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Bulletin of Indonesian Economic Studies
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Productivity growth in Indonesian agriculture,
1961–2000
Keith O. Fuglie
To cite this article: Keith O. Fuglie (2004) Productivity growth in Indonesian
agriculture, 1961–2000, Bulletin of Indonesian Economic Studies, 40:2, 209-225, DOI:
10.1080/0007491042000205286
To link to this article: http://dx.doi.org/10.1080/0007491042000205286
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Date: 19 January 2016, At: 19:58
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Bulletin of Indonesian Economic Studies, Vol. 40, No. 2, 2004: 209–25
Taylor & Francis Group
PRODUCTIVITY GROWTH IN
INDONESIAN AGRICULTURE, 1961–2000
Keith O. Fuglie
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International Potato Center, Bogor
This paper uses an index number approach to measure total factor productivity
(TFP) growth in Indonesian crop and livestock agriculture from 1961 to 2000.
Tornqvist–Theil chain-weighted indices of output, input and TFP are developed to
minimise biases that may result from relative changes in input and output price
aggregation weights. The results indicate that agricultural TFP growth accelerated
in the 1970s and 1980s but stagnated in the 1990s. Without new efforts to increase
productivity in agriculture, Indonesia’s goal of using agricultural growth to generate broad-based rural development and further reduce poverty may be undermined.
INTRODUCTION
During the latter half of the 20th century, rising output per hectare replaced
expansion of crop land as the predominant source of agricultural growth in most
of the world (Hayami and Ruttan 1985). This transition from agricultural extensification to intensification was most noticeable in Asia, where population density is relatively high and land scarcity most acute. In this paper, I examine the
performance of agricultural productivity in Indonesia during the last 40 years of
the 20th century. Although agriculture’s share of the economy declined over this
period, at the end of the century it still provided the main livelihood for half of
the nation’s population, or over 100 million people. Sustaining productivity
growth in agriculture can make a significant contribution to food security,
poverty reduction and broad-based economic growth.
Assessing the productivity performance of the agricultural sector in Indonesia
has been difficult because of the lack of appropriate data. Significant improvements in agricultural input and output measurement provided by Van der Eng
(1996) have, however, eased this task. In particular, the author provides improved
estimates of agricultural crop land in Indonesia that differ markedly from the
agricultural land use data provided by the Food and Agriculture Organization
(FAO), the primary source of data used in previous assessments of productivity
growth in Indonesia (e.g. Mundlak, Larson and Butze 2002; Suhariyanto 2001). In
addition, previous studies of agricultural growth have sometimes used changes
in agricultural value added rather than changes in output quantities (Van der Eng
1996; Mundlak, Larson and Butze 2002), a practice that confounds the effect of
changes in productivity and changes in terms of trade on agricultural growth.
The series I develop for the rate of real output growth differs greatly from the rate
of change in agricultural GDP.
ISSN 0007-4918 print/ISSN 1472-7234 online/04/020209-17
DOI: 10.1080/0007491042000205286
© 2004 Indonesia Project ANU
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210
Keith O. Fuglie
In this study, I use time series of 49 categories of crop and livestock outputs
and 19 categories of inputs, with their respective prices, to construct a Tornqvist–
Theil chain index of total factor productivity (TFP) between 1961 and 2000. A TFP
index is simply the ratio of an output index to an input index. Therefore, growth
in TFP is the residual share of output growth after accounting for percentage
changes in land, labour and other conventional factors of production. Changes in
TFP can be interpreted as a measure of the collective contribution of non-conventional inputs in agriculture, such as improvements in input quality, economies of
scale and technology (Alston, Norton and Pardey 1995). Using a Tornqvist–Theil
index reduces the bias that arises in fixed-weight indices when price weights
change over time. Recent studies of agricultural productivity growth in India
(Fan, Hazell and Thorat 1999) and China (Fan and Zhang 2002) have found this
approach to give significantly different results from TFP indices based on a constant set of aggregation weights.
In general, my approach finds a faster rate of input growth (especially more
land expansion) and a slower rate of output growth than previous studies of agricultural growth in Indonesia. In particular, I find that agricultural productivity
in Indonesia stagnated in the 1990s after two decades of rapid growth. What
emerges from this exercise is a picture that raises concern for the future performance of Indonesia’s agriculture and the welfare of the people who depend on it
for their livelihood. I conjecture that without major increases in public and private investment in the sector, agriculture will not return to a path of sustained
productivity growth.
METHODOLOGY
Productivity can be defined as a ratio of output to inputs used in production. An
increase in the amount of output per unit of inputs, equivalent to an outward
shift in a production function, is then measured as an increase in factor productivity. Partial productivity indices, such as output per worker, give the ratio of
output to a single factor of production. However, since the amounts of other
inputs may vary, increases in output per worker can result from either increases
in the use of other inputs or changes in technology. A TFP index compares
changes in output with changes in aggregate inputs. Changes in TFP provide a
better picture of changes in technology and other unmeasured improvements in
the means of production (such as improvements in input quality). In applications
of TFP measurements to agriculture, it is common to aggregate outputs and conventional inputs using market or shadow prices as weights. The theoretical
underpinnings of TFP index measurement assume that producers maximise profits, that output markets are competitive and that the production technology is
characterised by constant returns to scale. Under these conditions, total revenue
will equal total costs, the elasticity of output with respect to each input is equal
to its factor share, and TFP growth can be estimated using observed input and
output quantities and prices (Alston, Norton and Pardey 1995).
A well known limitation of index numbers is that changes in input prices over
time may lead to biases in the measurement of TFP because producers change the
mix of inputs in response to such price changes. To illustrate, figure 1 shows an
isoquant (Q) of a production function in which two inputs, L and A, are used to
Productivity Growth in Indonesian Agriculture, 1961–2000
211
FIGURE 1 Biases in TFP when Producers Change the Mix of Inputs a
A
Q
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A0
A1
b
c
w0
w1
L
a See
text for explanation of terms.
produce a commodity. The ratio of input prices is given by w0, and point b is the
cost-minimising combination of L and A to produce the level of output indicated
by the isoquant. Now suppose that technical change results in fewer inputs being
required to produce a given amount of output, so that the isoquant in the diagram now indicates a higher level of output than previously. A true index of productivity growth should measure the proportional change in the output level represented by this isoquant.
If the prices of inputs remain unchanged, this is given by the increase in output for the given aggregate input cost (A0, using A as the numeraire). However,
if the price of L relative to A declines (from w0 to w1), then the optimal input combination for the new, higher level of output becomes point c. At the old relative
price level, c is more costly than b, so if input costs are aggregated using observed
quantities together with the initial prices as aggregation weights, moving from
point b to point c would imply an increase in aggregate inputs to produce the now
higher level of output. This would result in underestimation of the actual change
in productivity. On the other hand, if the new prices are used to aggregate inputs,
then input combination c will appear cheaper (A1) than that at b, falsely magnifying the observed improvement in TFP. Similar problems are encountered in
aggregating across multiple outputs (Alston, Norton and Pardey 1995).
The Tornqvist–Theil chain-weighted index minimises the effect of changes in
price weights on output and input aggregation by allowing the weights to adjust
over time as prices change.
212
Keith O. Fuglie
The Tornqvist–Theil index of output in year t is defined as
n
ln (Yt Y t −1 ) =
∑
(Ri,t + Ri,t −1 ) ln⎛⎜
i =1
Yi, t ⎞
⎟
⎜Y
⎟
−
,
1
i
t
⎠
⎝
2
(1)
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where Ri,t is the revenue share of output i and Yi,t is the quantity of output i in
year t. Thus, the growth rate of aggregate output Y between period t and t –1 is a
sum of the growth rates of the n commodities that make up total output, each
weighted by the average of their revenue shares during t and t –1.
Similarly, the aggregate input index is defined as
m
ln (X t X t −1 ) =
∑
j =1
(S j,t + S j,t −1 ) ln⎛⎜
2
X j, t ⎞
⎟
⎜ X j, t − 1 ⎟
⎠
⎝
(2)
where the growth rate of aggregate input X between period t and t –1 is the sum
of the growth rates of the j = 1,2, … m categories of inputs, each weighted by the
average of their factor shares Sj during adjacent periods. (A ‘factor share’ is the
proportion of total cost paid for an input.)
The proportional change in TFP between period t and t –1 is then given as
⎛ TFPt ⎞
⎟ = ln (Yt Y t −1 ) − ln (X t X t −1 )
ln ⎜⎜
⎟
⎝ TFPt −1 ⎠
(3)
Estimation requires data on the quantities and prices of each output and input
for each period. For comparative purposes, I also estimate Paasche indices of output, input and TFP, in which end-period prices are used as fixed weights to aggregate items over the entire series. Comparison between the Tornqvist–Theil and
Paasche indices gives an indication of the size of the bias resulting from fixed
weights.
DATA
The lack of reliable time series data on agricultural production and input use in
Indonesia has been a limiting factor in previous attempts to quantify productivity growth (Booth 1988; Van der Eng 1996; Arnade 1998; Suhariyanto 2001;
Mundlak, Larson and Butze 2002). Agriculture is generally defined to include
crops, livestock, fresh water and marine fisheries, and forestry. But most studies
on agricultural productivity growth focus on crop and livestock agriculture
because of serious deficiencies in measuring outputs and inputs used in fisheries
and forestry (Craig, Pardey and Roseboom 1997). This study follows that practice
as well.
Measurement uncertainties also affect crop and livestock production and input
use. On the output side, some studies have used the national accounts value of
agricultural production or value added (GDP) as a measure of aggregate output
(e.g. Van der Eng 1996; Arnade 1998; Suhariyanto 2001; Mundlak, Larson and
Butze 2002). But using value added confounds quantity and terms of trade effects
on changes in output. For example, if agricultural prices fell relative to non-
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Productivity Growth in Indonesian Agriculture, 1961–2000
213
agricultural prices, this would reduce agricultural GDP even if output was constant. Further, deriving real changes in agricultural GDP depends critically on the
choice of inflation deflator. For periods of high inflation such as during the early
1960s, the CPI and GDP deflators available for Indonesia are subject to a wide
margin of error. Instead, I use revenue and factor shares calculated to current
prices to construct aggregation weights. This avoids having to use an inflation
deflator altogether. For inputs, FAO estimates of agricultural land in Indonesia
are highly suspect. While agricultural land is inherently difficult to define and
measure, Van der Eng (1996) has significantly improved the record of agricultural
land change in Indonesia.
