Chapter 4: Moral Representation

Brad Jones

18 August 2015

Up to this point, I have been primarily concerned with how individuals apply gen-eral moral values to specific political attitudes. In Chapter 1, I argued that moral valuesprecede rational considerations and are the raw materials from which (many) politicalattitudes are built. After an extended methodological diversion in Chapter 2 discussingthe measurement of these moral values, Chapter 3 showed that measures of individuals’moral foundations can do a great deal of work in explaining variation in political atti-tudes. This is obvious when we look at the moral divide between partisans of each of themajor political parties, but moral diversity within each of the major parties can explain thesometimes uncomfortable alliances that exist between people who are nominally “on thesame side.”

In this chapter, I change my focus from the individual level to the aggregate level. If itis true that general moral principles account for differences between individuals, it shouldalso be the case that these differences add up to aggregate effects. The larger story that Iam trying to tell in this project is that specific political attitudes are built upon intuitionsabout “rightness” and “wrongness” that constitute fundamental disagreements. Politicalelites have incentives to mobilize these basic concerns in the ways in which they framepolitical issues (as I will discuss more in the next chapter), and these attempts to structurepolitics around intuitive moral divides should show up in elite behavior like positiontaking.

I will take up the question of how basic moral values affect public policy by lookingat how variation in moral priorities across congressional districts affects congressionalbehavior. To do so, I will first need to construct measures of the moral foundations atthe congressional district level. I employ a model based strategy to generate small areaestimates. This approach is similar to that proposed by Lax and Phillips (2009), but thenature of the data I am using necessitate some adaptations to the method.

After demonstrating the face validity of my measures of the moral foundations at the

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congressional district level, I discuss some the challenges associated with finding realeffects amidst a sea of options available. It would be easy enough to find “statisticallysignificant” effects with purely random measures given the vast number of roll-call votesthat are cast during every legislative session, so I employ a non-parametric test to separateout the signal from the noise. I find that, even after controlling for district partisanship(a factor which explains over two thirds of the variance in intraparty roll-call votes), themoral foundations are robust and significant factors in a substantial number of cases.

Moral Representation

Recently, political scientists have devoted a lot of attention to the ways in which politicalpolarization potentially distorts representation. For example, Bafumi and Herron (2010)scale constituents and legislators into the same ideological space and show a substan-tial gap between representatives from both parties and almost every person in the masspublic. When party control shifts in a district, the hapless citizenry is “leapfrogged” byincreasingly extreme legislators. In a polarized political climate, it would seem that thereis little hope for the average citizen to have her interests represented in congress.

The story is perhaps not so bleak as painted by Bafumi and Herron. After all, onenotable difference between average citizens and the legislators who represent them is thatthe latter are embedded within institutions designed to shape their preferences along apartisan dimension (Cox and McCubbins, 2005). The average citizen is not nearly soconstrained in her opinions about specific issues (Converse, 2006). Indeed, as I arguein Chapter 2, individual preferences on political issues are shaped in large part by theirmoral intuitions,1 and these multidimensional predispositions cannot be cleanly dividedinto two camps. In a legislature however, one of the primary roles of the institution is toreduce the dimensionality of conflict thus making constructive legislative action possible(Shepsle and Weingast, 1981).

My contention in this chapter is that there is something more going on than just aleft-right divide in congressional voting. Despite (or perhaps because of) the overwhelm-ing pressure of the institution to structure conflict along a small number of dimensions,election-minded politicians will seek out opportunities to represent their constituents’moral concerns that are not so neatly structured. They are constrained in significant waysby the congressional agenda, but if they are acting in accordance with their constituents’

1Although, the most engaged segment of the populace does seem to let partisanship trump moral intu-itions at least some of the time.

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moral concerns, we ought to be able to detect systematic deviations from the predominantunidimensional pattern that we see in almost all votes.

In this chapter, I am arguing that political elites, in order to succeed in reelection,must become intuitive moral psychologists. Politicians should be selected for their abilityto capitalize on the underlying value structure in the electorate, and the system shouldcome to be oriented around these enduring values. Under this view, the structure ofindividual-level values operates as a significant constraint on elite behavior. At the elitestrategy level, we should observe elites operating under constraints placed upon them bythe moral concerns of their district. Elite appeals and position-taking behavior should betargeted at the particular concerns of the average voter in their districts.

Given the overwhelming explanatory power of one (or maybe two) dimensions inexplaining legislative decision making in the United States (Poole, 2005), it is perhapssurprising that there should be room for anything else in models of elite vote choice.Indeed, my measures of the moral foundations of each congressional district can only beplausibly extended back in time to around 2007. During this period, a simple bivariateprobit model with district partisanship2 correctly predicts 90% of the voting decisions inan average roll-call vote.3 In almost 15 percent of the votes (925), district partisanshipcleanly separates the “yea” votes from the “nays” with no errors.

In the subset of votes that I examine in this chapter,4 accounting only for the partisancomplexion of a legislator’s district (as measured by the two-party presidential vote in hisor her district) explains more than two-thirds of the variation on average. For some votes,the explanatory power of district partisanship exceeds 80%. Contemporary legislativevoting presents a difficult case for discovering countervailing factors.

Our understanding of legislative institutions suggests when these moral factors mightbe relevant. Legislators who find themselves in the majority party should be more con-strained along the single dominant dimension. Legislators in the minority are relativelymore free to act in accordance with their constituents’ demands. Fortunately (at least for

2In this chapter, when I refer to “district partisanship,” I am referring to the two-party vote for presidentin a member’s district in the presidential election preceding the vote. For example, when the dependentvariable is a vote that occurred in 2009, the district partisanship variable is defined as the proportion of thevote that went to Obama in the 2008 election excluding minor party candidates.

