UNIVERSITY OF CALGARY

What Drives Democratic Government Expenditures?

Evidence from Canadian Provincial Governments

by

Peter Ponsu

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF ARTS

DEPARTMENT OF ECONOMICS

CALGARY, ALBERTA

SEPTEMBER, 2013

© Peter Ponsu 2013

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Abstract

This thesis aims at analyzing the drivers of provincial government spending on health care,

education and municipal services from the perspectives of the median voter and political budget

cycle models. A panel of province-level data for the period 1989 to 2009 is used with FGLS and

GMM estimation methods. The results show that median income is positively related with both

health and education spending, while the tax-price faced by the median voter is negatively

associated with both health and municipal services spending. Population density has a positive

link with education spending but inversely related to municipal services spending. Increases in

income inequality raise provincial spending on education and municipal services. The effects of

the age-cohorts on these expenditures are program-specific.

The predictions of the political budget cycle models indicate that although there is a tendency

for the expenditures on these three programs to increase in election-years, only the cyclical

effects on health and municipal services are statistically significant.

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Acknowledgments

I would like to express my deepest gratitude to my supervisor, Dr. Jean-Francois Wen for his

constructive criticisms, patience, timely engagements and the excellent atmosphere provided me

throughout the learning process of this thesis.

I would like to thank the rest of my oral defense committee: Dr. Ronald David Kneebone, Dr.

David Kenney Stewart and Dr. Eugene C. Beaulieu for their insightful comments and sugges-

tions.

My appreciations also go to the following friends: Abigial Jackson, Irene Aboagye, Eric

Agyemang, Adriana Appau and Chindong Li for their diverse supports and the contributions.

Finally, I thank my parents, Isaac Ponsu and Mama Esther and all my family members for the

supports, encouragements and best wishes.

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Table of Contents Abstract…………………………………………………………………………………………..ii

Acknowledgements………………………………………………………………………….......iii

Table of Contents……………………………………………………………………..................iv

List of Tables………………………………………………………………………………….....v

List of Figures…………………………………………………………………………………....v

1. Introduction………………………………………………………………………………......1

1.1 Government Spending from the Perspective of the Median Voter Theorem………….2

1.2 Government Spending from the Perspective of the Political Budget Cycle Models….3

1.3 Overview of Provincial Government Disaggregated Expenditures…………………...5

1.3.1 Health Expenditure as a Percentage of GDP………………… ………………5

1.3.2 Expenditure on Education as a share of GDP………… ……………………...7

1.3.3 Expenditure on Municipal Services as a Share of GDP……………………….8

2. Literature Review…………………………………………………………………………..10

2.1 Median Voter Model………………………………………………………………….10

2.2 Political Budget Cycle Model…………………………………………………….......20

3. Data Description and Econometric Methodology………………………………………....34

3.1 Data Description ……………………………………………………………………....34

3.1.1 Data Description: Median Voter Variables………………………………….....35

3.1.2 Data Description: Political Variables………………………………………......37

3.2 Econometric Methodology……………………………………………………………..39

3.2.1 Models Estimated under Median Voter Hypothesis……………………...........42

3.2.2 Models Estimated under Political Budget Cycle Hypothesis.………................44

3.2.3 Estimation Strategy…………………………………………………………….45

4. Results and Policy Implications……………………………………………………………..51

4.1 Results and Policy Implications: Median Voter Model ……………………… …….....51

4.2 Results and Policy Implications: Political Budgets Cycles……………………………..56

4.3 Robustness Checks and Extensions……………………………………………………..59

5. Conclusion ....................................……………………………………………………….......63

References....................................................................................................................................67

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List of Tables

Table 3.1 Sources and Definition of Variables.............................................................................75

Table 3.2 Percentage Change (between 1989 and 2009) in the Median Variables......................77

Table 3.3-3.5 Initial Diagnostic Checks.......................................................................................79

Table 3.6 Summary Statistics of the Political Variables..............................................................81

Table 3.7 Summary Statistics of the Variables used in the Median model.................. ...............81

Table 4.1 Regression Results of the Median Voter Models

(Static Version with Tax-price as an Explanatory Variable)........................................................82

Table 4.2 Regression Results of the Median Voter Models

(Static Version with Gini Coefficient as an Explanatory Variable).............................................83

Table 4.3 Regression Results of the median Voter Models

(Dynamic Version with Gini Coefficient) ..................................................................................84

Table 4.4 Regression Results of the Median Voter Models

(Dynamic Version with Tax-price)...............................................................................................85

Table 4.5 Regression Results of Political Budget Cycle Models .

(Static Version) ............................................................................................................................86

Table 4.6 Regression Results of Political Budget Cycles Models

(Dynamic Version) ......................................................................................................................87

Table 4.7 Regression Results of the Median Voter Theorem

(with Gini Coefficient and Seemingly Unrelated Regression Estimation Method).....................88

Table 4.8 Regression Results under Median Voter Theorem

(with Tax-price and Seemingly Unrelated Regression Estimation Method)...............................89

Table 4.9 Regression Results of the Median Voter Models

(Static Version with Tax-price, Federal Transfers and Debt Servicing Costs).............................90

Table 4.10 Regression Results of the Median Voter Models

(Static Version with Tax-price, Federal Transfers Debt Servicing Costs)...................................91

Table 4.11 Regression Results of the Political Budget Cycle Models

(Static Version with Federal Transfers and Debt Servicing Costs)..............................................92

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List of Figures

Figure 1.1 Health Expenditure as a share of GDP....................................................................6

Figure 1.2 Expenditure on Education as a share of GDP.........................................................8

Figure 1.3 Expenditure on Municipal Services as share of GDP.............................................9

1

Chapter 1: Introduction

What drives government expenditures on goods and services? This constitutes a central question

in the public finance and public choice literatures. Over the past few decades, a number of

theories have been developed to account for public sector spending on goods and services.1 This

thesis aims at addressing a specific aspect of the above question relating to Canadian provincial

government spending on education, health care and municipal services by applying two of those

theories. The driving forces behind these three spending components are analyzed in the context

of the median voter and political budget cycle models.

Health care, education and municipal services are core public spending programs that jointly take

a large share of the provincial government’s budgets. It is therefore important to find out what

makes the provinces spend more or less resources on these programs. In doing so, two separate

empirical analyses are carried out, one to test the median voter hypothesis and the other for the

political budget cycle model. The provincial government disaggregated expenditure data on

education, health care and municipal services as well as data on provincial parliamentary

political variables from 1989 to 2009 are used.2

The constitution of Canada makes the provincial government spending and provincial

parliamentary political variables appropriate for testing both the median voter and political

budget cycle hypotheses, especially with respect to fiscal expenditures on health, education and

municipal services. Canada has a federal system of parliamentary government with

responsibilities and functions shared amongst federal, ten provincial and three territorial

1 See Hybeck (1998) for the discussion on the twelve-fold taxonomy on the growth of government.

2 Municipal spending in this analysis is made up of expenditures on protection of people and properties (courts of

law, correction and rehabilitation services, and policing and regulation measures) recreation and culture (recreation,

culture- libraries, art galleries and museums, broadcasting) and housing.

2

governments.3 The provincial legislature is a parliamentary democracy where the legislative

assembly is elected through competitive election. Moreover, in agreement with the constitution

each province is autonomous and the constitution act of 1982 gives exclusive provincial

jurisdiction over its level of expenditures and revenues generation.4 This

suggests that in the key

areas of health, education and municipal services that are considered to be core in public interest

are under the jurisdiction of provincial legislatures who are themselves directly accountable to

the electorate.5

1.1 Government Spending from the Perspective of the Median Voter Theorem

Since the pioneer works of Bowen (1943), Black (1948) and Downs (1957), there have been

various versions of the median voter model. Some of these models account for the size of the

public sector through policy-making decisions including the provision of public goods

(Borcherding and Deacon, 1972; Bergstrom Goodman, 1973).

The median voter model postulates that majority rule decision will always produce the outcome

most preferred by the median voter, irrespective of the institutional settings. Downs (1957)

asserts that in competitive elections where voters decide on two parties or two policy choices the

outcome of the elections always correspond to the position of the median voter. That is, policy-

makers are compelled by electoral competition to choose expenditure levels that maximize their

votes. They achieve this by adopting platforms that are closer to the ideal policies preferred by

3 The ten provinces are British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec, Newfoundland and

Labrador, Nova Scotia, New Brunswick and Prince Edward Island and the three territories are Northwest Territories,

Nunavut and Yukon. 4 See the Constitution Act , 1867 under Subsection 92(2) for the regulation governing provincial governments’

revenue generation. 5 In the area of health care and education, although the federal government supports the provincial governments

through Canada Health Transfer (CHT) and Canada Social Transfer (CST) its involvement is indirect as per the

constitution.

3

the median voter (Holcombe, 1989). Thus, the output produced by the public sector is

understood to be driven by the preferences of the median voter. The median voter model by

implication acts as a “matching function” which associates voters’ preferences to the ideal policy

choices. It follows that the demand for public sector output is driven by the demand of the

median voter. Therefore should any of the determinants of the median voter’s demand change it

would affect the expenditure level of government for that output.

In this thesis, the hypothesis that provincial government spending on health, education and

municipal services are stimulated by certain economic factors as well as some social and

demographic factors that represent the preferences of the median voter is tested. To the best of

my knowledge, there has not been empirical work in economics that has applied Canadian sub-

national government spending data to explain the size of provincial governments from median

voter perspective. One contribution of this thesis is to fill that vacuum.

1.2 Government Spending from the Perspective of the Political Budget Cycle Hypothesis

The political budget cycle hypothesis is based on the idea that incumbents behave

opportunistically and manipulate electoral outcomes by altering macroeconomic variables.

Specifically, opportunistic incumbent governments may capitalize on political-market

imperfections like information asymmetries to signal their competences in order to increase their

chances of re-election (Rogoff and Sibert, 1988 and Rogoff, 1990). They achieve this by

stimulating their economies with expansionary fiscal and monetary policies before elections. The

persistence of such pre-electoral manipulations result in “periodic fluctuations in a government’s

4

fiscal policies induced by elections” (Alt and Lassen, 2005) generating a political budget cycle

phenomenon which has its roots in the pioneering theoretical framework of Nordhaus (1975).6

Government fiscal and monetary policies have been found to be more susceptible to pre-electoral

manipulation in developing countries than developed economies (Shi and Svensson, 2002).

Rogoff’s (1990) model focuses on disaggregated government spending rather than the overall

expenditure level. The empirical research implication from the model is that government can

maintain a balanced budget but still will find means to score “political points” through altering

the composition of its spending. This signalling is accomplished by focusing on more ‘visible’

spending in the election-year than capital investment which takes longer period to be visible. A

rational voter in a developed economy like Canada is known to be aware of government budget

constraints at a point in time and intertemporally. Evidence shows that although voters like low

taxes and high government spending they are fiscally conservative. Voters rather punish

incumbents who engage in pre-electoral manipulation that result in deficit. Therefore an

opportunistic incumbent who understands the rules of the game and knows the environment he

finds himself in, is more likely to signal by altering the composition of the budget rather than the

overall level.

The hypothesis that provincial government spending on health care, education and municipal

services exhibit periodic fluctuations induced by elections is tested in this thesis.

6 Political Business Cycle (PBC) studies the possible effects of politics (elections) on the real economy such as

GDP, growth rate and unemployment. Due to possibly lack of empirical evidence (Drazen, 2001) and the fact that

government does not directly control real economic variables (Shi and Svensson, 2003) there have been shift of

focus from the study of political business cycle to political budget cycle.

5

1.3 Overview of Provincial Government Disaggregated Expenditures

The trend of provincial governments’ expenditures on health, education and municipal services

within the study period, 1989 to 2009 is examined below.

1.3.1 Provincial Governments’ Health Expenditures as a Percentage of GDP

The provincial government health expenditure as a percentage of GDP is depicted in figure 1.1.

In general, the provincial government health spending as a share of GDP shows a fairly steady

level for all the ten provinces from 1989-2009. However, between 1992-1996 there was

downswing in the percentage of GDP provincial governments devoted to health care but it started

rising steadily after 1997.7 In 1989 the average provincial governments’ health spending as a

percentage of GDP was 5.8, rising gradually to 6.9 by 1992. This declined swiftly afterwards

reaching 6.0 in 1997. It took an upward trend after 1997 and by 2009 the average provincial

government expenditure as a share of GDP has peaked to 7.8 percent. From 1989 to 2009, the

total growth rate in provincial health expenditure as percentage of GDP was highest in Prince

Edward Island with total growth rate of 55 percent and average annual growth rate of 2.6

percent, and lowest in Alberta with total growth rate of 11 percent and average annual growth

rate of approximately 0.5 percent.

The average real per capita provincial government health spending (with 2002 as the base year)

in 1989 was $1,588. Manitoba recorded the highest real per capita level expenditure of $1,733

and the least was Newfoundland, with real per capita spending of $1,366. By 2009 the average

7 The down swing in the percentage share of GDP provincial governments devoted to health within this period was

caused by the economic down turns and rising public debt which resulted in cut back in provincial expenditures including health spending. See Organisation for Economic Co-operation and Development (OECD) 2001 report,

“Policy Brief: OECD Health at glance- How Canada Compares”

6

provincial real per capita spending increased to $2,927, ranging from a high of approximately

$3,540 in Newfoundland to $2,625 in Quebec.

Figure 1.1 Provincial Government Health Expenditure as share of GDP

Source: Derived by dividing provincial governments’ health expenditures (Statistics Canada, table 385-0002)

by provincial GDP (Statistics Canada, table 384-0001)

The disparity among the provinces on how much of their GDP they devote to health spending

has persisted over the years. It clustered around 1989 with a percentage difference of only 2.4,

with New Brunswick having the highest contribution of 6.8 percent and Alberta least of 2.4

percent. However by 2009 the gap has widened by approximately 5.3 percentage difference, the

range between Prince Edward Island, highest of 10.2 and Alberta, lowest at 4.9 percent.

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

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According to the 2009 Canadian Institute for Health Information report, the difference among

provincial health spending has persisted as a result of many factors. Among them include the

differences in relative income levels, demographic, health care needs, health care system and

structure and health personal compensation. Also, some provinces exploit economies of scale in

providing these services. For instance, Prince Edward Island has the same spending

responsibilities but tiny GDP relative to a province like Ontario.

1.3.2 Provincial Government Expenditure on Education as a Percentage of GDP

Figure 1.2 shows the provincial governments’ expenditure on education as a percentage of GDP

from 1989 to 2009. The percentage of GDP provincial governments devoted to education

increased from an average of 5.1 percent in 1989 to 5.7 percent in 1993 representing average

annual growth rate of 2.9 percent. Thereafter it assumed a downward trend falling from the

provincial average of 5.7 percent in 1993 to a low of 4.1 percent in 2007. By 2009 the provincial

average has risen marginally again to 4.6 percent of GDP.

By 2009, Prince Edward Island was the leading contributor, devoting 6 percent of its GDP to

provincial education while Ontario was still behind, at 3.5 percent. Over the 21-year period

Ontario persistently lagged behind in terms of the proportion of GDP the provincial government

devoted to education. There was a fair deal of dispersion amongst the provinces in terms of what

percentage of their GDP they spend on education.

However, in terms of expenditure in levels, the real per capita (with 2002 as the base year)

provincial governments’ expenditure on education averaged $1,355 in 1989 and by 2009 it has

reached an average of $1,755, a 1.4 annual growth rate. This overall decline in the provincial

8

Figure 1.2 Provincial Government Expenditure on Education as a Percentage of GDP

Source: Derived by dividing provincial governments’ education expenditures (Statistics Canada, table

(385-0002) by provincial GDP (Statistics Canada, table 384-0001)

governments’ expenditures on education can be partly accounted for by a substitution of private

sector spending on education in place of government spending.8

1.3.3 Expenditure on Municipal Services as a Percentage of GDP

Figure 1.3 shows the percentages of GDP provincial governments spent on municipal service

from 1989 to 2009. For the 21-year span, provincial governments’ expenditure on municipal

services has generally been fluctuating within 1 to 2 percent of provincial GDP for all the

provinces. Newfoundland for the first ten years was using above 2 percent its GDP. Nova Scotia

8 See Organisation for Economic Co-operation and Development (OECD) 2008 report, “Policy Brief: OECD

Education at glance- How Canada Compares”

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

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on the other hand has been spending less than 1 percent of its GDP on municipal services from

1995 to 2009. The average provincial expenditure on municipal service as a percentage of GDP

was 1.5 percent in 1989 and rose slightly to 1.6 by 1992. Thereafter it started declining to its

lowest at 1.3 percent in 2007 and began to rise marginally to an average of 1.5 percent by 2009.

