Skip to Main Content
Table 5

Marginal effectsa

VariablesGainsProbHealthStateChildHealthParentHealth
Avert − 0.05 0.01 − 0.08 − 0.003
Gains  − 0.06 0.006 0.07
PriceAvert 0.00002 0.00003
TimeAvert 0.0009 − 0.0005 0.0006 0.002
WorkMore 0.14
WorkAbility 0.12
ChildRisk 0.005 0.002 − 0.018 − 0.009
ParentRisk 0.005 − 0.005 0.003 0.006
AAS 0.00003 0.0002 0.00009 − 0.0003
Age    − 0.002
Male    − 0.0009
VariablesGainsProbHealthStateChildHealthParentHealth
Avert − 0.05 0.01 − 0.08 − 0.003
Gains  − 0.06 0.006 0.07
PriceAvert 0.00002 0.00003
TimeAvert 0.0009 − 0.0005 0.0006 0.002
WorkMore 0.14
WorkAbility 0.12
ChildRisk 0.005 0.002 − 0.018 − 0.009
ParentRisk 0.005 − 0.005 0.003 0.006
AAS 0.00003 0.0002 0.00009 − 0.0003
Age    − 0.002
Male    − 0.0009

aThe effects of changes in independent variables in an ordered probit model are not easy to interpret. Keeping in mind that care must be taken in interpreting the coefficients that come from an ordered probit, marginal effects must be computed as partial derivatives for continuous variables and discrete changes must be computed for effects of binary variables (Greene 2003). For binary variables the interpretation is the increase or decrease in probability that the dependent variable takes on the value of 1 if the binary variable is 1. The marginal effects for the continuous variables can be interpreted as the approximate increased or decreased probability that the dependent variable takes on the value of 1, given one more unit of the explanatory variable, with other explanatory variables held at their mean. Even with these extra calculations researchers warn that marginal effects should be used with caution and for an overall impression only (Liao 1994).

Close Modal