1

Food Demand and Food Security in El Salvador

(Preliminary results)

Luis Sandoval

Texas Tech University

luis.sandovalmejia@ttu.edu

Carlos Carpio

Texas Tech University

carlos.carpio@ttu.edu

Selected Paper prepared for presentation at the Southern Agricultural Economics

Association’s 2016 Annual Meeting, San Antonio, Texas, February, 6-9 2016

Copyright 2016 by Luis Sandoval and Carlos Carpio. All rights reserved. Reader may make verbatim

copies of this document for non-commercial purposes by any means, provided that this copyright notice

appear on all such copies.

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Food Demand and Food Security in El Salvador

(Preliminary results)

Introduction

El Salvador is a small developing country located in Central America. The country is

characterized for being the most densely populated country in the region and for being one of the

most violent countries in the world (World Bank, 2015). According to the Salvadorian General

Direction of Statistics and Census (DIGESTYC), 7.6% of the country population lives in

extreme poverty and 24.3% in relative poverty. A household is categorized as extremely poor if

its income is lower than the per-capita cost of the basic food basket ($49.43 in the urban area and

$30.73 in the rural area) and categorized as relatively poor if its income is lower than the cost of

the “expanded” basic food basket (twice the cost of the basic food basket) but higher than the

cost of the basic food basket. Also, the average household in the El Salvador has an average of

3.72 members and has a monthly income of $539.70 (DIGESTYC, 2015).

Regarding the health status of children in the country, there are still a high proportion of

children under five that are stunted, especially among poor households. According to the 2009

National Family Health Survey (FESAL), in 2009 1 in 5 children were stunted at the national

level (National Survey of Family Health, 2009). Thirty six % of children from mothers with no

formal education and 24.2% of children living in rural areas suffer from stunting. The National

Family Health Survey (2009) also reports that that 23% of children suffer from anemia and

Stevens et al. (2015) estimate that 15% have Vitamin A deficiency which are some of the most

important nutritional problems influencing stunting and food and nutritional security. Finally, it

is important to note that information about the nutritional status of the Salvadorian population is

very limited and mostly focused in children.

The limited information about the nutritional status of the population is only one of the

many constraints to assess the food and nutrition security status of the population in the country.

Most of the food and nutrition security indicators currently used in El Salvador are macro-

indicators that tell the story of what happened over a determined period of time; however, this

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does not help to understand households’ response to economic or other shocks that might

compromise their food and nutrition security. This paper aims to estimate a food demand system

for El Salvador to better understand: 1) the consumption habits of the population, and 2) to

model households’ response to income and price shocks and subsequently their food and

nutrition security status. To the best of our knowledge, this is the first study reporting food

demand systems for El Salvador.

This paper uses data from the 2013 Households Expenditure Survey (which records food

expenditures for 52 food items, monthly consumer price index data and a recently developed

methodology to compensate for the absence of prices in the Household Expenditure Survey to

estimate a food demand system of 8 aggregate food products (Lewbel, 1989).

Conceptual framework

Stone-Lewbel price indices

The Stone-Lewbel prices indices assume that the between groups utility functions are weakly

separable and that the within groups utility functions are of the Cobb-Douglas form, and then

takes advantage of the household variation in food expenditures to estimate household level price

indices (Lewbel, 1989). The Stone-Lewbel prices have the form:

(1) 𝑃𝑙𝑖 =1

𝑘𝑖∏ (

𝑝𝑖𝑗

𝑤𝑙𝑖𝑗)

𝑤𝑙𝑖𝑗𝑛𝑖𝑗=1

where 𝑤𝑙𝑖𝑗 is the budget share of food item j in group i for household l; 𝑘𝑖 is a scaling factor for

food group i which is estimated using that food group budget shares of the representative

household 𝑘𝑖 = ∏ �̅�𝑖𝑗−𝑤̅̅ ̅̅ ̅𝑖𝑗𝑛𝑖

𝑗=1 ; and 𝑝𝑖𝑗is the price of food item j in food group i. For this research

we used monthly consumer price index data instead of the food prices.

