metadata: authors: 0: author: Arezki,Rabah
1: author: Fan,Yuting
2: author: Nguyen,Ha Minh
count: Middle East and North Africa
docna: 0: docna: Technology Adoption and the Middle-Income
            Trap : Lessons from the Middle East and East Asia
docty: Policy Research Working Paper
subtopic: Economic Growth,Industrial Economics,Economic Theory & Research,Innovation,Oil & Gas,Information Technology,Telecommunications Infrastructure
teratopic: Industry,Macroeconomics and Economic Growth,Science and Technology Development,Energy,Information and Communication Technologies
abstracts: cdata!: This paper documents the existence of a
            "middle-income trap" for the Middle East and North
            Africa region and contrasts the evidence with that of the
            East Asia and Pacific region. The results are two-folds.
            First, non-parametric regressions show that the average rate
            of economic growth in the Middle East and North Africa has
            not only been significantly lower than that in the East Asia
            and Pacific region, but it has also tended to drop at an
            earlier level of income. Second, econometric results point
            to Middle East and North Africa having experienced a
            relatively slow pace of technology adoption in
            general-purpose technologies and that a slower adoption pace
            of technology is associated with lower levels of economic
            growth. The paper concludes that barriers to the adoption of
            general-purpose technologies related to the lack of
            contestability in key sectors constitute an important
            channel of transmission for the middle-income trap.
repnme: repnme: Technology Adoption and the Middle-Income
            Trap : Lessons from the Middle East and East Asia
display_title: Technology Adoption and the
            Middle-Income Trap : Lessons from the Middle East and East Asia
listing_relative_url: /research/2019/05/31118742/technology-adoption-middle-income-trap-lessons-middle-east-east-asia
topicv3: Economic Growth m1326241 471,Oil and Gas Export m1326621 2290,Innovation and Productivity Growth
            m1327173 1944,Broadband Networks m1327576 291,Broadband m1627085 4739
publishtoextweb_dt: 2019-05-30T00:00:00Z
historic_topic: Industry,Macroeconomics and Economic Growth,Science and Technology Development,Energy,Information and Communication Technologies
no_of_pages: 38
author_keys: 0: author_key: 1596791
1: author_key: 1625028
2: author_key: 1057980
model_extractions: 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 trap” for the Middle East and North Africa region and contrasts the evidence with that of the East Asia and Pacific region. The results are two-folds. First, non-parametric regressions show that the average rate of economic growth in the Middle East and North Africa has not only been significantly lower than that in the East Asia and Pacific region, but it has also tended to drop at an earlier level of income. Second, econometric results point to Middle East and North Africa having experienced a relatively slow pace of technology adoption in general-purpose technologies and that a slower adoption pace of technology is associated with lower levels of economic growth. The paper concludes that barriers to the adoption of general-purpose technologies related to the lack of contestability in key sectors constitute an important channel of transmission for the middle-income trap.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger 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.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **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” <mark>refers to the possibility that economies could get stuck at a certain level of income.</mark> The debate on the trap has thus far focused mostly on the East Asia and Pacific region (EAP).<sup>1</sup> 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).<sup>2</sup> 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.

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middle-income trap.<sup>3</sup> 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**

<!-- Start of picture text --> 2000-2021<br>Lower<br>0.04 Upper Middle Middle<br>0.03<br>0.02 High Income<br>0.01<br>0.00<br>-0.01<br>-0.02<br>Bahrain Kuwait Oman Qatar Saudi Arabia United Arab Emirates Algeria Iran Iraq Lebanon Libya Djibouti Egypt Jordan Morocco Tunisia West Bank & Gaza Yemen, Rep.<br><!-- End of picture text --> 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).<sup>4</sup> 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.<sup>5</sup> 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).

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The remainder of the paper is organized as follows. Section II documents the evidence of a middleincome 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.<sup>6</sup> 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**

<!-- Start of picture text --> QAT<br>HKG SAU KWT ARE<br>OMNBHR<br>KOR CYP<br>PRT<br>LBN IRN<br>CHN DZA<br>IDNLKA TUNEGY JOR<br>LBY<br>BTN MAR<br>VNM INDLAO<br>0 1 2 3<br>log of real PPP GDP per capital relative to the U.S. in 1975<br>2.5<br>2<br>1.5<br>1<br>.5<br>0<br>log of real PPP GDP per capital relative to the U.S. in 2017<br><!-- End of picture text --> 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.

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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:<sup>7</sup> estimated growth rate of each region at each level of income: <u>yi</u> i U = , f ~~�~~ U ∆log (yy) ,ii+10 −∆log (yy) ii,ii+10 ff yy ii ii�+ εεii where i and U are overlapping annualized decadal growth in GDP per capita (or TFP) of country ∆log (yy) ,ii+10 and of the U.S. between time ∆log (yy) ii,ii+10 and time , <u>yUi</u> is the country’s relative income per capita relative to the U.S. at time ii . We use GDP per capita derived from output-side real tt tt+ 10 yy ii GDP at chained PPPs and total factor productivity (TFP), both from Penn World Table 9.0. The tt regressions also include overlapping decade fixed-effect, f , to control for common global shocks. ii Note that we are agnostic about the form of the function <u>yi</u> , f . Unlike linear regression, a ~~�~~ U � <mark>nonparametric regression is agnostic about the functional form between the outcome and the explanatory</mark> ff yy ii ii <mark>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.</mark> 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<sup>8</sup> . 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.

