PROCESSES OF ECONOMIC OPENNESS, GROWTH AND URBAN CONCENTRATION: ARE DEVELOPING COUNTRIES DIFFERENT?

PROCESSES OF ECONOMIC OPENNESS, GROWTH AND URBAN CONCENTRATION: ARE DEVELOPING COUNTRIES DIFFERENT? Fernando DePaolis, Ph.D. Associate Professor Graduate School of International Policy & Management 1

Introduction In the heyday of globalization studies circa 2000 policy makers in international organizations and national governments were looking for even stronger reasons to justify additional actions that would further accelerate the rate at which a handful of emerging economic powers were becoming more interdependent with the developed world. Such integration at the global scale was being facilitated by a remarkable new wave in trading relations, flourishing financial flow supported by the ever increasing expansion of new instruments and alliances, and by fascinating new technologies (try and visualize the days before the dot-com-bust of 2000). Thinkers and pundits were not predicting but stating as a fact that the death of several dimensions would give way to an entirely new world. These deaths included the death of history 1, the death of the nation-state 2, the death of distance 3, and when considered as a whole these deaths would certainly marshal in a new social, political, economic, and even a new spatial reality. This last dimension, space, had been incorporated into mainstream economic thinking just at the end of the previous decade by a rather small but influential group of economic thinkers, of which Paul Krugman was the most prominent. The integration of space into economics (a traditionally a-spatial discipline) was both a refreshing conceptual development and a formidable challenge for formal modelers. Krugman himself, in a 1996 seminal paper, addressed the newly conceptualized relations between trade and the configuration of urban systems through the simultaneous consideration of transport costs, increasing returns, and urban concentration. But, what did we know at that time about urbanization, urban concentration, and all other factors affecting the distribution of population and economic activity over space? A vast literature, extending more than 30 years, has addressed the interdependency among all components of the development process, particularly the relationship between urbanization and economic growth (starting with El-Shakhs, 1972). Although varied, urbanization processes are commonly measured by the change in the proportion of urban population and by urban primacy - a measure of the level of concentration of a large proportion of a country s urban population in one or a few urban areas. The well established tradition that asserts that a large share of the world urban population live in cities of excessive size (Jefferson, 1939) accepts the existence of an ideal distribution of city sizes at the national level, and the implicitly harmful effects of a departure from such a distribution (Linsky, 1965). Although versions of a rank-size ideal distribution have been systematically criticized because of their lack of relevant explanatory power in terms of effects on economic development they are still found in the contemporary literature. 1 Francis Fukuyama The End of History and the Last Man (1992) 2 Eric Hobsbawn Nations and Nationalism since 1780 (1990), and Ian Angell The Information Revolution and the Death of the Nation-state (1995) 3 Frances Cairncross The Death of Distance (1997) 2

Cities, metropolitan areas, and systems of urban centers do not follow the strict rules of physics or chemistry as molecules do when coalescing to form crystal structures. Urban entities emerge, develop, decline, and disappear as a consequence of, first, economic, and, later, political processes (Henderson at al., 2001 and Henderson 2002a). Any analysis of such an evolution has to begin, necessarily, with the modeling of those processes, not with an arbitrary mathematical regularity. The search for these patterns represented an important part of the earlier literature but their contribution was limited to descriptive analyses of developed countries first, and then an expanding list of developing countries (for example, Sokona, 1985). Only later did studies providing explanatory approaches become available (Junius, 1999). The controversy found in the literature between the merits or irrelevance of Zipf s regularity is appropriately discussed in Duranton (2002). Duranton dismisses the so-called Zipf s Law on the basis that, first, it is a poor approximation of existing distribution, and second,that it does not address the need for identifying the economic mechanisms that drive urban growth and decline. For the most part the search for this, and other, regularities have subsided, but not completely vanished. It is still possible to find contemporary analyses using Zipf-like approaches. Why, then, would we need to explore these issues any further, or create yet another model? 4 What, if anything, has been left out by the broad, but succinctly discussed, literature? We think there are some important policy implications that can be derived from an analysis that distinguishes between developed and developing countries. In this paper we examine the coevolution of economic growth, global integration, and patterns of urbanization. Using regression models with panel-data we test whether the determinants of a particular characteristic of urban systems, urban primacy as discussed by Krugman (1996), have been different for developed and developing countries in the second half of the twentieth century. Data and Measures We use the Heston-Summers-Aten database 5 (Penn World Tables, v.6.1) for measures of real GDP per capita (in constant 1996 dollars) and openness (exports plus imports as a ratio of total GDP). 6 We compute a measure of urbanization (ratio of urban population to total population), and a measure of primacy (the ratio of population of the largest city to total urban population) using data from the United Nations World Urbanization Prospect, 2003 revision. 7 4 For an excellent discussion of other models, see Glaeser (2008) 5 For a complete list of countries, see Appendix 6 In the literature, two types of openness measures are used: de jure measures that focus on a country s formal trade barriers (such as import tariffs and export taxes), and de facto measures that measure the flows themselves. Each has its strengths and weaknesses for different purposes; the de facto measure used here was chosen for its consistency with the rest of the data set and its more objective nature. 7 We are grateful to Thomas Buettner Assistant Director, Chief of Population Studies Branch at the United Nation s Population Division for providing valuable technical assistance. 3

