Measured stocks of capital are required to characterize the transformation process and to distinguish growth that is due to the accumulation of input factors from that due to changes in factor productivity. Moreover, in order to compare this transformation process among countries, a compatible measure is needed across countries and through time. In general, this has been lacking, especially for panels that include developing countries.<sup>4</sup> By necessity, empirical studies have therefore looked to proxies for agricultural capital, most often measures related to on-farm machines, livestock, and orchards, or have omitted capital entirely.<sup>5</sup> As we argue below, the composition of capital varies as the prevalent technologies change as part of the structural transformation of economies. Consequently, the roles played by on-farm machines or other proxies for a general measure of capital change. In general, machinery capital becomes less relevant as the stock of capital in agriculture grows; this creates problems for studies of agricultural productivity and growth.

Mundlak, Larson, and Butzer (1999) and Coelli and Rao (2005) list a number of early crosscountry studies of agricultural productivity, including important studies by Bhattacharjee (1955), Hayami and Ruttan (1970, 1971), Evenson and Kislev (1975), Nguyen (1979), Mundlak and Hellinghausen (1982), Kawagoe and Hayami (1983, 1985), Antle (1983), and Lau and Yotopoulos (1989). Varied capital measures, based on combinations of farm machinery, tractors, livestock, orchards, and, less frequently, data on irrigation and farm structures, were used. Recent econometric studies are less uniform in their methodology, but many still include capital proxies (most often in the form of livestock and machinery) in estimated production functions; examples include Fulginiti and Perrin (1993, 1998), Block (1994), Craig, Pardey, and Roseboom (1997), Wiebe et al. (2003), Cermeño, Maddala, and Trueblood (2003), Lio and Liu (2008), and Cermeño and Vázquez (2009).

Increasingly, agricultural productivity studies have also drawn on non-parametric or semiparametric approaches. These are related to index number theory, and frequently employ data envelope analysis (DEA) programming methods to estimate either input or output distance

> 4 Ball et al. (2008, 2004) construct panel data sets of agricultural capital stocks for various OECD countries starting in the early 1970s. The Groningen Growth and Development Centre (GGDC) houses the EU KLEMS database on output and input growth at the industry level for 25 European Union member states, as well as the United States and Japan from 1970 to 2007 (www.euklems.net).

> 5 Two-sector models of structural adjustment require comparable measures of capital in and out of agriculture. The lack of a consistent measure has prompted some researchers to exclude direct measures of agricultural capital from their analyses. See, for example, Restuccia, Yang, and Zhu (2008), Bah and Brada (2009), and references therein. In an examination of sectoral differences in TFP, Caselli (2005) used assumptions on factor shares to derive the sectoral (agricultural vs. nonagricultural) capital stocks necessary to compute TFP measures comparable across sectors.

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