© Lawrence Lam 2009
Anthro 174AW
Food Stress and Hunger
Starvation is defined as a severe reduction in vitamin, nutrient, and energy intake. It is an
extreme form of malnutrition, and is a phenomenon that has been observed throughout history. I
believe groups who live in harsher climates, and those who are more susceptible to natural
disasters (from weather or pests) are more prone to some level of food stress than other groups. It
is common knowledge that there different areas of the world have different climate types. Many
societies and groups may suffer from malnutrition from time to time, however some groups may
have more extreme cases than others. Some groups may have consistent sources of food, some
may have rare and occasional hunger, and others may experience periodic starvation. One major
cause of food stress and hunger is famine. Famine is a widespread scarcity of food which can be
caused by many reasons. Famine can be caused by people, such as war, tyrannical government,
and other extreme political conditions. Famine can also be caused by natural climate fluctuations
and natural disasters. Examples of such natural conditions are drought and weather and pest
disasters. Catastrophes such as drought and natural disasters can destroy agriculture and cause
food shortages. In this paper I will be using variables selected from the codebook of the Standard
Cross-Cultural Sample (SCCS). My dependent variable will be (v678) Food Stress and Hunger,
and I will be correlating it with (v1684) Threat of weather or pest disasters and (v857) climate
type.
Food stress and hunger is categorized in the SCCS codebook as:
678.
Food Stress or Hunger
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48
. = Missing data
47
1 = food constant
62
2 = occasional hunger or famine
26
3 = periodic or chronic hunger
3
4 = starvation or evidence of protein deficiency
The variable measures how much food stresses a society or group experiences and categorizes
them in one of the categories in the variable. What we are interested in is the ones who fall in the
last two categories. Societies that experience periodic or chronic hunger, or even experience
starvation must have a reason why. Generally, starvation is defined as having an inadequate
caloric intake. This can be caused by having problems in food supply. My hypothesis is that
those who live in harsher climates and those who are more susceptible to weather and pest
disasters will have problems and fluctuations in their food supply and thus, will be more prone to
food stresses than others.
There are many different climate types on this planet. The climates are categorized in the
SCCS codebook as:
857.
Climate Type - Ordered in terms of Open Access to Rich Ecological
Resources
6
D. White and M. Burton l986
1 = Polar
38
2 = Desert or cold steppe
50
3 = Tropical rainforest
39
4 = Moist temperate
45
5 = Tropical savanna
8
6 = Tropical highlands
Different climate types affect the kinds and amount of food available to the people. Climate also
affects agriculture. Despite technological advances, climate and weather is still a key factor in
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agricultural productivity. Many societies depend on agriculture as a source of food, and climate
becomes a factor in the productivity and output of agriculture. Fluctuations in climate may cause
fluctuations in agricultural output, which in turn will lead to some degrees of food stress for the
population. Because agriculture is so dependent on climate, it is logical to assume that areas with
the most stable and consistent climate will experience the least amount of food stress. This is
because the output of agriculture in a stable climate will be consistent and stable. Areas with
harsher or less stable climates will most likely have fluctuations in food sources. This is
supported by William Divale’s paper on Climatic Instability and Food Storage. Divale finds in
his research that “extremes in severe coldness and severe dryness produced the greatest
environmental instability and the greatest amount of starvation and famine” (Divale 1999).
Extremes in climate such as drought or floods, fire, heat waves, storms, and cold snaps can
potentially destroy crops.
Although climate and weather can certainly affect a population’s food supply, it is not the
only reason for populations to experience food stress. Another cause of food stress is pests. In
order to maximize food production, crops must be kept free from pests. If the pest presence is
left alone, the entire harvest can be destroyed. In Robert Dirks’s paper on starvation and famine,
Dirks writes that famine is especially prone to break out in environments of chronic hunger and
recurrent starvation. Although Dirks does not mention specifically that pests can directly lead to
a ruined food supply, it is common sense that pests that are left unchecked or out of control will
significantly damage the food supply. In other words, some areas are more prone to food stress
than others. He lists reasons given for famine in his research, and pests are among the top
reasons for famine (Dirks 1993).
