CHAPTER V. PRESENTATION OF RESULTS

78

CHAPTER V. PRESENTATION OF RESULTS

This study is designed to develop a conceptual model that describes the relationship between

personal financial wellness and worker job productivity. A part of the model was tested with a

randomly selected sample of white collar clerical workers in a employer in mid-eastern state

(N=474). A mail survey was conducted during January, February, and March 1998. This

chapter presents the data and research findings.

This chapter includes a discussion of the (a) return rate, (b) demographic characteristics of the

sample, (c) personal financial wellness profile of the sample, (d) personal financial wellness

changes with demographic characteristics, (e) personal financial wellness changes with financial

stressors, (f) the relationship between personal financial wellness and financial stress level, (g)

worker job productivity profile of the sample, (h) relationship between personal financial

wellness and worker job productivity, (i) relationship between financial stress and worker job

productivity, and (j) desired financial education programs of the sample.

Return Rate

Among the 474 questionnaires, 27 were returned as undeliverable because of termination of

employment, incorrect address, or death. Therefore, the total sample size was reduced to 447.

A total of 288 questionnaires were returned by the cut off date. This number represents a total

return rate of 64.4%. Seventeen of the returned questionnaires were unusable due to missing

information. Therefore, the usable return rate was 60.6%. Even though 271 usable

questionnaires were returned, the total number of responses for some questionnaire items were

not equal to 271 due to the missing information.

Demographic Characteristics of the Sample

This section presents the demographic characteristics of the respondents. Demographic

characteristics include gender, marital status, education, ethnicity, age, household income,

So-hyun Joo Chapter V. Results 79

number of financial dependents, housing tenure, and length of employment. Table 5 shows these

characteristics of the respondents.

The majority of the respondents were female (96.3%) and only 3.7% were male. It can be seen

that over two-thirds of the respondents were either married or lived with significant others

(69.9% not shown in Table 5), and the remaining respondents were never married (7.8%),

separated (3.0%), divorced (16.4%) or widowed (3.0%).

As a group, the respondents were moderately educated. By combining categories, over two-

thirds (74.1%, not shown in Table 5) of the respondents had some education beyond high

school. The largest group (31.9%) had some college education. The second largest group

(25.9%) were high school graduates, followed by those having a bachelor’s degree (17.0%). A

relatively small group of respondents received associate’s (13.7%), trade and vocational training

(8.5%), or graduate or professional (3.0%) degrees.

Almost all of the respondents were white (91.9%) while less than one-tenth (8.1%) were other

ethnic groups. The age was asked as an open-ended question. Respondents were asked to

report their age in years. Over one-third (35.7%) of the respondents were in their forties.

About a quarter (24.1%) of the respondents were between 30 to 39 years old and about one-

quarter (23.3%) of the respondents were between the age of 50 to 59. The percentage of

respondents who were in their twenties was 11.5%, and a small number of respondents were in

their sixties (5.4%). The mean age was calculated to be 43.15 years old. Compared to the mean

age of the research population (N=948, M=43.2), the sample could represent the population.

The respondents reported moderate income as a group. By combining categories, about half

(49.1%, not shown in Table 5) of the respondents had household incomes less than $40,000.

Less than one-tenth (9.2%, combining categories not shown in Table 5) of the respondents


Table 5

Demographic Characteristics of the Respondents (N=271)

Demographic Characteristics n %a

GenderMaleFemale

(N=271)10

2613.7

96.3Marital Status

Never MarriedNot married But Living With Significant OtherMarriedSeparatedDivorcedRemarried After DivorceRemarried After Spouse’s DeathWidowed

(Nb=269)2112

1518

442418

7.84.5

56.13.0

16.48.9.4

3.0Education

High SchoolTrade/Vocational TrainingAssociate’sSome CollegeBachelorsGraduate or Professional

(Nb=270)70233786468

25.98.5

13.731.917.03.0

EthnicityWhiteBlackHispanicNative AmericanAsianOther

(N=271)249114331

91.94.11.51.11.1.4

Age in Years20 to 2930 to 3940 to 4950 to 5960 to 69

(Nb=261)3063936114

11.524.135.723.35.4

a Percentages may not add to 100 due to rounding.b Number of respondents may not add to 271 due to non-response or non-applicability of the question.


Table 5. (Continued)

Demographic Characteristics n %a

Household IncomeLess Than $20,000$ 20,001 - $ 30,000$ 30,001 - $ 40,000$ 40,001 - $ 50,000$ 50,001 - $ 60,000$ 60,001 - $ 70,000$ 70,001 - $ 80,000$ 80,001 - $ 90,000$ 90,001 - $ 100,000More Than $ 100,000

(N=271)23654546392811752

8.524.016.617.014.410.34.12.61.8.7

Number of Financial Dependents12345More Than 6

(N b=267)8783484531

32.032.017.817.0

.8

.4Housing Tenure

OwnRentLive With Relative or Friend

(Nb=270)2065410

76.420.03.7

Length of employment1 to 2 years3 to 5 years6 to 9 years10 to 14 years15 to 19 years20 or more

(N=271)373641564061

13.713.315.120.714.822.5

a Percentages may not add to 100 due to rounding.b Number of respondents may not add to 271 due to non-response or non-applicability of the question.


reported above $70,000 household income. The largest group had a household income of

$20,001 to $30,000 (24.0%) followed by the next largest group having household income of

$40,000 to $50,000 (17.0%).

The average number of financial dependents was 2.24. About one-third (32.6%) of the

respondents had one financial dependent (including self), and another one-third (31.1%) of the

respondents had two financial dependents. About one-fifth (18.0%) of the respondents had

three financial dependents, and 16.9% of the respondents had four financial dependents.

About three-quarters (76.3%) of the respondents were homeowners, and one-fifth (20.0%) were

renters. The length of employment at the current employer varied. Over one-fifth (22.5%) of

the respondents were with the employer over 20 years, and another one-fifth (20.7%) of the

respondents had 10 to 14 years of employment with the current employer. Over ten percent

(13.3%) of the respondents had 3 to 5 years of employment. The number of respondents who

had 1 to 2 years of employment, 6 to 9 years of employment, and 15 to 19 years of employment

were 13.7%, 15.1%, and 14.8% respectively. By combining categories, over one-half (58.0%,

not shown in Table 5) of the respondents had more than 10 years of employment with the

current employer.

Representativeness of the Sample

In an effort to ascertain the generalizability of the findings of this study to broader populations, a

comparison was made of characteristics of the sample and those of households in the state

where the employer locates and the United States. Results, shown in Table 6, suggest that the

sample may represent the population. The mean age of the respondents and the population was

almost same. The distributions of the length of employment of the respondents and the

population were similar. The population had average 11.22 years of employment with the

current employer. In addition, the gender distribution of the respondents was similar to the

nationwide data for those who hold same job titles. The ethnicity distribution of the


Table 6

Comparison of Sample Characteristics with Those of Broader Population

Occupation SpecificDemographic Characteristics

Respondents Population United States

GenderMaleFemale

3.7%96.3%

(1992)a

5.0%95.0%

Age (Mean age) 43.15 43.2Ethnicity

WhiteBlackOther

91.9%4.1%4.1%

(1992)a

93.8%3.02%3.18%

Length of Employment1 to 2 years3 to 5 years6 to 9 years10 to 14 years15 to 19 years20 or more years

13.7%13.3%15.1%20.7%14.8%22.5%

13.9%14.6%18.6%20.8%13.2%18.9%

General DemographicCharacteristics

Respondents State United States

Housing TenureOwnOther

76.4%23.7%

(1996)b

73.6%26.4%

Household Income Meanc

$38,000Median(1992)d

$38,223Median(1996)e

$35,492Marital Status(Percentage of AdultPopulation Married)

65.4% 61.2%

a U.S. Department of Commerce. (1992). 1990 Census of Population: Social & Economic Characteristics - UnitedStates. Washington, DC: U.S. Government Printing Office.b Virginia Statistical Abstract. (1996). Charlottesville, VA: Weldon Cooper Center for Public Service. Universityof Virginia.c The mean was calculated from the categorical mean of 3.81. The category 3 was the income range $30, 001 to$40,000.d U.S. Department of Commerce. (1992). 1990 Census of Population: Social & Economic Characteristics -Virginia. Washington, DC: U.S. Government Printing Office.e Bureau of Census. (1996). Income 1996. [Data posted on World Wide Web]. Retrieved April 11, 1998 from theWorld Wide Web, http://www.census.gov/hhes/income/income96/in96sum.html


respondents was also similar to that of national data. Moreover, the three general demographic

characteristics shown in Table 6 suggests that the sample may represent a broader population.

However, in terms of the education level, the respondents, as a group, were slightly more

educated than the general population.

Research Question 1:

Personal Financial Wellness Profiles of the Sample

The first research question was the following: What is the personal financial wellness profile of

workers, especially in the areas of subjective perception of personal finance, behavioral

assessment of personal finance, objective scales of personal financial wellness, and satisfaction

with personal financial situations?

This section describes the personal financial wellness profile in the four areas of measurement.

To describe the personal financial wellness of the sample, descriptive analysis was used.

Subjective Perception of Personal Finance

Subjective perception of personal finance was measured with eight items in five areas: cash

management, credit management, income adequacy, personal financial management, and

consumer shopping skills. The respondents were asked to rank the eight items on a four-point

scale ranging from strongly agree to strongly disagree. Results are shown in Table 7.

Cash Management. The cash management item is the statement of “I am satisfied with the

money that I am able to save.” About three-quarters (74.7%, combining categories of strongly

disagree and tend to disagree) of the respondents reported that they are not satisfied with the

amount of money that they are able to save. Only 3.0% of the respondents strongly agreed that

they are satisfied with their ability of save.


Table 7

Percentage Responses to Subjective Perception of Personal Finance

Subjective Perception of Personal Finance: Perception of how respondents felt about their

financial situation utilizing the eight 4-point scale questions

Statement SA(1) TA(2) TD(3) SD(4) a Nb M SDCash ManagementI am satisfied with the amount of moneythat I am able to save.

3.0 22.3 31.2 43.5 269 3.15 .87

Credit ManagementI worry about how much money I owe. 23.3 31.1 27.8 17.8 270 2.40 1.03

I would have trouble borrowing $2,000cash if I needed it.

12.2 11.1 25.8 50.9 271 3.15 1.04

Income AdequacyI have difficulty living on my income. 17.3 38.4 28.8 15.5 271 2.42 .95

I worry about being able to pay monthlyliving expenses.

15.9 28.1 33.0 23.0 270 2.63 1.00

Personal Financial ManagementWhen I think of my financial situation, Iam optimistic about the future.

8.2 46.6 31.7 13.4 268 2.50 .83

I think I will have enough money to livecomfortably throughout retirement.

3.3 37.5 35.3 23.8 269 2.79 .84

Consumer Shopping SkillsI am knowledgeable about consumerprotection laws and regulations.

5.5 36.2 37.6 20.7 271 2.73 .85

a SA: Strongly Agree, TA: Tend to Agree, TD: Tend to Disagree, SD: Strongly Disagreeb Number of responses may not add to 271 due to non-response.


Credit Management. Credit management was measured with the two items of “I worry about

how much money I owe,” and “I would have trouble borrowing $2,000 cash if I needed it.”

Over one-half (54.4%, combined SA and TA, not shown in Table 7) of the respondents were

worried about their debts, and less than one-fifth (17.8%) of the respondents were not worried

about their debts. More than one-half (50.9%) of the respondents reported that they would not

have any trouble with borrowing $2,000 cash and about one-fourth (23.3%, combining SA and

TA in Table 7) reported they would have trouble borrowing $2,000 cash.

Income Adequacy. The items of “I have difficulty living on my income,” and “I worry about

being able to pay monthly living expenses” measured subject perception with income adequacy.

Those who strongly agreed with the statement that they have difficulty living on their income

were 17.3%, and those who tended to agree with that statement were 38.4%. Combining these

two categories, over 50% of the respondents had difficulty living on their income. On the other

hand, less than 50% of the respondents (44.0%, combined SA and TA in Table 7) were worried

about managing their monthly expenses. This percentage reflects that even though they have

financial problems with inadequate income, there are some people who do not worry about their

financial situation. The percentage (23.0%) of the respondents who strongly disagree with the

statement “I worry about being able to pay monthly living expenses” was more than that

(15.5%) of “I have difficulty living on my income.”

Personal Financial Management. Personal financial management was measured with two

statements about subjective perception of future financial situation. The two statements were

assumed to reflect personal financial management skills. More than half (54.8%, combined SA

and TA in Table 7) of the respondents were optimistic about their financial future, however less

than half (40.8%, combined SA and TA in Table 7) of the respondents thought that they would

have enough money for retirement. While 13.4% of the respondents strongly disagreed with the

optimism of their financial future, 22.8% of the respondents strongly disagreed with their

financial retirement preparedness.


Consumer Shopping Skills. Only 5.5% of the respondents were strongly self-assured with their

knowledge of consumer protection laws and regulations. Over one-fifth (20.7%) of the

respondents thought they were not knowledgeable about consumer protection laws and

regulations at all. By combining categories, more than one-half (58.3%, combined TD and SD

in Table 7) of the respondents were not knowledgeable consumers in the marketplace.

