Abstract
In a dataset on 83 countries covering the years 1960–2009, we find a negative indirect effect of the share of renewable natural capital in wealth on economic growth transmitted through demographic factors, more specifically, population fertility. In contrast, in countries with lower income inequality and higher institutional quality, the share of non-renewable natural capital in wealth has a direct positive impact on growth. We also find that countries with higher income per capita, human development, and institutional quality have a higher share of renewable natural capital per capita, but a lower share of renewable natural capital in wealth. Renewable natural capital is thus valuable for the population and of primary concern for empowered countries, even though it contributes less to wealth and economic growth. Our results raise serious questions about the way wealth and growth are defined in economics when one investigates the impact of natural capital and point to the importance of preserving natural capital, particularly, in less developed countries.
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Notes
Taxonomies of natural resources are typically controversial. The distinction between renewable and non-renewable natural capital focuses on whether or not the use of resources may be irreversible. Alternatively, the degree of exhaustibility could be considered to reflect the rate of use whereby a resource may be non-renewable but abundant. A classification may also be based on the capacity to generate economic rents through the exploitation of the resource.
There might also be a limit in the maximum usable amount of non-renewable resources due to the constrained planetary capacity to absorb the associated residues. Regarding fossil energies, for instance, 80% of fossil energy identified reserves should not be used in order to contain climate change (McGlade and Ekins 2015). Note, however, that scarcity cannot be assessed based on presently identified reserves, phosphate being a possible exception (Reynolds 1999).
Needless to say that this research output ought to be translated into a set of planned actions in which policy makers and industry players are actively involved.
The concept of ecosystem services precisely emphasizes the view that natural capital are assets that provide inputs and environmental services for economic production (Daily 1997), and their associated monetary value have been estimated (Costanza et al. 1997) though the precise meaning of a monetary value of services for which no markets exist has often been questioned.
Shahbaz et al. (2019) provide an interesting piece of empirical research as a survey on this controversy.
Rockström et al. (2009) discuss a set of nine boundaries of the planetary ecosystem.
BMA is known to account for uncertainty in the selection among models that describe the data-generating process and is gaining increasing popularity in the social sciences. See, for instance, Moral-Benito (2012) for an application to cross-country panel data. A recent recent survey on the use of BMA in economics is Steel (2019).
For more details on these points, see van der Ploeg (2010). Also, regarding the choice of natural resource indicators, when Lederman and Maloney (2008) use net exports of primary products per worker, Sachs and Warner (1995)’s negative impact of natural resources on economic growth based on share of gross exports of primary products in GDP disappears due to the possibility of re-exportation.
Alternative definitions of resource abundance have been proposed in the empirical literature. Norman (2009), for instance, defines resource abundance as the share of resource stocks in GDP or as resource stocks per capita and resource intensity as the ration of resource exports in GDP.
Fernandez et al. (2001) show the superiority of the BMA method over other techniques in selecting regressors for cross-country growth regressions.
There are a number of studies that use a wide variety of statistical methods to identify multiple economic growth regimes (Durlauf et al. 2005). See Owen et al. (2007) and Konte (2013) for an overview of how the presence of multiple economic growth regimes has been addressed by dividing the sample according to different theories, mainly, neoclassical, geography, demography, and institutions.
Even though averaging enables one to deal more adequately with business cycle effects, the sample size and the presence of heteroscedasticity and serial correlation constitute constraints on the time horizon over which this averaging exercise can be performed. Indeed, the longer this time span, the smaller the number of degrees of freedom and hence the less accurate the estimates are and the less explanatory power the regressors have (Durlauf et al. 2008b). In this paper, we use an averaging time span of 5 years.
A typical assumption is that the rate of technical progress and the physical capital depreciation rate add up to 5%, i.e., \(g + \delta\) = 0.05 (Mankiw et al. 1992).
Note that the presence of multiple regimes may be due to multiple steady-states or to non-linearity of the growth process.
The Chow test is based on an F-statistic given by \(\left[ {rss_{r} - \left( {rss_{1} + rss_{2} } \right)} \right]/K\left( {rss_{1} + rss_{2} } \right)\left( {n - 2K} \right)\) where \(rss_{r}\) is the residual sum of squares obtained from the full-sample model, \(rss_{1}\) and \(rss_{2}\) are the residual sum of squares of the two sub-sample models, \(K\) is the total number of independent variables (including the constant), and \(n\) is the total number of observations.
