revised literature review by dr
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This is the revised draft of the literature review.TRANSCRIPT
Productivity Growth in Philippine Agriculture: A Literature Review
Submitted by
Romeo G. Teruel
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1. Introduction
With the sizable contribution of agriculture to the Philippine economy, the
country’s economic future will continue to be highly affected by agriculture’s
performance. In recent years, the agricultural sector has accounted for
approximately 20 per cent of the Gross Domestic Product (GDP) and about 14
per cent of the country’s export earnings. In addition, it employs almost half of the
country’s labor force. Thus, the dependence of the majority of the rural poor on
the agriculture as the major source of livelihood remains high.
Recent discussions have expressed cause of concern regarding the future
role of Philippine agriculture in the process of economic development.
Agricultural production stagnated in the 1980s, growing at an average of 1 per
cent annually. From 1990 to 1995, the average annual growth rate increased to
1.4 per cent; by 1996-2000, however, this declined to 0.60 per cent (David,
1996a; Development Indicators for the Philippine Agriculture, 2002).
Furthermore, the past and present agricultural scenarios seem to suggest that
Philippine agriculture is lagging behind other agricultural economies in terms of
comparative competitiveness. The Philippines has been transformed into a net
agricultural importing country over the last decade. This trade scenario is the
complete opposite compared to the scenarios observed in other neighboring
countries, such as Indonesia, Malaysia and Thailand, which have consistently
posted an increasing agricultural surplus since the 1970s. It has also been
shown that the dismal growth of the agriculture correlates with the overall growth
of the economy. Several studies have shown that the decline in agricultural
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growth can be attributed to the continued deceleration of productivity (Mundlak,
2004 and Teruel and Kuroda, 2004, 2005).
The objectives of this paper are as follows:
1. To contribute a literature review on agricultural productivity analysis,
providing a critical assessment of the state-of-the-art at both
international and national levels;
2. To describe the standard and emerging empirical techniques used in
agricultural productivity studies, internationally and in the Philippines,
including econometric analysis and growth accounting;
3. To identify the similarities, relationships, and differences between the
techniques within a coherent framework;
4. To assess what is known about agricultural productivity growth in the
Philippines, critical gaps in our knowledge and data on the nature,
sources, and causes of productivity growth; and
5. To propose a theoretical framework and empirical technique for use in
the aggregate agriculture sector analysis of the PGPA
The review is composed of two stages: 1) theoretical and empirical. The
first stage aims to examine the five theoretical approaches to measuring
productivity growth such as: 1) the growth accounting, 2) index number, 3)
econometrics, 4) the distance function based-Malmqvist approach and 5) the
stochastic frontier approach. The second stage intends to review the empirical
studies on productivity in Philippine agriculture that employ these approaches.
The availability of data on output and input is essential for the accurate
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measurement of productivity; hence, this paper also seeks to review the
availability of agricultural data by looking at the different data sets assembled in
previous studies, the data gaps in actual empirical productivity measurement,
and other important data issues. This paper ends with a synthesis,
recommendation and conclusion.
2. The Concept of Productivity
This section discusses the concepts of and the issues surrounding
productivity. It also proposes to differentiate partial productivity from total factor
productivity (TFP).
The basic definition of productivity is expressed as a quantitative
relationship between output and input (Antle and Capalbo, 1988). It is also
defined as the ratio of some measure of output to some measure of input use or
simply an arithmetic ratio between the amount produced and the amount of any
resources used in the process of production. This definition of productivity can be
further simplified as the output per unit input or the efficiency with which
resources are utilized (Samuelson and Nordhaus, 1995).
As a concept, productivity can be a partial or total measure. The partial
measure of productivity or partial factor productivity (PFP) relates output to any
input implying that there will be as many definitions of productivity as inputs used
in production. In this case, productivity is the amount of output per unit of a
particular input or equally known as the average product. Commonly used partial
measures are: yield (output per unit of land), labor productivity (output per
economically active person or per agricultural per-hour) and capital productivity.
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Yield is usually used to assess the success of a particular production technology.
Labor productivity is used as an indicator to assess rural welfare or standard of
living since this captures the ability to acquire income through agricultural
production (Block, 1995).
PFP can sometimes be misleading. Changes in PFP can hardly be
explained since there is no clear indicator on why this measure of productivity
changes. An improvement for example in labor productivity can either be
attributed to the increased use of fertilizer or tractors. To account for at least
some of these problems, one can use the total measure of productivity or the
concept of total factor productivity (TFP). TFP relates the output produced with
an index of composite inputs; meaning the sum of all the inputs used in the
production process which may include land, labor, physical capital, livestock,
fertilizers and pesticides. TFP is computed as the ratio of an index of agricultural
output to an index of agricultural inputs. The index of agricultural output
(agricultural input) is a value-weighted sum of all agricultural production
components (conventional agricultural inputs).
Growth in TFP is termed in the literature as the Solow residual and it is a
measure of technological progress brought about by the changes in agricultural
research and development, extension services, human capital development,
infrastructure, government policies and environmental degradation. The change
in TFP may also be attributed to unmeasured inputs or imperfectly measured
inputs.
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3. Review of Methods
There are several approaches or techniques that can be used to measure
the change in output, to calculate the relative contribution of the different inputs
used in production to output growth and to identify the Solow residual or output
growth not due to increases in inputs. These major approaches are: 1) the
growth accounting, 2) index number, 3) econometrics and 4) the distance
function-based Malmquist approach and 5) the stochastic frontier approach.
Each of this approach has different data requirements, is suited to address
different questions, and has its relative strengths and weaknesses.
3.1 Growth Accounting Approach
The growth accounting approach involves compiling detailed accounts of
inputs and outputs, and aggregating these into input and output indices in order
to calculate the TFP index (Diewer, 1976, 1980). It decomposes the output
growth into two components: 1) the growth in different inputs like labor and
capital and 2) growth in TFP. This approach requires the specification of a
production function, which describes the technical relationship between the levels
of output that can be produced using certain amount of inputs.
This production function can be expressed as:
where Yt is the output at time t, At represents the technology or TFP at time t, Kt
is the capital stock at time t, and L is the amount of labor input at time t. Given
this production function, the use of growth accounting approach requires the
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1
following assumptions, namely: 1) the technology, as represented by At, is
separable as shown in Eq. 1, 2) the production function exhibits constant returns
to scale (CRS), 3) the producers are efficient and they attempt to maximize profit,
and 4) the markets are perfectly competitive.
Differentiating (1) with respect to t gives
where the dots indicate a first partial derivative with respect to time. Dividing Eq.
2 by Q gives:
Given the assumptions, the elasticity of output with respect to capital wk and the
elasticity of output with respect to labor wL can be written as:
and therefore, Eq. 3 can be written as:
Solving for , the growth rate of TFP is
The TFP growth can be interpreted as the residual share of output growth
after accounting for the changes in the production inputs. One of the
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disadvantages of using the growth accounting approach is that it imposes several
strong assumptions like the Hicks-neutrality of technological change, constant
returns to scale and long-run competitive equilibrium.
3.2 Index Number Approach
An alternative way to compute for TFP is through the use of the Index
Number Theory. This theory describes how to derive a single index for the
quantity of different outputs or goods produced and of inputs used over time
including their corresponding prices. The index number approach involves
dividing an output quantity index by an input quantity index resulting to a
productivity index. Therefore,
where At is TFP, Yt is an index of output quantities, Xt is an index of input
quantities and subscript t denotes the time period. After obtaining At, the
calculation of TFP growth rate is straightforward.
The difficulty associated with using the index number approach is in the
determination of the type of index to use and in gathering the price and quantity
data necessary to construct them. There are several index procedures that can
be used to measure productivity and these include the Laspeyres, Paasche,
Fisher and the Törnqvist index procedures. What follows is the formulation of
different output quantity indexes using the different index approaches. Input
quantity indexes can be similarly constructed using the data on input quantities
and prices.
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Given the vectors of prices and quantities
for the k different outputs produced in an economy at time t =
0,1,…….T, a Laspeyres index is computed as
where is quantity i’s nominal output share. Eq. 9 shows that the
Laspeyres index is a nominal share-weighted sum of quantities ratios.
The Paasche index is calculated by using period 0 prices instead of period
1 prices. This index formulation is obtained by using the following equation:
The Fisher index, on the other hand, is computed by getting the geometric
average of the Laspeyres and Paasche indexes. Finally, the Törnqvist index is
defined as
One can either use the economic or the axiomatic approach to decide on
what index formulation to use in constructing quantity indices for output and
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input. The economic approach selects an index number formulation on the basis
of an assumed underlying production function given the price taking, profit
maximizing behavior of producers (Diewert and Lawrence, 1999). The
Laspeyres index procedure is believed to be exact for, or at least imply, a linear
production function in which all inputs are perfect substitutes in the process.
Similarly, the Törnqvist index, which is a discrete approximation to the more
general Divisia index, implies a homogenous translog production function. On
the other hand, a geometric index like the Fisher index exacts the Cobb-Douglas
production function.
The axiomatic approach requires a comparison of the properties of the
different index number formulations with a number of desirable mathematical
properties. The index that satisfies the most tests is the “preferred” index
formulation. The axioms or desirable properties used for the test are as follows:
1. Constant quantities test: If quantities are the same in two periods, then
the output index should be the same in both periods irrespective of the
price of the goods in both periods;
2. Constant basket test: If prices are constant over two periods, then the
level of output in period 1 compared to period 0 is equal to the value of
output in period 1 divided by the value of output in period 0;
3. Proportional increase in quantity test: If all quantities in period t are
multiplied by a common factor, λ, then the quantity index in period t
compared to period 0 should be increased by λ also; and
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4. Time reversal test: If the price and quantities in period 0 and t are
interchanged, then the resulting output index should be the reciprocal of
the original index.
Diewert and Lawrence (1999) noted that among the index number
formulations, only the Fisher index passes all the axioms or desirable properties.
Both the Laspeyres and Paasche indexes are found to be inconsistent with the
time reversal test, while the Törnqvist index does not satisfy the constant basket
test. They also noted that using a more extensive list of axiomatic tests, the
Fisher index formulation continues to satisfy more tests than the other index
formulations.
Like the growth accounting approach, the construction of the output and
input quantity indexes using the different index procedures requires restrictive
assumptions such as the neutrality of technical change, constant returns to scale,
competitive markets and the separability of the underlying transformation function
in outputs and inputs. Another disadvantage is that the statistical methods
cannot be used to evaluate their reliability because index numbers are not
statistically generated. In addition, index numbers are not particularly informative
in identifying sources of growth. This approach however can be easily
implemented regardless of the number of observations.
3.3 The Econometric Approach
The econometric approach is based on an econometric estimation of the
production technology. Thus, this approach requires the use of econometric
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methodology; necessitates the specification of a function representing the
technology and the estimation of such function, its derivatives or both to
determine the value of the different parameters necessary for productivity
estimation.
The function can either be a primal function like the transformation
function or the production function or a dual function like the cost or profit
function. The estimation using the primal function involves the derivation of a
factor demand and supply function based on the necessary conditions of
optimization given a production function and the assumption of profit
maximization or cost minimization. The estimation using dual functions on the
other hand involves the derivation of a system of input demand and output supply
by appealing to the theory of duality. The fundamental principle of duality in
production is that the cost or profit function summarizes the economically
relevant characteristics of a technology. Thus, a firm’s technology as
represented by its production possibilities set or production function may also be
characterized by its cost or profit functions, provided that these obey certain
regularity conditions. To better understand the idea of this theory, the duality
between a cost function and production possibilities set or production function will
be discussed in turn1.
3.3.1 Duality Theory
1Between the two dual functions, cost function is frequently used in productivity estimation. Antle and Capalbo (1988) pointed out that TFP estimation using profit function is not straightforward. The link between TFP and cost function will be discussed in the latter part of this subsection.
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A production function is often assumed and used as a starting point in
many researches on production relationship. The analytical framework of
production functions can be extended with the inclusion of the duality theory
under the assumption that firm chooses input quantities in order to minimize the
cost of their production process, given the prices of these inputs. The duality
between production and cost functions can be explained by following Diewert
(1978): consider a production function with a single output given by Y = f(X)
where Y is the maximum output which can be produced by a firm given a k-
dimensional vector of inputs X at a certain point in time. The function f can be
said to describe the firm’s technology. On the other hand, if this firm has
minimum total cost of producing the level of output Y given the vector of input
prices P defined by C(Y,P), then its minimum C clearly depends on the
underlying production function f. The duality theory establishes the duality of
cost as well as production functions indicating that either of these functions, C or
f, can adequately describe the economically relevant characteristics of the same
underlying technology given certain restrictions on them.
More formally, the dual relationship between cost and production (or
transformation) functions implies that the minimum total cost of producing Y
given the production possibility set T = (X,Y) and a vector of positive input prices
P that correspond to X, that is, C(Y,P), can be constructed from T with C having
the following properties2:
2 The production possibilities set T has the following properties: (i) the set of producible outputs Y* is nonempty; (ii) for each Y in Y*, the input requirement set X(Y) is closed and for a nonzero output not contained the zero input vector; (iii) there is free disposal of inputs, that is, if an input vector X0 can produce output Y then a second input vector X1 that is at least as large as X0 in each component can also produce Y; (iv) the input requirement sets are strictly convex from below, that is, two input vectors X0 and X1 are in X(Y), then for a weighted combination of X0 and
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(a) C(Y,P) is a negative real valued function defined for all finite Y ≥ 0, P ≥
0k.
(b) C(Y,P) is nondecreasing in input prices P.
(c) C(Y,P) is nondecreasing in Y and tends to approach ∞ for every P ≥ 0k.
(d) C(Y,P) is positively linearly homogenous and quasi-concave in the
components P for every Y > 0.
Similarly, given C with properties (a) to (d) above, the production possibilities set
T with properties (i) to (iv) (please refer to footnote 2) can also be constructed
from C.3
By Shephard’s lemma, the total cost function C with properties (a) to (d)
above is related to the cost minimizing demand function for input X through its
first partial derivative with respect to the input price P. Suppose the first partial
derivative of C with respect to P is given by CP(Y, P). If C is continuously
differentiable in P, then Cp equals the total cost minimizing demand for input X, at
(Y,P). Moreover, if there is a unique cost-minimizing demand for input X at (Y,
P), then CP exists. The application of Shephard’s lemma produces a system of
equation representing the output supply and input demand functions which is
used to estimate parameters necessary for productivity measurement.
