Policy Research Working Paper 8870
Technology Adoption and the Middle-Income Trap
Lessons from the Middle East and East Asia
Rabah Arezki
Rachel Yuting Fan
Ha Nguyen
Middle East and North Africa Region
Office of the Chief Economist
May 2019
Policy Research Working Paper 8870
Abstract
This paper documents the existence of a “middle-income and North Africa having experienced a relatively slow pace
trap” for the Middle East and North Africa region and of technology adoption in general-purpose technologies
contrasts the evidence with that of the East Asia and Pacific and that a slower adoption pace of technology is associated
region. The results are two-folds. First, non-parametric with lower levels of economic growth. The paper concludes
regressions show that the average rate of economic growth that barriers to the adoption of general-purpose technol-
in the Middle East and North Africa has not only been ogies related to the lack of contestability in key sectors
significantly lower than that in the East Asia and Pacific constitute an important channel of transmission for the
region, but it has also tended to drop at an earlier level of middle-income trap.
income. Second, econometric results point to Middle East
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effort by the World Bank to provide open access to its research and make a contribution to development policy discussions
around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The
authors may be contacted at rarezki@worldbank.org.
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Technology Adoption and the Middle-Income Trap:
Lessons from the Middle East and East Asia
Rabah Arezki, Rachel Yuting Fan and Ha Nguyen
Updated May 30, 2020
JEL: O4
Keywords: middle-income, growth, technology adoption
I. Introduction
The term “middle-income trap” refers to the possibility that economies could get stuck at a certain level of
income. The debate on the trap has thus far focused mostly on the East Asia and Pacific region (EAP). 1
While economies in Middle East and North Africa region (MENA) have stalled, they have largely been
overlooked in the debate over the middle-income trap. Indeed, MENA has been characterized by
pervasively low growth. In the 1980s and 1990s, GDP growth per worker in the region was less than 1
percent per year, with continuous decline in total factor productivity (Yousef, 2004). In recent decades,
growth in MENA has remained relatively low (see Figure 1). 2 In the present paper, we document the
existence of a middle-income trap for MENA and contrast the evidence with that of EAP.
To do so, we adopt a non-parametric analysis of growth dynamics that helps flexibly capture sharp changes
in growth. Results from non-parametric regressions show that growth in GDP per capita and total factor
productivity (TFP) in MENA quickly decline as income levels rise. In contrast, growth in GDP per capita
and TFP in EAP is not only higher on average along the income ladder but also decline at much higher
levels of income. Importantly, we document that the slow pace of technology adoption of general-purpose
technologies (GPT) is associated with lower levels of economic growth. We then examine the adoption of
both older GPT and their applications such as electricity, and newer ones, such as broadband and internet.
For all technologies, when controlling for the level of income, MENA falls behind EAP in terms of the
adoption pace. Barriers to the adoption of general-purpose technologies thus constitute an important
channel of transmission for the middle-income trap.
This paper is most directly related to the strand of literature testing for the existence of a middle-income
trap. For example, Aiyar et al. (2013) uncover that middle-income countries are more likely to experience
growth slowdowns. Also, Eichengreen et al. (2013) determines that level of income within the $10,000-
$11,000 and $15,000-$16,0000 ranges. The jury is however still out on the empirical validity of the
1
The term “middle income trap” was first coined by Gill, Kharas and others (2007). Policymakers and
commentators have used the term abundantly in the media to characterize the risk of facing a ceiling on the level of
economic growth for countries such as Malaysia, Vietnam and China. Also, researchers have investigated the risk
associated with the trap in Asia and as well as the needed reforms to escape it (Ohno and Le, 2015 for Vietnam;
Fragen et al, 2013 for Malaysia; Eichengreen et al, 2012 and Glaw and Wagner, 2017 for China).
2
Figure 1 shows that for the period from 2000 to 2021, MENA countries, with the exceptions of Djibouti and
Morocco, are expected to experience lower growth in GDP per capita than the median of other countries in the same
income group. The years from 2019 to 2021 are projections.
2
middle-income trap. 3 The contribution of this paper is to provide evidence that MENA is subject to much
lower levels of growth along the income ladder compared to EAP.
Figure 1: MENA Growth performance has been subpar
2000-2021
Lower
0.04 Upper Middle Middle
0.03
0.02 High Income
0.01
0.00
-0.01
-0.02
Yemen, Rep.
Lebanon
Libya
Bahrain
Egypt
United Arab Emirates
Kuwait
Tunisia
Oman
Djibouti
West Bank & Gaza
Qatar
Jordan
Algeria
Saudi Arabia
Iran
Iraq
Morocco
Note: The blue diamonds are country average growth in GDP per capita. The red lines capture the
median growth in GDP per capita in non-MENA countries in the same income group. Source: World
Economic Outlook and World Bank’s Macro and Poverty Forecasts
This paper is also related to the literature on the link between innovation and economic growth. In
Schumpeterian growth theory, faster growth is associated with higher rates of firm creation and
destruction driven by R&D and innovation (Aghion and Howitt, 1992). In this environment, incumbent
firms’ innovation and productivity growth would be stimulated by competition and entry, particularly in
firms near the technology frontier (Aghion et al, 2014). 4 There is strong empirical evidence that
competition and productivity growth display an inverted-U shaped relationship: starting at an initially low
level of competition, higher competition stimulates innovation and growth; however, starting from a
higher initial level of competition, higher competition may hurt innovation and productivity growth. 5 This
paper documents MENA’s slow pace of adoption in GPT which can help explain the pervasively low
economic growth and TFP. This paper also provides evidence that technology adoption is slower when
concentration is higher in key (upstream) sectors of the economy.
3
Bulman et al. (2017) find that the fraction of countries “trapped” at the middle-income level is not larger than the
fraction of countries “trapped” at the low-income level. Similarly, Han and Wei (2017) find that the probability of
escaping from the middle-income level is not smaller than the probability of escaping from the low-income level.
4
See Aghion et al. (2014) for a recent review.
5 See for instance Aghion et al. (2005).
3
The remainder of the paper is organized as follows. Section II documents the evidence of a middle-
income trap for MENA. Section III explores the link between technology adoption and economic growth.
Section IV presents evidence of the relatively slow pace of technology adoption of GPT in MENA.
Section V concludes.
II. Empirical Evidence for the Middle East’s Middle-Income Trap
MENA countries are less likely to escape the middle-income trap than other countries around the globe.
