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Incorporating public transfers into the measurement of poverty
Anders Kjelsrud and Rohini Somanathan
July, 2013
The Problem
I Poverty measures in India, and elsewhere, are based on private consumption datafrom NSS-type surveys.
I Health and education needs are either ignored or incorporated into poverty linesin various ad-hoc ways, often using actual out-of-pocket expenses, or scaling up asubsistence basket by a fixed amount.
I If some communities get these through public goods, whose availability variessystematically with wealth, we have a serious measurement problem.
I This is the case for India, where richer communities often receive higher publictransfers and better public goods.
Question: How can measures of poverty and inequality incorporate public transfersand public goods? These methods will also help us assess the poverty-targeting ofpublic spending.
PDS and market prices across states
Table: Unit values 2009–10 (Rupees per kg)
Rice WheatMarket PDS Market PDS
Andhra Pradesh 22 2 24 12Assam 17 7 18 10Bihar 15 6 13 5Chhattisgarh 16 2 17 2Gujarat 22 3 15 2Haryana 22 8 12 5Jharkhand 16 3 15 2Karnataka 22 3 20 3Kerala 21 9 24 8Madhya Pradesh 18 5 12 3Maharashtra 20 6 15 6Orissa 14 2 18 8Punjab 25 12 13 4Rajasthan 25 18 14 5Tamil Nadu 23 1 25 8Uttar Pradesh 16 6 11 5West Bengal 18 2 16 7
Shares consuming any PDS rice or wheat
Unit values per kg.
Public schools and private educational expenses
Share of villages with schools (district averages), versus the median education expensesper school going child ( tuition/fees could include other thing than schooling).
APASM
BHR
GUJ
HAR
KTK
KRL
MP
MAH
ORS
PUN
RAJ
TN
UPWB
CHH
JHA
0.2
.4.6
.81
Shar
e of
villa
ges
with
mid
dle
scho
ol
0 50 100 150 200Out-of-pocket education expenses
Middle schools and median educ expenses
AP
ASM
BHR
GUJ
HAR
KTK
KRL
MP
MAH
ORS
PUN
RAJ
TN
UP
WBCHH
JHA0
.2.4
.6.8
Shar
e of
villa
ges
with
sen
ior s
choo
l
0 50 100 150 200Out-of-pocket education expenses
Senior schools and median educ expenses
Public health centers and private medical expenses
Median medical expenses (institutional and non-institutional) versus share of villageswith primary health centers and subcenters
AP
ASM BHRGUJ HARKTK
KRL
MPMAH
ORSPUNRAJ
TN
UPWBCHHJHA0
.2.4
.6Sh
are
of v
illage
s wi
th P
HC
0 20 40 60Out-of-pocket medical expenses
PHC and median medical expenses
AP
ASM
BHR
GUJ HAR
KTK
KRL
MPMAH
ORS
PUNRAJ
TN
UP
WB
CHH
JHA
0.1
.2.3
.4.5
Shar
e of
villa
ges
with
PHS
0 20 40 60Out-of-pocket medical expenses
PHS and median medical expenses
Indian poverty measures: early approaches
All India poverty lines:
I 1962: 20 and 25 rupees per capita per month for rural and urban areas,respectively, in 1960–61 prices.
I 1979:I calorie norms of 2400 and 2100 calories per capita per day for the rural and urban sectorI expenditure equivalents of these norms identified through the empirical expenditure
distribution observed in the NSS survey of 1973-74.I resulting poverty lines were 49 rupees (rural) and 57 rupees (urban).I no attempt to capture differences in prices or spending across states
State-wise lines: Lakdawala EG, 1993
I spatial price indices had been computed for the 1960s in two previous studiesbased on NSS data.
I these series were extended using the consumer price index for agricultural labours(CPIAL) and the consumer price index for industrial workers (CPIIW) for ruraland urban areas respectively.
I Both indices were reweighted to reflect the consumption patterns of the poor in1973–74.
So while health and education expenses were implicitly included in the PLB, there wasno special attention to them.
