Long-term economic consequences of the 1960 Chile earthquake
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Long-term economic consequences of
the 1960 Chile earthquake
Sonia Bhalotra Claudia Sanhueza Yicho Wu
University of Bristol Univ. Diego Portales University of Bristol
DRAFT, DO NOT CITE
COMMENTS ARE WELCOMED
May, 2011
Abstract
We investigate the long-term impacts of foetal and early childhood exposure to the
massive earthquake that struck Chile in May 1960. We study adult economic and
health outcomes. Mechanisms for foetal exposure are maternal stress and also public
infrastructure. We use census and survey data matched match to regional data on
earthquake intensity.
Key words: long term effects, earthquake, education, maternal stress
1. Introduction
Natural disasters always bring about substantial monetary and life loss all over the
1
world. However, Barker (1992) points out that another type of invisible impact of
these unexpected shocks exists as well. According to his research, unborn children
who experienced violent catastrophe would be influenced cognitively. This adverse
effect may further impact on their human capital accumulation and future
socioeconomic status, which leads to more social costs in the long term.
This study focuses on the strongest earthquake ever recorded, the 1960 Valdivia
earthquake in Chile, and analyses whether and how this disaster with following
tsunami affected certain individuals’ long run educational attainment, health outcomes
and socioeconomic status. The targeted group of sample in this analysis is the cohort
who was in utero and born in affected regions when the earthquake struck Chile. The
simplest way to test the earthquake impact is examining whether the schooling
performance and socioeconomic outcomes are relatively worse for the experimental
group in their adulthoods than other peers.
In this work, the 1992 and 2002 Chile censuses are exploited as the principal
datasets which provide information about respondents’ educational performance and
employment status. With restricting the year range, people who were born in the
period between 1950 and 1970 are selected as the sample of analysis. Within this
range, province of Valdivia as a geological dimension further divides the sample into
experimental group and comparison group. Nevertheless, individuals in both groups
might experience other confounding environmental trends which could also influence
results of interest. Therefore, the method of difference in difference is employed in
this study to pick up the net effect of the 1960 Valdivia earthquake.
Regression models with year and province fixed effects present results of whether
there was significant effect of the earthquake more quantitatively and precisely. The
outcomes of interest mainly consist of three aspects’ variables, educational attainment,
health outcomes and socioeconomic status, while the independent variables capture
birth year and province dummies for method of difference in difference, province
specific trends, gender gap and urban-rural difference. Regression results show that
there exist significant and adverse impacts of the 1960 Valdivia earthquake on
2
respondents’ schooling performance, but for health and socioeconomic status, the
disaster did not exert significant long term influence to the experimental group.
In terms of gender gap, females performed not as good as male students in school,
but women presented healthier outcomes and better socioeconomic status significantly
than men in their adulthoods. Furthermore, there is also difference existing between
cities and countryside. Specifically, people who were born in rural areas in the sample
generally obtained worse educational records and socioeconomic outcomes than those
living in cities. Compared between these two differences, the problem of urban-rural
gap was more serious in Chilean society.
In the following sections, background of the 1960 Valdivia earthquake is
introduced and several previous studies are reviewed. Then, Chapter 3 describes the
method of difference in difference and more specific models used in the quantitative
analysis. In Chapter 4, the datasets and variables of interest are displayed and
analysed qualitatively with point trends figures. Chapter 5 presents test results of all
three aspects of outcomes and simple explanations of them. Finally, the results of
empirical models are discussed in depth with comparisons to previous literature and
some potential weaknesses of this study are proposed in Chapter 6.
2. Background
A. The 1960 Valdivia Earthquake
On 22nd May 1960 (14:11 Chile time, 19:11 GMT), the greatest earthquake ever
recorded in history, with magnitude of 9.5 degrees on the Richter scale, shocked the
coastal regions of southern Chile. The overall stricken areas were located in the south
central Chile between latitude 37 degree and 43 degree S (Veblen and Ashton, 1978)
shown in the map of Figure 1.
Chile lies on the South American Plate when it joints the Nazca Plate and the
Antarctic Plate (see Figure 1.A), in which is called the Pacific Ring of Fire. The 1960
3
seismic event was produce by the release of mechanical stress between the
sub-ducting Nazca Plate and the South American Plate. The epicenter was relatively
shallow at 33 km and the main shock resulted from a rupture nearly 1,000 km long
(Cisternas et al. 2005, p404).
This severe earthquake then caused a more devastating tsunami, which spread out
the whole Pacific Ocean and affected Chile, Hawaii, Japan and the Philippines largely
(Plafker and Savage, 1970).
According to USGS reports, the estimates of the total mortality number ranged
from 2,000 to 5,000, which represent less than 0.1% of the country’s population
(7,300,000 people in 1960). There is not precise estimation of how many deaths were
caused by the earthquake and how many by the tsunami.
A total of 130,000 houses were destroyed -one in every three in the earthquake
zone- and approximately 2,000,000 people were left homeless (27% of the country’s
population).
The monetary loss was approximately 500 million US dollars at that time (USGS,
2010). The tsunami wave was almost 25 metres and flooded most areas of coastal
towns and cities, destroying the urban electricity and water supply system
(Pararas-Carayannis, 2010). This tsunami should account for the most casualty
numbers and property loss both in Chile and all over the other countries in the Pacific
basin affected (Martin, 1960).
Valdivia and another city called Puerto Montt were damaged most heavily with
intensity X to XI in Mercalli scale (USGS, 2010). In the province of Valdivia lived
255 thousand people and 40% of the concrete buildings collapsed (Wikipedia, 2010).
