Skip to main content
SpringerLink
Log in

Catching-up process in the transition countries

  • Published:
Economic Change and Restructuring Aims and scope Submit manuscript

Abstract

This paper revisits the catching-up hypothesis among the 29 transition countries using the time series approach to investigate income convergence. In this study, we propose a model which specifies a trend function incorporating both sharp and smooth breaks using dummy variables and Fourier functions, respectively. Our empirical results indicate that two convergence clubs are forming among the transition countries and one club is among the rich and the other club is among the poor countries, where most middle income countries will disappear and move into one of the two clubs. Also, our results indicate that the 1980s was an ominous decade for growth in the transition countries with income in most diverging from the USA. With recovery in the 1990s, we find that in the 2000s income per capita in most of these countries was catching up toward the USA.

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.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. . Whereas most of the transition countries were not official countries prior to the break-up of the former Soviet Union and the former Yugoslavia. Hence there is projected data for the countries in the database. The projections are informed primarily by analysis from Oxford Economic Forecasts, IHS Economics and Country Risk, the IMF World Economic Outlook, and other country-specific sources. Long-term projections are made under the assumption that global policies are largely held constant. Therefore, projections generally imply a smooth future path.

  2. “β-convergence” occurs when there is a negative relationship between the average growth rate of the relative income and its initial log per-capita level. On the other hand, “sigma convergence” is accepted when the standard deviation of the relative income decreases over time (Barro and Sala-i-Martin 1992).

  3. Whereas this paper is concentrated on time series approach of convergence hypothesis and also in order to save the space, we do not explain the technical debates on quantile regression and non-parametric regression. For quantile regression see Koenker (2000) and Sofi and Durai (2016) and for non-parametric regression see Laurini et al. (2005).

  4. See Epstein and Miao (2003), Quah (1997) and Durlauf and Quah (1999) for more details on distribution dynamics and its application in the debate of convergence.

  5. An important point to use Bai and Perron (BP) procedure to identify breaks is that the BP procedure is valid only if the time series is stationary. Given that we do not know before performing the stationary test whether the series is stationary or non-stationary, the CBL stationary test may prepare unreliable results. Much thanks from an anonymous referee for this note.

  6. As see in Eqs. (10) and (11), the conventional KPSS test is a one variety of Becker et al. (2006) when trigonometric component is ignored. As noted by Becker et al. (2006, p. 391) “the usual KPSS-type stationary tests will diverge when nonlinear trends are ignored. This leads to over-rejections of the true stationary null hypothesis in favour of the false unit-root hypothesis.” In order to test for presence of nonlinear terms, Becker et al (2006) offered a F(k) test. As noted by Becker et al (2006), the presence of the nuisance parameter causes the distribution of F(k) to be non-standard. Hence, in this paper we calculate the critical values for any series. For this end, first we generate 20,000 random series using the Gauss (version 10.0.0) RNDN procedure and under the null of linearity. Then using optimum frequency of any actual series, we calculated the F statistic for any of 20,000 pseudo series. In final step we obtained the critical values from sorted vector of pseudo F statistic.

  7. In order to determine the optimum frequency, we follow Becker et al. (2006) and first determine the maximum frequency equal to 5 and then calculate the sum of squared residuals (SSR hereafter) for any frequency. The optimum frequency is the minimum of the SSR.

  8. Whereas BP procedure needs to the series be stationary, hence if the null of stationary is rejected for a series, we cannot estimate Eq. (17) using our methodology.

References

  • Bai J, Perron Pierre (1998) Estimating and testing linear models with multiple structural changes. Econometrica 66:47–68

    Article  Google Scholar 

  • Barro R, Sala-i-Martin X (1992) Convergence. J Polit Econ 100:223–251

    Article  Google Scholar 

  • Becker R, Enders W, Lee J (2004) A general test for time dependence in parameters. J Appl Econom 19:899–906

    Article  Google Scholar 

  • Becker R, Enders W, Lee J (2006) A stationary test in the presence of an unknown number of smooth breaks. J Time Ser Anal 27:381–409

    Article  Google Scholar 

  • Bernard AB, Durlauf SN (1995) Convergence in international output. J Appl Econom 10:97–108

