Skip to main content

Advertisement

SpringerLink
Log in

Financial Liberalization, Fiscal Prudence and Growth: Panel Evidence from 1980–2003

  • Research Article
  • Published:
Open Economies Review Aims and scope Submit manuscript

Abstract

The main objective of this study is to investigate empirical links between financial liberalization, fiscal prudence and growth. More specifically, the hypothesis of whether financial liberalization coupled with fiscal prudence fosters or hinders growth is examined. We use an unbalanced panel dataset of 75 countries and quinquennial averages from 1980 to 2003. Through fixed effects estimations, we uncover that even though financial liberalization does not affect growth significantly, higher degree of financial liberalization in the presence of higher level of fiscal prudence leads to faster growth.

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

Similar content being viewed by others

Notes

  1. Arteta et al. (2003) argued that the relationship between capital account liberalization and growth could vary with the time period covered, the measure of openness and the method of estimation used.

  2. The composition of capital flows across countries is also important; however, due to unavailability of data, we did not incorporate it in our analysis. See Edwards et al. (2000), Wei and Wu (2002), Aizenman and Noy (2004) for further analysis of the composition of capital flows.

  3. See Arteta et al. (2003), Kaminsky and Schmukler (2003) and Chinn and Ito (2005) for further discussion.

  4. A general review of the recent empirical literature on cross-country growth can be found in Durlauf and Quah (1999).

  5. Openness is based on the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions and it ranges from 0 to 14 in increments of 0.5 with higher values representing more open accounts.

  6. Extreme bounds analysis introduced by Leamer (1983) is for investigating whether a particular explanatory variable is robustly significant; that is, the variable remains significant and preserves the theoretically predicted sign as the conditioning set of information changes.

  7. For a further discussion of the effects of financial liberalization during crises, among others, see Furman and Stiglitz (1998). Furman and Stiglitz claim that rapid financial liberalization without development of sound supervision and regulation play an important role in the East Asian crisis.

  8. For a data set of 20 emerging markets, Sachs et al. (1996) report that the behavior of current accounts, the size of capital inflows and the stance of fiscal policies during 1990–1994 do not explain why some countries suffer more than others in the aftermath of the Mexican Crisis.

  9. The countries excluded from the sample are eliminated due to data availability problems. Countries included in the sample are listed in Appendix 2.

  10. Sources of data can be found in Appendix 1.

  11. The variable is an observation of the natural log of real per capita GDP for 1980 in the 1980–1984 period, for 1985 in the 1985–1989 period, etc.

  12. For Honduras, Hungary, Pakistan, Sierra Leone, Ireland and Madagascar, secondary school enrollment rates were not available; therefore, for those countries, primary school enrollment rates were used.

  13. An alternative measure of openness, Share, is based on IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions. This annual report includes information about restrictions on capital and current account transactions imposed by countries. “Share” is simply the percentage of years a country has open accounts in a particular period of time. We prefer Quinn’s index over Share because of two major drawbacks of the latter. First, Share does not specify the years a country has open capital accounts. Second, it lacks information regarding degree of openness.

  14. Quinn’s measure is available for specific years only. We used Quinn’s available data for constructing 5-year averages. For example, for the 1980–1984 average, we used the only available year 1982 from Quinn’s data set.

  15. Trade to output ratio was used as an alternative to Quinn’s “openness”. The estimation results were robust to the choice of current account openness measure.

  16. The degree of financial liberalization and exchange rate regime choice may also be related. For a further discussion and empirical evidence on transitional economies, see von Hagen and Zhou (2005).

  17. We would like to note that we did not use the de jure classification of exchange rate regimes in our estimations. For comparative analyses of the two classifications see, among others, Ghosh et al. (2003) and Edwards and Levy-Yeyati (2003).

  18. More formally, overall budget balance is defined as current and capital revenue and official grants received less total expenditure and lending minus repayments, an increase implying an increase in fiscal prudence. Budget deficit share in GDP and total debt to GDP ratio were also used as alternative measures of fiscal prudence, with higher levels of both implying fiscal imprudence, however they were statistically insignificant and we chose overall budget balance to GDP ratio as the variable for fiscal prudence. The lack of significance of the debt to GDP ratio variable may be explained through the finding of von Hagen and Zhou (2005) that the stringency of capital controls empirically depends on the debt to GDP ratio and hence these variables may be collinear.

  19. For a detailed discussion of the relationship between democracy and growth, see Minier (1998) and Easterly (1999).

  20. The civil liberties and political rights indices used in the calculation of the democracy index variable are taken from Freedom in the World Country Ratings 1972 through 2003 of the Freedom House. The table of country ratings can be found at http://www.freedomhouse.org/ratings/index.htm.

  21. For further information on panel analysis, see Hsiao (1986).

  22. The only exception is the 2000–2003 period, in which a 4-year average is used.

  23. Geometric mean of growth of a variable X from t to t + n is equal to \(\sqrt[n]{{{{X_{t + n} } \mathord{\left/ {\vphantom {{X_{t + n} } X}} \right. \kern-\nulldelimiterspace} X}_t }} - 1\).

