Can a Heterodox Economist Use Cross-country Growth Regressions?
Matthew McCartney (School of Oriental and African Studies, UK)
© Copyright: Matthew McCartney 2006
This paper is concerned with how economic growth is analysed by economists. Over the last fifteen years an extremely common method has been through cross-country growth regressions. Section two shows how economic theory demonstrates that there should be a strong link between changes in economic policy and economic growth. Despite the implications of such theory empirical results using cross-country growth regressions remain disappointing. Section two demonstrates this using relevant empirical results in both a general manner and specifically those between economic growth and fiscal policy, investment, education and R+D.
The third section shows that the long-term averages typically used in cross-country growth regressions hide an important empirical reality of growth in developing countries. The medium-term growth averages used by cross-country regressions conceal the periods of stagnation, growth spurts, structural breaks, volatility and instability that actually characterise growth in developing countries. When confronted with these ‘empirical’ problems researchers typically stop and try to confront what they perceive as an empirical challenge. Researchers seek for better proxies for variables such as ‘education’, refine measures of ‘trade openness’ more precisely, and perhaps most commonly seek out longer and better data sets. Other methods have included the use of panel data and techniques such as ‘trimmed least squares’. Some researchers venture further realising that there may be more than an empirical problem at work, in particular that the theory relating growth and economic policy may be more complex than allowed for by simple cross-country regressions. If this is the case then the emphasis on improved data and technical refinements to the econometrics may be fruitless.
Section four explores a number of theoretical reasons why cross-country regressions may be an intrinsically poor method to isolate the link between changes in policy and changes in economic growth rates. Those analysed here are complementarity among policy variables, the relation between different theories of growth, hysteresis effects and dynamics. As demonstrated in this section, responses by researchers to these theoretical problems have been much more ad hoc.
Section five demonstrates the final problem of cross-country growth regressions that has rarely been faced by orthodox researchers. Far from being a positivist statistical exercise, cross-country growth regressions are bound to an underlying neo-classical assumption – that the growth process is universal. Each individual country in cross-section according to this view will provide evidence that can be used to elucidate the one underlying universal economic relation. An increase in openness for example is hypothesised to have the same effect on growth in all countries. There is a limited amount of evidence that can be teased out of existing cross-country growth regressions that suggests each growth experience should be treated as potentially unique, i.e. as a case study.
The last section concludes by suggesting that heterodox or post-autistic economists should open up the assumption of universalism to greater scrutiny and ask why the growth process may differ across time and space. In practical terms this would question the neo-classical assumption of universalism and with it the ‘one-size-fits-all’ programmes of liberalisation emanating from the World Bank, IMF and other institutions. A case study approach to economic growth would be justified on the assumption that growth processes are not universal. Comparative and historically informed case studies allow researchers to question the assumption of universality rather than be forced to assume it true a priori.
2. The Analysis of Growth and Empirical Results
This section shows there is a strong theoretical link between policy change and economic growth but also that the empirical evidence for this link is very weak. This is shown here in both a general sense and specifically in relation to fiscal policy, investment, education and R+D.
Policy provides the most straightforward explanation for changes in economic growth. A typical example is the World Bank1, which purports to show that ‘strong adjusters’ (policy) in Sub-Saharan Africa during the 1980s experienced increased rates of economic growth. The apparent link between policy and growth has been enhanced by theoretical developments. The original Solow growth model predicted that policy (investment) would only impact on the level of growth not the long-run growth rate. Endogenous growth models by contrast were motivated by the lack of convergence to steady-state among developing countries and the inability of traditional models to account for cross-country differences in income and growth rates.
Romer developed an equilibrium model of technological change in which optimising agents drove long-run growth through the accumulation of knowledge. The creation of knowledge by one firm has a positive external effect on the production possibilities of other firms2. Adding to capital and labour a third input (usually education) generates externalities. Due to the externality, these models yield a sub-optimal equilibrium/ market solution and in turn generate a potential role for the state. Policy is shown to effect growth through its impact on incentives to accumulate capital and knowledge and so generate technological change.
