Effects Of Public Sector Corruption On The Private Sector: Investigating The Market Value Of Political Connections

Omotoke Paul-Lawal*

Under the direction of Professor Arun Chandrasekhar

Stanford University Department of Economics

Honors Thesis

May 30, 2016

Abstract This paper exploits public announcements of corruption allegations amongst Nigerian political figures to estimate the impact of political connections on firm value. Specifically, I draw results from market reactions to 27 events to determine whether politically connected firms suffer more from financial malpractice amongst government officials. This paper finds heterogeneous impacts of corruption announcements for firms’ returns. For some events involving larger sums of misallocated funds, politically connected firms suffer greater losses in their returns than unconnected firms. For other events involving smaller amounts of missing funds, I find that unconnected firms are actually punished more than connected firms. Still for other events, there appears to be no significantly differential impact of a government corruption scandal on connected and unconnected firms. Furthermore, surprisingly, some events actually show a positive return for all firms, and when this is the case, politically connected firms experience a larger increase in returns than their unconnected counterparts. To identify an overall impact, I run a pooled regression aggregating over all the events studied. I find that on average, there is a negative albeit insignificant impact on the returns of politically connected firms relative to unconnected firms in the aftermath of a corruption scandal. Keywords: political connections, corruption, firm value, Nigeria

* Email address: omotokep@stanford.edu. I owe many thanks to my thesis advisor and Economics major advisor, Arun Chandrasekhar, whose advice, positivity, and endless support were invaluable in completing this work. My gratitude also goes to the Honors Director, Marcelo Clerici-Arias for organizing this program and always being available to answer questions. Special thanks go to Francisco Munoz for his comments and help in editing this paper. And last but not least, I am also greatly indebted to my family and friends without whose encouragement and confidence I could not have completed this project.

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1. Introduction

Historically, Nigeria has been infamous for its endemic fraud and corruption, and the

country’s economy has been fraught with the difficulties of overcoming the challenges posed by

these institutional problems. Nigeria ranked 136 out of 168 countries, with a score of only 26 out

of 100, on Transparency International’s widely quoted 2015 Corruptions Perceptions Index. As

a step towards combating these institutional challenges, in 2002, the Nigerian government

created an independent organization, the Economic and Financial Crimes Commission (EFCC), a

watchdog agency that acts as surveillance against money laundering, fraud, embezzlement, and

other forms of financial crime. Individuals and organizations report cases to the EFCC, who then

take up the case for research and investigation, often culminating in litigation. The importance of

the EFCC’s work is reflected in the widespread publicity of its cases – a fact that this paper

exploits.

When a case is reported to the EFCC, the organization usually takes the information to

the press, exposing details about the case, including names of the concerned individuals and their

associated organizations or government positions, as well as the amount alleged to be missing

under their control. This information often makes big news, and there has been much speculation

that the reports have large impacts on firms listed on the Nigerian Stock Exchange. This paper is

concerned with the extent to which the data supports this claim, and the extent to which investors

respond to such corruption announcements. The study focuses on high profile cases involving

public officials, with the aim of testing the impact of such notable cases of public sector

corruption on private firms. The fact that these cases involve well-known politicians, and that all

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the cases studied are reported in at least of one of the country’s major newspapers and/or online,

provides support for the belief that the information is readily available to the public and

investors. If the market response to the news of scandal is large, this informs us not only about

the importance of political connections, but also about the potential impact of corruption on the

financial economy, and the important role that fraud control agencies like the EFCC may play.

To investigate the value of political connections for firms, this paper studies 27 high-

profile corruption cases in which public officials are accused of some sort of financial

malpractice. The public officials include elected and appointed federal and state political figures.

Majority of the cases involve former governors of state, and the rest involve senators and

ministers of various government departments. This study takes a broad view of political

connections by defining a politically connected firm as a firm in which at least one member of its

board of directors had at any time in his or her past (before the event date) held a position such as

senator, member of the House of Representatives, or member of the administration, or had been a

director of an important government organization in the same party as the sitting party at the time

of the event. By taking a broad approach, I am able to examine whether the market punishes

firms with any type of link to the government, as defined above, in response to the actions of

one/a few government officials. If this is so, then there will be grave consequences of political

corruption on any scale for firms with potential access to political favors as suggested by their

connections.

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2. Literature Review

There is a growing body of empirical literature on the importance of political connections

to firm value. In different contexts, many researchers have found that political connections have

a significant positive impact on firm value. Fisman (2001) examines the impact of several news

announcements concerning the deteriorating health of President Suharto on firms in Indonesia

with political links to the Suharto family. He finds that well-connected firms suffer more than

less-connected firms in the event of a serious rumor, suggesting that a large share of a well-

connected firm’s value is derived from political connections. In her cross-country study, Faccio

(2006) finds that a company’s value increases when its large shareholders enter into politics, and

this result is most prominent in highly corrupt countries. Similarly, Do et al. (2015) find that

firms connected to elected governors experience a significant increase in their value compared to

those connected to the losing candidate. This study will enrich the existing international evidence

on the value of political connections (e.g. Faccio 2006; Fisman 2001; Prem 2015), whilst

complementing findings in the US (e.g. Do et al. 2015; Goldman, Rocholl, and So, 2009).

In addition to estimating the importance of political connections, a number of papers also

provide channels through which connections create value for politically connected firms. Several

studies have suggested that political connections are valuable to firms because they lead to

preferential access to bank finance (e.g. Gonzalez and Prem 2015; Li et al. 2008, Claessens et al.

2006; Kwhaja and Mian 2005). For example, Gonzalez and Prem (2015) argue that politically

connected firms are better able to make strategic investments when faced with a political

transition because of distortions in the credit market which favor connected firms, allowing them

to maintain their market dominance from the non-democratic regime into the new government.

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Kwhaja and Mian (2005) also find that political firms borrow more, despite having higher

default rates, and this preferential treatment occurs only in government banks and not in private

banks. Meanwhile, Faccio, Masulis, and McConnell (2006) suggest that connections create value

by showing that politically connected firms are more likely to be bailed out than their

unconnected counterparts.

This paper will also contribute to the literature that studies the value of political

connections through exploiting events that happened independently of the connections. Many

papers have exploited close election races, such as Do et al. (2015), which uses a regression

discontinuity design to examine the impact of a connected governor winning office on firm

value. Similarly, Goldman et al. (2008), Knight (2007), and Matozzi (2008) all exploit close

elections in US presidential races. Roberts (1990), Fisman et al. (2006), and Jayachandran (2006)

use events or news involving key political figures in the US, whilst Prem (2015), Fisman (2001),

and Ferguson and Voth (2008) exploit important political events in Chile, Indonesia, and Nazi

Germany, amongst other international studies of political connections (e.g. Johnson and Mitton

2003 in Malaysia; Imai and Shelton 2010 in Taiwan). This strategy of using independent or

unexpected events is favored in the literature because it provides a relatively clean solution to the

identification problem, whilst avoiding the reverse causation channel (Do et al. 2015).

Research on political connections in the Nigerian context is scarce. However, the few

existing results suggest that political connections do not play a huge role in the valuation of firms

in Nigeria. For example, Osamwonyi and Tafamel (2013) find that there is no significant

relationship between board composition, board political connections, and firm performance for

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their sample of thirty firms listed on the Nigerian Stock Exchange. Likewise, Aburime (2009)

finds that political affiliation has a positive but insignificant impact on bank profitability in

Nigeria. Some of the results of this paper seem to affirm these findings.

The current study is unique in the wealth of events it exploits to investigate the

comparative impact of bad news related to public officials on the value of politically connected

private firms and that of unconnected firms. The degree to which news of corruption affects the

health of the economy, as indicated by the value of firms that make up that economy, is

dependent on how much perceptions of government affect the willingness of investors to invest

in private Nigerian firms. This research will try to assess the magnitude of that dependence. If

the effect is significant, then this provides further impetus for government to clamp down on

instances of financial corruption, and informs corporate governance and institutional design.

3. Data and Descriptive Statistics

This paper uses two main bodies of data: it employs stock market data, and it also uses

information about the corruption events. The sample consists of all firms on the Nigerian Stock

Exchange (NSE) that remained listed between 2005 and 2015, the period of the events that this

paper studies. This leads to a total sample of 123 firms, of which I exclude those that lack

sufficient stock market and political connections data to observe returns for the corruption

announcements. This leads to a remaining base sample of 106 firms. All financial information

such as closing price, trade volumes, EPS, and market index values were retrieved from the

Nigerian Stock Exchange databases. Data about firms’ political connections was hand-collected

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by gathering the names of all members of the board in the relevant years from the firms’ annual

reports, and researching their career background in search of any political history. To aid this

process, I used online sources like Bloomberg and Reuters that detail biographies of reputable

business people, along with director introductions on the companies’ websites. I then verified the

political connections using Google searches that usually returned several articles mentioning the

political involvement of the director.

Data on the corruption events was acquired from the Economic and Financial Crimes

Commission (EFCC) in Nigeria. Relevant information about the corruption events include the

date that the case was first released to the public, the government personality involved, and the

amount alleged to be missing, stolen, or otherwise diverted. Where available, the date of EFCC’s

press release is used as day 1 of the event, or the event start date. When this was unavailable, I

used an online search to track down the first evident publication reporting the event in order to

determine the start date as this is key in identifying when the information first became publicly

accessible, allowing the stock market to react. One concern of this research is that particularly

connected investors or other insiders may have had access to the information before it became

public knowledge. If this is the case, then the public announcement may have no effect on the

firm, or the firm’s value may have adjusted earlier than the announcement date used. However, if

this is the case, then any findings here are lower-bounds on the true effects of the corruption

events. Furthermore, even if the market had anticipated the scandal, then the announcement

resolves any remaining uncertainty about whether the politician’s fraudulent actions were simply

rumors, or serious, actionable matters – a fact that should generate further market responses.

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As described above, this paper defines politically connected firms as those firms in which

at least one member of the board of directors had at any time in his or her past held a position

such as senator, member of the House of Representatives, or member of the administration, or

had been a director of an important government organization in the same party as the party in

government at the time of the event. These include both elected and appointed positions, but are

limited to federal and state government officials, who may be thought to be in a position to

provide significant access to political resources and favors. Furthermore, only one party has

been in government since Nigeria resumed elections in 1999 following the period of military

rule. This closed political structure, as well as the elitist club nature of politics in Nigeria, makes

this a valid definition of political connections.

Row 1 of Panel A in Table 1 reports descriptive statistics for the connections variable.

The firm with the greatest number of connections is United Bank of Africa (UBA) with a total of

7 connected board members over the studied years. In the first phase of the study, I assign a

value of 1 to all firms with any number of connected board members in the year of the event. On

average across the listed firms, the mean connections in the sample is 0.49. The average number

of connections conditional on a firm having at least one connection is 1.78.

