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The Evolution of Rice Price Policies over Four Decades: Thailand, Indonesia, and the Philippines
by
Kei Kajisa*
Takamasa Akiyama
Foundation for Advanced Studies on International Development, Tokyo Japan
April, 2004 *Corresponding author. Address: 2-2 Wakamatsu-cho, Shinjuku-ku Tokyo, 162-8677 Japan; E-mail: [email protected]; Phone: +81-(0)3-3341-0425; Fax: +81-(0)3-3341-0220. Authors would like to thank anonymous reviewers for their useful and constructive comments.
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The Evolution of Rice Price Policies over Four Decades: Thailand, Indonesia, and the Philippines
Abstract
Using time series data over the past four decades, 1960s-1990s, this paper examines rice
pricing policies in Thailand, Indonesia, and the Philippines. It analyzes the
determinants of the paths along which these policies have moved. While this analysis
confirms the findings of previous analyses, that price stabilization has been a major
policy achievement, it also reveals that stabilization was not necessarily sustained over
the entire survey period. It finds that politico-economic factors — such as entry into
the GATT, increase in per capita GDP and achievement of rice self-sufficiency — have
been among the determinants of rice pricing policy, but the ways in which these factors
have affected policy varies among these countries. Such variation, which previous
cross-country studies have not analyzed, is a reflection of variations in the roles of rice
and in the attitudes of policy makers in these economies. In its conclusion, this study
draws policy implications for each country taking into account differences in
determinant impact.
Keywords: rice; price policy; Thailand; Indonesia; the Philippines
JEL classification: F13, O13, Q18
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The Evolution of Rice Price Policies over Four Decades: Thailand, Indonesia, and the Philippines
1. Introduction
Rice has a special significance in Asia and to Asians. Because of its economic
and political sensitivity, it has been rare for any Asian government to allow the
international market to determine domestic rice prices. Instead, government
interventions in the domestic market — taxes, subsidies, and market operations — have
been prevalent.
Over the four decades examined in this study, 1960s-1990s, many Asian
countries experienced dramatic changes in their internal economic condition as well as
in the external economic environment. Dissemination of Green Revolution technology
since the early 1970s significantly increased rice production. Agricultural technology
and developments in non-agricultural sectors contributed to significant and long-term
increases in income level, such that some countries succeeded in moving out of poverty
into middle income status. Meanwhile the momentum of globalization was exerting
pressure in the direction of free trade. Each of these historical factors influenced the
rice pricing policies of Thailand, Indonesia and the Philippines, the three countries
chosen for this study, and their impact may have been intensified by entry into the
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GATT/WTO regime.
This study asks the question: Given these circumstances, what were the factors
that affected rice price policies in each of these three Asian economies and what were
the effects? More specifically, in each country what has been the relationship over
time between changes in macro- and rice-sector factors and rice price policies? And
secondarily, what was the impact on each country’s rice policy of significant events,
substantive or symbolic, such as the achievement of rice self-sufficiency or entry into
GATT? There are indications that protection of the rice sector in Asian developing
countries has been increasing despite clear indications from Asian developed countries,
Japan, Taiwan, and South Korea, that protection leads to inefficient rice farming with
huge financial burdens for consumers and governments. Thus, answering these
questions has practical relevance because without understanding the dynamics of price
policy formulation, it will be difficult to formulate and evaluate appropriate rice
policies.
The existing literature only partially answers these questions. The pattern that
has been most commonly observed is a policy shift toward protection as incomes
increase (van Bastelaer, 1998; Beghin and Kherallah, 1994; Zietz and Valdes, 1993;
Lindert, 1991; Timmer, 1988; Anderson and Hayami, 1986). Recent literature also
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reveals a practice of using taxes and subsidies to stabilize domestic prices (Dawe, 2001;
Timmer, 1993; Schiff and Valdes, 1992). These have been statistically confirmed in
Asian rice markets by David and Huang (1993). However, previous analyses have
generally relied on cross-country data using both developed and developing countries as
samples. Such analyses detect the factors that have influenced the sample countries,
but on the average rather than in the particular. For this reason, while the past
literature provides useful general ideas on the determinants of protection, it is inefficient
for the purpose of evaluating and designing a specific country’s rice pricing policies.
In order to formulate country specific policies, the generalized ideas provided by
cross-country analyses should be examined by country specific time series analyses, as
well.
This paper explores the forces driving the evolution of rice price policies over
the last four decades by conducting specific time series analyses for three major rice
producing countries: Thailand, Indonesia, and the Philippines. The period covered is
1961-2000 for Thailand and the Philippines and 1967-2000 for Indonesia. Akiyama
and Kajisa (forthcoming) analyze these countries’ price policies for major agricultural
commodities, including rice, over the period 1961-1995. Building on that work, this
paper focuses specifically on rice with extended newly available time series data.
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This paper is organized as follows: Section 2 provides details concerning
methods of measurement and an overview of rice price policies in the three countries
over the past four decades. In order to clarify the determinants of change, Section 3
presents an analytical framework. Section 4 shows the results of time series analyses.
Based on these results, policy implications and conclusions are drawn in Section 5.
Appendix 1 presents detailed information on the data used, by country. Appendix 2
explains the econometric issues of this analysis.
2. Methods of Measurement and an Overview of Rice Price Policies in the Three
Countries
2.1 Methods of Measurement
The impact of price policy is measured by the nominal protection rate (NPR)
defined as the percentage difference between the real domestic price (RDP) and the real
border price (RBP).
1−=−
=RBPRDP
RBPRBPRDPNPR
The domestic market price of rice deflated by the consumer price index (CPI) is
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used as RDP and the world price of rice at border converted at the official exchange rate
and deflated by CPI is used as RBP.1 NPR is negative when rice is taxed and is
positive when it is protected. The ratio (RDP/RBP) is called the nominal protection
coefficient (NPC), utilized for the econometric analysis in Section 4. Although there
are more complete protection measures, such as the producer support estimates of
OECD (OECD, 2002) or the effective protection rates, NPR is used in this analysis
because the ease of its estimation allows time series analysis to be extended long
enough for an examination of evolutionary path.
