Abstract
Agglomeration and income inequality at country level can be both understood as concentration of physical and human capital in the process of economic development. As such, it seems pertinent to analyse their impact on economic growth considering both phenomena together. By estimating a dynamic panel specification at country level, this paper analyses how agglomeration and inequality (both their levels and their evolution) influence long-run economic growth. In line with previous findings, our results suggest that while high-inequality levels are a limiting factor for long-run growth, agglomeration processes can be associated with economic growth, at least in countries at early stages of development. Moreover, we find that the growth-enhancing benefits from agglomeration processes depend not only on the country’s level of development, but also on its initial income distribution (something, to the best of our knowledge, not considered before). In fact, probably suggesting a social dimension to congestion diseconomies, increasing agglomeration is associated with lower growth when income distribution is particularly unequal.
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Notes
In particular, the high growth performance of East Asian countries presenting relatively low levels of inequality has been compared to the weak performance of Latin American countries, which have shown persistently high levels of inequality.
Ehrhart (2009) and Galor (2009) give a comprehensive review of these transmission channels and an overview of the empirical evidence on the effects of inequality on economic growth. Castells-Quintana and Royuela (2014) also review the theory and evidence on the transmission channels and provide evidence of a parallel positive and negative effect of inequality associated with two differentiated forms of inequality.
It has also been reported that the relative importance of each channel is likely to be associated with the profile of inequality. Inequality in different parts of the distribution is associated with different channels, and therefore, it has different implications for growth; top-end inequality fosters growth, while bottom-end inequality retards it (Voitchovsky 2005).
The literature distinguishes between agglomeration externalities of the Jacobs type, associated with the benefits from diversity in cities, and agglomeration externalities of the Marshall type, associated with localisation and specialisation. Duranton and Puga (2004) and Rosenthal and Strange (2004) provide a good theoretical survey on micro-foundations of agglomeration economies and an extensive review of the empirical evidence for both types. More recently, Spence et al. (2009) provide a comprehensive review linking the literature on agglomeration economies with the literature on urbanisation and growth.
“Urbanisation represents sectoral shifts within an economy as development proceeds, but is not a growth stimulus per se. However, the form that urbanisation takes, or the degree of urban concentration, strongly affects productivity growth” (Henderson 2003, p. 67).
As Brülhart and Sbergami note, different spatial scales imply that different mechanisms are at work, which may yield different results. At the small spatial scale, positive spillovers are associated with clustering activities (mainly knowledge spillovers) and agglomeration may have a positive impact on economic growth. The impact is probably even more marked in the more developed countries. However, the results these authors present are concerned with a larger spatial scale. In developing countries, the positive impact of agglomeration is more closely related to a reduction in transaction costs and a greater integration of markets. According to the authors, both these factors may become irrelevant or even detrimental to growth as development proceeds.
When urbanisation takes place as a result of the forced displacement of people from the rural areas—due to violence and social conflict, natural catastrophes or lack of opportunities, rather than motivated by free-market economic incentives—it is unlikely to be associated with economic growth. Bloom et al. (2008) compare industrialisation-driven urbanisation in Asia (considered as likely to enhance economic growth) with urbanisation due to population pressure and conflict in Africa, which is more than likely to be detrimental for growth. In Latin America, the absence of proper urban planning is also evident in certain countries (Angotti 1996).
Fallah and Partridge (2007) find that, for US counties and using cross-section data, there is a different inequality-growth linkage between urban and rural areas: positive in the former (as the agglomeration forces are stronger in urban areas) and negative in the latter (as social cohesion is more relevant in rural areas). Fallah and Partridge’s analysis might be as relevant at country level as it is at subnational level. Moreover, while their results suggest different cross-section effects of inequality on growth in urban and rural areas, they also further motivate a conjunct analysis of the effects of inequality and urbanisation on economic growth in a dynamic setting.
The fact that social conflict is expected to influence the efficiency of cities has already been recognised in the literature on optimal city size (Camagni et al. 2013).
Many authors have extensively defended the fundamental role of institutions for long-run growth. Robinson et al. (2005) relate institutions, along with a series of others factors, to “some degree of equality of opportunity in society”.
Barca et al. (2012) analyse the case of Europe where, they explain, economic growth is given in small-to-medium-sized cities.
In any case, the fundamental goal of our empirical approach is to reveal differentiated patterns regarding inequality, agglomeration and economic growth in the process of development through econometric analysis. Increasing urban concentration and increasing inequalities are here considered as two-pronged expression of concentration of resources at country level.
Durlauf et al. (2005) explain this common econometric setting in cross-country regressions derived from neoclassical economic growth theory. Sala-i-Martin et al. (2004), using cross-section regressions, and Barro (1998, 2000, 2003), using panel data, have both conducted in-depth analyses of these and other determinants of economic growth. Sala-i-Martin et al. (2004) explore 67 possible explanatory variables for long-run growth between 1960 and 1996 and find 18 that are significantly related to it. These results show that cross-country differences in long-run growth in per capita GDP are well explained using initial levels of per capita GDP—the neoclassical idea of conditional convergence—and variables of natural resource endowments, physical and human capital accumulation, macroeconomic stability and productive specialisation (a negative and significant effect being found for the fraction of primary exports in total exports). Barro (2003) also supports conditional convergence “given initial levels of human capital and values for other variables that reflect policies, institutions and national characteristics”.
