DP2011-33

Food Price Surges and Poverty in Urban
Colom bia: New Evidence fromHousehold Survey Data*
Laura Kik u ROD RIG UE Z-TA KEUCHI
Katsushi S. IMA I
December 5, 2011

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Food Price Surges and Poverty in Urban Colombia: New Evidence fromHousehold Survey Data

Laura Kiku Rodriguez-Takeuchi&
Katsushi S. Imai *School of Social Sciences, University of Manchester, UK and
Research Institute for Economics & Business Administration (RIEB),
Kobe University, Japan

5th December 2011

Abstract
The present study simulates the impacts of price surges in 2006-2008 on household
poverty in the main Colombian cities. It is found that the price surges increased both
extreme and moderate poverty in urban areas in short and medium terms. However, themagnitude of poverty rise is not homogeneous geographically or by household types –e.g., the poorest or less educated households were more badly affected than the wealthieror educated households. We suggest ‘demographic targeting’ or ‘geographical targeting’as a policy option that selects and supports poor households by demographic
characteristics or by geographical areas according to the degree of vulnerability.
Protecting those households from food price shocks would be still important now giventhat rising and volatile food prices have continued due to erratic climate patterns anddemand and supply conditions along with economic and financial crisis.

Keywords: food price surges, urban poverty, Colombia
JEL Codes: C21, I32, O15, O54*Corresponding Author:
Katsushi S. Imai (Dr)Economics, School of Social Sciences
University of Manchester, Arthur Lewis Building,
Oxford Road, Manchester M13 9PL, UK
Phone: +44-(0)161-275-4827
Fax: +44-(0)161-275-4928
E-mail: Katsushi.Imai@manchester.ac.uk
Acknowledgements
The authors greatly appreciate comments and advice from Patricia Cabrerizo, Maria del Carmen
Franco, Raghav Gaiha, and Juan Villa Lora. Only the authors are responsible for any errors.
1

Food Price Surges and Poverty in Urban Colombia: New Evidence fromHousehold Survey Data
1.

Introduction

From 2007 till the second quarter of 2008 international prices of basic staples experienced
major increases reflecting oil price surges. After the fall in late 2008 to early 2009, prices ofmajor commodities, such as cereals, oilseeds, meat have shown increasing trends till early2011 and high prices are expected in the near future (FAO, 2011a, b). In particular, large andunexpected price movements are likely to be harmful to people’s living standards,
particularly in developing countries (FAO, 2011b). Social and economic outcomes of foodprice are serious in developing countries – ranging from riots and protests, increases inpoverty, hunger and malnutrition (FAO, 2008a) to decline in investment in research, physicaland human capital (Prakash, 2010) and in aid flows the augmented cost of food aid programs(Oxfam, 2011).
The head of the World Food Program called the price surge episode the ‘silent tsunami ofhunger’ (Sheeran, 2011) and this was not a problem of shortages. Different voices (e.g.
Banerjee and Duflo, 2011) called the attention to the fact that there is enough food to feed theentire world population. Hunger is not of a situation where there is not food but one wheresome people do not have enough food and thus how people get entitlement and access to it isimportant (Sen, 1980).
The extent to which people are affected by food price surges depends on whether they are
net food producers or consumers (Ivanic and Martin, 2008). It is surmised in this context thatpoor people in urban areas were more vulnerable to recent price surges and that theirhousehold budgets were directly affected by a trade entitlement failure streaming from anendowment loss or a deterioration of terms of trade (Sen, 1980, 1999). When those people
purchase food, their ability to fulfill the right of being free from hunger is limited by2

precarious wages and employment, as well as discrimination in access to economic resources,
market places and the absence or malfunctioning of social security nets to provide a cushion
for hardship situations (High Commissioner for Human Rights & FAO, 2010).
Despite the importance of the issue and policy debates, there have been few rigorous toevaluate the recent price surges on poverty or hunger of households in developing countries
with a few exceptions (e.g., Ivanic & Martin, 2008; de Janvry and Sadoulet, 2010; Leyaro etal., 2010; Thurlow et al., 2011). The present study attempts to fill the gap by estimating theeffects of recent food price surges on expenditure and poverty of urban households in
Colombia where the food crisis was felt as well as the Central Bank missed the inflationary
target largely as a result of high food prices. Methodologically, we follow de Janvry andSadoulet, (2010) to simulate both short and medium term impacts of food price changes on
expenditure based on the estimates for price elasticities for various food items to take into
account demand responses of each household.
The rest of the paper is structured as follows. Section 2 provides a background for the
food crisis and poverty in Colombia. Section 3 briefly explains the data and Section 4
surveys the past papers which analyzed the effects of price changes on household welfare indeveloping countries. Section 5 presents and discusses the methodology. The results of theestimations are reported in Section 6. Section 7 discusses possible policy options. The final
section concludes with policy implications.

2.

Food price surge and poverty in Colombia

Although overall inflation had been generally moderate till the end of 2006, afterwards theCentral Bank of Colombia was under pressure as the inflation went much higher than thetarget range 3.5%-4.5% (Figure 1). In 2006-2008 annual changes of food CPI in Colombia

3

(5.7% in 2006; 8.5% in 2007; 13.2%) were much higher than those of general CPI (4.5% in
2006; 5.7% in 2007; 7.7% in 2009) (DANE, 2011).
[Figure 1 to be inserted around here]

Several factors contributed to the transmission of the global food price surge in Colombia.
First, international escalation of energy and oil prices in 2007-8 raised transportation costs,
input prices for agricultural production, and the prices of biofuel products, although revenues
from exports were increased (BanRep, 2008). Second, the revaluation of the peso, along withincreased food imports for local consumption, resulted in a reduction in the purchasing powerof households. High demand for food items in Colombia and in neighboring Venezuela also
contributed to price surges. Finally, extreme weather events had repercussions in agricultural
production and food inflation in the first months of 2007 (BanRep, 2007).
Even though Colombia has a moderately low hunger problem (FAO, 2010), thefulfillment of the right to food is limited in other aspects; 40.8% of households are classified
in National Nutritional State Survey-ENSIN as food insecure.1 Moreover, this survey showeda consistent correlation between food insecurity and poverty and with high food expenditures
across different household structures, ethnicity, incomes, and place of residence (ICBF,
2005). Poor people in developing countries spend much more of their incomes on food(roughly 75 to 80 percent) than middle income people in industrialized countries (15 to 20
percent) (Brandt and Otzen, 2007). The negative relationship between disposable income andfood expenditure share - Engel’s law - can help explain why even temporary movements inprices have considerable negative effects on poor households. The higher the share ofresources destined to food acquisition, the higher the risk of entitlement failure (Maxwell and1

