political investing: the common stock investments...

Political Investing: The Common Stock

Investments of Members of Congress 2004-2007

Andrew Eggers – Harvard UniversityJens Hainmueller – Harvard University

July 16, 2010

We examine an extensive, newly-collected dataset of the investment portfoliosof members of Congress between 2004 and 2007 in order to assess the extent towhich political factors are reflected in members’ investment behavior. In con-trast to the sole earlier study of Congressional investing, we find that members’portfolios on average do not outperform the market. However, we find higher re-turns among members with leadership positions on committees, members withseats on more influential committees, and members who have been consistentlyidentified as corrupt by a watchdog group. We also find that members tend toinvest disproportionately in companies in their own states and Congressionaldistricts, companies that contribute to their election campaigns through PACs,and companies that lobby on bills referred to their committees. These connec-tions appear to matter: a portfolio that mirrors the investments members makein companies from their own states outperforms the market by about 4% annu-ally, while a portfolio that mirrors investments members make in contributorsunderperforms the market by about 4% annually. (A portfolio that mirrors theinvestments members make in companies that lobby bills in their committeesunderperforms the market by almost as much.) Together, our results suggestthat politician-investors enjoy informational advantages in investing in con-stituent firms, but that they sacrifice financial gains to signal policy positionsand cement political deals with contributors and lobbying-oriented companies.

Andrew Eggers, PhD Candidate, Department of Government, 1737 Cambridge Street, Cambridge, MA02138. Email: [email protected]. Jens Hainmueller, PhD Candidate, Department of Government,1737 Cambridge Street, Cambridge, MA 02138. E-mail: [email protected]. Authors are listedin alphabetical order and contributed equally. Both authors are affiliated with Harvard’s Institute forQuantitative Social Science (IQSS), who generously provided funding for this project.

We thank Ryan Bubb, Justin Grimmer, Gary King, Gabe Lenz, Ken Shepsle, Alberto Tomba, JimSnyder, and seminar participants at Harvard and MIT for helpful comments.

We would especially like to thank the Center for Responsive Politics for sharing data. The usualdisclaimer applies.

I. Introduction

Research in empirical finance tells us that average investors do poorly relative to the market

(Barber & Odean 2000) and even professional investors fail to systematically earn excess

returns (Carhart 1997). In this paper, we examine the portfolio choices and investment

returns of members of Congress, a class of investors whose political roles put them in an in-

formationally privileged position but also impose unusual constraints on their investments.

In contrast to a previous paper analyzing Congressional investments using older and less

complete data (Ziobrowski et al. 2004), we do not find that members of Congress on av-

erage beat the market in the period 2004-2007. We do however find intriguing differences

in returns across members and types of investments, which, combined with evidence that

members tend to overweight local companies and campaign supporters in their portfolios,

improves our understanding of how politicians trade off conflicting financial and political

incentives while in office.

Why study the investments of members of Congress? Leaving aside the considerable

public interest in the question of whether legislators personally benefit from their political

positions (in violation of ethics rules), the investment choices and performance of members

of Congress may provide political scientists with valuable indirect evidence about the way

firms and legislators interact. Like all investors, members of Congress presumably invest in

stocks in order to preserve and increase their wealth. But members of Congress differ from

other investors in two principal ways that guide our investigation. First, they may possess

unusually valuable market-relevant information about public companies and the regulations

that affect them. Second, their political success depends to some extent on soliciting

political support and campaign contributions from corporations and their stakeholders, and

their investments (which after all are public) may play a role in establishing connections

with public companies. In short, the investment choices and investment performance of

members of Congress provide valuable clues about what kind of informational advantages

members may possess and how they use those advantages, as well as whether and in what

ways they use their investments to cement political relationships with firms and their

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stakeholders.

Our findings indicate that members of Congress do not on average outperform market in-

dices, in contrast to the sole previous study of Congressional stock investments (Ziobrowski

et al. 2004). But members appear to invest in a way that reflects political considerations,

strongly overweighting local firms, firms that give campaign contributions, and firms with

business before the member’s own committees. The performance of these connected port-

folios also diverges from market benchmarks: members’ local investments outperform the

market by about 4% a year, indicating substantial informational advantages from proxim-

ity and relationships with supporters, while their investments in contributing companies

underperform the market by about the same amount, suggesting that members invest in

contributors in part to cement political relationships.

II. Preliminary Discussion: What is Political about Politicians’Investments?

Despite evidence that both amateur and professional investors do not systematically beat

market indices, recent research in political economy provides substantial reason to believe

that members of Congress could be extraordinarily good investors. A substantial and

growing list of papers show that firm values are very sensitive to political factors:

• Roberts (1990) finds that the death of the ranking Democrat on the Senate Armed

Service Committee resulted in lower stock value of firms located in the Senator’s state

and higher stock values of firms connected to the Senator who inherited his position

on the committee.

• Jayachandran (2006) finds that the market value of Republican-connected firms dropped

when Senator Jeffords unexpectedly departed the Republican Party in 2001, shifting

the Senate majority to the Democrats.

• Goldman et al. (2008a) and Goldman et al. (2008b) show that companies that an-

nounce the appointment of a politically-connected director experience a positive ab-

normal return and that politically connected firms are more likely to secure procure-

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ment contracts.

Comparable evidence abounds for other countries as well (Fisman 2001, Johnson & Mitton

2003, Khwaja & Mian 2005, Faccio 2006, Ferguson & Voth 2008). The picture presented

by all of these studies is that politicians can significantly impact firm values. Presumably,

politicians know about the impact of their own actions and those of other politicians with

whom they work. If these studies do not greatly overstate the impact of politicians on

stock prices, an investment-minded member of Congress could handsomely profit from

information arbitrage.

Politicians may enjoy additional informational advantages simply by being in close

contact with corporate executives and industry lobbyists as part of their legislating and

fundraising routines. Recent research in empirical finance suggests that mutual fund man-

agers do better when they invest in companies to which they are connected through geo-

graphic proximity (Coval & Moskowitz 2001)1 or personal ties to executives (Cohen et al.

2008). Members of Congress necessarily have large personal networks and frequent con-

tact with corporate executives and lobbyists. Whether the firm approaches the legislator

asking for policy favors or the legislator approaches the firm asking for campaign dona-

tions, the firm may reveal information about its market prospects (either intentionally or

unintentionally) that the legislator could act on in her own investments.

While members of Congress likely enjoy considerable information advantages because

of their political power, they also face a number of constraints arising from their need to

appeal to political constituents. We focus on three such constraints, which we will refer to

as “signaling,” “bonding,” and “ethics.”

Signaling

Members’ investments are public (which is why this paper is possible) and occasionally

subject to journalistic scrutiny (e.g., Boller (1995)). To voters, firms, and other politicians,

a member’s stock holdings may convey a signal about the member’s policy preferences

1Although not in recent years; see below.

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or ideology. Suppose politicians agree on the expected return of a particular tobacco

company’s stock but differ in their opposition to tobacco companies: some are pro-tobacco

and some are anti-tobacco. Anti-tobacco types experience a higher private cost of owning

tobacco stock, perhaps because of the cognitive dissonance of having a financial stake in

a company they dislike. Suppose that there is also a group of constituents who oppose

the tobacco industry and want to elect an anti-tobacco politician. If the private cost to

anti-tobacco politicians of owning tobacco stock is high enough, there may be a separating

equilibrium where only pro-tobacco politicians choose to own the stock and the anti-tobacco

constituents vote for politicians who refuse to own tobacco stock.2

In addition to signaling political ideology, investments might be thought to signal prefer-

ences about public service versus private gain: by abstaining from using political knowledge

to bank windfall profits, politicians may signal to voters that they prioritize public service.

Consistent with the idea that members constrain themselves in order to send a signal to

voters, Ziobrowski et al. (2004) find smaller abnormal returns in 1996 and 1997, which they

suggest may be due to the unfavorable media attention drawn to well-timed stocks trades

among members by Boller (1995).

