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[7] CI > p*[ Y1(x1, x2)*, Y2(x1, x2)*] - pA [Y1(x1, x2)A, Y2(x1, x2)A]
information will not be gathered; hence input level will remain xA . In contrast, for a higher
level of x2, the above inequality will be reversed, thus inefficiency is eliminated.
Management structure: the impact of firm ownership on efficiency has long interested
agricultural economists. In sectors (such as agriculture) where economies of scale are weak and
technology is not overly complicated firms controlled by families may have an advantage over
those where a clear separation between ownership and control exists (see, e.g. Pollak, 1985).
Family-firms, in particular, may monitor resource use more efficiently; furthermore
"speculative" (as opposed to "purely productive") use of land may be less important for firms
where net incomes depend only on farming, as opposed to those where capital gains through
timing of purchase and sale of land may be a primary concern. The linkage between resource
ownership and efficiency, however, is clearly an empirical one: firms owned by outside
investors might be less concerned by risk considerations, tradition, or other constraints on
optimal resource use.
IV. DATA AND EMPIRICAL ANALYSIS
Analysis is made of the “worker” and “allocative” effects of the educational, firm size,
and firm ownership inputs, using production data for the most important agricultural region of
Argentina (provinces of Buenos Aires, Santa Fe and Cordoba). Decision-making units
correspond to partidos ("departamentos” for Cordoba and Santa Fé). A total of 146 crosssection
data points were available. Average values of production, revenue, land use and costs
for each of these were computed using a 20 year time period (1970-1989). Time period 0
corresponds to the 1970-79 period, and time period 1 to the 1980-89 period. Ideally, hypothesis
testing should proceed by fitting a production function to cross-section, time-series data. This
function allows estimation of the impact of human capital and the other variables on the
position of the production surface. Such an econometric exercise, however, requires a high11
quality data set, particularly if a multiple-output production process is to be modeled. Further,
errors due a badly specified technology might well mask inefficiency (or efficiency) levels. As
an alternative, simple measures of worker and allocative efficiency levels can be computed
without recourse to production function estimation. This is the approach to be taken here.
Total Factor Productivity: TFP change was calculated as the product of output change
times the reciprocal of change in input use. Change in total input use was derived as: [aE + (1-
a)T] where E represents change in cash production expenses, T represents change in land use
and a represents an estimate of the share of production expenses in total costs (production
expenses + land rent). A value a = 0.6 was assumed. 3
Returns to Fixed Factors: The extent to which price and technology changes are acted
upon by decision-makers can be gauged by comparing returns to fixed factors in a base period,
with returns in a future time period. Define change in returns as:
8] Dp10 = {pA[Y1(x1, x2), Y2(x1, x2)/H1]}/
{pA[Y1(x1, x2), Y2(x1, x2)/ H0]}
where Ht (t= 0,1) represents planted hectares in period 0 and 1. Change in TFP (expression [1])
as well as in returns to fixed factors (expression [8]) are used as dependent variables for
hypothesis-testing.
Land Input: planted hectares to the 5 principal crops constitutes the land input.
Variable Expenses: data at the "partido" level on variable expenses are not available
from formal surveys. Estimates of production expenses was obtained by using "engineering"
cost estimates reported in agricultural business publications (in particular from the
Agromercado monthly). These publications allow inter-zonal and inter-crop differences in
resource use patterns to be detected. Subjective corrections to data published in 1987 and 1999
were made in order to capture - at least roughly - differences in input use between the 1970's
and the 1980's.
Human Capital: A human capital (HC) proxy was derived by using the quotient:
where S and T represent population having assisted to secondary tertiary education, and Pop is
the population aged 14 and older. Two different HC values are derived . The first (HC1)
corresponds to human capital levels in the rural portion of each partido. This is "farm"
educational level. The second measure (HC2) is average HC of both rural and the urban
population. The rationale for this choice is the fact that "agricultural" decision-making skills
reside in farms as well as in urban areas that are surrounded by farms. Indeed, many farm
managers reside in urban areas; furthermore input and output markets depend crucially on the
level of decision-making skills that exist in "non-farm" businesses. It is expected that these
markets affect efficiency at the farm level. INDEC data of the Censo Nacional de Población y
Vivienda - Serie D Población (1980) was used to derive the HC indexes.