We also include a measure of institutional quality. From the widely recognized work by Kauffman, Kraay, and Ziodo-Lobatón on governance indicators we have selected government effectiveness as a proxy for institutional quality. Government effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies (Kauffman at all, 2009). The data availability for government effectiveness is limited to more recent years, only since 1996. We decided to capture the level of institutional quality by computing the 1996-2008 average and create a time-invariant country attribute. This seemingly arbitrary decision does not, in our opinion, compromise our results, as measures of institutional quality seem to be very stable over long periods of time. In addition we ran bivariate correlation analyses to assess the strength of government effectiveness as a substitute for the other five governance indicators. In the end, government effectiveness had the highest correlation with regulatory quality, rule of law, and control of corruption (0.957, 0.926, and 0.94 respectively) and correlation coefficients above 0.75 with voice and accountability and political stability. Therefore we are highly confident that our results would have remained unchanged had we used any other indicator or their combinations. We also include a measure of overall development by classifying countries into developed or developing using an income per capita-based approach similar to that of the World Bank. Other variables that account for geographic and geopolitical configuration are insularity (whether the country is an island, or if it is landlocked), and whether the largest urban agglomeration is the capital or a port. In terms of data coverage we decided to keep as many countries as possible in our datasets regardless of geography, political system, or any other attribute. It is common to find studies that truncate the data sets to reflect regions. For instance, Moonaw & Shatter (2003) included only countries in the Americas and Asia while removing socialist (or former socialist) countries. For us, it was more important to achieve country coverage that would assure the detection of existing patterns than to maximize the explanatory power of our models. Descriptives In general, countries we consider today as developed started as relatively urban places. Over long periods of time economic activity concentrated along waterways, seashores, and ancient trading roads; by the end of World War II, the starting point of our analysis, developed countries were highly urbanized compared to their less developed counterparts. The table below shows that developed countries grew richer since 1980 relative to developing countries, and they caught up with developing countries in terms of openness. Developing countries, on the other hand, become relatively more urban, even as developed countries continue to increase their level of urbanization. Primacy, although different in both groups of countries, remained virtually unchanged between the two time periods. 4

Developing Developing Primacy Developed Developed After 1980 Before 1980 1 Table 1 Selected Indicators by Type of Country (values are means of each variable) DEVELOPING DEVELOPED Ratios GDP per cap (*) $2,518 $9,988 3.97 Urbanization 35.4% 63.0% 1.78 Openness 66.8% 37.9% 0.57 Primacy 34.4% 26.0% 0.76 GDP per cap (*) $4,187 $19,645 4.69 Urbanization 49.6% 73.0% 1.47 Openness 75.4% 66.3% 0.88 Primacy 32.7% 25.0% 0.76 2 (*) GDP per capita in constant 1996 dollars) The scatterplots below show primacy as a function of each of the main explanatory variables examined in this study. 3 Figure 1 Primacy and Economic Growth 0 10000 20000 30000 Before80 After80 0.8 0.6 0.4 0.2 Before80 After80 0.8 0.6 0.4 0.2 0 10000 20000 30000 Real GDP (US$1996) N = 877, 2010-02-26, R 2.10.1 5

Developing Developing Primacy Developed Developed Developing Developing Primacy Developed Developed 4 Figure 2 Primacy and Economic Openness 0 100 200 300 Before80 After80 0.8 0.6 0.4 0.2 Before80 After80 0.0 0.8 0.6 0.4 0.2 0.0 0 100 200 300 Trade as share of GDP N = 879, 2010-02-26, R 2.10.1 5 Figure 3 Primacy and Urbanization Primacy and Urbanization 0.0 0.2 0.4 0.6 0.8 1.0 Before80 After80 0.8 0.6 0.4 0.2 Before80 After80 0.0 0.8 0.6 0.4 0.2 0.0 0.0 0.2 0.4 0.6 0.8 1.0 Urban population as share of total population N = 1232, 2010-02-26, R 2.10.1 6

The lines represent a multinomial smooth fitting of all points. Scatterplots show that for developed countries there is a strong inverse association between real GDP per capita and primacy. For developing countries, however, the relationship is ambiguous in both periods of time (before and after 1980). Openness seems to be weakly associated with primacy only in developed countries, and only in the early period. The relationship seems to disappear in all other cases. There is also a clear distinction between developed and developing countries in terms of the relationship between urbanization and primacy. In developed countries urbanization is more strongly related to primacy than in developing countries. Analysis We start the analysis by providing an overview of the initial and final conditions for the whole set of countries in terms of size and concentration of urban population, real GDP per capita, and level of economic openness. These indicators were chosen to assess empirically a series of postulates presented in the recent literature as stylized facts. We decided to split the dataset into two distinct periods, 1950-1980, and 1980-2000. The break point of 1980 was determined by running a principal component analysis to check for the stability of the indicators over the entire period. In doing so, we found that the level of economic openness loaded on two different factors, and the corresponding cut-off point was 1980. (See Component Matrix using Equamax Rotation Method in Appendix I). This seems to confirm our belief that the process of economic integration before 1980 was substantially different from the current process known as globalization. Although this paper does not focus on globalization we wanted to highlight this finding which we think has not been previously shown using this data set 8. Another important issue to consider is that we have limited our measure of globalization to trade in goods and services as share of GDP, without introducing any measure of financial flows. We strongly believe that different types of financial flows do have a differential impact on a country s development path, especially for newly industrialized countries and emergent markets. Patterns of physical concentration and fiscal centralization will be different if most of the flows are in the form of long term foreign direct investment, or if they are mostly short-term portfolio investments. For instance, if most of the financial flows into developing countries are in the form of long-term, production-based enterprises, the impact on the country s hinterland would be much greater than if the flows are short-tem, portfolio-based investment, which would have a direct impact on the local financial sector, usually located in the capital or largest urban agglomeration. The economic geography literature is rather limited in this regard. Financial flows are not usually included in empirical studies, in any relevant way, to measure the distribution of 8 The Penn World Tables provide a homogeneous set of time-series variables that allows consistent panel, cross-sectional and inter-temporal analyses. 7

