Efficiency and Technical Change in the Philippine Rice Sector:
A Malmquist Total Factor Productivity Analysis*
Chieko Umetsu 1,2, Thamana Lekprichakul 3 and Ujjayant Chakravorty 4
1
The Graduate School of Science and Technology, Kobe University, Kobe 657-8501, Japan
2
Program on Environment, East-West Center, Honolulu HI 96848, USA
3
Department of Economics, University of Hawaii at Manoa, Honolulu HI 96822, USA
4
Department of Economics, Emory University, Atlanta GA 30322, USA
Abstract
We account for regional differences in total factor productivity, efficiency and technological
change in the Philippine rice sector during the post-Green Revolution era by using Malmquist
productivity indices for the period 1971-90. The Malmquist indices were decomposed into
efficiency and technological change. The average annual Malmquist productivity growth was
found to be only slightly positive. Productivity growth was negative during the early 70’s, and
was followed by a period of positive growth, and negative growth in the late 80’s. The period of
positive growth coincided with the introduction of new rice varieties while the declines may have
been caused by intensification of rice production in lowland systems. Certain regions such as
Central Luzon, Western Visayas, South and North Mindanao exhibited higher rates of
technological change than others, which seems to have been contributed by higher investments in
irrigation, increased adoption of tractors, higher population growth rates and a better
agroclimatic environment.
JEL classification: O13; O33; Q16
Key words: Malmquist Productivity Index, Technical Change, Philippine Rice Sector
* This research was financially supported by the Foundation for Advanced Studies on
International Development, Tokyo, Japan. Please address all correspondence to Chieko Umetsu:
phone/fax: +81 (78) 803-5840; e-mail: umetsu@kobe-u.ac.jp.
Efficiency and Technical Change in the Philippine Rice Sector:
A Malmquist Total Factor Productivity Analysis
1.
Introduction
Agricultural intensification has long been considered the primary means by which
governments and international development agencies induce technological change in developing
countries characterized by high population pressure and low agricultural productivity. However,
some policy advocates now argue that contrary to the Boserup hypothesis which suggests that
population pressure is a sufficient condition for inducing technological change and productivity,
governments need to play a pro-active role in agricultural development by investing in rural
infrastructure and promoting higher input use through incentives such as input subsidies (Lele
and Stone, 1989).
However, the process of agricultural intensification itself may lead to productivity
declines. Recent observations of agricultural productivity in the post-Green Revolution era have
concluded that intensive monoculture production systems have contributed to declining or
stagnant productivity and farm incomes, as well as related environmental impacts such as
increased pest pressure, depletion of soil micronutrients, and changes in soil chemistry caused by
intensive cropping (Bouis, 1993; Pingali et al., 1995).
The purpose of this paper is to analyze trends in productivity growth in the Philippines
rice sector and examine the factors affecting productivity performance by region. We seek to
verify whether agricultural intensification in the Philippines was accompanied by technological
change and productivity growth during the post-Green Revolution era. This is done by applying
an input-oriented Malmquist Productivity Index. Traditionally, technical change has been
modeled in neoclassical growth models with the implicit assumption that a common production
function is available to all countries regardless of human capital, resource or institutional
1
endowment (Ruttan, 1995). This tradition is followed by productivity studies which incorporate
and measure the contribution of technology, human capital and other non-conventional factors of
production using specific functional forms for the production function. These studies usually
analyze national aggregate data to compare productivity among countries (Hayami and Ruttan,
1970, 1985; Kawagoe, Hayami and Ruttan, 1985; Lau and Yotopoulos, 1989). However, when
regional differences in factor endowment and technology are significant, cross-country
comparisons do not provide policy insights for regional development since they do not
incorporate location specific factors of production and technical change.
Other methods of productivity measurement such as growth accounting and index
number approaches also have some limitations. First, they assume that production is efficient by
theoretical construct. Inefficient production activity is largely ignored. Second, price data
requirements make it difficult to apply this method in developing countries where reliable price
information is often not available. Recent developments in nonparametric frontier approaches
provides more flexibility to productivity and technical change analysis. The Malmquist total
factor productivity (TFP) index was suggested by Färe, Grosskopf, Lindgren and Ross (1989)
following the work of Caves, Christensen, and Diewert (1982a). This index is based on the
Farrell measure of technical efficiency (Farrell, 1957) and Shephard's (1953) distance function.
One of the advantages of the Malmquist index is that it requires only quantity data, and it is not
constrained by a specific functional form of the production or cost function.1 The Malmquist
productivity index has recently been applied to cross-country comparisons of total factor
productivity (Thirtle, Hadley and Townsend, 1994; Färe et al., 1994b; Fulginiti and Perrin,
1997).
1
Diamond et al. (1978) assert that the traditional parametric approach to analyzing technology and technical change
may be sensitive to the particular parametric specification utilized.
2
Earlier studies on productivity and technical change in the Philippine agricultural sector
have relied on index number approaches for estimating total factor productivity (David, Barker
and Palacpac, 1985). While their analyses was based on aggregate data for the agricultural sector
of the Philippines, Antonio, Evenson and Sardido (1977) and Evenson and Sardido (1986)
looked at total factor productivity at the regional level. These studies assume that there is no
inefficiency in production. Färe, Grabowski and Grosskopf (1985) estimated the Farrell measure
of technical efficiency of the Philippine agricultural sector to account for production
inefficiency. However, their study is based on aggregate time series data used in David and
Barker (1979) and hence is restricted to national level data.
We estimate the input-oriented Malmquist total factor productivity index of the rice
sector based on panel data for 1971-90 for 12 regions of the Philippines. The method does not
impose specific functional forms on production technology. Results show that the rice sector in
some regions of the Philippines experienced negative long-run growth in total factor productivity
between 1971 and 1990, primarily due to input-biased intensification. In particular, productivity
growth was negative during the early 70’s, the second half of the Green Revolution era, followed
by positive growth in the late 70’s and early 80’s, leading to another negative trend in the late
80’s. The positive growth coincided with the introduction of new rice varieties, and the negative
growth suggests that rice production may have entered a phase where growth was primarily
achieved through input intensification. These findings confirm the results of Pingali (1992) and
others who suggest that intensive production systems may be causing productivity declines and
degradation of lowland rice environments in the Philippines and other agriculture-based
economies in Asia today. At the regional level, our analysis suggests that certain regions such as
Central Luzon, West Visayas and South and North Mindanao have performed markedly better
3
than others. Several factors contributed to this phenomena, including irrigation investments and
tractor use, as well as higher rates of population growth and a favorable agroclimatic
environment, although no single factor was found to be singularly important across regions.
The organization of the study is as follows. Section 2 briefly reviews trends in
agricultural development and factor/resource endowment for each region. Section 3 discusses the
theoretical construct and the method for estimating the Malmquist productivity index and its
decomposition into efficiency and technical change. Section 4 describes the panel data set used
in this study. Section 5 presents the results of the regional Malmquist index and the second stage
analysis. Section 6 concludes the paper.
2.
Overview of the Rice Sector and Factor/Resource Endowment in the Philippines
The total rice production in the Philippines more than doubled during the last 30 years
(IRRI, 1995; see Appendix Table 1). The introduction of high yielding varieties during the late
1960’s started the era of the "Green Revolution" in the Philippine rice sector. Otsuka, Gascon
and Asano (1994) divide the period for adopting modern high-yielding varieties into a first and
second generation. The first-generation was initiated in 1966 by the release of IR8 followed by
IR5 through IR34 and C4. These varieties were not resistant to pests and diseases. The second
generation started in 1976 when IR36 was introduced. The land planted by modern varieties
increased from 58% to 86% between 1971 and 1990 (Table 1). By 1979, more than 90 percent of
farmers in Central Luzon, the "rice bowl" of the Philippines, adopted second-generation varieties
(IR36-IR76) which almost doubled the rice yield compared to the traditional varieties. This trend
is indicated in Table 1.
Between 1971 and 1990, the average Philippine palay (rice) yield for high yielding
4
varieties increased from 1.74 to 2.69 metric tons per hectare. The average yield surpassed 3
metric tons per hectare in Cagayan Valley, Central Luzon, and Mindanao regions although not in
Western Mindanao. Southern Mindanao reached the highest yield per hectare in the late 80's
because of favorable soil conditions and a high adoption rate of modern varieties in the early
70's. High yielding varieties contributed to a significant increase in yield, with modern varieties
producing a third more on average than traditional varieties.
Although the average yield per hectare was increasing during 1971-90, the growth of rice
yields stagnated during the late 80’s. Average annual growth of rice yields was 1.6% in the early
70’s and jumped to 4.2% in the late 70’s when farmers switched to second-generation modern
varieties (PhilRice-BAS, 1994). In the early 80’s the growth rate of yields decreased sharply to
2.9% and then to 1.4%. Recently, many studies reported a declining trend of yield growth in Asia
during the post-Green Revolution era since the late 70’s:
"An assessment of farm-level data over time, ..... , shows that yield levels are being
maintained with increasingly higher input levels, indicating a long-term decline in total factor
productivity." (Pingali, Zeigler, Hossain and Prot, 1995)
In addition, the macroeconomic conditions during the 80’s were not favorable to rice
farming. Volatile political conditions, high inflation and a series of devaluations adversely
affected rice farming; and the Philippines 2, which once reached its self-sufficiency goal in 1978,
again turned into a net importer of rice in 1984. A severe debt crisis during the 80’s discouraged
production-oriented government investment projects such as research, irrigation, and credit
(Evenson and David, 1993).
The high yielding varieties required more labor for weeding, as well as increased
2
For a detailed discussion regarding macroeconomic impacts on environment in the Philippines, see Cruz and
Repetto (1992), and Montes and Lim (1996).