While the link between episodes of growth and stagnation and changes in policy seems intuitively reasonable and is supported by economic theory there is very little empirical evidence. Levine and Renelt took a number of variables commonly used in econometric growth analysis and ran them in thousands of regressions with different conditioning sets of other variables – judging them robust if they remained significantly related to growth. Their tests excluded variables that are only correlated with another factor that has a causal relationship with growth, i.e. those factors with an indirect impact on growth. They found only investment was robustly related to economic growth3. Even those factors many would accept as self-evidently related to economic growth, fiscal policy, investment, education and R+D have an ambiguous empirical relation to economic growth as revealed by conventional cross-country regression analysis. The relevant theory and empirical results concerning these four policy variables are analysed in turn.
2.1. Fiscal Policy and Growth.
Theory linking fiscal policy to economic growth is very clear. The link principally revolves around how increases in tax rates lower the return to private investment and hence permanently lowers the rate of investment. Barro measured government intervention as the ratio of real government consumption less spending on education and defence to real GDP. He found a significant negative association between this variable averaged over 1970-85 and real growth 1960-854. More generally there is no robust relation between growth and the ratio of total government expenditure to GDP, government consumption expenditure, government capital formation, or government educational expenditure5. The coefficient in Barro becomes insignificant when the ratio of exports to GDP is included in the conditioning set6. There are good theoretical reasons for the relation between government expenditure and growth to become more complex once trade openness is considered. Openness may increase the cost of government intervention by raising the elasticity of taxed factors7. Rodrik finds a positive correlation between a country’s exposure to international trade and the size of its government. A possible explanation he suggests is that government plays a risk-reducing role in economies exposed to a significant amount of external risk/ openness8.
There are severe empirical problems with any attempt to quantify the role of the state through regressing the rate of growth on the level of government expenditure. Keyensian demand management and automatic stabilisers imply that government expenditure will increase with poor economic performance. This will generate a spurious negative relation between the ‘size’ of government and economic growth. Governments also influence the economy in many ways that do not involve expenditure, such as regulation. Tax exemptions and fiscal transfers may have identical effects but have different implications for the measured size of government. The demand elasticity for government services is typically greater than one (Wagner’s Law). The level of government expenditure would then be determined endogenously.
2.2. Investment and Economic Growth
Investment was the one factor found robustly related to economic growth9. The average investment rate is frequently used as an independent variable in growth regressions, though there remain severe theoretical problems in identifying causality, and the traditional use of instruments as a ‘solution’ is problematic. “So many variables could be used to explain growth that it is difficult to find variables that are not only highly correlated with the endogenous variable but can also be plausibly excluded from the regression.”10
There is some closer empirical work on this question. There is a positive correlation between investment in specifically machinery and equipment and productivity growth. The relationship holds for countries with 1960 levels of GDP per worker greater than 25% of the US level, between 1960 and 1985 and the result is causal, robust, strong and statistically significant11. There is also a positive and significant relation between the ratio of imported to domestically produced capital goods for a large cross-country regression between 1960 and 198512. Ultimately such a causal link remains ambiguous. Between 1950 and 1988 the composition of investment in the OECD shifted sharply, the share of investment in producer durables, from 3 or 4% to more than 7% of GDP in France, Germany, the US and the UK and in Japan from 3.5% to 9%, growth showed no upward trend. Others have found growth induced subsequent capital formation in a large cross-section of countries between 1965 and 198513.
2.3. Education and Economic Growth
Intuitively education has an evident link with economic growth, but again there is no clear empirical link. Pritchett finds a robust and negative correlation between higher school enrolment and educational attainment and total factor productivity growth in developing countries. Between 1960 and 1985 educational capital grew faster in sub-Saharan Africa and South Asia than in East Asia even though the latter region grew more rapidly14. Bils and Klenow find only a weak relation between initial schooling and subsequent economic growth, even allowing for the indirect effects of schooling in permitting greater technology absorption. They find the relation either spurious, the expected return and incentive to acquire education increases in an expanding economy when the skilled wage is growing rapidly, or reflective of omitted variables related both to initial schooling rates and subsequent economic growth rates15.
A particular problem for regression analysis is finding a satisfactory measurement of human capital16. A large part of investment in education takes the form of forgone earnings by students. In addition, explicit spending on education takes place by the individual, family and state. Not all education expenditure is intended to generate productive human capital (for example the teaching of philosophy verses literacy). The typical proxy used in many cross-country regression equations is the share of the working-age population in secondary school. This fails to measure the quality of education, and the learning-on-the-job that takes place in the workforce. Good explanations of missing empirical support are possible external effects and the endogenous relation between education and economic growth.
