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Do Parties Matter for Economic Outcomes? A Regression-Discontinuity Approach

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A long-standing issue in political economics is to what extent party control makes a difference in determining fiscal and economics policies. This question is very difficult to answer empirically since parties are not randomly selected to govern political entities. This paper uses a regression-discontinuity design, i.e. party control changes discontinuously at 50 percent of the vote share, which can produce “near” experimental causal estimates of the effect of party control on economic outcomes. The method is applied to a large panel data set from Swedish local governments with a number of attractive features. The results show that there is an economically significant party effect: left-wing governments spend and tax 2-3 percent more than right-wing governments. Left-wing governments also have 7 percent lower unemployment rates, which is partly due to left-wing governments employing 4 percent more workers than right-wing governments.
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Do Parties Matter for Economic Outcomes ?
A Regression-Discontinuity Approach?


Per Pettersson-Lidbom?
July 12, 2007

Abstract
A long-standing issue in political economics is to what extent party control makes a
difference in determining fiscal and economics policies. This question is very difficult to
answer empirically since parties are not randomly selected to govern political entities.
This paper uses a regression-discontinuity design, i.e. party control changes
discontinuously at 50 percent of the vote share, which can produce “near” experimental
causal estimates of the effect of party control on economic outcomes. The method is
applied to a large panel data set from Swedish local governments with a number of
attractive features. The results show that there is an economically significant party effect:
left-wing governments spend and tax 2-3 percent more than right-wing governments.
Left-wing governments also have 7 percent lower unemployment rates, which is partly
due to left-wing governments employing 4 percent more workers than right-wing
governments.
Key words: political parties, party control, partisan politics, regression-discontinuity
design, natural experiments, unemployment, government employees, fiscal policy


? An earlier version of this paper has been circulated under the title “Do Parties Matter for Fiscal Policy
Choices? A Regression-Discontinuity Approach”. The idea of using a discontinuity as a source of
identifying information of the party effect originates from a conversation with David Strömberg. The author
gratefully acknowledges helpful comments from the editor Roberto Perotti, Torsten Persson, Jakob
Svensson, Justin Wolfers, two anonymous referees, and seminars participants at MIT, UC Berkeley,
Harvard University, Princeton University, University of Pennsylvania, Göteborg University and Uppsala
University. The views expressed in the paper are mine, as is the responsibility for any mistakes. Financial
support from Jan Wallander’s Research Foundation is gratefully acknowledged.
? Department of Economics, Stockholm University, S-106 91 Stockholm, Sweden; e-mail: pp@ne.su.se.



2
1. Introduction
This paper estimates the causal effect of party control on fiscal and economic policies.1
Estimating the party effect without bias is a very challenging identification problem since
parties are not randomly selected to govern political entities. For example, since voters
select parties to govern, there may be an omitted variable problem due to unmeasured
voter preferences.2 Thus, a correlation between party control and some policy outcome
does not necessarily imply causation. The large empirical literature dealing with partisan
cycles in macroeconomic outcomes (e.g., growth, unemployment and inflation) is also
plagued by similar problems of endogeneity of party control.3 Voters may, for example,
elect conservative governments when recession is anticipated which will lead to a
spurious relationship between party control and economic outcomes. While many studies
claim to find strong empirical support for partisan differences in some macroeconomic
outcomes, Faust and Irons (1999) argue that there is only weak evidence that party control
is of importance when issues of simultaneous causality bias and omitted variable bias are
properly accounted for in a vector autoregression framework.
The causal party effect could be convincingly estimated if parties in government
could be randomized over political entities since randomization ensures that there is no
systematic difference between political entities with governments of various stripes. In
that case, the average difference in economic outcomes between entities with different
party control is an unbiased estimate of the true party effect. However, such an
experiment would not be feasible since it would clash with our notion of democracy.
Thus, we are left with drawing inference from non-experimental data. Nevertheless, we
can still try to approximate the evidence generated by a randomized controlled trial,
namely using a quasi-experimental design.

