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Strictly Confidential © 2014
Poverty measurement:Housing
Why is housing relevant?
Gaerner and Short (2009): “…[including housing in the welfare aggregate]…allows for more reasonable inter-household comparisons, as well as international comparisons, of economic well-being.”
Consumption Aggregate ≈ Indirect Utility Function
It must be as comprehensive as possible(we must consider all goods and services)
Why is housing relevant?
Dominant component of total income(Norris and Pendakur, 2013)
Share of housing consumption increases with welfare levels (Blades, 2009)
13.413.8
14.1
15.916.4
16.817.117.217.2
17.517.617.717.717.918.118.1
18.2
19.119.5
19.819.820.120.2
20.5
21.421.521.7
24.9
10
12
14
16
18
20
22
24
Korea
Norway
Portugal
United States
Sloven
iaBe
lgium
Austria
Estonia
Ireland
Germany
Spain
France
Nethe
rland
sMexico
Australia
Swed
enHu
ngary
Italy
Luxembo
urg
Finland
Poland
Japan
United Kingdo
mSw
itzerland
Czech Re
public
Denm
ark
Slovak Rep
ublic
Greece
Housing as a share of total households’ net adjusted disposable income
Malawi
Burkina FasoKenya
Mauritania
Lao People's Democratic RepublicIndiaHondurasGuatemala
Belize
Azerbaijan
Colombia
Belarus
BulgariaMalaysiaMexico
ChileLithuania
Latvia
HungaryEstonia
Czech Republic
Malta
Israel
Greece
Cyprus
ItalyIceland
JapanGermanyGreenlandFranceCanadaBelgium
Finland
AustraliaAustriaIreland
Denmark
010
2030
Hou
sing
as
shar
e of
hou
seho
ld to
tal e
xpen
ditu
re
0 20000 40000 60000GDP per capita, US$
Source: OECD National Accounts, 2012 Source: authors elaboration on UNStat System of National Accounts
What are the problems?The basics…
The utility is the value of the flow of services from occupying the dwelling rather than the expenditure for purchase it over the period of analysis
Rental markets they work perfectly and all households rent their dwellings. Hence, market rents are a good approximation of dwelling services
However… Many households own their dwellings In other cases, households receive housing free of
charge or at subsidized rates by their employer, friends, relatives, government or other entities
Few households rent their dwelling
Source: authors elaboration on EU-SILC; SEDLAC; http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?fpt=table (USA); http://kostat.go.kr/portal/english/news/1/17/1/index.board?bmode=read&bSeq=&aSeq=273080&pageNo=2&rowNum=10&navCount=10&currPg=&sTarget=title&sTxt= (Korea); authors elaboration on HBS (Albania, Bangladesh, Iraq, Peru)
97.3
94.1
93.9
92.7
92.0
91.0
90.2
88.9
88.8
87.3
86.0
84.9
84.2
83.0
82.3
82.3
81.3
80.8
80.3
80.3
79.8
79.5
79.3
76.5
76.4
75.5
74.0
73.9
72.7
72.7
71.2
70.9
70.8
70.0
69.7
69.4
69.2
69.2
69.0
68.2
67.9
66.8
64.0
63.5
63.1
62.9
62.6
61.8
54.2
52.6
52.5
48.9
46.2
44.0
11 2.67.271
9.0 811.13 2 5.010
2 9 83
136 9 13
1021312
2227 29
11 921
33
20
264449
2 5 3.6 72
8 119.0513
8 1015
71411
814
211214
50 1
192210
0
18 22
7 5
17.7
19.2
20.5
26.1
28.8
29.2
30.0
30.8
31.0
31.8
32.1
36.0
36.5
36.9
37.4
38.2
45.8
47.4
56.0
0
10
20
30
40
50
60
70
80
90
100
RO LTAlbania, 2012
Iraq, 2012
HRPe
ru, 2013 SK
Banglade
sh, 2010
HU BGTh
ailand
, 2009
NO EE ES LV
Nicaragua, 2009 PL
Paraguay, 2011 EL SI IS
Vene
zuela, 2006 CZ FI
MT PT IT
Guatemala, 2011 LU DK
Brazil, 2012 NL
Costa Rica, 2011
Hond
uras, 2011
UK CY
Peru, 2012 BE
Ecuado
r, 2012
Mexico, 2012
El Salvado
r, 2012 SE
USA
Argentina, 2013
Chile, 2011 FR
Dominican
Rep
., 2011
Bolivia, 2012
Korea, 2010
Uruguay, 2012 AT DE CH
Colombia, 2012
Owner Tenant Subsidized Tenant Non Owners
Theoretical viewpoint When expenditure is used as a yardstick of welfare, it
is important to achieve comparability across household
Including the rent expenditure for renters and not the flow of the service of the dwelling for non-renters would lead to wrong conclusions: Tenants would be better off than Non-Tenants!
