Posturas pedagógicas ante el reto postmoderno

Following our discussion of the possible presence of the spatial effect and dynamic effect, we posit a general model in which an individual i’s income tax evasion (Eit) depends on individual j’s income tax evasion behavior, on that individual’s past

income tax evasion, and on a set of local socioeconomic variables:

it i it it N i j jt ij it E E u E = + ₋ + ′ + + ≠

∑

ε

ω

λ

∑

Since we use a cross-section of states over time, subscripts i and t represent an average individual in the state and time periods, respectively; ρ is a scalar parameter measuring the slope of the reaction function; ωijare spatial weights used to compute the effect of the

individual income tax evasion of states relevant to state i; ωij≠ 0 if individuals in states i

and j interact strategically, and, by convention, ωii=0; xitis (k×1) is a vector of individual

i’s socioeconomic conditions, γ the coefficient of persistence in evasion, and β is the corresponding vector of coefficients on the other conditioning variables. The first element of xit is unity to allow for the intercept. We assume that the parameters ρ, γ and β are

constant across time and space. The spatial and the dynamic analyses are special cases of the above general model. We will discuss the estimation technique in one type of analysis assuming that the other effect is absent for the sake of brevity. Further, we omit

discussion of the features of the model when neither spatial effects through the dependent variable or the error term nor the dynamic effects are present, for it then becomes a typical panel model whose features and estimation techniques are outlined in any

standard text on panel econometrics. However, it may be noted that even simple fixed or random effects model will produce biased results in this case since the audit rate is endogenous.

Estimation of Panel Model with Endogenous Audit Rate

Since the audit is endogenous, we need to find proper instruments to address it. Some of the past studies used the IRS cost per return as an instrument for this purpose. However, for us this is a bad instrument in that both the costs per return and the audit rate are jointly determined. Besides, the cost per return directly affects the level of tax evasion thus violating the fundamental assumption of being a valid instrument. In order to

substantiate our claim, we checked the validity of the instrument for audit rate. When cost per return alone is used as an instrument, it makes the audit coefficient statistically

insignificant; when used with other instruments the test of over-identifying restriction is rejected. Thus, one needs to look for instruments that have no direct effect on the level of tax evasion but influence the audit selection. These instruments are the political

affiliation of the President of the United States, the composition of both chambers of the U.S. Congress, and the party affiliation of the state Governor. The choice of these instruments is guided by Schulz and Wood (1998), who argue that audit enforcement is by and large governed by the responsive of the IRS to elected officials. Our assumption is that the audit rate of individual income tax would be lower if the party affiliation of the President is Democratic, the Senate and the House each have a Democratic majority and the state governor is Democratic. This negative association can be expected not because the Democrats are lenient to tax evaders but because they are proclaimed to be pro general public as opposed to pro corporation and rich. We checked the validity of instruments using Sargan (1958) test of over-identifying restrictions and found that our instruments as valid. This will be detailed while discussing the fixed and random effects instrumental variable estimates.

Estimation of Spatial Panel Dependence with Spatial Error Correlation

Note that the usual panel model, spatial panel model and dynamic panel model are all special cases of the above general model. For, instance, let us assume that the dynamic effect is absent. Then forming vectors of observations in t, the model becomes

t t t t t ρWE βX u E = + +I_Nη+ ; t = 1, …, T (5.3)

where Et=(E1t,…,ENt)′ is the (N×1) vector of individual income tax evasion for the cross-

section of N states at time t, Wtan (N×N) matrix of spatial weights, and Xtis an N×K

matrix with rows given by the set of vectors x′it; η is a (N×1) vector of the unobserved

heterogeneity, and utis the corresponding (N×1) error term vector.

Under this structure, tax evasion is determined endogenously in equilibrium because the spatial lag term, WtEt, is correlated with the error term, ut, and ordinary least

squares (OLS) yields inconsistent estimates of the parameters. It is therefore commonly estimated by using maximum likelihood techniques (Anselin 1988; Anselin and Hudak 1992). Removing the simultaneity in model (5.3), estimation can be carried out with alternative methods under the assumption that errors are iid. The reduced form equation is