Should government food assistance programs have nutritional requirements?

Some of the foods you can purchase through WIC.


There’s good reason to believe that adding nutritional requirements to government food programs is a better use of money and leads to better health outcomes for the people in said programs.

WIC (Women, Infants, and Children) is a state-run program that helps low-income women and children purchase healthy food. WIC has specific guidelines for the quantities and types of food recipients can purchase, all of which have to meet certain health standards. In this program there is no way to purchase soda, candy, pizza, baked sweets, ice cream, etc. SNAP (Supplemental Nutrition Assistance Program, often referred to as “food stamps”) is a federally-funded program helping low-income people purchase almost any food.

The USDA explains that SNAP is for purchasing any food or food product for home consumption and that this definition includes “soft drinks, candy, cookies, snack crackers, and ice cream” and similar items. Data suggest these types of purchases make up at least 17% of SNAP spending . In 2017, about 42 million people used SNAP at an average of $125.79 per person per month, meaning the government spent about $11.3 billion that year buying junk food for low-income people. What are the arguments for spending so much on junk rather than using those funds to ensure low-income people have high quality food?

Opponents of SNAP nutritional requirements give many reasons for why nutritional requirements are not feasible or effective: we can’t come up with clear standards for what is “healthy,” it would be too complicated and costly to implement such standards, restrictions wouldn’t stop people from buying unhealthy food with their own money, and people in higher income brackets purchase similar amounts of unhealthy food.

Yet WIC has managed to define what constitutes healthy food and implement a program based on those boundaries. In fact the USDA describes WIC as “one of the nation’s most successful and cost-effective nutrition intervention programs.” There is evidence to suggest people participating in WIC (especially children) have better nutrition and health outcomes than their peers. Conversely, there is evidence to suggest people who receive SNAP benefits have worse nutrition than income-eligible people who don’t participate in SNAP. For example:

Changing WIC changes what children eat – May 2013

Comparing July to December in 2008 and 2011, increases were observed in breastfeeding initiation (72.2-77.5%); delaying introduction of solid foods until after 4 months of age (90.1-93.8%); daily fruit (87.0-91.6%), vegetable (78.1-80.8%), and whole grain consumption (59.0-64.4%) by children aged 1-4 years; and switches from whole milk to low-/nonfat milk by children aged 2-4 years (66.4-69.4%). In 1-year-old children, the proportion ≥95th percentile weight-for-recumbent length decreased from 15.1 to 14.2%; the proportion of children 2- to 4-year-old with body mass index (BMI) ≥95th percentile decreased from 14.6 to 14.2%.

Trends in Obesity Among Participants Aged 2–4 Years in the Special Supplemental Nutrition Program for Women, Infants, and Children – November 2016

The prevalence of obesity among young children from low-income families participating in WIC in U.S. states and territories was 14.5% in 2014. This estimate was higher than the national estimate (8.9%) among all U.S. children in a slightly different age group (2–5 years) based on data from the 2011–2014 National Health and Nutrition Examination Survey (7). Since 2010, statistically significant downward trends in obesity prevalence among WIC young children have been observed overall, in all five racial/ethnic groups, and in 34 of the 56 WIC state agencies, suggesting that prevention initiatives are making progress, potentially by impacting the estimated excess of calories eaten versus energy expended for this vulnerable group (8).

The Supplemental Nutrition Assistance Program – September 2015

Child SNAP recipients consume more sugary beverages, processed meats, and high-fat dairy products, but fewer nuts, seeds, and legumes than income-eligible nonparticipants. Similarly, adult SNAP recipients consume more fruit juice, potatoes, red meat, and sugary beverages, but fewer whole grains than income-eligible nonparticipants. In another study, SNAP participants had lower dietary quality scores overall, and consumed significantly fewer fruits, vegetables, seafood, and plant proteins, but significantly more added sugar than income-eligible nonparticipants.

The study specifically compares SNAP nutrition to WIC nutrition:

In one study comparing the grocery store purchases of SNAP and WIC households in New England, SNAP households purchased more than double the amount of sugary beverages per month (399 ounces) than WIC households (169 ounces), 72% of which were paid for with SNAP dollars. In a 3-month study, new SNAP participants significantly increased their consumption of refined grains compared with low-income people who did not join. In a study of Hispanic Texan women, SNAP participants consumed 26% more sugary beverages and 38% more sweets and desserts than low-income nonparticipants.

Furthermore, most of the people who use SNAP believe the program should not allow recipients to purchase unhealthy food:

54% of SNAP participants supported removing sugary drinks from SNAP eligibility. In another survey of 522 SNAP stakeholders, 78% of respondents agreed that soda, and 74% agreed that “foods of low nutritional value” such as candy and sugar-sweetened fruit drinks should not be eligible for purchase with benefits. Seventy-seven percent of respondents believed that SNAP benefits should be consistent with the DGAs [Dietary Guidelines for Americans], and 54% thought that SNAP should be reformulated into a defined food package similar to WIC.

