Imperialism and Modern State Building Explode The Mexican

Imperialism and Modern
State Building Explode: The
Mexican Revolution
History 111 – World History since 1500
Spring 2022
Jorge Minella ([email protected])
Early Twentieth Century
-states and their empires.
Today’s Class
their
colonial subjects, also
class conflict.

-based clashes interacted with imperialism and
international warfare.
-1920.
-building and colonial legacy, imperialism, class
struggle, mobilization of
society.
Agreements.
The Mexican Revolution
Begins
Mexico Between
1810 and 1876
1845-1848.
-
1867.

1876.
Porfirio Diaz’s cabinet, ~1900.
The Porfiriato, 1876-1911
tion with U.S. business interests.
The Porfiriato’s
Decline
caused
resentment.

complexity.
excessive political control.
Image of the 1906 Cananea Strike, at the American-owned
Cananea copper mine, in Sonora, northern Mexico. Many
workers killed for demanding better working conditions.
Francisco Madero
investors.
an for president in 1910.
Broad coalition to oust Porfirio Diaz

erchants, middle-class
intellectuals.
and Pancho Villa)
Pancho Villa
family.
and poor conditions.
Emiliano Zapata
-oriented
sugar plantation into his village’s land.

1909.
means but failed due to the biased Porfirian
judiciary system.
Porfirio Ousted,
Madero President
defeated federal troops in Ciudad Juarez.
November 1911.
Madero in the 1911 electoral campaign
with Zapata’s troops.
The Mexican Revolution
Unfolds
Madero in Power

diverging goals.
of the coalition, mainly
Zapata’s peasants.
Zapata’s Plan of Ayala
nts.
demands.
The Federal Army

firian officers.
Ambassador.
Victoriano Huerta
The Tragic Ten
Days (Feb. 1913)
City.
Huerta.
Modern weaponry
employed in the battle.
Madero’s Coalition
Back Together

elites.
nd central
Mexico’s peasants.
new constitution.
reestablished Porfirian rule.
Pancho Villa
and his troops.
Late 1914, Conference of Aguascalientes
tion.

Constitutionalists
vs. Conventionists
y northern elites.
opposed Zapata’s land reform.
Zapata.
City.
Zapata and Villa’s armies enter Mexico
City, December 1914.
Constitutionalist Win

fight.
Mexico’s New Constitution
Social Reforms and the 1917 Constitution
rground resources.
Carranza’s Government, 1917-1920.

ata kept fighting.
Obregón’s Response
Constitution.
-class.
against Zapata.

died.
The Mexican Revolution and World
History
-state formation.
ial heritage.
Imperialism and Modern State Building Explode: The Mexican
RevolutionEarly Twentieth CenturyToday’s ClassThe Mexican
Revolution BeginsMexico Between 1810 and 1876The
Porfiriato, 1876-1911The Porfiriato’s DeclineFrancisco
MaderoBroad coalition to oust Porfirio DiazPancho
VillaEmiliano ZapataPorfirio Ousted, Madero PresidentThe
Mexican Revolution UnfoldsMadero in PowerZapata’s Plan of
AyalaThe Federal ArmyThe Tragic Ten Days (Feb.
1913)Madero’s Coalition Back TogetherLate 1914, Conference
of AguascalientesConstitutionalists vs.
ConventionistsConstitutionalist WinMexico’s New
ConstitutionSocial Reforms and the 1917
ConstitutionCarranza’s Government, 1917-1920.Obregón’s
ResponseThe Mexican Revolution and World History

Next Steps:
Now that you have submitted your data and have gotten
feedback, here are some next steps for you to
work on to advance your paper.
1. Compile the articles that you will reference in your paper.
2. Read them.*
3. Write a 1-2 paragraph summary of each paper.**
4. Construct the citation of the papers for the References Cited
section.
5. Begin writing your “Data Description” section.
6. Generate the Summary Statistics table.
7. Graph your variables.
8. Explore the correlation matrix.
9. Run regressions.
10. Generate your Regression Output table.
11. Decide on your overall (main) conclusion that your research
has helped you discover.
12. Write your Results section.
13. Write your Summary/Conclusion section.
14. Write your Limitations and Future Research section.
15. Write your Literature Review section.***
16. Write your Introduction section.
17. Write your Abstract.
18. Check the Grading Rubric.
19. Ensure your format is correct, and that proper information is
on your front page.****
*Not super intensely, just making sure you get the gist of what
can be learned from the research.
**You will not use this entire summary in your paper, it just

helps to write it RIGHT after reading it. You
will extract main findings later.
***Pay attention to how you cite a paper within text.
**** You must include your name(s), group number, “Spring
2020 EC204 [3:30 or 5pm] Section”, the
date and the abstract on the front page.
DETAILED VERSION ON FOLLOWING PAGE→
Next Steps (Detailed):
1. Compile the articles that you will reference in your paper.
a. Look for any that seem relevant to your research topic.
i. They may be about pre-discovered relationships with your DV
or IV, or both.
ii. They don’t need to address your exact research question.
Instead, they may
have discovered something about relationships that include
either of your main
variables. However, they need to “fit” into your overall
story/analyses and
should not seem completely unrelated.
2. Read them.
a. You just want to get the gist of what can be learned from the