1 For evidence on the party effect for fiscal policies, see Besley and Case (2003) for a survey of work on
U.S. states, and Blais et al. (1993) for a survey of cross-country studies and U.S. states. See also Imbeau et
al (2001) for a meta-analysis of studies using OECD data. For evidence on macroeconomic outcomes, see
Alesina et al. (1997) and the references cited therein.
2 For work that stresses the endogeneity of other political institutions see, for example, Aghion et al. (2004,
2005).
3 I use the word endogeneity as a catchall for problems with selection, omitted variables and simultaneous
causality since all these problems will make the explanatory variable (party control) correlated with the
error term.


3
In this paper, the source of identifying information of the party effect comes from
an institutional feature of the election system, that is, party control changes
discontinuously at 50 percent of the vote share which makes it possible to implement a
regression discontinuity design. The general idea of the regression-discontinuity design is
to compare the outcomes for units (e.g., political jurisdictions) whose value of an
underlying targeting variable (e.g., vote share) is “just below” and “just above” a fixed
threshold (e.g., 50 percent of the votes) since they will, on average, have similar
characteristics except for the treatment (e.g., party control). In other words, those units
slightly below the threshold will provide the counterfactual outcome for those units
slightly above, since the treatment status will be “as good as randomly assigned” in a
neighborhood of the treatment threshold. The inference from a regression discontinuity
analysis can therefore be as credible as that from a randomized experiment (e.g., Lee
2003). In particular, the regression discontinuity approach shares the same attractive
feature as a randomized controlled trial, namely that it can actually be tested whether
treatment status is likely to be “as if” randomized.
I employ the regression-discontinuity design on a data set from Swedish local
governments. The use of this data set offers some attractive features in the search for a
causal party effect on economic outcomes. First, it is a large panel data set (288
municipalities over a 21-year period) making it possible to use a regression discontinuity
design since there must be enough data “close” to the treatment threshold for the method
to be useful. Second, Swedish local governments are very homogeneous. In particular,
they operate within a common political framework and face the same institutional setting.
Thus, economic outcomes and political parties are quite comparable across political
entities, which is otherwise a major obstacle in cross-country studies. One potential
weakness with the data set, however, is the multi-party feature of the Swedish political
system. Nevertheless, the Swedish political map has been characterized by a very clear
dividing line between socialist and non-socialist parties leading to a quite stable two-bloc


4
system.4 Hence, to a first approximation we can treat the Swedish electoral system as
bipartisan.5
The results of this paper show that party control has a causal effect on spending,
taxes and unemployment. The party effect is also quite substantial. For example, left-wing
governments spend, as a share of income, about 2-3 percent more and have about 7
percent lower unemployment rates than right-wing governments. Left-wing governments
also employ about 4 percent more workers than right-wing governments. I also present
evidence in support for party control being as “good as randomly assigned” among those
municipalities that are close to the treatment threshold of 50 percent of the vote share,
which provides strong support for a causal interpretation of my results.
This paper is related to, but distinct from, the literature that investigates whether
representatives from different political parties vote differently.6 Specifically, Lee et al.
(2004) make use of a similar regression-discontinuity design in their study of the voting
records of Democratic and Republican congressmen in the U.S. House of Representatives
from 1946 to 1995.7 Although their analysis is interesting, it does not say whether, or to
what extent, parties are of importance for policy outcomes since the mapping between
votes and policy outcomes is not analyzed. For example, many votes in Congress are
supported by large supermajorities. If the difference in voting between Democrats and
Republicans mainly arises in these types of votes, then the effect on policy is nil.
The paper is organized as follows. Section 2 describes the regression discontinuity
design and how it is implemented in this paper. Section 3 describes the data, while
section 4 presents the results. Section 5 discusses the findings and concludes.

