Two (Game-Changing) Ways Cities can use Technology to Fight Inequality

The story of income inequality is not new – as lower and middle-class incomes stagnate while the highest income brackets race ahead, the wealthy have access to goods and services that are increasingly out of the average person’s reach.

But we now see its detrimental effects more clearly than ever. I live in the Silicon Valley, and when news of Donald Trump’s election broke, the overwhelming feeling was disbelief. It was unimaginable. Tears of anguish were shed, yet a large part of the country celebrated. To me, that moment captured the deeper impact of inequality – fragmentation of society. Our politics become polarized, we are unable to find middle ground in our interests, and we increasingly feel like a nation of enemies, not countrymen.

While the problem gets more serious, our typical approaches to tackling inequality are reaching their limits. Redistribution is a political hot potato that pits the interests of the “haves” and “have-nots” against each other. Investing heavily in educational opportunities has diminishing marginal returns on social mobility both in the absolute sense (because the future of jobs is increasingly uncertain) and in the relative sense (because wealthier parents give their children more and more advantages).

We are in desperate need of new paradigms to fight inequality in cities. Here are two ways I believe technology can be a powerful, game-changing force – if deployed thoughtfully by cities.

Inequality

Source: charterforcompassion.org http://bit.ly/1y8DPw1

First, cities should use technology to make life experiences in the city more and more independent of incomes.

 It would be impossible to close the income gap completely, short of communism. A society where incomes are totally equal is also undesirable, as it erodes the motivation to work.

However, I believe that technology can make life in the city increasingly independent of income, which can go a long way towards mitigating the daily experience of inequality.

Let me start with explaining the notion of an aspirational good – things that people wish they had money to buy. In transport, most people aspire towards owning a car. In housing, it is a condominium or a private home (American friends: as opposed to a publicly-built Housing Development Board apartment, which 80% of Singaporeans live in). In healthcare, it is a private doctor or hospital bed – at your choice and convenience. In education, it is getting into top schools and universities.

There is an unsustainable dynamic behind aspirational goods. Because these goods are limited in supply, the more people can afford it, the more expensive they get, and the further out of reach of the average citizen. Aspirational goods are the sources of a huge amount of angst in the middle class.

Technology has the potential to overturn the entire notion of an aspirational good. By creating new forms of value, it can make the alternatives so attractive that even those who have money choose not to buy the aspirational good. 

Take transportation for example. Owning a car is so attractive today because public transportation is an inferior option on many counts – the low cost cannot make up for its lack of time efficiency (it takes about twice the amount of time as a car ride), comfort (especially in humid weather), and customization (as a car owner, I know I can get a ride whenever I want).

What if public transport can be faster, more comfortable, more customized and cheaper than owning a car? With technology, this need not be a pipe dream. Imagine a day when you can wake up in the morning and your phone already knows where you need to be. It recommends the top three ways to get there. You select one, and within a minute, your ride shows up at your door – perhaps a shared car, or an electric bike if it’s sunny. It gets you to the train station just as your train pulls in. When you get out of the train, your minibus has just arrived to take you to the office. After work, you can summon a sleek designer vehicle for your dinner date. On the weekend, an autonomous jeep shows up at your door-step to take your family around for a day of fun.

You don’t need to buy multiple tickets – everything is paid through your phone. Or, you can even pay for transport just like a Netflix or Amazon Prime Subscription: a flat fee for unlimited rides. You never need to worry about parking again. With alternatives like this, how many people would still want to own a personal car? Even the wealthy may reconsider, especially if we simultaneously put in policies to make driving more inconvenient, such as no-drive zones in the city.

Just as technology brings about new forms of value (e.g. customization, flexibility) for those who don’t own a car, how can it do the same for other sectors?

  • How can technology help to transform Singapore’s public housing estates such that they offer new forms of value which private estates cannot provide? For example, how can we help HDB dwellers feel like the entire estate – with all its facilities and open spaces – is their home, one much bigger and diverse than any private estate? Digital communities and intra-town transportation may be aspects of this.
  • How can technology make a face-to-face doctors’ appointment something that people no longer seek as the “premium option”, for example, by making tele-health so attractive and pervasive?

