Market Size and Trade in Medical Services

Jonathan I. Dingel
Joshua D. Gottlieb
Maya LozinskiPauline Mourot
University of Chicago
April 2024

Economies of scale and trade in medical services
Perpetual policy discussion of geographic variation in medical services: Less populous places have worse health outcomes. . .
 . . . but US doctors are disproportionately in big cities (50% more per capita)

Evaluating this situation hinges on returns to scale and tradability
 Increasing returns → productivity benefits from concentrating production
 Trade costs for services → proximity-concentration trade-off Heterogeneity in patients’ travel costs → efficiency and equity considerations

How do local increasing returns and trade costs govern the geography of US
healthcare production and consumption? (18% of US GDP)1 / 50This paper
Approach: Setting: Medicare (regulated provider payments)
 Model: Trade costs & scale economies → market-size effects

 Implementation: Logit demand → gravity equation → scale elasticity

Estimates: Domestic trade in medical services mimics trade in manufactures
19% of production is exported; distance elasticity is about -1.7

 Market-size effect makes larger regions net exporters of medical services;
stronger effect in less common services
 Geographic concentration → ↑ service quality, ↑ specialization (α ≈ 0.8)
2 / 50Counterfactual scenarios
Simple model generates rich depiction of proximity-concentration tradeoffs:
 Changes in output quality ̸= changes in patient market access: improving
access need not mean subsidizing output in the region
 Production subsidies and travel subsidies can impose contrasting spillovers(“agglomeration shadows”) on neighboring regions
 Production may be too dispersed: marginal return is higher in larger regions
 Production subsidies in smaller regions benefit lower-income patients more
 Lower-SES patients need larger travel subsidies to equalize access Size of the United States contributes to inequality in access
3 / 50Contributions
Medical care: interregional trade & regional increasing returns
 Distribution of physicians/rural access Newhouse 1982a,b,c, 1990; Dranove, Shanley& Simon 1992; Buchmueller et al. 2006, Alexander & Richards, 2021; . . .

 Studies mostly treat markets as local Dartmouth; Baumgardner 1988a,b; Bresnahan& Reiss 1991; Chandra & Staiger 2007; Finkelstein, Gentzkow & Williams 2016

Home-market effect for trade in services Market size and goods: Davis and Weinstein 2003; Hanson and Xiang 2004; Dingel
2017; Bartelme et al. 2019 Acemoglu and Linn 2004; Costinot et al. 2019

 Trade in services: Lipsey ’09 Eaton & Kortum ’19 Eilat & Einav ’04, Muñoz ’22

Central place theory and “spatial shopping” literature Central place theory: Christaller 1933; Hsu, Holmes and Morgan 2014; Schiff 2015
 Credit-card trade matrices: Agarwal et al. 2017; Dunn and Gholizadeh 2021

4 / 50 Empirical setting and geographic patterns
 Theoretical framework
 Regional increasing returns in medical services- Estimating the scale elasticity- Strong home-market effect for aggregate medical services

 Mechanisms- How market-size effects vary with procedure characteristics- Sources of increasing returns: Division of labor and lumpy capital- Travel to access specialized services

 Health policy with trade and increasing returns

 Empirical setting and geographic patterns
 Theoretical framework
 Regional increasing returns in medical services- Estimating the scale elasticity- Strong home-market effect for aggregate medical services

 Mechanisms- How market-size effects vary with procedure characteristics- Sources of increasing returns: Division of labor and lumpy capital- Travel to access specialized services

 Health policy with trade and increasing returns

Medicare Medicare insures almost all Americans > 65 years old or disabled- 59 million beneficiaries and about 23% of healthcare expenditure (in 2017)- 39 million in Traditional Medicare (physicians & facilities bill Medicare) All willing providers covered; vast majority of doctors/hospitals- cf. private insurance: limited network, opaque pricing → patients havedifferent choice sets Medicare regulates payment (“reimbursement”) rates- Set based on estimates of average cost of care
- Professional fee → physician (by procedure code)- Facility fee → hospital (by main diagnosis or major procedure)- Limited geographic variation (89 regions for professional fees)- We standardize both professional and facility fees at national average prices
6 / 50Data
Medicare claims data for 2013-2017
 Claims files report procedure, provider, date, payment
 We include all care provided by MD/DO outside Emergency Department
 12,000+ 5-digit procedures in Healthcare Common Procedure Coding System 20% representative sample of patients contains ∼210 million claim lines ZIP codes of patient and place of service

