Anomalies and Frauds(?) in the Kenya 2017 Presidential ...wmebane/Kenya2017.pdf · from the Toolkit separately for each county.3 Such results appear in Tables 3{8.4 Tables 3 and 4

Anomalies and Frauds(?) in the Kenya 2017

Presidential Election∗

Walter R. Mebane, Jr.†

September 18, 2017

∗Thanks to Preston Due for assistance, and to many colleagues for helpful conversations.†Professor, Department of Political Science and Department of Statistics, University of

Michigan, Haven Hall, Ann Arbor, MI 48109-1045 (E-mail: [email protected]).

1 Introduction

The Presidential election in Kenya on August 8, 2017, was annulled by the Supreme Court,

probably because of problems with the paper records that should have been the official

record of the votes cast in the election (Freytas-Tamura 2017; Opalo 2017). Evidently there

were constitutional and procedural problems, but were there worse problems (Epstein

2017)? Were there frauds?

Every expert on Kenya that I’ve talked to over the past couple of weeks avers that, yes,

everyone knows there were frauds, but most people didn’t expect the frauds to be large in

magnitude. I’ve heard stories about eligible voter frauds in which IDs are rented from

legitimate voters and given to people who are too young to vote so that they may vote for

the candidate favored by the voter buyers. Another story relates to a Twitter post by a

U.S. official stating the Kenyan officials regretted frauds they had ordered once they

realized they could win without them, fearing that their efforts to manipulate the election

would be detected. Various people have told me that various locales in Kenya are

notoriously corrupt. So the general belief seems to be: small frauds, yes; huge frauds, not

likely.

Inspection of polling station vote count and eligible voter count data from Kenya using

election forensics tools (Hicken and Mebane 2015; Mebane 2016; Rozenas 2017) produces

results compatible with the belief that widespread but small frauds occurred. This paper

briefly reviews the evidence.

Before getting to that evidence I should say that Kenya presents a very hard case for

election forensics analysis. As will become clear, voting in Kenya is extremely polarized.

The polarization aligns candidates with ethnicities, and ethnicities tend to be highly

concentrated in terms of their geographic distribution. Also Kenyan elections feature

coalitions and other features of political behavior that involve voters (and candidates)

acting strategically. Electors’ and voters’ actions are strategic when they depend at least in

part on accurate expections about what other electors or voters will do. Both strategic

1

behavior and the existence of important and geographically concentrated covariates (such

as ethnicity) can distort election forensics results. False positive indicators for anomalies

are an important threat, but the distortions may also mean that indications of genuine

frauds are masked.1

I very briefly describe how I came to be analyzing these data, which I’ve done on an

entirely voluntary basis. On August 14, 2017, I was contacted by someone working on

behalf of NASA, seeking assistance looking into alleged problems with the August 8

election. After a couple of conference calls with various people in the U.S., Kenya and

elsewhere, with help from a research assistant I scraped polling station vote count data

from an IEBC website.2 We scraped vote count data both on 18aug2017 and 23aug2017.

From a source in Kenya I obtained a spreadsheet containing polling station eligible voter

counts. These counts appear to be the same as those published in the Kenya Gazette on

June 30, 2017. I estimated almost all the statistics I will report with both the 18aug2017

and the 23aug2017 data, with very similar results, but here I’m reporting only the results

from the 23aug2017 version of the data.

2 Statistics

As originally downloaded, the 23aug2017 vote count data includes 40,830 polling station

observations. The eligible voter count data includes 40,884 polling stations. After merging

the two files and removing unmatched observations and observations with missing data,

including observations that have vote counts of zero for all candidates, we are left with

40,818 polling station observations. Table 1 reports the sums of all the numerical variables

in the merged data file.

1My ongoing research attempts to discover exactly how such threats to valid inferences affect various ofthe statistics used in election forensics and to develop methods for adjusting for such threats. To get a senseof the caution I think we should have when using current election forensics tools with data from Kenya, seethe discussion of the Kenya 2013 election in Mebane (2015).

2The IEBC website is https://public.rts.iebc.or.ke/enr/index.html#/Kenya_Elections_

Presidential/1.

2

*** Table 1 about here ***

The first results, reported in Table 2, are a selection of statistics from the Election

Forensics Toolkit treating data from across the entire country as a whole. For a description

of the Toolkit and of the individual statistics in Table 2 see Hicken and Mebane (2015) and

Mebane (2016). Hicken and Mebane (2015) suggest that if several of the statistics that

indicate anomalies are triggered for an election, then frauds are likely to have occurred in

that election. For the Kenya election every indicator of anomalies is triggered for the votes

for Kenyatta or Odinga. These statistically significant anomalies appear in red. For

Turnout no significant anomalies are apparent (the 2BL statistic is not meaningful for

voter turnout). So these statistics might suggest that frauds affect the vote counts.


Caution is called for because of the way voting in Kenya is polarized. The plots of

empirical densities based on polling station data in Figures 1–6 illustrate this. Empirical

densities for turnout, Kenyatta and Odinga vote proportions across all of Kenya, in Figure

1, show that turnout appears to have a roughly unimodal distribution, but the

distributions of the candidates’ vote proportions are at least bimodal. In many polling

stations Kenyatta or Odinga receive almost all the votes and in many polling stations they

receive almost none of the votes. If such patterns are produced by ethnically polarized

voting, then ignoring the ethnic polarization might well inflate the number of apparently

anomalous Toolkit statistics. Indeed, most of the empirical densities for the same variables

in each county (including Diaspora and Prisons), in Figures 2–6, show that turnout most

often remains unimodal, albeit with a modal value that moves around a bit, and the vote

proportions are usually less pronouncedly bimodal than they are when the country is

treated as a whole. Most densities do exhibit bumps in their tails, however, and it’s hard to

say whether these are merely artifacts of having relatively small numbers of observations or

something more interesting.

3

*** Figures 1, 2, 3, 4 and 6 about here ***

In any case, the apparent diversity across counties motivates estimating the statistics

from the Toolkit separately for each county.3 Such results appear in Tables 3–8.4 Tables 3

and 4 show that the appearance of turnout not being manipulated persists when examined

separately for each county. Figures 5–8 show anomalies for the votes for both Kenyatta

and Odinga, although the appearance of anomalies is not as comprehensive as when the

country is analyzed as a whole. Anomalies are most frequent for the “P05s” statistic,

which may indicate that vote counts are being manipulated by the actions of multiple

agents who are either signaling that they did manipulate votes (Kalinin and Mebane 2011;

Kalinin 2017) or failing to disperse their efforts effectively due to coordination problems

(Rundlett and Svolik 2016). For several counties all or almost all of the statistics indicate

significant anomalies.

*** Tables 3, 4, 5, 6, 7 and 8 about here ***

Estimating the spikes test (Rozenas 2017) separately for each county shows small

percentages of frauds (in the sense of that model) in several counties. Figure 7, which

shows results from the spikes test being estimated for the whole country all together,

suggests that 1.4 percent of polling stations have “spikes frauds,” in particular an excess of

vote proportions of a certain value. Such excesses may be interpreted as symptoms of

excesses occurring due to coordination problems among agents manipulating votes, for

example through vote buying (Rundlett and Svolik 2016). The nationwide analysis of

Figure 7 suggests that such excesses involve excesses of vote proportions near zero or one,

but that impression is belied by running the test separately for each county. Figures 8–11

present these county-specific results. The following counties are estimated to have positive

percentages of polling stations with spikes frauds: Baringo, .40%; Bungoma, .29%; Busia,

3If I had data with which to observe ethnicities for each polling station, other forms of analysis would befeasible. Work to assemble such data or approximations to such data is underway.

4The Diaspora and Prisons “counties” are omitted because of the small numbers of observations in them.

