1. Weaponized Data and American Inequality (Part 3): We learned a lot in reading the faiV’s summary and corresponding links detailing the minimum wage debate consuming economists across the country. While we haven’t reached our own conclusion about whether a $13 minimum wage in Seattle is or isn’t too high, we are following how some state legislatures across the country are actively rolling back minimum wages established by municipal governments. Example? St. Louis was dealt a big blow and the city has received a lot of press this summer.
(ICYMI the debate, here and here are the two papers that offer opposing outcomes of Seattle’s minimum wage increase. If you don’t have time to read the papers, here’s a fun breakdown from Vice.)
2. Living for the City: CityLab profiled recent research on the intersection of urban development and economic inequality, making us think back to Stevie Wonder’s “Living for the City.” Still relevant. And beautiful. A new study out of the University of Idaho looks at 639 urban counties in the US and the factors that determined when they felt the effects of the 2006-2010 recession. Rarely do we see the Gini coefficient being used in the context of domestic inequality – but we should use this metric more often. Consequently, we were really excited to see this interactive map of the Gini coefficients of counties across the US.
For more on cities, another CityLab piece looks at how housing policies worldwide will only exacerbate urban inequality and housing crises. And this story on how inefficient tax codes, high cost of living, and migration, by both companies and residents, are sending the state of Connecticut spiraling, makes us rethink how we view the fiscal policies of traditionally blue, wealthy states.
3. Income Volatility, Short-Term Savings, Retirement (Oh My): Over the last 18+ months, our team has conducted a deep dive on both the impact income volatility – large fluctuations in week-to-week and month-to-month income – has on US households and potential solutions for mitigating the problem. Our latest briefs look at the role wage insurance could play in helping families cope with job loss or reduced wages and how shortfall savings can serve as a buffer during financial emergencies.
Because we care about both short-term financial stability and long-term security, we also spend our days thinking about comprehensive policy solutions to help expand access to retirement savings opportunities. In our process learning about more about income volatility, we’ve realized it’s particularly hard to save for the long-term when short-term savings are lacking. This new paper looks at the effect income shocks have on retirement savings (the stats aren’t pretty: “96 percent of Americans experience four or more income shocks by the time they reach 70”), and *mark your calendars* later this fall, we’ll be publishing two papers on how volatility affects retirement savings.
Week of June 26, 2017
1. Weaponized Data and American Inequality: Last week I linked to a paper finding minimal effects from minimum wage increases, unaware that a huge explosion of debate on this issue was about to occur. If you follow these things at all, you know that last Friday a paper on Seattle's minimum wage increase was released finding no job losses or cuts in hours. Monday, a different paper finding large losses for households with minimum wage jobs was released. There's a whole lot out there now on the two papers so I'm not going to rehash those arguments (if you need to catch up, try this or this or this or just scroll through Twitter). I want to focus on the backstory of why there were two papers released so close to each other because it's important for the future of research and policy-making. As detailed here, what appears to have happened is researchers at UW shared an early draft of their paper (using tax data that is rarely available in minimum wage studies) with the Seattle mayor's office. The mayor's office didn't like the conclusions so asked a different set of researchers to write their own paper--and release it just before the planned date for release of the UW paper. While I have no special insight into the exact details of what happened, the prospect that the report is accurate disturbs me a great deal. It's a blatant step toward what the author of the Seattle Weekly piece calls "weaponized data." Be afraid for evidence-based policy. Very afraid.
In other American inequality news on topics that yield strong confirmation bias reactions, Justin Fox reports on new work suggesting that occupational licensing actually crowded-in historically disadvantaged workers--seemingly the transparent rules of licensing reduced formal and informal discrimination that kept these groups underemployed. That's a very plausible story to me, though I generally also buy the anti-licensure arguments.
There's also new work on school vouchers, from Indiana, finding short-term declines in test scores, but later (over four years) gains. It's worth noting how claims for vouchers have down-shifted to "no harm and some students gain." But keeping on the weaponized data theme, the paper is not publicly available and was only obtained by ChalkBeat through public records requests. Apparently the study authors don't think it should be public until it's peer-reviewed, which illustrates the difference in norms in sociology and economics.