The LA/EASI demand system

The parametric model used for the estimation of the demand systems is the Exact Affine Stone

Index (EASI) demand system (Lewbel and Pendakur, 2009). We decided to use this demand

system because it allows for more flexible Engel curves across goods and to more directly

account for unobserved preference heterogeneity (Lewbel and Pendakur, 2009). Conveniently,

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the model can also be estimated by a linear approximation (LA) using the Stone price index

(Pendakur, 2009; Castellón et al., 2014). The budget share equations of the LA/EASI demand

system are defined as:

(2) 𝑤𝑙 = ∑ 𝑏𝑟𝑦𝑙𝑟 + 𝐶𝑧𝑙 + 𝐷𝑧𝑦𝑙 + 𝐴𝑝𝑙 + 𝐵𝑝𝑦𝑙 + 휀𝑙

5𝑟=1

where 𝑦𝑙 is real expenditures; 𝑧𝑙 is a vector of socio-demographic characteristics; and br, C, D, A

and B are matrices and vectors of parameters to be estimated. Real expenditures are defined by

the formula 𝑦𝑙 = ln 𝑥𝑙 − 𝑝′𝑙𝑤𝑙, where 𝑥𝑙 denotes total nominal expenditures, 𝑝𝑙 is a vector of the

food groups price indices, and 𝑤𝑙 is a vector of demand budget shares. Equation 2 is a reduced

form of Lewbel and Pendakur (2009) that has been used by other authors (Castellón et al., 2014)

and omits the interaction between socio-demographic characteristics and prices to reduce the

number of parameters to be estimated. The LA/EASI model does not provide us with traditional

demand functions but with implicit Marshallian demand equations (Lewbel and Pendakur, 2009).

Because of this, Marshallian demand elasticities cannot be directly derived. In this study we

follow and employ the equations previously derived by Castellón et al.( 2014) whom estimated

Hicksian demand and expenditure elasticities to later recover the Marshallian demand elasticities

via the Slutsky equation, as suggested by Lewbell and Pendakur (2009).

Data

The data used in this study comes from the 2013 Household Expenditure Survey (HES) from El

Salvador that is collected by DIGESTYC which is part of the Ministry of Economy of El

Salvador. According to DIGESTYC they collect information from 19,968 households, however

only 13,669 are used in this study. Of the 6,299 household that were eliminated, 5,480 were

eliminated because the household id couldn’t be identified1 and 819 households were eliminated

because they reported zero food expenditures. The main HES sections used in the study are

sections are the socio-demographic characteristics and food expenditures sections. Table 1 shows

the descriptive statistics of the demographic characteristics of the households used in this

research.

1 Households where there are more than one family get assigned the same id, regardless that they behave as

independent households under the same roof. Because of this, the additional households under the same id are not

clearly identifiable and therefore were eliminated.

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The food expenditures section collects data only on total food expenditures for a period

of two weeks for a total of 52 food items; however, no prices or quantities are recorded; thus we

used the monthly consumer price index data from January to December 2013 from the Economy

Ministry of El Salvador. Consistent with previous studies, we only consider 8 food groups for

the analysis (Castellon et al., 2014). Table 2 shows detailed information about the composition

of the different food groups, including what food items were used from the HES survey and what

food items were used to create the Stone-Lewbel prices from the consumer price index data.

As shown in Table 2, with the exception of sugar, the food items from the HES and the CPI data

are not always perfect matches. For example, in cereals food group the HES includes several

types of rice, beans and bread; however, CPI data only includes one of type of each product. The

same problem is observed in the dairy group: there are more food items included in HES relative

to what is available in the CPI data. An opposite problem is observed in the meat and eggs and

the fruits and vegetable groups: the HES reports a lower number of products types relative to the

CPI data. The food groups fats, non-alcoholic beverages and miscellaneous show no major

differences. In the end, all food items included in the HES where used to create each of the total

food groups expenditures but only 63 out of the 71 available in the CPI data where used to create

the food group Stone-Lewbel prices. The CPI for aggregated food products was created as the

average of the food items included in that food group. Stone-Lewbel prices were constructed by

replacing the monthly consumer price index values for the prices (pij) in equation (1). By doing

so, the Stone-Lewbel prices not only account for the monthly variation in prices but also the

household level price variation by using the subgroup household budget shares wlij.

Estimation Procedures

Since several households reported zero expenditure for some of the food groups (1% to 13%), we

estimated a Censored approximated LA/EASI model which allow us to account for the censored

distribution of the responses. More specifically, we follow the two step procedure suggested by

Shonkwiler and Yen (1999) which is based on the following equations:

3) 𝑤𝑙𝑖∗ = 𝑓(𝑝, 𝑧𝑙 , 𝑦𝑙; 𝜃𝑖) + 휀𝑙𝑖;, 𝑎𝑛𝑑 𝑑𝑙𝑖

∗ = 𝑠𝑙′𝜌𝑖 + 𝜇𝑙𝑖,

where 𝑑𝑙𝑖 = 1 𝑖𝑓 𝑑𝑙𝑖∗ > 0 and 𝑑𝑙𝑖 = 0 𝑖𝑓 𝑑𝑙𝑖

∗ ≤ 0; 𝑤𝑙𝑖 = 𝑑𝑙𝑖𝑤𝑙𝑖∗ ; 𝑤𝑙𝑖

∗ is a latent variable for the

ith food group in household l and 𝑑𝑙𝑖∗ is a latent variable which defines the sample selection; 𝑤𝑙𝑖

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are the observed budget shares, and 𝑑𝑙𝑖 are dummies indicating consumption of the food group

ith. 𝑓(𝑝, 𝑧𝑙 , 𝑦𝑙; 𝜃𝑖) is a demand equation where 𝜃𝑖 are parameters to be estimated, 𝑝 is a vector

prices, 𝑧𝑙 is a vector of socio-demographic characteristics and 𝑦𝑙 is real expenditures for

household l, 𝑠𝑙 is a vector o socio-demographic characteristics which explains the selection

process and 𝜌𝑖 is the vector of parameters of the selection process.

The estimation procedure is performed in three steps: 1) a probit model is used to obtain

estimates of 𝑝𝑖, which correspond to parameters of the sample selection model; 2) the parameter

estimates 𝑝�̂� are used to estimate the cdf (Φ̂𝑙𝑖) and pdf (�̂�𝑙𝑖) of 𝜇𝑙𝑖; and 3)estimates of 𝜃𝑖 are

obtained by using a modified version of the EASI model which includes Φ̂𝑙𝑖 and �̂�𝑙𝑖. The

censored EASI demand model is:

(5) 𝑤𝑙 = Φ̂𝑙(∑ 𝑏𝑟𝑦𝑙𝑟 + 𝐶𝑧𝑙 + 𝐷𝑧𝑦𝑙 + 𝐴𝑝𝑙 + 𝐵𝑝𝑦𝑙 + 휀𝑙

5𝑟=1 ) + �̂�𝑙𝛿 + 휀𝑙

where Φ̂𝑙 and �̂�𝑙 are identity matrices whose diagonal elements are substituted with the Φ̂𝑙𝑖 and

�̂�𝑙𝑖 elements, and 𝛿 is a new vector of parameters to be estimated. The compensated price

elasticities (𝑒𝑖𝑗∗ ) were obtained using:

6) 𝜉 = �̅�−1Φ(𝐴 + 𝐵𝑢) + Ω�̅� − 𝐼

where 𝜉 is an 8x8 matrix of compensated price elasticities, �̅� is an identity matrix that instead of

ones has the food group’s budget shares, Ω is an 8x8 matrix of ones and I is an identity matrix

(Castellón et. al, 2014). All the models were estimated using the SAS MODEL procedure.

Elasticities where computed using equation 6 and the estimate command of the SAS MODEL

procedure for three types of households: 1) those living in extreme poverty conditions, 2) those

living in poverty conditions and 3) those living in non-poverty conditions.

Results and Discussion

Since compensated price elasticities were estimated for three types of households

depending on their poverty conditions, they are shown in three separate Tables (Table 3-5).

With respect to own-price elasticities, households living in extreme poverty conditions are the

more responsive to increments in the prices of dairy products, exhibiting the highest value,

almost unit elastic, of the dairy food group own-price elasticity. However, all dairy own-price

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elasticities for the three household groups are inelastic. The own-price elasticity for the Fats and

oils food group is very similar in the three household groups, ranging from -0.81 to -0.84. The

case is the same for the own-price elasticity of fruits and vegetables, which is very similar for the

three household groups. Grains is the food group that exhibits the lowest values of the own-price

elasticities, ranging from -0.51 to -0.62 across the three household group. This comes as no

surprise since grains represent on average 41% percent of the households food expenditures and

the group contains the most important staples in the Salvadorian diet: corn, rice and beans.

Meats exhibits its highest values of own-price elasticities in households living in extreme

poverty conditions, were the own-price elasticity is elastic. For households in relative poverty

conditions and non-poor household the own-price elasticity of meats is inelastic exhibiting its

lowest values in the non-poor households. Non-alcoholic beverages exhibit elastic own-price

elasticities in households living in extreme and relative poverty conditions, and inelastic values

for the non-poor households. Sugar shows almost the same values for the own-price elasticities

across all three types of households. In the case of the food group others the values of the own-

price elasticities are extremely high but also very insignificant with a p-value of 0.999.