    Article  Google Scholar 

  • Campbell JY, Perron P (1991) Pit falls and opportunities: what macroeconpmists should know about unit roots. In: Blanchard OJ, Fisher S (eds) NBER Macroeconomics annual, vol 6. MIT press, Cambridge, pp 141–201

    Google Scholar 

  • Carlino G, Mills L (1993) Are U.S. regional economies converging? A time series analysis. J Monet Econ 32:335–346

    Article  Google Scholar 

  • Carlino G, Mills L (2006) The determinants of county growth. J Reg Sci 27(1):39–54

    Article  Google Scholar 

  • Carrion-i-Silvestre JL, Sansó A (2006) Testing the null of cointegration with structural breaks. Oxf Bull Econ Stat 68(5):623–646

    Article  Google Scholar 

  • Carrion-i-Silvestre JL, Del Barrio-Castro T, López-Bazo E (2005) Breaking the panels: an application to the GDP per capita. Econom J 8:159–175

    Article  Google Scholar 

  • Cheung YW, Pascualy AG (2004) Testing for output convergence: a re-examination. Oxf Econ Pap 56:45–63

    Article  Google Scholar 

  • Chong TTL, Hinich MJ, Liew VKS, Lim KP (2008) Time series test of nonlinear convergence and transitional dynamics. Econ Lett 100(3):337–339

    Article  Google Scholar 

  • Christopoulos DK, Leon-Ledesma MA (2008) Time-series output convergence tests and stationary covariates. Econ Lett 101:297–299

    Article  Google Scholar 

  • Cunado J, Perez de Gracia F (2006) Real convergence in Africa in the second-half of the 20th century. J Econ Bus 58:153–167

    Article  Google Scholar 

  • Datta A (2003) Time-series tests of convergence and transitional dynamics. Econ Lett 81:233–240

    Article  Google Scholar 

  • Dawson JW, Strazicich MC (2010) Time-series tests of income convergence with two structural breaks: evidence from 29 countries. Appl Econ Lett 17(9):909–912

    Article  Google Scholar 

  • Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74:427–431

    Google Scholar 

  • Durlauf SN, Quah DT (1999) The new empirics of economic growth. Handb Macroecon 1:235–308

    Article  Google Scholar 

  • Elliott G, Rothenberg TJ, Stock JH (1996) Efficiency tests for an autoregressive unit root. Econometrica 64:813–836

    Article  Google Scholar 

  • Enders W, Lee J (2012) A unit roots test using a Fourier series to approximate smooth breaks. Oxf Bull Econ Stat 74(4):574–599

    Article  Google Scholar 

  • Epstein LG, Miao J (2003) A two-person dynamic equilibrium under ambiguity. J Econ Dyn Control 27:1253–1288

    Article  Google Scholar 

  • Evans P, Karras G (1996) Convergence revisited. J Monet Econ 37:249–265

    Article  Google Scholar 

  • Fleissig A, Strauss J (2001) Panel unit-root tests of OECD stochastic convergence. Rev Int Econ 9(1):153–162

    Article  Google Scholar 

  • Freeman DG, Yerger DB (2001) Interpreting cross-section and time-series tests of convergence: the case of labor productivity in manufacturing. J Econ Bus 53:593–607

    Article  Google Scholar 

  • Friedman M (1992) Do old fallacies ever die? J Econ Lit 30:2129–2132

    Google Scholar 

  • Gallant R (1981) On the basis in flexible functional form and an essentially unbiased form: the flexible Fourier form. J Econom 15:211–353

    Article  Google Scholar 

  • Greasley D, Oxley L (1997) Time-series based tests of the convergence hypothesis: some positive results. Econ Lett 56:143–147

    Article  Google Scholar 

  • Hadri K (2000) Testing for stationary in heterogeneous panel data. Econom J 3:148–161

    Article  Google Scholar 

  • Islam N (2003) What have we learnt from the convergence debate? J Econ Surv 17:309–362

    Article  Google Scholar 

  • Kapetanios G, Shin Y, Snell A (2003) Testing for a unit root in the nonlinear STAR framework. J Econom 112:359–379

