Democracy and Education: Does the Type of Government Affect Access to Education? Matthew Tolan, Simon Fraser University
April 2011
Abstract:
In this article, I seek to find whether the type of government in a country affects the
amount of education provided. To do this, I propose a model where political power is
distributed throughout the population, and their choice to support a government is based
on an observable factor; whether the government provides access to education. Using the
results from this model, I propose 3 hypotheses: that post-primary education enrollment
will remain constant with changes in governance; that primary education enrollment will
increase with an increase in democracy; and that the effect democracy has on primary
education enrollment will be greater in predominantly rural countries than in
predominantly urban countries. I test these hypotheses using data from Africa between
1990 and 2008. Due to the results from the empirical analysis, I fail to reject the first two
hypotheses. Furthermore, I find inconclusive results for the third hypothesis.
1.
Introduction
This paper seeks to find whether the type of government in a country affects the
amount of education provided. In today’s political landscape, western nations are
required to interact with all types of governments, ranging from democracies to
autocracies. Through this interaction, western nations are gaining influence over the
political development of these countries, and as such they must understand the
ramifications of decisions they make regarding future political changes. To better
understand the effects the type of governance has on the provision of public goods in a
country, I address a specific subject within development; the link between type of
government in a country and the amount of education provided. To do this, I formulate a
model that treats the government as a rent-maximizing agent that is held accountable by
the subsections of the population with political influence. Depending on the type of
government, certain groups have more or less influence, and from this fact, provision of
public goods will cater to the influential groups while neglecting the ones who lack
influence. From this model I derive three hypotheses: that post-primary education
enrollment will remain constant with changes in governance; that primary education
enrollment will increase with an increase in democracy; and that the effect democracy
has on primary education enrollment will be greater in predominantly rural countries than
in predominantly urban countries. I test these hypotheses using panel data from African
countries between 1990 and 2008 and I find that the data fails to reject the first and
second hypotheses, while I am unable to draw any conclusion for third hypothesis due to
insufficient evidence.
The paper proceeds in the following format. Section 1 discusses the importance of
the topic and the previous works that have been carried out thus far. In section 2, I
present a theoretical model and derive my hypotheses. In sections 3 and 4, I discuss the
data and methodology I use to test the proposed hypotheses. In section 5, I analyze my
results and discuss their robustness. Finally, in section 6, I provide a conclusion. Section
7 contains references and Section 8 contains the appendices.
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1.1
Related Literature
There are a number of papers that analyze democracy and its effect on the
provision of public goods. Ansell (2006) and Stasavage (2005) analyze the effect an
increase in democracy has on the distribution and level of education expenditure in a
country. They both propose simple models where the government is rent seeking, and
both derive hypotheses that democratic governments will spend more on primary
education, and less on tertiary education then a corresponding autocracy. Ansell (2006)
then continues to analyze the effect democracy has on other public goods, but for the
purpose of this paper I need not discuss this further. The difference between the two
papers lies in their measurement for democracy. Ansell (2006) uses the polity index,
which he argues is the best available data for this measurement. Contrary to this,
Stasavage (2005) creates his own index based on dummy variables indicating whether
there is no political party, a single political party, or multiple political parties as his
measure of democracy. Despite the differences in measurement, they find that democracy
does increase primary education expenditure, while keeping secondary and tertiary
education expenditure constant.
Lake and Baum (2001) use a different approach; they study the effect a change in
democracy has on education and health indicators such as the literacy rate, gross
enrollment rates, and life expectancy. Similar to Ansell (2006), the authors use the polity
index to calculate their results. They find that democracy positively affects literacy rates,
gross enrollment rates, and life expectancy. However, the authors do not formulate a
mathematical model to explain the effects in this article. Due to this lack of a rigorous
representation of the government payoffs, the results are based on qualitative theories.
However, in a later paper, Lake and Baum (2003) attempt to explain how democracy
affects human capital growth, using a rigorous model as part of their argument. They
conclude that democracy has the largest effect on growth through an increase in life
expectancy in undeveloped countries, and an increase in secondary school enrollment in
developed countries.
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Deacon (2007), Kosack (2009), and Chen (2008) further validate the
aforementioned results in their articles. Deacon (2007) looks at a broader spectrum of
public goods, and finds that democracy affects many of them positively. Kosack (2009)
creates a new model where it is not democracy that directly affects education enrollment,
but rather the benefit that such policies have on the leader in terms of greater political
power and economic growth. Although the presentation of the argument is different, his
empirical analysis yields similar results. Finally, Chen (2008) runs a case study on East
Asia, and finds that democracy increases education expenditure and enrollment.
In this paper, I seek to build off of these papers but modify their methodology so
as to answer a different question. Similar to Stasavage (2005), the model in this paper
considers a rural region and an urban region; however, I further allow the regions to
contain both elite and commoners. I differentiate the groups based on their demand for
education as well as their political influence. Furthermore, I allow for variation in the
proportion of the population that lives in rural and urban regions. One of Stasavage’s
(2005) assumptions is that the public can view government expenditure. This assumption
is likely not valid. In the case of poor countries, the rural population likely does not have
the necessary skills to gather or interpret this information; furthermore, in non-democratic
countries this information is likely not available. In this regard, using access to education
as their deciding factor for political support is more realistic. This would manifest itself
in enrollment rates, which allows for a regression specification that is more similar to
Lake and Baum’s (2001). Similar to Ansell (2006) I will be using the polity index as my
measure of democracy. This is due to its accessibility as well as it being the generally
accepted measure in this field.
