SPATIAL HUMAN CAPITAL INTERACTION PATTERN TO INDONESIAN ECONOMIC GROWTH

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 9, Issue 1, January 2018, pp , Article ID: IJCIET_09_01_069 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed SPATIAL HUMAN CAPITAL INTERACTION PATTERN TO INDONESIAN ECONOMIC GROWTH Caroline Doctoral Candidate of Economics, Faculty of Economics and Business, Diponegoro University, Semarang-Indonesia, Sultan Fatah University Sugiyanto Faculty of Economics and Business, Diponegoro University, Semarang-Indonesia Kurnia. A,S Faculty of Economics and Business, Diponegoro University, Semarang-Indonesia Firmansyah Faculty of Economics and Business, Diponegoro University, Semarang-Indonesia ABSTRACT Indonesia with a population of 237 million people spreading over 34 provinces has a number of ethnic groups that range from Sabang to Merauke as a potential market to invest capital. Indonesia is visited by many foreign visitors because of its natural beauty, friendly people, good food such as meatballs, rendang, fried rice, and sate which are very famous abroad. Cultural diversity, ethnicity, and cuisine become the main attraction for Indonesia. This research wanted to know the pattern of spatial interaction of human capital to economic growth in Indonesia. This study uses the method of Local Identification Spatial Association (LISA). The conclusion of this research is the pattern of spatial interaction of human capital of Indonesia converging to LL area is percent of total sample which is in LL area that is North Kalimantan, Maluku, East Nusa Tenggara, Papua, West Papua, Riau, West Sulawesi, Central Sulawesi, Southeast Sulawesi, North Sumatra, West Sumatera, Gorontalo, East Kalimantan, North Sulawesi, North Maluku, the remaining percent of the total sample is in the LH area, percent of the total sample is in the HH area, and 5.88 percent is in the HL area. Keywords: Economic Growth, Human Capital, LISA, Global Moran s I Cite this Article: Caroline, Sugiyanto, Kurnia. A,S and Firmansyah, Spatial Human Capital Interaction Pattern to Indonesian Economic Growth, International Journal of Civil Engineering and Technology, 9(1), 2018, pp editor@iaeme.com

2 Spatial Human Capital Interaction Pattern to Indonesian Economic Growth 1. INTRODUCTION Indonesia consists of 34 provinces with a population of 237,641,300 people. Indonesia has a diversity of 1,300 ethnic groups or a classification of 31 ethnic groups spreading over 34 islands (Statistical Yearbook of Indonesia, 2016). With a large population, it makes Indonesia as a potential market for other countries to enter Indonesia, especially with the enactment of the ASEAN Economic Community in Figure 1 shows the highest Human Development Index Indonesia is the Province of DKI Jakarta, DI Yogyakarta, East Kalimantan, Bangka Islands Balitung, and Riau Islands Figure 1 Human Development Index Indonesia, This research wants to know the pattern of spatial interaction of human capital to economic growth in Indonesia This research develops economic growth model of Mankiw et al. (1992), and Moretti (2004) where the economic growth model of Mankiw et al. (1992) included human capital in the form of education into its growth model, while Moretti (2004) enlarged education into educated worker share (EWS), and uneducated worker share (UWS) into its growth model. 2. CONCEPTUAL FRAMEWORK Human capital in the form of education is embodied on labor. Formal education and special skills possessed by workers will increase internal productivity and external productivity, meaning that the formal education and special skills of the workers will increase the labor productivity itself, and the formal education and special skills of the workers will increase the productivity of other labor. The abundance of human capital occurs because of the abundance of human capital and it concentrates in a certain area with different quantity and quality become the consideration of workers to mobilize to other areas. This study also considers the spatial aspects. This study adopted the global method of Moran's I and Local Indicator Spatial Association (LISA) from Anselin (2001). 3. DATA AND METHODS 3.1. Data This research was conducted in 2016 in 34 provinces in Indonesia. Description of variables used per capita GDP, stock of capital, average length of school (RLS), Educated Worker Share (EWS), and labor. This study adopts the concept of autocorrelation of Anselin (1995) editor@iaeme.com