I want to live in a society where people are healthy and no one goes hungry. SNAP can and should serve both goals.

Who Bears the Cost of the Minimum Wage?

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From a forthcoming article in the American Economic Review (quoting from the draft version) on Hungarian minimum wage hikes:

Most firms responded to the minimum wage by raising wages instead of destroying jobs. Our estimates imply that out of 290 thousand minimum wage workers in Hungary, around 30 thousand (0.076% of aggregate employment) lost their job, while the remaining 260 thousand workers experienced a 60% increase in their wages. As a result, firms employing minimum wage workers experienced a large increase in their total labor cost that was mainly absorbed by higher output prices and higher total revenue. We also estimated that firms substituted labor with capital and their profits fell slightly. These results suggest that the incidence of the minimum wage fell mainly on consumers. Given the relatively small effect on employment, our results also suggest that minimum wages can redistribute income from consumers to low-wage workers without large efficiency losses. Our findings also indicate that the optimal level of the minimum wage is likely to vary across industries,cities and countries. In countries where low-wage jobs are concentrated in the local service sector (such as Germany or the U.S.) raising the minimum wage is likely to cause limited disemployment effects or efficiency losses. Moreover, in cities where mainly rich consumers enjoy the services provided by low wage workers this redistribution will be from rich to poor. The heterogenous responses across industries also underline the advantages of sector-specific minimum wage polices used in some European countries such as Italy or Austria. For instance, setting a higher minimum wage in the non-tradable sector than in the tradable sector can push up wages relatively more where it will generate more modest disemployment effects (pg. 23-24).

Passing the costs on to consumers fits with previous evidence. This also makes evident that the kind of industry (e.g., tradable vs. non-tradable) also matters when it comes to positive/negative effects of the minimum wage.

Is Student Loan Forgiveness for the Marginalized?

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I saw this floating around Facebook recently with the news of Elizabeth Warren’s student loan plan. For those unfamiliar with what Mayfield is referencing, here’s the entry from the HarperCollins Bible Dictionary:

As another Bible dictionary clarifies, “Though Leviticus 25 does not explicitly discuss debt cancellation, the return of an Israelite to his land plus the release of slaves implies the cancellation of debts that led to slavery or the loss of land.”

So does Warren’s plan benefit “the marginalized”?

According to Adam Looney at the Brookings Institution, Warren’s proposal is “regressive, expensive, and full of uncertainties…[T]he top 20 percent of households receive about 27 percent of all annual savings, and the top 40 percent about 66 percent. The bottom 20 percent of borrowers by income get only 4 percent of the savings. Borrowers with advanced degrees represent 27 percent of borrowers, but would claim 37 percent of the annual benefit.”

E Warren Distribution of benefit

He continues,

Debt relief for student loan borrowers, of course, only benefits those who have gone to college, and those who have gone to college generally fare much better in our economy than those who don’t. So any student-loan debt relief proposal needs first to confront a simple question: Why are those who went to college more deserving of aid than those who didn’t? More than 90 percent of children from the highest-income families have attended college by age 22 versus 35 percent from the lowest-income families. Workers with bachelor’s degrees earn about $500,000 more over the course of their careers than individuals with high school diplomas. That’s why about 50 percent of all student debt is owed by borrowers in the top quartile of the income distribution and only 10 percent owed by the bottom 25 percent. Indeed, the majority of all student debt is owed by borrowers with graduate degrees.

Drawing on 2016 data from the Federal Reserve’s Survey of Consumer Finances, Looney’s final analysis

shows that low-income borrowers save about $569 in annual payments under the proposal, compared to $900 in the top 10 percent and $2,653 in the 80th to 90th percentiles. Examining the distribution of benefits, top-quintile households receive about 27 percent of all annual savings, and the top 40 percent about 66 percent. The bottom 20 percent of borrowers by income get 4 percent of the savings…[W]hile households headed by individuals with advanced degrees represent only 27 percent of student borrowers, they would claim 37 percent of the annual savings. White-collar workers claim roughly half of all savings from the proposal. While the Survey of Consumer Finances does not publish detailed occupational classification data, the occupational group receiving the largest average (and total) amount of loan forgiveness is the category that includes lawyers, doctors, engineers, architects, managers, and executives.  Non-working borrowers are, by and large, already insured against having to make payments through income-based repayment or forbearances; most have already suspended their loan payments. While debt relief may improve their future finances or provide peace of mind, it doesn’t offer these borrowers much more relief than that available today.  

The Urban Institute’s analysis has similar findings (though their tone is more optimistic):

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I’m not sure whether or not Warren’s plan is a good one (I’m skeptical, especially given some of the results abroad). But I’m not big on acting like college graduates in a rich country are the marginalized of society.

How and Why to Rate Books and Things

Here’s the image that inspired this post:

Now, there’s an awful lot of political catnip in that post, but I’m actually going to ignore it. So, if you want to hate on Captain Marvel or defend Captain Marvel: this is not the post for you. I want to talk about an apolitical disagreement I have with this perspective.

