The Economic Effects of Democracy – An Empirical Analysis*

Carl Henrik Knutsen

Draft Working Paper, Political Science Department, University of Oslo, March 2008

Abstract

There is no existing consensus among academics on how democracy as a political regime type affects economic growth. There are many different theoretical arguments, sketching up potential causal mechanisms, pointing both in favor and disfavor of democracy when it comes to its ability to generate economic growth. Also when it comes to empirical research, results have been diverging, and this goes for case studies and small-n comparative studies, as well as statistical studies on the possible relationship. This is a purely empirical paper, and its aim is to investigate whether there is enough reasonable evidence to claim that democracy as a regime type has any systematic influence on national growth rates in GDP per capita. In addition, the effects of democracy on protection of property rights and corruption are also investigated, although in a less thorough manner.

The paper contributes to the already large existing body of empirical research on the topic by using a very extensive data set with the newest available data, and particularly by utilizing different, and more appropriate, statistical techniques when compared to a large bulk of the existing studies. This study, using country-years as the unit of analysis, investigates the possible relationship between democracy and economic factors by applying OLS with Panel Corrected Standard Errors, Fixed-Effects, Random-Effects, 2SLS and Matching-based techniques to the data. The study also describes some often overlooked methodological problems related to this issue.

According to the analyses in this paper, there is good reason to believe that democracy “on average” increases economic growth, increases protection of property rights and reduces corruption. Nevertheless, the results are not unequivocal, as the results vary with the specified model, the choice of democracy indicator as well as the statistical technique applied.

* Thanks to Michael Alvarez for sharing with me the updated version of the ACLP data set.

1. Introduction“This is a fairly boring and tedious paper, and is intentionally so. This paper provides no new interpretations of the East Asian experience to interest the historian, derives no new theoretical implications of the forces behind the East Asian growth process to motivate the theorist, and draws no new policy implications from the subtleties of East Asian government intervention to excite the policy activist.” (Young, 1995:641)

The quote above is taken from Alwyn Young’s article “The Tyranny of Numbers”, analyzing the sources of growth in four East Asian nations. If one substitutes “East Asian” with “global”, the quote fairly precisely describes the paper you are about to read as well. This paper is a purely empirical one, seeking to investigate whether democracy as a regime type over the last decades “on average” has influenced national economic growth rates and other economic factors in either a positive or negative direction. Democracy and economic development are more often than not viewed as inherent normative goods. Therefore, the question on whether these concepts are empirically related is an important question to address. Do we generally face a trade-off between two goods, or do the two go together? I will here ask the “whether/if”- and “how” questions, aiming at general description of aggregate relationships, and leave the “why” question to another day.

Does democracy systematically affect aspects of an economy, like economic growth rates? The answers to this question have come in at least four forms: “Yes, positively”, “Yes, negatively”, “No” or “We don’t know” (see Przeworski and Limongi (1993) and Knutsen (2006a:229-235) for literature overviews). The last two answers, that there is no general and strong effect from democracy on economic growth, or that it has at least not been detected, have been the prevailing views among scholars after Przeworski et al.’s seminal and methodologically thorough study from 20001. Nevertheless, the lack of consensus on the issue and the divergence in estimated results between papers warrants a new statistical investigation into this general and important topic. What can this paper possibly contribute with to the already large volume of research? First, it provides econometric models that I believe are well specified; it controls for relevant prior causal variables, and leaves out variables that should be excluded. Second, it specifies some methodological problems connected to the research question, which some but not all researchers seem to be aware of. Third it utilizes new data, and thereby extends the sample period when compared with earlier research. Fourth, and I believe this is the most important justification, it uses “new”, and relatively advanced statistical techniques, which might be better suited than for example OLS to deal with the question of interest.

Let it be clear from the outset: Empirical results not followed by specified theories, models or mechanisms provide limited insight. Additionally, the relationship between democracy and

1 Note that Przeworski et al. (2000) investigates the effect on GDP growth nationally, and not the effect on GDP per capita growth as I investigate in this analysis. Przeworski et al. (2000) actually find that democracy decreases population growth, and this effect ceteris paribus contributes to higher growth in GDP per capita for democracies.

1

economic growth is most certainly dependent on context and also on the type of democracy and authoritarian regime we are comparing, and average effects do not exhaust the complex nature of the relationship. Nevertheless, the general relationship is still of interest. People will not stop asking about general relationships even if there exist no deterministic laws of Newtonian character (even Newton’s laws are modified in certain contexts, like Einstein showed us). Relationships in the social sciences are nearly always probabilistic and “full of exceptions”. This being said; rather than claiming that regression-coefficients exhaust all potential information of the relationship, one should recognize that there are multiple plausible causal mechanisms that could be at work, and that the relative strength of these mechanisms depend on several contextual factors connected to the economic, political, social and maybe even cultural environment. I have earlier evaluated no less than 20 arguments, identifying 20 potential causal mechanisms with the degree of democracy in a country as a cause and the rate of growth in GDP per capita as an effect (Knutsen, 2006a), and analogously five arguments when protection of property rights is the effect variable (Knutsen, 2007). The arguments point in different directions when it comes to the effects of democracy, and some of the arguments seem to be more valid in specific situations (both spatially and temporally). This does not however preclude us from asking whether we can identify any overall pattern in the data. Do certain causal mechanisms dominate in the real world and thereby influence the empirically observed relationship in a systematic way? “Does a higher degree of democracy produce more economic growth in general?” is maybe an imprecise question, but we can still seek to provide some evidence for a rough sketch of an answer.

1.1 Less than one page on the “why” questionWhen it comes to democracy and economic growth, two arguments are central in pointing out the deficiencies of democracy. The first states that in democracies, the electorate will press demands for private and public consumption that democratically elected leaders, as opposed to authoritarian rulers, cannot afford to ignore. Increased consumption implies less saving and thereby less investment and ultimately less economic growth (Solow, 1956). The second argument is that because of certain political mechanisms, it will be harder for democratic leaders than more autonomous authoritarian leaders to push through contested reforms that are eventually economic growth-enhancing. On the other hand, democracies are argued to increase economic growth through among others facilitating innovation and diffusion of new technologies, which are crucial to long term growth rates (Romer, 1993 and Fagerberg, 2002). Democracy might also increase the legitimacy of economic reforms and also enhance growth through providing political checks on leaders that would otherwise have acted in ways that would have benefitted only themselves or narrow backing groups economically, at the cost of wider society (Knutsen 2006a). When it comes to the protection of property rights, democracy is also claimed to have a positive influence on this variable through the same political checks on elite-led grabbing of property. On the other hand, democracy might have a negative influence on the stability of existing property rights arrangements because a relatively poor majority will have incentives to redistribute from richer minorities (Knutsen, 2007). The effect from democracy on overall corruption in a society is often assumed to be negative because of the existence of several accountability generating mechanisms like elections and freedom of press. However, democracy might decentralize corruption (Knutsen 2006a:114-

2

115), and decentralized corruption is argued to be more harmful to economic growth than centralized corruption (Shleifer and Vishny, 1993). These are only a subset of the existing arguments on possible causal effects from degree of democracy on different aspects of the economy. Democracy is also argued to affect other aspects of the economy like income inequality and “human capital”, but these issues will not be addressed here.

1.2 Outline of the paperSection 2 of this paper deals with the conceptual definition and operationalization of the independent variable, political regime type, and the different dependent (economic) variables analyzed in this paper. Section 3 deals with a cluster of identified methodological problems related to testing hypotheses in this issue-area. Section 4 contains the description of the different methodologies and techniques used, and the empirical analysis of how democracy affects economic growth. Section 5 gives a brief overview over empirical results on how democracy affects protection of property rights and corruption. Section 6 draws up the main conclusions.

2. Definitions and operationalizations

2.1 DemocracyThere is no consensus on how to define democracy conceptually. Crick (2002:1) calls it “an essentially contested concept”. I have earlier presented and debated different ways of understanding and defining democracy conceptually, and I will not go deep into the question here (Knutsen, 2006a, 2006b and 2006c). Two categories of definitions are one the one hand those defining democracy according to a particular set of institutions and on the other hand the definitions that define democracy according to some underlying principles. The latter type of definition is sometimes referred to as “substantial” definitions of democracy. One such particular definition is the one proposed by David Beetham (1999), and I have earlier argued in favor of understanding democracy as a concept along the lines Beetham sketches up. Beetham proposes that “[T]he core idea of democracy is that of popular rule or popular control over collective decision making” (Beetham, 1999:90). Authoritarian governments are therefore related to the lack of popular control over collective decision making, since a low degree of democracy implies authoritarianism. To this first criterion, another one of political equality among citizens is added by Beetham. Democracy “is realized to the extent that such [public] decision-making actually is subject to the control of all members of the collectivity considered as equals” (Beetham, 1994:28). These two principles are the “first level” of the (democracy) concept; the deeper principles (Goertz, 2005). Then we have the “second level” of the concept (Goertz, 2005). This level points to the empirical features that help realize the underlying principles of democracy. Institutional structures like for example competitive elections of legislators are crucial elements. Beetham also argues that guaranteed political rights and civil liberties are necessary prerequisites for democracy. “The freedoms of speech, association, assembly and movement, the right to due legal process, and so on, are not something specific to a particular form of democracy called ‘liberal democracy’; they are essential to democracy as such, since without them no effective popular control over

3

government is possible” (Beetham, 1994:29). The third level of the democracy concept, the indicator-level, relates to the practical operationalization of democracy in terms of measurable characteristics, and the choice of operationalization is discussed below.

When it comes to institutionally based definitions, these have some obvious beneficial properties in terms of precision, and they can more easily be operationalized in stringent ways. Przeworski et al.’s (2000) definition of democracy provides a good example of such a definition. Another famous institutional definition of democracy is Schumpeter’s definition of “the democratic method” as “that institutional arrangement for arriving at political decisions in which individuals acquire the power to decide by means of a competitive struggle for the people’s vote” (Schumpeter,1976:269). However, as Beetham (1999) claims, if one is to dig deeper into institutional definitions of democracy, a pressing question is exactly why the particular proposed set of institutions makes up democracy. Any answer risks throwing us out into circular argumentation if we do not establish an underlying idea of what democracy actually is.

Since I am following Beetham’s substantial definition of democracy closely, it is clear that it is preferable to operationalize democracy in a relatively broad sense. This means that it does not suffice to look for existence of elections or degree of checks on the executive when I choose my favored operationalization. Existence of institutions clearly needs to be part of any operationalization, but in addition the actual functioning of these institutions must be included; that is, we are interested in whether these institutions actually enhance the two principles that serve as the foundation of the conceptual definition of democracy. The existence of multi-party elections and constitutional descriptions of freedom of speech and assembly are not sufficient for popular control over politics or political equality. We need subjective evaluations of the functioning of these institutional structures in practice, and we also need to incorporate the degree to which civil liberties and political rights are existent. I have therefore argued that the Freedom House Index (FHI), despite its many shortcomings, is the best available cross-country index, with a relatively extensive time series, for democracy (Knutsen 2006a, Knutsen 2006c). The FHI has been attacked, both because it does not sufficiently incorporate the actual functioning of institutional structures (Inglehart and Welzel, 2006), but also from the “opposite side” because of possible subjectivity bias and incorporation of policy outcome-related aspects (Strand, 2007). Strand (2007) also points out that there has been a change in the FHI coding practice over the course of time. Nevertheless, among the existing available democracy-indexes, I find the FHI to be the better alternative because it balances both the need to be subjective in order to capture “the political reality behind the formal and observable structures of government” (Grugel, 2002:22), and the problem of subjectivity bias and outcome-centeredness on the other hand.

The FHI consists of 25 main check questions2, 10 on political rights and 15 on civil liberties, leading to two different indexes, both ranging from 1 to 7. In this study, the “FHI-index”

2 For the complete list of check-questions, see Freedom House (2005a:5-8). The political rights questions are divided into three subgroups: Questions on A) the electoral process, B) political pluralism and participation, and C) functioning of government. The civil liberties questions are divided into four subgroups: D) freedom of expression and belief, E) associational and organizational rights, F) rule of law, and G) personal autonomy and individual rights.