economic activities over the territory. A potential explanation for this omission is the great difficulty researchers face when trying to obtain consistent data for a large number of countries and the fluctuation over time of investments by type. Explaining Primacy One explanation found in the literature is that primacy is the result of historical conditions, which over time have, in turn, stimulated the consolidation of most of the country s urban population in one or very few centers. In modern times, efforts to create national centers from scratch, such as the cases of Brazil in the 1950s, Nigeria in the early 1990s, or Kazakhstan in the late 1990s (and the failed attempts of Japan and Argentina in the 1980s), have brought to the foreground the massive complexity (and political controversy) of coordinating multiple aspects of development. In their attempt to short-circuiting history, and to build new institutional structures, national governments tried to relocate the national capital to a more central location in the hinterland. As in the case of Brazil and Nigeria the massive investment of financial and political capital made little or no impact in the evolution of their largest urban areas. São Paulo and Rio de Janeiro (the former capital) and Lagos are ports themselves or are in close proximity to ports (Santos in the case of São Paulo), and all of them remain the largest urban agglomerations in their respective countries, even decades after the inception of the new capital. 9 Clearly path dependency effects should not be summarily dismissed. When analyzing the case of Bangladesh, Naya et al. (2004) said that urbanization patterns are as much the result of path dependency and strong cumulative causation effects as they are of geography. They also put forth the idea that, if functional and political de-centralization efforts are to succeed, policies targeting urban de-concentration have to be in place in advance. In our opinion, in the Brazilian and Nigerian cases, the physical relocation of the capital did not (and probably could not) dismantle the system of economic and political linkages that had given the former capitals their territorial and economic preeminence. When considering the direct connection between primacy and economic expansion earlier studies (Richardson & Schwartz, 1988) found that economic factors do not appear to be associated with primacy levels. Later approaches to economic development, supported by an enhanced consideration of geography, asserted that as a country becomes more exposed to international trade the forward and backward linkages that initially forced industrial plants to be located in a central point (allegedly the primate city) lose their role as main determinants of industrial location (Krugman & Livas, 1996). 10 However, this is not so simple to analyze. Most analyses, including our own and others (Henderson 2002b), only consider the trade side of economic openness. This is a limitation if one 9 The role of government has also been considered to be a factor determining the size of the largest city. Ades and Glaeser (1995) provide compelling evidence of this effect in ancient and contemporary metropolises. 10 The oldest reference to the term primate city seems to be Jefferson (1939). 8

considers that financial flows are at least as important as trade flows, if not more important for some of the larger emergent economies such as China, India, the larger Latin American countries, or mineral-rich African countries. This modeling problem is, as we mentioned above, difficult to overcome as it is much harder to assemble the adequate financial flows data. Henderson s discussion of the ambiguity posed by location theory is refreshing (2002b, 107). Recent, and arguably more sophisticated, approaches such as increasing returns to scale, do not dispel the potentially undetermined outcome of external orientation of a country s economy. It is true that congestion costs, labor market rigidities, and the scarcity of suitable urban land will adversely affect a firm s decision to locate in the primate city, but there are other, less studied, factors. What is usually left out of models of industrial location and urban growth is what we call service provision thresholds. Most of the time firms do not have the option of locating their plants in the hinterland because access to reliable sources of energy, transportation networks, and telecommunication infrastructure is limited, if at all possible. Other necessary factors might also be scarce or absent in locations farther from the central urban area such as skilled labor, research and technology centers, and access to decision makers. 11 Although substantial progress has been made by new economic geography modelers most studies so far suffer the limitations of an assumed smooth distribution of infrastructure over the territory. The primate city, or its immediate surroundings, may very well be the only location worth considering when firms formulate plans to (re)locate in developing countries. Successive cycles of economic expansion end up consolidating this role rather than weakening it in favor of a more balanced growth in the hinterland. This is the point when policy design strategies geared to deconcentrate large agglomerations through infrastructure development clash with atrophic institutions, both at bureaucratic and political levels. Even in the best case scenario centripetal forces may be difficult to overcome in the short term, and the adjustment process that would lead to more development away from the primate center will certainly not be as instantaneous or costless as Krugman & Livas (1996) and Fujita et al. (1999) would have us believe. Those transition costs should be more relevant to policy makers than to modelers. Infrastructure expenditures designed to alleviate concentration and primacy usually stretch over a long time horizon, and their purported benefits are unreasonably high. Thus, it is understandable that the dissociation between the public s perception of costs and benefits is likely to result in an erosion of support for such policies. This in turn would jeopardize their proper funding during the implementation stages, and therefore their success, with the unintended outcome of further consolidating the growth and attractiveness of the primate city. 11 The quite expansive lobbying industry found in few city blocks in the United States national capital is a clear indication of the need for locating industry s agents in close proximity to decision makers. 9