    Article  Google Scholar 

  • Koenker R (2000) Galton, Edgeworth, Frisch, and prospects for quantile regression in econometrics. J Econom 95:347–374

    Article  Google Scholar 

  • Koenker R, Basset G (1978) Regression quantiles. Econometrica 46:33–50

    Article  Google Scholar 

  • Kwiatkowski D, Phillips PCB, Schmidt PJ, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root. J Econom 54:159–178

    Article  Google Scholar 

  • Laurini M, Andrade E, Valls Pereira PL (2005) Income convergence clubs for Brazilian Municipalities: a non-parametric analysis. Appl Econ 18:2099–2118

    Article  Google Scholar 

  • Lee J, Strazicich MC (2003) Minimum Lagrange multiplier unit root test with two structural breaks. Rev Econ Stat 85(4):1082–1089

    Article  Google Scholar 

  • Leybourne S, Newbold P, Vougas D (1998) Unit roots and smooth transitions. J Time Ser Anal 19(1):83–97

    Article  Google Scholar 

  • Li Q, Papell D (1999) Convergence of international output: time series evidence for 16 OECD countries. Int Rev Econ Financ 8:267–280

    Article  Google Scholar 

  • Liu J, Wu S, Zidek JV (1997) On segmented multivariate regression. Stat Sin 7:497–525

    Google Scholar 

  • Ng S, Perron P (2001) Lag length selection and the construction of unit root tests with good size and power. Econometrica 69(6):1519–1554

    Article  Google Scholar 

  • Perron P (1989) The great crash, the oil price shock, and the unit root hypothesis. Econometrica 55:277–302

    Google Scholar 

  • Perron P (1997) Further evidence on breaking trend functions in macroeconomic variables. J Econom 80:355–385

    Article  Google Scholar 

  • Phillips PCB, Perron P (1988) Testing for a unit root in time series regression. Biometrika 75:335–346

    Article  Google Scholar 

  • Quah D (1993) Galton’s fallacy and tests of the convergence hypothesis. Scand J Econ 95:427–443

    Article  Google Scholar 

  • Quah D (1997) Empirics for growth and distribution: stratification, polarization, and convergence clubs. J Econ Growth 2:27–59

    Article  Google Scholar 

  • Rassekh F (1998) The convergence hypothesis: history, theory and evidence. Open Econ Rev 9:85–105

    Article  Google Scholar 

  • Sofi AA, Durai SRS (2016) Income convergence in India: a nonparametric approach. Econ Change Restruct 49(1):23–40

    Article  Google Scholar 

  • Sollis R (2009) A simple unit root test against asymmetric STAR nonlinearity with an application to real exchange rates in Nordic countries. Econ Model 26:118–125

    Article  Google Scholar 

  • Strazicich Mark C, Lee J, Day E (2004) Are incomes converging among OECD countries? Time series evidence with two structural breaks. J Macroecon 26(1):131–145

    Article  Google Scholar 

  • Sul D, Phillips PCB, Choi CY (2005) Prewhitening bias in HAC estimation. Oxf Bull Econ Stat 67:517–546

    Article  Google Scholar 

  • Tomljanovich M, Vogelsang TJ (2002) Are U.S. regions converging? Using new econometric methods to examine old issues. Empir Econ 27:49–62

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Economics, Allameh Tabataba’i University, Tehran, Iran

    Omid Ranjbar

  2. Trade Promotion Organization of Iran, Tehran, Iran

    Omid Ranjbar

  3. Department of Finance, Feng Chia University, Taichung, Taiwan

    Tsangyao Chang

  4. Department of Finance, National Sun Yat-sen University, Kaohsiung, Taiwan

    Chien-Chiang Lee

  5. Faculty of Economics, University of Mazandaran, Babolsar, Iran

    Zahra Mila Elmi

Authors
  1. Omid Ranjbar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tsangyao Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chien-Chiang Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zahra Mila Elmi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omid Ranjbar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ranjbar, O., Chang, T., Lee, CC. et al. Catching-up process in the transition countries. Econ Change Restruct 51, 249–278 (2018). https://doi.org/10.1007/s10644-017-9214-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10644-017-9214-5

Keywords

JEL Classification

Search

Navigation