I believe if domain experts and technologists put their minds to this, they will be able to come up with much better ideas than these! In short, technology can help catapult currently “inferior” options to equal status as “aspirational” options by creating new forms of value.

2. Second, cities should use technology to distribute scarce land and human resources more equitably.

In most countries, there is a healthy debate on how progressive and equitable the tax and redistribution regime is. However, not as much attention is paid to how other scarce city resources – land and manpower – are used. These too, must be used equitably, and technology can help cities achieve this.

Land

Reducing the land used on roads is a great example of how we can use land more equitably. Roads and parking lots tend to be utilized disproportionately by those who own cars, who – in Singapore – tend to be wealthier. Can we cut down on roads and parking, and reallocate this land to purposes such as community facilities and public housing, which benefit a wider proportion of the population?

Yes, and technology is critical to this. How much land we need for roads and parking is determined by the concept of “peak demand” – the maximum number of vehicles on the road, ever. We can cut down peak demand by encouraging people to use shared mobility options rather than drive a private car (I write about how tech enables this here), and by investing in autonomous freight and utility so that these activities can be done at night, when roads are far emptier.

Public Sector Manpower

Similarly, we can use public sector manpower more equitably by investing in technology. Technology can significantly reduce the manpower we commit to customer services. For example, Govtech rolled out MyInfo, which enables citizens to automatically fill in their administrative information for Government schemes with the click of a button. Chatbots on Government websites will increasingly be able to answer public queries; phone lines will no longer be needed. Public sector manpower can now be dedicated to functions which are in great need of resources. One such area is social work and education. Families in the bottom rung of society often face a cocktail of challenges – divorce, low-income, lack of stable employment, cycles of incarceration and so on. Giving them (or their children) a real chance of breaking out involves an extremely high level of hand-holding and investment by social workers and schools. Resources are sorely needed here.

Access to top quality healthcare

Let’s take another scarce resource – top surgeons. People who can pay for their services access better quality care, and stand a higher chance at recovery. Technology can change this dynamic. Companies like Verb Surgical are using machine learning to propagate top surgeons’ expertise more widely. This is how it works: every time the best surgeons perform a procedure, every single action is recorded in a common machine “brain”. The “brain” is trained to associate each action with the probability of a successful surgery. As the “brain” records more and more surgeries, it gets smarter and smarter. Now, the “brain” is made accessible to ALL surgeons. At each step of their surgery, they are told what successful surgeons did. Now, the best surgical expertise is within the reach of the average citizen.

Technology that enables our scarce resources (e.g. land, public sector manpower and top surgeons) to benefit the broad population and serve those in acute need are the types of technologies that cities should invest in, and quickly enable through regulations.

Conclusion

If you google the “Smart City” movement, you’ll find many broad and loose definitions. Generally, it refers to how cities deploy technology to improve city life and allocate resources more efficiently, whether it is helping transport systems run more efficiently, making interactions with various Government services easier, or to adding fun to the city experience.

Unfortunately, such broad and loose definitions give cities little guidance to on what to focus on in prioritising investments and regulatory reform, which is an incredibly important conversation given the limited resources at most cities’ disposal. It also does not paint a compelling vision for why being a Smart City matters, which disengages most of the population. Personally, before I worked in tech, I felt absolutely no connection to the idea of a ‘Smart City’. Tech was cool, but I never thought it was crucial.

I believe that using technology to tackle inequality and its effects should be a Smart City’s ambitious goal and guiding force, providing focus and rallying support from its constituents. This article spelled out two ways to do so.

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“What I Wish They Knew”: 5 Answers from a Government Data Scientist

One of the goals of this blog is to bridge the worlds of tech and government: I believe we can do so much more by working together, yet we often don’t understand each other deeply enough to begin. I will be starting the “What I Wish They Knew” series, which features people who are familiar with both these communities.

