Spatial inequality refers to the unequal distribution of income and resources across geographical regions. Attributable to local differences in infrastructure, geographical features (presence of mountains, coastlines, particular climates, etc.) and economies of agglomeration, such inequality remains central to public policy discussions regarding economic inequality more broadly.
Whilst jobs located in urban areas tend to have higher nominal wages (unadjusted for differences in price levels or inflation) than rural areas, the cost-of-living and availability of skilled work correlates to regional divergences in real income and output. Additionally, the spatial component of public infrastructure affects access to quality healthcare and education (key elements of human capital and worker productivity, which directly impacts economic well-being).
Variation in both natural resource composition and quality of regional infrastructure are traditionally considered to be motivating factors for migration patterns between urban cities and rural areas. This, in turn, impacts the concentration of specific industries and sectors within a given area, as well as the investment choices made by local governments, thus perpetuating spatially-based disparities. However, there remain significant challenges in carrying out empirical research to quantify these disparities (particularly within a given nation, as opposed to across different nations), due to lack of region-specific datasets, the level of geographical disaggregation required to reveal such trends, as well as the inherent differences in incomes and living costs across different communities.
Urbanization and economies of agglomeration
The relationship between population density and productivity is a significant factor affecting the difference in economic capital, cultural capital, and social capital found between cities and rural areas. In particular, the clustering of agriculture activities versus manufacturing activities informs much of the urban-rural wage gap, as industrial jobs tend to earn higher wages than their counterparts in the agricultural sector. The rate at which this clustering of jobs occurs provides a partial explanation as to why different communities undergo urbanization at different rates. From this, the theory of the core-periphery model in urban economics suggests that manufacturing tends to form the "core" of an industrial cluster, with agricultural activity tending to take place on the "periphery" of such urban formations. This affects the organizational set-up of linkages throughout supply chains, as agricultural goods and resources (directly outputted from agricultural processes) are then transported inwards towards the urbanized center of the region. Such patterns permit greater economies of scale to be realized, as different economic activities become concentrated in regions that are best suited for such work, and transportation costs can be reduced accordingly.
Agglomeration economies refer to the benefits gained from such industrial clustering and city-formation. With the observed savings in transportation costs from this phenomenon being central to the study of economic geography, the positive externalities (indirect benefits gained from third-party activities) afforded by such urbanization (and the mechanisms by which they occur) remain to be of interest for academic studies and public policy considerations.
Population concentration and the clustering of particular industries also allows for the pooling of workers, which results in local business needs and workers' specific skillsets becoming better aligned. Such specialization also allows for knowledge spillovers and greater exchange of ideas, as similar firms can more easily and dynamically interact with one another. This can assist in gaining a comparative advantage with respect to a particular industry or sector, which can be especially beneficial for realizing gains from trade when interacting with other communities and regions which are not as specialized, thus resulting in more geography-based disparities in economic activity.
Natural resources and geographical features
Natural resource availability affects industry prevalence, as economic activities which are heavily dependent on specific natural resources tend to cluster around suitable geographical regions and climates.
Localities which have a heavy reliance on agricultural jobs require favorable climate conditions for crop production and harvesting. For instance, empirical evidence from Ghana points towards the impact of such spatial inequities on the quality of natural resources available. Although employment in the northern regions of the nation is heavily reliant on the agricultural sector, there is limited access to irrigation and modern implements needed for efficient farming. Such unsustainable farming practices have led to natural resource depreciation over time, including lower quality of soil and higher rates of erosion, which in turn impacts the region's ability to continue engaging in future crop production. In addition, in the face of erratic weather patterns, global warming, and climate change, these challenges have been exacerbated by distorted rainfall patterns and increasingly frequent crop failures.
The resource curse theory suggests that an over-reliance of employment on abundance of natural resources (including forestry, fossil fuels, mineral deposits, etc.) can lead to instability and volatile prices. However, the exogenously determined geographical features of the area directly determines the region's ability to produce traditional agricultural goods and exports. Therefore, such externally determined geographical and climate features informs the composition of employment in the region.
Regions with access to strong transportation networks (including highways, railways, airports etc.) are more likely to benefit from external trade in comparison to remote regions. As transportation costs and logistics inform much of the clustering of economic activity within a region, the geographical concentration of particular industries informs the extent to which particular physical infrastructures must be developed and invested in to support the needs of specific localities.
Social infrastructural components, which impact health and education standards (hospitals, schools, public libraries, etc.) additionally influence quality-of-life conditions and the well-being of workers, and thus their choices with respect to selecting regions/ communities to live in. As such, city planning and the provision of public infrastructure and services remains essential to public policy considerations for rapidly urbanizing communities.
