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Long-term economic consequences of the 1960 Chile earthquake

Sonia Bhalotra

University of Bristol

Claudia Sanhueza

Univ. Diego Portales

Yicho Wu

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DRAFT, DO NOT CITE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

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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

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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

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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

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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

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month latter2.

Historical documents3 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.

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/

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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   Traumatic  Effects  of  Specific  Types  of  Disasters,  The  National  Center  for  PTSD,  US.  

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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

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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

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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

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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

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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

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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.

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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

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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

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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

19  

 

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

20  

 

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

21  

 

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

22  

 

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

23  

 

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

24  

 

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

25  

 

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

26  

 

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

27  

 

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

28  

 

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.

29  

 

Among all the six indicators of schooling performance, year of education is the most

typical and also the only continuous variable. In this case, the earthquake affected the

schooling years of the 1960 cohort in Valdivia adversely, i.e. they obtained 0.273 year

(equivalent to over 3 months) less than other peers. According to the study by Almond

(2006) who analysed the long term impact of influenza, the coefficient of educational

years arranges from -0.071 to -0.176, or in other words foetuses exposed to the 1918

influenza would acquire about 1 to 2 months of schooling less than other cohorts in

their childhood. Fung and Wei (2009) examine the long run influence of Chinese

famine around 1960 on years of education as well. Their results show that people born

in the affected regions during the famine would receive 0.007 to 0.251 year

(equivalent to 1 to 3 months) of education less than other peers. Compared with these

previous works, the negative influence of this earthquake on schooling years was

relatively stronger.

Furthermore, the result of educational years reveals significant gender gap and

urban-rural difference. Female individuals obtained 1 month of education less than

others and people born in rural areas lost additional 2.703 years of schooling, which

demonstrates the urban-rural difference was much larger than the former problem.

However, previous studies do not provide consistent conclusion. In Almond’s work

(2006), influence of influenza to male respondents on schooling years was bigger than

that to females, and the gender gap was very significant. On the contrary, the size of

famine effect on years of schooling was bigger for female than male according to

Fung and Wei (2009), in which the impact on boys of 2 years old was even positive

and insignificant but that on girls was always negative and especially significant for

girls born in the famine year. After all, gender gap exists in deed in the influence of

natural disaster on individuals’ educational years.

Another group of results which is also interesting to be compared with other literature

show the graduation rates of primary school, secondary school and university. In this

30  

 

study, the 1960 Valdivia earthquake exerted adverse influence to these completion

rates for the 1960 cohort in Valdivia, but only the effect on secondary education was

significant with confident level of 10%. Comparing the three coefficient sizes could

reveal that the negative impact was stronger for secondary schooling but much weaker

for higher education. This conclusion is supported by previous studies. Fung and Wei

(2009) find the Chinese famine reduced the primary school graduation rate of those

affected cohorts by 0.2% to 1.8% points but not very significantly. This impact was

relatively smaller than the Chile earthquake, whereas Almond (2006) also shows the

1918 epidemic influenced the completion rate of secondary school less severely than

the Chile case. According to his finding, people affected by the influenza in utero

would have about 1.4% to 2.8% less likelihood to finish their secondary education

significantly. This contrasting displays the stronger educational influence of the 1960

Valdivia earthquake in the long term.

In terms of sexual difference in graduation rate, female students in Chile were

significantly less possible to complete primary and higher education but more likely

to finish their secondary schooling. Partly supporting this conclusion, Fung and Wei

(2009) show that primary education completion for female was influenced more by

the famine in China than for male, but Almond (2006) discovers women affected by

the 1918 epidemic were more likely to drop out during secondary education than men

significantly in the US. This inconsistency is worth noticing for secondary education

development in Chile. Moreover, similar with other variables, urban-rural gap of

graduation rate for these three types of education was larger than the gender

difference.

In summary, the 1960 Valdivia earthquake affected respondents’ educational

performance significantly especially for schooling years, schooling rate and literacy

rate. Compared with other cases of natural disaster, these adverse impacts were also

relatively stronger. In addition, both gender difference and urban-rural gap for these

31  

 

schooling outcomes existed in Chile, but the latter was more serious.

