Technology, education, and economic growth in Sub-Saharan Africa

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Technology, education, and economic growth in Sub-Saharan Africa Ficawoyi Donou-Adonsou Department of Economics & Finance, John Carroll University, 1 John Carroll Boulevard, University Heights, OH, 44118, USA

A R T IC LE I N F O

ABS TRA CT

Keywords: Telecommunications infrastructure Education Economic growth Sub-Saharan AfricaJEL classification: O30 O40 O55

This study examines whether telecommunications infrastructure promotes economic growth in countries with better access to education compared to those with less access. Using a panel of 45 Sub-Saharan African countries from 1993 to 2015, the results using the fixed-effects, two-step feasible efficient generalized method of moments estimator indicate that in countries with better access to education, the Internet contributes to economic growth, while mobile phones do not seem to do so. These results suggest that while education is regarded as pivotal for the Internet, it seems to be irrelevant for mobile phone usage.

1. Introduction The importance of technological progress has been underscored in the growth literature. An important feature of technological progress highlighted in the literature is telecommunications infrastructure. If developed countries seem to reap the most benefit – in terms of economic growth or reduction in corruption – from this type of technological progress, many developing countries do not seem to follow suit. Education could be one of the reasons as to why most developing countries are not getting the full benefit package of technological progress. Paying bills online, for example, does require not only the buyer to have computer and the Internet knowledge, but the vendor to develop and maintain such a billing technology. The analysis of Table 1 indicates that telecommunications infrastructure is used more in regions where the literacy rate is higher. For instance, in 2010, the adult literacy rate in high-income countries was 98.67%, while that of the low-income countries was 57.5%. For the same period, we observe that the Internet and mobile phone users outnumbered their corresponding value in low-income countries. Sub-Saharan Africa figures for the literacy rate, the Internet users, and mobile phone subscribers just confirm this pattern when each one of these two regions is compared to SUB-Saharan Africa. The intuitive implication could be that countries with better education access seem to make the best use of telecommunications infrastructure. A limited number of studies underscore the positive relationship between technological progress and economic growth in Africa (Chavula, 2013; Donou-Adonsou, Lim, & Mathey, 2016), as well as the importance of education for productivity growth in Africa (Gyimah-Brempong, Paddison, & Mitiku, 2006; Oketch, 2006). However, to date, there is no evidence on the role of education in the technology-growth relationship. In this paper, we investigate the impact of telecommunications infrastructure on economic growth in Sub-Saharan Africa. More specifically, we examine whether telecommunications infrastructure, measured by the Internet and mobile phone users, promote growth in countries with better access to education compared to those with less access. This study expands the literature in three ways. First, it sheds light on the importance of education in developing countries and how education can contribute to paving the road for technological progress. In other words, education plays an important role in nurturing knowledge indispensable for creation and innovation. This study highlights that interaction between education and

E-mail address: [email protected]. https://doi.org/10.1016/j.telpol.2018.08.005 Received 28 February 2018; Received in revised form 20 August 2018; Accepted 22 August 2018 0308-5961/ © 2018 Elsevier Ltd. All rights reserved.

Please cite this article as: Donou-Adonsou, F., Telecommunications Policy (2018), https://doi.org/10.1016/j.telpol.2018.08.005

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Table 1 Internet, mobile phone, and literacy in 2010. World Development Indicators, last update 12/28/2016

Internet users (per 100 people) Mobile cellular subscriptions (per 100 people) Adult literacy rate, population 15 + years, both sexes (%)

High income

Low income

Sub-Saharan Africa (all income levels)

