Abstract
This paper presents total factor productivity (TFP) growth analysis with a panel Stochastic Frontier Approach (SFA) for 26 Türkiye regions from 2004 to 2020. The study is a pioneer in the literature on decomposing TFP growth components at the regional level with the translog production function for the Turkish economy. This work has a fresh approach regarding adjusting the schooling of employment and involving macroeconomic factors in the inefficiency function. The numerous specification tests show considerable production inefficiencies. Therefore, the production frontier is estimated using the True Fixed Effects (TFE) model. The empirical findings reveal that human capital is the primary driver of output growth. Technical efficiency is estimated to be 90.6% on average, with 9.4% of potential output lost due to technical inefficiency. It has been determined that there has been technical progress in labor-saving. Trade has a negative effect on technical efficiency. These findings contribute to understanding TFP change in the Turkish economy with regional data.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The author confirms that all data generated or analysed during this study are included in this published article. The data set is available to researchers if desired.
References
Aguiar, D. I. R. (2014). Measuring the differences in productivities of nations: A stochastic frontier approach. Doctoral dissertation. Universidade Católica Portuguesa.
Aguiar, D., Costa, L., & Silva, E. (2017). An attempt to explain differences in economic growth: A stochastic frontier approach. Bulletin of Economic Research, 69(4), 42–65. https://doi.org/10.1111/boer.12102
Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37. https://doi.org/10.1016/0304-4076(77)90052-5
Altug, S., Filiztekin, A., & Pamuk, Ş. (2008). Sources of long-term economic growth for Turkey, 1880–2005. European Review of Economic History, 12(3), 393–430. https://doi.org/10.1017/S1361491608002293
Arcelus, F. J., & Arocena, P. (2000). Convergence and productive efficiency in fourteen OECD countries: A non-parametric frontier approach. International Journal of Production Economics, 66(2), 105–117. https://doi.org/10.1016/S0925-5273(99)00116-4
Atiyas, I., & Bakış, O. (2014). Aggregate and sectoral TFP growth in Turkey, a growth accounting exercise. İktisat İşletme Ve Finans, 29(341), 9–36. https://doi.org/10.3848/iif.2014.341.4019
Bakış, O., & Acar, U. (2021). Total factor productivity in the Turkish economy:1980–2019. Journal of Research in Economics, 5(1), 1–27. https://doi.org/10.29228/JORE.1
Battese, G. E., & Coelli, T. J. (1992). Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. Journal of Productivity Analysis, 3(1), 153–169. https://doi.org/10.1007/BF00158774
Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325–332. https://doi.org/10.1007/BF01205442
Belotti, F., Daidone, S., Ilardi, G., & Atella, V. (2013). Stochastic frontier analysis using Stata. The Stata Journal, 13(4), 719–758. https://doi.org/10.1177/1536867X1301300404
Bilenko, Y. (2022). Economic growth and total factor productivity in central and eastern european countries between two global crises and beyond. Baltic Journal of Economic Studies, 8(4), 8–18. https://doi.org/10.30525/2256-0742/2022-8-4-8-18
Bozkurt, E., & Toktaş, Y. (2018). A panel data analysis on sources of growth. International Journal of Management Economics and Business, 14(4), 883–896. https://doi.org/10.17130/ijmeb.2018445660
Chen, Y. H. (2018). Estimating the speeds of long-run technological catch-up and growth of total factor productivity for countries. The Journal of International Trade and Economic Development, 27(2), 220–234. https://doi.org/10.1080/09638199.2017.1339204
Coelli, T. J., Rao, D. S. P., & Battase, G. E. (1998). An introduction to efficiency and productivity analysis. Kluwer Academic.
Deliktas, E. (2002). Türkiye özel sektör imalât sanayiinde etkinlik ve toplam faktör verimliliği analizi. METU Studies in Development, 29(3–4), 247–284. https://bit.ly/3IETvfc
Diewert, W. E. (1981). The economic theory of index numbers: A survey. Cambridge University Press.