Given the elasticity results, some initial nutritional implications can be discussed. For,

example, a price increase in beefs can substantially affect the consumption of this group which

includes beef, pork, poultry, eggs and seafood. Reductions in the consumption of this food items

immediately affects the consumption of vitamins of the B complex, especially B12 that is only

found in animal food sources. Despite that the results suggest all the goods are perfect substitutes

no other food groups can be a complete nutritional substitute for the meats. It is interesting to

note that households living in extreme poverty conditions are the least responsive to increases in

the price of dairy products, with almost unit elastic values. This could be attributed to the fact

that dairy products are not easily available in rural areas, where the most of the households in

extreme poverty conditions are. Therefore, their consumption is so limited that increases in price

may go unnoticed leaving their demand un-affected. Increases in the price of grains will always

take its toll in their consumption, but the inelastic demand of this food group by the Salvadorian

families suggests that they will always maintain a relatively constant consumption of corn,

beans, and rice and that they will favor its consumption over the other food groups.

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Future research

In our future research we are contemplating at improving the estimation procedure of the model

and to derive nutrient elasticities. Nutrient elasticities have been derived by other authors such as

Ecker and Qaim (2011) and can be used to evaluate the nutritional impact of policies or income

and price shocks. This will allows us to evaluate the food and nutrition security of the

Salvadorian families by paying particular attention to nutrients of interest, such as iron and

vitamin A, which are considered the most important deficiencies in the country.

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Table 1. Descriptive statistics of the demographic characteristics.

Variable Definition Mean Standard

deviation

Min Max

MIEMH Number of household members 4.09 2.002 1 19

INGFA Family income (US$) 420.2 367.53 13.17 8929.47

GHALI Food expenditures (US$) 133.46 70.41 6.54 1000.78

POVEXT Households living in extreme poverty 0.1073 0.3094 0 1

POVREL Households living in relative poverty 0.2811 0.4495 0 1

NONPOR Non-poor households 0.6117 0.4874 0 1

REG I Households in the Western region 0.2415 0.4280 0 1

REG II Household in the Central I region 0.2099 0.4072 0 1

REG III Households in the Central II region 0.1711 0.3766 0 1

REG IV Households in the Eastern region 0.2616 0.4395 0 1

REG V Households in the San Salvador

Metropolitan area

0.1159 0.3201 0 1

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Table 2. Food items used to construct the food groups and the SL prices.

Household Survey data CPI data

Food group Food items Mean budget share Level of censoring Food items

Grains Tortilla

Bread

Sweet Bread

Parboiled rice

Regular rice

Yellow corn

White corn

Seda beans

Red beans

Sangre de toro beans

Black beans

Sandwhich bread

Corn flour

41% <1% Rice

Bread

Tortilla

Creole Corn

Beans

Dairy Whole milk

Skim milk

Partially skim milk

Preserved milk

Regular cream

Special cream

Regular quesillo

Special quesillo

Hard cheese

Fresh cheese

Hard-soft cheese

Milk powder

12% 13% Powder milk

Hard Cheese

Soft-hard cheese

Fresh cheese

Quesillo

Cream

Meat and eggs Angelina

Ground beef

Beef stew meat

Beef ribs

Rollizo loin

Regular loin

Posta negra

Solomo

Chicken

Sea food

Eggs

15% 6% Angelina

Ground beef

Beef stew meat

Beef ribs

Regular loin

Rollizo loin

Posta negra

Solomo

Chicken pieces

Tuna

Sardine

Corvina

Shark steak

Shrimp

Crab

Eggs

Fruits and vegetables Fresh fruits

Preserved fruit and other fruit

based products

Vegetables cultivated because

of their fruit (fresh or frozen)

Roots and bulbs

10% 5% Lemon

Oranges

Banana

Plantain

Apples

Avocado

Grapes

Coconut

Watermelon

Papaya

Pineapple

Green Bell Peppers

Zucchini

Tomato

Onions

Yucca

Carrots

Potato

Fats Cooking oil

Olive oil

Butter

Margarine

7% 3% Margarine

Cooking oil

Non-alcoholic beverages Granulated coffee

Soluble coffee

Soda

Fruits and vegetables juices

Tea

8% 9% Granulated coffee

Soluble coffee

Soda

Fruits and vegetables

juices

Sugar Sugar 6% 1% Sugar

Miscellaneous Salt

Spices

3% 1% Salt

Cooking sauce

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Table 3. Uncompensated price elasticities for households living in extreme poverty.