The subjective perception index (FAT) was obtained by computing all of the eight items. The

items of 1, 2, 3, and 8 of the questionnaire were reverse coded. As a scale, the eight items

showed .8429 Cronbach alpha coefficients. (The correlation matrix of the items in Appendix H.)

The principal component analysis of the eight items of subjective perception index showed two

components of factors (Appendix H). The questionnaire items 1 through 7 fell into one factor

and the 8th question composed one factor by itself. The reliability coefficient of the seven items

was .8766. The 8th item of the questionnaire (I am knowledgeable about consumer protection

laws and regulations) was included to measure consumer shopping skills. And conceptually, the

consumer shopping skills are important factors in personal financial wellness. In addition, the

Cronbach alpha of .8429 shows an acceptable reliability of the index. Therefore, the subjective

perception index was composed with all of the initial eight items of personal finance. The

possible value of the subjective perception index ranges from 8 to 32. The respondents’

subjective perception index ranged from 9 to 31 (Table 8). The mean of subjective perception

index of the respondents was 19.47 and the standard deviation as 5.15. The average of the

subjective perception index was converted to a percentage score. The converted percentage

average showed the personal financial wellness of the sample was below 50% (45.9%).

Behavioral Assessment of Personal Finance

The behavioral assessment of personal finance was measured with 12 items of cash management,

credit management, income adequacy, personal financial management, and


Table 8

Frequencies of Subjective Perception Indexa

Valid Cum

Value Label Value Frequency Percent Percent Percent

9.00 4 1.5 1.5 1.5

10.00 4 1.5 1.5 3.0

11.00 6 2.2 2.3 5.3

12.00 9 3.3 3.4 8.7

13.00 11 4.1 4.2 12.9

14.00 18 6.6 6.8 19.8

15.00 12 4.4 4.6 24.3

16.00 17 6.3 6.5 30.8

17.00 24 8.9 9.1 39.9

18.00 15 5.5 5.7 45.6

19.00 12 4.4 4.6 50.2

20.00 18 6.6 6.8 57.0

21.00 22 8.1 8.4 65.4

22.00 13 4.8 4.9 70.3

23.00 14 5.2 5.3 75.7

24.00 13 4.8 4.9 80.6

25.00 13 4.8 4.9 85.6

26.00 9 3.3 3.4 89.0

27.00 12 4.4 4.6 93.5

28.00 8 3.0 3.0 96.6

29.00 5 1.8 1.9 98.5

30.00 1 .4 .4 98.9

31.00 3 1.1 1.1 100.0

. 8 3.0 Missing

------- ------- -------

Total 271 100.0 100.0

Mean 19.468 Median 19.000 Mode 17.000

Std dev 5.148 Variance 26.502 Kurtosis -.771

S E Kurt .299 Skewness .100 S E Skew .150

Valid cases 263 Missing cases 8

a Perception of how respondents felt about their financial situation utilizing the sum of the eight 4-point scalequestions


consumer shopping skills. The respondents assessed their behaviors using four-point scales:

never, sometimes, usually, and always. Table 9 presents the results.

Cash Management. Almost one-fifth (18.1%) of the respondents never set money aside for

savings, and over two-fifths (43.3%) of the respondents never set money aside for retirement

savings (Table 9). On the other hand, three-tenths (30.0%) of the respondents always set money

aside for retirement, and less than three-tenths (26.6%) of the respondents always set money

aside for savings. In terms of spending, more than four-tenths (41.0%) of the respondents never

spent more money than they had. By combining categories, however, more than half (58.9%) of

the respondents reported sometimes, usually, or always spending more money than they had.

Among those 58.9% of the respondents, 3.0% were always spending more money than they had.

Credit Management. The credit management of the respondents was measured with two

questions about handling credits cards. While almost one-fifth (17.2%) of the respondents paid

credit card bills in full so that they avoided finance charges, almost two-fifths (37.1%) of the

respondents never paid credit card bills in full. By combining categories (never, sometimes, and

usually), over four-fifths (82.8%) of the respondents paid finance charges. Six-tenths of the

respondents (61.4%) never reached the maximum limit on a credit card. By combining

categories (sometimes, usually, and always), about two-fifths (38.6%) of the respondents

reached the maximum credit limits. Less than one-tenth (7.9%) of the respondents always

reached the credit limits.

Income Adequacy. Income adequacy was measured with three items: I had to cut living

expenses; I had to use a credit card because I ran out of cash; and I had financial troubles

because I did not have enough money. About one-tenth (7.8%) of the respondents reported that

they always had financial troubles because of inadequate income. A small percentage (3.4%) of


Table 9

Percentage Responses to Behavioral Assessment of Personal Finance

Behavioral Assessment: Assessment of respondents’ personal financial behaviors utilizing the

twelve 4-point questions

Statement N(1) S(2) U(3) A(4)a Nb M SD

Cash ManagementI set money aside for savings. 18.1 34.7 20.7 26.6 271 2.55 1.07I set money aside for retirement. 43.3 15.2 11.5 30.0 270 2.28 1.29I spent more money than I had. 41.0 44.0 11.9 3.0 268 1.77 .77

Credit ManagementI paid credit card bills in full andavoided finance charges.

37.1 28.1 17.6 17.2 267 2.15 1.10

I reached the maximum limit on acredit card.

61.4 23.2 7.5 7.9 267 1.62 .93

Income AdequacyI had to cut living expenses. 33.0 48.8 13.0 5.2 270 1.90 .81I had to use a credit card because Iran out of cash.

41.4 45.5 9.8 3.4 266 1.75 .77

I had financial troubles because I didnot have enough money.

46.3 33.6 12.3 7.8 268 1.81 .93

Personal Financial ManagementI had a plan to reach my financialgoals.

28.5 31.9 28.1 11.5 270 2.23 .99

I had a weekly or monthly budget thatI followed.

27.7 26.9 26.2 19.2 271 2.37 1.08

Consumer Shopping SkillsI comparison shopped at two or morestores for an expensive consumerproduct.

7.4 14.0 28.4 50.2 271 3.21 .95

I purchased something expensive thatI wanted, but really did not need.

34.7 56.5 8.5 .4 271 1.75 .62

a N: Never, S: Sometimes, U: Usually, A: Always.b Number of responses may not add to 271 due to non-response.


the respondents always had to use credit cards because they ran out of cash. A relatively small

number of the respondents (5.2%) reported that they always had to cut living expenses. On the

other hand, almost one-half (46.3%) of the respondents replied that they never had financial

troubles because of income inadequacy. This figure was relatively high compared to the number

of people who never cut their living expenses (33.0%) and those who never use a credit card

because of running out of cash (41.4%). This finding suggests that some people do not think

that cutting living expenses and withdrawing cash from credit cards are signs of financial

troubles.

Personal Financial Management. Personal financial management was measured by the practice

of financial goal setting and budgeting (Table 9). Only about one-tenth (11.5%) of the

respondents always utilized financial planning to achieve their financial goals. About three-

tenths of the respondents (28.1%) usually financially planned, and another three-tenths of the

respondents (31.9%) sometimes utilized financial planning. Over one-quarter of the respondents

(28.5%) never had a financial plan to reach their financial goals. In terms of budgeting, almost

two-tenths (19.2%) of the respondents always had a weekly or monthly budget and almost

three-tenths (27.7%) of the respondents never had a weekly or monthly budget. Over a quarter

of the respondents (26.9%) sometimes followed their weekly or monthly budget, and another

quarter of the respondents (26.2%) usually followed their budget.

Consumer Shopping Skills. The items for measuring consumer shopping skills were comparison

shopping and impulsive shopping (Table 9). Over one-half (50.2%) of the respondents always

comparison shopped for an expensive consumer products, and about one-tenth (7.4%) of the

respondents never comparison shopped. About three-tenths (28.4%) of the respondents usually

shopped at two or more stores before they bought an expensive product. More than one-half of

the respondents (56.5%) sometimes shopped impulsively and about one-third (34.7%) of the

respondents never bought something impulsively. A very small number of the respondents

(.4%) always purchased something expensive that they wanted but did not really need.


The behavioral assessment of personal financial wellness was computed to an index (Table 10).

All 12 items were included the behavioral assessment index (The correlation matrix of the scale

is in Appendix I) and as a scale and the Cronbach alpha coefficient was .8062 (standardized

alpha was .8073). The principal component analysis showed three factors (Appendix I). Factor

1 consisted of cash management, credit management, and income adequacy. The personal

financial management items made up one factor and consumer shopping skills items made

another factor. The reliability coefficient was .8563 for the first factor and .6501 for the second

factor. The third factor’s reliability coefficient was very low because the two items were weakly

correlated. Excluding the items of consumer shopping skills, the 10 items’ reliability coefficient

was .8286, which is a little higher than the original coefficient (.8062). Even though the

reliability coefficient was higher, the new index of the 10 items was not a better predictor of

personal financial wellness and worker job productivity. In addition, the original 12 items were

developed based on previous research and the conceptualization of the five domains of personal

finance. By including all 12 items in one index, personal financial wellness can be better

measured. Therefore, all of the original 12 items were included in the behavioral assessment

index. The possible value of the behavioral assessment index ranges from 12 to 48, and the

index of the respondents ranged from 16 to 48. The mean score of the behavioral assessment

index was 34.29, and the standard deviation was 6.50. The average score of the behavioral

assessment index was also converted to the percentage score. The converted mean of the

respondents was 61.9%.

Objective Scales of Personal Financial Wellness

Six objective scales of personal financial wellness were measured: solvency measure, amount of

reserve fund, credit payment per month, installment loan payments per month (excluding home

mortgage), amount of savings per month, and amount of voluntary supplementary tax-sheltered

employer-sponsored retirement contributions per month.


Table 10

Frequencies of Behavioral Assessment Index a

Valid Cum

Valid CumValue Label Value Frequency Percent Percent Percent

16.00 1 .4 .4 .4 17.00 1 .4 .4 .8 19.00 1 .4 .4 1.1 20.00 3 1.1 1.1 2.3 21.00 5 1.8 1.9 4.2 22.00 1 .4 .4 4.6 23.00 5 1.8 1.9 6.5 24.00 3 1.1 1.1 7.6 25.00 3 1.1 1.1 8.8 26.00 7 2.6 2.7 11.5 27.00 7 2.6 2.7 14.1 28.00 9 3.3 3.4 17.6 29.00 11 4.1 4.2 21.8 30.00 8 3.0 3.1 24.8 31.00 19 7.0 7.3 32.1 32.00 20 7.4 7.6 39.7 33.00 19 7.0 7.3 46.9 34.00 17 6.3 6.5 53.4 35.00 12 4.4 4.6 58.0 36.00 13 4.8 5.0 63.0 37.00 11 4.1 4.2 67.2 38.00 15 5.5 5.7 72.9 39.00 11 4.1 4.2 77.1 40.00 10 3.7 3.8 80.9 41.00 8 3.0 3.1 84.0 42.00 11 4.1 4.2 88.2 43.00 12 4.4 4.6 92.7 44.00 4 1.5 1.5 94.3 45.00 6 2.2 2.3 96.6 46.00 5 1.8 1.9 98.5 47.00 2 .7 .8 99.2 48.00 2 .7 .8 100.0 . 9 3.3 Missing ------- ------- ------- Total 271 100.0 100.0

Mean 34.286 Median 34.000 Mode 32.000Std dev 6.495 Variance 42.190 Kurtosis -.275S E Kurt .300 Skewness -.202 S E Skew .150


a Assessment of the personal financial behaviors of the respondents utilizing sum of the twelve 4-point questions


Solvency Measure. The solvency was measured with the question, “Suppose you were to sell all

of your major possessions (including your home), turn all of your investments and other assets

into cash, and pay all of your debts. Would you be in debt, break even, or have something left

over?” A very small number (3.3%) of people responded that they would be in serious debt.

About one-fifth of the respondents (17.5%) said they would about break even and over half of

people (53.5%) reported they would have money left over (Table 11).

Amount of Reserve Fund. The amount of reserve funds was measured with the following

question: “If you lost your job today, how many months could you live using your savings?”

Over one-third of the respondents (35.4%) reported that they did not have any reserve fund

(they could live 0 months with their savings, Table 12). Among those who had reserve funds,

the largest number of people (38.2% of those who had reserve funds) responded they could live

1 to 2 months with their savings. About one-fifth (19.1%) of those who had reserve funds

reported they could live off their savings for 3 to 4 months. About one-fifth (17.9%) of the

group reported 5 to 6 months, and by combining categories, about one-quarter (14.9%) of the

respondents could live 7 months or longer with their savings.

Monthly Credit Payment. Monthly credit payments and other monthly installment loan

payments, except mortgage payments, were measured to explore the personal financial wellness

of the respondents. These monthly debt payments are significant components of the objective

scales of personal financial wellness. About a third of the respondents (32.7%) reported they

usually paid $1 to $100 dollars toward their credit cards per month (Table 13). The second

largest group (21.2%) usually paid $101 to $200, followed by $201 to $300 group (15.2%).

Combining those three categories, 59.1% (not shown in Table 13) of the respondents usually

paid $1 to $300 toward their credit cards each month. About one-tenth of the respondents

(7.1%) paid $301 to $400 toward credit cards per month and 5.6% of the respondents paid


Table 11

Responses to Solvency Measure

Question: Suppose you were to sell all of your major possessions (including your home), turn all

of your investments and other assets into cash, and pay all of your debts. Would you be in debt,

break even or have something left over?