3.3.2 Primal and Dual Rate of Technological Change
In the econometric approach to productivity measurement, technological
change, which corresponds to the shifts of the production function, is
X1, for instance, X2 = λX1+ (1-λ)X0 where the scalar λ is 0<λ<1, there is an input vector X3 in X(Y) such that X2 is at least as large as X3 in every component.3 Please see Diewert (1971, 1978) and McFadden (1978) for the proofs of these duality relations between cost functions and production possibility sets.
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synonymous with productivity change under the assumption that production is
efficient. Technological change refers to changes in production process brought
about by the application of scientific knowledge. Technological change occurs
when there is an increase in output per unit of input due to the use of improved
production methods resulting in an increase of efficiency in the use of resources,
changes in input quality and the introduction of new processes and new inputs
(Antle and Capalbo, 1988). This production function shifts or the technological
change can actually be investigated using the duality relation by estimating the
dual cost (or profit functions). What follows are discussions on an econometric-
based productivity measurement drawn mainly from Antle and Capalbo (1988).
For the productivity estimation, assume that the aggregate production
function is given by
where Y and X are the aggregate output and the aggregate input vector,
respectively, and t denotes the state of technology. The prime rate of
technological change under the assumption of efficient production is given by
where Fi is the marginal product of Xi. Under a competitive market where price
equals marginal cost and inputs are paid in terms of their value of their marginal
products, Eq.12 can be written as
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14
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where is the factor cost share and the term is the
elasticity of cost with respect to output indicating returns to scale. The primal
rate of technological change given by Eq. 14 can be defined, therefore, as the
rate of change in output minus the scale-adjusted index of the rate of change in
input.
In the case of the dual cost function4, the dual rate of technological change
can be derived by differentiating totally the cost function C = C(Y, W, t) with
respect to time and by invoking the Shepard’s lemma. The dual rate of
technological change is algebraically expressed as
Eq. 15 shows that the dual rate is the sum of the index of the rate of change in
factor prices and the scale effects less the rate of change of total cost. The
relationship of the primal and dual rate of technological change can be shown by
the total differentiation of total cost with respect to time and by using
Eq.15. The relationship is indicated by the following equation:
Thus, the primal and dual rates of technological change are equal if the
elasticity of cost with respect to output, , is equal to 1. This means
4 The advantage of this so-called dual approach over the primal approach (use of production function) is that the derivation of the output supply and input demand functions is much simpler and easier. With this approach, it is not necessary to go through the sometimes bothersome algebra that is involved when solving the first order conditions as required in primal approach; one simply has to differentiate the cost function with respect to prices. Also, the cost function in general has factor prices as arguments or as independent variables rather than factor inputs as in the case of production function. The cost function, therefore, allows the use of input prices, which are actually exogenous to the firm instead of endogenous variables such as input quantities (Binswanger, 1974).
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that the primal and dual rates are equal if and only if the technology can be
described by constant returns to scale.
The derivation of the primal and dual rates of technological change can be
generalized to multiproduct technology represented by the following
transformation function
where and . Hulten (1978) defines the
primal rate of technological change in the case of multiproduct technology as
where R1 is defined as the revenue share of Y1 in total revenue. Here, the rate
of technological change is measured in terms of Y1. Using the multiproduct cost
function, , where Y and W are vectors of outputs and factor prices,
respectively, the dual rate of multiproduct technological change can be derived
by getting the time derivative of the transformation function and by using the first
order conditions for multiproduct profit maximization and the equilibrium
condition, The dual rate of multiproduct technological change can
be expressed as
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19
Eq. 19 shows that if the multiproduct technology exhibits constant returns
to scale, , the primal and dual measures of the rate of
technological change are equal.
Following Capalbo (1988), the relationship between the definition of
productivity as indicated by , which is the measure of technical change,
and the conventional definition of TFP = , which is the growth in outputs not
being accounted for by the growth in inputs, is shown by the following equation:
Under constant returns to scale, the measured growth
rate of TFP is equal to the rate of technical change. As previously mentioned, for
the single output case, the latter measures the marginal shift in the production
function. On the other hand, if the production structure is characterized by
increasing or decreasing returns to scale, then the TFP growth rate captures both
the technical change as well as the scale effect.
Similarly, it is also possible to show the link between the dual definition of
productivity and TFP. Suppose the aggregate cost function is
then by totally differentiating it with respect to time, invoking the Shephard’s
lemma, dividing everything by C and by simplifying, the expression for the
proportionate shift in the cost function is given by
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20
21
where . Eq. 22 indicates that is equal to the change in costs minus
the change in aggregate inputs and the scale effect. Following Ohta (1974), this
equation can be further simplified by the total differentiation of the cost equation
with respect to time, yielding
By substitution, Eq. 22 becomes
Combining Eq. 24 with the conventional definition of TFP, the dual cost function-
based definition of productivity is given as
Using Eq. 14 and Eq. 24, one can show that .
In the case of multiple outputs, the relationship between the shift in the cost
function and the growth in TFP is indicated by the following equation:5
.
5 See Capalbo (1988) for the detailed derivation of the link between the primal and dual definition of productivity and the conventional TFP.
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3.3.3 The Translog Functional Form
The use of the econometric approach in measuring productivity requires
the use of functional form for econometric estimation. The functional form,
however, should possess certain desirable properties, such as flexibility,
consistency and linearity. A functional form is said to be flexible if it can provide
a second order approximation to any arbitrary, twice continuously differentiable
function (cost, profit or production) having the appropriate theoretical properties.
The functional form should also be consistent, i.e. it must be consistent with the
appropriate theoretical properties that the function must have. In the case of the
cost function, these theoretical properties, as previously discussed, require
compliance for linear homogeneity in factor prices, concavity in factor prices and
monotonicity. Moreover, a functional form is desirable if the unknown
parameters appear in a linear fashion in the function as well as in the output
supply and input demand functions. A functional form that is linear in unknown
parameters allows the application of linear regression techniques on the
estimating equations of a particular function.
There are several functional forms that can be used for estimation: 1) the
transcendental logarithmic (translog), 2) normalized quadratic and, 3) the
generalized Leontief functional forms. Among these forms, the translog is
commonly used in productivity estimation using the econometric approach and it
also satisfies the flexibility, consistency and linearity properties. (Christensen,
Jorgenson and Lau, 1973).
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In a manner similar to Cobb-Douglas, changes in TFP can be estimated
using the translog production function specified as
In this function, a time trend denoted by t is included, indicating that the
technological change is not Hicks-neutral. Hicks neutrality can only be assumed
if parametric restriction is imposed, that is .
Applying Eq. 20 to Eq. 27, under the assumption of non-neutrality of
technological change, the is computed as
Assuming constant returns to scale, , Eq. 28 can be written as
On the other hand, if Hicks neutrality is assumed then
As shown above, the cost function-based TFP can also be derived by appealing
to the theory of duality. Specifically, a translog cost function can be used and
specified as
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31
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Applying Eq. 25 to the translog cost function given by Eq. 31 and assuming
constant returns to scale, non-neutrality of technological change and
homotheticity, the cost function-based definition of productivity is given by
The econometric approach has the advantage of being statistical, thus,
allowing inferential statistics or permitting hypothesis testing and calculation of
confidence intervals in order to test the reliability of the model estimated. This
approach specifically allows determining the contribution of each input to
aggregate output. Furthermore, if the flexible functional form is used, then the
use of the econometric approach would also mean the imposition of fewer
restrictive assumptions about technology as opposed to the growth accounting
and the index number approaches. The major disadvantage of the econometric
approach, however, is that it is more demanding in terms of data requirement
than the other approaches to productivity measurement. Oftentimes, the
constraint on data availability may make it difficult to implement the econometric
approach.
3.4 A Distance Function-based Malmquist Approach
The Malmquist approach is a non-parametric approach used to measure
productivity change. Unlike the Tornqvist-Theil index, the Malmquist productivity
index does not presume that production is always efficient. Hence, the
Malmquist index of productivity can be broken down into two components: 1) the
changes in efficiency (firms getting closer to the frontier) and 2) the changes in
technology (shifts in the frontier itself).
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In this approach, the productivity index is defined using two distance
functions: 1) the input distance function or 2) the output distance function.
These functions allow one to describe a multi-input and multi-output production
technology without assuming or specifying the cost-minimizing or profit-
maximizing behavior of the producers. The Malmquist productivity index can be
derived as follows:
Assume a production technology that can be described using the output
set, P(x). This output set P(x) represents the set of all output and input vectors
that are feasible, meaning that output vectors y can be produced using input
vectors x. For the sake of convenience, assuming that there is only one output
and one input, then the output set can be written as
P(x) = {y: x can produce y}
Given this output set, the output distance can be defined as
.
If y is an element of the feasible production set, P(x), then the distance
function will take a value less than one or equal to one. In particular, if
the value of is unity, then y is located on the outer boundary of the
feasible production set, otherwise, it is greater than one and located outside the
set.
This is the Malmquist productivity index defined by Caves, et al. (1982a
and b) with reference to the base period that is:
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34
35
In particular, they defined their productivity index as the ratio of the two
output distance functions both using the technology at time s (the base period).
The numerator is the output distance function at time t based on technology of
period s. On the other hand, the denominator is the output distance function at
time s based on technology of period s. Thus, the Malmquist productivity index
measures the distance between two data points of a particular unit (e.g. region or
country in two adjacent time period) by calculating the distances of each data
point relative to a common technology.
Alternatively, instead of using period s technology as the reference group,
it is also possible to develop the two output distance functions based on period
t‘s technology. The Malmquist productivity index in this case is given by
To avoid arbitrariness in the choice of benchmark technology, Fare et al. (1994)
suggested that the Malmquist productivity index computed, based on output
orientation, should be the geometric mean of the two indexes given by Eq. 35
and Eq. 36. That is
The value of M0 will indicate whether there are positive or negative changes in
productivity. If M0 is greater than one, this indicates positive productivity growth
from period s to period t, whereas if the value is less than one, then this shows a
decline in productivity.
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36
37
An equivalent way to writing this productivity index is
Fare et al (1994) gave the following interpretation to the two terms on the right-
hand side of Eq. 38:
Efficiency change =
Technical change = .
From Eq. 38, the Malmquist productivity index is just the product of the change in
relative efficiency that occurred from period s to period t and the change in
technology from period s to t.
One can estimate the distance functions necessary in the computation of
the productivity index based on the Malmquist approach using the Data
Envelopment Analysis (DEA).
In their seminal paper, Charnes, et al.(1978) described DEA as a
mathematical programming model applied to observational data that provides a
new way of obtaining empirical estimates of relations such as production
functions and/or efficient production possibility surfaces — the cornerstones of
modern economics. DEA is, therefore, a non-parametric analysis. It is data-
oriented and does not require the specification of any particular functional form to
describe the efficient frontier or envelopment surface.
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DEA is used to evaluate the performance of a homogenous set of peer
entities called “Decision Making Units” (DMUs). Under the DEA context, these
DMUs are compared against each other because they can individually identify
and vary their inputs and outputs. In this case, comparison is relative, meaning
all DMUs are compared with best performing DMUs. DEA is a methodology that
does not use central tendencies. Instead of fitting a regression plane through the
center of the data as in statistical regression, one floats a piecewise linear
surface to rest on top of the observations. The distance between the observed
data point and the frontier measures the relative technical efficiency of each
DMU. DEA is deterministic in nature and this approach does not differentiate
technical inefficiency from statistical noise effects.
DEA can either be input-oriented or output-oriented. In the input-oriented
DEA, the frontier is defined by searching for the maximum possible reduction in
input usage, with output held constant. While, in the output-oriented DEA, it
seeks the maximum proportional increase in output production, with input levels
held fixed. The two measures will give the same technical efficiency scores
under the assumption of constant returns to scale (CRS) technology, but will
register different scores when variable returns to scale are assumed.
The required distance measures necessary in the computation of output
oriented Malmquist productivity index can be calculated by using DEA-like linear
programs (Färe et al, 1994). A brief methodological explanation mainly taken
from Coelli and Rao (2003) follows.
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In the single output and input case, the basis for the inefficiency
measurement is usually the productivity δ that can be expressed as
.
If some DMUs are compared using the δ, then the one with a bigger δ is
considered a better performer or more efficient because less input is used for a
constant amount of output. The productivity δ is a relative measure because its
interpretation is facilitated by making a comparison among the different DMUs in
terms of efficiency.
Typically, DMUs have multiple inputs and outputs
that have to be weighted and can be summed up. Thus δ
is expressed as
.
The numerator and denominator describe the sum of the weighted output and the
sum of the weighted inputs, respectively. In the case of the output-oriented
approach, the objective is to minimize the , the reciprocal of the efficiency
score, by increasing proportionally the amount of outputs given the level of
inputs. The reciprocal of the efficiency score is just the ratio between the
weighted sum of inputs and the weighted sum of outputs.
Suppose there are n DMUs producing z outputs using m inputs. Then,
under the constant returns to scale assumption, the reciprocal of the relative
27
41
42
efficiency score of a hypothetical DMU p can be derived by solving the following
CCR model proposed by Charnes, Coopers and Rhodes (1978):
The fractional model above can be converted to a linear program by
setting the denominator equal to a constant usually unity. The resulting model is
given by
Every DMU has to choose input and output weights that minimize the , the
reciprocal of its efficiency score. Thus, the above model has to be run n times in
order to calculate the reciprocal of the efficiency score of all the DMUs.
Generally, a DMU is efficient when the reciprocal of its efficiency score is less 1,
and it is inefficient when the score is greater than 1.
However, the following dual form is the one usually calculated:
28
43
44
where λ is the dual variable or dual multiplier and the variable θ is the factor by
which DMU p’s output should be increased in order to achieve efficiency. A value
of one indicates that DMU p is efficient relative to other DMUs considered, while
a value higher than one indicates relative inefficiency. The ratio coincides
with the efficiency score δ which varies between zero to one.
For pth DMU, four distance functions have to be calculated between
periods s and t indicating solving four linear programming (LP) problems.