The first person to kick off this series is none other than my husband, Kenneth, who worked in Singapore’s Government Data Science unit before pursuing a PhD in Statistics at Stanford. By the way, you can find out more about Singapore’s Government Data Science unit on their blog: https://blog.gds-gov.tech/ – highly recommended.

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Kenneth at The Hive, where the Government Digital Services team is located.
  1. How did you get into Government Data Science? 

I’ve had a strong interest in mathematics and related quantitative fields like statistics and computer science for as long as I can remember, and studied math at Princeton as an undergrad. [NB: Kenneth’s mathematics blog can be found here.]

I began my career in the Singapore public service, hoping to give back to society in the small ways I could. While I was at the Ministries of Defence and Environment, most of my work did not involve any advanced math or data analysis.

I missed the intellectual challenge of quantitative thinking, and started taking online courses on the side. Andrew Ng’s “Machine Learning” course on Coursera first piqued my interest in data science. I learnt how we can use a small toolbox of algorithms to extract a whole lot of information from data, and I thought to myself, “How cool would it be if I could use some of these techniques in my work?”

Fortunately, a unit in Government was being set up to do just that: use state-of-the-art data analysis techniques to inform policy decision-making. I joined the Government Data Science unit in 2015 as a consultant. My work experience in policy and my quantitative undergraduate training put me in a rare position to understand the mindsets of both the policy officer and the data scientist. As such, I felt that I was an effective translator between the 2 parties.

  1. How about an example – what is one meaningful thing you did in Government Data Science?

An agency was very concerned about congestion during peak hours at the checkpoint between Singapore and Malaysia. Understanding cargo traffic patterns could help them design policies to reduce congestion. All they had was tens of millions of “permit data” entries, which captured the time that the cargo truck carrying the permit passed through a checkpoint, the industry code and value of the goods carried. I worked with the agency to define useful problem statements to shape the direction of analysis. One example: what are the top 5 industries moving cargo during peak hours? (Since policy interventions would be done at an industry-level).

Next, since each truck could hold multiple permits and the number of trucks was what we cared about, we went through a (non-trivial) process of turning “permit data” into “truck data”. We were able to identify the top industries moving cargo during peak hours, and further narrowed this group to those who were moving cargo on the busiest roads. We were also able to develop hypotheses on what influenced industry behaviour.

After completing the analysis, I shaped the narrative of our presentation in a way that delivered impactful policy insights, ruthlessly cutting down on unnecessary details. This is often the most painful part of the process for a data scientist – it’s so tempting to want to show ALL the great analysis we did.

After dozens of hours of work, there was nothing more satisfying in seeing the audience’s facial expressions saying, in effect, “before I was blind, now I see”! They never had a picture of the cargo traffic patterns until our analysis was done, and could now act upon it to improve congestion.

  1. What is one thing you wish non-data scientists knew about working with data scientists? 

That good data analysis requires significant collaboration between the data scientist and you, the domain expert.

Some people view the relationship between the domain expert and the data scientist as follows:

  1. Domain expert gives data scientist a bunch of Excel files.
  2. Data scientist crunches the numbers and churns out a report or presentation 3 months later. After all, the data scientist knows everything about data and that’s what we are paying them to do, right?

Nothing could be further from the truth! Domain expertise can speed up the data analysis process tremendously and direct it meaningfully, resulting in greater value from the project. Let me give two examples of this.

First, explaining the data to the data scientist, down to what each column means and how the data was collected, will save him/her much second-guessing angst. For example, if the patient check-out time was 18:00:00, does it mean that the person checked out at 6pm, or does it mean that the clinic closed at 6pm, and so everyone who hadn’t checked out yet was given a standard check-out time? Explaining the data will also give the data scientist a better sense of which variables are of greater importance and deserve more attention. In the example above, what does “checking-out” mean anyway, and is it significant?

Second, domain experts can pick up on insights that would escape data scientists. For example, a data scientist finds that Chromosome 21 seems to have an impact on a health outcome. Is that expected? Does it confirm some of the other hunches that we have? Or is it something completely unexpected, that suggests that the model is wrong? These are questions that a data scientist is unlikely to have any intuition about. However, with feedback from the domain expert, the data scientist can quickly decide to pursue or drop lines of inquiry.