Rows 2 to 4 in Panel A of Table 1 report basic financial information for the sample firms

from the Nigerian Stock Exchange database for 2014, supplemented by data from the financial

statements of the companies. Row 2 reports the firm’s size as the logarithm of total assets.

Return on equity is used for profitability, and leverage in Row 4 is the ratio of total debt to total

capital. Panel B reports differences in the means of these variables for connected and

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unconnected firms. As might be expected, connected firms are larger than unconnected ones (by

6.57%). Furthermore, connections appear to be significantly higher in firms in the finance and

agricultural industries. To ensure that these differences between connected and unconnected

firms are not driving the results, I conduct robustness checks that control for size and industry.

However, connected firms are not significantly more profitable, more leveraged, or older than

unconnected ones, so any differences we observe in market returns for the two types of firms

cannot be attributed to these characteristics.

4. Empirical Strategy

The econometric strategy exploits within firm variation and the corruption events as

exogenous announcements in a standard differences-in-differences analysis using panel data.

Since all firms are similar on most basic characteristics, as shown in the last section, observed

market responses reflect differences in political connections. A major concern of this research is

that the results may have been driven by other events in the macroeconomy. To control for the

fact that there were large market movements during the event windows, this paper calculates

abnormal returns for the event days. I follow Acemoglu 2013 and calculate abnormal returns

using the following market model:

Abnormal Returns or AR is calculated as

!"!" = !!" − [!!! + !!!!!"]

(1)

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where !"!" is the abnormal return for firm ! on event day t. !!" is the firm’s actual return, and

!!" is the market return, using the NSE’s All Shares Index (ASI). The parameters, !!! and !!!

are estimated from the pre-event relationship between market returns and firm returns:

!!" = !! + !!!!" + !!"

I follow the standard in the literature of using a pre-event period of 250 trading days ending 30

days before the event start date. The abnormal returns shows the actual returns to each firm

minus the predicted returns based on the firm’s performance relative to the market over the

estimation period.

After calculating abnormal returns in this way for all firms in each event window, the

main regression this research estimates is as follows:

!"!"# = !!!"#$!" × !"##!" + !!!"#$!" + !! + !! + !!"#

where !!!"# is the abnormal return for firm ! operating in industry group ! in time period !.

!"#$!" is an indicator variable that is equal to zero before the event and one in the period after

the event. Likewise, !"##!" is an indicator variable that takes the value of one if the firm was

politically connected at the time of the event. !! and !! are firm- and time-fixed effects

respectively, whilst !!"# is an error term. The main estimator of interest is !"#$!" × !"##!", the

interaction between the event and connections variables, which allows us to study the differential

impact of corruption announcements on the returns of connected and unconnected firms.

(2)

(3)

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I run two variations of equation (3). In the first, !"##!" is a simple dummy variable that

indicates whether a firm has any connections or not in the year of the event. I will refer to this as

the “simple” model. In the second specification, I classify politically connected firms according

to the number of actual connections that they have to examine the effects of varying degrees of

connections, so !"#$""%&''!" takes on this real number. I will refer to this specification as the

“extended” model.

5. Results

To estimate the impact of the events on the returns of firms, this study runs regressions

using a 5-day event window. Each column in Table 2 reports the effects of the different events

on firms’ stock prices for the simple model, with standard errors reported below the coefficients

in parentheses. The coefficient on !"#$!", the dummy variable for the period after the

announcement, is significant for many of the events, but takes on varied signs, making it unclear

in which direction the events affect firm returns by themselves. This may be due to the fact that

there appears to be more trading of stocks overall for both connected and unconnected firm on

event dates, causing prices to be more volatile when announcements are made.

Likewise, as Table 2 shows, the main regressor, !"#$!" × !"##!", takes on both negative

and positive signs depending on the event. In the simple model, only 3 out of the 27 events return

statistically significant differences for connected and unconnected firms. In one of these events,

“Alao-Akala 6Oct11” (Event 3), politically connected firms suffer an extra loss of .00554 in their

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abnormal returns, or an extra loss of 72% compared to unconnected firms. This is equivalent to

additional losses of 0.15 standard deviations for connected firms and it is statistically significant

at the 5% level. Figure 1 provides a visual representation of this result. Meanwhile, two events

(Events 7 and 25) return a positive coefficient on the main estimator, suggesting that

unconnected firms actually suffered greater losses than politically connected firms.

In the first case, all firms suffer a net loss in returns after the corruption announcement,

but politically connected firms suffered a smaller loss which is reflected by the negative

coefficient on !"#$!" and positive coefficient on the interaction term, !"#$!" × !"##!". This may

be seen in the results of Event 7 (“Botmang 18Jul08”, column 7) in which connected firms suffer

a loss of .00483 or 69% less than unconnected firms. This is equivalent to 0.13 standard

deviations in the returns of connected firms, and is significant at the 10% level. The second case

involving a positive interaction term is a particularly interesting finding of this study and is

observed in the results of Event 25 in the simple model. In this case, the coefficient on !"#$!" is

also positive, so all firms experienced an increase in returns following the corruption

announcement, with connected firms observing 3 times more gains in abnormal returns than

unconnected firms (0.15 standard deviations). Since all firms observe positive abnormal returns,

with connected firms seeing an extra gain, I will refer to this outcome as the “double positive”

result. These results are statistically significant at the 5% level. In the next section, I will discuss

some likely causes of this surprising result, and offer suggestions of possible mechanisms.

To examine the effects of varied amounts of political connections, I ran a variation of

equation (3) in which political connections is defined as the actual number of politically

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connected board members a firm has at the time of the event. Thus, I define a modified political

connections estimator, !"#$""%&''!", which takes the degree of connectedness into account

and is not a simple dummy variable like !"##!". Table 3 shows the results of the extended

model. Overall, the results here support the findings of the simple model, echoing existing

conclusions, whilst generating more statistically significant impacts. In this model, 12 of the 27

events show statistically significant variations of impact between connected and unconnected

firms. The three significant results from the simple model discussed above retain significance

and in fact, gain power in this extended model specification. As Table 3 shows, out of the 12

significant events in the extended model, six of them return a negative coefficient on the main

estimator as expected, where politically connected firms suffer a greater loss in their returns

compared to other firms. Two events return a positive coefficient on the main estimator,

suggesting that connected firms actually suffered slightly less in these events. The remaining

four significant events show a “double positive” coefficient where all the firms saw an increase

in abnormal returns after the event, but connected firms experienced a relatively greater increase

in returns. I offer possible explanations for these findings in the discussion section.

Given the heterogeneity in these results, I ran a pooled regression in order to examine the

average impact of a corruption event on the returns of politically connected and unconnected

firms. The equation that estimates this overall impact, aggregating over all events, is as follows:

!"!"# = !!!"#$!" × !"##!" + !!!"#$!" + !! !"#!" + !! + !! + !! + !!"#

(4)

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The main difference between this regression and the individual event regressions is the addition

of !!, a set of events-fixed effects. As before, !!!"# is the abnormal return for firm ! operating in

industry group ! in time period !. !"#$!" is an indicator variable that is zero before the event and

one after the event. !"##!"is an indicator variable that is equal to one if the firm was politically

connected at the time of the event. !! is a matrix of firm-fixed effects, and !! are time-fixed

effects. Lastly, !!"# is the error term. The key regressor is still the interaction between the event

and connections variables (!"#$!" × !"##!"), which provides the differential impact of the

corruption announcements on the returns of connected and unconnected firms.

Table 4 shows the results of this aggregate regression with clustered standard errors.

Columns 3 and 4 adjust for the size of the firms since this was found to be a key difference

between connected and unconnected firms. Using this refined specification, we observe that,

overall, politically connected firms suffer a negative albeit insignificant impact on their returns

of between 19.1% and 64.9% more than unconnected firms, based on the simple and extended

models respectively. In terms of magnitude, this corresponds to an extra decline of about 0.003

standard deviations for connected firms. The insignificance of the main estimator in the pooled

regression is unsurprising given the number of insignificant individual events in the sample, and

conflicting signs. However, the negative coefficient in the pooled regression is more in line with

previous research findings that politically connected firms suffer more relative to unconnected

firms in reaction to bad news.

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6. Robustness Checks

a) Firm Size

In order to be confident that my findings are due to the causal effect of political

connections, I run a series of robustness checks. Since I found that politically connected firms

are larger in size than unconnected firms, it was necessary to run a test to check that my results

are driven by political connections and not the size of the firm. To do this, I run variations of the

main regression, equation (3), for each significant event, controlling for size as follows:

!"!"# = !!!"#$!" × !"#$""!"##!" + !!!"#$""%&''!" + !!!"#$!" + !!!"#$! + !! + !!"#

!"#$! is the total assets for firm ! in 2014. !"#$!" is the dummy variable for the period after the

event as before, !"#$""!"##!" is the extended model definition of political connections – the

number of politically connected members in the board of directors of firm ! at the time of the

event. Lastly, !! are time fixed-effects, and !!"# is the error term.

Columns (b) of Table 5 show the results of this robustness check. 8 of the 12 events that

showed statistically significant differences in returns for connected and unconnected firms retain

significance at the 5% and 10% levels after controlling for size, suggesting that size is unlikely to

be an omitted variable driving the results for these events.

(5)

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b) Industry

I also found that politically connected firms tended to be concentrated in the finance and

agriculture industries. The differences in industry composition amongst connected and

unconnected firms was significant at the 5% level, generating concerns that any statistical

differences in returns between the two groups of firms was due to industry-based reactions, and

were not necessarily driven by differences in political connections. To ensure the validity of my

original results, I run the following specification that controls for industry:

!"!"# = !!!"#$!" × !"#$""!"##!" + !!!"#$""%&''!" + !!!"#$!" + !!!"#$%&'(! + !!

+ !!"#

Columns (c) of Table 5 show the results of this regression. As we can observe,

controlling for industry has barely any effect on the magnitude of the coefficients across all

events, so the events are robust to industry specifications. Columns (d) control for both size and

industry, and we can see that whilst results are slightly attenuated for some events, the main

estimator remains statistically significant for most events, so results are robust to these different

specifications.

7. Discussion & Analysis

This study found that the effect of a corruption announcement on the returns of firms is

largely event-specific. In some cases, all firms suffer a loss in their returns when news about

corruption allegations involving public officials is released. This reflects the fact that public

(6)

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corruption scandals trigger fear and uncertainty in financial markets, making investors less

confident in the business climate because they perceive that the investment environment is more

risky. In line with much of the past literature, I find that in most of these cases politically

connected firms suffer an extra loss in their returns to the tunes of 0.15 standard deviations

compared to unconnected firms. It makes sense that connected firms are punished more when

such scandals are announced because the scandal reflects instabilities in government, the source

of their advantage. This uncertainty in the coffers of power is associated with a reduced ability to

receive political favors such as preferential access to government contracts, loans, or bailouts.

