1. Impact of a Volatile Sex Ratio in Cambodia
1960 – 2010
Brent Jenkins
ECON 4980
Research Methods
Abstract:
Modern mortality estimates of the Khmer Rouge regime (1975-1979) range between 1.5 million
and 3 million. Many studies have been undertaken to assess the irreparable damages done to the
Cambodian society during this tragic time. This paper seeks to understand and explain one aspect of it,
the changes in the sex ratio, and the potential impact that this single variable might have upon the
economic welfare of the country. The focus variable for this study is the sex ratio, and the period of study
stretches from 1960 – 2010.
2. Table of Contents:
I
-
Introduction
-
Page 3
II
-
Historical Background
-
Page 3
III
-
Literature Background
-
Page 5
IV
-
Theory Background
-
Page 7
V
-
Data & Methods
-
Page 9
VI
-
Results & Interpretation
-
Page 10
VII -
Summary & Conclusion
-
Page 11
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3. I – Introduction
Cambodia is a beautiful country nestled in South-eastern Asia between Thailand and Vietnam.
This country is painfully familiar with genocide and the disastrous effects that are left over after decades
of strife and war. During the Khmer Rouge genocide (1975 – 1979), the sex ratio dropped to
unprecedented levels. This paper seeks to understand the impact of these low ratio levels. I predict that
we will see a positive coefficient for the sex ratio variable in the regression equation, meaning that the sex
ratio does, in fact, impact GDP.
II - Historical Background
In October of 1953, King Sihanouk Norodom proclaimed Cambodian independence from the
French government. From this time forth, Cambodia was a nation torn by civil wars, changing
administrations, foreign threats and economic instability. Despite large inflows of foreign aid, Cambodia
still struggled to find peace and stability. This instability continued throughout the 1950‟s and 1960‟s,
and only worsened in the coming years.
During the 5-year span of 1970-75, Cambodia experienced four regime changes, spillover from
the American-Vietnam War, and an invasion from the Vietnamese military. In 1975, there was a final
government coupe and the Kingship of Sihanouk Norodom was overthrown by Pol Pot and his
communist band known as the CPK – the Communist Party of Kampuchea. The first order of the CPK
was a nation-wide evacuation of all large cities. This was the initial step in the doomed vision of the
CPK: an agrarian utopia.
As the capital city Phnom Penh was razed, citizens began to see the ominous indicators of
hardship to come. In the next five years, Cambodia lost anywhere from 1.5 million – 3 million citizens:
nearly a quarter of the entire population (Heuveline, P., 1998). I have included Figure 1 (below) for a
visual presentation of the population growth over the period of 1960 – 2010, with the Khmer Rouge
period highlighted:
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4. Figure 1
Notice the period highlighted in red: nearly a quarter of the population in 1975 would be lost over the subsequent five years.
The population of Cambodia today is just over 14 million. This chart was custom generated using data from the World
Bank.
During this same period, the sex ratio in the country dropped from a healthy 0.5 to 0.46. To
explain further, the sex ratio in Cambodia had hovered about 0.500 as far back as 1960. That is to say
that for every 500 males in the population, there would be about 500 females, give or take 2-5 females.
Following the Khmer Rouge period, there would be only 460 males for every 500 females. At first
glance, this may seem like only a minor fluctuation, but we must keep in mind that the sex ratio is an
integral part of society‟s family structure. This number tells us that there are now fewer males per female,
leading to perhaps fewer marriages and nuclear family units.
A major cause of this is likely that the demographic that suffered most under the brutal CPK
regime was that of males ages 20-60 (Heuveline, P., 1998). My initial interest in this research topic was
based upon the hypothesis that because this is also the demographic that spent the most time in nondomestic production activities, we would see a heavy correlation between a falling sex ratio and the Gross
Domestic Product (GDP) of the country.
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Figure 2
5. Figure 2 that will synthesize the sex ratio in the period concerned:
Notice the period highlighted in red: the Khmer Rouge regime would cause a decrease from a consistent 1:1 ratio to 0.92:1
– In over 30 years since the end of the CPK regime, the sex ratio has still not recovered to the normal level (see red line). A
central tenant of the author’s hypothesis is that this battered sex ratio will show a positive correlation with GDP. This chart
was custom generated using data from the World Bank.
III – Literature Background
Much has been written in way of assessing the lasting damages and impacts of the crimes
committed by the Pol Pot regime and the CPK. As I have surveyed the literature background, it is easy to
see that many authors and researchers have gone before me. Perhaps the most extensive research on the
population demographics of Cambodia has been performed by Patrick Heuveline (occasionally coauthoring papers as well).
