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Macro starts with micro. When we think about big problems, we’re forced to grapple with big numbers. What would it take to solve problems for 1,000 people? Your first instinct might be to say: We’ll have to think about the big picture, because we can’t very well intervene individually with 1,000 people. But that notion, as it turns out, is exactly wrong. Notice how often the heroes in this book actually organized their work on a name-by-name basis. The teachers in Chicago assisted ninth graders using a by-name list. The team in Rockford housed homeless people using a by-name list. The domestic violence high-risk team protected women using a by-name list. All of these efforts were also aided by systems change, to be clear, but those changes were often sparked by a familiarity with individual cases. (The domestic violence team discovered that abusers needed to have their GPS bracelets added before they were released from jail—not two days later.) The lesson is clear: You can’t help a thousand people, or a million, until you understand how to help one. That’s because you don’t understand a problem until you’ve seen it up close. Until you’ve “gotten proximate” to the problem, as we explored in the chapter on leverage points. The leaders of the University of Chicago Crime Lab read the medical examiner’s reports on 200 homicide victims. How many people develop strong opinions about crime without bothering to train their intuition in that way? How many people develop strong opinions about homelessness without knowing any homeless people? It’s true that it’s harder to imagine this “by-name” methodology working with millions of people rather than hundreds or thousands. To affect millions requires systems change. But even systems change usually starts up close: Someone understands a problem so well that they formulate and lobby for a new policy at the city or state level, and it works, and later other state leaders see that the policy works and they embrace it, too. Remember the efforts of Dr. Bob Sanders in Tennessee, who lobbied for mandatory car seats? Macro starts with micro. If you want to help solve big problems in the world, seek out groups who have ambitious goals coupled with close-up experience.

People can’t see solutions to problems unless they experience it up close or in person. That is just how we are in general. While the expectation is that you can get super talented and intellectually rigorous people to tackle any problem in any field, that does not really hold true if they do not have context and have viewed the problem from up close before.

Thats not to say that the opposite can’t happen too. Someone who might be too close to the problem will not be able to make decisions for the “overall good”. Regardless, what I find most likely to work are people or leaders who take the effort to understand their users or customers up close and have a strong local context of their problems.

Ultimately, humans are just pattern recognition machines and we can’t do what we’re good at if we are just relying on spreadsheets and dashboards to train the “pattern recognition algorithm” in our brains.