The data has been subject to some criticisms (van der Ploeg and Poelhekke 2010). For instance, a discount rate of 4% per year is applied independently of the rate of economic growth, and a remaining lifetime of 20 years and the same elasticity of the cost of extraction are used independently of the type of resource, the country, and the date. In addition, Van der Ploeg (2010) argues that there is a caveat in using The World Bank data on resource stocks as a measure of abundance since it is based on rents.
See World Bank (2006).
While we realize that using 2000 data for natural capital may affect in a non-trivial way our results, we were constrained by data availability. In fact, as discussed in the concluding section of this paper, our analysis also points to the need to further develop better datasets on this crucial variable.
First, using the Fisher unit root test we find that the dependent variable is stationary in levels. Second, we verify whether or not we should pool the data by testing the appropriateness of random and fixed-effects panel data compared to the pool analysis through the goodness-of-fit results. Panel data is preferred to pool data, which implies that the parameters of the equation vary from one period to the other over the ten periods of available data. Third, using the Erlat LM-test, we find that there is heteroscedasticity in our data across panels and the Baltagi LM-test shows that there is serial correlation as well. We thus use the OLS and fixed-effects methods to adjust the standard errors for intragroup correlation, thus making the results robust to heteroscedasticity and serial correlation. The GMM method also controls for heteroscedasticity and we test the presence of serial correlation of order one and two. To develop the 2SLS method for the economic growth regressions, we use the approach developed by Driscoll and Kraay (1998) that guarantees that the covariance matrix estimator is consistent, independently of the cross-sectional dimension, in contrast to Parks-Kmenta and the Panel-Corrected Standard Errors (PCSE) approaches, which typically become inappropriate when the cross-sectional dimension of a microeconometric panel gets large.
The regression tree shows a preference for investment in physical capital to separate the sample. This suggests that investment in physical capital dominates the other variables in identifying multiple regimes in the data.
The DIF-GMM and SYS-GMM methods generate instruments that grow quadratically with \(T\), which can bias the estimates when the number of instruments is too large with respect to the number of observations. The weakness of specification tests is a particular concern for the SYS-GMM method whose instruments are only valid under non-trivial assumptions. We should hence take a conservative p value of the Hansen test (Roodman 2009).
See Brock and Durlauf (2001).
The 2SLS regression results are very similar to the BMA estimation results with uncertainty and the results are available from the authors upon request.
Note that the variables used as proxies for a theory may not be individually yet jointly significant. Given that the ratio of the number of observations to that of the independent variables should not fall below 5 (Bartlett et al. 2001), as in Durlauf et al. (2005), we exclude from the BMA regressions the variables that have weak explanatory power in our regressions compared to those presented in Table 8, namely, the religion variables corresponding to Buddhism, Catholicism, Judaism, and Orthodox religion. Checking for multicollinearity leads us to further exclude some additional variables, which are the regional heterogeneity variables East Asia and the Pacific and the institutional variables Liberal democracy, Public sector corruption, Legal formalism: Check (1), Legal formalism: Check (2), and Complex.
Note that within the BMA context, we no longer can appeal to the standard 10, 5, and 1% statistical significance levels or the p values.
These results are available from the authors upon request.
Our executive constraints variable reflects the outcomes of most recent elections (Glaeser et al. 2004). Cox and Weingast (2018) find that the quality of legislatures measured by the executive’s horizontal accountability is more important than the existence of free and fair elections for economic growth.
The results are available from the authors upon request.
The results are available from the authors upon request.
An ex-post exploration of the data, following the procedure of the preliminary analysis (see Table 11), shows that we cannot reject the presence of different growth regimes for the sub-samples based on income per capita (under a fixed-effects estimation with the F statistic significant at the 1% statistical level), income inequality (under OLS and fixed-effects estimations both with the F statistic significant at the 1% level), human development (under OLS and fixed effects estimations both with the F statistic significant at the 1% level), and institutional quality (under a fixed-effects estimation at with the F statistic significant at the 1% level). These results are available from the authors upon request.
The results are available from the authors upon request.
The results concerning the variable inequality are not unambiguous.
