False Beliefs about Food Stamps

In this post on the University of Illinois Policy Matters blog, Craig Gundersen tries to lay to rest a few false beliefs (or misconceptions) that may people (and policy makers) have about the Supplemental Nutrition Assistance Program (SNAP, also known as "food stamps").

Does SNAP participation lead to obesity obesity?  Gundersen writes:

“It is not clear why people would think SNAP leads to increases in obesity insofar as one doesn’t generally think that increasing someone’s ability to purchase food leads to higher weights. For example, one doesn’t usually think that a pay raise leads to increases in someone’s weight. Along with common sense, virtually all studies indicate that SNAP recipients are no more likely than eligible SNAP non-recipients to be obese, after controlling for selection into the program and other issues.”

Does SNAP participation cut down hunger?  Gundersen writes:

“the central goal of SNAP is to alleviate hunger and, in this role, SNAP has been enormously successful. (See Kreider et al., 2012, and references therein.) Along with these direct impacts on food intakes, SNAP has also been found to improve well-being over other dimensions including reductions in poverty (e.g., Tiehen et al. 2012), improvements in birth outcomes (Almond et al. 2011), lower mortality (Krueger et al. 2004), and better general health (Kreider et al. 2012). Moreover, by reducing food insecurity, the negative impacts of food insecurity on various health outcomes are diminished. (See Gundersen and Ziliak, 2015 for a review of these impacts on health.)”

If you've got a relatively decent income, it might be hard to imagine how SNAP could have such dramatic hunger and health effects, but it is important to keep in mind Engel's Law: the poor spend a larger proportion of their income on food than the rich.  That phenomenon is alive and well in the US, and I can see it in my Food Demand Survey (FooDS) data, which measures food expenditures.  Here's how estimated spending on food varies with household income, as measured by FooDS.

If you're on the right tail of the income distribution spending only about 5% of your income on food, then it is probably hard to imagine how food spending and eating will change when you're on the left tail of the distribution where food consumes almost 25% of the household's budget.

Finally, Gundersen takes on the idea that various health restrictions on SNAP spending will have much impact.  He writes: 

“If restrictions are imposed, there is unlikely to be any change in obesity in the U.S. Instead, the main consequence will be a reduction in the number of SNAP participants. This reduction is due to two factors, stigma and transaction costs. (I concentrate on the former here, for a discussion of the latter, see Gundersen, 2015.) Stigma would increase as participants would feel singled out as being irresponsible and incapable of making well-informed food purchases. More broadly, through its message that adults receiving SNAP are not responsible enough to make their own food choices, recipients would be further stigmatized. After all, the federal government doesn’t tell, say, government employees how to spend their earnings; why do some feel it is fine to tell SNAP recipients what they can purchase? This stigmatization due to restrictions is the central reason why the USDA has rejected proposed restrictions.

Due to increased stigma and increased transactions costs, participation in SNAP will decline as recipients leave the program and potential recipients are less likely to enter the program. (For at least some advocates of SNAP restrictions, this may be their central goal for imposing restrictions.) As a consequence, the positive benefits of SNAP will be realized for fewer Americans and, in particular, there will be an increase in food insecurity and, therefore, increases in negative health outcomes and subsequent health care costs (Tarasuk et al., 2015).”

To that I'll add that most SNAP participants can easily get around the restrictions on what they buy by rearranging what they buy with SNAP and what they buy with non-SNAP dollars (see my explanation of that phenomenon here).  

One might reasonably ask whether SNAP spending is too low or too high, and alternative variations on the program might be worth considering.  Either way, decisions about SNAP's future should be ideally based our our best understanding of the program's impacts rather than false beliefs. 

Effects of Crop Insurance Subsidies

The journal Applied Economics Perspectives and Policy just published my paper entitled, "Distributional Effects of Crop Insurance Subsidies."  Farmers of the major commodity crops (and increasingly even minor crops including fruits and vegetables) are eligible to buy subsidized crop insurance.  The insurance is, in principle, priced at actuarial fair rate (i.e., the price of the insurance is equal to the expected loss), but the government subsidizes the insurance premium paid by the farmer (in addition to some of the costs of the insurers).  The average subsidy is around 65% of the premium amount.  If there were a similar program for your car insurance, and the annual premium you pay for your car is $1000/year, you'd get back $650 in subsidy.  In addition to this premium subsidy, the latest farm bill also has provisions to subsidize the deducible in the case of a loss.  All this begs the question: what impact do these subsidies have on food prices and production?  

From the abstract:    

“Results indicate that the removal of the premium subsidy for crop insurance would have resulted in aggregate net economic benefits of $622, $932, and $522 million in 2012, 2013, and 2014, respectively. The deadweight loss amounts to about 9.6%, 14.4%, and 8.0% of the total crop insurance subsides paid to agricultural producers in 2012, 2013, and 2014, respectively. In aggregate, removal of the premium subsidy for crop insurance reduces farm producer surplus and consumer surplus, with taxpayers being the only aggregate beneficiary. The findings reveal that the costs of such farm policies are often hidden from food consumers in the form of a higher tax burden. On a disaggregate level, there is significant variation in effects of removal of the premium subsidy for crop insurance across states. Agricultural producers in several Western states, such as California, Oregon, and Washington, are projected to benefit from the removal of the premium subsides for crop insurance, whereas producers in the Plains States, such as North Dakota, South Dakota, and Kansas, are projected to be the biggest losers.”