Empirical Application
Market Tenants Owners and Non-market Tenants
We assume that tenants report the actual market rent
However, it is important to check if this is an accurate value
Impute what they would be paying if they were renting instead of owning
TEN
AN
TSO
WN
ERS
AN
D N
ON
MA
RK
ET T
ENA
NTS
Welfare value of the flow of services from dwelling
The rent paid by tenants as reported in the survey is supposed to be the true value of the flow of services for tenant, and included as such in the welfare aggregate
Information from external sources may be used
Econometric and statistical methods
Find the best fitting model for rents paid by tenants on the basis of observable dwelling characteristics
Apply the same model on dwellings’ characteristics of home owners and non-market tenants
Infer imputed rent for home owners and non-market tenants
Self-Assessment
Use the value reported by owners (and non-market tenants) as a reply to the question “If you would rent this dwelling, how much would you paid per month?”
Capital Market Approach
Find the capitaliz
ation rate from rents and
market value of
the dwelling
Apply the capitalization rate to the value of the owners occupied dwellings
Collect information on the return rate for housing and operating costs
Rent-to-
Value
User Cost
Payment Approach
Use information about the out-of-pocket expenditures faced by households. For owners, this is the sum of mortgage interest payments, taxes, insurance, maintenance and repairs
External Sources
(i) other households surveys
(ii) administrative register
(iii) newspaper or internet offer notices
(iv) multiple listing datasets
(v) mortgage transactions
(vi) key informant evaluations
Econometric and statistical methods
Find the best fitting model for rents
paid by tenants on the basis of
observable dwelling characteristics
Apply the same model on dwellings’ characteristics of home owners and
non-market tenants
Infer imputed rent for home owners and non-market
tenants
Econometric and statistical methods1. Hedonic models Utility derives from attributes or characteristics of goods
and not from goods per se (Lancaster, 1966) In equilibrium, economic agents observe the prices of
differentiated products and specific amounts of characteristics associated with them. This reveals the implicit prices for the different characteristics (Rosen, 1974)
Housing can be considered as a composite commodity:, ,
No consensus about the specific explicit form the hedonic price function might take
BUT: the relation between rent and characteristics should be non-linear
Location Structural Characteristics
Neighborhood
Econometric and statistical methods1. Hedonic models
The most generic functional specification is:R
In the literature, different specifications and estimation methods are found: Linear, semi-log and higher order Quantile regression, semi-parametric and non-parametric Heckman selection model Spatial Dependency
Econometric and statistical methods1. Hedonic models: Semi-Log
lnR
PROs: The coefficients show approximately the percentage change in the
imputed rent for a given unit-change in the covariate It mitigates the heteroskedasticity problem It is computationally simple It allows the marginal rent-value to be a non-linear function of the
size and quality of the dwellingBUT: The unobserved quality of the dwelling chosen by renters can be
different from that chosen by owners
Econometric and statistical methods1. Hedonic models: Heckman Selection If the choices of tenure type and characteristics of the dwelling are
not independent, the OLS estimation in the market rental sector might be inconsistent (Arevalo and Ruiz-Castillo, 2004)
SOLUTION: Heckman two-stages selection model
R ∑ 1 Where:
1 0
0
Econometric and statistical methods1. Hedonic models - COMMENT
PRO: Simple and fast to implement We can apply the model fitted on tenants on the entire
population and get an estimate of imputed rent for everybody
CONS: We risk to underestimate the prediction for those with
higher flow of services from housing and to overestimate the prediction for those with lower
There is no way to test whether the prediction for owners and non market tenants is accurate
Econometric and statistical methods1. Hedonic models – COMMENT (cont.)