5
fertilizer inputs and controlled water supply relative to the traditional varieties. The scheduled
irrigated water supply increased the yield with the adoption of high yielding varieties during the
dry season because traditional varieties are photo-period sensitive and not suitable for dry season
production (Otsuka et al., 1994). In areas where irrigated water and favorable rainfall were
available, farmers responded quickly to adopt high yielding varieties. Also, high yielding
varieties increased crop intensity due to a shorter growth duration compared to traditional
varieties.3 ,4 Otsuka (1991) points out that land reform was successfully implemented in areas
where the potential benefit from adopting high yielding varieties was large. These characteristics
of high yielding varieties induced higher input use in the rice producing sector during 1971-90.
Table 2 shows the ratios of factor use by region. Labor use per hectare was high during
the Green Revolution era (1971-1975) and decreased gradually during the post Green Revolution
era mainly due to the introduction of tractors. Central Visayas and Eastern Visayas had high
labor use per hectare reflecting slow adoption of labor saving technologies5 such as the hand
tractor. Fertilizer use per hectare, on the other hand, increased on average more than 1.5 times in
the Philippines during 1971-90, with the highest being in Central Luzon. The Philippine
government implemented various kinds of subsidies to increase fertilizer use. When the Fertilizer
Industry Authority (FIA) was established in 1973, fertilizer prices for priority crops (rice, corn,
feedgrains, and vegetables) were controlled by the government and set at 50-70% lower than
other crops (Francia, 1993). Direct cash subsidies were given to fertilizer companies between
1973 and 1982. In addition to direct subsidies, indirect subsidies in the form of tax exemptions
were given to fertilizer imports. In 1988, when the government launched the Rice Production
3
A crop intensity of more than one shows the existence of double or triple cropping.
The growth duration of traditional varieties, first-generation varieties, and second-generation varieties, are 155
days, 130 days and 115 days, respectively (Estudillo, 1995).
4
6
Enhancement Program (RPEP), fertilizers were again subsidized for farmers.
Labor saving technology such as tractors also increased during the study period showing
large regional differences in tractor use per hectare. Tractor use was particularly high in Central
Luzon, and lowest in Central Visayas. On the other hand, the carabao (water buffalo used for
animal draft) population did not decrease significantly. This may reflect the fact that during the
oil crisis, imported machinery became expensive and farmers substituted carabao for tractors
(Hayami et al., 1990).
Investment in irrigation expanded rapidly from 81 million pesos (US$12.6 million) in
1971 to 2,481 million pesos (US$88.6 million) in 1990 (IRRI, 1995). As a result, the percentage
of irrigated area out of planted area to rice increased from 37% to 53% in the Philippines as a
whole, and Cagayan Valley, followed by Central Luzon, had the highest proportion of irrigated
rice (see Appendix Table 2). The rapid growth of irrigation investment occurred only during the
70’s and the investment during the 80’s stagnated (IRRI, 1995). While irrigated land increased,
total rice area declined since 1970, which suggests that rice production intensified in the study
period. The quality of road infrastructure is high in Ilocos, Central Luzon, Southern Tagalog,
Bicol, and poor in all Mindanao regions.
Population growth in the 12 regions (not including the National Capital Region (NCR),
Cordillera Autonomous Region (CAR), and Autonomous Region in Muslim Mindanao (ARMM)
showed a declining trend during 1971-90. Population density was high in Luzon Island with
intense population pressure on agricultural lands. Ilocos, Central Luzon and Southern Tagalog
showed a population density of more than 10 persons per hectare of arable land and permanent
cropland during the study period. The growth of population density was high in Central Luzon
5
Other labor saving technologies include direct-seeding, herbicides, threshers which require less labor for plant
establishment and weeding (Otsuka, Gascon, and Asano, 1994).
7
and Western Visayas during the post Green Revolution period and declined sharply during the
1980’s.
Distribution of production to landlords in the region may be considered a proxy for the
degree of implementation of land reform policies. Roumasset and James (1979) argue that
regional differences in output share of landlords could be explained by land quality and
population. If land quality and population density are high, both will positively affect the
landlord share since a high physiological density will lead to low farm wages. In reality, the
landlord share of output has decreased during the study period in most of the regions, especially
in Cagayan Valley, Central Luzon and Southern Tagalog, although both population density and
population growth were relatively high in these regions6 (Appendix Table 3, 4). This is possibly
due to a combination of the following factors: land reform policies which reduced the share of
production accruing to landlords; land-saving technological change such as fertilizers which
decreased profits accruing to land (Roumasset and James, 1979); and finally, competition
between agriculture and other sectors of the economy for labor that did not depress farm wages
inspite of high population densities. On the other hand, Ilocos is the only region where
production share of the landlord was increasing over time and the area under share contracts was
the highest among all the regions. Total area of palay farms under share contract was 42.1% in
Ilocos, substantially higher than the 23.3% in Cagayan Valley and 17.4% in Central Luzon.
Agroclimatic conditions were favorable in Mindanao which is endowed with fertile soil.
According to Department of Agriculture (1993) estimates, average maximum potential palay
yield in Mindanao was over 6,139 metric tons per hectare (MT/ha), which was far higher than
6
The landlord share data in the Regional Rice Statistics Handbook may underestimate the actual shares which
usually fall between 30 and 50 per cent, according to farm-level studies (Roumasset, 1984).
8
that of Luzon (4,477 MT/ha) and of Visayas (4,679 MT/ha). On Luzon Island7, palay production
has already been quite intensive and there was not much potential for a major increase in yields.
Visayas and Mindanao, the southern part of the Philippines, have relatively less rainfall
compared to Luzon. Coastal regions which face the Pacific Ocean usually have a wet season8 of
more than nine months of the year. Eastern and Central Visayas, and Northern Mindanao are
especially disaster-prone areas. When the 1983 drought hit the Island, total rice area harvested in
Central Visayas declined by 43% compared to the previous year (PhilRice-BAS, 1994).
During 1970-89, soil erosion from the agricultural sector accounted for 22.4% of total
soil erosion in the Philippines (IRG, 1992). Soil erosion from agriculture was highest in Southern
Tagalog, 69 million metric tons (MMT). This was followed by Southern Mindanao (58 MMT)
and Bicol (51 MMT) where agricultural production was intensive. Among alternative land use
patterns, upland agriculture has significant effects on soil erosion compared to irrigated lowland
rice paddies (IRG, 1994).
In spite of the "Green Revolution" which brought high yielding varieties to the rice sector
of the Philippines, income of agricultural households did not increase relative to non-agricultural
households. A regional polarization of farmers became apparent after the Green Revolution. In
1971, regions where agricultural household income was lower than the Philippine average were
distributed throughout the country. In 1991, however, regions with above the national average
income can be found only in Luzon and ARMM. The introduction of high yielding varieties
contributed to an increasing income level of rice producing regions, especially on Luzon Island.
The income distribution within the rice sector has been found to be skewed in favor of large
farmers after the introduction of high yielding varieties (David et al., 1994). David argues,
7
8
In 1992, Luzon Island alone produced 63% of the total rice production in the Philippines.
Rainfall less than 100 mm per month is a dry month and more than 200 mm per month is a wet month.
9
however, that the high yielding varieties contributed significantly to increasing income for poor
farmers as well as landless farmers.
3.
Malmquist Total Factor Productivity Index
3.1 Farrell measure of technical efficiency and the distance function
In order to account for the regional total factor productivity, efficiency and technology
change in the Philippine rice sector, we apply the nonparametric method for estimating those
indices.
Farrell (1957) suggested the measurement of technical efficiency using piecewise linear
technology. Linear programming constructs a “best practice” frontier technology. Farrell’s
efficiency measure is the inverse of Shephard’s (1953) distance function, which provides the
theoretical base for the Malmquist productivity index.
The production technology is represented as the set of all feasible input and output
vectors for time period t. Let x t = ( x1t , x 2t ,..., x Nt ) denote an input vector at period t with i=1,..,N
inputs and y t = ( y1t , y 2t ,..., y Mt ) denote an output vector at period t with j=1,..,M outputs where
x t ∈ ℜ +N , and y t ∈ ℜ +M . The technology is expressed by the input requirement set, as follows:
Lt ( y t ) = {x t :( x t , y t ) ∈ S t }, t = 1,..., T .
(1)
where S t = {( x t , y t ): x t can produce y t } is the set of technology at period t. The input
requirement set, Lt ( y t ) provides all the feasible input vectors, x t ∈ ℜ +N , that can produce the
output vector, y t ∈ ℜ +M .
The Farrell measure is the radial measure of technical efficiency in which the efficiency
is obtained by radially reducing the level of inputs relative to the frontier technology holding the
10
level of output constant. The Farrell measure requires input and output quantity information and
is independent of input prices as well as behavioral assumptions on producers. Similarly, the
output-oriented Farrell measure can be defined by radially expanding the level of outputs relative
to the frontier technology holding the level of input constant. Figure 1 illustrates the inputoriented Farrell measure and distance function for a two-input case. The frontier technology is
given by the piecewise linear isoquant, Lt ( y t ) . Efficient production activity is the extreme point
of the convex hull of this frontier (B and C). Line segments extending from B and C, AB and
CD, indicate strong disposability of inputs i.e., disposal of surplus inputs is free. Production
activity c is inside of the input requirement set, thus inefficient. In terms of distance, the Farrell
measure of technical efficiency at period t is given by 0b/0c and the Shephard’s distance function
is the inverse 0c/0b. When the observation is efficient, both the Farrell measure and the distance
function are equal to one. The Farrell measure varies between zero and one, and the distance
function is equal to or greater than one.
k,t
Suppose there are k = 1,...,Kt number of firms which produce M outputs y m , m = 1,...,M,
using N inputs x nk,t , n = 1,...,N, at each time period t = 1,...,T. A piecewise linear input
requirement set at period t is defined as follows:
K
Lt ( y t ) = {x t : y mk',t ≤ ∑ z k ,t y mk,t , m = 1,..., M ,
k =1
K
x nk ',t ≥ ∑ z k ,t x nk ,t , n = 1,..., N ,
(2)
k =1
z k,t ≥ 0,
k = 1,..., K},
where z k ,t indicates intensity levels, which makes the activity of each observation expand or
contract to construct piecewise linear technology (Färe et al., 1994a). Let us define Fi t ( y t , x t ) as
the input-oriented Farrell measure, and Dit ( y t , x t ) as Shephard’s input-oriented distance
11
function at period t with constant returns to scale and strong disposability of inputs and outputs
assumption as:
Fi t ( y t , x t ) = min{λ: λx t ∈ Lt ( y t )},
(3)
Dit ( y t , x t ) = max{λ: ( x t / λ) ∈ Lt ( y t )}.
where Fi t ( y t , x t ) estimates the minimum possible expansion of x t while Dit ( y t , x t ) estimates
the maximum possible contraction of x t .