Food insecurity is defined in the ENSIN following adaptations of international measures by Alvarez
et al. (2006), in relation to the availability of money to buy food, the decrease in quantities or meals
consumed and the experience of hunger by members of the household.
4Smith, 1992), even putting at risk for loss of health or life of the poorest in developing
countries (Brandt and Otzen, 2007).
Engel’s law is consistent with Colombian urban households’ consumption pattern.
Average food expenditures represent 27.72% of a household’s monthly budget, whereas
figures for the lowest and highest quintiles are 35.6 and 17.6% respectively. 2 The same
pattern is observed for individual food items.
While the immediate effect of a price increase on households is loss in their purchasing
power ceteris paribus, the price shock could have further consequences as the households
adapt themselves for it, e.g., by selling assets, reducing the quantity, quality and variety offood consumed, or cutting other non-food expenditures such as health care and education(FAO, 2008a). Even if a price shock is short-lived, it could have a negative impact onhousehold welfare in the long run (FAO, 2011b) as it may debilitates households’ ability torespond to future distressful events in a self-reinforcing process. Thus, a failure thatoriginates in the deterioration of the terms of trade easily translates into a weaker endowment
position limiting the possibility to escape poverty.

3. Data
The present study requires the detailed and disaggregated price data as well as household
food expenditure data. The food expenditure data is based on the National Survey of Incomesand Expenditures (ENIG) conducted in 2006-2007 by the Colombian National Statistical
Department (DANE) in 296 municipalities. The present study uses only 14,695 households in
13 cities (out of 24 capital cities) in urban areas3.

2

Authors’ calculation based on the National Survey of Incomes and Expenditures Data.
13 cities are Bogota, Medellin, Cali, Barranquilla, Bucaramanga, Cartagena, Cucuta, Manizales,
Monteria, Neiva, Pasto, Pereira and Villavicencio. It is noted that the ENIG uses as a reference thecensus data in 1993 as a reference and our survey data are randomly sampled from 4,693,914
households (i.e. population).

35Local prices are taken from the CPI collected by DANE. Price data are matched with thefood expenditure data for each household according to its location and the sampling month.
We have used the CPI to calculate the variation over each food item and city during the 36
months comprising the 2006-2008 period.
Constrained by the availability of food price data, 13 major food items are used for ourestimations. They are beef, chicken, fish pork, eggs, sugar, panela, cooking oils, dry legumes,
rice, milk, and potatoes all of which (except pork) are among the most commonly consumed
in the country (ICBF, 2005).
The variable of our interest is household poverty. Ravallion (1998) defines a poverty line
as the minimum cost to achieve a reference welfare level. This cut-off point represents a
subsistence base below which a human could not physically survive (e.g. UNDP, 2004)
defining a poor person “as someone without enough to eat” (Banerjee and Duflo 2011, p.19),
either directly measuring nutritional or caloric intake, or indirectly calculating the necessary
income or expenditure to acquire that basket.
The present study uses the official Colombian lines for each city as poverty cut-off points,
the reason being that as the level of aggregation increases information is inevitably lost thus
it is better to use the most locally available information rather than a global absolute line
(ECLAC, 2005; Srinivasan, 2001; Reddy, 2004). The construction of the extreme poverty
line (EPL) in Colombia (Muñoz & Rivas, 2006; MERPD, 2006) is based on a basic foodconsumption basket for the poorest population in each city evaluated at implicit values (unitvalues) and also at market prices for robustness check4 5. In addition, there is another poverty

4

The criteria to include an item in the bundle to construct the EPL were five: either it was consumed
by at least 30% of the households, accounted for 1% of the total food expenditure, contributed to atleast 1% of the calories or proteins consumed, represented 0.5% of the total weight of food acquiredby the households or 5% of the subgroup of food items. Information on food quantities andcorresponding monetary expenditure is recorded on a weekly basis using recording sheets handled tothe households. When referring to food, the ENIG uses acquired consumption methodology which
accounts for goods and services acquired in the reference period which could or could not been totally
paid in the same period.
6

line - Moderate Poverty Line (MPL) - intended to reflect the amount of money necessary to
buy a bundle of basic commodities beyond food. This is constructed using a multipliermethod; the EPL is multiplied by the inverse Engel coefficient (Oshansky coefficient). To
make the poverty lines comparable with the household survey and track changes to makeconsumption comparisons, the official lines were adjusted using accumulated food CPI
variation from September 2005 to September 2007 following the approach of ECLAC (2005)
for each city. Household expenditures and the poverty lines for each city are all adjusted at2007 values so that they are comparable.6

4. Literature Survey
In this section, we review selected studies which evaluated price surges on household welfare
or poverty in the context of developing countries. Using 10 different sets of cross-sectional
household survey data for 9 developing countries (e.g. Bolivia, Peru, Vietnam) in various
periods (in 1998-2005), Ivanic and Martin (2008) simulated the possible effects of pricesurges in 2006-7 on aggregate poverty in those countries and found net food consumers tend
to be hurt by food prices increases. However, the distinction between producer and consumer
impacts which would be important for policy analysis (e.g. Chen and Ravallion, 2004) has
not been taken into account in Ivanic and Martin’s study due to the data constraints. Also,
5

