Accidental Factors

There was a great story in the NY Times about the craziness around taxi medallions in New York - the things that let you own a cab - and some of the trouble it got drivers into. The medallion system among NYC taxis put a sharp divide between those who drove taxis and who owned them, which lead to people making some bad decisions to get on the other side of that line, aided by intermediaries who were neither buyers or sellers, but profited off the transaction. The ridehailing apps came along and basically removed the barrier, but the scheme was so shaky it was destined to fail.

To meet the high cost of cabs, drivers needed to be efficient with their time. One of the other impacts of ridehailing apps in New York is that it's easier to get a ride in the boroughs outside Manhattan. Despite attempts (green cabs) to provide better service, there was a lot of pent up demand. Offering rides in the very dense Manhattan meant that wait time between fares was low, which made for efficient days for the drivers. Longer wait times made the cost of transacting with a rider in Long Island higher than in Manhattan for the driver.

Ridehailing reduced that cost (as well as many of the other friction points). While other areas might not be as dense, they are dense enough to offer a sufficiently low wait time for drivers and passengers if you have a global view of the drivers and riders and can put them together effectively.

There is another view of that change though: If you had been an outer-borough resident, and really wanted to know you could get a ride, you could buy a car, use a car service or even employ a driver. This was a larger outlay, but for a significantly lowered barriers to take a ride. Owning a car, hiring a driver, or even contracting with a regular car service are quite far from the experience of a taxi: you own this specific car, of this specific make and model, or you see this particular driver every day. These can be very nice things, but they are somewhat secondary to the core goal here - getting from A to B.

The kind of automated matching that makes ridehailing work requires a level of standardization - the product or service offered has to be interchangeable. Famously, standardizing wheat opened up competition across the world for a product that was previously seen as more locally differentiated, which benefited consumers of wheat (both businesses and individuals) but hurt farmers who had fixed cost structures for their products and were suddenly competing with other farmers with lower costs (though who also needed shipping).

Ridehailing apps standardised getting from A to B at a different level than taxi cabs. The standard was in some ways higher - tracking and credit card payments - and in some ways lower - higher range of cars, shared rides. The net was similar - good for consumers, bad for producers, though specifically for producers who were the most down this line of differentiation outside of the core A to B. If you owned a medallion, you had a lot invested in a specific taxi. If you just drove for someone else, your harm was the difference between what you could get driving ridehailing and what you could get driving a yellowcab, after paying to rent it. If you no longer or had never had a job as a taxi driver, the difference was positive.

More broadly, the more your work and work product is standardized, and the more it is easy to transact for that type of work in a marketplace, the less pricing power you will have: the ability to increase what you charge for your labor. The more your work is differentiated or hard to buy, the more pricing power you will have, in exchange for it being harder to find buyers. This is why some farmers travel long distances to city farmer's markets where they supplement their income by highlighting the unusual aspects of their produce.

A lot of people look for marketable skills when they are in education. They want the wide range of employers and opportunities given by skills for which there is a ready market. If that market crosses a tipping point by which those skills are too readily available, or the output of deploying those skills too predictable, any premiums are likely to be competed away.

Temp: How American Work, American Business, and the American Dream Became Temporary is an excellent review of the changes in the nature of work, and the future of work. Strongly recommended reading.

A quote that summarizes the core of the book well:

The end of the postwar prosperity in the 1970s may have been tragic, but handwringing over today’s jobs is farce. We are all terrified that the coming of Uber means the end of security, but we shouldn’t fear that: it is already gone. We already live in that world. We should not mourn the passing of a regime, moreover, that compelled us all to be afraid. Secure work began to erode the minute that Elmer Winter began to sell temporary labor. For some Americans—migrant laborers, African Americans—precariousness has always been part of the labor market. For white women, security was contingent on marriage to white men. Only for working-class white men, and even then not all of them, was job security a reality. Even for the high-paid executive, the office was a place of anxiety, and then possible downsizing. For decades, in ever more insidious ways, employers have found means to make workers disposable. For decades, this flexibility has benefited the employer, but for the first time, we are in a digital world where the flexibility might finally benefit the worker, who might, in the end, not need an employer after all.

Bloomberg columnist Matt Levine sometimes says that markets love completeness - if there are things which are bundled together, markets want to find ways to trade them individually.

