Quality and Accountability in
Health: Audit Evidence from
Primary Care Providers
Jishnu Das (World Bank and Centre for Policy Research)
With
Alaka Holla (World Bank)
Karthik Muralidharan (UCSD)
SITE
June 2013
The Problem
Strong theoretical reasons why health care should be public

U(government) ≠ U(Consumer): Pendergast (2003)



Patient satisfaction among narcotic addicted patients not a good
measure of how good the doctor is
Private sector aggregator of customer feedback
Medical care arguably a credence good


You don’t know what you need, but observe utility from what you get
Widely believed to produce inefficiencies in the market




Darby and Karni (1973): Over-treatment
Wolinsky (1993): You can’t observe what you bought; treat “low”, charge
“high”
Gruber and Owens (1996): Caesarian sections
Balfoutas and others (forthcoming): Greek taxi drivers (over provision and
over charging)
And yet…
77% of providers have no
degree, 18% have some other
degree (BAMS, BIMS, BUMS,
BHMS), and only 4% have an
MBBS degree (roughly
equivalent to MD in the U.S.).
Average village has 3.36
providers with no degree,
0.80 providers with some
degree, and 0.18 providers
with an MBBS degree



80% of first-contacts (primary care) in India in private sector
New nationwide study: 77 percent of private providers in rural areas do not have
medical training
Contrast: All public providers are (supposedly) trained, the majority with an MBBS
And yet…
Public share increases from 20%
to 35% in villages where there is
a public doctor but households
still visit private providers in 65%
of primary care cases.

Not (just) because there aren’t enough public sector
providers
Why?


What people demand from health care providers very different
from what the public sector provides
Hypothesis 1: Decreases quality, increases costs



Example: Demand for injections/steroids leads to lower quality for
higher cost
Peer and Administrative accountability from experts in regulated (and in
low-income settings) health care beats customer accountability through
the market
Hypothesis 2: Increases cost, but at increased quality


Example: Poor governance in public sector (Chaudhury and Hammer
2004, Chaudhury et. al. 2006, Das and Hammer 2007)
Low effort arising from poor administrative accountability is hard to
quantify

But is potential one large source of losses: `Quiet Corruption’
In health care: Arrow (1963)
This paper

Use audit studies with patient and provider fixed-effects
to assess quality in public and private sector


22 people recruited from the local community and
extensively trained visits multiple providers presenting the
same set of symptoms. Providers do not know that this is not
a real patient
Show effect of practicing in private sector on
Adherence to medically required checklists
 Under-treatment
 Over-treatment


Assess whether there is a price-quality relationship in
private sector
Three literatures it relates to

Customer Accountability in private sector versus
administrative accountability in public sector


What’s out there and what we don’t know
New empirical and theoretical literature on credence
goods
Dulleck and Kerschbamer (2006, 2011, 2012)
 Schneider (2012), Balfoutas(forthcoming)
 Bonroy and others (2012)


Audit studies in labor markets and services

Primary around issues of discrimination

What are we adding
This paper: What

Deploy “standardized patients” (audit study)



2 Related studies



People recruited from local communities and extensively trained to
present with the same symptoms to multiple providers
Largest such study to date (1105 interactions)
Compare market care to provider in public clinic (64% not doctors)
Compare same doctor in public and private practice
Note: unlike audit studies of car buying or home rentals, we always
observe a completed sale


Problems arising from potentially off-equilibrium behavior may still
remain
We try out various strategies to interpret these results in the light of
known issues with audits
Overview of Results

Significant evidence of over-treatment and under-treatment relative
to medical protocols



Conditions are not diagnosed or treated appropriately
Many medicines are not required
BUT

Public clinics provide similar care to private clinics


Joint effect of public sector with provider characteristics



Effects vary depending on measure of quality used
72% private sector providers have no medical training
The same provider in his/her private clinic provides better care than in
his/her public clinic across all quality measures
Customer accountability rewards better quality with higher prices

No link between provider wages in public sector and measures of
quality
Remainder of talk




Where we worked (and what does it look like)
What we did
What we found
Ruling out (some) interpretations of the data
 Worry
in particular about off-equilibrium behavior
This paper: Where?



All districts divided into 5 Socio-Cultural Regions (SCRs); one district from each SCR
20 randomly chosen villages from each district
Representative sample of all types of providers in 3 districts of Madhya Pradesh
(and public providers in 2 more); majority has no medical training

Additional sample from (urban) Delhi
MP Study: The sample
1
2
3
• In each sampled village, surveyors complete Participatory Resource Assessments (PRAs) in at
least 3 different geographical locations and ask for a list of all providers they visit for primary
illnesses
• A unique list is compiled and a Master Code File (MCF) is filled out. A short survey is
administered with each provider listed in the MCF
• Then a household census is completed in which members are asked about all illness in the last
one month and names and locations of providers they went to
4
• If more than 5% of households report visiting a provider in a location (village/town) outside
the village, that village/town is now considered a part of the health-market for the village.
These are referred as “clusters”, generally on the main highway near the village
5
• Once all clusters are identified, surveyors visit each cluster and conduct PRAs in the same
manner. All providers practicing in the clusters are added to the MCF and a survey is
implemented
Rural India: MP


100 villages in MP, randomly selected in 5 districts—located
>1000 health care providers
Snapshots of the two remotest districts
Standardized patients sample

Sample restrictions
 Ruled
out 2 remote districts entirely for private market
 Ruled out remote locations in other 3 districts
 Sampled
 All
MBBS private providers
 All public clinics in all districts

 All
But no more than 2 doctors per clinic
private clinics of public doctors in all districts
 Add in untrained till we have 6 providers per sampled
village
Basic Sample Description
Table 1: Health Market Attributes
Total
Inside village
(1)
(2)
Panel A: Number of providers
Total
Public MBBS
Public alternative qualification
Public paramedical
Public unqualified
Private MBBS
Private alternative qualification
Private unqualified
11.05
(1.25)
0.50
(0.11)
0.22
(0.05)
1.58
(0.19)
1.70
(0.17)
0.42
(0.16)
1.92
(0.36)
5.40
(0.60)
3.06
(0.37)
0.05
(0.02)
0.07
(0.03)
1.13
(0.15)
0.67
(0.10)
0.00
0.00
0.23
(0.07)
1.81
(0.22)
Outside village
(3)
7.99
(1.29)
0.45
(0.10)
0.15
(0.04)
0.45
(0.13)
1.03
(0.15)
0.42
(0.16)
1.69
(0.37)
3.59
(0.61)
(contd)
Table 1: Health Market Attributes
Total
Inside village
(1)
(2)
Panel B: Composition of demand
Population (2001 Census of India)
3885.00
1353.74
(385.46)
(103.56)
Probability household visited provider in
last 30 days
Probability visit was to private sector
Probability visit was to private sector in
villages with at least 1 public doctor
Probability visit was to MBBS doctor
Probability visit was to unqualified
doctor
Number of villages
Number of households
Number of reported household-visits
2531.26
(378.58)
0.46
(0.00)
Probability visited provider was
inside/outside village
Distance traveled to visited provider
(km)
Outside village
(3)
1.66
(0.02)
0.91
(0.00)
0.79
(0.01)
0.03
(0.00)
0.76
(0.00)
100
23306
18632
0.65
(0.00)
0.40
(0.01)
0.35
(0.00)
3.92
(0.03)
Standardized patients