In the remainder of the paper, data sources are represented by ‘FAO’ for the
FAO’s FAOSTAT series (FAO 2001), ‘BPS’ for statistics from the Indonesian
Central Statistics Agency (Statistical Yearbook of Indonesia unless otherwise noted)
(BPSa, BPSb, BPSc), ‘MOA’ for sources from the Ministry of Agriculture (MOA
1999, various issues) and ‘VDE’ for data series reported in Van der Eng (1996). I
discuss the land measurement issue in more detail below.
Crop and Livestock Output
FAO production data are used to measure outputs of food, horticultural and nonfood crops, and meat, milk and eggs since 1961. The FAO production data are
equivalent to the agricultural output data reported in BPSa, but include a larger
set of commodities. For crop and livestock production, I measure these in metric
tonnes net of seed and animal feed requirements.
Crop and Livestock Product Prices
Consistent, long-term time series of producer prices for agricultural commodities
in Indonesia are difficult to find, except for the most important crops such as rice,
rubber and palm oil. FAO provides a ‘producer’ price series for most commodities from the mid 1960s, but in fact these price series may represent farmgate,
rural or urban wholesale prices, depending on the country and commodity. VDE
also supplies price series for several major food and non-food crops that the
author calls ‘rural bazaar prices’. For many commodities, both the FAO and VDE
price data are close to or the same as the Jakarta wholesale prices reported in the
BPS statistical yearbooks. For this study I used BPS annual price series for 17
commodities reported in various issues of the annual Statistical Yearbook of
Indonesia and Agricultural Indicators. These commodities account for more than
three-quarters of the value of total crop–livestock output. Prices for another 18
commodities (mostly horticultural products) were taken from selected publications of the Ministry of Agriculture.1 MOA price data are also mostly urban or
Jakarta wholesale prices. FAO producer prices were used for another 14 commodities, accounting for about 7.5% of output value. VDE price data were used
for tobacco, and to supplement MOA data for palm oil and cane sugar prices
prior to 1986 and 1990 respectively.2
For some commodities, consistent price series were established in Indonesia
only in the late 1960s or early 1970s. For missing years, the average normalised
price (commodity price relative to the price of rice) for the nearest five-year
period for which price data were available was used to extend the series back to
1961. These relative prices were then used to construct revenue shares for those
214
Keith O. Fuglie
FIGURE 2 Estimates of Agricultural Crop Land in Indonesia a
(million hectares)
40
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30
20
BPS/VDE (crop land)
FAO (crop land)
10
FAO (area harvested)
0
1961
a See
1966
1971
1976
1981
1986
1991
1996
text for explanation of terms.
years. In summary, then, the agricultural product prices used in this study are a
generally consistent time series of prices at the Jakarta wholesale level.
Crop Land
Historical data on agricultural land use in Indonesia are generally unreliable or
unavailable except for recent years.3 Since the 1980s, BPSa has published reasonably good estimates, by province, of land area in sawah (bunded land for paddy
rice, irrigated and non-irrigated), land area planted to garden crops, land area
planted to upland crops (including fallow land) and land area planted to permanent crops (perennials). VDE extends these series back to 1880 and provides separate series for Java and non-Java. While FAO has been the primary source of data
on agricultural land for most previous studies of agricultural growth in Indonesia
(Arnade 1998; Suhariyanto 2001; Mundlak, Larson and Butze 2002), the FAO land
series differs markedly from the BPS and VDE land use estimates (figure 2). The
BPS/VDE data show that agricultural crop land in Indonesia expanded from
about 17 million hectares in the 1960s to over 37 million hectares in the 1990s,
with virtually all of the expansion occurring outside Java. This series tracks
closely the trend in area harvested reported by FAO.4 The FAO series on arable
land and land under permanent crops, on the other hand, shows a constant 26
million hectares of crop land in Indonesia until the mid 1980s, followed by
uneven growth to 31 million hectares by the mid 1990s. The FAO agricultural
land data are consistent neither with what we know from casual observation to
have occurred over these decades nor with published government statistics on
land use in the country. And the differences are substantial: while the FAO data
show a declining land-to-labour ratio in Indonesia, the BPS/VDE data show that
available crop land per agricultural worker actually increased during the
1961–2000 period for the country as a whole (but did decline in Java).
Productivity Growth in Indonesian Agriculture, 1961–2000
215
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In this study I use the BPS/VDE estimates of crop land, that is, land planted to
crops, fallow land and land planted to perennials (permanent crops). Crop land
is further divided into two land quality types: irrigated and non-irrigated crop
land. Since price series are unavailable for crop land, I follow Mundlak, Larson
and Butze (2002) in giving irrigated land 2.5 times the value of non-irrigated land.
The service flow (price weight) of irrigated and non-irrigated crop land is then
estimated as a residual—the remainder of total output after paying for agricultural labour, fertiliser, machinery and livestock.
Labour and Wages
Data on two categories of labour—adult male and adult female workers
employed in agriculture—are from FAO. Daily wages for male and female workers are from VDE and refer to average wages paid by plantations. Agricultural
wages are extended beyond 1996 using data from BPSc. To find total annual
labour costs, daily wages (in rice equivalents) are multiplied by 300 days worked
per year for men and 250 days worked per year for women. There is evidence that
farm work by households claiming agriculture as their principal occupation may
have declined over time as they diversified into more non-farm activities. Booth
(2002), for example, found that between the 1983 and 1993 agricultural censuses
the share of farm income in the total income of agricultural households declined
from 55% to 50%. The diversification into non-farm work was especially significant for households with very little land. To examine the implications of labour
diversification for the measurement of agricultural productivity, I developed an
alternative measure of labour inputs, by assuming that from 1970 onwards the
average time spent per worker in agriculture declined by two days per year. In
percentage terms this is slightly less than the decline in the share of farm income
in total household income estimated by Booth, to reflect (probably) higher average returns to labour in non-farm occupations. Aggregate input and productivity
indices with and without this adjustment for declining labour intensity in agriculture are reported in the results.
Fertiliser
Annual applications of chemical fertilisers—nitrogen (N), phosphorus (P2O5) and
potash (K2O)—are from FAO. Prices paid for fertiliser are from VDE, updated
since 1996 using BPSb. From the early 1970s until 1999, chemical fertilisers were
heavily subsidised in Indonesia, sometimes by as much as 50% of the actual cost.
This raises the question of whether fertiliser inputs should be weighted by their
social or private costs. Private costs (at the subsidy price) are used in this study
to conform with the theory underpinning the index number approach to measuring productivity. However, despite rapid growth in fertiliser application during
the last several decades, the share of fertiliser in total costs has remained small.
Even at the social cost of fertiliser, total (private and government) expenditure on
fertiliser has rarely exceeded 4% of the value of agricultural output.
Agricultural Machinery
Agricultural machinery is measured in terms of horsepower available from tractors and threshers used in agriculture. Machinery includes two-wheel tractors,
small, medium and large four-wheel tractors, and power threshers. Machinery
data are from FAO, supplemented by a more detailed breakdown of machinery
216
Keith O. Fuglie
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by type published in BPSa. Machinery prices are FAO import values amortised
over five years to derive an annual service flow.
Livestock
Livestock contribute to agriculture in several ways. In addition to yielding meat,
hides, milk and eggs, they provide power, fertiliser and offspring, and serve as a
store of wealth. I measure livestock inputs as the total annual stock of buffalo, beef
cattle, dairy cows, horses, pigs, small ruminants and poultry. Data on animal
stocks are from FAO. The relevant price weight for an animal input is the value of
services from that animal in a given year, and not its stock value. This is important
for animals that provide services for several years, such as large ruminants. Prices
for live animals are derived from FAO agricultural trade statistics. For cattle, buffalo and horses, the annual service flow is approximated by dividing the unit
value of imported live cattle by three. The assumption here is that the cattle price
reflects the present value of lifetime animal services, and that cattle have a working life of about three years on average. The unit value of imported sheep is used
for sheep and goats. Unit export values are used as prices for pigs and poultry.
RESULTS
Agricultural Output Growth
Indonesia produces a diverse set of agricultural commodities. Food crop production is dominated by rice, but also includes several secondary food crops, especially cassava and maize. Non-food crops include rubber, palm oil, coffee, tea,
cacao, sugar cane, coconut and numerous other species. Poultry has been the
fastest growing component of animal production, but large and small ruminants
and pigs are also significant. Besides crops and animal products, fresh water and
marine fisheries and forestry are important components of Indonesia’s agricultural sector. Table 1 shows the changing level and composition of agricultural
output in Indonesia since 1961. The first four columns report average yearly output levels during the first half of each decade in terms of ‘rice equivalents’, where
commodities are valued and aggregated by their current price relative to the price
of rice. Between 1961–65 and 1991–95, total annual agricultural output increased
from 50.5 million tonnes to 137.1 million tonnes of rice equivalents. Rice was by
far the most important commodity, accounting for about 25% of total agricultural
output in the early 1960s and nearly 35% of total output in the early 1990s.
Among non-food crops, palm oil grew very rapidly, especially after 1980. Meat
and milk production also gained in output share. According to the figures, production of forest products hardly grew at all over the four decades, but these figures are probably not reliable.
In assessing overall growth of the Indonesian agricultural sector since 1961, I
distinguish three periods. The first, 1961–67, was a time of economic and political instability in Indonesia. Non-food crop production fell sharply following the
nationalisation of foreign-owned estates, and food crop production barely kept
pace with population growth. Indonesian agriculture was probably operating
inefficiently during this period, inside the production function frontier. During
the second period, 1968–92, Indonesia sustained rapid growth in both crop and
animal production, with average annual output growth of around 4%. Green rev-
Productivity Growth in Indonesian Agriculture, 1961–2000
217
TABLE 1 Average Annual Level, Composition and
Growth Rate of Agricultural Output (selected periods)
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Production
(million tonnes of rice equivalents) a
Agricultural
outputs, all
Crop and animal
outputs, all
Food crops, all
Rice, paddy
Cassava
Maize
Horticultural
crops, all
Fruit
Vegetables
Non-food crops, all
Cane sugar
Rubber
Palm oil
Animal products, all
Meat
Milk
Fish products
Forest products
1961–
65
1971–
75
1981–
85
50.5
65.6
94.5
35.3
19.7
12.4
2.5
1.7
48.9
28.7
21.2
2.4
1.7
3.2
1.4
1.8
9.1
0.8
1.7
0.2
3.3
3.0
0.3
1991–
95
Growth Rate
(% p.a.)