In particular, people living in regions with poor infrastructure and public services are at a greater risk of poor health and wellbeing. This includes limited access to both healthcare, as well as quality and nutritious food. Such impacts compound over time, leaving individuals to become more susceptible to future health problems and illnesses. For instance, the spatial patterns of such environmental factors and hospital accessibility can impact public health outcomes, such as COVID-19 infection, spread, and mortality rates within a nation.
Furthermore, as families of similar incomes tend to cluster, further segregation of socio-economic classes is propagated by schooling environments. This adversely effects the opportunities available to children from low-income backgrounds, and reduces the ability for social mobility needed to escape the poverty trap and generational poverty. An example of this phenomenon in the United States includes redlining - a racially discriminatory historical practice, which resulted in subprime mortgages becoming highly concentrated to specific neighborhoods and geographies.
Investment choices, trade, and migration
As different communities may not have similar comparative advantage due to variations in natural resource composition and abundance, foreign trade and globalization are thought to play a key role in influencing spatial inequality as well. In particular, economies undergoing rapid trade liberalization have been observed to actually have increases in poverty rates and income inequality, in spite of nation-wide benefits of economic growth being realized, as urban-rural gaps tend to widen. Additionally, migration patterns from rural to urban areas in developing nations are observed to be a labor market adjustment to an increasing shift in importance from agriculture to manufacturing.
There remains no academic consensus on whether trends in spatial inequalities over time are causes of region-based differences in income, or rather the symptoms of other socio-economic disparities. Furthermore, the complex and intertwined relationships between geographical features, urbanization, availability of infrastructure, and access to public resources further complicates empirical research.
Output and productivity
The distribution of income within a nation can first be nominally estimated from local datasets, and then subsequently adjusted to account for regional differences in price levels. Such a procedure allows for comparisons to be made in real-terms and across different localities, which is especially pertinent when national-level inequalities are mostly influenced by regional disparities in income and cost of living. However, the level of disaggregation (granularity of geo-spatial units considered) and the number of localities selected for comparison varies across academic studies. For instance, geographic sub-groups can be considered at the state level, as an urban/rural divide, or even within-component (differences between households belonging to the same group or community). Typical econometric studies will then design and use regression models to analyze the effects of density, industry location, or related variables on regional differences in output or costs.
While nominal wages tend to be higher in cities and urban regions, the same is not necessarily true of real wages, as rising housing costs and expenses tend to offset these benefits.
The availability and reliability of local data remains a barrier to accurate estimation in academic studies. The typical limitations of econometric studies may also impact the soundness of empirical results and conclusions. As such, there remains no unified theory within economic geography to provide a broadly accepted causal explanation for spatial inequality.
In particular, an inherent difficulty in comparing urban and rural regions is the vast disparity in quality and variety of goods and services enjoyed by the typical household in either type of community. Furthermore, differences in disposable income and composition of spending pose further challenges to comparative approaches.
Whist the Gini coefficient and Theil index remain as popular income inequality metrics, these summary statistics do not allow for the decomposition of inequality into multiple dimensions, and thus are insufficient for the multi-faceted analysis required to study spatially dependent inequalities.
- Chakravorty, Sanjoy (2003 a), “Industrial Location in Post-reform India: Patterns of Inter-regional Divergence and Intra-regional Convergence”, Journal of Development Studies, Vol. 40, No. 2, pp. 120–52.
- Combes, Mayer and Thisse, Economic Geography: The Integration of Regions and Nations (Princeton University Press 2009)
- Deichmann, Uwe; Somik V. Lall; Stephen J. Redding and Anthony J. Venables (2008), “Industrial Location in Developing Countries”, The World Bank Research Observer, Vol. 23, No. 2, pp 219–46.
- Fujita, Masahisa (1996): “Economics of Agglomeration”, Journal of the Japanese and International Economies, Vol. 10, pp. 339–378.
- Fujita, Masahisa and Paul Krugman (2004): “The new economic geography: Past, present and the future”, Papers in Regional Science, Vol. 83, No. 1, pp. 139–164.
- Krugman, Paul (1991a), “Increasing Returns and Economic Geography”, The Journal of Political Economy, Vol. 99, No. 3, pp. 483–499.
- Lall, Somik V., Jun Koo and Sanjoy Chakravorty (2003): “Diversity Matters: The Economic Geography of Industry Location in India”, World Bank Policy Research Working Paper 3072, Washington DC.