Our starting point for systems thinking demands: What are the likely second-order effects? What will fill the void left by plastic bags, if they’re banned? Customers will either: (a) use more paper bags; (b) bring reusable bags; or (c) go without bags. Here’s where we reach our first surprise: While paper bags and reusable bags are far better than plastic ones from the perspective of keeping waterways clean, they are worse in other ways. They require far more energy to produce and ship than do plastic bags, which means they increase carbon emissions. A UK Environment Agency study calculated the “per use” effects of different bags on climate change and concluded that you’d need to use a paper bag 3 times and a cotton reusable bag 131 times to be on par with plastic bags. Not to mention that manufacturing paper and reusable bags causes more air and water pollution than plastic, and they are much harder to recycle. So now we’re forced to grapple with part/whole confusion: If protecting waterways and marine life, specifically, is our goal, then a plastic bag ban is a great idea. But if making the whole environment better is the goal, then it’s less clear. There are competing effects to consider. Another twist is that we’ve got to be very careful how we design the ban. In 2014, Chicago passed a law banning stores from offering thin, single-use plastic bags at checkout. So what did the stores do? They offered thicker plastic bags at checkout. The retailers’ supposed rationale was that customers could reuse these plastic bags, but of course most didn’t. That’s the cobra effect again: Trying to rid the environment of plastic led to more plastic. Experimentation leads to learning, which leads to better experiments. California voters passed a statewide ban in 2016, without the thicker-plastic loophole. One effect of the ban, though, was that sales of small and medium plastic trash bags shot up. (Presumably there were people who reused their grocery store plastic bags as trash bags at home—or for picking up dog poop—so in their absence they had to start buying alternatives.) A study by economist Rebecca Taylor found that 28.5% of the reduction in plastic caused by the ban had been nullified by this shift toward other bags. Still, that’s 28.5%, not 100%. The ban had significantly reduced single-use plastics. (And notice that in order to assess this issue at all, someone had to be carefully tracking the sales of substitute products, thus creating a source of feedback.) Then there were truly unanticipated consequences. Some people attributed a deadly 2017 hepatitis A outbreak in San Diego to the lack of plastic bags. Why? Homeless people had been in the habit of using the bags to dispose of their own waste. When the bags became less plentiful, the other alternatives turned out to be less sanitary. I wonder if you’re feeling now the way I was feeling when I first started trawling through this research: overwhelmed and dispirited, with a spritz of annoyance. What hope do we have of solving the hardest problems facing us when even plastic bag policies create a blizzard of complexity? It was Donella Meadows’s quote—about the need “not to bluff and not to freeze but to learn”—that pulled me out of my wallow. Because her point is: It’s hard, but we’re learning. As a society, we’re learning. Think of all the ingredients required even to analyze a policy like the plastic bag ban: the computer systems, the data collection, the network infrastructure, not to mention the ecosystem of smart people who know how to structure experiments that can shed light on city- and state-wide policies. This infrastructure of evidence has existed for a mere blip in human history. When it comes to upstream thinking, we’re just starting to get in the game. In 2016, Chicago scrapped the plastic bag ban that had led to the cobra effect. The city council replaced it with a 7-cent tax on all paper and plastic checkout bags that started in early 2017. And you know what? It’s working pretty well. A research team led by economist Tatiana Homonoff collected data from several large grocery stores. Before the tax, about 8 out of 10 customers used a paper or plastic bag. After the tax, that dropped to roughly 5 out of 10. What did the other 3 people do? Half the time they brought their own bag and half the time they carried out their purchases without a bag. And for those 5 customers who kept using bags, ka-ching, their voluntary tax payments provided the city with extra money to serve citizens.

2nd order effects is one of the key reasons why I always have trust issues with brands that market themselves as “sustainable” or so. The economic system is complex and it is really difficult to determine on an aggregate basis whether making a change in materials can really lead to overall environmental benefits.

It also depends on which environmental goal you are optimizing for. I remember reading that a metal straw could take up to 1000 times the carbon footprint of a plastic straw. Thus, if you wanted to optimize for ocean plastic pollution, banning plastic straws might help. On the other hand, it would make more sense to use plastic straws over metal straws for carbon footprint unless you are sure you can use the metal straw over a 1000 times.

The simplest way of course is to reduce overall consumption. I always found that there is an inherent contradiction between brands that promote greater consumption while touting environmentally sustainable products. Of course, it is impossible to know but I think this passage really brings about how it is important to have rigorous analysis and studies rather than taking claims or “common sense” on a superficial level.

A 2018 study by Harvard scholars Ethan Bernstein and Stephen Turban backs up Imber’s experience. They studied two Fortune 500 companies who were preparing to transition teams of employees to an open-office floorplan. Before and after the move, many staffers volunteered to wear “sociometric badges,” which captured their movements and logged how often they talked and to whom. (Their conversations were not recorded, just the fact they were talking.) The goal was to answer the most basic question about open floorplans: Do they boost face to face (F2F) interactions? The answer was almost laughably clear: F2F interactions plunged by about 70% in both companies. Meanwhile, email and messaging activity spiked. When people were placed closer together so that they’d talk more, they talked less. The cobra strikes again. What can be confusing, in situations like these, is that we must untangle contradictory strands of common sense. On one hand, you think: Of course, moving people closer together will lead them to collaborate more! That’s just basic sociology. On the other hand: No, look at subways or airplanes—when people are crammed in together, they find ways to retain some privacy through headphones or books or deeply unwelcoming glances. How can you know in advance which strand of common sense to trust? We usually won’t. As a result, we must experiment. “Remember, always, that everything you know, and everything everyone knows, is only a model,” said Donella Meadows, the systems thinker. “Get your model out there where it can be shot at. Invite others to challenge your assumptions and add their own.… The thing to do, when you don’t know, is not to bluff and not to freeze, but to learn. The way you learn is by experiment—or, as Buckminster Fuller put it, by trial and error, error, error.” Looking back on the open-office miscue, Imber said she wishes she had tried some experiments with her staff in the State Library Victoria in Melbourne. The library has many different kinds of environments, ranging from open, collaborative spaces to more solitary ones. Had the team sampled some of those different areas, observing how they affected the group’s productivity and happiness, that experience might have helped them design an office that served them better. For experimentation to succeed, we need prompt and reliable feedback. Consider navigation as an analogy: To travel somewhere new we need almost constant feedback about our location; we follow the arrow on a compass or the blue dot on Google Maps. Yet that kind of feedback is often missing from upstream interventions. Think of the open-office situation: How would you know whether collaboration was increasing or not? Most employers don’t have “sociometric badges” to log conversations. Maybe you’d add a question to the annual employee survey, asking for people’s feedback on the transition. But that kind of infrequent, point-in-time feedback isn’t enough to navigate. It’s like driving a car with no windows and, once every hour or so, getting beamed a photo of the outside environment. You’d never arrive at your destination, and given the risks, you’d be crazy to try.

