Roll-call analysis when you have too much data and are not in
the USA
Iain McLean
Nuffield College, Oxford OX1 1NF, UK
[email protected]
Paper prepared for presentation at the ECPR workshop on
Estimating the policy positions of political actors
Mannheim, March 1999
Roll-call analysis when you have too much data and are not in
the USA
1.
Introduction
One way to estimate the policy positions of political actors is to calculate them from votes in the
legislature. This is known in the USA as roll call analysis. The overwhelming majority of literature on
roll call analysis seems both to emanate from the USA and to refer to the US Congress. Why is roll
call analysis relatively little used in Europe? I suggest for the following reasons, which I arrange in
(what seems to me) ascending order of difficulty:
It is relatively high-tech political science, and most European political scientists have relatively
low-tech skills
Data collection is laborious and time-consuming
Party is strong in Europe and weak in the USA. Most votes in legislature are straight party votes.
Therefore (Europeans think) roll-call analysis has only a marginal role, perhaps in analysing votes
on matters of conscience, on the occasional cross-cutting issue, and on cheap-talk gestures such as
resolutions that are never debated (see, e.g., Finer et al 1961; Berrington 1968, 1973; McLean
1995)
There is still no consensus on the appropriate statistical techniques. Early studies (Aydelotte 1966,
1967, 1970, 1972, 1977; Leece and Berrington 1977) used Guttman scaling, which has fallen out
of favour. Modern studies tend to use factor analysis, but some social statisticians are suspicious
of factor analysis for this purpose, because they argue that the circumstances do not fit the model
assumptions that factor analysis requires
The models are hard to interpret because they cannot discriminate between the hypothesis that
ideology is causally prior and the hypothesis that the material interests of the legislator (and/or of
the district) are causally prior.
In this paper I introduce a body of British data on which I have been working since 1989 (McLean
1990, 1992, 1995a-c, 1998; forthcoming; McLean and Foster 1992). I discuss the analytical work done
so far, in the hope of opening a discussion on what further can and should be done.
2.
The Aydelotte dataset
In the mid 1950s the late W.O. Aydelotte, a professor of history at the University of Iowa, began the
task of a lifetime. He compiled information about every Member of Parliament who sat in the British
House of Commons that was elected in the 1841 General Election and dissolved immediately before
the 1847 General Election. There are 815 cases in his dataset – at any one time there were 658 MPs in
the house, and turnover during a parliament was greater then than now. For each case there are nearly
400 variables. About half of these are background variables on education, social class, and occupation.
The other half are rollcalls on 186 important divisions, and scale positions derived from the rollcalls.
Why the 1841 parliament? Although Aydelotte never, so far as I know, says explicitly why he selected
this particular parliament, he could not have chosen better. Here are three reasons why, over and above
its intrinsic interest to the historian, the political scientist ought to find it one of the most interesting UK
parliaments ever. I put these reasons in a deliberately anachronistic way.
1. It was the first ‘modern’ parliament in organisation. A strong Prime Minister (Sir Robert Peel)
controlled both houses through a majority party elected in a partisan campaign in which everybody
thought they knew what the parties stood for.
2. Peel’s was the first ‘modern’ administration in terms of its policy concerns. Although traditional
concerns of church and state remained prominent, there was substantial new legislation for new
problems. These included factory reform, utility and bank regulation (McLean and Foster 1992;
McLean 1995c), regulation of working hours, and public health.
3. The biggest unexplained policy reversal in modern British history took place in 1846. The Tories
under Peel were the party of the countryside. The material interest of the countryside was that
1
cereals produced in Britain should continue to be protected from overseas competition by the Corn
Laws, which put high and sometimes prohibitive tariffs on imports. Yet in October 1845,
confronted by the first evidence of looming potato famine in Ireland, Peel decided to repeal the
Corn Laws. He carried Repeal in the Commons on the votes of the entire Opposition and a third of
his own party. The other two-thirds went into opposition under Benjamin Disraeli, and soon
formed an alliance of convenience with the other parties to defeat Peel. Furthermore, Peel’s ally
the Duke of Wellington ensured that Corn Law repeal was carried in the House of Lords, which
could have vetoed it (and would have been expected to). The Lords represented the landed interest
even more exclusively than did the Commons.
