The Internet, English Proficiency and Economic Growth

Tamat Sarmidia*, Sulhi Ridzuana and Abu Hassan Shaari Md Nora

aSchool of EconomicsUniversiti Kebangsaan Malaysia

Abstract

The emergence of the Internet has revolutionised economic activity in terms of time and cost

efficiency. The Internet has also assisted in the dissemination of knowledge essential for the

factors of productivity and economic growth. However, in this article, we conjecture that the

efficient use of the Internet is conditional on the proficiency of the main language of the

Internet, which, for the time being, is English. Consequently, this paper investigates the

relationship between the Internet and economic growth under different levels of English

proficiency. By employing dynamic panel regressions to the Internet-growth model, our

empirical findings illustrate that the effectiveness of the Internet in accelerating economic

growth is contingent upon the level of English proficiency. Without a good command of

English, the advantages of having Internet access to speed-up economic growth may be

questionable. Interestingly, the finding, to some extent, may also indicate an evidence to

supports the language convergence hypothesis.

Keywords: the Internet; the English language; growth

1. Introduction

Communication technology has improved significantly over the past few decades with the

advent of the Internet. The Internet has significantly made local and international financial

transactions and business dealings more convenient; it has also made them more cost and

time efficient. However, no matter how advanced the communication technology is that we

experience, the medium of communication has not changed much. More specifically, it still

requires a competency in the lingua franca to communicate via the Internet effectively; this

language is currently the English language.

* Corresponding author: School of Economics, Faculty of Economics and Management, Universiti Kebangsaan Malaysia, 43600 UKM, Malaysia. Email: tamat@ukm.edu.my, Tel:+60389213448, Fax: +60389218759.

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Most business dealings around the world are conducting in English. New ideas or insights

are conducted in English. Therefore, the development of human capital through

dissemination of new knowledge will be sluggish without a good command of the English

language*. Consequently, an inability to have a good command of the English language may

impede the benefit of the Internet, as more than 50% of web content is in English.

Very little is currently known regarding the importance of language proficiency

concerning Internet access and economic growth. We conjecture that people with a low

English proficiency may not be able to benefit from the Internet to improve his/her new stock

of knowledge. With this motivation, the primary purpose of this paper is to investigate the

importance of the English language in affecting the role of the Internet in facilitating

economic growth.

This paper contributes to the literature in many ways. Firstly, it employs the cross-

countries dynamic panel analysis. Secondly, it revisits Choi and Yi (2009) and argues that

the effect of the Internet on economic growth may not be monotonic for the heterogeneous

nature of English proficiency levels between countries. Splitting the group of countries into

high and low English proficiency indicates the need to seriously consider English proficiency

as a vital ingredient to the Internet-economic growth relationship. In other words, the

effectiveness of the Internet, as a medium of communication between parties involved in

economic activities, is highly dependent on the level of English competency, at least for the

current century. Thirdly, this paper provides empirical evidence on the debate of the dynamic

development of language and whether it supports the language convergence hypothesis or

minority language survival (Zhang and Grenier, 2012).

The rest of the paper is organized as follows. Section 2 discusses the relationship between

the Internet, the English language and economic growth. Section 3 presents the model, the

methodology and the data used in the estimation. Section 4 discusses the empirical results

and Section 5 concludes.

2. The Internet, the English language and economic growth

A considerable amount of literature has been published on the Internet-economic growth

nexus; this includes Oliner and Sichel (2003), Choi and Yi (2009), Koutroumpis (2009) and

Farhadi, Ismail, Sarmidi and Kasimin (2013), among others. In general, scholars have

reached a conclusion that the Internet is crucial in boosting the spill over effect of knowledge * In 2014, as reported by W3Techs, English is used as a content language by more than 50% of all of the most popular websites. By contrast, no other language is used more than 10% of the time on the most popular websites.

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to accelerate further economic growth. Consequently, much of the current literature pays

particular attention to investigating how the Internet helps promote economic growth.

Previous research has illustrated that the Internet could accelerate further economic

growth through various channels, including significant reductions in transaction costs

(DePrince and Ford, 1999), improving the overall efficiency of day to day operational

procedures (Meijers, 2006), stimulating the Foreign Direct Investment (FDI) (Choi, 2003),

increasing labour productivity (Najarzadeh, Rahimzadeh and Reed, 2014), improving

transparency (Vinod, 1999), lowering inflation (Yi and Choi, 2005), enhancing local and

international service trade (Choi, 2010) and spurring international trade, especially in

developing countries (Meijers, 2014). To emphasize the importance of the Internet on the

economy, Noh and Yoo (2008) found that a country that inadequately invests in providing

Internet access to most of the community may experience a wider digital gap and be more

likely to suffer from income inequality distribution.

