age and income

8/2/2019 Age and Income

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Jing LIN

Chen GAO

Ruth ZHUANG

Nichole CHEN

Emploring Correlation between

Education Level, Income Leveland Job Satisfaction in Gippsland


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Contents

Abstract .................................................................................................. 3

1. Introduction ..................................................................................... 4

2. Previous findings.............................................................................. 5

3. Data source and independent variables .......................................... 7

3.1. Data source ................................................................................. 7

3.2. Independent variables ............................................................... 8

4. The Empirical Specification .......................................................... 11

4.1 Education difference and Income ........................................... 12

4.2 Age, Work Experience and Income ........................................ 16

4.3 Job Satisfaction ........................................................................ 19

5. Limitations ..................................................................................... 19

6. Conclusions .................................................................................... 21

7. Recommendations .......................................................................... 22

Appendix A........................................................................................... 23

Bibliography ......................................................................................... 29


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Abstract

Commodities and the sectors that are related to the commodity boom in Australia

such as finance and engineering is changing the demographics of the Australian

society. As more well-paying jobs open up in these sectors, young people, who would

otherwise choose farming, are moving to big cities to seek employment. These have

resulted in shortage of labour in regional areas, which has caused serious concern to

the public. This study is employed to assist regional area, especially Gippsland, to

attract young professionals to work in regional areas.

A survey named AGI & UoM Survey was conducted during July 2011 in Gippsland

to correlate education level with income level and job satisfaction level of people

involved in agribusiness. The purpose of the survey was to find out whether tertiary

education could lead to higher income and job satisfaction.

57 surveys were conducted during this period. Information such as the interviewees

characteristics and income level and job satisfaction is gathered by this survey.

Statistic analysis, such as correlation analysis and chart analysis, was employed on the

collected data.

Conclusion is drawn that positive correlation exists between education level and

income level for both agribusiness sector and farming sector. However, the

correlation of 3% in agribusiness sector is relatively weak comparing to 36 % of

farming sectors. The overall job satisfaction is very high regardless of the

interviewees education level, age and working experience.

It is recommended that an increased and unbiased sample should be employed to

further help justify the correlation between income level and education level. With a


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larger sample, regression models could be built to precisely identify the correlation,

not only with education level but also with other variables that would influence

income level.

Key words: Education, Income, Job satisfaction

1. IntroductionLucrative and booming sectors of mining, finance and engineering industries are

luring more and more young people to the big cities. Young people of regional areas,

who otherwise would normally choose farming as a career option, now seek to obtain

higher education in finance or engineering to get employment in professions that have

higher wages. There is also evidence of farmers seeking higher education in areas

related to agriculture to increase their earning potential.

This has resulted in shortage of labour in regional areas, which has caused serious

concern to the public. Sustained labour supply is essential to the viability and

competitiveness of regional areas agricultural industries. Therefore, it is imperative

to develop a new strategy to attract more labour, especially young professionals, to

regional areas.

This study is conducted by Agribusiness Gippsland and only focuses on Gippsland.

The study seeks to assist the development of agriculture and highlights the demand of

young professionals in Gippsland. In order to attract more young professionals, it is

important to identify the determinants of income growth and pathways to job

satisfaction. The improved understanding of income and job satisfaction


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determinations will assist both potential and existing workers in Gippsland to better

understand their career path and career options.

In this study, an examination of the determinants of income and job satisfaction,

especially the correlation between education level and income in Gippsland is

conducted using data gathered in Warragul, Phillip Island and Inverloch. The survey

provided inAppendix A. details demographic and economic features of the

participants.

This paper is organised as follows. Section 2 contains a review of previous research

related to correlation between education level and income as well as job satisfaction.

Section 3 describes the individual variables, as well as provides sample descriptive

statistics. Estimation results and correlation analysis are reported in Section 4.

Limitations, conclusions and recommendations are presented in Section 7, 8 and 9

respectively.

2. Previous findingsLiterature on correlation between education level and income level is extensive and

the results have been proved by continuous findings. However, literature on this

correlation of agriculture sector is relatively scarce.

