24(40)
Tabell 5.
Tooth extraction vs. Basic examination & diagnostics,
performed by dentist. Competition within 5 kilometers.
(1)
(2)
(3)
Interaction: K*competition5km
Elasticity
-0.00133
+
-0.00133
-0.00133
Standard error
(0.000730)
(0.00199)
(0.00199)
Constant
6.717
6.848
6.692
Clinic FE
No
Yes
Yes
Time dummy
No
No
Yes
0.613
0.772
0.814
adj.
0.613
0.738
0.786
17,596
17,596
17,596
Standard errors are clustered at the clinic level and the pooled model is estimated
with a public clinic dummy. All regressions are weighted with the number of patients
at each clinic per year.
+
p
< 0.10,
*
p
< 0.05,
**
p
< 0.01,
***
p
< 0.001
The models have been estimated separately for different types of
geographical areas. The elasticites are -0.0192% for “Metropolitan
municipalities” and -0.0121% for “Large cities”. These areas are defined
as municipalities with a population of over 200 000 inhabitants and a
population of 50 000–200 000 respectively. The point estimate is negative
but not statistically significant for “Forest counties”, defined as sparsely
populated counties (consisting of several municipalities) with a large
proportion of forest land. Municipalities that does not fit into any of the
aforementioned categories forms the group “All other areas”. The elasticity
for this group, consisting of about half of all municipalities, is -0.012%. The
significance level drops from 0.001 to 0.10 when adding clinic fixed effects.
The point estimates follow a similar pattern when using competition within
5 kilometres. The elasticities are around -0.011% for “Metropolitan
municipalities”, “Large cities” and “All other regions”, but only statistically
significant on the 10%-level when adding clinic fixed effects for “All other
regions”. The point estimates are negative but not statistically significant
for “Forest counties”. Definitions of the geographical areas are given in the
appendix.
Table 6 present results where the first-stage service is defined as “Full
examination, performed by dental hygienist“. A 1% increase in competition
is followed by a 0.017% decrease in the price for “Full examination”
realtive to “Tooth extraction”. The mean of clinics within 1 km is 16.3 for
the sample used in the analysis. Relating the point estimate to the mean
number of clinics gives that one extra clinic within 1 km is followed by an
increase in the price difference between full examinations and tooth
extractions with 0.102% (
6x0.017).
The sample is about half of that used in the baseline model. This can be
explained by the fact that auxiliary personnel such as dental hygienists are
much more common in public clinics. Therefore, many private clinics are
excluded from the estimation sample.