National Plan and Provider Enumeration System (NPPES) Physician ID, name, specialization, and location

We aggregate ZIP codes to hospital referral regions (HRRs)
7 / 50Production, consumption, trade, and market size

8,000

Dollars per resident
Medicare beneficiary

Production
6,000

Consumption
4,000

Imports

Exports

2,000

0
150k

300k

600k

1.2m

2.4m

4.8m

10m

Population
8 / 50

1
.8
.6

8

.4

6

.2

.1

0

−5

1

4

16
64
Distance (km)Within HRR

248

992

Across HRR

3,968

−3

−1

Share of pairs with positive trade.20Frequency

.3

4

.4

2

Trade (log, residualized)

10

Trade declines with distance1

Distance (log, residualized)
Log trade, residualized
Share of pairs with positive trade

9 / 50Understanding these patterns
The geography of US medical services: Large markets are net exporters
 Traveling for care is costly

Are large markets net exporters because they have more patients?
 Are there regional increasing returns to scale in medicine?
 Are they so large that higher demand generates net exports?

How might policies change these geographic patterns?
 Where is the marginal return to subsidizing production highest?
 What happens if government subsidizes travel?
10 / 50

 Empirical setting and geographic patterns
 Theoretical framework
 Regional increasing returns in medical services- Estimating the scale elasticity- Strong home-market effect for aggregate medical services

 Mechanisms- How market-size effects vary with procedure characteristics- Sources of increasing returns: Division of labor and lumpy capital- Travel to access specialized services

 Health policy with trade and increasing returns

Model of a market for a medical procedure (isoelastic case) Partial-equilibrium competitive model of one procedure with a fixed price Nj potential patients in region j. Patient k choosing care in region i gets

Uik = ln δi + ln ρij(k) + ϵik Provider in region i hiring L inputs to produce quality δ takes productivity
shifter Ai and regional output Qi as given. Output quantity is

Ai

H(Qi )LK(δ)

 Given government-set reimbursement rate R and factor price wi , thefree-entry condition defines an isocost curve in (Q, δ) space:R=

wi K(δi )≡ C(Qi , δi ; wi , Ai )Ai H(Qi )11 / 50

Autarky
Log δ

Higher Demand(∆N > 0)DemandC=R(α > 0)
δ′
δ

C
A
B

C=R
(α = 0)

Log Q

12 / 50

Common and rare procedures: 2 examples
 Colonoscopy (N =58,785 in our sample)
 Implanting LVAD—pump for severe heart failure patients (N =333)

13 / 50

Autarky: Common vs rare procedures

Log δ

δ′
δ

More Elastic
DemandLess Elastic
DemandC=R(α > 0)

δ′
δ

Log Q

14 / 50

Tradeiid

Preference shocks ϵik ∼ T1EV =⇒ Qij patients from j choosing i:X
δi ρijδi′ ρi′ j is patient market access in j
Nj
where Φj ≡E [Qij ] =Φj′iTrade follows gravity equation:
ln E [Qij ] = ln δi + ln

Nj
Φj


+ ln ρij

Market size and trade: Nj , δi , Φj
 Larger population (larger Nj ) raises import demand With increasing returns (α > 0): ↑ Ni → ↑ δi → ↑ gross exports & ↑ Φi
 With sufficiently strong increasing returns (α≫ 0):- ↑ Ni → ln δi increases faster than ln- This effect is larger for rare services

NiΦi: region i is net exporter

15 / 50

Trade: Weak home-market effectDemandLog δ

C=R(α  0)

Log Q

Exports(α  0)

Imports(α > 0)C=R(α > 0)δδ

0Exports(α > 0)
A

B

Log Q

Log N

16 / 50

Trade: Strong home-market effectDemandLog δ

C=R(α ≫ 0)
C

δ ′′
δ′
δ

A

C=R
(α > 0)

Log Q

Exports(α ≫ 0)