3At several points in this chapter, I will refer to the proportion of votes that were “correctly predicted.”For my purposes, correctly predicted refers to cases where the predicted probability of voting “yea” wasgreater than 0.5 and the legislator actually voted “yea,” or conversely the predicted probability of voting“yea” was less than 0.5 and the legislator actually voted “nay.”

4For reasons that I will detail below, I am looking only at intraparty variation in votes where there wassubstantial division within the party.

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the purposes of understanding the process), party control switched hands dramaticallyduring the period of my study. The cases I examine span the 110th through 113th con-gresses. During the 110th and 111th congress, Democrats held majority control of theHouse. The 2010 election saw a dramatic reversal in fortunes for the Democratic party,and the 112th and 113th congresses had Republican majorities. This variation in partycontrol allows a deeper look into the factors that contribute to legislative decision mak-ing.

Measurement

My empirical study of the ways in which constituent moral values influence congres-sional behavior requires measures of constituent moral concern at the congressional dis-trict level. In constructing these small-area estimates of the moral foundations, I draw ona large database of responses to the Moral Foundations Questionnaire collected by RaviIyer, Jonathan Haidt, and their colleagues at the website www.yourmorals.org (hereafter“YourMorals” data). The data in this paper were collected between June 2007 and May2013. After filling out some limited demographic and geographic information, individu-als filled out the moral foundations questionnaire and had the option of completing otherstudies at the website. Respondents who filled out the moral foundations questionnairewere self-selected and many came to the website after reading a newspaper editorial orblog post mentioning the research.5

The self-selection biases in the YourMorals data mean that it would be unwise tosimply disaggregate the data by congressional district to generate the needed measures.However, any model-based procedure that did not account for the unrepresentativenessof the sample would also be inappropriate. In this paper, I extend one model-based small-area estimation technique for use with unrepresentative samples. Multilevel Regressionwith Poststratification is one way to generate small area estimates of public opinion (Laxand Phillips, 2009). This method involves dividing the sample into demographically ho-mogeneous cells that can be matched to some other data source. A model is fit to thesample data, and the predictions from the model are then projected onto a new dataset(usually Census data broken up into the same set of homogeneous cells as the sample). Itis then a simple matter to generate the estimates of interest by using the predictions from

5The most common referring website was an editorial by Nicholas Kristof which ran in the New YorkTimes in May of 2007. The editorial was provocatively titled, “Would you slap your father? If so you’re aliberal.” The piece reported on some of Haidt’s research on the correlations between the moral foundationsand political ideology.

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the model applied to the population figures.6

This approach has been shown to work well when the aim is to smooth out the small-ncells by pulling them toward a group-level mean (e.g. Bayesian shrinkage).7 However,my problem is slightly different. Lax and Phillips (2009) develop their method with theaim of extrapolating the results of a nationally representative survey to develop state-level estimates. I am not so concerned with small sample sizes as I am with the unrep-resentativeness of the original sample. There are significant demographic biases in theYourMorals data (e.g. respondents are younger, more educated, less racially diverse),and the self-selected nature of the sample leads me to believe that it will not be sufficientto stratify only on variables that can be found in the Census.

MRP relies upon the idea that, conditional on observable factors, we can estimatesome parameter of interest about each of the cells in the model. Another way of statingthe problem that faces those who use unrepresentative data is that there is an unobservedfactor (selection into the survey) that we cannot condition on. By adding more stratifi-cation variables to the model, we might mitigate concerns about representativeness (e.g.the set of stratification variables is more likely to account for variance in the unobservedselection variable). In order to do this, we have to move beyond the narrow set of demo-graphic controls for which we have good population estimates in the Census.

For the purposes of my study, I stratify additionally on partisanship and ideology.The YourMorals database that I am relying upon has severe ideological biases. Morethan two-thirds of the individuals who filled out the Moral Foundations Questionnaireself-identify as “liberal.” This compares to about 20 percent of people in the general pop-ulation. Conditioning on ideology and partisanship corrects for one of the most egregiousdifferences between the self-selected respondents and the general population. The detailsof the estimation are relegated to an extended methodological appendix.

The resulting measures are displayed in Figure 1. The lower panels show the bivariaterelationships, and the upper panels show the correlations. There are modest correlationsbetween the moral foundations measures and the presidential vote variable (the high-est is between the Respect foundation at -0.3), and the correlations between the differentfoundations are small as well. In general, the bivariate relationships between the moralfoundations and the presidential vote are as we might expect from findings at the indi-vidual level (see Chapter 3). The correlations between the Care and Fairness foundationsare positive (districts that place relatively more emphasis on these foundations were more

6This is an approach that shares many similarities with that taken by Wang et al. (2014).7Although, Buttice and Highton (2013) raise some important concerns about the use of MRP in cases

where the sample size for the original opinion poll is relatively small.

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Figure 1: Scatterplot Matrix showing the relationships between the moral foundationsand Obama’s vote share in the 2008 election. This figure shows the measures from thepre-2011 districts, but the plot would look very similar with the post-2011 districts.

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supportive of Obama), and the Loyalty, Respect, and Sanctity foundations all had nega-tive correlations.

Case Selection

In selecting cases for analysis, my decision was guided by two overarching considera-tions. Firstly, I selected votes that substantially split one or both of the major parties.Formally, I considered only votes that received between 25 and 75 percent of a party’ssupport. This was done in part to exclude votes that cleanly divided the parties (withall or almost all of one party in support and all or almost all of the other in opposition).Votes that break along partisan lines are overdetermined in some sense. For the purposesof this chapter, I will consider only variation within each major party.