3 Caroline, Sugiyanto, Kurnia. A S and Firmansyah Table 1 Variable Description Variable Indicator Unit Source GDP per capita GDP per capita Rupiah Capital Stock Capital Stock Rupiah Means Years of Schooling (MYS) Educated Worker Share (EWS) Labour (L) Means Years of Schooling (MYS) People aged 15 years and over who worked during the past week according to the highest level of education (college / university) 34 provinces in Indonesia People aged 15 years and over who worked during the past week according to the highest education that was graduated (not / not yet a university school) in the country Year Person Person Statistical Indonesia Statistical Indonesia Statistical Indonesia Statistical Indonesia Statistical Indonesia 3.2. Methods This study uses the method of Global Moran's I and Local Indicator Spatial Association (LISA). The spatial pattern of human capital is obtained by Explanatory Spatial Analysis Data (ESDA) by calculating spatial autocorrelation through Global Moran's Index Statistics and Local Moran's Index Statistics. Local Moran's Index Statistics are seen in the Local Indicator Spatial Association (LISA) and Moran's scatterplot. Moran's scatterplot is one way to interpret Moran index statistics. Moran's scatterplot is to know the spatial relationships. Spatial relationships can be interpreted that 10 countries in accordance with the value of indicators of certain variables. Moran scatterplot displays a splash graph lag against standardized. Moran scatterplot produces four different quadrants scattered according to the four types of local Indonesian spatial relations with other ASEAN member countries. Moran scatterplot calculation results will appear on the Local Indicator Spatial Association (LISA). In the LISA analysis, if statistical tests do not show significant results identifying that the region has no spatial pattern, in this case the spatial pattern is random or random. If statistical tests indicate significant, then there are four possible spatial patterns occurring in the region, namely: a) Hot-spot cluster. In this cluster, the LISA index of the observed state, i is higher than the average of the observed variables. Likewise, neighboring countries around them have a LISA index higher than the average observed variables. In the LISA analysis is expressed with high-high (HH) association. b) The cold-spot cluster. In this cluster, the observed state LISA index, i and neighboring countries, j are lower than the average observed variables. In spatial LISA analysis is a low-low (LL) association. c) The observed state, i has a higher LISA index compared to other neighboring countries, j. This indicates a high-low (HL) association. In the LISA analysis referred to as spatial outliers editor@iaeme.com

4 Spatial Human Capital Interaction Pattern to Indonesian Economic Growth d) The observed state, i has a lower LISA index compared to other neighboring countries, j. This indicates a low-high (LH) association. In the LISA analysis referred to as spatial outliers. In the LISA analysis of concern is the hot-spot cluster (HH) and cold spot (LL). This study adopted the concept of LISA Anselin (1995). This study uses the initial year of research (2011) and final year of study (2015) to determine the pattern of spatial interaction of each variable Spatial Weight Matrix with Euclidean Distance Approach Spatial weight matrix with Euclidean Distance approach is a way to know the distance between provinces one with other provinces in Indonesia to obtain spatial weighted matrix or spatial weigher (W) that is using the distance of coordinate point x (latitude) and y coordinate point (longitude) from neighborhood. Euclidean Distance is used based on the following reasons: first, other Indonesia is not only limited by land, but there are countries that are bound by the ocean. Second, the use of spatial weight matrix through Euclidean Distance is to facilitate calculation. This study was designed using a spatial autocorrelation method approach through Global Moran's I and Local Moran's I. Global Moran's I and Local Moran's I calculations use the Euclidean Distance approach based on the coordinate point x and the coordinate point of a country. Euclidean Distance unit is mill where 1 Euclidean Distance = mills. Euclidean Distance calculation uses Geoda version The Indonesian spatial weight matrix can be seen in Table 2. Table 2 Spatial Weight Matrix with Euclidean Distance Province Shape Area Shape Length Latitude Longitude 1 Aceh Bali Bengkulu In Yogyakarta West Java Central Java East Java South Kalimantan North Kalimantan Bangka Islands of Balitung Lampung Maluku West Nusa Tenggara East Nusa Tenggara Papua West Papua Riau West Sulawesi South Sulawesi Central Sulawesi Southeast Sulawesi North Sumatra editor@iaeme.com