The underlying idea of this argument is that you should rate a movie based on how good or bad it is in some objective, cosmic sense. Or at least based on something other than how you felt about the movie. In this particular case, you should rate the movie based on some political ideal or in such a way as to promote the common good. Or something. No, you shouldn’t. ALl of these approaches are bad ideas.

That's not how this works

The correct way to rate a movie–or a book, or a restaurant, etc.–is to just give the rating that best reflects how much joy it brought you. That’s it!

Let’s see if I can convince you.

To begin with, I’m not saying that such a thing as objective quality doesn’t exist. I think it probably does. No one can really tell where subjective taste ends and objective quality begins, but I’m pretty sure that “chocolate or vanilla” is a matter of purely personal preference but “gives you food poisoning or does not” is a matter of objective quality.

So I’m not trying to tell you that you should use your subjective reactions because that’s all there is to go on. I think it’s quite possible to watch a movie and think to yourself, “This wasn’t for me because I don’t like period romances (personal taste), but I can recognize that the script, directing, and acting were all excellent (objective quality) so I’m going to give it 5-stars.”

It’s possible. A lot of people even think there’s some ethical obligation to do just that. As though personal preferences and biases were always something to hide and be ashamed of. None of that is true.

The superficial reason I think it’s a bad idea has to do with what I think ratings are for. The purpose of a rating–and by a rating I mean a single, numeric score that you give to a movie or a book, like 8 out of 10 or 5 stars–is to help other people find works that they will enjoy and avoid works that they won’t enjoy. Or, because you can do this, to help people specifically look for works that will challenge them and that they might not like, and maybe pass up a book that will be too familiar. You can do all kinds of things with ratings. But only if the ratings are simple and honest. Only if the ratings encode good data.

The ideal scenario is a bunch of people leaving simple, numeric ratings for a bunch of works. This isn’t Utopia, it’s Goodreads. (Or any of a number of similar sites.) What you can then do is load up your list of works that you’ve liked / disliked / not cared about and find other people out there who have similar tastes. They’ve liked a lot of the books you’ve liked, they’ve disliked a lot of the books you’ve disliked, and they’ve felt meh about a lot of the books you’ve felt meh about. Now, if this person has read a book you haven’t read and they gave it 5-stars: BAM! You’re quite possibly found your next great read.

You can do this manually yourself. In fact, it’s what all of us instinctively do when we start talking to people about movies. We compare notes. If we have a lot in common, we ask that person for recommendation. It’s what we do in face-to-face interactions. When we use big data sets and machine learning algorithms to automate the process, we call them recommender systems. (What I’m describing is the collaborative filtering approach as opposed to content-based filtering, which also has it’s place.)

This matters a lot to me for the simple reason that I don’t like much of what I read. So, it’s kind of a topic that’s near and dear to my heart. 5-star books are rare for me. Most of what I read is probably 3-stars. A lot of it is 1-star or 2-star. In a sea of entertainment, I’m thirsty. I don’t have any show that I enjoy watching right now. I’m reading a few really solid series, but they come out at a rate of 1 or 2 books a year, and I read more like 120 books a year. The promise of really deep collaborative filtering is really appealing if it means I can find is valuable.

But if you try to be a good citizen and rate books based on what you think they’re objective quality is, the whole system breaks down.

Imagine a bunch of sci-fi fans and a bunch of mystery fans that each read a mix of both genres. The sci-fi fans enjoy the sci-fi books better (and the mystery fans enjoy the mystery books more), but they try to be objective in their ratings. The result of this is that the two groups disappear from the data. You can no longer go in and find the group that aligns with your interests and then weight their recommendations more heavily. Instead of having a clear population that gives high marks to the sci-fi stuff and high-marks to the mystery stuff, you just have one, amorphous group that gives high (or maybe medium) marks to everything.

How is this helpful? It is not. Not as much as it could be, anyway.

In theoretical terms, you have to understand that your subjective reaction to a work is complex. It incorporates the objective quality of the work, your subjective taste, and then an entire universe of random chance. Maybe you were angry going into the theater, and so the comedy didn’t work for you the way it would normally have worked. Maybe you just found out you got a raise, and everything was ten times funnier than it might otherwise have been. This is statistical noise, but it’s unbiased noise. This means that it basically goes away if you have a high enough sample.

On the other hand, if you try to fish out the objective components of a work from the stew of subjective and circumstantial components, you’re almost guaranteed to get it wrong. You don’t know yourself very well. You don’t know for yourself where you objective assessment ends and your subjective taste begins. You don’t know for yourself what unconscious factors were at play when you read that book at that time of your life. You can’t disentangle the objective from the subjective, and if you try you’re just going to end up introducing error into the equation that is biased. (In the Captain Marvel example above, you’re explicitly introducing political assessments into your judgment of the movie. That’s silly, regardless of whether your politics make you inclined to like it or hate it.)

