Proceedings of Eurasia Business Research Conference
4 - 6 June 2015, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-77-1
Expenditure Patterns of Households in Erbil
Sarhang Razzaq Hamad and Cuma Akbay**
Household consumption patterns were investigated to determine the influence of
income and other sociodemographic factors on household expenditure and to
establish the possibility for demand growth in the area of Erbil-Irak. The data are from
the Iraq Household Socio-Economic Survey (IHSES), collected by the Central
Organization for Statistics and Information Technology (COSIT), The Kurdistan
Regional Statistics Organization (KRSO) and the World Bank during 2012. Household
data were collected through interview sessions with the principal decision-maker of
the household, The data contain information on consumption for a wide variety of
goods from 1913 households in the urban and rural area of Erbil. According to
results, household socio demographic and economic factors such as household
income, household size, education and working stauss of household head are
statistically significant effects on household expenditure for ten categories. Based on
our results, the expenditure for most categories in urban area of Erbil will continue to
increase. Moreover, expenditure and income elasticities were estimated for
household consumption categories by using Working Lesser Demand Model. Finding
indicates that income elasticities for durables and transporting expenditure categories
were highly elastic, while income elasticities for the food and health categories were
inelastic.
Key words: Expenditure categoies, Working Lesser Demand Model, expenditure
elasticity, Erbil.
1. Introduction
This paper contains the major results concerning the valuation of the expenditure
distribution of Erbil-Iraq in terms of inequality, levels and social welfare. In this paper
we illustrate the construction of a welfare indicator for the study of poverty and
inequality in Erbil-Iraq based on the Iraq Household Socio-Economic Survey
(IHSES,2007). The simplest way of thinking about consumption is to imagine a
basket of goods and services comprising items bought monthly by households in
Erbil. The IHSES collect information on quantity of consumption, consumption
expenditure, and other expenditures of households. the expenditure data from
IHSES 2012 are combined with the information on assets and demographics. Iraq’s
HDI value for 2012 is 0.590—in the medium human development category—
positioning the country at 131 out of 187 countries and territories.(HDI)
This study analyzed household expenditure patterns in a complete systems
framework where the possible endogeneity of source of income flows into the
household is taken into account in the estimation (Sekhampu and Niyimbanira,
2013). The several studies on expenditure household have reported widespread for
exapmle, few studies investigate have been used to estimate Engel’s curves using
OLS method on
general and foodstuff of expenditure elasticity in Iraq
(othman,2013), Syrovátka (2007) investigates the household demand pattern for
various meat and meat products by using the Engel’s curve and income elasticities
are estimated by employing the Dynamic Engel model with the help of Ordinary
Least Square (OLS) technique. ( T.J. Sekhampu and F. Niyimbanira, 2013) the
analyzed on expenditure patterns of households in a South African township of
**Prof. Dr. Cuma AKBAY, Department of Agricultural Economics, Agriculture Faculty,
Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey, Email: cumaadana@hotmail.com
Proceedings of Eurasia Business Research Conference
4 - 6 June 2015, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-77-1
Bophelong by using a multiple regression model was used to determine the factors
influencing household expenditure.
(Browne, Ortmann and Hendriks, 2007) Household consumption patterns were
investigated to determine the expenditure elasticities for consumer expendables,
durables and transport were highly elastic, while expenditure elasticities for the
aggregate food category were negative (October) and highly inelastic (March), but in
my paper expenditure and income elasticities were estimated for household
consumption categories by using Working Lesser Demand Model. Finding indicates
that income elasticities for durables and transporting expenditure categories were
highly elastic, while income elasticities for the food and health categories was
inelastic.
In This paper is contains as follows. Section two briefly explains the data set and
methods. Section three estimates of OLS and income and expenditure elasticity with
results. Section four is the last one paper comes to conclusion.
The major objective of this paper was to identify and quantify the relationship
between household expenditure and the socio-economic and demographic
characteristics of the household. The findings of the study are important to efforts in
understanding the factors that influence household expenditure in Erbil.
2. Data
Household expenditure and income cross section data was carried out by the
Central Organization for Statistics and Information Technology (COSIT), The
Kurdistan Regional Statistics Organization (KRSO), And the World Bank during
2012.
