Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/303194 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
I4R Discussion Paper Series No. 162
Verlag: 
Institute for Replication (I4R), s.l.
Zusammenfassung: 
Liu, Shamdasani, and Taraz (2023) examine, among other things, the effect of temperature and precipitation in India during the growing season (June-February) on the agricultural and non-agricultural worker share in Indian districts in the medium run (decades) and in the long run (30 years). In their preferred analytical specification, they find that a 1°C increase in temperature leads to a 17% increase in the agricultural labor share (corresponding to a logarithmic coefficient of 0.157) and an 8.2% decrease in the nonagricultural labor share (corresponding to a logarithmic coefficient of -0.086) in the medium term. The effects are significant at the 5% and 1% level (5% and 5% with Conley standard errors), respectively. For precipitation, they do not find effects significantly different from 0. First, we rerun the code with neither execution nor coding errors. Second, we reproduced the main tables in a different software language and did find the same results. Lastly, we tested the robustness by weighting the districts with the population size. Here, we find that the effects in the medium run become significantly smaller and not statistically significant anymore. However, the effects in the long run stay roughly the same. By splitting the sample in low and highly populated districts we find that the medium run effects are only present in low populated districts with no effect on highly populated ones.
Dokumentart: 
Working Paper

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