THE ECONOMICS OF HAPPINESS: ASSESSSING WELL-BEING THROUGH THE RELATIONSHIP BETWEEN INCOME AND HAPPINESS A THESIS Presented to The Faculty of the Department of Economics and Business The Colorado College In Partial Fulfillment of the Requirements for the Degree Bachelor of Arts By Keegan Flaherty Dollinger May 2011
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THE ECONOMICS OF HAPPINESS: ASSESSSING WELL-BEING THROUGH THE
RELATIONSHIP BETWEEN INCOME AND HAPPINESS
A THESIS
Presented to
The Faculty of the Department of Economics and Business
The Colorado College
In Partial Fulfillment of the Requirements for the Degree
Bachelor of Arts
By
Keegan Flaherty Dollinger
May 2011
THE ECONOMICS OF HAPPINESS: ASSESSSING WELL-BEING THROUGH THE
RELATIONSHIP BETWEEN INCOME AND HAPPINESS
Keegan Dollinger
May 2011
Economics and Business
Abstract
The investigation of the relationship between income and happiness can provide
important insights into human’s material aspirations. By redefining the application of the
term utility, economics can be used to understand how money affects our happiness.
Today and in past years at a given point in time those with higher incomes are indeed
happier than those with lower incomes. However, raising the incomes of a nation does
not make that nation any happier. These conclusions are suggested by data on reported
happiness and income collected in the United States over the past forty years. The
paradox that takes place in this relationship has important public policy implications and
raises doubts on the primary economic goal of growth in GDP. This thesis hopes to
stimulate a debate that questions some of the basic tenets of economic theory that regard
the use of GDP as a measure of welfare and the simplistic and limiting role that is applied
income1_6 = $75-109,999; income1_2 = $110,000 and above. The variable of health =
respondents general health, educ = highest level of education respondent achieved.
A heteroskedasticity test of the regression shows a chi-square of 9.71. The
problem of heteroskedasticity was fixed by implementing robust standard error.
TABLE 6.2
REGRESSION WITH HAPPINESS AS DEPENDENT VARIABLE
Linear Regression
Number of observations 1994
F ( 9, 1984) 24.59
Prob > F 0.0000
R-Squared 0.1029
Root MSE 1.12248
Happiness Coefficient t-statistic
Less than $10,000 -.518 -0.380
$10-19,999 -.249 -2.19
$20-29,999 -.248 -2.17
$30-49,999 -.241 -2.43
$50-74,999 .058 0.57
$75-109,999 .247 1.36
$110 and above .284 2.44
health -.374 -10.57
education -.00098 -0.11
_constant 3.230 19.62
40
The real GDP per capita was used as a measure to represent average income for
the years of 1972 to 2008. The average mean happiness was accumulated through the
GSS by finding the average mean happiness for each year using the same scale used in
the first regression of very happy = 4, pretty happy = 2 and not too happy = 0. The
following chart presents the real GDP per capita, average man happiness for each of the
following years:
TABLE 6.3
HAPPINESS AND REAL GDP PER CAPITA, UNITED STATES, 1972 -2008
Year
Real GDP per Capita
(2005 dollars)
Average Mean
Happiness
First Difference (2005
Dollars
1972 22,104 2.275
1973 23,155 2.456 1051
1974 22,827 2.496 -328
1975 22,563 2.396 -264
1976 23,536 2.431 973
1977 24,376 2.458 973
1978 25,462 2.496 1086
1980 25,621 2.412 159
1982 25,259 2.321 -362
1983 26,163 2.367 904
1984 27,798 2.436 1635
1985 28,690 2.345 892
1986 29,416 2.418 726
1987 30,139 2.313 723
1988 31,101 2.493 962
1989 31,899 2.459 798
1990 32,135 2.489 236
1991 31,657 2.402 -478
1993 32,713 2.410 1056
1994 33,618 2.331 905
1996 34,906 2.366 1288
1998 37,112 2.393 2206
2000 39,187 2.423 2075
2002 40,050 2.358 863
2004 41,137 2.358 1087
2006 42,725 2.355 1588
2008 43,250 2.281 525
Source for real GDP per capita: ERS International Macroeconomic Dataset
41
A simple time-regression using average mean happiness as the dependent
variable, the independent variables were real GDP per capita and the year for each
average mean happiness level. The equation for the time-series regression appears as
follows:
H = gdppercapita + year
The following chart displays the results of the regression.