Household data were collected through interview sessions with the principal
decision-maker of the household, The data contain information on consumption for a
wide variety of goods from 1913 households in governorate Erbil, During the survey,
the participating households were asked to report weekly spending.
This nationally representative and official household consumption survey collected
information on consumption of 1343 urban households and 570 rural households in
governorate in Erbil, Expenditures on 10 distinct categories which are exhaustive
and mutually exclusive, including food, education, health care, and entertainment
were considered for the analysis in this paper, though for certain groups
disaggregated items were examined to address specific issues, the expenditure data
from IHSES 2012 are combined with the information on assets and demographics .
The IHSES collects information on household acquisitions of food ( purchased for
own use, received as gift, received from an employer, and self-produced) for 10 days
in a row. Each household was provided with a daily ledger to help record all
consumed and purchased items. Interviewers visited each family over several
sessions to provide assistance, for example in measuring quantities, and to assess
whether the ledgers were being filled out regularly and accurately. While the IHSES
data provide us with the total value of items consumed and purchased in the
marketplace, for unpurchased items we used the self-reported value as estimated by
the respondents. The 2012 IHSES collects information on household acquisitions of
nonfood commodities and services in the previous 30 and 90 days and 12 months.
Proceedings of Eurasia Business Research Conference
4 - 6 June 2015, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-77-1
The 30-day recall was applied to a range of nonfood consumption goods, such as
fuel, pharmaceutical products, transportation, and a variety of other products. The
90-day reference period refers to clothing, medical services, and leisure and
entertainment, among others. The expenses made throughout the 12-month period
include the acquisition of durable goods (like houses, furniture, cars, and electronic
appliances).
3. Method
The first empirical model applied in this study is the Working-Leser model. The
original form of the Working-Leser model was discussed by Working (1943) and
Leser (1963). Intriligator, Bodkin and Hsiao (1996) and Deaton and Muellbauer
(1980a) provide a more detailed discussion of this functional form. In the WorkingLeser model, each share of the expenditure item is simply a linear function of the log
of expenditure category and of the total expenditure on all the expenditure items
under consideration(Chern, Ishibashi and Taniguchi, 2003). The Working-Leser
expenditure demand function can be expressed as:
∑
(1)
where (i) represents the 11 food items; wi is the total expenditure share of
expenditure category i among the 11 food items; and x is the total expenditure of all
expenditure items included in the model.
Hk includes dummy variables where k is 15:





URBAN = household lives in urban area;
DHSİZE = dummy variables for size of household (DHSİZE1,… DHSİZE4);
DE = dummy variables for education head of household (DE1,…DE3) ;
GENDER = sexy of head household (male, female);
DAGE= dummy variable for age of household head (DAGE1,…,DAGE5);
’s random disturbances assumed with zero mean and constant variance.
This model can be estimated for each food item by the ordinary least squares
(OLS).
Demand elasticity formulae for Working-Leser model
It is easy to show the elasticity formulae for the Working-Leser model. The
expenditure elasticity (ei) can be expressed as:
( )
(2)
4. Results
The descriptive statistics for selected variables used in this study showed in
Table 1. Within the dataset, 70.2% of the participating households are from urban,
the average monthly expenditure for households that participate in the survey is
2527.37 ID. Among the surveyed households, 88.4% are male and the average age
of the head of household is 44.07 and range from 16 year old to 99 year old. The
average household size was 5.57 person and range from 1 person to 16 persons.
58.2% of the household head reported that they can read and write or finished
Proceedings of Eurasia Business Research Conference
4 - 6 June 2015, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-77-1
elementary school, 27.7% middle school and 3.4% have attended high school. Share
of the household head less than 30 years old is about 20%, while share of household
head older than 60 is 25,2%.
