Using the Error in Pre-election Polls to Testfor the Presence of Pork
Jacob Allen*
Craig McIntosh**August 2008

Abstract

Polls are used by politicians, voters, and businesspeople alike to form
expectations over political outcomes. In an electoral system with ‘pork’ at thenational level, local economic prospects will be a direct function of politicalsupport. Because every poll comes with its margin of error, we can exploit theerror in sub-national polls to test for whether forward-looking investment in theeconomy adjusts to shocks in local political behavior. The covariance between
the strength of this adjustment and local-level characteristics allows us to identify
the locations in which strong ‘pork’ contracts exist. To illustrate the technique,we provide a theoretical foundation and an empirical exercise using data on
Ugandan micro-entrepreneurs during the 2001 presidential election. We findevidence of transfers which are explicitly conditional on electoral support, andthat businesses are most sensitive to surprise voting patterns in ‘core’ districts.

Keywords: elections, patronage, pork, polls
JEL classification: O16, N27, H42* PhD Student, International Relations/Pacific Studies, U.C.S.D.

jacoballen@ucsd.edu.
Assistant Professor, International Relations/Pacific Studies, U.C.
San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0519. email:ctmcintosh@ucsd.edu,
phone: 858 822 1125.
** Corresponding Author.

Thanks to Gary Cox, and to Stephen Haggard, Gordon Hanson, Ted Miguel, Devra Coren
Moehler, Dan Posner, Jeremy Weinstein, participants in seminars at UCSD, UCLA, and
APSA, and the staff of FINCA/Uganda; thanks also to Joanitah Kirunda, Robert Apell,
Mukalazi Deus, and Jennifer Bartlett for excellent research assistance.

1. Introduction.
Politicians have many channels through which they can attempt to engage the electorate in a contract,offering public largesse in return for support at the ballot box. We refer to any such contract, in which
the flow of public resources is made explicitly conditional on electoral support, as ‘pork’. It is not hard to
motivate why politicians would wish to form such contracts, but establishing the causal link between
political connections and the flow of expenditures has proven more challenging. If, for example, a region
or firm receives high public expenditures as a result of efficiency concerns alone, this should not be
considered pork even if the pattern of expenditures were correlated with political support. Several new
methods have been suggested in recent years to overcome this difficulty and identify the causal chain
through which the fortunes of political actors drive expenditures and therefore changes in investor
behavior. These range from the effect of political prediction markets (Snowberg et al. 2007) toredistricting (Ansolabehere et al. 2006) to rumors of the ill health of the Indonesian president (Fisman
2001). This paper suggests the error in subnational pre-election polls can be used as a new source ofidentification for this relationship.
Specifically, we form the ‘surprise vote share’ as the difference between the final pre-election poll
and the actual outcome of the 2001 Ugandan presidential election at the county level. The single broadly
publicized pre-election poll, conducted by the New Vision newspaper group, give us a straightforward
measure of expected voting patterns prior to the election, and the realization of the outcome of theelection generates a shock to the level of local support for the presidential victor. We then use panel dataon 21,000 microfinance clients to look for evidence of a correlation between this surprise vote share andabnormal investor borrowing and savings in the immediate aftermath of the 2001 presidential election.
The analysis therefore asks the following question: is investment behavior among microentrepreneurs
responsive to the strength of local political support for the winner of the election? The presence of such alink would suggest that counties with unexpectedly strong support for the incumbent expect to benefit (orunexpected opposition counties to suffer) when the favored incumbent wins the election.

1

A large theoretical literature proposes such an exchange between political actors and voters. In
established democracies these exchanges likely take the form of pork-barrel transfers (Cox and
McCubbins 1986, Dixit and Londregan 1996), but personal patronage may be more likely to emerge innew democracies (Keefer & Vlaicu 2007). We translate the logic of such models into an event study
framework, modeling the process of expectation formation by agents making forward-looking
investments. A natural unit over which expectations of voter behavior are formed is the mostdisaggregated unit at which electoral results are widely available, in our case the ‘county’. A simple
model predicts that the localized returns to private investment will be increasing in the scale of politicaltransfers to local actors (whether through pork-barrel or patronage), and so expected returns are an
increasing function of expected political support. The event which we consider is the difference between
the strength of local support as given by pre-election polls, and actual local support as revealed by theelection. This paper therefore proposes an indirect test for the presence of directly contingent public
expenditures: do shocks to information over local presidential support show up in investor behavior?
There are two steps in the logical chain of this test. First, the equilibrium between politicians andvoters must involve some use of pork or patronage to sway voters. The use of public resources to pursue
political ends in Africa has been widely documented (Miguel & Zaidi 2003, Kasara 2007). Hickey (2003)
writes that Uganda’s primary antipoverty programs ‘have tended to become highly politicized and subject
to both national clientelism and elite capture’. Second, the use of this pork must drive behavior among
the female microentrepreneurs covered by our panel of investment behavior, creating greater ability tosave and invest in the physical areas which received more pork. There has been less research on this
topic, but Hickey (2005) describes the unusual extent to which the Ugandan government has targetedpoverty assistance toward the ‘economically active’, and cross-country evidence exists as to the link
between overall growth and microfinance performance (Ahlin & Lin 2006). Our empirical exercise isessentially a joint test of both steps in the chain: for the informational shock to drive investment we musthave both a voting-dependent transfer and sensitivity to these transfers among microentrepreneurs.

2The 2001 Ugandan presidential election provides an interesting application for this test of pork
for several reasons. First, Uganda has a simple majoritarian presidential electoral system, which is more
analytically tractable than an electoral college system. Second, there was only one neutral, widely
publicized poll conducted in the month prior to the election, making it relatively straightforward to
calculate ‘expected’ voting. Third, this race featured a heavy national favorite, incumbent Yoweri
Museveni, squaring off against a rival with strong support in certain regions of the country but neverfavored to win the election. For reasons presented in the theory section, the technique outlined in thispaper is most straightforwardly applied to elections without surprises as to the winner of the overall
election. Finally, Uganda’s ‘no-party’ political system combines with the fact that the challenger Kizza
Besigye was from the same region and ethnicity as the incumbent to make the constituencies which
provided electoral support to each candidate rather fluid, and not explicitly ethnic. In this sense, the error
in the polls, revealed on election day, provides a meaningful informational shock.
This paper follows a host of new efforts to attempt to solve the identification problem in
estimating the causal effect of political support on public largesse. A voluminous empirical literature
examines which types of electoral districts receive the most transfers1, but establishing that theheterogeneity in transfers is driven by political behavior rather than some omitted variable remains achallenge (Dahlberg and Johansson 2002; Finan 2005; Magaloni, Diaz-Cayeros, and Estévez 2006;
Kasara 2007). Natural experiments such as redistricting (Ansolabehere, Gerber and Snyder 2006) or
national-to-local government party linkages (Arulampalam et al. 2008) provide a useful source ofvariation but do not completely resolve the ambiguity over the direction of causality between public
spending and political behavior.2 Hence there has been strong interest in new methods for establishing a
causal link between political support and economic behavior.

1 See, for example, Geddes (1994); Levitt and Snyder (1995); Fleck (1999); Case (2001); Khemani (2003); Miguel andZaidi (2003); Crampton 2004; Calvo & Murillo (2004); Milligan and Smart (2005); Leigh (2006); Cadot, Röller andStephan (2006).
2 Indeed, the concept of political business cycles (Drazen 2000) and performance-based incumbent support (Posner andSimon 2002) imply the reverse direction of causality: increases in local economic welfare cause increases in politicalsupport.

3

By using an event study methodology to examine the impact of the revelation of new information
on investor behavior, this paper is closest in spirit to work such as Fisman (2001), which uses rumors ofthen-president Suharto’s death to test for abnormal returns among Indonesian firms which were closely
connected to his administration. Goldman, Rocholl, and So (2006) show that U.S. firms connected to the
Republican Party had abnormal returns following the 2000 Presidential election, and Jayachandran (2006)shows that the impact of James Jeffords’ defection to the Democrats on firm values is correlated with thepolitical contributions that the firm had given to the Republicans.3 Herron (2000) uses Britain’s LIBOR
rates to estimate the counterfactual changes in interest rates and equity markets which would haveobtained had the 1992 British election gone to Labour. A related approach is to use the political‘connectedness’ of the directors of a firm to test for the probability that a firm gains access to government
procurement contracts (Goldman, Rocholl and So, 2008) or credit from public banks (Khwaja and Mian,
2005).
Political prediction markets provide a new and intriguing metric of political fortunes, attractive
both because we expect such markets to be efficient, and because they vary continuously over time.
Work such as Herron, Lavin, Cram, and Silver (1999) and Snowberg, Wolfers and Zitzewitz (2007) test
for a correlation between movements in prediction markets and the value of specific firms, arguing thatthe only reason such a link should exist is that investors perceive the financial fortunes of these firms to
be linked to the outcome of elections. Unfortunately, prediction markets do not exist for Uganda or mostdeveloping countries.
How, then, can we study the links between voting and economic activity in countries with few
publicly traded firms and no prediction markets? The answer provided here to use pre-election polls as
the metric of political expectations, and forward looking investment behavior among microentrepreneurs
as the optimized market outcome. This approach uses the local electoral district as the unit through which

3 A disagreement exists in the empirical literature as to whether political contributions in and of themselves increase
future firm value, with Cooper, Gulen, and Ovtchinnikov (2008) finding a positive relationship, whereas Aggarwal,
Meschke, and Wang (2008) cast donations as a result of poor corporate governance, and show them to be negatively
correlated with future value.

