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Social Networks, Female Unemployment, and the Urban-Rural Divide in Turkey: Evidence from Tree-Based Machine Learning Algorithms

Yıl 2021, , 73 - 93, 31.10.2021

Mehmet Güney Celbiş

https://doi.org/10.17233/sosyoekonomi.2021.04.04
Cited By: 1

Öz

This study takes a novel, algorithmic approach for understanding the underlying mechanisms related to the employment status of individuals. Using the data from the most recent survey of the International Social Survey Programme (ISSP) on Turkey, the present study examines how social connectivity and location play a role in the prediction of employment status through the use of two tree-based modern machine learning techniques, namely random forest, and extreme gradient boosting. We obtain a wide array of observations, with gender being the most prominent finding when periphery and rural locations are considered.

Anahtar Kelimeler

Machine Learning Unemployment Turkey Rural Urban

Kaynakça

  • Adam-Bourdarios, C. & G. Cowan & C. Germain & I. Guyon & B. Kegl & D. Rousseau (2015), “The Higgs Boson Machine Learning Challenge”, in: NIPS 2014 Workshop on High-energy Physics and Machine Learning, 19-55.
  • Adanacıoğlu, H. & S.G. Gümüş & F.A. Olgun (2012), “Rural Unemployment: The Problems which it Generates and Strategies to Reduce it: A Case-Study from Rural Turkey”, New Medit, 11(2), 50-57.
  • Athey, S. & G.W. Imbens (2019), “Machine Learning Methods that Economists Should Know About”, Annual Review of Economics, 11.
  • Athey, S. (2018), “The Impact of Machine Learning on Economics”, in: The economics of artifıcial intelligence: An agenda, University of Chicago Press, 507-547.
  • Berik, G. & C. Bilginsoy (2000), “Type of Work Matters: Women’s Labor Force Participation and the Child Sex Ratio in Turkey”, World Development, 28(5), 861-878.
  • Berik, G. (1987), “Women Carpet Weavers in Rural Turkey: Patterns of Employment”, Earnings and Status (Geneva: International Labour Office, 1987), 13-15.
  • Berik, G. (1989), “Born Factories: Women’s Labor in Carpet Workshops in Rural Turkey”, International Studies Notes, 14(3), 62.
  • Bock, B. (2004), “It Still Matters Where You Live: Rural Women’s Employment Throughout Europe”, in: H. Buller & K. Hoggart (eds.), Women in the European Countryside, Ashgate Publishing Limited, 14-41.
  • Breiman, L. & A. Cutler, Random Forests, <https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm>, 02.01.2020.
  • Breiman, L. & J.H. Friedman & R.A. Olshen & C.J. Stone (1984), Classification and Regression Trees, Wadsworth and Brooks, Monterey, CA.
  • Breiman, L. (1996), “Bagging Predictors”, Machine Learning, 24(2), 123-140.
  • Breiman, L. (2001), “Random Forests”, Machine Learning, 45(1), 5-32.
  • Calvo-Armengol, A. & M.O. Jackson (2004), “The Effects of Social Networks on Employment and Inequality”, American Economic Review, 94(3), 426-454.
  • Çarkoğlu, A. & E. Kalaycıoğlu (2020), International Social Survey Programme 2017: Social Networks and Social Resources - ISSP 2017 (Turkey).
  • Cartmel, F. & A. Furlong (2000), Youth Unemployment in Rural Areas, Number 18, York Publishing Services for the Joseph Rowntree Foundation York.
  • Chandler, J. (1989), “Youth Unemployment in Rural Areas: Local Government and Training Agencies”, Local Government Studies, 15(3), 59-73.
  • Chawla, N.V. & K.W. Bowyer & L.O. Hall & W.P. Kegelmeyer (2002), “SMOTE: Synthetic Minority Over-Sampling Technique”, Journal of Artificial Intelligence Research, 16, 321-357.
  • Chen, T. & C. Guestrin (2016), “XGBoost: A Scalable Tree Boosting System”, Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 785-794.
  • Chen, T. & T. He & M. Benesty & V. Khotilovich & Y. Tang (2015), “XGBboost: Extreme Gradient Boosting”, R package version 0.4-2, 1-4.
  • Chen, T. & T. He (2015), “Higgs Boson Discovery with Boosted Trees”, NIPS 2014 workshop on high-energy physics and machine learning, 69-80.
  • Conley, T.G. & G. Topa (2002), “Socio-Economic Distance and Spatial Patterns in Unemployment”, Journal of Applied Econometrics, 17(4), 303-327.
  • Cook, T. & A.S. Hall (2017), “Macroeconomic Indicator Forecasting with Deep Neural Networks”, Federal Reserve Bank of Kansas City, Research Working Paper, (17-11).
  • Friedman, J. & T. Hastie & R. Tibshirani (2001), The Elements of Statistical Learning, Volume 1. Springer Series in Statistics, New York.
  • Friedman, J.H. (2001), “Greedy Function Approximation: A Gradient Boosting Machine”, Annals of Statistics, 5, 1189-1232.
  • Friedman, J.H. (2002), “Stochastic Gradient Boosting”, Computational Statistics & Data Analysis, 38(4), 367-378.
  • Gash, N. (1935), “Rural Unemployment, 1815-34”, The Economic History Review, 6(1), 90.
  • Goldstein, A. & A. Kapelner & J. Bleich & E. Pitkin (2015), “Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation”, Journal of Computational and Graphical Statistics, 24(1), 44-65.
