Virtual special issue on regional inequality

ABSTRACT

This virtual special issue of Spatial Economic Analysis marks the keynote lecture by Professor Sergio Rey of the University of California – Riverside at the 58th Annual Congress of the European Regional Science Association in Cork, Ireland. It draws together nine papers from previous issues of the journal that deal with regional and spatial inequalities, a central theme of regional science in general and Rey’s research in particular, thereby providing an overview of the main sources of regional and spatial inequalities both within and across countries.

KEYWORDS:

• spatial models
• income distribution
• wage differentials
• growth

JEL:

• C23
• D31
• J31
• O11
• R11

This virtual special issue of Spatial Economic Analysis marks the keynote lecture by Professor Sergio Rey of the University of California – Riverside at the 58th Annual Congress of the European Regional Science Association in Cork, Ireland. A central theme of Rey’s research is spatial inequality dynamics. Now in its 13th year, Spatial Economic Analysis has become an agenda-setting journal for regional science, publishing important contributions on a variety of topics. This virtual special issue focuses on the theme of regional inequality. It draws together nine papers from previous issues of the journal so as to provide an overview of the insights into regional and spatial inequalities and to highlight the journal’s contribution to the topic from theoretical, applied econometric and policy perspectives.

This topic is currently critical as the focus of attention in explaining the rise of populist movements across the developed world, and specifically in the United States and Europe, turns towards regional inequalities. Regional inequalities are increasingly seen as a threat to economic performance, social cohesion and stability. This virtual special issue is timely, therefore, as it presents evidence of the dynamics and sources of regional and spatial inequalities both within countries and between countries.

The first paper, by Rey and Sastré-Gutiérrez (2010), provides a detailed empirical analysis of interregional inequality in the Mexican context. It uses 60 years of data and exploratory spatial data analysis to identify the dynamics of regional income inequality. The paper provides a distinctively geographical analysis, and highlights how different spatial scales can impact our understanding of inequality. By considering different regionalization schemes, the authors show differences in interregional inequality dynamics under each.

Sacchi and Salotti (2014) also provide an analysis of income inequality. They provide additional insights into the structure and effectiveness of governance on income disparities, specifically by examining the effects of fiscal decentralization. The authors use a sample of 23 Organisation for Economic Co-operation and Development (OECD) countries over the period 1971–2000 and employ a range of measures of fiscal decentralization covering the degree of both fiscal and political autonomy of subnational jurisdictions. The results highlight the importance of two aspects of regional governance for income inequality: the nature of fiscal decentralization – expenditure versus revenue; and the extent to which independent spending and taxation responsibilities are actually assigned to local governments. For instance, a higher degree of autonomy and responsibility over taxes assigned to subnational governments is associated with a more unequal distribution of income across households within a country.

De Dominicis (2014) analyzes a sample of 188 European regions during the period 1991–2004 to examine the impact of intra-regional income distribution on regional growth in Europe. The empirical estimations are based on a spatially augmented Solow model. The paper examines whether the clustering of income in a relatively small number of locations within a region promotes economic growth through benefits associated with agglomeration economies. When testing this hypothesis for less developed Objective 1 regions, strong evidence is found that agglomeration positively impacts subsequent growth.

Ezcurra and Rodríguez-Pose (2014) investigate the impact of trade openness on regional income inequality for a sample of emerging economies. They analyze 22 countries for the period 1990–2006. Specifically, the authors use data derived from the International Monetary Fund’s (IMF) World Economic Outlook report to examine the link between trade openness (measured as the ratio of total trade to national gross domestic product – GDP) and spatial inequality (measured using a Theil index). Their findings suggest that the degree of trade openness is positively correlated with spatial inequality. Moreover, the spatial impact of trade is found to be greater in poorer countries, meaning that, while trade on the whole may have a beneficial effect for aggregate economic performance in the emerging world, the poorest regions in the poorest countries – the poorest of the poor – are likely to lose out from greater engagement in international trade. This is a particularly interesting finding in the context of current moves, in the United States particularly, to impose new trade tariffs.

Pereira and Galego (2015) deviate from income measurements and instead provide an analysis of inequality by focusing on intra-regional wage differentials in Portugal. They use data from the period 1995–2005 and a quantile decomposition approach. An advantage of their approach is that it allows an analysis of economic inequality along the entire wage distribution. In addition, the methodology provides a way of understanding the causes of changes in wage inequality over time. Their results show that wage inequality evolution has been heterogeneous in Portugal, as inequality increased in some regions but decreased in others. They suggest that these differences can be explained by individual characteristics and by the industry structure of the region.

Paredes (2013) also considers the determinants of spatial inequalities in wages by focusing on the relative performance of rival, non-nested theories of wage determination in Chile. The two concepts underpinning the analysis are New Economic Geography (NEG) and resource endowment. When estimating the model, the author accounts for individual wage characteristics, such as age, gender, marital status and socioeconomic status, and regional-level variables associated with the two rival theories. The multilevel estimation method prevents statistical problems such as biased standard errors which would occur under normal ordinary least squares (OLS) estimation given the different spatial scales. In the analysis, the resource endowment model outperforms the NEG model as an explanation for wage disparities across regions.

Monastiriotis (2014) uses regional growth data for the period 1990–2008, contrasting the countries of Central and Eastern Europe (CEE) with the ‘old’ European Union member states, to investigate how different levels, or stages, of national economic development may be associated with different trajectories regarding regional growth and convergence. The author draws on the tradition of the regional Kuznets curve whereby inequalities first rise, as economies start to grow out from initially low levels of development, and then subside, as national economies advance. In this paper, a hybrid model of regional growth is developed which is then estimated for the two subsamples of Central–Eastern and Western European regions. The paper finds significant differences in the convergence process across different levels/stages of development and that the speed of convergence is not constant over time. The findings point to a non-linear path in convergence processes.

Tselios (2009) provides an analysis of income convergence across European regions in the period 1995–2000. The analysis is conducted using a conditional convergence approach. The novel element of the analysis is the application of state-of-the-art panel regression models with spatial interaction effects. Interestingly, the author finds conditional convergence in income per capita after controlling for educational attainment, unemployment, sectoral composition, spatially lagged growth of income per capita and regional fixed effects, but unconditional convergence in income inequality.

Gluschenko (2018) is concerned with the mechanics of analyzing spatial inequalities, presenting a discussion of the appropriate weighting mechanism when constructing regional inequality indices. The author claims that the common practice of population weighting when calculating indices of regional inequality, such as the Theil, Gini and Williamson coefficients, might lead to inconsistent outcomes and, at least, requires rethinking. Examples from regions within different countries are used to highlight how different weights impact inequality indices. The results suggest that population-weighted indices violate the anonymity principle, the principle of transfers, and do not have unambiguous maxima.

For all the papers in this virtual special issue, see http://explore.tandfonline.com/content/bes/virtual-special-issue-on-regional-inequality

ORCID

Declan Jordan http://orcid.org/0000-0002-2446-0111