TABLE 6.4
REGRESSION OF HAPPINESS AND REAL GDP PER CAPITA FROM, UNITED
STATES, 1972 – 2008
Time-series regression
Number of observations 27
F ( 9, 1984) 2.66
Prob > F 0.0908
R-Squared 0.1812
Root MSE 0.06055
Average mean happiness coefficient t-statistic
real GDP per capita 0.0000191 1.31
year 0.00884 -1.56
constant 17.250 1.71
Due to a very low R-square, very low coefficients and lack of significance a first
difference variable was incorporated into the regression. The first difference may
provide a better representation of the significance between the difference in real GDP per
capita and the happiness levels from year to year. The following chart displays the results
for the first difference regression:
42
TABLE 6.5
REGRESSION OF HAPPINESS AND REAL GDP PER CAPITA WITH FIRST
DIFFERENCE
Time-series regression
Number of observations 26
F ( 9, 1984) 4.22
Prob > F 0.0275
R-Squared 0.2048
Root MSE 0.05406
Average mean happiness coefficient t-statistic
real GDP per capita -5.43e-06 -2.90
first difference 0.000023 1.31
constant 2.552 47.03
43
CHAPTER VII
RESULTS
The data shown in Table 1 between income and happiness reveals the positive
relationship between the two variables following the predictions made in the hypothesis.
The lowest income bracket has the lowest average mean happiness and the income
increases consecutively to a higher income bracket the average mean happiness increases.
This does not establish causality or significance but offers an intriguing view of the two
variables. The results of the regression do not follow the hypothesis in regards to the
theory of diminishing marginal utility.
The results for the regression conducted using happiness as a dependent variable
and the income, health and education levels as independent variables are shown in Table
2. The coefficients and t-statistics of the regression indicate that being a respondent in
the four lowest income brackets or having a household income lower than $50,000 has a
negative relationship with happiness. The lowest income bracket ($10,000 or less) has
the highest significance of all the income dummy variables, -3.80, and the highest
coefficient of -.518. This indicates that respondents with the lowest income bracket have
the highest dependency upon income to achieve happiness.
The next three income brackets ($10,000 to 49,000) have very similar coefficients
and t-statistics with one another. All three variables are statistically significant and have
negative coefficients. The coefficients indicate that the respondents for these three
income levels share similar characteristics in relation to income and happiness.
44
Respondents in each of these income brackets are not as unhappy as the respondents from
the lowest income bracket but still maintain a negative relationship between the
dependent variable of happiness and the independent variable of income. The following
income level ($50,000 to 74,999) lacks significance and has the lowest coefficient of all
the independent variables besides education. The income level of $75,000 to 109,999
also lacks significance but still falls just below achieving significance and has a much
higher coefficient. The highest income level ($110,000 and above) has a relatively high
coefficient and a high significance with a t-statistic of 2.84.
The variables of health and education were included to provide context for the
dependent variable, happiness. Health, as expected achieved a high coefficient of -.374
and was very significant with a t-statistic of -10.57. The high coefficient and high t-
statistic are to be expected; the reason that they are negative is due to the input of data.
The rating system ranked the highest level of health at 1 with the lowest at 4. The
variable of education had a very low coefficient and lacked significance with a t-statistic
of -.11. Education was expected to have a positive relationship with happiness. The
results of the regression suggest that there is not a relationship between education and
happiness.
The reason that the lower income brackets were so dependent upon income for
happiness can be explained by the fact that these income brackets cannot achieve enough
money to satisfy their financial security. The income bracket of $110,000 and above is
statistically significant reinforcing the conclusion that higher income levels produce
higher levels of happiness. The highest income bracket also had a higher coefficient than
the preceding income bracket rejecting the predicted hypothesis. The results do not
45
follow the law of diminishing marginal utility that expected the coefficients of the two
brackets to show no significant difference.
