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№ 2/2020
BRYZHAN Iryna 1, CHEVHANOVA Vira 2, HRYHORYEVA Оlesya 3, SVYSTUN Lyudmyla 4
1Project "Integrated Development in Ukraine" in Poltava
2National University «Yuri Kondratyuk Poltava Polytechnic»
3National University «Yuri Kondratyuk Poltava Polytechnic»
4National University «Yuri Kondratyuk Poltava Polytechnic»
APPROACHES TO FORECASTING DEMOGRAPHY TRENDS IN THE MANAGEMENT OF INTEGRATED AREA DEVELOPMENT
Ekon. prognozuvannâ 2020; 2:21-42 |
ABSTRACT ▼
The article is devoted to the innovative approach in the management of the area development for Ukraine based on demographic forecasting. Demographic forecasting is an essential element of informational supply for development and implementation of mid- and long-term social-economic development strategy and public administration of the area development.
It is emphasized that the approach to solve this problem should be comprehensive. One of the modern options to settle the problem is based on borrowing European expertise on integrated development, which results, apart from social-economic growth and environment improvement, in significant increase in the number of European urban dwellers. Detailed demographic forecast should make a ground for decision-making and development of integrated area plans. Integrated development of areas, primarily urban ones, involves the development of all urban environment elements: transport, economy, economic and social infrastructure, etc. Therefore, it requires vertical integration, on one hand, of various public administration levels – national, regional, and local ones, and, on the other, of private sector and public society.
Based on the analysis of demographic forecasting methods, the authors propose their own approach to area population forecasting, combining the component method that considers the pure migration indices, the future employment estimating method and the similarity (correlation) method. The authors offer their own approach for area population forecasting based on a combination of cohort group method (considers the pure migration indices), future employment estimate and similarity (correlation) methods. The common indices (birth and death rates, migration) should be the key components. However, the factors for their future changes should be defined individually based on the trends in the city’s social-economic development.
The proposed method takes into account the impact of the key drivers capable to change significantly the demography forecasting when developing normative and functional demo-forecast options, and should make up the basis for social-economic strategic plans of urban development to be implemented by local authorities and self-government bodies.
The theoretical provisions are supported with practical data of demographic forecasting for the implementation of integrated development strategy for the town of Poltava (Ukraine). Authors argue that demographic forecasting is optimal under the following conditions: detailed social-economic analysis of the city; and identification of strengths and weaknesses, and opportunities and threats. Based on the performed analysis and the objectives of perspective development, one can assess the opportunities for urban demography improvement.
Keywords:innovation in the management of area development, integrated area development, demographic forecast, demography forecasting methods, demographic development drivers
JEL: J11, C15, C36, О18, R58
Article in Ukrainian (pp. 21 - 42) | Download | Downloads :376 |
REFERENCES ▼
iac.enbek.kz/sites/default/ files/.МИРОВОЙ.pdf [in Russian].
2. Steshenko, V., Homra, O. & Stefanovskiy, A. (1999). Demographic perspectives
of Ukraine until 2026. Кyiv: Institute of Economics, NAS of Ukraine [in Ukrainian].
3. Libanovа, E.M. (Eds.). (2006). Complex demographic forecast of Ukraine for the
period up to 2050. Кyiv: Ukrainian Center for Social Reforms [in Ukrainian]
4. Croix, David de la & Gobbi, Paula E. (2017). Population density, fertility, and
demographic convergence in developing countries. Journal of Development Eco-
nomics, 127, 13-24.
5. Vyshnevskyi, A.H. (2015). After the demographic transition: divergence, conver-
gence or diversity? Obschestvennyie nauki i sovremennost – Social Sciences and
Modernity, 2, 112-129 [in Ukrainian].
6. Merrick, Т. & Tordella, S. (1988). Demographics: people and markets. Popula-
tion Bulletin, 43, 16-24.
7. Murdock, Steve H., Kelley, Chris, Jordan, Jeffry, Peccote, Beverly & Luedke,
Alvin. (2006). Demographics: а guide to methods and data sources for media, busi-
ness, and government. London: Boulder.
