Tuesday, July 17, 2018

Drug Poisonings and Chronic Health Conditions

In an earlier post, I wrote that about 20-25% of drug poisoning deaths involved some kind of chronic health condition. See what I wrote. I think this calls into question whether these were truly drug overdoses. If a death certificate for a supposed opioid poisoning has “sleep apnea” and “obesity” listed on it, that calls into question whether, in a “but for” sense, the death was caused by the drugs. It's more accurate to say that the death was a combination of a drug overdose (drug interaction, in most cases) and a pre-existing health condition.

I’ve done some more thorough analysis, and it looks like I was on the right track with the 20-25% figure. I can’t give any details just now, but I’m glad I stumbled on this path. Will share details and acknowledgements when I can.

I also know quite a lot more about how the death certificate is filled out, how the causes of death get coded in the ICD-10 codes, how an “underlying cause” is selected from the various causes listed on the certificate, how the different parts of the certificate work, etc. When I wrote that post (linked above) a couple of years ago, I had no idea about any of this. I guess I just assumed it was a bunch of contributing causes listed in no particular order. The story is a little more complicated than that. I may have to do a “death certificate explainer” post when the paper I’m working on comes out. I don’t think the stuff I know now invalidates anything I wrote before, but the details are interesting.

Shoulder Flexibility

I’ve been taking private gymnastics lessons, one or two a months since last November. I’d always wanted to do those cool gymnastics tricks (front handsprings, back handsprings, back flips, aerial cartwheels, etc.) After eight lessons, I have a solid front handspring, and I’m close to a back flip and an aerial. My back handspring is not at all ready for prime time, but I can do them on a trampoline. I’m fairly pleased with my progress. In my most recent lesson (last week), we figured out I could do a roundoff after about five minutes of instruction. That was satisfying.

I said this before in a previous post : It’s really important to get a coach with stuff like this. You can sometimes make impressive gains on your own, but there’s no substitute for having an expert telling you what you’re doing wrong. Try this for anything you want to get better at. Are you struggling with a computer language or some other skill at work? Find someone who knows better than you and ask them. Even offer to pay a tutor. Come up with a set of questions or skills you want to learn, then set up a meeting with a co-worker or paid tutor who is willing to teach you. You might find you’re not even asking the right questions.

One thing I never would have thought about is my shoulder flexibility. I had very stiff shoulders, which my gymnastics coach spotted right away. I’ve been stretching them daily since November, and they’re a lot more flexible now. I have very good leg flexibility (almost down to the splits), but never stretched my shoulders. This deficiency was masked from my view, because in my mind “I’m pretty flexible.” I never would have thought to work on this. My front handspring (which I could already do to a reasonable standard before I started stretching) has gotten much better. The push off the ground isn’t at the proper angle unless you can reach your arms far enough above/behind your head.

The moral of the story: get a coach.

Monday, July 16, 2018

Personal Debt of CEOs as Pre-commitment

I’m interested in the topic of very high executive pay, which I’ve written about several times on this blog. The short version of my take: the expected payoff for getting the very best person, as compared to the second or third-best person, can be worth millions or even billions of dollars. So it’s worth shelling out for top talent. Also, once you snag this person, it’s worth coming up with some kind of incentive scheme to actually motivate them. A relatively low base-salary with generous bonuses for performance is probably better than a flat multi-million dollar base salary.

Another problem might be the following: Once you snag the best person, how do you ensure they stay snagged? If someone paid me a $5 million salary, I’d be very tempted to work for one year and then retire. A tiny fraction of the pay that a high-powered corporate executive makes over the course of their career would make a substantial nest egg for any mere mortal. Warren Buffet’s austerity aside, many CEOs have reputations for lavish spending and luxurious homes.