  • Greenwell, B.M. (2017), “pdp: An R Package for Constructing Partial Dependence Plots”, The R Journal, 9(1), 421-436.
  • Gülümser, A.A. & T. Baycan-Levent & P. Nijkamp (2011), “Changing Trends in Rural Self-Employment in Europe and Turkey”, in: A. Torre & J. Traversac (eds.), Territorial Governance, Physica-Verlag HD, 3-25.
  • Halden, D. & R. McQuaid & M. Greig (2005), Relationships Between Transport and the Rural Economies, The Countryside Agency, Derek Halden Consultancy and Employment Research Institute, <http://www.dhc1.co.uk/projects/transport_rural_economies.html>, 07.12.2020.
  • Harding, M. & J. Hersh (2018), “Big Data in Economics”, IZA World of Labor, (451).
  • İlkaracan, I. & I. Tunalı & B. Karapınar & F. Adaman & G. Özertan (2011), “Agricultural Transformation and the Rural Labor Market in Turkey”, Rethinking Structural Reform in Turkish Agriculture: Beyond the World Bank’s Strategy, 105-48.
  • James, G. & D. Witten & T. Hastie & R. Tibshirani (2013), An Introduction to Statistical Learning, Volume 112, Springer, New York.
  • Jones, L.P. (1991), “Unemployment: The Effect on Social Networks, Depression, and Reemployment”, Journal of Social Service Research, 15(1-2), 1-22.
  • Jones, M.K. (2004), “Rural Labour Markets: The Welsh Example”, Local Economy: The Journal of the Local Economy Policy Unit, 19(3), 226-248.
  • Kreiner, A. & J.V. Duca (2019), “Can Machine Learning on Economic Data Better Forecast the Unemployment Rate?”, Applied Economics Letters, 1-4.
  • Lasley, P. & P.F. Korsching (1984), “Examining Rural Unemployment”, Journal of Extension, 22(5), 32-36.
  • Liaw, A. & M. Wiener (2002), “Classifıcation and Regression by Randomforest”, R News, 2(3), 18-22.
  • Lindsay, C. & M. McCracken & R.W. McQuaid (2003), “Unemployment Duration and Employability in Remote Rural Labour Markets”, Journal of Rural Studies, 19(2), 187-200.
  • Lindsay, C. (2009), “In a Lonely Place? Social Networks, Job Seeking and the Experience of Long-Term Unemployment”, Social Policy and Society, 9(1), 25-37.
  • Lyu, H. & Z. Dong & M. Roobavannan & J. Kandasamy & S. Pande (2019), Rural Unemployment Pushes Migrants to Urban Areas in Jiangsu Province, China, Palgrave Communications, 5(1).
  • Maru, T. (2016), “How Social Customs Restrict EU Accession Effects on Female Labor Participation in Agricultural Production in Rural Adana, Turkey: A Simulation Analysis”, The Japanese Journal of Rural Economics, 18(0), 17-31.
  • McQuaid, R. & C. Lindsay & M. Greig (2004), “‘Reconnecting’ The Unemployed information and Communication Technology and Services for Jobseekers in Rural Areas”, Information, Communication & Society, 7(3), 364-388.
  • McQuaid, R.W. & C. Lindsay (2003), “Delivering Job Search Services for Unemployed People in Rural Areas: The Role of ICT”, 43rd Congress of the European Regional Science Association: “Peripheries, Centres, and Spatial Development in the New Europe”, 27th - 30th August 2003, Jyväskylä, Finland.
  • Mullainathan, S. & J. Spiess (2017), “Machine Learning: An Applied Econometric Approach”, Journal of Economic Perspectives, 31(2), 87-106.
  • OECD (2020), Working Age Population (Indicator), <https://data.oecd.org/pop/working-age-population.htm>, 07.04.2020.
  • Olhan, E. (2011), “The Structure of Rural Employment in Turkey”, FAO Turkey Report 2011.
  • Özgüzel, C. (2020), “Agglomeration Effects in a Developing Economy: Evidence from Turkey”, PSE Working Papers, 2020-41.
  • Russell, H. (1999), “Friends in Low Places: Gender, Unemployment and Sociability”, Work, Employment and Society, 13(2), 205-224.
  • Schonlau, M. (2005), “Boosted Regression (Boosting): An Introductory Tutorial and A Stata Plugin”, The Stata Journal, 5(3), 330-354.
  • Topa, G. (2001), “Social Interactions, Local Spillovers and Unemployment”, The Review of Economic Studies, 68(2), 261-295.
  • Türk, U. (2020), “Gelir Dağılımında Fırsat Eşitsizliği ve Alt Kırılımları: Türkiye Üzerine Bir Araştırma”, Alternatif Politika, 12(2), 311-335.
  • Ulukan, U. & N. Ciğerci-Ulukan (2019), “Is Agriculture Feminized? Female Labor in Contemporary Rural Turkey”, in: M. Meciar & K. Gökten & A.A. Eren (eds.), Economic & Business Issues in Retrospect & Prospect, IJOPEC Publication Limited, London.
  • Unay-Gailhard, I. (2016), “Job Access After Leaving Education: A Comparative Analysis of Young Women and Men in Rural Germany”, Journal of Youth Studies, 19(10), 1355-1381.
  • Varian, H.R. (2014), “Big Data: New Tricks for Econometrics”, Journal of Economic Perspectives, 28(2), 3-28.
  • Wickham, H. (2011), “ggplot2”, Wiley Interdisciplinary Reviews: Computational Statistics, 3(2), 180-185.
  • Xu, W. & Z. Li & C. Cheng & T. Zheng (2013), Data Mining for Unemployment Rate Prediction Using Search Engine Query Data, Volume 7, Springer, 33-42.
  • Zenou, Y. (2011), “Rural-Urban Migration and Unemployment: Theory and Policy Implications”, Journal of Regional Science, 51(1), 65-82.