4politicians and voters interact, and political ‘connectedness’ is given by the degree of expected support for
the eventual winner of the election. The fundamental logic is closely related to other political event
studies in that we suggest there is no reason that investment behavior should be correlated with shocks topolitical support unless the investment climate is a direct function of political outcomes.
The strength of the empirical approach outlined in this paper is that it mimics experimental
assignment of political support: because the most likely source for the surprise vote share is the
sampling error in the polls, we have a strong argument that the informational shocks are in fact randomly
assigned. The weakness of the approach is its indirectness, an issue which is related to the question ofwhat we mean by the use of the term ‘pork’. Most studies of pork analyze specific types of public
expenditure as the dependant variable, and define pork-barrel spending as the provision of localized
public goods in return for electoral support. We infer heterogeneity in the scale of public transfers basedon the behavior of local investors. Our empirical definition of pork is then any form of public
expenditure which is explicitly conditional on local electoral support, and whose use alters the decisionsof local microentrepreneurs. We may equally expect such entrepreneurs to respond to politically driven
public investment (road construction, electrification, telephony) as to clientelistic patronage (bribes andvote buying putting money in the pockets of local consumers).
We use a model based on Dixit & Londregan (1996) to derive optimality conditions forpoliticians’ use of public resources to sway the electorate. In the simplest version of the model, thepolitician will push transfers towards each electoral unit until the increase in vote share per dollartransferred to voters is equalized across units.4 Heterogeneity in the marginal transfer per vote sharearises as a result of ‘leaky bucket’ effects, through which politicians are not equally efficient attransferring to all constituencies. Aggregate political uncertainty around the time of an election can be
decomposed into a national and a local component. National uncertainty relates to the actual outcome ofthe election, while local uncertainty concerns the extent of support for the eventual winner of the election.
4 This constant pork-per-voteshare contract emerges in simple majoritarian systems (where voteshare in each district isequally valuable), while systems with electoral colleges will feature more money transferred for a unit of increasedtransfers in ‘swing’ districts (where the probability of tipping the majority is highest).

5In our empirical application, we study the 2001 presidential election in Uganda (a race with a heavy
national favorite) so as to be able to abstract away from the question of who won the election and focus on
the question of the extent to which a region backed the winner. In other words, do local investors expectthat unexpectedly weak local support for a victorious incumbent will prove negative for the local
economic climate? The relationship between the surprise vote share and discontinuous shocks ininvestment provide the basis for this test.
To preview our results, we find evidence that investment jumps immediately following thepresidential election in a manner correlated with the size of the surprise support for the victor, Yoweri
Museveni. These effects are statistically significant but not large in magnitude; a 10% surprise increasein a county’s support for Museveni results in a $1.37 increase in average loan sizes, against an overall
average loan size of $135 and an average residual loan change of $.50.5 This jump in investment islargest in the counties which form Museveni’s core support, as well as among well-educated, small ethnic
groups. This is consistent with a ‘leaky bucket’ effect in which the efficiency with which the government
can make transfers to groups will determine who the core supporters are. The marginal response to a
positive surprise is larger than the response to a negative surprise.

2. Model.
We begin with a derivation of optimal transfers which is based on a simplification of the theoreticalmodel of Dixit & Londregan (1996, hereafter D&L).6 They posit a CDF of votes for a particular partyΦ i ( X i ) , where X i denotes ideological preference for that party.

D&L use the index i quite generally(variously referring to garment workers, ethnic blocs, and states); our application is spatial and so wedefine i as the county, the smallest electoral unit at which returns data are available.

Transfers can be

5 This change is calculated by regressing loan changes on a set of month and loan cycle fixed effects and taking the
average residual.
6 Specifically, we consider only positive political transfers while they consider redistributive taxation and spending; ourmodel therefore considers how best to allocate a strictly positive ‘pork’ budget.

6

used to shift the ‘cut point’ within this distribution, because they increase utility from consumption. κ igives the extent to which voters are willing to change their behavior in response to transfers, and wefollow D&L in writing voter utility as U i (Ci ) = κ i C1−ε / (1 − ε ) , where ε captures the degree ofdiminishing marginal returns to private consumption (marginal utility is κ i C −ε ). Ti is the transfer given
by a politician and ti = θiTi is the transfer received by the voter; θ i therefore gives the ‘leaky-bucket’
efficiency of the politician at making transfers to a specific electoral unit. As θ i rises towards one itmeans that the politician can transfer to a unit with perfect efficiency, and as it goes to zero it means thatthe politician is utterly unable to transfer benefits.7 Consumption is given by Ci = Yi + θ Ti .
We model a two-party race in which both parties face the same budget constraint B (the ‘spoils of
victory’ define B) and we assume that κ i , the extent to which voters can be bought, is symmetric acrossthe two parties. Without loss of generality, we consider the decisions of the party that eventually wins theelection, with the ‘opposition’ party denoted by a tilde. Thus the only difference across the two parties is
the transfer efficiency (written as θ i for the winning party and θi for the opposition party). For anelectoral unit with N i members, equation 12 in D&L establishes the Lagrangian of the constrained
maximization problem as:
(1)

G

G

i =1

i =1

L = ∑ N i Φ i ( X i ) + λ ( B − ∑ N iTi ) ,which has the associated FOC
(2)

⎛
⎞
dt
N i ⎜ φi ( X i )U i′(Ci ) i − λ ⎟ = 0 ∀ i
dTi⎝⎠

where φi is the pdf of the vote share in unit i across received transfers ti .
7 This formulation differs from D&L, who separately consider efficiency in taxation and redistribution. We study only
the use of positive transfers to buy votes using a fixed pool of resources, rather than the explicit use of taxation as aredistributive game. This model is more analytically tractable than a model of redistributive politics, although theintuition of the first-order conditions is very similar. Also note that D&L define θ as the inefficiency of transfers (meaninga share 1 − θ reaches the electorate), while we define it as the efficiency.

7

Substituting in for the derivative of the utility function, usingdti= θi , and rearranging terms, (2)dTi

can be written as:(3)

φiκ i (Yi + Ti )−ε θi = λ ∀ i .

The term φi gives the slope of the CDF of the vote share, or the increase in the vote share that is
effected by a marginal increase in received transfers.8 The term κ i (Yi + Ti ) −ε θi gives the extent to which
voter behavior is altered by transfers; when κ i is high voters are willing to change their votes in returnfor particularistic benefits, and when θ i is high then the politician is effective at getting transfers to thevoters. The number of voters in an electoral unit N i falls out of the maximization because large counties
are symmetrically more important electorally and more expensive to provide a given transfer to each
voter. Hence equation (3) says that the real efficacy of vote share purchased per dollar given must be
equalized across all electoral units in equilibrium.9 The equilibrium transfers for the opposition party are
the same as given by (3), except that θ i is replaced by θi , the opposition party’s efficiency at making
transfers to unit i.
Equation (3) can be thought of as establishing the terms of a contract between politicians and theelectorate: λ gives the politician’s willingness to pay for incremental vote share in equilibrium. A
additional dollar transferred to voters in an electoral unit will drive up the vote share by a term which isthe product of the willingness of local voters to trade ideology for money ( κ i (Yi + Ti ) −ε θi ) and the

8 Our theory abstracts away from the question of the credibility of politicians.

A previous version of this paper featured
a more complex model which explicitly considered credibility; that formulation generates marginal effects of ‘credibility’which are observationally equivalent to county-level variation in the slope of φ . Hence in this simpler formulation, we

can think of θ as implicitly capturing the credibility channel; if a politician is less credible in a specific county, thenpromises to that county will result in fewer votes bought and therefore a dampened sensitivity to transfers.
9 Note that the common slope of marginal transfers across units is a product of the assumption in equation (1) of asimple majoritarian election. In a system with an electoral college, marginal vote share in uncontested units has little
value to the politician, and transfers focus on those units which are ‘in play’. This means that the slope of the transfer
function is steepest when the vote share of the unit is closest to 50%; we propose and execute a test of this propositionin Section 4.4.

8

density of voters at the equilibrium cutpoint ( φi ).