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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.

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# **Figure 3: Growth in PPP GDP per capita**

Panel A: Relative to the U.S.

<!-- Start of picture text --> East Asia and Pacific Middle East and North Africa<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>relative income to the U.S., percentage relative income to the U.S., percentage<br>Panel B: Actual growth (not relative to the U.S.)<br>East Asia and Pacific Middle East and North Africa<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>relative income to the U.S., percentage relative income to the U.S., percentage<br>.05 .05<br>.04 .04<br>.03 .03<br>.02 .02<br>.01 .01<br>0 0<br>-.01 -.01<br>-.02 -.02<br>relative growth in income -.03 relative growth in income-.03<br>-.04 -.04<br>-.05 -.05<br>.05 .05<br>.04 .04<br>.03 .03<br>.02 .02<br>.01 .01<br>0 0<br>-.01 -.01<br>-.02 -.02<br>absolute growth in income absolute growth in income<br>-.03 -.03<br>-.04 -.04<br>-.05 -.05<br><!-- End of picture text --> 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 underperforms 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
<!-- Start of picture text --> MENA-GCC MENA-Other oil-exporting countries MENA- Other oil-importing countries<br>growth in incomein gcc growth in income in oec growth in incomein oic<br>88<br>aa2= 28r-g r<br>i: i: Hy<br>fs i: f<br>g4<br>3<br>8<br>901 2 8s 1 0 5 Cn ee 5 Ey 5 ® 5 © emacue = % »<br><!-- End of picture text --> <!-- Start of picture text --> MENA-GCC MENA-Other oil-exporting countries MENA- Other oil-importing countries<br>grow th in income in gee grow th in income in oec growth in incomein oic<br>5 isz !<br>iba i:S i<br>a‘i) 10 2 x a x i) 70 a 2 100 3) 5 10 15 2 3 x 3s Qo 5 10 15 2 3 x<br>reat income reat income relate income<br><!-- End of picture text -->
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.<sup>12</sup> 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.

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**Table 1. Technology Adoption Readiness and Growth**

||(1)<br>Rela|(2)<br>tive decadal gr|(3)<br>owth|
|---|---|---|---|
|Relative income|-0.0130***<br>(0.00186)|-0.0135***<br>(0.00168)|0.0302***<br>(0.00328)|
|Average technology readiness (-)|0.000243***<br>(0.0000146)|0.000243***<br>(0.0000133)||
|Relative income # Average<br>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).<sup>13</sup> 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

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**Table 2. Growth and technology, OLS and IV regressions**

||||Relative dec<br>|adal growth<br>|||
|---|---|---|---|---|---|---|
||(1)<br>OLS|(2)<br>IV|(3)<br>OLS|(4)<br>IV|(5)<br>OLS|(6)<br>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<br>readiness (-)|0.000383***|0.000413***|0.000407***|0.000389***|||
||(0.0000262)|(0.0000347)|(0.0000234)|(0.0000305)|||
|Relative income x<br>Average technology<br>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.

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Figure 5. Correlations between Technology Readiness and Attitude Traits <!-- Start of picture text --> Technology Readiness Technology Readiness<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Power Distance Individualism<br>Technology Readiness<br>Technology Readiness<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Indulgence<br>Long-term Orientation<br>Technology Readiness Technology Readiness<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Masculinity Uncertainty Avoidance<br>0 0<br>50 50<br>100 100<br>150 150<br>0 0<br>50 50<br>100 100<br>150 150<br>0 0<br>50 50<br>100 100<br>150 150<br><!-- End of picture text --> Source: World Economic Forum, The Global Competitiveness Index dataset 2007-2017; and Hofstede Insights.

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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.