The Colombian government released in September 2011 a new methodology to calculate the poverty
line. This updates the source for consumption patterns (2006-07 ENIG) and introduces a series ofchanges to the estimation procedure. The most relevant revisions include a common poverty threshold
applied across all cities and urban areas, the change in the reference population from the poorest to
the median population, the reduction of criterions of inclusion of a food item and the use of a fixedand exogenous Orshansky coefficient (see DNP, 2011 for details). However, the present study uses
the national poverty lines which have been long used in the empirical literature of poverty studies on
Colombia as (i) the use of the reference population based on the bottom 25% group would better
capture the behavioural responses of the poor and (ii) they reflect local information, such as,
differences of the food prices. It is also noted that any poverty line is used only as a reference point
and the overall pattern of the simulation results will not change regardless of our choice of poverty
cut-off point.
6
While social, cultural, gender, educational and other dimensions conjointly determine the real accessto goods and services and the achievement of a living standard (Sen, 1999), we use only the monetarymeasures of poverty because of the data constraints.
7

they used international prices and assume full transmission into domestic markets. In
addition to the question of using an appropriate deflator to convert prices into a comparable
currency (Horton,1998), the degree and speed of transmission can differ between producers
and consumers, among different regions within a country, and across countries. de Janvry
and Sadoulet (2010) focus on the 2006-2008 crisis in Guatemala and exemplify this findingthat poverty impacts are much lower than when full price transmission is assumed.
The impacts of price volatility are also uneven. For instance, a “one-time 10 percent
increase in the price of rice may be comfortably absorbed by consumers in developed
countries, but not so in many low-income countries” (Prakash 2010, p.5). Ulimwengu et al.
(2009) found that losses in food consumption and nutrient intake are unevenly distributed
geographically. Similarly, Chen and Ravallion (2004) find that absolute gains from pricechanges attributed to WTO accession are higher in richest provinces in China.
Focusing on differentiated impacts by household characteristics, FAO (2008a) estimatedthat the poorest, landless and female-headed households were hit harder. In Ethiopia
households with lower levels of assets and those where the head of the household was acasual worker were more adversely affected in their general consumption and self-reported
more distress and cuts in food consumption (Alem and Söderbom, 2010). Most studies
consider both producer and consumers, whereas Dessus et al. (2008) calculate aggregate
poverty changes considering solely urban households, assuming that food inflation affects
only their consumption as their incomes are not greatly derived from food production
activities. Finally, most of the studies incorporate only first round effects, but loses may be
mitigated over the medium term by changes in consumption quantities (de Janvry andSadoulet, 2010). Following de Janvry and Sadoulet, we will simulate both short-term (or firstround) effects and medium term (or second round) effects.

85. Methodology
As in most of the previous studies reviewed in the previous section, the cross-sectional
structure of the ENIG data does not allow us to carry out before/after comparisons to derive a
direct estimate of household welfare changes due to price surges over the years. Therefore,
the study relies on simulations of potential scenarios following closely the methodologicalapproaches used by Chen and Ravallion (2004), Ivanic and Martin (2008) and de Janvry andSadoulet (2008, 2010). 7 Our study goes beyond the first round effects by incorporating
demand substitution responses in the medium term.
The underlying model can be described as follows. A household (h) has a utility function  ,  ,  that depends on a quantity of food and non-food demanded by the household(  and  ) and labor  (Chen and Ravallion, 2004). Because we deal with urbanhouseholds, we assume that all the household income are based on the verctor of wage rateand labour supplied (i.e.,  ). All the household income is assumed to be consumed

(no savings) and thus the budget constraint is written as       where (or  ) is the price vector for food consumption (or non food consumption) and  (or  )is a vector of food quantities (or non-food quantities) demanded by the househod, and isthe vector of wage rates. The indirect utility function is specified as, follwing Chen andRavallion (2004) but subsuming the profit term:  ,  ,max ,,   ,  , |    

 

(1)

Taking the first derivatives of the indirect utility function:

7

The present study presents a partial equilibrium analysis due to the data constraints. In contrast, ageneral equilibrium analysis would need to include the overall economy responses to the crisis as
done by Chen and Ravaillion (2004) for China and by Thurlow et al. (2011) for Vietnam including
the effect on employment and wages, transfers to households from remittances and governmenttransfers and subsidies among others. de Janvry and Sadoulet (2008, p.12) argue, however, that‘partial equilibrium effects with behavioral responses will capture most of the large effects that wewant to measure”.
9







   !     !    !

(2)

The welfare change (in monetary terms) of any price increase is given as a simplified version(where the household production is subsumed) of de Janvry and Sadoulet’s (2008, p.8)
equation (3):"#   ! #   !    !



(3)As this is a partial equilibrium analysis, other prices in the economy remain constant while
only food prices fluctuate (that is,  ! and  !

 are both zero). Considering also that

 ! $  ⁄ and disaggregating among different food items (g) (de Janvry & Sadoulet,2010):


where ()*"

∑* '#()*


+,

+,

-

(4)

* * is the monetary expenditure of each household on good g. " is thecompensated variation or the monetary amount that households should be given to maintainthe same utility level as before (de Janvry and Sadoulet, 2008). It represents the expected loss
in consumption caused by the food price increase. Though it is obvious that the monetary
expenditure represents only a part of household welfare, ‘household welfare’ is proxied by‘household (monetary) expenditure’ in the present study due to the data constraints, such aslack of non-monetary measures of welfare in our data (e.g. qualitative measures of happinessor life satisfaction or health conditions of household members). Expenditure is a more
appropriate welfare measure than income when estimating the effects of price changes(Leyaro et al., 2010) as earnings may not translate into actual consumption of goods andservices from which individuals derive utility and are more prone to seasonal patterns offluctuations and volatility (Deaton, 1997).
To address the household specific short-term welfare impacts using the equation (4) we
calculate the ‘new achievable level of expenditure’ for each household, adding initial
10expenditure and the compensated variation (Ivanic and Martin, 2008). The achievable levelof expenditure of a household is defined as the sum of current monetary and non-monetary
expenditures. Non-monetary expenditures included are: estimation of the economic benefit ofhome ownership, in kind benefits, expenditure that would have to be made when the children
receive food at school. Self-consumption is not included because it is not reported in the
ENIG. Non-discretionary expenditures, such as taxes and social security contributions arealso excluded. This imputations are made following ECLAC (2005) recommendations andthe methodological document of the ENIG (DANE, 2009).
The poverty line needs to be updated to account for change in the cost of the consumption
basket (de Janvry and Sadoulet, 2008). To do this, the additional expenditure that would beneeded to maintain the marginally poor at the same level of utility is obtained by multiplying
food expenditures by the change in their prices. Aggregate food price and total foodexpenditure are used, and the marginally poor are those within a 5% upward and downward
boundary of the poverty threshold (ibid., 2008).