Nonetheless, the results also show that in some cases, whilst all firms see a loss in their returns,

politically connected firms actually suffer slightly less. This may be the case if investors believe

that politically connected firms are more shielded from economic uncertainties associated with

the corruption case. Indeed, in such environments with weak political and legal institutions, the

advantage of political connections is exactly that it offers opportunities to benefit from the

corrupt system and to engage in more profitable rent-seeking. Thus, firms without such an

advantage may be more vulnerable when the political environment is uncertain.

However, in still other cases, I find the surprising “double positive” result where the

announcement of a political scandal actually increased the returns of all firms, and politically

connected firms experienced a disproportionately larger increase relative to other firms. This

result is indeed unexpected, but several factors unique to the events studied in this paper and to

the Nigerian context may help to explain this fact. As noted earlier, the events studied in this

paper are drawn from cases under investigation by the EFCC, an agency created with the aim of

tackling financial crimes. The agency has largely been hailed for its work, especially as a

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deterrence force. Thus, when the EFCC releases new cases of corruption scandals to the press, it

may be that investors actually interpret the news as evidence that the agency is efficiently

clamping down on corruption, and this generates greater confidence in the investment climate.

Hence, what appears to be bad news on its face, is actually good news to the financial markets,

leading to the positive results we observe in some cases. If this is the case, then politically

connected firms will see an even greater increase in their returns than other firms because they

have closer ties to a political environment that is perceived to be working more efficiently.

Another factor that may explain the “double positive” result is the “immunity clause” in

Nigeria’s constitution. The immunity clause shields the president and governor from prosecution

while in office, so the EFCC does not bring cases against most public officials until after they

have left government. At this point, the agency’s prosecution of the (ex-)official may be

interpreted by the markets as effective governance reclaiming justice from those who abused

public office, leading to positive reactions in the stock market. Furthermore, there may have been

rumors about the corruption allegations before the news is officially released, so investors with

inside knowledge may have acted upon their beliefs about government corruption before the

news became public. This means that, for some events, when EFCC takes the news to the press,

the actual corruption allegation or amount involved may be less serious than the initial

expectations of investors, also leading to positive impacts on stock prices.

Lastly, we saw from the pooled regression aggregating over all events that overall,

corruption scandals lead to negative abnormal returns for all firms, and politically unconnected

firms suffer greater but insignificant losses than their unconnected counterparts. Indeed, this

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apparent insignificance of political connections is in line with prior findings of other political

connection studies in Nigeria (Osamwonyi and Tafamel, 2013; Aburime, 2009) as discussed in

the literature review above. Furthermore, the average number of connections in this paper was

0.49, with 52 out of 106 firms being politically connected. This is a rather large fraction, and

leads to questions about what it actually means to be politically connected in Nigeria. Since

connections are so common, it may be the case that they seize to confer any special advantage

beyond the dignitary to connected firms. In addition, not only are political connections common,

but announcements of public scandals are also not a rare event in Nigeria. Therefore, it may be

the case that investors are fairly immune to the political environment and have already factored

the difficult institutional climate into their initial investment decision. A final explanation for the

insignificance of the overall results is that general perceptions of government may not be critical

to the daily investment decisions of investors in Nigerian firms. Instead, it may be that close

personal connections to the government officials involved in these cases is more important than

general access to political resources as was studied in this paper. It would be interesting for

future research to investigate this hypothesis.

Whilst it is possible to explain the different impacts we observe in the results, an

important question for this research is to understand the root of the heterogeneity, and why

certain events have more of a differential impact on politically connected firms than others do.

One common factor of all the events with a statistically significant main estimator is that they

involve larger amounts of looted or missing funds, with a mean of 25 billion Naira, compared to

the whole sample mean of 12 billion Naira. This difference is statistically significant at the 5%

level. The regressions in Table 6 investigate how the sign of the coefficient on the main

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estimator (!!) and its significance change as the amount involved in the scandal varies. Columns

(1) and (2) show that !!becomes more negative as the amount involved in the scandal increases,

and this relationship is significant at the 10% level for all events, and at the 1% level for events

with a significant !!in the main regression. This result is also depicted in Figure 3. The amount

involved in a scandal might be seen as a measure of the intensity of the corruption event, so this

finding suggests that politically connected firms suffer more relative to unconnected firms when

there is a more serious scandal. Similarly, columns (3) and (4) show that !! and !! from the

main regression (equation (3)) are more likely to both be negative when the amount involved in a

scandal is larger, suggesting that all firms suffer greater losses in returns when there is a more

serious scandal. Lastly, column (5) of Table 6 shows the intuitive result that an event is more

likely to have a significant impact on firms’ returns when the amount involved in the scandal is

larger.

In sum, there are several possible explanations for the varied effects of corruption

allegations on firm value that this study finds. The impacts are event-specific, and may depend

on how much inside knowledge existed about the case before it became public, the nature of this

knowledge and its relation to the truth, and other network effects. Overall, I pose two main

opposing effects that may explain the heterogeneous findings in this paper. The first is the

reputation effect of the EFCC’s work, which has a tendency to increase the returns to firms when

a corruption scandal is uncovered because it signals the competency of that agency, and

increases confidence in the investment climate. The second effect is the political effect, which

reduces the returns to firms when a public scandal is announced due to increased uncertainty

regarding access to political favors. The results suggest that the first effect is dominant when the

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amount involved in the scandal is small, but as the amount of diverted funds increases and the

scandal is perceived as being more serious, the second effect prevails, increasing the tendency of

firms to suffer reductions in their value. In both cases, politically connected firms tend to

experience a larger impact than unconnected firms.

8. Conclusion

This paper has studied the effect of different corruption announcements on the market

returns of firms listed on the Nigerian Stock Exchange. Exploiting within-firm variation and

exogenous events, I was able to analyze how politically connected and unconnected firms are

impacted by news of corruption scandals concerning public figures. I found that for some events,

firms that are politically connected experience greater impacts on their stock returns than

unconnected firms. For other events, there were no statistically significantly results. These

findings are robust to different specifications and are not dependent on firm characteristics.

The loss in returns associated with some events may reflect the market’s beliefs that

during a period of uncertainty and instability in government created by missing funds, firms

whose business depend upon access to political resources would be the first to lose out if such

events make government less willing to distribute favors or more likely to implement tighter

regulatory controls. Yet, many events did not appear to have a significantly different impact on

politically connected firms. This may reflect that the broad measure of political connections

investigated in this paper does not capture some important intricacies of firm connections in

OmotokePaul-Lawal

22

Nigeria; perhaps close personal ties to politicians as examined in a large part of the prior

literature on connections is more important than general access to political resources.

Nonetheless, corruption scandals amongst public officials do seem to impact the private

sector in some significant ways. This impact appears to be greater for politically connected firms

in certain cases, and creates instability in the stock markets in all cases as investors adjust to the

news and seek a portfolio that minimizes exposure to the uncertainty and confusion in the public

sector. Hence, this paper provides impetus for Nigeria and other countries to implement tighter

controls against fraud in the high seats of power, as it damages not only the country’s reputation,

but also affects the private sector, tarnishing its attractiveness for investment. To my knowledge,

this is the first paper studying such corruption events in the Nigerian context, and the first study

returning a multitude of heterogeneous impacts. Future research is needed to see if these results

hold amongst other sets of events. In addition, future research should also aim to investigate the

mechanisms through which political connections may be important in Nigeria, and should further

investigate the characteristics of economically significant public scandals.

OmotokePaul-Lawal

23

CHARTS&TABLES

TABLE1:DESCRIPTIVESTATISTICS

Asterisksdenotesignificancelevelsofatwo-tailedt-test

***p<0.01,**p<0.05,*p<0.1

PanelA:SummaryStatistics Mean Min Median Max St.Dev. N

(1)Connections

(2)Size

(3)Profitability

(4)Leverage

(5)Age

.49

7.08

.073

.295

49.5

0

4

-4.04

-5.39

18

0

7.04

.101

.244

48.5

1

9.64

2.05

4.63

122

.50

1.09

.515

.778

16.57

106

103

103

103

106

PanelB:Politicallyconnectedvs.Unconnected Connected Unconnected Diff

(1)Size

(2)Profitability

(3)Leverage

(4)Age

(5)Industry

Observations

7.30

-.022

.202

49.6

5.5

52

6.85

.167

.379

49.5

6.58

54

0.45*

-.189

.6

0.1

-1.58**

Notes:Firmsizeisreportedasthelogarithmoftotalassets.Returnonequityisusedforprofitability,andleverageistheratiooftotaldebttototalcapital.PanelBreportsdifferencesin

themeansofthesevariablesforconnectedandunconnectedfirms.

OmotokePaul-Lawal

24

TABLE2:REGRESSIONOFABNORMALRETURNSONPOLITICALCONNECTIONS(SIMPLEMODEL)

(1) (2) (3) (4) (5) (6) (7)

VARIABLESAbdullahi22Feb10

Abubakar8Apr14

Alao-Akala11Oct11

Audu18Mar13

Bafarawa9Dec09

Borishade1Jul08

Botmang18Jul08

PostXConn -0.00153 -0.000801 -0.00554** -0.000720 -2.59e-05 -0.00539 0.00482*

(0.00223) (0.00214) (0.00237) (0.00255) (0.00238) (0.00403) (0.00279)

Post 0.00221 0.000740 -0.00765*** -0.00546* 0.00341 0.00489 -0.00701***

(0.00268) (0.00274) (0.00281) (0.00323) (0.00297) (0.00498) (0.00183)

Constant 0.00192 -0.00128 0.00611*** 0.00130 0.000460 -0.0303*** -0.0240***

(0.00175) (0.00177) (0.00183) (0.00211) (0.00195) (0.00330) (0.00102)

Observations 1,000 1,111 1,000 1,122 1,111 1,111 1,100

R-squared 0.148 0.102 0.185 0.089 0.095 0.880 0.955

(8) (9) (10) (11) (12) (13) (14)

VARIABLESDaniel13Oct11

Dariye20Jan06

Doma19Oct11

Electrification19May09

George7Aug08

Goje7Oct11

Grange-Aduku27Mar08

PostXConn 0.000637 0.00234 0.00264 0.00359 -0.00644 -0.00307 0.00525

(0.00181) (0.00246) (0.00173) (0.00319) (0.00443) (0.00227) (0.00450)

Post 0.000800 -0.00616** 0.00729*** -0.00826** -0.00799 -0.00383 0.00719

(0.00228) (0.00298) (0.00217) (0.00398) (0.00546) (0.00270) (0.00488)

Constant -0.00104 0.000688 -0.00400*** 0.0218*** -0.0154*** 0.00446** -0.0197***

(0.00150) (0.00199) (0.00143) (0.00262) (0.00362) (0.00175) (0.00316)