Several other provinces of the country were affected: Chiloe (population 98,7
thousand) and Llanquihue (population 166 thousand), Osorno (population 144
thousand), which altogether account for the 10% of the population in Chile in 1960.
Triggered by the earthquake, many landslides destructed railway and highway
transportation, broke bridges and telecommunication systems in the southern Chile
(Martin, 1960). The whole disaster continued for several months and, in addition to
4
the earthquake and tsunami, more other types of natural disasters followed afterwards.
For example, countless landslides happened chiefly in the valley of the southern
Andes (Wikipedia, 2010), and two volcanoes, named Puyehue and Calbuco, erupted
in 1960 and 1961 after the earthquake (Veblen and Ashton, 1978).
When the earthquake of 1960 hit Chile, President Jorge Alessandri Rodriguez
was in the government who was elected jus two years before. In 1959, the Chilean
economy had a great recession with per capita GDP decreasing in 8% with respect to
1958, which recovered in 1960 with a per capita GDP growth of 6%. However, after
the earthquake the country's economy grew at a lower rate of 2% in 1961 to 1963, and
has a second recession in 1964-65 (See Figure 2.B).
The exact cost of the disaster remains unknown but is estimated at approximately
$550 million in losses. In repairing the damage, the Chilean state invested 136.4
million U.S. dollars from abroad in the form of donations and 292.6 million from
government coffers.
After the earthquake, the government created the “Minister of Economy,
Development and Reconstruction” which was in charged of recovering the south of
Chile. Following information of the press in those years, only after two years the
province of Valdivia was more recovered1. However, it took several decades to come
back to what it was.
The earthquake triggered numerous landslides, principally in the steep glacial
valley of the southern Andes. One landslide however caused the alarm following its
blockage of the outflow of Riñihue Lake, the Riñihuazo. The lake was increasing its
level and the danger of a collapsing was imminent. The problem was that Valdivia
was in the way and therefore the city could have finished flooded. Because of this, the
government started a controlled evacuation of the city, starting for the children, which
were taken to other parts of the country. However, engineers and more than one
hundred people finally avoid the flooding and make the normal path of the river two
1
See El Mercurio, 22 May 1962 in
http://issuu.com/terremoto1960/docs/el-mercurio-22-de-mayo-de-1962
5
month latter2.
Historical documents 3 point put that there was a significant effect on the
migration of the population of Valdivia and cities nearby to other parts of the country.
The devastation was of great magnitude and people move to other places to live. At
that time, the population of Valdivia was conformed, in part, by a German migration
realized in the XIX century. Many of them lost their business and therefore move
from the city of Valdivia as well. In fact, household survey data (CASEN 2009)
shows that the percentage of people living in Valdivia that stayed living in Valdivia in
2009 is lower than compared to several other municipalities and to the rest of the
country. In fact, it has the lower rate of people still living in the same municipality.
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B. Psychological consequences of earthquake’s exposure
The main effects of a large earthquake in mental health are related to post traumatic
stress disorders (PTSD). PTSD is an anxiety problem that develops in some people
after extremely traumatic events, such as combat, crime, an accident or natural
2
See Historia de Valdivia in
http://historiadevaldivia-chile.blogspot.com/2010/06/terremoto-maremoto-1960.html
3
http://www.terremoto1960.cl/
6
disaster. People with PTSD may re-experience the event via intrusive memories,
flashbacks and nightmares; avoid anything that reminds them of the trauma; and have
anxious feelings they did not have before that are so intense their lives are disrupted
(American Psychological Association). Per definition, the symptoms last more than
six months and cause significant impairment in social, occupational, or other
important areas of functioning.
Evidence collected by PILOTS database (Published International Literature on
Traumatic Stress4) on the traumatic effects of specific types of disasters, points out
that earthquakes have the highest risk of severe damage and injury, compared to other
natural disasters (Carr, et al., 1997; Galea, et al., 2005; Najarian, et al., 2001;
Goenjian, et al., 1995; Roussos, et al., 2005). Factors that contribute to the level of
health damage are how populated is the affected area, the length of the event is longer,
which affect people's lives over a prolonged period and persistent or recurring
disruptions. The collected research papers find that general distress levels following
an earthquake appear to return to normal after about 12 months, but posttraumatic
stress reactions do not fade until 18 months after the earthquake. The prevalence of
PTSD varies widely in earthquake survivors. In adults, 92% have been found to have
PTSD, while in children, varies from 4.5% to 95% affected by PTSD. This variability
is due in large part to differing levels of trauma exposure and proximity to the
epicenter of the earthquake. For example, research on the 1993 Turkey earthquake
(Karanci and Rustemli, 1995) found that the majority of the survivors stated that these
emotional problems still distressed them after sixteen months. As is true after most
disasters, females were particularly likely to be distressed. Research on 1988 Yunnan
earthquake in China (McFarlane and Hua, 1993) shows that psychiatric morbidity
rates doubled in the most severely affected regions, even after 6 months. In this rural
Chinese population, much of the posttraumatic morbidity expressed itself as somatic
4
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7
symptoms. Research on 1995 Kobe earthquake in Japan (Shinfuku, 1999) found that
three years after the earthquake, victims are still suffering from psychological
difficulties resulting mostly from living isolated lives in temporary housing.