4

refers to the (unweighted) average of these two indexes. I will also use two additional operationalizations, in order to check the robustness of my findings. The first is the Polity IV measure of democracy (see Marshall and Jaggers (2002) for a discussion of operationalization), which runs from -10 to 10. The Alvarez-Cheibub-Limongi-Przeworski (ACLP) data set’s dichotomous measure of democracy is the second alternative measure (see Przeworski et al. (2000) for a discussion on operationalization). Note, when interpreting estimates, that a high value on the FHI and ACLP-measure of regime type indicates “less” democratic, and that a high value on the Polity indicates “more” democratic. The FHI data used in the analyses run from 1972 to 2005, the Polity data used in this study are from 1960 to 2004, and the ACLP data used here go from 1960 to 2002.

2.2 Economic growthEconomic growth in a geographic area is defined as the increase in the aggregate level of production during a year. I operationalize the concept by using growth in PPP-adjusted GDP per capita, with data collected from the World Development Indicators (WDI). I use PPP-adjustment instead of exchange adjustment because my main underlying concern is welfare effects, and I therefore need to take into account the local price level. There are several methodological problems with using GDP per capita growth as a proxy for economic growth. Depreciation of capital is not calculated into the concept, and it does not take into account depletion of natural resources either (oil and mineral extraction taken from known reserves). The most problematic issue when it comes to using it on a global sample however, is that it does not take into account the size/growth of the informal economy, which is relatively large especially in some developing countries

2.3 Protection of property rights and corruption“A right of ownership is associated with property that establishes the good as being "one's own thing" in relation to other individuals or groups, assuring the owner the right to dispense with the property in a manner he or she sees fit, whether to use or not use, exclude others from using, or to transfer ownership” (http://en.wikipedia.org/wiki/Property_rights). This is one way of defining property rights. Development- and institutional economists in particular have been focusing on the importance of a well-functioning system of private property rights for the economy. “Property rights are seen as a key institutional feature because they provide actors with proper incentives in economic life, and well-structured property rights therefore enhance efficient resource allocation in an economy.” (Knutsen, 2007:8). I use the World Governance Indicator’s “Rule of Law” dimension as a proxy for the protection of private property rights in an economy, since a large bulk of the indicators used to construct it are related to different factors affecting the protection of these rights. However, as the name indicates, there are also some indicators (among the 50 or so utilized in the construction of the index) relating to other aspects of rule of law in a country. The index is constructed through a principal component analysis, with a specific weighting procedure when it comes establishing the relative importance of the indicators in the index (Kaufman et al., 2007).

*

5

Government corruption is defined as “the sale by government officials of government property for personal gain” (Shleifer and Vishny, 1993:599). The importance of control of government corruption for a well functioning economy, both when it comes to efficiency and distribution, has also been a focus in development economics and institutional economics. I once again use data from the WGI data set, more specifically their “Control of Corruption” index, which is constructed analogously to the Rule of Law index treated above. The more specific description of the procedures related to constructing these indexes can be found in Kaufman et al., (2007), which also provides estimates of the measurement errors related to the indexes. One problem when it comes to the operationalization of corruption for the purpose of this paper, is that several of the components of the index are related to subjective estimates of corruption (by for example international investors and experts). This could create a bias in my results, since an open and free press writing about the problems of government corruption could affect the perception of the corruption level in the country. If a hypothetical country goes through a democratization process, and establishes freedom of press in practice, the perception of government corruption levels could therefore increase, even if the actual level of corruption stays the same. This problem is however even larger for the Corruption Perception Index, produced by Transparency International. This index relies solely on subjective estimates of corruption, whereas the WGI also incorporates more “objective” data.

Unfortunately, the time-series for the WGI data are still very short, which increases the uncertainty in our estimates. The first data are from 1996, and the last are from 2006, with exception for the years 1997, 1999 and 2001. However, the number of countries and territories included, 212, is impressive. An alternative operationalization of property rights protection is the “Rule of law and the protection of private property rights”-index, which is provided by the Gwartney and Lawson “Economic Freedom of the World” dataset. This index will be used for the purpose of robustness checks, and it provides data for the years 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2001, 2002, 2003 and 2004 for up to 129 countries.

6

3. Methodological problems

From Hume (1739;1969), social scientists and others have been made aware of the fact that causality cannot be unambiguously observed and identified. When it comes to statistical studies, the problem is often referred to as one of how to infer from observed correlation to causation between two concepts. X and Y can be correlated because X tends to produce Y, or because Y tends to produce X. The third option is that another entity, Z, tends to produce X and Y in a systematic fashion. When using regression-based methodologies, one way to deal with the latter case is to include control variables, and thereby reduce the possibility of so-called omitted variable bias. However, when applying so-called “kitchen-sink regressions” (including everything but the kitchen-sink in the model!), we face a second problem. If we are interested in the total causal effects of a variable, we risk controlling away indirect effects by including as controls the causal channels through which an independent variable affects the dependent variable. This is especially troublesome when it comes to the economic effects of democracy. Two illustrations are provided by Tavares and Wacziarg (2001) and Helliwell (1994). The first article claims that democracy positively affects economic growth through human capital/education and capital investment, and the second that democracy affects growth through its positive effect on property rights protection. In both cases the direct effect of democracy is found to be relatively small. If one wants to find the total causal effect of democracy on growth, one therefore needs to be very careful when specifying the regression model. One should only include as controls those variables that are mainly to be considered as prior variables; alternatively one should choose a better suited estimation procedure, for example by using multiple equations.Figure 1: Stylized causal diagram. Grey arrows show effect that accrue over substantial period of time

3.1 Lack of data and selection biasAccording to Ha-Joon Chang (2006:145), in statistical studies of economic development 1960 is often considered as “year zero”, and this is due to the fact that the World Development

7

Prior variables: Historical and socio-economic structures

Causal channels: e.g. investment, education, economic policies

Political regime type; degree of democracy

Economic outcomes/factors: e.g growth, inequality

Indicators start recording much of its data in this year. This points to an obvious limitation in quantitative research on development topics; the data sample is often limited to the nearest history3. Many variables even have time-series that start well after 1960. However, even if the difficulty of generalizing over a larger temporal dimension is a problem because of lacking data, there exists one maybe even bigger problem. This problem is the one of lacking data for particular nations. If the lack of national data is randomly generated, this would only have created a higher level of uncertainty in estimates, due to fewer observations. However, there are good reasons to believe that we have a systematic selection of countries without data on economic variables. Halperin et al. recognized this “frequently overlooked, recording problem” (2005:32), and calculated that from 1960 to 2001 a quarter of the autocratic countries lacked economic growth data, while only five percent of the country-years were missing among democracies (Halperin et al., 2005:33). If we recognize that there are incentives for authoritarian rulers presiding over “development disasters” to hide data or obstruct data-collection, we could be led to think that it is the worst performing autocracies that are excluded from the data sets. Some examples of authoritarian regimes with missing data serve as anecdotal evidence for this claim: North Korea, Myanmar, Eritrea and pre-invasion Iraq and Afghanistan. If authoritarian growth disasters are not reporting data, then we will have a source of systematic bias in the analysis, with regression coefficients estimating a too positive effect from authoritarianism on economic growth on average.

One possible solution is to expand the available data by including estimates from several different sources. Since the Penn World Tables incorporate growth and GDP data for some countries that are not included in the WDI (and vice-versa), one strategy is to include these numbers for countries lacking data on the WDI, or maybe even better to incorporate the predicted values on the WDI data estimated from a bivariat regression with PWT data as independent and WDI data as dependent variable. Incorporation of such estimates will be done in future research, thereby expanding the number of countries available to use in analysis. There also exist data from outside these two larger databases for some countries. The Bank of Korea (South Korea) for example keeps estimates on GDP in North Korea (Nanto, 2006). It is of course difficult to judge the credibility of these data, but the alternative is to systematically leave such bad-performing countries out of the analysis, thereby skewing results.

Even though I have here dwelled on the problems of lacking data, this study incorporates one of the most extensive data-sample of any study on the economic effects of democracy. The newest and most updated data are used, and in some cases the analyses incorporates almost 4500 country-years as a basis for inference. I have constructed a data-matrix in a cross-section – time-series format, based on available data from several different sources like for example the World Bank, Penn World Tables, Polity, Freedom House, UNESCO and UNCTAD. There are 227 countries, countries that no longer exist and more or less independent territories in the matrix, and the time-series run from 1950-2006, but most variables have data for a substantially shorter period. 3 For a very interesting empirical study on how absolutist monarchies in Western Europe impeeded economic development when compared to less absolutist forms of governments from 1000 to 1800, read De Long and Shleifer (1992).

8

3.2 Measurement error and attenuation biasIf there is random measurement error connected to one of the independent variables in a regression, there is a well-established finding that the estimated regression coefficient will be drawn towards zero4 (and other coefficients might be biased upwards/downwards because they pick up some of the variation). If democracy is measured with an error, which is almost certainly the case whether we rely on Polity or Freedom House or any other source, then the effect of political regime type will tend to be underestimated due to this bias. Kenneth Bollen is one of the researchers that have focused on measurement errors related to different indexes of democracy (Bollen, 1980, Bollen, 1990, Bollen, 1993 and Bollen and Paxton, 2000). He has used techniques based on factor analysis to estimate both systematic and random measurement errors for different democracy indexes. In general, Bollen (1993) and Bollen and Paxton (2000) find that the indexes constructed by Freedom House have relatively high validity when compared to indexes created by Banks and by Sussman. Especially the Political Rights index from Freedom House fares well. Bollen (1993) estimates that 6% of the variation in this index is due to a systematic “Method Factor Error”, and that the estimated random measurement error is actually 0%. The Civil Liberties index fares less well with the respective numbers being 16% and 6%. This indicates that the FHI, if the estimates from Bollen are accepted as true, are not as plagued by measurement error as some of its critics have claimed is the case. Nevertheless, the question on whether these estimates of systematic and random measurement errors are actually correct is open to dispute.

If we leave the question of methodology aside, and also forget about the systematic biases for a second, what does the estimates from Bollen on random measurement errors imply? If we believe that Bollen’s estimates at least are in the right ballpark, we can assume for example that the random measurement component of the aggregated FHI is 5% of the variance. In a bi-variate regression model, where the independent variable has a random measurement error of 5%, the covariation of X and Y now has to be divided by 95% of the observed variance in the measured X variable (the true variance), if we are to find the “true β”. This implies that the “observed” β-coefficient will as a rule of thumb have to be divided by 0,95 in order to give the consistent estimate of the real effect. If we observe a β of 0,3, as we approximately do in some of the analyses where the FHI is used as an independent variable, then the “correct” coefficient-estimate is really 0,316. This translates into an estimated positive effect of 1,9% extra annual growth when going from 7 to 1 on the FHI, instead of 1,8%. If the random measurement error is instead 10%, the “correct effect”, according to these calculations, is 2%. Then again, these are only crude calculations, and attenuation bias in a multivariate regression model is more complicated to calculate, even if we assume that only the democracy variable is measured with an error. The problems of measurement errors and resulting attenuation bias will be dealt with more thoroughly in later research.

4 See Biørn (2003:353) for an algebraic proof of the asymptotic bias of a regression coefficient in a bivariat regression analysis when there is measurement error [in Norwegian].