The total mortality has been predicted to be between 1.2 and 3.4 million, most likely near 2.5
million (Heuveline, P., 1998). These estimates were obtained using a non-traditional method, but still
fell within the ranges previously produced by a variety of researchers.
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6. There are also extensive research findings on the marriage/family structure of the society pre- and
post-conflict. In summation, they conclude that the conflict had a major destabilizing effect on the family
structure, beginning with the frequency and circumstances of marriage during and immediately after the
conflict (Heuveline, P., Poch, B., 2006). Additionally, we find that this conflict had enormous
consequences on the birth rates and death rates among the general population (Heuveline, P., Poch, B.,
2007). Birth rates plummeted as death rates skyrocketed, as one might surmise during this period.
Figure 3 is provided to illustrate:
Figure 3
Rates in shown per 1,000 people - Notice the period highlighted in red: the Khmer Rouge regime would cause a decrease in
the birth rate (red line) and an increase in the death rate (blue line). Notice also that these trends began before the Khmer
Rouge took power in 1975. This is perhaps an effect of civil wars and government coupes that had taken place. This chart
was custom generated using data from the World Bank.
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7. Briefly leaving the history of Cambodia, I‟ve researched the work of others that have performed similar
studies for other data sets. In China, Wei and Zhang found that provinces with higher average sex ratios
were more likely to produce sustained growth in GDP (Wei, S., Zhang, X., 2011). Their analysis has
been extremely similar to what I‟ve proposed to perform – I was able to learn a lot and apply a very
similar method of regression.
One of the key principles from the literature review in determining the importance of the sex ratio
is to understand that men and women may be equal contributors to society, but the methods with which
their contributions are measured have not been equal over the years. For example, a married couple may
both contribute in meaningful ways: traditionally in Southeast Asia, the women will take charge of
domestic activities such as raising children, preparing meals, cleaning the house, running errands and so
on. The man would also contribute, but by working outside of the home in a factory, office building or
guild of some type. When considering the types of measures that go into GDP, most of the activities
would only be what the man has performed (outside the home) and not what the women has performed
(inside the home). This is not to say that one contributes more than the other, but in terms of the
measurements used, it may well be that the man has a higher impact of statistics like GDP.
IV – Theory Background
To sum up the goal of this research project, I want to know how the sex ratio has influenced GDP
over time. This could also be explained as the change in GDP divided by the change in the sex ratio:
The core of the theory used in this research is based on the standard Cobb-Douglas production
function. This function helps us to estimate an output that is based upon the inputs of labor (L) and
capital (K):
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8. In my analysis, I‟ve modified this basic function to include the influence of the sex ratio. I‟ve
included this with the labor term, making the assumption that the sex ratio is a factor of labor:
In determining the influence of the sex ratio on GDP, we will need to know the values of both α
and β. We can find these pieces of the puzzle by running a regression analysis. Before completing a
regression, we must linearize the entire equation. In its current state, the above equation is not linear
because it contains by α and β as powers. The linear equation will look like this:
We are now able to estimate α and β because they are linear pieces of this equation.
To find the
(elasticity), we know that it must equal the following evaluation:
[
]
[ ]
α = Elasticity
By stating the hypothesis as follows, I indicate that I am hoping to reject the null hypothesis and
find that the sex ratio has indeed influenced GDP:
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9. V – Data & Methods
The primary data source for this research project was the World DataBank, under the „World
Development Indicators‟ section. One key issue (and perhaps downfall) of this analysis was an utter lack
of data from the earlier parts of this period for Cambodia. The dependent variable of this analysis is GDP
– that is the total economic output of the country.
For the labor term “L” of the production function, I used the population multiplied by the life
expectancy. I used the total population instead of specific age demographics more likely to be in the
labor force because of a lack of age-specific data. Total population ought to suffice because it is
representative of those who might be in the labor force. Additionally, regression analysis seeks to pick
out trends and correlations, so using total population instead of a specific labor force indicator should not
skew the results to any significant level.
For the capital term “K”, a statistic such as capital stock, or net national investment would have
been ideal, but unfortunately, no such data exists for those statistics. The only available data that spanned
the time period in question is Net Official Development Aid. This represents all capital inflows from
other nations for the purpose of relief or national development. This will suffice until a future time when
more data may be available or derived.
As mentioned in the previous section (See “Theory Background” above), the entire equation had
to be multiplied by its log in order to linearize the equation – this now makes it appropriate for a
regression analysis. Once the analysis has been completed, I will walk through the steps of de-linearizing
the equation into its original form. This will give us tangible, meaningful results that we can then
interpret.