Because price stabilization is claimed to be one of the major objectives of price
policy, two price stability indices are calculated. One is the ratio of the coefficient of
variation (CV) of real border price to that of real domestic price, which is greater than
one when the real domestic price is more stable around its mean than is the real border
price. Because the CV can be affected if the underlying data have some trend, a
second index has been calculated, the ratio of absolute average values of annual
percentage changes in RBP to those of RDP over the decades.2 This ratio is greater
than one when the domestic price is more stable than the border price; that is, when the 1 For details of the sources and the computational method of RDP and RBP for each country, see
Appendix 1.
2 Because these analyses use real prices and because the period considered is relatively short (i.e. 10
years each), the CV is little affected by trend.
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year-to-year RDP percentage changes are smaller on average than those of RBP. Since
the first index examines deviations from the average and the second examines annual
changes, the former can be regarded as a long-term stability index and the latter as a
short-term stability index.
2.2 Thailand
These indices for Thailand are presented in Figure 1 and Table 1. Figure 1
shows the trend of RDP and RBP from 1961 to 2000 and Table 1 shows the average
NPR and the two stability indices for Thai rice. Thailand exported rice and taxed it
throughout the period but the data reveal a changing pattern. Figure 1 and the NPR
averages in Table 1 indicate that tax rates were very high in the 1960s and 1970s (on
average 30% in the 1960s and 24% in the 1970s even after exclusion of the commodity
crisis years) and then were reduced appreciably in the 1980s (10%) and in the 1990s
(4%). The stability indices in Table 1 reflect a policy of domestic price stabilization
from the early 1970s to the early 1980s; after that the price stabilization indices become
closer to one, or even smaller, suggesting that in the early 1980s the government
abandoned the stabilization policy. This does not mean that Thai rice farmers have
been exposed to the world market without recourse to any government support. Since
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1981 the Bank for Agriculture and Agricultural Cooperatives has operated a rice
pledging program (paddy mortgage scheme), an optional program which allows farmers
who choose to opt-in to mortgage their rice when market price is low and redeem it
when the price returns to normal. This program, however, reflects a switch in the
government’s stance from one of direct support to all rice farmers through commodity
market intervention to one of price stabilization linked to farmers’ management of their
own risk. These data, the reduction in taxation and the cessation of direct intervention
together point to a definite shift toward free trade in Thai government rice policy
starting in the early-1980s .
Thailand’s development plan does not clearly articulate agricultural policies but
the generally held view is that the rice policy was designed to generate national revenue
through diversification of taxable resources and, simultaneously, to provide wage goods
at low and stable prices (Dixon, 1999; Siamwalla, et al., 1993). An implication of this,
which we revisit in Section 4.1, is that as these objectives became less urgent in the
early 1980s, the government shifted its stance. It is also relevant that Thailand signed
the GATT agreement in November 1982. The government abolished the rice
reservation deposit in that year, and several types of rice taxes in 1986. In this regard,
the statistical section which follows (Section 4.1) pays particular attention to the
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economic and political rationale underlying tax reductions prior to the early-1980s in
determining whether a structural break occurred in the policy-making mechanism at that
time.
2.3 Indonesia
Key statistics on Indonesian rice prices for the period 1967-2000 are shown in
Figure 2 and Table 2. They indicate that rice was moderately protected at around 15%
throughout the survey period — excluding the very early years and the commodity crisis
and the Asian currency crisis years when RBP surged. The high values of the stability
indices show that the domestic price was more stable than the border price, especially in
the 1970s and 1980s. These outcomes are attributed to marketing operations designed
and implemented by the parastatal BULOG (Bandan Urusan Logistic, National
Logistics Board).
BULOG was established to achieve two major policy goals. First –
achievement of rice self-sufficiency, one of the top national priorities in the face of a
chronic rice deficit at the macro level (Gonzales, et al., 1993; Timmer, 1993). Second
– attainment of price stabilization because this (along with rice availability) was thought
to bear on the avoidance of social unrest (Gonzales et al., 1993; Timmer, 1993). To
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achieve these goals, policies were designed to set rice prices sufficiently high to
stimulate production without transferring international price fluctuations into the
domestic market.
The performance of BULOG from the mid 1970s until the late 1980s is
considered a major success. The stable RDP line relative to the RBP line in Figure 2
and the high stabilization indexes in Table 2 for the 1970s and 1980s indicate that
BULOG intervention was effective in protecting the domestic market from international
fluctuations. — The positive NPRs in the same periods also reflect this. — And
protection was implemented in such a way that the deviation of the domestic price from
the world price was limited (Timmer, 1993; Hill, 2000). Moreover, protection was a
contributing factor to the achievement of rice self-sufficiency, publicly announced in
1984 and internationally recognized by the award of a gold medal to President Suharto
by the Food and Agriculture Organization in tribute to BULOG’s success.
The achievement of rice self-sufficiency brought BULOG to a decision point, to
remove or to retain protection; but no clear decision seems to have been made (a
situation which is explained in Section 4.2, below). The slight reduction in NRP in
recent years, as shown in Table 2, may be a result of decreased protection. Conversely,
some analysts maintain that that protection policies have remained in place because
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BULOG has been unmoved by advice that at this stage liberalization will promote
efficiency and effectiveness (The World Bank, 1986; Timmer, 1993). As evidence of
this entrenched position, analysts point to the removal of the pro-liberalization Minister
of Trade and the transfer of that ministry’s functions into the more conservative
Ministry of Industry, which is oriented toward state-led import substitution (Amelina,
forthcoming).
In the absence of more definitive indicators, the effect of macro or sector
variables – especially the achievement of self-sufficiency and the recent rise of
protectionism – on BULOG pricing policies will be examined statistically in Section 4.
In the case of Indonesia, entry into GATT occurred before the survey period (1950) and,
hence, will not be analyzed.
2.4 The Philippines
As in the case of Indonesia, in the Philippines a parastatal has been responsible
for the implementation of rice price policy.3 Unlike the case of Indonesia, however,
the Philippines parastatal is not authorized to design policies but only to implement
them. Market intervention by the parastatal is shown in Figure 3, which characterizes
3 This parastatal was called the Rice and Corn Administration (RCA) until 1972 when it became the
National Grain Authority (NGA). In 1981 it became the National Food Authority (NFA).
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Philippines rice price policy as starting with protection, switching to taxation in the
early 1970s, and then returning to protection in the early 1980s.