Repeal of the Corn Laws is fascinating because none of the main schools of political economy, be they
Marxist, Namierite, Virginian, or Chicagoan, can explain it. All of these diverse schools share the
belief that politicians act in their material interest and/or in that of their constituents. In 1846 the
median MP and the median peer were both landowners. The median MP represented a rural
constituency. The median peer represented himself. From Marx (1852) to Schonhardt-Bailey (1994),
political economists have confronted these facts. They either gloss over the fact that it was the Tory
government that repealed the Corn Laws or attempt to explain it in material terms. The best that
political economists can do with conventional tools is to produce a regression line with the right slope
but the wrong intercept. Schonhardt-Bailey and others have shown that the more free-trading an MP’s
constituency interests, the more likely he was to vote for Repeal. The Aydelotte data also show that
this is true of MPs’ personal interests. Nevertheless, Repeal carried in the Commons by 327 to 229 and
in the Lords by 211 to 166. The median member of each house voted in favour of Repeal, whereas any
model based on material interest predicts that he would have voted against.
American political scientists are extremely interested in this case. This was probably Aydelotte’s
original motivation, or one of them. At least three times in the last 10 years, there have been panels at
the annual meeting of the American Political Science Association devoted, wholly or partly, to the
Repeal of the Corn Laws. Most scholars working on the question are aware of the Aydelotte dataset
but few have tried to use it. This is partly because of its daunting size and age. Both of these are a
tribute to Aydelotte. He was thirty years ahead of his time when he started, and ten years ahead of it
when he finished. Although he was one of the main inspirations of the mid-western school of
empirical political science, and specifically of roll-call analysis (he was one of the founders of
Legislative Studies Quarterly), his own raw data have not been mined as they should have been. When
I started work on them in 1989, they came in 80-column punched card format, with ‘control cards’
(older readers will perhaps remember them) coded in one of the earliest versions of SPSS. Ten years’
work, on and off (mostly off), has seen them transmuted to a file manipulable in the current version of
SPSS. They have been merged with Schonhardt-Bailey’s independently collected data. I have added
data on the ideology of each MP and the religious makeup of his constituency. None of this would
have been possible without Mrs Aydelotte’s discovery of the name key to the database in her late
husband’s papers. The dataset is freely available for reanalysis. I hope it will be mounted on my Web
page by the time this paper is discussed.
Aydelotte was fascinated by the dimensionality of voting in the House of 1841—7. His main use of the
rollcalls was to construct 24 Guttman scales. Indeed, this absorbed so much time that he seemed to
have little energy left to report his substantive findings. His motivation for constructing the scales is
best explained in his own words:
The purpose of the scale analysis is to make possible generalizations about voting patterns in
this parliament…. The technique of cumulative scaling … has … been applied … to the study
of roll calls in legislatures. Its utility consists in the fact that it ranks in order both the issues
voted upon and the men voting upon them, and ranks each of these in terms of the other…. It
is particularly useful in that it reveals connections not only between items on which voting
was identical but also between items that were favored by the respondents [sic] to different
degrees but in a clearly marked order of preference. It can be used to test the consistency of
voting on single issues and also the degree to which patterns of voting on single issues can be
combined into more general patterns that include votes on several different topics. (Aydelotte
1970, p.2)
A particular advantage of scaling in this context is that it saves information that might otherwise be
discarded. Many divisions were quite thinly attended. Many MPs rarely voted. By relying primarily
2
on scales rather than on raw rollcalls the analyst can incorporate information about the thin divisions
and the infrequent attenders that would otherwise be thrown away.
Guttman scaling has gone so far out of fashion that the program for it is not even in the current version
of SPSS. Most statisticians would no longer accept that it was the right tool for analysis of data of this
sort. But they do not seem to agree what is. Some say factor analysis; others say not, on the grounds
that rollcall data do not fit the required assumptions for factor analysis to be appropriate. One recent
contender is the NOMINATE program written by Poole and Rosenthal. The larger version, DNOMINATE, ‘was designed specifically for the CYBER 205 supercomputer’ (Poole and Rosenthal
1997 p.233). Not having one of those, I have not attempted to use it. They also wrote a version, WNOMINATE, for use on desktop PCs. If I were a professor in a research department of political
science in the USA I would be employing a graduate research assistant to rerun the Aydelotte data
through W-NOMINATE to see how far it reproduces Aydelotte’s findings. But as I am not I cannot,
and am coming to this workshop instead.