Modeling results Krugman (1996) uses stylized facts to describe the determinants of urban concentration. According to Krugman urban concentration (a) falls with income per capita; (b) increases with the concentration of political power; (c) is impacted by changes in transportation infrastructure; and (d) declines with trade openness. The regression results using panel data as shown in Table 2 (first three result columns) show that Krugman s stylized facts (a) and (d) hold, for the most part, as predicted: urban concentration does fall with GDP per capita (coefficients are non-significant for the 1950-1980 period) and does decline with openness. A much more interesting picture emerges when separate regressions are performed for more and less developed countries (last six columns in Table 2). The partial regression coefficients are remarkably larger for developed countries, meaning that the effect of openness on urban primacy are 3.6 times larger for all years, 3.4 times larger for the period prior to 1980, and even larger (3.7 times) for the period after 1980. 6 Table 2 Modeling Results ALL COUNTRIES DEVELOPING COUNTRIES DEVELOPED COUNTRIES Dependent Variable: 1950-1950- 1980-1950- 1950-1980- 1950-1950- 1980- Log Primacy 2000 1980 2000 2000 1980 2000 2000 1980 2000 Log GDP per capita -0.073* -0.044-0.062* 0.002 0.104* 0.005 0.213* -0.006-0.054 (0.022) (0.036) (0.029) (0.028) (0.045) (0.039) 0.084 (0.111) (0.174) Log Urban Pop. (size urbpop) -0.287* (0.011) -0.276* (0.016) -0.347* (0.015) -0.295* (0.011) -0.268* (0.045) -0.350* (0.016) -0.228* (0.030) -0.197* (0.041) -0.303* (0.042) Log Urbanization (urbpop/totpop) 0.247* (0.036) 0.194* (0.049) 0.210* (0.060) 0.243* (0.038) 0.143* (0.050) 0.178* (0.064) -0.677* (0.130) -0.533* (0.159) -0.710* 0.220 Log Openness (X+M)/GDP -0.090* (0.019) -0.096* (0.025) -0.197* (0.036) -0.064* (0.020) -0.056* (0.025) -0.117* (0.037) -0.232* (0.063) -0.191* (0.084) -0.434* (0.093) Constant 2.338* 1.904* 3.248* 1.731* 0.519 2.408* -0.897 0.683 3.302 Observations 874 430 444 660 316 344 214 114 100 Adjusted R 2 0.491 0.455 0.568 0.553 0.529 0.616 0.439 0.442 0.550 *Significant at 5% or better. Numbers in parenthesis are robust standard errors. All logs are natural logarithms. Source: Author calculations based on data from Heston et al (2002) and United Nations (2004). If one considers that one relevant dimension along which more and less developed countries differ is governance, and more specifically how effective their government structures are in designing and implementing public policy, the next step in our analysis would be to add such a measure. The results in Table 3 show just that. For developed countries, in all three time periods, higher governance quality (in this case measured through government effectiveness) is significantly associated with declining urban primacy. 10

7 Table 3 Modeling Results [Government effectiveness] 8 Table 4 Modeling results [Government effectiveness and geography] ALL COUNTRIES DEVELOPING COUNTRIES DEVELOPED COUNTRIES Dependent Variable: Log Primacy 1950-1950- 1980-1950- 1950-1980- 1950-1950- 1980-2000 1980 2000 2000 1980 2000 2000 1980 2000 Log GDP per capita 0.042 0.051 0.055 0.013 0.107* 0.006 0.313* 0.129 0.236 (0.028) (0.043) (0.043) (0.031) (0.046) (0.046) (0.079) (0.112) (0.191) Log Urbanization (share urbpop) 0.220* (0.036) 0.187* (0.048) 0.143* (0.062) 0.246* (0.038) 0.145* (0.051) 0.182* (0.065) -0.422* (0.126) -0.412* (0.156) -0.427* (0.230) Log Openness (X+M)/GDP -0.118* (0.019) -0.111* (0.025) -0.203* (0.036) -0.071* (0.021) -0.057* (0.025) -0.125* (0.037) -0.256* (0.058) -0.174* (0.080) -0.444* (0.089) Log Urban Pop. (size urbpop) -0.290* (0.010) -0.269* (0.015) -0.347* (0.015) -0.295* (0.011) -0.267* (0.016) -0.350* (0.016) -0.298* (0.030) -0.241* (0.041) -0.365* (0.045) Government Effectiveness -0.129* (0.020) -0.110* (0.028) -0.115* (0.031) -0.035 (0.024) -0.008 (0.034) -0.015 (0.036) -0.439* (0.071) -0.370* (0.106) -0.355* (0.115) Constant 1.523* 1.160* 2.250* 1.66* 0.501 2.428* -0.253 0.498 1.778 Observations 874 430 444 660 316 344 214 114 100 Adjusted R 2 0.516 0.473 0.583 0.555 0.528 0.617 0.524 0.494 0.587 *Significant at 5% or better. Numbers in parenthesis are robust standard errors. All logs are natural logarithms. Source: Author calculations based on data from Heston et al (2002) and United Nations (2004). ALL COUNTRIES DEVELOPING COUNTRIES DEVELOPED COUNTRIES Dependent Variable: Log Primacy 1950-1950- 1980-1950- 1950-1980- 1950-1950- 1980-2000 1980 2000 2000 1980 2000 2000 1980 2000 Log GDP per capita 0.019 0.014 0.048-0.006 0.046-0.0030 0.233* -0.018 0.085 (0.026) (0.039) (0.043) (0.029) (0.040) (0.045) (0.085) (0.126) (0.214) Log Urbanization (share urbpop) 0.239* (0.034) 0.227* (0.044) 0.145 (0.063) 0.246* (0.035) 0.184* (0.044) 0.179* (0.065) -0.069 (0.152) -0.317 (0.201) -0.585 (0.354) Log Openness (X+M)/GDP -0.129* (0.018) -0.135* (0.023) -0.196* (0.035) -0.075* (0.019) -0.080* (0.022) -0.118* (0.036) -0.254* (0.061) -0.031 (0.099) -0.294* (0.107) Log Urban Pop. (size urbpop) -0.260* (0.010) -0.237* (0.015) -0.332* (0.017) -0.264* (0.011) -0.236* (0.015) -0.334* (0.018) -0.272* (0.033) -0.164* (0.048) -0.314* (0.059) Government Effectiveness -0.144* (0.019) -0.146* (0.025) -0.122* (0.030) -0.012 (0.025) -0.030 (0.029) -0.012 (0.035) -0.611* (0.091) -0.471* (0.130) -0.281 (0.157) Landlocked 0.089* 0.061 0.011 0.112* 0.075 0.023 0.455* 0.416* 0.105 (0.039) (0.056) (0.054) (0.040) (0.056) (0.023) (0.139) (0.186) (0.240) Island 0.116* 0.094 0.164* 0.035 0.036 0.086 0.121 0.262 0.513* (0.042) (0.057) (0.061) (0.048) (0.062) (0.068) (0.111) (0.177) (0.199) Capital_Port 0.241* 0.304* 0.124* 0.298* 0.352* 0.167* 0.364* 0.152-0.074 (0.030) (0.041) (0.043) (0.032) (0.040) (0.045) (0.116) (0.166) (0.199) Capital_Largest 0.109* 0.161* 0.033 0.093* 0.136* 0.004-0.060-0.080 0.058 (0.031) (0.042) (0.045) (0.034) (0.045) (0.049) (0.086) (0.123) (0.129) Constant 1.313* 1.072* 2.065* 1.391* 0.611 2.254* 0.542 0.825 1.926 Observations 874 430 444 660 316 344 214 114 100 Adjusted R 2 0.578 0.581 0.606 0.626 0.653 0.637 0.597 0.539 0.627 *Significant at 5% or better. Numbers in parenthesis are robust standard errors. All logs are natural logarithms. Source: Author calculations based on data from Heston et al (2002) and United Nations (2004). 11