Broadly speaking, the periods of protection and taxation correspond respectively
to the periods of rice importation and exportation, implying the achievement of rice
self-sufficiency as major motive for intervention. Historical accounts are consistent
with this pattern. When the country experienced severe food shortages in 1971-72, the
government imposed rice price controls and also launched a program called Masagana
99 that gave assistance to rice farmers in the form of credit, irrigation, extension
services and fertilizer subsidies. In conjunction with this program, the breakthrough of
the Green Revolution significantly increased rice production; rice was even exported in
the mid-1970s. Beginning at this time, the parastatal started setting the domestic price
lower than the world price, a practice which continued until the mid-1980s when the
economic and political turmoil marking the end of the Marcos era caused the country to
revert to the position of importer.
Table 3 shows the dramatic changes in indexes from the 1980s to the 1990s.
The protection rate increased from 16% to 41% and domestic price stabilization
declined from 1.8 to 0.9 in the ratio of CV, implying a structural break in that period.
Many researchers trace this to a combination of incompetence and insufficient funding
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at the rice marketing parastatal, the National Food Authority (NFA) (Balisacan, 1989;
Dawe, 1993; Amelina, forthcoming). NFA, which was an international rice trading
monopoly, was required to import rice and to release it in the event of a shortage in
domestic supply to defend the price set by the government . However, inaccurate
projections of the amount to import during times of shortage often caused domestic
price surges; at the same time, NFA’s capacity for market intervention was limited by
the stringency of its budget. Price fluctuation due to domestic shortages under
controlled importation accelerated in the 1990s when the El Niño and La Niña weather
phenomena damaged the agricultural sector. This emerging administrative inadequacy
in the 1990s is captured as an element of structural change in our statistical analysis.
The Philippines signed the GATT in December 1979 but rice was exempt until
2004 (as of 2002). The government seemed reluctant to open the rice market,
implying no structural change in 1979. In the statistical section which follows, we will
detect the timing of a structural break and identity the determinants of pricing policies
with special attention to the self sufficiency rate as the most probable candidate.
3. Analytical Framework
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We start with a simple price-transmission model in which the relationship
between the real domestic price (RDP) and the real border price (RBP) of rice in year t
is described as
βα tt RBPRDP = (1)
where α reflects tax or protection policies determined independently of the level of RBP,
as well as marketing and transportation costs if these components are not included in
RBP, and β captures the elasticity of price transmission. When β is one, the full
percentage change of RBP is transmitted to the domestic price. Although unusual, the
value of β can be negative if movements of the two prices are opposing. When β is
zero, RDPt is equal to α regardless of the level of RBPt, implying a constant percentage
tax/subsidy. The magnitude of β becomes close to zero either when price stabilization
policies are effective or when domestic prices fluctuate as much as border prices but in a
different direction. Therefore, in order to see the effect of price stabilization policies
over time, the magnitude of β must be interpreted by the stability indexes presented in
Tables 1 to 3.
To facilitate analysis of NPC, equation (1) is modified as
ttt
t RBPNPCRBPRDP )1('lnln −+==
βα (2)
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where α’ is lnα. Both coefficients, α’ and β, may change over time due to the effects
of some variables. Assuming a linear relationship between the coefficients and these
variables, each coefficient is replaced with α’ and β with subscript t and be specified as
iti
it Xln' 0 ∑+= ααα (3)
iti
it Zln0 ∑+= βββ (4)
where Xi and Zi are determinants of taxation/protection policies.
Combining equations (2), (3), and (4) and adding a statistical error term, the
following equation can be estimated:
[ ] ttiti
ititi
it eRBPZRBPXNPC ++−++= ∑∑ lnlnln)1(lnln 00 ββαα (5)
Estimating this equation allows us to evaluate how αs and βs have changed over time
with the effects of determinants Xit and Zit on NPC. Furthermore, by computing βt
based on equation (4), we can trace the change in the elasticity of transmission over
time.
Previous cross-country studies suggest candidates for Xi and Zi. First, income
level is used in order to capture politico-economic factors which encourage taxation on
the rice sector in low income countries; these factors include the government’s need for
revenue from the dominant agricultural sub-sector and the desirability of providing
people staple foods at low price (Lindert, 1991; Timmer, 1988; Anderson and Hayami,
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1986). These same studies also argue that as incomes grow, factors promoting
protection become more powerful; these factors include as nostalgia for rural life which
often results in leniency toward an unprofitable agricultural sector. Therefore, a
positive relationship is expected between income level and protection rate. In the
present analysis, per capita GDP (PGDP) is used to measure income level.4 Second,
the self-sufficiency ratio of rice (SSR), measured as the ratio of production to
consumption, is used to capture the extent to which rice importing countries protect the
sub sector in order to achieve food security (David and Huang, 1993; Schiff and Valdes,
1992; Lindert, 1991). Third, the relative wage ratio of the agriculture sector to the
industrial sector is used because several previous studies (van Bastelaer, 1998; Beghin
and Kherallah, 1994; Zietz and Valdes, 1993; Anderson and Hayami, 1986) argue that
governments tend to subsidize the agricultural sector when agricultural wages tend to
lag behind industrial wages. This is measured by the ratio of agricultural GDP per
total agricultural population to non-agricultural GDP per total non-agricultural
population (GDPRTO). Fourth, the competitiveness of the rice sector is measured by
rice yield per hectare (YLD) to test the argument of David and Huang (1993) that an
increase in productivity may nullify the need for protection and may even make the
4 The GDP per capita used in our analysis is calculated such that local currency GDP is converted to
US$ GDP using the 1995 official exchange rate and is then divided by total population.
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sector tolerant to taxation. Fifth, in order to examine the influence of powerful interest
groups, the relative size of the agricultural sector is used, measured by the share of
agriculture in the labor force (AGPOPSHR) or in GDP (AGGDPSHR) as proposed by
van Bastelaer (1998); Beghin and Kherallah (1994); Lindert (1991); and Miller (1991).
In our time series analysis, we examine the effects of these six variables on the path of
nominal protection rate, NPC.