The following shows the linear programming problem for (2) for estimating the Farrell
measure, or the inverse of the distance function.
Fi t ( y k ',t , x k ',t ) = [ Dit ( y k ',t , x k ',t ) ]− 1 = min λ,
(4)
subject to
K
y mk',t ≤∑ z k ,t y mk,t , m = 1,..., M ,
k =1
K
λx nk ',t ≥ ∑ z k ,t x nk ,t , n = 1,..., N ,
(5)
k =1
z k,t ≥ 0,
k = 1,..., K.
In equation (5), the left hand side of the input and output inequality shows the analysis
set, the observation to be evaluated, and the right hand side shows the reference set. This linear
program evaluates observations at period t relative to the reference (frontier) technology at
period t. In general, both Farrell measure and distance function can be defined with any type of
returns to scale assumptions such as non-increasing returns to scale as well as variable returns to
scale which includes constant, decreasing and increasing returns to scale. By controlling the
K
intensity variables with additional constraints,
∑
k =1
z k ,t = 1 , and
K
∑z
k ,t
≤1 in the linear program,
k =1
variable returns to scale and non-increasing returns to scale can be imposed (Afriat, 1972). Also,
the strong disposability assumption can be relaxed (Grosskopf, 1986).
12
In order to estimate the Malmquist productivity index from period t to t+1, additional
distance functions are required as follows:
Dit ( y t + 1 , x t + 1 ) = max{λ: ( x t + 1 / λ) ∈ Lt ( y t + 1 )},
(6)
Dit + 1 ( y t , x t ) = max{λ: ( x t / λ) ∈ Lt + 1 ( y t )},
(7)
Dit + 1 ( y t + 1 , x t + 1 ) = max{λ: ( x t + 1 / λ) ∈ Lt + 1 ( y t + 1 )}.
(8)
and
The cross-period distance function, Dit ( y t + 1 , x t + 1 ) , indicates the efficiency measure using the
observation at period t+1 relative to the frontier technology at period t, and Dit + 1 ( y t , x t ) shows
the efficiency measure using the observation at period t relative to the frontier technology at
period t+1. In Figure 1, the input requirement set for period t+1 is illustrated by Lt + 1 ( y t + 1 ) , and
Dit ( y t + 1 , x t + 1 ) and Dit + 1 ( y t , x t ) are given by 0e/0f and 0c/0a respectively. Cross period distance
functions take value less than, equal to, or more than one. Similarly, Dit + 1 ( y t + 1 , x t + 1 ) is given by
0e/0d. A linear programming problems of the equation (6), (7), and (8) are similar to equation (5)
once the respective analysis set (observation) and the reference set (frontier technology) are
defined.
3.2
The Malmquist productivity index to measure total factor productivity
The Malmquist productivity index (Färe, Grosskopf, Kindgren and Roos, 1989) is the
geometric mean of two Malmquist indices which were suggested by Caves, Christensen, and
Diewert (1982a). The input-oriented Malmquist productivity index consists of four inputoriented distance functions. The change of productivity between period t and t+1 is defined as:
13
1
D t ( y t + 1 , x t + 1 ) Dit + 1 ( y t + 1 , x t + 1 ) 2
Mit + 1 ( y t + 1 , x t + 1 , y t , x t ) =  i t t t

Dit + 1 ( y t , x t ) 
 Di ( y , x )
(9)
where Dit + 1 ( y t , x t ) and Dit ( y t + 1 , x t + 1 ) are cross-period distance functions.
The Malmquist productivity index can be decomposed into changes in efficiency and
changes in technology as:
1
D t + 1 ( y t + 1 , x t + 1 )  Dit ( y t + 1 , x t + 1 ) Dit ( y t , x t ) 2
Mit + 1 ( y t + 1 , x t + 1 , y t , x t ) = i t t t
⋅ t + 1 t + 1 t + 1
t+ 1
t
t 
Di ( y , x )
Di ( y , x ) Di ( y , x ) 
(10)
where the first term defines changes in efficiency from period t and t+1. The second geometric
mean in the bracket indicates changes in technology, i.e., a shift in the frontier from period t to
period t+1. This decomposition provides useful indices for the study of efficiency and technical
change. In the input-oriented case, all three terms, i.e., the change in productivity, and its
decomposition to the change in efficiency and the change in technology, are interpreted as
progress, no change, and regress, when their values are less than one, equal to one, and greater
than one, respectively.
4.
Data Set
The time series data set for 1971-90 for 12 regions of the Philippines rice sector was
constructed for estimating regional input-oriented Malmquist productivity indices. The twelve
regions are: Ilocos (Region 1), Cagayan Valley (Region 2), Central Luzon (Region 3), Southern
Tagalog (Region 4), Bicol (Region 5), Western Visayas (Region 6), Central Visayas (Region 7),
Eastern Visayas (Region 8), Western Mindanao (Region 9), Northeastern Mindanao (Region 10),
Southeastern Mindanao (Region 11), and Central Mindanao (Region 12). The Cordillera
Autonomous Region (CAR) was not considered as an independent region due to a lack of time
14
series data before the establishment of CAR in 1988. Therefore, data for Region 1 and Region 2
after 1988 does not include the provinces which were reorganized under CAR. The National
Capital Region (NCA) and the Autonomous Region in Muslim Mindanao (ARMM) were
ignored simply due to their small agricultural sectors.
Two outputs and six inputs were used for estimating the input-oriented Malmquist index.
Due to a lack of regional input data for the rice sector, some data was generated by separating it
from aggregate agricultural input data using distribution parameters from the actual regional
data. Outputs consist of total annual production of high yielding varieties (HYV) and traditional
varieties (TV) by region (PhilRice-BAS, 1994).
Inputs are classified into traditional and modern inputs. Traditional inputs are land
harvested of HYV, land harvested of TV (thousand hectares per year), labor (total man-days per
year), and work animals (carabao head per year). The labor inputs were estimated from the total
cost data per hectare for rice production, i.e., cash cost (hired labor), non-cash cost (hired labor
in kind), and imputed cost (unpaid family and operation and exchange labor) between 1985-90.
Work carabaos by region were estimated from various unpublished data sets from the Bureau of
Agricultural Statistics.
Modern inputs such as fertilizer and machinery are considered to embody technology.
Fertilizer use data by grades was converted into actual nutrient sums of nitrogen, phosphorus and
potash ([N+P2O3+K2O] kg per hectare per year). The number of hand tractors used per year were
considered as machinery inputs. This underestimates the number of four-wheel tractors used in
rice production. However, as Otsuka et al. (1994) describe, four-wheel tractors were largely
replaced by two-wheel tractors by the end of the 70’s. Therefore, hand tractors may be a good
approximation of tractor use since separate data for four-wheel tractor use in rice production was
15
not available.
For estimating the Philippine average for the Malmquist index and its components for
each period, a simple geometric mean of each region discriminates against productivity indices
for relatively large rice producing regions. Bjurek and Hjalmarsson (1995) adopted input-shares
as a weight for estimating industry wide input-oriented efficiency. Due to multiple inputs, in this
paper we used an average output-share, i.e., a share of total palay production in each region, to
obtain a weighted average index for the Philippines. Regional Farrell measures were aggregated
using output-shares for period t for efficiency scores for single-period Farrell measures and the
average output-share of period t and t+1 for mixed-period Farrell measures to derive a
"structural" distance function for the Philippines.
For the 2nd-stage analysis, the following explanatory variables are considered.
Infrastructure variables include irrigation and transport. Rural infrastructure such as irrigation
and roads have a significant impact on technological change in the region (Binswanger et al.,
1993). Irrigation infrastructure is measured in terms of the ratio of irrigated area planted to rice.
The percentage of paved road per total length of road was used as a proxy for transport, which
indicates road quality.
According to the Boserup hypothesis (1965), population pressure induces technical
change and intensification of agriculture (Pingali et al., 1987; Lele and Stone, 1989; Thirtle et al.,
1994). However, if a Boserupean transformation is not possible, population pressure is expected
to cause detrimental effects on production (James and Roumasset, 1992). Regional population
pressure is expressed by population per arable and permanent crop land.9 The economically
active population in agriculture is a suitable estimate of population pressure; however, reliable
16
regional time series data was not available.
Technology variables include education and a dummy variable for the second-generation
modern rice variety. Schultz (1964) states that "the acquired capabilities of farm people are of
primary importance in modernizing agriculture." Higher education enrollment per total
population was used as a proxy for the education variable. According to Otsuka's (1994)
classification of modern varieties, the switching from first-generation to second-generation
varieties occurred during 1976-79. Therefore, the dummies for detecting the effects of secondgeneration varieties on productivity change are zero for 1971-77 and one for 1978-80. The share
tenancy ratio, as well as land holdings are strong candidates as explanatory variables in
explaining owner operators’and large farmers' incentive to adopt technology.10 However, since
the land holding data was available for census years only, we used the landlord share of
production from tenants (PhilRice-BAS, 1994). Exogenous variables include rainfall and
disasters. The ratio of monthly average rainfall to the average of the highest three months of
rainfall was used as suggested by Nugent and Sanchez (1995). Rice area damaged by typhoon
and other causes was used as a proxy for disasters.
