The Relationship between the Dynamics of Total Factor Productivity and the Age Structure in Russian Regions
DOI:
https://doi.org/10.17059/ekon.reg.2025-1-9Keywords:
Solow residual method, dual measure of the Solow residual, total factor productivity, age structure of the population, spatial correlation, models of spatial econometrics, regions of RussiaAbstract
In modern society, population aging has become one of the most pressing demographic challenges. Increasing life expectancy and low fertility rates are reshaping age pyramids across many countries. This article examines how factors describing the age structure of the population influence the growth rate of total factor productivity (TFP). The study tests the hypothesis that population aging has a positive relationship with TFP growth. To calculate TFP growth, the study employs an approach based on the dual method of estimating the Solow residual, which accounts for potential distortions in the regional distribution of capital in Russia. Due to data availability, the analysis considers statistical data for Russian regions up to 2021. The calculated Solow residuals vary significantly across regions. For example, in Moscow, the Solow residual is 0.06 % (just 2.5 % of GRP growth), whereas Russia’s average annual GRP growth rate is 0.75 % (17.6 % of the average annual growth rate of real GRP per capita). The study examines several indicators of age structure, including the dependency ratio, median age, mean age, and the ratio of mean to median age. Moran’s index calculations confirm the expected significance of spatial correlation. Spatial econometric models, specifically the spatial lag model (SLM), reveal that age structure factors significantly influence TFP growth. The results show that both median and mean age positively impact TFP growth. The estimated effects of changes in age structure on TFP can be used to forecast regional economic development while considering demographic trends.
References
Ahmad, M., & Khan, R. E. A. (2018). Age-structure, human capital and economic growth in developing economies: A disaggregated analysis. Pakistan Journal of Commerce and Social Sciences (PJCSS), 12 (1), 229–252.
Aiyar, M. S., & Mody, M. A. (2013). The demographic dividend: Evidence from the Indian States. India Policy Forum, National Council of Applied Economic Research, 9 (1), 105–148. https://doi.org/10.5089/9781455217885.001
Artamonov, N. V., Kurbatskii, A. N., & Khalimov, T. M. (2021). Relationship between economic development and population age structure in the Russian regions. Terra Economicus, 19 (2), 77–90. https://doi.org/10.18522/2073–6606-2021-19-2-77-90 (In Russ.)
Barro, R., & Sala-i-Martin, X. (2004). Economic growth (2nd ed.). MIT Press.
Belyakov, A. O., Kurbatskiy, A. N., & Priymak, I. I. (2023). The relationship between the dynamics of overall factor productivity in Russian regions and their age structure. V Rossiyskiy ekonomicheskiy kongress REK-2023. Tematicheskaya konferentsiya Prikladnaya ekonometrika (sbornik tezisov dokladov) [V Russian Economic Congress “REC-2023”. Thematic conference “Applied Econometrics” (collection of abstracts)], 4, (pp. 72–74). Moscow. (In Russ.)
Bloom, D. E., & Williamson, J. G. (1998). Demographic transitions and economic miracles in emerging Asia. The World Bank Economic Review, 12 (3), 419–455. https://doi.org/10.1093/wber/12.3.419
Bloom, D. E., Sachs, J. D., Collier, P., & Udry, C. (1998). Geography, demography, and economic growth in Africa. Brookings papers on economic activity, 1998 (2), 207–295. https://doi.org/10.2307/2534695
Burda, M. C., & Severgnini, B. (2014). Solow residuals without capital stocks. Journal of Development Economics, 109, 154–171. https://doi.org/10.1016/j.jdeveco.2014.03.007
Choudhry, M. T., & Elhorst, J. P. (2010). Demographic transition and economic growth in China, India and Pakistan. Economic Systems, 34 (3), 218–236. https://doi.org/10.1016/j.ecosys.2010.02.001
Demidova, O. A., & Ivanov, D. S. (2016). Models of Economic Growth with Heterogenous Spatial Effects: The Case of Russian Regions. Ekonomicheskii zhurnal Vysshei shkoly ekonomiki [HSE Economic Journal], 20 (1), 52–75. (In Russ.)
