Silviase/jgov_v1.4_full_ann huggingface dataset:
sample. The results are similar to the previous results to a great extent.

In summary, FDI has a positive estimated effect on quality improvement but this diminishes as the relative quality level a increases. Although the evidence on the effect of R&D is unclear, we find some evidence to support the hypothesis that the quality improvements that are due to R&D are larger than those that are due to FDI when the quality level is close to the frontier level.

### 4.3 Testing the Predictions of the Model

Proposition 2 of the model suggests that there are two types of country. Countries with large endowments of skilled labor and knowledge converge to a steady state in which the level of R&D is positive, whereas others converge to a steady state characterized by FDI without local R&D activities. Figure 3 presents broad empirical support for these results. In this figure, one segment shows changes in FDI inflows and R&D expenditures from the 1980s to the 1990s for each country, and one end of a segment with a country code denotes averages for the 1980s while the other without a code denotes averages for the 1990s. Countries are divided into two groups, one of which comprises countries with low TFP in 1980, while the other comprises those with high TFP.

Included in the top figure for countries with low initial TFP are countries such as Costa Rica (country code, CRI), Indonesia (IDN), Thailand (THA), Togo (TGO), and Trinidad and Tobago (TTO), in which FDI inflows increased while R&D expenditures remained low or declined. Moreover, the number of skilled workers, represented by the number of workers with a secondary-school degree, in countries such as Costa Rica and Togo is relatively small, as predicted by the theoretical model. $ ^{8} $ By contrast, as the bottom figure shows, R&D expenditures increased or were stable at a high level in most countries with high initial TFP, with exceptions such as Mexico (MEX) and Argentina (ARG).

Furthermore, we tested theoretical predictions by using formal regression analysis. $ ^{9} $ The difficulty is that the data do not clearly distinguish between the
two types of country. One approach is to classify countries by the growth rate of R&D expenditures. However, this exogenous division may not reflect the true difference between the countries. Therefore, this paper uses the switching-regression model with unobservable regime change developed by Quandt (1972) and introduced to the growth literature by Bloom et al. (2003). Specifically, the empirical model is constituted by the following three equations.

$$ z^{*}=\eta_{0}+\eta_{1}\ln a_{i,t-1}+\eta_{2}\ln H_{i t}+\varepsilon_{c,i t}, $$

where $ z^{*} $ is a latent variable, and

$$ \Delta\ln R\&D_{it}=\gamma_{r0}+\gamma_{r1}\ln R\&D_{i,t-1}+\gamma_{r2}\ln H_{it}+\varepsilon_{r,it},\qquad if\quad z^{*}\geq0, $$

$$ \Delta\ln R\&D_{it}=\gamma_{m0}+\gamma_{m1}\ln R\&D_{i,t-1}+\gamma_{m2}\ln H_{it}+\varepsilon_{m,it},\qquad if\quad z^{*}<0. $$

Equation (22) indicates that countries are classified according to their endowments of skilled labor and knowledge. Equation (23) represents the dynamics of R&D expenditures for the type of country that converges to a positive-R&D steady state, while equation (24) represents the dynamics for the type of country that converges to the zero-R&D steady state. Assuming that the variance–covariance matrix for the disturbances of the empirical model is given by

$$ \Sigma=\left(\begin{array}{c c c}{\sigma_{r r}}&{\sigma_{r m}}&{\sigma_{r c}}\\ {\sigma_{r m}}&{\sigma_{m m}}&{\sigma_{m c}}\\ {\sigma_{r c}}&{\sigma_{m c}}&{1}\\ \end{array}\right), $$

we can estimate equations (22), (23), and (24) jointly by maximum likelihood (ML). $ ^{10} $

Although our approach is similar to that of Bloom et al. (2003), their empirical model is regression of levels of income per capita whereas ours is regression of the growth in R&D expenditures. This is because we assume that the ratio of R&D expenditures to GDP does not reach its steady state in many countries. In addition, although Bloom et al. (2003) avoid using endogenous variables
as regressor, this paper uses the number of skilled workers and the TFP level, which are endogenous. However, using lagged regressors should alleviate the endogeneity problem.

