Final report
Small research and development activity
project
The effect of research on agricultural
productivity in Indonesia
project number
AGB/2010/018
date published
March 2011
prepared by
Professor Peter Warr
co-authors/
contributors/
collaborators
Indonesian Centre for Agriculture, Socio-Economic and Policy Studies,
Government of Indonesia, Bogor
approved by
David Shearer, Agribusiness, ACIAR
final report number
FR2011-04
ISBN
978 1 921738 50 0
published by
ACIAR
GPO Box 1571
Canberra ACT 2601
Australia
This publication is published by ACIAR ABN 34 864 955 427. Care is taken to ensure the accuracy of the information
contained in this publication. However ACIAR cannot accept responsibility for the accuracy or completeness of the
information or opinions contained in the publication. You should make your own enquiries before making decisions
concerning your interests.
© Australian Centre for International Agricultural Research (ACIAR) 2011 - This work is copyright. Apart from any use as
permitted under the Copyright Act 1968, no part may be reproduced by any process without prior written permission from
ACIAR, GPO Box 1571, Canberra ACT 2601, Australia, aciar@aciar.gov.au.
Final report: The effect of research on agricultural productivity in Indonesia
Contents
1
Acknowledgments .................................................................................... 3
2
Executive summary .................................................................................. 4
3
Introduction ............................................................................................... 5
4
Objectives ................................................................................................. 7
5
Methodology ............................................................................................. 9
6
Achievements against activities and outputs/milestones .................. 11
7
Key results and discussion ................................................................... 12
8
Impacts .................................................................................................... 17
8.1
Scientific impacts – now and in 5 years .............................................................................17
8.2
Capacity impacts – now and in 5 years .............................................................................17
8.3
Community impacts – now and in 5 years .........................................................................17
8.4
Communication and dissemination activities .....................................................................17
9
Conclusions and recommendations ..................................................... 19
9.1
Conclusions ........................................................................................................................19
9.2
Recommendations .............................................................................................................19
10
References .............................................................................................. 20
10.1 References cited in report ..................................................................................................20
10.2 List of publications produced by project .............................................................................22
11
Appendixes ............................................................................................. 23
11.1 Appendix 1: ........................................................................................................................23
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Final report: The effect of research on agricultural productivity in Indonesia
1
Acknowledgments
The following persons and institutions kindly assisted in provision of data:
Dr Keith Fuglie, Economic Research Service, US Department of Agriculture, provided his
previously published estimates of total factor productivity growth in Indonesian agriculture.
Dr Waleerat Suphannachart, Faculty of Economics, Kasetsart University, Bangkok,
provided her previously published data on foreign investment in agricultural research.
ICASEPS, AARD, Government of Indonesia, provided data on Indonesian government
expenditure on agricultural research, agricultural extension, investment in irrigation
facilities and length of rural roads.
In addition, the following persons contributed to the analysis of the data:
Dr Keith Fuglie, Economic Research Service, US Department of Agriculture, commented
helpfully on the statistical methodology used in this study.
Dr Waleerat Suphannachart advised on the details of the statistical methods used.
Ms Hemantha Ekanayake, Arndt-Corden Department of Economics, Australian National
University, provided outstanding research assistance in undertaking the quantitative
analysis presented in this study.
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Final report: The effect of research on agricultural productivity in Indonesia
2
Executive summary
Agriculture in Indonesia is a vital source of food production and rural income. Sustaining
agricultural growth is thus important for maintaining Indonesia’s food security and
improving the living standards of the majority of poor people residing in rural areas and
directly involved in agricultural production.
Growth of total factor productivity (TFP) has been shown to contribute significantly to
output growth in the Indonesian agricultural sector and its contribution has been greater
than in the non-agricultural sectors. However, there may have been a slowdown in
agricultural TFP growth in recent years. Refocusing attention on what determines TFP in
Indonesian agriculture is thus important for understanding and sustaining long-term
agricultural growth and thereby maintaining its contribution to overall economic
performance.
This study (AGB/2010/018) aims to examine the extent to which agricultural research
within Indonesia contributes to the enhancement of productivity growth, while allowing for
other possible determinants of agricultural productivity growth, including , international
agricultural research, infrastructure investments, extension, weather changes and
epidemics. It draws upon the existing literature which estimates the rate of TFP growth in
Indonesian agriculture and to attempt to explain its determinants, in particular the
contribution of agricultural research.
The data assembled for this research shows that the research intensity of agricultural
production in Indonesia (the ratio of agricultural research expenditure to total value-added
in agriculture) has declined from around 0.13% in the decade from the mid 1970s to mid
1980s to around 0.04% in the decade from the mid-1990s to the mid-2000s.
This study provides a statistical analysis of the relationship between government
expenditure in agricultural research, expressed in constant prices, and the level of total
factor productivity in Indonesian agriculture. The data used relate to the years 1974 to
2006. The methodology is based on the error correction econometric procedure, designed
for the analysis of time series data.
The results showed a significant effect of expenditure on agricultural research on total
factor productivity in Indonesian agricultural production. The impact elasticity (per cent
change in total factor productivity from a 1% increased in research expenditure) was
estimated at 0.0774.
Based on these econometric results a projection was made of the impact on total factor
productivity within Indonesian agriculture of a 1 billion Rupiah increase in agricultural
research occurring in the year 2007. Impacts on the change in the value of Indonesian
agricultural output were estimated from this analysis. From this it was possible to estimate
the real rate of return (at constant prices) from a marginal increase in investment in
Indonesian agricultural research. The estimated annual real rate of return was 13%.
The estimated real rate of return is well above rates normally required for public
investments. It is concluded that Indonesia has under-invested in this form of public
expenditure and an increase is warranted. If means could be found to increase the
efficiency of publicly funded agricultural research this would further enhance the case for
increased funding.