Figure 1.3: Expenditure on Municipal Services as a Percentage of GDP

Source: Derived by dividing provincial governments expenditure on municipal services (Statistics Canada, table

385-0002) by provincial GDP (Statistics Canada, table 384-0001)

The rest of this thesis is organised as follows. Chapter 2 has two separate sections under

literature review: one on the median voter theorem and the other on the political budget cycle

hypothesis. Chapter 3 describes the data used in the analysis as well as the econometric

methodology adopted to model the data. Chapter 4 is devoted to the results with their attendant

policy implications. Chapter 5 concludes this thesis.

1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

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Chapter 2: Literature Review

This chapter has two main sections. The first part reviews the related literature on the median

voter hypothesis, while the second part is devoted to the literature on the political budget cycle

models.

2.1 Median Voter Theorem

The median voter model since its inception has been adapted to explain various public sector

decisions-making by operationalizing it to fit the institutional structure. The prediction of the

median model under its traditional assumptions is that majority rule decision will always produce

the outcome most preferred by the median voter, irrespective of the institutional settings.

Hotelling (1929) in his article “Stability in Competition” describes how a political competition

can be related to the competition faced by entrepreneurs in private markets. He argues that in a

product market, entrepreneurs standardize their product so that it does not look entirely different

from the existing products on the market. This is done so that entrepreneurs do not lose the old

buyers of the products in addition to the new customers being targeted through a price war. He

relates this to the political contest by arguing that the competition for votes between two political

parties compels them not to take too sharp opposing positions for which the voter can clearly

choose one. The competing parties make their political platform similar in order to capture a

majority of the votes. This is because a departure from this common median position is likely to

lead to massive vote lost to the party. He concludes that in equilibrium both parties adopt

platforms that appear to converge on the median during majoritarian voting.

After Hotelling (1929) revealed the essence of the median position in decision making, Bowen

(1943) was the first to indicate how the median voter influences the outcome of voting systems.

11

Using an example of referendum voting on the amount of public education to provide, he

illustrates that the optimum quantity depends on the preference of the median voter. In Bowen’s

setup, voting is used as a proxy to determine the preference of the individuals which reflects their

marginal rate of substitution between a social good and the amount paid in taxes. He shows that

when the cost associated with the provision of the good is known, the median position gives an

indication as to the quantity that can be provided. Moreover he posits that when the individuals

vote on increments rather than on the quantity to be provided, the ideal position will be where the

marginal rate of substitution equals marginal cost. That position corresponds to the median

position where the votes are equally divided. In the equilibrium, the decision on the quantity of

public good to be provided under the simple majority decision in a referendum is the quantity

most preferred by the median voter.

However, the median model was clearly specified with its underlying assumptions by Black

(1948). In his quest to develop economic theory concerning how voting or a group decision is

arrived at in a setting where actors hold diverse views, he analyzed how a committee chooses an

outcome by majority rule. He made the following explicit assumptions underlying the median

voter model.

1. All alternatives to be voted on can be placed in one continuous dimensional space.

2. The preferences of each voter have a single peak and voters prefer outcomes closer to

their most preferred choice than distant ones. That means voters have ideal point on their

preference scale that give them the highest utility.

3. Voters can rank the alternatives in some decisive order of preferences and they vote

according to that order.

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4. The outcome is determined using simple majority voting rule.

Based on these assumptions he argues that when motions are tabled and each alternative is

placed against one another, the winning motion is the one most preferred by the median voter

and it defeats each other by a simple majority rule. He demonstrates the importance of single

peakedness in such a voting system. Without that assumption there will be no motion which will

get at least a simple majority over the others to emerge as the clear winner under such a majority

voting system. In a committee with an even number of members he shows that once the position

of the median voter’s optimum is given the voting outcome can be determined.

In addition, Downs (1957) conceptually enriches the median model by providing deeper analysis

of how the median voter theorem determines the outcome in a political competition of a two-

party system as well as a multi-party system. Under the assumption of single peakedness of

voters’ preferences and left-to-right ordering of political parties in accordance with voters’

agreement, he advances Hotelling’s (1929) argument. He shows that in a competition between

two parties, the political parties’ platform converge to the preference of the median voter if the

distribution of voters follows the normal distribution. He argues that voters favor a candidate

whose policies are closer to their most preferred choice. The party who gets the median voter’s

vote also gets most of the votes on both side of the median voter and since majority of the votes

is centered at the median position. It follows that the party who wins the election is the one that

wins the median voter’s vote. This motivates both parties to move their political platform from

the extremes towards the center where the median voter is located. Under such circumstance the

policies of both parties does not differ entirely. In equilibrium it leads to efficient stable

democracy. He shows that when the distribution of voters has a unique mode, a multiparty

system does not survive except in a two-party system.

13

Below is an illustration of how the median voter model applies to a two-party contest.

In the diagram, the horizontal axis represents left to right ordering of the alternatives to be voted

on. The vertical axis represents the number of voters who vote for a particular alternative at the

optimum position. Point M is the median position and the distribution of votes is symmetric. In a

contest between two parties (party X and party Y), for a party X to win it needs one more than

half of the votes to favor a measure for it to be approved, under simple majority voting rule.

Suppose there are possible policies, ranked from most conservative to most liberal where L,

represent most liberal and R, represent most conservative.

If party X advocates for position B on the diagram, for party Y to maximize its number of votes

it will announce a point just barely to the left of B, B-ε. ln this case, the total votes share of party

X will be the area under the curve from B to the right plus a share of the area between B and B-

ε, depending on how close the policy choices are to the ideal preferences of the voters located

14

between the positions. Party Y will get the area under the curve to the left of B-ε plus a share of

the area between B and B-ε, depending how close the preferences of the voters in that range to

the prefer policy choices. In all, party Y will get the largest share of votes. But party X knowing

the position advocated by party Y will announce a different position still to the left of B-ε. This

process will repeat itself until one party first advocates for the position M. Similarly, if party X

first advocates for position A on the diagram, party Y will announce a point just infinitesimally

to the right of A, A+ε. The process will repeat just like the above. The party which first

announces M gets half of the votes (either to the left or right depending on the position of the

other party) plus a share of the votes between the median position, M and the other party’s

position. Hence the party which first advocates for M ends up winning the election. At M, there

is no incentive for any of the two parties to continue re-strategizing. Point M is the stable

equilibrium position for a two party systems.

In the case of three-party system (party X, Y and Z), if party X advocates for A- ε and party Y

for A, for the third party entering to maximize votes, it will announce a position close to party X

or Y , but not in the middle. In this case party Z is most likely to choose a point to the right of A,

A+ε. Party Z will end up getting the largest share of the votes but party X and Y knowing party

Z’s position will announce new positions. This process will repeat itself endlessly. Assume now,

that party X is at point A-ε and point Y is at B-ε, then the new party entering is likely to

announce a position in the middle since that is likely to give him a largest share of the votes. It

will get a share of votes between its position and party X and a share of votes between its

position and party Y. If this ends up giving party Z the largest votes share, the other two parties

15

outside will converge on the middle one. Party Z will then leap to the outside to optimize its

votes. In a three-party system, these processes will repeat endlessly, with no stable equilibrium.

Hence the median voter does not apply to a three or multiparty system. Nevertheless, researchers

have used the median voter model as a metaphor for the view that electoral competition tends to

cater to the preferences of “mainstream” voters, and not to the extremes of the political spectrum.

After these theoretical frameworks were laid, the median voter model has since been applied to a

lot of decision-making processes in public choice. Bradford and Oates (1971) apply it to public

policy. Using Black’s (1948) result that under simple majority rule the outcome is the one most

preferred by the median voter, they show from their model the economic effects of both lump-

sum grant and matching intergovernmental grants to political units. They maintain that if the

members of a community have a fixed tax-share and the quantity of a single public good to be

provided is determined under simple majority rule, matching intergovernmental grants will yield

higher spending on the public good than the effect the same amount of grant will produce if the

community receives such grant in the form of lump-sum.

Similarly, Meltzer and Richard (1981) apply the majority rule decision to estimate the size of

government measured by the volume of public income to be redistributed. In their general

equilibrium model they argue that when the franchise is extended it changes the position of the

median voter in the distribution of income which changes productivity relatively. They conclude

that in equilibrium under majority rule an increase in the relative mean income of the median

voter increase the size of government since the tax share balances the budget and pays for voters’

the choice.

16

The first authors to use the median model as a formal basis for econometric analysis were Barr

and Davis (1966). Based on the theoretical framework developed, an ordinary least squares

estimation method was used to analyze cross-sectional data from the local governments’

expenditures in Pennsylvania. The effects of per-capita value of all taxable property and the

percentage of electorates that own property were used as explanatory variables for various

expenditure components including general government, judiciary and highways spending. Per-

capita values of all taxable properties were found to account for all the spending compositions

analyzed.

The prominence of the median voter model as a popular structural model of local public

spending for both theoretical and empirical analysis was advanced by Borcherding and Deacon

(1972) and Bergstrom and Goodman (1973). The median voter was assumed to maximize his

utility by consuming private and public goods given a budget constraint. In estimating the

median voter’s demand for a public good they incorporated the characteristics of the median

voter like the median income, the tax price, as well as some demographic, social and geographic

factors in their empirical estimation. Borcherding and Deacon (1972) use data on US states level

expenditure whiles Bergstrom and Goodman (1973) use cross section data on 826 municipalities

in US. In both studies the empirical results shows that the estimated coefficients were

statistically significant for both income and price elasticities; the income elasticities were

positive while the tax- price elasticities were negative.

Since then a lot of empirical papers have tried to test the degree of accuracy and the predictive

power of the median voter theorem. Holcombe (1980) uses referendum data on 257 Michigan

school districts to test how accurate the prediction from the median voter model is for explaining

empirical reality. He finds that the actual expenditure of an average district differs from the

17

predicted value based on the median model by less than only three percentage points. He

concludes that the median voter model best predicts election outcomes compared to competing

options. Inman (1978) tests whether there are extraneous factors other than the characteristics of

the median voter (like the median income) in a population that explain spending outcomes. In a

sample of 58 Long Island districts, more than 75 percent of the predictions were consistent with

the median voter hypothesis that the median voter in a population is a family with median

characteristics.

Pommerehne and Frey (1976) and Pommerehne (1978) both compare the explanatory power of

median income relative to mean income in a set up which utilizes public spending data from

Swiss municipalities. Pommerehne and Frey (1976) use cross section data from 74 counties and

based on the comparison between the goodness-of-fit of the two alternative specifications they

conclude that the median voter model offers a better explanation for the demand for public goods

than the alternative estimation method which made use of mean income. Pommerehne (1978),

however, polishes their original findings. In a data from 110 largest Swiss cities which were

categorized into different democratic systems, he concludes that the median income variable

performs better than mean income for a direct democracy government, while its performance

becomes weak in representative government as compare to the mean income variable.

Kӧksal (2008) applied generalized method of moment estimation to a panel data from 79 Turkish

provincial governments from 1995 to 2001. He finds the result to be consistent with the

theoretical model with income elasticity of 0.51 and price elasticity of -0.52. Similarly,

Thompson (2004) uses pooled dataset consisting of 26 sub-datasets from 12 different studies of

collective decision-making to compare the predictive power between median voter model and the

mean voter theorem. Under different cultural and institutional settings, his findings suggest that

18

under simple majority voting rule the median model predicts more accurately than the mean

voter counterpart. Aronsson and Wikstrӧm (1996) also compare the degree of predictive

precision between the median model and a statistical alternative where the median voter is

assumed in the model to be the voter with median income. Although there was little support for

the median model the estimation results were similar in both models.

Corcoran and Evans (2010) test the relationship between inequality and public spending in the

context of the median voter model. Their result is consistent with the theoretical predictions of

the median voter model that inequality reduces the median voter’s tax share, which results in

higher local spending. They apply an econometric technique to compare the performance of the

median voter model to an oligarchy choice model in a group-decision-making setting. They

employ a nested test to examine both models based on data on demand for military activities by

10 NATO alliances. The median voter model was able to explain the public demand for military

spending for some allies like Canada, Britain, and Denmark. Gerber and Lewis (2004) in their

quest to find out whether legislators actually act by the dictate of the people in their districts use

2.8 million individual-level votes to study the distribution of voters’ preferences. Their findings

indicate that in districts where the individual preferences are homogenous legislators are

constrained by the preferences of the median voter. Ahmed and Greene (2002) use a non-nested

test to compare the performance of the median model to other competing models which try to

explain government size through redistribution, theories of interest group and political

institutions. Based on the public spending data from New York counties, they find that the

median model outperforms the other matching models but did not discount the importance of the

other models when other factors that help in explaining public expenditure are taken into

account.

19

Notwithstanding the support that the median voter model has received from numerous empirical

studies, there are a few other studies especially on the theoretical front, which have questioned

the inference the model draws. Notable among them is the work by Niskanen (1971). After using

the median voter hypothesis to build a model of the demand side of bureaucratic resource

allocation of the public sector, he superimposed on it the supply side of the provision of public

goods. Based on the model, he concludes that in the equilibrium, government produces goods

and services that exceed the amount preferred by the median voter.

Critics of the median model took Niskanen’s model and its conclusion to mean that the median

model has less power to explain public sector resource allocations. Mueller (1987) observed that

the median voter is descriptive under certain conditions. Romer and Rosenthal (1982) modelled

one referenda institution in the State of Oregon and they find that school district spending

decisions follow the comparative static predictions of neither the median voter hypothesis nor the

median voter demand aggregation model.

In what seems to be a response to critics of the median model, Holcombe (1989) reviews the

literature on the role of the median voter model in explaining public sector resource allocation

decisions. In his paper after reviewing both theoretical and empirical works in support and

against the median model, he posits that the median voter model is a demand side aggregation.

This is equivalent to the market demand under simple majority decision and therefore may serve

as the basis for understanding the public sector demand. He points out that the model does not

suggest the public sector produces exactly what the median voter wants but the model suggests

that under majority rule the demand for the public sector is that of the median voter. He draws

the equivalences of the median model in the public sector to the perfect competition model in the

20

private sector. He states that supply side of the model as well as multi-peakedness of preferences

and agenda control can be incorporated to serve the desired purpose.

2.2 Political Budget Cycle Model

There is a vast literature covering the degree, nature and timing of economic policies by policy-

makers that are driven by partisan and electoral incentives in democratic political economy.

Political budget cycles emerged from a broader scope of study known as the political business

cycles. Traditionally, authors in the field of political business cycles were looking at the effects

(timing, characteristics and outcome) of elections or politics as a whole on macroeconomic

variables: inflation, interest rate, unemployment level and real GDP growth rate.

Theoretically, there are two main approaches that attempt to capture the relation between politics

and economic policy decisions by policy makers: the opportunistic approach which was initially

modelled by Nordhaus (1975) and the partisan approach of Hibbs (1977).

Nordhaus (1975) formalizes his opportunistic political business cycle model based on the simple

Philips Curve under the following assumptions:

1. Expectation is adaptive. That is, economic agents’ expectation of current policy

depends on the past policies. They also use past behaviours of policy makers to judge

future performance in order to form their voting decision. In their evaluation, they

assign more weight to recent outcomes than distant ones.

2. Voters have in their preference function inflation, unemployment level and output.

Therefore they favour incumbents who preside over stables prices, low level of

21

unemployment and high real output. Voters do not understand the short run trade-off

between inflation and unemployment.

3. Incumbents are in charge of stimulative policy instruments and they are opportunistic

in seeking re-election.

Based on these assumptions Nordhaus argues that, with the election date exogenously fixed,

office-motivated incumbents will exploit the Philips Curve’s trade-off between inflation and

unemployment. They achieve this by conducting stimulative fiscal and monetary policies to

improve the macroeconomic variables prior to elections. Naive economic agents who vote on the

basis of the performance of the economy right before election get them re-elected. After election,

a government conducts contractionary policy to contain inflation and also prepare the economy

for the next election. The persistence of this election-induced expansion and contraction of the

economy creates opportunistic political business cycles.

Although the Nordhaus model was theoretically accepted, empirically the model found little

supportive evidence. Many authors that tested the model using United States or OECD countries’

data concluded that there was a lack of significant and consistent evidence that there exist

electoral cycles in macroeconomic variables.9

In the context of the partisan approach, Hibbs (1987) argues, based on his two-party model, that

the fluctuations in the real economy associated with election outcomes are driven by ideological

differences of the political parties. He indicates that the kind of economic policies a government

pursues depends on the political party a candidate represents and their position on the political

9 See the findings of McCallum (1978), Golden and Poterba (1980), Hibbs (1987), Alesina (1988), Alesina and

Sachs (1988), Alesina and Roubini (1992) Alesina et al (1992, 1993, 1997), Alesina and Rosenthal (1995).

22

spectrum. In particular, a left wing party prefers trading-off a high level of inflation to reduce

unemployment whiles the right wing prefers lower inflation to higher unemployment.