I’m curious if lavish spending can be a sort of commitment strategy for highly-paid executives. In The Smartest Guys in the Room, a book about the Enron crisis, there is a brief discussion of Ken Lay’s personal finances at the end of his tenure. Enron was basically trying to oust him, but he wouldn’t go quietly. It seems he’d racked up $20 million or so in personal debt, some no doubt from his own consumption, some from supporting various benefits and charities. He made sure to negotiate a nice severance for himself, because he had backed himself into a tough spot. (This is from memory, from a book I read in 2011. If any of the details don’t match the actual story, I’m sorry. In that case, please take this example as a hypothetical.)

In that particular case, the pre-commitment strategy backfired. Lay’s debt made it harder for Enron to get rid of him when they wanted to. But in normal times and for normal companies, it’s probably a good thing for a firm if their CEO is sweating a little about their personal finances. It means they’ll stick around. It solves the “What’s keeping this guy from working for a single year then leaving?” problem.

There are other solutions to this problem. “Debt as a pre-commitment” is surely a small piece of the answer. There’s always the good old contract. Just have a clause saying the new CEO will stay on for at least 5 years or something. This isn’t perfect. You might get two great years and then three lack-luster years as the CEO loses interest in the company and starts salivating over his early retirement. Contract or no contract, it’s impossible to make someone show up to work and perform to the peak of their ability if they just don’t wanna. Reputation has to be part of the answer, too. People have personal reputations for things like ambition, honesty, and so on. The board will consider these traits when they appoint their next CEO, looking for some assurance that the person will stick around. Maybe for the kind of person who is likely to climb to the top of a corporation, the “work one year, then retire early” option isn’t even appealing. Maybe the vetting/promotion process selects for people with a lot of raw ambition.

There is a broad literature on this topic of incentivizing executive performance. In the actuarial exam syllabus, there is a discussion of whether or not investors should let companies hedge their risks, and why or why not. The investors can hedge their own risks by investing broadly, so they don’t want the individual companies to blow a lot of money on expensive insurance policies or hedging strategies. But one reason to allow hedging has to do with incentivizing the executives. You want the executive to make a substantial investment in the company, the thinking goes, so they have skin in the game. But it hurts to lose skin, so CEOs will hesitate to invest more than they absolutely have to. Allow the CEO to hedge and protect their investment, and they’ll be prone to invest more of their own private wealth in the company. With more skin in the game, they’re on the hook for bad decisions, they’re more likely to take a long term view, and so on. This is another way to solve the problem described above. It will be hard for a CEO to leave and sell off their investment in their employer without losing a substantial chunk of their investment. A CEO’s surprise retirement is likely to make a company’s stock take a dive. Similar with a major shareholder selling off large quantities of stock. This is a good way for a CEO to tie himself to the mast.

Interestingly, Jeff Skilling, who succeeded Lay at Enron, seems to have tried some version of “work a single year then leave” strategy. Except he didn't quite make it a full year: 

On February 12, 2001, Skilling was named CEO of Enron, receiving $132 million during a single year….Skilling unexpectedly resigned on August 14 of that year, citing personal reasons, and he soon sold large amounts of his shares in the corporation.

Oops! Apparently it’s important to worry about this problem. The Smartest Guys In the Room fills in the details of his short tenure and unexpected resignation.

Wednesday, July 11, 2018

What If There Were Mutants?

Suppose Magneto exists. Or Superman, or Jean Grey, or Green Lantern. There’s a population of people with unusual gifts that allow them to be extremely productive. What happens? As Tyler Cowen might say, solve for the equilibrium. (Ignoring for a moment the offensive potential of these gifts. A Magneto or Superman could bring a nation to its knees or destabilize an existing global order. Pretend for a moment that these gifts make certain individuals more productive without allowing them to be offensive. That's not what this post is about.)

These people would be vastly more productive at certain kinds of jobs. Moving very large objects with surgical precision, without having to move in heavy equipment (cranes and the like) could make big construction projects a snap. Sure, we still need architects and engineers to direct the work. But “put all of these giant girders in their places” and “move a thousand tons of earth, okay now another, now another” becomes easy.

These people are likely to be highly paid. If your construction firm can build a tall building in a few days, while the other developers are still taking months or years for similar projects, you can get a lot more done and earn a lot more money. So your firm should be willing to bid a very high price for the unique talents that allow you to accomplish quick production. The Green Lanterns and Magnetos will be at least millionaires if not billionaires.