Observations 1,111 1,133 1,111 1,111 1,100 1,010 909

R-squared 0.226 0.130 0.256 0.333 0.894 0.173 0.848

Standarderrorsinparentheses***p<0.01,**p<0.05,*p<0.1

Standarderrorsinparentheses***p<0.01,**p<0.05,*p<0.1

OmotokePaul-Lawal

25

TABLE2(CONT’D):REGRESSIONOFABNORMALRETURNSONPOLITICALCONNECTIONS(SIMPLEMODEL)

(15) (16) (17) (18) (19) (20) (21)

VARIABLESHaruna8Aug08

Igbinedion21Jan08

Ladoja29Aug08

Lamido4Feb14

Lamido-Abubakar9Jul15

Lawal&Co11May11

Nyako-Aliyu-Abba8Jul15

PostXConn -0.000781 0.00275 0.000404 -0.00386 0.00339 -0.00313 0.00278

(0.00436) (0.00345) (0.00309) (0.00257) (0.00223) (0.00210) (0.00218)

Post -0.00520 -0.000844 0.00318 0.00154 0.00256 -0.00322 0.00242

(0.00540) (0.00425) (0.00383) (0.00329) (0.00285) (0.00264) (0.00279)

Constant -0.0179*** -0.0154*** -0.0266*** -0.00385* -0.00602*** 0.00557*** -0.0248***

(0.00357) (0.00282) (0.00253) (0.00212) (0.00184) (0.00173) (0.00179)

Observations 1,111 1,100 1,111 1,122 1,122 1,111 1,122

R-squared 0.903 0.946 0.963 0.100 0.166 0.136 0.203

Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1

(22) (23) (24) (25) (26) (27)

VARIABLESNyame23Jul07

Obasanjo-Bello8Apr08

Okokuro-Opuala24Mar10

Suswan-Okolobia3Nov15

Sylvia24Feb12

Turaki11Jul07

PostXConn 0.00104 -0.0109 0.00127 0.00554** -0.00309 -0.00119

(0.00462) (0.00749) (0.00273) (0.00223) (0.00193) (0.00418)

Post 0.00324 -0.0121 0.00510 0.00178 0.00421* 0.00782

(0.00572) (0.00923) (0.00342) (0.00285) (0.00242) (0.00517)

Constant -0.0331*** -0.0214*** -0.000887 -0.0139*** 0.00233 -0.0271***

(0.00378) (0.00612) (0.00225) (0.00184) (0.00159) (0.00342)

Observations 1,111 1,100 1,100 1,122 1,111 1,111R-squared 0.902 0.755 0.201 0.184 0.281 0.882Standarderrorsinparentheses

***p<0.01,**p<0.05,*p<0.1

OmotokePaul-Lawal

26

TABLE3:REGRESSIONOFABNORMALRETURNSONPOLITICALCONNECTIONS(EXTENDEDMODEL)

(1) (2) (3) (4) (5) (6) (7)

VARIABLESAbdullahi22Feb10

Abubakar8Apr14

Alao-Akala11Oct11

Audu18Mar13

Bafarawa9Dec09

Borishade1Jul08

Botmang18Jul08

PostXDegreeConn -0.000982 8.28e-07 -0.00539*** 0.000303 -0.000476 -0.00476*** 0.00242**

(0.000946) (0.000884) (0.00106) (0.00101) (0.00107) (0.00178) (0.00123)

Post 0.00233 0.000320 -0.00573** -0.00610* 0.00377 0.00622 -0.0120***

(0.00260) (0.00263) (0.00270) (0.00313) (0.00289) (0.00485) (0.00335)

Constant 0.00192 -0.00128 0.00611*** 0.00130 0.000460 -0.0303*** -0.0194***

(0.00175) (0.00177) (0.00181) (0.00211) (0.00195) (0.00329) (0.00227)

Observations 1,000 1,111 1,000 1,122 1,111 1,111 1,100

R-squared 0.149 0.102 0.203 0.089 0.095 0.881 0.956

Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1

(8) (9) (10) (11) (12) (13) (14)

VARIABLESDaniel13Oct11

Dariye20Jan06

Doma19Oct11

Electrification19May09

George7Aug08 Goje7Oct11

Grange-Aduku

27Mar08

PostXDegreeConn -0.000238 0.000818 0.00230*** 0.00386*** -0.00338* -0.00444*** 0.00131

(0.000776) (0.00129) (0.000783) (0.00144) (0.00195) (0.00102) (0.00200)

Post 0.00130 -0.00574* 0.00660*** -0.00968** -0.00815 -0.00156 0.00846*

(0.00221) (0.00293) (0.00211) (0.00386) (0.00533) (0.00260) (0.00473)

Constant -0.00104 0.000688 -0.00400*** 0.0218*** -0.0154*** 0.00446** -0.0197***

(0.00150) (0.00199) (0.00142) (0.00261) (0.00362) (0.00174) (0.00316)

Observations 1,111 1,133 1,111 1,111 1,100 1,010 909

R-squared 0.226 0.129 0.260 0.337 0.894 0.189 0.848

Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1

OmotokePaul-Lawal

27

TABLE3(CONT’D):REGRESSIONOFABNORMALRETURNSONPOLITICAL

CONNECTIONS(EXTENDEDMODEL)

(15) (16) (17) (18) (19) (20) (21)

VARIABLES Haruna8Aug08Igbinedion21Jan08

Ladoja29Aug08

Lamido4Feb14

Lamido-Abubakar9Jul15

Lawal&Co11May11

Nyako-Aliyu-Abba8Jul15

PostXDegreeConn -0.000304 0.00217 -0.000556 -0.00148 0.00169* -0.00296*** 0.00154*

(0.00194) (0.00152) (0.00137) (0.00106) (0.000919) (0.000950) (0.000898)

Post -0.00531 -0.00133 0.00379 0.000863 0.00271 -0.00221 0.00240

(0.00527) (0.00415) (0.00374) (0.00315) (0.00276) (0.00256) (0.00270)

Constant -0.0179*** -0.0154*** -0.0266*** -0.00385* -0.00604*** 0.00557*** -0.0248***

(0.00357) (0.00282) (0.00253) (0.00212) (0.00185) (0.00172) (0.00181)

Observations 1,111 1,100 1,111 1,122 1,111 1,111 1,111

R-squared 0.903 0.947 0.963 0.100 0.166 0.143 0.203

Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1

(22) (23) (24) (25) (26) (27)

VARIABLESNyame23Jul07

Obasanjo-Bello8Apr08

Okokuro-Opuala24Mar10

Suswan-Okolobia3Nov15

Sylvia24Feb12

Turaki11Jul07

PostXDegreeConn 5.41e-05 -0.000603 0.000973 0.00362*** -0.00174** 0.000120

(0.00226) (0.00331) (0.00116) (0.000915) (0.000823) (0.00204)

Post 0.00366 -0.0163* 0.00487 0.00130 0.00424* 0.00722

(0.00559) (0.00903) (0.00332) (0.00275) (0.00235) (0.00506)

Constant -0.0331*** -0.0214*** -0.000887 -0.0139*** 0.00233 -0.0271***

(0.00378) (0.00613) (0.00225) (0.00185) (0.00159) (0.00342)

Observations 1,111 1,100 1,100 1,111 1,111 1,111R-squared 0.902 0.755 0.201 0.191 0.282 0.882Standarderrorsinparentheses

***p<0.01,**p<0.05,*p<0.1

OmotokePaul-Lawal

28

TABLE4:AGGREGATEREGRESSIONOFABNORMALRETURNSONPOLITICAL

CONNECTIONS

(1) (2) (3) (4)

VARIABLES Simple Extended Simple Extended

PostXConn -0.000242

-0.000131

(0.000751)

(0.000773)

Post -4.50e-05 -5.42e-05 -0.000110 -7.40e-05

(0.000417) (0.000397) (0.000431) (0.000411)

Conn -0.000522

0.00801

(0.00633)

(0.00629)

PostXDegreeConn

-0.000127

-0.000122

(0.000272)

(0.000292)

DegreeConn

-0.00105

0.00278

(0.00189)

(0.00226)

Size

0.000611 0.000626

(0.000553) (0.000536)

Constant 0.000574 0.00121 -0.0131 -0.0119

(0.00486) (0.00443) (0.00956) (0.00916)

Observations 29,494 29,461 28,326 28,293

R-squared 0.290 0.290 0.036 0.035

Robuststandarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1

OmotokePaul-Lawal

29

TABLE5:ROBUSTNESSCHECKSNotes:Thetableshowstheresultsofrobustnesschecksforeventsfoundtohavesignificantresults.Thespecificationincolumnslabeled(a) is a replication of the standard regression of the extendedmodel in Table 3. Columns labeled (b)control for size, (c)controls forindustry,andcolumnslabeled(d)controlforbothsizeandindustry. (1a) (1b) (1c) (1d) (2a) (2b) (2c) (2d)

Alao-Akala11Oct11

Alao-Akala11Oct11

Alao-Akala11Oct11

Alao-Akala11Oct11

Borishade1Jul08

Borishade1Jul08

Borishade1Jul08

Borishade1Jul08

PostXDegreeConn -0.00539*** -0.00605*** -0.00539*** -0.00605*** -0.00476*** -0.00478 -0.00476 -0.00478

(0.00106) (0.00113) (0.00109) (0.00113) (0.00178) (0.00522) (0.00488) (0.00522)

DegreeConn

0.00447*** 0.00413*** 0.00457***

0.0118*** 0.0113*** 0.0121***

(0.000876) (0.000846) (0.000879)

(0.00386) (0.00363) (0.00387)

Post -0.00573** -0.00445 -0.00573** -0.00445 0.00622 0.00689 0.00622 0.00689

(0.00270) (0.00282) (0.00278) (0.00281) (0.00485) (0.0138) (0.0133) (0.0138)

Size

-0.000185

-0.000148

0.00151

0.00163

(0.000241)

(0.000243)

(0.00114)

(0.00115)

Industry

0.000252 0.000269

0.000745 0.000848

(0.000213) (0.000215)

(0.000974) (0.00101)

Constant 0.00611*** 0.00502 0.00113 0.00268 -0.0303*** -0.0650*** -0.0436*** -0.0725***

(0.00181) (0.00439) (0.00242) (0.00478) (0.00329) (0.0210) (0.0113) (0.0227)

Observations 1,000 960 1,000 960 1,111 1,067 1,111 1,067

R-squared 0.203 0.066 0.065 0.067 0.881 0.021 0.019 0.022

Standarderrorsinparentheses***p<0.01,**p<0.05,*p<0.1

(3a) (3b) (3c) (3d) (4a) (4b) (4c) (4d)

Botmang18Jul08

Botmang18Jul08

Botmang18Jul08

Botmang18Jul08

Doma19Oct11

Doma19Oct11

Doma19Oct11

Doma19Oct11

PostXDegreeConn 0.00242** 0.00224 0.00242 0.00224 0.00230*** 0.00258*** 0.00230*** 0.00258***

(0.00123) (0.00596) (0.00557) (0.00596) (0.000783) (0.000864) (0.000852) (0.000864)

