long term health effects of the 1999 ecuadorian financial crisis · 2017. 6. 17. · publicada en...

Long term health effects of the 1999 Ecuadorian financial crisis Ana Larrea, Ph.D. candidate, Department of Applied Economics, UAB.

Abstract: We evaluate the effect of the 1999 Ecuadorian financial crisis on health outcomes in 2012. By means of a regression discontinuity model, we identify treatment and control groups by using a financial tax imposed on 1-Jan-1999 as a cut-off. We argue this triggered a bank run that was not anticipated by deposit holders, and, as such, can be used as an exogenous shock. We compare those born just before/after the cut-off, creating a counter-factual which has similar (un)observable characteristics. We find a deleterious effect on 2012 health outcomes and argue pre-natal maternal stress is likely to be the transmission mechanism.

Introduction Most of us are aware of the harmful effects of drug use during pregnancy, specifically, how it can directly

affect the nervous system of the fetus. However, most of us are unaware of the fact that stress hormones,

such as cortisol, have the ability to pass from the mother to the fetus via the placenta. We are unaware

that high levels of stress hormones can have deleterious effects, not only on fetal development, but also

on post-natal development.

Stress is difficult to measure as it can be objective or subjective. Objective stress refers to the measurable

amount of hardship endured by an individual. While it would be unacceptable to subject an individual to

objective stress in order to measure its effects, some exogenous life events, such as sudden death or natural

disasters, may reproduce conditions similar to those found in a lab. In this study we use the 1999

Ecuadorian financial crisis as one of these exogenous stress-inducing life events.

The objective is to study the effects of this shock on the health outcomes of the child. We observe the 2012

z-score of height for age of children born just before and just after the crisis hit in 1999. We estimate the

average treatment effect by measuring the difference in outcomes. In order to control for the observable

and unobservable characteristics of the children which might affects these outcomes we propose a

regression discontinuity model.

We identify treatment and control groups by using a financial tax imposed on 1 Jan 1999 as a cut-off point.

We argue this triggered a bank run that was not anticipated by deposit holders, and, as such, can be used

as an exogenous shock. The method compares children born just before/after the cut-off, creating a

counter-factual which can be assumed to have similar observable and unobservable characteristics. This

allows us to identify a causal effect of the shock.

Our findings suggest a deleterious long term effect on health outcomes of the children who were in-utero

during the financial crisis. We argue that prenatal maternal stress is likely to be the transmission

mechanism. Based on the fetal origins hypothesis, we argue that the fetal environment determines the

long term health outcomes of individuals. According to this hypothesis, fetal conditions are persistent and

they reflect a specific biological mechanism, a series of “switches” which determine the epigenome,1 such

that, the health effects of an intra-uterine shock remain latent though the life cycle. Although we cannot

1 Fetal conditions affect a series of “switches” that determine whether parts of a genome are expressed or not

test this hypothesis directly, we propose that the precipitation of the financial crash on 1 January 1999

created an intra-uterine shock through prenatal maternal stress (Almond & Currie, 2011).

This study is a contribution because it finds a natural experiment where an exogenous cut-off allows us to

measure long term effects of an exogenous stress shock on health. This paints a more comprehensive

picture of the consequences of prenatal maternal stress. Additionally, it may allow policy makers to better

assess the optimum age of preventative health care interventions.

This paper is divided into six parts: (1) The context explains the origin, collapse and aftermath of the 1999

Ecuadorian crisis; (2) the mechanisms explain the fetal origins hypothesis and how it applies to this case;

(3) the methodology gives the econometric account of the model; (4) the data and results explain the

survey and the results; (5) the robustness checks go through every case where a regression discontinuity

model might fail; and finally we end with (6) the conclusion and discussion.

Context: the financial crisis of 1999 1. Run-up to the crisis: the 1994-1998

a. Liberalization of financial markets: 1994

This crisis started as most financial crises do: with a far reaching liberalization of the financial system in

1994. This period of financial liberalization coincided with a parallel increase of capital inflows attracted to

higher domestic returns. Between 1993 and 1994 the Central Bank of Ecuador’s (CBE) international

reserves doubled (Jacome, 2004; Martinez, 2006).

In 1994 the Law of Financial System Institutions was enacted. This was essentially part of a greater

liberalization process which had begun in the early 1990’s in which liberalized interest and exchange rates

were instituted. The law promoted the free entry and exit of institutions in the financial market (the number

of financial institutions increased from 33 in 1993 to 44 in 1994) and allowed for an expansion of bank

operations particularly in foreign currency and in offshore branches. CBE was named lender of last resort

(LOLR) and was only allowed to provide liquidity assistance in the local currency (sucres) not in foreign

currency. Additionally, the amount of liquidity assistance allowed was unlimited and the deposit guarantees

would rely on CBE funds. Finally, there was a rapid reduction in bank reserve requirements from 28% to

10% in domestic currency and from 35% to 10% in foreign currency (Jacome, 2004).

In this environment, the Superintendence of Banks and Insurance Companies, failed to effectively monitor

operations, particularly in offshore branches. This allowed banks to circumvent regulations and controls.

This institutional framework did not encourage agents or principals to minimize risk. Financial

intermediaries engaged in transactions in foreign currency including currency and maturity mismatches in

the denomination of assets and liabilities,2 connected lending, large amounts of non-performing loans and,

in some cases, even fraudulent operations. They failed to gauge the risk in lending operations (credits

increased 40% in 1993 and 50% in 1994) and performed operations such as lending overnight in the

bouyant money market and then turning around and lending it to their customers at a higher rates and

longer maturities (Jacome, 2004; Martinez, 2006).

b. Exogenous liquidity crunch of 1996

The Mexican peso crisis (December 1994) and the border conflict with Peru (January 26 – February 28,

1995) led to a liquidity crunch in Ecuador. CBE stabilized the exchange rate in order to control inflation by

contracting money through Open Market Operations (OMO) pushing the nominal interest rate up to 50%

(and the real interest rate up to 30%). This increase in the interest rate created liquidity problems for banks

with maturity mismatches. Banco Continental failed and was acquired by the State, however, the CBE

isolated the crisis by providing liquidity support to banks. An ominous equilibrium ensued in 1997 and with

the liquidity conditions restored the interest rate went back down. Notwithstanding, the banking system

remained fragile due to poor quality of bank assets and a resulting equity shortage (Jacome, 2004).

c. Low oil prices and El Niño 1997-1998

In the winter of 1997-1998 Ecuador suffered the worst El Niño phenomenon in its history. This destroyed

agricultural areas, particularly in the coastal regions, impairing banking assets. Additionally, the Brazilian

crisis began to spill over to the rest of Latin America while the price of oil was 10$ a barrel making foreign

currency scarce and hurting public finance (Jacome, 2004).

d. Political unrest and bank failures 1997-1998

In a nutshell, in August of 1998 banks stated showing signs of failure. The new administration rushed in to

protect deposits by dishing out a bailout financed with a rapid monetary expansion (Martinez, 2006).

Amongst widespread social unrest and protest, on February 6th 1997, the sitting president (Adbala

Bucaram) was removed from office by congress and declared unfit to govern. His vice-president (Rosalia

2 Currency or maturity of assets not equal to currency or maturity of liabilities.

Arteaga) was sworn into office on February 9th 1997 and removed from it on February 11th 1997. The

president of congress, (Fabian Alarcon) was named interim president on February 11th 1997 and was in

office until August 10th 1998.

