Revisiting The Nexus of FDI and DDI: Evidence from Tourism (Hotel and Restaurant) sector in Indonesia
Herlitah1, Saparuddin Mukhtar2, Susi Indriani3, Muhammad Fawaiq4
1Universitas Negeri Jakarta, [email protected]
2Universitas Negeri Jakarta, [email protected]
3 Universitas Negeri Jakarta, [email protected]
4Indonesian Trade Ministry, [email protected]
Abstract
This study analyzed the influence of FDI (Foreign Direct Investment) and DDI (Domestic Direct Investment) in Hotel and Restaurant in Indonesia. The results of this study indicate a one-way relationship between FDI and DDI. The data used in this study are secondary data obtained from related institutions. That is, data realization Foreign Direct Investment (FDI) and Direct Investment (DI) obtained from the Investment Coordinating Board (BKPM)This is evident from the results of the analysis using VECM that FDI provides a positive short-term and long-term impact on DDI, as well as showing strong influence. However, the DDI variable does not affect the FDI either short or long term. Therefore, the government needs to add strategies to improve FDI in the field of Hotels and Restaurants in Indonesia, especially in areas that have potential in terms of tourism.
JEL Classification: F21, F23, E22, Z32
INTRODUCTION
Tourism is a very important part in helping to improve the economy of a State especially in developing countries. Tourism has become one of the most significant export sectors in many developing countries (Samimi, 2011). Where now countries, both developed and developing countries increase promotion in the field of tourism.
Which aims to improve the competitiveness of local businesses, open up opportunities for foreign investment and investment from domestic to develop its human resources.
The examples of countries that are growing rapidly because the tourism sector obtained from the World Travel and Tourism Council data is Iceland, Japan, Mexico, New Zealand, Qatar, Saudi Arabia, Thailand, and Uganda ( money.cnn.com ). These countries are on average able to increase the growth in the tourism sector above 5 percent to tens of percent per year after running a promotional program of tourism. In this case the Indonesia's tourism sector has contributed about 4% of the total economy in 2017. By 2019, the Government of Indonesia wants to increase this figure to double
to 8% of GDP, an enormous target and should be implemented within the next 4 years.
(www.indonesia-investments.com)
One of the ways to improve the tourism industry in Indonesia is by improving infrastructure like Qatar, the Qatari government has made investment in infrastructure as a way to continue developing the tourism industry ( www.wttc.org ). Infrastructure becomes the key of any country's tourism industry (www.bkpm.go.id), The lack of a viable infrastructure in Indonesia is a major and sustained problem for the Government in improving the tourism industry, not just making logistics costs increase, making the investment climate less attractive but also reduces the smooth journey and comfort for both domestic and foreign tourists.
Looking back Infrastructure in Bali, Lombok, Yogyakarta, and especially in Jakarta infrastructure is very remarkable, but outside the area most of the infrastructure is still very less feasible, especially in eastern Indonesia due to lack of airports, ports, roads, restaurants and hotels and facilities other support. The lack of connectivity between regions or between islands makes a large number of areas in Indonesia with good tourism potentials difficult to reach. For that reason the role of government is needed in the development of infrastructure areas that have tourism potential
Infrastructure development cannot be carried out by the government independently, because in building large capital required and it cannot be fully done by the government. The spirit of government in improving the tourism sector in Indonesia has received support from businessmen in Indonesia and foreign. Investors will invest as long as their investment is guaranteed and protected by the government. Therefore, the task of government and society today to work together to make tourist locations in Indonesia become more attractive and liked tourists.
Basically investment is the formation of capital that makes the role of private sector in helping the government run the economy. According to Harrod-Domar (M.Todaro, 2006), in support of economic growth, new investments are needed as capital stocks such as domestic investment (PMDN / DI) and foreign investment (PMA / FDI). For that the Government always increases the capital for the development can be done well.
Of the largest FDI available, most investment is still mostly done for the development of star hotels, which is about 57%. And then followed the investment in consulting, management, restaurants, and water tourism. The biggest investment is in Jakarta as the gateway of Great Jakarta. Then the second Bali, followed by West Java, North Sulawesi, and West Nusa Tenggara. The amount of FDI on
The hotel and restaurant services sector is due to the full commitment that makes tourism into tourism sector a priority sector. (www.beritasatu.com). The amount of FDI can be caused because the investor is indeed seeing the potential of tourism areas, especially for the services of hotels and restaurants or already exist DI first develop new foreign investors glance at the area. Or on the contrary, there are already foreign investors who invest in the area that makes domestic investors interested to also invest in the area, because from year to year FDI and DI tend to always rise.
