An introduction to spatial econometric models and methods is provided that discusses spatial autoregressive processes that can be used to extend conventional regression models. Estimation and interpretation of these models are illustrated with an applied example that examines the relationship between commuting to work times and transportation mode choice for a sample of 3,110 US counties in the year 2000. These extensions to conventional regression models are useful when modeling cross-sectional regional observations or and panel data samples collected from regions over both space and time can be easily implemented using publicly available software. Use of these models for the case of non-spatial structured dependence is also discussed.

/pdf/introduction-to-spatial-econometrics-9xcsp6v7d6.pdf

Introduction to spatial econometrics

Local Indicators of Spatial Aggregation (LISA) can be used as objectives in a multicriteria framework when highly autocorrelated areas (hot-spots) must be identified and geographically located in complex areas. To do so, a Multi-Objective Evolutionary Algorithm (MOEA) based on SPEA2 (Strength Pareto Evolutionary Algorithm v.2) has been designed to evaluate three different fitness functions (fine-grained strength, the weighted sum of objectives and fuzzy evaluation of weighted objectives) and three LISA methods. MOEA makes it possible to achieve a compromise between spatial econometric methods as it highlights areas where a specific phenomenon shows significantly high autocorrelation. The spatial distribution of financially compromised olive-tree farms in Andalusia (Spain) was selected for analysis and two fuzzy hot-spots were statistically identified and spatially located. Hot-spots can be considered to be spatial fuzzy sets where the spatial units have a membership degree that can also be calculated.

/pdf/multiobjective-evolutionary-algorithms-to-identify-highly-4g00yfahq1.pdf

Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms

Although spatial econometrics is being used more frequently in political science, most applications are still based on geographic notions of distance. Here we argue that it is often more fruitful to consider political economy notions of distance, such as relative trade or common dyad membership. We also argue that the spatially autoregressive model usually (but not always) should be preferred to the spatially lagged error model. Finally, we consider the role of spatial econometrics in analyzing time-series–cross-section data, and show that a plausible (and testable) assumption allows for the simple introduction of space (however defined) into such analyses. We present examples of spatial analyses involving trade and democracy.

Space Is More than Geography: Using Spatial Econometrics in the Study of Political Economy

We provide a comprehensive overview of the strengths and weaknesses of different spatial econometric model specifications in terms of spillover effects. Based on this overview, we advocate taking the SLX model as point of departure in case a well-founded theory indicating which model is most appropriate is lacking. In contrast to other spatial econometric models, the SLX model also allows for the spatial weights matrix W to be parameterized and the application of standard econometric techniques to test for endogenous explanatory variables. This starkly contrasts commonly used spatial econometric specification strategies and is a complement to the critique of spatial econometrics raised in a special theme issue of the Journal of Regional Science (Volume 52, Issue 2). To illustrate the pitfalls of the standard spatial econometrics approach and the benefits of our proposed alternative approach in an empirical setting, the Baltagi and Li (2004) cigarette demand model is estimated.

The SLX model

This study helps to explain why measured school inputs appear to have little effect on student outcomes, particularly for cohorts educated since 1960. Teachers' unionization can explain how public schools simultaneously can have more generous inputs and worse student performance. Using panel data on United States school districts, I identify the effect of teachers' unionization through differences in the timing of collective bargaining, especially timing determined by the passage of state laws that facilitate teachers' unionization. I find that teachers' unions increase school inputs but reduce productivity sufficiently to have a negative overall effect on student performance. Union effects are magnified where schools have market power.

How teachers unions affect education production

On model specification and parameter space definitions in higher order spatial econometric models

A popular approach to examining the effects of public policy has been to rely on a spatial
data sample of border counties as in Holmes (1998)Nborder counties from a sample
of states that are used in conjunction with least-squares estimation techniques in
an attempt to isolate the policy impact while controlling for spatial dependence that
often arises from latent or unobserved variables. This technique is in the spirit of
control-group methodologies from the laboratory sciences. This paper contrasts
border-county estimation results from Holmes' (1998) approach and those from a related
methodology set forth in Holcombe and Lacombe (2003), with estimates from a
spatial autoregressive model explicitly accounting for within-state and between-state
public policy effects. As an illustration, the paper examines the effects of Aid to Families
with Dependent Children (AFDC) and Food Stamp payments on female-headed
households and female labor force participation using the three different methods.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.2004.tb01128.x

Does Econometric Methodology Matter? An Analysis of Public Policy Using Spatial Econometric Techniques

This study examines the impact of changes in marginal state income tax rates on per capita income by comparing income growth in counties on state borders with income growth in adjacent counties across the state border. Compared to a standard cross-sectional analysis, this border-matching technique is a better way to hold constant many factors that can vary for geographical reasons, such as climate, culture, and proximity to markets. The results show that over the 30-year period from 1960 to 1990, states that raised their income tax rates more than their neighbors had slower income growth and, on average, a 3.4% reduction in per capita income.

The Effect of State Income Taxation on Per Capita Income Growth

Province-level convergence of China’s carbon dioxide emissions

We survey the literature on spatial bio-economic and land-use modelling and assess its thematic development. Unobserved site-specific heterogeneity is a feature of almost all the surveyed works, and this feature, it seems, has stimulated significant methodological innovation. In an attempt to improve the suitability with which the prototype incorporates heterogeneity, we consider modelling alternatives and extensions. We discuss solutions and conjecture others.

Donald J. Lacombe

Papers

On model specification and parameter space definitions in higher order spatial econometric models

Does Econometric Methodology Matter? An Analysis of Public Policy Using Spatial Econometric Techniques

The Effect of State Income Taxation on Per Capita Income Growth

Province-level convergence of China’s carbon dioxide emissions

Spatial Econometric Issues for Bio-Economic and Land-Use Modelling