Employment is currently a big bundle of different properties, and some of what we have seen over the past few years is the unbundling of some of those features. A business is a series of activities coordinated to generate a profit, and the mix of how the necessary activities get done is, according to Coase and others, decided by the transaction costs of the different bundles. A business might hire a manager to look after a facility, contract with a vendor to have the facility cleaned, and hire a freelance graphic designer to put together the signage. Socially, we strongly privilege some of these over others.

Relations between companies and individuals are generally regarded as being in a few different forms: Owner/Shareholder, Employee, Non-employee work contractor, piecework and so on. Each of these is a general category for a range of options though. For example, is a member of the board of directors an employee? Is the zero-hour contract teenager at a fast food store in the same employment category as the troubleshooting consultant brought in to revamp a failing product launch? Internationally, we see other differences, such as between career track employees and non-regular ones in Japan. Over the last few years the categories have got murkier as the lowered transaction costs from digital technologies and smartphones added new options to the mix.

The bundle involves not just the work-to-be-done part of employment that the hiring companies most care about, but a bunch of policies around work - particularly how benefits like sick pay, healthcare, and retirement are funded and managed. One of the problems with approaches like California's AB-5 is that they view the world through a fairly binary set of employment relations, and so impact everything from ridehailing drivers to freelance journalists. Policy would be better served by finding ways to more cleanly separate the responsibilities, so that a range of employment types could contribute to the outcomes policymakers are looking for rather than them attempting to move specific relationships in to one or other arbitrary bucket.

We're still figuring our how marketplaces and matching work here: you might use an app to call a Lyft or to hire a window cleaner, but the relationship you have can look very different if you are (legally) contracting directly with the window cleaner. There are new venues and places for work too - fully remote companies like Gitlab are now not all that uncommon, and we are seeing work done in virtual spaces, mainly games, making the territory in which the employment takes place more ambiguous.

The other part of the lowering of transaction costs, though connected to the unbundling, is the intermediating of management by algorithm. Part of what made hiring people necessary was complexity of instructions: it's easier for a chef to describe to a line cook how they want a vegetable prepared than call up suppliers for that preparation. In many industries, the "manager" is now giving conditions to a machine, which translates those into individualized instructions for workers.

In many ways, that's what the ridehailing apps do when they give drivers routes and pick ups - no human is asking them to do that, but at the same time there is nothing inherently inhuman about receiving the instructions: a taxi dispatcher did roughly the same thing. The interesting question is around who gets to define the conditions, and how they can create and tailor those kind of logical processes. How can you, as someone with insight and experience, direct an "instruction generating" algorithm to distribute work intelligently to others, at a much greater scale than you could enable through human intermediaries.

In Chaos Monkeys, Antonio García Martínez suggests that every job will either be giving instructions to machines, or taking them from machines. The reality is likely to be messier: many more people will follow algorithmic instructions in one sphere, but also be responsible for generating them in another. The line of where an employment relationship begins and ends will only get fuzzier.

From a Stumbling and Mumbling post:

A key insight here comes from Marko Tervio. He argues that what matters isn’t so much talent as proven talent. Many hirers would prefer the known quantity who is just above a threshold of competence to the unknown one who might be brilliant but might also be a duffer. In hiring a factory manager, you want someone who isn’t going to blow the place up. In hiring a journalist, you want someone who can be relied upon to file something literate and on time. And so on.

This situation goes both ways - choosing predictability over variance reduces your chances of hiring a duffer, but also of hiring a genius. Growing companies can afford more variance, and benefit more from the stars, so tend to take more chances.

I'd say there is also a factor where many hiring managers don't actually have a good concise way of describing the job they actually need done. Part of the reason automation isn't going to suddenly replace everyone is that there is a lot of ambiguity in many jobs, at all levels, but that same ambiguity opens the door for all manner of biases.

Or to be precise, monosonys: where there is a dominant supplier that gains pricing power through lack of competition. Timothy Taylor has an excellent post looking at the economics and linking (as always) to some solid research on the matter. There are some pecularities in job markets that make employment more vulnerable to big, powerful employers than you would otherwise think. To re-quote, the core point around the difference from product markets is the bi-directional matching:

Compare buying a car in the product market and searching for a job. Both are important, high-stakes choices that are taken with care. However, there is a crucial difference. In a car sale, only the buyer cares about the identity, nature, and features of the product in question — the car. The seller cares nothing about the buyer or (in most cases) what the buyer plans do with the car. In employment, the employer cares about the identity and characteristics of the employee and the employee cares about the identity and characteristics of the employer. Complexity runs in both directions rather than in one. Employers search for employees who are not just qualified, but also who possess skills and personality that are a good match to the culture and needs of that employer. At the same time, employees are looking for an employer with a workplace and working conditions that are a good match for their needs, preferences, and family situation. Only when these two sets of preferences and requirements "match" will a hire be made.