research.
b. If the econometrics is beyond the scope of our course
(Metrics 1!), then just make sure
you can understand their main conclusions.
3. Write a 1-2 paragraph summary of each paper.
a. Make sure YOU ARE NOT copying/pasting from the abstract,
or from anywhere else.
4. Construct the citation of the paper for the References Cited
section.
a. APA style
i. By author:
1.
https://owl.purdue.edu/owl/research_and_citation/apa_style/apa
_for
matting_and_style_guide/reference_list_author_authors.html
ii. More detailed info on references to articles in particular:
1.
_for
matting_and_style_guide/reference_list_articles_in_periodicals.
html
b. MLA style is also fine if that’s the one on which you are
already well-versed (just be
consistent within your References Cited section).
5. Begin writing your “Data Description” section.
a. This should be straight to the point:
i. Source(s) of data

ii. Describe your data.
1. Who or what make up your elements/observations?
2. Describe your variables:
a. Include tables: Variables’ Descriptions and Summary
Statistics.
i. You can copy/paste the one you have submitted, with
any changes I recommended in feedback.
b. Don’t include polynomial or log terms in your description,
just
describe the underlying X
b. Example: “Data were obtained from the World Bank and span
1980-2015. The list of
countries is provided below the Summary Statistics Table
(Table 2) and the variables are
described in Table 1.”
i. Of course include anything else about your data that you think
may be
important for the reader to know, including missing countries
for example, or
which “population of interest” you think the sample was drawn
from.
c. Note: this is NOT the place to mention potential sources of
omitted variable bias.
CONTINUES ON FOLLOWING PAGE→
_formatting_and_style_guide/reference_list_author_authors.htm

l
_formatting_and_style_guide/reference_list_author_authors.htm
l
_formatting_and_style_guide/reference_list_articles_in_periodi
cals.html
_formatting_and_style_guide/reference_list_articles_in_periodi
cals.html
6. Generate the Summary Statistics table.
a. Use the resources on Blackboard. Find the do file and
example of a summary statistics
table under “Research Project Stuff→Stata Stuff→Stata Help
Videos, Commands,
etc.→Producing Publication-Style Tables in Stata→Summary
Statistics Tables”
b. Make sure you use the label option in the esttab command to
ensure that Stata uses the
labels of the variables and not the (often ugly or uninformative)
variable names
themselves.
7. Graph your variables.
a. Scatterplots, bar charts, pie charts… whatever help you better
understand your data and
whatever may help your reader either better understand or
become more motivated by
your research.
b. This is where you can set yourself apart from the rest of the

class, by providing a graph
that is super informative and more than a simple basic
scatterplot.
8. Explore the correlation matrix.
a. Use the corr command to see the correlation matrix.
b. This may help you describe omitted variable bias that is
removed once the variable is no
longer omitted, or also help you discover other possible control
variables or other
interesting relationships!
i. The correlation matrix does NOT need to be reported in your
paper unless you
think it should be/is extremely helpful to make a point you are
trying to make.
9. Run regressions.
a. Play with various model specifications: logs, polynomials,
interaction terms…
10. Generate your Regression Output table.
a. Use outreg2.
i. Find Resources on Blackboard under “Research Project
Stuff→Stata Stuff→Stata
Help Videos, Commands, etc.→Producing Publication-Style
Tables in
Stata→Regression Results Tables
b. The first column will be just your DV regressed on your main
variable of interest
i. No controls, no “fixed effects” (for panel data)
c. The subsequent columns will display the various models you

attempted
i. Don’t add in controls one at a time
ii. Do add in time and entity fixed effects one at a time (if panel
data)
d. Be sure your table reports the adjusted R2 for each model, as
well as the number of
observations.
e. You should only have ONE regression output table (MAYBE
two if you run LOTS of
interesting regressions)
i. Each different regression is a different column, not a different
table
ii. You can often fit up to 5-7 columns in one table
iii. Make sure that your columns clearly indicate what your DV
is.
11. Decide on your overall (main) conclusion that your research
has helped you discover.
a. What’s the main finding? It may or may not be exactly what
you set out to discover.
b. This will shape the way you “frame” your research paper,
keeping a consistent flow
between sections/ideas that have an overall (and consistent)
point throughout, all
leading to this overall conclusion.
CONTINUES ON FOLLOWING PAGE→

12. Write your Results section.
a. This should also be consistent with the “frame” of the paper.
b. It describes things you can learn from the regression output
table.
13. Write your Summary/Conclusion section.
a. This should also be consistent with the “frame” of the paper.
b. Unlike your Results section, it only discusses the main
takeaway.
c. This is where any policy implications would be discussed.
How does what we have
learned from your research help us better understand what w e
can do to achieve a
particular goal?
i. Note that some papers may not have “policy implications” but
instead may
prescribe a perspective we should share, a new way to look at
something, or
provide advice on how to approach future decision making.
14. Write your Limitations and Future Research section.
a. This is where you discuss the limitations of your research,
including missing data that
may be biasing your results
b. You also mention a possible direction (or two, or three) that
research can take.
15. Write your Literature Review section.***
a. Be sure your lit review section is very concise, only
mentioning the main takeaways from
papers that help us better understand something about either
your DV or your main IV