A possible extension of this research would be to estimate the contribution of agricultural
research in Indonesia to poverty reduction in rural areas and in the total population. This
research would take account of the estimated impact of research on total factor
productivity and then relate this impact to the rate of poverty reduction.
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Final report: The effect of research on agricultural productivity in Indonesia
3
Introduction
There is widespread concern that food prices may rise substantially in the coming
decades because of a combination of increasing population, land and water constraints,
increasing food demand per person; potential increases in demand for biofuels; and
climate change (Evans 2009; Fischer, Byerlee and Edmeades 2009; Msangi and
Rosegrant 2009). As shown by van der Mensbrugghe, Osorio-Rodarte, Burns and Baffes
(2009), these factors could result in substantial increases in food prices, with potentially
adverse implications for poverty (Ivanic and Martin 2008).
As these food prices rise there will be a serious impact on poverty in developing countries.
Due to this negative impact, national governments (such as the Government of Indonesia)
and research for development agencies (such as ACIAR) are considering appropriate
responses, such as increased investment into agricultural research and how this will affect
agricultural productivity as a key driver in poverty reduction in developing countries.
While the evidence on a slowdown in agricultural productivity is mixed (Fuglie 2008;
Alston and Pardey 2009), there is considerable evidence that total factor productivity
growth in agriculture was higher than in the rest of the economy during the period of the
green revolution (Martin and Mitra 2001). Informed commentators also believe that there
are scientific possibilities for substantial further increases in productivity (Fischer, Byerlee
and Edmeades 2009). There is also strong evidence of serious under-investment in
research on agricultural productivity, as evidenced by very high rates of return on
government investments in research and development (Alston and Pardey 2000).
Recent analysis from Ivanic and Martin (2010) indicate that higher agricultural productivity
resulting from increased investments in research and development could offset these
impacts and contribute to poverty reduction. Their results (2010) refer only to very broad
scenarios of productivity growth across regions and commodities and any decisions about
resource allocation must be taken for individual countries—or even regions within
countries—and the results of decisions in these contexts may be quite different. In
particular, improvements in productivity at the individual region level are much less likely
to have many of the offsetting effects on commodity prices that are experienced at the
global level.
In Indonesia, sustaining agricultural growth is important for maintaining Indonesia’s food
security and improving the living standards of the majority of poor people residing in rural
areas and directly involved in agricultural production. The ‘food crisis’ enabled a number
of policy options to be tested, such as restrictions of trade, which have since been
analysed to increase poverty, rather than improve welfare. As such, Indonesia is
interested in alternative policy responses, such as an increase investment in agricultural
research as a way to improving the living standards of the majority of poor people.
The Australian Government’s Food Security though Rural Development initiative
recognises the centrality of agriculture in developing countries and targets support
towards food production globally and strengthening the ability of developing countries in
the Asia-Pacific and Africa to address food security. Part of the targeted support is in
agricultural research and more knowledge is required on the likely impact of agricultural
research, firstly on productivity and then on poverty reduction.
Due to the food price effect on poverty, the need for a greater specificity of the impact of
research in agricultural productivity and the desire for more appropriate responses, by
both the Indonesian and Australian governments, the present study has the potential to
contribute to improved decision making.
It has long been recognized that agricultural growth is important for overall economic
development and poverty reduction, especially in developing countries (Johnston and
Mellor, 1961). This recognition is one of the main principles of ACIAR existence and is an
Page 5
Final report: The effect of research on agricultural productivity in Indonesia
important reason for the Indonesian government’s commitment to the development of the
agricultural sector.
Research-induced productivity growth offers a potential solution to the challenge of
maintaining a continuous increase in agricultural output in a manner that minimizes input
use and protects the natural resource base (CGIAR, 2009). However, further analysis that
is country (and commodity) specific is required to substantiate this claim of impact.
Although the returns to research are believed to be high, the empirical evidence on a
country by country basis is very limited. Suphannachart and Warr (2011 forthcoming)
have provided evidence for Thailand which indicates that public investment in R&D in
Thailand’s crop agriculture has contributed significantly to the impressive growth of TFP in
crop production. The high measured rate of return from publicly funded agricultural
research also implies underinvestment in it.
As each country has a variety of policy tools that could induce more research investment,
including improving intellectual property protection and providing subsidies, an
understanding of the poverty impact from agricultural research is critical in developing a
balanced and appropriate policy response. If the significance of agricultural research is
well recognized and it is to be used effectively as a policy tool to maintain agricultural
output using fewer resources, then a greater policy commitment is necessary to overcome
the inadequacy of present levels of investment.
The significant role of foreign research (Suphannachart and Warr, 2011) on productivity
suggests public resources could be saved if Thailand is able to choose what will be most
useful to borrow from the international research system. This may or may not be the case
in Indonesia. Detailed analysis is required. Public or other types of local research should
be strengthened in a way that makes it capable of adapting and making efficient use of
foreign technology. In the case of Thailand the statistical insignificance of the interaction
term between domestic and foreign research seems to signal weak collaboration in
Thailand, but this is unknown for Indonesia. The government could play a more active role
in encouraging increased collaboration among major research performers.
Given the slowdown in the levels of funding for this research, the results of the study by
Suphannachart and Warr (2011), suggest that Thailand should now invest more heavily in
its own agricultural science capacity. This may also be the case in Indonesia, and the
present study is intended to provide evidence on this point. The results are therefore
expected to provide some empirically based advice for the Indonesian government and for
ACIAR.
