ISSN 0911-1247

March 2024

Special Issue“Time Series Analysis on Macroeonomy in Japan”
Editor’s Introduction

Hiroshi Morita

1

Price and Wage Dynamics and Labor Market Conditions in Japan

Hiroshi Morita

3

Shota Araki, Bin Ni, Hiroshi Morita

23

Tadashi Nakanishi

35

Narihisa Nakashimaa

47

Macroeconomic Shocks and Firms' Overseas Expansion:
Evidence from the Factor-Augmented VAR Approach

The NG-SVAR Model under the Pearson Family of Distributions:
Implementation with R Packages

Ways to Conserve the Land Title of the Orang Rimba,
Hunter-Gatherers of Jambi, Indonesia

THE INSTITUTE OF COMPARATIVE ECONOMIC STUDIESHOSEI UNIVERSITYTokyo, Japan

JOURNAL OF INTERNATIONAL ECONOMIC STUDIES
Editorial Board

Akiko Tamura (Editor in Chief)Tadashi Sakai
Miki Sugiura
Fumio Makino
Advisory Board

Masayoshi Tsurumi (Professor Emeritus, Hosei University)
Hideki Esho (Professor Emeritus, Hosei University)
Fumio Makino (Professor Emeritus, Hosei University)
Bishwanath Goldar (Professor, institute of Economic Growth, University of Delhi)
Jinping Yu (Professor, School of Economics, Nanjing University)
Jongsoo Park (Professor, Department of Economics, Gyeongsang National University)
Editorial Secretary

Nanako Shirasaka
Chiyoko Kojima
Yoshie Shimizu

この用紙は再生紙を利用しています。

Journal of International Economic Studies (2024), No.38, 1‒2©2024 The Institute of Comparative Economic Studies, Hosei University“Time Series Analysis on Macroeonomy in Japan”
Editor’s Introduction

Hiroshi Morita
I am pleased to introduce a special issue of the Journal of International Economic Studies,
entitled “Time Series Analysis on Macroeonomy in Japan.” As an outcome of the research project“Analysis on Macroeconomic Structure in Japan” of the Institution of Comparative Economic
Studies, this issue comprises three articles which tackle important macroeconomic problems using a
sophisticated time-series empirical method. I believe that all the papers achieve the principal purposeof our project, which is to provide the readers with an understanding of empirical methods andinsight into macroeconomic structure.
The first paper, entitled “Price and Wage Dynamics and Labor Market Conditions in Japan”
written by Hiroshi Morita, examines the state-dependent relationship between labor market tightness
and price-wage rigidities. In the phase of increasing inflationary pressure, it is often discussed
whether the pass-through of such an increase in prices to nominal wages is sufficient and timely.
Indeed, if this is case, real wages do not decline even in the inflationary phase and households should
not suffer very much from inflation. However, amid the long-lasting deflation in Japan, the channel
from prices to wages seems not to be functioning adequately, and thus real wages often decline in
response to price increases stemming from external factors such as hikes in oil prices and commodity
prices. The primary aim of this paper is to examine a transmission mechanism of structural shocks
to prices and wages, and then reveal a source of insufficient pass-through between them. In theanalysis, the Smooth Transition Vector Autoregressive (STVAR) model is employed to reveal thestate-dependent effects of demand and supply shocks on prices and wages. The STVAR framework
allows the dynamic responses of prices and wages to vary depending on the labor market tightness.
Furthermore, this paper addresses the structural interpretation of the source of a state-dependency byconducting an impulse response matching technique based on the Dynamic Stochastic General
Equilibrium (DSGE) model. From the methodological viewpoint, this paper combines two major
methods in the macroeconomic analysis: the VAR model and the DSGE model, leading to a bridging
of the gap between reduced-form analysis and structural interpretation. The results are summarized
as follows. First, price and wage indexation in Japan is rather low. Second, as the labor market
tightens, price becomes stickier, while wage becomes less sticky. Finally, the standard theoreticalmodel can be used to sufficiently describe Japan’s economy at the period of labor market tightness.
As the labor market loosens, however, the distance between the empirical and theoretical responses
gradually grows.
The second paper, entitled “Macroeconomic Shocks and Firms’ Overseas Expansion: Evidence
from the Factor-Augmented VAR Approach,” written by Shota Araki, Bin Ni, and Hiroshi Morita,
analyzes the effects of macroeconomic variations, such as exchange rate and global GDP, on
Japanese firms’ overseas expansion behaviors. Using a novel dataset of individual firm’s panel data,
the authors examine how macroeconomic shocks affect the distribution of overseas subsidiaries ofindividual firms. The key technical contribution of this paper is to propose the Factor Augmented
1

(Editor’s Introduction)

VAR (FAVAR) model with Tobit specification. The FAVAR framework is utilized to capture thetransmission effects of one macroeconomic shock to the activities of a group of firms in a single
model, while Tobit specification can deal with a truncated number of oversea subsidiaries. Namely,
the number of overseas subsidiaries for some firms might be zero in some period, but Tobit
specification allows us to include such data samples in the analysis. By using the panel data of firms,
the analysis can derive the implication of the effects of macroeconomic shock on the firms’
distribution. The proposed method in this paper is a bridge connecting macro data and micro data.
The results can be summarized as follows. First, the authors show that most firms increase overseas
subsidiaries in response to the appreciation of the exchange rate. However, the results of forecast
error variance decomposition show that, compared with the exchange rate, global GDP shocks playa more important role in the variation of Japanese firms’ overseas expansion. Additionally, the results
indicate that the variation of the exchange rate has only a temporary effect on overseas expansion
behaviors.
The third paper, entitled “The NG-SVAR Model under the Pearson Family of Distributions:
Implementation with R Packages” written by Tadashi Nakanishi, explains the pseudo-likelihood
estimation method of the VAR model with structural shocks following a non-Gaussian distribution
as well as a presentation of the R program for conducting the estimation. The paper applies
independent component analysis, which is an unfamiliar method in the field of econometrics, being
used mainly in engineering, to separate and estimate the structural error term of the structural VAR
model from the inductive system error term. In the R software, the use of “fastICA” allows for
estimation. The distribution of the separated and estimated structural error terms is then investigatedusing “PearsonMSC,” which selects an approximate distribution of the estimated distribution fromthe Pearson family of distributions and estimates parameters that describe the shape of the
distribution. Based on this information, a probability density function is constructed and estimated.
In practice, this is not estimation but optimization and the optimization process can be implemented
by using “optim.” Although “optim” is used to obtain the contemporaneous matrix in the VAR
model to be estimated by the pseudo-maximum likelihood method, initial values are needed toperform optimization. Therefore, the analysis uses the value obtained from the estimation method(“id.ngml”), which assumes the t-distribution and is a prior study of the pseudo-maximum likelihood
method. The paper uses as initial values the values obtained from the value estimated by “id.ngml”
plus a random number generated from a uniform distribution to set the initial value. By following
these steps, a contemporaneous matrix can be estimated. The estimated matrix is used to draw the
IRF and measure the policy effect of monetary policy.
Finally, I would like to express my deepest appreciation to the authors of this special issue for
their cooperation. I am also very grateful to the members of my own research project.

2

Journal of International Economic Studies (2024), No.38, 3‒22©2024 The Institute of Comparative Economic Studies, Hosei University

Price and Wage Dynamics and Labor Market Conditionsin Japan
Hiroshi Morita
Tokyo Tech.

Abstract
This study estimates structural parameters characterizing price and wage dynamics in Japan byaccounting for labor market tightness. To reveal the state dependency of price and wage dynamics,
we first estimate a smooth transition vector autoregressive model, in which the unemployment rate
is regarded as a transition variable. Thereafter, the structural parameters in each state of the labormarket are estimated using the impulse response matching technique. The results are summarized asfollows. First, price and wage indexation in Japan is rather low. Second, as the labor market tightens,
price becomes stickier, while wage becomes less sticky. Finally, the standard theoretical model canbe used to sufficiently describe Japan’s economy at the period of labor market tightness. As the labormarket loosens, however, the distance between the empirical and theoretical responses gradually
grows.
Keywords: price and wage dynamics, ST-VAR model, DSGE model
JEL classification: C32, E24, E31, E52

1. Introduction
The recent literature has been actively discussing whether inflation pressure from the economic
stimulus policy implemented by the Abe Cabinet has sufficiently carried on nominal wages.
Obviously, real wage declines in response to demand-stimulating policies, such as fiscal andmonetary policies, particularly if the response of nominal wage is less than that of price. In this case,evaluations of such policies may deem resultant benefits inadequate. From an academic viewpoint,
it is important to investigate the relationship between price and wage. As is widely known, bothvariables have a mutual linkage through the New Keynesian Phillips Curve (NKPC) and New
Keynesian Wage Phillips Curve (NKWPC) in the dynamic stochastic general equilibrium (DSGE)
model. Analyzing the dynamic relationship between price and wage, therefore, can reveal not only
the effectiveness of economic policies but also the structure of the real economy.
This study explores the state dependency of price and wage dynamics in a labor market situation
and attempts to understand the source and pattern of variation in price and wage dynamics in Japan.
To accomplish these objectives, we adopt the smooth transition vector autoregressive (STVAR)
model and impulse response matching technique. The STVAR model is a vector version of thesmooth transition autoregressive (STAR) model developed by Granger and Terasvirta (1993), andhas recently been used in Auerbach and Gorodnichenko (2012). The model allows the coefficients3

Price and wage dynamics and labor market conditions in Japanand variance-covariance structure to smoothly vary by the value of a transition variable, and thus itis suitable for our analysis, which sheds light on the state dependency of price and wage dynamics.
However, the results from the VAR model cannot be used to further discuss the structure of an
economy (i.e., what causes a variation of dynamics in price and wage). Thus, we perform a structural
estimation using impulse response matching, as proposed by Christiano et al. (2011). By estimating
the DSGE model, the structural parameters in each state of the labor market are revealed, allowing
us to identify the causes underpinning the changes in price and wage dynamics.
More precisely, our analysis is implemented as follows. First, we estimate the STVAR model,
in which the unemployment rate is regarded as a transition variable. This first-step exercise gives us
reduced-form results for the relationship between price and wage dynamics and labor market
tightness. We examine the effects of supply and demand shocks on price and real wage based onlong-run restrictions. Thereafter, the structural parameters are estimated to minimize the distance
between the empirical and theoretical responses using the impulse response matching technique.
These estimations are implemented using the Bayesian method.
The literature contains numerous studies that analyze the NKPC and NKWPC in Japan. Theseprevious studies are categorized into two strands of approaches: one that estimates the NKPC andNKWPC as part of the DSGE model (Iiboshi et al., 2006, Sugo and Ueda, 2008) and the other does
so separately (Fuchi and Watanabe, 2002, Koga and Nishizaki, 2006, Muto and Shintani, 2014). The
approach in this study follows the former in that the DSGE model is completely estimated. However,this study may have certain advantages. First, a time variation of structural parameters cannot be
estimated using an ordinary method to estimate the DSGE model. Although some recent studies(Farmer et al., 2011, Liu et al., 2011, Iiboshi, 2015) have estimated the Markov-switching (MS)DSGE model, this model cannot be successfully adopted if the sample size in a specific state isextremely small. On the contrary, the impulse response-matching technique can estimate thestructural parameters in the DSGE model, even with a small sample size, as our method regards theimpulse response functions (IRFs) as data. Compared with the latter, our estimation is desirable atthe point of directly estimating the structural parameters. The coefficients in the separate estimation
are formulated as a mixture of structural parameters, including price and wage stickiness and theinverse of Frisch labor elasticity. In addition, the components lying outside of the NKPC andNKWPC (e.g., a stance of monetary authority) are ignored.
This study’s results are briefly summarized as follows. First, the state dependency of price andwage dynamics is observed. In particular, the response of real wage to demand shock shows a sign
switch depending on labor market tightness. Second, as the labor market tightens, price becomesstickier, while wage becomes less sticky. Third, the standard DSGE model can sufficiently replicate
the dynamics of the real economy when the labor market is tight. However, as the labor market
loosens, the dynamics of the real economy deviates from the prediction of the theoretical model.
The remainder of this paper is organized as follows. Section 2 explains the STVAR model andreports the reduced-form empirical results. In Section 3, we construct the NK-type DSGE model,
which is estimated using the Bayesian impulse response matching technique. Section 4 documents
the estimation of the DSGE model using the impulse response-matching and then presents theestimates of the structural parameters. Section 5 provides concluding remarks.

2. VAR analysis
2.1. Smoothed transition VAR model
To reveal the variations in price and wage dynamics, depending on labor market tightness, weestimate the STVAR model in which unemployment rate is regarded as a transition variable. The
4

Hiroshi Morita

STVAR model is specified as follows:

(1)

Σ

0, Σ
exp

(2)
(3)
(4)

where c(t) is defined to include all the deterministic components of data, Bi (L), i = 0,1 is a polynomialin the lag operator, and Σi, i = 0,1 denotes the variance–covariance matrix of the reduced-form
residuals, ut. F(z), formulated as in equation (4), denotes the transition function with z as a transition
variable, which allows the model to smoothly change according to the transition variable value.
As stated in Auerbach and Gorodnichenko (2012), the principal advantage of using the STVAR
model, as compared to separately estimating the structural VAR (SVAR) model for each regime, isthe utilization of all sample information to estimate a particular regime in which few observations
are included. In addition, we also emphasize several advantages relative to the MSVAR and timevarying parameters (TVP) VAR models. Since the coefficients and variance-covariance matrix in the
STVAR model smoothly vary by state, we can specify the model at the arbitrary points among thediscrete states. For concreteness, we analyze the dynamics of Yt at the mid-point of the two states by
setting F(z)=0.5, and this cannot be done using the MSVAR model, wherein the states are perfectly
separated. It is difficult to consider that the economy is divided into perfect discrete states, and thus,
the STVAR model is more suitable to represent the real economy compared with the MSVAR model.
On the other hand, the TVPVAR model describes continuous variations of the estimation model.
However, it cannot explicitly answer what causes the time variation of the coefficients and covariancestructure. In contrast, the STVAR model relates a transition variable to the variation of parametersin the model, and thus, we obtain the reason underpinning changes in the model throughout the
sample period. For example, this study can reveal the relationship between price and wage stickinessand the unemployment rate, allowing for a more sophisticated micro-foundation for the theoreticalmodel; however, this point remains beyond the focus of this study. In short, the STVAR model
elucidates the state dependency of the model associated with the variable in which we are interested.

2.2. Identification of structural shocks
Based on the STVAR model mentioned above, we identify labor productivity and monetary
policy shocks as supply and demand shocks using the long-run restriction. To achieve theidentification of structural shocks and the estimation of structural parameters in the theoreticalmodel, constructed in Section 3, our VAR model comprises the log first difference of real wage (wt),price (pt), and monetary base (mt).1 Real wage is a proxy of labor productivity. For simplicity,represented by the SVAR model and not the STVAR model, our 3-variables VAR system can be
written in the form of a structural vector moving average as follows:1

The source and construction of data are explained in detail in Section 2.3.

5

Price and wage dynamics and labor market conditions in Japan(5)

where C(L) is a polynomial in the lag operator. The innovations eta, etm, and etnon, respectively,
denote labor productivity shock, monetary policy shock, and non-specified shock; these shocks are
assumed to be mutually orthogonal. Following the seminal work of Blanchard and Quah (1989), we
impose the restriction that only labor productivity shock has a permanent effect on real wage, that is,
C12 (1)=0 and C13 (1)=0.2 Moreover, we allow the labor productivity and monetary policy shocks tohave a long-run effect on prices. In other words, a non-specified shock, which is considered as a
mixture of all temporary shocks, is assumed to have no long-run effects on prices, that is, C23 (1)=0.
This is based on the theoretical prediction that a monetary policy measure, such as a temporary
increase in the growth rate of money or decrease in the short-term interest rate, permanently shifts
price level, although it has no permanent effect on inflation rate.