The following model takes strengths from each of these papers, and combines
them to make a new model, which better explains how the type of government affects the
amount of education provided. Like the above papers, the model only addresses the
supply side of education, taking the demand side as given.
4
2.
Model
2.1
Structure of Society
Consider two societies that differ only in their political systems; one is a
democracy and the other is not. Each society contains two regions, a rural region (j=r)
and an urban region (j=u). The proportion of the population that lives in one region
versus the other varies, and each region has a different distribution of individuals’
demand for education. To account for this, the proportion of the population that lives in
the urban region is represented by 𝛽 while the proportion that live in the rural region is
represented by 1 − 𝛽. Each individual (i) in each region has a demand for education 𝜃!,! .
I assume that individual’s demand for education is uniformly distributed between 0 and
𝜃!! for each region. I further assume that the upper limit is different between the two
regions such that 𝜃!! < 𝜃!! . The intuition behind this assumption is that individuals in the
rural population are predominantly agriculture based, and their maximum demand for
education is lower than those who live in the urban region, who are predominantly
manufacturing and service based, which requires more education. This demand for
education can be satisfied with a combination of two levels of education (e); primary (p)
and post-primary (s) schooling. There is a threshold value of 𝜃 = 𝜃 !→! such that people
who have a 𝜃!,! < 𝜃 !→! only demand primary education while those who have
𝜃!,! ≥ 𝜃 !→! demand both primary and post-primary education. I also assume that
𝐸[𝜃!,! ] < 𝜃 !→! < 𝐸[𝜃!,! ]. This assumption states that the average rural person demands
primary education while the average urban person demands both primary and postprimary education. Furthermore, there are elite in both the urban and rural regions. These
elite are the political, military, and business leaders who have political influence
regardless of the political system. The proportion of the elite in the urban region is 𝜀
while the proportion of the elite living in the rural region is 1 − 𝜀. Since these elite are
major leaders, I assume that they live predominantly in the urban region, and therefore
𝜀 > 0.5. Furthermore, these elite demand both primary and post-primary education. A
graphical representation of the above information can be found in Appendix A, Figure 1.
5
The government (g) can choose whether to provide access to education in each
region for both primary and post-primary education separately. I represent this access
with an indicator variable, 𝑎!! , that equals 1 if access is provided, and 0 if not. Access can
be interpreted as the government providing a specific level of schooling in a specific
region at a price that is affordable to all individuals in that region. However, providing
this access is costly to the government, which I represent with a cost function,
𝑐(𝑎!! , 𝑎!! , 𝑎!! , 𝑎!! ) where 𝜕𝑐(𝑎!! ) 𝜕𝑎!! > 0. Subtracting this cost from the benefit the
government receives (E), the government receives utility, 𝑈! = 𝐸 − 𝑐(𝑎!! , 𝑎!! , 𝑎!! , 𝑎!! ), if
the government is in power, and utility, 𝑈! = 0, if it is not in power. Since governments
prefer to be in power, I assume that 𝐸 − 𝑐(𝑎!! = 1, 𝑎!! = 1, 𝑎!! = 1, 𝑎!! = 1) > 0 .
Depending on which political system is present, the government can only get in power if
enough individuals support it. All individuals who’s 𝜃!,! the government satisfies support
the government while all those who’s 𝜃!,! they don’t satisfy, do not.
2.2
Democracy
In a democracy, both the commoners and the elite have power in the form of a vote. This
leads to the following events:
1. The government chooses an education access policy (𝑎!! , 𝑎!! , 𝑎!! , 𝑎!! ).
2. An election is held. Individuals who support the government vote for it, and those
who do not support the government, do not vote for it. The outcome of the
election is determined by whether the median voter supports the government.
3. Regardless of the outcome of the vote, the elite decide whether to revolt and
overturn the government depending on whether their needs are satisfied. Whether
this revolt occurs depends on whether the median elite supports the government.
4. Government provides (𝑎!! , 𝑎!! , 𝑎!! , 𝑎!! ) if it succeeds past step 2 and 3.
Solving this model for the government’s optimal access policy, two possible
solutions occur; one for the case where 𝛽 > 0.5, and one for the case where 𝛽 < 0.5.
6
From event 3, we see that the optimal policy must satisfy the urban elite demand for
education (since the median elite is in the urban region). This leads to an access policy
which includes 𝑎!! = 1 and 𝑎!! = 1. By providing access to both primary and secondary
education in the urban region, the government gets support from all of the urban voters.
This means that the government is supported by 𝛽 of the population. If there are more
people in the urban region than there are in the rural region (𝛽 > 0.5), the government
will receive the median voters vote; therefore, the government does not need to provide
any access to education in the rural region. This means that the government’s optimal
access policy is (𝑎!! = 0, 𝑎!! = 0, 𝑎!! = 1, 𝑎!! = 1).
However, if there are more people in the rural region (𝛽 < 0.5), the government
will need to gain support from some people in the rural region to receive the median
voters vote. Let 𝜃!∗ be the median voters demand for education. The optimal access
policy will satisfy the following equation:
!!∗
𝛽+
!