Following Färe et al (1994), under the assumption of constant returns to scale
(CRS) technology, the pth DMU requires the following LPs:
29
45
46
The change in the technical efficiency can be further decomposed into two
components: 1) the change in pure efficiency and 2) the change in scale
efficiency. This decomposition can be done by using the variable returns to scale
(VRS) version of the above model. This version was introduced by Banker,
Charnes and Cooper (1984) and is denoted as the BCC model. This BCC model
has additional convexity constraint:
30
47
48
49
50
This constraint allows capturing the returns to scale characteristics. The BCC
model estimates the reciprocal of the pure technical efficiency, the overall
technical efficiency estimated by the CCR model, net of the scale efficiency. The
scale efficiency measures the capability of the DMU to fully exploit production
possibilities and it is affected by external factors such as credit constraints,
market demand and the likes. It can be obtained by calculating the ratio between
the overall technical efficiency and the pure technical efficiency. If the values of
the pure technical efficiency and the scale efficiency are less than one, then the
DMUs are inefficient, but if the values are equal to one, then the units are
efficient.
The change in the pure efficiency is, therefore, whereas the change
in the scale of efficiency can be derived as .
The use of BCC model requires calculating two additional distance functions by
solving the following two additional linear programming problems:
31
51
DEA offers some benefits but also has certain limitations that have to be
kept in mind when using it. DEA is able to handle multiple inputs and outputs
cases and as mentioned earlier, it does not require a functional form that relates
inputs and outputs nor any specific behavioral assumptions of the firms/unit
under consideration often expressed as cost minimization, or profit or revenue
maximization. It can also handle inputs and outputs without knowing their prices
or weights.
With regards to its limitation, DEA can only calculate the relative efficiency
measures and as a non-parametric technique, statistical hypothesis tests are
quite difficult (Charnes, A., et al., 1994, Schmid, F. A., 1994, Anderson, T. 1996,
Hamburg, C., 2000). In DEA, it is also possible that some of the inefficient DMUs
are in fact better overall performers than certain efficient ones. This is because
of the unrestricted weight flexibility problem in DEA. Thus, a DMU can achieve a
high relative efficiency score by being involved in an unreasonable scheme.
Such DMUs heavily weigh few favorable measures and completely ignore other
inputs and outputs. These DMUs can be considered as niche members and are
not good overall performers.
32
52
3.5 Stochastic Frontier Approach
In recent years, the measurement of technical efficiency with the use of
the Stochastic Frontier Approach (SFA) has become a common approach. Like
in the DEA-Malmquist productivity index approach, the level of technical
efficiency of the firm in the SFA also refers to its ability to transform inputs into
outputs relative to a sample of similar firm. A firm is deemed efficient if it can
potentially increase its output level without reducing its input level. This potential
is dependent on the productive capabilities of comparable firms in the sector and
is represented by a production frontier which displays the boundary or highest
possible output levels for all input levels (Kumbhakar and Lovell, 2000 and Coelli
et al., 2005). As a result, SFA differs with the other productivity measurement
techniques but is similar with the Malmquist productivity index approach because
it uses a frontier approach capable of capturing the two components of
productivity: the efficiency change and the technical change. Unlike the DEA-
based approach, however, the SFA is parametric in nature because it also
depends on the choice of the estimation method, particularly on the specification
of the functional form for technology and the choice of distribution for the
inefficiency error term.
The choice of functional form appears to be arbitrary, but the Cobb-
Douglas (C-D) framework is generally adopted. The C-D functional form has
been used by many researchers because of its parsimony and simplicity. But this
simplicity comes with a cost due to strong restrictions imposed such as the
33
unitary elasticities of substitution. As a result, a number of alternative flexible
functional forms to C-D framework have been proposed in the literature, but the
transcendental logarithmic form (commonly known as translog form) is by far the
most popular despite the observation that the dominance of one functional form
over the other depends on the data set (Kumbhabar and Lovell, 2000).
The many studies on efficiency measurement started from the seminal
papers of Aigner, Lovell and Schmidt (1977) and Meeusen and Van den Broeck.
The general stochastic frontier production function is expressed as
where i = l,……N indicates the units being studied, Yi is the output, Xi are factors
of production and b is a vector of unknown parameters. The term, vi - ui, is the
composed error term, where vi and ui capture the statistical noise and technical
inefficiency in production, respectively.
There are two models under the parametric SFA: the deterministic and
stochastic frontier models. The deterministic model involves a process of
enveloping all observations and identifying the distance between the observed
production and the maximum production defined by the frontier and the available
technology. This distance measures the technical inefficiency, hence, in the
deterministic approach, will be equal to zero.
There are several econometric techniques that can be employed to
estimate the deterministic frontier model. One can use the corrected ordinary
least square method where the parameters of the model, excluding the intercept
term, can be estimated consistently by using the ordinary least squares. This
34
53
method is considered robust even in the case of non-normality (Richmond,
1974). The consistent estimator for the intercept term can be obtained if the
estimated intercept term is corrected by shifting it upward until no residual is
positive and at least one is zero.
Assume the following model:
The corrected parameter estimates are given by
then the individual efficiency can be computed as
The stochastic frontier model differs from the deterministic model because
the former can capture the effects of exogenous shocks not under the control of
the units being investigated. In the stochastic model, other errors related to the
data measurement are also being taken into account. The error capturing the
statistical noise in Eq. (53) is assumed to be identical, independent and
identically distributed. With respect to the one-sided (inefficiency) error the
distributional assumption is a requirement. The commonly assumed distributions
35
54
55
56
in the literature are: the half-normal, truncated from below at zero and the
exponential distribution. If the two error terms are assumed to be independent of
each other and of the input variables, and assuming a particular distribution, then
the likelihood function can be defined and the maximum likelihood estimates can
be determined.
From the estimation, one can obtain the fitted value for the composed
error term . These two error terms, therefore, have to be separated in order
to facilitate the measurement of technical efficiency. Jondrow, Lovell, Materov
and Shmidt (1982) separated the ramdomness or statistical noise from the
technical efficiency by developing an explicit formula for the expected value of u
conditional on the composed error term in the half-normal and
exponential cases.
Half-normal case:
where Ø(۰) is the density of the standard normal distribution and Ф(۰) the
cumulative density function.
Exponential case:
where
36
57
58
Truncated case:
Following Greene (1993), the conditional technical efficiencies for the
truncated model are obtained by replacing in the expression for the half-
normal case, with
Finally, individual (conditioned) technical efficiency scores can be
expressed as
The measurement of technical efficiency has been extended to the panel
data and this approach captures the behavior of the producer as it evolves
across time. There are several benefits that can be derived from the use of
panel data: there will be more data variability, collinearity is less of a problem and
there will be more degrees of freedom necessary for econometric estimation
(Baltagi, 1995).
For the general panel data specification, the generic form of stochastic
frontier production function given by Eq. (53) can be extended by adding the
subscript t to the output, inputs and the error term. The stochastic frontier
production function can then be expressed as:
where yit is the output of the ith unit (i = 1,2,. . . . . ,N) in period t (t =1,2,. . . .,T);
f(.) is the production technology; xit is a vector of j inputs; t is the time trend
variable; uit is a non-negative random variable and output-oriented technical
37
59
60
61
inefficiency term; and vit is assumed to be an iid N(0, ) random variable,
independently distributed of the uit.
The technical efficiency (uit) in Eq. (61) is assumed to be invariant through
time. This time-invariant efficiency assumption is only plausible with very short
panels, but highly unlikely if the time-dimension of the panel data spans over a
longer period of time. It can be argued that a firm’s efficiency or technology is
less likely to remain constant over an extended period of time. This issue on
technical efficiency (uit) has been addressed in the literature and several
specifications have been proposed to make the technical inefficiency term uit time
varying. Kumbhakar (1990) and Battese and Coelli (1992), respectively,
proposed the following time-varying efficiency specifications
where t is time and α, β, and η are the parameters to be estimated. The
shortcoming of these two specifications is the imposition of the same temporal
pattern of efficiency across time making the efficiency rankings of the firms
invariant through time. Cuesta (2000) has addressed this problem by proposing
an efficiency specification following the firm-specific pattern as indicated by
where ξi are firm-specific parameters responsive to different patterns of temporal
variation among firms.
Battese and Coelli (1995) have further extended the approach of
Kumbhakar, Ghosh and McGuckin (1991) to panel data by specifying the
38
62.a
63
64
62.b
technical efficiency (uit) as a function of a set of explanatory variables zit, and an
unknown vector of coefficients δ:
They pointed out that the random wit is defined by the truncation of the normal
distribution with zero mean and variance σ2, such that the point of truncation is -
zitδ, that is, wit ≥ -zitδ. As a result, uit is obtained by truncation at zero of the
normal distribution with mean zitδ and variance σ2. The normal assumption that
the uits and vits independently distributed for all i =1,2,……N and t = 1,2, . . .,T is
obviously a simplifying but restrictive condition.
The measure for technical efficiency uit is the proportion by which the
actual output yit falls short of the maximum possible output which is considered
the frontier output f(x,t). Therefore technical efficiency (TE) can be defined by:
In addition to the measure of technical efficicency uit, the productivity
change interpreted as the exogenous technical change can also be measured
using the time trend variable appended in Eq. (61). It is specifically calculated by
taking the log derivative of the stochastic frontier production function with respect
to time (Kumbhakar, 2000). That is, technical change (TC) is defined as:
Since the technical efficiency is time-varying, the overall productivity
change is not only dependent on the technical change but also on the change in
39
65
66
technical efficiency (TEit). That is, given the input level, the productivity change is
given by
where
TCit is positive if the exogenous technical change shifts the production frontier
upward given inputs. TECit is negative if technical efficiency (TEit) declines over
time or positive if a producer is moving closer to the production frontier (technical
efficiency increasing over time, ceteris paribus).
When input quantities change, the productivity change can be measured
by TFP change and is defined as:
where Sjit is the share of the jth input for ith firm at time t. Following Kumbhakar
(2000), the overall technical efficiency change is decomposed into a pure
technical efficiency change effect and scale efficiency change effect. This overall
technical efficiency can be measured by differentiating Eq. (61) totally and using
the definition of TFP change in Eq. (70). The TFP change can be expressed as
where is the measurement of the returns to scale and
inputs elasticities defined at the production frontier ,and
when is the marginal product of input (assuming that inefficiency effects
40
68
69
70
71
are not functions of inputs). As shown by Eq. 71, TFP change is composed of
different components: the first term is the scale effect, the second term is the
pure technical change, the third term is the technical efficiency change, and the
last term is the input allocative effect.
In this parametric SFA, the measures of technical efficiency and technical
change can be used to calculate the Malmquist index via Eqs. 38-40. Eq. (65)
can be employed to compute for efficiency change component. Suppose
and , then the efficiency change can be
expressed as
This measure is the same with Eq. 39. On the other hand, the technical change
index between periods s and t for the ith firm can also be calculated using the
parameter estimates derived by taking the time derivative of the production
function. This index may vary for different input vectors due to the assumption of
non-neutrality of the technical change. Hence, there is a need to use the
geometric mean to measure the technical change index between two periods.
This can be expressed as
This measure is related to Eq. 40. To compute for the Malmquist productivity
index, the efficiency change and technical change indices can be derived using
Eqs. (72) and (73), respectively, have to be multiplied together. This Malmqvist
productivity index can also be compared with Eq. 38.
41
72
73
4. Review of Applications in the Philippines
This section presents the results obtained in selected studies that have
attempted to measure the productivity levels or growth rates of Philippine
agriculture. This does not attempt to explain the robustness of the estimation
results from these studies, rather, it demonstrates that a number of studies have
already been done to measure the growth of productivity in Philippine agriculture
using different data sets, timeframes and measurement techniques. This section
also presents the different data sets used to estimate productivity estimates for
Philippine agriculture.
Researches on agricultural productivity in the Philippines can be classified
according to the types of study conducted or the analysis used in the study. A
research can be a cross-country study or a country-specific study. The first type
is conducted purposely to examine productivity gaps among different countries or
42
to analyze convergence. Thus, in this case, productivity estimates are usually
calculated at the national or aggregate level. Examples of these studies are
those of Fulginiti and Perrin (1993, 1998), Craig, et al., (1997), Mundlak, et al.
(2004), Arnade (1998), Suharriyanto and Thirtle (2001), Trueblood and Coggins
(1997), and Coelli and Rao (2003). The second type intends to estimate country-
specific productivity levels or growth rates usually for purposes of a trend
analysis and these can be either national or regional estimates (see Evenson
and Sardido, 1986, Cororaton and Cuenca, 2001 and Teruel and Kuroda, 2004,
2005).
In the Philippines, only a handful of empirical studies have dealt with
productivity estimation in agriculture. This paper is limited to a review of the
studies conducted since the 1980s onwards. Table 1 summarizes the different
productivity studies in Philippine agriculture showing the different timeframes,
productivity estimates and the productivity approaches used.
4. 1 Application in the Philippines
4.1.1 Cross-country Studies
Pioneering cross-country studies started with the work done by Clark
(1940) and Bhattachrjee (1953) followed by prominent studies by Hayami and
Ruttan (1969, 1970). Thereafter, most of the studies conducted were
refinements of the earlier ones through inclusion of variables missing in the
previous models such as R&D, infrastructure, use of different functional forms,
pooling techniques, as well as improved measurements of input and output with
43
quality adjustment (Evenson and Kislev, 1975; Antle, 1983; Nguyen, 1979;
Mundlaak and Hellinghausen, 1982; Kawagoe, Hayami and Ruttan, 1985; Lau
and Yotopolous, 1989; Craig, Pardey, and Roseboom, 1997). What follows are
accounts of cross-country studies on productivity offering some estimates for
Philippine agriculture.
Using a Cobb Douglas production function, Fulginiti and Perrin (1993)
specifically studied the effects of price discrimination and other related policies
on agricultural productivity from 1961-1985 for eighteen (18) developing
countries. Empirical evidence shows that price-depressing policies reduce
productivity with an elasticity estimate of 1.3 per cent. They pointed out that
those countries with higher taxation show more regression than those with little
or no taxation at all. Based on their analysis, Philippine agricultural productivity
could have been increased by 1.3 per cent through the elimination of direct
government intervention (commodity price intervention) and 4.1 per cent through
the removal of indirect intervention (real exchange rate distortion and protection
afforded to the nonagricultural sector).