Putting people together compels them to interact more face-to-face. Yet in this case, putting them close together actually reduced overall f2f interactions. This is where common sense or intuitive ideas can contradict. In some ways, team members can end up arguing against each other all day using “logic” and “rational” but we will never know the actual result.

As mentioned by the author, the key is to experiment. Always test your assumptions and try to prove them wrong. However, sometimes, what is valid at a certain scale is no longer accurate at a larger volume, so experimentation might not necessarily be the silver bullet that we are looking for.

This might seem very tiring and challenging but the key is to always test your assumptions and not assume what worked before will work again. While probabilistically, past experience might work most of the time. It doesn’t hurt  to adopt an alternative view point or simply change things up once in a while. You might find that in different situations, your previous assumptions might no longer hold true.

The lesson of the high-risk team’s success seems to be: Surround the problem with the right people; give them early notice of that problem; and align their efforts toward preventing specific instances of that problem. To clarify that last point, this was not a group that was organized to discuss “policy issues around domestic violence.” This was a group assembled to stop particular women from being killed. Note the similarity to the Chicago Public Schools story earlier in the book. Remember this quote from Paige Ponder, who led the district’s Freshman On-Track efforts: “The beautiful thing about teachers—you can have whatever philosophy you want, but if you’re engaged in a conversation about Michael, you care about Michael. It all boils down to something real that people actually care about.… ‘What are we going to do about Michael next week?’ ” That same motivation led the work in Newburyport. The cops and DAs and advocates and health workers all had different institutional priorities. But what they shared was a desire not to see one of their neighbors murdered by her abusive husband. And that shared aim became the fuel for their coordination. The other point of connection between the two stories is the primacy of data, which was a theme I observed repeatedly in my research, and one that surprised me. I knew data would be important for generating insights and measuring progress, but I didn’t anticipate that it would be the centerpiece of many upstream efforts. I mean this even in a literal sense—what the teachers and counselors in Chicago were doing, and what the high-risk team members in Newburyport were doing, was sitting around a table together and looking at data. Discussing how the fresh data in front of them would inform the next week’s work. In Chicago, the data was: Has Michael been coming to school since we last met? How are his grades in all of his courses? How can we help him this week? In Newburyport, the data was: Where was Nicole’s abuser? What has he been doing? How can we help her this week? This kind of system is what Joe McCannon calls “data for the purpose of learning.” McCannon is an expert in scaling up efforts in the social sector—a former nonprofit and government leader, he has advised movements in many countries. McCannon distinguishes “data for the purpose of learning” from “data for the purpose of inspection.” When data is used for inspection, it sounds like this: Smith, you didn’t meet your sales targets last quarter—what happened? Williams, your customer satisfaction numbers are going down—that’s unacceptable. Using data for inspection is so common that leaders are sometimes oblivious to any other model. McCannon said that when he consults with social sector leaders, he’ll ask them, What are your priorities when it comes to data and measurement? “And I never hear back ‘It’s important to set up data systems that are useful for people on the front lines.’ Never,” he said. “But that’s the first principle! When you design the system, you should be thinking: How will this data be used by teachers to improve their classrooms? How will this data be used by doctors and nurses to improve patient care? How can the local community use the information? But that’s rarely how the systems are designed.” McCannon believes that groups do their best work when they are given a clear, compelling aim and a useful, real-time stream of data to measure their progress, and then… left alone. The situation at Expedia, with its millions of unnecessary calls, provides a model. A cross-functional group is presented with a goal: Help millions of our customers avoid the nuisance of calling us. That’s a valuable and challenging target. And then the group is basically locked in a room together, armed with regularly updated data to see if the number of calls is going up or down. The team members come up with theories and then they test them. They watch what works. That’s the “data for learning” part. They don’t need a boss standing over them, hollering out specific targets: “We need to cut four percent call volume by tomorrow!”