3.
Dimensionality and heresthetic
The previous section assumed that opinion in the legislature was unidimensional. Deep down, all the
schools of political economy so far discussed depend for their conclusions on the median voter
theorem, as did the formulation of the intellectual puzzle of repeal in the last section. If opinion is
unidimensional, then the median voter’s optimum policy is a Condorcet winner. On any well-behaved
voting procedure, it will be followed. The majoritarian voting procedures of both houses of parliament
is well-behaved in this sense. Therefore the median legislator’s optimum ought to have been chosen.
It was not. Political economy explanations have failed to find a convincing reason. So perhaps the
right thing to do is to relax the assumption that opinion was unidimensional. It may be relaxed at two
levels: the level of parliamentarians, and the level of the executive – that is, Peel’s Cabinet.
If opinion is structured in more than one dimensions, then by the well-known ‘chaos results’ of social
choice theory, the median voter theorem does not hold. Not only does it not hold, but if it fails it may
fail utterly. All possible options may be in a global cycle such that the status quo, whatever it is, will
always lose to something, and that to something else, and that to something else….. and that to the
status quo by a chain of finite length of successive defeats by majority vote. (The best non-technical
summary of these results remains Riker 1982).
Riker (1982, 1986) pointed out that there were two sides to this chaos. With naïve actors, chaos might
indeed lead to policy outcomes wandering all over the place (although it rarely seems to). Perhaps the
reason that outcomes rarely wander far is that they are constrained by institutions – ‘structure-induced
equilibrium’ in the ungainly phrase of Shepsle and Weingast (1981). But with sophisticated actors
there are opportunities to jump out of the currently dominant set of policies to a completely new set.
This may be attractive to persistent losers. Riker (1982, 1986) and Weingast (1998) construct a story
of the origins of the American Civil War along these lines. Could Repeal of the Corn Laws be
explained in the same way?
Riker (1986, p. ix) coined the word ‘heresthetic’ to denote ‘structuring the world so [that] you can win’.
A politician who can change the issue dimensions by introducing or suppressing issues and getting
voting aligned in unexpected ways is a heresthetician. At first sight, this is a promising candidate to
explain Repeal, especially given that all other explanations fail. Some of my work on the Parliament of
1841--7 (especially McLean and Foster 1992; McLean 1995, forthcoming) focuses on the high politics.
I believe it can be shown that Peel and Wellington were herestheticians in Riker’s sense. They made
high politics two-dimensional by linking Irish famine with corn policy. This, together with Peel’s
decisiveness and Lord John Russell’s dithering, gave Peel the opportunity to construct a repealing
majority in one house and Wellington in the other.
As this paper is already long and its focus is methodological rather than substantive, I have to ask the
reader to take these results on trust, and/or from my web site. The rest of this paper tries to establish
the dimensionality of roll-call voting in the Commons. Alas, Aydelotte’s data do not cover the Lords,
which had a veto and which is overlooked in almost all studies of repeal.
3
4.
Interests and ideology in the House of Commons
4.1
Data
In this section we study in more detail what sorts of Tory MPs supported and opposed Peel. Our data
sources are the merged datasets of Aydelotte (1970) and Schonhardt-Bailey (1991a, b, 1994), with
some extra variables.1 Schonhardt-Bailey’s further data, originally collected for her doctoral
dissertation, were intended to facilitate a direct test of endogenous tariff theory (a variant of Chicago
trade theory, for which see especially Magee 1997). Accordingly, her data record the economic interest
of each constituency and the degree of portfolio diversification since 1815. Of her variables, the one
reported below, now called CSBDPREF2, records the expected trade orientation of each constituency.
The more it depended on industry vulnerable to imports (specifically agriculture), the more its MP
could be expected to have a constituency interest in voting for protection. Conversely, the more it
depended on exporting industry (such as textiles, machinery, or cutlery), or on international transport
(docks), the more its MP could be expected to have a constituency interest in voting for free trade.
Both the Aydelotte and the Schonhardt-Bailey variables concentrate on interests rather than ideology.
