Abstract
This paper examines the linkages between population growth and standard-of-living growth in 21 countries over the period of 1870–2013. We apply the bootstrap panel causality test proposed by Kónya (Econ Model 23:978–992, 2006), which accounts for both dependency and heterogeneity across countries. We find one-way Granger causality running from population growth to standard-of-living growth for Finland, France, Portugal, and Sweden, one-way Granger causality running from standard-of-living growth to population growth for Canada, Germany, Japan, Norway and Switzerland, two-way causality for Austria and Italy, and no causal relationship for Belgium, Brazil, Denmark, Netherlands, New Zealand, Spain, Sri Lanka, the UK, the USA, and Uruguay. Dividing the sample into two subsamples due to a structural break yields different results over the two periods of 1871–1951 and 1952–2013. Our empirical results suggest important policy implications for these 21 countries as the directions of causality differ across countries and time period.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The literature typically focuses on the relationship between population growth and economic growth, rather than standard of living growth. An older, but comprehensive literature review can be found in Cassen (1976).
The 21 countries include Austria, Belgium, Brazil, Canada, Denmark, Finland, France, Germany, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Spain, Sri Lanka, Sweden, Switzerland, the U.K., the U.S. and Uruguay. Researchers estimated growth regressions over the years that use a large number of variables to explain economic growth. In our case, however, the lack of continuous data on these variables over the entire sample period restricts our analysis to a bivariate model rather than a multivariate model.
Summary statistics reveal that Japan and Uruguay experience the highest and lowest per capita real GDP growth rate of 2.4 and 1.1 %, respectively. Brazil and France experience the highest and lowest mean population growth rate of 2.13 and 0.37 %, respectively.
We use POG to represent population growth and PEG to represent standard-of-living growth. That is, standard-of-living growth equals the growth rate of real GDP minus population growth.
The alternative panel Granger causality test was developed by Hurlin (2008). The method, however, does not control for cross-sectional dependence, and only provide results for the full-sample.
See Kónya (2006) for more details of the bootstrapping method and of country-specific critical values.
As indicated by Kónya (2006), this is an important step because the causality test results may depend critically on the lag structure. In general, lag decisions may cause different estimation results. Too few lags mean that some important variables are omitted from the model and this specification error will usually cause incorrect estimation in the retained regression coefficients, leading to biased results. On the other hand, too many lags will waste observations and this specification error will usually increase the standard errors of the estimated coefficients, leading to inefficient results. Based on Schwarz Bayesian Criterion, we find the optimal lag is 6 for our estimated model.
To save space, we do not report the results from the lag selection procedure, but these results are available on request.
T > N is the basic requirement for our bootstrap panel causality test.
The reader is referred to Kónya (2006) for explanations of the bootstrap procedure and how the country-specific critical values are generated. Note that the sign of the causal effect is derived from the sum of the coefficients of the variable considered as independent in a specific equation. So in our case, the sign is based on the sum of the coefficients on the six lags of the causal variable.
Multiple other break dates exists, as rightly pointed out by an anonymous referee, in both the standard of living and population growth rate equations, based on the CUSM and Bai and Perron (2003) tests of structural breaks applied to each of the 21 countries separately—details of which are available on request. The approach that we undertake, however, does not allow us to model breaks using dummy variables (as suggested by the referee). Hence, we had to rely on the break determined by the CUSUM test based on cross-sectional averages. Using the Bai and Perron (2003) test on the cross-sectional averages also identified multiple structural breaks. But, we could not split our samples, since some of the sub-samples would imply that T is no longer greater than N, and would make the Kónya (2006) approach infeasible. In such a situation, an ideal methodology to pursue would be time-varying causality. Time-varying causality, however, is currently restricted to only time-series data. Hence, while our panel approach allows us to analyze causality for each of the cross-sectional units explicitly, unlike standard panel data approaches that provide an overall estimate for the panel, the inability to model breaks using dummy variables can be considered as a drawback of our approach.
As suggested by an anonymous referee, we conducted the analysis for the full-sample, as well as the sub-samples by dropping Uruguay and Sri Lanka from our panel of 21 countries. For the 19 countries considered, the results for the sub-samples were virtually the same for the 21 countries. The only exceptions were that under the null that population growth does not Granger cause standard-of-living growth, we could not reject the null hypothesis for Austria and Finland. The differences between the 19 country case and the 21 country case were quite stark when we dropped Uruguay and Sri Lanka from the full-sample. Under the null hypothesis that population growth (standard-of-living growth) does not Granger cause standard-of-living growth (population growth), we reject the null hypothesis for only 3 (2) countries, namely Austria, Italy and the Netherlands (Italy and New Zealand). We believe that the weak results for the full-sample could reflect the existence of cross-sectional dependence between the 21 countries (i.e., with Uruguay and Sri Lanka included). Further, note that it is quite well-accepted that panel data results are sensitive to the cross-sectional units chosen, since there selection bias may exist. This is specifically why, we did not choose countries based on certain pre-conceived categorization, but went with these 21 countries for which data were available over the entire sample period of 1871–2013. But having said this, it is also true that Granger causality tests are sensitive to structural breaks. So, when we rely on the sub-sample analysis, our results are consistent for the included countries across the 19 and 21 countries cases.
