The Global Alliance for Genomics & Health (GA4GH) recently honored me by inviting me to blog about one of my recently published studies.
You can read the blog post "The perceived risks of sharing genomic data with researchers" here:
www.ga4gh.org/news_item/th...
Lastly, a shout-out to my co-author Manisha Jain, the study participants, and the Dutch Research Council (NWO) for making this research possible. (4/4)
- People with higher risk tolerance were much more likely to participate, increasing the risk of sampling bias on risk preferences.
- To improve participation rates, funds should prioritize data security, better communication materials, and an insurance scheme. (3/4)
- People were, on average, hesitant to share their DNA with researchers, primarily due to the risk of privacy violations.
- Financial incentives were not very effective in persuading people to accept these risks. (2/4)
Happy to share our new (open access) article in the Canadian Journal of Economics on people’s willingness to donate DNA for science and its relationship with economic incentives and preferences. A brief thread on what we learned (1/4):
doi.org/10.1111/caje...
The scenes in the Oval Office today will shame the US for decades. They’ll never be forgotten.
A world leader, fighting for his country against the 21st century’s Nazis, came to the US and was attacked & abused by a gangster regime siding with the fascist invaders.
A historic disgrace.
Seems like central banks have completely lost control of the only thing they are supposed to control: the money supply.
The preprint is also available on medRxiv:
www.medrxiv.org/content/10.1...
I mislabelled this cut-out version of the figure, it should read "Risk of any disease for males (MDC-males)" and NOT "prostate cancer".
While assuming 50% genetic test takeup, the figure displays minimum implicit tax up to the 80th percentile of risk within the standard risk class for the single-disease and multiple-disease CII contracts, in the four scenarios. The striped blue area corresponds to the range of the implicit tax observed by Hendren (2013, Econometrica) in market segments that had not unraveled, and the striped red area corresponds to the range for market segments that had unraveled. The breaks at the top of the tallest bars indicate that these bars' heights exceed the figure's.
(4/4) We also find that if the take-up rate remains <50%, or if the genetic-test accuracy does not improve as much as expected, then the selection pressure may remain more manageable. In this case, complete bans on genetic information in critical illness insurance markets could be feasible policy.
The figure displays minimum implicit tax up to the 80th percentile of risk within the standard risk class for the single-disease and multiple-disease CII contracts, in the four scenarios. The striped blue area corresponds to the range of the implicit tax observed by Hendren (2013, Econometrica) in market segments that had not unraveled, and the striped red area corresponds to the range for market segments that had unraveled. The breaks at the top of the tallest bars indicate that these bars' heights exceed the figure's.
(3/4) Under widespread take-up of genetic tests (>50%), we find evidence of substantial opportunity for selection from future PGIs (both at lower/upper bounds). Disease bundling reduces the selection noticeably, but is not sufficient to circumvent the problem fully. But most people are not tested.
Left panel: distribution of the risk of contracting any of the six diseases in the male multiple-disease contract by age 65, conditional on various information scenarios. The vertical blue line marks the average risk in the standard risk class. The standard risk class individuals are shown in blue; insurers treat these individuals identically, so the blue distribution corresponds to the distribution of private risk for these individuals. Right panel: implicit tax for consumers in the standard risk class as a function of their percentile private risk of contracting one of the diseases, for each scenario.
(2/4) We first predict the risk of seven critical illnesses, e.g., prostate cancer, while adjusting for both (i) observable risk factors, and (ii) the measurement error of the current PGI technology. We then leverage these predictions to model a realistic insurance policy covering many conditions.
(1/4) Excited to announce our new method that can estimate the predictive performance of _future_ genetic tests for common diseases (polygenic indexes, PGIs). We showcase the method by evaluating the impact of this fast-improving technology on critical illness insurance. SSRN: tinyurl.com/37rtbujx
It appears that the law of demand is not applicable to asset/speculative demand, because often as the price of an asset rises, so does the demand for the appreciating asset. This must in part explain the inherent instability of markets, Minskey-style?!
Sounds awesome, but could be hit or miss. What is the main argument?
We are going to look back at a lot of empty steel, glass, and concrete, wishing we had built a positive return capital stock instead.
We have now had at least 25 years of crisis rates, QE, or a combination of both. The capital stock built up during this time will be found unproductive in the coming years (educations with negative returns, hotels/offices/malls/airbnb with negatjve returns, CRE in general).
Few people have experience woth factoring in this uncertainty in their evonomic or financial decisions.
When central banks make use of interest rates as a gas and break pedal, the interest rate will never be exactly right for the prevailing circumstances. Artificially set interest rates will therefore cause misinvestent. The deeper the misinvestment, the more painful the next crisis will be.
In not too long, counterparty risk will be a serious consideration and transaction cost that most ordinary people typically do not experience when ordering a new car or kitchen.
And if the mark-to-market value would lead to financial institutions failing, the central banks are likely to let the financial institutions get rid of their Bitcoin based on historical cost accounting = massive wealth transfer.
Thought of this morning: as the interest in Bitcoin among financial institutions increases, it is not unthinkable that Bitcoin will end up on the balance sheet of central banks during future quantitative easing efforts.
I often recommend The Effect to my thesis students to fresh up on regression methodology and inference: theeffectbook.net