Following the Hibbs (1978) model, many empirical works have found the model to be consistent

with the electoral and ideological incentives for macroeconomic policies of the Democratic Party

in the United State and the Socialist Party in Europe who are more inclined to lower inflation and

more averse to unemployment than their counterpart Republicans in United States and

Conservatives in Europe. However the partisan approach has little application in the developing

economies where the differences in economic and ideological objectives among the political

parties cannot be clearly defined as compared to the left-right dichotomy in the Western

countries.

The main distinctions between these two models and the research motivation leading to the

subsequent formalization of the other models in political business or budget cycles were captured

in Alesina’s (1988) paper on the classification of models. It deals with the expectation and

evaluation process of economic agents and the motivation for policy choices.

First is the argument of whether policy-makers decide on macroeconomic policy instruments

based on their opportunistic (office-seeking) motives in the sense that they are only about

winning elections, or their decisions are driven by partisan motivations. This results in

opportunistic or partisan approach groupings. Second is whether voters are not forward-looking

and they evaluate policy-makers based on the past behaviors or previous policies to take their

voting decisions. This corresponds to adaptive as opposed to rational expectation.

Based on these, the literature on electoral business cycles can be categorized into:

23

1. Opportunistic and adaptive expectations models: Nordhaus (1975), Tufte (1978)

2. Opportunistic and rational expectations models: Cukierman and Meltzer (1986),

Rogoff and Sibert (1988), Rogoff (1990)

3. Partisan and adaptive expectations models: Hibbs (1977, 1989)

4. Partisan and rational expectations model: Alesina (1987, 1988), Alesina & Rosenthal

(1995), Alesina and et al (1997)

Alesina (1987, 1988) in his seminal papers “rational partisan theory” provides a theoretical

framework and argues on the need to incorporate rational expectations into the partisan models

for it to be consistent with economic theory. In the rational partisan theory he posits that since

economic actors are rational and take into consideration decisions of policy makers, the cycles in

macroeconomic variables can only be created when monetary and fiscal policies effects happen

unexpectedly. In such a situation, when a left government is elected, once the expected change

was not anticipated, growth, employment and inflation will rise while they fall in case of a right

government. But as time elapses economic agents agree on new prices and sign new wage

contracts depending on the level of inflation. In equilibrium, real growth and employment return

to the natural level while inflation still remains. Alesina et al. (1997) concludes that the

difference between rational and non-rational partisan theories depends on whether the changes in

the macro- variables persist or disappear over the incumbent’s term in office.

Unlike Nordhaus’ opportunistic theory, both rational and non-rational partisan theories enjoyed a

lot of empirical support. Alesina et al. (1997) in separate studies with post war data from U.S and

OECD countries find evidence to be consistent with the rational partisan model with the

24

evidence being more pronounced in two-party systems. Wilensky (1976, 1981) using data from

19 OECD countries from 1965 to 1971, finds that there exist a number of correlations between

partisanship and various government policies including social, welfare and fiscal policies. But he

points out that the significance level was minimal. In the analysis of time series data from 1960

to 1977, Pommerehne and Schneider (1980) find that Australian Liberal party (right) and Labor

party (left) pursue partisan spending and tax policies. Hicks and Swank (1984a, 1984b), Swank

(1988, 1992), Garrett (1995, 1998) and Hallerberg and Basinger (1998) all find some evidence of

partisan cycles.

On the contrary, besides the lack of empirical evidences in favour of pre-electoral manipulations

in aggregate economic variables (Drazen, 2001), some of the assumptions behind the

opportunistic political business cycles models were not consistent with economic theory. The

assumption of adaptive expectations in the initial political business cycles models were

overcome by incorporating rational thinking on the part of economic actors into the subsequent

models. However, there were still some arguments against the model on the grounds that

governments do not have direct control over real economic variables (Shi and Svensson, 2003),

particularly where the country’s central bank is independent. Even currently there is ongoing

debate in the literature whether government macroeconomic policy instruments, fiscal or

monetary, have an immediate effect on the real economy.

Subsequent researches in macroeconomic game theory led to the development of models which

address those conceptual setbacks. Cukierman and Meltzer (1986), Rogoff and Sibert (1988) and

Rogoff (1990) were the earlier proponents of political budget cycle theories - the study of

fluctuations in a government’s fiscal policies especially its expenditure (levels or composition)

stimulated by elections. In particular, Rogoff and Sibert (1988) and Rogoff (1990) develop

25

signalling models. In their models incumbent governments take advantage of the political market

information asymmetries especially regarding candidates’ abilities or about the political

environment to engage in pre-electoral manipulation in macroeconomic policies like spending

(either in levels or composition), taxes and deficits to score political point on the incumbents’

competence.

In Rogoff’s model pre-electoral manipulation may be achieved by shifting government

expenditure from investments which are not readily observable by the electorates towards more

easily and promptly observed current spending. This signals the incumbent’s competence in

administering the production of public good which heightens his chances of being re-elected.

In the model, Rogoff assumes that each political candidate has a certain level of competence and

voters basically choose between candidates based on their competence level. The utility of voters

was assumed to depend on public consumption, public investment and a random shock. This

means voters rationally favour the more competent candidate because to them he has the ability

to deliver more public goods with same level of taxes. Thus the more competent policy-maker

provides higher welfare. Competence is serially correlated which makes it realistic for voters to

vote for the candidate they perceive to be more competent before the elections. The expectation

is that he will administer more public goods after the election than the one with a low level of

competence. The “competence” shock follows a moving average process of order one. That

makes successive elections independent.

The production of public good at time t by a leader with unobserved administrative competence

is given by

+ = +

26

Where is the public consumption good and is the public investment good τ is the lump-sum

taxes levied by the government. Public investment is chosen at period t and only becomes

observable and productive at period t+1. Due to information asymmetry voters do not observe

the incumbents’ current competence before elections but are aware of only the past competence

level of government. However the government knows its current competence. Under this

imperfect information voters infer about incumbent’s ability based on their observation of g.

Since the incumbent is rational and office-seeking, there is an incentive for him to signal his

competence by administering higher g with the same level of τ. Rogoff argues that signalling

takes the form of shifting government expenditure away from investment expenditure (whose

effect is observed with lag) to easily observable consumption expenditure. In a separating

equilibrium, the incumbent choice of the fiscal policy reveals his competence type. The

empirical research implication from the model is that government can maintain a balanced

budget but still will find means to score political points through altering the composition of its

spending.

Tabellini (2000), Gonzalez (1999, 2002) and Shi and Svensson (2002) all offered an extension of

Rogoff’s model to account for pre-electoral manipulation. Tabellini (2000) and Shi and Svensson

(2002) proposed political budget cycle models based on moral hazard10

of competence approach.

Their model differs slightly from the previous signalling models to the degree that in their model

both the voters and the political candidates cannot observe the politicians’ competence at the

same time. This makes the politicians uncertain about how they will overcome the economic

challenges during their tenure especially regarding the provision of the desired level of public

goods having in mind the government budget constraint. Knowing that voters are rational and

10

Lohmann (1998) was the first to adopt a moral hazard approach to study political budget cycle.

27

forward-looking who will vote for the more competent candidate, that can provide more public

goods after elections, in the model they assume that politicians can put to use a “hidden effort”

(like government short term borrowing). The electorates only observe hidden effort with a lag.

This works because voters base their voting decisions on the performance of observable

macroeconomic indicators. In the equilibrium, voters are not naive: they understand the political

environment and know the incumbent’s intentions, hence they can distinguish his actual

competence from the apparent exertion of the “hidden effort”. The conclusion from the model is

that such hidden policy used by the government to increase its performance index leads to budget

deficits before elections due to excess borrowing.

Shi and Svensson (2002) argue that the intensity of the political budget cycle depends on the

politico-institutional settings which are mainly a function of the politician’s rents from remaining

in office and the proportion of the voters who are well informed.11

They show that policy-makers

have higher incentives to remain in office through influencing electorate’s perceptions before

elections by engaging in pre-electoral manipulations if they recognize that there are more private

benefits that go with holding office. Likewise they conclude that the lower the proportion of

voters who are more informed to identify the true competence of incumbents, the higher the

chance for incumbents to manipulate fiscal policies prior to elections.

Gonzalez (1999a, 2002b) and Shi and Svensson (2000) capture the effect of the degree of

democracy on the relative size of political budget cycles. In Gonzalez’s model, the effect of two

key variables were introduced into the Rogoff’s (1990) signalling model: the cost of removing a

policy-maker from office, which captures the degree of democracy, and the probability that

11

Shi and Svensson accounted for the well informed voters by using information accessibility (availability of radio

and media freedom)

28

electorates can discover the incumbent’s true competence devoid of signalling, which measures

the level of transparency. The main prediction from Gonzalez’z model is that with a high cost of

removing policy-makers from office, incumbent governments tend to remain in office. Thus

electoral budget cycles occur in jurisdictions where removing incumbents from office come with

relatively less cost. Also she predicts that pre-electoral manipulations are reduced as the degree

of transparency is enhanced. She establishes that there is positive association between the degree

of democracy and transparency, and political budget cycles emerge in jurisdictions where the

level of democracy can be described as intermediate, where both the degree of transparency and

the level of democracy are also intermediate.

On the empirical front there have been various studies covering different aspects of political

budget cycles. Most of these studies use auto-regressive specifications where the response

variable is a policy outcome (GDP, revenue, expenditures, deficits etc) and is modelled on a set

of control and predictor variables. The control and predictor variables include some economic

variables that affect the policy outcome variables, a dummy variable capturing the effect of

elections and other unobserved variables.

Brender and Drazen (2005) provide insight as to why the strength of political budget cycles in

developed countries differs from the developing countries. They argue that political budget cycle

is a phenomenon in countries with new democracies, including both developed and developing

countries and the significance of the drift persists in a few elections after the country has made a

transition from non-democratic to democratic. They indicate that in studies that use large sample

to explain political budget cycles, it is the effect of these new democratic countries that makes

the result entirely different. Without the inclusion of the new democratic countries in the sample,

the cycle disappears. Their conclusion is that the new democracy phenomena helps explain why

29

budget cycles are larger in developing countries and countries with weak democracies since most

of the developing countries had recently made a transition into democratic rule. A key

explanation for their result is that voters in new democracies are naive to understand the

complexities of electoral manipulations, coupled with the difficulty in assessing credible

information to evaluate politicians.

Early empirical works on political budget cycles were mainly focused on the advanced

industrialized countries specifically in United States and other OECD countries. Tufte (1978)

was the first to use United States data to study pre-electoral opportunistic manipulations in fiscal

instruments. The result indicates that incumbents manipulate transfers payments like social

security and payments to veterans prior to elections. Alesina (1988) based on time series data

from 1961 to 1985 finds electoral cycle in net transfers proportional to GNP in the United States.

However, he added that when the data is extended to cover the period up to 1949, the electoral

effects go away. Similarly, Keech and Park (1989) find evidence of electoral cycles in veteran’s

benefit in United States using data from 1961 to 1978 but also remark that the evidence has

disappeared afterwards. Alt and Lassen (2006) find a positive correlation between the magnitude

of electoral cycle and the degree of fiscal transparency. Alesina et al. (1992, 1997) uses a panel

data set comprising 13 OECD from 1961 to 1993 and find that the government budget deficit

increases by 0.6 percent of GDP in election years. Barreira and Baleiras (2006) find that

government in established democracies manipulate budget composition towards current

expenditure in election elections. Their data comprises 15 European Union central government

expenditure data from 1970 to 2001. Eflthyvoulou (2011) finds evidence of electoral

manipulation in the form of fiscal deficit where the magnitude becomes stronger when the

30

election dates are fixed exogenously from a data set comprising 27 members of the European

Union from 1997 to 2008.

Recent studies have shown that electoral cycles exist in all countries, the developed and the

developing, as well as in all level of democracies. This is contrary to the initial arguments that

electoral cycles exist in only the developing countries. Ben-Porath (1975) finds in data from

1952 to 1973 that incumbent governments in Israel cut taxes before elections but adjusts them

upwards after winning elections. Likewise Brender (1999) finds evidence of pre-electoral

manipulation in fiscal policies but points out that such manipulations did not work in favor of

incumbents. Krueger and Turan (1993) find that there was pre-electoral manipulation in Turkey

particularly from 1950 to 1980. Gonzalez (1999) finds evidence of electoral cycles in

government spending in Mexico over the period of 1958-1997 in both presidential and

congressional elections.

Some other studies focused on cross-countries have affirmed the existence of budget cycles.

Ames (1987) in a study involving 17 Latin American countries from data that covers the period

from 1947 to 1982, finds that on the average government expenditure increases by more than 6

percent prior to elections and reduces by almost 8 percent after elections. In a study of 44 Sub-

Saharan African countries by Blocks (2000a), he finds evidence of both fiscal and monetary

cycles with the deficit increasing by 1.2 percent before elections. Blocks (2002b) using a sample

of 69 developing countries, finds evidence consistent with Rogoff’s budget cycle model. In his

analysis which involves only presidential elections between 1975 and 1990, the results show that

government spending shifts towards projects which are readily visible (current expenditure)

away from capital investment. .

31

Schuknecht (1996) in a study of 35 developing countries over the period of 1970-1992, finds that

governments’ fiscal balances are worsened by approximately 0.6 percent of GDP in election

years. Shi and Svensson (2002) use data set spanning from1975 to 1999, which is made up of 91

countries (both developed and developing) to investigate whether budget cycles pertains to only

developed countries. They find that on the average, the fiscal deficit increases by one percent

relative to GDP in elections years with the magnitude in the budget cycles being higher in the

developing countries than in the developed countries. The election-year fiscal balance worsens

by 1.4 percent for the developing countries as compared to 0.6 percent in the developed

countries. The source of deficit comes from a decline in tax revenue by a margin of 0.4 percent

of GDP while spending increases by 0.5 percent of GDP.

Persson and Tabellini (2002) considered how electoral rules and forms of governments affect

political budget cycles. In a data set comprising of 60 democratic economies for election periods

from 1960 to 1998, they find countries with majoritarian elections reduce its fiscal expenditure in

election years while those with proportional electoral rules increase their expenditures on

welfare, but they found election years tax-cuts as a common phenomena of both electoral

systems. Presidential governments tend to cut spending before elections and raise taxes after

elections as compared to their parliamentary counterparts. Vergne (2009) finds that election year

spending shifts from capital expenditure to more visible current spending in the areas of wages

and subsidies in data on 42 developing countries from 1975 to 2011. Barberia and Avelino

(2011) in a panel data set of 18 Latin America countries from 1973 to 2008, find that government

spending increases while tax revenue reduces by about 10 percent. Brender and Drazen (2005)

examine central governments’ expenditures, revenues and fiscal balances from 106 democratic

32

and non-democratic countries. They find that pre-electoral manipulation is higher in new

democracies than in well established democracies.

Some other studies have investigated the effects of fiscal performance on re-election fortunes of

incumbent governments. Notable among them is Brender and Drazen (2005). In a sample made

up of 74 democratic countries from 1960 to 2003, they find no support for the hypothesis that

expansionary fiscal policies help incumbent government to get re-elected. The results show that

it rather reduces their chances of being re-elected especially in the developed countries. They

find that in the developed countries where their democracies are stronger, voters punish

incumbents who preside over deficits over their term in office and election years. Their empirical

result indicates that in the developed countries a percentage increase in surplus relative to GDP

over the incumbent’s term in office induces 3 to 4.5 percent chances for incumbent’s re-election

prospect but the probability increases to between 7 and 9 percentage points if the surplus occurs

in an election year.

A lot of extensive works have been done at the sub-national level which confirms that political

budgets cycles are not confined to only the central governments. Among them are United States:

(Peltzman, 1992), Canada: (Kneebone and McKenzie, 2001; Reid, 1998), Columbia: (Drazen

and Eslava, 2005), India: (Khemani, 2004), Israel: (Brender, 2003), Russia: (Akhmedov and

Zhuravskaya, 2004) Germany: (Galli and Rossi, 2002) Italy: (Cioffi et al, 2012) and Brazil:

(Klein 2010)

Akhmedov and Zhuravskaya (2004) in their monthly data from 1996 to 2003 find evidence of

political budget cycle in local government spending after Russia made a transition into a

democratic rule. However they observe that the effect dwindles away with time and finally

33

disappears after two successive elections. Cioffi et al. (2012) test the existence of pre-electoral

manipulations in Italian municipalities from 1998 to 2006 and find that in election-years

municipal government spending increased by almost 40 euros per capita mostly from capital

expenses. Reid (1998) finds that provincial government spending, and non-borrowed revenue

exhibit electoral cycles with expenditure in the areas of transfer to persons, businesses and fixed

capital formation growing more rapidly, while revenue decreases during election periods.