The previously-discussed literature unanimously agreed that the Internet has had a

substantial positive effect on economic growth. Despite this agreement, these studies have

failed to address the issue of how language competency affects the efficient use of the

Internet in business dealings and the dissemination of knowledge. This is because the Internet

only works to facilitate cost effective, fast, and convenient communication. The medium of

communication for the dissemination of knowledge still does not change.

Two or more parties involved in an economic activity can communicate effectively with

each other via the Internet when they are using the same medium of communication (i.e. the

preferred language or language that has been agreed upon in advance). Lee (2012)

hypothesised that a better command of English leads to a better marginal rate of absorption of

the new stock of knowledge. Therefore, we conjecture that the effective use of the Internet in

economic activities is conditional on English proficiency.

Since most of the currently available Internet content is English, the effective use of the

English language is important to effectively use the Internet. This can, in turn, spur economic

growth. This can also lead to interesting discussions on the survival of a minority language

or the convergence language hypothesis (Zhang and Grenier, 2012; Brenton, 2000; and

Lazear, 1999).

The hypothesis suggests that a group of people from different language backgrounds can

cooperate and opt to use a common language, the lingua franca, to communicate with each

other (Giles and Philip, 1979) in their daily interactions. This is directly related to the daily

economic activities that affect utility maximization (Grin, 1990). Based on this framework,

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Grin (2003) and Grin (1993) built an economic model to study the effect of the dominant

language choice in communication, due to liberalization in the European Union (EU). They

found that the formation of the EU provided an indication of the threatening of the survival of

the traditional minority languages. Thus, as one world community uses the English based

communication via the Internet, the intensity of the Internet use in the dissemination of

knowledge may threaten the survival of the global minority language such as Malay in

Malaysia, Thai in Thailand or Tamil in India. The advent of the Internet may speed up the

language convergence hypothesis.

3. Model specification, methodology and data

Following Choi and Yi (2009), the growth equation is as follows:

Growth¿=α Growth¿−1+β0+β1 Internet ¿+βs' X+u¿ (1)

where: uit = ηi + vt + εit, ηi is a country effect, vt is a year effect, and εit is independently and

identically distributed. The subscripts, i and t, denote the country and the year, respectively.

Growth is real Gross Domestic Product (GDP) per capita growth; the Internet is the ratio of

the Internet users to the population; Eng is English language proficiency and X are the

controlled variables that influence growth (e.g. investment, government spending, openness

and financial development). We include the lagged dependent variable, as economic growth

may be influenced by past ones.

Choi and Yi (2009) found that the effect of the Internet on economic growth was

positively linear. Our hypothesis is that the impact of the Internet on economic growth

depends upon countries’ English proficiency. The positive (negative) impact of the Internet

only appears for countries with good (poor) English-language skills. To test the hypothesis,

the model takes the following form:

Growth¿=α Growth¿−1+β0+β1 Internet ¿+β1¿ Internet ¿× Engi+β s

' X+u¿ (2)

Adding the interaction term between the Internet and Eng captures the extent to which

English proficiency increases or lowers the impact of the Internet on growth. We conjectured

that people with a low English proficiency may not benefit from the Internet, since most web

content are in English. In the meanwhile, high English proficiency improves the ability to

absorb knowledge from the Internet; thus, we expect β1¿>0. At the margin, the partial impact

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of an increase in the use of the Internet on economic growth can be examined by the partial

derivative:

∂ Growth∂ Internet

=β1+ β1¿ Eng (3)

It is expected that either β1>0 or β1<0 , because the impact of the Internet on growth may

be driven by countries’ English proficiency. Eng is not included independently in the model,

because the variable has been observed as being constant over the years; thus, it will be

correlated with the country-fixed effects (Demetriades and Fielding, 2012; Ibrahim and Law,

2014).

Two proxies are used to represent English proficiency (Eng): the English Proficiency

Index (EPI) and a dummy variable. The EPI index ranges from 0 to 100, where the higher

score indicates a higher level of English proficiency. For robustness checking, we use a

dummy variable, where the dummy variable equals 1 if the country is English native

speaking or with an EPI equal to or greater than 55. This EPI score is the lowest index for a

country to be categorised as high English proficiency (http:// http://www.ef.com/epi/). We

integrate these two sources of classification (native speakers and high English competency

based on EPI), because people living in some non-English speaking countries have a good

command of English (Lee, 2012).

The presence of a lagged dependent variable and unobserved heterogeneity implies that

estimating Eq. (2) with a pooled OLS, fixed effect estimator and random effect estimator is

not appropriate (Nickell, 1981). The dynamic General Method of Moment (GMM) estimator

proposed by Arellano and Bond (1991) deals with unobserved heterogeneity by taking the

first differences of the empirical equations. Furthermore, a set of instrumental variables is

used to solve the problem of potential endogeneity among the regressors (Choi and Yi, 2009).