67.57 115.45 98.67

3.28 29.08 57.5

10.02 44.41 60.37

technology to determine economic outcomes. In better educational systems, technology is likely to foster economic growth. If so, developing countries can derive growth effects by improving their educational systems. Secondly, evidence in Africa is limited, and prior studies have not considered spatial correlation across countries. In this study, we account for cross-sectional dependence. Using a sample of 45 Sub-Saharan African countries from 1993 to 2015, the results using the fixed-effects, generalized method of moments estimator indicate that in countries with better access to education, the Internet contributes to economic growth, whereas mobile phones do not seem to do so. The rest of the paper is organized as follows. In Section 2, we provide the theoretical background and related literature. In Section 3, we describe the empirical model, the methodology, and the data. Section 4 provides the results and discusses them, while Section 5 concludes. 2. Theoretical background and related literature Technology in the growth model started with Solow (1956) and was propelled by Barro (1991), Barro and Sala-i-Martin (1991, 1992), and Mankiw, Romer, and Weil (1992). These studies identified technological progress as an important factor of economic growth. However, contrary to the Solow growth model wherein technology is considered as exogenous, a new growth model emerged to endogenize technological progress. Lucas (1988), Romer (1990, 1993), Grossman and Helpman (1991), Aghion and Howitt (1992), and Aschauer (1989) have endogenized technological progress. For example, Lucas (1988) considers that technological progress depends on human capital, while Romer's (1990) model goes beyond and considers that it also depends on the search for new ideas. That is, he considers that the search for new ideas affects technological progress which then determines economic growth. Firms in this model are motivated by profit-maximization and make the necessary research investment. This view aligns with that of Grossman and Helpman (1991), who identify innovations and improvements to existing products as the engine for growth. Oliner and Sichel (1994) use the neoclassical framework and incorporate information technology into the growth model. In their model, they show that the growth rate of output depends not only on computing equipment (stock of computers), other types of capital, labor, and multifactor productivity, but also on their respective shares of output. Oliner and Sichel (2000) extending the basic model find that hardware, software, and communication equipment together account for two-thirds of U.S. labor productivity growth in the second half of the 1990s, and communication equipment contributed about 0.1 percentage point annually to output growth. Although there is an abundant empirical literature that discusses telecommunications infrastructure in the growth model,1 there is less evidence in Sub-Saharan Africa. Lee, Levendis, and Gutierrez (2012) examine the effect of mobile cellular phones on economic growth in Sub-Saharan Africa. Using linear Generalized Method of Moments (GMM) estimator, their study indicates that mobile cellular phone expansion is an important determinant of the rate of economic growth in Sub-Saharan Africa. More importantly, they find that the marginal impact of mobile telecommunication services is higher in regions with less land line phones. Chavula (2013) also investigates the impact of telecommunications penetration on per capita income growth. Using a pooled OLS regression in 49 countries in Africa, the results show that the telephone main lines and mobile phones have a significant impact on economic growth, while the Internet usage does not have a significant impact upon economic growth. However, mobile phones and the Internet have a significant effect in the upper-middle income countries, whereas in low and lower-middle income countries, only mobile phones have a significant effect. Donou-Adonsou et al. (2016), using instrumental variables-Generalized Method of Moments approach, also examine the impact of telecommunications infrastructure on economic growth in 47 countries in Sub-Saharan Africa. Their results indicate that both the Internet and mobile phones have increased economic growth. More importantly, they indicate that the contribution of the Internet is four times that of mobile phones. Haftu (2018) finds similar results with the system GMM approach, especially as far as mobile phones are concerned. At the country level, Katz and Koutroumpis (2014) have estimated the impact of the broadband on economic growth in Senegal. Their results indicate no significant impact of fixed broadband on growth, while the evidence suggests a significant impact of mobile broadband. However, none of these studies, whether in Africa or around the world, has considered the influence education could have on the relationship between technology and income or income growth. This consideration could be important given that the use of the Internet may require prior computer literacy. Billon, Crespo, and Lera-López (2017) have attempted to address the issue but have instead considered educational inequality for a sample of developed and developing countries. Their results indicate that educational inequality negatively affects the impact of the Internet on economic growth. Put differently, the Internet may promote growth in 1 See for instance Correa (2006), Jorgenson and Motohashi (2005), Martínez, Rodríguez, and Torres (2008), Vu (2013), Chakraborty and Nandi (2003, 2009), Sridhar and Sridhar (2007), Koutroumpis (2009), Bertschek et al. (2013), Roller and Waverman (2001), Qiang, Rossotto, and Kimura (2009), Thompson Jr and Garbacz (2011), Holt and Jamison (2009), Czernich, Falck, Kretschmer, and Woessmann (2011), Saidi and Mongi (2018).