It is worth noting that the relationship between natural capital and income growth and income is complex. For instance, some OPEC countries have very high-income levels per capita, mainly linked to the oil sector, but have experienced a negative real growth over the past few decades.
Such a negative result raises the question of some variables possibly being omitted from our empirical framework, which certainly deserves further investigation efforts.
The natural capital dependence effect and, when considering long-term horizons, the decrease of human capital, the capital shallowing (Solow 1956) and the congestion of fixed resources (Malthus 1798) effects seem to be the most relevant hypotheses through which the role of fertility in economic growth can be examined (Ashraf et al. 2013). The "Dutch disease" argument, according to which the over-development of a natural resource sector affects negatively the overall economy, could also be invoked to explain low economic growth in countries where the share of renewable natural capital in wealth is very high (Bruno and Sachs 1982).
We have omitted to take into account the numerous complementarities between the different sub-components of the variable natural capital, as this would not be relevant for exploring the separate role of each of these sub-components in explaining the final results of our analysis.
The quality of data will certainly benefit from initiatives such as the "Ecosystem natural capital accounts" project that considers different kinds of renewable natural capital and offers to keep distinct accounts for water, carbon, and ecosystem infrastructures (Weber 2014). Indeed, developing adequate accounting methods for natural capital so that its different components are properly recorded and integrated into social rules and economic regulation is necessary for the success of policies aimed at improving societal welfare.
We have indeed compared our results for the period 1960-2009 and those of Durlauf et al. (2008a) for the period 1965–1994. The data used by Durlauf et al. (2008a) concerns 57 countries of which 54 are also present in our work. In particular, both analyses are based on 11 countries from Asia and Oceania, 13 countries from Latin America and Caribbean, 19 countries from North America and Europe and 11 countries from Middle East and Africa. In addition, we include 1 country from Europe, 14 countries from Middle East and Africa, seven countries from Latin America and Caribbean and 7 countries from Asia and Oceania. While Durlauf et al. (2008a) find that among new growth theories, macroeconomic policy is a robust determinant, in our BMA analysis for the full sample we find that demography, religion, and institutions instead are the robust determinants. This suggests that the results are contingent on both the time frame considered and the country sample analyzed.
There exists a lengthy literature seeking to find the characteristics that enable to have a positive relationship between natural capital and economic growth with a special focus on institutional endowments. See Omgba (2015), among others.
On the issue of the social value of natural capital, in particular, its role in fighting poverty, and the need for societies to allocate extremely rewarding efforts for recording it, P. Dasgupta states: "Poverty will only be made history when nature enters economic calculations in the same way that buildings, machines, and roads do." (Conservation International 2019). Along similar lines, J. Stiglitz states: "Business is always evaluated by both its income statement and its balance sheet (assets and liabilities, or wealth). Similarly, a prospective homeowner can obtain a mortgage only by demonstrating both his or her income and net assets. Income in any given year can always be made to look good by selling off assets, but liquidating assets undermines the ability to generate income in the future. The true picture of economic health requires looking at both income and wealth. The economic performance of countries, however, is only evaluated based on national income (World Bank 2006, 2011, 2018)." For more on this issue and related ones, see Stiglitz et al. (2009).
See Stanford Advisory Council (2006). This project aims at integrating the value that nature provides to society into major policy decisions. Its objectives are clearly stated as follows: "Our ultimate objective is to improve the well-being of all people and nature by motivating greater and more targeted natural capital investments. Centered at Stanford University, we operate as a partnership between the Chinese Academy of Sciences, the University of Minnesota, the Stockholm Resilience Centre, The Nature Conservancy, and the World Wildlife Fund. We are an interdisciplinary team of academics, software engineers, and real-world professionals all working to make valuing natural capital easier and more accessible to everyone."
