COUNTRY SIZE AND GOVERNMENT SIZE

COUNTRY SIZE AND GOVERNMENT SIZE: A REASSESSMENT MICHAEL JETTER AND CHRISTOPHER F. PARMETER

Abstract. This paper uncovers a robust effect of the urbanization rate on government spending, with population size playing only a minor role. We extend Alesina and Wacziarg’s (1998) theoretical analysis and predict a positive effect of the urbanization rate on government size, while agreeing with their theoretical findings regarding the ambiguity of population size as a possible determinant of government size. Our panel data analysis of 161 countries confirms this prediction: urbanization causes higher government spending, whereas the negative effect of population size appears much weaker. We observe a strong positive effect of urbanization on the size of government, especially for the OECD countries, Asia, and Eastern Europe. Our results are robust to the inclusion of various control variables, fixed effects, dynamic estimations, and the removal of outliers, influential observations or small nations.

1. Introduction The world population has more than doubled over the past 50 years, from around 3 billion people in 1960 to over 6.8 billion in 2010. A New York Times article in 2007 titled “UN Predicts Urban Population Explosion” highlights where this rise in population is mostly taking place: urban areas. The proportion of people living in urban areas has strongly increased over time as illustrated in figures 1 – 3, from 37 % in 1975 to 50 % in 2009, with a prediction of almost 57 % for the year 2025.1 The article also touches on some possible consequences from this substantial increase in urbanization: “Poverty is increasing more rapidly in urban areas, and governments need to plan for where the poor will live rather than leaving them to settle illegally in shanties without sewerage and other services.” Furthermore, the article suggests that if governments ignore urban growth, severe problems of insecurity and violence could be on the horizon. Indeed, governments might play a crucial role in responding to changes in population size and its distribution. But why and how? Alesina and Wacziarg (1998) provide an explanation for why government consumption per capita may shrink in response to general population growth: the non-rivalry of public goods makes per capita costs decrease when spread out over more people. We are adding to their analysis, both by providing a deeper look at population size and by highlighting the role of the urbanization rate in this context. The general urban lifestyle differs fundamentally from living in a rural area. Being closer to fellow citizens, you are bound to have more encounters with more people, in professional as well Date: March 16, 2012. Key words and phrases. Government Size, Population Size, Urbanization. Christopher F. Parmeter, Corresponding Author, Department of Economics, University of Miami, 305-284-4397, e-mail: [email protected]. 1 See http://esa.un.org/unpd/wup/maps_1_2025.htm for more details.

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MICHAEL JETTER AND CHRISTOPHER F. PARMETER

as in private settings. Consequently, an urban citizen experiences more points of friction with other people, which may require a better definition of rules and regulations. Take the simple example of playing loud music in your apartment: with your neighbors living a block away, nobody might complain about your taste in music. Living in a high-rise building increases the chances of complaints substantially. In the latter case, governments are eventually more likely to get involved by enforcing existing regulations and possibly by creating new ones.2 Another difference between urban and rural areas lies in the awareness of income gaps. Taking the metro to work, you likely see various neighborhoods and different income groups on your trip, not to mention the variety of people sharing your metro ride. In consequence, income gaps, but also differences in health and education standards, are more visible in a city. This may result both in a stronger urge for security and/or more consciousness about redistribution – in short, a more detailed and possibly an expanded definition of social rules and regulations. Finally, consider the metro itself: setting up and maintaining an expensive public transportation system is a job which governments are bound to execute in urbanized areas. In general terms, cities require enormous public infrastructure investments, as noted by Henderson (2005). Notice that these effects are neither related to population size nor to population density: consider two countries with the same land area. Country A counts 1,000,000 people spread out over 10 towns as opposed to country B with 1,000,000 people living in one big city. Population size and density are the same, but a resident of country B is very likely to have more encounters with her fellow citizens. Finally, there may very well be an element of self-selection involved in this connection between urbanization and government size. Somebody who chooses to live in a city may well differ from a rural habitant in terms of her genuine social attitude. Thus, people’s social attitude could affect both the urbanization rate and the size of government. Regarding previous works on urbanization and its macroeconomic effects, various angles have been taken. For example, urbanization has been shown to decrease urban-rural inequality (Lu and Chen, 2004) and labeled as one of the engines of growth (Bertinelli and Black, 2004, or Zhang, 2002).3 Henderson (2002) argues for an ideal individual degree of urbanization, depending on size and development of a country.4 Ravallion (2001) identifies conditions under which the poor urbanize faster than the non-poor, putting urbanization in the context of poverty. Finally, various case studies – such as Hope (1998) for Africa, Weber and Puissant (2003) for Tunis, Zhang and Song (2003) plus Chen (2007) for China to name a few – are analyzing the aspects of urbanization in specific areas. However, none of these studies develop a general connection between urbanization and the size of government. Similarly, the literature on determinants of government size is very diverse. One approach considers the political aspect, such as the influence of interest groups, both in general (e.g. Mueller and Murrell, 1990), but also in the context of inequality within vs. across groups (Lind 2007). 2

A similar example would be traffic accidents. Other papers such as Henderson (2005) and Bloom, Canning, and Fink (2008) find a weaker effect on growth though. 4 For his detailed analysis of urbanization and growth, see Henderson (2005). 3

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There exists a stream of literature analyzing fiscal decentralization in this context, in addition to theoretical and empirical work on whether income and its distribution are significant determinants of government size.5 An ongoing debate regarding the effects of trade openness has been opened by Rodrik (1998), who argues that government spending could play a risk-reducing role when countries decide to open their trade borders, suggesting that the relationship between trade openness and government size is positive. Various papers have since further investigated this relationship with mixed results.6 In addition, more natural explanations of government size are proposed, such as deindustrialization (Iversen and Cusack, 2000) or the increase in female labor force participation (Cavalcanti and Tavares, 2011). Finally, the paper closest to our approach is the previously mentioned work by Alesina and Wacziarg (1998, A&W from here on), which suggests a negative causality running from the size of population to public spending. We extend their theoretical model by introducing urbanization as a potential factor regarding preferences, but also add to their empirical analysis in this context. Specifically, we are able to use panel data spanning from 1960-2009, which increases our sample considerably from 137 observations in A&W to 1380 observations in our basic estimations. Moreover, all values of our independent variables are taken prior to the dependent variable, whereas A&W regress the average of government consumption from 1985-1989 on the urbanization rate of 1990 – which may be one reason why urbanization has not been recognized before as a serious determinant of government size. Our main results suggest that the urbanization rate has a robust positive effect on the extent of public spending. Population size on the other hand appears to be a much weaker determinant, contrary to A& W’s (1998) results. This conclusion is specifically pronounced in the OECD countries, Asia, and Eastern Europe. As an extension, we test our theory on specific forms of government spending (health, military, and education) and find that urbanization specifically affects public health expenditures.7 Finally, our results are robust to various robustness checks, removing outliers, influential observations and small nations, etc. The remainder of the paper is structured as follows. Section 2 introduces our theoretical model. Section 3 presents our empirical results starting with the description of our data and methodology, leading into main findings, the move to a balanced panel, dynamic regressions, a comparison across regions and time, and eventually a look at different forms of government spending. Section 4 offers conclusions and avenues for further research. 2. The Model 5

