Introduction
A goal underlying economic policy is to positively
impact the quality of life. This paper analyzes which variables can predict
quality of life, and which quality of life aspects are more easily predicted.
This information is largely relevant to policymakers across the world, since
knowing quality of life predictors could be very useful in crafting economic
policy. It is also relevant to concerned and active voters interested in
choosing the policymakers most qualified for the situation at hand. For
instance, if statistics indicate a nation has a relatively low life expectancy,
policymakers may wish to orient policy in a manner attempting to manipulate the
variables that indicate potential influence on life expectancy.
The Theoretical
Models
I used three possible measures for
Quality of Life: Life Expectancy, Gross Domestic Product per Capita
(abbreviated GDP/Capita), and Gross Tertiary Enrollment Ratio (GTER). Each
measure is used as a dependent variable in its own multiple regression model
with three to five explanatory variables and 5% significance. Data for all
models are from the year 2010 and come from DataWorldBank
online. Because of the many missing data points, a few compromises were
necessary in the analysis. Some variables were omitted due to insufficient
data, and many countries were left out of the analysis because they lacked data
for one or more explanatory variables. A list of countries may be found in the
appendix, 2. The models, accompanying economic theory, and descriptive data
statistics are discussed below.
Life Expectancy
Model
In the first model, the dependent variable is life expectancy
at birth (denoted LE). In evaluating the relative prosperity of a nation, one would
likely take into account the country’s life expectancy. Policymakers striving
for prosperity may be interested in which variables, if any, can predict life
expectancy with statistical significance. A likely follow-up question is which
of the significant predictors, if any, have a causal relationship with life
expectancy. The answers to these questions would be useful in deciding which
variables to manipulate in economic policy. Chen and Ching of built a similar
multiple regression model to that described above. In their analysis, Life
Expectancy was predicted by Gross National Product Per Capita, Annual
Population Growth, Fertility Rate, Aids Prevalence, Tuberculosis Prevalence,
School Enrollment Rate, Percent of Population with Clean Water Access, and
Annual Rate of Deforestation. While our model used slightly different
explanatory variables, Chin and Cheng’s analysis used data from the year 2000
rather than 2010.
The first variable used to predict
life expectancy is public health expenditures as a percentage of Gross Domestic
Product (denoted HEALTH$). The chosen indicator is a proportion rather than the
value of gross expenditures, which adjusts for differences in scope of
countries’ economies. With increased federal spending on health, there are
likely to be resultant increases in: access to medical supplies, access to
medical facilities, and number of physicians – all of which should positively
impact life expectancy.
The second variable used to predict
life expectancy is percent of total population living in urban areas (denoted
%URBAN). Chen and Cheng describe cities in relation to life expectancy as,
“centers of medicine and modern advance, but also quarters of overpopulation
and overcrowding.”(Chen, Ching, 5) Since there is typically greater access to
medical facilities in cities, it makes intuitive sense that a larger proportion
of a population living in urban areas would result in a larger proportion with
access to medical care and thus a higher life expectancy.
The third explanatory variable in
the life expectancy regression model is percentage of urban population with
access to clean water (denoted %CLEAN). Few will argue that environmental
factors will influence life expectancy in a country. In Chen and Ching’s model,
they address this by saying, “Environmental soundness measured in the forms of
clean drinking water…[it] is a reflection on the salutary conditions of the
country.”(Chen, Ching, 7) Because a deprivation of clean water access is likely
to decrease life expectancy, a positive relationship and thus a positive
coefficient is expected for this variable.
The fourth and final explanatory
variable in this model is value of international trade as a percentage of Gross
Domestic Product (denoted TRADE). Unlike Net Exports, this indicator is
calculated by [(X+M)/GDP]. Similar to with public health expenditures, the
international trade indicator is a proportion, which adjusts for the size
differences in economies. Countries that isolate themselves from international
trade could very well be missing valuable medical supplies available abroad. It
makes sense then that an increased value of international trade could increase
access to medical supplies and thus increase life expectancy – implying a
positive relationship and positive coefficient value.