(or both).
b. Be sure you think about how you are framing your research
paper to make sure these
additions fit nicely and flow smoothly toward your own main
conclusions.
16. Write your Introduction section.
a. This is generally saved for last because by now you know
EXACTLY how you are framing
your research conclusions.
b. This section contains a (brief) description of your research
question, the motivation
(why is it worth studying), and your main conclusions.
17. Write your Abstract.
a. A VERY brief description of your research question and your
main conclusions.
18. Check the Grading Rubric.
a. This will help ensure you hit all/most of the necessary pieces.
19. Ensure your format is correct, and that proper information is
on your front page.****
**** You must include your name(s), group number, “Spring
2020 EC204 [3:30 or 5pm ]Section”, the
date and the abstract on the front page.
GDP GROWTH ON MORTALITY RATE IN 2000 AND 2014

ACROSS THE WORLD
Yuxuan Tang
Jianing Wang
EC204 Empirical Economics II, Fall 2021
ABSTRACT
Journal of Population of Economics stated “For the period
1800–2000, an increase in
GDP by 1% decreased mortality by 0.7%. This overall
relationship is due to a strong
counter-cyclical relationship in the nineteenth century, which
disappeared in the twentieth
century” (Svensson, M., Krüger, 2010). Based on the
WorldBankData2years panel data in the
year of 2000 and 2014, this research mainly focused on the
effects of GDP growth on mortality
rate, with different variables involved. The results showed a
statistically insignificant relationship
between GDP growth and mortality rate. And 43.39% of the
variation of the mortality rate can be
explained by GDP growth within the country when holding
country level and time fixed effect.
1

https://learn.bu.edu/bbcswebdav/pid-9575630-dt-content-rid-
59011850_1/xid-59011850_1
I. INTRODUCTION
This research aims to discover if GDP per capita affects
mortality rate. Past research
shows that GDP per capita is inversely related to mortality rate
during 1901-2000 in the United
States (M Harvey Brenner, 2005). In this article, Thomas
McKeown demonstrated that
economic development is of fundamental importance to the
decline of classic infectious and
childhood disease. With rapid economic growth in the 20th
century, more people tend to have
vaccinations and are less vulnerable to infectious and
childhood disease, which leads to a
decline in mortality rate. As a result, an inverse relationship
between GDP per capita and
mortality rate worldwide was expected at the beginning.
After the hypothesis was conducted, we described and utilized a
panel data across the
world in 2000 and 2014, and regressed GDP per capita and
mortality rate with some variables

including improved sanitation facilities of po, co2 emissions
metric tons per capita, improved
water, urban population growth annual spurb, prevalence of hiv
total of population,
immunization measles of children age and others are tested with
GDP per capita to find out how
it affects mortality rate. Then, we compiled our findings and
found there is a statistically
insignificant relationship between the two main variables.
Therefore, we use interactive variables
to test if the effects of GDP growth per capita on mortality rate
depends on other variables listed
above. Then we created a graph that involves a linear
regression and scatter plot were used to
make further comparison of fitness. Also, with the quadratic
model being graphed, the turning
point is at 0.164310932, and after this turning point, the
relationship between GDP growth and
mortality rate becomes positive contrary to our expectations.
2
2. Literature Review
Many researchers had done studies relative to the effects of

GDP growth on mortality rate
for years, and the reasons could be complicated. Mikael
Svensson and Niclas A. Kruger used
wavelet methods to analyze the relationship between mortality
rate and economic growth from
1800 to 2000 in Sweden. (Mikael Svensson and Niclas A.
Krüger, 2012) According to the article,
it was found that in the early period of the 19th century, people
were more vulnerable to disease
and health problems when the economy went downward. As a
result, the mortality rate was
higher when the economy was poor. However, when we entered
the 20th century, the augment
changed. People were more likely to stress out due to reasons
including work stress, family
pressure due to unemployment, which leads to higher death rate.
Furthermore, the research found
out some more specific factors that associate mortality rate with
GDP growth, including stroke,
accident, suicide, cancer, and infection.
More findings were found by M Harvey Brenner. Using the time
series model, with
variables of “ long-term effects of economic growth over 0–11
years,” “long-term effects of

unemployment over 0–11 years,” and “interactive effect of
unemployment and GDP per capita
over 0–11 years”, it was found out that for a short period,
increased mortality rate was due to
higher GDP growth, because of better technology with longer
working period and speed.
However, for a longer period, GDP growth leads to the decline
of mortality rate.（M Harvey
Brenner, 2005） More evidence was found by Brenner and
Haines to prove this theory. According
to the article written by Haines in 2003, it was found that the
United States experienced a rapid
economic growth but rising mortality rate between 1830 and
1860 due to deterioration of the
biological standard of living (Hanis 2003). During this period,
the fast urban growth, mass
migration from abroad, changes in transportation infrastructure,
rapid commercialization,
3
worsened the mortality environment which caused the mortality
rate to rise. For a longer period,
Varvarigos constructed a model of a growing economy with

pollution and testified that economic
growth and mortality rates are negatively related due to the
difference of environment-related
structural parameters, such as lower p (units of pollution per
output generating), which improves
the environmental conditions and reduces mortality rate
(Varvarigos, 2013).
3. Data Description
Table 1
This research used panel data at country level worldwidely in
the year of 2000 and 2014
from world bank data to analyze the relationship between GDP
growth and mortality rate. A total
of 369 observations are collected from world bank data with 6
variables, including sanitation
facilities of po, co2 emissions metric tons per capita, improved
water, urban population growth
annual spurb, prevalence of hiv total of population, and
immunization measles of children age.
These 6 variables, together the two main variables are tested to
find out the relationship between
GDP growth and mortality rate. The six variables are chosen
because we realized that the higher

