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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).

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