AN EMPIRICAL ANALYSIS OF ASYMMETRIC DUOPOLY IN THE INDONESIAN CRUDE PALM OIL INDUSTRY

The apparent increase in market concentration and vertical integration in the Indonesian crude palm oil (CPO) industry has led to concerns about the presence of market power. For the Indonesian CPO industry, such concerns attract more attention because of the importance of this sector to the Indonesian economy. CPO is used as the main raw material for cooking oil (which is an essential commodity in Indonesia) and it contributes significantly to export earnings and employment. However, dominant producers argue that the increase in economies of scale and scope lead to an increase in the efficiency, which eventually will be beneficial for the end consumers and export earnings. This research seeks to examine whether the dominant producers do behave competitively and pass the efficiency gains to the end consumers, or they enhance inefficiency through market power instead. In order to identify the most suitable model to measure market power in the Indonesian CPO industry, different market power models are explored. These models can be divided into static and dynamic models. In general, all of them accept the price–cost margins as a measure of market power. However, static models fail to reveal the dynamic behaviour that determines market power; hence the dynamic models are likely to be more appropriate to modelling market power. Among these dynamic models, the adjustment model with a linear quadratic specification is considered to be a more appropriate model to measure market power in the Indonesian CPO industry. In the Indonesian CPO industry, producers can be divided into three groups, namely the public estates, private companies and smallholders. However, based on their ability to influence market price, smallholders are not considered as one of the dominant groups. By using the adjustment cost model, the market power of the dominant groups is estimated. The model is estimated using a Bayesian technique annual data spanning 1968–2003. The public estates and private companies are assumed to engage in a noncooperative game. They are assumed to use Markovian strategies, which permit firms to respond to changes in the state vector. In this case, the vector comprises the firms and their rivals’ previous action, implying that firms respond to changes in their rivals’ previous action. The key contribution of this thesis is the relaxation of the symmetry assumption in the estimation process. Although the existence of an asymmetric condition often complicates the estimation process, the different characteristics of the public estates and private companies lead to a need for relaxing such an assumption. In addition, the adjustment system—which can be seen as a type of reaction function—is not restricted to have downward slopes. Negative reaction functions are commonly assumed for a quantity setting game. However, the reverse may occur in particular circumstances. Without such restrictions, the analysis could reveal the type of interaction between the public estates and private companies. In addition, it provides insights into empirical examples of conditions that might lead to the positive reaction function. Furthermore, the analysis adds to the understanding of the impact of positive reaction functions to avoid the complicated estimation of the asymmetric case. As expected, the public estates act as the leader, while the private companies are the follower. Interestingly, results indicate that as well as the private companies, public estates do exert some degree of market power. Moreover, the public estates enjoy even higher market power than the private companies, as indicated by market power indices of -0.46 and -0.72, respectively. The exertion of market power by both the public estates and the private companies cast some doubts about the effectiveness of some current policies in the Indonesian CPO industry. With market power, the underlying assumption of a perfectly competitive market condition—that serves as the basis for the government interventions—is no longer applicable. Hence, many government interventions are unlikely to have the desired effect. The Indonesian competition law that has been imposed since 1999 might be effective in preventing firms to sign collusive contracts. In fact, even without such an agreement, firms in the CPO industry are likely to exert some degree of market power. As an alternative, eliminating the ‘sources’ of market power might be a better solution. If the public estates have the aim of maximising welfare, privatisation might improve their efficiency, hence they have ability to suppress the private companies’ market power. However, if in fact, the public estates deliberately reduce output to gain higher profit, privatisation might increase the degree of market power of both groups of companies even further. In such a condition, addressing the long term barriers of entry stemming from the requirement of high investment might be a better alternative to address the market power problem in the CPO industry

Flowchart showing the experimental design for fetal rats at day 18.

<p>The fetal rats were delivered from 2 dams. LCM =  laser-capture microdissection, AO EC = endothelial cells from the descending aorta, AO SMC = smooth muscle cells from the descending aorta, DA EC = endothelial cells from the ductus arteriosus, DA SMC = smooth muscle cells from the ductus arteriosus, PFA 4% = paraformaldehyde 4%, LCM = laser-capture microdissection, MA = microarray. The same experimental design was used for the experiments at day 21.</p

Visual representation of microarray results.