Material interests are easier to measure than ideology, but we have been able to import a few measures
of ideology to the database. From Hilton (1988) and his sources, we marked all those known to be
religious evangelicals (and the few known vocal opponents of evangelicalism). From the records of the
History of Parliament, which end in 1832, we coded for the attitudes of the longer-serving MPs in our
set to the religious and constitutional crises of 1829—32 over the position of the Catholic and Anglican
churches3. And from the 1851 census we add details of religious attendance in England, Scotland and
Wales (1851 being the only time in British history that these data have been recorded). There was no
census of religious attendance in Ireland, but the 1851 census yields data of excess mortality for the
Irish census districts, 1841—51, which we have coded as a new variable DEATH for Irish members.
(It is not significant in the analyses below, perhaps because the horror of the Famine had not yet struck
in full force by June 1846.)
Riker’s ideas on heresthetic and dimensionality (see previous section) have been fiercely criticised
(most effectively by Green and Shapiro 1994, pp. 107--13). I agree with the critics that he probably
exaggerated the frequency that cyclical outcomes arise in real politics. But that does not invalidate his
stress on the extraordinary political events when they may do. The election of Abraham Lincoln in
1860 was one such case. Repeal of the Corn Laws was another. Peel structured the world so that
almost the whole of the opposition in the Commons voted for Repeal. (Only ten non-Tory MPs voted
against repeal in the third reading vote, on May 18, 1846, on which this analysis is based).
The Aydelotte dataset encompass votes not only on Repeal but also, inter alia, on railway, bank, and
factory regulation; political reform; educational reform; working-class distress; individual railway
proposals; and the abolition of flogging in the army. It can therefore be used to test, not only the roles
of interest and ideology in determining MPs’ votes, but also whether the number of issue dimensions
was high enough to promote chaos and heresthetics. Earlier work (McLean and Foster 1992; McLean
1995, 1998) confirms Aydelotte’s original finding that politics in that parliament were multidimensional. Aydelotte (1970) originally found no fewer than 24 dimensions to the voting, using
Guttman scaling. This is both too many and too few. It is too many in that most of the scales are
inadequately labelled, uninterpreted, and seriously overlapping. It is too few in that it does not pick up
the dimension of regulation. An MP’s position on Aydelotte’s Big Scale, which links the Corn Laws,
1
Grateful thanks to the Leverhulme Trust for funding, and to Beata Rozumilowicz and Camilla
Bustani for locating and coding data. Either I or Dr Schonhardt-Bailey can supply the merged dataset,
in SPSS format, to interested users for further reanalysis.
2
This is a revision of her earlier variable DISTPREF, the results from which have been reported
in both her and my earlier work. Some dubious cases have been reclassified with new data, and the
new variable gives better results. The other variables imported from the Schonhardt-Bailey dataset
were found to have no independent significance.
3
By kind permission of Valerie Cromwell, Director of the History of Parliament, and of D. R.
Fisher, editor of the projected volumes for 1820—32.
4
Ireland, and relief of working-class distress into a single ideological dimension, fails to predict his
position on regulation. For instance, if he was ‘right-wing’ on the Big Scale, he was equally likely to
be ‘right-wing’ or ‘left-wing’ on regulation. This is not too surprising, as it was then as now not clear
whether being in favour of stringent government regulation is a right-wing or a left-wing position.
Attitudes to regulation formed a dimension orthogonal to that of the Big Scale. However, regulation is
not a big enough exception to the generalisation that opinion among MPs was one-dimensional.
Although it is true that Peel threatened to resign if factory legislation he opposed were carried, and it is
also true that the Ten Hours Act 1847 was carried by a ‘left-right’ coalition to punish the repealers of
the Corn Laws, Peel’s threat was not really credible. Governments did not stand or fall on issues of
regulation.
4.2
Results of bivariate analysis
All the analyses reported below take the dependent variable as vote on the 3 rd Reading of the bill to
repeal the Corn Laws, on 15 May 1846. (The vote on the 2 nd reading, on 27 March 1846, was almost
identical – the analysis is not affected by choosing one vote rather than the other). The first step is to
take likely predictors one at a time. Taking all cases, the results look quite promising. MPs who voted
for repeal sat for more urban seats than those who voted against. Viewing the data the other way
round, the probability of voting for repeal rose for each class of constituency except the last (there was
no difference in vote between MPs representing moderately free-trade and strongly free-trade
constituency interests). Table 1 summarises the main results.
