My friend, Magrey, just alerted me to a recent study which examined the dollar value of the emotional effects of life events such as marriage, home purchase, child birth, and divorce. Positive dollar amounts indicated “psychic benefits”; negative dollar amounts showed “psychic costs.” The study found.
• A man getting married feels like he just received $32,000. To women, it only feels like $15,600.
• Divorce feels like a $110,000 loss to a man, but only a $9,000 loss to a woman.
• The death of a lovd one feels like a $130,900 loss to a woman, and a whopping $627,300 deficit to a man.
• And moving into a new home? A positive $2,600 for a woman, but a negative $16,000 to a man.
Do any of the numbers surprise you?
Some of these numbers don’t add up to me. For example I read the full article and it says women value a birth at $8,700 while men feel like they just raked in $32,600. I don’t have children but that’s still difficult for me to believe. It also seems odd that the there is such a disparity between men and women when it comes to the death of loved one.
I’m not sure we can put assign a monetary value to certain things in life, that’s why we have the word “priceless.”
DCT, it’s not that women value birth at $8700, it’s that their (average? the article didn’t say) happiness following birth is roughly equivalent to getting $8700.
The method “…involves a comparison between the Discounted Life Satisfaction (DLS) of a life event (e.g. death of spouse), which incorporates both anticipation and adaptation e¤ects, with the DLS of a positive nancial shock (e.g. winning the lottery).”
As for the disparity between results for death of a loved one I suspect that because men generally have a smaller emotional support network, their relative unhappiness with a death is more / longer than a woman’s.
The actual study is at: http://ftp.iza.org/dp3604.pdf
IZA = Institute for the Study of Labor
Finally, while many people would call certain life events priceless, those events have to be quantified for legal purposes and this paper has a new method for making those determinations. The data comes from Australia, so I don’t know how applicable the numerical results are outside of Australia.