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  1. PPS232S.01Microeconomics of International Development Policy 5. Land

  2. Introduction One of the oldest puzzles in development economics is the inverse relationship between farm size (e.g., hectares) and productivity (e.g., kilograms per hectare). That is, as farms get larger, their productivity (in terms of output per unit of land, not total output) falls. Why is this at odds with economic theory?

  3. Introduction If land sales or land rental markets function as they should, then land should either be sold or leased out from low- to high-productivity households. Thus, with well-functioning land (sales or rental) markets, productivity should be equal between households. In other words, at the margin, there should not exist any arbitrage opportunities – again, there should be no dollar bills left on the sidewalk.

  4. Introduction Moreover, within a farm operated by a single household, factor productivity (i.e., land, but also labor, capital, human capital etc. productivity) should be equalized across plots (recall Udry, 1996). Otherwise, the household could merely (and costlessly) reallocate inputs among its plots to increase output.

  5. Introduction One possible explanation is that the technology used in agricultural production exhibits non-constant returns to scale (Carter, 1984). The CRS assumption – can you remember what it says? –is so well-supported by the data for developing countries, however, that no one even tests for it anymore.

  6. Introduction Consequently, a prima facie, naïve policy recommendation to increase aggregate output (and reduce food insecurity) would be to redistribute land from high- to low-productivity households. Not only would this be highly unfair and risk causing a revolution, it would also be plain wrong. Let’s see why.

  7. Introduction In most places, even when the land sales market is thin or does not exist, the land lease market will compensate. So this really begs the question: Why do we observe the inverse productivity relationship almost consistently throughout the developing world?

  8. Chayanov (1926) The first one to notice this inverse farm size—productivity relationship was Chayanov (1926). Under Lenin’s New Economic Policy (when kulaks were still allowed to market their farm surplus and profit from it), Chayanov noticed that larger farms where less productive on average than smaller farms.

  9. Sen (1962) and a Few Exceptions Likewise, Sen (1962) noticed the same phenomenon for Indian farms. To be fair, Hill (1972, 1977) and Kevane (1994) have actually observed a positive relationship in Nigeria and Sudan, respectively. This is also at odds with economic theory, but such “positive relationship” findings are the exception rather than the norm.

  10. Sen (1966) This was the first attempt at explaining the inverse relationship. Assume two types of farms: small peasant farms and large capitalist farms. Also assume a wage gap due to a lower real cost of labor on peasant farms than the wage rate on capitalist farms (say, because of supervision costs.)

  11. Sen (1966) In that case, small peasant farms will overuse labor, simply because their first-order condition for profit maximization entails doing so. Thus, small farms are more productive than large capitalist farms.

  12. What Could Cause the Inverse Relationship? There really are two hypotheses in the literature: Market Imperfections: At the level of the village or the household, there exist some market imperfections that give rise to the inverse relationship. Sen’s labor explanation was just such an explanation. Solution: Use village and household fixed effects and see if the inverse relationship disappears.

  13. What Could Cause the Inverse Relationship? Omitted Variables Soil Quality Measurements: Include those soil quality measurements, but those are extremely difficult and costly to collect. Measurement Error: This is somewhat fuzzy… Collect better data?

  14. What Could Cause the Inverse Relationship? Inverse Relationship Statistical Endogeneity Market Imperfections Omitted Soil Quality Measurements Measurement Error

  15. The Ideal Data Set So really, the ideal data set would include: 1. Plot-level production information in several village, for several households, with several plots per household; 2. Longitudinal data, so as to observe the above over time; 3. Precise soil quality measurements; and 4. No measurement error.

  16. What Do We Know About the Inverse Relationship? Different studies find that different hypotheses account for a fraction of the inverse relationship between farm size and productivity, but no study can actually pinpoint a single cause. In fact, it looks as though all three explanations are valid in specific data sets: market imperfections (Barrett, 1996), omitted soil quality measurements (Bhalla and Roy, 1988; Assunção and Braido, 2007; Barrett et al., 2010), and measurement error (Lamb, 2003; Carletto et al., 2011).

  17. Land Rental Markets and Agrarian Contracts Let’s now look at land rental markets. Land rentals involve a contract between a landowner and her potential tenants, so this section will focus on agrarian contracts (e.g., wage; fixed rent; sharecropping.) This section will thus focus heavily on principal-agent problems and on transaction costs.

  18. What is Sharecropping? For Adam Smith, sharecropping was “an unsatisfactory intermediate stage between slavery and the English (i.e., fixed rent) system.” For Marxians, it is a mechanism through which capital exploits labor (Byres, 1983). Further, freed slaves in the post-bellum South typically entered sharecropping agreements with their landlords.

  19. What is Sharecropping? So sharecropping has generally had bad press in the social sciences. Yet an economist would ask: “If sharecropping is so bad, why has it existed for thousands of years and all over the world?” Let’s start by defining the terms of the contract themselves.

  20. What is Sharecropping? Sharecropping is an agrarian contract by which a landowner leases out her land to a tenant in exchange for a share of the crop. By “share,” we mean a fraction or proportion of the crop, and not a given quantity per unit of land. For example, a sharecropping contract would thus specify that rent is 50 percent of the crop and not 500 kg per hectare.