Demidova, O. A., & Kamalova, E. (2021). Spatial Econometric Modeling of Economic Growth in Russian Regions: Do Institutions Matter? Ekonomicheskaya politika [Economic Policy], 16 (2), 34–59. (In Russ.)
Elhorst, J. P. (2014). Spatial econometrics: from cross-sectional data to spatial panels. Springer. https://doi.org/10.1007/978-3-642-40340-8_1
Hsieh, C. T. (2002). What explains the industrial revolution in East Asia? Evidence from the factor markets. American Economic Review, 92 (3), 502–526. https://doi.org/10.1257/00028280260136372
Iqbal, K., Yasmin, N., & Yaseen, M. R. (2015). Impact of demographic transition on economic growth of Pakistan. Journal of Finance and Economics, 3 (2), 44–50. https://doi.org/10.12691/jfe-3-2-3
Kadochnikova, E. I. (2020). Convergence of economic growth and digitalization of households: spatial analysis of interrelation with regional panel data. Aktual’nye problemy ekonomiki i prava [Actual Problems of Economics and Law], 14 (3), 487–507. https://doi.org/10.21202/1993-047X.14.2020.3.487-507 (In Russ.)
Kalabikhina, I. E., & Kazbekova, Z. G. (2022). The impact of the fi rst demographic dividend on economic growth considering human capital. Zhurnal Novoi Ekonomicheskoi Assotsiatsii [Journal of the New Economic Association], (3), 81–100. https://doi.org/10.31737/2221–2264-2022-55-3-5 (In Russ.)
Kazbekova, Z. G. (2018). Impact of the demographic dividend on economic growth. Naselenie i ekonomika [Population and Economics], 2 (4), 85–135. https://doi.org/10.3897/popecon.2.e36061 (In Russ.)
Korshunov, V. A., & Raynkhardt, R. O. (2017). Assessment of the Solow residuals for real and potential GDP: Practical calculation for member countries of OECD. Vestnik Instituta ekonomiki Rossiiskoi akademii nauk [The Bulletin of the Institute of Economics of the Russian Academy of Sciences], (3), 137–149. (In Russ.)
Millo, G. (2015). Testing for serial correlation in spatial panels. Mimeo.
Millo, G., & Piras, G. (2012). splm: Spatial panel data models in R. Journal of statistical software, 47 (1), 1–38. https://doi.org/10.18637/JSS.V047.I01
Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37 (1-2), 17–23. https://doi.org/10.1093/BIOMET/37.1-2.17
Pace, R. K., & LeSage, J. P. (2008). A spatial Hausman test. Economics Letters, 101 (3), 282–284. https://doi.org/10.1016/j.econlet.2008.09.003
Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of applied econometrics, 22 (2), 265–312. https://doi.org/10.1002/jae.951
Piras, G. (2014). Impact estimates for static spatial panel data models in R. Letters in Spatial and Resource Sciences, 7, 213–223. https://doi.org/10.1007/s12076-013-0113-8
Shirov, A. A. (Ed.) (2022). Potentsial’nye vozmozhnosti rosta rossiiskoi ekonomiki: analiz i prognoz. Nauchnyi doklad INP RAN [Potential for growth of the Russian economy: analysis and forecast. Scientific report — Institute of Economic Forecasting RAS]. Moscow: IEF RAS. https://doi.org/10.47711/sr2-2022 (In Russ.)
Solow, R. M. (1957). Technical Change and the Aggregate Production Function. The Review of Economics and Statistics, 39 (3), 312–320. https://doi.org/10.2307/1926047
Young, A. (1995). The tyranny of numbers: confronting the statistical realities of the East Asian growth experience. The quarterly journal of economics, 110 (3), 641–680. https://doi.org/10.2307/2946695
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Беляков Антон Олегович , Курбацкий Алексей Николаевич, Приймак Ирина Игоревна
This work is licensed under a Creative Commons Attribution 4.0 International License.