The key questions to be answered are therefore:
-
has total factor productivity growth in Indonesia been increasing or decreasing;
-
has the level of publicly funded agricultural research in Indonesia been increasing or
decreasing relative to the value of agricultural output;
-
how does the level of publicly funded agricultural research in Indonesia compare with
other countries;
-
does publicly funded agricultural research in Indonesia contribute to the development
of Indonesian agriculture through an enhancement of total factor productivity growth;
-
what is the economic rate of return to public investment in Indonesian agricultural
research; and
-
based on these results, is an increase in public funding for agricultural research in
Indonesia justified?
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Final report: The effect of research on agricultural productivity in Indonesia
4
Objectives
The central objective of the study is to examine the extent to which agricultural research
within Indonesia contributes to the enhancement of productivity growth. In this
examination, the study allows for other possible determinants of agricultural productivity
growth, including international research, infrastructure investments, extension, weather
changes and epidemics. Without this component of the analysis the statistical results
could wrongly attribute to included variables, such as publicly funded agricultural research
within Indonesia, to excluded variables, such as those listed above. In statistical terms,
this attribution error is likely to occur if the excluded variables are correlated though time
with the included variables. In avoiding this bias, the study draws upon and improves the
existing literature which estimates the rate of TFP growth in Indonesian agriculture and
which attempts to explain its determinants.
As such the objective of the study is to:
1.
Understand the extent to which agricultural research contributes to the enhancement
of productivity growth in Indonesia
It is hoped that the research will produce a research paper for an international journal, and
a suitable Indonesian publication, on the impact of agricultural research on agricultural
productivity within Indonesia. A policy brief, based on the results of the research is to be
produced in conjunction with the key collaborator in Indonesia, the Indonesian Centre for
Agriculture, Socio-Economic and Policy Studies.
The central contribution of the research is to provide evidence on the degree to which
agricultural research contributes to productivity growth and the rate of return to this form of
public investment. This evidence will contribute to policy making within Indonesia, and
possibly other countries, on the degree of funding priority that should be accorded to
agricultural research. In addition, the results will contribute to strategic decision making by
ACIAR in relationship to research for development investment in Indonesia.
To achieve the objective of understanding the extent to which agricultural research
contributes to the enhancement of productivity growth in Indonesia, the project will
(a) Assemble existing estimates of total factor productivity growth within Indonesian
agriculture.
(b) Assemble a supplementary data set on the following variables (relating to
Indonesia unless otherwise specified):
-
government expenditure on research
-
expenditure on related international research within the CGIAR group
-
government expenditure on extension
-
infrastructure data on length of road
-
government expenditure to extend irrigation
-
years in which severe economic disruptions may have affected productivity
-
years in which Avian Flu may have affected livestock productivity
-
years in which significant weather changes may have affected crop
productivity.
2.
Undertake an econometric analysis on the relationship between productivity growth in
agriculture (dependent variable) and each of the determinants listed in (b).
3.
Utilising the results from 2, estimate the economic rate of return to investment in
Indonesian agriculture.
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Final report: The effect of research on agricultural productivity in Indonesia
4.
From this empirical foundation
4.1 draw inferences on the extent to which agricultural research has contributed to
productivity growth, controlling for the other possible determinants of productivity
growth.
4.2 draw policy recommendations regarding appropriate policy towards agricultural
research in Indonesia.
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Final report: The effect of research on agricultural productivity in Indonesia
5
Methodology
The methodology is based on a time-series econometric analysis of the relationship
between productivity growth in agriculture (dependent variable) and a range of variables
such as government expenditure on research and related international research. From
this analysis, conclusions can be drawn on the extent to which agricultural research has
contributed to productivity growth, controlling for the other possible determinants of
productivity growth.
The first two activities, described under Objectives above as (a) and (b), are based on the
assembly of data. Data assembly was done primarily in Indonesia, by the ICASEPS
team in Bogor, using data sources available there. To the extent that published data could
be used, data collection was done in Canberra by the ANU team.
The econometric analysis is the most significant activity within the project. It is
undertaken to determine the relationship between productivity growth in agriculture
(dependent variable) and each of the determinants using the data assembled in the first
activity. This part of the work was undertaken by the ANU team in Canberra.
The econometric analysis examines the impact that public agricultural research has on
TFP in agricultural production. Subsequent analysis measures the implied social rate of
return. The statistical analysis uses time series data and error correction modelling (ECM)
techniques covering an appropriate period. Given the data available, this proved to be the
period 1974 to 2006.
The statistical relationship between research and productivity involves important issues of
research lags and possible omitted variable bias resulting from ignoring the role of
international research and other major factors affecting productivity (Evenson, 2001,
Fuglie and Heisey, 2007, Griliches, 1979). In dealing with lags in the impact of research
the usual practice has been to impose arbitrary restrictions on the lag structure such as
the second-degree polynomial distributed lag (bell-shaped lag structure).
However, imposing a lag structure that is too short or is otherwise inappropriate tends to
bias upwardly the estimated research impact and associated rate of return (Alston et al.,
1998a, Alston et al., 2000). Error correction modelling (ECM) offers an improved method
to estimate the long-run dynamic relationship among time series economic variables
(Makki et al., 1999). The ECM does not impose any restrictive form of lags and allows for
both short-term and long-term relationships among variables. It also guards against the
possibility of spurious regression, which can arise from the use of time series data
(Hendry, 1995).
Most empirical studies at the country level ignore all research done abroad, although there
is evidence that international technology transfers influence local productivity (Alston et
al., 1998b, Alston, 2002). Ignoring benefits from international research tends to produce
an upward bias in estimates of the returns to local research investment. In the case of
Indonesian agriculture, there is a possibility that foreign research results, such as rice
varieties developed by the International Rice Research Institute (IRRI), may have
benefited local productivity. Hence, this study incorporates international research and
other potential factors affecting TFP.