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Climate fluctuations and weather and pest disasters are prime culprits for many episodes
of food stress and hunger. It is evidenced in the papers written by Divale and Dirks. Extremes in
climate produce the greatest environmental instability and in turn, produce the greatest amount of
food stress. Dirks lists the reasons for famine in his research, and weather disasters as well as
pests are among the top reasons why populations would experience famine. It is quite clear that
both authors support the fact that climate and weather and pest disasters are top causes of food
stress and hunger. However, not all areas are at risk for such disasters. Some areas of the world
are more at risk for disasters than others. The SCCS codebook categorizes the risk as low,
moderate, moderately high, and high.
In order to support my hypothesis, I used Eff and Dow’s program called R (--> in R) in
order to gather statistics on the variables. Eff and Dow’s method provides an alternative to
another form of survey research. Eff and Dow’s method provides a better alternative when it
comes to survey research, since it addresses the problem of missing data and autocorrelation. The
most common procedure for handling missing data is listwise deletion, where any observation
with missing data is deleted. This is very inefficient since, according to Eff and Dow’s article
Multiple Imputation of Missing Data in Cross-Cultural Samples, “on average 65% of the sample
units will have at least one missing value.” (Eff, Dow, 2009). The alternative to listwise deletion
is multiple imputation, where values are estimated for missing observations using auxiliary data.
With Eff and Dow’s method and program, I was able to modify the program with my own
variables selected from the SCCS codebook and run a regression tailored towards my topic.
In my earlier stages of this project, my hypothesis was different. My earlier hypothesis
still had my dependent variable as food stress and hunger (v678); however my earlier hypothesis
stated that societies and groups are more at risk for food stress and hunger when they are further
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from a water source. I ran the program with food stress and hunger (v678) as my dependent
variable, and I also added fishing contribution to food supply (v7), ocean and lake within 100
miles (v1889 and v1890), agricultural improvements (v1812) and changes and introduction of
water transport (v1816) as independent variables. After running the program, I found that all of
my newly added independent variables were all insignificant in relation to my dependent
variable. After reading Robert Dirks’s article on starvation and famine, I decided to add another
variable, (v1684) threat of weather or pest disasters, to my program. After running the program
again, I was pleased to find out that the variable (v1684) threat of weather or pest disasters was
significant. Also, another significant variable that caught my attention was the variable (v857),
climate type. I then formed a new hypothesis with these two variables, stating that groups who
live in harsher climates, and those who are more susceptible to natural disasters (from weather or
pests) are more prone to some level of food stress than other groups.
After culling down my variable list, I was down to only a handful of independent
variables. Among those variables are (v1684) threat of weather and pest disasters, and (v857)
climate type. The name given to (v1684) in the program is “weatherpest” and (v857) is named
“ecorich”. The result of the regression was:
>
bbb
coef Fstat
ddf pvalue
VIF
(Intercept)
0.221 0.026
3978.758
0.872
fyll
0.210 0.071
3033.455
0.790 1.305
fydd
0.546 2.377 18249.668
0.123 1.369
-0.112 5.160 20834.907
0.023 1.068
ecorich
NA
moralgods
0.127 4.754
1289.122
0.029 1.067
himilexp
-0.320 5.698
3288.341
0.017 1.068
weatherpest
0.100 2.868
267.651
0.092 1.056
agrlateboy
0.061 3.126
1758.514
0.077 1.054
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>
r2
R2:final model R2:IV(distance) R2:IV(language)
0.1634943
>
0.9616244
0.9408496
ccc
Fstat
RESET
df pvalue
0.184
5783.607
0.668
Wald on restrs. 13.422
79.897
0.000
NCV
1.763
533.089
0.185
SWnormal
3.556
540.919
0.060
lagll
0.276 418888.582
0.599
lagdd
1.271 352082.077
0.260
>
As stated earlier, climate type and weather and pest disasters are significant in relation to food
stress and hunger. This reinforces my hypothesis that a group or society’s climate or risk of
weather and pest disasters can affect the level of risk for food stress. The coefficient for weather
and pest disasters is positive, meaning that as the risk for weather and pest disasters is increased,
so is the level of food stress. The coefficient of climate type (ecorich) is negative, meaning that
as we move up the variable (mentioned earlier in the paper), the risk of food stress goes down.