The Tendulkar expert group: overall approach
The PDS:
I Treated as a price effect
I Lumps the PDS items with the relevant market items before computing unitvalues =⇒ Little effect on the unit values and the state-wise price comparisons
Education and Health
I Education and health are two out of 23 sub price indices used to construct anoverall state-wise price index
I Derived by looking at the median household out-of-pocket expenses in each state
The Tendulkar expert group – education ”prices”
1. Find the number of children in the age group of 5-15 enrolled in school in eachhousehold
2. Find the household’s total expenses on tuition and stationery (not just schooling)
3. Divide the total expenditure on education by the number of school going children
4. Compute the median hh expenditure on education by state and sector
5. Compute the “price” as the median divided by the weighted all-India average ineach sector
Rural UrbanAndhra Pradesh 1.61 1.31Assam 0.53 0.65Bihar 0.65 0.49Chhattisgarh 0.56 0.62Gujarat 0.96 1.39Haryana 2.25 1.22Jharkhand 0.55 1.06Karnataka 0.75 0.99Kerala 2.32 1.09Madhya Pradesh 0.52 0.68Maharashtra 0.55 1.09Orissa 0.81 0.68Punjab 2.04 1.22Rajasthan 0.91 1.05Tamil Nadu 0.83 0.78Uttar Pradesh 0.96 0.75West Bengal 1.29 0.82
State-wise EG prices and MPCE
If all hhs face the same prices and education is a normal good, the EG “prices” will behigher in richer states.
AP
TN
ASM
UPRAJ
KRL
MP
PUN
BHR
HAR
ORS
GUJ
WB
JHA MAH
KTK
CHH
.51
1.5
22.
5EG
's pr
ice o
f edu
catio
n
600 800 1000 1200Median MPCE
Rural
KTK
MAH
UP
GUJ
TN
KRL
BHR
HAR
CHH
RAJ
AP
MPORS
WB
PUN
ASM
JHA
.4.6
.81
1.2
1.4
EG's
price
of e
duca
tion
800 1000 1200 1400 1600Median MPCE
Urban
Our approach
I Collect primary data on consumer expenditure and transfers through the PDS.
I Impute values to public education and health facilities.
I Arrive at a new distribution of expenditures using these imputed values.
I Raise poverty lines to account for median transfers- this gives us roughly thesame fraction poor
I Study changes the overall distribution of consumer expenditure and the spatialdistribution of poverty.
In this presentation, we focus on the PDS and Education.
Primary data
Field survey conducted in Bihar in the period September-December 2012
I Drew 10 districts with probabilities in proportion to population size (census 2001figures), 5 from the northern NSS region and 5 from the NSS southern region.
I Sampled 4 villages at random in each district.
3 parts:
I Household survey: 50 randomly chosen households from each village
I Village survey: basic village characteristics
I Public facility survey: visits to the main public and private school, and to mainpublic and private health facility.
Map of sample villages
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Gaya
Patna
RohtasJamui
Purnia
Banka
Araria
Saran
Bhabua
Katihar
SiwanSupaul
Madhubani
Bhojpur
Nawada
Buxar
Nalanda
Muzaffarpur
Aurangabad
Vaishali Samastipur
Bhagalpur
Pashchim Champaran
Purba Champaran Sitamarhi
Darbhanga
Saharsa
Gopalganj
Begusarai
Munger
Kishanganj
Khagaria
Madhepura
Jehanabad Lakhisarai
Sheohar
Sheikhpura
Table: Access to selected facilities
Share Distance*Mean Min Max
(1) (2) (3) (4)Schooling
Government school with grades 1-5 0.93 0.4 0.1 0.5Government school with grades 6-8 0.70 1.4 0.1 3.0Private school with grades 1-5 0.17 3.7 0.5 18.0Private school with grades 6-8 0.12 4.8 0.5 20.0High school 0.12 4.8 0.5 20.0Anganwadi centre 0.95 0.8 0.5 1.0
HealthGovernment PHC 0.03 7.3 0.5 20.0Government hospital 0.00 22.6 5.0 45.0Private clinic 0.23 5.6 0.5 15.0Private hospital 0.05 14.9 1.0 40.0
OtherPDS shop 0.55 1.8 0.1 4.0Bus stop 0.17 4.9 0.3 20.0Train station 0.00 14.2 2.0 36.0Commercial bank 0.15 3.4 0.5 12.0
Note: * Conditional on not having the particular facility within the village.
Construction of poverty lines
Adjust the Planning Commission poverty line for Bihar in 2009–10, to Sep-Dec 2012by the CPIAL: base is jan, 2009, 22% increase between NSS data and our survey data
11.
11.
21.
31.
