Scottish Longitudinal Study
Development & Support Unit

Current Projects

Project Title:

Neighborhood Dynamics: Pollution and Poverty Traps

Project Number:

2012_004

Researchers:

Stephan Heblich (University of Stirling)
Christopher Timmins (Duke University, USA)
Robert Wright (University of Strathclyde)

Start Date:

25 September 2012

Summary:

A large body of literature summarized in Kuminoff et al. (2010) recognizes the influence of local (dis)amenities on households’ residential choice. It turns out that households with children are willing to pay higher house prices to sort into neighborhoods with a better school quality. Similarly, low crime rates and local public goods are positively valued. On the other hand, negative amenities from being exposed to pollutants or noise are considered negative amenities that decrease individuals’ willingness to pay to live in a neighborhood. Typically, one observes a larger concentration of low-income households in areas characterized by disamenities whereas high-income households tend to locate in neighborhoods with positive amenities.

This observation involves two types of research questions. First, we would like to understand how this observed equilibrium emerges. Is it the result of disproportionate (possibly discriminatory) siting of amenities or is it the result of residential sorting that follows siting. In the latter case, even equitable site placement would be undone in subsequent sorting of households according to their valuation of (dis)amenities (cf. Depro et al. 2011). To solve this chicken and egg problem, we plan to exploit information from the 1991 and 2001 census on individuals’ location, migration and corresponding characteristics of their neighborhoods. Between these two censuses, the 1995 Environment Act led to better availability of information about local pollution levels. This pollution information allows us (i) to analyze the siting of pollution (with 1994 pollution levels as status quo) using residential information from 1991 and (ii) to analyze residential sorting following better information about local pollution (from 1994 on) using changes in residency between 1991 and 2001. Any systematic changes in the composition of neighborhoods with a low (high) level of pollution—holding other neighborhood characteristics constant—would indicate residential sorting.

Second, starting from the observed negative relationship between pollution and income, we would like to understand the long-term consequences of growing up in a polluted area. Besides the obvious negative health effects there is also evidence that being exposed to pollutants during childhood can affect cognitive capacities and educational outcomes (Currie 2009; Morello-Frosch et al. 2002; Pastor et al. 2004). Another strand of literature provides rich evidence that lower educational attainments are linked restricted labor market opportunities (cf. Card 1999; Meghir and Rivkin, 2011). This research project links these two stands of literature and analyzes the causal chain between pollution exposure in early childhood, subsequent educational attainments and future job opportunities. SLS is a valuable data source for this long-term analyzes because it allows us to follow individuals from birth to work life and it provides the opportunity to match additional information on the individual level (individual health status and schooling) and the neighborhood level (socio-demographic and socio-economics characteristics provided by SLS and additional information on pollution provided by the National Atmospheric Emissions Inventory (NAEI) that can be matched on the output area level). We believe that this research bears important policy implications as it provides a new possible explanation why some neighborhoods are caught in poverty traps (Bowles et al. 2006).

We intend to analyse how local disamenities may affect human capital development in the long run in two different ways. First, we are interested in an environmental justice argument: is the observation that poorer neighbourhoods are often exposed to more pollution the result of a disproportionate (possibly discriminatory) siting of disamenities or is it the result of residential sorting that follows siting. In the latter case, even equitable site placement would be undone in subsequent household sorting. Our second hypothesis takes the observed spatial distribution of households across neighbourhoods of a given pollution level and analyses if environmental pollution affects individuals’ cognitive capacities. If so, this may subsequently lead to lower academic performance and labour market outcomes. Pollution levels from toxic releases can be calculated on a fine geographic scale using information from pollution monitors and information about the location of firms in certain. If proximity to toxic releases affects children’s health and the development of cognitive abilities, we hope to observe some early evidence in the detailed child health records. Subsequently, we plan to look at educational achievements and eventually labour market outcomes. If poor families were more likely to be exposed to pollutants that affect their children’s future educational achievements and labour market opportunities, the underlying relationship provides one possible explanation for observed traps of continued poverty and social immobility in certain neighbourhoods. SLS provides a suitable data basis to analyse both questions. To assess residential sorting, we can analyse residential changes between the 1991 and 2001 census. As the 1995 Environment Act led to a better availability of information about local pollution levels, any systematic changes in polluted areas residential composition (while holding other characteristics constant) might indicate residential sorting. To assess the second question, we plan to exploit the detailed individual information from early childhood to schooling and even the working life (especially if we included the upcoming 2011 census). To determine causal relationships that quantify the effect of local pollution on future outcomes we will additionally add detailed information about the socio-demographic structure of individuals’ neighbourhoods to control for possibly confounding characteristics.

References:

Bowles, S., S. Durlauf, and K. Hoff (2006). Poverty Traps. Princeton University Press. Princeton, NJ.

Card, D. (1999). The Causal Effect of Education on Earnings, in: O. Ashenfelter and D. Card (Eds.): Handbook of Labor Economics Vol. 3A. Amsterdam: Elsevier, 1801-1863.

Currie, J. (2009). Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development, Journal of Economic Literature, 47(1): 87–122.

Depro, B., C. Timmins, and M. O’Neil (2011). Meeting Urban Housing Needs: Do People Really Come to the Nuisance?, NBER Working Paper 18109.

Kuminoff, N., V.K. Smith, and C. Timmins (2010). The New Economics of Equilibrium Sorting and its Transformational Role for Policy Evaluation, NBER Working Paper 16349.

Morello-Frosch, R., M. Pastor, C. Porras and J. Sadd (2002). “Environmental Justice and Regional Inequality in Southern California: Implications for Future Research,” Environmental Health Perspectives, 110(2), 149-154.

Meghir, C. and S. Rivkin (2011). Econometric Methods for Research in Education, in: E. Hanushek, S.

Matchin and L. Woessmann (Eds.) Handbook of the Economics of Education, Vol 3., Amsterdam: Elsevier, 1-88.

Pastor, M., J. Sadd, and R. Morello-Frosch (2004). “Reading, writing, and toxics: children’s health, academic performance, and environmental justice in Los Angeles,” Environment and Planning C: Government and Policy. 22:271-290.

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