[Table 1 about here]
Table 1 gives reasonable support to endogenous tariff theory (ETT): the probability of voting for
Repeal varies in the expected direction for constituency characteristics, and also for whether the MP
was an active businessman. It also shows that ideology mattered: for instance, either MPs’ attitudes to
the constitutional questions of 15 years earlier coloured their attitudes to Repeal, or that both were
coloured by some common background factor. The influence of evangelicalism fits the inverted Ushaped pattern predicted by Hilton (1988), although this effect is less strong and there is some risk of
circularity in the data. Hilton’s hypothesis is that ‘moderate’ evangelicals welcomed Repeal whereas
‘extreme’ ones merely saw in the famine evidence for God’s punishment for Britain’s wickedness
(rather blasphemously implying that God has a poor aim – punishing the English by killing the Irish).
But in such data there is a risk of defining evangelicalism from public statements made during the Corn
Law crisis, thus reversing the direction of causation.
However, these conclusions need to be shaded and qualified. First and most important, although the
trends are as predicted by ETT, the outcome is not. If the data were regression data, we would say that
the slope was correct but the intercept was wrong for ETT. True, the more rural the constituency, the
likelier was its MP to vote against repeal. But in all categories except the most rural, a majority of
Members voted for Repeal. Disregarding coding errors and missing data, the median constituency is in
category 2 (next-to-most protectionist). But Table 1 shows that a majority of Members who sat for
category 2 constituencies voted for Repeal. Second, the analysis so far assumes that party is irrelevant.
This is in the tradition of ETT and of American roll-call analysis. In these traditions, party is treated as
an intervening variable, which confuses more than it clarifies. Even in the weak party system of the
1840s, that approach is wrong for UK data. True, there is a reasonable association 4 between rurality
and Toryism, but it is far from perfect. The strong Tory government was a strong constraint on those
whose party label was Tory, even though it could only induce a third of them to vote with the
government.
Therefore, we need to repeat the analysis of Table 1 for Tories only. Peel’s heresthetics, and Russell’s
characteristic combination of firmness (the Edinburgh letter) and dithering (the failure to form a
government) had delivered all the Whig, Liberal, Reform, and Repealer votes, bar ten, to the
Government. The swing voters would be those Tories who, torn between their government on one
hand and their traditions and interests on the other, chose the former. Table 2 shows the results of
repeating the analysis of Table 1 on just the Tory MPs.
4
Crosstabulating CSBDPREF (district trade orientation) against PARTYBK5 (5-way party
breakdown) gives a Kendall’s tau-β value of 0.345
5
[Table 2 here]
In Table 2, three of the five predictors drop away. These variables are themselves strongly associated
with party, so their significance in the all-MPs analysis tells us nothing about the comparison between
the swing voters (the Peelites) and the protectionist Tories. We are left with trade orientation and
evangelicalism, both of them still subject to the qualifications just mentioned. Note in particular that
the median MP in this category still sits for a seat with the next-to-most protectionist trade orientation.
As expected, a majority of them, like a majority of all Tory MPs, voted against Repeal, but 38% of
them voted in favour – roughly the same proportion as among Tory MPs at large, and enough to secure
Peel his Commons majority.
4.3.
Logistic regression analysis
If bivariate analysis tells us little about the Peelites, can multivariate analysis do any better? In earlier
work (McLean 1998), we reported that an ideological model performed better than an interest-based
one, but that both were poor predictors. The recent improvements to the trade preference variable
CSBDPREF change that conclusion, but not by very much.
All the variables that were tried for the bivariate associations reported above were tried again, including
those found not to be significant in the bivariate model (since bivariate analysis can conceal true
associations as well as reveal misleading ones). As the dependent variable (vote on the 3rd Reading of
the Corn Law bill) is binary, we use logistic regression. Beginning with a saturated model, we
progressively eliminated non-significant predictors. The best-fitting model has the properties reported
in Table 3.
[Table 3 here]
Logistic regression is notoriously hard to interpret, and none of the standard packages make it easy for
the reader. The first block of Table 3 shows the prediction without the model (Initial Log Likelihood)
and with the model. One measure of the information improvement is the model chi-square as a
proportion of the initial log likelihood. The classification table shows how many cases the model
predicts successfully. Note that it is good at predicting negative votes (that is, votes against Repeal),
but bad at predicting positive votes, in favour of Repeal. It successfully predicts only 40% of these.