2. Our Algorithmic Overlords: Also a few weeks ago I linked to a story about how to tell if borrowers on online lending platforms were going to default, and to the book, Everybody Lies, from which it came. I said I was going to read the book and I started this week--and was immediately dismayed. The opening of the book discusses what search data--particularly searches on pornography websites--can tell us about Americans' hidden desires. You can see a summary in this deeply disappointing Vox piece (isn't Vox supposed to be better at thinking critically about this stuff?). There is no discussion of how such data might be biased or inaccurate, how a site's interface may interact with what people search for, or why we should believe that search data closely corresponds to "real life." In other words, it's an object lesson in the dangers of using data and algorithms without understanding the data or the people, social structures and institutions that generate it. So of course it's a best seller. Suffice it to say that I have radically revised down my faith in any of the book's conclusions.
In other data-generating processes of uncertain usefulness news, Google will stop showing ads inside Gmail based on scans of email content (illustrating the sucker's game that is attention, I had no idea they were still doing this; I hadn't noticed an ad in years). The nominal reason is combating hesitance from corporates to adopt Gmail and Google's suite of web apps. As someone in my Twitter feed noted, the real reason is that Google already gets better information to drive ads to you than your email.
Week of June 19, 2017
1. Indebtedness: A few weeks ago I mentioned the wave of agricultural loan waivers in a variety of Indian states, a pattern that has been repeated over decades (and not just in India; and perhaps I should say repeated over millennia) with all sorts of moral hazard implications for lenders and borrowers (here's Xavi Gine explaining the impact of the 2008 agricultural debt relief program). Shamika Ravi looks at data from the current round of farmer distress examining how poverty, indebtedness and political power interact since straightforward explanations don't hold up to scrutiny.
2. Our Algorithmic Overlords (and some Data Viz): Sometimes it's helpful to take a step back and see where artificial intelligence is still struggling. Reassuringly while AI can negotiate it still produces aphorisms like: Death when it comes will have no sheep. But maybe that's a negotiating tactic? Meanwhile, apparently machine learning still struggles to tell the difference between labradoodles and fried chicken (I suppose that would be more frightening than funny to chickens and labradoodles).
And while not about algorithms, here's another one of those cool illustrations of how data visualization influences how we interpret data that are so popular.
3. American Inequality: One of the clear themes of recent research on poverty and inequality in the United States is the rise of month-to-month and year-to-year volatility of incomes, while real wages have stagnated. The safety net in the US, such as it is, is especially unable to deal with income volatility. Here's the story of a family in Texas with volatile income who has adopted a number of medically fragile children: because of the way the state administers Medicaid the family has to re-certify eligibility almost every month. While this is somewhat unusual, the language of the Senate Republicans healthcare/Medicaid legislation would enable states to require all recipients to re-certify eligibility monthly.
Meanwhile here's Cengiz, Dube, Lindner and Zipperer with a new look at the perpetual question of what raising minimum wages does to jobs, finding little evidence for job losses or labor substitution. And here's a piece from HBR on the household effects of unstable work.
Week of June 12, 2017
1. St. Monday, American Inequality and Class Struggle: One of my favorite things about writing the faiV is when I get the chance to point readers to something they would likely never come across otherwise. So how about a blog post from a woodworking tool vendor about 19th century labor practices, craft unions and the gig economy? Once you read that, you'll want to remind yourself about this piece from Sendhil Mullainathan about employment as a commitment device (paper here), and this paper from Dupas, Robinson and Saavedra on Kenyan bike taxi drivers' version of St. Monday.
Back to modern America, here's Matt Bruenig on class struggle and wealth inequality through the lens of American Airlines, Thomas Picketty and Suresh Naidu. I feel a particular affinity for this item this week having watched American Airlines employees for a solid 12 hours try to do their jobs while simultaneously giving up the pretense that they have any idea what is going on.
2. Our Algorithmic Overlords: Facebook is investing a lot in machine learning and artificial intelligence. Sometimes that work isn't about getting you to spend more time on Facebook...or is it? With researchers at Georgia Tech, Facebook has been working on teaching machines to negotiate by "watching" human negotiations. One of the first things the machines learned was to "deceive." I use quotes here because while it's the word the researchers use, I'm not sure you can use the word deceive in this context. And that's not the only part of the description that seems overly anthropomorphic.