5 Caroline, Sugiyanto, Kurnia. A S and Firmansyah 23 West Sumatra South Sumatra North Sumatra Central Kalimantan Gorontalo West Kalimantan Riau islands DKI Jakarta East Kalimantan North Maluku Jambi Banten Source: data processed with GeoDa version Spatial Autocorrelation Model The method used in this research is the spatial autocorrelation of Global Moran's Index Stastistic and Local Moran's Index Stastistic Global Moran s IndexStastistic Spatial autocorrelation occurs when the spatial distribution of the observed variables exhibits a systematic pattern (Cliff and Ord, 1982). Positive / negative spatial autocorrelation occurs when a region geographically tends to be surrounded by neighbors with equal or different values of the variables studied. This study used the size of Moran's I statistic to detect spatial autocorrelation in the data. The Global Equation of Moran's Index is written (Anselin, 1995): ( )( )( ) ( ) Remark: is the amount of time observed (1 year) = standardization of data = observed variables = region observed neighboring territory = average from (1) ( ) ( ) = spatial weight connectivity iandj ( ) = matrix value N x N ( ) ( ) where d(i,j) is the distance from point i to point j; m is 2 (coordinate point x and coordinate point y); A is 1. Expected Value (I) = - ( ) The Global Moran's Positive Index score indicates that the observed region has similarities with its neighboring region, whereas the negative Global Moran's Index indicates editor@iaeme.com

6 Spatial Human Capital Interaction Pattern to Indonesian Economic Growth that the observed region has nothing in common with its neighbors. The Moran Index is worth between -1 I 1. Decision making H_ (0) is rejected or there is autocorrelation among ASEAN member countries if, which means that there is a positive autocorrelation of 34 provinces in Indonesia. means there is no positive autocorrelation of 34 provinces in Indonesia Local Moran s Index Statistic Local spatial statistics are often referred to as the Local Indicators of Spatial Association (LISA) which is a technique for providing visual graphs of spatial groupings such as Moran's Scatterplot (Fotheringham, Brunsdon et al., 2000; Haining, 2003). Local spatial autocorrelation indicates individual contributions to global spatial autocorrelation. Local spatial autocorrelation is the value observed i positive (having similarity) or negative (different) with neighbor observation, j. The Moran Index is worth between -1 I 1. This study adopted Local Moran I statistic from Anselin (1995). The study time is one year i.e The Moran's-I-statistic model of spatial autocorrelation is locally written, ( )(2.) where: ( ) [ ] [ ] E[ ] B = ( ) ( )( ) ( ) ( ( ) ) [ [ ] ] Remark: islocalmoran s-i-statistic = 34 Provinces in Indonesia; is the average value of ;. is the observed variable; is an element of the spatial weight matrix that links observations of the provinces i (observed provinces) with neighboring provinces, j using the Euclidean Distance approach based on the x coordinate point and the coordinate point of a country. Determination of spatial pattern of human capital is used The Moran Scatter Plot. The Moran Scatter Plot is divided into four quadrants, The High-High Quadrant (HH), The High- Low (HL) quadrant, The Low-High (LH) quadrant, The Low-Low (LL) quadrant. Quadrant editor@iaeme.com

7 Caroline, Sugiyanto, Kurnia. A S and Firmansyah position determination on The Moran Scatter Plot based on the variable value in the observed province *; and variable values in neighboring provinces,. where:,, Figure 2 The Moran Scatter Plot 4. RESULTS AND DISCUSSION This research uses Moran's I and LISA Global Methods by adopting an Anselin spatial autocorrelation model (1995, 2001) LISA GDP per Capita Table 3 shows the Spatial Weight Matrix with Euclidean Distance from GDP per Capita of 34 Provinces in Indonesia. Tabel 3 Spatial Weight Matrix with Euclidean Distance Province Wz Z 1 Aceh Bali Bengkulu In Yogyakarta West Java Central Java East Java South Kalimantan North Kalimantan Bangka Islands of Balitung Lampung Maluku West Nusa Tenggara East Nusa Tenggara editor@iaeme.com

8 Spatial Human Capital Interaction Pattern to Indonesian Economic Growth 15 Papua West Papua Riau West Sulawesi South Sulawesi Central Sulawesi Southeast Sulawesi North Sumatra West Sumatra South Sumatra North Sumatra Central Kalimantan Gorontalo West Kalimantan Riau islands DKI Jakarta East Kalimantan North Maluku Jambi Banten Source: data processed with GeoDa version 16.8 Table 4 and Figure 3 show the distribution patterns of LISA GDP per capita Indonesia 2016 that percent of the total samples are located in the LL areas of DI Yogyakarta, Bali, West Java, Central Java, Aceh, Lampung, Maluku, Southeast Sulawesi, North Sulawesi, Sumatera West Java, North Sumatera, North Maluku, Banten, the rest of the LISA GDP per capita distribution pattern of percent of the total sample is in LH, percent of the total sample in the HL area, and 8.82 percent of the total sample is in the HH area. Table 4 Distribution pattern of LISA GDP per Capita 2016 HH LH LL HL West Papua Bengkulu In Yogyakarta North Kalimantan Riau islands East Java Bali South Sulawesi East Kalimantan South Kalimantan West Java Riau Bangka Islands of Balitung Central Java DKI Jakarta West Nusa Tenggara Aceh East Nusa Tenggara Lampung Papua Maluku Central Sulawesi Southeast Sulawesi South Sumatra North Sulawesi Gorontalo West Sumatra Central Kalimantan South Sumatra Jambi North Sumatra North Maluku Banten editor@iaeme.com

