Table 7 Demographic variables impact.

From: Understanding drivers when investing for impact: an experimental study

 TIF vs. IIFImpact descriptionRisk factorTax deductionVisual aid
Intercept2.09***2.87***1.49*** − 0.172.38***
 (0.20)(0.22)(0.19)(0.48)(0.64)
Male0.47***0.50***0.65***0.110.78**
 (0.09)(0.09)(0.09)(0.23)(0.30)
Expert0.74***0.65***0.342.23*1.76
 (0.19)(0.20)(0.18)(1.05)(1.09)
Age0.02***0.010.02***0.04**0.01
 (0.00)(0.00)(0.00)(0.01)(0.01)
Higher education −0.11 −0.160.25* −0.15 −0.06
 (0.12)(0.12)(0.11)(0.29)(0.38)
Postgraduate education0.050.240.110.200.35
 (0.14)(0.14)(0.13)(0.36)(0.50)
Other0.310.200.380.30 − 0.29
 (0.23)(0.24)(0.22)(0.61)(0.70)
Previous knowledge0.150.060.060.22 −0.17
 (0.12)(0.12)(0.11)(0.28)(0.39)
Video displayed0.35***0.27*0.27**0.98***0.00
 (0.10)(0.10)(0.10)(0.26)(0.34)
Delta0.97***0.96***0.71***  
 (0.04)(0.04)(0.04)  
AIC3262.163084.173545.96551.29352.44
BIC3322.263144.273606.06590.89392.04
Log likelihood −1621.08 −1532.08 −1762.98 −266.64 −167.22
Deviance3242.163064.173525.96533.29334.44
No. of obs.301030103010602602
  1. Each model (column) corresponds to a regression for each framing type. TIF vs. IIF considers data from Q1 and Q2; impact description from Q3 and Q4; risk factor from Q5 and Q6; tax deduction from Q7; visual aid from Q8. We consider three different levels for education: higher education, postgraduate education, and other (i.e., non-curricular education besides basic education programmes); delta coefficients correspond to the return differences between TIF and IIF. Observations correspond to all participants’ responses to questions according to the framing type, columns from left to right: Q1 and Q2, Q3 and Q4, Q5 and Q6, Q7, and Q8. Values in bold correspond to statistically significant coefficients.
  2. ***p < 0.001, **p < 0.01, *p < 0.05.