The paper goes on to point out the difficulties this creates particularly for lower-income individuals and those in two-earner households who need to find at least a geographic match for both people. I wonder if there has been any effect from the gig-work platforms: it seems plausible that a low-paid worker with a car could earn more as a Lyft or Uber driver, which, if true, would hopefully blunt some of that monopsony power. Interestingly, both those companies have actively worked on systems to remove (economic) barriers to entry, namely having a qualifying car, through their managed rental car programs. In some ways their competition for drivers is traditional employment, so it makes sense for them to make the opportunity widely available.

Eugene Wei's excellent Status as a Service post rang a bell for me in terms of the puzzle of the jobless prime age men in the US. The difference from just unemployment is that the folks in the jobless cohort have opted out of even trying to get work. They're most white and in mostly former manufacturing heavy areas. They score lower on optimism than their counterparts in most of the world, and interestingly even lower than US black and latino men in similar states (See Graham and Pinto's recent work). They view their life as worse than their parents, and don't expect it to get better.

One nugget I heard was that many other groups of people will take on care work (which has boomed most everywhere), but that the men in this group are somewhat resistant to it, even when available. Care is, in my read of society, a somewhat lower status industry. The manufacturing jobs that these folks, or their fathers, had possessed a strong image as an industry, supported by some of the most significant unions, that helped make them broadly respected.

As well as social status, those jobs provided reasonable wages and a social environment, but my impression is that without a reasonable status reward people are going to be be hesitant to re-enter the job market - if they can be comfortable enough, and get their status another way. For example, time use studies show that games have become a significant part of jobless men's days, which can be a great provider of status.

One of my takeaways from Wei's piece was that everyone needs a place in their lives to have high status, and that people will prioritize status over economic advantage, at least partially. I suspect even if you could inject a good employer that required a similar qualification/education level (say a forward looking call center) into an area hit with high joblessness, you'd still fail at making a dent in those statistics because of this.

I mentioned an increase in "rote" jobs in an earlier post, but I wasn’t too clear about exactly what I was referring to. I’ve been thinking about this a lot in terms of the questions around wages and productivity, inspired partly by Scott Alexander’s recent article, which is detailed and appropriately confusing. Luckily, the question at hand doesn’t require definitively asserting the link, but instead thinking about how that productivity increase happens in general.

For a manual output, a workers effort and competence entirely determine their productive output. There really aren’t many jobs that fall too much into this category, but as an example I saw a crew today resurfacing a section of road: a truck was slowly unloading material which a team were raking in to place. On an individual level, the output for each of those workers is going to be entirely determined by how much time they put in.

The next stage is what I mean by rote jobs. This is where a workers output is entirely mediated by technology. For example, imagine a supermarket worker manning a checkout who is moved to oversee a bank of self-checkout machines (note: I haven’t run one of these, so this is speculative!) This is an immediate increase in their productive output (in economic activity), but is totally mediated by the effectiveness of those machines. If the machines work well, the worker will be primarily getting people to the open machines, and resolving issues like the receipt printer running out of paper, and if the machines work less well they will be resolving entry errors and other malfunctions.

The best the human can do is operate the technology to the designed capacity: if someone sat down and analysed the throughput of the machine with a worker that performed that maintainance in the minimum of time, they would calculate a real ceiling for the amount of output any real world worker could generate. I think of the workers input as a sigmoid function: the best they can do is 100% of the machine capacity.

What makes this a rote job is that the training investment to get to that 100% of capacity is minimal, and when faithfully executed represents a very high percentage of mastery. That is, if I can take a relatively motivated, but otherwise average individual and provide them with a small amount of training, I can effectively operate near design capacity.

This sets up an incentive to optimise the machine capabilities, not the human capabilities, and encourages jobs to be more predictable, more rote, and have less opportunity for development or growth: though supermarket workers can and do develop, I assume they do so through transitioning into different roles, not in increasing capabilities with the self-checkout.