Fulginiti and Perrin (1998) conducted another cross-country study to
confirm previous findings that agricultural productivity is declining in developing
countries. In this study, they covered the same time period and used four
different productivity estimation procedures. The empirical results show that at
least 50 per cent of the countries investigated have experienced a decline in
productivity. For Philippine agriculture, wide discrepancy in results is observed as
indicated by the estimated growth rates of productivity. Using the Malmquist
44
index approach, the average annual productivity growth rate was -0.30 per cent
from 1961-85. On the other hand, using the traditional growth accounting
approach, the average rate of growth was –2.50 per cent for the same period.
Conversely, the production function- (variable and fixed coefficient) based
estimations resulted in positive estimates of 0.1 and 1.80 per cent, respectively.
Another, but more comprehensive, multi-country study using the
Malmquist index approach was conducted by Trueblood and Coggins (1997).
This study computed the productivity growth rates of 115 developed and
developing countries. For the years covered 1961-1991, this study shows that
most countries had modest agricultural productivity growth rates. The developed
countries had an overall weighted average growth rate of 1.6 per cent for the
entire period. There was, however, a marked decline in productivity in
developing countries pointing to the widening gap of productivity among these
countries. The widening productivity gap was particularly evident during the
decades of the 1960s and the 1970s, though there was a reversion in this trend
in the 1980s. When viewing the period as a whole, Trueblood and Coggins
(1997) approximated a 1.19 per cent annual average productivity growth rate for
Philippine agriculture.
Arnade (1998) also investigated the productivity changes among the
different countries. Like Thirtle, et al. (1995) and Fulginiti and Perrin (1997,
1998), he used the conventional Malmquist approach by constructing an index
with respect to a contemporaneous frontier technology, in which the frontier in
year t+1 is compared with that of the previous year, t, while ignoring past history.
45
Based on this study, Philippine agriculture recorded a negative productivity
growth rate of 0.40 per cent indicating a deceleration of productivity growth for
the years 1961-93. This also indicates that productivity has not been the source
of growth in Philippine agriculture for the entire period.
Though the approach adopted by Arnade (1998) is used extensively in
examining cross-country productivity differences, it is noted to suffer a
dimensionality problem especially when the number of observations in the cross-
section is small relative to the total number of inputs and outputs. This problem
wa emphasized by Suhariyanto and Thirtle (2001) in their study and they
addressed this by measuring the agricultural total factor productivity using the
Malmquist index calculated with respect to the sequential frontier. Based on the
empirical evidence, more than 50 per cent of the 18 Asian countries were found
to have lost their productivity from 1965 to 1996. This is a corroboration of the
empirical results on productivity loss obtained by Fulginiti and Perrin (1998) and
Arnade (1998). On the contrary, Philippines agriculture posted an annual
positive growth rate of 1.33 per cent.
For the period 1962-1992, Martin and Mitra (1999) estimated the total
factor productivity for the agricultural as well as the manufacturing sectors in a
relatively wide range of countries for convergence analysis. Under the
assumption of constant returns to scale, they used primal functions such as the
translog and the Cobb Douglas production functions. They also computed for
productivity growth rates using the growth accounting approach that is based on
the actual factor shares. For the Philippines, agricultural productivity grew at an
46
average annual rate of 1.64 per cent using the translog production function and
at 1.57 per cent based on the Cobb Douglas production function. A relatively
higher annual growth rate of productivity of 2.07 per cent was calculated using
the growth accounting procedure.
Using the Malmquist index approach, Coelli and Rao (2003) examined the
level and trends of productivity of 93 developed and developing countries that
account for 97 per cent of the world’s agricultural output and 98 per cent of the
world’s population. Using the Malmquist index approach on recent data from the
Food and Agriculture Organization of the United Nations, they calculated an
annual average rate of growth in productivity of 2.1 per cent from year 1980 to
2000. They found little evidence of technological regression and this is in
contrast with the empirical findings of a number of studies. For Philippine
agriculture, they computed 0.80 per cent annual rate of productivity growth for
the same period. They, however, got a higher productivity growth rate of 1.3 per
cent using the Törnqvist index number procedure.
Mundlak (2002, 2004) estimated productivity growth rates that departed
from the use of a traditional production function that assumes that technology is
homogenous. They argued that the level of output was dependent on the
implemented technology and the inputs used. Thus, the aggregate output is the
sum of outputs produced using more than one technique, making the technology
heterogeneous. In the case of heterogeneous technology, Mundlak (2002,
2004) adopted an optimization problem at the firm level expressed as the choice
of the techniques to be implemented and their level of intensity, given the
47
available technology, product demand, factor supply, and constraints, referred to
as state variables. The state variables in this study were referred to as the
carriers of the implemented technology and these included roads, representing
the physical infrastructure, measures of education, health representing human
capital and incentives. For the empirical estimation, the aggregate production
function was specified and applied to three ASEAN Countries: Philippines,
Thailand and Indonesia. This production function is like a Cobb-Douglas
function, but the coefficients are functions of the state variables and possibly of
the inputs. Basing on the empirical evidence, the state variables accounted for
an important part of the changes in the total factor productivity. Decreasing
productivity was noted among the three countries, with the steepest decline
observed in Philippine agriculture, from 0.98 per cent in 1961-80 to 0.13 per cent
in 1980-98. On the average, the productivity growth rate was 0.25 for the entire
period.
4.1.2 Country-specific Studies
Using a growth accounting procedure, Evenson and Sardido (1986)
conducted a country-specific study and obtained an estimated average annual
productivity growth rate of 1.90 per cent for Philippine agriculture over the years
1955-1984.
Cororaton and Cuenca (2001) also attempted to estimate the productivity
growth rate for the entire Philippines and for the different sectors including
agriculture. They also applied the growth accounting approach using national
48
and sectoral databases covering years 1980-1998. The sectoral database was
constructed from the national level data using some distributional shares. In their
productivity estimation, they only included two conventional inputs such as
capital and labor. For the entire period, agricultural productivity growth rate was
negative 0.56 per cent. This indicates that the growth in output for the period
1980-1989 was driven by production inputs.
On the other hand, using a recent time-series cross-sectional agricultural
data set covering 12 regions and covering the years 1974-2000, Teruel and
Kuroda (2004, 2005) computed the productivity growth rates in Philippine
agriculture by using a translog variable cost function (with land assumed to be a
quasi-fixed input), a translog cost function (with price of land calculated as the
residual of total revenue, net of measured costs for agricultural labor, fertilizer,
seeds, and machinery and animal services), the growth accounting approach
(the factor shares were based on a Cobb Douglas production function estimation)
and an index number approach using the Törnqvist–Theil approximation method
of the Divisia index. It is worth emphasizing, that Teruel and Kuroda (2005)
applied these different approaches to the same dataset with the same timeframe.
For the entire 27-year period, Teruel and Kuroda (2004, 2005) obtained a
conservative estimate of the average annual productivity growth rate of 0.51 per
cent based on the econometric approach using a translog variable cost function.
The estimate based on a Cobb–Douglas production function approach is also
modest, giving an annual average productivity growth rate of 0.99 per cent for
Philippine agriculture. For the translog cost function and index number
49
techniques, they, however, obtained higher estimates of 1.42 and 1.62 per cent,
respectively.
4.1.3 Productivity Estimates by Subperiod
Evenson and Sardido (1986) and Teruel and Kuroda (2004, 2005)
summarized and presented estimates of productivity by subperiods. These
studies were conducted to assess productivity growth and trends across time.
These estimates are presented in Table 2. Evenson and Sardido (1986)
reported productivity estimates using a 5-year and 10-year subperiods, while
Cororaton and Cuenca (2001) maintained 9-year subperiods. Teruel and Kuroda
(2004, 2005), on the other hand, computed productivity growth rates by
considering the following time periods: 1974-1980, 1981-1990 and 1991-2000.
Mundlak, et al. (2004) for their study maintained longer episodes such as 1961-
1980 and 1980-1998.
Using a growth accounting procedure and agricultural data from 1950-
1984, Evenson and Sardido (1986) obtained positive estimates for the different
5-year subperiods except for 1980-1984. The agricultural sector performed better
during the 1950s as indicated by productivity growing at an annual rate higher
than 2 per cent, followed by a decline in the 1960s and a recovery in the 1970s.
The productivity growth rate particularly was at its peak during the period of
1975-1979 with a growth rate of 5.3 per cent. This subperiod, however,
considered as the post Green Revolution period by Evenson and Sardido (1986),
was followed by a deceleration of productivity as evidenced by a negative annual
50
growth rate of 1.10 per cent during the subperiod 1980-1984. This poor
performance of the agricultural sector in the early 1980s seems to find support
from the study carried out by Cororaton and Cuenca (2001). They computed an
annual productivity growth rate of -5.36 per cent for years 1980-1989 followed by
a positive growth during the 1990s at 4.25 per cent.
Teruel and Kuroda (2004, 2005), based on a translog variable cost
function, found that the highest productivity level of 0.77 per cent in Philippine
agriculture also occurred during the post-Green Revolution period (1974–1980).
From this subperiod, the productivity growth declined in the 1980s and even
further in the 1990s as evidenced by the computed annual average rate of 0.50
and 0.35 per cent, respectively. This further indicates that productivity level
during the Green Revolution era has not been sustained or paralleled, despite
substantial policy changes put in place since 1986 to invigorate agriculture in the
Philippines. These productivity trends are also reinforced qualitatively by
estimates derived from the translog cost function as shown by growth rates 2.01,
1.16 and 0.84 per cent for the subperiods 1974-1980, 1981-1990 and 1990-2000,
respectively.
On the other hand, these temporal patterns of productivity are not
supported by estimates obtained from productivity measurement using the
growth accounting and the Törnqvist index approaches. For the growth
accounting approach, Philippine agricultural productivity grew annually at a rate
of 2.19 per cent in the late 1970s and then followed by a deceleration and
recovery in the 1980s and 1990s as explicitly shown by annual growth rates of -
51
0.53 and 1.44 per cent, respectively. Similarly, for the Törnqvist index approach,
the calculated productivity growth rates are 2.50, 0.50 and 2.60 per cent across
time periods. However, Mundlak, et al (2004), using Cobb-Douglas production
function with state variables, seem to validate the evidence on declining
productivity as shown by their estimates indicating a relatively rapid growth
during years the 1961-1980 (0.98%) and slower growth during the years 1980-
1998 (0.13%).
4.1.4 Regional Productivity Estimates.
Table 3 presents productivity estimates for different regions. Evenson and
Sardido (1986) reported estimates for productivity growth rates for 9 regions
using the growth accounting approach. For the entire 1950-1984, the rapid
productivity growth was observed in Northern-Eastern Mindanao (2.54%),
followed by two regions in Luzon, namely: Southern Tagalog (2.39%) and the
Ilocos region (1.60%). Western-Southern Mindanao and Eastern Visayas had
productivity growth rates within the range of 1.09 to 1.46 per cent. Other regions
showed modest growth with a rate of less than 1 per cent except for Central
Luzon that did not reflect any productivity growth.
Teruel and Kuroda (2005) also computed for the productivity estimates of
the different regions using the translog variable cost function and the index
number approach. Compared with Evenson and Sardido, the empirical
estimates based on the translog variable cost function show a marked difference
52
in terms of the productivity performance of the different regions over the period
1974-2000. The highest annual average productivity growth of 1.56 per cent was
posted by the Ilocos region, followed by Central Luzon with productivity growing
at a rate of 1.45 per cent. On the other hand, negative productivity estimates
were noted in three of the twelve regions, namely: Bicol, Western Visayas and
Western Mindanao.
Using the index number approach, among the regional production areas,
Central Luzon had the highest annual productivity rate of growth of 3.28 per cent.
This region was followed by Ilocos, Southern Tagalog and Northern Mindanao
having an annual productivity growth rate of 2.16 per cent. Other regions with
productivity growth rates above 2 per cent per annum were Cagayan Valley and
Southern Mindanao. The remaining regions with positive productivity growth
rates included Western Visayas (0.55%), Central Visayas (1.07%), Eastern
Visayas (0.91%), Western Mindanao (0.17%) and Central Mindanao (0.85%).
On the contrary, between the years 1975 to 2000, only Bicol region posted an
annual negative productivity growth rate of 0.29 per cent.
It can be noted from Table 3 that out of the 12 regional agricultural
production areas, eight has productivity levels explaining more than 50 per cent
of output growth and these included all the regions in Luzon and two regions
each from the Visayas and Mindanao. This illustrates that agricultural production
in the Philippines during this period was driven by productivity and the regions in
Luzon were relatively more productive than those in the Visayas and Mindanao.
53
On the average, productivity contributed 59 per cent to output growth with the
remaining 41 per cent attributed to the increased use of inputs.
4.1.5 Productivity Estimates by Approach
This subsection classifies productivity studies by type of approaches used
in the estimation. As indicated by Table 4, four studies used growth accounting
approach to estimate productivity growth. Regardless of timeframes and data
sets, the average productivity growth rate based on this approach fell within the
range of -2.50 to 2.07 per cent
For the index number approach, two studies attempted to compute for the
rate of growth of productivity using the Tornqvist-Theil index number procedure.
The mean growth rate was relatively higher at 1.46 per cent annually.
There are five studies identified to have used the econometric approach
using both the primal and the dual functions. These studies adopted one or a
combination of these functions for empirical exposition purposes. Three studies
estimated productivity based on a production function either using the Cobb-
Douglas or the Translog functional forms. On the other hand, two studies
employed a dual cost function using a translog functional form. Ignoring the
differences in the methodologies, the time periods, the functional forms and the
data used in the estimation, the productivity growth rate based on the
econometric approach averaged 1.14 per cent; lower than the average estimate
based on the index number approach.
54
Moreover, a number of productivity studies conducted more recently
offered productivity estimates for Philippine agriculture using the DEA-based
Malmquist approach. With this approach, the productivity estimates fell within
the range of -0.30 to 1.19 per cent and averaged 0.32 per cent. This indicates
that average annual productivity growth rate calculated using DEA-based
Malmquist approach generated more conservative than results than those
obtained using other productivity measurement techniques.
4.2 Contribution of Non-conventional Inputs to Productivity
This section discusses the non-conventional inputs in relation to productivity.
