Exploring the Impact of Democratic Capital on Prosperity Lisa L. Verdon*

SUMMARY

Capital accumulation has long been considered one of the driving forces behind economic

growth. The idea that democratic experience accumulates and depreciates like other forms of

capital is a relatively unexplored idea. Much like financial and physical capital, it has been

suggested that democracy is a necessary but insufficient condition for economic growth.

Building on the work of Persson and Tabellini (2006b), this paper constructs several alternative

calculations of democratic capital. These values of democratic capital are then compared to

GDP, as a measure of prosperity, and economic freedom for 161 countries. The results of this

analysis support the idea that democracy acts indirectly through economic freedom to enhance

prosperity. The causal relationships between democracy, economic freedom, and prosperity

appear to be self-perpetuating.

ABSTRACT

Capital accumulation is one of the driving forces behind economic growth. The idea that

democratic experience accumulates and depreciates like other forms of capital is a relatively

unexplored idea. Like other forms of capital, it has been suggested that democracy is a necessary

but insufficient condition for economic growth. This paper constructs several alternative

calculations of democratic capital. These values of democratic capital are then compared to

GDP per capita and economic freedom for 161 countries. The results support the idea that

democracy acts indirectly through economic freedom to enhance prosperity and the relationship

is self-perpetuating.

JEL CLASSIFICATIONS: E02, O43

* Assistant Professor of Economics, College of Wooster, 206 Morgan Hall, Wooster, OH 44691, 1 (330) 263-2216, lverdon@wooster.edu

I. INTRODUCTION

It is a standing tenant of growth theory that capital is an essential component. This capital comes

in many forms including physical, human, and financial capital. For any form of capital to grow

there must be continuous investment in excess of depreciation and other losses.

A relatively new idea is that democracy can be thought of as a form of capital. The idea of

democratic capital is that the more experience or history a country has with a democratic system,

the more likely that country is to develop and maintain the institutions of democracy. Although

democracy and free-markets are not synonymous, there is a very strong correlation between

these two ideas.

The purpose of this paper is to more clearly identify the impact of democratic capital on

prosperity. I begin by recreating a basic measure of democratic capital based on the work of

Persson and Tabellini (2006b). I then suggest and calculate alternative measures of democratic

capital. Using these measures of democratic capital, I measure the impact of democratic capital

on prosperity, measured as GDP per capita. Finally, I identify the direction of the relationship

between democratic capital, economic freedom, and GDP per capita.

II. LITERATURE REVIEW

The positive connection between democracy and capitalism has been believed for many years.

Almond (1991) provides a survey of economists’ views on this relationship. Among those

economists is Schumpeter who suggests that democracy is supportive of capitalism. The counter

argument by Marx that there will always be a tension between democracy and capitalism has

proven invalid, or at the very least, inconsequential. However, understanding and quantifying

this relationship is a matter that has only been addressed in the past few decades.

Helliwell’s (1994) analysis of the relationship between democracy and growth is the basis of

most of the literature in this line if inquiry. Helliwell looked at 125 countries from 1960 to 1985

and determined that income has a positive impact on democracy and that democracy has a

positive but indirect affect on growth through increases in education and investment. A multitude

of papers follow which suggest different measurements and/or model specifications. These

differences result in mixed results on the nature of the relationship between democracy and

growth.

There are many papers that identify a positive correlation between democracy and growth. Arat

(1988) identifies the positive correlation but determines that economic growth is not a sufficient

condition for democracy. Alesina and Perotti (1994) find that there is a strong positive

correlation between democracy, GDP per capita, and education. Alesina and Perotti then turn

their focus to the stability of political regimes rather than the structure, finding that instability

reduces growth regardless of the type of regime. However, Feng (1997) determines that

democracy reduces the probability of a regime change and thus indirectly promotes growth

through stability.

Taking a different approach, Burkhart and Lewis-Beck (1994) use Granger causality to

determine that growth Granger causes democracy but democracy does not Granger cause growth.

This causality relationship holds up even in the poorest countries. However, Heo and Tan (2001)

suggest that the relationship between democracy and growth could go either way and use

Granger causality on 32 individual, developing countries. Heo and Tan find that 34 percent show

growth Granger causing democracy, 31 percent show democracy Granger causing growth, 9

percent show a feedback loop, and 25 percent show no relationship.