Du, K. (2017). Translog: Stata Module to Create New Variables for a Translog Function, Statistical Software Components S458318, Boston College Department of Economics. https://ideas.repec.org/c/boc/bocode/s458318.html
Ekinci, R. (2020). The effect of spatial dependence on measurement of technical efficiency: Spatial stochastic frontier analysis findings for manufacturing industry. Dokuz Eylul University the Journal of Graduate School of Social Sciences, 22(3), 995–1021. https://doi.org/10.16953/deusosbil.733488
Färe, R., Grosskopf, S., Norris, M., & Zhongyang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. The American Economic Review, 84, 66–83. http://www.jstor.org/stable/2117971.
Forstner, H., & Isaksson, A. (2002). Productivity, technology, and efficiency: An analysis of the world technology frontier when memory is infinite. Statistics and Information Networks Branch of UNIDO, Working Paper No.7.
Greene, W. (2005a). Fixed and random effects in stochastic frontier models. Journal of Productivity Analysis, 23(1), 7–32. https://doi.org/10.1007/s11123-004-8545-1
Greene, W. (2005b). Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics, 126(2), 269–303. https://doi.org/10.1016/j.jeconom.2004.05.003
Hoover, H. C., & Hoover, L. H. (1912). Georgius agricola de re metallica. Translated from the latin edition of 1556, Reprinted 1950. New York: Dover.
Hou, Z., Roseta-Palma, C., & Ramalho, J. J. (2020). Directed technological change, energy and more: A modern story. Environment and Development Economics, 25(6), 611–633. https://doi.org/10.1017/S1355770X2000008X
IMF (2021). Commodity Terms of Trade: A New Database. https://bit.ly/3NcFokS
Ismihan, M., & Metin-Ozcan, K. (2009). Productivity and growth in an unstable emerging market economy: The case of Turkey, 1960–2004. Emerging Markets Finance and Trade, 45(5), 4–18. https://doi.org/10.2753/REE1540-496X450501
Jones, C. I., & Vollrath, D. (2013). Introduction to Economic Growth (3rd ed.). Norton.
Kim, S., & Lee, Y. H. (2006). The productivity debate of East Asia revisited: A stochastic frontier approach. Applied Economics, 38(14), 1697–1706. https://doi.org/10.1080/00036840500426884
Kim, S., Park, D., & Park, J. H. (2010). Productivity growth across the world, 1991–2003 (Asian Development Bank Economics Working Paper Series No: 212). https://doi.org/10.2139/ssrn.167044
Kumbhakar, S. C. (1990). Production frontiers, panel data, and time-varying technical inefficiency. Journal of Econometrics, 46(1–2), 201–211. https://doi.org/10.1016/0304-4076(90)90055-X
Kumbhakar, S. C., Ghosh, S., & McGuckin, J. T. (1991). A generalized production frontier approach for estimating determinants of inefficiency in U.S. dairy farms. Journal of Business and Economics Statistics, 9(3), 279–286. https://doi.org/10.1080/07350015.1991.10509853
Kumbhakar, S. C., Lien, G., & Hardaker, J. B. (2014). Technical efficiency in competing panel data models: A study of Norwegian grain farming. Journal of Productivity Analysis, 41(2), 321–337. https://doi.org/10.1007/s11123-012-0303-1
Kumbhakar, S. C., Wang, H.-J., & Horncastle, A. P. (2015). A Practitioner’s guide to stochastic frontier analysis using Stata. Cambridge University Press.
Lee, Y. H., & Park, J. (2023). Middle-income country trap and total factor productivity growth [Early Access]. Pacific Economic Review. https://doi.org/10.1111/1468-0106.12439
Limam, Y. R., Miller, S. M., & Garzarelli, G. (2019). Output growth decomposition in the presence of input quality effects: A stochastic frontier approach. German Economic Review, 20(4), 383–409. https://doi.org/10.1111/geer.12147
Ma, W. D., Wu, C. C., & Tang, D. S. (2022). Analysis on regional characteristics of economic growth based on total factor productivity. Discrete Dynamics in Nature and Society, 2022, 1–10. https://doi.org/10.1155/2022/9747559
Meeusen, W., & Van Den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18, 435–444. https://doi.org/10.2307/2525757
Mincer, J. A. (1974). Schooling, experience and earnings. Columbia University Press.