The results of the model imply the following predictions for the coefficients of the equations. First, since countries converging to a positive-R&D steady state should have sufficiently large endowments of skilled labor and knowledge according to Proposition 2, we expect $ \eta_{1}>0 $ and $ \eta_{2}>0 $ . Second, the standard convergence argument suggests $ \gamma_{r1}<0 $ and $ \gamma_{m1}<0 $ : a country well below its steady-state level grows quickly. Third, since Proposition 3 suggests that a larger endowment of skilled labor H leads to a larger level of R&D activities in the positive-R&D steady state, we expect $ \gamma_{r2}>0 $ . However, since H does not affect R&D activities in the zero-R&D steady state, we expect $ \gamma_{m2}=0 $ . Finally, equation (23) implies that the share of R&D expenditures in GDP in the positive-R&D steady state for country i is given by $ \exp\left(-\left(\gamma_{r0}+\gamma_{r2}\ln H_{i}\right)/\gamma_{r1}\right) $ , which is expected to be positive. However, its value in the zero-R&D steady state, given by $ \exp\left(-\left(\gamma_{m0}+\gamma_{m2}\ln H_{i}\right)/\gamma_{m1}\right) $ , is expected to be zero.

Column (1) in Table 2 reports the results from a simple ML estimation in which we assume no regime change, while columns (2), (3), and (4) indicate the results for equations (22), (23), and (24), respectively. The likelihood ratio statistic from the values of the likelihood functions of the two models is 64.3, rejecting the null hypothesis that the model without regime change is the true specification. $ ^{11} $

Column (2) shows that the coefficients of $ \ln a_{i,t-1} $ and $ \ln H_{it} $ in equation (22) are positive and significant, as the theory predicts. The estimated coefficients suggest that the probability of converging to the zero-R&D steady state is, for example, 0.98 for Togo, 0.45 for Thailand, and 0.15 for Korea.

In addition, column (4) indicates that the predictions relating to countries
that converge to the zero-R&D steady state are supported. The coefficient of $ \ln R\&D $ is negative and significant, while the coefficient of $ \ln H $ is insignificant. According to these results, in the zero-R&D steady state for a country with the average number of skilled workers (11.1 million), R&D expenditures are estimated to be 0.08 percent of GDP. A Wald statistic reported in the last row of Table 2 indicates that this point estimate is not significantly different from zero.

The results relating to countries that converge to the positive-R&D steady state presented in column (3) are not clearly consistent with the theoretical predictions. Although the coefficient of $ \ln R\&D $ is negative, it is not significantly different from zero. The coefficient of $ \ln H $ is negative and significant, rather than positive as predicted. According to the results, in the positive-R&D steady state, R&D expenditures for the average country are estimated to be 9.54 percent of GDP. The fact that this estimated R&D share is much greater than the corresponding share in the zero-R&D steady state supports the theoretical prediction of multiple equilibria. However, a Wald test cannot reject the hypothesis that the R&D share in the positive-R&D steady state is zero, probably because of the large standard error of the coefficient of $ \ln R\&D $ .

A possible reason for this partial inconsistency with the theoretical predictions may be the use of the absolute number of skilled workers as the key regressor in the estimation. Although this is justified by Propositions 2 and 3, one may derive another conclusion that the share of skilled workers in the total labor force, rather than the number, influences the equilibrium of the model. This is possible by assuming that the number of intermediate goods produced in each country is proportional to the size of its total labor force. $ ^{12} $

Thus, using the share of H in the total labor force, denoted by h, we replicate the estimations above. The results reported in columns (5)-(8) in Table 2 show evidence that is generally consistent with the model. First, the likelihood statistic from the ML regression without regime change and the switching re-
gression suggests that the latter is the true specification. The results from the regime equation shown in column (6) indicate that the initial TFP level is a significant factor for the classification of countries into two regimes, while the impact of the share of skilled labor is insignificant. The estimated probability of converging to the zero-R&D steady state is 0.65 for Togo, 0.41 for Thailand, and 0.19 for Korea. Second, in contrast to the result in column (3), the effect of skilled labor in equation (23) is positive, although it is significantly different from zero only at the 10 percent level. Third, the share of R&D expenditures in GDP in the positive-R&D steady state implied from the estimation for the average country is 2.79 percent. The p value from a Wald test is 5.3 percent, suggesting that this is different from zero at a reasonable significance level. The magnitude of the estimate is plausible, since the share of R&D expenditures in the U.S. has been stable around 2.5-3 percent. The share of R&D expenditures in the zero-R&D steady state is smaller and 0.17 percent, and its p value is 9.2 percent. The estimated shares in the positive- and zero-R&D steady states are equal to each other with a p value of 7.3 percent.