1961–
2000
1961–
67
1968–
92
1993–
2000
137.1
2.9
0.7
4.0
1.0
75.6
45.9
35.8
2.8
2.7
112.8
62.3
47.5
3.4
4.3
3.4
3.5
3.9
1.2
6.9
1.1
1.2
1.7
–0.5
9.7
4.7
5.0
5.5
1.9
7.7
1.2
0.4
0.7
0.2
2.5
4.5
2.0
2.5
11.1
1.2
1.9
0.4
4.6
4.2
0.4
5.5
2.9
2.7
16.4
2.0
2.4
1.2
7.8
6.7
1.1
8.7
3.8
4.9
27.2
2.9
3.4
4.4
14.6
12.4
2.1
3.5
3.8
3.8
3.4
2.9
2.3
10.7
4.0
3.8
6.0
2.7
2.4
3.0
0.6
1.4
0.4
2.6
1.5
1.6
0.1
3.5
3.5
3.9
4.3
5.6
2.8
12.7
5.7
12.6
8.1
4.2
5.7
3.9
2.7
–4.1
1.8
10.2
0.7
3.9
3.5
2.9
3.9
6.4
11.1
4.4
4.6
4.4
4.3
12.3
12.8
12.4
13.2
–0.5
–1.5
0.7
–3.5
a To aggregate agricultural output, I weight each commodity by the ratio of its current price
to the current price of rice. Thus, output is measured as millions of tonnes of ‘rice equivalents’, or its value in terms of rice. I chose rice as the output numeraire because of its strategic importance to Indonesia and because in most years it accounts for more than 30% of
agricultural output.
Sources: Commodity production: FAO (2001); commodity prices: BPSa, BPSb; MOA (various issues); FAO (2001); Van der Eng (1996).
olution technologies brought large increases in rice yield, and the area under nonfood crops grew rapidly outside Java. However, growth began to slow during the
late 1980s and stagnated in the 1990s. During the third period, 1993–2000, output
growth averaged only 1% per year. Indonesia suffered from weather-induced and
macroeconomic shocks during the latter part of the 1990s that exacerbated the
slowdown in output growth, but the slowdown appears to have begun before
these shocks.
218
Keith O. Fuglie
FIGURE 3 Agricultural Output Indices (1961 = 100) a
600
Tornqvist-Theil
500
Paasche
400
FAO
Agricultural GDP
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300
200
100
0
1961
a See
1966
1971
1976
1981
1986
1991
1996
text for explanation of terms.
Indices of agricultural output are shown in figure 3, with annual growth rates
for different periods given in table 2. (Here, agricultural production includes only
crop and animal components; fisheries and forestry production have been excluded because of the unreliability of the data.) For comparative purposes, I have
also constructed a Paasche index, which uses fixed weights to aggregate outputs
based on average end-period prices (the average value of normalised commodity
prices during 1996–2000). The Tornqvist–Theil, Paasche and FAO indices are
quantity-based, while the agricultural GDP index is a value-of-production index.
The Tornqvist–Theil index shows that agricultural output increased by 272%
between 1961 and 2000, slightly faster than is indicated by the Paasche index
(264% over the period). This difference between the two indices gives an indication of the extent of bias from using fixed weights for aggregation. The FAO production index also uses fixed weights based on a global set of commodity prices.
It shows somewhat slower growth in agricultural output: 254% over the 40 years.
All three indices show that the rate of output growth in Indonesian agriculture
slowed significantly in the 1990s.
All the output indices show that the years between 1968 and 1992 were exceptionally good for Indonesian agriculture, with annual growth averaging 4.6–4.8%
(table 2). However, the quantity-based indices indicate that growth in the years
before and after this green revolution period was considerably slower, at 1.1–1.3%
p.a. The very high growth shown by the GDP index during the hyperinflation
years of 1961–67 may partly be attributable to errors in the CPI. However, choice
of deflator does not explain the differences in growth between agricultural GDP
and the quantity-based indices after 1993. It is possible that the growth in value
of output (after discounting for general inflation) during the 1990s was primarily
due to terms of trade effects, and not to increases in the quantity of production.
Productivity Growth in Indonesian Agriculture, 1961–2000
219
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TABLE 2 Average Annual Growth in Agricultural Output
(%)
Tornqvist–Theil
Index
(chain
weights)
Paasche
Index
(fixed
weights)
FAO
Index
(fixed
weights)
Index of
Agricultural GDP
(deflated by
CPI)
By decade
1961–70
1971–80
1981–90
1991–2000
3.7
4.0
4.6
1.7
3.3
4.0
4.7
1.8
3.2
3.8
4.6
1.9
13.5
1.5
5.4
3.0
By period
1961–2000
1961–67
1968–92
1993–2000
3.5
1.2
4.8
1.1
3.4
1.1
4.7
1.2
3.4
1.2
4.6
1.3
5.7
12.2
4.7
3.7
Sources: Tornqvist–Theil and Paasche indices: author’s estimates; FAO index: FAO (2001);
agricultural GDP and CPI: BPSa.
Agricultural Input Use
Table 3 provides a breakdown of inputs used in Indonesian agriculture during
the first half of each of the past four decades, plus average annual growth rates
over the entire period and during the three periods of instability, growth and
stagnation described above. Crop land expanded by about 2% per year during
1961–2000, even in the last decade of the century. Virtually all of the expansion
occurred outside Java, especially on the relatively sparsely populated islands of
Kalimantan, Sumatra and Sulawesi. Irrigated crop land expanded by 1.8% per
year, slightly less than the overall growth in crop land. The agricultural labour
force also grew continuously over the four decades, and was still growing by
about 1% per year in the 1990s. However, this may overstate actual growth in
labour if agricultural households expanded their non-farm activities during this
period (and reduced the amount of time spent in agricultural pursuits).
The number of farm animals grew by an average of 2.3% p.a. during 1961–
2000, but growth was highly variable within this period. A large share of this
growth was in poultry, which relied heavily on imported feed. The number of
livestock fell sharply during the economic crisis of 1997–98, when the cost of
imported feed increased several-fold as the rupiah lost most of its exchange value.
Growth in industrial inputs used in agriculture, namely fertiliser and machinery, was very rapid, averaging more than 10% p.a. between 1961 and 2000.
However, use of these inputs started from a very small base, and industrial input
use per hectare remained small by Asian standards (Mundlak, Larson and Butze
2002). From 1993 there was virtually no growth in fertiliser use, and per hectare
application actually declined. The slowdown in fertiliser use can be attributed in
220
Keith O. Fuglie
TABLE 3 Average Annual Quantity and Growth Rate of Inputs Used in Agriculture
Quantity
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1961–
65
Crop land
(million hectares)
Area harvested
(million hectares)
Irrigated crop land
(million hectares)
1971–
75
Growth Rate (% p.a.)
1981–
85
1991–
95
1961–
2000
1961–
67
1968–
92
1993–
2000
17.6
18.9
26.0
32.2
2.0
0.3
2.3
2.1
17.5
19.6
22.7
28.3
1.7
1.1
2.1
0.8
2.4
2.7
3.3
4.6
1.8
1.4
2.3
0.3
28.6
31.7
37.5
46.0
1.5
0.8
1.7
1.1
0.1
0.4
1.7
2.5
10.6
1.7
16.0
0.1
Labour
(million workers)
Fertiliser
(million tonnes)
Machinery (million
horsepower)
Animals (million
head of stock)
0.1
0.2
0.2
0.6
11.5
7.5
14.3
5.9
10.5
10.9
14.7
24.8
2.3
–0.1
3.6
–0.3
Fertiliser/crop land
(kg/ha)
6.9
22.7
64.0
76.3
8.5
1.3
13.6
–2.0
Sources: Crop land and irrigated crop land: BPSa; Van der Eng (1996); others: FAO (2001).
FIGURE 4 Aggregate Input Use and TFP in Agriculture (1961 = 100) a
250
200
150
Tornqvist-Theil input
100
Paasche input
Tornqvist-Theil TFP
50
Paasche TFP
0
1961
a See
1966
1971
text for explanation of terms.
1976
1981
1986
1991
1996
Productivity Growth in Indonesian Agriculture, 1961–2000
221
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TABLE 4 Average Annual Growth in Aggregate Input Use and TFP in Agriculture
(%)
Paasche
Input
Index
(fixed
weights)
Tornqvist–Theil
Input Index
Paasche
TFP
Index
(fixed
weights)
(chain
weights)
(declining
labour
intensity)
By decade
1961–70
1971–80
1981–90
1991–2000
0.6
1.8
3.1
1.4
0.6
1.9
3.1
1.3
By period
1961–2000
1961–67
1968–92
1993–2000
1.8
0.5
2.2
1.2
1.8
0.5
2.2
1.2
Tornqvist–Theil
TFP Index
(chain
weights)
(declining
labour
intensity)
0.6
1.8
3.0
1.3
2.7
2.1
1.5
0.4
3.1
2.1
1.5
0.4
3.1
2.1
1.6
0.4
1.7
0.5
2.2
1.1
1.7
0.6
2.5
–0.1
1.7
0.8
2.5
–0.1
1.8
0.8
2.6
0.0
Source: Author’s estimates.
part to farmers’ rising real costs. The level of fertiliser subsidy was as high as 50%
from the mid 1970s to the mid 1980s, but then declined gradually. The subsidy
finally ended in 1999.
Trends in aggregate input use in Indonesian agriculture are shown in figure 4.