The dynamic GMM estimator can be either a one-step GMM estimator or a two-step

GMM estimator. We apply the two-step GMM estimator in this study to better deal with the

iid error terms. The consistency of the dynamic GMM estimator depends on two specification

tests: the Hansen test of over-identifying restrictions (Hansen, 1982) and a serial correlation

test in the transformed residuals (Arellano and Bond, 1991). Failure to reject both of the null

hypotheses would imply that the instruments used in the models and the estimated

coefficients are valid.

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The measures for growth, investment, government and inflation were previously used by

Choi and Yi (2009). For the Internet, the number of people with access to the worldwide

network (the number of Internet users per 100 people) is used. An additional control variable

is added to represent institution; Financial Development (defined as M2, % of GDP) and

Openness (total export and import, % of GDP). The present study employs annual data for

166 countries from 2004–2013. Data for the English proficiency variable was extracted from

the Nations Online classification (http://www.nationsonline.org/) and the EF Education First

(http:// http://www.ef.com/epi/). The rest of the data were extracted from the World

Development Indicators.

3. Results

Table 1 reports the Arellano-Bond two step GMM results. In general, the estimations met

all of the required specifications, where the p-values of AR(2) and the Hansen over-

identification tests indicated that all of the models were correctly specified. There was no

evidence of autocorrelation or invalid instruments. All control variables that were statistically

significant are shown with a theoretically correct sign.

Columns 1 – 4 report the estimation results for two different subsamples (high and low

English proficiency), which support our conjecture that the Internet only has a positive effect

on economic growth for high English proficiency economies. The sign of the Internet

variable appears to be sensitive to the sample selection. The estimated coefficients of the

Internet for high English proficiency indicate that a 10% increase in the Internet use per 100

population is related to increases in expected economic growth by roughly 2.6 to 4.7% points.

For low English proficiency economies, an increase in Internet access has not produced a

desired positive level to economic growth. The result is of no surprise, since the effective

dissemination of the new stock of knowledge and business communications through the

Internet may largely depend on the marginal absorptive capacity to new knowledge, which is

primarily in English. Hence, the spill over effect of knowledge is higher for countries with a

good command of English.

A multiplicative term between the Internet and English was then included into the main

regression (column 5-8) to confirm the differential effect of the Internet by high and low

English proficiency. The results in Columns 5 and 6 illustrate the effect of the interaction

between the Internet and the English proficiency dummy. The result shows that the

coefficient of the Internet is – 0.132 + (0.169 × Engi) and – 0.233 + (0.199 × Engi),

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respectively. The marginal effect of a high English proficiency has a positive contribution to

economic growth and ranges from 0.169 to 0.199. The coefficient of the Internet is negative,

whereas the interaction term is positive and statistically significant. This result implies that

the marginal effect of the Internet on Growth is contingent on the level of English

proficiency.

The results are consistent using the EPI index, as shown in Columns 7 and 8. The

coefficient of the Internet is –1.35 + (0.025 × Engi) and –1.963 + (0.037 × Engi), respectively.

The interaction terms illustrates that the marginal effect of the interaction terms (the Internet

and EPI index) significantly improves economic growth, but only over and above 54 of the

EPI index which is slightly lower than the lowest score of the high proficiency index i.e. 55.

The findings confirm the non-monotonocity relationship between Internet-economic growth

and the positive effect of the Internet on economic growth, only after certain level of English

proficiency.

5. Conclusions and policy implications

Even though the advent of the Internet has significantly changed the orientation of

economics and business dealings, the importance of English proficiency as a lingua franca

remains crucial in disseminating the stock of knowledge. Using a dynamic panel estimation

method for 166 countries from 2004 to 2013, our findings confirm the fact that the Internet

has a positive effect on economic growth. However, the positive effect of the Internet on

economic growth is conditioned with the level of English proficiency. A national

development policy that aims to improve Internet accessibility to accelerate economic growth

should not ignore the importance of English competency, at least to a level of high English

competency. Below than high English competency level, Internet access may not produce the

expected contribution to economic growth. Interestingly, to some extent, these findings also

provide support to the language convergence hypothesis. Thus, learning English is not merely

important for literature, it is important for economics too.

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Table 1

Descriptive statistics.

Variable Obs. Mean Std. Dev. Min. Max.

Growth 1446 2.514 3.864 -16.589 15.507

Investment 1446 24.291 8.615 1.525 81.567

Government 1446 15.732 6.146 2.754 88.788

Inflation 1446 6.970 31.146 -18.108 1096.678

Internet 1446 30.865 27.503 0.031 96.546

EPI 574 52.910 6.820 38.160 68.690

Fin. Development 1393 71.721 66.857 4.530 662.729

Openness 1358 71.931 42.857 17.693 398.883

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Table 2

Classification of Countries Based on English Proficiency*.