Would be inserious debt

Would aboutbreak even

Would havemoney left over

Value 1 2 3 4 5

n 9 21 47 48 144

% 3.3 7.8 17.5 17.8 53.5

Na =269

M = 4.10

SD=1.15

a Number of responses does not add to 271 due to non-response.


Table 12

Responses to the Amount of Reserve Fund

Question: If you lost your job today, how many months could you live using your savings?

Value n % %a

0 Months 95 35.4

1-2 Months 66 24.6 38.2

3-4 Months 33 12.3 19.1

5-6 Months 31 11.6 17.9

7-12 Months 12 4.5 6.9

13-24 Months 6 2.2 3.5

Over 24 Months 25 9.3 14.5

Nb =268 100.0 Nc =173

a Percentage of the total of 173 respondents those who had savings for emergency.b Number of responses does not add to 271 due to non-response.c Total number of respondents those who could live more than 1 month with their savings.


Table 13

Credit and Installment Loan Payments per Month

Credit Payment Other Loan Payments

Value n % n %

$ 0 25 9.3 70 26.4

$1-$100 88 32.7 10 3.8

$101-$200 57 21.2 25 9.4

$201-$300 41 15.2 52 19.6

$301-$400 19 7.1 29 10.9

$401-$500 15 5.6 33 12.5

$501-$600 7 2.6 16 6.0

$601-$700 6 2.6 11 4.2

$701-$800 2 .7 7 2.6

$801-$900 2 .7 4 1.5

$901-$1,000 3 1.1 1 .4

Above $1,000 4 1.5 7 2.6

Na =269 100.0 Na=265 100.0



$401 to $500. Combining categories, less than one-tenth (8.8%) of the respondents’ monthly

credit cards payments were over $500. There were five respondents whose credit payments

were over $1,000 per month.

Monthly Installment Loan Payments. Over one-quarter of the respondents (26.4%) did not have

any monthly installment loan payments. About one-fifth (19.6%) of the respondents paid

between $201 and $300 for monthly installment loan payments, followed by 12.5% of the

respondents who paid $401 to $500. By combining categories (not shown in Table 13), almost

one-fifth (17.3%) of the respondents had over $500 in monthly installment payments. Less than

one-tenth of the respondents (9.4%) reported $101 to $200 in monthly installment payments and

slightly over one-tenth (10.9%) of the respondents reported $301 to $400. A small number

(3.8%) of people had less than $100 in monthly installment payments.

Amount of Savings per Month. Over three-tenths (31.0%) of the respondents did not save any

money each month (Table 14). Over one-third (39.5%) of the respondents saved less than $100

per month. Over one-tenth of the respondents (11.8%) saved between $101 and $200.

Combining the two groups, just over one-half (51.3%, not in Table 14) of the respondents saved

less than $200 per month. Those who had over $300 in monthly savings were 10.4%

(combining categories, not in the Table 14).

Amount of the Voluntary Supplementary Tax-Sheltered Employer-Sponsored Retirement

Contribution Program Per Month. Three-quarters of the respondents (77.3%) did not

contribute to the voluntary supplementary tax-sheltered employer-sponsored retirement

contribution program (Table 14). Slightly over one-tenth of the respondents (10.4%)

contributed less than $100. By combining categories, those who contribute over $100 into the

voluntary supplementary tax-sheltered employer-sponsored retirement contribution programs

Were 12.4%.


Table 14

Savings and Voluntary Supplementary Tax-Sheltered Employer-Sponsored Retirement

Contributions per Month

Savings Retirement

Contributions

Value n % n %

$ 0 84 31.0 208 77.3

$1-$100 107 39.5 28 10.4

$101-$200 32 11.8 15 5.6

$201-$300 20 7.4 8 3.4

$301-$400 7 2.6 4 1.5

$401-$500 9 3.3 1 .4

$501-$600 4 1.5 3 1.1

$601-$700 1 .4 0 0

$701-$800 2 .7 1 .4

$801-$900 1 .4 0 0

$901-$1,000 3 1.1 0 0

Above $1,000 1 .4 1 .4

N=271 100.0 Na =269 100.0



Respondents were asked to report the primary reason for not contributing to the voluntary

supplementary tax-sheltered employer-sponsored retirement contribution program. As shown in

Table 15, the largest number (47.2%) of the respondents reported that they “do not have enough

money” as the primary reason. The second most frequent answer (34.7%) was “I do not know

enough about the retirement plan.” A relatively small number of people responded “I am not

convinced that I need to invest” and “I do not know how much money to save and invest”

(3.0% and 3.0%, respectively). Over one-tenth (12.1%) of the respondents wrote their own

reasons for not contributing. The specified reasons were investing in other programs, waiting

for children to finish college, and not having chances to consult with the company.

Overall financial wellness scales of Personal Financial Wellness

The overall financial wellness scales of personal financial wellness consisted of three measures;

satisfaction with personal financial situations, perceived financial wellness, and feelings about

financial situations. The descriptive results are shown in Graph 1, Table 16, and Table 17.

Satisfaction with Personal Financial Situation. The satisfaction with personal financial situations

was measured by a question with a 10-step measure. Those who were dissatisfied with their

financial situations marked lower steps and those who were satisfied with their financial

situations marked higher steps. As Graph 1 shows, only 1.2% of the respondents marked the

highest level of financial satisfaction, but 7.5% of the respondents marked the lowest step. By

combining the categories (1, 2, 3, and 4), more than one-half (52.6%) of the respondents were

dissatisfied with their financial situation. Slightly over one-fifth (21.8%, combining 7, 8, 9, and

10) were satisfied with their financial situation, and about a quarter (25.7% combining 5 and 6)

were in the middle.


Table 15

Responses to the Primary Reason for Not Contributing to the Voluntary Supplementary Tax-

Sheltered Employer-Sponsored Retirement Contributions

Reason n %

I do not have enough money 94 47.2

I do not know enough about the retirement plan 69 34.7

I am not convinced that I need to invest 6 3.0

I do not know how much money to save and invest 6 3.0

Other 24 12.1

N= 199a 100.0

a The total number does not add up 208 (number of non-contributor) due to the missing data.


Graph 1

Percentage Distribution of Satisfaction with Personal Financial Situationa

Question: How satisfied you are with your present financial situation. Those who are not

satisfied will be towards the lower steps. Those who are satisfied will be towards the higher

steps.

8.3

11.99.9

15.8

18.219.4

7.57.5

10.009.008.007.006.005.004.003.002.001.00

30.0

20.0

10.0

0.0

Nb = 253

M = 4.57

SD = 2.08

a The percentage of the respondents who answered “9” was 0.4. The percentage of the respondents who answered “10” was 1.2.b Number of respondents doe not add to 271 due to non-response.


Perceived Financial Wellness. Perceived financial wellness was measured with the question of

“how well are you financially?” As shown in Table 16, respondents answered a 5-point scale

ranging from “feel like I am always in financial trouble” to “feel like I am doing pretty well.” By

combining categories, about three-tenths (29.9%) of the respondents answered that they were

doing pretty well, and about three-tenths (29.2%) said they were always in financial trouble.

The largest number of people (41.0%) said they were in the middle.

Feeling about Financial Situations. Feeling about financial situation was measured with the

question “how do you feel about your financial situation?” As shown in Table 17, the answers

ranged from 1 to 5, and were labeled “I find it is hard to pay bills” for the lowest level and “I

save more than spend” as the highest level. Responses of the measure were different from the

perceived financial wellness. Slightly over one-third of the respondents (35.1%) said they find it

hard to pay bills (combining categories). By combining categories, only 12.2% of the

respondents marked higher levels (save more than they spend) and the largest number of people

(52.8%) responded that they were in the middle. While the number of people who chose the

middle point were higher in this scale than the perceived financial wellness (52.8% for feeling

about financial situation versus 41.0% for perceived financial wellness), the number of

respondents who chose higher levels of financial wellness was fewer than the perceived financial

wellness measure (12.2% for feeling about financial situation versus 29.9% with perceived

financial wellness).

Unfortunately, the statement of “I save more than spend” on the scale of “Feeling about

Financial Situation” was not the best choice because for the great majority of people it is

mathematically impossible to save more than one spends. A better choice to contrast to the

other side would be “I find it is easy to save.” However, many respondents (12.2%) still

selected this portion of the scale perhaps as an indicator of their situation which they may have

perceived as being in contrast to the other side of the scale (“I find it is hard to pay bills”).


Table 16

Responses to Perceived Financial Wellness

Question: How well are you financially?

Feel like I am always infinancial trouble

Feel like I am doing pretty well

Value 1 2 3 4 5

n 30 49 111 59 22

% 11.1 18.1 41.0 21.8 8.1

29.2% 29.9%

N =271

M= 2.98

SD = 1.08


Table 17

Responses to Feeling about Financial Situation

Question: How do you feel about your financial situation?

I find it is hard to pay bills I save more than spenda

Value 1 2 3 4 5

n 29 66 143 29 4

% 10.7 24.4 52.8 10.7 1.5

35.1% 12.2%

N=271

M=2.68

SD=.86

a Unfortunately, this statement on the scale of “Feeling about Financial Situation” was not the best choice becausefor the great majority of people it is mathematically impossible to save more than one spends. A better choicewould be “I find it is easy to save.” However, many respondents (12.2%) still selected this portion of the scaleperhaps as an indicator of their situation which they may have perceived as being in contrast to the other side ofthe scale (“I find it is hard to pay bills”).


The three overall financial wellness scales of personal financial wellness were next computed

into one index. The first overall financial wellness scale (i.e., satisfaction with personal financial

situation) was recoded to a 5-point scale for computing with the other two scales. The

reliability coefficient (Chronbach’s alpha) of the overall satisfaction index was .8940. The

possible range of the overall satisfaction index was 3 to 15 and the overall satisfaction index of

the respondents ranged 3 to 15 (Table 18). The mean of the overall satisfaction index was 8.20

and standard deviation was 2.74. The mean of the overall satisfaction index was converted to a

percentage score. The converted score of the overall satisfaction index of personal financial

wellness was 43.32% which is lower than midpoint of 50%.


Demographic Characteristics and Personal Financial Wellness

The second research question of this study was: “How does the personal financial wellness

profile differ by the demographic characteristics?” This section describes personal financial

wellness according to the demographic characteristics. The demographic characteristics

included gender (GENDER), marital status (MS), education (EDU), ethnicity (ETHN), age

(AGE), household income(INCOME), number of financial dependents (Number), housing

tenure(HOUSING), and length of employment at a current employer (YEAR). Personal

financial wellness includes subjective perception of personal finance, behavioral assessment of

personal finance, objective scales, and overall financial wellness scales. The variables of

personal financial wellness in the regression equations are the subjective perception index (FAT),

the behavioral assessment index (FBT), satisfaction with financial situation (FM1), perceived

financial wellness (FM2), feeling about financial situation (FM3), solvency measure (FO1),

amount of reserve funds (FO2), credit payments per month (FO3), other installment payments

per month (FO4), savings per month (FO5), and voluntary supplementary tax-sheltered

employer-sponsored retirement contribution per month (FO6).


Table 18

Frequencies of Overall Satisfaction Indexa

Valid CumValue Label Value Frequency Percent Percent Percent

3.00 16 5.9 6.3 6.3 4.00 13 4.8 5.1 11.5 5.00 15 5.5 5.9 17.4 6.00 24 8.9 9.5 26.9 7.00 27 10.0 10.7 37.5 8.00 42 15.5 16.6 54.2 9.00 35 12.9 13.8 68.0 10.00 24 8.9 9.5 77.5 11.00 25 9.2 9.9 87.4 12.00 20 7.4 7.9 95.3 13.00 8 3.0 3.2 98.4 14.00 3 1.1 1.2 99.6 15.00 1 .4 .4 100.0 . 18 6.6 Missing ------- ------- ------- Total 271 100.0 100.0

Mean 8.202 Std err .172 Median 8.000Mode 8.000 Std dev 2.738 Variance 7.495Kurtosis -.599 S E Kurt .305 Skewness -.083S E Skew .153


a The sum of the three overall financial wellness scales: satisfaction with financial situation, perceived financialwellness, and feeling about financial situation. The satisfaction with financial situation was recoded to 5-pointscale. Therefore, the overall satisfaction index consisted of the sum of the three 5-point overall financial wellnessscale.


Correlation Results

Table 19 shows the correlation matrix among the variables. Among the demographic

characteristics, age, education, household income, number of financial dependents, and length of

employment were entered into the correlation matrix.

Age showed significant positive relationships with FAT, FBT, FM1, FM2, FM3, FO1, FO2,

FO5, and FO6. Age and FO4 had a negative correlation. Those who were older tended to have

better personal financial wellness profiles in subjective perception, behavioral assessment, overall

satisfaction, perceived financial wellness, feeling about personal financial situation, solvency,

reserve fund, monthly savings, and voluntary supplementary tax-sheltered employer-sponsored

retirement contributions. Those who were older tended to have less monthly installment

payments than younger respondents had. Education showed a significant relationship with credit

payments per month (FO3). Those who had more education tended to pay more monthly credit

payments. Household income had significant positive relationships with all of the measures of

personal financial wellness. That means that if a respondent had more household income, his or

her personal financial wellness tended to be higher. The number of financial dependents had a

significant positive relationship with monthly installment loan payments. Those who had more

financial dependents had more monthly installment payments. Length of employment showed

significant positive relationship with FAT, FM1, FM2, FM3, FO1, FO2, and FO5. Therefore,

those who worked a longer period of time with the current employer tended to have higher

levels of personal financial wellness.

