Hospital Models
Group 1:
Hospital costs (Y) depend on: the number of beds (X1), the number of outpatients (X2),
the number of inpatients (X3), and the case mix (X4).
The costs related to the number of beds and outpatients are primarily fixed costs as these
determine the amount of infrastructure that is required for the hospital.
The costs related to the number of inpatients are primarily variable costs. That is, they
will determine how intensely the hospital is used in any given year.
The types of specialization and the need for specialized equipment matter as some types
of conditions are more expensive to treat than others. As a proxy for these effects on
costs, the case mix variable can be used.
General form of the model:
Y = f(X1, X2, X3, X4, ε)
where Y = costs
X1 = number of beds
X2 = number of outpatient visits
X3 = number of inpatient days
X4 = case mix
ε = a random error term that captures the effects of any other factors on hospital
costs.
Y
Y
Y
Y
 0,
 0,
 0,
 0.
It is expected that
X 1
X 2
X 3
X 4
Group 3:
Hospital costs (Y) depend on:
a. the number of patients,
b. the number of specialists,
c. the number and types of specialization
d. political policies (that may effect factors such as wages)
e. natural disasters
f. the cost and amount of research
g. the standard of living of the people living in the area
h. the number of nearby hospitals
i. the type of hospital
f. the insurance structure
For (a) - (g), it is believed that costs are positively associated with these variables. For
(h), it is expected that costs would be negatively correlated with this variable. For (i) and
(j), the direction relationship would depend on how these factors are measured.
Not all of these variables are observable in the data set.
General form of the model:
Y = f(X1, X2, X3, X4, ε)
where Y = costs
X1 = number of inpatients (corresponds to a.)beds
X2 = case mix (used as a proxy for impacts of b. and c. )
X3 = Nonprof (used as a measure of the type of hospital and may also pick up
insurance effects)
X4 = Forprof (used as a measure of the type of hospital and may also pick up
insurance effects)
ε = a random error term that captures the effects of all of the other variables listed
and any other factors on hospital costs.
Y
Y
 0,
 0 The signs of the other impacts are difficult to
X 1
X 2
predict as they depend on whether or not management decisions differ across types of
hospital and whether or not for profit hospitals can ‘cherry pick’ which patients they
accept.
It is expected that
Group 5:
Hospital costs (Y) depend on:
(1) Externalities / uncertainties / unpredictabilities”
disasters
epidemics
pandemics
(2) Technology Development
short-term: cost of purchase
long-term: more efficient  decrease in costs
(3) Insurance system (affects profit levels)
(4) Staff training / education
increase in costs if hospital paying for classes
decrease in costs if staff efficiency improves
(5) Number of beds and case mix
(1) consists of random events that will be captured through a random error term
(2) and (4) consists of factors potentially observable factors for which we do not have
data
(3) the type of hospital (for profit or not for profit) may be related to how many insured
patients and what types of insured patients are treated. So, the category of hospital could
be thought of as a proxy for this.
(5) these data are in our data set.
General form of the model:
Y = f(X1, X2, X3, X4, ε)
where Y = costs
X1 = number of beds
X2 = case mix
X3 = Nonprof (used as a proxy for insurance system)
X4 = Forprof (used as a proxy for insurance system)
ε = a random error term that captures the effects of all of the other variables listed
in (1), (3) and (4) any other factors on hospital costs.
Y
Y
 0,
 0 The signs of the other impacts are difficult to
X 1
X 2
predict, especially since they depend on whether or not the type of hospital is a good
proxy for the insurance system.
It is expected that
Unemployment Models
Group 4:
Alberta’s unemployment rate (Y) is a function of:
(a) the interest rate (as interest rates go up  investment falls  decreased
demand for labour  increased unemployment)
(b) wages (as wages increase  labour becomes more expensive  demand for
labour falls  unemployment increases)
(c) GDP growth (as the economy grows  more labour is needed to produce the
extra goods and services  unemployment falls)
(d) Average work week in the Canada (when workers can get plenty of hours 
less likely to move to Alberta to find work  lower supply of labour 
unemployment rate falls)
(e) Exchange rate (as the Canadian dollar becomes more expensive for US
customers  exports fall  demand for labour falls  unemployment rate
increases)
General form of the model:
Y = f(X1, X2, X3, X4, X5, ε)
where Y = Alberta’s unemployment rate
X1 = interest rate (BR or possibly the prime rate)
X2 = wages
X3 = GDP growth
X4 = AWW
X5 = exchange rate (Canadian dollar price of a US dollar)
ε = a random error term that captures the effects of any other factors on
unemployment.
Currently, the data set does not include X2 or X3. If these data are not collected, these
variables will have to be relegated to the error term.
It is expected that
Y
Y
Y
Y
Y
 0,
 0,
 0,
 0,
 0.
X 1
X 2
X 3
X 4
X 5
Group 6:
Alberta’s unemployment rate (Y) is a function of:
(i) the inflation rate (positive)
(ii) political policies that affect job opportunities
(iii) the exchange rate (due to impacts on exports and imports)
(iv) the mortgage rate (positive)
General form of the model:
Y = f(X1, X2, X3, X4, ε)
where Y = Alberta’s unemployment rate
X1 = inflation rate
X2 = exchange rate (Canadian dollar price of a US dollar)
X3 = mortgage rate
ε = a random error term that captures the effects of political policies and any other
factors on unemployment.
Currently, the data set does not include X1 or X3. If these data are not collected, these
variables will have to be relegated to the error term.
It is hypothesized that
Y
Y
Y
 0,
 0,
 0.
X 1
X 2
X 3
(There may be ‘competing macroeconomic theories’ that predict different signs for the
impacts of X1 and X3)