Currently, Indonesia has liberalized the Tourism Sector, especially the Hotel and Restaurant fields by allowing maximum capital ownership of 65%. It is expected as a solution to get capital for Indonesia's development. The consequence of this entry into foreign capital or FDI is the potential to generate competition with domestic investment and vice versa where the entry of FDI may also encourage DI.
This Paper emphasizes the importance of foreign direct investment and domestic direct investment for Tourism in Indonesia using VECM approach. The rest of this paper is organized as follows: Section 2 reviews the Literature and relevant empirical studies. Section 3 describes the data and methodology. The next section presents the empirical results. Finally, the paper concludes.
Literature: Analysis of investment in the Tourism sector becomes interesting thing to do considering Tourism become supporting aspect of the economy. The research related to this research is Samimi, Sadeghi & Sadeghi (2011), which discuss about the causality relationship between FDI with the number of foreign tourist arrivals by the method of vector error correction model (VECM) in 20 developing countries with period 1995- 2008. In this study confirmed that the arrival of tourists strongly encourages economic growth both in the short and long term. For that the tourism sector proved to play a big role in increase economy. In terms of investment Cok Istri Sinta Regina Trisnur (2014), examines the influence of PMDN and PMA to GRDP in Bali Province with the method of multiple linear regression with the period 1990-2012. In the analysis results PMDN and PMA simultaneously have a significant effect and partially have a positive and significant impact on GRDP and efficiency level of investment implementation classified as very efficient. Sacred Safitriani (2016) examines the effects of international trade flows and FDI in Indonesia by using time series analysis of VECM. In this study, holy indicates that FDI has a positive long-term impact on exports, while in the short run, FDI has a negative impact on exports. However, in imports, it was found that FDI had a positive impact although not statistically significant. From both research above, Cok Istri (2014) and Suci (2016), said that the investment, both FDI and DDI will bring very good impact for Indonesia which is a developing country.
However, there are several studies that suggest different findings in terms of FDI that are not in line with DDI which always has a positive impact. As in Lee, H., and Dominique (2001) research, the host country may not benefit from FDI if there is still a distortion of economic policy occurring in domestic policy. It is also supported by Naya (1990) indicates that FDI liberalization can reduce economic welfare of the State with a protected economy. This is because protection in the host country will encourage foreign investors to make foreign direct investment decisions that are not optimal.
However, a positive influence was shown in Fry's (1993) study that found that FDI inflows contributed significantly to economic growth in developing countries, especially East Asia where there were still domestic distortions, such as trade controls and relatively low financial arrangements. What distinguishes this research is that previous research only looked at the effect of investment with other things such as international trade, economic growth, and see how important the role of tourism sector
in a country. And in this research, the researcher want to know that actually the Investment it is whether to influence each other and whether the positive or negative influence, especially in the tourism sector, because interested parties must know which should take precedence whether to increase FDI or DDI in developing the Tourism sector.
The empirical model and Analysis Method
The data used in this study are secondary data obtained from related institutions.
That is, data realization Foreign Direct Investment (FDI) and Direct Investment (DI) obtained from the Investment Coordinating Board (BKPM).
Vector Error Correction Model (VECM)
a) The causal relationship between FDI and DI in tourism sector.
The entry of foreign investment is one form of government to raise capital to build the tourism industry, especially the field of hotels and restaurants in Indonesia.
The entry of foreign investment in Indonesia measured in this research is through foreign direct investment (FDI). The entry of FDI will affect other investment in this case that is domestic investment (PMDN) or domestic investment (DI). The relationship between the two variables (FDI and DI) will be tested using the Panel-VECM-Granger model shown in equation (1) and equation (2) as follows.