GDP a country has, the more conscious people have of their
health. And as a result, more people
4
are getting vaccinated and actions or policies are taken for the
sake of citizens’ health, which
leads to the decline of mortality rate.
The data of this research all come from world bank data, and
two tables were created by
different years to describe the mean and standard deviation of
the variables. Out of all the
variables, improved water has the highest mean value of 83.2%
and 89.0 % in 2000 and 2014,
whereas urban population growth annual spurb have the lowest
mean values around 2% in both
years.
Table 2
5
4. Model:
After we collected the data, we constructed a model of mortality
rate as a function of GDP

growth at the country level of time fixed effect.
Within this fixed effect model, by holding year t and country i
at constant level, mortrate
represents mortality rate, the continuous dependent variable in
this equation, in year t and
country i. The main independent variable of this equation is
gdpgrowth, which is continuous in
country i and year t, and is predicted to have a positive
relationship with the main variable
mortality rate. The model is predicted as a linear regression as
shown in the scatterplot graph. As
we used a time fixed effect model, the 6 other variables with i
are absorbed into the ai variable
which change based on different countries. According to graphs
shown below, most countries
with different mortality rates are scattered between 0% to 20%
growth of GDP in both 2000 and
2014.
Also, in this model, the panel data at country level analyzes
data from both year of 2000
and year of 2014 by using the dummy variable d00t and u is the
error term. Graph1 represents
the worldwide GDP growth rate and mortality rate in 2000, and

graph 2 displays GDP growth
rate and mortality rate in both the years of 2000 and 2014.
However, by looking at the two
graphs below, we can see there is no inverse relationship
between the GDP growth rate and
mortality rate, but instead a positive relationship. However, we
cannot conclude that there is a
definitely positive relationship between GDP growth rate and
mortality rate, as the dots mostly
concentrated in the middle of the graph rather than displaying a
linear relationship. And there are
6
countries including Liberia, Equatorial Guinea , and Timor-
Leste which are more than 3 standard
deviations away from the mean fall into the category of
becoming outliers of the group.
Therefore, we used some interactive variables to test if there is
a non linear relationship between
the two main variables. (shown in table-3)
Graph1: Scatterplot of Worldwide GDP Growth Rate and
Mortality Rate in 2000.
7

Graph 2: Two-way Scatterplot of Worldwide GDP Growth and
Mortality rate in 2000
and 2014.
8
5.RESULTS
Table 3: Regression Results
Looking at table 3, the coefficient of GDP growth rate has a
statistically insignificant
relationship with mortality rate, and we can not conclude that
GDP growth rate has a linear
relationship with mortality rate. Therefore, we added 6 more
variables as shown in Table 3 that
are relative to mortality rate to test their relationships. The
results in Model 2 show that the
coefficient of improved sanitation facility sanitation and CO2
emissions are statistically
insignificant with mortality rate. And the coefficient of
Immunization measles of children's age,
prevalence of HIV total of population, and improved water are
statistically significant at 1%

level with mortality rate, with P-value equals to 0. Urban
population growth annual spurb is
statistically significant at 5% level on the country level, with P-
value equals to 0.047. Therefore,
we removed these two insignificant variables and ran the
regression (Model 3). Since it’s a panel
data, in order to make sure different countries have the same
coefficient effect, we uses country
9
level fixed effects, as we can see in Model 4, the coefficient of
GDP growth rate still has a
statistically insignificant relationship with mortality rate, even
after we controlling for the effects
of time (Model 5).
Furthermore with the data, we decided to add interactive
variables of immunization and
GDP growth rate in Model 6, within in a country and after
controlling for the effects of year,
with variable we testified significance before , the data shows
that the coefficient of GDP growth
rate still has a statistically significant relationship with
mortality rate at 1% level because the

effect of GDP growth on mortality rate depends on the
percentage of Kids Immunization (12-13
months), and 77.5% of the variable of data in mortality rate
explained by GDP growth rate
within country when the effects of time controlled.
In Model 7, we tested if the relationship of GDP growth rate to
mortality rate depends on
other 3 significance variables. The results showed that the other
3 coefficients of interactive
variables are statistically insignificant which does not affect the
relationship of GDP growth rate
to mortality rate. The results shown are not as consistent with
our hypothesis, as immunization is
the factor that would affect the relationship between the two
main variables.
6. Conclusions
Based on our findings on the model, GDP growth does not have
a statistically significant
relationship with the mortality rate. However, when holding
countries and time fixed, and we
added the interactive variable immunization, the results showed
relationship between GDP growth rate and mortality rate. As a

result, we can conclude that GDP
growth rate and mortality rate depends on a third variable of
immunization measles of children's
age. And our findings do not completely support the hypothesis
that there is a linear relationship
10
between GDP growth rate and mortality rate. As shown in graph
1 and graph 2, the countries are
scattered altogether in a group instead of displaying a linear
relationship. While the independent
variable being statistically insignificant, the causes can be
complicated due to many other
factors.
7. Limitations and future results
For further research in the future, we would apply more
different variables which could
contribute to the change of the dependent variable, including
import and export on the country
level. Also, besides testing the interactive variables, maybe we
would use the log and square of
the independent variable after we collected the data. And more
research will be done not only on

a country level, but within a country as well. There are some
limitations in this research,
including not enough variables that are relative to the dependent
variable. More variables will be
applied in the future research.
11
REFERENCES CITED
● Svensson, M., Krüger, N.A. Mortality and economic
fluctuations. J Popul Econ 25,
1215–1235 (2012). https://doi.org/10.1007/s00148-010-0342-8
● M Harvey Brenner, “Commentary: Economic growth is the
basis of mortality rate decline
in the 20th century—experience of the United States 1901–
2000”, International Journal
of Epidemiology, Volume 34, Issue 6, December 2005, Pages
1214–1221. Retrieved
from https://doi.org/10.1093/ije/dyi146
● Svensson, Mikael, and Niclas A. Krüger. “Mortality and
Economic Fluctuations:
Evidence from Wavelet Analysis for Sweden 1800—2000.”
Journal of Population

