YE_TasteDiff_final

Can taste differentials explain the urban-rural nutrition gap
in Indonesia?∗
Tan Yang En
April 2016
Abstract
This paper investigates whether taste differentials qualify as a plausible explanation for the
urban-rural nutrition gap using household data from Indonesian provinces. To do so, I employ
the use of Deaton and Muellbauer’s (1980) Almost ldeal Demand System (AIDS) to estimate
provincial tastes for each food and compare to see if they are significantly different between urban
and rural subsamples. Subsequently, I regress current tastes on lags of logged prices to test for
presence of food habits in consumption. Taste differentials between urban and rural households
can explain the persistence of the nutrition gap provided that tastes are not arbitrary but instead
developed through food habits. My results confirm the existence of taste differentials for most
foods, and that urban households have higher tastes for protein and iron-rich foods compared to
their rural counterparts. I also find past prices to be significant predictors of current tastes for
all foods in general, but not so for protein and iron-rich foods. Moreover, the lagged price effects
are surprisingly positive and are not different between urban and rural households. This suggests
that current tastes could be developed through a separate channel via a “quasi-positional goods”
effect, though this reasoning has its own limitations. Overall, I do not find sufficient evidence to
support the habit formation hypothesis, and therefore am unable to attribute the persistence of
the urban-rural nutrition gap to taste differentials.
∗The author would like to thank his supervisor, Dr Gharad Bryan, for his guidance and critical feedback, as well
as Dr Matthew Levy, Dr Judith Shapiro, Vincenzo Scrutinio, Dr Matthew Gentry, Dr Maria Molina-Domene, Laura
Castillo-Martinez and all EC331 seminar participants for their time, input and kind words of encouragement.
1

Contents
1 Introduction 3
2 Conceptual Framework 5
2.1 Theoretical Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Identification Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2.1 Examining urban-rural taste differentials . . . . . . . . . . . . . . . . . . 7
2.2.2 Testing for presence of habit formation . . . . . . . . . . . . . . . . . . . 9
3 Data 10
4 Results 12
4.1 Significant current taste differentials for iron-rich foods . . . . . . . . . . . . . . . 12
4.2 Past prices are significant predictors of current tastes . . . . . . . . . . . . . . . . 13
4.2.1 Baseline specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2.2 Heterogeneity of lagged price effects . . . . . . . . . . . . . . . . . . . . . 15
4.2.3 Robustness check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
5 Discussion and Limitations 18
5.1 Positive relationship between current tastes and past prices . . . . . . . . . . . . 18
5.2 Policy implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
5.3 Potential concerns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
6 Conclusion 21
A Derivation of AIDS 25
B Data sources 26
C Additional tables 27
2

1 Introduction
Urban-rural disparities have been longstanding since Indonesia’s rapid development in the 1970s. A
2012 report by UNICEF highlights poverty concentration in rural areas as one of the influencing
factors contributing to the urban-rural divide, along with others such as geographic isolation, poor
infrastructure, high transport costs and poor quality of services. One of the most notable manifest-
ations of such disparity is the urban-rural nutrition gap, in which many rural households are not
consuming sufficient amounts of the right foods needed for a healthy and balanced diet. It is widely
known that Iron Deficiency Anaemia (IDA) is prevalent in Indonesia, with just under half of pre-
school children and pregnant women suffering from lack of iron. It has also been found that 37%
of children under the age of five are stunted.1
Several other sources, such as Thorbecke and Van
Der Pluijm (1992) who reported that anaemia is a huge problem in Indonesia, especially for rural
households, have confirmed this phenomenon. A more recent paper by Ngwenya and Ray (2007)
that examined changes in Indonesian food consumption during 1996-2002 found low levels of protein
intake for rural households, and their results revealed a distinct urban-rural nutrition gap. Since
iron deficiency has a large impact on immunity, productivity, mental performance and pregnancy
outcomes, it is unsurprising that reducing IDA is one of the main goals of the Department of Health
in Indonesia (Kodyat et al., 1998).
Various analysis in the literature identify two key reasons to account for the urban-rural nutrition
gap. Firstly, this could be due to urban-rural income gaps that generate distinct consumption patterns
between urban and rural households (Becker and Morrison, 1999). Since mean urban incomes are
much higher than those of rural households, the difference in demand functions is a result of Engel
“forces”. For example, rural households that are poorer tend to spend a larger share of their food
budget on less expensive calorie sources such as staples purely for nourishment. On the other hand,
urban households facing higher incomes can afford to spend a greater portion of their budget on
“luxury” foods such as meat and fish which provide them with more variety in their diet. Another
explanation is that urban tastes in food may differ from rural tastes, leading both types to consume
differently and hence affecting their caloric intake (Haughton and Khandker, 2009). I posit that
tastes in this setting refer to the residual “preference measure” for a given food, after income effects
or other observable factors that affect budget shares have been controlled for. This latter explanation,
however, is less explored and there has been little to no previous empirical work done on it. Therefore
this paper seeks to explore whether taste differentials can explain the urban-rural nutrition gap, using
household data from Indonesian provinces.
1Figures obtained from a World Bank Nutrition Report on Indonesia.
3

I follow the approach of Atkin (2013) in defining habit formation to be a process by which
household tastes evolve over time to favour foods consumed as a child. Urban and rural households
face different prices in the past that affect their consumption levels. Since rural households face higher
relative prices than their urban counterparts due to lower food accessibility,2
the first generation
of adults in these households consume larger quantities of less nutritious food which tend to be
cheaper e.g. cassava and instant noodles. Their children are raised on these less nutritious foods and
subsequently develop particular tastes for them when they become adults. As food habits develop
endogenously, the lack of more nutritious foods such as protein and iron-rich foods in household
consumption emerges over time. Therefore, taste differentials between rural and urban households
will be able to explain the longstanding nutrition gap provided that tastes are not arbitrary,3
but are
instead developed through food habits. I test this hypothesis of habit formation using four waves of
household survey data from 13 Indonesian provinces.
The two primary objectives of my paper are thus as follows: (1) To estimate tastes and test if
they are significantly different between urban and rural households for protein and iron-rich foods,
(2) To test if habit formation is present in food consumption and is a significant driver of tastes.
To address both objectives, I first employ the use of Deaton and Muellbauer’s (1980) Almost Ideal
Demand System (AIDS) to estimate provincial tastes for each food. Subsequently, I regress these
current taste estimates on lags of logged prices to test for presence of food habits in consumption.
My results reveal significant differences in tastes for iron-rich foods such as meat, poultry, and green
vegetables, which verifies the presence of an urban-rural nutrition gap in my sample. I also find
that past prices matter for current tastes and budget shares for all foods in general, but not so
much for protein and iron-rich foods. Moreover, the lagged price effects are not negative as I would
have expected them to be. This suggests that current tastes could be developed through a separate
channel via a “quasi-positional goods” effect, though this reasoning has its own limitations. Even
if this alternative explanation holds, the difference in lagged price effects between urban and rural
households remain insignificant. Therefore, I cannot find sufficient evidence to support the habit
formation hypothesis, and am unable to attribute the persistence of the urban-rural nutrition gap to
taste differentials.
Though there has been little empirical work done linking tastes with habits, there exist numerous
literature that test the habit formation hypothesis using a different approach. Dynan (2000), for
example, uses a simple model of habit formation relating the strength of habits to the evolution
2Though I could not find any formal work on this, there were several online articles which discussed the problem of
low food accessibility in rural areas, and vulnerability of these households to frequent price fluctuations.
3Arbitrary tastes might explain differences in consumption in a single time period, but certainly cannot explain why
the nutrition gap has been so persistent.
4