For fiscal decentralization, see Marlow (1988), Grossman (1989), Persson and Tabellini (1994), Jin and Zou (2002), and Cassettee and Paty (2010). Looking at income as a possible determinant, Wagner’s Law comes to mind, suggesting that higher income is accompanied by a bigger government. See Meltzer and Richard (1981) for a theoretical model relating income inequality to government size, but also Gouveia and Masia (1998) for an empirical rebuttal of their hypothesis. 6 For instance, Molana, Montagna, and Violato (2004), Liberati (2006), Benarroch and Pandey (2008), and Ram (2009) do not support Rodrik’s (1998) hypothesis. On the other hand, papers such as Epifani and Gancia (2009) support and extend Rodrik’s conclusion. 7 However, the latter result comes after losing a significant amount of observations due to data availability, so it should be taken with caution.

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2.1. Setup. This section provides a basic theoretical intuition for the causality running from population size and urbanization to the size of government by expanding Alesina and Wacziarg’s (1998) model. Consider n individuals, each earning an income of y and caring about 2 goods: aggregate consumption of the non-rival public good, g, and per capita consumption of the private good, c. All people have identical preferences with one distinction: s individuals (with s≤n) live in urban settings as opposed to (n − s) living in rural areas. In the following, µ determines everybody’s preference for the public good and α the elasticity of substitution. However, we assert that urban people have an additional desire for the provision of the public good, stemming from living closer to their fellow citizens. This additional preference is displayed by δ.8 In light of A&W, we conjecture the social planner’s problem of the optimal goods’ provision for the entire society as a CES utility function given by Some general questions: Should we have a fixed population or allow population growth? What would happen if we allowed y to be randomly distributed across individuals? Also can we develop an optimal urbanization rate or an optimal split of rural/urban populations? Does the private good have to be constant over individuals? What would happen if we let s be endogenous in the model as opposed to exogenously given? 1/α U = (n − s)µg + (n − s)(1 − µ)c + s(µ + δ)g + s(1 − µ − δ)c ,

(1)

α

α

α

α

where the first two terms address the rural population’s utility of the public and private good, while terms three and four consider the utility of our urban population. This utility function can be simplified to 1 s α α s α 1/α g + 1−µ−δ c . (2) U =n µ+δ n n As a final assumption, consider the overall budget constraint with individual income y:

(3)

ny = nc + g.

Notice that this is an exact replication of A&W’s budget constraint underlining the non-rivalry of the public good.9 With these assumptions in mind, we now turn to the solution of our simple model. 8See the introduction for a detailed explanation. Completing our setup of society’s utility are the usual assumptions

of α≤1 and µ + δ < 1 in this case. 9The assumption of non-rivalry can be relaxed if one argues that many public goods are in fact rival, like transfer payments. In this case, we can attach an additional “price term” to the public good, for example y = c + g(γ + 1/n). However, this would not change our qualitative results, so we simplify by assuming that the representative public good is of a non-rival form. Similarly, per capita and societal budget constraints are in this case equal since y = c + g/n is equivalent to equation (3).

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2.2. Theoretical Results. Given the above assumptions, it is straightforward to conclude the optimal provision of both goods. For the public good, i.e. the ideal size of government in this setup, we get y

g∗ =

(4)

1 n

+

s 1−µ−δ n µn+δs

1 1−α

which leads to a ratio of public spending to aggregate GDP of (5)

g yn

∗

1

= 1+n

s 1−µ−δ n µn+δs

1 1−α

.

Two main conclusions can be drawn from using comparative statics here: (i) The effect of an increase in population on government size is ambiguous – it depends on the parameters as well as on the actual urbanization rate. For instance, a country with an entirely rural population (corresponding to s = 0) allows three possible conclusions, depending on the degree of substitutability between the public and private good: if α < 0, an increase in population has a negative effect on government size. For 0 < α < 1 the effect is positive and if α = 0 there is no effect. This result is an exact reproduction of A&W. However, for 0 < s < n the conclusion becomes more complicated as the result also depends on the given preference parameters, δ and µ, as well as the degree of substitutability α. Thus, as in A&W we have to leave the determination of the effect of population size on government size to the empirics. Could we have a figure or phase diagram that shows where the different boundaries lie? (ii) An increase in the urbanization rate – an increase in the ratio ns – unambiguously leads to ∂

g

yn a bigger government as ∂s > 0. Notice that even for α = 0 (unit elasticity of substitution) the effect remains positive, as opposed to above, when looking at population size.

In summary, our theoretical model predicts a clear positive causality running from the urbanization rate to government spending. The relationship between the general size of population and the size of government depends on parameters which are very difficult to observe such as the degree of substitutability or preferences for public and private goods and services, which may be country-specific. This makes general analysis of the impact of country size difficult to assess. The following sections take our model to the data, testing whether the result for the urbanization rate is confirmed as well as investigating what effect the size of population actually has on government size. 3. Empirical Part 3.1. Data and Methodology. All variables in this analysis come from the World Development Indicators, a data set released by the World Bank. Our dependent variable is the 5-year average