A study supported by ABERE examined the determination of total factor productivity

(TFP) in the Australian grains industry. The relationship between education level and

TFP could be treated as a supplementary indicator for correlation between educational

level and income level. It was found that investing in human capital through

education is likely to have a positive effect on TFP. The positive impact of education


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on productivity could be associated with a number of factors, including better

resource allocation, better business risk management strategies, or faster adoptions of

productivity enhancing innovations by more educated farmers (Shiji Zhao, 2009).

Other findings related to this correlation are demonstrated by major reports from the

US. A study by US Census Bureau suggested that, people with a higher level of

education made more money than those with less education. It is interesting to note

that this relationship between education and earnings potential has been known since

the 1970's, and has been consistently demonstrated by government surveys (the US

Census Bureau, 2002).

The correlation between education level and job satisfaction in general has been

proved by various institutions. However, different institutions showed different result

based on the surveys they used. Tom Smith from the University of Chicago suggested

that Job satisfaction was higher among those with more education (Simth, 2007). His

research was based on the General Social Surveys (GSSs), which led to an overall

view of American societys job satisfaction. On the contrary, Keith Bender and John

Heywood from the University of Wisconsin-Milwaukee focused on higher educated

group by using survey of doctorate recipients (SDR) and found out that additional

education resulted in lower job satisfaction (Keith A. Bender). The usual explanation

relied on expectations. The more educated had higher expectations for the pecuniary

and non-pecuniary returns from their jobs, and so were more easily disappointed and

dissatisfied (Clark.A.E, 1996).

Correlations between education level and income level/job satisfaction level of the

whole Australia area and of other fields/other countries have been widely identified


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The information collected in this study was collected during period from Jun 2010 to

Jun 2011. Ideally only individuals who are younger than 45 years old should be

included in our study. Except when analysing the correlation between age and other

factors, all the valid surveys were included. Surveys with incomplete information

were excluded from this analysis.

3.2.Independent variablesIndependent variables were grouped into three categories:

Individuals characteristics: Variables are individuals gender, age, workingexperience, business type, the role in work, formal education, On-going Education,

and sources of information

Income level: Variables are, currentl taxable income, off-farm income Job satisfaction :variables are primary reason for entering agribusiness sector,financial expectation, current job satisfaction level

All the variables included in Individuals characteristics are assumed to have the

potential to influence the income level and job satisfaction.

The income and job satisfaction can be highly related to individuals age and working

experience. As these two factors change over time, if this survey is conducted yearly,

a clear trending of income and job satisfaction at an individual level can be found out.

The other individuals characteristics stay the same in a short term, which may

determine individuals income level and job satisfaction in a long run. Taxable

income is employed in this survey instead ofnet income or gross income to

avoid the incomparability derived from different tax rate and interest payment. The


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percentage of off-farm income is included, as the income data is more reliable and

comparable if only on-farm income is counted for farmers and only off-farm income

is counted for agribusiness sector. The job satisfaction sector consists of motivation to

work in Gippsland, job satisfaction at the current moment and expectation for the

future, which could on the whole reflect individuals job satisfaction level.

Table A belowshows the means or percentage of the variables in each sector. The

survey data indicated the average age is 34.4 years old and the average working

experience is 10.5 years. Among them, 26.3% of surveyed people work in

agribusiness, 21.1% in agricultural R&D and 1.8% in agripolitics. For the farming

sector, dairy industry absorbs the most labour, which is 28.1% of the whole surveyed

sample. It is followed by mix farming, cropping/other farming and beef. In the sample,

45.6% are agribusiness employee, 21.1% are farm owner and 14% are agribusiness

manager. The operator/employee is the least group (3.5%). More than two thirds of

people in the sample are highly educated. Almost 80% of them have a bachelor

degree and the other 20% have either the master degree or a doctor degree. Based on

the total 57 surveys, internet, agriculture consultants, newspapers and magazines and

department of primary industries are the main channel of information.

About the income level, one third of the surveyed people earned $40, 000 to $60,000

in the current year and another one third earned $60, 000 to $80,000. 28% of the

people can earn more than $80,000 and only 5.3% of the peoples income is less than

$40,000. Of the total taxable income, average 57.1% is generated from off-farm. This

figure is high, mainly due to the containing of a majority of agribusiness workers in

this sample.


