This document outlines a research proposal examining the relationship between economic development and environmental health. The researchers plan to test the theory that environmental damage initially increases with economic growth, but eventually decreases after a certain point, forming an inverse U-shaped curve. They will run regressions using CO2 emissions, deforestation, and water quality as dependent variables, and GDP per capita, industrialization, urbanization, and land area as independent variables. The researchers expect to find an initially negative then positive relationship between GDP and each environmental measure. They collected panel data from 2001-2014 from World Bank sources, but are missing some data points.

1. 1
Michael Barry, Brennan Haley, Rainsford Reel
Prof. Anderson
ECON-203
5 April 2016
Research Proposal
Environmental health and policy has been a growing concern in recent years, not just in
the United States but at an international level, and politicians have been paying closer attention
to this topic. Additionally, there has been a long-standing debate about the relationship between
economic growth and the health of the environment, with some claiming development degrades
the environment and others saying growth will inevitably resolve environmental issues and that
as a nation’s wealth increases, those living in it will become increasingly concerned with the
non-economic factors of their living conditions. Shafik (1994) points out that some have come
to view caring for nature as a luxury, only feasible with high incomes, but also notes that some of
the poorest tribal people in the world are very concerned with the preservation of their
environment too. This is an interesting parallel with one of the prevailing theories on the
relationship between economic growth and environmental health, which is that there is an
inverse U-shaped relationship between level of economic growth and level of damage done to
the environment. We will examine the relationship between increases in economic development
and environmental health. Based on previous research done on the subject, we expect the shape
of the relationship, with GDP on the X-axis and environmental health level on the Y-axis, to be
that of a U.
There have been several works published in relation to this topic, though findings are not
consistent. Grossman and Krueger (1994) state that environmental damage follows an inverse-U
pattern when related to economic development. Grossman and Krueger use a variety of measures

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for environmental health and economic growth. Their findings suggest an initial phase of
deterioration followed by improvement. They go on to mention theoretical reasons of why this
pattern may emerge. However, Shafik states that the pattern of the relationship depends on the
environmental measure being observed. Specifically, she measures the relationship between
income and several environmental factors, noting that the changes seem to depend on more than
just economic growth. Some of the issues (such as lack of access to clean water and urban
sanitation) had high social and private costs and were addressed early in a country’s economic
growth. Others (such as CO2 emissions) do not have as significant private costs, and are not
addressed until countries reach high income levels, if ever.
In a 2015 study of Greece, Katrakilidis, Kyritsis, and Patsika examined the relationship
between economic growth (GDP per capita), environmental quality (CO2 emissions), and health
quality (infant mortality rate). They, too, acknowledge the findings of the Environmental
Kuznets Curve (EKC), which represents the relationship between economic growth and
environmental health and is shown as an inverted U-shaped curve.
High economic growth initially leads to environmental decline until it reaches a critical
point, after which the highest economic growth may reduce the environmental burden as
citizens begin to apply pressure towards the implementation of policies to protect the
environment and minimize pollution. (Katrakilidis et al. 217)
Using Kuznets-type models, they conclude that economic growth leads to environmental
degradation.
All this considered, our research is attempting to capitalize on the recent popularity of
environmental issues by using new and improved data to help answer some of the questions that
arise after reading past research on the subject. By examining the relationship between economic
development and environmental health, we can determine what actions should be taken and what
policies to support in order to maintain high levels of environmental health and economic