4 For an overview of the Swedish political system, see Petersson (1994). For a detailed description of local
governments in Sweden, see Gustafsson (1988).
5 For example, Alesina et al. (1997) also classify Sweden as a bipartisan system (along with the U.S. and
other political systems with a clear left-right division) in their empirical analysis.
6 See, for example, Levitt (1995), Snyder and Groseclose (2000), and McCarty et al. (2001).
7 The first version of this paper was written in May 2001 (Pettersson-Lidbom 2001) while the first version
of the Lee et al. paper is from 2002. My paper is cited in their working paper, Lee et al. (2002), but not in
the published version Lee et al. (2004).


5
2. Empirical framework
In this section, I will discuss the regression-discontinuity method and how it is
implemented in this paper.
In the “sharp” regression-discontinuity design, treatment status is a deterministic
function of some underlying continuous variable, that is,

(1)
Ti=T(xi) =1[xi ? x ],

where 1[.] is an indicator function and x is a continuous variable or an assignment
variable, and x is a treatment threshold separating the units into two mutually exclusive
groups: those units receiving treatment (T=1) and those which do not (T=0). The idea is to
compare the outcomes for units whose value of the underlying targeting variable is “just
below” and “just above” the treatment threshold x , since they will on average have
similar characteristics except for the treatment. In other words, those units slightly below
the threshold will provide the counterfactual outcome for those units slightly above, since
the treatment status will be randomized in a neighborhood of treatment threshold. In our
context, the vote share is the assignment variable that assigns parties to political entities
and where the treatment threshold is at 50 percent of the proportion of votes.

In practice, the regression-discontinuity design can be implemented in a number
of ways.8 The simplest possible approach is to just compare average outcomes in a small
neighborhood on either side of the treatment threshold. This approach could, however,
produce very imprecise measures of the treatment effect, since the regression-
discontinuity method is subject to a large degree of sampling variability and this
procedure would therefore require very large sample sizes. An equivalent, but much more
efficient, method is to use all available data and a control function approach, that is, to
regress the outcome of interest, say Yi, on a low-order polynomial in the treatment-
determining covariate xi, i.e., the control function, and the binary treatment indicator Ti.
This procedure will yield an unbiased estimate of the treatment effect, unless the control
function is misspecified, since xi is the only systematic determinant of Ti and therefore the

8 See Hahn et al (2002) for a non-parametric approach.


6
control function will capture any correlation between Ti and the population error term.
The control function approach is my preferred method since there is only a limited
number of observations close to the threshold in my data set (i.e., there are only 89
municipalities within ±2 percentage points from the 50 percent threshold). Nevertheless, I
will also present results where I only use data close to the threshold, i.e., in the range [48,
52], as a specification check since the estimate from the control function approach and the
discontinuity sample should be the same (except for sampling variability) if the control
function is correctly specified.

In this paper, a panel data set from Swedish local governments will be used to
estimate regression models of the form



(2)
Yit= µi + ?t + ?Tit + f(Left vote share)? + vit

where Yit is an economic outcome (e.g., spending per capita, taxes, unemployment, and
government employees per capita) for local government i in time period t, µi is a locality-
fixed effect, ?t is a time-specific effect, Tit is a treatment indicator taking the value of 1 for
left-wing governments and zero for right-wing governments, and f(Left vote share) is a
control function, i.e., some low-order polynomial in Left vote share. The parameter of
interest is ? ? the party effect ? which measures the average difference in economic
outcomes between left- and right-wing governments.9 The main reason for including
fixed municipality and time effects is to enhance efficiency since there is no need to
include additional covariates except for f(.) in (2) to get an unbiased estimate of ?.
However, Hoxby (2000) argues that a “within-unit” regression-discontinuity method is
“more powerful and less subject to bias” than a cross-section discontinuity analysis when
there is only a limited number of observations close to the threshold. Thus, specification
(2) takes into account her concern since it only uses the within-municipality variation to
identify the party effect. A number of other controls (e.g., income, population size,
proportion of people below 15, and proportion of people above 65) will also be added to