Economics, vol. 25, no. 4, Springer, 2012, pp. 1215–35,
http://www.jstor.org/stable/23354789.
● M Harvey Brenner, Commentary: Economic growth is the
2000, International Journal of
Epidemiology, Volume 34, Issue 6, December 2005, Pages
1214–1221,
https://doi-org.ezproxy.bu.edu/10.1093/ije/dyi146
● Varvarigos. (2013). ENVIRONMENTAL DYNAMICS AND
THE LINKS BETWEEN
GROWTH, VOLATILITY AND MORTALITY. Bulletin of
Economic Research, 65(4),
314–331. https://doi.org/10.1111/j.1467-8586.2011.00410.x
● Haines, Craig, L. A., & Weiss, T. (2003). The Short and the
Dead: Nutrition, Mortality,
and the “Antebellum Puzzle” in the United States. The Journal
of Economic History,
63(2), 382–413. https://doi.org/10.1017/S0022050703001839
12
https://doi.org/10.1007/s00148-010-0342-8
https://doi.org/10.1093/ije/dyi146

http://www.jstor.org/stable/23354789
https://doi.org/10.1111/j.1467-8586.2011.00410.x
https://doi.org/10.1017/S0022050703001839
APPENDIX A
DO FILE
clear all
set more off
capture log close
cd/Users/sarah
use "/Users/sarah/Downloads/WorldBankData2years (7).dta"
sum mortrate gdpgrowth immunizationmeaslesofchildrenage
prevalenceofhivtotalofpopulation
improvedsanitationfacilitiesofpo
co2emissionsmetrictonspercapitae
urbanpopulationgrowthannualspurb improvedwater
*Graph 2
bysort year: eststo: estpost sum mortrate gdpgrowth
immunizationmeaslesofchildrenage

esttab using summary_stats_table.rtf, cells((mean(fmt(%10.2f))
sd(fmt(%10.2f)))) label
title(Summary Statistics) nonumber nomtitle replace
label var mortrate "Mortality Rate"
label var gdpgrowth "GDP Growth"
label var immunizationmeaslesofchildrenage "% of Kids
Immunization (12-13 months)"
label var prevalenceofhivtotalofpopulation "HIV population"
label var improvedsanitationfacilitiesofpo "improved sanitation
facility"
label var co2emissionsmetrictonspercapitae "CO2 emissions"
label var urbanpopulationgrowthannualspurb "Urban Population
Growth"
13
label var improvedwater "Imporve Water"
#delimit ;
esttab using SummaryStats1.doc, main(mean) aux(sd)
nonotes rtf replace label varwidth(30) modelwidth(9) b(%9.2f)

nonumbers
mtitle("2000" "2014")
title("Table 1: Summary Statistics by Year")
addnotes("NOTE: Table reports the mean and standard deviation
in 2000 and 2014. 'The
mean' is above 'standard deviation' for each variable")
;
#delimit cr
*Graphing in 2000 and 2014
twoway (lfitci mortrate gdpgrowth )(scatter mortrate
gdpgrowth), ytitle(Mortality Rate)
*title(Two-way Scatterplot of GDP Growth Rate and Mortality
Rate)
*graph save "Graph" "/Users/sarah/Desktop/Two-way
Scatterplot 2000 and 2014.gph"
*Graphing in 2000
twoway (scatter mortrate gdpgrowth if year == 2000,
mlabel(countryname)) (lfit mortrate
gdpgrowth)
*ytitle(Mortality Rate) xtitle(GDP Growth)
*title(Two-way Scatterplot of GDP Growth and Mortality Rate)

*graph save "Graph" "/Users/sarah/Desktop/Scatterplot
2000.gph"
*Graph 3
*regress X and Y
reg mortrate gdpgrowth, r
14
outreg2 using ResearchRegression.doc, replace label title
("Regression Results") adjr2 addtext
(Country FE, NO, YEAR FE, NO)
outreg2 using ResearchRegressionTable.doc, adjr2 addtext
(Country FE, NO, YEAR FE, No)
*regression on full model
reg mortrate gdpgrowth immunizationmeaslesofchildrenage
urbanpopulationgrowthannualspurb improvedwater,r
*running an F test on the insignificant variables

test gdpgrowth improvedsanitationfacilitiesofpo
*The F test shows these variables are jointly insignificance, so
we kick these variable out
*To see if gdpgrowth is jointly significance with other variable,
let's pick
test gdpgrowth immunizationmeaslesofchildrenage
*Regression on Fixed Effect
xtset countrynum year
xtreg mortrate gdpgrowth, r fe
(Country FE, YES, YEAR FE, No)