Meanwhile, Lant Pritchett has a new post at CGD that ties together Silicon Valley, robots, labor unions, migration and development. And probably some other things as well. If I read Lant correctly, he would approve of Facebook's negotiating 'bots since negotiation is a scarce and expensive resource (though outsourcing negotiation is filled with principal-agent problems). I guess that means a world where robots are negotiating labor contracts for low- and mid-skill workers would be a better one than the one we're currently in?
3. Statistics, Research Quality and External Validity: Here's another piece from Lant on external validity and multi-dimensional considerations when trying to systematize education evidence. A simpler way to put it: He's got some intriguing 3-dimensional charts that allow for thinking a bit more carefully about likely outcomes of interventions, given multiple factors influence how much a child learns in school. It closely parallels some early conversations I've had for my next book with Susan Athey and Guido Imbens, so I'm paying close attention. And if you can't get enough Lant, you could always check out my current book. Yes, both of those sentences are shameless plugs.
Week of June 5, 2017
1. Social Enterprise: A few weeks ago I noted that Etsy was under pressure from an activist investor for behaving like a B Corp (which it is (was?)). I missed the notice that the investor won: Etsy layed off 80 employees and fired the CEO/Chairman. Here's a piece reflecting on the Etsy saga that is emblematic of much of what I think is wrong in social enterprise rhetoric. The argument that social enterprises have to be ruthless competitors may sound good (to some) but it ignores the exact issue that is at the heart of social enterprises: how do you manage the trade-offs. It's worthless--less than worthless, I should probably say "actively harmful"--to pretend there are no trade-offs or to imply that there is value in advice like "be ruthlessly competitive except for in these parts of your business model." It's why efforts like B Corporations that don't have any governance teeth are a distraction, and why even efforts life For Benefit Corporations that do have governance teeth are fraught.
In other social enterprise-ish news, I can't resist a story about a star rapper, off-grid solar power in Senegal and Chinese investors. You can't either can you? On a more practical level here's Devanshi Vaid on the lack of information flow on social enterprise in India.
And here's Felix Salmon with some remarkably clear reframing of an important wing of social investment: if a foundation endowment can't get high investment returns in the near term, don't cut back on grantmaking, accelerate it!
2. Our Algorithmic Overlords: The Atlantic has a long piece on how cryptocurrencies like Bitcoin, purportedly designed to limit centralized authority, actually can become tools of authoritarianism. You don't have to go all the way to cryptocurrencies though, as I try to frequently point out. Digital currency of any sort can easily become weaponized by authority, even authority that isn't fully authoritarian.
I wasn't sure whether to include this in "Social Enterprise" or "Our Algorithmic Overlords" because it's a bit of both, through an extraordinary lens: Venezuela's bonds. As Matt Levine relates, Goldman Sachs (sort-of) bought some bonds from Venezuela (sort-of) that (sort-of) prop up an authoritarian government apparently bent on starving people. But no one is really responsible for this decision because of the way governance of the investment funds is set-up and which all point back to an index by which fund manager performance is measured. (I know, this is confusing and complicated, but it's worth it). In this case everyone is pointing to some arbitrary set of decisions as responsible for their behavior and denying any responsibility for moral judgment. If we struggle with these issues already, how much worse are they going to get with the arbitrary set of decisions are made by an algorithm that we don't really understand?
But people are more worried about algorithms driving their cars, than about algorithms ruling their moral decisions.
Week of May 29, 2017
1. Income Instability and the Cost of Living: Those who have studied financial management among low-income people know that instability and unpredictability of income are a main source of difficulty. The U.S. Financial Diaries project and book bring this challenge to life. This week Jonathan Morduch is quoted in the New York Times examining how the norm of a steady paycheck has been replaced by turbulence, affecting pretty much everyone who makes under $105,000 per year.
Speaking of $105,000 being a new marker for financial challenges, according to the U.S. Department of Housing and Urban Development’s new income limits, in parts of the high cost Bay Area, a family of four earning $105,350 is considered low income. On the other extreme, in the recent CGAP national survey of smallholders in Bangladesh, 75% of households reported that an annual income of $1,524 would cover their household needs.
