Making policy is so 20th Century

The board for the coop building where I live has issued a memo introducing the idea of making our collective residence a non smoking building.  Evidently, they were spurred into action by a passionate discussion about non smokers' rights during the recent monthly public meeting.  They write:  "owner-expressed concerns about second hand smoke as well as prevailing standards and practices related to smoking informed the discussion."  While the memo neglected to define non smoking building, it asked owners for a yea or nay vote about whether we should "transition" to a non-smoking building.  A better question might have been:  do we need a policy to solve the second hand smoke problem?

There are too many problems with the board's approach to enumerate here.  Initially, the word bungled comes to mind.  They are handling this matter as if they themselves are members of the US Congress or Council of the European Union.  That means chaotic, short-sighted, out of touch with reality, truly uninformed, and motivated by power and hegemony.  Their memo illustrates:  how to stir up angst and competition among neighbors who otherwise live together peacefully.  

The board's actions also illustrate the reckless practice of using policy as an intervention when it's unnecessary and furthermore, without considering how it may cause more harm than good.  In this case, addressing the problem of second hand smoke with a blanket no smoking anywhere policy is like using a cannonball to kill a flea.  Policy implementation is not a panacea for public problems, and in many situations policy effects may be more undesirable than living with the perceived problem.  The field of biomedicine provides a useful example.  Policies that fund and promote vaccine and antibiotic use save lives.  Upon discovery, they revolutionized advances in ability to produce optimal public health.  They reduce human morbidity and mortality, and unquestionably improve quality of life and life expectancy.  However, using them in an unrestrained manner is rendering them ineffective at best and iatrogenic at worst due to sequelae such as, antibiotic resistant bacteria, vaccine side effects, and populations with overall weakened immune responses.

It's worth noting that conventionally recognized strategies for solving public problems are limited to two intervention options:  (1) regulation through public policy or (2) privatization using economic markets.  Alternatively however, Elinor Ostrom reminds us that there are multiple remedies for any public problem and a limitless range of potential solutions for equitable allocation of public resources, referenced by economists as the commons.  Mostly, these approaches can be characterized as self-governing.

In this youtube video, The Role of Culture in Solving Social Dilemmas, Dr. Elinor Ostrom speaks about how local and regional farmers in Africa self-govern to manage access to water, a precious, limited, commons resource that is not owned by any individual or corporation, or regulated by any government.  Interestingly, her comments include exceptional attention to effectiveness and productivity resulting from these interventions.  It's also worth noting that Dr. Ostrom was trained as an anthropologist, not an economist.  Ponder that fact for a minute.  

Another example of self-governing is set in Manhattan.  From the New York TImes, At Strawberry Fields, Feuding Musicians Give Peace a Chance is a story about a group of street musicians who have organized themselves to allocate benefits derived from a famous, magical, coveted park bench.  The musicians rely on access to the bench, a commons resource, to produce income from performing.  By self-governing, they no longer need to call the police to settle disputes or risk being shunned by would-be patrons, who choose to avoid public obscenity and risk of becoming collateral damage from a full blown public brawl, rather than listen to music in Central Park.  The musicians have made a beautiful discovery.  Behaving cooperatively to achieve a common solution to a public problem protects the commons and ultimately their livelihood.

As for the second hand smoke problem at the coop, I'm wondering how to introduce self-governing concepts to my neighbors and the board, without sounding too weird.  It seems to me it's possible to solve the problem of exposure to second hand smoke without:  (1) dividing the community into yea or nay camps, (2) working tirelessly on developing a policy that will never satisfy everyone, and (3) monitoring and enforcing compliance once they implement yet another regulation.  What's irritating me and diminishing my quality of life are my neighbors' car alarms that spontaneously ignite just outside my window at 2 am or any other time for that matter.  These car owners make up the subpopulation I'll call other-side-of-the-building-residents who are oblivious to the noise from unattended auto noise that pollutes my-side-of-the-building environment.  I think the coop may have just opened a Pandora's Box.        

Unfortunate disconnect between the facts and policymaking

The New York Times published an op-ed by U.S. Congressman Andy Harris (R-MD 1^st District) in which he proposes a policy solution for the problem of, not enough innovative thinking to optimize the value of dollars spent on biomedical research.  Harris presents a fairly disparaging picture of the National Institutes of Health (NIH) and its poor ability to administer the money that Congress appropriates each year.  Whether this claim is true or not, one of his criticisms in particular is that NIH is funding the wrong people, investigators who, “though well-regarded in their fields,” are too old to make particularly valuable contributions to science. 

