Poverty, emergence, boom and affluence: a new classification of economies

Abstract

The paper offers a new country classification system defined in relative terms and jointly based on the level and the medium–long term rate of growth of per capita income. The classification system identifies four categories of economies: poor (low income–low growth), emerging (low income–high growth), booming (high income–high growth) and affluent (high income–low growth). After classifying 122 countries in periods 1985–1999 and 2000–2014, the paper focuses on the comparison of poor and emerging economies and, in parallel, of emerging and high-income economies, and characterizes their transitions across categories. In line with the empirical literature on economic growth, the results of multinomial logit analysis suggest that higher growth rates of export and investment are the main factors distinguishing emerging from poor economies. Further, a better institutional setting, measured by various dimensions of economic freedom, plays an important role in driving the transition of low income countries from low to high growth. Moreover, along with a better technological advancement, it also represents the crucial attribute differentiating high-income from emerging economies.

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Fig. 1
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Notes

  1. 1.

    As Vaggi (2017, p. 64–65) notices “the original thresholds were meant to provide a comparative classification of countries and were supposedly considered as being relative thresholds […]. However, the annual adjustment of the thresholds is based only on “international inflation”, which leads to the fact that the thresholds are rather ‘sticky’; they become similar to absolute thresholds and provide a partial view of the changes in the global economic scenario.”

  2. 2.

    In the World Economic Outlook, the IMF classifies the world into two groups—advanced economies and emerging market and developing economies—according to per capita income level, export diversification and the degree of integration into the global financial system.

  3. 3.

    The average per capita income of country i (i = 1,…,n) over period T (T = 1,….,t) is calculated as \(\bar{y}_{i,T} = \frac{{\mathop \sum \nolimits_{T = 1}^{t} y_{i,T} }}{t}\), while the average annual rate of growth of country i’s per capita income is given by \(\bar{r}_{i.T} = \left( {\sqrt[{t - 1}]{{\frac{{y_{i,t} }}{{y_{i,1} }}}} - 1} \right) \times 100\).

  4. 4.

    An alternative way of determining the two thresholds is to weight countries’ per capita income by their share of world population in the computation of \(\bar{y}_{T}\), that corresponds to calculate the individual world average income and its rate of growth. In this case, the benchmark would not be the average economy but rather the average individual at a world level. Even if it is a common approach adopted by many studies about global income inequality and poverty (Deaglio 1994, 2004; Milanovic 2013), it does not match our focus, that is to evaluate each country’s economic conditions against an average condition. Another alternative way would be to select as a benchmark the median economy. However, Nielsen (2011) formally demonstrates that using the mean outcome as the threshold value dividing countries into two categories is the most appropriate choice, since it minimizes the error associated to treating all countries belonging to the same category as alike. Moreover, it is interesting to note that both the median economy and the average individual methods, applied to periods 1985–1999 and 2000–2014, get thresholds of per capita income close to the cut-off level used by the World Bank in order to divide high-income countries from low and middle-income countries, that has been highly criticized for undervaluing the number of low income countries (to this purpose, see Vaggi 2017).

  5. 5.

    The concept of emerging markets, introduced in 1981 by economists at the International Finance Corporation (WB Group), has been largely defined by global institutions as well as financial companies and is mainly based on countries’ financial structure.

  6. 6.

    In both periods, the percentage of countries clustered around the averages (with a deviation lower than 10%) was below one-tenth and only one country had a ‘light’ membership in terms of both income and rate of growth (Malaysia in 2000–2014).

  7. 7.

    Our calculations based on the Total Economy Database, Conference Board.

  8. 8.

    Estonia, Korea, Malaysia, Slovak Republic, and Trinidad and Tobago.

  9. 9.

    Unfortunately, we cannot consider booming and affluent economies—i.e. the two high income economies- as two different outcomes because the number of countries in these categories is limited. In particular, in period 1985–1999 our sample counts only ten economies classified as affluent, while for period 2000–2014 booming economies are just 9. We then merge the two categories and consider them as one single group.

  10. 10.

    In order to identify the model, a reference outcome j must be arbitrarily selected and the respective coefficients \(\beta_{j}\) must be set equal to 0.

  11. 11.