1−𝛽
1
𝑑𝜃
=
, 0 ≤ 𝛽 < 0.5
𝜃!!
2
or
𝜃!∗ = 1 −
1
𝜃 ! , 0 ≤ 𝛽 < 0.5
2(1 − 𝛽) !
Since, over the domain 0 ≤ 𝛽 < 0.5 , 𝜃!∗ (𝛽, 𝜃!! ) is continuous in 𝛽 and
𝜕𝜃!∗ 𝜕𝛽 < 0 , the range of 𝜃!∗ is 0 < 𝜃!∗ ≤ 0.5𝜃!! . By the stated assumption that
0.5𝜃!! = 𝐸[𝜃!,! ] < 𝜃 !→! < 𝐸[𝜃!,! ], and the fact that the median and the mean are equal
in uniform distributions, the optimal access policy is (𝑎!! = 1, 𝑎!! = 0, 𝑎!! = 1, 𝑎!! = 1).
Restating the above two cases as one solution, the optimal education access policy
under a democratic system, dependent upon the distribution of the population, is:
!
𝑂𝑝𝑡𝑖𝑚𝑎𝑙 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 𝑎𝑐𝑐𝑒𝑠𝑠 =
!
(𝑎! = 1, 𝑎!! = 0, 𝑎! = 1, 𝑎!! = 1),
!
!
(𝑎! = 0, 𝑎!! = 0, 𝑎! = 1, 𝑎!! = 1),
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0 ≤ 𝛽 < 0.5
0.5 ≤ 𝛽 ≤ 1
2.3
Non-Democracy
In a non-democracy, the commoners (non-elite) have no political power and
therefore event 2 in the democratic situation is not necessary. This leads to the following
events occurring in a non-democracy:
1. The government chooses an education access policy (𝑎!! , 𝑎!! , 𝑎!! , 𝑎!! ).
2. The elite decide whether to revolt and overturn the government depending on
whether their needs are satisfied. Whether this revolt occurs depends on whether
the median elite supports the government.
3. Government provides (𝑎!! , 𝑎!! , 𝑎!! , 𝑎!! ) if it succeeds past step 2.
From the above events, the optimal government access policy must only satisfy
the median elite’s requirements. Therefore, (𝑎!! = 0, 𝑎!! = 0, 𝑎!! = 1, 𝑎!! = 1) is the
government’s optimal education access policy. Since the commoners have no power, and
the median elite is in the urban region, the government has no incentive to provide
education access to anyone in the rural region, regardless of the distribution of population
between the rural and urban regions.
2.4
Hypotheses
I pose 3 hypotheses based on the findings from the above model:
1. A change from a non-democratic system to a democratic system will have no
effect on post-primary access and therefore enrollment rates.
This hypothesis is drawn from the changes in the 𝑎!! variables when a government
moves from a non-democracy to a democracy. With regards to urban access to postprimary education, 𝑎!! , the government must provide access to stay in power under both
systems of government. Furthermore, the government will not offer access to postprimary education in the rural region regardless of the form of government. From this, I
conclude that, with an increase in democracy, access to post-primary education does not
change and therefore enrollment rates do not change.
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2. A change from a non-democratic system to a democratic system will lead to more
primary school access and therefore higher primary enrollment rates.
This hypothesis is the result of the difference in the rural access to primary
education variable, 𝑎!! , between non-democratic and democratic governments with
countries that have a predominantly rural population. Since most of my sample is
composed of predominantly rural countries, an increase in democracy should lead to an
increase in primary education enrollment rates.
3. The effect an increase in democracy has on primary education enrollment will be
greater in predominantly rural countries than in predominantly urban countries.
I propose the third hypothesis based on the two different outcomes that occur in
democracies, depending on the percentage of the population that is rural. Since the
optimal education access policy in a democracy is dependent upon the percentage of the
population that is rural, a positive effect on the primary enrollment rate should only be
observed for countries with predominantly rural populations. There should be no
significant difference in primary enrollment rates between in countries with
predominantly urban populations.
3.
Data
3.1
Sample
My sample includes all the African countries between 1990-2008. The full list of
countries is available in Appendix C. Africa is the natural choice to test my hypotheses
due to the high variability in democracy both between countries and over time, and the
variability of educational enrollment between countries and over time. Furthermore, by
using such an important sample with regards to development, the need for external
validity is not as strong as that if I used a different, less prominent sample.
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3.1
Dependent Variables
In this paper, my dependent variables are net enrollment in primary school, net
enrollment in secondary school, and gross enrollment in tertiary schools (including
universities and technical colleges) from the World Development Indicators database by
The World Bank (2011). Net enrollment is defined as the quotient of the number of
students enrolled in school who are school aged and the total number of school aged
people. Gross enrollment is defined as the quotient of the number of students enrolled in
school and the number of school aged people. Gross enrollment is used for tertiary school
because there is not a defined age limit to tertiary education, and therefore identifying
whether the students are of an appropriate age is difficult. Since the denominator of the
fraction is the school aged population, and the limits to this population are hard to
identify, I must assume that there is more measurement error in the measure of gross
tertiary school enrollment than in the primary and secondary school enrollment measures.