C. Long term causal effect of birth conditions and environmental factors
In this section, the previous literature is discussed with relevance to the research
question of this study. Several analyses focus on the long term causal effect of birth
conditions and environmental factors (Johnson and Schoeni, 2007; Van den Berg et
al., 2009).
Johnson and Schoeni (2007) build a two generation model to analyse the general
relationship between initial birth status and adulthood health and cognitive outcomes.
Specifically, the researchers firstly observe whether inherited factors and prenatal
socioeconomic status affect the infants’ birth outcomes. Secondly, they focus on how
this initial birth conditions influence the later life consequences including health,
educational performance, and employment status. They exploit the US national data
and the Panel Study of Income Dynamics for the main tests and estimate the long run
impact with sibling fixed effects. According to their estimation, firstly, in utero
socioeconomic outcomes such as parental income increase may be positively related
to the birth health indicators; secondly, healthy birth and early life development will
then significantly benefit the future health status, educational attainment and
employment outcomes. In other words, insufficient prenatal investment, poor health
status of birth and in childhood and limited development of cognition in early life
together significantly impact on the later life health and human capital. Furthermore,
controlling for sibling fixed effects, the effect becomes more significant. Therefore,
this study presents evidence on the transmission effect and practical guide for parents
that investment to their children prenatally and in the early life will significantly help
8
their schooling performance and labour market competitive power.
Similarly, Van den Berg et al. (2009) explore the long run effect of the childhood
living conditions on the adulthood mortality rate in the Netherlands. Different from
the study by Johnson and Schoeni, the living conditions here are mainly confined to
macro economic trends such as business cycle and other environmental conditions
like weather. These exogenous trends and shock that have no endogenous linkage to
the future mortality rates are helpful for the long range impact analysis. The Danish
Twin Registry Data is applied to this study, which may easily generate some
macroeconomic index in the childhood of respondents. The analysts employ a
Proportional Hazards model to estimate the long-term influence with including the
year of birth trend controls. The estimating outcomes demonstrate that business cycle
at the birth time plays an important role in the future mortality, or specifically if a
infant born in a booming period he will have a lower mortality rate in his adulthood.
On the contrary, other economic indices like food price and salary do not exert
significant impact on mortality, possibly since these indices are yearly scale or too
aggregate geographically. In addition, the model also expands its target to infants in
utero and of 1 year old, but the size of effects is smaller than the former case.
A majority of papers examines the long range impact of natural disasters such as
pandemic influenza and famine (Almond, 2006; Fung and Wei, 2009; Scholte et al.,
2010).
Almond (2006) analyses the impact of the 1918 flu epidemic on the long term human
capital attainment and socioeconomic status. As the influenza broke out suddenly in
1918 and only spread for several months, this pandemic could be considered as an
unexpected natural disaster which might engender long run influence to the people
born during that period or people who were exposed to the influenza in utero. The
1960-1980 US Census Micro Data is used to examine the long term effect, which
9
enables the study to restrict the sample within the US and identify the quarter of birth.
The latter advantage may lead to a clearer identification of the respondents who were
foetuses when the 1918 pandemic arrived. With controlling the fixed effect of birth
cohort and state of birth, Almond tests the influenza impact on educational attainment,
health and employment status, and further identifies the various influences of different
pandemic stages and different intensity among states. According to the result, people
who were in utero around the epidemic period have fewer years of education and
lower human capital outcomes. In addition, their employment status is significantly
worse than other cohorts and they are more likely to be disabled. With identifying the
year and geographic differences, the effect of exposure to the influenza becomes more
significant.
Fung and Wei (2009) observes the long range influence of the Chinese Famine from
1959 to 1961 on socioeconomic outcomes of both individuals born during the famine
and children whose parents born during the famine. Therefore, compared with other
studies, this work examines not only the long run effect of the famine, but also
intergenerational influence of the famine, since they consider such kind of negative
impact may transmit into their next generation. They employ the Chinese Health and
Nutrition Survey that covered information about respondents’ health condition,
socioeconomic status, demographic characteristics, and especially identified family
relationship. An empirical model is designed for the parental generation who were
born in the period of three years famine with explanatory variables of the famine
intensity when they were conceived, 1 year old and 2 years old separately and with
year and province fixed effect as well. For the second generation whose parents were
born within the famine period, regressions also distinguish the father’s and mother’s
transmission effect. In terms of the results, for the parental generation, as they
suffered the famine, they may experience malnutrition which influences their health
status, and they have lower schooling attainment; for the next generation, there is still
negative influence on their health and growth, but the impact on their educational
10
outcomes is not significant. Moreover, the adverse effect for people whose mother
suffered the famine is stronger than that of father born during the three years.
Another research on the long run negative effect of famine is the work about the
Dutch 1944 to 1945 Hunger Winter (Scholte et al., 2010). During that winter, because
of misallocation and the early arrived winter, there was a temporary food shortage in
Netherland, which may seem as a natural disturbance. Scholte et al. analyse how this
famine influenced the health, social and economic status of children who experienced
the food shortage in their early life by exploiting the historical data from Association
of Netherlands Municipalities. This set of data provides rich information of time and
regional intensity of the famine and controlling these variations is beneficial to
eliminate other confounding effect. In the empirical models, the independent variables
of interest are dummies of whether children’s early life was in the affected regions
and around the winter. Additionally, other controls mainly include individuals’
demographic characteristics and birth year and province fixed effects. Since the
famine did not last for a long time, to clearly define the affected infants, the further
inclusion of month of birth seems necessary. Results of health status indicate that
early life malnutrition causes high hospitalisation rate in later life significantly for
both male and female. However, results of other indicators show that there is no
significant impact of the famine on their future income, and disability is also not
different between people suffering the famine or not in their childhood. The
fundamental reason for the insignificance is not clear, but it might be selective
mortality.