To the extent that signaling is indeed a significant determinant of members’ investment

portfolios, we might expect average returns to be modest, since basic portfolio theory tells

us that restricting possible investments (particularly based on non-financial considerations)

cannot enhance returns.

Bonding

The second political constraint on members’ investment decisions also comes from their

relationship with voters and constituent firms. As is widely discussed in the political econ-

omy literature, politicians face a commitment problem with respect to voters and potential

campaign donors. Suppose that a firm is considering offering a campaign contribution to a

politician, but is unsure of whether the politician will pursue its interests in the legislature.

2In terms of Spence’s educational signaling model, the constituents are the employers, the politiciansare the workers, not buying stock is the costly credential, and the anti-tobacco politicians are the highability types for whom the credential is less costly.

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This is clearly an incomplete contracting situation: it is impossible to write down all of

the ways in which the politician could serve or not serve the firm’s interests, and at any

rate courts would not enforce such a quid pro quo. The firm may then find it beneficial to

require the politician to take an equity stake in the company, bringing the firm’s and the

politician’s policy interests into closer alignment.3 In short, members may take on equity

in constituent companies in order to make policy promises credible. If the politician is

able to influence policy to help the firm in a way the market did not anticipate, we might

expect these investments to be profitable; in an inefficient market, though, there can be no

such expectation: since the politician engages in the informal contract principally to earn

political returns, it may be that the ex ante financial returns are nonexistent or negative.

Ethics

The final political constraint is ethics regulation. Members of Congress face no special

restrictions on their investment choices (other than the requirement to file annual disclo-

sures), but ethics rules state broadly that members should not financially profit from their

political positions (Code of Conduct, 2005). A member of Congress who invested very

aggressively might face ethics charges in addition to journalistic scrutiny (Boller 1995).

III. Related literature

The empirical literature examining the investments of members of Congress consists of

one published paper and one working paper. Ziobrowski et al. (2004) uses transactions

reported in the 1990s to demonstrate that Senators experienced large abnormal returns.

As an indication of the uncanny timing exhibited by Senators in the period they consider,

stocks sold by Senators outperformed the market by 25 percent during the 12 months

prior to the sell date and remained fairly flat thereafter, while stocks that they purchased

beat the market by only 3 percent prior to the buying date, but by almost 28 percent in

3It is standard for corporate directors to be required to own large equity stakes in the companies onwhose boards they serve in order to reduce slack in the shareholder/director relationship. Directors areusually contractually required to hold the stock, which brings us back to the commitment problem here:it may not be time consistent for the politician to continue holding the company’s stock once the firm’scheck has been cashed.

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the year following the transaction. In subgroup analysis they fail to find a difference in

the returns of Democrats and Republicans, and if anything junior Senators appear to be

smarter traders. Intriguingly, they find smaller abnormal returns in 1996 and 1997, which

they suggest may be due to the unfavorable media attention drawn to well-timed stocks

trades among members by Boller (1995). The Ziobrowski et al paper generated attention

in the media4 and in Congress itself.5

Consistent with the idea that members face considerable constraints that offset their

informational advantages, Lenz (2009) finds that members of Congress did not experience

any abnormal wealth gains between 1995 and 2005 compared to similar subjects in the

Panel Study of Income Dynamics (PSID). The finding extends to stock holdings as well.

A large literature in empirical finance examines the investment behavior and perfor-

mance of other groups of investors, providing useful benchmarks and techniques. Barber

& Odean (2000) study the stock portfolios of over 65,000 retail investors between 1991

and 1996, using data from a discount brokerage. They find that individual investors on

average underperform the market (earning annual returns of 16.4 percent in a period when

the market returned 17.9 percent annually), and that more active traders did significantly

worse, largely due to trading fees.

Towards the other extreme of investor sophistication, Jeng et al. (2003) examine trades

reported by corporate executives (who are required by insider trading rules to report sales

and purchases they make of their own company stock). As did Ziobrowski et al. (2004),

Jeng et al. evaluate the timing of insider trades by creating portfolios based on the reported

trades: a “buy” portfolio that reflects the stocks purchased by insiders and a “sell” portfolio

that reflects the stocks sold by insiders. They find that the sell portfolio does just as well

4The study was cited on the New Yorker ’s “Financial Page” of October 31, 2005; it was describedin a Washington Spectator article on January 1, 2006, “An Ethics Quagmire: Senators Beat the StockMarket—and Get Rich—With Insider Information”; and it was featured on “Nieman Watchdog – Questionsthe press should ask” on March 10, 2006.

5The “Stop Trading on Congressional Knowledge” (STOCK) Act was introduced in 2006 as H.R. 5015by Reps Slaughter and Baird and reintroduced in 2007 (by the same members) as H.R. 2341. The billproposed to prohibit members of Congress, congressional staffers, and members of the executive branchfrom trading on “material non-public information,” defined as information members acquire as a resultof their employment by the federal government. For more on policy issues surrounding stock trading bymembers of Congress, see George (2008).

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as the market but that the buy portfolio beats the market by more than 6% per year, a

handsome return for any fund manager.

Cohen et al. (2008) examine the portfolio choices of mutual fund managers in the context

of their social connections with the managers of public companies. They find that portfolio

managers tend to invest more intensively in companies to which they are connected by

educational ties (e.g., the portfolio manager attended the same business school at the

same time as the CEO) and that these “connected” investments perform better than non-

connected investments.6 They further show that returns are concentrated around news

announcements by portfolio companies, suggesting that social ties allow connected portfolio

managers to obtain market-relevant news from company executives in advance of other

investors. Intriguingly, they find that returns are higher the closer the connection is: on

average portfolio managers do better when they invest in companies whose CEO attended

the same university at the same time they did, for example, than when the CEO attended

the same school at a different time. They interpret their findings as evidence that valuable

market information travels through social networks.

Coval & Moskowitz (1999, 2001) also examine portfolio choices and performance by

mutual fund managers. The principal finding of Coval & Moskowitz (1999) is that mutual

fund managers prefer to invest in companies that are headquartered closer to their homes.

The authors calculate the distance from each fund manager to the top holdings in that

manager’s portfolio (weighted by the size of the holdings) and the distance from the fund

manager to the entire market (weighted by market capitalization of the companies), and

find that fund managers are about 180 kilometers closer to their portfolios than they

are to the market, indicating a substantial “domestic home bias.” In their 2001 paper,

6They find that managers place a disproportionate weight on connected companies conditional on in-vesting in those companies; unconditionally, they tend to underweight connected companies. In otherwords, the portfolio weight a fund manager assigns to a particular company, conditional on holding it, ishigher if the manager and firm management are connected, but connected companies as a group have asmaller share of fund managers’ portfolios than their market capitalization would suggest. The combina-tion of conditionally high but unconditionally low portfolio weights might make sense if fund managersare choosing carefully among the connected companies: they perform well on connected companies bothby choosing some companies to hold and others to avoid. Still, Cohen et al. are unable to explain whyfund managers would choose not to hold a larger stake in connected companies if they beat the market sohandily on this sub-portfolio.

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they show that fund managers on average exhibit a modest bias toward the stocks of

local (defined as headquartered within 100 kilometers) companies, with the average fund

manager investing a little under 7% of her assets locally, even though only 6.16% of the

market is located within her local area. Consistent with Cohen et al. (2008), they find

that mutual fund managers enjoy a modest information advantage with respect to local

companies: between 1975 and 1984, the local component of their portfolios outperformed

the nonlocal component by about 2% per year. They find no evidence, however, that

fund managers’ local portfolios outperformed the market after 1985. Examining brokerage

accounts in the 1991-1996 period, Zhu (2002) also finds no evidence that investors with

a stronger propensity to invest locally enjoy higher returns, and shows that investment

behavior seems more driven by familiarity (either through proximity or advertising) than

by responses to fundamental information.