<p>A. Spectral map bioplot. The first two principal components (PC) of the weighted spectral map analysis (SPM) of normalized microarray data are plotted. The samples are depicted in coloured squares with numbers. The colours are explained in the figure. AO SMC = smooth muscle cells from the descending aorta, DA EC = endothelial cells from the ductus arteriosus, DA SMC = smooth muscle cells from the ductus arteriosus. 18 d = day 18 of gestation, 21 d = day 21 of gestation. Distances between the squares are a measure for the similarity between samples. Genes that do not contribute to the differences are indicated as dots in the cloud around the centroid (represented by the cross). The ten most significantly contributing genes are annotated by their gene symbol. The first PC (PC1) explains 29% of the variance in the dataset and discriminates samples from day 18 (n = 24) from those of day 21 (n = 24). The second PC (PC2) explains 8% of the variance and discriminates between ECs and SMCs. B. Histogram showing the most significant differentially expressed genes between DA and aorta. The annotation of the genes is on the x-axis. The adjusted p-values are on the y-axis. Red bars represent genes that are enriched in the aorta. Green bars represent genes that are upregulated in the DA. C. Volcano plot. The volcano plot constructed with LIMMA analysis summarizes the fold changes between the two types of the samples (<i>i.e.</i>, DA versus aorta) and the log10 transformed p-values. The negative log10 transformed p-values (y-axis) are plotted against the log ratios between the samples (log₂ fold change). For our study we selected 4 genes. The position in the upper left (<i>Rgs5, Tfap2B, Dlx1</i>) is the result of a high ratio of differential expression.</p

The 16 most significantly differentially expressed genes between DA and aorta in alphabetical order.

<p>The 16 most significantly differentially expressed genes between DA and aorta in alphabetical order.</p

Gene expression of <i>Rgs5</i> (a), <i>Dlx1</i> (b), <i>Tfap2B</i> (c) and <i>Pcp4</i> (d) by rtqPCR.

<p>The same mRNA preparations were used for microarray (shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086892#pone-0086892-g004" target="_blank">figure 4</a>) and rtqPCR. Relative expression levels are shown for each sample. Horizontal bars depict the means. The levels are peptidylprolylisomerase B (<i>Ppib</i>) normalized. Red symbols represent endothelial cells (EC) and green symbols represent smooth muscle cells (SMC). AO = aorta, DA = ductus arteriosus. 18 = day 18, 21 = day 21.</p

Sections used for laser-capture microdissection.

<p>The level of laser-capture microdissection (LCM level) is indicated in <b>a</b> and <b>d</b>. The arrow represents the area of LCM in the aorta (AO) of a fetal rat at 18 days (18 d) and the ductus arteriosus (DA) at 21 days (21 d). Smooth muscle cells are labeled with anti-smooth muscle actin in green (<b>b</b>, <b>f</b>) and endothelial cells are labeled with CD31 in red (<b>c,g</b>). Bars represent 100 µm. Images from the microdissection cap show the dissected endothelium of two aorta sections with fluorescence (<b>d</b>) and strips of SMCs from the DA without fluorescence (<b>h</b>). These photomicrographs are taken from the microdissection cap that was used for capturing the tissue samples from the sections.</p

Gene expression of <i>Rgs5</i> (a), <i>Dlx1</i> (b), <i>Tfap2B</i> (c) and <i>Pcp4</i> (d) by microarray.

<p>Expression levels are expressed as fluorescent signal intensity measured on the array after normalization. The dots represent individual samples. Horizontal bars represent the means. The same colors are used in a–d. Red = ECs from the aorta at day 18 (AO EC 18), yellow = ECs from the aorta at day 21 (AO EC 21), light green = SMCs from the aorta at day 18 (AO SMC 18), dark green = SMCs from the aorta at day 21 (AO SMC 21), turquoise = ECs from the DA at day 18 (DA EC 18), blue = ECs from the DA at day 21 (DA EC 21), purple = SMCs from the DA at day 18 (DA SMC 18), pink = SMCs from the DA at day 21).</p