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Analysis

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:

Di = -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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