3.2
Explanatory Variable
My explanatory variable is the Polity IV index created by the Polity IV Project
(2011). This index has a range from -10 to 10; however, I standardized the index so the
regression coefficients represent the change if the polity index increases 1 standard
deviation. This allows for better interpretation of my results. I further create a second
explanatory variable that is a democracy dummy variable. This democracy dummy has a
value of 1 when a country has received a Polity IV index value of 6 or greater, otherwise
it is 0. The value of 6 was chosen because this is the threshold value for democracy
recommended by the Polity IV project (2011) authors.
3.3
Control Variables
I use the following control variables in my empirical analysis: GDP per capita
(PPP, 2008 USD), net official foreign aid and official foreign assistance received, rural
population as a percentage of total population, and a variable called “New Regime” that
designates whether a regime is 3 or fewer years old. The GDP per capita, official foreign
aid and foreign assistance, and rural population percentage are from the World
10
Development Indicators database (The World Bank, 2011). The new regime variable is
created from the measure of durability in the Polity IV (2011) dataset. The durability
measure in the Polity IV dataset is the number of years since a regime change. The “new
regime” dummy is equal to 1 when the durability measure is less than or equal to 3, and
equal to 0 otherwise.
3.4
Omitted Data Points
There are two main causes for omitted data points; missing data, and conflict
indicators in the polity index. Unfortunately, due to data limitations, data is not available
for all countries and over all years in the sample. In instances where there is a missing
data field, I omit the data point; however, since all the regressions contain country fixed
effects, differences in the sample due to a country systematically not recording data
would show up in the country fixed effect. The second instance when I omit data points is
when the polity index indicates that the country is in a domestic conflict such as a civil
war or other internal conflicts. During these periods, education enrollment rates should be
systematically lower and measurement error should be more prevalent. By omitting these
data points, I reduce the potential for bias due to this.
4.
Methodology
To test my 3 hypotheses I run 16 regressions; 12 regressions to test hypotheses 1
and 2, and 4 regressions to test hypothesis 3. Section 4.1 contains the methodology for
Hypothesis 1 and 2, while Section 4.2 contains the methodology for Hypothesis 3.
4.1
Hypothesis 1 and 2
To test hypotheses 1 and 2, I run 12 regressions, 4 regressions for each level of
education on the full dataset. In regressions (1) and (2) I use the polity index for the
country as the explanatory variable. This index allows for a higher level of variation in
the level of democracy than the democracy dummy. To test whether these results hold
true with greater measurement error, I replace the polity index with the democracy
dummy for regressions (3) and (4). In all four regressions, I lag both of these two
11
variables two years. I chose a two-year lag because it leads to the best fit for the data
(highest R2). The R2 for regressions (1) and (2) using lags from 0 to 5 years can be seen
can be seen in Appendix B, Table 2.
For all 12 regressions, I control for the natural logarithm of GDP, natural
logarithm of foreign aid, and the percentage of the population who are rural. These
control variables vary over time and affect education enrollment and, as such, need to be
included. GDP is included because countries with higher GDPs may have higher skilled
industries that require more education to support; thus leading to a higher natural amount
of education in the country. Foreign aid is included because governments may choose
education policies that lead to receiving higher levels of foreign aid; specifically,
providing more access to education and therefore become eligible to receive more foreign
aid. Such a policy is not chosen on the basis of being democratic but rather on receiving
as much foreign aid as possible. This effect is not an effect I am interested in and
therefore I control for it. I control for the percentage of the population that is living in
rural areas because rural regions tend to focus on agriculture, which has a lower
education requirement than urban occupations. This variable is included to make
countries with different levels of urbanization comparable. In addition, I control for
whether the regime is 3 years old or less, as well as an interaction term between being
having a new regime and the level of democracy. This interaction term is included
because the effect a new democratic regime has on the decision to attend school may be
different from the effect a new autocratic regime would have on such a decision.
In regression (1) and (3) I use only a country fixed effect. This specification is
chosen because there is no reason to observe a trend value for either education enrollment
or democracy. Absorbing the changes in enrollment over time with a time fixed-effect
variable forces democracy to explain the variation in excess of this trend. I am arguing
that this trend is due to changes in democracy, and therefore should not be included.
However, to further test for robustness, I include both a time and country fixed-effect
variables in regressions (2) and (4).
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In the following regressions I have used the following labels: the polity index is
P, democracy dummy is D, whether the regime is new is N, GDP is Y, foreign aid is A,
and the percentage of the population that is rural is R. With this, my regressions
specifications are:
1. 𝐸𝑛!" = 𝛼! + 𝛽! 𝑃!,!!! + 𝛽! 𝑃!,!!! ∗ 𝑁!,!!! + 𝛽! 𝑁!,!!! + 𝛽! 𝑙𝑛(𝑌!,! ) + 𝛽! 𝑙𝑛(𝐴!,! ) +
𝛽! 𝑅!,! + 𝜀!,!
2. 𝐸𝑛!" = 𝛼! + 𝛾! + 𝛽! 𝑃!,!!! + 𝛽! 𝑃!,!!! ∗ 𝑁!,!!! + 𝛽! 𝑁!,!!! + 𝛽! 𝑙𝑛(𝑌!,! ) +
𝛽! 𝑙𝑛(𝐴!,! ) + 𝛽! 𝑅!,! + 𝜀!,!