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Final report: The effect of research on agricultural productivity in Indonesia
The model of the long-run determinants of TFP is based on the production function
framework in which TFP growth is identified as a shift in the production function
representing technical change. It is measured as that part of output growth not explained
by growth of measured factor inputs (Solow, 1957, Jorgenson and Griliches, 1967,
Jorgenson, 1995). Measured TFP growth therefore includes not only pure technical
change, but also factors and measurement errors left unaccounted for by measurable
conventional inputs (Ruttan, 1987, Alston et al., 1998b, APO, 2001, Oguchi, 2004). It thus
includes, but is not confined to, the effects of advances of knowledge or technological
progress (Denison, 1967, Griliches, 1996).
The statistical analysis is based on a conceptual model in which the determinants of TFP
include agricultural research production as well as other economic and non-economic
factors such as extension services directed to agricultural technology, infrastructure such
as roads and irrigation, and weather.
Having obtained an estimated equation for the determinants of TFP growth in agriculture
which satisfies the conventional statistical diagnostic tests, the analysis then uses this
equation to project the effect on the level of TFP of a 1 billion Rupiah increase in
agricultural research in 2007, holding all other estimated determinants of TFP growth
constant. This increase in TFP is then converted to value terms, using the level of value
added in agriculture in 2006 to obtain an estimate of the value of the stream of additional
output that derives from the increased level of agricultural research, valued at constant
2006 prices. The value of this stream of projected additional output is then compared with
the cost – the 1 billion Rupiah of initial investment, to obtain an estimate of the economic
rate of return to agricultural research in Indonesia.
From the economic analysis, conclusions are then drawn on the extent to which
agricultural research has contributed to productivity growth, controlling for the other
possible determinants of productivity growth and whether there is under or over
investment in agricultural research in Indonesia from an economic perspective.
It is intended that the study will produce a research paper for an international journal, and
a suitable Indonesian publication, on the impact of agricultural research on agricultural
productivity within Indonesia. This paper will form the basis for presentations of the
research both within Indonesia and within Australia. A policy brief, based on the results
of the research will be produced by the key collaborator in Indonesia, the Indonesian
Centre for Agriculture, Socio-Economic and Policy Studies. The users of these outputs will
be Indonesian policy makers involved with agricultural research policy and academic
scholars interested in these policy issues.
It is expected that the results will contribute to policy making and academic discussion
within Indonesia, and possibly other countries, on the degree of funding priority that
should be accorded to agricultural research within Indonesia. This impact will occur
through dissemination and discussion of the research findings within Indonesia and
elsewhere. In addition, the results will contribute to strategic decision making by ACIAR in
relationship to research for development investment in Indonesia.
Page 10
Final report: The effect of research on agricultural productivity in Indonesia
6
Achievements against activities and
outputs/milestones
Objective 1: To understand the extent to which agricultural research contributes to
the enhancement of productivity growth in Indonesia.
no.
activity
outputs/
milestones
completion
date
comments
1.
Data
Completed
November
2010
Data assembly completed, full data set
contained in Appendix to this report
2
Econometric analysis
Completed
February
2010
Econometrics completed, reported
below
3.
Estimate rate of return
Completed
February
2011
Estimation completed, reported below
4.1
Conclusion on
contribution of
agricultural research
Completed
February
2011
Contained in this report
4.2
Policy
recommendations
Completed
February
2011
Contained in this report
PC = partner country, A = Australia
Page 11
Final report: The effect of research on agricultural productivity in Indonesia
7
Key results and discussion
All data used in the study are included in the Appendix.
The Research Intensity of Indonesian agriculture (the ration of government expenditure on
agricultural research to the level of value-added in Indonesian agriculture) is reported in
Figure 1.
Figure 1 Research intensity of agricultural production in Indonesia: 1972 to 2006
0.0018
0.0016
0.0014
Ratio
0.0012
0.001
Research intensity
0.0008
Linear (Research
intensity)
0.0006
0.0004
RI = -3E-05t + 0.0014
0.0002
0
1972
1976 1980 1984
1988 1992 1996
2000 2004
Source: Author’s calculations.
Note: Research intensity is the ratio of investment in agricultural research to total value-added in
agricultural production.
Two significant points can be made about these data. First, the level of Research Intensity
in Indonesia is particularly low by international standards. The mean level in Indonesia is
0.077%. The mean level for Thailand over the same period was 0.49% (Suphannachart
and Warr 2011) more than six times the level in Indonesia and Thailand’s Research
Intensity is not high by international standards. Second, Indonesia’s Research Intensity
has been declining. Estimating a simple linear trend line to the above data indicates a
significant negative trend, as shown in Figure 1.1
From these facts alone it is clear that while Indonesia’s agricultural research may or may
not have had a statistically significant impact on the level of TFP (see the results below),
the level of agricultural research in Indonesia is so low that it is unlikely to have raised the
level of TFP by a large amount.
1
RI  bt  a , where t is time. The estimated coefficients were, with t-statistics in
â = 0.0014 (3.03) and b̂ = -3 E-05 (2.04). The null hypothesis that the coefficient b is zero was
The estimated equation is
parentheses,
rejected at the 5 % level (p= 0.049).
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Final report: The effect of research on agricultural productivity in Indonesia
Since time series data are to be used in the study, it is now normal to apply a unit root test
to the variables to examine their time series properties. These results are shown in
Table 2.
Table 1: Results of unit root tests
Null hypothesis: Series has a unit root
Variable
ADF statistic for level
ADF statistic for 1st
difference
with time
trend
without time
trend
with time
trend
without
time trend
Total factor productivity
-2.724
-0.382
-6.558***
-6.667***
Govt. expenditure in
research: Flow
-3.160
-2.938
-4.291***
-4.346***
Govt. expenditure in
research: Stock
-3.484*
-3.143**
Govt. expenditure in
extension :Flow
-5.794***
-5.269***
Govt. expenditure in
extension :Stock
-11.44***
-21.329***
Foreign research
expenditure: Flow
-4.570***
-6.229***
Foreign research
expenditure :Stock
3.324*
-16.033***
Order of
integration
I(1)
I(I)
I(0)
I(0)
I(0)
I(0)
I(0)
Source: Author’s calculations.
