Empirical methods in business location research

Figures & data

Figure 1. Simple graphical representation of the demand threshold framework.

Source: Authors, drawing on Christaller’s (1966) classic work.

Figure 2. Simple graphical representation of random profit maximization.

Figure 3. Business location modeling decision tree.

Note: This figure visualizes the interaction between data types and the associated feasible model types. For more detailed discussion of the additional interaction and overlay of the potential for spatial models/effects, see Table 2. ADV, advantage; and IIA, independence of irrelevant alternatives.

Table 1. Aspatial estimators in business locational determinant research.

	Separate participation and amount decisions	Allows opposite participation and amount partial effects	Allows participation and amount correlation	Multiple zero creation processes
Corner response estimators
Type I Tobit (T1T)	No	No		No
Truncated normal hurdle (TNH)	Yes	Yes	No	No
Lognormal hurdle model (LNH)	Yes	Yes	No	No
Exponential Type II Tobit (ET2T)	Yes	Yes	Yes	No
Count data estimators
Poisson	No	No		No
Negative binomial (NB)	No	No		No
Hurdle Poisson (HP)	Yes	Yes	Yes	No
Zero-inflated Poisson (ZIP)	Yes	Yes	Yes	Yes
Zero-inflated negative binomial (ZINB)	Yes	Yes	Yes	Yes

Note: T1T, also known as ‘standard censored regression’ (Tobin, 1958); TNH, also known as ‘two-tier’, ‘two-stage’ or ‘double-hurdle’ (Cragg, 1971); LNH (Cragg, 1971); ET2T (Cragg, 1971; Duan et al., 1984; Heckman, 1979); HP, also known as ‘hurdle model’ (Mullahy, 1986) or ‘zero-altered Poison’ (Heilbron, 1989); ZIP (Greene, 1994; Lambert, 1992); and ZINB (Greene, 1994).

Table 2. Spatial estimators in demand threshold and business locational research.

	Dependent variable spillover	Explanatory variable spillover	Error spillover
Theoretical framework typical a priori expectation
Demand threshold	Yes	Yes
Spatial monopsony	Yes	Yes
Random profit max		Yes	Yes
Behavioural		Yes	Yes
Compatibility with regression models
Spatial lag of X (SLX)		Yes
Spatial autoregressive models (SAR)	Yes
Spatial error model (SEM)			Yes
Spatial autocorrelation model (SAC)	Yes		Yes
Spatial Durbin error model (SDEM)		Yes	Yes
Spatial Durbin model (SDM)	Yes	Yes
Spatial Tobit or Probit	Yes	Yes
Spatial Poisson		Yes

Note: SAR is also known as the spatial lag model (SLM). The theoretical framework a priori expectations listed are those that are often made by researchers; of course, exceptional works exist.

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