References
Acemoglu D, Robinson J (2012) Why nations fail: the origins of power, prosperity and poverty. Crown Publishers, New York
Acemoglu D, Johnson S, Robinson J (2002) The colonial origins of comparative development: an empirical investigation. Am Econ Rev 91(5):1369–1401
Acemoglu D, Johnson S, Robinson J (2005) Institutions as a fundamental cause of long-run growth. In: Aghion P, Durlauf S (eds) Handbook of economic growth. Elsevier, Amsterdam
Ades A, Di Tella R (1999) Rents, competition and corruption. Am Econ Rev 89(4):982–993
Alesina A, Ozler S, Roubini N, Swagel P (1996) Political instability and economic growth. J Econ Growth 1(2):189–211
Alesina A, Devleeschauwer A, Easterly W, Kurlat S, Wacziarg R (2003) Fractionalization. J Econ Growth 8(2):155–194
Aliyev I (2011) Understanding the resource impact using matching. CERGE-EI Working Papers Series No. 451
Andersson BA, Råde I (2002) Material constraints on technology evolution: the case of scarce metals and emerging energy technologies. In: Ayres RU, Ayres LW (eds) Handbook of industrial ecology. Edward Elgar, Cheltenham
Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58(2):277–297
Arellano M, Bover O (1995) Another look at the instrumental variable estimation of error-components models. J Econ 68:29–51
Ashraf QH, Weil DN, Wilde J (2013) The effects of fertility reduction of economic growth. Popul Dev Rev 39(1):97–130
Barbier EB (2014) Accounting for depreciation of natural capital. Nature 515:32–33
Barbier EB (2017) Natural capital and wealth in the 21st century. Eastern Econ J 43(3):391–405
Bardini C (1997) Without coal in the age of steam: a factor-endowment explanation of the Italian industrial lag before World War I. J Econ Hist 57:633–653
Barrett DB, Johnson TM, Kurian GT (2001) World christian encyclopedia: a comparative survey of churches and religions in the modern world. Oxford University Press
Barro R (1991) Economic growth in a cross section of countries. Quat J Econ 106(2):407–443
Barro R (1996) Democracy and growth. J Econ Growth 1(1):1–27
Barro R (1997) Determinants of economic growth. MIT Press, Cambridge
Barro R, Lee JW (1994) Sources of economic growth. Carnegie-Rochester Conf Ser Public Policy 40(1):1–46
Barro R, Lee JW (2014) A new data set of educational attainment in the world, 1950–2010. J Dev Econ 104:184–198
Barro R, McCleary R (2003) Religion and economic growth across countries. Am Sociol Rev 68(5):760–781
Barro RJ, Sala-i-Martin X (1992) Convergence. J Polit Econ 100(2):223–251
Bartlett JE, Kotrlik JW, Higgins CC (2001) Organizational research: determining appropriate sample size in survey research. Inf Technol Learn Perform J 19(1):43–50
Beck T, Clarke G, Groff A, Keefer P, Walsh P (2001) New tools in comparative political economy: the database of political institutions. World Bank Econ Rev 15(1):165–176
Bloom DE, Sachs JD, Collier P, Udry C (1998) Geography, demography, and economic growth in Africa. Brook Pap Econ Act 2:207–273
Boschini A, Pettersson J, Roine J (2013) The resource curse and its potential reversal. World Dev 43:19–41
Brock W, Durlauf SN (2001) Growth empirics and reality. World Bank Econ Rev 15(2):229–272
Brock W, Durlauf SN, West K (2003) Policy analysis in uncertain economic environments (with discussion). Brook Pap Econ Act 1:235–322
Brunnschweiler CN, Bulte EH (2008) Linking natural resources to slow growth and more conflict. Science 320(5876):616–617
Bruno M, Easterly W (1998) Inflation crises and long-run growth. J Monet Econ 41(1):3–26
Bruno M, Sachs J (1982) Energy and resource allocation: a dynamic model of the “Dutch Disease.”. Rev Econ Stud 49(5):845–859
Capolupo R (2009) The new growth theories and their empirics after twenty years. Open Assess E J Kiel Inst World Econ 3:1–72
Caselli F (2005) Accounting for cross-country income differences. In: Aghion P, Durlauf S (eds) Handbook of economic growth. Elsevier, Amsterdam
Caselli F, Esquivel G, Lefort F (1996) Reopening the convergence debate: a new look at cross-country growth empirics. J Econ Growth 1(3):363–389
Central Intelligence Agency (2009) The world fact book 2009, ISSN 1553-8133, Washington DC, USA
Cerny A, Filer RK (2007) Natural resources: are they really a curse? CERGE-EI Working Paper Series No. 321