15
xtreg mortrate i.year gdpgrowth, r fe
(Country FE, YES, YEAR FE, YES)
xtreg mortrate i.year
c.gdpgrowth##c.immunizationmeaslesofchildrenage
urbanpopulationgrowthannualspurb improvedwater,r fe
xtreg mortrate i.year immunizationmeaslesofchildrenage
c.gdpgrowth##c.prevalenceofhivtotalofpopulation
urbanpopulationgrowthannualspurb
improvedwater,r fe
c.gdpgrowth##c.urbanpopulationgrowthannualspurb
improvedwater,r fe

c.gdpgrowth##c.improvedwater,r fe
log close
16
GDP GROWTH ON MORTALITY RATE IN 2000 AND 2014
ACROSS THE WORLD
Yuxuan Tang
Jianing Wang
EC204 Empirical Economics II, Fall 2021
ABSTRACT
Journal of Population of Economics stated “For the period
1800–2000, an increase in
GDP by 1% decreased mortality by 0.7%. This overall
relationship is due to a strong
counter-cyclical relationship in the nineteenth century, which

disappeared in the twentieth
century” (Svensson, M., Krüger, 2010). Based on the
WorldBankData2years panel data in the
year of 2000 and 2014, this research mainly focused on the
effects of GDP growth on mortality
rate, with different variables involved. The results showed a
statistically insignificant relationship
between GDP growth and mortality rate. And 43.39% of the
variation of the mortality rate can be
explained by GDP growth within the country when holding
country level and time fixed effect.
1
⻄
https://learn.bu.edu/bbcswebdav/pid-9575630-dt-content-rid-
59011850_1/xid-59011850_1
I. INTRODUCTION
This research aims to discover if GDP per capita affects
mortality rate. Past research
shows that GDP per capita is inversely related to mortality rate
during 1901-2000 in the United
States (M Harvey Brenner, 2005). In this article, Thomas
McKeown demonstrated that

economic development is of fundamental importance to the
decline of classic infectious and
childhood disease. With rapid economic growth in the 20th
century, more people tend to have
vaccinations and are less vulnerable to infectious and
childhood disease, which leads to a
decline in mortality rate. As a result, an inverse relationship
between GDP per capita and
mortality rate worldwide was expected at the beginning.
After the hypothesis was conducted, we described and utilized a
panel data across the
world in 2000 and 2014, and regressed GDP per capita and
mortality rate with some variables
including improved sanitation facilities of po, co2 emissions
metric tons per capita, improved
water, urban population growth annual spurb, prevalence of hiv
total of population,
immunization measles of children age and others are tested with
GDP per capita to find out how
it affects mortality rate. Then, we compiled our findings and
found there is a statistically
insignificant relationship between the two main variables.
Therefore, we use interactive variables

to test if the effects of GDP growth per capita on mortality rate
depends on other variables listed
above. Then we created a graph that involves a linear
regression and scatter plot were used to
make further comparison of fitness. Also, with the quadratic
model being graphed, the turning
point is at 0.164310932, and after this turning point, the
relationship between GDP growth and
mortality rate becomes positive contrary to our expectations.
2
2. Literature Review
Many researchers had done studies relative to the effects of
GDP growth on mortality rate
for years, and the reasons could be complicated. Mikael
Svensson and Niclas A. Kruger used
wavelet methods to analyze the relationship between mortality
rate and economic growth from
1800 to 2000 in Sweden. (Mikael Svensson and Niclas A.
Krüger, 2012) According to the article,
it was found that in the early period of the 19th century, people
were more vulnerable to disease
and health problems when the economy went downward. As a

result, the mortality rate was
higher when the economy was poor. However, when we entered
the 20th century, the augment
changed. People were more likely to stress out due to reasons
including work stress, family
pressure due to unemployment, which leads to higher death rate.
Furthermore, the research found
out some more specific factors that associate mortality rate with
GDP growth, including stroke,
accident, suicide, cancer, and infection.
More findings were found by M Harvey Brenner. Using the time
series model, with
variables of “ long-term effects of economic growth over 0–11
years,” “long-term effects of
unemployment over 0–11 years,” and “interactive effect of
unemployment and GDP per capita
over 0–11 years”, it was found out that for a short period,
increased mortality rate was due to
higher GDP growth, because of better technology with longer
working period and speed.
However, for a longer period, GDP growth leads to the decline
of mortality rate.（M Harvey
Brenner, 2005） More evidence was found by Brenner and
Haines to prove this theory. According

to the article written by Haines in 2003, it was found that the
United States experienced a rapid
economic growth but rising mortality rate between 1830 and
1860 due to deterioration of the
biological standard of living (Hanis 2003). During this period,
the fast urban growth, mass
migration from abroad, changes in transportation infrastructure,
rapid commercialization,
3
worsened the mortality environment which caused the mortality
rate to rise. For a longer period,
Varvarigos constructed a model of a growing economy with
pollution and testified that economic
growth and mortality rates are negatively related due to the
difference of environment-related
structural parameters, such as lower p (units of pollution per
output generating), which improves
the environmental conditions and reduces mortality rate
(Varvarigos, 2013).
3. Data Description
Table 1

