In the social sciences, online surveys using convenience samples are growing in popularity. These online platforms and recruitment through commercial internet panels has proved to be a cheaper alternative to the collection of data from the field, including data on the university students. Further, the alternative of using administrative data, especially in developing nations like India, is riddled with multiple shortcomings. First, observational data collected often by administration, lack the details conforming to international standards. This difficulty is exacerbated by the disparity in nature between the data necessary for academic research and the administrative data that is usually collected to monitor programs. Second, the data collection process is infrequent and at irregular intervals. Third, given the limited state capacity in developing nations, the cost of collection of data is considerably high. This adversely affects coverage of observational datasets that are often compromised even when nationally representative. Finally, these datasets lack a comprehensive focus on the whole range of behavioral outcomes. For instance, none of India's administrative datasets provide information on behavioral characteristics such as time and risk preferences. However, it is now well established that these behavioral traits have important micro and macro implications (Akerlof, 2002; Frederick et al., 2002; Levitt and List, 2007).
| Project Team : | Soham Sahoo, Ritwik Banerjee and Satarupa Mitra |
| Sponsor : | IIM Bangalore |
| Project Status: | Ongoing (Initiated in September 2022) |
| Area : | Economics & Social Science |
| Abstract : | In the social sciences, online surveys using convenience samples are growing in popularity. These online platforms and recruitment through commercial internet panels has proved to be a cheaper alternative to the collection of data from the field, including data on the university students. Further, the alternative of using administrative data, especially in developing nations like India, is riddled with multiple shortcomings. First, observational data collected often by administration, lack the details conforming to international standards. This difficulty is exacerbated by the disparity in nature between the data necessary for academic research and the administrative data that is usually collected to monitor programs. Second, the data collection process is infrequent and at irregular intervals. Third, given the limited state capacity in developing nations, the cost of collection of data is considerably high. This adversely affects coverage of observational datasets that are often compromised even when nationally representative. Finally, these datasets lack a comprehensive focus on the whole range of behavioral outcomes. For instance, none of India's administrative datasets provide information on behavioral characteristics such as time and risk preferences. However, it is now well established that these behavioral traits have important micro and macro implications (Akerlof, 2002; Frederick et al., 2002; Levitt and List, 2007). |