For example, the climate categories such as polar and desert will have a higher risk of food stress
than tropical savanna and tropical highlands.
Taking a look at the diagnostics in this restricted model, there are a few things to consider.
The high p-value for the “RESET” category suggests that there are no nonlinear transformations
of independent variables. The low p-value of the category “Wald on restrs.” suggests that there
are significant excluded variables. The high p-value of the “NCV” category means that the
autocorrelation errors are not bunched. The p-value of “SWnormal” means that autocorrelation
errors are normally distributed. The p-value of “lagll” means that there are no additional network
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effects in this variable. Finally, the p-value of “lagdd” means that there are no additional network
effects in this variable.
In conclusion, I think it is safe to say that the statistical data found from the regression
program does support my hypothesis. It is clear that climate type and risk of weather and pest
disasters has an effect on the level of food stress a society encounters. However, the low p-value
of the diagnostic “Wald on restrs.” suggests that there are significant excluded variables. This
means that we are not seeing the entire picture for what food stress and hunger is dependent on.
Even in the R program, however, the Wald test suggests that one might be able to find another of
the independent variables that could be added to the Restricted model and help complete the
picture. In the book on famine by G. Ainsworth Harrison, the author states that “Weather and
consequent crop failure is not, and never has been, the only or even the main cause for famine”
(Harrison, pg. 9). This is also illustrated in the maps in this paper. There are a total of 29 cases
illustrated on the map for periodic hunger and starvation, whereas there are a total 55 cases
where risk of weather and pest disasters are moderately high to high risk. There are many other
factors that can contribute to food stress, such as inefficient governments, poverty, and many
more. Although the R program helped us see that climate and disasters are a factor in food stress,
it does not show us the full picture. Perhaps further study will help enlighten us on what
contributes to food stress.
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Maps of Food Stress
3 = Periodic or chronic hunger (26 cases)
4 = Starvation or evidence of protein deficiency (3 cases)
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Maps for risk of Weather or Pest Disasters
3 = Moderately high threat of severe natural disruptors of food supply (20 cases)
4 = High risk (32 cases)
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Works Cited
Dirks, Robert. "Starvation and Famine: Cross-Cultural Codes and Some Hypothesis Tests."
Starvation and Famine: Cross-Cultural Codes and Some Hypothesis Tests (1993). Web.
<http://ccr.sagepub.com/cgi/content/abstract/27/1-2/28>.
Divale, William. "Climatic Instability, Food Storage, and the Development of Numerical
Counting: A Cross-Cultural Study." Climatic Instability, Food Storage, and the
Development of Numerical Counting: A Cross-Cultural Study (1999).
Http://www.sagepublications.com. Web.
<http://ccr.sagepub.com/cgi/content/abstract/33/4/341>.
Dow, Malcolm M., and E. Anthon Eff. "Multiple Imputation of Missing Data in Cross-Cultural
Samples." Multiple Imputation of Missing Data in Cross-Cultural Samples (2009).
Http://www.sagepublications.com. Web.
<http://ccr.sagepub.com/cgi/content/abstract/43/3/206>.
Eff, E. Anthon, and Malcolm M. Dow. "How to Deal with Missing Data and Galton?s Problem
in Cross-Cultural Survey Research: A Primer For R." How to Deal with Missing Data
and Galton?s Problem in Cross-Cultural Survey Research: A Primer For R (2009). UC
Irvine, Structure and Dynamics, Social Dynamics and Complexity, Institute for
Mathematical Behavioral Sciences. Web. <http://escholarship.org/uc/item/7cm1f10b>.
Harrison, G. Ainsworth. Famine. New York: Oxford UP, 1988. Print.
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