  1. As a policy-maker, what is one thing you wish more data scientists paid attention to?

That data analysis is not for the data scientist, but for the policy-maker (or client). As such, good data analysis always puts findings and insights in the proper context.

Consider the sentence: “Our prediction model for which patients will be re-admitted over the next 6 months is 34.56% more accurate than the existing model.” Upon seeing this sentence, several questions come to mind:

  • What do you mean by accuracy? Is the measure for accuracy that you are using appropriate? (See this https://en.wikipedia.org/wiki/Confusion_matrix for a whole zoo of accuracy measures.)
  • 56% seems overly precise: can we really compare performance down to 2 decimal places? (35% might be better.)
  • Does 35% more accuracy translate to a meaningful difference? For example, will this allow us to tailor our services better to 100,000 patients, or 10 patients? (If possible, relate the finding to something the policy-maker cares about, like dollars or man-hours saved.)
  • Is this even a meaningful thing to predict?? (Hopefully, the domain expert would have said so. See answer to question 2.)

Good data analysis also provides enough detail to illuminate, but not too much till it confuses. For example, saying nothing about the modeling process could make me wonder whether you did your homework in choosing the most appropriate model (and whether I should have spent all that money hiring you). At the other extreme, I will not appreciate going through slide after slide of raw SAS/R output.

At this point I cannot overstate the importance of appropriate data visualisation. These visualisations have to be thought through: good charts clarify, poor ones confuse. Unfortunately, it’s a lot easier to make the latter. (See this for examples not to follow.)

kenneth-graph
Truly, a (well-designed) picture is worth a thousand words

 

5. What is your hope for the field of data science?

The economist Ronald Coase famously said “Torture the data, and it will confess to anything.” In an era of subjective reporting and “fake news”, this concern is more pertinent than ever.

My hope is that the general population will have enough statistical knowledge so that they can call a bluff when they see one, and demand quantitative evidence for decisions their leaders make. To this end, I hope to see reforms in statistics education at the high-school level so that it becomes a subject that people feel is relevant and interesting, rather than abstract and theoretical (which is often how it is taught today).

Thanks for reading! Know anyone who should be featured in this series? Do let me know at karentay@gmail.com.

Tackling AI-driven job displacement: A Primer

I’m starting a series of posts on solutions to AI-driven job displacement.

I want to answer one big question: in light of AI-driven job displacement, what technology and policy innovations do we need to give people a great sense of hope for what the future holds?

I use the word “hope” because it is the prospect of a better future within reach which will inspire people to continually re-invent themselves when they face setbacks like job loss, rather than to give up. It’s not enough to give them concessions.

Lots of great articles have diagnosed the problem of technology-driven job displacement. A quick summary with links to articles I really enjoyed:

  • In the long run, there will be a net positive in jobs, but in the short run there will be pain: especially among the middle class. The White House published a useful overview on the impact of AI on society last December and Gary Bolles has great resources to understand the hollowing out of the middle class. One very concrete example of middle-class job loss: as shared mobility becomes an increasingly attractive option for city-dwellers, less people will feel the need to own a car. The GMs, Fords, Toyotas and Tier-1 suppliers of the world will scale down their manufacturing business and move into new lines such as mobility services. New jobs will most definitely be created – for example, operators who manage fleets of autonomous vehicles and software engineers whose algorithms will continually improve the cost and energy efficiency of fleets. It’s likely these jobs will hire new people, not those who were displaced.
  • Progress is at stake if we can’t give people the confidence that the new system will work in their favor. We see this in widespread support for anti-trade platforms, even though no economist doubts the overall benefits of trade. America has taken a few steps back, and while some of this may be attributed to the ignorance of one man, it is also the collective anxiety of people speaking through their votes – and this anxiety is not unfounded. This is not a particularly American problem. Brexit is the other most popularly cited example, but you see it everywhere, both in the developed and developing world: Egypt, the Philippines, Singapore: a push back among those who feel that the system no longer works in their favor.