∆FDIi,t = α1,i + φ1,iECTi,t-1 + 1,j,i ∆FDIi,t-j + 1,j,i∆DIi,t-j + ɛ1,i,t
……….…...(1)
∆DI i,t = α2,i + φ2,iECTi,t-1 + 2,j,i ∆FDIi,t-j + 2,j,i∆DIi,t-j + ɛ2,i,t
…….…....….(2)
Where i is the province, t (period), and j is the optimum lag. As for Δ is the difference between operators, ECT is a lagged error-correction term obtained from long-term co-integration relations, φ1 and φ2 are coefficients and ɛ1, i, t and ɛ2, i. The stages in this research are stationery test, co-integration test, lag length criteria, Granger causality test with VECM Panel. Non-stationarity testing uses Im, Pesaran and Shin (IPS) unit root test. The first test is done at the level, if the data have not been stationer it will be continued on the first difference. The test is performed on each variable until the same order is found in both with the assumption that both variables (FDI and DI) are integrated in the same order.
The co-integration test for panel data in this study used the approach proposed by Pedroni (1999). This test aims to determine the need for control over long-run equilibrium relationships between variables in the econometric specification (Samimi, et.al., 2011).
Furthermore, lag testing aimed to measure the optimum lag length used in subsequent tests (Safitriani, 2014). The lag test in this study was measured using Schwarz information criterion (SC) and Hannan-Quinn information criterion. After the
testing process, the final test is the Granger Kasalitas test of the VECM Granger Panel Model.
Some stages in this research are stationary testing and then co-integration testing, then lag length criteria testing of Granger causality with VECM Panel. The explanation of each stage of the tests is as follows: (1) Unit Test root data panel, ie Non-stationary testing using I'm, Pesaran and Shin (IPS) unit root test. Because this test uses Eviews 8 program, then hypothesis testing done on the root unit will be done at level and first difference. (2) Co-integration test, ie Cointegration Test for panel data in this study using the approach proposed by Pedroni (1999). This test aims to determine the need for control over long-run equilibrium relationships between variables in the econometric specification (Samimi, et.al., 2011). (3) The Length Criteria Test The purpose of this purpose is to measure the optimum lag length used in subsequent tests (Safitriani, 2014). The lag test in this study was measured using Schwarz information criterion (SC) and Hannan-Quinn information criterion. (4) Causality Test with VECM-Granger Panel, Granger Causality Testing using VECM Granger Panel. This test is conducted to test the relationship of long-term, short-term causality and combination between the two.
The short-term causality test uses F-statistics from the Wald Test results on the coefficient Ɵ1 or coefficient γ2.
Empirical Result
In nominal terms, FDI in Indonesia, especially in Hotel and Restaurant areas always increase from year to year, especially in big cities like DKI Jakarta, Bali and Daerah Istimewa Yogyakarta. In addition, in other areas such as North Sumatra, Riau Islands, West Java, Central Java, East Java, Banten and West Nusa Tenggara, although the increase in FDI is not as high as 3 big cities above.
As with DDI, the investment value in Hotel and Restaurant field in Indonesia is not as big as FDI for this field or other field. The entry of DDI in this field can be said to have occurred only in 2009, although previous years already exist, but only seen graphically (graph 1.1) occurred in 2009 in the major cities of the Special Capital Region of Jakarta, Central Java, West Java, and Bali. For other cities significant increase in investment value occurred in 2012. It is as shown in graph 1.1 below.
Prior to the estimation through VECM must first perform the initial test of stationary test. The output resulting from the test performed as follows:
Table 1.1. Stasionerity Testing at Level and First Difference
Variable Testing Method Probabilities (Level)
Probabilities (1st Difference)
DDI Levin, Lin & Chu t* 0.0448 0,0000
Im, Pesaran and Shin W-stat 0.0001 0.0000
ADF - Fisher Chi-square 0.0002 0.0000
PP - Fisher Chi-square 0.0000 0.0000
FDI Levin, Lin & Chu t* 0.1749 0.0000 Im, Pesaran and Shin W-stat 0.0004 0.0000
ADF - Fisher Chi-square 0.0001 0.0000
PP - Fisher Chi-square 0.0000 0.0000
Source: Analysis Results
Description: Maximum 5 percent significance level.