Firm Size: is defined as Land in Farms/Number of Farms. This measure is imperfect, as
it does not take into account differences in potential output due to differences in land quality.
Further, for this and the next variable, data was obtained from the Censo Nacional
Agropecuario (1988). Ideally, data from the late 1970's or early 1980's would have been more
appropriate given that the objective was to compare changes in efficiency levels between the
1970's and the 1980's.
Firm Ownership: separation between ownership and control was defined as the quotient:
[10] Owner = (PP + FP)/Total Land
where PP and FP represents, respectively, land area under personal and family property. The
denominator ("Total Land") includes PP and FP as well as firms under a "corporate" legal
form.
Hypothesis testing is carried out by regressing DTFP10 and Dp10 on human capital (rural
and urban), firm size and ownership variables. Further, dummy variables were used to attempt
to capture regional differences in technology and production potential (land- and weather
13
induced). A total of 5 production regions were defined. 4 These correspond roughly to the
classification of production areas frequently employed in agricultural business publications.
V. RESULTS
Data Description: Graphs 5-7 report variability of independent variables used in
regression models. Human Capital: Educational levels are lower in rural than in urban areas
(Graph 5a). The most frequent interval of the former is 10; of the latter 22 (as mentioned
previously these figures reflect percentage of population having assisted to secondary or
tertiary education).5 Further, human capital levels appears considerably less variable in rural
than in urban settings. Rural areas thus can be characterized by uniformly low educational
levels; in urban settings education is higher but also more variable. The regression model uses
as independent variable both rural as well as average (rural + urban) human capital. Graph 5b
reports variability of this last measure in the sample. As shown, in some 3/4 of observations the
proportion of individual who assisted to secondary or tertiary education ranges from 14 to 22
%.
Firm size is concentrated in the intervals spanning 0 - 600 hectares (Graph 6). Despite
the long-run trend to firm growth that appears pervasive in many agricultural economies, firms
(in 1988) were "medium sized". In 1999 dollars, assuming 100 % land ownership, total land
investments for a 300-hectare "modal" farm was approximately US$ 600.000. Total investment
will of course be larger due to (non-land) capital inputs; however land typically represents
about 80 % of total investment. In summary, the agricultural firm as analyzed here corresponds
(in total investment) to typical "medium sized enterprises" such as small manufacturing or
service firms. Net returns to the owner - operator (assuming a 5 percent return on capital) might
total US$ 30.000 a year, not much higher than that obtained by a supervisory white-collar
worker.
Firm Ownership: Personal or family property of land resources the dominant form of
organization. In 2/3 of the "partidos", this type of ownersip accounts for more than 3/4 of total
land controlled (Graph 7). Thus, "corporate" forms of organization are relatively infrequent.
Regression Results: Appendix 1 reports regression results for models:
[11] DTFP10 = f(HC1, Size, Owner)
[12] Dp10 = f(HC2, Size, Owner)
A one-tailed test (p = 0.10) is used to reject the null hypothesis of no effect of the
independent variables. The following results can be highlighted:
1. A relatively small (20 - 30 percent) of variation in DTFP and Dp can be explained
by variables included in the models. Difficulty in predicting these dimensions of
efficiency - even when including dummy variables for different agronomic areas - is
readily apparent.
2. The hypothesis of no relationship between HC and efficiency is not rejected for both
measures of HC and for both dependent variables. Thus, human capital does not
appear to be a variable explaining TFP productivity differentials (1980's vs 1970's),
or changes in returns to fixed factors in the same time period.
3. Firm size appears - as hypothesized - to have a positive effect on efficiency. t-values
for all models are highly significant. The existence of fixed costs in informationgathering
and technology adoption is therefore likely.
4. "Family" firms appear to be associated with higher TFP. However the evidence on
the impacts of ownership type on allocative efficiency is inconclusive.
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