<mark>suggest that statistical adjustments can reduce, if not eliminate, coverage bias in the situation we study. Schonlau et al. (2009) study a survey of respondents that have Internet access in the Health and Retirement Study. They analyze whether propensity score and Genetic Matching algorithm can correct for imbalances between Internet and non-Internet samples for variables used for adjustment and for variables not used in the adjustment. Using these methods, they find that the estimated bias is almost always reduced, but significant differences in many cases remain.</mark> <mark>Mercer et al (2018) study public opinion polls and find that the choice of adjustment variables is more important than any reweighting method for the accuracy of estimates. R</mark> aking performs nearly as well as more elaborate techniques based on matching. In their application, b <mark>oth the demographic and political variables resulted in lower average bias than adjusting on demographics alone. V</mark> ery large sample sizes do not fix the shortcomings of online opt-in samples. While an online opt-in survey with 8,000 interviews may sound more impressive than one with 2,000, the study finds virtually no difference in accuracy. Ferri-Garcia (2020) uses Machine Learning methods as an <mark>alternative to propensity score adjustment (PSA) for reducing or eliminating selection bias in online surveys. The results indicate that the ML algorithms are more effective in removing selection bias than logistic regression when used for PSA, but this difference is not important when the dimensionality of the data is low, and the covariates are not very discriminant.</mark> <mark>In sum, the existing literature broadly suggests that</mark> various weighting schemes do not adequately reduce the difference between weighted Internet survey data and the reference survey. Propensity score adjustment seems to work better than post-stratification in settings where they have been compared directly, but other <mark>studies show that the choice of adjustment variables is more important than any reweighting method for the accuracy of estimates. Only one study examines economic indicators (wages) from web surveys. W</mark> hile this study was able to match demography and human capital variables with reference samples, they were unable to match wage distributions. Clearly, all studies are based on high income countries, with little research on developing countries. In this study, we try to address these gaps by studying the accuracy of Internet surveys in a set of six developing countries using the propensity score adjustment method.

# **<mark>3. Data and Descriptive Statistics</mark>**

# **<mark>3.1. Data Sources</mark>**

<mark>The study uses several sources of data. The primary data source is the web-survey data collected through the Random Domain Intercept Technology. The survey is offered to the online population at random, provided they incur a browsing data input error. Specifically, if an individual stumbles across a broken link, they will be invited to participate in the survey. After excluding bots, users are invited to participate in a survey in their country’s language. The targeting is based on the IP-address that also reveals information about different regions across the country. Respondents are identified by creating a unique non-personally identifiable respondent code which is a combination of the respondent’s IP address, device details such as device type, internet browser, operating system, and other device details. Using this code, respondents are filtered out from taking the survey more than once, as the various survey waves are designed as repeated cross-sections. While the same person could potentially take the survey from multiple devices, the opportunity cost of time could prevent them from doing so.</mark> <mark>The survey collected information on basic demographic details, such as age, gender, education, and household size. Beyond these basic details, the survey obtained information on respondents’</mark> 10