What does this all mean? It means that it’s not important to rate things objectively (you can’t, and you’ll just mess it up), but it is helpful to rate thing frequently. The more people we have rating things in a way that can be sorted and organized, the more use everyone can get from those ratings. In this sense, ratings have positive externalities.

Now, some caveats:

Ratings vs. Reviews

A rating (in my terminology, I don’t claim this is the Absolute True Definition) is a single, numeric score. A review is a mini-essay where you get to explain your rating. The review is the place where you should try to disentangle the objective from the subjective. You’ll still fail, of course, but (1) it won’t dirty the data and (2) your failure to be objective can still be interesting and even illuminating. Reviews–the poor man’s version of criticism–is a different beast and it plays by different rules.

So: don’t think hard about your ratings. Just give a number and move on.

Do think hard about your reviews (if you have time!) Make them thoughtful and introspective and personal.

Misuse of the Data

There is a peril to everyone giving simplistic ratings, which is that publishers (movie studios, book publishers, whatever) will be tempted to try and reverse-engineer guaranteed money makers.

Yeah, that’s a problem, but it’s not like they’re not doing that anyway. The reason that movie studios keep making sequels, reboots, and remakes is that they are already over-relying on ratings. But they don’t rely on Goodreads or Rotten Tomatoes. They rely on money.

This is imperfect, too, given the different timing of digital vs. physical media channels, etc. but the point is that adding your honest ratings to Goodreads isn’t going to make traditional publishing any more likely to try and republish last years cult hit. They’re doing to do that anyway, and they already have better data (for their purposes) than you can give them.

Ratings vs. Journalism

My advice applies to entertainment. I’m not saying that you should just rate everything without worrying about objectivity. This should go without saying but, just in case, I said it.

You shouldn’t apply this reasoning to journalism because one vital function of journalism for society is to provide a common pool of facts that everyone can then debate about. One reason our society is so sadly warped and full of hatred is that we’ve lost that kind of journalism.

Of it’s probably impossible to be perfectly objective. The term is meaningless. Human beings do not passively receive input from our senses. Every aspect of learning–from decoding sounds into speech to the way vision works–is an active endeavor that depends on biases and assumptions.

When we say we want journalists to be objective, what we really mean is that (1) we want them to stick to objectively verifiable facts (or at least not do violence to them) and (2) we would like them to embody, insofar as possible, the common biases of the society they’re reporting to. There was a time when we, as Americans, knew that we had certain values in common. I believe that for the most part we still do. We’re suckers for underdogs, we value individualism, we revere hard work, and we are optimistic and energetic. A journalistic establishment that embraces those values is probably one that will serve us well (although I haven’t thought about it that hard, and it still has to follow rule #1 about getting the facts right). That’s bias, but it’s a bias that is positive: a bias towards truth, justice, and the American way.

What we can’t afford, but we unfortunately have to live with, is journalism that takes sides within the boundaries of our society.

Strategic Voting

There are some places other than entertainment where this logic does hold, however, and one of them is voting. One of the problems of American voting is that we go with majority-take-all voting, which is like the horse-and-buggy era of voting technology. Majority-take-all voting is probably much worse for us than a 2-party system, because it encourages strategic voting.

Just like rating Captain Marvel higher or lower because your politics make you want it to succeed or fail, strategic voting is where you vote for the candidate that you think can win rather than the candidate that you actually like the most.

There are alternatives that (mostly) eliminate this problem, the most well-known of which is instant-runoff voting. Instead of voting for just one candidate, you rank the candidates in the order that you prefer them. This means that you can vote for your favorite candidate first even if he or she is a longshot. If they don’t win, no problem. Your vote isn’t thrown away. In essence, it’s automatically moved to your second-favorite candidate. You don’t actually need to have multiple run-off elections. You just vote once with your full list of preferences and then it’s as if you were having a bunch of runoffs.

There are other important reasons why I think it’s better to vote for simple, subjective evaluations of the state of the country instead of trying to figure out who has the best policy choices, but I’ll leave that discussion for another day.


The idea of simple, subjective ratings is not a cure-all. As I noted above, it’s not appropriate for all scenarios (like journalism). It’s also not infinitely powerful. The more people you have and the more things they rate (especially when lots of diverse people are rating the same thing), the better. If you have 1,000 people, maybe you can detect who likes what genre. If you have 10,000 people, maybe you can also detect sub-genres. If you have 100,000 people, maybe you can detect sub-genres and other characteristics, like literary style.

But no matter how many people you have, you’re never going to be able to pick up every possible relevant factor in the data because there are too many and we don’t even know what they are. And, even if you could, that still wouldn’t make predictions perfect because people are weird. Our tastes aren’t just a list of items (spaceships: yes, dragons: no). They are interactive. You might really like spaceships in the context of gritty action movies and hate spaceships in your romance movies. And you might be the only person with that tick. (OK, that tick would probably be pretty common, but you can think of others that are less so.)

