Skip to main content
SpringerLink
Log in

Testing differences in efficiency of regions within a country: the case of Ukraine

  • Published:
Journal of Productivity Analysis Aims and scope Submit manuscript

Abstract

In this paper we synthesize and adopt the recently developed methods in efficiency analysis to the case of comparison of regions within a country. We take Ukrainian regions as a subject of investigation, yet the same toolkit can be applied to test disputable differences in productivity for many other countries where such questions can be of national concern (e.g., Belgium, Great Britain, Spain, etc.). Contrary to common perception of economists focusing on Ukraine, we find no significant differences in distributions and aggregate efficiencies between the agricultural and industrial regions, neither between western (mostly Ukrainian speaking) and eastern (mostly Russian speaking) regions of Ukraine. However, we find strong support for a rapidly increasing gap between the capital (Kyiv) and all the regions since 2001. Using truncated regression analysis with bootstrap we also find robust evidence that the inefficiency of regions is positively related to alcohol and tobacco consumption, the amount of foreign direct investment and inversely related to criminality in the region. On the other hand, we also find strong evidence that amount of capital in the region and its wealth is positively associated with efficiency level of this region.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Also see more recent work of Badunenko et al. (2008) and longer reference list cited therein.

  2. We should mention that a more complete study would also involve bad outputs of the regions (e.g., air or water pollutants).

  3. The only somewhat related works we are aware of are unpublished manuscripts by Tsyrennikov (2002) and Markova (2004), which analyze income convergence between Ukrainian regions.

  4. DEA was originally introduced by Charnes et al. (1978); conceptual background, however, dates back to at least Farrell (1957).

  5. Consistency of DEA is proved in Kneip et al. (1998) under the conditions of: (1) i.i.d. sampling of observations{(x i , y i ), i = 1,…,n}; (2) free disposability and convexity of the production set; (3) positive probability mass being in a neighborhood of the true frontier and (4) sufficient smoothness of the true frontier.

  6. For “moving window” analysis we construct data samples as combination of 2 year data series from the available set of years (1996–2002) and compare to the latest possible frontier, e.g., 1996 and 1997 to 1997 frontier or 2000 and 2001 to 2001 frontier, etc. One of the first applications of “moving window” was made by Charnes et al. (1985).

  7. For estimation of densities, we use Silverman (1986) reflection method with Gaussian kernel and bandwidth selected using Sheather and Jones (1991) method. See Simar and Zelenyuk (2006) for details.

  8. One may argue of the reverse relationship of alcohol consumption and economic development, i.e., people drink more alcohol because they are poor. Even if this statement is true, recall that poverty does not have one-to-one relationship to inefficiency. Indeed, we see some regions being less efficient, yet richer (in terms of income per capita), whether having lower or higher consumption of alcohol and tobacco per capita. Moreover, drinking level in Ukraine is not something that appeared or significantly changed during the period of study—it is something that is going back centuries ago (see McKee 1999) and so can hardly be a variable depending on the efficiency levels of 1996–2002.

  9. Other specifications showed similar results, and in this sense results are robust. Some specifications caused problems of numerical convergence of optimization of linear function due to high multicollinearity.

  10. As noted by one of the referees, the efficiency score for Kyiv (Table 18) appears to be relatively small in 1999 and 2000, which also may be a reason of the insignificance of the dummy variable.

References

  • Aigner D, Lovell CAK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econom 6:21–37. doi:10.1016/0304-4076(77)90052-5

    Article  Google Scholar 

  • Åslund A (2005) The economic policy of Ukraine after the Orange Revolution. Eurasian Geogr Econ 45(5):327–353. doi:10.2747/1538-7216.46.5.327

    Article  Google Scholar 

  • Åslund A, de Menil G (eds) (2000) Economic reform in Ukraine: the unfinished Agenda. M. E. Sharp Inc., New York, p 294

    Google Scholar 

  • Badunenko O, Henderson DJ, Zelenyuk V (2008) Technological change and transition: relative contributions to worldwide growth during the 1990s, Oxford Bulletin of Economics and Statistics, Department of Economics. University of Oxford, 70(4), 461–492

  • Battese GE, Coelli TJ (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir Econ 20:325–332. doi:10.1007/BF01205442

    Article  Google Scholar 

  • Blanchard O, Kremer M (1997) Disorganization. Q J Econ 112:1091–1126. doi:10.1162/003355300555439

    Article  Google Scholar 

  • Cazals C, Florens JP, Simar L (2002) Nonparametric frontier estimation: a robust approach. J Econom 106:1–25. doi:10.1016/S0304-4076(01)00080-X

    Article  Google Scholar 

  • Charnes A, Clark CT, Cooper WW, Golany B (1985) A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the US air forces. Ann Oper Res 2(1):95–112. doi:10.1007/BF01874734