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# **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: i i −1 0 1 2M M 3M 4M M ×<sup>yyi</sup> 5M<sup>yyi</sup> + f i Uxx U U U<sup>−1−1</sup> ttfftth yy xx ii ,ii = ββ + ββ ,ii−1 + ββ + ββ EE+ ββ ,ii−1 + ββ EE× ,ii−1 + εε where ttfftth i captures technology adoption of technology yy in county yy at time ; henceyy Uixx captures xx iieecchxx technology adoption relative to the U.S. Technology adoption depends on the country’s development ttfftth xx ii tt iieecch ,ii level, proxied by lagged per capita income relative to the U.S. ~~(~~ <u>yi</u> −1 ), time fixed effects f , region U fixed effects, and the interaction of the region fixed effects and per capita income relative to the United yy ,ii−1 ii 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 selfcontained 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)<br>Bandwidth per<br>Internet User|(2)<br>Computer Per<br>Mil Capita|(3)<br>Internet Users<br>(%)|(4)<br>Number of ATM<br>per Mil Capita|(5)<br> <br>Payments by<br>Credit and<br>Debit Cards per<br>Mil Capita|(6)<br> <br>Tractor per Mil<br>Capita|(7)<br>Electricity<br>Production per<br>Mil Capita|
|---|---|---|---|---|---|---|---|
|Relative Income to the US|6.554***<br>(1.227)|0.718***<br>(0.0181)|0.835***<br>(0.0214)|0.621***<br>(0.0539)|0.649***<br>(0.0569)|1.307***<br>(0.0339)|0.848***<br>(0.0317)|
|EAP|114.2***<br>(30.67)|3.716***<br>(1.108)|9.477***<br>(1.336)|200.6***<br>(39.10)|38.39**<br>(15.99)|-2.956*<br>(1.676)|1.678*<br>(0.871)|
|MNA|149.8***<br>(32.20)|7.814***<br>(0.653)|11.98***<br>(1.311)|60.16*<br>(32.83)|15.19***<br>(5.540)|11.83***<br>(0.851)|14.08***<br>(1.193)|
|EAP * Relative Income|-4.136***<br>(1.358)|0.0430<br>(0.0395)|-0.361***<br>(0.0431)|-1.951***<br>(0.377)|-0.416**<br>(0.166)|-0.323***<br>(0.0859)|-0.0633<br>(0.0396)|
|MNA * Relative Income|-6.382***<br>(1.228)|-0.640***<br>(0.0185)|-0.653***<br>(0.0275)|-1.205***<br>(0.320)|-0.709***<br>(0.0775)|-1.317***<br>(0.0340)|-0.786***<br>(0.0338)|
|Constant|-76.26*<br>(42.48)|14.97*<br>(8.378)|-21.00***<br>(1.883)|4.214<br>(9.867)|-27.73**<br>(13.66)|-25.35***<br>(5.750)|-0.178<br>(10.90)|
|Observations|2985|1281|4193|368|372|4115|3151|
|r2<br>* p<0.10 ** p<0.05 *** p<0|0.161<br>.01|0.796|0.673|0.432|0.262|0.536|0.579|

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.

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<!-- Start of picture text --> bandwicth Computer per mil capita Intemet users (Sb)<br>3 a a<br>F 5 Poe ee an 5<br>8° = fo Bo<br>;<br>z : é :<br>ay : on Es 58a o<br>a8 - 8<br>= —~ EF EF<br>ggo<br>°<br>g8<br>9 0 2 DH 8 HM MM 8B 1d o 110 2 30 40 50 60 Fo BO 90 1 a 0 #20 @ 40 & 8 FO #o oO 100<br>relative Income relative income relative income<br>—#— rei EAP vs ROW —#— ret: MNA vs ROW He et BAP ve ROW —— red: MINA ve ROW<br>ATM per mil capita 5 Credit and debit card payment per mil capita g Electricity production per ma capita<br>5. & fo<br>BR zs E<br>ri 4 =<br>'b Fi ut<br>Ee i E<br>= F nt<br>a ——. a> i<br>Ee == a E g<br>i 8<br>ia =<br>gS1 o am 2 40 40 oa 0 7 8 © 100a a o om 20 3 40ee5 6 7D SO oD 100 i Tan a eT<br>—#— rei EAP vs ROW =—i— rot: MMA vs ROW —#— ei: EAP ys ROW —#— rei: MNA ws ROW —#— rt: EAP ve ROW —i— rei MNA ve ROW<br>Tractor per mil capits<br>&<br>i.<br>+<br>1<br>ks<br>a1<br>Gi1<br>o i TM 40 40 S50 & 7oO Bo 90 1h0<br>relative income<br>—#— rei: EAP ve ROW —i— roi: MNA ve ROW<br><!-- End of picture text -->
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 <mark>Herfindahl-Hirschman Index (H</mark> HI) 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 c <mark>oncentration 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.</mark> 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)<sup>14</sup> . To account for possible nonlinearities 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).<sup>15</sup> 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”.

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<!-- Start of picture text --> 8 mobile operator concentration < bank concentration<br>oO<br>c Cc<br>382S3s Be38<br>aa<br>GoeCS G®<br>5s =i<br>x) Ss<br>=So<br>is) S) 82°<br>8a<br>010 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>relative income relative income<br>—®— Ic1: EAP vs ROW —®— Ic1: MNA vs ROW —®— Ic1: EAP vs ROW —®— Ici: MNA vs ROW<br><!-- End of picture text -->
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**

||bandwidth per<br>internet user|internet users<br>(%)|Number of<br>ATMs per mil<br>capita|Payments by credit<br>and debit cards per<br>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<br>(0.00000167)|0.000000167*<br>(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|

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“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.<sup>16</sup>

> 16 World Bank (2019) shows that MENA has the highest youth unemployment rates in the world and these rates are highest among the educated.

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