So when it comes to job displacement, it’s not a matter of “us” and “them”. It affects all of us, albeit differently.

Here’s the challenge: how can we make the future full of hope for everyone – so that broad-based support for technological progress will push us all forward?

A Framework to Understand our Target Group

To start off, it’s useful to have a framework to understand the universe of people we need to reach. I offer two dimensions that differentiate people in our target group.

Slide2.jpg

*these are just illustrative examples! Obviously, the reality is a lot more nuanced.

Proactive vs Reactive

On one end, we have people who are “proactive” about keeping themselves relevant to the job market. Geeks like my husband, who completed a dozen Coursera/Udacity courses, taught himself new coding languages, mastered Tableau and ran a successful math blog, all while managing his day job.

On the other end we have people who are “reactive” – they are hardly thinking about the possibility of losing their job; even if they are, they aren’t taking action. This could be the 45-year old who worked a 9-5 job for the past 20 years or a new parent who has no bandwidth to think about anything but the present.

Inherent personality traits are one determinant of where a person falls on this spectrum. Life stage is another – it affects how much time we have to think about alternative plans. The prevailing culture of family and community also shapes what we value such adventure or stability, ambition or work-life balance. Much of this is determined by geography.

Where someone falls on this spectrum is never static. People can shift left and right, and motivation is key.

 New Entrant vs Old Timer

The “new entrant” vs “old timer” distinction is useful in framing solutions. To illustrate this, let’s imagine Uber can fully automate all its fleets by 2025, and will no longer need human drivers.

“Old timers” are existing Uber drivers. The challenge is to give them sufficient fore-warning of impending job loss, and opportunities to pivot into new industries. The difficulty is in encouraging them to take action before it’s too late, and to ensure there are good alternatives.

“New entrants” are those who will be entering the job market in 2025. The challenge is to equip them with skills that will be relevant to the job market in 2025, including skills required to operate Uber’s fleet of autonomous vehicles in localities throughout the world. The difficulty is that no one knows exactly what skills will be needed in 2025, and how many jobs will require these skills.

Here are some topics I’d like to address based on this framework.

First, most solutions today address those who are “proactive” – it’s the easiest group to address because it’s sufficient to make learning resources available; they’ll find them and take them up. How can we motivate or guide those who fall into the “reactive” half of the framework? What can tech companies do? What can Governments and employers do? How do these efforts tie together?

Second, how can tech work with higher education institutions to prepare new entrants (our children today) for the future job market? About five years ago, some predicted the death of higher education institutions as Massive Open Online Courses took off. Now, most of us acknowledge that this is not going to happen at a large scale any time soon. Most people aren’t as confident enough to ditch traditional paths, even if these don’t make sense anymore. How can tech companies help to reform higher education and make it far more responsive to the job market than today? I will draw on my experience working in higher education to address this question.

Third, what social security policies do we need in an era of accelerating job displacement? In the Silicon Valley, the idea of a Universal Basic Income – where everyone receives a basic monthly stipend from the state – has gained much traction as a policy solution to job-displacement. How will a Universal Basic Income impact the different groups in this framework? What are the alternatives or complements to a Universal Basic Income? I will draw on my experience working in and studying social policy to address this.

A summary here: Stay tuned!

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The Dark Side of the Sharing Economy in Transport (and Three Solutions)

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photosource: cnbc

The shared economy is a net positive for society…

In my previous posts, I’ve talked about how the shared economy – expedited by technology – can have tremendous benefits for society. For example, it can mitigate the feeling the inequality by closing gaps in the transportation experience. The benefits are even greater when private companies work with Governments to reach the elderly, poor and underserved.

I’m pretty sure that the shared economy in transport has a net positive effect on the economy too, though evidence is nascent. Last March, Lawrence Katz and Alan Krueger conducted the RAND-Princeton Contingent Worker Survey, which showed a substantial rise in the incidence of alternative work arrangements[1] for workers in the US, from 10.1% in 2005 to 15.8% in late 2015. Strikingly, this implies that all of the net employment growth in the US from 2005–15 appears to have occurred in alternative work arrangements.