7
(2) as a way of checking whether party control is as good as randomly assigned. The
inclusion of these additional covariates should not significantly affect the estimate of the
party effect since party control should be as good as randomly assigned conditional on
f(.). Here, it is important to not include variables that are themselves affected by the
treatment, such as intermediate outcomes, since these will bias the estimate of the
treatment effect.10 For example, including the lagged economic outcome Yit-1 among the
control variables is not advisable in our context of measuring the causal effect of party
control since it is an intermediate outcome,11 and therefore affected by the treatment
itself, i.e., party control. Nevertheless, it is possible to include the economic outcome
from a previous treatment since that guarantees that it is a pretreatment variable, i.e., it
was measured before the current treatment was chosen. Thus, one should only control for
pretreatment characteristics to avoid bias. In practice, however, the covariates are often
recorded at the same time as the outcome, subsequent to treatment. In this case, it must be
assessed on a case-by case basis whether a particular covariate should be used as a control
variable.
A final comment about specification (2) is that it is only the party effect ? that has
a causal interpretation since f(.) is allowed to be correlated with the error term vit. Thus, it
is not valid to interpret the coefficient on vote share ? as measuring the causal impact of
voter preferences on economic outcomes. In other words, in the regression discontinuity
approach, it is totally irrelevant whether the vote share can be considered as a good
measure for voter preferences.

9 The estimated treatment effect from a regression-discontinuity design will typically not be the average
treatment effect but a marginal treatment effect (see, e.g. Hahn et al 2001). This issue will be discussed
below.
10 See Rosenbaum (1984) and Imbens (2004) for a discussion of the choice of covariates.
11 This is related to the term-in-office being longer than one year. The term-in-office in Sweden is three
years.


8
3. Data
To test whether party control is of importance for economic outcomes, I will use a panel
data set from Swedish local governments, but before turning to the description of the
data, it might be helpful to briefly digress on the workings of Swedish local governments.
As of 2005, there are 291 local governments (or municipalities) in Sweden which
cover the entire country. Local governments play an important role in the Swedish
economy, both in terms of the allocation of functions among different levels of
government and economic significance. They are, for example, responsible for the
provision of day care, education, care of the elderly and social welfare services. To
quantify their economic importance, note that in the 1980s and 1990s their share of
spending out of GDP was in the range 20 to 25 percent and they employed roughly 20
percent of the total Swedish workforce. Swedish local governments also have the
constitutional right of self-government, no restriction on borrowing and no balanced
budget rules.12 Moreover, only 20 percent of their income come from grants, whereas the
rest mostly comes from a proportional income tax, which each municipality can set freely.
In other words, they have a relatively large degree of fiscal freedom.
To implement the regression-discontinuity method, the mechanics of the Swedish
election system need to be discussed in some detail. The election schedule is fixed and
elections were held every third year on the third Sunday of September during the sample
period.13 During the same period, voter turnout has been very high, close to 90 percent, in
the local elections. The decision-making body in each of the municipalities is an elected
municipal council and the Swedish Elections Act prescribes that in elections to the
municipal council, seats should be proportionally distributed among parties on the basis
of the election results in each constituency, where the distribution is based on the adjusted
odd-number method. As a result, the election system is entirely party based, i.e., a closed-
list system, and has several political parties.14 The multi-party issue raises the question of

12 As from 2000, however, there is a balanced budget rule in place.
13 As from 1994, elections are held every fourth year.
14 Whether the proportional election system is a cause of the multitude of parties or whether the number of
parties is caused by a heterogeneous distribution of voter preferences is still in dispute.