SPs
 22
SPs recruited from the local community
 Important
so that their appearance and manner conform
closely to providers’ expectations
 Thoroughly
trained to make plausible excuses to avoid
invasive exams
 “palm” medicines if required


150+ hours of training
First tried in Delhi pilot
 No
adverse events; <1% detection rate
Standardized patients


Three standardized cases

Unstable Agina: “Doctor, this morning I had a pain in my chest” – Ramlal, Male, 45 years
old

Proxy Dysentery: “Doctor, my 2 year old child has been suffering from diarrhea for 2
days” – Shankarlal, Male, 25 years old

Asthma: “Doctor, last night I had a lot of difficulty in breathing” – Rajesh (Male) or Radha
(Female), 25 years old
Cases chosen such that


Relevance to the Indian context

Increasing incidence of cardiovascular and respiratory illness in India

Diarrheal diseases kill approximately 200,000 children per year (Black et al. 2008)
No invasive treatment required

Important to minimize any potential harm to SPs
Standardized patients

What is measured
 Quality
of care through adherence to required and
essential checklist of questions and examinations that
the provider should complete for each patient
 Why
this may be preferable
 Treatment:
correctness, incorrectness, use of antibiotics
and steroids for cases where they are not required
 Diagnosis: whether given, whether correct
 Time spent, total questions asked, total examinations
completed
Relation between quality measures
Figure 1a
Figure 1b
Consultation Time and Adherence to Checklist
0
10
20
Total Time Spent (Mins)
Consultation Time and Adherence
30
Density of Time Spent
.03
.01
.02
Density of Adherence
.4
0
.1
0
.2
.1
.3
.2
Correct Treatment Given
0
10
20
30
Density of Time Spent
40
.5
.3
50
Adherence to Checklist and Treatment
0
20
40
60
Adherence to Checklist (Percent)
Checklist Adherence and Treatment
80
Adherence Density
1. Doctors under-treat because they figured out that these were not “real patients”. But then,
we should see that “correct treatment” is less likely for doctors who spend more time and
complete more of the checklist, since they would be more likely to figure out that the
patient is not “real”. We find exactly the opposite
2. Little evidence of signaling through medically irrelevant costly effort: more effort leads to
better treatment through 90 percent of the distribution
Basic Sample Description
Table 1. Characteristics of Providers and Practices
Audit 1
Panel A: Provider characteristics
Age of Provider
More than 12 years of basic education
Has MBBS degree
No medical training
Has multiple practices
Panel B: Practice characteristics
Tenure in years at current location
Dispense medicine
Consultation fee (Rs.)
Average number of patients per day
Electricity
Stethoscope
Blood pressure cuff
Thermometer
Weighing Scale
Handwash facility
Number of providers
Notes:
Audit 2
Public
Private
(1)
(2)
p-value of
(1)-(2)
(3)
47.03
0.59
0.26
0.62
1.18
43.33
0.53
0.08
0.67
1.07
15.49
1.00
0.95
28.33
0.95
0.97
0.84
0.95
0.87
0.89
39
13.45
0.82
12.92
16.25
0.95
0.95
0.76
0.92
0.51
0.83
206
Dual
p-value of
(4)-(5)
(6)
Public
Private
(4)
(5)
0.059
0.507
0.001
0.473
0.022
44.53
0.62
1.00
0.00
1.83
45.43
0.69
1.00
0.00
2.16
0.523
0.277
0.260
0.003
0.000
0.000
0.920
0.477
0.274
0.584
0.000
0.315
7.09
0.57
28.78
24.40
0.98
0.99
0.99
0.94
0.94
0.86
143
8.08
0.37
41.34
17.05
1.00
1.00
1.00
0.98
0.84
0.83
94
0.318
0.004
0.002
0.027
0.166
0.427
0.427
0.212
0.011
0.537
Public
Private
(7)
(8)
p-value of
(7)-(8)
(9)
45.43
0.69
1.00
0.00
0.000
8.08
0.37
41.34
17.05
0.96
0.99
0.99
0.95
0.93
0.83
94
1.00
1.00
1.00
0.98
0.84
0.83
94
0.087
0.328
0.328
0.435
0.074
0.915
Results