IV. Data

Our study is based on the most comprehensive dataset of congressional investments yet

assembled, complemented by extensive data about connections between members and com-

panies defined by company headquarters, PAC contributions, committee jurisdictions, bill

referrals, and lobbying contracts. The core of the data consists of assets and transactions

reported in 2,235 annual Financial Disclosure Reports for 650 Congressmen who served

between 2004 and 2007.7 The FDRs contain 130,215 reported asset holdings with an ap-

proximate value of $9.2 billion and 68,346 transactions with an approximate value of about

$3 billion. The reports include assets owned by members’ spouses and dependent children,

but excludes securities held in blind trusts. Members are required to disclose the value

of their assets and transactions within broad ranges.8 For all value-based analysis we rely

7The Center for Responsive Politics (CRP) (www.opensecrets.org transcribed the reports and madethem available to us. The average number of FDRs per member is about 3.5 since not all members servefor the entire four years: 70 % have 4 reports, 12 % have 3 reports, 9 % have 2 reports, and 9 % have only1 report. There is also a very small number of missing reports.

8The ranges are $1,001-$15,000; $15,001-$50,000; $50,001-$100,000; $100,001-$250,000; $250,001-$500,000; $500,001-$1,000,000; and over $1,000,000.

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on midpoints and cap all transactions above $1 million.9 Our analysis focuses on com-

mon stock investments, thus ignoring real estate, bonds, and mutual funds reported in the

FDRs.

For every reported asset or transaction corresponding to a public company, we matched

the name to a company in the Center for Research and Security Prices (CRSP) database and

retrieved daily quotes and company data. CRSP only covers companies that were publicly

traded on NYSE, AMEX, or NASDAQ, so this led to the exclusion of other exchanges.10

Overall we ended up with 54,003 stock holdings with an approximate value of $2.5 billion

and 45,135 stock trades (20,025 buys and 25,110 sells) worth about $1 billion in total. The

stocks cover a total of 3,132 unique companies, with about 58% listed on the NYSE, 38%

on the NASDAQ, and 3% on the AMEX. About 88% of the companies are U.S. companies.

Figure 1 shows the empirical cumulative distribution functions (CDF) of the number of

matched stock holdings and stock transactions (buys plus sells) of each member averaged

over the 2004-2007 period. The upper panel shows the CDFs for the average number of

stocks holdings and transactions; the lower panel shows the CDFs for the average value the

stocks holdings and transactions. The functions show that both holdings and transactions

are fairly concentrated. On average 24% (48%) of members report zero stock holdings

(transactions) per year. The average number of holdings (transactions) per year among

members who hold stocks is 32 (25); the median number held (traded) is 5 (0.7) per year.

The distribution of the value of the stock holdings and transactions is also skewed. Each

year, the average value of the stock holdings (transaction volume) per member is about

$1.4 million ($570,000). The median member held stocks worth of $110,000 and traded

with a volume of $12,000. Each year the 20 most active members account for about 48%

of holdings (64 % of transactions).

On average the reported stock holdings account for about 18% (median 13%) of a

9This follows Ziobrowski et al. except that they capped at $250,000.10A very small number of companies listed on those exchanges did not return quotes. Notice that we do

not consider privately held companies despite that the fact that there are several examples of links betweenprivate companies and legislators. Numerous such cases are listed in the Citizens for Responsibility andEthics in Washington’s annual reports (2005-2008) on the “Most Corrupt members of Congress.” Seehttp://www.crewsmostcorrupt.org/report.

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member’s total reported assets and this proportion is increasing with a member’s total

assets. The annual trading volume varies widely across members. Each year the median

member trades about 13% of the value of her stock holdings, but a few members trade

a much higher proportion: Hillary Clinton, for example, has trades totaling 110% of her

holdings.

V. Portfolio Choices

A. Measures of Connectedness

We first examine portfolio choices of Members of Congress, focusing on the extent to which

Members focus their investments on local firms, firms that provide campaign donations, and

firms that are particularly affected by congressional legislation. In particular, we employ

regression analysis to investigate how the weight that a Member puts on a company in his

portfolio varies as a function of the connections he has with the firm. (See Cohen et al.

(2008) et al for another example of this kind of analysis.)

For each of the 453 Members with stock holdings we construct their portfolio weights in

basis points by computing the share of holdings of each firm relative to their total holdings

averaged over the 2004-2008 period. We include all 2, 617 firms that are held by at least

one Member in this period resulting in 1, 185, 501 possible Member-firm holdings. Together

these firms make up more than 94 % of the total market value in the entire universe of

CRSP common stocks and thus provide an accurate coverage of the universe of firms among

which Members are likely to chose their stocks allocations.

To compare allocations in stocks to which Members are connected politically, relative

to stocks to which they are not connected we define three sets of measures of “connected”

holdings. First, we classify stocks that are connected to Members through geographic

proximity. It is well known in empirical finance that mutual fund managers and individ-

ual investors prefer to invest in local stocks since investors are more familiar with local

firms (Coval & Moskowitz (1999, 2001), Zhu (2002)). For Members we expect a similar

local familiarity bias, but it may be even stronger since politicians have various additional

dealings and frequent contact with local companies that ask their representative for policy

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favors. We define In State as a binary indicator for stocks to which a Member is connected

because the company has its headquarter in the Member’s home state.11

Second, we classify stocks that are connected because the company PACs provided

campaign donations to a Member.12 We define the binary indicator Contributions that

codes a company and member as connected if, in the period 2004 to 2008, the company’s

PAC gave any contribution to the member. To capture the increasing degree of strength of

this link we also code another binary indicator Contributions (> p50) that codes stocks of

companies whose contributions exceeded the median amount of contributions given to each

Member. Finally, Contributions Strength measures the share of a company’s contributions

as a fraction of a Member’s total contributions in basis points.

Third, we classify connections between firms and Members based on the Member’s

policy portfolio based on actual corporate lobbying. In specific, we tally up how much each

company lobbied on bills referred to each committee, and define a company as connected to

a member if the company lobbied a bill before one of the committees on which the member

sat during 2004-2008.13 We again use three measures: Lobbying is a binary indicator for

companies that did any lobbying of this sort, Lobbying (> p50) codes companies whose

lobbying exceeded the median amount of lobbying for each Member, and Lobbying Strength

measures the share of a company’s lobbying as a fraction of a Member’s total lobbying in

basis points.

11We extract the headquarter location for firms from Google Finance.12PAC contributions data comes from the FEC via watchdog.net13In this approach we thus use bill referrals rather than statutory jurisdictions to define committee policy

areas (King 1994), and we use bill lobbying rather than industrial classifications to determine which policyareas companies view as important to them. We considered an alternative coding based on a mappingbetween industries and committees based on the committees’ stated jurisdictions, extending Myers (2007)’smapping of House committees to two-digit SIC codes. (The approach of linking committees to industriesthrough jurisdictions has previously been used by Munger (1989), Endersby & Munger (1992)). However,the industry classifications are far too coarse in some instances, making many companies appear connectedto members when they are not, and in other cases clear connections are overlooked. For example, NorthropGrumman, a major defense contractor, falls under SIC code 38, “Instruments and Related Products,”along with photographic equipment companies like Kodak, Fuji, and Canon and a host of medical devicecompanies. According to Myers’ mapping, this industry comes under the jurisdiction of the armed servicescommittee, but not the defense subcommittee of the appropriations committees. The problems withusing statutory committee assignments were noted by King (1994). In our view the lobbying/bill-referralapproach gives the best representation of which members had a special role in shaping legislation thatmattered to companies.

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Overall, about 4 % of all stocks are coded as connected by the In State metric, about

4 % of all stocks are coded as connected by the Contributions metric, and about 18 %

of all stocks are coded as connected by the Lobbying metric. Apart from the connections

outlined above, Members may choose stocks based on a number of other motivations (such

as the general popularity of certain firms, the level of diversification, etc.). Since some

of these factors may be correlated with our connection measures, we include a full set of

Member and company fixed effects into the regression to difference out these two sources of

unobserved heterogeneity. The model is therefore identified based on within-Member and

within-company variation and we can rule out the possibility that the results are driven

by unobserved factors that vary across Members and or firms.14 We cluster our standard

errors by Members in order to account for the fact that a Member’s investments may not

be independent.