of consumption over time. He estimates the model using food consumption data from the Panel
Study on Income Dynamics (PSID) and finds no evidence of habit formation. Heien and Durham
(1991) employ a similar lagged dependent variable approach using BLS Interview Panel Data and
find habit effects to be highly significant. However, the closest study to this paper is the recent work
of Atkin (2013), which explores the causes and consequences of regional taste differences in India.
By introducing habit formation into a standard general equilibrium model, his results show strong
evidence of habit formation in food consumption and that tastes relate positively to endowments. He
goes on to conclude that regional taste differences are a result of household tastes which evolve over
many years to favour crops relatively well-suited to local agro-climatic endowments. This paper adopts
a similar approach as his, but uses a different source of price variation which is due to differing market
structures i.e. higher relative prices in rural areas due to the presence of monopoly food suppliers as
compared to a monopolistic competitive structure of food vendors in urban areas. Furthermore, I am
not solely interested in testing for habit formation in food consumption in general, but also examining
the heterogeneity of the lagged price effects between urban and rural households for a specific class
of foods i.e. protein and iron-rich foods.
The remainder of the paper is organised as follows. Section 2 describes the theoretical framework,
as well as the identification strategy by which the two objectives of my paper are addressed. Section 3
describes the data used. Section 4 presents the key findings of my two testable implications. Section
5 discusses these results, their possible policy implications, and limitations of my paper. Section 6
concludes.
2 Conceptual Framework
2.1 Theoretical Framework
In this subsection, I present a theoretical framework describing how current (adult) tastes θt are
formed through food habits. First, assuming that most foods are normal goods and no Giffen-good
effects are present, it is clear from the law of demand that past consumption ct−n depends negatively
on past prices pt−n. Following Atkin (2013), it is also reasonable to assume that adults have higher
tastes for particular foods that he or she consumed relatively more as a child as food habits develop
over time. For example, a person is likely to have a higher preference or taste for rice over other foods
if he grew up consuming it often since it is relatively cheaper. Higher taste θt for a food then translates
into a higher level of consumption ct of the food. This could be due to the fact that taste, modelled
as a “habit stock”, raises the marginal utility of consumption of that particular food as put forth by
5

Stigler and Becker (1977), or that stronger tastes increases the “psychologically and physiologically
necessary quantity of the food” thus leading to higher consumption levels (Pollak, 1970). Figure 1
provides an illustration of this framework, showing how in the presence of food habits, current tastes
and consumption depend on past consumption, which in turn depends on past prices.
Figure 2.1: Current tastes are developed through food habits
In order for this framework to be relevant in my context, it must be that urban and rural
Indonesian households faced different price levels in the past for similar foods, which affect subsequent
tastes and consumption levels. I assume that this price variation stems wholly from differences in
market structure due to accessibility of food produce within an area. With more extensive and
cheaper transportation networks in urban areas, it is easier for a greater quantity and larger variety
of food to be transported there. This means that the market will be able to support many small firms
that compete under a monopolistic competitive structure as a result of greater supply. In comparison,
food suppliers tend to be monopolies in rural areas due to lower accessibility.4
Since rural households
in Indonesia have comparatively fewer access to street food vendors and grocery stores than urban
households, they therefore face higher relative prices in general. Table 1, which shows the mean
prices faced by urban and rural households, confirms this for my food category of interest - protein
and iron-rich foods.5
As a result, rural households switch to cheaper calorie sources e.g. staples,
instant noodles that are less nutritious, and devote a larger share of their budget to these foods.
Consequently, they have lower tastes for protein and iron-rich foods e.g. meat and poultry that tend
to be more expensive.
Table 1: Mean prices (in Rp) faced by urban and rural households and as a % of weekly food expenditure
Food item
Urban Rural
Price/unit (Rp) % Price/unit (Rp) %
Beef 12091.57 17.28 11074.50 23.40
Chicken 4797.93 6.85 5028.01 10.62
Fresh fish 4182.80 5.97 3368.02 7.12
Green vegetables 190.14 0.27 150.69 0.32
4It is common for rural households to obtain their food from a central wet market which is a monopoly in the region.
5Note that urban prices are still higher than those of rural in absolute terms, which are likely due to higher rental
costs in urban areas. However, here I am comparing prices each type of household face as a percentage of their weekly
food expenditure (proxy for income).
6

It is also important to note that under this framework, tastes play a significant role in influencing
current consumption and nutrition levels of households. A rural household that sees an increase in
family income (and hence purchasing power) and subsequently moves to an urban area might still not
be consuming sufficient amounts of protein and iron-rich foods if their tastes for staple and instant
foods run “deep” and are developed through food habits over time. This would necessarily imply
that taste differentials between rural and urban households are non-trivial and could well be an area
worth looking into when addressing issues related to nutrition security.
2.2 Identification Strategy
The objectives of this paper are two-fold: The first seeks to estimate tastes and test if they are
significantly different between urban and rural households for my foods of interest i.e. protein and
iron-rich foods which I define to be meat, poultry, fish and green vegetables. The second explores
whether habit formation is present in food consumption and is a significant driver of tastes. I will
proceed to describe the empirical methods by which these two objectives are addressed, as well as
the testable implications.
2.2.1 Examining urban-rural taste differentials
In order to compare differences in tastes between urban and rural households, I first require a model
to estimate taste, which I define to be a “habit stock” that raises the budget share spend on a food,
ceteris paribus. Following Atkin’s (2013) approach, I estimate taste for a particular food using the
Almost Ideal Demand System (AIDS) of Deaton and Muellbauer (1980). This is done by expressing
the budget share of a good sgi as a function of prices for every good ln pki, real household expenditure
which proxies for income lnmi
Pi
, and a good-province specific constant θgp (in this case referring to
taste for a good in each province):
sgi = θgp +
k
αgkln pki + βgln
mi
Pi
(2.1)
It is crucial to note that since urban and rural households in each province face different prices,
the provinces identified here (denoted by subscript p) refer to the 13 Indonesian Family Life Survey
(IFLS) provinces which were further split into their residential type i.e. urban or rural,6
forming 26
different categories altogether.
6Provinces are labelled as Prov1-Urban, Prov1-Rural, Prov2-Urban, Prov2-Rural etc.
7