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development. We plan to answer our question through the use of panel data and run a regression
of various environmental measures on measures of economic development, such as GDP per
capita and industrialization levels.
Literature Review
There has been some contention over the relationship between economic growth and the
health of the environment, with some claiming development degrades the environment and
others claiming growth will inevitably resolve environmental issues. A study was conducted by
Grossman and Krueger using data from the Global Environmental Monitoring System (GEMS),
and included a variety of measures of air and water quality. They note that the data from the
GEMS fails to include economic health measures like deforestation, loss of biodiversity, and
industrial waste. Grossman and Krueger use a reduced-form approach to determine the
relationship between GDP and pollution, while also including other relevant exogenous variables
depending on the pollution measure.
Grossman and Krueger’s findings show that for their measures of air quality, economic
development and environmental health have an inverted-U relationship. That is, pollution
appears to rise with GDP at lower levels of income, but eventually reaches a peak and falls at
higher levels of income (14). Their findings in regards to water quality measures differ over the
different measures. Similar to air quality, they find an inverted-U shape relation between
measures of oxygen in water and income. They also note that the apex of pollution for this
measure of water quality tended to come later than those of air quality measures. Another metric
was the measure of fecal contamination in water. For this measure, the relationship initially
shows an inverted-U shape, but then has a sharp increase. Unfortunately, they include no theory
for explaining this relationship. Lastly, their findings with regard to heavy metal concentration in

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water vary with the type of metal. While lead and cadmium had a negative correlation, arsenic’s
relation to income followed the inverted-U shape.
Overall, Grossman and Krueger find that most indicators of economic growth also bring
an initial deterioration in the environment, which then levels out and begins to trend in the
opposite direction (18). In the conclusion of their paper, they note that there are several things to
consider when interpreting their findings; one example being how policy affects environmental
health. As countries experience greater economic prosperity, “their citizens demand that more
attention be paid to the non-economic aspects of their living conditions” (19). A second thing to
consider is that as countries develop, they often stop producing “pollution-intensive goods” and
instead have them imported.
Shafik addresses this “inverted-U” shape, claiming that many see caring for the quality of
the environment as a luxury good, only being necessary at high levels of income (Shafik, 758).
However, she also points out that some of the poorest people in the world, especially those in
tribal settings, have some of the best relationships with their surrounding environments (758).
This narrative supports her theory that environmental health is best at the highest and lowest
levels of income, with deterioration, followed by improvement, in between (due to economic
development). She examined the relationship between economic development and many
variables relating to environmental health. She finds that the relationship is consistently positive
for access to clean water and sanitation, positive and increasing for emissions and amounts of
waste, inverted U-shaped for deforestation, suspended particulate matter, and ambient sulfur
dioxide, and, interestingly, the relationship for fecal coliform is consistent with Grossman and
Krueger’s findings, with an inverted U-shape followed by another sharp increase (764). Most
importantly, Shafik shows that there is not one level of economic development that coincides

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with the healthiest environment. Instead, the factors respond differently to income growth, and
the relationships that change sign do so at different points, depending in part on how easily their
negative effects can be externalized (758).
This issue has also been examined at a more individual level by Hanna and Oliva in their
case study of India, published in 2015. The study uses data from a natural experiment created by
a government asset transfer program which randomly selected 50% of poor families in
Marshibad, West Bengal to receive aid (242). They wanted to see how increased wealth would
affect consumption of fuel, specifically in the home. Would people replace cheaper, dirty fuel
with more expensive, clean fuel for cooking and lighting (242)? The results show that both the
substitution and wealth effects were present in the case of lighting, resulting in more
consumption of dirty fuels, despite some households switching away from them (244). Cooking
fuel did not show these effects, most likely because of the fixed cost associated with a new, clean
fuel burning oven (244). Hanna and Oliva’s findings add to the idea that there seem to be critical
income levels at which people are willing to spend more to be more environmentally friendly,
but before these levels the wealth effect will increase consumption of the less environmentally
friendly goods. However, these critical points are inconsistent and could also be due to other
factors such as benefits from health effects of using cleaner fuels (242).
Researchers Katrakilidis, Kyritsis, and Patsika used a slightly different approach in
examining the relationship between health, environment, and economic growth. They used data
on Greece from the World Bank Database (years 1960-2012) to study the dynamic linkages
between the index of carbon dioxide emissions (environmental pollution), index of infant
mortality per thousand (health quality), and GDP per capita (economic growth) (Katrakilidis et
al. 218). They acknowledge the previous studies findings, citing the U-shaped Environmental