Most recently, Persson and Tabellini have produced a series of working papers looking at the

relationship of democracy and growth. In their 2006 paper they look at the experience a country

has with democracy, identifying that experience as “democratic capital.” They use this idea of

democratic capital for a country and its neighbors to model the probability of a country

becoming democratic and the probability of staying democratic. They find a “dynamic, positive

feedback loop” between democratic capital and growth. They find that becoming a democracy

accelerates growth by 0.75 percentage point and that this affect is still positive and significant

when paired with market reforms. In their 2007 paper they suggest that previous measures of the

effects of democracy on growth have been significantly underestimated. They find that the

positive impact of democracy is up to 1.8 times greater than any previously reported estimates

and the negative impact of autocracy is at least 1.8 times greater than previous estimates.

III. MEASURES OF DEMOCRATIC CAPITAL

Quantifying the accumulation of democratic capital, or any other type of institutional capital, is

full of challenges. The most obvious challenge is accounting for periods of external control. I

approached this challenge as having several possibilities. The first possibility is that a nation is

formed after being a colony. The second possibility is that a nation is in existence but is occupied

and controlled by another country for a period of time. The third possibility is that a smaller

nation splits from a larger existing nation. Finally, there is the possibility that two or more

nations join together.

The case where a country splits from a larger country seems the easiest to handle. A country like

Bangladesh, that was part of Pakistan until 1972, shares a democratic and institutional history

with Pakistan. So when Bangladesh became its own country in 1972, it started with the same

democratic capital as Pakistan at that time. The opposite of this is where two or more countries

join together to form one country. This is the case for Germany that was once split between East

and West. For this situation, the current country shares both histories. For these cases I use the

simple average of the countries’ democratic capital during the period(s) of separation1. For

countries that were formerly colonies, there is evidence that the legal institutions tend to stick.

The same cannot be said for political institutions. For that reason, I calculate democratic capital

for former colonies starting from zero at the time they become an independent country.

The most interesting challenge is for countries that are occupied and controlled by another

country. This situation has occurred to many countries and on several occasions. The most

glaring examples are the countries of the former Soviet Union. Many of these countries were

independent and democratic before they were overtaken. During the period of foreign control

there are two possibilities regarding democracy. People either buy into or accept the new regime

or they resist and resent the change. It seems unlikely that democratic capital can grow in this

scenario but it is also not realistic to assume that the country takes on the democratic capital of

its oppressors. Therefore, for these situations the democratic capital accumulated before

occupation is simply depreciated while occupied.

1 It could be argued that a weighted average, based on population or some other measure of relative power, should be used. I use the simple average to avoid questions of determining relative power.

Table 1 – Measures of Democratic Capital

My calculations of democratic capital are based on the work of Persson and Tabellini. Persson

and Tabellini (2006a) identify two different types of democratic capital, domestic and foreign.

For the purposes of this paper, I concern myself only with the domestic measure of democratic

capital. To calculate democratic capital, Persson and Tabellini use the Polity II value to identify

if a country is democratic or autocratic. If a country has a positive Polity II value then it is

considered democratic. To calculate the democratic capital, countries receive a 1 each year they

are democratic, 0 otherwise, but depreciate only in years of autocracy. They estimate a

depreciation rate between 0.06 and 0.01. Like Persson and Tabellini, I will use the depreciation

rate of 0.06 for δ throughout the remainder of the paper.

The binary variable of democracy is represented in formula 1 as Dt. The stock of democratic

capital is represented as DCt-1. My calculations based on this method2, identified as Binary, are

reported for select countries in Table 1.

(1)

This calculation is a good place to start. A potential problem with this measure is that it only

depreciates democratic capital in years of autocracy. In contrast, when we look at other types of

capital accumulation, depreciation of the capital stock occurs every year. Beyond this technical

argument, there is an idea in the public choice literature that suggests people become complacent

in their valuation of democracy. That is, the longer they live in a democracy, the more they take

it for granted. Van Den Doel and Van Velthoven (1993) support this idea with theory and

evidence. In particular, they describe Olson’s model of participation in democracy as supporting

very low participation when policy is either very near optimal or very far from optimal. When

policy is near optimal, people believe things will continue to go well so their participation is

unnecessary. When policy is far from optimal, people believe their contributions will have no

impact so refrain from participation. With these arguments in mind I calculate a second measure

of democratic capital, identified as Binary Depreciated, based on formula 2.