Of the four predictors that work in this model, one is ideological, one relates to personal interests and
the other two are environmental. Most of the work is done by the constituency variable TYPECON2,
which distinguishes county members from the rest. Note that in this model, small borough members do
not behave distinctively from anybody else. Being in a constituency with a high proportion of
Anglican churchgoers on census Sunday 1851 predisposes the member against Repeal. The personalinterest variable that works is GENTRY. Earlier work had shown that the relationship between Repeal
vote and membership of the landed interest was non-linear. The very largest landowners in the
Commons actually voted in a majority in favour of repeal. They were likelier to have diversified asset
holdings than the next class down, classified as GENTRY, the only set of landowners to vote by a
majority against Repeal. This finding is consistent with earlier economic history (cf Moore 1965;
Schonhardt-Bailey 1996, 1997). Support for Catholic emancipation in 1828—30 predisposes a
Member in favour of Repeal. The variables relating to district trade orientation and evangelicalism
have dropped out.
Another measure of the success of the model in classifying cases is p. This is a proportional reduction
of error statistic of the form (Errors without model – errors with model)/(errors without model). At
0.54, it is reasonably high. But note that this is because there are very few incorrectly predicted votes
against Repeal. There are a lot of incorrectly predicted votes for Repeal. The best available
multivariate model cannot account for 55 out of the 93 Peelites for whom data are available.
In earlier work (McLean 1998), we produced a better prediction for Repeal by incorporating the
residuals from models predicting MPs’ Aydelotte scale positions. However, this approach now seems
unnecessarily complicated. The residual from the model predicting an MP’s scale position from his
personal and constituency characteristics means just that part of his general ideological predisposition
that is not predicted from his material background. That part is quite high (McLean 1998). To enter it
6
as a prediction of his vote on Repeal is to incorporate what we already know – viz., that there is a lot of
unexplained ideology in the decision to go with Peel. Thus we are led to a conclusion with which only
Namierites, Marxists, new trade theorists, and public choice theorists could disagree: ideology and
interests both matter.
5.
Conclusion and prospects for further work
This is work in progress and conclusions must be tentative. From the part of the work programme not
reported here I have concluded that Peel was indeed a heresthetician in Riker’s sense. What about the
dimensionality of parliamentary opinion? I have been unable to give substantive content to most of
Aydelotte’s 24 scale dimensions – I suspect that most of them tap the same dimension in different
ways. And the one dimension that clearly crosscuts Aydelotte’s Big Scale – that of attitudes to
regulation – did not concern a sufficiently salient issue to make or break governments.
However, Peel’s heresthetic did have something to work on. First of all, he found a platform such that
all the Commons opposition bar ten voted for it. No other peacetime Prime Minister has managed that.
And the logistic regression shows that issues of religion do help to predict which Tories went with Peel
and which stayed with Disraeli (or on another way of labelling, which stayed with Peel and which went
with Disraeli). Most claims made about religion in the 1841 Parliament are unsubstantiated. But here
is one valid one. The more ‘liberal’ a Tory MP in matters of religion, and/or the fewer Anglicans lived
in his district, the more likely he was to follow Peel, all other things being equal.
The data are rich and still largely unexploited. There is surely much more waiting to be discovered.
7
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trade: domestic determinants of the Repeal of the Corn Laws’, World Politics 43: 545—69.
Schonhardt-Bailey, C. (1991b) ‘Lessons in lobbying for free trade in 19th-century Britain: to
concentrate or not’, American Political Science Review 85: 37-58.
Schonhardt-Bailey, C. (1994), ‘Linking constituency interests to legislative voting behavior: the role of
district economic and electoral composition in the Repeal of the Corn Laws’, Parliamentary History
13: 86—118.
Schonhardt-Bailey, C. (ed.) (1996), Free Trade: the repeal of the Corn Laws Bristol: Thoemmes
Press
8
Schonhardt-Bailey, C. (ed.) (1997) The Rise of Free Trade 4 vols London: Routledge
Shepsle, K. A, and Weingast, B. R, (1981), ‘Structure-induced equilibrium and legislative choice’,
Public Choice 37: 509—19
Weingast, B. R. (1998), ‘Political stability and Civil War: institutions, commitment, and American
democracy’ in R. Bates et al., Analytic Narratives (Princeton, NJ: Princeton University Press)
9
Table 1. Bivariate analysis: all votes
VARIABLE
PROB. OF
VOTING
FOR
REPEAL
N. of CASES df
1 (most
protectionist)
2
3
4
5 (most free
trade)
0.19
99
0.56
0.72
0.89
0.86
176
79
36
22
Anti
No info
evangelical
Extreme evang.