Checklist adherence
IRT Scores
Treatment
Diagnosis
Prices
Checklist adherence
Checklist adherence
Checklist adherence
Full Sample
Audit 1
(1)
(2)
(3)
(4)
Dependent Variable: Percentage of Checklist Items
Mean
22.38
22.32
21.74
21.81
SD
16.82
16.39
16.49
16.26
Is a public provider
-7.932*** -8.302*** -8.046*** -7.749***
(1.637)
(1.688)
(2.655)
(2.664)
Has MBBS
2.368
2.173
(2.706)
(2.700)
Has some qualification
2.205
2.144
(1.562)
(1.567)
Age of provider
-0.023
-0.004
(0.057)
(0.056)
Gender of provider (1=Male)
-1.097
-0.708
(3.846)
(3.911)
PCA of clinic infrastructure
1.101*
1.238**
(0.583)
(0.578)
Number of patients waiting
-0.196
-0.112
(0.321)
(0.440)
Case II (Dysentery)
19.881*** 1.167
-1.155
-1.370
(2.528)
(4.725)
(4.655)
(4.657)
Case III (Asthma)
24.476*** 6.193
3.250
3.666
(2.280)
(4.829)
(4.869)
(4.789)
Constant
26.782*** 27.064*** 24.724*** 25.076***
(4.226)
(4.841)
(3.951)
(4.898)
Market fixed effects
Yes
Yes
Yes
Yes
Provider fixed effects
F-stat (All SP FE = 0)
65.3
11.6
12.1
11.8
R2
0.334
0.321
0.245
0.253
Number of observations
1,129
1,013
668
640
Note: All regressions include SP fixed effects
Audit 2
(5)
(6)
Dual
(7)
(8)
(9)
(10)
23.29
23.19
23.54
23.03
23.10
23.54
17.26
16.61
17.04
17.61
17.13
17.49
-7.226*** -7.808*** -7.483*** -7.390*** -7.726*** -7.280***
(1.973)
(2.432)
(2.434)
(1.949)
(2.419)
(2.384)
-0.390***
-0.301***
(0.138)
(0.092)
4.314
(4.160)
0.046
-0.447
-0.620
-0.452
(1.968)
(1.786)
(1.788)
(1.795)
-0.261
-0.333
-0.290
-0.320
(0.516)
(0.481)
(0.776)
(0.713)
58.333*** -0.596
0.445
61.149***
0.488
2.751
(4.519)
(3.897)
(3.687)
(5.270)
(4.531)
(4.412)
25.138*** -31.835*** -31.739*** 26.613*** -32.176*** -31.452***
(2.638)
(2.415)
(2.360)
(3.272)
(3.106)
(2.997)
32.769*** 48.043*** 34.973*** 30.319*** 48.658*** 33.866***
(2.725)
(4.823)
(3.893)
(3.602)
(4.439)
(4.574)
Yes
Yes
Yes
Yes
Yes
Yes
81.8
38.4
40.7
80.0
33.6
38.0
0.479
0.465
0.505
0.476
0.477
0.480
461
373
414
331
272
292
Table 3. Public-private differences in the treatment of Standardized Patients
Unstable Angina
Dysentery
Asthma
(1)
(2)
(3)
(4)
(5)
(6)
Dependent Variable: Percentage of Checklist Items
Mean
24.690
24.687
19.033
18.762
23.796
23.911
SD
(19.881) (19.229) (15.531) (15.017) (14.724) (14.528)
Is a public provider
-10.344* -12.717* -5.406** -4.486 -8.222*** -9.335***
(6.021)
(7.545)
(2.484)
(2.877)
(2.433)
(2.896)
Has MBBS
3.687
-1.771
4.481
(7.363)
(4.382)
(3.438)
Has some qualification
1.283
3.749
-0.066
(2.912)
(3.115)
(2.154)
Age of provider
0.077
-0.166
0.026
(0.194)
(0.133)
(0.133)
Gender of provider (1=Male)
-0.000
-3.168
2.755
(6.419)
(6.788)
(7.590)
PCA of clinic infrastructure
2.997**
-0.785
1.945*
(1.416)
(0.885)
(1.058)
Number of patients waiting
0.021
-0.178
-0.787
(0.666)
(0.756)
(0.632)
Constant
28.010*** 22.539*** 28.807*** 38.857*** 60.972*** 23.841**
(6.491)
(8.652)
(1.999)
(5.853)
(2.558)
(9.488)
F-stat (All SP FE = 0)
3.59
3.34
12.51
11.25
32.65
3.89
R2
0.526
0.532
0.513
0.523
0.535
0.530
Number of observations
327
294
398
358
404
361
Notes: 1) All regressions include market and SP fixed effects
2) *** Significant at 1%, ** Significant at 5%, * Significant at 10%
IRT Score
Table 2. Public-private differences in the treatment of Standardized Patients
Dependent Variable: IRT
Score (Plausible Values)
Is a public provider
Has MBBS
Has some qualification
Age of provider
Gender of provider (1=Male)
Constant
Full Sample
(1)
-0.666***
(0.176)
0.289
(0.282)
0.172
(0.152)
0.003
(0.009)
-0.002
(0.333)
-0.722
(0.534)
Yes
390
Audit 1
(2)
-0.490
(0.310)
0.246
(0.265)
0.187
(0.139)
0.004
(0.008)
0.037
(0.320)
-1.792
(0.560)
Yes
223
Audit 2
(3)
-0.768***
(0.261)
Dual
(4)
-0.791***
(0.234)
-0.044
(0.030)
0.732
(0.619)
0.359
(0.770)
Yes
167
-0.051**
(0.031)
-1.181***
(0.373)
2.531***
(1.020)
Yes
126
Market fixed effects
Number of providers
Notes:
1) Robust standard errors clustered at the market level are in parenthesis
2) *** Significant at 1%, ** Significant at 5%, * Significant at 10%
Table 1. Treatment of Standardized Patients by Case
Unstable Angina (N=327)
Correct treatment
Aspirin
Anti-platelet agents
Referred
ECG
ECG & Referred
Antibiotic
Unnecessary or harmful treatment