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of government size (from time t until t + 4), defined as “general government final consumption expenditure in % of GDP”, whereas all our explanatory variables are taken at time t. Our main independent variables are the urbanization rate, the natural logs of population size, life expectancy, population density, trade, and GDP per capita.10 In addition, we also use public spending on health, military, and education, each as total % of GDP, in one particular subsection of our results. We are using yearly data spanning from 1960 – 2009, with our pooled sample consisting of 161 countries (1380 observations), a second sample only consisting of countries with at least 5 available observations of 5-year intervals (908 data points), and our balanced panel from 1970 – 2005 with 90 countries (720 observations). Table (10) displays each country’s data availability. The characteristics of our data confirm previous observations regarding urbanization: the world population is concentrating itself in cities over time. Figure 4 presents kernel density estimates of the urbanization rate for our balanced panel at 10 year intervals, beginning in 1965.11 As we immediately note, the density of the urbanization rate has experienced a severe rightward shift, suggesting that more and more people are moving to urban areas. Figure 5 shows the quartiles for both the urbanization rate and the logarithm of population over time. We can see that across all three quartiles the distribution of both variables is moving up over time. Government size is also increasing over time: in 1960 government spending marked 11.7 % of GDP on average, whereas in 2009 this number rose to 16.5% in our pooled sample (balanced sample: 14 % in 1970 (Should this be 1960?) vs. 17 % in 2009). 3.2. Main Results. In our baseline analysis we use all observations available for a given specification. Table 1 contains estimates from pooled OLS estimation with our main focus on the effects of the urbanization rate and population size. In the first and most simple specification, we only use these two covariates as determinants of government size. As predicted, the urbanization rate shows a significantly positive effect on public spending. Population size returns a significantly negative coefficient, which confirms A&W’s main conclusion. The interpretation of the coefficient on the urbanization rate suggests that ceteris paribus a 1% point increase in the urbanization rate leads to a 0.04% point increase in government spending (as a percentage of GDP). Given that population size is measured logarithmically, the semi-elasticity estimate suggests that a 1% increase in the size of a country leads to a 0.01% point increase in government spending, ceteris paribus. From this simple specification we see that increases in urban populations lead to approximately a four fold increase in government spending (in absolute magnitude) than equivalent changes in population. While these effects may appear small, consider a simple back-of-the-envelope calculation. For a country whose GDP is 10 billion, a 0.04% point change results in an additional 400 million in spending, so these effects are economically meaningful as well. 10A city with over 750,000 people counts as urban. Trade is measured as percentage of GDP. I am not sure what

the 750,000 people counts as urban means? Is this the World Bank’s definition? Is there anyway we could test the robustness of our findings to this result? 11We use a normal kernel with rule-of-thumb bandwidth.

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In specifications (2) – (6) we gradually add explanatory variables deemed important in determining the size of the public sector: life expectancy, population density, trade, and GDP/capita. Given our broad mix of countries and their individual historical backgrounds in terms of political aspects, wars, cultural and regional differences etc., we include country fixed effects in columns (2), (4), and (6), which amounts to within estimation in a panel framework. We immediately notice two features. First, urbanperc is positive and statistically significant in every specification except (5), while ln(pop) is negative and statistically significant in the specifications that do not include country specific fixed effects. A generic explanation for the latter result is that ln(pop) is slow moving over time, not leaving enough period to period variation to reliably uncover the impact that changes in population have on government spending. Further, accounting for country specific effects we see that our R2 is above 0.6, suggesting that our model does an adequate job explaining variation in government spending across countries and time. To get a better idea of how our variables are related, table 3 presents the correlation matrix amongst our key variables. Notice that urbanperc is highly correlated with both ln(lif e) and ln(gdp). These high correlations may underscore the insignificance we witnessed in column (5) in table 1, as opposed to a true lack of effect of urbanperc on government spending. In fact, if we check the correlation between urbanperc and ln(lif e) and ln(gdp), removing country specific variation, we obtain a much lower correlation between urbanperc and ln(gdp) (almost 50% lower), which coincides with our findings in columns (4) and (6) of table 1. The high correlation with output is also interesting from an applied point of view, suggesting richer countries have higher urbanization rates. Table 2 contains estimates from the models in table 1, but including an interaction between urbanperc and ln(pop). The interaction can be thought of as an absolute measure of urban population since urbanperc is measured as the number of people living in an urban environment divided by the total population of the country. This will not be an exact absolute measure since the interaction 12 Thus, the models presented in Table 2 will equal ln(pop) pop ·# people living in an urban environment. capture both relative and absolute effects of urbanization on government spending. We see that the interaction term is significant across all of the specifications. However, even with this significance, the models which include fixed effects produce a large standard error on the coefficient estimate for ln(pop) which implies that the partial effect of ln(pop) on government spending is insignificant across wide ranges of urbanperc. In figure 6, we plot out the partial effects of urbanperc and ln(pop) on government spending from the coefficient estimates in table 2, column (2). The solid lines are the estimated effect while the dashed lines are the 95% confidence bands. We have also plotted out the origin to gauge where these effects are statistically significant. The impact of urbanperc on government spending is highly statistically significant and positive for ln(pop) > 15. In other words, for countries with more than approximately 3.3 million people, the urbanization rate seems to have a strong positive 12Notice that the scaling ln(x)/x converges to zero very quickly.

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effect on the size of government.13 This suggests that the governments of large countries spend even more money than smaller countries with identical urbanization rates. Again, this could reflect an absolute versus a relative effect. Moreover, we see that as countries become more urbanized, the impact of the size of the population is negative (or constant), but statistically insignificant. 3.3. Moving to a Balanced Panel and Including Time Fixed Effects. Michael, I changed this a little bit. I did not see much value to the table with fixed, time and fixed and time effects in it to our overall analysis. We can add it back in. I have simply commented it out and added in my own discussion. To present a more homogeneous set of results we further refine our analysis. First, we remove observations where any of the six covariates or government spendings is missing. Second, we focus our attention on three specific subsamples from this filtered dataset. Our primary subsample includes all observations where no missing values occur in any variable across the time period 19652005. Our next subsample restricts the analysis to countries who have no missing values and have at least five of the nine time periods available. Lastly, our balanced sample includes only countries which have all eight time periods available over the period 1970-2005.14 These results appear in table 1. Columns with an ‘A’ represent the primary subsample, columns with a ‘B’ our filtered, but unbalanced sample and columns with a ‘C’ our balanced sample. We immediately notice that the effects on both urbanization rate and population are larger than they were in our earlier analysis. This suggests that our more homogeneous sample provides even further evidence on the impact of urbanization on government spending. Moreover, aside from the baseline models which do not include additional determinants of government spending, population size is strongly statistically insignificant whereas urbanization is strongly statistically significant in every specification except (3C). It is interesting to note that the impact of urbanization diminishes (while remaining economically and statistically significant) as we add more predictors of government spending. Initially the impact is around 0.1% points but decreases with the full set of determinants to 0.075% points, a 25% fall in magnitude. In summary, it appears that the urbanization rate is a more consistent predictor of government size than population size – a result that contrasts with the main findings of Alesina and Wacziarg (1998), but confirms our theoretical predictions. Finally, in our most restrictive specifications, (3A) – (3C), the urbanization rate remains significant on the 10 % level for the pooled sample and keeps its predicted sign when moving to the balanced panel. On the contrary, population size is not significant and even switches signs. 3.4. Dynamic Regressions including lagged Variable of Government Size. Michael, no time effects are in these dynamic regressions. They all include country fixed effects 13Of the 216 countries in our dataset, 135 have populations over 3.3 million people. 14We did estimate a balanced panel covering the period 1965-2005, however, this resulted in the loss of 25 countries

– many of which were OECD nations – and so we decided to start our balanced panel in 1970. Regarding robustness checks, removing small nations, influential observations or outliers did not amount to notable changes in our results. Also, we did not find notable differences in results when looking across quantiles. All these specifications are available upon request.

