The New Growth Evidence

by

Jonathan Temple

Journal of Economic Literature

March 1999

The Outline

• The evolution of the growth literature

• Six important empirical questions

• Methodology and Complications

• The Evidence

• Conclusion

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.

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?

Empirical Methodology

• Historical case studies

• Cross-country growth regressions

growthi = constant + Xi + i

• Growth accounting y/y = A/A + K/K + /H +(1-L/L

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

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 i. Not always easy.

• Measurement error– In general, may create attenuation bias.

– Not always, though…

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?

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)?

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).

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.

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.

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.

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

The Evidence (cont.)

– R&D and Growth:• Why is R&D important? R&D A/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.

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 y/y ?

• A slight negative effect is the consensus, but should we worry about y/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.

The Evidence (cont.)• Sometime people test fertility y/y ; with

that y/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.

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.

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.

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.

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.

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.

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…