Many studies have applied portfolio theory to explain acreage allocation in production agriculture . These applications and many since have been primarily applied to crop acreage decisions assuming linear technology and most are in a static setting. This literature, which grew out of Nerlovian models of supply response , is generally based upon adaptive interpretations of risk, and has evolved into a more rational risk approach . The general finding of this, by now, large literature is that the allocation of total acreage to specific production activities is significantly influenced by risk as generally modeled with variances and covariances. The most common finding is that an increase in the own variance of price or revenue reduces the acreage allocated to that activity. This is generally interpreted as the impact of risk aversion.From the perspective of more recent developments in portfolio theory, two general findings beg application in this empirical agricultural risk literature. First, explicit attempts to measure risk aversion structurally such as those in the equity premium puzzle are preferred . Only this way will researchers be able to distinguish risk aversion from other behaviors. Second, the structural approach provides a way to determine whether estimated risk aversion is credible . Thus, we argue that the structural approach is a sensible way to proceed at least at this stage in the development of risk literature in agriculture.
Specifically,maceta 5 litros this literature suggests advantages for a more integrative examination of the broader portfolio problem in agriculture that includes consumption, investment, and other risk sharing activities as well as production. Modern agriculture is characterized by much off-farm investment . At the very least, reduced form production- or acreage-oriented models may misinterpret the level of risk aversion . Worse, parameters can be biased if relevant variables are omitted. For example, if markets are incomplete, Fisher separation may not hold implying inconsistent estimation of parameters . A third issue concerns the advantages and disadvantages of using typical Euler equation representations of inter temporal arbitrage. Euler equations may yield important information from which to identify parameters, but imply that the dynamics must be properly specified . For example, one must choose between the non-expected utility model of Kreps and Porteus and standard model of discounting with additive preferences . After building a dynamic model of consumption, investment, and production, we obtain fundamental arbitrage equations that govern allocations of wealth to financial assets and agricultural capital as well as the allocation of acreage. This enables econometric choice from a larger set of first-order conditions in order to estimate risk preferences.The crucial variable of interest driving decisions is consumption, which is facilitated by accumulation of net worth .For agricultural households, these are both notoriously difficult to measure.After developing the arbitrage conditions, empirical estimates are obtained by generalized method of moments for eight states in the North Central region of the U.S. using stock market returns, bonds, and agricultural land allocations.
For these eight states in the period 1991-2000, reasonably good measurements of wealth are available which are essential for our approach.While this is a relatively short and somewhat anomalous time period compared to typical studies in finance, we suggest this comprehensive approach to arbitrage structure can be beneficial compared to typical incomplete approaches to estimation of risk behavior in agriculture. Using contemporaneous arbitrage equations implied by Euler conditions, an econometric model is specified over future wealth and excess returns conditional on a current information set. In spite of limited data, we find evidence of aggregate risk aversion that is rationalized by a single set of representative consumer preferences using an unconventional but reasonable specification. Although the organizational form of farms varies, a recent report by Hoppe and Banker finds that 98 percent of U.S. farms remained family farms as of 2003. In a family farm, the entrepreneur controls the means of production and makes investment, consumption, and production decisions. We begin by modeling the intertemporal interactions of these decisions. The starting point is a model similar in spirit to Hansen and Singleton’s but generalized to include consumption decisions and farm investments as well as financial investments and production decisions. No carefully-constructed publicly-available panel of agricultural data including farm and off-farm decisions and wealth variables exists. The periodic Survey of Consumer Finances and the Panel Study of Income Dynamics has too little farm information to give a very complete picture of decisions and representation of farm households. The best available data on wealth are found in the Agricultural Resource Management Survey and the U.S. Census of Agriculture, which are conducted by the NASS. For reasons explained above, this survey does not suffice for application of our model at the micro level.