2.3. Data and specification
We employ monthly real wage, price, and monetary base data for 1990M1–2014M12. The
series of hourly real wage is constructed as follows. First, we obtain the seasonally adjusted seriesof real wage indices (establishments with five employees or more and total cash earnings) and total
hours worked indices (establishments with five employees or more) from the Monthly Labor Survey(Ministry of Health, Labour and Welfare). Second, each index is translated into a real value using thevalue of the base year. Finally, we calculate hourly real wage by dividing real wage by total hours
worked. For price, the series of non-seasonally adjusted Consumer Price Index (CPI) (all items, lessfresh food) is downloaded from the CPI (Ministry of Internal Affairs and Communications). We first
subtract 1.4% in 1997 and 2.0% in 2014 from the year-on-year inflation rate to eliminate the effects
of an increase in consumption tax.3 Then, we recalculate the level series and perform seasonal
adjustments using X-12 ARIMA. The seasonally adjusted series of the monetary base (with adjusted
reserve requirement rate change) is collected from the Bank of Japan.
For transition variable z, we use the unemployment rate published in the Labour Force Survey(Ministry of Internal Affairs and Communications). As shown in Figure 1(a), the original series forthe unemployment rate has an upward trend in our sample period, and thus we de-trend it byregressing on a constant and linear trend (Figure 1(b)). Furthermore, the data for period t-1 is used
to take account of the possibility that labor market tightness cannot be contemporaneously reflectedin wage negotiation.

2
3

6

Francis and Ramey (2005) also adopt the long-run restriction associated with real wage.
These values are based on a trial calculation by the Bank of Japan.

Hiroshi Morita

Figure 1: Unemployment rate

Note: Figure 1(a) and 1(b) denote the original series of and the detrended series of unemployment rate, respectively. The original data
is obtained from Labor Force Survey. For the detrended series, the linear trend and constant term are excluded from the original series.

In the VAR model, all variables are included in the form of the first difference of the natural logarithm,
and a constant term and linear trend are contained as a deterministic component. In addition, the lag
length is set as two. This is because the principal aim of the VAR analysis in this study is to obtain
the impulse responses necessary to estimate the DSGE model using the impulse response matching
technique. Accordingly, we choose two lags as a lag length that can at least capture the dynamics ofa system.

2.4. Estimation procedure
The estimation of the STVAR model is implemented using the Bayesian Markov Chain Monte
Carlo (MCMC) method, particularly the random-walk Metropolis-Hastings (MH) algorithm.
Basically, we follow Auerbach and Gorodnichenko (2012). Let Y ̃={Yt}Tt=1 be the sequence of Yt andΘ=[γ, Σ0,Σ1, c(t), B0 (L), B1(L)] be the parameters in which we are interested. The posterior loglikelihood for the model is then specified as follows:

1
2

|Σ |

1
2

Σ

(6)

where ut=Yt-c(t)-{1-F(z t-1)} B0 (L) Yt-1-F(z t-1) B1(L)Yt-1. Given the above, the estimation
procedure of a random-walk MH algorithm is summarized as follows:7

Price and wage dynamics and labor market conditions in Japan

1. Derive the posterior mode
2. Set the initial value Θ

3. Draw Θ

to maximize
, and

Θ

6. Set.

from the following random-walk model:Θ

4. Calculate the acceptance rate

5. Accept Θ.

using Θwith probability

,

and Θ

as follows:,1 .

and reject it with probability

when accepted and Θ. Set

when rejected.

and return to Step 3 until the number of iterations reaches

times.
In the actual estimation, we randomly generate only Θ’=[γ, chol(Σ0), chol(Σ1)] because, given
Θ’, the lag polynomials [B0 (L), B1 (L)] can be estimated with weighted least squares to maximizeln L(Θ Y ̃). Moreover, this simplification saves the time necessary for convergence.
In this study, N is set to be 120,000 and the first N0 = 20,000 samples are discarded as a burn-in.
To ensure the stationarity of the VAR system, the unit circle is selected only for draws within which
the roots of the lag polynomials [B0 (L), B1 (0)] are present. With respect to the priors, the gamma
distribution is associated with γ. In the benchmark, we set the mean and standard deviation of theprior to 10 and 0.1.

2.5. Impulse response function
Figure 2 shows the estimated transition probability, F(z), and indicates that the higher thetransition probability, the more the labor market tightness. The figure also allows us to understandlabor market transition in Japan. As shown, the degree of tightness in the labor market is still highjust after the economic bubble burst in the early 1990s, but it gradually declines. The state then falls
F(z) = 0 (i.e., low tightness state) perfectly from the second half of the 1990s to the first half of the
2000s. Thereafter, the labor supply and demand situation improve by benefiting from the economic
recovery in the 2000s. However, after the temporary economic deterioration by the Lehman shock,it tightens again because of the Great East Japan earthquake and the economic stimulus policy
implemented by Prime Minister Shinzo Abe, or the so-called “Abenomics.”8

Hiroshi Morita

Figure 2: Estimated transition probability

Figures 3 and 4 depict the IRFs to supply and demand shocks at the state of F(z)=0,0.5, and 1. The
solid lines and shaded areas denote the median of sampled IRFs and 90% Bayesian credible intervals.
At first glance, we observe that the signs of several responses switch depending on labor market
tightness. The most notable is a response of real wage to demand shock, where the response changes
from negative to positive as the labor market tightens. This result indicates the possibility that thestructure of price and wage dynamics, that is, the structural parameters of a real economy, alters
according to labor market tightness.
In addition, we also recognize that the sign of some responses at F(z)=0 is opposite to a normal
prediction of the standard theoretical model. For instance, the most prominent one is a positive (butinsignificant) response of price to supply shock, as shown in the middle-left chart of Figure 3. A
negative response of money growth rate to both shocks also seems to be counterintuitive because thestandard model, generally, presumes that both shocks stimulate consumption and, as a result,increase money demand. Consequently, as mentioned in Section 4, the calculated IRFs at F(z)=0using the DSGE model do not effectively trace the VAR model. On the other hand, the responses at
F(z)=1 are consistent with the usual expectation: a permanent labor productivity shock reduces
price, while a monetary policy shock increases price and money growth rate. Furthermore, we findthat a positive demand (monetary policy) shock increases real wage and price levels. The responsesat F(z)=0.5 are in the center of the two extreme cases. The impact of responses seems weak comparedwith F(z)=0 and F(z)=1, especially in the responses of prices to supply shock and real wage todemand shock. Below, we estimate the value of structural parameters for each state via the impulse
response matching technique using the IRFs obtained in this section.

9

Price and wage dynamics and labor market conditions in Japan

Figure 3: Responses of variables to supply shock

Note: Solid lines and shaded areas indicate the median responses and 90% credible intervals, respectively.

Figure 4: Responses of variables to demand shock

Note: Solid lines and shaded areas indicate the median responses and 90% credible intervals, respectively.

10

Hiroshi Morita

3. Theoretical model
To interpret the (reduced-form) empirical results obtained from the VAR analysis in a structuralmanner, we estimate the DSGE model using the impulse response matching technique. This section
presents the simple NK model employed in the Bayesian estimation.
The NK model in this study comprises Calvo-type (1983) sticky prices and wages, price andwage indexation, consumption habit, money growth rate, and no capital.4 To capture the situation inwhich the Bank of Japan has been setting the policy rate to be almost zero and adopting quantitativeeasing (QE) after the second half of the 1990s, we employ a money growth rule as a monetary policy
rule instead of the Taylor rule. Since the focus of this study is to ascertain the state dependency of
price and wage dynamics, price and wage indexation is incorporated into the model. Furthermore,
the stochastic process of permanent technology and transitory monetary policy shocks is introduced
as a counterpart of supply and demand shocks in the VAR analysis.

3.1. Households
The economy consists of a unit mass of identical households indexed by imaximization problem of each household can be written as follows:[0,1]. The utility(7)(7)
subject to

Π
Π

Π
Π

(8)
(8)

where the uppercase and lowercase letters denote nominal and real variables. The household obtains
positive utility from consumption c, and real money holdings m and a negative utility from hours
worked n. In return, the budget constraint represents that a household allocates the sum of real labor
incoment (i); inflation-adjusted money holding

its interest payment; inflation-adjusted total sum of bond andbt-1 (i); and real dividends dt to consumption, money holdings, andbond. Here, P, Π, and R denote aggregate price, inflation rate, and gross nominal interest rate,
respectively. Based on this optimal problem, each household chooses the optimal level ofconsumption and money holdings.

3.2. Labor market and wage setting
To introduce wage stickiness, we assume a continuum of differentiated labor input indexed byl [0,1]. Furthermore, it is assumed that there exist labor unions corresponding to each differentiated4

Watanabe (2009) emphasizes the importance of the consumption habit in Japan.

11Price and wage dynamics and labor market conditions in Japanlabor input, and each union sets their wage rate. As adopted in Schmitt-Grohe and Uribe (2006),
each household member is assumed to provide each possible type of labor input. In other words,
each household belongs to every labor union. This assumption rules out the possibility that eachhousehold receives different labor income in equilibrium without adopting a contingent claim. In
addition, as adopted in Gaíl et al. (2007), labor supply is assumed to be determined by labor demand(not by the optimal choice of households), given a wage fixed by the labor union.
A perfectly competitive labor-bundler firm bundles differentiated labor input nt (l) into effective
labor denoted by n_t as follows:(9)
where εw denotes the elasticity of substitution among labor inputs. As a result of the labor bundler’s
optimal problem, the demand function for each differentiated labor input is expressed as(10)Then, aggregate nominal wage is equal to(11)

Given the demand function for labor input l, the labor union sets its nominal wage Wt (l) to maximize
the lifetime utility of each household. Under the Calvo-type (1983) wage stickiness, it is assumed
that the fraction 1−ρw of labor unions can reset the optimal nominal wage Wt* (l) in each period. The
rest of the labor unions, which do not obtain an opportunity to re-optimize their nominal wage, sets
Wt (l) according to(12)where γw is a degree of wage indexation. The optimal problem for wage union l can then be written
as

max
∗

12

Λ

(13)

Hiroshi Morita

subject to

∗

and

Π

Π
∗

where Λ t,t+s =

(14)

(15)

denotes the stochastic discount factor. From equations (11) and (12), thelaw of motion of aggregate wages can be given as

(16)

∗

3.3. Firm
The production sector comprises two types of firms: monopolistically competitive firms that
produce differentiated intermediate goods and perfectively competitive firms that produce single
final goods using intermediate goods as input. Each intermediate goods firm indexed by j [0,1]produces an intermediate good yt (j), and its production function is simply assumed to be a linear
function:(17)
where at denotes the total factor productivity (TFP), which is exogenously given. As a result of the
profit maximization problem for intermediate goods firms, we simply obtain real marginal cost mct
as follows:

.

(18)

On the other hand, the final goods firms transform intermediate goods to final goods using thefollowing Dixit–Stiglitz-type production function:13

Price and wage dynamics and labor market conditions in Japan(19)

where εp is the elasticity of substitution across each type of intermediate goods. Then, the demand
function for intermediate goods is obtained as(20)
and the final goods pricing rule is written as(21)

3.4. Price setting
As with wage setting, intermediate goods firms set their prices according to the Calvo (1983)
mechanism with price indexation. For any given period t, the fraction 1−ρp of intermediate goods
firms can revise their prices to the optimal price level P*t ( j), whereas the fraction ρp does not havethe opportunity to reset their price and set their prices as(22)
where γp indicates a degree of price indexation. The optimal price P*t (j) is determined by solving
the problem:

max∗

subject to

∗

Λ

y

j

P
P

P

(23)

P∗ j

y

.

As in wage setting, the law of motion of aggregate prices can be written as

14

(24)

Hiroshi Morita

P
P

P∗

P

P

(25)

.

3.5. Rest of the modelWith respect to the monetary policy, we assume that the monetary authority adopts the money
growth rule as follows:

M

exp

M

⇔m

m
.
Π

exp

(26)

Here, νt denotes the money growth rate, which depends on its own lags and inflation rate, such as
ν

ν

(27)

π

where a hat means a log deviation from a steady state value, and εmt is a monetary policy shock that
follows an i.i.d. normal distribution. On the other hand, the stochastic process of TFP is assumed tofollow the random-walk process:(28)
Finally, the market clearing condition in this study is given by

yt

(29)

ct

because our model ignores the government sector.

3.6. Log-linearized de-trended equilibrium conditions
To eliminate a stochastic trend and ensure model stationarity, we divide real variables ct, yt, and
Wt /Pt by the productivity at . Let xt̃ indicate the log deviations of a stationary variable from its
steady states. The log-linearized de-trended equilibrium conditions in our model are summarized as
the following nine equations:
1
1

h

1 h
Eπ
γ 1 h t t 1Et ct 11βEπβ γp t t 11 hr
γ 1 h t
πt

1

h
1

γp
π
β γp t 1

h

at

ct

1

ρp
ρp 1

h
1

h

ct 1

1 ρp β
wt
β γp

h
1

h

at 1

(30)
(31)
15

Price and wage dynamics and labor market conditions in Japan

1

1

1
̃

1

1

1

1

γ

rt

h

1

ct
νt

νt

ρm νt 1

1

(32)

(33)
(34)

hγ
1

̃

1

nt

mt

at

1

ct

yt
1

1

1
yt

μm t

1

1

h

ct 1

mt 1

1

h

at

hγ
1

a
h t 1

(35)

E tπt 1

(36)

m
t

(37)

p πt

at 1

γ

a
t

(38)

where Γ=ρw /(1+βρw2 ) and κw = (1-βρw )(1-ρw )/ρw.

4. Structural estimation
4.1. Methodology
The presented DSGE model is estimated using the Bayesian minimum distance technique (i.e.,
impulse response matching), as adopted in Altig et al. (2011), Christiano et al. (2011), and Hofmannet al. (2012). More specifically, the structural parameters in the DSGE model are derived to minimize
the distance between the IRFs obtained from the VAR and DSGE models. Since we estimate theVAR model using the Bayesian method, there is no estimated point at which we can match the IRFs,
calculated using the DSGE model. Hence, we regard the median of the sampled IRFs, shown in
Figure 3 and 4, as reference points to minimize the distance from the theoretical responses.
Following the description of Christiano et al. (2011), let be the stacked vector of the sampled
IRFs, which has a dimension of 12 (number of horizons) times 2 (number of shocks) times 3 (numberof variables). For the number of observations T, the standard asymptotic theory says that

√

ψ

ψ θ

θ ,ζ

(39)

where θ0 indicates the true values of the parameters and ζ0 denotes those of the parameters of shocks
that are in the model. As noted in Christiano et al. (2011), ζ0 does not formally appear in this analysis.
can be written as
Given the above, the asymptotic distribution of16

Hiroshi Morita

ψ

ψ θ

θ ,ζ

V θ ,ζ ,T ≡W θ ,ζ
.
T

where

Subsequently, we regard
function of θ can be written as

ψ∣θ

(40)

,

(41)

as data, and then the approximate likelihood of the data

as a

1 2
2π
1

θ ,ζ
exp

∣ 2

1
ψ
2

(42)
ψ θ

θ ,ζ

1

ψ

ψ θ

where N denotes the number of elements in . With respect to the matrix V, its estimator is set todepend on the second moments of the sample impulse response function. More precisely, we use thematrix that has on its diagonal the diagonal elements of
1

ψ

ψ ψ

ψ

(43)

1

as based on Christiano et al. (2011). Here, is a median of the sampled IRFs and M is the numberof samples. Given the likelihood of , as represented in (43), the Bayesian posterior distribution ofand is specified byθ conditioned on

θ∣ψ

ψ∣θ

ψ

θ

(44)

where p(θ) indicates the priors for θ, and f( ) denotes a marginal density of . Based on equation(44), we estimate the structural parameters in the DSGE model using a random-walk MH algorithm,
as explained in Section 2.4.