A common theme I’ve observed is that in most organizations, the frontline staff are the ones with least access to the data. Most data is used for “introspection” as in this case, and most information is usually descriptive rather than prescriptive.

Knowing for example, your number of visitors everyday, is a descriptive statistic. There is usually no clear actionable or revealing of problem from it. However, these statistics and metrics are usually the easiest to generate.

Having to dive deep into data to reveal certain insights or actions you should take is using data for “learning”, but these are generally less straightforward. Furthermore, the ones whom need to use these data (people closer to the frontline rather than high level managers), are usually not super data driven. Even if they were, they probably wouldn’t get any the attention from the data team, who would quite rightly prioritize the optics from working with high level managers.

In such cases, the organization then slowly shifts towards being more reactive towards customer trends and behaviours, focusing on “downstream” activities, as this book would put it.

And this is true in many parts of society. So often in life, we get stuck in a cycle of response. We put out fires. We deal with emergencies. We handle one problem after another, but we never get around to fixing the systems that caused the problems. Therapists rehabilitate people addicted to drugs, and corporate recruiters replace talented executives who leave, and pediatricians prescribe inhalers to kids with breathing problems. And obviously it’s great that there are professionals who can address these problems, but wouldn’t it be better if the addicts never tried drugs, and the executives were happy to stay put, and the kids never got asthma? So why do our efforts skew so heavily toward reaction rather than prevention? Back in 2009, I spoke with a deputy chief of police in a Canadian city; it was one of the conversations that sparked my interest in upstream thinking. He believed that the police force was unduly focused on reacting to crimes as opposed to preventing them. “A lot of people on the force want to play cops and robbers,” he said. “It’s much easier to say ‘I arrested this guy’ than to say ‘I spent some time talking to this wayward kid.’ ” He gave an example of two police officers: The first officer spends half a shift standing on a street corner where many accidents happen; her visible presence makes drivers more careful and might prevent collisions. The second officer hides around the corner, nabbing cars for prohibited-turn violations. It’s the first officer who did more to help public safety, said the deputy chief, but it’s the second officer who will be rewarded, because she has a stack full of tickets to show for her efforts. That’s one reason why we tend to favor reaction: Because it’s more tangible. Downstream work is easier to see. Easier to measure. There is a maddening ambiguity about upstream efforts. One day, there’s a family that does not get into a car accident because a police officer’s presence made them incrementally more cautious. That family has no idea what didn’t happen, and neither does the officer. How do you prove what did not happen? Your only hope, as a police chief, is to keep such good evidence of crashes that you can detect success when the numbers start falling. But even if you feel confident your efforts accomplished something, you’ll still never know who you helped. You’ll just see some numbers decline on a page. Your victories are stories written in data, starring invisible heroes who save invisible victims.

Solving problems as they pop up can be cathartic in some sense. It gives you a feeling of accomplishment as you resolve issue to issue and you get a tangible output.

Preventing them from happen in the first place? Not so fancy and engaging. Nobody lauds the security guard or measures that might have prevented a probable attack. But everyone would congratulate and celebrate those who apprehended the culprits.

This book covers this phenomenon in detail, and describes how we end up focusing on downstream impacts and how we can all make our lives easier by identifying upstream issues. I’ll be sharing certain excerpts and examples that I found interesting.