In conclusion, although the empirical tests do not provide full support for the theoretical predictions, they do provide some support. Countries with large endowments of knowledge and skilled labor have a large probability of converging to a steady state in which R&D expenditures are positive, whereas countries with small endowments tend to converge to a steady state in which there are no R&D expenditures.

## 5 Conclusions

This paper has developed an endogenous growth model that incorporates quality improvement, and focuses on follower countries behind the world technology frontier that adopt new technology with adaptation to local circumstances. There are two forms of adaptation, minor and inventive, which constitute adaptation with and without local R&D activities, respectively. In practice, the adoption of new technology with minor adaptation is often undertaken by MNEs.
that do not engage in R&D activities in the host country, while inventive adaptation involves R&D activities by either MNEs or local firms that have licensing agreements with foreign firms. We assume that adoption without local R&D does not require local knowledge, whereas the degree of technology adopted through R&D is positively correlated with the level of local knowledge. Furthermore, we assume that technology adoption with local R&D expands future local knowledge, while adoption with R&D does not.

Our main finding is that countries with large endowments of local knowledge and skilled labor depend on adoption without local R&D activities initially but then increasingly depend on local R&D. When endowments of local knowledge and skilled labor are sufficiently small, dependence on adoption without R&D persists, and countries converge to a steady state in which there are no local R&D activities. The reason for the multiple dynamic processes is that there is a 'biased' positive dynamic externality in local R&D in the sense that current R&D improves the productivity of future R&D, whereas there is no externality in technology adoption without R&D. Moreover, because of this externality, subsidizing R&D is likely to increase output per capita in the long run.

Empirical evidence from country-level panel data was used to test the main assumptions and predictions of the theoretical model. In particular, we use a switching-regression approach with unobservable regime change to test for multiple steady-state equilibria in R&D expenditures. The empirical results generally support the theoretical predictions, although some results are not statistically significant or consistent with the theory.
## References

Acemoglu, Daron, Philippe Aghion, and Fabrizio Zilibotti, “Distance to Frontier, Selection, and Economic Growth,” NBER Working Paper, 2002, 9066.

Aghion, Philippe and Peter Howitt, Endogenous Growth Theory, Cambridge: The MIT Press, 1998.

— and _, “Growth with Quality-Improving Innovations: An Integrated Framework,” in Philippe Aghion and Steven N. Durlauf, eds., Handbook of Economic Growth, Amsterdam: North Holland, forthcoming.

Barro, Robert J. and Jong-Wha Lee, “International Data on Educational Attainment: Updates and Implications,” Oxford Economic Papers, 2001, 3, 541–563.

— and Xavier Sala-i-Martin, “Technological Diffusion, Convergence, and Growth,” Journal of Economic Growth, 1997, 2, 1–26.

Bloom, David E., David Canning, and Jaypee Sevilla, “Technological Diffusion, Conditional Convergence, and Economic Growth,” NBER Working Paper, 2002, 8713.

—, —, and —, “Geography and Poverty Traps,” Journal of Economic Growth, 2003, 8, 355–378.

Borensztein, Eduardo, Jose De Gregorio, and Jong-Wha Lee, “How does Foreign Direct Investment Affect Economic Growth?,” Journal of International Economics, 1998, 45, 115–135.

Branstetter, Lee, “Are Knowledge Spillovers International or Intranational in Scope? Microeconometric Evidence from the U.S. and Japan,” Journal of International Economics, 2001, 53 (1), 53–79.

Conley, Timothy G. and Ethan Ligon, “Economic Distance and Cross-Country Spillovers,” Journal of Economic Growth, 2002, 7, 157–187.
Dickens, William T. and Kevin Lang, “A Test of Dual Labor Market Theory,” American Economic Review, 1985, 75 (4), 792–805.

Dinopoulos, Elias and Peter Thompson, “Schumpeterian Growth without Scale Effects,” Journal of Economic Growth, 1998, 3, 313–335.

Eaton, Jonathan and Samuel Kortum, “International Technology Diffusion: Theory and Measurement,” International Economic Review, 1995, 40(3), 537–570.

Ethier, Wilfred J. and James R. Markusen, “Multinational firms, technology diffusion and trade,” Journal of International Economics, 1996, 41, 1–28.