Note: *, **, ***, denote rejection of the null hypothesis at 10%, 5% and 1% significance levels,
respectively.
The results indicate that the variables TFP and government expenditure on research are
each I(1) variable and all others are I(0). It follows that a time series statistical
methodology must be used which can accommodate this combination of variables without
running the risk of spurious regression. Accordingly, the error correction modelling (ECM)
procedure of Hendry (1995) is employed.2 This approach minimizes the possibility of
estimating spurious relationships while retaining long-run information without arbitrarily
restricting any particular lag structure (Hendry, 1995). The ECM also provides a precise
estimate with valid t-statistics even in the presence of endogenous explanatory variables
(Inder, 1993).
Under the ECM, the long-run relationship is embedded within a sufficiently detailed
dynamic specification, including both lagged dependent and independent variables, which
helps minimize the possibility of estimating a spurious regression. The short- and long-run
parameters in an ECM can be separately identified. The ECM can be estimated by OLS.
Equation (5) is the ‘maintained hypothesis’ for specification search. The full model is
2
This method is used in many time-series studies but has apparently not yet been used in TFP determinants
studies except in Suphannachart and Warr (2011).
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Final report: The effect of research on agricultural productivity in Indonesia
‘tested down’ by dropping statistically insignificant lag terms using the standard testing
procedure to obtain a parsimonious ECM.
The final preferred model is required to satisfy standard diagnostic tests, including the
Breush-Godfrey LM test for serial correlation in the regression residual, the Ramsey test
for functional form mis-specification (RESET), the Jarque-Bera test of normality of the
residual (JBN), Engle’s autoregressive conditional heteroskedasticity test (ARCH) and the
Augmented Dickey-Fuller test for residual stationarity (ADF).
The estimated equation is reported below in Table 3.
Table 2: Econometric results
Variable
Coefficient
t-statistic
p-value
Constant
2.2731**
(-2.8617)
[0.0086]
lnTFP(-1)
-0.4727**
(-2.9212)
[0.0075]
lnRIS(-1)
0.0366*
(2.0822)
[0.0482]
0.0774**
lnRFS(-1)
0.001372
(1.3407)
[0.1926]
0.0029
dlnRIS
0.02074
(0.8504)
[0.4035]
dlnRFS
-0.03795
(-1.2391)
[0.2273]
D1
0.04960
(6.9971)
[0.0000]
D2
-0.05749
(-8.1935)
[0.0000]
dlnTFP(-1)
0.10779
(-0.5397)
[0.5943]
Number of observations
Mean dependent var.
0.01517
0.479
S.D. dependent var
0.02581
0.306
Akaike info criterion
-4.6139
F statistic
2.759
Schwarz criterion
-4.2058
Prob (F-Statistic)
0.0258
Durbin-Watson stat
2.5063
S.E of regression
0.02151
Sum squared residuals
0.0111
JBN, chi-square
0.48 [0.78]
R2
Adjusted
R2
33 (1974 to 2006)
Long -run
elasticity
Log likelihood
85.12981
Notes: t statistics are in the ( ) parenthesis and p values are in [ ]. *, ** and *** denote 10% , 5%
and 1% significance levels, respectively. Standard Errors are corrected for Heteroskedasticity. The
long run elasticity is computed by dividing the estimated coefficient of the level term by the positive
value of the coefficient of the level dependent(lag) variable(lntfp(-1)).
Variable definitions:
(ln indicates natural logarithm; dln indicates annual change in natural logarithm)
TFP: Logarithm of level of total factor productivity
RIS: Indonesian government expenditure in research – stock variable
lnRFS: Foreign research – stock variable
D1 dummy variable for 1980, a year of unusually favourable weather
D2 dummy variable for 1997, year of Asian financial crisis.
Page 14
Final report: The effect of research on agricultural productivity in Indonesia
The choice of dropping or keeping variables in the final model was statistical acceptance
in terms of the joint variable deletion tests against the maintained hypothesis. Other
variables listed in the Data Appendix were found to be statistically highly insignificant by
this test and were dropped from the estimation process, applying the Hendry general-tospecific estimation procedure. This included government expenditure on extension, length
of roads and extended irrigation.
The overall equation was statistically significant at the 5% level (p = 0.0258) in terms of
the F-test. The equation passes the standard diagnostic tests. The error correction
coefficient (TFPt-1) has the expected negative sign and is statistically significant at the 1%
level. The coefficient on this variable was moderately large (-0.47) indicating moderately
rapid adjustment in the dependent variable to dissipate a shock in the independent
variables. Since all variables except the dummy variables are measured in logarithms, the
regression coefficients can be interpreted as elasticities and the size of the coefficients
also indicate the magnitude of their relative influence.
Government expenditure on research was significantly related to TFP growth at the 5 %
level of significance. In the short-run a 1% increase in agricultural research leads to an
increase in TFP of 0.02%. In the long-run the corresponding increase is 0.08%. These
responses may be compared with the results obtained for Thailand by in Suphannachart
and Warr (2011), where the corresponding long run elasticity was estimated at 0.067%.
That is, Indonesia’s long run response elasticity is about 16% higher. Compared with
Thailand, the response in Indonesia to an increase in foreign agricultural research within
the CGIAR group is much lower and more highly significant. This comparison suggests
that Thailand’s agricultural system makes more effective use of foreign research findings
than is the case in Indonesia. The dummy variables for the favourable conditions in 1980
and the economic crisis in 1997 were both highly significant with the expected signs.
