dec6

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Initially, descriptive statistic and binomial correlations were prepared to give an overview of the data. The results are presented in Table One.

TABLE 1

Descriptive Statistics and Correlations

Variables	N	Mean	Std. Dev.	Fail?	Fund	Age	Loc.	# Emp	Sex	Occ.
Fail?	23 1	0.5022	0.5011	1.000
Funding	13 8	0.4420	0.8196	-.118	1.000
Age	10 6	4.0000	5.3666	-.173	.126	1.000
Location	22 6	1.2800	0.4500	*-.135	.159	-.122	1.000
# Emp.	18 4	1.5200	0.9700	-.094	** .347	.037	.085	1.000
Sex	22 4	1.5313	0.5264	** -.181	.166	.050	** .371	* .176	1.000
Occupation	19 0	2.0105	0.8482	** -.403	** .244	.085	** .388	.132	** .335	1.000

To test the hypothesis that not all firm disappearances are failures, Chi–Square Goodness of Fit test was used. The Chi–Square clearly rejects the null hypothesis that all disappearances are failures, as can be seen in Table Two.

TABLE 2

Chi-Squared Goodness of Fit

Disappearance	Observed N	Expected N	Residual	Test Statistics
Closed	115	0	115	Chi–Square	5724936
Failed	116	231	-115	df	1
Total	231	231		Asymp. Sig.	*** p<0.000

Once the first hypothesis was supported, a Kruskal–Wallis one–way analysis of variance test was employed to show the significance of the association between each of the independent variables and the outcomes of failure or other type of closure. These results are presented in Table Three.

Discriminant analysis was used to test the effect and significance of the independent variables on whether a disappeared firm had actually failed or had closed for other reasons. The independent (discriminant) variables were entered in a stepwise method, selecting the variable that resulted in the smallest Wilkes’ lambda, because the relationship between variables was not predicted by prior research. While there was some concern about the assumption of the multivariate normality of the variables, other assumptions of the test were met. Box’s M, insignificant at p=.9139, did not allow the rejection of the null hypothesis that the SSCP matrices were equal, therefore homogeneity of variance among the variables was confirmed.

TABLE 3

Kruskal–Wallis One–Way Analysis of Variance

Independent Variables	Dependent Variable (Disappearance)	N	Mean Rank	Test Statistics
Source of Funding	Closed Failed Total	49 89 138	74.86 66.74	Chi–Square df Asymp. Sig.	2.465 1.000 p=0.116
Age of Business	Closed Failed Total	38 68 106	58.84 50.64	Chi–Square df Asymp. Sig.	1.773 1.000 p=0.183
Location of Business	Closed Failed Total	110 116 226	121.56 105.85	Chi–Square df Asymp. Sig.	5.352 1.000 * p=0.021
Number of Employees	Closed Failed Total	75 109 184	97.87 88.80	Chi–Square df Asymp. Sig.	1.777 1.000 p=0.183
Sex of Owner(s)	Closed Failed Total	108 116 224	123.10 102.63	Chi–Square df Asymp. Sig.	7.348 1.000 ** p=0.007
Occupation after Closure	Closed Failed Total	83 107 190	119.16 77.15	Chi–Square df Asymp. Sig.	30.807 1.000 *** p<0.001

TABLE 4

Discriminant Analysis: Classification Results

Actual Group	No. of Cases	Predicted Group 0	Predicted Group 1
Closure (0)	83	50 = 60.2%	33 = 39.8%
Failure (1)	107	19 = 17.8%	88 = 82.2%
Ungrouped Cases	6	02 = 33.3%	04 = 66.7%
Percent of "grouped" cases correctly classified: 72.63%

A total of 95 cases were included in the analysis (cases with at least one missing variable were eliminated). The resulting discriminant function was significant (p<0.001) and explained more than 22% of the variance in firm disappearance based on a cannonical correlation of 0.473. The overall hit rate was 72.63%. These results are in Table Four. The discriminant function is:

D_i = -2.59 + 0.71(JOB)

As can be seen, only independent variable entered into the equation was the occupation of the owner after the business disappeared. Interestingly, we found that the owners of closed (rather than failed) firms were more likely to have started other firms or to be employed elsewhere, while the owners of failed firms were not. This is consistent with the predictions of the efficiency wage theory and is explained more fully in the next section.

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