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Nearly half of the surveyed people got into agribusiness sector because of primary

career choice and 21.1% of them inherit the family business. The other 33.3%

switched into agriculture sector due to current employment or experience in previous

professional area. A majority of people have a positive prospect for the future and a

high job satisfaction is demonstrated by a scale of 8 out of 10.

Table A Summary Statistics

Mean/PercentageIndividuals characteristicsAge

34.4Working experience

10.5Business type

Agribusiness

Agricultural R&D

Agripolitics

Beef

CroppingDairy

Mixed farming

Other farming

26.3%

21.1%

1.8%

1.8%

3.5%28.1%

14.0%

3.5%

The role in work

Agribusiness Employee

Agribusiness Manager

Farm Manager

Farm Owner

Farm Partner

Operator/employee

45.6%

14.0%

8.8%

21.1%

7.0%

3.5%

Formal education

TAFE

University

University higher

Year10-12

19.3%

52.6%

15.8%

12.3%

Source of Information


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Newspapers and magazines

Radio/TV

Internet/OnlineDepartment of PrimaryIndustries

Agricultural consultants

Agribusiness retailers/salespeople

Other farmers

Other

15.80%

7.30%

20%

15.80%

17%

7.90%

13.90%

2.40%

Income level

Current taxable income

($0,$40,000)

($40,000,$60,000)

($60,000,$80,000)

($80,000,$100,000)($100,000,$120,000)($120,000,$140,000)

(>$160,000)

5.3%

33.3%33.3%

15.8%3.5%5.3%3.5%

Off-farm income 57.1%

Job satisfactionPrimary reason for entering agribusiness sector

Family business

Primary choiceOther choice

21.1%

45.6%33.3%

Financial expectation

Better off

Worse offSimilar positionDon't know

73.7%

1.8%17.5%7.0%

Current job satisfaction level 8

4. The Empirical Specification

Statistic analysis, such as correlation analysis and chart analysis, was employed in our report.

Instead of just analyzing individuals who were younger than 45, which was mention in

Section 3.1, all data collected were used. This is due to the smallness of the sample.


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4.1 Education difference and Income

Education level was grouped into four major categories: year 10-12, TAFE, university

degree and university higher degree. As Figure 1 presents, more than half of the

participants in the survey received university degree and each of the other three

groups consist of around 15% of the sample equally. For people who hold university

degree, 65% of them majored in either agriculture or science. Basically, agribusiness

sector receives higher degree than the farming sector in the sample. For instance, 26

people from agribusiness sector received university or university higher degree, while

13 people from farming group achieved the same education.

13%

15%

57%

15%

Figure 1:Education ClassificationSource: AGI & UoM Survey

Year10-12 TAFE University University Higher


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sector, the correlation between education level and income level is 36%. This positive

correlation figure indicates that as the level of education increases, the annual taxable

income raises as well. The evidence that only 36% was explained by the education

factor, however, indicates that other important factors may exist in explaining the

differentiations of income level. One possible factor is farm size, which is referred by

many interviewees as an essential determinant of annual income. Historically, farms

of bigger size usually generate higher income than the relative smaller size farms.

With regards to agribusiness sector, the correlation of 3% suggests no significant

difference in taxable income regarding to different education level.


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0

2

4

6

8

10

12

14Numbers

in Sample

Figure 3: Income Level of Different Education Backgroung inAgribusiness SectorSource:AGI & UoM Survey

University Higher

University

TAFE

Year10-12

0

1

2

3

4

5

6

7

8

Numbers insample

Figure 4: Income Level of Different Education Backgroung inFarming SectorSource:AGI & UoM Survey

University

Higher

University

TAFE

Year10-12


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4.2 Age, Work Experience and IncomeFactors besides education that could affect the income level, such as age and work

experience, were also studied during the survey analysis. In our sample, the average

age of a farmer is 35 years old whereas the average age of an employee in the

Agribusiness sector is 33.9 years old. An average work experience, in terms of years,

for a farmer and an employee of an agribusiness sector is 13.6 years and 8.5 years

respectively. The discrepancy in the work experience, of the two groups concerned,

can be explained by a simple reason. More than one-third of the employees of the

agribusiness sector have never worked in a different profession. Farmers, on the hand,

tend to try different professions before settling in on farming. More than 83% of the

farmers surveyed have tried an occupation other than farming.

