Incorporating public transfers into the measurement of poverty · Pashchim_Champaran Siwan Vaishali...

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





Arwal_JehanabadArwal_JehanabadArwal_JehanabadArwal_Jehanabad

AurangabadAurangabadAurangabadAurangabad





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











.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





Arwal_JehanabadArwal_JehanabadArwal_JehanabadArwal_JehanabadAurangabadAurangabadAurangabadAurangabad





1520

2530

3540

.2 .25 .3 .35 .4 .45Head count

Mean p.c. transfer

Pashchim_Champaran











-.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


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


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.

Incorporating public transfers into the measurement of poverty · Pashchim_Champaran Siwan Vaishali...

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