The concept of TFP, as residual share of output growth after accounting for the
changes in production inputs, originated from the seminal papers of Tinbergen
(1942) and Solow (1957). Abromovitz (1956) called this residual part of the
output growth as “measure of our ignorance”, since it does not only contain
technological change but also other unnecessary components, namely
measurement errors, omitted variables, model misspecification, etc.
Accurate measurement is at the heart of productivity estimation and
comparison. There are two types of measurement errors: in factor utilization and
in the quality changes of the production factors. Thus, given the importance of
TFP in understanding the growth process, one of the recent directions taken by
researchers, as indicated in the literature, has been on measuring better the
55
factors of production, including corrections for quality of factors (Jorgenson and
Griliches, 1967; Greenwood and Jovanovic, 2000). Some researchers have
addressed the issues pertaining to omitted variables and model specification by
incorporating non-conventional inputs to TFP estimation. These non-
conventional inputs include education, research and development, extension,
government programmes and policies and infrastructure. Several empirical
studies have been conducted to determine their contribution to productivity.
Some of these non-conventional inputs have been used to account for input
quality changes. This will be discussed in turn.
Education is always assumed to be related to the quality of the agricultural
labor force. Education is an investment in “human capital”. It provides
individuals with general skills to solve problems. However, data on the
educational level of the agricultural labor are not available especially in most
developing countries. Consequently, for productivity analysis, national proxies
are usually used in empirical studies: literacy and life expectancy (Craig, Pardey
and Roseboom, 1997), historic calorie availability (Frisvold and Ingram, 1995)
and the number of agricultural college graduates as a proxy for the level of
advanced technical education in agriculture (Hayami and Ruttan, 1985).
Many researchers also have attempted to account for land quality in
productivity estimation in order not to attribute the differences in production that
are actually due to changes in land quality to other inputs. Some tried to control
for differences in land quality by including a land quality index in productivity
estimation. Developed by Peterson (1987), in this land quality index, land quality
56
is characterized as a function of historic precipitation and the share of a
country's land area devoted to pasture and crops. Other researchers have made
adjustments for the impact of land quality on productivity by using proxy variables
such as the mean rainfall and the percentage of land area that is arable and
irrigated.
Research and development is also undertaken to improve the production
capacity of the agricultural sector given resource constraints and to minimize the
environmental degradation caused by agricultural production. Research and
development has been shown to have significant contribution to productivity. On
the average, the productivity contribution of agricultural research ranged from 20
to 60 per cent (Ruttan, 1980, 1982; Echeverria, 1990; Huffman and Evenson,
1993; and Fuglie et al., 1996). For productivity analysis, public agricultural
research expenditures are generally used as proxy for research and
development. To measure the impact of research and development to
productivity, expenditures in research are lagged for a number of years to
account for the time required for research to reach fruition. This is done
specifically because a particular research may require several years for
completion and the farmer’s learning curve for the new innovation may also take
time. This approach of lagging agricultural research expenditures, however,
does not account for the spillover effects of research to other countries or
regions. Agricultural research is performed both by public and private sectors.
Private agricultural research is equally important but related information is
incomplete or not yet available especially in the case of developing countries. It
57
has been shown in the productivity literature that public investment in research
stimulates private research efforts (Pray, Neumeyer and Upadhyaya, 1988).
Related to research are extension services. This non-conventional input
has been shown to have positive contribution to agricultural productivity.
Agricultural extension involves the dissemination of agricultural information, the
demonstration of new production technologies as well as direct consultation with
farmers regarding specific problems related to production and farm management.
Agricultural extension is undertaken to reduce the time lag between the
development of new technologies and adoption. It is assumed that the impact of
extension on productivity is more immediate than research. Based on empirical
findings, the contribution of extension to agricultural productivity is more mixed
than research and this problem is data-related since reports on public extension
expenditures are incomplete and even more non-existent than research
especially in most developing countries.
On the other hand, the effects of government programs and policies to
productivity have also been revealed by a number of studies (Fulginiti and Perrin,
1993; Hu and Antle, 1993; Block, 1995; Fulginiti and Perrin, 1997; Frisvold and
Ingram, 1995). It has been indicated that the prices of agricultural outputs and
inputs affect the technology chosen by the farmers and thus the productivity
trends. These prices may be affected by government policies that tax or
subsidize agriculture. In some productivity estimation studies, the depreciation of
the real exchange rate, past export growth rate and export instability are used as
proxies for government policy and policy reforms. Relatively, little research has
58
attempted to investigate the impact of government programs on productivity in
agriculture, but some have shown evidence of positive relationship (Huffman and
Evenson, 1993 and Makki and Tweeten, 1993).
Like other non-conventional inputs, public investments in infrastructure
can also increase agricultural productivity by lowering the cost of inputs at the
farm level and increasing farmers' access to marketing opportunities. To account
for the impact of the provision of public infrastructure to productivity, proxy
variables are also used such as the paved road density adopted by Craig,
Pardey and Roseboom (1997) and the gross domestic product of each country's
transportation and communication sectors employed by Hu and Antle (1993).
Other proxy variables include water and sewer systems, schools, hospitals,
conservation structures and mass transit. Using a Cobb-Douglas (C-D)
aggregate production function, Aschauer (1989) was the first to empirically show
the strong positive impact of the ratio of the public to the private capital stock to
productivity in the United States. Specifically, he found out that a 1 per cent
increase in the public capital stock would result in an increase in total factor
productivity (TFP) by almost 0.40 per cent. Thus, he attributed the decline in US
TFP growth in the 70s to lower public investment spending.
In the Philippines, some studies attempted to incorporate these non-
conventional inputs to account for the changes in the quality of production inputs
and to determine their contribution to productivity.
One empirical study was conducted by Evenson and Quizon (1991) and
they employed a normalized quadratic profit function with infrastructure,
59
technology and policy or program variables and used pooled dataset covering
the years 1948-1984 and 9 regions.6 Infrastructure variables include roads, rural
electrification and general rural development expenditures. To capture the
impact of policies or programs to productivity, they used variables such as land
transfers under the agrarian reform program. On the other hand, they also
included technology variables such as high-yielding rice varieties, the regional
and national research stock variables and extension. Evenson and Quizon
(1991) have evaluated the productivity effects of infrastructure, technology and
policy via the output supply and input demand equations using elasticities, but
not in the sense of productivity decomposition analysis. The major findings can
be summarized as follows:
i. HYVs stimulated input demands and had positive impact on output;
ii. Regional research program had substantial impact to output
relative to national research program, although though combined
research investment had a higher marginal rate of return of 70 per
cent;
iii. The net impact of extension was positive but small, indicating a
lower rate of return;
iv. Roads were shown to have a significant impact on inputs and
outputs with a substantial net profit effects;
v. Rural electrification appeared to have minimal impact on output;
and
6 Evenson and Quizon (1991) used the data set constructed by Evenson and Sardido.
60
vi. Land reform had a small but significant output and net productivity
effect.
Teruel and Kuroda (2000) also examined the impact of public
infrastructure on the productivity performance of Philippine agriculture. Instead
of the profit function, they used a translog cost function framework augmented
with public infrastructure such as irrigation, roads and rural electrification7. They
used more a recent data set covering 12 regions and period 1974-2000.
From their study, a higher TFP estimate was noted during the late 1970s,
but this was followed by a discernible decline in the 1980s and 1990s. The higher
productivity growth in the period 1974-1980 was driven by public infrastructure.
Its productivity contribution, however, decreased markedly in the 1980s. During
this decade, it was technological change that spurred the growth of productivity,
although its contribution was not sufficient to sustain the higher productivity level
of the late 1970s. On the other hand, in the last decade, there was a recovery of
contribution of public infrastructure to productivity, though this did not reverse the
overall trend in TFP growth. For the entire period, TFP grew with an annual
average growth rate of 1.42 per cent. The contributions of technological change
and public infrastructure to TFP were comparable (1.81 and 1.99 per cent
respectively), with provision of farm-to-market roads seen to play an important
role. This is in line with the findings of Evenson and Quizon (1991) emphasizing
the importance of roads relative to rural electrification in effecting significant
changes in input demand, output supply as well as in profit. Overall, in this
7 Teruel and Kuroda (2005) also estimated a profit function. However, we did not further pursue this line of inquiry since there were negative profits in some regions for some years.
61
study, Teruel and Kuroda (2005) provided empirical evidence showing that the
decline of productivity in Philippine agriculture could be partly explained by the
reduced provision of rural infrastructure.
4.3. Data Sets
In order to estimate productivity levels or growth rates, data are needed on
agricultural output and input. In the case of the cross-country or even the
country-specific study using time and cross sectional data, comparable and
consistent data are necessary to make comparisons over time and space. This
subsection deals with the measurement of inputs and outputs that may influence
measured productivity. First, it begins with a discussion of the measurement of
the quantity of output and proceeds with a discussion of the measurement of the
different inputs. Discussion will be done by type of productivity studies starting
with cross-country and moving on to country-specific studies. Specifically, the
different data sets will be presented: those used by cross-country studies to
include that of Mundlak, et al. (2002) in their study involving three ASEAN
countries, and those employed for country-specific analysis such as the works of
Evenson and Sardido (1986) and Teruel and Kuroda (2004, 2005).
6.1 Cross-country Studies
For the past decade, the number of cross-country studies examining the
differences in agricultural productivity levels and their growth rates has increased
significantly. This can be attributed to three factors: 1) the availability of some
62
panel data sets, 2) the development of new empirical techniques to analyze this
type of data, and 3) the desire to assess the impact of Green Revolution and
other programs to agricultural productivity in developing countries (Coelli and
Rao, 2003).
Most of the cross-country studies reviewed in this paper used the Food
and Agriculture Organization of the United Nations (FAO) panel data, spanning
for several decades from the 1960s to 1990s8. Most of these data are typically
measured in relatively simple physical terms especially the conventional inputs.
The shortcomings of the FAO data have been cited in the literature (Thirtle, et al.,
1995; Fulginiti and Perrin, 1997, 1998; Arnade, 1998). This data set does not
account for the differences in input quality especially land, the chemical inputs
considered only fertilizers and excluded other chemicals for crop protection like
pesticides, and the machinery did not include animal draft power, equipment and
other machinery.
6.1.1 FAO Data Set
The FAO developed a measure of output that aggregates each country’s
output in a manner that minimizes exchange rate distortions and facilitates inter-
country comparisons. This measure is called the “international dollar.” This
measure involves the calculation of weighted world prices for each commodity,
and multiplies each country’s commodity quantities by their weighted world
prices. Aside from the international dollar measure, there are other ways to
aggregate agricultural output and these include the wheat unit approach and the
8 Other researchers used the data set from Hayami and Ruttan series of studies.
63
use of official exchange rates. The former was developed by Hayami and Ruttan
(1985) and this involved the calculation of the ratio of each individual commodity
price to the price of wheat in India, the United States and Japan, while the latter
converted output in local currency units to dollars.
Land typically is measured as hectares of agricultural land that is arable
and permanent cropland. Some studies exercised control over the differences in
land quality by using the international land quality index of Peterson (1987).
Fertilizer input is measured as the sum, in nutrient-equivalent terms, of
nitrogen (N), potassium (P2O2), and phosphate (K2O) contained in the
commercial fertilizers consumed in each country.
Labor includes economically active agricultural population. Economically
active population is defined as all persons engaged or seeking employment in an
economic activity, whether as employers, own-account workers, salaried
employees or unpaid workers assisting in the operation of a family farm or
business. Specifically, the FAO definition of economically active population in
agriculture includes workers in agriculture, forestry and fisheries. A few
researchers have attempted to correct for the quality of the agricultural labor
force by considering non-conventional inputs such as national-level measures of
education or literacy. In addition, other researches adjusted the quality of
agricultural labor force directly by sex and age.
Livestock in the FAO dataset are measured by aggregating different
animals (Cattle, sheep, goats, pigs, mules, horses, asses, buffaloes, camels,
64
ducks, chicken, and turkeys) using different weights usually taken from Hayami
and Ruttan (1985).
Machinery is measured as the total number of wheel and crawler tractors
used in agriculture. This, however, excludes garden tractors.
6.1.2 The Mundlak, et al. Data Set
For Mundlak, et al. (2002), the data set included time series data on the
quantity of output and inputs such as agricultural land, fertilizers, capital stock in
agricultural machines and in non-agricultural origin (livestock and orchards), and
labor. The data were taken from the different sources: National Statistical
Coordination Board (NSCB), FAO, Fertilizer and Pesticide Authority (FPA) and
from different surveys conducted by National Statistics Office. From the
Mundlak, et al. study (2002), the data series construction will be discussed in
turn.
The agricultural Gross Domestic Product series includes Forestry and
Fishery. National accounts were obtained in constant and current market prices
(pesos) from the Economic and Social Statistics Office of the National Statistical
Coordination Board (NSCB).
Data on the area in hectares of agricultural land were taken from the
statistical databases found in FAO's website. This data series included the arable
and permanent cropland, along with permanent pastures. Data on the area in
hectares of irrigated land were also downloaded from the statistical databases of
65
the FAO website. Data on the consumption of fertilizers in metric tons were
reported by the FPA.
The data series on the agricultural capital stock in agricultural machines
as well as in livestock and orchard were estimated using the method of Larson,
Butzer, Mundlak, and Crego (2000). Data on gross domestic capital formation in
agricultural machinery and tractors in current pesos were taken from the
Philippine Statistical Yearbook. These were used to calculate capital stock. The
investment data were then converted to constant values using the agricultural,
fishery, and forestry GDP deflator before aggregating these to the capital stock
series. On the other hand, the data on gross domestic capital formation in
breeding stock and orchard development were in constant and current market
prices (pesos) taken from NSCB. These were also used in calculating capital
stock.
Labor force data were obtained from the Philippine Statistical Yearbook
and the Bureau of the Census and Statistics' (BCS) Survey of Households, now
known as the National Statistics Office (NSO). When available, data on total
agricultural employment were taken from the October survey of NSO.
6.2 Country-specific Studies
For country-specific studies, several data sets were assembled for
Philippine agriculture for the analysis of production structure and for productivity
estimation. Evenson and Sardido (1986) constructed a data set for years 1948-
1974 for 9 regions. They also assembled agricultural data series for the years
66
1974-1984 using a 12-region classification. The most recent data set can be
attributed to Teruel and Kuroda (2004, 2005). This cross-sectional and time-
series dataset on Philippine agriculture included 12 regions and spanned 27-year
period. This period spans the post-Green Revolution era and the period
characterized by substantial changes in policies affecting Philippine agriculture.