Openness. The measure of openness used in this study (trade as share of GDP) is consistently significant as a significant regressor of primacy for all periods and both groups of countries. The magnitude of its impact is, however, remarkably different for more versus less developed countries. In both periods, before and after 1980, the expected reduction in primacy due to changes in openness is around three times larger in developed countries (the coefficients are -0.057 and -0.174 for the 1950-1980 period, and -0.125 and -0.444 for the 1980-2000 period, for developing and developed countries respectively). This seems to indicate that more developed countries are able to channel the effects of globalization in such a way that the expectations from the theoretical point of view do, in fact, materialize. One question remains: why less developed countries cannot realize the same, arguably positive, results? Urbanization In order to understand how the processes differ between more and less developed countries it is useful to look at the coefficients of the urbanization variable. The aggregate results show that as the ratio of urban to total population increases primacy also increases (positive and significant partial regression coefficients). The disaggregated results indicate that the effect is driven mostly by the outcomes corresponding to developing countries. The interpretation seems to be straight forward. As developing countries become more urban, most of the urban growth takes place in very few locations (if not just in one megalopolis). There also seems to be a slight acceleration of concentration in the period after 1980. Conversely, as developed countries become more urbanized, primacy declines, especially in the period after 1980. 12 Combining the results for both measures openness and urbanization the process is consistently different for developed and developing countries. In developing countries the process contains a set of opposing forces. On the one hand the economic transformation derived from increased linkages with the rest of the world seems to decelerate the growth of the largest metropolitan areas. On the other hand, an expansion of the urban population (mainly due to unabating rural-to-urban migration) seems to provide a force of opposite direction and, likely, greater intensity in both periods of time. A possible explanation is the presence of institutional rigidities that preclude the emergence of second-tier urban centers as major forces in economic growth that in turn would reduce the importance and size of the primal city. Naya et al. (2004) discussed the relationship between decentralization and de-concentration. The former concept refers to the ability of regional and local government to take over functions previously performed by the national government. This is certainly related to one of the factors that positively correlate with urban concentration, namely political power. 12 One could speculate about the role of anti-sprawl initiatives and their manifestation on transportation infrastructure planning and funding. 12