This is a feature, not a bug. If it were possible to build a perfect recommendation it would also be possible to build (at least in theory) an algorithm to generate optimal content. I can’t think of anything more hideous or dystopian. At least, not as far as artistic content goes.

I’d like a better set of data because I know that there are an awful lot of books out there right now that I would love to read. And I can’t find them. I’d like better guidance.

But I wouldn’t ever want to turn over my reading entirely to a prediction algorithm, no matter how good it is. Or at least, not a deterministic one. I prefer my search algorithms to have some randomness built in, like simulated annealing.

I’d say about 1/3rd of what I read is fiction I expect to like, about 1/3rd is non-fiction I expect to like, and 1/3rd is random stuff. That random stuff is so important. It helps me find stuff that no prediction algorithm could ever help me find.

It also helps the system over all, because it means I’m not trapped in a little clique with other people who are all reading the same books. Reading outside your comfort zone–and rating them–is a way to build bridges between fandom.

So, yeah. This approach is limited. And that’s OK. The solution is to periodically shake things up a bit. So those are my rules: read a lot, rate everything you read as simply and subjectively as you can, and make sure that you’re reading some random stuff every now and then to keep yourself out of a rut and to build bridges to people with different tastes then your own.

Is Contract Enforcement Important for Firm Productivity?

Contract enforcement is a major player in measuring the ease of doing business in a country. A new working paper demonstrates the importance of enforceable contracts to firm productivity:

In Boehm and Oberfield (2018) we study the use of intermediate inputs (materials) by manufacturing plants in India and link the patterns we find to a major institutional failure: the long delays that petitioners face when trying to enforce contracts in a court of justice. India has long struggled with the sluggishness of its judicial system. Since the 1950’s, the Law Commission of India has repeatedly highlighted the enormous backlogs and suggested policies to alleviate the problem, but with little success. Some of these delays make international headlines, such as in 2010, when eight executives were convicted in the first instance for culpability in the 1984 Bhopal gas leak disaster. One of them had already passed away, and the other seven appealed the conviction (Financial Times 2010)

These delays are not only a social problem, but also an economic problem. When enforcement is weak, firms may choose to purchase from suppliers that they trust (relatives, or long-standing business partners), or avoid purchasing the inputs altogether such as by vertically integrating and making the components themselves, or by switching to a different production process. These decisions can be costly. Components that are tailored specifically to the buyer (‘relationship-specific’ intermediate inputs) are more prone to hold-up problems, and are therefore more dependent on formal court enforcement.

…Our results suggest that courts may be important in shaping aggregate productivity. For each state we ask how much aggregate productivity of the manufacturing sector would rise if court congestion were reduced to be in line with the least congested state. On average across states, the boost to productivity is roughly 5%, and the gains for the states with the most congested courts are roughly 10% (Figure 3).

Are Immigrants a Threat?

Image result for immigration

From a new working paper:

The empirical evidence comes down decidedly on the side of immigrants being less likely to commit crimes. A large body of empirical research concludes that immigrants are less likely than similar US natives to commit crimes, and the incarceration rate is lower among the foreign-born than among the native-born (see, for example, Butcher and Piehl 1998a, 1998b, 2007; Hagan and Palloni 1999; Rumbaut et al. 2006). Among men ages 18 to 39—prime ages for engaging in criminal behavior—the incarceration rate among immigrants is one-fourth the rate among US natives (National Academies of Sciences, Engineering, and Medicine 2015).

…There is some evidence that the lower propensity of immigrants to commit crimes does not carry over to immigrants’ children. The US-born children of immigrants—often called the “second generation”— appear to engage in criminal behavior at rates similar to other US natives (Bersani 2014a, 2014b). This 4 “downward assimilation” may be surprising, since the second generation tends to considerably outperform their immigrant parents in terms of education and labor-market outcomes and therefore might be expected to have even lower rates of criminal behavior (National Academies of Sciences, Engineering, and Medicine 2015). Instead, immigrants’ children are much like their peers in terms of criminal behavior. This evidence mirrors findings that the immigrant advantage over US natives in terms of health tends to not carry over to the second generation (e.g., Acevedo-Garcia et al. 2010).