In the estimation of time series equation (5), we use the autoregressive
distributed lag (ARDL) approach (See Appendix 2 for detail). One interpretational
advantage of this model is that long-term effects can be computed by reparameterization
of the OLS estimates. We present not only OLS results but also the long-run effects on
ln(NPC) from the selected explanatory variables. For the detection of structural breaks
the cumulative sum of residual (CUSUM) and the cumulative sum of residual square
(CUSUM of square) tests, which are commonly used for this purpose, are used in our
analyses.5 In order not to overlook any structural breaks, we also rely on the method
of plotting residual variance over time (Hansen, 2001).6
5 The CUSUM test can be applicable to our dynamic model which contains a lagged dependent variable
among the regressors (Kramer et al., 1988).
6 A sample is split into two at a possible break year and then OLS regressions are run separately on each
segment. Repeating this process, we compute the residual variances for all the years (sum of squared
errors divided by the sample size) and plot them against years. The years having global/local minimum
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4. Regression Results
The regression results are reported in Tables 4 to 6. Our analysis found some
variables such as AGPOPSHR, AGGDPSHR, and YLD to be insignificant and they are
not shown in the tables. The results indicate that some particular variables may be
significant in one country during a certain time period but not necessarily in another
country. This suggests that the variables found to be significant in cross-country
analyses are not necessarily significant in country- and commodity-specific time series
analyses. The following discussion is based on time series analyses specific to each
country and uses the results of the long-term response unless otherwise indicated.
4.1 Thailand
The path of Thailand’s NPC is determined by per capita GDP (PGDP) and real
border price (RBP). Before interpreting these variables, however, we test for a
structural break because it will change the ways PGDP and RBP affect NPC. Results
are candidates for break years.
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reported in column (T-1) in Table 4 were obtained under the assumption of no structural
break. Using the same explanatory variables in the model (T-1), CUSUM, CUSUM of
squares and the residual variance are plotted (Figures A1-(a) to A1-(c)). These suggest
structural breaks in the years 1970 and in 1982.7 Inclusion of a period dummy, which
becomes one in and after 1982, and its interaction terms with other factors improves the
results substantially. Hence, we can conclude that Thailand’s rice price policy
experienced a structural break in 1982, which is the year Thailand signed the GATT
agreement and also abolished the rice reservation deposit.
How is the impact of PGDP different before and after the break? According to
the results in the model (T-2) with the period dummy (DUMMY82), the partial
derivative of ln(NPC) with respect to ln(PGDP) evaluated at the mean ln(RBP) indicates
that the impact of PGDP was much greater on NPC before 1982 than after. For
example, a 5% annual increase in per capita income reduces the tax from 20% to 17%
before the break; whereas after the break, the impact from the growth of PGDP becomes
almost negligible (from 20% to 19.4% with the same income growth rate). For
comparison, we run a regression using the relative agricultural wage, GDPRTO;
however, it does not explain NPC as well as PGDP does. These results suggest that
7 Although both CUSUM and CUSUM of squares are within the band of 5% significance, the residual
variance suggests possible breaks in 1970 and in 1982 where the variances take lower values.
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before the break point, the government did not pay attention to the relative poverty of
the agricultural sector but instead reacted to the increase in absolute income level.
As noted in Section 2.2, one of the probable reasons for this is that a tax on rice
became less necessary as alternative taxable sources increased with the growth of the
economy. Figure 4 shows that the proportion of rice revenue in central government
revenue declined dramatically during the 1960s and became very small in the 1970s
even though the rice tax rate remained the same (about 30 % on average). Another
reason is that the need to provide low wage goods had declined with the growth of the
economy. Figure 5 shows that during the late 1970s, minimum non-agricultural wages
doubled in real term. This suggests that over this time the provision of rice at low
prices became politically less necessary due to increases in income among the
non-agricultural poor. Given these two reasons and also the finding that GDPRTO,
relative agricultural wage or relative poverty in agriculture, did not explain NPC’s path
well, we can conclude that taxes on rice were not reduced because the government
wanted to reduce the burden on rice farmers, or even to protect them; rather because the
need for the taxes diminished as a consequence of economic development. Therefore,
by the time of the break point the tax had been reduced enough that PGDP had lost its
impact as a relevant determinant.
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How does the other important determinant, RBP, relate to NPC? The evolution
of the transmission elasticity in Figure 6, which is computed as the coefficient βt in
equation (4), indicates that until the break point was reached, the elasticity had been
declining; that is, the domestic market had became more and more insulated over time.
This insulation was achieved through adjustment of the tax or premium rates such that
abrupt changes of the world prices were neutralized (Dixon, 1999). Domestic prices
were stabilized as reflected in the high stability indices in the 1970s in Table 1.
However, after the break, the transmission elasticity jumped to unity and since 1982
domestic price has been moving almost in tandem with border price.
In summary, our results imply that when the need for a tax on rice diminished in
the early-1980s, the government used entry into GATT as an opportunity to change its
pricing policy, withdrawing completely from the domestic market and allowing the
price of rice to be determined by the world market. As noted earlier, however,
producers still have access to stabilization benefits through the rice pledging program if
they so choose.
4.2 Indonesia
The CUSUM, CUSUM squares, and the residual variance plots in Figures
23
A2-(a) to A2-(c) are computed using the explanatory variables in the Model (I-1) in
Table 5, suggesting possible structural breaks in 1985 and/or in 1992. The results
shown as Model (I-2), which include DUMMY85 and DUMMY92 interacted with RBP
and PGDP, fit the actual NPC path best. This suggests that Indonesia’s pricing policies
experienced structural breaks firstly as a shift of constant term in 1985 and secondly as
changes of magnitudes of the coefficients in 1992.
The effect of the first structural break is captured by DUMMY85. The
negative value for the coefficient for this dummy indicates that the protection rate was
reduced from 15% to 3% after 1985 and was kept at that level. It is worth noting here
that this break year occurred one year after Indonesia’s public announcement of
achievement in rice self-sufficiency. This clarifies the ambiguity referred to in Section
2.3; once BULOG became convinced that rice self-sufficiency had been achieved, it
reduced the protection that it had used to achieve that objective. Interestingly, at the
same time the results show that the annual change of self-sufficiency ratio (SSR) has no
explanatory power for the evolution of NPC in the long term. This holds even if
DUMMY85 is excluded as shown in Model (I-1). These findings indicate that
BULOG did not change its policy of protecting against short term fluctuations of SSR
(Figure 7) until it was convinced that rice self-sufficiency had been achieved.