Figure 2 illustrates that by comparing levels of income reached in 1975 to the ones in 2017. We follow
Bulman et al. (2017) in grouping countries into three relative income groups, namely low-income,
middle-income and high-income depending on their GDP per capita relative to that of the United States in
the same year. 6 Countries in the middle-left quadrant escaped the low-income group in 1975 and shifted
to the middle-income group in 2017. Countries in the top-middle quadrant escaped the middle-income
group and shifted to the high-income group. Countries in the center quadrant have been trapped in the
middle-income group for more than four decades. Among MENA countries, aside from the six countries
that have remained high-income, five have been trapped in the middle-income group (Algeria, Egypt,
Jordan, Morocco, and Tunisia), three fell from the high-income to middle-income group (the Islamic
Republic of Iran, Lebanon, and Libya), while none have become “escapees”. The Republic of Korea;
Hong Kong SAR, China; Cyprus and Portugal have become escapees.
Figure 2: Illustrating the Middle-Income Trap
log of real PPP GDP per capital relative to the U.S. in 2017
2.5
QAT
KWT ARE
HKG
2
SAU
BHR
OMN
KOR CYP
PRT
IRN
1.5
LBN
CHN
DZA
LKA
IDN EGY JOR
TUN
LBY
BTN MAR
LAO
VNM IND
0 .5 1
0 1 2 3
log of real PPP GDP per capital relative to the U.S. in 1975
Sources: International Monetary Fund, World Economic Outlook database, and authors’ calculations.
Note: Data labels use International Organization for Standardization (ISO) country codes. Regions follows
World Bank country groups.
6
A country is defined as low-income if its per capita GDP is lower than or equal to 10 percent of that of the United
States; middle-income if between 10 percent and 50 percent of U.S. GDP, and high-income if above 50 percent.
4
To document more systematically the evidence of a middle-income trap for MENA relative to EAP, we
use the non-parametric local-linear regression technique that give the mean and standard errors of the
estimated growth rate of each region at each level of income: 7
������������
∆log (������������)������������������������,������������+10 − ∆log (������������)������������������������������������,������������+10 = ������������ ������������� ������������������������ , ������������������������������������ � + ������������������������
������������������������������������
where ∆log (������������)������������������������,������������+10 and ∆log (������������)������������������������������������,������������+10 are overlapping annualized decadal growth in GDP per capita
������������������������������������
(or TFP) of country ������������ and of the U.S. between time ������������ and time ������������ + 10, is the country’s relative
������������������������������������������������
income per capita relative to the U.S. at time ������������. We use GDP per capita derived from output-side real
GDP at chained PPPs and total factor productivity (TFP), both from Penn World Table 9.0. The
regressions also include overlapping decade fixed-effect, ������������������������������������ , to control for common global shocks.
������������������������������������
Note that we are agnostic about the form of the function ������������ ������������� , ������������������������������������ �. Unlike linear regression, a
������������������������������������
nonparametric regression is agnostic about the functional form between the outcome and the explanatory
variables and is therefore not subject to misspecification error. In our context, a non-parametric regression
could capture sharp changes in growth rates as relative income rises, a key advantage for us to identify an
income trap.
For each region, the non-parametric regressions (with 100 bootstrap replications) help provide the average
predicted values and confidence intervals of annualized decadal growth in GDP per capita at different
levels of relative income. Average predicted relative growth in GDP per capita relative to the U.S. (and its
95% confidence interval) for MENA and EAP are visually shown in Panel A of Figure 3, while those of
absolute growth in GDP per capita are shown in Panel B 8. Their numerical values are reported in
Appendix Table A1. The results for other regions are also reported in Appendix Figure A2, although not
discussed in the text.
For EAP countries, both average relative and absolute growth in GDP do not significantly decline until
the countries reach 60 percent of U.S. GDP per capita. At 50 percent or below, EAP economies maintain
a stable growth rate at 4 to 4.5 percent (Panel B), or 2 to 2.5 percent higher than the U.S. (Panel A)
indicating that these countries are catching up. In contrast, the growth performance of MENA countries is
much weaker. Although starting at the same level of growth as EAP, around 4 percent, growth for MENA
7
The STATA command is npregress kernel y x1 x2, where y is the dependent variable, and x1 and x2 are the
explanatory variables. See Fan and Gijbels (1996) for a reference on local-linear regressions.
8
Note that we restrict the estimation at below 100 percent of U.S. income because we focus on the middle-income
level. In addition, at above 100 percent of U.S. income, there are fewer observations making the estimations
imprecise.
5
quickly and steadily declines. At 20 percent of U.S. GDP, the average growth rate for MENA is about 3
percent (Panel B), only 1 percent higher than that of the U.S. (Panel A) as opposed to almost 3 percent
gap as in EAP. At 40 percent of U.S. income, MENA relative growth in GDP per capita becomes
insignificantly different to that of the U.S., and starting from 60 percent of U.S. income, MENA growth is
lower than that of the United States. The steady decline in per capita GDP in MENA along the income
ladder indicates stronger evidence of the middle-income trap for MENA than for EAP—the region most
prominently associated with the debate about the middle-income trap.
6
Figure 3: Growth in PPP GDP per capita
Panel A: Relative to the U.S.
East Asia and Pacific Middle East and North Africa
.01 .02 .03 .04 .05
.01 .02 .03 .04 .05
relative growth in income
relative growth in income
-.05 -.04 -.03 -.02 -.01 0
-.05 -.04 -.03 -.02 -.01 0
0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100
relative income to the U.S., percentage relative income to the U.S., percentage
Panel B: Actual growth (not relative to the U.S.)
East Asia and Pacific Middle East and North Africa
-.05 -.04 -.03 -.02 -.01 0 .01 .02 .03 .04 .05
-.05 -.04 -.03 -.02 -.01 0 .01 .02 .03 .04 .05
absolute growth in income
absolute growth in income
0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100
relative income to the U.S., percentage relative income to the U.S., percentage
Note: MENA includes Algeria, Bahrain, Djibouti, the Arab Republic of Egypt, the Islamic Republic of Iran, Iraq,
Jordan, Kuwait, Lebanon, Morocco, Oman, Qatar, Saudi Arabia, the Syrian Arab Republic, Tunisia, United Arab
Emirates, and the Republic of Yemen; EAP includes Australia, Brunei Darussalam, Cambodia, China, Fiji, Hong
Kong SAR-China, Indonesia, Japan, the Republic of Korea, Lao PDR, Macao SAR-China, Malaysia, Mongolia,
Myanmar, New Zealand, Philippines, Singapore, Thailand, and Vietnam.
A similar pattern emerges when we explore the evolution of TFP growth. Figure A1 in the Appendix
shows the results of non-parametric regressions for relative and absolute TFP growth for EAP and
MENA. In both relative TFP growth (Panel A) and absolute TFP growth (Panel B), MENA under-
performs compared to EAP along the income ladder. MENA’s absolute TFP growth is downward-sloping
and quickly falls below zero when the countries reach 20 percent of U.S. GDP per capita. EAP’s absolute
TFP growth, on the other hand, is stable at 1 percent level. In relative terms, MENA’s TFP growth is
almost always below that of the United States.
