The use of ad blocking software has risen sharply with online advertising and is recognized as challenging the survival of the ad supported web. However, the effects of ad blocking on consumer behavior have been studied scarcely.

This paper uses propensity score matching techniques on a longitudinal survey of 4,411 Internet users in Spain to show that ad blocking has a causal positive effect on their number of online purchases. This could be attributed to the positive effects of ad blocking, such as a safer and enhanced navigation.

This striking result reinforces the controversial debate of whether current online ads are too bothersome for consumers.

[Keywords: Ad blockers, advertising avoidance, e-commerce, propensity score matching]

…This study employs a rich dataset coming from a longitudinal survey. The source of the data is a survey conducted by the Spanish Markets and Competition Authority on the same sample of interviewees in the 4^th quarter of 2017 and in the second quarter of 2018 (CNMCData2019). The sample was designed to be representative of the population living in private households in Spain. The information was provided by 4,411 Internet users ≥16 years old. At the baseline time point (fourth quarter of 2017) these individuals were asked if they regularly used ad blocking tools when navigating the web. Additionally, the survey collected information on their socio-demographic characteristics (age, gender, education level and employment status) and on how they used Internet (frequency of use of online services like: GPS navigation services, instant messaging, mobile gaming, social networks, e-mail and watching videos on the phone). 6 months later (second quarter of 2018), the same individuals were asked how many online purchases they had made during the previous 6 months (these included goods and services purchases, irrespective of the form of payment). Thus, the outcome variable (number of online purchases) occurred later than the collection of the ad blocking information and the rest of variables (our X covariates).

Analysis	N	Treated	Controls	Difference (ATT)	95% LCI	95% UCI	p-value
Unmatched	4411	5.084	2.735	2.348	—	—	—
PSM—NN	1648	5.084	3.325	1.759	0.994	2.523	<0.001
PSM—KM	4411	5.084	3.733	1.351	0.658	2.044	<0.001
Stratification on PS quintiles	4411	5.084	3.686	1.398	0.724	2.072	<0.001
Stratification on PS deciles	4411	5.084	3.774	1.310	0.626	1.994	<0.001
PSM—NN after CEM pruning (1)	1160	4.979	3.773	1.206	0.165	2.246	0.023
PSM—NN after CEM pruning (2)	1622	5.082	3.476	1.605	0.830	2.380	<0.001

Table 2: Estimated average treatment effects of ad blockers on online shopping (number of purchases in 6 months). [ATT: average treatment effect on the treated. PSM: propensity score matching. NN: nearest neighbor. KM: kernel matching. PS: propensity scores. CEM: coarsened exact matching. LCI: lower confidence interval. UCI: upper confidence interval. (1) CEM pruning by using use of Internet apps covariates. (2) CEM pruning by using socio-demographic covariates.]