Because producers in different states grow different crops, the effects of the subsidies aren't equally dispersed.  I write:

“Take for example the comparison of California, which generated about $32.6 billion in annual food-related agricultural output from 2008 to 2012 and Kansas, which generated about $11.2 billion over the same time period. Despite the fact that California generates about three times more agricultural output than Kansas, Kansas farmers received 2.65 times the amount of crop insurance subsidies and attributed overhead ($618 million vs. $233 million) in 2013. Moreover, the states are radically different in terms of the types of agricultural commodities grown. Just under 70% of the value of all food-related agricultural output in California comes from fruits, vegetables, and tree nuts; for Kansas, the figure is only 0.04%. ”

These differences in commodities produced lead to differences in the uptake of crop insurance subsidies and the prices paid in each location.

“To illustrate how this heterogeneity comes about, again consider California and Kansas and the results from 2013. Removal of premium subsidies is projected to increase vegetable (a major California crop) prices by 1.4% and wheat (a major Kansas crop) prices by 7.9% (aggregate reductions in quantities are 0.2% and 3.1%, respectively). The implicit subsidy lost by California producers of vegetables is only 0.16%, whereas the implicit subsidy lost by Kansas producers of wheat is 12%. Thus, California vegetable producers gain an effective price advantage of 1.4% −0.16% = 1.24% whereas Kansas wheat producers experience an effective price change of 7.9% −12% = −4.1%. Therefore, California vegetable producers sell about the same amount of output at about 1% higher effective prices, but Kansas wheat growers sell less output at about 4% lower effective prices. As a result, California producers benefit and Kansas producers lose from the removal of food-related crop insurance premium subsidies.

Even the results in figure 4 mask within-state heterogeneity. For example, despite the fact that Kansas wheat farmers are net losers, California wheat farmers are net winners. Why? Because the implicit price subsidy to California wheat farmers is much lower than the one to Kansas (3.6% vs. 12%). But, not all California producers benefit. California barley, hog, poultry, and egg producers are projected to be net losers from the removal of crop insurance subsidies. Within Kansas, wheat producers lose about $86 million but cattle producers gain about $12 million annually from the removal of the premium subsidy for crop insurance”

How Fat Taxes Affect the Rich and the Poor

I'm pleased that the Economic Journal has decided to publish the paper Distributional Impacts of Fat Taxes and Thin Subsidies I wrote with  Laurent Muller, Anne Lacroix, and Bernard Ruffieux of the University of Grenoble and the French National Institute for Agricultural Research.  

Here is an excerpt

“How do the price policies differentially affect women at different points in the income distribution? Beliefs about the relative effects of fat taxes and thin subsidies on the poor relative to the non-poor are often premised on two assumptions. First is the assumption that the poor consume less healthful diets than the non-poor, perhaps due to the higher costs of more healthy diets (e.g., Drewnowski and Specter, 2004). The second assumption is that price policies are more likely to benefit low income consumers because low income consumers have more room for improvement, and because of their financial situation, they are likely to be more responsive to price changes. In short, a common view is that price policies can help the poor “catch up” to the non-poor in terms of the healthfulness of their diets.

Our experimental results confirm the first assumption: poor women tended to purchase less healthy food than the non-poor women. The implication is that, holding initial consumption patterns constant, policies which tax unhealthy food and subsidise healthy food will be regressive, favouring the non-poor more than the poor. But, people can change consumption patterns in response to price policies. If the poor are more responsive to price policies than are the non-poor, then inequalities will be dampened. This hypothesis, however, was rejected. Behavioural adjustments to the price policies amplified rather than dampened the divergent fiscal impacts of the price policies.”

In short:

“The tax/subsidy policies serve to widen the gap between the poor and non-poor, increasing the inequality in health and fiscal outcomes. Fat taxes cause the poor to pay disproportionally more for food than the non-poor and thin subsidies primarily flow to the non-poor. These effects occur because the non-poor already consume healthier diets but also because the non-poor are more price responsive than the poor”

Our approach to addressing this issue is quite different than that of previous studies.  Here's what's unique about our appoarch

“The advantage of the experimental set-up is that people’s choice behaviours are directly observed (rather than inferred as in a simulation study). In addition, the setting does not require the use of econometric models to infer behavioural responses. There is no need to assume a functional form or structure for responses; each individual can respond in their own unique way according to their own preferences. The experiment attempts to measure the overall fiscal effect (based on a day’s food choices) rather than simply focusing on one or two foods or a few food product categories. The experiment environment also allows us to study larger price variations (+/- 30%) than would likely have been feasible outside the lab, and as such, makes the price changes particularly salient.”