The Tornqvist–Theil input index is based on actual factor shares paid for inputs,
while the Paasche input index uses average real input prices during 1996–2000 as
fixed weights for input aggregation. These indices generate nearly identical
results, both showing that aggregate input use increased by 96% between 1961
and 2000. If we assume a decline in labour intensity since 1970 (fewer days
worked per agricultural worker per year), this reduces the estimated gain in
aggregate agricultural inputs by 4% (from 96% to 92% over the whole period).
Agricultural TFP
With estimates of aggregate outputs and inputs, the index of TFP for Indonesian
agriculture is found simply by taking the ratio of the output index to the input
index (figure 4, with annual growth rates given in table 4). The Tornqvist–Theil
TFP index indicates that TFP grew by 90% between 1961 and 2000, or by 93%
assuming declining labour intensity on the part of agricultural workers. The error
in the Paasche output index (which underestimated growth) is also reflected in
the Paasche TFP index (underestimating TFP growth by 4 percentage points over
the 40-year period). All three indices show that growth in TFP was rapid during
the green revolution decades, but began to slow in the 1980s and fell to less than
0.5% p.a. in the 1990s. Since 1993 there has been no growth at all in TFP.
222
Keith O. Fuglie
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TABLE 5 Average Annual Growth in Productivity in Agriculture
(%)
1961–2000
1961–67
1968–92
1993–2000
Total outputs (crop and animal)
Total inputs
TFP
3.5
1.8
1.7
1.2
0.5
0.7
4.8
2.2
2.6
1.1
1.2
–0.1
Labour productivity
Land productivity
Land/worker
2.0
1.5
0.5
0.3
0.8
–0.4
2.9
2.4
0.6
0.1
–0.9
1.0
Food crop output/population
Rice output/population
2.5
2.5
0.2
1.0
4.0
3.7
–0.4
–0.3
Sources: Total output, input and TFP growth is derived from the Tornqvist–Theil output,
input and TFP given in tables 2 and 4. Labour is found by dividing aggregate output (see
table 1 for sources) by total agricultural labour (see table 3 for sources). Land productivity
is derived in the same way. Land/worker is crop land per agricultural worker (see table 3
for sources). Food crop output is the sum of rice, cassava, maize, bean and sweet potato
output measured in rice equivalents (see table 1 for sources). Population is from BPSa.
With these estimates, it is possible to decompose total growth in output into a
component due to increases in conventional factors of production and a component due to productivity improvement. Table 5 summarises the results. Between
1961 and 2000, average annual growth in agricultural crop and livestock production in Indonesia was 3.5%, of which 1.8% was due to increases in land, labour
and other conventional inputs and 1.7% was due to improvements in TFP.
Increases in TFP were most evident in 1968–92, with little change either before or
since these years. The growth in agricultural output since 1993 (about 1.1% p.a.)
was entirely due to increases in conventional agricultural inputs, especially land
and labour. These general findings are not appreciably affected by our assumptions about labour intensity per worker in agriculture.
Output per agricultural worker increased by an average of 2% p.a. between
1961 and 2000. Most of this was due to increases in output per hectare of crop
land, but crop land per worker also increased. In fact, land per worker was growing by about 1% p.a. even as late as the 1990s, despite the continued increase in
the number of workers employed in agriculture. Virtually all of the land expansion occurred outside Java and Madura; these densely populated islands actually
lost crop land in the 1990s (BPSa).
The final two rows of table 5 give an indication of food security in Indonesia
by comparing growth in food production to population growth. By these measures Indonesia’s food economy performed remarkably well during the final
decades of the 20th century. Per capita production of both rice and all food crops
grew at an annual rate of 2.5% during 1961–2000. However, per capita food production went into decline in the 1990s, when TFP growth in Indonesian agriculture stagnated.
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Productivity Growth in Indonesian Agriculture, 1961–2000
223
CONCLUSIONS AND IMPLICATIONS
Improvements in the data series for Indonesian agriculture provide an opportunity to reassess the productivity performance of this sector during the closing
decades of the 20th century. By constructing a Tornqvist–Theil index of input,
output and TFP growth, I have been able to minimise the bias inherent in index
measures when price weights change over time. My estimation showed that
between 1961 and 2000, about half of the growth in agricultural output could be
attributed to an expansion of conventional inputs (crop land, labour, livestock,
fertiliser and machinery) and about half to TFP. The use of modern industrial
inputs (chemical fertilisers and power machinery) grew rapidly from very low
initial levels. Nearly all of the growth in TFP occurred between 1968 and 1992,
however. By the 1990s, agricultural growth once more relied almost entirely on
increases in conventional factors as productivity stagnated.
Recent work on agricultural productivity assessments for China (Fan and
Zhang 2002) and India (Fan, Hazell and Thorat 1999) allow some comparisons
among the performances of the three largest agricultural economies in Asia since
1970. Indonesia depended upon resource expansion (in particular, bringing new
crop land into production) more heavily than either China or India. In China, TFP
improvements accounted for 62% of agricultural growth, and in India, more than
70%. Both China and India were able to sustain growth in their agricultural sectors during the 1990s whereas agricultural growth in Indonesia slowed significantly as TFP stagnated.
While a rigorous examination of the underlying causes of the productivity
slowdown in Indonesian agriculture is beyond the scope of this study, I can suggest some probable causes. Drought-inducing El Niño and macroeconomic shocks
took their toll on agricultural production in the late 1990s (especially in 1996–98),
but the slowdown in productivity appears to have preceded these events, and has
continued since. Probably a more important explanation for the productivity
slowdown is that once the initial gains made possible by the green revolution
were exhausted, public and private investments in agriculture were not sufficient
to sustain further productivity growth. Both government spending and private
investment in agriculture have fallen since the mid 1980s as a percentage of agricultural GDP (Fuglie and Piggott, forthcoming). While much of the decline in government support for agriculture came about through a reduction in fertiliser subsidies (which probably had low returns, especially in later years), the decline also
extended to investments with a higher pay-off such as rural infrastructure and
agricultural research and extension (Fuglie and Piggott, forthcoming).5 Private
sector investment in agriculture fell drastically during the financial crisis of
1997–98. It is unlikely to recover unless the overall investment climate in
Indonesia improves and complementary infrastructure investments that can only
be made by the public sector are forthcoming.
The stagnation of TFP growth has serious implications for the long-term performance of Indonesian agriculture. Indonesia will find it increasingly difficult to
expand agricultural land, and without growth in TFP, agricultural output will
continue to grow only very slowly. This will speed the drain of resources away
from agriculture and rural areas to other sectors of the economy. While movement of labour out of agriculture is a natural consequence of economic development, this transition will be suboptimal if high pay-off investment opportunities
224
Keith O. Fuglie
in agriculture are forgone, thus undermining the potential contribution of agricultural development to reducing poverty and generating broad-based economic
growth for the Indonesian economy.
NOTES
1
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2
3
4
5
These sources include Vademekum Pemasaran for horticultural crops (Jakarta wholesale
prices) (MOA 1999) and Statistics for Estate Crops of Indonesia for cacao beans, palm oil
and cane sugar (domestic market prices) (MOA, various issues).
VDE and MOA prices for palm oil were identical for the overlapping years 1986–96, so
these series appear to be from the same source. For cane sugar, VDE reports a ‘Surabaya export price’ that was consistently below the MOA domestic market price of cane
sugar by 8–14% for the overlapping years in the series (1990–96). To develop a consistent price series for 1961–2000, I used MOA prices for 1990–2000 and VDE prices for
1961–89 multiplied by 1.12.
See Booth (1988: 264–71) for a thorough discussion of Indonesian land use statistics.
Harvested area differs from crop land in that the former does not include fallow land
or land planted to immature perennials, and double counts land where two crops are
harvested in a year.
The studies on agricultural growth in India and China by Fan and his colleagues found
strong correlations between public spending on agricultural research/rural infrastructure and agricultural productivity growth in those countries (Fan and Zhang 2002; Fan,
Hazell and Thorat 1999). For Indonesia, Salmon (1991) and Evenson et al. (1997) found
high rates of return to food crop research in the 1970s and 1980s, mainly because the
programs studied helped to adapt and disseminate high-yielding varieties made available by international investments in the green revolution. Once the easy gains from
these technologies were achieved, productivity growth was not sustained. Unlike its
larger Asian neighbours, Indonesia does not yet appear to have developed an agricultural research system capable of sustaining productivity growth.
REFERENCES
Alston, Julian M., George W. Norton and Philip G. Pardey (1995), Science under Scarcity:
Principles and Practice for Agricultural Research Evaluation and Priority Setting, Cornell
University Press, Ithaca NY.
Arnade, Carlos (1998), ‘Using a Programming Approach to Measure International
Agricultural Efficiency and Productivity’, Journal of Agricultural Economics 49: 67–84.
Booth, Anne (1988), Agricultural Development in Indonesia, Allen & Unwin, Sydney.
Booth, Anne (2002), ‘The Changing Role of Non-farm Activities in Agricultural
Households in Indonesia: Some Insights from the Agricultural Censuses’, Bulletin of
Indonesian Economic Studies 38 (2): 179–200.
BPSa (Badan Pusat Statistik) (various issues), Statistical Yearbook of Indonesia, Jakarta.
BPSb (various issues), Agricultural Indicators, Jakarta.
BPSc (various issues), Farm Cost Structure of Paddy and Secondary Food Crops, Jakarta.
Craig, Barbara J., Philip G. Pardey and Johannes Roseboom (1997), ‘International
Productivity Patterns: Accounting for Input Quality, Infrastructure, and Research’,
American Journal of Agricultural Economics 79 (November): 1,064–76.
Evenson, Robert E., Edi Abdurachman, Budiman Hutabarat and Achmat C. Tubagus
(1997), ‘Contribution of Research on Food and Horticultural Crops in Indonesia: An
Economic Analysis’, Ekonomi dan Keuangan Indonesia XLV (4): 551–78.
Fan, Shenggen, and Xiaobo Zhang (2002), ‘Production and Productivity Growth in
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Chinese Agriculture: New National and Regional Measures’, Economic Development and
Cultural Change 50 (4): 819–38.