(2)

Both of the measures produced to this point have been based on a binary value of whether a

country is democratic or autocratic in a given year. The Polity II values that this binary is

2 Because they are concerned with probabilities, Persson and Tabellini adjust their calculation to stay bounded between 0 and 1. My calculation does not impose this restriction.

calculated from can provide more information. The Polity II value ranges from negative ten to

positive ten. This scale provides more information (and potentially reduces measurement error)

in that two countries may be considered democratic because they have positive Polity II values

but one may have a score of 2 while the other has a score of 8, implying that the second is far

more democratic than the first.

Figure 1 – Comparison of Measures (Ghana)

If you believe that all autocracy has the same impact, it would be appropriate to only use the

positive values of Polity II. In this case, the annual measure of democracy is equal to the Polity II

value if it is positive and zero otherwise. Using these values or democracy in equation 2 provides

a measure referred to as Positive Polity II. Finally, using the full range of Polity II provides a

measure that allows for the varying degrees of both democracy and autocracy. Using the full

range of Polity II values for democracy in formula 2 provides the final measure of democratic

capital which I refer to as Polity II.

Comparing the alternative measures, it is clear that the binary measures provide very smooth

time paths. There is often little difference between the Binary and Binary Depreciated values as

shown in Figures 1 and 2. However the Positive Polity II and Polity II time paths show much

more variation and volatility. Additionally, there are obvious breaks in trajectory that can

generally be tied to major political events. This is an important distinction that the use of

different measurements makes inherent assumption about government stability which must be

recognized. If your view is that political institutions are generally stable over time, use of the

binary measures fits this assumption. If your view is that dramatic shocks to the political system

are important and far reaching, then the broader measures of Positive Polity II and Polity II are

more appropriate. It should be obvious at this point that whichever assumption is made regarding

political institutions will affect any subsequent analysis.

Figure 2 – Comparison of Measures (Chile)

IV. DATA ANALYSIS

My approach to the analysis involves three parts. The first part employs a graphical analysis

focusing on a few select countries. The second part uses regression analysis on the panel dataset.

The third and final part looks at causality with a series of Granger type tests. The data set is

based on the calculations of democratic capital described in the section three. The democratic

capital measures cover the period 1800 to 2004 for approximately 160 countries3. Addition data

includes economic freedom from the Fraser Institute’s Economic Freedom of the World covering

the period from 1975 to 2005 and GDP per capita from the World Bank for the same period. The

difference in scale of these variables makes the use of logs very appealing. However, the

democratic capital value based on the full Polity II scale ranges into negative values. Taking logs

of this variable cuts the sample size in half. For this reason, it is necessary to conduct some of the

analysis using raw values.

1. Graphic Analysis

For the graphic analysis, I compare five countries: China, Estonia, United States, Venezuela, and

Zimbabwe. These five countries represent a wide range of political histories and economic

prosperity. I first look at the Binary democratic capital versus the Polity II measure. This visual

inspection confirms the general accuracy of the measures as we see the US at the top and China

at the bottom. It is more interesting to note that Venezuela is above Estonia in both measures.

However, a closer look at the Polity II measure reveals Estonia to be on a steep upward path4

while Venezuela’s path is headed downward. If we revisit this data in 50 years and both

countries stay on their current trajectories, Estonia will have greater democratic capital than

Venezuela.

3 The Polity IV project reports values for all independent states with populations greater than 500,000. The number of countries changes when states split, merge, etc. 4 The democratic capital for Estonia and other former Soviet block countries start (or re-start) in the mid-1990’s as they established their independence. See section III for further explanation.

Table 2- Summary Statistics

Figure 3 – Binary Democratic Capital

Moving on to economic freedom, we see again that the US is at the top with Estonia close

behind, again showing a steep upward trend until recent years. An important difference

comparing economic freedom and democratic capital is that China is in the middle of the

economic freedom graph. This reflects the many steps that China has taken towards economic

liberation while holding to their politically autocratic traditions. It is also not surprising to find

Zimbabwe at the bottom. Zimbabwe’s trend is downward sloping in the Polity II democratic

capital. Zimbabwe’s economic freedom shows a similar pattern, though the downturn starts later.