Compare this to a situation where the technology doesn’t mediate, but instead mixes with human capability. For this situation, imagine a construction worker who has been trained in operating a digger or backhoe. Like the self checkout there is a training required to develop competence, and operate the machine to its designed capacity. However, there is also significant scope for mastery: an experienced operator can expand the range of tasks their digger can accomplish beyond what the designer may have expected, and produce more output for the same (hour) input.

The digger is an open-ended tool which is operated in environments which are uncertain. As systemic as construction is now, there is still significant variability in conditions that require and reward the ability to adapt. It also requires that tools used have the ability to adapt. A supermarket is a significantly more constrained and designed system, in which the staff can be treated somewhat more mechanistically.

Businesses, of course, are trying to contain and constrain varibility and ambiguity. The more you can nail down a process, the easier it is to optimise, but the less scope there is for humans in that process to out-perform.

That said, there are some examples where adaptability and mastery have been integrated into more rote procedures. The Toyota Production System‘s emphasis on individuals adapting their jobs based on their insights on an assembly line is a great example: the innovation was subordinating the line to the individual worker, giving them the ability to halt it or suggest changes to procedures. Similar inversion of control has been successful in areas from software development to flying airliners.

Part of the challenge of implementing it in many modern organisations may be the impact of software based design of these systems. I wonder if Amazon warehouse workers have similar opportunities - can a worker stop the package line if something isn’t going right? If they can, how easy is it to adapt the (world‘s best) logistics system to their local insight?

Bit of a grab bag on some recent work on the government policy side. Timothy Taylor had a post a couple of weeks ago on active labor market policies - those are policies which directly encourage work rather than subsidize unemployment. There's a useful table in the post of various policy interventions and the assessed effectiveness:

  • Job placement services - positive results
  • Training programs - positive in medium run
  • Employer subsidies - mixed results
  • Wage insurance - generally positive
  • Public works - negative
  • Self-employment - insufficient evidence

From the view of my own pet theory at the moment, the positive training results makes sense: the government intervention addresses the coordination failure around long-term investment in staff. The Aspen Institute's (excellent) recent report is calling for some of the same - supporting community college and apprenticeships, for example.

One thing I don't quite get though is why job placement services work as a public intervention. Matching is a definite problem, but it feels like a problem that the private sector would be quite incentived to fix. Hiring is expensive, and failing to hire even more so (longer period without person in role, more interviews etc.)

There clearly are some very successful businesses doing job listings, but presumably at a certain job/wage level they aren't making their way through. This could be because there just isn't the margin in matching minimum wage jobs, though I wonder if this is similar to a note that stood out to me in Evicted: though looking for an apartment was hard, the people followed in the book didn't use the library with its free internet access to help them look, even if they were regularly using that access for other reasons.

I do also worry about the long-term effectiveness of improving matching for low-productivity work. The Aspen report has a good study looking at the relationship between wages and productivity (it is strong, which is reassuring!):

Given the strength of the link between pay and productivity, it is important for public policy to attempt to make workers, particularly low-wage workers, more productive. Policies to increase the skills of, and training available to, workers—for example, reforms to our K-12 education system and the expansion of apprenticeships and other forms of work- based learning—should be enacted. Earnings subsidies should be expanded to draw more people into the workforce. Policies to encourage business investment should also be considered. Labor market regulations that serve as barriers to workers and reduce the quality of matches between workers and jobs should be removed.

Speaking of the barrier to workers, also worth a look is Taylor's recent article on capitalism with Scandinavian characteristics. Some of the interventions made have a pro-work effect, by effectively removing barriers to joining or continuing in the workforce:

But Kleven also points out that the higher Scandinavian taxes finance government policies that make it easier for many people to work — in particular "provision of child care, preschool, and elderly care." He writes: "Even though these programs are typically universal (and therefore available to both working and nonworking families), they effectively subsidize labor supply by lowering the prices of goods that are complementary to working. … [T]he Scandinavian countries … spend more on such [labor] participation subsidies … than any other country. …"

In prior decades, hiring an external firm to do something that was an internal function would have looked mostly like outsourcing: hire a company to provide the same service more cheaply, often by through geographical labor price differences. Now, however, an increasing amount of that work is done by subscription to a software-as-a-service and similar products.