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and only use the balanced panel. We can use a larger panel, but this causes problems since we need lags to have viable observations. So sticking with balance keeps things manageable. Also, R2 in this setting is not really enlightening, so I did not include it. Table 5 reports estimates using the fully balanced panel from a dynamic panel framework (Arellano and Bond, 1991). We keep the same structure as our earlier specifications, adding in a single lag of government spending as well as either one or two lags of both urbanperc and ln(pop). We see that across all specifications the lag of government spending is heavily statistically significant. More interesting is that with the presence of the lag of government spending, the impact of urbanization on current government spending is more than double what we saw in our baseline, balanced panel data estimates. These estimated effects are also highly statistically significant. The lag structure for both urbanperc and ln(pop) does not appear robust. Adding in a two period lag for both of these variables does not result in individually (or jointly) statistically significant first lags for these variables. Moreover, the impact of urbanperc is very stable in the dynamic, fixed effects setting as additional controls are added. We also witness evidence that population size has a statistically significant effect on government spending in this setup suggesting that population dynamics may play an important role in the A&W model. 3.5. Looking Across Regions. As mentioned before, our sample includes countries with a variety of different backgrounds – in terms of history, geography, climate and/or politics, just to name a few aspects. Given these differences, analyzing our theory for different groups of countries might give interesting insights as to where our findings are robust and where not. In this light, we create 7 subsamples corresponding to traditional regions: OECD stands for the OECD countries, SSA for Sub-Saharan African nations, LAC for Latin America and the Caribbean, MENA for the Middle East and North African region, Asia for Asian nations, EER for the group of countries within the Eastern European Bloc, and SEA for South East Asian countries. Table 6 presents estimates across regions including country fixed effects and different controls to match up with the models estimated in Table 1. One can immediately notice that the effect of urbanperc on government spending is strongly significantly positive in the OECD nations and Asia. EER first shows only a weak significance for the urbanization rate, but then the effect becomes stronger in our final specification including all control variables. The effect is insignificant, and sometimes even negative, for SSA, LAC, MEAN, and SEA. As for population size, there is no distinct pattern noticeable for either region – confirming our previous findings when we control for country fixed effects in table 1 for instance. Figure 7 presents the estimates on urbanperc and ln(pop) across regions, including country specific fixed effects, along with 95% confidence bands (This is panel 1 from table 6, right? Using panel 3 from that table might be nicer, mainly because urban in EER is also significant, but population size is not in Asia. Given that we’d rather want a new finding of the urbanization rate being stronger than pop. size to distinguish from A&W, it might be better to choose panel 3. Minor question of displaying results

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though...). The estimates to the far right on each plot are the full sample estimates found in table 1. The bottom panel is also interesting as it further illustrates what we have seen from our earlier estimates. The effect of country size on government spending appears to vary across regions and this effect is hard to pin down when we include country specific fixed effects. Only Asia has an effect that provides qualitatively the same insights regarding ln(pop) as our initial results. Note that these results do not appear to stem from sample size issues as the SSA and LAC regions have more observations in sum than both the OECD and Asia regions. Furthermore, the Asia region only counts 6 more observations than the EER region. Thus, the impacts we are estimating here are more precise for reasons other than differences in sample sizes across the regions. Michael, see table ??, this does not include country specific fixed effects. I think this is better. To summarize our findings across regions, we note that urbanization plays a significant role in determining government size specifically for the OECD countries, Asia, and EER countries. Moreover, population size remains insignificant when adding control variables, confirming our previous findings. 3.6. Looking Across Time. After splitting our sample in different regions, we now examine particular time periods. To assess if the impact of both urbanization and population are changing over time we estimate our benchmark model from Table 4, column (1a), including time specific fixed effects. The period effect coefficient estimates are presented in Figure 8. The presented estimates measure the difference between the baseline period (1965) and the current period. An effect which is not statistically different from 0 implies that the impact of this variable (either urbanpec or ln(pop)) has remained constant over time. As is immediately noticeable, the estimated impact of urbanization does not appear to be changing over time. However, the impact of population appears to be increasing over time (relative to 1965). This effect is suggestive that over time larger countries are spending more. This could be the result of responding to a need for increased infrastructure, providing better public transportation services, etc. than existed in 1965. 3.7. Looking Across Various Forms of Government Spending. So far, we have looked at overall government expenditure. Given the above findings, we now focus on different forms of spending to localize where the urbanization rate and population size actually have their strongest impact. To assess this, we use data on health, military, and education expenditures of the government. Even though our sample size is decreasing quite a bit (now 461, 624, and 732 data points respectively), this still represents a decent number of observations. Tables 7-9 present our three model specifications using an unbalanced panel of countries for government spending on health care, military, and education. The first 3 columns present results using all available observations for the specific measure of government spending. The next set of columns, labeled “All 3 Measures” uses only the country-year observations for which all three measures of government spending exist simultaneously.

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We notice that urbanization strongly maintains its positive effect on health care spending, whereas military and education spending is generally not affected by the rate of urbanization here. Intuitively, this follows our suggestion from the introduction: a stronger visibility of different income groups could provoke a stronger desire for improving health situations of the poor. Another explanation could be that when you interact with more people, you have a stronger self-interest of them being healthy to minimize the danger from transmission of diseases and illnesses. Population size on the other hand appears to have no noticeable effect on either specific form of government spending, except for the basic specification regarding military expenditures. Since the effect of population size theoretically stems from the non-rivalry of the public good (after A&W), this makes sense as military spending represents a non-rival public good. Overall our previous results are confirmed as the urbanization rate seems to be stronger than the size of population as a predictor of government size. However the limited availability of these detailed variables – government spending on health, military, and education – signals caution for the interpretation of these specific results. 4. Conclusions This paper provides an in-depth look at the causality running from population size and urbanization to the size of government. We extend Alesina and Wacziarg’s (1998) model, which relates an increase in population to the degree of public spending, by incorporating the urbanization rate. As people are living more and more in cities, we acknowledge differences between the life of an urban vs. a rural citizen. We assume that living in a city exposes yourself to more interactions with other people, creating stronger enforcement and more detail on part of the social contract. As a result, government gets more involved. Thus, our model predicts a positive causality running from urbanization to government spending. In addition, we confirm the ambiguous effect of population size on public expenditure found in Alesina and Wacziarg’s (1998) model and notice that this ambiguity is strengthened by the introduction of urbanization in this context. Our panel data analysis of 161 countries (90 in the balanced panel) confirms the theoretical predictions as we find the urbanization rate to be a positive significant predictor of government size, especially in countries with over 3.3 million citizens. This effect is also more robust than the negative link between population size and public spending, contrary to Alesina and Wacziarg’s (1998) empirical findings. Beyond our main result, we look across different regions, time periods, and finally distinguish between different forms of government spending. The urbanization rate is specifically a strong positive determinant of public spending in the OECD countries, Asia, and Eastern Europe, whereas population size per se is an insignificant predictor across all regions. Also, when looking at different forms of government spending, the urbanization rate maintains its positive effect on public health expenditures.15 Our results are robust to a dynamic panel data analysis, removing influential countries, small countries or outliers.