Secondly, the only health indicator is disability rate. In this study, the negative effect

of the earthquake on disability rate was not big and significant probably due to the

small proportion of disabled people in Chile. This insignificant outcome might also be

the result of variable selection, as in the datasets there is not any other available

indicators of health status, and disability rate could not reflect the whole picture of

respondents’ health conditions. However, males and people born in countryside were

more likely to be disabled significantly. Dissimilar with other indicators, the sexual

and rural-urban gaps were not very large in this case and with almost the same size.

Such a result is backed up by the work of Scholte et al. (2010), who find that men

affected by the Dutch famine had higher possibility to be disabled than women and

citizens were less probable to have the risk of disability and the gaps were larger than

the earthquake case.

Finally, referring to the socioeconomic status, the 1960 Valdivia earthquake only

significantly affected the household infrastructure of respondents born during the

disaster. Significance for this case seems essential because without significance the

earthquake even positively benefitted the employment status (about 1% less probable

to be unemployed) which does not make any sense. However, comparing these three

coefficients reveals that the quality of household infrastructure was more likely to be

influenced by the earthquake with regard to the coefficient’s size and significance.

The difference of asset and infrastructure is necessary here since infrastructure reflect

a basic living condition while asset only stands for some furniture and electrical

appliances. Apparently, people with more human capital usually have better job and

higher salary, which help them live with good household infrastructure like piped

water supply. However, on the other hand, less educated individuals can also afford to

those electronic appliances. Therefore, combined with significant results of

educational attainment, the negative impact on infrastructure looks more sensible.

32  

 

Another interesting point needed to be discussed is the gender gap for both disability

rate and socioeconomic status. As summarised in Chapter 5, female was less likely to

be disabled and unemployed, and in addition women owned significantly more asset

and better infrastructure than male fellows. All of these four indicators were

advantageous to Chilean women in the sample, which demonstrates that the gender

inequality was not very serious in Chile at least for these four variables. Compared

with this, urban-rural difference was more obvious. Except unemployment rate which

was higher in cities, respondents born in urban areas obtained more educational

attainment, had lower disability rate, and owned more asset and household

infrastructure. This urban-rural difference for each variable was usually larger than

the sexual gap respectively, which reflects the former one was a relatively more

serious social problem requiring more attention from the Chilean government.

Nevertheless, some implausible results in Chapter 5 might also be a consequence of

potential weakness in methodology and data selection. First of all, the sampling of

experimental group and comparison group is not precise due to the lack of further

supportive data and this inaccurate selection might generate errors of estimation. The

definition of two groups considers time and place of birth.

In terms of birth time, this study focuses on the long term impact on foetuses exposed

to the 1960 Valdivia earthquake. Therefore, strictly speaking, the respondents of

interest are individuals who were in utero when the earthquake struck Chile. However,

both 1992 and 2002 censuses do not provide information about month of birth, but

only display age of respondents. With imprecise calculation, it cannot be

distinguished that people who were born before or after May 1960, i.e. individuals

who were born in 1960 could be in utero or had been born when the earthquake

happened.

33  

 

Referring to the geological data, as there is no exact data indicating the earthquake

intensity of each province, it is hard to sequence the 25 provinces by earthquake

magnitude. Fortunately, one study (Veblen and Ashton, 1978) has identified a latitude

range of affected regions by the earthquake, but without longitude figures it is still

difficult to define specific affected and unaffected provinces. Therefore, in this

analysis only province of Valdivia is regarded as the affected province, and all other

provinces are considered as comparison group, since Valdivia suffered the most.

Furthermore, within each province the earthquake influence was largely different.

Secondly, the disability rate is the only indicator of respondents’ health status which

could not reflect the complete health outcomes. As discussed in Chapter 4, people

who were disabled only accounted for 1.53% in the 1992 dataset and 1.78% in the

2002 census. Therefore, what the results of disability rate explain is not typical in

Chilean society. It would be better to exploit more medical datasets which contain

respondents’ complete health outcomes, like the work by Scholte et al. (2010) using

hospitalisation rate which reflects the health status more generally, and the study by

Akresh et al. (2007) employing height for age Z scores that usually reveals the

nutrition intake of children.

On the other hand, some indicators in both datasets are too specific like dummies of

electric appliance ownership, compared with more general variables such as wage

which two censuses do not contain. Therefore, in this case, with Principal Component

Analysis two indices are generated to reflect respondents’ asset and infrastructure

ownership. This generation process itself, however, loses part information of the raw

data, and furthermore these two indices do not include complete features of the

ownership. Hence, these indices calculated with incomplete variables may generate

bias of estimation.