3. 𝐸𝑛!" = 𝛼! + 𝛽! 𝐷!,!!! + 𝛽! 𝐷!,!!! ∗ 𝑁!,!!! + 𝛽! 𝑁!,!!! + 𝛽! 𝑙𝑛(𝑌!,! ) + 𝛽! 𝑙𝑛(𝐴!,! ) +
𝛽! 𝑅!,! + 𝜀!,!
4. 𝐸𝑛!" = 𝛼! + 𝛾! + 𝛽! 𝐷!,!!! + 𝛽! 𝐷!,!!! ∗ 𝑁!,!!! + 𝛽! 𝑁!,!!! + 𝛽! 𝑙𝑛(𝑌!,! ) +
𝛽! 𝑙𝑛(𝐴!,! ) + 𝛽! 𝑅!,! + 𝜀!,!
These regressions are repeated for net primary education enrollment, net
secondary education enrollment, and gross tertiary education enrollment for a total of 12
regressions.
4.2
Hypothesis 3
To test Hypothesis 3, I create two different data sets from my original dataset; one
that contains countries with predominantly rural populations and one that contains
countries with predominantly urban populations. The rural dataset is composed of data
points where the percentage of the population that is rural is greater than 50%. The urban
dataset contains all the remaining data points. I then run regressions (1) and (2) on each
data set with primary enrollment as the dependent variable. The specifications remain
identical to those used in section 4.1. I omit regressions (3) and (4) because the datasets
are much smaller. Since with this reduced size, increasing the measurement error will
further understate the coefficients, leading to a large amount of bias.
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5.
Results
5.1
Descriptive Statistics
The descriptive statistics for my full dataset are presented in Appendix B, Table 1.
The mean and median for primary enrollment are 69.17% and 71.51%, which indicates
that primary enrollment is negatively skewed. This is apparent due to a minimum value
of 21.24 and a maximum value of 99.60. Furthermore, a standard deviation of 20.66
signifies that there is a high level of variability in this measure. The means for secondary
and tertiary enrollment are 28.8% and 5.67% respectively. Despite the low means,
standard deviations of 18.77% and 7.94% indicate that there is sufficient variation to test
my hypotheses. The skews for secondary enrollment and tertiary enrollment are opposite
that of primary enrollment, with means greater than their medians. The Polity IV index is
standardized with a maximum value of 1.815 and a minimum value of -1.665. The
democracy dummy indicates that 35 percent of my data points are democratic, which is a
sufficient amount of variation to make the results of those regressions meaningful.
Finally, the mean percentage of rural population is 62.67% with a median of 63.66%.
This high mean value indicates that the majority of my data points have predominantly
rural populations. From the results of the model, this means that changes in democracy
should have an effect on primary education enrollment for the majority of the data points.
A maximum value of 94.60% and minimum value of 12.70% show that there is a high
level of variation in this sample, thus leading to more valid results.
The descriptive statistics for the reduced data sets are presented in Appendix B,
Tables 3 and 4. Variations in the democracy measures by country for the predominantly
urban data set are presented in Appendix B, Table 5. Table 3 indicates that the countries
in the rural dataset are more democratic, have lower primary enrollment rates, and
significantly lower secondary and tertiary enrollment rates than countries in the urban
dataset. They also have a lower average GDP. Table 4 contains descriptive statistics for
the urban dataset. One notable observation is that the distributions of education
enrollment are very negatively skewed. This is likely due to the small sample size. This
leads to a major issue with the urban dataset; there is not much variation in the level of
14
democracy. Table 5 contains all the countries in the urban dataset, the standard deviations
of their democracy variables, and the number of data points present. Of the 12 countries
in the dataset, only 4 have a standard deviation greater than 0.2 for their polity index.
This low level of variation in the data will cause the regression results to be very
inaccurate and therefore conclusions cannot be drawn when using this dataset.
5.2
Panel Regression Results
The regression results for the specifications proposed to test my first two
hypotheses (in section 4.1) are located in Appendix B, Tables 6 and 7.
From regressions (1), (3), and (5) in Table 6, a 1 standard deviation increase in the
polity IV index is associated with a 4.98% increase in primary education enrollment, no
significant increase in secondary education enrollment, and a 0.70% decrease in tertiary
education enrollment. These regressions explain between 33% and 43% of the variation
in the data. However, in regressions (2), (4), and (6), which include a time fixed effect
variable, a similar change yields an increase of 2.42% in primary enrollment, no
significant change in secondary enrollment, and a 1.29% decrease in tertiary enrollment.