9

3.3 Selection of regimes: When do countries change to and from democracy, and how does it affect results?One type of endogeneity when it comes to the democracy-variable in regressions where economic factors are used as dependent variables, is the “selection” of regimes after economic performance. Which situations tend to produce a change in regime to more or less democratic types of regimes? Przeworski and Limongi brought up this issue in an article from 1993, and the issue was further explored in Przeworski et al. (2000). One central insight is that “everyone seems to believe that durability of any regime depends on its economic performance. Economic crises are a threat to democracies as well as to dictatorships. The probability that a regime survives a crisis need not be the same, however, for democracies and dictatorships” (Przeworski and Limongi, 1993:62). The key implication is that if “democratic regimes are more likely to occur at a higher level of development or if democracies and dictatorships have a different chance of survival under various economic conditions, then regimes are endogenously selected”, and this will lead to biases when using OLS-based techniques. One solution is to use modeling techniques that incorporate the selection-bias, like the one suggested by Heckman (1978), but as Przeworski and Limongi note “[S]election models turn out to be exceedingly sensitive: minor modifications of the equation that specifies how regimes survive can affect the signs in the equations that explain growth. Standard regression techniques yield biased (and inconsistent) inferences, but selection models are not robust” (1993:64). One less ambitious way of dealing with this issue is to incorporate the level of economic development at all times as a control variable, in order to secure that we compare relatively countries when it comes to prior developmental characteristics, and then use panel-data to infer effects also from intra-national variation in growth rates over time (with changes in the regime variable). We do then rely not only on cross-sectional aggregated measures. These remedies do not however fully solve the problem, but it reduces it to some extent. Empirical studies have shown that coup probabilities for autocrats are higher when there has recently been slower economic growth (Clague et al., 2003:166). If we couple this with a probability of entering democracy some time after the coup (which is certainly not a given!), we would observe fewer years with economic crisis in authoritarian regimes relative to “boom-years”, than if the coup-probability was independent of growth rates. The same argument could analogously be made for democracy, if assuming that the probability of democratic breakdown increases in periods of economic crisis. Indeed, this has been estimated to be the case: “When democracies face a decline in incomes, they die at the rate of 0.0523 and can be expected to last nineteen years, but when incomes are growing, they die at the rate of 0.0160, with an expected life of sixty-four years” (Przeworski and Limongi, 1997). The two effects taken together might partially cancel each other out, but if they do so is an empirical question. Using Instrumental Variable techniques that utilizes exogenous variation in the regime variable might be an even better way of dealing with the problem.

*

When it comes to the three specific methodological problems sketched up above, these will be taken more thoroughly into account in later research, but the readers should keep them in mind when interpreting the results in the following sections. The implication when it comes to

10

the direction of the bias is a priori uncertain in the last problem of endogeneity of regime type. The implication from the attenuation bias is that our regression results tend to understate the “real” effect; that is, if we for example estimate democracy to have a positive effect on growth, the true effect is maybe even stronger than our estimates imply. The implication of the lacking data-problem is that we probably underestimate the positive effect of democracy on growth in statistical analyses.

4. Does democracy affect economic growth?

4.1 OLS with Panel Corrected Standard ErrorsThe starting point of my analysis is a Pooled Cross-Section – Time-Series technique called OLS with Panel Corrected Standard Errors. This technique is as the name indicates based on OLS-methodology when it comes to calculating estimators, but it takes into account that standard errors can be heterogeneous across panels, that they can be autocorrelated within panels (AR1), and that we can have contemporaneous correlation (correlation between cross-section units within same period). All of these moments are crucial when we investigate a data set with the type of structure I have used, with country-year as unit of analysis. The technique allows us to base our inferences not only on cross-sectional variation, but also on variation within cross-section units along the time dimension. The amount of information used when making inferences is therefore considerably larger than if we were to base inferences on OLS on averages for the cross-section units alone. There are alternative PCSTS techniques available, the most common being FGLS with correlated disturbances. However, as Beck and Katz (1995) argue based on Monte Carlo simulations, OLS with Panel Corrected Standard Errors performs better in a data structure where the cross-section units are relatively many and the time-series are relatively short. To be more specific, FGLS-based techniques tend to underestimate the size of standard errors in these contexts, thereby making it easier to claim that coefficients are significantly different from zero.

As mentioned in section 3, one needs to be careful when selecting control variables, and in the baseline model I only incorporate controls that are more likely from a theoretical point of view to be prior variables in the causal structure than intermediate variables in the causal chain from regime type to economic factors. The choice of control variables can certainly be disputed, but I have chosen to incorporate level of PPP-adjusted GDP per capita, log of population size, both collected from the WDI, as well as dummies for region, plurality religion and colonizer5

5 For a description of the operationalization of these three dummies, see Knutsen (2007: 67-68). Here, you will also find some arguments on why these variables should be used as controls in analyses seeking to explore effects of regime type. Unfortunately, the paper is already too long to include a good discussion on the choice of control variables. The colonizer dummies are: (1) British and American rule, (2) French rule, (3) Spanish rule, (4) Portuguese rule, (5) Dutch rule (6) Belgian rule and (7) Italian rule. The plurality religion dummies are: (1) Protestantism (combined with Anglicanism and other varieties of Christendom related to or springing out of Protestantism), (2) Greek/Russian/Armenian Orthodox Christendom, (3) Catholicism, (4) Sunni Islam, (5) Shia Islam, (6) Buddhism (combined with Taoism and Confucianism), (7) Hinduism and (8) a dummy for indigenous beliefs. The dummies for region are (1) Western Europe with North America plus Australia and New Zealand (dropped in order to avoid perfect multi-colinearity), (2) Eastern Europe and (ex-) Soviet Union, (3) Africa south

11

The baseline regression model is given by the equation below:

Another model, which reduces the possibility of omitted variable bias, but at the risk of controlling away indirect causal effects from democracy includes a linear time trend, logarithm of regime duration +1 (proxy for political stability), with data on regime duration gathered from Polity, the degree of urbanization (percentage living in urban areas) and energy production in kilotons of oil equivalents/GDP, with both the two latter variables stemming from the WDI. See Knutsen (2007:68-69) for the benefits and drawbacks of adding time trend-, regime duration- and energy production variables to the model.

The extended regression model is given by the equation below:

The results from the baseline and extended models, in terms of the estimated democracy-coefficient, are given in Table 1. I ran regressions with the FHI-, Polity-, and ACLP-measures alternating as indicator for democracy. As stated in section 2, I will put the main weight on the FHI, since I believe it is the most valid operationalization of democracy defined substantially. According to the analyses, democracy as operationalized by FHI is significantly different from zero on the 1%-level when we use the baseline model, and on the 5%-level when we use the extended model. The estimated effects of democracy in both models are positive, and actually quite substantial in terms of estimated size. The result from the extended model indicates that a country going from most authoritarian (FHI=7) to most democratic (FHI=1) will increase its annual growth rate in GDP per capita with approximately 1,6%, holding GDP per capita, population size, colonizer, region, religion, share of people living in cities, age of the regime and energy production per GDP constant, as well as controlling for a potential time trend. 1,6% might not seem like a large number at first glance, but let me illustrate with the following example: Two countries, A and B are initially equal countries in terms of GDP per capita. Country A, a democracy, is growing at a 1,6% higher annual rate than country B, an autocracy. This implies that over a 15-year period, A has become 27% wealthier in per capita terms than B, and that A is more than twice as rich as B after 44 years. These are actually dramatic differences in welfare for the next generation of A- and B citizens6.

of the Sahara, (4) Asia, (5) Middle East and North Africa , (6) Oceania (without Australia and New Zealand) and (7) Latin America.

6 I have not put weight on the robustness of results in terms of changes in estimates stemming from selection and inclusion of variables in the model. Nevertheless, I added some possibly (they might be viewed as endogenous to regime type!) relevant variables to the extended FHI-model and checked the results: The

12

The results are however not completely robust in the sense that some of the models using other democracy indicators show less convincing results, when it comes to a significant, positive effect from democracy on economic growth. All specifications tested show an estimated positive effect from democracy, but the effect is insignificant even at the lenient 10%-level for the extended Polity model. The two specifications using the ACLP-measure show coefficients that are significantly different from zero at the 10%, but not at the 5%-level. That the estimated coefficients are not statistically different from zero does not mean however that the estimated effects are small substantially, should they be true (we could be making a sizeable Type II error!). Even for the extended Polity model, the estimated effect of going from -10 to 10 on the Polity index is an extra annual growth of 0,8%. Then again, we cannot with this specification reject the claim that the effect on growth is equal to zero, based on criteria for statistical inference.

Table 1: Results from the OLS with Panel Corrected Standard Errors analyses 7

ModelDemocracy-indicator Coefficient t-value Observations Territories

Baseline FHI -0,28 -3,01*** 4473 167Baseline Polity 0,05 2,22** 3683 146Baseline ACLP -0,61 -1,75* 3983 167Extended FHI -0,26 -2,29** 3016 118Extended Polity 0,04 1,57 2927 117Extended ACLP -0,56 -1,66* 2783 118

There are three crucial differences between the Polity- and FHI variables, other than the obvious differences in operationalization (FHI for example includes civil liberties): These are first that Polity has a longer time-series than the FHI, and the Polity-regressions include observations from 1960, whereas 1972 is the corresponding first year for the FHI. The second is that countries that go through periods of state collapse, anarchy or foreign invasion are not

estimated effect was still significant at the 5%-level, and the absolute size of the estimate actually increased when I controlled for trade as a percentage of GDP, final government consumption expenditure as a percentage of GDP, percentage of GDP in agriculture and the latest assigned Gini-coefficients (assigned also for the five years before first score, many years have equal scores). I have argued that investment level (investment level as share of GDP) and human capital (here proxied by gross enrollment in secondary schooling) are to be viewed as causal channels from democracy on growth. However, if I check the robustness of my statistical findings by including these variables as well, the FHI-measure is still significant at the 5%-level, and actually now even significant at the 1%-level. If I also add ores and metal export as a percentage of merchandise export as an extra control, the FHI-coefficient is still significant at the 5%-level but not at the 1%-level. The FHI-coefficient in this very extensive model is estimated to be -0,35. These inclusions of extra control variables to my extended model indicate that at least in the case of PCSTS using FHI as operationalization, the effect from democracy on growth is robust. When I included the ores- and metal variable in the model without investment and human capital however, the FHI-coefficient was not significant at the 5%-level, even though it still had the same sign.

7 * indicates that the coefficient is significant at the 10%-level, ** at the 5%-level, and *** at the 1%-level. This is also the case for later tables.

13

included in the Polity-regressions, whereas these are often labeled as relatively authoritarian country-years by the FHI (because of lack of political and civil rights and liberties in these years). The third is that Polity does not have data for relatively small countries with a population below 500 000 people as of 2002 (Marshall and Jaggers, 2002:4). In the extended model, no data from before 1972 is included, since some of the control variables only have data for more recent years. In order to check whether the differences in data sample can explain the insignificant results in the extended Polity model, I run regressions on a data sample that incorporates only those countries that have data for both operationalizations of democracy. The Polity-estimates are those in Table 1, but the FHI-estimate decreases in absolute value to -0,21, but the effect is still significant at the 5%-level, with a p-value of 0,049. The conclusion is that the differences in data sample might drive some of the differences in results, but it is also likely that some of the difference is linked to genuine differences in operationalization of democracy.

When it comes to the other independent variables (other than democracy), there are in general few that are significant at the 5%-level, and this could be an indication that we have some degree of multi-colinearity between the variables. I have to remind the reader that we control for region (Western Europe and North America is dropped, and thereby used as a reference category in order to avoid perfect multi-colinearity), religion, and colonizer by incorporating a set of dummies. In the only model (extended Polity) where the democracy-coefficient is insignificant at all conventional levels, there are only two variables significant at the 5%-level, and those are the regime duration and Belgian-colony variables. Given these results, it is maybe not a crucial blow to the robustness of the findings that the democracy-variable is insignificantly different from zero at the same level in some of the specifications8.

*

One of the few contextual investigations I am going to perform here, is led by the question: Did something happen to the relationship between democracy and growth after 1990? I have earlier argued (Knutsen, 2006b) that both international political reasons (the collapse of the Second World, increased international focus on human rights and democracy and contingent aid and trade) and economic reasons (increased trade and FDI, the “emergence of the knowledge economy”) might lead one to believe that the effect from democracy on growth could have become more positive on aggregate after that point in time. I have earlier attempted to validate the hypothesis through investigating the effects of democracy in a global sample starting in 1990 and ending in 2005, and then comparing these results with the results from among others Przeworski et al. (2000), whose data extend to 1990. Table 2 gives the results when the same models as in Table 1 were used on the temporally restricted sample. The point estimates for the extended model specifications are especially large in relative terms, when compared to the results from Table 1. I will here propose a more formal test of the hypothesis that the economic growth effect of democracy is different pre- and post-1990. I will use a so-called Chow-test, and incorporate an interaction variable consisting of a post-1990 dummy multiplied with the degree of democracy. If this interaction variable is

8 However, an auxilliary regression with Polity as the dependent variable shows an R-squared of 0,34, which is not very high.