φi =

λ

and

κ i (Yi + Ti )−ε θitheoptimalThe density of voters at the cutpoint in equilibrium isquantity

of

pork

which

will

go

to

each

county,1/ ε⎛φκ θ ⎞
Ti = −Yi + ⎜ i i i ⎟ . Counties which receive pork are therefore poor, have many swing voters, and⎝ λ ⎠*feature efficient transfers to voters who are easily bought.
The pork contract: The left-hand side of (3) is the derivative of vote share Φ i with respect totransfers Ti , which can be written more generally asd Φi= λ . This says that the politician offers a fixeddTiamount to each electoral unit in return for 1% more support on the margin when the game is inequilibrium. In order to present a model of optimal transfers which is as transparent as possible, we
consider a linearization of the slope of the pork contractd Φiaround the equilibrium. This linearizationdTi

allows us to abstract away from second-order effects, and will provide a reasonable approximation for
local perturbations. Retaining heterogeneity through a unit-specific intercept, we write the vote share asa function of transfers with the expression Φ i = α i + λTi . The transfers actually received by the voter are

ti = θiTi , and so this expression can be inverted to give optimal transfers actually received by the voters
as a function of local support;(4) ti* =1λ( −α i + θi Φ i ) .

Optimal transfers from the losing politician would be ti* =1

λ( −α + θ (1 − Φ ) ) .
i

ii

The inverted derivative gives the change in optimal transfers with respect to changes in the voteshare; this can be written as:(5)dti* θi= .
d Φi λ9Instead of describing how politicians respond to voting, Equation (5) presents the contract from theperspective of local citizens, giving the money in pocket transferred to them in exchange for support. It
tells us that while the politician will be willing to pay an equal amount for increased vote share anywhere,that the amount received by voters for a given increase in vote share will be larger the more efficient is
the politician in transferring pork to that constituency.
Political expectations: With this machinery in place, we can specify the way in which preelection polls shape expectations of the outcome. The first of these is through P , which gives the ex ante
probability that the eventual winner will win the election. The second is through pi , which gives the
local vote share for the eventual winner, thereby providing an expectation over the strength of regional
support for the winner. If the actual outcome of the vote is Φ i = vi , then the ‘surprise vote share’ can be
defined as si = vi − pi , and the surprise present in the outcome of the election itself is given by 1 − P .
The variance of the surprise vote share is generated by the standard error of the poll itself, whereas thevariance of the national outcome is given by P (1 − P ) .10
The ‘surprise’ created by an election can be decomposed into two parts; the resolution of theuncertainty over the winner of the election (national), and the surprise over the extent to which an
electoral unit backed the winner (local). The expected transfer based on pre-election polls will be

E (ti* ( P, pi )) = PE (ti* ( pi )) + (1 − P ) E (ti* (1 − pi )) . Following the linearized from in (4), this can be
written as:(6)

E (ti* ( P, pi )) =

(

)

1⎡
P ( −α i + θi pi ) + (1 − P) −α i + θi (1 − pi ) ⎤⎦λ⎣

10 Since the surprise vote share goes to zero as polls become perfectly accurate, we may expect that a ‘poll of polls’ indeveloped countries which feature many different polls and polling organizations provides less variation off of which toidentify this effect. To the extent that all polling houses share sampling mistakes (e.g. turnout patterns themselves were
surprising) then elections with multiple polls may provide surprise over local vote shares.

10

The actual transfer made once the outcome of the election is revealed will be ti* (vi ) = −αi+ θi vi .
λDefining the shock to optimal transfers that arises from the election as Δti* ≡ ti* (vi ) − E (ti* ( P, pi )) , we
can then write(7)

Δti* =1⎡s θ + (1 − P) ⎡⎣ piθi − (1 − p)i θi ⎤⎦ ⎤⎦ .
λ⎣ i iThis equation allows us to decompose the uncertainty in transfers into a national and a local component
by letting si go to zero or P to one, respectively.
As si goes to zero, we remove sampling error from the local poll result and thereby focus on thesurprise caused by the outcome of the election itself. In this case, we see the way in which national-level
political uncertainty enters decision-making:(7a)Δti* =(1 − P)λ⎡⎣ piθi − (1 − p)i θi ⎤⎦ .

Because we have defined the outcome of the election as P = 1 , the surprise is given by thequantity 1 − P . Given this, the shifts will be largest where voters are responsive to transfers, and will bepositive if piθi > (1 − p )i θi , This term compares the received transfers from each politician and ispositive if there is heavy support for and efficient transfers from the winner (so the outcome of theelection is a pleasant surprise for locals). If the voters supported the opposition, or could be transferred tomore efficiently by the losing politician, the outcome of the election represents a negative shock to
expected transfers. This term is the product of two questions: “am I surprised?” and “do I care?”. If theanswer to either question is no, then there is no national-level shock to expected transfers from electoral
realizations.
Alternatively, when we eliminate the surprise in national elections by sending P to 1, theremaining term Δti* =

siθi

λ

gives the extent to which local pork is driven by the local-level surprise vote11

share. In other words, when we consider an election in which the outcome itself was not a surprise, theonly uncertainty remaining in an election is the degree of local support for the winner:(7b)

d Δti* θi
= >0,
dsi
λ

The change in optimal transfers as election results are revealed is now a simple linear function of thesurprise vote share. The slope is steepest where transfers are most efficient, and so (7b) allows us not
only to test for the presence of conditional transfers, but to identify the units for which the bucket leaks
least. This response is based on ‘did we back the winner?’.
The Ugandan case, being an electoral system which had a heavy winner in a simple majoritarianpresidential system, allows us to analyze the case established by equation (7b), where the primary
uncertainty is over the degree of local support. If we were able directly to observe transfers ti , we could
use the relationship with the surprise vote share to measure the cross-unit determinants of the term θ i .
We present some aggregated results which suggest that such a reapportionment of explicit budgetary
spending across districts did occur (the simple correlation between the surprise vote share and the change
in district-level budgets before and after the election is .43). However, many forms of political transfers
are intentionally hidden, and so a more interesting empirical test would use an outcome which was
responsive both to formal transfers but also to patronage and explicitly corrupt means for affecting
redistribution.
Investor response to transfers: In order to operationalize the theory, therefore, we propose theuse of forward-looking behaviors which are optimized intertemporally on the basis of expectations, andwhich we expect should be sensitive to a broad variety of political transfers. Candidates include business
investment, housing starts, stock prices of local firms with government contracts, and so on. In this paper
we use investment loan decisions taken by the clients of a microfinance lender with offices covering mostof Uganda. We denote this metric by π i (ti ) ; in our application this represents the size of small-businessloans.

12

We abstract away from heterogeneity in this business response by assuming thatdπ i
= π ' isdti

linear and constant across electoral units, and so investment responds to transfers in a similar way acrosselectoral units. We can then calculate the responsiveness of local business to the surprise vote share as
the product of business response to transfers and the transfer response to the surprise vote share:(8)dπ i d π i dti*θ= *=π ' i .
dsi dti dsi
λThis result can now be taken to the data by measuring the relationship between the discontinuous
jump in investment and the surprise vote share. The significance of the average slope of this relationshipacross all electoral units gives us a test for the presence of the use of pork, and variation in the slope
across different units allows us to study which units are most efficiently courted with conditional,
particularistic benefits.

3. Data.
Investor data: We measure changes in investor behavior using borrowing records for the clients of
FINCA/Uganda, the country’s largest microfinance lender. These clients are exclusively female,primarily urban or semi-urban, and typically invest in high-turnover trading businesses. Loans are issued
to groups of roughly 30 borrowers for a 16-week term, and the effective annual rate of interest is over
80%. The lender makes efforts to ensure that this credit is actually being invested in the business, sochanges in borrowing give us an indication of the borrowers’ expected demand in their enterprise over the
four-month period which follows the taking of the loan. We include results for the volume of savings;
however due to the fact that savings in FINCA serve as collateral for loans and the formula for maximum
loan sizes is a repayment grade times the current savings balance, we tend to see savings and investment
co-move.
Are microfinance loans a valid measure for fluctuations in local economic activity? Theirdisadvantage is their indirectness; ideally we would prefer a more direct observation on transfers. Our

13empirical test requires both that voting affects transfers, and that transfers affect average local borrowing.
Extralegal transfers in particular, however, will be hidden from view and therefore not amenable to direct
study. FINCA borrowers are running the bars, small restaurants, groceries, and mobile airtime vendors
who serve the local economy and therefore may be a reasonable place to look for the effects ofexpenditures which are hidden. Further, the discontinuity which could be formed on actual spending
would be quite weak (actual expenditures may not adjust immediately). Because we hypothesize thatmicroentrepreneurs take loans which are increasing in the size of their expected turnover, they incorporate
longer-term expectations into decisions taken days before and days after the election, and therefore may
present a real discontinuity at the time of the election. The independent nature of FINCA’s funding
sources and its centralized infrastructure make it an attractive venue for studying the election. Data
collection is constant across units and time, and it seems unlikely that FINCA had itself become a venue
for distributing pork on the supply side.
The FINCA data is a rolling panel in which each individual features only every four months (ormore) as they ‘recapitalize’ their loans. For this reason, it is difficult to interpret discontinuous changes
between time periods because they consist of different individuals.11 In order to minimize differences
between cohorts we difference outcomes. The dependent variable used throughout the study isπijt = π ijt − π ij for individuals j; the study uses only those individuals who took two or more loans (so as
to avoid entering a zero change for the 11,206 individuals who took only one loan). We thus analyze how
explanatory variables relate to deviations from individual mean outcomes for the 21,140 individuals who
took multiple loans from FINCA during this period. We have accounting data for FINCA clients locatedin 71 out of Uganda’s 214 counties, and in 22 out of the 54 districts (these districts represent 56% of the
population of eligible voters in the country). Figure 1 provides a map of the location of all districts in
Uganda and all FINCA groups.