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10. VI – Results & Interpretation
Once the regression had been assembled and ran, the original regression equation produced is
shown below:
ln(Y) = -6.06 + 2.97(ln*sr) + 4.0(ln*L) + 0.0815(ln*K)
In this current form, the equation is difficult to dissect. Once we have de-linearized the equation by
multiplying each variable with the antilog, we see this: Y = 0.0023 + 19.49 sr + 54.6 L + 1.08
Y = 0.0023 + 19.49(sr) + 54.6(L) + 1.08(K)
***NOTE: While preparing data for the regression, I changed the sex ratio from a 0 – 1 scale to a 0 – 100
scale to make the numbers more convenient to work with. An appropriate estimation of the equation
using the correct scale for sex ratio would be as follows:
Y = 0.0023 + 1949(sr) + 54.6(L) + 1.08(K)
From the P-Value tests, we are able to evaluate the significance of each variable. Under SR, we
see an extremely large P-Value – This tells us that we cannot accept SR as a significant variable.
Under L and K, we see much smaller P-Values, each under 0.05. This tells us that that variables for L and
K seem to be insignificant.
We also know that from the Adjusted R-Squared statistic that this model can account for
approximately 81% of the fluctuations in y, or GDP.
Holding L (population) constant, we have now determined that for every 1.0 unit of change in the
sex ratio, GDP will have changed by $1949 - This is the co-efficient of the variable, but keep in mind that
this is insignificant. I‟ve included it merely because it was the focus of this analysis. We can also
conclude that for every additional contributing person in the population, GDP may increase by $54.60.
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11. Finally, for every $1.00 increase if the capital term, Net Official Development Assistance received, GDP
will increase by $1.08. Much more study would be needed to determine the accuracy and sustainability
of this new-found „assistance multiplier‟.
VII – Summary & Conclusion
Based on the meager data available on the topic, and the following regression, I have concluded
that the fluctuating sex ratio in Cambodia has not had an impact on the GDP over the 50 year span that
I‟ve studied. I emphasis “meager” as a description of the data selection because this may prove to be a
key concern about the overall accuracy of the regression‟s conclusion. I will follow with a few
conclusive points that must be synthesized and perhaps improved:
Does the sex ratio really matter, in terms of economic output?
Is this data set sufficient?
What other factors matter to this study?
a – Why does the sex ratio matter?
In terms of economic output, most would agree that men and women are quickly becoming equal
contributors – income gaps between males and females have been closing for decades, we are now
finding more and more women in the workplace and those who are competing for jobs, pay raises and
promotions are competing well against their cohorts. If we were talking about the situation today, it may
be the case that the sex ratio has little to no impact, since men and women are generally regarded as equal
contributors. However, since this study began in a primitive 1960‟s Cambodia, this has not always been
the case. We see that the more primitive we become (especially in developing and under-developed
economies), the more paternal societies are. In using the term „paternal‟ to describe a society, I mean that
in these societies, it is customary and traditional for the husband or man of the household to be the
breadwinner. Please note that I do not mean to say that the real contributions of females have a lower
value than that of males; but we must understand that the contributions made inside the home, which are
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12. usually part of a domestic agreement between partners, are not counted as a part of the economic
statistics used in this project.
In the early, eastern traditions of Cambodia, it would have mostly been men who worked outside
of the home, hence contributing to GDP. This statement is most likely fully true until the mid-1990‟s,
when a wave of westernization and modernization (not necessarily the same thing) swept across the
country, bringing with it increased levels of education for both men and women, and an increased
tolerance and acceptance of women working outside the home/ village.
To summarize this argument, it is the assumption and original hypothesis of the author that a
higher sex ratio would lead to a higher GDP, and inversely, a lower sex ratio would lead to lower
economic performance. This is because males have traditionally been the breadwinners of Cambodian
society, working outside the home in capacities that would be counted toward GDP. Again I must
emphasis that I am not meaning that females contribute less to society, but rather we must understand that
the general statistic available for this period does not take into account the contributions of those working
outside of the general workforce, without official employment.
b – Is this data set sufficient?
In performing this research, I followed another study very closely: Sex Ratios, Entrepreneurship,
and Economic Growth in the People’s Republic of China by Shang-Jin Wei and Xiaobo Zhang. These
two brilliant researchers ran a similar regression to test the influence of sex ratios on GDP. Under the
term for labor, I used sex ration (SR) and the total population – extremely similar to this previous study
performed in China. The capital term, however, may not be as robust and accurate as is needed. These
two authors used statistics that included that capital stock, domestic investment, and foreign investment,
all as shares of GDP. For Cambodia, none of this data was available. In fact, very little data was
available before the early 1990‟s. The only available capital-oriented statistic that stretched further back
than 1981 was the net official development assistance received (net ODA).