Teruel and Kuroda’s (2004, 2005) data set was constructed using assumptions
mostly taken from Evenson and Sardido (1986).
6.2.1. The Evenson and Sardido’s Data set
Evenson and Sardido (1986) define agricultural output as the gross value
of production of agricultural crops and livestock. Most of the data for this series
were taken from the Raw Materials Resources Survey for Agriculture (RMRSA)
of the Department of Agriculture and Natural Resources (DANR) and from the
Crop and Livestock Survey (CLS) of the Bureau of Agricultural Economics
(BAEcon), DANR. The data on agricultural output were reported on a calendar
year basis.
Regional agricultural crop production included palay, corn, coconut,
sugarcane, fruits, and other crop production such as root crops, onions, potatoes,
beans and peas, vegetables, coffee, cacao, peanuts, abaca, tobacco, cotton,
kapok, ramie, rubber, maguey, and other commercial and food crops. Annual
crop prices were computed by dividing the crop value by the quantity of
production.
67
For the regional livestock and poultry production, Evenson and Sardido
(1986) included meat, milk and egg production of farm households as well as
changes in the livestock and the poultry inventories. Annual changes in the
inventories from 1948 to 1984 were computed using the annual regional
population estimates of carabaos, cattle, hogs, horses, goats, sheep, chicken,
ducks, geese, and turkeys from the RMRSA and the CLS. Dressed weights of
the slaughtered livestock and poultry in each region were also obtained from the
RMRSA and the CLS. Missing years were filled in by using the ratios of dressed
weights of slaughtered animals to their corresponding January populations.
Prices of livestock and poultry were computed in the same manner as
crop prices. Meat prices were computed as the price of a particular animal
divided by the average dressed weight of similar slaughtered animals. These
dressed weight equivalents were obtained from the DANR.
The input series were subjected to the following qualifications:
1. Agricultural land was classified as either (a) land planted to temporary
and permanent crops or cultivated land, and (b) all other agricultural land (land
under temporary and permanent pastures, land temporarily idle/fallow, etc.). Area
measures of other agricultural land were not available from the RMRSA and the
CLS. This series was constructed by initially using the regional estimates of all
other agricultural land from the 1948, 1960, 1971 and 1980 Censuses of
Agriculture. Interpolation and extrapolation were used to complete the missing
years.
68
2. Cultivated land is reported as crop area capturing the effects of multiple
cropping. Evenson and Sardido (1986) converted this crop area into physical
land area by first computing the multiple cropping indices as the ratio of crop
area to physical area in each region. Then, these multiple cropping indices were
completed for the missing years by interpolation and extrapolation using the
1948, 1960, 1971 and 1981 BCS Censuses of Agriculture. Finally, to purge the
land data series of the effects of multiple cropping, crop areas were subsequently
divided by their respective multiple cropping indices. For each type of land, they
constructed a rental series for the 1948-74 data and assumed constant shares
for each type of land for recent period fixed at 0.3.
3. Labor was measured in equivalent man-days spent in agricultural
production. The labor data series was based on annual data from the Philippine
Statistical Survey of Households (PSSH) of the BCS. However, the PSSH
surveys (labor force survey) reported employment in agriculture, forestry, hunting
and fishing, as a group. Evenson and Sardido (1986) assumed that labor
employment in agriculture was a constant-proportion (92%) of the reported total
employment for this group of economic activities. This assumption was adopted
from Paris (1971).
4. Prior to the construction of the series of equivalent man-days spent in
agriculture, several adjustments or estimations were employed to come up with
regional data on employment in agriculture purposely to complete the missing
years. This was done especially for the years prior to 1967.
69
In computing for the equivalent man-days (LMDt), the regional
employment in agriculture had to be broken down by age and sex. For age
distribution, Evenson and Sardido (1986) used the national percentage
distribution of agricultural employment by age for each year and applied this
uniformly to the different regions. On the other hand, the within-region
distributions of employment by sex were used to estimate the distribution of
employment by sex for each age group in each region.
Equivalent man-days spent in agriculture per year were computed using
the equation:
where M = number of male workers,
F = number of female workers,
a = adult, i.e., 15 years and above,
c = children, i.e., 14 years and below,
m = average number of hours worked per week by male adults,
f = average number of hours worked per week by female adults,
c = average number of hours worked per week by children,
t = time
This equation implies that females and children have working capacities
equal to 75 and 50 per cent, respectively, of the working adult males. The
equation further assumes that the adult male workers work 23 weeks a year
while female adults and children work only 15 weeks a year. This assumption
70
74
was adopted from Oppenfeld, et al. (1957). Regional agricultural wages were
taken from BAEcon surveys and referred to wages without meals.
The farm machinery data series was based heavily on the annual national
stock data for four-wheel tractors, hand tractors, plows, harrows, and other
implements. To estimate the annual national stock of farm equipment, the
following formula was used:
Kt = (1-d) Kt = 1 + I t
where K refers to the stock of farm equipment, I to gross domestic capital
formation, d to the annual rate of depreciation and t to the time subscript. To
construct this input series, Evenson and Sardido (1986) gathered the data on the
stock of farm equipment from the 1948 Census of Agriculture and the data on
gross domestic capital formation from the 1956 Capital Formation Study of
BaEcon and from the annual estimates of gross domestic capital formation in
durable agricultural machinery as well as implements from the National
Economic and Development Authority’s National Income Account. Evenson and
Sardido (1986) computed the annual depreciation rate using the 1948 and the
1956 benchmarks and the annual gross domestic capital formation data. In a
similar manner with the labor input, adjustments or estimations were done to fill
in data gaps in the farm machinery data series.
The amount of capital services of agricultural machinery in each region
was assumed to be 16.2 percent of the total value of each region's agricultural
capital stock. This was based on the interest rate assumed to be 10 per cent and
the depreciation rate of 6.2 per cent. Implicit price indices for farm equipment
71
75
were computed from the National Income Accounts. They were assumed to be
equal across regions in any given year.
The fertilizer input series was constructed using data from the different
sources. For the years 1956-1975, the total supply of fertilizer in nutrient
equivalents (the sum of domestic production and imports unadjusted for year-end
stocks) was taken from Anden (1976). This series was extended backwards to
1948 by converting total supply of fertilizer (unadjusted for yearend stocks)
available for the years 1948 to 1955 into their nutrient equivalents using the
constant conversion factor computed as the average ratio of total nutrient supply
to total fertilizer supply for the years 1956 to 1960.
For the years 1974-1984, fertilizer consumption was based on the fertilizer
sales of the distributors obtained from the Fertilizer and Pesticide Authority.
Regional series construction was done by distributing the total sales to the
different regions using the regional proportion of fertilizer distributors. The
regional fertilizer sales by nutrient were then computed based on the proportion
of N, P, and K to the total nutrient demand and then multiplied by the total sales.
The series on work animal was based on the 1948, 1960, 1971 and 1980
Censuses of Agriculture. Working carabaos and cattle stocks were estimated for
each region by interpolation between censuses. The service flow included an
adjustment for feed.
6.2.2 Teruel and Kuroda’s Data Set
72
A regional data set on agricultural products and inputs was assembled for
the years 1974-2000. Like Evenson and Sardido (1986), the data were reported
on a calendar year basis using the 12-region classification for the Philippines9.
The data set accounted for 88 per cent of the total volume of crop production and
almost 100 per cent of the total poultry and livestock production.
The data on the quantities and prices of the different agricultural products,
inputs, and areas planted were sourced from the several occasional publications
of the Bureau of Agricultural Statistics (BAS). These publications include the
Crop Statistics, Selected Crop Statistics, Prices Received by Farmers, Rice
Statistics Handbook and Selected Statistics on Agriculture. In the data set,
quantities were reported in thousands of metric tons, prices in pesos per
kilogram, and areas in hectares.
The crop categories include rice, corn, sugarcane, coconut, tobacco,
rootcrops (camote, cassava, gabi, pao galiang, tugui, and ubi or yam), fruits
(banana, mango and pineapple), and vegetables (cabbage, eggplant, garlic,
radish and tomato). Livestock and poultry products, on the other hand, included
meat of cattle, carabao, hogs, goat, chicken, and ducks, as well as chicken and
duck eggs. The prices reported in the data set were farmgate prices. Gaps in the
price data were filled in through estimation.
The data on land variable is the sum of the areas for all the crops (i.e. rice,
corn, sugarcane, coconut, tobacco, rootcrops, fruits, and vegetables).
9 The twelve regions are as follows: Ilocos, Cagayan Valley, Central Luzon, Southern Tagalog, Bicol, Western Visayas, Central Visayas, Eastern Visayas, Western Mindanao, Northern Mindanao, Southern Mindanao and Central Mindanao.
73
Labor was reported in terms of equivalent man-days (MD) spent in
agricultural production. Equivalent man-days were computed based on the
number of male and female workers aged 15 years old and over who were
employed in agriculture. Equivalent man-days spent in agricultural production
were computed using
MD M F 160 105 0 75.
where M refers to the number of male workers and F refers to the number of
female workers, following Quizon (1980) and Evenson and Sardido (1986).10 The
equivalent mandays equation reflected the assumption that a female worker had
75% of the working capacity of a male worker, and that an adult male agricultural
worker worked 160 days in a year while an adult female worked 105 days in a
year.11 Regional agricultural wages referred to the average daily wage (without
meal allowance) received by farm workers in all agriculture,12 as reported by the
Bureau of Agricultural Statistics.
Equivalent animal work days were computed based on the number of
work carabaos and work cattle by assuming that these animals worked an
average of 220 and 150 days a year, respectively.13 The cost of services of work
animals per work day was assumed to be one-half of the daily wage rate of
agricultural labor.14
10These authors included the number of male and female children who were employed in agriculture in their computation of equivalent mandays. This was not done in this study however due to the unavailability of such data for most of the years covered by the data series.11The latter assumption on the number of workdays of males and females was based on a study by Oppenfeld, et. al.(1957).12This is a weighted average of the wages received by farm workers in rice, corn, coconut and sugarcane farms.13The assumptions on the number of workdays were adopted from Quizon (1980).14This was the assumption used in the Evenson data set.
74
76
Fertilizer quantities were reported in metric tons of nutrients, i.e. nitrogen,
phosphorus and potassium. Raw quantity data were taken from the Fertilizer
Statistics, a publication of Fertilizer and Pesticide Authority (FPA) and were
reported by fertilizer grade (e.g. 46-0-0, 14-14-14, etc.). The data on the volume
of consumption/sales15 by fertilizer grade were converted to their nutrient
equivalents. The fertilizer grade indicates the nutrient content of the fertilizer.
For example, let x-y-z be a fertilizer grade. Then the nitrogen content of this
fertilizer is X% of its weight, phosphorous is y%, and potassium is Z% of its
weight. Regional consumption of nitrogen, phosphorus, and potassium fertilizers
was computed by getting the nutrient content of all the fertilizer grades consumed
in the region, and summing these by type of nutrient (i.e. nitrogen, phosphorus
and potassium) for all the grades.
Fertilizer prices were reported in the data set in pesos per kilogram of
nutrient. For a given region, the price per kilogram of nutrient in a particular
fertilizer grade was computed as follows:
Let x-y-z be a fertilizer grade, and let Px-y-z be its retail price per kilogram in region i. Then the price per kilogram of nitrogen in x-y-z is Px-y-z/x%; of phosphorus is Px-y-z/y%; and of potassium is Px-y-z/z%.
These prices were computed for all the fertilizer grades consumed in the
region. Thus, for a given region, there were as many prices per kilogram of
particular nutrient as there were fertilizer grades containing this nutrient.16 To
15FPA defines consumption/sales as withdrawals from importers’ and manufactures’ warehouses. No data on actual consumption were available.16 For example, for the year 1974, there were five fertilizer grades containing nitrogen (46-0-0, 21-0-0, 16-20-0 and 14-14-0, 14-14-14), two fertilizer grades containing phosphorus (16-20-0 and 14-14-14), and two fertilizer grades containing potassium (0-0-60 and 14-14-14). Thus there were four computed prices of nitrogen, one for each grade, and two prices each of phosphorus and potassium.
75
derive the final price estimates for a given region, the prices per kilogram of a
particular nutrient, say nitrogen, computed for the various fertilizer grades were
weighted by the ratio of the nitrogen content of a particular grade to the total
nitrogen content of all the grades consumed in the region. For example, the
computation of the price per kilogram of nitrogen for region i in 1974, denoted by
PN,i is as follows:
P PN
total NN i N gg
g, ,
where PN,g is the region’s price per kilogram of nitrogen in fertilizer grade g, g=
46-0-0, 21-0-0, 16-20-0, 14-14-14; Ng is the nitrogen content of fertilizer grade g;
and total N = N46-0-0 + N21-0-0 +N16-20-0 +N14-14-14.
The data on seeds, which included rice and corn seeds, were taken from
the Supply-Use data of BAS. The price of seeds was based on the farmgate
prices of corn and rice.
The sources of the data they used to construct the data series on
agricultural machinery were: (i) the 1978 BAEcon Capital formation Study and (ii)
the annual national estimates of gross domestic capital formation17 from the
Economic and Social Statistics Office (ESSO) of the National Statistical
Coordination Board.
17Based on the Manual on the Philippine System of National Account, gross domestic capital formation consists of two major components; the gross fixed capital formation and the change in stocks. The gross fixed capital formation refers to the outlays on construction, durable equipment and breeding stocks, orchard development and afforestation. Change in stocks, on the other hand, refers to the difference between ending and beginning inventories such as finished goods, work-in-progress, and raw materials, which have been produced or purchased but not yet sold or consumed as intermediate inputs during the accounting period.
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77
To estimate the national stock values of agricultural machinery for 1974-
2000,18 the following equation was used:
K d K It t t ( )1 1
where K refers to the stock value of agricultural machinery, I to investment in
agricultural machinery, d to the depreciation rate, and t to the time subscript. The
benchmark figure used for k was the 1973 value of agricultural machinery
(=185.6 million pesos, in current prices) reported in the 1978 BAEcon Capital
Formation Study, and the depreciation rate was assumed to be 10%. The
investment data used were the data on gross domestic capital formation in
agricultural machinery and tractors (in current prices) taken from Economic and
Social Statistics Office (ESSO) of the National Statistical Coordinating Board.