2. A Raise or More Frequent Paychecks?: Here’s an interesting fact: More than one in four millennials prefer real-time pay to a raise. A report by the Aspen Institute’s Expanding Prosperity Impact Collaborative (EPIC), employers and governments are exploring new options for workers to collect their pay more frequently and when they need it. The jury is still out on the effects for managing household finances. We suspect this might be be helpful for short-term volatility management, but may result in difficulty with long-term savings and may diminish the commitment element that some people prefer in being able to keep money at a distance under some circumstances. Let’s hope Uber and Lyft are learning from all the data they have!
3. Global Tech Trends: TechCrunch summarized the 2017 Internet Trends report this week, and there are lots of great insights here. With over 700 million mobile internet users, the volume of mobile pay in China doubled last year to reach $5 trillion. There are 3.4 billion internet users in the world, up 10% since last year. As consumers increasing look online for shopping and retailers offer customer service through “chat”, we are curious what these trends will mean for call centers, a big industry for several developing countries, like the Philippines. There are also increasing apps developed in emerging markets, such as Kampala’s fast-growing Safeboda which allows riders the convenience of the common motorcycle taxi with the promise of a trained and safe driver.
Meanwhile, on-demand car and bike services are exploding in China and throughout Southeast Asia. Bike-for-rent services experienced a 100% month on month growth, reaching 20 million users in 2016 (a scale which could mean meaningful reductions in CO2 emissions. Helpful given recent developments in US politics!).
Week of May 22, 2017
1. The Value of Management: If you pay any attention to the development economics world, you were probably already aware that there was unrest at the World Bank since Paul Romer became Chief Economist. Yesterday that unrest came out into full public view with stories about Romer being relieved of management responsibilities for the Development Economics Group. The news stories make everyone look bad, and don't reflect my experience with the parties involved (which is admittedly quite limited). But rather than adjudicate any of the issues, I'm going to pivot to my ongoing amazement that economists of all people seem to have so little appreciation of the value of management and specifically specialization in management. It's a learned skill! The idea that someone should be managing a department of more than 600 people because they happen to be a leading economist is bonkers.
Just look at what a little bit of management training for school principals can do for schools and test scores. Or what professional management training can do for quality of care in hospitals. That's right, management can save lives! Here's hoping that skilled management will advance the very legitimate goals of clear and useful communication in Bank reports. I can't be the only one glancing through the stories about the gender studies hoax paper and thinking it wouldn't be that hard to do the same thing for a World Bank research report.
In closing, I'm not good enough of a person to avoid noting that "and" is 16% of the World Bank's actual name and linking to Ryan Briggs' Drunk World Bank twitter account.
2. Immigration: If you weren't distracted by counting the number of "and"s in your latest piece of writing, you may have seen another controversy bubbling up in social media: Michael Clemens and Justine Hunt have a new paper suggesting that Borjas' finding of losses for low-wage workers from the Mariel boatlift are actually a result of a change in the composition of wage survey samples. Borjas responded first by accusing Clemens and Hunt of being tools of Silicon Valley open border enthusiasts--and essentially saying that no grant-supported research can be trusted--and only later with an attempt to defend his results with data. That attempt looks plausible until you realize that he ends up charting the outcomes for less than 20 people. David Roodman--whose earlier work on this specific issue Borjas also managed to slander by calling it "fake news"--weighs in with some typically substantive and clear points (maybe he could do some coaching for World Bank writers?). The major one from my perspective being: Borjas already had to pick through data to find a narrow slice of the population that might have been negatively affected by sudden mass immigration, and can only defend that result with a sample better suited to a local news broadcast than serious economic inquiry.
If this kind of thing fascinates you, rather than tires you, Borjas has an additional reply that is more substantive and ultimately arrives at a useful point. But the process to get there remains bizarre.
In other immigration news, here's a look at the effect of differing state approaches to immigration law enforcement, and here's an animation of Mushfiq Mobarrak making the case for the gains from migration.
Week of May 15, 2017
1. Data (and Our Algorithmic Overlords): Many of you probably saw the Economist piece on data becoming the world's most valuable resource. It does a darn good job at producing conflicting reactions for me: Yes, we should be paying more attention to the accumulation and use of data among private companies! But governments aren't to be be trusted with this kind of data any more than private companies! And you're spending way too much time in Silicon Valley--we're a long long way from data being more valuable than physical resources for most of the people in the world!
