Foundations of spatial microeconometrics modeling
A micro-level approach to spatial econometrics
This book is devoted to the spatial econometric analysis of individual micro-data observed as points in the economic space (Dube and Legros, 2014), sometimes referred to as “spatial microeconometrics” (Arbia et al., 2016). This branch is rapidly emerging onto the stage of spatial econometrics, building upon results from various branches of spatial statistics (Diggle, 2003) and on the earlier contributions of Arbia and Espa (1996), Duranton and Overman (2005), Marcon and Puech (2003; 2009; 2010) and Arbia et al. (2008; 2010; 2014a; 2014b; 2015b). In a relatively recent paper Pinkse and Slade (2010) heavily criticized the current developments of spatial econometrics, observing:
The theory is in many ways in its infancy relative to the complexity of many applications (in sharp contrast to time-series econometrics, where the theory is well developed) ... due to the fact that it is almost invariably directed by what appears to be the most obvious extension of what is currently available rather than being inspired by actual empirical applications.
and:
Many generic large sample results treat locations as both exogenous and fixed and assume that they are observations at particular locations of an underlying spatial process. ... Economists have studied the locational choices of individuals ... and of firms ... but generally treat the characteristics of locales as given. The purpose of much spatial work, however, is to uncover the interaction among (authorities of) geographic units, who choose, e.g., tax rates to attract firms or social services to attract households. ... An ideal model would marry the two; it would provide a model explaining both individuals’ location decisions and the action of, say, local authorities. (Pinkse and Slade, 2010)
This new modelling strategy, which treats location as endogenous by taking into account simultaneously both individuals’ locational choices and their economic decisions in their chosen location, represents the scope of the growing field of spatial microeconometrics.
As a matter of fact, a spatial microeconometric approach (unconceivable until only a few decades ago) is now more and more feasible due to the increasing availability of very large geo-referenced databases in all fields of economic analysis. For instance, the US Census Bureau’s Longitudinal Business Database provides annual observations for every private-sector establishment with a payroll and includes approximately 4 million establishments and 70 million employees each year. Sourced from US tax records and Census Bureau surveys, the micro-records document the universe of establishments and firms characterized by their latitude-longitude spatial coordinates (Glaeser and Kerr, 2009). Examples of this kind can be increasingly found in all branches of economics including education, health economics, agricultural economics, labor economics, industrial economics, house prices, technological diffusion and many others. We will discuss them in the next section.
The availability of these detailed geographical databases now makes it possible to model individuals’ economic behavior in space to gain information about economic trends at a regional or macro-level. A spatial microeconometric approach had already been suggested some 30 years ago by Durlauf (1989), at a time when data allowing this kind of approach were not yet available, appropriate models had not been developed and computing power was limited. Durlauf criticized the mainstream macroeconomy, pointing out that “macroeconomic modeling currently relies upon the representative agent paradigm to describe the evolution of time series. There is a folk wisdom that heterogeneity of agents renders these models unsatisfactory approximations of the macroeconomy”. He then proceeded to describe a “lattice economy” where a “collection of agents are distributed across space and time” and “macroeconomy consists of many simple agents simultaneously interacting”. Durlauf (1989; 1999) suggested a parallel between physics and economic analysis. In particular he concentrated on the links existing between formal individual choice models and the formalism of statistical mechanics, which suggested that there are many useful tools that applied economists could borrow from physics. Just as in statistical mechanics models explain how a collection of atoms can exhibit the correlated behavior necessary to produce a magnet, in economics one may devise models aimed at explaining spatially interdependent behaviors. The basic idea in statistical mechanics, that the behavior of one atom is influenced by the behavior of other atoms located nearby, is indeed very similar to the hypothesis that forms the basis of all spatial econometric studies that individual or collective decisions depend upon the decisions taken in other neighboring regions or by neighboring economic agents.
According to Kirman (1992) the traditional approach considers “the aggregate behavior of the economy as though it were the behavior of a single representative agent”. However there is strong evidence that “heterogeneity and dispersion of agents’ characteristics may lead to regularity in aggregate behavior” and that “once we allow for interdependence ... consistency between microeconomic characteristics and macroeconomic characteristics may be lost” and, finally, “strong local random interacting agents who are a priori identical may produce macroeconomic irregularities”. Kirman concludes his work by stating that we must change our attitude and start thinking “of the economy as a self-organizing system, rather than a glorified individual”.
Perhaps the most radical criticism in this respect is, however, presented by Danny Quah (1993), who states:
Modern macroeconomics concerns itself, almost by definition with substitution of consumption and production across time. The macroeconomist wishes to understand the dynamic of inflation and asset prices, output and employment, growth and business cycles. Whether in doing so, one uses ideas of search and nonconvexities, intertemporal substitution and real business cycles, sticky prices and wages, or dynamic externalities, one implicitly assumes that it is the variation in economic activity across time that is the most useful to analyse. But why must that variation be the most important?
In doing so the macroeconomist “almost exclusively focuses on aggregate (rather than disaggregate) shocks as the source of economic fluctuations” ignoring “rich cross-sectional evidence on economic behaviour” and losing “the ability to say anything about the rich heterogeneous observations on economic activity across space, industries, firms and agents”. These criticisms should be distinguished from those implying the
failure of aggregation to a representative agent (e.g. Forni and Lippi, 1997; Kirman, 1992). There, the researcher points out the inability to represent aggregate behaviour because of individual heterogeneity. Here I assert instead that it is individual heterogeneity that is more interesting even from the perspective of wishing to understand macroeconomic behaviour.
However in introducing such concepts into the discussion and ignoring the empirical tools, “researchers have used empirical ideas that are altogether uninformative. Those econometricians who model dynamic adjustment have done so not because adjustment occurs only in time and not in space, but because time series methods are already readily available for the former and not the latter” (Quah, 1993). The quoted sentences can be considered in some sense the manifesto of spatial microeconometrics.