This is not to say that variation in roll-call voting behavior between the two majorparties is not structured in some way by the moral concerns of constituents. Indeed, manyof the issues that divide along party lines are justified in moral language (see Chapter 2for individual level results on point and Chapter 5 for evidence on how the parties frameissues in moral terms), but it is difficult to convincingly separate partisanship and anyconstituent pressure empirically. Especially in a time of such deep polarization, inter-party competition swamps almost all other effects.8 Considering each party separatelyallows more space for uncovering other factors in the voting decision.9

Empirical Expectations

In examining such a broad cross-section of votes, I do not formulate specific expectationsconcerning the set of moral foundations that should matter in each particular case. Itwould be possible to cherry-pick a handful of illustrative cases that confirm the story thatI am trying to tell here, but it would also be possible to find cases that contradict it. Thereare too many factors at play in any particular roll-call vote to satisfactorily model them all.For my purposes, I am most interested in how much explanatory power is added by themoral foundations measures to a simple baseline model. In the analyses that follow, I willfocus only on variation within the two major parties, and all models will include controlsfor district partisanship (as measured by presidential election returns). This sets up a very

8As Aldrich, Montgomery and Sparks (2014) have shown, looking within each party reveals greaterideological diversity than is apparent when considering the parties together.

9Although, as I mentioned earlier, even in this subset of cases nearly 70 percent of the variation in roll-call voting within the parties is accounted for by district partisanship.

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conservative test, as the lion’s share of the variation in congressional behavior falls alongone dimension. For the 1276 votes that satisfy my selection criteria, the baseline modelcorrectly predicts (yields a predicted probability of greater than 0.5 for “yea” votes andless than 0.5 for “nay” votes) more than two-thirds of the votes on average. In one case,this figure reaches as high as 87% and in almost every case it is over 50%. Knowing thepartisanship of a district explains a good deal of congressional behavior.

My main task in the remaining part of this chapter will be to demonstrate that themoral foundations have something to contribute over and beyond what is explained bydistrict partisanship. I will detail below how I will deal with the multiple comparisonsproblem that confronts a project such as this, but mainly I will be looking at improvedprediction over and above just district partisanship.

After establishing that there is a “there” there, I will turn to the question of whenthe moral foundations matter for congressional voting. My expectations here are thatthe moral foundations will be most likely to matter in cases where party pressure is ex-pected to be low and representatives are more free to vote their (constituency’s) con-science. When a party is in the majority, their party has almost complete control over theagenda (Cox and McCubbins, 2005), and I expect that majority party members will be un-der pressure to support their party’s agenda at the expense of their (or their constituents’)moral concerns when the vote might be close (e.g. out party support is low). Using asimilar logic, when a party is in the minority, they will be under the most pressure to votea particular way when their votes will not matter towards the passage of the legislation(e.g. it already has a great deal of support from their own party, or it has little supportfrom both parties). By examining the dynamics of partisan support for each bill, we cantest these expectations about partisan pressure.

Results

As I am interested in how constituent moral values affect elite congressional behaviorover and beyond the role of congressional ideology, I use a simple baseline model to com-pare the results against. District partisanship can be roughly approximated by lookingat the presidential race broken out by congressional district. The share of the two partyvote that goes to the Democratic candidate correlates strongly with other measures ofcongressional ideology (e.g. NOMINATE, ADA scores, etc.). District partisanship is alsohas the advantage of not being constructed from roll-call votes. However, its inclusionalso sets up a conservative test for my hypotheses. In Chapter 2, I showed how the moralfoundations are related to presidential vote at the individual level, so including presiden-

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tial vote shares in the model will most likely understate the effect of the aggregate moralfoundations measures. I have selected all votes that meet these criteria from the 110h to113th congresses.10

To test my claims about the role of the moral foundations in affecting elite behavior,I compare the performance of a very simple baseline model (district partisanship) to themodel that includes the moral foundations measures as well as district partisanship. Foreach vote that split one or both of the major parties (e.g. more than 25% and less than75% of Democrats voted in favor of a particular bill), I fit two probit models. One withonly district partisanship, and one with district partisanship and the aggregate moralfoundations measures. It is then a simple matter to calculate the proportional reductionin error (PRE) of the moral foundations model compared against the baseline model.11

The specter of multiple comparisons looms large over this project. Indeed, with morethan 1200 votes under consideration and 6 dependent variables, false positives are a vir-tual certainty. In an effort to address these problems, I adopt a permutation strategy toserve as the baseline for statistical significance rather than the standard approach of com-paring p-values to some threshold. Permutation is a well-studied computational tech-nique for addressing many of the problems that can attain when we violate some of theassumptions of classical statistical theory, namely, the assumption of independent signif-icance tests (Gibbons and Chakraborti, 2011).12

10This approximately matches the time during which the YourMorals data were collected. The vote datawas downloaded from http://voteview.com.

11PRE is simply the difference between the proportion of cases that were correctly predicted under themoral foundations model and the proportion of cases that were correctly predicted under the baselinemodel divided by the proportion that were correctly predicted under the baseline model. It generallyranges from 0 to 1. Where a score of zero means no improvement over the baseline model and a score of1 would mean that all of the unexplained cases from the baseline model are explained by the new model.In practice, the score can dip negative in rare cases where the baseline model actually outperforms lessrestrictive model with more covariates.

12Other strategies are available. For example, the Bonferroni multiple comparisons correction adjusts thep-value for the number of tests conducted. However, this test is generally seen as too restrictive and ismore helpful as a heuristic than an actual decision rule. With advances is computing power, it is relativelystraightforward to construct more direct tests involving resampling or permutation.