I chose the above expenditure function since tastes are additively separable from price and
income effects, and that it takes into account the price of a good relative to others.7
Other reasons
include the flexible functional form of the expenditure function, and how it avoids the need for
non-linear estimation, making it relatively simple to estimate.
Using the AIDS framework in equation (2.1), I proceed to regress the household budget share
of food g on prices, income and household characteristics for each food using OLS in equation (2.2)
below. Since I assume that tastes act as pure budget shifters in the demand equation, I estimate them
using the unexplained province-level variation in food budget shares. Accordingly, the coefficients
θgp on my province-food dummies are the taste measures. When estimating using AIDS, there are
three necessary conditions to be fulfilled: adding-up, homogeneity and symmetry,8
provided every
household consumes every food item. However, given there are no households in my sample that
consumed all food items, I follow Deaton (1997) and Atkin (2013) in interpreting equation (2.2) as a
“linear approximation to the conditional budget share averaging over zero and non-zero purchases”.
sgi = θgpdgp +
k
αgkln pks + βgln
foodi
P∗
i
+ γgHHsizei + δgHHsize2
i + µgreligioni + εgi (2.2)
I now explain each of the variables in equation (2.2), and how they might differ for empirical
purposes from the original demand system in equation (2.1). The dependent variable sgi is the share
of food expenditure foodi spent on food g for each household i. For the price variable, I am concerned
that prices at the household level might be measured incorrectly, and measurement error in ln pki
would result in a downward bias of αgk towards zero. Therefore, to address the issue of endogeneity,
I use median subdistrict prices ln pks as proxy for prices that households face.9
I also replace total
household expenditure mi with total food expenditure foodi, and use a Stone index (1954) given
by ln P∗
i = g sgi ln pgi to approximate the price index in order to make the system linear.10
The
term ln foodi
P ∗
i
then represents total real food expenditure, having taken inflation into account. The
relevant household characteristics that I use as controls include household size and religion, since (i)
a bigger household could necessarily imply a larger budget share of staples needed for sustenance,
and (ii) certain religions have prohibitions on the type of food households can consume.
7Thus allowing for relationships between different goods, i.e. substitutes or complements
8Adding-up implies that all budget shares sum to unity. Homogeneity implies that a proportional change in all
prices and expenditure has an effect on the quantities purchased. Symmetry implies the consistency of consumers’
choices.
9Median subdistrict prices were used instead of mean since they are more robust to outliers
10This linear approximation using the Stone index has been employed in most empirical literature which use the
AIDS approach.
8

The taste estimates that are obtained from the OLS regressions will then be used directly in
my subsequent specification for Proposition 2. However, I would first like to test for significant taste
differences between urban and rural households for certain foods of interest, i.e. protein and iron-rich
foods, namely meat, poultry, fish and green vegetables. This would support the evidence in various
literature that urban and rural households differ in consumption levels for more nutritious food, and
verify the presence of a nutrition gap. I should expect that urban households have higher tastes for
protein and iron-rich foods relative to their rural counterparts. For the purpose of comparing tastes
between the two residential types, I run a slightly different specification from equation (2.2) that
would allow me to easily perform a t-test to see if taste differentials are significant:
sgi = αg + λgdgr +
k
αgkln pks + βgln
foodi
P∗
i
i + µgreligioni + εgi (2.3)
This specification replaces the province-food dummies with a good constant αg, and includes a
dummy variable dgr that indicates if the household is from a rural area. The average taste of urban
households for food g is then given by αg, while that of rural households is αg + λg correspondingly.
Proposition 1. Urban households have higher tastes for protein and iron-rich foods i.e. meat,
poultry, fish and green vegetables relative to rural households: λg < 0 ∀ g ∈ {meat, poultry, fish, green
vegetables}
2.2.2 Testing for presence of habit formation
As discussed under the framework in Section 2.1, I would expect current taste for food g to be
decreasing in past prices of food g if food habits are present in consumption. Therefore, I choose to
regress current provincial tastes on lags of logged subdistrict prices. Tastes for each province and
residential type have been calculated, using equation (2.2), separately for each of the four IFLS survey
rounds indicated by t. Also, I use unlogged tastes since some of the taste estimates are negative.
However, this simple specification might not suffice. In particular, I suspect that the coefficients on
the lagged price variables suffer from an omitted variables bias. Lagged initial tastes could affect
prices positively if firms decide to exploit higher tastes (and hence demand) by raising prices in
the same or subsequent periods during which the tastes were formed. By the definition of habit
formation in this context, these initial tastes also necessarily influence current tastes.11
As a result,
the coefficients on the lagged price variables will be upward-biased if initial tastes are not accounted
11Tastes across the different survey rounds are more or less similar, thus current tastes are highly correlated with
initial tastes.
9

for. By controlling for initial tastes, I am able to isolate the effect on current provincial tastes that is
a result of supply-side factors (i.e. due to past prices) and obtain consistent estimates. Moreover, I
include province-time fixed effects to absorb any general changes in price levels within each province
over time. The final baseline specification is then laid out in equation (2.4) as follows:
θgp,t = α + β0ln (pgs,t−1) + β1ln (pgs,t−2) + β2θgp,t−2 + dpt + εgp,t (2.4)
I run the fixed effects regression by including only two lags of logged prices since my panel only
consists of four periods. Additionally, by using fixed effects, I am making a strong assumption of
strict exogeneity i.e. E [εgp,t|ln(pgs,t−1), ln(pgs,t−2), θgp,t−1, θgp,t−2] = 0 for t = 3, 4. However, this
assumption necessarily fails if I include both lags of taste θgp,t−1 and θgp,t−2, since θgp,4 is correlated
with εgp,3 by construction. Therefore, I use only tastes lagged by two survey rounds (i.e. θgp,t−2) to
proxy for initial tastes.
The key coefficients of interest are β0 and β1. Under the presence of habit formation in general
food consumption, I would expect β0 and β1 to be both negative and significant. Conversely, if
food habits are not driving tastes, then I would not be able to reject the null hypothesis of βn =
0 for n = 0, 1 at the 1, 5 or 10% significance level. I use the above specification to test for food
consumption in general, and proceed to run subsample regressions to test for heterogeneity in lagged
price effects between urban and rural households. The results from the baseline specification are
presented in Section 4.
Proposition 2. (a) Food habits are significant drivers of tastes across all households i.e. habit
formation is present in food consumption for both urban and rural households: βn < 0 at the 1, 5 or
10% significance level; (b) Habit formation in the consumption of protein and iron-rich foods is
stronger for urban than rural households: β urban
n > β rural
n ∀n = 0, 1
3 Data
This paper employs the use of household data from the Indonesian Family Life Survey (IFLS), which
was carried out in four survey rounds in 1993, 1997, 2000 and 2007.12
Though the IFLS covers only
13 of the 27 provinces in Indonesia, it is representative of 83% of Indonesia’s population with approx-
imately 8000 unique households surveyed in each round.13
The surveys are rather comprehensive as
12Data for the fifth IFLS survey round was released at the time of writing and thus not incorporated.
13Figure obtained from RAND website: http://www.rand.org/labor/FLS/IFLS.html
10