    The same hypothesis can be tested through a Wald test, that however turns out to be less reliable for small samples, as in our case (Agresti 2007).

  12. 12.

    The two additional observations for period 2000–2014 correspond to Ethiopia and Tajikistan.

  13. 13.

    For each of the independent variables, the LR test is performed as follows. First, the full model is estimated. Second, a reduced model that excludes the tested variable is estimated. The difference in the LR2 is then computed and used to test the hypothesis that the tested variable does not affect the outcome (Freese and Long 2000).

  14. 14.

    When the beginning-of-period value is not available, we use the first non-missing value over the first 5 years of the fifteen-year period.

  15. 15.

    More easily, it may also be a reflection of the sample composition by category.

  16. 16.

    When a proxy for technological advancement is added to the full model by ignoring the rule of ten observations per regressor (necessary to guarantee the empirical validity in multinomial logit models), predictably some coefficients lose their significance (even if the most part of them maintains the same sign and size) and this is very likely due to the violation of the rule and the subsequent lack of validity, while the coefficient of technological advancement is never significant in the two periods.

  17. 17.

    We also use a dummy variable indicating whether the country is landlocked, but the coefficient is not significant.

  18. 18.

    The WVS also provides a series of detailed data on religion and religiousness that, although more refined than the variables here used, can not be exploited in our analysis because of the significant drop in the number of observations.

  19. 19.

    Guiso et al. (2003) showed that religious denominations are strictly related to trust, whose level increases if a person is Catholic or Protestant.

  20. 20.

    If we look at the mean level of the variables, in both periods they were characterized by a higher fertility rate, a lower educational attainment of people aged 20–24, a higher Gini index and a lower share of urban population.

  21. 21.

    The fourth Asian Tiger, South Korea, was a booming economy in the second period but in the first period it was classified as emerging.

  22. 22.

    This is of course an average relation, that does not capture single cases of huge inequalities like experienced by China and India during their process of emergence.

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Acknowledgements

We thank the members of the Turin Centre on Emerging Economies (OEET) and, in particular, Professor Vittorio Valli for their stimulating discussions and suggestions.

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Correspondence to Donatella Saccone.

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Appendices

Appendix I: List of indicators and sources

Age dependency ratio, World Development Indicators, World Bank.

Agricultural raw materials exports (% of merchandise exports), World Development Indicators, World Bank.

Conflicts, Polity IV Project, Center for Systemic Peace.

Educational attainment (15 + and 2024), Barro-Lee Educational Attainment Data, Barro and Lee (2013).

Export and import growth rates, our calculations on World Development Indicators, World Bank.

Exports and imports as a percentage of GDP, World Development Indicators, World Bank.

FDI as a percentage of GDP, World Development Indicators, World Bank.

FDI growth rates, our calculations on World Development Indicators, World Bank.

Fertility rate, World Development Indicators, World Bank.

Fiscal balance, World Development Indicators, World Bank.

Geographical variables, GeoDist, CEPII.

Gini index, Global Income Dataset, Global Consumption and Income Project (GCIP).

Illiteracy rate, Barro-Lee Educational Attainment Data, Barro and Lee (2013).

Income share of top 1 %, Global Income Dataset, Global Consumption and Income Project (GCIP).

Index of economic freedom, Economic Freedom of the World, Fraser Institute.

Inflation, World Development Indicators, World Bank.

Investment as a percentage of GDP, World Development Indicators, World Bank.

Investment growth rates, our calculations on World Development Indicators, World Bank.

Percentage of urban population, World Development Indicators, World Bank.

Political regime, Polity IV Project, Center for Systemic Peace.

Real GDP per capita with EKS PPP, Total Economy Database, Conference Board.

Religious diversity index and dominant religions, Pew Research Center.

Researchers in R&D (per million people), World Development Indicators, World Bank.

R&D expenditure (% GDP), World Development Indicators, World Bank.