There are also some studies using social events as natural experiments to test their
long run influence (Almond et al. 2009; Akresh et al. 2007).
Almond et al. (2009) examine the cognitive influence of Chernobyl’s radioactive
fallout to Swedish foetuses in 1986. Different levels of rainfall in different regions of
11
Sweden caused various geographic distributions of the radiation, and moreover
children in utero are more likely to be affected by the radiation, both of which provide
a natural experiment to test the long range impact of exposure to radioactive fallout.
They observe the outcome of health and educational attainment for people’s birth year
from 1983 to 1988 using administrative data. The main method is to measure the
effect size of radiation to foetuses in affected regions when controlling the
discontinuous fallout degrees, respondents’ characteristics, and year, month and place
of birth fixed effects. A more precise estimation includes continuous degrees of
radiation and family fixed effects. The regression results do not reveal any significant
relationship between the in utero exposure to the radiation and their later health status.
In terms of schooling attainment, the prenatal impact is obvious that people who
experienced the Chernobyl’s radioactive fallout in their childhood have lower average
grade and especially lower mathematics scores. With family fixed effect and siblings’
comparison, the impact becomes more significant, which excludes the cause of family
heterogeneity to some extent.
Akresh et al. (2007) investigate the long term impact of civil conflicts on children’s
later life health outcome in Rwanda. From 1987 to 1991, Rwanda experienced
economic recession which might be due to the crop production decline, and then a
civil war broke out in 1990. They identify and limit the geographic and time range of
the crop failure and civil conflicts separately, and explore how these two events
influence the children’s health later. The researchers exploit the UNICEF survey for
Rwanda to calculate the central health indicator, height for age Z scores, which
usually reveals the nutrition intake of children. With information of infant born
characteristics, the empirical regression employs the method of difference in
difference which controls the year and month of birth fixed effects and province of
birth fixed effects. Referring to the results, children whose births were affected by the
civil wars have significantly lower Z scores of height for age, in which females are
further lower scored than male. With restriction of a measure of wealth, results show
12
that children in poor families are affected more significantly than those in rich
families by the civil conflict. If controlling further the family fixed effects, children
born within the period have worse health status in later life than their siblings born
before or after the conflicts, and the gender difference in this case is no longer
significant.
In terms of earthquake, most papers discuss the relationship between earthquake
experience and future health outcomes both physically and psychologically (Matsuoka
et al., 2000; Bland et al., 1996; Bland et al., 2000; Kilic and Ulusoy, 2003) , while
some others cover variables like human capital and socioeconomic status (Lin et al.
2002).
Matsuoka et al. (2000) analyse the health influence of the 1995 Hanshin Awaji
earthquake in Japan. They investigate the hospital record in affected regions and state
that the morbidity rates of several severe diseases are highly related to the earthquake
intensity degrees. Some Italian researchers observe the reports of survivors of the
1980 earthquake in southern Italy and state that individuals who suffered earthquakes
before are more likely to have long term psychological distress, which also relates to
the wealth loss of respondents (Bland et al., 1996).
With respect to psychological influence, the same Italian researchers later assess the
impact of earthquake on heart disease for the same group of respondents (Bland et al.,
2000). They find that there is no direct relationship between the earthquake
experience and later heart disease risk, but earthquake impacts on the health status
only through the channel of wealth loss in disasters. Similarly, Kilic and Ulusoy
(2003) analyse the post-traumatic stress from the earthquake in Turkey in 1999 and
they discover that this psychological effects is higher in the regions nearer the
epicentre.
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Referring to some socioeconomic outcomes, Lin et al. (2002) study the long run effect
of the Chi-Chi earthquake in Taiwan in 1999 on the quality of life for respondents
who suffered this disaster. They test physical and psychological health status, and
other social attainment, and find that old people who survived in the earthquake
evaluate their life quality lowly, but individuals whose wealth lost in the disaster score
the quality of life very highly.
3. Methodology
This chapter introduces the main methods employed to analyse the long term impact
of the 1960 Valdivia earthquake on individuals’ educational attainment, health
outcomes and socioeconomic status.
A simple idea to evaluate the long run effect is to model those outcomes of interest
depending on a dummy indicating whether the cohort was born in Valdivia in 1960.
However, there could be other factors also influencing the behaviours of the
experimental group, the 1960 cohort born in Valdivia, for example business cycles. In
order to estimate the net effect of the earthquake, these types of bias should be
eliminated, which requires the method of difference in difference. Since these
confounding factors are mostly macro trends or environmental factors, dependent
variables in unaffected provinces may be affected due to such bias as well.
Therefore, it is helpful to include the cohorts not born in 1960 or not in Valdivia as
comparison groups that experienced the same trends and macro conditions but only
without being affected by the earthquake. The difference of dependent variables for
the comparison group before and after the earthquake could be a proper
approximation of the additional bias of common trends and macro conditions. To
evaluate the net effect of the 1960 Valdivia earthquake, the basic regression model
using the method of difference in difference is shown below.
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(1)
In this regression, is the dependent variable for individual i, born in province s
and in year t. On the right hand side, there lists year dummy Yob1960 indicating the
birth year of 1960, province dummy Val demonstrating the birth province of Valdivia,
and their interaction, Val1960, a dummy revealing whether the individual was
affected by the earthquake in utero. The coefficient of interest here measures the
net effect of the 1960 Valdivia earthquake. In addition, Female and Urban represent
whether the respondent is a female and living in urban areas, and therefore, the
coefficients and may reveal the gender gap and urban-rural difference. Finally,
is the error term.