Fan, Shenggen, Peter Hazell and Sukhadeo Thorat (1999), ‘Linkages between Government
Spending, Growth, and Poverty in Rural India’, Research Report 110, International Food
Policy Research Institute, Washington DC.
FAO (Food and Agriculture Organization) (2001), FAOSTAT Agricultural Databases, Rome.
Fuglie, Keith O., and Roley R. Piggott (forthcoming), ‘Agricultural R&D Policy in
Indonesia’, in P.G. Pardey, J.M. Alston and R.R. Piggott (eds), Agricultural Research Policy
in the Developing World, International Food Policy Research Institute, Washington DC.
Hayami, Yujiro, and Vernon W. Ruttan (1985), Agricultural Development: An International
Perspective, Johns Hopkins University Press, Baltimore MD.
MOA (Ministry of Agriculture) (1999), Vademekum Pemasaran 1990–1999, Directorate
General of Food Crops and Horticulture, Ministry of Agriculture, Jakarta.
MOA (various issues), Statistics for Estate Crops of Indonesia 1990–1999, Directorate General
of Estate Crops, Ministry of Agriculture, Jakarta.
Mundlak, Yair, Donald F. Larson and Rita Butze (2002), ‘Determinants of Agricultural
Growth in Thailand, Indonesia, and the Philippines’, Discussion Paper No. 302, Center
for Agricultural Economic Research, Hebrew University of Jerusalem.
Salmon, David. C. (1991), ‘Rice Productivity and the Returns to Rice Research in
Indonesia’, in R.E. Evenson and C.E. Pray (eds), Research and Productivity in Asian
Agriculture, Cornell University Press, Ithaca NY: 133–57.
Suhariyanto, Kecuk (2001), ‘Agricultural Productivity Growth in Asian Countries’, in G.H.
Peters and P. Pingali (eds), Tomorrow’s Agriculture: Incentives, Institutions, Infrastructure
and Innovations, Proceedings of the 24th International Conference of Agricultural
Economists, Berlin, 13–18 August 2000, Ashgate, Burlington VT: 376–82.
Van der Eng, Pierre (1996), Agricultural Growth in Indonesia: Productivity Change and Policy
Impact since 1880, St Martin’s Press, New York.
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Productivity growth in Indonesian agriculture,
1961–2000
Keith O. Fuglie
To cite this article: Keith O. Fuglie (2004) Productivity growth in Indonesian
agriculture, 1961–2000, Bulletin of Indonesian Economic Studies, 40:2, 209-225, DOI:
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Bulletin of Indonesian Economic Studies, Vol. 40, No. 2, 2004: 209–25
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PRODUCTIVITY GROWTH IN
INDONESIAN AGRICULTURE, 1961–2000
Keith O. Fuglie
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International Potato Center, Bogor
This paper uses an index number approach to measure total factor productivity
(TFP) growth in Indonesian crop and livestock agriculture from 1961 to 2000.
Tornqvist–Theil chain-weighted indices of output, input and TFP are developed to
minimise biases that may result from relative changes in input and output price
aggregation weights. The results indicate that agricultural TFP growth accelerated
in the 1970s and 1980s but stagnated in the 1990s. Without new efforts to increase
productivity in agriculture, Indonesia’s goal of using agricultural growth to generate broad-based rural development and further reduce poverty may be undermined.
INTRODUCTION
During the latter half of the 20th century, rising output per hectare replaced
expansion of crop land as the predominant source of agricultural growth in most
of the world (Hayami and Ruttan 1985). This transition from agricultural extensification to intensification was most noticeable in Asia, where population density is relatively high and land scarcity most acute. In this paper, I examine the
performance of agricultural productivity in Indonesia during the last 40 years of
the 20th century. Although agriculture’s share of the economy declined over this
period, at the end of the century it still provided the main livelihood for half of
the nation’s population, or over 100 million people. Sustaining productivity
growth in agriculture can make a significant contribution to food security,
poverty reduction and broad-based economic growth.
Assessing the productivity performance of the agricultural sector in Indonesia
has been difficult because of the lack of appropriate data. Significant improvements in agricultural input and output measurement provided by Van der Eng
(1996) have, however, eased this task. In particular, the author provides improved
estimates of agricultural crop land in Indonesia that differ markedly from the
agricultural land use data provided by the Food and Agriculture Organization
(FAO), the primary source of data used in previous assessments of productivity
growth in Indonesia (e.g. Mundlak, Larson and Butze 2002; Suhariyanto 2001). In
addition, previous studies of agricultural growth have sometimes used changes
in agricultural value added rather than changes in output quantities (Van der Eng
1996; Mundlak, Larson and Butze 2002), a practice that confounds the effect of
changes in productivity and changes in terms of trade on agricultural growth.
The series I develop for the rate of real output growth differs greatly from the rate
of change in agricultural GDP.
ISSN 0007-4918 print/ISSN 1472-7234 online/04/020209-17
DOI: 10.1080/0007491042000205286
© 2004 Indonesia Project ANU
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210
Keith O. Fuglie
In this study, I use time series of 49 categories of crop and livestock outputs
and 19 categories of inputs, with their respective prices, to construct a Tornqvist–
Theil chain index of total factor productivity (TFP) between 1961 and 2000. A TFP
index is simply the ratio of an output index to an input index. Therefore, growth
in TFP is the residual share of output growth after accounting for percentage
changes in land, labour and other conventional factors of production. Changes in
TFP can be interpreted as a measure of the collective contribution of non-conventional inputs in agriculture, such as improvements in input quality, economies of
scale and technology (Alston, Norton and Pardey 1995). Using a Tornqvist–Theil
index reduces the bias that arises in fixed-weight indices when price weights
change over time. Recent studies of agricultural productivity growth in India
(Fan, Hazell and Thorat 1999) and China (Fan and Zhang 2002) have found this
approach to give significantly different results from TFP indices based on a constant set of aggregation weights.
In general, my approach finds a faster rate of input growth (especially more
land expansion) and a slower rate of output growth than previous studies of agricultural growth in Indonesia. In particular, I find that agricultural productivity
in Indonesia stagnated in the 1990s after two decades of rapid growth. What
emerges from this exercise is a picture that raises concern for the future performance of Indonesia’s agriculture and the welfare of the people who depend on it
for their livelihood. I conjecture that without major increases in public and private investment in the sector, agriculture will not return to a path of sustained
productivity growth.
METHODOLOGY
Productivity can be defined as a ratio of output to inputs used in production. An
increase in the amount of output per unit of inputs, equivalent to an outward
shift in a production function, is then measured as an increase in factor productivity. Partial productivity indices, such as output per worker, give the ratio of
output to a single factor of production. However, since the amounts of other
inputs may vary, increases in output per worker can result from either increases
in the use of other inputs or changes in technology. A TFP index compares
changes in output with changes in aggregate inputs. Changes in TFP provide a
better picture of changes in technology and other unmeasured improvements in
the means of production (such as improvements in input quality). In applications
of TFP measurements to agriculture, it is common to aggregate outputs and conventional inputs using market or shadow prices as weights. The theoretical
underpinnings of TFP index measurement assume that producers maximise profits, that output markets are competitive and that the production technology is
characterised by constant returns to scale. Under these conditions, total revenue
will equal total costs, the elasticity of output with respect to each input is equal
to its factor share, and TFP growth can be estimated using observed input and
output quantities and prices (Alston, Norton and Pardey 1995).
A well known limitation of index numbers is that changes in input prices over
time may lead to biases in the measurement of TFP because producers change the
mix of inputs in response to such price changes. To illustrate, figure 1 shows an
isoquant (Q) of a production function in which two inputs, L and A, are used to
Productivity Growth in Indonesian Agriculture, 1961–2000
211
FIGURE 1 Biases in TFP when Producers Change the Mix of Inputs a
A
Q
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A0
A1
b
c
w0
w1
L
a See
text for explanation of terms.
produce a commodity. The ratio of input prices is given by w0, and point b is the
cost-minimising combination of L and A to produce the level of output indicated
by the isoquant. Now suppose that technical change results in fewer inputs being
required to produce a given amount of output, so that the isoquant in the diagram now indicates a higher level of output than previously. A true index of productivity growth should measure the proportional change in the output level represented by this isoquant.
If the prices of inputs remain unchanged, this is given by the increase in output for the given aggregate input cost (A0, using A as the numeraire). However,
if the price of L relative to A declines (from w0 to w1), then the optimal input combination for the new, higher level of output becomes point c. At the old relative
price level, c is more costly than b, so if input costs are aggregated using observed
quantities together with the initial prices as aggregation weights, moving from
point b to point c would imply an increase in aggregate inputs to produce the now
higher level of output. This would result in underestimation of the actual change
in productivity. On the other hand, if the new prices are used to aggregate inputs,
then input combination c will appear cheaper (A1) than that at b, falsely magnifying the observed improvement in TFP. Similar problems are encountered in
aggregating across multiple outputs (Alston, Norton and Pardey 1995).
The Tornqvist–Theil chain-weighted index minimises the effect of changes in
price weights on output and input aggregation by allowing the weights to adjust
over time as prices change.
212
Keith O. Fuglie
The Tornqvist–Theil index of output in year t is defined as
n
ln (Yt Y t −1 ) =
∑
(Ri,t + Ri,t −1 ) ln⎛⎜
i =1
Yi, t ⎞
⎟
⎜Y
⎟
−
,
1
i
t
⎠
⎝
2
(1)
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where Ri,t is the revenue share of output i and Yi,t is the quantity of output i in
year t. Thus, the growth rate of aggregate output Y between period t and t –1 is a
sum of the growth rates of the n commodities that make up total output, each
weighted by the average of their revenue shares during t and t –1.
Similarly, the aggregate input index is defined as
m
ln (X t X t −1 ) =
∑
j =1
(S j,t + S j,t −1 ) ln⎛⎜
2
X j, t ⎞
⎟
⎜ X j, t − 1 ⎟
⎠
⎝
(2)
where the growth rate of aggregate input X between period t and t –1 is the sum
of the growth rates of the j = 1,2, … m categories of inputs, each weighted by the
average of their factor shares Sj during adjacent periods. (A ‘factor share’ is the
proportion of total cost paid for an input.)
