Capital is important to agricultural production, so policies that improve access to agricultural capital will facilitate growth, if the capital is used efficiently. In this analysis of capital's role in agricultural production, a new construction of data on capital allowed Mundlak, Larson, and Butzer to advance the cross-country study of production functions. The model reveals the relative importance of capital, a finding quite robust to modifications of the model and the disaggregation of capital to its two components. The model is also consistent with the view that lack of physical capital serves as a constraint on agricultural growth. The shift to more productive techniques is associated with a decline in labor, reflecting labor-saving technical changes. This is not news, but it is emphasized here because it comes out of an integral view of the process which distinguishes between the core technology and the changes that took place over time and between countries. Not only is capital important to agricultural production, and agricultural development dependent on the economic environment, but agriculture is more cost-capital-intensive than nonagriculture. Capital is all the more important as a factor of production in that land (also important) varies little over time. The availability of agricultural capital determines whether the gap between available and applied technologies can be closed. Prices have little direct, immediate impact on agricultural growth, beyond their impact through inputs and choice of technology. The legacy of past policies that distorted the relative returns to economic activity is enshrined in current stocks, which may respond slowly to policy reform. The analysis assumes that the production technology is heterogeneous and the implemented technology is endogenous and determined jointly with the level of unconstrained inputs. Thus, a change in the state variables affects both the technology and the inputs, so the production function is not identified. To overcome that problem, changes in productivity are decomposed to three orthogonal components caused by the fundamentally different processes underlying panel data. The statistical framework explains the unstable results observed in production functions derived from panel data. Statistically, the results depend on how the data are projected. Comparisons between units over time or of deviations from unit-means or time-means all describe different processes. This is based on theory but has an intuitive appeal as well. In this case, the spread in productivity among countries is different from the spread in productivity for a country through time. The factors explaining the spread will differ. The modeling approach should explicitly recognize the fact that panel data measure a combination of economic phenomena. [author]
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