This book is not about specific development policy. It is not a book about whether one way of tackling the HIV–AIDS problem is better than another, or if one education strategy yields better results than another. It is about how to finance the development agenda so that, whatever the development policy, economic prosperity might be realized. Dongo will only change if its fundamental model of aid-dependency is abandoned and the Dead Aid proposal of this book adopted wholesale, in its entirety. The choice of development finance is at least as important as the policies a government adopts. You can have the best development policy in the world, but without the right financial tools to implement it, the agenda is rendered impotent. Put differently, it matters little whether Dongo is capitalist or socialist in development orientation – of paramount importance is how Dongo finances its economic development. Indeed, neither a capitalist nor a socialist economic agenda can be truly achieved in the longer term without a financing strategy based on free-market tools. Implicit in the proposals that follow are financing solutions that have their roots in the free-market system. This invites the question: is it possible for a government to raise money in a free-market way and spend it on a socialist agenda (for example, provide free education and healthcare)? The answer is yes: Sweden, Denmark and Norway are just three examples. Whatever the social, political and economic ideology a country chooses, there is a menu of financial alternatives (all better than aid) that can finance its agenda. Can a government use free-market tools and still maintain its core socialist values? The answer is not only yes, it can, but, perhaps more importantly, it has to. And even when a government finances itself using socialist-like tools (for example, high taxes), it must still rely on some market-based financing tools in order to successfully achieve its economic goals.

Last excerpt I’ll be sharing from this book. The key message is that economic ideology between socialism and capitalism is a false dichotomy when it comes to development. The financing solutions that are effective in developing a nation has to be grounded in free-market tools.

These include things like micro-finance, finance access to centralised banking services rather than the inefficient and informal methods, trade and direct investment. Without being able to build a sustainable middle class through these methods, any aid only increases the competition for resources and prevents a sustainable middle class from ever developing.

Another short passage from another aspect of the book that highlights this as follows:

An aid-driven economy also leads to the politicization of the country – so that even when a middle class (albeit small) appears to thrive, its success or failure is wholly contingent on its political allegiance. So much so, as Bauer puts it, that aid ‘diverts people’s attention from productive economic activity to political life’, fatally weakening the social construction of a country.”.

Democracy, the argument goes, gives a greater percentage of the population access to the political decision-making process, and this in turn ensures contract enforcement through an independent judiciary. Not only will democracy protect you, but it will also help you better yourself. Democracy promises that businesses, however small, will be protected under the democratic rule of law. Democracy also offers the poor and disadvantaged the opportunity to redress any unfair distribution via the state. It is after all under democratic governments, the American economist and social scientist Mancur Olson posited, that the protection of property rights and the security of contracts, crucial for stimulating economic activity, were more likely. In essence, democracy engenders a peace dividend, introduces a form of political stability that makes it a precursor for economic growth. In Olson’s world, democratic regimes engage in activities that assist private production in two ways: either by maintaining a framework (regulatory, legal, etc.) for private activity or by directly supplying inputs which are not efficiently delivered by the market (for example, a road connecting a small remote village to a larger trading town). By their very nature, democracies have an incentive to provide public goods which benefit each and everyone, and wealth creation is more likely under democratic regimes than non-democracies, such as, say, autocratic or dictatorial regimes. Under this sky, democracy is seen as Africa’s economic salvation: erasing corruption, economic cronyism, and anticompetitive and inefficient practices, and removing once and for all the ability for a sitting incumbent to capriciously seize wealth. Democracies pursue more equitable and transparent economic policies, the types of policies that are conducive to sustainable economic growth in the long run. Moreover, the Nobel Laureate Amartya Sen argues that because democratically elected policymakers run the risk of losing political office, they are more vigilant about averting economic disasters. Among mainly developing economies another study found that democratically accountable governments met the basic needs of their citizens by ‘as much as 70 per cent more’ than non-democratic states. But, perhaps most of all, donors are convinced that across the political spectrum democracy (and only democracy) is positively correlated to economic growth. Although the potential positive aspects of democracy have dominated discourse (and aid policy), Western donors and policymakers have essentially chosen to ignore the protests of those who argue that democracy, at the early stages of development, is irrelevant, and may even be harmful. In an aid-dependent environment such views are easy to envisage. Aid-funded democracy does not guard against a government bent on altering property rights for its own benefit. Of course, this lowers the incentive for investment and chokes off growth. The uncomfortable truth is that far from being a prerequisite for economic growth, democracy can hamper development as democratic regimes find it difficult to push through economically beneficial legislation amid rival parties and jockeying interests. In a perfect world, what poor countries at the lowest rungs of economic development need is not a multi-party democracy, but in fact a decisive benevolent dictator to push through the reforms required to get the economy moving (unfortunately, too often countries end up with more dictator and less benevolence). The Western mindset erroneously equates a political system of multi-party democracy with high-quality institutions (for example, effective rule of law, respected property rights and an independent judiciary, etc.). But the two are not synonymous. One only has to look to the history of Asian economies (China, Indonesia, Korea, Malaysia, Singapore, Taiwan and Thailand) to see how this is borne out. And even beyond Asia, Pinochet’s Chile and Fujimori’s Peru are examples of economic success in lands bereft of democracy. The reason for this ‘anomaly’ is that each of these dictators, whatever their faults (and there were many), was able to ensure some semblance of property rights, functioning institutions, growth-promoting economic policies (for example, in fiscal and monetary management) and an investment climate that buttressed growth – the things that democracy promises to do. This is not to say that Pinochet’s Chile was a great place to live; it does, however, demonstrate that democracy is not the only route to economic triumph. (Thanks to its economic success Chile has matured into a fully fledged democratic state, with the added accolade of, in 2006, installing South America’s first woman President – Michelle Bachelet.)