4.2. Results
Table 1 reports the priors and posteriors of the estimated parameters for the states of F(z)=0,
F(z)=0.5, and F(z)=1. The other parameters are fixed as follows. The discount factor is set as β=0.999 because the interest rate in the sample period is markedly low due to the zero-interest rate17

Price and wage dynamics and labor market conditions in Japan

policy and QE.5 The preference parameters are chosen as γ=1.5, μ=1, and ϕ=1. The values of the
fixed parameters and priors depend on previous studies.
From the result of Table 1, we first observe a drastic variation of structural parameters according
to the labor market state. For example, the price stickiness parameter changes from 0.01 at F(z)=0 to
0.67 at F(z)=1, while the wage stickiness parameter changes from 0.32 to 0.01.6 These variations
suggest the necessity of considering the relationship between price and wage dynamics and labormarket tightness. Accordingly, this result indicates the possibility that the labor market condition
affects the price and wage decision. In return, the indexations of price and wage are estimated asconsiderably small and stable through all the states, and this is consistent with Muto and Shintani’s(2014) results.7 Compared with the estimated results for the United States, as reported by Hofmannet al. (2012), this degree of price and wage indexation in Japan is substantially modest. This finding(i.e., the indexation in Japan is smaller than that in the United States) also conforms to the claims in
Kuroda and Yamamoto (2007) and Muto and Shintani (2014).
Subsequently, we focus on the state dependency of price and wage stickiness. As mentioned
above, price stickiness tends to be high as the labor market becomes tight. For the price stickiness,
if we regard the period of F(z)=1 as an economic boom, this result implies that it is unlikely that theprice will rise in response to an increase in demand resulting from economic recovery. On thecontrary, it seems to be easy to downwardly adjust a price to a decrease in demand when F(z)=0.
This result accords with the Japanese economy’s experience in the “lost two decades,” that is, longlasting deflation. On the other hand, wage is stickier when the labor supply and demand situation isloose. This is also consistent with a phenomenon called the downward rigidity of wage, as indicatedin Kuroda and Yamamoto (2007).
Finally, we mention the parameters in the monetary policy rule. One can confirm that themonetary authority takes an active stance at F(z)=0, in which the sample from the second half of the
1990s to the 2000s is mainly included. Accordingly, reflecting the adoption of the QE policy, themonetary policy at the time was more persistent and aggressive to deflation.

5 Under β=0.999, the net nominal interest rate is assumed to equal 0.01.
6 The estimates of price and wage stickiness in this study are somewhat small compared with the results reported inprevious studies (Iiboshi et al., 2006, Sugo and Ueda, 2008). It can be considered that this difference emanates from thevariation in the sample period. The sample period in the extant literature is restricted to the late 1990s to avoid the periodof zero lower bound on nominal interest rate. In fact, the estimated result in Muto and Shintani (2014), who adopted 1980–
2013 as the sample period, is similar to ours.
7 Although the median values of price indexation at F(z)=0 and wage indexation at F(z)=1 seem to be large, the credibleintervals associated with these parameters are significantly wide. Hence, we consider these values as largely insignificant.

18

Hiroshi Morita

Table 1: Prior and posterior estimates of the DSGE model

Figure 5 shows the 90 percentiles of the IRFs of the DSGE model and those from the VAR
analysis. The red dotted lines and shaded areas indicate the credible intervals corresponding to the
DSGE and VAR models, respectively. As can be seen from Figure 5(c), the IRFs obtained from theDSGE model correspond fairly well with those from the VAR model at F(z)=1. However, as thelabor market loosens, the deviation between theoretical and empirical responses gradually spreads.
At F(z)=0, the theoretical responses almost completely fail to trace the empirical response, except
for the response of price to demand shock. This finding indicates the possibility that it may bedifficult to describe the Japanese economy from the second half of the 1990s to the first half of the
2000s using the standard DSGE model. In other words, the Japanese economy in this period is aunique situation not to be explained by the standard DSGE model. Therefore, we will have toinvestigate Japan-specific factors that cause the real economy to become estranged from thetheoretical prediction. Unfortunately, this is beyond the focus of this study and thus, we hope toaddress this in the future.

19

Price and wage dynamics and labor market conditions in Japan

Figure 5: Impulse responses of VAR and DSGE model for F(z)=0,0.5,and 1.

20

Hiroshi Morita

5. Conclusion
In this study, we analyzed price and wage dynamics in Japan by accounting for the labor market.
Combining the reduced-form results derived from the STVAR model and the structural analysis
using impulse response matching technique, we estimated the structural parameters in the Japanese
economy based on labor market tightness. The main findings are summarized as follows.
First and most importantly, price and wage dynamics change according to labor market
tightness. The sign of several IRFs in reduced-form analysis switches as the labor market tightens,
and thus, the structural parameters characterizing price and wage dynamics also show a drastic
variation. Second, it is found that price becomes stickier, while wage becomes less sticky as thelabor market becomes tight. The former is consistent with the historical fact that it is difficult forfirms to raise price in response to an increase in demand, even in an economic boom. On the other
hand, the latter is in accord with the downward rigidity of wage, which is often mentioned in Japan.
Related to price and wage rigidity, it also turns out that the indexation in price and wage in Japan isquite modest. Third, the standard DSGE model fairly replicates the dynamics of the Japanese
economy when the labor market is tight. However, as the labor market loosens, its explanatory
power to describe the real economy seems to gradually dampen.
Nevertheless, this study has certain limitations. As stated above, it reveals the state dependency
of price and wage dynamics; however, the theoretical model that we estimate captures the differencesin the dynamics that exogenously stem from the labor market situation. Therefore, in an extensionof this study, we will attempt to construct a theoretical model in which the price and wage decision
is endogenously determined based on labor market tightness. Enhancing the ability of the DSGE
model to explain the real economy will allow us to better understand the structure of the Japanese
economy.

References[1] Altig, D., Christiano, L.J., Eichenbaum, M., Lindé, J., Firm-specific capital, nominal rigidities
and the business cycle. Review of Economic Dynamics 14(2), 225-247.
[2] Auerbach, A.J., Gorodnichenko, Y., 2012. Measuring the output responses to fiscal policy.
American Economic Journal: Economic Policy 4(2), 1-27.
[3] Blanchard, O.J., Quah, D., 1989. The dynamic effects of aggregate demand and supply
disturbances. American Economic Review 79(4), 655-673.
[4] Calvo, G.A., 1983. Staggered prices in a utility-maximizing framework. Journal of Monetary
Economics 12(3), 383-398.
[5] Christiano, L.J., Trabandt, M., Walentin, K., 2011. DSGE models for monetary policy analysis.
Handbook of Monetary Economics 3A, 285-367.
[6] Farmer, R., Waggoner, D.F., Zha, T., 2011. Minimal state variable solutions to Markov-switchingrational expectations model. Journal of Economic Dynamics and Control 35(12), 2150-2166.
[7] Francis, N., Ramey, V.A., 2005. Is the technology-driven real business cycle hypothesis dead?
Shocks and aggregate fluctuations revisited. Journal of Monetary Economics 52(8), 1379-1399.
[8] Fuchi, H., and Watanabe, T., 2002. Phillips curve and price stickiness: estimation using industrylevel data. Monetary and Economic Studies 21(1), 35-69 (in Japanese).
[9] Galí, J., López-Salido, D., Vallés, J., 2007. Understanding the effects of government spending on
consumption. Journal of the European Economic Association 5(1), 227-270.
[10] Granger, C.W.J., Terasvirta, T., 1993. Modelling Nonlinear Economic Relationships. New
21

Price and wage dynamics and labor market conditions in Japan

York: Oxford University Press.
[11] Hofman, B., Peersman, G., Straub, R., 2012. Time variation in U.S. wage dynamics. Journal of
Monetary Economics 59(8), 769-783.
[12] Iiboshi, H., 2015. Monetary policy regime shifts under the zero lower bound: An application ofa stochastic rational expectations equilibrium to a Markov switching DSGE model. Economic
Modelling, In press.
[13] Iiboshi, H., Nishiyama, S-I., Watanabe, T., 2006. An estimated dynamic stochastic generalequilibrium model of the Japanese economy: A Bayesian analysis. mimeo.
[14] Koga, M., Nishizaki, K., 2006. Estimating price and wage Phillips curve: sticky price and wage
model. Monetary and Economic Studies 25(3), 73-106 (in Japanese).
[15] Kuroda, S., Yamamoto, I., 2007. Why are nominal wages downwardly rigid, but less so inJapan? An explanation based on behavioral economics and labor market/macroeconomic
differences. Monetary and Economic Studies 25(2), 45-88.
[16] Muto, I., Shintani, K., 2014. An empirical study on the New Keynesian Wage Phillips Curve:
Japan and the US. Bank of Japan Working Paper Series No.14-E4.
[17] Liu, Z., Waggoner, D.F., Zha, T., 2011. Sources of macroeconomic fluctuations: A regimeswitching DSGE approach. Quantitative Economics 2(2), 251-301.
[18] Schmitt-Grohe, S., Uribe, M., 2006. Comparing two variants of Calvo-type wage stickiness.
NBER Working Paper No.12740.
[19] Sugo, T., Ueda, K., 2008. Estimating a dynamic stochastic general equilibrium model for Japan.
Journal of the Japanese and International Economies 22(4), 476-506.
[20] Watanabe, T., 2009. The application of DSGE-VAR model to macroeconomic data in Japan.
ESRI Discussion Paper Series No.225-E.

22

Journal of International Economic Studies (2024), No.38, 23‒34©2024 The Institute of Comparative Economic Studies, Hosei University

Macroeconomic Shocks and Firms’ Overseas Expansion:
Evidence from the Factor-Augmented VAR Approach*
Shota Araki 1), Bin Ni 2), Hiroshi Morita 3)
RIETI 1), Hosei University 2), Tokyo Tech. 3)

Abstract
This paper analyzes the effects of macroeconomic variations, such as exchange rate and global
GDP, on Japanese firms’ overseas expansion behaviors. Particularly, we examine how macroeconomic
shocks affect the number of overseas subsidiaries of individual firms under the framework of thefactor-augmented VAR (FAVAR) model. Moreover, we combine the Tobit and FAVAR models toincorporate firms that own no overseas subsidiaries into our empirical analysis. The results can besummarized as follows. First, we show that most firms increase overseas subsidiaries in response to
the appreciation of the exchange rate. However, the results of forecast error variance decomposition
show that, compared with the exchange rate, global GDP shocks play a more important role in thevariation of Japanese firms’ overseas expansion. Additionally, our results indicate that the variationof the exchange rate has only a temporary effect on overseas expansion behaviors.
Keywords: exchange rate, overseas affiliate, factor-augmented VAR model, Tobit model
JEL classification: F31, F44, C32

1. Introduction
The causality between the variation of the exchange rate and foreign direct investment (FDI)
flows has been verified by many existing studies. A large stream of empirical studies has focused on
the first and second moments of the exchange rate, that is, how the depreciation or devaluation of thehost country’s currency is associated with FDI inflows into the country or how exchange rate
volatility would affect FDI inflows.
Several studies have empirically examined the effects of the exchange rate on FDI (Froot andStein 1991; Klein and Rosengren 1994; Blonigen 1997; Bayoumi and Lipworth 1998; Goldberg andKlein 1998; Ito 2000; Sazanami and Wong 1997; Sazanami et al. 2003; Kiyota and Urata 2004).
However, among the few studies that focused on the impacts of exchange rate volatility, the findings
are mixed. For instance, Cushman (1985 and 1988) and Goldberg and Kolstad (1995) find a positive
impact of exchange rate volatility on FDI, while Urata and Kawai (2000) and Bénassy-Quéré,
Fontagné and Lahrèche-Révil (2001) find a negative impact.
* This research was financially supported by JSPS KAKENHI Grant Number 22K13425 and the Tokyo Center for Economic
Research.
The authors would like to thank the participants of the HSI Workshop at Hitotsubashi University and the ICES International
Conference at Hosei University for their valuable comments and suggestions.

23Macroeconomic Shocks and Firms’ Overseas Expansion:Evidence from the Factor-Augmented VAR Approach

There are at least two reasons for the mixed results concerning the impacts of exchange
rate volatility on FDI. One reason is the aggregation problem, as suggested by Kiyota and Urata(2004). 1 Most previous studies use aggregated national- or industry-level data without further
breakdowns. However, as shown by Froot and Stein (1991) and Sazanami et al. (2003), the analysis
of national-level data may result in ambiguous results because exchange rate volatilities amongindustries may offset one another. Similarly, industry-level data may also be too aggregated. As
Melitz and Redding (2014) indicate, there is only a limited number of firms within an industry or
area that choose to be engaged in FDI. Therefore, the aggregated index cannot differentiate theheterogeneity of FDI firms from that of non-FDI firms. To better capture the impact of the exchange
rate on firms’ decision making for overseas investments, we combine firm-level data from the Toyo
Keizai’s Overseas Japanese companies’ database with financial information from Nikkei Needs
Financial Quest, while also controlling for other macroeconomic factors.

Figure 1: Correlation between the number of Japanese firms’ overseas affiliates and thereal effective exchange rate (authors’ calculation based on the Basic Survey on Overseas
Business Activities).

Further, we use the number of foreign affiliates owned by individual Japanese firms as a proxyfor FDI. 2 From Figure 1, the relationship between the number of Japanese firms’ overseas affiliates
and real effective exchange rate (REER) seems positive. Although firm-level data can help overcome
the heterogeneity issue, when we use these data, all firms in the sample will be faced with the same
exchange rate at a certain time point. This approach will complicate our identification because theimpact of the exchange rate on FDI will be contaminated by other macroeconomic factors. Usingfirms’ historical export/import data, previous studies attempt to derive the ease with which firms canreact to exchange rate variations and then control for it as a proxy of firm heterogeneity so that the
1 Kiyota and Urata (2004) use industry-level outward FDI data on Japan for analysis.
2 It has been argued that the revenue-weighted index of each overseas affiliate would be a more reasonable proxy;
however, we do not have access to information on affiliates. We will consider analyzing this issue in future studies.

24

Shota Araki, Bin Ni, Hiroshi Morita

impact of the exchange rate can be singled out (e.g., Klein et al. 2003; Moser et al. 2010; Nucci and
Pozzolo 2010).
Analogous to these studies, we apply the FAVAR model proposed by Bernanke et al. (2005).
Specifically, we extract one unobserved factor from the number of overseas affiliates of eachmanufacturing firm and then estimate the VAR model that builds on this single factor and additional
macroeconomic variables. By applying this method, we can clarify the dynamic relationship between
the factor and macro variables, as well as that between the factor and the number of an individual
firm’s overseas affiliates. Furthermore, unlike previous studies on the static relationship between
exchange rate volatility and one-time FDI, this paper attempts to capture the dynamic variations in
the number of overseas affiliates after macroeconomic shocks occur through the lens of the VAR
model. One practical caveat is that, when we focus on the number of overseas affiliates, the firmsthat have no affiliates will be dropped from the sample. To deal with this truncation problem, we use
the Tobit model nested within the FAVAR setting. In other words, for firms that have zero overseas
affiliates, we assume that firms that have a potentially negative number of affiliates also exist (andare unobservable) and estimate this potential number. To the best of our knowledge, this study is apioneer in exploring the impact of macroeconomic variation on individual firms’ outward FDI
decisions, while also extending the conventional FAVAR model by its combination with the Tobit
model.
The findings can be summarized as follows. First, the appreciation of the exchange rate leads
to an increasing number of overseas affiliates of the firms. However, the impact of the exchange rate
on FDI is temporary. Second, after we control for other macroeconomic factors, we show that world
GDP has a more long-lasting and profound influence on firms’ decisions for outward FDI.
In view of the above, this study is related to two literature streams. The first one lies in thecontext of labor economics, specifically, the question of whether the hollowing out phenomenon didarise in Japan and to what extent we can relate it to the appreciation of Japanese currency. The
second one derives from macroeconomic modeling and shows how firms’ decision making evolves
over time under the impact of exchange rate volatility. The former literature stream focuses on
economic intuition, whereas the latter tends to approach the issue from a more technical perspective.
However, both point in the same direction regarding the impact of the exchange rate variation.
Therefore, clarifying this theoretical puzzle will have significant policy implications for othernations as well.
The remainder of the paper is organized as follows. The next section introduces the backgroundto why we use FAVAR model. Section 3 presents the estimation strategy and data used for this study.
Section 4 shows the results and Section 5 concludes the paper.

2. Background to the application of the FAVAR model
The FAVAR model was originally proposed by Bernanke et al. (2005) to cope with the problemof sparse information sets in typical VAR analyses. Building on the dynamic factor model, developedby Stock and Watson (2002), the relatively small set of factors extracted from the large dataset, andthe variables of interest (e.g., the Federal Fund rate) composes the system of the FAVAR model, andthus it is free from the degrees-of-freedom limitation despite including large amounts of information.
Bernanke et al. (2005) document three advantages of using the FAVAR model with a large series
dataset. First, the FAVAR model reflects the information possessed by economic agents better than
the standard VAR model and thus mitigates the possibility of contaminating policy innovations.
Second, it excludes arbitrariness, which occurs for the choice of a time series, including in the VAR
system. Finally, we can examine the responses of a number of variables in the system to structural
25

Macroeconomic Shocks and Firms’ Overseas Expansion:Evidence from the Factor-Augmented VAR Approachinnovations at the same time, which cannot be done in the standard VAR model because the inclusionof variables is limited by the degrees of freedom.
Our analysis mainly relies on the third point. As noted above, the FAVAR model allows us to
identify the influence of macroeconomic shocks, such as the variations in the exchange rate and thebusiness cycle worldwide, on the behaviors of numerous firms comprehensively and simultaneously.
Therefore, we apply the FAVAR model in our analysis.