According to Figure 5 and Figure 6, there is a negative correlation (-5%) between age

and income for the Agribusiness sector; whereas, there is a positive correlation (30%)

between work experience and income. When compared to the correlation of 3%,

between education and income, it could be concluded that work experience is the

most important factor that determines the income level of an employee in

Agribusiness sector.


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For the farmers, the correlation between age and income is 29 %; whereas,the

correlation between work experience and income is only 4%. As mentioned above,

the correlation between education and income is 36%, which is greater than the

correlation of age and income. A conclusion could be drawn based on the above

comparison that positive relationship does exist between education and income level

among farmers and is stronger than any other factors that have been taken into

account in the survey.

0

20,000

40,000

60,000

80,000

100,000

120,000

0 10 20 30 40 50 60

In

come

Age

Figure 5: Income and Age in Agribusiness SectorSource: AGI & UoM Survey

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

0 5 10 15 20 25 30 35

Income

Work Experience

Figure 6: Income and Work Experience in Agribusiness SectorSource:AGI & UoM Survey


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0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

0 10 20 30 40 50 60 70 80

Income

Age

Figure 7: Income and Age in FarmingSectorSource: AGI & UoM Survey

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

0 10 20 30 40 50 60

Income

Work Experience

Figure 8: Income and Age in Agribusiness SectorSource: AGI & UoM Survey


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4.3 Job Satisfaction

In the survey, a scale of 0 to 10, 0 being the lowest rating and 10 being the highest

rating, was used to measure the job satisfaction level among the participants. As

Figure 9 illustrates, 97% of people love their jobs. The average of job satisfaction

level is 8. It implies that there is no direct relationship between the job satisfaction

level and the income and education level. Generally speaking, people from

agribusiness sector are more educated than farmers. However, it is interesting to note

that both groups of people have higher level of job satisfaction irrespective of their

income and education level.

5. Limitations

During the process of data collecting and analysis, there are three groups of

limitations in the scope of our analysis, which aided in the systematic and accurate

analysis. The first group of limitations were brought by the design of the survey.

97%

3%

Figure 9: Job Satisfaction

Source: AGI & UoM Survey

High Low


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This survey is only designed and conducted for the current period. Though most of

the individual characteristics remain unchanged, individuals income can fluctuate

every year. The fluctuation may result from the unexpected market condition,

changeable weather, strategic expenditure or unpredicted expense, which can distort

the influence carried out by the main examined factors.

The measurement of income also has certain defects. In this survey, taxable income

was used as the indicator of income level. But taxable income would not be reliable

enough to reflect the well-being of an individual. Take a farm owner for an example,

if he/she has just invested in new equipment, his investment can significantly reduce

the current years taxable income due to the offset of expense. It is pointed out by the

farmers interviewed that it is more crucial for the farmers to accumulate their assets

on the farms rather than focus on short-term profits, like current years taxable income.

This statement further proves that taxable income is not reliable and comparable,

especially in the farming sector. Another issue with the survey is that the on-farm

income of farmers and off-farm income of agribusiness is unable to be computed as

the interval is adopted to present the income level. Thus the total taxable income is

implied, in our analysis, as the effective income.

Limitations also rose from the data collecting process. First, there is a limited access

to the target population during the two weeks survey conducting. A small sample of

57 interviewees was set up and only 23 farmers were approached. Hence simple

correlation analysis is applied here to seek the linkage between incomes and

individual variables for the empirical specification, rather than a more scientific

analysis such as the panel data regression model.


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correlation between income and work experience. Other factors may explain the rest

of the correlations for income difference. For agribusiness sector, there is not

significant correlation between education level and income level. The relatively

strong positive relation only exists between work experience and income level.