Solbanco was the first (small) bank to close in April 1998. This led to a wave of withdrawals in other banks

given medium and large size deposit holders lost their savings (Jacome, 2004) after a very close general

presidential election which was held on May 30th 1998, Jamil Mahuat was sworn in as president on August

10th 1998. In the same month a medium sized bank failed, and in September 1998 Filanbanco along with

11 other financial institutions requested lender of last resort (LOLR) assistance from the CBE. OMOs meant

to mop up the liquidity were being performed by the CBE. This increased the interest rate on government

bonds and devaluates the local currency (the sucre) (Jacome, 2004).

Up until January 1999, there was an absence of effective bank resolution instruments. The CBE providing

LOLR assistance which reached 30% of the money base. In order to hold down the depreciation of the

currency, the CBE tried to mop up liquidity by simultaneously selling bonds (Bonos de estabilizacion

monetaria, BEMs) through OMOs. This proved insufficient as the Sucre depreciated by 24%, inflation

reached 15% and international reserves fell by 7.6% between September and November of 1998. Finally,

in the last quarter of 1998 banks foreign credit lines experienced a US$300 million cut due to the Russian

and Brazilian crisis (Jacome, 2004).

2. The AGD and the 1% tax: first trimester of 1999

The a 1% tax in place on all financial transactions starting on 1 January 1999 was created in legislation3

which passed Congress on 1 December 1998. The same law created the Deposit Guarantee Agency (AGD

given its name in Spanish - Agencia de Garantia de Depositos), which was entitled to “purchase and assume

operations” of financial institutions and began operating 1 December 1998 (Cantos Bonilla, 2006; Jacome,

2004).

Enacting a 1% tax on all financial transactions was devastating for the financial system given it engendered

a massive liquidity flight. This accelerated the collapse of various financial institutions, notably the largest

bank in terms of deposits (Banco del Progreso). Total deposits plummeted in this month (see Figure 1)

putting more pressure on the exchange rate and fulling a speculative run on the sucre as preferences

3 Ley de Reordenamiento en Materia Económica en el Área Tributario - Financiera. Publicada en el Suplemento del Registro Oficial No. 78 del I de diciembre de 1998.

shifted to the Dollar. International reserves of the CBE shrank and by February of 1999 they floated the

sucre resulting in an almost immediate 50% devaluation (Jacome, 2004).

Despite having enacted the AGD which was meant to protect deposits, the State closed 6 banks between

December of 1998 and January 1999. The blanket guarantee of deposits was not honored until April 1999

using CBE funds. This fueled withdrawals from other banks, eroded credibility of the blanket guarantee and

stimulated contagion (Jacome, 2004).

In the early days of March 1999 Banco del Progresso experienced a massive run on deposits. This, coupled

with the currency crisis and the systematic lack of confidence, led the government to declare a bank holiday

on Monday March 8th 1999 (meaning that banks remained closed). This holiday lasted a week and finalized

in the widespread freezing of all bank accounts with a balance over 500 USD in order to avoid further capital

flight. Savings accounts would be frozen for a year and checking accounts for 6 months (Jacome, 2004).

3. The aftermath: mid 1999 to early 2000

In July 1999 the government began to progressively unfreeze bank deposits without having any assurance

of being able to provide the appropriate liquidity safeguards. This measure was gradual so not all deposit

holders benefited. This led to a wave of withdrawals and a hike in the demand for foreign currency fueling

a second currency crisis. In September 1999 liquidity assistance peaked, total monetization associated with

the banking system reached 12% of GDP (Jacome, 2004). During this month the government suspended

payments on Discount and PDI Brady Bonds. By October of 1999 it also defaulted on its remaining Brady

Bonds and it’s Eurobonds (Sturzenegger and Zettelmeyer, 2008). The default on public debt impacted both

foreign investors and domestic banks who had invested in government securities. By October of 1999 half

of Ecuadorian private banks had failed and in March of 2000 Ecuador adopted the US dollar as legal tender

as a measure to stop inflation and restore confidence. Finally, on July 27, 2000, Ecuador launched a

renegotiation of its public debt (Sturzenegger and Zettelmeyer, 2008).4

4. Discussion on our crisis threshold

Waves of withdrawals occurred fairly regularly in Ecuador. However, as we can see in Figure 1, between

the outbreak of the crisis (first bank failure in April 1998) and December of the same year total deposits

continued to increase. Notwithstanding, total deposits fell drastically in January 1999. Figure 2 shows how

4 “[The] offer [was] to exchange its defaulted Bradys and Eurobonds for new uncollateralized bonds maturing in 2030 with a step-up coupon starting at 4% and rising to 10%, in 1% steps, by 2006” (Sturzenegger and Zettelmeyer, 2008)

the largest liquidity crunch faced by the banks also occurred in January of 1999 which was only stopped

with the freezing of bank deposits in March 1999. Why would there be a bank run in January 1999 if banks

were closing since April 1998 and inflation and devaluation was ramped since September of the same year

(see Figure 3 and see Figure 15 p.31 for inflation shock in Sep 98)? We argue that it is this financial tax

which precipitated individuals to change their behavior from being cautiously optimistic to losing

confidence in the government’s capacity to support the financial system. That, in order to circumvent the

tax, individuals preferred to operate transactions outside of the financial system creating a violent squeeze

on bank deposits and liquidity. We maintain that the financial tax directly triggered panic among deposit

holders and a massive bank run and indirectly, through the bank run, engendered the collapse in foreign

currency reserves of the central bank,5 the float of the sucre in February and the freezing of bank deposits

in March. Essentially, we suggest that the financial tax precipitated the crash of the financial system.

Figure 1Total Deposits and Currency Issue (Billions of sucres)

Source: Jacome, 2004

5 as it struggled to inject liquidity and mop up liquidity simultaneously

Figure 2 Liquidity and credit crunch

Figure 3 Real effective exchange rate and inflation

Source: Jacome, 2004.

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We believe this trigger is exogenous because of how counter-intuitive a tax on a failing financial system

would seem to the rational individual. Taxation normally increases prices, reducing demand. Why would

the government use taxation as a means of re-activating the financial system? According to many sources

(Jacome, 2004; Cantos Bonilla, 2006) this tax was instituted as a means to increase government revenue.

However, the same law that created this tax also eliminated all tax on income. If the objective was to

increase government revenues, why not also maintain the income tax? Or, if anything, create a tax that

would not directly affect the financial system. We argue that, for a rational individual, this policy would be

quite difficult to predict or anticipate.

We argue this was an exogenous shock despite the fact that the tax was instituted on 1 December 1999.

One month before the bank run deposit holders were aware of the tax they would have to pay starting on

1 January 1999. This may have allowed them to anticipate the tax and withdraw their deposits beforehand.

However, the data shows that total deposits grew between November and December 1998 (Figure 1).

Therefore, individuals did not react until the tax was actually instituted.

Additionally, an anticipation of the effects of the tax would imply that an individual was capable of

forecasting the collapse of the financial system. Specifically, that an individual would withdraw her deposits

because she was aware that she would lose all of her financial assets rather than merely afraid of having to

pay a tax on her financial transactions. We believe the violent panic was triggered by some individuals

circumventing the tax. That throughout the month of January this behavior spread and quickly exploded

into a bank run which, in retrospect, began specifically on 1 January 1999. This bank run, we argue, was

something that individuals could not have anticipated.

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Figure 4 Chronology of Ecuador's 1999 Financial Crisis

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Mechanism: intra-uterine shocks 1. What are intra-uterine shocks? How could they affect long run health outcomes?

a. What is the fetal origins hypothesis?