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countries with a more homogenous educational distribution. Moreover, Minges (2016) highlights some methodological and data issues that prevent conclusive findings in the previous studies. Whether with cross-sectional, panel data, time series, or non-linear models, there is no consensus about the findings. In this study, we take a step further and account for the possibility of cross-sectional dependence that may arise due to spillover effects across the panel units. Previous studies have failed to consider this possibility, especially as far as Sub-Saharan Africa is concerned. 3. Methodology and data Barro's (1991) cross-country growth model is usually extended to include technology. Datta and Agarwal (2004), Chavula (2013), and Donou-Adonsou et al. (2016) have used this extended version of the model. We build on the extended version and include education and an interactive term between education and technology defined by the telecommunications infrastructure. The following dynamic panel data model is estimated.

Growthi, t = α + θi + ϕ1 Growthi, t − 1 + ϕ2 Techni, t + ϕ3 Educationi, t + ϕ4 (Techn ∗Education)i, t + Xi′, t β + εi, t

(1)

In equation (1), Growth refers to the growth rate of per capita GDP in country i = 1, 2, …, N (N is the total number of countries) at time t = 1, 2, …, T (T is the total number of years), while Growtht-1 captures the short-run autoregressive behavior of the dependent variable. Techn refers to telecommunications infrastructure that is measured by Internet users and mobile phone subscribers. Consistent with the literature on growth and technology, these two variables are expected to positively influence growth. Education denotes access to education. We construct this education variable based on primary school enrollment.2 We calculate the average primary school enrollment across our sample (92.53%)3 and define Education as a dummy variable that equals one when primary enrollment in a given country is greater than average enrollment and zero, otherwise. Therefore, countries coded one are viewed as having better access to education compared to countries coded zero. We then interact this education dummy with each of the telecommunications infrastructure. We expect stronger growth effects of telecommunications infrastructure in countries with better access to education. We recall that better access to education does not imply effective school systems-based community support, teacher supervision, textbooks and materials, facilities, school leadership, flexibility and autonomy, student assessments and examinations, school climates, and teaching or learning processes (Heneveld & Craig, 1996). It is just an indicator of how easy access to school is. X is a vector of explanatory variables as defined in Datta and Agarwal (2004) and Donou-Adonsou et al. (2016). These variables include lagged GDP per capita that accounts for initial economic conditions and accounts for convergence in the growth rate, government expenditures as a percentage of GDP, investment as measured by the share of gross capital formation in GDP, inflation rate, and openness to trade as measured by the share of trade in GDP. Such variables are consistent with the growth literature. From the neoclassical growth theory perspective, a negative coefficient for the GDPt-1 is expected to lend support to the convergence hypothesis that higher levels of initial GDP tend to lower growth because of the diminishing returns to capital. However, this scenario could be different in the face of rapid technological progress in recent era where technology is helping continuously to shift the production function by increasing the productivity in many ways. Thus, the sign of the coefficient of GDPt-1 can be expected to go either way. Investment is considered as input and is expected to have a positive effect on economic growth (Barro, 1991; Mankiw et al., 1992). Government expenditures, openness to trade, and inflation are policy factors that proxy for macroeconomic environment (Donou-Adonsou, 2014; Donou-Adonsou & Sylwester, 2017; Levine, Loayza, & Beck, 2000). While openness to trade is expected to have a positive effect, the literature points to mixed evidence of government expenditures and inflation on economic growth. θi represents unobservable time-invariant country specific effect and εit is the error term. θi is assumed to be random and independent of εit. Also, both εit and θi are each independently and identically distributed (i.i.d.) with zero mean and finite variance. Equation (1) is estimated using the fixed-effects estimator (FE) that accounts for the first-order autocorrelation disturbance introduced by the lagged of the dependent variable. The specification of equation (1) includes a dummy variable in the right-hand side of the model. Given the dummy nature of the education variable, this variable is likely to be correlated with the time-invariant country specific effect, and so using the fixed-effects estimator is likely to cause multicollinearity. This would be particularly true if the education dummy were time-invariant. In this study, the education variable does vary. Nevertheless, the FE estimates are likely to be biased given the possible reverse causality between telecommunications infrastructure and each one of the dependent variables. The literature above highlights a bidirectional causality between growth and telecommunications infrastructure (see Chakraborty & Nandi, 2009). To address this endogeneity issue, we follow Schaffer's (2015) fixed-effects, two-step feasible efficient generalized method of moments (GMM). This method extends the standard IV/2SLS method which assumes homoscedasticity and non-autocorrelation in the error terms in the variance-covariance matrix. The extended GMM estimator implements IV/GMM estimation of the fixed-effects panel data models with endogenous regressors. The extended GMM estimator, compared to the standard IV/2SLS estimator, requires robust variance-covariance estimator (see Baum, Schaffer, & Stillman, 2007). We then cluster the data by country and year to have heteroscedastic and autocorrelation2 We choose primary school enrollment over secondary and tertiary enrollment for data availability and for its effectiveness in developing countries (particularly in Sub-Saharan Africa) as commonly suggested in the literature (e.g., Psacharopoulos, 1994; Psacharopoulos & Patrinos, 2018; World Bank, 1993). 3 This sample average is consistent with the millennium development goal 2 (MDG2) of achieving universal primary education. According to MDG2, enrollment in primary education in developing regions has reached 91% in 2015 (http://www.un.org/millenniumgoals/education.shtml).