15However, in this specific analysis we encounter a strong loss in data points, from 1380 observations in our main

regressions to 461.

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References [1] Alesina, A. and Wacziarg, R. (1998). Openness, country size and government. Journal of Public Economics, Volume 69, Issue 3, Pages 305 – 321 [2] Arellano, M. and Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, Volume 58, Issue 2, Pages 277 – 297 [3] Benarroch, M. and Pandey, M. (2008). Trade openness and government size. Economics Letters, Volume 101, Issue 3, Pages 157 – 159 [4] Bertinelli, L. and Black, D. (2004). Urbanization and growth. Journal of Urban Economics, Volume 56, Issue 1, Pages 80 – 96 [5] Bloom, D.E., Canning, D., and Fink, G. (2008). Urbanization and the Wealth of Nations. Science, Volume 319, Number 5864, Pages 772 – 775 [6] Cassette, A. and Paty, S. (2010). Fiscal decentralization and the size of government: a European country empirical analysis. Public Choice, Volume 143, Numbers 1-2, Pages 173 – 189 [7] Cavalcanti, T. and Tavares, J. (2010). Women Prefer Larger Governments: Growth, Structural Transformation, and Government Size. Economic Inquiry, Volume 49, Issue 1, Pages 155 – 171 [8] Chen, J. (2007). Rapid urbanization in China: A real challenge to soil protection and food security. CATENA, Volume 69, Issue 1, Pages 1 – 15 [9] Coughlin, P.J., Mueller, D.C., and Murrell, P. (2007). Electoral Politics, Interest Groups, and the Size of Government. Economic Inquiry, Volume 28, Issue 4, Pages 682 – 705 [10] Epifani, P. and Gancia, P. (2009). Openness, Government Size and the Terms of Trade. Review of Economic Studies, Volume 76, Issue 2, Pages 629 – 668 [11] Gouveia, M. and Masia, N.A. (1998). Does the median voter model explain the size of government? Evidence from the states. Public Choice, Volume 97, Numbers 1-2, Pages 159 – 177 [12] Grossman, P.J. (1989). Fiscal decentralization and government size: An extension. Public Choice, Volume 62, Number 1, Pages 63 – 69 [13] Henderson, J. V. (2002). The Urbanization Process and Economic Growth: The So-What Question. Journal of Economic Growth, Volume 8, Number 1, Pages 47 – 71 [14] Henderson, J. V. (2005). Urbanization and Growth. Handbook of Economic Growth, Volume 1, Part B, Pages 1543 – 1591 [15] Hope, K.R. (1998). Urbanization and Urban Growth in Africa. Journal of Asian and African Studies, Volume 33, Number 4, Pages 345 – 358 [16] Iversen, T. and Cusack, T. (2000). The causes of welfare state expansion: Deindustrialization or Globalization? World Politics, Volume 52, Pages 313 – 349 [17] Jin, J. and Zou, H.-F. (2002). How does fiscal decentralization affect aggregate, national, and subnational government size? Journal of Urban Economics, Volume 52, Issue 2, Pages 270 – 293 [18] Liberati, P. (2006). Trade Openness, Financial Openness and Government Size. Unpublished Manuscript, http: // www. dauphine. fr/ globalisation/ liberati. pdf [19] Lind, J.T. (2007). Fractionalization and the size of government. Journal of Public Economics, Volume 91, Issues 1-2, Pages 51 – 76 [20] Lu, M. and Cheng, Z. (2004). Urbanization, Urban-Biased Economic Policies and Urban-Rural Inequality. Economic Research (in Chinese), Volume 21, Issue 6, Pages 50 – 58 [21] Marlow, M. (1988). Fiscal decentralization and government size. Public Choice, Volume 56, Number 3, Pages 259 – 269 [22] Meltzer, A.H. and Richard, S.F. (1981). A Rational Theory of the Size of Government. Journal of Political Economy, Volume 89, Number 5, Pages 914 – 927

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[23] Molana, H., Montagna, C., and Violato, M. (2004). On the Causal Relationship between Trade Openness and Government Size: Evidence from 23 OECD Countries. Working Paper, http: // papers. ssrn. com/ sol3/ papers. cfm? abstract_ id= 716164 [24] Mueller, D.C. and Murrell, P. (1986). Interest groups and the size of government. Public Choice, Volume 48, Number 2, Pages 125 – 145 [25] Persson, T. and Tabellini, G. (1994). Does centralization increase the size of government? European Economic Review, Volume 38, Issue 3-4, Pages 765 – 773 [26] Ram, R. 2009. Openness, country size, and government size: Additional evidence from a large cross-country panel. Journal of Public Economics, Volume 93, Issues 1-2, Pages 213 – 218 [27] Ravallion, M. (2002). On the urbanization of poverty. Journal of Development Economics, Volume 68, Issue 2, Pages 435 – 442 [28] Rodrik, D. 1998. Why do More Open Economies Have Bigger Governments? Journal of Political Economy, Volume 106, No. 5, Pages 997 – 1032 [29] Weber, C. and Puissant, A. (2003). Urbanization pressure and modeling of urban growth: example of the Tunis Metropolitan Area. Remote Sensing of Environment, Volume 86, Issue 3, Pages 341 – 352 [30] Zhang, J. (2002). Urbanization, population transition, and growth. Exford Economic Papers, Volume 54, Number 1, Pages 91 – 117 [31] , Zhang, K. H. and Song, S. (2003). Ruralurban migration and urbanization in China: Evidence from time-series and cross-section analyses. China Economic Review, Volume 14, Issue 4, Pages 386 – 400

14


Figure 1. Urban Agglomerations in 1975, proportion urban of the world: 37.2 %. World Urbanization Prospects, the 2009 Revision.


URBANIZATION


15

16


0.010 0.005

Density

0.015

Figure 4. Kernel density estimates for the Urbanization Rate. Bandwidths are obtained using the Silverman rule-of-thumb selection method.