Dysentery (N=398)
Correct treatment
ORS
Asked to see child
Antibiotic
Unnecessary or harmful treatment
Asthma (N=404)
Correct treatment
Bronchodilators
Theophylline
Oral Corticosteroids
Antibiotic
Unnecessary or harmful treatment
Audit 1
Audit 2
Full
Sample
Public
Private
Public
Private
0.39
0.06
0.01
0.25
0.26
0.13
0.21
0.61
0.44
0.03
0.03
0.28
0.23
0.10
0.13
0.51
0.35
0.04
0.01
0.24
0.23
0.12
0.17
0.53
0.37
0.03
0.00
0.24
0.30
0.13
0.33
0.75
0.56
0.20
0.02
0.27
0.34
0.15
0.27
0.80
0.18
0.18
0.24
0.63
0.54
0.10
0.10
0.32
0.46
0.56
0.13
0.13
0.14
0.61
0.45
0.32
0.32
0.25
0.76
0.63
0.20
0.20
0.43
0.59
0.60
0.54
0.44
0.24
0.25
0.48
0.79
0.37
0.32
0.12
0.15
0.41
0.76
0.50
0.36
0.22
0.31
0.40
0.70
0.57
0.51
0.27
0.17
0.63
0.93
0.68
0.59
0.29
0.27
0.52
0.86
Table2A. Public-Private Differences in Treatment
Full Sample
(1)
Dependent variable: Correct Treatment
Mean
0.39
SD
(0.49)
Is a public provider
-0.029
(0.030)
Has MBBS
Has some qualification
Number of patients waiting
Age of provider
Gender of provider (1=Male)
Case II (Dysentery)
Case III (Asthma)
Constant
0.151**
(0.070)
0.585***
(0.056)
0.364***
(0.082)
Yes
(2)
Audit 1
(3)
Audit 2
(4)
(5)
0.39
0.39
0.34
0.45
(0.49)
(0.49)
(0.47)
(0.50)
-0.026
-0.027
0.034
-0.052
(0.042)
(0.045)
(0.064)
(0.042)
0.178**
0.178**
(0.073)
(0.070)
0.075
0.075
(0.052)
(0.050)
-0.015**
-0.027***
(0.007)
(0.008)
-0.002
-0.002
(0.002)
(0.002)
0.041
0.071
(0.102)
(0.100)
-0.027 -0.347*** -0.092 -0.189***
(0.189)
(0.039)
(0.115)
(0.062)
0.399**
0.096* 0.379*** 0.180***
(0.182)
(0.053)
(0.128)
(0.069)
0.306* 0.487*** 0.289* 0.481***
(0.158)
(0.034)
(0.153)
(0.059)
Yes
Yes
Yes
Yes
District fixed effects
Market fixed effects
Provider fixed Effects
R2
0.166
0.297
0.209
Number of observations
1,087
1,011
608
Notes: 1) All regressions include SP fixed effects
1) Standard errors clustered at the market level in parenthesis
0.274
605
0.126
406
Dual
(6)
(7)
(8)
0.45
(0.50)
-0.071
(0.057)
0.46
(0.50)
-0.051
(0.048)
0.46
(0.50)
-0.069
(0.053)
0.000
(0.011)
-0.002
(0.011)
-0.002
(0.477)
0.585*
(0.311)
0.344
(0.222)
0.666
(0.428)
-0.013
(0.014)
-0.203*** -0.197**
(0.077)
(0.077)
0.152*
0.154*
(0.087)
(0.079)
0.505*** 0.528***
(0.070)
(0.076)
Yes
Yes
0.362
406
0.115
330
Yes
0.312
330
Table2B. Public-Private Differences in Correct Treatment
Coefficient on Public Reported
Panel A: Unstable Angina
Correct Treatment
Gave/prescribed Aspirin
Referred to another provider
Asked/recommended EKG
Both refer and EKG
Panel B: Dysentery
Correct Treatment
ORS
Asked to see child
Panel C: Asthma
Correct Treatment
Gave/prescribed Bronchodilators
Gave/prescribed Steroids
Full Sample
(1)
(2)
Audit 1
Audit 2
(3)
(4)
(5)
Dual
(6)
(7)
(8)
-0.021
(0.072)
-0.101***
(0.038)
0.022
(0.064)
-0.015
(0.068)
-0.011
(0.047)
0.195
(0.158)
-0.033
(0.070)
0.071
(0.138)
-0.024
(0.146)
-0.078
(0.108)
0.155
(0.101)
-0.017
(0.032)
0.043
(0.085)
0.048
(0.093)
-0.022
(0.059)
0.232*
(0.133)
-0.034
(0.060)
0.104
(0.115)
0.008
(0.122)
-0.050
(0.090)
-0.195*
(0.107)
-0.167**
(0.076)
-0.006
(0.097)
-0.075
(0.108)
-0.009
(0.078)
-0.258**
(0.111)
-0.202**
(0.085)
-0.109
(0.105)
-0.079
(0.120)
-0.078
(0.084)
0.053
(0.047)
0.053
(0.047)
-0.007
(0.062)
0.079
(0.054)
0.079
(0.054)
0.011
(0.070)
-0.054
(0.061)
-0.054
(0.061)
0.197**
(0.092)
-0.014
(0.075)
-0.014
(0.075)
0.297***
(0.096)
0.135**
(0.066)
0.135**
(0.066)
-0.153**
(0.076)
0.125
(0.086)
0.125
(0.086)
-0.203*
(0.108)
0.125
(0.078)
0.125
(0.078)
-0.205**
(0.097)
0.102
(0.068)
0.102
(0.068)
-0.182**
(0.089)
-0.146***
(0.056)
-0.082
(0.054)
-0.106**
(0.052)
Yes
-0.116
(0.072)
-0.070
(0.070)
-0.073
(0.054)
-0.144*
(0.086)
-0.036
(0.086)
-0.117
(0.101)
Yes
-0.127
(0.130)
-0.008
(0.116)
-0.100
(0.097)
-0.140*
(0.075)
-0.118
(0.076)
-0.088
(0.061)
Yes
-0.139
(0.098)
-0.135
(0.099)
-0.062
(0.073)
-0.139
(0.088)
-0.135
(0.087)
-0.064
(0.065)
Yes
-0.147*
(0.077)
-0.111
(0.081)
-0.095
(0.061)
District fixed effects
Market fixed effects
Yes
Yes
Provider Fixed Effects
Yes
Other controls
SP fixed effects, age, gender, qualification, number of patients waiting
Notes:
1) Standard errors clustered at the market level in parenthesis
Yes
What exactly is happening with
treatment?