Descriptive
Statistics
Descriptive Statistics
|
Dependent Variable
|
Variable 1
|
Variable 2
|
Variable 3
|
Variable 4
|
Life Expectancy
|
HEALTH$
|
URBAN%
|
%CLEAN
|
TRADE
|
|
Mean
|
70.278
|
7.0799
|
57.123
|
95.212
|
24.126
|
Standard Deviation
|
9.486
|
2.597
|
22.456
|
7.209
|
24.447
|
Observations
|
147
|
147
|
147
|
147
|
147
|
Analysis Results
= 2.819 + .045(HEALTH$) + .175(URBAN%)* +
.599(%CLEAN)* + .028(TRADE)
(.214) (.027) (.084) (.028)
F=46.20, p<.05; adj-R2=.555;
dfbetween=4; observations=146
The regression results indicate that
the coefficient parameter for public health expenditures is insignificant in
predicting life expectancy.
As expected, the coefficient value
for percentage of urban population living in urban areas was positive and
significant. The model suggests that the marginal effect of this is explanatory
variable is as follows: each additional percentage of a population living in an
urban area increases life expectancy by .175 years. Chen and Ching’s
description of cities as “medical centers” is likely the primary explanation
for the positive relationship.
The coefficient value for percentage
of urban population with clean water access was also positive and significant.
The marginal effect of this variable is: each additional percentage point of a
nation’s urban population with clean water access adds .599 years to life
expectancy. That increased access to clean water will allow for a longer life
is relatively straightforward and needs little explanation.
Similar to public health
expenditures, the model suggests international trade is not a significant
predictor of life expectancy.
Gross Domestic
Product per Capita Model
The first variable used to predict
GDP/Capita is international trade value as a percentage of GDP (TRADE). Just as in the last model, this indicator sums
exports and imports and takes the value as a proportion of GDP. A regression
analysis by Bidlingermaier examines the relationship between income and
international trade. He lists the following description of the gains from trade
theory:
“These gains stem
from specialization in production due to international trade. If countries
specialize according to comparative advantage enhanced resource allocation can
be achieved…Consequently, the welfare (income) of all trading nations is
improved.” (Bidlingmaier, 1)
It
makes intuitive sense that both exports and imports would positively impact
GDP/Capita. With increased exports will come increased sales for domestic
producers. Furthermore, increased imports imply cheaper production inputs for
domestic producers. Because of these two factors, a positive coefficient value
is expected for this variable.
The second explanatory variable used in
the GDP/Capita model is population density (denoted POPDENSE). Chen and Ching suggest that
the relevance of population growth comes from the idea that a larger population
implies the same resources must be spread across more people, thus decreasing
prosperity. From this, an inverse relationship and a negative coefficient value
is expected for population density.
The third variable used to predict
GDP/Capita is net immigration (denoted NETMIG). This indicator is calculated by
subtracting the total number of emigrants from the number of immigrants for a
five-year interval. Borjas’s labor market model helped build a prediction for
the coefficient sign for this variable.
“When immigrants enter the country, the supply of labour expands… and the
market wage falls to W1 (all other things being equal). As a result,
native workers earn a lower wage. Total employment increases. The economy’s
total output also expands. Total output is represented by the area under the
marginal product curve and to the left of the supply curve. This area is larger
following the increase in labour supply. The expansion in output generates an
increase in income for the owners of capital in local firms (and, of course,
income for immigrants). Under certain conditions the loss in income for native
workers is more than offset by the increase in income accruing to the owners of
capital. The result is a net increase in national income.” (Moody, 11)
From
this information, a positive coefficient value is expected for this independent
variable.