7
Figure 4: Growth in PPP GDP per capita – MENA sub-regions
Panel A: Relative growth to the U.S.
Panel B: Absolute growth.
Note: Gulf Cooperation Council (GCC) consists of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab
Emirates. Other oil exporting countries include Algeria, the Islamic Republic of Iran, Iraq, the Syrian Arab Republic
and the Republic of Yemen. Other oil importing countries include Djibouti, the Arab Republic of Egypt, Jordan,
Lebanon, Morocco, and Tunisia.
The pattern of the middle-income trap is robust across three sub-regions in MENA. 9 All the sub-regions
have experienced a decline in GDP per capita at early levels of income (Figure 4), consistent with the
regional overall pattern shown in Figure 3. 10 GCC countries perform best in terms of growth. Growth in
GDP per capita in the GCC does not drop to below zero when the countries are still below the U.S. level
of per capita income. 11 In contrast, growth in per capita income of other oil exporting countries and oil
importing countries quickly drops as their income rises. Specifically, growth falls below zero at about 30
9
Countries in the Gulf Cooperation Council (GCC) are Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United
Arab Emirates. Other oil exporting countries are Algeria, the Islamic Republic of Iran, Iraq, the Syrian Arab
Republic, and the Republic of Yemen. Other oil importing countries are Djibouti, the Arab Republic of Egypt,
Jordan, Lebanon, Morocco, and Tunisia.
10
Unfortunately, the TFP data for MENA do not allow us to run non-parametric regressions at the sub-region level.
11
The focus of our paper being on middle-income, we do not examine the performance of the GCC when their per
capita income is higher than that of the United States.
8
percent of the U.S. per capita income for other oil exporting countries and at about 20 percent of the U.S.
per capita income for other oil importing countries.
III. Technology Adoption and Economic Growth
There are many possible causes for MENA’s relatively slow growth. In this section, we focus on
poor technology adoption. The literature has identified technology adoption as one important
cause for economic growth (see Parente and Prescott, 1994 and Temple, 1999).
To do so, we simply regress decadal growth on the initial level of income, a measure of
technology adoption that is Technology Readiness obtained from the World Economic Forum
and the interaction between the latter two terms. Technology readiness captures availability of
latest technologies, firm-level technology absorption, FDI and technology transfers, and other
indicators of technology adoption. 12 The interaction allows to explore the importance of the
technology adoption in driving growth at different levels of income.
Results presented in Table 1 show that higher technology adoption is associated with higher
economic growth and that the effect of technology also differ depending on the initial level of
income. Indeed, the coefficient of the interaction term is significantly positive in all three
columns, indicating that given the same initial income level, a high ranking of technology
readiness is associated with higher economic growth. According to column (3), for a country
whose initial GDP per capita is 50 percent of that of the US, increasing average technology
readiness ranking by 10, would increase annual growth of GDP per capita in the next decade by
0.8 percent.
12
Data for Technology Readiness are from Global Competitiveness Index. The index captures: availability of latest
technologies, firm-level technology absorption, FDI and technology transfer, individuals using internet, fixed
broadband internet subscriptions, international internet bandwidth, and mobile broadband subscriptions. In the
following we use the terms “technology adoption” and “technology readiness” interchangeably.
9
Table 1. Technology Adoption Readiness and Growth
(1) (2) (3)
Relative decadal growth
Relative income -0.0130*** -0.0135*** 0.0302***
(0.00186) (0.00168) (0.00328)
Average technology readiness (-) 0.000243*** 0.000243***
(0.0000146) (0.0000133)
Relative income # Average
technology readiness (-) 0.000335*** 0.000259*** 0.00169***
(0.0000459) (0.0000415) (0.0000846)
Observations 6319 6319 6319
Country fixed effect no no yes
Year fixed effect no yes yes
R-square 0.115 0.286 0.451
Notes: Coefficient estimates from ordinary least squares regressions at the country-year level. Standard errors are
given in parentheses. * p<0.1, ** p<0.05, *** p<0.01. The dependent variable is the relative annualized overlapping
decadal growth of real GDP per capita, compared to the growth in the US. Relative income is the relative real GDP
per capita from the initial year of the decade (US’s real GDP per capita at the same year equals 1). Average
technology readiness in the regression represents the average ranking for technology readiness. A higher number
means a better ranking and higher technology readiness. The main variable of interest in all columns are the
technology readiness, interacted with relative income from the initial year. The coefficient estimates associated with
the constant are not reported to save space. Column (1) has no fixed effects, while column (2) is added with year
fixed effects. In column (3), we added country fixed effect to replace the linear term of average technology
readiness, in order to capture country-specific characteristics in addition to technology readiness. See Appendix
Table A2 for the list of countries.
To address concerns about endogeneity associated with technology adopted, we instrumented
Technology Readiness with variable capturing variables capturing the attitude toward innovation
and risks presented in Hofstede et al (2010). 13 Attitudes toward innovation vary considerably
across countries. These attitudes play a critical role in driving decision of governments, firms,
individuals toward adoption of technology and innovation. Figure 5 provides illustrative
evidence of the powerful relationship between attitude traits and technology readiness. The
correlations validate that the most relevant psychological traits are power distance (the way in
which power is distributed), avoidance of uncertainty, and individualism (see Figure 5). Other
dimensions that might affect are tough versus tender, (short-term) normative versus (long-term)
pragmatic, and indulgence versus restraint. We use all six dimensions to instrument technology
readiness in the first table, and the results are presented in Table 2.