Evenson, Robert E. and Larry E. Westpal, “Technological Change and Technology Strategy,” in Jere R Behrman and T.N. Srinivasan, eds., Handbook of Development Economics, Vol. 3A, Amsterdam: North Holland, 1995.

Fiaschi, Davide and Andrea Mario Lavezzi, “Distribution Dynamics and Nonlinear Growth,” Journal of Economic Growth, 2003, 8, 379–401.

Findlay, Ronald, “Relative Backwardness, Direct Foreign Investment, and the Transfer of Technology: A Simple Dynamic Model,” Quarterly Journal of Economics, 1978, 92, 1–16.

Fosfuri, Andrea, Massimo Motta, and Thomas Ronde, “Foreign Direct Investment and spillovers through workers’ mobility,” Journal of International Economics, 2001, 53, 205–222.

Glass, Amy J. and Kamal Saggi, “Foreign Direct Investment and the Nature of R&D,” Canadian Journal of Economics, 1999, 32(1), 92–117.

— and _, “Licensing versus Direct Investment: Implications for Economic Growth,” Journal of International Economics, 2002, 56 (1), 131–153.
Griffith, Rachel, Stephen Redding, and John Van Reenen, “Mapping the Two Faces of R&D: Productivity Growth in a Panel of OECD Countries,” Centre for Economic Policy Research Discussion Paper, 2000, 2457.

Grossman, Gene M. and Elhanan Helpman, Innovation and Growth in the Global Economy, Cambridge: The MIT Press, 1991.

Guellec, Dominique and Bruno V. P. de la Potteire, "R&D and Productivity Growth: Panel Data Analysis of 16 OECD Countries," 2001.

Hall, Robert E. and Charles I. Jones, “Why Do Some Countries Produce So Much More Output per Worker than Others?,” Quarterly Journal of Economics, 1999, 114, 83–116.

Haskel, Jonathan E., Sonia C. Pereira, and Matthew J. Slaughter, "Does Inward Foreign Direct Investment Boost the Productivity of Domestic Firms?," NBER Working Paper, 2002, 8724.

Ito, Takatoshi and Anne O. Krueger, The Role of Foreign Direct Investment in East Asian Economic Development, Chicago: The University of Chicago Press, 2000.

Jones, Charles I., “Growth: With or Without Scale Effect,” American Economic Review Papers and Proceedings, 1998, 89, 139–144.

Keller, Wolfgang, “International Technology Diffusion,” NBER Working Paper, 2001, 8573.

– , “Geographic Localization of International Technology Diffusion,” American Economic Review, 2002, 92 (1), 120–142.

Kim, Hyung-Ki and Jun Ma, “The Role of Government in Acquiring Technological Capability: The Case of the Petrochemical Industry in East Asia,” in Masahiko Aoki, Hyung-Ki Kim, and Masahiro Okuno-Fujiwara, eds., The Role of Government in East Asian Economic Development, Oxford: Oxford University Press, 1997.
Lall, Sanjaya, “Technological Change and Industrialization in the Asian Newly Industrializing Economies: Achievements and Challenges,” in Linsu Kim and Richard R. Nelson, eds., Technology, Learning, & Innovation: Experiences of Newly Industrializing Economies, Cambridge: Cambridge University Press, 2000.

Peretto, Pietro F., “Technological Change and Population Growth,” Journal of Economic Growth, 1998, 3, 283–311.

Psacharopoulos, George, “Returns to Investment in Education: A Global Update,” World Development, 1994, 22 (9), 1325–43.

Quah, Danny, “Empirical Cross-Section Dynamics in Economic Growth,” European Economic Review, 1993, 37, 426–434.

Quandt, Richard E., “A New Approach to Estimating Switching Regressions,” Journal of the American Statistical Association, 1972, 67 (338), 306–310.

Rodriguez-Clare, Andres, “Multinationals, Linkages, and Economic Development,” American Economic Review, 1996, 86(4), 852–873.

Saggi, Kamal, “Foreign Direct Investment, Licensing, and Incentives for Innovation,” Review of International Economics, 1999, 7 (4), 699–714.

_, “Trade, Foreign Direct Investment, and International Technology Transfer: A Survey,” World Bank Research Observer, 2002, 17 (2), 191–235.

Sjöholm, Fredrik, “Productivity Growth in Indonesia: The Role of Regional Characteristics and Direct Foreign Investment,” Economic Development and Cultural Change, 1999, 47(3), 559–584.