This again supports the idea that there is a strong relationship between democratic capital and

economic freedom, though that relationship may be lagged.

Figure 4 – Polity II Democratic Capital

Figure 5 – Economic Freedom

Finally, I turn to GDP per capita. The picture here is not surprising either. The order of the

countries reflects the same order as economic freedom. The US and Estonia both have strong

upward sloping GDP per capita while Venezuela and Zimbabwe have little or no growth. This,

once again, demonstrates the strong relationship between economic freedom and prosperity, as

measured by GDP per capita.

Figure 6 – GDP per capita

2. Regression Analysis

The next step is an attempt to quantify the relationships between democratic capital, economic

freedom, and GDP per capita. I model the relationship of GDP per capita as dependent on lags of

itself, lags of economic freedom, and lags of democratic capital. I choose to use a fixed effects

model with in-panel AR(1) disturbances. The fixed effect absorbs the country specific

characteristics, which allows our results to be a general representation of this relationship.

Including the AR(1) component provides for a consistent estimator5. I consider up to five lags of

each variable. The best fitting model is the simple model that contains one lag of each variable,

represented in equation 3.

(3)

Although it is difficult to interpret the exact relationships between the variables, because of the

large variation in units, the relationships are positive and statistically significant with the

exception of democratic capital. The binary measure of democratic capital is the only version

that is statistically significant. This is a first hint that how democratic capital is measured will be

important to our results. One might challenge the use of OLS or the AR(1) disturbances. As

robustness check, I ran the same OLS regression without the AR(1) disturbances as well as GLS

with and without the AR(1) disturbances. There is little variation in the results of these alternate

methods.

To have a better understanding of the relationship, I rerun the regression using logged values.

Using the logged values results in all of the democratic capital measures being statistically

significant, though some are weakly significant and all are relatively small. From these results

we can see that GDP per capita is highly dependent on previous period GDP per capita values.

For economic freedom, we see that a 1 percent increase in economic freedom is associated with

an approximate increase in GDP per capita of 0.1 percent. The impact of a 1 percent increase in

democratic capital is less than a tenth of a percent.

5 The consistency of the fixed effects, AR(1) estimator holds in the face of heteroskedasticity under most assumptions. It is not necessarily efficient. (Wooldridge 2002)

Table 3 – Estimates of Equation 3

Another approach to this type of problem is to use an Arellano-Bond estimation. In this method,

past realizations of the dependent variable affect its current level and past realizations, beyond

the specified lag(s), of the independent variables are used as instrumental variables. The results

of the Arellano-Bond estimation are presented in Table 56 and are very similar to the previous

results. This provides yet another robustness check. All of the coefficients are still positive and

statistically significant. The primary difference is the estimated impact of democratic capital is

larger while the impact of economic freedom and previous GDP per capita coefficient estimates

are smaller under this method.

6 The model is not estimated using the logged values of Polity II Democratic Capital because the sample was reduced by more than half due to the negative values of the raw variable.

Table 4 – Estimates of Equation 3 with Logs

This set of results has at least two major implications. The first is that democratic capital may not

directly affect GDP, or the direct affect is relatively small. This is not a surprising result, as it has

been suggested by Feng (1997), Helliwell (1994), and Leblang (1996) that the impact of

democracy is indirect. The second, and more important, implication is that how you measure

democratic capital, and democracy by extension, impacts the statistical significance of your

results. This supports the implications of the graphic analysis of Figures 1 and 2 in section 4.1.

Table 5 – Arellano-Bond Estimates

The body of research in this area often goes back and forth as to whether democracy is

important. The results here suggest that those papers that are based on different measures of

democracy are likely to get different results. This is particularly poignant when one considers the

first two results here; Tables 3 and 47 are based on the exact same data and econometric

approach but result in different estimates. Using different econometric methods, as presented in

Table 5, simply adds to the confusion.