13
Data are from Hofstede Insights: https://hi.hofstede-insights.com/national-culture
10
Table 2. Growth and technology, OLS and IV regressions
Relative decadal growth
(1) (2) (3) (4) (5) (6)
OLS IV OLS IV OLS IV
Relative income -0.0525*** -0.0555*** -0.0558*** -0.0544*** -0.0654*** -0.0862***
(0.00297) (0.00366) (0.00266) (0.00323) (0.00656) (0.00721)
Average technology
readiness (-) 0.000383*** 0.000413*** 0.000407*** 0.000389***
(0.0000262) (0.0000347) (0.0000234) (0.0000305)
Relative income x
Average technology
readiness (-) 0.000691*** 0.000582*** 0.000662*** 0.000524*** 0.00275*** 0.00202***
(0.0000756) (0.0000874) (0.0000672) (0.0000770) (0.000174) (0.000205)
Observations 2794 2794 2794 2794 2794 2794
Year fixed effect no no yes yes yes yes
Country fixed effect no no no no yes yes
R-square 0.201 0.200 0.382 0.380 0.555 0.552
First-stage F-stat 249.5 255.1 1009.8
First-stage Sargan-stat 70.56 95.94 107.0
Notes: Coefficient estimates from ordinary least squares regressions at the country-year level. Standard errors are
given in parentheses. * p<0.1, ** p<0.05, *** p<0.01. The dependent variable is the relative annualized decadal
growth of real GDP per capita, compared to the growth in the US. Relative income is the relative real GDP per
capita from the initial year of the decade (US’s real GDP per capita at the same year equals 1). Average technology
readiness in the regression represents the average ranking for technology readiness. A higher number means a better
ranking, and higher technology readiness. This variable is instrumented in column (2) (4) (6), by 6-dimensions of
country specific attitudes. First stage F-stat and Sargan test for over-identification are both reported. Regressions in
all columns have the same sample to ease comparison. The coefficient estimates on constant are not reported to save
space. Column (1) and (2) has no fixed effects, while column (3) and (4) is added with year fixed effects. In column
(5) and (6), we added country fixed effect to replace the linear term of average technology readiness, in order to
capture country specific characteristics in addition to technology readiness. The main variable of interest in all
columns are the technology readiness, interacted with relative income from the initial year. This coefficient has been
significant through all columns. Countries involved this regression are listed below. The relationship between
technology readiness and 6 dimensions of attitude are graphed in Figure 5. First stage regressions of column (4) is
provided in Appendix Table A3. We have also conducted regressions in Table 2 with quadratic term of relative
income, and confirmed the relationship between decadal growth and initial income to be negative in the segment of
interest.
11
Figure 5. Correlations between Technology Readiness and Attitude Traits
Technology Readiness Technology Readiness
0
0
50
50
100
100
150
150
0 20 40 60 80 100 0 20 40 60 80 100
Power Distance Individualism
Technology Readiness Technology Readiness
0
0
50
50
100
100
150
150
0 20 40 60 80 100 0 20 40 60 80 100
Indulgence Long-term Orientation
Technology Readiness Technology Readiness
0
0
50
50
100
100
150
150
0 20 40 60 80 100 0 20 40 60 80 100
Masculinity Uncertainty Avoidance
Source: World Economic Forum, The Global Competitiveness Index dataset 2007-2017; and Hofstede Insights.
12
Note: Technology readiness in y-axis represents the average ranking for technology readiness. A smaller number
means a better ranking, and higher technology readiness. The y-axis is reversed.
The results from the instrumental regressions using attitude traits as instruments for technology
readiness confirm that there is a causal relationship between technology adoption and economic
growth. Indeed, Table 2 shows that the individual coefficients associated with technology
readiness and interactions with the level of initial income are statically significant and with the
expected signs. Due to the lack of complete 6-dimensions of attitude for some countries,
regressions in Table 2 are conducted again with only two dimensions, namely long-term
orientation and indulgence. The coefficients of the interaction term remain significantly positive,
indicating a causal relationship between technology on economic growth (Appendix Table A4).
To streamline the instrumentation, we use the first component of the 6-dimensions of the attitude
using a principal component analysis in the IV regressions presented in Table 2. The regression
table is provided in Appendix Table A5. The first stage regression is provided in Appendix Table
A6, and the weights in the first principle component is reported in Appendix Table A7. The
results confirm the causal relationship between technology adoption and economic growth.
13
IV. Empirical Evidence for the Slow Pace of Technology Adoption in MENA
In this section, we show that MENA’s technology adoption in general purpose industries (GPT) has been
poor. We do so within a framework of cross-country panel regressions, specifically contrasting
technology adoption between MENA and EAP. The specification is as follows:
������������
������������������������������������ℎ������������ ������������ ������������������������������������−1 ������������������������������������ −1 ������������������������������������−1
������������ = ������������0 + ������������1 + ������������2 ������������������������������������������������ + ������������3 ������������������������������������ + ������������4 ������������������������������������������������ × + ������������5 ������������������������������������ × + ������������������������������������ + ������������������������������������
������������������������������������ℎ������������������������,������������ ������������������������������������,������������−1 ������������������������������������,������������−1 ������������������������������������,������������−1
������������ ������������������������������������ℎ������������
where ������������������������������������ℎ������������������������ captures technology adoption of technology ������������ in county ������������ at time ������������; hence ������������������������������������ℎ������������
������������������������
captures
������������������������,������������
technology adoption relative to the U.S. Technology adoption depends on the country’s development
������������������������������������−1
level, proxied by lagged per capita income relative to the U.S. (������������ ), time fixed effects ������������������������������������ , region
������������������������,������������−1
fixed effects, and the interaction of the region fixed effects and per capita income relative to the United
States. The EAP and MENA fixed effects capture region-specific difference in technology adoption
relative to the rest of the world. The interactions capture the speed of technology adoption in MENA and
EAP, relative to other countries with the same level of income, as income rises.
Technology adoption is proxied by (1) bandwidth per internet user (bits per second), (2) number of self-
contained computers designed for use by one person, (3) internet users in percentage of population, (4)
number of ATMs per million capita (5) number of payments by credit and debit cards per million capita,
(6) tractors used in agriculture per million capita, and (7) gross output of electric energy per million
capita. Data are mainly from the CHAT database (Comin and Hobjin, 2010), except that bandwidth and
internet users in percentage of population are from World Telecommunication Database (ITU).
For MENA, the pace of technology adoption for all technologies as income rises is slower compared to
other countries with the same income. The results are shown in Table 3 and illustrated graphically in
Figure 6. The coefficients associated with the interaction between the MENA regional dummy and
relative income are all negative and statistically significant, which translates into downward-sloping lines
for MENA (in red) in Figure 6. For EAP, the coefficients associated with the interaction between the EAP
regional dummy and relative income are also negative and significant, but the magnitudes are much
smaller than those for MENA. That implies the speed of technology adoption as income rises in EAP is
larger than that in MENA. This is shown by the gaps between the blue lines (EAP) and the red lines
(MENA) in Figure 6.
It is noteworthy that both MENA and EAP have positive and significant coefficients for the region fixed
effects (translating into positive intercepts of the blue and red lines in Figure 7). That suggests that at
(very) low levels of income, EAP and MENA countries have a faster pace of technology adoption relative
14
to the rest of the world. However, when income rises, that initial advantage quickly fades away because of
the lower speed of adoption. Results are robust to using all regional dummies (see Appendix Table A9).
