The Impact of Broadband Speed on the Household Income: Comparing OECD and Brics
23 Pages Posted: 4 Mar 2013 Last revised: 27 Sep 2013
Date Written: March 1, 2013
This paper aims to measure the impact of broadband speed on the household income based on set of survey data conducted by Ericsson Consumer Lab in eight OECD and three BRIC countries in 2010 with the sample of study comprises a total of 20.000 respondents. The analysis is crucial as the broadband speed affects not only the end-users (increasing growth capacity, providing variety of services, and linking the users to other socio-economic variables, namely health and education) but also the supply-side, as the availability of speed also increases productivity and efficiency of firms. Moreover, the study is novel as most previous studies on broadband emphasize the penetration rate as the variable of interest.
This study adopts the framework of the return to schooling models (as Mincer, 1974 and Card, 2001). In this discourse, variety of estimations has been employed to capture the difference of schooling quality, and gap between male–female. However, the impact is difficult to be measured as, for instance, people are different in their tacit ability (skills and motivation). This study introduces “additional skills and experiences” by presenting variables related to the access and use of ICT in addition to the standard return to schooling model where income is affected by education, skills (managerial competencies), and variety of socio-economic variables (e.g., age, gender, type of occupation, marital status, etc.). The access and use are believed to play important roles in increasing knowledge and skills as shown in many previous studies (James, 2011; van Deursen & van Dijk, 2011; Hargittai, 2010). In addition, the idea behind this approach is to eliminate the problem of endogeneity of speed variable and reverse causality that speeds subscribed by the users are in fact influenced by their income levels.
To operationalize this approach, a treatment effect model is employed using the Propensity Score Matching (PSM). The basic idea behind the method is to estimate the counterfactual outcome of the income for people who have connected to the broadband would have achieved had they not connected to the device. Two aspects are investigated –the impact of the access to broadband on income and the impact of varying broadband speeds on income. On the access identification, the samples are one with the broadband access at a particular speed level against the other without the broadband access. On the speed upgrades, a comparison is conducted at various speed levels, e.g., users 2 Mbps, with users at 0.5 Mbps.
On access to broadband, the results in OECD countries show that gaining access to 0.5 Mbps would not be expected to yield an increased income as the threshold is somewhere between 2 Mbps-4 Mbps. For BRIC countries, on the contrary, the impact is already visible at 0.5 Mbps. Around 800 USD additional annual household income is expected to be obtained by introducing 0.5 Mbps broadband connection which is equivalent to 70 USD per month per household.
On speed upgrades, the speed level giving the highest benefit to income in BRIC and OECD are the same (4 to 8 Mbps), even though higher speed levels (8 to 24 Mbps) contributes more in OECD than in BRIC. Moving from 4-8 Mbps, the incremental income generated in OECD country is around 4% (with average income 37.000 USD) and around 1.5 % in BRIC countries (with the average income 10.000 and 12.000 USD for China and Brazil respectively). However, the BRIC countries can obtain higher impact by upgrading the speed only from 0.5 to 4 Mbps. At this scale, the countries will gain an additional household income of 2.2% for China and 4.7% for Brazil. Note that the survey was carried out in 2010 where the sample average speed level in OECD countries is only around 4-5 Mbps and 2 Mbps in BRIC countries.
Keywords: broadband, speed, household income, OECD, BRICs, propensity-score matching, treatment effect
JEL Classification: O11, O14, O32, O33, N84
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