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Institutions and savings in developing and emerging economies

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Abstract

Domestic savings are an important prerequisite for capital formation and growth. In this paper we analyze a new channel through which institutions influence aggregate savings and economic development. Whereas research in the field of savings decisions concentrates largely on the level of the individual, the literature on institutions and growth as well as on aggregate savings formation focuses on the aggregate, national level. First, we develop a framework that brings together both lines of reasoning, arguing that institutions may influence the individual savings decision as well as national savings in aggregate. This potential for institutional quality to influence economic performance has been neglected so far. Second, we build upon the empirical literature on aggregate savings formation and provide results supporting our hypothesis that better economic institutions drive aggregate savings formation upwards. By contrast, we do not find such effects in the case of the political environment. Our findings are robust when checked against a number of changes in explanatory variables, estimation methods and the treatment of instruments.

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Notes

  1. See Apergis and Tsoumas (2009) for a detailed survey.

  2. See Glaeser et al. (2004) for a short and critical overview.

  3. Note that some buffer stock savings are held in form of goods like real estate, gems, gold or jewelry. These are not counted as savings in the Systems of National Accounts, but as consumption or investment.

  4. This may partly explain why some studies find an effect of urbanization on savings, as there are more banking branches available in towns than in rural areas (Sen and Athukorala 2003).

  5. This is closely related to the ‘time preference’ concept, but as the latter has an exact definition with certain assumptions in microeconomics, we do not use this term here.

  6. This includes also a well-functioning system of land property registration because of collateral requirements and is therefore related indirectly to public administration and rule of law.

  7. On the other hand, actual micro-evidence challenges this view: if lifetime income is dependent on investment in education, parents try to save money when the children are young and use these funds to finance higher education when their children enter adulthood (Chamon and Prasad 2010).

  8. This explanation even holds despite the fact that Chinese savings have not resulted from voluntary private decisions, but rather have been politically motivated.

  9. We do not use a measure of the current account balance, like some earlier empirical studies. It can easily be shown that in this case the actual savings rate is estimated by a fraction of its own value, as the current account balance nearly equals gross national savings—our dependent variable—minus gross capital formation. More importantly, the usual way of interpreting the current account balance as an international borrowing constraint is not correct, as this would mean, in a cardinal interpretation inherent in every linear estimation framework, that current account surpluses are a sign of strong borrowing constraints and only current account deficits are a sign of borrowing ability. Whereas the latter should hold on average, the first aspect clearly does not.

  10. A list of countries is given in the Appendix.

  11. A broad description of the other control variables including their sources and treatment can be found in the Appendix; our institutional variables are described in Sect. 5.

  12. Sobel and Coyne (2011) hint at this problem indirectly with their analysis of the time series properties of different institutional indicators.

  13. We use the xtabond2 routine of Roodman (2006) for the STATA software package.

  14. We relax the assumption of exogenous institutions in our robustness analysis; see our Table 11.

  15. Note that, in their seminal paper, Loayza et al. (2000) treated all variables as predetermined, using not second but first lags as instruments, thereby improving the efficiency of their estimates considerably at the cost of coping with the endogeneity problem.

  16. An alternative test would be the Sargan test. As this test is not robust to the presence of heteroscedasticity, it very often fails to reject the null of inappropriate instruments in our case. This may, for instance, also explain why this standard test is also not reported in Terrones and Cardarelli (2005).

  17. We have also tested an FE version with year dummies. No year dummy has been significant. The change in coefficients is well within the range of the standard errors in the model without year dummies, so we excluded them for comparability with the system GMM estimations. Results are, of course, available upon request.

  18. See, for example, Loayza et al. (2000: 173, model No. 6) or (Terrones and Cardarelli 2005, Table 2.2).

  19. The calculation of the long-run coefficients is based on a simple autoregressive distributed lag model with one lag of the dependent and zero lags of the additional variable. In this case, the long-run effect is simply the ratio of the estimated coefficient divided by one minus the coefficient of the lagged dependent variable.

  20. See the extensive and very inspiring research project by Christiansen et al. (2009).

  21. Results not reported; they are available upon request.

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Acknowledgements

We are thankful for valuable comments from the participants of the ESSA Meeting in Stellenbosch, September 2011 and the Martin Paldam workshop in Aarhus, September 2012. We thank especially Christian Bjørnskov, Co-Pierre Georg and William Shughart as well as two anonymous referees for helpful comments. All errors and inconsistencies remain with the authors.

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  1. Friedrich-Schiller-University Jena, Jena, Germany

    Andreas Freytag & Sebastian Voll

  2. University of Stellenbosch, Stellenbosch, South Africa

    Andreas Freytag

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  1. Andreas Freytag
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Correspondence to Andreas Freytag.

