Online surveys are convenient, cost-effective, speedy, and increasingly popular instruments for data collection. This study investigates whether online surveys that used Random Domain Intercept Technology to recruit respondents were accurately measured labor market outcomes in six middle-income countries in the aftermath of the COVID-19 pandemic. Compared with the national average, online surveys oversampled males, youth, those with higher levels of education, and those in smaller households. Reweighting using propensity score estimates fails to equalize the means of variables excluded from the model. When comparing the employment-to-population ratio from the internet surveys to the most recent relevant nationally representative surveys, the average deviation is 30 percent. Reweighting using propensity scores in that case worsened the bias. Internet survey estimates of informal and self-employment rates also tend to be inconsistent with benchmark data, although the latter are available for fewer countries. Overall, the results suggest that despite the advantages and convenience of recruiting internet survey participants through Random Domain Intercept Technology, the resulting sample is not representative and even after propensity score reweighting, it can yield estimates that are at odds with nationally representative surveys.