{ "original_study": { "claim": { "hypothesis": "The relationship between (in)security and election fraud should be in the form of an inverted U-shape. Fraud increases with violence up to a certain level, but then decreases again.", "hypothesis_location": "Section: MANIPULATION TACTICS; Subsection: Violence and Fraud; p. 58", "statement": "We find support…for the inverted U-shaped relationship between violence and fraud", "statement_location": "Section: CONCLUSION AND POLICY IMPLICATIONS; p. 74", "study_type": "Observational" }, "data": [ { "source": "International Election Commission", "wave_or_subset": "1-3", "sample_size": "27,163", "unit_of_analysis": "polling-station level", "access_details": "not stated", "notes": "After removing missing, sample size drops to 22,858." }, { "source": "SIGACT reports", "wave_or_subset": "not stated", "sample_size": "not stated", "unit_of_analysis": "violent incident", "access_details": "not stated", "notes": "The authors discuss that they use this data, but do not describe the characteristics of the data (e.g. sample size)." }, { "source": "National Risk and Vulnerability Assessment", "wave_or_subset": "2007", "sample_size": "20,576", "unit_of_analysis": "household", "access_details": "not stated", "notes": "" }, { "source": "GTOPO30", "wave_or_subset": "NA", "sample_size": "NA", "unit_of_analysis": "elevation", "access_details": "Available at http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html. 2007.", "notes": "This is geological raster data for Afghanistan so there is not real sample size." }, { "source": "LandScan population data", "wave_or_subset": "not stated", "sample_size": "not stated", "unit_of_analysis": "30 x 30 latitude/longitude cells", "access_details": "not stated", "notes": "They do not directly discuss access, but they leave the reference to the data: Oak Ridge National Laboratory, LandScan Global Population database, 2008. For this paper, the data is aggregated to the district level." } ] }, "method": { "description": "Use logit (for the last-digit fraud measure) and OLS models (for the recount-based fraud measure) and regress fraud on violence and its squared term to test the inverted U-shaped prediction.", "steps": "(1) Clean, merge, and aggregate the data (remove missing, etc.) when necessary; (2) Group polling stations by district and apply the Beber–Scacco last-digit test to the total vote count; (3) Code a binary dependent variable for fraud; (4) Estimate the effects using logit and OLS models.", "models": "logit and OLS", "outcome_variable": "Fraud", "independent_variables": "Violence; Violence squared", "control_variables": "number of closed centres, economic development and geographic accessibility", "tools_software": "not stated" }, "results": { "summary": "Since the linear term of the violence measure receives a positive and significant coefficient, and the squared term a negative one, this implies the U-shaped prediction is credible.", "numerical_results": [ { "outcome_name": "Election Fraud (last-digit)", "value": "13.748", "unit": "not stated", "effect_size": "not stated", "confidence_interval": { "lower": "not stated", "upper": "not stated", "level": "not stated" }, "p_value": "<0.001", "statistical_significance": "0.1% level", "direction": "Negative", "notes": "Confidence intervals are not presented, but standard errors are presented instead (4.720)." }, { "outcome_name": "Election Fraud (Recount)", "value": "1.438", "unit": "not stated", "effect_size": "not stated", "confidence_interval": { "lower": "not stated", "upper": "not stated", "level": "not stated" }, "p_value": "<0.001", "statistical_significance": "0.1% level", "direction": "Negative", "notes": "Confidence intervals are not presented, but standard errors are presented instead (0.375)." } ] }, "metadata": { "original_paper_id": "10.1017/S0007123412000191", "original_paper_title": "Violence and Election Fraud: Evidence from Afghanistan", "original_paper_code": "http://dvn.iq.harvard.edu/dvn/dv/nilsw", "original_paper_data": "http://dvn.iq.harvard.edu/dvn/dv/nilsw" } }