Although immigrants are less likely to commit crimes than similar US natives, they are disproportionately male and relatively young—characteristics associated with crime. Does this difference in demographic composition mean that the average immigrant is more likely than the average US native to commit crimes? Studies comparing immigrants’ and US natives’ criminal behavior and incarceration rates tend to focus on relatively young men, leaving the broader question unanswered. However, indirect evidence is available from looking at the relationship between immigration and crime rates. If the average immigrant is more likely than the average US native to commit crimes, areas with more immigrants should have higher crime rates than areas with fewer immigrants. The evidence here is clear: crime rates are no higher, and are perhaps lower, in areas with more immigrants. An extensive body of research examines how changes in the foreign-born share of the population affect changes in crime rates. Focusing on changes allows researchers to control for unobservable differences across areas. The finding of either a null relationship or a small negative relationship holds in raw comparisons, in studies that control for other variables that could underlie the results from raw comparisons, and in studies that use instrumental variables to identify immigrant inflows that are independent of factors that also affect crime rates, such as underlying economic conditions (see, for example, Butcher and Piehl 1998b; Lee, Martinez, and Rosenfeld 2001; Reid et al. 2005; Graif and Sampson 2009; Ousey and Kubrin 2009; Stowell et al. 2009; Wadsworth 2010; MacDonald, Hipp, and Gill 2013; Adelman et al. 2017). The lack of a positive relationship is generally robust to using different measures of immigration, looking at different types of crimes, and examining different geographic levels.2 Further, the lack of a positive relationship suggests that immigration does not cause US natives to commit more crimes. This might occur if immigration worsens natives’ labor market opportunities, for example.

The few studies that examine crime among unauthorized immigrants have findings that are consistent with the broader pattern among immigrants—namely, unauthorized immigrants are less likely to commit crimes than similar US natives (apart from immigration-related offenses).4 Likewise, studies that examine the link between the estimated number of unauthorized immigrants as a share of an area’s population and crime rates in that area typically find evidence of null or negative effects (pg. 3-5).

Comparatively, the effects of border control on crime is mixed. The authors conclude,

A crucial fact seems to have been forgotten by some policy makers as they have ramped up immigration enforcement over the last two decades: immigrants are less likely to commit crimes than similar US natives. This is not to say that immigrants never commit crimes. But the evidence is clear that they are not more likely to do so than US natives. The comprehensive 2015 National Academies of Sciences, Engineering, and Medicine report on immigration integration concludes that the finding that immigrants are less likely to commit crimes than US natives “seems to apply to all racial and ethnic groups of immigrants, as well as applying over different decades and across varying historical contexts” (328). Unauthorized immigrants may be slightly more likely than legal immigrants to commit crimes, but they are still less likely than their US-born peers to do so. Further, areas with more immigrants tend to have lower rates of violent and property crimes. In the face of such evidence, policies aimed at reducing the number of immigrants, including unauthorized immigrants, seem unlikely to reduce crime and increase public safety (pg. 11).

Does Female Autonomy Lead to Long-Term Economic Growth?

From a new study:

A number of development economists have found higher gender inequality to be associated with slower development. Amartya Sen (1990) estimated a large number of ‘missing women’, which resulted in skewed sex ratios, and argued that this has been one of history’s crucial development hurdles. Stephan Klasen, with various co-authors, used macroeconomic regressions to show that gender inequality has usually been associated with lower GDP growth in developing countries during the last few decades (Klasen and Lamanna 2009, Gruen and Klasen 2008). This resulted in development policies targeted specifically at women. In 2005, for example, UN Secretary General Kofi Annan stated that gender equality is a prerequisite for eliminating poverty, reducing infant mortality, and reaching universal education (UN 2005). In recent periods, however, a number of doubts have been made public by development economists. Esther Duflo (2012) suggested that there is no automatic effect of gender equality on poverty reduction, citing a number of studies. The causal direction from poverty to gender inequality might be at least as strong as in the opposite direction, according to this view.

…In a new study, we directly assess the growth effects of female autonomy in a dynamic historical context (Baten and de Pleijt 2018). Given the obviously crucial role of endogeneity issues in this debate, we carefully consider the causal nature of the relationship. More specifically, we exploit relatively exogenous variation of (migration-adjusted) lactose tolerance and pasture suitability as instrumental variables for female autonomy. The idea is that high lactose tolerance increased the demand for dairy farming, whereas similarly, a high share of land suitable for pasture farming allowed more supply. In dairy farming, women traditionally had a strong role, which allowed them to participate substantially in income generation during the late medieval and early modern period (Voigtländer and Voth 2013). In contrast, female participation was limited in grain farming, as it requires substantial upper-body strength (Alesina et al. 2013). Hence, the genetic factor of lactose tolerance and pasture suitability influences long-term differences in gender-specific agricultural specialisation. In instrumental variable regressions, we show that the relationship between female autonomy and human capital is likely to be causal (and also address additional econometric issues, such as the exclusion restriction, using Oster ratios, etc.). 

Age-heaping-based numeracy estimates reflect a crucial component of human capital formation. Recent evidence documents that numerical skills are the ones that matter most for economic growth. Hanushek and Woessmann (2012) argued that maths and science skills were crucial for economic success in the 20th century. They observed that these kinds of skills outperform simple measures of school enrolment in explaining economic development. Hence, in the new study we focus on math-related indicators of basic numeracy. We use two different datasets: first, we use a panel dataset of European countries from 1500 to 1850, which covers a long time horizon; second we study 268 regions in Europe, stretching from the Ural mountains in the east to Spain in the southwest and the UK in the northwest. 