3. Methodology and data
3-1. Factor-Augmented VAR model with censored variable
The details of the empirical strategy are as follows. First, we define Ft as an m×1 vector thatis an n(t)×1 vector of the latent number of firm
represents unobserved factors. 3
i’s overseas affiliates in year t, denoted by x*it . As shown in equation (1), we assume Ft has a dynamic
impact on X t*. As some firms might go bankrupt at some time point during the analysis period, theseobservations will be dropped from the sample, meaning we can only estimate n(t) based on
unbalanced panel data.
∗

.

(1)

In equation (1), zt-1 is an l×n(t) vector that includes the observed exogenous variables that. Λ is called
might affect X t* other than Ft. R is a matrix with the diagonal elements
factor loading and represents an n(t)×m vector that shows the relationship between extracted factors
and the number of each firm’s overseas affiliates. Furthermore, as indicated in equation (2), we
define xit as x*it if the threshold value is above 0 and xit =0 otherwise. In practice, a Tobit model canbetter capture this mechanism.
∗

∗

(2)

∗

Next, we define Yt as a k×1 vector that includes the observed macroeconomic variables. The
dynamics between Ft and Yt can be described using the following VAR model:.

(3)

In equation (3), Φ is the coefficient matrix, whereas Q represents the variance-covariancematrix of error term ut. To clarify how exogenous factors affect firms’ overseas investment behavior,
we define Yt as a 2 × 1 vector that includes world GDP and REER at time t. In contrast to theconventional VAR model that only includes observable endogenous variables Yt , the current
specification adds unobserved Ft to the estimation system, and we call equation (3) the factoraugmented VAR model.
We conduct the analysis outlined above using firm-level micro data. Since we can identify the3

26

Throughout the text, we alternatively use “factor shock” to indicate Ft.

Shota Araki, Bin Ni, Hiroshi Morita

channel through which Yt affects Ft and the relationship between Ft and xit* is captured by Λ, we
can then derive the extent to which the shock in Yt affects xit* . By doing so, we can quantify individual
firms’ reactions to the macroeconomic shock, especially in terms of the exchange rate.

3-2. MCMC estimation
The FAVAR model mentioned above is estimated by the Bayesian Markov Chain Monte Carlo(MCMC) method via the Gibbs sampler. To do so, we construct the state-space model, where
equation (3) is regarded as the state-equation and equation (1)’, which is the transformation of (1),as the observation equation:
∗

0

0

.

(1)’

In this system, Y t is also treated as the latent variable, as well as F t . Since our modelcomprises several parameters and the latent variables denoted by Θ, where, the posterior distribution is too complicated to calculate
analytically and, thus, the MCMC method is suitable for estimation. Given observed dataand prior density functions π(Θ), the samples from the posterior distribution
π(Θ y) are obtained as follows:1.

Set initial values of

2.

Draw

,

from

3.

Draw

and

4.

Draw

from

5.

Draw

∗

,

,
∣

from

in

truncated between
6.

Draw

from

7.

Draw

from

8.

Return to step 2 until

,

∗

,

,

,

,

∗

∣
∣

, and

1.

.
,

∗

from

∗

∗

and 0.

∗

∣

.
.
,

.

iterations have been completed.

For the above process, N is set at 25,000, but the initial 5,000 samples are discarded as burn-in.
In the following, we briefly explain the process of sampling for each step. In step 2, we employ the
Kalman filter and Kalman smoother to our state-space specification to sample the latent factor
27Macroeconomic Shocks and Firms’ Overseas Expansion:Evidence from the Factor-Augmented VAR ApproachΛ∗.
. To draw Λ and β simultaneously in step 3, equation(1) is transformed as:

Λ

∗

.

Specifically, the equation can be expressed as:

∗

∗

∗

⋮

∗

⎡
⎜
⎜⋮

∗

⋮

∗

⎡
⎜
⎜⋮

⋱
⋱

⋮
0

⋮
⋮

⋮
⋮

⋱
⋱

⋮
0

⋮
⋮

⋮
⋮

⎜
⎜⋮

⋱
⋱

⋯

⋯

⋮

⋮
⋮

⋮

⋮
⋮

.

⋮

.

As mentioned above, our model is estimated using the Bayesian
method, such that λ1 is⋮normalized to be 1 to identify the latent factor uniquely.4 Thus, the system for estimation in thisstudy is given by

∗

∗

⋮

∗

⋮
0

⋮
⋮

Here, let us denote

⋮

⋮
⋮

⋮
⋮

⋮

⎜
⎜⎜ ⎜
⎜⎜ ⎜
⋮⎜

.

and

⋱
⋱

⋮
0

⋮
⋮

⋮
⋮

⋮
⋮

,
⎜

and then assume Λ̃〜N (h0, H0) for the prior of Λ̃. The posterior distribution of Λ̃ is obtained as:

where

4

28

and

. In step 4, under theThis restriction for identifying the latent factor is adopted by Bernanke et al. (2005) and Belviso and Milani (2006).

Shota Araki, Bin Ni, Hiroshi Morita

prior distribution ofof Rii-1 is drawn from

, where Rii is the (i,i) element of R, the random sample

,

∣

,

∗

, Λ,

2

,

,

2

where
Since xit* follows a normal distribution of mean λi Ft+z’i,t-1 β and variance Rii, as shown inequation (1), for the observations that are xit=0, latent variable xit* is generated from:∗

As for Φ and Q in steps 6 and 7, we regard the sampling result of {Ft }Tt=1 as data and set thenormal distribution and Wishart distribution for the prior of Φ and Q-1 as follows:.
Then, the conditional posterior density functions for Φ and Q-1 are, respectively:

Φ∣
Here,

,
,

.
, where

,
,

, and

.

3-3. Data
Firm-level FDI data (1999–2014)5 are taken from Toyo Keizai’s Kaigai Sinshutsu Kigyo Soran,consisting of green-field and acquisition (20% or higher equity acquisitions) investments. We focus
on manufacturing firms listed on either the first or the second section of the Tokyo Stock Exchange.
We also limit our estimation to firms that have observations for at least eight years. In other words,
we omit firms that have too many missing values or do not have enough observations because ofbankruptcy during the estimation period. Finally, 715 firms were used for analysis.
5

Since the one-period lag is included in the VAR model, the data used in practice range from 2000 to 2014.

29

Macroeconomic Shocks and Firms’ Overseas Expansion:Evidence from the Factor-Augmented VAR Approach

First, we extract the unobserved factors based on the variation of these firms’ overseas affiliates.
Second, as for explanatory variables zt-1 in equation (1), overseas export revenue/total revenue,
R&D expenditure/total revenue, capital investment/total revenue, and liabilities/assets are used.
These data are all obtained from Nikkei’s Needs Financial Quest. The information on REER is takenfrom the website of the Bank of Japan, whereas the world GDP comes from Constant GDP per capita
for the World, constructed by the Federal Reserve Bank of St. Louis.
It is worth noticing the identification of the VAR model. The number of overseas affiliates inyear t is based on the value in October, REER is the average value from January to December, andworld GDP is the flow value aggregated from January to December. Because of the deviation in thetiming of observations, the order of the variables used in the VAR model of equation (3) is asfollows: factors, world GDP, and REER. By applying the recursive formulation of the Cholesky
factorization, we can identify the structural shock.6
Concerning the specification of the VAR model, the lag length of our system is set as one year,
and all variables are included at this level. Furthermore, in equation (1), we control for firm-level
fixed effects and, in equation (3), we add the constant term. Because the estimated coefficients will
be interpreted as elasticity, it is reasonable for the variables to take logarithm values. While this will
work for REER and world GDP, since the number of overseas affiliates have zero values, whichmakes it impossible to take the logarithm, we apply the inverse hyperbolic sine transformation tosolve the problem. 7
Finally, for the prior distribution of parameters, we assume that Λ̃ =[Λ’ β ’ ]’ and Φ follow thenormal distribution, the inverse of Rii follows the Gamma distribution, and the inverse matrix of Q
is subject to the Wishart distribution.
Λ

,
40
,
2

,

0.001
2

(4)

.

4. Estimation results
4-1. Factors’ impulse response function and variance decomposition
Figure 2 shows the impulse response function of the factors (for the number of overseas
affiliates) with respect to the world GDP (a) and the exchange rate (b). The impulse responses to a
one standard deviation macroeconomic shocks are included—the blue solid line indicates the median
response based on all sampled responses, whereas the red dotted line indicates the 68% confidence
intervals.

6
7

30

The financial data are all based on observations during the accounting period in March.
The inverse hyperbolic sine transformation of a certain x is defined as ln(x+(x2+1)1/2 ).

Shota Araki, Bin Ni, Hiroshi Morita

Figure 2: Impulse response function of factors with respect to world GDP and exchange
rate shocks.
（a）World GDP shock（b）Exchange rate shock

Notes: Figures 2(a) and 2(b) show the responses of the overseas affiliate factor to world GDP and exchange rate shocks, respectively. The
solid blue line indicates the average responses of the sample responses, whereas the red dotted line indicates 68% confidence intervals.

From the results, the world GDP has a positive impact on the factors of overseas affiliates andit is significant for 68% confidence intervals. In the meantime, the responses to the exchange rate
shock are confirmed not to be significant. By the median response, the factors of the overseas
affiliates show a continuous response to the world GDP shock, whereas the response to the exchange
rate converges to zero.
Furthermore, to confirm the scale of each shock’s influence on the factors, we use Table 1 tosummarize the results of the variance decomposition by shocks. Variance decomposition is themethodology that quantifies the impact of each macroeconomic shock on the unpredictable volatilityof the variables included in the VAR system. Table 1 shows the relative contributions of each shockon the factor one, two, and five years ahead, respectively, in terms of the average square of the error
term. Since this study conducts a Bayesian estimation based on the MCMC method, we calculate thevariance decomposition for each group of sampling observations and show the average value,
together with the 68% confidence intervals.

Table 1: Variance decomposition by shocks
Factor shock

World GDP shock

Exchange rateshock

1 year later

98.6[97.4, 99.8]

0.7[0.0, 1.5]

0.7
[0.0, 1.3]

2 years later

96.5[93.5, 99.4]

2.0[0.1, 4.1]

1.5
[0.1, 3.0]

5 years later

89.7[81.0, 98.1]

7.1[0.4, 14.2]

3.2[0.2, 6.5]

10 years later

81.0[64.4, 96.5]

14.5[0.8, 30.0]

4.4[0.3, 8.8]

Notes: The values show the contribution of each shock to the volatility of the dependent variable, while the values between parentheses
indicate the 68% confidence intervals.

31

Macroeconomic Shocks and Firms’ Overseas Expansion:Evidence from the Factor-Augmented VAR Approach

From Table 1, the variation of the number of overseas affiliates can be mostly explained by thefactor shock. This is due to the fact that we adopt the recursive constraint based on the Cholesky
decomposition. In other words, because the factor shock under the recursive constraint is the only
one that affects the change in the number of overseas affiliates when the shock occurs, its impact islarger than that of the other shocks. One thing that worth noticing is that, in the second and thirdcolumns, the world GDP shock has a larger long-term impact on the dependent variable than theexchange rate does. In comparison to the exchange rate, the impact of the world GDP shock is twiceas large after five years and three times as large after 10 years. In this way, for the determinants ofindividual firms’ overseas investment, rather than exchange rate, the world GDP plays an even more
important role. This is in accordance with the results of the impulse response shown in Figure 2.

4-2. Response to the number of overseas affiliates
Hitherto, we have verified the impacts of world GDP and exchange rate on the decision making
for overseas investment by Japanese firms in general. To take this a step further, we can calculate Λin equation (1) and thus identify the macroeconomic shocks’ impact on each individual firm. That is,using the FAVAR model, it is possible to systematically estimate the impact on the 715 firms by
respectively combining equations (1)’ and (3). However, due to space constraints, we only show thehistogram based on 715 firms’ impulse response function in Figure 3.

Figure 3: Individual firms’ responses

Notes: The horizontal axis shows the response, while the vertical axis indicates the ratio of firms within each interval.

Figures 3(a) and 3(b) are the histograms of the impulse response functions for world GDP andexchange rate, respectively. The horizontal axis shows the response, while the vertical axis indicatesthe ratio of firms within each interval. In Figure 3(a), the interval is 0.1, whereas it is 0.02 in Figure3(b). The histograms are based on the median value of each firm’s response. The blue solid line
indicates the distribution of the responses one year after the shock. The red and green dotted lines
are for five and 10 years, respectively.
First, in Figure 3(a), the blue line shows that the peak of the histogram is near zero and nearly
half of the firms do not have any responses. However, after five years, the firms that increase anddecrease their number of overseas affiliates diverge. Furthermore, the shapes of the responses after32

Shota Araki, Bin Ni, Hiroshi Morita

five and 10 years are similar, which means that the influence of the shock is durable. In Figure 2(a),the factor of the overseas affiliates shows a positive response to the world GDP shock. However,when we look at individual firms’ responses, the histogram skews to the left, which means that there
are more firms that decrease their number of overseas affiliates after a positive world GDP shock.
The result is opposite to our intuition, and to better understand the mechanism behind it, we assume
it is necessary to divide the samples and conduct more detailed analyses.8
On the other hand, one year after the exchange rate shock, the distribution tail of the responses
becomes wider, which means that firms react at a relatively early stage once they are affected by theshock. While in the short run, more than half of the firms increase their overseas affiliates in responseto an increase in exchange rate, the peak of the histogram is near zero after 10 years, showing thatthe impact of the exchange rate shock on firms’ overseas investment decisions might be temporary.

5. Conclusions
This study analyzes the dynamic relationship between macroeconomic shocks such as exchange
rate and individual firms’ overseas investment decision in terms of the number overseas affiliates.
Specifically, we extract one unobserved factor from the number of overseas affiliates of eachmanufacturing firm and then estimate a VAR model that builds on this single factor and macroeconomic
variables. Specifically, we embed a Tobit model in the FAVAR context and apply firm-level micro
data while controlling for year fixed effects. By using a VAR model for time series analysis, we cancapture not only the static relationship between macroeconomic variables and firm behavior, butalso how economic shocks dynamically affect firms’ responses. This is the major contribution of thisstudy.
By analyzing impulse response and variance decomposition based on the factors extracted fromthe number of Japanese firms’ overseas affiliates, we verify that both the exchange rate and world
GDP variation affect firms’ decisions to invest abroad. When there is an increase in the exchange
rate, most firms will increase their number of overseas affiliates; however, the impact of the world’s
GDP is even larger. Additionally, in contrast to the fact that the impact of the exchange rate on firms’
overseas investment is temporary, the world GDP has a continuous influence on firms’ decisions interms of their outward FDI. By far the largest number of studies has focused on the determinants ofinward FDI, as FDI is an important channel through which a host country can improve its technology,
as well as resource allocation efficiency. By contrast, in this paper, we attempt to lay the micro-level
foundation for quantifying the influence that macroeconomic factors have on individual firms’
outward FDI. The new insights from the supply side will have tremendous policy implications for
future study.
However, the current study has its limitations. As outlined in the previous section, the result on
the influence that world GDP has on firms’ reactions is the opposite of our expectations, which needs
further justification. We might divide the destinations by different patterns or regions. Furthermore,structural shocks, such as financial policy and risk premium, can also change the exchange rate;
however, the current Cholesky factorization under recursive constraint does not consider thesefactors. To mitigate such problems, we will apply a similar FAVAR model with sign restrictions, as
used by Ahmadi and Uhlig (2009). In this case, a theoretical model to describe the mechanism of
8 For example, when the economic situation improves in developing countries, firms will shut down several foreign
affiliates in advanced countries and open new affiliates in those developing nations. In this paper, such behavior is regarded
as decreasing overseas affiliates, such that it is necessary to divide the sample by region and conduct further analyses infuture studies.

33

Macroeconomic Shocks and Firms’ Overseas Expansion:Evidence from the Factor-Augmented VAR Approachfirms’ overseas investments will be proposed in future studies and more endogenous variables will
be included in our VAR model.