Olomola, P. A., & Osinubi, T. T. (2018). Determinants of total factor productivity in Mexico, Indonesia, Nigeria, and Turkey (1980–2014). Emerging Economy Studies, 4(2), 192–217. https://doi.org/10.1177/2394901518795072
Önder, A., Deliktaş, E., & Lenger, A. (2003). Efficiency in the manufacturing ındustry of selected provinces in Turkey: A stochastic frontier analysis. Emerging Markets Finance and Trade, 39(2), 98–113. https://doi.org/10.1080/1540496X.2003.11052537
Pablo-Romero, M. D. P., & Gómez-Calero, M. D. L. P. (2013). A translog production function for the Spanish Provinces: Impact of the human and physical capital in economic growth. Economic Modelling, 32, 77–87.
Pitt, M. M., & Lee, L. F. (1981). The measurement and sources of technical inefficiency in the Indonesian weaving industry. Journal of Development Economics, 9(1), 43–64. https://doi.org/10.1016/0304-3878(81)90004-3
Ricardo, D. (1951). On the principles of political economy and taxation, In Sraffa, P. (ed.), Cambridge: Cambridge University Press.
Salman, M., Wang, G. M., & Zha, D. L. (2022). Modeling the convergence analysis of sustainable production and consumption in terms of ecological footprints and human development index in Belt and Road Initiative countries. Sustainable Production and Consumption, 30, 233–254. https://doi.org/10.1016/j.spc.2021.12.008
Smith, A. (1976). The wealth of nations. The University of Chicago Press.
Solow, R. M. (1956). A contribution to the theory of economic growth. Journal of Economics, 70(1), 65–94. https://doi.org/10.2307/1884513
Solow, R. M. (1957). Technical change and the aggregate production function. Review of Economics and Statistics, 39(3), 312–320. https://doi.org/10.2307/1926047
Taskin, F., & Zaim, O. (1997). Catching-up and innovation in high-and low-income countries. Economics Letters, 54(1), 93–100. https://doi.org/10.1016/S0165-1765(97)00004-9
Taymaz, E., & Saatci, G. (1997). Technical change and efficiency in Turkish manufacturing industries. Journal of Productivity Analysis, 8(4), 461–475. https://doi.org/10.1023/A:1007796311574
Taymaz, E., Voyvoda, E., & Yılmaz, K. (2008). Türkiye imalat sanayiinde yapısal dönüşüm, üretkenlik ve teknolojik değişme dinamikleri (Economic Research Center (ERC) Working Papers in Economics 08/04). https://bit.ly/3uqR3nB
TURKSTAT (2021a). National Accounts Statistics. https://bit.ly/3uxy3UK
TURKSTAT (2021b). Energy Consumption Statistics. https://bit.ly/3qy3z3o
TURKSTAT (2021c). Labour Force Statistics. https://bit.ly/3IHx3Ci
TURKSTAT (2021d). Population Statistics. https://bit.ly/3LedDa0
TURKSTAT (2021e). Foreign Trade Statistics. https://bit.ly/3Nm5UbK
Van, H. V., Van Dao, L., Hoang, L. K., & Van Hien, N. (2023). The efficiency of government finanical expenditures before and during the COVID-19 pandemic: A cross-country investigation. Finance Research Letters, 54, 1–7. https://doi.org/10.1016/j.frl.2023.103697
Wang, H. J. (2002). Heteroscedasticity and non-monotonic efficiency effects of a stochastic frontier model. Journal of Productivity Analysis, 18(3), 241–253. https://doi.org/10.1023/A:1020638827640