Follow-up from the previous passage. It gets taken for granted that democracy is positively correlated with economic growth and hence it must be a prerequisite for growth. Obviously, for most of us whom have grown in Asian Economies, it doesn’t necessarily hold true.

Strong leadership appears to be crucial in getting a country in the early stage of development to band together and develop. However, it can be a double-edged sword. You’ll never know whether you’ll be getting a benevolent dictatorship or one whom be using the power to enrich themselves and their cronies. Even with a well intentioned leader, right intentions doesn’t necessarily lead to right results.

I’ve always felt that the more developed a country is, the risk of of lack of accountability in a leadership eventually becomes higher than the risk of having weak leadership. Nevertheless, I’m going on a tangent here and lets get back to the books message.

Ultimately, the key message from this passage is that forcing democracy in developing nations might actually backfire and cause more infighting and hinder growth. Tying democratic reforms to aid might actually promote infighting within the country for access to the aid. The situation is always far more nuanced and complex than we can expect, and complex situations deserve more than simple ideological answers.

On a random note, when hiring, never trust the person who always has ‘the right answer’.

The correlation is certainly suggestive, even if the causation may be debated. Over the past thirty years, according to Moyo, the most aid-dependent countries have exhibited an average annual growth rate of minus 0.2 per cent. Between 1970 and 1998, when aid flows to Africa were at their peak, the poverty rate in Africa actually rose from 11 per cent to a staggering 66 per cent. Why? Moyo’s crucial insight is that the receipt of concessional (non-emergency) loans and grants has much same effect in Africa as the possession of a valuable natural resource: it’s a kind of curse because it encourages corruption and conflict, while at the same time discouraging free enterprise. Moyo recounts some of the more egregious examples of aid-fuelled corruption. In the course of his disastrous reign, Zaire’s President Mobutu Sese Seko is estimated to have stolen a sum equivalent to the entire external debt of his country: US$5 billion. No sooner had he requested a reduction in interest payments on the debt than he leased Concorde to fly his daughter to her wedding in the Ivory Coast. According to one estimate, at least US$10 billion – nearly half of Africa’s 2003 foreign aid receipts – leave the continent every year. The provision of loans and grants on relatively easy terms encourages this kind of thing as surely as the existence of copious oil reserves or diamond mines. Not only is aid easy to steal, as it is usually provided directly to African governments, but it also makes control over government worth fighting for. And, perhaps most importantly, the influx of aid can undermine domestic saving and investment. She cites the example of the African mosquito net manufacturer who is put out of business by well-intentioned aid agencies doling out free nets. Moyo offers four alternative sources of funding for African economies, none of which has the same deleterious side effects as aid. First, African governments should follow Asian emerging markets in accessing the international bond markets and taking advantage of the falling yields paid by sovereign borrowers over the past decade. Second, they should encourage the Chinese policy of large-scale direct investment in infrastructure. (China invested US$900 million in Africa in 2004, compared with just US$20 million in 1975.) Third, they should continue to press for genuine free trade in agricultural products, which means that the US, the EU and Japan must scrap the various subsidies they pay to their farmers, enabling African countries to increase their earnings from primary product exports. Fourth, they should encourage financial intermediation. Specifically, they need to foster the spread of microfinance institutions of the sort that have flourished in Asia and Latin America. They should also follow the Peruvian economist Hernando de Soto’s advice and grant the inhabitants of shanty towns secure legal title to their homes, so that these can be used as collateral. And they should make it cheaper for emigrants to send remittances back home.