Based on the stationary test results in Table 1.1, the statistical probability of the FDI variable has been smaller than the 5 percent significance level for some tests, but there is still one test that has a probability greater than 5 percent. This indicates that the FDI variable is not stationary at the level, but stationary at first difference. After passing the stationary test, in VECM testing, it has been determined that both variables have long-term relationships. To test for a long-term relationship, a co-integration test is conducted. Co-integration test results obtained by forming the residual obtained by way of regression independent variable to the dependent variable with the results shown in table 1.2 as follows
Table 1.2. Co-integration Testing Results with Pedroni
Mehod Statistic Nilai Probabilitas
Panel rho-Statistic -10.99541 0.0000 Panel PP-Statistic -9.561090 0.0000 Panel ADF-Statistic -4.329518 0.0000 Group rho-Statistic -6.188155 0.0000 Group PP-Statistic -7.964258 0.0000 Group ADF-Statistic -5.036133 0.0000 Source: Analysis Results
Description: Maximum 5 percent significance level.
Based on the results of co-integration testing in Table 1.2, it can be seen that the value of statistical probability is lower than 5 percent and 1 percent. This indicates that the FDI and DDI variables have long-term relationships at the 99 percent confidence level. Given the very close co-integration relationship at this 1 percent significance level, both variables can be estimated using VECM.
VECM Estimates With DDI Tied Variables
The first rarity of the equation is to test the short-term, long-term coefficients of the effect of FDI on DDI in lag 1 as well as the strength of the relationship between FDI and DDI. The results of the coefficient test are presented in Table 1.3 as follows.
Table 1.3. Short Term, Long Term Granger Causality Relationships, and relationship strengths for DDI bound variables
Short term Long term causality
Test Statistic Value df Probability Value df Probability Value df Probability
t-statistic -3.976798 224 0.0001 -7.021056 224 0.0000
F-statistic 15.81492 (1, 224) 0.0001 49.29523 (1, 224) 0.0000 24.98945 (2, 224) 0.0000
Chi-square 15.81492 1 0.0001 49.29523 1 0.0000 49.97889 2 0.0000
Source: Analysis results
Description: Maximum 5 percent significance level.
Table 1.3 shows that the short-term, long-term probability, and strength of the relationship with the dependent variable DDI gives results that have been less than 5 percent, for short- and long-term Granger causality indicates that the inclusion of FDI encourages domestic investment in Indonesia. Furthermore, to see the strength of FDI's influence on DDI, a joint test of Long Term Granger Causality and short-term Causality is performed. The relationship of Granger causality to the dependent variable DDI shows that the probability is less than 5 percent. This means that a strong FDI variable affects DDI at a 5 percent level. Thus, the entry of investors in the field of hotel services and foreign restaurants encourages domestic investors to invest in the same field.
VECM Estimates With FDI Tied Variables
Table 1.4. Short Term Granger Causality Relationships for FDI bound Variables
Short Term Long Term variables Bound
Test
Statistic Value df Probability Value df Probability Value df Probability
t-statistic -1.799794 224 0.0732 1.564445 224 0.1191
F-statistic 3.239260 (1, 224) 0.0732 2.447488 (1, 224) 0.1191 1.704605 (2 224) 0.1842
Chi-square 3.239260 1 0.0719 2.447488 1 0.1177 3.409210 2 0.1818
Source: Analysis results
Description: Maximum 5 percent significance level.
In Table 1.4 shows that the probability value is greater than 5 percent in the Short Run. This means that the DDI variable does not affect the FDI variable in the short run. Furthermore, long term causality relationships show that the probability value is greater than the 5 percent level of significance. This means that the DDI variable does not affect the FDI variable in the long run. Although in the short or long term DDI variable does not affect the FDI variable. Thus, the inclusion or increase of domestic investors in the field of hotels and restaurants in Indonesia will not encourage foreign investors to invest in the same field.
Short-term and long-term Granger causality indicates that the inclusion of FDI encourages domestic investment in Indonesia. When viewed from the strength of the influence of FDI on DDI, then conducted a joint test between Long Term Granger Causality and Short Term. Shows that strong FDI variable affects DDI at the 5 percent level. Thus, the entry of investors in the field of hotel services and foreign restaurants encourages domestic investors to invest in the same field. But different things are shown from the results for DDI. Although in the short or long term DDI variable does not affect the FDI variable. Which means, the entry or increase of domestic investors in the field of hotels and restaurants in Indonesia will not encourage foreign investors to
invest in the same field. But that does not mean the Indonesian government does not increase the DDI for the Tourism sector. Because although it does not have an impact on tourism FDI but can give Multiflier an effect on other things.
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