Chan KMA, Satterfield T, Goldstein J (2012) Rethinking ecosystem services to better address and navigate cultural values. Ecol Econ 74:8–18
Chen D, Ma X, Mu H, Li P (2010) The inequality of natural resources consumption and its relationship with the social development level based on the ecological footprint and the HDI. J Environ Assess Policy Manag 12(1):69–86
Conservation international (2019) Valuing natural capital: Accounting for the benefits that nature provides. https://www.conservation.org/projects/valuing-and-accounting-for-natural-capital
Costanza R, d’Arge R, de Groot R et al (1997) The value of the world’s ecosystem services and natural capital. Nature 387:253–260
Costanza R, de Groot R, Sutton P, van der Ploeg S, Anderson SJ, Kubiszewski I, Farber S, Turner RK (2014) Changes in the global value of ecosystem services. Glob Environ Change 26:152–158
Cox GW, Weingast BR (2018) Executive constraint, political stability and economic growth. Comp Polit Stud 51(3):279–303
Daily G (1997) Introduction: what are ecosystem services? In: Daily G (ed) Nature’s services. Societal dependence on natural ecosystems. Island Press, Washington DC
Daly HE, Farley J (2004) Ecological economics: principles and applications. Island Press, Washington
De Long JB, Williamson JB (1994) Natural resources and convergence in the nineteenth and twentieth centuries. Harvard University, USA
Diaz S, Unai P, Stenseke M et al (2018) Assessing nature’s contributions to people. Science 359(6373):270–272
Ding N, Field BC (2005) Natural resource abundance and economic growth. Land Econ 81(4):496–502
Dinh HT, Dinh R (2016) Managing natural resources for growth and prosperity in low income countries. OCP Policy Center Policy Paper 16/01
Djankov S, La Porta R, Lopez-de-Silanes F, Shleifer A (2002) The regulation of entry. Quart J Econ 117(1):1–37
Djankov S, La Porta R, Lopez-de-Silanes F, Shleifer A (2003) Courts. Quart J Econ 118:453–518
Driscoll J, Kraay AC (1998) Consistent covariance matrix estimation with spatially dependent data. Rev Econ Stat 80:549–560
Durlauf SN, Johnson PA (1995) Multiple regimes and cross-country growth behaviour. J Appl Econ 10(4):365–384
Durlauf SN, Quah DT (1999) The new empirics of economic growth. In: Taylor JB, Woodford M (eds) Handbook of macroeconomics, vol 1. Elsevier, Amsterdam
Durlauf SN, Johnson PA, Temple JRW (2005) Growth econometrics. In: Durlauf SN, Aghion P (eds) Handbook of economic growth. Elsevier, Amsterdam
Durlauf SN, Kourtellos A, Tan CM (2008a) Are any growth theories robust? Econ J 118(527):329–346
Durlauf SN, Kourtellos A, Tan CM (2008b) Empirics of growth and development. In: Dutt AK, Ros J (eds) International handbook of development economics, vol 1. Edward Elgar, Cheltenham
Durlauf SN, Kourtellos A, Tan CM (2012) Is god in the details? A re-examination of the role of religion in economic growth. J Appl Econ 27(7):1059–1075
Easterly W (2001) The lost decades: explaining developing countries’ stagnation in spite of policy reform 1980–1998. J Econ Growth 6(2):135–157
Easterly W, Levine R (1997) Africa’s growth tragedy: policies and ethnic divisions. Quart J Econ 112(4):1203–1250
Edenhofer O (2015) King coal and the queen of subsidies. Science 349(6254):1286–1287
Fernandez C, Ley E, Steel MFJ (2001) Model uncertainty in cross-country growth regressions. J Appl Econ 16(5):563–576
Frankel (2010) The natural resource curse: a survey. National Bureau of Economic Research Working Paper No. 15836
Glaeser EL, La Porta R, Lopez-de-Silanes F, Shleifer A (2004) Do institutions cause growth? J Econ Growth 9(3):271–303
Goedkoop M, Heijungs R, Huijbregts M, De Schryver A, Struijs J, Van Zelm R (2008) ReCiPe 2008: a life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level. Report I: characterisation, 1st edn. Dutch Ministry of the Environment, The Hague, The Netherlands
Gowdy JM, Howarth RB, Tisdell C (2009) Discounting, ethics and options for maintaining biodiversity and ecosystem integrity. In: Kumar P (ed) The economics of ecosystems and biodiversity (TEEB): ecological and economic foundations. Routledge, Abingdon
Gylfason T (2001) Natural resources, education, and economic development. Eur Econ Rev 45(4):847–859