Reliable regional input price data was most difficult to obtain except for wage and
fertilizer prices. In order to obtain the effect of input price ratios, i.e., fertilizer to land price, and
labor to machinery price, land price was approximated by the value of production distribution to
the landlord and the same machinery price index was used for all regions.
5. Regional Malmquist Productivity Index in the Philippine Rice Sector
9
Binswanger and Pingali (1988) suggest using agroclimatic population density based on production potential of
each country rather than using population per unit area. This method is useful for adjusting regional differences in
land quality. However, due to limited data availability, it was not used in this analysis.
17
5.1 Mamlquist indices during 1971-1990
Table 3 provides a summary of the input-oriented Malmquist total factor productivity
indices of the Philippine rice sector, and their decomposition into efficiency change and
technological change. For these input-oriented measures, an index of less than one represents
progress, and an index of more than one represents regress. However, for an easy interpretation,
the numbers in these tables are the reciprocals of the real indices multiplied by 100 so that an
index of more than 100 indicates progress, an index of less than 100 indicates regress, and an
index equal to 100 shows no change. The numbers in Table 3 show annual averages of five and
twenty-year intervals between 1971 and 1990.
Two indices for the Philippines are the weighted arithmetic mean (WAM) and the
geometric mean (GM) for all 12 regions. During 1971-90, the weighted average Malmquist
productivity growth for the Philippine rice sector was 0.7%, indicating slightly positive growth.
This is similar to earlier results by Evenson and Sardido (1986) who reported a 0.21% average
increase in total factor productivity of the Philippine agricultural sector between 1975-84. On the
other hand, the geometric mean showed negative growth of –0.6%. However, the simple
geometric mean tends to underestimate the productivity change in relatively large rice regions
such as Central Luzon which has been at the production frontier, i.e., the best rice technology,
throughout the study period. We therefore take the weighted mean to be a better representation of
productivity change for the Philippine rice sector.
Productivity growth was found to be negative in the in the early 70’s (–2.0%), the second
half of the Green Revolution era, followed by positive growth during the late 70’s (2.4%), and
the early 80’s (3.6%), and again negative growth in the late 80’s (–1.8%). The positive TFP
10
Ruttan (1977) generalized the adoption of HYV and stated that neither farm size nor tenure constrained the
adoption of HYV.
18
growth coincides with a period during the late 70’s when IR36 and other second-generation
modern varieties were introduced and rapidly adopted by farmers. These results are supported by
the findings of Evenson and Sardido (1986) who show that the highest total factor productivity
growth of agriculture occurred between 1975-84. The growth of total factor productivity is
mostly attributable to technological change (0.7%) during the study period.
Figure 2 illustrates the trend of efficiency, technological change, and Malmquist
productivity indices (WAM) between 1971-90, using 1971 as a base year.11 The technological
change component, i.e., a shift of the frontier technology, displays similar movement to the
Malmquist productivity index, indicating that a change in total factor productivity largely
consists of technological change in the Philippines rice sector during this period. Two oil price
shocks in 1973 and 79 contributed to negative total factor productivity by increasing the import
price of fertilizer, and as a result, domestic fertilizer sales decreased drastically (PhilRice-BAS,
1994). Compared to technological change, the change in efficiency was quite small, and was not
a major source of productivity growth over the twenty years studied.
At the regional level, the average annual growth of the Malmquist index was positive
only in five regions during 1971-90 (Table 3). These regions are Central Luzon, Bicol, Western
Visayas, Northern Mindanao, and Southern Mindanao. Except for Bicol, these regions are
characterized by high rice yields per hectare, high adoption rates of HYV, and high fertilizer and
tractor use per hectare. The annual average total factor productivity growth is highest in Central
Luzon (8.2%) and the other four regions exhibited modest growth of less than 2%. On the other
hand, seven regions resulted in negative annual productivity growth, with the lowest growth in
Central Mindanao (–7.3%) followed by Central Visayas (–4.8%).
Regional differences in Malmquist productivity are also largely due to regional
19
differences in technological progress. Figure 3, 4 and 5 show the technological change index in
Luzon, Visayas, and Mindanao Island, respectively, with 1971 as the base year. Within Luzon
Island, Central Luzon showed much higher technological progress compared to the other four
regions, with more than 2.5 times the relative shift of the production frontier (Figure 3). On
Visayan Island, Western Visayas exhibited technological progress while the other two regions,
Eastern and Central Visayas, lagged behind, and are among the poorest agricultural areas in the
Philippines (Figure 4). In 1991, average income for agricultural households in Central Visayas
was 27,634 pesos/year and in Eastern Visayas was 29,349 pesos/year. These income levels are
the lowest of agricultural households in all regions (NSO, 1991). On Mindanao Island, Northern
and Southern Mindanao made good technological progress, although Western and Central
Mindanao showed negative progress in the study period (Figure 5).
5.2
Factors affecting changes in productivity, efficiency, and technology
The second stage regression analysis attempts to identify factors affecting the Malmquist
TFP as well as efficiency and technical change indices. Three regional blocks, the Island of
Luzon, Visayas and Mindanao, as well as the Philippines as a whole are considered. Luzon
Island is characterized by good infrastructure and high population density. Visayan Islands, on
the other hand, have relatively lower quality infrastructure but favorable rain fall in the coastal
areas. Mindanao Island has low population density and high infrastructure with relatively good
soil quality and a high production potential.
Table 4 presents the results of regression analysis of the Malmquist TFP index and its
components of efficiency and technological change. The Malmquist index, efficiency and
technological change components for 12 regions are grouped into 3 blocks and regressed against
11
The index starts with 1972 because the index in 1972 requires data from 1971 and 1972.
20
the explanatory variables mentioned above. In each regional block model, heteroskedasticity and
autocorrelation were adjusted for when detected. For the Philippine model, panel data was used
for estimating groupwise regression models.
Among the infrastructure variables, coefficients for irrigation are significant in enhancing
TFP, efficiency and technological change in Luzon and have a negative effect in Visayas.
However, the overall effect of irrigation on TFP in Mindanao and the Philippines was not
significant. Since the adoption of modern varieties largely depends on the availability of irrigated
water, this result is counter intuitive. However, Bouis (1993) also reported that both irrigation
and fertilizer contributed only a small portion of yield increases due to stagnation in the growth
of the irrigated area during the 80's. For transportation, on the other hand, coefficients were
significant and positive in all three indices in Luzon and the Philippines and negative for
technological change in the Philippines.
The parameter for population per arable land can show whether the Boserup hypothesis,
i.e., population induced technological change, can be supported by this analysis. The results,
however, are mixed. Population pressure affected TFP positively in the Philippines and in
Visayas but negatively in Mindanao. On the other hand, population pressure affects
technological change positively in Mindanao where population growth was most rapid during
this period. One reason may be that the favorable agroclimatic condition coupled with the
investment in irrigation and high adoption rate of modern varieties in Mindanao contributed to a
shifting out of the production function. Furthermore, population pressure negatively affected
efficiency in Mindanao, Luzon and the Philippines.
The level of education positively affected TFP, efficiency and technological change in
the Philippines. However, some parameters turned out negative, results for which may be
21
difficult to explain, and may be due to regional variations in higher education enrollment. The
dummy variable for modern variety II (second-generation varieties) was not significant in
explaining TFP change in the Philippines. Moreover, the efficiency change parameter is negative
in Mindanao, which could be because of input-biased technological change during the study
period. When new technologies are introduced, it is possible that inefficient production occurs
because of unfamiliarity with new technology (Arnade, 1994). Distribution of production share
to landlords mostly had a negative effect showing that higher production shares accruing to
landlords has a negative effect on TFP, efficiency and technological change.
The Hayami-Ruttan hypothesis of induced innovation can be considered a two-stage
hypothesis. In the first stage, relative scarcity of resources induces a relative increase in the
factor price of scarce resources. In the second stage, a change in relative factor prices induces
technology to save relatively costlier factors of production. Recently, Olmstead and Rhode
(1993) challenged the plausibility of the first-stage indicating that in the U. S., a change in
relative factor prices did not follow a change in relative scarcity of factors at a regional level.
The purpose of the regression against factor price ratios is to test whether changes in relative
price ratios of inputs positively affected technological change, i.e. a shift in the "best practice
frontier", which represents the second half of the Hayami-Ruttan hypothesis. Following the
Olmstead and Rhode (1993) critique that the relative scarcity of factors does not represent
relative factor prices at regional levels, a factor price ratio instead of a factor endowment ratio
was considered as an independent variable.
All factor price ratios, i.e., an increase in land prices relative to fertilizer prices, wages
relative to machinery, and land relative to wages, have significant impacts on increasing
efficiency. In Visayas and the Philippines, however, a land price increase relative to fertilizer
22
negatively affected technological change. This technological regress through a relative decrease
in fertilizer price may be partly contributing to stagnant technological progress in the Philippines
through excessive use of fertilizers caused by its lower relative price leading to a worsening
input-output combination and a backward shift of the production frontier. However, it increased
efficiency and the overall effect of an increase in the land/fertilizer price ratio on TFP was
positive. A wage increase relative to the price of machinery as well as land prices relative to
wages positively affected efficiency and technological change. In particular, all coefficients for
the labor/machinery price ratio on TFP, efficiency and technological change were highly
significant in Central Luzon where the introduction of hand tractors, a labor-saving technology,
was the fastest (Table 2).
Factor intensity variables can be used to test the Boserup hypothesis which postulates that
agricultural intensification leads to technological change. The overall impact of fertilizer
intensity on TFP was negative in the Philippines although fertilizer intensity contributed to
technological change to some extent. These results are somewhat surprising because it is
believed that the level of fertilizer use in many developing countries, the representative mode of
intensification, is still not enough to accelerate agricultural productivity (Lele and Stone, 1989).
Pingali (1992) asserts that the long-term stagnation in yields potential under intensive irrigated
rice production can be attributed to degradation of the paddy environment due to production
intensification and current yield gains can be sustained only with increasing levels of chemical
fertilizers. On the other hand, in Mindanao where intensification positively affected TFP growth,
there is scope for further intensive use of fertilizer to increase productivity.