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Example Cases

Federal Employees Paid Leave Act

The Federal Employees Paid Parental Leave Act proposed to provide two months of paidleave to new mothers and fathers. This act had the overwhelming support of Democrats(only one Democrat voted against the bill). It drew only tepid support from fifty Re-publicans (just over 25% of the caucus). In a statement made after the bill’s passage, thelone Republican co-sponsor Tom Davis (VA-11) said “This measure, which I’ve pushedfor years, says to employees, ‘We care about you and your family. We want you to staywith us.’ But it’s more than a recruitment and retention tool. It’s a matter of fairness.”13

Democrats went to great lengths in the floor debate to frame support for the bill as actingin the interests of families. The bill’s sponsor, Representative Carolyn Maloney (NY-14),said on the floor: “Members on both sides of the aisle talk about family values, but one ofthe most concrete ways we can help families is to give parents more time with their newchildren, without losing their paycheck.”

Table 1 shows the results of the regressions predicting Republican support for thisbill. The first column shows the results of the regression with only the partisanship ofthe district. The second column shows the results when we add the moral foundationsmeasures to the model.

The baseline model shows that district partisanship is strongly related to a member’sposition on the bill. Members representing districts that went strongly for Bush in 2004were more likely to vote against the bill than members representing more marginal dis-tricts. When we add the moral foundations measures, we can see that, independent ofdistrict partisanship, members who represented districts who scored particularly high onthe loyalty foundation were more likely to support the bill as well. Indeed, adding themoral foundations scores increases the predictive power of the model considerably.

Patriot Act Extension (nee Medicare Physician Payment Reform Act)

To take another example, in a bizarre case of legislative maneuvering,14 the house consid-ered a bill extending the Patriot act in 2009. This bill (H.R. 3961), which was confusinglycalled the Medicare Physician Payment Reform Act, extended several controversial pro-

13Quoted in a press release available from Representative Carolyn Maloney’s website(http://maloney.house.gov/media-center/press-releases/paid-parental-leave-bill-passes-house).

14Sen. Harry Reid “amended” the H.R. 3961 by striking its content entirely and replacing them with thePatriot act extensions.

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Table 1: Regression Results: Parental Leave Act

Dependent variable:Baseline Model Moral Foundations

(1) (2)

Care/Harm 0.055(0.105)

Fairness/Cheating 0.225∗

(0.118)

Loyalty/Betrayal 0.296∗∗

(0.120)

Respect/Subversion −0.063(0.115)

Sanctity/Degradation 0.175(0.121)

District Partisanship 0.072∗∗∗ 0.082∗∗∗

(0.015) (0.017)

Constant −3.894∗∗∗ −4.372∗∗∗

(0.697) (0.778)

Observations 195 195Log Likelihood −97.184 −92.729Akaike Inf. Crit. 198.368 199.458

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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grams that were set to expire. Ultimately, more than 90 percent of the Republican caucussupported the measure, but about 35 percent of the Democratic caucus voted against thebill. Table 2 shows the results of the vote for Democratic members.

The first column in the table shows that, within the Democratic caucus, members whorepresented the most Democratic districts were least likely to support the bill. The secondcolumn shows that this pattern persists once we account for the moral foundations ofeach district, but we also see that members who represent districts that place the mostemphasis on the Care and Fairness foundations were significantly more likely to voteagainst the bill as well.

Water Quality Financing Act

Not all of the statistically significant findings could be easily interpreted. The above ex-amples are relatively intuitive. It makes sense that Republicans who represent districtsconcerned with loyalty and fairness might be more amenable to arguments about parentalleave that center around family values and creating government programs that reflectthose that already exist in many private enterprises as Rep. Davis pointed out. It alsomakes sense that Democrats who represent districts with greater concern form Care andFairness might be especially wary about extending the provisions of the Patriot act thatare seen by many as violations of privacy and unfairly target already marginalized groupswithin American society.

In some cases however, the observed pattern could not be so readily understood. Forexample, the Water Quality Financing Act of 2007 drew votes from over 40 percent ofRepublican members. The main source of controversy in the bill seemed to be a provisionthat required any workers hired on federally funded water quality improvement projectsto be paid a “local prevailing wage.” A priori, it would seem that fairness concerns shouldhave dominated this debate. However, we see something different in the data. In Table3, it is the Sanctity dimension that seems to be affecting voting behavior more than anyother.15

These are just a selection from the more than 1200 comparisons reported in this paper.How can we be sure that these are real effects and not just artifacts of making hundredsof comparisons? By permuting the values of the moral foundations measures (randomly

15One could try to make the sanctity dimension fit here. Others have noted how environmentalists adoptSanctity-laden language when talking about the necessity of protecting the environment. However, perusalof the relevant speeches in support of this bill did not support the idea that this kind of concern for thepotential degradation of the environment was what was driving support for the bill.

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Table 2: Regression Results: Patriot Act Extension

Dependent variable:Baseline Model Moral Foundations

(1) (2)

Care/Harm −0.237∗∗

(0.103)

Fairness/Cheating −0.259∗∗

(0.109)

Loyalty/Betrayal 0.001(0.097)

Respect/Subversion −0.022(0.106)

Sanctity/Degradation 0.146(0.102)

District Partisanship −0.041∗∗∗ −0.038∗∗∗

(0.007) (0.007)

Constant 2.946∗∗∗ 2.828∗∗∗

(0.445) (0.467)

Observations 249 249Log Likelihood −141.821 −136.402Akaike Inf. Crit. 287.642 286.804

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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Table 3: Regression Results: Water Quality Financing Act

Dependent variable:Baseline Model Moral Foundations

(1) (2)

Care/Harm −0.012(0.105)

Fairness/Cheating 0.175(0.115)

Loyalty/Betrayal 0.054(0.113)

Respect/Subversion 0.189(0.117)

Sanctity/Degradation 0.234∗∗

(0.116)

District Partisanship 0.070∗∗∗ 0.091∗∗∗

(0.014) (0.016)

Constant −3.295∗∗∗ −4.283∗∗∗

(0.614) (0.728)

Observations 187 187Log Likelihood −112.493 −105.479Akaike Inf. Crit. 228.986 224.958

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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ordering the row subscripts), we can get a sense of the probability that we would seepatterns at least as extreme as those we observe in the tests against random chance. InFigure ??, I show the distribution of PRE statistics obtained from 10,000 regressions withdifferent permutations of the moral foundations scores. The vertical line shows the PREfrom the un-permuted data. Less than one percent of the permutations yielded a largerPRE than the actual data.