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Table 2 Variable definitions and summary statistics. Variables

Definition of variables

Obs.

Mean

St. Dev.

Source

GDP per capita Growth Internet Mobile phones Education

GDP per capita, constant 2010 US$ GDP per capita growth (annual %) Internet users (per 100 people) Mobile cellular subscriptions (per 100 people) Gross enrollment ratio, primary, both sexes (%). Redefined as a dummy that equals 1 when the ratio is above the sample average of 92.53% and 0, otherwise. General government final consumption expenditure (% of GDP) Gross capital formation (% of GDP) Inflation, consumer prices (annual %) Trade (% of GDP)

1035 1035 915 1034 804

1977.63 2.03 4.99 26.25 0.56

2984.75 8.32 9.05 36.63 0.49

957 978 956 993

15.59 22.10 77.72 79.18

7.66 17.37 1116.06 51.13

WDI WDI WDI WDI Education Statistics WDI WDI WDI WDI

Government Investment Inflation Openness to trade

Note: WDI stands for World Development Indicators.

consistent estimates. Clustering is a common technique used when errors are likely to be correlated within clusters. However, it is also possible that errors be correlated across clusters. This correlation, known as cross-sectional or spatial dependence, is suspected in the data generating process when the cross-sectional data are not the result of independent sampling. Thus, cross-sectional dependence can arise due to spatial or spillover effects, as well as to unobserved (or unobservable) common factors (Wooldridge, 2010, p. 134). R &D – technology in general – is a great example of spillover (Baltagi & Li, 2001). Consequently, we use the Bartlett kernel function with a bandwidth that ensures that estimates are kernel-robust to common correlated disturbances as described in Driscoll and Kraay (1998). The vast majority of the previous studies have failed to account for cross-sectional dependence, even more so in the context of Sub-Saharan Africa. Finally, we use the lags of the endogenous regressors for identification purposes. Griliches and Hausman (1986) suggest the use of those in the absence of proper external instruments. Other studies, such as Vu (2013) and Elbahnasawy (2014), have also used the internally generated instruments in similar studies to address the endogeneity issue. The sample period goes from 1993 to 2015 and covers 45 countries in Sub-Saharan Africa. The choice of this period is justified by the positive economic growth rates over the last two decades in Sub-Saharan Africa (Donou-Adonsou et al., 2016). Table 2 shows the summary statistics. 4. Results and discussion Table 3 summarizes the fixed-effects results. In the first two columns, technological progress is measured by telecommunications infrastructure, namely the Internet (column 1) and mobile phones (column 2). In all regressions, we do not see significant evidence that technological progress promotes economic growth in countries with primary gross enrollment ratio above 92.53%. Put differently, better access to education does not seem to affect the relationship between technological progress and economic growth. However, telecommunications infrastructure (the Internet and mobile phones) positively affect economic growth in Sub-Saharan Africa. Nevertheless, these results are likely to be biased given the reverse causality between technological progress and economic growth. In Table 5, we repeat the same regression using the two-step, feasible efficient GMM estimator to address the endogeneity concern, along with other concerns as mentioned above. Except for the first lag of growth and GDP variables, we treat all other variables as potentially endogenous. As discussed earlier, we use the first two lags of each one of those variables as instruments. The fixed-effects regression of growth on these instruments that controls for the first-order autoregressive disturbance is reported in Table 4. These results suggest that except for the first lag of education and trade variables, these instruments do not significantly impact growth. However, we do not drop these two instruments from the growth equation as doing so does not change the results. Turning to Table 5, we find significant evidence that in countries with better access to education, the Internet does promote economic growth as shown in column 1. This is significant at the 1% level. Economically, the result indicates that in countries wherein primary gross enrollment is higher than 92.53%, a one-percentage point increase in Internet access increases economic growth by 0.224 percentage points. The Hansen p-value of 0.459 confirms the validity of the model. In columns (2) and (3), we do not find significant impacts of mobile phones on growth in countries with better access to education, which is consistent with the fixedeffects autoregressive results. It is important, however, to point out that the interaction between mobile phones and education has a positive impact on economic growth. Note: The fixed-effects estimates are based on the first-order autoregressive disturbance. t-1 denotes the first lag, while t-2 denotes the second lag. In this table, the dependent variable, growth, is regressed on the instruments represented by the first lag and second lag of each regressor. Standard errors are in parentheses. ***, **, and * denote significance at 1%, 5%, and 10% levels. To summarize, and consistent with the fixed-effects, two-step GMM regressions, the interaction between the Internet and education does have an impact on economic growth, contrary to the interaction between education and mobile phones. While education seems to play a pivotal role for the Internet to foster economic growth, it does not seem a priori to matter for the association between mobile phones and growth. Intuitively, one does not need to be literate to use a cell phone, and this seems to be the case in SubSaharan Africa, where poor farmers also make use of cell phones. In Togo, for instance, there is a program launched in September 2016 known as AgriPME that helps farmers place orders for fertilizers and other inputs directly from the cell phones they use as an 4