4

.67%; Elgeyo/Marakwet, .36%; Embu, .41%; Isiolo, 1.45%; Kajiado, 1.14%; Kakamega,

.29%; Kisii, .55%; Kisumu, 5.67%; Kitui, .07%; Laikipia, .90%; Lamu, 2.27%; Machakos,

.14%; Mandera, .00%; Meru, .34%; Migori, 4.16%; Mombasa, .89%; Murang’a, .43%;

Nairobi City, .30%; Nandi, 1.09%; Narok, 1.30%; Nyamira, 1.26%; Nyandarua, 2.22%;

Nyeri, 1.45%; Samburu, 1.27%; Taita-Taveta, 1.03%; Tana River, 2.39%; Trans Nzoia,

.90%; Uasin Gishu, .63%; Vihiga, 1.28%; Wajir, 1.70%; West Pokot, 1.36%. The highest

percentages—greater than two percent—are for Kisimu, Migori, Tana River, Nyandarua

and Lamu counties. The number of votes for Kenyatta in polling stations with proportions

that are identified as spikes-fraudulently high is 444,102.5 Note that this model asserts that

the proportions of votes for Kenyatta in these polling stations is excessive, not that all the

votes in the polling stations are fraudulent. If the test is detecting genuine frauds, it is

likely that some votes were somehow added to sets of otherwise authentic votes.

*** Figures 7, 8, 9 and 10, 11 about here ***

I also estimate the spikes test separately for those polling stations in constituencies with

Forms 34B that NASA complained to the Supreme Court about (Epstein 2017).6 Figures

12–18 show these results. Spikes frauds are not generally more frequent in polling stations

included in problematic Forms 34B than they are in polling stations that do not have

problematic Forms 34B.

*** Figures 12, 13, 14, 15, 16, 17 and 18 about here ***

Finally I estimate likelihood finite mixture models (Mebane 2016) that implement the

positive conception of frauds introduced by Klimek, Yegorov, Hanel and Thurner (2012).

Estimating the model for the country as a whole with Kenyatta specified to be the

candidate who may benefit from frauds gives an incremental fraud probability of fI = .030,

5This count was found by modifying the plot.out function in the spikes package (Rozenas 2016).6The constituencies that are the target of these complaints are listed in FORM 34C AND 34B VERIFICATION

REPORT.doc.

5

extreme fraud probability fE = 0 and α = .9. These estimates imply that incremental

frauds produced an expected 74,127 votes for Kenyatta. But in light of the serious

bimodality of the vote distribution (recall Figure 1) that we strongly believe stems

primarily from ethnic polarization, it is not appropriate to estimate the finite mixture

model for the whole country all at once. Instead estimates done separately for each county

are somewhat more plausible.

Estimates for the counties for which the model could be estimated appear in Table 9.7

Because it is plausible that both Kenyatta and Odinga benefitted from frauds, but models

that allow multiple candidates to benefit from frauds do not yet exist, I estimate the model

for each county twice: once with Kenyatta specified as the beneficiary of any frauds; and

once with Odinga specified as the beneficiary of any frauds.8 Extreme frauds are estimated

never to occur: always the estimate is fE = 0. When estimable the probability of

incremental fraud is usually positive. Often the positive fI estimate is less than .01, but

sometimes fI is larger than .02. This happens 18 times when Kenyatta is specified to

benefit from any frauds and 11 times when Odinga is specified to benefit from any frauds.

The largest value estimated for fI is fI = .327 for Kenyatta in Lamu county.


The large value for fI in Lamu county exemplifies the dilemma that remains because of

the extremity of ethnic polarization. In Figure 3 the distributions of vote proportions for

both Kenyatta and Odinga are clearly and substantially bimodal. Experts on Kenya tell

me the bimodality in the county—in which Odinga finished ahead of Kenyatta—stems

from the mix of ethnicities there. But some of these experts also tell me that, nonetheless,

the county has a reputation for corruption. The estimate of fI = .327 is surely too high,

but apparently ethnicities in Lamu county both mask any frauds the finite mixture model

7In the counties for which estimation failed, the program never managed to find starting values.8Such an approach is generally undesirable but it’s what I can do at the moment.

6

might detect there and are associated with the frauds we might like to detect.9

The estimated values of α suggest that sometimes votes are stolen from other

candidates (α < 1) and sometimes votes are manufactured from nonvoters (α > 1). The

instances where α is substantially less than 1.0—say .5 or less—all occur for Kenyatta: in

Bomet, Nairobi City, Nyandarua, Nyeri, Samburu and Tana River; Kenyatta received the

most votes in all these counties except Nairobi City and Tana River. Instances where α is

substantially greater than 1.0—say 2 or greater—occur frequently for both Kenyatta and

Odinga. Such results comport with the prevalence of ID-renting I’ve been told about.

Using the county- and candidate-specific estimates of the likelihood finite mixture

model to derive expected number of votes produced by incremental frauds suggests these

numbers are generally small. As Table 10 shows, the total number of votes due to

incremental frauds across the counties for which estimates could be produced is 36,907 for

Kenyatta and 25,093 for Odinga. Table 11 expresses these incremental fraud estimates for

each county as proportions of the valid votes in the county. Only in Lamu and Nairobi City

(for Kenyatta) and in Narok and Usain Gishu (for Odinga) is the number of votes due to

incremental frauds as large as one percent.

*** Tables 10 and 11 about here ***

3 Discussion

Election forensics analysis suggests that frauds may have been widespread throughout

Kenya in the August 8 presidential election but that the magnitude of the frauds was

small. Only a few percent of votes, at most, are likely to have been produced by frauds.

A complication is that I do not know whether the vote counts scraped from the IEBC

website correspond to counts actually produced by tallying votes at each polling station.

Some of the procedural defects the Supreme Court confronted may have produced

9Work continues on models that can include covariates (Ferrari and Mebane 2017), but such models arenot ready to use, yet.

7

deviations between the Forms 34A and the counts available at the website. Many of the

Forms 34A themselves are alleged to have deficiencies and may not contain accurate tallies.

Another major complication is that features of politics in Kenya, in particular the very

high degree of ethnic polarization, presents challenges that some of the statistics used to

conduct the election forensics analysis cannot or can barely deal with. Analyzing data

separately for each county mitigates the problems to some extent, but difficulties still

intrude: many counties are ethnically heterogeneous even as voting remains polarized. Also

analysis is not able, yet, to take the implications of coalition behavior fully into account.

With luck developments currently underway regarding these techniques will soon support

improved investigations.

8

References

Epstein, Helen. 2017. “Kenya: The Election & the Cover-Up.” New York

Review of Books . August 30, http://www.nybooks.com/daily/2017/08/30/

kenya-the-election-and-the-cover-up/.

Ferrari, Diogo and Walter R. Mebane, Jr. 2017. “Developments in Positive Empirical Mod-

els of Election Frauds.” Paper presented at the 2017 Summer Meeting of the Political

Methodology Society, Madison, WI, July 13–15, 2017.

Freytas-Tamura, Kimiko de. 2017. “Kenya Supreme Court Nullifies Presidential Elec-

tion.” New York Times . September 1, https://www.nytimes.com/2017/09/01/world/

africa/kenya-election-kenyatta-odinga.html?_r=1.

Hicken, Allen and Walter R. Mebane, Jr. 2015. “A Guide to Election Forensics.” Working pa-

per for IIE/USAID subaward #DFG-10-APS-UM, “Development of an Election Forensics

Toolkit: Using Subnational Data to Detect Anomalies.” URL: http://www-personal.

umich.edu/~wmebane/USAID15/guide.pdf.

Kalinin, Kirill. 2017. “The Essays on Election Fraud in Authoritarian Regimes.” PhD thesis,

University of Michigan.

Kalinin, Kirill and Walter R. Mebane, Jr. 2011. “Understanding Electoral Frauds through

Evolution of Russian Federalism: from “Bargaining Loyalty” to “Signaling Loyalty”.”

Paper presented at the 2011 Annual Meeting of the Midwest Political Science Association,

Chicago, IL, March 31–April 2.

Klimek, Peter, Yuri Yegorov, Rudolf Hanel and Stefan Thurner. 2012. “Statistical Detection

of Systematic Election Irregularities.” Proceedings of the National Academy of Sciences

109(41):16469–16473.