Table 3: Technology adoption in MENA and EAP
(1) (2) (3) (4) (5) (6) (7)
Bandwidth per Computer Per Internet Users Number of ATM Payments by Tractor per Mil Electricity
Internet User Mil Capita (%) per Mil Capita Credit and Capita Production per
Debit Cards per Mil Capita
Mil Capita
Relative Income to the US 6.554*** 0.718*** 0.835*** 0.621*** 0.649*** 1.307*** 0.848***
(1.227) (0.0181) (0.0214) (0.0539) (0.0569) (0.0339) (0.0317)
EAP 114.2*** 3.716*** 9.477*** 200.6*** 38.39** -2.956* 1.678*
(30.67) (1.108) (1.336) (39.10) (15.99) (1.676) (0.871)
MNA 149.8*** 7.814*** 11.98*** 60.16* 15.19*** 11.83*** 14.08***
(32.20) (0.653) (1.311) (32.83) (5.540) (0.851) (1.193)
EAP * Relative Income -4.136*** 0.0430 -0.361*** -1.951*** -0.416** -0.323*** -0.0633
(1.358) (0.0395) (0.0431) (0.377) (0.166) (0.0859) (0.0396)
MNA * Relative Income -6.382*** -0.640*** -0.653*** -1.205*** -0.709*** -1.317*** -0.786***
(1.228) (0.0185) (0.0275) (0.320) (0.0775) (0.0340) (0.0338)
Constant -76.26* 14.97* -21.00*** 4.214 -27.73** -25.35*** -0.178
(42.48) (8.378) (1.883) (9.867) (13.66) (5.750) (10.90)
Observations 2985 1281 4193 368 372 4115 3151
r2 0.161 0.796 0.673 0.432 0.262 0.536 0.579
* p<0.10 ** p<0.05 *** p<0.01
Note: (1) Bandwidth per internet user (bits per second), (2) number of self-contained computers designed for use by
one person, (3) internet users in percentage of population, (4) numbers of ATM per million capita (5) payments by
credit and debit cards per million capita, (6) tractors used in agriculture per million capita, (7) Gross output of
electric energy per million capita. Time-fixed effects are included in all regressions. See the list of countries
included in these regressions presented in Appendix Table A8.
15
Figure 6: Technology adoption in MENA and EAP
16
Generally, there could be several reasons behind to the lack of technology adoption. The literature has
identified many factors that could affect a country’s technology adoption, such as human capital
(Wozniak, 1987; Benhabib and Spiegel, 2005; Che and Zhang, 2018), trade and FDI (see Keller, 2004 for
a review), and competition (Aghion et al, 2005; Seim and Viard, 2011).
With a cross-country regression framework, we show that the lack of competition could be one of the
reasons behind MENA’s lack of technology adoption. Our measure for market competition is market
concentration. The argument is that, comparing within the same industry, countries or regions with higher
market concentration tend to have weaker competition (see Berger and Hannan, 1989 and Bikker and
Haaf, 2002 in the banking industry, and Sung, 2014 for the telecom industry). Market concentration is
widely used to proxy for market competition. The calculation of market concentration indices such as
Herfindahl-Hirschman Index (HHI) has been a starting point for assessing the state of market competition
(see for example, U.S. Department of Justice and the Federal Trade Commission, 2010). Obviously,
market concentration is just one indicator and does not contain all relevant information about competition.
However, given our data limitation in cross-country regressions, it is our best choice.
In general purpose industries (GPT) such as telecom and finance, there is a high level of market
concentration in MENA. Table 4 and the associated graphical illustration in Figure 6 show that market
concentration for Mobile Operators and Banking in MENA increases significantly faster as income rises
than other countries with the same income (see the red lines in Figure 7) 14. To account for possible non-
linearities we include a quadratic term. Figure 7 shows that for mobile operators, while market
concentration is smaller in MENA when income is low, it quickly increases with a steep positive slope,
while the slope for EAP is negative. For banking, asset concentration for both EAP and MENA is rising
faster than the rest of the world, but MENA is above EAP in levels of asset concentration. This evidence
is consistent with a popular notion that MENA does not fare well in market competition. For example,
according to the World Bank’s Doing Business data, MENA countries are generally ranked very low in
starting a business (e.g. Saudi Arabia is ranked 141, Egypt 109, Algeria 150, Iraq 155). 15 Results are
robust to using regional dummies for all regions of the world (see Appendix Table A10).
14
For Mobile Operators, market concentration is calculated as annual average of quarterly HHI, based on market
share of mobile operators provided by GSMA. For Banking, market concentration is calculated as assets of three
largest banks as a share of assets of all commercial banks, data source is World Bank Database on Financial
Development and Structure which was first constructed by Beck et al (2000).
15
As Arezki et al. (2018) argue, “MENA governments seeking to protect incumbents, especially in sectors like
banking and telecommunications, impose excessive and outdated regulations that deter new actors from entering the
market. This short-circuits competition, undermines the diffusion of general-purpose technology, and blocks the
type of adaptation and evolution that underpins a vibrant private sector”.
17
Table 4: Market Concentration in Telecom and Finance
(1) (2)
Mobile Bank
Operators Concentration
Concentration
Relative Income to the US -13.72*** -0.0472***
(1.394) (0.0146)
EAP 1477.3*** -6.485***
(161.0) (1.894)
MNA -112.6 -1.523
(168.7) (1.559)
EAP * Relative Income -5.327* 0.105***
(3.123) (0.0255)
MNA * Relative Income 23.21*** 0.0655***
(2.166) (0.0205)
Constant 6791.0*** 72.84***
(207.0) (2.118)
Observations 3321 2983
r2 0.194 0.0246
* p<0.10 ** p<0.05 *** p<0.01
Note: Time-fixed effects are included in all regressions.
Figure 7: Visual illustration for Table 2
18
A large literature has helped document both theoretically and empirically that weak competition is
arguably harmful for innovation and productivity growth (see Aghion and Hewitt, 1992 and Aghion et al,
2014). Results from cross-country regressions presented in Table 5 show that given the same level of
relative income, mobile and banking concentrations are negatively correlated with technology adoption in
the corresponding sector. In other words, a higher level of concentration is associated with lower
penetration of the technology. Figure 8 provides a graphical illustration of the results in Table 5. The lines
for bandwidth, internet users and ATMs are downward-sloping. For credit and debit card payments, the
line slopes upward when bank concentration is very high. However, the confidence band becomes large.
To summarize, this section shows that market concentration in GPT such as banking and telecom in
MENA becomes higher than other comparators as income rises. This translates to lower adoption of the
GPT technologies in MENA.