B. Results

Results from the portfolio choice analysis are presented in table 1. We find a strong

political skew in the Members’ portfolio allocations. Looking at column 1, Members’ invest

an additional 15 basis points if a companies is in their home state. Compared to the

average weight of 3.8 basis points this constitutes an increase of more than 400 %, a degree

of overweighing that far exceeds previous estimates of local bias for other types of investors

(NEED CITES HERE). As another benchmark, in their study of education connections

Cohen et al. (2008) find that fund managers place an additional 8 basis points on companies

where a senior officer (CEO, CFO, or Chairman) attended the same school, with the same

degree, and at a similar time as the fund manager.15

We find that Members’ also heavily overweight companies that provide campaign con-

tributions. Compared to non-contributors, they place an additional 14 basis points in com-

panies with any PAC contributions (a 360 % increase over the mean weight). Moreover, as

column 3 and 4 suggest this premium is increasing with the strength of the contribution

14Note that the use of both fixed effects extends the approach used by Cohen et al. (2008) who includeeither firm or fund fixed effects but never both.

15Their table 2 model 8 with firm and quarter fixed effects (but not fund fixed effects).

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connection. Members place an additional 24 basis points on companies that are among the

top 50 percent of their contributors (column 3) and a 10 basis point increase in the relative

share of contributions from a company results in a 0.5 basis point increase in the Member’s

portfolio weight (column 4).

Looking at the measures of lobbying connections, we find no bias towards firms that

lobby committees that Members sat on. The points estimate for Lobbying is almost zero

and the same is true once we narrow in on companies whose lobbying exceeds the median

amount lobbying for each Member (column 3) or use the relative measure of Lobbying

Strength (column 5).

Finally, in column 2 we consider whether the overweighing is increasing for companies

that are connected through multiple connections by including one dummy variable for each

possible combination of In State, Contributions, and Lobbying. The estimates of the con-

ditional average weights for each of the combinations are also displayed in Figure 2. The

overweighing is clearly increasing for multiple connections. Compared to unconnected com-

panies, Member’s place an additional 10 basis points in companies that are solely connected

through state, and an additional 11 basis points in companies that are solely connected

only through contributions. Companies that are both contributors and in the home state

receive a striking 76 additional basis points on average. This premium increases further to

96 basis points if, in addition, the company is also connected through committee lobbying.

This reinforcing effect of additional connections is confirmed column 5 where we interact

our continuous measures of contributions and lobbying strength with the home state indi-

cator. In both models the interaction terms enter positive and significant indicating that

the portfolio weights are increasing in contributions and lobbying to a much stronger extent

if the companies are located in a Members’ home state.

Taken together these results suggest that there is a large political bias in a Members

portfolio choice: Members place considerably larger bets in politically connected companies.

We have replicated this finding with several different measures (such as different cutpoints,

based on ranks, etc.) and the results are very robust to alternative specifications. Moreover,

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we have also replicated the analysis conditioning only on stocks that Members actively

choose to hold. The results, which are displayed in table 2 in appendix A are very similar

to the to the unconditional overweighing. That is, even comparing only among the stocks

that Members choose to actively hold, they place much larger bets on politically connected

companies. For example, compared to an average weight of 297 basis points they place

an additional 104 basis points on home state firms and an additional 42 basis points on

firms that provide campaign contributions. The overweighing is similarly increasing in the

strength and combinations of the connections. For example, a home state companies that

also contribute receive an additional 357 basis points on average.

C. Measures of Member-Firm Connections

D. Portfolio weight regressions

As an alternative way of assessing members’ portfolio choices, A key advantage of this

approach is that it helps somewhat to disentangle the relationships among these different

connections, as e.g. many contributors are likely also local companies and/or lobby on bills

referred to a member’s committees. Note however that the question addressed is somewhat

different from the one posed above: here we examine the weight a member puts on a

company, conditional on holding it, as a function of the connections between the company

and the member. (Above, the comparison was between the total portfolio weight given to a

set of connected companies by a member and the weight given to that set by all members.)

Table 5 provides the regression results. The regression includes dummies for state

connection, contribution connection, and committee lobbying connection (our preferred

measure of policy oversight by the member of the firm16 ) as well as fixed effects for

16Although the approach of linking industries to committees based on committees’ statutory jurisdictionshas been used in many previous papers (see cites above), the industry classifications are far too coarse insome instances, making many companies appear connected to members when they are not, and in othercases clear connections are overlooked. For example, Northrop Grumman, a major defense contractor, fallsunder SIC code 38, “Instruments and Related Products,” along with photographic equipment companieslike Kodak, Fuji, and Canon and a host of medical device companies. According to Myers’ mapping, thisindustry comes under the jurisdiction of the armed services committee, but not the defense subcommitteeof the appropriations committees. The problems with using statutory committee assignments were notedby King (1994). In our view the lobbying/bill-referral approach gives the best representation of whichmembers had a special role in shaping legislation that mattered to companies.

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member, company, and year.17 With the average portfolio weight in the sample being just

over 5%, the weight given to a connected company is a full 2 percentage points higher

if the company is in-state, and about half a percentage point higher if the company is a

contributor or lobbies bills before the member’s committees.

It may seem inconsistent that committee lobbying produces a positive bias here (con-

ditional on owning stock in the company) but no bias above (looking at the portfolio share

of connected firms). The two findings are, however, consistent with a situation in which

the choice not to hold some connected companies is balanced by the choice to take bigger

stakes in the companies the members do hold.

E. Portfolio weight regressions

As an alternative way of assessing members’ portfolio choices, we examine the weight that

a member puts on a company in his portfolio as a function of the connections he has with

the company. (See Cohen et al. (2008) et al for another example of this kind of analysis.)

A key advantage of this approach is that it helps somewhat to disentangle the relationships

among these different connections, as e.g. many contributors are likely also local companies

and/or lobby on bills referred to a member’s committees. Note however that the question

addressed is somewhat different from the one posed above: here we examine the weight

a member puts on a company, conditional on holding it, as a function of the connections

between the company and the member. (Above, the comparison was between the total

portfolio weight given to a set of connected companies by a member and the weight given

to that set by all members.)

Table 5 provides the regression results. The regression includes dummies for state

connection, contribution connection, and committee lobbying connection (our preferred

measure of policy oversight by the member of the firm18 ) as well as fixed effects for

17Because of the fixed effects, we can rule out the possibility that our results are based on e.g. correlationsbetween members’ overall level of diversification and connectedness to companies, or correlations betweencompanies’ popularity among investors generally and their amount of lobbying or political contributions.

18Although the approach of linking industries to committees based on committees’ statutory jurisdictionshas been used in many previous papers (see cites above), the industry classifications are far too coarse insome instances, making many companies appear connected to members when they are not, and in other

15

member, company, and year.19 With the average portfolio weight in the sample being just

over 5%, the weight given to a connected company is a full 2 percentage points higher

if the company is in-state, and about half a percentage point higher if the company is a

contributor or lobbies bills before the member’s committees.

It may seem inconsistent that committee lobbying produces a positive bias here (con-

ditional on owning stock in the company) but no bias above (looking at the portfolio share

of connected firms). The two findings are, however, consistent with a situation in which

the choice not to hold some connected companies is balanced by the choice to take bigger

stakes in the companies the members do hold.

