<mark>is poorer for these indicators, with the average relative absolute deviation of 39.1 percent, and for those in self-employment, it is even higher with a deviation of 166.9 percent.</mark> <mark>Previous studies from developed countries that attempt to reduce bias in online surveys find that the propensity score-based reweighting methods do not help in bringing the web survey</mark><sup>7</sup> <mark>estimates close to the probability-based reference survey/sample (Smyk et al. 2020; Schonlau et al. 2009; Yeager et al. 2011). We add to this literature by showing that these findings also tend to hold in developing countries. We compare the reduction in bias after adjusting the estimates based on propensity scores to estimates when we do not adjust at all. For the employment-topopulation ratio, the propensity score adjustment increased the average bias ratio across countries/waves by 18 percent, indicating that reweighting using propensity scores worsened the accuracy of the estimates. The employment measures that we are interested in are not included in propensity score matching models. To the best of our knowledge, other studies have only examined the accuracy of variables used in the propensity score model or in other calibration methods, and not the non-model variables. We are the first to attempt to study whether nonmodel variables can be estimated accurately using such adjustment techniques. We also contribute to the literature by testing our method across six countries, which gives a coarse sense of whether Internet surveys may be less biased in some settings over others.</mark> <mark>Overall, the results suggest considerable caution when interpreting labor market indicators collected through random intercept domain technology. Although the internet survey results do match benchmark data in some cases, the sample of internet responders is highly skewed, and in most cases, there are large discrepancies in outcomes even after reweighting. While there may be more room to experiment with the collection of additional demographic data to reweight the sample more effectively, achieving credibly representative estimates with internet surveys continues to be a major ongoing challenge.</mark> <mark>The next sections are laid out in the following format. Section 2 presents the previous literature on the methods and issues in utilizing online surveys to be representative of a larger target population. Section 3 presents the data sources. Section 4 describes the methodology. Section 5 presents the results, and section 6 concludes the study.</mark>

# **<mark>2. Literature and Methods Review</mark>**

## **<mark>2.1.Overview of Issues in Online Surveys</mark>**

There are two broad types of web surveys. The first one is a probability-based sample where there is a sampling frame with e-mail addresses available for all eligible persons and a random sample is asked to visit a website and complete a survey. The second type, in contrast, is a _nonprobability-based_ web survey, like entertainment surveys, self-selected web surveys, and surveys made up of volunteer panels of Internet users. In the latter type of surveys, the probability of selection differs for different members of the population, making the surveys less representative. For instance, in volunteer web surveys, open invitations on websites are used to select respondents.<sup>8</sup> The probability of receiving such an invitation and the probability of accepting it are unknown. Due to the lack of a sampling frame and volunteer-based data collection, it is difficult to ascertain the extent to which the results from these surveys can be generalized for the whole population (Valliant and Dever, 2011).

> 7 The terms web surveys and online surveys will be used interchangeably. 8 Many market research and survey companies use convenience sampling to track sentiment, behavior, consumer preferences, etc.

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