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Table 3 Growth, technology, and education: The fixed-effects estimates Dependent variable: Growth.

Growtht-1 Education Internet Internet × Education

(1)

(2)

−0.333*** (0.028) 1.171* (0.681) 0.100* (0.056) −0.033 (0.054)

−0.017 (0.035) 0.289 (0.789)

Mobile Mobile × Education GDPt-1 Government Investment Trade Inflation Constant R-squared (within) F-test # of countries # of observations

−0.002*** (0.000) −0.096 (0.090) −0.065** (0.033) 0.037*** (0.014) −0.000 (0.000) 5.044*** (0.970) 0.21 19.39*** 44 731

0.023** (0.010) 0.011 (0.014) −0.003*** (0.000) −0.129 (0.081) −0.036 (0.033) 0.038*** (0.014) −0.000 (0.000) 7.331*** (1.668) 0.23 25.20*** 44 794

Note: The fixed-effects estimates are based on the first-order autoregressive disturbance. Standard errors are in parentheses. ***, **, and * denote significance at 1%, 5%, and 10% levels. Table 4 The fixed-effects regression of growth on the instrumentsDependent variable: Growth. t-1 −0.274*** (0.053) −0.002** (0.001) −2.059** (0.932) −0.054 (0.204) 0.204 (0.138) 0.033 (0.042) −0.004 (0.021) 0.050 (0.099) −0.001 (0.053) 0.048* (0.025) 0.000 (0.001) 3.481** (1.503) 0.15 2.86*** 35 436

Growth GDP Education Internet Internet × Education Mobile Mobile × Education Government Investment Trade Inflation Constant R-squared (within) F-test # of countries # of observations

5

t-2

−0.187 (0.876) −0.089 (0.220) −0.062 (0.131) 0.004 (0.046) −0.013 (0.023) 0.085 (0.105) 0.011 (0.055) −0.033 (0.026) −0.022 (0.015)

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Table 5 Growth, technology, and education: The fixed-effects two-step GMM estimates Dependent variable: Growth.

Growtht-1 Education Internet Internet × Education

(1)

(2)

0.088** (0.039) 0.680 (1.409) −0.061 (0.045) 0.224*** (0.073)

0.028 (0.025) −2.909*** (0.989)

Mobile Mobile × Education GDPt-1 Government Investment Trade Inflation Hansen test p-value F-test # of countries # of observations

−0.002*** (0.000) −0.183* (0.111) 0.249*** (0.093) −0.068* (0.036) −0.000 (0.000) 0.459 284.85*** 43 670

−0.006 (0.016) 0.032 (0.022) −0.002*** (0.000) 0.166 (0.143) 0.165* (0.087) 0.010 (0.024) −0.001** (0.000) 0.687 538.71*** 44 770