In audit 1, the two groups behave similarly, but there are some differences across cases

MI identical in both

Dysentery public sector providers 20-30% more likely to ask to see child, no difference in
ORS

Asthma public sector providers 12-14% less likely to give correct treatment (not
statistically significant)

Across all cases, public 13% less likely to give antibiotics
Dual sample, with and without provider fixed effects

MI: Equal likelihood of EKG/Referral but private more likely to give Aspirin

Dysentery: Public 10-12% (not significant) more likely to give ORS, private 18-20% more
likely to ask to see child

Asthma: Public 13-15% less likely to get it correct
Table3A. Public-Private Differences in Incorrect Treatment
Full Sample
Audit 1
Audit 2
(1)
(2)
(3)
(4)
(5)
(6)
Dependent variable: Unnecessary or harmful treatment
Mean
0.65
0.64
0.57
0.57
0.76
0.76
SD
(0.48)
(0.48)
(0.50)
(0.50)
(0.43)
(0.43)
Is a public provider
0.052
0.010
0.011
-0.017
0.020
0.025
(0.035)
(0.040)
(0.068)
(0.066)
(0.037)
(0.051)
Has MBBS
0.229***
0.236***
(0.074)
(0.073)
Has some qualification
-0.010
-0.011
(0.055)
(0.053)
Number of patients waiting
0.005
0.005
0.004
(0.006)
(0.009)
(0.009)
Age of provider
-0.001
-0.000
-0.011
(0.002)
(0.002)
(0.011)
Gender of provider (1=Male)
0.076
0.086
0.218
(0.095)
(0.096)
(0.468)
Case II (Dysentery)
-0.083**
0.055
-0.051
0.004 -0.152*** -0.577
(0.041)
(0.261)
(0.055)
(0.107)
(0.059)
(0.443)
Case III (Asthma)
0.171***
0.258
0.181*** 0.198* 0.128***
0.305
(0.027)
(0.255)
(0.035)
(0.113)
(0.044)
(0.279)
Constant
0.597*** 0.322** 0.523*** 0.309** 0.756*** 0.994**
(0.028)
(0.149)
(0.034)
(0.147)
(0.046)
(0.416)
District fixed effects
Yes
Yes
Yes
Market fixed effects
Yes
Yes
Yes
Provider fixed Effects
R2
0.095
0.252
0.059
0.170
0.091
0.358
Number of observations
1,129
1,052
668
644
461
408
Notes: 1) All regressions include SP fixed effects
1) Standard errors clustered at the market level in parenthesis
Dual
(7)
(8)
0.75
(0.43)
0.027
(0.042)
0.75
(0.43)
0.042
(0.046)
0.018
(0.014)
-0.131* -0.124*
(0.077)
(0.067)
0.143** 0.153**
(0.058)
(0.061)
0.732*** 0.695***
(0.060)
(0.064)
Yes
0.315
331
Yes
0.300
331
Table3B. Public-Private Differences in Incorrect Treatment
Coefficient on Public Reported
Panel A: Unstable Angina
Unnecessary or harmful treatment
Gave/prescribed antibiotics
Gave/prescribed steroids
Panel B: Dysentery
Unnecessary or harmful treatment
Gave/prescribed antibiotics
Panel C: Asthma
Unnecessary or harmful treatment
Gave/prescribed antibiotics
Full Sample
(1)
(2)
Audit 1
Audit 2
(3)
(4)
(5)
Dual
(6)
(7)
(8)
-0.083
(0.069)
0.012
(0.054)
-0.021
(0.022)
-0.168
(0.148)
-0.071
(0.104)
-0.028
(0.044)
-0.134
(0.119)
-0.040
(0.055)
-0.029
(0.039)
-0.176
(0.128)
-0.075
(0.089)
-0.031
(0.038)
-0.070
(0.079)
0.032
(0.094)
-0.014
(0.037)
-0.047
(0.094)
0.065
(0.108)
-0.015
(0.042)
0.025
(0.061)
0.029
(0.062)
0.076
(0.070)
-0.005
(0.073)
0.028
(0.110)
-0.181*
(0.099)
0.193*
(0.110)
-0.238**
(0.119)
0.005
(0.075)
0.164**
(0.073)
0.000
(0.086)
0.149
(0.097)
0.027
(0.090)
0.140
(0.087)
0.016
(0.078)
0.123
(0.075)
0.065
(0.045)
0.068
(0.062)
Yes
0.026
(0.053)
0.008
(0.074)
0.021
(0.098)
-0.011
(0.110)
Yes
-0.079
(0.092)
-0.132
(0.124)
0.078*
(0.047)
0.115
(0.080)
Yes
0.081
(0.068)
0.110
(0.105)
0.080
(0.061)
0.109
(0.094)
Yes
0.095*
(0.056)
0.117
(0.081)
District fixed effects
Market fixed effects
Yes
Yes
Provider Fixed Effects
Yes
Yes
Other controls
SP fixed effects, age, gender, qualifications, number of patients waiting
Notes:
1) Standard errors clustered at the market level in parenthesis
Diagnosis