Table 1: Definitions and Sample Statistics of Explanatory Variables
Variable Definition of variables
Mean Standard
name
Deviation
LNEXP
LN for Expenditure (Dependent Variable)
7,683
0,557
Residential area of household
Urban
1,298
0,457
(1: Urban; 2: Rural)
Size of the household (1:if household size ≤3;
0,198
DHSİZE1
0.380
0: Others
DHSİZE2 1:if household size between 4 and 5; 0: Others
0,331
0,471
DHSİZE3 1:if household size between 6 and 7; 0: Others
0,266
0,442
DHSİZE4 1:if household size ≥8; 0: Others
0,205
0,404
0,539
De1
1: if Education (1:Illitrate,2:Reads & Writes)
0.505
1: if Education(3:Elementary, 4:Intermediate,
De2
0,416
0,493
5:primary, 6:Preparatory, 7:Diploma);0: Others
1: if Education ( 8:Bachelor, 9:Higher Diploma,
De3
0,045
0,207
10:Master , 11:PHD,12:Other; 0: Others
Gender of household head
Gender
1,125
0,331
(1: Male; 2: Female)
Age of the household (1:if household age ≤30;
0,205
DAGE1
0.412
0: Others
1:if household age between 30 and 40; 0:
DAGE2
0,298
0,457
Others
1:if household age between 40 and 50; 0:
DAGE3
0,186
0,389
Others
1:if household age between 50 and 60; 0:
DAGE4
0,160
0,367
Others
Age of the household (1:if household age >60;
DAGE5
0,151
0,358
0: Others
Household expenditures groups are calculated in the form of natural LN and then
are used as an in dependant variable in this regression analysis. Using The dummy
variable for residential household , size of household, education head of household ,
gender of head household and age of head household.
Table 2 shows the avearge expenditure and budget sahre of each expenditure
categegories in total expenture. Households highest spend on average 656.55 per
month on food and Households lowest spend on average 13.09 per month on
Alcoholic beverages and tobacco.The data set from the budget share table gives
adetailed desccription of household spending patterns in Erbil. Food expenditure
highest as a percentage of total expenditures ranged from 25.98 percent and
Alcoholic beverages and tobacco as a percentage of total expenditures ranged from
0.52 percent.R3
Proceedings of Eurasia Business Research Conference
4 - 6 June 2015, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-77-1
Table 2: Average Expenditure and Budget share of Expenditure Categories
Expenditure Categories
Average
Standard
Budget
ID/1000
Deviation
Share
Food and non-alcoholic beverages
656,55
219,416
25,98
Housing, Water, Electricity and LPG
461,55
522,329
18,26
Transportation and Communication445,89
483,430
17,64
Clothes and footwear
207,72
130,818
8,22
House supplies, appliances
195,33
195,028
7,73
&maintenance
Buy car, fridge other machine
169,76
485,645
6,72
Health Expenditure
102,24
158,103
4,05
Education
24,75
54,154
0,98
Alcoholic beverages and tobacco
13,09
32,664
0,52
Others (Culture, recreation, , hotel,
250,60
140,257
9,92
Miscellaneous services and Goods)
Total Expenditure
2527,37
1415,699
100,00
By using Working Lesser Model, the estimated parameters for the each mojer of
expenditure categories are presented Table 3 and 4. In addition to the parameters,
robust consistent with expectations and t-statistics are presented. It also includes a ttest for the null hypothesis that the estimated parameters for the expenditure
category data are different to each other. The estimated parameters between the
expendiutre category are nigative for most of the variables, which is corroborated by
the low t-tests, while the majority shows the differences not being statistically
significant. While the matched sample seems to offer good enough information for
statistical inferences, as was previously shown in tables 3 and 4, there are some
biases on the parameters which are statistically significant. Most of the coefficient
estimates on the demographic variables are not statistical significant. Most of The
size of household are statistical significant ( and some of them positive and some of
them has negative effect. In general are important special small size household.
The educational attainments (DE) of the head of household are significant predictors
negative effect of the each household expenditure category only for the food
expenditure are statistical significant but not positive. The gender of the head of
household most of them is positive but non-statistical significant. The ages (DAGE)
of the head of household for each group were not important and negative in
explaining the variations in household expenditure categories. The statistical nonsignificance of these coefficients suggests that these variables are not important
factors with regard to explaining household expenditure patterns.
The model containing all explanatory variables was significant, indicating that the
model was able to distinguish between the various explanatory variables used in the
model.