Reynolds (2012) examines the tuition fees and required fees at public four-year institutions of

higher education in United States in the context of political budget cycles. He finds that tuition

and other required fees decrease by 1.5 percent during gubernatorial election years than no

election years. He finds the magnitude of the cycle increases with the level of competition in

state house elections and the effects were mostly concentrated in the districts held by an

opposition party. Kneebone and McKenzie (2001) use panel data set from 1966 to 1997 to

examine the presence of partisan or electoral cycle in Canadian provincial government spending.

They find that all political parties have a common set of attitude to suspend tax increases in

election-years. There were also evidences of opportunistic cycles where spending in visible areas

tend to increase prior to elections. On the partisan front they find that spending programs exhibit

partisan responses and such responses do not appear in policies regarding revenue.

34

Chapter 3: Data Description and Econometric Methodology

This chapter is partitioned into two parts. The first part is devoted to the data description on both

the median voter and the political budget cycle models. The second part of this chapter focuses

on the econometric methodology adopted to estimate both models.

3.1 Data Description

The data used is a panel data set covering the 10 provinces in Canada from 1989-2009.12

The

panel approach provides 210 observations through the pooling of the 10 cross-sections for 21

years.

The fiscal variables used in this analysis, both under the median voter and political budget cycle

models are provincial health care, education and municipal services expenditures. The three

spending categories are major program expenditure components and are core public spending.

These fiscal variables are divided by the provincial total population and adjusted for inflation

using the provincial consumer price index (2002=100) to obtain the real per capita provincial

government spending on these categories.13

Specifically, in the median voter models these fiscal

variables (measured in real per capita terms) are used as the dependent variables. However in the

political budget cycle models these fiscal variables are further converted to growth rates to obtain

annual growth rate in real per capita terms.

12

A consistent data set on disaggregated government spending at the provincial level is only available for that time

period. 13

Data on the disaggregated spending components are taken from CANSIM table 385-0002 and are measured in

nominal dollars, provincial population data from CANSIM table 051-0001 and the CPI data from CANSIM table

326-0021. The GDP data is taken from 384-0001 and is measured in millions of nominal dollars.

35

3.1.1 Data Description: Median Voter Variables

Based on the median voter model, the levels of government spending of all categories are driven

by factors that influence the demand function of the median voter. These include some

economic, demographic and social factors that characterize the preferences of the median voter.

The major economic factors are income and price. Since the median voter is not known ex ante,

following the argument of Bergstrom and Goodman (1973), the median voter is assumed to be

the individual in a particular province with median characteristics. The median voter’s income

and the income of all families are expected to be highly correlated.14

Therefore the after-tax real

median income of all families is used as a proxy for the income of the median voter. The utility

of the median voter increases in spending (consumption), therefore it is expected that taking all

other factors as given, as income increases the median voter will want to spend more and hence

the coefficient of the income variable is expected to be positive provided the good in question is

a normal good. Therefore the aggregate spending on the various programs is expected to rise

with income. The after-tax median income data is taken from CANSIM and it is converted to

2002 constant dollars using provincial CPI (2002=100).15

When demanding some quantity of public good, the median voter takes into account the cost of

the product. He considers how much of his taxes that goes into the delivery of the product as

well as the unit cost of the product. Therefore the price effect in this analysis is measured by the

tax-price index, which is the product of the tax-share of the median voter times the relative price

14

All families in this analysis include economic family of two or more persons and unattached individuals. Median

income of all families is used instead of median income of individuals so as it will be consistent with the tax-share

measure which also uses all families share of total income tax paid by the third quintile. The all families’ median

income is highly correlated with the median income of individuals, hence it can be used without giving rise to a bias

estimate. The median income of individuals is later used to check the robustness of the estimates. 15

The after -tax median income of all families is obtained from CANSIM table 202-0605 and is converted to 2002

constant dollars.

36

of the good. The share of the total income taxes paid by the third quintile of the provincial

income distribution is used as a proxy for the median tax-share while the relative prices of the

fiscal variables (health, education and municipal services) are computed as the ratio of the

respective program’s CPIs (province level) to overall provincial CPI (2002=100)16

. The median

voter becomes less inclined to demand more as the tax share or unit price of the good increases,

therefore the coefficient of the tax price index is expected to be negative.

The effect of income inequality on provincial government spending is captured by the gini index

which measures the dispersion in the income distribution.17

Meltzer and Richard (1981) argue

within the context of the median voter model that as the pivotal voter’s income drops below the

average income level, the size of government budget rises through redistribution. Thus, the

expenditures on these fiscal programs will increase if we assume that these programs are

themselves redistributive. Also it can be argued that with a progressive tax system in place, as

the level of inequality increases the tax revenue allocated to redistributive income transfers will

rise, leaving less money for other programs. The theoretical arguments of inequality and

government spending are inconclusive. Therefore the sign of the coefficient of this variable

cannot be pre-determined theoretically.

16

The CPI indexes are taken from CANSIM table 326-0021 and the tax share of the third quintile is taken from

CANSIM table 202-0501. Although the health care CPI has dental and eye care components which are generally not

financed by provincial governments, there is a strong correlation between the entire care CPI and those two

components. Therefore using health care CPI to calculate tax-price of health without isolating those two components

does not give a bias estimates. There is no provincial level CPI data for the protection of people and properties

component of the municipal services. Therefore the average CPI of housing and recreation and culture is used to

represent the CPI of municipal services. 17

The gini index of an after-tax income of all families is taken from CANSIM table 202-0705.

37

Population density is expected to affect government spending through two channels.18

First, as

the size of the population increases relative to a fixed land mass, the cost for providing these

public goods and services would decrease due to economics of scale. This will reduce the tax

share of the median voter and therefore it pays for him to demand more, leading to an increase in

government spending. Second as the population density increases depending on the degree of

publicness of the good, the utility of the median voter may decrease and therefore he will vote

for more of these goods and services in order for him to maintain the same level of utility.

Provincial government spending is likely to be influenced by some demographic factors that

reflect the preferences of the median voter. This is captured through three age structures:

proportion of the children between ages 0 to five years, proportion of school going age, from

ages 6-18 years, and the fraction of the elderly population, from 65 years and above19

. The

delivery of health services to the aged as well as the young children are associated with higher

costs due to their special health needs. Also, the age groups may have different levels of

resistance to tax increases. The effects of the age groups on these fiscal spending cannot be pre-

determined.

3.1.2 Data Description: Political Budget Cycles Variables

The political variables used in this thesis closely follow the set of variables used by Reid (1998)

to study the effects of politics on government transfer payments, current expenditure on goods

and services and fixed capital formation. The same set of political variables are employed in

18

Population density obtained as the ratio between provincial total population and land mass in kilometers square.

The data on total population is from CANSIM table 051-0001 and the land mass is taken from Statistics Canada

2012, Population and dwelling count for Canada, provinces and territories. The land area excludes area under inland

water bodies, natural claims to continental shelf and exclusive economic zone. 19

The various calculations are based on the population and census table from CANSIM table 051-0001. The age

limits for proportion of school going age is based on the average provincial age for completing high school.

However in the analysis it was also experiment with age limit 6-24 years, capturing the effect of school going age up

to the university level.

38

this thesis because such variables are core in characterizing the effects that provincial elections

will exert on government expenditures on education, health care and municipal services. In order

to capture the real effect elections have on these fiscal variables there is a need to control for the

other factors that might influence government expenditure. The set of control variables

accounting for the effect of the prevailing macroeconomic conditions on these fiscal variables

are the provincial real GDP growth rate and the level of provincial unemployment rate.20

From

the perspective of the economic approach “all other things being equal”, the amount of resources

devoted to these spending categories will exhibit cyclical patterns as a result of changes in the

level of GDP and unemployment rate.

The predictor variable of interest is the election dummy variable expected to test the effect of the

budget cycle hypothesis. It takes the value one for years where provincial elections were held

and zero in all non-election years. Opportunistic spending usually occurs in advance before the

actual election date, therefore following Alesina et al. (1993) the election data were associated

with the economic data by setting the election dummy to zero if the election occurred in the first

half of year , and one for the year . From 1989 to 2009, there were 55 elections held in all

the provinces. Five of the provinces held 6 elections each while the remaining five held 5

elections each. The average duration between two successive elections was 4 years.

The other explanatory variables are the “percentage of seats” and “proportion of votes” won by

the party in government. These actually measure the size or authority of the ruling government.

It is expected that a larger size should exert an upward pressure on government spending. The

minority variable is a dummy which takes on the value one if government in office is a minority

20

. The provincial unemployment data is taken from Labour Force Survey estimate, CANSIM table 282-0086 and

the GDP data from CANSIM table 384-0001. All the political variables are obtained from Canadian Parliamentary Guide.

39

and zero otherwise. The effect of the minority variable on these spending categories can be small

or huge depending on the preferences of all legislators as a minority government has less

discretionary powers over unapproved expenditures. The “change variable” equals zero for a

continuation government and one if the current incumbent is different from the previous one

prior to the elections. Left versus right dichotomy variable indicates the position of the

government in power on the political spectrum. It assumes the value zero for right-of-center

parties and one for left-of-center parties. It measures the ideological preferences of the parties on

these programs. New Democratic Party (NDP) and Parti Québécois fall under left-of-center

dichotomy.

3.2 Econometric Methodology

In order to account for the persistence and the partial adjustments over time in the budgeting

processes and also to take care of the province-specific heterogeneity, a dynamic panel model in

the form of a one-way fixed effect error component model is considered for this analysis. The

choice of a fixed effect model over the random effect model is due to three reasons. Besides the

province-specific heterogeneity bias that fixed effects seek to correct, the economic theory

supporting the selection of these two models suggests that the random effect model is most

appropriate if the data generating process is random. This selection criterion is not applicable to

this study since the sample data comprises all the population (the ten provinces in Canada).

40

Finally, the Hausman (1978) model specification test conducted is also consistent with the fixed

effect model at one percent significance level.21

Moreover, the joint F-test conducted to find out if the dummies for all years are equal to zero

failed to reject the null hypothesis that for all years coefficients are jointly equal to zero. This

gives rise to a one-way error components model.22

The generic model estimated for both median voter theorem and the political budget cycle is of

the form:

= +

+

+ (3.1a)

where = + (3.2a)

where the superscript indexes the type of fiscal expenditure under consideration, indexes

the province, t the year, . is the common intercept, β is a

vector where is the number of explanatory variables, is the

observation of the

regressor of the fiscal variable , is the composite error term which is made up of , a

province-specific error term, and , an idiosyncratic disturbance term.

21

The Hausman test was a preliminary test conducted on the six models to be estimated. The null hypothesis

is that the preferred model is random effects against the alternative hypothesis that preferred model is fixed effect. It tests whether the unobserved effects are correlated with the regressors. The results under the various equations are ( education: Prob > Chi2 = 0000; health: Prob > Chi2 = 0.0007; municipal services: Prob > chi2 = 0.0000)

22 Under Ho: All years’ coefficient are jointly equal to zero against H1: Not all year coefficient are jointly zero.

The result are ( education: F( 20, 173) = 6.25 with Prob > F = 0.000; health: F(20, 173) = 4.33 with Prob > F = 0.0045 municipal services F( 20, 173) = 2.18 Prob > F = 0.0039)

41

The following standard dynamic panel assumptions are made with respect to equation (3.1a).

These assumptions are further tested to ascertain the best estimator to be used in analyzing the

data.

i. The error variance is constant within panels but may vary across panels. That is the

panels are heteroscedastic. ( ) = (

) but ( ) (

)

ii. The errors are contemporaneously correlated. That is = 0 but

= 0 and =

iii. The common unit-specific autocorrelated errors is of order one, AR (1)

= + where │θ│< 1 and ~ ( )

Converting (3.1a) and (3.2a) to vectors

S = + γ + + ℰ (3.3a)

with = + (3.4a)

is vector ones of dimension NT, s is NT 1 vector, is NT K,

ℰ = (ℰ ℰ ℰ ℰ ℰ ℰ ) are the stacked errors

) and = ( , . . . , . . . )

⨂ , is the matrix of individual provincial dummies to be used in estimation, is an

identity matrix of dimension N, is vector matrix

Combining (3.3a) and (3.4a)

S = + γ + + + υ = Zϕ + + υ (3.5a)

where Z = and

42

Conditioned on the assumptions of contemporaneous correlation between province and and

cross-section heteroscedasticity of the error , it follows that:

E ( ) =

E ( ) = 0 for all with

The covariance matrix of the stacked error term computed in similar fashion as

seemingly unrelated regression fashion with feasible GLS will be equal to

Ω = E =

= ∑ ⊗

where Σ = [σij] the N×N skedasticity matrix of each observation, is the T-dimensional identity

matrix and ⊗ denotes the matrix Kronecker product.

3.2.1 Estimated Model: Median Voter

The first set of the systems of equations which are used to explain provincial government

spending from the perspective of the median voter model are expressed as follows:

= + + + β1 + β2 + β3 + β4 + β5 +

β6 + β7 (3.1b)

= + + + β1 + β2 + β3 + β4 +β5 +

β6 + β7 (3.2b)

= + + + β1 + β2 + β3 + β4 + β5

+β6 + β7 (3.3b)

43

“ln” is a notation for natural logarithm. The log-log specification gives the flexibility for

calculating the elasticities.

The explanation of the variables used in the in the above specifications are as follows:

By repeated forward substitution for fiscal variable , it implies that an increase in one unit of

an explanatory variable yields an increase of in year t and

in year t+1

in year t +2

.

.

.

in year t + k

Variable Definition

rpcee Real per capita expenditure on education

rpceh Real per capita health expenditure

rpcems Real per capita expenditure on municipal services

rmii Real median income of individuals

gc Gini Coefficient

pd Population density

pc Percentage of children (aged 5 years and below)

ppe Percentage of elderly population (aged 65 and above)

psga Percentage of school going age population

taxpricee Tax-price of education

taxpriceh Tax-price of health

taxpricem Tax-price of municipal services

44

Given that │ │< 1, it generates an exponential series which decays as t increases.

Therefore the total increase in fiscal spending over all current and future years resulting from

a unit increase in explanatory variable is given by

Thus in the steady state (long run) the elasticity of explanatory variables is given by

=

, where = 1, 2, ...7

3.2.2 Estimated Model: Political Budget Cycles

The second set of the systems of equations used to explain government spending from the

perspective of the political budget cycles are expressed as follows:

= + + + β1 + β2 + β3 +

β4 +β5 + β6 + β7 (3.1c)

= + + + + β2 + β3 + β4 +

β5 + β6 +β7 (3.2c)

= + + + β1 + β2 + β3 + β4 +

β5 + β6 + β7 (3.3c)

45

The definitions of the variables are given in the table below

3.2.3 Estimation Strategy

In order to determine the appropriate method for estimating equations (3.1b) to (3.3b) and (3.1c)

to (3.3c) some diagnostic checks are carried out to assess the properties of the data as well as the

structure of the error terms.

First, panel unit root tests are carried out to ascertain the trending behaviour of the variables.

Unit root is a major problem in the sense that regressions that involve non-stationary variables

produce standard errors that are biased, hence inferences that are drawn based on such results are

misleading. Regressions from such variables produce results that seem to give a good fit and

predict a statistically significant relationship between variables where none really exist. These

spurious regression results occur due to common trending in the variables, rather than the

underlying economic relationships (Granger and Newbold, 1974). In this study two of such unit

roots tests: Im-Pesearan-Shin (IPS) root test and Hadri LM test are conducted to determine the

Variable Definition

rpceh Annual growth rate in real per capita health spending

rpceee Annual growth rate in real per capita expenditure on education

rpcems Annual growth rate in real per capita spending on municipal services

rgdppc Annual growth in real GDP per capita

ur Change in unemployment Rate

left Left-of-center government

electdum Election dummy variable

change Change of government

perseat Proportion of seats

pervote Proportion of votes

46

order of integration.23

Since most economic variables increase over time, a deterministic trend

component is included in the test.24

Also the choice of lag order to be included is determined by

the Schwert (1989) lag order selection criterion.

The results from the IPS root test and Hadri’s test are shown Table 3.3. The IPS unit root tests

show the null hypothesis could be rejected at a significance level of 5 percent for all the variables

except unemployment rates, population density, proportion of school going age and proportion of

children. Since the power of the IPS test depends on the lag level and the length of time period,

Hadri (2000) residual-based LM test which is built on Kwiatkowski- Phillips-Schmidt-Shin test

(KPSS) for time series data is employed to aid in drawing a better conclusion. At 5 percent

significance level most of the results were consistent with the IPS test results. However, unlike

the IPS test, the results indicate that unemployment rate, proportion of school going age and

proportion of children could not be rejected under the null hypothesis that the panels are

stationary. Reconciling the two tests results and also relying on the fact none of the policy

variables shows evidence of a unit root, this study will proceed by assuming the panels are

stationary.

