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MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Healthcare Spending Inequality:
Evidence from Hungarian Administrative Data
Anikó Bíró, Dániel Prinz
MTA KRTK KTI
1 April 2019
MTA KRTK KTI
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Introduction
• New evidence on the distribution of healthcare spending in HungarySample: full-time workers
• Universal health insurance, but
• Health varies with income• Geographic inequalities in the availability of healthcare services
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Data
• Administrative data, covering years 2003-2011 on a random 50%sample of the 2003 population aged 5-74
• Health expenditures are observed on the annual level
• Location is observed in 2003
• Sample restrictions: individuals aged 18-55, earning at least theminimum wage each month and not receiving any public transfers
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Methods
• Maps of healthcare spending for 3 types of spending (outpatient,inpatient, prescription drugs)
• Heterogeneity by income:• Relate the current year's healthcare expenditures to the previous
year's income• Calculate income percentiles (deciles)• Income of those who spent zero days on sick-leave the previous year• Adjustment for age and gender
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Outpatient expenditures
(9958,11465](8596,9958](8159,8596](7574,8159][6610,7574]
77% di�erence between highest and lowest spending counties
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Inpatient expenditures
(8406,8773](7921,8406](7663,7921](7357,7663][7069,7357]
27% di�erence between highest and lowest spending counties
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Prescription drug expenditures
(12821,14265](12439,12821](12178,12439](11653,12178][10353,11653]
37% di�erence between highest and lowest spending counties
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Outpatient expenditures
7,00
08,
000
9,00
010
,000
11,0
00O
utpa
tient
Spe
ndin
g (H
UF)
0 20 40 60 80 100Percentile of Wage
34% di�erence between the 90th and 10th percentile
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Inpatient expenditures
5,00
06,
000
7,00
08,
000
9,00
010
,000
Inpa
tient
Spe
ndin
g (H
UF)
0 20 40 60 80 100Percentile of Wage
33% di�erence between the 90th and 10th percentile
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Prescription drug expenditures
10,0
0012
,000
14,0
0016
,000
Rx
Spen
ding
by
Soci
al In
sura
nce
(HU
F)
0 20 40 60 80 100Percentile of Wage
27% di�erence between the 90th and 10th percentile
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Outpatient expenditures
6,00
08,
000
10,0
0012
,000
0 2 4 6 8 10decile
Budapest Győr-Moson-SopronSzabolcs-Szatmár-Bereg
If we eliminated cross-county variation, variance in spending would bereduced by 71%
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Inpatient expenditures
4,00
06,
000
8,00
010
,000
12,0
00
0 2 4 6 8 10decile
Budapest Győr-Moson-SopronSzabolcs-Szatmár-Bereg
If we eliminated cross-county variation, variance in spending would bereduced by 47%
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Prescription drug expenditures
10,0
0012
,000
14,0
0016
,000
0 2 4 6 8 10decile
Budapest Győr-Moson-SopronSzabolcs-Szatmár-Bereg
If we eliminated cross-county variation, variance in spending would bereduced by 43%
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
MTA KRTK KTI
MethodsGeographic heterogeneity
Income heterogeneityIncome heterogeneity by county
Discussion
Next steps: explanations
• Health factors? � Next wave of linked administrative data
• Access to care?• Number of hospital beds, physicians, pharmacies?• Distance to healthcare facilities?• Information, connections, informal payments?
Anikó Bíró, Dániel PrinzHealthcare Spending Inequality: Evidence from Hungarian Administrative Data