One of Congressman Harris’s more clearly defined solutions is based on an over-simplified problem definition, which presumes an inverse relationship between an investigator’s age and her propensity to produce innovative discoveries.  According to this framework, his proposed policy solution is to increase the value of dollars Congress allocates for biomedical research by lowering the average age of NIH investigators.  The sole basis for this solution rests on the results of a single study published by the National Bureau of Economic Research (NBER) entitled, Age and Great Invention (Jones, 2005).   It is strikingly noteworthy that The Review of Economics and Statistics published a peer-reviewed version of this study (Jones, 2010) and concluded--in the abstract--that, "innovators have become especially unproductive at younger ages.”  Furthermore, the Harris solution to increasing scientific productivity subtly and erroneously presumes that the relationship between age and productivity is causal.  In other words, young age causes productivity.  The NBER study does not seem to provide any empirical evidence to support that claim.  A more accurate interpretation suggests that the average age of “great innovation has trended upwards by approximately 6 years over the course of the 20^th Century” (Jones, 2005, p. 17), and reasonably so, because the demand for increasing time requirements of education and knowledge acquisition functions as an opportunity cost of preparing aspiring great achievers to produce.  Given a more accurate interpretation of empirical observations, alternative funding policies would have a greater probability of producing desired results by incorporating evidence of the trade-off between training investment and age at time of great achievements.

Too often, policy formulation is based on belief, custom, and a need to do something rather than an in-depth understanding about the true nature of the problem.  In the matter of how to promote and ensure innovative productivity in biomedical research, a truly wise and potentially more effective solution would be to invest increased resources in and attribute greater value to policy research and evaluation efforts designed to determine which factors and circumstances predict production of research discoveries that lead to marketable discoveries that are capable of improving public health.  Congressman Harris’s statements illustrate the inherent disconnect between policy research and how its results get haphazardly translated into quick-fix policy solutions with little promise for influencing desirable outputs.  It also points out the importance of defining a public problem succinctly and rationally, using empirical evidence as a guide. 

Here's another point worth noting:  Andy Harris will be sitting on the Labor Subcommittee of the House Appropriations Committee this fall.  There's a god chance that his proposed policy will become a reality, or at the very least, a total nightmare for those bureaucrats charged with preparing talking points for the upcoming budget hearings.

Data collection for health outcomes research: twenty-first century options

Here is an interesting article outlining an effort supported by the government of India to compile vital statistics for determining the leading causes of death in its country between 2001 and 2014 (New York Times, Door by Door, India Strives to Know More About Death, May 22, 2014).  While the sentiment is admirable, the approach seems byzantine and early 20th century.  Government registry officials visit the homes of deceased citizens using autopsy forms as a guide to verbal information gathering about circumstances surrounding cause of death for those individuals who died at home without medical supervision.  Good old fashioned epidemiology.  Of course collecting data this way provides huge opportunity for bias due to errors affecting its reliability, including those introduced by language barriers, human recall, and lack of respondents' willingness to disclose intimate family details to a stranger representing the national government.  Not to mention time-consuming:  results will not be final for at least five years.  With great insight, the article's author notes a more modernistic objection to building data sets of this type, that might not have been considered even 20 years ago:  "A great reservoir of information will be valuable to public health specialists, but will probably bring little to the families who were its subjects."

Contrast conventional vital statistics data collection methods with crowdsourcing approaches.  The crowdsourcing concept is characterized by collecting volumes of data, in a systematic manner, at no or very low cost to any single entity.  Millions of ordinary people, fueled by passion for the cause, each contributes information about an observation made during the course of their normal activities, at but a micro-cost to themselves.  Individual contributions are turned into digital data and amassed into a single, gigantic data set so the statisticians can do their magic.  In terms an economist would understand, using crowdsourcing to create a useful data set is like harnessing the antithesis of forces that lead to the tragedy of the commons.

An interesting article also published by the New York Times (Crowdsourcing, for the Birds, August 19, 2013) provides an illustration of two variations of crowdsourcing used by wildlife biologists to monitor bird populations.  It provides an articulate description of both efforts, each of which harness the power of crowdsourcing techniques to produce data for bird epidemiology research:  (1) the Breeding Bird Survey coordinated by the United States Geological Survey (USGS) and (2) the eBird project, a non-profit, global ornithological network of high tech data collectors.  The app-driven eBird project seems more productive, efficient, and appealing, despite criticisms lobbed against it by more conventional Bird Survey proponents, including the usual generic validity and reliability snarks.  But the Bird Survey is adept at organizing and training volunteer bird-watchers to count birds in a systematic, quasi-controlled manner, and transfer observations by hand to a paper survey form.  It requires conventional data processing methods to key in and compile data points to produce a final data set.  Still, final data set products from both projects are freely available to the public so that anyone can use the data for their own analysis.  Open data!  A highlight of the data gathering process for the eBird project is that it provides the ability to produce a real-time view of bird populations around the world using heat maps, a visually appealing, intuitively easy to understand depiction of bird species population density, location, and migration over time.  Imagine being able to track human health epidemics, diseases, conditions, and health outcomes in response to various interventions using similar methods.

