Autism research funding allocation: can economics tell us if we have got it right?

Berkovits et al. (2014) wrote a position paper about autism research money.

They asked if cost-benefit math can pick the best studies to fund.

The paper says we need clear goals and real-world payoff data first.

The team found that dollar formulas alone cannot set the budget.

Without stated aims and proof that labs turn cash into cures, any ranking is guesswork.

Mukherjee et al. (2015) agree that discovery must link to clinic use.

They warn that gene findings stay stuck unless behavior data travel with them.

da Silva Montenegro et al. (2020) give a live example: adding 30 Brazilian families to a 20 000-person pool spotted a new autism gene and gave 23 % a clear genetic answer.

That number is the kind of payoff metric D et al. say is missing.

Schaaf et al. (2015) look at the other end. They show support-needs scales, not old adaptive tests, predict personal funding.

Together the papers say: pick the right ruler for the right level—genes for labs, needs for kids.

You sit on grant panels or pick assessments. Push for two things. Ask applicants to state real-life targets like “cut parent stress 20 %” and to show past results. When you write goals in a treatment plan, copy the same rule—tie each hour to a visible daily skill. Money and time follow clear outcomes.