    Article  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444. doi:10.1016/0377-2217(78)90138-8

    Article  Google Scholar 

  • Debreu G (1951) The coefficient of resource utilization. Econometrica 19:273–292. doi:10.2307/1906814

    Article  Google Scholar 

  • Färe R, Grosskopf S, Norris M, Zhang Z (1994) Productivity growth, technical progress, and efficiency change in industrialized countries. Am Econ Rev 84(1):63–83

    Google Scholar 

  • Färe R, Grosskopf S, Zelenyuk V (2004) Aggregation bias and its bounds in measuring technical efficiency. Appl Econ Lett 11(10):657–660

    Article  Google Scholar 

  • Färe R, Primont D (1995) Multi-output production and duality: theory and applications. Kluwer, Boston

    Google Scholar 

  • Färe R, Zelenyuk V (2002) Input aggregation and technical efficiency. Appl Econ Lett 9:635–636. doi:10.1080/13504850110118165

    Article  Google Scholar 

  • Farrell MJ (1957) The measurement of productive efficiency. J R Stat Soc [Ser A] 120(part 3):253–281. doi:10.2307/2343100

    Article  Google Scholar 

  • Henderson DJ, Russell RR (2005a) Human capital and convergence: a production frontier approach. Int Econ Rev 46:1167–1205. doi:10.1111/j.1468-2354.2005.00364.x

    Article  Google Scholar 

  • Henderson DJ, Russell RR (2005b) Human capital and convergence: a production frontier approach. Int Econ Rev 46:1167–1205. doi:10.1111/j.1468-2354.2005.00364.x

    Article  Google Scholar 

  • Henderson DJ, Zelenyuk V (2007) Testing for catching-up: statistical analysis of DEA efficiency estimates. Southern Econ J 73(4):1003–1019

    Google Scholar 

  • Johnson S, Kaufmann D, Shleifer A (1997) The unofficial economy in transition, brookings papers on economic activity. Fall, Washington, DC

    Google Scholar 

  • Johnson S, McMillan J, Woodruff C (1999) Why do firms hide? Bribes and unofficial economy after communism, discussion paper # 2105, CEPR discussion papers

  • Kneip A, Park B, Simar L (1998) A note on the convergence of nonparametric DEA estimators for production efficiency scores. Econom Theory 14:783–793. doi:10.1017/S0266466698146042

    Article  Google Scholar 

  • Kneip A, Simar L, Wilson PW (2008) Asymptotics and consistent bootstraps for DEA estimators in non-parametric frontier models. Econom Theory 24:1663–1697. doi:10.1017/S0266466608080651

    Article  Google Scholar 

  • Koropeckyj IS (1992) The Ukrainian economy: achievements, problems, challenges. Harvard Ukrainian Research Institute, Harvard University Press, Cambridge

  • Kumar S, Russell RR (2002) Technological change, technological catch-up, and capital deepening: relative contributions to growth and convergence. Am Econ Rev 92(3):527–548. doi:10.1257/00028280260136381

    Article  Google Scholar 

  • Li Q (1996) Nonparametric testing of closeness between two unknown distribution functions. Econom Rev 15:261–274

    Google Scholar 

  • Markova T (2004) An analysis of regional income disparities in Ukraine during transition, unpublished thesis. EERC, Kyiv

  • McKee M (1999) Alcohol in Russia. Invited commentary. Alcohol Alcohol (Oxford, Oxfordshire) 34(6):824–829. doi:10.1093/alcalc/34.6.824

    Google Scholar 

  • Meeusen W, van den Broeck J (1977) Efficiency estimation from Cobb-Douglas production functions with composed error. Int Econ Rev 18:435–444. doi:10.2307/2525757

    Article  Google Scholar 

  • Park BU, Simar L, Zelenyuk V (2008) Local likelihood estimation of truncated regression and its partial derivatives: theory and application. J Econom 146(1):185–198. doi:10.1016/j.jeconom.2008.08.007

    Article  Google Scholar 

  • Schneider F (2002) Size and measurement of the informal economy in 110 countries around the world, paper presented at the Workshop of Australian National Tax Centre, ANU, Canberra, Australia

  • Sheather SJ, Jones MC (1991) A reliable data based bandwidth selection method for Kernel density estimation. J R Stat Soc [Ser A] 3:683–690

    Google Scholar 

  • Silverman BW (1986) Density estimation for statistics and data analysis. Chapman and Hall, London

    Google Scholar 

  • Simar L, Wilson PW (2007) Estimation and inference in two-stage, semi-parametric models of production processes. J Econom 136(1):31–64. doi:10.1016/j.jeconom.2005.07.009