The process is somewhat self-reinforcing. Using one SaaS enables using others: you take on an HR-as-a-service tool, which integrates seamlessly with a selection of payroll-as-a-service products. From the employment standpoint, this has a couple of interesting effects. There are likely many fewer people working on even a very large SaaS - compare Salesforce to all of the customer tracking systems companies used to maintain internally. However, those jobs are, generally, pretty good: because of that exact same scale, employees of the SaaS vendors are productive, and their work well compensated. On top of this, firms need to staff internally to work with and maintain their use of these SaaS systems. These introduce new, skilled occupations, some very successful.

However, there is also a pressure towards a certain type of internal standardization within firms. In prior years the internal working of firms could be quite idiosyncratic. The only cost was acclamation for new workers, who were coming from a sufficient diverse range of former employers that there was limited pressure to "do things a certain way". I suspect we're going to see that become less and less true: the costs of idiosyncrasies are fewer SaaS options that fit your needs, and more cost to transition to (and later upgrade) different SaaS offerings.

The lack of internal standardization though is where much of the ongoing need for more traditional outsourcing comes from: rather than being able to contract for software or transactional services (e.g. QA) companies need actual humans who can learn that idiosyncratic internal process, and deliver against it.

There is an ongoing story about the "shadow workforce" in Silicon Valley - the temps, vendors and contractors that make up over half of many of the large companies staffing. There is an interesting puzzle in this, that Russ Roberts and Noah Smith posed in an episode of Econtalk last year. These smart, dynamic Valley firms have very large staffs of contingent worker for which they are paying a significant premium: its not clear, though, what they are getting for that premium.

All of these companies are sophisticated in how they buy-in services: they all use SaaS from other providers, and augment with traditional outsourcing to deal with their own processes and systems. On top of that, they outsource roles where the company itself has no real competence: hiring bus drivers via a transportation firm for staff shuttles or contracting with a maintenance company for an office complex's HVAC systems. While there are valid questions about how these folks are treated, the reason why the firms aren't hiring directly are pretty clear.

The questions is in the third class of outsourcing: the large class of contingent workers that have roles which are direct parallels of full time staff: skilled program managers, test engineers, support team members and so on. Overwhelmingly, these are vendors - traditional outsourcing relationship where the company will hire (for example) a full QA, customer service or content reviewer team from the likes of Accenture, HCL or a more specialized shop.

This isn't cheap - the hiring companies are paying the full cost of the people working, plus a markup. This is not to say the costs are the same as full timers: benefits vary, often contractors are housed in less-plush offices, and the contract relationships are nominally more elastic. However (from my own anecdotal survey) many of those workers would work for the hiring company directly for the same deal, and the length of the tenure is not massively different than the average full time staff tenure. So why pay the premium?

There are some factors which undoubtedly contribute to incentivize outsourced hiring:

  1. Hiring is expensive. It is significantly easier and faster to add staffing through a pre-established contract than it is to go through recruiting within a firm, even for common roles.
  2. OpEx is easier to get than headcount. Its often easier to drum up budget that can be spent on a contractor than it is to get headcount, and managers in the firm are often are aided by account managers from the contract companies.
  3. Regional availability. Contract companies offer an ability to get in-country staff in a region where the firm does not have an office.

I think there is something deeper going on though (meaning I don't have any kind of useful answer for Robert's question). I'm of the opinion that if you removed almost all of the staff from (say) Google tomorrow, the company would happily continue to generate revenue for quite a while (and significantly more profit!) The majority of full time employees at Google are building Google itself, and the work they do scales. However, if I imagine removing all of the vendor workers things would go south a lot faster: advertisers wouldn't be able to get the questions answered to run campaigns, bad content would appear in search results, and myriad other human-reliant, hard-to-scale aspects would start to fail.

In effect, Google, and I suspect most of the Silicon Valley firms, have two workforces: one full-time, well remunerated workforce investing in building the company's capabilities, and one contingent, outsourced workforce focused on operating the company. For that, they are willing to pay a premium.

Slightly longer post, as this is working through a bigger picture I've been thinking about for the last week. There is a puzzle in the US: unemployment is low and wages are rising, but so is joblessness and alternative (and much less employee friendly) working structures. From looking at various explanations, several of the same factors seem to come up, resulting in pressure on long-term planning. I've tried to capture some of that in the note below:

The mega-trend is digitization: the process of making things more amenable to processing by software. In Rodney Brooks' example, the transponders on an automatic toll bridge are an example of this process: they make what was physical exchange digital, and allow it to happen entirely in software. One of the largest side effects of the trend is lower transaction costs for many, many things.