Sosyal Ağlar, Kadın İşsizliği ve Türkiye’de Kentsel-Kırsal Farklılaşmaları: Ağaç Bazlı Makine Öğrenmesi Algoritmalarından Bulgular

Yıl 2021, , 73 - 93, 31.10.2021

Mehmet Güney Celbiş

https://doi.org/10.17233/sosyoekonomi.2021.04.04
Cited By: 1

Öz

Bu çalışma kişi bazında işsizlik durumlarının nedenlerini anlamak amacıyla yeni ve algoritmik bir yaklaşım getirmektedir. Uluslararası Sosyal Anket Programı’nın (International Social Survey Programme, ISSP) Türkiye üzerine olan en güncel verilerini kullanarak kişilerin sosyal bağlantılarının ve bulundukları lokasyonların işsizlik statülerini tahmin etmede oynadıkları roller iki farklı modern makine öğrenmesi tekniği ile irdelenmektedir. Bu teknikler rassal orman ve ekstrem gradyan artırma modelleridir. Çalışmanın bulgularından yola çıkarak kırsal ve çevre bölgeler özelinde cinsiyet faktörünün rolünün en önde gözüktüğü bir dizi gözlem yapılmaktadır.

Anahtar Kelimeler

Makine Öğrenmesi İşsizlik Türkiye Kırsal Kentsel

Kaynakça

  • Adam-Bourdarios, C. & G. Cowan & C. Germain & I. Guyon & B. Kegl & D. Rousseau (2015), “The Higgs Boson Machine Learning Challenge”, in: NIPS 2014 Workshop on High-energy Physics and Machine Learning, 19-55.
  • Adanacıoğlu, H. & S.G. Gümüş & F.A. Olgun (2012), “Rural Unemployment: The Problems which it Generates and Strategies to Reduce it: A Case-Study from Rural Turkey”, New Medit, 11(2), 50-57.
  • Athey, S. & G.W. Imbens (2019), “Machine Learning Methods that Economists Should Know About”, Annual Review of Economics, 11.
  • Athey, S. (2018), “The Impact of Machine Learning on Economics”, in: The economics of artifıcial intelligence: An agenda, University of Chicago Press, 507-547.
  • Berik, G. & C. Bilginsoy (2000), “Type of Work Matters: Women’s Labor Force Participation and the Child Sex Ratio in Turkey”, World Development, 28(5), 861-878.
  • Berik, G. (1987), “Women Carpet Weavers in Rural Turkey: Patterns of Employment”, Earnings and Status (Geneva: International Labour Office, 1987), 13-15.
  • Berik, G. (1989), “Born Factories: Women’s Labor in Carpet Workshops in Rural Turkey”, International Studies Notes, 14(3), 62.
  • Bock, B. (2004), “It Still Matters Where You Live: Rural Women’s Employment Throughout Europe”, in: H. Buller & K. Hoggart (eds.), Women in the European Countryside, Ashgate Publishing Limited, 14-41.
  • Breiman, L. & A. Cutler, Random Forests, <https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm>, 02.01.2020.
  • Breiman, L. & J.H. Friedman & R.A. Olshen & C.J. Stone (1984), Classification and Regression Trees, Wadsworth and Brooks, Monterey, CA.
  • Breiman, L. (1996), “Bagging Predictors”, Machine Learning, 24(2), 123-140.
  • Breiman, L. (2001), “Random Forests”, Machine Learning, 45(1), 5-32.
  • Calvo-Armengol, A. & M.O. Jackson (2004), “The Effects of Social Networks on Employment and Inequality”, American Economic Review, 94(3), 426-454.
  • Çarkoğlu, A. & E. Kalaycıoğlu (2020), International Social Survey Programme 2017: Social Networks and Social Resources - ISSP 2017 (Turkey).
  • Cartmel, F. & A. Furlong (2000), Youth Unemployment in Rural Areas, Number 18, York Publishing Services for the Joseph Rowntree Foundation York.
  • Chandler, J. (1989), “Youth Unemployment in Rural Areas: Local Government and Training Agencies”, Local Government Studies, 15(3), 59-73.