There are certain problems due to the subjective nature of the happiness level as
well as the relatively small sample pool. In the lowest income bracket there are
respondents that describe themselves as very happy and respondents in the highest
income bracket that describe themselves as not too happy. Certain individuals do no nee
high levels of income to be happy. But the data shows that for most people a higher
income leads to higher level of happiness.
Secondly, data was accumulated from the GSS for the years of 1972 to 2008 for
the variable happiness and an average mean happiness was calculated for each year. A
more accurate depiction of happiness over time would have required that the same
respondents were surveyed year after year. Unfortunately, the GSS does not survey the
same respondents for each year requiring an average mean to be calculated for each year.
To account for income, instead of using the incomes of each respondent, data was
accumulated for each year for the real Gross Domestic Product per capita. The real GDP
is a more accurate measure of the average income of a United State‘s citizen than
averaging the income of each of the respondents.
The data presented in Table 3 shows a lack of a relationship between the income
(real GDP per capita) and the happiness levels. The real GDP per capita nearly doubles
from the year 1972 to 2008 but the average mean happiness level merely increases from
2.275 to 2.281. A discrepancy is shown between the clear relationship of income and
happiness during 2006 and a lack of any relationship across the time span of 1972 to
2008.
46
A time series regression was conducted on the real GDP per capita and the
average mean happiness. The results of the regression are displayed in Table 5. The real
GDP per capita has a very low coefficient, which is to be expected, while the t-statistic
lacks significance. From 1972 to 2008 there is no statistically significant relationship
between income and happiness rejecting the hypothesis predicted. To try and account for
differences between years a regression was conducted including the first difference for
each year of real GDP per capita.
The data displayed in Table 5 reflects the results of running the regression for
each year after applying a first difference. The first difference was used to determine if
there was a significant relationship between the changes in the real GDP per capita for
each year. Surprisingly, there was a significant relationship with a t-statistic of -2.90.
Although it is significant, the fact that the t-statistic is negative rejects the null hypothesis
suggesting the opposite of what was expected.
In general, the change in the real GDP per capita should result in a positive t-
statistic indicating that with a one unit increase in the change in the real GDP per capita
would mean a unit increase in the average mean happiness level should increase by one
unit as well. The coefficient was also practically negligible, -5.43e-06. Although the
coefficient is significant the rate at which it is significant is so small that the significance
loses merit. By multiplying the real GDP per capita coefficient with real GDP per capita
to provide the incremental difference in the happiness level we can achieve a better idea
of scope:
-5.43e-06 x $22,104 = 0.120
47
Multiplying the coefficient of the income variable with the real GDP per capita of 1972
results in a small number but is significant to the average mean happiness level. This is
explained not by the actual significance in the change in the happiness level but by the
constant change of the real GDP per capita. The significance of the t-statistic is
determined significant but not clinical in that the significance does not reflect the
relationship between income and happiness accurately.
The discrepancy found between happiness over time and a point in time can be
explained by a number of different reasons. Happiness is affected by many different
variables; the regression only accounts for one of those variables, income. A more
detailed approach that incorporates variables and understands year-by-year fluctuations is
necessary to understand why the average mean happiness is lower in some years than in
others. An argument can be made to justify the lack of significance against the measure
of happiness. The measure of happiness proved to be valid for the regression run in
2006; why would the measure not be valid over the almost forty years time period? This
may be due to changed preferences of the respondents. A deeper analysis of how happy
people consider themselves each year is necessary to account for the subjective nature of
happiness. The culture and the people surveyed changed dramatically from 1972 to
2008 and could explain fluctuations of the happiness levels.
Although there are many different variables that need to be accounted for using
happiness as a dependent variable, the data and the results are still useful and compelling.
The comparison between average mean happiness and real GDP per capita presents a
rather stark difference to the comparison between income and happiness just in 2006.
This discrepancy is consistent with relevant time-series economic research done on time-
48
series and cross-sectional data from Europe and countries in Asia. In almost every study
done there is a significant positive bivariate relationship between happiness and income.
