Farming Doesn’t Pay….For a Reason

NOTE: The Growth Economics Blog has moved sites. Click here to find this post at the new site.

An op-ed in the NYT showed up the other day, by Bren Smith, a farmer who lamented the fact that farmers like him (small, local suppliers) were having trouble making money. This despite the surge in farm-to-table restaurants and the “locavore” movement.

Why? Why haven’t the new crop of small, local, hands-on farmers been able to make money? For the same reasons that farmers throughout history have not been able to make money. Their particular product is homogenous across producers, and almost perfectly substitutable with other products. Farmers have essentially no market power. No market power, no profits. Farming is probably the closest thing we have to the perfectly competitive market of Econ 101 textbooks.

Your hand-grown tomato (or kale, or beef, or whatever) faces both competition from other hand-grown tomatoes, as well as competition from conventionally-grown tomatoes. Even more, there is competition from other foods; even farm-to-table restaurants will change their menus to use lower-cost foods if your tomatoes cost too much. So even if your tomato is the greatest, most loved, best-grown tomato ever, you aren’t going to be able to get a premium for it. And hence it’s going to be hard to make money farming those tomatoes, especially if you are using really inefficient methods that require lots of manual labor.

Understanding the economics of farming takes us back to David Ricardo: farmers don’t make money, landowners make money.

If you read beneath the surface of Mr. Smith’s suggested remedies, he understands this. In his words:

But now it’s time for farmers to shape our own agenda. We need to fight for loan forgiveness for college grads who pursue agriculture; programs to turn farmers from tenants into landowners; guaranteed affordable health care; and shifting subsidies from factory farms to family farms. We need to take the lead in shaping a new food economy by building our own production hubs and distribution systems. And we need to support workers up and down the supply chain who are fighting for better wages so that their families can afford to buy the food we grow.

If you want farmers to have money, then you have to give it them directly (subsidies), give it to them indirectly (loan forgiveness or cheap health care), or give them a money-producing asset (land or a distribution chain). But competition is a bitch, and there is no world (including a higher-wage world) in which the pure act of producing food is going to make money for farmers.

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Herrendorf and Schoellman on Labor Allocations

NOTE: The Growth Economics Blog has moved sites. Click here to find this post at the new site.

The next post on the NBER growth session. Berthold Herrendorf (ASU) and Todd Schoellman (ASU) looked at the (surprise!) misallocation of labor between agriculture and non-agriculture. They look at the wage gap between ag and non-ag in a panel of 39 countries. The question is how much of the gap is due to human capital differences between ag and non-ag workers.

Across their sample, the average wage premium for non-ag is 1.79. That is, non-ag workers earn about 79% more than ag workers per hour. If you control for human capital using a standard Mincerian return to years of schooling of 10%, then this average premium falls to 1.36. The wage \textit{per unit of human capital} in non-ag is 36% higher than in agriculture. The raw wage premium is 1.79 because non-ag workers have higher average education levels.

What Todd and Berthold do to advance on this is to consider the possibility that the returns to education are different between sectors. They provide evidence that this is in fact the case. For each year of schooling, agricultural workers get a smaller bump in wage than do non-ag workers. Thus non-ag workers have even higher implied human capital than ag workers. They have more years of schooling, and those years of schooling provide them with more human capital. If you make this adjustment, then the average wage premium for non is 0.92, or non-ag workers earn about 8% \textit{less} per unit of human capital than in ag. Essentially, Todd and Berthold can account for the entire observed wage gap.

This is intriguing because it suggests that the labor markets in these countries are getting things roughly right. This doesn’t mean ag workers earn the same as non-ag workers, they don’t. But this is because ag workers provide less human capital to the market than non-ag workers, not because ag workers are underpaid for their human capital. I’ll do some self-promotion in that their work complements my own finding that wage gaps between sectors in developing countries are not a big source of aggregate productivity losses.

One conclusion from their work is that movements of workers between sectors are not by themselves a source of growth. With the marginal return to HC being the same across sectors, there is no boost to productivity coming just from moving workers around. If we do observe shifts of labor from ag. to non-ag then that represents shifts due to differential productivity growth in the sectors or to non-homothetic preferences.

Restuccia and Santaeulalia-Llopis on Land Misallocation

NOTE: The Growth Economics Blog has moved sites. Click here to find this post at the new site.

Next entry in the NBER growth session recap. Diego Restuccia (Toronto) and Raul Santaeulalia-Llopis (Wash. U.) presented their paper on land misallocation and productivity. Essentially, what Diego and Raul are trying to do is apply the firm-level study of Hsieh and Klenow (2009) methodology to farms (you might be starting to sense a theme to the day by now). Specifically, they have detailed data on farm plots from Malawi, and use this to see how much the marginal product of land varies across farming households.