DegreeConn

0.00636 0.00567 0.00696

-0.00193*** -0.00172*** -0.00191***

(0.00440) (0.00414) (0.00442)

(0.000638) (0.000633) (0.000641)

Post -0.0120*** -0.0115 -0.0120 -0.0115 0.00660*** 0.00555** 0.00660*** 0.00555**

(0.00335) (0.0158) (0.0152) (0.0158) (0.00211) (0.00227) (0.00230) (0.00227)

Size

0.00102

0.00126

0.000290

0.000299

(0.00131)

(0.00132)

(0.000187)

(0.000189)

Industry

0.00169 0.00170

2.37e-06 5.83e-05

(0.00111) (0.00115)

(0.000170) (0.000167)

Constant -0.0194*** -0.0408* -0.0341*** -0.0556** -0.00400*** -0.00665* -0.00259 -0.00716*

(0.00227) (0.0240) (0.0130) (0.0260) (0.00142) (0.00343) (0.00197) (0.00373)

Observations 1,100 1,056 1,100 1,056 1,111 1,067 1,111 1,067

R-squared 0.956 0.008 0.008 0.010 0.260 0.041 0.038 0.041Standarderrorsinparentheses***p<0.01,**p<0.05,*p<0.1

OmotokePaul-Lawal

30

TABLE5(Cont’d):ROBUSTNESSCHECKSNotes:Thetableshowstheresultsofrobustnesschecksforeventsfoundtohavesignificantresults.Thespecificationincolumnslabeled(a)isareplicationofthestandardregressionoftheextendedmodelinTable3.Columnslabeled(b)controlforsize,(c)controlsforindustry,

andcolumnslabeled(d)controlforbothsizeandindustry. (5a) (5b) (5c) (5d) (6a) (6b) (6c) (6d)

Electrification19May09

Electrification19May09

Electrification19May09

Electrification19May09

George7Aug08

George7Aug08

George7Aug08

George7Aug08

PostXDegreeConn 0.00386*** 0.00422** 0.00386** 0.00422** -0.00338* -0.00268 -0.00338 -0.00268

(0.00144) (0.00169) (0.00161) (0.00169) (0.00195) (0.00612) (0.00571) (0.00612)

DegreeConn

0.00107 0.000829 0.000973

0.00763* 0.00777* 0.00788*

(0.00125) (0.00119) (0.00125)

(0.00452) (0.00424) (0.00454)

Post -0.00968** -0.0107** -0.00968** -0.0107** -0.00815 -0.00813 -0.00815 -0.00813

(0.00386) (0.00442) (0.00433) (0.00442) (0.00533) (0.0162) (0.0156) (0.0162)

Size

9.63e-05

5.96e-05

0.000790

0.000892

(0.000365)

(0.000368)

(0.00134)

(0.00135)

Industry

-0.000395 -0.000257

0.000707 0.000711

(0.000316) (0.000322)

(0.00114) (0.00118)

Constant 0.0218*** 0.0196*** 0.0235*** 0.0219*** -0.0154*** -0.0347 -0.0257* -0.0409

(0.00261) (0.00668) (0.00369) (0.00725) (0.00362) (0.0246) (0.0133) (0.0267)

Observations 1,111 1,067 1,111 1,067 1,100 1,056 1,100 1,056

R-squared 0.337 0.088 0.086 0.088 0.894 0.006 0.006 0.006

Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1

(7a) (7b) (7c) (7d) (8a) (8b) (8c) (8d)

Goje7Oct11

Goje7Oct11

Goje7Oct11

Goje7Oct11

Lamido-Abu.9Jul15

Lamido-Abu.9Jul15

Lamido-Abu.9Jul15

Lamido-Abu.9Jul15

PostXDegreeConn -0.00444*** -0.00466*** -0.00444*** -0.00466*** 0.00169* 0.00183* 0.00169* 0.00183*

(0.00102) (0.00109) (0.00104) (0.00109) (0.000919) (0.000935) (0.000910) (0.000935)

DegreeConn

0.00272*** 0.00268*** 0.00277***

-0.00128* -0.00122* -0.00120*

(0.000842) (0.000813) (0.000846)

(0.000691) (0.000679) (0.000697)

Post -0.00156 -0.000834 -0.00156 -0.000834 0.00271 0.00227 0.00271 0.00227

(0.00260) (0.00270) (0.00266) (0.00270) (0.00276) (0.00281) (0.00274) (0.00281)

Size

-0.000324

-0.000304

-8.81e-05

-6.75e-05

(0.000231)

(0.000233)

(0.000228)

(0.000229)

Industry

0.000149 0.000132

0.000202 0.000188

(0.000205) (0.000207)

(0.000204) (0.000207)

Constant 0.00446** 0.00724* 0.00132 0.00608 -0.00604*** -0.00265 -0.00612*** -0.00421

(0.00174) (0.00422) (0.00232) (0.00459) (0.00185) (0.00417) (0.00236) (0.00451)

Observations 1,010 970 1,010 970 1,111 1,067 1,111 1,067

R-squared 0.189 0.057 0.058 0.058 0.166 0.105 0.102 0.105

Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1

OmotokePaul-Lawal

31

TABLE5(Cont’d):ROBUSTNESSCHECKS

Notes:Thetableshowstheresultsofrobustnesschecksforeventsfoundtohavesignificantresults.Thespecificationincolumnslabeled

(a)isareplicationofthestandardregressionoftheextendedmodelinTable3.Columnslabeled(b)controlforsize,(c)controlsforindustry,andcolumnslabeled(d)controlforbothsizeandindustry.

(9a) (9b) (9c) (10d) (10a) (10b) (10c) (10d)

VARIABLESLawal&Co11May11

Lawal&Co11May11

Lawal&Co11May11

Lawal&Co11May11

Nyako-Aliyu-Abba8Jul15

Nyako-Aliyu-Abba8Jul15

Nyako-Aliyu-Abba8Jul15

Nyako-Aliyu-Abba8Jul15

PostXDegreeConn -0.00296*** -0.00334*** -0.00296*** -0.00334*** 0.00154* 0.00141 0.00154* 0.00141

(0.000950) (0.00101) (0.000965) (0.00101) (0.000898) (0.000916) (0.000890) (0.000916)

DegreeConn

0.00282*** 0.00261*** 0.00276***

-0.00114* -0.00123* -0.00107

(0.000744) (0.000717) (0.000748)

(0.000677) (0.000664) (0.000682)

Post -0.00221 -0.00223 -0.00221 -0.00223 0.00240 0.00242 0.00240 0.00242

(0.00256) (0.00265) (0.00260) (0.00265) (0.00270) (0.00275) (0.00268) (0.00275)

Size

3.93e-05

1.93e-05

-7.88e-05

-6.11e-05

(0.000218)

(0.000220)

(0.000223)

(0.000225)

Industry

-0.000143 -0.000138

0.000163 0.000161

(0.000192) (0.000195)

(0.000200) (0.000203)

Constant 0.00557*** 0.00274 0.00428* 0.00395 -0.0248*** -0.0225*** -0.0246*** -0.0238***

(0.00172) (0.00400) (0.00223) (0.00435) (0.00181) (0.00409) (0.00231) (0.00442)

Observations 1,111 1,067 1,111 1,067 1,111 1,067 1,111 1,067

R-squared 0.143 0.027 0.029 0.028 0.203 0.140 0.139 0.140Standarderrorsinparentheses***p<0.01,**p<0.05,*p<0.1

(11a) (11b) (11c) (11d) (12a) (12b) (12c) (12d)

VARIABLESSuswan-Oko3Nov15

Suswan-Oko3Nov15

Suswan-Oko3Nov15

Suswan-Oko3Nov15

Sylvia24Feb12

Sylvia24Feb12

Sylvia24Feb12

Sylvia24Feb12

PostXDegreeConn 0.00362*** 0.00346*** 0.00362*** 0.00346*** -0.00174** -0.00162* -0.00174* -0.00162*

(0.000915) (0.000872) (0.000897) (0.000872) (0.000823) (0.000933) (0.000904) (0.000930)

DegreeConn

-0.00228*** -0.00255*** -0.00236***

0.00342*** 0.00333*** 0.00358***

(0.000645) (0.000669) (0.000650)

(0.000689) (0.000671) (0.000690)

Post 0.00130 -8.88e-05 0.00130 -8.88e-05 0.00424* 0.00422 0.00424 0.00422

(0.00275) (0.00262) (0.00270) (0.00262) (0.00235) (0.00260) (0.00258) (0.00259)

Size

2.08e-05

1.63e-07

6.51e-05

0.000135

(0.000213)

(0.000214)

(0.000213)

(0.000214)

Industry

-0.000189 -0.000188

0.000495*** 0.000491**

(0.000201) (0.000193)

(0.000191) (0.000191)

Constant -0.0139*** -0.0120*** -0.0103*** -0.0105** 0.00233 -0.00240 -0.00351 -0.00669

(0.00185) (0.00389) (0.00233) (0.00421) (0.00159) (0.00391) (0.00221) (0.00424)

Observations 1,111 1,067 1,111 1,067 1,111 1,067 1,111 1,067

R-squared 0.191 0.163 0.146 0.163 0.282 0.051 0.051 0.057

OmotokePaul-Lawal

32

TABLE6:INVESTIGATINGTHEDIRECTIONOFIMPACT

Regressionofmainestimatorsignsonamountinvolvedinscandal

(1) (2) (3) (4) (5)

VARIABLES Sign Sign BothNeg BothNeg Significance

Amount -0.0820* -0.265*** 0.0745* 0.196** 0.0732*

(0.0421) (0.0791) (0.0367) (0.0835) (0.0420)

Constant 2.172*** 3.916*** -0.367 -1.547* 0.792**

(0.366) (0.743) (0.319) (0.785) (0.365)

Observations 27 11 27 11 27

R-squared 0.132 0.555 0.141 0.379 0.108

Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1 Notes:ThetableshowsunivariateregressionsusingthelogarithmoftheNairaamountinvolvedin

thescandalastheindependentvariable.Column(1)showshowtheNairaamountchangesthe

signof!! themainestimator,PostXDegreeConn;Column(2)showshowthenairaamountaffectstheestimator’ssignforeventswithasignificantcoefficientonthemainestimator.Column(3)showshowtheamountaffectsthetendencyofbothPostandPostXDegreeConntobenegative.Column(4)runsthesametestforthesignificanteventsfromTable3,whilstthelastcolumnrunsaregressionofhoweventsignificancedependsonamountinvolvedinthescandal.

