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Identifying Electoral Fraud: A Novel Test and New Data From Nigeria
Unformatted Document Text:  SAP results 0 .08 Mean .12 .22 0 1 2 3 4 5 6 7 8 9 MSP results 0 .08 Mean .12 .22 0 1 2 3 4 5 6 7 8 9 Share of last digits Number of registered voters 0 .08 Mean .12 .22 0 1 2 3 4 5 6 7 8 9 Figure 2: Frequencies of last digits, Sweden 2002 within a municipality. The vertical axis gives the statistic of interest. For the first graph, it denotes the extent to which the last and penultimate digits within a given municipality are the same, relative to the lower confidence bound. Municipalities marked in black above the dashed line at 0 have suspiciously few repetitions. The second graph shows the extent to which digits are adjacent in a given municipality, relative to the upper confidence bound. Points above 0 have worryingly many pairs of adjacent digits. The third graph displays the degree to which we observe pairs of non-neighboring digits, relative to the lower confidence bound. The black dots indicate municipalities with suspiciously few pairs of non-adjacent digits. There are a small number of municipalities that are seemingly suspicious, but this is the result of the fact that we plot unadjusted 95% confidence bounds for a test of many hypotheses—one for each municipality. Since we plotted just short of 200 municipalities (in order to facilitate comparison with our analysis of Nigeria’s 2003 election), it is not surprising that a small number of them will lie beyond the 95% confidence interval purely by chance. Again, we cannot reject the null hypothesis of a “clean” election. 4.2 Data and results from Nigeria We now use our digit-based test to examine electoral returns from Nigeria. In particular we analyze data at the polling station level for Plateau state, which is located in the “middle belt” region of the country. We were able to retrieve this data in 2006, and it is, to our knowledge, the first time that post-colonial election data at this level of aggregation has been available outside of Nigeria. The body of data we gathered consists of a nearly complete record of the 2003 presidential, gubernatorial, and parliamentary elections for Plateau and, to some extent, neighboring Kaduna state. We here analyze presidential election returns for Plateau state. All results were entered from original, handwritten electoral ward report sheets used by local authorities, and we focus on the ward as our target of analysis. Figure 4 provides an example of such a ward-level return sheet, highlighting the digits we will analyze below. For 9

Authors: Beber, Bernd. and Scacco, Alexandra.
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background image
SAP results
0
.08
Mean
.12
.22
0
1
2
3
4
5
6
7
8
9
MSP results
0
.08
Mean
.12
.22
0
1
2
3
4
5
6
7
8
9
Share of last digits
Number of registered voters
0
.08
Mean
.12
.22
0
1
2
3
4
5
6
7
8
9
Figure 2: Frequencies of last digits, Sweden 2002
within a municipality.
The vertical axis gives the statistic of interest. For the first graph, it denotes the extent
to which the last and penultimate digits within a given municipality are the same, relative
to the lower confidence bound. Municipalities marked in black above the dashed line at 0
have suspiciously few repetitions. The second graph shows the extent to which digits are
adjacent in a given municipality, relative to the upper confidence bound. Points above 0 have
worryingly many pairs of adjacent digits. The third graph displays the degree to which we
observe pairs of non-neighboring digits, relative to the lower confidence bound. The black
dots indicate municipalities with suspiciously few pairs of non-adjacent digits.
There are a small number of municipalities that are seemingly suspicious, but this is
the result of the fact that we plot unadjusted 95% confidence bounds for a test of many
hypotheses—one for each municipality. Since we plotted just short of 200 municipalities (in
order to facilitate comparison with our analysis of Nigeria’s 2003 election), it is not surprising
that a small number of them will lie beyond the 95% confidence interval purely by chance.
Again, we cannot reject the null hypothesis of a “clean” election.
4.2
Data and results from Nigeria
We now use our digit-based test to examine electoral returns from Nigeria. In particular we
analyze data at the polling station level for Plateau state, which is located in the “middle
belt” region of the country. We were able to retrieve this data in 2006, and it is, to our
knowledge, the first time that post-colonial election data at this level of aggregation has been
available outside of Nigeria. The body of data we gathered consists of a nearly complete
record of the 2003 presidential, gubernatorial, and parliamentary elections for Plateau and,
to some extent, neighboring Kaduna state. We here analyze presidential election returns for
Plateau state.
All results were entered from original, handwritten electoral ward report sheets used by
local authorities, and we focus on the ward as our target of analysis. Figure 4 provides an
example of such a ward-level return sheet, highlighting the digits we will analyze below. For
9


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