Where z is the EPL, . represents the change in the cost of food consumption caused by

the price increase:.

() +

+


(5)

() is per capita expenditure of the household on food and + is the proportional change in+food prices faced by the household h. The adjustment of the EPL, ./ , is the average . forthe marginally poor. The MPL is adjusted by the same amount.
.0.1  ./

(6)

The third step is to address the medium term impacts by allowing for demand responses tothe food price increase.

11

Janvry and Sadoulet (2010) consider the medium-term welfare effect of food price changes
by simulating demand and supply responses where the food price surges are allowed toincrease supplies or decrease demands, but they did not take into account cross-priceelasticities, that is, they assumed that the price of a particular commodity affects only its own
supply or demand. Because we focus on only urban households, we incorporate only demand
responses in the medium term. Following the methodology in de Janvry and Sadoulet (2008,2010), the medium term welfare change can be written as:"# ∑* 2() 31  '5 6* + -7 + 8
0


+,
,


+,

(7)

,

Consequently, and because detailed price data was available for each city, the proceduretakes a standard Working-Leser model as explained in Chern et al. (2003). For each item andeach expenditure quintile, a consumption equation is defined in terms of budget shares.
Because not all households consume all commodities, it does not take a logarithmic
specification which would only work for households with positive consumption.
9*

:*  ;* ln>()  ?* )  @* !*  @A !A  *

(8)

9* is the household budget share for a particular food item g (e.g. potatoes), >() ishousehold’s total expenditure and ) a vector of household characteristics (e.g. family size,
dependency ratio, head of household sex and educational level). To control for increases inother prices, we include * , the price of a particular item and A , the overall food price. *

is an error term, independent and identically distributed. The equation (8) is estimated foreach food item.
Price elasticities are (Chern et al., 2003):
6*B@
C *BD9* E # F*B

12

(9)

F*B is the Kroneker delta8 and as cross-price elasticities are not considered F*B

1.

Results of absolute and proportional losses - to initial expenditure per capita - will bepresented. They will be disaggregated by city, initial expenditure level and household
characteristics to assess whether there are concentrations of losers and enable the analysis ofpotential distributional impacts of the price surge.

6. Results
The overall average baseline extreme poverty headcount was 6.26% and moderate poverty
35.04%. These figures are slightly higher than the trend shown in official poverty figures inColombia 9 which are calculated using incomes instead of expenditures. Across cities,
extreme poverty ranges from 2.96% in Bogota to 17.04% in Barranquilla. Moderate poverty
was the lowest in Pasto (25.26%) and the highest in Manizales (57.81%). Cities with higher
extreme poverty are also the ones with higher moderate poverty although not exactly in thesame order.
When using a uniform 30% price increase (column (a) of Table 1) the MPL goes up by anaverage of COP$13,820. In contrast when using the actual food price change for the 20062008 period (column (b)), the increase is slightly lower (COP$13,339) except in cities forwhich overall food inflation was over 30% (Barranquilla, Neiva and Cucuta) (Table 1). For
the remainder results obtained using the actual price change will be highlighted as they betterreflect the conditions in each city allowing a closer picture of the real impacts.
[Table 1 to be inserted around here]

1 GH I J M
30 GH I L J
9Due to the methodological change, no official poverty measure is available for 2007. We thus
compare the results with those of 2005 and 2008.
8

F*B

13

Because poverty lines are highly elastic to relative food prices (Bresciani and Valdes,
2007), changes in them result in variations of poverty prevalence. This is consistent with thefindings, given that adjusting the lines already increases extreme poverty headcount by 1.8%
and moderate by 2.37% on average for the 13 cities. The greater increase is observed in
extreme poverty as expected, because households closer to the EPL are the ones with larger
food budget shares and thus more negatively affected by the rise in the cost of the basic
consumption basket. However, this result is not consistent across cities and, for seven of thethirteen, extreme poverty varies less than moderate poverty (in percentage terms).
Table 2 reports the results for poverty head count ratios for each city.10 The first column‘Baseline’ shows the actual poverty head counts, while the column titled ‘EPL adjustment’ or‘MPL adjustment’ reports the poverty headcount ratios where the poverty thresholds areadjusted to reflect the change of the cost of the consumption basket due to the price surges in
2006-2008 (de Janvry and Sadoulet, 2008). The following two columns show the poverty
headcounts after the households’ welfare changes are accounted. Due to the food price surge,
the households which were under the extreme poverty line on average increased from 6.26%
to 9.75% in the short term where only (actual) price changes are taken into account, and to
9.15% in the middle term where demand response is incorporated. Those under the moderate
poverty line will increase from 35.0% to 39.2% in the short run and to 38.3% in the middle
run. The poverty headcount ratios will marginally increase in both short and middle run fromthe cases where the poverty lines are adjusted for the food price surges. While poverty
headcount ratios in all the cities rise due to the price surges, the extent they are affecteddiffers among different cities, though any generalization would be difficult. For example,
because of the price surges, Medellin would experience only small increase in both extreme

10

The results for poverty gap show the similar pattern and will be provided from the author onrequest.
14

and moderate poverty in the short and middle run, while the cities like, Barranquilla,Monteria and Nevia would see a relatively large poverty increase in extreme poverty.
[Table 2 to be inserted around here]

Tables A.1 and A.2 in Appendix summarize the results of consumption equation
regressions used to estimate price elasticities based on the equation (8). As explained in theprevious section, the budget share of each food item is regressed on its own price controlling
for total household expenditure, other household characteristics and overall food price level.
The CPI coefficient represents the proportional change in the share of the budget of a specificfood item when its price changes.
Elasticities were estimated for each quintile using CPI in levels and its monthly andannual variation as shown in Table A.3 in Appendix 11 . As expected, basic staples areinelastic for most quintiles; this is the case for rice, potatoes, legumes, eggs and milk. Bread,pork, fish, oils and panela were found to be elastic goods. Beef12, milk and oils showed apattern of being relatively inelastic for the lowest expenditure quintiles and more elastic forthe highest ones, while sugar and panela have an opposite pattern.
Based on these estimates of price elasticies, the welfare changes in the short and middle
run can be derived at household levels using the equations (4) and (7). Household level
estimates are then aggregated for each city. The results of short and middle term welfarechanges are reported in the last two columns of Table 2. The average welfare reduction for