4
2009 2010 2011 2012
CPIAL Bihar
Poverty measures
Table: Poverty and inequality measures
Poverty InequalityHC PG Gini GE1 d9/d1(1) (2) (3) (4) (5)
Arwal/Jehanabad 44.5 12.4 33.1 20.0 4.0Aurangabad 41.3 9.3 30.4 19.2 3.0Begusarai 20.5 7.2 37.7 25.8 4.9Jamui 34.9 9.3 30.7 17.9 3.6Katihar 28.8 5.8 36.7 26.4 4.5Lakhisarai 41.7 9.0 35.1 30.0 3.3Nawada 44.3 10.7 32.3 19.9 3.6Pashchim Champaran 25.1 4.4 29.3 16.7 3.4Siwan 18.3 3.3 36.8 27.4 4.3Vaishali 25.9 7.2 42.7 42.5 5.3All 32.5 7.9 36.4 27.5 4.2
Access and average consumption (II)
Pashchim_Champaran
SiwanSiwanSiwanSiwan
VaishaliVaishaliVaishaliVaishali
BegusaraiBegusaraiBegusaraiBegusarai
LakhisaraiLakhisaraiLakhisaraiLakhisarai
Arwal_JehanabadArwal_JehanabadArwal_JehanabadArwal_JehanabadAurangabadAurangabadAurangabadAurangabad
NawadaNawadaNawadaNawada
JamuiJamuiJamuiJamui
Pashchim_ChamparanPashchim_ChamparanPashchim_Champaran
KatiharKatiharKatiharKatihar
.2.3
.4.5
.6
.2 .25 .3 .35 .4 .45Head count
Share of HHs with any PDS grain cons.
Pashchim_Champaran
SiwanSiwanSiwanSiwan
VaishaliVaishaliVaishaliVaishali
BegusaraiBegusaraiBegusaraiBegusarai
LakhisaraiLakhisaraiLakhisaraiLakhisarai
Arwal_JehanabadArwal_JehanabadArwal_JehanabadArwal_Jehanabad
AurangabadAurangabadAurangabadAurangabad
NawadaNawadaNawadaNawada
JamuiJamuiJamuiJamui
Pashchim_ChamparanPashchim_ChamparanPashchim_Champaran
KatiharKatiharKatiharKatihar
4.8
55.
25.
45.
6
.2 .25 .3 .35 .4 .45Head count
Conditional average p.c. cons.
Note: The right graph displays average consumption conditional on any consumption.
Imputation: Subsidized grains as income transfers
1. We compute district-wise median unit values for rice and wheat, separately formarket and PDS purchases and separately for BPL and Antyodaya HHs.
2. We evaluate the household specific quantity consumed from the PDS by the localmarket unit value. Since the PDS prices are lower than the market prices, thisraises the expenditure level of households reporting PDS consumption.
Note: 97% of the hhs consuming PDS rice made rice purchases in the regular market,while 90% of the hhs consuming PDS wheat bought wheat in the regular market=⇒ indicates that it is reasonable to treat the subsidy as an income transfer.
Median unit values – market vs. the PDS (by district)
05
1015
20
.2 .25 .3 .35 .4 .45Head count
Unit values rice
05
1015
20.2 .25 .3 .35 .4 .45
Head count
Unit values wheat
Market BPL Antyodaya
Share of PDS purchases made by Antyodaya HHs (by district)
Relatively fewer Antyodaya PDS purchases in the poorest districts.