Alert: In some countries, housing and rental markets are not
well enough developed to permit any serious estimate of rental value, and attempts to repair the deficiency using data from a small number of households are unlikely to be effective, however sophisticated the econometric technique (Deaton and Zaidi, 2002, p.38-39)
Econometric and statistical methods2. Stratification
Define a set of relevant characteristics Each characteristic has a set of possible realizations:
(e.g. Region1, Region2, Region3)(e.g. Detached House, Flat)(e.g. 1room, 2rooms, 3rooms, >3rooms)
From which we can define strata of dwellings with homogeneous characteristics:
Take the average rent for each stratum and assign it to each owner-occupied or non-market tenant dwelling in the same stratum
Econometric and statistical methods2. Stratification
PRO: Dwellings in the same strata will be of a more
homogeneous quality, leading to more precise estimates for owners and non-market tenants, since the model is defined on dwellings with similar characteristics
BUT: Increasing the number of strata reduces the average
number of observations per stratum What we learn: We may obtain better predictions for owners if we infer
their rents using information from tenants with dwellings having similar characteristics, possible there is no overlap
Self-assessment This approach is based on data collected about
owners’ estimates of a fictitious market rent homeowners are asked to estimate how much they would pay if they were renting their home
Assumption: owners can estimate rental equivalences This should be less problematic in regions where rental
market is active and well developed (Lanjouw, 2009) BUT…owner-occupiers may over-estimate the true
rental value of their dwelling given the affinity to their property or neighborhood (owner pride factor)
Should be treated with caution and should be tested Sergio’s presentation next Thursday!
III. Distributional Impacts
A theoretical framework Population , made of 1,2,… , individuals, ∈ , , … , distribution of welfare aggregate (w/o
rent) level of welfare aggregate for individual Assume ⋯ , , … , distribution of rents Consider three possible scenarios:
. ∀ ∈ , 0
. ∀ ∈ , ∈ 0,1
. ∀ ∈ , ∈ 0,1
A theoretical framework
wel
fare
agg
rega
te
Individuals
Baseline Scenario A Scenario B Scenario C
Distributional Impact of Rent Imputation
We are interested in:
Distribution Poverty
InequalityLevels
Profiles
Fixed poverty
line
New poverty
line
Ranking
Shape
Shared Prosperity
010
3040
20
0 20 40 60 80 100Percentiles of Population
010
2030
4050
0 20 40 60 80 100Percentiles of Population
DistributionTheoretical Framework
Define a Rent-Incidence Curve:
010
2030
4050
0 20 40 60 80 100Percentiles of population
Scenario A
Scenario B Scenario C
Inequality Decreases
Inequality is constant
Unpredictable Effect
DistributionEvidence from the Literature In general, the literature finds that including rents
reduces inequality Mainly developed countries Mostly income as welfare aggregate Some examples: Guenardand S. Mesple-Somps (2010) for Madagascar and
Cote D’Ivoire: the poor are more likely to own their homes Gasparini and Escudero (2004) for the Greater Buenos
Aires area: large proportion of house-owners at the bottom of income distribution, and income elasticity in housing expenditure <1
Törmälehto and Sauli (2010, 2013) for 29 EU-SILC countries: combined effect of the equalizing gap-effect and dis-equalizing re-ranking effect
DistributionOur Findings – Rent Incidence Curve
10
20
30
40
50
Ren
t inc
iden
ce c
urve
0 20 40 60 80 100Percentiles
OLS Heckman MatchingSelf Assessed Original
IRQ, 2007
10
20
30
40
50
Ren
t inc
iden
ce c
urve
0 20 40 60 80 100Percentiles
OLS Heckman MatchingSelf Assessed Original
IRQ, 2012
DistributionOur Findings - Inequality
Peru, 2013Iraq, 2012
0.2
0.25
0.3
0.35
0.4
Gini Gini Gini
Overall Owners Tenants
Norent OLS Heckman Matching Self_Assessed Original
Inequality increases when using OLS as the imputation method