14

significantly different from zero, there is reasonable evidence for the hypothesis that the effect has changed over time.

Table 2: Results from the OLS with Panel Corrected Standard Errors analyses (1990-2005 (2004 for Polity))

ModelDemocracy-indicator Coefficient t-value Observations Territories

Baseline FHI -0,38 -2,77*** 2611 167Polity 0,06 1,71* 2069 146ACLP -0,59 -1,34 2125 167

Extended FHI -0,79 -3,87*** 1710 118Polity 0,08 1,79* 1652 117ACLP -1,21 -2,02** 1477 118

The Chow-tests do not reject the null-hypothesis that the effect of democracy on growth has been similar before and after 1990, and this is true even if we choose the 10% significance level. The hypothesis that democracy has been more beneficial for growth after the fall of the Berlin-Wall, generated from the arguments in Knutsen (2006b), does therefore not receive support from the data.

4.2 Fixed- and Random-Effects and Arellano Bond-estimationWhat if there are country specific factors that we do not include in the regression analysis framework those determine both the rate of growth and the degree of democracy in a country? In that case, the methodology used above is inappropriate, since we should have controlled for country-specific effects. One solution is to run a so-called Fixed-Effects regression, incorporating dummy-variables for all the cross-section units. Thereby one will only infer the effect of democracy from investigating variation within nations along the time dimension as they become more or less democratic9. In this sense Fixed Effects analysis is a very restrictive analysis, since it does not allow us to infer anything about causal effects from cross-national variation. This could be a problem, especially if there are long lags in effects and we have short panels. Clague et al. (2003) indicates that “[M]any of the benefits of democracy seem to accrue only over a substantial period of time” (Clague et al, 2003:157), writing about the effect on property rights. If this is the situation also for economic growth, Fixed-Effects might be an inappropriate estimation technique. If we stylistically assume that cross-sectional variation in economic performance is due to the fact that some countries have come farther in their democratization processes than others (assumed to be on the same path), we should have used techniques that utilize cross-national variation as well. However, I run Fixed-Effects

9 We could perhaps speak of the effects of “democratization”, but we do have long time-series as our basis, and we would have needed to assume that the effects from “democratization” and “autocratization” are symmetric. A better test on the effects of “democratization” is proposed in Knutsen (2006a: 258-268), who looks at 45 historical instances of relatively substantial shifts towards more democratic government over a short time span, and assesses the pre- and post growth rates. No considerable systematic effect on growth rates was found.

15

analysis, controlling for country-specific factors, and I use both the basic and extensive models. In the extensive models, however, I do not include the linear time trend. The reason is that we now only base inferences on intra-national variation along the time dimension, and we might expect a high degree of colinearity between the time trend and a country’s change in the regime variable. If a country democratizes gradually over time, the estimation procedure will then have problems with assigning the causal effect between the democracy variable and the time trend, due to its utilization of “a mechanical procedure”. My view is that there are stronger a priori reasons to believe that it is the regime change rather than the passage of time that might have causal effects on national growth rates. This choice might however be disputed, and when checking the robustness of the results, several of the results from the extended models are sensitive to the incorporation of the time trend; in general, the estimated effect of democracy becomes less strong when we incorporate the time trend, and it often becomes insignificant on conventional levels.

The results from the Fixed-Effects analysis are presented below. Table 3 gives us the results when we use ordinary standard errors, and Table 4 gives us the results when we use so-called “robust” standard errors that incorporate the possibility of heteroskedastic standard errors.

Table 3: Results from Fixed-Effects analyses with ordinary standard errors

Model Democracy-indicator Coefficient t-value Observations TerritoriesBaseline FHI -0,27 -2,83*** 4473 167

Polity 0,05 1,94* 3683 146ACLP -0,96 -2,77*** 3983 167

Extended FHI -0,37 -3,09*** 3016 118Polity 0,10 3,42*** 2927 117ACLP -1,01 -2,58*** 2783 118

Table 4: Results from Fixed-Effects analyses with robust standard errors

ModelDemocracy-indicator Coefficient t-value Observations Territories

Baseline FHI -0,27 -2,91*** 4473 167Polity 0,05 2,07** 3683 146ACLP -0,96 -2,88*** 3983 167

Extended FHI -0,37 -2,95*** 3016 118Polity 0,10 3,11*** 2927 117ACLP -1,01 -2,53** 2783 118

The results from the Fixed-Effects analysis are very strong, in the sense that all the specifications in Tables 3 and 4 show a positive effect from democracy, significant at least at the 10%-level. Only one specification (baseline Polity with ordinary standard errors) is insignificant at the 5%-level, and most specifications are significant at the restrictive 1%-level. According to these results, there are good reasons to “falsify” the claim that democracy

16

as a regime type has no effect on economic growth. The estimated effect on economic growth from these analyses are all positive, and the sizes of the coefficients correspond relatively well to those found when using OLS with Panel-Corrected Standard Errors.

*

Fixed-Effects analysis assumes that each country has it own specific intercept in the regression. Random-Effects analysis moderates this assumption. Random-Effects, like Fixed-Effects, postulates a different intercept for each cross-section unit, “but it interprets these differing intercepts in a novel way. This procedure views the different intercepts as having been drawn from a bowl of possible intercepts, so they may be interpreted as random … and treated as though they were part of the error term” (Kennedy, 2003:304). Under the assumption that the intercept is truly randomly selected, that is they will have to be uncorrelated with the independent variables, RE gives increased efficiency to the estimates when compared to FE. However, RE will be biased if the error term is correlated with any of the independent variables. In our case, there might be reason to believe that for example region, democracy and income level will be correlated with the error term (if we have country-specific factors that generate higher than predicted growth from our model, and that these countries are systematically clustered along one or more of our independent variables), and that results from RE could therefore be biased. This should lead us to interpret RE-results with some caution. I use a Maximum Likelihood-version (ML) of Random-Effects, and the results when I use ordinary standard errors are given in Table 5.

Table 5: Results from Random-Effects analyses (ML) with ordinary standard errors

ModelDemocracy-indicator Coefficient t-value Observations Territories

Baseline FHI -0,27 -3,55*** 4473 167Polity 0,05 2,65*** 3683 146ACLP -0,66 -2,35** 3983 167

Extended FHI -0,30 -3,26*** 3016 118Polity 0,06 2,72*** 2927 117ACLP -0,57 -1,79* 2783 118

The results from the RE analyses in Table 5 are relatively similar to those found in the FE analyses in Table 3 and Table 4, both in terms of t-values and size of coefficients. All six models show a positive effect from democracy, which is significantly different from zero at least on the 10%-level. Five of the six specifications give significant effects on the 5%-level, and four on the 1%-level. These results once again indicate that there is decent evidence in favor of the view that democracy on aggregate increases annual growth in GDP per capita.

When I test for a different specification of the standard errors, namely a calculation of standard errors based on a “Bootstrap” simulation procedure (100 replications), I basically get the same results qualitatively. The only main difference is that the extended Polity model gives us an insignificant democracy-coefficient on the 5%-level. However, when I use the

17

Generalized Least Squares (GLS)-based version of RE instead of the ML version, the results are generally even stronger in terms of absolute size of the t-values, and the Polity variable in the extended model is once again significant on the 5%-level, even when we use Bootstrap-based standard errors.

The main conclusion from the Random-Effects exercise is therefore that democracy seems to have a clear, positive and significant impact on economic growth, and the results are very robust, surviving different specifications of estimation procedure and ways to calculate standard errors.

*

Fixed-Effects analysis in short panels are biased, but this bias can be corrected by using a method developed by Arellano and Bond (1991). This dynamic panel-data method is based on the Generalized Method of Moments (GMM). Even though most panels here are not considered sufficiently short enough to create a bias, I test the different models, both when I include one and two lags of the dependent variable (growth) on the right hand side of the equation. The results from the Arellano-Bond regressions are more mixed than what is the case for Fixed- and Random-Effects. The analysis is a very demanding one, since it controls for the lags of the dependent variable (Democracy in t-1 is probably correlated with growth in t-1 and democracy in t, and one therefore controls away possible relevant effects. One could however include lagged values of democracy, and test whether these together are significant). However, for the basic model, the FHI-variable is positive and significant at the 5%-level. This is true both when I include one and two lags of the dependent variable on the right hand side For the ACLP-measure, the coefficient is significantly different from zero at the 5%-level when I use one lag of the dependent variable, and the p-value is 0,054 when I use two lags. But, the other specifications, both when it comes to the extended model for FHI and ACLP, and for all models with the Polity-index, are insignificant at all conventional levels, and p-values are sometimes as low as 0,5 in absolute value. The signs in all specifications nevertheless indicate that there is an estimated positive effect from democracy on economic growth, even if we often can not reject the null-hypothesis of no effect. The results in terms of the significance of the effect from democracy are therefore more mixed when using Arellano-Bond, than when using Fixed-Effects and Random-Effects.

4.3 Instrumental Variables/2SLSAs was discussed in section 3, the issues of reverse causality and omitted variable bias have to be dealt with in the study of the economic effects of democracy. The economy influences political life and structures, such as regime type (Lipset, 1959, Diamond, 1992, Przeworski et al., 2000, Inglehart and Welzel, 2006), and a correlation between democracy and economic factors does not imply that we have established a causal effect from democracy on these economic factors. We tried to deal with the problem of omitted variable bias in the above analyses, first of all by incorporating a relatively extensive set of control variables, and then by incorporating country specific dummies in the Fixed-Effects analysis. Lagging the independent variable could be seen as one way to try to deal with the issue of reverse causality, by exploiting the temporal sequence of cause and effect. However, there is still the

18

problem that both democracy and growth are positively autocorrelated with their respective past values, and this means that lagging variables is not a fool-proof strategy. We need another strategy to try to deal with the issue of reverse causation. One proposed solution, very often used in econometric literature is to find so-called instrumental variables, or instruments for endogenous independent variables. There are two requirements for a variable to be a valid and “decent” instrument for the independent variable it is supposed to stand as an instrument for. First, it should be correlated with the independent variable. If the correlation is low we will often face very large standard errors for the estimated coefficients. Second, an instrument should not be directly related to the dependent variable. This means that it should only be correlated with the dependent variable through the independent variable it instruments for. The intuition behind the procedure is that we only utilize the “exogenous” part of the variation in the independent variable that is related to the exogenous instrument, and we thereby get a better estimate of the causal effect from the independent variable on the dependent. If this second condition is not satisfied, the resulting estimates from the analysis will not be consistent.

Figure 2: Causal structure underlying Instrumental Variable analysis

The technique I use is often referred to as Two Stage Least Squares (2SLS), and the procedure followed is to first use OLS on an equation where democracy (the endogenous independent variable in the original regression) is the dependent variable, and the instrument(s) and the control variables from the original regression are entered as right hand side variables. We then take the predicted, instead of the actual, democracy values from this first regression and enter them into the original regression equation, and obtain consistent estimates. One “problem” with 2SLS is that we tend to get relatively large standard errors for the coefficient of the endogenous independent variable, especially if the correlation with the instrument is low, and it is therefore often difficult to find significant results.

We now need an instrument for democracy; that is we need a variable that is correlated with democracy but that does not directly affect national economic factors. The empirical literature on the economic effects of democracy has not seen too many IV-analyses when compared to the usage of this econometric technique in other fields. This is maybe not surprising: It is difficult to find instruments that match the two criteria set up above when it comes to this thematic. One exception is provided by Helliwell (1994) who uses past values on the democracy variable as an instrument for present values. I have however earlier claimed to have come up with an interesting suggestion for a valid instrument, when analyzing the effect on democracy on protection of property rights (Knutsen, 2007). The instrument draws on Samuel Huntington’s (1991) observation that the share of democracies as a regime type at a global level has tended to increase in a clustered fashion temporally10. Huntington’s argument

10 The following description of the instruments build on Knutsen (2007).

19

Instrument Democracy Economic factor

is that democratization has historically proceeded in three temporal waves (see Przeworski et al., 2000:??? for nuances and criticisms of this claim).