Regression Results

To describe personal financial wellness according to demographic characteristics, regression

analysis was used. The independent variables were age, education, gender dummy variable

(GenderD), housing tenure dummy variable (HousingD), income, marital status dummy variable

(Marital StatusD), number of financial dependents, ethnicity dummy variable (EthnicityD),


Table 19

Correlation Matrix of Demographic Characteristics and 11 Measures of Personal Financial

Wellness

FATa FBT FM1 FM2 FM3 FO1

AGE .2133 .2013 .2396 .1883 .1508 .2924

( 254) ( 254) ( 243) ( 261) ( 261) ( 259)

P= .001 P= .001 P= .000 P= .002 P= .015 P= .000

EDU -.0893 .0158 -.0989 -.0847 -.0800 -.1028

( 262) ( 261) ( 252) ( 270) ( 270) ( 268)

P= .150 P= .799 P= .117 P= .165 P= .190 P= .093

INCOME .3903 .2840 .3932 .3849 .3516 .2750

( 263) ( 262) ( 253) ( 271) ( 271) ( 269)

P= .000 P= .000 P= .000 P= .000 P= .000 P= .000

NUMBER -.0398 .0137 -.0904 -.0369 -.0700 -.0228

( 259) ( 258) ( 249) ( 267) ( 267) ( 265)

P= .524 P= .827 P= .155 P= .549 P= .254 P= .712

YEAR .2182 .0785 .2175 .1656 .1290 .1950

( 263) ( 262) ( 253) ( 271) ( 271) ( 269)

P= .000 P= .206 P= .000 P= .006 P= .034 P= .001

a FAT: Subjective perception of personal finance (Perception of how respondents felt about their financialsituation utilizing the eight 4-point questions) FBT: Behavioral assessment of personal finance (Assessment of respondents’ personal financial behaviorsutilizing the twelve 4-point questions) FM1: Satisfaction with financial situation (Respondents’ satisfaction level with their present financial situationmeasured with a 10-point question) FM2: Perceived financial wellness (Respondents’ perception about their financial wellness measured with a 5-point question) FM3: Feeling about personal financial situation (Respondents feelings about their financial situation measuredwith a 5-point question) FO1: Solvency measure AGE: Age of the respondents in years EDU: Education INCOME: Household income NUMBER: Number of financial dependents YEAR: Length of employment with a current employer


Table 19 (Continued)

FO2a FO3 FO4 FO5 FO6

AGE .3649 .0650 -.2339 .1446 .1969

( 258) ( 260) ( 256) ( 261) ( 259)

P= .000 P= .296 P= .000 P= .019 P= .001

EDU -.0649 .1360 -.0605 -.0747 .0786

( 267) ( 268) ( 264) ( 270) ( 268)

P= .291 P= .026 P= .327 P= .221 P= .200

INCOME .3056 .2434 .1397 .3933 .2708

( 268) ( 269) ( 265) ( 271) ( 269)

P= .000 P= .000 P= .023 P= .000 P= .000

NUMBER -.0847 .0441 .2243 .0339 .0504

( 264) ( 265) ( 261) ( 267) ( 265)

P= .170 P= .474 P= .000 P= .581 P= .414

YEAR .2175 -.0468 -.0932 .1726 .1193

( 268) ( 269) ( 265) ( 271) ( 269)

P= .000 P= .445 P= .130 P= .004 P= .051

a FO2: Amount of reserve funds FO3: Monthly credit payments FO4: Monthly installment loan payments FO5: Monthly savings FO6: Monthly voluntary supplementary tax-sheltered employer-sponsored retirement contributions AGE: Age of the respondents in years EDU: Education INCOME: Household income NUMBER: Number of financial dependents YEAR: Length of employment with a current employer


and length of employment. The dependent variables were the 11 personal financial wellness

measures.

Subjective Perception of Personal Finance. Table 20 shows the regression results. The

correlations among the independent variables and the collinearity test result are in Appendix J.

The nine demographic characteristics explain 23.1% of the variance of the dependent variable.

The R square suggests that there are other factors that explain the subjective perception of

personal finance besides the nine demographic characteristics used in this research. Among the

nine demographic characteristics, housing tenure, income, number of financial dependents, and

length of employment had significant coefficients. Controlling other conditions, the predicted

subjective perception of personal finance levels of the homeowners were higher than the others

(renters and those who live with others). Household income has a positive relationship with the

subjective perception of personal finance. With other demographic characteristics being equal,

the predicted level of one’s subjective perception of personal finance was higher for those who

have higher household income than lower income groups. The number of financial dependents

had a negative relationship with the subjective perception index. Those who had more financial

dependents showed lower levels of personal financial wellness in the subjective perception index.

Those who worked longer years with the current employer tended to have higher levels of the

personal financial wellness profile in the subjective perception index. The Beta coefficients

represent the relative contributions of the variables in the equation (Howell, 1992; Pedhazur,

1982). Household income was a relatively more significant predictor of the subjective

perception of personal finance than the other three variables (HousingD, NUMBER, and Length

of employment).

Behavioral Assessment of Personal Finance. As shown in Table 21, household income was the

only significant variable in explaining the behavioral assessment of personal finance (FBT).

Those who had more household income were more likely to show a better behavioral


Table 20

Regression Results of Personal Financial Wellness with Demographic Characteristics as

Independent Variables and the Subjective Perception Indexa as the Dependent Variable (N =

248)

Variableb b Beta t Sig.

Constant 11.884 4.428 .000

Age -2.51E-03 -.005 -.072 .943

Education 1.453E-02 .004 .071 .944

GenderD 1.998 .074 1.270 .205

HousingD 1.932 .164* 2.478 .014

Income .794 .314** 4.352 .000

Marital StatusD 1.043 .094 1.222 .223

Number -.976 -.222** -3.270 .001

EthnicityD 1.126 .061 1.025 .306

Length of employment .411 .139* 2.028 .044

R2 = .231

F = 7.961**

* p < .05. ** p <.01.

a Perception of how respondents felt about their financial situation utilizing the sum of the eight 4-point questionsb Age: Respondent’s age in years Education : Respondent’s education level GenderD: Dummy variable for Gender : 1 if female, 0 if male HousingD: Dummy variable for Housing: 1 if home-owner, otherwise, 0 Income: Respondent’s household income Marital StatusD: Dummy variable for Marital status: 1 if married, otherwise, 0 Number: Number of financial dependents EthnicityD: Dummy variable for Ethnicity: 1 if white, otherwise, 0


Table 21

Regression Result of Personal Financial Wellness with Demographic Characteristics as

Independent Variables and the Behavioral Assessment Indexa as the Dependent Variable

(N=249)


Constant 24.301 6.779 .000

Age 6.899E-02 .110 1.455 .147

Education .225 .054 .797 .427

GenderD 3.546 .108 1.726 .086

HousingD .867 .057 .807 .420

Income .827 .254** 3.340 .001

Marital StatusD -1.03E-02 -.001 -.009 .993

Number -.226 -.041 -.549 .583

EthnicityD -.817 -.001 -.543 .588

Length of employment -3.23E-03 -.001 -.012 .991

R2 = .111

F = 3.327**

* p < .05. ** p <.01.

a Assessment of the personal financial behaviors of the respondents utilizing the sum of the twelve 4-pointquestionsb Age: Respondent’s age in years Education : Respondent’s education level GenderD: Dummy variable for Gender : 1 if female, 0 if male HousingD: Dummy variable for Housing: 1 if home-owner, otherwise, 0 Income: Respondent’s household income Marital StatusD: Dummy variable for Marital status: 1 if married, otherwise, 0 Number: Number of financial dependents EthnicityD: Dummy variable for Ethnicity: 1 if white, otherwise, 0


assessment index. The total of nine demographic characteristics explained 11.1% of the

behavioral assessment variance. The R square suggests that there are other factors that explain

the behavioral assessment of personal finance besides the nine demographic characteristics

examined in this study.

Satisfaction with Financial Situation. The regression results of demographic variables and

satisfaction with financial situation are shown in Table 22. The nine demographic variables

explained 25.7% of the variance of satisfaction with personal financial situations. This also

suggests that the potential other factors that explain the satisfaction with financial situation of

the respondents. Three demographic characteristics were significant at the 0.05 level: housing

tenure, household income, and the number of financial dependents. The satisfaction with

financial situation of the homeowners was higher than others, household income had a positive

impact on the overall satisfaction, and the number of financial dependents had a negative

relationship with the overall satisfaction. By comparing the beta coefficients among those three

significant variables, household income was the best predictor of the satisfaction with financial

situation. The number of financial dependents was the second most important variable.

Perceived Financial Wellness. As shown in Table 23, the totals of four independent variables

were significant: housing tenure, household income, number of financial dependents, and

ethnicity. If the respondents were homeowners, their perceived financial wellness was likely to

be higher than the perceived financial wellness of others. If household income was higher, those

respondents tended to have better perceived financial wellness. The number of financial

dependents negatively influenced the perceived financial wellness. Those who had more

financial dependents showed lower levels of perceived financial wellness. If the respondent were

white, their perceived financial wellness was higher than other ethnic groups. The equation

explained 23.2 % of the variance of perceived financial wellness. The R square suggests that the

existence of other factors that explain the variance of perceived financial wellness.


Table 22

Regression Result of Personal Financial Wellness with Demographic Characteristics as

Independent Variables and the Satisfaction with Financial Situationa as the Dependent Variable

(N=237)


Constant 2.187 2.054 .041

Age -5.79E-04 -.003 -.041 .967

Education -1.35E-02 -.010 -.161 .873

GenderD .383 .038 .640 .523

HousingD .912 .192** 2.908 .004

Income .317 .312** 4.341 .000

Marital StatusD .524 .117 1.525 .129

Number -.497 -.279** -4.103 .000

EthnicityD .389 .048 .809 .419

Years of Employment .148 .123 1.808 .072

R2 = .257

F = 8.706**

* p < .05. ** p <.01.

a The questionnaire item of this variable was: “On the stair steps of financial wellness, mark (with a circle) how

satisfied you are with your present financial situation.” The answer ranged 1(lowest level) to 10 (highest level).b Age: Respondent’s age in years Education : Respondent’s education level GenderD: Dummy variable for Gender : 1 if female, 0 if male HousingD: Dummy variable for Housing: 1 if home-owner, otherwise, 0 Income: Respondent’s household income Marital StatusD: Dummy variable for Marital status: 1 if married, otherwise, 0 Number: Number of financial dependents EthnicityD: Dummy variable for Ethnicity: 1 if white, otherwise, 0


Table 23


Independent Variables and the Perceived Financial Wellnessa as the Dependent Variable

(N=255)


Constant 1.287 2.335 .020

Age -5.46E-04 -.005 -.075 .940

Education 7.249E-03 .010 .169 .866

GenderD .449 .082 1.421 .157

HousingD .439 .163** 2.693 .008

Income .173 .321** 4.532 .000

Marital StatusD .210 .089 1.187 .236

Number -.220 -.237** -3.526 .001

EthnicityD .461 .116* 1.993 .047

Length of employment 4.401E-02 .070 1.044 .298

R2 = .232

F = 8.203**

* p < .05. ** p <.01.

a The questionnaire item of this variable was: “How well are you financially?”b Age: Respondent’s age in years Education : Respondent’s education level GenderD: Dummy variable for Gender : 1 if female, 0 if male HousingD: Dummy variable for Housing: 1 if home-owner, otherwise, 0 Income: Respondent’s household income Marital StatusD: Dummy variable for Marital status: 1 if married, otherwise, 0 Number: Number of financial dependents EthnicityD: Dummy variable for Ethnicity: 1 if white, otherwise, 0


In terms of relative contribution, household income had the greatest effect followed by the

number of financial dependents. The housing tenure was the third most important variable, and

ethnicity was the least important of the four significant predictors.

Feeling about Financial Situation. Household income and number of financial dependents were

significant variables in explaining the feeling about financial situations (Table 24). Income had a

positive relationship with the dependent variable while the number of financial dependents had a

negative relationship with the feeling about financial situation. The nine demographic variables

explained 17.8% of the variance of the dependent variable. This also suggests that the existence

of other factors that explain the variance of the feeling about financial situation. Relatively,

household income contributed more to the personal financial wellness than the number of

financial dependents.

Solvency Measure. The nine demographic variables explained 27.5% of the variance in the

solvency measure. The R square suggests that there are other factors that explain the solvency

measure. Housing tenure and the number of financial dependents were significant variables.

Those who own their houses were more likely to be solvent than the others (Table 25). Those

who had more financial dependents were less likely to be solvent besides the nine demographic

characteristics used in this research. Relatively, housing tenure contributed more to explaining

the predicted solvency of the respondents.