11

When using price variations, whether monthly or accumulated, the elasticities are almost always
unity. Due to this lack of variability, only the first set (CPI level) of elasticities are used in the final
estimation. In addition, these are consistent with the estimations of Cortés and Pérez (2010) who
find a food elasticity of -1.407. For the 13 items and averaging across quintiles our estimation is 1.101. This is expected considering the fact that basic commodities tend to be more inelastic.
12
Although beef is relatively expensive, it was inelastic for the lowest quintiles. This was possibly
because its consumption is already low for these groups it is not easy to further reduce it even when
they faced with a price increase.
15

the whole sample is COP$16666.3, but when consumption responses are allowed this isalmost halved.
Absolute change in the capital is the largest, but this is also the city with the highest initialexpenditure level highlighting the importance of consider proportional changes as well. The
magnitude of the welfare change is larger for the cities classified as those with middle cost ofliving (Romero, 2007) 13 (Figure 2) which relates to the fact that these four were among theones with largest price increases.
[Figure 2 to be inserted around here]

The relationship between absolute and relative welfare changes and initial expenditures is
not completely clear, perhaps because the distribution of initial expenditure is highly
concentrated in the lower bound. Nonetheless, when looking at the average proportionalchanges by quintile (Figures 3 and 4), the lowest quintiles are clearly the worst hit, while theaverage household in the first quintile loses 4.5% of their welfare and the one in the highest
quintile loses only 0.73%.
[Figures 3 and 4 to be inserted around here]

By dependency rate, the result is similar; loses of households with lower dependency rates
are smaller in proportion to their incomes but, as the dependency rate increases therelationship loses strength. This is in line with Alem and Söderbom (2010) finding of theabsence of significance of demographic characteristics. However, in contrast to the results for
Ethiopia, in Colombia the educational level seems to be an important determinant of the13

High living costs cities are Cartagena, Medellin, Cali, Bogota and Barranquilla, the five biggest
cities in the country. Cities with intermediate living cost are Manizales, Pasto, Pereira and Cucuta.
Low cost cities are Bucaramanga, Neiva, Villavicencio and Monteria.
16

severity of the outcomes as those households for which the head of the household had higher
educational attainment level are less affected (Table 3).
[Table 3 to be inserted around here]

An interesting result is that households that (subjectively) perceive that their incomes are
not enough to cover the basic expenditures loose more than twice of their proportional
expenditure than those who believe their incomes are enough. Female headed households arealso more negatively affected in absolute and relative terms, but the average difference of thewelfare impact in female headed against male headed households (COP$ 722.4) is not aslarge as those found by initial expenditure or educational level.
The relationship between the initial share of food expenditure in total household
expenditure and the proportional welfare changes is shown in Figure 5. It is observed thatthe variance of proportional welfare changes is larger for households with higher levels offood expenditure share. This means that for households with higher food budget shares, theproportional losses could be large or small, while for households with lower food shares thelosses are usually not so large.
[Figure 5 to be inserted around here]

We have compared the aggregate poverty levels for each city before and after the foodprice surges.14 A city that seems to be highly affected by the food price surge is Barranquilla;
it was among the top in both extreme and moderate poverty increases in the short andmedium terms. Here extreme poverty rises by 9.05% and 7.39% in the short and medium
term respectively, which is more than double the average (3.48% and 2.88%). Moreover, thisis the city where general poverty increases the most, although is not the one with the largest

14

A full set of results will be available on request.
17

initial level. Another concerning city case is Monteria where extreme poverty increases
substantially, reaching 24.93% over the medium term. In contrast the rise in moderate
poverty is not as large, although it is above the Colombian average. The two cities with thehighest initial poverty, Monteria and Manizales, were not necessarily those with the biggest
increases. Additionally, Bogota, which had the lowest initial poverty headcount, had poverty
increases above the average.
Finally, it is important to see how the effect in poverty is reversed slightly when demand
responses are allowed. Quantities consumed decrease as households try to smoothconsumption, slightly reducing expenditure. These second order effects are often small
compared to the initial households’ responses (de Janvry and Sadoulet, 2008). Figures 6.1 –6.4 show a visual representation of what would be the evolution of the poverty indicators
across cities.
[Figures 6.1 -6.4 to be inserted around here]

According to the poverty gap calculations, the extent to which individuals in the 13
Colombian cities fall below extreme poverty was on average 1.5% and 4.2% in the moderatecase. Barranquilla was again the city where the extreme poverty gap was highest and also theone where its increase was the largest in all three simulation steps. On the contrary, Bogota
was the one with the lowest score (0.5%) and remained in this position after the priceincreases took place (0.94% over the medium term).
If we consider the poverty gap as the measure of the fiscal cost of eliminating poverty,Monteria , Barranquilla and Manizales where the three cases where the price increase could
be more costly to address. As with the extreme poverty gap, the moderate poverty gap
remained highly concentrated in some cities after the food price surge, ranging from 2.65%
in Bogota to 14.88% in Barranquilla.