Gylfason T (2011) Natural resource endowment: a mixed blessing? In: Sy A, Arezki R, Gylfason T (eds) Beyond the curse: policies to harness the power of natural resources. IMF e-Library, Washington
Hall RE, Jones CI (1999) Why do some countries produce so much more output per worker than others? Q J Econ 114(1):83–116
Hox J (2002) Multilevel analysis. Techniques and applications. Lawrence Erlbaum, Mahwah
Hox J (2010) Multilevel analysis: techniques and applications, 2nd edn. Routledge, New York
IPBES (2019) Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. https://www.ipbes.net/news/ipbes-global-assessment-summary-policymakers-pdf
Kaufmann D, Kraay A, Mastruzzi M (2005) Governance matters IV: governance indicators for 1996–2004. World Bank Policy Research Working Paper 3630
Kelley AC, Schmidt RM (1995) Aggregate population and economic growth correlations: the role of the components of demographic change. Econ Demogr 32(4):543–555
Konte M (2013) A curse or a blessing? Natural resources in a multiple growth regimes analysis. Appl Econ 45(26):3760–3769
La Porta R, Lopez-de-Silanes F, Shleifer A, Vishny R (1999) The quality of government. J Law Econ Organ 15(1):222–279
Laurans Y, Rankovic A, Billé R, Pirard R, Mermet L (2013) Use of ecosystem services economic valuation for decision making: questioning a literature blindspot. J Environ Manag 15(119):208–219
Lederman D, Maloney W (2002) Open questions about the link between natural resources and economic growth: Sachs and Warner revisited. Central Bank of Chile Working Paper 141
Lederman D, Maloney W (2008) In search of the missing resource curse. Policy Research Working Paper No. 4766. World Bank
Lenzen M, Moran D, Kanemoto K et al (2012) International trade drives biodiversity threats in developing nations. Nature 486:109–112
MAEDI (2014) Innovative financing mechanism for biodiversity and the identification of mechanisms with strong potential. French Ministry of Foreign Affairs and International Development, Paris, France
Malthus TR (1798) An essay on the principle of population. Oxford University Press, Oxford
Mankiw NG, Romer D, Weil DN (1992) A contribution to the empirics of economic growth. Q J Econ 107(2):407–437
Mansano O, Ribogon R (2001) Resource curse or debt overhang? National Bureau of Economic Research Working Paper No. 8390
McGlade C, Ekins P (2015) The geographical distribution of fossil fuels unused when limiting global warming to 2 [deg] C. Nature 517:187–190
Mehlum H, Moene K, Torvik R (2006) Institutions and the resource curse. Econ J 116(508):1–20
Millennium Ecosystem Assessment (MEA) (2005) Four volumes: current state and trends; scenarios; policy responses; sub-global assessments. Island Press, Washington
Moral-Benito E (2012) Determinants of economic growth: a Bayesian panel data approach. Rev Econ Stat 94(2):566–579
Newbold T, Hudson NL, Arnell AP et al (2016) Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353(6296):288–291
Norman C (2009) Rule of law and the resource curse: abundance versus intensity. Environ Resour Econ 43(2):183–207
Omgba LD (2015) Why do some oil-producing countries succeed in democracy while other fail? World Dev 76:180–189
Owen A, Videras J, Davis L (2007) Do all countries follow the same growth process? J Econ Growth 14(4):265–286
Pearce DW, Markandya A, Barbier E (1989) Blueprint for a green economy. Earthscan, London
Petersen JE, Gocheva K (2015) EU reference document on Natural Capital Accounting. Prepared as part of the EU MAES process (Mapping and Assessment of Ecosystems and their Services), revised draft for consultation, January, 6, 2015
Pomeranz K (2000) The great divergence: China, Europe, and the making of the modern world economy. Princeton University Press, Princeton
Recuero Virto L, Weber JL, Jeantil M (2018) Natural capital accounts and public policy decisions: findings from a survey. Ecol Econ 144:244–259
Repetto R, Magrath W, Wells M, Beer C, Rossini F (1989) Wasting assets: natural resources in the national income accounts. World Resource Institute, NewYork
Reynolds DB (1999) The mineral economy: how prices and costs can falsely signal decreasing scarcity. Ecol Econ 31(1):155–166
Rockström J, Steffen W, Noone K et al (2009) A safe operating space for humanity. Nature 461:472–475