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13. Given that no other statistics for capital were available, the regression used in this project
included a capital term that was made up of only foreign aid (configured as a per capita statistic). This
must necessarily and wholly represent the capital term due to lack of alternatives. This may potentially
prove to be a concern in determining the complete accuracy of the regression. I have made the
assumption that this is why the capital term in the regression has proved to be insignificant – surely with a
more accurate picture of the capital stock we could perform a more robust regression. It may be absurd to
think that the capital stock has no impact or influence on the GDP, which is why I leave the capital term
of the regression with a question mark.
c – What other events or factors may be significant for this study?
In terms of potentially significant externalities, I would like to focus on two events that have had
lasting economic impacts on the productive capabilities of Cambodia: The first is the Khmer Rouge
genocide (perhaps the motivating element behind this research topic being chosen) and the second is a
short period of massive inflows of aid capital from foreign nations during the mid-1990‟s.
As previously mentioned, the hellish period from 1975-1980 was ruled by the CPK – Communist
Party of Kampuchea – and the Khmer Rouge. This was a period of massive starvation, economic
destruction, state brutality and death. Nearly one quarter of the population was lost during this period, the
capital city was destroyed, and the intellectual and progressive parts of the population were specifically
sought out and eradicated. The male population suffered more heavily than did the female population –
the sex ratio has still not recovered after 30 years. Population and GDP eventually caught up to pre-crisis
levels, but the lingering effect of the genocide will be poignant for decades and generations to come. This
is a confounding impact because there is no concrete method to assess the lasting damages and
opportunity costs associated with this crisis.
Secondly, there have always been large amounts of capital flowing into the country as foreign
aid. Indeed the net official development assistance was capital K variable of the production function used
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14. in this project. During the mid-1990‟s, there were several billion dollars that flowed into the Cambodian
economy from many different sources: the European Union with its many aid organizations, the IMF,
ASEAN, the United States, and Japan among many other sources. Below is a chart of net ODA per
capita:
Notice the massive spikes in capital inflows immediately before and immediately after the crisis. During the Khmer Rouge
regime, the borders were closed and no cross-borders trade or transaction was allowed. Also notice the spike in the mid1990’s. The US Dollar was unofficially adopted as the common currency in 1993. This chart was custom generated using
data from the World Bank.
Major and erratic inflows of foreign capital may also be an influential externality. The model
built and used in this project may not be sufficient to account for all capital inflows, as the data is
insufficient and the only portion of incoming capital accounted for in this study is official development
assistance.
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15. Conclusion
In conclusion, I find that we cannot reject the null hypothesis because of the large P-Value. I
believe that in a revised version of this study, additional data that may be derived or uncovered will
extend the model and give us more insight into the fluctuating economic activity of Cambodia over the
last 50 years.
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16. Works Cited
Heuveline, P. (1998), “„Between One and Three Million‟: Towards the Demographic Reconstruction of a
Decade of Cambodian History (1970-79),” Population Studies, Vol. 52, No. 1 (March), pp. 49-65
Wang, Y., Yao, Y., (1999), “Sources of China‟s Economic Growth, 1952-99: Incorporating Human
Capital Accumulation,” Research Working papers, (November), pp. 1-24
Heuveline, P., Bunnak, P. (2006), “Do Marriages Forget Their Past? Marital Stability in Post-Khmer
Rouge Cambodia,” Demography, Vol. 43, No. 1 (February), pp. 99-125
Heuveline, P., Bunnak, P. (2007), “The Phoenix Population: Demographic Crisis and Rebound in
Cambodia,” Demography, Vol. 44, No. 2 (May), pp. 405-426
Wei, S., Xiaobo Z., (2011), “Sex ratios, entrepreneurship, and economic growth in the People‟s Republic
of China,” National Bureau of Economic Research, No. w16800
Hagan, MGA. The Solow Growth Model - University of Pittsburgh. n.d.
http://www.pitt.edu/~mgahagan/Solow.htm (accessed February 2013).
Solow Growth Model - Iowa State University. n.d.
http://www2.econ.iastate.edu/classes/econ302/alexander/Spring2006/SOLOW/SOLOWGROWT
HMODEL.htm (accessed February 2013).
World DataBank. n.d. http://databank.worldbank.org/data/home.aspx (accessed February 2013).
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