Amount of Reserve Funds. The regression equation explained 21.3% in the variance of the

amount of reserve funds. The R square suggests that there are other factors that explain the

variance of the amount of reserve funds. Age and household income of the respondents were

significant variables. The predicted reserve funds of the old respondents were more than those

of the younger respondents. Household income had a positive impact on a person’s reserve

funds. Household income was relatively more significant than the respondent’s age (Table 26).


Table 24


Independent Variables and the Feeling about Financial Situationa as the Dependent Variable

(N=255)


Constant 2.205 4.909 .000

Age -1.01E-03 -.012 -.170 .865

Education -1.73E-02 -.032 -.494 .622

GenderD -5.85E-02 -.013 -.227 .821

HousingD .213 .109 1.607 .109

Income .139 .328** 4.482 .000

Marital StatusD .179 .097 1.243 .215

Number -.178 -.243** -3.495 .001

EthnicityD .126 .040 .666 .506

Length of employment 2.644E-02 .054 .769 .442

R2 = .178

F = 5.899**

** p <.01.

a The questionnaire item of this question was: “ How do you feel about your financial situation?”b Age: Respondent’s age in years Education : Respondent’s education level GenderD: Dummy variable for Gender : 1 if female, 0 if male HousingD: Dummy variable for Housing: 1 if home-owner, otherwise, 0 Income: Respondent’s household income Marital StatusD: Dummy variable for Marital status: 1 if married, otherwise, 0 Number: Number of financial dependents EthnicityD: Dummy variable for Ethnicity: 1 if white, otherwise, 0


Table 25


Independent Variables and the Solvency Measure as the Dependent Variable (N= 253)

Variablea b Beta t Sig.

Constant 2.097 3.686 .000

Age 1.039E-02 .094 1.376 .170

Education -5.63E-04 -.001 -.013 .993

GenderD .370 .063 1.133 .258

HousingD 1.033 .387** 6.070 .000

Income 7.587E-02 .133 1.927 .055

Marital StatusD .113 .045 .615 .539

Number -.138 -.140* -2.137 .034

EthnicityD .294 .070 1.232 .219


R2 = .275

F = 10.232**

* p < .05. ** p <.01.

a Age: Respondent’s age in years Education : Respondent’s education level GenderD: Dummy variable for Gender : 1 if female, 0 if male HousingD: Dummy variable for Housing: 1 if home-owner, otherwise, 0 Income: Respondent’s household income Marital StatusD: Dummy variable for Marital status: 1 if married, otherwise, 0 Number: Number of financial dependents EthnicityD: Dummy variable for Ethnicity: 1 if white, otherwise, 0


Table 26


Independent Variables and the Amount of Reserve Funds as the Dependent Variable (N=252)


Constant -7.87E-03 -.008 .994

Age 4.318E-02 .239** 3.367 .001

Education -2.59E-02 -.022 -.344 .731

GenderD -.387 -.041 -.702 .483

HousingD .324 .075 1.128 .260

Income .273 .296** 4.121 .000

Marital StatusD -.103 -.026 -.335 .738

Number -.199 -.125 -1.827 .069

EthnicityD .365 .054 .905 .366


R2 = .213

F = 7.289**

** p <.01.



Monthly Credit Payments. Household income was the only significant variable in explaining

monthly credit payments at or beyond the 0.05 significant level (Table 27). Household income

had a positive relationship with the monthly credit payments. Therefore, those who had higher

household incomes tended to pay more on credit bills per month. The equation explained 8.6%

of the variance of the dependent variable. This suggests that there are other factors that explain

the monthly credit payments besides the nine demographic characteristics that were researched

in this study.

Monthly Installment Loan Payments. The regression equation explained 12.6% of the variance

of the dependent variable (Table 28). The R square suggests that there are other factors that

explain the variance of the monthly installment loan payments besides the nine demographic

characteristics used in this study. The age of the respondents and marital status were significant

variables at the .05 level. The older respondents had fewer monthly installment loan payments,

and the married respondents had higher monthly installment loan payments. Relatively, age is a

more significant factor in explaining the variance of monthly installment loan payments.

Savings per Month. As shown in Table 29, household income was the only significant variable

in explaining the amount of money that is put into a savings account in each month. Household

income positively influenced the monthly savings. The total of nine demographic characteristics

explained 20.4% of the variance of monthly savings. This also suggest the existence of other

variables that explain the variance of the savings per month.

Voluntary Supplementary Tax-Sheltered Employer-Sponsored Retirement Contributions per

Month. Table 30 shows the regression results of the monthly voluntary supplementary tax-

sheltered employer-sponsored retirement contributions with demographic characteristics.

Household income was the only significant variable. Household income had a positive

relationship with voluntary supplementary tax-sheltered employer-sponsored retirement


Table 27


Independent Variables and the Monthly Credit Payments as the Dependent Variable (N=254)


Constant 1.186 .947 .345

Age 1.676E-02 .077 1.014 .311

Education .190 .132 1.942 .053

GenderD -.183 -.016 -.254 .800

HousingD -.252 -.048 -.678 .498

Income .286 .255** 3.301 .001

Marital StatusD 2.050E-02 .004 .051 .959

Number -1.56E-02 -.008 -.110 .913

EthnicityD .367 .043 .686 .493


R2 = .086

F = 2.549**

** p <.01.



Table 28


Independent Variables and the Monthly Installment Loan Payments as the Dependent Variable

(N=250)


Constant 6.613 4.428 .000

Age -4.88E-02 -.184* -2.477 .014

Education -8.95E-02 -.051 -.765 .445

GenderD -1.213 -.088 -1.424 .156

HousingD -.116 -.018 -.260 .795

Income 8.556E-02 .063 .827 .409

Marital StatusD 1.011 .170* 2.096 .037

Number .216 .092 1.274 .204

EthnicityD -.309 -.031 -.496 .620


R2 = .126

F = 3.846**

* p < .05. ** p <.01.



Table 29


Independent Variables and the Monthly Savings as the Dependent Variable (N=255)


Constant -6.75E-02 -.070 .945

Age 5.188E-03 .029 .406 .685

Education 1.072E-03 .001 .014 .989

GenderD .306 .032 .551 .582

HousingD 7.860E-02 .018 .274 .784

Income .381 .410** 5.684 .000

Marital StatusD .105 .026 .337 .736

Number -.146 -.091 -1.330 .185

EthnicityD .372 .054 .914 .362

Length of employment .107 .099 1.442 .151

R2 = .204

F = 6.956**

** p <.01.



Table 30


Independent Variables and the Voluntary Supplementary Tax-Sheltered Employer-Sponsored

Retirement Contributions per Month as the Dependent Variable (N= 253)


Constant -.397 -.538 .591

Age 1.771E-02 .134 1.182 .071

Education 9.799E-02 .112 1.693 .092

GenderD -.337 -.049 -.797 .427

HousingD .107 .034 .488 .626

Income .204 .302** 4.005 .000

Marital StatusD -.400 -.135 -1.682 .094

Number 6.117E-02 .053 .733 .464

EthnicityD .255 .051 .824 .410


R2 = .131

F = 4.084**

** p <.01.



contributions per month. Those who had a higher household income level tended to contribute

more toward their retirement. The total of nine independent variables explained 13.1% of the

variance of the dependent variable. This suggests that there are other factors that explain the

variance of the dependent variable.

Summary of Demographic Characteristics and Personal Financial Wellness

In summary, some of the demographic characteristics showed significant relationships with

various personal financial wellness measures. Household income explained a significant amount

of the variance of the subjective perception index, the behavioral assessment index, the overall

satisfaction scale, perceived financial wellness, feeling about financial situation, amount of

reserve funds, monthly savings, and monthly voluntary supplementary tax-sheltered employer-

sponsored retirement contributions. Household income positively affected the personal financial

wellness of the respondents. Housing tenure had positive relationships with the subjective

perception index, the overall satisfaction scale, perceived financial wellness, and solvency

measure. The personal financial wellness of homeowners was higher than the others. Those

who had more financial dependents tended to show lower levels of personal financial wellness.

If the number of financial dependents increased, the level of the subjective perception of

personal finance, overall satisfaction, perceived financial wellness, and feeling about financial

situation decreased. Ethnicity affected perceived financial wellness. Marital status had a

significant relationship with monthly installment loan payments. The whites were more

financially well than other ethnic groups, and married respondents had more monthly installment

payments than the singles. Age showed a positive relationship with reserve funds and a negative

relationship with the monthly installment payments. The older respondents had more reserve

funds and fewer monthly installment loan payments. The R squares of regression results suggest

that there are other factors that explain the variance of the personal financial wellness besides the

nine demographic characteristics that were examined in this study.



Financial Stressors and Personal Financial Wellness

The third research question of this study was: “What is the relationship between financial

stressors and personal financial wellness?” This section presents personal financial wellness

according to the financial stressors of the respondents. The financial stressors included personal

stressors, family events, and financial stress situations. A total of 24 financial stressors were

investigated. Those stressors were decrease in income, job change, investment and/or business

loss, injury, disability, illness, marriage, divorce or separation, child birth, children go to college,

retirement, job loss, death, moving from one residence to another, house repair, vehicle accident,

vehicle repair, vehicle repossession, mortgage foreclosure, wage garnishment, personal

bankruptcy, overdue notices from creditors, serious medical bills, and legal problems.

Table 31 shows the frequency of the financial stressors among the respondents. About one-fifth

(17.7%) of the respondents reported that they have not suffered from any financial stress events

during the past year.

The most frequent financial stressor was “a major vehicle repair expenses” (30.3%). Almost

three-tenths of the respondents (28.4%) had received an overdue notice from a creditor. Almost

two-tenths (17.3%) of the respondents had a major house repair. Almost one-fifth (17.0%) of

the respondents had some family members who went to college. Less than one-fifth (16.6%) of

the respondents experienced a family member’s death. Over one-tenth (12.9%) of the

respondents changed their jobs. Over one-tenth (12.2%) of the respondents moved from one

residence to another during the past year. Over one-tenth (11.1%) of the respondents had a

family member who married during last year (including self). Less than one-tenth (9.6%) of the

respondents had some family members who lost their jobs during last year. Less than one-tenth


Table 31

Responses to Financial Stressors

Financial stressors: 24 financially stressful events that occurred during the past year

Yes NoItem n % n % N

I had a major vehicle repair expense. 82 30.3 189 69.7 271

I received an overdue notice from a creditor. 77 28.4 194 71.6 271

I had a major house repair. 47 17.3 224 82.7 271

Some(or one) of my family members went to college. 46 17.0 225 83.0 271

Some(or one) of my family members died. 45 16.6 226 83.4 271

I changed my job. 35 12.9 236 87.1 271

I moved from one residence to another. 33 12.2 238 87.8 271

Some(or one) of my family members got married. 30 11.1 241 88.9 271

Some(or one) of my family members lost a job. 26 9.6 245 90.4 271

I was seriously ill. 22 8.1 249 91.9 271

My income decreased. 21 7.7 250 92.3 271

I had serious medical bills. 16 5.9 255 94.1 271

I had a vehicle accident that cost a lot of money. 15 5.5 256 94.5 271

I had a legal problem. 14 5.2 257 94.8 271

Some(or one) of my family members retired. 12 4.4 259 95.6 271

I experienced divorce or separation from a spouse. 12 4.4 259 95.6 271

I had an investment and/or business loss. 10 3.7 261 96.3 271

My wages were garnished. 4 1.5 267 98.5 271


Table 31 (Continued)

Yes NoItem n % n % N

I (or my spouse) gave birth to a child. 3 1.1 268 98.9 271

I filed for personal bankruptcy. 2 0.7 269 99.3 271

I was seriously injured at the job. 1 0.4 270 99.6 271

My vehicle was repossessed. 1 0.4 270 99.6 271

I was diagnosed as disabled. 0 0.0 271 100.0 271

My home mortgage loan was foreclosed. 0 0.0 271 100.0 271

None of above events have happened to me or myfamily during the past year.

48 17.7 223 82.3


(8.1%) of the respondents were seriously ill. Less than one-tenth (7.7%) of the respondents

reported their income has been decreased. About 5% of the respondents (5.9%) had serious

medical bills. About 5% of the respondents (5.5%) had a major vehicle accident. About 5%

(5.2%) of the respondents had legal problems. A small number of the respondents had retired

family members (4.4%), were divorced or separated from a spouse (4.4%), reported investment

or business loss (3.7%), had wages garnishments (1.5%), or had a child birth (1.1%). Two

respondents filed personal bankruptcy. There was only one respondent who was seriously

injured at the job. There was only one respondent whose vehicle was repossessed. No one

experienced home mortgage foreclosure. None reported that they were diagnosed as disabled.

The financial stressors were summed up in an index. Each stress event was assumed to have an

equal amount of stress; therefore, in summing, no weighting was given to any stressor. The

frequency of the total stressor index is shown in Table 32. About one-fifth (17.7%) of the

respondents did not have any financial stressors during the past year. About one-quarter

(26.2%) of the respondents suffered from one stressor, and about one-quarter (25.5%)

experienced two stressors. Over one-tenth (12. 9%) of the respondents experienced three stress

events, and about one-tenth (9.2%) suffered from four. There was one respondent who

reported nine stress events during the past year. The mean of the stress events was 2.04 and the

standard deviation was 1.74.