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Kuznets Curve (EKC) as the most popular approach to observing this relationship. They
differentiate their research by investigating the interactive relationship between the variables, not
asserting “a one-way causal relationship” that the EKC theory focuses on (218). Ultimately, they
found a “long-run causal effect from GDP and CO2 to infant mortality”, “short-run causal effects
from GDP to CO2, and from GDP to infant mortality” (219). While their conclusions are similar
to those of other researchers, their consideration of all three studied indicators affecting each
other differently made for a fresh perspective.
Theory and Model:
Incorporating results from previous research on the subject, we expect to find that as
economic development increases, environmental health levels will initially decrease, but will
eventually start to increase again once development reaches a certain point. To test this theory
we will run three regressions, each with a different dependent variable to measure environmental
health: CO2 emissions, deforestation level, and annual freshwater withdrawals. We believe that
these three variables are good representatives of the overall health of the environment as they
incorporate effects on land, water, and air.
We believe that the effect on CO2 will allow us to see how emission of greenhouse gasses,
a popular issue in recent times, responds to increases in development levels. Despite Shafik’s
findings, which show carbon emissions increasing at an increasing rate, we expect that the
relationship with economic growth will decrease at a certain level of development, or at least
appear to approach an asymptote. We expect our findings to differ from Shafik’s due to the
increased attention governments and other regulating bodies have been paying to emissions since
the time she wrote her paper, both from producers and the products they produce. It seems that,
in wealthier countries, people have become willing to spend more money for products that are

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environmentally friendly, as opposed to developing countries where people tend to produce and
buy the least expensive products, regardless of their environmental impact.
Similarly, we expect that, initially, as economic development increases so will levels of
deforestation. This is because as countries’ economies are growing they are often willing to use
all resources at their disposal to maintain and increase growth, no matter the consequences.
However, deforestation will likely level off and begin decreasing once economies are large
enough. This could be due to many factors, some of which could be changes in industry away
from lumber towards more profitable sectors, or increased levels of government regulation once
the country has sufficiently benefited from the use of their forests.
For annual freshwater withdrawals, we expect a similar story once again. As stated in the
deforestation paragraph above, as countries initially develop they will use all their resources to
maximize their economic progress. We expect that eventually they will change their practices in
order to start preserving water, as it is a valuable resource that needs to be more carefully
monitored.
To test these theories, and the general theory that the relationship between environmental
damage and economic production will have an inverse U-shaped relationship, we will run
regressions with CO2 emissions, deforestation level, and SPM in water. The independent
variables in these equations will be GDP per capita, percentage of economy that is industrial,
percentage of population that lives in cities, land area and country fixed effects. We include the
variables for urbanization and industrialization to measure the effects of different housing and
economic situations. We use GDP per capita as the main indicator of economic development
and land area as its own variable to control for the intensity of production in nations. This is

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important because if the same goods were produced in different sized areas, the production
would harm the environment to different extents.
Given these variables we estimate our base regression model will be as follows:
𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑀𝑒𝑎𝑠𝑢𝑟𝑒
= 𝛽0 + 𝛽1 (
𝐺𝐷𝑃
𝑐𝑎𝑝𝑖𝑡𝑎
) + 𝛽2(
𝐺𝐷𝑃
𝑐𝑎𝑝𝑖𝑡𝑎
)2 + 𝛽3( 𝐼𝑁𝐷) + 𝛽4( 𝑈𝑅𝐵𝐴𝑁) + 𝛽5( 𝑆𝑄𝐾𝑀) + 𝜖
Variable Key:
GDP/sqkm - gross domestic product per square kilometer
IND - industrialization level (% of economy that is manufacturing based)
URBAN - % of population that lives in cities
Using this model, our theory anticipates that 𝛽1would be negative, 𝛽2will be positive, 𝛽3will be
negative, and 𝛽4 would be positive. Since, as stated in the the previous section, we anticipate an
inverse U pattern we expect the stated signs on 𝛽1and 𝛽2 to give this overall pattern. A negative
sign on 𝛽1 will lead to the initial downturn in economic health, while the positive sign on 𝛽2 will
allow for the measure to increase as (GDP/sqm)2 takes over. We expect 𝛽3 to be negative
because as industrialization occurs and an economy has higher levels of factory based industry,
pollution increases. As such, an increase in industrialization leads to lower environmental health.
For urban versus rural housing we expect the sign of the coefficient to be negative. As more
people shift from rural to urban housing, people become more concentrated over the same
amount of space which can lead to more use of public transportation and less land usage as a
whole, limiting negative effects on the environment.
We plan to use panel data for these regressions. This is because panel data combines
time series and cross sectional methods by comparing different countries over a range of time to
create a more encompassing representation of the true story. Using panel data will also allow us
to control for country and time fixed effects, which are variables that change from country to
country or year to year. This is crucial for our research and model because, without the ability to