18

7. Policy Discussions
Assessing disaggregated impacts of the food price surge is important for policy purposes.
This view is shared throughout the literature; for example Ulimwengu et al. (2009, p.15)
argue that “effective policy responses should account for geographic heterogeneity inhousehold consumption behavior ... targeting national averages might not be efficient”. The
responses implemented by countries worldwide during and after the food price crisis can bedivided into three broad categories: producer oriented, consumer oriented or trade oriented(Coloumbe and Wodon, 2008; Demeke et al., 2011; FAO, 2008b). Despite some variation,
the general pattern was a change of policy emphasis as many developing countries have
moved towards an attempt to isolate the domestic agricultural commodity market from theworld market to protect domestic producers (e.g. Demeke et al., 2011). However, de Janvry
and Sadoulet (2010, p.1336) argue that “(w)hen the price shock originates in the international
market, limited price transmission is an advantage. Had the price shock originated indomestic production, greater integration into the international market would have been an
advantage. Hence, food security must not go the way of autarky, but of using trade policies tomitigate the transmission of acute price spikes to shelter the welfare of the poor”.
Colombia has followed the attempt to rely less on food imports and used mostly producer
oriented strategies. For instance, in December 2007, the government, the biggest supply
centers and food retail chains agreed to freeze food prices for almost two months without
much success. In February 2008, the Treasury proposed a new agreement, but it was notbacked up by trade unions and think tanks which believed it did not addressed the root causes
of inflation (Dominguez, 2008).
Nonetheless, macro strategies need to be complemented with household oriented policies
given the heterogeneity of the impacts of price surges on different cities or different
19households as shown by the present study. Releasing stocks and providing consumption
subsidies were common demand oriented responses in many countries (Demeke et al., 2011),
but in Colombia, the reaction to the crisis was mainly directed to control prices and increase
supply, and not much on food programs and social safety nets.
These programs are not inexistent. On the contrary, conditional cash transfer andnutritional programs have been implemented at both the national and municipal levels. A
renowned one is “Bogotá sin hambre” (Bogota without hunger) and, although it has
expanded and spread out to other municipalities in the past years, this was not necessarily adeliberate effort to respond to the price crisis as was the case in other Latin Americancountries such as Brazil, Ecuador and Mexico (Demeke et al., 2011). The strategy of raising
the transfer and expanding coverage of existing programs could be extremely importantbecause constraints at the micro level help to explain the partial achievements of macro level
policies to promote food security (Rapsomanikis, 2009).
In fact, although these conditional cash and in kind transfer programs have been found tobe effective and authors, such as Tiba (2011) who regards them as the best option, it takes
time for these programs to be in operation and this was a barrier to comprise a rapid responsein the 2008 crisis (Demeke et al., 2011). On the other hand, universal subsidies or transfers,
although quicker and easier to launch, are costly and do not necessarily reach those who needmore support (Rapsomanikis, 2009).
Three methods of targeting have been proposed - by individual eligibility, by category or
by self-selection (Tiba, 2011). The methodology and results presented here could be used totarget either by selecting beneficiaries by category (demographic targeting) or by location(geographical targeting). This may be particularly helpful when time, information andresource restrictions are present and the problem is particularly acute in a specific city or ademographic group. Coulombe and Wodon (2008) support geographical targeting methods

20and advocate for the use of poverty maps to do so. This would be possible with the same
methodology and adding geographical referencing data to construct inter-city extensions of
this study to set up poverty maps. Our results suggest that the cities with higher initialpoverty rates (e.g. Barranquilla) are likely to be vulnerable and should be targeted. In
addition, the households in the lower quintiles, with less educational level and female headedhouseholds should be protected as they are more likely to be vulnerable to price shocks.
However, these targeting policies are not, of course, free of selection biases and should be
combined with other alternatives.
A second possible application of the results is to facilitate the determination of the level ofbenefit when the program consists of a cash transfer. This is a difficult task as “(a) benefit set
too high will cause fiscal burden and may generate dependency, reduce work incentives andcrowd out private transfers. If, on the other hand, the benefit is too low, the program will lack
impact and fail to achieve its objectives, while incurring high administrative costs relative tothe size of the transfer” (Tiba, 2011, p.500). If the purpose is to lift beneficiaries’ achievable
expenditure up to the point where they were before the price shock, the measure of thewelfare change (" ) could be used. If the intention goes further on, the adjusted poverty
line or new poverty gap could be used to compute the appropriate cash transfer needed for ahousehold to be able to buy the minimum food basket. For example, according to the results
for the adjusted MPL, a program in Bogota would need to lift achievable monetaryconsumption to COP$ 122,776 and in Cartagena that value would be COP$ 135,702. Also
the disaggregated results could be used to set a variable benefit among different types ofhouseholds.
It should be noticed that these strategies may work only in the short and middle term.
They serve the role of insurance mechanisms “in an environment of rapidly increasing foodprices, the provision of subsidized food or cash to poor through safety nets improves their

21

ability to cope with increased expenditure and prevents households from divesting in assetsthat are important for their well-being” (Rapsomanikis, 2009, p. 57).
In the long run, household assets, livelihood options and social security nets may be more
important than transfers. These could be helpful to ‘protect entitlements’ but complimentarypolicies would be needed to ‘promote entitlements’ (Maxwell and Smith, 1992).
Nevertheless, although the first purpose of these programs is to avoid starvation andmalnutrition, they have the potential to achieve parallel objectives and they are now often
designed to do so. For instance, a program providing free school meals can not only sustain
nutritional intake of children but also avoid parents taking children out of school because of
economic distress.
Finally, the analysis addresses only national and sub national policy responses. However,
the international agricultural and price-setting context could also have important implications
that are not referred to here. Specific implications of international negotiations on
agricultural trade are also important for countries to consider when addressing food pricevolatility and its effects.

8. Concluding remarks
From the end of 2006 to 2008 the price of food stepped up. The present study was an attempt
to measure the impacts of this on the welfare of consumer households and on aggregate
poverty in the main Colombian cities. According to our estimations, the average welfare loss
was COP$16,666.3 over the short term and COP$8,479.6 in the medium term as households
adjust consumed quantities. Extreme poverty increased almost three percentage points andmoderate poverty 3.3% to reach a level of 38.33%. The average value to which poor
households fell below the poverty threshold also showed an increase of 1.9% across the 13
cities.