F. Discussion

We view the finding that members of Congress invest disproportionately in companies to

which they are politically connected as quite striking. The bias is strongest toward local

companies; as noted above, mutual fund managers also appear to favor local companies but

apparently not to the degree that members of Congress do. The portfolio weight assigned

to contributor companies and committee-lobbying companies is smaller but suggests that

members invest almost 10% more in connected than in non-connected companies, con-

ditional on state connection and member and firm fixed effects, which is quite a sizable

difference.20

cases clear connections are overlooked. For example, Northrop Grumman, a major defense contractor, fallsunder SIC code 38, “Instruments and Related Products,” along with photographic equipment companieslike Kodak, Fuji, and Canon and a host of medical device companies. According to Myers’ mapping, thisindustry comes under the jurisdiction of the armed services committee, but not the defense subcommitteeof the appropriations committees. The problems with using statutory committee assignments were notedby King (1994). In our view the lobbying/bill-referral approach gives the best representation of whichmembers had a special role in shaping legislation that mattered to companies.

19Because of the fixed effects, we can rule out the possibility that our results are based on e.g. correlationsbetween members’ overall level of diversification and connectedness to companies, or correlations betweencompanies’ popularity among investors generally and their amount of lobbying or political contributions.

20For comparison, this bias in portfolio weights is about the same as the bias toward connected compa-nies found in Cohen et al. (2008), whose central finding is that mutual fund managers make bigger bets onconnected companies. (Connected companies there are defined based on shared educational backgroundbetween mutual fund managers and the firm’s managers). Meanwhile, the unconditional home bias esti-mates in that paper actually show a bias away from connected companies compared to the market as awhole, while here we find strong pro-connection bias by both measures.

16

The biases toward constituent and contributor companies that we observe are consistent

with the idea that members invest based on information about firms, as well as the signaling

and bonding stories: members probably know more about local and contributor companies,

and they may be particularly interested in signaling and/or aligning incentives with them.

The bias away from companies whose industries match one’s committee jurisdictions is not

consistent with the idea that members have information about regulation and invest based

on that information, but this is somewhat contradicted by a bias toward companies that

lobby bills referred to one’s committee.

In order to assess the mix of informational advantages and political considerations

that drive members’ investing behavior (and particularly their portfolio bias toward local

companies and contributors), we now turn to an examination of the returns of members’

portfolios, paying particular attention to differences in the performance of investments

made in connected and unconnected portfolios.

VI. Event Study of Timing of Transactions

We now turn to the performance of members’ investments.

A. Methodology

We use an event study approach to examine whether members have well-timed stock trans-

actions. The basic idea is to calculate, for each trading day around a transaction (e.g.

-2,-1,0,1,2) the average return for the traded stocks, and to see whether stocks on average

rose or fell before and after the member chose to sell or buy. Let t be an event-day indicator

that ranges from t = (−255,−254, ..., 255) with t = 0 denoting the day at which a member

sold or bought a particular stock. Let i = (1, ..., N) be an indicator of the traded stocks

in a particular sample. For several samples of buy and sell transactions, we compute the

cumulative abnormal return (CAR) on each event-day. First, we compute the daily average

abnormal return for the sample transactions as

ARt =

∑Ni wi(Ri,t −Rm,t)∑N

i wi

17

where Ri,t is the return from sample transaction i on the calendar day that corresponds to

event day t, Rm,t is the return on the CRSP value weighted market index, and wi is the

trade weight of transaction i. We use transaction values as our trade weights. As noted

above, we use midpoints of the ranges reported in the FDRs to obtain the transaction value

unless the exact amount is reported. The cumulative abnormal return for a given day t is

then computed as

CARt =t∑

T=−255

ARt

To make the figures more easily interpretable, we normalize each CAR series by subtracting

the value of CAR0 so that the CAR is always zero at the trading day t = 0. In addition

to the value-weighted approach for the CARs described above we also compute an equal-

member weighted CAR where the ARt is first computed for the transactions of each member

separately and then averaged across members. Intuitively, the value weighted approach

examines how the value-weighted average of all transactions performed relative to the

market, while the equal-member weighted approach examines how the transactions of the

average member performed relative to the market.

Notice that we use the CAR analysis primarily as a descriptive tool to describe the

timing and performance of the members transactions vis-a-vis the market. Further below

we provide more formal tests based on the calendar time portfolio approach.

B. Results of CAR Analysis

B.1. Overall CAR

Figure 3 shows the CAR plots for the buy and sell samples of all members (value weighted

and equal-member weighted), as well as the subsamples of only the Democratic and Repub-

lican members. Figure 4 shows the CAR plots for best and worst five selling and buying

members.

18

B.2. State Connected versus Unconnected

In this section we compare the timing of stock trades of companies headquartered in a

member’s state relative to stocks trades of companies that are not. Figure 5 shows the

CAR plots for the buy and sell samples of selected subsamples: all members, Republicans,

House Republicans, and Senate Democrats. For these subsets, members tend to have better

timed stock transactions with companies that are in their own state or district compared

to out-of-district companies. The same pattern roughly holds for many other subsamples.

Figure 6 shows similar CAR plots for four selected members. Later we conduct systematic

analysis to examine these patterns in more detail.

B.3. Contribution-Connected versus Unconnected

In this section we compare the performance of stock trades of companies which contributed

to a members financially, relative to stocks trades of companies that did not contribute.

Figure 7 shows the CAR plots for the buy and sell samples of selected subsamples: All

members, Rebulicans, House Rebulicans, and Senate Democrats. The results suggest that

members fare worse with stocks transactions of companies that contributed to the members

compared to their transactions with companies that did not contribute to them.

B.4. Committee Connected versus Unconnected

TBA

VII. Analysis of Calendar Time Portfolio Returns

A. Methodology

We use a standard calendar time portfolio approach to examine the risk adjusted returns

that members earned on their portfolios. For members h ∈ (1, ..., H) we observe their

holdings at the end of each year as well as the transactions that occurred within each year.

We use this information to construct a member’s monthly portfolios returns Rh,t for each

of t ∈ (1, ..., T ) months between January 2004 and January 2008. Let i ∈ (1, ..., Nt) be

an indicator for stocks held by member h in month t, each with a dollar amount of wi. A

19

member’s monthly portfolios return is computed as the weighted average of the monthly

returns of the portfolio’s underlying stocks, Rh,t =∑N

i=1 wh,iRi,t∑Ni=1 wi,t

. Weights are computed at

the beginning of each month; we therefore assume (as is standard) that all transactions

reported in a given month take place at the end of the month.21

We compute two kinds of portfolios, corresponding to different ways of averaging returns

across members. Value weighted calendar time portfolios Rp,t are computed by averaging

across members, weighting individual member portfolios by the members total dollar hold-

ings in that month, i.e. Rp,t =∑

h=1Hwh,tRh,t∑h=1Hwh,t

where wh,t =∑N

i=1wi,t is the total value

invested by member h in month t. This approach corresponds to an investment strategy of

investing in a portfolio that mimics dollar for dollar the aggregate Congressional portfolio.

We also compute monthly equal-member weighted calendar time portfolios by taking a

simple average across members portfolios for each calendar month, so that wh,t = 1 and

every member is weighted equally regardless of how much she invested.

To test whether members outperform the market we regress the risk-adjusted calendar

time portfolios on a standard set of controls. (This is known in empirical finance as the

“four-factor” model - a regression of an excesss return series on the monthly returns from

the three Fama & French (1993) factors and Carharts (1997) momentum factor..) The

regression yields an intercept (commonly called “alpha”) that calculates the risk-adjusted

monthly returns on the portfolio:

Rp,t −Rf,t = α + β(Rm,t −Rf,t) + sSMBt + hHMLt + wWMSt + et,

where Rf,t is the risk-free rate of return (ie. the return on a 1-month Treasury bill),

Rm, t is the normal market return, SMBt is the size premium (small minus big), HMLt

is the value premium (high minus low), WMLt is the momentum factor (winners minus

losers).22 To compare the excess returns for connected and unconnected portfolios we also

consider a hedged portfolio in which the dependent variable is the difference between the

21Barber & Odean (2000) show that these simplifying assumptions only cause minor differences in thereturn calculations even with high portfolio turnover. In our data, the turnover rates are low so our returncalculations should only marginally be affected by ignoring the intra-month trading activity.