References[1] Ahmadi, Pooyan Amir and Harald Uhlig. 2009. “[TB4] Measuring the Dynamic Effects ofMonetary Policy Shocks: A Bayesian FAVAR Approach with Sign Restrictions.” Mimeo.
[2] Bayoumi, Tamim and Gabrielle Lipworth. 1999. “Japanese Foreign Direct Investment andRegional Trade.” Journal of Asian Economics, 9(4), 581-607.
[3] Belviso, Francesco and Fabio Milani. 2006. “Structural Factor-Augmented VARs (SFAVARs)
and the Effects of Monetary Policy.” B.E. Journal of Macroeconomics, 6(3), 1-46.
[4] Bernanke, Ben S; Jean Boivin and Piotr Eliasz. 2005. “Measuring the Effects of Monetary
Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach.” The Quarterly
Journal of Economics, 120(1), 387-422.
[5] Blonigen, Bruce A. 1997. “Firm-Specific Assets and the Link between Exchange Rates andForeign Direct Investment.” The American Economic Review, 87(3), 447-65.
[6] Froot, Kenneth A and Jeremy C Stein. 1991. “Exchange Rates and Foreign Direct Investment:
An Imperfect Capital Markets Approach.” The Quarterly Journal of Economics, 106(4), 1191217.
[7] Goldberg, Linda S and Michael W Klein. 1997. Foreign Direct Investment, Trade and RealExchange Rate Linkages in Southeast Asia and Latin America.
[8] Ito, Takatoshi. 2000. “Capital Flows in Asia,” Capital Flows and the Emerging Economies:
Theory, Evidence, and Controversies. University of Chicago Press, 255-96.
[9] Kiyota, Kozo and Shujiro Urata. 2004. “Exchange Rate, Exchange Rate Volatility and Foreign
Direct Investment.” The World Economy, 27(10), 1501-36.
[10] Klein, Michael W and Eric Rosengren. 1994. “The Real Exchange Rate and Foreign Direct
Investment in the United States: Relative Wealth vs. Relative Wage Effects.” Journal ofInternational Economics, 36(3), 373-89.
[11] Klein, Michael W; Scott Schuh and Robert K Triest. 2003. “Job Creation, Job Destruction, andthe Real Exchange Rate.” Journal of International Economics, 59(2), 239-65.
[12] Melitz, Marc J and Stephen J Redding. 2014. “Missing Gains from Trade?” The American
Economic Review, 104(5), 317-21.
[13] Moser, Christoph; Dieter Urban and Beatrice Weder di Mauro. 2010. “International
Competitiveness, Job Creation and Job Destruction—An Establishment-Level Study of German
Job Flows.” Journal of International Economics, 80(2), 302-17.
[14] Nucci, Francesco and Alberto Franco Pozzolo. 2010. “The Exchange Rate, Employment andHours: What Firm-Level Data Say.” Journal of International Economics, 82(2), 112-23.
[15] Sazanami, Yoko and Wong Yu Ching. 1997. “Strategies of Japanese Multinationals: Changes inthe Locational Importance of Asia, the EC and North America.” Multinational Firms andInternational Relocation, Cheltenham, UK and Brookfield, US: Edward Elgar, 103-22.
[16] Sazanami, Yoko; Seiji Yoshimura and Kozo Kiyota. 2003. “Japanese Foreign Direct Investment
to East Asia and Exchange Rate Policies: Some Longer Term.” Keio Economic Studies, 40(1),1-26.
[17] Stock, James; Mark Watson. 2002. “Macroeconomic Forecasting Using Diffusion Indexes.”
Journal of Business Economics and Statistics, 20(2), 147-62.

34

Journal of International Economic Studies (2024), No.38, 35‒46©2024 The Institute of Comparative Economic Studies, Hosei University

The NG-SVAR Model under the Pearson Family ofDistributions: Implementation with R Packages＊
Tadashi Nakanishi
Hokkaido University

Abstract
In traditional econometric analysis, Structural Error Terms (SETs) of Structural Vector
Autoregressive (SVAR) models are assumed to follow a normal distribution. However, in actual data
analysis, SETs follow non-Gaussian distributions in more than a few cases. We have not foundnormally distributed data in the SETs of models constructed from data (mainly CPI, MB, Nikkei225,
real effective exchange rate, 10-year Japanese Government Bond interest rate, etc.) that we havetreated in our empirical studies. These experiences made us realize the need to develop a nonGaussian Structural Vector Autoregressive (NG-SVAR) model. This paper omits the theoreticalcontent, simulation results, etc., and details the procedure for applying Maekawa et al. [11] to theempirical analysis using the R program.
Keywords: Monetary policy, VAR model, non-Gaussian distribution
JEL classification: E52, C32

1. Introduction
We consider estimation problems in SVAR models in which disturbances have non-Gaussian
distribution. We call this model the NG-SVAR model. Since the estimation problem of this model isclosely related to the Independent Component Analysis (ICA) developed in machine learning andsignal processing, we apply the theory of ICA to our estimation problem. However, since we do notknow the true non-Gaussian distribution in practice, we cannot construct the exact log likelihood
function. The pseudo log likelihood approach proposed in Maekawa et al. [11] and ‛id.ngml’assuming t-distribution are known to have consistent estimators and asymptotic normality as the
sample size increases from a semiparametric statistics standpoint. See Amari [1], Amari et al. [2, 3]
for details. For recent studies of NG-SVAR models, see Lanne et al. [8]; Maekawa [9, 10]; Maekawa
et al. [11]; Shimizu et al. [13].
＊I am grateful for the comments and discussions from participants at a seminar of Joint Usage and Research Center
Programs at the Institute of Economic Research, Hitotsubashi University at Hiroshima University of Economics in March
2023, Japanese Joint Statistical Meeting 2023 at Kyoto University in September 2023 and a Modern Economics and StatisticsEconometrics Seminar at Hokkaido University in July 2023. I am also grateful to Professor K. Maekawa of Hiroshima
University of Economics, Professor N. Kunitomo of The Institute of Statistical Mathematics, Professor Y. Kakizawa,
Professor T. Suzuki of Hokkaido University and Professor H. Morita of Tokyo Institute of Technology for their valuable
discussions and comments. This work was supported by JSPS KAKENHI Grant Numbers, JP18K01555 and JP22K20151.

35

The NG-SVAR Model under the Pearson Family of Distributions: Implementation with R Packages

Consider the NG-SVAR model consisting of k variables with p-lags as in equation (1). The NGSVAR model is written as:1
t = 1,…, T
where yt = (y1t,…, ykt)’ is a vector of k×1 observed values at time t for k variables, and εt = (ε1t,…,εkt)’
is a vector of k×1 that is assumed to follow a non-Gaussian distribution under fairly generalassumptions. Furthermore, the t-distribution was used to derive an exact log likelihood function andderived asymptotic properties of the maximum likelihood estimator (MLE) in the model.
Rewriting the equation (1), it can be written as:

2
By defining B0-1 = (I-B0)-1 (note that the diagonal element is 1), the reduced form of Eq. (2)becomes3
where Ai = B0-1 Ai, i = 1,…, p.

Typically, Ai is assumed to satisfy the stability condition

detA z| |.

4

Our aim is to estimate B0-1 without knowing the Probability Density Function (PDF) of the true
PDF of non-Gaussian εt. Maekawa et al. [11] proposed the following method.
(1) Obtain reduced form residuals ut from the reduced form.
(2) Decompose ut into the two parts B̂ 0-1 and ε̂ t by equation (4) using an ICA algorithm.
(3) Find the most suitably fitted PDF for εt from the Pearson family of distributions, whichcontains a wide range of distributions.
(4) Use the fitted PDF as a pseudo PDF for εt(5) Using a pseudo PDF, construct a pseudo log likelihood function and estimate B̂ 0-1 .
36

Tadashi Nakanishi

In this paper, the above methods are presented using the R program.

2. Estimate VAR analysis
The variables used in the model presented as an example in this paper are real Gross Domestic
Product (GDP), Consumer Price Index (CPI) excluding fresh food and energy prices, Monetary Base(MB), 10-year Japanese Government Bond interest rate, and Nikkei225. The lag of the VAR model
was set to 2 based on the results of an investigation of the optimal lag using the information criterion.
The R program ‘VARselect’ calculates Akaike’s Information Criteria (AIC), the Hannan-Quinn
criterion (HQ), Schwarz Criterion (SC) and the Final Prediction Error criterion (FPE). The estimatedresults of optimal lags of the VAR model are shown in Table 1. In this paper, the optimal lag wasselected with reference to HQ and SC. Stationarity of the VAR model was checked using theAugmented Dickey-Fuller (ADF) test. The results were non-stationary. Therefore, the first-orderdifference was taken. Some of the results of the ADF test after taking the first-order difference areshown in Table 2. Since this model was found to be a stationary process, we estimate a VAR model.
The estimated reduced form residuals are shown in Figure 1.
> v1 <- vars::VAR( data.selected , p=2 )# estimate VAR(2)> X <- residuals(v1) # residuals obtained> plot(ts(X))

Table 1. Selection of optimal lags of the VAR model
selection
AIC(n)

HQ(n)

SC(n)

FPE(n)

12

2

2

10

1

2

3

4

5

6

AIC(n)

3.40E+01

3.44E+01

3.45E+01

3.45E+01

3.46E+01

3.46E+01

HQ(n)3.38E+01

3.41E+01

3.41E+01

3.40E+01

3.39E+01

3.37E+01SC(n)3.36E+01

3.37E+01

3.34E+01

3.31E+01

3.28E+01

3.25E+01

FPE(n)

1.71E-15

1.12E-15

1.01E-15

1.01E-15

9.72E-16

9.80E-16

7

8

9

10

11

12

AIC(n)

3.45E+01

3.45E+01

3.46E+01

3.47E+01

3.47E+01

3.47E+01

HQ(n)3.35E+01

3.33E+01

3.33E+01

3.33E+01

3.31E+01

3.31E+01SC(n)3.20E+01

3.17E+01

3.15E+01

3.12E+01

3.09E+01

3.06E+01

FPE(n)

1.07E-15

1.10E-15

9.86E-16

8.71E-16

9.19E-16

8.73E-16

criteria

37

The NG-SVAR Model under the Pearson Family of Distributions: Implementation with R Packages

Table 2. Result of the ADF test
Augmented Dickey-Fuller Test
Dickey-Fuller-6.8114

Lag order2

p-value

0.01

alternative hypothesis: stationary

Figure 1. Reduced form residuals

3. ‘fastlCA’
The relationship between the ICA model and the VAR model is briefly described. Equation (5)is a general ICA model.

x in equation (5) is known as an “observed signal” and corresponds to ut in equation (4). A inequation (5) is called a “mixing matrix” and corresponds to B0-1 in equation (4). s in equation (5) iscalled a “source signal” and corresponds to εt in equation (4).
Comparing equations (4) and (5) shows that the structures are similar. Hence, we can apply thetheory and algorithm in ICA to estimate the SVAR model.
38

Tadashi Nakanishi

The reduced form of the SVAR model is estimated to obtain û t. Inputting the induced form
residuals into “fastICA” provides SETs that are separated and estimated by the ICA algorithm. This
process provides a good estimation because it performs whitening even in the presence of correlations.
However, the ICA method cannot be applied when two or more ut are normally distributed.
>b <- fastICA(X, 5, alg.typ = “parallel”, fun = “logcosh”, alpha = 1,method = “C”, row.norm>= FALSE, maxit = 200,tol = 0.0001, verbose = TRUE)
The ‘X’ in the code is the estimated reduced form residuals. The estimated results are stored in‘b’. The ε̂ t, which is separated and estimated from the induced system residuals, can be viewed
inside by entering b$s. Also, ‘b$X’ is called pre-processed data, and the input induction system
residuals can be viewed. The correlation matrix of ε̂ t (Table 3) shows that each value is small anduncorrelated. The distribution of each structural error term is shown in Figure 2. For more information
on ICA theory, see Hyvärinen et al. [7] and Moneta et al. [12].

Table 3. Correlation matrix of ε̂ t
1.000000E+00

7.703835E-15

2.277426E-16-4.207289E-15

3.415022E-15

7.703835E-15

1.000000E+00

2.769406E-16

1.070747E-16

5.048078E-16

2.277426E-16

2.769406E-16

1.000000E+00-2.565982E-16-1.095701E-16-4.207289E-15

1.070747E-16-2.565982E-16

1.000000E+00-1.924934E-17

3.415022E-15

5.048078E-16-1.095701E-16-1.924934E-17

1.000000E+00

Figure 2. Histograms of distribution of each ε̂ t

39

The NG-SVAR Model under the Pearson Family of Distributions: Implementation with R Packages

In the next section, the approximate distribution of each estimated structural error term isselected from the Pearson distribution family.
4. ‘PearsonMSC’
The approximate distribution of ε̂ t obtained from the above results is selected by ‘pearsonMSC’in the R package ‘PearsonDS’. As an example, use ‘pearonMSC’ to select an approximate distribution
for ε̂ 1t .
>result1<-pearsonMSC(e_hat1)
Using the above R code, the approximate distribution of ε̂ 1t can be selected from the Pearson
family of distributions. The information criteria used in this process are Maximum Likelihood (ML),
AIC, corrected AIC, Bayes Information Criterion (BIC), and Hannan-Quinn criterion (HQC). Fromeach information criterion, the optimal Pearson distribution type and some parameters are obtained.
The analyst must choose which information criteria to use. In this paper, AIC will be employed. The
results for the approximate distribution of ε̂ 1t are shown in Tables 4 and 5. Pearson distribution type4 was chosen as the approximate distribution of ε̂ 1t . However, the parameters of the distribution
when fitted to a distribution other than the optimal distribution are also obtained.

Table 4. Selection of type of Pearson distribution of ε̂ 1t
MSCs
0

1

2

3

ML

777.5783

749.3435

777.5851

2135.874AIC

781.5783

757.3435

783.5851

2141.874

AICs

781.6226

757.4922

783.674

2141.963

BIC

788.8046

771.796794.4245

2152.713

HQC

784.4787

763.1444

787.9358

2146.224

4

5

6

7

ML

646.3415

740.8067

741.2063

649.3327

AIC

654.3415

746.8067

749.2063

655.3327

AICs

654.4902

746.8956

749.355

655.4216

BIC

668.794757.6461

763.6588

666.1721

HQC

660.1424

751.1573

755.0071

659.6833

0

1

2

3

-3.89E+02-2.75E+02-388.7926

4

5

6

7

-323.1708-370.4033-370.6031-324.6663

Log-Likelihood

40-1067.9369

Tadashi Nakanishi

Table 5. Selection of type of Pearson distribution and parameters of ε̂ 1tFitted Distributions
mean

SD

1.45E-17

1.00E+00

ablocationscale

72889.74434

37.04834-11654.12752

11660.04866

alocationscale

147868.3289-543.9983

1087.9966

shapelocationscale

0.4394017

3.6792755-1.5690128

mnu

locationscale

1.9688366

0.4601418

0.2350211

0.9395913

shapelocationscale

115.81485

10.09428-1158.78507

ab

locationscale

244.383856

123.808464

8.527508-4.284872

DF

locationscale

3.37881989

0.06671337

0.56239978

Type

m

nu

location

scale

Best$ML4

1.9688366

0.4601418

0.2350211

0.9395913

Best$AIC

4

1.9688366

0.4601418

0.2350211

0.9395913

Type

m

nu

locationscale

4

1.9688366

0.4601418

0.2350211

0.9395913

Type

df

location

scale

Best$BIC

7

3.37881989

0.06671337

0.56239978

Best$HQC

7

3.37881989

0.06671337

0.56239978

Type 0Type 1

Type 2Type 3

Type 4

Type 5

Type 6

Type 7

Best$AICs

In this paper, rather than using all of the selected approximate distributions and parameters foreach distribution, we use the average of the parameters that represent the most frequently adopted
distribution types and their shapes among the selected approximate distributions. In the future, theR program will need to be modified with a view to using the type of approximate distribution and its
parameters for each error term. In this example, the average values of each parameter were used, but
the median value could also be used. Three Pearson distribution type 4 and two Pearson distribution
type 7 were selected for this analysis. The average values for each parameter of the selected Pearson
41

The NG-SVAR Model under the Pearson Family of Distributions: Implementation with R Packages

distribution type 4 are as follows.
>params4 <- cbind(3.122808967, 0.2509609, 0.091948133, 1.6145176)
We use pseudo-PDF to construct a pseudo log likelihood function of B0-1 . We introduce PDFs
for Pearson family of distribution types 4 and 7. See Figures 3 and 4 for the R codes of the respective
Pearson distributions.
Pearson type 4 :22

1 1 1 1,2 2 2,2

For

For

.