1.00
0.58
0.79
0.30
3
573
14
10
Not active
bus’man
Active
bus’man
0.55
498
0.72
102
Was against
No info
Was pro
0.24
0.59
0.71
33
525
42
1 (Strongly anti
reform)
2
3 (no info)
4
5 (Strongly pro
reform)
0.42
24
0.33
0.54
0.80
0.93
3
498
20
55
VALUE
F
Sig. F
4
26.844
.000
3
2.625
.050
1
9.180
.003
2
9.704
.000
4
9.843
.000
Predicted trade
orientation
Evangelicalism
Business
Catholic
emancipation
Church and
state
10
Table 2. Bivariate analysis: Tory votes only
VALUE
PROB. OF
VOTING
FOR
REPEAL
N. of CASES df
1 (most
protectionist)
2
3
4
5 (most free
trade)
0.10
86
0.38
0.46
0.64
0.40
116
41
11
5
No info
evangelical
Extreme evang.
0.31
0.67
0.22
337
9
9
VARIABLE
Sig. F
F
Predicted trade
orientation
4
8.189
.000
2
2.719
.067
Evangelicalism
For Tories, attitudes to Catholicism or church and state, and whether the MP was an active
businessman, were not significant predictors of vote on Repeal.
NB. Only 10 non-Tories voted against Repeal. They were all from deeply agricultural seats (mean
value of the trade orientation variable = 1.67; mean trade orientation for non-Tory pro-Repeal voters =
2.89). Taking the vote as the dependent variable, trade orientation was the only significant predictor
for the non-Tories (F = 8.189 for 4 d.f; sig. of F = 000)
11
Table 3. Logistic Regression (Tory votes only)
_
Number of selected cases:
469
Number rejected because of missing data: 173
Number of cases included in the analysis: 296
Value
Freq
1.00
2.00
3.00
133
103
60
TYPECON2
county
small borough
large borough
Dependent Variable..
DIV018
Beginning Block Number
-2 Log Likelihood
0.
Parameter
Coding
(1)
(2)
1.000
.000
.000
.000
1.000
.000
CORN LAWS 15MAY1846 605
Initial Log Likelihood Function
368.46766
* Constant is included in the model.
Beginning Block Number
Variable(s) Entered
1..
COFE
CATHOLIC
TYPECON2
GENTRY
1.
Method: Enter
on Step Number
(C of E attenders/population, 1851) * 100
attitude to roman catholics
newly coded constituency type
member of gentry
Estimation terminated at iteration number 4 because
Log Likelihood decreased by less than .01 percent.
-2 Log Likelihood
Goodness of Fit
315.678
296.820
Chi-Square
Model
52.790
df Significance
5
.0000
Classification Table for DIV018
The Cut Value is .50
Observed
NEGATIVE VOTE
N
POSITIVE VOTE
P
12
Predicted
NEGATIVE VOTE POSITIVE VOTE
Percent Correct
N
I
P
+--------------+--------------+
I
176
I
27
I
86.70%
+--------------+--------------+
I
55
I
38
I
40.86%
+--------------+--------------+
Overall 72.30%
[Table 3, continued]
----------------------- Variables in the Equation -----------------------Variable
COFE
CATHOLIC
TYPECON2
TYPECON2(1)
TYPECON2(2)
GENTRY
Constant
NB.
B
S.E.
Wald
df
Sig
R
Exp(B)
-.0590
.6675
.0224
.3817
.3808
.3426
.3221
.5122
1
1
2
1
1
1
1
.0085
.0803
.0000
.0002
.2596
.0250
.0606
-.1155
.0536
.2603
-.1762
.0000
-.0906
.9427
1.9494
-1.3957
.3863
-.7218
.9613
6.9151
3.0584
28.9675
13.4348
1.2710
5.0231
3.5221
.2477
1.4715
.4859
Tau-p for this model= 0.54 (cf Menard 1995, pp. 24-9)
Cox & Snell - R^2 for this model=0.163. This is a version of
proportion of sum of squares explained in terms of likelihoods rather
than sums of squares (personal communication, Sir David Cox).