However, data from this survey has been used within ERS to estimate average farm household net worth by state for the period 1991-2001. These data include eight states in the North Central Region of the U.S.: Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, and Wisconsin. These data are not without issues but seem to be the best available source for net worth and are actively used by government personnel in the ERS for research. Alternative data would omit non-farm assets, which are a substantial portion of farm households’ net worth and are intended as a key source of identification for this study. Although the time-period is short and in some ways atypical due to the run up of the stock market in the 1990s, this variation is ideal for identifying the arbitrage effects on agriculture of returns to financial assets. This period also has the advantage that the impact of government policy on crop substitution is relatively reduced and less complicated. For example, the Freedom to Farm Act of 1996 culminated a growing effort to decouple farm subsidies from acreage allocation decisions. A canonical prediction of economic theory is that high wages increase labor productivity. In settings where workers are salaried or paid by the hour, this is the concept of efficiency wages . In settings where workers are paid in proportion to their output , the theoretical connection between wages and productivity is even clearer.1 However, it has proven difficult to empirically estimate the responsiveness of labor productivity to piece rate wages, since much of these wages’ variation is driven by endogenous characteristics of the production process. In this paper, I provide the first quasi experimental estimate of the elasticity of labor productivity with respect to piece rate wages. Specifically, I analyze a high-frequency panel of worker-level production data from over 2,000 California blueberry pickers paid by piece rates. Surprisingly, I find that on average, labor productivity is very inelastic with respect to wages. Piece rate wages are interesting to study because they offer such a direct, clear, and salient link between a worker’s effort and reward. In general, optimal labor contracts can be quite complex, as they must effectively incentivize worker effort while simultaneously accounting for issues like risk aversion, asymmetric information, and moral hazard . However, these complications are less of a concern in settings where a firm can cheaply monitor both worker productivity and product quality. In such cases, theory suggests piece rate wages will outperform other common incentive schemes .2 Understanding how workers respond to changes in a piece rate wage is important in sectors where these wages can vary over time, like in specialty agriculture, the auto repair industry, or the growing ride share market .3 Econometricians face a fundamental challenge when trying to estimate the causal effect of piece rate wages on labor productivity: these wages are inherently endogenous. As an example, consider blueberry picking. When ripe berries are scarce and spread out ,cultivo de la frambuesa average worker productivity is low. When ripe berries are abundant and dense , it is easier for workers to pick berries quickly, and average productivity is markedly higher. Because farmers aim to keep their workers’ average effective hourly pay relatively stable over time, they set piece rate wages higher when picking is more difficult, and lower when picking is easier. In order to account for piece rates wages’ endogeneity, I adopt a two-pronged identification strategy. First, exploiting the richness of my multidimensional panel data, I econometrically control for environmental factors like seasonality and temperature that directly affect the berry picking production function. Second, I use the market price for blueberries as an instrument for piece rate wages.
This price is a valid instrument because it affects a farmer’s willingness to raise piece rates , but is otherwise uncorrelated with picker productivity. Furthermore, the market price for California blueberries is set by global demand and global supply. As a result, individual farms are too small to directly affect the market price, and supply shocks at the farm level can be considered orthogonal to aggregate supply shocks. I find that, on average, labor productivity is very inelastic with respect to piece rate wages, and I can reject even modest elasticities of up to 0.7. This finding contrasts with both canonical economic theory and previous structural estimates: relying on a calibrated structural model of worker effort, Paarsch and Shearer estimate a labor effort elasticity of 2.14 in the British Columbia tree-planting industry, and Haley estimates a labor effort elasticity of 1.51 in the U.S. mid-west logging industry. Why, then, do blueberry pickers not seem to respond to changes in their wage? One explanation of my findings could be that blueberry pickers respond to average effective hourly wages rather than marginal piece rate wages, similar to how electricity consumers respond to average prices rather than marginal prices . This is unlikely, both because piece rate wages are highly salient in the context I study, and because my identification strategy specifically isolates marginal effects from average effects. Instead, I find suggestive evidence that blueberry pickers face some binding constraint on physical effort that is related to temperature. Specifically, I find that at moderate to hot temperatures, I cannot reject that the piece rate wage level has no effect on labor productivity. However, at temperatures below 60 degrees Fahrenheit , a one cent per pound increase in the piece rate wage increases worker productivity by nearly 0.3 pounds per hour, implying a positive and statistically significant productivity elasticity of approximately 1.6. In other words, blueberry pickers respond to the piece rate wage level at cool temperatures, but seem not to respond to changes in their wage at higher temperatures. Temperature also affects productivity directly in economically meaningful ways. Specifically, I find that blueberry pickers’ productivity drops precipitously at very hot temperatures: workers are 12% less productive at temperatures above 100 degrees Fahrenheit than they are at temperatures between 80 and 85 degrees Fahrenheit . However, I also find negative effects at cool temperatures. Workers are nearly 17% less productive at temperatures below 60 degrees Fahrenheit than at temperatures in the low eighties. The most likely explanation of this finding is that berry pickers lose finger dexterity at cool temperatures and find it uncomfortable to maintain high levels of productivity. This hypothesis is supported by evidence from the ergonomics literature , and highlights that temperature’s effects on labor productivity depend on the particularities of the relevant production process. To demonstrate the robustness of my findings, I address several threats to my identification strategy. First, I investigate berry pickers’ labor supply on both the intensive and extensive margins. I show that neither temperature nor wages have a statistically significant effect on these measures. Next, I address the fact that there exists a minimum hourly wage rule in the setting I study. This constraint binds for approximately 15.8% of my observations, raising concerns that workers falling below this threshold have an incentive to shirk or “slack off.” I re-estimate my results using only those observations where workers earn more than the minimum wage and see no qualitative change in my findings. Finally, I confront the possibility of adverse selection in my sample by limiting my sample to only the observations from workers who work more than thirty days in a single season. My results highlight the importance of environmental conditions in outdoor industries. Previous studies have shown, and I confirm, that temperature affects labor productivity directly.However, I am the first to demonstrate that temperature also affects labor productivity indirectly by disrupting the economic relationship between wages and worker effort.