Imports(α > 0)
Imports(α ≫ 0)
Exports(α > 0)

B

Log Q

Log N

17 / 50

 Empirical setting and geographic patterns
 Theoretical framework
 Regional increasing returns in medical services- Estimating the scale elasticity- Strong home-market effect for aggregate medical services

 Mechanisms- How market-size effects vary with procedure characteristics- Sources of increasing returns: Division of labor and lumpy capital- Travel to access specialized services

 Health policy with trade and increasing returns

 Empirical setting and geographic patterns
 Theoretical framework
 Regional increasing returns in medical services- Estimating the scale elasticity- Strong home-market effect for aggregate medical services

 Mechanisms- How market-size effects vary with procedure characteristics- Sources of increasing returns: Division of labor and lumpy capital- Travel to access specialized services

 Health policy with trade and increasing returns

Estimating regional quality and the scale elasticity
1. Exporter fixed effects from gravity regression reveal regional quality:
ln E (Sij ) =

ln δ
|{z}i

exporter FE

+

ln θj
|{z}

+ γ ln distanceij

importer FE= Nj /Φj

2. Isocost curve implies estimating equation for α:dlnδi = α ln Qi − ln wi + ln Ai + ln R
Higher-quality output can reflect:- larger scale (Qi ↑),- cheaper inputs (wi ↓),- exogenous productivity (Ai ↑) [e.g., sunk investments in quality]

18 / 50

Estimating the scale elasticity: empirical considerations

ln E (Sij ) =

ln δ
|{z}i

exporter FE

+

ln θj
|{z}

+ γ ln distanceij

importer FE Estimate by Poisson pseudo-maximum likelihood (PPML) due to zeros
 Distance: Log-linear, log-quadratic, or decile dummiesdln
δi = α ln Qi + ln R − ln wi + ln Ai
 3 instruments for ln Qi : population, 1940 population, bedrock depth Auxiliary evidence on factor prices wi
 First-difference using 2013–2017 changes
19 / 50

Estimated HRR quality positively correlated with health outcomesln E (Sij ) =

ln δ
|{z}i

+

exporter FE+ γ ln distanceij

importer FE

CA−Los Angeles

2

CA−Los Angeles

2
MN−Rochester

MA−Boston
NY−New YorkMI−Royal Oak
IL−Evanston
PA−Allentown
NJ−Hackensack

0

GA−Columbus
TX−El Paso

CA−Stockton
OR−Bend
NC−Hickory WI−Appleton
ID−Idaho Falls

NJ−Ridgewood
OH−Elyria−2

WA−Yakima

MA−Boston
NY−New YorkMI−Royal Oak
MN−St Cloud
IL−Blue Island
TX−Mcallen

0

CA−Fresno

Exporter FE

Exporter quality (fixed effects, δi)

ln θj
|{z}

GA−Columbus
FL−Bradenton
AR−SpringdaleGA−Albany
IA−Mason City
FL−Ocala
NC−Hickory
WI−Neenah

OH−Elyria−2

ND−Minot

−4

−4
2

2

N = 305; R = 0.0632
Y = −0.6540 − 0.2129 [0.0469] X + ε

−6
−4

−2

0

2

Best estimate for overall mortality (Chandra et al., 2023)

N = 306; R = 0.1385
Y = 4.5390 − 0.4588 [0.0608] X + ε

−6
4

9

10

11

12

13

14

Overall mortality rate across conditions

 Further support for clinical quality: Fischer et al., 2022; Battaglia, 2022; Petek, 2022

20 / 50

Quality ∼ isoelastic with respect to scale
2

Quality lnδi

0

−2

−4
HI− Honolulu

AK− Anchorage

−6
4

5

6

7

8

9

Production lnQi
21 / 50

Scale elasticity estimate α̂ (HRRs)
All services

Baseline

No Diagonal

Controls

OLS: 2017

0.806(0.031)

0.961(0.047)0.786(0.041)

OLS: 2013–2017 difference

0.999(0.079)1.045(0.083)1.018(0.082)

2SLS: population (log)0.800(0.037)[2141]

0.905(0.057)[2141]

0.777(0.050)[1621]

2SLS: population (1940, log)

0.697
(0.063)
[163]