This passage was actually taken from the foreword, which I believe was written by Niall Ferguson. I thought it to be a pretty good summary that introduced the book concepts, and decided to highlight it here.

Not that I think it should or can be simplified, but the central concept in this book is that giving aid to developing nations can actually set them back. Of course, a lot depends on what kind of aid is given and how it is deployed. Generally, unfetterred access to aid from local governments only increase corruption while crowding our local enterprises.

It also reminds me of this concept called systemic vulnerability I once read from a paper, used as a framework to explain the rise of states such as Singapore, Taiwan and South Korea. The fact that these states had ‘broad coalitions’, a potential ‘external threat’ and lack of natural resources actually compelled these nations to maximise their resources and build strong developmental states instead. Whereas our neighbours, which might have had more natural resources, were never in position where their ruling elites needed to do so.

In certain cases, less is more. I find parallels with that of promoting creativity as well. Constraints and limited resources compel you to find new innovative solutions instead of simply spending your way out.

Does vocational study really so tarnish your image? While it’s tempting to declare, “The jury is still out,” the truth is more like, “The jury has yet to be convened.” To my knowledge, this lamented stigma remains unmeasured. Still, the critics probably go too far. In our society, even incurable snobs rank vocational students above high school dropouts. The signal vocational ed sends is weak, not bad. In any case, matching course content to job openings remains the most direct way to ballpark vocational ed’s signaling share. All classes prepare students for some job. Auto shop teaches students how to repair cars; history teaches students how to do history. From a signaling standpoint, the issue is always, “How often do students use the skills they learn?” Vocational ed stands out because it prepares students for common jobs. According to the Bureau of Labor Statistics, the United States has roughly 900,000 carpenters, 700,000 auto mechanics, and 400,000 plumbers. Classic college-prep classes like literature, foreign language, and history fall short because they prepare students for rare jobs. The whole U.S. employs only 129,000 writers, 64,000 translators, and 3,800 historians. What then is vocational education’s signaling share? Bearing both stigma and job relevance in mind, half of normal is a reasonable guess. Suppose my earlier 80% signaling figure is correct, so 40% of vocational education’s payoff stems from signaling. Then ignoring the selfish advantages of learning a trade—extra income, higher employment, better high school completion rates—the social return for vocational ed surpasses regular high school’s by at least four percentage points. The social return for Poor Students—especially male Poor Students—exceeds 7%. Fiddling with the signaling assumption naturally shifts the bottom line, but as long as conventional schooling’s signaling share exceeds 50%, halving it dramatically boosts social returns. What makes vocational ed’s social return so ample? Status is zero-sum; skill is not. Conventional education mostly helps students by raising their status, but average status cannot rise. Vocational education mostly helps students by building their skills—and average skill can rise. Why are social returns especially ample for Poor Students? Because vocational ed trains these crime-prone students for productive work without igniting severe credential inflation.

Vocation training, which might be conventionally viewed by some as not the most desired of education outcomes, actually helps fill up the majority of the workforce needs.