23

For the IPS test, the null hypothesis is that all individual panels follow a unit root process, against the alternate

that some of the panels have a unit root. Thus,

= for all i against =

On the hand, for the Hadri’s LM test, the null hypothesis is that there is no unit root in any series (stationary) against

the alternate that the panels have a unit root. 24

Economic theory is of less help as to whether deterministic trend needs to be included in a unit root test or not.

Moreover segmented trends and structural breaks (see Perron, 1989; Zivot and Andrews, 1992) are not considered in

this analysis as it considered beyond the scope of this study. The possible existence of structural breaks in the series

is neglected because neither fundamental abrupt changes in economic policy nor tremendous exogenous shocks

could be detected in the period of 1989-2009.

47

The inclusion of province-specific effects may not resolve all the possible complications

associated with panel data. In order to produce optimal results, the estimation method largely

depends on the structure of the error process which usually tends to be non-spherical. Three post-

estimation tests are carried out to find the properties of the error structure. The Wooldridge

(2002) AR (1) serial correlation test under the null hypothesis that the residuals in the specified

model do not exhibit first order serial correlation within the panels is applied. The results

indicate that at significance level of five percent the null hypothesis could be rejected in five of

the models.25

Also in this study of disaggregated expenditures it is likely that large errors in one program

spending may be associated with the errors in the other categories in the same year. And with

each province having different endowments and social characteristics, such contemporaneous

correlation may differ by province. Using Modified Breuch–Pagan LM test of independence, the

null hypothesis that the residual across panels are contemporaneously not correlated was rejected

in the entire six models, suggesting the presence of spatial correlation.26

A modified version of a Wald test was conducted to assess if the variances of the error structure

differ from unit to unit. The null hypothesis is rejected, suggesting the presence of groupwise

25

The Wooldridge AR(1) test results for three specification under the median voter model are: (Education: F (1,

20)= 3.607, prob > F = 0.0521; Health: F (1, 20)= 5.885, prob > F = 0.0249; Municipal Services F (1, 20)= 3.163,

prob > F = 0. 0905). The results for the three models under the political budget cycles are: (Education: F (1, 20)=

2.477, prob > F = 0.1312; Health: F(1, 20)= 24.443, prob > F = 0.0001; Municipal Services F(1, 20)= 5.245, prob >

F = 0.0330). 26

The Breusch –Pagan LM test of independence results for the three models under median voter model are:

(Education: Chi2 (45) = 72.608, prob > Chi2 = 0.00057; Health: Chi2 (45) =107.760, prob > Chi2 = 0.0000

Municipal Services: Chi2 (45) = 146.578, prob > Chi2 = 0.0000). The results for the three model under political

budget cycles are: (Education: Chi2 (45) = 170.703, prob > Chi2 = 0.0000; Health: Chi2(45) = 501.606, prob >

Chi2 = 0.0000 Municipal Services : Chi2(45) = 179.241, prob > Chi2 = 0.0000)

48

heteroscedasciticy.27

When these errors complications are not accounted for in the estimation

procedure the results produced tend not be consistent and efficient. Another important diagnostic

that has the tendency to affect the standard errors of the estimates and also provide imprecise

estimates of the marginal effects of the explanatory variables leading to wrong inference is the

presence of multicollinearity. In this study, the correlation matrix and the Variance Inflation

Factor (VIF) of the explanatory variables were examined to determine the degree of collinearity.

Applying the common rule of thumb for the Variance Inflation Factor found in the literature

where VIF of up to 10 is considered not to be a problem statistically, the results show no

evidence of multicollinearity as a problem.

In estimating the equations of the median model, the gini coefficient and the tax-price indexes

are not included in the model simultaneously. This is because the tax-price is presumably

endogenous to the level of inequality in the society. For example, a very unequal society may

choose to rely on a highly progressive tax structure to address redistributive concerns. Thus the

tax-price is a function of inequality. Hence if the Gini coefficient is contained in the regression,

the tax-price is omitted.

Specifications (3.1b) to (3.3b) and (3.1c) to (3.3c) are standard dynamic panel models. By

construction the lagged dependent variable in each equation is correlated with its respective error

term even if the structure of the error term is not serially correlated. The endogeneity caused by

27

The modified Wald test has a null hypothesis of common error variance across all units. The results under the

median model is given by: (Education: Chi2 (21) = 1139.39, prob > Chi2 = 0.0000; Health: Chi2 (21) = 1791.48,

prob > Chi2 = 0.0000; Municipal Services: Chi2 (21) = 131.79, prob > Chi2 = 0.0000. Similarly, the result under the

three political budget cycles models are: (Education: Chi2 (21) = 106.06, prob > Chi2 = 0.0000; Health: Chi2 (21) =

1791.48, prob > Chi2 = 0.0000; Municipal Services: Chi2 (21) = 95.50, prob > Chi2 = 0.0000.

49

the inclusion of the lagged dependent variable implies that estimating the six equations with

Ordinary Least Squares renders the estimates biased and inconsistent. According to Baltagi

(1995) and Kiviet (1995), even estimating the equations with the standard panel estimators like

“within group” or Least Squares dummy variables transformation to remove the individual

effects produces biased and inconsistent results because the correlation remains between

transformed lagged dependent variables and the transformed error terms. The asymptotic

properties of these estimators suggest that as the time period increases, the effects of such bias

becomes minimal. According to (Nickell, 1998) the bias is of order

. This thesis did not

employ such estimation methods because the time period of 21-years involved in this study is

arguably small to overcome the bias.

To overcome these econometric challenges, the estimation method adopted involves two steps.

First, the static versions of (3.1b) to (3.3b) and (3.1c) to (3.3c) are estimated using Feasible

Generalized Least Squares with Fixed Effects (FGLS-FE) designed by Park (1967) and Kmenta

(1986) purposefully for time series cross-sectional data. The Parks-Kmenta method has the

potential to address group-wise heteroskedascity, first-order serial correlation and spatial

dependence in the residuals as the characteristic of the data set in this study. The individual

dummies are included to cater for province-specific heterogeneity. Table 4.1 shows the

estimation results for equations (3.1b) to (3.3b) without the gini coefficient as an explanatory

variable and table 4.2 shows the results with the gini coefficient as an explanatory variable but

without the tax price indexes.

50

In the second stage, the full version of the model is estimated by adopting the Generalized

Method of Moments (GMM) estimator developed for dynamic panel data by Arellano and Bond

(1991). This estimator has the ability to control the effect caused by the unobserved province-

specific effects as well as the bias caused by the right-hand side endogenous variables using

further lags. The standard errors are adjusted for clustering on province. Tables 4.3 and 4.4 show

the results for equations (3.1b) to (3.3b) from the GMM estimation technique. The estimation

method for equations (3.1c) to (3.3c), the political budget cycle models, are the same as the

methods described above for the median voter models. The static versions of (3.1c) to (3.3c) are

estimated first with FGLS estimator followed by the dynamic version with GMM estimator. The

results are presented in tables 4.5 and 4.6

The results from the static and dynamic estimation methods are not necessarily expected to be

the same. This is because theoretically, FGLS is designed for panel data with longer periods

relative to cross-sectional unit while GMM is the opposite. Moreover the inclusion of the lag

dependent variable can suppress the explanatory power of the other independent variables

(Achen, 2001). The main purpose for the second estimation method is just to find out the

dynamics in the adjustments and the degree of persistence in governments spending. Therefore,

the GMM results must be interpreted with care and I stand by the more conservative estimates of

FGLS.

51

Chapter 4: Results and Policy Implications

This chapter presents the results and policy implications of provincial governments’ expenditures

on health, education and municipal services examined under the two hypotheses. The results of

median voter models are discussed first followed by the political budget cycle models.

4.1 Results and Policy Implications of the Median Voter Model

Table 4.1 shows the regression results for provincial government’s expenditure on health,

education and municipal services from the median voter model perspective. The results are

equation-by-equation estimation of the static version of models (3.1b) to (3.3b) using feasible

generalized least squares estimator with fixed effect (FGLS-FE). The Chi-Square value of Wald

test statistic indicates that all the three models are correctly specified.

The results show that within the period 1989-2009, provincial governments’ expenditures on

health care and education have been driven by the level of real median income. Real median

income has, as expected, a positive coefficient for all the spending categories and is statistically

significant at 1% level in both the health and education equations. A percentage rise in income of

the median voter on average causes provincial government real per capita expenditure on health

and education to increase by approximately 0.30% and 0.37% respectively. These coefficients

indicate that provincial government expenditures on these programs are income inelastic. The

two programs are considered as a normal good (specifically as a necessity and not a luxury) by

provincial governments. Median income does not significantly explain the changes in

government spending on municipal services.

Consistent with the economic theory, the effect of price on government spending captured by tax

price indexes is negative for all the spending programs. The implication is that as the costs of

52

these programs rise the median voter becomes less inclined to demand more of these services.

This leads to a decline in provincial government expenditures on these programs. The estimated

coefficient for the tax-price of health is significant at the 5% significance level. The tax-price of

municipal services is also statistically significant at 1% significance level. A percentage rise in

the tax-price of health care leads to 0.11% fall in the per capita health expenditure and a

percentage increase in the price of municipal services leads to 0.26% decrease in municipal

services spending. The estimated coefficient for the price of education is not statistically

significant.

Population density which may also indicate the degree of urbanisation has a positive relationship

with education and health care spending but it is negatively associated with municipal services

spending. The estimated coefficient is statistically significant at 1% level for both education and

municipal services expenditures. This suggests that as the population of a province increases

relative to a fixed land mass, the per capita expenditure on education increases while municipal

services spending falls. The negative coefficient for municipal services may result from the fact

that provinces with large populations relative to a fixed land mass presents lower average costs

for the government to provide these services. Governments in these provinces may explore the

advantages of economies scale in providing these services. Economies of scale may be enjoyed

by provincial governments in producing these services at least up to the congestion level where

the average and marginal costs will start rising.

The effects of the three age groups on provincial governments’ spending are varied and the sign

of estimated coefficients depend on the type of program. The proportion of children under five

years is negatively related to health spending but the estimated coefficient is positive in both

education and municipal services equations. The estimated coefficient is statistically significant

53

at the 5% level for both health and municipal services spending and at 10% level for education

expenditure. This suggests that as the number of children under five years relative to the total

population increases, it leads to a fall in provincial government health spending, but expenditures

on education and municipal services increase. Similarly as the number of school going age

increases with respect to the total population, per-capita government expenditure on education,

health and municipal services decrease. The estimated coefficient for the proportion of school

going age is negative and statistically significant at 1% level in all the three equations. The

parameter estimate for the proportion of elderly population is positive and statistically significant

for health spending at 10% significance level. However the estimated coefficient for the elderly

cohort is negative and statistically significant at 5% level for education and at 1% level for

municipal services spending.

The positive coefficient estimate for the proportion of elderly in the health spending equation

confirms the popular expectation that the ageing population significantly drives up health

spending. The evidence suggests that within the study period, a percentage rise in the proportion

of elderly above 65 years increases per capita spending on health by almost 0.29%. The negative

effects of the ageing population on education and municipal services can be explained in the

context of the median voter theorem. If the elderly becomes the median voter, they will vote for

programs that are directly related to the elderly like social security and health care as against

education. This is likely to happen if education happens to compete against the other programs

for the same budget share.

The negative links between proportion of school going age and education spending and the

children below five years and health spending may be as a result of economies of scale exploit

by provincial governments in delivering health care services and education to these age groups.

54

Table 4.2 shows the regression results for the three spending categories when the gini coefficient,

a measure of income inequality, is included as an explanatory variable instead of a tax-price

measure. The gini index has a positive association with all the three spending programs and is

statistically significant at 10% level for education spending and 1% level for municipal services

expenditure. This implies that as the level of income inequality increases provincial governments

spend more on education and municipal services. The possible explanation might be that as the

level of inequality increases, the median voter exerts political pressure on the government for a

redistributive government intervention. In this case education and municipal services can be

interpreted to be redistributive government programs.

The other explanatory variables that were statistically significant in the previous models (see

table 4.1) are still significant and have maintained their signs. However, with inclusion of the

gini index the magnitude of the estimated coefficients show slight variation. For instance, in the

health equation the estimated coefficient of the median income has decreased from 0.30 in the

previous model to 0.27 but they are both statistically significant at 1% significance level.

Tables 4.3 shows the regression results of the dynamic version of equations (3.1b) to (3.3b)

estimated using generalized method of moment. Table 4.4 presents the regression results of these

equations when the tax-price variable is replaced with the gini index. In both cases, the lag

dependent variables in the three spending equations are all positive but are statistically

significant at 1% level for only the health and municipal services expenditures. The implication

is that previous period expenditures contribute to the adjustment process in the level of current

spending for these two programs spending. In the education equation (the model with tax price as

55

an explanatory variable but no gini index) the inclusion of the lagged dependent variable has

eroded the explanatory power of population density, proportion of children and elderly variables.

Also, with the inclusion of the lagged dependent variable, generally the standard errors have

increased leading to a reduction in the significance level.

Since various governments’ programs expenditure appropriation is made from the composite

budget it is expected that the unobserved characteristics may affect all the equations. Therefore,

the error terms across the system of equations is expected to be correlated. This is tested using

Breusch-Pagan (1989) specification test of whether or not the residuals from the pooled cross

section, time series regression is correlated across the fiscal variables. The result shows that the

errors across the system of equations are contemporaneously correlated.28

This means estimating

the system of equations simultaneously induces some efficiency gains. Following that, equations

(3.1b) to (3.3b) are estimated simultaneously using seemingly unrelated regression (SUR). The

results are presented in tables 4.7 and 4.8. These results are not discussed but are included for the

purpose of comparing to the results of other studies that use Ordinary Least Squares (OLS) as the

estimation method.

The results of this thesis for the median voter model compare favorably with the results of some

other studies. Stuart et al. (2006) uses a panel of province-level data from 1989 to 2003 to test

the hypothesis that health spending has crowded out other types of spending. They found that the

coefficients of the income variable (personal income) in the health and education equations to be

positive, but only the coefficient in the education equation was statistically significant. Both

children under five years and the elderly population are found to be negatively associated with

28

The Breusch-Pagan test has the null hypothesis the residuals across the equations are independent. The result is given by Chi2 (3) = 29.270, prob > Chi2 = 0.0000

56

health spending but positive with education. Di Matteo (2004) also found income to as a

significant determinant of provincial health spending. The study uses Canadian provincial data

from 1975 to 2000. However the measure of his income variable is real GDP per capita. He

found the proportion of the population aged 65 and above to be positive and significant

determinant of health spending. The negative association between the expenditure on education

and the proportion of school going age population has been by some authors like Strawczynski

and Zeira (2003) for Israel; Medeiros and Barcelos (2007) for Brazilian municipalities and

Fernandez and Rogerson (2001) for US states.

4.2 Results and Policy Implications of the Political Budget Cycles

Table 4.5 shows the regression results for growth in real per capita health, education and

municipal services expenditures examined under the political budget cycles model.

Health care and municipal services spending respond positively to changes in unemployment rate

and GDP growth, while education spending responds negatively to the changes GDP growth.

The provincial government expenditures on these spending programs rise as the level of

unemployment increases. The coefficient estimate is statistically significant at 1% level for

municipal services and education spending. The estimated coefficient for the employment rate is

not statistically significant in the health spending equation.

The proportion of votes and percentage of seats won by the incumbent government, which

measure the size and the popularity of the incumbent government have different effects on

different spending components. The “proportion of votes” is positively related to health

spending. The estimated coefficient is statistically significant at the 5% level. However, the

57

proportion of votes won the incumbent has no significant effects on education and municipal

spending. The percentage of seats won by the incumbent government is negatively correlated

with all the three spending programs but it significantly explains only the changes in health

spending at 1% significance level. This suggests that as the government becomes more popular,

the per capita amount of resources devoted to health and municipal services also increases. On

the contrary, as the number of seats of the incumbent party increases, the growth in health,

education and municipal services spending decline. The possible reason may be that as the size

of the government increases, it feels more secured and becomes complacent.

Incumbent government assuming office for the first term devotes higher resources to education

and health care than “continuity government”. The coefficient estimate for first term incumbent

government is positive and statistically significant at 10% significance level in the health

equation. First term government needs to win the confidence of the people in order to get a

second term. This is usually achieved by honouring the various campaign promises. Health care

and education are core programs most politicians promote during campaigns and therefore it

normal to expect that first term governments will devote more resource for these programs.

Governments whose ideological perspectives are located at left-of-center on the political

spectrum spend more on health care and education than the right-of-center governments. The

estimated coefficient is statistically significant at 1% significance level for health spending. The

dummy variable capturing the effect of minority government on the three spending programs is

positive in all the spending equations. However the estimated coefficient is statistically

significant at 1% level for health and at 5% for municipal services expenditures. This implies

that minority governments spend more on health, education and municipal services than majority

governments. Minority governments rely on the support of other parties to remain in office.