11 The temporal sorting process is non-random, a fact which is easily verified by picking ‘placebo’ discontinuity dates, ofwhich nearly 50% are significant.

14

Voting data: Because we know the district and county in which each borrower is located, we cantake the detailed panel of outcomes provided by FINCA’s institutional data and lay onto it a variety of
information pertaining to political events.12 The first of these is the county-level election results. We use
the percentage of the vote in each county cast for the incumbent Movement candidacy of Yoweri
Museveni, versus the total percentage cast for the opposition candidates combined (Kizza Besigye, alongwith Moody Awori, Chapaa Karuhanga, Kibirige Mayanja, and Francis Bwengye).
Polling data: To construct the surprise vote share, we utilize the last neutral opinion poll which
received wide publicity prior to the election. This poll, released by the New Vision on February 28th
2001, got front-page attention in both national English-language papers and in the local-language press.
This poll was designed to be representative at the district level, and was based on 2,994 surveys with amargin of error of 5%. Results exist for only 19 of Uganda’s 56 districts. FINCA groups exist in 22
districts, of which only 11 overlap with those covered by the opinion polls. From these we haveconstructed a county-level prediction of voting based on that found in the district as a whole, or that in theclosest district for which data exist. While this measure is imperfect, it also represents the best
information available to voters in the run-up to the election as to likely voting outcomes; no other polls
existed, and it is not unreasonable to think that voters were using polls to form expectations in a similarmanner. 13
The mean surprise vote share for Museveni was 1.1%, with a large standard deviation of 8.8%.
Whether from large sampling errors in the polls, poor sampling design, or because the polls are at the
district rather than the county level, we have large surprise votes occurring in some locations. While this

12 It is important to note that our use of institutional data in effect weights the impacts we measure by the distribution of
FINCA’s clients, and not the distribution of voters in Uganda. Hence we can make no claim that the results we estimate
are representative for the average Ugandan voter.
13 We have experimented with two alternative ways of defining the surprise vote share. The first of these is to use either
the average of the two pre-election polls or the linear extrapolation based on the difference between them as themeasure of ‘expected’ voting. The results are not sensitive to these changes in the manner of predicting the vote. The
second is to calculate the vote share only for Museveni and his main opponent in the election, Kizza Besigye, excluding
votes for minor candidates. Because the 4 other candidates in the race received less than 2.5% of the votes, the results
again are not sensitive to alternate ways of calculating vote shares.

15

implies an imprecise prediction process, it also generates a large amount of variance which can be utilizedin our empirical analysis.
Content Analysis: The 2001 election between incumbent Yoweri Museveni and the upstart
Movement insider Col. Kizza Besigye provides an interesting case study because of the absence of an
explicitly ethnic dimension14 and because it featured an opposition organization built from the ground up
in less than a year. In the absence of entrenched ethnic interests an electoral competition opened up
which was unusually issue-oriented, with a vote for the opposition sending a message of change. The
opposition scored well among the educated, urban, young, and male, and Museveni’s core supporters
were the older rural women with strong memories of the country’s troubled past.
Because we want to avoid confusing the impact of the surprise vote share with other unexpected
events that occurred during the course of the election campaign, we conducted a newspaper content
analysis for the months of November 2000 through July 2001. Uganda has two major newspapers—the
New Vision, owned by the Movement government, and the Monitor, owned by the Aga Khan’s Nation
Media Group. We coded all events of political violence, threats, promises of patronage, and contestationof election results which could be attributed to a specific time and place. The advantage of having suchdata is not only to control for concurrent shocks; comparing the impact of promises of patronage to theimpact of the surprise vote share gives us an interesting check on the direction & magnitude of impact of
the surprise vote share.
We aggregated the events in the content analysis into eight types of political shock:1. Acts of violence include any attacks, beatings, shootings, etc. which are politically motivated.
2. Threats against the opposition, which includes the following categories from the content analysis:
Government threats to opposition candidates, Government arrests opposing supporters, Government
threats to opposing officials, Government supporters threaten opposition, and Physical attack on
opposition officials/supporters.
3. Threats against the Movement, includes Opponent supporters threaten government officials, Citizens
threaten government, and Physical attack on government official/supporters.
4. Threats against citizens, includes Opponent supporters threaten citizens, Government/police
attack/arrest citizens, Citizens struggle with citizens, and Increased security (included because it wasan implied threat, and a response to violence).
14 Besigye is from the same ethnic group and region as the incumbent, served as Museveni’s personal doctor during thebush war in the 1980’s, and is married to Museveni’s ex-lover.

16

5. Election results contested.
6. Election was close: This is a dummy which switches on in the month after the election that equals
one if the poll was between 45% and 55%; this variable captures whether the response to the election
is a function of local-level uncertainty over the outcome.
7. Movement promise of patronage made to a county by a Movement politician. These include promisesof electrification, road construction, or similar statements which can be assigned to a specificlocation. We refer to these promises as ‘patronage’ to avoid confusing them with the ‘pork’ measured
through the primary analysis of the paper.
8. Opposition promise of patronage made to a county by any opposition politician.
Figure 2 illustrates the growing political tension in the months leading up to the March 15th election
date, and the rapid decline after the election; the three months prior to the election saw around 100 events
each, while only 10 occurred in the month afterwards. Table 1 shows the distribution of these eventsfrom the content analysis by district, along with the numbers of FINCA clients in each district. It is clear
from inspection that areas likely to see political events are likely to have FINCA clients. Only Rukungiri(home to both Museveni and Besigye), Mbale, and Tororo districts saw substantial political activity in theabsence of FINCA clients. Districts in which FINCA clients were present accounted for 74% of the actsof violence, 73% of threats against opposition, 78% of threats against the Movement, 52% of threatsagainst citizens, 67% of the Movement patronage promises, and 100% of the opposition patronage
promises.

4. Analysis.
As a first step in our empirical analysis, we perform a simple exercise to verify that a positive
relationship exists between business borrowing and transfers from the government. We calculate theaverage jump in microfinance loans at the county level from the two weeks prior to the election to the two
weeks after the election. This jump is then compared to the swing in total funds received by districts fromthe central government (as a percentage of total budget) from the fiscal years before and after the election.
While the number of observations here is limited (there are only seven districts for which FINCA data
and budget data exist), the correlation between the jump in loans and the percent change in budgets is .75,
and the correlation between loan changes and the budget share actually received by the district is .83.

17Figure 3 takes the next step and plots the district-level change in loans against the surprise vote share; theraw correlation between these two quantities is .39, and the positive relationship is visually clear.
4.1. The mean response to the surprise vote share.
Table 2 gives the basic regression estimates of the impact of the surprise vote share on loan
volume. The first two columns of Table 2 examine a four-month window around the election, using avariable which is equal to the surprise vote share in the month after the election and zero else. Using thissimple regression we see that the surprise vote share for Museveni displays a positive correlation withdiscontinuous increases in borrowing and savings. This implies that the shock of the surprise vote shareinduces a meaningful increase in investment at the local level.
The third and fourth columns of Table 2 use data from 14 months around the election (June 2000 –July 2001, with the election on March 15th 2001), remove time trends with month dummies, and control
for the impact of concurrent political shocks through the events from the content analysis. We use pooled
OLS with standard errors clustered at the district level.15 This regression specification for individual j incounty i at time t is:8πijt = β 0 + δ t + β1 χ ijt + ∑ βl Llit + γ sit + uijt
l =1

for the differenced outcome πijt . χ ijt is a vector of controls (loan cycle number and cycle squared) thatvary at the individual level over time, and Llit is a set of eight dummies equal to one in the month after
new political events are revealed to have occurred in a county. Given the time dummies, these indicatorvariables measure how outcomes in counties in the month after each kind of shock has occurred differ
from the average differenced outcomes in that month. sit equals the surprise vote share in a county in themonth after the election and zero all other times, so γ measures the extent to which county-specificchanges in economic outcomes in the month after the election are correlated with the surprise vote share.
Because our dependent variables are defined in differences from individual means, the random effects
15 Results are very similar if the regressions are run using county-level instead of district-level clustering.

