Replication Data for: Mining and Local Corruption in Africa
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'We investigate whether mining affects local corruption in Africa. Several cross-country analyses report that natural resources have adverse effects on political institutions by increasing corruption, whereas other country-level studies show no evidence of such 'political resource curses'. These studies face well-known endogeneity and other methodological issues, and employing micro-level data would allow for drawing stronger inferences. Hence, we connect 92,762 Afrobarometer survey respondents to spatial data on 496 industrial mines. Using a difference-in-differences strategy we find that mining increases bribe payments, and this result is robust to using alternative models. Mines are initially located in less corrupt areas, but mining areas turn more corrupt after mines open. When exploring mechanisms we nd that local economic activity' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Corruption","Minerals","Mining","Natural resources"]} <|eot_id|>
 Here is the Description of a dataset. Description:'We investigate whether mining affects local corruption in Africa. Several cross-country analyses report that natural resources have adverse effects on political institutions by increasing corruption, whereas other country-level studies show no evidence of such 'political resource curses'. These studies face well-known endogeneity and other methodological issues, and employing micro-level data would allow for drawing stronger inferences. Hence, we connect 92,762 Afrobarometer survey respondents to spatial data on 496 industrial mines. Using a difference-in-differences strategy we find that mining increases bribe payments, and this result is robust to using alternative models. Mines are initially located in less corrupt areas, but mining areas turn more corrupt after mines open. When exploring mechanisms we nd that local economic activity' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
We investigate whether mining affects local corruption in Africa. Several cross-country analyses report that natural resources have adverse effects on political institutions by increasing corruption, whereas other country-level studies show no evidence of such 'political resource curses'. These studies face well-known endogeneity and other methodological issues, and employing micro-level data would allow for drawing stronger inferences. Hence, we connect 92,762 Afrobarometer survey respondents to spatial data on 496 industrial mines. Using a difference-in-differences strategy we find that mining increases bribe payments, and this result is robust to using alternative models. Mines are initially located in less corrupt areas, but mining areas turn more corrupt after mines open. When exploring mechanisms we nd that local economic activity
Corruption; Minerals; Mining; Natural resources