BEST CLIENT

Who is the best client?



Who is the best client?

This paper has examined the impact of household wealth on transition to self-employment in a data set using values of recently received inheritances and house type movements as instruments for financial wealth. The existing literature indicates that household self-employment entry is predicted by household wealth and also by receipt of 'windfall' payments such as inheritances, lottery winnings and bonus payments. This relationship pointed towards the existence of liquidity constraints preventing low wealth households form entering self-employment.

By exploiting the panel dimension of the data set used in this paper, entry to self-employment is shown to be weakly dependent on household net worth. Controlling for household characteristics, incomes, educational background and recent labour market experience an increase in net worth of £100,000 is associated with a 27% increase in the probability of entering self-employment. This relationship is also shown to be non-linear: the association between wealth and transition appears to be wholly driven by households at the higher end of the wealth distribution.

Employment Status

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

unemployed

698

51.6

52.8

52.8

Retired

118

8.7

8.9

61.7

Employed Part-Time

189

14.0

14.3

76.0

Employed Full-Time

303

22.4

22.9

98.9

Not in Labor Force

5

.4

.4

99.3

Never Employed

9

.7

.7

100.0

Total

1322

97.8

100.0

Missing

System

30

2.2

Total

1352

100.0

Most of the sample comprises of unemployed individuals.

Gender

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

male

540

39.9

40.0

40.0

female

811

60.0

60.0

100.0

Total

1351

99.9

100.0

Missing

System

1

.1

Total

1352

100.0

Children

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

0

668

49.4

50.0

50.0

1

298

22.0

22.3

72.3

2

190

14.1

14.2

86.5

3

114

8.4

8.5

95.1

4

38

2.8

2.8

97.9

5

19

1.4

1.4

99.3

6

8

.6

.6

99.9

9

1

.1

.1

100.0

Total

1336

98.8

100.0

Missing

System

16

1.2

Total

1352

100.0

Total Cash Benefits

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

0

376

27.8

27.8

27.8

1

755

55.8

55.8

83.7

2

193

14.3

14.3

97.9

3

24

1.8

1.8

99.7

4

2

.1

.1

99.9

5

2

.1

.1

100.0

Total

1352

100.0

100.0

Housing Type

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Unregulated Rental

414

30.6

30.6

30.6

Public Housing/Rent Subsidized

279

20.6

20.6

51.3

Rent Controlled/Stabilized

175

12.9

12.9

64.2

Living with Family

166

12.3

12.3

76.5

Owned Housing with Mortgage

50

3.7

3.7

80.2

Shelter

5

.4

.4

80.5

Apartment Rental

154

11.4

11.4

91.9

Home Rental

16

1.2

1.2

93.1

Supportive Housing

18

1.3

1.3

94.5

Other

60

4.4

4.4

98.9

Homeless

2

.1

.1

99.0

Living with Friends

13

1.0

1.0

100.0

Total

1352

100.0

100.0

Most of the sample comprises of Unregulated Rental housing type.

Employment Status * Gender Crosstabulation

Count

Gender

Total

male

female

Employment Status

unemployed

293

404

697

Retired

41

77

118

Employed Part-Time

63

126

189

Employed Full-Time

131

172

303

Not in Labor Force

0

5

5

Never Employed

2

7

9

Total

530

791

1321

From our study we observed that most of the sample comprises from unemployed individuals from both genders.

Housing Type * Gender Crosstabulation

Count

Gender

Total

male

female

Housing Type

Unregulated Rental

158

256

414

Public Housing/Rent Subsidized

82

197

279

Rent Controlled/Stabilized

65

110

175

Living with Family

95

70

165

Owned Housing with Mortgage

17

33

50

Shelter

3

2

5

Apartment Rental

73

81

154

Home Rental

7

9

16

Supportive Housing

4

14

18

Other

25

35

60

Homeless

0

2

2

Living with Friends

11

2

13

Total

540

811

1351

From our study we observed that most of the sample comprises from Unregulated Rental housing type from both genders.

Correlations

Employment Status

Gender

Housing Type

Employment Status

Pearson Correlation

1

.023

-.123**

Sig. (2-tailed)

.397

.000

N

1322

1321

1322

Gender

Pearson Correlation

.023

1

-.086**

Sig. (2-tailed)

.397

.002

N

1321

1351

1351

Housing Type

Pearson Correlation

-.123**

-.086**

1

Sig. (2-tailed)

.000

.002

N

1322

1351

1352

**. Correlation is significant at the 0.01 level (2-tailed).

From correlation analysis we observed that Employment Status and Gender have positive correlation where as Employment Status and Housing Type have negative correlation. Gender and Housing Type have negative correlation.

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.123a

.015

.014

2.667

a. Predictors: (Constant), Employment Status

We run regression analysis among Employment Status and housing type and observed that the value of adjusted R square is .015 which indicates a weak relationship among these two variables or we can say that housing type isn't effected by Employment Status.

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

143.483

1

143.483

20.172

.000a

Residual

9389.231

1320

7.113

Total

9532.714

1321

a. Predictors: (Constant), Employment Status

b. Dependent Variable: Housing Type

From ANOVA table we observed that p value is less than .05 so we can say that housing type isn't effected by Employment Status.

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

3.929

.139

28.244

.000

Employment Status

-.251

.056

-.123

-4.491

.000

a. Dependent Variable: Housing Type

Regression Equation:

Housing Type = 3.929 - (0.251* Employment Status)

A standard response to this problem of interpretation utilises positive 'shocks' to household financial wealth as a potential instrument for the unravelling of liquidity constraints facing would-be self-employed households. Depending on the particular study, different indicators of 'shocks' have been used: inheritances, redundancy payments, lottery wins and changes in self-reported housing wealth are all examples. An obvious problem with some of these indicators is that they ...