Abstract
“Middle-income trap” is a relatively new term in economics that has, however, drawn tremendous attention in the economic development and growth field. Han, Wei (Re-examining the middle-income trap hypothesis (MITH) what to reject and what to revive? NBER Working Paper Series 23126, National Bureau of Economic Research, Mass, Cambridge (2017)) emphasize the importance of the issue by introducing a paper written by Gill, Kharas (The middle-income trap turns ten, 7403, World Bank, Washington, D.C. (2015), the inventors of this term. Using a search of Google Scholar, Gill, Kharas (The middle-income trap turns ten, 7403, World Bank, Washington, D.C. (2015) identify more than 3,000 articles that include the term “middle-income trap”. Many of these studies have analyzed the middle-income trap issue to clarify the phenomenon and investigate the determinants. In this study, we focus on the issue more from the perspective of changes in the industrial structure during economic development and discuss early deindustrialization in middle-income countries as a key factor of this trap.
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Notes
- 1.
Gill and Kharas (2015), p. 6.
- 2.
For example, see (Chenery and Syrquin 1975).
- 3.
Todaro and Smith (2011), p. 121.
- 4.
Gill and Kharas (2015), p. 4.
- 5.
Ibid.
- 6.
Ibid.
- 7.
Ibid., p. 20.
- 8.
Eichengreen et al. (2013), p. 10.
- 9.
Ibid.
- 10.
Bulman et al. (2014), p. 20.
- 11.
Han and Wei (2017), p. 26.
- 12.
Dasgupta and Singh (2006), p. 1. Moreover, in this paper, we use “industry” and “manufacturing” synonymously in most cases. This is especially important in Sect. 1.4, where we employ sector level data of industry rather than of manufacturing. Manufacturing data are simply not available at the time this paper is written.
- 13.
Dasgupta and Singh (2006), p. 3.
- 14.
Felipe et al. (2014), p. 22.
- 15.
Ibid., p. 5.
- 16.
Ibid., p. 2.
- 17.
The World Bank data for each income level are only available since the 1990s (World Bank 2017).
- 18.
Our sample countries in this analysis are Australia, Bangladesh, China, Hong Kong, India, Indonesia, Japan, South Korea, Malaysia, New Zealand, Pakistan, the Philippines, Singapore, Sri Lanka and Thailand (15 countries).
- 19.
Dasgupta and Singh (2006), p. 5.
- 20.
It should be noted, however, that in the regressions for GDP, Appendices 1.1 to 1.4 show the year dummy variable as statistically significant only for the 1960s, but not for other years. Moreover, the dummy variable for the 2000s in the regressions for employment is not statistically significant at the 10% level.
References
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Dasgupta, S., and A. Singh. 2006. Manufacturing, services and premature deindustrialization in developing countries. United Nations University, World Institute for Development Economics Research (UNU-WIDER), Research Paper No. 2006/49, May.
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Acknowledgements
We gratefully acknowledge the financial support of the Japan Society for the Promotion of Science (JSPS) “Grants-in-Aid for Scientific Research” on this research (Kakenhi (c): No.15K03480). This is a modified version of “The effect of premature deindustrialization on labor productivity and economic growth in Asia,” which was initially presented at the 74th Conference of the Japan Society of International Economics (JSIE), Senshu University, November 8, 2015. This is moreover the second version of the paper presented at the JAAE Spring Session, Kurume University, June 17–18, 2017.
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Appendices
Appendix 1.1: Regression Result 1 (Dependent Variable: LYIND)
All Countries | East Asia | South Asia | ||||
|---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 1 | Model 2 | Model 1 | Model 2 | |
Constant | -2.255 | -0.452 | -0.628 | 0.213 | -5.698 | -4.944 |
***(0.337) | (0.297) | **(0.282) | (0.357) | ***(1.396) | ***(1.493) | |
LGDPH | 1.454 | 1.081 | 1.114 | 0.916 | 2.503 | 2.311 |
***(0.088) | ***(0.082) | ***(0.080) | ***(0.087) | ***(0.437) | ***(0.466) | |
SLGDPH | -0.089 | -0.070 | -0.072 | -0.061 | -0.172 | -0.160 |
***(0.006) | ***(0.005) | ***(0.005) | ***(0.006) | ***(0.034) | ***(0.036) | |
Year60s | -0.155 | -0.217 | -0.079 | |||