9 Caroline, Sugiyanto, Kurnia. A S and Firmansyah Figure 3 Moran Scatter Plot GDP per Capita Indonesia 4.2. LISA Capital Table 5 shows the Spatial Weight Matrix with Euclidean Distance from Capital of 34 Provinces in Indonesia. Table 5. Matrix of Spatial Capital Weight with Euclidean Distance Approach Province Wz Z 1 Aceh Bali Bengkulu DI Yogyakarta Jawa Barat Jawa Tengah Jawa Timur Kalimantan Selatan Kalimantan Utara Kepulauan Bangka Balitung Lampung Maluku Nusa Tenggara Barat Nusa Tenggara Timur Papua Papua Barat Riau Sulawesi Barat Sulawesi Selatan Sulawesi Tengah Sulawesi Tenggara Sumatera Utara Sumatera Barat editor@iaeme.com

10 Spatial Human Capital Interaction Pattern to Indonesian Economic Growth 24 Sumatera Selatan Sumatera Utara Kalimantan Tengah Gorontalo Kalimantan Barat Kepulauan Riau DKI Jakarta Kalimantan Timur Maluku Utara Jambi Banten Source: data prosessed with GeoDa versi 16.8 Figure 4 Moran Scatter Plot Capital Indonesia Figure 4 and Table 6 show the distribution patterns of LISA capital of Indonesia 2016 that percent of the total samples are in the LH areas of Bengkulu, Central Java, East Java, Lampung, Papua, West Sulawesi, South Sumatra, DKI Jakarta, Riau Islands and Jambi, the remaining percent of the total sample is in the HH and LL areas, and percent are in the HL area. Table 6 LISA Capital Distribution Pattern HH LH LL HL In Yogyakarta Bengkulu Aceh West Papua West Java Central Java Maluku North Sulawesi South Kalimantan East Java West Nusa Tenggara West Sumatra North Kalimantan Lampung East Nusa Tenggara East Kalimantan Bangka Islands of Balitung Papua South Sulawesi Bali Riau West Sulawesi Central Sulawesi North Sumatra Central Kalimantan South Sumatra Southeast Sulawesi Banten DKI Jakarta Gorontalo editor@iaeme.com

11 Caroline, Sugiyanto, Kurnia. A S and Firmansyah Bengkulu Riau islands North Maluku Jambi 4.3. LISA EWS Table 7 shows the Spatial Weight Matrix with Euclidean Distance from EWS 34 Provinces in Indonesia. Table 7 Spatial Weight Matrix of EWS with Euclidean Distance Province Wz Z 1 Aceh Bali Bengkulu In Yogyakarta West Java Central Java East Java South Kalimantan North Kalimantan Bangka Islands of Balitung Lampung Maluku West Nusa Tenggara East Nusa Tenggara Papua West Papua Riau West Sulawesi South Sulawesi Central Sulawesi Southeast Sulawesi North Sumatra West Sumatra South Sumatra North Sumatra Central Kalimantan Gorontalo West Kalimantan Riau islands DKI Jakarta East Kalimantan North Maluku Jambi Banten Source: data processed with GeoDa version editor@iaeme.com

12 Spatial Human Capital Interaction Pattern to Indonesian Economic Growth Figure 5 Moran Scatter Plot EWS Indonesia Figure 5 and Table 8 show the LISA EWS Indonesia 2016 distribution pattern that percent of the total samples are in the LL areas of Bengkulu, Papua, West Sulawesi, Riau Islands, North Kalimantan, Riau, Central Kalimantan, Maluku, West Nusa Tenggara, East Nusa Tenggara, South Sulawesi, Central Sulawesi, Southeast Sulawesi, Gorontalo, North Maluku, West Papua, West Sumatera, East Kalimantan, the remaining percent of the total sample is in the LH area, percent of the total sample is in the HH area, and 2.94 percent HL area. Table 8 LIS EWS Distribution Patterns HH LH LL HL Central Java Aceh Bengkulu North Sumatra East Java Bali Papua Lampung In Yogyakarta West Sulawesi North Sulawesi South Kalimantan Riau islands South Sumatra Bangka Belitung Islands North Kalimantan DKI Jakarta West Nusa Tenggara Riau Banten In Jakarta Central Kalimantan Jambi Maluku West Nusa Tenggara East Nusa Tenggara South Sulawesi Central Sulawesi Southeast Sulawesi Gorontalo North Maluku West Papua West Sumatra East Kalimantan editor@iaeme.com

