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Objectives and MotivationsBackground and Data

MethodologyConclusions

Estimating the Heterogeneous Effects of Aggregate Shockson Caloric Adequacy:

The Case of Hurricane Mitch in Nicaragua

Erdgin Mane

PhD Thesis

PhD in Econometrics and Empirical Economics

Rome, 12 September 2011

Erdgin Mane Heterogeneous Effects of Aggregate Shocks on Nutrition

Outline

1 Objectives and Motivations

2 Background and DataHurricane MitchThe Data

3 MethodologyDifference-in-differenceQuantile Treatment EffectsIntra-household analysis

4 Conclusions

Main Objectives

This study has three main objectives:

Evaluate the persistence of the effect on nutrition 7 years after theoccurrence of hurricane Mitch in Nicaragua.

Analyze the heterogeneity of the effects across households by using theQuantile Treatment Effect (QTE) methodology.

Shed some light on the role of humanitarian response by looking at thehurricane’s impact on intra-household outcomes when onlyhousehold-level data are available. We adopt a methodology introducedby Chesher (JRSS Series A, 1997).

Main Objectives

Motivations

All the evaluation studies on the impact of Mitch have focused on theshort and medium-term average effects on different welfare outcomes byusing 1998, 1999 and 2001 LSMS panel data on Nicaraguanhouseholds. For example: household consumption growth (Premand,2008), child’s wellbeing (Baez and Santos, 2007), household budget andschooling (Ureta, 2005).

Why focusing on nutrition?

1 Policy point of view: Food aid is the main component in humanitarianinterventions. It is often argued that the urgency of interventions ’justifies’the lack of analytical understanding on its impact, especially, in the long-termperspective.

Motivations

Hurricane MitchThe Data

Summary

4 Conclusions

Hurricane Mitch: The Numbers

Hurricane Mitch hit Central America, with one of the most devastating stormsto ever hit the region, in November 1998. (shortly after the completion of datacollection for the 1998 Nicaragua LSMS Survey (Encuesta Nacional deHogares sobre Medicion de Nivel de Vida - EMNV’98).

Over 130 cm of rain fell in five days.

The death toll in the region as a whole was estimated as high as 10,000with 3,000 or more dead in Nicaragua alone.

The Nicaraguan government estimated that about 1,000,000 peoplewould need housing (approximately 20 percent of the population).Approximately 45,000 households in 72 municipalities were affected insome way by Mitch.

The western and north-western sections of the country were especiallyhard hit. Nearly 300 schools were destroyed or damaged so badly thatthey cannot be used. Dozens of health clinics, civic buildings and publicmarkets were damaged or destroyed.

More than one-third of the country’s agricultural crops were destroyed.(Source: World Bank, 2001)

Hurricane Mitch: The Numbers

Hurricane Mitch hit Central America, with one of the most devastating stormsto ever hit the region, in November 1998. (shortly after the completion of datacollection for the 1998 Nicaragua LSMS Survey (Encuesta Nacional deHogares sobre Medicion de Nivel de Vida - EMNV’98).

Over 130 cm of rain fell in five days.

The death toll in the region as a whole was estimated as high as 10,000with 3,000 or more dead in Nicaragua alone.

The Nicaraguan government estimated that about 1,000,000 peoplewould need housing (approximately 20 percent of the population).Approximately 45,000 households in 72 municipalities were affected insome way by Mitch.

The western and north-western sections of the country were especiallyhard hit. Nearly 300 schools were destroyed or damaged so badly thatthey cannot be used. Dozens of health clinics, civic buildings and publicmarkets were damaged or destroyed.

More than one-third of the country’s agricultural crops were destroyed.(Source: World Bank, 2001)

Summary

4 Conclusions

The Data

Longitudinal data from the Nicaraguan Living Standards MeasurementStudies (LSMS) carried out by the National Institute of Statistics (INEC)in 1998, 1999, 2001 and 2005 with support from the WB.

A unique opportunity to assess the impact of the hurricane since ithappened right after the EMNV’98 survey.