2.4. R+D and Economic Growth
Theory and intuition suggest there is a clear link between R+D and economic growth. Again, this link has not been satisfactorily uncovered by empirical analysis. Between 1950 and 1988 the total number of scientists engaged in R+D in the US increased from 200,000 to over 1,000,000. A similar pattern was evident in Germany, France and Japan. Measured by R+D expenditure the results are similar. Despite this extra R+D there has been no permanent increase in growth in these countries17.
3. An Empirical Problem: Episodes of Growth and Stagnation in Least Developed Countries
This section shows that using long-run averages typical of cross-country growth regressions hide an important empirical reality of growth in contemporary developing countries. Growth averages over the medium-term (25-30 years) conceal the periods of stagnation, growth spurts, structural breaks, volatility and instability that actually characterise growth in developing countries.
3.1. The Historical Experience of Developing Countries
Theoretical and empirical research on growth has focused on averages over the medium-term (25-30 years)18. A decade of ten-percent growth followed by another of zero-percent drops into a Barro-type regression with the same average as two decades of five-percent growth. This problem has real implications for the analysis of growth in developing countries. Brazil enjoyed rapid growth between 1965 and 1980, and stagnated during the 1980s. A medium-term average doesn’t distinguish between the average of 3.1% between 1960-92 and the importance of the structural break. Per capita GDP in Cote D’Ivoire increased by 3.1% p.a. between 1960 and 1980 and declined by an average of 4.1% p.a. between 1980 and 1992. Ignoring the structural break average growth was 0.22%, almost the same as Senegal (0.18%) which stagnated throughout the whole period19.
Growth averages over the medium-term (25-30 years) conceal the periods of stagnation, growth spurts, structural breaks, volatility and instability that actually characterise growth experiences in developing countries. The overall average is not a good summary indicator of growth performance. Countries show shifts in growth rates, often in clear episodes, such as the slowdown in Latin America in the 1980s. GDP growth is not well characterised by a single exponential trend. For forty percent of least developed countries the R2 on such a trend is less than 0.5, suggesting that shifts and fluctuations are the dominant feature of GDP growth. There are instead six distinct patterns of growth, before and after statistically chosen structural breaks, steep hills, hills, plateaus, mountains, plains and accelerations20. Growth is very unstable across time periods. The correlation of per capita growth between 1977-92 and 1960-76 across 135 countries is only 0.0821.
There are very striking instances of growth accelerations and collapses among developing countries. There are 14 episodes of growth in Africa between 1960 and 1996 including South Africa between 1960 and 1974 (5.1%), Cote D-Ivoire 1960 to 1978 (9.5%), Gabon 1965 to 1976 (13.1%), and Namibia 1961 to 1979 (6.4%)22. Ten countries in Africa between 1967 and 1980 had growth of more than 6% p.a., including Gabon, Botswana, Congo, Nigeria, Kenya and Cote D’Ivoire all which were outperforming both Malaysia and Indonesia23. Hausmann et al conducted a very broad empirical test to locate episodes of growth by finding the year that maximises the F-statistic of a spline regression with a break at the relevant year24. Countries can have more than one acceleration. This filter yields 83 growth accelerations, capturing most well-known episodes such as China 1978, Argentina 1990, Mauritius 1971, Korea 1962, Indonesia 1967, Brazil 1967, Chile 1986, and Uganda 1989. The magnitude of accelerations is striking. Their definition is conditional on a growth acceleration of at least 2% p.a.; the average acceleration though was 4.7% p.a. There are many episodes with acceleration of 7% or more such as Ghana 1965 (8.4%), Pakistan 1962 (7.1%), and Argentina 1990 (9.2%). The occurrence of an episode is quite common, of 110 countries in their sample between 1957 and 1992 54.5% had at least one episode of growth and 20.9% two. The occurrence is also common across space: 21 episodes occurred in Asia, 18 in Africa, 17 in Latin America, 12 in Europe and 10 in the Middle East and North Africa.
4. Cross-country Growth Regressions: Theoretical Problems
Recent theorising on endogenous growth models is clear that there should be a strong link between policy and growth. This section shows that any empirical link between changes in public policy and changes in growth rates will be difficult to isolate using traditional cross-country regression analysis for theoretical not just empirical reasons. These theoretical problems include complementarity among policy variables, the relation between different theories of growth, hysteresis effects, and dynamics.