  24. Arithmetic mean is a sum-based value, which is appropriate for additive processes. However, growth is not additive, it is multiplicative, and the appropriate measure for growth factors is geometric mean, which is a product-based value.

  25. See Islam (1995) for a further discussion of how fixed effects estimation in panels partly solves the problem of parameter heterogeneity.

  26. The recent empirical literature employs system GMM estimation á la Arellano and Bover (1995) and Blundell and Bond (1998) system estimator to overcome the endogeneity problem. We also attempted to use this method. However, the requirement of lagged variables as instruments left us with insufficient degrees of freedom since we have large cross-sectional observations (N = 75) but short time series dimension (T = 5).

  27. The significance of the correlation coefficients can be tested as follows: If the statistic R denotes the correlation coefficient of a random sample from the bivariate normal distribution, then the random variable \(W = \frac{1}{2}\ln \frac{{\left( {1 + R} \right)}}{{\left( {1 - R} \right)}}\) has an approximate normal distribution with mean \(\frac{1}{2}\ln \frac{{\left( {1 + \rho } \right)}}{{\left( {1 - \rho } \right)}}\) and standard deviation \(\sqrt {\frac{1}{{\left( {n - 3} \right)}}} \) Hence, using the sample correlation R between the series, one can test whether it is significant or not (H o: ρ = 0) using the aforementioned distribution. If R ≤ cut-off point, then the series are said to be insignificantly correlated. The cut-off point corresponds to the value required to reject the null hypothesis, and is equal to 0.23 at the 5% level of significance, and 0.30 at the 1% level of significance for our sample of 75 countries.

  28. Cross correlations for the five sub-periods into which the data is divided (1980–1984, 1985–1989 etc.) are listed in Appendix 3.

  29. Among others, see Levine and Renelt (1992), Quinn (1997) and Levy-Yeyati and Sturzenegger (2003).

  30. Levy-Yeyati and Sturzenegger (2003) report negative and significant coefficient of the de facto exchange rate regime choice variable for developing countries only, indicating that flexible exchange rate regime choice is positively associated with growth.

References

  • Aizenman J, Noy I (2004) On the Two Way Feedback Between Financial and Trade Openness. NBER Working Paper No. w10496

  • Arellano M, Bover O (1995) Another look at the instrumental variable estimation of error-components models. J Econometrics 68:29–51

    Article  Google Scholar 

  • Arteta C, Eichengreen B, Wyplosz C (2003) When does capital account liberalization help more than it hurts? In: Helpman E, Sadka E (eds) Economic policy in the international economy: essays in honor of Assaf Razin. Cambridge University Press, Cambridge, pp 177–206

    Google Scholar 

  • Barro RJ (1991) Economic growth in a cross section of countries. Q J Econ 106(2):407–443

    Article  Google Scholar 

  • Blundell R, Bond S (1998) Initial conditions and moment conditions in dynamic panel data models. J Econometrics 87:115–143

    Article  Google Scholar 

  • Bollen KA (1993) Liberal democracy: validity and factors in cross-national measures. Am J Polit Sci 37:1207–1230

    Article  Google Scholar 

  • Brock WA, Durlauf SN (2001) Growth empirics and reality. World Bank Econ Rev 15(2):229–272

    Article  Google Scholar 

  • Chinn MD, Ito H (2005) What Matters for Financial Development? Capital Controls, Institutions, and Interactions. NBER Working Paper No. w11370

  • Durlauf SN, Quah DT (1999) The new empirics of economic growth. In: Taylor J, Woodford M (eds) Handbook of macroeconomics. Amsterdam, North Holland, pp 235–303

    Google Scholar 

  • Easterly W (1999) Life during growth. J Econ Growth 4(3):239–275

    Article  Google Scholar 

  • Edwards S (2001) Capital mobility and economic performance: are emerging economies different? In: Siebert H (ed) The world’s new financial landscape: challenges for economic policy. Springer, New York, pp 219–244

    Google Scholar 

  • Edwards S, Levy-Yeyati E (2003) Flexible exchange rates as shock absorbers. NBER Working Paper No. w9867

  • Edwards S, Gregorio JD, Valdes RO (2000) Controls on capital inflows: do they work? J Dev Econ 63(1):59–83

    Article  Google Scholar 

  • Eichengreen B, Leblang D (2003) Capital account liberalization and growth: was Mr. Mahathir right? Int J Finance Econ 8(3):205–224

    Article  Google Scholar 

  • Furman J, Stiglitz JE (1998) Economic crises: evidence and insights from East Asia. Brookings Papers on Economic Activity 2:1–135

    Article  Google Scholar 

  • Ghosh A, Gulde A, Holger W (2003) Exchange rate regime: choices and consequences. MIT Press, Cambridge and London