The correlation matrix of the financial stressor index and personal financial wellness measures is

presented in Table 33. As shown in the table, financial stressors had significant relationships

with some of the personal financial wellness measures: subjective perception index (FAT),

behavioral assessment index (FBT), overall satisfaction with personal financial situation (FM1),

perceived financial wellness (FM2), feeling about financial situations (FM3), solvency measure

(FO1), reserve funds (FO2), monthly savings (FO5), and monthly voluntary supplementary tax-

sheltered employer-sponsored retirement contributions (FO6). The financial stressor index

(FST) had negative relationships with personal financial wellness. As the number of stress


Table 32

Frequencies of the Financial Stressor Indexa

Valid Cum


no financial stressors .00 48 17.7 17.7 17.7

1 financial stressor 1.00 71 26.2 26.2 43.9

2 financial stressors 2.00 69 25.5 25.5 69.4







9 financial stressors 9.00 1 .4 .4 100.0

------- ------- -------

Total 271 100.0 100.0

Mean 2.044 Median 2.000 Mode 1.000

Std dev 1.740 Variance 3.028 Kurtosis 1.785

S E Kurt .295 Skewness 1.215 S E Skew .148


a The sum of the 24 financially stressful events that occurred during the past year.


Table 33

Correlation Matrix of the Financial Stressors Index and 11 Measures of Personal Financial

Wellness

FAT a FBT FM1 FM2 FM3 FO1

FST -.3764 -.2368 -.3746 -.3734 -.3623 -.3297

( 263) ( 262) ( 253) ( 271) ( 271) ( 269)

P= .000 P= .000 P= .000 P= .000 P= .000 P= .000

FO2 FO3 FO4 FO5 FO6

FST -.3123 -.0378 .0970 -.2007 -.1729

( 268) ( 269) ( 265) ( 271) ( 269)

P= .000 P= .537 P= .115 P= .001 P= .004

a FAT: Subjective perception of personal finance (Perception of how respondents felt about their financialsituation utilizing the eight 4-point questions) FBT: Behavioral assessment of personal finance (Assessment of respondents’ personal financial behaviorsutilizing the twelve 4-point questions) FM1: Satisfaction with financial situation (Respondents’ satisfaction level with their present financial situationmeasured with a 10-point question) FM2: Perceived financial wellness (Respondents’ perception about their financial wellness measured with a 5-point question) FM3: Feeling about personal financial situation (Respondents feelings about their financial situation measuredwith a 5-point question) FO1: Solvency measure FO2: Amount of reserve funds FO3: Amount of credit payments per month FO4: Amount of installment loan payments per month FO5: Amount of savings per month FO6: Amount of voluntary supplementary tax-sheltered employer-sponsored retirement contributions per month FST: Financial stressor index


events increased, the subjective perception scores and behavioral assessment scores of the

respondents tended to decrease. The overall satisfaction with personal financial situation and

the level of perceived financial wellness decreased as the numbers of financial stressors

increased. If the financial stressors increased, respondents felt their financial situation was poor.

Those who experienced more financial stressors tended to be less solvent and tended to have

less reserve funds. Those who experienced more financial stressors tended to save less both for

general purposes and retirement.


Personal Financial Wellness and Financial Stress

The conceptual model of this study assumed that personal financial wellness affects financial

stress levels. The fourth research question of this study was: “What is the relationship between

personal financial wellness and financial stress levels?” This section describes the relationship

between personal financial wellness and financial stress levels. The financial stress level of the

respondents was measured with the question of “how do you rate your financial stress level?”

Answers ranged from 1 (no stress at all) to 10 (extremely stressful). The responses about

financial stress levels are presented in Graph 2. Approximately 3% of the respondents (2.6%)

reported no stress. By combining categories (combining 1, 2, 3, and 4), about three-tenths of

the respondents (28.8%) reported a lower level of stress, and more than one-third of

respondents (37.6%) reported a medium level of stress. By combining categories (combining 7,

8, 9, and 10), about one-third of respondents (33.6%) reported a high level of stress. The

percentage of the respondents who reported extreme stress was 4.4%.

The correlation matrix (Table 34) shows the relationships between personal financial wellness

and financial stress levels. Financial stress levels (FS) had negative relationships with subjective

perception index (FAT), behavioral assessment index (FBT), solvency measure (FO1), reserve

funds (FO2), amount of savings per month (FO5), amount of voluntary supplementary tax-


Graph 2

Distribution of The Financial Stress level of Respondents

Question: How do you rate your financial stress level?

On a scale of “no stress at all” (1) to “Extremely stressful (10)”

4.46.3

10.712.2

15.5

22.1

10.39.6

6.3

2.6

10.009.008.007.006.005.004.003.002.001.00

30.0

20.0

10.0

0.0

N = 271

M= 5.6

SD = 2.2


Table 34

Correlation Matrix of the 11 Measures of Personal Financial Wellness and Financial Stress Level

FAT a FBT FM1 FM2 FM3 FO1

FS -.7068 -.6305 -.6493 -.7031 -.6855 -.5366

( 263) ( 262) ( 253) ( 271) ( 271) ( 269)

P= .000 P= .000 P= .000 P= .000 P= .000 P= .000

FO2 FO3 FO4 FO5 FO6

FS -.5281 .1369 .2535 -.3991 -.2277

( 268) ( 269) ( 265) ( 271) ( 269)

P= .000 P= .025 P= .000 P= .000 P= .000

a FAT: Subjective perception of personal finance (Perception of how respondents felt about their financialsituation utilizing the eight 4-point questions) FBT: Behavioral assessment of personal finance (Assessment of respondents’ personal financial behaviorsutilizing the twelve 4-point questions) FM1: Satisfaction with financial situation (Respondents’ satisfaction level with their present financial situationmeasured with a 10-point question) FM2: Perceived financial wellness (Respondents’ perception about their financial wellness measured with a 5-point question) FM3: Feeling about personal financial situation (Respondents feelings about their financial situation measuredwith a 5-point question) FO1: Solvency measure FO2: Amount of reserve funds FO3: Amount of credit payments per month FO4: Amount of installment loan payments per month FO5: Amount of savings per month FO6: Amount of voluntary supplementary tax-sheltered employer-sponsored retirement contributions per month FS: Financial stress level (Questionnaire item: “How do you rate your financial stress level?”)


sheltered employer-sponsored retirement contribution per month (FO6), overall satisfaction with

financial situations (FM1), perceived financial wellness (FM2), and feelings about financial

situations (FM3). Among those significant relationships, financial stress level was highly

correlated with FAT, FBT, FM1, FM2, and FM3, moderately correlated with FO1 and FO1, and

weakly correlated with FO6 and FO7. If one’s subjective perception of the personal finance

index and behavioral assessment index were higher, the person’s financial stress was lower.

Respondents who were more solvent, had more reserve funds, put more money into their

savings account each month, and contributed more money for their retirement had less financial

stress. Respondents who were more satisfied with their financial situation felt less stressed.

Financial stress levels had positive relationships with monthly credit payments and monthly

installment loan payments. If one’s monthly credit payment and installment payments were

higher, he or she reported higher level of financial stress.


Worker Job Productivity Profile

The fifth research question was: “What is the worker job productivity profile in the self-reports

of job productivity change, performance rating, absenteeism, worker’s compensation claims, and

work time used for personal financial matters?” This section describes the worker job

productivity profile. The descriptive results are presented in the following tables.

Self-Reports of Productivity Change. Self-reports of productivity change were measured with a

9-point scale (Table 35). Less than one-tenth of workers reported their productivity decreased

this past year compared to the previous year (combining categories, 8.1%). The majority of the

respondents reported their productivity had increased (68.3%). Among those whose

productivity has been increased, the largest group reported (25.5%) that their productivity has

been increased by 2%. Less than one-tenth (8.5%) of the respondents reported their

productivity increased 1%. About one-fifth of the total respondents (16.6%) reported their


Table 35

Responses to Self-Reported Productivity Change

Question: Compared to a year ago, how has your work productivity changed?

Value n % Combined Value %

-4% of Greater Decrease 7 2.6

-3% 1 .4 Decreased 8.1

-2% 2 .7

-1% 12 4.4

No change 64 23.6 No Change 23.6

+1% 23 8.5

+2% 69 25.5

+3% 45 16.6 Increased 68.3

+4% or Greater Increase 48 17.7

N=271 100.0 100.0


productivity increase as 3%, and another one-fifth of the respondents (17.7%) reported as 4% or

more.

Performance Rating From the Boss. Four-fifths (80.4%, combining categories not shown in

Table 36) of the respondents reported their performance rating as above average. Almost four-

tenths (37.4%) of the respondents reported exceptional performance ratings. The largest

number of people (42.8%) described their performance rating as between average and

exceptional. Only a small number of people (1.1%) reported their performance rating as below

average.

Absenteeism. Absenteeism was measured by the number of missed work days over the past year

except vacation and holidays. As shown in Table 37, the responses concerning absenteeism

were well-distributed. Only a small number of respondents (5.6%) reported no absences during

the past year. About one-quarter (24.1%) reported 1 to 3 days of absences. Three-tenths

(30.6%) reported 4 to 6 days of absences. About one-sixth of the respondents (15.5%) were

absent 7 to 9 days. One-tenth (10.0%) of the respondents were absent from work 10 to 12 days

during the past year. Over one-tenth of the respondents (14.1%) were absent more than 12

days.

Worker’s Compensation Claim. There were only 16 respondents who used worker’s

compensation claims during the past three years. The percentage who used worker’s

compensation claims was only 5.9%.

Work Time Used for Personal Financial Matters. A total of eight items that related to the

personal financial matters at the workplace were measured: discussion of money related matters

with coworkers, consultation with a lender about a loan, phone conversation regarding an

overdue credit payments, phone conversation to friends and/or relatives about financial matters,


Table 36

Responses to Performance Ratings from Boss

Question: Describe your “performance rating” this past year from your boss.

Poor Average ExceptionalValue 1 2 3 4 5

n 0 3 50 116 101

% 0 1.1 18.5 42.8 37.3

Na =270

M = 4.16

SD=.76



Table 37

Responses to Absenteeism

Question: How many work days were you absent over the past year (excluding vacation and

holidays)?

Value n %

None 15 5.6

1-3 Days 65 24.1

4-6 Days 83 30.7

7-9 Days 42 15.6

10-12 Days 27 10.0

More than 12 Days 38 14.1

Total Na =270 100.0



phone conversation with a lawyer, consultation with a financial planner, arrangement of a vehicle

loan, and consultation with credit or budget counselors.

About one-third of the respondents (33.0%) never dealt with financial matters at work (Table

38). About one-half of the respondents (44.8%) talked with their coworkers about financial

matters during work time. About one-quarter (23.7%) talked with a lender about loan. Over

one-tenth (12.6%) of the respondents made calls regarding overdue credit payments during

work times. About one-tenth of the respondents made calls to friends and/or relatives about

financial matters, made calls to a lawyer, and talked with a financial planner (12.2%, 11.5%, and

10.4% respectively). About one-tenth of the respondents (8.9%) arranged a vehicle loan at

work and only a small number of the respondents (3.0%) made calls to a credit/budget counselor

at work.

The work time used for personal financial matters was computed into an index. The number of

personal financial matters that were dealt with at the workplace was summed without any

weighting. Table 39 shows the frequencies of work time used. About two-thirds (67.0%) of the

workers used work time for their personal financial matters. About a third of the respondents

(33.7%) dealt with personal finance matters at work in one of the eight listed ways. About one-

sixth (15.6%) of the respondents dealt with personal financial matters in two ways. About one-

tenth (11.1%) of the respondents used three different ways. A small number of respondents

(7.0%) dealt with personal financial matters in four or more ways. The average number of ways

that work time is used for personal financial matters was 1.27.

Health Condition. The job productivity of the respondents was assumed to have a relationship

with health conditions of workers. The self-reports of personal health conditions was measured

with a 5-point scale ranging from “not very healthy” to “very healthy”. Table 40 shows the

frequencies. Only a small number of the respondents (1.5%) reported that they are not very


Table 38

Work Time Used for Personal Financial Matters Over the Past Year

Yes NoItem n % n % Na

Talked With coworkers about money related matters 121 44.8 149 55.2 270

Talked with a lender about a loan 64 23.7 206 76.3 270

Made calls regarding an overdue credit payments 34 12.6 236 87.4 270

Made calls to friends and/or relatives about financialmatters

33 12.2 237 87.8 270

Made Calls to a Lawyer 31 11.5 239 88.5 270

Talked with a Financial Planner 28 10.4 242 89.6 270

Made calls to arrange a vehicle loan 24 8.9 246 91.1 270

Made calls to a credit/budget counselor 8 3.0 262 97.0 270

Never dealt with financial problems at work 89 33.0 181 67.0 270

a Numbers across a low may not add to 271 due to non-response.