they detail consumption data for each household over the past week for a list of 35 different foods,14
prices of foods at the community level, and other household characteristics. I primarily utilise data
on prices that households face as well as household consumption, where the latter includes both
household expenditure as well as own production. I value own production at prevailing market prices
within the subdistrict given it is an opportunity cost since the household could have sold what they
produced at those prices. The geographical coverage of the IFLS surveys and the full set of food
items are described in Appendix B.
One main problem I faced when cleaning up the dataset was matching of the price for each food
item to household consumption. This proved to be a challenging task as the former was recorded
at the community-facility level and the latter at the household level. Not only was the price data
at the community level available for a smaller category of food items, there were also differences in
the way several food items were described. I highlight the main assumptions I made in the matching
process in Table 2 below. I also ensured that the prices were matched at the same measurement unit
as household consumption wherever possible,15
with kilogram as the unit for solid foods and litre for
liquid foods. Altogether, I successfully matched for 20 out of the 35 food items, which accounted for
70.91 percent of total food expenditure on average.16
Table 2: Matching categories of household consumption and price data
Consumption data Price data Action
Rice
High, average and
low quality rice
Take the average price of the three
types of rice as proxy for price of
rice
Other staples (include
potato, sweet potato, and
yam)
Sweet potato
Use the price of sweet potato as
proxy for price of other staples
Fruits Bananas, papayas
Take the average price of bananas
and papayas as proxy for price of
fruits
Current adult tastes are identified using the cross-sectional data in a single survey round e.g.
IFLS-4/2007. In order to test for presence of food habits, I require consumption and price data
from multiple survey rounds. To do so, I construct my panel by merging data from all four waves
of the IFLS. My main dataset is thus a panel with four time periods. It is unbalanced since not
all households were present throughout the four rounds due to attrition, and new households were
included in subsequent survey rounds. Attrition, however, is not a major source of concern as the
14The 35 different foods include staples, vegetables, dried foods, meat and fish, milk, eggs, spices, beverages and
other consumer products such as tobacco, cigarettes and betel nuts. See Appendix B for the full list.
15There were instances where the units of measurement were not specified by the households.
16Although not completely ideal, this is the best I can do given how I am constrained by the paucity of price data
available.
11

selectivity of those who attrit appears to depend on characteristics unobservable at the baseline survey
in 1993 (D. Thomas et al., 2012). Also, I choose to use the cross-section analysis weights provided by
IFLS that was intended to correct for sample attrition across the survey waves, and also to correct for
the fact that the baseline survey sample design included over-sampling in urban areas and off-Java.
4 Results
4.1 Significant current taste differentials for iron-rich foods
The average tastes of rural and urban households for each food g are estimated via the aforementioned
equation (2.3) using OLS with standard errors clustered at the household level:
sgi = αg + λgdgr +
k
αgkln pks + βgln
foodi
P∗
i
i + µgreligioni + εgi
Since the coefficient λg on the rural dummy measures the difference in tastes between the two groups,
I perform a t-test on λg directly with the null hypothesis of λg = 0 (i.e. urban and rural households
have the same tastes for food g) for each of the foods, and conclude that significant taste differentials
exist for most foods. Figure 4.1 shows the p-value for each of the 20 foods in the IFLS-4/2007
survey round, while Table 3 indicates the mean urban-rural taste estimates for protein and iron-rich
foods. My results illustrate differences in tastes for all iron-rich foods, apart from fresh fish where
the difference in means has a p-value of 0.72 and is not significant. It is also evident that urban
households have higher tastes for these foods compared to rural households, verifying the presence of
a nutrition gap since rural households are consuming significantly lower quantities of more nutritious
foods.
Figure 4.1: Scatterplot of p-values for all 20 food items
12

Table 3: Mean taste estimates of iron-rich foods for urban and rural households
Food item Urban tastes Rural tastes Difference
Beef 0.0062 0.0008 0.0054*
Chicken 0.0617 0.0523 0.0094*
Fresh fish 0.2008 0.1996 0.0012
Green vegetable -0.0604 -0.0675 0.0071*
As tastes are the unexplained components of food budget shares, it is tautological that tastes
and budget shares move in the same direction. Therefore, to check that I have specified my demand
system correctly, I should find no significant difference in mean budget shares between urban and
rural households for foods where no significant differences in tastes were found. These foods include
sweet potato, fresh fish, sugar, noodles, tofu and cigarettes. Table 4 compares the mean urban-rural
budget shares for those foods in the IFLS-4/2007 survey round. Using a t-test, I confirm that the
difference in means of the two residential types are not significant.
Table 4: Mean urban-rural budget shares for foods with no significant differences in tastes
Food item
Budget share
Urban Rural Difference
Sweet potato 0.0068 0.0061 0.0007
Fresh fish 0.0442 0.0496 -0.0054
Sugar 0.0280 0.0317 -0.0037
Noodles 0.0438 0.0442 -0.0004
Tofu 0.0357 0.0372 -0.0015
Cigarettes 0.0954 0.0944 0.0010
4.2 Past prices are significant predictors of current tastes
4.2.1 Baseline specification
Table 5 presents the results from my baseline specification of:
θgp,t = α + β0ln (pgs,t−1) + β1ln (pgs,t−2) + β2θgp,t−2 + dpt + εgp,t
The pooled OLS specification in column (1) where I simply regress current taste on lags of logged
prices and lagged tastes produces inconsistent estimates of β0 and β1, since unobserved and time-
invariant good-province heterogeneity e.g. quality of the food in each province arising from different
agro-climatic endowments is not controlled for. Therefore, I run fixed effects instead which would
give me consistent estimates on the lagged price variables. Performing a Hausman test allows me to
reject the random effects assumption i.e. the null hypothesis that these fixed effects are uncorrelated
with my regressors, at the 1% significance level.
13

