Measuring Labor Market Status Using Remote Sensing Data

In this study, we utilize remote sensing data to estimate employment in Bangladesh. Since labor force surveys are only conducted occasionally in Bangladesh, total employment is measured statistically based on scarce survey data. To exploit the real-time availability and regionally subdivided characteristic of remote sensing data, we estimated annual employment in terms of job quality in 64 districts using interpolation. Using the employment data interpolated by job quality, which show how many people have good-quality employment, such as a good employer or regular-paid job, we constructed a forecasting model for annual employment data with mixed frequency control variables in quarterly frequency, as well as remote sensing data. Then, we extended the model for estimating quarterly employment status using a monthly series, which included remote sensing data such as nighttime light and enhanced vegetation index. Since there was no true observation in our target variable, there was no way to verify the accuracy of the forecasting model. Instead, we extended our approach to perform an out-of-sample forecast, since we had real-time data up to the first half of 2022. As a result, the remote sensing data may not have captured the overall trend of employment due to the domination of control variables such as industrial production. However, the remote sensing data showed idiosyncratic movement by districts. Therefore, our result is helpful for policymakers in implementing labor policies for specific districts because it allows for observing remote sensing data trends in real time.