9
how to define treatment or party control. However, as previously discussed, the Swedish
political map has been characterized by a very clear dividing line between socialist and
non-socialist parties leading to a quite stable two-bloc system.15 Hence, to a first
approximation we can treat the Swedish electoral system as bipartisan,16 and define the
treatment indicator Ti as 1 for left-wing majorities and zero otherwise. The party effect
should thus more accurately be addressed as a majority coalition effect but, for simplicity,
I retain the former name.17
There is also one caveat with my data that needs to be mentioned: the existence of
several small parties?often one-issue parties?at the local level which are not part of the
two blocs. These parties sometimes hold the balance of power, which creates a problem
in defining party control since these are not easily classified along the left-right
ideological spectrum. I call these kinds of constellations undefined majorities.18 The
problem with undefined majorities is solved by including a separate dummy variable for
the undefined majority, however. The party effect will now be correctly identified as the
average difference in policy outcomes between left-wing and right-wing majorities.19
Table 1 summarizes the number of left-wing, right-wing and undefined
governments in every election period during the sample period 1974-1994. There was a
left-wing majority in 826 cases, and a right-wing majority in 833 cases. Thus, the two
blocs have been in power almost the same number of times.20 Table 1 also shows that
there has been an undefined majority in 312 cases, which corresponds to 15 % of all

15 For a general overview of the Swedish political system, see Petersson (1994). For a detailed description
of local governments in Sweden during the period of investigation, see Gustafsson (1988).
16 For example, Alesina et al. (1997) also classify Sweden as a bipartisan system (along with the U.S. and
other political systems with a clear left-right division) in their empirical analysis.
17 To define the left-wing majorities and the right-wing majorities, I have relied on the standard
classifications of parties along the left-right spectrum as discussed by Petersson (1994). According to this
classification, the left-wing bloc includes the Social Democratic Party and the Leftist Party while the right-
wing bloc includes five parties: the Conservative Party, the Centrist Party, the Liberal Party, the Christian
Democratic Party and the New Democratic Party. The Christian Democratic Party is only included in the
right-wing majority from the year 1988, however, and the New Democratic Party only from the year 1991.
18 This classification is compiled from the distribution of seats in local councils. If either of the blocs
receives more than 50 percent of the seats it is defined accordingly, otherwise it is classified as undefined.
19 Another approach would be to altogether exclude these observations from the analysis. It turns out that it
is of no importance for the results about the party effect presented below which of these two approaches I
use.
20 This might be surprising given the Social Democratic party hegemony at the national level.


10
observations. Table 2 shows the frequency of government changes for the localities. The
number of government changes is very unequally dispersed among the different
municipalities. For example, 122 municipalities (42 percent of the total sample) had no
change of power (69 had left-wing and 45 right-wing governments). It is important to
stress that the 122 municipalities with zero turnovers will not be part of identifying the
party effect, since only the within-municipality variation will be used, as discussed in
section 2.
Turning to the economic outcomes, nine different variables will be used in the
empirical analysis: total expenditures per capita, total expenditures as a share of income,
current expenditures per capita, current expenditures as a share of income, total revenues
per capita, total revenues as a share of income, proportional income tax rate, the
unemployment rate, and the number of local government employees per capita. The
difference between total and current expenditures per capita is mainly that investments
are included in the former. Roughly 85 percent of total spending is classified as current
spending. Total revenues per capita include tax receipts from a proportional income tax
rate, fees and governmental grants. Since total revenues might reflect non-discretionary
local government decisions, using the income tax rate itself is a more discretionary
measure.21 The unemployment rate is only available from 1979 and therefore I will lose 5
years of data, as compared to the other outcomes, when I use this variable as the
economic outcome of interest. Total expenditures, current expenditures, total revenues
and income are expressed in 1991 prices. Total expenditures as a share of income, current
spending as a share of income, total revenues as share of income, the proportional tax
rate, the unemployment rate and government employment per capita are expressed as
percentages.22 Table 3 presents summary statistics for the nine outcome variables. Table 3
also presents summary statistics for a standard set of controls in the local public finance
literature (see e.g., Besley and Case 2003): average income, proportion of people of age 0
to 15, proportion of people older than 65 and population size. I consider these variables as
not affected by the treatment, which is the key requirement for using them as controls as

21 On average, about 55 % of the total revenues come from the income tax.

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