Households were included in the Post-Mitch (EMNV’99) survey only ifthey were: (a) located in areas that were affected by the hurricane; (b)included in the original EMNV’98 survey. The final sample size wasaround 600 households.

EMNV’98 survey consisted of 4200 households, of which, around 3100were re-interviewed in 2001 and around 2500 were re-interviewed in the2005 survey.

The final sample sizes in 2001 and 2005 have been respectively around4200 and 7000 households. Given these numbers, attrition needs to becarefully considered in the study.

The Data

Difference-in-differenceQuantile Treatment EffectsIntra-household analysis

Methodology

Natural Experiment This study, as others have done before (Ureta, 2005,Baez and Santos, 2007), considers the hurricane Mitch as anatural experiment.

The Treatment Dummy variable indicating the households “affected” by thehurricane.

Definition The criteria used by INEC to establish the affectedhouseholds are not completely clear. The World Bank (2001)specifies: “By November 1998, personnel from LSMS visitedthe country to identify the affected areas. Months later,interviewers went back to households that met two conditions:i) they were located in segments of municipalities affected bythe hurricane as determined previously in the November visitsand, ii) they were surveyed in the 1998 round”.

Other Options Premand (2008) adopted satellite-based rainfall data atmunicipality level. He states that “there is no evidence toreject INEC community-level definition in favour of ahigher-level definition”.

Nutritional Outcome Caloric Adequacy defined as: CAi = DECiMDERi

Methodology

Descriptive Results

Summary

4 Conclusions

Difference-in-difference estimates

We perform pairwise estimation 1998-2001 and 1998-2005.

We are interested in estimating the average treatment on the treatedeffect (TTE) defined as E [lnCA1i |Di = 1]−E [lnCA0i |Di = 1]

TTE can be can be consistently estimated using thedifference-in-difference (DiD) approach.

We obtain DiD estimates through the fixed-effect estimator (OLS regress(lnCAit − lnCAi ) on Mi and (xit −xi )).

Hausman test on the difference between FE and RE estimates issignificantly different from zero.

Although we are interested in marginal effects of Mitch, the covariatesare important to control for exogenous selection.

Fixed-effects models on Mitch

(1998-2001) (1998-2005) (1998-2001) (1998-2005)Dependent Variable: lnCAi (1) (2) (3) (4)Mitch (Mi = Ti × ti ) -0.0814 -0.130*** -0.0761 -0.114**

[0.0550] [0.0435] [0.0549] [0.0460]Affected Groups (Ti ) . . . .Year dummy (ti ) 0.00387 -0.00399 0.00953 -0.000778

[0.00886] [0.00405] [0.00817] [0.00339]Household Size -0.0765*** -0.0763***

[0.0116] [0.00809]Female Head -0.00555 0.0569

[0.0826] [0.0649]Share < 15 0.0287 0.140

[0.139] [0.0920]Share > 60 0.408** 0.166

[0.193] [0.128]Age of Head -0.00477 -0.00436

[0.0114] [0.00503]Age of Head Squared 0.0000299 0.0000470

[0.000112] [0.0000480]Head education 0.00650 0.00610

[0.0124] [0.00427]Land Owned 0.00191 0.000331

[0.00128] [0.000710]Livestock Tropical Units 0.00979** 0.00579**

[0.00400] [0.00238]Number of rooms -0.00339 -0.0144

[0.0203] [0.0173]NGO Participation 0.0215 0.0500**

[0.0210] [0.0194]Constant -6.913 8.700 -18.74 1.847Observations 2205 2216 2238 2238Adjusted R2 0.070 0.108 0.001 0.006Notes: Standard errors in brackets. Dummies on districts were also included in models (1) and (2).