18

estimator is identical to pooled OLS. The use of fixed effects in this context would only demean the
RHS, and hence the FE estimate is very similar to pooled OLS in all specifications.
Table 2 shows that the surprise vote share has a significant effect on both borrowing and savings
behavior. The implication is that counties which backed Museveni to a surprising extent saw significant
increases in both investment and illiquid savings, indicating an environment which is both better forinvestment and more secure. The outcomes are in US Dollars, so the coefficients imply that a 10%
increase in county-level political support leads local investors to invest $1.37 and to save $.50 more informal financial institutions.
The impact of the controls from the content analysis are of interest in their own right. Both threatsand promises have measurable effects on borrower behavior. There is some evidence that threats against
the Movement drive up loan sizes, but threats against citizens have a stronger effect; here we see
increased borrowing and sharply decreased savings. This is the only place in the entire analysis in whichsavings and loans display divergent responses: the implication is that threats against citizens trigger anunusual response in which households maximize their short-term liquidity by taking larger loans andputting less money into FINCA’s relatively illiquid savings product. The response to a patronage promise
is positive, significant, and resembles the effects of the surprise vote share in significance although thecoefficients indicate that the surprise vote share would have to be on the order of 20-40% to rival thequantity effects of a direct promise of patronage. Intriguingly, the effects of patronage promises by
opposition politicians seem to be larger in both magnitude and significance than those made by theMovement. It may be that they were more unexpected, causing a bigger discontinuous shift.
There seems to be support here for the idea that changes in business outcomes are related to thesurprise vote share. Figure 4 plots the response to the surprise vote share as we increase the length of the
post-election window used to measure the ‘shock’. Supporting the theory that this informational shock isboth immediate and transitory we see the largest effect using a two-week window and find that by thetime the window has expanded to two months the measured effects become insignificant.
4.2. Is the Surprise Vote Share random?

19

The identifying assumption of the technique presented here is that the surprise vote share comesfrom sub-national sampling error in pre-election polls, and therefore should be randomly distributed.
There are several reasons to view the assumption that the error in the polls is random with suspicion. If
the polling firm makes systematic mistakes, or if the ‘surprise’ picks up real last-minute swings inpolitical preferences, then there may be a direct correlation between the surprise vote and local
characteristics which does not pass through the shock to expectations. We can explore this assumption
due to the fact that if si is indeed a classical error term, the surprise vote will be uncorrelated not onlywith pre-election covariates, but with pre-election outcomes as well.
To test this, we use a similar specification to our impact tests, but use the surprise vote share as thedependent variable in order to test for pre-election orthogonality. We find that pre-election borrowing,savings, and repayment performance are not related to the surprise vote share, and a wide range ofindividual- and county-level characteristics are similarly insignificant (see Table 3). The sole clear
correlation is that education (measured either at the individual level or from district-level data) is strongly
negatively related to the surprise vote share. In other words, educated counties swung more strongly than
expected against Museveni’s Movement government.

Whether this correlation arises from randomvariation, mis-sampled polling, or arises because of real last-minute changes in political preferences, its
existence moves us from the realm of experimental to quasi-experimental identification.
We can investigate whether education is itself an important contributor to changes in outcomesby regressing changes in the months prior to the election on education. We accomplish this by interacting
the month dummies for the three months prior to the election with district-level education. In no case arethese interactions significant, and their signs flip from positive to negative. Since education appears to be
having little direct effect on outcomes, the correlation between surprise vote share and education would
not appear to be introducing bias into the causal effect of the surprise vote share on investor outcomes.

4.3. Heterogeneity in the response to surprise vote share.

20

Given this moderate average effect, there are several interesting dimensions across which we canlook at heterogeneity in the responsiveness of local businesses to a given surprise vote share. Equation(7b) of our theoretical model shows thatd Δπ iθ= π ' i ; given the assumption that the business responsedsi
λto a given shift in transfers is the same across districts, the heterogeneity in the slope of the response to
the surprise vote share is driven by the term θ i . In other words, the response will be strongest in counties
where Museveni is most efficient at delivering transfers.
District level heterogeneity: The empirical study of this response to heterogeneity can beaccomplished through the use of an interaction analysis. For some fixed (and demeaned) district-level
characteristics Z i , we can use OLS to estimate:8πijt = β 0 + δ t + β1 χ ijt + ∑ βl Llit + β 2 Z i + γ sit + η ( Z i * sit ) + uijt , where η measures the slope of
l =1

dπ i
dsi

across Z i . Assuming that the responsiveness of π i to pork does not differ across counties, the interaction
term η gives us the determinants of θ i : which counties have the buckets that leak least?
Table 4 shows some differentiation of the responsiveness of business to vote share across districtlevel characteristics Z i . The districts which show a strong response to the surprise vote share are
educated and rural. This means that educated rural counties have the values of θ i closest to 1, and so
represent a natural core constituency for Museveni.
Ethnic Heterogeneity: Given the (apparently) ethnic nature of many African electoral contests,
another interesting dimension along which to examine variation in slopes in response to surprise voteshare is ethnicity. Using the language spoken in group meetings as a proxy for ethnicity we again findstrong differentiation in slopes, although we have data on language spoken for only 37,635 of our 58,731
observations. Table 5 presents the results of interactions performed in the same way as described above
where we dummy out seven language categories, leaving Kiswahili-speakers as the omitted category.

21

Swahili is the language of trade in East Africa but is not widely spoken in Uganda, and so these groups
are likely to be ethnically heterogeneous and to be trading over larger distances than other groups.
We find that neither English-speaking groups (which may be the most highly educated) nor the major
ethnicities of southern Uganda (the Baganda and Basoga) have slopes which differ from Kiswahili
speakers. However, two kinds of ethnicities have sharply divergent responses. On one hand ethnicities
from the war-torn north of Uganda, where Museveni has struggled to establish authority, show much
lower response to the surprise vote share. On the other hand the four small, non-northern ethnicities showmuch larger responses. The implication is that Museveni is more efficient at transferring resources tosmall southern ethnic groups than he is to the large groups which dominate southern Uganda. The
complete inability to transfer resources to the North implied by these results is interesting in light of thelong-running civil war being fought by the Movement government against the Lord’s Resistance Army
(LRA) on Northern soil. This conclusion is intuitively appealing and may be driven by the fact that small
ethnic groups can more easily overcome collective action problems in order to provide an efficient
political machine (Olson 1965, Bates 1981).
4.4. Core versus Swing voters.
A major theoretical debate has taken place over whether politicians will target pork to core or
swing constituencies. Cox and McCubbins (1986) assume risk-averse incumbents, concluding that they
will steer transfers disproportionately to their core supporters in order to maintain coalitional stability.
Dixit and Londregan (1996), on the other hand, argue that the incumbent’s core supporters will only
benefit when the incumbent has an organizational advantage in directing favors to the core. Otherwise,
welfare transfers will be directed to voters whose value for material utility is high relative to theirideological persuasions. By definition these voters are typically swing voters.16 Perhaps not surprisingly

16 Lindbeck and Weibull (1987) reach a similar conclusion. Case (2001), Miguel and Zaidi (2003), Levitt and Snyder(1995), Stokes (2005), and Magaloni, Diaz-Cayeros, and Estévez (2006) find evidence supporting transfers to the core.
The evidence of Dahlberg and Johansson (2002), Kehmani (2003) and Kasara (2007), all support the Dixit-Londreganswing model.