The proportional change in TFP between period t and t –1 is then given as
⎛ TFPt ⎞
⎟ = ln (Yt Y t −1 ) − ln (X t X t −1 )
ln ⎜⎜
⎟
⎝ TFPt −1 ⎠
(3)
Estimation requires data on the quantities and prices of each output and input
for each period. For comparative purposes, I also estimate Paasche indices of output, input and TFP, in which end-period prices are used as fixed weights to aggregate items over the entire series. Comparison between the Tornqvist–Theil and
Paasche indices gives an indication of the size of the bias resulting from fixed
weights.
DATA
The lack of reliable time series data on agricultural production and input use in
Indonesia has been a limiting factor in previous attempts to quantify productivity growth (Booth 1988; Van der Eng 1996; Arnade 1998; Suhariyanto 2001;
Mundlak, Larson and Butze 2002). Agriculture is generally defined to include
crops, livestock, fresh water and marine fisheries, and forestry. But most studies
on agricultural productivity growth focus on crop and livestock agriculture
because of serious deficiencies in measuring outputs and inputs used in fisheries
and forestry (Craig, Pardey and Roseboom 1997). This study follows that practice
as well.
Measurement uncertainties also affect crop and livestock production and input
use. On the output side, some studies have used the national accounts value of
agricultural production or value added (GDP) as a measure of aggregate output
(e.g. Van der Eng 1996; Arnade 1998; Suhariyanto 2001; Mundlak, Larson and
Butze 2002). But using value added confounds quantity and terms of trade effects
on changes in output. For example, if agricultural prices fell relative to non-
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Productivity Growth in Indonesian Agriculture, 1961–2000
213
agricultural prices, this would reduce agricultural GDP even if output was constant. Further, deriving real changes in agricultural GDP depends critically on the
choice of inflation deflator. For periods of high inflation such as during the early
1960s, the CPI and GDP deflators available for Indonesia are subject to a wide
margin of error. Instead, I use revenue and factor shares calculated to current
prices to construct aggregation weights. This avoids having to use an inflation
deflator altogether. For inputs, FAO estimates of agricultural land in Indonesia
are highly suspect. While agricultural land is inherently difficult to define and
measure, Van der Eng (1996) has significantly improved the record of agricultural
land change in Indonesia.
In the remainder of the paper, data sources are represented by ‘FAO’ for the
FAO’s FAOSTAT series (FAO 2001), ‘BPS’ for statistics from the Indonesian
Central Statistics Agency (Statistical Yearbook of Indonesia unless otherwise noted)
(BPSa, BPSb, BPSc), ‘MOA’ for sources from the Ministry of Agriculture (MOA
1999, various issues) and ‘VDE’ for data series reported in Van der Eng (1996). I
discuss the land measurement issue in more detail below.
Crop and Livestock Output
FAO production data are used to measure outputs of food, horticultural and nonfood crops, and meat, milk and eggs since 1961. The FAO production data are
equivalent to the agricultural output data reported in BPSa, but include a larger
set of commodities. For crop and livestock production, I measure these in metric
tonnes net of seed and animal feed requirements.
Crop and Livestock Product Prices
Consistent, long-term time series of producer prices for agricultural commodities
in Indonesia are difficult to find, except for the most important crops such as rice,
rubber and palm oil. FAO provides a ‘producer’ price series for most commodities from the mid 1960s, but in fact these price series may represent farmgate,
rural or urban wholesale prices, depending on the country and commodity. VDE
also supplies price series for several major food and non-food crops that the
author calls ‘rural bazaar prices’. For many commodities, both the FAO and VDE
price data are close to or the same as the Jakarta wholesale prices reported in the
BPS statistical yearbooks. For this study I used BPS annual price series for 17
commodities reported in various issues of the annual Statistical Yearbook of
Indonesia and Agricultural Indicators. These commodities account for more than
three-quarters of the value of total crop–livestock output. Prices for another 18
commodities (mostly horticultural products) were taken from selected publications of the Ministry of Agriculture.1 MOA price data are also mostly urban or
Jakarta wholesale prices. FAO producer prices were used for another 14 commodities, accounting for about 7.5% of output value. VDE price data were used
for tobacco, and to supplement MOA data for palm oil and cane sugar prices
prior to 1986 and 1990 respectively.2
For some commodities, consistent price series were established in Indonesia
only in the late 1960s or early 1970s. For missing years, the average normalised
price (commodity price relative to the price of rice) for the nearest five-year
period for which price data were available was used to extend the series back to
1961. These relative prices were then used to construct revenue shares for those
214
Keith O. Fuglie
FIGURE 2 Estimates of Agricultural Crop Land in Indonesia a
(million hectares)
40
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30
20
BPS/VDE (crop land)
FAO (crop land)
10
FAO (area harvested)
0
1961
a See
1966
1971
1976
1981
1986
1991
1996
text for explanation of terms.
years. In summary, then, the agricultural product prices used in this study are a
generally consistent time series of prices at the Jakarta wholesale level.
Crop Land
Historical data on agricultural land use in Indonesia are generally unreliable or
unavailable except for recent years.3 Since the 1980s, BPSa has published reasonably good estimates, by province, of land area in sawah (bunded land for paddy
rice, irrigated and non-irrigated), land area planted to garden crops, land area
planted to upland crops (including fallow land) and land area planted to permanent crops (perennials). VDE extends these series back to 1880 and provides separate series for Java and non-Java. While FAO has been the primary source of data
on agricultural land for most previous studies of agricultural growth in Indonesia
(Arnade 1998; Suhariyanto 2001; Mundlak, Larson and Butze 2002), the FAO land
series differs markedly from the BPS and VDE land use estimates (figure 2). The
BPS/VDE data show that agricultural crop land in Indonesia expanded from
about 17 million hectares in the 1960s to over 37 million hectares in the 1990s,
with virtually all of the expansion occurring outside Java. This series tracks
closely the trend in area harvested reported by FAO.4 The FAO series on arable
land and land under permanent crops, on the other hand, shows a constant 26
million hectares of crop land in Indonesia until the mid 1980s, followed by
uneven growth to 31 million hectares by the mid 1990s. The FAO agricultural
land data are consistent neither with what we know from casual observation to
have occurred over these decades nor with published government statistics on
land use in the country. And the differences are substantial: while the FAO data
show a declining land-to-labour ratio in Indonesia, the BPS/VDE data show that
available crop land per agricultural worker actually increased during the
1961–2000 period for the country as a whole (but did decline in Java).
Productivity Growth in Indonesian Agriculture, 1961–2000
215
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In this study I use the BPS/VDE estimates of crop land, that is, land planted to
crops, fallow land and land planted to perennials (permanent crops). Crop land
is further divided into two land quality types: irrigated and non-irrigated crop
land. Since price series are unavailable for crop land, I follow Mundlak, Larson
and Butze (2002) in giving irrigated land 2.5 times the value of non-irrigated land.
The service flow (price weight) of irrigated and non-irrigated crop land is then
estimated as a residual—the remainder of total output after paying for agricultural labour, fertiliser, machinery and livestock.
Labour and Wages
Data on two categories of labour—adult male and adult female workers
employed in agriculture—are from FAO. Daily wages for male and female workers are from VDE and refer to average wages paid by plantations. Agricultural
wages are extended beyond 1996 using data from BPSc. To find total annual
labour costs, daily wages (in rice equivalents) are multiplied by 300 days worked
per year for men and 250 days worked per year for women. There is evidence that
farm work by households claiming agriculture as their principal occupation may
have declined over time as they diversified into more non-farm activities. Booth
(2002), for example, found that between the 1983 and 1993 agricultural censuses
the share of farm income in the total income of agricultural households declined
from 55% to 50%. The diversification into non-farm work was especially significant for households with very little land. To examine the implications of labour
diversification for the measurement of agricultural productivity, I developed an
alternative measure of labour inputs, by assuming that from 1970 onwards the
average time spent per worker in agriculture declined by two days per year. In
percentage terms this is slightly less than the decline in the share of farm income
in total household income estimated by Booth, to reflect (probably) higher average returns to labour in non-farm occupations. Aggregate input and productivity
indices with and without this adjustment for declining labour intensity in agriculture are reported in the results.
Fertiliser
Annual applications of chemical fertilisers—nitrogen (N), phosphorus (P2O5) and
potash (K2O)—are from FAO. Prices paid for fertiliser are from VDE, updated
since 1996 using BPSb. From the early 1970s until 1999, chemical fertilisers were
heavily subsidised in Indonesia, sometimes by as much as 50% of the actual cost.
This raises the question of whether fertiliser inputs should be weighted by their
social or private costs. Private costs (at the subsidy price) are used in this study
to conform with the theory underpinning the index number approach to measuring productivity. However, despite rapid growth in fertiliser application during
the last several decades, the share of fertiliser in total costs has remained small.
Even at the social cost of fertiliser, total (private and government) expenditure on
fertiliser has rarely exceeded 4% of the value of agricultural output.
Agricultural Machinery
Agricultural machinery is measured in terms of horsepower available from tractors and threshers used in agriculture. Machinery includes two-wheel tractors,
small, medium and large four-wheel tractors, and power threshers. Machinery
data are from FAO, supplemented by a more detailed breakdown of machinery
216
Keith O. Fuglie
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by type published in BPSa. Machinery prices are FAO import values amortised
over five years to derive an annual service flow.
Livestock
Livestock contribute to agriculture in several ways. In addition to yielding meat,
hides, milk and eggs, they provide power, fertiliser and offspring, and serve as a
store of wealth. I measure livestock inputs as the total annual stock of buffalo, beef
cattle, dairy cows, horses, pigs, small ruminants and poultry. Data on animal
stocks are from FAO. The relevant price weight for an animal input is the value of
services from that animal in a given year, and not its stock value. This is important
for animals that provide services for several years, such as large ruminants. Prices
for live animals are derived from FAO agricultural trade statistics. For cattle, buffalo and horses, the annual service flow is approximated by dividing the unit
value of imported live cattle by three. The assumption here is that the cattle price
reflects the present value of lifetime animal services, and that cattle have a working life of about three years on average. The unit value of imported sheep is used
for sheep and goats. Unit export values are used as prices for pigs and poultry.