This research used panel data at country level worldwidely in
the year of 2000 and 2014
from world bank data to analyze the relationship between GDP
growth and mortality rate. A total
of 369 observations are collected from world bank data with 6
variables, including sanitation
facilities of po, co2 emissions metric tons per capita, improved
water, urban population growth
annual spurb, prevalence of hiv total of population, and
immunization measles of children age.
These 6 variables, together the two main variables are tested to
find out the relationship between
GDP growth and mortality rate. The six variables are chosen
because we realized that the higher
GDP a country has, the more conscious people have of their
health. And as a result, more people
4
are getting vaccinated and actions or policies are taken for the
sake of citizens’ health, which
leads to the decline of mortality rate.
The data of this research all come from world bank data, and
two tables were created by

different years to describe the mean and standard deviation of
the variables. Out of all the
variables, improved water has the highest mean value of 83.2%
and 89.0 % in 2000 and 2014,
whereas urban population growth annual spurb have the lowest
mean values around 2% in both
years.
Table 2
5
4. Model:
After we collected the data, we constructed a model of mortality
rate as a function of GDP
growth at the country level of time fixed effect.
Within this fixed effect model, by holding year t and country i
at constant level, mortrate
represents mortality rate, the continuous dependent variable in
this equation, in year t and
country i. The main independent variable of this equation is
gdpgrowth, which is continuous in
country i and year t, and is predicted to have a positive
relationship with the main variable

mortality rate. The model is predicted as a linear regression as
shown in the scatterplot graph. As
we used a time fixed effect model, the 6 other variables with i
are absorbed into the ai variable
which change based on different countries. According to graphs
shown below, most countries
with different mortality rates are scattered between 0% to 20%
growth of GDP in both 2000 and
2014.
Also, in this model, the panel data at country level analyzes
data from both year of 2000
and year of 2014 by using the dummy variable d00t and u is the
error term. Graph1 represents
the worldwide GDP growth rate and mortality rate in 2000, and
graph 2 displays GDP growth
rate and mortality rate in both the years of 2000 and 2014.
However, by looking at the two
graphs below, we can see there is no inverse relationship
between the GDP growth rate and
mortality rate, but instead a positive relationship. However, we
cannot conclude that there is a
definitely positive relationship between GDP growth rate and
mortality rate, as the dots mostly
concentrated in the middle of the graph rather than displaying a

linear relationship. And there are
6
countries including Liberia, Equatorial Guinea , and Timor-
Leste which are more than 3 standard
deviations away from the mean fall into the category of
becoming outliers of the group.
Therefore, we used some interactive variables to test if there is
a non linear relationship between
the two main variables. (shown in table-3)
Graph1: Scatterplot of Worldwide GDP Growth Rate and
Mortality Rate in 2000.
7
Graph 2: Two-way Scatterplot of Worldwide GDP Growth and
Mortality rate in 2000
and 2014.
8
5.RESULTS
Table 3: Regression Results

Looking at table 3, the coefficient of GDP growth rate has a
statistically insignificant
relationship with mortality rate, and we can not conclude that
GDP growth rate has a linear
relationship with mortality rate. Therefore, we added 6 more
variables as shown in Table 3 that
are relative to mortality rate to test their relationships. The
results in Model 2 show that the
coefficient of improved sanitation facility sanitation and CO2
emissions are statistically
insignificant with mortality rate. And the coefficient of
Immunization measles of children's age,
prevalence of HIV total of population, and improved water are
statistically significant at 1%
level with mortality rate, with P-value equals to 0. Urban
population growth annual spurb is
statistically significant at 5% level on the country level, with P-
value equals to 0.047. Therefore,
we removed these two insignificant variables and ran the
regression (Model 3). Since it’s a panel
data, in order to make sure different countries have the same
coefficient effect, we uses country
9

level fixed effects, as we can see in Model 4, the coefficient of
GDP growth rate still has a
statistically insignificant relationship with mortality rate, even
after we controlling for the effects
of time (Model 5).
Furthermore with the data, we decided to add interactive
variables of immunization and
GDP growth rate in Model 6, within in a country and after
controlling for the effects of year,
with variable we testified significance before , the data shows
that the coefficient of GDP growth
rate still has a statistically significant relationship with
mortality rate at 1% level because the
effect of GDP growth on mortality rate depends on the
percentage of Kids Immunization (12-13
months), and 77.5% of the variable of data in mortality rate
explained by GDP growth rate
within country when the effects of time controlled.
In Model 7, we tested if the relationship of GDP growth rate to
mortality rate depends on
other 3 significance variables. The results showed that the other
3 coefficients of interactive

variables are statistically insignificant which does not affect the
relationship of GDP growth rate
to mortality rate. The results shown are not as consistent with
our hypothesis, as immunization is
the factor that would affect the relationship between the two
main variables.
6. Conclusions
Based on our findings on the model, GDP growth does not have
relationship with the mortality rate. However, when holding
countries and time fixed, and we
added the interactive variable immunization, the results showed
relationship between GDP growth rate and mortality rate. As a
result, we can conclude that GDP
growth rate and mortality rate depends on a third variable of
immunization measles of children's
age. And our findings do not completely support the hypothesis
that there is a linear relationship
10
between GDP growth rate and mortality rate. As shown in graph
1 and graph 2, the countries are

scattered altogether in a group instead of displaying a linear
relationship. While the independent
variable being statistically insignificant, the causes can be
complicated due to many other
factors.
7. Limitations and future results
For further research in the future, we would apply more
different variables which could
contribute to the change of the dependent variable, including
import and export on the country
level. Also, besides testing the interactive variables, maybe we
would use the log and square of
the independent variable after we collected the data. And more
research will be done not only on
a country level, but within a country as well. There are some
limitations in this research,
including not enough variables that are relative to the dependent
variable. More variables will be
applied in the future research.
11
REFERENCES CITED