Proceedings of Eurasia Business Research Conference
4 - 6 June 2015, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-77-1
Table 3: Regression analysis of expenditure group patterns
İndepene
nt
varaiable
CONSTA
NT
Food
2,0881
(66,0387)***
Clothes
Housing
Durable
Maintance
0,2813
-0,5785
-0,5726
0,0363
(16,5205)*** (-12,8562)*** (-19,4707)* (1,7308)***
-0,0286
-0,2242
0,0959
0,0815
0,0050
LNEXP
((-56,5191)***
(16,9864)*** (22,0962)*** (1,9019)*
13,4094)***
-0,0011
-0,0006
0,0145
-0,0080
0,0039
URBAN
(-0,2303)
(-0,2480)
(2,1844)**
(-1,8373)*
(1,2480)
-0,0150
0,0292
-0,0230
-0,0006
-0,0027
DHSİZE2
(-2,4676)**
(8,9182)***
(-2,6583)***
(-0,1038)
(-0,6641)
0,0007
0,0397
-0,0339
-0,0100
-0,0019
DHSİZE3
(0,1027)
(11,4669)*** (-3,6991)***
(-1,6786)*
(-0,4404)
0,0176
0,0457
-0,0501
-0,0182
-0,0058
DHSİZE4
(2,5414)**
(12,2165)*** (-5,0754)*** (-2,8115)***
(-1,2608)
-0,0115
0,0030
-0,0018
-0,0018
0,0020
DE2
(-2,4752)**
(1,1979)
(-0,2759)
(-0,4209)
(0,6472)
-0,0199
0,0038
-0,0029
-0,0039
-0,0010
DE3
(-1,8944)*
(0,6718)*
(-0,1912)
(-0,3984)
(-0,1502)
-0,0510
0,0019
0,0121
0,0096
0,0005
GENDER
(-7,3209)***
(0,4988)
(1,2198)
(1,4842)
(0,1046)
0,0260
-0,0065
-0,0012
-0,0078
-0,0012
DAGE2
(4,2835)***
(-2,0051)**
(-0,1353)
(-1,3807)
(-0,2953)
0,0461
-0,0049
-0,0094
-0,0143
-0,0052
DAGE3
(6,7771)***
(-1,3301)
(-0,9655)
(-2,2579)**
(-1,1621)
0,0457
-0,0047
-0,0121
-0,0222
-0,0028
DAGE4
(6,3332)***
(-1,2100)
(-1,1817)
(-3,3045)***
(-0,5845)
-0,0003
0,0017
0,0041
0,0004
-0,0007
DAGE5
(-0,0447)
(0,4230)
(0,3744)
(0,0509)
(-0,1396)
Note :(***p-values>1%,** p-values>5%, *p-values>10%
Proceedings of Eurasia Business Research Conference
4 - 6 June 2015, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-77-1
Table 4: Contiuos .
İndepenent
varaiable
Health
Transpot
Eduction
0,2006
-0,6141
0,035
(9,0438)*** (-15,4181)***
(4.332)***
-0,0218
0,0989
-0,0043
LNEXP
(-7,8439)*** (19,7962)*** (-4,1270)***
0,0073
-0,0157
-0,0015
URBAN
(2,2290)**
(-2,6702)***
(-1,2311)
-0,0024
0,0084
0,0069
DHSİZE2
(-0,5570)
(1,0929)
(4,3784)***
-0,0110
0,0073
0,0094
DHSİZE3
(-2,4439)**
(0,9022)
(5,6213)***
-0,0119
0,0114
0,0088
DHSİZE4
(-2,4354)**
(1,3001)
(4,8762)***
0,0016
0,0102
-0,0013
DE2
(0,4747)
(1,7357)*
(-1,0466)
0,0062
0,0123
0,0041
DE3
(0,8338)
(0,9247)
)1,4977)
0,0100
0,0146
0,0030
GENDER
(2,0550)**
(1,6591)*
(1,6737)*
-0,0007
-0,0084
0,0002
DAGE2
(-0,1570)
(-1,0951)
(0,1336)
0,0009
-0,0122
0,0008
DAGE3
(0,1943)
(-1,4239)
(0,4770)
-0,0005
-0,0043
0,0000
DAGE4
(-0,0919)
(-0,4711)
(0,0007)
-0,0019
-0,0018
0,0010
DAGE5
(-0,3467)
(-0,1901)
(0,6192)
Note :(***p-values>1%,** p-values>5%, *p-values>10%)
CONSTANT
Tobacco
Others
0,0232
0,1032
(4,1878)*** (40,9289)***
-0,0023
-0,0004
(-3,3756)***
(-1,2308)
0,0007
0,0006
(0,8507)
(1,5813)
-0,0005
-0,0008
(-0,5124)
(-1,5494)
0,0000
-0,0007
(-0,0152)
(-1,4402)
0,0028
-0,0007
(2,2780)**
(-1,2967)
0,0000
-0,0004
(-0,0126)
(-1,1139)
0,0015
-0,0003
(0,8408)
(-0,3908)
-0,0005
-0,0003
(-0,4458)
(-0,4556)
0,0003
-0,0008
(0,2360)
(-1,5933)
-0,0012
-0,0007
(-1,0072)
(-1,3784)
0,0017
-0,0008
(1,3299)
(-1,3063)
-0,0016
-0,0010
(-1,1849)***
(-1,6126)
Estimates of expenditure elasticities are presented in Table 5. The first column
presents Coefficient estimates for the expenditure category, the second column
presents expenditure elasticity estimates, while the other columns report for income
elasticity.