8. Leipzig charter on Sustainable European Cities. Retrieved from
ec.europa.eu/regional _policy/archive /themes /urban/leipzig charter
9. Guiding Principles for Sustainable Spatial Development of the European Conti-
nent. Retrieved from www.mdrap.ro/_documente/dezvoltare_teritoriala/ doc-
umente_strategice/Sustainable%20Spatial%20Development.pdf
10. Dennis, R., Howick, R. & Stewart, N. (2007). Methods of Estimating Population
and Household Projections. Science Report. Environment Agency, Rio House, Wa-
terside Drive, Aztec West, Almondsbury, Bristol.
11. Smith, Stanley, Tayman, Jeffrey & Swanson David. (2002). State and Local
Population Projections. Methodology and Analysis. New York, Boston, Dordrecht,
London, Moscow: Kluwer Academic Publishers.
12. Klosterman, Richard Е. (1990). Community Analysis and Planning Techniques.
Rowman& Littlefield.
13. Chapin, Tim & Diaz-Venegas, Carlos. (2007). Local Government Guide to Pop-
ulation Estimation and Projection Techniques. A Guide to Data Sources and Metho-
dologies for Forecasting Population Growth. Florida Department of Community Af-
fairs. Division of Community Planning.
14. Mussio, Irene & Tondo, Christian. (2009, June). The implications of the current
German demographic evolution. Insight. Retrieved from
ssrn.com/abstract=1445410
15. Tuljapurkar, Shripad. (2006). Population Forecasts, Fiscal Policy, and Risk. Final
paper for the conference, “Government Spending on the Elderly” at The Levy Eco-
nomics Institute of Bard College, April 28-29, Stanford University. Working Paper
No. 471.
16. Alho, Juha M. (2014). Forecasting demographic forecasts. International Journal
of Forecasting, 30: 4, 1128-1135.
17. Lassila, Jukka, Valkonena, Tarmo & Alho, Juha M. (2014). Demographic fore-
casts and fiscal policy rules. International Journal of Forecasting, 30: 4, 1098-1109.
18. Wilson, Tom. (2013). Quantifying the uncertainty of regional demographic fore-
casts. Applied Geography, 42, 108-115.
19. De Iaco, Sandra & Maggio, Sabrina. (2016). A dynamic model for age-specific
fertility rates in Italy. Spatial Statistics, 17, 105-120.
20. Shangad, Han Lin, Smith, Peter W.F., Bijak, Jakub & Wiśniowski, Arkadiusz.
(2016). A multilevel functional data method for forecasting population, with an ap-
plication to the United Kingdom. International Journal of Forecasting, 32: 3, 629-
649.
21. Rueda, Cristina & Rodríguez, Pilar. (2010). State space models for estimating
and forecasting fertility. International Journal of Forecasting, 26: 4, 712-724.
22. United Nations (1974). Manuals on methods of estimating population. MANUAL
VIII. Methods for Projections of Urbanand Rural Population. New York.
23. Malynovska, O.A. Internal migration and temporary displacement in Ukraine in
conditions of political and socio-economic threats. niss.gov.ua. Retrieved from
www.niss.gov.ua/content/ articles/ files/ vnutrishnya_migratsia-45aa1.pdf [in
Ukrainian].
24. Ukrainian society: migration dimension (2018). Ptoukha Institute of Demography
and Social Studies of NAS of Ukraine. Кyiv. [in Ukrainian].
25. Migration in Ukraine: Facts and Figures. iom.org.ua. Retrieved from
iom.org.ua/sites/default/files/ff_ukr_21_10_press.pdf [in Ukrainian]
26. Poltava-2030. Concept of Integrated Urban Development (2018). Institute of
Urban Development CO. PCC. Poltava. Retrieved from
drive.google.com/file/d/1Rzf_AaUJ29PNKfNoDts2Ar4BF7Z7Nt1D/view
[in Ukrainian].
