How do tablets affect economic growth

In fact, even as early as , it was estimated that there were around , jobs created as a result of the app economy. It is worth noting that the estimate does not include the design, manufacture, or marketing of smartphones and other mobile devices. One of the other benefits of the so-called app economy is the dramatically lower barrier to entry that this new sector offers.

Instead of requiring a massive manufacturing capacity or labor pool, entrepreneurial individuals can take a great idea, learn how or hire someone to code, and utilize the platforms built by Apple, Google, or Microsoft to distribute their software solution to a truly global market. Perhaps the best example of this is Uber, the ridesharing app that is taking the world by storm and severely disrupting the taxi industry.

Uber allows anyone with a car, presuming the car is new enough and well maintained, to use it to get paid for giving other people rides. Uber, through the use of their smartphone app, is able to manage drivers with riders very effectively, reducing costs associated with getting a ride.

They reveal that return from IT capital investment is positive and significant for the developed countries in the sample but not statistically significant for the developing ones. This study attributes this gap to the low level of IT investment as well as lack of complementary assets in developing countries. They explain that complementary investments in infrastructure, human capital, and knowledge-based structures are prerequisite for IT investments to be productive which are mostly available in developed countries rather than developing ones.

Moreover, Lee et al. In line with this result, Edquist [17] conclude that the vague impact of ICT on economic growth in developing countries may account for the late introduction of ICT in these countries; for example, Internet service was not available in most developing countries until the late s. In this point of view developing countries may have an advantage over advanced countries with respect to ICT diffusion. Generally, we can divide the empirical evidence of the impact of ICT on economic growth to two categories based on the methodology used in these literatures.

The first is studies employing the growth accounting technique, which weights growth in inputs by their share in the value of output and express the contribution of ICT to economic growth in percentage point.

It should be noted that all the above evidences are at the national level whereas there are some other studies at the firm or industry level.

For instance, O'Mahony and Vecchi [29] , applying heterogeneous dynamic panels method with a unique dataset covering the entire non-agricultural market economy at the industry level for the US and UK from to , find a positive and significant effect of ICT on economic growth and excess returns to ICT compared with non-ICT assets. The second category consists of researches that use cross country regression techniques to investigate the impact of ICT on economic growth.

Madden and Savage [30] , using the sample of 27 Central and Eastern European countries, show a positive and significant impact of telecommunication investment on economic growth during the period — Roller and Waverman [31] also confirm a causal relationship among telecommunication investment and economic growth for 21 OECD countries over the period to Jacobsen [32] and Waverman et al.

Another study conducted by Koutroumpis [34] for 22 OECD countries during to , shows that there is a positive casual link among broadband infrastructure as a driving factor of ICT and economic growth, especially in the presence of critical infrastructure mass. Applying panel data of 29 countries, Seo et al.

They only verify the positive impact of ICT on economic growth in the s. The positive and significant effect of mobile telecommunications diffusion on both economic growth and productivity growth has proven by Gruber and Koutroumpis [36] for countries over the period — Although ICT is well known as a driving engine of economic growth, there are few evidences that show the negative effect of ICT on economic growth. For example, Kiley [37] , applying the traditional growth accounting framework in the US, explains the negative contribution of computers to economic growth due to adjustment costs.

He indicates that the introduction of a new investment good like computers can impose large adjustment costs to the economy and decrease economic growth. Moreover, Pohjola [2] finds no significant relationship between ICT investment and economic growth for the sample of 43 countries over the period of — In another research, Jacobsen [32] reveals no significant positive impact of computer penetration on the economic growth of 84 countries during —, although he confirms the positive link among mobile phone and growth.

However, the empirical results of the previous studies are somewhat fragile and depend on data period specifications and econometric techniques, the dominant impact of ICT as a production input on economic growth and productivity is positive [38] , [39] , [24] , [40] , [41] , [42]. Evidently, most of the literatures in the field of ICT effect on economic growth and productivity, concentrate on the ICT investment as a whole and evidence on the impact of ICT use on economic growth and productivity is scarce.

Only a few studies investigate the effect of ICT use on economic performance applying different proxies such as telephone penetration estimated by number of telephones per persons [43] and teledensity defined as the number of fixed-line and mobile phone subscribers per persons [44] , [45]. Therefore, the main hypothesis of this paper is that the effects of ICT use as measured by the number of internet users, fixed broadband internet subscribers and the number of mobile subscription per inhabitants on economic growth is positive and significant.

Combining data for the countries, we find that ICT use has a positive impact on output growth. This study uses a dynamic panel data model [47] to investigate the impact of ICT use on economic growth.

The model is shown as follows:. In the above equation, the fixed effects , such as regional and demographics which are also called time-invariant country characteristics, might be correlated with the explanatory variables which violates the assumptions underlying the classical linear regression model [48].

Moreover with regards to dynamics, the presence of lagged dependent variables will increase the autocorrelation. First differencing can solve this problem by removing such fixed effects, as follows:.