22

These results were not homogeneous geographically or by household type. Although thehigher increases were not found in the poorest cities, there was a high correlation between theinitial and final poverty levels, implying persistence in aggregate poverty. In addition, thefindings suggest that the urban poorest and less educated households were the most affected.
Lower quintile population, who spend larger shares of their budgets on food, suffered larger
proportional welfare loses. Consistent with this, for most of the cities extreme poverty
escalated the most. The fact that the largest impacts are more acute in specific groups andcities could be used to better direct efforts to adopt policies aimed at mitigating the adverse
effects of the price surge and protect households’ welfare, especially considering the
restrictions in time, information and resources. The findings are important given the likely
reoccurrence of this type of price episode. Food prices have continued to rise and be volatile
in more recent years (World Bank, 2011; FAO, 2011b) and climate change and erratic
climate patterns along with economic crisis and price volatility will aggravate hunger
problems around the world (FAO, 2011b) and Colombia will be no exception.
Additionally, although the study only focuses on urban households, it is plausible thatrural ones may also be negatively affected; this has also been found in previous empiricalstudies in clear opposition to theoretical predictions (de Janvry and Sadoulet, 2010; Ivanicand Martin, 2008). In fact, in 2008, towards the end of the crisis there was a growth in world
wheat production but it mainly involved developed countries, implying that net producer
households in the poorest countries would not necessarily benefit from higher prices (FAO,
2008b). Studies are required to estimate the effects of food price changes on households inboth rural and urban areas of Colombia.
Finally, this study was an attempt to link macroeconomic events with theirmicroeconomic consequences. It was shown how price movements felt at the global level had
particular manifestations at the national level and were in turn transmitted to households. A

23

further line of research would be to explore the issue of poverty dynamics using bothmonetary and non-monetary measures with a focus on the long-term livelihood strategies ofpoor households facing price shocks.

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29

Table 1
New poverty lines(Unit: COP$)

EPL(Extreme Poverty Line)

MPL(Moderate Poverty Line)

City

(a)

(b)

(a)

(b)

Medellin132933132154323054322275

Barranquilla

137993139763257893259663

Bogota123444122776284498283831

Cartagena

138775135702246955243881

Manizales

143016142228387516386727

Monteria

129938128440272511271013Neiva

137560139965309343311749Villavicencio

124541123727274270273456

Pasto

91676.789363.2

205268202954

Cucuta112726113315243679244269Pereira137780137663336363336247

Bucaramanga

122716122474290450290208Cali
132845130433302598300186
Source: Authors’ calculations based on ENIG data.
Note: Case (a) assumes a uniform 30% price increase, while Case (b) uses the actual food price change for the 2006-2008
period.

30

Table 2
Short-term and middle-term effects of food price surges on poverty headcount ratios
Extreme poverty
(%)
EPL
Short
adjustment
term

Moderate poverty
(%)
MPL
Short
adjustment
term

Middleterm

Welfare Changes(COP$)
Short
Medium
term
term

46.34

45.86

-10975

-5403

52.15

55.26

53.73

-17984

-9266

25.57

28.22

29.87

29.12

-20580-10334

21.9

48.71

50.85

53.01

51.87

-11018

-5782

19.47

57.81

59.62

62.54

61.37

-17839

-9256

24.93

59.09

61.2

63.9

62.8

-15181

-8060

18.17

17.09

48.84

51.69

53.35

52.85

-14037

-7497

6.014

8.191

7.161

31.73

34.23

37.47

35.98

-16020

-8286

4.588

5.592

5.156

25.26

27.45

30.23

28.59

-19838-10240

5.492

7.527

10.6

9.093

37.7

39.85

42.15

40.54

-13297

-6662

9.744

13.23

15.54

14.78

54.82

57.39

58.63

57.82

-16897-8886

Middleterm

Baseline

12.39

11.91

44.74

45.76

21.82

26.09

24.43

49.09

2.69

3.465

4.525

4.284

Cartagena

16.64

20.11

23.31

Manizales

13.17

17.4

20.37

Monteria

17.01

22.36

26.49

Neiva

10.73

15.22

Villavicencio

4.665

Pasto

3.261

CucutaPereiraCity

Baseline

Medellin

9.46

11.17

Barranquilla

17.04

BogotaBucaramanga

2.4

4.402

6.156

5.217

33.05

35.71

38.01

37.01

-14694

-7108

Cali

4.267

6.012

7.574

7.065

34.6

37.25

39.53

38.37

-12261

-6696

6.26
8.04
9.75
Total
Source: Authors’ calculations based on ENIG data.

9.15

35

37.4

39.2

38.3

-16666-8480

31

Table 3
Welfare change by educational level
Absolute

Proportional(COP$)(%)

Incomplete Primary-5143.3-8.38

Primary-8100.3-2.96

Incomplete Secondary-7721.4-2.63

Secondary-8551.9-2.35

University-9397.2-1.28

No information

-6145

-3.49

Source: Authors’ calculations based on ENIG data.

32

Figure 1
Annual Consumer inflation in Colombia

Source: BanRep, 200833

Figure 2
Effects of price surges: proportional change of household welfare by cost of living

Proportional change
by cost of living
Low

MediumHighmean of pdw2b_l

0

-.01

-.02
-.021069-.025368-.029611-.03

Source: Authors’ calculations based on ENIG data.

34

Figure 3
Effects of price surges: absolute changes by household expenditure quintile groups

Absolute changeby quintile
1

2

3

4

5

Total
0

mean of dw2b_l-2,000-4,000-6,000-5881.04-6350.1-8,000-8479.59-8723.07-10,000-10836.1 -10608.7

Source: Authors’ calculations based on ENIG data

35

Figure 4
Effects of price surges: proportional change by household expenditure quintile groups

Porportional changeby quintile
1

2

3

4

5

Total
0

mean of pdw2b_l-.0073-.01-.0176-.0229-.02-.0238-.0261

-.03

-.04

-.045
-.05

Source: Authors’ calculations based on ENIG data

36

Figure 5
Effects of price surges: proportional change of household welfare by the share of foodexpenditure in total household expenditure

Proportional change
by food share0

pdw2b_l

-.1

-.2

0

.2

.4

.6

.8

1

-.3

foodshSource: Authors’ calculations based on ENIG data.

37

Figure 6.1
Changes of poverty headcount ratios by city, for moderate poverty

Poverty head countEffect evolution by city
Medellin
Bogota60

Barranquilla
Cartagena
Manizales

50

Monteria
Neiva
Villavicencio
Pasto

40

CucutaPereiraBucaramanga

30

Cali

20
Baseline

Adjusted line

t m
i
e

Short term

Medium term

Source: Authors’ calculations based on ENIG data.