However, a drawback of this basic model is that the dummies can only divide the
whole population into four groups, people born in 1960 or not, and born in Valdivia
or not. Nevertheless, there might be more differences within the comparison group.
For example, around 1960, there were 25 provinces in Chile and the comparison
group incorporates 24 provinces and regards them as a whole. In order to pick up the
differences among provinces and cohorts, our model further controls year and
province fixed effects.
(2)
In this model, dummies of Val and Yob1960 are replaced by and , which reflect
the province and year fixed effects respectively. There also includes an interaction
term, Valt, capturing the affected province trend, which considers within Valdivia
there might be a year trend influencing outcomes of interest. For example, younger
cohort in Valdivia could have more years of education.
15
4. Data and Descriptive Analysis
4.1 Data
The main data source of this study is 2% samples of the 1992 and 2002 Chile
censuses. These two censuses include respondents’ information in three main aspects.
Firstly, they both consist of demographic records of the population in Chile, such as
their ages, genders, birth places and so on, which are chiefly used for classification
and comparison between groups of people. Secondly, information about individuals’
educational performance and their employment status are also covered in the datasets,
which may be the principal variables of interest for this study. Finally, a large amount
of indices are concerning about the asset of interviewees, for example, their furniture
and electrical appliances, which combining infrastructures of their households may
fully indicate the wealth status of respondents.
On the contrary, there are several differences between the two datasets as well. First,
the samples are different. The sample size of the 1992 Chile census is about 1.3
million and the 2002 Chile census records about 1.5 million observations. In addition,
both datasets only contain people aged from 0 to 99, and this means the 1992 Census
investigates individuals born from 1893 to 1992 while the 2002 census covers the
birth year from 1903 to 2002. Second, the 2002 Chile census adds some new variables
and indices, so that the classification may be more comprehensive, and more
essentially, the inclusion of respondents’ new type assets provides a more complete
description of wealth status. For example, whether individual has internet access and
computer becomes an important criterion for his wealth conditions.
In terms of sampling, both two datasets record millions of observations, but in this
study, birth year range should be restricted further. Since the group of interest is
respondents born in Valdivia in 1960, it is feasible to select a sample born between
1950 and 1970, which contains 10 years’ data before and after the earthquake
16
respectively. Firstly, it is suggested that narrowing down the observed window is
beneficial to avoid other influence of confounding factors (Fung and Wei, 2009).
Secondly, for the 1992 Chile census, respondents born in this sampling range were
from 22 to 42 years old in the record year, and they had finished their education,
suitable for the analysis of earthquake impact on educational performance. Lastly, for
the 2002 census, individuals in this sample range were from 32 to 52 years old, and
they should have steady health and socioeconomic status. Hence, accompanying with
the advantage of inclusion of new asset variables, the 2002 Chile census is useful for
the analysis of earthquake long term effect on socioeconomic outcomes.
However, due to the nature of two datasets, there involves two aspects of concern
when defining the sample: year and place of birth. Firstly, neither of two censuses
presents the precise time of birth, and the only available information is the age of
respondents which can help calculate the approximate year of birth. Take the 1992
census for example, year of birth = 1992 – age. Nevertheless, since the earthquake
broke out in May 1960, it cannot be identified whether individual whose birth year is
1960 had been born or in utero when the disaster arrived. Secondly, information of
birth place is essential to identify whether the respondents were born in affected
regions. However, in both datasets only province of birth is available. In the last
century, Chile experienced several regions and province reorganisation (Statoids,
2010), but between 1929 and 1976 the administration division was not changed which
guarantees the geographical steadiness within the sample year range. As shown in
Figure 2, around 1960 there were 25 provinces in Chile compared with nowadays’ 54
provinces in 15 regions, and two datasets both provide information about province of
the 1960 format which is consistent with other data about the 1960 Valdivia
earthquake.
Consequently, the experimental group, defined as individuals born in province of
Valdivia in 1960, contains about 804 persons in the 1992 census and 962 persons in
17
the 2002 census, taking up 0.18% and 0.22% of the observed samples respectively.
Including the comparison groups, the whole sample size for the 1992 dataset is
450,829, about 33.77% of the whole census population, and the sample size for the
2002 dataset is 446,549, about 29.50% of the whole census population.
4.2 Descriptive Analysis
In this study, three major aspects of data as dependent variables will be examined:
educational attainment, health outcomes and socioeconomic status. In the following
descriptive analyses, Figure 3 to Figure 10 plot these variables of interest by birth of
year separately, which are helpful to examine their year trends. In each figure, trends
for people born in Valdivia and in other provinces are compared directly, which
enable further to identify whether the trend break was due to the 1960 Valdivia
earthquake or the whole country shared the same trends.
Figure 3 depicts the trend of educational years for both people born in Valdivia and
other provinces. It can be seen that both trends reveal individuals receive increasingly
more years of education than their predecessors, where specifically, people born in
1970 obtained around 2 more years of schooling than those born in 1950. However,
respondents born in Valdivia acquired 1 year less than people born in other provinces
averagely, which illustrates maybe the educational status in Valdivia was worse than
the national average conditions. More attractively, in 1960 as the reference line plots,
there was a sudden break of the trend in Valdivia, i.e. children born in 1960 received
0.5 year less than those born in 1959. Afterwards the trend continued its increasing
but with a lower slope. Yet, the trend for other provinces shows a much smaller drop
in 1960 which may reflect there was a much smaller long term earthquake impact on
people born in other provinces.