***(0.054) | ***(0.083) | (0.049) | ||||
Year70s | -0.055 | -0.037 | -0.061 | |||
(0.050) | (0.080) | (0.044) | ||||
Year80s | -0.008 | 0.022 | -0.052 | |||
(0.045) | (0.075) | (0.037) | ||||
Year90s | 0.047 | 0.100 | -0.016 | |||
(0.045) | (0.075) | (0.034) | ||||
Year00s | -0.002 | 0.005 | -0.015 | |||
(0.053) | (0.088) | (0.032) | ||||
RegionEA | 0.012 | |||||
(0.042) | ||||||
RegionSA | -0.366 | |||||
***(0.045) | ||||||
Obs | 687 | 687 | 395 | 395 | 216 | 216 |
F-test | 145.49 | 158.66 | 140.34 | 78.88 | 166.37 | 53.79 |
Prob > F | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
R\(^{2}\) | 0.394 | 0.559 | 0.314 | 0.353 | 0.605 | 0.612 |
Appendix 1.2: Regression Result 2 (Dependent Variable: LYIND)
All Countries | East Asia | South Asia | ||||
|---|---|---|---|---|---|---|
Model 3 | Model 4 | Model 3 | Model 4 | Model 3 | Model 4 | |
Constant | -0.732 | -14.636 | -1.200 | -47.774 | 15.214 | -12.528 |
(0.989) | ***(2.144) | (2.158) | ***(4.269) | ***(1.585) | ***(3.292) | |
LGDPH | 1.472 | 1.874 | 1.755 | 2.070 | 1.674 | 1.593 |
***(0.110) | ***(0.191) | ***(0.162) | ***(0.205) | ***(0.296) | ***(0.316) | |
SLGDPH | -0.092 | -0.117 | -0.107 | -0.122 | -0.124 | -0.112 |
***(0.007) | ***(0.012) | ***(0.010) | ***(0.012) | ***(0.023) | ***(0.025) | |
LPOP | -0.312 | 1.162 | -0.310 | 4.259 | -1.710 | 1.043 |
***(0.086) | ***(0.194) | (0.190) | ***(0.416) | ***(0.159) | ***(0.336) | |
SLPOP | 0.009 | -0.029 | 0.011 | -0.108 | 0.043 | -0.029 |
***(0.002) | **(0.005) | **(0.004) | ***(0.011) | ***(0.004) | ***(0.009) | |
LOPEN | -0.073 | -0.065 | 0.012 | 0.234 | -0.344 | -0.234 |
(0.048) | (0.049) | (0.074) | ***(0.051) | ***(0.029) | ***(0.037) | |
LGFCF | -0.021 | -0.081 | -0.141 | -0.078 | 0.236 | 0.194 |
(0.055) | *(0.048) | **(0.061) | (0.079) | ***(0.043) | ***(0.055) | |
LSECEDU | 0.111 | 0.227 | 0.246 | |||
**(0.046) | ***(0.083) | ***(0.048) | ||||
LTEREDU | -0.055 | -0.192 | 0.036 | |||
**(0.025) | ***(0.043) | (0.034) | ||||
Year60s | -0.113 | 0.076 | -0.494 | |||
*(0.068) | (0.140) | ***(0.053) | ||||
Year70s | -0.087 | -0.088 | 0.083 | 0.191 | -0.410 | -0.004 |
(0.061) | (0.072) | (0.123) | *(0.103) | ***(0.047) | (0.071) | |
Year80s | -0.015 | 0.035 | 0.145 | 0.245 | -0.372 | -0.020 |
(0.055) | (0.064) | (0.104) | ***(0.081) | ***(0.041) | (0.054) | |
Year90s | 0.040 | 0.035 | 0.173 | 0.161 | -0.224 | 0.002 |
(0.046) | (0.052) | **(0.082) | **(0.069) | ***(0.037) | (0.046) | |
Year00s | 0.001 | 0.007 | 0.023 | 0.056 | -0.083 | 0.011 |
(0.051) | (0.047) | (0.085) | (0.055) | ***(0.028) | (0.027) | |
RegionEA | 0.058 | -0.428 | ||||
(0.037) | ***(0.085) | |||||
RegionSA | -0.283 | -0.755 | ||||
***(0.037) | ***(0.084) | |||||
Obs | 605 | 389 | 357 | 250 | 172 | 84 |
F-test | 251.08 | 133.30 | 47.29 | 62.91 | 166.98 | 118.36 |
Prob > F | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
R\(^{2}\) | 0.577 | 0.682 | 0.393 | 0.734 | 0.869 | 0.941 |
Appendix 1.3: Regression Result 3 (Dependent Variable: LEMPIND)
All Countries | East Asia | South Asia | ||||
|---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 1 | Model 2 | Model 1 | Model 2 | |
Constant | -0.850 | -2.765 | -1.286 | -2.060 | -10.121 | -11.895 |
*(0.512) | ***(0.803) | (0.994) | **(0.967) | ***(2.779) | ***(2.879) | |
LGDPH | 0.864 | 1.180 | 0.926 | 1.050 | 3.704 | 4.098 |
***(0.126) | ***(0.188) | ***(0.237) | ***(0.226) | ***(0.858) | ***(0.871) | |
SLGDPH | -0.045 | -0.061 | -0.046 | -0.053 | -0.259 | -0.280 |
***(0.008) | ***(0.011) | ***(0.014) | ***(0.013) | ***(0.066) | ***(0.066) | |
Year80s | 0.304 | 0.312 | 0.275 | |||
***(0.068) | ***(0.087) | ***(0.071) | ||||
Year90s | 0.235 | 0.273 | 0.094 | |||
***(0.066) | ***(0.084) | *(0.055) | ||||
Year00s | 0.109 | 0.107 | 0.081 | |||
(0.067) | (0.086) | (0.053) | ||||
RegionEA | 0.181 | |||||
***(0.027) | ||||||
RegionSA | 0.446 | |||||
***(0.051) | ||||||
Obs | 403 | 403 | 282 | 282 | 67 | 67 |
F-test | 104.90 | 70.69 | 53.42 | 69.25 | 41.86 | 24.66 |
Prob > F | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
R\(^{2}\) | 0.370 | 0.504 | 0.386 | 0.465 | 0.673 | 0.774 |
Appendix 1.4: Regression Result 4 (Dependent Variable: LEMPIND)
Notes for Appendices 1.1, 1.2, 1.3, and 1.4: the pooled OLS regressions.