The question that remains is as GDP grows substantially in countries why is the level of
happiness not growing?
One reason that has achieved popularity is the idea of relative income. People
judge their income relative to the incomes of people around them. This would explain
the phenomenon that as GDP is doubling; people‘s levels of happiness are not. No matter
how much money you receive it is dependent on how much money people receive around
you. As everyone‘s income levels grew there is no change in their level of happiness
even though they have more wealth and material things. This concept has serious policy
implications; do we really need to continue to make policies that attempt to accelerate our
growth? Is an increase in GDP per capita an actual measure of well-being or strictly a
measure of the amount of good/services produced within a country? The next chapter
will provide a more detailed explanation behind the mystery of the paradox of happiness.
49
CHAPTER VIII
CONCLUSION
―We must acquire a life style which has at its goal maximum freedom and happiness for the
individual, not a maximum Gross National Product.‖
Paul Erlich
By analyzing human well-being through the relationship between income and
happiness we can refine our judgment of social well-being while also opening the debate
to basic and accepted principles of economics. Economic theory is founded on the
objective of economic growth, efficient allocation and fair distribution of resources; even
though this market-based neo-classical ideology is effective it is limited by certain
assumptions. Economic growth serves as an ideology that promises financial security
and a higher level of well-being. The body of research reviewed here examines the
successfulness of economic growth but also raises serious questions with respect to the
use of GDP as a measure of our welfare, and the limiting and inhuman role assigned to
individuals in economic models. Recognizing certain shortcomings in economic theory is
critical for the United States and the way we utilize the study of economics.
Examining the relationship between respondents‘ measure of their own happiness
and their household income has established that those with higher incomes are for the
most part happier. Then by analyzing respondents‘ measures of their own happiness over
the past 40 years against the United States real GDP per capita we can observe that higher
income is not tied to greater happiness at a macro-level. Economists and government
officials both use economics to construct policies that increase our GDP on the basis that
50
this will increase our national well-being but a rigid application of this economic theory
may be erroneous.
A strong relationship between income and happiness is to be expected. Results
from the regression conducted on data from 2006 reveal that individuals with higher
incomes are generally happier. Although many do not like to believe that money buys
happiness, the reasons for this to occur may be rather simple. Earning enough money is
necessary to provide basic human needs like food and shelter. Beyond our basic needs
money can provide material things that increase our happiness. Earning a higher income
can also provide people with a feeling of status and superiority over people around them.
Interesting, the law of diminishing marginal utility of income was not evident in the
results. One hypothesis to explain this absence is that once people can satisfy their basic
human needs and free of the stress and discomfort of struggling to survive they are free to
be happy. Therefore the correlation we observe between income and happiness may be
driven by the impact on personal well-being of reaching a threshold survival-enabling
income, which allows individuals to experience happiness regardless of the incremental
monetary value they obtain from income past this threshold. Given this hypothesis we
would expect money, or income, past a certain point, is in fact immaterial to happiness.
The strong relationship found between happiness and income in 2006 is
incongruent with the non-existent relationship found between the two variables between
the years 1972 and 2008. The results found that there was a statistically significant
relationship between income and happiness in 2006 and a lack of significance for the
years 1972 to 2008. The apparent paradox that occurs between the two variables at a
point in time and the two variables over time is important and disconcerting. The results
51
suggest that our tremendous growth in GDP does not in fact improve our subjective well-
being. These findings are congruent with findings in past studies that analyze different
indicators for income and happiness throughout the United States, across Europe, and
into Asia.
Determining exactly why the paradox occurs between happiness and income at a
point in time and over time is difficult. There are many theories attempting to explain the
puzzling anomaly. A prominent and accepted theory of relative income suggests that
utility depends on income relative to some reference or comparison income of individuals
or households in similar economic circumstances as your own. Samuel Johnson was
accurate when he declared, ―Life is a progress from want to want, not from enjoyment to
enjoyment‖.1 This would explain that as households receive higher incomes they are not
becoming happier.