Rodrik D (2003) Search of prosperity: analytic narratives on economic growth. Princeton University Press, Princeton
Roodman D (2009) A note on the theme of too many instruments. Oxf Bull Econ Stat 71(1):135–158
Ross M (2015) What have we learned from the resource curse? Annu Rev Polit Sci 18:1–499
Sachs J (2003) Institutions don’t rule: direct effects of geography on per capita income. National Bureau of Economic Research Working Paper No. 9490
Sachs J, Warner A (1995) Economic reform and the process of global integration. Brook Pap Econ Act 26(1):1–118
Sachs J, Warner A (2001) The curse of natural resources. Eur Econ Rev 45(4–6):827–838
Sala-i-Martin X, Doppelhofer G, Miller R (2004) Determinants of long-term growth: a Bayesian averaging of classical estimates (BACE) approach. Am Econ Rev 94(4):813–835
Shahbaz M, Destek MA, Okumus I, Sinha A (2019) An empirical note on comparison between resource abundance and resource dependence in resource abundant countries. Resour Policy 60:47–55
Shastry GK, Weil DN (2003) How much of cross-country income variation is explained by health. J Eur Econ Assoc 1:387–396
Sleeswijk A, van Oers L, Guinee JB, Struijs J, Huijbregts MAJ (2008) Normalisation in product life cycle assessment: an LCA of the global and European economic systems in the year 2000. Sci Total Environ 390(1):227–240
Solow RM (1956) A contribution to the theory of economic growth. Q J Econ 70(1):65–94
Stanford Advisory Council (2006) The natural capital project. Stanford University. https://naturalcapitalproject.stanford.edu
Steel MFJ (2019) Model averaging and its use in economics. J Econ Lit. https://doi.org/10.1257/jel.20191385
Stiglitz JE, Sen A, Fitoussi JP (2009) The measurement of economic performance and social progress revisited. Document de Travail de l’OFCE No 2009-33
Stijns JP (2005) Natural resource abundance and economic growth revisited. Resour Policy 30(2):107–130
Sutton P, Anderson SJ, Costanza R, Kubiszewski I (2016) The ecological economics of land degradation: impacts on ecosystem service values. Ecol Econ 129:182–192
Ten Brink P, Russi D, Tinch R, Schoumacher C, Agarwala M, Bateman I (2015) The use of (economic and social) values of NC/ES in national accounting. D3.4 Discussion Paper, European Commission (OPERA project)
The Economics of Ecosystems and Biodiversity-TEEB (2008) An interim report. European Communities, Brussels, Belgium
Torres N, Afonso O, Soares I (2013) A survey of literature on the resource curse: critical analysis of the main explanations, empirical tests and resource proxies. CEF.UP Working Papers 1302, Faculdade de Economia do Port, Universidade do Porto
Torvik R (2009) Why do some resource-abundant countries succeed while others do not? Oxf Rev Econ Policy 25(2):241–256
UNEP (2015) The UNEP environmental data explorer, as compiled from World Resources Institute. United Nations Environment Programme, Nairobi, Kenya
van der Ploeg F (2010) Natural resources: curse or blessing? J Econ Lit 49(2):366–420
van der Ploeg F, Poelhekke S (2010) The pungent smell of ‘red herrings’: subsoil assets, rents, volatility and the resource curse. J Environ Econ Manag 60(1):44–55
van der Ploeg F, Venables AJ (2013) Absorbing a windfall of foreign exchange: Dutch disease dynamics. J Dev Econ 103:229–243
Weber JL (2014) Ecosystem natural capital accounts: a quick start package. CBD Technical Series No. 77
Weil DN (2005) Accounting for the effect of health on economic growth. National Bureau of Economic Research Working Paper 11455
World Bank (2006) A guide to valuing natural resources wealth. Environment Department, World Bank, USA
World Bank (2011) The changing wealth of nations: Measuring sustainable development in the new millennium. Environment and development. World Bank Group
World Bank (2018) The changing wealth of nations: Building a sustainable future. In: Lange GM, Wodon Q, Carey K (eds) World Bank Group
World Christian Encyclopedia (2001) Oxford University Press, Oxford
Worldwide Fund for Nature (WWF) (2015) From obstacles to opportunities: towards an action plan for improved natural capital and ecosystem accounting implementation. WWF, Brussels
Acknowledgements