The positive and significant impact of public research is consistent with the theory and
findings from previous studies. This supports the general belief that research-induced
technical change is a significant driving force behind the impressive growth of TFP in
Indonesian agriculture, despite the low level of that form of public expenditure relative to
other countries. It is also consistent with the finding from studies of many countries that
agricultural research is an important source of technical change that improves productivity
(Evenson, 1993, Fuglie, 1999, Ruttan, 2002, Thirtle et al., 2003).
What is the rate of return to agricultural research in Indonesia? This question is explored
by means of the following projection, based on the econometric estimates reviewed
above. First, we project the effect of a hypothetical 1 billion Rp. increase in agricultural
research in 2007 only, relative to its observed level in 2006, with all other right hand side
variables held constant at their 2006 levels. That is, the increase in the level of investment
is for one year only. After that it reverts to its previous level. The projected level of TFP
arising from this simulation is then calculated for each year beginning in 2008 and ending
in 2036. This stream is then compared with the results of a second projection in which the
hypothetical increase in agricultural research expenditure does not occur – its value, along
with the values of all other right-hand-side variables – remains at its 2006 level. The
difference between the level of TFP in these two projections is the estimated impact of the
1 billion Rp. spending increase on the level of TFP.
Next, this projected increase in TFP for each year from 2008 to 2036 is converted to an
increase in the value of agricultural output using the level of value-added in agriculture in
2006 as a base. The result is a stream of value marginal products for the impact of the
initial 1 billion Rp. on the value of agricultural output in each year, measured in constant
2006 prices. The net present value of this stream of output is the discounted value of the
stream of value marginal products, discounted to 2007. The rate of return to the
investment in agricultural research is the discount rate at which this net present value,
Page 15
Final report: The effect of research on agricultural productivity in Indonesia
minus the 1 billion Rp. initial investment is equal to zero. That is, the rate of return to the
investment in agricultural research is calculated from the value of r, such that
 VMP /(1  r )   1  0 .
2036
t
t  2008
(1)
t
The stream of value marginal products computed from the regression results in Table 3
above is indicated in Figure 2 below. It is important to recall that no pre-specified lag
distribution structure was imposed on the estimated results. The results indicate that the
annual impact of research reaches a peak after 4 to 5 years and then declines steadily.
Half of the impact is dissipated after 19 years.
Figure 2 Value marginal product (VMP) projected from 1 billion Rupiah additional
investment in agricultural research in 2007: 2008 to 2036)
(Units: billions of Rupiah at 2006 prices)
0.20
0.18
0.16
0.14
0.12
VMP
0.10
0.08
0.06
0.04
0.02
0
2007
2010
2013
2016
2019
2022
2025
2028
2031
2034
Source: Author’s calculations.
Note: See text for explanation of econometric estimates and subsequent calculations.
The estimated real rate of return implied by these results, as calculated from equation (1)
is 13%. This real rate of return is well above the opportunity cost of public funds in
Indonesia and high enough to justify expanded public investment in agricultural research
in Indonesia. However, the estimated rates of return in Thailand were substantially higher,
in both crop and livestock research, suggesting the likelihood that considerable space
exists for the effectiveness of Indonesia’s agricultural research system to be enhanced.
Page 16
Final report: The effect of research on agricultural productivity in Indonesia
8
8.1
Impacts
Scientific impacts – now and in 5 years
More attention needs to be given in Indonesia to the determinants of the productivity of
agriculture and the policy actions that can be taken to enhance agricultural productivity.
Research is one of those variables. The results of this study have indicated the value of
analysing the payoff to agricultural research using modern statistical methods. The
research has been limited by the available data. Efforts could be taken to enhance the
data available for this kind of analysis.
8.2
Capacity impacts – now and in 5 years
Capacity needs to be developed within Indonesia in basic statistical analysis and its
interpretation. This study may assist in showing the value of that work.
8.3
Community impacts – now and in 5 years
Economic impacts
Enhancement of Indonesia’s agricultural research capacity, if it occurs as a result of this
study, will have a large economic payoff, indicated by the rate of return measured in the
study.
Social impacts
By raising agricultural productivity, improved agricultural research can reduce poverty.
This occurs by raising incomes within the agricultural sector and through reducing the
price of food available to consumers, both of which are important determinants of the
overall level of poverty incidence in the country.
Environmental impacts
Raising agricultural productivity can reduce the pressure on Indonesia’s natural resources
by reducing the necessity to deforest new land in order to meet the country’s growing
requirements for food. This is an outcome that is important to the global community and
not only to Indonesians. The results of this study show that improving the level and quality
of agricultural research are potential means of achieving this outcome.
8.4
Communication and dissemination activities
The results of this study need to be disseminated within Indonesian policy and academic
circles. That can be done through the activities outlined above:
-
A research paper for an international journal, and a suitable Indonesian publication,
on the impact of agricultural research on agricultural productivity within Indonesia.
This paper will form the basis for presentations of the research both within Indonesia
and within Australia.
-
A policy brief, based on the results of the research will be produced by the key
collaborator in Indonesia, the Indonesian Centre for Agriculture, Socio-Economic and
Policy Studies.
The users of these outputs will be Indonesian policy makers involved with agricultural
research policy and academic scholars interested in these policy issues. It is expected
Page 17
Final report: The effect of research on agricultural productivity in Indonesia
that the results will contribute to policy making and academic discussion within Indonesia,
and possibly other countries, on the degree of funding priority that should be accorded to
agricultural research within Indonesia. This impact will occur through dissemination and
discussion of the research findings within Indonesia and elsewhere. In addition, the results
will contribute to strategic decision making by ACIAR in relationship to research for
development investment in Indonesia.