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The second structural break is related to the impact of PGDP which for
Indonesia, as for Thailand, was one of the most important factors affecting protection
rate. Unlike Thailand, however, PGDP became more influential after the structural
break in 1992. After the break, the coefficient of PGDP increases from 0.58 to 1.138,
meaning, for example, that while the nominal protection rate increases from 15% to
18% with a 5% increase of GDP per capita before the break, it increases from 15% to
23% with the same increase of GDP per capita after the break.8 This is consistent with
anecdotal evidence that BULOG’s interventions in this period were oriented toward
import substitution, partly due to the merger of the Ministry of Trade with the more
conservative Ministry of Industry and also partly due to replacement of the management
board of BULOG (Amelina, forthcoming). Hence, one explanation for the increase of
protection with PGDP at an increased rate after 1992 is an increase in demand for
protectionism by policymakers.9 Our conclusion is that although the achievement of
self-sufficiency resulted in reduced protection after 1985, political demand increased it
8 In 1998, the year of the Asian currency crises, NPC sharply dropped with PGDP. While there might
be concern that the magnitude of the positive coefficient of PGDP after the break would be exaggerated
by that event, additional regression analyses show that the exclusion of that year does not change the
results appreciably, especially regarding the coefficient of PGDP.
9 Regression implication that the drop of SSR in the late 1990s (Figure 7) does not significantly explain
the rise of protection lends credibility to this explanation.
25
again after 1992.
The transmission elasticity for Indonesia in Figure 6 shows that the effect of the
structural break on the transmission elasticity is not discernable. Although the positive
and significant coefficient of the interaction term of RBP with GDPRTO indicates the
evolution of beta, the aggregated impact is so small that Indonesia’s transmission
elasticity is low for the entire survey period (around 0.35), a reflection of the immunity
of domestic prices to world price fluctuations. The stabilization indices in Table 3
re-enforce this, showing that at least until the late 1980s BULOG stabilized the
domestic price against world market prices. In the 1990s, stabilization was achieved to
some extent, but not as strongly as in the previous decades. These findings suggest
that while it might have been accomplished during the period until the late-1980s, after
that time the achievement of price stabilization for the entire county by a single
parastatal became increasingly difficult.
In summary, Indonesia’s price policies effectively achieved stabilization of
domestic price in the 1980s but became less effective after the early 1990s. The
achievement of SSR had a one-time reduction impact on protection in 1984, but the
protection rate significantly increased with PGDP growth after 1992. This was
presumably due to a rise in demand for protectionism by policymakers and reinforces
26
indications that Indonesia embarked on a rice protection path similar to that traveled by
Japan, South Korea, and Taiwan at an even earlier stage in its development process,
when analyzed with reference to its GDP level.
4.3 The Philippines
According to the residual variance plot in Table A3-(c), there are possible
structural breaks in the Filipino rice pricing policies in 1979 (the year of entry into
GATT), 1985 (a year before the end of the Marcos regime) and/or 1991 (when
inefficiency at the NFA emerged), also. On the other hand, CUSUM and CUSUM
squares in Tables A3-(a) and A3-(b) do not suggest any structural breaks. Among the
three possibilities, only the inclusion of DUMMY91 and its interaction terms produces
significant coefficients as shown in the Model (P-2). From this we judge that the
structure of Filipino rice pricing policies changed in 1991.
A structural break in 1991 is reflected in the change of the transmission elasticity
in Figure 6. Until 1991, elasticity stayed very close to zero, indicating that the Filipino
rice price was immune from fluctuations in world prices. This insulation of the market
was accompanied by price stabilization, as shown in the stability indices in Table 3.
This data is consistent with the claim that although imperfectly, the goal of price
27
stabilization was reasonably well achieved, particularly in the 1970s (Dawe, 1993;
Unnevehr, 1985). However, after the break, transmission elasticity was around -1.4, an
unusual value which indicates that domestic prices were moving in the opposite
direction from border prices. The fluctuation depicted in Figure 3 and the CV ratio in
Table 3 (less than 1 in the 1990s) reflect the change in transmission elasticity. As
pointed out in Section 2.3, many researchers attribute this change in large degree to a
lack of competence and funding at the NFA (Balisacan, 1989; Dawe, 1993; Amelina,
forthcoming).
Rice self-sufficiency ratio (SSR) is another important determinant. In the
Philippines, unlike Indonesia, indications are that the government adjusted price policies
in accordance with annual changes in SSR. As we have seen in Section 2.4, when rice
was exported as a result of the Green Revolution in the late 1970s, it was taxed.
However, rice production did not keep pace with population increase so that the country
reverted to the position of importer in the mid-1980s. The SSR dropped further in the
1990s (Figure 7). Facing this trend, NFA purchased paddy at subsidized prices with
the aim of stimulating domestic production, resulting in positive protection rates (FAO,
2001). NFA’s mismanaged import control further increased the domestic price in years
of low self-sufficiency, strengthening the negative relationship between SSR and NPC.
28
In summary, for the entire period considered the level of rice self-sufficiency and
price stabilization have been two influential forces driving Philippines rice price polices.
As we suggested in Section 2.4, the entry of the Philippines into GATT did not cause a
structural break as it did in the case of Thailand. Like Indonesia, parastatal market
interventions aimed at price stabilization were effective until the 1980s. The
disappearance of the price stabilization effect is more obvious in the case of the
Philippines than for the other countries; in fact, the market intervention exacerbated
domestic price fluctuation in the 1990s. Unlike Thailand the recent rise of protection
is explained mainly by the decline in rice self-sufficiency rather than by income level.
5. Policy Implications and Conclusions
For each of these three countries, the analyses confirm what has been claimed by
the existing literature, one of the main achievements of pricing policy is price
stabilization. The time series analyses in this paper, however, also reveal that
stabilization was not necessarily sustained over the entire four decades. The Thai
government has been letting the market determine rice price since the early 1980s. In
29
Indonesia and, especially, in the Philippines price stabilization policy was less effective
in the 1990s than in earlier periods, reflecting the increasing difficulty of achieving
price stability for an entire country through a single marketing parastatal such as
BULOG in Indonesia or NFA in the Philippines. Taking account of the differences in
the dynamic mechanisms of the rice price policies, some policy implications can be
drawn for each country.