RESULTS
Agricultural Output Growth
Indonesia produces a diverse set of agricultural commodities. Food crop production is dominated by rice, but also includes several secondary food crops, especially cassava and maize. Non-food crops include rubber, palm oil, coffee, tea,
cacao, sugar cane, coconut and numerous other species. Poultry has been the
fastest growing component of animal production, but large and small ruminants
and pigs are also significant. Besides crops and animal products, fresh water and
marine fisheries and forestry are important components of Indonesia’s agricultural sector. Table 1 shows the changing level and composition of agricultural
output in Indonesia since 1961. The first four columns report average yearly output levels during the first half of each decade in terms of ‘rice equivalents’, where
commodities are valued and aggregated by their current price relative to the price
of rice. Between 1961–65 and 1991–95, total annual agricultural output increased
from 50.5 million tonnes to 137.1 million tonnes of rice equivalents. Rice was by
far the most important commodity, accounting for about 25% of total agricultural
output in the early 1960s and nearly 35% of total output in the early 1990s.
Among non-food crops, palm oil grew very rapidly, especially after 1980. Meat
and milk production also gained in output share. According to the figures, production of forest products hardly grew at all over the four decades, but these figures are probably not reliable.
In assessing overall growth of the Indonesian agricultural sector since 1961, I
distinguish three periods. The first, 1961–67, was a time of economic and political instability in Indonesia. Non-food crop production fell sharply following the
nationalisation of foreign-owned estates, and food crop production barely kept
pace with population growth. Indonesian agriculture was probably operating
inefficiently during this period, inside the production function frontier. During
the second period, 1968–92, Indonesia sustained rapid growth in both crop and
animal production, with average annual output growth of around 4%. Green rev-
Productivity Growth in Indonesian Agriculture, 1961–2000
217
TABLE 1 Average Annual Level, Composition and
Growth Rate of Agricultural Output (selected periods)
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Production
(million tonnes of rice equivalents) a
Agricultural
outputs, all
Crop and animal
outputs, all
Food crops, all
Rice, paddy
Cassava
Maize
Horticultural
crops, all
Fruit
Vegetables
Non-food crops, all
Cane sugar
Rubber
Palm oil
Animal products, all
Meat
Milk
Fish products
Forest products
1961–
65
1971–
75
1981–
85
50.5
65.6
94.5
35.3
19.7
12.4
2.5
1.7
48.9
28.7
21.2
2.4
1.7
3.2
1.4
1.8
9.1
0.8
1.7
0.2
3.3
3.0
0.3
1991–
95
Growth Rate
(% p.a.)
1961–
2000
1961–
67
1968–
92
1993–
2000
137.1
2.9
0.7
4.0
1.0
75.6
45.9
35.8
2.8
2.7
112.8
62.3
47.5
3.4
4.3
3.4
3.5
3.9
1.2
6.9
1.1
1.2
1.7
–0.5
9.7
4.7
5.0
5.5
1.9
7.7
1.2
0.4
0.7
0.2
2.5
4.5
2.0
2.5
11.1
1.2
1.9
0.4
4.6
4.2
0.4
5.5
2.9
2.7
16.4
2.0
2.4
1.2
7.8
6.7
1.1
8.7
3.8
4.9
27.2
2.9
3.4
4.4
14.6
12.4
2.1
3.5
3.8
3.8
3.4
2.9
2.3
10.7
4.0
3.8
6.0
2.7
2.4
3.0
0.6
1.4
0.4
2.6
1.5
1.6
0.1
3.5
3.5
3.9
4.3
5.6
2.8
12.7
5.7
12.6
8.1
4.2
5.7
3.9
2.7
–4.1
1.8
10.2
0.7
3.9
3.5
2.9
3.9
6.4
11.1
4.4
4.6
4.4
4.3
12.3
12.8
12.4
13.2
–0.5
–1.5
0.7
–3.5
a To aggregate agricultural output, I weight each commodity by the ratio of its current price
to the current price of rice. Thus, output is measured as millions of tonnes of ‘rice equivalents’, or its value in terms of rice. I chose rice as the output numeraire because of its strategic importance to Indonesia and because in most years it accounts for more than 30% of
agricultural output.
Sources: Commodity production: FAO (2001); commodity prices: BPSa, BPSb; MOA (various issues); FAO (2001); Van der Eng (1996).
olution technologies brought large increases in rice yield, and the area under nonfood crops grew rapidly outside Java. However, growth began to slow during the
late 1980s and stagnated in the 1990s. During the third period, 1993–2000, output
growth averaged only 1% per year. Indonesia suffered from weather-induced and
macroeconomic shocks during the latter part of the 1990s that exacerbated the
slowdown in output growth, but the slowdown appears to have begun before
these shocks.
218
Keith O. Fuglie
FIGURE 3 Agricultural Output Indices (1961 = 100) a
600
Tornqvist-Theil
500
Paasche
400
FAO
Agricultural GDP
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300
200
100
0
1961
a See
1966
1971
1976
1981
1986
1991
1996
text for explanation of terms.
Indices of agricultural output are shown in figure 3, with annual growth rates
for different periods given in table 2. (Here, agricultural production includes only
crop and animal components; fisheries and forestry production have been excluded because of the unreliability of the data.) For comparative purposes, I have
also constructed a Paasche index, which uses fixed weights to aggregate outputs
based on average end-period prices (the average value of normalised commodity
prices during 1996–2000). The Tornqvist–Theil, Paasche and FAO indices are
quantity-based, while the agricultural GDP index is a value-of-production index.
The Tornqvist–Theil index shows that agricultural output increased by 272%
between 1961 and 2000, slightly faster than is indicated by the Paasche index
(264% over the period). This difference between the two indices gives an indication of the extent of bias from using fixed weights for aggregation. The FAO production index also uses fixed weights based on a global set of commodity prices.
It shows somewhat slower growth in agricultural output: 254% over the 40 years.
All three indices show that the rate of output growth in Indonesian agriculture
slowed significantly in the 1990s.
All the output indices show that the years between 1968 and 1992 were exceptionally good for Indonesian agriculture, with annual growth averaging 4.6–4.8%
(table 2). However, the quantity-based indices indicate that growth in the years
before and after this green revolution period was considerably slower, at 1.1–1.3%
p.a. The very high growth shown by the GDP index during the hyperinflation
years of 1961–67 may partly be attributable to errors in the CPI. However, choice
of deflator does not explain the differences in growth between agricultural GDP
and the quantity-based indices after 1993. It is possible that the growth in value
of output (after discounting for general inflation) during the 1990s was primarily
due to terms of trade effects, and not to increases in the quantity of production.
Productivity Growth in Indonesian Agriculture, 1961–2000
219
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TABLE 2 Average Annual Growth in Agricultural Output
(%)
Tornqvist–Theil
Index
(chain
weights)
Paasche
Index
(fixed
weights)
FAO
Index
(fixed
weights)
Index of
Agricultural GDP
(deflated by
CPI)
By decade
1961–70
1971–80
1981–90
1991–2000
3.7
4.0
4.6
1.7
3.3
4.0
4.7
1.8
3.2
3.8
4.6
1.9
13.5
1.5
5.4
3.0
By period
1961–2000
1961–67
1968–92
1993–2000
3.5
1.2
4.8
1.1
3.4
1.1
4.7
1.2
3.4
1.2
4.6
1.3
5.7
12.2
4.7
3.7
Sources: Tornqvist–Theil and Paasche indices: author’s estimates; FAO index: FAO (2001);
agricultural GDP and CPI: BPSa.
Agricultural Input Use
Table 3 provides a breakdown of inputs used in Indonesian agriculture during
the first half of each of the past four decades, plus average annual growth rates
over the entire period and during the three periods of instability, growth and
stagnation described above. Crop land expanded by about 2% per year during
1961–2000, even in the last decade of the century. Virtually all of the expansion
occurred outside Java, especially on the relatively sparsely populated islands of
Kalimantan, Sumatra and Sulawesi. Irrigated crop land expanded by 1.8% per
year, slightly less than the overall growth in crop land. The agricultural labour
force also grew continuously over the four decades, and was still growing by
about 1% per year in the 1990s. However, this may overstate actual growth in
labour if agricultural households expanded their non-farm activities during this
period (and reduced the amount of time spent in agricultural pursuits).
The number of farm animals grew by an average of 2.3% p.a. during 1961–
2000, but growth was highly variable within this period. A large share of this
growth was in poultry, which relied heavily on imported feed. The number of
livestock fell sharply during the economic crisis of 1997–98, when the cost of
imported feed increased several-fold as the rupiah lost most of its exchange value.
Growth in industrial inputs used in agriculture, namely fertiliser and machinery, was very rapid, averaging more than 10% p.a. between 1961 and 2000.
However, use of these inputs started from a very small base, and industrial input
use per hectare remained small by Asian standards (Mundlak, Larson and Butze
2002). From 1993 there was virtually no growth in fertiliser use, and per hectare
application actually declined. The slowdown in fertiliser use can be attributed in
220
Keith O. Fuglie
TABLE 3 Average Annual Quantity and Growth Rate of Inputs Used in Agriculture
Quantity
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1961–
65
Crop land
(million hectares)
Area harvested
(million hectares)
Irrigated crop land
(million hectares)
1971–
75
Growth Rate (% p.a.)
1981–
85
1991–
95
1961–
2000
1961–
67
1968–
92
1993–
2000
17.6
18.9
26.0
32.2
2.0
0.3
2.3
2.1
17.5
19.6
22.7
28.3
1.7
1.1
2.1
0.8
2.4
2.7
3.3
4.6
1.8
1.4
2.3
0.3
28.6
31.7
37.5
46.0
1.5
0.8
1.7
1.1
0.1
0.4
1.7
2.5
10.6
1.7
16.0
0.1
Labour
(million workers)
Fertiliser
(million tonnes)
Machinery (million
horsepower)
Animals (million
head of stock)
0.1
0.2
0.2
0.6
11.5
7.5
14.3
5.9
10.5
10.9
14.7
24.8
2.3
–0.1
3.6
–0.3
Fertiliser/crop land
(kg/ha)
6.9
22.7
64.0
76.3
8.5
1.3
13.6
–2.0
Sources: Crop land and irrigated crop land: BPSa; Van der Eng (1996); others: FAO (2001).