Average age at marriage is used as a proxy for female autonomy. Low age at marriage is usually associated with low female autonomy. Age at marriage is highly correlated with other indicators of female autonomy, such as the share of female household heads or the share of couples in which the wife was older than the husband. Age at marriage is particularly interesting because of the microeconomic channel that runs from labour experience to an increase in women’s human capital. After marriage, women typically dropped out of the labour market, and switched to work in the household economy (Diebolt and Perrin 2013). Consequently, after early marriage women provided less teaching and self-learning encouragement to their children, including numeracy and other skills. Early-married women sometimes also valued these skills less because they did not ‘belong to their sphere’, i.e. these skills did not allow identification (Baten et al. 2017).

What do they find?

Figure 3 depicts a strong and positive relationship between average age at marriage and numeracy for the two half centuries following 1700 and 1800. Most countries are close to the regression line. Denmark, the Netherlands, Germany, Sweden, and other countries had high values of female autonomy and numeracy – interestingly, many of the countries of the Second Industrial Revolution of the late 19th century, rather than the UK, the first industrial nation. In contrast, Russia, Poland, Slovakia, Italy, Spain, and Ireland had low values in both periods.

In our regression analyses, we include a large number of control variables, such as religion, serfdom, international trade, and political institutions. We find that the relationship between female autonomy and numeracy is very robust.

We also study the relationship between female autonomy and human capital formation at the regional level in the 19th century. Numeracy and age at marriage (after controlling for country-fixed effects and other control variables) yield an upward sloping regression line (Figure 4). 

…In sum, the empirical results suggest that economies with more female autonomy became (or remained) superstars in economic development. The female part of the population needed to contribute to overall human capital formation and prosperity, otherwise the competition with other economies was lost. Institutions that excluded women from developing human capital – such as being married early, and hence, often dropping out of independent, skill-demanding economic activities – prevented many economies from being successful in human history.


What Are the Effects of Economic Freedom at the State Level?

A brand new paper from the Mission Foods Texas-Mexico Center at SMU:

In this paper, we examine the relationship between institutional quality and bilateral trade patterns between Mexican states and U.S. states. We are contributing to the small, but growing, literature which uses gravity models to examine economic exchange at the subnational level (see Havranek and Irsova 2017 for a recent review of this literature). We are the first to explicitly incorporate institutional quality into a model of trade between the U.S. states and Mexican states, and the first to examine these sorts of relationships between the U.S. and Mexican states more generally. Poor institutions can be viewed as a cost for potential trading partners, and economic theory tells us that when an action becomes more costly, less of that action will be undertaken. Conversely, when an action becomes less costly, more of that action will be undertaken. We find that states with better institutional environments as measured by the Economic Freedom of North America index do, indeed, realize higher levels of trade. We also contribute to the literature examining trade border effects (Hillberry and Hummels 2002; Chen 2004; Head and Ries 2001) by examining the impact the border has on trade between the U.S. states and Mexican states. Finally, we use our dataset to examine the relationship between trade volume and three measures of economic prosperity (pg. 6).

The authors lay out their key findings and policy recommendations:

Economic institutions matter.

Minimum Wage & Low-Skilled Workers: More Evidence

Image result for minimum wage

Ready for yet another post on the minimum wage? From a recent paper in the Journal of Public Economics:

Our empirical analysis uses the fact that the 2007 through 2009 increases in the federal minimum wage were differentially binding across states. We base our “bound” designation on whether a state’s January 2008 minimum wage was below $6.55, rendering it bound by the entirety of the July 2009 increase. In the states we describe as “unbound,” the effective minimum wage rose, on average, by $1.42 between 2006 and 2012. In the states we describe as “bound,” the effective minimum wage rose, on average, by $2.04. Of the long-run differential, $0.58 took effect on July 24, 2009.

We use monthly, individual-level panel data from the 2008 panel of the Survey of Income and Program Participation (SIPP) to implement a combination of difference-in-differences and triple difference research designs. Because we use longitudinal employment records with data on wage rates, our implementation of these research designs has two key advantages. First, we are able to pinpoint “target” groups more intensely affected by minimum wage increases than the analysis groups in many studies. Second, we are able to pinpoint workers who were not directly affected yet, as evidenced by their wage rates, were only moderately more skilled than the “target” workers. We incorporate this second group of workers into our analysis as a “within-state control” group. That is, we use this group to construct a set of counterfactuals that proxy for otherwise unobserved shocks to the low-skilled labor market (pg. 53).

What do they find?:

  • “We find that increases in the minimum wage significantly reduced the employment of low-skilled workers. By the second year following the $7.25 minimum wage’s implementation, we estimate that targeted individuals’ employment rates had fallen by 6.6 percentage points (9%) more in bound states than in unbound states. The implied elasticity of our target group’s employment with respect to the minimum wage is −1, which is large within the context of the existing literature” (pg. 54).
  • The average monthly incomes of low-skilled individuals decreased. “Relative to low-skilled workers in unbound states, targeted individuals’ average monthly incomes fell by $90 over the first year and by an additional $50 over the following 2 years. While surprising at first glance, we show that these estimates can be straightforwardly explained through our estimated effects on employment, the likelihood of working without pay, and subsequent lost wage growth associated with lost experience. We estimate, for example, that targeted workers experienced a 5 percentage point decline in their medium-run probability of reaching earnings greater than $1500 per month” (pg. 54).