For the job satisfaction, the average job satisfaction is scale of 8 out of 10 for the total

sample. It implies that there is no direct relationship of job satisfaction with income

level and education in agriculture business.

7.

Recommendations

It is suggested that the survey should be conducted every year within a certain period

to exclude the influence of unrelated factors. These also raise our concerns that some

factors, such as farm size, land use intensity, business type, number of partners and

management costs and the stage of a farm should also be considered in the survey. So

that curtain fluctuation of income caused by these essential factors could be excluded

from the analysis.

As taxable income could not be a reliable and comparable income indicator,

especially for the farming sector, the assets of farmers should also be taken into

account in the survey form.

Furthermore, the limitation of the sample suggests increasing sample size, especially

in the number of farmers. Based on a larger sample, a regression model can be run to

estimate a more related relationship between education level and income level.

Once the sample is increased, it is also recommended that more efficient income

measurements could be adopted. For example, exact taxable income is required in the

survey to solve the calculation problem of on-farm/off-farm income. Otherwise,


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calculation of deducting the on-farm/off-farm income is suggested to be performed on

the spot of interview. The asset investment amounts of farm owner should be also

taken into account to avoid disturbance of such factor.


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Appendix A

AGI & UoM Survey

LEARN MORE = EARN MORE?

University of Melbourne/Agribusiness Gippsland survey, Gippsland June/July 2011

Section A. Personal Details

1. Gender:2. Age:3. How long have you worked in farming/agribusiness?4. Are you primarily involved in:

Student

Other farming

(horticulture/forestry/lifestyle

farm)


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5. What is your role in your work?Owner

Manager

Farm Partner

Sharefarmer/Leasee

Operator/employee

Agribusiness owner

Agribusiness manager

Agribusiness employee (includes

R&D)

6. Education Level1) What is your highest level of formal education

Apprenticeship

- III

TAFE Diploma / Advanced Diploma

2) What is the title of your highest qualification?_____________________________________

3) What was the subject of your post-secondary education?


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Specify) __________________________

4) What is been your ongoing education? How many days/half-days have your spent in the last year at:

_______

_______

_______

How many days/half-days have your spent in the past three years at:Short course(s) at an educational institution _______

_______

_______

7. Rank these in order of importance as sources of informationNewspapers/Journals/Magazine

Radio/TV

Internet/Online

Department of Primary Industries

Agricultural consultants

Agribusiness retailers /salespeople

Other famers

Other (Specify) _______

____________________________________________

Section B. Income Level

1. Estimate your current taxable income:


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- $60,000

- $80,000

- $100,000

- $120,000

- $140,000

- $160,000

- $180,000

>$160,000

2. How much of this income is generated off-farm?_____________________________________

Section C. Outlook

1. Did you enter agribusiness/farming because it was:A family business

A primary career choice

A career choice after employment or experience in another

job/business/profession (Specify) ____________________________________

2. Financially, in five years time do you expect to be:Better off

Worse off

In a similar position to now

Dont know


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3. Do you expect to still be in this career in five years time?Yes

No

Dont know

Can you specify a reason?

_____________________________________

4. On a scale of 0-10 are you happy / satisfied with your career choice in farming?(0 very unhappy, 10 delighted)

1 2 3 4 5 6 7 8 9 10

5. All things being equal, do you think you personally could have a moresatisfying life style outside farming?

Yes

No

Who knows?

THANKYOU


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Bibliography

Clark.A.E, O. (1996). Satisfaction and comparison income. Journal of Public Economics , 359-381.

Keith A. Bender, J. S.Job satisfaction of the highly educated: the role of gender, academic tenure and

comparison income. Department of economics and graduate progrma in human resources.

Shiji Zhao, E. Y. (2009).Exploring determinants of total factor productivity in Australias broadacre

gain farms. ABARE.

Simth, T. W. (2007).Job satisfaction in America: trends and socio-demongraphic correlates. Chicago:

the university of Chicago.

the US Census Bureau. (2002).Education And Income. Retrieved from Education of Online Search:

http://www.education-online-search.com/articles/special_topics/education_and_income

age and income

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