The estimated annual national stock values of agricultural machinery in current
prices were deflated using implicit price indices computed from the ESSO data.
The amount of capital service of agricultural machinery in each region
depended on the (deflated) value of the capital stock, the interest rate and the
depreciation rate. The relationship can be expressed as
K d r Kst t
where Kst is the value of capital service in year t, K t is the deflated value of capital
stock in year t, d is the annual depreciation rate which they have assumed to be
10% and r is the annual interest rate, also assumed to be 10%. Implicit price
indices were computed from the ESSO data as the ratio of the current price to
18The ESSO reported data on gross domestic capital formation separately for agricultural machinery and for tractors other than steam. We define agricultural machinery to be inclusive of tractors, so the sum of the gross domestic capital formation data for these two categories of durable equipment were used in the estimation.
77
78
79
the constant price estimates of gross domestic capital formation in agricultural
machinery and tractors. The computed indices had 1985 as base year; these
indices were rebased to 1974.
6. Literature Gaps
6.1 Key General Findings
Most of the cross-country and country-specific studies reviewed offer
evidence of positive productivity growth rates for Philippine agriculture.
Numerically, however, there were marked discrepancies among these estimates.
These can be attributed to the differences in the theoretical constructs or
methodologies used or probably due to the use of different data sets assembled
using different assumptions and time periods. Although this paper highlights the
alternative approaches to productivity measurement and data sources, a
consistent general picture emerges with regard to the recent agricultural
productivity performance of the Philippines.
78
The performance during the 1950s was generally good. This was followed
by a decline in the decade of the 1960s and a recovery between 1970s and
1980s as shown by a relatively strong growth in productivity. A subsequent
decline, however, followed through until 2000. These empirical results seem to
corroborate relatively few studies arguing that the poor performance of
agriculture can be attributed to the prolonged and relatively rapid decline in
agricultural productivity, which characterizes many developing countries (Fulginiti
and Perrin, 1997, 1998, 1999; Kawagoe et al., 1985; Lau and Yotopoulos, 1989
and Kawagoe and Hayami, 1985). At the regional level, the regions in Luzon are
generally more productive than in the Visayas and Mindanao especially in recent
years. The regions of Ilocos and Central Luzon are identified as the relatively
more productive regions. Bicol region, on the other hand, is consistently the
least productive of the regions and in some years experienced negative growth
rates.
6.2 Estimation
In addition to cross-country studies, there are a handful of country-specific
studies on productivity estimation conducted in the Philippines. From 1980
onwards, the common approaches to productivity measurement used by these
studies are the growth accounting approach and the econometric approach. The
latter approach specifically employed the Cobb-Douglas production function, the
translog production function and the dual translog cost function. All dual
estimations dealt with a single output case, indicating that multiple output dual
79
models have not been fully exploited in studying agricultural productivity in
Philippine agriculture. The index number procedure is also seldom used, while
the DEA-based Malmquist index procedure has not been applied so far to
Philippine agriculture using regions as the DMUs in spite of its advantage of
handling inputs and outputs without requiring information on prices and weights,
which are often problematic under the Philippine context. A number of these
studies based on non-parametric approach were considered cross-country
studies. Just like the DEA-based Malmquist index approach, the stochastic
frontier approach has not also been used to empirically estimate productivity
levels or growth rates in Philippine agriculture.
6.3 Data
Regardless of the source of data, there are several data issues that are
widely recognized in estimating productivity in Philippine agriculture, to wit:
1.1 The constraint imposed by data availability or small data dimension
indicating limited number of observations which consequently
diminishes the number of options for sophisticated approaches to
productivity measurement or the use of more advanced techniques
such as the econometric and the parametric SFA.
1.2 The insufficient disaggregation of the inputs implies the inability to
assign inputs to particular outputs. Given the diverse and highly
80
specialized nature of modern agriculture, it will be interesting to have
forecasts of the productivity growth of the different commodities.
1.3 Missing data on some intermediate inputs such as pesticides,
herbicides, organic fertilizers and on non-conventional inputs affecting
productivity, among them, research and development, extension,
government programs and education is also a concern. Missing
variables will have an upward bias on productivity estimates.
Likewise, the problem on measurement errors also needs to be
addressed in the estimation process. One technique is by accounting
for the changes in the quality of inputs over time or by through
minimizing under/over measurement of inputs.
1.4 The most recent data set maintained a 12-region classification. There
is also a need to look at the possibility of reconstituting the data to
come up with a panel data for the current classification composed of
14 regions or, if possible, even for a more detailed dimension like at
the provincial level.
1.5 The use of the following assumptions in constructing the data set need
to be validated:
i. The assumption used in the computation of the equivalent
man-days regarding the working capacity of a female worker
relative to the working capacity of a male worker.
ii. The assumption that an adult male agricultural worker works
160 days a year while an adult female works 105 days.
81
iii. The assumption used in the calculation of the equivalent
animal work day that the carabao and the cattle work an
average of 220 and 150 days a year, respectively.
iv. The cost of services of the work animals per workday was
assumed to be one half of the daily wage of agricultural
labor.
v. The assumption that labor employment in agriculture is a
constant-proportion (92%) of the reported total employment
in the agricultural sector.
vi. The use of fertilizer consumption/sales data (withdrawals
from importers’ and manufactures’ warehouses) in lieu of
data on the actual fertilizer consumption.
vii. The assumption regarding the depreciation as well as the
interest rates used for the computation of the amount of
capital service of the agricultural machinery.
viii. The imputation of land price as the residual of total revenue
net of measured costs for agricultural labor, fertilizer, seeds,
and machinery and animal services.
1.6 The implication of the use of interpolation, extrapolation and other
techniques such as the application of regional shares to distribute
national data to the different regions to fill in the missing years. An
example of this is the distribution of deflated capital stock figures to the
regions using the regional distribution of interpolated number of
82
tractors. These techniques are commonly used and these might have
caused some biases in the estimates for productivity growth or levels.
7. Recommendations and Conclusion
In spite of the fact that all studies reviewed in this paper used different
data sets, time frames and theoretical constructs, most of these studies revealed
positive growth rates for Philippine agriculture. A review of the existing literature,
however, poses a challenge with regard to the veracity of productivity estimates
offered by the studies due to the reported problems such as the data gaps or
difficulties in constructing relevant data series especially at the regional level and
also in terms of the use of restrictive assumptions to fill in the missing years, the
measurement errors attributed to missing variables and over/under measurement
of the output and input data, to name a few.
In order to provide better estimates of productivity, particularly in
Philippine agriculture, more work needs to be done on the following areas:
83
On the issue of methodology, there is a need to highlight the use of
models with functional forms that are flexible enough to take into account the
complexity of relations between output and input and between various inputs to
include intermediate inputs such as pesticides, herbicides, organic fertilizers, etc.
due their increasing importance in agricultural production. These relationships
can be easily investigated by using parametric approaches like the econometric
and the SFA. Aside from productivity estimates, these approaches can provide
information on the production structure and the nature of technology in Philippine
agriculture not readily available when using for example the index number
procedure. For Philippine agriculture, one can also use the DEA-based
Malmqvist productivity index approach to address the issue on the availability of
quality input data series. This approach provides additional information on
efficiency which is assumed to be constant in the case of econometric approach.
There is also a need to identify the data requirements for chosen methods
and conduct a detailed quality assessment of all readily available data series and
consider alternatives for correcting the common data problems. The econometric
approach is more demanding in terms of data requirement than the Malmquist
index approach. The former requires both data on quantity and prices of output
and input, while the latter provides options to use either the data on output or
input quantity. However, both are sensitive to measurement errors due to
missing variables and over/under measurement. These measurement errors
have been shown to cause biases in the parameter estimates that are used in
productivity estimation.
84
In preparing for the data series, there is also a need to develop options for
obtaining the missing data of interest and to assess the feasibility of obtaining the
required data. The missing data problem is more common in all inputs than in
output data and more conspicuous in pooled time-series and cross-sectional data
set.
The measurement of agricultural productivity growth has been in the policy
agenda for the longest time due to its welfare implication. Because of its
importance, a huge literature on agricultural productivity measurement has been
developed. In spite of the extensiveness of the literature, one important
characteristic stands out, that is, most publications measure sectoral productivity
and neglect commodity-specific productivity growth. This observation also holds
true in Philippine agriculture and this can be attributed to the difficulty of
allocating inputs to individual outputs. There is a need, therefore, to review
research studies addressing this empirical issue such as those of Lence and
Miller 1998; Paris and Howitt, 1998 and Just, Zilberman and Hochman, 1983).
The studies reviewed in this paper, particularly the country-specific
studies, that have focused on Philippine agriculture constitute a small part
relative to the extensiveness of a broad body of international work that has been
published with regard to productivity since the 1980s. The attempt to investigate
key issues related to data and estimation will not only bring research on
Philippine agricultural productivity at par with international studies, it will also
provide broader evidence significantly to rejuvenate Philippine agriculture.
85
Table 1: Productivity Studies on Philippine Agriculture: 1986-2005
Authors Year Years Covered
Productivity Estimates
Methodology
Cross-country Studies
Trueblood and Coggins
1997 1961-1991 0.0119 Malmqvist Index
Arnade 1997 1961-1993 -0.0040 Malmqvist IndexMartin and Mitra 1999 1967-1992 0.0164
0.0157
Translog Production Function
Cobb-Douglas Production Function
Martin and Mitra 1999 1967-1992 0.0207 Growth Accounting (Actual Factor Share)
Fulginiti and Perrin
1998 1961-1985 -0.0250 Growth Accounting Method
Fulginiti and Perrin
1998 1961-1985 0.0010
0.0180
Production Function (Variable Coefficient)
Production Function(Fixed Coefficient)
86
Fulginiti and Perrin
1998 1961-1985 -0.0030 Malmqvist Index
Coelli and Rao 2003 1980-2000 0.0080 Malmqvist IndexCoelli and Rao 2003 1980-2000 0.0130 Index Number Approach
(Törnqvist Index Procedure)Mundlak, Larson
and Butzer2004 1961-1998 0.0025 Production function
Country-specificStudies
Evenson and Sardido
1986 1950-1984 0.0190 Growth Accounting Method
Cororaton and Cuenca
2001 1980-1998 -0.0056 Growth Accounting Method
Teruel and Kuroda
2004 1974-2000 0.0051 Translog Variable Cost Function
Teruel and Kuroda
2005 1974-2000 0.0162 Index Number Approach(Törnqvist Index Procedure)
Teruel and Kuroda
2005 1974-2000 0.0091 Cobb-Douglas Production Function
Teruel and Kuroda
2005 1974-2000 0.0142 Translog Cost Function
Table 2: Productivity Estimates Across Subperiods in Philippine AgricultureAuthors Year Years
CoveredProductivity Estimates Methodology
Evenson and Sardido
(5-year subperiod)
1986 1950-19541955-19591960-19641965-19691970-19741975-19791980-1984
0.0210.0240.0180.0080.0140.053-0.011
Growth Accounting Method(Land Rents Based)
Evenson and Sardido
(5-year subperiod)
1986 1950-19541955-19591960-19641965-19691970-19741975-19791980-1984
0.0270.0250.0190.0100.0210.053-0.011
Growth Accounting Method(Fixed Share = 0.3)
Evenson and Sardido
(10-year subperiod)
1986 1955-19641965-19741975-1984
0.0210.0130.021
Growth Accounting Method(Land Rents-based)
Evenson and Sardido
(10-year subperiod)
1986 1955-19641965-19741975-1984
0.0220.0160.021
Growth Accounting Method(Fixed Share = 0.3)
Corroraton and 2001 1981-1989 -0.054 Growth Accounting Method
87
Cuenca 1990-1998 0.042Mundlak, Larson
and Butzer2004 1961-1980
1980-19980.00980.0013
Production Function(With State Variables)
Teruel and Kuroda 2004 1974-19801981-19901991-2000
0.00770.00500.0035
Translog Variable Cost Function
Teruel and Kuroda 2005 1974-19801981-19901991-2000
0.0219-0.00530.0144
Cobb-Douglas Production Function
Teruel and Kuroda 2005 1974-19801981-19901991-2000
0.02500.00500.0260
Index Number Approach(Törnqvist Index Procedure)
Teruel and Kuroda 2005 1974-19801981-19901991-2000
0.02010.01160.0084
Translog Cost Function
Table 3: Productivity Estimates by Regions in Philippine AgricultureRegions Growth Accounting
Approach
(Evenson and Sardido, 1986)
9-Region Classification
1950-1984
Translog Variable Cost Function
(Teruel and Kuroda, 2004)
12-Region Classification
1974-2000
Index Number Approach
(Teruel and Kuroda, 2004)
12-Region Classification
1974-2000
Ilocos 0.0160 0.0156 0.0216
Cagayan Valley 0.0073 0.0084 0.0204
Central Luzon 0.0000 0.0145 0.0328
Southern Tagalog 0.0239 0.0066 0.0216
Bicol 0.0047 -0.0025 -0.0029
Western Visayas 0.0083 -0.0059 0.0055
88
Central Visayas - 0.0059 0.0107
Eastern Visayas 0.0146 0.0021 0.0091
Northern-Eastern Mindanao
0.0254 - -
Western-Southern Mindanao
0.0109 - -
Western Mindanao - -0.0028 0.0017
Northern Mindanao - 0.0085 0.0216
Southern Mindanao - 0.0082 0.0201
Central Mindanao - 0.0032 0.0085
Table 4: Productivity Studies on Philippine Agriculture by Approaches: 1986-2005
Authors Year Years Covered
Productivity Estimates Methodology
Growth Accounting Approach
Evenson and Sardido 1986 1950-1984 0.0190 Growth Accounting Method
Fulginiti and Perrin 1998 1961-1985 -0.0250 Growth Accounting MethodMartin and Mitra 1999 1967-1992 0.0207 Growth Accounting
(Actual Factor Share)Cororaton and Cuenca 2001 1980-1998 -0.0056 Growth Accounting Method
Index Number Approach
Teruel and Kuroda 2005 1974-2000 0.0162 Index Number Approach(Törnqvist Index Procedure)
Coelli and Rao 2003 1980-2000 0.0130 Index Number Approach(Törnqvist Index Procedure)
Econometric Approach
Fulginiti and Perrin 1998 1961-1985 0.0010 Production Function
89
0.0180
(Variable Coefficient)
Production Function(Fixed Coefficient)
Martin and Mitra 1999 1967-1992 0.0164
0.0157
Translog Production Function
Cobb-Douglas Production Function
Mundlak, Larson and Butzer
2004 1961-1998 0.0025 Production Function (With State Variables)
Teruel and Kuroda 2004 1974-2000 0.0051 Translog Variable Cost Function
Teruel and Kuroda 2005 1974-2000 0.0091 Cobb-Douglas Production Function
Teruel and Kuroda 2005 1974-2000 0.0142 Translog Cost FunctionMalmquist Approach
Trueblood and Coggins 1997 1961-1991 0.0119 Malmqvist IndexArnade 1997 1961-1993 -0.0040 Malmqvist Index
Fulginiti and Perrin 1998 1961-1985 -0.0030 Malmqvist IndexCoelli and Rao 2003 1980-2000 0.0080 Malmqvist Index
8. References
Abramovitz, M. (1956). “Resource and Output Trends in the United States since 1870“. American Economic Review, 46, 2, 5-23.