Problem: 67% interactions there is no diagnosis
Noted in pilot
Final survey: randomized SSPs into 2 groups
 1 group turns around as they are leaving and ask the
provider “Doctor, what is wrong with me?”
 Increases rate of diagnosis provision by 20-25 p.p. in all
groups
First look at likelihood of providing diagnosis
Second, use randomization as instrument in selection model
with binary dependent variables to deduce correct diagnosis
rates
Summary of Diagnosis by Public and by SP Empowered, by Sample
Full Sample
Yes
No
Panel A: By Public (Yes = Public)
Gave Diagnosis
0.260
0.353
373
756
Audit 1
Yes
No
Audit 2
Yes
No
Yes
No
0.190
121
0.356
547
0.294
252
0.344
209
0.270
163
0.381
168
0.652
23
0.513
195
0.507
73
0.611
72
0.270
163
0.381
168
Panel B: By SP Empowerment (Yes = Empowered)
Gave Diagnosis
0.461
0.200
0.470
529
600
302
0.208
366
0.449
227
0.188
234
0.446
168
0.202
163
0.526
76
0.515
101
0.659
44
0.413
92
0.579
57
Diagnosis Correct
Diagnosis Correct
0.542
96
0.523
243
0.539
267
0.575
120
0.528
142
Notes:
2) For each sample, group means and number of observations
Dual
Public-private differences in diagnosis
Full Sample
Audit 1
Audit 2
Dual
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dependent Variable: Diagnosis was given
Mean
0.32
0.33
0.33
0.33
0.32
0.32
0.33
0.33
0.33
SD
(0.47)
(0.47)
(0.47)
(0.47)
(0.47)
(0.47)
(0.47)
(0.47)
(0.47)
Is a public provider
-0.093*** -0.120*** -0.142*** -0.150*** -0.064
-0.095* -0.112** -0.107** -0.095*
(0.029)
(0.036)
(0.031)
(0.044)
(0.047)
(0.055)
(0.053)
(0.051)
(0.053)
SP was empowered
0.243***
0.251***
0.226***
0.230*** 0.228***
(0.033)
(0.039)
(0.055)
(0.057)
(0.061)
Case II (Dysentery)
-0.283***
-0.273***
-0.305***
-0.319*** -0.340***
(0.037)
(0.047)
(0.057)
(0.066)
(0.073)
Case III (Asthma)
-0.077**
-0.107**
-0.054
-0.045
-0.072
(0.038)
(0.043)
(0.069)
(0.074)
(0.084)
Number of patients waiting
0.004
0.004
0.003
-0.004
0.001
(0.006)
(0.007)
(0.012)
(0.014)
(0.019)
Has MBBS
-0.081
-0.075
(0.087)
(0.083)
Has some qualification
-0.002
-0.002
(0.055)
(0.053)
Age of provider
-0.001
-0.001
-0.002
(0.002)
(0.002)
(0.003)
Gender of provider (1=Male)
-0.073
-0.052
-0.042
(0.093)
(0.092)
(0.076)
Constant
0.353*** 0.536*** 0.352*** 0.458*** 0.351*** 0.390*** 0.381*** 0.532*** 0.419***
(0.015)
(0.106)
(0.017)
(0.099)
(0.030)
(0.063)
(0.034)
(0.148)
(0.082)
District fixed effects
Yes
Yes
Yes
Yes
Market fixed effects
Yes
Yes
Yes
Yes
Provider fixed effects
Yes
R2
0.018
0.268
0.025
0.211
0.014
0.381
0.027
0.188
0.332
Number of observations
1,129
1,052
668
644
461
461
331
302
331
Notes:
Public-private differences in diagnosis, by case
Unstable Angina
Dysentery
Asthma
(1)
(2)
(3)
(4)
(5)
(6)
Dependent Variable: Diagnosis was given
Mean
0.46
0.46
0.17
0.17
0.37
0.38
SD
(0.50)
(0.50)
(0.37)
(0.38)
(0.48)
(0.48)
Is a public provider
-0.064
-0.100 -0.086** -0.097* -0.124** -0.126
(0.058)
(0.120)
(0.037)
(0.053)
(0.051)
(0.086)
SP was empowered
0.104
0.228***
0.311***
(0.096)
(0.049)
(0.072)
Number of patients waiting
-0.002
0.005
0.016
(0.010)
(0.014)
(0.029)
Has MBBS
-0.092
-0.007
-0.123
(0.177)
(0.101)
(0.165)
Has some qualification
0.053
0.032
-0.078
(0.127)
(0.081)
(0.080)
Age of provider
-0.001
-0.003
-0.001
(0.005)
(0.003)
(0.004)
Gender of provider (1=Male)
0.108
-0.071
-0.227
(0.202)
(0.120)
(0.227)
Constant
0.476***
0.415
0.194*** 0.280* 0.410*** 0.570**
(0.034)
(0.314)
(0.024)
(0.156)
(0.027)
(0.234)
District fixed effects
Yes
Yes
Yes
Market fixed effects
Yes
Yes
Yes
R2
0.016
0.503
0.046
0.471
0.026
0.457
Number of observations
327
304
398
372
404
376
Notes:
1) Standard errors clustered at the market level in parenthesis
Difference in Diagnosis by Public, by Sample
Full
(1)
Y-variable: Diagnosis Given was Correct
(marginal effects (dy/dx) reported)
Is a public provider
0.014
(0.060)
Case II (Dysentery)
0.058
(0.071)
Case III (Asthma)
0.137**
(0.056)
Number of patients waiting
Has MBBS
Has some qualification
Age of provider
Gender of provider (1=Male)
Audit 1
Audit 2
Dual
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.009
(0.067)
0.080
(0.073)
0.137**
(0.059)
-0.000
(0.012)
0.138*
(0.076)
0.049
(0.079)
-0.000
(0.003)
-0.064
(0.100)
Yes
1,050
0.154
(0.111)
0.139
(0.092)
0.050
(0.075)
0.184
(0.116)
0.138
(0.092)
0.053
(0.074)
-0.001
(0.014)
0.303**
(0.145)
0.043
(0.080)
0.000
(0.003)
-0.103
(0.158)
Yes
644
-0.075
(0.076)
-0.064
(0.108)
0.210**
(0.097)
-0.072
(0.073)
-0.012
(0.101)
0.210*
(0.112)
-0.004
(0.017)
-0.151*
(0.090)
-0.056
(0.116)
0.170
(0.112)
-0.119
(0.089)
-0.038
(0.109)
0.136
(0.113)
-0.022
(0.021)
District fixed effects
Yes
Number of observations
1,127
Notes:
1) Standard errors calculated using the Delta method
2) All regressions include district fixed effects
Yes
668
Yes
459
-0.004
(0.004)
0.019
(0.109)
Yes
406
Yes
330
-0.002
(0.004)
-0.002
(0.120)
Yes
301
Prices in the Private Sector


Each vertical line represents a box-plot of prices charged by a provider to real
patients. Providers are sorted on the x-axis by quality (measured by number of
questions asked and examinations conducted)
Huge variation
within providers
True for all MBBS,
other qualified, and
unqualified
providers
Prices, Checklists and Treatment


Greater compliance with the checklist is always
rewarded in higher prices
Correct treatment leads to higher prices , but
vanishes once we control for checklist adherence
 Stronger