The fetal origins hypothesis proposes that the fetal environment determines the long term health

outcomes of individuals. The British physician and epidemiologist J.D. Baker (1990) puts forward that intra-

uterine shocks affect the “programing” of certain metabolic characteristics which may have effects later in

life. The hypothesis connects three central ideas: that fetal conditions are persistent, that they reflect a

specific biological mechanism, and, therefore, that the health effects remain latent through time.

Specifically, fetal conditions affect a series of “switches” that determine whether parts of a genome are

expressed or not, called the epigenome (Almond & Currie, 2011).

a. What empirical evidence supports it?

Backer’s initial work was essentially correlational (Backer and Osmond, 1986; Backer, 1995). Other studies

attempt to identify a causal effect between famine and long term health outcomes using birthdates to

determine the intra-utero exposure. Stein, Susser, Saeger, and Marolla (1975) analyze the effects of pre-

natal exposure to the Dutch famine of 1944 (known as the “Hunger Winter”) and the anthropometric

measures of 400000 conscripted 18-year old men. They found obesity rates were twice as high among

those who had first trimester exposure. Hoek, Brown and Susser (1998) later found an increase in

schizophrenia among those affected by the same famine. The findings have been replicated for the Chinese

famine of 1959-1961 (St Clair, et al., 2005). However, no effect was found for individuals inflicted by the

siege of Leningrad (Stanner, et al., 1997) nor for those who affected by the Finnish famine of 1866-1868

(Stein, et al., 1975; Hoek, et al., 1998; St Clair, et al., 2005; Stanner, et al., 1997; Kannisto, et al., 1997;

Almond & Currie, 2011).

b. How does it relate to our study?

Our study does not focus on famine. We analyze the long term health effects of a financial crisis. We argue

that the 1% tax on all financial transactions is an exogenous shock which increased the probability of pre-

natal maternal stress or other non-observables intra-uterine shocks which may affect fetal maturation

(such as an increase in the price level of basic staples, or a reduction in access to pre-natal medical services).

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2. The proposed mechanism: pre-natal maternal stress.

Stress increases our vulnerability to a wide range of health problems, from a down-regulated immune

system to a wear and tear on the cardiovascular system (Wilkinson & Pickett, 2009). Stress among pregnant

women can increase levels of CRH (Corticotropin-releasing hormone) which regulates the duration of

pregnancy and fetal maturation, it can increase the probability of infection and maternal vascular disease,

as shown in Figure 5 (Holzman et al, 2001). This type of stress is called pre-natal maternal stress, it is a

known determinant of adverse birth outcomes (Beydoun & Saftlas, 2008, Mansour and Rees, 2011,

Camacho, 2008).

Beydoun and Saftlas (2008), in their review of the literature on the effect of pre-natal maternal stress

(PNMS) on fetal growth, find that 9 out of 10 studies report significant effects of PNMS on birth weight, low

birth weight (LBW) or fetal growth restriction. Couzin (2002) summarizes how endocrinologist Hobathan

Seckl, of Western General Hospital in Edinburgh, U.K., believes that excess levels of stress hormones in the

fetus “reset” an important arbitrator of stress in the body, making it hypersensitive to even banal events.

Almond and Currie (2011) find numerous studies providing evidence of the long-term consequences of a

wide variety of intrauterine shocks (Almond & Currie, 2011; Camacho, 2008; Eskenazi et al., 2007). For

example, Camacho (2008) finds that the intensity of random landmine explosions during a woman’s first

trimester of pregnancy has a significant negative impact on child birth weight.

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Figure 5 Prenatal maternal stress pathway

Source: C. Holzman, et al., 2001, Pregnancy outcomes and community health: the POUCH study of preterm delivery, Paediatric and perinatal

Epidemiology, 15(2), pp. 138.

There are various studies which find that children with LBW are at higher risk of suffering chronic

malnutrition. Marins and Almeida (2002) find that LBW could be characterized as important under-

nutrition risk factor, and that, birthweight deficits appear to have effects on a child’s growth that extend

for years after birth (Marins & Almeida, 2002). Willey et. al. (2009) found an increased likelihood of stunting

(chronic malnutrition) was seen in LBW children (Willey et. al., 2009). Aerts, et al. (2004) perform a cross-

sectional population-based study of determinants of growth retardation (chronic malnutrition) in children

under five and find that one of the main determinants was LBW (Aerts, et al., 2004). Taguri et al. (2009),

who used a multivariate analysis in order to ascertain predictors of stunting in children under five, found

LBW to be of the main risk factors (El Taguri, et al., 2009). Adair and David (2007) used a multivariate

discrete time hazard model to estimate the likelihood of becoming stunted in each two month intervals

and found a significant association with LBW which was strongest in the first year. They argue that breast-

feeding, preventive health care and taller maternal stature significantly decreased the likelihood of

stunting. (Adair & David, 1997).

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In this study, we are not able to measure pre-natal maternal stress directly. Firstly given we have cross

section data from 2012. The survey does not ask about the pre-natal stress conditions of mothers.

Additionally, it is difficult to measure pre-natal maternal stress through biomarkers. Generally, the

diagnosis is made through a description of the symptoms of the patient. However, we propose that this

may be one of the mechanisms through which the shock of the financial crisis may have affected the long

term health outcomes of children born in January 1999 as compared to those born in December. Those

born in January were still in-utero when the financial system collapsed and are otherwise very similar to

those born in December in their observable and unobservable characteristics.

3. Alternative mechanism: lack of access to medical services.

Other non-stress related intra-uterine shocks which may link the 1% tax to the long term nutritional

outcomes of children could include a lack of access to pre-natal medical services which may increase the

risk of untreated infection or maternal vascular disease. Again, this is not a variable which we are able to

measure directly. However, the environment of panic and the various failing macro and micro economic

variables could have led to a reduced probability of accessing the appropriate medical services required at

the end of the term. We also consider this an intra-uterine shock (Holzman et al, 2001).

Methodology If we have an assignment variable Si which determines whether the individual receives the “treatment”

(the tax shock before giving birth) with an eligibility cut-off at s∗ (1 Jan 1999) we are able to model the

effect of the shock on the individual outcomes yi (z-score of height for age) using the RD method. This

allocation mechanism generates a non-linear relation between “treatment” and date of birth (𝑆𝑖). In

general, the estimating equation is 𝑦𝑖 = 𝛽𝑆𝑖 + 𝜀𝑖 , where individuals (children) with 𝑠𝑖 ≥ 𝑠∗ (born on or

after 1 Jan 1999) receive the “treatment” and individuals with 𝑠𝑖 < 𝑠∗ (born before 1 Jan 1999) do not. If

we assume that limits exist on either side of the threshold 𝑠∗, the impact estimation for an arbitrarily small

𝜀 > 0 around that threshold would be as follows (Shahidur, et al., 2010; Lee & Lemieux, 2010):

𝐸[𝑦𝑖|𝑠∗ − 𝜀] − 𝐸[𝑦𝑖|𝑠∗ + 𝜀] = 𝐸[𝛽𝑆𝑖|𝑠∗ − 𝜀] − 𝐸[𝛽𝑆𝑖|𝑠∗ + 𝜀] (1)

When taking the limit of both sides of equation (16) as 𝜀 → 0 we identify β as the ratio of the difference in

outcomes of individuals just above and below the threshold, weighted by the difference in their realizations

of 𝑆𝑖 as follows (Shahidur, et al., 2010; Lee & Lemieux, 2010):

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lim𝜀→0

𝐸[𝑦𝑖|𝑠∗ − 𝜀] − lim𝜀→0

𝐸[𝑦𝑖|𝑠∗ + 𝜀] = 𝑦− − 𝑦+ = 𝛽(𝑆− − 𝑆+) ⇒ 𝛽 =𝑦−−𝑦+

𝑆−−𝑆+ (2)

We assume, that individuals are assigned to “treatment” solely on the basis of the assignment variable

(date of birth). Therefore, the assignment variable (date of birth) is deterministic in receiving the

“treatment.”