1965 1975 1985 1995 2000

0

20

40

60

Urbanization Rate

80

100

URBANIZATION

17

Figure 5. Quartiles over Time.

Urbanization Rate Log of Population

14 13 1970

1980

1990 Year

2000

Log of Population

15

50 40 30 20

Urbanization Rate

60

16

70

Quartiles over Time

18


8

10

12

14

16

18

20

22

−2

0 1 2

Logarithm of Population

−4

Effect on Government Spending

0.05 0.15

Urban Percentage

−0.10


Figure 6. Estimated partial effects of urbanperc and ln(pop) on government spending along with 95% confidence bands. The estimates stem from the coefficients in Table 2 with fixed effects.

0

20

10

12

14

16

18

20

22

14

16

18

Logarithm of Population c) Model (6)

20

22

40

60

100

80

100

30 10 −10 −30


0.15 0.05

12

80

30

20

Urban Percentage b) Model (4)

−0.05


10

100

10 0

Logarithm of Population b) Model (4)

8

80

−30 −10


0.2 0.1 8

60

Urban Percentage a) Model (2)

0.0


Logarithm of Population a) Model (2)

40

0

20

40

60

Urban Percentage c) Model (6)

URBANIZATION

19

0.2 0.4

Urban Percentage

● ● ● ● ●

●

● ●

−0.2


Figure 7. Estimated effects of urbanperc and ln(pop) on government spending along with 95% confidence bands across regions.

OECD

SSA

LAC

MENA

Asia

EER

SEA

Overall

5


0

● ●

●

●

● ●

−10 −5


Region

●

●

OECD

SSA

LAC

MENA

Asia

Region

EER

SEA

Overall

20


0.04

Urban Percentage

●

−0.02

●

●

● ●

●

● ●

−0.08


Figure 8. Estimated effects of urbanperc and ln(pop) on government spending along with 95% confidence bands across time.

1970

1975

1980

1985

1990

1995

2000

2005


0.0 0.2 0.4 0.6


Time

● ●

●

●

●

● ● ●

1970

1975

1980

1985

1990 Time

1995

2000

2005

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21

Table 1. OLS Results. Dependent variable is government consumption. Standard errors are reported in parentheses beneath each estimate.

urbanperc ln(pop) ln(lif e) ln(popd)

(1) (2) (3) 0.048 0.074 0.024 (0.007) (0.028) (0.010) -1.166 -1.083 -1.016 (0.091) (0.702) (0.096) 5.951 (1.357) -0.909 (0.116)

(4) 0.103 ( 0.031) 0.170 (15.271) 0.869 ( 2.384) -2.960 (15.345)

ln(trade) ln(gdp) Country Effects # obs ¯2 R

No 1380 0.127

Yes 1380 0.621

No 1268 0.153

Yes 1268 0.628

(5) -0.011 (0.012) -0.412 (0.127) 1.711 (1.574) -1.002 (0.116) 2.099 (0.377) 0.885 (0.218) No 1184 0.175

(6) 0.067 ( 0.031) -4.087 (14.824) 0.036 ( 2.345) 0.508 (14.894) 1.505 ( 0.476) 0.708 ( 0.430) Yes 1184 0.672

22


Table 2. OLS Results. Dependent variable is government consumption. Standard errors are reported in parentheses beneath each estimate. (1) (2) (3) (4) urbanperc -0.387 -0.285 -0.318 -0.296 (0.059) (0.133) (0.061) ( 0.147) ln(pop) -2.465 -2.008 -2.082 -4.179 (0.197) (0.775) (0.211) (15.304) urbanperc · ln(pop) 0.028 0.023 0.022 0.025 (0.004) (0.008) (0.004) ( 0.009) ln(lif e) 4.797 0.008 (1.356) ( 2.397) ln(popd) -0.780 0.519 (0.117) (15.349) ln(trade) ln(gdp) Country Effects # obs. ¯2 R

No 1380 0.16

Yes 1380 0.623

No 1268 0.174

Yes 1268 0.631

(5) -0.315 (0.062) -1.416 (0.238) 0.020 (0.004) 1.680 (1.558) -0.891 (0.117) 2.096 (0.373) 0.616 (0.222) No 1184 0.192

(6) -0.197 ( 0.146) -6.807 (14.879) 0.017 ( 0.009) -0.350 ( 2.351) 2.692 (14.922) 1.329 ( 0.485) 0.600 ( 0.433) Yes 1184 0.672

Table 3. Correlations across main covariates in government-urbanization nexus model.

ln(pop) ln(lif e) ln(popd) ln(trade) ln(gdp)

urbanperc ln(pop) ln(lif e) ln(popd) ln(trade) 0.039 0.715 -0.015 0.172 0.060 0.338 0.227 -0.564 0.303 0.199 0.803 -0.105 0.786 0.208 0.269

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23

Table 4. Panel data regression estimates. Dependent variable is government consumption. Standard errors are reported in parentheses beneath each estimate.

urbanperc ln(pop) ln(lif e) ln(popd)

(1A) (1B) (1C) (2A) 0.11 0.105 0.114 0.098 (0.032) (0.033) (0.035) ( 0.033) -4.30 -4.134 -2.948 -4.365 (0.860) (0.888) (0.923) (14.676) -0.475 (14.757) 3.860 ( 2.442)

(2B) 0.092 ( 0.034) -6.459 (15.180) 1.775 (15.268) 4.081 ( 2.508)

(2C) 0.104 ( 0.036) 19.159 (25.772) -22.652 (25.785) 3.777 ( 2.596)

0.027 908

0.02 720

ln(trade) ln(gdp) ¯2 R # obs.

0.024 1040

0.024 908

0.016 720

0.027 1040

(3A) 0.075 ( 0.034) -2.595 (14.570) -2.243 (14.650) 1.636 ( 2.487) 1.341 ( 0.512) 0.940 ( 0.467) 0.041 1040

(3B) 0.066 ( 0.035) -5.050 (15.027) 0.311 (15.114) 1.657 ( 2.546) 1.534 ( 0.552) 1.068 ( 0.505) 0.046 908

(3C) 0.037 ( 0.038) 14.886 (25.215) -17.887 (25.233) 0.875 ( 2.606) 1.313 ( 0.598) 2.154 ( 0.566) 0.059 720

24


Table 5. Dynamic panel data regression estimates. Dependent variable is government consumption. Standard errors are reported in parentheses beneath each estimate. These estimates include a set of year fixed effects. (1) (2) 0.866 0.876 (0.035) ( 0.038) urbanperc 0.271 0.277 (0.059) ( 0.060) urbanperc− 1 -0.227 -0.232 (0.059) ( 0.058) urbanperc− 2

gf ive− 1

ln(pop) ln(pop)− 1

3.665 21.940 (1.679) (18.203) -4.992 -5.043 (1.782) ( 1.773)

ln(pop)− 2 ln(lif e) ln(popd) ln(trade) ln(gdp)