However, the amount of variation explained by the model decreases to values between
11% and 14%. In both cases, the results support my first and second hypotheses as
primary education enrollment does increase with democracy, and secondary education
enrollment remains the same. The decrease in tertiary enrollment is not explained in my
theoretical model, however this is likely due to the budget constraint of the government
and the greater emphasis on primary education spending. If the government is required to
spend more on primary education, and also spend on non-education related programs,
tertiary education expenditure will likely decrease, and therefore the number of seats in
tertiary institutions would decrease with it. Since my model does not take the government
budget constraint and expenditure patterns into account, this result is not observed. There
are two possible reasons for the change in the R2 between the two specifications. The first
possibility is that the time fixed-effect variable is absorbing most of the variation due to
an increase in education enrollment over time, despite the increase in democracy over
time. The second possibility is that there is an omitted variable that is increasing
15
enrollment over time and if we exclude a time-fixed effect, we are wrongly attributing
this change to changes in democracy. The first possibility occurs because both democracy
and education enrollment is increasing over time. By including a time fixed-effect, we are
forcing changes in democracy to explain the variation in excess of the trend; however
changes in democracy may be the cause of the trend itself. In this case, if we exclude the
time fixed-effect we will get a less biased measure of the effect. The second possibility is
that an omitted variable that is relatively constant across all countries is increasing
enrollment over time, by including a time fixed-effect, we are allowing it to absorb an
appropriate amount of variation, leading to the coefficient on the democracy variable to
be less biased than without it. In this case, the numerical figures in the regressions with
the time fixed-effect are less biased. Due to these conflicting possibilities, the numerical
values of the regression coefficients cannot be assumed to be the correct values; however,
since in both cases the results maintain their signs and significance, I can conclude that
democracy does have a significant effect on education enrollment rates.
The results in Appendix B Table 7 show that, in the regressions using the
democracy dummy, a change from a non-democratic system to a democratic system leads
to a 9.38% (or 5.32% with a time-fixed effect) in primary education enrollment, no
significant change in secondary education enrollment, and a 1.56% (2.05%) decrease in
tertiary education enrollment. Despite the increase in measurement error, the sign and
significance of the coefficients remain, further supporting the results in Table 6. The
same possible explanation behind the effect the time fixed-effect has had on the results
remains from the previous regressions; however, the fact that the coefficients have all
retained their sign and significance indicates that changes in democracy have an effect on
education enrollment. From these results, I fail to reject both hypotheses 1 and 2. This is
true regardless of whether I am using a country fixed-effect or both country and time
fixed-effects.
The results from the regressions proposed in section 4.2 are in Table 8. From
regressions (1), we can see that an increase in the level of democracy leads to a positive
and significant change in primary enrollment. However, when a time fixed-effect variable
is included, this relationship disappears. The reasons for this are the same as those for the
16
results in Tables 6 and 7. The results in regression (1) are more valid than those in
regression (2) for two reasons; theoretically there should be no trend in education
enrollment and therefore a time fixed-effect variable is not necessary, and the R2 of the
regressions drops significantly when the time fixed-effect is included. Furthermore,
despite the coefficients in regression (2) being insignificant, the sign remains the same. In
regressions (3) and (4) there are no significant results. This seemingly validates my third
hypothesis; however, this is not true. As stated in section 5.1, the urban dataset is too
small and does not contain enough variation in the democracy measures to draw
conclusions. This is apparent in the signs of the coefficient, which alternate for the
interaction term, the new variable, and the rural variable. In the case of the rural variable,
both coefficients are significant at the 10% and 5% level, however the signs are opposite.
From this, I conclude that the regressions (3) and (4) do not provide adequate evidence
that there is no significant effect between democracy and primary education enrollment in
predominantly urban countries. Therefore, I am unable to test hypothesis 3 adequately
with this dataset, and as such, the results in Table 8 are inconclusive.
5.3
Robustness
There are three potential causes for bias in my results; reverse causality,
measurement error, and endogeneity. By calculating the optimal lag time in Appendix B,
Table 2, the data indicates that reverse causality is likely not an issue. This is because the
data is best fit with a 2-year lag between changes in democracy and changes in
enrollment. The second best fit is for a lag of 3 years. The regressions with a country
fixed-effect as well as those with both time and country fixed-effects support this. These
results support the assumption that changes in democracy cause changes in enrollment.
The Polity IV index is created by academics on a subjective basis. This means
that the indicator is soft and therefore contains measurement error. This measurement
error causes my results in Appendix B, Tables 6, 7, and 8 to be understated; however,
since the most important aspects of these results are the signs and significance, and they
are constant throughout all estimations for the full dataset, measurement error is not an
issue. Furthermore, despite injecting more measurement error by creating the democracy
17
dummy, the signs and significance of my variables remains the same. This further
validates that these results are not affected by measurement error.
Endogeneity is an issue that would cause the results to be biased. There are two
sources of endogeneity possible in this analysis; endogeneity due to historical factors that
do not vary over time, and endogeneity due to factors occurring during the period
analyzed. The first form of endogeneity is not an issue since it would not vary over time.
This means that the omitted endogenous variation in the polity index would be accounted
for in the country fixed effect variable. The second form of endogeneity is not accounted
for in my paper. In other literature, Ansell (2006) uses two instruments to address this
issue; the average regional polity index, and a 5 year lagged term of the polity index.
Unfortunately, the lack of strength both these instruments have when using this sample
makes them unsuitable for this application. However, the results Ansell (2006) finds,
when estimating the effect democracy (using the polity index) has on public school
expenditure, do not vary much in magnitude or significance between the IV regression
and the regular fixed-effect regressions. This indicates that the endogenous portion of the
polity index does not cause much bias when analyzing education indicators. This issue is
not exclusive to this paper; Baum & Lake (2003) and Chen (2008) indicate that they were
unable to find a suitable instrument for their polity index.
6.
