Habiba, a shepherd in rural Lebanon. (Credit: UN Women/Joe Saade/Flickr)
Habiba, a shepherd in rural Lebanon. (Credit: UN Women/Joe Saade, 'Lebanon - Rural Lebanese women weave economic opportunity while conserving the local environment'/Flickr licensed under CC BY-NC-ND 2.0) (via: bit.ly)

In recent years, after the Arab Spring events of 2010 and beyond, civil wars and disarray in Egypt, Iraq, Libya, Syria, and Yemen have undermined economic growth in countries of the MENA region. Yet for over half a century before that, MENA was a relatively successful region in terms of growth, with per capita GDP growth rates between 1960 and 2010 generally higher than world averages except during the 1980s, when the Iran-Iraq war and the 1986 collapse of oil prices had a significant impact. See Fig. 1.

Figure. 1. Annual growth rates of per capita GDP in MENA countries as compared to the whole world in 1961-2014

Will the next economic miracle happen in the Middle East? 1

Source: World Development Indicators Database.

While growth rates in the per capita GDP of MENA countries in the postwar period were well below that of the East Asian ‘tigers’ and ‘dragons’ (Fig. 2, 3), and a little below that of South Asia as well, they were higher than in other regions of the global south, such as Sub-Saharan Africa, Latin America, and the former Soviet Union. Three countries in the MENA region – Israel, Oman, and Tunisia – were among the 20 fastest growing countries in the world between 1950 and 2010 (Fig. 4, Table 1).

Figure 2. PPP GDP per capita in countries that took off after World War Two (Japan, Taiwan, Hong Kong Singapore, S. Korea)

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Source: Maddison (2013).

Figure 3. PPP GDP per capita in countries that took off in the 1960s and later (SEA and China)

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Source: Maddison (2013).

Figure 4. PPP GDP per capita in some countries outside East Asia that took off in the 1960s and later (India, Tunisia, Botswana)

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Source: Maddison (2013).

In terms of social progress – education and life expectancy – the achievements of the MENA region were even more spectacular. Many MENA countries increased their life expectancy greatly between 1960 and 2010; in most of them, it exceeded 70 years (in comparison, it was 72 years for Russia in 2015). See Fig. 5.

Human Development Index (HDI) – an average of calibrated indicators of per capita income, educational levels (enrolment and years of schooling), and life expectancy – scores increased by 65% in Arab countries between 1970 and 2010, which is more than in any other region of the world except for East Asia (96%) and South Asia (72%) – See Table 2. The increase in life expectancy between 1970 and 2010 in Arab countries was the highest in the world, whereas the increase in school enrolment and literacy was higher than in all other regions of the world except Sub-Saharan Africa, which started from a very low base level (Table 2).

As Table 3 shows, out of 22 countries which most increased their HDI between 1980 and 2010, six are Arab countries, seven are MENA countries, and 11 are Muslim countries. Of the 10 countries with the greatest increase in HDI between 1970 and 2010, there are five MENA countries (Oman, Saudi Arabia, Tunisia, Algeria, Morocco). And 6 out of 10 leaders in the improvement of non-income HDI (education and life expectancy) are also from the MENA region – Oman, Saudi Arabia, Lybia, Algeria, Tunisia, Iran (Table 4).

Table 1. Fastest growing countries – average annual per capita real GDP growth rates in 1950-2013 (MENA states highlighted in bold)

Country / PeriodGrowth rate in 1950-2010


Growth rate in 1960-2010


Growth rate in 1960-2013 (WDI)
S. Korea5.545.915.97
Hong Kong SAR, China4.484.674.17
Botswana (Maddison data – until 2008)4.465.075.66
Burma (Myanmar)3.803.842.77 (1960-2004)
Israel 3.252.872.98
Montenegro3.18 (1952-2010)3.18
Ireland3.143.333.14 (1971-2013)
Tunisia 2.953.162.94
Lesotho (Maddison data – until 2008)2.882.943.06
Sri Lanka2.452.883.42

Source: Maddison, 2013; World Development Indicators database.

