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.
Week of April 24, 2017
1. Sweatshops: You've probably seen the New York Times piece by Chris Blattman and Stefan Dercon about their experiment with Ethiopian factory employment--finding that while many people wanted the jobs initially, they quickly learned that they didn't want them after all. The jobs are dangerous and unpleasant, and people who didn't get the jobs did just as well if not better via self-employment. Meanwhile, Lee et. al. look at urban-to-rural remittances from Bangladesh factory workers and find large positive effects for the folks back home, while the factory workers were less likely to be poor, but also less healthy. Morduch (one of the et als) also notes the workers felt pressure to work more despite poor conditions in order to send money home. It's an interesting compare/contrast.
I'm of several minds about this. First, the Blattman/Dercon piece notes that much of the problem in the Ethiopian factories is that they were poorly run, not that the owners were deliberately trying to exploit workers. If you're a reader of the faiV you know I'm somewhat obsessed with the "technology of management" and how to spread it (and that there's a good bit of evidence that its a big problem, Google Nick Bloom for more). Second, there's the perennial issue of external validity: what do these experiments tell us about sweatshops more generally in other places and times. Here's an overview by Heath and Mobarak (HT Asif Dowla) on the impact of factory labor on Bangladeshi women; and here are some emerging financial diaries of garment workers in several different countries. Third, factory jobs have almost always been terrible, despite the romanticization of those jobs in developed countries of late--and they still are even in places like the United States. So what to make of the fact that they do seem instrumental in the process of countries and households becoming wealthier? And what of my strong prior that most people in developing countries are "frustrated employees and not frustrated entrepreneurs"?
2. Our Algorithmic Overlords: Continuing last week's theme on Seeing Like A State and algorithms, the Royal Society has a new report suggesting easier access to public data sets so that machine learning can help improve policy. You'll be shocked, shocked, to learn that Google DeepMind, Amazon and Uber leaders were all part of drafting the report. The New Inquiry has used data to create a predictive algorithm and heat map for people and places likely to commit white-collar crime. Here's the methodology behind it, which you should definitely at least glance through through to see Figure 4 on page 4. On a related note, here's a story about racial and gender biases being "learned" by machine learning programs.
The white collar crime piece came via Matt Levine, and it's worth scrolling down to his item on Facebook for this gem: "What if human history isn't a video game at all?" Hopefully that will soon be a standard response to FinTech triumphalism: "What if people's financial lives aren't a video game at all?" It all brings to mind this piece from several years ago: The Reductive Seduction of Other People's Problems. You should definitely read it. It's about social entrepreneurs from developed countries traveling to developing countries but it does easily apply to algorithms, fintech and seeing like a state. Hat tip to Lee Crawfurd and Justin Sandefur for reminding me about it.
Week of April 17, 2017
1. FinTech Like a State: Aadhaar, the Indian government's unique identifier system, is now ubiquitous with 99% of citizens over 18 having an ID. That makes it a powerful platform for delivering both government programs and digital financial services. But it also raises a lot of concerns about what the government might do--or what others could do if they gain access to or corrupt the system--when it can track and/or regulate citizen behavior at a detailed level. That certainly plays into the longer-term ramifications of Indian demonetization, especially since it appears that it has driven many more people to digital transactions. CGD held an event this week with Annie Lowery interviewing Arvind Subramanian about Aadhaar, demonetization and universal basic income. I haven't gotten all the way through it yet, so I don't know whether my pre-submitted question was asked, "Which governments should be trusted with the power to deny people the ability to transact legally?"
And for some reason I feel like this piece, nominally about why Silicon Valley keeps getting biotechnology wrong, is really about FinTech.
2. Financial Literacy Like A State (University): "Shut Up About Financial Literacy" says Sara Goldrick-Rab contemplating how higher education institutions blame a lack of financial literacy for the problems students have paying for college. Here's Helaine Olen documenting the head of Penn State University's FinLit program saying: "The real problem is not the rising cost of education, it is in the... lack of financial literacy..." Goldrick-Rab cites a new paper from Sandy Darity and Darrick Hamilton (and here's a Chronicle of Higher Education write-up) making the case that the financial literacy movement as a whole tends to blame the victim rather than acknowledging that many of the choices that look like "low financial literacy" are in fact choices born of poverty and the racial wealth gap. That's a key element of Scott's Seeing Like A State: The drive to solve problems at scale often leads to simplified measurement systems that obscure important distinctions, or miss reality altogether, and ultimately reinforce the problems they are meant to address or create worse ones.
3. Financial Services Regulation: You pretty much have to do financial services regulation like a state. In the United States one of the main financial regulators is the Office of the Comptroller of the Currency (OCC). This week we learned that the OCC had received more than 700 whistleblower complaints about Wells Fargo's practice of opening accounts without customer knowledge or consent, but did nothing. Well not quite nothing. Matt Levine points to part of the OCC's report where it admits it focused too much on process and not enough on outcomes: "You spend so much time making sure that there are processes to stop bad things that you forget to actually stop the bad things." [You have to scroll past the amazing JuiceTech story] That's certainly another part of seeing like a state. And it's a particular concern when you get isomorphic mimicry, in Lant Pritchett's application, of financial services regulation.