What happens to the rest of us? Do hundreds of thousands (millions?) of displaced construction workers languish in permanent unemployment? Is there a super-rich society of mutants who do everything in the economy? (And do they thus own everything?)  And the rest of us can only eat by sifting though their garbage for scraps? Of course not. Comparative advantage is still in play. If Magneto is doing the work of thousands of people, he’ll likely want to come home to an already-cooked meal, or an already-cleaned home. Maybe he could do these tasks himself better than any mere mortal simply by using his powers, but it is better still for him to spend more hours at work and use his massive pay-check to hire some servants. Having some very rich, productive people nearby creates employment opportunities.

Or let me put it another way. For whom is all this production happening? Suppose a small group of mutants are responsible for half the world’s production. What exactly are they doing? Are they simply building gigantic homes for other mutants? Are some mutants mass-producing consumer goods for other mutants, who repay them with thousands of tons of bulk materials? No, this production is bound to benefit everyone, even if highly productive individuals get compensated for their contribution. Transportation, food, consumer goods, and housing get cheaper in general.  

Or let me put this yet another way. In 1800, 83% of the US labor force was in agriculture. 
Today it’s around 2%. So compared to 1800, each agricultural worker is doing the work of about 40 people. Mutants indeed! Do they own everything? Did this 2% take over the economy? Is it even remotely sensible to say that today’s agricultural workers are doing 4/5th of America’s production? Again, of course not.  The people who would have been farmers in an older economy found other useful things to do. Like become telephone operators, photographic film developers, and video-store workers. Oops, and then those jobs basically disappeared. Are former telephone operators and video-store workers languishing in permanent unemployment? Again, no. Most people have a very bad intuition about what happens when production get automated, when a few people can now do what used to take many people. And it leads to some very silly commentary.

A tangential point. Suppose there are a lot of good CEOs of various construction firms. If you tried to throw an average person in that role, they would flounder and fail and ruin the business. But there’s a wide enough pool of talented CEOs to pick from. Now, suppose there is one who is uniquely skilled at management for a particular firm. He knows that particular market really well, he can forecast demand just slightly better than any of his competitors, he’s slightly better at keeping his workers motivated and his managers on track. Maybe there are 10 or 100 people who could do the job reasonably well, but the very best guy for the job will build one more medium-sized building each year with the same amount of resources. This person’s unique contribution to the world’s production is one building per year. But for this individual, we’d be short one building per year of his/her working life.  That’s quite a lot for one person to accomplish. It’s like having a mutant superpower. Of course, he’s not literally moving the building materials with his mind, shaping raw steel and cement into a structure. He’s doing it by making hundreds or thousands of workers just a little bit more productive, efficient, and focused. It’s no less impressive for that.

There are people who like to turn the topic of CEO pay into outrage-porn. I think they are deeply mistaken and there is nothing to be outraged about. These executives really are very productive, pretty much in line with their enormous paychecks. There are some “respectable”, “academic” attacks on CEO pay. Some academics have attempted to “prove” that CEO’s aren't particularly talented or good at their jobs. I admit I do not know this literature, so maybe it’s more convincing than I’m giving it credit for. (On the other hand. Perhaps something is happening in the world that’s hard to measure? Perhaps the difference between the very best candidate and the second- or third-best is something ineffable? It seems to me that the people shelling out for top talent have skin in the game and make decisions based on things that are hard to measure or define. Particularly at the very top, decision-makers have to be a little speculative. Contrast this with academics, with no skin in the game and who are only good at discussing things that are externally visible and measurable.) This post by Steven Landsburg says it well. It’s really important to have the very best person in the top spot. Some critics draw exactly the wrong lesson from CEO screw-ups. “See, these guys don’t know what they’re doing!” But a big screw-up is a big deal. If the best person has just a 1 or 2% lower chance of making a big screw-up, that can be worth millions of dollars. Watching someone pour over spreadsheets and grill his staff for information for 12 hours a day might not be quite as sexy as watching a telepath move hundreds of tons of material with his mind. But the outcome is no less impressive. 