22We are grateful to Kenneth R. French for providing the factor data in his data library at http:

//mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

20

(risk free adjusted) return on the connected portfolio minus the return on the unconnected

portfolio. This mimics an investment strategy of going long in the connected and short in

the unconnected stocks.

B. Results of Portfolio Tests

B.1. All members

Table 6 shows the annualized alpha estimates for the value-weighted portfolios constructed

from all members.23 The first row shows the average excess returns for the portfolio of all

stocks. We find that the Congressional portfolio performs about as well as the market; the

excess return is an insignificant minus .4 percent. This finding stands in contrast to the

results from Ziobrowski et al. who found that in the 1990’s the stock transactions of Sen-

ators produced excess returns. Notice that in contrast to this earlier analysis, our returns

estimates are based on real portfolios and therefore approximate the actual returns.24

Rows 2-4 show the results when we split the portfolios into stocks of companies that are

located in and out of a member’s own state. We find that the in-state portfolio on average

outperforms the market by about 3.3 percent annually. The returns on the out-of-state

portfolio slightly underperformed the market. The returns on the hedged portfolio suggest

that on average the connected stocks outperform the unconnected stocks by about 4.2

percent annually. As we saw earlier, members also tend to overweight in-state companies

in their portfolios. Based on the fact that the in-state portfolio outperforms the market, this

appears to be a wise strategy, suggesting that members are not only bonding themselves

to local interests by investing locally but also picking winners.

Rows 5-7 show the results when we split the portfolios into stocks of companies that

contribute to a members’ campaign funds and stocks of companies that do not contribute.

Here a stock is coded as connected if the company is among the top 20 PAC contributors

over the period 2004-2007.25 We find that the portfolio of contribution-connected stocks

23The member-equal weighted estimates are qualitatively similar and not shown here.24Ziobrowski et al. (2004) create a buy and sell portfolio from the record of transactions by assuming

that stocks are held for a certain amount of time after the transaction.25Results for other definitions, such as the top 40 contributors or the top 1% of contributors, are quali-

tatively similar.

21

on average underperform the market by about 4 percent annually. The returns on the

unconnected portfolio is indistinguishable from the normal market return. The returns

on the hedged portfolio suggest that on average the connected stocks underperform the

unconnected stocks by about 3.6 percent annually. So despite the fact that members invest

more heavily in companies that provide campaign contributions, these investments perform

worse than those in companies that do not contribute. This is consistent with the idea that

members invest in companies in part to seal political exchanges rather than to profit from

an informational advantage.

Rows 8-10 examine the differences in returns for the committee jurisdiction connection.

We find no differences in the returns of the connected and the unconnected portfolio. As

indicated above, this may be because our definition of committee-connected companies

provides only a weak signal. If it is indeed true that members do not enjoy excess returns

in trading committee-connected stocks, it would appear that members either do not have

an informational advantage due to their regulatory power or they do not use it in investing.

Rows 11-13 examine the differences in returns for the committee lobbying connection

(ie companies that lobbied bills before a member’s committees). Here we find that the con-

nected portfolio underperforms the market by almost 2% while the unconnected portfolio

outperforms the market by about 1.3% annually. (The first point estimate has a p-value

of .07 and the second of .47.) The difference between them is about -3.3, with a p-value

of .12. Thus while the estimate is somewhat imprecise, these connected investments also

seem to underperform compared to unconnected investments, suggesting that whatever

regulation-related market knowledge they have is not translated into abnormal investment

returns.

B.2. Members

In this section we examine the distribution of alpha returns across members. We compute

annualized four-factor alphas for each of the 429 members who report at least two years of

stock holdings using the calendar time portfolio approach outlined above. Figure plots the

return estimates against the average annual value of a member’s investment. There is a

22

significant variation in members’ returns ranging from 45 % annual excess returns for John

Yarmuth (D-Ky) to -43 % for Bob Inglis (R-SC). In line with the overall portfolio results

shown above, the average returns across members is -2.9 %. The returns are roughly nor-

mally distributed. On average members who invest more earn higher returns as indicated

by the linear fit.

What else accounts for the variation in member’s returns? We regress the member’s

return on a set of explanatory variables including the member’s party, the year first elected

to Congress, a dummy for whether the member served in a leadership position (committee

chairman or ranking member), a “revolving door score” obtained from the CRSP which

consists of the number of a member’s staffers who either came to Capitol Hill after repre-

senting private interests or left the member’s staff for a lobbying position. We also include

a two dummy variables that capture allegations of unethical activities. The first dummy,

Named Corrupt 1, is coded one for those members that have once or twice between men-

tioned in the CREW’s list of the 20 Most Corrupt members of Congress over the last four

years. The second dummy, Named Corrupt 2, is coded one for those members that have

been named three or more times over the last four years. Table 7 shows regression results.26

We find that higher revolving door scores are associated with higher returns. Ten addi-

tional revolving door staffers are associated with about a 0.1 increase in the average annual

returns. Seniority is negatively correlated with returns. Members that enter Congress one

year later have 0.1 lower returns on average. Somewhat remarkably, we find that the 128

members with leadership position earn 2.5 higher annual excess returns on average. Simi-

larly, those members named more than twice on the list of corrupt members on average earn

about 4.8 higher returns. Notcie that only 4 members fall into this group, however. Finally,

we find no significant different betwee the average returns of Republican and Democratic

members.

VIII. Conclusion

TBA

26Notice that this regression ignores the estimation uncertainty in the members’ alpha returns.

23

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25

Tables

Table 1: Portfolio Weights as a Function of Member-Firm Connections

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

Dependent Variable: Portfolio WeightMean: 3.82

In State 15.18 9.86 15.13 15.13 11.85(1.59) (1.36) (1.59) (1.61) (1.66)

Lobbying 0.47 0.58(0.62) (0.60)

Contributions 14.72 11.49(2.43) (4.23)

In State & Lobbying 12.82(4.25)

In State & Contributions 75.99(21.83)

Lobbying & Contributions 6.14(2.65)

In State & Contributions & Lobbying 96.18(16.35)

Lobbying (> p50) 0.90(1.22)

Contributions (> p50) 24.06(4.28)

Lobbying Strength -0.0001 -0.014(0.0274) (0.026)

Contribution Strength 0.059 0.035(0.017) (0.018)

Lobbying Strength · In State 0.55(0.24)

Contribution Strength · In State 0.085(0.053)

Members Fixed Effects x x x x xFirms Fixed Effects x x x x x

N 1,185,501 1,185,501 1,185,501 1,185,501 1,185,501

Note: Regression coefficients with standards errors (clustered by Members) in parenthesis. The dependent variable is theportfolio weight, i.e. the share of holdings of a firm in a Member’s portfolio (in basis points). Members’ portfolios arecomputed as average holdings over the 2004-2008 period. In State is a binary indicator for firms that are connected toa Member since they are located in a Member’s home state. Lobbying is a binary indicator for firms that are connectedto a Member since they lobbied a Committee on which the Member served. Contributions is a binary indicator for firmsthat are connected to a Member since they provided her with campaign contributions. Lobbying (> p50) and Contributions(> p50 ) are binary indicators for firms that provided more than the median lobbying or contribution amount for eachMember. Lobbying Strength and Contribution Strength measure a firm’s share of lobbying or contributions relative to eachMember’s total lobbying or contributions (in basis points). All regressions include a full set of Members and firms fixedeffects (coefficients not shown here).