● Svensson, M., Krüger, N.A. Mortality and economic
fluctuations. J Popul Econ 25,
1215–1235 (2012). https://doi.org/10.1007/s00148-010-0342-8
● M Harvey Brenner, “Commentary: Economic growth is the
2000”, International Journal
of Epidemiology, Volume 34, Issue 6, December 2005, Pages
1214–1221. Retrieved
from https://doi.org/10.1093/ije/dyi146
● Svensson, Mikael, and Niclas A. Krüger. “Mortality and
Economic Fluctuations:
Evidence from Wavelet Analysis for Sweden 1800—2000.”
Journal of Population
Economics, vol. 25, no. 4, Springer, 2012, pp. 1215–35,
http://www.jstor.org/stable/23354789.
● M Harvey Brenner, Commentary: Economic growth is the
2000, International Journal of
Epidemiology, Volume 34, Issue 6, December 2005, Pages
1214–1221,

● Varvarigos. (2013). ENVIRONMENTAL DYNAMICS AND
THE LINKS BETWEEN
GROWTH, VOLATILITY AND MORTALITY. Bulletin of
Economic Research, 65(4),
314–331. https://doi.org/10.1111/j.1467-8586.2011.00410.x
● Haines, Craig, L. A., & Weiss, T. (2003). The Short and the
Dead: Nutrition, Mortality,
and the “Antebellum Puzzle” in the United States. The Journal
of Economic History,
63(2), 382–413. https://doi.org/10.1017/S0022050703001839
12
https://doi.org/10.1007/s00148-010-0342-8
https://doi.org/10.1093/ije/dyi146
http://www.jstor.org/stable/23354789
https://doi.org/10.1111/j.1467-8586.2011.00410.x
https://doi.org/10.1017/S0022050703001839
APPENDIX A
DO FILE
clear all
set more off
capture log close

cd/Users/sarah
use "/Users/sarah/Downloads/WorldBankData2years (7).dta"
sum mortrate gdpgrowth immunizationmeaslesofchildrenage
*Graph 2
bysort year: eststo: estpost sum mortrate gdpgrowth
esttab using summary_stats_table.rtf, cells((mean(fmt(%10.2f))
sd(fmt(%10.2f)))) label
title(Summary Statistics) nonumber nomtitle replace
label var mortrate "Mortality Rate"
label var gdpgrowth "GDP Growth"
label var immunizationmeaslesofchildrenage "% of Kids
Immunization (12-13 months)"

label var prevalenceofhivtotalofpopulation "HIV population"
label var improvedsanitationfacilitiesofpo "improved sanitation
facility"
label var co2emissionsmetrictonspercapitae "CO2 emissions"
label var urbanpopulationgrowthannualspurb "Urban Population
Growth"
13
label var improvedwater "Imporve Water"
#delimit ;
esttab using SummaryStats1.doc, main(mean) aux(sd)
nonotes rtf replace label varwidth(30) modelwidth(9) b(%9.2f)
nonumbers
mtitle("2000" "2014")
title("Table 1: Summary Statistics by Year")
addnotes("NOTE: Table reports the mean and standard deviation
in 2000 and 2014. 'The
mean' is above 'standard deviation' for each variable")
;
#delimit cr

*Graphing in 2000 and 2014
twoway (lfitci mortrate gdpgrowth )(scatter mortrate
gdpgrowth), ytitle(Mortality Rate)
*title(Two-way Scatterplot of GDP Growth Rate and Mortality
Rate)
*graph save "Graph" "/Users/sarah/Desktop/Two-way
Scatterplot 2000 and 2014.gph"
*Graphing in 2000
twoway (scatter mortrate gdpgrowth if year == 2000,
mlabel(countryname)) (lfit mortrate
gdpgrowth)
*ytitle(Mortality Rate) xtitle(GDP Growth)
*title(Two-way Scatterplot of GDP Growth and Mortality Rate)
*graph save "Graph" "/Users/sarah/Desktop/Scatterplot
2000.gph"
*Graph 3
*regress X and Y
reg mortrate gdpgrowth, r
14
outreg2 using ResearchRegression.doc, replace label title

("Regression Results") adjr2 addtext
(Country FE, NO, YEAR FE, NO)
*running an F test on the insignificant variables
test gdpgrowth improvedsanitationfacilitiesofpo
*The F test shows these variables are jointly insignificance, so
we kick these variable out
*To see if gdpgrowth is jointly significance with other variable,
let's pick

test gdpgrowth immunizationmeaslesofchildrenage
*Regression on Fixed Effect
xtset countrynum year
xtreg mortrate gdpgrowth, r fe
(Country FE, YES, YEAR FE, No)
15
xtreg mortrate i.year gdpgrowth, r fe
xtreg mortrate i.year
c.gdpgrowth##c.immunizationmeaslesofchildrenage
urbanpopulationgrowthannualspurb improvedwater,r fe

c.gdpgrowth##c.prevalenceofhivtotalofpopulation
improvedwater,r fe
c.gdpgrowth##c.urbanpopulationgrowthannualspurb
improvedwater,r fe
c.gdpgrowth##c.improvedwater,r fe
log close
16

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