.

).

).

Pearson type 7:

√

2

2

.

The Pearson type 7 distribution has n degrees of freedom, location parameter λ, and scaleparameter s. This distribution reduces to the student’s t-distribution when s=1 and λ=0.

Figure 3. The R code of the log-Likelihood function of Pearson type 442Tadashi Nakanishi

Figure 4. The R code of the log-Likelihood function of Pearson type 7

5. ‘id.ngml’
‘id.ngml’ is a program in R that constructs a likelihood function assuming the t-distribution as
the distribution of εt and performs a maximum likelihood estimation. Maekawa [10] states that theresults of Monte Carlo experiments under various non-Gaussian distribution assumptions show thatthe pseudo-maximum likelihood estimation and tests based on the t-distribution are not bad. Whenan approximate probability density function is obtained from ε̂ t , the t-distribution and distributions
close to it are often selected. When the true distribution is unknown, the t-distribution is flexible as
an approximation to the various non-Gaussian distributions that tend to appear in data analysis. It isalso easy to use, with a wide variety of options available to meet the various requirements of theanalyst.
‘id.ngml’ is optimized using the reduced form residuals obtained from the VAR estimation.
Here, the residuals obtained from the previously estimated VAR (2) are given and estimated. The
estimation results are shown in Table 6.

43

The NG-SVAR Model under the Pearson Family of Distributions: Implementation with R Packages

Table 6. Result of using ‘id.ngml’
Identification Results
Method: Non-Gaussian maximum likelihood
Sample size

274

Log-Likelihood

3014.7

AIC-5859.5

Estimated degree of freedom

2.23898.22392.45542.18754.6149Standard errors of
estimated degrees of freedom

0.60353.5046

1.34390.05701.5236

GDP

CPI

MB

IR

STK

GDP

0.0047-8.76E-05-0.0002-9.05E-05-0.0002CPI

0.0114

1.03E-01

0.0041

1.46E-02

0.0024

MB-0.0008-7.26E-04

0.0185-3.32E-03-0.0022IR

0.0089-3.46E-03-0.0011

2.08E-01-0.0034

STK

0.0223

5.12E-04

0.0085

4.71E-02

0.0518

GDP

CPI

MB

IR

STK

GDP

1

-0.0009-0.0082-0.0004-0.0004

CPI

2.39901

0.07010.0455

MB-0.1727-0.00701-0.0160-0.0419

IR

1.8819-0.0034-0.05931-0.0647

STK

4.70360.00500.46030.2268

1

GDP

CPI

MB

IR

STK

GDP

0

0.00180.01050.00210.0036CPI

2.629900.38040.07910.1616

MB

0.21080.005200.00770.0162IR

2.65750.04530.372300.1570STK

1.41740.0336

0.27220.05500

Estimated scale of the standardized B

0.00470.10280.01850.20770.0518

Standard errors of the scale

0.00530.00540.01960.02580.0047

Estimated B Matrix(unique decomposition of the covariance matrix)

Estimated standardized B matrix

Standard errors of standardized B matrix

44

Tadashi Nakanishi-1
6. The Estimation of B̂ 0

To obtain B̂ 0-1 using the pseudo maximum likelihood method proposed by Maekawa et al. [11],an initial value is needed. As the initial value, we use B̂ 0-1 estimated by ‘id.ngml’ plus a small
random number. For the initial value, a random number generated from the standard normal
distribution and reduced to 1/50 is added. By using the nonlinear optimization function “optim”, thepseudo-MLE of B̂ 0-1 is calculated by the pseudo log likelihood function. The optimized results areshown in Figure 5.

Figure 5. The result of our method

This method is primarily intended to accurately measure B̂ 0-1 . The reason for this is to estimate
the IRF, and the Ai and B̂ 0-1 matrices are used in calculating the IRF. Therefore, B̂ 0-1 must be
estimated accurately. This method has been shown, based on the results of Monte Carlo experiments,
to yield values close to the true value. For details of the results of the Monte Carlo experiment, see
Maekawa et al. [11].

7. Conclusion
In this paper, we have described the method of implementation of the pseudo-maximum
likelihood method proposed by Maekawa et al. [11] in R. At present, the most frequently selected
approximate distributions from the Pearson family of distributions and their average values are
adopted, and a pseudo log likelihood function is constructed from the PDF of the approximate
distributions for optimization. In the future, we would like to modify the program so that optimizationis performed using the distribution of each SET and its average value.
In economic analysis, some data, such as GDP, are only available in quarterly data. A possible
solution to such problems is the application of the Mixed Data Sampling (MIDAS) model. Therefore,
we would like to consider developing a model that assumes non-normality in the MIDAS model.
45

The NG-SVAR Model under the Pearson Family of Distributions: Implementation with R Packages

References[1] Amari S. Estimating Functions of Independent Component Analysis for Temporally Correlated
Signals. Neural Computation. 2000;12(9): 2083-2107.
[2] Amari S, Cardoso JF. Blind Source Separation-Semiparametric Statistical Approach. IEEE
Transactions on Signal Processing. 1997;45(11):2692-2700.
[3] Amari S, Kawanabe M. Information geometry of estimating functions in semi-parametricstatistical models. Bernoulli. 1997;3(1):29-54.
[4] Gouriéroux C, Monfort A, Renne J. Statistical inference for independent component analysis:
Application to structural VAR models. Journal of Econometrics. 2017;196(1):111-126.
[5] Hyvärinen A, Karhunen J, Oja E. Independent component analysis. New York (NY): JOHNWILEY & SONS, INC; 2001.
[6] Hyvärinen A, Oja E. Independent component analysis: Algorithms and applications. Neural
Networks. 2000;13(4-5): 411-430.
[7] Hyvärinen A, Zhang K, Shimizu S, et al. Estimation of structural vector autoregression modelusing non-gaussianity. The Journal of Machine Learning Research. 2010;11:1709-1731.
[8] Lanne M, Meitz M, Saikkonen P. Identification and estimation of non-gaussian structural vector
autoregressions. Journal of Econometrics. 2017;196(2):288-304.
[9] Maekawa K. Causal inference by non-Gaussian SVAR model: With an application to Japan’squantitative easing policy. Hiroshima University of Economics 50th Anniversary
Commemorative Papers. 2017;1:25-62.
[10] Maekawa K. Identification and estimation of structural VAR model (II): under the non-Gaussian
errors. HUE Journal of Economics and Business. 2021;44(2):21-32.
[11] Maekawa K, Nakanishi T. Estimation of non-Gaussian SVAR models: A pseudo-log-likelihood
function approach. Journal of Statistical Computation and Simulation. 2023;93(11):1830-1850.
[12] Moneta A, Entner D, Hoyer PO, et al, Causal inference by independent component analysis:
theory and applications. Oxford Bulletin Economics and Statistics. 2013;75(5):705-730.
[13] Shimizu S, Hoyer PO, Hyvärinen A, et al. A linear non-gaussian acyclic model for causal
discovery. Journal of Machine Leaning Research. 2006:2003-2030.

46

Journal of International Economic Studies (2024), No.38, 47‒61©2024 The Institute of Comparative Economic Studies, Hosei University

Ways to Conserve the Land Title of the Orang Rimba,
Hunter-Gatherers of Jambi, Indonesia
Narihisa Nakashima(Professor Emeritus of Hosei University, Dr of International Public Policy, Osaka University)

Abstract
Since the 1980’s, the Orang Rimba, hunter-gatherers of Jambi Province, Indonesia, have been
in a dire situation due to displacement and land dispossession accompanying oil palm plantation andtimber plantation development. The Communal Land Rights recognized by Indonesia’s Basic
Agrarian Law of 1960 were designed with sedentary agrarian communities in mind. Consequently,
the government has shown reluctance to extend these rights to the nomadic Orang Rimba.
This article discusses the difficulty of preserving Orang Rimba land rights, even in the postSuharto Reform Era that commenced in 1998. Although during the COVID-19 pandemic, 80 percent
of Orang Rimba were officially issued Indonesian Residential Identity cards (KTP) inscribed with
their indigenous beliefs, uncertainties remain. Specifically in question is whether the Land forAgrarian Reform Policy of the Joko Widodo administration will effectively protect communities
predominantly maintaining a semi-nomadic lifestyle, such as the Orang Rimba.

1. Introduction, Impact of the Rights of Indigenous People on Indonesia
In September 2007, the UN General Assembly adopted the Declaration on the Rights ofIndigenous Peoples. However, there was opposition from some countries to the right to selfdetermination and the corresponding obligation to obtain FPIC (Free, Prior, Informed Consent) for
decisions and policies affecting indigenous peoples. Indonesia voted in favor of the resolution, but
shortly thereafter, Muhammad Anshor, then Ambassador to the UN, criticized the lack of a strict
definition of indigenous peoples in the Declaration, and announced that Indonesia would pursue her
own policy regarding indigenous peoples.1
In fact, however, the concept of Indigenous People, a term which has been defined in a series
of incremental changes since the 1970s (Osakada, 2017, pp.57-62), has provided substantial
grounding for NGO movements supporting land disputes. The English term, “indigenous people,” isoften used in Indonesia, but the Indonesian equivalent has been extensively debated. Eventually, theterm Masyarakat Adat 2 (indigenous society) was settled on, but a complex political struggle
occurred in the process leading up to this decision.
Sandra Moniaga, who observed the process of the founding of AMAN (The Indigenous Peoples’
1 United Nations, General Assembly Adopts Declaration on Rights of Indigenous People, 13 September 2007.
2 Adat (Customary Practice and Law) is a system of general social norms in each ethnic community, governing customs
such as land use, marriage regulations, and inheritance.

47

Ways to Conserve the Land Title of the Orang Rimba, Hunter-Gatherers of Jambi, Indonesia

Alliance of Nusantara3) wrote; “In 1993, a meeting in South Sulawesi decided upon the words,
Masyarakat Adat for referring to indigenous peoples. Suharto’s authoritarian regime fell in 1998.
This provided greater political space for civil organizations to operate. In 2003 nearly 1,000 local
and regional indigenous organizations were allied with AMAN.” (Moniaga, 2008, pp.281-83).
Lucas & Warren, known for their study of land conflicts in Indonesia, explained the politicalcontext of the term, “indigenous people” as follows (Lucas & Warren, 2003, p.100).
The term was regarded as unacceptable by the Indonesian government because of its politicallyawkward connotations in international law and because all but the small percentage of mainly
ethnic Chinese Indonesians could be regarded as “indigenous.” “Indigenous minorities” isnevertheless an appropriate means of distinguishing these marginalized groups from the largest
Javanese ethnic group that has dominated Indonesia’s political and cultural landscape since
independence. As indigenous rights became a global human rights issue, this discourse wasimported and increasingly began to influence representations of the cultural minorities referred
to collectively as the adat peoples (masyarakat adat) of Indonesia.
On the other hand, the Indonesian government continues to use Masyarakat Hukum Adat
(Customary Law Community), a descendent of the Adat-Recht Gemeinscaft concept of adat
(Customary Practice and Law) during the Dutch colonial period.4
Article 3 of the Basic Agrarian Law of 1960 (abbreviated BAL below), despite the intention tocreate a unified Indonesian law system after independence, defines the Customary Law Community,
the essence of which is not separable from the existence of Communal Land Rights (Hak Ulayat).
However, the Communal Land Rights granted by the BAL were denied to the people living inside
the Forest Area (Kawasan Hutan) defined by the Basic Forestry Law of 1967, Article 17.5 The Forest
Area, as defined by the Ministry of Forestry, covers 70% of the total area of Indonesia, and was thedriving force behind the development policy that followed (Bedner, 2016, p.70).
Law No. 39 Year 1999 - Concerning Human Rights - Article 6 stipulates:(1) In the interest of upholding human rights, the differences and needs of indigenous peoples
must be taken into consideration and protected by the law, the public and the Government.
(2) The cultural identity of indigenous peoples, including indigenous land rights, must be
upheld, in accordance with the development of the times. The difference between the original
Indonesian text and the English translation is that here, “Customary Law Community” is consistently
used for “Masyarakat Hukum Adat,” not “Masyarakat Adat.”
Although the term, Masyarakat Adat, is used in some Indonesian laws,6 its use is discouraged
by legislative bodies. Administratively, “Customary Law Community” (Masyarakat Hukum Adat) ispreferred. Masyarakat Hukum Adat and Masyarakat Adat are very different in their connotations.
The concept and use of the English term, “Customary Law Community” has its roots in the colonial
3 AMAN is the abbreviation of Aliansi Masyarakat Adat Nusantara. Nusantara is an elegant old Javanese term to describe
the Indonesian Archipelago.
4 Reviewing the long history of Dutch Colonial Studies in Indonesia, Franz and Keebet Benda-Beckmann, remarked;“Tanah ulayat (communal land) has not received sufficient attention by Vollenhoven and subsequent Dutch common law
scholars. Since the beginning of the 20th century, the perception of ulayat has changed among residents, administrators,
and researchers, and since the beginning of the reform era after 1998, tanah ulayat has been advocated by residents who
advocate the restoration of suppressed rights, but this is not in line with what customary law researchers and administrators
believe.” (Von Benda-Beckmann and Von Benda-Beckmann, 2011, pp.167-188).
5 Undang-undang No. 5 Tahun 1967 Tentang Ketentuan-Ketentuan Pokok Kehutanan.
6 For example, Law on the Management of Coastal Areas and Small Islands (No 27/2007).

48

Narihisa Nakashima

period, originating from late 19th and early 20th century conditions when colonizers exploited the“unique” culture of indigenous peoples through indirect rule. In contrast, Masyarakat Adat
(Indigenous Society) implies both the victims of Suharto’s Development Policy, and expresses
resistance to discrimination (Arizona & Cahyadi, 2013, pp.52-55).
This article clarifies the precarious situation of the Orang Rimba, formerly hunter-gatherers ofJambi Province, Indonesia. Development policies initiated in the 1980’s have led to the nearextinction of the forests that sustained them.7 Due to the facts mentioned above, their legal status iscomplicated. Internationally they are recognized as indigenous and domestically, termed Masyarakat
Adat by NGO organizations. Yet, they are not categorized as Masyarakat Hukum Adat under thestringent criteria of the Indonesian administration. In analyzing the difficulty of guaranteeing theirland rights, the author highlights shifts occurring during the COVID-19 pandemic and contemplates
potential futures facing these communities.

2. Leadership and the Land of the Orang Rimba
The number of Orang Rimba (People in the Forest) was estimated to be 3,650 in 2008 for theentire province of Jambi, according to a census conducted in collaboration with the Department of
Statistics and KKI WARSI in 2013.8 Of these, 1,775 people lived in the Bukit Duabelas National
Park (abbreviated BDNP below) area,9 which is exactly the geographical center of the Orang Rimba
lands.
The Residential ID Card,10 which all Indonesian citizens are required to carry at all times, has
not been issued to Orang Rimba, and they have not traditionally been treated as Indonesian citizens.
According to the electronic version of the Antara News,11 “The Jambi Provincial Demographic
Office announced that “the demographics of the Orang Rimba will be included in the 2020 population
statistics as well as the 2010 population statistics.” Regardless of how they are represented indemographic statistics, the fact remains that they were not accorded the rights of normal Indonesian
citizens prior to receiving ID cards during the COVID-19 pandemic. This issue will be explored in
more detail below.
Sager, who submitted his doctoral dissertation on Orang Rimba rituals and beliefs to ANU,
describes the inland inhabitants of Jambi as follows. “The traditional economies of upstream peoples
combined horticulture, foraging, and the gathering of forest products for trade. Over the last several
centuries, the upstream regions have received significant numbers of migrants from the matrilineal
Minangkabau people. Since the early 1980s, the upstream regions have been flooded by peasantsfrom Java who have come with the Indonesian government’s transmigration program and primarily
work in attached palm oil plantations (Sager, 2008, p.3).”
Orang Rimba societies are federations under a Tumenggung (senior leader). There are thirteen
Tumenggung around BDNP, three in Tebo Province, and one in Bungo Province. Tumenggung areleaders of a group of hunter-gatherer Orang Rimba who share Jelajah (an area of foraging andhunting) with fluid boundaries. Leadership is not merit based or assigned to a superior individual(Bigman), but is passed down through family lineage (Nakashima, 2021, p. 91).
According to Sager, their nomadism primarily occurs following the death of a group member
7 According to the statistics mentioned in the internet news platform, Mongabay, September 3, 2021, the forest of Jambi
Province in 1990 was 2,771 ha, but the area drastically dropped to 900 ha in 2019.
8 See KKI WARSI, Sejarah Pendirian.
9 Taman Nasional Bukit Duabelas, Tumenggung Suku Anak Dalam di Kawasan BDNP.
10 KTP, Kartu Tanda Penduduk.
11 Antara News, 19 September 2019. The Census is held every ten years by BPS (Central Agency on Statistics).