  • Hsiao C (1986) Analysis of panel data. Cambridge University Press, Cambridge

    Google Scholar 

  • Islam N (1995) Growth empirics: a panel data approach. Q J Econ 110(4):1127–1170

    Article  Google Scholar 

  • Kaminsky G, Schmukler S (2003) Short-run pain, long-run gain: the effects of financial liberalization. NBER Working Paper No. w9787

  • Klein MW (2003) Capital account openness and the varieties of growth experience. NBER Working Paper No. w9500

  • Leamer E (1983) Sensitivity analysis would help. Am Econ Rev 75:308–313

    Google Scholar 

  • Levine R, Renelt D (1992) A sensitivity analysis of cross-country growth regressions. Am Econ Rev 82(4):942–963

    Google Scholar 

  • Levy-Yeyati E, Sturzenegger F (2003) To float or to trail: evidence on the impact of exchange rate regimes. Am Econ Rev 93(4):1173–1193

    Article  Google Scholar 

  • Minier JA (1998) Democracy and growth: alternative approaches. J Econ Growth 3:241–266

    Article  Google Scholar 

  • Quinn D (1997) The correlates of change in international financial regulation. Am Polit Sci Rev 91(3):531–551

    Article  Google Scholar 

  • Sachs JD, Tornell A, Velasco A (1996) Financial crises in emerging markets: the lessons from 1995. Brookings Papers on Economic Activity 1:147–215

    Article  Google Scholar 

  • Sala-i-Martin XX (1997) I just ran four million regressions. Am Econ Rev 87(2):178–183

    Google Scholar 

  • Von Hagen J, Zhou J (2005) The determination of capital controls: which role do exchange rate regimes play? J Bank Finance 29:227–248

    Article  Google Scholar 

  • Wei SJ, Wu Y (2002) Negative alchemy? Corruption, composition of capital flows, and currency crises. In: Edwards S, Frankel J (eds) Preventing currency crises in emerging markets. University of Chicago Press, Chicago, pp 461–501

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Economics and Center for Economics and Econometrics, Bogazici University, Istanbul, 34342, Turkey

    C. Emre Alper

  2. University of Bonn, 53113, Bonn, Germany

    S. Meral Cakici

Authors
  1. C. Emre Alper
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Meral Cakici
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Emre Alper.

Additional information

The authors wish to thank without implicating Fikret Adaman, O. Pinar Ardic and Burcay Erus. Alper acknowledges financial support from TUBA-GEBIP (Turkish Academy of Sciences, Young Scientist Scholarship Program).

Appendices

Appendix 1. Data

Data sources: IMF’s International Financial Statistics, February 2005; World Bank’s World Development Indicators, 2004; Global Development Finance, 2002; Freedom House

GGDP:

Growth of real per-capita GDP; source, GDP volume (1995 = 100), IFS line 99BV

GPOP:

Growth of population; source, population, IFS line 99Z

LINI:

Log of initial real per-capita GDP; source, GDP (in constant US$), WDI

LSCH:

Log of school enrollment; source, secondary school enrollment (percent gross), WDI

LINV:

Log of investment share in GDP; source, gross fixed capital formation, IFS line 93E; nominal GDP, IFS line 99B

INF:

Inflation rate; source, consumer prices, IFS line 64

GOV:

Government consumption share in GDP; source, government consumption, IFS line 91F whenever available; government expenditure, IFS line 82 otherwise; nominal GDP, IFS line 99B

EXC:

De facto classification of exchange rate regimes; source, Levy-Yeyati and Sturzenegger (2003)

DEM:

Democracy index; source, Freedom House

CAP:

Capital account openness measure; source, Quinn (1997)

CUR:

Current account openness measure; source, Quinn (1997)

CUR2:

Trade to output ratio; source, exports of goods and services, IFS line 90C; imports of goods and services, IFS line 98C; nominal GDP, IFS line 99B

DEF:

Budget deficit share in GDP; source, government deficit/surplus, IFS line 80; nominal GDP, IFS line 99B

OBB:

Overall budget balance; source, WDI

DEBT:

Percentage change in total debt; source, total debt stocks (in US$), WDI; domestic debt, IFS line 88A; foreign debt, IFS line 89A; central government debt, GDF

Appendix 2

Table 3 List of countries
Full size table

Appendix 3. Sub-period correlations (75 countries)

3.1 Appendix 3a

Table 4 1980–1984 averages
Full size table

3.2 Appendix 3b

Table 5 1985–1989 averages
Full size table

3.3 Appendix 3c

Table 6 1990–1994 averages
Full size table

3.4 Appendix 3d

Table 7 1995–1999 averages
Full size table

3.5 Appendix 3e

Table 8 2000–2003 averages
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Alper, C.E., Cakici, S.M. Financial Liberalization, Fiscal Prudence and Growth: Panel Evidence from 1980–2003. Open Econ Rev 20, 509–524 (2009). https://doi.org/10.1007/s11079-008-9094-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11079-008-9094-4

Keywords

JEL Classification

Search

Navigation