Appendix II: Country membership by period and transition matrix

Country membership by period

Country 1985–1999 Level of membership stability 2000–2014 Level of membership stability
Albania Emerging Nearly stable Emerging Nearly stable
Algeria Poor Stable Poor Nearly stable
Angola Poor Unstable Emerging Stable
Argentina Emerging Unstable Poor Stable
Armenia Poor Nearly stable Emerging Stable
Australia Booming Stable Affluent Stable
Austria Booming Stable Affluent Stable
Azerbaijan Poor Nearly stable Emerging Nearly stable
Bahrain Affluent Nearly stable Affluent Stable
Bangladesh Emerging Nearly stable Emerging Stable
Barbados Emerging Nearly stable Poor Stable
Belarus Poor Unstable Emerging Stable
Belgium Booming Stable Affluent Stable
Bolivia Emerging Unstable Poor Unstable
Bosnia and Herzegovina n.a. Unstable Poor Unstable
Brazil Poor Nearly stable Poor Stable
Bulgaria Poor Stable Emerging Nearly stable
Burkina Faso Emerging Stable Poor Nearly stable
Cambodia Emerging Nearly stable Emerging Stable
Cameroon Poor Stable Poor Nearly stable
Canada Booming Stable Affluent Stable
Chile Emerging Stable Emerging Nearly stable
China Emerging Stable Emerging Stable
Colombia Emerging Nearly stable Emerging Nearly stable
Congo, Dem. Rep. Poor Stable Poor Nearly stable
Costa Rica Emerging Stable Emerging Unstable
Cote d’Ivoire Poor Nearly stable Poor Nearly stable
Croatia Affluent Unstable Poor Nearly stable
Cyprus Booming Nearly stable Affluent Stable
Czech Republic Booming Unstable Affluent Nearly stable
Denmark Booming Unstable Affluent Stable
Dominican Republic Emerging Stable Emerging Nearly stable
Ecuador Poor Nearly stable Emerging Nearly stable
Egypt, Arab Rep. Emerging Nearly stable Poor Nearly stable
Estonia Emerging Unstable Booming Nearly stable
Ethiopia Poor Nearly stable Emerging Nearly stable
Finland Booming Nearly stable Affluent Stable
France Booming Nearly stable Affluent Stable
Georgia Poor Nearly stable Emerging Stable
Germany Booming Nearly stable Affluent Stable
Ghana Emerging Stable Emerging Nearly stable
Greece Booming Stable Affluent Nearly stable
Guatemala Emerging Nearly stable Poor Stable
Hong Kong SAR, China Booming Nearly stable Booming Nearly stable
Hungary Affluent Unstable Affluent Nearly stable
Iceland Booming Stable Affluent Stable
India Emerging Stable Emerging Stable
Indonesia Emerging Nearly stable Emerging Nearly stable
Iran, Islamic Rep. Poor Unstable Poor Unstable
Iraq Poor Unstable Emerging Nearly stable
Ireland Booming Stable Affluent Nearly stable
Israel Booming Nearly stable Affluent Stable
Italy Booming Nearly stable Affluent Stable
Jamaica Emerging Nearly stable Poor Stable
Japan Booming Nearly stable Affluent Stable
Jordan Poor Nearly stable Poor Unstable
Kazakhstan Poor Nearly stable Emerging Stable
Kenya Poor Nearly stable Poor Nearly stable
Korea, Rep. Emerging Nearly stable Booming Stable
Kuwait Affluent Unstable Affluent Unstable
Kyrgyz Republic Poor Nearly stable Emerging Stable
Latvia Poor Unstable Emerging Nearly stable
Lithuania Poor Unstable Emerging Unstable
Luxembourg Booming Stable Affluent Stable
Macedonia, FYR Poor Stable Poor Nearly stable
Madagascar Poor Stable Poor Stable
Malawi Poor Unstable Poor Nearly stable
Malaysia Emerging Nearly stable Booming Unstable
Mali Emerging Nearly stable Poor Nearly stable
Malta Booming Nearly stable Affluent Nearly stable
Mexico Emerging Unstable Poor Stable
Moldova Poor Nearly stable Emerging Stable
Morocco Emerging Stable Emerging Stable
Mozambique Emerging Stable Emerging Stable
Myanmar Emerging Nearly stable Emerging Stable
Netherlands Booming Stable Affluent Stable
New Zealand Booming Unstable Affluent Stable
Niger Poor Stable Poor Nearly stable
Nigeria Emerging Stable Emerging Stable
Norway Booming Nearly stable Affluent Stable
Oman Booming Unstable Affluent Unstable
Pakistan Emerging Nearly stable Poor Nearly stable
Peru Poor Nearly stable Emerging Nearly stable
Philippines Emerging Unstable Emerging Unstable
Poland Emerging Nearly stable Emerging Unstable
Portugal Booming Stable Affluent Stable
Qatar Affluent Unstable Affluent Nearly stable
Romania Poor Stable Emerging Stable
Russian Federation Affluent Nearly stable Booming Nearly stable
Saudi Arabia Affluent Nearly stable Affluent Nearly stable
Senegal Poor Nearly stable Poor Stable
Serbia and Montenegro Poor Stable Emerging Nearly stable
Singapore Booming Nearly stable Booming Unstable
Slovak Republic Emerging Nearly stable Booming Unstable
Slovenia Affluent Nearly stable Affluent Nearly stable
South Africa Poor Stable Poor Stable
Spain Booming Stable Affluent Stable
Sri Lanka Emerging Stable Emerging Nearly stable
St. Lucia Emerging Stable Poor Stable
Sudan Emerging Unstable Poor Unstable
Sweden Booming Stable Affluent Stable
Switzerland Booming Nearly stable Affluent Stable
Taiwan, China Booming Nearly stable Booming Unstable
Tajikistan Poor Stable Emerging Stable
Tanzania Poor Stable Emerging Stable
Thailand Emerging Nearly stable Emerging Nearly stable
Trinidad and Tobago Emerging Nearly stable Booming Nearly stable
Tunisia Emerging Nearly stable Poor Unstable
Turkey Emerging Nearly stable Poor Unstable
Turkmenistan Poor Nearly stable Emerging Stable
Uganda Emerging Nearly stable Emerging Nearly stable
Ukraine Poor Nearly stable Emerging Unstable
United Arab Emirates Affluent Stable Affluent Stable
United Kingdom Booming Stable Affluent Stable
United States Booming Stable Affluent Stable
Uruguay Emerging Nearly stable Emerging Nearly stable
Uzbekistan Poor Stable Emerging Stable
Venezuela, RB Affluent Unstable Poor Nearly stable
Vietnam Emerging Stable Emerging Stable
Yemen, Rep. Emerging Unstable Poor Stable
Zambia Poor Stable Emerging Stable
Zimbabwe Poor Nearly stable Poor Nearly stable