Monday, July 9, 2018

Light Blogging

I’ve been blogging less recently, but I hope to return to it as a full-time hobby at some point.

I’ve been spending a lot of time trying to make myself better at my job. Reading books about R programming, taking courses on Datacamp, and reading about various machine-learning topics. I’m really trying to up my game here. I’d like to have a thorough understanding of exactly how all of these algorithms work. Like, if I wanted to build a simple gbm neural net on a small dataset, I want to show what these look like as (for example) a series of formulas in an Excel workbook. Any monkey can use these methods. Just plug the arguments into a function that somebody else wrote for you. It’s not even that hard to explain what they are doing, in basic English in summary form anyway. I’m trying to make myself stand out by understanding how they work in excruciating detail. 

I’m also working on a paper on opioids, which should get published in Cato. I’ll share more details when I can but I’m trying to 1) maintain some level of anonymity and 2) avoid scooping the paper in any way. I've learned some very interesting things about this topic in the past few months. Maybe I'll discuss some of them here after the paper comes out. 

Thanks for your patience. I’ll get back in full swing in the coming months. There is much to discuss!

Thursday, June 14, 2018

Multi-Drug Poisonings Over Time

I’ve written several previous posts about multi-drug interactions. My write-ups of the 2014, 2015, and 2016 drug overdose data all show cross-tables as well as tables displaying the distribution of numbers of substances found in accidental drug overdoses.

I didn’t have the ability to see what this looked like over time until recently. If you want to see, for example, how many people died from a combination of benzodiazepines and prescription opioids, you can’t just go to the CDC’s Wonder database and pull that information. You have to go to the large file that lists all (roughly) two-and-a-half million deaths that happen each year along with each cause listed on the death certificate. Then you have to define your own counting logic. To see trends over time, you have to pull together the detailed files for several years. That is what I have done.

I’m looking to see which drugs are “associated” with each other. (Below I use the term "correlation" loosely; I am not using Pearson correlation or any other well-known statistic. I'm literally saying "These two things are co-related.")  Roughly speaking, this means they appear together more often than I would expect by chance. Here’s an example of what I mean. In 2016, there were 55,071 accidental drug poisoning deaths (presuming those are correctly labeled, which is an open question). 14,483 of these were prescription opioid overdose deaths (which, by my own definition, includes the “Other Opioid” drug category or methadone, ICD10 codes T40.2 and T40.3); 9,177 of these were benzodiazepines-related (the death record listed the benzodiazepine code T42.4); 4,873 of these listed both. Taking some ratios, 26.3% of accidental drug poisonings involved prescription opioids (14483/55071), 16.7% involved benzodiazepines, and 8.8% involved both. What might it mean for two drugs to be associated with each other? To show up together more often than “by chance?” Crudely we can think of the drugs listed on death records as being randomly distributed across those records, in other words they are “independent” observations. (Maybe there is a better way of specifying what independence means, but I think it works for my purposes.) We “expect” to see 0.263 x 0.167 = 4.4% of accidental poisoning deaths to include both substances if the independence assumption holds. Instead we observed that 8.8% included both. So these substances show up together 2.02 times as often as we’d expect given independence. We can say, crudely speaking, that these substances are positively associated. (You can enlarge the tables below by clicking directly on them.)

That is no surprise, and it’s no coincidence that I chose these substances to illustrate my point. It’s well known that benzos can exacerbate the respiratory suppression of opioids and vice versa. (Benzos have their own effect on respiration independent of other substances, just not quite as extreme. Someone I know recently suffered altitude sickness in Colorado. He told me (presumably he heard this while getting medical attention) that you can’t have benzos if you’re having a panic attack at altitude. I was perplexed for a minute then realized, “Oh, right, they suppress your breathing.” Possibly dangerous at high altitude.)