Table5:Currenttastesandpastprices
(1)(2)(3)(4)(5)(6)
VARIABLESPooledOLSFixedEffectsFixedEffectsFixedEffectsAllfoodsexcl
cigarettes
Iron-richfoods
ln(pgs,t−1)0.0499**0.04500.135***0.135***0.145***-0.00346
(0.0216)(0.0324)(0.0386)(0.0364)(0.0373)(0.0101)
ln(pgs,t−2)-0.02270.0256*0.120***0.120***0.123***0.00821***
(0.0229)(0.0136)(0.0259)(0.0309)(0.0313)(0.00265)
ln(pgs,t−3)0.0845***
(0.0199)
θgp,t−1-1.074***
(0.188)
θgp,t−2-0.970***0.349***0.1940.1940.142-0.249
(0.145)(0.126)(0.136)(0.176)(0.193)(0.011)
Observations395869869869843191
R2
0.4600.0330.0840.0840.0850.960
No.ofprovince-food
clusters
47447447446199
Province-timeFEYESYESYESYES
ClusteredSEYESYESYES
Note:Thedependentvariableiscurrenttastesθgp,twhichhavebeenestimatedusingunexplainedprovincialvariationoffoodbudget
shares.ln(pgs)arelogsofmediansubdistrictprices.tdenotesconsecutiveIFLSsurveyrounds1993,1997,2000and2007.Robust
standarderrorsareclusteredattheprovince-goodlevelandgiveninparentheses.***p<0.01,**p<0.05,*p<0.1
14

Across the fixed effects specifications in columns (2) to (4), the coefficients (β0 and β1) on lagged
price terms ln (pgs,t−1) and ln (pgs,t−2) are consistently positive and mostly significant, with current
tastes for food increasing in past prices. Looking at the basic fixed effects model in column (2), β0
and β1 are rather small and only the second lagged price term is just significant at the 10% level.
However, with the inclusion of province-time fixed effects, there is a huge increase in the magnitudes
of β0 and β1 which can be clearly seen in column (3), and both are now very significantly different
from zero (null strongly rejected at the 1% level). This suggests that controlling for price trends
within each province over time has a large effect on β0 and β1.
In my most robust specification i.e. column (4), I further cluster my standard errors at the
province-food level which account for serial correlation between the error terms from different periods
within each province-food unit. This ensures that my coefficients β0 and β1 will be consistent as
shown by Kezdi (2005). The estimates of β0 and β1 are 0.135 and 0.120 respectively and are strongly
significant at the 1% level. Assuming that my specification has a causal interpretation, this means
that a 100% increase in price (i.e. doubling the price) a decade earlier increases the budget share of
food today by 12% on average. This provides strong evidence that past prices are significant predictors
of current tastes for food consumption in general. However, it is puzzling that the coefficients are
consistently positive since I would have expected otherwise. I explore a potential reason for this in
Section 5.1.
I proceed to run fixed effects for each subsample by food type. The full set of results for each
of the 20 foods can be found in Appendix C. Since cigarettes, being an indisputably addictive good,
was considered in the list of food items, I suspect that it might be an anomaly driving the coefficients
on lagged price variables to be significant. Hence as a specification check, I repeated my most robust
specification, this time excluding cigarettes in the food items considered. I find that there is not much
change in the magnitude of coefficients β0 and β1 in column (5), and they remain highly significant.
I also limit my sample to protein and iron-rich foods since that is the food category of interest. My
results in column (6) indicate much smaller positive coefficients on the lagged price variables and
only the coefficient on the second price lag is significant, suggesting that the effect of past prices on
current tastes is much weaker for this class of foods.
4.2.2 Heterogeneity of lagged price effects
Table 6 presents the results of my specification by residential type for both all foods and iron-rich
foods samples.
15

Table 6: Heterogeneous lagged price effects for urban and rural households
(1) (2) (3) (4)
All foods Iron-rich foods
VARIABLES Urban Rural Urban Rural
ln (pgs,t−1) 0.172*** 0.130*** 0.00618 0.0116
(0.0591) (0.0490) (0.0132) (0.00915)
ln (pgs,t−2) 0.131*** 0.121*** 0.0120*** 0.00558*
(0.0463) (0.0456) (0.00404) (0.00299)
Observations 441 402 102 89
R2
0.088 0.088 0.963 0.972
Province-food clusters 239 222 52 47
Province-Time FE YES YES YES YES
Clustered SE YES YES YES YES
Note: Robust standard errors are clustered at the province-food level and given in parentheses.
*** p<0.01, ** p<0.05, * p<0.1
In order to test if the coefficients on lagged price variables β0 and β1 are significantly different
across the urban and rural subsamples, I perform a Chow test using the following specification:
θgp,t = α + β0ln (pgs,t−1) + β1ln (pgs,t−2) + β2θgp,t−2 + dpt + γ0dr + γ1 [ln (pgs,t−1) ∗ dr](4.1)
+γ2 [ln (pgs,t−2) ∗ dr] + γ3 [θgp,t−2 ∗ dr] + [dpt ∗ dr] + εgp,t
where dr is the dummy for rural households, and [ ln(pgs,t−1)∗dr], [ln(pgs,t−2)∗dr], [θgp,t−2 ∗dr],
[dpt ∗ dr] are the interactions of the rural dummy with the original regressors. The coefficients on
each of the interaction terms then capture differences in lagged price effects between the urban and
rural subsamples. A joint F-test of γ1 and γ2 produces a p-value of 0.859 for all foods in general
and 0.237 for protein and iron-rich foods, both of which do not allow me to reject the null at the
10% significance level. I thus conclude that lagged price effects are not significantly different between
urban and rural households.
From columns (3) and (4), we see that a doubling of price one decade earlier increases the current
budget share of iron-rich foods for urban households by 1.2% and that for rural households by 0.56%.
Although the effect of past prices for urban households is more than twice that of rural households,
these effects are relatively small and not significantly different from each other. This suggest that
tastes for iron-rich foods across all households evolve rather slowly over time.
16