Pashchim_Champaran
SiwanSiwanSiwanSiwan
VaishaliVaishaliVaishaliVaishali
BegusaraiBegusaraiBegusaraiBegusarai
LakhisaraiLakhisaraiLakhisaraiLakhisarai
Arwal_JehanabadArwal_JehanabadArwal_JehanabadArwal_Jehanabad
AurangabadAurangabadAurangabadAurangabad
NawadaNawadaNawadaNawada
JamuiJamuiJamuiJamui
Pashchim_ChamparanPashchim_ChamparanPashchim_Champaran
KatiharKatiharKatiharKatihar
.05
.1.1
5.2
.2 .25 .3 .35 .4 .45Head count
Adjusted poverty lines
In all the calculation when we adjust for public facilities we also adjust the poverty lineas follows:
1. Look at households ± 5 per cent of the original poverty line
2. Calculate the average imputed value for the particular public facility
3. Add this amount to the poverty line and apply this new line for all households
Mean per capita transfer and changes in HCs
Pashchim_Champaran
SiwanSiwanSiwanSiwan
VaishaliVaishaliVaishaliVaishali
BegusaraiBegusaraiBegusaraiBegusarai
LakhisaraiLakhisaraiLakhisaraiLakhisarai
Arwal_JehanabadArwal_JehanabadArwal_JehanabadArwal_JehanabadAurangabadAurangabadAurangabadAurangabad
NawadaNawadaNawadaNawada
JamuiJamuiJamuiJamui
Pashchim_ChamparanPashchim_ChamparanPashchim_Champaran
KatiharKatiharKatiharKatihar
1520
2530
3540
.2 .25 .3 .35 .4 .45Head count
Mean p.c. transfer
Pashchim_Champaran
SiwanSiwanSiwanSiwan
VaishaliVaishaliVaishaliVaishali
BegusaraiBegusaraiBegusaraiBegusarai
LakhisaraiLakhisaraiLakhisaraiLakhisarai
Arwal_JehanabadArwal_JehanabadArwal_JehanabadArwal_Jehanabad
AurangabadAurangabadAurangabadAurangabad
NawadaNawadaNawadaNawada
JamuiJamuiJamuiJamui
Pashchim_ChamparanPashchim_ChamparanPashchim_Champaran
KatiharKatiharKatiharKatihar
-.02
-.01
0.0
1.0
2
.2 .25 .3 .35 .4 .45Head count
Change in HC
Private school rates from NSS 2009–10 (rural)
Grade level 1-8 1-5 6-8Andhra Pradesh 0.26 0.30 0.22Assam 0.06 0.04 0.07Bihar 0.04 0.04 0.04Chhattisgarh 0.04 0.04 0.03Gujarat 0.11 0.07 0.14Haryana 0.41 0.40 0.42Jharkhand 0.06 0.07 0.05Karnataka 0.16 0.16 0.16Kerala 0.56 0.60 0.54Madhya Pradesh 0.14 0.14 0.13Maharashtra 0.28 0.13 0.42Orissa 0.05 0.04 0.05Punjab 0.39 0.44 0.34Rajasthan 0.30 0.28 0.32Tamil Nadu 0.22 0.26 0.18Uttar Pradesh 0.44 0.41 0.49West Bengal 0.05 0.06 0.04All India rural 0.21 0.21 0.20
Private teaching and schooling – shares and median (annual) expenses
“Private teaching” could include teaching at a coaching centre, extra teaching at theschool after the regular hours (for money) or teaching from a private teacher at home.
Private school rates are 0.08 for grades 1-5 and 0.04 for grades 6-8.
Private schooling Private teaching outside schoolIn public school In private school
Share Tuition Share Costs Share CostsArwal/Jehanabad 0.02 1117 0.33 750 0.33 2100Aurangabad 0.09 2400 0.27 1200 0.64 1020Begusarai 0.08 1800 0.38 1200 0.37 4000Jamui 0.02 700 0.44 1200 0.17 1200Katihar 0.00 – 0.33 1200 – –Lakhisarai 0.04 2700 0.37 1200 0.50 2400Nawada 0.12 1200 0.30 720 0.36 1500Pashchim Champaran 0.15 1040 0.27 1200 0.11 1500Siwan 0.09 1800 0.43 1200 0.77 1200Vaishali 0.04 2200 0.58 1200 0.80 1500All 0.07 1200 0.37 1200 0.42 1200
Private teaching vs. private schooling
Villages with low private school rates generally have a higher share of kids receivingprivate teaching.
0.1
.2.3
.4Pr
ivat
e sc
hool
sha
res
0 .2 .4 .6 .8Private teaching shares
Pashchim_Champaran
Siwan
Vaishali
Begusarai
Lakhisarai
Arwal_Jehanabad
Aurangabad
Nawada
Jamui
Katihar0.0
5.1
.15
Priv
ate
scho
ol s
hare
s
.2 .3 .4 .5 .6Private teaching shares
Note: The shares are calculated based on all kids enrolled in grades 1-8.
Impute values for public schooling
Three steps:
1. Find all students enrolled in grade levels one to eight at a public school
2. Add the imputed value of being enrolled in the public school
3. Sum over all such students in the household and convert this amount to monthlyper capita expenditure
Consider three methods for imputing school values:
I Naive: Add the median expense on tuition+school books, separately for grade1-5 and 6-8 (1200rs and 1800rs).
I Similar HHs: Use the median expenses on tuition+school books from “similar”households.
I Quality: Use the median expenses on tuition+school books from private schoolsof “similar” quality.