There are two immediate results on business. The first is that, due to lower transaction costs, it makes much more sense to buy in services rather than hire people to complete similar tasks. We saw the start of this with outsourcing, then SaaS businesses, and now just as explosion of business services: think of WeWork versus hiring an office manager. However, this rate of change also causes uncertainty - in effect, it raises the discount rate for long term projects, which makes businesses less likely to invest.

That point comes with a caveat: there are definitely some companies that are investing long-term, on very speculative things. The dividing line is comfort with intangibles: intangible assets have come to represent a bigger part of the economy, so companies who are comfortable working with them and reasoning about them have a better sense of long term potential. Therefore, they can price their investments more accurately.

One of the specific side effects of investing less in the long-term is investing less in people for the long term. Training and career employment rest on the fact that this transaction is good for both sides: if companies aren't sure what their world will look like in 10 years, they're probably not trying to develop their workforce for that world. This, I think, is part of the coordination problem: the future is likely to demand more skills, more talent, and the best way to get those people is to grow them.

Companies are looking for the current-day equivalents of those people right now though, and (in general) not finding them, or only finding them at very high prices in certain places. This perceived skill gap, combined with the better availability of services, encourages companies to invest more in automation. This doesn't necessarily mean getting rid of a job, but it often means conforming the job to work within the bounds of the machinery or software. This creates "rote" jobs: jobs where there is a low bar of variance or individual skill required, which encourage companies to treat workers as (basically) fungible. This serves the company in the short term, but gives them no base to react to larger shifts: they have, in effect, locked in a certain way of doing something. They are trading away the prospect of innovation coming from the front lines in exchange for an optimizable process.

These factors contribute to "bad jobs": no development prospects, little autonomy, high chance of job loss. They also give rise to alternative, short term forms of work, from 1099 contractors to app-based gig work, where a function has been outsourced to a service. This is one element in the precarious financial situation for workers.

By this, I don't mean just the precariat, people who are on the edge of poverty or homelessness. There are successful software engineers living in Palo Alto who would not be able to sustain their lifestyle if they experienced a 10% drop in income: they'd have to move, their kids change school, and so on. They're at (approximately) zero risk of poverty, and often have high net worth, but they share an important features with those living on the edge: they are extremely risk adverse in the face of income changes because of the substantial spillover effects in lifestyle. The precariousness has a scarcity tunneling effect, encouraging short term thinking and making it harder for individuals to do the long term investments that could move them to more productive careers: workers become increasingly inflexible in terms of responding to demand for skills and geographic distribution of jobs.

Two big factors exasperating this lack of flexibility are employment-based benefits, which tie workers to current employment and often state/city administrative regions, and housing policy which pushes up cost of living in the most dynamic, high productivity areas. The availability of alternative employment often gives people ways of boosting short term income at very low risk, even though it is unlikely to lead to higher levels of productivity itself - its just a way of monetizing more of their time.

I think this is a poor equilibrium. The top level trends will continue, which will only increase the need for capable, autonomous staff able to flex to a rapidly changing environment, at all levels of income and impact. That requires investment - investment in getting people to the right areas, and in getting people the right skills. That in turn requires the perspective to defer immediate benefits on the part of both employees and employers, and right now that is not happening.

There is a parallel to the the seishain system in Japan, particular in the post-war period:

Taiichi Ohno, Chief Engineer at Toyota during the 1950s, learned many valuable lessons from Detroit [...] To make this system work, Ohno needed both an extremely skilled and a highly motivated work-force. As a result of a post-war strike, an agreement was worked out between the company and the union: employees were guaranteed lifetime employment and their pay would be steeply graded by seniority rather than by specific job function and was to be tied to company profitability through bonus payments. This essentially made the employees members of the Toyota family, with rights of access to Toyota facilities (housing, recreation, clubs, etc.). The employees also agreed to be flexible in work assignments and to initiate improvements rather than merely respond to problems. Hence, the Japanese labor policy was born (Womack et al., 1990).

The economic conditions provided an external impetus for coordination, and the unions made it possible: there was a single negotiating point between Toyota and its workers that could be used to find a better outcome for everyone. This approach yielded at least a couple of decades of extremely positive results.

I don't mean to imply the seishain system is appropriate now: I think the current state in Japanese employment shows the limits. Even if it were appropriate though, its difficult to see what would prompt better coordination around this right now, particularly in a generally strong economic climate.