Finally, the lack of background information restricts deeper analysis and explanation

34  

 

of results. Take the gender difference for example. Many results indicate that Chilean

women were advantageous in terms of the long run impact on employment rate and

socioeconomic status which manifests females do not belong to the so called

vulnerable group. If accompanied with some social research about female status in

Chile, the result discussion would be more plausible.

7. Conclusion

A destructive earthquake with consequent tsunami ravaged Chile in 1960 and exerted

long term influence to foetuses exposed to the catastrophe. According to Barker’s

research (1992), unborn children who experienced violent shock like natural disaster

would be affected cognitively. This adverse influence may further impact on their

human capital accumulation and future labour market competitive power. This study

mainly examines whether and how the 1960 Valdivia earthquake affected certain

individuals’ long run educational attainment, health outcomes and socioeconomic

status.

The 1992 and 2002 Chile censuses are exploited in this case as the main datasets

which provide information about respondents’ educational performance and

employment status. However, both datasets contain people aged from 0 to 100, and

therefore a 20 years range sample is selected for further analysis. With the help of

point trends diagrams, outcomes of interest can be compared between individuals

affected and unaffected by the earthquake in utero and whether there appeared break

of trends of all the three types of outcomes for the 1960 cohort born in Valdivia is

obvious in the figures.

Nevertheless, to examine whether there was significant effect of the earthquake and to

obtain precise estimation, more quantitative tests are carried out further. In this study,

the method of difference in difference is used to estimate the net impact on the 1960

cohort born in Valdivia who might be in utero when the earthquake struck Chile. With

35  

 

birth year and province fixed effect, regression results show that there exists

significant and adverse impact of the 1960 Valdivia earthquake on respondents’

schooling performance, but for health and socioeconomic status, the results are not

significant.

Specifically, in terms of educational outcomes, individuals who were born in Valdivia

in 1960 received 3 less months of education than other cohorts significantly, and they

were usually less likely to have ever attended school or to be literate. However, the

earthquake did not influence the graduation rate negatively except for that of

secondary education with difference of 2.9% possibility. With regard to health

outcomes, the only indicator of disability rate does not reveal any significant

discrepancy between the two groups, maybe because it cannot represent individuals’

complete health status. Referring to socioeconomic results, people who were

influenced by the 1960 Valdivia earthquake in utero were not disadvantageous in

terms of unemployment rate or asset ownership than other cohorts, but they might

possess worse quality of household infrastructure than others significantly.

Regression models also reveal both gender difference and urban-rural gap for all the

three aspects of results. Firstly, females performed in school comparatively worse

than male fellows, for example, girls might obtain 1 less year of education than boys

and they were 1.2% less likely to complete their higher education. Secondly, for both

health outcomes and socioeconomic status, Chilean women took more advantages

than men in the sample. Females were less probable to be disabled and unemployed

significantly, and moreover they might own more assets like TV sets and better

infrastructure such as piped water than males. Thirdly, urban-rural difference was

reflected in the results as well. Individuals born in cities averagely obtained more

educational attainment, lower disability rate, and more assets and household

infrastructure than people living in countryside. However, citizens usually had higher

unemployment possibility significantly than countryside born people. Finally,

36  

 

urban-rural difference exerted more influence to individuals’ education, health and

employment than gender gap, since the effect size of coefficients of Urban is

generally bigger than that of Female, which indicates that the urban-rural gap was the

principal social problem in Chile.

With some potential weakness identified, this study could be improved through

sampling experimental group precisely, selecting variables more typically, and

incorporating more background information for analyses.