  • Chawla, N.V. & K.W. Bowyer & L.O. Hall & W.P. Kegelmeyer (2002), “SMOTE: Synthetic Minority Over-Sampling Technique”, Journal of Artificial Intelligence Research, 16, 321-357.
  • Chen, T. & C. Guestrin (2016), “XGBoost: A Scalable Tree Boosting System”, Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 785-794.
  • Chen, T. & T. He & M. Benesty & V. Khotilovich & Y. Tang (2015), “XGBboost: Extreme Gradient Boosting”, R package version 0.4-2, 1-4.
  • Chen, T. & T. He (2015), “Higgs Boson Discovery with Boosted Trees”, NIPS 2014 workshop on high-energy physics and machine learning, 69-80.
  • Conley, T.G. & G. Topa (2002), “Socio-Economic Distance and Spatial Patterns in Unemployment”, Journal of Applied Econometrics, 17(4), 303-327.
  • Cook, T. & A.S. Hall (2017), “Macroeconomic Indicator Forecasting with Deep Neural Networks”, Federal Reserve Bank of Kansas City, Research Working Paper, (17-11).
  • Friedman, J. & T. Hastie & R. Tibshirani (2001), The Elements of Statistical Learning, Volume 1. Springer Series in Statistics, New York.
  • Friedman, J.H. (2001), “Greedy Function Approximation: A Gradient Boosting Machine”, Annals of Statistics, 5, 1189-1232.
  • Friedman, J.H. (2002), “Stochastic Gradient Boosting”, Computational Statistics & Data Analysis, 38(4), 367-378.
  • Gash, N. (1935), “Rural Unemployment, 1815-34”, The Economic History Review, 6(1), 90.
  • Goldstein, A. & A. Kapelner & J. Bleich & E. Pitkin (2015), “Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation”, Journal of Computational and Graphical Statistics, 24(1), 44-65.
  • Greenwell, B.M. (2017), “pdp: An R Package for Constructing Partial Dependence Plots”, The R Journal, 9(1), 421-436.
  • Gülümser, A.A. & T. Baycan-Levent & P. Nijkamp (2011), “Changing Trends in Rural Self-Employment in Europe and Turkey”, in: A. Torre & J. Traversac (eds.), Territorial Governance, Physica-Verlag HD, 3-25.
  • Halden, D. & R. McQuaid & M. Greig (2005), Relationships Between Transport and the Rural Economies, The Countryside Agency, Derek Halden Consultancy and Employment Research Institute, <http://www.dhc1.co.uk/projects/transport_rural_economies.html>, 07.12.2020.
  • Harding, M. & J. Hersh (2018), “Big Data in Economics”, IZA World of Labor, (451).
  • İlkaracan, I. & I. Tunalı & B. Karapınar & F. Adaman & G. Özertan (2011), “Agricultural Transformation and the Rural Labor Market in Turkey”, Rethinking Structural Reform in Turkish Agriculture: Beyond the World Bank’s Strategy, 105-48.
  • James, G. & D. Witten & T. Hastie & R. Tibshirani (2013), An Introduction to Statistical Learning, Volume 112, Springer, New York.
  • Jones, L.P. (1991), “Unemployment: The Effect on Social Networks, Depression, and Reemployment”, Journal of Social Service Research, 15(1-2), 1-22.
  • Jones, M.K. (2004), “Rural Labour Markets: The Welsh Example”, Local Economy: The Journal of the Local Economy Policy Unit, 19(3), 226-248.
  • Kreiner, A. & J.V. Duca (2019), “Can Machine Learning on Economic Data Better Forecast the Unemployment Rate?”, Applied Economics Letters, 1-4.
  • Lasley, P. & P.F. Korsching (1984), “Examining Rural Unemployment”, Journal of Extension, 22(5), 32-36.
  • Liaw, A. & M. Wiener (2002), “Classifıcation and Regression by Randomforest”, R News, 2(3), 18-22.