Figure. 5. Life expectancy in selected Middle Eastern countries

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Source: World Development Indicators database.

Table 2. Human Development Index and its components for major regions, 1970-2010

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Source: Human Development Report. UNDP

Table 3.  Countries with the highest growth in Human Development Index scores, 1980-2010, percentage increases (Arab countries highlighted in bold, Muslim non-Arab countries in italics)

Country% IncreaseCountry% Increase
Egypt58Papua and New Guinea46
Tunisia56El Salvador45

Source: Human Development Report. UNDP.

Table 4. Top 10 countries with highest increases in HDI and its components, 1970-2010

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Source: Table 2.2 of Human Development Report 2010. UNDP.

To put it differently, in terms of economic progress, MENA countries may not have been the leaders between 1950 and 2010, but they were not the laggards, either, falling in between rapidly growing East and South Asia and more slowly growing Latin America, OECD countries, Sub-Saharan Africa, and the former Soviet Union. But regarding social progress in the several decades before the Arab Spring (1970-2010), MENA regions did better than all the other regions of the developing world.

On top of that, inequalities in the MENA region were lower than in other countries with a similar level of economic development. Controlling for many factors (size, population density, per capita income, urbanisation, democracy, a communist past, government effectiveness index) it turns out that Muslim countries have Gini coefficients for income distribution that are five percentage points lower than in other countries.[1]

But the most important advantage of MENA countries that is very often lacking in other parts of the developing world is the strength of state institutions – a crucial prerequisite for stable and strong economic growth. State institutional capacity is defined here as the ability of the state to enforce rules and regulations. Subjective measures of this state capacity – indices of government effectiveness, the rule of law, corruption, etc. – have a number of shortcomings (Popov, 2011), but there are objective indicators, such as crime rate, murder rate, the proportional size of the shadow economy, and the ability of the state to enforce its monopoly on violence and taxation.

The general rule is that East Asian, South Asian, and MENA countries have murder rates of 1-10 murders per 100,000 inhabitants and a shadow economy at less than 30% of GDP, whereas in Sub-Saharan Africa, Latin America, and some former Soviet Union republics (the Baltics, Belarus, Kazakhstan, Moldova, Russia, Ukraine) the murder rate is higher by an order of magnitude of 10-100 murders per 100,000 and the shadow economy represents way over 30% of GDP (Figs. 6-8, see Popov, 2014 for details). Economic growth in large regions of the global south correlate strongly with the murder rate and the size of the shadow economy: the higher the murder rate and the shadow economy, the lower the growth. East Asia is ahead of everyone in terms of growth, followed by South Asia and the MENA region, while Latin America, Sub-Saharan Africa, and the former Soviet Union are falling behind.

In fact, the murder rate and the share of the shadow economy – objective indicators of the institutional capacity of the state – turn out to be the best institutional predictors of the long-term growth rates of GDP per capita. In regressions for over 50 years (1960-2013) for 80 countries for which data are available, up to 40% of variations in GDP per capita growth are explained by the level of development (GDP per capita) and institutional indicators (murder rate and share of shadow economy)[2]. These regressions are quite robust and hold for different sub-periods (1960-75, 1975-2000, 2000-13). Among variables that are not directly related to growth, such as investment rate, population growth rates, etc., state institutional capacity turns out to be the single most important predictor of growth (Popov, 2015). The negative relationship between growth rate and state institutional capacity as measured by the murder rate and the shadow economy’s share of GDP can be observed with the naked eye in Figs. 9 and 10. And countries with high income and wealth inequalities usually have higher murder rates and larger shadow economies (Figs. 11 and 12).

Figure. 6. Average murder rates in 1960-2013 by decades, per 100,000 inhabitants, log scale (countries for which data are available for three or more decades)

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Source: List of countries by intentional homicide rate. Data are taken from different sources (mostly national data provided to WHO) and sometimes are not strictly comparable.