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Federal Employees Paid Parental Leave Act

PRE

Fre

quen

cy

−0.1 0.0 0.1 0.2 0.3

050

010

0015

0020

0025

00Patriot Act Extension

PRE

Fre

quen

cy

−0.10 −0.05 0.00 0.05 0.10 0.15 0.20

050

010

0015

0020

0025

00(a) (b)

Water Quality Financing Act

PRE

Fre

quen

cy

−0.2 −0.1 0.0 0.1 0.2 0.3

050

010

0015

0020

0025

0030

00

(c)

Figure 2: Distribution of PRE scores obtained from 10,000 permutations of the moral foun-dations matrix. The vertical line shows the PRE from the unpermuted data for the threeexample cases. In the case of the Paid Family Leave Act (a) and the Patriot Act extension(b), the observed increase in model fit was much larger than what would have been pre-dicted by chance. In the case of the Water Quality Financing Act (c), the increase in modelfit was not significantly greater than what might have been observed by random chance.

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All Votes

I repeated the above procedure (measuring how much the model fit increased whenadding the moral foundations measures and then permuting the moral foundations ma-trix to test the robustness of the relationship) for all of the 1100+ votes under considera-tion. Figures 3 and 4 show the results for the votes within the Republican and Democraticparties respectively. In each figure, the horizontal axis shows proportion of the permuta-tion tests that returned PRE statistics greater than the actual data (e.g. randomly orderingthe moral foundations measures actually performed better than the unpermuted data). Incases where the permuted datasets performed consistently more poorly than the originaldata, we can be more confident that the observed effect is not just an artifact of the data.

Figures 3 and 4 reveal the importance of these kinds of efforts toward addressingmultiple comparisons when conducting hundreds of statistical tests. Most of the apparentfindings in the data are suspect. This is true in many instances where adding the moralfoundations measures to the regression seems to substantially boost the performance ofthe model (e.g. PRE > 0.1).

After culling out spurious correlations with the permutation procedure, about 17%of the roll call votes under consideration showed robust effects from adding the moralfoundation measures. This rate was relatively constant across both parties (17.6% forDemocrats, 16.8% for Republicans).

Table 4: Numbers of Statistically Significant Relationships by Party

Republicans DemocratsCare 19 30

Fairness 31 35

Loyalty 27 10

Respect 45 22

Sanctity 69 42

A closer examination of the particular moral dimensions that mattered for each party(determined by looking at the particular foundation or foundations that were statisti-cally significant for the subset of robust cases) shows that, relative to their Republicancolleagues, Democrats were more often affected by the Care and Fairness dimensions.Table 4 shows the counts of statistically significant coefficients for each moral foundation(again, only among the subset of robust relationships) by party. For example, the first

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Figure 3: Plot of PRE scores for votes that split the Republican party. The horizontal axisshows the results of the permutation tests; namely, the proportion of permuted datasetsthat returned PRE scores higher than the PRE score obtained from the unpermuted data.The vertical axis shows the PRE score from the unpermuted data. The vertical line showsthose tests where less than 5% of the permutations returned scores higher than the origi-nal data. Blue points correspond with votes that were taken while the Democrats held amajority (red points for Republican majority).

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Figure 4: Plot of PRE scores for votes that split the Democratic party. The horizontal axisshows the results of the permutation tests; namely, the proportion of permuted datasetsthat returned PRE scores higher than the PRE score obtained from the unpermuted data.The vertical axis shows the PRE score from the unpermuted data. The vertical line showsthose tests where less than 5% of the permutations returned scores higher than the origi-nal data. Blue points correspond with votes that were taken while the Democrats held amajority (red points for Republican majority).

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row of the table shows that there were 19 bills that showed a robust statistically signifi-cant relationship between constituent emphasis on the Care foundation and variation invoting with the Republican caucus. Correspondingly, there were 30 bills that showed asimilar relationship within the Democratic caucus between the Care foundation and rollcall voting. Although partisans do appear to be differentially responsive to constituentmoral concerns, members from both parties showed at least some level of responsivenessacross all of the different domains.

When Morality Matters

The results from the previous section showed that in a substantial number of cases themoral foundations allow us to better understand congressional voting behavior. In thissection, I will turn my attention to the question of the conditions that make these “moralvotes” more likely to occur. I expect to find a role for the moral foundations when partypressure is low. To investigate the conditions under which we might expect to see “moralvotes,” I used the results from the previous section to create a dichotomous indicator forwhether or not a vote was substantially affected by constituent moral concerns. This vari-able takes a value of 1 for votes that showed a robust increase in model fit from addingthe moral foundations measures (e.g. less than 5% of the permuted datasets had a bet-ter fit than the unpermuted data). Tables 5 and 6 show some summary results for theproportions of moral votes by majority status and the result of the vote.

Table 5: Republicans: Proportions of moral votes by majority status and vote resultResult P(Moral)

Majority Failed 0.14Minority Failed 0.24Majority Passed 0.08Minority Passed 0.20

Given the majority party’s control over the agenda in the contemporary House, I ex-pect party pressure to be higher for members of the majority party. The results in thetables confirm that expectation. Independent of a bill’s prospects (as measured by postfacto performance), members in the majority were less likely to be significantly affectedby their constituents’ moral concerns. Interestingly, Republicans seemed to be under themost pressure to bow to the party’s wishes on bills that ultimately passed the house, whileDemocrats seemed to be under more pressure on bills that ultimately failed to pass.