Conclusion
In this paper, I provide a theoretical framework that helps explain the relationship
between the type of government and access to education. From this I propose 3
Hypotheses: that post-primary education enrollment will remain constant with changes in
governance; that primary education enrollment will increase with an increase in
democracy; and that the effect democracy has on primary education enrollment will be
greater in predominantly rural countries than in predominantly urban countries. I test
these hypotheses using and empirical analysis of African nations between 1990 and 2008.
Using a fixed-effects model I find evidence that supports the hypotheses 1 and 2 and this
evidence holds up to my robustness checks. Due these results, I fail to reject the first two
hypotheses. Finally, due to limitations of my dataset, I am unable to draw a conclusion on
18
the third hypothesis. Further empirical analysis, with an expanded dataset or using a
different time period, is required to test hypothesis 3. Despite this, the results from this
paper show that the type of government does have an effect on access to education.
Therefore, the type of government should be considered when creating policies that seek
to affect access to education in developing countries.
19
7.
References
Stasavage, D. (2005) Democracy and Education Spending: Has Africa’s Move to
Multiparty Elections Made a Difference for Policy? American Journal of Political
Science, (Vol 49. No. 2)
Ansell, B. W. (2006) Traders, Teachers, and Tyrants: Democracy, Globalization, and
Public investment in Education, Weatherhead Center for International AffairsWorking Paper Series, (06-01)
Lake D. A., Baum M. A. (2001) The Invisible Hand of Democracy: Political Control and
the Provision of Public Services, Comparative Political Studies (24:587)
Lake D. A., Baum M. A. (2003) The Political Economy of Growth: Democracy and
Human Capital, American Journal of Political Science (Vol 47. No 2.)
Deacon R. T. (2007) Public Good Provision under Dictatorship and Democracy. Public
Choice, (139: 241-262)
Kosack S. (2009) Realizing Education for All: Defining and Using the Political Will to
Invest in Primary Education, Comparative Education (45:4)
Chen J. (2008) Democratization and Government Education Provision in East Asia,
Journal of East Asian Studies (8)
The World Bank (2011) World Development Indicators, accessed February 2011 at
http://data.worldbank.org/
Polity IV Project (2011) Global Report 2009, accessed February 2011 at
http://www.systemicpeace.org/polity/polity4.htm
20
8.
Appendices
8.1
Appendix A
Figure 1: Demand for Education
21
8.2
Appendix B
Table 1: Descriptive Statistics for Full Data Set
Variable
Mean
Median
Std. Dev.
Min.
Max.
Primary
Enrollment
69.17
71.51
20.66
21.24
99.60
Secondary
Enrollment
28.80
24.97
18.77
2.55
80.10
Tertiary
Enrollment
5.67
2.903
7.94
0.00
55.743
Polity IV
0.00
-0.27
1.00
-1.665
1.815
Democracy
Dummy
0.35
~
~
~
~
ln(GDP)
7.41
7.10
0.96
5.72
10.35
ln(Foreign Aid)
19.63
19.84
1.24
15.28
23.28
Rural
Population
62.67
63.66
17.65
12.70
94.60
New Government
0.31
~
~
~
~
Table 2: R2 for Regressions (1) and (2) for lags of 0 to 5 time years
Lag Primary
0
0.388
1
0.416
2
0.424
3
0.408
4
0.356
5
0.394
Country Fixed Effect
Secondary Tertiary
0.447
0.313
0.432
0.303
0.426
0.339
0.417
0.336
0.391
0.324
0.426
0.311
Sum
1.148
1.151
1.189
1.161
1.071
1.131
22
Time and Country Fixed Effect
Primary Secondary Tertiary Sum
0.062
0.112
0.162
0.336
0.094
0.116
0.136
0.346
0.112
0.120
0.147
0.379
0.114
0.101
0.136
0.351
0.067
0.096
0.118
0.281
0.083
0.041
0.098
0.222
Table 3: Descriptive Statistics for Predominantly Rural Countries
Variable
Mean
Median
Std. Dev.
Min.
Max.
Primary
Enrollment
66.88
69.29
19.29
21.24
99.60
Secondary
Enrollment
24.69
22.50
15.85
2.55
80.10
Tertiary
Enrollment
3.77
2.41
5.00
0.29
35.18
Polity IV
0.06
-0.10
1.01
-1.66
1.81
Democracy
Dummy
0.39
~
~
~
~
ln(GDP)
7.14
6.95
0.78
5.83
10.35
ln(Foreign Aid)
19.78
19.95
1.14
16.74
23.28
Rural
Population
69.95
69.38
10.70
50.10
94.60
New Government
0.34
~
~
~
~
23
Table 4: Descriptive Statistics for Predominantly Urban Countries
Variable
Mean
Median
Std. Dev.
Min.
Max.
Primary
Enrollment
76.89
87.46
23.20
26.54
98.66
Secondary
Enrollment
49.75
60.73
18.71
13.98
71.85
Tertiary
Enrollment
12.58
10.75
11.91
0.00
55.74
Polity IV
-0.21
-0.62
0.93
-1.32
1.64
Democracy
Dummy
0.19
~
~
~
~
ln(GDP)
8.39
8.64
0.94
5.72
9.65
ln(Foreign Aid)
19.07
19.31
1.43
15.28
21.44
Rural
Population
36.05
40.54
11.37
12.70
50.00
New Government
0.19
~
~
~
~
Table 5: Democracy Variation by Country for Predominantly Urban Countries
Country
Algeria
Angola
Botswana
Cameroon
Djibouti
Gabon
The Gambia
Liberia
Libya
Morocco
South Africa
Tunisia
Polity
Std. Dev.