My favorite quote from the article is, "Birds are notoriously hard to count."  This observation, as if birds are harder to count than any other species?  Scientists who count birds must not have any cross-training in the epidemiology of human health.  How is it that birds are any harder to count than babies?  Adults?  People with heart disease?  People living in poverty or substandard living conditions?  My mind almost short-circuited with thoughts about how crowdsourcing might be applied to compiling data sets for the study of human health and the effects of public policy and other interventions on health outcomes.  Crowdsourcing:  an untapped resource for producing data that could answer crucial questions in medical care research, particularly useful for very large, heterogeneous populations that were heretofore impossible to study because of the impracticalities of producing requisite data for a nominal cost.

Twenty-five hundred grams

Twenty-five hundred grams (2,500 g) is the upper cut-off point for classifying infant birth weight as undesirably low.  A customary public health metric, low birth weight is defined as an infant weight of less than 2,500 g (equivalent to 5 pounds, 8 ounces), measured within one hour of birth.  As it relates to infant health at birth, as infant size declines, risk for early death and acute and chronic morbidity increases.  Low birth weight is a noteworthy public health concern because:  (1) it is a major risk factor for infant mortality; (2) it consumes a disproportionately high level of health care resources; and (3) its prevalence continues to rise despite investment of public resources aimed toward improving birth outcomes.      

Infant birth weight is one of the primary measures of infant health at birth, making it a central target of much public health policy in both the U.S. and globally.  It is a well documented predictor of neonatal health, infant survival, and future health and productivity (Currie, 2011, p.12; Martin et al., 2011; Almond, Chay, & Lee, 2005).  Public health entities and policy makers traditionally view infant birth weight as an indicator of overall health at the population level because poor birth weight outcomes reflect maternal undernutrition, chronic ill health, excessive physical exertion or stress, and poor health care during pregnancy (The World Health Organization [WHO], n.d.; Stevens-Simon & Orleans, M., 1999).  The WHO and others also note its importance as an indicator of a country or region's health care system's effectiveness in delivering life-saving, life enhancing interventions to its citizens (Almond et al., 2005; WHO, 2000).

Federal and state governments are heavily invested in financing medical care for childbirth associated maternal and infant health outcomes.  In the U.S., public funding sources finance the cost of health insurance coverage for a substantial number of infant deliveries and subsequent medical care.  The Federal-State Medical Assistance Program, better known as Medicaid, is the primary source of insurance for publicly financed infant deliveries.  Federal and state governments share the cost of providing pubic insurance for low-income residents who meet certain eligibility criteria, and states administer their respective programs.

In 2010, the U.S. recorded 3,854,224 million births.  Of those, Medicaid provided insurance coverage for 45% (1.75 million) of associated deliveries and just under half (48% or 1.86 million) were billed to private payers; those that made up the remaining portion (3% or 131,205) were either uninsured or covered by other miscellaneous government programs (Source:  Agency for Healthcare Research and Quality [AHRQ], Nationwide Inpatient Sample [NIS], n.d.).  AHRQ indicator data that tracks sources of insurance coverage for all U.S. hospital deliveries illustrate a steadily rising trend in public insurance financing for infant deliveries under current eligibility criteria.  And as federal health care reform progresses toward full implementation, eligibility criteria are likely to expand, at least in many states.

 

Sex & Econometrics

You think it's impossible that these two topics are related?  Not so.  Imagine my delight when I came upon the following quote, embedded in a dry old journal article published in 1983, by The Journal of Economic Review.

"Methodology, like sex, is better demonstrated than discussed, though often better anticipated than experienced" (Leamer, 1983, p. 41).

I think it's poetic. 

Although Lerner wrote this paper in 1983 (based on a public lecture presented at the University of Toronto in 1982), it is crammed full of wisdom relative to current events in applied health research.  Take, for one example, the presumed belief in the relative power of experimental over nonexperimental data to produce credible inferences.  Customary assumptions rely on the belief that the randomized controlled trial (RCT) is the gold standard in medical care research.  Maintaining control over randomization automatically equates to "a rigorous study."  However, Lerner addresses this question directly when he asks, "Is Randomization Essential" (p. 31)?  The answer is:  "the randomized experiment and the nonrandomized experiment are exactly the same" (p. 32), at least in terms of drawing credible inferences.