Figure 4 shows the relationship between year of birth and literacy rate for the two
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geographical groups of respondents. According to this diagram, both trends were
rising like the case of schooling years above, but, generally speaking, the average
literacy rate in Chile was very high, fluctuating within a range between 95% and 98%.
However, not like the previous case, these two trends did not separate a lot and even
sometimes they were very close to each other, which may explain that the literacy rate
all over the country were very high and even. In terms of the reference year, 1960, the
trend for people born in other provinces went through 1960 smoothly, but for people
who were born in Valdivia in 1960, they showed about 2% points of the literacy rate
lower than the cohorts born 1 year before or after. Since the whole fluctuation range
for this 20 years’ period was 3% points, this abrupt jump in 1960 seemed to be very
obvious and significant. Nevertheless, the value in 1961 appeared to return to the
original trend and all the change in 1960 was very temporary.
From Figure 5 to Figure 7, they together demonstrate the year trends of graduation
rates for primary school, secondary school and university. Comparing these three
graphs can reveal the following four features of graduation rate in Chile. Firstly, in
terms of the average range, primary school graduation rate was higher than that of
secondary school, and the rate of secondary school was further higher than that of
university, which reflect the current circumstances all over the world. Secondly,
referring to the absolute value, primary school graduation rate increased from around
70% for people born in 1950 to above 90% for the 1970 cohort which revealed the
success of Chilean primary education. For the secondary education, however, the
range of graduation rates was only from 20% to 40%. Different from the two rising
trends, graduation rate of university declined from the 1950 cohort’s 6% to 1970
cohort’s 4%, and for the people born in Valdivia in 1970, the value even dropped into
1%. Hence, the two latter graphs show the failure of Chilean higher education.
Thirdly, in all three diagrams, the absolute value for people born in Valdivia was
relatively lower than that for people in other provinces, and these two trends separated
clearly, reflecting an obvious difference (about 7% for Figure 6, 10% for Figure 7 and
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2% for Figure 8) of educational outcomes between Valdivia and other provinces
averagely. Finally, with respect to the reference year 1960, there cannot find any
break of trends for people born in other provinces in all the three graphs. For people
born in Valdivia, yet, breaks appeared in both Figure 6 and Figure 7. On the contrary,
the drop of university graduation rate in Valdivia in 1960 seemed not obvious in
Figure 8.
Human capital accumulated from earlier education will be used to gain satisfied
socioeconomic status. In this analysis, the unemployment rate could reflect
respondents’ employment status, whose year trend is shown in Figure 8. The overall
trends for the two groups seemed to be cyclical, but apparently the unemployment
rates for people born in Valdivia were higher than that of people born in other
provinces, which is consistent with the educational performance between the two
groups. In the trend for people in other provinces, respondents born in 1960 were
more likely to be unemployed compared with people born around 1960, as the line
reached a peak in 1960 within that fluctuating cycle. However, the peaks of
unemployment rate for people in Valdivia appeared in 1957 and 1962, and there was
no sudden rising of unemployment rate in 1960 within this business cycle.
Referring to the wealth status, the two censuses provide a large number of asset and
infrastructure indicators, such as whether he or she owns TV, automobile and so on.
Therefore, employing Principal Component Analysis, those indicators could be
calculated into two simple indices, named Asset and Infrastructure. As all points are
weighted and calculated from a group of variables, the absolute value of both indices
does not provide any actual meaning.
In the graph of asset index, both groups experienced a gradual decrease. In other
words, individual born in 1970 (32 years old) owned fewer assets than the 1950
cohort (52 years old), possibly because young cohort were still working hard but the
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old people might have steady asset ownership. Moreover, these two trends separated
clearly and index for Valdivia people was significantly lower than the other one.
Examining the reference line, however, does not show any rupture of both trends,
although the dot trend presented more fluctuating.
Finally, Figure 10 plots the trend of infrastructure index for both groups of
respondents. Generally speaking, both trends do not show any clear rise or fall
tendency. For people born in Valdivia, their index points were located very irregularly
but waving within a large range. On the contrary, the infrastructure index trend for
people in other provinces nearly kept constant from 1950 to 1970, probably because
32 years old people may have the same infrastructure like electricity and water supply
with the 52 years cohort.
5. Results
5.1 Educational Attainment
This section mainly examines if the 1960 Valdivia earthquake affects educational
performance of people born in 1960 in the long term. According to Chapter 3, the
following analysis employs the method of difference in difference and models with
birth year and province fixed effects to evaluate the change of education attainment
which includes years of schooling, and dummies showing whether or not they have
ever attended school, whether they have completed primary school, secondary school
or university studies, and whether they are literate.
Table 1 displays the results of the basic model for all six variables. As can be seen, the
impact of the 1960 Valdivia earthquake on years of schooling is shown in the first
column. The coefficient of Val1960, or in equation (1), indicates that there was a
negative and significant effect of the earthquake on educational years. Specifically,
people born in Valdivia in 1960 had about 0.249 year of education (equivalent to
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about 3 months) less than the others significantly, which accords with diagrams in
Chapter 4. Considering individual’s characteristics like gender and living, female
students’ schooling years were about 0.130 year (equivalent to one and a half month)
less than males’ with 1% level of confidence, and respondents living in urban area
usually spent 3.16 years more than people in other regions in education significantly.