0.924
(0.093)
[163]

0.633
(0.070)[206]
22 / 50

 Empirical setting and geographic patterns
 Theoretical framework
 Regional increasing returns in medical services- Estimating the scale elasticity- Strong home-market effect for aggregate medical services

 Mechanisms- How market-size effects vary with procedure characteristics- Sources of increasing returns: Division of labor and lumpy capital- Travel to access specialized services

 Health policy with trade and increasing returns

Estimating home-market effects using gravity regressions
Test for HME using the gravity equation à la Costinot et al. (2019): 
Njln E [Qij ] = ln δi + ln
+ γ ln distanceij
Φjln E (Qij ) = λX ln populationi + λM ln populationj + γ ln distanceij
 λX > 0 is a weak home-market effect: ↑ Ni =⇒ ↑ gross exports

 λX > λM is a strong home-market effect: ↑ Ni =⇒ ↑ net exports

Panel estimation using 2013–2017 population changes:
ln E (Qijt ) = µX ln populationit + µM ln populationjt + ϕij + γt ln distanceij
23 / 50

Gravity regression: Strong HME for aggregate medical services

λX Provider-market population (log)λM Patient-market population (log)Distance (log)Distance (log, squared)

Cross-sectional PPML

IV: 1940
population

2013–2017
panel

0.671
0.681
0.671
(0.0543) (0.0505) (0.0366)
0.260
0.252
0.286
(0.0547) (0.0501) (0.0346)-1.6270.344(0.0489) (0.304)-0.199(0.0305)

0.757(0.0547)0.284(0.0467)0.377(0.250)-0.201(0.0247)

0.939(0.151)-0.205(0.148)Distance (log) × 2017
p-value for H0 : λX ≤ λM
Observations
Fixed effects
Distance elasticity at mean
Distance deciles-0.00117(0.00667)<0.001
93,636<0.001
93,636<0.001
93,636-1.59<0.001
93,636<0.001
162,678
ij

-1.57
Yes

24 / 50

 Empirical setting and geographic patterns
 Theoretical framework
 Regional increasing returns in medical services- Estimating the scale elasticity- Strong home-market effect for aggregate medical services

 Mechanisms- How market-size effects vary with procedure characteristics- Sources of increasing returns: Division of labor and lumpy capital- Travel to access specialized services

 Health policy with trade and increasing returns

 Empirical setting and geographic patterns
 Theoretical framework
 Regional increasing returns in medical services- Estimating the scale elasticity- Strong home-market effect for aggregate medical services

 Mechanisms- How market-size effects vary with procedure characteristics- Sources of increasing returns: Division of labor and lumpy capital- Travel to access specialized services

 Health policy with trade and increasing returns

Larger markets produce greater set of procedures

4

Distinct procedures
consumed (relative)

Distinct procedures
produced (relative)

4

3

2

1

3

2

1
150k

300k

600k

1.2m

2.4m

4.8m

10m

150k

300k

600k

Population
Surgery
Anesthesia
Pathology
Other

Evaluation & Management
Radiology
Medical

1.2m

2.4m

4.8m

10m

Population
Surgery
Anesthesia
Pathology
Other

Evaluation & Management
Radiology
Medical25 / 50

Estimating procedure-level population elasticities
Examine spatial concentration of production and consumption by procedure Qpi is the count of procedure p produced in region i Qpi /Mi is production per Medicare beneficiary residing in region i

Estimate population elasticities of production and consumption by Poisson PML:
"
#
Qpi
ln E
ln populationi = ζp + βp ln populationi
Mi
Then relate estimated population elasticity βbp to p’s national frequency
26 / 50

Population elasticity of quantity
per Medicare beneficiary

Population elasticity of production declines with frequency
.4

.2

0
32

256

2,048

16,384

131,072

1,048,576

National Volume of Procedure
production
Production fitted line: y = −0.025 (0.002) * x + 0.380 (0.016)
This plot depicts estimated population elasticities per Medicare beneficiary for 8,210 procedures
produced at least 20 times nationally.