First, looking at the Philippines: Given the negative and significant coefficient
of the self-sufficiency ratio (SSR), achievement of food self-sufficiency appears to have
been one of the major objectives of price policies in the Philippines. We question
whether the NFA is an appropriate agent for this purpose in view of the administrative
inefficiency revealed by the exaggerated price fluctuations of recent years. Reform of
the NFA could be one option, alternatively, opening the rice market could be another
option for achieving the underlying objectives, assuming dependability of the world rice
market. Opening the rice market may be a more reliable and satisfactory way to
achieve food security, rather than food self-sufficiency. Opening the market would
also help smooth price fluctuations, at least to the international level; though in this
regard, the dependability of the international rice market must be demonstrated to the
Philippine government. Although, the world rice market used to be regarded as thin,
30
recent research by Dawe (2002) shows that rice prices have been low and quite stable
since Thailand and Vietnam returned to the market and predicts that they will remain so
in the future. Research of this sort can be valuable input to policymakers’ decisions.
Second, the case of Indonesia: The income growth since 1992 indicates that
despite having attained self-sufficiency, the government continues to subsidize domestic
price relative to world price. Protection in rice-producing developed countries is a
common pattern; however, it generally results in inefficiency and huge burdens on
consumers and should serve as an example of what not to do. Information about the
negative consequences of price protection is limited and it might be that Indonesia’s
policymakers are ill-informed. Researchers should disseminate information about the
potentially significant adverse effects of large-scale protectionism.
Third, better price stabilization mechanisms should be adopted in Indonesia and,
especially, in the Philippines. In both countries, price stabilization was effectively
achieved in the past but was not sustained. While intervention efforts have not
weakened, stabilization has been less successful indicating the increasing difficulty of
achieving price stabilization for an entire country through the procurement and trade
controls of a single parastatal. The nature of the parastatal should perhaps be changed
from that of agent stabilizing prices to that of agent providing instruments through
31
which individuals, themselves, stabilize prices. Thailand’s rice pledging program is
one example of such an instrument. However, this kind of program would need to be
introduced carefully in the Philippines and Indonesia. Poverty is more prevalent in
these countries than in Thailand and there is a risk that poor farmers may experience
long-term ill effects from a one-time negative price shock. Introduction of new
programs should be accompanied by safety nets.
Fourth, the Thai rice protection policy is interesting in that it switched from
price intervention to other means in the early 1980s. The results of the present analysis
indicate that increases in income induced a reduction in taxes until the structural break
in 1982 but did not induce an increase in protection through pricing policies. This is
not the same thing as saying that no protection was given to Thai farmers. Since 1981
the major support program for rice farmers in Thailand has been the rice pledging
program (the impact of which is difficult to capture by NPC). However, it should be
noted that the quantity of pledged rice at subsidized rates has been increasing.
According to a review by WTO, this support for rice farmers is designed to address the
income gap between agriculture and industry (WTO, 2000; GATT, 1991). This
suggests that as relative poverty in the agricultural sector becomes a social problem,
protection through non-price policies has increased in Thailand. Based on Japan’s
32
experience, Hayami (1988) argues that relative poverty becomes a social problem when
the economy enters the mid-income level, a stage which Thailand appears to have
entered in the mid-1980s. The future path of protection in Thailand could be more
clearly revealed by examining the relationship between non-price policies and poverty
in the agricultural sector relative to the non-agricultural sector.
These country-specific time series analyses allow us to understand the impact of
politico-economic factors — such as entry into GATT, increase in per capita GDP or the
achievement of rice self-sufficiency — in ways that earlier cross-country studies have
not considered: Firstly, that these factors may be relevant determinants in one country
during one time period but not necessarily in another country during that same period.
Secondly, that the pattern of impact on price policy of these politico-economic factors
differs among the countries; in some cases the impact is continuous and uninterrupted
and in other cases there is a structural break. These differences reflect the differences
in the role and importance of rice in the economy and in the attitudes of policymakers.
Earlier cross-country studies have erroneously assumed that the same factors explain
each country’s evolutionary path in the same way.
The present study has found that policy making mechanisms cannot be readily
generalized and that country specific time series analyses are needed for each
33
developing country and commodity. The ramifications of policy alternatives are
country-specific. This is an important reality that the researcher must accommodate.
34
Appendix 1: Description and Sources of Data Used
All the data except prices are from either the World Development Indicators (2002) by
the World Bank (WDI hereafter) or FAOSTAT by FAO.
Variable Source Official exchange rate (LCU per US$, period average) WDI Consumer price index (1995 = 100) WDI GDP (constant 1995 US$) WDI GDP share of agriculture % WDI Population, total (000) FAOSTATAgricultural population FAOSTATRice export FAOSTATRice import FAOSTATRice production (MT) paddy FAOSTATRice yield per hectare FAOSTAT
The rice prices are obtained in the following manner for each of the three countries.
Thailand
The domestic and international prices from 1960 to 1985 are from Siamwalla and
Setboonsarng (1989) (S&S hereafter). From 1986 to 2000, both domestic and border
prices are extrapolated using the results from the regression of the prices from S&S on
the prices from Rice Statistics by IRRI (2002) (Rice Stat hereafter). The results are:
35
(Domestic Price from S&S) = 0.6082137*(Wholesale Price from Rice Stat), adjusted R2
=0.999
(Border price from S&S) = 0.5641606*(Border price from RiceStat), adjusted R2=0.999
The coefficients are significant at the 1% level.
Since Thailand is one of the largest rice exporters, border price is influenced by
Thailand’s taxation. Hence, the adjusted border price, which is the border price under
the assumption of no taxation in Thailand, is computed using the formula in S&S on p.
47.
Philippines
The domestic prices from 1960 to 1979 are the wholesale equivalent domestic prices
from Intal and Power (1989) (I&P hereafter). From 1980 to 2000, we use the
wholesale price from Rice Stat without adjustment because the prices from Rice Stat
and I&P are quite close. The border prices are computed as the sum of Thai fob rice
price and transportation costs which are assumed to be US$ 10 from 1961 to 1973,
US$ 20 from 1974 to 1978, US$ 25 from 1979 to 85, and US$ 15 from 1976 to 2000
based on oil prices at the world market.