4.1. Complementarity among Policies
Policy variables typically enter the right hand side of regressions separately without diagnostic tests allowing for any but very limited interaction among them. Theory does suggest complementarity is important. For example, investment may be causally related to growth only in the presence of strong property rights, reforms causally linked to growth only if considered credible or if correctly sequenced. There is some limited empirical support for the importance of complementarity between policies. Mosley finds that complementarity between inflation, openness and the government share to be minimal but when corrected for sequencing the coefficient(s) increases and becomes significant25.
Econometrics has coped with this problem in an ad-hoc manner, splitting country samples by region or income level to look for changes in the strength and direction of causal relations or including occasional ad-hoc interaction effects between two variables. It would in theory be possible to add all possible interaction effects by adding multiplicative relations in a regression between all combinations of variables and adding a welter of dummy variables for all possible structural breaks and geographical regions. The resulting loss of degrees of freedom would then render the regression all but meaningless.
4.2. The Relation between Different Theories of Growth
There are numerous cross-country econometric studies finding some indicator of national policy to be linked to economic growth. There is however no clear consensus on what policy variables to include in cross-country regression analysis. Economic theory rarely generates a complete listing of variables to be held constant when trying to gauge the impact on the relation between the dependent and independent variable. Mauro for example adds measures for corruption and Knack and Keefer likewise add proxies for trust to standard Barro-type regressions26. There is no means to compare the merits of these two approaches and the relationship between these and other theories remains confusing. A causal relation between two variables (e.g. trade and growth) does not imply the falsity of another (e.g. democracy and growth). Levine and Renelt find “statistical relationships between long-run average growth rates and almost every particular policy indicator considered by the profession are fragile: small alterations in the ‘other’ explanatory variables overturn past results”27.
4.3. Growth and Hysteresis Effects
Hysteresis effects are likely to exist in the process of economic growth. Hysteresis implies that a temporary economic shock can have a permanent impact on future growth. The implication being that growth is not a linear process and regression analysis will have trouble capturing this effect. There may be virtuous and vicious circles at work in growth connected with threshold effects28. If a country has a critical initial mass of human and physical capital, growth will be virtuous, capital accumulation attracting yet more capital. Green revolution technology for example depends on the availability of both seeds and fertilisers through access to adequate credit (and hence collateral). Households with enough collateral can invest in the necessary skills and technology to get the virtuous circle going. The option is not open for poor households without collateral. With an exogenous shock there is the potential for hysteresis effects. A disaster can wipe out the liquid assets of a household and leave it in a poverty trap, unable thereafter to invest in green revolution technology. Potential poverty traps make households and an entire economy more vulnerable to shocks. If a country were near the critical mass level of capital, then a terms-of-trade shock that rendered part of that country’s capital stock useless might shift that country from strong growth to decline. The same shock may have little effect on a country far from the threshold. This implies there may be no primary causal factor but an interlocking circular process with feedback. Neat econometric models with fixed coefficients will by definition then be impossible to find.
4.5. Cross-Country Growth Regressions and Dynamics
Theories of cyclical and adjustment dynamics of output are not well developed within growth theories. Reliable data sets for many traditional growth determinants (inflation, government expenditure, tariffs, inequality etc) have typically run for twenty-five plus years. Averages over this sample length are too short for history and too long to model macroeconomic policy changes and short-run dynamics. In cross-sectional regression analysis it is not clear whether variables affect long-term growth, the steady-state, or both. Some growth effects are contemporaneous (macroeconomic and cyclical factors), some take several years (transitional dynamics due to changed investment incentives), others even decades (incentives effecting the rate of technical change). Some right-hand-side variables may have output/ growth effects at all three horizons - cyclical, transitional and steady-state. There is no reason to assume these are of the same magnitude or even the same sign29. What little ad hoc empirical work has been carried out finds regression parameters commonly are unstable over time. Knack and Keefer for example look at the relation between social characteristics and growth and find that social variables have different signs on growth before and after 198030. With such findings common attempted explanations are notable only by their absence.