* p < 0.10, ** p < 0.05, *** p < 0.01

T-tests on being affected from Mitch (Affected: Ti = 1) (1)

1998 2001 2005Groups T = 0 T = 1 T = 0 T = 1 T = 0 T = 1

Household Characteristics

Household Size 6.25 6.46 6.03* 6.38* 5.75 5.93Female Head 0.2 0.17 0.22 0.19 0.24 0.2Age Head 46.37 46.59 49.2 48.49 49.45 49.92Married Head 0.4 0.42 0.39*** 0.50*** 0.39* 0.33*Education Average (15-60) 3.42 3.3 3.83 3.82 5.05 5.15Indigenous 0.02*** 0.07*** 0.02*** 0.07*** 0.03*** 0.11***Rural Household 1 1 0.92*** 0.99*** 0.93 0.96

Nutritional Outcomes

Expenditure per capita 4327.54** 3876.97** 5607.38** 4908.87** 7733.66** 6764.64**MDER per capita 1711.64 1723.09 1738.14 1743.98 1774.5 1790.05ADER per capita 2149.03 2167.26 2194.46 2200.62 2258.7 2283.84Calories per capita 2728.81 2913.84 2864.5 2767.6 2640.16 2444.18Calories per adult equivalent 3563.71 3756.79 3674.63 3531.19 3343.11* 3031.99*Undernourished MDER 0.28 0.27 0.28 0.29 0.25** 0.32**Undernourished ADER 0.45 0.40 0.44 0.47 0.50** 0.59**Caloric Adequacy (CA) 1.59 1.58 1.64 1.58 1.49* 1.35*Log Caloric Adequacy (lnCA) 0.28 0.32 0.31 0.28 0.28** 0.20**Caloric Gap 0.29 0.33 0.26 0.29 0.2 0.22* significant at 10%; ** significant at 5%; *** significant at 1%

T-tests on being affected from Mitch (2)

1998 2001 2005Groups T = 0 T = 1 T = 0 T = 1 T = 0 T = 1

Assets

Land Owned 8.22** 4.06** 6.13 7.11 7.46 6.65Irrigated Land 0.01 0 0.02 0 0.01 0.02Agricultural Assets Index 2646.02 1881.61 3235.07 1750.21 3173.26* 2287.41*Agricultural Wealth 0.04 -0.05 0.11 0.01 0.04** -0.09**Livestock in Tropical Units 2.42** 1.50** 2.28 2.29 3.14 2.85Animal Value 3488.95 2680.83 5471.96 6265.73 12298.68 9619.92Number of Rooms 1.93 1.83 1.99 1.98 2.44 2.32Home Owned 0.85** 0.78** 0.87* 0.82* 0.88 0.87Durable Index 1160.06** 695.67** 2042.08** 1087.98** 3610.27* 2439.76*

Social Protection

Governmental Program 0.97*** 1.23*** 1.29*** 2.23*** 1.41 1.42NGO Program 0.4 0.48 0.30*** 0.75*** 0.26 0.33

Access to Basic Services

Distance Health Centre 4.82*** 3.82*** 5.48 4.82 4.34 4.16Distance Primary School 0.20* 0.12* 13.19 13.66 0.08 0.1Running Water 0.25*** 0.15*** 0.32*** 0.21*** 0.33** 0.25**Electricity 0.35* 0.29* 0.43 0.42 0.49* 0.55*Infrastructure Index -0.09 -0.13 0.06** -0.08** 0.12 0.11

* significant at 10%; ** significant at 5%; *** significant at 1%

Summary

4 Conclusions

Quantile Treatment Effects

The standard QTE methodology. The potential outcome for a householdi will be lnCAi (1) if affected and lnCAi (0) otherwise. Hence, theobserved outcome is defined as:lnCAi ≡ lnCAi (1) ·Mi + lnCAi (0) · (1−Mi ).

The unconditional QTE for the quantile τ is defined as:

∆τ = Qτ

lnCA1−Qτ

lnCA0, (1)

where, Qτ

lnCA refers to the τ-th quantile of the outcome variable lnCA.