Table 39

Frequencies of Work Time Use Indexa

Valid Cum


Never dealt with .00 89 32.8 33.0 33.0

Dealt with 1 1.00 91 33.6 33.7 66.7

2 2.00 41 15.1 15.2 81.9

3 3.00 30 11.1 11.1 93.0

4 4.00 16 5.9 5.9 98.9

5 5.00 2 .7 .7 99.6

6 6.00 1 .4 .4 100.0

. 1 .4 Missing

------- ------- -------

Total 271 100.0 100.0

Mean 1.270 Median 1.000 Mode 1.000

Std dev 1.266 Variance 1.603 Kurtosis .350

S E Kurt .295 Skewness .963 S E Skew .148


a Sum of the answers based on the list of eight items of personal financial matters that were dealt with duringwork hours


Table 40

Responses to Health Condition

Not veryHealthy

Healthy VeryHealthy

Value 1 2 3 4 5

n 4 30 101 80 56

% 1.5 11.1 37.3 29.5 20.7

N=271

M=3.56

SD=.99


healthy. About one-tenth (11.1%) of the respondents reported their health condition as between

“not very healthy” and “healthy”. The largest group of the respondents (37.3%) reported that

they are healthy. By combining categories, more than one-half of the respondents (50.2%)

reported that they are either very healthy or in between healthy and very healthy.

Table 41 shows the correlation matrix of some of the productivity measures, length of

employment, and health condition. The 9-point scale self-report productivity change (P1), 5-

point scale performance rating (P2), 6-point scale absenteeism (P3), and the work time use index

(WT) were examined. Health condition had significant relationships with each of the four

productivity measures. Health condition had positive relationships with the self-report

productivity change and performance rating. Those who reported better health conditions

performed better at work. Health condition had a negative relationship with absenteeism;

therefore, those who were healthier were absent less from work. The health condition also had

a negative correlation with the work time use; therefore, those who were not very healthy

tended to deal more with personal financial matters at work.

Length of Employment with Current Employer. Table 41 also shows the relationship between

job productivity and length of employment with the current employer of the respondents.

Length of employment had a significant positive relationship with performance ratings. Those

who had worked longer for the current employer had a higher performance rating. The length

of employment showed a negative correlation with the work time use index. Therefore, those

who worked longer years tended to deal with fewer personal financial matters at work.


Personal Financial Wellness and Productivity

The sixth research question was: “What is the relationship between personal financial wellness

and worker job productivity?” To examine the relationship between personal financial wellness


Table 41

Correlation Matrix of Four Productivity Measures with Health Condition and Length of

Employment

P1a P2 P3 WT

HEALTH .1514 .2302 -.3436 -.1595

( 271) ( 270) ( 270) ( 270)

P= .013 P= .000 P= .000 P= .009

YEAR .0213 .1259 .0398 -.1406

( 271) ( 270) ( 270) ( 270)

P= .727 P= .039 P= .515 P= .021

a P1: Self-reported productivity change P2: Performance rating P3: Absenteeism WT: Work time use index Health: Respondents’ self-reported health condition Year: Length of employment with the current employer


and productivity, correlations were utilized. Also, multiple regression analysis was conducted.

To control the demographic characteristics and financial stressors, the multiple regression

equation included selected demographic variables and the financial stressors index if those were

significantly correlated with a dependent productivity measure.

Correlation Results

The correlation matrix (Table 42) shows the relationship among the personal financial wellness

measures and productivity measures. The self-reports of productivity change (P1) showed a

positive significant correlation with monthly credit payments. Those who paid more money

toward their credit card bills each month tended to report higher levels of productivity.

The performance rating (P2) showed significant positive correlation coefficients with the

subjective perception index (FAT), the behavioral assessment index (FBT), the overall

satisfaction index (FMT), reserve fund (FO2), monthly credit payment (FO3), monthly savings

(FO5), and voluntary supplementary tax-sheltered employer-sponsored retirement contributions

per month (FO6). Those who had higher levels of personal financial wellness in terms of their

subjective perception reported higher levels of performance ratings and those who had a higher

behavior assessment score also reported higher performance ratings. The overall satisfaction of

personal financial wellness (FMT) was positively related to the performance ratings. Those who

had more reserve funds tended to perform better. Those who put more money toward their

credit card bills, savings accounts, and voluntary supplementary tax-sheltered employer-

sponsored retirement contributions performed better.

Absenteeism (P3) had a significant correlation with FAT, FBT, FO1 (solvency measure), FO2,

and FO4 (monthly installment loan payments). Those who had a higher score on their subjective

perception of personal finance and behavioral assessment of personal finance


Table 42

Correlation Matrix of 11 Measures of Personal Financial Wellness and Four Measures ofProductivity P1a P2 P3 WT

FAT .0939 .1779 -.1287 -.2223 ( 263) ( 262) ( 262) ( 262) P= .129 P= .004 P= .037 P= .000

FBT .0479 .1665 -.1611 -.1822 ( 262) ( 261) ( 261) ( 261) P= .441 P= .007 P= .009 P= .003

FMT .0624 .1580 -.1231 -.2509 ( 253) ( 252) ( 252) ( 252) P= .323 P= .012 P= .051 P= .000

FO1 .1001 .1088 -.1663 -.2921 ( 269) ( 268) ( 268) ( 268) P= .101 P= .075 P= .006 P= .000

FO2 .0598 .1249 -.1743 -.1985 ( 268) ( 267) ( 267) ( 267) P= .329 P= .041 P= .004 P= .001

FO3 .1248 .1712 -.0379 -.0843 ( 269) ( 268) ( 268) ( 268) P= .041 P= .005 P= .537 P= .169

FO4 .0582 .0440 .1932 .2310 ( 265) ( 264) ( 264) ( 264) P= .345 P= .477 P= .002 P= .000

FO5 .0367 .1519 -.0118 -.1223 ( 271) ( 270) ( 270) ( 270) P= .547 P= .012 P= .847 P= .045

FO6 .0920 .1361 -.0534 .0482 ( 269) ( 268) ( 268) ( 268) P= .132 P= .026 P= .384 P= .432

a P1: Self-reported productivity change P2: Performance rating P3: Absenteeism WT: Work time use index FAT: Subjective perception of personal finance FBT: Behavioral assessment of personal finance FMT: Overall satisfaction with financial situation index FO1: Solvency measure FO2: Amount of reserve funds FO3: Monthly credit payments FO4: Monthly installment loan payments FO5: Monthly savings FO6: Monthly voluntary supplementary tax-sheltered employer-sponsored retirement contributions


reported fewer absences. Those who were more solvent and had more reserve funds reported

fewer absences. And those who had to pay more money toward their monthly installment loans

reported more absences. Moreover, absenteeism and personal financial wellness profiles were

negatively correlated. Those who had better personal financial wellness profiles were absent less

frequently from work.

The total work time used for personal financial matters (WT) showed significant relationships

with FAT (subjective perception index), FBT (behavioral assessment index), FMT (overall

satisfaction with financial situation index), FO1 (solvency measure), FO2 (amount of reserve

funds), FO4 (monthly installment loan payments), and FO5 (savings per month). The total work

time use (WT) was negatively related with FAT, FBT, FMT, FO1, FO2, and FO5. That means

those who had higher personal financial wellness profiles used less work time for personal

financial matters. As the level of subjective perception and behavioral assessment of personal

finance increased, the work time used for personal financial matters decreased. If respondents

were more satisfied with their personal financial wellness, they tended to deal with fewer

personal financial matters at work. Those who had more reserve funds and more monthly

savings dealt with personal financial matters less at work. The total work time use was

positively related with monthly installment payments (FO4); therefore, those who had more

monthly installment payments dealt with more personal financial matters at work.

Regression Results

To examine the relationship between personal financial wellness and worker job productivity,

multiple regression was conducted. Dependent variables were self-reports of productivity

change, performance ratings, absenteeism, and the work time use index. The compensation

claims of workers were not tested as one of the dependent variables because of skewness. There


were only 5.9% of the respondents that utilized the worker’s compensation claim. Due to the

skewness of the variable, worker’s compensation claim was not examined for further analysisa .

The independent variables were personal financial wellness measures, demographic

characteristics, and financial stressors. Among the nine personal financial measures (subjective

perception, behavioral assessment, overall satisfaction index, solvency measure, amount of

reserve funds, monthly credit payments, monthly installment loan payments, monthly savings,

and voluntary supplementary tax-sheltered employer-sponsored retirement contributions), the

ones that showed significant correlation with the productivity measures were entered into the

regression equation. Each regression equation included demographic variables and the financial

stressors index if they were significantly related to the dependent productivity measure.

Self-Reports of Productivity Change. Among the nine demographic characteristics – age,

education, gender, housing tenure, household income, marital status, number of financial

dependents, ethnicity, and length of employment — household income showed a significant

correlation with self-reports of productivity change. (The correlation matrix is in Appendix K).

The financial stressor index was not significantly correlated with the self-reports of productivity

change. As shown earlier in Table 42, only monthly credit payments was significantly correlated

with the self-reports of productivity change. Therefore, the household income and monthly

credit payments were included in the regression equation. The regression results showed no

significant variables in the equation.

Performance Rating. The listwise correlation analysis showed significant correlations between

household income, gender, marital status, length of employment, and financial stressors with

performance ratings (Appendix K). Among the personal financial wellness measures, the

subjective perception index, behavioral assessment index, overall satisfaction index, solvency

a While worker’s compensation was hypothesized as a variable that may be associated with job productivity, theskewness prohibits further analysis. A large database and/or different sample might provide useful insight in afuture research study.


measure, reserve funds, monthly credit payments, and monthly savings were significantly related

to the performance rating. Therefore, seven different regression equations were developed. All

of the seven equations were statistically significant in terms of F ratios. Because the research

question of this study was to examine the relationship between personal financial wellness and

worker job productivity, only those equations that included statically significant personal

financial wellness measures are reported. Monthly credit payments showed a statistically

significant impact on the performance rating. As shown in Table 43, the equation explained

15.2% of the variance in performance ratings. The independent variables explained 15.2% of the

variance of performance rating. This suggests that there are other factors that explain the

variance of self-reports of productivity change besides gender, housing tenure, household

income, marital status, length of employment, financial stressors, and monthly credit payments.

Other than monthly credit payments, household income, gender, and financial stressors were

significant variables. Monthly credit payments had a positive impact on performance ratings.

Those who paid more on their credit bills each month tended to report higher levels of

performance ratings, controlling other demographic variables and financial stressors.

Absenteeism. Among those nine demographic characteristics, age was included in the regression

equations. The financial stressor index showed a significant correlation with absenteeism;

therefore, it was also included in the equations (Appendix K). A total of five different

regression equations with different personal financial measures were tested. The behavioral

assessment index and monthly installment payments showed statistically significant relationships

with absenteeism. As shown in Table 44, behavioral assessment of personal financial wellness

negatively influenced absenteeism. Those who had higher levels of behavioral assessment

scores, in other words those who behaved well financially, tended to be absent less from work,

controlling for financial stressors and the age of the respondents. The three independent

variables — age, financial stressors, and behavioral assessment index —


Table 43

Regression Result of Personal Financial Wellness and Productivity with Monthly Credit

Payments as One of the Independent Variables and the Performance Ratings as the Dependent

Variable (N=255)


Constant 3.139 11.613 .000

GenderD .673 .170** 2.936 .004

HousingD .005 .003 .048 .962

Income .050 .131 1.775 .077

Marital StatusD .119 .072 1.006 .315

Length of employment .033 .074 1.162 .246

Financial Stressors -.081 -.187** -3.065 .002

FO3 .044 .129* 2.168 .031

R2 = .152

F = 6.584**

* p <.05. ** p <.01.

a GenderD: Dummy variable for Gender : 1 if female, 0 if male HousingD: Dummy variable for Housing: 1 if home-owner, otherwise, 0 Income: Respondent’s household income Marital StatusD: Dummy variable for Marital status: 1 if married, otherwise, 0 Financial Stressor: The financial stressors index FO3: Monthly credit payments


Table 44

Regression Result of Personal Financial Wellness and Productivity With Behavioral Assessment

Index as One of the Independent Variables and the Absenteeism as the Dependent Variable

(N=253)


Constant 4.617 7.475 .000

Age -1.06E-02 -.074 -1.183 .238

Financial Stressors .126 .151* 2.376 .018

FBT -2.90E-02 -.128* -2.002 .046

R2 = .061

F = 5.402**

* p <.05. ** p <.01.

a Age: Respondent’s age in years Financial Stressor: The financial stressors index FBT: Behavioral assessment index


explained 6.1% of the variance of absenteeism. This suggests that there are other factors that

explain the variance of absenteeism.

Table 45 shows the regression results with monthly installment loan payments. Monthly

installment loan payments had a significant relationship with one’s absenteeism. The positive

regression coefficient means a positive relationship exist between the amount of monthly

installment loan payments and days of absence from work. Those who had more monthly

installment loan payments tended to be absent more from work, controlling for financial

stressors and the age of the respondents. The equation explained 8.4% of the variance of

absenteeism. This also suggests the existence of other factors that explain absenteeism.

Work time used for Personal Financial Matters. Among the nine demographic variables, age,

housing tenure, and number of financial dependents showed significant correlations with the

work time use index. Financial stressors also showed a significant relationship. Therefore, the

regression equations included the three demographic characteristics, financial stressors, and

significant personal financial measures. A total of seven different regression equations were

tested. Among the significant seven personal financial wellness measures —subjective

perception index, behavioral assessment index, overall satisfaction index, solvency measures,

reserve funds, monthly installment loan payments, and amount of monthly savings — five

showed statistically significant impacts on the work time use index. Those five were the

subjective perception index, behavioral assessment index, overall satisfaction index, solvency

measure, and monthly installment payments.