Note: Except for the lag of growth and GDP, we treat all other variables as potentially endogenous. We use the first two lags of each one of those variables as instruments. Data are clustered by country and year. The bandwidth for the Bartlett kernel function is given by T1/3, with T the number of observations. Robust standard errors are in parentheses. ***, **, and * denote significance at 1%, 5%, and 10% levels.

electronic wallet. That is, cell phones are credited with government funds in form of subsidies that farmers use to buy fertilizers or pesticides without any intermediary. Chhachhar and Hassan (2013) argue that mobile phones provide an opportunity for farmers to get information about marketing and the weather. Tadesse and Bahiigwa (2015) echo the same argument, although they point out that the number of farmers in Ethiopia who use mobile phones for information searching is small. According to Donovan (2011) and Howard and Mazaheri (2009), enhanced regulatory environments, technological progress, and payment options attractive to poor people have all empowered the rapid adoption of mobile phones. Education, thus, seems a priori irrelevant for the use of mobile phones.

5. Correlation analysis In this section, we undertake some robustness checks with the secondary and tertiary school enrollments.4 As discussed earlier, we choose primary school enrollment over secondary and tertiary enrollments for data availability and for the effectiveness of primary school enrollment in developing countries – particularly in Sub-Saharan Africa – as commonly suggested in the literature. Psacharopoulos and Patrinos (2018), for instance, point out the effectiveness of the primary education over the secondary and tertiary education for developing regions. However, critics could arise that primary education is not a good proxy of a country level of literacy, less even for telecommunications literacy in which case the results may say little of the objective of the paper. Given the data issue regarding secondary and tertiary education, this section attempts to address these critics by estimating a correlation matrix for the different levels of education for countries where such data exist. In that regard, we estimate the Pearson correlation coefficients as well as and the Spearman correlation coefficients. The results, reported in Table 6, show that primary school enrollment is positively correlated with secondary school enrollment and tertiary school enrollment, respectively. These results, significant at the 1% significance level, are robust to both Pearson and Spearman coefficients and imply that there is a significant linear relationship between primary school enrollment and secondary school enrollment, on the one hand, and between primary school enrollment and tertiary school enrollment, on the other. These significant linear relationships lend support to our initial results based on primary school enrollment and suggest that an interaction exists between secondary/tertiary education and telecommunications infrastructure which 4 Data for these variables are based on Education Statistics database. Secondary school enrollment is defined as gross enrollment ratio, secondary, both sexes (%); tertiary school enrollment is measured by gross enrollment ratio, tertiary, both sexes (%).

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Table 6 Correlation matrix. Pearson correlation coefficients Primary Primary

1

Secondary

0.459*** (0.000) 0.233*** (0.000)

Tertiary

Secondary

Spearman correlation coefficients Tertiary

Primary

Secondary

Tertiary

1 1 0.748*** (0.000)

0.549*** (0.000) 0.328*** (0.000)

1

1 0.784*** (0.000)

1

Note: p-values are reported in parentheses. ***, **, and * denote significance at the 1, 5, and 10% levels, respectively.

may contribute to economic growth. This implication is further confirmed with the Pearson's positive and significant correlation coefficients between telecommunications infrastructure and secondary and tertiary education, respectively. For countries where data exist, these coefficients are respectively 0.629 and 0.675 with the Internet and 0.555 and 0.667 with mobile phones. 6. Conclusion In this study, we examine whether access to education influences the relationship between technological progress and economic growth in Sub-Saharan Africa. Technological progress is measured by telecommunications infrastructure, namely the Internet and mobile phones. Using the fixed-effects GMM estimator, our results indicate that in countries with better access to education, the Internet contributes to economic growth, whereas mobile phones do not seem to do so. These results suggest that better access to education is indispensable for the Internet to generate economic value, while it seems to be irrelevant for mobile phone usage. It is also important to underscore the interlink between the Internet and mobiles phones. According to the World Development Indicators database, Internet users are individuals who have used the Internet via a computer, mobile phone, personal digital assistant, games machine, or digital TV in the last 12 months. Put differently, people who subscribe for mobile phones may have access to the Internet in which case mobile phones go beyond their primary role of making and receiving calls to serve other purposes, such as video calls, doing research, looking at the map, just to mention a few. Our results may thus suggest that only mobile phones with no access to the Internet may be irrelevant to economic growth despite better access to education. Equally, the use of smartphones may be beneficial for economic growth. 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