Data Analysis Methods Factors Influencing Indonesian Cocoa Export to the European Union

Table 6. Budget recapitulation of cocoa program for three years 2009 - 2011 No Source Cost Milion 1 Central Government National Budget 2.521.634,7 2 Province Government Local Budget I 257.594,5 3 Regency Government Local Budget II 786.482,2 4 Banking plantation revitalization 6.716.289,3 5 Private quality standard socialization 2.500 6 Farmer labour 3.464.989,8 Total 13.749.490,5 Source: Portal Nasional Republik Indonesia, 2009 According to the General Directorate of Plantations 2009, locations of te program cover nine provinces in 40 districts, they are: a. West Sulawesi in five districts: Mamasa, Polewali Mandar, Majene, Mamuju and North Mamuju. b. South Sulawesi in 10 districts: Bantaeng, Bone, Soppeng, Wajo, SindenrengRappang, Pinrang, Enrekang, Luwu, North Luwu. c. Southeast Sulawesi in five districts:Konawe, Kolaka North, South and MunaKonawe. d. Central Sulawesi in eight districts: Donggala, Moutong, Parigi, Poso, Morowali, Banggai, ToliToli, Buol and Tojo Una-Una. e. East Nusa Tenggara in Sikka and Ende, Tabanan and Jembrana. f. Maluku in the District of West Seram and Buru. g. West Papua in Manokwari and Sorong. h. Yapen Islands of Papua in Sarmi, Keerom and Jayapura districts. There are some activities which are conducted in this Production Improvement and Quality of National Cocoa Program. The main activities are rejuvenating 70,000 ha plantations, rehabilitating 235.000 ha plantations, intensification which covered 145,000 ha areas, farmer training for 450,000 people to realize quality improvement. Supporting activities are training 360 people, constructing sub-station research, building four units of experimental garden and strengthening seven units of field laboratories, manufacturing cocoa cultivation technology database systems, rehabilitating 90 units of UPP, soil and leaf analysis for fertilizer recommendation, monitoring and evaluating are done by universities. Cocoa production and quality improvement program involves various parties to exploit potential available resources. They include central government, provinces, foreign countries, private companies, banks and farmers with the duties and responsibilities as follows: 1. Central government: providing financing for planting materials, fertilizers, rejuvenation, rehabilitation and intensification, labor assistance for farmers, pest control tools and materials, professional assistants, farmer empowerment, development of sub-station study, strengthening and developing labs and field application of quality or socialize the implementation of quality standards. 2. Provincial government: allocating budget to support program implementation and cocoa certification and providing land for sub-station construction. 3. District Government: providing budget to support the program and selecting farmer candidate and land candidate. 4. Banking: providing credit to finance revitalization of farm fertilizers, pesticides, agricultural tools and land certificate. 5. Private: providing financing for SNI Implementation. 6. Farmers: providing shade trees and labor. The implementation of cocoa production and quality improvement program will provide these benefits: 1. Increasing cocoa productivity in program location. 2. Increasing cocoa production in program location. 3. Increasing farmer income in program location. 4. Increasing money supply in rural location. 5. Increasing foreign exchange earning in program location. 6. Increasing cocoa quality in accordance with SNI. 7. Fulfilling raw material needs of domestic industry. In April 2010 Indonesian government started to impose tax policy for cocoa bean under decree No. 672010. The Finance Minister imposed a five percent tax on exported cocoa beans, and priced ranging from US 2000 - 2750 per ton. This tax rate is increased to 10 percent for beans sold for more than 2750. This tax policy was aimed to push domestic cocoa downstream industry. Government considers that the cocoa tax policy will revive the cocoa industry. It was made to encourage more production of cocoa beans in Indonesia, to improve the benefit from marketing value-added product for the country. It would benefit not only the cocoa industry but also cocoa farmers, who currently have more options in selling their beans. Instead of being depended on exports; farmers have the option to sell their beans to domestic processors. Government can also use the funds gathered from tax to help cocoa farmers, particularly to improve their productivity as well as the quality of their products. On the other hand, there are some organizations or people who do not agree with this policy. They believe the tax would lead to decreased competitiveness of the nation’s cocoa export, compared to the competitors such as Ivory Coast and Ghana. They argue that domestic processors were able to get more than enough cocoa beans already. The domestic industry will not suffer from the lack of raw materials if an export is not imposed.

VI. RESULT AND DISCUSSION

6.1 HS 1801 Cocoa Beans, Whole or Broken, Raw or Roasted

Overall there are seven variables used in the analysis of HS 1801 with the gravity model. Those are GDP of exporting country, GDP of importing country, population of exporting country, population of importing country, physical distance, exchange rate and export tax. The Analysis starts with pooled least square as the basic model and then is extended with fixed effect. Analysis of code HS 1801 cocoa beans, whole or broken, raw or roasted with export tax is divided into two analyses. Firstly, export tax is treated as dummy variable and secondly, export tax is analyzed as percentage value. From these two analyses, it is intended to know the effect of the export tax to European Union as whole, before and after export tax policy and also the effect of export tax in percentage value to trade flows. The result of those econometrics models are shown in Table 7. Table 5 shows that the coefficients and standard error for each model vary. All of models are jointly significant, as known from an F test of all of models which are less than 0.10. For export tax as a dummy, we can see that R-Squared is 0.1444 or 14.44 percent. It means that 14.44 percent of export value is explained by the input variables’ variance GDP of exporting country, GDP of importing country, population of exporting country, population of importing country, exchange rate, physical distance and export tax. P-value of each coefficient provides the likelihood that they are real results and did not occur by chance. The lower the P-value, the higher the likelihood that coefficient is valid. In this paper, we can justify one variable as a significant variable if p-value of each variable is under 0.10 10 percent. Table 7. Fixed Effect Regression of HS 1801 Description Export Tax as a Dummy Export Tax as percentage value Ln gdpi 4.24738 0.085 4.439333 0.070 Ln gdpj 4.29403 0.352 4.340007 0.349 Ln population -43.4491 0.023 -43.82033 0.022 Ln population -0.5542183 0.964 -0.5566403 0.963 Ln Exchangerate -0.4414836 0.055 -0.4486994 0.051 Ln Physicaldistance Omitted Omitted Exporttax 2.048882 0.038 0.0955555 0.041 Constant 782.834 0.033 788.0725 0.033 Observations 109 109 R-Squared 0.1444 0.1291 F-test 0.0022 0.0023 significant at 10 percent level Physical distance is omitted by stata. The omission of variables can happen if there is collinearity between variables in the model. Under real condition, physical distance will not influence the cocoa trade flows so much. There is only one exporting country Indonesia and eleven importing countries. These eleven importing countries are closely located to each other. We would conclude that one percentage change in GDP of exporting country results in around a 4.24738 percent change in export value. One percentage change in GDP of importing country results in around a 4.29403 percent change in export value. One percentage change in population of exporter country results in around 43.4491 percent change in export value decreasing. One percentage change in population of importer country results in around