<mark>weights, and a combination of propensity score weighting and calibration, Lee and Valliant (2009) find a bias reduction in the range of 1 percent to 94 percent for health status, and 18 percent to 98 percent for health insurance coverage. However, both these variables themselves were used in the propensity score weight estimation, and health status was used in the calibration of weights. Specifically, the study finds that matching after adjustments is better in cases where the variable in question itself is used for the adjustment. In other words, the matching for model variables performs better than non-model variables, a result we also observe.</mark>

# **<mark>6. Conclusion</mark>**

<mark>Traditional surveys that track economic and social indicators use probability sampling techniques based on a complete sampling frame. While these surveys accurately represent the national population, they are expensive and difficult to conduct in national crisis situations, such as the COVID-19 pandemic or fragile or conflict affected environments. Quicker, less expensive, and more convenient methods of data collection have emerged, including phone surveys and online surveys. While Random Digit Dialing technique-based phone surveys are based on a representative sampling frame, they may be biased, since the population of phone owners who would answer the phone and respond to the survey may differ from the general population. Internet surveys are even more convenient and inexpensive to administer, but their scope in developing countries could be curtailed by lower levels of internet access and educational attainment. These surveys are based on non-probabilistic sampling frames because it is difficult to ascertain the universe of Internet users.</mark> <mark>In this study, we utilized three waves of online surveys conducted in six countries during the COVID-19 pandemic between December 2020 and August 2021. The objectives were to capture key economic variables during the pandemic that would have otherwise been harder to collect through physical door-to-door surveys. We compared economic indicators from these surveys with regular labor force surveys conducted in these countries around the same time in order to assess whether these surveys accurately captured and tracked indicators. We tracked three key indicators, namely, the employment-to-population ratio, the formal share of employment, and the share of self-employment.</mark> <mark>Examining the demographic representation of the online survey data, we find that the raw or unweighted online-survey data are not nationally representative. They overrepresent men, younger individuals, and the highly educated. To attempt to overcome these biases, we estimated a propensity score model to estimate weights to make the internet survey data more representative. For this, we estimated a model to predict the selection of observations in the online survey compared to a nationally representative survey from the Global Monitoring Database (GMD) using a variety of independent variables. From this model, we further predicted inverse probability weights for adjustments. The means of variables included in the propensity score model seem to be relatively close to the GMD means, in comparison to the non-model variables whose online survey means deviated widely from the GMD mean. For example, the deviation for model variables was 32.0 percent in Brazil, but 56.8 percent for the non-model variables. In Indonesia, while the deviation for model variables was 15.3 percent, the deviation was 582.0 percent for non-model variables.</mark> <mark>Applying these estimated weights, we compared the economic indicators from the online survey with those derived from nationally representative comparative labor force surveys available during the pandemic. The results indicate that there is substantial deviation of the online survey</mark> 22