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Table 6: Democrats: Proportions of moral votes by majority status and vote resultResult P(Moral)

Majority Failed 0.03Minority Failed 0.14Majority Passed 0.18Minority Passed 0.23

Including additional information about the bills under consideration in a multipleregression framework (e.g. controls for substantive issue content,16 dummy variables forthe specific congress, and the proportion of each party that supported the bill), does notalter the pattern revealed in the table above.17 Members in the minority appear to be moreoften in positions where their votes are affected by their constituents moral foundationsrather than the dominant dimension of conflict in Congress.

Discussion and Conclusion

In this chapter, I have shown a significant relationship between constituency moral foun-dations and congressional voting behavior in some instances. This was true even aftercontrolling for the partisan complexion of the district (a factor which explains the lion’sshare of variance in roll call votes). In more than one in six roll-call votes, there ap-peared to be a statistically significant and robust relationship between constituent moralconcerns and their representatives’ roll call votes. This relationship was, perhaps para-doxically, strongest for members of the minority party. Members in the minority appearto be more free to vote their districts’ preferences. For a large number of votes, membersof congress seem to be responsive to their constituents’ moral concerns.

Despite this apparent responsiveness, the results I have presented in this chapter holdsome troubling implications for normative theories about democracy and representation.The precise moment when one’s preferred party has assumed power and is in a positionto implement policy changes is also the moment when one’s representative is under themost pressure to ignore her constituency’s preferences. This result might go part of theway toward explaining the extreme dissatisfaction that most people seem to have withgovernmental institutions.

16Issue area information was taken from the Congressional Bills Project “Major Issue” codes availablehere: http://www.congressionalbills.org/.

17Full regression results can be found in Appendix D

21

One unanswered (and unanswerable given the data here) question relates to the pre-cise mechanism by which constituent moral foundations affect congressional voting be-havior. I have presented some evidence here that they matter in some cases, but we knowfrom the literature on political interest and attention at the individual-level that membersof the general public pay little to no attention to specific roll-call votes.

There are several potential avenues. First, it may be the case that individuals selectcandidates based (in part) on the extent to which their moral values align. In Chapter 3, Ipresented some evidence that this appears to be the case for presidential voting. It wouldnot be too far of a stretch to apply that same logic to voting for and against members ofcongress. Second, Tracy Sulkin’s work (2005; 2011) shows how challengers can be instru-mental in holding incumbents to account for deviating too far from district preferences.Perhaps, legislators who anticipate (or have had recent experience with) challengers whomight better represent the moral concerns of their constituents adjust their behavior ac-cordingly.

Another interesting question which the data in this chapter unfortunately cannot an-swer is the extent to which the patterns shown here are time-bound. Measures of themoral foundations do not exist prior to 2007, but it is interesting to speculate about whatthe picture would look like if we had access to comparable measures of constituent-levelmoral concern going back fifty years. In a less polarized party system, would the moralfoundations of constituents serve as a more powerful constraint on elite behavior?

22

References

Aldrich, John H, Jacob M Montgomery and David B Sparks. 2014. “Polarization andideology: partisan sources of low dimensionality in scaled roll call analyses.” PoliticalAnalysis 22(4):435–456.

Bafumi, Joseph and Michael C Herron. 2010. “Leapfrog representation and extremism:A study of American voters and their members in Congress.” American Political ScienceReview 104(03):519–542.

Buttice, Matthew K and Benjamin Highton. 2013. “How Does Multilevel Regressionand Poststratification Perform with Conventional National Surveys?” Political Analysis21(4):449–467.

Converse, Philip E. 2006. “The nature of belief systems in mass publics (1964).” CriticalReview 18(1-3):1–74.

Cox, Gary W and Mathew D McCubbins. 2005. Setting the agenda: Responsible party gov-ernment in the US House of Representatives. Cambridge University Press.

Gibbons, Jean Dickinson and Subhabrata Chakraborti. 2011. Nonparametric statistical in-ference. Springer.

Lax, Jeffrey R and Justin H Phillips. 2009. “How should we estimate public opinion in thestates?” American Journal of Political Science 53(1):107–121.

Plummer, Martyn et al. 2003. JAGS: A program for analysis of Bayesian graphical mod-els using Gibbs sampling. In Proceedings of the 3rd international workshop on distributedstatistical computing. Vol. 124 Vienna p. 125.

Poole, Keith T. 2005. Spatial models of parliamentary voting. Cambridge University Press.

Shepsle, Kenneth A and Barry R Weingast. 1981. “Structure-induced equilibrium andlegislative choice.” Public choice 37(3):503–519.

Sulkin, Tracy. 2005. Issue politics in Congress. Cambridge University Press.

Sulkin, Tracy. 2011. The legislative legacy of congressional campaigns. Cambridge UniversityPress.

Wang, Wei, David Rothschild, Sharad Goel and Andrew Gelman. 2014. “Forecastingelections with non-representative polls.” International Journal of Forecasting .

23

Appendices

A Estimating Partisanship and Ideology

For my purposes, I adapt the MRP method to include multiple sources of data. Specifi-cally, I am interested in correcting for the partisan and ideological biases of the sample inaddition to the demographic imbalances. The approach is a straightforward two-stage ap-plication of MRP. First I will use an independent source of data (actually several sources)to estimate the partisan and ideological leanings of each cell in the data and use the ex-panded cell space to generate estimates of the moral foundations.

Stage 1: Partisanship and Ideology

I use an aggregation of Pew surveys and data from the CCES to estimate partisanshipand ideology.18 This is a straightforward application of MRP methods that have beendescribed elsewhere (e.g. Lax and Phillips (2009)). I stratify on age (3 categories), sex, ed-ucation (2 categories; less than college, college), race (2 categories, non-Hispanic White,other), income (2 categories; less than $50k, more than $50k), and 436 congressional dis-tricts (including D.C.).19 In addition to the demographic cells, I also stratify based on thesource of the data (Pew or CCES).20 This yields 436 ∗ 3 ∗ 2 ∗ 2 ∗ 2 ∗ 2 ∗ 2 = 41856 cells.