In a way the previous discussion provides further justification for the inclusion of government effectiveness in our models. The modeling results are such that government effectiveness is negative and very significant for all countries combined, and all periods of time. This translates as countries that have ineffective governments also tend to have high levels of primacy. Not a completely surprising finding. But when the results in Table 3 are disaggregated by type of country one can see that institutional quality is not significant for developing countries for any period of time, although it retains its (expected) negative sign. Why is government effectiveness not a significant explanatory factor for developing countries? Let s address the mathematical issue first. The coefficient of variation of government effectiveness for developing countries is ten times larger than for developed countries (3.051 and 0.304 respectively). 13 The larger heterogeneity of developing countries could result in a lack of fit in the regression model. It is possible, however, to conjecture that developing countries were unable to realize the benefits of de-concentration derived from economic openness because of excessive concentration of government functions and therefore power in the primal city, especially in the case where the capital (or main port) is the primal city. 14 Although not explicitly modeled in this paper it is certainly possible to argue, without much effort, that developing countries in general have a distorted distribution of urban centers over territory. What is not so easy to discern is the causality direction. Our contention is that because ineffective governments have a much more difficult time governing beyond the capital city the capital city retains its attractiveness, a widely recognized characteristic of Sub-Saharan countries. It is not unreasonable, however, to argue in favor of the opposite direction of causality. Gigantic urbanizations have grown beyond the limits of adequate management, compromising governments ability to perform effectively. The reasons for this interplay between the physical and political dimensions are multiple, but we can mention just a few. Regions such as Sub-Saharan Africa and South-East Asia show a more or less rapid succession of colonial rule, incipient independence, industrialization-as incomplete as it might have been-, and finally the attempt to participate in the world economy through trade and financial liberalization. The journey through those stages have left many developing countries with an eclectic array of technologies and institutions that far from facilitating an ordered process of sustained development as proposed by W.W. Rostow (1960) have had the opposite result. More often than not countries find themselves prisoners of their own legacy of physical and administrative infrastructure, and the necessary resources to overcome such a burden are perhaps as large as the resources needed to achieve higher levels of development. Apparently, the tradeoffs between structural change that would enable sustained and sustainable long-term growth and the short term benefits of growth are tilted in favor of the latter. For many developing 13 The range of values of government effectiveness for developing countries goes from about -2 to +2, while for developed countries goes from +0.2 to +2.2 14 Both variables (capital_port and capital_largest) are significant for developing countries in both periods. 13

countries, it might not be possible to even consider the total overhaul of their infrastructure and, as a result, development policies are designed and executed under those constraints. Regression Model Diagnostics Multicollinearity An early step in the analysis was to examine the bivariate correlation between all variables in the model. Although the dependent variable (log of primacy) exhibited moderate and significant correlation with all the independent variables many of the independent variables were correlated among themselves which could indicate potential multicollinearity problems. The diagnostics tool we used was to examine the variance inflation factors for all regression models. We found that in all cases the VIFs were below 4, thus confirming that no data reduction was need. 15 Homogeneity of variance The second diagnostic tool we used was a set of standardized scatter plots to examine the potential violation of the assumptions about error term distribution. The scatter plots showed that the models were well fitted, displaying undistorted distribution patterns of standardized residuals within ± 2 standard deviations over the entire spectrum of standardized fitted values in all cases by time period and country type (before/after1980, and developed/developing). Normality of residuals For partial correlation coefficients (and the p-values) to be valid one must additionally test for the normality of residuals. To that effect we performed two different tests; in the first one we used Normal Q-Q Plots of Unstandardized Residuals to detect possible distortions in the distribution of residual from the perfect normal distribution. We also recorded the residuals from each regression model in new variables and examined the distributions. Based on the values of kurtosis and skewness (both near zero), and the Kolmogorov-Smirnov statistics (non-significant), we concluded that distributions of residuals (for all models) did conform linear regression assumptions. Autocorrelation Since the dataset s underlying structure correspond to a time-series it was important to verify potential autocorrelation effects. In all cases, the Durbin-Watson statistics were around 2±0.3, which implied that the models did not violate autocorrelation assumptions. Simultaneity Given the nature of the matter under discussion (determinants of urban primacy), the most serious threat to the validity of our finding was the presence of simultaneity between GDP and primacy. If that were the case there would be important methodological and theoretical implications. From the modeling point of view the results would be questionable and our findings about the determinants of primacy in different types of countries at different points in time 15 Only one variable, log of GDP, in one model (developing countries, after 1980), has a VIF larger than 4 (4.6), which is still well within the acceptable values to dismiss potential multicollinearity effects. 14

would be erroneous. From the theoretical perspective, the reversal of causality (from the one specified in our models) would imply a substantial change in the composition of policy variables. Or worse yet, primacy is not influenced by growth in GDP and acceleration in economic openness, but another, unobserved variable determines all three, making our claims operationally invalid, and crippling our depiction of the economic structure-urban structure policy problem. We already discussed the potential inversion in the direction of causality between primacy and government effectiveness, but this did not seem to be a problem at least from the co-linearity tests. Some potential solutions are: (1) Build randomized controlled experiment; (2) Develop and estimate a complete, simultaneous equation model of both directions of causality. This is very complex and difficult to do (some examples are the highly sophisticated macroeconomic models developed by the Bank of England, and the U.S. Federal Reserve); and (3) use instrumental variables (IV) regression to estimate the causal effect of interest (effect of X on Y, ignoring reverse effect). In the end, and after testing the model in Table 2, we decided against implementing an IV model for two reasons. First the tests did not confirm that estimators in our model were biased or inefficient; and second because the nature of the variables already included in all our models, there were almost no variables left to be used as valid and relevant instruments. A few attempts produced under-performing models. A typology of dynamic evolution Primacy and Economic growth In this section we discuss the co-evolution of primacy, openness, and income. To that effect we have used vector charts to show the path from 1950 to 1980 to 2000. The sample includes 45 countries, for which complete data exist for all three periods of time (18 developed and 27 developing countries). The objective here, as in previous sections, is to provide empirical evidence to the theoretical claims in the literature, specifically about the expected decline in primacy as openness and GDP increase (Krugman, 1996; Rodrik et al., 2002). In Figure 1 we use vectors to represent the co-evolution of variables between 1950 and 1980, 1980 and 2000, and the resultant 1950-2000. Panels A and B show the co-evolution of primacy and real GDP per capita 16. The charts show that in 12 developed countries primacy declined as they grew richer, while in 6 others primacy increased with increases in GDP. 17 Similarly, 17 developing countries show a decline in primacy, while it increased in ten. In one country, Bolivia, both GDP and primacy declined from 1950 to 2000, and in two countries, Nicaragua and Nigeria, primacy increased while GDP shrunk. There are some interesting departures from the expected reduction in primacy as GDP increases. Eight 16 GDP refers to GDP per capita and is measured in constant 1996 dollars. 17 The others are Austria, Canada, Japan, New Zealand, and Spain. 15