Tractor intensity, on the other hand, needs a somewhat different interpretation. Except for
Visayas, none of the parameters were significant to increase TFP. Intensity of tractor use, i.e.,
23
hand tractor use per land harvested, decreased technological change. These results may be
explained by the fact that hand tractor use as a labor-saving technology was not saving labor fast
enough to produce positive TFP growth. This result is in contrast to the wage/machinery price
ratio which significantly increased TFP growth in Luzon.
The impact of weather (rainfall) on TFP was not significant while rainfall negatively
affected efficiency and technological change in the Philippines. Disasters did not significantly
affect TFP, although they negatively affected TFP in Luzon. In disaster-prone Visayas, the
weather variable was a significant negative effect on efficiency and technology.
6.
Summary and Conclusions
During 1971-90, the average annual Malmquist productivity growth of the Philippine rice
sector was only slightly positive. We find that productivity growth was negative during the early
70’s, the second half of the Green Revolution era, followed by positive growth in the late 70’s
and early 80’s, and finally another negative trend in the late 80’s. The positive TFP growth
coincided with a period during the late 70’s when IR36 and other second-generation modern
varieties were introduced. Negative growth during the early 70’s, the second half of the Green
Revolution era, may indicate that the input-output combination was not favorable for the first
generation modern varieties to continue to yield positive TFP growth. Also, the negative growth
in the late 80’s suggests that growth in the level of inputs outweighed the output growth of the
second-generation modern varieties.
The pattern of growth is mostly attributable to technological progress (0.7%) which
occurred during this period. The technological progress component displays a strikingly similar
trend as the Malmquist productivity index, which suggests that the total factor productivity
24
largely consists of technological change. Technological advances were particularly significant in
the regions of Central Luzon, Western Visayas, Southern Mindanao and Northern Mindanao.
Other regions experienced technological regress primarily due to input-biased intensification.
Compared to technological change, the changes in efficiency were quite small.
The factors affecting productivity, efficiency and technological change were analyzed by
second-stage regression analysis. Irrigation infrastructure had a positive effect on TFP in Luzon.
The impact of population pressure on technological and efficiency change gave mixed results.
The effect of population pressure was positive on technological change in Mindanao and was
negative on efficiency change in Mindanao and Luzon. Production distribution to landlords had a
negative effect on TFP, efficiency and technological change. Relative input price changes in
favor of traditional factor- (land and labor) saving and modern factor- (fertilizer and machinery)
using technological change positively affected technological and efficiency changes. Fertilizer
intensification negatively affected total factor productivity in Visayas.
The results suggest that even though there was overall technological progress in the
Philippine rice sector, there were periods of negative growth. These findings are consistent with
cross-country studies of technological change in the agriculture sector of developing countries
which have found productivity declines even in countries where green revolution varieties of rice
and wheat have been widely adopted (Fulginiti and Perrin, 1997; Lau and Yotopoulos, 1989). In
this study , we find periods of productivity decline at the beginning and end of the study period
(1971-90). It seems quite likely that technological change was most rapid immediately after the
introduction of the second generation of rice varieties (IR36) in 1976. However, preceding the
introduction of the new rice technology, productivity declines could have been caused by
intensification of input use from a decline in yield growth of the first generation of rice varieties.
25
The rapid increase in productivity following the introduction of the new varieties could not be
sustained in the late 80’s because of several reasons including possible input intensification and
macroeconomic policies that discouraged production oriented investment and led to input pricing
policies that led to overuse of inputs by farmers.
At the regional level, our analysis suggests that certain regions, in particular, Central
Luzon, West Visayas and South and North Mindanao performed markedly better than others.
Although there is no immediately obvious reason why these areas exhibited higher rates of
technological progress, certain key factors emerge from the regression analysis. For instance,
irrigation investments and tractor use were a major contributor to productivity growth in Central
Luzon. High labor use intensity, as in Central and Eastern Visayas could have contributed to a
slower rate of technology adoption relative to regions with low labor use per hectare. High initial
rates of population growth, as in Central Luzon and Western Visayas, may have led to
modernization of the rice sector, as predicted by the Boserup hypothesis – a conclusion
supported by the observation that population growth rates declined sharply in the two regions in
the late 80’s. Good soil quality and a favorable agroclimatic environment may have also
contributed to productivity gains from modern technology, as in the case of Mindanao. These
results are supported by Evenson and Sardido (1986) who point out that the Mindanao regions
have benefited from their frontier status as well as from improved infrastructure. However, their
conclusions on regional gains in productivity are somewhat different because they have
examined a complete basket of crops and not just rice.
A possible extension of this study would involve classifying regions under different
criteria such as adoption rates of modern varieties, degrees of population pressure, and
proportion of irrigated area, among others. This would provide further insights into the precise
26
causes of agricultural intensification or the lack thereof. Regional research and extension efforts
have significant impacts on productivity gain in food grains and could enrich the analysis
(Evenson and David, 1993). Environmental factors such as soil quality may be important for
explaining stagnant productivity growth. If enough panel data is available, these factors would be
important explanatory variables for productivity growth in the Philippines.
The study of regional-level agricultural intensification and technological change needs to
be supplemented by micro-level analysis that could support policy analysis. In particular, this
study does not cover upland rice production since not enough statistical information is available.
Some upland areas in the Philippines are experiencing rapid population growth through
migration and dynamic transformation of production technologies as well as environmental
problems such as deforestation and soil erosion. An in-depth analysis of these phenomena would
be helpful in understanding the impact of population pressure on production systems and the
environment.
Finally, the Malmquist total factor productivity approach does not directly address
welfare changes of farmers, i.e., whether or not producer surplus increased in the period
analyzed. It is quite possible that farmers' welfare increased even when total factor productivity
growth was negative. More careful interpretation should be made of changes in input and output
prices, technological change and farmer's producer surplus along the direction of total factor
productivity growth.
27
References
Afriat, S. N. (1972). "Efficiency Estimation of Production Functions." International Economic Review.
13:568-598.
Arnade, C. A. (1994). Using Data Envelopment Analysis to Measure International Agricultural
Efficiency and Productivity. Washington, D.C., Economic Research Service, U.S.
Department of Agriculture.
Binswanger, H. P., S. R. Khandker, and M. R. Rosenzweig. (1993). "How Infrastructure and
Financial Institutions Affect Agricultural Output and Investment in India." Journal of
Development Economics 41: 337-366.
Bjurek, H. and L. Hjalmarsson. (1995). "Productivity in Multiple Output Public Service: A
Quadratic Frontier Function and Malmquist Index Approach." Journal of Public Economics.
56:447-460.
Boserup, E. (1965). The Conditions of Agricultural Growth. London: George Allen & Unwin Ltd.
Bouis, H. E. (1993). "Measuring the Source of Growth in Rice Yields: Are Growth Rates
Declining in Asia?" Food Research Institute Studies. 22:305-330.
Caves, D. W., L. R. Christensen, and W. E. Diewert. (1982). "The Economic Theory of Index Numbers
and the Measurement of Input, Output, and Productivity." Econometrica 50(6): 1393-1414.
Cruz, W. and R. Repetto (1992). The Environmental Effects of Stabilization and Structural
Adjustment Programs: The Philippine Case. Washington, D.C., World Resources
Institute
David, C. C., V. G. Cordova, and K. Otsuka. (1994). Technological Change, Land Reform, and Income
Distribution in the Philippines. Modern Rice Technology and Income Distribution in Asia. C. C.
David and K. Otsuka, Eds. Boulder, Colorado, Lynne Rienner Publishers.
Department of Agriculture (1993a). Crop Development and Soil Conservation Framework for Luzon
Island 1993. Bureau of Soils and Water Management.
Department of Agriculture (1993b). Crop Development and Soil Conservation Framework for Visaya
Island 1993. Bureau of Soils and Water Management.
Department of Agriculture (1993c). Crop Development and Soil Conservation Framework for
Mindanao Island 1993. Bureau of Soils and Water Management.
Diamond, P., D. McFadden, and M. Rodriguez. (1978). Measurement of the Elasticity of Factor
Substitution and Bias of Technical Change. Production Economics: A Dual Approach to Theory
and Application. M. Fuss and D. McFadden, Eds. Amsterdam, North-Holland: ch. IV.2.
Estudillo, J. P. (1995). Income Inequality in the Philippines, 1961-91: Trends and Factors. Ph. D
Dissertation. Department of Economics. Honolulu, Hawaii, University of Hawaii at Manoa.
Evenson, R. and C. David (1993). Adjustment and Technology: The Case of Rice. Paris, Development
Centre of the Organization for Economics Co-operation and Development.
28
Evenson, R. E. and M. L. Sardido (1986). "Regional Total Factor Productivity Change in Philippine
Agriculture." Journal of Philippine Development. 13: 40-61.
Färe, R., R. Grabowski, and S. Grosskopf. (1985). "Technical Efficiency of Philippine Agriculture."
Applied Economics. 17: 205-214.
Färe, R. and S. Grosskopf (1992). "Malmquist Productivity Indexes and Fisher Ideal Indexes." The
Economic Journal. 102:158-160.
Färe, R., S. Grosskopf, B. Lindgren, and P. Roos. (1989). Productivity Developments in Swedish
Hospitals: A Malmquist Output Index Approach. Department of Economics, Southern Illinois
University, Carbondale.
Färe, R., S. Grosskopf, B. Lindgren, and P. Roos. (1992). "Productivity Changes in Sweedish
Pharmacies 1980-1989: A Non-Parametric Malmquist Approach." The Journal of
Productivity Analysis. 3:85-101.
Färe, R., S. Grosskopf, B. Lindgren, and P. Roos. (1994). Productivity Developments in Swedish
Hospitals: A Malmquist Output Index Approach. Data Envelopment Analysis: The Theory,
Applications and the Process. Charnes, A., W. W. Cooper, A. Y. Lewin and L. M. Seiford, Eds.