Summers, Robert and Alan Heston, “The Penn World Table (Mark 5): An Expanded Set of International Comparisons: 1950-88,” Quarterly Journal of Economics, 1991, 106, 327–368.
Todo, Yasuyuki and Koji Miyamoto, “Knowledge Diffusion from Multinational Enterprises: The Role of Domestic and Foreign Knowledge-Enhancing Activities,” Technical Paper, OECD Development Centre, 2002, 196.

UNCTAD, World Investment Report, New York: United Nations, 2003.

UNESCO, Statistical Yearbook, Paris: United Nations, 1999.

Walz, Uwe, “Innovation, Foreign Direct Investment and Growth,” Economica, 1997, 64, 63–79.

Wang, Jian-Ye and Magnus Blomström, “Foreign Direct Investment and Technology Transfer: A Simple Model,” European Economic Review, 1992, 36, 137–155.

Wooldridge, Jeffrey M., Econometric Analysis of Cross Section and Panel Data, Cambridge: The MIT Press, 2002.

World Bank, World Development Indicators, Washington, D.C.: World Bank, 2003.

Young, Alwyn, “Growth without Scale Effects,” Journal of Political Economy, 1998, 106(1), 41–63.

Yusuf, Shahid, Innovative East Asia: The Future of Growth, Oxford: Oxford University Press, 2003.
Table 1: Testing the Assumptions of the Model Dependent variable: $ \Delta $ (growth rate of TFP)

Sample	All countries	Countries with low initial TFP
(1)	(2)	(3)	(4)
OLS	Efficient GMM	OLS	Efficient GMM
Δa	-0.1111	-0.1180	-0.1199	-0.1217
(0.0179)**	(0.0219)**	(0.0260)**	(0.0201)**
ΔlnFDI	0.0149	0.0329	0.0146	0.0279
(0.0039)**	(0.0137)*	(0.0065)*	(0.0081)**
Δ(lnFDI×a)	-0.0163	-0.0404	-0.0190	-0.0378
(0.0065)*	(0.0178)*	(0.0106)	(0.0094)**
ΔlnR&D	0.0055	0.0082	0.0027	-0.0011
(0.0049)	(0.0108)	(0.0074)	(0.0069)
Δ(lnR&D×a)	-0.0032	-0.0070	0.0049	0.0117
(0.0069)	(0.0138)	(0.0115)	(0.0086)
Constant	-0.0072	-0.0056	-0.0064	-0.0088
(0.0036)	(0.0042)	(0.0064)	(0.0064)
Observations	41	41	23	23
R-squared	0.673	0.498	0.689	0.615
Hansen's J	0.006	0.026

Note: The dependent variable and all the regressors are first differenced. Standard errors are in parentheses. $ * $ denotes statistical significance at the 5% level, while $ \*\* $ denotes statistical significance at the 1% level.
Table 2: Testing the Predictions of the Model

(1)	(2)	(3)	(4)
ML regression without regime change	Switching regression
Regime	Positive-R&D steady state	Zero-R&D steady state
a	3.076
(1.272)**
$ \ln H $	0.319	0.395	-0.787	-1.718
(0.654)	(0.172)**	(0.255)**	(1.883)
$ \ln R\&D_{0} $	-2.400	-0.615	-11.091
(1.017)**	(0.551)	(1.914)**
Constant	-2.583	-4.720	8.722	-12.419
(5.3389)	(2.011)**	(2.166)**	(10.854)
Observations	48	48
Log likelihood	-164.342	-132.177
Implied S.S. R&D	9.54	0.08
Wald statistic	0.21	1.93

(5)	(6)	(7)	(8)
ML regression without regime change	Switching regression
Regime	Positive-R&D steady state	Zero-R&D steady state
a	2.028
(0.979)**
$ \ln h $	7.016	0.190	2.034	4.279
(1.495)**	(0.472)	(1.217) $ ^{+} $	(2.440) $ ^{+} $
$ \ln R\&D_{0} $	-3.736	-2.120	-11.868
(0.875)**	(0.870)**	(1.432)**
Constant	-25.975	-1.295	-5.427	-36.798
(5.612)**	(1.390)	(4.663)	(5.442)**
Observations	48	48
Log likelihood	-155.396	-135.615
Implied S.S. R&D	2.79	0.17
Wald statistic	3.72	2.83