Transition matrix

  2000–2014
Poverty Emergence Boom Affluence Total
1985–1999
 Poverty 15 23 0 0 38
 Probability 39.5 60.5 0.0 0.0 100
 Emergence 16 22 5 0 43
 Probability 37.2 51.2 11.6 0.0 100
 Boom 0 0 3 28 31
 Probability 0.0 0.0 9.7 90.3 100
 Affluence 2 0 1 7 10
 Probability 20.0 0.0 10.0 70.0 100
Total 33 45 9 35 122

Appendix III: Classification of economies by region

  1985–1999 2000–2014
n % n %
Poor economies
East Asia and Pacific 0 0.0 0 0.0
Europe and Central Asia 17 44.7 4 12.1
LAC 3 7.9 9 27.3
MENA 4 10.5 6 18.2
North America 0 0.0 0 0.0
South Asia 0 0.0 1 3.0
Sub Saharan Africa 14 36.8 13 39.4
Total 38 100.0 33 100.0
Emerging economies
East Asia and Pacific 9 20.9 7 15.6
Europe and Central Asia 6 14.0 18 40.0
LAC 13 30.2 7 15.6
MENA 4 9.3 2 4.4
North America 0 0.0 0 0.0
South Asia 4 9.3 3 6.7
Sub Saharan Africa 7 16.3 8 17.8
Total 43 100.0 45 100.0
Booming economies
East Asia and Pacific 6 19.4 5 55.6
Europe and Central Asia 20 64.5 3 33.3
LAC 0 0.0 1 11.1
MENA 3 9.7 0 0.0
North America 2 6.5 0 0.0
South Asia 0 0.0 0 0.0
Sub Saharan Africa 0 0.0 0 0.0
Total 31 100.0 9 100.0
Affluent economies
East Asia and Pacific 0 0.0 3 8.6
Europe and Central Asia 4 40.0 22 62.9
LAC 1 10.0 0 0.0
MENA 5 50.0 8 22.9
North America 0 0.0 2 5.7
South Asia 0 0.0 0 0.0
Sub Saharan Africa 0 0.0 0 0.0
Total 10 100.0 35 100.0
  1. Source: our calculations based on the Total Economy Database, Conference Board

