiener M (2002) Classification and regression by randomforest. R News 2(3):18–22

    Google Scholar 

  • Liu Y. (2019). Predicting labor market competition and employee mobility — a machine learning approach. PhD Dissertation, University of Iowa. https://doi.org/10.17077/etd.3x1x-s7p3

  • Liu Y, Xie T (2019) Machine learning versus econometrics: prediction of box office. Appl Econ Lett 26(2):124–130

    Article  Google Scholar 

  • Lopez-Yucra K, Libaque-Saenz C, Lazo J, Bravo E (2018) Could machine learning improve the prediction of child labor in Peru? In: Information Management and Big Data: 4th Annual International Symposium, SIMBig 2017, Lima, Peru, September 4–6, 2017, Revised Selected Papers, vol 795. Springer, Berlin, p 15

    Chapter  Google Scholar 

  • Mittal M, Goyal LM, Sethi JK, Hemanth DJ (2019) Monitoring the impact of economic crisis on crime in India using machine learning. Comput Econ 53(4):1467–1485

    Article  Google Scholar 

  • Mullainathan S, Spiess J (2017) Machine learning: an applied econometric approach. J Econ Perspect 31(2):87–106

    Article  Google Scholar 

  • Nigam A, Jaiswal P, Arora T, Girkar U (2019) Migration through machine learning lens– predicting sexual and reproductive health vulnerability of young migrants. arXiv preprint arXiv:1910.02390

    Google Scholar 

  • Nijkamp P, Poot J, Vindigni G (2001) Spatial dynamics and government policy: an artificial intelligence approach to comparing complex systems. In: Fischer M, Frolich J (eds) Knowledge, complexity and innovation systems. Springer, Boston, pp 369–401

    Chapter  Google Scholar 

  • Nyman R, Ormerod P (2017) Predicting economic recessions using machine learning algorithms. arXiv preprint arXiv:1701.01428

    Google Scholar 

  • Nyman R, Ormerod P (2020) Understanding the great recession using machine learning algorithms. arXiv preprint arXiv:2001.02115

    Google Scholar 

  • Ridgeway G, Ridgeway MG (2004) The gbm package, vol 5. R Foundation for Statistical Computing, Vienna, p 3

    Google Scholar 

  • Robinson C, Dilkina B (2018) A machine learning approach to modeling human migration. In: Proceedings of the 1st ACM SIGCAS conference on computing and sustainable societies, pp 1–8

    Google Scholar 

  • Robinson C, Hohman F, Dilkina B (2017) A deep learning approach for population estimation from satellite imagery. In: Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities, pp 47–54

    Chapter  Google Scholar 

  • Saavedra M, Twinam T (2019) A machine learning approach to improving occupational income scores. Explor Econ Hist 75:101304

    Article  Google Scholar 

  • Tarasyev AA, Agarkov GA, Hosseini SI (2018) Machine learning in labor migration prediction. In: AIP Conference Proceedings, vol 1978. AIP Publishing LLC, Melville, p 440004

    Google Scholar 

  • Varian HR (2014) Big data: new tricks for econometrics. J Econ Perspect 28(2):3–28

    Article  Google Scholar 

  • Wu W, Jiang S, Liu R, Jin W, Ma C (2020) Economic development, demographic characteristics, road network and traffic accidents in Zhongshan, China: gradient boosting decision tree model. Trans A: Trans Sci 16:1–33

    Google Scholar 

  • Xu W, Li Z, Cheng C, Zheng T (2013) Data mining for unemployment rate prediction using search engine query data. SOCA 7:33–42

    Article  Google Scholar 

  • Zhang C, Zhang J (2014) Analysing Chinese citizens’ intentions of outbound travel: a machine learning approach. Curr Issue Tour 17(7):592–609

    Article  Google Scholar 

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Authors and Affiliations

  1. Yeditepe University, Department of Economics, and United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology, Maastricht, The Netherlands

    Mehmet Güney Celbiş

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  1. Mehmet Güney Celbiş
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Correspondence to Mehmet Güney Celbiş .

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Editors and Affiliations

  1. School of Social Science, University of Waikato, Hamilton, Waikato, New Zealand

    William Cochrane

  2. School of Accounting, Finance and Economics, University of Waikato, Hamilton, Waikato, New Zealand

    Michael P. Cameron

  3. School of Accounting, Finance and Economics, University of Waikato, Hamilton, Waikato, New Zealand

    Omoniyi Alimi

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Celbiş, M.G. (2021). Applications of Machine Learning Models in Regional and Demographic Economic Analysis: A Literature Survey. In: Cochrane, W., Cameron, M.P., Alimi, O. (eds) Labor Markets, Migration, and Mobility. New Frontiers in Regional Science: Asian Perspectives, vol 45. Springer, Singapore. https://doi.org/10.1007/978-981-15-9275-1_10

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