    Article  Google Scholar 

  • Simar L, Wilson PW (2009) Estimation and inference in cross-sectional, stochastic frontier models, discussion paper 0524, revised version 2007, Institut de Statistique, Universite Catholique de Louvain, in press Econometric Review

  • Simar L, Zelenyuk V (2006) On testing equality of distributions of technical efficiency scores. Econom Rev 25(4):497–522. doi:10.1080/07474930600972582

    Article  Google Scholar 

  • Simar L, Zelenyuk V (2007) Statistical inference for aggregates of Farrell-type efficiencies. J Appl Econom 22(7):1367–1394. doi:10.1002/jae.991

    Article  Google Scholar 

  • Tsyrennikov V (2002) Determinants of regional growth, a thesis submitted in partial fulfilment of the requirements for the degree of Master of Arts. Kyiv, EERC

  • Zelenyuk V, Zheka V (2006) Corporate governance and Firm’s efficiency: the case of a transitional country, Ukraine. J Prod Anal 25(1):143–157. doi:10.1007/s11123-006-7136-8

    Article  Google Scholar 

Sources of Data

  • Official website of Cabinet of Ministers of Ukraine www.kmu.gov.ua

  • Official website of National Bank of Ukraine www.bank.gov.ua/

  • State Statistical Committee of Ukraine (2000, 2004), Osnovni Fondy (Capital Assets), Statistical Compendium, Kyiv, Ukraine

  • State Statistical Committee of Ukraine (2003, 2005), Pratsya Ukrayiny (Labor of Ukraine) 2002, 2004, Statistical Compendium, Kyiv, Ukraine

  • State Statistical Committee of Ukraine (2004), Valova Dodana Vartist’ po Rehionah v 2001–2002 (Gross Value Added in Regions in 2001–2002), Statistical Compendium, Kyiv, Ukraine

  • State Statistical Committee of Ukraine (2000, 2004), Stattystychnyj Shchorichnyk Ukrayiny (Statistical Yearbook of Ukraine) 2000, 2004, Kyiv, Ukraine

  • State Statistical Committee of Ukraine (2001, 2002, 2003), Vytraty i Resursy Domohospodarstv Ukrayiny u (Household Expenditures and Resources in) 1999–2000, 2000–2001, 2001–2002, Statistical Bulletin, Kyiv, Ukraine

  • Ukrainian Economic Trends, UEPLAC. Various issues

Download references

Acknowledgments

We thank Anders Åslund, Tom Coupé, Joyce Gleason, Natalya Voynarovska and anonymous referees, as well as participants of seminars and workshops of UPEG at EERC-Kiev and NAPW IV for valuable comments. We remain solely responsible for the views expressed and mistakes made.

Author information

Authors and Affiliations

  1. Rice University, Houston, TX, USA

    Pavlo Demchuk

  2. Kyiv Economics Institute, Kyiv, Ukraine

    Valentin Zelenyuk

  3. Kyiv School of Economics, Kyiv, Ukraine

    Valentin Zelenyuk

Authors
  1. Pavlo Demchuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Valentin Zelenyuk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valentin Zelenyuk.

Additional information

The original paper was written while Pavlo Demchuk was at Kyiv Economics Institute, Kyiv, Ukraine.

Appendices

Appendix 1

See Tables 10, 11, 12, 13, 14, 15, 16, and 17.

Table 10 Gross value added (mln. UAH), yearly indicators
Full size table
Table 11 Capital (mln. UAH), yearly indicators
Full size table
Table 12 Labor cost (mln. UAH), yearly indicators
Full size table
Table 13 Labor (mln. employees)
Full size table
Table 14 Population (thou), yearly indicators
Full size table
Table 15 Alcohol and tobacco consumption (UAH per person), yearly indicators
Full size table
Table 16 Net foreign direct investment (mln. USD), yearly indicators
Full size table
Table 17 Number of crimes (thou. Crimes), yearly indicators
Full size table

Appendix 2

See Fig. 4.

Fig. 4
figure 4

Maps of Ukraine with division into Agrarian versus Industrial and Right (West) versus Left (East) Bank regions

Full size image

Appendix 3

See Tables 18, 19, and 20.

Table 18 Efficiency scores of regions under 2002 technology and CRS assumption (pooled case)
Full size table
Table 19 Efficiency scores of regions under CRS technology assumption, yearly indicators
Full size table
Table 20 Efficiency scores for “Moving Window” observations
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Demchuk, P., Zelenyuk, V. Testing differences in efficiency of regions within a country: the case of Ukraine. J Prod Anal 32, 81–102 (2009). https://doi.org/10.1007/s11123-009-0136-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11123-009-0136-8

Keywords

JEL Classification

Search

Navigation