Method 2: Similar HHs
The MPCE numbers are biased due to the present of public facilities. We use theshare of total calories from rice and wheat as an indicator of welfare.
0.2
.4.6
.81
Ric
e an
d w
heat
cal
orie
sha
re
0 2000 4000 6000 8000mpce
bandwidth = .8
Method 2: Similar HHs
We divide households into quartiles based on the calorie shares (highest share=quartile1 and so on). The table below displays the median expense among those enrolled in aprivate school within each quartile
There are to few children in group 1 and 2 for grade 6-8 for a meaningful comparison.Therefore: this is for grades 1-8 combined.
Table: Private schooling – expenses on tuition and school books
Median No of children1 1240 272 1800 373 2600 534 2960 59
Method 3: Schools of similar quality
The government and private schools are very different in nature: the governmentschools are larger, have more proper buildings, more students per teacher and perclassroom and are less likely to offer teaching in English.
Quality is not necessarily reflected by the same set of characteristics acrossgovernment and private schools.
Government Privatemean sd n mean sd n
(1) (2) (3) (4) (5) (6)Enrolled students 405 210 40 248 153 39Attendance on day of visit 0.64 0.16 40 0.77 0.16 37Students per teacher 58 25 40 23 9 39Students per classroom 63 35 40 29 16 39
No of latrines per 100 student 0.93 0.98 40 1.89 2.01 39Building made of pucca 0.88 0.33 40 0.59 0.50 39Proper floor in building 0.93 0.27 40 0.67 0.48 39Serves more than 3 midday meals a week 0.60 0.50 40 0.05 0.23 37
Main teaching language English 0.00 0.00 40 0.22 0.42 37Any teaching in English (all grades) 0.38 0.49 40 0.85 0.36 40Tests in math and reading (all grades) 0.05 0.22 40 0.95 0.23 38
Explaining village-wise variation in median private school expenses
Dependent variable: median private school expenses (tuition + school books).
(1) (2) (3) (4)No of latrines per 100 student 366.5 389.9 372.9 371.7
(126.6) (127.6) (128.4) (129.0)
School building made of pucca 637.3 320.8 519.6(556.2) (630.7) (674.3)
Proper floor in building 679.1 659.3(642.4) (645.9)
Any teaching in English (all grades) 686.7(796.5)
Constant 2084.8 1671.0 1448.4 772.1(360.9) (509.2) (550.1) (959.7)
Observations 31 31 31 31R2 0.224 0.259 0.288 0.308
Impute values for public schools
Use the estimated coefficients from the regression and characteristics from the publicschools to predict annual values.
Validation I: Private shool rates
Lower private school rates in villages with public schools of (estimated) good quality.
0.1
.2.3
.4Pr
ivat
e sc
hool
sha
re
1000 2000 3000 4000Predicted public school values
Validation II: Household evaluation of school quality
Positive correlation between the households’ own evaluation of the local public schooland our quality measure.
.1.2
.3.4
.5Av
erag
e sc
hool
eva
luat
ion
1000 2000 3000 4000Predicted public school values
Note: “How would you evaluate the follow characteristics of the government school in your village? (good,mediocre or bad). In the graph we give value 1 if good, 0 otherwise. Average over the following categories:
teaching quality, teaching material and classroom, drinking water, latrines and meals.
Distribution across villages (deciles)
010
2030
4050
0 2 4 6 8 10
Schooling (Naive) PDS
Across villages
Note: The graph groups villages in 10 groups based on average MPCE in each village (4 villages in each group).
Average distribution within villages (deciles)
010
2030
4050
0 2 4 6 8 10
Schooling (Naive) PDS
Average within villages
Note: The graph first divides households into 10 groups within each village. It then takes the average of eachgroup across villages.
Distribution across all HHs (deciles)
010
2030
4050
0 2 4 6 8 10
Schooling (Naive) PDS
Across all households
Note:The graph groups households in 10 groups based on MPCE.
Summing up
I Public spending on the PDS is largely un-targeted.
I Transfers through public schools are marginally progressive: better schools arelocated in richer villages, but within villages the poor attend these at higher rates.
I Results in other states may be very different because the share of publicschooling, quality and the PDS varies substantially by state.
I Can we use these methods to get accurate poverty rates, that account for allpublic transfers? Hard, because of the number of public programs. Better atdetecting targeting.
I Doing this requires micro data on government programs matched to consumptiondata.
I With universal access to high quality public goods, these sources of measurementerror go down.
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