36
Indonesia
There are no previous studies along the lines of S&S and I&P for Indonesia. For
domestic prices, retail prices issued from BULOG rather than wholesale prices from
Rice Stat are used. This is because, according to a specialist from the International
Rice Research Institute, recent wholesale prices have still been determined using prices
of varieties that are no longer popular in the domestic market. The border price is
computed as was done for the Philippines. It should be noted that if the real domestic
price had been based on the wholesale price series constructed by the same varieties
used in the BULOG series, the nominal protection rate in Indonesia would have been
lower than the figures reported in Table 2.
37
Appendix 2: Econometric Issues
As time series data are used in the estimation of equation (5), what is required is
essentially a co-integration analysis of a set of variables. This implies that the choice
of specific analytical framework is very important. There are several choices
including the two-step Engle-Granger method (TSEG), Johansson’s maximum
likelihood approach, the fully-modified Hansen-Phillips least squares approach
(FM-OLS), and the autoregressive distributed lag (ARDL) approach. The results
presented and discussed here are based on the ARDL (dynamic OLS) approach. In
addition to its relative computational simplicity, this approach requires fewer
assumptions, has attractive large sample properties (Pesaran and Shin, 1997), and
permits valid inferences on the long-run parameters. It can be applied regardless of
whether the variables are trend- or difference-stationary, making it unnecessary to apply
unit-root tests that have low power in small samples.
We considered a simple illustration of the approach based on an ARDL(1,1)
model for two I(1) variables y and x, i.e
ttttt xxyy εγγφ +++= −− 11011 11 <φ (6)
which has the dynamic representation
ttt
tt uxLL
xLLy +=
−+
−+
= )(11 11
10 θφε
φγγ (7)
38
The long-run relationship implied by this model is
xxy1
10
1)1(
φγγθ
−+
== (8)
where the long-run coefficient is )1(θ . The standard errors of long-term coefficients
can be obtained by a variety of methods including the delta-method. The model given
above can be generalized to allow for additional lags in both y and x and for additional
stationary variables such as dummy variables.
In practice, information on the order of lags in the model is not available.
Model selection criteria can be used to select the appropriate order. In this paper we
use the Schwarz criterion, which tends to favor a relatively parsimonious
parameterization. We also apply specification tests for serial correlation to confirm
that the model is free from misspecification.
39
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Dawe, D. (1993) Price Stabilization and the Macroeconomy in Low Income Countries,
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Hayami, Y. (1988) Japanese Agriculture under Siege: The Political Economy of
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Figure 1 and Table 1: Real Rice Prices, NPR, and Stabilization Indexes in Thailand from 1961 to 2000
Period 1961-1970 1971-1980 1981-1990 1991-2000
Average NPR -30.7 -29.6 (-24.4)*
-10.5 -4.2
Ratio of CV 1.1 2.9 1.6 0.8
Ratio of Average
Absolute
Percentage
Change
0.9 2.8 1.1 0.8
* NPR excluding commodity crisis years from 1973 to 1975.
0.00
5.00
10.00
15.00
20.00
25.0019
61
1971
1981
1991
Year
000
Bah
t/ton
RDP RBP
Figure 2 and Table 2: Real Rice Prices, NPR, and Stabilization Indexes in Indonesia from 1967 to 2000
Period Averages
1967-1970 1971-1980 1981-1990 1991-2000
Average NPR
-7.0 1.45 (14.76)*
17.46 13.02 (18.61)**
Ratio of CV
1.2 3.6 3.6 1.8
Ratio of Average
Absolute
Percentage
Change
0.7 3.7 4.1 2.5
* NPR excluding commodity crisis years from 1973 to 1975.
**NPR excluding 1998 when the exchange rate surged due to the Asian currency crisis.
Note: Retail price is used for RDP.
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,00019
61
1971
1981
1991
Year
000
Rup
iah/
ton
RDP RBP
Figure 3 and Table 3: Real Rice Prices, NPR, and Stabilization Indexes in the Philippines from 1961 to
2000
Period Averages 1961-1970 1971-1980 1981-1990 1991-2000 Average NPR
16.5 -10.1 (4.66)*
16.1
41.8
(-16.4)** Ratio of CV
2.3 3.0 1.8 0.9
Ratio of Average
Absolute
Percentage
Change
2.0 5.4 1.6 1.3
* NPR excluding commodity crisis years from 1973 to 1975.
** From 1976 to 1981 when the domestic rice prices had been set lower than the border prices.
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
1961
1971
1981
1991
Year
000
Pes
o/to
n
RDP RBP
Figure 4: Proportion of Rice Revenue in Total Central Government Revenue in Thailand from 1961
to1984
Source: Siamwalla, A., and S. Setboonsarng (1989) "Trade, Exchange Rate, and Agricultural Pricing
Policies in Thailand," Washington D. C.: The World Bank.
0.0
5.0
10.0
15.0
20.0
25.019
61
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
Year
%
Figure 5: Real Minimum Wage Rate Enforced by the Government in Non-agricultural Sector in Thailand
from 1973 to 1999
Source: Ministry of Labor and Social Welfare (Thailand)
Note: Nominal minimum wages are converted to the real terms using the consumer price index (base year
=1995).