One reason is that the shared economy provides part-time or intermittent work for people who otherwise cannot find a suitable job. All these Uber drivers I’ve met before – the young man who lost his job and needs time to search for new employment. The father fighting a costly custody battle, who needs a flexible job so he can show up in court. The lower-income mother who needs to supplement her day job to pay for her mortgage. Uber driving is a particularly attractive part-time job – typical part-timers get paid disproportionately less than full-timers. In contrast, Alan Krueger and Jonathan Hall found that no matter how many (or few) hours Uber drivers work, their hourly earnings were the same.

…BUT the distribution of benefits matters, and here’s how cities should think about it

Even though the shared economy creates tremendous new value, the distribution of value favours some groups over others. The tensions that arise can undermine companies operating in the shared economy, as we’ve seen in several cities worldwide. Cities need to consider how they may ensure a fair distribution of the shared economy’s benefits along three dimensions:

  • Workers vs companies
  • Incumbents vs new entrants
  • Now vs 10 years’ time, especially with the advent of autonomous vehicles

Companies would be wise to work collaboratively with cities to resolve these issues early on, rather than lock horns in costly legislative battles, or get blocked from new markets.

  1. Fair Distribution of Benefits Between Drivers and Companies

The shared economy benefits both ride-sharing companies and their drivers, but arguably companies benefit a lot more. The business model of ride-sharing companies like Uber, Lyft and Grab is to provide a technology platform which enables matching of riders to drivers. These drivers are essentially self-employed contractors who log on to the platform whenever they wish. Many are able to find work and supplement their income through this platform.

However, these drivers are not employed by ride-sharing companies and hence do not receive certain benefits. In Singapore, employers are required to contribute up to 17% of their employee’s salary to a savings account for housing, retirement and healthcare. In the U.S., many receive healthcare insurance through their employers, who are often able to get better rates than individuals. In the UK, employees are protected by the minimum wage legislation. Drivers for Uber and Grab do not receive such benefits because they are not considered employees.

As companies rely more on these self-employed workers to fuel their business, risks are passed from companies to workers. As Singapore’s Deputy Prime Minister Tharman Shanmugaratnam has said “….it serves the interests of the company because they’re really pushing risk to the contract worker…who actually can’t take much risk – the risk of instability in wages and the risk of not being prepared for retirement because of a lack of social security contributions.”

Cities need to start shifting their social policies to accommodate the rising proportion of self-employed workers. At the same time, companies need to discuss reasonable ways to spread benefits and share risks with workers who are self-employed. It need not be all or nothing. There has been talk about creating a “third classification” of workers who have some benefits of employees, while retaining the independence of a contractor. This gives both the business and worker flexibility while providing some social protection.

Working with cities on some “in-between” solutions will help business avoid lengthy legal battles down the road. For example, Uber is appealing a ruling by London’s employment tribunal recently that it should treat its drivers like employees, including paying the national minimum wage. I would go so far as to say that it is the responsibility of ride-sharing companies to start engaging in these discussions, because many of their workers face an uncertain future when autonomy arrives (third point).

  1. Fair Distribution of Benefits Between Incumbents vs New Entrants

The arrival of ride-sharing companies like Uber very rapidly redistributed benefits in the transportation system, creating new winners (e.g. consumers, new drivers) and losers (e.g. taxi drivers). Yes, there were probably more winners than losers, but the losers suffered a huge impact on their livelihoods. For exampe, many taxi drivers in the U.S. invested large sums in their license – in Chicago, the median cost of a taxi medalllion in late 2013 was USD$357,000. Having the value of your medallion plummet is like losing your home!

Cities dealing with disruptive innovation need to quickly level the playing field between incumbents and new entrants, to ensure that the distribution of benefits is not overly skewed in the direction of new entrants. 

