Monday, 15 May 2017

Does the Internet make people happier?

Following on from the paper I discussed yesterday about Facebook use being associated with lower measures of wellbeing, I thought this 2013 paper by Thierry Penard (University of Rennes), Nicolas Poussing (INSEAD), and Raphael Suire (University of Rennes), published in the Journal of Socio-Economics, was a good way to follow up (it's open access, but just in case there's an ungated earlier version here).

The paper is titled "Does the Internet make people happier?", and the authors used data from the 2008 European Social Survey (but only for 1332 respondents from Luxembourg). Intensity of internet use is their main variable of interest, which: measured by four dummies: Onlineday+ if the Internet is used several times per day (38%), OnlineDay if it is used only once per day (22.3%), OnlineMonth if it is seldom connected (17.1%), and NoInternet if the individual never uses the Internet (22.6%).
They essentially look at how life satisfaction (measured on a ten-point scale) changes with intensity of internet use. So far, so good. Except for the fact that the data is based on a cross-section, so it's only going to show correlations, the approach seems reasonable. The main problem arises later in the analysis. They find:
...a significant negative relation between the non-use of the Internet and life satisfaction. However, among the Internet users, there is no significant difference between the heavy and light users. This suggests that being deprived of Internet access (i.e. being on the wrong side of the digital divide) has a detrimental effect on the well-being.
These results hold up as they add more explanatory variables to their model, but as soon as they add health and income , their main result becomes only weakly statistically significant. Here's where the analysis becomes problematic. Penard et al. introduction interactions between internet intensity and other variables (age, marital status, gender, sociability, and income), but in those interactions they treat the ordinal variable of online intensity (described above as four categories) as a continuous variable (0,1,2,3). Treating an ordinal variable as continuous is unjustifiable in this case - there isn't any reason to believe that the difference between no internet use (0) and online once a month (1) is the same as the difference between online once per day (2) and online several times per day (3), but that is how it is treated in this case.

Once they add these dodgy variables into their analysis, it makes all of the internet intensity variables statistically significant, and of the expected sign. However, the results can't be believed because the additional variables introduce bias into the analysis.

As an aside, I'm always skeptical when a paper suddenly does one of three things after finding weak or statistically insignificant results in their main analysis: (1) looking at sub-groups or subsets of the data; (2) introducing interaction variables; or (3) quantile regression techniques. I may talk more about those in a later post, but if they weren't part of the original plan they really cry out that the researchers were clutching at straws looking for something to report.

There is further evidence that the initial results by Penard et al. lack robustness, and that comes from their own robustness checks reported in the paper. The authors rightly point out that:
It is possible that omitted variables in the estimated models influence both the intensity of Internet use and well-being, or that people who are more satisfied with their life are more likely to use the Internet (inverse causality).
So, they apply an instrumental variables (IV) analysis (which I've described earlier here). This involves finding a variable that you know affects internet use, but which won't have a direct effect on life satisfaction. Penard et al. use internet use by other family members. [*] Once they run the IV analysis, none of their internet intensity variables are statistically significant (even when they include the dodgy interaction variables).

Overall, despite the title this paper doesn't really contribute much to our understanding of whether internet use makes people happier or not. I'd be interested to know what happens to their analysis if you replaced the dodgy interaction variables with interactions based on the proper categorical variables, but I wouldn't expect it to change much (else they would probably have reported those results instead!).


[*] If I wasn't feeling generous I would point out that this variable fails the exclusion restriction. If the rest of your family uses the internet, perhaps they don't spend so much time on interacting with you, which could directly affect your life satisfaction (positively or negatively, depending on your family!).

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