4.2.3 Robustness check
In order to ensure that my baseline specification was specified correctly, I consider an alternative
method to test for the presence of habit formation in food consumption. Current budget shares
should depend on past prices after controlling for current prices and income if food habits are present.
Therefore, I choose to retain the use of AIDS but now include the lags of logged subdistrict prices
directly in it. I also use data from multiple survey rounds. Equation (2.2) is thus modified as such:
sgi,t = θgpdgp+
2
n=0 k
αt−n
gk ln pks,t−n+βgln
foodi,t
P∗
i,t
+γgHHsizei,t+δgHHsize2
i,t+µgreligioni,t+εgi,t
(4.2)
As before, I run fixed effects with two lags of logged prices on equation (4.2) for each food. I
then perform a F-test on each of the lagged price terms. Under the null, past prices do not affect
current budget shares, i.e. αt−n
gk = 0 ∀ n, g, k. My results in Table 7 indicate a p-value of 0.00 for
almost all foods which allow me to reject the null, although it is not immediately clear that the
coefficients on the lagged price terms are positive for most foods. However, the key takeaway remains
that my findings strongly support my conclusion in the previous subsection, implying that past prices
are significant predictors of current tastes, and hence current budget shares, for food consumption in
general.
Table 7: Significance of past prices in the demand system (all foods)
t-1 prices t-2 prices
Food item F-stat p-value F-stat p-value
Rice 5.19 0.00 6.42 0.00
Cassava 7.10 0.00 3.42 0.00
Sweet potato 6.68 0.00 65.67 0.00
Tapioca 1.49 0.09 1.36 0.15
Beef 1.99 0.00 2.02 0.00
Chicken 2.66 0.00 3.32 0.00
Fresh fish 4.21 0.00 2.82 0.00
Dried fish 5.53 0.00 5.48 0.00
Bread 12.89 0.00 10.75 0.00
Cooking oil 3.61 0.00 3.52 0.00
Sugar 4.28 0.00 2.74 0.00
Salt 2.42 0.00 1.13 0.32
Milk 2.37 0.00 1.93 0.00
Noodles 4.22 0.00 4.32 0.00
Tofu 4.66 0.00 2.02 0.00
Beancurd 2.26 0.00 3.13 0.00
Cigarettes 7.37 0.00 3.40 0.00
Green vegetables 15.31 0.00 4.27 0.00
Fruits 81.95 0.00 18.50 0.00
Water 2.91 0.00 2.83 0.00
17

5 Discussion and Limitations
5.1 Positive relationship between current tastes and past prices
Looking at the results in Tables 5 and 6, it is clear that past prices matter for current tastes in
general food consumption, but not so much for the consumption of protein and iron-rich foods since
the coefficients were much smaller. A doubling of price one decade earlier increases the current
budget share of iron-rich foods for urban households by 1.2% and that for rural households by 0.56%,
which are very small effects. Moreover, I find no difference for these effects between urban and rural
households. Therefore, there is insufficient evidence to support the habit formation hypothesis. This
signifies that taste differentials are unable to explain the low consumption of protein and iron-rich
foods in rural households relative to urban households. However, there remains a puzzling fact -
the positive relationship between past prices and current tastes that appears consistently across my
results.
Based on the theoretical framework outlined in Section 2.1, I would have expected that current
tastes depend negatively on past prices due to the law of demand. Nonetheless, my results have
proven my hypothesis to be incorrect. This could mean two possibilities: either the law of demand
does not hold (which is highly unlikely), or that current tastes are developed through a different
channel other than food habits arising from what they consumed as a child. One possible explanation
to account for the observed positive relationship uses a theory analogous to that of “positional goods”.
The term, coined by Fred Hirsch in 1976, refers to “things whose value depends relatively strongly on
how they compare with things owned by others” (Frank, 1985). Applied to this context, it could well
be that children growing up in most Indonesian households consider protein and iron-rich foods to be
“luxury” foods, as signalled by their higher prices and relative scarcity compared to other foods such
as staples. Since they do not consume much of these “quasi-positional foods” during childhood,17
they
carry this desire to consume them all the way until adulthood when they have greater purchasing
power, and consume more than they would have as a result. This explains why higher past prices
could potentially lead to higher current tastes and budget shares. However, it is important to note
that this “quasi-positional goods” argument has its limitation as well, that it would only hold for
certain classes of foods such as protein and iron-rich foods since a comparison group is present (which
are cheaper and less nutritious foods in this case), and not for all foods in general. Nonetheless, even
if we consider this alternative explanation, the difference in lagged price effects (or “quasi-positional
17These luxury foods cannot be considered strictly ’positional’ since the theory of ’positional goods’ does not apply
to goods which are privately consumed. However, although rural households do not directly observe the diets of their
urban counterparts, they are likely to learn from other sources about the types of food they consume, and therefore
able to distinguish between normal and luxury foods. Hence, I refer to these luxury foods as “quasi-positional foods”.
18

goods” effect in this case) between the two residential types remain insignificant. Thus, it does not
seem reasonable for current taste differentials to result from this channel.
5.2 Policy implications
To sum up, I present in Figure 5.1 two potential channels by which current tastes develop - the first
is through food habits in which my original hypothesis was based upon (Channel A), the second
through the “quasi-positional goods” reasoning (Channel B).
Figure 5.1: Two possible channels through which current tastes are developed
Taste differentials can account for the nutrition gap provided that (i) the lagged price effects on
current tastes for protein and iron-rich foods are relatively large and significant thereby signalling
strong evidence of food habits, and (ii) these effects are significantly different between urban and
rural households. However as seen in previous sections, the coefficients, though large for all foods in
general, are small for protein and iron-rich foods. Moreover, the lagged price effects are no different
for urban and rural households in the consumption of all foods as well as iron-rich foods. Overall, I
conclude that urban-rural taste differentials, whether they result from Channel A or B, are unable to
account for the nutrition gap.
This in turn points to the significance of other factors causing differential food consumption
between urban and rural households. Policy should then be concentrated on areas such as reducing
income poverty in rural areas. This could come in the form of direct welfare grants, or skills training
programs that increase job and income opportunities for rural households. More importantly, meas-
19

ures should be focused on increasing the intake of protein and iron-rich foods among rural households
by improving dietary diversity, as well as the accessibility of food supplies to rural areas, rather than
merely increasing the production of staple crops (Thompson, 2010). Certain cost-effective farming
methods e.g. using an A-frame structure that enable households to grow iron-rich leafy vegetables
using less water and fertiliser have also been implemented in places such as West Timor, and could be
easily replicated in other rural areas. This would not only allow rural households to be self-sufficient
and reduce their reliance on market supplies, but also enable them to consume more nutritious food
at lower costs.
5.3 Potential concerns
There are two main limitations of my paper which I shall now discuss. The first is the issue of rural-
urban migration which was not addressed due to data limitations. Rural-urban migration has been
an important phenomenon since Indonesia’s rapid development in the 1970s. In fact, the percentage
of Indonesia’s population living in urban areas rose from 30% to 44% between 1990 and 2010,18
the
period over which the four waves of IFLS surveys were conducted. Therefore, being able to identify
rural households that subsequently moved to urban areas would have allowed me to determine if
tastes were any different after they moved, and if they did not, attribute it to the work of food habits.
Although it was mentioned in the data whether the household had moved, I was not able to retrieve
information on which particular member of the household unit moved, the residential type where
they moved to, or link them to their subsequent household IDs. Therefore, I could only examine the
effect of lagged prices for households which maintained the same IDs over the four survey waves, on
the assumption that children in a particular household stayed on in the same unit even when they
reached adulthood.
Another potential concern is the misspecification of the demand system. Estimating tastes is a
complex task and I might not have accounted for all factors affecting the budget share for a food in my
demand system. A further extension of this work would be to investigate the effect of composition,
caste as well as primary activity of the household on consumption levels and include them as controls.
Specifying the demand system as accurately as possible would make the taste estimates less noisy.
This is crucial since these same estimates will be used directly in the subsequent baseline specification.
18Figures taken from CIA World Factbook Indonesia
20