OmotokePaul-Lawal

33

FIGURE1:ABNORMALANDCUMULATIVERETURNS(NEGATIVEINTERACTION)ALAO-AKALA6OCT11

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0.01

0.012

-4 -3 -2 -1 0 1 2 3 4 5

EVENT DAY

Panel A: Abnormal Returns

Connected

Unconnected

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

-4 -3 -2 -1 0 1 2 3 4 5 EVENT DAY

Panel B: Cumulative Abnormal Returns

Connected

Unconnected

Notes:PanelApresentsdailystockmarketabnormalreturnsforfirmswithpoliticalconnections(redline)andthosewithnone(blueline)atthetimeoftheeventonOctober6th,2011.Meanwhile,PanelBshowsthecumulativeabnormalreturnsaroundtheeventperiod.Cumulativeabnormalreturnsaredefinedas!"# (0,!)! = ∑ !"!"!

!!! . Thedashedverticallinesinbothpanelsdenotethestartsoftheevent.

OmotokePaul-Lawal

34

FIGURE2:ABNORMALANDCUMULATIVERETURNS(“DOUBLEPOSITIVE”)DANIEL16APR12

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

-5 -4 -3 -2 -1 0 1 2 3 4 5

EVENT DAY

Panel A: Abnormal Returns

Unconnected

Connected

-0.01

0

0.01

0.02

0.03

0.04

0.05

-5 -4 -3 -2 -1 0 1 2 3 4 5

EVENT DAY

Panel B: Cumulative Abnormal Returns

Unconnected

Connected

Notes:PanelApresentsdailystockmarketabnormalreturnsforfirmswithpoliticalconnections(redline)andthosewithnone(blueline)atthetimeoftheeventonApril16th,2012.Meanwhile,PanelBshowsthecumulativeabnormalreturnsaroundtheeventperiod.Cumulativeabnormalreturnsaredefinedas!"# (0,!)! = ∑ !"!"!

!!! . Thedashedverticallinesinbothpanelsdenotethestartsoftheevent.

OmotokePaul-Lawal

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FIGURE3:GRAPHOFPOSTXPOLITICALCONNECTIONSCOEFFICIENT(!!)AGAINSTAMOUNTINVOLVED

Notes:Thegraphplotsthevaluesof!!,thecoefficientonthemainestimator–theinteractionoftheeventdummyandconnectionsdummyvariables(!"#$!" ×!"##!")–againstthelogarithmofthemonetaryamountinvolvedinthecorruptionscandal.

OmotokePaul-Lawal

36

APPENDIX

TABLEA1:SUMMARYOFEVENTSSTUDIED

PublicOfficial Title/PositionCurrentpublic

officeCharge

(statecounts)AmountInvolved

(Naira)CaseInheritedbyEFCCon

EarliestPressDate

AdenikeGrange,GabrielAduku FormerMinisterofHealth N/A 56 300million 3-Apr-08 27-Mar-08

JoshuaDariye FormerGovernor,PlateauStateSenatorforPlateauCentral 23 700million 13-Jul-07 20-Jan-06

SaminuTuraki FormerGovernor,JigawaState N/A 32 36billion 13-Jul-07 11-Jul-07

IyaboObasanjo-Bello ServingSenator,OgunState N/A 56 10million 2-Apr-08 8-Apr-08

LuckyIgbinedion FormerGovernor,EdoState N/A 191 4.3billion 23-Jan-08 21-Jan-08

JollyNyame FormerGovernor,TarabaState N/A 21 180million 13-Jul-07 23-Jul-07

MichaelBotmang FormerGovernor,PlateauState N/A 31 1.5billion 18-Jul-08 18-Jul-08

BabalolaBorishade,FemiFani-Kayode FormerMinistersofAviation N/A - 19.5billion June2008. 1-Jul-08

BoniHaruna FormerGovernor,AdamawaState

MinisterforYouthDevelopment 28 254million Aug-08 8-Aug-08

BodeGeorge Chieftainoftherulingparty,PDP N/A 163 84billion Aug-08 7-Aug-08

RasheedLadoja FormerGovernor,OyoState N/A 33 6billion - 29-Aug-08NicholasUgbane,Hon.NdudiElumeluHon.MohammedJibo,Hon.PaulinusIgwe(servingmembersofHouseofRepresentatives)DrAliyuAbdullahi(servingfed.perm.sec)Mr.SamuelIbi.Mr.SimonNanle,MrLawrenceOrekoya,MrKayodeOyedeji,Mr.A.GarbaJahun

"RuralElectrificationAgencyCase"involvingthreeservingmembersoftheHouseofRepresentatives,thepermanentsecretaryoftheministryofpowerandotherhighprofilepublicofficers

-

130 5.2billion 15-May-09 19-May-09

OmotokePaul-Lawal

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AdamuAbudullahi FormerGovernor,NasarawaStateSenatorforNasarawaWest 149 15billion

Feb22/March32010 22-Feb-10

AttahiruBafarawa FormerGovernor,SokotoState N/A 47 15billion 9-Dec-09 9-Dec-09

FrancisOkokuro,Dr.CharlesSilvaOpuala

AccountantGeneralandCommissionerforFinanceandBudgetofBayelsaStaterespectively N/A 6 2.4billion 24-Mar-10 24-Mar-10

AliyuAkweDoma FormerGovernor,NasarawaState N/A 17 15billion 18-Oct-11 19-Oct-11

DanjumaGoje FormerGovernor,GombeState N/A 18 52.9billion

7-Oct-11

DrHassanLawal(and9others) FormerMinisterofWorksandHousing N/A 44 75.7billion 11-May-11 11-May-11

AdebayoAlao-Akala FormerGovernor,OyoState N/A 11 11.5billion 11-Oct-11 11-Oct-11

MrTimipreSylvia FormerGovernor,BayelsaState N/A 6 6.46billion 5-Jun-12 24-Feb-12

GbengaDaniel FormerGovernor,OgunState N/A 13 58billion 12-Oct-11 13-Oct-11

AbubakarAudu FormerGovernor,KogiState N/A 36 11billion 18-Mar-13 19-Mar-13

MohammedBelloAbubakarandAbubakarAbdullahiAhmed

PermanentSecretaryandDeputyDirectorofSokotoStateMinistryofEducationrespectively

43 100million 7-Apr-14 10-Apr-14

MurtalaNyako,Sen.Abdul-AzizNyako,AbubakarAliyu,andZulkifikkAbba

FormerGovernorofAdamawaState,hisson,andothers N/A 37 29billion 8-Jul-15 9-Jul-15

SuleLamido,AminuLamido,MustaphaLamido,andAminuWadaAbubakar

FormerGovernorofJigawaState,hissons,andanother N/A 28 1.35billion 8-Jul-15 8-Jul-15

GabrielSuswam,andOmodachiOkolobiaFormerGovernorofBenueState,andhisFinanceCommissioner N/A

3.1billion 10-Nov-15 3-Nov-15

SanusiLamido Ex-governorofcentralbank N/A N/A 20billion(USD) N/A 4-Feb-14

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TABLEA2:FIRMSSTUDIEDLISTEDONTHENIGERIANSTOCKEXCHANGE(NSE)

Symbol FirmName ISO_ISIN Industry7UP 7-UpBottlingComp.Plc. NG7UP0000004 ConsumerGoodsACADEMY AcademyPressPlc. NGACADEMY008 ServicesACCESS AccessBankPlc. NGACCESS0005 FinancialServicesAFRPAINTS AfricanPaints(Nigeria)Plc. NGAFRPAINTS8 IndustrialGoodsAGLEVENT A.G.LeventisNigeriaPlc. NGAGLEVENT01 ConglomeratesAIICO AiicoInsurancePlc. NGAIICO00006 FinancialServicesALEX AluminiumExtrusionInd.Plc. NGALEX000003 NaturalResourcesALUMACO AluminiumManufacturingCompanyPlc NGALUMACO008 NaturalResourcesANINO AninoInternationalPlc. NGANINO00003 OilandgasARBICO ArbicoPlc. NGARBICO0007 Construction/RealEstateASHAKACEM AshakaCemPlc NGASHAKACEM8 IndustrialGoodsAVONCROWN AvonCrowncaps&Containers NGAVONCROWN7 IndustrialGoodsBERGER BergerPaintsPlc NGBERGER0000 IndustrialGoodsBETAGLAS BetaGlassCoPlc. NGBETAGLAS04 IndustrialGoodsBOCGAS B.O.C.GasesPlc. NGBOCGAS0008 NaturalResourcesCADBURY CadburyNigeriaPlc. NGCADBURY001 ConsumerGoodsCAP CapPlc NGCAP0000009 IndustrialGoodsCCNN CementCo.OfNorth.Nig.Plc NGCCNN000003 IndustrialGoodsCHAMPION ChampionBrew.Plc. NGCHAMPION00 ConsumerGoodsCHELLARAM ChellaramsPlc. NGCHELLARAM5 ConglomeratesCILEASING C&ILeasingPlc. NGCILEASING2 ServicesCONOIL ConoilPlc NGCONOIL0003 OilandgasCORNERST CornerstoneInsuranceCompanyPlc. NGCORNERST03 FinancialServicesCOSTAIN Costain(WA)Plc. NGCOSTAIN006 Construction/RealEstateCUTIX CutixPlc. NGCUTIX00002 IndustrialGoodsDNMEYER DnMeyerPlc. NGDNMEYER001 IndustrialGoodsDUNLOP DnTyre&RubberPlc NGDUNLOP0005 ConsumerGoodsELLAHLAKES EllahLakesPlc. NGELLAHLAKE8 AgricultureENAMELWA NigerianEnamelwarePlc.

ConsumerGoods

EQUITYASUR EquityAssurancePlc. NGEQUITYASS2 FinancialServicesETERNA EternaPlc. NGETERNAOIL1 OilandgasEVANSMED EvansMedicalPlc. NGEVANSMED04 HealthcareFBNH FbnHoldingsPlc NGFBNH000009 FinancialServicesFCMB FcmbGroupPlc. NGFCMB000005 FinancialServicesFIRSTALUM FirstAluminiumNigeriaPlc NGFIRSTALUM7 IndustrialGoodsFLOURMILL FlourMillsNig.Plc. NGFLOURMILL0 ConsumerGoodsFO ForteOilPlc. NGAP00000004 OilandgasGLAXOSMITH GlaxoSmithklineConsumerNig.Plc. NGGLAXOSMTH8 HealthcareGUARANTY GuarantyTrustBankPlc. NGGUARANTY06 FinancialServicesGUINEAINS GuineaInsurancePlc. NGGUINEAINS0 FinancialServicesGUINNESS GuinnessNigPlc NGGUINNESS07 ConsumerGoodsINTBREW InternationalBreweriesPlc. NGINTBREW005 ConsumerGoodsINTERLINK InterlinkedTechnologiesPlc NGINTERLINK3 ServicesIPWA IpwaPlc NGIPWA000006 IndustrialGoodsJBERGER JuliusBergerNig.Plc. NGJBERGER009 Construction/RealEstateJOHNHOLT JohnHoltPlc. NGJOHNHOLT05 ConglomeratesJULI JuliPlc. NGJULI000003 ServicesLASACO LasacoAssurancePlc. NGLASACO0002 FinancialServicesLAWUNION LawUnionAndRockIns.Plc. NGLAWUNION02 FinancialServices