premium among MBBS providers
Works across all cases, weaker for asthma
Prices and Checklist Adherence
.02
0
.01
40
20
0
Fees Charged
60
Density of Adherence
80
.03
Prices and Checklist Adherence
0
20
40
60
Checklist Adherence (%)
80
Fees and Checklist Adherence: All Private
Density of Adherence
Fees and Checklist Adherence: Only Dual
Table 4. Prices Charged and Checklist Items (Full, Private Only)
(1)
(2)
Dependent Variable: Prices in Rs.
Mean
28.51
28.46
SD
26.44
26.45
Fixed effects
District
District
Percentage of checklist items
0.434*** 0.419***
(0.072)
(0.056)
Dispensed medicine
20.093***
(1.691)
Has MBBS
22.127***
(4.061)
Has some qualification
7.614***
(2.334)
Referred in Unstable Angina
-8.257***
(2.060)
Asked for child in Dysentery
-2.446
(2.085)
Time spent with SP (minutes)
R2
Number of observations
0.122
755
0.292
742
(3)
(4)
28.59
26.59
District
0.455***
(0.069)
20.946***
(1.707)
22.144***
(3.943)
7.488***
(2.465)
-8.179***
(2.003)
-0.296
(2.082)
28.44
26.22
District
0.340***
(0.082)
20.364***
(1.740)
22.368***
(3.814)
7.362***
(2.567)
-8.716***
(1.958)
-0.534
(1.925)
1.108***
(0.284)
0.339
705
0.309
724
(5)
(6)
28.59
28.44
26.59
26.22
Market
Market
0.450*** 0.346***
(0.075)
(0.090)
21.154*** 20.703***
(1.925)
(2.005)
25.688** 25.888**
(10.453)
(10.272)
3.117
3.428
(2.849)
(2.865)
-9.972*** -10.000***
(2.732)
(2.797)
1.132
1.506
(2.044)
(2.200)
0.984***
(0.338)
0.440
0.462
724
705
(7)
28.51
26.44
Provider
0.297***
(0.078)
24.301***
(2.985)
1.322***
(0.383)
0.626
755
Prices Charged and Correct Treatment (Full Sample, Private Only)
(1)
(2)
Dependent Variable: Prices in Rs.
Mean
28.07
28.07
SD
(26.35)
(26.40)
Fixed Effects
District
District
Correct treatment
6.903*** 8.119***
(1.913)
(1.988)
Dispensed medicine
20.299***
(1.759)
Has MBBS
23.310***
(4.427)
Has some qualification
7.818***
(2.321)
Referred in Unstable Angina
-8.609***
(2.270)
Asked for child in Dysentery
-3.048
(2.770)
Percentage of checklist items
Time spent with SP (minutes)
R2
Number of observations
0.062
721
0.248
709
(3)
(4)
(5)
(6)
(7)
(8)
28.21
28.07
28.21
28.21
28.21
28.07
(26.55)
(26.35)
(26.55)
(26.55)
(26.55)
(26.35)
Market Provider Market
Market
Market Provider
7.210*** 8.135***
2.801
4.538**
1.930
2.890
(2.072)
(2.440)
(2.458)
(2.246)
(2.510)
(2.689)
21.357*** 24.189*** 21.826*** 21.063*** 21.508*** 24.866***
(1.999)
(3.062)
(1.940)
(2.048)
(2.020)
(3.159)
25.059**
23.703** 25.311** 24.196**
(10.403)
(10.257) (11.500) (10.971)
1.983
1.876
2.748
2.443
(2.538)
(2.639)
(2.513)
(2.584)
-9.015***
-9.525*** -7.774** -8.526***
(3.054)
(2.877)
(3.120)
(3.035)
-1.701
-0.176
-1.090
-0.097
(2.584)
(2.071)
(2.124)
(2.152)
0.385***
0.296*** 0.243***
(0.081)
(0.092)
(0.081)
1.633*** 1.159*** 1.314***
(0.333)
(0.357)
(0.432)
0.409
0.594
0.447
0.442
0.461
0.631
691
721
691
691
691
721
continued on next slide
continued from previous slide
(1)
Average steroid (0-1)
Average antibiotic (0-1)
Number of patients waiting
Age of provider
Gender of provider (1=Male)
Constant
(2)
(3)
(4)
(5)
-2.726
3.850
(7.595)
(7.695)
9.486***
9.167***
(3.465)
(3.476)
0.338
-0.029
0.314
(0.419)
(0.469)
(0.444)
-0.130
-0.108
(0.096)
(0.097)
-3.220
-3.628
(4.955)
(4.988)
25.425*** 9.711*** 14.777** 14.753*** 5.927
(1.852)
(1.702)
(6.965)
(1.788)
(7.985)
Yes
Yes
Yes
Yes
Yes
0.062
0.248
0.409
0.594
0.447
721
709
691
721
691
District fixed effects
Market fixed effects
Provider Fixed Effects
R2
Number of observations
Note:
1) Robust standard errors clustered at the village level in parenthesis
2) *** Significant at 1%, ** Significant at 5%, * Significant at 10%
(6)
-0.345
(7.576)
9.012***
(3.287)
0.450
(0.433)
-0.115
(0.099)
-5.548
(5.048)
10.765
(7.605)
(7)
4.012
(7.617)
8.905***
(3.402)
0.399
(0.439)
-0.103
(0.099)
-5.185
(5.162)
5.135
(8.324)
Yes
Yes
0.442
691
0.461
691
(8)
-0.034
(0.416)
5.870**
(2.605)
Yes
0.631
721
Does the public sector reward quality?



Public sector pay in India follows a matrix
Composed of: rank, tenure, qualifications
Zero effect of checklist adherence, treatment,
likelihood of discussing diagnosis on wages
Variation in Wages in the Public Sector By Job Position
(1)
(2)
Dependent Variable: Log of Monthly Wages
Percentage of checklist items
-0.001
(0.002)
Time spent with SP (minutes)
-0.033**
(0.013)
Correct treatment
Provider discussed diagnosis
Is a doctor
Is a nurse
Age of provider
Gender of provider (1=Male)
Born in same district
Is a dual provider
Constant
(3)
(4)
-0.056
(0.044)
0.026
(0.080)
0.908*** 0.903*** 0.935*** 0.899***
(0.168)
(0.165)
(0.177)
(0.171)
-0.104
-0.097
-0.098
-0.114
(0.170)
(0.168)
(0.172)
(0.173)
0.017*** 0.017*** 0.017*** 0.017***
(0.006)
(0.006)
(0.006)
(0.006)
0.032
0.034
0.039
0.032
(0.157)
(0.156)
(0.154)
(0.156)
-0.015
-0.009
-0.018
-0.016
(0.115)
(0.113)
(0.115)
(0.114)
0.290*** 0.266** 0.280** 0.294***
(0.110)
(0.108)
(0.109)
(0.112)
8.351*** 8.407*** 8.346*** 8.331***
(0.253)
(0.251)
(0.262)
(0.265)
Yes
Yes
Yes
Yes
0.453
0.459
0.442
0.453
308
308
301
308
District fixed effects
R2
Number of observations
Notes:
1) Standard errors clustered at the market level in parenthesis
2) *** Significant at 1%, ** Significant at 5%, * Significant at 10%
(5)
0.001
(0.002)
-0.035**
(0.016)
-0.040
(0.047)
0.053
(0.088)
0.925***
(0.172)
-0.085
(0.167)
0.017***
(0.006)
0.042
(0.152)
-0.010
(0.109)
0.256**
(0.107)
8.395***
(0.266)
0.449
301
Variation in Wages in the Public Sector By Qualification
(1)
(2)
Dependent Variable: Log of Monthly Wages
Percentage of checklist items
-0.002
(0.002)
Time spent with SP (minutes)
-0.016
(0.012)
Correct treatment
(3)
Has some qualification
Age of provider
Gender of provider (1=Male)
Born in same district
Is a dual provider
Constant
1.283***
(0.169)
0.871***
(0.297)
0.019***
(0.006)
0.130
(0.106)
0.015
(0.080)
0.145*
(0.085)
8.022***
(0.308)
Yes
0.628
308
1.271***
(0.168)
0.857***
(0.290)
0.019***
(0.006)
0.130
(0.105)
0.017
(0.080)
0.137
(0.086)
8.040***
(0.315)
Yes
0.629
308
(5)
0.020
(0.065)
1.275***
(0.167)
0.865***
(0.293)
0.019***
(0.006)
0.131
(0.104)
0.014
(0.081)
0.149*
(0.087)
7.995***
(0.329)
Yes
0.627
308
-0.000
(0.002)
-0.017
(0.014)
-0.038
(0.044)
0.052
(0.073)
1.303***
(0.179)
0.878***
(0.299)
0.019***
(0.006)
0.121
(0.105)
0.022
(0.081)
0.140
(0.086)
8.020***
(0.344)
Yes
0.622
301
-0.047
(0.042)
Provider discussed diagnosis
Has MBBS
(4)
1.308***
(0.174)
0.892***
(0.299)
0.019***
(0.006)
0.122
(0.107)
0.015
(0.081)
0.152*
(0.086)
7.993***
(0.337)
Yes
0.620
301
District fixed effects
R2
Number of observations
Notes:
1) Standard errors clustered at the market level in parenthesis
2) *** Significant at 1%, ** Significant at 5%, * Significant at 10%
Quick back of the envelope