BRYZHAN Iryna 1, CHEVHANOVA Vira 2, HRYHORYEVA Оlesya 3, SVYSTUN Lyudmyla 4
1Project "Integrated Development in Ukraine" in Poltava
2National University «Yuri Kondratyuk Poltava Polytechnic»
3National University «Yuri Kondratyuk Poltava Polytechnic»
4National University «Yuri Kondratyuk Poltava Polytechnic»
APPROACHES TO FORECASTING DEMOGRAPHY TRENDS IN THE MANAGEMENT OF INTEGRATED AREA DEVELOPMENT
Ekon. prognozuvannâ 2020; 2:21-42 | https://doi.org/10.15407/eip2020.02.000 |
ABSTRACT ▼
The article is devoted to the innovative approach in the management of the area development for Ukraine based on demographic forecasting. Demographic forecasting is an essential element of informational supply for development and implementation of mid- and long-term social-economic development strategy and public administration of the area development.
It is emphasized that the approach to solve this problem should be comprehensive. One of the modern options to settle the problem is based on borrowing European expertise on integrated development, which results, apart from social-economic growth and environment improvement, in significant increase in the number of European urban dwellers. Detailed demographic forecast should make a ground for decision-making and development of integrated area plans. Integrated development of areas, primarily urban ones, involves the development of all urban environment elements: transport, economy, economic and social infrastructure, etc. Therefore, it requires vertical integration, on one hand, of various public administration levels – national, regional, and local ones, and, on the other, of private sector and public society.
Based on the analysis of demographic forecasting methods, the authors propose their own approach to area population forecasting, combining the component method that considers the pure migration indices, the future employment estimating method and the similarity (correlation) method. The authors offer their own approach for area population forecasting based on a combination of cohort group method (considers the pure migration indices), future employment estimate and similarity (correlation) methods. The common indices (birth and death rates, migration) should be the key components. However, the factors for their future changes should be defined individually based on the trends in the city’s social-economic development.
The proposed method takes into account the impact of the key drivers capable to change significantly the demography forecasting when developing normative and functional demo-forecast options, and should make up the basis for social-economic strategic plans of urban development to be implemented by local authorities and self-government bodies.
The theoretical provisions are supported with practical data of demographic forecasting for the implementation of integrated development strategy for the town of Poltava (Ukraine). Authors argue that demographic forecasting is optimal under the following conditions: detailed social-economic analysis of the city; and identification of strengths and weaknesses, and opportunities and threats. Based on the performed analysis and the objectives of perspective development, one can assess the opportunities for urban demography improvement.
Keywords:innovation in the management of area development, integrated area development, demographic forecast, demography forecasting methods, demographic development drivers
JEL: J11, C15, C36, О18, R58
REFERENCES ▼
2.Steshenko, V., Homra, O. & Stefanovskiy, A. (1999). Demographic perspectives of Ukraine until 2026. Кyiv: Institute of Economics, NAS of Ukraine [in Ukrainian].
3.Libanovа, E.M. (Eds.). (2006). Complex demographic forecast of Ukraine for the period up to 2050. Кyiv: Ukrainian Center for Social Reforms [in Ukrainian]
4.Croix, David de la & Gobbi, Paula E. (2017). Population density, fertility, and demographic convergence in developing countries. Journal of Development Economics, 127, 13-24. doi.org/10.1016/j.jdeveco.2017.02.003
5.Vyshnevskyi, A.H. (2015). After the demographic transition: divergence, convergence or diversity? Obschestvennyie nauki i sovremennost – Social Sciences and Modernity, 2, 112-129 [in Ukrainian].
6.Merrick, Т. & Tordella, S. (1988). Demographics: people and markets. Population Bulletin, 43, 16-24.
7.Murdock, Steve H., Kelley, Chris, Jordan, Jeffry, Peccote, Beverly & Luedke, Alvin. (2006). Demographics: а guide to methods and data sources for media, business, and government. London: Boulder.