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42  

 

Figure 1 Earthquake Affected Areas of Chile

Figure 1.A: World Tectonic Plates

Source: http://pubs.usgs.gov/gip/dynamic/slabs.html

43  

 

Figure 2 Provincial Map of Chile

44  

 

Figure 2.B

 

 

 

Figure 3 Years of Schooling Trends

45  

 

Figure 4 Literacy Rate Trends

Figure 5 Primary School Graduation Rate Trends

46  

 

Figure 6 Secondary School Graduation Rate Trends

Figure 7 University Graduation Rate Trends

47  

 

Figure 8 Unemployment Rate Trends

48  

 

Figure 5 Asset Index Trends

Figure 10 Infrastructure Index Trends

49  

 

Table 1 Effect of the Earthquake on Educational Attainment

Years of Schooling Literacy Ever Attended Schooling (1) (2) (3) (1) (2) (3) (1) (2) (3) Val1960

-.249* [.136]

-.276** [.135]

-.275** [.135]

-.016*** [.006]

-.017*** [.006]

-.017*** [.006]

-.012** [.005]

-.013** [.005]

-.013** [.005]

Val -.652*** [.031]

-.624*** [.031]

-.624*** [.031]

-.001 [.001]

-.000 [.001]

-.000 [.001]

-.000 [.001]

.000 [.001]

.000 [.001]

Yob1960

-.028 [.025]

.041* [.025]

-.055** [.026]

.002* [.001]

.003*** [.001]

.001 [.001]

.003*** [.001]

.004*** [.001]

.001 [.001]

Yob .077*** [.001]

10.352*** [.685]

.001*** [.000]

.262*** [.031]

.001*** [.000]

.302*** [.025]

Yobsq

-.003*** [.000]

-.000*** [.000]

-.000*** [.000]

Female

-.130*** [.011]

-.125*** [.011]

-.125*** [.011]

-.001 [.000]

-.001 [.000]

-.001 [.000]

-.003*** [.001]

-.003*** [.000]

-.003*** [.000]

Urban

3.161*** [.015]

3.182*** [.015]

3.180*** [.015]

.055*** [.001]

.055*** [.001]

.055*** [.001]

.026*** [.001]

.026*** [.001]

.026*** [.001]

Constant

3.772*** [.029]

-146.481*** [1.821]

-10217.95*** [671.646]

.871*** [.001]

-1.730*** [.082]

-257.478*** [30.161]

.935*** [.001]

-1.109*** [.067]

-296.013*** [24.746]

Obs. 450829

450829 450829 450829

450829

450829 450829

450829

450829

Standard errors in brackets * Significant at 10% **Significant at 5% ***Significant at 1%

50  

 

Table 2 Effect of the Earthquake on Graduation Rate

Primary School Secondary School University (1) (2) (3) (1) (2) (3) (1) (2) (3) Val1960

-.018 [.013]

-.021 [.013]

-.021 [.013]

-.026 [.017]

-.028* [.017]

-.028* [.017]

-.003 [.008]

-.003 [.008]

-.003 [.008]

Val -.041*** [.003]

-.039*** [.003]

-.039*** [.003]

-.071*** [.004]

-.068*** [.004]

-.068*** [.004]

-.012*** [.002]

-.012*** [.002]

-.012*** [.002]

Yob1960

.008*** [.002]

.014*** [.002]

.004*** [.002]

-.019*** [.003]

-.012*** [.003]

-.013*** [.003]

.000 [.001]

.001 [.001]

.001 [.001]

Yob .007*** [.000]

1.110*** [.064]

.008*** [.000]

.084 [.088]

-.001*** [.000]

-.151*** [.038]

Yobsq -.000*** [.000]

-.000 [.000]

.000*** [.000]

Female

-.008*** [.001]

-.007*** [.001]

-.007*** [.001]

.017*** [.001]

.018*** [.001]

.018*** [.001]

-.013*** [.001]

-.013*** [.001]

-.013*** [.001]

Urban .215*** [.001]

.217*** [.001]

.217*** [.001]

.294*** [.002]

.297*** [.002]

.297*** [.002]

.044*** [.001]

.043*** [.001]

.043*** [.001]

Constant

.462*** [.003]

-13.974*** [.170]

-1094.928*** [62.759]

-.180*** [.004]

-15.246*** [.233]

-90.514*** [85.830]

-.030*** [.002]

2.128*** [.102]

149.134*** [37.601]

Obs. 450829

450829 450829 450829

450829 450829 450829

450829

450829

Standard errors in brackets * Significant at 10% **Significant at 5% ***Significant at 1%

51  

 

Table 3 Fixed Effects Results of Educational Attainment

Yrschl School Literacy Primary Secondary University Val1960 -.273**

[.133] -.012** [.005]

-.016*** [.006]

-.019 [.013]