Figure. 7. Murder rate in countries with less than 1.5 murders per 100,000 inhabitants in 2008

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Source: UNODC.

Figure. 8. Murder rate in countries with over 15 murders per 100,000 inhabitants in 2008

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Source: UNODC.

Figure 9. Murder rate in 2002 per 100,000 inhabitants and average annual per capita GDP growth rates in 1960-2013

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Source: WDI; WHO.


Figure 10. Shadow economy in 2005 and annual average growth rates of per capita GDP in 1960-2013

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Source: WDI; Schneider (2007).


Figure 11. Shadow economy and Gini coefficient of income inequalities in 1990-2005

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Source: WDI; Schneider (2007).


Figure 12. Murder rate and Gini coefficient of income inequalities in 1990-2005

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Source: WHO; WDI; Schneider (2007).

Manufacturing growth is like cooking a tasty dish – all necessary ingredients should be in the right proportion. If even one of them is under or overrepresented, the ‘chemistry of growth’ will not happen. Fast economic growth can materialise in practice only if several necessary conditions are met at the same time.

Rapid growth is a complicated process that requires some crucial inputs – infrastructure, human capital, strong state institutions, even distribution of land in agrarian countries, and economic stimuli, among other things. Once one of these crucial ingredients is missing, growth simply does not take off. Rodrik, Hausmann and Velasco (2005) talk about “binding constraints” that hold back economic growth; finding these constraints is the task of “growth diagnostics”. In some cases, these constraints are associated with the lack of market liberalisation, in others, with the lack of state capacity or human capital or infrastructure.

Why did economic liberalisation work in central Europe, but not in Sub-Saharan Africa and Latin America? The answer, according to the approach outlined above, would be that in central Europe the missing ingredient actually was economic liberalisation, whereas in SSA and Latin America it was a lack of state capacity instead. Why did economic liberalisation work in China and central Europe and not in the Commonwealth of Independent States that took shape in the wake of the Soviet Union’s demise? Because in the CIS it was carried out in a manner that undermined state capacity – the precious heritage of socialist past – whereas in central Europe, and even more so in China, state capacity did not decline substantially during the transition.

MENA countries have lower inequality and stronger state institutional capacity, i.e., the ability to enforce rules and regulations – including the ability to control crime and the shadow economy – whatever those rules and regulations may be. For the purposes of comparison, the following statistics are illuminating: in China, the murder rate was 1 per 100,000 inhabitants under Mao, about 2 in the 1990s-2000s and again 1 in 2010 (Fig. 13), whereas in Russia it went up from 7-10 in late Soviet times to 30 in the rocky 1990s and declined again to about 10 by 2014 (Fig. 14). In MENA countries, these rates during peacetime were normally in the range of 1 to 5 per 100,000 inhabitants (but less than 1.5 for Algeria, Tunisia, Egypt, Qatar, Oman, Bahrain; see Fig. 7). No wonder such strong institutional capacity contributed to relatively strong economic performance and exceptional increases in life expectancy and educational attainments in recent decades, especially in periods with no wars and civil conflicts.

Figure. 13. Murder rate in China per 100,000

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Source: UNODC.

Figure. 14. Crime rate (left scale), murder rates and suicide rate (right scale) per 100,000 inhabitants

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Source: Federal Statistical Service of the Russian Federation.

As they look to the future, MENA countries still possess many crucial ingredients of economic growth: natural resources; human capital; relatively low inequality; and, most importantly, strong state institutions, ensuring low crime and shadow economy rates. They were making rapid economic and social progress between 1960 and 2010, and many still retain the necessary pre-conditions to become growth miracles in the future, provided that ethnic and religious conflicts are effectively managed and peace prevails.


Vladimir Popov

Research Director, Dialogue of Civilizations Research Institute



Popov, V. (2011). Developing New Measurements of State Institutional Capacity. PONARS Eurasia Policy Memo No. 158, May 2011.