6 Conclusion
This paper studies whether taste differentials can explain the urban-rural nutrition gap using house-
hold data from Indonesian provinces. To do so, I employ the use of Deaton and Muellbauer’s (1980)
AlDS demand system to estimate provincial tastes for each food and compare to see if they are sig-
nificantly different between urban and rural subsamples. I find that taste differentials exist for most
foods, especially for protein and iron-rich foods. I then run my baseline specification, where I regress
current tastes on lags of logged prices, to test for presence of food habits in consumption. My results
reveal that past prices are significant predictors of current tastes for all foods in general, but not so for
protein and iron-rich foods. From my estimates, a doubling of price one decade earlier increases the
current budget share of iron-rich foods for urban households by 1.2% and that for rural households
by 0.56%, both of which are very small effects. Moreover, the effect of lagged prices are surprisingly
positive and are not different between urban and rural households. Thus, I cannot conclude this to
be evidence of habit formation, and therefore am unable to attribute the persistence of the urban-
rural nutrition gap to taste differentials. As a robustness check, I included the two lags of logged
price terms directly into the demand system by which tastes were estimated. My findings support
my baseline result that current tastes and budget shares are affected by past prices for general food
consumption.
One way to reconcile the positive relationship between current tastes and past prices is to use the
“quasi-positional goods” reasoning - where children considered iron-rich foods which were relatively
scarce and more expensive to be luxury foods, and grew up with a yearning to consume them. They
then devote a larger budget share to these “quasi-positional” foods once they reach adulthood and
have greater purchasing power. However, this can only explain the positive lagged price effects for
the class of iron-rich foods and not all categories of foods. Moreover, these “quasi-positional goods”
effects are not different between urban and rural households, rendering this explanation unsuitable
as well.
Although my empirical results do not seem to be in favour of the habit formation hypothesis in
this particular Indonesian context, I am not quick to dismiss the validity of the theory. In Atkin’s
(2013) study of regional taste differences in India, he found the effect of past prices on current tastes
to be negative and significant, though his coefficients suggest a slow evolution of tastes. Therefore,
for purpose of internal validity, a similar approach could be used but with a different Indonesian
household dataset e.g. Susenas which has (almost) yearly data since 1976. An extension of this paper
could also include the fifth survey wave i.e. IFLS-5/2014 which was released at the time of writing
21

to form a longer panel.
The fact that past prices are significant predictors of current tastes and budget shares in general
food consumption is a consistent result in this paper as well as Atkin’s (2013) study, and should
not be overlooked. This implies that current tastes for foods are not arbitrary but are formed over
time through a deeper mechanism. A more concrete understanding of the forces that shape current
tastes is thus required and should be looked further into. Other avenues for future research could
also explore alternative methods of estimating tastes using a different demand system from the one
used in this study, and carefully consider the source of price variations in the past that influenced
the formation of current tastes.
22

References
Atkin, D. (2013). Trade, Tastes, and Nutrition in India. American Economic Review, 103(5), 1629-
1663. http://dx.doi.org/10.1257/aer.103.5.1629
CM Becker and AR Morrison. (1999). Chapter 43 Urbanization in transforming economies. Handbook
of Regional and Urban Economics , 3 , 1673-1790.
Deaton, A., & Muellbauer, J.. (1980). An Almost Ideal Demand System. The American Economic
Review, 70(3), 312–326. Retrieved from http://www.jstor.org/stable/1805222
Dynan, K. (2000). Habit Formation in Consumer Preferences: Evidence from Panel Data. American
Economic Review, 90(3), 391-406. http://dx.doi.org/10.1257/aer.90.3.391
Food security in Indonesia. Inside Indonesia. Retrieved 20 April 2016, from http://www.insideindones-
ia.org/food-security-in-indonesia-2
Frank, R. H.. (1985). The Demand for Unobservable and Other Nonpositional Goods. The American
Economic Review, 75(1), 101–116. Retrieved from http://www.jstor.org/stable/1812706
Haughton, J., & Khandker, S. (2009). Handbook on poverty and inequality (pp. 57-58). Washington,
DC: World Bank.
Heien, D., & Durham, C. (1991). A Test of the Habit Formation Hypothesis Using Household Data.
The Review Of Economics And Statistics, 73(2), 189. http://dx.doi.org/10.2307/2109508
Improving Nutrition and Food Security in Indonesia. UMCOR. Retrieved 20 April 2016, from
https://www.umcor.org/umcor/resources/news-stories/2015/october/1015westtimorveg
Kezdi, G. Robust Standard Error Estimation in Fixed-Eﬀects Panel Models. SSRN Electronic
Journal. http://dx.doi.org/10.2139/ssrn.596988
Kodyat, B., Kosen, S., & de Pee, S. (1998). Iron deﬁciency in Indonesia: Current situation and inter-
vention. Nutrition Research, 18(12), 1953-1963. http://dx.doi.org/10.1016/s0271-5317(98)00165-1
MDGs, Equity and Children: The way forward for Indonesia. (2012). UNICEF Indonesia Issue Brief
Oct 2012. Retrieved 20 April 2016, from http://www.unicef.org/indonesia/A1-_E_Issue_Brief_MD-
G_REV.pdf
23