Appendix: Dataset, description of the main variables and selection process

Appendix: Dataset, description of the main variables and selection process

  • Gross national savings to GDP: taken from the IMF World Economic Outlook 2012 database.

  • Government savings (deficits): we use the ratio of general government net lending to GDP from the IMF World Economic Outlook database as a proxy for governmental budget deficits. We dropped 14 observations, for which deficits exceeded −25 % of GDP, as extreme outliers.

  • Private savings: gross national savings minus government savings. We dropped one case wherein private savings exceeded 150 % of GDP as an inconsistent or extreme outlier.

  • Actual inflation: consumer price inflation taken from the World Development Indicators. We truncated inflation values above 50 % (which is the defined border for hyperinflation) and set all values above 50 % to this. That truncation was applied to 325 of 4512 observations in the basic dataset.

  • Three-year average inflation: inflation average over the last three years before the year under consideration.

  • Real interest rates: taken from the World Bank Database and truncated to −10 % to 50 %, which replaced 190 values below −10 % and 34 over 50 % out of 3534 observations.

  • Real GDP per capita, log: the log of the variable ‘rgdpch’, taken from the PWT 7.0. This is the real GDP, chain-linked series in PPP prices with 2005 as the base year.

  • Real GDP growth: the variable ‘grgdpch’ taken from the PWT 7.0, which is the real growth rate of the ‘rgdpch’ series.

  • Domestic credit to the private sector to GDP (also: broad money to GDP, M2 and quasi-money to GDP, credit supplied by the banking sector to GDP): taken from the World Bank database. Values of zero were treated as missing observations.

  • Old age (youth) dependency ratio: ratio of people over 65 (below 15) years to working age people between 15 and 65. Data are from the World Bank database.

  • Population: population in millions was taken from PWT 7.0. Countries with populations of less than one million people were dropped to avoid problems due to large capital account-based transactions in so-called tax havens such as the Bahamas.

  • Oil trade balance: the ratio of volume of oil exports minus oil imports to GDP, taken from the World Economic Outlook database 2011. In one country, imports were counted as a negative entry in the dataset, which we corrected.

  • EFW-indices: the interpolated chain-linked versions taken from Gwartney and Lawson (2009). As the institutional data are available only in five-year intervals from 1970 to 2000 and annually thereafter, we do linear interpolations between two data points where necessary for our yearly estimations. Such a procedure has been applied previously by, for instance, de Soysa and Neumayer (2005). As institutions develop slowly over time, a linear estimation comes close to capturing the gradual development inherent in the evolution of institutional quality. Furthermore, as the indices are constructed from different sources, including surveys, the normal measurement error and errors caused by linear interpolation are two sides of the same coin, leading us to the conclusion that our error can be tolerated given the long time span and wide country coverage of our sample.

  • IMF capital account openness: we use the Chinn and Ito (2008) index based on the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER) in its 2012 version.

  • Trade openness: the ratio of imports plus exports to GDP in 2005 constant prices, the variable openk taken from the Penn World Tables 7.0.

  • Freedom House—PolityIV: the ‘imputed revised combined polity2’ score from Hadenius and Teorell (2005) is a combination of the Freedom House civil liberty and political rights as well as the p_polity2 score of the PolityIV project. Overall, this index tries to capture the degree of democratic participation in a country. It ranges from 0 to 10, with higher values indicating more democracy.

  • PolityIV: chief executive constraints: measure of the power of the highest executive body in a country and the system of checks and balances against it. The measure takes values between one and seven, equal to one if the chief executive has unlimited power and seven if his/her power is balanced by the controls available to other political actors.

  • ICRG: quality of governance: is the aggregate indicator from the International Country Risk Guide of the Political Risk Group, which we have taken from Teorell et al. (2011).

  • List of countries in our baseline models:

    Albania, Argentina, Bangladesh, Belarus, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cambodia, Chile, China, Colombia, Cote d’Ivoire, Ecuador, El Salvador, Estonia, Ethiopia, Gabon, Ghana, India, Jamaica, Jordan, Kenya, Latvia, Macedonia, Malaysia, Mexico, Moldova, Mongolia, Mozambique, Namibia, Niger, Pakistan, Panama, Papua New Guinea, Paraguay, Philippines, Poland, Russian Federation, Singapore, Sri Lanka, Sudan, Swaziland, Syrian Arab Republic, Tanzania, Thailand, Togo, Tunisia, Uganda, Ukraine, Venezuela, Vietnam.

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Freytag, A., Voll, S. Institutions and savings in developing and emerging economies. Public Choice 157, 475–509 (2013). https://doi.org/10.1007/s11127-013-0121-7

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