  • Lindsay, C. & M. McCracken & R.W. McQuaid (2003), “Unemployment Duration and Employability in Remote Rural Labour Markets”, Journal of Rural Studies, 19(2), 187-200.
  • Lindsay, C. (2009), “In a Lonely Place? Social Networks, Job Seeking and the Experience of Long-Term Unemployment”, Social Policy and Society, 9(1), 25-37.
  • Lyu, H. & Z. Dong & M. Roobavannan & J. Kandasamy & S. Pande (2019), Rural Unemployment Pushes Migrants to Urban Areas in Jiangsu Province, China, Palgrave Communications, 5(1).
  • Maru, T. (2016), “How Social Customs Restrict EU Accession Effects on Female Labor Participation in Agricultural Production in Rural Adana, Turkey: A Simulation Analysis”, The Japanese Journal of Rural Economics, 18(0), 17-31.
  • McQuaid, R. & C. Lindsay & M. Greig (2004), “‘Reconnecting’ The Unemployed information and Communication Technology and Services for Jobseekers in Rural Areas”, Information, Communication & Society, 7(3), 364-388.
  • McQuaid, R.W. & C. Lindsay (2003), “Delivering Job Search Services for Unemployed People in Rural Areas: The Role of ICT”, 43rd Congress of the European Regional Science Association: “Peripheries, Centres, and Spatial Development in the New Europe”, 27th - 30th August 2003, Jyväskylä, Finland.
  • Mullainathan, S. & J. Spiess (2017), “Machine Learning: An Applied Econometric Approach”, Journal of Economic Perspectives, 31(2), 87-106.
  • OECD (2020), Working Age Population (Indicator), <https://data.oecd.org/pop/working-age-population.htm>, 07.04.2020.
  • Olhan, E. (2011), “The Structure of Rural Employment in Turkey”, FAO Turkey Report 2011.
  • Özgüzel, C. (2020), “Agglomeration Effects in a Developing Economy: Evidence from Turkey”, PSE Working Papers, 2020-41.
  • Russell, H. (1999), “Friends in Low Places: Gender, Unemployment and Sociability”, Work, Employment and Society, 13(2), 205-224.
  • Schonlau, M. (2005), “Boosted Regression (Boosting): An Introductory Tutorial and A Stata Plugin”, The Stata Journal, 5(3), 330-354.
  • Topa, G. (2001), “Social Interactions, Local Spillovers and Unemployment”, The Review of Economic Studies, 68(2), 261-295.
  • Türk, U. (2020), “Gelir Dağılımında Fırsat Eşitsizliği ve Alt Kırılımları: Türkiye Üzerine Bir Araştırma”, Alternatif Politika, 12(2), 311-335.
  • Ulukan, U. & N. Ciğerci-Ulukan (2019), “Is Agriculture Feminized? Female Labor in Contemporary Rural Turkey”, in: M. Meciar & K. Gökten & A.A. Eren (eds.), Economic & Business Issues in Retrospect & Prospect, IJOPEC Publication Limited, London.
  • Unay-Gailhard, I. (2016), “Job Access After Leaving Education: A Comparative Analysis of Young Women and Men in Rural Germany”, Journal of Youth Studies, 19(10), 1355-1381.
  • Varian, H.R. (2014), “Big Data: New Tricks for Econometrics”, Journal of Economic Perspectives, 28(2), 3-28.
  • Wickham, H. (2011), “ggplot2”, Wiley Interdisciplinary Reviews: Computational Statistics, 3(2), 180-185.
  • Xu, W. & Z. Li & C. Cheng & T. Zheng (2013), Data Mining for Unemployment Rate Prediction Using Search Engine Query Data, Volume 7, Springer, 33-42.
  • Zenou, Y. (2011), “Rural-Urban Migration and Unemployment: Theory and Policy Implications”, Journal of Regional Science, 51(1), 65-82.
Toplam 58 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ekonomi
Bölüm Makaleler
Yazarlar