5. Universalism: A Problem of Neo-classical Economics?
In order to run large cross-country regressions researchers are tightly constrained to the assumption of universalism. Conventional growth analysis assumes growth parameters are identical across countries. Far from being a positivist statistical exercise, cross-country growth regressions are bound to an underlying neo-classical assumption – that the growth process is universal. Each individual country in cross-section according to this view will provide evidence that can be used to elucidate the one underlying universal economic relation. An increase in openness for example is hypothesised to have the same effect on growth in all countries. There are a small number of exceptions that for example allow the constant term to differ across countries (controls for fixed effects) using panel data or on occasional a dummy variable is added for regions and notable events. The over-riding assumption behind cross-country growth regressions is a that of a universalist growth process.
Many studies explain Africa’s slower growth as a function of different levels of explanatory variables31. They seek to explain growth as the result of a common growth process beginning from different levels of the same explanatory variables. Significant regional dummies remain common in much of this literature, and especially so for Sub-Saharan Africa. The usual assumption is that significant dummy variables are capturing the influence of missing variables, which must then be unearthed. This has led researchers to propose ever more variables in the hope that the dummy variable will be rendered insignificant and growth will finally be ‘explained’32. The alternative route is to relax the assumption that only the levels of explanatory variables are different and explore the idea that the growth process in Africa works differently. There are a limited number of studies that suggest this latter notion may be true. The implication being that cross-country growth regressions are an intrinsically poor mechanism to analyse growth and each growth experience should be treated as potentially unique i.e. as a case study.
Block conducts a more flexible analysis and allows for the slope coefficients to differ and finds openness in Sub-Saharan Africa has a much stronger effect on growth and that growth is less responsive to fiscal policy than his sample average33. Brock and Durlauf find “the operation of ethnic heterogeneity on growth is different in Africa, not just the levels of ethnic heterogeneity. A comparison of other regressor coefficients for Africa with those of the rest of the world makes clear the growth observations for African countries should not be treated as partially exchangeable with the growth rates of the rest of the world.”34. Asiedu finds that FDI is less responsive to openness in Africa than in other regions. For a given level of trade openness, infrastructure and return on capital, Sub-Saharan Africa receives less FDI35. Mosley finds that inequality only has a negative impact on growth in regions other than sub-Saharan Africa36.
Cross-country growth regressions assume that economic growth operates according to universal laws across all economies through time and space. This is one of the key ideological foundations of neo-classical economics. There are only a few exceptions and the occasional dummy variable for regions and notable events. Discussion in this paper has demonstrated that there is evidence the growth process differs significantly between different regions and countries and over time. Finding fragility and heterogeneity of regression coefficients by region and country is only a beginning. Opening up the assumption of universalism to greater scrutiny leaves us asking why the growth process may differ. In doing so we would throw doubt on the neo-classical assumption of universalism and with it the ‘one-size-fits-all’ programmes of liberalisation emanating from the World Bank, IMF and other institutions. A case study approach to economic growth would be justified on the assumption that growth processes are not universal. The recent collection of country case studies together with an introduction drawing general lessons edited by Rodrik (2003) is a useful step in this direction37. Comparative and historically informed case studies allow researchers to question the assumption of universality rather than be forced to assume it true a priori. A heterodox or post-autistic economist should begin with case studies and only then proceed to cross-country growth regressions with all due caution.
1. World Bank ‘Adjustment in Africa: Reforms, Results and the Road Ahead’, (Oxford University Press, New York 1994).
2. P.M.Romer, ‘Increasing Returns and Long-Run Growth’, Journal of Political Economy, 94:5 (1986) p1003.
3. R.Levine and D.Renelt, ‘A Sensitivity of Cross-Country Growth Regressions’, Amercian Economic Review, 82:4 (1992), p942-963.
4. R.J.Barro, ‘Economic Growth in a Gross Section of Countries’, Quarterly Journal of Economics, 106, (1991), p407-43l.
5. Levine and Renelt (1992).
6. Barro (1991) and Levine and Renelt (1992:951).
7. J.Slemrod, ‘What do Cross-Country Studies Teach About Government Involvement, Prosperity and Growth’, Brookings Papers on Economic Activity, 2 (1995), p405.
8. D.Rodrik, ‘Why Do More Open Economies Have Bigger Governments?’, Journal of Political Economy, 106:5 (1998), p997-1032.