0
20
40
60
80
100
120
140
160
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
Year
Bah
t/day
Counry Average Bangkok
Figure 6: Evolution of Estimated Transmission Elasticity in Thailand, Indonesia, and the Philippines from
1961 to 2000
-1.5
-1
-0.5
0
0.5
1
1.5
1961
1971
1981
1991
Year
Ela
stic
ity
Thailand Indonesia Philippines
Figure 7: Self Sufficiency Ratio of Rice in Indonesia and the Philippines
0.70
1.00
1961
1971
1981
1991
Year
SSR
Indonesia Philippines
Table 4: Dynamic OLS estimation of Rice NPC in Thailand from 1961 to 2000 Model (T-1) Model (T-2) OLS results Long-term
response OLS results Long-term
response
ln(NPR)(-1) 0.265** (0.125)
0.073 (0.094)
ln(NPR)(-2) -0.418*** (0.108)
-0.302*** (0.089)
Alphas Constant 1.122***
(0.214)
0.973*** (0.178)
3.175*** (0.536)
2.582*** (0.482)
ln(PGDP) -0.723** (0.303)
-0.627** (0.264)
7.460*** (1.917)
6.066*** (1.660)
Betas ln(RBP) -0.328***
(0.053)
-0.284*** (0.041)
-0.796*** (0.120)
-0.647*** (0.107)
ln(RBP)*ln(PGDP) 0.211*** (0.074)
0.183*** (0.064)
-1.634*** (0.429)
-1.329*** (0.372)
Dummies for Structural Break a DUMMY82 -3.476***
(1.056)
-2.827*** (0.935)
ln(RBP)*(DUMMY82) 0.823*** (0.258)
0.669*** (0.227)
ln(PGDP)*(DUMMY82) -7.688*** (2.137)
-6.251*** (1.764)
ln(RBP)*ln(PGDP)*(DUMMY82) 1.734*** (0.494)
1.410*** (0.406)
Adj-R2 0.89 0.95 Serial Correlation: CHSQ(1) 1.10
[0.30] 1.407
[0.24]
Observations 39 39 Period 1961-2000 1961-2000 Numbers in parentheses are standard errors. Numbers in brackets are p-values. *significant at 10% level; **significant at 5% level; ***significant at 1% level. a Structural break dummy becomes one if year is greater than or equal to 1982.
Table 5: Dynamic OLS estimation of Rice NPC in Indonesia from 1967 to 2000 Model (I-1) Model (I-2) OLS results Long-term
response OLS results Long-term
response
ln(NPR)(-1) 0.326** (0.149)
Alphas Constant 4.217***
(1.059)
6.253*** (0.650)
6.329*** (0.378)
6.328*** (0.378)
ln(PGDP) 0.425*** (0.106)
0.631*** (0.010)
0.577*** (0.140)
0.577*** (0.140)
ln(SSR) -1.016** (0.369)
-0.282 (0.570)
-0.628* (0.344)
0.527 (0.424)
ln(SSR)(-1) 0.825* (0.406)
1.154*** (0.355)
Betas ln(RBP) -0.726***
(0.057)
-0.544*** (0.066)
-0.670*** (0.048)
-0.560*** (0.045)
ln(RBP)(-1) 0.360*** (0.125)
0.110** (0.041)
ln(RBP)*ln(GDPRTO) 0.059*** (0.017)
0.087*** (0.019)
0.079*** (0.016)
0.079*** (0.016)
Dummies for Structural Break a DUMMY85
-0.112* (0.058)
-0.112* (0.058)
ln(RBP)*(DUMMY92)
0.013** (0.006)
0.013** (0.006)
ln(PGDP)*(DUMMY92)
0.800*** (0.266)
0.800*** (0.266)
Adj-R2 0.91 0.94 Serial Correlation: CHSQ(1) 1.75
[0.19] 1.57
[0.22]
Observations 33 33 Period 1967-2000 1967-2000 Numbers in parentheses are standard errors. Numbers in brackets are p-values. *significant at 10% level; **significant at 5% level; ***significant at 1% level. a Structural break dummy becomes one if year is greater than or equal to 1985 for DUMMY85 and 1992 for DUMMY92.
Table 6: Dynamic OLS estimation of Rice NPC in the Philippines from 1961 to 2000 Model (P-1) Model (P-2) OLS results Long-term
response OLS results Long-term
response
ln(NPR)(-1) 0.554*** (0.122)
0.580*** (0.121)
Alphas Constant -0.740***
(0.192)
-1.662*** (0.207)
-0.682*** (0.186)
-1.622*** (0.238)
ln(SSR) -0.492** (0.210)
-1.104** (0.493)
-0.614*** (0.218)
-1.46** (0.628)
Betas ln(RBP) -0.991***
(0.059)
-0.841*** (0.098)
-0.977*** (0.059)
-0.815*** (0.117)
ln(RBP)(-1) 0.616*** (0.127)
0.635*** (0.123)
ln(RBP)*ln(PGDP) 0.148*** (0.046)
0.332*** (0.111)
0.136** (0.053)
0.324** (0.121)
Dummies for Structural Break a DUMMY91
-1.548** (0.704)
-3.69* (2.098)
ln(RBP)*(DUMMY91)
-0.657** (0.297)
-1.564* (0.882)
Adj-R2 0.92 0.94 Serial Correlation: CHSQ(1) 0.01
[0.92] 0.64
[0.42]
Observations 40 40 Period 1961-2000 1961-2000 Numbers in parentheses are standard errors. Numbers in brackets are p-values. *significant at 10% level; **significant at 5% level; ***significant at 1% level. a Structural break dummy becomes one if year is greater than or equal to 1991.
Figure A1: Plot of CUSUM, CUSUM squares, and Residual Variance in the Model for Thailand
(a)
(b)
(c)
-20
-10
0
10
20
70 75 80 85 90 95 00
CUSUM 5% Significance
-0.4
0.0
0.4
0.8
1.2
1.6
70 75 80 85 90 95 00
CUSUM of Squares 5% Significance
0.000500
0.000700
0.000900
0.001100
0.001300
0.001500
0.001700
0.001900
0.002100
0.002300
0.002500
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
Figure A2: Plot of CUSUM, CUSUM squares, and Residual Variance in the Model for Indonesia
(a)
(b)
(c)
-15
-10
-5
0
5
10
15
76 78 80 82 84 86 88 90 92 94 96 98 00
CUSUM 5% Significance
-0.4
0.0
0.4
0.8
1.2
1.6
76 78 80 82 84 86 88 90 92 94 96 98 00
CUSUM of Squares 5% Significance
0.001000
0.001100
0.001200
0.001300
0.001400
0.001500
0.001600
1977
1979
1981
1983
1985
1987
1989
1991
Figure A3: Plot of CUSUM, CUSUM squares, and Residual Variance in the Model for the Philippines
(a)
(b)
(c)
-20
-10
0
10
20
70 75 80 85 90 95 00
CUSUM 5% Significance
-0.4
0.0
0.4
0.8
1.2
1.6
70 75 80 85 90 95 00
CUSUM of Squares 5% Significance
0.00300
0.00320
0.00340
0.00360
0.00380
0.00400
0.00420
0.00440
0.00460
1968
1971
1974
1977
1980
1983
1986
1989
1992