Note: Standard errors are in parentheses. $ ^{\dagger} $ , $ ^{*} $ , and $ ^{**} $ denote statistical significance at the 10, 5, and 1 percent level, respectively.
Case (ii): $\phi + \rho > 1$ and $H > \theta$

Case (iii): $\phi + \rho > 1$ and $H < \theta$
Case (i): $ \phi + \rho < 1 $

Case (ii): $\phi + \rho > 1$ and $H > \theta$

Case (iiia): $ \phi + \rho > 1 $ and $ (1 - \tau)^{\frac{1 - \phi}{\rho - (1 - \phi) \mu}} \theta < H < \theta $

Case (iiib): $\phi + \rho > 1$ and $H < (1 - \tau)^{\overline{\rho - (1 - \phi)\mu}} \theta$
Figure 3: Changes in FDI inflows and R&D Expenditures from the 1980s to the 1990s
Appendix Table: Cross-Country Data

Country	Code	a1980	a1990	FDI1980	FDI1990	R&D1980	R&D1990	H1980	H1990	h1980	h1990
Argentina	ARG	1.03	0.54	0.65	2.59	0.45	0.40	2,717	4,259	26.5	37.3
Australia	AUS	1.09	0.99	1.71	1.75	1.21	1.59	4,634	5,781	68.7	71.0
Austria	AUT	1.16	1.10	0.31	1.06	1.27	1.59	2,196	2,433	65.1	66.3
Belgium	BEL	1.28	1.14	1.77	9.30	1.67	1.74	1,583	1,908	40.2	45.9
Brazil	BRA	1.12	0.75	0.65	1.51	0.50	0.81	5,285	8,828	11.9	16.3
Canada	CAN	1.05	1.00	0.99	1.76	1.41	1.66	8,950	10,557	74.2	76.3
Chile	CHL	0.60	0.52	1.85	5.11	0.40	0.66	1,287	2,215	34.1	46.2
China	CHN	0.19	0.21	0.52	4.11	0.67	0.69	122,318	249,920	22.7	36.4
Colombia	COL	0.66	0.57	1.30	2.14	0.12	0.37	1,878	2,968	21.3	26.0
Costa Rica	CRI	0.73	0.53	1.78	3.13	0.19	0.20	146	282	18.7	27.0
Cyprus	CYP	0.84	0.91	2.48	2.52	0.10	0.20	101	162	35.4	51.2
Denmark	DNK	1.05	0.90	0.25	2.57	1.35	1.90	1,645	1,989	60.8	69.5
Egypt, Arab Rep.	EGY	0.45	0.45	2.66	1.38	0.20	0.21	1,501	3,450	13.5	23.7
El Salvador	SLV	0.51	0.37	0.33	1.28	1.54	2.20	182	257	12.0	16.1
Finland	FIN	1.10	1.06	0.26	1.19	1.73	2.50	1,162	1,599	48.8	64.8
France	FRA	1.30	1.17	0.50	1.62	2.20	2.34	10,413	12,837	43.0	48.3
Greece	GRC	1.28	0.89	1.14	0.87	0.29	0.50	1,002	1,452	27.3	37.4
Iceland	ISL	1.34	1.06	0.46	0.61	0.78	1.58	41	62	35.0	45.8
India	IND	0.25	0.27	0.04	0.40	0.75	0.83	42,848	60,411	16.2	18.2
Indonesia	IDN	0.37	0.43	0.37	1.06	0.31	0.07	5,743	13,376	10.4	19.1
Ireland	IRL	0.89	0.92	0.65	4.92	0.86	1.28	600	788	47.7	58.3
Israel	ISR	1.11	0.95	0.39	1.21	2.97	2.65	863	1,084	59.7	59.5
Italy	ITA	1.55	1.35	0.30	0.35	1.05	1.12	7,226	9,315	33.3	40.5
Japan	JPN	1.02	1.05	0.02	0.06	2.62	2.90	39,228	51,414	54.3	65.7
Korea, Rep.	KOR	0.67	0.87	0.27	0.67	1.30	2.39	6,747	12,016	45.8	67.3
Malaysia	MYS	0.71	0.70	3.18	6.64	0.10	0.32	1,137	2,226	21.3	29.9
Mauritius	MUS	0.52	0.58	0.61	0.81	0.36	0.28	127	200	24.2	33.0

Note: See text for the definitions of the variables.