Boston, Kluwer Academic.
Färe, R., S. Grosskopf, and C. A. N. Lovell. (1985). The Measurement of Efficiency of Production.
Boston: Kluwer-Nijhoff.
Färe, R., S. Grosskopf, and C. A. N. Lovell. (1994a). Production Frontiers. New York, Cambridge
University Press.
Färe, R., S. Grosskopf, M. Norris, and Z. Zhang. (1994b). "Productivity Growth, Technical Progress, and
Efficiency Change in Industrialized Countries." American Economic Review.
84:66-83.
Farrell, M. J. (1957). "The Measurement of Productive Efficiency." Journal of the Royal Statistical
Society. 120 (Part III):11-290.
Francia, S. O. (1993). The Philippines: Executive Summary. Fertilizer Policy in Asia and the
Philippines. S. Ahmed and A. Clark, Eds. Tokyo, Japan, Asian Productivity Organization.
Fulginiti, L. E. and R. K. Perrin (1997) "LDC agriculture: Nonparametric Malmquist productivity
indexes." Journal of Development Economics. 53:373-390.
Grosskopf, S. (1986). "The Role of the Reference Technology in Measuring Productive Efficiency." The
Economic Journal. 96:499-513.
Hayami, Y., M. Kikuchi, et al. (1990). Transformation of a Laguna Village in the Two Decades of Green
Revolution. Manila, Philippines, International Rice Research Institute.
Hayami, Y. and V. W. Ruttan (1970). "Factor Prices and Technical Change in Agricultural Development:
The United States and Japan, 1880-1960." Journal of Political Economy 78: 1115-1141.
29
Hayami, Y. and V. W. Ruttan (1985). Agricultural Development: An International Perspective.
Baltimore.
International Resources Group. (1992). The Philippine Natural Resources Accounting Project (NRAPPHASE I) Main Report. Manila, Philippines.
IRRI (1995). World Rice Statistics, 1993-94. Manila, Philippines, International Rice Research
Institute.
Lau, L. J. and P. A. Yotopoulos (1989). "The Meta-Production Function Approach to Technological
Change in World Agriculture." Journal of Development Economics 31: 241-269.
Lele, U. and S. Stone (1989). Population Pressure, the Environment and Agricultural Intensification:
Variations on the Boserup Hypothesis. Washington, D.C., The World Bank.
Montes, M., F. and J. Y. Lim (1996). "Macroeconomic Volatility, Investment Anemia and
Environmental Struggles in the Philippines." World Development. 24(2).
National Statistical Office (1991) Unpublished Family Income & Expenditure Surveys.
Nugent, J. B. and N. Sanchez (1995). The Local Variability of Rainfall and Tribal Institutions: The Case
of Sudan. University of Southern California and College of the Holy Cross.
Olmstead, A. L. and P. Rhode (1993). "Induced Innovation in American Agriculture: A
Reconsideration." Journal of Political Economy 101(1): 100-118.
Otsuka, K. (1991). "Determinants and Consequences of Land Reform Implementation in the
Philippines." Journal of Development Economics 35: 339-355.
Otsuka, K., F. Gascon, and S. Asano. (1994). "Green Revolution and Labour Demand in Rice Farming:
The Case of Central Luzon, 1966-90." Journal of Development Studies 31(1): 82-109.
PhilRice-BAS (1994). Regional Rice Statistics Handbook, 1970-1992. The Philippine Rice
Research Institute and the Bureau of Agricultural Statistics.
Pingali, P., Y. Bigot, H. P. Binswanger. (1987). Agricultural Mechanization and the Evolution of
Farming Systems in Sub-Saharan Africa. Washington, D.C., Johns Hopkins University Press.
Pingali, P. L. (1992) “Agriculture-Environment Interactions in the Southeast Asian Humid Tropics”,
Southeast Asian Journal of Agricultural Economics, 1(2): 107-124.
Pingali, P. L., R. S. Zeigler, M. Hossain, and J.-C. Prot. (1995). "Humid Tropics of Asia: Coping with
Human Pressure and Environmental Stress." Paper prepared for the Planning Meeting of the
Ecological Initiative for the Humid Tropics at the International Rice Research Institute, Los
Baños, Laguna, Philippines, 18-22 September 1995.
Roumasset, J. and W. James (1979). "Explaining Variations in Share Contracts: Land Quality,
Population Pressure and Technological Change." Australian Journal of Agricultural
Economics 23(2): 116-127.
30
Ruttan, V. W. and Y. Hayami (1995). "Induced Innovation Theory and Agricultural Development: A
Personal Account." In B. M. Koppel (ed.), Induced Innovation Theory and International
Agricultural Development: A Reassessment. Baltimore: Johns Hopkins University Press.
Shephard, R. W. (1953). Cost and Production Functions. Princeton, N.J., Princeton University Press.
Schultz, T. W. (1964). Transforming Traditional Agriculture. New Haven, Yale University Press.
Thirtle, C., D. Haddey, and R. Townsend. (1995). "Policy Induced Innovation in Sub-Saharan African
Agriculture: A Multilateral Malmquist Productivity Index Approach." mimeo. Department of
Agricultural Economics and Management, University of Reading.
31
Input
x1
D
e
d
xt
f
C
c
x t+1
b
a
Lt (yt )
B
A
Lt+1(yt+1)
Input
x2
0
Figure 1. Input-oriented Distance Function
and the Malmquist Productivity Index
32
150
Efficiency
Technology
Malmquist
140
130
Index
120
110
100
90
80
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
Figure 2. Efficiency, Technological Change and Malmquist Indices (CRS)
for the Rice Sector, Philippines, 1971-1990 (1971=100, weighted mean).
350
300
Index
250
Ilocos
Cagayan
C. Luzon
S. Tagalog
Bicol
200
150
100
50
0
1972
1974
1976
1978
1980
1982
1984
1986
1988
Figure 3. Technological Change Index (CRS) for the Rice Sector,
Luzon Island, Philippines (1971=100).
33
1990
180
160
W.Visayas
C. Visayas
E. Visayas
140
Index
120
100
80
60
40
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
Figure 4. Technological Change Index (CRS) for the Rice Sector,
Visayas Island, Philippines (1971=100).
240
220
200
180
W. Mindanao
N. Mindanao
S. Mindanao
C. Mindanao
Index
160
140
120
100
80
60
40
20
1972
1974
1976
1978
1980
1982
1984
1986
1988
Figure 5. Technological Change Index (CRS) for the Rice Sector,
Mindanao Island, Philippines (1971=100).
34
1990
Table 1. Estimated Average Yield of PALAY Production by Varieties and Adoption of Modern Variety by Regions,
Region
Philippines
Ilocos
Cagayan Valley
Central Luzon
Southern Tagalog
Bicol
Western Visayas
Central Visayas
Eastern Visayas
Western Mindanao
Northern Mindanao
Southern Mindanao
Central Mindanao
Total (MT/Ha)
HYV (MT/Ha)
TV (MT/Ha)
1971-75 1976-80 1981-85 1986-90
1971-75 1976-80 1981-85 1986-90
1971-75 1976-80 1981-85 1986-90
1971-75
1.54
1.97
2.33
2.56
1.74
2.18
2.50
2.69
1.24
1.34
1.53
1.78
58.46
1.53
1.93
2.40
2.64
1.64
2.04
2.49
2.68
1.37
1.70
1.89
2.02
58.84
1.77
2.06
2.49
3.08
1.97
2.25
2.64
3.18
1.49
1.54
1.63
1.98
54.48
1.94
2.76
3.18
3.20
1.98
2.81
3.21
3.23
1.83
2.26
2.37
2.37
72.88
1.54
1.89
2.24
2.51
1.85
2.29
2.45
2.68
1.20
1.27
1.32
1.64
51.60
1.69
2.06
2.03
2.18
1.88
2.24
2.19
2.28
1.27
1.21
1.19
1.47
68.14
1.57
2.00
2.38
2.48
1.73
2.14
2.45
2.52
1.24
1.15
1.21
1.47
64.60
1.33
1.62
1.41
1.54
1.40
1.73
1.52
1.66
1.22
1.30
1.08
1.08
55.59
1.23
1.47
1.81
1.86
1.41
1.70
1.95
1.98
1.01
1.04
1.24
1.27
54.28
1.62
2.49
2.30
2.56
2.04
2.95
2.67
2.81
1.21
1.33
1.56
1.82
46.11
1.25
1.61
2.56
3.07
1.43
1.76
2.70
3.27
0.90
0.97
1.43
1.97
63.51
1.76
2.37
2.92
3.40
2.01
2.62
3.11
3.50
1.17
1.45
2.03
2.80
70.92
1.43
1.72
2.88
2.93
1.71
2.06
Source: Regional Rice Statistics Handbook (1994).
Note: HYV = High Yielding Variety, and TV = Traditional Variety.
3.24
3.18
1.17
1.32
1.89
2.12
47.87
Table 2. Factor Input Ratios by Regions, 1971-1990.