8. Leipzig charter on Sustainable European Cities. Retrieved from ec.europa.eu/regional _policy/archive /themes /urban/leipzig charter
9.Guiding Principles for Sustainable Spatial Development of the European Continent. Retrieved from www.mdrap.ro/_documente/dezvoltare_teritoriala/ documente_strategice/Sustainable%20Spatial%20Development.pdf
10.Dennis, R., Howick, R. & Stewart, N. (2007). Methods of Estimating Population and Household Projections. Science Report. Environment Agency, Rio House, Waterside Drive, Aztec West, Almondsbury, Bristol.
11.Smith, Stanley, Tayman, Jeffrey & Swanson David. (2002). State and Local Population Projections. Methodology and Analysis. New York, Boston, Dordrecht, London, Moscow: Kluwer Academic Publishers. doi.org/10.1007/0-306-47372-0
12.Klosterman, Richard Е. (1990). Community Analysis and Planning Techniques. Rowman& Littlefield.
13.Chapin, Tim & Diaz-Venegas, Carlos. (2007). Local Government Guide to Population Estimation and Projection Techniques. A Guide to Data Sources and Methodologies for Forecasting Population Growth. Florida Department of Community Affairs. Division of Community Planning.
14.Mussio, Irene & Tondo, Christian. (2009, June). The implications of the current German demographic evolution. Insight. doi.org/10.2139/ssrn.1445410
15.Tuljapurkar, Shripad. (2006). Population Forecasts, Fiscal Policy, and Risk. Final paper for the conference, “Government Spending on the Elderly” at The Levy Economics Institute of Bard College, April 28-29, Stanford University. Working Paper No. 471. doi.org/10.2139/ssrn.924627
16.Alho, Juha M. (2014). Forecasting demographic forecasts. International Journal of Forecasting, 30: 4, 1128-1135. doi.org/10.1016/j.ijforecast.2014.02.005
17.Lassila, Jukka, Valkonena, Tarmo & Alho, Juha M. (2014). Demographic forecasts and fiscal policy rules. International Journal of Forecasting, 30: 4, 1098-1109. doi.org/10.1016/j.ijforecast.2014.02.009
18.Wilson, Tom. (2013). Quantifying the uncertainty of regional demographic forecasts. Applied Geography, 42, 108-115. doi.org/10.1016/j.apgeog.2013.05.006
19.De Iaco, Sandra & Maggio, Sabrina. (2016). A dynamic model for age-specific fertility rates in Italy. Spatial Statistics, 17, 105-120. doi.org/10.1016/j.spasta.2016.05.002
20.Shangad, Han Lin, Smith, Peter W.F., Bijak, Jakub & Wiśniowski, Arkadiusz. (2016). A multilevel functional data method for forecasting population, with an application to the United Kingdom. International Journal of Forecasting, 32: 3, 629-649. doi.org/10.1016/j.ijforecast.2015.10.002
21.Rueda, Cristina & Rodríguez, Pilar. (2010). State space models for estimating and forecasting fertility. International Journal of Forecasting, 26: 4, 712-724. doi.org/10.1016/j.ijforecast.2009.09.008
22.United Nations (1974). Manuals on methods of estimating population. MANUAL VIII. Methods for Projections of Urbanand Rural Population. New York.
23.Malynovska, O.A. Internal migration and temporary displacement in Ukraine in conditions of political and socio-economic threats. niss.gov.ua. Retrieved from www.niss.gov.ua/content/ articles/ files/ vnutrishnya_migratsia-45aa1.pdf [in Ukrainian].
24.Ukrainian society: migration dimension (2018). Ptoukha Institute of Demography and Social Studies of NAS of Ukraine. Кyiv. [in Ukrainian].
25.Migration in Ukraine: Facts and Figures. iom.org.ua. Retrieved from iom.org.ua/sites/default/files/ff_ukr_21_10_press.pdf [in Ukrainian]
26.Poltava-2030. Concept of Integrated Urban Development (2018). Institute of Urban Development CO. PCC. Poltava. Retrieved from drive.google.com/file/d/1Rzf_AaUJ29PNKfNoDts2Ar4BF7Z7Nt1D/view [in Ukrainian].
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