9. Levine and Renelt (1992).
10. J.Temple 1999:128) ‘The New Growth Evidence’, The Journal of Economic Literature, 38, (1999), p128.
11. J.B.De Long and L.H.Summers, ‘Equipment Investment and Economic Growth’, Quarterly Journal of Economics, 106, (1991), p445-502.
12. J-W.Lee, ‘Capital Goods Imports and Long-run Growth’, Journal of Development Economics, 48, (1995), p91-110.
13. M.Blomstrom, R.E.Lipsey and M.Zejan, ‘Is Fixed Investment the Key to Economic Growth’, Quarterly Journal of Economics, 111 (1996), p269-276.
14. L.Pritchett, ‘Where has all the Education Gone?’, World Bank (1999), p3.
15. M.Bils and P.J.Klenow, ‘Does Schooling Cause Growth?’, American Economic Review, 90:5 (2000), p1160-1183.
16. N.G.Mankiw, D.Romer and D.N.Weil, ‘A Contribution to the Empirics of Economic Growth’, Quarterly Journal of Economics, 107 (1992), p418-9.
17. C.I.Jones, C.I, ‘R+D Based Models of Economic Growth’, Journal of Political Economy, 103:4 (1995), p759-784.
18. Beginning with Barro (1991).
19. L.Pritchett, ‘Understanding Patterns of Economic Growth: Searching for Hills among Plateaus, Mountains, and Plains’, World Bank Economic Review, 14:2 (2000), p221-50.
20. Pritchett (2000).
21. W.Easterly and R.Levine (2001:195) ‘It’s Not Factor Accumulation: Stylised Facts and Growth Models’, World Bank Economic Review, 15:2 (2001) p195, see also Temple (1999:116).
22. Defined as a ten-year plus period in which the five-year moving average of annual GDP growth exceeds 3.5%, J-C.Berthelemy and L.Soderling, ‘The Role of Capital Accumulation, Adjustment and Structural Change for Economic Take-Off: Empirical Evidence from African Growth Episodes’, World Development, 29:2 (2001), p323-343.
23. T.Mkanawire, ‘Thinking about Developmental States in Africa’, Cambridge Journal of Economics, 25 (2001) p2989-313).
24. R.Hausmann, L.Pritchett and D.Rodrik, ‘Growth Accelerations’, JFK School of Govt, Harvard Universitry (2004).
25. Adding a premium for example when reforms were conducted in the correct sequence, deflation before devaluation, liberalisation of the current before capital account etc, P.Mosley, ‘Globalisation, Economic Policy and Convergence’, World Economy, 23:5 (2000) p613-634.
26. P.Mauro, ‘Corruption and Economic Growth’, Quarterly Journal of Economics, 110 (1995) p681-712). S.Knack and P.Keefer, ‘Does Social Capital Have an Economic Payoff: A Cross-Country Investigation’, Quarterly Journal of Economics, 112 (1997), p1251-1288.
27. Levine and Renelt (1992:943).
28. W.Easterly, ‘The Elusive Quest for Growth: Economists’ Adventures and Misadvantures in the Tropics’, (MIT Press, Cambridge, 2001), Chapter 10.
29. Temple (1999:124).
30. Knack and Keefer (1997).
31. W.Easterly and R.Levine, ‘Africa’s Growth Tragedy: Policies and Ethnic Divisions’, Quarterly Journal of Economics, 112 (1997), J.D.Sachs and A.M.Warner, ‘Fundamental Sources of Long-Run Growth’, American Economic Review, 87:2, (1997) p184-8 among many others.
32. A good example of an exhaustive attempt to remove the significant dummy variable is P.Collier and J.Gunning ‘Explaining African Economic Performance’, Journal of Economic Literature, 37:1 (1999), p64-111.
33. S.A.Block, ‘Does Africa Grow Differently?’, Journal of Development Economics, 65, (2001), p443-467.
34. W.A.Brock and S.N.Durlauf, ‘Growth Empirics and Reality: What Have We Learned from a Decade of Empirical Research on Growth?’, World Bank Economic Review 15:2, (2001), p264.
35. E.Asiedu, ‘On the Determinants of Foreign Direct Investment in Developing Countries: Is Africa Different?’, World Development, 30:1 (2002), p107-119.
36. Mosley (2000).
37. D.Rodrik, ‘In Search of Prosperity: Analytic Narratives on Economic Growth’, (Princeton University Press, Princeton, 2003).