Table 5: Concentration in mobile and banking operators and technology adoption
Number of Payments by credit
bandwidth per internet users ATMs per mil and debit cards per
internet user (%) capita mil capita
relative income (t-1) 3.505*** 0.616*** 0.478*** 0.490***
(0.690) (0.0253) (0.0722) (0.0691)
mobile concentration -0.0213 -0.00595***
(0.0224) (0.00121)
mobile concentration^2 0.000000682 0.000000167*
(0.00000167) (9.10e-08)
bank concentration -1.198** -2.410***
(0.547) (0.781)
bank concentration^2 0.00649 0.0181***
(0.00425) (0.00657)
Constant 66.30 31.01*** 73.31*** 73.07***
(79.92) (4.211) (19.88) (24.41)
Observations 2464 2658 178 182
year fixed effects y y y y
r2 0.108 0.621 0.385 0.344
19
Figure 8: Visualization for Table 3
V. Conclusion
This paper documented the existence of a “middle-income trap” for the Middle East and North Africa
region (MENA). It argued that MENA economic woes offer new insights into the debate on the trap,
which has thus far focused on the East Asia and Pacific region (EAP). The results are two-folds. First,
non-parametric regressions show that the average rate of economic growth in MENA has not only been
significantly lower than EAP but has also tended to drop at an earlier level of income. Second, a slower
pace of technology adoption is associated with slower levels of economic growth and MENA has
experienced a relatively slow pace of technology adoption in general purpose technologies (GPT).
These results suggest that barriers to GPT adoption constitute an important channel of transmission for
the middle-income trap. Indeed, the pervasive lack of market contestability in MENA markets and the
resulting slow pace of technology adoption including in key sectors can help explain why more generally
economies tend to get stuck. To the extent that governments play a key role in the regulation of entry
including in key “upstream” sectors, the literature focus on firm level dynamics only shed lights on
20
“downstream” matters. Instead, the lack of availability of frontier GPT can seclude firms into low
productivity activities, limiting trade and economic growth. Further research on the interplay between the
causes and consequences of lack of (government induced) GPT adoption would help understand the
nature and consequences of upstream factors impeding productivity gains and growth.
From a policy perspective, one proposal put forward by Arezki et al (2018) to break with “business as
usual” in the MENA region is for the authorities to embrace a “moonshot approach” to the adoption of
information technology and communications. MENA countries could emulate President John F.
Kennedy’s 1961 decision to unleash an extraordinary collective national effort that achieved its seemingly
impossible goal: a manned lunar landing in mid-1969. A MENA moonshot would involve a collective
regional commitment to achieve parity with advanced economies in information and communications
technology by 2021. MENA countries would seek to equal or better OECD countries in terms of their
level of access to the internet, capacity to transmit data (bandwidth) and the number of financial
transactions carried out electronically. This would unleash the potential of the young and educated
population—who have been subject to abnormally high levels of unemployment—and spur growth. 16
16
World Bank (2019) shows that MENA has the highest youth unemployment rates in the world and these rates are
highest among the educated.
21
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25
Table A1: Coefficients for the non-parametric regressions: Relative growth versus relative income
EAP
Average Percentile
relative
Predicted Std.Err z P>|z|
income [95% Conf.
Growth
Interval]
0 0.021 0.001 14.4 0 0.018 0.021
10 0.022 0.001 19.76 0 0.021 0.023
20 0.026 0.002 16.71 0 0.025 0.028
30 0.026 0.001 28.82 0 0.025 0.028
40 0.025 0.001 25.62 0 0.024 0.026
50 0.023 0.001 22.65 0 0.022 0.024
60 0.016 0.001 14.9 0 0.013 0.016
70 0.008 0.001 11.25 0 0.006 0.008
80 0.004 0 8.88 0 0.003 0.004
90 0.006 0.002 3.89 0 0.004 0.009
100 0.009 0.003 2.96 0.003 0.003 0.011
MENA
Average Percentile
relative
Predicted Std.Err z P>|z|
income [95% Conf.
Growth
Interval]
0 0.024 0.003 8.123 0 0.018 0.029
10 0.016 0.002 6.683 0 0.011 0.02
20 0.01 0.002 4.693 0 0.007 0.015
30 0.006 0.002 2.509 0.012 0.002 0.011
40 0.002 0.002 0.869 0.385 -0.002 0.007
50 -0.001 0.003 -0.242 0.809 -0.005 0.004
60 -0.004 0.003 -1.241 0.215 -0.009 0.001
70 -0.007 0.003 -2.117 0.034 -0.013 -0.002
80 -0.011 0.004 -2.775 0.006 -0.018 -0.004
90 -0.015 0.004 -3.497 0 -0.023 -0.007
100 -0.02 0.004 -4.784 0 -0.028 -0.013
26
Figure A1: Growth in TFP
Panel A: Relative TFP Growth (to the U.S.)
East Asia and Pacif ic Middle East and North Africa
-.05 -.04 -.03 -.02 -.01 0 .01 .02 .03 .04 .05
-.05 -.04 -.03 -.02 -.01 0 .01 .02 .03 .04 .05
relative growth in TFP
relative growth in TFP
0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100
relative income to the U.S., percentage relative income to the U.S., percentage
Panel B: Absolute TFP growth
East Asia and Pacif ic Middle East and North Af rica
-.05 -.04 -.03 -.02 -.01 0 .01 .02 .03 .04 .05
-.05 -.04 -.03 -.02 -.01 0 .01 .02 .03 .04 .05
absolute growth in TFP
absolute growth in TFP
0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100
relative income to the U.S., percentage relative income to the U.S., percentage
27
Figure A2: Relative GDP growth for other regions
Europe and Central Asia Latin America and the Caribbean
-.01 0 .01 .02 .03 .04 .05
relative growth in income
relative growth in income
-.01 0 .01.02.03.04.05
-.05-.04-.03-.02
-.02
-.03
-.04
-.05
0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100
relative income to the U.S., percentage relative income to the U.S., percentage
South Asia Sub-Saharan Af rica
-.05-.04-.03-.02-.01 0 .01 .02 .03 .04 .05
relative growth in income
relative growth in income
-.05-.03-.01.01 .03 .05
0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100
relative income to the U.S., percentage relative income to the U.S., percentage
Note: All regions are defined following World Bank country groups 17.
17
See http://databank.worldbank.org/data/download/site-content/CLASS.xls for the current classification.