English Speaking Countries

Antigua, Barbuda, Australia, Bahamas, Barbados, Belize, Botswana, Brunei Darussalam,

Cameroon, Canada, Dominica, Fiji, Gambia, Ghana, Grenada, Guyana, India, Ireland,

Jamaica, Kenya, Lesotho, Liberia, Malawi, Mauritius, Namibia, New Zealand, Nigeria,

Pakistan, Philippines, Rwanda, Sierra Leone, Singapore, Solomon Islands, South Africa,

Sri Lanka, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Swaziland,

Tonga, Trinidad and Tobago, Uganda, United Kingdom, United States, Vanuatu, Zambia,

and Zimbabwe.

Non-English Speaking Countries with EPI > 55

Austria, Belgium, Denmark, Estonia, Finland, Germany, Hungary, Latvia, Malaysia,

Netherlands, Norway, Poland, Portugal, Slovenia, Sweden, and Switzerland.

Non-English Speaking Countries with EPI ≤ 55

Afghanistan, Albania, Algeria, Angola, Armenia, Aruba, Azerbaijan, Bahrain, Bangladesh,

Belarus, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Brazil, Bulgaria, Burkina Faso,

Burundi, Cabo Verde, Cambodia, Central African Republic, Chad, Chile, China, Colombia,

Comoros, Democratic Republic of Congo, Republic of Congo, Costa Rica, Cote d'Ivoire,

Croatia, Cyprus, Czech, Djibouti, Dominican Republic, Ecuador, Egypt, Arab Rep., El

Salvador, Equatorial Guinea, Ethiopia, France, Gabon, Georgia, Greece, Guatemala,

Guinea, Haiti, Honduras, Hong Kong , Iceland, Indonesia, Iran, Iraq, Israel, Italy, Japan,

Jordan, Kazakhstan, Republic of Korea, Kuwait, Kyrgyz Republic, Lao PDR, Lebanon,

Libya, Lithuania, Luxembourg, Macao SAR, Macedonia FYR, Madagascar, Mali, Malta,

Mauritania, Mexico, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Nepal,

Nicaragua, Niger, Oman, Panama, Papua New Guinea, Paraguay, Peru, Qatar, Romania,

Russian Federation, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Slovak

Republic, Spain, Sudan, Suriname, Syrian Arab Republic, Tajikistan, Tanzania, Thailand,

Timor-Leste, Togo, Tunisia, Turkey, Ukraine, United Arab Emirates, Uruguay, Venezuela,

Vietnam, West Bank, Gaza, and Yemen Republic.

* Classification is based on the One World Nations Online 2011 where index above 55 considered as high English competency.

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Table 3Arellano-Bond GMM results.

High English Proficiency Low English Proficiency Full Sample(1) (2) (3) (4) (5) (6) (7) (8)

Growth (-1) -0.409*** -0.418*** -0.301*** -0.141* -0.155** -0.185*** -0.422*** -0.567***(0.084) (0.130) (0.069) (0.075) (0.060) (0.050) (0.076) (0.077)

Investment 0.146 0.083 0.173 0.111 0.115 0.007 0.379 1.084** (0.114) (0.255) (0.134) (0.133) (0.128) (0.115) (0.233) (0.490)

Government -0.446 -0.515 -1.775*** -0.673 -1.764*** -0.986*** -0.627 1.115 (0.314) (0.257) (0.508) (0.472) (0.272) (0.230) (0.444) 1.069

Inflation 0.139 0.755 -0.021 0.113 -0.020 -0.023 0.139 (0.417) (0.105) (0.527) (0.061) (0.092) (0.039) (0.054) (0.212) 0.570

Internet 0.260** 0.473** -0.115*** -0.154*** -0.132*** -0.223*** -1.35*** -1.963*(0.109) (0.230) (0.035) (0.038) (0.047) (0.045) (0.478) (1.112)

Internet *Eng 0.169** 0.199***(0.068) (0.063)

Internet *EPI 0.025*** 0.037* (0.009) (0.022)

Fin. Development 0.006 -0.030 -0.015 -0.072(0.072) (0.026) (0.018) (0.061)

Openness -0.044 0.237*** 0.184*** 0.003 (0.155) (0.088) (0.032) 0.061

Hansen test (p-value) 0.13 0.28 0.54 0.32 0.27 0.51 0.88 0.98AR(2) (p-value) 0.18 0.68 0.46 0.26 0.30 0.17 0.12 0.57Observations 327 324 551 573 878 842 322 301Countries 58 60 106 107 164 166 54 56

Notes: Standard errors in parentheses. We use Windmeijer’s (2005) finite sample corrected standard errors. ***, **, and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. EPI = EF English Proficiency Index 2013. We do

not include EPI independently in the model, because the variable is fixed every year; thus, it will be correlated with the country-fixed effects (Demetriades and Fielding, 2012; Ibrahim and Law, 2014).

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