  21. What is Sharecropping? In mathematical terms, let • a be the share of the crop that goes to the tenant and (1 – a) be the share of the crop that goes to the landlord; and • b be a monetary side payment from the landlord to the tenant (a fixed wage if b > 0; a fixed rent if b < 0) We can thus simply define a contract as the pair {a,b}.

  22. What is Sharecropping? There are three possible contracts: • Sharecropping: a is in (0,1) and bcan be anything • Wage: a = 0 and b > 0 • Fixed Rent: a = 1 and b <0 The landlord’s payout is πL = (1 – a)q – b The tenant’s payout is πT = aq + b And the landlord’s objective is to maximize her utility by choosing {a,b}. Even with this very simple framework, it is very difficult to show (in theory) why sharecropping should emerge as optimal (it was perceived as an “irrational” contract until the mid-1970s).

  23. Two Schools of Thought Stiglitz established the rationality of sharecropping. Before we address his contribution, however, let’s discuss the history of economic thinking on sharecropping. Smith (1776, 1976), Marshall (1890, 1920): Sharecropping is inefficient. Indeed, if the tenant only receives half of the product of his labor, why would he provide maximum effort? The landlord can do better by asking for a fixed (cash or crop) rent.

  24. Two Schools of Thought Marshall (1920) offered a diagrammatic proof of why sharecropping causes moral hazard. Since then, moral hazard in this context is referred to as “Marshallian inefficiency.” w, MPL w MPL aMPL Labor

  25. Two Schools of Thought For Cheung (1968, 1969), if such contracts emerged, it was because there was no supervision problem. In fact, for Cheung, the landlord can perfectly enforce his chosen level of effort. In other words, effort is costlessly enforceable. This may seem unrealistic in contrast to Marshall’s assumption that enforcement is prohibitively costly, but ultimately, whether tenant effort is enforceable is an empirical question.

  26. Two Schools of Thought Two schools of thought were thus born: • The Marshallian school of thought, which assumes that landlords cannot enforce their preferred level of effort on the tenant’s part; and • The Cheungian (or “transactions costs”) school, which assumes that landlords can costlessly enforce their preferred level of effort on the tenant’s part. Let’s now turn to Stiglitz’ contribution.

  27. Stiglitz (1974) Assume that the landlord is risk-neutral and the tenant is risk-averse. In this case: • Fixed Rent: Leaves all the production risk to the risk-averse tenant; • Wage: Provides little or no incentives to the tenant via Marshallian inefficiency; • Sharecropping: Allows for risk-sharing (w.r.t. fixed rent) while providing strong incentives (w.r.t. wage). Thus, sharecropping trades off risk sharing and incentives. In fact, this was the title of Stiglitz (1974): “Risk sharing and incentives in sharecropping.”

  28. Stiglitz (1974) Based on his contribution, Stiglitz observed that sharecropping usually emerges due to missing insurance markets. If there exists a complete market for agricultural insurance, we should only observe fixed rent contracts. Indeed, as insurance markets develop, share tenancy tends to disappear, but the presence of transaction costs can still cause it to exist. (What if the landlord is risk-averse and the tenant is risk-neutral?)

  29. Empirical Evidence In the applied literature on sharecropping (much like in the applied contract theoretic literature), there exist two strands (Prendergast, 1999): 1. Do incentives matter? For sharecropping, is there evidence of Marshallian inefficiency? Generally, the answer is yes. 2. Do the observed contracts correspond to the predictions of the theory? In other words, do we observe sharecropping (versus fixed rent or wage) when the theory says we should? On this, the evidence is somewhat muddy.

  30. Empirical Evidence Shaban (1987) Observes that one needs to control for individual unobserved heterogeneity in testing for the incentive effects of sharecropping – maybe different types of agent select into different contracts. Using tenant fixed effects (he looks only at sub-sample of tenants who enter both fixed rent and sharecropping), he conducts a test of Marshallian inefficiency for all the inputs used in production.

  31. Empirical Evidence Shaban (1987) This allows to do away with individual unobserved characteristics (e.g., technical ability, risk aversion, preferences, etc.) and really test for Marshallian inefficiency. Using this method, Shaban finds evidence of Marshallian inefficiency.

  32. Empirical Evidence Ackerberg and Botticini (2002) Using data from Early Renaissance Tuscany, they first wrote a paper in which they found that risk-aversion did not matter in the choice of contract (Ackerberg and Botticini, 2000) Yet, after thinking about the problem for a while, they realized that matching between landlords and tenants is non-random, i.e., endogenous matching.

  33. Empirical Evidence Ackerberg and Botticini (2002) Failing to account for this endogenous matching may lead to biased coefficient estimates. Controlling for endogenous matching, risk-aversion, it turns out, does matter! The strength of Ackerberg and Botticini’s paper was to highlight the problem with ignoring the matching process between landlords and tenants.