In the case of ride-sharing, issues like driver training requirements take the forefront. For example, Singapore placed 10 hours of training requirements on Uber/Grab drivers, while significantly cutting down the training hours required for taxi drivers (now, they only need to spend 16 hours on in-class training, compared to about 60 hours previously). We also cut down course fees for taxi drivers, and scrapped the daily minimum mileage – a move which helps taxi drivers minimise empty cruising just to meet their quota.

It is in the interest of disruptors to avoid a total regulatory lockdown by avoiding a zero sum mentality in these negotiations.

  1. Fair Distribution of Benefits Between Present and Future

Finally, while we reap many benefits now, there are two important long-term considerations for cities working with ride-sharing companies.

First, many ride-sharing companies are at the stage where they are flush with investment, and can afford keep their ride prices artificially low. What happens if cities “outsource” their transportation to ride- companies, which eventually raise the prices beyond what regular citizens can afford? How can cities set up their transport systems such that competition can easily arise – keeping prices in check – or that public options can bounce back quickly? A city needs to ensure that even as people reap the benefits of the shared economy today, these benefits can be sustained over time.

Second, the big elephant in the room is autonomy. Full autonomy = no more need for drivers.

Autonomy will further redistribute the benefits away from drivers towards companies, and for all we say about new jobs being created, I’m pretty sure many of these drivers won’t be the ones to do it.

Because many drivers are self-employed workers not covered by social protections, they will be in particularly difficult situations.

It will be some time before full autonomy at scale is realised, so it is not too late to start conversations on how to ensure that drivers don’t get the short end of the stick when their jobs are replaced. One immediate action companies need to take is to give drivers information. Drivers, while not employees, are stakeholders in the company’s business and should be informed about the timeframes and implications of autonomy as the field evolves. In addition, much more can be done to help them with skills and future employment, a topic I will cover soon.

Conclusion

Over a series of posts, I’ve argued that the shared economy is a net positive for society and economy. This post, I posit that we need to work together to ensure that these benefits are distributed fairly between drivers and companies, incumbents and new entrants, present and future.

This is not the ambit of cities or Governments alone; companies seeking a sustainable business model in essential public services like transportation would be wise to work closely with cities rather than to be caught in costly legislative battles, be locked out of markets, or worse still – to be exploitative in their practices.

[1] “temporary help agency workers, on-call workers, contract company workers, independent contractors or freelances)

 

Disrupting Elderly Caregiving (and why Uberisation won’t work)

I came across a really encouraging article about tech start-ups that are trying to fix the elderly caregiving sector. This is incredibly important work.

The double whammy is here 

The graphic below, from the UN, shows how our global population is aging. It will happen faster in developed countries. In Japan people above 65 already constitute more than a quarter of the population. Singapore will get there in the 2030s, and the U.S. in the 2040s.

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This will be a double whammy on societies. Countries will have a shrinking tax base to support the growing number of elderly who need care. There won’t be abundant resources to build new nursing homes and hospital beds. At the same time, it will be more difficult for the elderly to continue living at home – with shrinking family sizes, the responsibility for caregiving will fall on one or two children, instead of three or four.

Advances in biotechnology, personalised medicine and genomics will go a long way towards mitigating these challenges. For example, if companies like Calico and Unity Biotechnology manage to reverse aging, people can have longer, healthier, independent lives. The periods of time when they need caregiving will plummet. This is referred to as a longer “healthspan” (as opposed to just “lifespan”).

Unfortunately, many developed countries are already starting to face the double whammy and need a more immediate solution. From a policy perspective, it does not make sense to build new infrastructure for the elderly because these will become redundant when subsequent elderly cohorts are smaller.

The best solution is to help people receive care in their own homes as they age. It is also what a large majority (some statistics suggest 90%) of people prefer.

Why is Home Caregiving such a tough nut to crack? 

A fragmented home care sector with low standards and revolving door caregivers is the norm in many cities. I don’t see a level of innovation and investment flowing into this sector that is commensurate to its importance. Three likely reasons why this is such a tough nut to crack.