The expenditure elasticity for housing, transport, house supplies and durable
exceeds one were (1.601, 1.667, 1.031, 3.123) respectively. Other commodities are
relatively
Expenditure-inelastic. The expenditure elasticity for Durable (1.93) was the highest
among the 10 goods, and the expenditure elasticity for housing was the second
highest (1.667). It is interesting to note that study shows food, clothes, health,
education, tobacco, and others to be a normal good, although housing, transport,
house supplies and durable are superior goods.
The income elasticity for expenditure housing, transport, clothes, house supplies,
durable and others exceeds one were (2.502, 2.606, 1.067, 1.612, 4.881, 1.557)
respectively. Other commodities are relatively income-inelastic. The income elasticity
for expenditure Durable (4.881) was the highest among the 10 goods, and the
income elasticity for expenditure transport was the second highest (2.606).
Proceedings of Eurasia Business Research Conference
4 - 6 June 2015, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-77-1
Table 5: Expenditure and income elasticitiy of expenditure groups.
Expenditure
Income
Expenditure Categories
Coefficient
Elasticities Elasticities
Food and non-alcoholic beverages
-0,2242
0,306
0,478
Housing, Water, Electricity and LPG
0,0959
1,601
2,502
Transportation and Communication
0,0989
1,667
2,606
Clothes and footwear
-0,0286
0,683
1,067
House supplies, appliances &
maintenance
0,0050
1,031
1,612
Buy car, fridge other machine
0,0815
3,123
4,881
Health
-0,0218
0,546
0,853
Education
-0,0043
0,610
0,954
Alcoholic beverages and tobacco
-0,0023
0,603
0,942
Others (Culture, recreation, hotel,
Miscellaneous services and Goods)
-0,0004
0,996
1,557
5. Conclusion
This report documents the research results from model household expenditure by
using household expenditure and income data(IHSES) for 2012. The IHSES data
provide a rich source of information for estimating expenditure in Erbil. The report
presents empirical household expenditure for 10 aggregate expenditure. The
empirical results are sets of estimated expenditure elasticities for households
segmented at five different income levels. And also the income levels segmented at
tow residential area. More will be to learn more about Erbil household expenditure
from this database and from similar data for 2007.
An objective of this study was to estimate household expenditure patterns by
using elasticity demand system. This study showed that including expenditure
category into the expenditure and income elasticity improves the estimation results
and goodness of fit an important amount. Application of the model for household
survey data showed that some calculated OLS are statistically significant and some
of them not statistical significant. All expenditure categories have positive elasticities
Value. Even thought housing, transport, house supplies and durable expenditure
elasticities were bigger than one. Income elasticities are positive and Housing,
Transportation, Clothes, House supplies, durable and Others were bigger than one.
Demographic variables have generally small effect on household expenditure result
of the estimated model. But some of the dummy variable and demographic variables
have effect on food consumption. For example, household sizes have four equation
and age of household head in eight equations, education of household head in five
equations most have effect. Even though the restricted elasticity model gave
acceptable results and fit with the data, one can use also LA/AIDS model because it
is very rich functional form. Because of the time and space limitation, result of
different elasticity formula, which used by Chern, Ishibashi, Taniguchi and Tokoyama
(2003) .
Proceedings of Eurasia Business Research Conference
4 - 6 June 2015, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-77-1
Further researcher should address the issue of different elasticity formula.
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