Yiğiteli, N., & Öztürk, F. (2021). Total factor productivity in OECD countries: A panel data application with stochastic frontier approach. Ankara Hacı Bayram Veli University Journal of the Faculty of Economics and Administrative Sciences, 23(3), 1108–1147. https://doi.org/10.26745/ahbvuibfd.875248
Yiğiteli, N., & Şanlı, D. (2020). Calculation of human development indices in Turkish provinces: A comprehensive panel dataset for the 2009–2018 period. Journal of Economy Culture and Society, 61, 1–40. https://doi.org/10.26650/JECS2019-0068
Acknowledgements
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by DS and NGY. The first draft of the manuscript was written by DS and NGY. Both authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
Ethics approval and consent to participate
Not applicable. This article does not contain any studies with human participants or animals performed by any of the authors.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix 1. Description of the data set
Variable | Description | Value | Data sources | Web site |
|---|---|---|---|---|
Y | Gross value-added output | Logarithmic level | TURKSTAT (2021a) | |
K | Energy consumption-proxy variable for capital stock. It includes consumption in household, commercial, government, industrial, illumination, and other | Logarithmic level | TURKSTAT (2021b) | |
L | Employed Persons | Level | TURKSTAT (2021c) | https://data.tuik.gov.tr/Kategori/GetKategori?p=istihdam-issizlik-ve-ucret-108&dil=1 |
SCH | Years of schooling | Level | Yiğiteli and Şanlı (2020) | |
LQ | Quality-adjusted persons employed, based on years of schooling and returns to education | Logarithmic level | Author's calculation | |
N | Population | Level | TURKSTAT (2021d) | https://data.tuik.gov.tr/Kategori/GetKategori?p=nufus-ve-demografi-109&dil=1 |
YR | Gross value added share, obtained by dividing regional Gross Value Added to Türkiye' Gross Value Added | Logarithmic level | Author's calculation | |
NR | Population share, obtained by dividing regional population to Türkiye' population | Logarithmic level | Author's calculation | |
X | Total exports | Level | TURKSTAT (2021e) | |
M | Total imports | Level | TURKSTAT (2021e) | |
PX | Export price Index | Level | IMF | https://data.imf.org/?sk=2CDDCCB8-0B59-43E9-B6A0-59210D5605D2 |
XL | Exports per employed person | Logarithmic level | Author's calculation | |
ML | Imports per employed person | Logarithmic level | Author's calculation |
Appendix 2. Correlation matrix
Variable | lnY | lnK | lnLQ | lnKlnLQ | lnYR | lnNR | lnXL | lnML |
|---|---|---|---|---|---|---|---|---|
lnY | 1.0000 | |||||||
lnK | 0.9086a | 1.0000 | ||||||
0.0000 | ||||||||
lnLQ | 0.9638a | 0.8823a | 1.0000 | |||||
0.0000 | 0.0000 | |||||||
lnKlnLQ | − 0.2092a | − 0.4448a | − 0.2722a | 1.0000 | ||||
0.0000 | 0.0000 | 0.0000 | ||||||
lnYR | 0.9693a | 0.8743a | 0.9227a | − 0.1825a | 1.0000 | |||
0.0000 | 0.0000 | 0.0000 | 0.0003 | |||||
lnNR | 0.8946a | 0.7793a | 0.8614a | − 0.1429a | 0.9243a | 1.0000 | ||
0.0000 | 0.0000 | 0.0000 | 0.0047 | 0.0000 | ||||
lnXL | 0.7373a | 0.8004a | 0.6860a | − 0.2955a | 0.7139a | 0.6677a | 1.0000 | |
0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |||
lnML | 0.7164a | 0.8203a | 0.6504a | − 0.2107a | 0.7150a | 0.6090a | 0.8290a | 1.0000 |