Mebane, Jr., Walter R. 2015. “Election Forensics Toolkit DRG Center Working Pa-

per.” Working paper for IIE/USAID subaward #DFG-10-APS-UM, “Development of

an Election Forensics Toolkit: Using Subnational Data to Detect Anomalies.” URL:

http://www-personal.umich.edu/~wmebane/USAID15/report.pdf.

9

Mebane, Jr., Walter R. 2016. “Election Forensics: Frauds Tests and Observation-level Frauds

Probabilities.” Paper presented at the 2016 Annual Meeting of the Midwest Political Sci-

ence Association, Chicago, April 7–10, 2016.

Opalo, Ken. 2017. “Kenya’s Supreme Court just declared the Aug. 8 elections

invalid. Here’s what this means.” Monkey Cage, Washington Post . Septem-

ber 5, https://www.washingtonpost.com/news/monkey-cage/wp/2017/09/05/

kenyas-supreme-court-just-declared-the-aug-8-elections-invalid-heres-what-this-means/

?utm_term=.7a023cbfea91&wpisrc=nl_cage&wpmm=1.

Rozenas, Arturas. 2016. spikes: Detecting Election Fraud from Irregularities in Vote-Share

Distributions. R package version 1.1.

Rozenas, Arturas. 2017. “Detecting Election Fraud from Irregularities in Vote-Share Distri-

butions.” Political Analysis 25(1):41–56.

Rundlett, Ashlea and Milan W. Svolik. 2016. “Deliver the Vote! Micromotives and Mac-

robehavior in Electoral Fraud.” American Political Science Review 110(1):180–197.

10

Table 1: Sums in Merged Polling Station Data

ValidVotes DisputedVotes RejectedVotes objectedVotes15164124 5189 403416 2812

Kenyatta Odinga Nyagah Dida8212671 6816240 37985 37964

Aukot Kaluyu Jirongo Mwaura27376 11749 11268 8856

VOTERS19588995

Note: sums for listed variables across n = 40818 polling stations in the 23aug2017 data. Allvariables except “VOTERS” come from scraped data. “VOTERS” comes from the eligiblevoter data.

Table 2: Distribution and Digit Tests, Kenya 2017 Presidential

Name 2BL LastC P05s C05s DipT ObsTurnout 4.439 4.515 .201 .202 .945 40818

(4.412, 4.468) (4.487, 4.541) (.197, .205) (.198, .206) −−Kenyatta 4.302 4.297 .231 .214 0 40818

(4.272, 4.331) (4.268, 4.325) (.227, .235) (.21, .217) −−Odinga 4.278 4.29 .24 .206 0 40818

(4.25, 4.31) (4.261, 4.318) (.235, .244) (.202, .21) −−

Note: “2BL,” second-digit mean; “LastC,” last-digit mean; “C05s,” mean of variableindicating whether the last digit of the vote count is zero or five; “P05s,” mean of variableindicating whether the last digit of the rounded percentage of votes for the referent partyor candidate is zero or five; “DipT,” p-value from test of unimodality; “Obs,” number ofpolling station observations. Values in parentheses are nonparametric bootstrap confidenceintervals. Estimates that differ significantly from values expected if there are no anomoliesare shown in red.

Table 3: Distribution and Digit Tests, Kenya 2017 Presidential, Counties

County Name 2BL LastC P05s C05s DipT Obs

BARINGO Turnout 4.144 4.612 .192 .196 .99 892(3.94, 4.333) (4.42, 4.81) (.165, .217) (.168, .225) −−

BOMET Turnout 4.184 4.432 .209 .198 .997 727(3.985, 4.392) (4.21, 4.645) (.177, .238) (.169, .226) −−

BUNGOMA Turnout 4.717 4.594 .197 .192 .995 1185(4.557, 4.883) (4.425, 4.753) (.174, .219) (.17, .214) −−

BUSIA Turnout 4.34 4.393 .192 .196 .981 759(4.127, 4.537) (4.196, 4.593) (.162, .22) (.167, .225) −−

ELGEYO/MARAKWET Turnout 4.432 4.36 .195 .195 .886 528(4.172, 4.669) (4.133, 4.606) (.163, .231) (.161, .227) −−

EMBU Turnout 4.392 4.556 .224 .183 .997 709(4.166, 4.606) (4.351, 4.763) (.195, .254) (.154, .212) −−

GARISSA Turnout 4.495 4.318 .239 .226 .842 380(4.203, 4.766) (4.021, 4.608) (.192, .287) (.184, .268) −−

HOMA BAY Turnout 4.188 4.525 .217 .209 .992 1061(4.013, 4.36) (4.355, 4.684) (.191, .241) (.185, .235) −−

ISIOLO Turnout 4.389 4.428 .196 .201 .914 194(4.014, 4.741) (4.036, 4.845) (.134, .252) (.144, .258) −−

KAJIADO Turnout 4.489 4.445 .208 .224 .134 795(4.278, 4.691) (4.244, 4.655) (.177, .236) (.194, .253) −−

KAKAMEGA Turnout 4.692 4.394 .21 .199 .992 1496(4.544, 4.841) (4.255, 4.541) (.191, .231) (.178, .219) −−

KERICHO Turnout 4.503 4.49 .195 .186 .985 774(4.306, 4.71) (4.28, 4.703) (.168, .224) (.156, .212) −−

KIAMBU Turnout 4.398 4.528 .217 .202 .999 1962(4.274, 4.515) (4.403, 4.657) (.198, .235) (.185, .221) −−

KILIFI Turnout 4.53 4.457 .2 .22 .979 987(4.351, 4.715) (4.28, 4.643) (.172, .223) (.194, .243) −−

KIRINYAGA Turnout 4.46 4.52 .198 .225 .998 658(4.248, 4.673) (4.307, 4.755) (.167, .228) (.193, .257) −−

KISII Turnout 4.684 4.458 .219 .223 1 1125(4.509, 4.849) (4.285, 4.613) (.194, .244) (.198, .246) −−

KISUMU Turnout 4.447 4.495 .195 .196 .895 1026(4.28, 4.627) (4.322, 4.683) (.172, .219) (.171, .221) −−

KITUI Turnout 4.401 4.583 .191 .196 .992 1452(4.267, 4.545) (4.438, 4.732) (.17, .211) (.174, .216) −−

KWALE Turnout 4.203 4.692 .191 .196 .992 611(3.953, 4.431) (4.458, 4.908) (.16, .223) (.162, .227) −−

LAIKIPIA Turnout 4.406 4.651 .189 .181 .928 530(4.165, 4.692) (4.406, 4.898) (.157, .223) (.147, .215) −−

LAMU Turnout 4.584 4.578 .211 .157 .26 166(4.157, 5.054) (4.133, 5) (.151, .271) (.102, .211) −−

MACHAKOS Turnout 4.476 4.625 .177 .204 .988 1331(4.319, 4.613) (4.47, 4.768) (.157, .197) (.183, .224) −−

MAKUENI Turnout 4.33 4.56 .198 .192 .997 1058(4.149, 4.503) (4.392, 4.729) (.173, .222) (.168, .217) −−



MANDERA Turnout 4.246 4.393 .222 .162 .936 400(3.931, 4.541) (4.1, 4.665) (.182, .265) (.125, .2) −−

MARSABIT Turnout 4.646 4.596 .202 .199 .819 381(4.375, 4.921) (4.325, 4.895) (.16, .241) (.155, .239) −−

MERU Turnout 4.539 4.525 .2 .205 .991 1471(4.4, 4.692) (4.374, 4.668) (.178, .22) (.184, .225) −−

MIGORI Turnout 4.332 4.442 .213 .193 .991 825(4.13, 4.51) (4.255, 4.632) (.183, .24) (.167, .218) −−

MOMBASA Turnout 4.509 4.311 .209 .218 .985 933(4.323, 4.706) (4.116, 4.51) (.184, .236) (.192, .244) −−

MURANG’A Turnout 4.467 4.514 .198 .211 .725 1130(4.293, 4.633) (4.35, 4.673) (.175, .22) (.187, .235) −−