26

Table 2: Portfolio Weights as a Function of Member-Firm Connections (Conditional onHolding)

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

Dependent Variable: Portfolio WeightMean: 297.81

In State 104.6 32.49 105.3 97.56 75.40(37.3) (36.03) (36.76) (36.50) (36.38)

Lobbying 17.3 28.84(17.9) (18.19)

Contributions 42.9 70.86(21.5) (53.97)

In State & Lobbying 62.03(72.82)

In State & Contributions 357.3(177.8)

Lobbying & Contributions 40.56(27.15)

In State & Contributions & Lobbying 268.0(87.45)

Lobbying (> p50) 1.155(19.84)

Contributions (> p50 ) 54.87(29.49)

Lobbying Strength 0.018 0.035(0.028) (0.028)

Contribution Strength 0.040 0.014(0.022) (0.023)

Lobbying Strength · In State -0.12(0.09)

Contribution Strength · In State 0.11(0.05)

Members Fixed Effects x x x x xFrims Fixed Effects x x x x x

N 15,211 15,211 15,211 15,211 15,211

Note: Regression coefficients with standards errors (clustered by Members) in parenthesis. The dependent variable is theportfolio weight, i.e. the share of holdings of a firm in a Member’s portfolio (in basis points). Members’ portfolios arecomputed as average holdings over the 2004-2008 period. In State is a binary indicator for firms that are connected toa Member since they are located in a Member’s home state. Lobbying is a binary indicator for firms that are connectedto a Member since they lobbied a Committee on which the Member served. Contributions is a binary indicator for firmsthat are connected to a Member since they provided her with campaign contributions. Lobbying (> p50) and Contributions(> p50 ) are binary indicators for firms that provided more than the median lobbying or contribution amount for eachMember. Lobbying Strength and Contribution Strength measure a firm’s share of lobbying or contributions relative to eachMember’s total lobbying or contributions (in basis points). All regressions include a full set of Members and firms fixedeffects (coefficients not shown here).

27

Table 3: Top 50 members by Number of Stock Holdings

Stock Holdings Stock Tradesmember Number Value ($K) Number Value ($K) RankElizabeth Dole (R-NC) 416 147,525 285 3,648 1John Kerry (D) 234 92,086 614 77,834 2Robin Hayes (R-NC) 198 54,413 8 126 3Peter G. Fitzgerald (R-Ill) 53 37,436 0 0 4Jane Harman (D-Calif) 822 31,645 544 20,400 5Dianne Feinstein (D-Calif) 339 24,659 1 438 6Lincoln D. Chafee (R-RI) 56 18,734 31 7,944 7Rodney Frelinghuysen (R-NJ) 144 17,168 51 2,875 8F. James Sensenbrenner Jr. (R-Wis) 262 12,631 82 2,218 9Sheldon Whitehouse (D-RI) 104 9,063 0 0 10Chris Chocola (R-Ind) 98 8,880 440 25,763 11John Edwards (D) 88 7,459 0 0 12Nancy Pelosi (D-Calif) 49 7,374 22 6,751 13Mark Dayton (D-Minn) 351 6,958 246 2,325 14Bob Corker (R-Tenn) 348 6,942 0 0 15Michael McCaul (R-Texas) 206 6,782 224 3,598 16Fred Upton (R-Mich) 68 6,537 3 26 17Doug Ose (R-Calif) 50 5,478 0 0 18Tom Petri (R-Wis) 15 5,463 9 723 19Johnny Isakson (R-Ga) 52 4,660 34 2,474 20Cass Ballenger (R-NC) 144 3,998 134 1,511 21James L. Oberstar (D-Minn) 60 3,905 62 6,352 22Kenny Ewell Marchant (R-Texas) 484 3,885 955 6,413 23Amo Houghton (R-NY) 529 3,617 0 0 24Jim Leach (R-Iowa) 181 3,463 257 3,649 25Tom Osborne (R-Neb) 406 3,179 481 3,845 26Nita M. Lowey (D-NY) 50 3,172 19 584 27Ken Lucas (D-Ky) 4 3,125 0 0 28Lloyd Doggett (D-Texas) 60 3,090 6 89 29Anne M. Northup (R-Ky) 76 3,078 30 668 30Stephen Ira Cohen (D-Tenn) 82 2,959 8 138 31Judy Biggert (R-Ill) 103 2,948 49 858 32Vernon Buchanan (R-Fla) 134 2,887 212 5,420 33Frank R. Lautenberg (D-NJ) 68 2,872 32 2,537 34Otter (R-Idaho) 69 2,761 52 992 35Carolyn B. Maloney (D-NY) 43 2,726 16 713 36Tom Lantos (D-Calif) 16 2,523 5 142 37Kay Bailey Hutchison (R-Texas) 56 2,390 10 112 38Dave Hobson (R-Ohio) 56 2,246 17 349 39Charlie Wilson (D-Ohio) 31 2,230 20 312 40Don Sherwood (R-Pa) 20 2,186 2 177 41James M. Inhofe (R-Okla) 57 2,162 42 507 42Rob Simmons (R-Conn) 72 2,119 34 725 43Dave Camp (R-Mich) 58 2,054 29 658 44Virgil H. Goode Jr. (R-Va) 30 1,994 11 228 45Jay Rockefeller (D-WVa) 4 1,938 4 762 46Lamar Alexander (R-Tenn) 3 1,938 2 550 47John W. Warner (R-Va) 124 1,937 98 1,137 48Ben Nelson (D-Neb) 7 1,910 5 289 49Shelley Moore Capito (R-WVa) 31 1,840 12 686 50

28

Table 4: Average Home Bias in Members’ Stock HoldingsConnection si,i s+,i mean bias p.value

State 0.18 0.04 0.15 0.00State (House only) 0.19 0.04 0.16 0.00

District (House only) 0.05 0.002 0.04 0.00Contributor 0.24 0.17 0.09 0.00

Contributor (top 40) 0.16 0.09 0.07 0.00Contributor (top 20) 0.10 0.05 0.05 0.00

Contributor (out of state) 0.18 0.15 0.02 0.02Committee jurisdiction 0.15 0.17 -0.04 0.01

Committee jurisdiction (house only) 0.13 0.15 -0.04 0.02Committee lobbying 0.55 0.55 0.00 0.39

Note: si,i refers to the share of member i’s portfolio devoted to companies connected to him in the specified way(e.g. by sharing a state or by contributing money to he member through a PAC); si,i is the average of this overmembers. s+,i is the average share of members’ portfolios devoted to companies connected to member i in thespecified way; s+,i is the average of this over members. When the two numbers are the same, members on averageshow no bias for or against companies to which they are connected. Bias is calculated as described in the paper;it is roughly equal to the difference between the first two columns. The p-value is based on a permutation testthat asks whether how often a bias as large as the one observed would result if in fact there were no connectionbetween company connections and portfolio choices.

Table 5: Portfolio Weights as a Function of Member-Firm ConnectionsCoef SE P-val

State Connected 0.021 0.004 0.000Contribution Connected 0.005 0.002 0.013

Committee Lobbying Connected 0.005 0.002 0.015Note: Regression coefficients, robust standards errors, and p-values shown. N=26,085(member-asset-years). The dependent variable is the portfolio weight for a particularcompany in a member’s yearly portfolio. The regression include and full set of memberand company fixed effects (coefficients not shown here). As a benchmark, the averageportfolio weight across observations is 0.054.

29

Table 6: Annualized Excess Returns from Four-Factor Fama-French ModelsAlpha

Return SE P-ValAll Stocks -0.41 1.07 0.70

State ConnectionConnected 3.34 1.56 0.04Unconnected -0.86 1.14 0.45Connected vs. Unconnected 4.20 1.84 0.03

Contribution ConnectionConnected -3.98 2.19 0.08Unconnected -0.32 1.19 0.79Connected vs. Unconnected -3.66 2.54 0.16

Committee Jurisdiction ConnectionConnected 0.00 2.53 1.00Unconnected -0.49 1.05 0.64Connected vs. Unconnected 0.49 2.48 0.84Committee Lobbying ConnectionConnected -1.98 1.07 0.07Unconnected 1.33 1.84 0.47Connected vs. Unconnected -3.30 2.06 0.12Note: Annualized alphas (ie. the annual excess returns in percent) from calendar-time portfo-lio regression with four factor Fama-French models. Standard errors and p-values are shownnext to the alpha estimates. N = 49 in all regressions. Each row represent the alphas froma separate regression for a different portfolio. The dependent variable is the return of theportfolio in a particular month. The independent variables are the normal market return, asize premium (small minus big), a value premium (high minus low), and a momentum factor(winners minus losers). All portfolios are value weighted portfolios that consist of the averagereturn across all stock held by all members and weighted by the value of the holdings. Astock is connected by state, if the company is headquartered in a member’s state. A stock isconnected by contribution if the company is among the member’s top 20 contributors.A company is connected by lobbying if the company reports lobbying expenditures for lob-bying that refers to a committee that a members serves on.Connected (unconnected) portfolios includes all connected (unconnected) stocks weightedby the value of holdings. Connected vs. unconnected is a hedged portfolio that is long inthe connected and short in the unconnected stocks. Members include all Congressmen thatserved between 2004-2007. Stocks includes all stocks traded on the NYSE, AMEX, NASDAQ.Value of holdings are reported in bands; midpoints are used for the value weights.