-.029* [.017]

-.003 [.007]

Valt -.003 [.005]

.000 [.000]

.000 [.000]

.002*** [.000]

-.001** [.001]

-.000 [.000]

Female -.099*** [.011]

-.003*** [.000]

-.000 [.000]

-.006*** [.001]

.020*** [.001]

-.012*** [.001]

Urban 2.703*** [.016]

.024*** [.001]

.050*** [.001]

.189*** [.001]

.250*** [.002]

.034*** [.001]

Constant 4.253*** [.058]

.920*** [.002]

.865*** [.003]

.453*** [.006]

-.114*** [.008]

.016*** [.003]

Obs. 439076 439076 439076 439076 439076 439076 Standard errors in brackets * Significant at 10% **Significant at 5% ***Significant at 1%

Table 4 Effect of the Earthquake on Health and Employment Status

Disability Rate Unemployment Rate (1) (2) (3) (1) (2) (3) Val1960 -.004

[.004] -.004 [.004]

-.004 [.004]

--.010 [.013]

--.010 [.013]

--.010 [.013]

Val .001 [.001]

.001 [.001]

.001 [.001]

.009*** [.003]

.009*** [.003]

.009*** [.003]

Yob1960 .000 [.001]

-.001 [.001]

.001 [.001]

.007*** [.003]

.007*** [.003]

.009*** [.003]

Yob -.001*** [.000]

-.164*** [.025]

.001*** [.000]

-.197*** [.073]

Yobsq .000*** [.000]

.000*** [.000]

Female -.005*** [.000]

-.005*** [.000]

-.005*** [.000]

-.015*** [.001]

-.015*** [.001]

-.015*** [.001]

Urban -.007*** [.001]

-.007*** [.001]

-.007*** [.001]

.007*** [.002]

.007*** [.002]

.007*** [.002]

Constant .034*** [.001]

1.250*** [.066]

161.551*** [24.451]

.103*** [.004]

-.956*** [.194]

192.502*** [71.447]

Obs. 446549 446549 446549 301811 301811 301811 Standard errors in brackets * Significant at 10% **Significant at 5% ***Significant at 1%

52  

 

Table 5 Effect of the Earthquake on Socioeconomic Status

Asset Infrastructure (1) (2) (3) (1) (2) (3) Val1960 -.024

[.057] -.018 [.057]

-.017 [.057]

-.126*** [.042]

-.126*** [.042]

-.126*** [.042]

Val -.453*** [.014]

-.459*** [.014]

-.459*** [.014]

.005 [.010]

.004 [.010]

.004 [.010]

Yob1960 -.024** [.011]

-.043*** [.011]

-.060*** [.011]

-.018** [.008]

-.019** [.008]

-.024** [.008]

Yob -.021*** [.000]

1.741*** [.317]

-.001*** [.000]

.540*** [.231]

Yobsq -.000*** [.000]

-.000*** [.000]

Female .141*** [.005]

.141*** [.005]

.141*** [.005]

.056*** [.004]

.056*** [.004]

.056*** [.004]

Urban 1.631*** [.008]

1.629*** [.008]

1.629*** [.008]

1.982*** [.005]

1.982*** [.005]

1.982*** [.005]

Constant -3.001*** [.014]

1.250*** [.066]

-1689.407*** [310.625]

-3.717*** [.011]

-.978 [.614]

192.502*** [71.447]

Obs. 438888 438888 438888 436920 436920 436920 Standard errors in brackets * Significant at 10% **Significant at 5% ***Significant at 1%

Table 6 Fixed Effects Results of Health and Socioeconomic Status

Disability Rate Unemployment Asset Infrastructure Val1960 -.004

[.005] -.010 [.013]

-.015 [.055]

-.125*** [.042]

Valt -.000 [.000]

.000 [.001]

-.004* [.002]

-.001 [.002]

Female -.006*** [.000]

-.015*** [.001]

.145*** [.005]

.058*** [.004]

Urban -.007*** [.001]

.007*** [.002]

1.391*** [.008]

1.922*** [.006]

Constant .044*** [.002]

.134*** [.007]

-2.480*** [.027]

-3.629*** [.020]

Obs. 428915 287084 421876 420136 Standard errors in brackets * Significant at 10% **Significant at 5% ***Significant at 1%