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Final report: The effect of research on agricultural productivity in Indonesia
9
9.1
Conclusions and recommendations
Conclusions
The results of this study indicate underinvestment in agricultural research within
Indonesia. Given the government’s objective of raising the level of Indonesia’s food selfsufficiency, combined with rapid population growth, diminishing returns on traditional
factor inputs, declining availability of arable land, fresh water supplies and other natural
resources, concern over climate change and environmental degradation, along with high
fuel and fertilizer prices, it is clear that agricultural research deserves a much higher policy
priority within Indonesia than it has received in recent years.
9.2
Recommendations
Consideration should be given to upgrading Indonesia’s agricultural research capacity.
Both the level and quality of this research effort may be capable of being improved
significantly and the results of this study suggest the value of doing so. In addition, the
degree to with foreign research findings are utilized and adapted within the Indonesian
agricultural research system should be reviewed as it may also be capable of
considerable improvement.
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Final report: The effect of research on agricultural productivity in Indonesia
10 References
10.1 References cited in report
Alston, J. M. (2002) Spillovers. The Australian Journal of Agricultural and Resource
Economics, 46, 315-346.
Alston, J. M., Craig, B. & Pardey, P. G. (1998a) Dynamics in the Creation and
Depreciation of Knowledge, and the Returns to Research. EPTD Discussion Paper
No. 35. Washington, D.C., Environment and Production Technology Division,
International Food Policy Research Institute.
Alston, J. M., Marra, M. C., Pardey, P. G. & Wyatt, T. J. (2000) Research Return Redux: A
Meta-Analysis of the Returns to Agricultural R&D. Australian Journal of Agricultural
and Resource Economics, 44, 185-215.
Alston, J. M., Norton, G. W. & Pardey, P. G. (1998b) Science Under Scarcity: Principles
and Practices for Agricultural Research Evaluation and Priority Setting, Wallington,
CABI Publishing.
APO (2001) Measuring Total Factor Productivity: Survey Report, Tokyo, Asian
Productivity Organization.
Athukorala, P. & Sen, K. (2002) Appendix 2: The Econometric Methodology. Saving,
Investment and Growth in India. Oxford University Press.
CGIAR (2009) Why Agricultural Research Matters? , Consultative Group on International
Agricultural Research. Available at http://cgiar.org/who/index.html.
Chang, H.-S. & Zepeda, L. (2001) Agricultural Productivity for Sustainable Food Security
in Asia and the Pacific: the Role of Investment. In Zepeda, L. (Ed.) Agricultural
Investment and Productivity in Developing Countries. FAO Economic and Social
Development Papers 148. Rome, Food and Agriculture Organization of the United
Nations. Available at http://www.fao.org/docrep/003/X9447E/x9447e07.htm.
Davidson, J. E. H., Hendry, D. F., Srba, F. & Yeo, S. (1978) Econometric Modelling of the
Aggregate Time-Series Relationship Between Consumers' Expenditure and
Income in the United Kingdom. The Economic Journal, 88, 661-692.
Engle, R. F. & Granger, C. W. J. (1987) Co-Integration and Error Correction:
Representation, Estimation, and Testing. Econometrica, 55, 251-276.
Evenson, R. E. (1993) Research and Extension Impacts on Food Crop Production in
Indonesia. Upland Agriculture in Asia: Proceeding of a Workshop Bogor,
Indonesia.
Evenson, R. E. (2001) Economic Impacts of Agricultural Research and Extension. In
Gardner, B. L. and Rausser, G. C. (Eds.) Handbook of Agricultural Economics,
edition 1, volume 1, chapter 11, pages 573-628. Elsevier.
Evenson, R. E. & Pray, C. E. (1991) Research and Productivity in Asian Agriculture,
Ithaca, Cornell University Press.
Fuglie, K., O., (1999) Investing in Agricultural Productivity in Indonesia. Forum Penelitian
Agro Ekonom, 17, 1-16.
Fuglie, K. O. (2001) Private Investment in Agricultural Research: Thailand. Agricultural
Economic Report No. (AER 805). Washington, D.C., Economic Research Service
of the U.S. Department of Agriculture (ERS/USDA).
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Fuglie, K., O., & Heisey, P. W. (2007) Economic Returns to Public Agricultural Research.
USDA Economic Brief No.10. United States Department of Agriculture, Economic
Research Service. Available at
http://www.ers.usda.gov/publications/eb10/eb10.pdf.
Fuglie, K. O. 2010. "Sources of Growth in Indonesian Agriculture." Journal of Productivity Analysis
33, no. 3: 225-240.
Granger, C. W. J. & Newbold, P. (1974) Spurious Regressions in Econometrics. Journal
of Econometrics, 2, 111-120.
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Change. Econometrica, 25, 501-522.
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Productivity Growth. The Bell Journal of Economics, 10, 92-116.
Griliches, Z. (1996) The Discovery of the Residual: A Historical Note. Journal of Economic
Literature, 34, 1324-1330.
Hendry, D. F. (1995) Dynamic Econometrics, Oxford, Oxford University Press.
Hendry, D. F., Pagan, A. & Sargan, J. D. (1984) Dynamic Specification. In Griliches, Z.
and Intriligator, M. D. (Eds.) The Handbook of Econometrics Vol. II. Amsterdam,
North-Holland.
Huffman, W. E. & Evenson, R. E. (2006) Do Formula or Competitive Grant Funds Have
Greater Impacts on State Agricultural Productivity? American Journal of
Agricultural Economics, 88, 783-798.
Inder, B. (1993) Estimating Long-Run Relationships in Economics: A Comparison of
Different Approaches. Journal of Econometrics, 57, 53-68.
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Available at http://www.irri.org/media/facts/pdfs/THAILAND.pdf.
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Jorgenson, D.W. (1988) "Productivity and Postwar U.S. Economic Growth," Journal of
Economic Perspectives, 2 (4), 23-42.
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Growth, London, The MIT Press.