38

Figure 6.2
Changes of poverty headcount ratios by city, for extreme poverty

Extreme poverty head countEffect evolution by city
Medellin
Bogota25

Barranquilla
Cartagena
Manizales

20

Monteria
Neiva
15

Villavicencio
Pasto
Cucuta10

PereiraBucaramanga
5

Cali

0
Baseline

Adjusted line

t m
i
e

Short term

Medium term

Source: Authors’ calculations based on ENIG data.

39

Figure 6.3
Changes of poverty gap by city, for moderate poverty

Poverty gap
Effect evolution by city
Medellin
20

BogotaBarranquilla
Cartagena
Manizales

15

Monteria
Neiva
Villavicencio

10

Pasto
CucutaPereira5

Bucaramanga
Cali

0
Baseline

Adjusted line

t im

e

Short term

Medium term

Source: Authors’ calculations based on ENIG data.

40

Figure 6.4
Changes of poverty gap by city, for extreme poverty

Extreme poverty gap
Effect evolution by city
Medellin
8

BogotaBarranquilla
Cartagena
Manizales

6Monteria
Neiva
Villavicencio
4

Pasto
CucutaPereira2

Bucaramanga
Cali

0
Baseline

Adjusted line

t im

e

Short term

Medium term

Source: Authors’ calculations based on ENIG data.

41

Appendix
Table A.1
Consumption -Summary
Quintile
1

2

3

4

5

Beef

Rice

Bread

Potatoes

Legumes

Pork

CPI coefficient

0.0934*0.0321-0.0625***0.0172***0.00768-0.0661*p-value

0.02

0.175

0

0

0.64

0.015

R-squared

0.023

0.131

0.116

0.061

0.09

0.076

CPI coefficient

0.0282-0.0226-0.0447**

0.00898**

0.0180*-0.062p-value

0.486

0.254

0.002

0.001

0.018

0.053

R-squared

0.018

0.073

0.118

0.083

0.11

0.051

CPI coefficient-0.00640.0384**-0.0227*

0.00721*0.00086-0.0511p-value

0.854

0.003

0.021

0.028

0.888

0.05

R-squared

0.019

0.068

0.068

0.131

0.087

0.109

CPI coefficient-0.0380.0369**-0.0133

0.00714**0.0196*-0.0662***

p-value

0.383

0.008

0.168

0.008

0.026

0

R-squared

0.035

0.142

0.086

0.169

0.102

0.114

CPI coefficient-0.0366

0.00689-0.0190**0.00523***

0.00393-0.0434***

p-value

0.529

0.51

0.001

0

0.397

0

R-squared

0.017

0.103

0.227

0.132

0.211

0.162

Significance level:
*

0.1

**

0

***

0.001

42

Table A.2
Consumption -Summary
Quintile
1

2

3

4

5

Chicken

Fish

Eggs

Milk

Oil

Sugar

PanelaCPI coefficient

0.0282-0.0388**0.0300***0.0179-0.00738-0.0074-0.0290***p-value

0.272

0.003

0

0.552

0.572

0.359

0

R-squared

0.086

0.083

0.107

0.081

0.081

0.113

0.208

CPI coefficient

0.0295-0.0350***

0.007910.00529-1.39E-06-0.0114-0.0116***

p-value

0.312

0

0.119

0.796

1

0.068

0

R-squared

0.049

0.042

0.07

0.113

0.091

0.045

0.095

CPI coefficient-0.0141-0.0266**

0.009480.00026-0.0132

0.00065-0.00959***

p-value

0.56

0.008

0.076

0.988

0.289

0.891

0

R-squared

0.093

0.055

0.09

0.101

0.046

0.059

0.134

CPI coefficient

0.0417*-0.0098-0.0066

0.00601-0.0173

0.00313-0.0038

p-value

0.03

0.434

0.178

0.614

0.055

0.432

0.082

R-squared

0.083

0.108

0.096

0.09

0.068

0.056

0.086

CPI coefficient-0.0038-0.0168

0.0042-0.0101-0.01290.0019-0.0018

p-value

0.742

0.332

0.149

0.326

0.124

0.439

0.081

R-squared

0.186

0.157

0.176

0.152

0.099

0.118

0.132

Significance level:
*

0.1

**

0

***

0.001

43

Table A.3
Price elasticities
Quintile

Beef

Rice

Bread

Potatoes

Legumes

Pork

Chicken

Fish

Eggs

Milk

Oil

Sugar

Panela1-0.366-0.693-1.906-0.591-0.866-1.802-0.683-1.572-0.47-0.823-1.123-1.202-1.7

2-0.788-1.307-1.677

-0.73

-0.62

-1.836-0.677-1.668-0.823-0.94-1-1.415-1.4293-1.048-0.336-1.331-0.776

-0.98

-1.81

-1.162-1.508-0.765-0.997-1.296-0.975-1.4984-1.291-0.275-1.259-0.744-0.542-2.072-0.47-1.176-1.189-0.927-1.43-0.866-1.2115-1.289-0.814-1.382-0.766-0.889-1.887-1.051-1.34-0.855-1.137-1.416-0.889-1.181

1-0.998-1.029-1.008-0.997-1.004-0.998-0.993-0.999-1-1.002-0.986-1.01-1.0082-1.003-1.039-1.019-1-0.998-0.951-1.003-0.997-0.996-0.995-0.998-0.998-1.0063-0.999-1.002-0.994-0.994-1.002-0.999-1.01-0.982-1.002-0.991-1.031-0.993-1.0064-0.998-0.995-1.015-0.999-0.998-0.998-0.985-0.987-1.001-0.991-1.007-1.001-1.0045-1-1.004-0.992-0.998-0.999-1.017-0.986-0.99-1-0.995-1.001-1-1.003

LevelsMonthly variation

Accumulated variation
1-0.994-1.002-1.008-0.996-1.002-1.023-0.994-1.004-0.999-0.996-1.003-1-1.0062-0.998-1.005-1.009-0.997-0.999-1.011-0.981-0.997-1-1-1.004-1-1.0063-0.999-1-1.002-0.997-1-1.018-0.99-0.998-1-0.998-0.998-1-1.0054-1.001-1.001-1.002-0.997-0.997-1.019-1.003-0.999-1-0.999-1.001-1.001-1.0045-1.001-1-1.006-0.997-1.001-1.013-1.002-0.999-1.001-1.002-1.005-0.999-1.00544