According to Huntington’s categorization, the first long wave started in 1828 and ended in 1926, and the second wave lasted from 1943 to 1962 (Huntington, 1991:16). The third wave of democratization “began, implausibly and unwittingly, at twenty-five minutes after midnight, Thursday, April 25, 1974, in Lisbon, Portugal, when a radio station played the song “Grandola Vila Morena.” The broadcast was the go-ahead signal for the military units in and around Lisbon to carry out plans for the coup d ‘etat” (Huntington, 1991:3). So-called reverse waves have come between the three waves, and in these reverse waves authoritarianism has triumphed relatively. The first reverse wave lasted from 1922 to 1942 and the second from 1958 to 1975. Since Huntington wrote his book in the early 1990’s, there is no concrete suggestion to when the third wave ended, if it ever has. However, Freedom House Executive Director Jennifer Windsor has stated that “[A]lthough the past 30 years have seen significant gains for political freedom around the world, the number of Free countries has remained largely unchanged since the high point in 1998. Our assessment points to a freedom stagnation that has developed in the last decade”. I will therefore (somewhat arbitrarily) assign 1998 as the end-year of the third wave of democratization.

I will not go deep into the underlying argument of why democratization globally has followed the described pattern, but Huntington links the first wave to the American and French revolutions and the second wave to the allied victory in World War II. Even though Huntington asserts that the third wave started in Southern Europe in the mid-70’s, and soon transplanted to Latin America, many of the democratization processes in Eastern Europe, Central Asia and Africa can be connected to the fall of the Berlin Wall and the Soviet Empire, which are decisive geo-political events. However, not only important geo-political events make democratization come in clusters. We probably also have “contagion effects” or “spill-over effects” between countries, for example neighbours. When Portugal went through democratization, it set a precedent for those in Spain fighting for democracy, and we could perhaps speak of causal effects from one country’s democratization process on other countries’ political processes. This political spill-over phenomenon is certainly not new. The 1848 revolution in Paris set a precedent for revolutionary attempts in Poland and the Netherlands as well as in several “German” states, even though the communication and transport technology of the day lead to some time-lags (letters brought on horsebacks). Historians have recognized the relatively direct effect from the events in France on the (timing of) revolutionary attempts elsewhere (Palmer and Colton, 2002:???). No matter if the direct cause of change in a nations regime comes from underlying global political-economic structures or contagion from neighbouring or other countries, the timing and clustering of democratization experiences can be attributed to factors that are considered as exogenous to national politics. If democratization waves represent such exogenous factors that affect the probability of regime transition to democracy, and these waves are not directly related to economic factors, regime transition for a country within one of these three waves can serve as a valid instrument for democracy.

20

When it comes to the concrete operationalization of the instrumental variable, I use the same methodological approach as Persson and Tabellini (2003), when they propose instruments for type of constitutional arrangement and electoral system11. I use the starting year of the political regime according to Polity as a point of departure, and score countries after whether their existing regime (at a particular point in time) originated in a reverse wave or not. When it comes to constructing instruments based on Huntington’s work, I actually could have used two slightly different operationalizations. The reason is that Huntington’s categorizations are somewhat ambiguous, with overlapping waves and reverse waves. According to Huntington’s categorization of waves, these lasted from 1828-1926, 1943-1962 and 1974-1998 (endpoint not given by Huntington (1991)). However, Huntington also stated that the reverse waves lasted from 1922-1942 and from 1958-1975 (Huntington, 1991: 16). I add the period after 1998 as a reverse wave as well, even if this choice could be discussed. The difference between the two potential IVs, “In Wave” and “Not in Reverse Wave” is that countries with regime change in 1922-1926, 1958-1962 and 1974-1975 are scored differently on the two specifications, and so is the USA (the only country with a regime originating from before the first wave according to Polity). The first, “Wave”, would be scored a 1 if the country-year had its latest regime transition according to Polity within one of the three waves, and 0 if not. The second potential dummy-instrument “Not in Reverse Wave” would be scored a 1 if the last regime change was not in a reverse wave. According to the econometric theory of Instrumental Variable analysis, the preferred instrument is the one with the highest correlation with the endogenous independent variable it is supposed to stand as an instrument for (given that the IVs are exogenous). Both instruments would be consistent, but the one with higher correlation with democracy is expected to be more efficient. According to Table 6, the reverse wave-dummy is the one with the highest correlation with democracy, and I therefore use this variable in the analysis below.

Table 6: Correlation between Huntington instruments and democracy indexes

InstrumentOperationalization of democracy

Aggregated FHI Polity-index ACLP-measure

In Wave -0,15 0,18 -0,24

Not in Reverse Wave -0,33 0,37 -0,38

Number of observations 4893 6900 6772

After having chosen an instrument, I proceed with the 2SLS-analysis, using both the baseline and extended models. The first and second stage regression equations are given below for the baseline model:

First stage:

11 Persson and Tabellini (2003) have no underlying theory for their scoring-procedure, but use relative frequencies of emergence of specific constitutional types and electoral systems globally as their criteria for categorizing time-periods.

21

Second stage12:

Table 7: Results from IV-analysis, with “Not in Reverse Wave” as instrument

ModelDemocracy-indicator Coefficient t-value

t-value instrument in 1st stage Observations

Baseline FHI 0,39 1,22 -14,85 3809Baseline Polity -0,04 -0,62 18,46 3682Baseline ACLP 1,08 0,92 -13,94 3518Extended FHI 0,86 1,66* -9,3 3015Extended Polity -0,12 -1,35 12,8 2926Extended ACLP 1,50 0,76 -8,17 2782

The results from the IV analysis, given in Table 7, do not confirm the general picture from the other analyses conducted above, and give us reason to be careful before concluding that democracy has a positive and significant effect on economic growth. Not only are all results from the different models insignificant at the 5%-level, but the estimated effects from democracy are also negative13. In the extended model using FHI as a democracy indicator, the negative effect from democracy is actually significant at the 10%-level. In contrast, a similar IV-analysis conducted in Knutsen (2007) indicated that democracy in many models had a significant and positive effect on protection of private property rights. The results here deal a blow to the hypothesis that democracy generally affects growth positively, since the 2SLS procedure is deemed to be consistent, even if the democracy variable is endogenous.

However, even if the 2SLS procedure is consistent, it is plagued by large standard errors and uncertainties connected to the estimates, especially if the correlation between the instruments and the endogenous independent variable is relatively low. We can improve upon the certainty in the estimates by including more instruments in the first stage. We do this at a risk however, if we are uncertain about whether the instruments are directly related to the 12 The ^ over the democracy variable in the second stage equation refers to the fact that it is the predicted values of democracy from the first stage regression rather than the actual values that are entered into the equation.

13 Hausman tests show that for some of the models, there is reason to believe that the difference between the PCSTS-estimates and the IV-estimates are systematic. For example when it comes to the baseline model where democracy is operationalized by FHI, we can reject Ho: “difference in coefficients is not systematic”, if we set the significance level to 5%.

22

dependent variable in the second stage. Good suggestions for instruments will be needed to make better 2SLS estimates, and this is something that I hope can be a topic for future research. At this stage, I try to improve upon the efficiency of the 2SLS estimates by using religion- and colonizer dummies as instruments rather than as exogenous independent variables in the second stage of the regression. The standard error for the FHI in the baseline model for example, is almost halved. What is interesting as well is that the models again estimate a quite strong positive effect from democracy on growth, with the baseline FHI- and ACLP models being significant at the 5%-level, and this almost also holds true for the Polity-version with a t-value of 1,94. The extended models however, do not show significant effects14. The problem with these estimates are however that we have assumed that plurality religion and identity of colonizer do not have any direct effect on economic growth outside the influence it has through its effects on the nature of the political regime. If there are independent cultural effects on growth from religion, or if the effect of colonization history works through other channels than this particular institutional one, then the instruments are not valid. Arguments could easily be made that this might be the case, but if these direct effects are small, they might not matter much for the estimates. I did not include geographical region-dummies as instruments, but chose to keep them as controls in the second stage, since there are obvious possible direct economic growth effects from geographical location (demand-spillover from neighbors (Krugman, 1991), climatical and disease-environment effects (Sachs, 2005) or even spread of political instability and civil war (Bøås and Dokken, 2002)). The results from the 2SLS-analyses with colonizer- and religion-dummies as additional instruments are given in Table 8.

Table 8: Results from 2SLS when “Not In Reverse Wave”, colonizer- and religion dummies are used as IV’s

ModelDemocracy-indicator Coefficient t-value

R-squared 1st stage Observations

Baseline FHI -0,39 -2,36** 0,63 3809Baseline Polity 0,07 1,94* 0,55 3682Baseline ACLP -1,35 -2,08** 0,49 3518Extended FHI -0,12 -0,65 0,67 3015Extended Polity 0,03 0,78 0,65 2926Extended ACLP -1,10 -1,53 0,55 2782

4.4 Matching-techniquesRegression-based techniques assume linear effects, and this could be problematic when investigating the research question of this paper. The probable complexity of the relationship means that imposing a linearity restriction might be a too strong assumption to make, and thereby lead to a too crude estimation procedure. Recently, there has been some interest in so-

14 Hausman tests cannot lead us to claim that the difference in coefficients between these model specifications, and the ones from the PCSTS-analysis are different, even at the 10%-level of significance, and one could therefore argue that one could stick with the PCSTS-estimates rather than the IV-estimates, since the former are more efficient, even if they are inconsistent if H0 of similar coefficients is violated.

23

called Matching-techniques among researchers studying political-economic topics, even though the usage is still far from widespread. Persson and Tabellini (2003) use Matching in their study of the economic effects of different forms of constitutions and electoral systems. Matching is a so-called non-parametric estimation technique, where we relax assumptions of functional form. We do not have to make an initial assumption on whether the relationship is linear or have any other particular functional form, and we do not have to assume that the effect is independent of values on contextual variables. As is so often the case in econometric/statistical work however, relaxing strict assumptions bears with it a cost in terms of reduced efficiency in the estimates; that is, we tend to get relatively large standard errors. This is analogous to the situation when we moved from OLS-based PCSTS which assumed that democracy was exogenous, to the more robust, but less efficient 2SLS technique (there is no free lunch, as economists like to point out).

Matching-techniques draw on experimental logic: “The central idea in matching is to approach the evaluation of causal effects as one would in an experiment. If we are willing to make a conditional independence assumption, we can largely re-create the conditions of a randomized experiment, even though we have access only to observational data” (Persson and Tabellini, 2003:138). The main underlying idea is that we split our independent variables into two groups, the control variables, which here consist of all independent variables except for democracy, and the treatment variable, regime type. Further, we need to dichotomize the treatment variable, and assume so-called conditional independence; that is, we need to assume that the selection of democracy is uncorrelated with economic growth and even more specifically that regime type is randomly selected, after we have controlled for the other independent variables. As was discussed in section 3, this assumption might not be warranted (Przeworski and Limongi, 1993).