-0.779 ( 1.933) -18.097 (18.062)

(3) (4) (5) 0.795 0.723 0.740 ( 0.048) (0.042) ( 0.044) 0.216 0.288 0.301 ( 0.065) (0.086) ( 0.090) -0.206 -0.152 -0.164 ( 0.062) (0.154) ( 0.156) -0.032 -0.027 (0.109) ( 0.111) 16.117 7.891 26.615 (17.540) (2.130) (13.388) -3.203 -2.770 -3.273 ( 1.565) (1.402) ( 1.570) -7.200 -6.871 (2.705) ( 2.670) -1.259 -2.084 ( 1.973) ( 1.960) -10.410 -18.421 (17.322) (13.013) 0.313 ( 0.516) 3.370 ( 0.933)

(6) 0.714 ( 0.054) 0.255 ( 0.093) -0.213 ( 0.162) -0.005 ( 0.114) 19.474 (12.442) -1.424 ( 1.653) -5.896 ( 3.185) -2.773 ( 1.995) -10.095 (12.131) -0.652 ( 0.550) 3.942 ( 1.165)

URBANIZATION

25

Table 6. OLS Results. Dependent variable is government consumption. Standard errors are reported in parentheses beneath each estimate. Country specific fixed effects are included. OECD 0.375 (0.052) ln(pop) 0.731 (2.012) ¯2 R 0.782 urbanperc 0.282 ( 0.058) ln(pop) 7.986 (678.314) ln(lif e) 15.506 ( 7.532) ln(popd) -10.244 (678.578) ¯2 R 0.798 urbanperc 0.243 ( 0.061) ln(pop) -616.054 (692.686) ln(lif e) 31.445 ( 11.536) ln(popd) 613.914 (692.872) ln(trade) -2.995 ( 1.104) ln(gdp) -0.226 ( 1.456) ¯2 R 0.817 # obs. 175 urbanperc

SSA -0.044 (0.056) 0.234 (1.297) 0.509 -0.034 ( 0.062) 10643.872 (6482.530) 2.892 ( 3.933) -10645.165 (6482.447) 0.503 -0.054 ( 0.065) 11367.227 (6330.022) 1.316 ( 4.034) -11369.203 (6329.962) 3.704 ( 1.142) 1.300 ( 1.018) 0.54 380

LAC -0.028 (0.057) 3.066 (1.596) 0.702 0.041 ( 0.069) 31.114 (24.585) -6.115 ( 6.897) -28.322 (24.460) 0.717 0.062 ( 0.073) 33.257 (24.787) -5.134 ( 7.096) -30.358 (24.623) -0.147 ( 0.938) -1.445 ( 1.177) 0.717 276

MENA 0.103 (0.136) 1.198 (3.248) 0.413 0.258 ( 0.233) -82.840 (91.147) -3.794 (17.671) 80.314 (92.401) 0.459 -0.054 ( 0.176) -35.583 (59.285) 41.494 (15.668) 25.545 (60.683) -0.924 ( 2.219) -3.128 ( 1.768) 0.736 108

Asia EER SEA 0.221 0.165 -0.099 (0.066) (0.10) (0.082) -7.919 -0.846 0.941 (2.052) (4.24) (1.340) 0.77 0.424 0.834 0.252 0.205 -0.383 ( 0.058) ( 0.133) (0.116) 11.655 12.667 -18.314 (60.627) (185.185) (9.043) -1.997 -13.536 12.443 ( 8.811) ( 13.857) (5.505) -21.545 -10.564 18.302 (61.186) (185.688) (9.748) 0.8 0.399 0.867 0.121 0.309 -0.212 ( 0.054) ( 0.14) ( 0.143) 14.886 13.075 -12.036 (51.974) (158.01) (12.176) -19.710 -14.742 7.469 ( 9.185) ( 13.94) ( 6.049) -22.333 -16.752 7.938 (52.512) (158.68) (14.228) 1.487 -0.312 1.357 ( 1.070) ( 1.51) ( 1.129) 2.872 1.251 2.537 ( 0.912) ( 1.24) ( 1.879) 0.817 0.53 0.87 149 143 58

26


Table 7. OLS Results. Dependent variable is government consumption. Standard errors are reported in parentheses beneath each estimate. OECD urbanperc 0.114 (0.023) ln(pop) 0.093 (0.167) ¯2 R 0.113 urbanperc 0.092 (0.024) ln(pop) 0.058 (0.171) ln(lif e) 21.674 (7.357) ln(popd) 0.185 (0.176) ¯2 R 0.798 urbanperc 0.100 ( 0.026) ln(pop) 0.647 ( 0.321) ln(lif e) 14.536 (10.446) ln(popd) -0.232 ( 0.255) ln(trade) 2.312 ( 1.018) ln(gdp) -0.540 ( 0.948) ¯2 R 0.817 # obs. 175

SSA -0.007 (0.024) -2.067 (0.263) 0.137 -0.105 (0.028) -1.790 (0.279) 13.482 (2.786) -1.014 (0.268) 0.503 -0.156 (0.033) -0.814 (0.345) 10.053 (3.099) -0.833 (0.272) 4.807 (0.926) 0.111 (0.583) 0.54 380

LAC 0.10 (0.020) -1.71 (0.184) 0.236 0.061 (0.031) -1.679 (0.268) 9.501 (4.554) -0.565 (0.297) 0.717 0.081 (0.033) -1.330 (0.335) 7.964 (5.459) -0.478 (0.328) 1.712 (0.941) -0.505 (0.732) 0.717 276

MENA 0.129 (0.038) 0.243 (0.573) 0.1 0.254 (0.068) 0.187 (0.651) -11.094 (8.808) -1.208 (0.492) 0.459 0.240 (0.069) 0.213 (0.856) -17.701 (9.440) -1.362 (0.522) 2.055 (2.615) 0.369 (0.895) 0.736 108

Asia -0.089 (0.021) -1.926 (0.218) 0.394 -0.021 (0.029) -1.301 (0.209) 3.666 (5.299) -1.467 (0.387) 0.8 -0.069 (0.027) -0.778 (0.213) -7.235 (4.704) -1.890 (0.293) -0.386 (0.678) 2.799 (0.502) 0.817 149

EER 0.132 (0.033) -0.590 (0.345) 0.113 0.094 (0.037) -0.284 (0.388) 11.607 (6.858) 0.235 (0.620) 0.399 0.033 (0.043) 0.336 (0.408) 0.154 (7.305) 1.086 (0.674) 3.709 (1.007) 0.727 (0.494) 0.53 143