Here is one of the key figures from the paper.  The figure shows the distribution of price indices (i.e., the relative change in prices paid) after the introduction of a combined unhealthy-food-tax and healthy-food-subsidy policy for low income women as compared to a reference group (i.e, "normal" income women).

The Laspeyres index calculates the change in prices paid relative to the initial pattern of consumption; the Paasche index is similar except that it weights prices paid using the new pattern of consumption.  A greater difference between the two indices reveals greater substitution and responsiveness to the policy.

The figure above shows that 25-30% of  the low income consumers paid more for food after the price policy (they had an index greater than 100), and given the similarity of the two red lines, were less responsive (perhaps because of being more habit prone) than the richer consumers.  Moreover, at the individual level, the Paasche index was higher than the Laspeyres index for 35.9% of low income individuals.  These individuals did not shift their diet in the intended direction.

We ended the paper as follows:

“Whatever health benefits these policies might create, this paper suggests they need to be weighed against the adverse monetary effects they have on some of the poorest people in society.”

Possible Impacts of Massachusetts Ballot Question on Animal Welfare

Joshua Miler of the Boston Globe has a piece on a ballot initiative in Massachusetts.  He writes:

“The proposed Massachusetts ballot initiative, backed by a coalition called Citizens for Farm Animal Protection, has met the first and most arduous signature-gathering hurdle to make the ballot and is expected to clear the other obstacles that remain to make the November ballot.

It would ban the production and sale in the state of eggs from hens and meat from pigs and calves kept in tight enclosures starting in January 2022. For selling of shell eggs in Massachusetts, each hen would have to have access to at least 1.5 square feet of usable floor space.”

What are the possible cost implications?  

On the one hand:

“a consulting firm being paid by advocates to conduct an economic analysis of the ballot question’s impact, said the price increase would be modest. He predicted something on the order of 1 or 2 cents per egg, 12 to 24 cents a dozen”

On the other hand:

“[a top executive at Sauder’s Eggs, a big producer in Pennsylvania which ships many eggs to Massachusetts] estimates that the Massachusetts ballot question would raise the price by 70 or 80 cents per dozen, maybe more.”

Here was my take, as cited in the story:

“Some experts in the field say the best place to look to compare prices is California, where the sale of eggs from hens kept in small “battery cages” became illegal at the start of last year.

In a recent paper, Jayson L. Lusk, a professor of agricultural economics at Oklahoma State University, and another researcher used grocery store scanner data from California and other states to estimate how California’s animal welfare law changed the price of eggs. Per a dozen eggs, they found it raised prices by around 75 cents on average, a 22 percent increase over what the price of eggs would have been had the laws not gone into effect.

Lusk acknowledged that there are several confounding variables in extrapolating that data to Massachusetts, from last year’s avian influenza outbreak to Massachusetts importing more of its eggs than California (which could make the increase bigger) to the growth of the cage-free industry by 2022 (which could make the increase smaller).

But the overarching conclusion was clear.

“Egg prices are going to increase in Massachusetts” if the ballot measure passes, he said, “I don’t think there’s any doubt about that. The question really is ‘how much?’ ””

The cited research papers are discussed in this post.

Country of Origin Labeling Conspiracy

I've seen the following meme going around on social media.    

I don't know whether the source is actually March Against Monsanto, but whoever put it out is obviously trying to stoke paranoia without any context or background.  And, like most good lies, the meme contains an element of truth.

First, it is true that both Houses of Congress repealed mandatory country of origin labeling (MCOOL).  This happened about a month ago.  What the meme doesn't revel is why.

MCOOL has been controversial since it's inception more than a decade ago, and the policy was fought by the largest beef and pork producer organizations.  Moreover, our trading partners were less than happy with the law.  Canada and Mexico filed suit with the World Trade Organization (WTO) claiming the law represented a non tariff trade barrier.  The USA lost the first round and several appeals (here's the timeline from the WTO).  What the meme doesn't reveal is that if Congress didn't repeal MCOOL then Canada and Mexico could slap on more than a billion dollars in retaliatory tariffs; my understanding is that these tariffs could have been on any products, not just meat.

What's not true about the meme?  

Well, despite the existence of MCOOL, our research shows most consumers didn't know where their beef or pork was coming from anyway; the vast majority of consumer's weren't checking the label.  More importantly, it simply isn't true that "now you will not know which country your meat comes from" simply because the government doesn't require the information.  If consumers really want the information and are willing to pay to have it, why wouldn't a retailer voluntarily advertise origin?  The restaurant chain Wendy's, for example, advertised for a while it only used North American beef.  Lots of small producers sell the animal products they raise at farmers' markets and at local restaurants.  There are thousands of products that voluntarily (without a government mandate) use  the "Made in the USA" logo to try to garner more sales.   There are lots of things I want (a new BMW; a private jet; perhaps origin information on meat), but just because I want something doesn't mean the government should mandate that it be provided. 

For more background, here's a recent report on issues surrounding MCOOL by the Congressional Research Service; here's another recent report summarizing the research on the subject prepared by my friends Glynn Tonsor, Ted Schroeder, and Joe Parcell for the USDA Chief Economist.