The second column presents the result of the earthquake impact on literacy. The
coefficient of interest is still negative and significant with confident level of 1%.
Respondents who were born in Valdivia in 1960 were 1.6% points less likely to be
literate. The coefficient of female and its significance reflect that there was not any
significant gender difference in literacy in Chile. However, urban individuals were
about 5.5% more possible to be literate than those living in rural areas in 1% level of
confidence, which shows a huge urban-rural gap on literacy.
The influence of the 1960 Valdivia earthquake on whether the respondents have ever
attended school is shown in the third column. In this case, people born in Valdivia in
1960 were commonly 1.2% points less probable to receive any education than
individuals born in other provinces and in other years. In addition, males might be
0.3% point more likely to have attended school than female respondents. Nevertheless,
although the result is significant, the size of the effect is comparatively small, which
may imply that the gender difference is not so obvious. In terms of urban-rural gap,
people who were born in rural area were about 2.6% points less probable to receive
schooling than citizens significantly.
The next column examines the impact on the completion of primary schooling. The
basic model does not provide significant result even with confident level of 10%, but
the coefficient is negative, which indicates that the earthquake disaster might
influence the primary education negatively for people born in Valdivia in 1960,
although the influence was not so significant. Referring to the gender difference, the
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coefficient of female is significant and negative. Particularly, females were 0.8%
point less likely to finish their primary study than males significantly, though this
difference is relatively small. Finally, the coefficient of urban shows that people living
in urban area had about 21.5% points more possible to graduate from primary school,
which indicates a dramatic urban-rural gap of the primary school graduate rate in
Chile.
Similarly, the effect of the earthquake on completion of secondary school study is
tested and shown in the fifth column. Commonly speaking, people who were born
during the earthquake period were 2.6% points less probable to complete their
secondary school education than other respondents, although the effect was not so
significant. Interestingly, the coefficient of female is positive and significant, which
means that females were about 1.7% more possible to finish their secondary schooling
than males significantly. This might be a gender difference contrary to other situations.
In terms of urban-rural gap, the difference is huger than that of primary school. People
born in cities might have 29.4% possibility higher than people in rural areas on
completing their secondary schooling.
Finally, the last column in Table 1 exhibits the impact of the earthquake on finishing
university study or higher education. As shown in the table, although the earthquake
effect is negative, about 0.3% point, the coefficient is not significant even with
confident levels of 10% and the effect size is comparatively small. However, the
gender difference of university study is obvious here, since males were 1.3% points
more likely to finish their higher education than female students significantly.
Furthermore, the urban and rural difference in this case is not as big as that in primary
and secondary education, and has narrowed down to 4.4%, which indicates that
citizens did not have so much advantage on the completion of higher education.
With controlling the year and province fixed effects, results of equation (2) shown in
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Chapter 3 for all the six indicators of educational performance are presented in Table
2 below. As shown in the table, coefficient of Val1960 is still the parameter of interest,
and the new included term Valt reveals the province trend during the period between
1950 and 1970.
Similarly with the results of the basic model above, the 1960 Valdivia earthquake
exerted negative and significant effect on years of schooling, ever attended schooling
rate, and literacy rate of individuals born in Valdivia in 1960. Specifically, the 1960
cohort born in Valdivia averagely received 0.273 year (equivalent to over 3 months)
less than other respondents in the sample, and seemed 1.2% points less likely to have
ever obtain formal education and 1.6% points less probably to be literate, where the
long term impact of the latter was more significant. In terms of the province trend, in
these three cases, the size and significance of the coefficients are very small which
reflects there might be no specific trend within the province of Valdivia. Finally,
compared with urban-rural difference, the parameters of Female provide very small
scale negative effect on year of education (about 1 month less) and schooling rate, and
the influence to literacy rate was insignificant at all. However, individuals born in
urban area during the sample year range took many advantages on educational
attainment. For example, their schooling time was 2.7 years more than the rural peers,
and they were 2.4% points more possibly to have attended to schools and highly 5.0%
points more likely to be literate with confident level of 1%.
With regard to the graduation rates of primary school, secondary school and
university, only the completion of secondary education was affected significantly by
the 1960 Valdivia earthquake with only 10% level of confidence. These effects, yet,
were all negative even though not so significant, and in terms of scale the impact on
university graduation rate was relatively smaller than the other two. Secondly,
according to the coefficients of Valt, although two of them are significant, their scales
are too small to explain whether there were province specific trends. Thirdly, the
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gender difference of graduation rates was more apparent and interesting than the
former three variables. Male students were about 0.6% more probably to complete
primary school, but on the contrary female students seemed to be highly 2% more
likely to graduate from secondary school than male peers, both significantly.
Nevertheless, males were a little more advantageous on higher education completion
at last. Finally, the urban-rural difference for these three outcomes was more obvious
than the gender difference. Children born in rural regions were 18.9%, 25.0% and
3.4% less possibly than those born in cities to finish their primary, secondary and
higher education respectively all with 1% level of confident. Actually, the difference
for university graduation rates was comparatively smaller than the other two.
5.2 Health and Socioeconomic Status
In this section, two aspects of results are presented to test whether the 1960 Valdivia
earthquake influences health and socioeconomic outcomes of people born in 1960 in
Valdivia. There is only one indicator of health outcomes in both datasets, the
disability rate, and variables showing respondents’ socioeconomic conditions consist
of their employment status, and two indices revealing their assets and infrastructure
generated by Principal Component Analysis.