58

Hence minority government can easily be held accountable for the campaign promises and may

also be compelled to implement programs that benefit the majority of the voting populace. This

is likely to result in higher spending on these core programs.

The main variable of interest, election dummy, which captures the effect elections have on

provincial health care, education and municipal services expenditures, is positive in all the

spending equations. The coefficient estimate for the election dummy variable is statistically

significant at 10% level for health spending and at 5% level for municipal services spending.

This means election-years observe a rise in the amount of resource devoted to heath care and

municipal services. Although the coefficient estimate for education is positive, it is not

statistically significant. The joint F-test of the province specific effect is not statistically

significant hence they are not reported.

Table 4.6 shows the regression results when these three equations are estimated as dynamic

panel model with generalized method of moment estimation method. The estimated coefficients

for the lagged health and education are both negative and statistically significant at 1%.

However, the inclusion of lagged dependent variables as additional predictor variables has

absorbed the explanatory power of the main predictor variable, election dummy.

These empirical results are consistent with Rogoff’s (1990) theoretical argument that suggests

that incumbent governments may signal their competence prior to elections by increasing

expenditures on “readily visible” programs without necessarily altering the aggregate

expenditure. Health, education and municipal services are core programs and increasing the

amount of resource in these areas can easily be seen and felt by the electorates in election years.

However the approach used in this does not fully test the Rogoff’s(1990) model. It cannot be

59

established from these results that expenditures on capital projects were reduced giving rise to

the increase in these programs’ expenditures as all programs spending could increase

simultaneously. Such analysis is left for future research.

Kneebone and McKenzie (2001) use a panel data from 1966-1997 on the 10 Canadian provinces

to test the opportunistic responses to spending on various governments programs. They found

that there are higher chances for spending on Education, Transportation and Communication and

Recreation and Culture to increase in election-years. Health, Social Services and Industrial

development are found to decrease in election-years. These results found for education and

municipal services are consistent with the one found in this study where spending on education,

health and municipal services all increase during the election years. It is not surprising that the

results found on health care contradict their finding. This is because the two studies use different

time periods and provincial health care spending and its financing have changed in last few

years.

4.3 Robustness Checks and Extensions

Provincial government spending decisions are sometimes influenced by the level of federal

government transfer payments to the provinces as well as the government’s debt which reflects

the level of borrowing. The federal transfers are made up of two components: the specific

purpose transfers which comprises Canada Health Transfer (CHT) and Canada Social Transfer

(CST) and the general purpose transfers. In the original models estimated to test both the median

and political budget cycle hypotheses, federal transfers and provincial governments’ debts were

not included.

60

The underlying assumption for not including government’s debt was that increases in provincial

governments spending through borrowing would be captured by the tax-price index. However,

governments’ debts via borrowing generally represent future tax increases. Hence the expected

future taxes should have been included in the model to capture the effect of government

borrowing. However, expected future tax variable is not available, hence provincial government

debt servicing cost is used as a proxy.

Similarly, in the original median voter models, the effect of federal transfers to provincial

governments was expected to be captured by the after-tax median income as such transfers

relieves provincial governments from using taxes to pay for their expenditures, which may

translate into higher after-tax median income. Although this may generally hold, it may not

always be true. Federal transfers for health and social welfare may influence provincial

government spending patterns. To check this, both federal transfers and provincial government

debt servicing costs are included in the two models (median voter and political budget cycle) as

an additional control variable. In the median voter models both transfers and government debt

servicing costs are measured in real per capita terms while in the political budget cycle models

they are measured in growth rates.

Also, in the original median voter models, only the program’s own price effects were estimated.

The underlying assumption was that the demand for one good is independent in demand of one

another: they are neither substitutes nor complements. However the processes involved in

government’s budget appropriations suggest that an increase in the price of one good may affect

the expenditure of the other. To check this, cross-price elasticities of demand among the various

programs are estimated.

61

Table 4.9 shows the results when federal transfer and debt servicing costs are introduced into the

median models with the gini coefficient, while Table 4.10 shows the results of these two

variables with the tax-price. Table 4.11 is the results of the political budget cycle models with

these two additional control variables. Also, in these tables are social services spending, another

expenditure category. Expenditures on social services are quite different from the other

expenditure categories as it mostly represents transfer payments to individuals. Political support

for transfer payments to lower income individuals must be motivated by altruism. A median

voter model with altruism is, however, unlikely to be coherent, as the single-peak requirement of

utility as a function of income is violated. For this reason, the original models of the median

voter left out social services.

In the median voter models, federal transfers are statistically significant in both the health and

education equations. As expected, in both equations it has a positive sign. This suggests that an

increase in federal transfer payments to provincial governments, generally leads to an increase in

provincial government spending on these programs. Although the effect of federal transfers on

both municipal services and social services expenditures are positive, the effects are not

statistically significant.

In table 4.10, the results show that the level of provincial government debt is negatively related

to all expenditure categories except social services. The coefficient estimates are statistically

significant for only municipal and social services expenditures. Thus, a rise in provincial

government debt leads to a cut in almost all program expenditures. These results reflect what

happened in the late 1990’s as provincial governments budgetary choices were constrained with

rising debt. The estimated coefficients of the median income variable in both tables are negative

62

for social services spending.29

This shows that as the income level decreases government

spending on social services rises. The results make sense as most of these expenditures represent

redistributive transfer. Also, the estimated coefficient for the cross-price elasticity is statistically

significant and positive between education and health but significant and negative between

municipal services and social services30

. This implies that increases in the price of health care

causes municipal services and social services expenditures to fall, while increases in the price of

municipal services lead to a fall in health spending.

In the context of the political budget cycle models, the estimated coefficients for federal transfers

are statistically significant for education and health expenditures and both have a positive sign.

This shows that increases in federal transfers lead to growth in health and education spending.

However, growth in government debt has a significant and negative impact on municipal

services spending, which suggest that provincial governments cut its spending on municipal

services as its debt level increases. All the other coefficients maintained their signs as in the

previous models, except the election dummy variable measuring the effect of elections on health

care spending. It is now insignificant although it has maintained its positive sign. It seems the

effect is absorbed by the federal transfer variable. This raises a question of whether transfer to

provincial government purposely for health care increases significantly in the election years of

that province?

29

Since federal transfers are included in the models, the before-tax median income of individual is used instead of

after-tax income. Federal transfers, debt servicing costs and social services expenditures are taken from CANSIM

table 385-0002 30

The tax-price of social services is calculated similar to the other programs. However the relative price of social

services component of the tax-price is given by the ratio of GDP price index on general government expenditures on

goods and services to the overall implicit GDP price index. The GDP price index taken from CANSIM

table 384-0036.

63

Chapter 5: Conclusion

The expenditure levels of various governments programs have been increasing in recent years.

This has been a major source of concern for both policy makers and researchers. To better

understand the driving forces behind these programs spending, much attention has been focussed

on getting a better understanding of the factors that go into the public spending decision-making

process. As a result many theories have been developed to explain the determinants of

government spending on goods and services. This thesis applies two of those theories: the

median voter and political budget cycle models to analyze the drivers of the provincial

governments’ health care, education and municipal services expenditures. Disaggregated data

from 1989-2009 on these three spending programs as well as political variables from the

Canadian provincial governments are used in this thesis.

In the context of the median voter model, it is generally argued that government expenditures on

goods and services are driven by the preferences of the median voter. When the determinants of

the median voters’ demand of a particular good or service change, it may induce a corresponding

change in government spending on that program. The hypothesis that government spending on

these three programs respond to changes in some economic, social and demographic factors that

represent the preferences of the median voter is tested. Specifically, the impact of median

income, tax-price, gini coefficient, population density and age cohort on the three spending

categories are examined.

In the context of political budget cycle models, Rogoff (1990) argues that incumbent

governments may signal their competence during an election year without changing the overall

level of the budget. Incumbent government can achieve this by shifting spending from capital

projects which takes longer period to be observed by the electorates to “readily visible

64

programs”. These three core programs are tested to assess whether its expenditures exhibit

fluctuations induced by elections.

The model specification employed in this thesis is a dynamic model in the form of a one-way

error components model. The estimation method adopted is in two steps. First, a static version of

the full model is estimated with Parks-Kmenta’s Feasible Generalized Least Squares (FGLS)

method. This method is designed purposefully for panel data with more time periods than cross

sectional units as in the case of this study. It also has the ability to control for cross-sectional

dependence of the error term, autocorrelation and heteroskedascity. In the second stage of the

estimation process, the full version of the model is estimated with Generalized Method of

Moment estimation method by Arrelano and Bond (1991).

The findings are generally consistent with the results found in the international literature with

regards to the signs and the level of significance of the key explanatory variables.

The main results from the analysis suggest that: Real median income is a positive and significant

determinant of provincial governments’ expenditures on health and education. The income

elasticities computed show that provincial governments regard education and health care as a

normal good. The tax-price variable is inversely related to health care and municipal services

spending, with the estimated coefficient being statistically significant in both equations. The tax-

price of education does not significantly explain the changes in provincial government

expenditure on education. Increases in the level of inequality contribute positively to an increase

government spending on municipal services and education but does not significantly explain

health care expenditure.

65

There is positive association between population density and the amount of resources provincial

governments devote to health care and education. However, provincial governments exploit

economies of scale in terms of its per capita spending on municipal services as the estimated

coefficient for population density is negative. The effects of the age groups on the government

expenditure are program-specific. The proportion of the elderly population is positively

associated with health spending but its impacts on both education and municipal services

expenditures is negative. The proportions of children under five years is has a positive link with

education spending but inversely related to health and municipal services expenditures. The

school going age population was found to be negatively correlated with all the three spending

categories.

The results from the political budget cycle model show that: The marginal effect of elections on

health care and municipal services expenditure is positive and statistically significant. Although,

there is positive correlation between education spending and elections, the link is not statistically

significant.

Minority governments are significantly associated with higher spending on health, education and

municipal services. First term incumbent governments are noted for higher spending on health

and education than continuous government. Moreover, there is tendency for the per capita

resource devoted for all spending components to increase if the government in office finds its

ideological position located in the left-of-center political spectrum. The popularity of the

incumbent government does not significantly explain the changes in any of spending

components. However, as the number of seats of the incumbents increase it negatively affects its

spending on education and health.

66

The main econometric challenge encountered in this research has to do with finding an estimator

equivalent to the Parks-Kmenta’ estimator specifically designed for dynamic panel model with

more time periods than cross sectional units. The common estimators like GMM, 2SLS, OLS all

rely on some asymptotic properties. Theoretically, GMM is designed for panel data with more

cross sectional unit than time periods. The lack such an estimator makes it difficult to directly

compare the results of the dynamic model to the static model.

67

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Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. The MIT press.

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75

Table 3.1 Sources and Definition of Variables

Label Variable(s) Created From Source Definition

rpcee Education expenditure

CPI

T: 385-0002

T: 326-0021

Real per capita expenditure on education

rpceh Health expenditure

CPI

T: 385-0002

T: 326-0021

Real per capita health expenditure

rpcems Housing

Policing and protection

Recreation and culture

CPI

T: 385-0002

T: 385-0002

T: 326-0002

T: 326-0021

Real per capita expenditure on municipal services

rmii After tax median income

CPI

T: 202-0605

T: 326-0021

Real median income of all families

pd Population

Land mass

T: 051-0001

CA: Population and

Dwelling count, 2012

Population density

gc Gini coefficient T: 202-0705 Gini coefficient

pc Population T: 051-0001 Percentage of the total population aged 5years and below

psga Population T: 051-0001 Percentage of school going age population

ppe Population T: 051-0001 Percentage of the elderly population( aged 65+)

taxpricee

CPI

3rd

Quintile’s tax-share

T: 326-0021

T: 202-0501

Tax-price of education

taxpriceh CPI

3rd

Quintile’s tax-share

T: 326-0021

T: 202-0501

Tax-price of health

taxpricem CPI

3rd

Quintile’s tax-share

T: 326-0021

T: 202-0501

Tax-price of municipal services

NB: T represents Statistics Canada Table and CA stands for Population and Dwelling count for Canada

76

Table 3.1 Sources and Definition of Variables

T represents Statistics Canada Table

Label Variable(s) Created From Source Definition

rpceh Education expenditure

CPI

T: 385-0002

T: 326-0021

Annual growth rate in real per capita health Spending

rpcee Education expenditure

CPI

T: 385-0002

T: 326-0021

Annual growth rate in real per capita expenditure on education

rpcems

Housing

Policing and protection

Recreation and culture

CPI

T: 385-0002

T: 385-0002

T: 326-0002

T: 326-0021

Annual growth rate in real capita spending on municipal services

rgdppc

GDP

CPI

Population

T: 384-0001

T: 326-0021

T: 051-0001

Annual growth rate in real GDP per capita

ur Unemployment T: 282-0086 Change in unemployment rate

left

Canadian Parliamentary Guide

Left-of-center government

electdum Election dummy

perseat Proportion of seats

pervote Proportion of votes

77

Table 3.2 Percentage Change (between 1989 and 2009) in the median variables

Province

Year

Median

Income

Health

Exp.

Education

Exp.

Municipal

services

Gini

Coefficient

Pop.

Density

NL

1989 41021.42 1365.71 1609.26 484.827 0.264 1.5561

2009 41845.140 3539.36 2152.08 917.422 0.299 1.37341

% change 1.968504 159.159 33.73103 89.22667 13.25758 -11.7402

PE

1989 35447.15 1400.7 1380.1 411.971 0.26 22.8912

2009 43089.43 2945.97 1798.97 549.351 0.258 24.8374

% change 17.73585 110.3213 30.3507 33.34701 -0.76923 8.501957

NS

1989 39119.8 1617.95 1387.45 416.383 0.274 17.0731

2009 39690.3 2973.57 1684.87 579.087 0.304 17.7617

% change 1.437372 83.78627 21.43645 39.07556 10.94891 4.033245

NB

1989 40083.33 1626.22 1546.62 307.516 0.265 10.2992

2009 40666.67 3116.66 1760.98 471.083 0.285 10.5073

% change 1.434426 91.65058 13.8599 53.18975 7.54717 2.020545

QB

1989 43279.8 1494.05 1652.53 434.156 0.266 5.10497

2009 42603.55 2624.81 1623.76 466.893 0.286 5.76972

% change -1.5873 75.68421 -1.74097 7.540377 7.518797 13.02162

ON

1989 53372.19 1730.59 1085.33 344.247 0.278 11.1195

2009 51623.65 2709.69 1377.32 411.338 0.323 14.3876

% change -3.3871 56.57608 26.90334 19.4892 16.18705 29.39071

MB

1989 41582.49 1733.64 1220.38 390.225 0.275 1.99843

2009 47053.87 2860.35 1425.5 502.484 0.289 2.20735

% change 11.62791 64.991 16.80788 28.76776 5.090909 10.45421

SK

1989 38114.75 1529.93 1100.69 415.786 0.288 1.73303

2009 47377.05 3141.85 1862.55 797.286 0.307 1.74984

% change 19.55017 105.3591 69.21658 91.75393 6.597222 0.969977

AB

1989 45107.4 1686.88 1471.23 507.533 0.284 3.90313

2009 57040.57 2658.08 2219.4 524.981 0.332 5.7363

% change 20.9205 57.57375 50.85337 3.437806 16.90141 46.96667

BC

1989 48154.51 1692.76 1091.51 311.559 0.27 3.46525

2009 47553.65 2695.21 1640.2 451.431 0.326 4.83458

% change -1.26354 59.21985 50.26889 44.89423 20.74074 39.51605

NB: Income and expenditures values are in 2002 constant dollars.

78

Table 3.2 Percentage Change (between 1989 and 2009) in the median variables

Province

Year

School

Aged pop.

Pro. of

Children

Elderly

pop.