The technique is then based on the underlying idea that we should compare “most similar countries”. In this sense, the logic does not only reflect that of experiments, but also that of the Most Similar Systems logic utilized in small-n studies in comparative politics (John Stuart Mill’s “Method of Difference”). We make “local” comparisons over units that are relatively similar on all but the variable we are interested in investigating (political regime here), and then we go on and infer something about the effect of this variable after we have looked at how matched units differ on the dependent variable. As Persson and Tabellini (2003:139) puts it, we try to find “twins” or a “close set of close relatives” to each observation, but these most similar countries need to differ on the treatment variable, democracy. The estimated effect of democracy is computed for each of these comparisons, and we then go on and calculate an average of these effects to get our final (generalized) estimate. “Matching allows us to draw inferences from local comparisons only: as we compare countries with similar values of X [characteristics in terms of values on the control variables], we rely on counterfactuals that are not very different from the factuals observed” (Persson and Tabellini, 2003:139)

There are several specifications that need to be made. One is the number of “similar” experiences we want to compare with when estimating effects. A second is whether we want to adjust for possible biases in specific ways (Abadie and Imbens, 2002). A third is how we want to calculate the standard errors. When it comes to the first, I explore different

24

specifications and investigate whether the results differ. Using several cases as matches of course increases the amount of information one bases inferences upon, but we are then at risk of comparing against country-years that are relatively more dissimilar to each other. I therefore explore different specifications and match with the one, two, four and ten most similar observations in different analyses. When it comes to bias-adjustment, I explore both a specification without such adjustment, and a bias-adjustment procedure specified by Abadie and Imbens (2002). Since there is good reason to believe that the standard errors are heteroskedastic (and also correlated) in my data-set, I use “robust standard errors” when I estimate results. STATA calculates such robust standard errors by running through a second matching process (I use one match in this second stage), and in this second stage matches are done between observations that have similar values on the treatment variable (democracy or authoritarian). The resulting standard errors are now heteroskedasticity-consistent, but the problem of autocorrelation of residuals might still pose a problem to the analysis, since there is a panel structure in my data set.

The unit of analysis is country-years. This mean that we also incorporate variation that is due to a country going from non-democratic to democratic or vice-versa in the estimating procedure, as well as information from relatively similar countries that have not changed along this dimension of regime type (cross-sectional information). When keeping the panel-structure, we base our inferences on a very large amount of data, and we do not face methodological problems which will result from comparing countries that have gone through a regime change and are arbitrarily assigned as either democracies or authoritarian regimes due to their specific average over a longer time period. One of the main methodological problems that still do exist however is that we have to dichotomize the democracy - authoritarian continuum, into two arbitrarily selected categories when using the FHI. I deal with this arbitrary element by choosing two different cut-off points for the FHI. The most extensive democracy definition includes countries that have an FHI-score lower than or equal to 3,5, and the more restricted one classifies those countries that receive a score lower than or equal to 2,5 on the FHI as democracies. The ACLP-measure is already a dichotomous one, and is therefore particularly suitable for Matching analysis. I use this measure in addition to the FHI-based dichotomies.

Table 9 provides the results from the analyses based on FHI-constructed dummies, and Table 10 shows the results when using the ACLP-measure.

Table 9: Results from the Matching estimation based on FHI (coefficients show average treatment effect of going from authoritarianism = 0 to democracy = 1).

Model FHI cut-off Matches Coefficient t-value ObservationsBaseline 3,5 1 1,08 2,54** 4473Baseline 2,5 1 1,10 3,42*** 4473Extended 3,5 1 0,48 1,06 3016Extended 2,5 1 0,30 1,00 3016Baseline 3,5 2 0,92 3,04*** 4473Baseline 2,5 2 1,11 4,15*** 4473Extended 3,5 2 0,59 1,98** 3016

25

Extended 2,5 2 0,60 2,59*** 3016Baseline 3,5 4 0,75 2,79*** 4473Baseline 2,5 4 0,95 3,50*** 4473Extended 3,5 4 0,69 2,88*** 3016Extended 2,5 4 0,59 2,51** 3016Baseline 3,5 10 0,74 3,66*** 4473Baseline 2,5 10 0,71 3,31*** 4473Extended 3,5 10 0,86 4,09*** 3016Extended 2,5 10 0,83 4,41*** 3016

Table 10: Results from the Matching estimation based on the ACLP-measure (coefficients show average treatment effect of going from democracy = 0 to authoritarianism = 1).

Model Matches Coefficient t-value ObservationsBaseline 1 -0,36 -0,98 3983Extended 1 -0,45 -1,25 2783Baseline 2 -0,47 -1,51 3983Extended 2 -0,77 -2,56** 2783Baseline 4 -0,45 -1,52 3983Extended 4 -0,71 -2,82*** 2783Baseline 10 -0,50 -2,26** 3983Extended 10 -0,60 -2,69*** 2783

The results in Table 9 generally support the hypothesis that there is a positive effect from democracy on economic growth. The estimated average effect on growth for a country when moving from the (relatively) authoritarian to the (relatively) democratic category varies between extra annual growth of 0,3% and 1,1%. 14 of the 16 models reported in Table 9 show significant effects at the 5%-level, and 11 of the models show effects that are significant at the 1%-level. None of the models utilizing more than one match have insignificant effects from democracy, when we choose the 5%-significance level. The results in Table 10 are a bit more ambiguous when it comes to significance of effects. All specifications using the ACLP-measure show an estimated negative effect of authoritarianism on economic growth, but only half of them are significant at the 5%-level. The estimated effect on annual growth rate of going from a democratic to an authoritarian regime varies between -0,36% and -0,77%. The extended models are here generally the ones with the highest absolute t-values.

However, the results are not robust since the significant coefficients whither away when I use the bias-corrected version of the estimation technique developed by Abadie and Imbens (2002). I checked several of the specifications, but none gave significant results even on the 10%-level. Nevertheless, the sign was always in the same direction as in Tables 9 and 10; that is, the estimated effect from democracy on growth is positive. We can therefore not conclude unequivocally that Matching-techniques indicate a significant positive effect from democracy

26

on economic growth, since it depends on the specifications we make initially of the estimation process.

I have used so-called replacement when I estimate the average treatment effect. This implies that several observations can have the same match. This is according to Abadie and Imbens (2002) the proper way to estimate: “Matching with replacement allows us to reduce biases, since it produces matches of higher quality than matching without replacement… In addition, matching with replacement allows us to consider estimators that match all units, treated as well as controls, so that the estimated effect is the average treatment effect” (Abadie and Imbens, 2002:3). However, there is one problem associated with this property, which might require some further investigation: Some of the observations might excessively drive the results, since they are used over and over again as the best match, and this produces an amount of possible arbitrariness in the results. This point might indicate the relevance of using more matches in the analysis as well, as some single observations are less likely to drive the results “alone”. I investigated the observations used as matches in the extended FHI model with cut-off point 3,5 and one match. Indeed, some of the observations are used several times as matches, with Spain in 1976 being the obvious outlier (a good match for democratic, Western country-years!). Spain was the favored match in a total of 421 instances. No other match came even close, with Zimbabwe 1980 registering as a match in 146 instances, Portugal 1975 in 114 instances Tonga 1982 in 98 instances and South Africa 1981 in 82 instances. Few other observations were used as matches more than 50 times, and the majority of matches were used a far lower number of times (many only used in one or two instances). These considerations point to a “lack of observable data” that can be used as decent counterfactuals, which is generating uncertainty in any analysis attempting to estimate the effect of democracy on growth: There are few relatively rich, modernized countries that are authoritarian, and in earlier years, there were also few relatively poor countries that could be classified as democracies. These problems become extra obvious when we rely on matching techniques, where we attempt to find data that can function as approximate counterfactuals, but this clearly also permeates any regression-based technique15.

In order to check the robustness of the results, I first skipped Spain (all years) from the sample. I then reran the analyses on the extended one-match FHI models, which previously showed insignificant effects. After removing Spain (which was the match for about 10% of the observations), the democracy variable was significant on the 5%-level for both models. The coefficients estimated were far higher than before, with the coefficient for the 3,5 cutoff-specification now being 1,98, indicating an almost 2% increase in annual growth rate when

15 The best test on whether democracy generates growth (in a low-development context) is therefore maybe produced by estimating the effect of democracy after 1990 in the developing world. Partial answers can be provided from my earlier analysis on democracy and growth on specific continents (Knutsen, 2006a). The results can be summarized in this way: There is a very strong and robust positive effect from democracy on growth in Africa, a less strong, but still significant, positive effect from democracy on growth in Latin America, and there is an estimated (and significant) negative effect from democracy on growth in Asia. Especially the performances of authoritarian regimes vary strongly between continents, and this leads us straight into a debate on different types of authoritarian regimes and their effects on the economy (Knutsen, 2006a, Knutsen, 2007).

27

going from a relatively authoritarian to a relatively democratic regime, everything else being equal. If we compare this to the results from the regressions analysis, this is larger than many of the estimated effects of going from 7 to 1 on the FHI, and we of course have to remember that we in the matching analysis dichotomize the variable and measure a group of relatively democratic countries against a group of relatively authoritarian. This is not the same as estimating the difference between the most democratic and most authoritarian countries, as we can do when assuming linearity of effect in regressions. This latter result could of course be taken as evidence for the hypothesis that democracy promotes growth, but it could also be taken as a sign that the matching technique utilized here is not very robust, given the structure of the sample (this is already obvious from the relatively large standard errors we generally face). I then proceeded by deleting the other influential observations mentioned above, Portugal, South Africa, Tonga and Zimbabwe (all years). For the 3,5-specification, the estimated size of the coefficient now drops somewhat to 1,65, but the t-value actually increases from 2,31 to 4,97, indicating that the standard errors have decreased substantially. The positive effect from democracy is now significant at the 1%-level. When we look at the 2,5-version, the results are very similar: the coefficient drops from 1,89 (only Spain out) to 1,38 (Spain, Zimbabwe, Portugal, South Africa and Tonga out), but the t-value increases from 2,41 to 3,50.

4.5 Summing up the resultsXavier Sala-i-Martin wrote an article in 1997 called “I just ran 2 million regressions”, and the follow-up question was what he learned from this exercise? I have not run quite as many regressions on my computer, but I have probably still conducted enough analyses to make the reader bored. What is the point of conducting so many analyses? The point is that we cannot rely on one specification of model, econometric method or democracy indicator, when we conclude when testing our hypotheses, since the results could be driven by the specification. By using many different ways of analyzing the question, one addresses this issue, and does not “hide” some of the uncertainties related to statistical analysis. Moreover, there is no obvious consensus on one best way to estimate the possible relationship. Should we include cross-country variation, or should we control for country-specific factors, as we do in Fixed-Effects? Should we estimate the relationship by assuming linear relationships, or should we rather rely strictly on “local” comparisons, as in matching? Are endogeneity problems grave enough to use the less efficient 2SLS analysis? Should we control for factors like the passage of time? Should we include subjective measures of political rights and civil liberties as we do in Freedom House, or should we rely strictly on observable institutional structures, with the risk of missing elements related to democracy defined substantially? Until anyone comes up with definite answers to these questions, which is unlikely, there is a gain from testing several specifications. When applying a specific method, model and operationalization, we implicitly conduct an “if-then” analysis. When we use Fixed-Effects and the FHI to obtain results, we could interpret the results in the following way: “IF it is relevant to control for country specific effects AND democracy is best operationalized by the FHI AND endogeneity problems are not severe THEN democracy has a positive effect on economic growth significant at the 1%-level”. The long quote from Montgomery and Nyhan (2008) below underlines an analogous point about uncertainty and model selection, but in the more

28

particular case of which independent variables to include in your model16. Presenting one or two selected specifications gives the reader a paper that is easy and simple to read, but also a fake sense of confidence in the results.

“Political scientists who analyze observational data frequently encounter uncertainty about what variables to include in their statistical models. A typical researcher develops theory about a few key explanatory variables and then must choose from a set of possible control variables over which she has much weaker prior beliefs. In such cases, the appropriate set of control variables is often highly uncertain. As a result, researchers frequently estimate a variety of models before selecting one to include in the published version of their research. This practice leads to a number of pathologies. First, it understates our uncertainty about the effects of the variables of interest. Basing inferences on a single model implicitly assumes that the probability that the reported model generated the data is 1, an assumption that is surely mistaken” (Montgomery and Nyhan, 2008: 1-2).

*

Even though the effect from democracy on growth is insignificant at the 5%- level for several important specifications, the statistical evidence in this paper more or less point in one direction: Democracy as a regime type seems on average to be beneficial for economic growth. Nevertheless, this is not the final word on the issue, and the analysis can still be improved upon, especially viewed in light of the methodological problems sketched up in the beginning of this paper. The biggest question mark related to the conclusion that democracy is good for growth is still the endogeneity problem, underlined by the fact that the 2SLS analysis was the analysis that provided the least clear-cut results. This will be an important topic to address in future research.