  34. Empirical Evidence Dubois (2002) Also looks at the shape of the observed contracts rather than testing for incentive effects. Principal is risk-neutral, and agent is risk-neutral. Develops a dynamic Principal-Agent model in which the slope of the contract (the incentive power) can affect future production possibilities.

  35. What Does the Data Have to Say About Sharecropping? Dubois (2002) In other words, the level of production now affects fertility tomorrow. In the one-period context, the problem reverts to the canonical model, i.e., fixed rent. (Why?) In the dynamic model, even though the principal signs short-term contracts, sharecropping emerges as the optimal choice.

  36. What Does the Data Have to Say About Sharecropping? Dubois (2002) This is because a fixed rent contract would entail land overuse on the part of the tenant, whereas a sharecropping contract curbs land overuse. Thus, if the landlord cares about soil fertility, moral hazard is the premium she is willing to pay to preserve the fertility of her plot.

  37. What Does the Data Have to Say About Sharecropping? Dubois (2002) Using data from a rural area of the Philippines, Dubois finds support for his theoretical hypothesis. This is a nice example of the canonical Stiglitzian model failing to explain the stylized facts.

  38. Empirical Evidence Bellemare and Brown (2010) Most of the empirical studies on sharecropping (risk sharing, really) use some proxy for risk aversion, usually wealth or income. Most of those studies then test that the wealth levels of the principal and the agent increase the amount of risk each is exposed to at the margin, under the assumption that risk aversion is decreasing in wealth.

  39. Empirical Evidence Bellemare and Brown (2010) We show that using income or wealth as a proxy for risk aversion leads to tests that are completely unidentified. In other words, any statistically significant result for wealth in a regression of a on wealth levels is altogether spurious. What does this say about Laffont and Matoussi (1995), Ackerberg and Botticini (2002), etc.?

  40. Land: Why Worry About Property Rights? In The Mystery of Capital, de Soto (2000) claims that the poor in developing countries own US$1 trillion worth of assets, but that the lack of property rights in those countries prevents the poor from capitalizing on those assets. de Soto’s monograph – like any other “silver bullet” explanation for underdevelopment – has received (too) much attention.

  41. Land Rights as Institutions More credibly, there is an important literature looking at the causal relationship that flows from the quality of institutions and economic performance. In this context, “institutions” refers to legal institutions, i.e., property rights and contracting institutions, but others have looked at legal origins (i.e., common vs. civil law), financial institutions, etc.

  42. Land Rights as Institutions Acemoglu et al. (2001) pioneered this line of research. The problem, of course, is that it is very difficult to establish a causal relationship from institutions to economic development. Presumably, institutions evolve as a response to the level of economic development and economic development depends on institutions.

  43. Land Rights as Institutions Acemoglu et al. (2001) use early settler mortality rates as a source of credibly exogenous variation in explaining institutional differences: “Europeans adopted very different colonization policies in different colonies, with different associated institutions. In places where Europeans faced high mortality rates, they could not settle and were more likely to set up extractive institutions. Exploiting differences in (…) mortality rates as an instrument for current institutions, we estimate large effects of institutions on income per capita. Once the effect of institutions is controlled for, countries in Africa or those closer to the equator do not have lower incomes.”

  44. Land Rights In this section we investigate the link between institutions and economic performance at the micro level by looking at land, which is one of the most important inputs in the aggregate production function. Having studied land productivity and land rental markets in the previous sections, we now turn to land rights, which provide landowners with the incentives to improve and maintain their land assets.

  45. Land Rights and Agricultural Productivity More specifically, we will look at the relationship between land rights and agricultural productivity, which is used here as a proxy for economic development. (Why?) Economists posit that there are three channels through which land rights can increase agricultural productivity (Feder and Noronha, 1987; Feder and Feeny, 1991; Migot-Adholla et al., 1991).

  46. Land Rights and Agricultural Productivity • Property rights allow landowners to sell their plots or lease them out to more productive individuals, or to choose more productive contracts; • Property rights give landowners stronger incentives to maintain and improve their plots; and

  47. Land Rights and Agricultural Productivity • Property rights allow landowners to use their plots as collateral to obtain loans that can be used to finance investments in land or the purchase of production inputs. In other words, clearly defined and well-enforced property rights should lead to productivity gains, ceteris paribus.

  48. Land Rights and Agricultural Productivity As a consequence, land rights (and land reform) have been part of the development Zeitgeist for quite some time now, although there is mounting evidence that land reform is more effective when it emerges endogenously (i.e., bottom up) than imposed exogenously (i.e., top down). (See also Platteau, 1994a and 1994b for how one cannot simply “transplant” market institutions to places where they did not emerge in the first place.)

  49. Empirical Evidence Atwood (1990) was among the first to take a serious look at the impact of land titling, focusing on Africa. He took issue with the conventional view that holds that land registration improves agricultural productivity and tried to provide an alternative viewpoint.

  50. Empirical Evidence Atwood’s reasoning was that land registration policies are extremely costly and that its impacts may actually be contrary to expectations. • “Customary institutions prevent land transfers”: Atwood notes that this is false in most places, where land transactions (both sales and rentals) are widespread.