Price of

Health

Price of

Education

Price of

Municipal

NL

1989 22.5779 0.067897 0.093168 16.34771 9.416581 16.81805

2009 13.8965 0.046545 0.147855 9.732227 8.689791 11.79486

% change -38.4509 -31.4476 58.69719 -67.975 -8.36372 -42.588

PE

1989 19.988 0.075135 0.128987 15.06225 9.980023 16.89067

2009 15.796 0.049894 0.153032 13.77581 14.75551 13.07059

% change -20.9726 -33.5942 18.64141 -9.33841 32.36411 -29.2266

NS

1989 18.3655 0.06759 0.12264 15.49748 8.433042 17.09679

2009 14.1072 0.047438 0.157431 12.3131 12.8649 12.42939

% change -23.1864 -29.8151 28.3684 -25.8617 34.4492 -37.5514

NB

1989 19.5298 0.066323 0.116586 13.86667 8.892749 16.60604

2009 14.2707 0.047952 0.154909 12.66241 14.66471 12.85472

% change -26.9286 -27.6993 32.87101 -9.51049 39.35954 -29.1825

QB

1989 17.4936 0.06259 0.105626 14.74381 8.413367 17.44562

2009 14.2368 0.05318 0.149461 12.63173 13.24877 11.94216

% change -18.6171 -15.0344 41.5002 -16.7204 36.49699 -46.0843

ON

1989 17.2505 0.069198 0.111689 14.26283 7.664791 16.88246

2009 15.4879 0.054231 0.136977 12.381 13.08996 11.31398

% change -10.2177 -21.6292 22.64144 -15.1993 41.44526 -49.2176

MB

1989 18.7715 0.075156 0.129938 13.06411 8.835891 15.63134

2009 17.0405 0.061627 0.138152 13.75456 13.69444 14.0997

% change -9.22143 -18.0012 6.321476 5.019761 35.47825 -10.863

SK

1989 20.3347 0.081422 0.134144 15.57861 8.316514 15.97167

2009 16.8179 0.063924 0.147648 11.01307 11.81281 14.24514

% change -17.2946 -21.4905 10.06679 -41.4557 29.59749 -12.1201

AB

1989 19.3017 0.083893 0.08656 17.43269 8.982671 17.88916

2009 15.7179 0.064717 0.10486 12.08732 12.41563 13.76663

% change -18.5673 -22.8577 21.1414 -44.2229 27.65028 -29.9458

BC

1989 17.1097 0.068363 0.125655 16.26719 12.80899 18.9685

2009 14.1743 0.049284 0.147164 12.22181 16.80237 11.67676

% change -17.1563 -27.9084 17.1175 -33.0997 23.76675 -62.4467

NB: Income and expenditures values are in 2002 constant dollars.

79

Table 3.3 Results of Im-Pesearan-Shin and Hadri LM unit root tests

NB: Δ represents first difference of the variable

Im-Pesearan-Shin Root Test Hadri LM Test

Variables Test Statistic P-Value Variables Test Statistic P-Value

Inrpcee

Δ

-2.3098

-13.8823

0.0105

0.0000

Inrpcee

Δ

1.7818

1.1832

0.0374

0.1184

Inrpceh

Δ

-4.7705

-6.2240

0.0000

0.0000

Inrpceh

Δ

0.6172

-0.1538

0.2686

0.5611

Inrpcems

Δ

-2.9992

-3.6047

0.0014

0.0002

Inrpcems

Δ

1.7262

1.4495

0.1010

0.0736

Inrmii

Δ

-3.9215

-4.0987

0.0000

0.0000

Inrmii

Δ

-0.0550

0.0315

0.5219

0.4875

Ingc

Δ

-9.9698

-5.3859

0.0000

0.0000

Ingc

Δ

-1.8903

0.9495

0.9706

0.1712

Inpd

Δ

-1.3488

-5.3959

0.0887

0.0000

Inpd

Δ

0.3773

-1.2780

0.3530

0.8994

Inpc

Δ

-2.88440

-2.8139

0.9978

0.0024

Inpc

Δ

1.4398

1.2780

0.0750

0.8994

Inppe

Δ

-4.2916

-1.7476

0.0000

0.0403

Inppe

Δ

-2.3963

0.2054

0.9917

0.4186

Intaxrpicee

Δ

-4.2975

-3.1251

0.0000

0.0030

Intaxpricee

Δ

3.5045

2.5524

0.0002

0.0058

Intaxpriceh

Δ

-5.8769

-5.8769

0.0000

0.0000

Intaxpriceh

Δ

-0.5048

0.8690

0.6930

0.1924

Intaxpricem

Δ

-3.0364

-12.3625

0.0012

0.0000

Intaxpricem

Δ

2.4628

-2.5578

0.0069

0.9946

Inur

Δ

-3.4815

-3.0020

0.9998

0.0013

Inur

Δ

4.3976

0.8995

03250

0.1842

Inpsga

Δ

-0.8623

-3.4612

0.1943

0.0003

Inpsga

Δ

2.6805

2.6478

0.1537

0.0041

Inrgdppc

Δ

-10.4203

-7.0278

0.0000

0.0000

Inrgdppc

Δ

-2.7085

0.6702

0.9966

0.2508

80

Table 3.4 Correlation Matrix for the Median Voter Variables

lntax

pricee lntax

pricems lntax

priceh Inrmii lngc lnpd lnpc lnpsga lnppe

lntaxpricee 1.0000

lntaxpricem -0.2572 1.0000

lntaxpriceh -0.3602 0.8155 1.0000

Inrmii 0.1105 -0.2213 -0.1229 1.0000

lngc 0.2503 -0.5501 -0.5292 0.5511 1.0000

lnpd 0.0305 0.0632 0.1130 0.0408 -0.2932 1.0000

lnpc -0.4964 0.7364 0.6931 -0.0111 -0.2712 -0.1623 1.0000

lnpsga -0.3764 0.6179 0.6649 -0.3083 -0.3379 -0.2341 0.7784 1.0000

lnppe 0.4021 -0.3790 -0.4842 -0.1561 0.0285 0.1065 -0.4441 -0.4133 1.0000

Table 3.5 Results of the Variance Inflation Factor Test

Variables Education Health Care Municipal Services

VIF 1/VIF VIF 1/VIF VIF 1/VIF

Inrmii 2.11 0.474319 2.13 0.468871 2.07 0.482272

Inpd 1.34 0.747813 1.40 0.716460 1.36 0.737741

lngc 2.03 0.491775 3.49 0.395186 2.37 0.421546

Intaxpricee 1.49 0.669966

Intaxpriceh 3.35 0.298377

Intaxpricem 3.23 0.309799

Inpc 3.65 0.274072 3.49 0.286774 4.55 0.219735

Inpsga 3.67 0.272530 3.90 0.256148 3.60 0.278139

Inppe 1.49 0.670097 1.52 0.658418 1.41 0.706962

Mean VIF 2.25 2.62 2.66

81

Table 3.6 Summary Statistics of the Political Variables

Variables Code Frequency

election 0 158

1 52

change 0 133

1 77

left 0 158

1 52

Minority 0 201

1 9

Time Elapse

1 55

2 53

3 51

4 41

5 10

Table 3.7 Summary Statistics of the Median Voter Variable

Variable Observation Mean Std. Dev Minimum Maximum

Real per capita Health Exp. 210 2041.386 424.4628 1365.71 3539.36

Real per capita Education Exp 210 1469.927 271.7423 854.9526 2845.457

Real per capita Exp on

Municipal

210 435.3629 94.4897 307.516 917.422

Real Median Income 210 40955.1 5133.391 33414.63 58233.89

Gini Coefficient 210 0.2917 0.0189 0.238 0.341

Population Density 210 8.4236 7.2278 1.3667 24.8374

Proportion of Children 210 0.0608 0.0094 0.0447 0.0839

Percentage of School Aged 210 0.1760 0.0167 0.1390 0.2258

Proportion of Elderly 210 0.1283 0.0155 0.0866 0.1574

Price of Education 210 13.0881 2.2500 8.2374 20.3026

Price of Health 210 12.5521 1.2550 8.8490 16.0314

Price of Municipal Service 210 14.8470 1.6780 10.9806 19.0543

82

Table 4.1: Regression Results for Median Voter Models (Static Version without Gini Coefficient as an explanatory Variable)

Estimation Method: Generalized Least Squares with Fixed Effect

Variable Education Expenditure Health Expenditure Expenditure on Municipal Services

Coefficient (s.e) P-value Coefficient (s.e) P-value Coefficient(s.e) P-Value

Median income 0.3666(0.1097) 0.001 0.2973(0.0032) 0.003 0.0256(0.1160) 0.825

Tax-price of Education -0.0033(0.0362) 0.925

Tax-price of Health -0.1088(0.0515) 0.035

Tax-price of municipal services -0.2636(0.0751) 0.000

Population density 0.4781(0.1510) 0.002 0.0305(0.1305) 0.815 -0.5238(0.1565) 0.001

Aged below 5yrs 0.1365(0.0811) 0.095 -0.1880(0.0885) 0.034 0.2341(0.1082) 0.031

School going age -1.0230(0.1600) 0.000 -1.5956(0.2091) 0.000 -0.8807(0.2580) 0.000

Aged 65+ -0.4250(0.1777) 0.017 0.2936(0.1609) 0.068 -2.2786(0.2765) 0.001

Constant 5.5942(1.2928) 0.000 9.4993(1.1868) 0.000 12.6526(1.4776) 0.000

Newfoundland -0.4682(0.1920) 0.015 -0.0617(0.1649) 0.708 -0.1674(0.2069) 0.419

Prince Edward Island -0.4944(0.1627) 0.002 -0.2785(1.4090) 0.048 -0.3223(0.1692) 0.057

Nova Scotia -0.1660(0.0484) 0.001 -0.0435(0.0469) 0.354 -0.2138(0.0621) 0.001

New Brunswick -0.7372(0.2943) 0.012 -0.2578(0.2486) 0.300 0.2322(0.3044) 0.445

Quebec 0.3200(0.0994) 0.001 -0.1012(0.0699) 0.148 -0.3956(0.1155) 0.001

Ontario -1.1566(0.3706) 0.002 -0.2948(0.3148) 0.349 0.6789(0.3815) 0.075

Manitoba -1.3335(0.3133) 0.000 -0.2707(0.2741) 0.323 0.3587(0.3291) 0.276

Saskatchewan -1.0719(0.4091) 0.009 -0.2074(0.3505) 0.554 1.0291(0.4201) 0.014

Alberta -0.5261(0.2028) 0.009 0.3458(0.1719) 0.044 -0.0727(0.2131) 0.733

British Columbia

Wald Test Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000

NB: Standard error in parenthesis the dependent variables are in per capita levels. BC is the baseline or reference category for the dummies

83

Table 4.2: Regression Results for the Median Voter Models (Static Version with Gini Coefficient as an explanatory variable)

Estimation Method: Generalized Least Squares with Fixed Effect

Variable Education Expenditure Health Expenditure Expenditure on Municipal Services

Coefficient (s.e) P-value Coefficient (s.e) P-value Coefficient(s.e) P-Value

Median income 0.4153(0.1084) 0.000 0.2742(0.0995) 0.006 0.1324(0.1161) 0.254

Gini coefficient 0.2406(0.1277) 0.059 0.1756(0.1206) 0.145 0.5289(0.1217) 0.000

Population density 0.4037(0.1497) 0.007 0.0331(0.1311) 0.801 -0.4531(0.1490) 0.002

Aged below 5yrs 0.1854(0.0780) 0.017 -0.1833(0.0914) 0.045 0.2697(0.1091) 0.013

School age -1.0106(0.1620) 0.000 -1.6761(0.2042) 0.000 -2.2789(0.2633) 0.000

Aged 65+ -0.4203(0.1755) 0.017 0.2784(0.1632) 0.088 -0.7570(0.2473) 0.002

Constant 5.4849(1.2388) 0.000 9.9010(1.1900) 0.000 10.5067(1.4584) 0.000

Newfoundland -0.4060(0.1902) 0.000 -0.0812(0.1647) 0.622 -0.2294(0.1951) 0.240

Prince Edward Island -0.4278(0.1617) 0.033 -0.2927(0.1407) 0.037 -0.4014(0.1565) 0.010

Nova Scotia -0.1446(0.0482) 0.008 -0.0436(0.0465) 0.349 -0.2142(0.0608) 0.000

New Brunswick -0.5740(2955) 0.003 -0.2676(0.2535) 0.291 0.1584(0.2871) 0.581

Quebec 0.3339(0.0973) 0.052 -0.1014(0.0703) 0.149 -0.2992(0.1151) 0.009

Ontario -0.9595(0.3712) 0.010 -0.3076(0.3193) 0.335 0.5526(0.3597) 0.124

Manitoba -1.1952(0.3106) 0.000 -0.2875(0.2744) 0.295 0.2133(0.3096) 0.491

Saskatchewan -0.8337(0.4116) 0.043 -0.1976(0.3576) 0.581 0.9261(0.4011) 0.021

Alberta -0.4179(0.2049) 0.041 -0.3516(0.1754) 0.045 -0.0962(0.2009) 0.632

British Columbia

Wald Test Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000

NB: Standard errors are in parenthesis and the dependent variables are in per capita levels. BC is the baseline category for the dummies

84

Table 4.3 Regression results for the median voter Models (Dynamic Version with Gini Coefficient)

Estimation Method: Generalized Method of Moments

Variable Education Expenditure Health Expenditure Expenditure on Municipal Services

Coefficient (s.e) P-value Coefficient (s.e) P-value Coefficient(s.e) P-value

Lagged Dependent Variable 0.0697(0.1232) 0.572 0.5677(0.0851) 0.000 0.6154(0.0839) 0.000

Median Income 0.4873(0.2278) 0.032 0.4090(0.1734) 0.018 0.1663(0.1872) 0.374

Gini Coefficient -0.3375(0.1927) 0.080 0.3527(0.1414) 0.013 0.3909(0.1941) 0.044

Population Density -0.0835(0.2772) 0.763 -0.2117(0.1069) 0.048 -0.1952(0.0708) 0.006

Aged below 5yrs -0.2796(0.1228) 0.023 -0.1056(0.0746) 0.157 0.1348(0.1325) 0.309

School age Population -0.8686(0.4671) 0.063 -0.6909(0.3114) 0.027 -0.3796(0.3073) 0.217

Aged 65+ -0.1024(0.4027) 0.799 0.0091(0.3115) 0.963 -1.0281(0.3722) 0.006

Constant 4.9407(2.6677) 0.064 1.4270(2.5634) 0.578 3.9328(2.9019) 0.175

Wald Test Prob > chi2 = 0.000 Prob > chi2 = 0.000 Prob > chi2 = 0.000

AR(2) Test Prob > Z = 0.3610 Prob > Z = 0.2489 Prob > Z = 0.2716

Standard errors are in parenthesis and the dependent variables are in the per capita levels.

85

Table 4.4 Regression results for the median voter Models (Dynamic Version without Gini Coefficient)

Estimation Method: Generalized Method of Moments

Variable Education Expenditure Health Expenditure Expenditure on Municipal Services

Coefficient (s.e) P-value Coefficient (s.e) P-value Coefficient(s.e) P-value Lagged Dependent Variable 0.0646(0.1291) 0.617 -0.5632(0.0796) 0.000 0.6115(0.0963) 0.000

Median Income 0.3913(0.2372) 0.099 0.4267(0.1562) 0.006 0.1255(0.1961) 0.522

Tax-price of Education -0.884(0.1177) 0.453

Tax-price of Health -0.2300(0.0897) 0.010

Tax-price of Municipal Services -0.2088(0.0983) 0.034

Population Density 0.1887(0.2752) 0.493 -0.1659(0.1141) 0.146 -0.2419(0.0683) 0.000

Aged below 5yrs 0.1067(0.1301) 0.412 -0.1383(0.0735) 0.060 0.0944(0.1342) 0.482

School age Population -0.9319(0.5508) 0.722 -0.5988(0.2696) 0.643 -1.0723(0.3899) 0.100

Aged 65+ -0.1704(0.4791) 0.091 -0.0890(0.1919) 0.026 -0.5590(0.3402) 0.006

Constant 5.1800(3.2150) 0.107 0.8200(2.4426) 0.737 4.1938(2.8702) 0.144

Wald Test Prob > chi2 = 0.000 Prob > chi2 = 0.000 Prob > chi2 = 0.000

AR(2) Test Prob > Z = 0.3009 Prob > Z = 0.2198 Prob > Z = 0.2520

NB: Standard errors are in parenthesis and the dependent variables are in per capita levels.