Similarly with Section 5.1, the following analysis employs the method of difference in
difference and models with birth year and province fixed effects to evaluate the long
term impact of the 1960 Valdivia earthquake. To begin with, Table 3 demonstrates the
results of the basic model for all the four variables discussed above.
As shown in the first column, the coefficient of Val1960 (-0.004) explains that people
born in Valdivia in 1960 had 0.4% point less likely to be disabled than other
respondents. Commonly speaking, people born in the period of a natural disaster
should have higher possibility of being disabled, which contradicts the result above.
However, observing the size of effect and its significance could reveal that even
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though the earthquake positively influenced the health status of the 1960 cohort, this
impact was so small and insignificant that it should not be accepted to be valid
evidence showing the earthquake long run effects on health. On the other hand,
gender difference and urban-rural gap also affect the disability rate significantly.
Individuals born in cities were averagely 0.7% point less probable to become disabled
which might be due to the good medical and nutrition condition. Females, yet, in this
case showed they were significantly healthy than males, 0.5% less likely to be
disabled. However, these two gaps were not very large.
The second column demonstrates how the earthquake influenced unemployment rates
for people born in 1960 in Valdivia. Contrary to the common understanding, here,
individuals born in Valdivia in 1960 had 1% point more than others to be employed.
According to the test results of educational attainment in Section 5.1, the 1960 cohort
in Valdivia performed worse than other cohort in other provinces, which consequently
should provide a consistent result for their employment status, for example relatively
higher unemployment rate. Analogously, this coefficient is insignificant even with
10% level of confidence, which explains that the positive impact of earthquake on
individuals’ employment status was not valid enough. Another interesting finding
exists in the result of urban-rural gap, since citizens born in 1960 in Valdivia would
be 0.7% more probable to lose their jobs. This higher unemployment rate in cities
might reflect the more competitive labour market than that in rural areas. The same
gender difference occurred in the unemployment rate. Female individuals in this case
were still less possible to be unemployed than male fellows, and the difference of
unemployment rate enlarged to 1.5% significantly.
For the index of asset, it is weighted by eight different variables including the
availability of telephone, cell phone, internet, automobiles, hot water heater, computer,
refrigerator and television set. The coefficient of interest is negative but insignificant,
which revealed that people born during the earthquake generally owned fewer assets
26
such as automobiles than other cohorts of the sample in their adulthood. However, the
absolute value of these parameters does not indicate any real meaning, and the
insignificance shows that the earthquake was not likely to influence the 1960 cohort’s
asset so adversely. In terms of the sexual difference, interestingly, women owned a
few more assets than men significantly, which illustrates the gender gap was
advantageous to Chilean women in the sample. In addition, the urban-rural gap was
more obvious for this index, which was a typical difference in living standards.
However, when comparing the coefficients of Female and Urban, it can be discovered
that the size of urban-rural difference was much bigger than the gender gap in the
sampled years in Chile.
The second index of infrastructure is generated dummies of electricity, water supply,
sewage, kitchen and toilet availability, which indicates the general living conditions.
The result for this index is shown in the last column of Table 5. The negative impact
of the 1960 earthquake on infrastructure was significant, different from the former
three variables, which points out the 1960 cohort born in Valdivia had significantly
less infrastructure than other peers, i.e. this cohort might have no piped water supply
or their own kitchen and toilet. With regard to the effect size of the 1960 earthquake
on these two indices, the adverse impact on infrastructure was comparatively bigger
than that on asset. Referring to the gender difference, female respondents still had
more infrastructure than male adults significantly, but the difference was relatively
smaller than that of asset ownership. However, the urban-rural gap for infrastructure
was still large and significant. For example, people born in cities might have higher
possibility to own advanced sewage system in their houses and neighbourhood, which
reflects a basic difference of living conditions between cities and countryside.
Secondly, the estimations with year and province fixed effects for all the four
variables are presented in Table 4. Nearly the same as the results above, the 1960
Valdivia earthquake exerted negative but insignificant impacts on disability rate and
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unemployment rate of individuals born in Valdivia in 1960. At the same time, women
had 0.6% and 1.5% higher possibility than male fellows to be healthy and employed
respectively, which revealed the advantages of these two aspects for women even
though the advantages were not obvious enough. In addition, citizens had 0.7% lower
disability rate than people born in rural areas, but they were also 0.7% more likely to
lose their jobs than people in countryside.
Similarly, people born in Valdivia during the earthquake period would possess fewer
assets and lower quality of household infrastructure in their adulthoods than other
peers, and the earthquake impact on infrastructure was very significant. Interestingly,
Chilean women owned significantly more assets and better infrastructure than men
which showed the opposite situation of sexual difference in other countries. On the
other hand, the urban-rural difference was more obvious for these two indices, even
10 to 40 times larger than the former two which reveals the principal social problem
in Chile is the gap between cities and countryside but not between men and women.
6. Discussion
In this chapter, results of the 1960 Valdivia earthquake impact on educational
attainment, health and socioeconomic status are summarised and discussed in detail.
In addition, outcomes in this study are also compared with results in previous
literature to explore the similarity and dissimilarity among analyses about natural
disasters’ long term effect and further reveal the specific national circumstances of
Chile.
To begin with, people born in Valdivia in 1960 achieved averagely less educational
attainment than other cohorts born in other provinces of Chile, for example they were
less likely to receive any formal education or more probable to be illiterate
significantly based on the results in Chapter 5.
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