According the Lee and Lemieux (2010) there are four instances where a RD model can fail. (1) individuals

may are able to anticipate or manipulate the cut-off; (2) the effect is not exclusive to the cut-off point, i.e.

there are placebo effects; (3) the effect is caused by a covariate that has the same cut-off which is driving

in the outcome; (4) a linear functional form of the regression could mislead us to conclude there is a

significant jump where actually there is simply a non-linear association. We address all of these issues in

the robustness checks section (Lee & Lemieux, 2010).

Data and Results 1. Data

The National Health and Nutrition Survey (ENSANUT given its name in Spanish: Encuesta Nacional de Salud

y Nutrición) is a cross-section database built by the National Institute for Statistics and Censes (INEC given

its name in Spanish: Instituto Nacional de Estadísticas y Censos) in Ecuador between 2011 and 2013. It

covers various health topics including anthropometric measures for children adolescents and adults. It has

a total sample of 92502 individuals out of which we have a sample of 32426 children between the ages of

5 and 19 with our outcome variable z-score of height for age (Ministerio de Salud Publica; Instituto National

de Estadisticas y Censos, 2013).

The z-score was calculated by the INEC and the Ministry of Health using the method proposed by the

World Health Organization (WHO). The normalized z-score (equation 1) establishes the growth standard

of children by defining a normal growth curve (World Health Organization, 2013; World Health

Organization, 1997).

𝑧 𝑠𝑐𝑜𝑟𝑒 =(𝑥𝑖 − 𝑥𝑚𝑒𝑑𝑖𝑎𝑛)

𝜎𝑥⁄ (3)

Where 𝑥𝑖 is the height of child i, 𝑥𝑚𝑒𝑑𝑖𝑎𝑛 is the median height from the reference population of the same

age and gender and 𝜎𝑥 is the standard deviation of 𝑥 of the same reference population (Imai, et al., 2014;

World Health Organization, 1997). They use anthropometric data available in the LSMS (2006) to calculate

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the normalized z-score for each individual. In this case we are interested in children between the ages of 5

and 19.

The z-score ranges from −∞ to ∞ as it is measured in standard deviations from the mean which is zero. If

a child’s z-score is under -2, that is to say, under two standard deviations below the mean, the child is

chronically malnourished or “stunted” (World Health Organization, 1997). Figure 6.a and 6.b shows the z-

score growth curve by age in days for boys and girls.

In Figure 2 we can see the distribution of the z-score of height for age in Ecuador. The average z-score for

children between 5 and 19 is -1.18 (below the expected average of zero - blue line). Approximately, 20% of

children in this age range are chronically malnourished, that is, have a z-score under -2 (red line).

Figure 6 Distribution of z-score of height for age in 2012 among 5 to 19 year olds in Ecuador

a. Distribution of full sample b. Distribution of sub-sample of children close to the cut-off point

In this study we focus exclusively on the children born just before and just after 1 January 1999 (12 to 13

year olds). Therefore, in Figure 7 we present the distribution of z-score of height for age in 2012 by month

of birth around the cut-off. We can see that the average stays consistently below zero for every month.

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Figure 7 Box-plot z-score height for age by month of birth for sample of children born just before/after crisis

In Figure 8 we can see every observation of the z-score of height for age by day of birth. Here, the x-axis is

measured in days, and the day of the crisis (1 January 1999) is equal to zero. The days before the crisis are

negative and the days after the crisis are positive. In this way we are able to set up a regression discontinuity

model where the child receives the “treatment” if S≥0 and is in the “control” group if S<0. This graph

includes the entire sample of children between 5 and 19 years of age. However, as we mentioned above,

for the purposes of this study, we will focus in on the children born just before and just after 1 January

1999.

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Figure 8 Z-score of height for age in 2012 by day of birth among 5-19 year olds in Ecuador

Days before the bank run Crisis Days after the bank run

It is difficult to see the jump in the z-score of height for age at the cut-off point in Figure 8. In Figure 9 we

present a local polynomial regression one on each side of the cut-off point. Here, can clearly see the jump

in the mean z-score at the cut-off point.

Figure 9 Local polynomials for z-score of height for age over day of birth using 1 Jan 1999 as cut-off

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2. Results

As suggested by Lee and Lemieux (2010), we report a number of bandwidth and local polynomial order

specifications to demonstrate to what extent the results are sensitive to each. In section (b) we attempt to

estimate the optimal polynomial order and in section (c) we attempt to estimate the optimal bandwidth.

a. Linear and non-linear models with various bandwidths

For each model we use 25, 30, 50 and 60 day bandwidths. Table 1 presents the linear RD model, Table 2

presents the second degree polynomial RD model, and, finally, Table 3 presents the third degree polynomial

RD model with their respective sample sizes by treatment and control groups.

As we can see in Table 1, every linear model is negative and significant except for the 60 day bandwidth.

The magnitude of the effect decreases and the size of the bandwidth increases (-1.07 for 25 days, -0.8 for

30 days, -0.38 for 50 days, -0.28 for 60 days which is not significant).

Table 1 Linear RD model using 1 January 1999 as cut-off with 30 and 60 days bandwidths

------------------------------------------------------------------------------

_zhfa | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

25 days | -1.071095 .2862077 -3.74 0.000 -1.632051 -.5101378

30 days | -.8060477 .2710277 -2.97 0.003 -1.337252 -.2748431

50 days | -.380916 .2277 -1.67 0.094 -.8271998 .0653678

60 days | -.2870547 .2086706 -1.38 0.169 -.6960416 .1219321

------------------------------------------------------------------------------

Additionally, in Table 2, every non-linear second degree polynomial model is negative and significant. The

magnitude of the effect decreases and the size of the bandwidth increases (-2.2 for 25 days, -2.7 for 30

days, -0.9 for 50 days, -0.78 for 60 days).

Table 2 RD with second degree polynomial

Order loc. poly. (p): 2 Order bias (q): 3 BW loc. poly. (h/b): 1 Kernel type Tri.

-------------------------------------------------------------------------------------------------

Method | Coef. Std. Err. z P>|z| [95% Conf. Interval] n.control n. treat

-------------+-----------------------------------------------------------------------------------

25 days

Conventional | -2.2205 .44939 -4.9412 0.000 -3.10133 -1.33975 105 160

Robust | - - -3.5184 0.000 -3.31245 -.942283 105 160

-------------+-----------------------------------------------------------------------------------

30 days

Conventional | -1.7981 .38847 -4.6286 0.000 -2.55949 -1.03671 149 201

Robust | - - -4.4215 0.000 -3.49209 -1.347 149 201

-------------+----------------------------------------------------------------------------------

50 days

Conventional | -.96845 .30954 -3.1286 0.002 -1.57514 -.361751 232 337

Robust | - - -4.4900 0.000 -2.62424 -1.02936 232 337

-------------+-----------------------------------------------------------------------------------

60 days

Conventional | -.78583 .28986 -2.7111 0.007 -1.35394 -.217723 267 402

Robust | - - -3.9866 0.000 -2.20911 -.752885 267 402

20

-------------------------------------------------------------------------------------------------

As we can see in Table 3, every non-linear third degree polynomial model is negative and significant. The

magnitude of the effect decreases moderately as the size of the bandwidth increases (-2.1 for 25 days, -2.4

for 30 days, -1.8 for 50 days, -1.4 for 60 days). In Figures 10 - 14 we present the linear and non-linear graphic

representations of the jump for the 25, 30, 50 and 60 day bandwidths.