0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
ap < 0.01, bp < 0.05, cp < 0.1
Appendix 3. Standart deviation of TFP change decomposition by region
Rank | NUTS-2 | TE | TEC | TC | SE | TFPC | Frequency |
|---|---|---|---|---|---|---|---|
1 | TRA2 | 0.035 | 0.030 | 0.009 | 0.228 | 0.234 | 15 |
2 | TR82 | 0.017 | 0.014 | 0.009 | 0.229 | 0.231 | 15 |
3 | TR81 | 0.069 | 0.058 | 0.009 | 0.090 | 0.122 | 15 |
4 | TRB2 | 0.037 | 0.029 | 0.008 | 0.096 | 0.104 | 15 |
5 | TRA1 | 0.015 | 0.016 | 0.009 | 0.095 | 0.101 | 15 |
6 | TRC3 | 0.056 | 0.029 | 0.008 | 0.068 | 0.078 | 15 |
7 | TRB1 | 0.024 | 0.024 | 0.008 | 0.053 | 0.064 | 15 |
8 | TRC2 | 0.040 | 0.027 | 0.007 | 0.050 | 0.056 | 15 |
9 | TR63 | 0.048 | 0.029 | 0.009 | 0.034 | 0.053 | 15 |
10 | TR61 | 0.053 | 0.061 | 0.009 | 0.023 | 0.053 | 15 |
11 | TR52 | 0.053 | 0.032 | 0.009 | 0.029 | 0.050 | 15 |
12 | TR42 | 0.040 | 0.042 | 0.008 | 0.029 | 0.049 | 15 |
13 | TR31 | 0.024 | 0.021 | 0.008 | 0.039 | 0.047 | 15 |
14 | TRC1 | 0.070 | 0.037 | 0.008 | 0.025 | 0.045 | 15 |
15 | TR22 | 0.010 | 0.013 | 0.009 | 0.035 | 0.044 | 15 |
16 | TR83 | 0.032 | 0.027 | 0.009 | 0.028 | 0.044 | 15 |
17 | TR72 | 0.032 | 0.024 | 0.008 | 0.036 | 0.043 | 15 |
18 | TR90 | 0.030 | 0.028 | 0.009 | 0.022 | 0.041 | 15 |
19 | TR71 | 0.021 | 0.021 | 0.009 | 0.034 | 0.040 | 15 |
20 | TR32 | 0.031 | 0.032 | 0.008 | 0.023 | 0.037 | 15 |
21 | TR21 | 0.020 | 0.020 | 0.008 | 0.029 | 0.035 | 15 |
22 | TR62 | 0.027 | 0.022 | 0.009 | 0.022 | 0.034 | 15 |
23 | TR51 | 0.021 | 0.024 | 0.008 | 0.018 | 0.032 | 15 |
24 | TR33 | 0.027 | 0.026 | 0.009 | 0.027 | 0.029 | 15 |
25 | TR10 | 0.039 | 0.027 | 0.008 | 0.012 | 0.024 | 15 |
26 | TR41 | 0.038 | 0.035 | 0.009 | 0.019 | 0.021 | 15 |
TR | 0.106 | 0.030 | 0.009 | 0.077 | 0.084 | 416 |
Appendix 4. 26 Statistical Regions of Türkiye
Rank | Region | NUTS-2 |
|---|---|---|
1 | İstanbul | TR10 |
2 | Tekirdağ, Edirne, Kırklareli | TR21 |
3 | Balıkesir, Çanakkale | TR22 |
4 | İzmir | TR31 |
5 | Aydın, Denizli, Muğla | TR32 |
6 | Manisa, Afyonkarahisar, Kütahya, Uşak | TR33 |
7 | Bursa, Eskişehir, Bilecik | TR41 |
8 | Kocaeli, Sakarya, Düzce, Bolu, Yalova | TR42 |
9 | Ankara | TR51 |
10 | Konya, Karaman | TR52 |
11 | Antalya, Isparta, Burdur | TR61 |
12 | Adana, Mersin | TR62 |
13 | Hatay, Kahramanmaraş, Osmaniye | TR63 |
14 | Kırıkkale, Aksaray, Niğde, Nevşehir, Kırşehir | TR71 |
15 | Kayseri, Sivas, Yozgat | TR72 |
16 | Zonguldak, Karabük, Bartın | TR81 |
17 | Kastamonu, Çankırı, Sinop | TR82 |
18 | Samsun, Tokat, Çorum, Amasya | TR83 |
19 | Trabzon, Ordu, Giresun, Rize, Artvin, Gümüşhane | TR90 |
20 | Erzurum, Erzincan, Bayburt | TRA1 |
21 | Ağrı, Kars, Iğdır, Ardahan | TRA2 |
22 | Malatya, Elazığ, Bingöl, Tunceli | TRB1 |
23 | Van, Muş, Bitlis, Hakkari | TRB2 |
24 | Gaziantep, Adıyaman, Kilis | TRC1 |
25 | Şanlıurfa, Diyarbakır | TRC2 |
26 | Mardin, Batman, Şırnak, Siirt | TRC3 |
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Yiğiteli, N.G., Şanlı, D. Decomposition of total factor productivity growth in Türkiye regions: a panel stochastic frontier approach. Eurasian Econ Rev 14, 275–300 (2024). https://doi.org/10.1007/s40822-023-00255-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40822-023-00255-7