What was super interesting in this passage also was about “status being zero sum and skill being not”. From the perspective of society, if everyone worked to simply improve their “prestige”, then it is an unfortunate use of resources on the aggregate and akin to a ‘prisoners dilemma’. Training our youths for the large variety of skills that society really needs (such as mechanics, carpentry, plumbing) instead of signalling would be a more efficient use of resources on the aggregate.

Of course, the counterpoint is that learning things like ‘literature’ or ‘philosophy’ generates value in ways that is more than just about job matching. I can’t say that I can go as far as the author to suggest that education is simply just 80% signalling (as he would state in the book). My favourite and most impactful class in 4 years of uni education was an intro to philosophy module. I do believe that there is utility in going through the full education and in public funding of this education.

Nevertheless, we can’t deny the “signalling” effect that Caplan speaks about. Technically, most of what we learn in our universities can be also learned in the Massive Open Online Courses (MOOCs). If it was purely just about skills, then MOOCs should technically begin serving as a good substitute for hiring. I find that for most, they would have a position about this in between the 2 extremes.

These truisms extend to educational signaling. Credentials are undeniably important at the hiring stage. Yet once you’re hired, your employer comes to know you as an individual. If your education understates your skill, the boss will fear to lose you. Expect good raises, or even a promotion. If your education overstates your skill, your employer might hope to lose you. Expect meager raises, or even a pink slip. As time goes by, then, employers should lose interest in mere credentials. This logic is impeccable but dodges crucial questions. Employers eventually get to know the Real You. But how long is “eventually”? In the end, employers pay you what you’re Really Worth. But when is “the end”? Economists have spent twenty years searching for answers, measuring what they call “the speed of employer learning.” When they attack this problem, economists never measure employer learning directly. Instead, they infer what employers know from what employers pay. As workers gain experience, does the payoff for education go down and the payoff for cognitive ability go up? Then researchers infer learning: as employers get to know workers, they pay less and less for superficial credentials, and more and more for underlying merits. When payoffs for education and cognitive ability plateau, researchers often conclude employers have reached the truth. What does this approach reveal? For most workers, employer learning takes years or even decades, not months. Two seminal studies of employer learning found that during your first decade in the workforce, the ability premium sharply rises, while the education premium falls 25–30%. A subsequent prize-winning article found the education and ability premiums plateau after roughly ten years of experience; the education premium stops falling, and the ability premium stops rising. Employers seem to see through college graduates much more quickly than less-educated workers. One early researcher confirmed academic ability is a strong predictor of job performance in both blue- and white-collar jobs. Unlike college graduates, however, high school graduates capture little or no job reward for academic ability during their first eight years in the labor force. A recent high-profile study claims employers see college graduates’ ability “nearly perfectly” as soon as they join the labor market. Yet the same piece finds less-educated workers wait over a decade to get full credit for their talent. The logic of employer learning also suggests sheepskin effects matter less and less as careers progress. The only paper to test this prediction finds sheepskin effects take about two decades to disappear. In light of all the evidence, I’d call employer learning slow. Yes, a few studies hail employer’s “perfect” or “almost perfect” knowledge. When closely read, however, they paint a sluggish picture. Take the study that provocatively claims employers see college graduates’ ability “nearly perfectly.” The same piece reports high school dropouts, high school graduates, and college dropouts enjoy virtually zero payoff for their ability when they first join the labor force. Full catch-up takes over ten years. In other words: to win your rightful place in the world, you must either enter the labor force and work for a decade-plus, or graduate from a four-year college. Somber news for “diamonds in the rough” whose skills surpass their credentials.

It takes employers much longer time for them to recognise your true abilities without a college degree. I’m not sure what is the exact mechanism that causes this. It could simply be a form of bias. Anecdotally, I have also seen some competent people that I have worked with whom did not have a university degree. Yet, they were always seen as “having less management potential” despite them being able to perform their work at a high level.

These signals might also manifest themselves in the self perception of those in the workforce without a degree. Self-doubt and limiting themselves to their own ceiling might impact their ambitions or their confidence in contributions.

Coming from industries where I may have only have applied 5-10% of what I’ve learned from uni, I think one thing that my degree has helped in career progression is more about “shared vocabulary” and possibly network. I would know things that other university students would know, and it is easier to have common talking points with managers (most of whom were graduates anyways).