The theory of relative income suggests that because of our human nature as we
become richer we do not become happier. The theory encourages questions about both
economic science and economic policy for blind obeisance to aggregate material
―progress,‖ and for neglect of its costly side effects. Disillusioned critics indict economic
growth making the claim that it distorts national priorities, worsens the distribution of
income, and irreparably damages the environment.2 A critical assumption of economic
growth disregarding its negative implications stated above is an increase in economic
well-being. Economic growth is charged by the notion that it sustains our market,
1 Easterlin, "Income and happiness: Towards a unified theory," The Economic Journal (2001):
465-484.
2 William D. Nordhaus and James Tobin,‖Is Growth Obsolete?‖ The Measurement of Economic
and Social Performance (1973): 510-532.
52
increases the wealth, and ultimately promotes our well-being. But what if this
assumption is wrong and that economic growth does not truly have the desired effects?
Ideally economic growth provides more money, more jobs and spurs
technological advancement. The neoclassical model institutes measures that advance
technological knowledge and measures that increase the share of potential output devoted
to accumulation of physical or human capital. Capitalism is essential to advancing but
there needs to be a balance between the capitalistic drive to advance in science and
technology, and using sustainable practices and accounting for our social well-being.
This thesis intends to raise questions that examine the effect of economic growth on our
happiness as well as the impact it has on our environment. The idea is to put value on
aspects of our economy that do not have price tags but are invaluable like open space and
air, our oceans and forests.
How can we account for our natural resources and well-being while sustaining our
economic growth? Governments should strive to incorporate more personal and socially
conscientious measures within their policies. Economic growth is not the only avenue to
increased well-being; we have to understand how to increase our well-being through
sustainable economic development.
Along with criticisms of economic growth as the sole objective, economists have
raised doubts to the accuracy of Gross Domestic Product as measure of economic
welfare. An obvious shortcoming is that it is an index of production rather than
consumption, while the goal of economic activity is consumption. Nordhaus and Tobin
constructed an alternative to GDP and propose a ―measure of economic welfare‖ (MEW)
in an attempt to account for certain discrepancies that occur between the GDP and
53
economic welfare.3 Their adjustments to GDP fall into three major categories:
reclassification of GDP expenditures as consumption, investment, and intermediate
products; imputation for the services of consumer capital, for leisure, and for the product
of household work; correction for some of the disadvantages of urbanization. Although
the MEW still does not provide a measure of happiness, it is arguably a better measure
than the GDP or GNP.
The study of well-being through the lens of economics hopes to make progress
not only through the application of utility but in the limiting and simple form of that
economics applies to individuals in models of behavior. Departing from the narrow
application of utility instituted during the Ordinalist revolution, utility can offer more
than insights into the preferences of a consumer and can allow us to examine the relation
of a human being to a good or service. The historical use of utility in standard economics
did not allow for the measurement of well-being or happiness because of its subjective
nature. The results discovered in this thesis demonstrate how the narrow application of
utility can be expanded and provide useful insights into the interaction between the study
of economics and human nature. The expansive structure of economics allows
economists to break away from the traditional economic studies and use empirical
evidence to measure more subjective matters like happiness.
Beyond the term utility, the real limiting agent of economics is found in an
excessively restrictive portrait of human nature and human motivation; namely in
individualistic anthropology, which states that behind economic action there is an
individual who has no other determination than that of homo oeconomicus (the
3 Ibid.
54
assumption that an individual will only pursue his selfish economic gain).4 More
specifically, the ineptitude of conventional economic theory to adequately address the
happiness issue is to be attributed to a philosophical stance for which individuals are
isolated in an atomistic theoretical construct.5 As a result, models are generated where
what is analyzed is essentially selfish and egotistical behavior: the understanding of
mental states of the other is simply not considered at all, nor are the sympathy and
empathy given a proper role in the explanation of economic decisions in interactive
contexts.
Emma Rothschild reports from the 1770s regarding how ―economic life was
intertwined, in these turbulent times, with the life of politics and the life of the mind.
Economic thought was intertwined with political, philosophical, and religious reflection.
