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The first question we examine in our reanalysis is the magnitude of the drop in the minority-status coefficient when “meets guidelines” and “unable to ver- ify” are included in a denial equation. The Browne and Tootell (1995) and Tootell (1996b) conclusions on this point are not convincing, because they are based on a specification that is more parsimonious than many of the specifica- tions investigated by Munnell et al. (1996) and others.

We reexamine the model with a more complete specification that includes additional loan, borrower, and neighborhood characteristics. We start with a base specification that does not include any credit history variables but does include an extensive list of control variables: housing-expense-to-income ratio; debt-to-income ratio; net worth; a proxy for the likelihood of unemployment; term; dummy variables for loan-to-value ratio (LTV) between 0.6 and 0.8, LTV between 0.8 and 0.95, and LTV above 0.95; fixed-rate mortgage; down payment

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includes gift; special program application; cosigner; age over 35; gender; mari- tal status; self-employed; percent minority in tract above 30; median income in tract over $39,111; multifamily unit; owner-occupied unit; private mortgage insurance application denied; and, of course, minority status (which means, as in Munnell et al., black or Hispanic).8_{We then successively add the Boston}

Fed Study’s credit history variables, the “unable to verify” variable, and the “meets guidelines” variable. The pseudo-R2_{or goodness-of-fit values for these}

four models are 0.454, 0.511, 0.568, and 0.611.9_{Thus, each additional variable}

or set of variables has a substantial impact on the fit of the model. The minority- status coefficients (t-statistics in parentheses) for the four models are 0.537 (6.39), 0.356 (3.97), 0.327 (3.43), and 0.218 (2.047). When all of these variables are added, therefore, minority status is still significant, but only at the 5 percent level (two-tailed test) instead of the 1 percent level. Moreover, the effect of minority status on loan denial rates falls dramatically as these variables or sets of variables are included. Based on the characteristics of the minority appli- cants, the average effect of minority status on the probability of denial is 12.3, 7.7, 6.2, and 3.3 percentage points for the four models.10

We conclude that the two variables in question—“unable to verify” and “meets guidelines”—do, indeed, have a dramatic impact on the minority- status coefficient. As a result, the key question is whether it is appropriate to include these variables in the equation, include them as endogenous variables, or exclude them altogether. To answer this question, we first reestimate the model using a simultaneous equations technique that is appropriate for detect- ing a simple form of endogeneity when the dependent variable is binary (i.e., accept or reject).11_{This simple form arises when the unobservable factors in}

the equation to explain denial are correlated with the unobservable factors in an equation to explain one of the explanatory variables in the denial equation, that is, when some unobserved factors simultaneously influence both variables. The procedure we use allows us to calculate the correlation between unobservable factors across equations; a high value for this correlation indicates that endo- geneity may be a serious problem.12

Consider first the case of “unable to verify,” which may be endogenous because the probability of denial influences data verification efforts. We find that the across-equation correlation in unobserved factors is quite large (0.574 with a t-statistic of 0.94). This correlation could arise either because some underwriting variables are omitted from the specification or because “unable to verify” is influenced by the probability of denial. In either case, a loan denial equation that treats “unable to verify” as exogenous will yield biased results, but the simultaneous equations model we estimate will not. In this model, the estimated coefficient of “unable to verify” in the loan denial equation is 0.410 (with a t-statistic of 0.27), which is small and statistically insignificant. The coefficient of “unable to verify” without the endogeneity correction, 1.747 (with a t-statistic of 13.58), is obviously biased. Moreover, the estimated minority- status coefficient is basically unaffected by the inclusion of “unable to verify” when this variable is treated as endogenous. To be specific, it changes from 0.356 (with a t-statistic of 3.97) to 0.364 (3.68).13_{The probability of loan denial}

is 7.4 percentage points higher for minorities than for whites with “unable to verify” treated as endogenous, which is close to the 7.7-percentage-point effect

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based on a single-equation specification without the “unable to verify” vari- able.14_{We conclude that, when properly treated as endogenous, the “unable to}

verify” variable has little or no impact on the minority-status coefficient, and we drop it from all further analysis.15