The model looks like this:18I collected every nationally representative survey that I could find between 2001 and 2011 from the

Roper iPoll database conducted by Pew (n > 164, 000). The CCES data are from the common contentadministered in 2008, 2009, 2010, 2011, and 2012 (n > 176, 000).

19While the CCES includes information on respondents’ congressional districts, the Pew data does not. Inmany cases, the Pew datasets did include respondent zip codes, and in a few cases where zip codes were notavailable, the data included county information. There is not a perfect correspondence between zip codeand congressional district (about 30% of respondents lived in a zip code that was split between two or morecongressional districts), and in the case of counties the correspondence is significantly worse. In cases wherethe geographic data was ambiguous, respondents were assigned to congressional districts probabilisticallybased on the proportion of the geography that falls into each district. For example, if a county is splitbetween three congressional districts such that 60% of the county falls into one district and 30% into anotherand 10% into a third, that individual was assigned to each district with a probability of 0.6, 0.3, and 0.1respectively. These proportions were calculated with the help of the (tremendously useful) GeoCorr websitehosted by the University of Missouri (http://mcdc.missouri.edu/websas/geocorr12.html).

20This seems especially important given the dramatically different modes employed by the two surveys.The Pew surveys were all nationally representative telephone surveys, and the CCES is an opt-in internetsurvey.

24

yi. ∼Multinomial(pi., ni)

pij = f(βageij + βsex

ij + βraceij + βincome

ij + βCDij + βsource

ij )

The dependent variable (yij) holds a count of the number of people identifying as, forexample, “liberal” and “Democrat” in cell i. There are nine possibilities correspondingwith the 3× 3 tabulation of ideology (liberal, moderate, conservative),21 and partisanship(Democrat, independent, and Republican).22 For example, in the combined Pew data,there were 15 people who were white, male, under 30, with less than a college degree,making less than $50,000 dollars per year living in Alabama’s 1st District. Of these 15individuals, none identified as liberal Democrats, 3 identified as moderate Democrats,and 1 identified as a conservative Democrat. The model “borrows strength” from similarcells (e.g. the political identifications of other young, white, males in Alabama), and tellsus that we should expect about 3% of individuals who match these demographics in thepopulation to identify as liberal Democrats, 10% to identify as moderate Democrats anda little less than 5% as conservative Democrats.

The model was fit in JAGS (Plummer et al., 2003) with vague hyperparameters for theβ parameters.23 Separate models were estimated for the partisan-ideological breakdownin the pre-2011 redistricting districts and the post districting districts. These cell propor-tions will be used in the next stage of the model to stratify additionally on ideology andpartisanship.

We can check the face-validity of the model by looking at the correlation between es-timates of district partisanship and ideology with presidential vote. This is accomplishedeasily enough by projecting the cell estimates onto a comparable set of population esti-mates. I use the 2008-2011 American Community Survey to get estimates of the popu-lations of each cell in the model.24 The estimates of district partisanship and ideologycorrelate strongly with the presidential vote at the congressional district level (e.g. thepost-districting estimated proportion of Democratic identifiers in a district correlates with

21People who responded “Don’t Know” or refused to respond were classified as “moderate.”22People who responded “Don’t Know,” “Other,” or refused to respond were classified as independents.

People who said they “leaned” toward one party or the other were classified as independents as well.23See Appendix B for the JAGS code used. After discarding the first 1,000 iterations, the next 1,000

iterations were retained as an approximation of the posterior distribution. Visual inspection of the chainssuggests that the models had converged on the appropriate solution.

24As with the survey data, there is not a perfect correspondence between the level of geography availablein the ACS (public use microdata areas [PUMAs]) and the congressional district. There are 2,069 PUMAsin the ACS many of which fall entirely within one congressional district. For PUMAs that spilled intomultiple congressional districts, I assigned individuals to congressional districts probabilistically based onthe proportion of the population of the PUMA that lives in each district.

25

Obama’s 2012 share of the two party vote at r = 0.69).

Stage 2: Moral Foundations

I follow a very similar procedure for estimating the moral foundations of each district.In the second stage model, I include fewer demographic categories to keep the numberof cells to a more manageable level.25 The model includes 3 categories of partisanship(Democrat, independent, Republican), 3 categories of ideology (liberal, moderate, conser-vative), 2 categories of education, sex, a questionnaire indicator,26 and 436 congressionaldistricts (plus D.C.). This yields 3 ∗ 3 ∗ 2 ∗ 2 ∗ 2 ∗ 436 = 31, 392 total cells.

The model for the moral foundations was slightly different. In the partisanship andideology case, I had counts that were modeled as being multinomially distributed. In themoral foundations case, I have cell averages of the moral foundations scores.27 Again, themodel was estimated in JAGS and the code can be found in Appendix B. I projected theestimates from the model onto the population data to produce estimates of each of themoral foundations at the congressional district level.

25Including all of the demographic categories in the second stage would lead to a model with nearly190,000 cells. This is a larger number of cells than I have data, and it would make the results too dependentupon the model.

26The data mostly come from the large database of responses to the MFQ30 collected at YourMorals.org.However, a subset of the data include responses from the MFQ48. The MFQ48 includes all of the itemsfrom the MFQ30 with an additional 18 that were designed to address some shortcomings in the estimationof the fairness domain in particular. In Chapter 2 I discuss how the MFQ30 and MFQ48 scale together atgreat length.