The fourth explanatory variable used
in this model is average insolvency time (denoted INSOLVENCY). This indicator
refers to the average number of years passed between initial court filing and
resolution for distressed assets cases. A larger value for this indicator may
imply a larger level of instability and risk, and most likely implies a lower
level of liquidity. Reduced risk would likely incentivize investment spending
and loans, since there is a greater chance of being repaid. Also, a reduction
in liquidity would likely result in a lower level of consumption and investment
spending, both of which are components of GDP. These factors suggest that this
variable will be inversely related to GDP and will have a negative coefficient.
The fifth and final explanatory
variable in the GDP/Capita model is the Logistics Performance Index (denoted
LPI). This indicator ranges from one to five, with a higher score suggesting
more impressive logistics. Metrics used
to build this composite indicator include: quality of trade and transport-related
infrastructure, frequency of shipments arriving on schedule, and ability to
track and trace shipments. Because infrastructure is a key component (and somewhat
of a pre-requisite) of economic growth, this predictor variable is expected to
be positively related to GDP/Capita and have a positive coefficient.
Descriptive Statistics
Dependent Variable
|
Variable 1
|
Variable 2
|
Variable 3
|
Variable 4
|
Variable 5
|
|
GDP/CAP
|
TRADE
|
POPDENSE
|
NETMIG
|
IINSOLVENCY
|
LPI
|
|
Mean
|
14250.04
|
2304.045
|
249.791
|
-1058.72
|
2693.701
|
2950.234
|
Standard Dev.
|
18828.015
|
2403.578
|
887.332
|
777602.14
|
1277.44
|
554.27
|
Observations
|
129
|
129
|
129
|
129
|
129
|
129
|
Analysis Results
= 58502.65 +
2.043(TRADE)* + (-2.978)(POPDENSE)* + .0027(NETMIG)*
(.387)
(1.061)
(.0011)
+ (-1.356)(INSOLVENCY) +
24.56(LPI)*
(.761) (1.792)
F=71.69, p<.05; adj-R2=.737;
dfbetween=5; observations=127
In the GDP/Capita model, four of the
five explanatory variables had significant parameter estimates. The adjusted R
Square statistic suggests that the model explains 73.72% of variation in
GDP/Capita among nations. The F-test indicates the overall model has
statistical significance.
The coefficient value for
international trade was positive and significant. The marginal effect of
international trade on GPD/Capita suggested by the model is: each incremental
percentage increase in international trade value as a percentage of GDP
increases GDP/Capita by $2043.08. Trade theory’s explanation of improved
efficiency in resource allocation resulting in cheaper domestic inputs and
increased domestic sales abroad is the most probable explanation of the
positive relationship between international trade and GDP/Capita.
As expected, the coefficient value
for population density was negative and significant. Calculating the marginal
effect of population density on GPD/Capita gives us: each incremental increase
in population density leads to a decrease in GDP/Capita equal to $2.98. The
most probable explanation for the negative coefficient is Chen and Ching’s
argument of resources becoming scarcer when population is increased.
A positive coefficient value was hypothesized
correctly for net migration, which was found to be significant. The marginal
effect of net migration on GDP/Capita is as follows: each incremental increase
in net migration (increase by one person) leads to an increase in GDP/Capita of
$.003. Borjas’s labor market model offers a possible explanation for the
positive relationship in saying that increased immigration implies an increased
labor supply which in turn leads to a net increase in national income. Since
the market wage rate also falls, this opposing force could explain the small
coefficient value.
The results of the regression
analysis suggest that average insolvency time has no predictive significance
with respect to GDP/Capita.
Finally, the coefficient sign was
correctly hypothesized as positive and significant for the explanatory variable
Logistics Performance Index. The marginal effect of this variable on GDP/Capita
is as follows: each increase in Logistics Performance Index by .001 leads to an
increase in GDP/Capita by $24.56. As stated in the model explanation,
infrastructure is somewhat of a pre-requisite for economic growth. A weak
infrastructure makes economic development significantly more difficult. This is
a likely explanation for the positive relationship among these two variables.
