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alternative hypothesis that high levels of horizontal decentralization
induce greater efforts at cross-district coordination (and hence lower
in¬‚ation scores).
The results were also supportive of the joint centralization
hypothesis. Horizontal and vertical centralization together determine
the size of the aggregation payoff. Aggregation is best where power is
concentrated both horizontally and vertically. The interaction models
suggested that the marginal effect of vertical centralization on in¬‚ation

25
In model 4, a rise in decentralization from 0 to 1 yields a change in the predicted in¬‚ation
of .07 (from .06 to .20). (All other variables are held at their mean “ see Table 3.9.)
Testing the Theory 65

is conditional on the level of horizontal centralization. Vertical
centralization is indeed associated with a lower in¬‚ation score, but only
where there is also substantial horizontal centralization. In short, dif-
fusion of power within the national government is suf¬cient
to undermine the effect of vertical centralization on in¬‚ation, as
hypothesized. Finally, the proxy for the size of the aggregation payoff,
the decentralization index, is positively related to party system in¬‚a-
tion, as expected.


3.4 the probability of capturing the
prize “ parliamentary systems
The size of the payoff to being the largest party in government is not the
only thing that shapes aggregation incentives. The probability that the
largest party will capture that prize is also important. If the largest
legislative party has little chance of capturing the reins of government,
particularly executive of¬ce, then even a potentially large payoff will
not be enough to induce national coordination. In parliamentary sys-
tems, the probability of capturing the prize depends on whether the
leader of the largest party becomes the prime minister. If someone other
than the leader of the largest party gets the chance to form and head the
government, then this can undermine the incentives to coordinate
across districts to forge a large national party.

Prime Minister Hypothesis: In parliamentary systems, in¬‚ation will
be negatively related to the probability that the leader of the
largest party becomes prime minister.

I operationalize the probability variable as a moving average of the
number of elections since 1970 where the prime minister has not been
a member of the largest party in the legislature (probability). The
variable is continuous and ranges from 0 (the leader of the largest
party has always been the prime minister) to 1 (the leader of the
largest party has never been the prime minister). In short, probability
captures the probability that the prime minister will n o t come from
the largest legislative party. Higher values on probability should be
associated with a higher in¬‚ation score. I believe probability is a rea-
sonable proxy for the expectation candidates and party leaders might
have when devising a coordination strategy. However, as a robustness
Building Party Systems in Developing Democracies
66

check I also calculated two dichotomous variables. Probability2 is
coded 1 once someone other than the leader of the largest party serves
as prime minister and probability3 is 1 if someone other than the
leader of the largest party served as prime minister in the last election.
These two cruder proxies produce results consistent with probability,
though they are occasionally insigni¬cant. All three variables are
calculated from data in the Database of Political Institutions (Beck
et al. 2001).


Probability-Payoff Interaction
Recall that it is the interaction of the size of the payoff and the prob-
ability of capturing that prize that together jointly shape the expected
utility of becoming the largest legislative party. In short, probability
modi¬es the effect of decentralization on aggregation incentives.

Expected Utility Hypothesis: The effect of the size of the payoff on
in¬‚ation is conditional on the probability of capturing that pay-
off. Speci¬cally, the marginal effect of decentralization on in¬‚a-
tion is increasing in probability.

I test these hypotheses using a dataset of 156 elections in 21
parliamentary democracies from 1970 to 2002. I use both cross-
sectional and pooled models. Given the structure of the data I again use
OLS with robust standard errors clustered by country for the pooled
analysis.


3.4.1. Results

Table 3.5 displays the results of the hypothesis testing “ again divided
into cross-sectional and pooled analyses. Given the small number of
observations in the cross-sectional models, the results should be
interpreted with some caution. It is heartening, therefore, to see the
cross-sectional results replicated in the pooled analysis. Columns 1 and
4 contain the results of a simple additive model. We can see that
probability is statistically signi¬cant with the right sign when added to
the model in this way. The higher is the probability that the leader of
the largest party will n o t capture the payoff, the higher is the asso-
ciated in¬‚ation score. This is true, even controlling for the size of the
table 3.5. Aggregation in Parliamentary Democracies (Dependent Variable: In¬‚ation)