0.48
0.07
0
0
0.80
0
0
0.39
0
0.08
0.23
0.13
Democracy Dummy
Std. Dev
0
0
0
0
0
0
0
0.38
0
0
0
0
24
n
19
8
12
8
19
18
8
9
19
16
17
19
Table 6: Polity Index Panel Regression Results
Education Enrollment by Type
Variables
Primary
Secondary
(3)
(4)
(1)
(2)
Polity
(t-2)
4.98***
(0.09)
2.42**
(0.97)
0.26
(0.95)
Polity*New
(t-2)
-2.36**
(1.12)
-1.34
(1.02)
New
(t-2)
-4.52***
(1.13)
ln(GDP)
Tertiary
(5)
(6)
-0.66
(1.01)
-0.70**
(0.35)
-1.29***
(0.35)
1.46*
(0.88)
1.89**
(0.87)
1.00***
(0.38)
1.51***
(0.39)
-2.76***
(1.04)
-4.75***
(0.89)
-3.65***
(0.87)
-0.33
(0.38)
-0.39
(0.38)
5.65*
(3.04)
-5.31*
(2.98)
13.66***
(4.3)
6.46
(4.26)
10.9***
(1.34)
7.25***
(1.50)
ln(Aid)
5.15***
(1.11)
4.44***
(1.03)
-1.29
(0.79)
-1.88**
(0.77)
0.75**
(0.37)
0.62
(0.39)
Rural
-1.53***
(0.20)
0.19
(0.26)
-0.55**
(0.24)
0.21
(0.29)
-0.20**
(0.09)
0.11
(0.11)
Fixed
Effect
Country
Country
Time
Country
Country
Time
Country
Country
Time
R2
0.42
0.11
0.43
0.12
0.33
0.14
N
397
397
223
223
365
365
Standard errors are in parenthesis (*=p<0.1; **=p<0.05; ***=p<0.01)
25
Table 7: Democracy Dummy Panel Regression Results
Education Enrollment by Type
Variables
Primary
Secondary
(3)
(4)
(1)
(2)
Democracy
(t-2)
9.38***
(2.10)
5.32***
(1.93)
2.05
(1.63)
Democ.*New
(t-2)
-2.85
(2.00)
-0.38
(1.83)
New Regime
(t-2)
-3.31**
(1.32)
ln(GDP)
Tertiary
(5)
(6)
0.72
(1.60)
-1.56**
(0.72)
-2.05***
(0.73)
1.69
(1.43)
2.21
(1.41)
1.24*
(0.70)
1.91***
(0.70)
-2.77**
(1.20)
-4.84***
(1.01)
-3.90***
(1.01)
-0.71
(0.45)
-1.05**
(0.45)
5.78*
(3.04)
-5.58*
(2.96)
12.53***
(4.18)
5.58
(4.22)
10.79***
(1.34)
7.48***
(1.52)
ln(Aid)
4.79***
(1.12)
4.18***
(1.04)
-1.40*
(0.79)
-1.99**
(0.77)
0.79**
(0.37)
0.66*
(0.39)
Rural
-1.69***
(0.19)
0.17
(0.26)
-0.52**
(0.23)
0.17
(0.29)
-0.18**
(0.08)
0.13
(0.11)
Fixed Effect
Country
Country
Time
Country
Country
Time
Country
Country
Time
R2
0.43
0.12
0.44
0.13
0.33
0.12
N
397
397
223
223
365
365
Standard errors are in parenthesis (*=p<0.1; **=p<0.05; ***=p<0.01)
26
Table 8: Reduced Sample Panel Regression Results
Primary Enrollment
Variables
Rural
Urban
(1)
(2)
(3)
(4)
Polity
(t-2)
3.72***
(1.36)
1.63
(1.22)
3.28
(2.21)
0.58
(2.76)
Polity*New
(t-2)
-0.80
(1.44)
-0.43
(1.31)
-0.01
(1.68)
2.62
(2.58)
New
(t-2)
-5.49***
(1.31)
-3.10**
(1.23)
0.26
(1.84)
-0.61
(2.08)
ln(GDP)
5.04
(3.54)
-5.73*
(3.41)
22.58***
(6.70)
3.95
(10.73)
ln(Aid)
5.28***
(1.30)
4.89***
(1.18)
-0.88
(1.95)
-1.06
(2.24)
Rural
-2.47***
(0.28)
-0.82**
(0.33)
-0.15**
(0.37)
2.17*
(1.15)
Fixed Effect
Country
Country
Time
Country
Country
Time
R2
0.50
0.15
0.33
0.12
N
301
301
78
78
Standard errors are in parenthesis (*=p<0.1; **=p<0.05; ***=p<0.01)
27
8.3
Appendix C
Sample Countries
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Rep.
Chad
Comoros
Cote d’Ivoire
Congo, Dem. Rep.
Congo, Rep.
Djibouti
Egypt
Equatorial Guinea
Eritrea
Ethiopia
Gabon
The Gambia
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Libya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Morocco
Mozambique
Namibia
28
Niger
Nigeria
Rwanda
Sao Tome and Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Tunisia
Uganda
Zambia
Zimbabwe