College Enrollment
Model
In discussing quality of life,
education is rarely a topic that will go unmentioned. Furthermore, most people
would regard increased education opportunities as an increase in the standard
of living. The indicator used to measure post-secondary enrollment is called
Gross Tertiary Enrollment Ratio, which is calculated by dividing the total
number of students enrolled in post-secondary school by the number of citizens
in the five-year age group following completion of secondary school.
The first variable used to predict
college enrollment is preprimary enrollment rate (denoted PREPRIMARY). This
indicator is calculated by total number of students enrolled in preprimary
education by the total number of citizens in the official preprimary education
age group. A higher preprimary enrollment rate could have a few possible
implications. First, a higher proportion of students enrolled in preprimary
education could imply greater access to education opportunities. Second, the
higher proportion could imply a higher societal value placed on education –
since parents who more highly value education would likely enroll their
children at a younger age. Because of these two factors, a positive
relationship and positive coefficient is expected for preprimary enrollment rate.
The second explanatory variable used
in this model is Female to Male Secondary Enrollment Ratio (denoted
FEMALE:MALE). This indicator is calculated by dividing the number of females
enrolled in secondary education by the number of males enrolled. A higher
female to male ratio is likely an indicator of increased gender equality with
respect to education. A possible implication of increased gender education
equality is a higher societal value placed on education. Also, a larger
proportion of women in secondary school will likely result in a larger
proportion of women enrolled in college. Because a low value for this indicator
could imply gender inequality with respect to women, a high value likely
implies more women enrolled in college and thus a higher Gross Tertiary
Enrollment Ratio. The coefficient for this predictor is expected to be
positive.
The third and final predictor used
in the post-secondary education model is prevalence of HIV (denoted HIV). This
indicator refers to the percentage of citizens aged 15-49 infected with the HIV
virus. An increase in the proportion of a population infected with HIV would
likely result in a reduction in post-secondary enrollment, since sickness and
death would likely affect many enrolled in post-secondary school. It makes
intuitive sense that these two variables would be inversely related, giving
this explanatory variable an expected negative coefficient.
Descriptive
Statistics
Descriptive Statistics
|
Dependent Variable
|
Variable 1
|
Variable 2
|
Variable 3
|
GTER
|
PREPRIMARY
|
FEMALE:MALE
|
HIV
|
|
Mean
|
46.142
|
68.741
|
96.793
|
68.933
|
Standard Deviation
|
28.463
|
35.971
|
12.133
|
97.919
|
Observations
|
75
|
75
|
75
|
75
|
Analysis Results
= 8.524 + .451(PREPRIMARY)* +
.129(FEMALE:MALE) + (-.086)(HIV)*
(.073) (.229) (.025)
F=46.20, p<.05; adj-R2=.554;
dfbetween=3; observations=75
In the Post-Secondary Enrollment model, two of the
three explanatory variables had predictive significance. Explanatory variables omitted
from the model may be found in the appendix, footnote 1.
The regression results returned a positive and
significant coefficient value for Pre-Primary Enrollment Rate, which matched
expectations. Calculating the marginal effect of this variable gives us: each
percentage increase in pre-primary enrollment yields an increase of .451% of
Gross Tertiary Enrollment Ratio.
Regarding female:male secondary
enrollment ratio as an explanatory variable, the analysis suggests no
significance in predicting life expectancy.
As expected, the coefficient value
for HIV prevalence was negative and significant. The marginal effect of HIV
prevalence on Post-Secondary Enrollment is as follows: each 1% increase in HIV
prevalence yields a reduction in Gross Tertiary Enrollment Ratio of 8.6%.
Conclusion
As it turned out, GDP/Capita was the
easiest quality of life variable to predict. The Life Expectancy and
Post-Secondary Enrollment offered less explanatory ability than did the
earnings model. While all R Square terms are above .53, the analysis results
should be interpreted with caution. Rather than establish significant explanatory
variables as causal factors for quality of life, this paper merely lays
groundwork for a further analysis of determining causality. Possible
improvements that could have been made given more time include adding more
explanatory variables to increase predictive ability and increasing size of
data sets to include more countries and reduce bias in the data.