Appendix IV: Likelihood-ratio tests on independent variables

Period 1985–1999

  Chi2 P > Chi2
Age dependency ratio 4.494 0.106
Export growth 4.930 0.085
Investment growth 3.247 0.197
Urban population  % 9.409 0.009
Inflation 0.731 0.694
Gini 1.383 0.501
Illiteracy rate 0.405 0.817
Economic freedom index 6.236 0.044

Period 2000–2014

  Chi2 P > Chi2
Age dependency ratio 23.004 0.000
Export growth 19.980 0.000
Investment growth 15.823 0.000
Urban population  % 14.483 0.001
Inflation 3.440 0.179
Gini 16.429 0.000
Illiteracy rate 4.496 0.106
Economic freedom index 9.146 0.010

Appendix V: Akaike information criterion (AIC) and Bayesian information criterion (BIC) values

Period 1985–1999

  Full model
(column 3)
Reduced model
(column 4)
AIC 1.536 1.528
BIC − 210.528 − 235.834
BIC′ − 16.520 − 41.826

Period 2000–2014

  Full model (column 3) Reduced model (column 4)
AIC 0.959 0.968
BIC − 260.724 − 269.786
BIC′ − 69.014 − 78.076

Appendix VI: Sensitivity analysis

Period 1985–1999

  (1) (2) (3) (4) (5) (6) (7) (8) (9)
Poor economies
Demography   1.0289***   0.0325*      
Economic openness − 0.1386* − 0.1632* − 0.2267** − 0.1757** − 0.1585* − 0.3222** − 0.1411* − 0.0129 − 0.1169
Physical capital accum.          
Structural change 0.0048 0.0311 0.0103 − 0.0762 0.0211 0.0168 0.0032 0.0427 0.0151
Macroeconomic policy       − 0.0603    
Inequality      0.1052*     
Human capital   − 0.0395        
Institutional quality − 0.2854   − 0.3851 − 0.1556 − 0.2898 − 0.6086 − 0.2670 − 0.3416 0.0613
Latitude        0.1893   
Technological advancement         0.2021 0.0001
Booming and affluent economies
 Demography   − 2.6895***   − 0.1264***      
Economic openness 0.0178 0.0671 − 0.1024 − 0.0364 0.0370 − 0.0282 − 0.0003 0.0164 − 0.0467
Physical capital accumulation          
Structural change 0.0898*** 0.1011*** 0.0885*** − 0.1745* 0.0844*** 0.0905*** 0.0695*** 0.0623* 0.0438
Macroeconomic policy       − 0.3817*    
Inequality      − 0.2419*     
Human capital   0.0788        
Institutional quality 1.1050***   1.0320*** 1.4613*** 1.2061*** 1.5032** 1.1004** 0.7732 1.1085**
Latitude        5.4802*   
Technological advancement         2.1338** 0.0013***
Obs. 87 87 87 87 87 70 87 60 55
  1. The table reports the results of the reduced forms obtained by using alternative proxies for the regressors. Column 1 replicates the original reduced form (as in Table 1) in order to facilitate comparisons; in column 2 the age dependency ratio is substituted by the fertility rate; in column 3 economic openness is measured by the rate of import growth; in column 4 the structural change is proxied by the share of agricultural raw materials exports as a percentage of merchandise exports; in column 5 the Gini coefficient is replaced by the share of income held by the top 1 %; in column 6 the fiscal balance (% of GDP) is a proxy for macroeconomic policy; in column 7 a variable measuring the distance from the equator is added to the reduced form; in columns 8 and 9 technological advancement is added to the reduced form and is measured respectively by the R&D expenditure (% GDP) and the number of researchers (per million people)
  2. Results are not reported for the regressions where the alternative proxies did not pass the likelihood-ratio test at a 90 % confidence level. This is the case of exports/GDP, imports/GDP, FDI/GDP and FDI growth for economic openness; investment as a percentage of GDP for physical capital accumulation; average years of schooling (people aged 15 + and 20–24) for education; access to sound money (sub-component of economic freedom), regulation of credit, labor and business (sub-component of economic freedom), conflicts (dummy = 1 if presence of conflicts), and political regime (dummy = 1 if democratic regime) for institutional quality. Constants are not reported in order to save space. ***p < 0.01, **p < 0.02, *p < 0.05