SEA 0.196 (0.050) -1.367 (0.168) 0.558 0.172 (0.057) -0.988 (0.193) 3.178 (3.443) -1.529 (0.383) 0.867 -0.002 (0.064) 0.009 (0.317) -6.035 (4.904) -2.281 (0.351) 0.296 (1.315) 5.276 (1.044) 0.87 58

URBANIZATION

Table 8. Panel data estimates. Dependent variable is government consumption of healthcare services. Standard errors are reported in parentheses beneath each estimate. Only Health (1) (2) urbanperc 0.085 0.084 (0.020) (0.020) ln(pop) 0.834 2.280 (0.474) (4.999) ln(lif e) -1.838 (5.013) ln(popd) 1.815 (0.908) ln(trade) ln(gdp) # obs. ¯2 R

0.108

Care All 3 Measures (3) (1) (2) (3) 0.079 0.062 0.062 0.054 (0.020) (0.026) (0.026) (0.027) 2.597 1.354 2.452 2.414 (5.001) (0.603) (6.141) (6.149) -2.254 -1.424 -1.449 (5.018) (6.128) (6.141) 1.576 1.375 1.059 (0.922) (0.947) (0.971) 0.229 0.041 (0.187) (0.244) 0.131 0.330 (0.181) (0.259) 461 334 0.115 0.118 0.1 0.105 0.109

Table 9. Panel data estimates. Dependent variable is government consumption of military services. Standard errors are reported in parentheses beneath each estimate.

Only Military All 3 Measures (1) (2) (3) (1) (2) (3) urbanperc -0.015 -0.013 -0.013 -0.017 -0.017 -0.026 (0.015) (0.015) (0.016) (0.030) (0.03) (0.031) ln(pop) -2.199 -6.825 -6.391 -1.368 -1.798 -2.025 (0.390) (6.007) (5.969) (0.695) (7.05) (7.057) ln(lif e) 4.843 4.311 0.941 1.147 (6.029) (5.990) (7.03) (7.048) ln(popd) -1.868 -2.038 -2.160 -2.505 (0.822) (0.842) (1.09) (1.115) ln(trade) 0.494 -0.087 (0.179) (0.280) ln(gdp) -0.317 0.421 (0.201) (0.298) # obs. 624 334 ¯2 R 0.118 0.125 0.136 0.036 0.047 0.052

27

28


Table 10. Panel data estimates. Dependent variable is government consumption of educational services. Standard errors are reported in parentheses beneath each estimate. Only Education All 3 Measures (1) (2) (3) (1) (2) (3) urbanperc 0.035 0.222 0.196 0.017 0.019 0.023 (0.129) ( 0.129) ( 0.131) (0.036) (0.037) (0.038) ln(pop) 3.672 -38.598 -37.876 1.351 7.937 8.160 (3.412) (63.630) (63.200) (0.833) (8.502) (8.552) ln(lif e) 50.337 49.131 -6.812 -7.060 (64.045) (63.604) (8.484) (8.541) ln(popd) -60.401 -66.718 0.892 1.030 ( 9.194) ( 9.408) (1.312) (1.351) ln(trade) 6.683 0.129 ( 2.053) (0.340) ln(gdp) -0.975 -0.211 ( 1.853) (0.361) # obs. 732 334 ¯2 R 0.007 0.061 0.075 0.025 0.028 0.029

URBANIZATION

Table 11: Country-Year pairs for analysis in Paper. Country Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica

Years 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1985, 1990 1965, 1970, 1975, 1985, 1990, 1995, 2000, 2005 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1990, 1995, 2000, 2005 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1985, 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005

29

30


Table 11: Country-Year pairs for analysis in Paper. Country Cote d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominican Republic Ecuador El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Fiji Finland France Gabon Gambia, The Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hong Kong SAR, China Hungary Iceland India Indonesia Ireland

Years 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1975, 1980, 1985, 1990, 1995, 2000, 2005 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1985, 1990, 1995, 2000, 2005 1995, 2000, 2005 1995, 2000, 2005 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1975, 1980, 1985, 1990, 1995, 2000, 2005 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005

URBANIZATION

Table 11: Country-Year pairs for analysis in Paper. Country Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Rep. Kuwait Latvia Lebanon Lesotho Liberia Libya Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Mauritania Mauritius Mexico Moldova Mongolia Montenegro Morocco Mozambique Namibia Nepal Netherlands New Zealand Nicaragua Niger

Years 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1980, 1985, 1990, 1995, 2000, 2005 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1995, 2000, 2005 1990, 1995, 2000, 2005 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 2000, 2005 2000, 2005 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1990, 1995, 2000, 2005 1985, 1990, 1995, 2000, 2005 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1980, 1985, 1990, 1995, 2000, 2005 1980, 1985, 1990, 1995, 2000, 2005 1975, 1980, 1985, 1990, 1995, 2000, 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005

31

32


Table 11: Country-Year pairs for analysis in Paper. Country Years Norway 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Oman 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Pakistan 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Panama 1980, 1985, 1990, 1995, 2000, 2005 Papua New Guinea 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Paraguay 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Peru 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Philippines 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Poland 1990, 1995, 2000, 2005 Portugal 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Puerto Rico 1965, 1970, 1975, 1980, 1985 Qatar 2000, 2005 Romania 1990, 1995, 2000, 2005 Rwanda 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Saudi Arabia 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Senegal 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Serbia 2000, 2005 Seychelles 2005 Sierra Leone 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Singapore 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Slovak Republic 1990, 1995, 2000, 2005 Slovenia 1990, 1995, 2000, 2005 Solomon Islands 1995, 2000, 2005 South Africa 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Spain 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Sri Lanka 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 St. Lucia 1980, 1985, 1990, 1995, 2000, 2005 St. Vincent and the Grenadines 1980, 1985, 1990, 1995, 2000, 2005 Sudan 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Suriname 1975, 1980, 1985, 1990, 1995, 2000, 2005 Swaziland 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Sweden 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 Switzerland 1980, 1985, 1990, 1995, 2000, 2005 Tajikistan 1990, 1995, 2000, 2005 Tanzania 1990, 1995, 2000, 2005 Thailand 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005

URBANIZATION

Table 11: Country-Year pairs for analysis in Paper. Country Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Vietnam Zambia Zimbabwe

Years 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1995, 2000, 2005 1985, 1990, 1995, 2000, 2005 1990, 1995, 2000, 2005 2005 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1990, 1995, 2000, 2005 1980, 1985, 1990, 1995, 2000, 2005 1990, 1995, 2000, 2005 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 1975, 1980, 1985, 1990, 1995, 2000, 2005

33