NAIROBI CITY Turnout 4.395 4.43 .208 .2 .352 3377(4.285, 4.497) (4.342, 4.524) (.195, .221) (.186, .213) −−

NAKURU Turnout 4.536 4.618 .194 .191 .992 1805(4.402, 4.662) (4.48, 4.766) (.177, .211) (.173, .211) −−

NANDI Turnout 4.337 4.502 .208 .196 .996 795(4.121, 4.548) (4.311, 4.699) (.177, .236) (.167, .224) −−

NAROK Turnout 4.298 4.692 .167 .195 .903 749(4.097, 4.512) (4.466, 4.888) (.14, .194) (.168, .222) −−

NYAMIRA Turnout 4.672 4.672 .205 .187 .859 552(4.44, 4.924) (4.428, 4.938) (.169, .237) (.152, .217) −−

NYANDARUA Turnout 4.547 4.475 .196 .193 .994 653(4.345, 4.76) (4.248, 4.691) (.165, .225) (.162, .222) −−

NYERI Turnout 4.215 4.447 .191 .214 .996 916(4.032, 4.392) (4.248, 4.643) (.164, .217) (.189, .241) −−

SAMBURU Turnout 4.415 4.41 .219 .226 .269 283(4.074, 4.738) (4.071, 4.763) (.17, .265) (.173, .276) −−

SIAYA Turnout 4.442 4.398 .183 .201 .991 915(4.258, 4.633) (4.187, 4.573) (.157, .205) (.175, .227) −−

TAITA TAVETA Turnout 4.482 4.49 .187 .195 .663 353(4.181, 4.782) (4.173, 4.81) (.144, .221) (.156, .238) −−

TANA RIVER Turnout 4.266 4.359 .219 .193 .999 306(3.94, 4.574) (4.013, 4.689) (.17, .265) (.147, .235) −−

THARAKA - NITHI Turnout 4.345 4.63 .192 .229 .931 624(4.121, 4.57) (4.405, 4.859) (.162, .223) (.197, .26) −−

TRANS NZOIA Turnout 4.429 4.455 .196 .23 .959 638(4.202, 4.658) (4.23, 4.683) (.165, .226) (.198, .262) −−

TURKANA Turnout 4.418 4.787 .192 .187 .733 642(4.194, 4.649) (4.559, 5.026) (.16, .224) (.157, .217) −−

UASIN GISHU Turnout 4.484 4.468 .186 .208 .992 866(4.293, 4.667) (4.275, 4.654) (.158, .21) (.182, .234) −−

VIHIGA Turnout 4.669 4.592 .212 .218 .836 547(4.399, 4.93) (4.364, 4.826) (.176, .245) (.183, .25) −−

WAJIR Turnout 4.506 4.764 .206 .16 .969 432(4.235, 4.803) (4.5, 5.051) (.167, .243) (.123, .194) −−

WEST POKOT Turnout 4.305 4.643 .204 .25 .98 711(4.103, 4.512) (4.432, 4.856) (.174, .235) (.218, .283) −−



BARINGO Kenyatta 4.151 4.635 .161 .209 .188 892(3.962, 4.341) (4.457, 4.818) (.135, .185) (.181, .232) −−

BOMET Kenyatta 4.664 4.406 .199 .231 .967 727(4.457, 4.867) (4.189, 4.612) (.169, .227) (.201, .263) −−

BUNGOMA Kenyatta 4.018 4.558 .196 .206 0 1185(3.849, 4.185) (4.397, 4.723) (.176, .217) (.181, .228) −−

BUSIA Kenyatta 4.143 4.51 .192 .223 .994 759(3.935, 4.372) (4.311, 4.713) (.165, .22) (.191, .253) −−

ELGEYO/MARAKWET Kenyatta 4.307 4.392 .172 .229 .766 528(4.066, 4.549) (4.148, 4.634) (.138, .205) (.193, .265) −−

EMBU Kenyatta 4.507 4.362 .147 .202 .992 709(4.285, 4.709) (4.157, 4.573) (.121, .172) (.173, .23) −−

GARISSA Kenyatta 4.011 4.426 .187 .232 .022 380(3.722, 4.288) (4.126, 4.713) (.145, .224) (.189, .274) −−

HOMA BAY Kenyatta 2.833 1.566 .664 .4 0 1061(1.719, 3.961) (1.459, 1.674) (.637, .692) (.37, .429) −−

ISIOLO Kenyatta 4.449 4.34 .206 .175 .669 194(4.021, 4.869) (3.928, 4.768) (.144, .263) (.119, .227) −−

KAJIADO Kenyatta 4.442 4.296 .204 .213 .942 795(4.249, 4.635) (4.088, 4.484) (.176, .233) (.182, .24) −−

KAKAMEGA Kenyatta 4.42 4.564 .202 .2 .995 1496(4.261, 4.564) (4.414, 4.708) (.182, .223) (.18, .221) −−

KERICHO Kenyatta 4.533 4.521 .15 .207 .991 774(4.305, 4.727) (4.314, 4.709) (.124, .176) (.176, .236) −−

KIAMBU Kenyatta 4.462 4.529 .208 .189 .1 1962(4.336, 4.586) (4.401, 4.662) (.191, .226) (.171, .205) −−

KILIFI Kenyatta 4.13 4.509 .195 .22 .992 987(3.93, 4.335) (4.331, 4.696) (.168, .218) (.195, .244) −−

KIRINYAGA Kenyatta 4.304 4.763 .157 .184 .594 658(4.096, 4.526) (4.538, 4.98) (.129, .184) (.155, .213) −−

KISII Kenyatta 4.128 4.386 .198 .188 .676 1125(3.972, 4.281) (4.224, 4.556) (.173, .221) (.164, .21) −−

KISUMU Kenyatta 3.943 2.739 .472 .268 0 1026(3.582, 4.28) (2.57, 2.891) (.441, .499) (.242, .295) −−

KITUI Kenyatta 4.153 4.461 .207 .219 .994 1452(3.982, 4.31) (4.302, 4.607) (.187, .229) (.198, .24) −−

KWALE Kenyatta 4.062 4.245 .206 .187 .802 611(3.837, 4.28) (4.031, 4.47) (.173, .237) (.155, .218) −−

LAIKIPIA Kenyatta 4.306 4.343 .185 .221 .961 530(4.055, 4.552) (4.119, 4.57) (.153, .217) (.183, .257) −−

LAMU Kenyatta 4.412 4.404 .223 .223 0 166(3.928, 4.897) (3.988, 4.855) (.157, .289) (.163, .289) −−

MACHAKOS Kenyatta 4.174 4.623 .215 .183 .993 1331(4.011, 4.33) (4.462, 4.771) (.193, .238) (.161, .203) −−

MAKUENI Kenyatta 4.249 4.523 .201 .198 .995 1058(4.05, 4.44) (4.343, 4.707) (.178, .225) (.174, .223) −−



MANDERA Kenyatta 4.367 4.327 .208 .198 .997 400(4.065, 4.653) (4.05, 4.617) (.167, .245) (.16, .237) −−

MARSABIT Kenyatta 4.424 4.504 .228 .186 .536 381(4.132, 4.703) (4.234, 4.793) (.184, .268) (.147, .223) −−

MERU Kenyatta 4.489 4.455 .195 .215 .874 1471(4.326, 4.645) (4.309, 4.599) (.174, .216) (.196, .236) −−

MIGORI Kenyatta 4.171 2.697 .527 .366 0 825(3.868, 4.511) (2.514, 2.88) (.496, .56) (.335, .398) −−

MOMBASA Kenyatta 3.652 4.397 .219 .193 .996 933(3.465, 3.824) (4.21, 4.573) (.19, .245) (.167, .22) −−

MURANG’A Kenyatta 4.477 4.468 .265 .23 0 1130(4.308, 4.647) (4.3, 4.638) (.241, .289) (.204, .257) −−

