The New Growth Evidence

1. The New Growth Evidence by Jonathan Temple Journal of Economic Literature March 1999

2. The Outline • The evolution of the growth literature • Six important empirical questions • Methodology and Complications • The Evidence • Conclusion

3. The Literature • Patterns of economic growth and development important question but ignored until recently. Why? • No reliable cross-country data; • Technology important but no adequate theory that would explain its evolution. • Last two decades progress on both fronts.

4. Six Empirical Questions • The world income distribution. • Is there convergence as Solow model predicts? • Are there diminishing returns to K and H? How fast do they kick in? • Factor accumulation vs. Technological Progress? • Explaining the long Run differences in growth. • The Long Run equilibrium? How will the WID look like in 2155?

5. Empirical Methodology • Historical case studies • Cross-country growth regressions growthi = constant + b*Xi + ei • Growth accounting Dy/y = DA/A + aDK/K + b)DH/H +(1-a-b)DL/L

6. Issues & Complexities • PPP-adjusted GDPs • Prices vary across countries  a $ does not afford same standard of living everywhere. • Data problems (availability, accuracy and measurement) • Outliers • Visual inspection and elimination • Cook’s test • Model uncertainty • Robustness checks

7. Issues & Complexities (cont.) • Endogeneity • Y  X (solution: lag X) • But, if Z  X and Z  Y, then lagging won’t help • Instrumental Variables (IV): find some other Z that is related to X but not to ei. Not always easy. • Measurement error • In general, may create attenuation bias. • Not always, though…

8. What Have We Learned? • Table 1: confirms the wide disparities in the world income distribution (WID). • There does not seem to be “convergence” as Solow model predicts. Figure 1. • What about conditional convergence?

9. The Evidence (cont.) • Conditional convergence • Most studies find that y converges to its steady state at the rate of 2 % per year. This is consistent with Decreasing Marginal Product (of K and H). • If there is conditional convergence, what does it say about factor accumulation vs. productivity (or the role technical change)?

10. The Evidence (cont.) • Mankiw, Romer, and Weil (1992) show that 80 % of the variation in y across countries can be accounted for with differences in ik ih and n. • But their approach has its problems. • Klenow and Rodriguez-Clare (1999) look at primary enrollment rates and find that it is more like 50 %. • Some argue that Japan and East-Asian NICs cannot be solely explained by factor accumulation. Hsieh (2002).

11. The Evidence (cont.) • The World Technology Frontier • Theory predicts that technologies should be transferable across countries. If so, what should income per capita trends look like? • Indeed, Evans (1996) finds that the growth of income (among rich countries) stays within some bounds.

12. The Evidence (cont.) • What drives this convergence? • If A’s vary across the countries, then DMPK and DMPH cannot be driving it alone; it also has the be A transfers across countries. • Ventura (1997) has shown that if countries trade with one another, then the DMPK property applies at the global level; if must be A transfers that drive convergence.

13. The Evidence (cont.) • Inputs and Growth • K accumulation and Growth: • Some supporting evidence but remember theory says effect should be temporary. • Investment is endogenous to the environment; Y I or Z Y and Z  I. Thus, results may be biased. • H accumulation and Growth: • MRW find that Y = AK1/3H1/3L1/3 is about right.

14. The Evidence (cont.) • Since then, other have raised questions about this large role. • Part of the problem: H is much broader than school enrollment; it covers experience, training, health, LE, etc. Another problem: • Proxies for H: years of schooling (Y  h) enrollment rates (stock vs. flow?) • Micro studies could be helpful here. How? Mincerian regressions

15. The Evidence (cont.) • R&D and Growth: • Why is R&D important? R&D  DA/A > 0. • Microeconomic studies support that the returns to R&D are significant at the firm level. • At the macro level, there are spillover effects (i.e. externalities +/-). Need to account for them.

16. The Evidence (cont.) • R&D productivity has fallen in the OECD in the post-WWII era despite improvements in I, H, openness to trade, etc. (Jones, 1995a, 1995b). • Wider Influences on Growth • Population Growth: n  Dy/y ? • A slight negative effect is the consensus, but should we worry about Dy/y  n ? Not so much. It would be a bigger issue with n  y . • Where does this negative effect come from? High n seems to lower k, h and labor productivity. The last is somewhat of a puzzle, but Hanushek (1992) shows that high n  lower student achievement.

17. The Evidence (cont.) • Sometime people test fertility  Dy/y ; with that Dy/y  fertility a bigger concern? Why? • Trade and Growth: • Many of the typical problems: outliers, endogeneity, specification errors, etc. • How to quantify trade friendliness of the economy is another problem. • Two standard measures are OPENNESS and BLACK MARKET PREMIA. • Sachs and Werner (1995), updated by Wacziarg and Horn-Welsh (2004), create dummies for trade liberalization; find strong evidence for the role of trade lib. On growth.

18. The Evidence (cont.) • Causality a big deal here; Rodrik (1998). So it may pay to look at natural experiments; Ben-David (1994) EU integration. • Finance and Growth: • Early growth theories ignored it; assumed financial development would emerge in a growing economy almost as a “side show.” Recent evidence rejects this (non)treatment; Levine (many papers). Of course, gotta deal with reverse causality properly.

19. The Evidence (cont.) • Government and Growth: • A charged issue but evidence mixed at best. • Hall and Jones (1997) do show that higher G  lower Y. But this is not about growth; it is about level of Y. Endogeneity is an important issue in these tests but in Hall-Jones spec, this biases results to understate the negative effect of G on Y. • Infrastructure and Growth: • Telephone networks and electricity capacity seem to boost growth. Easterly and Rebelo (1993) show that the gov’t share in I in public transport and communication also help.

20. The Evidence (cont.) • Inequality and Growth: • Evidence suggests a robust negative link; one exception is Forbes (2000) but she studies medium-run growth. • But why the negative link? • Traditional argument was Political Economy, Persson and Tabellini (1994). • Easy to test: does inequality raise taxation? • Evidence not supportive. • Other channels: fertility, education, politico-social stability.

21. The Evidence (cont.) • Polity and Growth: • Difficulties in measurement. • Some focus on democracy and growth; evidence is weak but Barro (1997) suggests an invereted-U relationship. Why? • Growth and Welfare: • Not clear; Easterly (1997) suggests the effect on welfare is mixed. Dollar and Kraay (2003) suggest growth helps the poor.

22. Conclusion • Poor countries are not catching up to the rich ones. WID has become more polarized. • Countries converge to their own steady states but with lots of uncertainty. This suggests that both DMPK/DMPH and differences in technology adoption play a role in convergence.

23. Conclusion • Solow model factors help to explain growth but many others not in the model also have been found to play roles (i.e. finance, inequality, R&D-driven technological change). • Big government? Openness to Trade? Democracy? Jury is still out…