28
Table A2. List of countries
Albania Denmark Latvia Qatar
Dominican
Algeria Republic Lesotho Romania
Argentina Ecuador Lithuania Russia
Armenia Egypt Luxembourg Saudi Arabia
Australia El Salvador Macedonia, FYR Senegal
Austria Estonia Madagascar Serbia
Azerbaijan Ethiopia Malaysia Singapore
Bahrain Finland Mali Slovak Republic
Bangladesh France Mauritania Slovenia
Barbados Gambia, The Mauritius South Africa
Belgium Georgia Mexico Spain
Benin Germany Mongolia Sri Lanka
Bolivia Greece Montenegro, Rep. of Sweden
Bosnia and Herzegovina Guatemala Morocco Switzerland
Botswana Honduras Mozambique Syria
Brazil Hong Kong SAR Namibia Tajikistan
Bulgaria Hungary Nepal Tanzania
Burkina Faso Iceland Netherlands Thailand
Burundi India New Zealand Trinidad and Tobago
Cambodia Indonesia Nicaragua Tunisia
Cameroon Ireland Nigeria Turkey
Canada Italy Norway Uganda
Chad Jamaica Oman Ukraine
Chile Japan Pakistan United Arab Emirates
China Jordan Panama United Kingdom
Colombia Kazakhstan Paraguay United States
Costa Rica Kenya Peru Uruguay
Croatia Korea Philippines Venezuela
Cyprus Kuwait Poland Vietnam
Czech Republic Kyrgyz Republic Portugal Zambia
Zimbabwe
Note: The table presents countries included in Table 1.
29
Table A3. First stage regression of Column (4) in Table 2.
(1) (2)
Average technology Relative income x Average
readiness (-) technology readiness (-)
Power Distance 0.0279 0.0618***
(0.0381) (0.0108)
Individualism 0.783*** -0.0757***
(0.0322) (0.00912)
Masculinity -0.563*** -0.0604***
(0.0361) (0.0102)
Uncertainty Avoidance 0.161*** 0.00136
(0.0251) (0.00712)
Long-term Orientation 0.733*** -0.00253
(0.0240) (0.00680)
Indulgence 0.568*** 0.0195***
(0.0250) (0.00709)
Power Distance x Relative income -0.301*** -0.343***
(0.0847) (0.0240)
Individualism x Relative income -1.112*** 0.337***
(0.0623) (0.0177)
Masculinity x Relative income 0.567*** -0.0726***
(0.0626) (0.0177)
Uncertainty Avoidance x Relative income -0.653*** -0.264***
(0.0562) (0.0159)
Long-term Orientation x Relative income -0.695*** 0.384***
(0.0498) (0.0141)
Indulgence x Relative income -1.347*** -0.238***
(0.0732) (0.0208)
Relative income x Relative income 247.5*** 2.194
(9.711) (2.754)
Observations 2794 2794
Note: Standard errors are given in parentheses. * p<0.1, ** p<0.05, *** p<0.01. Coefficients of constants are not
reported to save space.
30
Table A4. Growth and technology, OLS and IV regression (2 dimensions)
Relative decadal growth
(1) (2) (3) (4) (5) (6)
OLS IV OLS IV OLS IV
Relative income -0.0605*** -0.0727*** -0.0609*** -0.0639*** -0.0245*** -0.109***
(0.00344) (0.00537) (0.00309) (0.00474) (0.00828) (0.0144)
Relative income x
Average technology
readiness (-) 0.000720*** 0.000619*** 0.000603*** 0.000562*** 0.00325*** 0.000900**
(0.0000768) (0.000147) (0.0000689) (0.000130) (0.000192) (0.000381)
Average technology
readiness (-) 0.000492*** 0.000602*** 0.000498*** 0.000525***
(0.0000255) (0.0000457) (0.0000229) (0.0000402)
Observations 3726 3726 3726 3726 3726 3726
Year fixed effect - - yes yes yes yes
Country fixed effect - - - - yes yes
R-square 0.188 0.184 0.361 0.361 0.496 0.475
First-stage F-stat 222.8 221.2 613.3
First-stage Sargan-test 9.674 10.42 29.15
Notes: Coefficient estimates from ordinary least squares regressions at the country-year level. Standard errors are
given in parentheses. * p<0.1, ** p<0.05, *** p<0.01. The dependent variable is the relative annualized decadal
growth of real GDP per capita, compared to the growth in the US. Relative income is the relative real GDP per
capita from the initial year of the decade (US’s real GDP per capita at the same year equals 1). Average technology
readiness in the regression represents the average ranking for technology readiness. A higher number means a better
ranking, and higher technology readiness. This variable is instrumented in column (2) (4) (6), by 2-dimensions of
country specific attitudes, namely long-term orientation, and indulgence. First stage F-stat and Sargan test for over-
identification are both reported. Regressions in all columns have the same sample to ease comparison. The
coefficient estimates on constant are not reported to save space. Column (1) and (2) has no fixed effects, while
column (3) and (4) is added with year fixed effects. In column (5) and (6), we added country fixed effect to replace
the linear term of average technology readiness, in order to capture country specific characteristics in addition to
technology readiness. The main variable of interest in all columns are the technology readiness, interacted with
relative income from the initial year. This coefficient has been significant through all columns. Countries involved
this regression are listed below.
31
Table A5. Technology and growth, OLS and IV (first principle component)
Relative decadal growth
(1) (2) (3) (4) (5) (6)
OLS IV OLS IV OLS IV
Relative income -0.0525*** -0.0315*** -0.0558*** -0.0534*** -0.0654*** 0.0776***
(0.00297) (0.0104) (0.00266) (0.0101) (0.00656) (0.0261)
Relative income x
Average technology
readiness (-) 0.000691*** 0.000380 0.000662*** 0.000865*** 0.00275*** 0.00776***
(0.0000756) (0.000248) (0.0000672) (0.000236) (0.000174) (0.000900)
Average technology
readiness (-) 0.000383*** 0.000150 0.000407*** 0.000386***
(0.0000262) (0.000114) (0.0000234) (0.000111)
Observations 2794 2794 2794 2794 2794 2794
Country fixed effect - - - - yes yes
Year fixed effect - - yes yes yes yes
R-square 0.201 0.151 0.382 0.380 0.555 0.416
First-stage F-stat 31.60 23.44 131.2
Notes: Coefficient estimates from ordinary least squares regressions at the country-year level. Standard errors are
given in parentheses. * p<0.1, ** p<0.05, *** p<0.01. The dependent variable is the relative annualized decadal
growth of real GDP per capita, compared to the growth in the US. Relative income is the relative real GDP per
capita from the initial year of the decade (US’s real GDP per capita at the same year equals 1). Average technology
readiness in the regression represents the average ranking for technology readiness. A higher number means a better
ranking, and higher technology readiness. This variable is instrumented in column (2) (4) (6), by the first principle
component of the 6 dimensions of attitude. First stage F-stat is reported. Regressions in all columns have the same
sample to ease comparison. The coefficient estimates on constant are not reported to save space. Column (1) and (2)
has no fixed effects, while column (3) and (4) is added with year fixed effects. In column (5) and (6), we added
country fixed effect to replace the linear term of average technology readiness, in order to capture country specific
characteristics in addition to technology readiness. The main variable of interest in all columns are the technology
readiness, interacted with relative income from the initial year. First stage regression of column (4) is provided in
Appendix Table A6, and the weights in the first component is reported in Appendix Table A7.