What about other substances? Heroin and benzodiazepines seem to be roughly “independent.” When I do the same exercise explained above, I get something that’s close to 1.00. See the table below for details. (I'm counting deaths that involved either heroin or synthetic opioids as heroin deaths, unless I specify otherwise. The reason is that some of these people are intending to take heroin and end up taking fentanyl or something stronger instead due to black market uncertainties about drug quality. These deaths could be marked with either or both substances. There are technically codes to differentiate these: T40.1 for heroin and T40.4 for "other synthetic narcotics." But in practice I don't think medical examiners are scrupulously checking to see which substance was involved.)

Cocaine and heroin were slightly “dissociated” from each other in 1999, but the correlation seems to have grown over time. Joint cocaine/heroin overdoses appear 1.2 times as often as expected by chance in 2016. It's interesting that this jumped from ~1.0 to ~1.2 in 2011, the first year of heroin's very recent dramatic increase. (You can see the slope change dramatically in the charts in this post.) Notice that cocaine deaths peaked in 2006, then came back down. But their dramatic recent rise, as in the past couple of years, is largely being driven by heroin. If someone were just tracking cocaine deaths and reported some kind of cocaine epidemic, they would be mistaken. This phenomenon of multi-substance poisoning is important. It would be misleading to look at trends in any one substance in isolation.

Taking a look at many possible combinations, I see the following table. (Other Opioids means specifically code T40.2; as stated above methodone has its own code T40.3. For anti-depressants, I'm looking at code T43.2, "other and unspecified antidepressants." Heroin in this table refers specifically to T40.1 and not T40.4. The last column shows how these two codes are correlated.)

I found the very last column interesting. Heroin and other synthetic narcotics were significantly dissociated from each other until 2014, when deaths listing T40.4 began to rise dramatically. That suggests these were very different phenomenon until 2013. There were some people dying of fentanyl and similar substances, but these were probably pain patients with legitimate prescriptions to the fentanyl patch or something like it. It looks like there is a sudden "regime switch." I would have expected the number to be bigger. A lot of the heroin deaths in the years 2014-2016 were actually taking heroin adulterated with fentanyl, but then again maybe there is a tendency for medical examiners to only write one substance on the death certificate. Like I had said, I don't think they are scrupulously checking to see if it's really heroin or if it's something else.

Take a look at the heroin and antidepressants column. It looks like these are dissociated, occurring together less frequently than we'd expect by chance. Other Opioids (code T40.2, not including methadone like the first table in this post) seems to overlap significantly with benzodiazepines and antidepressants. I'd summarize this table by saying that pharmaceuticals tend to go together, and tend not to go with heroin. I might be tempted to say something like, "There are street drug users, and then there are pharmaceutical drug users. These are distinct categories, even if they overlap." That's not quite right, because we're looking at just the population of users who died and not the full population. That's a pretty strong "filter" for our data, and presumably some kind of selection bias creeps in here. But I'd be surprised if you didn't see the same kind of pattern on SAMHSA drug survey data. Once again there is overlap on any given pair of substances, but we're trying to see if there is more or less than we'd expect by chance.

Here are some more substance correlations. Again "heroin" means just heroin (code T40.1) and Other Opioids means just code T40.2. "Cocaine Or Heroin" means just that, one or the other substance is present. So in the third column I'm roughly speaking looking for the correlation between prescription opioid deaths and street drugs. "Stimulants" refers to code T43.6, usually called "Psychostimulants with abuse potential", which includes things like methamphetamine and prescription ADHD medications. (Yes, the government counts them in the same category, because they are chemically similar and have very similar effects at similar doses. Insert stock comment about the hypocrisy of American drug policy here.) The second and third column sort of corroborate the story that "There are street drug users, and then there are prescription drug users." Methadone has interesting correlations with other substances. I wasn't sure what to expect, but I would have thought a lot of methadone poisonings were heroin users in rehab who maybe fell off the wagon. If that were true, I would have expected a number substantially higher than 1.00, but instead it's substantially below. Similar with Other Opioids and Methadone. (Does this mean methadone is often the sole drug found in an overdose death? Not particularly; see the tables in any of the three links at the top. Most methadone deaths involve multiple substances; the distribution of "number of substances involved" is similar to other things on the list.)