FIGURE 4 Aggregate Input Use and TFP in Agriculture (1961 = 100) a
250
200
150
Tornqvist-Theil input
100
Paasche input
Tornqvist-Theil TFP
50
Paasche TFP
0
1961
a See
1966
1971
text for explanation of terms.
1976
1981
1986
1991
1996
Productivity Growth in Indonesian Agriculture, 1961–2000
221
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TABLE 4 Average Annual Growth in Aggregate Input Use and TFP in Agriculture
(%)
Paasche
Input
Index
(fixed
weights)
Tornqvist–Theil
Input Index
Paasche
TFP
Index
(fixed
weights)
(chain
weights)
(declining
labour
intensity)
By decade
1961–70
1971–80
1981–90
1991–2000
0.6
1.8
3.1
1.4
0.6
1.9
3.1
1.3
By period
1961–2000
1961–67
1968–92
1993–2000
1.8
0.5
2.2
1.2
1.8
0.5
2.2
1.2
Tornqvist–Theil
TFP Index
(chain
weights)
(declining
labour
intensity)
0.6
1.8
3.0
1.3
2.7
2.1
1.5
0.4
3.1
2.1
1.5
0.4
3.1
2.1
1.6
0.4
1.7
0.5
2.2
1.1
1.7
0.6
2.5
–0.1
1.7
0.8
2.5
–0.1
1.8
0.8
2.6
0.0
Source: Author’s estimates.
part to farmers’ rising real costs. The level of fertiliser subsidy was as high as 50%
from the mid 1970s to the mid 1980s, but then declined gradually. The subsidy
finally ended in 1999.
Trends in aggregate input use in Indonesian agriculture are shown in figure 4.
The Tornqvist–Theil input index is based on actual factor shares paid for inputs,
while the Paasche input index uses average real input prices during 1996–2000 as
fixed weights for input aggregation. These indices generate nearly identical
results, both showing that aggregate input use increased by 96% between 1961
and 2000. If we assume a decline in labour intensity since 1970 (fewer days
worked per agricultural worker per year), this reduces the estimated gain in
aggregate agricultural inputs by 4% (from 96% to 92% over the whole period).
Agricultural TFP
With estimates of aggregate outputs and inputs, the index of TFP for Indonesian
agriculture is found simply by taking the ratio of the output index to the input
index (figure 4, with annual growth rates given in table 4). The Tornqvist–Theil
TFP index indicates that TFP grew by 90% between 1961 and 2000, or by 93%
assuming declining labour intensity on the part of agricultural workers. The error
in the Paasche output index (which underestimated growth) is also reflected in
the Paasche TFP index (underestimating TFP growth by 4 percentage points over
the 40-year period). All three indices show that growth in TFP was rapid during
the green revolution decades, but began to slow in the 1980s and fell to less than
0.5% p.a. in the 1990s. Since 1993 there has been no growth at all in TFP.
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Keith O. Fuglie
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TABLE 5 Average Annual Growth in Productivity in Agriculture
(%)
1961–2000
1961–67
1968–92
1993–2000
Total outputs (crop and animal)
Total inputs
TFP
3.5
1.8
1.7
1.2
0.5
0.7
4.8
2.2
2.6
1.1
1.2
–0.1
Labour productivity
Land productivity
Land/worker
2.0
1.5
0.5
0.3
0.8
–0.4
2.9
2.4
0.6
0.1
–0.9
1.0
Food crop output/population
Rice output/population
2.5
2.5
0.2
1.0
4.0
3.7
–0.4
–0.3
Sources: Total output, input and TFP growth is derived from the Tornqvist–Theil output,
input and TFP given in tables 2 and 4. Labour is found by dividing aggregate output (see
table 1 for sources) by total agricultural labour (see table 3 for sources). Land productivity
is derived in the same way. Land/worker is crop land per agricultural worker (see table 3
for sources). Food crop output is the sum of rice, cassava, maize, bean and sweet potato
output measured in rice equivalents (see table 1 for sources). Population is from BPSa.
With these estimates, it is possible to decompose total growth in output into a
component due to increases in conventional factors of production and a component due to productivity improvement. Table 5 summarises the results. Between
1961 and 2000, average annual growth in agricultural crop and livestock production in Indonesia was 3.5%, of which 1.8% was due to increases in land, labour
and other conventional inputs and 1.7% was due to improvements in TFP.
Increases in TFP were most evident in 1968–92, with little change either before or
since these years. The growth in agricultural output since 1993 (about 1.1% p.a.)
was entirely due to increases in conventional agricultural inputs, especially land
and labour. These general findings are not appreciably affected by our assumptions about labour intensity per worker in agriculture.
Output per agricultural worker increased by an average of 2% p.a. between
1961 and 2000. Most of this was due to increases in output per hectare of crop
land, but crop land per worker also increased. In fact, land per worker was growing by about 1% p.a. even as late as the 1990s, despite the continued increase in
the number of workers employed in agriculture. Virtually all of the land expansion occurred outside Java and Madura; these densely populated islands actually
lost crop land in the 1990s (BPSa).
The final two rows of table 5 give an indication of food security in Indonesia
by comparing growth in food production to population growth. By these measures Indonesia’s food economy performed remarkably well during the final
decades of the 20th century. Per capita production of both rice and all food crops
grew at an annual rate of 2.5% during 1961–2000. However, per capita food production went into decline in the 1990s, when TFP growth in Indonesian agriculture stagnated.
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Productivity Growth in Indonesian Agriculture, 1961–2000
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CONCLUSIONS AND IMPLICATIONS
Improvements in the data series for Indonesian agriculture provide an opportunity to reassess the productivity performance of this sector during the closing
decades of the 20th century. By constructing a Tornqvist–Theil index of input,
output and TFP growth, I have been able to minimise the bias inherent in index
measures when price weights change over time. My estimation showed that
between 1961 and 2000, about half of the growth in agricultural output could be
attributed to an expansion of conventional inputs (crop land, labour, livestock,
fertiliser and machinery) and about half to TFP. The use of modern industrial
inputs (chemical fertilisers and power machinery) grew rapidly from very low
initial levels. Nearly all of the growth in TFP occurred between 1968 and 1992,
however. By the 1990s, agricultural growth once more relied almost entirely on
increases in conventional factors as productivity stagnated.
Recent work on agricultural productivity assessments for China (Fan and
Zhang 2002) and India (Fan, Hazell and Thorat 1999) allow some comparisons
among the performances of the three largest agricultural economies in Asia since
1970. Indonesia depended upon resource expansion (in particular, bringing new
crop land into production) more heavily than either China or India. In China, TFP
improvements accounted for 62% of agricultural growth, and in India, more than
70%. Both China and India were able to sustain growth in their agricultural sectors during the 1990s whereas agricultural growth in Indonesia slowed significantly as TFP stagnated.
While a rigorous examination of the underlying causes of the productivity
slowdown in Indonesian agriculture is beyond the scope of this study, I can suggest some probable causes. Drought-inducing El Niño and macroeconomic shocks
took their toll on agricultural production in the late 1990s (especially in 1996–98),
but the slowdown in productivity appears to have preceded these events, and has
continued since. Probably a more important explanation for the productivity
slowdown is that once the initial gains made possible by the green revolution
were exhausted, public and private investments in agriculture were not sufficient
to sustain further productivity growth. Both government spending and private
investment in agriculture have fallen since the mid 1980s as a percentage of agricultural GDP (Fuglie and Piggott, forthcoming). While much of the decline in government support for agriculture came about through a reduction in fertiliser subsidies (which probably had low returns, especially in later years), the decline also
extended to investments with a higher pay-off such as rural infrastructure and
agricultural research and extension (Fuglie and Piggott, forthcoming).5 Private
sector investment in agriculture fell drastically during the financial crisis of
1997–98. It is unlikely to recover unless the overall investment climate in
Indonesia improves and complementary infrastructure investments that can only
be made by the public sector are forthcoming.
The stagnation of TFP growth has serious implications for the long-term performance of Indonesian agriculture. Indonesia will find it increasingly difficult to
expand agricultural land, and without growth in TFP, agricultural output will
continue to grow only very slowly. This will speed the drain of resources away
from agriculture and rural areas to other sectors of the economy. While movement of labour out of agriculture is a natural consequence of economic development, this transition will be suboptimal if high pay-off investment opportunities
224
Keith O. Fuglie
in agriculture are forgone, thus undermining the potential contribution of agricultural development to reducing poverty and generating broad-based economic
growth for the Indonesian economy.
NOTES
1
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2
3
4
5
These sources include Vademekum Pemasaran for horticultural crops (Jakarta wholesale
prices) (MOA 1999) and Statistics for Estate Crops of Indonesia for cacao beans, palm oil
and cane sugar (domestic market prices) (MOA, various issues).
VDE and MOA prices for palm oil were identical for the overlapping years 1986–96, so
these series appear to be from the same source. For cane sugar, VDE reports a ‘Surabaya export price’ that was consistently below the MOA domestic market price of cane
sugar by 8–14% for the overlapping years in the series (1990–96). To develop a consistent price series for 1961–2000, I used MOA prices for 1990–2000 and VDE prices for
1961–89 multiplied by 1.12.
See Booth (1988: 264–71) for a thorough discussion of Indonesian land use statistics.
Harvested area differs from crop land in that the former does not include fallow land
or land planted to immature perennials, and double counts land where two crops are
harvested in a year.
The studies on agricultural growth in India and China by Fan and his colleagues found
strong correlations between public spending on agricultural research/rural infrastructure and agricultural productivity growth in those countries (Fan and Zhang 2002; Fan,
Hazell and Thorat 1999). For Indonesia, Salmon (1991) and Evenson et al. (1997) found
high rates of return to food crop research in the 1970s and 1980s, mainly because the
programs studied helped to adapt and disseminate high-yielding varieties made available by international investments in the green revolution. Once the easy gains from
these technologies were achieved, productivity growth was not sustained. Unlike its
larger Asian neighbours, Indonesia does not yet appear to have developed an agricultural research system capable of sustaining productivity growth.
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