Mehmet Güney Celbiş 0000-0002-2790-6035

Yayımlanma Tarihi 31 Ekim 2021
Gönderilme Tarihi 10 Şubat 2021
Yayımlandığı Sayı Yıl 2021

Kaynak Göster

APA Celbiş, M. G. (2021). Social Networks, Female Unemployment, and the Urban-Rural Divide in Turkey: Evidence from Tree-Based Machine Learning Algorithms. Sosyoekonomi, 29(50), 73-93. https://doi.org/10.17233/sosyoekonomi.2021.04.04
AMA Celbiş MG. Social Networks, Female Unemployment, and the Urban-Rural Divide in Turkey: Evidence from Tree-Based Machine Learning Algorithms. Sosyoekonomi. Ekim 2021;29(50):73-93. doi:10.17233/sosyoekonomi.2021.04.04
Chicago Celbiş, Mehmet Güney. “Social Networks, Female Unemployment, and the Urban-Rural Divide in Turkey: Evidence from Tree-Based Machine Learning Algorithms”. Sosyoekonomi 29, sy. 50 (Ekim 2021): 73-93. https://doi.org/10.17233/sosyoekonomi.2021.04.04.
EndNote Celbiş MG (01 Ekim 2021) Social Networks, Female Unemployment, and the Urban-Rural Divide in Turkey: Evidence from Tree-Based Machine Learning Algorithms. Sosyoekonomi 29 50 73–93.
IEEE M. G. Celbiş, “Social Networks, Female Unemployment, and the Urban-Rural Divide in Turkey: Evidence from Tree-Based Machine Learning Algorithms”, Sosyoekonomi, c. 29, sy. 50, ss. 73–93, 2021, doi: 10.17233/sosyoekonomi.2021.04.04.
ISNAD Celbiş, Mehmet Güney. “Social Networks, Female Unemployment, and the Urban-Rural Divide in Turkey: Evidence from Tree-Based Machine Learning Algorithms”. Sosyoekonomi 29/50 (Ekim 2021), 73-93. https://doi.org/10.17233/sosyoekonomi.2021.04.04.
JAMA Celbiş MG. Social Networks, Female Unemployment, and the Urban-Rural Divide in Turkey: Evidence from Tree-Based Machine Learning Algorithms. Sosyoekonomi. 2021;29:73–93.
MLA Celbiş, Mehmet Güney. “Social Networks, Female Unemployment, and the Urban-Rural Divide in Turkey: Evidence from Tree-Based Machine Learning Algorithms”. Sosyoekonomi, c. 29, sy. 50, 2021, ss. 73-93, doi:10.17233/sosyoekonomi.2021.04.04.
Vancouver Celbiş MG. Social Networks, Female Unemployment, and the Urban-Rural Divide in Turkey: Evidence from Tree-Based Machine Learning Algorithms. Sosyoekonomi. 2021;29(50):73-9.

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