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Review of Economic Studies, 34, 249-283.
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Research: Results from a Cointegration Model. Journal of Policy Modeling, 21,
185-211.
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Pardey, P. G., Alston, J. M. & Piggot, R. R. (2006) Agricultural R&D in the Developing
World: Too Little, Too Late?, Washington, DC.
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Pray, C. E. & Fuglie, K. (2001) Private Investment in Agricultural Research and
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805). Washington, D.C., Economic Research Service of the U.S. Department of
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Final report: The effect of research on agricultural productivity in Indonesia
Agriculture (ERS/USDA). Available at
http://www.ers.usda.gov/Publications/AER805/.
Ruttan, V. W. (1987) Agricultural Research Policy and Development, Rome, Food and
Agriculture Organization of the United Nations.
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Journal of Economic Perspectives, 16, 161-184.
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Solow, R. M. (1957) Technical Change and Aggregate Production Function. Review of
Economics and Statistics, 39, 312-320.
Thirtle, C., Lin, L. & Piesse, J. (2003) The Impact of Research-Led Agricultural
Productivity Growth on Poverty Reduction in Africa, Asia and Latin America. World
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van der Eng, P. (1996) Agricultural Growth in Indonesia: Productivity Change and Policy
Inpact Since 1880, Macmillan, New York.
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Studies No. 200606, Center for Economics and Development Studies, Department
of Economics, Padjadjaran University, Bandung, Indonesia.
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Series?: A Study in Sampling and the Nature of Time Series. Journal of the Royal
Statistical Society, 89, 1-64.
10.2 List of publications produced by project
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Final report: The effect of research on agricultural productivity in Indonesia
11 Appendixes
11.1 Appendix 1:
Data appendix: Data used in econometric analysis
(1961=
100)
Govt
expend:
research
million
Rp.
A
B
TFP
Level
Year
Govt
expend:
Govt
expend:
extension
million Rp.
extended
irrigation
million. Rp.
Foreign
Research
expenditu
re in log
Road
length km.
D
E
F
G
Agriculture,
value added
billion Rp.
H
1974
140
1429.4
2,812
25,075
2.74084
27,889
3,517,180
1975
139
1457.4
2,913
42,971
2.867899
45,887
4,026,100
1976
138
4660.0
5,841
62,688
3.095578
68,532
4,839,760
1977
140
4799.0
8,852
86,470
3.206803
95,325
5,940,070
1978
144
6483.1
10,502
122,534
3.453157
133,039
6,744,700
1979
146
8674.6
10,985
144,155
3.605498
155,144
9,374,000
1980
157
10517.0
14,930
182,500
3.740048
197,434
11,725,500
1981
161
13530.0
17,600
224,596
3.86073
242,200
13,648,900
1982
157
15500.0
16,715
260,728
3.94739
277,447
15,000,500
1983
159
14560.9
24,860
262,800
4.032469
287,664
17,764,700
1984
165
8160.0
24,123
241,400
4.084294
265,527
20,419,701
1985
168
12039.0
28,014
243,500
4.130355
271,518
22,509,999
1986
173
9924.0
22,765
111,021
4.146304
133,790
24,870,000
1987
168
8941.1
12,052
119,897
4.19419
131,952
29,120,000
1988
173
3944.2
14,201
55,474
4.238445
69,680
33,650,599
1989
176
4140.0
22,045
82,810
4.314818
104,859
38,893,999
1990
177
11828.0
30,900
232,179
4.371976
263,083
40,929,998
1991
177
22465.6
44,846
355,231
4.366913
400,082
45,635,999
1992
186
23467.5
25,790
431,984
4.467057
457,778
52,745,998
1993
184
33629.0
37,722
494,781
4.434382
532,507
58,963,400
1994
179
44672.0
18,324
378,067
4.455509
396,395
66,071,499
1995
190
49409.0
19,350
1,110,228
4.428433
1,129,582
77,896,236
1996
188
60956.0
32,096
599,937
4.433195
632,037
88,791,797
1997
182
67462.0
26,041
1,062,471
4.563306
1,088,517
101,009,502
1998
186
73470.0
29,326
1,343,892
4.520701
1,373,222
172,827,571
1999
192
75434.0
28,366
1,403,947
4.612146
1,432,318
215,686,731
Page 23
Final report: The effect of research on agricultural productivity in Indonesia
Govt
expend:
Year
(1961=
100)
Govt
expend:
research
million
Rp.
2000
196
112101.0
89,201
861,078
4.636669
950,284
216,831,500
2001
196
178104.9
172,779
1,044,865
4.61611
1,217,648
251,727,000
2002
202
356698.6
149,308
2,073,298
4.634729
2,222,611
281,590,800
2003
212
550044.3
182,426
1,743,882
4.672829
1,926,313
305,783,500
2004
222
395689.0
222,399
2,643,798
4.597138
2,866,202
329,124,600
2005
221
135793.6
117,118
2,590,534
4.706824
2,707,657
364,169,300
2006
226
156913.2
79,736
2,880,450
4.741448
2,960,191
433,223,400
TFP
Level
Govt
expend:
extension
million Rp.
extended
irrigation
million. Rp.
Foreign
Research
expenditu
re in log
Road
length km.
Agriculture,
value added
billion Rp.
Data sources:
Column A from Fuglie, K. O. 2010. "Sources of Growth in Indonesian Agriculture." Journal
of Productivity Analysis 33, no. 3: 225-240.
Column F from Suphannachart, W and Warr, P (2011) “Research and Productivity in Thai
Agriculture” Australian Journal of Agricultural and Resource Economics, (forthcoming).
Column H from World Bank, World Development Indicators.
All other data from Indonesian Centre for Agriculture, Socio-Economic and Policy Studies,
Government of Indonesia, Bogor.
Page 24