Table 3 RD with third degree polynomial

Order loc. poly. (p): 3 Order bias (q): 4 BW loc. poly. (h): 25 rho (h/b): 1 Kernel type Tri.

-------------------------------------------------------------------------------------------------


-------------+-----------------------------------------------------------------------------------

25 days

Conventional | -2.1274 .60465 -3.5184 0.000 -3.31245 -.942283 105 160

Robust | - - -2.2249 0.026 -3.40735 -.21568 105 160

-------------+-----------------------------------------------------------------------------------

30 days

Conventional | -2.4195 .54723 -4.4215 0.000 -3.49209 -1.347 149 201

Robust | - - -2.7918 0.005 -3.31544 -.580397 149 201

-------------+-----------------------------------------------------------------------------------

50 days

Conventional | -1.8268 .40686 -4.4900 0.000 -2.62424 -1.02936 232 337

Robust | - - -4.6963 0.000 -3.47079 -1.4268 232 337

-------------+-----------------------------------------------------------------------------------

60 days

Conventional | -1.481 .37149 -3.9866 0.000 -2.20911 -.752885 267 402

Robust | - - -4.7485 0.000 -3.13725 -1.30408 267 402

-------------------------------------------------------------------------------------------------

21

Figure 10 Linear and non-linear models: 25 day bandwidth



22


23

b. Correct functional form

In this section we apply a more formal guidance on the choice of polynomial, as recommended by Lee and

Lemieux (2010). We perform simple test where we add a set of bin dummies to the polynomial regression

and jointly test the significance of the bin dummies.

When we introduced the RD design in our methodology we followed the Lee and Lemieux (2010) linear

model:

𝑌 = 𝛼 + 𝜏𝐷 + 𝑋𝛽 + 𝜀 (4)

Where 𝜏 is the effect of the treatment. Given we want to test the functional form of the outcome and

assignment variable we use this variation of the equation (Lee & Lemieux, 2010):

𝑌 = 𝛼𝑙 + 𝑓𝑙(𝑋 − 𝑐) + 𝜀 (5)

𝑌 = 𝛼𝑟 + 𝑓𝑟(𝑋 − 𝑐) + 𝜀 (6)

The treatment effect can then be computed as the difference between the two regressions intercepts, 𝛼𝑙

and 𝛼𝑟, on the two sides of the cutoff point (Lee & Lemieux, 2010).

𝑌 = 𝛼𝑙 + 𝜏𝐷 + 𝑓(𝑋 − 𝑐) + 𝜀 (7)

Where 𝜏 = 𝛼𝑟 − 𝛼𝑙 and𝑓(𝑋 − 𝑐) = 𝑓𝑙(𝑋 − 𝑐) + 𝐷[𝑓𝑟(𝑋 − 𝑐) − 𝑓𝑙(𝑋 − 𝑐)]. Here we can let the

regression function differ on either or both sides of the cut-off point. The linear model would be written

(Lee & Lemieux, 2010):

𝑌 = 𝛼𝑙 + 𝜏𝐷 + 𝛽𝑙(𝑥 − 𝑐) + (𝛽𝑟 − 𝛽𝑙)𝐷(𝑋 − 𝑐) + 𝜀 (8)

Where 𝑓𝑙(𝑋 − 𝑐) = 𝛽𝑙(𝑋 − 𝑐) and 𝑓𝑟(𝑋 − 𝑐) = 𝛽𝑟(𝑋 − 𝑐). Restricting the linear model to have the same

slope on each side of the cut-off point would amount to 𝛽𝑟 = 𝛽𝑙. We do not apply this technique because

this would amount to using data on the right hand side to estimate 𝛼𝑙. As we can see in the graphs, even

in the linear models, we allow each side of the cut-off to have its own slope. Applying third order

polynomials to the model is as follows in equation (9) (Lee & Lemieux, 2010).

𝑌 = 𝛼𝑙 + 𝜏𝐷 + 𝛽𝑙1(𝑥 − 𝑐) + 𝛽𝑙2(𝑥 − 𝑐)2 + 𝛽𝑙3(𝑥 − 𝑐)3 + (𝛽𝑟1 − 𝛽𝑙1)𝐷(𝑋 − 𝑐) + (𝛽𝑟2 − 𝛽𝑙2)𝐷(𝑋 −

𝑐)2 + (𝛽𝑟3 − 𝛽𝑙3)𝐷(𝑋 − 𝑐)3 + 𝜀 (9)

In order to perform the simple test where we add a set of bin dummies to the regression and jointly test

the significance of the bin dummies (Lee & Lemieux, 2010).

𝑌 = 𝛼𝑙 + 𝜏𝐷 + 𝛽𝑙1(𝑥 − 𝑐) + (𝛽𝑟1 − 𝛽𝑙1)𝐷(𝑋 − 𝑐) + ∑ 𝜙𝑘𝛽𝑘𝐾−1𝑘=2 + 𝜀 (10)

24

The test can be computed by including K- 2 bin dummies Bk, for k = 2 to K- 1, in the model and testing the

null hypothesis 𝜙1 = 𝜙2 = ⋯ = 𝜙𝑘−1 = 0. Another major advantage of this technique is that it allows us

to test for the presence of discontinuities in other points of the assignment variable than the cut-off. This

is a sort of alternative placebo effect test. We limit the sample to children born 60 days before/after sample

and create one bin for every 3 days. As we can see in the regression below, the only significant jumps are

at the cut-off point and no other bin dummies are significant in either the linear or the non-linear models

(Lee & Lemieux, 2010).

Table 4 RD with linear an non-linear polynomial with bin dummies OLS1 OLS2 OLS3

b/se b/se b/se

Z99 -0.016 -0.010 -0.024

(0.03) (0.03) (0.04)

Z992 0.000 0.000

(0.00) (0.00)

Z993 0.000

(0.00)

1bn.bin . . .

. . .

2.bin 0.541* 0.727** 0.669*

(0.30) (0.35) (0.37)

3.bin 0.366 0.697 0.617

(0.40) (0.52) (0.55)

4.bin 0.532 0.953 0.872

(0.46) (0.62) (0.65)

5.bin -0.062 0.484 0.423

(0.61) (0.81) (0.82)

6.bin 0.528 1.168 1.145

(0.73) (0.96) (0.96)

7.bin -0.084 0.596 0.600

(0.83) (1.07) (1.07)

8.bin 0.954 1.660 1.689

(0.88) (1.12) (1.12)

9.bin 0.328 1.074 1.156

(0.98) (1.22) (1.24)

10.bin -0.305 0.463 0.596

(1.11) (1.34) (1.38)

11.bin 0.312 1.090 1.311

(1.24) (1.46) (1.55)

12.bin 1.290 2.055 2.356

(1.39) (1.58) (1.73)

13.bin -0.122 0.629 0.969

(1.45) (1.63) (1.81)

14.bin 0.488 1.219 1.595

(1.53) (1.69) (1.90)

15.bin 0.892 1.604 2.008

(1.58) (1.73) (1.96)

16.bin 1.230 1.897 2.350

(1.68) (1.80) (2.08)

17.bin 1.164 1.739 2.260

(1.83) (1.92) (2.26)