LYIND: the share of industry sector output in GDP (%, log).
LEMPIND: the share of industry sector employment in total employment (%, log).
LGDPH, and SLGDPH: per capita GDP (constant international dollar, 2005 price, log), and its squared value, respectively.
LPOP, and SLPOP: population (log), and its squared value, respectively.
LOPEN: trade openness in GDP (%, log).
LGFCF: the share of gross fixed capital formation in GDP (%, log).
LSECEDU, LTEREDU: the secondary education enrollment ratio, and the tertiary education enrollment ratio, respectively (%, log).
Year60s, Year70s, Year80s, Year90s, and Year00s: the dummy variables for 1960s, 1970s, 1980s, 1990s, and 2000s, respectively.
RegionEA, and RegionSA: the regional dummy variables for East Asia, and South Asia, respectively.
Obs: the number of observations.
***, **, and *: statistical significance at 1, 5, 10%, respectively.
All Countries | East Asia | South Asia | ||||
|---|---|---|---|---|---|---|
Model 3 | Model 4 | Model 3 | Model 4 | Model 3 | Model 4 | |
Constant | 6.868 | -2.045 | 14.431 | -10.510 | -17.706 | -6.902 |
***(1.482) | ***(1.857) | ***(1.960) | *(5.363) | ***(4.511) | (7.691) | |
LGDPH | 1.667 | 2.015 | 1.914 | 1.807 | 3.666 | 3.733 |
***(0.185) | ***(0.232) | ***(0.271) | ***(0.325) | ***(1.127) | **(1.669) | |
SLGDPH | -0.089 | -0.107 | -0.106 | -0.096 | -0.240 | -0.258 |
***(0.011) | ***(0.013) | ***(0.016) | ***(0.019) | ***(0.087) | *(0.132) | |
LPOP | -1.262 | -0.361 | -2.018 | 0.447 | 0.685 | -0.344 |
***(0.122) | **(0.160) | ***(0.171) | (0.542) | (0.552) | (0.910) | |
SLPOP | 0.035 | 0.011 | 0.055 | -0.011 | -0.017 | 0.010 |
***(0.003) | **(0.004) | ***(0.004) | (0.014) | (0.014) | (0.022) | |
LOPEN | -0.169 | -0.192 | -0.281 | -0.125 | 0.145 | 0.229 |
***(0.031) | ***(0.036) | ***(0.042) | *(0.069) | (0.157) | *(0.111) | |
LGFCF | 0.044 | 0.024 | -0.059 | 0.210 | -0.162 | -0.078 |
(0.061) | (0.063) | (0.069) | **(0.096) | (0.128) | (0.172) | |
LSECEDU | 0.020 | 0.386 | -0.280 | |||
(0.072) | ***(0.120) | (0.183) | ||||
LTEREDU | -0.170 | -0.285 | 0.002 | |||
***(0.027) | ***(0.045) | (0.145) | ||||
Year80s | 0.203 | -0.019 | 0.010 | -0.056 | 0.407 | 0.008 |
***(0.066) | (0.072) | (0.080) | (0.103) | ***(0.119) | (0.152) | |
Year90s | 0.204 | 0.048 | 0.140 | 0.008 | 0.187 | -0.029 |
***(0.059) | (0.061) | **(0.067) | (0.075) | *(0.098) | (0.107) | |
Year00s | 0.125 | 0.056 | 0.071 | 0.059 | 0.125 | -0.095 |
**(0.055) | (0.052) | (0.063) | (0.057) | *(0.068) | (0.059) | |
RegionEA | 0.324 | 0.120 | ||||
***(0.033) | **(0.055) | |||||
RegionSA | 0.605 | 0.231 | ||||
***(0.044) | **(0.085) | |||||
Obs | 392 | 271 | 272 | 201 | 66 | 29 |
F-test | 161.71 | 128.32 | 121.75 | 163.18 | 16.93 | 17.15 |
Prob > F | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
R\(^{2}\) | 0.660 | 0.693 | 0.688 | 0.720 | 0.795 | 0.880 |
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Osaka, H. (2018). The Middle-Income Trap Reconsidered: The Case of Asia. In: Hosoe, M., Kim, I., Yabuta, M., Lee, W. (eds) Applied Analysis of Growth, Trade, and Public Policy. Springer, Singapore. https://doi.org/10.1007/978-981-13-1876-4_1
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