Period 2000–2014

  (1) (2) (3) (4) (5) (6) (7) (8)
Poor economies
Demography 0.1498*** 2.0025*** 0.1239*** 0.1422*** 0.1974** 0.1917* 0.2236** 0.2395**
Economic openness − 0.2517 − 0.2441 − 0.0377 − 0.0050 − 0.4175* − 0.3281 − 0.0108 − 0.3579°
Physical capital accumulation − 0.4806*** − 0.6974*** − 0.4807*** − 0.4517*** − 0.7892*** − 0.5035** − 1.2080*** − 0.4311**
Structural change 0.0593* 0.0721** 0.0535* 0.0475* 0.1004** 0.0627* 0.0991* 0.1111*
Inequality 0.0960 0.0993 0.1199   − 0.0366 0.0139 13.6398 9.6998
Human capital      0.1141    
Institutional quality − 1.3047° − 1.4680* − 1.2194 − 1.0345° − 0.7775 − 1.4534° − 1.7985° − 1.3649*
Latitude       6.0869   
Technological advancement        1.8248 0.0021
Booming and affluent economies
Demography − 0.3157* − 1.3439 − 0.2509* − 0.2399 − 1.1965* − 0.5526* − 0.3397 4.8682
Economic openness − 1.1959** − 0.8520*** − 0.4778*** 0.1534* − 3.1321* − 1.3200* − 1.6038** − 22.5280
Physical capital accumulation 0.1046 − 0.0531 − 0.0824 0.6036** − 0.3900 0.2104 − 0.2632 − 6.2078
Structural change 0.0699* 0.0597* 0.0656* 0.1109** 0.1917* 0.1478* 0.0471 2.7244
Inequality − 0.2908* − 0.2346* − 0.1216   − 0.0292* − 0.0486* − 33.6174° − 426.6601
Human capital      1.1999**    
Institutional quality 3.2408** 2.2720** 2.3375** 2.4083** 8.1259* 3.3499* 4.1343** 38.3601
Latitude       − 12.5449   
Technological advancement        2.8668 0.0847
Obs. 87 87 87 87 87 87 79 79
  1. The table reports the results of the reduced forms obtained by using alternative proxies for the regressors. Column 1 replicates the original reduced form (as in Table 3) in order to facilitate comparisons; in column 2 the age dependency ratio is substituted with the fertility rate; in column 3 and 4 economic openness is measured by the rate of import growth and the exports-to-GDP ratio respectively; in column 5 the Gini coefficient is replaced with the share of income held by the top 1 %; in column 6 a variable measuring the distance from the equator is added to the reduced form; in columns 7 and 8 technological advancement is added to the reduced form and is measured respectively by the R&D expenditure (% GDP) and the number of researchers (per million people)
  2. Results are generally not reported for the regressions where the alternative proxies did not pass the likelihood-ratio test at a 90 % confidence level. This is the case of imports/GDP, FDI/GDP and FDI growth for economic openness; investment as a percentage of GDP for physical capital accumulation; share of agricultural raw materials exports as a percentage of merchandise exports for structural change; average years of schooling (people aged 15 + and 20–24) for education; sub-components of economic freedom, conflicts (dummy = 1 if presence of conflicts) and political regime (dummy = 1 if democratic regime) for institutional quality. Constants are not reported in order to save space. ***p < 0.01, **p < 0.02, *p < 0.05, °p < 0.10

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Saccone, D., Deaglio, M. Poverty, emergence, boom and affluence: a new classification of economies. Econ Polit 37, 267–306 (2020). https://doi.org/10.1007/s40888-019-00166-4

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Keywords

  • Country classification systems
  • Per capita income
  • Economic growth
  • Emerging economies
  • Comparative economics

JEL Classification

  • O10
  • O11
  • O20
  • O47