countries (Colombia, Egypt, El Salvador, Honduras, Mexico, Pakistan, Peru, and Uganda) displayed increasing primacy with increasing GDP over the 1950-2000 period. In terms of trajectory, that is the consideration of the intermediate positions before and after 1980, the behavior of developing countries appears to be more complex. In order to assess this specific component without relying on graphic methods we used a very simple procedure: we calculated the area delimited by the three vectors. In the case of developed countries, the triangles are rather small in area, even for countries with very significant increases in GDP. In some cases, such as Australia, France, and Denmark, the vectors are almost perfectly co-linear, resulting in a triangle of negligible area. The exception is Finland, which is one of the few developed countries that experienced an increase in primacy. Finland s trajectory includes a significant decline in primacy in the earlier period, followed by an equally significant increase in primacy from 1980 to 2000. The result is a moderate increase in primacy, but through a dramatic reversal in the evolutionary path. Conversely, New Zealand displays a much more marked increase in primacy, but through a relatively stable path from 1950 to 1980 to 2000. Ireland, for instance, shows a moderate initial growth followed by spectacular growth, with a rather modest reduction in primacy. Spain shows rapid growth first (under the Franco regime) followed by a sluggish increase in economic growth, while primacy increases in both periods. This path of co-evolution between primacy and economic growth is hardly uniform, and, although we are not questioning the usefulness of existing theoretical models that predict such regularity, we want to highlight the wide variety of cases found in the empirical evidence. 16

Primacy Primacy Primacy Primacy 9 Figure 4 Co-evolution of Primacy, Economic Growth and Openness PANEL A-Developed Countries PANEL B- Developing Countries 0.5 0.6 IRL PAN 0.4 JPN AUT 0.5 ISR PRT CRI URY 0.3 NZL FIN 0.4 0.3 UGA NIC HND PER EGY SLV THA ARG 0.2 ESP FRA GBR AUSDNK NOR CHE CAN 0.2 BOL ETH PHL PAK KEN MAR NGA GTM COL TUR VEN MEX 0.1 ITA NLD BEL 0.1 BRA ZAF USA IND 5000 10000 15000 20000 25000 30000 GDP 5000 10000 15000 GDP PANEL C- Developed Countries PANEL D- Developing Countries 0.5 0.6 IRL PAN 0.4 JPN AUT 0.5 ISR PRT URY CRI 0.3 NZL FIN 0.4 0.3 PER ARG UGA EGY SLV THA HND NIC 0.2 AUS DNK NOR FRA CHE CAN ESP GBR 0.2 PAK COL BOL ETH PHL MAR TUR KEN GTM VEN MEX NGA 0.1 ITA NLD BEL 0.1 BRA ZAF USA IND 50 100 150 Openness 20 40 60 80 100 120 140 Openness The case of developing countries presents a much more varied set of behaviors. In most cases the vectors define very large areas, indicating very sharp changes in the trajectory of primacy and GDP. One particular case surprises the unaware observer: Guatemala. In 1976 a violent earthquake devastated the capital, Guatemala City, with long lasting effects in the urbanization 17

process of the entire country. This is captured by an abrupt drop in the primacy coordinate, most markedly in the first period (1950-1980). On the far left of the GDP-Primacy chart, four African countries (Ethiopia, Kenya, Nigeria, and Uganda) display extreme behaviors in terms of changes in primacy. For example, Ethiopia s entire increase in primacy between 1950 and 1980 gets reversed in the 1980-2000 period with almost no change in GDP. The reverse case is Turkey, which had a significant change in GDP with almost no change in primacy in either period. The case of Venezuela might be explained by the expansion of ports and industrial centers related to the oil and petrochemical sectors (Barquisimeto, Puerto La Cruz, Ciudad Guayana, etc.). In other cases, natural disasters or other sudden events may have produced this punctuated evolution, such as the case of Guatemala mentioned above, the Southeast Asia tsunami of 2004, and most recently the case of Haiti after the 2010 earthquake. Primacy and globalization The two panels at the bottom of Figure 4 show the co-evolution of primacy and our measure of economic openness. 18 In Panels C and D, we again use vectors to represent the co-evolution of those measures. According to the literature we would expect most 1950-2000 vectors to be pointing downward and to the right, showing the increasing openness-decreasing primacy relation. However one can see that in most of the cases with significant increase in trade-to-gdp ratios primacy is not reduced, and some cases actually increases. The first attribute to be noticed is that while all developed countries increased their exposure to the rest of the world (i.e. monotonic increase in openness) six developing countries Egypt, Kenya, Morocco, Pakistan, Panama, and Venezuela did not. Developed countries exhibit moderate changes over the 50 years covered by this study (hence the small triangles in the chart, with the exception of Finland). Conversely, developing countries show very large changes in direction, including reversing direction entirely. This simple methodology was developed to provide more efficient descriptive indicators in search of global patterns, rather than to demonstrate the intricacies of the highly complex processes. As a result, a typology seems to emerge to illustrate the different modalities of economic transformations and their spatial manifestations. This evidence is probably not enough to challenge established theory, but it seems to be strong enough to consider the appropriateness of the stylized facts used to derive those postulates. Such a typology is presented in the tables below, where countries are listed according to their position for each indicator and period. 18 Openness is defined as the trade to GDP ratios, that is (X+M)/GDP. 18