Dairy Fats and

Oils

Fruits and

vegetables Grains Meats Others

Non-

alcoholic

beverages

Sugar

Dairy -0.9870*

(0.0981)

0.0439

(0.0231)

0.0210

(0.0231)

0.1932*

(0.0205)

0.0780*

(0.0275)

0.1490*

(0.0415)

0.0740*

(0.0205)

0.0863*

(0.0286)

Fats and Oils

-0.8128*

(0.0402)

0.0478*

(0.0073)

0.0897*

(0.0061)

0.0650*

(0.0084)

0.0495*

(0.0221)

0.0480*

(0.0083)

0.0318*

(0.0136)

Fruits and

vegetables

-0.8372*

(0.0604)

0.1207*

(0.0127)

0.0551*

(0.0178)

0.0833*

(0.0249)

0.0486*

(0.0130)

0.0473*

(0.0180)

Grains

-0.5101*

(0.0001)

0.4900*

(0.0001)

0.4900*

(0.0001)

0.4900*

(0.0001)

0.4900*

(0.0001)

Meats

-1.0112*

(0.0514)

0.1583*

(0.0232)

0.1243*

(0.0109)

0.1380*

(0.0221)

Others

299.1201

(484361)

0.0473*

(0.0178)

0.0052

(0.0292)

Non-

alcoholic

beverages

-1.0812*

(0.1563)

0.0138

(0.0267)

Sugar -0.9169*

(0.0174)

Standard errors are in parenthesis.

*Denotes significance at α=0.05.

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Table 4. Uncompensated price elasticities for households living in relative poverty.

Dairy Fats and

Oils

Fruits and

vegetables Grains Meats Others

Non-

alcoholic

beverages

Sugar

Dairy -0.9094*

(0.0505)

0.0813*

(0.0118)

0.0694*

(0.0116)

0.1580*

(0.0106)

0.0991*

(0.0137)

0.1372*

(0.0218)

0.0992*

(0.0104)

0.1020*

(0.0148)

Fats and Oils

-0.8456*

(0.0250)

0.0507*

(0.0044)

0.0766*

(0.0038)

0.0629*

(0.0050)

0.0512*

(0.0143)

0.0518*

(0.0050)

0.0347*

(0.0084)

Fruits and

vegetables

-0.8478*

(0.0296)

0.1145*

(0.0062)

0.0793*

(0.0083)

0.0931*

(0.0125)

0.0781*

(0.0063)

0.0750*

(0.0090)

Grains

-0.5587*

(0.0001)

0.4419*

(0.0001)

0.4419*

(0.0001)

0.4418*

(0.0001)

0.4418*

(0.0001)

Meats

-0.9040*

(0.0211)

0.1601*

(0.0099)

0.1446*

(0.0044)

0.1462*

(0.0088)

Others

261.14

(394307)

0.0419*

(0.0142)

0.0065

(0.0234)

Non-

alcoholic

beverages

-1.0073*

(0.1128)

0.0194

(0.0188)

Sugar

-0.9138

(0.0161)

Standard errors are in parenthesis.

*Denotes significance at α=0.05.

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Table 5. Uncompensated price elasticities for households living in non-poverty conditions.

Dairy Fats and

Oils

Fruits and

vegetables

Grains Meats Others Non-

alcoholic

beverages

Sugar

Dairy -0.8667*

(0.0232)

0.1150*

(0.0054)

0.1094*

(0.0052)

0.1497*

(0.0049)

0.1232*

(0.0060)

0.1413*

(0.0103)

0.1244*

(0.0047)

0.1237*

(0.0069)

Fats and Oils

-0.8498*

(0.0201)

0.0521*

(0.0034)

0.0725*

(0.0030)

0.0631*

(0.0038)

0.0519*

(0.0118)

0.0538*

(0.0039)

0.0337*

(0.0067)

Fruits and

vegetables

-0.8606*

(0.0109)

0.1183*

(0.0023)

0.1042*

(0.0029)

0.1092*

(0.0047)

0.1047*

(0.0023)

0.1024*

(0.0034)

Grains

-0.6217*

(0.0002)

0.3802*

(0.0001)

0.3800*

(0.0001)

0.3700*

(0.0001)

0.3700*

(0.0001)

Meats

-0.8491*

(0.0082)

0.1728*

(0.0040)

0.1661*

(0.0017)

0.1648*

(0.0033)

Others

244.97

(344951)

0.0383*

(0.0122)

0.0063

(0.0202)

Non-

alcoholic

beverages

-0.9483*

(0.0648)

0.0392*

(0.0105)

Sugar

-0.9110*

(0.0148)

Standard errors are in parenthesis.

*Denotes significance at α=0.05.

14

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