27Chapter 2 details the response model that was used to extract latent parameters from individual re-sponses to the Moral Foundations Questionnaire. In that chapter, I discuss some of the limitations of thestandard procedure to scaling the MFQ (namely adding up responses to items that have been assigned apriori to a particular dimension), and propose a new method that allows items to load onto multiple di-mensions. I show that the new measure fits the responses better than alternative measures and producesestimates of moral concern that perform better than other measures.

This measurement model is the foundation for the aggregate measures relied upon in this chapter. Afterusing a subset of responses from the larger database to estimate the item parameters in the model, I esti-mated the latent moral concern parameters for the remaining respondents from the large YourMorals.orgdatabase. This was accomplished through a simple optimization procedure to find the values of the latent“ability” parameters that maximized the likelihood of observing an individual’s response pattern condi-tional on the item parameters.

The resulting estimates display the same characteristics as those explored in Chapter 2. The estimates ofthe moral foundations are less correlated with one another, and the measures perform measurably better interms of their relationship with politically relevant dependent variables.

26

mij ∼ Normal(µij, σj/√ni)

µij = βPartyij + βIdeo

ij + βSexij + βEduc

ij + βCDij + βsource

ij

In the second stage, the dependent variable (mij) holds the average of the moral foun-dations score for all of the individuals in a particular cell. This average is considered tobe drawn from a normal distribution centered at µij and with a standard deviation that isa function of the number of individuals in the cell.28 As in the party-ideology model, themain parameter of interest is decomposed into several different parts (party, ideology, ge-ography, etc.). In practice, this means our cell estimates will be borrowing strength fromsimilar cells.

B JAGS Code

The first stage model was fit to a collection of Pew surveys and CCES surveys described inthe main body of the text. These surveys used roughly comparable partisanship and ide-ology items and included all of the necessary demographics. The estimated parametersfrom the model were used to generate estimates of the proportion of individuals in eachcell who identified with a particular party and ideology in order to incorporate these vari-ables into the second stage model to estimate the moral foundations at the CongressionalDistrict level.

model {

for (j in 1:N.cells) { #loop through the non empty cells

y[j,1:9] ˜ dmulti(p[j,1:9], n[j]) #party-ideology is distributed

#multinomial conditional on the cell probabilities and the number of

#respondents in each cell

for (k in 1:9) { #loop through the 9 possible response options

p.star[j,k] <- exp(b.cd[cd[j],k] + #exponentiate to ensure

#positive results

28This is the central limit theorem in action. The standard deviation of our prediction is decreases withthe square root of the sample size, and it is mathematically equivalent to running the much more computa-tionally intensive model with the individual scores rather than the grouped means.

27

b.sex[sex[j],k] +

b.ed[ed[j],k] +

b.race[race[j],k] +

b.inc[inc[j],k] +

b.src[src[j],k] +

b.age[age[j],k])

}

p[j,1:9] <- p.star[j,1:9]/sum(p.star[j,1:9]) #divide by the sum to

#ensure probabilities sum to 1

}

for (k in 1:9) {

for (j in 1:51) {

alpha0[j,k] ˜ dnorm(0, 1) #state effects

}

for (j in 1:CD) {

b.cd[j,k] ˜ dnorm(alpha0[state[j],k], .1) #CD effects

}

for (j in 1:2) { ##other demographic effects

b.sex[j,k] ˜ dnorm(0, .1)

b.ed[j,k] ˜ dnorm(0, .1)

b.race[j,k] ˜ dnorm(0, .1)

b.inc[j,k] ˜ dnorm(0, .1)

b.src[j,k] ˜ dnorm(0, .1)

}

for (j in 1:3) { #age effects

b.age[j,k] ˜ dnorm(0, .1)

}

}

}

28

C Estimating Moral Foundations

The stage two model was similar:

model {

for (j in 1:N.cells) { #loop through non-empty cells

for (k in 1:5) { #loop through the five moral foundations

theta[j,k] ˜ dnorm(mu[j,k], n[j]*tau[k]) #JAGS uses the precision,

#so rather than dividing by the square root of n, we multiply by n

mu[j,k] <- b.party[party[j],k] + b.ideo[ideo[j],k] +

b.sex[sex[j],k] + b.educ[educ[j],k] + b.src[src[j],k] +

b.cd[cd[j],k]

}

}

for (k in 1:5) { #5 Moral Foundations

tau[k] ˜ dgamma(.1, .1) #Precision parameters

for (j in 1:2) { #demographic effects

b.sex[j,k] ˜ dnorm(0, .1)

b.educ[j,k] ˜ dnorm(0, .1)

b.src[j,k] ˜ dnorm(0, .1)

}

for (j in 1:3) {

b.party[j,k] ˜ dnorm(0, .1)

b.ideo[j,k] ˜ dnorm(0, .1)

}

for (j in 1:436) { #CD effects clustered by state

b.cd[j,k] ˜ dnorm(b.state[state[j],k], .1)

}

for (j in 1:51) { #State hyperparameters clustered by region

b.state[j,k] ˜ dnorm(b.region[region[j],k], .1)

}

for (j in 1:9) { #Region parameters

b.region[j,k] ˜ dnorm(0, .1)

}

29

}

}

30

D Supplementary Regression Results

Table 7: When Morality Matters: Probit Models

Dependent variable:

Democrats Republicans

Majority −1.253∗∗ −0.918∗∗∗

(0.533) (0.257)Passed House 0.367∗∗ −0.095

(0.186) (0.151)Democratic Support −0.091 −0.100

(0.531) (0.131)Republican Support −0.505∗∗ 0.032

(0.197) (0.388)Majority*Passed 0.707 −0.160

(0.553) (0.249)Constant −0.312 −0.439

(0.596) (0.519)

Observations 467 809Log Likelihood −187.693 −339.502Akaike Inf. Crit. 431.386 737.005

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01Models include controls for substantive issue area

and the congress.

31