The life of cold and rational reflection was intertwined with the life of sentiment and
imagination.‖6 Perhaps we need to refer back to these times and understand that
economics is more effective and accurate when it is applied using tools and knowledge
gained from other disciplines.
In applying economics to the investigation of happiness, economics can utilize a
more holistic approach that incorporates human nature into models of human behavior.
This radical notion is simple and logical—treat humans like humans. Incorporating
findings of the human psyche from psychology, sociology, and politics into economic
models of behavior creates a more valid and realistic model. Studying happiness through
4 Luigino Bruni and Pier Luigi Porta, Economics and happiness: framing the analysis, (Oxford;
New York: Oxford University Press, 2005), 1-366.
5 Bruni and Porta, 303.
6 Emma Rothschild, Economic Sentiments: Adam Smith, Condorcet, and the Enlightenment,
(Cambridge: Havard University Press, 2002), 1-366.
55
economics should allow governments to incorporate a more mindful approach to
constructing public policy. All academics strive to progress and make advancements in
their particular study. Important in progression is challenging fundamental accepted
truths. The research here strives to challenge the accepted notion that money buys
happiness and that economic growth promotes the betterment of society. The ultimate
goal is to prompt a debate that questions the anthropological foundations of economic
discourse and to make the inquiry into not simply the wealth of nations but rather the
happiness of nations.
56
CHAPTER IX
APPENDIX
Survey:
Questions from the General Social Survey for each variable used in the regression:
HAPPY: Categorical (Single) Taken all together, how would you say things are these days--would you say that you are very happy, {response to happytxt1}, or not too happy? Categories: {very_happy} Very happy {pretty_happy} {response to happytxt2} {not_too_happy} Not too happy {dontknow} DON'T KNOW {refused} REFUSED
Variable income06 : TOTAL FAMILY INCOME
Literal Question
In which of these groups did your total family income, from all sources, fall last year –
2005 – before taxes, that is. Just tell me the letter.
Categories: {a} A. UNDER $1,000 {b} B. $1,000 to 2,999 {c} C. $3,000 to 3,999 {d} D. $4,000 to 4,999 {e} E. $5,000 to 5,999 {f} F. $6,000 to 6,999 {g} G. $7,000 to 7,999 {h} H. $8,000 to 9,999 {i} I. $10,000 to 12,499 {j} J. $12,500 to 14,999 {k} K. $15,000 to 17,499 {l} L. $17,500 to 19,999 {m} M. $20,000 to 22,499 {n} N. $22,500 to 24,999 {o} O. $25,000 to 29,999 {p} P. $30,000 to 34,999 {q} Q. $35,000 to 39,999 {r} R. $40,000 to 49,999 {s} S. $50,000 to 59,999 {t} T. $60,000 to 74,999
57
{u} U. $75,000 to $89,999 {v} V. $90,000 to $109,999 {w} W. $110,000 to $129,999 {x} X. $130,000 to $149,999 {y} Y. $150,000 or over {dontknow} DON'T KNOW {refused} REFUSED HEALTH: Categorical (Single) Would you say your own health, in general, is excellent, good, fair, or poor? Categories: {excellent} Excellent {good} Good {fair} Fair {poor} Poor {dontknow} DON'T KNOW {refused} REFUSED Variable educ : HIGHEST YEAR OF SCHOOL COMPLETED
Literal Question
Now just a few more background questions.
15 - 22. ASK ALL PARTS OF QUESTION ABOUT RESPONDENT BEFORE GOING ON TO ASK
ABOUT R'S FATHER; AND THEN R'S MOTHER; THEN R'S SPOUSE, IF R IS CURRENTLY
MARRIED.
A. What is the highest grade in elementary school or high school that (you/your father/ your mother/your
[husband/wife]) finished and got credit for?
CODE EXACT GRADE.
B. IF FINISHED 9th-12th GRADE OR DK*:
Did (you/he/she) ever get a high school diploma or a GED certificate? [SEE D BELOW.]
C. Did (you/he/she) complete one or more years of college for credit--not including schooling such as
business college, technical or vocational school?
IF YES: How many years did (you/he/she) complete?
58
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