So I'm going to use it mostly as a foil to introduce two pieces that you should read that you probably don't think are relevant to the faiV. First, here's a piece by Ted Knutson, a protagonist in the development and use of "advanced statistics" in football/soccer, about why he developed and continues to use a terrible visualization of data to evaluate player performance. Second, here's a piece about how adapting behavior based on data in baseball has helped some players but hurt others so that there is zero net gain. The point here being, understanding data is hard enough. Using data is even harder. Figuring out how to help people change based on data--without just turning everything over to our algorithmic overlords--is the toughest of all. And if you don't believe, that let me remind of you of one of my all-time favorite papers about seaweed farmers. Take that, "vast empirical literature"!
2. Theories of Change (and Demonetization): In my book of interviews of development economists on RCTs etc. the throughline is theory of change. How do ideas get translated into policy and into making the world a better place? I argue that a lot of debate about methodology is really debate about theory of change, particularly around the role of experts and the value of small vs. large changes. This Planet Money episode about the Indian demonetization has the most jaw-dropping "theory of change" story I think have ever encountered. The short version is an engineer developed--through divine inspiration--a model of the Indian economy, complete with cheesy illustrations, and just kept talking about it until a powerful politician took notice and decided to introduce one of the biggest economic shocks in modern history. If you know someone graduating from high school or college, perhaps you should make them listen to the episode rather than buying them a copy of Oh, The Places You'll Go. (Oh, and that feeling when you visit the Smithsonian with your kid and get to talk about how even our heroes fail us.)
3. Digital Finance: Over at CGAP, IPA has a post about fees for 21 mobile money services in seven different countries, with an eye to how the highest fees are paid on the smallest transactions, presumably serving as an effective tax on the poorest customers. This of course is the same issue we've been talking about in microfinance for decades: small transactions don't cost less to process than large ones and so small transactions are more expensive. While it's less of an issue in things like digital services than in-person services it doesn't entirely go away and so providers have to make decisions about whether they are going to over-charge their relatively wealthier clients to subsidize their poorer ones, or tax their poorer ones for their inability to transact in larger amounts. The problem with the former is that there is almost certainly going to be a competitor who is willing to take those wealthier clients by not asking them to subsidize costs for smaller transactions.
This also raises one of my long-term fascinations: people tend to react strongly to poor customers being charged more for financial services but not for telephone services--even when it's the same company doing it! The same poor customers who are paying more for mobile money transfers are almost certainly paying more for cellphone minutes by buying them in small increments, but I don't ever see that being charted.
Week of May 8, 2017
Editor's Note: You might think you've seen this announcement before, but it's different. The US Financial Diaries project has an upcoming free webinar on May 16th. If you're in the New York City area, join us on May 23rd at New America NYC.
1. American Inequality: The exceptionalism of the United States in promoting home ownership asthe signifier of middle class status and/or upward mobility, and a generally accepted keystone of building wealth has persisted despite the Great Recession/housing crisis. But that doesn't mean that things haven't changed--the availability of housing that costs less than 30% of a household's income has dramatically decreased. Matt Desmond, author of Evicted, writes in the New York Times magazine that the American emphasis on home ownership has become one of the primary engines of inequality. Non-profits--or at least how we measure and fund them--are another (unintended) engine of inequality. In New York state, non-profits pay wages just above retail and food service (and 80 percent of these workers are women, and 50 percent people of color).
2. Our Algorithmic Overlords: The goal of machine-learning and using algorithms to analyze data is to yield better decisions, at least better than human beings would make given biases and the challenges of causal inference. A(nother) new book looking into the way this works is Everybody Lies. I haven't read it yet, but I'm looking forward to it. In the meantime, there's an excerpt in the Science of Us, taking a look at one of those areas that humans always struggle to make good decisions: who is credit-worthy. The substitution of bias against minorities (or at least people different from the loan officer) and the poor for careful judgment is well documented and wide-spread. Netzer, Lemaire and Herzenstein turn the machine loose on data from Prosper, an online platform for peer-to-peer lending, and find that the words that borrowers use are predictive of repayment behavior. You should read the whole excerpt because it does focus on the unintended consequences of using machine learning and big data. I, of course, immediately wonder how quickly borrowers and lenders will adapt to the findings.