NAIROBI CITY Kenyatta 4.344 4.512 .207 .2 .906 3377(4.256, 4.44) (4.411, 4.607) (.193, .22) (.187, .213) −−

NAKURU Kenyatta 4.494 4.671 .183 .19 .994 1805(4.37, 4.628) (4.543, 4.809) (.164, .199) (.171, .207) −−

NANDI Kenyatta 4.47 4.516 .131 .201 .941 795(4.26, 4.659) (4.312, 4.713) (.108, .153) (.175, .229) −−

NAROK Kenyatta 4.413 4.367 .202 .211 0 749(4.202, 4.625) (4.172, 4.571) (.172, .228) (.182, .238) −−

NYAMIRA Kenyatta 4.397 4.656 .181 .207 .992 552(4.151, 4.631) (4.412, 4.909) (.149, .214) (.169, .239) −−

NYANDARUA Kenyatta 4.588 4.629 .345 .184 0 653(4.378, 4.787) (4.386, 4.833) (.308, .381) (.153, .211) −−

NYERI Kenyatta 4.261 4.529 .19 .203 .006 916(4.086, 4.443) (4.342, 4.709) (.164, .213) (.177, .229) −−

SAMBURU Kenyatta 4.225 4.551 .184 .155 .593 283(3.867, 4.575) (4.223, 4.869) (.134, .23) (.11, .198) −−

SIAYA Kenyatta 1.821 2.449 .492 .244 0 915(.916, 2.606) (2.31, 2.59) (.459, .523) (.214, .271) −−

TAITA TAVETA Kenyatta 4.174 4.799 .232 .195 .92 353(3.892, 4.478) (4.504, 5.099) (.187, .278) (.153, .235) −−

TANA RIVER Kenyatta 3.936 4.497 .196 .222 .001 306(3.602, 4.26) (4.164, 4.833) (.15, .239) (.176, .271) −−

THARAKA - NITHI Kenyatta 4.345 4.538 .189 .194 .991 624(4.121, 4.57) (4.303, 4.774) (.157, .22) (.162, .224) −−

TRANS NZOIA Kenyatta 4.113 4.447 .18 .185 .903 638(3.908, 4.32) (4.232, 4.674) (.15, .208) (.155, .213) −−

TURKANA Kenyatta 4.118 4.511 .213 .192 .994 642(3.883, 4.358) (4.283, 4.735) (.179, .245) (.16, .221) −−

UASIN GISHU Kenyatta 4.684 4.514 .174 .2 .402 866(4.477, 4.887) (4.323, 4.699) (.149, .201) (.172, .225) −−

VIHIGA Kenyatta 4.099 4.461 .181 .205 .993 547(3.835, 4.353) (4.216, 4.697) (.148, .212) (.172, .238) −−

WAJIR Kenyatta 4.472 4.477 .211 .245 .814 432(4.21, 4.736) (4.199, 4.762) (.171, .248) (.204, .285) −−

WEST POKOT Kenyatta 3.987 4.387 .184 .198 .818 711(3.777, 4.186) (4.174, 4.581) (.155, .214) (.17, .226) −−



BARINGO Odinga 3.85 4.03 .168 .195 .007 892(3.565, 4.112) (3.859, 4.214) (.143, .193) (.169, .222) −−

BOMET Odinga 4.456 4.696 .201 .183 .993 727(4.222, 4.672) (4.473, 4.912) (.171, .228) (.154, .208) −−

BUNGOMA Odinga 4.351 4.47 .203 .193 0 1185(4.172, 4.518) (4.306, 4.628) (.181, .225) (.171, .216) −−

BUSIA Odinga 4.569 4.51 .183 .221 .865 759(4.363, 4.785) (4.286, 4.713) (.154, .211) (.191, .252) −−

ELGEYO/MARAKWET Odinga 3.607 4.223 .199 .218 .17 528(3.246, 3.907) (3.977, 4.449) (.163, .233) (.184, .252) −−

EMBU Odinga 3.678 4.553 .12 .168 .968 709(3.425, 3.954) (4.353, 4.755) (.096, .144) (.141, .195) −−

GARISSA Odinga 4.396 4.274 .179 .203 .044 380(4.089, 4.738) (4.003, 4.555) (.139, .218) (.161, .242) −−

HOMA BAY Odinga 4.171 4.602 .525 .199 0 1061(3.997, 4.339) (4.435, 4.766) (.496, .555) (.174, .224) −−

ISIOLO Odinga 4.587 4.907 .196 .186 .428 194(4.168, 5.016) (4.521, 5.299) (.139, .253) (.129, .242) −−

KAJIADO Odinga 4.367 4.395 .215 .218 .989 795(4.165, 4.577) (4.192, 4.582) (.186, .242) (.19, .249) −−

KAKAMEGA Odinga 4.516 4.553 .195 .201 .992 1496(4.371, 4.664) (4.404, 4.686) (.174, .216) (.18, .221) −−

KERICHO Odinga 3.75 4.355 .165 .22 .995 774(3.484, 4.003) (4.164, 4.577) (.138, .191) (.19, .248) −−

KIAMBU Odinga 4.077 3.958 .257 .199 0 1962(3.901, 4.244) (3.824, 4.086) (.237, .276) (.181, .216) −−

KILIFI Odinga 4.418 4.498 .222 .181 .956 987(4.242, 4.609) (4.306, 4.686) (.197, .246) (.156, .205) −−

KIRINYAGA Odinga 3.414 3.271 .34 .193 0 658(2.88, 3.963) (3.097, 3.426) (.305, .375) (.161, .222) −−

KISII Odinga 4.288 4.543 .196 .198 .629 1125(4.12, 4.447) (4.363, 4.701) (.174, .22) (.175, .22) −−

KISUMU Odinga 4.506 4.486 .352 .196 0 1026(4.319, 4.669) (4.316, 4.656) (.326, .382) (.172, .219) −−

KITUI Odinga 4.162 4.468 .196 .187 .994 1452(4.017, 4.31) (4.32, 4.615) (.175, .216) (.165, .207) −−

KWALE Odinga 4.559 4.563 .182 .203 .812 611(4.345, 4.778) (4.332, 4.799) (.151, .213) (.172, .232) −−

LAIKIPIA Odinga 3.954 4.013 .221 .209 .201 530(3.633, 4.3) (3.774, 4.255) (.187, .258) (.174, .243) −−

LAMU Odinga 4.209 4.536 .217 .193 0 166(3.787, 4.646) (4.114, 4.946) (.151, .277) (.133, .247) −−

MACHAKOS Odinga 4.493 4.527 .196 .204 .996 1331(4.339, 4.656) (4.361, 4.687) (.174, .216) (.182, .225) −−

MAKUENI Odinga 4.647 4.457 .201 .215 1 1058(4.479, 4.819) (4.272, 4.635) (.177, .224) (.189, .238) −−



MANDERA Odinga 4.104 4.18 .182 .232 .715 400(3.805, 4.441) (3.91, 4.47) (.145, .218) (.19, .272) −−

MARSABIT Odinga 4.281 4.223 .236 .22 .178 381(3.936, 4.601) (3.932, 4.509) (.194, .276) (.178, .262) −−

MERU Odinga 4.378 4.533 .201 .194 .992 1471(4.235, 4.525) (4.391, 4.676) (.181, .221) (.174, .215) −−

MIGORI Odinga 4.273 4.384 .439 .211 0 825(4.07, 4.456) (4.194, 4.589) (.405, .473) (.184, .241) −−

MOMBASA Odinga 4.886 4.418 .212 .19 .996 933(4.695, 5.08) (4.238, 4.602) (.186, .239) (.166, .213) −−

MURANG’A Odinga 3.824 2.69 .476 .25 0 1130(3.41, 4.228) (2.552, 2.827) (.447, .504) (.223, .273) −−

NAIROBI CITY Odinga 4.259 4.515 .204 .213 .908 3377(4.165, 4.362) (4.414, 4.61) (.19, .217) (.2, .227) −−

NAKURU Odinga 3.991 4.171 .216 .21 .154 1805(3.84, 4.148) (4.04, 4.302) (.196, .233) (.192, .23) −−