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control for these fixed effects, we would need to include far more control variables in order to
isolate the effects of GDP growth.
Data:
We collected all of our data from the the archives of the World Bank in order to maintain
consistency. We used data from 2001 to 2014 in order to produce a recent dataset and examine
how economic development and environmental health are interrelated in the modern world. We
included a variety of countries, first and third world, developing and developed, in order to
observe a wide range of stages of economic development and environmental health. To
determine levels of economic growth while accounting for the size of the country, we use both a
measure of GDP per capita and square kilometer. After examining all of our desired statistics,
we compiled them into a data table to analyze using our regression technique laid out in the
methodology section.
As expected, we ran into some problems when collecting our data. Though the World
Bank has an extensive library of data for research, there are some holes. Unfortunately, they did
not have data for all years and countries we examined. The CO2 (carbon dioxide emissions in
metric tons per capita) dataset ends at 2012, so we do not have data for the last two years of our
timeframe. Similarly, the Forest Area percentage dataset does not have data for 2014. The
Industrialization dataset is missing some data sporadically, which is just a result of the individual
countries’ decision to publish that specific statistic for a given year. Our software detected some
multicollinearity due to the fact that some countries were missing data for certain measures. For
example, when we regressed CO2 on the measures of economic development, Stata dropped
Afghanistan, The Democratic Republic of the Congo, Kenya, and Serbia as these countries were
lacking in data supply. Also, the World Bank only collected data for the measure of water quality

10. 10
on a five-year basis so there are fewer observations but still enough to use regression analysis.
Finally, as is typical with economic studies using regression analysis, we chose to correct for
heteroskedasticity.
Table 1 shows the descriptive statistics for our dependent variables and key independent
variables. The CO2 variable represents carbon dioxide emissions in metric tons per capita. The
minimum of 0 shows that some countries (Afghanistan and The Congo) do not emit a significant
amount of CO2, while most other countries have much higher levels of output. We expect that
the highest CO2 emissions will not be from the wealthiest countries, but from those that are in
the midst of developing.
Forest area is the percentage of a country’s land that is covered by forests. The
Table 1: Descriptive statistics for observed variables
Variable Observations Mean Std. Dev. Min Max
CO2 Emissions
(metric tons per
capita)
307 5.879 4.907 0 19.7
Forest Area (%) 371 32.022 22.576 .1 73.5
Freshwater
Withdrawals
(cubic tons)
69 71.614 153.585 .58 761
GDP Per Capita
(USD)
406 16,469.73 17,852.18 111.53 56928.82
Land Area (sq.
km)
406 2,057,286 2,900,906 33,670 9,388,214
Industrialization
Percentage (%)
296 31.439 6.67 5.1 47
Urban Percentage
(%)
406 63.622 21.409 14.74 93.021

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range (.1 to 73.5) shows large variations in forest area, from almost no land to close to three
quarters of the country’s land. Some of this will be due to differences in climate and geography,
but also may be due to differences in our independent variables.
Freshwater shows annual freshwater withdrawals by a country in billions of cubic meters.
The data shows that the countries in our data set have a very wide range of water usage, from
less than a billion cubic meters to 761 billion cubic meters. With a standard deviation greater
than the mean, it is clear that the amount of fresh water used varies greatly, possibly in response
to our independent variables. Additionally, the minimum value of .58 billion cubic meters shows
that the data set includes at least one country that has very little access to clean water, likely due
to a combination of geography and low income. While the number of observations of freshwater
withdrawals is much lower than those for the other variables, we believe we have enough data to
run a proper regression.
Industrialization (measured in percent of men working in industry) and urban population
(%) are the most straightforward and self explanatory variables we use. While industry seems to
be relatively consistent across countries, there is a wide range in the percentage of countries’
populations living in urban areas.