22.53
3.54
4.55
5.56
p90p10 p90p10 p90p10
Overall Owners Tenants
Norent OLS Heckman Matching Self_Assessed Original
0.2
0.25
0.3
0.35
0.4
Gini Gini Gini
Overall Owners Tenants
Norent OLS Heckman Matching Self_Assessed Original
22.53
3.54
4.55
5.56
p90p10 p90p10 p90p10
Overall Owners Tenants
Norent OLS Heckman Matching Self_Assessed Original
DistributionOur Findings - Re-ranking
PE
RU
, 20
13
Even if inequality does
not change much, there is still re-ranking
OLS1 2 3 4 5 6 7 8 9 10
1 92 7 0 0 0 0 02 8 79 12 1 0 0 0 0 03 14 69 14 3 0 0 0 0 04 19 62 16 2 1 0 0 05 23 59 15 3 0 06 23 56 18 2 0 07 26 56 16 2 08 22 65 13 19 17 73 1010 11 89
Heckman1 2 3 4 5 6 7 8 9 10
1 98 1 0 02 2 96 2 0 0 03 3 92 5 0 0 04 5 89 5 1 0 0 05 6 87 5 1 0 06 7 84 7 1 07 10 82 7 18 9 83 7 09 8 87 510 5 95
Matching1 2 3 4 5 6 7 8 9 10
1 93 6 1 0 0 0 0 0 02 7 81 10 1 0 0 0 0 03 13 71 12 3 1 0 0 04 19 64 14 2 1 0 0 05 22 58 15 3 1 0 06 25 55 16 3 1 07 28 55 13 4 08 24 62 13 19 20 69 1110 13 87
Self_Assessed1 2 3 4 5 6 7 8 9 10
1 93 6 0 0 02 7 82 10 1 0 0 0 03 12 75 11 2 1 0 0 04 15 69 13 2 0 0 0 05 19 63 14 3 1 0 06 22 59 15 3 1 07 24 60 13 2 08 21 65 13 19 18 72 1010 12 88
Original1 2 3 4 5 6 7 8 9 10
1 93 6 0 0 02 7 82 10 1 0 0 0 03 12 74 11 2 0 0 0 04 15 69 13 2 0 0 0 05 19 62 14 3 1 0 06 23 59 15 3 1 07 24 60 13 3 08 21 65 13 19 18 72 1010 12 88
IRA
Q,
2012
DistributionShared prosperity
6
8
10
12
0 20 40 60 80 100pctl
No rent OLS HeckmanMatching Self Original
PER, 2010 - 2013
5
6
7
8
9
0 20 40 60 80 100pctl
No rent OLS HeckmanMatching Self Original
IRQ, 2007 - 2012
Poverty IndicesTheoretical Framework – Fixed Poverty Line
Poverty does not decrease and
rank among the poor is preserved under scenarios
A and B
Poverty does not decrease BUT
rank among the poor might NOT
BE preserved under scenario C
Poverty IndicesTheoretical Framework – Adjusted Poverty Line
Poverty might increase…
…Or remain the same …
…Or decrease
Even under the simplest scenario A …
Poverty Indices – FGT0Our Findings – adjusting poverty line
Iraq, 2012Absolute, Fixed Poverty Line
Iraq, 2012Absolute, Adjusted Poverty Line
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
All Owners Tenants Rural Urban
Tenure Status Type of settlement
Norent OLS Heckman Matching Self_Assessed Original
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
All Owners Tenants Rural Urban
Tenure Status Type of settlement
Norent OLS Heckman Matching Self_Assessed Original
Poverty ProfilesTheoretical Framework – Fixed Poverty Line
R1
R2
R3
, ,
R1
R2
R3 z
1/3 of poor individuals
live in region 1, 1/3 in
region 2 and 1/3 in region
3
2/3 of poor individuals
live in region 1 and 1/3 in
region 2
Profile is the same as the
original distribution. Notice re-ranking!
R1
R2
R3
,
R3
R2
R1
Other examples: urban/rural, minorities, education…
Poverty Profiles – FGT0Our Findings
Iraq, 2013Absolute, Fixed Poverty Line
Iraq, 2013Absolute, Adjusted Poverty Line
00.10.20.30.40.50.60.70.80.9
1
Mal
e
Mar
ried
Empl
oyed
Tena
nt
edu_
1
edu_
2
edu_
3
edu_
4
Coa
stal
Cen
tral
Mou
ntai
n
Demographic Education GeographicalRegion
Share of poor individuals
No Rent OLS Heckman
Matching Self Assessed Original
00.10.20.30.40.50.60.70.80.9
1
Mal
e
Mar
ried
Empl
oyed
Tena
nt
edu_
1
edu_
2
edu_
3
edu_
4
Coa
stal
Cen
tral
Mou
ntai
n
Demographic Education GeographicalRegion
Share of poor individuals
No Rent OLS Heckman
Matching Self Assessed Original
PovertySummary of Results
As expected, poverty decreases by keeping fixed the poverty line and adding imputed rents to the consumption aggregate
Poverty changes very little when the poverty line is adjusted by adding imputed rent in the non-food component
With an adjusted poverty line poverty tends to slightly decrease for tenants and to increase in rural areas (particularly in Iraq and Peru)
The profile of the poor does not seem to change, both keeping fixed and varying the poverty line, according to the imputation method used
Further Readings
Strictly Confidential © 2014