Aigner, D. J., C.A. Knox Lovell, and P. Schmidt (1977). “Formulation and Estimation of Stochastic Frontier Production Function Models.” J. Econometrics 6: 21-37.
Anderson, K., Dimaranan, B., Hertel, T. and Martin, W. (1996), “Asia-Pacific food markets and trade in 2005: a global, economy-wide perspective” Australian Journal of Agricultural and Resource Economics, 41(1): 19-44.
Antle, John M., (1983). “Infrastructure and Aggregate Agricultural Productivity: International Evidence”, Economic Development and Cultural Change, 31, 609-618.
Amado, F., (1995), “Output Supply and Input Demand in Philippine Agriculture: A Profit Function Approach”. (Unpublished) School of Economics, University of the Philippines, Diliman, Quezon City.
90
Banker, R.D., Charnes, A.,& Cooper, W.W.(1984). “Some models for estimating technical and scale inefficiencies in data envelopment analysis”. Management Science, 30(90), 1078-1092.
Baltagi, B. H. (1995). Econometric Analysis of Panel Data. New York: John Wiley and Sons
Battese, G.E and Coelli, T. J. (1992). “Frontier Production Functions, Technical Efficiency and Panel Data: With an Application to Paddy Farmers in India’ Journal of Productivity Analysis“, 3: 153-169
Battese, G.E and Coelli, T. J. (1995) “A Model for Technical Inefficiency Effects in a Stochastic Frontier Function for Panel Data“, Empirical Economics, 20: 325-332
Bendt ER (1991). “The practice of econometrics: Classic and contemporary”. Addison-Wesley, Boston, MA.
Bhattacharjee, Jyoti P, (1955). “Resource Use and Productivity in World Agriculture,” Journal of Farm Economics 37 57-71.
Binswanger, H. P., (1974). “A cost function approach to the measurement of factor demand elasticities and elasticities of substitution”. American Journal of Agricultural Economics, 56 (2), pp.377-386.
Butzer, R., Larson, D., and Mundlak, Y, (2004). “Agricultural Dynamics in Thailand, Indonesia and the Philippines: The Australian Journal of Agricultural and Resource Economics, 48:1, pp. 95-126.
Butzer, R., Larson, D., and Mundlak, Y., (2002). “Determinants of Agricultural Growth in Indonesia, the Philippines, and Thailand”. The World Bank Development Research Group Rural Development, Policy Research Working Paper 28 03
Caves, D.W., Christensen, L.R. and Diewert, W.E. (1982a), “Multilateral Comparisons of Output, Input and Productivity using Superlative Index Numbers”, Economic Journal 92, 73-86.
Caves, D.W., Christensen, L.R. and Diewert, W.E. (1982b), “The Economic Theory of Index Numbers and the Measurement of Input, Output and Productivity”, Econometrica 50, 1393-1414.
Charnes, A., W.W Cooper and E. Rhodes (1978), “Measuring the efficiency of decision-making units”, European Journal of Operational Research 2, 429-444.
91
Charnes, A., Cooper, W.W., Lewin, A. Y., & Seiford, L. M. (Eds.). (1994). “Data envelopment analysis: Theory, methodology, and applications”. Boston: Kluwer.
Christensen, L.R., Jorgenson, D.W. and Lau, L.J. (1973), “Transcendental logarithmic production functions”, Review of Economics and Statistics, vol. 55, pp. 28–45.
Clark, Colin, (1940). “The Conditions of Economic Progress” (Macmillan & Co., London, first edition).
Coelli, T. J., and Rao, P. (2003). “Total Factor Productivity Growth in Agriculture: A Malmquist Index Analysis of 93 Countries, 1980-2000“, Working Paper Series No. 02/2003, School of Economics, Centre for Efficiency and Productivity Anaalysis, University of Queensland, Australia.
Coelli, T. J., Rao, P., O’ Donnell, C. J. and Battese, G. E. (2005). An introduction to Efficiency and Productivity Analysis. 2nd Edition. New York: Springer
Coggins, J. and Trueblood, M.A., (1997), “Intercountry Agricultural Efficiency and Productivity: A Malmquist Index Approach,” U.S. Department of Agriculture, Cooperative State Research Service, Research Initiative Grant #9302723.
Cororaton, C., Cuenca, S., (2001). “Estimates of Total Factor Productivity in the Philippines”. Discussion Paper Series No. 2001-02.
Craig, B., P. Pardey, and J. Roseboom, (1997), “International Productivity Patterns: Accounting for Input Quality, Infrastructure and Research”, American Journal of Agricultural Economics, 79, 1064-1076.
Cuesta, R. A. (2000) ‘A Production Model With Firm-Specific Temporal Variation in Technical Efficiency: With Application to Spanish Dairy Farms’ Journal Productivity Analysis, 13:139-158
Diewert, W.E. (1978), “Superlative Index Numbers and Consistency in Aggregation”, Econometrica 46, 883-900.
Echeverria, R.G. (1990). “Assessing the Impact of Agricultural Research. In Methods for Diagnosing Research System Constraints and Assessing the Impact of Agricultural Research“. Proceedings of the ISNAR/Rutgers Agricultural Technology Management Workshop. The Hague: ISNAR.
Evenson, R., Kislev, Y., (1975). “Agricultural Research and Productivity”. Yale Univ. Press, New Haven.
92
Evenson, R.E., Sardido M.L., (1986). “Regional Total Factor Productivity Change in Philippine Agriculture”: A Journal of Philippine Development. Number Twenty –Three, Volume XII, 1986.
Evenson, R. E. and Quizon J., (1991), “Technology, infrastructure, output supply, and factor demand in Philippine agriculture“. In R. E. Evenson & C. E. Pray (Eds.), Research and productivity in asian agriculture. Ithaca and London: Cornell University Press.
Färe, R., Grosskopf, S., Norris, M. and Zhang, Z. (1994). “Productivity growth, technical progress and efficiency change in industrial countries”, American Economic Review 84: 66-83
Flynn, R.P. and Siepel, C.,(2003), “Calculating Fertilizer Costs. Cooperative Extension Services”, College of Agriculture and Home Economics, New Mexico State University.
Frisvold, G. and Ingram, K. (1995). “Sources of Agricultural Productivity Growth and Stagnation in sub-Saharan Africa”, Agricultural Economics, 13, pp. 51-61.
Fuglie, Keith, N. Ballenger, K. Day, C. Klotz, M. Ollinger, J. Reilly, U. Vasavada, and J. Yee. (1996). ”Agricultural Research and Development: Public and Private Investments Under Alternative Markets and Institutions,” USDA-ERS, Agriculture Economic Report No. 735.
Fulginiti, L.E, and Perrin, R.K., (1997), “LDC agriculture: nonparametric malmquist productivity indexes“. Journal of Development Economics, 53, pp. 373-390.
Fulginiti, L.E., Perrin R.K., (1998). “Agricultural Productivity in Developing Countries”. Faculty Publications: Agricultural Economics.
Fulginiti, L.E, and Perrin, R.K. (1999). “Have price policies damaged LDC agricultural productivity“. Contemporary Economic Policy, 17, pp. 469-475.
Greene, W.M., (1993). “The econometric approach to efficiency analysis, in: Harol O. Fried, C.A.K. Lovell and S.S. Schmidt, eds., The Measurement of Productive Efficiency": Techniques and Applications (Oxford University Press) 68-119.
Hayami, Y., Kawagoe, T., and V. Ruttan, (1985). “The Intercountry Agricultural Production Function and Productivity Differences Among Countries”, Journal of Development Economics, 17, 113-132.
Homburg, C., (2002). “Benchmarking durch Data Envelopment Analysis” Wirtschaftswissenchaftliches Studium (29), pp. 583-587.
93
Huffman, Wallace E. and R. E. Evenson. (1993). Science for Agriculture A Long-Term Perspective. Ames, IA: Iowa State University Press, 1993
Hu, F., and J.M. Antle (1993), “Agricultural Policy and Productivity: International Evidence,” Review of Agricultural Economics, 15, 495-505.
Jondrow, J., C. A. Knox Lovell, I. S. Materov, and P. Schimdt (1982). “On the Estimation of Technical Inefficiency in the Stochastic Frontier Production Function Model.” J. Econometrics 19 233-38.
Jorgenson, D.W. and Griliches, Z. (1967). The Explanation of Productivity Change. Review of Economic Studies, 34, 349-83.
Just, R., D. Zilberman, and E. Hochman. (1983). “Estimation of multi-crop production Functions“, American Journal of Agricultural Economics, 65, 770-780.
Kawagoe, T., Hayami, Y. (1985). “ “An intercountry comparison of agricultural production efficiency“. Am. J. Agric. Econom. 67, 87-92.
Kumbhakar, S. C. (1990) “Production Frontiers, Panel Data, and Time-Varying Technical Inefficiency“, Journal of Econometrics, 46:210-212
Kumbhakar, S. C., Ghosh, S. and McGuckin, J. T. (1991) “A generalized Production Frontier Approach for Estimating Determinants of Inefficiency in US Dairy Farms“, Journal of Business and Economic Statistics,9(3): 279-286
Kumbhakar, S. C. “Estimation and Decomposition of Productivity Change When Production is not Efficient: A Panel Data Approach. “Economic Reviews 19(2000): 425-460.
Kumbhakar SC & Lovell CAK (2000). “Stochastic frontier analysis”. Oxford University Press, Oxford.
Lau, L. and P. Yotopolous, (1989), “The Meta-Production Function Approach to Technological Change in World Agriculture”, Journal of Development Economics, 31, 241-269.
Lence, H.L., and D. Miller. (1998a). “Recovering output-specific inputs from aggregate input data: A generalized cross-entropy approach“, American Journal of Agricultural Economics, 80, 852-867.
Lence, H.L., and D. Miller. (1998b). “Estimation of multi-output production functions with incomplete data: A generalized maximum entropy approach“. European Review of Agricultural Economics, 25, 188-209.
94
Makki, S.S., and Tweeten, L.G.. (1993). “Impact of Research, Extension, Commodity Programs, and Prices on Agricultural Productivity.” Paper presented at the 1993 meetings of the American Agricultural Economics Association, Orlando, Florida.
Martin, Will and Devanish Mitra (2001).”Productivity Growth and Convergence in Agriculture and Manufacturing”. Economic Development and Cultural Change, 49 (2). pp.403-21.
McFadden, D., (1978). “Cost, Revenue and Profit Functions in Production Economics: A Dual approach to theory and Applications”, Vol. 1. In M. Fuss and D. McFadden (Eds.), Amsterdam: North-Holland Publishing Company.
Meeusen, W. and van den Broeck, J. (1977) “Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error“, International Economic Review. 18(2): 435-444
Mundlak, Y. and R. Hellinghausen, (1982), “The Intercountry Agricultural Production Function: Another View”, American Journal of Agricultural Economics, 64, 664-672.
Nguyen, Dũng, (1979). “On Agricultural Productivity Differences among Countries”, American Journal of Agricultural Economics 61 565-570
Ohta, M., (1974), “A note on the duality between production and cost functions: rate of returns to scale and rate of technical progress”. Economic Studies Quarterly, 25 (3), pp. 63-65
Oppenfeld, H. von, Oppenfeld, J. von, Sta. Iglesia, J. C., and Sandoval, P. R., (1957). “Farm management, Land use, and Tenancy in the Philippines”. Central Experiment Station Bulletin 1, University of the Philippines.
Paris, Q., and Howitt, R.E., (1998), “An analysis of ill-posed production problems using Maximum Entropy“, American Journal of Agricultural Economics, 80(1), 124-138.
Prasada Rao, D.S. & Coelli, T.J. (2003). “Catch-up and Convergence in Global Agricultural Productivity”. Brisbane. Center for Efficiency and Productivity Analysis. University of Queensland. 24p.. (unpublished).
Pray, C E., Neumeyer, C., and Upadhyaya, S. (1988). “Private Sector Food and Agricultural Research in the United States: Trends and Determinants of R & D Expenditures,” unpublished paper.
95
Peterson, Willis (1987). International Land Quality Indexes, Department of Agricultural and Applied Economics Staff Paper P87-10, University of Mennisota
Quizon, J. B., (1980), “Factor Gains and Losses in Agriculture: An Application of Cost and Profit Functions”. (Dissertation) University of the Philippines, Manila.
Richmond W (1974). “Estimating the efficiency of production”. International Economic Review 15:515-521.
Ruttan,V. W. (1980) “Bureaucratic Productivity: The Case of Agricultural Research“, Public Choice, 35.
Solow, R. M. (1957). “Technical Change and the Aggregate Production Function“. Review of Economics and Statistics, 39, 312-320.
Teruel, R., and Kuroda, K., 2004, An empirical analysis of productivity in Philippine agriculture, 1974-2000. Asian Economic Journal 18 (3), pp. 319-344.
Teruel, Romeo.G. and Yoshimi Kuroda (2005). “Public Infrastructure and Productivity Growth in Philippine Agriculture”, 1974-2000: Journal of Asian Economics 16 555-576.
Tinbergen, J. (1942). Zur Theorie der Langfirstigen Wirtschaftsentwiicklung. Weltwirst. Archiv., 1, Amsterdam : North-Holland Publishing Company, 511-549.
96