18.bin 1.555 2.074 2.623

(1.92) (1.99) (2.35)

19.bin 1.032 1.449 2.033

(2.04) (2.08) (2.48)

20.bin 0.895 1.243 1.843

(2.11) (2.14) (2.55)

21.bin 1.028 1.280 1.890

25

(2.21) (2.22) (2.63)

22.bin 1.013 1.195 1.807

(2.27) (2.28) (2.68)

23.bin 0.689 0.707 1.305

(2.40) (2.40) (2.77)

24.bin 1.588 1.414 1.975

(2.54) (2.55) (2.86)

25.bin 1.863 1.630 2.176

(2.59) (2.60) (2.88)

26.bin 0.983 0.683 1.207

(2.63) (2.65) (2.91)

27.bin 1.744 1.329 1.811

(2.70) (2.73) (2.95)

28.bin 1.690 0.962 1.299

(2.88) (2.97) (3.07)

29.bin 1.402 0.543 0.807

(2.96) (3.08) (3.14)

30.bin 1.576 0.614 0.816

(3.01) (3.15) (3.19)

_cons -1.881 -2.632 -2.971

(1.45) (1.62) (1.80)

r2 0.164 0.165 0.165

N 675 675 675

c. Optimal bandwidth

In the results presented above, we include various bandwidths. However, the rdbwselect command

(Calonico, et al., 2014) allows us to calculate three “optimal” bandwidths: CCT bandwidth, IK and CV

bandwidth selectors are presented in Table 5. The bandwidths proposed are 551, 681 and 101 days. We

use the smallest optimal bandwidth and find a negative and significant effect both in the linear (Table 6)

and in the third degree polynomial RD models (Table 7) with a magnitude of 0.26 and 0.58 respectively.

Table 5 Optimal bandwidths

Bandwidth estimators for RD local polynomial regression

Cutoff c = 0 | Left of c Right of c Number of obs = 32426

----------------------+---------------------- NN matches = 3

Number of obs | 8947 23479 Kernel type = Triangular

Order loc. poly. (p) | 1 1 Min BW grid = 0.00000

Order bias (q) | 2 2 Max BW grid = 2020.00000

Range of Z99 | 2020.000 3234.000 Length BW grid = 101.00000

----------------------------------------------

Method | h b rho

----------+-----------------------------------

CCT | 531.6602 826.7741 .6430538

IK | 681.5333 1047.444 .6506633

CV | 101 NA NA

----------------------------------------------

Table 6 Linear RD models using optimal bandwidths

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

101 days | -.2622825 .147038 -1.78 0.074 -.5504718 .0259067

-------------+----------------------------------------------------------------

Table 7 Second degree polynomial RD models using optimal bandwidth 101 days

-------------------------------------------------------------------------------------------------

26


-------------+-----------------------------------------------------------------------------------

101 days:

Conventional | -.42696 .22643 -1.8856 0.059 -.870757 .016835 527 678

Robust | - - -1.9998 0.046 -1.14986 -.011568 527 678

-------------------------------------------------------------------------------------------------

Table 8 Third degree polynomial RD models using optimal bandwidth 101 days

-------------------------------------------------------------------------------------------------


-------------+-----------------------------------------------------------------------------------

Conventional | -.58072 .29039 -1.9998 0.046 -1.14986 -.011568 527 678

Robust | - - -3.5826 0.000 -1.93358 -.566075 527 678

-------------------------------------------------------------------------------------------------

Robustness checks According the Lee and Lemieux (2010) there are four instances where a RD model can fail. (1) individuals

may are able to anticipate or manipulate the cut-off; (2) the effect is not exclusive to the cut-off point, i.e.

there are placebo effects; (3) the effect is caused by a covariate that has the same cut-off which is driving

in the outcome; (4) a linear functional form of the regression could mislead us to conclude there is a

significant jump where actually there is simply a non-linear association. We address all of these issues in

the robustness checks section (Lee & Lemieux, 2010).

1. Individuals are able to anticipate or manipulate the cut-off

For the purposes of this paper, an anticipation of the cut-off would imply foreseeing the bank run and a

manipulation of the cut-off would translate to inducing child birth with the purpose of avoiding being

exposed to the stress of the financial crisis while pregnant. If individuals were able to anticipate or

manipulate the cut-off this would produce a contamination of the control group, making it an

unappropriated counter-factual to the treatment group.

It may be argued that individuals could anticipate the imposition of the financial tax given the law which

instituted6 it was passed in Congress on 1 December 1998. However, the data shows that total deposits

grew in December 1998 and only fell violently in January 1999 (Figure XX). Additionally, an anticipation of

the effects of the tax would imply that an individual was capable of forecasting the collapse of the financial

system. We believe the violent panic was triggered by some individuals circumventing the tax. That

throughout the month of January this behavior spread and quickly exploded into a bank run which, in

6 Ley de Reordenamiento en Materia Económica en el Área Tributario - Financiera. Publicada en el Suplemento del Registro Oficial No. 78 del I de diciembre de 1998.

27

retrospect, began specifically on 1 January 1999. This bank run, we argue, was something that individuals

could not have anticipated.

There is also an argument pertaining to the anticipation of a financial break down on a grander scale. The

behaviour of financial agents was risky because they anticipated a bailout (Martinez, 2006). However, it is

difficult to argue that financial principals were aware of the risks taken by agents given deposits continued

to increase until December 1998 and only fell violently in January 1999 (Figure 1).

Finally, in relation to inducing labour in anticipation of the crisis, we accept that there is always a percentage

of women who are faced with the decision to induce labour before the due date. This may happen for a

variety of reasons, be it relating to the mother or the baby’s health (high blood pressure, risk of infection,

etc.). However, we contend that this percentage is similar in the treatment and control groups because

women who induced labour before 1 Jan 1999 could not have been motivated by avoiding a crisis they

could not foresee.

2. The effect is not exclusive to the cut-off point: there are placebo effects

Another way to see if a non-observables affect the outcome is by setting up placebo effect tests. In this

paper we have two types of tests. The first refers to a sub-sample of individuals with no access to financial

services and who should not have been affected by the bank run. The second refers to placebo effects in

the months before the crisis. That is to say, to see if the control group was in fact affected by an anticipation

of the crisis. If there is a significant jump on our outcome of interest any time before 1 January 1999 this

may indicate a non-observable is driving the effect. Similarly, if there is a significant jump in the outcomes

of individuals who do not have access to banking services, they may also indicate a non-observable is driving

the effect.

a. Placebo effects in months running up to crisis.

We test for placebo effects in the months running up to the New Year. This will help us rule out that the

closing of small banks in this trimester allowed individuals to anticipate the magnitude of the crisis. We run

third degree polynomial models for 1 December, 1 October, 1 September, and, 1 August 1998 and find no

effect. We do find a positive significant effect for 1 November 1998. This is difficult to explain, mainly

because the effect is positive. If it were negative we could argue that there is an anticipation bias. However,

we need to research the circumstances of November that might have produced this effect.