On the bright side, I worry a bit less about the progress of our algorithmic overlords when apparently none of the deep learning programs noticed that videos about Wells Fargo like this or this (and many, many, many others) have been on YouTube since at least 2010. But then there's also this about how United's algorithms led to it's disastrous decision-making.
Week of April 10, 2017
1. Social Investment Dissent: Last week I had an item about "social investment wars"--unfortunately Felix Salmon's critical take ("How Not to Invest $1 Billion") on the Ford Foundation's announcement came out just a bit too late to be included. It does pair nicely with a video of Xav Briggs of the Ford Foundation talking about the decision and the future of impact investing.
In the item last week I criticized the sector for not acknowledging trade-offs, principal-agent problems and the like. To be fair, there are people in the sector talking about these issues. Here's a piece from Omidyar Network staff in SSIR about a "returns continuum" rather than "no tradeoffs." Here's a piece from Ceniarth staff concurring. And there are two recent pieces from the CFI blog on responsible exits from social investments: first, pointing out that who a social investor sells to should be part of the impact calculation, and second making an important point about the "missing middle" in social investment (though they don't use that term).
The missing middle they are pointing out is investors who are willing to buy on the secondary market but maintain social goals. This echoes a long-standing problem in foundation philanthropy: most large foundations want to be first movers and believe that there are "followers" who will come after them to support organizations or programs after the initial grants. It seems in both cases, the followers just don't meaningfully exist.
2. Financial Literacy: April is financial literacy month in the United States at least. I continue to use financial literacy as my barometer for the evidence-based policy movement: if evidence isn't making an impact here, why should we expect to have an influence elsewhere? But on to the links. Here's perhaps the dumbest idea currently circulating--making financial literacy a requirement for high school graduation. Here's Graham Wright de-mythifying financial education in the developing world. And on a brighter note, here is IPA's review of what's been learned from impact evaluations of financial literacy programs around the world (it's not just "they don't work!").
3. The Technology of Management: Having written a couple of books about Toyota, this is a particular fascination of mine--and of course I therefore think other people should be paying more attention to it. Management matters a lot to firm performance (explaining about 20% of firm-to-firm productivity gaps), which in turn matters a lot to wages and job creation/growth. Here's Nick Bloom in Harvard Business Review on rising firm inequality. Here's Bloom et al. on why the technology of management diverges (or alternatively, doesn't converge as much as expected given the returns).
Read MoreWeek of April 3, 2017
1. Cash vs Chickens Wars: Within development circles, the most widely read point/counterpoint began with Chris Blattman's piece in Vox, written almost as a letter to Bill Gates. Blattman takes issue with Gates' idea to provide livestock, specifically chickens, to poor households and instead proposes a test of the benefit of just giving cash. To be clear Blattman isn't saying that cash is better, but that we don't know--and we do know that giving chickens is much more expensive (and everyone who's been involved in aid knows at least one story about how "the chickens all died")--so we should run a test and compare. Lant Pritchett responds on CGD's blog, saying in all his years in development, never once has the question of "chickens versus cash" arisen as a pressing question. One reason is that Pritchett believes the goal of development shouldn't be marginal improvements for the poorest but generating the kind of growth that has seen hundreds of millions escape poverty in China, Vietnam, Indonesia and other countries. Of course, Blattman responds and does a good job keeping the focus on what I would call the competing theories of change proposed by Chris and Lant. In fact, I have called it that, and if you're interested in a deeper dive into the issues in this debate, I know a good book you should read (or at least check out Marc Bellemare's and Jeff Bloem's review of it).
2. Mortality Wars: Those in the US policy community, on the other hand, have probably been too occupied following the "mortality wars" to even know there's a battle between cash and chickens happening next door. Here's the quick background: Anne Case and Angus Deaton have a new paper about mortality rates in the US--I would say more about their results but, of course, this wouldn't be a war if there wasn't vehement disagreement over what their results actually are. As with an earlier paper, Jonathan Auerbach and Andrew Gelman take issue particularly around the composition of Case's and Deaton's aggregate results, and provides charts decomposing mortality rates by race, gender and state. There are a lot of other critiques, including about the data visualization in Case's and Deaton's paper, but you can save yourself a lot of time by just reading Noah Smith's excellent post about the data and the debate which brings the attention squarely to where it should be: that mortality rates for white Americans stopped following the trajectory of other developed countries and a massive gap has opened up between the US and others.
Then there's a secondary discussion of why this is happening and what it all means so here's some supplementary reading on that, courtesy of Jeff Guo at the Washington Post: An interview with Case and Deaton; "if white Americans are in crisis, what have black Americans been living through?"; and it's more than opioids. But if there's one related thing you aren't likely to read, but should, it's this article from Bloomberg on automobile manufacturing in the South.
This also seems like the best place to insert my favorite new aphorism: "Being a statistician means never having to say you are certain." (via Tim Harford)