To estimate ∆τ we use the semiparametric two-stage approachdeveloped by Firpo (2007) defined as ∆τ ≡ Qτ

lnCA1− Qτ

lnCA0, where for j

= 0, 1,

lnCAj≡ arg min

∑i=1

ωj ,i ·ρτ (lnCAi −Q). (2)

∆τ = Qτ

lnCA1−Qτ

lnCA0, (1)

where, Qτ

lnCA1− Qτ

lnCA0, where for j

= 0, 1,

lnCAj≡ arg min

∑i=1

∆τ = Qτ

lnCA1−Qτ

lnCA0, (1)

where, Qτ

lnCA1− Qτ

lnCA0, where for j

= 0, 1,

lnCAj≡ arg min

∑i=1

Quantile Treatment Effects (2)

The check function ρτ (·) evaluated at a real number a isρτ (a) = a · (τ−1{a≤ 0}) and the weights ωj ,i are

ω1,i =Mi

Pr(M = 1|Xi )and ω0,i =

1−Mi1−Pr(M = 1|Xi )

But, we are interested in the quantile treatment on the treated effect(QTTE). We need the equivalent of DiD for the quantiles.

QTTE can be consistently estimated by performing the Firpo’s methodon the first differences. Therefore, lnCAi is substituted bylnCAi (t)− lnCAi (t−1) and the covariates Xi by ∆Xi .

Estimation was implemented using the IVQTE Stata command byFrolich and Melly (2008), where 95% confidence intervals areconstructed using standard errors according to Firpo (2007).

ω1,i =Mi

1−Mi1−Pr(M = 1|Xi )

ω1,i =Mi

1−Mi1−Pr(M = 1|Xi )

ω1,i =Mi

1−Mi1−Pr(M = 1|Xi )

Unconditional QTTE: Firpo’s (2007) Estimator

Summary

4 Conclusions

Chesher’s Model (1997)

Average energy intakes by sex and age are estimated through asemiparametric model developed by Chesher (1997).Household consumption process. We denote the average caloric intakeof each household member p conditional on individual characteristics xpand household characteristics z by:

E [cp|x ,z] = f (xp,z). (4)

Unfortunately, we observe only the total household energy consumptionc which can be expressed as the sum of the individual functions:

E [c|x ,z] =P

∑p=1

f (xp,z). (5)

The function f (xp,z) can be estimated by utilizing the momentconditions:

E [{c−P

∑p=1

f (xp,z)}g(xp,z)|xp,z] = 0, (6)

which hold for arbitrary functions g(xp,z).Erdgin Mane Heterogeneous Effects of Aggregate Shocks on Nutrition

E [cp|x ,z] = f (xp,z). (4)

E [c|x ,z] =P

∑p=1

f (xp,z). (5)

E [{c−P

∑p=1

f (xp,z)}g(xp,z)|xp,z] = 0, (6)

E [cp|x ,z] = f (xp,z). (4)

E [c|x ,z] =P

∑p=1

f (xp,z). (5)

E [{c−P

∑p=1

f (xp,z)}g(xp,z)|xp,z] = 0, (6)

E [cp|x ,z] = f (xp,z). (4)

E [c|x ,z] =P

∑p=1

f (xp,z). (5)

E [{c−P

∑p=1

f (xp,z)}g(xp,z)|xp,z] = 0, (6)

Chesher’s Model (1997) 2

The model allows for different functions for males and females:

f (xp) = sp · fM (agep) + (1−sp) · fM (agep), (7)

where sp = 1 if the person p is a male and sp = 0 if female.Let’s also denote wp = (wp,0, ...,wp,99) a vector of binary indicators of theage. Then, the expected household consumption can be written as:

E [c|x ,z] = [β0 +P

∑p=1{spw

′pβ

M + (1−sp)w′pβ

F }]exp(z′γ) (8)

= (β0 + n′

M βM + n

F βF )exp(z

′γ), (9)

where ns is a vector containing counts of household member of sex S ateach completed year of age and exp(z

′γ) is the parametric form

assumed for u(z).Consistent estimators of β M ,β F and γ can be obtained by non-linearleast squares methods represented by the following estimators:

arg minβ M ,β F ,γ

∑i=1{ci − (β0 + n

M βM + n

F βF )exp(z

′γ)}2. (10)