The views expressed in this paper are only our own and do not necessarily reflect those of the institutions we are affiliated to. We are grateful to Carlos Romero, Antonio Casimiro Herruzo Martinez, Miguel Marchamalo Sacristan, Tiago Domingos, Emilio Jaime Cerda Tena, and Alejandro Caparros Gass for their suggestions. We thank as well the participants to the Séminaire du Centre International de Recherche sur l’Environnement et le Développement (CIRED), January 2017, at the Séminaire Développement Durable Environnement et Economie Publique (DDEEP-EconomiX), Paris Nanterre, January 2017, and at the Séminaire Café-scientifique du Centre d’Ecologie et des Sciences de la Conservation (CESCO), Museum National d’Histoire Naturelle, March 2017. We would also like to thank Steven Durlauf and Grigoris Emvalomatis for insightful thoughts as well as for sharing data and econometric programs at the 2nd Advanced summer school in economics and econometrics, University of Crete, Rethymno, August 2007. We thank Enrique Moral-Benito for suggestions and sharing data and Steven Poelhekke for sharing data. Farid Gasmi acknowledges funding from the French National Research Agency (ANR) under the Investments for the Future (Investissements d’Avenir) program, Grant ANR-17-EURE-0010.
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Appendices
Appendix 1: Data Set Sources
The unbalanced panel data set constructed for this study contains observations for 10 five-year periods from 1960 to 2009 on 83 countries from the following regions for which we have data on our variables of interest, namely, neoclassical variables, natural capital in wealth and natural capital per capita:
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Latin America and the Caribbean (20): Argentina, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Peru, Trinidad and Tobago, Uruguay, and Venezuela.
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Middle East and North Africa (10): Bahrain, Brunei, Egypt, Iran, Israel, Jordan, Kuwait, Saudi Arabia, Tunisia, and United Arab Emirates.
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Sub-Saharan Africa (15): Cameroun, Congo, Gabon, Ghana, Kenya, Malawi, Mauritius, Mozambique, Senegal, Sierra Leone, South Africa, Sudan, Uganda, Zambia, and Zimbabwe.
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East Asia and the Pacific (13): Australia, China, Fiji, Indonesia, Japan, Malaysia, New Zealand, Papua New Guinea, Philippines, Republic of Korea, Singapore, Thailand, and Tonga.
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South Asia (5): Bangladesh, India, Maldives, Pakistan, and Sri Lanka.
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North America, Europe and Central Asia (20): Austria, Belgium, Canada, Denmark, Finland, France, Greece, Hungary, Italy, Ireland, Norway, Poland, Portugal, Spain, Sweden, Switzerland, The Netherlands, Turkey, United Kingdom, and United States.
We have collected data on variables regrouped in five categories: neoclassical, demography, macroeconomic policy, regional heterogeneity, religion, natural capital, geography, fractionalization, institutions, and other. The definition of these variables and the data sources are given in Table 6. The choice of the eight new growth theories and the associated variables is largely inspired by the work of Durlauf et al. (2008a). These authors explore the question of what the robust determinants of economic growth are building on 43 growth theories and 145 regressors, each of these theories being statistically significant in at least one study (Durlauf et al. 2005). Besides the eight new growth theories, we also include a category named “Other” to account for some instrument and time dummy-driven growth effects (Tables 6, 7).
Appendix 2: Descriptive Statistics and Preliminary Results
See Tables 8, 9, 10, 11, 12, 13 and 14.
Appendix 3: BMA Estimation Results
See Tables 15, 16, 17, 18, 19, 20, 21, 22 and 23.
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Gasmi, F., Recuero Virto, L. & Couvet, D. The Impact of Renewable Versus Non-renewable Natural Capital on Economic Growth. Environ Resource Econ 77, 271–333 (2020). https://doi.org/10.1007/s10640-020-00495-0
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DOI: https://doi.org/10.1007/s10640-020-00495-0