I wrote this post thinking the analysis is a good way of tracking this sort of question: Are deaths due to X being driven up by an increase in deaths from Y? If "Yes" I would expect some kind of change in this number. I don't exactly see any smoking gun here. It definitely looks like "other synthetic narcotics" went from being a pharmaceutical-type drug to a street-drug in the past three years, no surprise there. And it looks like heroin is driving up cocaine-related deaths; my "correlation" statistic jumps from ~1 to ~1.2 in 2011, which is when heroin-related deaths started really trending upward.

Something about this analysis bothers me, regarding how I've defined "independence." I have a certain number of bodies, say 55,071 in 2016. Then I'm effectively saying, "I have 14,483 'prescription opioid' tokens, 9,177 'benzodiazepine' tokens, 27,035 'heroin' tokens, 9,979 'cocaine' tokens. 'Independence' means I'm going to randomly distribute these tokens to the 55,071 bodies. 'Dependence' means having one kind of token makes it more likely that you'll get certain kinds of tokens and less likely that you'll get other kinds of tokens." I think this is right, but something about it feels weird. Like I should be fully specifying a rigorous statistical model. Someone more familiar with the tools of social science can pick up the torch from here if they like. If anyone knows, does this "correlation" statistic I've cooked up correspond to an existing concept? If so, please share and I'll try to tighten this analysis up with a more familiar set of tools and concepts.

Tuesday, June 12, 2018

The Nature of the Firm

Why don’t auto workers just manufacture pieces of automobiles and sell their work product on the open market? Why don’t I provide “actuarial analysis” from my home, sitting in my pajamas at a laptop, and sell it to the highest bidder? Why don’t software engineers write code in their basement and sell it piecemeal to interested buyers? No doubt some of this kind of freelance labor exists, but for the most part people work for firms. That is, most workers have long-term arrangements to work exclusively for a single company. They are paid an hourly wage or a salary. Some might get a sort of “performance pay” or rate for piece work, but by and large they are not selling their work product in anything like an open market.

A long time ago, this looked like an interesting puzzle to Ronald Coase. The economy as a whole is characterized by open markets and the free buying and selling of goods and services. But within the economy there are these tiny islands of socialism called “firms.” If open-market buying and selling is relatively efficient for the economy as a whole, why should individual firms resort to centralized planning? In his paper The Nature of the Firm, Coase tries to solve this puzzle.

I won’t give you a full review of the paper, mostly because I haven’t read it. You can find good discussions of it elsewhere, see here or here for example. (Seriously, listen to the Econtalk with Mike Munger. "Bunny slippers. Tell me about bunny slippers." It's priceless.) And besides it’s only 20 pages, so shame on me for not having read it yet. But I’ll give the cliff-notes version and talk about why corporate bureaucracy in practice is disappointing.

Coase’s answer was that firms reduce transaction costs between individuals within the organization. The programmer writes and maintains code for his employer for a specific purpose. He gets paid whether it’s a busy time or a relatively easy time. If the department that uses his code finds an error, he’s right there to fix it. Versus if he were selling code piecemeal, there would be no guarantee he’d have the free time to fix it. Mr. Freelance Programmer could perhaps shove a contract in a disappointed clients face and say, “Honor served. I did what you asked of me.” Of course the client could sue the programmer if they disagree about the contract's fulfillment, but this would take time and resources. Transaction costs loom large in this world of freelance labor. It would be time-consuming to pre-specify every likely contingency or disappointment in a contract, and it’s even more time-consuming to sue for breach of contract. Long-term relationships and dominance hierarchies can solve these problems. If the code breaks, your boss simply tells you to fix it. He doesn’t need to sue you, because the threat of losing your job for insubordination keeps you in line. Your annual pay-raises and bonuses are, at least vaguely and on average, related to the quality of your work, so you have an incentive to create a good work product. This is the ultimate repeat-business relationship.