As shown in Table 46, the subjective perception of personal finance influenced the work time

used for personal financial matters. The subjective perception index was the only significant

variable at or beyond the .05 level in explaining the dependent variable. The negative regression


Table 45

Regression Result of Personal Financial Wellness and Productivity with Monthly Installment

Loan Payments as One of the Independent Variables and the Absenteeism as the Dependent

Variable (N= 255)


Constant 3.013 6.439 .000

Age -7.66E-03 -.053 -.854 .394

Financial Stressors .144 .174** 2.851 .005

FO4 .103 .190** 3.056 .002

R2 = .084

F = 7.678**

** p <.01.

a Age: Respondent’s age in years Financial Stressor: The financial stressors index FO4: Monthly installment loan payments


Table 46

Regression Result of Personal Financial Wellness and Productivity with Subjective Perception

Index as One of the Independent Variables and the Work Time Use Index as the Dependent

Variable (N=248)


Constant 2.435 4.609 .000

Age -1.41E-02 -.113 -1.542 .124

HousingD -.147 -.050 -.706 .481

Number 8.575E-02 .078 1.182 .238


Financial Stressors 9.487E-02 .132 1.953 .052

FAT -3.89E-02 -.155* -2.303 .022

R2 = .115

F = 5.241**

* p <.05. ** p <.01.

a Age: Respondent’s age in years HousingD: Dummy variable for Housing: 1 if home-owner, otherwise, 0 Number: Number of financial dependents Marital StatusD: Dummy variable for Marital status: 1 if married, otherwise, 0 FAT: Subjective perception index (Perception of how respondents felt about their financial situation utilizingthe eight 4-point questions)


coefficient shows that those who had higher levels of subjective perception scores in personal

financial wellness dealt with less personal financial matters at work, controlling for financial

stressors and age. Those with subjectively perceived low levels of personal financial wellness

dealt with more personal financial matters at work. The regression equation explained 11.5% of

the variance of work time used for personal financial matters. The R square suggests that there

are other factors that explain the work time used for personal financial matters besides age,

housing tenure, number of financial dependents, length of employment with current employer,

financial stressors, and the subjective perception index.

The behavioral assessment of personal finance also influenced the work time use index. Table

47 shows the regression results. The behavioral assessments of personal finance also had a

negative relationship with work time use. Those who reported high levels of personal financial

wellness in terms of their behavioral assessment dealt with fewer personal financial matters at

work, controlling for financial stressors and age. In this equation, the financial stressors index

showed a significant relationship. Those who experienced more financial stress events took

work time to attend to more personal financial matters. The equation explained 11% of the

variance in work time use. This also suggests the existence of other factors that explains the

dependent variable.

Table 48 shows the regression results with the overall satisfaction index. The overall

satisfaction index is a composite index of the satisfaction with personal financial situations

(FM1), perceived financial wellness (FM2), and feeling about financial situation (FM3). The

overall satisfaction index had a negative regression coefficient, meaning those who are more

satisfied with their financial situation, perceived a higher financial wellness level, and felt more

comfortable with their financial situation tended to deal with fewer personal financial matters at


Table 47

Regression Result of Personal Financial Wellness and Productivity with Behavioral Assessment


Variable (N=248)


Constant 2.581 4.509 .000

Age -1.57E-02 -.128 -1.721 .087

HousingD -.168 -.057 -.817 .415

Number .107 .099 1.491 .137



FBT -2.65E-02 -.135* -2.128 .034

R2 = .110

F = 4.965**

* p <.05. ** p <.01.

a Age: Respondent’s age in years HousingD: Dummy variable for Housing: 1 if home-owner, otherwise, 0 Number: Number of financial dependents Marital StatusD: Dummy variable for Marital status: 1 if married, otherwise, 0 FBT: Behavioral assessment (Assessment of respondents’ personal financial behaviors utilizing the twelve 4-point questions)


Table 48

Regression Result of Personal Financial Wellness and Productivity with Overall Satisfaction


Variable (N= 237)


Constant 2.501 4.917 .000

Age -1.56.E-02 -.124 -1.679 .094

HousingD -.144 -.049 -.686 .493

Number 8.944E-02 .080 1.202 .230



FMT -9.41E-02 -.197* -2.846 .005

R2 = .129

F = 5.698**

* p < .05. ** p <.01.

a Age: Respondent’s age in years HousingD: Dummy variable for Housing: 1 if home-owner, otherwise, 0 Number: Number of financial dependents Marital StatusD: Dummy variable for Marital status: 1 if married, otherwise, 0 FMT: Overall satisfaction index


work, controlling for financial stressors and age. The equation explained 12.9% of the variance

of work time use. This suggests the existence of other variables that explain the work time used

for personal financial matters. The overall satisfaction index was the only significant variable in

explaining work time use.

Table 49 reports the regression results with solvency measure. Those who were more solvent

dealt with fewer personal financial matters at work, controlling other variables. In other words,

those who had heavier debt burdens tended to deal with more personal financial matters at work.

The measure of work time used for personal financial matters included: talking with coworkers

about money related matters, talking with a lender about a loan, making calls regarding an

overdue credit payment, making calls to friends and or relatives about financial matters, making

calls to a lawyer, talking with a financial planner, making calls to arrange a vehicle loan, and

making calls to a credit and/or budget counselor. The equation explained 14.3% of the variance

of the work time use. This also suggests the existence of other variables that explain the

dependent variable.

The amount of monthly installment loan payments also influenced work time use (Table 50).

The positive regression coefficient represents that those who had more monthly installment loan

payments dealt with more personal financial matters at work, controlling for financial stressors

and age. The equation explains 12.8% of the variance of the work time use index. The R

square also suggests that there are other factors that explain the variance of the work time used

for personal financial matters. In this equation, the financial stressors index was another

significant variable. Those who experienced more financial stress events tended to deal with

more personal financial matters at work.


Table 49

Regression Result of Personal Financial Wellness and Productivity with Solvency Measure as

One of the Independent Variables and the Work Time Use Index as the Dependent Variable

(N=253)


Constant 2.603 5.333 .000

Age -1.41E-02 -.113 -1.577 .116

HousingD -.1.08E-02 -.004 -.049 .961

Number .100 .091 1.422 .156



FO1 -.255 -.230** -3.279 .001

R2 = .143

F = 6.828**

** p <.01.

a Age: Respondent’s age in years HousingD: Dummy variable for Housing: 1 if home-owner, otherwise, 0 Number: Number of financial dependents Marital StatusD: Dummy variable for Marital status: 1 if married, otherwise, 0 FO1: Solvency measure


Table 50

Regression Result of Personal Financial Wellness and Productivity with Monthly Installment

Loan Payments as One of the Independent Variables and the Work Time Use Index as the

Dependent Variables (N=250)


Constant 1.349 3.001 .003

Age -1.14E-02 -.092 -1.255 .211

HousingD -.304 -.103 -1.490 .137

Number 6.146E-02 .056 .844 .399



FO4 8.774E-02 .186** 2.952 .003

R2 = .128

F = 5.929**

* p <.05. ** p <.01.

a Age: Respondent’s age in years HousingD: Dummy variable for Housing: 1 if home-owner, otherwise, 0 Number: Number of financial dependents Marital StatusD: Dummy variable for Marital status: 1 if married, otherwise, 0 FO4: Monthly installment payments



Financial Stress and Productivity

The seventh research question was: “What is the relationship between financial stress and

worker job productivity?” This section describes the relationship between financial stress and

productivity. To inspect the relationship between financial stress and worker job productivity

correlations were examined. Table 51 shows correlations between financial stress level and

productivity measures. The work time used for personal financial matters (WT) was

significantly correlated with financial stress. The higher financial stress levels, the more personal

financial matters were dealt with at work.

The relationship between financial stress levels and worker job productivity was also examined

using regression analysis. To control for demographic characteristics and financial stressors,

those variables that showed significant correlations with each productivity measure were

included in the equation. The financial stress level was also included as one of the independent

variables. Among the four measures of productivity, only work time use showed a significant

relationship with financial stress levels. The regression equation included age, housing tenure,

number of financial dependents, length of employment, and financial stressors as controlling

variables. The financial stress level was also included in the regression equation. As shown in

Table 52, financial stress level influenced the work time use index. The positive regression

coefficients show that those who reported high levels of financial stress dealt with more personal

financial matters at work, controlling for age, housing tenure, number of financial dependents,

length of employment with current employer, and financial stressors. In this equation, financial

stress level was the only significant variable at or beyond the .05 level. The equation explained

13.5% of the variance of the work time use index. The R square suggests that there are other

factors that explain the variance of the work time use index.


Table 51

Correlation Matrix of Financial Stress and Four Measures of Productivity

P1a P2 P3 WT

FS .0889 -.1000 .1142 .2422

( 271) ( 270) ( 270) ( 270)

P= .144 P= .101 P= .061 P= .000

a P1: Self-reported productivity change P2: Performance rating P3: Absenteeism FS: Financial stress level


Table 52

Regression Result of Financial Stress Level and Productivity with Financial Stress Level as One

of the Independent Variables and the Work Time Use Index as the Dependent Variable (N=254)


Constant 1.130 2.364 .019

Age -.015 -.118 -1.656 .991

HousingD -.179 -.061 -.884 .378

Number .086 .078 1.212 .227

Length of employment -.029 -.039 -.578 .564

Financial Stressors .089 .124 1.885 .061

Financial Stress Level .110 .187** 2.889 .004

R2 = .135

F = 6.445**

** p <.01.

a Age: Respondent’s age in years HousingD: Dummy variable for Housing: 1 if home-owner, otherwise, 0 Number: Number of financial dependents Marital StatusD: Dummy variable for Marital status: 1 if married, otherwise, 0



Desired Financial Education

The eighth research question of this study was to explore the desired future financial education

programs. A total of 13 categories of workplace financial education programs were

investigated. Respondents indicated all of their desired future workplace financial education

programs. Table 53 shows the results. Six-tenths (60.5%) of the respondents wanted to

participate in retirement planning programs. Almost one-half (48.7%) of the respondents

reported that they would participate in investment education programs. About four-tenths

(42.1%) of the respondents said they would participate in budgeting programs. More than one-

third (36.2%) of the respondents desired a financial program that deals with reducing consumer

debt. About one-third of the respondents (33.6%) reported that they would participate in

workplace financial education if there was a program that dealt with understanding benefits.

Almost three-tenths (27.7%) of the respondents reported that they want estate planning

education. About a quarter (25.8%) wanted tax planning education. Credit management

programs were selected by over one-fifth (22.1%) of the respondents. They also indicated

interest about consumer protection, as more than two-tenths (22.1%) of consumers chose

consumer protection law programs as a desired future financial education program. About one-

fifth (20.3%) desired education on college planning. Home buying education was selected by

16.2 % of the respondents. Over one-tenth (14.4%) answered that they were interested in

education on buying insurance. A small number of people (3.3%) who reported other desired

programs, including: reversing bad credit rating, writing a will and a living will, comprehensive

financial planning, and ways to make a second income from home. About 6% of respondents

(6.6%) did not answer the questions about their desired financial education programs.

The respondents were asked to choose all of the financial education programs that interested

them. About one-fifth of the respondents (20.7%) picked two financial education programs.

About one-sixth (16.2%) of the respondents chose three programs, 13.7% chose four, 11.8%

chose five, and one-tenth (10%) of the respondents chose one program (Table 54).


Table 53

Responses to Desired Future Financial Education Programs

Yes NoItem n % n %

Retirement planning 164 60.5 107 39.5

Investing 132 48.7 139 51.3

Budgeting 114 42.1 157 57.9

Getting out of debt 98 36.2 173 63.8

Understanding benefits 91 33.6 180 66.4

Estate planning 75 27.7 196 72.3

Tax planning 70 25.8 201 74.2

Managing credit 60 22.1 211 77.9

Consumer Protection laws 60 22.1 211 77.9

College planning 55 20.3 216 79.7

Buying a home 44 16.2 227 83.8

Buying insurance 39 14.4 232 85.6

Other 9 3.3 262 96.7

No answer 18 6.6 253 93.4


Table 54

Number of Desired Financial Education Programs Chosen by Respondentsa

Number n %

0 18 6.6

1 27 10.0

2 56 20.7

3 44 16.2

4 37 13.7

5 32 11.8

6 23 8.5

7 13 4.8

8 6 2.2

9 4 1.5

more than 10 11 4.0

Total 271 100.0

M = 3.73

SD = 2.54

a Sum of the 13 list of desired financial education program chosen by respondents


Summary of Results

This chapter presented the data collected to empirically test a relationship between personal

financial wellness and worker job productivity. Return rates and the demographic characteristics

of the sample (N=446) were described. The personal financial wellness profile was described

according to the four scales —subjective perception of personal finance, behavioral assessmet of

personal finance, objective scales, and overall financial wellness scales. Personal financial

wellness was described with demographic characteristics. Regression results of personal

financial wellness was presented. The relationship between personal financial wellness and

financial stressors was described. The relationship between personal financial wellness and

financial stress level was also described. The worker job productivity profile of the sample was

presented. The relationship between personal financial wellness and worker job productivity

was described according to the regression results. The relationship between financial stress level

and worker job productivity was presented. The desired workplace financial education of

workers were presented.

CHAPTER V. PRESENTATION OF RESULTS

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Transcript of CHAPTER V. PRESENTATION OF RESULTS