Region
Labor/Land (Mandays/Ha)
1971-75 1976-80 1981-85 1986-90
Philippines
95.06 93.64 93.02 93.26
83.43 82.32 82.11 83.30
Ilocos
95.02 93.65 93.59 95.20
Cagayan Valley
80.80 81.14 76.02 78.65
Central Luzon
Southern Tagalog
71.86 72.21 71.49 72.05
Bicol
95.67 94.38 91.23 92.09
96.13 96.89 96.95 98.79
Western Visayas
119.40 114.92 112.24 111.23
Central Visayas
112.14 108.57 112.32 109.36
Eastern Visayas
Western Mindanao
95.60 94.36 93.58 93.30
Northern Mindanao 106.71 97.84 100.01 98.25
Southern Mindanao 98.46 96.78 98.23 96.14
95.69 98.73 98.04 98.76
Central Mindanao
a/
Fertilizer/Land
(NPK Kg/Ha)
1971-75 1976-80 1981-85 1986-90
Tractor/Land (Unit/'000 Ha)
1971-75 1976-80 1981-85 1986-90
49.46
53.50
51.30
73.85
21.45
42.88
66.98
72.20
73.16
95.72
34.50
66.65 115.66 202.03
51.56
57.84
55.46
87.82
13.00
35.78
100.05 101.36
84.29 114.23
99.48 166.16
96.67 112.49
88.42 170.33 240.65 294.29
54.17
55.49
51.48
70.65
43.34
61.33
97.47 121.62
39.65
45.89
40.43
60.43
18.57
48.40
97.20 132.15
74.25
69.29
62.04
83.80
26.44
50.36 105.03 162.03
35.85
35.84
31.50
55.22
8.20
11.27
12.13
11.54
32.48
34.10
33.60
55.98
11.92
32.93
66.48
89.60
51.29
59.30
53.64
75.79
7.73
20.99
51.78
73.01
47.18
51.31
53.10
72.63
30.77
54.84 137.75 184.25
35.32
45.80
44.97
69.81
36.37
74.40 143.10 188.09
37.73 44.03 48.38 67.77
13.26 20.02 56.73 73.35
Source: Census Of Agriculture (1971, 1980, 1991); National Statistical Information Center unpublished data;
Regional Rice Statistics Handbook (1994).
Note:
a
Land includes both areas applying and not applying inorganic fertilizers.
35
Ca
1971-75
Table 3. Efficiency Change (E), Technological Progress (T), and Malmquist Total Factor Productivity (M) Indi
in PALAY Production with CRS Technology by Regions, 1971-1990.
Region
E
Philippines(WAM)
Philippines(GM)
Ilocos
Cagayan Valley
Central Luzon
Southern Tagalog
Bicol
Western Visayas
Central Visayas
Eastern Visayas
Western Mindanao
Northern Mindanao
Southern Mindanao
Central Mindanao
1971-75
T
M
E
1976-80
T
M
E
1981-85
T
M
E
1986-90
T
100.2
97.8
98.0
100.1
102.3
102.4
99.9
103.7
103.6
99.6
99.6
97.2
96.9
100.7
101.4
102.1
99.8
101.8
101.6
99.4
98.6
97.3
98.1
101.3
99.4
96.5
96.7
93.4
103.4
98.8
102.1
98.0
102.6
100.0
90.9
90.9
97.0
105.3
102.2
103.1
103.5
106.7
100.0
97.6
100.0
108.2
108.2
100.0
107.7
107.7
100.0
109.9
109.9
100.0
106.9
101.1
96.1
97.1
98.7
100.5
99.2
101.3
99.0
100.3
100.0
99.1
101.7
98.3
99.9
97.1
107.3
104.1
97.7
102.7
100.3
102.6
94.4
103.9
98.5
102.3
98.8
110.2
108.8
100.6
100.9
101.5
95.4
97.7
101.2
92.7
93.7
101.7
95.7
97.4
100.0
93.0
93.0
100.0
96.5
98.3
95.5
93.9
103.7
100.6
104.3
96.1
103.2
99.2
97.4
93.8
103.2
100.2
103.5
97.1
98.1
95.2
99.7
102.6
102.4
103.3
92.0
90.8
97.3
88.4
108.6
107.3
116.6
98.2
105.0
103.1
102.6
95.6
100.0
100.2
100.2
100.0
101.0
101.0
100.0
106.6
106.6
100.0
95.7
100.0
88.4
88.4
100.0
92.6
92.6
100.0
96.0
96.0
100.0
93.3
Note: Index of 100 means no change, less than 100 means deterioration and greater than 100 means an improvement.
Figures are geometric mean of 5 year and 20 year periods.
WAM: weighted arithmetic mean using output share of each region as a weight; GM: geometric mean of regional score
36
Table 4. Regression Analysis of Efficiency Change,Technological Change, and Malmquist TFP Index by Regions, 1971-1990.
Efficiency Change
Variables
PHILS
Luzon
Visayas
Technological Change
Mindanao
PHILS
Luzon
Visayas
Malmquist TFP Index
Mindanao
PHILS
Luzon
Visayas
Mindanao
Infrastructure Variables
Irrigation
Transportation
0.036
0.218
(0.84)
(2.25)
0.070
**
(3.50)
0.269
**
-0.656
**
(4.01)
**
(4.96)
0.155
0.018
0.115
(0.76)
(1.54)
(4.63)
-0.203
0.217
(1.22)
(2.37)
**
-0.019
**
(4.19)
0.037
**
**
(3.06)
-0.155
**
0.013
(3.63)
(1.65)
0.015
-0.005
(0.37)
(1.75)
*
*
-0.086
0.803
(0.96)
(2.71)
0.129
**
(3.66)
0.832
**
-0.707
**
(4.36)
**
(5.50)
0.400
(1.55)
-0.046
0.025
(0.29)
(0.22)
Demographic Variable
Population/Land
-0.131
**
(4.82)
-0.272
**
(3.94)
0.808
**
-0.813
**
0.070
**
(11.92)
-0.006
0.007
0.087
(0.27)
(0.08)
(19.90)
(2.40)
(2.94)
0.086
-2.274
-0.488
0.054
0.440
0.141
(0.16)
(1.49)
(0.85)
(2.01)
(3.33)
(1.20)
(5.46)
-0.026
0.024
-0.254
(0.42)
(0.23)
(2.40)
**
0.086
*
(1.67)
-0.235
1.124
(1.00)
(3.63)
**
-0.313
**
(2.71)
Technology Related Variables
Higher Education
0.602
**
(4.66)
Modern Variety II
0.092
**
(2.44)
**
**
0.349
**
**
0.661
**
(2.79)
-3.372
**
-3.654
(2.29)
(2.79)
**
0.954
(1.37)
0.000
-0.012
-0.023
0.001
0.011
0.097
-0.103
0.035
(0.02)
(1.06)
(1.07)
(0.15)
(0.18)
(0.58)
(1.12)
(0.27)
-0.002
-0.996
-0.367
-0.896
(0.19)
(6.69)
(1.48)
(3.82)
Institutional Variable
Landlord Share
-0.729
**
(8.14)
-0.383
**
(2.10)
-0.836
**
(3.22)
-0.623
**
-0.019
-0.110
(3.13)
(1.11)
(3.26)
**
-0.153
**
(2.14)
**
-0.908
*
(1.82)
**
Factor Price Variables
Land/Fertilizer Prices
0.482
*
(1.93)
Labor/Machinery Prices
14.433
**
(5.37)
Land/Labor Prices
0.590
0.635
**
(2.26)
17.114
**
(3.20)
**
(7.88)
0.712
*
(1.85)
1.265
*
-0.643
-0.135
(1.81)
(0.59)
(2.17)
14.119
-9.526
1.269
(1.46)
(1.07)
(1.90)
0.556
*
(1.93)
0.326
0.073
(1.53)
(4.83)
**
0.123
**
(2.19)
*
1.830
0.125
**
(2.19)
**
(2.17)
**
-0.365
5.524
**
(1.97)
0.255
1.539
0.139
0.807
1.167
(0.44)
(3.14)
(0.17)
(1.17)
(1.24)
0.197
4.982
38.224
(0.58)
(0.86)
(2.52)
**
(2.00)
**
0.007
0.467
(0.88)
(3.24)
-0.009
0.002
-0.119
(0.37)
(0.74)
(2.48)
0.039
-0.016
(1.21)
(3.48)
(2.84)
**
-0.012
**
2.396
**
**
1.454
4.724
(0.15)
(0.40)
0.087
0.548
(2.08)
(0.30)
(2.27)
0.312
-0.127
0.221
(1.11)
(1.64)
(1.96)
**
Factor Input Intensity Variables
Fertilizer/Land
Hand Tractor/Land
0.054
0.310
(0.84)
(2.96)
-0.086
*
(1.86)
**
-0.153
*
(1.89)
**
-0.009
0.032
(0.08)
(5.27)
0.007
**
-0.021
**
0.061
**
(3.26)
**
-0.070
**
0.039
0.347
(0.53)
(2.88)
(6.19)
(3.61)
-0.012
-0.030
-0.025
-0.006
-0.005
0.022
(0.73)
(0.73)
(0.53)
(2.17)
(1.58)
(1.77)
0.018
0.000
0.000
-0.022
(0.67)
(0.05)
(0.15)
(2.96)
(5.54)
**
**
**
0.023
0.252
0.390
(0.45)
(1.37)
(3.50)
(1.33)
**
-0.178
Purely Exogenous Variables
Weather
-0.013
**
(1.98)
Disasters
-0.016
**
(2.52)
0.038
**
(2.52)
-0.060
**
(2.12)
**
*
**
0.001
0.014
0.040
0.025
-0.022
(1.10)
(0.51)
(0.87)
(0.54)
(0.57)
-0.001
-0.006
-0.119
(0.81)
(0.56)
(2.81)
**
-0.035
0.011
(1.23)
(0.45)
Statistics
Number of Observations
216
90
54
76
216
90
57
72
216
90
54
72
Log-Likelihood Function
118.41
-3.91
-0.70
43.84
461.60
145.76
74.92
237.03
-38.84
-86.75
-3.33
-33.95
Homoscedasticity Test1
221.15
**
80.06
**
1.42
21.23
**
230.99
**
34.08
**
7.72
26.87
**
16.47
**
12.07
**
22.23
**
145.59
**
13.22
**
13.28
755.76
**
227.57
**
31.98
**
114.20
**
482.40
**
81.83
**
13.02
Groupwise Correlation Test2
Autocorrelation Test3
**
22.92
**
169.80
**
22.04
**
18.60
12.29
**
28.27
**
26.86
**
14.76
**
15.13
11.16
**
98.60
**
321.17
**
32.28
**
37.06
118.97
**
**
** denotes significant level of at least 5% and * denotes significant level of at least 10%. Figures in parentheses are z statistic.