NANDI Odinga 3.888 4.348 .158 .228 .993 795(3.642, 4.097) (4.156, 4.519) (.132, .185) (.196, .255) −−

NAROK Odinga 4.272 4.503 .174 .218 0 749(4.058, 4.48) (4.304, 4.704) (.148, .2) (.187, .246) −−

NYAMIRA Odinga 4.288 4.491 .205 .181 .983 552(4.042, 4.518) (4.232, 4.716) (.172, .239) (.147, .214) −−

NYANDARUA Odinga 2.714 2.381 .533 .292 0 653(1.95, 3.336) (2.202, 2.55) (.499, .568) (.257, .328) −−

NYERI Odinga 3.587 2.91 .388 .226 0 916(3.134, 4.063) (2.753, 3.059) (.356, .421) (.199, .252) −−

SAMBURU Odinga 4.39 4.562 .219 .226 .795 283(4.043, 4.73) (4.202, 4.89) (.17, .265) (.173, .276) −−

SIAYA Odinga 4.348 4.605 .339 .208 0 915(4.157, 4.532) (4.406, 4.8) (.309, .369) (.18, .234) −−

TAITA TAVETA Odinga 4.547 4.739 .187 .21 .993 353(4.213, 4.884) (4.448, 5.059) (.147, .227) (.167, .255) −−

TANA RIVER Odinga 3.812 4.248 .229 .196 0 306(3.475, 4.165) (3.902, 4.569) (.183, .275) (.15, .242) −−

THARAKA - NITHI Odinga 4.127 4.502 .189 .197 .862 624(3.829, 4.414) (4.276, 4.718) (.157, .223) (.165, .228) −−

TRANS NZOIA Odinga 3.917 4.401 .221 .212 .712 638(3.695, 4.115) (4.174, 4.618) (.19, .254) (.18, .243) −−

TURKANA Odinga 4.393 4.607 .204 .184 .992 642(4.178, 4.598) (4.365, 4.813) (.17, .238) (.153, .213) −−

UASIN GISHU Odinga 4.127 4.5 .164 .18 .261 866(3.919, 4.35) (4.319, 4.697) (.139, .188) (.155, .206) −−

VIHIGA Odinga 4.437 4.781 .203 .185 .993 547(4.194, 4.662) (4.556, 5.022) (.168, .234) (.152, .219) −−

WAJIR Odinga 4.252 4.273 .227 .208 .668 432(3.993, 4.528) (4.021, 4.56) (.188, .269) (.169, .245) −−

WEST POKOT Odinga 4.041 4.231 .174 .236 .57 711(3.829, 4.256) (4.01, 4.446) (.148, .203) (.205, .264) −−

Table 9: Candidates and “Incremental Fraud” Estimates by County

Kenyatta Odinga Kenyatta OdingaCounty p fI α fI α County p fI α fI αBaringo .85 K .003 7.4 Marsabit .84 K .014 1.7 .178 13.Bomet .87 K .000 .1 .162 11.9 Meru .89 K .000 1.3 .014 13.Bungoma .68 O .091 1.7 .014 .7 Migori .85 O .002 .5Busia .87 O .171 9.0 .008 .8 Mombasa .70 O .005 2.9 .011 3.1Elgeyo/Marakwet .95 K .002 .9 .047 12. Murang’a .98 K 0 —Embu .92 K .003 8.8 Nairobi City .51 O .053 .5 0 —Garissa .48 K .008 .9 .08 1.1 Nakuru .85 K .044 .6 .237 6.7Homa Bay .99 O .006 1.7 Nandi .87 K .000 5.1Isiolo .49 K .013 1.1 .077 10.0 Narok .53 K .001 4.9 .065 .6Kajiado .57 K .004 .8 .004 1.7 Nyamira .52 K .005 1.5 .008 1.6Kakamega .87 O .044 5.6 .002 1.6 Nyandarua .99 K .001 .5Kericho .93 K 0 — Nyeri .98 K .002 .3Kiambu .93 K .001 .9 .251 14. Samburu .50 K .006 .2 .014 1.1Kilifi .84 O .101 7.8 .012 3.2 Siaya .99 OKirinyaga .99 K 0 — Taita Taveta .71 O .027 3.7 .012 1.7Kisii .55 O .005 1.2 .006 1.4 Tana River .52 O .026 .1 0 —Kisumu .98 O .002 .8 Tharaka - Nithi .93 K .000 1.4 .037 13.Kitui .80 O .124 10. .003 4.4 Trans Nzoia .54 O .135 .8 .004 2.7Kwale .75 O .145 4.2 .005 1.6 Turkana .54 O .052 1.3 .026 .9Laikipia .89 K .004 2.0 Uasin Gishu .78 K .026 .6 .354 6.6Lamu .49 O .327 4.5 .014 .7 Vihiga .90 O .100 4.3 .015 1.1Machakos .81 O .047 3.2 .002 .8 Wajir .51 K .030 1.2 .014 1.7Makueni .91 O .042 9.3 .001 2.0 West Pokot .65 K .001 13. .001 13.Mandera .83 K 0 — .098 10. Prisons .54 O 0 — .000 1.4

Note: n = 40818 polling stations. County- and candidate-specific estimates of parameters(fI and α) of the finite mixture model (Mebane 2016). p reports the proportion of votes forthe candidate with the most votes in each county and identifies that county-leader byinitital (“K” for “Kenyatta” and “O” for “Odinga”). Blank parameters could not beestimted.

Table 10: Estimated Numbers of Votes Due to “Incremental Fraud”

County Kenyatta Odinga County Kenyatta OdingaBaringo 17 — Marsabit 79 810Bomet 0 1537 Meru 0 221Bungoma 1456 362 Migori — 73Busia 1573 101 Mombasa 56 297Elgeyo/Marakwet 0 491 Murang’a 0 —Embu 36 — Nairobi City 17601 0Garissa 918 896 Nakuru 2966 3602Homa Bay — 26 Nandi 0 —Isiolo 1 238 Narok 6 3706Kajiado 155 139 Nyamira 56 144Kakamega 583 24 Nyandarua 2 —Kericho 0 — Nyeri 0 —Kiambu 0 6327 Samburu 136 127Kilifi 1517 277 Siaya — —Kirinyaga 0 — Taita Taveta 165 56Kisii 91 86 Tana River 779 0Kisumu — 0 Tharaka - Nithi 0 273Kitui 1110 16 Trans Nzoia 1810 39Kwale 1550 598 Turkana 490 372Laikipia 0 — Uasin Gishu 347 3421Lamu 1987 89 Vihiga 451 0Machakos 248 0 Wajir 417 184Makueni 284 5 West Pokot 18 19Mandera 0 534 Prisons 0 1

Total 36907 25093

Note: expected counts of votes in each county due to “incremental fraud” based on county-and candidate-specific estimates of the finite mixture model (Mebane 2016). Estimates forthe candidate with the most votes in each county is highlighted in grey. “—” indicates avalue that could not be calculated because the model could not be estimated.

Table 11: Vote Proportions and “Incremental Fraud”Vote Proportions

Votes “Frauds” Votes “Frauds”County Keny. Odin. Keny. Odin. County Keny. Odin. Keny. Odin.