  • Home caregiving is highly personal. When someone takes an Uber ride, they don’t have to talk to the driver if they don’t want to. In contrast, a caregiver gets deep into your personal space if they bathe, feed and change your diaper. You’re obviously going to be very picky about who you choose. Pickiness is not a one-way street – caregivers have their preferences too!
  •  The job is just not a lot of fun. In fact, that’s why many people out-source care to paid contractors, even care for those they love most. It’s not glamorous, it’s repetitive, it can be highly physical, and in some cases, you interact with suffering on a daily basis. For such a difficult job, you mostly work alone without a network of support and accountability. Benefits, training and wages are patchy. Motivation is low. It is more difficult to recruit a good part-time Caregiver than a good part-time Uber driver.

What are the implications for tech companies and smart cities?

Disrupting the flailing home care sector is a classic problem that the tech, business, Government and non-profit sectors need to go at together. Here are three things that anyone working in this space needs to consider.

  1. Tackle the root problem – motivation.  

The FT article features Josh Bruno,who left Bain Capital to set up Hometeam. He started with a tech solution in mind – perhaps the equivalent of Uber for in-home care, he thought – a platform that would match caregivers to elderly and take a cut.

After volunteering in about 40 elderly care organisations, he quickly realised that the deepest issue in the caregiving sector was not matching, but motivation – “People blame them as lazy, but they are the worst working conditions — low pay, no training.” Defying all industry standards, he decided to hire the caregivers, give them good benefits  of paid holidays, maternity leave, training and retirement.

I think Josh is getting to the heart of the issue – how can you expect people to deliver quality care if you don’t care for them? The first step is turn caregiving into a worthy career. Business can’t tackle this alone, which leads me to point two.

2. Partner to make home care an economically viable business 

Josh’s company was turned away by many VCs because hiring the caregivers was not deemed to be maximally profitable.

I believe many  have not yet realized the economic benefits of a strong home care sector. When elderly are able to continue living in their own homes, a whole ecosystem of services such as tele-health, food delivery and transport has a larger market. For large healthcare providers like Kaiser Permanente, a strong home care sector combined with tele-health can help manage its elderly clients outside the expensive hospital setting. This optimizes the use of hospital facilities and doctors’ time, making the business more profitable. For the same reasons, countries where healthcare systems are operated by the Government should consider investing public funds in the home care space, for example, by extending subsidies in nursing homes to the home care sector.

Furthermore, to help the home care sector grow, the public and non-profit sectors should play a role in helping to overcome inherent”diseconomies of scale”. It does not make sense for small home care companies to have their own training programmes and qualifications system. One idea is for Governments or non-profits to offer basic, modularised caregiver training as a “shared resource” for home care companies to utilise.

3. Use technology to build a care ecosystem around the elderly 

Although it may not currently feel like it, home caregivers are really part of a team of family members, healthcare professionals and community members who help make living at home comfortable and enjoyable.

Technology can enable better communication and support between different members of this team. The home caregiver should be able to ask the healthcare professional whether a situation requires medical attention, and share information that would help the healthcare professional with diagnosis and prescription. Community members should be able to volunteer in bite-sized chunks of time, a topic I wrote about in my article “Three ways to build a transportation system that services the vulnerable”. Ultimately, when members of the team communicate, they can collectively give better care and receive support from each other. This can help to tackle the motivation problem for caregivers.

Technology can be part of the care team too! It can enable an elderly person to live independently as long as possible, only activating their caregiving team when necessary. One of the greatest fears of an elderly person is that something happens (a fall, a stroke) when they are alone. The solution, 24-hour care, is expensive and intrusive.

The Singapore Management University has been piloting sensor-enabled homes in Singapore through their SHINESeniors initiative. These sensors observe and analyze the elderly’s living patterns and immediately activate his care team when a change in pattern suggests a deterioration. Besides enabling independent living, this also allows companies and family members to allocate their time and resources efficiently.

Conclusion

Disrupting the flailing home care sector is essential to the quality of life of older folk, and the sustainability of healthcare systems when populations age. It’s a tough nut to crack, but I can’t think of a more important issue to work on right now, because our parents – and then us – are going to be the beneficiaries of positive disruption!