Elsevier

Ecological Economics

Volume 175, September 2020, 106693
Ecological Economics

Analysis
Spotlight on Spatial Spillovers: An Econometric Analysis of Wastewater Treatment in Mexican Municipalities

https://doi.org/10.1016/j.ecolecon.2020.106693Get rights and content

Highlights

  • Analysis of the spatial spillover of wastewater treatment in Mexican municipalities.

  • Evaluation of the impact of socioeconomic, demographic and institutional factors.

  • Findings suggest wastewater treatment spills over among neighbouring municipalities.

  • We recommend to implement environmental pilot projects to trigger domino effects.

Abstract

Environmental pollution levels are still high in most developing and emerging countries. However, substantial variations exist across and within countries. We investigate the impact of spatial spillovers, a factor that has received very little attention in explaining why variations occur. Spatial spillovers imply that the likelihood of implementing an environmental measure in a specific region is positively influenced by the successful implementation of this measure in neighbouring regions. We analyse whether spatial spillovers exist on the example of municipal wastewater treatment in Mexican municipalities. We apply a spatial econometrics approach for our empirical analysis and control for socioeconomic, demographic and institutional impacts. Our findings suggest that the successful implementation of municipal wastewater treatment spills over among neighbouring Mexican municipalities.

Introduction

Despite many achievements, environmental pollution levels are still generally high in most developing and emerging countries. For example, more than 80% of wastewater resulting from human activities is discharged into the environment without any pollutant removal (UNDP, 2016). However, substantial variations exist across regions, nations, and within countries. The Environmental Performance Index (EPI) indicates the current state and trends of water quality, biodiversity, forestry, and greenhouse gas emissions for different countries, and shows substantial heterogeneity among countries (Jabbour et al., 2012; Hsu et al., 2014). As to subnational levels, World Health Organization data on outdoor air quality in 1600 cities across 91 countries reveals higher variation among cities within developing countries than within their counterparts in developed countries (WHO, 2014). Similarly, the quality of inland waters differs substantially among river basins (UNEP, 2016). These variations beg the question of what are the reasons that some countries or regions in developing and emerging countries are more successful than others in coping with environmental problems.

Our paper addresses a factor that has received very little attention in explaining differences in environmental performance. We focus on spatial spillovers of a successfully implemented measure to reduce pollution in a way that neighbouring administrative entities also implement this environmental measure (henceforth referred to as spatial spillovers). Our working hypothesis is that the likelihood of implementing an environmental measure in a specific region is positively influenced by the successful implementation of this measure in neighbouring regions.

Spatial spillovers may exist for three reasons (Simmons et al., 2006). (1) Spatial contiguity catalyse the diffusion of environmental measures as it allows territorial authorities to learn from each other or mimic each other's behaviour (Drezner, 2001; Shipan and Volden, 2008; Perkins and Neumayer, 2009; Verdolinia and Galeotti, 2011). (2) Competition for residents and investments between contiguous municipalities result in a race to the top in the provision of clean and safe living environments, and result in the convergence of environmental performance (Tiebout, 1956; Holzinger et al., 2008). (3) The negative externality character of transboundary pollution and the resulting public good character of environmental measures induce neighbours to align environmental measures. Once a jurisdiction abates pollution it gives a positive example of proper environmental conduct, which put pressure on neighbours to follow (Maddison, 2007; Amin, 2016).

We test our working hypothesis on the example of municipal wastewater treatment in Mexican municipalities and investigate whether municipal wastewater treatment spills over among neighbouring municipalities. We apply a spatial econometrics approach for our empirical analysis and control for socioeconomic, demographic and institutional characteristics. We rely on cross-sectional data to investigate whether wastewater treatment takes place in a municipality. We would have preferred to use a panel data approach. However, this was not feasible due to data limitation. Mexico is well suited to be a case study as municipal wastewater treatment differs greatly among Mexican municipalities. In our sample of 2299 Mexican municipalities, 1526 or 66.4% of the municipalities did not treat wastewater in 2010 whereas the remaining 773 municipalities had plants that operated in 2010.

Our study focuses on an environmental problem of high relevance as water pollution continues to be a key environmental threat in developing and emerging countries (Semarnat, 2008). 2.2 billion people in less-developed regions suffer annually from waterborne diseases (Diaz and Rosenberg, 2008; Corcoran et al., 2010; Wang and Yang, 2016) and a major pollution source is the discharge of untreated wastewater (Azizullah et al., 2011; Baum et al., 2013; Malik et al., 2015).