22for such a complex strategic relationship, the empirical evidence on the core/swing redistribution debate
has been mixed.
The method suggested here provides new angle on this debate. While our test does not allow us tomeasure the total quantity of transfers, we are able to measure county-level variation in the efficiency oftransfers. We may think of core voters either as being those with strongly aligned ideological preferences,or as those groups to whom the politician is most efficiently able to transfer pork. Without some kind of‘leaky bucket’ effect, one cannot easily motivate transfers focusing on core voters. It should therefore be
the case that the local response to the surprise vote share should be strongest in core districts, to whichtransfers are most efficient. In order to test this, we define dummies that define core Movement andswing counties (Movement vote percentages of >60%, and >40% and <60%, respectively), and interact
these dummies with the surprise vote share variable, using the same specification as above.
The first column of Table 6 reports the results of analysis using these interactions, using loanchanges as the dependent variable (results for savings were virtually identical). We find that core
Movement counties show a significantly greater sensitivity to the surprise vote share than opposition core
counties, while swing counties do not respond differently. Columns 2-4 of Table 6 illustrate the same fact
a different way, by partitioning the counties according to their status and runs three separate sets ofregressions. The loan response is highly divergent, with a strongly significant positive response in core
Movement counties and not elsewhere. The implication is that while the security effects of a positive
vote share (measured by savings) are not divergent across core and swing counties, the discontinuous
improvement in business opportunities engendered by credible pork is strictly limited to core Movement
counties.
4.5. Majoritarian voting.
A clear upshot of the theory on majoritarian systems is that since only total vote share matters,
votes in all electoral units are equally valuable and no discontinuity should exist over ‘winning’ a specific23

unit.17 In a majoritarian system the surprise vote share should have the same effect whether or not the
surprise tipped the county to a surprise majority change. In other words,

whether the surprise crosses 50%.

d Δπ ishould be invariant to
dsi

Because this variable is itself an informational shock (unlike

swing/core status), we test for it in a fashion similar to Section 4.1. We use the same specification
outlined above and add the trichotomous variable mit which switches on in the month after the electionand equals 1 if the surprise makes Museveni the county winner and -1 if an opposition politician takes thecounty by surprise. We also define separate dummies for the move in each direction.
When one of these variables is used to explain Δπ i (controlling for the surprise vote share), it tests
for whether the magnitude of Δπ i differs for counties that tip allegiances. If it is interacted with thesurprise vote share, the interaction (mit * sit ) measures whether the slope ofd Δπ idiffers across mi .
dsi

Columns 5 and 6 of Table 6 report the results of this regression (suppressing shocks already reported
above). Using the trichotomous variable (which measures symmetric positive effects in counties that tip
to the Movement and negative effects in those that tip away), as well as the dummies for the change ineach direction, we find no differential shocks across counties that tip.
The results support the theory—the dummy indicating a majority tip in the election is insignificant
whether included as a separate shock or in interaction with the surprise vote share. Both the magnitude of
the response (conditional on the surprise vote share) and the sensitivity of businesses to surprises are
similar in counties that tipped allegiance by surprise and those that did not. In regressions not reported,
we test for the presence of a non-linear effect of the surprise vote share, and fail to find any evidence ofsuch an effect. In order to make the confirmation of the theory more complete it would be useful toperform similar tests in countries with electoral colleges to verify whether such surprises do indeed
17 This is in contrast to a system with an electoral college which assigns all local votes to the majority winner.

Electoral
college systems will direct resources at counties with split votes. This can be thought of as replacing Φ i withPr(Φ i > .5) , a problem which makes politicians more willing to spend to achieve marginal vote share in counties wherethe vote share is evenly divided. Thus pork goes to the swing.

24

demonstrate differential responses, but in this case we do find that business response is indeed a linear
function of the surprise vote share as we would expect in a majoritarian system.
One final empirical issue which is easily addressed is the symmetry of upward and downward
shocks to expected political support. Our simple theory model generates a symmetric response, but inpractice it may be the case that it is different to unexpectedly vote for the incumbent than for the
opposition. In order to examine this question, we define two separate variables, one for a positive
surprise vote share (equal to zero else) and the other for a negative surprise vote share. Table 7 presents
results using this alternate way of defining surprises, and we find the marginal effect to be significant only
for positive surprises. An F-test for the equality of the marginal effect of upwards and downwards shocks
can be rejected at the 95% confidence level. Column 2 of Table 7 then takes the analysis one step further
by including the dummy for ‘Movement Core’ and interacting this dummy with positive surprise voteshare; the interaction is strongly significant. This analysis implies that the significant overall effect of
surprise vote share is being driven by a surge in investment in counties which were already in Museveni’s
corner and then turn out in his favor even more heavily than expected. This suggests that the effect arises
as a result of anticipation of future benefit from Museveni’s re-election, rather than fear of punishment
among those who did not support him.

5. Robustness Checks.
5.1. Is the election outcome itself a shock?
If the election itself were causing huge swings in the outcomes that we measure, it would be moredifficult to argue that the cross-county differences in these shocks were related to nothing but the surprise
vote share. We assumed in Section 2 that P , the probability of re-election, is close to one, but if theoutcome of the national election itself is a surprise then our identification relies on the assumption that the
local business response to the resolution of this uncertainty is orthogonal to the surprise vote share. As
way of testing whether the electoral result itself was an economic shock, we generate smoothed picturesof how the de-meaned outcome used in the analysis changed in the weeks around the election. The

25

results are presented in Figures 5 and 6. We see that the week prior to the election had savings andborrowing volumes that were below the mean, but that this difference was just significant and that there
were no significant departures from trend on average in the four weeks after the election. Regressions
confirm this lack of significance. Hence we conclude that the outcomes were not responding as if theresult of the election itself was a major shock.
5.2. Is the surprise vote share endogenous?
Could an unobserved shock be driving both voting changes and borrowing changes? We cannot
test this in the month of the election, but we can see if the surprise vote share is able to explain the
direction of changes in borrowing in the months before and after the election.
We generate two new variables to form this test. Like the test of the surprise vote share, thesevariables equal zero in all other months and the surprise vote share in a specific month: one for the month
before the election, and one for the period one to two months after the election.

Conditional on the

results of the election, this tests whether correlation exists between unexplained changes in outcomes in
the month prior to the election and the surprise vote share that occurred at the end of that month. The
results, presented in Table 8, are not consistent with endogeneity; in all specifications si is not
significantly correlated with Δπ it −1 or Δπ it +1 and is significantly correlated with Δπ it . This test indicatesthat there is no reverse causality, and so we can interpret correlation between si and Δπ it as causal.

6. Conclusion.
Using a new measurement technique we find that business investment in Uganda responds in amanner consistent with the presence of contingent political redistribution, and that not all constituencies
engage in this game with equal intensity. We find that businesses in educated, small, rural ethnic groups
are most responsive to surprise vote share, and ethnicities from the North of Uganda are uniquely
unresponsive. We find evidence that the behavior of microentrepreneurs is more sensitive to voting

26

surprises among core rather than swing voters; business investment and savings are most responsive tosurprisingly strong support for the incumbent victor in counties which already supported him strongly.
This indicates that these core supporters expect to benefit most from having backed the president,
contradicting Kasara (2007) who finds that core supporters are hit with the highest agricultural taxes.
These findings contribute to the literature on pork in several ways. First, we illustrate the use of thechange in expectations induced by errors in pre-election polls at the local level. This technique isidentified off of the relationship between changes in investment and changes in political expectations, andthereby gives a clean measurement of the extent to which political transfers are perceived to be explicitly
conditional on voting behavior. Using variation in the intensity of the response to surprise vote share, we
add a new angle to the study of ethnicity in African politics by locating the specific ethnic groups towhom the government is able to make transfers most efficiently. Finally, we provide a new way of
informing the old debate over the use of transfers to support core versus swing voters, finding thatinvestors in the core are most sensitive to voting.
A primary attraction of the empirical method suggested in this paper is its relatively generalapplicability. The robustness checks conducted here provide general tests for the conditions under which
the assumptions underlying the method are valid. In any such election for which polling outcomes andforward-looking business behavior are observable at a sub-national level, we can use this technique to test
both for the presence of pork and for its cross-sectional determinants. We need not be able to observe
pork or patronage directly; instead we assume that local businesspeople are well informed, and we use
their responses to informational innovations to test for the presence of a link between politics andbusiness. The resulting ability to use micro data and sub-national variation provides a new angle fromwhich to test theories of electoral competition.

27

References
Aggarwal, R. K., Felix Meschke, and Tracy Yue Wang. 2008 "Corporate Political Contributions:
Investment or Agency?" SSRN Working Paper.
Ahlin, C., and Jocelyn Lin. (2006). “Luck or Skill? MFI Performance in Macroeconomic Context”.
BREAD Working Paper No. 132.
Ansolabehere, S., A. Gerber and J. Snyder. (2006). “Equal Votes, Equal Money: Court-Ordered
Redistricting and Public Expenditures in the American States.” American Political Science Review,Vol. 96, No. 4, pp. 767-777.
Arulampalam, W., S. Dasgupta, A. Dhillon and B. Dutta. 2008. “Electoral goals and center-statetransfers: A theoretical model and empirical evidence from India,” Journal of Development
Economics, forthcoming.
Bates, R., (1981). Markets and States in Tropical Africa. Berkeley, CA: University of California Press
Case, A., (2001). ‘Election Goals and Income Redistribution:
European Economic Review, 45. pp. 405-423.

Recent Evidence from Albania’.