28

Table 9 Third degree polynomial RD model for the using 1 December 1998 as the cut-off

------------------------------------------------------------------------------------------------


-------------+-----------------------------------------------------------------------------------

30 day bandwidth

Conventional | .76411 1.1598 0.6588 0.510 -1.50909 3.0373 107 157

Robust | - - 0.2622 0.793 -3.1702 4.14943 107 157

-------------------------------------------------------------------------------------------------

Table 10 Third degree polynomial RD model for the using 1 November 1998 as the cut-off

-------------------------------------------------------------------------------------------------


-------------+-----------------------------------------------------------------------------------

30 day bandwidth

Conventional | 2.4993 .93625 2.6695 0.008 .664298 4.33434 215 109

Robust | - - 2.0097 0.044 .060202 4.80931 215 109

-------------------------------------------------------------------------------------------------

Table 11 Third degree polynomial RD model for the using 1 October 1998 as the cut-off

-------------------------------------------------------------------------------------------------


-------------+-----------------------------------------------------------------------------------

30 day bandwidth

Conventional | -.20702 .70299 -0.2945 0.768 -1.58485 1.17081 162 221

Robust | - - -0.3926 0.695 -2.13695 1.42379 162 221

-------------------------------------------------------------------------------------------------

Table 12 Third degree polynomial RD model for the using 1 September 1998 as the cut-off

-------------------------------------------------------------------------------------------------


-------------+-----------------------------------------------------------------------------------

30 day bandwidth

Conventional | -.16544 .4556 -0.3631 0.717 -1.05841 .727533 192 164

Robust | - - 0.2901 0.772 -1.0299 1.38778 192 164

-------------------------------------------------------------------------------------------------

Table 13 Third degree polynomial RD model for the using 1 August 1998 as the cut-off

-------------------------------------------------------------------------------------------------


-------------+-----------------------------------------------------------------------------------

Conventional | .42131 .43934 0.9589 0.338 -.439792 1.28241 177 217

Robust | - - 1.0367 0.300 -.615615 1.99812 177 217

-------------------------------------------------------------------------------------------------

We also run placebo effects for the years presiding Jan 1999. That is, Jan 1998 and Jan 1997, given there

may be a “new year” unobservable effect that might be determining the outcome. In Table 12 we present

the linear RD models where we find no effect for 1 January 1997 and a negative significant effect for 1

January 1998. In Table 13 we present second degree polynomial RD models and find no effect 1997 nor in

1998. Finally, in Table 14 we present the third degree polynomial RD models and find no effect in 1997 nor

in 1998.

Table 14 Linear RD model placebo

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

1 January 1997

30 days | -.2867261 .1908434 -1.50 0.133 -.6607723 .0873201

15 days | -.0976621 .3065324 -0.32 0.750 -.6984546 .5031305

60 days | -.0777697 .1485041 -0.52 0.600 -.3688323 .2132929

------------------------------------------------------------------------------

29

1 January 1998

30 days | -.5510791 .2488749 -2.21 0.027 -1.038865 -.0632933

15 days | -.7497808 .3497156 -2.14 0.032 -1.435211 -.0643509

60 days | -.3973136 .2304558 -1.72 0.085 -.8489986 .0543714

------------------------------------------------------------------------------

Table 15 second degree polynomial RD model placebo

-------------------------------------------------------------------------------------------------


-------------+-----------------------------------------------------------------------------------

1 January 1997 (30 days bw)

Conventional | -.11159 .3264 -0.3419 0.732 -.751314 .528139 143 113

Robust | - - -0.2682 0.789 -.968417 .735311 143 113

-------------------------------------------------------------------------------------------------

1 January 1998 (30 day bandwidth)

Conventional | -.88182 .36406 -2.4222 0.015 -1.59537 -.168265 123 211

Robust | - - -0.9415 0.346 -1.14783 .402925 123 211

-------------------------------------------------------------------------------------------------

Table 16 Third degree polynomial RD model placebo

-------------------------------------------------------------------------------------------------


-------------+-----------------------------------------------------------------------------------


Conventional | -.11655 .43463 -0.2682 0.789 -.968417 .735311 143 113

Robust | - - 0.6601 0.509 -.740471 1.49255 143 113

-------------------------------------------------------------------------------------------------


Conventional | -.37245 .39561 -0.9415 0.346 -1.14783 .402925 123 211

Robust | - - -0.0695 0.945 -.897545 .836039 123 211

-------------------------------------------------------------------------------------------------

b. Placebo effects: sub-sample of individuals who have no access to banking services

The effect of a bank run on individuals who have no access to financial services would be indicative of a

non-observable driving the effect. We do not have information on whether the parents had access to

banking services. However, we are able to identify the parents who belong to the lower end of the income

distribution. We propose that they are less likely to have access to banking and financial services and test

the effect of the bank run on this subgroup. We find no effect.

In the following table, we can see the median household income per capita by quantiles. The poorest 5%

of the population have a median (household per capita) income of 20 USD per month. We run the sharp

RD model on the sub-sample of individuals who belong to the bottom 5% of the income distribution, that

is, households which make 20 USD per month per capita. We find a positive significant effect in the linear

model, no effect (in robust second and third degree) non-linear models. We also run a sharp RD model on

the bottom 10% of the income distribution using linear and non-linear models and find no effect.

Table 17 median income by quintiles of the income distribution

---------------------------------------------------------------------------

Quantile |

group | Quantile % of median Share, % L(p), % GL(p)

----------+----------------------------------------------------------------

1 | 20.00 22.22 0.57 0.57 0.76

2 | 30.00 33.33 1.12 1.69 2.26

30

3 | 37.78 41.98 0.95 2.64 3.52

4 | 46.25 51.39 1.57 4.21 5.62

5 | 50.00 55.56 1.84 6.05 8.07

6 | 60.00 66.67 3.10 9.15 12.21

7 | 66.67 74.07 2.01 11.15 14.89

8 | 73.33 81.48 1.99 13.15 17.55

9 | 80.00 88.89 3.38 16.53 22.06

10 | 90.00 100.00 3.16 19.69 26.28

11 | 100.00 111.11 5.55 25.24 33.69

12 | 106.67 118.52 1.29 26.53 35.41

13 | 120.00 133.33 4.52 31.05 41.44

14 | 136.00 151.11 4.60 35.64 47.58

15 | 152.50 169.44 5.37 41.01 54.75

16 | 175.00 194.44 6.17 47.19 62.99

17 | 207.25 230.28 7.18 54.37 72.57

18 | 266.67 296.30 9.02 63.39 84.62

19 | 379.33 421.48 11.70 75.09 100.23

20 | 24.91 100.00 133.48

---------------------------------------------------------------------------

Share = quantile group share of total ingpc;

L(p)=cumulative group share; GL(p)=L(p)*mean(ingpc)

Table 18 Linear RD model for the bottom x% of income distribution using 1 January 1999 as the cut-off

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

Bottom 5%:

160 days | .6255344 .3295368 1.90 0.058 -.0203458 1.271415

------------------------------------------------------------------------------

Bottom 10%:

90 days | .4114897 .753258 0.55 0.585 -1.064869 1.887848

------------------------------------------------------------------------------

Table 19 Second degree polynomial RD model for the bottom x% of income distribution using 1 January 1999 as the cut-off

-------------------------------------------------------------------------------------------------


-------------+-----------------------------------------------------------------------------------

Bottom 5% (160 days)

Conventional | .76735 .4549 1.6869 0.092 -.124227 1.65893 109 100

Robust | - - 1.1990 0.231 -.557232 2.31327 109 100

-------------------------------------------------------------------------------------------------


Conventional | 2.0718 .68032 3.0454 0.002 .738435 3.40526 105 102

Robust | - - 1.6374 0.102 -.33186 3.70128 105 102

-------------------------------------------------------------------------------------------------

Table 20 Third degree polynomial RD model for the bottom x% of income distribution using 1 January 1999 as the cut-off

-------------------------------------------------------------------------------------------------


-------------+-----------------------------------------------------------------------------------


Conventional | .87802 .73228 1.1990 0.231 -.557232 2.31327 109 100

Robust | - - 0.9500 0.342 -1.10135 3.17348 109 100

-------------------------------------------------------------------------------------------------


Conventional | 1.6847 1.0289 1.6374 0.102 -.33186 3.70128 105 102

Robust | - - -0.4505 0.652 -2.98329 1.86811 105 102

-------------------------------------------------------------------------------------------------

3. The effect is caused by covariates with same cut-off driving the outcome

Touching on a previous point, despite women not being able to foresee the crisis, it is possible that those

who were pregnant after 1 Jan 1999 were not able to access the medical services they needed at the end

31

of their pregnancy. That is to say, that the birth outcomes were not determined by the prenatal stress due

to the bank run but rather by the lack of access to healthcare caused by the financial crisis.