Baringo .849 .146 .00009 — Marsabit .836 .145 .00071 .00742Bomet .870 .121 .00000 .00586 Meru .889 .103 .00000 .00041Bungoma .302 .681 .00342 .00085 Migori .142 .853 — .00023Busia .124 .868 .00573 .00036 Mombasa .291 .699 .00016 .00087Elgeyo/Marakwet .947 .048 .00000 .00325 Murang’a .979 .018 .00000 —Embu .922 .070 .00014 — Nairobi City .485 .510 .01075 .00000Garissa .483 .483 .00810 .00795 Nakuru .848 .148 .00392 .00476Homa Bay .005 .993 — .00006 Nandi .868 .126 .00000 —Isiolo .493 .350 .00002 .00456 Narok .531 .461 .00002 .01309Kajiado .571 .425 .00048 .00043 Nyamira .521 .465 .00027 .00071Kakamega .115 .874 .00105 .00004 Nyandarua .990 .008 .00001 —Kericho .928 .065 .00000 — Nyeri .984 .012 .00000 —Kiambu .927 .070 .00000 .00641 Samburu .496 .495 .00212 .00202Kilifi .152 .836 .00465 .00085 Siaya .007 .991 — —Kirinyaga .986 .010 .00000 — Taita Taveta .277 .710 .00151 .00052Kisii .432 .554 .00023 .00021 Tana River .462 .521 .00899 .00000Kisumu .018 .979 — .00000 Tharaka - Nithi .932 .059 .00000 .00156Kitui .180 .799 .00311 .00004 Trans Nzoia .445 .544 .00734 .00015Kwale .237 .750 .00842 .00321 Turkana .449 .541 .00375 .00283Laikipia .891 .105 .00000 — Uasin Gishu .782 .212 .00101 .01000Lamu .490 .493 .03967 .00183 Vihiga .091 .896 .00226 .00000Machakos .176 .809 .00052 .00000 Wajir .511 .443 .00349 .00155Makueni .083 .906 .00086 .00002 West Pokot .648 .344 .00013 .00012Mandera .830 .133 .00000 .00391 Prisons .457 .536 .00000 .00029

Note: proportion of valid votes for each candidate and expected votes in each county due to“incremental fraud” expressed as proportions of the valid votes in each county. “—” indicates avalue that could not be calculated because the model could not be estimated.

Figure 1: Turnout and Kenyatta and Odinga Vote Proportions: Empirical Densities

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Legend: solid black line, turnout; blue dashed line, Kenyatta; red dotted line, Odinga.Note: empirical densities using 40,818 polling stations from across Kenya. Vote count datascraped from ... on August 23, 2017. Eligible voter data from ....


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Kenya 2017: KAKAMEGA 037

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Kenya 2017: KIAMBU 022

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Kenya 2017: KILIFI 003

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Kenya 2017: KWALE 002

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01

23

4

Kenya 2017: LAMU 005

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

02

46

8

Kenya 2017: MACHAKOS 016

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

02

46

810

Kenya 2017: MAKUENI 017

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

01

23

45

Kenya 2017: MANDERA 009

proportion

dens

ity


0.0 0.2 0.4 0.6 0.8 1.0

01

23

45

Kenya 2017: MARSABIT 010

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

02

46

8

Kenya 2017: MERU 012

proportion

dens

ity0.0 0.2 0.4 0.6 0.8 1.0

02

46

810

Kenya 2017: MIGORI 044

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

01

23

45

67

Kenya 2017: MOMBASA 001

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

020

4060

Kenya 2017: MURANG'A 021

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

01

23

45

67

Kenya 2017: NAIROBI CITY 047

proportionde

nsity

0.0 0.2 0.4 0.6 0.8 1.0

01

23

45

67

Kenya 2017: NAKURU 032

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

02

46

810

12

Kenya 2017: NANDI 029

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

01

23

45

Kenya 2017: NAROK 033

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

02

46

8

Kenya 2017: NYAMIRA 046

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

020

4060

80

Kenya 2017: NYANDARUA 018

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

010

2030

4050

60

Kenya 2017: NYERI 019

proportion

dens

ity


0.0 0.2 0.4 0.6 0.8 1.0

01

23

4

Kenya 2017: SAMBURU 025

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

020

4060

80

Kenya 2017: SIAYA 041

proportion

dens

ity0.0 0.2 0.4 0.6 0.8 1.0

02

46

Kenya 2017: TAITA TAVETA 006

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

01

23

4

Kenya 2017: TANA RIVER 004

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

02

46

810

12

Kenya 2017: THARAKA − NITHI 013

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

02

46

8

Kenya 2017: TRANS NZOIA 026

proportionde

nsity

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Kenya 2017: TURKANA 023

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

01

23

45

67

Kenya 2017: UASIN GISHU 027

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

02

46

810

Kenya 2017: VIHIGA 038

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

01

23

4

Kenya 2017: WAJIR 008

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

02

46

Kenya 2017: WEST POKOT 024

proportion

dens

ity


0.0 0.2 0.4 0.6 0.8 1.0

05

1015

2025

Kenya 2017: DIASPORA 048

proportion

dens

ity

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.5

1.0

1.5

2.0

Kenya 2017: PRISONS 049

proportion

dens

ity

Figure 7: Kenyatta Vote Percentages: Spikes Test

020

040

060

080

0

Kenya 2017

Vote−share

Den

sity

F = 1.40

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Note: Vote shares are the proportion of valid votes for Kenyatta. Vote count data scrapedon 23aug2017. n = 40818 polling stations.

Figure 8: Kenyatta Vote Percentages: Spikes Test0

500

1000

1500

Kenya 2017: BARINGO

Vote−share

Den

sity

F = 0.40

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

0

Kenya 2017: BOMET

Vote−shareD

ensi

ty

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

100

150

200

250

300

Kenya 2017: BUNGOMA

Vote−share

Den

sity

F = 0.29

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0

Kenya 2017: BUSIA

Vote−share

Den

sity

F = 0.67

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

0012

00

Kenya 2017: ELGEYO/MARAKWET

Vote−share

Den

sity

F = 0.36

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: EMBU

Vote−shareD

ensi

ty

F = 0.41

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: GARISSA

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

0040

0060

0080

0012

000

Kenya 2017: HOMA BAY

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: ISIOLO

Vote−share

Den

sity

F = 1.45

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

100

150

200

250

Kenya 2017: KAJIADO

Vote−share

Den

sity

F = 1.14

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0

Kenya 2017: KAKAMEGA

Vote−share

Den

sity

F = 0.29

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: KERICHO

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Note: Vote shares are the proportion of valid votes for Kenyatta. Vote count data scrapedon 23aug2017.