All independent variables are in log form except factor price variables, factor input intensity variables, disaster and dummy variable for period of
second modern variety.
1
2
3
Log ratio statistic is used and its distribution is χ2 to test the null of homoscedastic variance against heteroscedasticity.
Log ratio statistic to test the null of no cross group correlation against groupwise correlation.
Box-Ljung statistic to test the null of no autocorrelation. The statistic distributes as χ2 with 10 degree of freedom.
37
Appendix Table 1. Estimated Average PALAY Production by Varieties by Regions, 1971-1990.
Region
Total (Million MT)
1971-75 1976-80 1981-85 1986-90
HYV (Million MT)
TV (Million MT)
1971-75 1976-80 1981-85 1986-90
1971-75 1976-80 1981-85 1986-90
Philippines
5.537 7.272 8.035 9.107
3.712 5.920 7.243 8.346
1.825 1.352 0.791 0.761
CAR
0.134 0.164 0.164 0.173
0.044 0.070 0.097 0.120
0.090 0.095 0.066 0.053
Ilocos
0.511 0.561 0.725 0.794
0.320 0.394 0.649 0.757
0.191 0.167 0.075 0.037
Cagayan Valley
0.616 0.730 0.797 1.077
0.378 0.584 0.720 1.018
0.238 0.147 0.077 0.058
Central Luzon
0.912 1.181 1.487 1.603
0.690 1.086 1.443 1.568
0.223 0.095 0.043 0.034
Southern Tagalog
0.699 0.837 0.839 0.963
0.438 0.621 0.742 0.856
0.262 0.216 0.097 0.107
Bicol
0.558 0.650 0.631 0.670
0.423 0.579 0.586 0.613
0.135 0.071 0.045 0.056
Western Visayas
0.651 0.993 1.082 1.082
0.471 0.907 1.047 1.056
0.181 0.087 0.035 0.026
Central Visayas
0.119 0.155 0.148 0.174
0.071 0.120 0.120 0.148
0.048 0.034 0.028 0.025
Eastern Visayas
0.220 0.269 0.357 0.389
0.138 0.207 0.307 0.341
0.082 0.062 0.049 0.047
Western Mindanao
0.224 0.364 0.307 0.357
0.137 0.310 0.236 0.291
0.087 0.054 0.071 0.066
Northern Mindanao 0.167 0.257 0.302 0.408
0.124 0.229 0.284 0.366
0.043 0.029 0.018 0.042
Southern Mindanao 0.268 0.428 0.534 0.678
0.216 0.368 0.468 0.596
0.051 0.059 0.066 0.082
Central Mindanao
0.458 0.682 0.662 0.740
0.264 0.445 0.543 0.613
0.194 0.237 0.119 0.127
Source: Regional Rice Statistics Handbook (1994).
Note: HYV = High Yielding Variety, and TV = Traditional Variety; Figures are annual averages of five-year per
Appendix Table 2. Rice Production Area under Irrigation by Regions, 1971-1990.
Region
Irrigated Area ('000 Ha)
1971-75 1976-80 1981-85 1986-90
Growth of Irrigated Area (%)
1971-75 1976-80 1981-85 1986-90
% Irrigated Area/Arable Land
1971-75 1976-80 1981-85 1986-90
Philippines
1362.8 1475.7 1668.8 1892.2
2.23
3.76
3.90
2.41
19.23 19.04 19.80
Ilocos
122.6 121.7 149.9 161.4
-3.94
0.89
4.26
2.28
48.71 46.10 54.16
Cagayan Valley
179.4 190.7 204.8 280.0
-4.50 -1.39
9.24
3.28
52.68 51.39 50.00
Central Luzon
267.6 295.0 328.8 370.1
4.69
1.76
0.70
3.78
57.89 64.82 69.50
Southern Tagalog
180.0 186.2 185.7 200.3
3.08 -1.98
3.54
1.04
21.87 19.98 17.59
Bicol
156.8 145.1 160.5 169.7
-0.39
2.53
1.08
0.24
21.37 17.63 18.27
Western Visayas
90.8 120.4 153.1 158.7
3.02
8.17
2.36
0.28
14.85 19.30 23.35
Central Visayas
31.1
29.9
36.0
46.7
-2.98 13.89 -1.29 11.25
7.94
6.57
7.25
Eastern Visayas
44.4
54.8
73.5
77.3
-4.35 11.11
8.98 -4.91
7.63
8.56 10.92
Western Mindanao
48.5
50.8
54.9
61.2
14.38
2.50
6.48
1.93
10.80 10.10
9.51
Northern Mindanao
59.7
82.4
79.2
90.7
10.97
1.79
2.14
7.29
10.84 12.37 10.17
Southern Mindanao
84.5
91.6 117.8 132.8
9.31
0.19
6.22
3.09
12.17 10.92 11.97
Central Mindanao
97.5 107.0 124.6 143.3
-2.57
5.70
3.11 -0.69
28.77 28.30 27.99
Source: Regional Rice Statistics Handbook (1994); Census Of Agriculture (1971, 1980, 1991).
38
% Irrig
1971-75
20.41
36.86
55.45
36.65
61.31
49.76
71.54
54.11
16.85
38.78
18.87
44.30
22.63
21.74
8.97
31.55
11.31
23.47
9.12
34.69
10.28
40.17
11.86
53.98
26.53
30.69
Appendix Table 3. Landlord Share, Population with Higher Education, and Paved Road by Regions, 1971-1990.
Region
% Production Paid to Landlord
1971-75 1976-80 1981-85 1986-90
Philippines
14.55 12.17 12.42 10.68
Ilocos
11.78 16.01 19.20 19.54
Cagayan Valley
18.96 11.66 11.33
9.46
Central Luzon
16.59 13.31 11.59
8.11
Southern Tagalog
20.22 16.54 13.33 13.03
Bicol
15.99 13.82 14.23 12.97
Western Visayas
17.05 14.02 15.39 11.82
Central Visayas
15.45 12.19 15.14 10.16
Eastern Visayas
19.12 17.34 17.15 17.01
Western Mindanao
11.15
6.89
9.79
8.17
Northern Mindanao 12.79
9.14
8.40
8.34
Southern Mindanao 10.83 10.00
9.19
8.07
Central Mindanao
9.62
9.96
9.34
7.76
Source: RRSH (1994); IRRI unpublished data.
% Population ≥ 15 Yrs. Educ.
1971-75 1976-80 1981-85 1986-90
% Paved Road
1971-75 1976-80 1981-85 1986-90
6.07
6.51
6.91
7.32
13.35
14.18
11.13
11.69
6.79
7.36
7.78
8.31
18.40
19.78
15.64
20.11
6.03
6.42
6.87
7.27
8.28
8.54
7.13
7.13
7.02
7.44
7.88
8.30
30.99
32.87
19.38
20.17
7.92
7.53
7.53
7.93
34.66
29.35
18.91
19.03
6.27
6.85
7.47
7.83
22.42
23.39
25.79
21.67
6.23
6.67
7.10
7.51
16.67
14.83
13.48
13.80
5.78
6.13
6.45
6.81
13.39
13.33
13.36
15.03
5.38
5.81
6.22
6.56
10.91
13.17
12.80
18.56
4.92
5.40
5.65
6.10
9.07
9.85
6.92
6.36
6.40
6.91
7.33
7.59
12.56
12.39
8.56
9.13
6.18
6.68
7.06
7.41
3.44
6.48
5.27
5.05
4.62
5.43
5.99
6.63
9.14
8.40
4.78
4.86
Appendix Table 4. Population, Population Pressure and Growth Rates by Regions, 1971-1990.
Region
Population ('000 Persons)
1971-75 1976-80 1981-85 1986-90
Philippines
Ilocos
Cagayan Valley
Central Luzon
Southern Tagalog
Bicol
Western Visayas
Central Visayas
Eastern Visayas
Western Mindanao
Northern Mindanao
Southern Mindanao
Central Mindanao
Population Growth (%)
1971-75 1976-80 1981-85 1986-90
Pop./Arable Land (Person/Ha)
1971-75 1976-80 1981-85 1986-90
Growth
1971-75
39256
44913
50837
57252
2.53
2.54
2.37
1.99
5.91
6.06
6.20
6.33
0.52
2631
2844
3091
3400
1.79
1.40
1.86
2.10
10.45
10.77
11.17
11.68
0.84
1592
1831
2081
2331
2.72
2.72
2.50
1.42
4.67
4.93
5.08
5.10
1.03
3985
4581
5199
5881
2.99
2.68
2.45
2.64
8.61
10.06
10.98
11.36
3.28
9514
11300
12993
15026
3.75
3.04
2.86
3.38
11.55
12.11
12.30
12.63
1.30
3112
3372
3746
4105
1.48
1.70
2.34
-0.01
4.24
4.10
4.26
4.56
-0.72
3940
4385
4868
5387
2.75
1.73
2.31
1.21
6.44
7.03
7.42
7.68
2.39
3252
3637
4033
4445
2.25
2.19
2.00
1.88
8.29
7.98
8.10
8.54
-0.73
2517
2725
2964
3184
1.77
1.46
1.82
-0.09
4.32
4.26
4.40
4.65
-0.08
1986
2345
2735
3056
1.89
4.21
2.34
2.04
4.42
4.66
4.74
4.56
-0.32
2179
2590
3014
3420
3.40
3.49
2.73
2.06
3.96
3.88
3.87
3.87
-0.40
2517
3105
3647
4162
4.21
4.17
2.60
3.10
3.62
3.69
3.70
3.71
0.47
2030
2197
2468
2854
Source: RRSH (1994); COA (1971, 1980, 1991).
1.36
1.74
2.62
4.17
5.99
5.81
5.54
5.27
-0.77
Note: Figures for Philippines do not include National Capital Region (NCR), Cordillera Autonomous Region (CAR
and Autonomous Region in Muslim Mindanao (ARMM).
39