Meanwhile, here's a Quora forum with Jennifer Doleac on the American criminal justice system, which dwells a lot on how machine learning is affecting decisions in another area humans have a lot of trouble with: who's guilty and who is a threat for recidivism. And of course, on the unintended consequences of our efforts to punish people. And here's a speculation that Donald Trump is a dynamic neural network/machine-learning algorithm with narrow goals. Here's an alternate version of the same argument, which in addition to being even more frightening, provides additional insight into the potential unintended consequences of data analysis without theory (of Mind).
3. Digital Finance: The item on Prosper and algorithms determining credit-worthiness based on language used by borrowers is about digital finance of course. But in the domain of more traditional ways of thinking about digital finance, here's a story about M-Pawa in Tanzania, interesting for it's integration of savings, lending and education. The bottom line: more savings, larger loans, better repayment. In other news, M-Pesa is supporting proposed regulations for cross-platform transfers in Kenya. And MicroSave has some ideas on how to enable digital finance among the illiterate, since traditional approaches to inclusion through digital have the unintended consequence of excluding the illiterate.
More specifically on the "unintended consequences" theme, though having relatively little to do with digital finance, here's some new research on how global de-risking in banking has cut the number of correspondent banking relationships (what makes cross-border payments even somewhat efficient) have declined by 25% since 2009, pushing whole regions out of the regulated banking sector.
Week of May 1, 2017
1. Households Matter!: If you've followed research on microfinance at all, you've probably come across work by de Mel, McKenzie and Woodruff about giving cash grants to microenterprises (in Sri Lanka and Ghana), finding that the returns to investment in women's firms is much lower (and close to 0) than in men's enterprises. It's a bit of puzzle for several reasons (e.g. why do women borrow if their returns are so low, and why don't men borrow more if their returns are so high?) and there have been various explanations tried out (you can see one of mine in this paper). Bernhardt, Field, Pande and Rigol (paper here, overview from Markus Goldstein here) have a new one that seems pretty compelling based on reanalyzing data from several experiments, including the cash grant experiments. It's an explanation that points back to Gary Becker and Robert Townsend ideas (household's maximizing returns across the household assuming money is fully fungible) about how households work, and away from Viviana Zelizer's (money is often not, in fact, fungible and different income streams in the household are treated differently) or in some ways against Yunus's idea of focusing on women. Bernhardt et al. see that in general when it appears that when women's enterprises show little or no return to capital it's often because the household has another microenterprise that the capital is invested in instead--and those enterprises (where data is available) show gains from the capital injection into the household. When women own the only microenterprise in the household, they see returns (and are often in similar industries) as men.
This is a big deal and it emphasizes how far we still have to go in understanding household finance. This doesn't say that Zelizer's insights are wrong--they are clearly right in lots of cases--but we don't have a solid grasp on when we should think of households as a single utility-maximizing unit and when we should disaggregate.
2. Pre-K Matters? (and other scale-ups): One of the things that households--or if you read some of the charity marketing that has dominated the last decade or so, only women--invest in is their children's education. Unfortunately, it seems that they often under-invest in education and so a lot of effort is invested in getting children into and keeping them in school. In the United States, the current frontier is about universal Pre-K since most every child is enrolled through the beginning of secondary school. The idea is that children from poorer households start school already well behind their wealthier peers, those gaps persist and if we close them early, well the gaps will stay closed. There are some studies that suggest that's true and Jim Heckman in particular among economists has been a big advocate of significantly increasing investment in early childhood education programs. But there are other studies that suggest it's not. I called the arguments on this "Pre-K" wars in my book because a lot of the argument has been over experimental design and methodological issues in the studies.
Russ Whitehurst at Brookings has a new post on the Pre-K wars that I learned a lot from, including new data from Tennessee that shows the returns from pre-K there were negative and the randomization in the famous Abecedarian study was violated in ways that are impossible to correct for. The bottom line for Whitehurst is that while small-scale, intensive interventions with very high-skill staff can make a big difference, programs at scale don't have any solid evidence they work. Which sounds a lot like some of the things we're seeing from scale up of successful programs in other areas of development.