The same goes for many other kinds of relationships. The auto-worker needs to reliably show up to work on the assembly line, which requires inputs from a hundred other people to keep moving. Someone could try to write a contract binding all these people together on a day-to-day basis and distribute the proceeds of selling the piece-work equitably. But it’s a lot easier for a single employer to simply buy all the machines and other capital and employ the workers on some kind of ongoing basis. Potential conflicts and legal disputes between n(n-1)/2 pairs of individuals are thus minimized. Those workers are more productive, thus there is a greater surplus for all involved parties to share, thus their pay is higher than it would be in a freelance world. Or consider a free-lance capital owner, someone who buys or builds the assembly line with the intention of renting it out at "the market price" to freelance laborers. In this messy and confusing world, I think everyone would quickly see the advantage of forming long-term relationships and dominance hierarchies. 

So why does this go so horribly wrong in the real world? Why is corporate bureaucracy so stifling? Why do some bureaucracies actively fight the lessons of Coase and erect barriers to communication? If corporate bureaucracy is supposed to minimize transaction costs, why does it seem to increase them? 

Most companies have some kind of acquisitions process, perhaps even an entire department. This is to some degree necessary. You can’t just say “yes” every time an employee asks for a new tool or shiny new toy. But often the process is ill-defined and clumsy. There aren’t enough acquisitions staff to handle the requests, or they don’t know how to prioritize the flood of requests. Almost everyone has seen something like this: “If the Acquisitions Department would just buy the damned software, the company would save thousands of dollars worth of employee hours every year. What’s the damn hold-up!?” But someone has to sift through the requests to see if they have merit or don’t. And sometimes the requester is mistaken about the value-added. Still, the process often takes way too long, or it stops because you didn’t fill out the right request form and nobody told you your request got halted. There are times when I’ve said, “If I could pay out-of-pocket and install the software or hardware myself, I would do so, because the added productivity is worth that much to me. The surplus to the company as a whole is even greater than that, but whatever. That’s their loss.” And in fact I paid for a year’s subscription to Datacamp rather than waiting for my employer to do so, because I felt I was wasting time by waiting. It was a good investment. Why did my employer erect these huge barriers to free communication? Why did it fail to reduce transaction costs? It's pretty bad when your employees are longing for an open market in order to actually get shit done. 

Some departments come up with schemes for keeping track of special requests. These can be stifling. “Fill out this form, and submit it via this web portal.” Fuck you, how about this. How about I have a conversation with one of my colleagues, one professional to another, and that colleague performs a task for me because we’re both trying to add value for our employer? “Oh, you want me to submit a ticket through the obscure internal portal that I use once a year? Okay, do you remember the instructions. No? Okay, whose department is that? Oh, I don’t have permission to use that portal? I’ll have to get access, then set up a user name and password. Oops, it won’t add me as a user. Which apparently means I already have an account? Okay, how do I recover my username or reset my password?” 

I have tremendous respect for one of my former colleagues. He basically "hired" one of our programmers to build something useful for him by buying him a few rounds of golf. This guy knew how to get some shit done, and he went outside the bureaucracy to do it. On the other hand, this is a sign of a dysfunctional bureaucracy. My friend should have been able to get the programming resources necessary to complete his project. Bad management made this impossible. The company was failing, in this instance, to perform its Coasian role of reducing transaction costs between employees. Some particularly dedicated employees essentially said, "Screw it, I'll just go out and buy one."

I don't think my past employers were particularly dysfunctional. I think it's probably like this everywhere. I don't have a solution, either. It is genuinely difficult to "manage" a very large group consisting of thousands of individuals, who sometimes have conflicting goals. Maybe organizations should "institutionalize" the process of going around the bureaucracy. Each department has a flexible spending budget for instances of "Screw it, I'll go out and buy one." The procurement department tracks these to identify where its processes are broken. Revealed preference helps identify requests that are truly needed, as opposed to requests that are frivolous. As in, "If they're willing to spend out of their own budget for this, even out of their own pockets sometimes, maybe that means it's really useful. We should probably just shell out for it."