13
APPENDIX I. VARIABLES USED IN THE ANALYSIS
Measures of MP’s ideology
1.
VARNAME
BIGBIGSC
BIGENCAN
CATHOLIC
DESCRIPTION
Scale position on Big
Scale
Scale position on enlarged
Canada Wheat scale
MP’s attitude to Roman
Catholics
Values
1.00
5.00
1.00
5.00
-1.00
.00
1.00
CHUSTATE
MP’s attitude to churchstate relations
DENOM
MP’s denomination if not
C of E
EVANGEL
MP’s attitude to
evangelicalism
MILITARY
Any military service
PEELITE
Tory who voted with Peel
in May 1846 in favour of
repealing the Corn Laws
whether MP gave pledges
PLEDGE
2.
-1.00
.00
1.00
Most ‘left-wing’
Most ‘right-wing’
Most ‘left-wing’
Most ‘right-wing’
Against Emancipation or
change
No information
Pro Emancipation or
change
Strongly anti-change
Weakly anti-change
No information
Weakly pro-change
Strongly pro-change
No information
RC
Church of Scotland
Free Ch. of Scotland
Quaker
Unitarian
Other Nonconformist
Anti-Evangelical
No information
Evangelical
Extreme Evangelical
No
Yes
No
Yes
Aydelotte, recoded
Gave no pledges
No information
Gave a pledge (to
anything)
Dod
Aydelotte, recoded
DNB, Dod, Thorne,
Histparl
DNB, Dod, Thorne,
Histparl
DNB, Dod, Histparl
DNB, Dod, Hilton,
Histparl, Thorne, Brent,
Bradley
Regrouping of Aydelotte
vars Army1, Navy1
Aydelotte (recoded)
Measures of constituency ideology
VARNAME
COFE
PROTDISS
RC
RELIGIOS
14
-1.00
-.50
.00
.50
1.00
.00
1.00
2.00
3.00
4.00
5.00
9.00
-1.00
.00
1.00
2.00
.00
1.00
.00
1.00
Source
DESCRIPTION
(C of E attenders /
population, 1851)*100
(Protestant dissenters /
population, 1851)*100
(Roman Catholics
/population, 1851)*100
(total attenders
/population, 1851)*100
Values
Source
1851 census
1851 census
1851 census
1851 census
3.
Measures of MP’s interests
VARNAME
AGE1841
BANK1
BUSINT
CONTEST
GENTRY
DESCRIPTION
MP’s age in 1841
MP had interests in
banking
MP had any business
interests
Election to 1841
parliament contested
Member of gentry
SOCCLSUM
N. of livings of which
patron
Social class - summary
WEALTH
Wealth at death
PATRON
4.
Values
Source
Aydelotte
Aydelotte (recoded)
.00
1.00
.00
1.00
.00
1.00
.00
1.00
No
Yes
No
Yes
No
Yes
No
Yes
1.00
2.00
3.00
4.00
5.00
6.00
Peerage
Gentry
Baronetage
Otherwise Related
Related by Marriage
Unrelated to Landed
Aydelotte (recoded)
Aydelotte (recoded)
Derived from
SOCCLSUM
Dod
Aydelotte
Aydelotte
Measures of constituency interests
VARNAME
CSBDPREF
DCDIST
DIVS15CO
EOVRSSUM
NENGLAND
POVRESUM
SCOTLAND
DESCRIPTION
District trade
preference
Nature of district
Diversification score
1850
Electors per seat in the
constituency
Constit. in
England/Wales north of
line from Wash to
Avon
Population/electorate in
the constituency
Constit. in Scotland
TYPECON2
Constit. in Ireland excl.
Ulster
Type of constituency
ULSTER
Constit. in Ulster
SIRELAND
Values
1.00
5.00
.00
1.00
2.00
Most Pro-Protection
Most Pro-Free Trade
Agricultural
Mixed
Industrial
Source
Schonhardt-Bailey
Deane & Cole
Schonhardt-Bailey
Aydelotte
.00
1.00
No
Yes
Aydelotte
Aydelotte
.00
1.00
.00
1.00
1.00
2.00
3.00
4.00
.00
1.00
No
Yes
No
Yes
County
Small borough
Large borough
University
No
Yes
Aydelotte
Aydelotte
Aydelotte
Aydelotte
15