27 / 50

Population elasticity of quantity
per Medicare beneficiary

Population elasticity of consumption declines less with frequency
.4

.2

0
32

256

2,048

16,384

131,072

1,048,576

National volume of procedure
production

consumption

Production fitted line: y = −0.025 (0.002) * x + 0.380 (0.016)
Consumption fitted line: y = −0.007 (0.002) * x + 0.138 (0.014)
This plot depicts estimated population elasticities per Medicare beneficiary for 8,210 procedures
produced at least 20 times nationally.

28 / 50

Example procedures: Trade in colonoscopy & LVAD

CodeN
Physiciansβ̂pproductionβ̂pconsumption
Share traded (HRR)
Share traded (CBSA)Median distance traveled (km)Share > 100km

Colonoscopy

LVAD Implant

G0121
58,785
13,469
-0.01
-0.01
0.10
0.11
13.83
0.04

33979
333
177
0.87
0.03
0.48
0.48
65.27
0.37
29 / 50

Stronger home-market effect for rarer proceduresln E (Sij ) = λX ln populationi + λM ln populationj + γ ln distanceij

Market−size elasticity

1

.5

0

−.5
0

2

4

6

8

10

Procedure frequency decile
Provider population

Patient population

30 / 50

Stronger HME for rarer procedures (with region-pair FEs)
Procedure(1)(2)
λX Provider-market population (log)

Procedure(3)(4)

Procedure(5)(6)

Diagnosis
(7)
(8)

0.618
(0.0516)0.360(0.0519)0.605(0.0493)0.364(0.0492)

0.603
0.606
(0.0493)(0.0488)
0.366
0.364
(0.0492)(0.0486)
0.344
0.329
0.362
(0.0447) (0.0405) (0.0452)-0.241-0.239-0.250(0.0606) (0.0587) (0.0612)

0.601(0.0489)
0.371(0.0486)
0.317
0.120
0.110
(0.0392) (0.0232) (0.0206)-0.220-0.0986 -0.0915(0.0564) (0.0186) (0.0172)

p-value for H0 : λX ≤ λM
p-value for H0 : µX ≤ µM

0.005

0.006

0.007
<0.001<0.001

0.005<0.001<0.0010.008<0.001<0.001

Observations
Distance Controls
Distance [quadratic] controls
Patient-provider-market-pair FEs

187,272
Yes

110,402
Yes

110,402
Yes

110,402110,402109,658

109,658

Yes

Yes
Yes

Yes

Yes
Yes

λM Patient-market population (log)µX Provider-market population (log) × rareµM Patient-market population (log) × rare

110,402
Yes

Yes

31 / 50

Strong HME for specific common & rare services

Procedure:
HCPCS code:
λX Provider-market population (log)λM Patient-market population (log)Distance (log)Distance (log, squared)

p-value for H0 : λX ≤ λM
Observations
Distance elasticity at mean
Total count(1)
Colonoscopy
G0121

(2)
Cataract surgery
66982

(3)
Brain tumor
61510

(4)
Brain radiosurgery
61798

(5)
LVAD
33979

(6)
Colon removal
44155

0.454
(0.0600)
0.391(0.0609)-0.497(0.339)-0.116(0.0338)

0.349(0.0819)0.521(0.0711)-0.0800(0.479)-0.170(0.0474)

0.932(0.0936)
0.162(0.0767)
1.033(0.513)-0.266(0.0553)

1.107(0.126)
0.191(0.0943)
1.125(0.608)-0.272(0.0628)

1.332(0.158)0.115(0.136)2.042(0.942)-0.352(0.0940)0.871(0.180)-0.0729(0.159)6.620(2.978)-0.850(0.307)0.280
93,636-1.62
58,7850.891
93,636-1.66
43,547<0.001
93,636-1.56
1,922<0.001
93,636-1.54
754<0.001
93,636
-1.50
333

0.001
93,636-1.58
112

32 / 50

Scale elasticity estimates α̂ for rare procedures
Rare services

Baseline

No Diagonal

Controls

OLS: 2017

0.972(0.035)1.119(0.048)0.938(0.041)

OLS: 2013–2017 difference

1.326(0.264)0.859(0.542)1.348(0.278)2SLS: population (log)0.941(0.041)[1581]

1.074(0.053)[1575]

0.897(0.053)[1143]

2SLS: population (1940, log)