Cadot, O., L-H Röller and A. Stephan. 2006. “Contribution to productivity or pork barrel? The two facesof infrastructure investment,” Journal of Public Economics, 90 (6-7): 1133-1153.
Calvo, E. and M. V. Murillo, (2004). ‘Who Delivers? Partisan Clients in the Argentine Electoral Market’.
American Journal of Political Science, Vol. 48, No. 4, pp. 742-757.
Cooper, M. J., Huseyin Gulen, and Alexei V. Ovtchinnikov. 2008. "Corporate Political Contributions andStock Returns.” SSRN Working Paper.
Cox, G. and M. McCubbins (1986). ‘Electoral Politics as a Redistributive Game.’ The Journal of
Politics, Vol. 48, No. 2, pp. 370-389.
Crampton, E. 2004. “Distributive politics in a strong party system: Evidence from Canadian job grant
programs,” Discussion paper, University of Canterbury.
Dahlberg, M. and E. Johansson. (2002). ‘On the Vote-Purchasing Behavior of Incumbent Governments.’
American Political Science Review, Vol. 96, No. 1, pp. 27-40.
Dixit, A., and J. Londregan, (1996). ‘The Determinants of Success of Special Interests in Redistributive
Politics’. The Journal of Politics, Vol 58, No. 4, pp. 1132-1155.
Drazen, A. (2000). Political Economy in Macroeconomics. Princeton, NJ: Princeton University Press.
Finan, F. (2005). ‘Political Patronage and Local Development: A Brazilian Case Study’. Working
Paper, UC Berkeley.
Fisman, R. (2001). ‘Estimating the Value of Political Connections’. American Economic Review, Vol.
91 No. 4, pp. 1095-1102.

28

Fleck, R. (1999). ‘The Value Of The Vote: A Model And Test Of The Effects Of Turnout On Distributive
Policy.’ Economic Inquiry, Vol. 37, No. 4, pp. 609-623.
Geddes, B. (1994). The Politician’s Dilemma: Building State Capacity in Latin America. Berkeley:
University of California Press.
Goldman, E., Jörg Rocholl and Jongil So. 2006. “Does Political Connectedness Affect Firm Value?”
Working Paper. University of North Carolina. Forthcoming in Review of Financial Studies.)
Goldman, E., Jörg Rocholl and Jongil So. 2008. “Political Connections and the Allocation of Procurement
Contracts.” SSRN Working Paper
Herron, M., J. Lavin, D. Cram, and J. Silver. (1999). “Measurement of Political Effects in the UnitedStates Economy: A Study of the 1992 Presidential Election.” Economics and Politics, Vol. 11, No.
1, pp. 51-81.
Herron, M. (2000). “Estimating the Economic Impact of Political Party Competition in the 1992 British
Election.” American Journal of Political Science, Vol. 44, No. 2, pp. 326-337.
Hickey, S. (2003). “The Politics of Staying Poor in Uganda.” Chronic Poverty Research Center
Working Paper No. 37.
Hickey, S. (2005). “The Politics of Staying Poor: Exploring the Political Space for Poverty Reduction in
Uganda.” World Development, Volume 33 Issue 6, pp. 995-1009.
Jayachandran, S. (2006). “The Jeffords Effect.” Journal of Law and Economics, Vol. 49, pp. 397-425.
Kasara, K. (2007). ‘Tax Me If You Can: Ethnic Geography, Democracy, and the Taxation ofAgriculture in Africa’. American Political Science Review, Vol. 101, No. 1, pp. 159-172.
Keefer, Philip, and Razvan Vlaicu (2007). ‘Democracy, Credibility, and Clientelism’. Journal of Law,
Economics, and Organization, Vol 10, pp. 1-36.
Khemani, S. (2003). ‘Partisan Politics and Intergovernmental Transfers in India.’ Working Paper, WorldBank.
Khwaja, A. I. and A. Mian, (2005). ‘Do Lenders Favor Politically Connected Firms? Rent Provision in an
Emerging Financial Market’. Quarterly Journal of Economics, Vol. 120, No. 4, pp. 1371-1411.
Leigh, A. 2006. “Bringing Home the Bacon: An Empirical Analysis of the Extent and Effects of PorkBarrelling in Australian Politics”. Mimeo, Australian National University.
Levitt, S. and J. Snyder. (1995). ‘Political Parties and the Distribution of Federal Outlays.’ American
Journal of Political Science, Vol. 39, No. 4, pp. 958-980.
Lindbeck, A. and J. Weibull, (1987). ‘Balanced-budget Redistribution as the Outcome of Political
Competition’. Public Choice, Vol. 52, pp. 273-297.
Magaloni, B., Diaz-Cayeros, A., and F. Estevez, (2006). ‘Clientelism and Portfolio Diversification: A
Model of Electoral Investment with Applications to Mexico’. In Patrons, Clients, and Policies:29Patterns of Democratic Accountability and Political Competition, ed. by Herbert Kitschelt andSteven I. Wilkinson. Cambridge: Cambridge University Press.
Miguel, E., and F. Zaidi (2003). ‘Do Politicians Reward their Supporters?
Incumbency Advantage in Ghana’. Working Paper, UC Berkeley.

Public Spending andMilligan, Kevin and Michael Smart. 2005. “Regional Grants as Pork Barrel Politics”. CESifo Working
Paper 1453.
Olson, M., (1965). The Logic of Collective Action. Cambridge, MA: Harvard University Press.
Posner, D. and D. Simon. (2002). ‘Economic Conditions and Incumbent Support in Africa’s New
Democracies: Evidence from Zambia.’ Comparative Political Studies, Vol. 35, No. 3, pp. 313-336.
Snowberg, E., J. Wolfers and E. Zitzewitz. (2007). ‘Partisan Impacts on the Economy: Evidence from
Prediction Markets and Close Elections.’ Quarterly Journal of Economics, Vol. 122, No. 2, pp.
807-829.
Stokes, S., (2005). ‘Perverse Accountability: A Formal Model of Machine Politics with Evidence from
Argentina’. American Political Science Review, Vol. 99, No. 3, pp. 315-326.

30

Appendix.
Table 1. Distribution of Borrowers & Political Events by District.
Number of Events of
FINCA
political
District
Clients
violence
APAC
576
4
ARUA
1676
0
BUGIRI
678
0
BUKEDA
0
1
BUNDIBUGYO
0
1
BUSIA
400
10
BUSOGA
0
0
GULU
0
0
HOIMA
178
0
IGANA
2966
0
JINJA
3725
5
KABALE00
KABAROLE
0
1
KAMPALA
7065
17
KAMULI
1766
3
KAMWENGE
00
KARAMOJA00KASESE
0
0
KASSE
0
0
KAYUNGA
74
1
KIBALE
0
0
KIBOGA
377
0
KITGUM
0
0
KUMI
0
1
LIRA
1534
0
LUWERO935
3
MASAKA
2879
3
MASINDI
1294
0
MBALE
0
6
MBARARA
45
3
MENGO
0
0
MPIGI
732
1
MUBENDE
648
0
MUKONO
3123
3
NAKASONGOLA
98
0
NAMIREMBE
0
0
NEBBI
0
0
RAKAI
350
1
RUKUNGIRI
0
6
SEMBABULE
00
SOROTI
01
TESO
00
TORORO
0
2
WAKISO
1442
0
32561
73

Threats
against
Opposition
4
2
0
1
1
1
1
2
0
1
4
1
2
38
1
1
0
0
0
1
0
0
0
2
1
7
8
2
7
7
0
6
0
6
1
1
1
1
10
1
0
0
3
1
126

Threats
againstMovement
2
1
0
0
0
0
0
0
0
0
1
1
0
2
1
0
0
0
0
0
0
0
0
0
0
5
1
0
0
0
0
0
0
1
0
0
0
0
1
0
1
0
1
0
18

Threats
againstCitizens
0
1
1
0
0
0
0
2
0
0
3
0
0
9
0
1
0
1
0
0
0
0
1
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
6
0
0
0
1
0
29

Electionresults
contested
3
0
1
0
0
0
0
0
0
0
0
1
0
3
0
1
0
0
0
0
0
0
0
0
0
0
1
0
4
3
0
0
0
1
0
0
0
0
1
0
0
0
0
0
19

Movement Opposition
promisespromisespatronage
patronage
0
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
3

31

Table 2. Basic Estimation of the Impact of the Surprise Vote Share.
Four month window
around election

Surprise Vote Share
Borrower loan cycle #Borrower loan cycle # squared
Linear time trend
Quadratic time trend
Violence
Threats against Opposition
Threats against Movement
Threats against Citizens
Election Results Contested
Vote was Close (outcome >45% & <55%)
Movement Promises Patronage
Opposition Promises Patronage

All data

(1)

(2)

(3)

(4)

Loans

SavingLoans

Saving

0.107

0.063

0.137

0.053

(2.07)(2.64)*(3.10)**(2.11)*-0.025-0.062

0.609

0.251

(0.03)(0.25)(2.56)*(3.47)**

0-0.011-0.024-0.0110.00(0.60)(1.64)(2.45)*-0.586-5.088(0.05)(1.14)

0.17

0.379

(0.23)(1.41)-1.297-0.747(0.44)(0.62)

0.879

0.763

(0.39)(0.85)

8.765

1.798

(1.67)(1.32)

4.646

-2.68

(1.53)(3.86)**

0.738-1.398(0.17)(1.90)

0.146

0.129

(0.07)(0.10)

4.492

0.563

(1.50)(1.07)

7.104

2.764

(4.18)**(3.89)**

Number of Observations:
19,83019,83058,73158,729T statistics in parentheses, standard errors clustered at the District level. The dependent variables
are in differences from individual means, and columns (3) and (4) use fixed effects for each calendar
month, with data covering the 14 months from June 2000 through July 2001. Loans and Savings aremeasured in US Dollars.
* significant at 5%, ** significant at 1%

32

Table 3. Tests of the Orthogonality of the Surprise Vote Share.