The researchers conclude,

We use data from the SIPP to investigate the effects of the 2007 to 2009 increases in the federal minimum wage on the employment and income trajectories of low-skilled workers. We estimate that the minimum wage increases enacted during the Great Recession had negative effects on affected individuals’ employment, income, and income growth. The SIPP data suggest that this period’s minimum wage increases reduced aggregate employment rates by at least half of a percentage point in states that were fully bound by the federal minimum wage’s rise from $5.15 to $7.25 (pg. 67).

Stuff I Say at School – Part VI: Economic Freedom & Corruption

This is part of the Stuff I Say at School series.

The Assignment

Response to a group’s summary of Jakob Svensson’s “Eight Questions About Corruption.”

The Stuff I Said

The Fraser Institute’s Economic Freedom of the World (EFW) Index, published in its annual Economic Freedom of the World reports, defines economic freedom based on five major areas: (1) size of the central government, (2) legal system and the security of property rights, (3) stability of the currency, (4) freedom to trade internationally, and (5) regulation of labour, credit, and business. According to its 2018 report (which looks at data from 2016), countries with more economic freedom have substantially higher per-capita incomes, greater economic growth, and lower rates of poverty. Drawing on the EFW Index, Georgetown political philosophers Jason Brennan and Peter Jaworski point to a strong positive correlation between a country’s degree of economic freedom and its lack of public sector corruption.

Granted, a lack of corruption could very well give rise to market reforms and increased economic freedom instead of the other way around. However, recent research on China’s anti-corruption reforms suggests that markets may actually pave the way for anti-corruption reforms. Summarizing the implications of this research, Lin et al. explain,

Reducing corruption creates more value where market reforms are already more fully implemented. If officials, rather than markets, allocate resources, bribes can be essential to grease bureaucratic gears to get anything done. Thus, non-[state owned enterprises’] stocks actually decline in China’s least liberalised provinces – e.g. Tibet and Tsinghai – on news of reduced expected corruption. These very real costs of reducing corruption can stymie reforms, and may explain why anticorruption reforms often have little traction in low-income countries where markets also work poorly. China has shown the world something interesting: prior market reforms clear away the defensible part of opposition to anticorruption reforms.Once market forces are functioning, bribe-soliciting officials become a nuisance rather than tools for getting things done. Eliminating pests is more popular than taking tools away … A virtuous cycle ensues – persistent anticorruption efforts encourage market-oriented behaviour, which makes anticorruption reforms more effective, which further encourages market oriented behaviour.

Interesting enough, there is some evidence that suggests that more government hands in the pies increases corruption. For example, a 2017 study found that larger municipality councils in Sweden result in more corruption problems. A 2009 study found that more government tiers and more public employees lead to more bribery. Finally, a 2015 study showed that high levels of regulation are associated with higher levels of corruption (likely because of regulatory capture).

Do Most Americans Really Want What They Say They Want?

I hear a lot about how “most Americans” are in favor of “Policy XYZ.” The problem is that the social science shows that most Americans don’t know what they’re talking about. Do opinions change with more information or when costs are introduced? Two surveys from the Cato Institute seem to answer this in the affirmative.

The first is on federal paid leave. Seventy-four percent of the 1,700 Americans surveyed “a new federal government program to provide 12 weeks of paid leave to new parents or to people to deal with their own or a family member’s serious medical condition…Support slips and consensus fractures for a federal paid leave program, however, after costs are considered.” A 20 percentage point drop in support occurs when a $200 price tag is attached. Less than half are willing to pay $450 more in taxes for the program. When other potential costs are introduced (e.g., smaller future raises, reduction in other benefits, women less likely to be promoted, cut funding to other government programs), the majority of Americans find themselves opposing the program.

Less than half of men would be willing to pay even $200 more, while 55% of women would still be willing to pay $450 more. Support for the program drops across all political parties as costs are introduced, with 60% of Democrats still willing to fork over $1,200 a year to implement it (but only 22% of Republicans and 45% of Independents). “In sum,” writes Cato researcher Emily Ekins, “Democrats have a much higher tolerance threshold for taxes than the average American.”

Another survey looked at support for the Affordable Care Act’s pre-existing condition regulation. Out of the 2,498 Americans questioned, 65% support this aspect of the ACA. However, when costs are introduced, support drops. Furthermore, wealthier Americans are more willing to entertain trade-offs than lower-income ones.

Thomas Sowell has written, “There are no solutions; there are only trade-offs.” What “most Americans” want depends on whether or not trade-offs are kept in the dark.