We can provide a back of the envelope measure of
costs and quality in the public and private sectors
 This
is rough—public and private sector providers
provide other services beyond primary care
Cost and Checklist Completion in Public Sector
Per Patient Cost in the Public Sector Facility
Average doctors per facility
2.03
Monthly salary cost of doctors
per month per facility
Rs.64,199
Average number patients per
facility per month
984
Average cost per patient
Rs.101
Public Checklist Usage
25th percentile 8.33%
50th percentile
15.79%
75th percentile
99th percentile
27.27%
63.18%
Cost and Checklist Completion in Private Sector
Private Facility (parameters from regression)
Base price (constant)
Rs.2.05
Cost per percentage checklist item
Rs.0.46
MBBS premium
Rs.22.14
Some qualification premium
Rs.7.49
Percentage Checklist Items
0%
25%
50%
75%
100%
MBBS
Doctor
Rs.24
Rs.36
Rs.47
Rs.59
Rs.70
Some
No
Qualification Qualification
Rs.10
Rs.2
Rs.21
Rs.14
Rs.33
Rs.25
Rs.44
Rs.37
Rs.56
Rs.48
Some further interpretation results




Audit patients present the same symptoms and same script to multiple doctors in different
conditions. This may be off-equilibrium behavior. 3 sets of issues

“Serious” cases never go to the public sector. Therefore, if they do, it is an indication to the
doctor (who is on the equilibrium path) that the patient is not serious

If the same case goes, the patient presents in a different way in the public to the private
sector, accounting for lower incentives to put effort
The public-private difference for the same doctors may reflect incentive effects due to the
presence of the private sector clinic
Difficult problems: in past led to differing results between audit studies and observational
data

Famously, Ayres and Spiegelman (1995) versus Goldberg (1996)

More recently, discrimination against African American (names): Bertrand-Mullianathan
versus Fryer and Levitt
What we do in addition to the audit

First, observe equilibrium path behavior with real patients and check to see if the results
are the same

Second, try to assess patient sorting

Third, try to rule out deliberately lower effort due to dual practice
Some further interpretation results

Is it the case that they treat “real” patients this way?


Yes, we sat in their clinics for 1 day each and find identical results
on things that we can measure in both (time spent, questions
asked, examinations done) (link to table)
Is it the case that the “regular” patient body is very
different for public/private



We did exit surveys with patients from all practices. Patients were
not very different in illness and severity, but in private had more
access to transport and had more mobile phones (72 vs. 64%)
When we include (means) of the regular patient populations in
the audit regressions, nothing changes
It seems like people use the public clinics precisely like they use
unqualified providers (link to table)
Some further interpretation results

Is it the case that patients “expect” something very
different from public and private?
 If
the patients know what they have, then it is likely that
there will be complete separation by quality and price
 Cases deliberately chosen so that same symptom can
reflect a minor or major condition
Some further interpretation results

Is it the case that public providers were “directing” patients to their private
clinics?

Or, would we expect very different care among public sector providers if they
did not have a private clinic?



None of our SPs were directed to the private clinic of the public provider.
Referrals lower among dual practice
People already know where the private clinic is (and sometimes this is not in the same
place)



Fully segmented markets
Some effect of location on estimated impact in checklist and time-spent, but not on treatment
and diagnosis
 Further, the guys with clinics in the same location are also worse in their private practice,
suggesting that these guys are just selected worse
We cannot tell what would happen where there are is no dual-practice


We note that it is not allowed, but 80 percent of providers have them
The providers who have dual practice versus the 20 percent who do not, behave identically
in their public practice in treatment and have lower referral rates, but also have lower
checklist completion and diagnosis rates
Some further interpretation results

Is it the case more educated patients would get different results
from the public sector? What about urban areas?


On all process measures, public providers in Delhi are worse
But on treatment, they are better; could reflect higher competence since
we did not observe the same doctor in both practices
Time spent
Public
MBBS
Some qualification
Dysentery
Patient load during visit
-5.42***
(0.47)
0.80
(0.71)
2.24***
(0.60)
0.12
(0.093)
0.00015
(0.018)
Percentage of
IRT score
checklist items
-16.1***
(2.23)
6.14***
(1.17)
4.48
(2.53)
6.75***
(0.22)
-0.0069
(0.054)
-1.25***
(0.068)
0.34***
(0.042)
0.32***
(0.054)
0.31***
(0.017)
-0.0054
(0.0047)
Uttered
diagnosis
Correct
treatment
Incorrect
treatment
Antibiotics
-0.16
(0.086)
-0.14
(0.11)
-0.014
(0.11)
-0.34***
(0.0078)
0.00058
(0.0015)
0.29**
(0.090)
0.037
(0.11)
-0.019
(0.15)
0.13***
(0.016)
-0.0063**
(0.0025)
-0.21
(0.13)
0.17
(0.14)
-0.13***
(0.023)
-0.28***
(0.014)
0.0020
(0.0039)
-0.21
(0.15)
0.17
(0.16)
-0.068*
(0.034)
0.025**
(0.0084)
0.0011
(0.0023)
Conclusion




Significant over and under treatment in both sectors
Pure public versus private comparison for the same provider suggests that
public sector has
 Lower compliance with checklist
 Lower correct treatment and diagnosis rates
 Similar rates of over-treatment
Public versus private clinics is more complex
 Lower compliance with checklist in public sector
 BUT, no difference in treatment and higher correct diagnosis rates
 Several potential explanations (accountability, local, rules of thumb)
Prices reward quality as measured through adherence to checklist and
treatment in private, but not in public clinics
Implications

What to do with the public sector
 Location
subsidies?
 Massive investments?
 Better administrative accountability?
 Reorganizing geographical locations?

What to do with the private sector
 Better
knowledge?
 What about equity—how to introduce subsidies