References
Afzal M (2009) Population growth and economic development in Pakistan. Open Demogr J 2:1–17
Avakov Alexander V (2010) Two thousand years of economic statistics: world population. GDP and PPP, Algora Publishing, New York
Bai J, Perron P (2003) Computation and analysis of multiple structural change models. J Appl Econom 18(1):72–78
Balcilar M, van Eyden R, Inglesi-Lotz R, Gupta R (2014) Time-varying linkages between tourism receipts and economic growth in South Africa. Appl Econ 46(36):4381–4398
Birdsall N, Kelley AC, Sinding SW (eds) (2001) Population matters: demographic change, economic growth, and poverty in the developing world. Oxford University Press, New York
Breusch TS, Pagan AR (1980) The Lagrange multiplier test and its applications to model specification in econometrics. Rev Econ Stud 47(1):239–253
Cassen R (1976) Population and development: a survey. World Dev 4:785–830
Darrat AF, Al-Yousif YK (1999) On the long-run relationship between population and economic growth; some time series analysis evidence for developing countries. East Econ J 25:310–312
Dasgupta P (2000) Population and resources: an exploration of reproductive and environmental externalities. Popul Dev Rev 26:643–689
Drèze J, Murthi M (2001) Fertility, education, and development: evidence from India. Popul Dev Rev 27:33–63
Easterlin RA (1967) Effects of population growth on the economic development of 18 developing countries. Ann Am Acad Polit Soc Sci 369:98–108
Espenshade TJ (1978) Zero population growth and the economies of developed nations. Popul Dev Rev 4(4):645–680
Galor O, Weil D (1996) The gender gap, fertility, and growth. Am Econ Rev 86:374–387
Galor O, Weil D (2000) Population, technology and growth: from Malthus stagnation to the demographic transition and beyond. Am Econ Rev 90:806–828
Huang T-H, Xie Z (2013) Population and economic growth: a simultaneous equation perspective. Appl Econ 45:3820–3826
Hurlin C (2008) Testing for Granger non causality in heterogeneous panels, mimeo. Department of Economics: University of Orleans, Orléans
Kónya L (2006) Exports and growth: granger causality analysis on OECD countries with a panel data approach. Econ Model 23:978–992
Kremer M (1993) Population growth and technological change: one million B.C. to 1990. Q J Econ 108(3):681
Kuznets S (1955) Economic growth and income inequality. Am Econ Rev (March) 45(1):1–28
Kuznets S (1960) Population change and aggregate output. In: Roberts GB (ed) Demographic and economic change in developed countries. Universities-national Bureau Committee for economic research, Columbia University Press, New York, pp 324–351
Kuznets S (1976) Demographic aspects of the size distribution ofincome. Econ Dev Cult Change 25(1):1–94
Li H, Zhang J (2007) Do high birth rates hamper economic growth? Rev Econ Stat 30:67–80
Luigi P, Ferrara A, Varra P (2010) Population and economic growth: evidence from EU and emerging countries, Gottingen summer school 2010. Relations between the EU and the emerging global players
Malthus TR (1798) An essay on the principle of population. J. Johnson, London
Mason Andrew (1988) Saving, economic growth, and demographic change. Popul Dev Rev 14(1):113–144
McNicoll G (1984) Consequences of rapid population growth: an overview and assessment. Popul Dev Rev 10:177–240
Pesaran MH (2004) General diagnostic tests for cross section dependence in panels. Cambridge working papers in economics No. 0435. Faculty of Economics, University of Cambridge, Cambridge
Pesaran MH, Yamagata T (2008) Testing slope homogeneity in large panels. J Econom 142:50–93
Pesaran MH, Ullah A, Yamagata T (2008) A bias-adjusted LM test of error cross-section independence. Econom J 11:105–127
Simon JL (1987) Population growth, economic growth and foreign aid. Cato J (spring/summer) 7(1):159–193
Solow Robert M (1956) A contribution to the theory of economic growth. Q J Econ 70(1):65
Swamy PAVB (1970) Efficient inference in a random coefficient regression model. Econometrica 38:311–323
Thornton J (2001) Population growth and economic growth: long-run evidence from Latin America. South Econ J 68:464–468
Todaro MP, Smith SC (2006) Economic development. Pearson Education, London
Toney MB, Stinner WF, Kim Y (1981) The population debate: a survey of opinions of a professional organization’s membership. Popul Environ 4(3):156–173
Yao W, Kinugasa T, Hamori S (2013) An empirical analysis of the relationship between economic development and population growth in China. Appl Econ 45:4651–4661
Zellner A (1962) An efficient method of estimating seemingly unrelated regression equations and tests of aggregation bias. J Am Stat Assoc 57:500–509
Acknowledgments
We would like to thank two anonymous referees for many helpful comments.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chang, T., Chu, HP., Deale, F.W. et al. The relationship between population growth and standard-of-living growth over 1870–2013: evidence from a bootstrapped panel Granger causality test. Empirica 44, 175–201 (2017). https://doi.org/10.1007/s10663-016-9315-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10663-016-9315-9