Popov, V. (2014). Mixed Fortunes: An Economic History of China, Russia and the West. Oxford University Press, April 2014.

Popov, V. (2015). Catching Up: Developing Countries in Pursuit of Growth. MPRA Paper No. 65878, August 2015.

Rodic, D, Hausmann, R., and Velasco, A. (2005). Growth Diagnostics.


[1] y = -0.0003*** Ycap75  – 0.03*MURDERS –0.14***SHADOW  + 5.32***

(-4.95)              (1.67)                          (-4.82)            (8.55)

N=80,     R2 = 0.38, robust standard errors, T-statistics in brackets below;

y = 0.003***POPDENS – 0.0002*** Ycap75  – 0.023 MURDERS –0.067***SHADOW  + 5.04***

(4.08)                     (-4.33)                      (-1.62)                                 (-4.40)        (7.67)

N=80,  R2 = 0.40, robust standard errors, T-statistics in brackets below, where


y –annual average growth rates of per capita GDP in 1960-2013, %,

POPDENS – number of residents per 1 square km in 2000,

Ycap75 – per capita PPP GDP in 1975 in dollars,

MURDERS – number of murders per 100,000 inhabitants in 2002,

SHADOW – share of the shadow economy in GDP in 2005, %.

Data on growth, population density and PPP GDP per capita are from WDI, data on murders are from WHO, data on the shadow economy are from Schneider, 2007 (measures of the shadow economy are derived from the divergence between output dynamics and electricity consumption, demand for real cash balances, etc.)

[2] Regression equation linking inequalities with various determinants is given below:

Ineq = –0.26*** Y95us + 0.016*** PopDens + 6.47*1007***AREA – 832.1***Y99/Area +  0.18***URBAN – 4.11**Islam + 12.24*** TRANS – 4.07**GE2002 1.17*DEM –  0.09**EXfuel + 46.4***,

N = 114,   R-squared =  0.6089,


IneqGini coefficient of income distribution in 1990-2005 (last available year),

 Y95us – PPP GDP per capita in 1995 as a % of the US level,

PopDens population density in 2002 (number of persons per 1 sq. km),

TRANSdummy variable for the communist past,

DEM – average index of authoritarianism in 1970-2002 (average index of political rights from Freedom House, varies from 1 to 7, higher values indicate more authoritarianism)

GE2002 –  government effectiveness index in 2002,

URBAN –share of urban population in 2002,

Y99/Area – ratio of PPP GDP in 1999 per 1 square km of national territory,

Islam – dummy variable for the membership in Organization of Islamic Conference.


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Vladimir Popov

Research Director in Economics & Political sciences, DOC Research Institute, RU

Vladimir Popov is a Principal Researcher in the Central Economics and Mathematics Institute of the Russian Academy of Sciences. He is also a professor emeritus at the New Economic School in Moscow, and an adjunct research professor at the Institute of European and Russian Studies at Carleton University in Ottawa. In 2009-15 he worked in DESA, UN, as a Senior Economic Affairs Officer and Inter-regional Adviser. He has published extensively on world economy and development issues (he is the editor of three books, and author of ten books and hundreds of articles, including in the Journal of Comparative Economics, World Development, Comparative Economic Studies, Cambridge Journal of Economics, New Left Review, as well as essays in the media). His books and articles have been published in Chinese, English, French, German, Italian, Japanese, Korean, Norwegian, Portuguese, Russian, Spanish, and Turkish. His most recent book is “Mixed Fortunes: An Economic History of China, Russia, and the West” (Oxford University Press, 2014). He graduated from the Economics Department of the Moscow State University in 1976, and holds PhDs (Candidate of Science, 1980; and Doctor of Science, 1990) from the Institute for US and Canadian Studies of the Academy of Sciences of the USSR. More info can be found at his website: http://www.nes.ru/~vpopov