Decreasing OPENNESS Increasing Decreasing OPENNESS Increasing Decreasing GDP Increasing Decreasing GDP Increasing 10 Table 5 Country positions Primacy-Real GDP 1950-1980 Decreasing Italy, Thailand, France, Belgium, Guatemala, Ethiopia, Denmark, Norway, Israel, South Africa, USA, Finland, Switzerland, Netherlands, India, Brazil, Canada, Ireland, Morocco, Portugal, Turkey, Nigeria. PRIMACY Increasing Austria, Spain, Japan, Egypt, Costa Rica, Mexico, Philippines, Pakistan, Colombia, Kenya, Peru, Panama, Uganda. Uruguay, Venezuela, UK, Bolivia, Argentina, Australia. Honduras, New Zealand, El Salvador, Nicaragua. Country positions Primacy-Real GDP 1980-2000 Decreasing Kenya, Portugal, Ethiopia, Thailand. PRIMACY Increasing USA, Netherlands, Turkey, Austria, Ireland, Uganda, UK, India, Egypt, Spain, Norway, Austria, Japan, Finland, Israel. Guatemala, Philippines, Denmark, Venezuela, Morocco, Bolivia, Italy, Panama, Mexico, Brazil, Uruguay, Argentina. Costa Rica, Belgium, South Africa, Pakistan, France, Switzerland, Honduras, Colombia, Nicaragua, Nigeria, Peru, Canada, El Salvador, New Zealand. Country positions Primacy-Openness 1950-1980 PRIMACY Decreasing Increasing Ethiopia, Denmark, Israel, USA, Finland, Switzerland, Netherlands, Austria, Spain, Japan, Costa Rica, India, Thailand, France, Belgium, Italy, Nicaragua, Uganda. Ireland, Bolivia, Nigeria. Guatemala, Uruguay, Norway, Venezuela, UK, South Africa, Australia, Brazil, Canada, Argentina, Morocco, Portugal, Turkey Honduras, Egypt, Philippines, Pakistan, Colombia, New Zealand, El Salvador, Kenya, Mexico, Peru, Panama. Country positions Primacy-Openness 1980-2000 PRIMACY Decreasing Increasing USA, Costa Rica, Netherlands, Belgium, Ireland, France, Uganda, Switzerland, Denmark, Ethiopia, Italy, Thailand. Spain, Austria, Nicaragua, Nigeria, Japan, Finland, Israel. Kenya, Guatemala, Philippines, Portugal, Venezuela, Morocco, Bolivia, Panama, Mexico, Brazil, Uruguay, Argentina. Turkey, Australia, UK, India, South Africa, Pakistan, Egypt, Norway, Honduras, Colombia, Peru, Canada, El Salvador, New Zealand. 19

Conclusion The first item of our closing arguments has to do with the policy relevance of our findings. Many times, either through the actions of developed countries foreign aid agencies or international financial institution, them being large investment banks and regional development banks, theory finds its way into public policy. McCleery and DePaolis (2008) argue that institutions actions are not deprived of ideological content, and sometimes, they contain nothing but ideology. The benefits of globalization, at least in their spatial dimension, have been oversold. Although we have not explored cases to the extent needed to make convincing claims of inappropriate application of policies it seems at least plausible that unintended as they might have been the promised outcome of economic growth and more equitable distribution of population and economic activity over the national territory has not materialized in developing countries as a result of heightened economic openness. We have shown that the empirical evidence does in fact correspond with the theory as hypothesized by Krugman, and others but only when all countries are considered as a unified whole. By building simple models we have shown, also, that the set of processes collectively known as globalization (happening in the last two decades of the past century) is indeed rather different in its empirical regularity from the economic expansion prior to 1980. We have also shown that that global economic integration after 1980 has had a different effect on urbanization growth and urban concentration than previous cycles of global economic expansion, such as the period immediately after WWII. When analyzed with disregard of the country s level of development the results do match those anticipated by models that used stylized facts. One finds difficult not to think that the decisions made by policymakers based on the predictions of such models have hindered, rather than helped, the balanced spatial growth of developing countries, and perhaps even compromised the chances of sustainable economic growth. Tempted by the promise of positive outcome policymakers might have overlooked country-specific conditions, so often dismissed by the proponents of over-stylized models. We have also commented on the validity of the findings, in terms of how much they could inform policies geared toward reducing urban concentration in the primate city/cities, and spur development in other urban centers, under a different global integration regime. There are a few areas that should be further explored in order to fine-tune the large number of analytical models now available in the literature. One is the impact of foreign direct investment on urbanization processes. Another expansion is to include a measure of the change in the composition of trade over time, beyond what is included in our models. More detailed models are likely to shed light on the issue of mutual influence between deep economic transformation and its physical manifestation through the urbanization process. Countries may have a similar measure of overall openness and still have very different shares of imports and exports, and different trade composition (i.e. proportions corresponding to primary products, consumer goods, 20