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Results:
CO2:
Table 2: Regressions of CO2 emissions on economic development measures
To examine the relationship between economic development and carbon dioxide output
we ran two regressions with two different models. The first model was polynomial to test for a
Kuznets shaped relationship. To do this, we used co2 as the dependent variable, with GDP per
capita, GDP per capita squared as the key independent variables, and with industrialization level,
urbanization level, size of country, and country fixed effects as linear control variables. The
results from this regression are shown in column 1 of Table 2. We find that the coefficients for

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GDP per capita and GDP per capita squared are both statistically significant and demonstrate the
inverse U shape predicted by theory.
Although statistically significant, at first glance the small size of the coefficients makes
them seem like the real effects may not be very important. However, it is important to remember
that CO2 emissions are measured at a per capita level, and the coefficients represent the change
in CO2 output in response to an increase in just one dollar of GDP per capita. Because of this, in
order to better understand the implications of our findings we tested the coefficients for clinical
significance. For this model we found that, at the mean level of GDP per capita, a change in one
standard deviation of CO2 will be associated with a change of just under 25% of one standard
deviation of CO2. This shows that, despite their small values, the coefficients reveal changes in
economic development levels lead to significant changes in carbon dioxide output. The
calculations for clinical significance can be found in Appendix A.
The second regression, shown in Table 2 column 2, uses a double-log model, where we
use the natural log of CO2 emissions as the dependent variable and the natural log of GDP per
capita as the independent variable, while keeping the same independent control variables from
the polynomial model. Although the double-log model cannot show changes in sign of the
relationship at different levels of development, we believe this model is important to examine in
addition to the polynomial model since it examines the relationship in terms of percent level
changes instead of unit changes. The coefficient for log GDP per capita shows that a 1%
increase in GDP per capita is associated with a .068% increase in metric tons of CO2 emitted per
capita. In addition to being statistically significant at the 1% level, this coefficient is also
clinically significant, with a standard deviation change from the first quartile of GDP per capita

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leading to a change in CO2 emissions of just over 20% of a standard deviation change of CO2
from the first quartile.
For both models the R-squared and adjusted R-squared values are above .99, showing
that our models explain at least 99% of the variation in the dependent variable. These very high
values are due to the use of country fixed effects, which control for country specific differences
not specified by independent variables. Because of this, it is important not to put too much
emphasis on these statistics for our models.
Deforestation:
Table 3 shows results from the same polynomial and double-log regression models, this
time using Forest area as a percent of a country’s land as the dependent variable. In the
polynomial model, the coefficients for GDP per capita and GDP per capita squared are not
statistically or clinically significant. Despite this, the signs of the coefficients, negative and
positive respectively, point to the relationship we theorized. The negative sign for GDP per
capita shows that development in low income countries tends to decrease forest area levels, and
the positive coefficient for GDP per capita squared shows that at a high enough level of
economic development, the effects of development should change, and forest area would begin
to increase in response to increased income.
The coefficient for log GDP per capita is statistically significant at the 1% level.

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Table 3: Regressionof forest area on economic development measures
Its value of -.0196 shows that an increase of 1% in GDP per capita will be associated with
decline of .0196% in forest area. Despite its, statistical significance, the coefficient is not
clinically significant, with standard changes in GDP per capita levels associated with small
changes relative to the standard deviation for forest area. Since these models also used country

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fixed effects, the high r-squared values should not be used to evaluate explanatory abilities of
variables for GDP per capita and our key control variables.
Freshwater Withdrawals:
Table 4: Regressionof freshwater withdrawals on economic development measures
To examine the relationship between economic development and freshwater withdrawals
we used used the same two models once again, this time with freshwater withdrawals (measured
in cubic tons) as the dependent variable. As shown in Table 4, in this case neither model found
statistically significant coefficients for our measures of economic development, suggesting that