Now consider the “meets guidelines” variable, which may be endogenous because the actual underwriting outcome may influence a conclusion about whether an application meets a lender’s guidelines. As a point of reference, the minority-status coefficient in an equation that includes the “meets guide- lines” variable but not the “unable to verify” variable is 0.245 (with a t-statistic of 2.43), and the effect of minority status on the probability of denial is 4.1 per- centage points. Our simple simultaneous equations procedure estimates that the correlation between the unobservable factors in the loan denial and “meets guidelines” equations is –0.214 (with a t-statistic of 0.81). The estimated minority-status coefficient from a model in which “meets guidelines” is allowed both to be endogenous and to influence approval is 0.270 (with a t-statistic of 2.44), and the influence of minority status on the probability of denial is 6.5 percentage points. This “corrected” estimate of the impact of minority status on loan denial (6.5 percentage points) is bracketed by the single- equation estimate with the “meets guidelines” variable included (4.1 points) and the single-equation estimate with this variable excluded (7.7 points), but it obviously is closer to the latter. Thus, treating “meets guidelines” as endoge- nous eliminates most of its impact on the minority-status coefficient; correcting for the endogeneity of this variable makes a big difference.

This result may arise because this simultaneous equations model is too simple. As noted earlier, Browne and Tootell (1995) contend that the “meets guidelines” variable represents an after-the-fact judgment concerning the entire loan file made when someone filled out the Boston Fed Study’s survey. If this contention is correct, the issue is not simply whether unobservable factors are correlated across equations, but whether the loan denial decision itself influ- ences the “meets guidelines” variable. Thus, we must ask whether denied applications are more likely than accepted applications to be coded as “does not meet guidelines,” all else equal. If so, then one would have to reject the Day and Liebowitz (1996) claim that “meets guidelines” is determined entirely by additional credit history details.

A full examination of these issues requires a complex model in which (a) both loan denial and the “meets guidelines” indicator depend on a loan officer’s unobserved opinion concerning whether the applicant meets the lender’s credit history standards and (b) the “meets guidelines” statement by another bank employee could be influenced by the loan denial decision. With this formulation, the “meets guidelines” variable itself cannot influence the denial decision because it is set at a later point in time. Instead, only one com- ponent of this variable can influence loan denial, namely, the component that reflects the initial loan officer’s evaluation of the applicant’s creditworthiness. This component is assumed to be the same for the loan officer who makes the loan denial decision and the bank official who later fills out the survey form. We develop and estimate such a model.16

The estimation results reveal that loan denial has a large influence on the “meets guidelines” variable; the coefficient is –2.040 (with a t-statistic of 2.48).

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This finding leads us to reject the view that lenders fill in the “meets guide- lines” variable simply by comparing an applicant’s external credit score with some standard. Moreover, the fact that an application is coded as meeting the lender’s standards has a large influence on the likelihood of the loan being denied; the coefficient is –0.437 (with a t-statistic of 8.33). The estimated minority-status coefficient in the denial equation after controlling for the like- lihood that the original loan officer felt that the application met the lender’s standards is 0.248 (2.60), and the effect of minority status on the probability of denial is 5.3 percentage points. Thus, the effect of minority status on the loan denial probability is still bracketed by the results from the two single-equation specifications, one excluding and the other including the “meets guidelines” variable. In other words, although “meets guidelines” is clearly influenced by the denial decision, it still has some impact on the minority-status coefficient even when this influence is taken into account.

Because we are ultimately interested in only the loan denial equation, not the “meets guidelines” equation, we also explore a simpler estimating proce- dure for use in our subsequent analysis. To be specific, we use the results of our complex estimating procedure to construct a variable, based solely on exogenous information, to measure the likelihood that an applicant meets a lender’s credit standards, as seen by the original loan officer. In other words, we “cleanse” the usual “meets guidelines” variable of any influence that flows from the loan denial decision. The introduction of this cleansed variable into a single-equation estimate of loan denial yields coefficient estimates that are very similar to those obtained with our more complicated simultaneous equations procedure. In addition, the effect of minority status on loan denial in the sim- plified procedure, 5.6 percentage points, is almost the same as the effect with the full model, 5.3 percentage points. In future models, therefore, we make use of the simpler procedure.