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Ways to Conserve the Land Title of the Orang Rimba, Hunter-Gatherers of Jambi, Indonesia

but can also occur or be extended due to mere preference for the lifestyle. It is traditionally combinedwith hunting, trapping, fishing, damming and poisoning rivers, and collection of forest products for
trade (Sager, 2008, p.7).
Elkholy, who conducted field work in Tebo Regency, said; “remayow is a term replete withmeaning, as it signifies a condition of living that the Orang Rimba hold sacred; one that is embodied
in the practice of moving through the forest and living off the land through hunting and gathering.
The notion of remayow also serves as a euphemism for individual and collective authority and a stateof ontological purity (murni) (Elkholy, 2016, p.105).”
Prasetijo, a lecturer at Diponegoro University, divides the Orang Rimba into three types
according to their degree of sedentarization; (1) Not sedentary, (2) Semi-sedentary, staying in boththe forests and villages, and (3) Sedentary, and typifies the characteristics of each, using fourteen
indicators. Indicators influencing this include religion, practice of rituals and taboos, housing,
attitudes toward settlement, attitudes toward policies, degree of assimilation and access to public
facilities (Prasetijo, 2017, 267-68). The Orang Rimba people discussed in this article belong to type(2). They live on the fringe of BDNP.

3. Land Grabs by Oil Palm Plantations
SAL Operations and Resistance
The Transmigration Policy of the 1970s and 1980s brought large numbers of migrants fromJava to Jambi Province. They settled in the vicinity of BDNP, in Hitam Hulu and Air Hitam, quickly
clearing the surrounding forests. This was followed by oil palm development and further forest
50

Narihisa Nakashima

depletion.
SAL (PT Sari Aditya Loka) began operations in 1988. SAL is a subsidy of Astra Group, anIndonesian multi-national conglomerate. SAL is composed of two companies, SAL1 and SAL2,
which together own 33,867 ha of plantations.12 The author was witness to the following testimonyof how the Orang Rimba lost their living environment through a series of measures.13
The land was our Jelajah until SAL came. At that time, there were not many Tumenggung, andmany Depati (leaders under the Tumenggung) were assisting Tummenggung. Then, thepopulation grew, and the group branched out. We have thirteen Tumenggung now. However, inthe 1980s, the government suddenly announced, “This land will become SAL land.” Thegovernment suddenly demanded that we vacate the land because it was going to become SAL
land. We were evicted from our land without any compensation. There was no memorandum of
understanding or sales contract of any kind.
The following is a summary of a WARSI report entitled, “Conflict Settlements between PT
SAL and the Orang Rimba.”14 There are 505 Orang Rimba people in 130 households in elevenlocations around SAL plantations.15 The trees are essential to the birth and naming of the Orang
Rimba. This custom used to be central to Orang Rimba life, but no longer exists. Some Orang Rimba
live in government-supplied barracks, and some live in tents on the plantation.16 Conflicts with oilpalm plantations have resulted in the deaths of fourteen Orang Rimba in the last fifteen years. Their
livelihood depends on gathering snakes, lizards, rubber nuts/fallen oil palm/pinang nuts, stink beans
such as jengkol,17 and other food sources that are not used by the villagers.18 Problems arise when
what is a means of earning a living for them is seen as theft by the big plantation and plasma(smallholder) farmers.
The main causes of trouble are categorized as: (1) stealing from villagers’ plantations and oilpalm orchards, (2) entering plantations and smallholders’ plantations to hunt and cause trouble withguards and villagers, and (3) picking up brondolan (oil palm nuts that have fallen in the plantations)and selling them to middlemen. The Orang Rimba who have lost their forest habitat typically haveno skills or knowledge outside of hunting and gathering, with the concept of land ownership being
foreign to them. Their only recourse for survival is to enter the plantations to hunt game and collectbrondoran to sell. Tragically, encounters with plantation security guards or plasma (smallholder)farmers, result in their being forcibly expelled and in extreme cases, fatal violence.19 Many of theseincidents are resolved through “customary law,” to avoid police involvement, which culminates in
the Orang Rimba being given only meager compensation.
In 2000, the BDNP was established by expanding a previously protected area. The whole areaof BDNP is only 60,500 ha. Nevertheless, the government seems to assume that the establishment
12 PT. Sari Aditya Loka 1, Company Profile.
13 Interviews conducted on August 20 and 21, 2019.
14 Peluang Solusi Konflik Orang Rimba dengan PT SAL, Pekat IB Sumbar, Year Not Mentioned, Unpublished Paper.
15 The latest data by PT SAL shows that there are 217 house heads or 898 people who had been suffering damage fromthe operation of PT SAL (Personal communication from WARSI).
16 Of the five people interviewed, two live in tents and three live in government-provided barracks.
17 The plant belongs to the legume family and has a strange odor. It is a popular ingredient in Indonesian cuisine.
18 They hunt crocodiles about once a month. They shoot sleeping crocodiles at night and eat the meat. According to the
WARSI’s report, the crocodile penis is also included, but as an energizer.
19 Such troubles increased during the COVID-19 pandemic, because Orang Rimba faced food shortages. Mongabay, aninternet news platform, reported the serious trouble of some Orang Rimba people with PT SAL in Merangin regency on
May 19, 2020.

51

Ways to Conserve the Land Title of the Orang Rimba, Hunter-Gatherers of Jambi, Indonesiaof the BDNP has solved all the Orang Rimba problems. On the contrary, the land designated as
BDNP was previously industrially forested, and there is very little good (diverse) forest there.
Approximately 900 Orang Rimba live in the park, but many Orang Rimba live outside the park, andthe issue of how to protect the rights of both groups has not been addressed at all. Merely providing
small barracks to the Orang Rimba who have lost their forests and means of survival, is not asolution.

Orang Rimba Demanding Land Return
In the 2000s, people started insisting that SAL return their land, but SAL stated no more thanthat it would improve the welfare of the people through CSR. The Orang Rimba demanded that (1)No extension of SAL’s HGU (Commercial Use Rights) (35 years, valid until 2030) would be madeand that: (2) As part of SAL’s CSR, each household should be given 2 ha of land in an accessiblearea and receive livelihood compensation. The corporation refused these requests.
However, things have changed dramatically since that time. Article 7.1.c of the 2018 Presidential
Decree No. 86 on Agrarian Reform stipulates that “the project concessionaire must return at least
20% of the state land (the land on which the project concession was issued) to the original owners
upon renewal or termination of the project concession.”20 On the basis of this so-called TORA (Landfor Agrarian Reform) policy, the Orang Rimba are demanding that 20% of the 5,479 ha of SAL1’s
core plantations, or 1,095 ha of land, be returned to them. This is because the living areas of each of
the five groups the author interviewed are within SAL1’s core plantations (Nakashima, 2021,100102).
These claims of lost land rights are also beginning to be considered by an Orang Rimba groupliving in another area, Terab, Batang Hari Regency.21 In fact, 167 Orang Rimba households underfour Tumenggung reside in and around BDNP. The estate of the PT Wana Perintis rubber plantation
used to be in the Jelajah of those Orang Rimba people. As the land clearing was not done by Wana
Perintis, these Orang Rimba people were given the right to engage in Social Forestry22 there. Thisdecision was made by the Ministry of Environment and Forestry to avoid conflict. Through engaging
in partnership (kemitraan) Social Forestry, Orang Rimba households now earn 500,000-1,500,000
rupiah per month.23
They also have been experiencing tensions with the oil palm plantation BKS (PT Bahana Karya
Semesta). In June 2016, there was a major conflict with BKS when BKS security guards took
physical action against the protesters. As a result, two people were injured and a car, five motorcycles,
and 1,000 pieces of clothing were burned. The corporation has since discussed compensation for thedamage and future countermeasures, but no progress has been made in negotiations other thancompensation for the two injured individuals (Nakashima, 2021, pp. 102-103).24

20 Peraturan Presiden Republik Indonesia Nomor 86, Tahun 2018 Tentang Reforma Agraria.
21 Batin 24 County, Batang Hari Regency.
22 Social Forestry was legislated in 2016 by the Regulation of the Minister of Environment and Forestry No. 83
23 According to the information from Mr Rudi Syaf, it is the women who are involved in rubber cultivation. Most of themen are engaging in hunting and collecting. The exchange rate at the time made this equivalent to approximately US$50.
24 On this incident, I interviewed persons locally on August 26, 2019. They have Sialang trees for wild honey collectionin BDNP and the cash income raised from their honey collection is significant. A leader said, “We just collected one ton ofhoney; with two Sialang trees, we could harvest one ton of honey and had a cash income of 40 million rupiah (US $4,000).”
Sialang is a tree in which bees build their nest.

52

Narihisa Nakashima

4. Institutional Efforts to Improve the Orang Rimba’s Situation
Legal Status of the Orang Rimba
It is difficult to locate the exact legal status of Orang Rimba in the current Indonesian legal
system. It has not been the subject of remedies in the various laws aimed at resolving land issues
during the reform era. To resolve land disputes which frequently occurred after Suharto’s resignationin 1998, a decision was made by the National Land Agency Head, Ministry of Agrarian Affairs. No.
3.25 Article 2(2) of the Agrarian Affairs Act stipulates the following requirements for the recognitionof a Customary Law Community (Masyarakat Hukum Adat): a) the existence of people who
recognize the existence of a shared customary law system, b) the existence of a specific Communal
Land (tanah ulayat) where the people live, and c) the existence of communal land operated, managed,
and used by the people. Bedner, a professor at Leiden University, explains that certification requires
a study by an external third party (a university or research institution) (Bedner, 2016, 72).
Article 97 of the 2014 Law of The Republic of Indonesia, Number 6, concerning Village,26 lists
the following three requirements for recognizing a customary law community: a. its unity ofCustomary Law Community and traditional rights still actually exist, whether they shall be territorial,
genealogical, or functional; b. its unity of Customary Law Community and traditional rights are
deemed in accordance with the development of society; and c. its unity of Customary Law
Community and traditional rights in accordance with the principles of the Republic of Indonesia. In
other words, what is key in defining a community is that the Masyarakat Hukum Adat members
reside in a certain area, have their own adat and the adat is being successfully managed. It is assumed
that communities cooperate with the development policies of the Indonesian government.
Currently, two customary law communities (Masyarakat Hukum Adat) are recognized in Jambi
Province.27 Both are located in remote areas and are very small communities that meet the criteria
above. Although this recognition imposes restrictions on their ability to buy and sell communal
lands, it can be a means to combat development and illegal settlement.28 However, as discussed
below, these criteria cannot remedy the current situation of the Orang Rimba (Nakashima, 2021,
p.105).

WARSI Support
WARSI began supporting the Orang Rimba around 1997. Their action was focused on teaching
three subjects: reading, writing and counting. WARSI has a local office on the southwestern borderof the BDNP. There, more than a dozen children (ages 7-8 to 15-16, all boys) belonging to theTumenggung Nggrip group live and study with the staff. They can study in WARSI facilities, or they
can go to elementary school and receive an institutional education. Alternatively, they can study in
the BDNP when WARSI staff come through.29
However, there is still a tendency among some Orang Rimba to reject these activities. For them,the outside world is considered harmful to the Orang Rimba and they need to keep as much distancefrom it as possible. Diseases and misfortunes in their community are considered to be the result of
25 Peraturan Menteri Negara Agraria / Kepala Badan Pertanahan Nasional Nomor 5 Tahun 1999 Tentang Pedoman
Penelesaian Masalah Hak Ulayat Masyarakat Hukum Adat.
26 https://www.pbatlas.net/uploads/7/0/6/1/70619115/law_6__2014_.pdf
27 One is Datuk Sinaro Putih, Bungo Regency, which was recognized in 2006, and the other is Serampus, Murangin
Regency, which was recognized in 2016.
28 Villagers’ opinion in a Serampus survey.
29 I visited this place on August 21, 2018, and talked to the staff of WARSI (Nakashima, 2021, p.109).

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Ways to Conserve the Land Title of the Orang Rimba, Hunter-Gatherers of Jambi, Indonesia

these negative influences from the outside world.30 Institutional education is also thought of in thisway, and there are still some groups that keep their relationship with the outside world to a minimum.31
The forest dwellers still observe strict food taboos, an indicator of their differentiation from theoutside population. Specifically, to prevent contamination from the Orang Terang (the outside
peoples), they forbid foods made from farmed animals, such as chickens, eggs, goats, cows and theirmilk, etc. This “prohibition” is called Patang’on (Prasetijo, 2017, p.273).
At the same time, some Orang Rimba are seeking to adapt to the outside world. If their children
wish to continue their studies further in secondary school, the decision regarding this will be madethrough negotiations among parents, leaders and WARSI representatives. Such children may receive
government support, and some have already become police officers and military personnel. WARSI
also provides FM radio broadcasts for the Orang Rimba living in the BDNP fringe area, conveying
important news, providing other necessary information, introducing people of interest, and playing
popular Dandut (an Indonesian song genre).32
The majority of the Tumenggung Nggrip group still lives in the BDNP, while 17 families live
outside of the forest in tents; 40 families live in government-provided houses near where WARSI’s
local office is located. “Even if we are provided with houses, we have no food. There is not enoughforest in the surrounding area for us, Orang Rimba, to survive. We cannot hunt the game we need tosurvive. There are not enough wild animals, especially wild pigs (Babi Hutan),” they complain.

Placing Hope in Social Forestry with Wild Rubber
Indonesia classifies natural rubber into two types: Karet Alam (wild rubber) and Karet Sintetis(cultivation rubber). The latter nomenclature is derived from the scientific name, hevea brasiliensis,
for the species brought from Brazil. The most productive variety is the excellent cultivation rubber,
Karet Unggul. It becomes harvestable 5 to 7 years after planting and reaches its maximum yield
between the 15th and 20th years. The yield is more than five times higher than that of wild rubber,
but it requires intensive management, depletes the soil and must be replanted after 20 years(Nakashima, 2021, pp. 113-114).
Some Orang Rimba hope that instead of the excellent cultivation rubber (Karet Unggul) being
planted by companies, wild rubber (Karet Alam) planting, which is supportive of the community,
will be allowed.33 This shift is essential for them since fruit and wild rubber can coexist. Although
the monoculture plantation system forces people to obey company regulations, some Orang Rimba
have planted fruit among the cultivation Karet Unggul rubber trees despite plantation companies
banning the practice (ibid, p. 113).
In the last chapter of Stories of Rubber (Gomu Monogatari, 1984), the author Tsurutaro
Nakagawa states, “In the early 1980s, the demand ratio for artificial to natural rubber was two to one,but as natural rubber (hevea brasiliensis) generates less heat during vibration, it has been in demand
for tires for heavy trucks, large buses, and airplanes. In addition, the price of synthetic rubber is
30 During the COVID-19 pandemic, they practiced a custom called besasandingon, or social distancing from the plague.
31 The parents of Orang Rimba who managed to permit WARSI educational activities wanted education to take place
far away from their residence. This is because they are afraid of being possessed by the external demon, Natong Luar
(Manurung, 2007, p.306).
32 KKI WARSI, Sidia Dan Benor FM Radionya Orang Rimba.
33 I interviewed Tummenggung Tarib, a prominent leader of Orang Rimba, in August 2018. He told me that cultivationof Karet Alam enhances ecosystem diversity. Although it is true that Karet Unggul produces greater quantities of rubber
product than Karet Alam, the latter can thrive for up to a century, he continued, and can allow for the coexistence of fruits
and wild animals. On the contrary, he stressed that Karet Unggul trees last only 20 years, and have a detrimental effect on
the soil and environment (Nakashima, 2021, p.123-124).