Ngwenya, E and Ray, R (2007) Changes in Indonesian Food Consumption Patterns and their Nutri-
tional Implications. Discussion Paper. School of Economics and Finance, Hobart.
Nutrition at a glance: Indonesia. World Bank Nutrition Report. Retrieved 20 April 2016, from
http://siteresources.worldbank.org/NUTRITION/Resources/281846-1271963823772/Indonesia.pdf
Pollak, R. (1970). Habit Formation and Dynamic Demand Functions. Journal Of Political Economy,
78(4, Part 1), 745-763. http://dx.doi.org/10.1086/259667
RAND Indonesian Family Life Survey (IFLS). Retrieved 20 April 2016, from http://www.rand.org/lab-
or/FLS/IFLS.html
Stigler, G. J., & Becker, G. S.. (1977). De Gustibus Non Est Disputandum. The American Economic
Review, 67(2), 76–90. Retrieved from http://www.jstor.org/stable/1807222
Stone, R. (1954). The measurement of consumers’ expenditure and behaviour in the United Kingdom,
1920-1938. Cambridge [England]: University Press.
The World Factbook: Indonesia. (2016). CIA. Retrieved 20 April 2016, from https://www.cia.gov/lib-
rary/publications/the-world-factbook/geos/id.html
Thomas, D., Witoelar, F., Frankenberg, E., Sikoki, B., Strauss, J., Sumantri, C., & Suriastini, W.
(2012). Cutting the costs of attrition: Results from the Indonesia Family Life Survey. Journal Of
Development Economics, 98(1), 108-123. http://dx.doi.org/10.1016/j.jdeveco.2010.08.015
Thompson, B., & Meerman, J. (2010). Narrowing The Nutrition Gap: Investing in Agriculture to Im-
prove Dietary Diversity. Retrieved from http://www.fao.org/ﬁleadmin/user_upload/agn/pdf/Narro-
wing_Nutrition_Gap_2013.pdf
Thorbecke, E., & Pluijm, T. (1993). Rural Indonesia (pp. 222-223). New York, NY: Published for
the International Fund for Agricultural Development by New York University Press.
24

A Derivation of AIDS
I present the derivation of the Almost Ideal Demand System (AIDS) equation that was set out by
Deaton and Muellbauer (1980) in their paper. They begin by assuming expenditure functions for
utility u and price vector p to be defined as:
log(e(u, p)) = (1 − u)log(a(p)) + ulog(b(p)) (A.1)
Subsequently, they take specific functional forms for log(a(p)) and log(b(p)) to be the following:
log(a(p)) = α0 +
k
αklogpk +
1
2 j k
γ∗
kjlogpk logpj
log(b(p)) = log(a(p)) + β0
k
pβk
k (A.2)
Thereafter, the AIDS cost function can be written as:
log(c(u, p)) = α0 +
k
αklogpk +
1
2 j k
γ∗
kjlogpk logpj + uβ0 pβk
k (A.3)
It can be verified that c(u, p) is homogeneous in p provided the adding-up property is satisfied i.e.
i αi = 1, j γ∗
kj = k γ∗
kj = j βj = 0. Using Shephards Lemma, where dc(u, p)/dpi = qi, and
multiplying both sides by pi/c(u, p), they find that d logc(u,p)
d logpi
= piqi
c(u,p) = si, where siis the budget
share of good i. Performing a logarithmic differentiation of (A.3) then gives the budget shares as a
function of prices and utility:
si = αi +
j
γijlogpj + βiuβ0 pβk
k (A.4)
Since total expenditure x is equal to c(u, p), this can be inverted to give u as a function of p and
x i.e. the indirect utility function. Doing this for (A.3) and substituting this result into (A.4) gives
the AIDS demand function in budget share form in (A.5), where budget shares are expressed as a
function of p and x. x is the total expenditure on the group, γij = 1
2 (γ∗
ij + γ∗
ji) and P is a price index
for the group which ’deflates’ income.
si = αi +
j
γijlogpj + βilog(
x
P
) (A.5)
25

B Data sources
The IFLS data used in this paper are described in Section 3. Here, I include a map showing the
geographical coverage of the IFLS across 13 of the 27 provinces in Indonesia. These include four
provinces on Sumatra (North Sumatra, West Sumatra, South Sumatra, and Lampung), all five of the
Javanese provinces (DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java), and four
provinces covering the remaining major island groups (Bali, West Nusa Tenggara, South Kalimantan,
and South Sulawesi). I also present, in the tables below, the full set of 35 food items present in the
household expenditure data, and the set of 20 food items after matching with the available price data.
These 20 food items accounted for 70.91 percent of total food expenditure on average.
Figure B.1: Map of the 13 IFLS provinces in Indonesia
Food items in household
expenditure data
Rice Soy sauce
Corn Salt
Tapioca Shrimp paste
Cassava Chilli sauce
Other staples Spices
Green vegetables Water
Bean curd Butter
Fruits Cooking oil
Noodles Javanese sugar
Bread White sugar
Beef Coffee
Chicken Tea
Fresh fish Cocoa
Salted fish Soft drinks
Canned meat Alcoholic drinks
Tofu Betel nut
Eggs Cigarettes
Fresh milk
Table 8: Full set of 35 food items
Food items where expenditure
and price data could be matched
Rice Chicken
Cassava Fresh fish
Tapioca Salted fish
Sweet potato Tofu
Green vegetables Fresh milk
Bean curd Salt
Fruits Sugar
Noodles Water
Bread Cooking oil
Beef Cigarettes
Table 9: List of 20 food items that were success-
fully matched
26

CAdditionaltables
(1)(2)(3)(4)(5)(6)(7)(8)(9)
VARIABLESRiceCassavaSweetPotatoTapiocaBeefChickenFreshFishDriedFishMilk
ln(pgs,t−1)0.001680.0007430.000104-0.00311***-0.0583**-0.0008280.008410.000996-0.0106
(0.0311)(0.000928)(0.000566)(0.000377)(0.0249)(0.00664)(0.00498)(0.000973)(0.0121)
ln(pgs,t−2)0.0009800.00111*-0.0104***1.86e-05-0.0111-0.001280.004780.00275**-0.0116
(0.0156)(0.000606)(0.000944)(0.000468)(0.0175)(0.00160)(0.00335)(0.00109)(0.00950)
(10)(11)(12)(13)(14)(15)(16)(17)
VARIABLESGreenVegeFruitsCookingOilSugarSaltNoodlesBeancurdCigarettes
ln(pgs,t−1)0.00724*-0.00752*0.0171*-0.04320.004130.00785***0.00385***-0.00739**
(0.00390)(0.00383)(0.00986)(0.0345)(0.00390)(0.00253)(0.00123)(0.00314)
ln(pgs,t−2)0.009530.001690.000537-0.007210.002830.00997***0.00208**0.00529*
(0.00779)(0.00464)(0.0125)(0.00571)(0.00316)(0.00276)(0.000937)(0.00261)
Robuststandarderrorsinparentheses,***p<0.01,**p<0.05,*p<0.1
Table10:Currenttastesandpastprices(foreachindividualfooditem)
27

YE_TasteDiff_final

Recommended

Recommended

More Related Content

Similar to YE_TasteDiff_final

Similar to YE_TasteDiff_final (20)

YE_TasteDiff_final