7 The model in Table 4 is not estimated using logged values of Polity II Democratic Capital. See the previous footnote.

3. Granger Causality

Clearly, the three variables of GDP per capita, economic freedom and democratic capital are

closely connected. There is a strong theoretical connection that has been demonstrated both

graphically and statistically in this paper. This begs the question of which one causes the others

to move. The standard approach to answering this question is Granger causality tests.

Table 6 – Granger Causality using VAR on Binary Democratic Capital

Table 7 – Granger Causality using Wald Tests on Binary Democratic Capital

The challenge here is there is no exact way to translate the traditional Granger causality test to

panel data. For this reason, I took several approaches to the calculations. The first approach is to

collapse the data to a measure of central tendency8, then use the traditional Granger test. The

second approach is to run a series of panel regressions with Wald tests. For these regressions I

used the same fixed effect, AR(1) disturbance model employed in section 4.2.

Determining the appropriate number of lags can be important. Regression results suggest that

one lag is sufficient for this relationship. However, the Granger test can be biased if too few lags

are included. Hsiao testing suggests that the correct number of lags is two. Since including more

lags does not bias the Granger test, I include five lags.

Table 8 – Granger Causality using VAR on Polity II Democratic Capital

Table 9 – Granger Causality using Wald Tests on Polity II Democratic Capital

8 I chose to collapse to the median, instead of the mean, to reduce the impact of outliers.

The results of both approaches were generally consistent. Using the binary measure of

democratic capital the results are exactly the same under both methods. Democratic capital

Granger causes economic freedom and vice versa. Similarly, economic freedom Granger causes

GDP per capita and vice versa. However, GDP per capita Granger causes democratic capital but

democratic capital does not Granger cause GDP per capita.

(4)

Another observation from these results is that improvements in these factors seem to be self-

perpetuating. That is, increases in democracy supports increases of economic freedom which, in

turn, supports increases in democracy and GDP which then supports increases in economic

freedom, and so on. The downside of this is that it also implies a negative loop for those

countries that are low in any of these three areas.

The results are slightly different when using the Polity II democratic capital measure. The results

based on median values suggest that economic freedom Granger causes democratic capital but

the relationship does not go the other direction. A more significant deviation is under the Wald

tests, we find that democratic capital Granger causes GDP per capita but not the other way

around. This is the only place where the results suggest a direct relationship of democratic capital

causing GDP per capita. These variations in results may reflect the volatility of the Polity II

measure that we see in the graphs in section 3 or it may be that there is more noise in this

measure.

Finally, I use simultaneous equations to see if we can better understand the complicated

relationship between democratic capital and GDP per capita. It appears that the complication

comes from the relative strength of relationships. This estimation uses the logged values so one

can simply compare the coefficient estimates. The coefficient estimate on economic freedom, in

the GDP per capita equation, is over 15 times greater than the coefficient estimate on democratic

capital. This implies that even though democratic capital does contribute to GDP per capita, its

contribution is often overshadowed by economic freedom.

Table 10 – Simultaneous Equations

V. CONCLUSION

I have put forth three original measures of democratic capital. Comparisons of these measures

suggest that the most appropriate measure depends on your assumptions about political stability.

I have also demonstrated that econometric results are sensitive to the measure of democratic

capital employed. This sensitivity helps to explain the conflicting results in this line of research.

Using a fixed effects model with in-panel AR(1) disturbances, I show that GDP per capita

depends on lags of itself, economic freedom, and democratic capital. Based on these results the

relationship between GDP per capita and democratic capital is either direct or indirect,

depending on which measure is used. Further analysis, using Granger causality tests, support the

indirect relationship of democratic capital acting through economic freedom to positively

influence prosperity.

VI. FURTHER RESEARCH

The depreciation used to calculate the various measures of democratic capital are based on

calibration of a different model. One extension would be to calibrate each of these measures

separately. Given that two of the measures are based on binary values while the other two have

far larger ranges suggests that the depreciation rates should be different, although the different

depreciations rates may not have a significant impact.

Another area of extension is to include other variables important to prosperity such as other types

of capital and geographic factors. Including these requires dealing with more endogeneity issues

but it will also reduce the unidentified factors that are currently being absorbed into the country

fixed effects and would lend further strength to these arguments.

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