OmotokePaul-Lawal

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LEARNAFRCA LearnAfricaPlc NGLONGMAN007 ServicesLINKASSURE LinkageAssurancePlc NGLINKASSUR7 FinancialServicesLIVESTOCK LivestockFeedsPlc. NGLIVESTOCK5 AgricultureMANDRID P.S.MandridesPlc

ConsumerGoods

MAYBAKER May&BakerNigeriaPlc. NGMAYBAKER01 HealthcareMBENEFIT MutualBenefitsAssurancePlc. NGMBENEFT000 FinancialServicesMOBIL MobilOilNigPlc. NGMOBIL00007 OilandgasMORISON MorisonIndustriesPlc. NGMORISON000 HealthcareMRS MrsOilNigeriaPlc. NGCHEVRON008 OilandgasNASCON NasconAlliedIndustriesPlc NGNASCON0005 ConsumerGoodsNB NigerianBrew.Plc. NGNB00000005 ConsumerGoodsNCR Ncr(Nigeria)Plc. NGNCR0000008 ICTNEIMETH NeimethInternationalPharmaceuticalsPlc NGNEIMETH001 HealthcareNEM N.E.MInsuranceCo(Nig)Plc. NGNEM0000005 FinancialServicesNESF NigeriaEnerygySectorFund NGNESF000003 FinancialServicesNESTLE NestleNigeriaPlc. NGNESTLE0006 ConsumerGoodsNIG-GERMAN Nigeria-GermanChemicalsPlc. NGNIGGERMAN3 HealthcareNIGERINS NigerInsuranceCo.Plc. NGNIGERINS04 FinancialServicesNIGROPES NigerianRopesPlc NGNIGROPES04 IndustrialGoodsNNFM NorthernNigeriaFlourMills

ConsumerGoods

OANDO OandoPlc NGOANDO00002 OilandgasOKOMUOIL OkomuOilPalmPlc. NGOKOMUOIL00 AgriculturePHARMDEKO Pharma-DekoPlc. NGPHARMDEKO7 HealthcarePREMPAINTS PremierPaintsPlc. NGPREMPAINT2 NaturalResourcesPRESCO PrescoPlc NGPRESCO0005 AgriculturePRESTIGE PrestigeAssuranceCo.Plc. NGPRESTIGE00 FinancialServicesPZ PZCussonsNigeriaPlc. NGPZ00000005 ConsumerGoodsRAKUNITY PaintsAndCoatingsManufacturesPlc NGPAINTCOM0 OilandgasROADS RoadsNigPlc. NGROADS00004 Construction/RealEstateROKANA RokanaIndustriesPlc. NGROKANA0001 ConsumerGoodsROYALEX RoyalExchangePlc. NGROYALEX007 FinancialServicesRTBRISCOE RTBriscoePlc. NGRTBRISCOE9 ServicesSCOA SCOANig.Plc. NGSCOA000009 ConglomeratesSMURFIT SmartProductsNigeriaPlc NGSMURFIT002 Construction/RealEstateSTDINSURE StandardAllianceInsurancePlc. NGSTDINSURE7 FinancialServicesSTERLNBANK SterlingBankPlc. NGSTERLNBNK7 FinancialServicesTHOMASWY ThomasWyattNig.Plc. NGTHOMASWY07 NaturalResourcesTOTAL TotalNigeriaPlc. NGTOTAL00001 OilandgasTOURIST TouristCompanyOfNigeriaPlc. NGTOURIST009 ServicesTRANSEXPR Trans-NationwideExpressPlc. NGTRANSEXPR4 ServicesTRIPPLEG TrippleGeeAndCompanyPlc. NGTRIPPLEG04 ICTUAC-PROP UacnPropertyDevelopmentCo.Limited NGUACPROP006 Construction/RealEstateUACN UACNPlc. NGUACN000006 ConglomeratesUBA UnitedBankForAfricaPlc NGUBA0000001 FinancialServicesUBN UnionBankNig.Plc. NGUBN0000004 FinancialServicesUNIC UnicInsurancePlc. NGUNIC000008 FinancialServicesUNILEVER UnileverNigeriaPlc. NGUNILEVER07 ConsumerGoodsUNIONDICON UnionDiconSaltPlc. NGUNIONDICO1 ConsumerGoodsUPL UniversityPressPlc. NGUPL0000008 ServicesUTC UTCNig.Plc. NGUTC0000009 ConsumerGoodsVANLEER VitafoamNigPlc. NGVITAFOAM00 NaturalResourcesVITAFOAM VonoProductsPlc. NGVONO000005 ConsumerGoodsVONO WAGlassInd.Plc. NGWAGLASS003 ConsumerGoods

OmotokePaul-Lawal

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WAPCO WestAfricanPortlandCement

NaturalResourcesWAPIC WapicInsurancePlc NGWAPIC00004 FinancialServicesWEMABANK WemaBankPlc. NGWEMABANK07 FinancialServicesZENITHBANK ZenithInternationalBankPlc NGZENITHBNK9 FinancialServices

TABLEA3:DESCRIPTIVESTATISTICSBYINDUSTRY

No. Industry ConnectedFirms

UnconnectedFirms

AverageConnections

Observations

1 Agriculture 3 1 0.75 42 Conglomerates 3 2 0.6 5

3Construction/RealEstate 3 2 0.6 5

4 ConsumerGoods 11 9 0.55 205 FinancialServices 14 11 0.56 256 Healthcare 4 3 0.57 77 ICT 2 0 1 28 IndustrialGoods 3 9 0.25 129 NaturalResources 2 5 0.29 710 Oilandgas 3 5 0.38 811 Services 4 5 0.44 9

Total 52 52

104

OmotokePaul-Lawal

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TABLEA4:LISTOFCONNECTEDFIRMS&THEIRCONNECTIONS

Firm BoardMember GovtPosition7UP DrAdekunleOjora LagosHighChief

MrsOluwatoyinOjoraSaraki

WifeofformerGovernorofKwaraStateandCurrentPresidentoftheSenateofNigeria,SenatorBukolaSaraki

ACCESS ObaS.A.Sule TraditionalrulerofOdonselu-AlaroinIjebu,North-EastOgunState

AIAig-Imoukhuede ChairthePresidentialCommitteeontheVerificationofFuelSubsidies(2012)

ErnestNdukwe

FormerChiefExecutiveOfficeroftheNigerianCommunicationsCommission,NCC

AjoritsedereAwosika

PermanentSecretaryattheFederalMinistryofInternalAffairs,theFederalMinistryofScience&TechnologyandtheFederalMinistryofPowerrespectively

AFROILChristopherEhikhuemenOlu

ChristopherEkpenyong

AGLEVENT

AmbassadorHamzatAhmadu

PrincipalSecretarytothreeofNigerian'sHeadofState,MajorGeneralAguiyi-Ironsi,GeneralYakubuGowon,andGeneralMurtalaMuhammed.

AIICO SenatorTokunboOgunbanjo SenatoroftheNationalAssemblyrepresentingOgunEastSenatorialDistrict

ChiefEugeneOkwor CommissionerforInsurance

ALUMACO JosephOyeyaniMakoju SpecialAdviseronElectricPower

ASHAKACEMEngrMuhammedMustaphahDaggash HonourableCommissionerforWorksandHousin

AlhajiBubaYerima Adamawastateex-Governor,MurtalaNyako'sAdviser

MrsHamraImam CommissionerandPermanentSecretaryinBornoState

Sen.MuhammedMumhammedOFR Bauchistatesenator

DrAbubakarAliGombe MinisterofStateforHealth

ChiefKolawoleBabalolaJamodu MinisterofIndustry

CADBURY Mr.AtedoPeterside HonorarySpecialAdvisortotheExecutiveGovernorofRiversState.

CHELLARAMOtunbaRichardAdeniyiAdebayo GovernorofEkitiState

AlhajiAhmedAdamuAbdulkadir

GovernorofCBN;SpecialAdvisertothePresidentonManufacturingandPrivateSector;PresidentialCommitteeonTariffandIncentives.

CORNERST ChristopherKoladeNigerianHighCommissionertotheUK;ChairmanoftheSubsidyReinvestmentandEmpowermentProgramme

DNMEYER EreluAngelaAdebayo FirstladyofEkitiState

ELLAHLAKES ZamaniLekwot MilitaryGovernorofRiversState

FrankChukwudiEllah SenatorofRiversstate

ENAMELWA AlhajiInuwaWada MinisterofDefence

ETERNA AdetoyeSode MilitaryAdministratorof

MahmudTukur dformerministerforCommerceandIndustry

AfolabiAdeola(Mr)

ChairmanoftheNationalPensionCommission,Vice-PresidentialcandidateoftheActionCongressofNigeria(ACN)candidateoftheActionCongressofNigeria(ACN)

EVANSMED AlhajiIbrahimDamcidaFBNH UmaruAbdulMutallab Federalminister

AyoolaOOtudeko ChairmanoftheNationalMaritimeAuthority;OFR

GarbaDuba Governor,Sokoto&BauchiState

AjibolaAfonja FederalMinisterofLabourandProductivityandNationalPoliticalConference

OmotokePaul-Lawal

42

AmbroseFeese FederalMinisterofStateforWorksandHousing

AdebolaOsibogun

MemberofthePresidentialCommitteeonHousingandUrbanDevelopment,thePresidentialCommitteeonMortgageFinanceandtheNigerianRealEstateDevelopersAssociation

FCMB AlhajiIbrahimDamcida

Three-timePermanentSecretaryinthreedifferentministries(MinistryofTrade(1966to1970),MinistryofDefence(1970to1975)andMinistryofFinancein1975)

FLOURMILL JeryyGana MinisterofInformation

GLAXOSMITH TundeLemo DeputyGovernorinchargeofOperations

GUARANTY OwelleGilbertPOChikelu MinisterofEstablishment

OluwoleSOduyemi DeputyGovofCBN

FaroukBelloBunza Senator

OlabodeAgusto

DirectoroftheNationalPensionCommission,MemberoftheCentralBankofNigeriaMonetaryPolicyCommitteeandDirector-GeneraloftheBudgetOfficeoftheFederation

GUINEAINS FredUdechukwu CommissionerofFinanceAnambra

SenatorMohammedSanusiDaggash SenatorforBornoNorth;MinisterforNationalPlanning

GUINNESS SundayTomasDogonyaro Ambassador

IPWA ChiefSilasBandeleDaniyan MinisterofNationalPlanning

EmmanuelOlatunjiAdesoye MinisterofWorks

FolorunshoDaniyan SpecialAdvisertoKogiStategovernment

AbubakarSadiqZakariyaMaimalari MilitaryAdministratorofJigawaState

JBERGER NuraImam MinisterwithresponsibilityfortheMines,Power&Steel

JafaruDamulak MemberoftheHouseofRepresentatives

JULI PrinceJuliusAdelusi-Adeluyi MinisterofHealthandSocialServices

SirRemiOmotoso,MFR Ekitistategovernor

LASACO IsmailAdebayoAdewusi CommissionerforFinanceandEconomicPlanningandBudget

AshimAdebowaleOyekan CommissionerforEnvironmentandPhysicalPlannin

AderinolaDisu SpecialAdvisertoLagosStateGovernoronCentralBusinessDistrict

SanniNdanusa

NigerState’sCommissionerforWorksandInfrastructuralDevelopment,MinisterofYouths,Sports&SocialDevelopmentin2008