This is an important point, it calls into question the argument that the bank run is deterministic in the health

outcomes of children rather than probabilistically increasing prenatal stress. In response to this, we

propose building either a fuzzy regression discontinuity model, where the bank run determines the

probability of prenatal stress which, in turn, affects the health outcome of their children. However, this is

difficult given prenatal stress is an unobservable. As we mention in the introduction, the only ethical way

of measuring prenatal stress is by finding some exogenous life event, such as sudden death or, in our case,

a financial crisis, where we may reproduce conditions hardship endured by an individual.

It was also suggested that other baseline covariates might have experienced a jump on 1 Jan 1999. One

particularly important variable would be the price level. During months running up to the crisis there was

a non-negligible increase in prices leading to a consequent depreciation of the minimum wage which might

also have created an intra-uterine shock through a reduction in the access to adequate nutrition. As we

can see in Figure 15 there was an inflation shock in August of 1998 with a 5% hike in prices and another

one in March 1999 with an additional 14% hike in process. None of these shocks happen simultaneously

with the 1% tax or the bank run, indicating that the cause of the long term effect do not lie in a hike in

prices.

Figure 14 CPI and inflation 1998 - 1999

295.8

354.3

13.5%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

0

50

100

150

200

250

300

350

400

450

500

CPI base year=1995 Inflation

32

Additionally, in order to control for any baseline covariates that are measureable, we present an OLS

regression in Table XX where we control for income, age, gender, education of the mother, and where we

include fixed effects for ethnicity and geo-graphic sub-regions. We find a significant deleterious effect of

the dummy for treatment. Note we are restricting the sample to those born 60/50/30/25 days before/after

1 Jan 1999 in order to reduce any unobservable heterogeneity to a minimum. We find a significant negative

effect with a 60 and 50 day bandwidth. It is important to keep in mind that this model captures the

difference between treatment and control groups which is effectively a linear model.

OLS1-60 OLS2-50 OLS3-30 OLS4-25

b/se b/se b/se b/se

dtreat99 -0.247** -0.268** -0.047 -0.142

(0.09) (0.09) (0.12) (0.13)

lningpc 0.090 0.103 0.056 -0.023

(0.05) (0.06) (0.07) (0.08)

edadmes -1.648 -8.529 -13.300 -7.890

(3.79) (4.66) (8.43) (10.39)

edadmes2 0.005 0.026 0.040 0.023

(0.01) (0.01) (0.03) (0.03)

escolmadre 0.050*** 0.060*** 0.077*** 0.101***

(0.01) (0.01) (0.02) (0.02)

dmujer -0.169* -0.208* -0.319** -0.394**

(0.08) (0.09) (0.11) (0.13)

dind 0.008 0.098 -0.093 -0.117

(0.12) (0.13) (0.15) (0.17)

dafro 0.121 0.225 0.145 -0.971

(0.32) (0.35) (0.35) (0.65)

dmont 0.992*** 1.058*** -0.057 -0.053

(0.22) (0.24) (0.53) (0.54)

dsr 0.010 0.232 0.244 0.303

(0.13) (0.15) (0.21) (0.24)

dcu 0.413** 0.499** 0.815*** 0.648*

(0.14) (0.16) (0.22) (0.29)

dcr -0.348* -0.255 -0.213 -0.296

(0.17) (0.19) (0.24) (0.26)

dau 0.438** 0.524*** 0.702*** 0.800**

(0.14) (0.16) (0.21) (0.24)

dar 0.161 0.269 0.442* 0.321

(0.14) (0.15) (0.21) (0.23)

dgp 0.571** 0.689** 0.874** 0.888**

(0.22) (0.24) (0.28) (0.30)

dq 0.385* 0.332 0.308 0.045

(0.16) (0.19) (0.28) (0.30)

dg 0.250 0.206 0.099 -0.035

(0.22) (0.26) (0.29) (0.31)

_cons 138.565 706.808 1104.623 662.914

(311.64) (383.37) (693.57) (855.83)

r2 0.194 0.209 0.300 0.341

N 572 504 321 264

. eststo clear

OLS1 OLS2 OLS3 OLS4

4. The functional form is incorrect

Conclusion and discussion

33

In this study we have attempted to measure the 2012 anthropometric effects of the 1999 Ecuadorian

financial crisis. We find a significant deleterious effect on the z-score of height for age in 2012 when

comparing those born just before and just after 1 January 1999. We are able to model the causal effect of

the shock on individual anthropometric outcomes by using a sharp regression discontinuity (RD) model.

This natural experiment finds an exogenous cut-off which allows us to measure long term effects of the

crisis on the health outcomes of children. Our findings suggest a deleterious significant effect.

We argue that prenatal maternal stress is likely to be the transmission mechanism. We propose that pre-

natal maternal stress induced by the exogenous shock affected fetal maturation, the length of the

pregnancy, birthweight and ultimately growth during infancy and adolescence (Almond & Currie, 2011).

Although we do not test it directly, we assume that this, like other sudden life events (death or natural

disaster), constituted an unanticipated stress shock which can be considered a natural experiment.

The most important component of a threshold in a RD model is that it is exogenous. One important caveat

of this study is that individuals would be able to anticipate the effects of the financial tax. This is a possibility

given the law which instituted the tax was passed in Congress on 1 December 1998. However, the data

shows that total deposits grew in December 1998 and only fell violently in January 1999 (Figure 1).

Additionally, an anticipation of the effects of the tax would imply that an individual was capable of

forecasting the collapse of the financial system. We believe the violent panic was triggered by some

individuals circumventing the tax. That throughout the month of January this behavior spread and quickly

exploded into a bank run which, in retrospect, began specifically on 1 January 1999. This bank run, we

argue, was something that individuals could not have anticipated.

Another possible limitation of this study is that it assumes that the cut-off is deterministic in increasing

stress levels. There is an argument to be made that the relationship should be probabilistic, in that, stress

can be caused by other unobservables in the treatment group and can occur in the control group before

the crisis. We argue that there is always a certain percentage of mothers who suffer from prenatal maternal

stress, and that this percentage would have otherwise been similar in the treatment and control group.

The only change in the percentage would be that caused by the financial crisis.

This study paints a more comprehensive picture of the consequences of prenatal maternal stress.

Additionally, it may allow policy makers to better assess the optimum age of preventative health care

interventions, particularly during financial shocks such as this one. The nutritional outcomes of children are

important because they are instrumental in their schooling achievements and their potential revenue in

34

adulthood. Therefore, nutrition plays an important role in the inter-generational transmission of poverty.

Avoiding in-utero shocks may be key to avoiding poverty traps (Granthan-MacGregor, et al., 2007;

Grantham-McGregor, et al., 2000; Walker, et al., 2000; Walker, et al., 2007).

35

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