We might not only be interested in whether democracy has an aggregate effect that is statistically significant from zero, but take our model estimates more seriously and address the question: What is our best guess on the estimated size of the effect on annual economic growth from democracy? Our best guess, not inferred in a strict way, but only by observing the point-estimates in Figure 3, could be approximately 1,6% when using the continuous FHI and comparing the most democratic and most authoritarian countries. When we look at the Matching analyses, now using a dichotomous regime variable, and compare the two categories democratic and authoritarian, the best guess on the effect could be somewhere around 0,7%, viewed in light of Figure 4. We do have to remember of course, that even if these guesses are based on different methodologies and specifications, they all use the same underlying data material. This would make us underestimate the uncertainty in our claims on the reliability of our guesses, if we only look at the spread of the different point estimates in the two figures.

Figure 3: The estimated effect on percentage annual growth of going from 7 to 1 on the Freedom House Index in the PCSTS, FE and RE-analyses from Tables 1,3 and 5.

16 The issue of variable inclusion and exclusion in the regression models will be investigated more thoroughly in future research, both through theoretical reasoning and empirical work.

29

Figure 4: The estimated effects of democracy (dichotomous measures) on annual economic growth from Matching analyses in Tables 9 and 10. Grey arrows show insignificant results at 5%-level

30

0 1 2

0 0,5 1

5. The effects from democracy on protection of private property rights and corruption; a short description of results17.

5.1 Property rightsThe results reported on the effect of democracy on protection of private property rights are drawn from an earlier work (Knutsen, 2007), and just referred to very shortly here. Indeed, protection of private property rights could be seen as one of the potential channels through which democracy could affect economic growth18. This is the argument from Przeworski and Limongi (1993), but they claim that the sign of the effect from democracy on protection of private property rights is inconclusive. This view could be contrasted with the view held by Douglass North (1991) and Mancur Olson (2003) who both argue that a democratic regime type is conducive to property rights protection. Knutsen (2007) surveys five different theoretical arguments on the link between democracy and property rights, and claims that there is no universal a priori conclusion on the sign of the effect, and that the effect probably is highly context dependent, along several specified dimensions of contextual variables. Empirically however, several studies claim that there is on aggregate a strong positive effect from democracy on property rights protection (Helliwell, 1994 and Boix, 2003). I also found a strong, positive and relatively (if not completely) robust effect from a democratic regime type

17 There is one particular issue that is problematic when it comes to using the FHI in this section, namely that property rights protection and corruption related issues enter into the scoring of the FHI itself (Knutsen, 2007). The FHI includes a check question on the degree of property protection in a country, which makes up 1of 25 main check-questions. To be more specific it constitutes 1/15 of the potential score on the civil liberties index. This will lead to a small positive bias between the democracy and property rights measures used, if we claim that these two concepts should be kept analytically separated. “The check question from Freedom House is a dichotomous measure, with a “1” designed to countries assigned a “Yes” to: “Do citizens have the right to own property and establish private businesses? Is private business activity unduly influenced by government officials, the security forces, political parties/organizations, or organized crime?” (Freedom House, 2006). Charles Tilly discusses how for example Jamaica got a degraded score on civil liberties because of violence and crimes to private property (Tilly, 2007:4-6)” (Knutsen, 2007:7).

The same is true for corruption: One of the Civil Liberties 15 check questions sound “Is the government free from pervasive corruption?”. If we claim that democracy is analytically separable from corruption, this is a problem. However, some authors have claimed that rampant corruption actually reduces the degree of democracy itself, by making political institutions function in a way that hinders popular control over public decision making and that retards political equality (Inglehart and Welzel, 2006 and Knutsen, 2006c). Inglehart and Welzel (2006) therefore claim that the FHI actually does not put enough weight on corruption in its scoring procedure, and they propose an alternative measure. I have however argued that this measure is strongly affected by some serious reliability and validity problems (Knutsen, 2006c).

18 In the institiutional- and development economics literature, the effect from a stable property rights system on growth is most often theoreticized to be positive, and econometric studies confirm this prediction (Torstensson, 1994 and Helliwell, 1994). However, the economic growth effects of changing property rights arrangements, depend on how the change is produced, and what the earlier property rights arrangement looked like. One particular change in property rights structures that is probably conducive to economic growth is land reform in inegalitarian societies (Banerjee and Duflo, 2005 and Banerjee et al., 2002).

31

on protection of private property rights. As can be seen in Tables 11 and 12, imported from Knutsen (2007), this was the case both when OLS with Panel Corrected Standard Errors and Fixed-Effects analysis was used. This was also the case when I used IV-analysis with the instruments based (only) on “waves” and “reverse waves” from Huntington (1991), as described above. The estimated size of the coefficients in these analyses were often larger than the estimated effects from for example the PCSTS-analyses, but due to the large standard errors, they were more often insignificant at the 5%-level. Nevertheless, several IV-estimates were significantly different from zero on this level.

Table 11: Main results from PCSTS-analyses: Coefficients for political regime. Property rights protection is dependent variable 19 .

Model Democracy measure Property rights indicator Coeff. P-value Obs. Countries

Basic FHI WGI (1996-2006) -0,21 0,000 1140 167

Basic FHI G&L (1970-2004) -0,20 0,000 1061 126

Basic Polity WGI (1996-2006) 0,02 0,000 845 145

Basic Polity G&L (1970-2004) 0,03 0,006 991 121

Extended FHI WGI (1996-2006) -0,19 0,000 705 118

Extended FHI G&L (1970-2004) -0,18 0,000 890 105

Extended Polity WGI (1996-2006) 0,03 0,000 684 116

Extended Polity G&L (1970-2004) 0,01 0,243 870 105

Table 12: Main results from Fixed-Effects analyses: Coefficients for political regime. Property rights protection is dependent variable 20

Model Democracy measure Property rights indicator Coeff. P-value Obs. Countries

Basic FHI WGI (1996-2006) -0,08 0,000 1140 167

Basic FHI G&L (1970-2004) -0,11 0,004 1061 126

Basic Polity WGI (1996-2006) 0,01 0,105 845 145

Basic Polity G&L (1970-2004) 0,02 0,004 991 121

Extended FHI WGI (1996-2006) -0,10 0,000 705 118

Extended FHI G&L (1970-2004) -0,02 0,686 890 105

Extended Polity WGI (1996-2006) 0,01 0,002 684 116

Extended Polity G&L (1970-2004) -0,01 0,228 870 105

19 The ”basic” and ”extended” models are the same as those used in this paper, with the exception that population is entered linearly and not log-linearly, that degree of urbanization is not included in the extended model, and of course that the dependent variable is not not growth but protection of property rights.

20 The Fixed-Effects analysis with property rights as dependent variable included a linear time trend as a control, in contrast with the other FE as well as RE and Arellano-Bond analyses in this paper.

32

In conclusion, there seems to be relatively good evidence for claiming that democracy on average has generated better protection of private property rights, and further analysis from Knutsen (2007) indicated that for example military regimes were among the worst regimes when it came to establishing secure property rights. Authoritarian monarchies on the other hand fared almost as well as democracies. Authoritarian regimes that faced mainly internal security threats to continuation in power were also worse at enforcing general protection of private property than those that faced external security threats.

5.2 CorruptionWhat about corruption? As for property rights, we lack long time series and reliable data on this variable, but we can in any case try to estimate an average effect. We of course have to remember that these features of the data generate large uncertainties in the results. As we see from Table 13, the t-values of the democracy indicators in PCSTS regressions are even larger when corruption is the dependent variable, than what was the case for economic growth. According to results based on this technique, democracy significantly reduces the amount of corruption as measured by the World Bank.

Table 13: OLS with Panel Corrected Standard Errors: The effects of democracy on control of corruption:

ModelDemocracy indicator Coefficient t-value Observations Countries

Baseline FHI -0,17 -24,77*** 1126 167Baseline Polity 0,02 8,79*** 834 145Baseline ACLP -0,12 -2,76*** 642 167Extended FHI -0,12 -15,07*** 704 118Extended Polity 0,02 5,83*** 683 116Extended ACLP -0,08 -1,72* 473 118

The results from the Fixed-Effects analyses, are as we see from Table 14, not as clear cut as from the PCSTS analyses, in the sense that the two models using Polity show democracy coefficients that are only significant at the 10%-level. The estimated effects are also smaller in size. The ACLP models even show an estimated positive effect from authoritarianism on control of corruption, but we have to remember that the WGI-data start in 1996, and the ACLP-data end in 2002, whereas the Polity-data end in 2004 and the Freedom House-data in 2006. In any case, the coefficients for the ACLP-measure is far from significant at the 5%-level.

Table 14: Fixed-Effects with ordinary standard errors: The effects of democracy on control of corruption:

ModelDemocracy indicator Coefficient t-value Observations Countries

Baseline FHI -0,09 -5,66*** 1126 167Baseline Polity 0,01 1,82* 834 145

33

Baseline ACLP 0,07 1,12 642 167Extended FHI -0,10 -5,37*** 704 118Extended Polity 0,01 1,93* 683 116Extended ACLP 0,06 1,06 473 118

The results from the 2SLS/IV analyses are even more mixed when it comes to evidence in favor of rejecting a H0 of no effect from democracy on corruption. As mentioned earlier, this type of analysis generates large standard errors, especially if the R2 in the first stage is low. Only for the baseline model using FHI do we find a significant effect on the 5%-level, and this result indicates that a higher degree of democracy lowers corruption in a country. These results were generated when only the “Not in Reverse Wave”-dummy were used as an instrument. When I included the religion and colonizer dummies as instruments, both the extended and the baseline models showed that democracy had a positive and significant effect at the 1%-level. However, when I switched to the Polity index, this did not hold true, with the Polity-coefficient being insignificant even at the 10%-level. The ACLP-measure, when used in both the baseline and extended models, gave differing signs, but the t-values for both models were below 1 in absolute values. As in the IV-analysis of democracy’s effect on growth and (but less so) property rights protection, IV-analysis here yielded inconclusive results; that is the results depended on the specification of model and measure of democracy.

5.3 Protection of property rights and control of corruption as causal channelsSome of the main arguments on why democracy might matter for economic growth are often linked to how democracy is assumed to influence economic institutions and economic policies, with protection of property rights and restrictions on corruption being two important factors (Przeworski and Limongi, 1993, Leblang, 1994 and Knutsen, 2006a). Even if there are a priori uncertainty on how democracy might affect these factors, the empirical results from section 5 would lead us to believe that democracy in general increases protection of property rights and reduces corruption (even if the evidence is weaker on corruption). These latter findings probably contribute to an explanation on why democracy is found in section 4 to have a likely positive effect on economic growth.

6. ConclusionsStudents of East- and Southeast Asian politics have often concluded that authoritarianism is the best way to achieve economic growth and development, and that democracy is to be viewed as some kind of luxury. Amartya Sen puts it best:

“[A] great many people in different countries of the world are systematically denied political liberty and basic civil rights. It is sometimes claimed that the denial of these rights helps to stimulate economic growth and is “good” for economic development. Some have even championed harsher political systems – with denial of basic civil and political rights – for their alleged advantage in promoting economic development” (Sen, 1999: 15).

34

The idea is that first you should go about fulfilling the economic needs of the populace, and then, maybe at a later stage, grant them political and civil rights and liberties. Authoritarian regimes are in the eyes of these observers necessary to curb consumption, and thereby increase savings and investments, and they are necessary in order for the state to have some autonomy from partial interest groups that lobby for their own benefit at the expense of society in general. And who other than strong autocrats could push through painful reforms? The South Korean, Singaporean and now Chinese near histories seem to give the ultimate empirical evidence. But as Przeworski et al. (2000) rightfully claims: In order “to assess the impacts of political regimes, we must examine their full record, not just the best performers” (Przeworski et al., 2000:4). We need to take into account all the evidence before making general claims! Maybe it is something idiosyncratic to the East Asian style of autocracy that is particularly beneficial to economic growth (Hveem et al, 2008)? The growth record in African and Latin American dictatorships are not as impressive as for their Asian counterparts, and even the Chilean record under Pinochet, which Rodrik (1997:2) calls “exhibit number two” for the claim that authoritarianism is growth-enhancing, is matched by the growth-record under later democratic Chilean governments. The same is actually (almost) also the case for the South Korean and Taiwanese democratic governments, when they are compared with their authoritarian predecessors.

