(denoted by superscripts) i.e., and , plus the natural logarithm amount donated (plus a scalar C – of positive unity), conditional on a leadership PAC donation being made i.e., and . More – formally, a pair of double hurdle regression models are estimated to test the theory’s predictions in relation to women group size (denoted by for male and female Senators’ colleague w) valuation decisions with respect to incumbent and challenger women: and The variable is a binary measure that accounts for female-male donor differences, Donor and is equal to 1 for female donors, 0 for male donors. Each equation also has a generic kth dimension vector of control variables at election cycle comprising donor-specific effects, X t recipient-specific effects, donor-recipient dyadic specific effects, plus a disturbance term, denoted as or in the equations above. Moreover, because the data include multiple v ε observations per donor-recipient dyad which may exhibit dependence across election cycles, robust standard errors clustered on this dimension are calculated and reported. Equation (5.1a) models the probability of a donation decision being made to a female incumbent Senator from a male Senator (α1I) or a female Senator (α1I + ), conditional on the proportion of women (w), α2I via a Probit equation. Equation (5.1b) models the expected value of the natural log of donations, conditional on one being made, female incumbent Senators obtain from their male (β1I) and female (β1I + Senate colleagues in relation to by truncated normal regression methods. w β2I) Equations (5.2a) and (5.2b) represent analogous specifications for the challenger models which capture Senators’ valuation of prospective colleagues.
(denoted by C superscripts) – i.e., and , plus the natural logarithm amount donated (plus a scalar of positive unity), conditional on a leadership PAC donation being made – i.e., and . More formally, a pair of double hurdle regression models are estimated to test the theory’s predictions in relation to women group size (denoted by w) for male and female Senators’ colleague valuation decisions with respect to incumbent and challenger women: and The variable Donor is a binary measure that accounts for female-male donor differences, and is equal to 1 for female donors, 0 for male donors. Each equation also has a generic kth dimension X vector of control variables at election cycle t comprising donor-specific effects, recipient-specific effects, donor-recipient dyadic specific effects, plus a disturbance term, denoted as v or ε in the equations above. Moreover, because the data include multiple observations per donor-recipient dyad which may exhibit dependence across election cycles, robust standard errors clustered on this dimension are calculated and reported. Equation (5.1a) models the probability of a donation decision being made to a female incumbent Senator from a male Senator (α1I) or a female Senator (α1I + α2I ), conditional on the proportion of women (w), via a Probit equation. Equation (5.1b) models the expected value of the natural log of donations, conditional on one being made, female incumbent Senators obtain from their male (β1I) and female (β1I + β2I) Senate colleagues in relation to w by truncated normal regression methods. Equations (5.2a) and (5.2b) represent analogous specifications for the challenger models which capture Senators’ valuation of prospective colleagues.
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