0.857
(0.065)
[129]

1.078
(0.089)
[128]

0.797
(0.072)[164]
33 / 50

 Empirical setting and geographic patterns
 Theoretical framework
 Regional increasing returns in medical services- Estimating the scale elasticity- Strong home-market effect for aggregate medical services

 Mechanisms- How market-size effects vary with procedure characteristics- Sources of increasing returns: Division of labor and lumpy capital- Travel to access specialized services

 Health policy with trade and increasing returns

Rare specialties have higher population elasticities

Population elasticity of MDs in
specialization per capita

1.5

One source of increasing
returns could be divisionof labor among physicians

1
Transplant Surgery
Pediatric Emergency Medicine
Transplant Hepatology Reproductive Endocrinology.5

Medical Toxicology
Forensic Pathology
Foot and Ankle Surgery
Retina Specialist
Thoracic Surgery

0Internal Medicine

Family Medicine

−.5
4

32

256

2048

16384

Pearson correlation: -0.446.
Fitted line: y =−0.072(0.010) ln x+0.798(0.065)
Plot excludes 2 observations withelasticity greater than 1.60.

131072

Number of physicians in specialty
Slope: −0.052 (0.009)

34 / 50

Population elasticity of equipment use
frequency per capita

Rarely used equipment has higher population elasticities
Augmentative Communication − WhitKit Head Support

2

Another mechanism could
be lumpy capital, namely
expensive medical
equipment

CO2 Infrared Analyzer (COSMO)

1
Articulator (Model 96H2 − HANAU)

Flow Cytometry Analytic Software

RGP Lens Modification Unit
Mayo Stand0

Table, Exam
CPAP/BiPAP Remote Clinical Unit
Densitometry Unit, Whole Body, DXA−1Whirlpool (Hubbard Tank)

10

100

1k

10k

100k

1m

10m

100m

Pearson correlation: -0.450.
Fitted line: y =−0.056(0.006) ln x+0.946(0.076)
Plot excludes 11 observation withelasticity greater than 2.29.

National equipment use frequency
Slope: −0.056 (0.006)

35 / 50

 Empirical setting and geographic patterns
 Theoretical framework
 Regional increasing returns in medical services- Estimating the scale elasticity- Strong home-market effect for aggregate medical services

 Mechanisms- How market-size effects vary with procedure characteristics- Sources of increasing returns: Division of labor and lumpy capital- Travel to access specialized services

 Health policy with trade and increasing returns

Trade expands access to specialists, experience, and equipment

 Traded procedures are specialist-intensive. . .
 . . . especially smaller markets’ imports
 Small markets’ locally-produced care uses “non-standard” specialties more
 Larger regions & imported care have more experienced physicians Larger regions & imported care use rare equipment more

36 / 50

Traded procedures are specialist-intensive8 Classify a procedure as“generalist” if performed
by Internal Medicine,
Family Medicine, andGeneral Practice ≥ 70%(2,492 procedures)

Frequency

6
4
2
0
.2

.3

.4

.5

.6

Share of specialist procedures
Local

Imports.7

 Classify as “specialist” if
top two specializations do≥ 70% (7,533 procedures) Imports are more likely to
be specialty care thanlocally produced
consumption

37 / 50

Share performed by specialist

Smaller places more likely to import specialty procedures

.6

.55

.5

.45
150k

300k

600k

1.2m

2.4m

4.8m

Population
Imports

Locally produced

Import slope: −0.018 (0.003)
Locally produced slope: −0.002 (0.004)

38 / 50

Share performed
by non−standard specialists

Care provided by “non-standard” specialties in smaller places.08

In smaller regions,.07

 locally produced caremore likely performedby “non-standard”
specialist

.06

.05
150k

300k

600k

1.2m

Population
Imports

Locally produced

2.4m

4.8m

 imports less likely
performed by“non-standard”
specialist

Import slope: −0.000 (0.001)
Locally produced slope: −0.004 (0.001)

39 / 50

Mean normalized experience

Larger regions & imported care have more experienced physicians
 Physician experience:
number of times
billing the service codeover past year (scaledby code’s mean)

1.3

1.2

1.1

 Average experience
across codes, weighted
by spending (scaled bymean across HRRs)