There are still following econometric problems in the estimation of equation 2 which should be considered:. In this case the assumptions of stationarity of all the variables included in the regression and homogeneity of cross-country coefficients are violated. In this case, the simple ordinary least squares OLS approach can produce highly misleading results [50] , [51]. Therefore, the empirical analysis for the estimation of equation 2 should employ a methodology that accounts for heterogeneous dynamic panels [52].

To overcome these issues, economists recommend the use of instrumental variables, and more recently panel data techniques such as Pooled Mean Group PMG , discussed in Pesaran et al. However, when the number of cross-section observations is quite large and the time-series dimension is relatively small, as is the case in this paper, the GMM estimator can produce more consistent estimates [52].

Shortly, GMM estimator is useful for panel data with relatively small time dimension, as compared to the number of cross sections [54]. In this method lags of the dependent and independent variables are used as instruments. In this study, we consider lags up to four years and the dynamic panel data model is then applied to the complete panel dataset.

The ICT use index includes three indicators, Internet user penetration, fixed broadband penetration, and mobile broadband penetration and captures the level of ICT use in more than countries worldwide. Our estimated results based on the GMM -dynamic panel data- are summarized in Table 1. As table 1 shows, the signs of all variables are consistent with theory predictions.

It means that the more a country use ICT, the greater is its economic growth. The coefficient of ICT use index is equal to 0. Additionally, the coefficient of the first lagged ICT use index is equal to 0. It means that one percent change in ICT use index of the previous year can increase the growth rate of GDP per capita by 0. The statistics presented by the ITU and other international organizations indicate an increasing trend of ICT use indicators in most of these countries, it means that these countries recognized the important effect of ICT on their economic growth.

They also verify the hypothesis of this paper that ICT use has a significant growth generating effect. The signs of the first lagged of ICT use index and GDP per capita coefficient are positive and highly significant that implies the positive effect of these variables on economic growth.

In the context of GMM, the over-identifying restrictions may be tested via both the Sargan and Hansen test. In comparison the Hansen J statistic is more robust than Sargan. For example, Sargan is not distributed as chi-squared under heteroskedasticity and Hansen is, and if this problem is present then it could cause Sargan to incorrectly reject the null. Table 2 also shows that the first lagged of ICT use index for all income groups is positive; however it is only significant in upper middle and lower middle income countries.

These empirical results are consistent with the findings of Lam and Shiu [45]. To shed more lights on the differences of countries regarding to ICT use index, following figures are presented. Figure 2 indicates the increasing trend of the ICT use index over the period to separately for each income group. As depicted in this figure, ICT use index has a nearly fixed growth in high income countries while other income groups have been experiencing an increasing growth of ICT use indicators.

Figure 3 also illustrates the different level of the average ICT use index in four income groups. As expected the highest value of the ICT use index is allocated to high income countries and the lowest value of this index is related to the group of low income countries. These findings can confirm the between groups differences in the estimated coefficients of ICT use index in table 2. This paper concentrated on exploring the effect of ICT use index on economic growth.

The results show that ICT use has a significant effect on the economic growth of these countries. The coefficient measuring the effect of the ICT use on economic growth was positive, indicating that ICT affect economic growth of the sample countries in a positive way. Furthermore, in high income counties ICT use index has the strongest effect on real GDP per capita among the others while this effect is the lowest in countries with low level of income.

Moreover, the performance of the both higher middle and lower middle income groups in the effect of ICT use index is somewhat lagging. Consequently, ICT plays a vital role as a mean for economic growth.

Therefore, it seems necessary for all countries to increase their ICT use index through increasing the number of internet users, fixed broadband internet subscribers and the number of mobile subscription per inhabitants in order to boost economic growth. It is also essential for the governments to provide the society with information, up-to-date structures and educate people in order to use ICT efficiently.

The major research limitation of this study was the failure to collect data for a longer time period. Therefore future research for a longer time span would shed more light in the assessment of the relationship between ICT use and economic growth. Conceived and designed the experiments: RI.

Performed the experiments: M. Analyzed the data: M. Wrote the paper: M. Farhadi M. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract In recent years, progress in information and communication technology ICT has caused many structural changes such as reorganizing of economics, globalization, and trade extension, which leads to capital flows and enhancing information availability. Funding: The authors have no support or funding to report.

Introduction At the present time, ICT has become a serious part of economy. Download: PPT. Literature Review The effect of ICT on economic growth has been analyzed by many authors in last decades.

Methodology and Data Conceptual form This study uses a dynamic panel data model [47] to investigate the impact of ICT use on economic growth. First differencing can solve this problem by removing such fixed effects, as follows: 2 There are still following econometric problems in the estimation of equation 2 which should be considered: 1- There is correlation between the new error term and the differenced lagged-dependent variable.

In this case the assumptions of stationarity of all the variables included in the regression and homogeneity of cross-country coefficients are violated. Table 2. Conclusions and Implications This paper concentrated on exploring the effect of ICT use index on economic growth. Supporting Information. Table S1. ICT Use Index, — Author Contributions Conceived and designed the experiments: RI.

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Journal of Economic Perspectives 14 4 3— View Article Google Scholar 8. Advances, In Computers 43 February — View Article Google Scholar 9. STI Reviews 20, p.

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