86

Table 4.5 Regression Results for Political Budget Cycles Models (Static Version)

Estimation Method: Generalized Least Squares with Fixed Effect

Variable Education Expenditure Health Expenditure Expenditure on Municipal Services

Coefficient(s.e) p-value Coefficient(s.e) p-value Coefficient(s.e) p-value

Real GDP growth -0.0287(0.1024) 0.779 0.0442(0.0715) 0.536 0.0124(0.0881) 0.888

Unemployment rate 1.8674(0.6282) 0.003 0.5431(0.3964) 0.171 2.0275(0.4351) 0.000

Percentage of seats -0.0477(0.0523) 0.362 -0.1164(0.0332) 0.000 -0.0238(0.0509) 0.640

Proportion of votes -0.1286(0.1286) 0.317 0.2651(0.0904) 0.003 0.0925(0.1468) 0.529

Change 1.3058(0.7269) 0.072 0.8362(0.4826) 0.083 -0.3184(0.7312) 0.663

Left 0.5197(0.7305) 0.477 1.7537(0.6516) 0.007 -1.1608(1.0346) 0.876

Minority 2.7215(2.123) 0.200 4.2113(1.4048) 0.003 4.5400(1.8355) 0.013

Election dummy 0.3902(0.8324) 0.639 0.9874(0.5872) 0.093 1.6071(0.7747) 0.038

Constant 11.5662(7.6557) 0.131 -2.0961(3.4828) 0.547 0.5288(5.4955) 0.923

Wald Test Prob > chi2 = 0.000 Prob > chi2 = 0.000 Prob > chi2 = 0.000

NB: Standard errors are in parenthesis and the dependent variables are measured in per capita levels

87

Table 4.6 Regression Results for Political Budget Cycles Models (Dynamic Version)

Estimation Method: General Method of Moment

Variable Education Expenditure Health Expenditure Expenditure on Municipal Services

Coefficient(s.e) p-value Coefficient(s.e) p-value Coefficient(s.e) p-value

Lagged dependent variable -0.5108(0.0373) 0.000 -0.2059(0.0750) 0.006 -0.1710(0.1363) 0.210

Real GDP growth 0.0022(0.3356) 0.995 0.0964(0.1462) 0.510 0.0870(0.0485) 0.077

Unemployment rate 2.3049(1.5648) 0.141 1.7380(0.6173) 0.005 1.7178(0.5572) 0.002

Percentage of seats -0.2626(0.1597) 0.100 -0.1008(0.0942) 0.285 -0.0985(0.1151) 0.392

Proportion of votes -0.2521(0.2552) 0.323 0.2290(0.1224) 0.061 0.3136(0.2607) 0.229

Change 4.9909(3.6941) 0.177 0.1678(1.4334) 0.907 -2.4068(1.7414) 0.167

Left -1.1436(2.4482) 0.640 0.18665(1.8063) 0.301 -2.3082(2.3471) 0.325

Minority 1.9060(2.5564) 0.456 4.7569(2.9842) 0.111 4.0649(2.7620) 0.141

Election dummy -2.2455(2.0037) 0.262 0.4375(1.0828) 0.686 0.2402(0.9515) 0.801

Constant 30.7667(15.0922) 0.041 -1.3957(5.3480) 0.794 -5.2405(6.4945) 0.420

Wald Test Prob > chi2 = 0.000 Prob > chi2 = 0.000 Prob > chi2 = 0.000

AR(2) Prob > Z = 0.3003 Prob > Z = 0.1413 Prob > Z = 0.2212 NB: Standard errors are in parenthesis and the dependent variables are in per capita growth rates

88

Table 4.7 Regression Results under Median Voter Theorem (Dynamic version without tax price as an explanatory variable)

Estimation Method: Seemingly Unrelated Regression with Fixed Effect

Variable Education Expenditure Health Expenditure Expenditure on Municipal

Services

Coefficient (s.e) P-value Coefficient (s.e) P-value Coefficient(s.e) P-Value Lagged Dependent Variable 0.1325(.0647) 0.041 0.5977(0.0493) 0.000 0.6643(.0476) 0.000

Median income 0.4886(0.2077) 0.019 0.3509(0.0962) 0.000 0.2429(0.1232) 0.049

Gini coefficient 0.2671(0.2903) 0.358 0.3711(0.1341) 0.006 0.2863(0.1728) 0.098

Population density 0.1313(0.2028) 0.517 -0.2338(0.0929) 0.012 -0.3159(.1255) 0.012

Aged below 5yrs 0.3021(0.1532) 0.049 -0.1225( 0.0706) 0.083 0.0582(.0892) 0.514

School age -0.8994(0.3446) 0.009 -0.6792(0.1766) 0.000 -0.9247(0.2141) 0.000

Aged 65+ -0.1174(3275) 0.720 -0.0643(0.1504) 0.669 -0.4375(0.1964 0.026

Constant 4.8715(2.6354) 0.065 1.3918( 1.2699) 0.273 1.9284( 1.5352) 0.209

Newfoundland

Prince Edward Island -0.4510(0.5741) 0.432 0.7169(0.2631) 0.006 0.9170(0.3548) 0.010

Nova Scotia -0.6323(.5130) 0.218 0.5573(0.2339) 0.017 0.7138(0.3130) 0.023

New Brunswick -0.4008(4057) 0.323 0.4361(0.1853) 0.019 0.4943(0.2449 0.044

Quebec -0.4100(2750) 0.136 0.2282(0.1249) 0.068 0.2743( 0.1647) 0.096

Ontario -0.9356(4262) 0.028 0.3870(0.1921) 0.044 0.4865(0.2545) 0.056

Manitoba -0.4226(.1202) 0.000 0.1178(0.0541) 0.030 0.1163(0.0692) 0.093

Saskatchewan -0.3482(.1337) 0.009 0.1120(.0611) 0.067 0.9900(0.0791) 0.012

Alberta -0.4119(.2283) 0.071 0.1999(0.1040) 0.055 0.1390(0.1343) 0.300

British Columbia -0.4826(.2152) 0.025 0.1490(0.0969) 0.124 0.1230( 0.1251) 0.326

Wald Test Prob>chi2 = 0.0000 Prob>chi2 = 0.0000 Prob>chi2 = 0.0000

R- Square 0.6830 0.9294 0.8848

NB: Standard errors are in the parenthesis the dependent variables are measured in per capita levels. Newfoundland is the baseline category

89

Table 4.8 Regression Results under Median Voter Theorem (Dynamic version with tax price as an explanatory variable)

Estimation Method: Seemingly Unrelated Regression with Fixed Effect

Variable Education Expenditure Health Expenditure Expenditure on Municipal Services

Coefficient (s.e) P-value Coefficient (s.e) P-value Coefficient(s.e) P-Value Lagged dependent Variable 0.1455(0.0652) 0.026 -0.6092(0.0495) 0.000 0.6731(0.0471) 0.000

Median income 0.4673(0.2068) 0.024 0.3350(0.0959) 0.000 0.2188(0.1210) 0.070

Price of Education -0.0385(0.0722) 0.594

Price of health -0.1554(0.0669) 0.020

Price of Municipal Services -0.1610(0.0970) 0.097

Population density 0.1508(0.2016) 0.454 -0.1793(0.0904) 0.047 -0.3340(0.1309) 0.009

Aged below 5yrs 0.2723(0.1541) 0.077 -0.1708(0.0655) 0.009 -0.5589(0.2054) 0.652

School age -0.9051(0.3448) 0.009 -0.5946(0.1819) 0.473 -0.9136(0.2141) 0.009

Aged 65+ -0.1136(3296) 0.730 -0.1083(0.1510) 0.001 -0.5389(0.2054) 0.000

Constant 4.5031(2.6504) 0.089 0.8953(1.2723) 0.482 (1.8893)1.5338 0.218

Newfoundland

Prince Edward Island -0.5352(0.5615) 0.341 0.5485(0.2540) 0.031 0.9942(0.3734) 0.008

Nova Scotia -0.6826(0.5087) 0.180 0.4418(0.2286) 0.053 0.8084(0.3344) 0.016

New Brunswick -0.4489(0.3998) 0.261 0.3445(0.1807) 0.057 0.5683(0.2619) 0.030

Quebec -0.4401(-0.2713) 0.105 0.1710(0.1225) 0.163 0.3249(0.1755) 0.064

Ontario -0.9441(0.4279) 0.027 0.3180(0.1905) 0.095 0.5830(0.2728) 0.033

Manitoba -0.4238(0.1228) 0.001 0.1265(0.0543) 0.020 0.1697(0.0758) 0.025

Saskatchewan -0.3339(0.1341) 0.013 0.1376(0.0604) 0.023 0.2552(0.0812) 0.002

Alberta -0.4091(0.2291) 0.074 0.1796(0.1041) 0.084 0.1985(0.1425) 0.164

British Columbia -0.4902(0.2149) 0.023 0.1344(0.0969) 0.165 0.2080(0.1414) 0.141

F-Test Prob>F = 0.0000 Prob>F = 0.0000 Prob>F = 0.0000

R-Square 0.6805 0.9289 0.8849

NB: Standard errors are in the parenthesis and the dependent variables are measured in per capital levels. Newfoundland is the baseline category

90

Table 4.9 Regression Results under Median Voter Theorem (Static version with Gini Index Federal Transfers and Government Debt)

Estimation Method: Feasible Generalized Least Squares

Variable Education

Expenditure

Health

Expenditure

Expenditure on

Municipal Services

Expenditure on

Social Services

Coefficient (s.e) p-value Coefficient (s.e) p-value Coefficient(s.e) p-value Coefficient(s.e) p-value

Median income 0.100(0.068) 0.139 0.131(0.756) 0.084 0.159(0.092) 0.084 -0.10(0.063) 0.115

Transfer Payment 0.123(0.029) 0.000 0.041(0.019) 0.029 0.030(0.025) 0.222 0.005(0.014) 0.726

Debt Servicing Cost -0.002(0.026) 0.994 0.021(0.223) 0.334 -0.041(0.036) 0.261 0.114(0.028) 0.000

Gini coefficient -0.030(0.127) 0.817 0.114(0.108) 0.295 0.552(0.118) 0.000 0.677(0.093) 0.468

Population density 0.700(0.1872) 0.000 0.256(0.148) 0.083 -0.435(0.186) 0.019 0.588(0.180) 0.001

Aged below 5yrs -0.003(0.074) 0.969 -0.259(0.081) 0.001 0.209(0.108) 0.052 0.768(0.093) 0.000

School age -1.131(0.161) 0.000 -1.933(0.204) 0.204 -2.137(0.279) 0.000 -1.340(0.187) 0.000

Aged 65+ -0.638(0.187) 0.001 0.050(0.161) 0.756 -0.632(0.249) 0.011 0.283(0.168) 0.093

Constant 6.90(0.992) 0.000 10.940(0.982) 0.000 11.271(1.192) 0.000 13.455(0.896) 0.000

Newfoundland -0.656(0.228) 0.004 -0.334(0.185) 0.071 -0.207(0.220) 0.348 -0.425(0.212) 0.093

Prince Edward Island -0.647(0.197) 0.001 -0.507(0.159) 0.001 -0.387(0.186) 0.038 -0.477(0.81) 0.009

Nova Scotia -0.255(0.060) 0.000 -0.123(0.0530 0.020 -120(0.070) 0.004 -0.045(0.069) 0.513

New Brunswick -1.267(0.353) 0.000 -0.760(0.280) 0.007 0.145(0.342) 0.671 -1.222(0.336) 0.000

Quebec 0.159(0.103) 0.124 -0.182(0.073) 0.013 -0.266(0.122) 0.029 0.247(0.084) 0.003

Ontario -1.797(0.447) 0.000 -0.916(0.355) 0.010 -0.521(0.435) 0.231 -1.679(0.427) 0.000

Manitoba -1.687(0.396) 0.000 -0.728(0.318) 0.022 0.220(0.387) 0.570 -1.102(0.377) 0.003

Saskatchewan -1.780(0.494) 0.000 -0.847(0.392) 0.031 0.871(0.481) 0.070 -1.735(0.481) 0.000

Alberta -0.837(0.239) 0.000 -0.686(0.194) 0.000 -0.086(0.233) 0.712 -0.337(0.228) 0.139

British Columbia

Wald Test Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000

NB: Standard errors are in parenthesis and the dependent variables are measured in per capita levels. British Columbia is the baseline category.

91

Table 4.10 Regression Results under Median Voter Theorem (Static Version with Tax-price, Federal Transfers and Government Debt )

Estimation Method: Feasible Generalized Least Squares

Variable Education

Expenditure

Health

Expenditure

Expenditure on

Municipal Services

Expenditure on

Social Services

Coefficient (s.e) p-value Coefficient (s.e) p-value Coefficient(s.e) p-value Coefficient(s.e) p-value

Median Income 0.201(0.094) 0.032 0.156(0.075) 0.038 0.179(0.095) 0.059 -0.113(0.073) 0.073

Transfer Payment 0.112(0.031) 0.000 0.023(0.021) 0.270 0.010(0.025) 0.680 0.005(0.151) 0.751

Debt Servicing Cost -0.018(0.028) 0.527 -0.002(0.025) 0.920 -0.063(0.037) 0.093 0.102(0.027) 0.00

Price of Education 0.053(0.054) 0.068 -0.026(0.046) 0.574 0.089(0.061) 0.148 -0.094(0.058) 0.107

Price of Health 0.126(0.071) 0.076 -0.075(0.0540 0.168 -0.169(0.071) 0.018 -0.105(0.050) 0.035

Price of Municipal services -0.368(0.153) 0.016 -0.224(0.094) 0.017 -0.355(0.128) 0.006 0.170(0.095) 0.075

Price of Social Services 0.199(0.109) 0.068 0.185(0.069) 0.007 0.116(0.107) 0.280 0.102(0.027) 0.041

Population density 0.652(0.196) 0.001 0.169(0.160) 0.288 -0.575(0.196) 0.003 0.692(0.183) 0.000

Aged below 5yrs 0.088(0.084) 0.300 -0.254(0.088) 0.004 0.342(0.109) 0.000 0.623(0.128) 0.000

School age -1.174(0.169) 0.000 -1.854(0.206) 0.000 -2.058(0.265) 0.000 -1.230(0.186) 0.000

Aged 65+ -0.724(0.199) 0.000 0.022(0.169) 0.896 -0.799(0.245) 0.001 0.441(0.168) 0.008

Constant 6.354(1.266) 0.000 10.981(1.00) 0.000 11.432(1.234) 0.000 12.867(0.909) 0.000

Newfoundland -0.684(0.233) 0.003 -0.278(0.194) 0.152 -0.132(0.227) 0.559 -0.471(0.203) 0.021

Prince Edward Island -0.621(0.203) 0.002 -0425(0.169) 0.012 -0.261(0.196) 0.183 -0.555(0.177) 0.002

Nova Scotia -0.251(0.063) 0.000 -0.096(0.053) 0.072 -0.186(0.070) 0.008 -0.542(0.066) 0.409

New Brunswick -1.164(0.366) 0.001 -0.588(0.303) 0.053 0.400(0.368) 0.277 -1.416(0.340) 0.000

Quebec 0.182(0.105) 0.083 -0.184(0.072) 0.011 -0.304(0.115) 0.008 0.279(0.081) 0.001

Ontario -1.664(0.466) 0.000 -0.698(0.385) 0.070 0.865(0.467) 0.064 -1.932(0.435) 0.000

Manitoba -1.644(0.410) 0.000 -0.580(0.342) 0.090 0.481(0.409) 0.239 -1.286(0.377) 0.001

Saskatchewan -1.640(0.518) 0.002 -0.624(0.422) 0.139 1.182(0.247) 0.894 -2.032(0.487) 0.000

Alberta -0.799(0.246) 0.001 -0.592(0.208) 0.004 0.331(0.247) 0.894 -0.443(0.225) 0.049

British Columbia

Wald Test Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 NB: Standard errors are in parenthesis and the dependent variables are measured in per capita levels. British Columbia is the baseline category.

92

Table 4.11 Regression Results of Political Budget Cycle Models (Static Version with Federal Transfers and Government Debt)

Estimation Method: Feasible Generalized Least Squares with Fixed Effect

Variable Education

Expenditure

Health

Expenditure

Expenditure on

Municipal Services

Expenditure on

Social Services

Coefficient (s.e) p-value Coefficient (s.e) p-value Coefficient(s.e) p-value Coefficient(s.e) p-alue

Real GDP growth 0.005(0.113) 0.963 0.038(0.073) 0.602 -0.005(0.086) 0.958 0.0108(0.074) 0.885

Unemployment rate 1.369(0.604) 0.023 3.889(2.010) 0.112 2.024(0.426) 0.000 1.761(0.432) 0.000

Growth in federal transfer 19.766(3.500) 0.000 0.632(0.398) 0.064 -0.975(2.229) 0.662 0.667(1.696) 0.694

Growth in government debt 2.793(5.038) 0.579 -1.049(4.411) 0.812 -12.769(4.489) 0.009 6.271(4.535) 0.167

Percentage of seats -0.560(0.057) 0.323 -0.112(0.033) 0.001 -0.014(0.048) 0.780 -0.075(0.048) 0.065

Proportion of votes -0.088(0.148) 0.554 0.244(0.096) 0.011 0.049(0.141) 0.727 0.237(0.104) 0.065

Change 0.774(0.810) 0.339 0.883(480) 0.066 -0.348(0.712) 0.625 -0.922(0.656) 0.160

Left 0.964(0.797) 0.339 1.710(0.625) 0.006 -0.442(0.994) 0.657 1.876(0.946) 0.047

Minority 1.843(2.213) 0.405 3.874(1.440) 0.007 3.762(1.778) 0.034 3.520(1.398) 0.012

Election dummy 0.629(0.885) 0.477 0.954(0.598) 0.111 1.880(0.735) 0.011 -0.034(0.621) 0.956

Constant 9.227(7.079) 0.192 -1.430(3.775) 0.705 0.9570(5.330) 0.858 -4.586(4.445) 0.302

Wald Test Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000

NB: Standard errors are in parenthesis and the dependent variables are measured in per capita growth rates