- Lall, Somik V. and Sanjoy Chakravorty (2005), “Industrial Location and Spatial Inequality: Theory and Evidence from India”, Review of Development Economics, Vol.9, No. 1, pp. 47–68
- Wei, Yehua Dennis (ed.) (2015), "Spatial Inequality", Applied Geography, Vol.61, pp. 1-116.
- Ravi, K.; Venables, A. J. (2005). "Spatial Inequality and Development". In Ravi, K.; Venables, A. J. (eds.). Spatial inequality and development. Oxford University Press. pp. 3–12. doi:10.1093/0199278636.003.0001. ISBN 9780191602191.
- Romero, Jessie and Schwartzman, Felipe F. Inequality in and across Cities. October 2018, No. 18-10. Federal Reserve Bank of Richmond Economic Brief.
- Lall, S. V.; Chakravorty, S. (2005). "Industrial Location and Spatial Inequality: Theory and Evidence from India". Review of Development Economics. 9 (1): 47–68. doi:10.1111/j.1467-9361.2005.00263.x.
- Kim, Sukkoo. 2008. Spatial Inequality and Economic Development : Theories, Facts, and Policies. Commission on Growth and Development Working Paper;No. 16. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/28050 License: CC BY 3.0 IGO.
- Aten, B.; Heston, A. (2005). "Regional Output Differences in International Perspective". In Ravi, K.; Venables, A. J. (eds.). Spatial Inequality and Development. Oxford University Press. pp. 15–36. doi:10.1093/0199278636.003.0002. ISBN 9780191602191.
- “Lall, Somik V.; Selod, Harris; Shalizi, Zmarak. 2006. Rural-Urban Migration in Developing Countries : A Survey of Theoretical Predictions and Empirical Findings. Policy Research Working Paper; No. 3915. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/8669 License: CC BY 3.0 IGO.”
- Elbers, C.; Lanjouw, P.; Mistiaen, J.; Özler, B.; Simler, K. R. (2005). "Are Neighbours Equal? Estimating Local Inequality in Three Developing Countries". In Ravi, K.; Venables, A. J. (eds.). Spatial Inequality and Development. Oxford University Press. pp. 37–60. doi:10.1093/0199278636.003.0003. ISBN 9780191602191.
- Boulant, Brezzi, M., & Veneri, P. (2016). Income Levels And Inequality in Metropolitan Areas A Comparative Approach in OECD Countries / Justine Boulant, Monica Brezzi and Paolo Veneri. In Income Levels And Inequality in Metropolitan Areas A Comparative Approach in OECD Countries. OECD Publishing.
- Israel, E.; Frenkel, A. (2018). "Social justice and spatial inequality: Toward a conceptual framework". Progress in Human Geography. 42 (5): 647–665. doi:10.1177/0309132517702969.
- Krugman, P. (1991). "Increasing Returns and Economic Geography". Journal of Political Economy. 99 (3): 483–499. doi:10.1086/261763.
- Glaeser, Edward L. (February 2010). "Agglomeration Economics". National Bureau of Economic Research (NBER). University of Chicago Press: 1–14. ISBN 978-0-226-29789-7.
- Tanaka, Tomomi; Nuamah, Camille; Geiger, Michael (December 14, 2018). "Ghana's challenges: Widening regional inequality and natural resource depreciation".
- Hernandez, Jesus (June 2009). "Redlining Revisited: Mortgage Lending Patterns in Sacramento 1930–2004". International Journal of Urban and Regional Research. 33 (2): 291–313. doi:10.1111/j.1468-2427.2009.00873.x.
- Ahimah-Agyakwah, S.; Nketiah-Amponsah, E.; Agyire-Tettey, F. (2022). "Urbanization and poverty in Sub-Saharan Africa: evidence from dynamic panel data analysis of selected urbanizing countries". Cogent Economics & Finance. 10 (1): Article 2109282. doi:10.1080/23322039.2022.2109282.
- Sun, Y.; Hu, X.; Xie, J. (2021). "Spatial inequalities of COVID-19 mortality rate in relation to socioeconomic and environmental factors across England". Science of the Total Environment. 758: Article 143595. doi:10.1016/j.scitotenv.2020.143595.
- Nijman, J.; Wei, Y. D. (2020). "Urban inequalities in the 21st century economy". Applied Geography. 117: Article 102188. doi:10.1016/j.apgeog.2020.102188.
- "Spatial inequalities: across states or between rural and urban areas?". OECD Economic Surveys: India 2017. OECD Publishing. 2017. pp. 141–142. doi:10.1787/eco_surveys-ind-2017-8-en. ISBN 978-92-64-26935-4.