Randomization is customarily the linchpin for determining the value of research today, but really it might better be viewed as one consideration among many others within the context of overall study design.  Here is a perspective that levels the playing field between the value of research produced by RCTs and econometric methods as far as their potential to generate valid inferences.  Regardless, any applied research is dependent on measurement whether using experimental or nonexperimental data.

My third favorite point:  "The job of a researcher is then to report economically and informatively the mapping from assumptions into inferences" (p. 38).  Sensitivity analyses ameliorate doubt about the reality of some assumptions, but others are not objective nor are they value free.  When this is the case, changes in assumptions can change inferences.

In conclusion, most facts are really opinions, and many opinions are based on conventions rather than truth.  So it seems that, just as there are similarities between sex and econometrics, questioning assumptions about econometric modeling conventions might be a process that is similar to how we determine the truths by which we live our lives.

Minimum Wage Outcomes

While it seems like raising the minimum wage is the right thing to do, it is probably a woefully inadequate intervention for improving the living standards of the population many believe it will benefit.  A recent CBO analysis projects the effects for two levels of a minimum wage hike to either $9.00 or $10.10 (it's $7.25 now).  Here are some bullets from the $10.10 scenario:  

• Many low-wage workers would see an increase in their earnings.
• Some of the people earning slightly more than $10.10 would also have higher earnings.
• Increased earnings for low-wage workers would not go only to low-income families, because many low-wage workers are not members of low-income families. Just 19 percent would accrue to families with earnings below the poverty threshold, whereas 29 percent would accrue to families earning more than three times the poverty threshold.
• Some people will became jobless because of the minimum-wage increase.
• Business owners and consumers will face higher prices.

Analyzing policies based on economic realities complicates simple realities.  And it may be one explanation for the prevailing prejudice liberals have about economists -- that they're all Republicans.  At $10.10 per hour, assuming that the number of hours in a typical work year is 2080, a minimum wage worker could expect to earn an annual income of $21,008, that's before taxes and don't forget health insurance premiums.   I can't see how anyone could manage a decent standard of living on such a salary without subsidies from somewhere and forget it if they're responsible for any dependents.

Nicholas Kristof Insults Academics

What is he saying?  It seems as if Mr. Kristof is speaking out of both sides of his mouth in his recent article Professors We Need You!  In Sunday's New York Times, his title suggests a sympathetic stance about the value those in ivory towers might offer in solving our world's ills, if only they weren't repeatedly marginalized by "American life."  But he provides overwhelming evidence that there are bona fide reasons for marginalizing professors and he even goes so far as to blame them for marginalizing themselves.  They've neglected the qualitative in favor of the quantitative.  Most political science professors can't formulate a policy prescription.  Professors submit meaningless gibberish to scholarly journals.  Kristof repeats insult after insult, sort of like gossip, to prove the point that professors have nothing valuable to provide in formulating useful public policy.

He begins by discussing professors although soon changes the target population to academics.  Is he referring to all academics?  Is that everyone who does research or just professors?  Categorizing people who differ in many ways into a singe group and comprehending them as being all the same is a dreadfully dangerous practice for anyone who presents him or herself as a humanist.  An epidemiologist would understand that there are sub-populations of academics and that they function differently, for different target audiences, therefore with differing influence.  A sociologist would likely explain that ascribing a group of people to a single category based on one obvious, easily observed trait is a fundamental requisite for creating and perpetuating inequality and prejudice.  An econometrician would understand that there are varying magnitudes of ineptitude (or value, depending on your perspective) expressed by academics according to multiple factors that vary in the effects each one has on each individual.  Lumping everyone into one group and saying they're marginalized illustrates fundamental thinking.  And what was he thinking when he categorized economists (except for Paul Krugman) as Republicans? 

Reframing the problem might be more productive than arguing about whether or not academics are marginalized.  The real issue may be that the research produced by academics or professors is often ignored, never has a voice, and may not even be conducted in the first place, in the context of our current government structure and political system.  Conscientious, well constructed, quantitative research studies can provide valuable insight into the effects public policy has on health, education, and general well-being of various individuals in a general population.  And ideally, research produced by multiple disciplines might be used to diminish factors that harm people, and create both public and private incentives for more humanistic policy outcomes.  But without the political will to manifest the American ideal of equality, it's not just academicians who are marginalized.