30

Table 7: The Correlates of member Specific Returnscoef se p-val

Revolving Door Score 0.013 0.007 0.047Year First Elected 0.102 0.066 0.125Named Corrupt 1 -0.733 2.161 0.735Named Corrupt 2 4.821 1.612 0.003Leadership Position 2.596 1.421 0.068Republican -0.318 0.937 0.734Constant -207.830 132.367 0.117Note: Regression coefficients, robust standards errors, and p-values shown. N=429.The dependent variable is each members’s four-factor annualized alpha return.

31

Figure 1: Cumulative Distribution Functions: Stock Holdings and Stock Transactions ofmembers 2004-2007

0.0

0.2

0.4

0.6

0.8

1.0

number

CD

F(x

)

●

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●● ●●●●●●● ●● ● ● ●

0 1 10 100 1000

●

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●●●●●●●●●●●●●●●● ●●●●● ●●●●●●●●●●●●●●●●●●● ●●●●●●●●●● ●●●●●●●● ● ●

●

●

Reported Stock HoldingsReported Stock Transactions

0.0

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0.4

0.6

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1.0

value ($ 1000s)

CD

F(x

)

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●●●●●●●●●●●●●●●●●

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●●●●●●●●●●●●●●●● ● ●● ● ●● ● ● ●

0 1 10 100 1000 10000 1e+05

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●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●

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●

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Reported Stock HoldingsReported Stock Transactions

32

Figure 2: Portfolio Weights as a Function of Portfolio Weights as a Function of Member-Firm Connections

Portfolio Weight (Basis Points)

Con

nect

ion

In State & Contribution & Lobbying

Contribution & Lobbying

In State & Contribution

In State & Lobbying

Contribution Only

Lobbying Only

In State Only

Unconnected

●

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0 20 40 60 80 100 120

33

Figure 3: Daily Cumulative Abnormal Returns for Common Stocks Bought and Sold bymembers 2004-2007 (EW = member Equal Weighted; VW = Value Weighted)

−200 −100 0 100 200

−0.

04−

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0.00

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All Members (EW)

trading day

Cum

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al R

etur

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buyssells

−200 −100 0 100 200

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All Members (VW)

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Cum

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etur

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−200 −100 0 100 200

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Republicans (VW)

trading day

Cum

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etur

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Democrats (VW)

trading day

Cum

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ive

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al R

etur

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buyssells

34

Figure 4: Daily Cumulative Abnormal Returns: Best and Worst Five Sellers and Buyers2004-2007

−200 −100 0 100 200

−0.

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0

Worst Five Buyers

trading day

Cum

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ive

Abn

orm

al R

etur

n

Christopher S. 'Kit' Bond (R−Mo)Pat Roberts (R−Kan)Hal Rogers (R−Ky)James L. Oberstar (D−Minn)Howard P. "Buck" McKeon (R−Calif)

−200 −100 0 100 200

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050.

000.

050.

10

Worst Five Sellers

trading day

Cum

ulat

ive

Abn

orm

al R

etur

n

Jim Gibbons (R−Nev)C. L. "Butch" Otter (R−Idaho)John Campbell (R−Calif)Shelley Berkley (D−Nev)Jane Harman (D−Calif)

−200 −100 0 100 200

−0.

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000.

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100.

15

Best Five Buyers

trading day

Cum

ulat

ive

Abn

orm

al R

etur

n

Heath Shuler (D−NC)Tom Coburn (R−Okla)Jack Kingston (R−Ga)Richard Burr (R−NC)Shelley Berkley (D−Nev)

−200 −100 0 100 200

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00.

1

Best Five Sellers

trading day

Cum

ulat

ive

Abn

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al R

etur

n

Zoe Lofgren (D−Calif)Nancy E. Boyda (D−Kan)Richard Burr (R−NC)Pat Roberts (R−Kan)Gary Miller (R−Calif)

35

Figure 5: Daily Cumulative Abnormal Returns of State Connected and Unconnected Com-mon Stocks Bought and Sold by members 2004-2007 (EW = member Equal Weighted; VW= Value Weighted)

−200 −100 0 100 200

−0.

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0.05

0.00

0.05

All Members (VW)

trading day

Cum

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al R

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n

connected buysunconnected buysconnected sellsunconnected sells

−200 −100 0 100 200

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0.00

0.05

House Republicans (VW)

trading day

Cum

ulat

ive

Abn

orm

al R

etur

n


−200 −100 0 100 200

−0.

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−0.

050.

000.

05

Republicans (VW)

trading day

Cum

ulat

ive

Abn

orm

al R

etur

n


−200 −100 0 100 200

−0.

10−

0.05

0.00

0.05

Senate Democrats (VW)

trading day

Cum

ulat

ive

Abn

orm

al R

etur

n


36

Figure 6: Daily Cumulative Abnormal Returns of State Connected and Unconnected Com-mon Stocks Bought and Sold by Selected members 2004-2007

−200 −100 0 100 200

−0.

4−

0.3

−0.

2−

0.1

0.0

0.1

Zoe Lofgren (D−Calif)

trading day

Cum

ulat

ive

Abn

orm

al R

etur

n

connected buys N=33unconnected buys N=168connected sells N=26unconnected sells N=95

−200 −100 0 100 200

−0.

050.

000.

050.

100.

15

Jane Harman (D−Calif)

trading day

Cum

ulat

ive

Abn

orm

al R

etur

n


−200 −100 0 100 200

−0.

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0.0

John Kerry (D)

trading day

Cum

ulat

ive

Abn

orm

al R

etur

n


−200 −100 0 100 200

−0.

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0.2

−0.

10.

00.

1

Kenny Ewell Marchant (R−Texas)

trading day

Cum

ulat

ive

Abn

orm

al R

etur

n


37

Figure 7: Daily Cumulative Abnormal Returns of Contribution Connected and Uncon-nected Common Stocks Bought and Sold by members 2004-2007 (EW = member EqualWeighted; VW = Value Weighted)

−200 −100 0 100 200

−0.

020.

000.

020.

04

All Members (VW)

trading day

Cum

ulat

ive

Abn

orm

al R

etur

n


−200 −100 0 100 200

−0.

04−

0.02

0.00

0.02

0.04

0.06

Democrats (VW)

trading day

Cum

ulat

ive

Abn

orm

al R

etur

n


−200 −100 0 100 200

−0.

050.

000.

050.

10

House Democrats (VW)

trading day

Cum

ulat

ive

Abn

orm

al R

etur

n


−200 −100 0 100 200

−0.

10−

0.05

0.00

0.05

Republicans (VW)

trading day

Cum

ulat

ive

Abn

orm

al R

etur

n


38

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● ● ● ●

trad

ing

year

s: 1

trad

ing

year

s: 2

trad

ing

year

s: 3

trad

ing

year

s: 4

Note

:A

nnual

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retu

rnis

the

annualized

four-

facto

ralp

ha

obta

ined

from

acale

ndar

tim

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ort

folio

regre

ssio

nfo

reach

mem

ber.

39

political investing: the common stock investments...

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