E [c|x ,z] = [β0 +P

∑p=1{spw

′pβ

M + (1−sp)w′pβ

F }]exp(z′γ) (8)

= (β0 + n′

M βM + n

F βF )exp(z

′γ), (9)

∑i=1{ci − (β0 + n

M βM + n

F βF )exp(z

′γ)}2. (10)

E [c|x ,z] = [β0 +P

∑p=1{spw

′pβ

M + (1−sp)w′pβ

F }]exp(z′γ) (8)

= (β0 + n′

M βM + n

F βF )exp(z

′γ), (9)

∑i=1{ci − (β0 + n

M βM + n

F βF )exp(z

′γ)}2. (10)

Smoothing: Roughness Penalty Approach

A roughness penalty approach (Green and Silverman, 1994) is appliedto smooth the curve. It will add to our minimization function the term:

PS = λ2S

∫ d2fS(x)

dx , S ∈ {M,F}. (11)

The discrete analogue of (11) is the sum of squared second differencesof the elements of β S , which is given by λ 2

S(Aβ S)′(Aβ S),S ∈ {M,F}.λS controls for the degree of smoothing. λS = 0 means no-smoothingand λS → ∞ means linear intake-age relation.The smoothing is applied to the range of n years using the matrix A((n-2) × n):

1 −2 1 0 · · · 0 0 00 1 −2 1 · · · 0 0 00 0 1 −2 · · · 0 0 0...

......

.... . .

......

...0 0 0 0 · · · −2 1 00 0 0 0 · · · 1 −2 1

which, produces second differences if applied to any vector.Erdgin Mane Heterogeneous Effects of Aggregate Shocks on Nutrition

PS = λ2S

∫ d2fS(x)

dx , S ∈ {M,F}. (11)

1 −2 1 0 · · · 0 0 00 1 −2 1 · · · 0 0 00 0 1 −2 · · · 0 0 0...

......

.... . .

......

...0 0 0 0 · · · −2 1 00 0 0 0 · · · 1 −2 1

PS = λ2S

∫ d2fS(x)

dx , S ∈ {M,F}. (11)

1 −2 1 0 · · · 0 0 00 1 −2 1 · · · 0 0 00 0 1 −2 · · · 0 0 0...

......

.... . .

......

...0 0 0 0 · · · −2 1 00 0 0 0 · · · 1 −2 1

PS = λ2S

∫ d2fS(x)

dx , S ∈ {M,F}. (11)

1 −2 1 0 · · · 0 0 00 1 −2 1 · · · 0 0 00 0 1 −2 · · · 0 0 0...

......

.... . .

......

...0 0 0 0 · · · −2 1 00 0 0 0 · · · 1 −2 1

Gender-Age Frequencies in the Rural Sample

Energy Intake Curves and MDER by Age: Males

Energy Intake Curves and MDER by Age: Females

Energy Intake-Age Curves and MDER by Gender, in 1998

Impact of Mitch using Diff-in-Diff by Age and Gender

Conclusions

The diff-in-diff estimates evidence that the humanitarian response (foodaid) has mitigated the short-term effects of the hurricane on households’caloric adequacy, but longer-term effects persist.

The QTTE estimates have shown that, in the long-term, the negativeeffects are not homogeneous across households. They are morerelevant on better-off rural households, which have probably lost part oftheir agricultural production capacity. Moreover, the poorer were lessaffected, since had probably nothing to lose in terms of agriculturalassets.

Caloric intake-age curves have shown that humanitarian response hasmitigated the negative effects of Mitch on young (under-20) and elderly(over-60) population. Moreover, the long-term negative effects weremore relevant for women, probably meaning that men were able to copebetter without humanitarian assistance.

Conclusions

chapter2_mane

Education

householdlevel data

data hurricane mitch

impact of mitch

household budget

intrahousehold outcomes

mediumterm average effects

lsms panel data

hurricanes impact