On the basis of the vast amount of statistical analyses conducted in this paper, there is no evidence that authoritarianism produces higher economic growth in general. On the contrary, democracy is often found to have a positive and significant impact on economic growth rates on aggregate. This study utilizes statistical methodologies that are much sounder than the previously often used OLS on average growth rates over long time spans, and the results are therefore more credible than the results from most previous studies. Non-parametric techniques, like matching for example, have to my knowledge not been used to study this issue before, and it has allowed us to estimate average effects without assuming that the effect is linear and independent of context. In addition to the methodology-issue, this study also improves upon most earlier studies in terms of the amount of data used, and the models do not falsely exclude the important causal channels through which political regime type is expected to influence growth. Our best guess should therefore no longer be that political regime type does not systematically influence economic growth, as many prominent researchers have proposed. Even though the national variations in economic growth rates, especially among authoritarian regimes, is large (Rodrik, 2000 and Knutsen, 2006a), and we indeed have some authoritarian “Tigers”, democracy on average probably enhances the economic growth rate. The same also seems to be true when it comes to curbing corruption and protecting private property rights. “In the end, there is not a single reason to sacrifice democracy at the altar of development” (Przeworski, 2004: 1).

35

7. LiteratureAbadie, Alberto and Guido Imbens (2002). “Simple an bias-corrected matching estimators for average treatment effects”. National Bureau of Economic Research, Cambridge, MA. Technical Working Paper 283.

Alvarez, Michael E., José Antonio Cheibub, Fernando Limongi and Adam Przeworski. “ACLP Data Set. Updated Version”.

Arellano, Manuel and Stephen Bond (1991). “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations”. Review of Economic Studies 58 (2): 277-297.

Banerjee, Abhijit V., and Esther Duflo (2005). “Growth Theory through the Lens of Development Economics.” Chapter 7 (473-552) in: Philippe Aghion and Steven N. Durlauf, [Eds.] Handbook of Economic Growth Volume 1. North Holland: Elsevier.

Banerjee, Abhijit V., Paul Gertler and Maitreesh Ghatak (2002). “Empowerment and Efficiency - Tenancy Reform in West Bengal”. Journal of Political Economy 110: 239-280.

Beck, Nathaniel and Jonathan N. Katz (1995). “What to do (and not to do) with Time-Series – Cross-Section Data”. The American Political Science Review 89: 634-647.

Beetham, David (1994). “Key Principles and Indices for a Democratic Audit” in David Beetham [ed.] Defining and Measuring Democracy. London: Sage Publications.

Beetham, David (1999). Democracy and Human Rights. Cambridge: Polity Press.

Boix, Carles (2003). Democracy and Redistribution. New York: Cambridge University Press.

Bollen, Kenneth A. (1980). “Issues in the Comparative Measurement of Political Democracy”. American Sociological Review: 370-390.

Bollen, Kenneth A. (1990). ”Political Democracy: Conceptual and Measurement Traps”. Studies in Comparative International Development 25: 7-24.

Bollen, Kenneth A. (1993). ”Liberal Democracy: Validity and Method Factors in Cross-National Measures”. American Journal of Political Science 37 (4): 1207-1230.

Bollen, Kenneth A. and Pamela Paxton (1998). “Subjective Measures of Liberal Democracy”. Comparative Political Studies 33: 58-86.

Bøås, Morten and Karin Dokken (2002). Internasjonal politikk og utenrikspolitikk i Afrika sør for Sahara. [International politics and foreign politics in Africa south of the Sahara]. Oslo: Universitetsforlaget.

36

Chang, Ha-Joon (2006). ”How important were the ’initial conditions’ for economic development?: East Asia vs. Sub-Saharan Africa” in Ha-Joon Chang: The East Asian Development Experience. The Miracle, the Crisis and the Future. London: Zed Books.

Clague, Cristopher, Philip Keefer, Stephen Knack and Mancur Olson (2003). “Property and Contract Rights in Autocracies and Democracies” in Stephen Knack [ed.] Democracy, Governance and Growth. Ann Arbor: The University of Michigan Press.

Crick, Bernard (2002). Democracy – A Very Short Introduction. Oxford: Oxford University Press.

De Long, Bradford and Andrei Shleifer (1992). “Princes and Merchants: European City Growth before the Industrial Revolution”. Harvard University and NBER. Working Paper.

Diamond, Larry (1992). “Economic Development and Democracy Reconsidered”. American Behavorial Scientist 35: 450-499.

Fagerberg, Jan (2002). “A Layman’s Guide to Evolutionary Economics”. Oslo: Centre for Technology, Innovation and Culture, University of Oslo. Paper.

Freedom House. “Freedom in the World, Country Ratings”. Data to be found at: http://www.freedomhouse.org/

Goertz, Gary (2005). Social Science Concepts: A User’s Guide. Princeton: Princeton University Press.

Grugel, Jean (2002). Democratization: A Critical Introduction. New York: Palgrave.

Gwartney, James and Robert Lawson. “Economic Freedom of the World Data Set”. Can be downloaded from http://www.freetheworld.com/release.html

Halperin, Morton H., Joseph T. Siegle and Michael M. Weinstein (2005). The Democracy Advantage. How Democracies Promote Prosperity and Peace. New York: Routledge.

Heckman, James J. (1978). "Dummy Endogenous Variables in a Simultaneous Equation System". Econometrics 46: 931-59.

Helliwel, John F. (1994). “Empirical Linkages between Democracy and Economic Growth”. British Journal of Political Science 24: 225-248.

Heston, Alan, Robert Summers and Bettina Aten, (2002). “Penn World Table Version 6.1” Philadelphia: Center for International Comparisons at the University of Pennsylvania (CICUP), October 2002. Data to be found at: http://pwt.econ.upenn.edu/php_site/pwt61_form.php

Hume, David (1969). A Treatise of Human Nature. Harmondsworth: Penguin Books.

37

Huntington, Samuel P. (1991). The Third Wave. Democratization in the Late Twentieth Century. Oklahoma: The University of Oklahoma Press.

Hveem, Helge, Eva Hansson, Carl Henrik Knutsen and Ari Kokko (2008) “Globalisation and national political systems”. Research programme proposal within the ‘Sigtuna Network on East Asia’.

Inglehart, Ronald and Christian Welzel (2006). Modernization, Cultural Change and Democracy – The Human Development Sequence. Cambridge: Cambridge University Press.

Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2007). “Governance Matters VI: Aggregate and Individual Governance Indicators 1996-2006”. Washington D.C.: The World Bank. Paper.

Kennedy, Peter (2003). A Guide to Econometrics. Fifth edition. Cambridge, MA: The MIT Press.

Knutsen, Carl Henrik (2006a). “Political regime types and economic growth: Are democracies better at increasing prosperity”. Oslo: University of Oslo, Departement of Political Science. Master thesis.

Knutsen, Carl Henrik (2006b). “Democracy and growth in the Post-Cold War era”. Oslo: University of Oslo. Article written for Oslo Summer School in Comparative Social Science Studies.

Knutsen, Carl Henrik (2006c). “Measuring effective democracy”. Oslo: University of Oslo. Article written for Oslo Summer School in Comparative Social Science Studies.

Knutsen, Carl Henrik (2007). “Democracy and Property Rights - A theoretical and empirical analysis on the effects of political regime type on property rights arrangements?” Oslo: University of Oslo, Department of Economics. Master Thesis.

Krugman, Paul (1991). Geography and Trade.Cambridge, MA: The MIT Press.

Lipset, Seymor Martin (1959). “Some Social Requisites of Democracy: Economic Development and Political Legitimacy”. The American Political Science Review: 69-105.

Marshall, Monthy G. and Keith Jaggers (2002). “Polity IV Project – Dataset Users’ Manual”. College Park: Program Center for International Development and Conflict Management, University of Maryland. Manual.

Montgomery, Jacob and Brendan Nyhan (2008). “Bayesian Model Averaging: Theoretical developments and practical applications”. Durham: Duke University. Paper.

Nanto, Dick K. (2006). “North Korea’s Economic Crisis, Reforms and Policy Implications” in Young Whan Kihl and Hong Nack Kim [eds.] North Korea – The Politics of Regime Survival. Armonk, NY: M.E. Sharpe.

38

North, Douglass C. (1990). Institutions, Institutional Change and Economic Performance. New York: Cambridge University Press.

North, Douglass C. (2000). “Institutions and Economic Growth: A Historical Introduction” in Jeffrey a Frieden and David Lake [eds.] International Political Economy. Perspectives on Global Power and Wealth. Boston: Bedford/St. Martin’s.

Olson, Mancur (2003). “Dictatorship, Democracy and Development”. Chapter 6 in Stephen Knack [ed.] Democracy, Governance and Growth. Ann Arbor: The University of Michigan Press.

Palmer, R.R., Joel Colton and Lloyd Kramer (2002). A History of the Modern World. New York: McGraw-Hill.

Persson, Torsten and Guido Tabellini (2003). The Economic Effects of Constitutions. Cambridge, MA.: The MIT Press.

Przeworski, Adam (2004). “Democracy and Economic Development” in Edward D. Mansfield and Richard Sisson [eds.] The Evolution of Political Knowledge. Columbus: Ohio State University Press. Online version at http://politics.as.nyu.edu/docs/IO/2800/sisson.pdf.

Przeworski, Adam and Fernando Limongi (1993). “Political Regimes and Economic Growth”. The Journal of Economic Perspectives 7 (3): 51-69.

Przeworski, Adam and Fernando Limongi ( 1997). “Modernization: Theories and Facts.” World Politics 49 (2): 155-183.

Przeworski, Adam, Michael E. Alvarez, José Antonio Cheibub and Fernando Limongi (2000). Democracy and Development: Political Institutions and Well-being in the World, 1950-1990. New York: Cambridge University Press.

Rodrik, Dani (1997). “Democracy and Economic Performance”. Prepared for conference on democratization and economic reform in Cape Town, South Africa. Conference paper

Rodrik, Dani (2000). “Institutions for High-Quality Growth: What They Are and How to Acquire Them”. Cambridge MA.: National Bureau of Economic Research. NBER Working Paper no 7540.

Romer, Paul (1990). “Endogenous Technological Change”. Journal of Political Economy 98: 71-102.

Sachs, Jeffrey (2005). The End of Poverty. London: Penguin Books.

Sala-i-Martin, Xavier X. (1997). “I Just Ran Two Million Regressions”. The American Economic Review 87: 178-183.

39

Shleifer, Andrei and Robert W. Vishny (1993). “Corruption”. The Quarterly Journal of Economics 108: 599-617.

Sen, Amartya (1999). Development as Freedom. New York: Anchor Books.

Solow, Robert M. (1956). “A Contribution to the Theory of Economic Growth”. The Quarterly Journal of Economics 70: 65-94.

Strand, Håvard (2007). “Measures and Mismeasures of Democracy”. Oslo: University of Oslo, Department of Political Science. Working Paper.

Tavares, José and Romain Wacziarg (2001). “How democracy affects growth”. European Economic Review 45: 1341-1378.

Tilly, Charles (2007). Democracy. New York: Cambridge University Press.

Torstensson, Johan (1994). “Property Rights and Economic Growth: An Empirical Study”. Kyklos 47: 231-247.

World Bank. (2007). World Development Indicators 2007. Washington D.C.: World Bank. Edited internet version at: http://devdata.worldbank.org/wdi2007/contents/cover.htm

Young, Alwyn (1995). “The Tyranny of Numbers: Confronting the Statistical Realities of the East Asian Growth Experience”. Quarterly Journal of Economics 110: 641-680.

40