It is important to know whether spatial spillovers exist. From a policy point of view, their existence may be used to design environmental measures. For example, spatial spillovers may be an argument for a specific spatial allocation of environmental measures as pilot projects with the idea that the measures are located in a way that they can spread to neighbouring administrative entities. From a scientific point of view, it is important to know whether spatial spillovers exist because if they are unaccounted for in econometric analyses of drivers of the successful implementation of environmental measures, results of such analyses might be incorrect. For example, the successful implementation of an environmental measure might be wrongly attributed to a spatially clustered driver such as income per capita whereas it may in fact (also) be due to spatial spillovers.

We are not the first who investigate reasons for differences in environmental performance. Studies typically investigated the impact of socioeconomic, demographic and institutional factors on environmental pollution. Many authors scrutinized the nexus between income levels and environmental performance addressing areas such as air pollution (Grossman and Krueger, 1991; Narayan et al., 2010), water pollution (Shafik, 1994; Wong and Lewis, 2013) and deforestation (Culas, 2007; Choumert et al., 2013). Others analysed the relationship between environmental performance and institutional factors such as the decentralization of governmental decisions (Fredriksson and Wollscheid, 2014), corruption levels in the public sector (Halkos and Tzeremes, 2014), the prevalence of democratic participation or autocratic government regimes (Wan-Hai et al., 2015; Farzanegan and Markwardt, 2018), and the efficiency and soundness of institutions (Costantini and Monni, 2008; Costantini et al., 2013). Further emphasis has been put on the role of demographic and socioeconomic factors such as racial and ethnic composition of the population (Zwickl et al., 2014), gender discrimination (Germani et al., 2014), differences in education levels (Meyer, 2015), and income inequality (Berthe and Elie, 2015).

Although it is an established hypothesis that policymaking in general spreads geographically (Hosseini and Kaneko, 2013; Amin, 2016), empirical studies that scrutinize the spatial spillover potential of environmental measures are rare. A few studies exist for developed countries (Fredriksson and Millimet, 2002; Maddison, 2007) whereas we are aware of only two studies for developing and emerging countries. Amin (2016) analyses for 48 sub-Saharan countries the spatial interdependence of biodiversity conservation policymaking, and Sauquet et al. (2012) investigate whether neighbouring municipalities influence the practice to establish protected areas in 399 municipalities in the Brazilian state of Paraná. Our work is different from both studies as we focus on environmental pollution and not biodiversity conservation. Moreover, unlike Amin (2016) we address spatial spillovers within and not between countries. Unlike us, Sauquet et al. (2012) are not interested in the spatial spread of successful environmental measures but on spatial interaction in general. In fact, they find a negative spatial interrelation, which they explain by higher incentives to develop land economically in municipalities whose neighbours already created protected areas. Moreover, Sauquet et al. (2012) do not control for similarities in the socioeconomic, demographic and institutional structure of contiguous municipalities as an explanation for spatial patterns.

Our results suggest that municipal wastewater treatment spills over across neighbouring jurisdictions. In addition, we find that per capita GDP, income distribution, urbanization, education level, the creation of a public water enterprise and the municipality's location in a particular federal state have a significant influence on the probability that a Mexican municipality will treat wastewater.

Section snippets

Data and Econometric Methodology

Fig. 1 contains a flowchart, which gives an overview of our methodological approach for the empirical analysis.

Considering information availability and the institutional background, we specified as dependent variable whether wastewater treatment takes place or not in a Mexican municipality. In addition to spatial spillovers, we identified further independent variables that may influence whether wastewater is treated or not. We were able to gather secondary data on these variables for 2299

Baseline Results

Table 2 presents the estimation results. Column (1) contains the estimated parameter of the general probit model of socioeconomic, demographic and institutional factors. Columns (2) to (8) present the respective parameters and ρ-values for SAR probit for different specifications of k.

As for independent variables, the general probit model and SAR probit for all k specifications display relatively similar estimation results in terms of magnitude and significance. Our socioeconomic, demographic

Discussion and Conclusions

In this paper, we focus on a factor that has received little attention in explaining environmental performance in developing and emerging countries – the spatial proximity of a successfully implemented measure to reduce pollution. As an example for an environmental measure we take wastewater treatment in Mexican municipalities. For a sample of 2299 municipalities, we find that a municipal administration is the more likely to treat wastewater the higher the percentage of neighbouring

Acknowledgements

We thank the reviewers of the paper and the participants of the research colloquium of the Department of Environmental Economics at the Brandenburg University of Technology, the symposium “Political Economy of Climate Change and Natural Disaster with Reference to Iran and the Arab World” at Philipps-University Marburg and the 2019 IIPF Annual Congress for their valuable comments and suggestions.

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