Cross-Sectional Analyses Pooled Analyses

Explanatory
Variables 1 2 3 (No Thailand) 4 5 6 (No Thailand)
0.07** 0.04 0.05 0.08** 0.05 0.05*
decentralization
(0.03) (0.05) (0.05) (0.03) (0.03) (0.03)
0.23** 0.16*
probability À0.14 À0.08 À0.12 À0.03
(0.10) (0.32) (0.30) (0.09) (0.18) (0.17)
0.21 0.10 0.18 0.07
decent*prob
(0.17) (0.17) (0.13) (0.12)
0.36*** 0.32** 0.30** 0.53*** 0.51*** 0.48**
ef
(0.11) (0.11) (0.12) (0.16) (0.16) (0.17)
log_govage À0.07** À0.07*** À0.07*** À0.03** À0.02** À0.02**
(0.02) (0.02) (0.02) (0.01) (0.01) (0.01)
0.01 0.02 0.02 0.01 0.01 0.01
log_districts
(0.02) (0.02) (0.02) (0.02) (0.03) (0.03)
0.11 0.18 0.18
Constant À0.05 À0.01 À0.01
(0.13) (0.14) (0.14) (0.10) (0.10) (0.10)
0.74
R-squared .74 .65 .66 .67 .64
19 19 18 101 101 98
Observations
* p < .1, ** p < .05, *** p < .01; standard errors in parentheses




67
Building Party Systems in Developing Democracies
68

payoff (decentralization). Note too that the effect of decentralization
on in¬‚ation remains signi¬cant in these models.
Columns 2 and 5 summarize the results of the interactive models.
To get a better sense of the interactive dynamics at work in these
models, I once again include marginal effects graphs. In this case, the
graphs display the marginal effect of decentralization as probability
changes. Figures 3.2a and 3.2b show that the marginal effect of de-
centralization on in¬‚ation is always positive, though when proba-
bility is at or near 0 this positive effect is not statistically signi¬cant at
the 95% level (though it is at the 90% level).26 This suggests that
the marginal effect of the size of the payoff is weakest where actors
are virtually certain that the largest party will capture the
premiership. However, as the probability that someone other than the
leader of the largest party will become prime minister increases, the
marginal impact of decentralization in¬‚ation increases as well. As
hypothesized, the marginal effect of decentralization increases in
probability. As I argued in Chapter 2, aggregation appears to be the
poorest where both the size of payoff is small (decentralization is
high) and the chance of capturing that payoff is low (probability is
high). More concretely, the model in column 5 suggests that where
power is highly decentralized (decentralization ¼ 2) and there is little
chance of capturing the payoff (probability ¼ 1), the in¬‚ation score is
a very high .42.27 By contrast, where there is certainly that the largest
legislative party will capture a valuable prize (probability and
decentralization are both 0), the predicted in¬‚ation score is .08 (see
Table 3.9).
Plots of the observations suggest that Thailand is an outlier with both
an unusually high probability score and an equally high in¬‚ation score.
Even though this result is consistent with the theory (as I will demonstrate
in Chapter 5), I want to be certain the results are not being driven entirely
by the Thai case. As a remedy, I reran the interactive models with
Thailand excluded and display the results in columns 2 and 4. We can see
that dropping the Thai case does indeed reduce the size of the coef¬cients
on both probability and the interaction term. The marginal effects graphs

26
In the pooled analyses, substituting expenditures for revenues in the decentralization
index produces consistent but weaker results. Substituting expenditures makes no
difference in the cross-sectional analyses.
27
Holding all other variables at their means.
Testing the Theory 69

95% Confidence Interval

Marginal Effect of Decentralization on
Marginal Effect of Decentralization on (b)
(a)
Inflation as Probability Changes (Pooled)
Inflation as Probability Changes (XS)




Marginal Effect of Decentralization
Marginal Effect of Decentralization



.6 .5

.4
.4
.3
.2
.2

0 .1

0
“.2
0 .5 1 0 .5 1
Probability of NOT Capturing Payoff
Probability of NOT Capturing Payoff


(c) (d)
Marginal Effect of Decentralization on Marginal Effect of Decentralization on
Inflation as Probability Changes““No Thailand (XS) Inflation as Probability Changes““No Thailand (Pooled)


Marginal Effect of Decentralization
Marginal Effect of Decentralization




.4 .3

.3
.2
.2
.1
.1

0
0

“.1
“0.1
0 .5 1 0 .5 1
.5
Probability of NOT Capturing Payoff Probability of NOT Capturing Payoff

(e) Marginal Effect of Probability on Marginal Effect of Probability on Inflation
(f)
Inflation as Decentralization Changes (XS) as Decentralization Changes (Pooled)
Probability of NOT Capturing Payoff




Probability of NOT Capturing Payoff




.5
1


.5


0 0


“.5


“.5
“.1
0 1 2 3 0 .5 1 2
1.5
Decentralization Decentralization

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