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water usage may be less dependent on income than our other two measure of environmental
health. That being said, the positive coefficient for GDP per capita and negative coefficient for
GDP per capita squared once again point to the existence of a Kuznets shaped relationship
between our key variables, suggesting that freshwater withdrawals will increase along with GDP
per capita until a level of income where they will begin to decline. As in the first two sets of
regressions, the high R-squared values are the result of controlling for country fixed effects.
Conclusions:
Although not always statistically significant, the coefficients for economic development,
as well the coefficients for our control variables, tend to align well with previous research, and
seem to confirm most of our theories. We were especially happy with the statistically significant
coefficient for industrialization level in all regressions but one. This points to the fact that our
coefficients for development are not merely showing effects of different levels of
industrialization that may be associated with higher and lower levels of GDP per capita.
However, we were surprised to find that the coefficients for industrialization were not clinically
significant (calculations in Appendix A). We believe that this calls for further research on the
relationship between industrialization and and economic health, possibly using different
functional forms and different measures of industrialization levels.
We were also surprised to find that the signs of the coefficients for urbanization did not
align with our theory in our regressions for CO2 and freshwater withdrawals. We expected that
these coefficients would be negative to due to increased efficiency associated with life in urban
settings. However, we believe that these incorrect signs were the result of our urbanization
variable serving as a proxy for quality of life, as wealthier countries tend to be more urban. As

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with industrialization, we think our findings for the relationship between urbanization and
environmental health call for further research.
Although our findings do not suggest any direct policy implications, we believe they can
serve as a good point of reference for the relationship between economic development and
environmental health. The polynomial model can offer some comfort to those concerned with
the negative effects of development, as it predicts that marginal environmental harm will decline
as development increases. However, the double-log model can serve as a reminder that, despite
the light at the end of the tunnel, we must continue to be mindful of the harmful effects that can
arise due to economic pursuits.

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Works Cited
Grossman, Gene M., and Alan B. Krueger. “Economic Growth and the Environment”. The
Quarterly Journal of Economics 110.2 (1995): 353–377. Web.
Katrakilidis, C., I. Kyritsis, and V. Patsika. "The Dynamic Linkages between Economic
Growth, Environmental Quality and Health in Greece." Applied Economics Letters 23.3
(2016): 217-21. Web.
Shafik, Nemat. “Economic Development and Environmental Quality: An Econometric
Analysis”. Oxford Economic Papers 46 (1994): 757–773. Web.
Hanna, Rema, and Paulina Oliva. "Moving Up the Energy Ladder: The Effect of an Increase In
Economic Well-Being On the Fuel Consumption Choices of the Poor in India." American
Economic Review 105.5 (2015): 242-246. EconLit with Full Text. Web. 13 Mar. 2016.

20. 20
Appendix A (clinical significance calculations):
Polynomial model for CO2:
F(mean+SD) – F(mean) = 1.211, SD(CO2) = 4.9,
1.211 / 4.9 = 24.7%
Double-log model for CO2 at Q1:
β(GDP/cap) * SD(GDP/cap)/Q1(GDP/cap)=Δ compared to SD(CO2)/Q1(CO2)
=
.06819 * 17,852/2093 = .5816 vs. 4.907/1.7 = 2.8865
.5816/2.8865= 20.1%
Industrialization in CO2 model:
β(Industrialization) * SD (industrialization)= Δ compared to SD(CO2)
.0569 * 6.67 = .3795 SD(CO2) = 4.907
.3795/4.907 = 7.7%
*note: We use results from the polynomial model for CO2 to evaluate industrialization’s clinical
significance because CO2 has the smallest standard deviation, and the beta coefficient for
industrialization in this model was the second largest from all of our regressions. It is safe to
assume that, because it is not clinically significant in this model, the coefficient for
industrialization is not clinically significant in any of the models. We believe that the lack of
clinical significance could be due to the fact that it was only included in our models with linear
coefficients, and that testing different functional forms may reveal greater clinical significance.