LAWUNION RemiBabalola MinisterofStateforFinance

LINKASSURE UdomaUdoUdoma SenatorfortheAkwa-IbomSouth

BukarUsman Federalpermanentsecretary

SilvaOpuala-Charles Commissionerforfinanceandbudget

JohnAndersonEseimokumoh CommissionerforBayelsa

PatmoreDuateIyabi CommissionerforFinance,BayelsaState

IkobhoAnthonyHowells DirectoratMinistryofFinanceincorporated,BayelsaState

LIVESTOCK SefiuAdegbengaKaka DeputyGovernorofOgunState

MAYBAKER TheophilusYakubuDanjuma FederalMinisterofDefence

DavidDankaro PermanentSecretaryoftheMinistryofFinanceandEconomicDevelopment

EmeUfotEkaette SenatorforAkwaIbomSouth

EdugieAbebe PermanentSecretaryoftheFederalRepublicofNigeria

MBENEFIT FestusPortbeni AmbassadortoEquatorialGuinea;MinisterofTransport

MORISON MuhammedAMuhammed Bauchistatesenator

OmotokePaul-Lawal

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MRS SamailaKewa Com-missionerforFinanceandCommissionerforEducation

NBChiefKolawoleBabalolaJamodu MinisterofIndustry

NenadiEUsman MinisterofFinance

FrankNweke

MinisterofYouth,MinisterofInformationandthenMinisterofInformationandCommunications

IfuekoMOmoiguiOkauru ExecutiveChairmanoftheFederalInlandRevenueServiceofNigeria

NCRAmbassadorHamzatAhmadu

PrincipalSecretarytothreeofNigerian'sHeadofState,MajorGeneralAguiyi-Ironsi,GeneralYakubuGowon,andGeneralMurtalaMuhammed.

NEMAlhajiMohammedMunirJa'afaru

HonourableCommissionerforLocalGovernmentandCommunityDevelopment,HonourableCommissionerforInformation,HomeAffairsandCulture,HonourableCommissionerforAgricultureandNaturalResources,KadunaState

NIGERINS YusufHamisuAbubakarKadunaStateCommissionerforHealthandSocialDevelopment,CommissionerforFinanceandEconomicPlanning

NIGROPES BodeOlajumoke SenatorforOndo

NNFMAlhajiAminuAlhassanDantata KanoStateCommissioner

OANDO GeneralMMagoro FederalCommissionerofTransport,MinisterforInternalAffairs

OmamofeBoyo MinisterofPetroleum

Ms.AmalInyingialaPepple HeadoftheCivilServiceoftheFRN;Permanentsecinnumerousministries

AmmunaLawanAli PermanentSecretaryandservedinvariousMinistries

TanimuYakubu

ChiefEconomicAdviserandChiefofStafftothePresident;HonourableCommissioner,MinistryofFinance,BudgetandEconomicPlanning,KatsinaState

PHARMADEKO AfolabiAdeola(Mr)

ChairmanoftheNationalPensionCommission,Vice-PresidentialcandidateoftheActionCongressofNigeria(ACN)candidateoftheActionCongressofNigeria(ACN)

PRESCO JamesBErhuero PermanentSecretary,DeltaStae

AtedoPeterside HonorarySpecialAdvisortotheExecutiveGovernorofRiversState.

ShettimaMustafa

MinisterofAgricultureandNaturalResources;between1990and1992,HonourableMinisterofDefencefrom2008to2009andMinisterofInteriorbetween2009and2010.

AiguobasinmwinAkenzua SpecialAdvisertotheExecutiveGovernorofEdoState

PZ EmmanuelCEdozien EconomicAdvisertoPresidentShagari

LawalBatagarawa

PermanentSecretaryinKadunastateandbetween1999and2003hewasMinisterforEducationandlateraMinisterforDefence;between2003and2007hewastheSpecialAdvisertothePresidentonIntra-PartyRelations.

KolaJamodu MinisterofIndustry.

RAKUNITYAlhajiMallamMuhammedLawanBuba CommissionerforHealthandMemberoftheStateExecutiveCouncil

ROKANA ChukwuemekaUgwuh MinisterofCommerceandIndustry

RTBRISCOE AlhajiSanusiAdoBayero PermanentSecretary

SMURFIT MosesOAjaja OndoStateCommissionerforCommerce&Industry

STDINSURE AlhajiYahayaSa'ad SpecialAdviseronSpecialDutiestotheExecutiveGovernorofKadunaState.

DominicOneya AdministratorofKanoandBenueState

PaulObi AdministratorofBayelsa

RamseyMowoe PermanentSecretaryintheFederalCivilService

STERLING TamarakareYekwe CommissionerforJustice,BayelsaState

THOMASWY NenadiEUsmanCommissionerofWomenAffairs,EnvironmentandHealthinKadunaState;ministeroffinance

TOURIST AlexanderIbru MinisterofInternalAffairs

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FelixIbru GovernorandSenatorofDelta

TRANSEXPR OtunbaTBAdebayo CommissionerforOgunState

AhmanPategi MemberoftheHouseofReps

UmarJimada

InformationOfficerandtheAdministrativeOfficerintheGovernor’sofficeandCabinetDepartmentrespectively

TRIPPLEG FelixKolawoleBajomo MemberoftheSenatefortheOgunWestconstituencyofOgunState

UAC MohammedInuwaWushishi CommissionerforIndustries;ChiefofArmyStaff

AwunebaAjumogobia WifetoMinisterofStateforPetroleumandForeignMinister

UAC-PROP IbrahimAlawoMohammed MemberoftheKwaraStateHouseofAssemblyfrom1979to1983

HalimaTAlao

HonourableMinisterofStateforEducation;MinisterofStateforHealth,andHonourableMinisterofEnvironment,HousingandUrbanDevelopment

OkonAnsa

CommissionerforAgricultureandCommissionerforCommerce&IndustriesinAkwaIbomState

UBA JosephChieduKeshiPermanentSecretary,CabinetSecretariat,thePresidency;andPermanentSecretary,MinistryofForeignAffairs.

FolukeAbdul-Razaq

CommissionerintheMinistriesofFinanceandWomenAffairsinLagosState(97-99)

OwanariDuke FirstLadyofCrossRiverStateofNigeria

Ja'afaruPaki

SpecialAssistantonPetroleumMatterstoNigeria’sPresidentOlusegunObasanjo

AdekunleOlumide FederalPermanentSecretary

FerdinandAlabraba SpecialAdvisertotheGovernmentofRiversStateofNigeria

UBN IbrahimAbdullahiGobir SenatorforSokotoEast

OnikepoAkande MinisterofIndustry

UdomaUdoUdoma SenatorfortheAkwa-IbomSouth

UNIC EAOShonekan FormerPresidentofNigeria

UNILEVER UUdoUdoma SenatorfortheAkwa-IbomSouth

Mr.AtedoPeterside HonorarySpecialAdvisortotheExecutiveGovernorofRiversState.

AbbaKyari CommissionerforForestryandAnimalResources

IgweNnaemkaAAchebe ObiofOnitsha

UNIONDICON AliyuIsmailaPermanentSecretary,Political&EconomicAffairstotheOfficeoftheSecretarytotheGovernmentoftheFederation

UTC AfolabiAdeola(Mr)

ChairmanoftheNationalPensionCommission,Vice-PresidentialcandidateoftheActionCongressofNigeria(ACN)candidateoftheActionCongressofNigeria(ACN)

VANLEER OlunkleAdebayoObadinaFormerPresidentialcandidate;PresidentialAdviseronBudgetAffairsandDirectorofBudget

UAMutallab

FederalCommissioner(i.e.Minister)forEconomicDevelopment&Reconstruction

WAPCODr.ShamsuddeenUsman,CON FinanceMinister;MinisterofNationalPlanning

WEMABANK OmololuSMeroyi OndoSouthSenator

PatrickAyoAkinyelure OndoCentralSenator

ZENITH AlhajiBabaTela Senator

AmalPepple HeadoftheCivilServiceoftheFRN;Permanentsecinnumerousministries

HarunaUsmanSanusi

PermanentSecretary,Budget,FederalMinistryofFinance;PermanentSecretary,MinistryofDefence,aswellasPermanentSecretary,OfficeoftheHeadofCivilServiceoftheFederation

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to finance: the role of campaign contributions. Journal of Financial Economics 88(3), 554-80.

Do, Quoc-Anh, Yen Teik Lee, and Bang Dang Nguyen (2015), "Political Connections

and Firm Value: Evidence from the Regression Discontinuity Design of Close Gubernatorial Elections." CEPR Discussion Paper No. DP10526.

Faccio, M. (2006), “Politically Connected Firms”, American Economic Review, 96: 369-

386. Faccio, M., Masulis, R., McConnell. J. 2006. Political connections and corporate

bailouts. Journal of Finance 61(6), 2597-635. Ferguson, T., Voth, H. (2008), Betting on Hitler – the value of political connections in

Nazi Germany. Quarterly Journal of Economics 123(1), 101-37. Fisman, David, Raymond J. Fisman, Julia Galef, Rakesh Khurana, and Yongxiang

Wang (2012), "Estimating the Value of Connections to Vice-President Cheney." The B.E. Journal of Economic Analysis & Policy 12.3

Fisman, R. (2001), “Estimating the Value of Political Connections”, American Economic

Review, 91: 1095-1102 Goldman, E., Rocholl, J., So, J. (2009), Do politically connected boards affect firm

value?” Review of Financial Studies, 22(6), 2331-60. Gonzalez Felipé, Prem Mounu (2016), “Losing Your Dictator: Firms During Political

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Evidence from the 2008 Taiwanese Presidential Election." Journal of Public Economics 95(7-8), 837-49.

Jayachandran, S. (2006), The Jeffords Effect. Journal of Law and Economics 49(2), 397-425. Johnson, S. Mitton, T. 2003. Cronyism and capital controls: evidence from Malaysia.

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Knight, B. (2007), Are policy platforms capitalized into equity prices? Evidence from

the Bush/Gore 2000 Presidential Election. Journal of Public Economics 91(1-2) 389-409.

Li, H., Meng, L., Wang, Q., Zhou, L.-A. (2008), Political connections, financing and

firm performance: Evidence from Chinese private firms. Journal of Development Economics, 87(2), 283-99.

Mattozzi, A. (2008), Can we insure against political uncertainty? Evidence from the U.S.

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