200

400

600

800

1000

1200

Kenya 2017: KIAMBU

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

0

Kenya 2017: KILIFI

Vote−shareD

ensi

ty

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

010

0015

0020

00

Kenya 2017: KIRINYAGA

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

100

150

200

250

Kenya 2017: KISII

Vote−share

Den

sity

F = 0.55

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

0040

0060

0080

00

Kenya 2017: KISUMU

Vote−share

Den

sity

F = 5.67

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

100

150

200

250

300

Kenya 2017: KITUI

Vote−shareD

ensi

ty

F = 0.07

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0

Kenya 2017: KWALE

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

010

0015

00

Kenya 2017: LAIKIPIA

Vote−share

Den

sity

F = 0.90

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: LAMU

Vote−share

Den

sity

F = 2.27

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

100

150

200

250

300

Kenya 2017: MACHAKOS

Vote−share

Den

sity

F = 0.14

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

0

Kenya 2017: MAKUENI

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: MANDERA

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0



500

1000

1500

Kenya 2017: MARSABIT

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

0

Kenya 2017: MERU

Vote−shareD

ensi

ty

F = 0.34

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

0040

0060

0080

0010

000

Kenya 2017: MIGORI

Vote−share

Den

sity

F = 4.16

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

100

150

200

250

300

Kenya 2017: MOMBASA

Vote−share

Den

sity

F = 0.89

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

0020

0030

00

Kenya 2017: MURANG'A

Vote−share

Den

sity

F = 0.43

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

4060

8010

012

0

Kenya 2017: NAIROBI CITY

Vote−shareD

ensi

ty

F = 0.30

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: NAKURU

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

060

070

0

Kenya 2017: NANDI

Vote−share

Den

sity

F = 1.09

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

100

150

200

250

300

Kenya 2017: NAROK

Vote−share

Den

sity

F = 1.30

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0

Kenya 2017: NYAMIRA

Vote−share

Den

sity

F = 1.26

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

0020

0030

0040

0050

00

Kenya 2017: NYANDARUA

Vote−share

Den

sity

F = 2.22

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

010

0015

0020

0025

0030

00

Kenya 2017: NYERI

Vote−share

Den

sity

F = 1.45

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0



100

200

300

400

500

Kenya 2017: SAMBURU

Vote−share

Den

sity

F = 1.27

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

0040

0060

00

Kenya 2017: SIAYA

Vote−shareD

ensi

ty

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

0

Kenya 2017: TAITA TAVETA

Vote−share

Den

sity

F = 1.03

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

010

00

Kenya 2017: TANA RIVER

Vote−share

Den

sity

F = 2.39

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

00

Kenya 2017: THARAKA − NITHI

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

100

150

200

250

300

350

Kenya 2017: TRANS NZOIA

Vote−shareD

ensi

ty

F = 0.90

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

060

070

0

Kenya 2017: TURKANA

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0

Kenya 2017: UASIN GISHU

Vote−share

Den

sity

F = 0.63

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

060

070

0

Kenya 2017: VIHIGA

Vote−share

Den

sity

F = 1.28

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0

Kenya 2017: WAJIR

Vote−share

Den

sity

F = 1.70

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

0

Kenya 2017: WEST POKOT

Vote−share

Den

sity

F = 1.36

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0


Figure 12: Kenyatta Vote Percentages: Spikes Test, Unproblematic Form 34B0

500

1000

1500

Kenya 2017: BARINGO

Vote−share

Den

sity

F = 0.34

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: BOMET

Vote−shareD

ensi

ty

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

0

Kenya 2017: BUNGOMA

Vote−share

Den

sity

F = 0.60

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

060

0

Kenya 2017: BUSIA

Vote−share

Den

sity

F = 1.21

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

0012

0014

00

Kenya 2017: ELGEYO/MARAKWET

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

00

Kenya 2017: EMBU

Vote−shareD

ensi

ty

F = 0.24

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: GARISSA

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

0040

0060

0080

0012

000


Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

00

Kenya 2017: ISIOLO

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0

Kenya 2017: KAJIADO

Vote−share

Den

sity

F = 1.19

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

060

0


Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: KERICHO

Vote−share

Den

sity

F = 0.28

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Note: Vote shares are the proportion of valid votes for Kenyatta. Vote count data scrapedon 23aug2017. Using polling stations not in a constituency with a problematic form 34B.


200

400

600

800

1000

1200

Kenya 2017: KIAMBU

Vote−share

Den

sity

F = 0.07

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0

Kenya 2017: KILIFI

Vote−shareD

ensi

ty

F = 1.27

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

010

0015

00

Kenya 2017: KIRINYAGA

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

100

150

200

Kenya 2017: KISII

Vote−share

Den

sity

F = 0.56

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

0040

0060

0080

0010

000

Kenya 2017: KISUMU

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0

Kenya 2017: KITUI

Vote−shareD

ensi

ty

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0

Kenya 2017: KWALE

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

010

0015

00

Kenya 2017: LAIKIPIA

Vote−share

Den

sity

F = 0.90

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

00

Kenya 2017: LAMU

Vote−share

Den

sity

F = 2.40

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

100

150

200

250

300


Vote−share

Den

sity

F = 0.32

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

060

0

Kenya 2017: MAKUENI

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

0012

00

Kenya 2017: MANDERA

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0



500

1000

1500

Kenya 2017: MARSABIT

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0

Kenya 2017: MERU

Vote−shareD

ensi

ty

F = 0.09

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

0040

0060

0080

0010

000

1200

0

Kenya 2017: MIGORI

Vote−share

Den

sity

F = 2.24

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0

Kenya 2017: MOMBASA

Vote−share

Den

sity

F = 1.22

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

010

0015

0020

0025

0030

00

Kenya 2017: MURANG'A

Vote−share

Den

sity

F = 0.17

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

100

150


Vote−shareD

ensi

ty

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: NAKURU

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: NANDI

Vote−share

Den

sity

F = 1.05

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

100

150

200

250

Kenya 2017: NAROK

Vote−share

Den

sity

F = 1.30

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

060

0

Kenya 2017: NYAMIRA

Vote−share

Den

sity

F = 3.14

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

0020

0030

0040

0050

00


Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

010

0015

0020

0025

00

Kenya 2017: NYERI

Vote−share

Den

sity

F = 0.23

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0



100

200

300

400

500

600

700

Kenya 2017: SAMBURU

Vote−share

Den

sity

F = 1.01

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

0040

0060

0080

00

Kenya 2017: SIAYA

Vote−shareD

ensi

ty

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

0

Kenya 2017: TAITA TAVETA

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

00


Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0


Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0


Vote−shareD

ensi

ty

F = 0.48

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0

Kenya 2017: TURKANA

Vote−share

Den

sity

F = 0.60

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

060

0


Vote−share

Den

sity

F = 0.24

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

060

070

0

Kenya 2017: VIHIGA

Vote−share

Den

sity

F = 1.28

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

00

Kenya 2017: WAJIR

Vote−share

Den

sity

F = 2.91

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0


Vote−share

Den

sity

F = 1.22

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

0020

0030

0040

0050

00

Kenya 2017: PRISONS

Vote−share

Den

sity

F = 4.97

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0


Figure 16: Kenyatta Vote Percentages: Spikes Test, Problematic Form 34B0

500

1000

1500

Kenya 2017: BARINGO

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: BOMET

Vote−shareD

ensi

ty

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

0

Kenya 2017: BUNGOMA

Vote−share

Den

sity

F = 0.64

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

0

Kenya 2017: BUSIA

Vote−share

Den

sity

F = 0.56

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

0012

0014

00

Kenya 2017: EMBU

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

0040

0060

0080

0010

000


Vote−shareD

ensi

ty

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

0

Kenya 2017: KAJIADO

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

0


Vote−share

Den

sity

F = 1.26

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

010

0015

0020

00

Kenya 2017: KERICHO

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

010

0015

0020

00

Kenya 2017: KIAMBU

Vote−share

Den

sity

F = 2.67

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

0012

00

Kenya 2017: KILIFI

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

0020

0030

0040

0050

00

Kenya 2017: KISUMU

Vote−share

Den

sity

F = 0.42

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Note: Vote shares are the proportion of valid votes for Kenyatta. Vote count data scrapedon 23aug2017. Using polling stations in a constituency with a problematic form 34B.


200

400

600

800

1000

Kenya 2017: KITUI

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: KWALE

Vote−shareD

ensi

ty

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

010

0015

0020

0025

00

Kenya 2017: LAMU

Vote−share

Den

sity

F = 2.26

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

0012

0014

00


Vote−share

Den

sity

F = 1.60

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: MAKUENI

Vote−share

Den

sity

F = 0.24

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

0012

00

Kenya 2017: MANDERA

Vote−shareD

ensi

ty

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: MERU

Vote−share

Den

sity

F = 1.89

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

0012

00

Kenya 2017: MIGORI

Vote−share

Den

sity

F = 2.32

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

0

Kenya 2017: MOMBASA

Vote−share

Den

sity

F = 2.88

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

100

150


Vote−share

Den

sity

F = 0.74

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

00

Kenya 2017: NAKURU

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

0012

0014

00

Kenya 2017: NANDI

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0



100

200

300

400

500

Kenya 2017: NYAMIRA

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

0040

0060

0080

00


Vote−shareD

ensi

ty

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

0020

0030

0040

0050

00

Kenya 2017: NYERI

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

040

060

080

010

0012

00

Kenya 2017: SAMBURU

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

020

0040

0060

0080

00

Kenya 2017: SIAYA

Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

010

0015

00


Vote−shareD

ensi

ty

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

050

010

0015

00


Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

0


Vote−share

Den

sity

F = 0.65

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

060

0


Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

050

0

Kenya 2017: WAJIR

Vote−share

Den

sity

F = 1.90

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

010

020

030

040

0


Vote−share

Den

sity

F = 0.00

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0


Documents

Anomalies and Frauds(?) in the Kenya 2017 Presidential ...wmebane/Kenya2017.pdf · from the Toolkit separately for each county.3 Such results appear in Tables 3{8.4 Tables 3 and 4