32
Table A6. First stage regression of column (4) in Table A5
(1) (2)
Average technology Relative income x Average
readiness (-) technology readiness (-)
Attitude -9.434*** 2.189***
(0.703) (0.237)
Attitude x Relative income 16.19*** -6.931***
(1.197) (0.404)
Relative income 91.99*** -8.251***
(1.947) (0.657)
Constant -73.92*** -8.618***
(3.235) (1.092)
Observations 2794 2794
Table A7. Principle Component of Attitude
Principle
Component
Power Distance 0.6
Individualism -0.6
Masculinity 0.1
Uncertainty Avoidance 0.3
Long-term Orientation 0.2
Indulgence -0.4
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Table A8: List of countries
United States Guatemala Vietnam Sudan
United Kingdom Haiti Algeria Swaziland
Austria Honduras Angola Tanzania
Belgium Mexico Botswana Togo
Denmark Nicaragua Burundi Tunisia
France Panama Cameroon Uganda
Germany Paraguay Central African Republic Burkina Faso
Italy Peru Chad Zambia
Congo, Democratic
Netherlands Uruguay Republic of the Armenia
Norway Belize Benin Azerbaijan
Sweden Suriname Equatorial Guinea Belarus
Switzerland Iran Ethiopia Albania
Canada Jordan Gabon Georgia
Japan Kuwait Ghana Kazakhstan
Finland Lebanon Guinea-Bissau Bulgaria
Greece Oman Guinea Moldova
Iceland Saudi Arabia Kenya Russia
Ireland Syria Lesotho Tajikistan
Portugal United Arab Emirates Liberia China
Spain Egypt Madagascar Turkmenistan
Turkey Yemen Malawi Ukraine
Australia Bangladesh Mali Uzbekistan
New Zealand Cambodia Mauritania Czech Republic
South Africa Sri Lanka Mauritius Slovak Republic
Argentina India Morocco Estonia
Bolivia Indonesia Mozambique Latvia
Brazil Korea Niger Hungary
Chile Malaysia Nigeria Lithuania
Colombia Nepal Zimbabwe Mongolia
Costa Rica Pakistan Rwanda Croatia
Dominican
Republic Philippines Senegal Slovenia
Ecuador Singapore Sierra Leone Poland
El Salvador Thailand Namibia Romania
Note: The table presents countries included in Table 3.
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Table A9: Estimating Technology Adoption with Regional Dummies
(1) (2) (3) (4) (5) (6) (7)
Bandwidth Computer Internet Number of Payments Tractor Electricity
per internet per mil users (%) ATM per by credit per mil production
user capita mil capita and debit capita per mil
cards per capita
mil capita
Relative income -3.618*** 2.220*** 0.387 -1.072*** 0.547** -1.847*** -2.979***
(0.818) (0.215) (0.279) (0.231) (0.266) (0.143) (0.174)
EAP -435.4*** 118.4*** -48.61* 8.481 -24.39 -295.0*** -405.9***
(80.72) (20.76) (25.21) (44.27) (29.85) (13.63) (17.35)
ECA -792.3*** 113.2*** -44.66* -191.1*** -57.90** -249.8*** -406.8***
(123.2) (20.78) (25.21) (21.64) (25.45) (14.02) (17.70)
LAC -374.4*** 120.0*** -51.10** -293.7*** -403.0***
(85.68) (20.76) (25.20) (13.58) (17.37)
MNA -444.1*** 122.6*** -47.76* -132.7*** -46.98* -279.9*** -394.4***
(80.24) (20.74) (25.20) (37.24) (25.79) (13.58) (17.40)
SAR -452.5*** 121.6*** -62.83** -287.4*** -401.7***
(80.33) (20.79) (25.23) (13.69) (17.39)
SSA -449.5*** 121.2*** -60.60** -288.0*** -401.1***
(80.14) (20.77) (25.18) (13.57) (17.38)
EAP x Relative income 6.038*** -1.454*** 0.0865 -0.259 -0.311 2.837*** 3.753***
(1.002) (0.218) (0.282) (0.437) (0.309) (0.162) (0.175)
ECA x Relative income 14.05*** -1.510*** 0.296 1.628*** -0.0432 2.733*** 3.778***
(2.290) (0.217) (0.281) (0.234) (0.268) (0.155) (0.186)
LAC x Relative income 3.616*** -1.840*** 0.243 2.638*** 3.316***
(0.974) (0.218) (0.282) (0.149) (0.175)
MNA x Relative income 3.812*** -2.142*** -0.204 0.494 -0.614** 1.836*** 3.041***
(0.819) (0.215) (0.280) (0.376) (0.272) (0.143) (0.174)
SAR x Relative income 4.654*** -2.064*** 0.474 2.356*** 3.208***
(0.932) (0.287) (0.304) (0.306) (0.220)
SSA x Relative income 4.117*** -2.026*** -0.0954 2.105*** 3.187***
(0.855) (0.220) (0.286) (0.146) (0.177)
Observations 2967 1281 4170 368 372 4115 3151
R-square 0.219 0.839 0.709 0.545 0.476 0.611 0.671
Note: This table reports all regional dummies with North America as the default region. Standard errors are given in
parentheses. * p<0.1, ** p<0.05, *** p<0.01. Time-fixed effects are included in all regressions. Coefficients of
constants are not reported to save space.
35
Table A10: Market Concentration in Telecom and Finance
(1) (2)
Mobile operators Concentration Bank concentration
Relative income -33.65 -1.831***
(20.52) (0.223)
EAP 1328.9 -147.9***
(1834.6) (21.49)
ECA -1088.4 -146.1***
(1830.8) (21.45)
LAC -272.5 -144.6***
(1837.4) (21.48)
MNA -211.6 -144.3***
(1835.7) (21.46)
SAR -1835.0 -164.0***
(1852.6) (21.76)
SSA 13.51 -135.7***
(1830.6) (21.43)
EAP x Relative income 14.63 1.890***
(20.71) (0.224)
ECA x Relative income 29.33 1.870***
(20.59) (0.224)
LAC x Relative income 34.90 1.757***
(21.75) (0.231)
MNA x Relative income 43.11** 1.852***
(20.59) (0.223)
SAR x Relative income 119.8*** 3.063***
(28.16) (0.450)
SSA x Relative income 53.61** 1.929***
(21.08) (0.235)
Observations 3303 2962
R-square 0.248 0.111
Note: This table reports all regional dummies with North America as the default region. Standard errors are given in
parentheses. * p<0.1, ** p<0.05, *** p<0.01. Time-fixed effects are included in all regressions. Coefficients of
constants are not reported to save space.
36