Alesina and Rodrik use cross-section data and include income and land (as a proxy for wealth) distribution variables along with control variables for initial level of income and primary school enrolment ratio, taking 1960–1985 and 1970–1985 time horizons. As control variables, Perotti includes the initial level of income, the initial average years of secondary schooling in the male and female population (MSE and FSE) and the initial PPP value of investment deflator relative to the US Forbes also adopts Perotti’s specification but uses panel data. Other authors include additional control variables. Clarke’s cross-section study, for instance, includes the initial level of income, primary and secondary enrolment rates lagged ten years, the average number of revolutions and coups per year between 1970 and 1985, the deviation of the price level for investment in 1970 from the sample mean and the average government spending as percentage of GDP between 1970 and 1988. His time horizon is 1970–1988.
Rather than including lagged levels and first differences, an alternative, but intrinsically equivalent, specification would be to include contemporaneous levels and lagged levels, as in Brülhart and Mathys (2008) estimating agglomeration effects on labour productivity for European regions. We choose the specification detailed in model 1 for consistency with traditional econometric settings of cross-country economic growth models in which right-hand-side variables are not introduced contemporaneously. In this regard, our specification is closer to Brülhart and Sbergami (2009).
The main and most complete dataset on Gini coefficients comes from the World Income Inequality Database (WIID-WIDER). Besides quality, there are three important details of the construction of Gini coefficients relevant to take into account when we use these coefficients to study interactions between inequality and economic growth: (1) the object of measure: gross income, net income, expenditure or consumption; (2) the unit of measure: individual, family or household; and (3) the coverage of data: urban, rural or all.
The following missing values for Green’s Gini coefficients have been filled based on trends and/or interpolations: Bolivia 1980 and 2000, Ecuador 1980, Egypt 1980, Honduras 1980, Korea 1980, Nepal 1990, Peru 1980 South Africa 1980, Tanzania 1980 and Zambia 1990.
We also consider other measures of agglomeration at country level: the share of population concentrated in the largest city (PRIMACY), as well as two other variables employed in the related literature, the geographical concentration of population (GEO_CONC) and the average population per square km (DENSITY).
The sample includes: 11 countries form Latin America and the Caribbean, 2 from North America, 10 from Africa, 13 from Asia, 1 from Oceania and 14 from Europe.
System-GMM estimation techniques have already been used in the two fields in which the present research focuses: in the study of the effects of inequality on economic growth, in works such as Voitchovsky (2005), and in the study of the effects of agglomeration on economic growth, in Brülhart and Sbergami (2009). Both papers present a good practical explanation and discussion of the advantages of System-GMM estimators in short dynamic panels with highly persistent variables.
An additional concern worth noticing with GMM estimations of the effect of inequality on economic growth, according to Banerjee and Duflo (2003), is that for inequality, we need to be aware that the use of lagged levels to instrument for first differences is likely to be biased. This happens because, while low levels of inequality are not significantly correlated with increases in inequality, high levels of inequality are significantly correlated with decreases, which are positively correlated with economic growth. As there are more decreases than increases in their dataset, the coefficient for the effect of inequality on economic growth is positively biased when estimating by traditional GMM. In that case, using Sys-GMM, rather than traditional GMM, has an additional advantage of compensating the mentioned bias. Furthermore, in our sample, there are actually more increases—86—than decreases—67, and both are common in countries with initially high as in countries with initially low levels of inequality.
We report ar1 and Hansen tests for validity of instruments in the results tables. Due to the shortness of our panel and the use of variables in changes, ar2 tests can only be computed as robustness checks from estimations similar than those presented but omitting the variables in changes (in order to gain an extra time period). Key results for the rest of the variables do not change, and serial correlation does not appear to be a problem. As for evidence regarding the strength of our instrument set, as Bazzi and Clemens (2013) highlight, there is yet no reliable and straightforward test for Sys-GMM estimations. However, an analysis of correlations for our key variables reveals substantial explanatory power for lagged differences to explain levels and for lagged levels to explain first differences.
Table 3 Estimations using \(UC\) as measure for agglomeration Table 4 Estimations using URB as measure for agglomeration While urban concentration rates only give us information on the role of large agglomerations, more likely to be subject of congestion diseconomies, urbanisation rates also inform us of the role of small-to-medium-sized cities. When we experimented with the other measures considered for agglomeration at country level (PRIMACY, GEO_CONC and DENSITY), our key results did not vary much. Here, we only present results for URB and UC. These urbanisation measures, besides being the most widely used, capture the agglomeration of population and economic activity and seem to relate more closely to the analysis conducted here, as our results show.
Following recent evidence suggesting that economic growth today is given in small-to-medium-sized cities, especially in developed countries (McCann 2012). If we look at the association between economic growth and urbanisation processes decade by decade in our sample, we find that while in the 1980s and 1990s, economic growth seems more closely associated with increasing urban concentration, during the 2000s, economic growth is far more correlated with increasing urbanisation in small-to-medium-sized cities—urbanisation that does not take place in agglomeration of more than 1 million inhabitants
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Acknowledgments
We thank Mark Partridge, Enrique Lopez-Bazo and two anonymous referees for valuable comments. We are also grateful for comments received at the AQR-IREA Seminars-2011, at the Encuentros de Economía Aplicada 2012, at several Regional Science Association International (RSAI) conferences (the 9th World Congress, 52nd European Congress and the 59th Annual North American Meetings) and at the Sixth World Bank Urban Research and Knowledge Symposium (and to the anonymous referee assigned for such Symposium). Both authors acknowledge the financial support of CICYT ECO2010-16006.
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