Regression of the Surprise Vote Share on:
Changes in Loans (US $)Changes in Savings (US$)

Multivariate
regression,
includingMonth FEs

Pairwise
Regression

0.002

0.007

(0.47)(1.46)-0.0110.002(1.71)(0.27)-0.4290.673(1.03)(1.69)

0.551

0.117

(0.80)(0.23)-0.0240.009(0.82)(0.33)

4.416-3.119(1.21)-0.6700.484-0.639(0.25)(0.23)

Group is Ethnically Heterogeneous-1.109-1.553(0.63)(0.53)

District-level Share with Secondary Education-415.452-146.567(3.15)**(4.12)**

0.305-0.568(0.28)(0.22)-0.2070.407(0.11)(0.18)-23.287

7.682(1.89)(1.59)

41345

41345

Repayment Performance ('Grade Point Average', 1 (worst) - 4 (best))
Loan Cycle Number
Loan Cycle Number Squared
Does Group Conduct a Rotating Savings & Cash as well?
Did the Group Pre-exist the formation of the MF group?

District-level Population
District-level % with Secondary Education
District Average time to nearest Trading Center
Observations

T statistics in parentheses, standard errors clustered at the District level. The dependent variable is the countylevel surprise vote share (deviation between pre-election poll & actual election result) and the regressions use
data from the 14 months from June 2000 through July 2001 in the data other than the month after the election.
Column (1) presents multivariate regression of the Surprise Vote Share on all variables including month fixed
effects, and Column (2) presents simple pairwise correlations.
* significant at 5%, ** significant at 1%

33

Table 4. District-level Interactions with Surprise Vote Share.
Interaction Effects using District-Level Characteristics:

Loans

Saving% with Secondary Education * Surprise Vote Share

3.277

2.169

(1.73)(2.61)*-0.050-0.027(1.58)(1.70)-0.035-0.026(1.32)(1.91)

Population * Surprise Vote ShareSex ratio (M/F) * Surprise Vote ShareDistrict Area * Surprise Vote Share
Average distance from Trading center * Surprise Vote Share% with Secondary Education
Population
Sex ratio (M/F)
District Area
Average distance from Trading center
Surprise Vote Share
Observations

0.001

0.002

(0.35)(1.61)

0.406

0.128

(2.30)*(1.72)-9.649-1.126(1.31)(0.30)

0.038

0.015

(0.45)(0.40)-0.001

0.032(0.02)(0.85)

0.002

0.000

(0.25)(0.12)-1.139-0.311(1.83)(0.97)

1.500

1.805

(0.51)(1.14)

53875

53873

T statistics in parentheses, standard errors clustered at the District level. The dependent variables
are in differences from individual means, and regressions use fixed effects for each calendar month,with data covering the 14 months from June 2000 through July 2001. Loans and Savings aremeasured in US Dollars.
* significant at 5%, ** significant at 1%

34

Table 5. Language Group Interactions with Surprise Vote Share.
OLS, clustered SEs
Omitted group: Kiswahili-speakers

Loans

SavingSmall groups * Surprise Vote Share

0.862

0.139

(2.63)*(1.09)-0.057-0.047(0.17)(0.36)

0.181-0.015(0.49)(0.09)

0.062-0.033(0.19)(0.26)-0.931-0.469(2.42)*(2.39)*

0.039-0.145(0.12)(1.18)

1.093

0.699

(0.37)(0.62)-1.618-0.705(0.91)(0.79)-0.8400.122(0.50)(0.16)-1.800-0.488(1.19)(0.85)-3.854-0.735(2.15)*(0.95)-1.029-0.011(0.63)(0.02)

0.058

0.155

(0.18)(1.24)

37635

37635

English * Surprise Vote Share
Luganda * Surprise Vote Share
Lunyoro * Surprise Vote Share
Northern * Surprise Vote Share
Lusoga * Surprise Vote Share
Small groups (Kakua, Lugbara, Lulamogi,
Kinubi)EnglishLugandaLunyoroNorthern (Lango, Luo/Acholi)
LusogaSurprise Vote Share
Observations# in group:
2098

83, 729, 445, 30
595
25089
1536
343, 1486
11971

T statistics in parentheses, standard errors clustered at the District level. Dependent variables arecalculated as deviations from individual means, regression uses all observations in the 14 months
from June 2000 through July 2001. Loans and Savings measured in US Dollars.
* significant at 5%, ** significant at 1%

35

Table 6. Heterogeneity in the Response to the Surprise Vote Share by Core/Swing Status:
Dependent Variable: Changes in Loans.
(1)
Whole
SampleSurprise Vote Share
Dummy for Movement Core County

(2)

(3)

(4)

Movement
Opposition
Core only Swing only Core only

(5)

(6)

Whole Sample-0.378

0.217

0.036

-0.185

0.175

0.147

(1.51)(3.58)**(0.48)(0.70)(1.24)(0.64)-1.517-1.186(0.30)(0.20)

1.985(2.30)*

Dummy for Swing County

2.11(2.73)*

Movement Core * Surprise Vote Share

0.56(2.20)*

Swing * Surprise Vote Share

0.337(1.36)

Surprise vote crosses 50%
Surprise vote crosses 50% * Surprise Vote Share

0.03(0.26)

Observations

58731

30098

24790

3843

58731

58731

T statistics in parentheses, standard errors clustered at the District level. Dependent variables are calculated as deviations
from individual means, regression uses all observations in the 13 months between July 2000 through July 2001.
* significant at 5%, ** significant at 1%

36

Table 7. Positive versus Negative Shocks.
(1)
Dependent Variable: Loans
Marginal Effect of Positive Surprise Vote Share
Marginal Effect of Negative Surprise Vote Share
Dummy for Movement Core

(2)

Whole Sample
0.16569-0.06752(3.29)**-0.63-0.16667-0.13776(1.35)-1.09-0.50365-1.5

Positive Surprise Vote Share * Movement Core

0.24267(2.70)*

Observations
58731
58731
T statistics in parentheses, standard errors clustered at the District level.
Dependent variables are calculated as deviations from individual means,regression uses all observations in the 14 months from June 2000 through July
2001. Loans measured in US Dollars.
* significant at 5%, ** significant at 1%

37

Table 8. Robustness Check.

Dependent Variable: Loans.

(1)

(2)

Surprise Vote Share, 1 month prior to election

0.057

0.057

(1.27)(1.27)

0.113

0.114

0.115

(2.12)*(2.21)*(2.20)*

Surprise Vote Share, month of election
Surprise Vote Share, 1 month after election(3)-0.034-0.034(0.24)(0.24)

Number of Observations:
58,73158,73158,731
T statistics in parentheses, standard errors clustered at the District level. Dependent variables arecalculated as deviations from individual means, regression uses all observations in the 14 months from
June 2000 through July 2001. Election was March 15 2001; month before election refers to loans madeFeb15-Mar 14; month of election refers to Mar 15-Apr 15, month after election refers to Apr 15-May 15.
Loans are in US Dollars.
* significant at 5%, ** significant at 1%

38

Figure 1. Map of the Locations of FINCA Borrowers(Dots represent location of FINCA groups)

39

Figure 2. Number of Political Events from Content analysis, by Month.

140
120
100
80
60
40

Election

200Nov. 2000

Dec. 2000

Jan. 2001

Feb. 2001

Mar. 2001

Apr. 2001

May. 2001

Jul. 2001

Figure 3. District-Level Change in Loan Size by Surprise Vote Share.

Change in Loan Size, $
0
10
20

30

BUSIA

MPIGI

LUWEROMASINDIBUGIRI

ARUA
APAC

KAMULI
MASAKA

IGANGAKAMPALA

JINJA

-10

MUKONO

-20

LIRA

-2

0

2
Surprise Vote Share

4

6

Loan change calculated at District level using average two weeks prior to and two weeks after election

40

Figure 4. Effects of the Surprise Vote Share as ‘Window’ Length is Increased:

41

Figure 5. Temporal Pattern of Loans around Election.

15

Deviations from Average

10
5
0
-5 -6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

-10
-15
-20
-25
-30
Loans
Lower 95
Upper 95

Weeks from Election

Figure 6. Temporal Pattern of Savings around Election.

Deviations from Average

6
4
2
0
-2 -6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

-4
-6
-8

-10
-12

Weeks from Election

Savings
Lower 95
Upper 95

42