1

.9
150k

300k

600k

1.2m

Population
Imports

Locally produced

Imports slope: 0.033 (0.011)
Locally produced slope: 0.035 (0.017)

2.4m

4.8m

 Imported care provided
by more experienced
physicians than locallyprovided care, at any
population size
40 / 50

Larger regions & imported care use rare equipment more

Share performed
with rare equipment

.022

.018

 Imports are more likely
performed with rareequipment

.014

.01
150k

300k

600k

1.2m

Population
Imports

2.4m

4.8m

 Larger regions more
likely to use rareequipment for locally
produced care

Locally produced

Import slope: −0.000 (0.000)
Locally produced slope: 0.001 (0.000)

41 / 50

 Empirical setting and geographic patterns
 Theoretical framework
 Regional increasing returns in medical services- Estimating the scale elasticity- Strong home-market effect for aggregate medical services

 Mechanisms- How market-size effects vary with procedure characteristics- Sources of increasing returns: Division of labor and lumpy capital- Travel to access specialized services

 Health policy with trade and increasing returns

Counterfactual scenarios

1. Increase reimbursements in one region- Spatial impacts on quality and access- Spillovers on neighbors- Heterogeneity by income

2. Subsidize imported care in one region- Spillovers on neighbors- Differences by population size
- Subsidies required by income

3. Increase proximity to make US geography ∼ Germany- Reduces market access-income gradient

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Counterfactual: Increase reimbursements in Rochester, MN

Change (%) in output quality δiChange (%) in patient market access Φi

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Counterfactual: Increase reimbursements in Paducah, KY
Change (%) in output quality δiChange (%) in patient market access Φi

 Spillover negative with exports to Paducah Net spillovers depend on whether market is net exporter
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Higher-SES patients are more willing to travel

Distance elasticity of trade

−1.5

−2

−2.5
1

2

3

4

5

6

7

8

9

10

Income decile
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Nationwide percentage increasein patient market accessper percentage increase in expenditure

Counterfactual scenarios: Raise reimbursements in one region
Two sources of contrast:

1.5

 Lower-income patients
disproportionately live insmaller markets

1

 Higher-income patients
travel more to high-quality
providers.5

Boston example:
0
150k

300k

600k

1.2m

2.4m

4.8m

Population size of treated region (log scale)
Total
Tercile 2

Tercile 1
Tercile 3

10m

 Tercile 3 gains 70% morethan tercile 1
 Difference due to shareimported from Boston
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Counterfactual: Subsidize imports for Paducah residents
Change (%) in output quality δiChange (%) in patient market access Φi

 Paducahans’ imports → agglomeration benefits in neigboring regions

 Positive spillovers correlated with baseline exports to Paducah

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Annual mortality rate per 100,000 inhabitants

Could geography explain US’s steeper health-income gradient?
GER
USA
2000

1500

mortality = 1400 − 0.012 incomemortality = 1700 − 0.019 income

1000 United States has asteeper health-income
gradient across regions
than other OECD
countries (e.g.
Germany) Does USA’s large size
→ costs ofremoteness?

500$20,000$40,000$60,000
Per capita net disposable income in USD (2017 PPP)

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Counterfactual: Match German market-potential distribution

Market access (log scale)

10,000Baseline
Counterfactual

 Transforming the US
geography to that ofGermany (equivalent tobroad travel subsidy). . .
 . . . reduces the Φi -income
elasticity by 21%

1,000 Rural areas have lower
incomes
100

market access = −11 + 1.7 income
market access = −6.5 + 1.3 income$16,000$22,500$32,000$45,250$64,000
Per capita net disposable income (log scale) Rural areas gain most in
this counterfactual

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Conclusions

Market Size and Trade in Medical Services
Findings:
 Domestic trade in medical services mimics trade in manufactures- But larger distance elasticity- Low-SES patients more sensitive to distance Scale economies → large markets are net exporters
 Market-size effects largest in lower-volume services

Counterfactual outcomes:
 Spillovers of production subsidies depend on net trade flows
 For net importers, travel subsidies have opposite spillovers
 Aggregate return highest in larger regions, but lower-income patients benefit
from subsidizing smaller regions
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Thank you