54

Narihisa Nakashimarising due to soaring petroleum prices, and it is no longer possible to use petroleum, without restraint,
to produce synthetic rubber as in the past” (Nakagawa, 1984, pp.213-215). However, Nakagawa was
not aware that natural rubber could be further classified into wild rubber and cultivation rubber.
The Ministry of Agriculture of Indonesia expresses the following view on the difference
between the properties of wild and cultivation rubber: “Wild rubber is gaining popularity indeveloped countries due to its superiority over cultivation rubber in terms of heat generation during
vibration. In fact, the demand for wild rubber has increased in recent years, as tires for vans, trucks,
buses, and other vehicles have been found to perform better when composed of 45-55% wildrubber.”34
The book, Everything about Rubber (Panduan Lengkap Karet, 2008), describes the difference
between the properties of wild rubber (Karet Alam, abbreviated KA) and cultivation rubber (Karet
Sintetis, abbreviated KS) as follows. KS has a much higher utilization value than KA. KS is moreresistant to chemicals. KS has not experienced any difficulties in transportation or supply during thepast century. KA, on the other hand, has advantages over KS, such as perfect elasticity, easy
processing, high fluidity and heat resistance. It is also less prone to cracking than KS. However, KS
has higher resistance to chemicals and more stable prices. There has never been a shortage in productdelivery or supply of KS. These advantageous characteristics are not true of KA, which has
inconsistent prices and supplies which sometimes fluctuate widely. Despite these disadvantages,some producers prefer KA” (Tim Penulis PS, 2008, pp.17-18).
In addition to engaging in Social Forestry on 114 hectares of the PT Wana Perintis rubber
plantation, the people of Terab, Batang Hari Regency have been cultivating their own rubber estatesin which fruits such as mango, jack fruit and durian are planted among wild rubber trees.35 Although
it is probable that both the government and the company are aware of this situation, they havechosen not to pursue any tangible measures in response. Their reluctance may stem from a motive
to evade negative publicity. At present, Social Forestry regulations strictly prohibit changing
designated land use, but it is necessary to firmly emphasize to companies and government authorities
that there is a growing demand for wild rubber based on its characteristics, and that this demand will
also lead to the preservation of tropical forests as well as that of the Orang Rimba living environment.

Sokola Rimba
Butet Manurung’s book, Sokola Rimba (2007), which describes the formal education of Orang
Rimba and their reactions to it, received great international acclaim and she was awarded theMagsaysay Award in 2014. In 2013, Indonesian director, Riri Riza, made a film based on this book,which was also highly acclaimed.36 Butet Manurung worked for WARSI’s educational activities(1999-2003) before leaving WARSI and founding Sekola Rimba/The Jungle School in 2003,37 an
NGO that works not only with the Orang Rimba but also with the Dani people in Papua. Within
Sekola Rimba/The Jungle School is KMB, an organization of the Orang Rimba for the Orang Rimba.
More specifically, it is an organization that supports the 533 members of Tumenggung Jelitai who
reside upstream of the Makekal River in BDNP.38
34 Kementrian Pertaian, Keungulan Karet Alam dibandinga Karet Sintetis, 23-September-2013.
35 Personal communication from Mr. Rudi Syaf.
36 Sokola Rimba: The Jungle School (Miles Film) won awards at various international film festivals and the AudienceAward at the 2014 Fukuoka Film Festival.
37 The Indonesian word Sekolah Rimba (Forest School) is “Sokola Rimba” in the Orang Rimba language.
38 Data of the Orang Rimba Upstream of the River Makekal, 2016 (KMB, Data Penduduk Orang Rimba Makekal Hulu,
Maret 2016) based on statistics. The author was able to interview more than a dozen Orang Rimba youth on August 24,
2018, at the KMB office in the city of Bangko, the capital of the province of Murangin regency.

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Ways to Conserve the Land Title of the Orang Rimba, Hunter-Gatherers of Jambi, Indonesia

The major goal of KMB activity is to establish a 6,500-ha portion of the Tumenggung Jelitai
group’s Jelajah in BDNP as a Customary Law Forest (Hutan Adat). Their goal is to preserve it “asa place of ritual and worship for the Orang Rimba.” Cutting trees will not be allowed, potato
cultivation and rubber plantations will not be permitted, but hunting and rattan/honey gathering willbe allowed. If successful, they hope to be officially recognized by the President, and at the sametime, by the local government.
In 2012, the Indonesian Ministry of Forestry segmented the BDNP into seven zones forconservation purposes. This division faced criticism for not accurately reflecting the land usage of
the Tumenggung Jelitai group who have resided there for generations. In September of 2018, arevision in the BDNP zoning was declared,39 motivated by several factors: (1) The Orang Rimba
communities had already established their presence before the inception of BDNP zoning, (2) the
current zoning has few religious zones, and (3) restrictions on economic activities were imposed
within the traditional zones. This commitment signifies the BDNP’s commitment to honoring the
traditional land-use practices of the Orang Rimba (Nakashima, 2021, p.116). As a result, KMB’s
objective to secure a place of ritual and worship for the Orang Rimba is drawing closer to realization.
Butet Manurung founded the Sokola Rimba organization with the belief that addressing thepressing challenges faced by the Orang Rimba required a unique approach, one that, while alignedwith the nationwide organization AMAN, diverged in its methods and activities. Similarly, based on
his experience in interviewing KMB members, this author felt that it would be difficult for the Orang
Rimba to have an equal relationship with AMAN, both in terms of Indonesian language literacy
skills and education level. KMB is an attempt to build an Orang Rimba-led rights movement(Nakashima, 2021, pp.115-118).40

Sedentarization and ID Cards
Since the colonial period, the central government has consistently implemented Orang Rimba
sedentarization policies with little success. Today, the Orang Rimba are accepting some forms ofsedentarization because the forest is already gone and the traditional way of life is no longer
possible.41 However, although buildings have been erected, they are seldom used. The forest dwellers
are not familiar with the idea of settlement, and the buildings themselves, such as barracks, are so
inferior that many Orang Rimba return to the forest after a short period of attempting to inhabit
them. Most importantly, the lack of forest in the vicinity of the buildings renders the residences
almost completely unappealing (Nakashima, 2021, p.118).
On October 30, 2015, President Joko Widodo (Jokowi) visited Air Hitam County, Sarolangun
Regency, and was greeted by several hundred Orang Rimba. After speaking with residents living intents within oil palm plantations, the President met with four proactive Tumenggung. President
Jokowi was the first president to visit Orang Rimba lands, but no further progress was made.
President Jokowi’s visit was commendable in that it demonstrated the administration’s commitment
to indigenous peoples, but no significant outcomes transpired other than a promise to build housing
to promote resettlement.42 What was expected of him was not the provision of housing to promote
resettlement, but a statement from a broader perspective on how to guarantee the indigenous rights
39 Taman Nasional Bukit Duabelas, Zonasi Taman Nasional Bukit Duabelas.
40 WARSI is critical of this KMB activity. It says that KMB only helps one particular group and does little for other
Orang Rimba.
41 Sager remarked: “Before 1960, most of the area had forest cover. By 1980, 73% had forest cover, and by 1990, when
the World Bank ended its support for the transmigration program, the figure had again dropped to just over 50%” (Sager,
2008,p.13).
42 Detiknews, Berikut Kronologi Pertemuan Presiden Jokowi dengan Suku Anak Dalam, 2 November 2015.

56

Narihisa Nakashimaand land rights of the Orang Rimba. This was not forthcoming.
After an interview at the park administration office in Sarolangun Regency, the author was toldthat some Tumenggung are allowed to have Indigenous Beliefs listed in the religion section of their
ID card. However, there was no legal basis for this at the time, and it was understood to be a local
response. A landmark ruling was issued in 2017. “Indonesia’s Constitutional Court has announced
that it will allow ‘Indigenous Beliefs’ (Aliran Kepercayaan)43 to be inscribed in the faith section of
the ID Card.”44 Responding to this constitutional decision, President Jokowi ordered theadministrators of state and local governments to honor the decision.45 Notwithstanding this movefrom the central government and growing criticism throughout Indonesia of ongoing politicalrepression of religious minorities in Muslim-majority Indonesia, the administrators of state and local
governments are still unwilling to accept the ruling.46
On May 28, 2021, Suara.com reported on the Orang Rimba response to the death of a 70-yearold woman at a market in Sarolangun Regency. The woman had suddenly fallen, and died in themarket. Witnesses of the unexpected event were taken aback, speculating that the coronavirus had
infiltrated their region. Immediately, the Tumenggung Nggrip group residing at a nearby barracks
decided to withdraw into the jungle to protect themselves from the perceived threat. In their culturalcontext this is known as besasandingon, a social distancing strategy. They maintained distances ofseveral meters from each other. This proved effective against potential COVID-19 transmission, but
led to food scarcity.47 Following this incident, other Orang Rimba groups also adopted this custom.
The Indonesian government became aware of their difficulties and assisted them with cash, food andmedical services including vaccinations at community health centers. WARSI supported thegovernment in issuing ID cards to Orang Rimba, and currently it is estimated that 80 percent possessthem. Their recorded faith was officially recognized as Aliran Kepercayaan.48

5. Concluding Remarks
Communal Land Rights granted to each ethnic group in Indonesia under BAL were greatly
restricted by the Basic Forestry Law of 1967. However, Law No. 41 Concerning Forestry, Part II,
Section (1) stipulates that “forests shall be divided into state forests and rights forests according totheir status,” and Section (2) stipulates that “state forests shall constitute Customary Law Forests”(Hutan Adat). AMAN appealed the inconsistency of this provision to the Constitutional Court,
which ruled on May 16, 2013, by Constitutional Court Decision No. 35 (hereinafter referred to asMK35) that customary forests are not state-owned forests. Customary forests are forests within
indigenous lands that exist provided the forests and the indigenous communities that recognize their
existence continue to exist.
MK 35 considers the Customary Law Community to be the “bearer of rights” and the subjectof law in customary lands. It is hoped that this ruling will contribute solutions to the issues of
43 This refers to several syncretistic religions with characteristics of Javanese mysticism but means “other religions.”“Other” refers to ones not among the six recognized religions.
44 It admits that to deny other religions than the six religions (Islam, Catholicism, Protestantism, Hinduism, Buddhism
and Confucianism) listed in Law No. 23 of 2006 concerning People Administration is anti-constitutional. Kompas. Com(online), MK: Kolom Agama di KTP dan KK Dapat Ditulis “Penghayat Kepercayaan”, 7 November 2017.
45 Beritagar.id., KTP untuk penghayat kepercayaan sesuai pilkada.
46 VOA (Voice of America), Indonesian Groups Call for Minority Religious Protections, Sep. 14, 201847 Suara.com, (2021). Antara News reported that 22,854 people were infected with COVID-19, and 511 people died of it
in Jambi Province at that time (Antara News 2021).
48 Personal communication from Mr. Rudi Syaf. See the news of LIPUTAN 6 on March 12, 2021, concerning issuing ID
cards to Orang Rimba.

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Ways to Conserve the Land Title of the Orang Rimba, Hunter-Gatherers of Jambi, Indonesia

indigenous societies in the future. While some are pleased with this decision out of hand, Siscawati
lays out the problems that exist in this decision (Siscawati, 2014, p.13). It is problematic, forexample, that AMAN, which filed the case, pointed out the inconsistencies in the definition ofCustomary Law Community in the 1999 Forestry Law, but refused to request a review of it. It would
be premature to expect that the rights of indigenous communities will be significantly protected bythis Constitutional Court decision, as none of the indigenous communities, such as the Orang Rimba
keep written records to confirm their rights.
Defining communal land rights among the Orang Rimba, who are/were migratory huntergatherers rather than sedentary agriculturalists, is a difficult task. They did not occupy the land evenin a collective manner but were people who moved around in Jelajah (an area of foraging andhunting) and lived on the periphery of the current SAL plantation just prior to the arrival of the SAL
corporation. It is not clear how long they lived there. Whenever there was a death in their familygroup, they would fold up their tents, move for several months, and look for a new place to live. Thiswas possible in the days when the forests were rich, but now that the land has been encroached uponby oil palm plantations, industrial plantations and migrants, it is difficult for the Orang Rimba tocontinue this way of life.
It is difficult at this point to recognize the entire population of Orang Rimba scattered in four
regencies of Jambi as one Customary Law Community. It may be possible if authority is given to thegovernor of the province, rather than to the regencies. Alternatively, if this were done under theauthority of the President, it would be more likely to have an effect. However, the idea of establishingan all-Orang Rimba Customary Law Community is not currently being considered.
Indonesia has only endorsed the UN resolution recognizing the rights of indigenous peoples
and has not actively pursued policies that would push for the expansion of indigenous rights indomestic law.49 The government’s recommendation for recognition of Masyarakat Hukum Adat is
not a remedy for the current situation of the Orang Rimba, as it requires (1) territoriality, (2) the
existence of autonomous customary law, and (3) a permanent management entity. Working from thefoundation of the Jokowi Administration’s Land for Agrarian Reform policy (TORA), it might bepossible that the Orang Rimba, who have conventionally lived in the vicinity of SAL corporation
operations and had a portion of their land confiscated by these operations, be given 5 ha of land perhousehold to cultivate wild rubber and other crops.
On October 26, 2021, the Sarolangun Regency government announced that for the first time,
the governor of Sarolangun Regency issued 105 TORA certifications to the indigenous inhabitantsof Desa Spintun, Sarolangun Regency in Jambi. Furthermore, they confirmed plans to extend the
TORA certification to additional areas within the Regency. Antara News also highlighted that in
2023, the Ministry of Forestry in Jambi intends to broaden the scope of the TORA program.50
Interestingly, the recipients of these certificates were not the Orang Rimba, but rather, the Batin
Sembilan people. Both groups are commonly referred to as SAD (Suku Anak Dalam), whichtranslates to “peoples in outback.” Historically, the Batin Sembilan people were involved in upland
rice cultivation, and those awarded the certificates had been residents of the area, engaging in
49 Regrettably, the 2023 IWGIA report reads: “Throughout 2022, Indigenous Peoples in Indonesia demonstrated theirresilience to the political pressure and policies that were increasingly moving away from the agenda of recognizing andprotecting Indigenous Peoples. The fact that recognition has been confirmed in the Constitution does not necessarily meanthat the State is carrying out its mandate to establish the Indigenous Peoples Law. After a long process of more than adecade, the Indigenous Peoples Bill remains simply a bill in Parliament. By the end of 2022, the Indigenous Peoples Bill
had again failed to be adopted into law, as in previous years. In the midst of this situation, there have recently been a numberof discriminatory laws and regulations enacted that have placed Indigenous Peoples in an increasingly threatened position.”
50 Pemerintah Kabupaten Sarolangun (2021), and Antara Jambi (2022).

58

Narihisa Nakashima

swidden rice cultivation long before the arrival of SAL. To date, no Orang Rimba has received a
TORA certificate, and there is no clear indication that they will in the foreseeable future.51
The TORA program, under the Joko Widodo administration, seems to offer significant
opportunities for indigenous populations. However, it appears to show a preference for completely
sedentary peoples, sidelining the Orang Rimba who, while settled, still aspire to maintain a seminomadic way of life. Given the divide between the Orang Rimba and governmental policy, it remains
crucial to explore strategies that safeguard the land rights of the Orang Rimba.

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Abbreviations and Indonesian Words
Adat
AMAN

Customary Practice and Law
Aliansi Masyrakat Adat Nusantara (Nusantara Association of Customary
Law Society)
BDNP
Bukit Duabelas National Park
Hak Ulayat
Communal Land Rights
JelajahArea of Foraging and Hunting
Masyarakat Adat
Indigenous (Adat) Society
Masyarakat Hukum Adat Customary Law Community
Tanah Ulayat
Communal Land
Tumenggung
Senior Leader of a Nomadic Group of Orang Rimba
WARSI / KKI WARSIThe Indonesian Conservation Community

Acknowledgements
This article is a revised and condensed version of Chapter 3 from the author’s book in Japanese,“Palm Oil Plantation Development and Land Disputes in Sumatra” (Aburayashi Nouen Kaihatsu toTochi-funsou, Sumatoratou no Fieldwork, Hosei University Press, 2021, pp.76-140). Informationand insights obtained through personal communication with WARSI have been incorporated.
This article is a research result of “Cultural Anthropological Studies of the Land Title of theOrang Rimba, Hunter and Gatherer of Indonesia,” Grants-in-Aid for Scientific Research (KAKENHI,
C, April 2020 – March 2024, No. 20K01198, the author is the representative). Field work in Jambi
Province took place on several occasions with the aid of “Socio-Cultural Study of National Parks ofIndonesia under Oil Palm Development,” KAKENHI (C, April 2017 – March 2020, No. 17K03295,
the author is the representative).
I would like to extend my gratitude to Mr. Rudi Syaf, former Director and current Advisor at
WARSI, for his invaluable support in my research.

61

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