They then ask the counter-factual question of: how much would agricultural output rise if we re-allocated land across farmers to equalize the marginal product of land? Essentially, if I could make sure that high-productivity farmers were able to receive more land, and low-productivity farmers less, how much could we raise aggregate output? Their finding is that output would go up by a factor of 3! This is way off the charts compared to anything that people have done with firms (where output might rise by a factor 1.5).

Big numbers like that lead to questions. The obvious concern is that they are incorrectly measuring the marginal product of land. Working in their favor is that they have clear measures of actual real output (bushels of maize). This means they do not have to make any assumptions about prices to try and deflate revenues, as we often need to do with firms.

You might also be worried that they are attributing land-quality differnces to households. That is, household A looks really productivity in the data because they have good land, not because they are really good at farming. Diego and Raul have really fine-grained measures of land characteristics. Conceptually they can control for land quality. But how do you construct a single-valued index of land quality from 11 different characteristics (slope, elevation, acidity, etc…). There is conceivably some kind of “true” agronomic function that transforms these into a single measure of quality, but it is almost certainly highly non-linear and involves all sorts of weird covariances between the measures. So if you want to be skeptical about their results, I think this is the primary worry.

Let’s take their results as true for the moment, though. One explanation for the vast degree of misallocation goes back to subsistence constraints, as I discussed in a different context before. If I need to ensure that all farming households can achieve a minimum output (and markets are not developed enough for people to borrow/lend over time if they can’t), then you’re bound to have big misallocations. You’d in fact have to give bad farmers even more land than good farmers. A very low absolute level of productivity is thus a determinant of misallocation.

On the other hand, if all the land is really highly productive, then small plots are sufficient to ensure subsistence. This means that we can give all the bad farmers small plots, and let the good farmers operate the surplus land. So a high level of absolute productivity will ensure a better allocation. Inherent land productivity thus has two separate influences on outcomes: a direct one through increasing output, and an indirect one in ensuring better allocations of land.

Some Self-Promotion

NOTE: The Growth Economics Blog has moved sites. Click here to find this post at the new site.

The CSAE blog has put up a research summary about my paper with Markus Eberhardt, on agricultural technology and agricultural productivity (which are different things).

Agriculture and Growth Reading List

NOTE: The Growth Economics Blog has moved sites. Click here to find this post at the new site.

I will be teaching graduate growth and development this fall, and I’m trying to get a head start on my reading list. Today’s effort is looking at the relationship of agriculture to overall economic development.

I think a really convenient way to see the main issues in this literature is the following figure. It plots output per worker (in logs) in both the agricultural and non-agricultural sectors against the share of labor in non-agriculture. Moving to the right, then, is associated with industrialization and/or structural transformation, whatever you want to call it. The data is from Francesco Caselli’s handbook chapter (data here).

Ag and Non-ag Output per Worker
The figure contains within it several important stylized facts that motivate current research. First, think about how to explain the variance in aggregate output per worker between countries. This depends on variance in agricultural output per worker and variance in non-agricultural output per worker. For agricultural output per worker, note that there is a much higher variance. The range along the y-axis is much bigger, from 6 (roughly $403 per worker) to almost 11 (nearly $60,000 per worker). The variance in non-agricultural output per worker is much smaller, ranging only from about 8.5 ($4900) to about 11 (again $60,000). A huge part of the variance in overall output per worker across countries is driven by differences in how productive agricultural workers are.

The second fact that jumps out is the tight correlation of the non-agricultural labor share and agricultural output per worker. Countries that have the lowest agricultural output per worker also tend to have the most people in that sector. The poorest countries – Nepal (NPL), Uganda (UGA), and Mozambique (MOZ) for example – are poor because their agricultural workers produce very little, and most of their workers are agricultural workers. It’s a double-whammy.

Third, there is a gap in output per worker between agricultural and non-agricultural workers, but this gap shrinks as the non-agricultural labor share rises. For the poorest countries, the gap implies that a non-agricultural worker produces something like 30 or 40 times more than an agricultural worker (and there are even more extreme examples, like Nepal, where the ratio is 130-1). But as the non-agricultural share of workers rises, these gaps fall to something like 1.5 to 1.

In thinking about why some countries are rich and some are poor, these stylized facts are of first-order importance. Standard one-sector growth theory sweeps all this under the rug. The papers I teach in this area take these facts as a jumping-off point. They generally work with a model that has a low income elasticity for agricultural goods (Engel’s Law), so that as productivity goes up in either sector, labor is pushed/pulled out of agriculture. Other papers take off from this to consider the existence of the productivity gaps, either trying to account for them more accurately, provide some explanation for their existence, and/or explain why they disappear as countries industrialize.

The reading list itself can be found on the “Papers” page on the site, below the introductory papers. It includes both a pdf and a BibTeX file.