13 Caroline, Sugiyanto, Kurnia. A S and Firmansyah 4.4. LISA Labor Table 9 shows the Spatial Weight Matrix with Euclidean Distance from Labor 34 Provinces in Indonesia. Table 9 Spatial Weight Matrix of Labor with Euclidean Distance Province Wz Z 1 Aceh Bali Bengkulu In Yogyakarta West Java Central Java East Java South Kalimantan North Kalimantan Bangka Islands of Balitung Lampung Maluku West Nusa Tenggara East Nusa Tenggara Papua West Papua Riau West Sulawesi South Sulawesi Central Sulawesi Southeast Sulawesi North Sumatra West Sumatra South Sumatra North Sumatra Central Kalimantan Gorontalo West Kalimantan Riau islands DKI Jakarta East Kalimantan North Maluku Jambi Banten Source: data processed with GeoDa version editor@iaeme.com

14 Spatial Human Capital Interaction Pattern to Indonesian Economic Growth Figure 6 Moran Scatter Plot Labor Indonesia Figure 6 and Table 10 show the LISA EWS Indonesia 2016 distribution pattern that percent of the total samples are in the LL areas of North Borneo, Maluku, East Nusa Tenggara, Papua, West Papua, Riau, West Sulawesi, Central Sulawesi, Southeast Sulawesi, North Sumatera, West Sumatera, Gorontalo, East Kalimantan, North Sulawesi, North Maluku, the remaining percent of the total sample is in the LH area, percent of the total sample is in the HH area, and 5.88 percent are in the HL area. Table 10 Distribution Pattern of LISA Labor 2016 HH LH LL HL Lampung Aceh North Kalimantan North Sumatra South Sumatera Bali Maluku South Sulawesi DKI Jakarta Bengkulu East Nusa Tenggara Banten In Yogyakarta Papua West Java South Kalimantan West Papua Central Java Bangka Islands of Balitung Riau East Java West Nusa Tenggara West Sulawesi West Kalimantan Central Sulawesi Riau islands Southeast Sulawesi Jambi North Sumatra West Sumatra Gorontalo East Kalimantan North Sulawesi North Maluku editor@iaeme.com

15 Caroline, Sugiyanto, Kurnia. A S and Firmansyah 5. CONCLUSIONS Spatial interaction patterns of Indonesia's human capital converge to the LL area by percent of the total sample are in the LL region of North Kalimantan, Maluku, East Nusa Tenggara, Papua, West Papua, Riau, West Sulawesi, Central Sulawesi, Southeast Sulawesi, North Sumatra, Sumatra West, Gorontalo, East Kalimantan, North Sulawesi, North Maluku, the remaining percent of the total sample is in the LH area, percent of the total sample is in the HH area, and 5.88 percent are in the HL area. REFERENCES [1] Anselin, L. (1995). Local indicators of spatial association LISA. Geographical analysis, 27(2), [2] Anselin, L., & Hudak, S. (1992). Spatial econometrics in practice: A review of software options. Regional science and urban economics, 22(3), [3] Dattorro, J. (2010). Convex optimization& Euclidean distance geometry. Lulu. com. [4] Dube, J. (2014). Spatial econometrics using microdata. John Wiley & Sons. [5] Indonesia Statistical Yearbook, 2017 [6] Elhorst, J. P. (2012). Dynamic spatial panels: models, methods, and inferences. Journal of geographical systems, 14(1), [7] LeSage, J. P. (2009). Spatial econometrics. [8] LeSage, J. P., & Pace, R. K. (2010). Spatial econometric models. Handbook of applied spatial analysis, [9] Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A Contribution to The Empirics of Economic Growth. The Quarterly Journal of Economics. [10] Moretti, E. (2004). Workers' Education, Spillovers, and Productivity Evidence from Plant- Level Production Functions. The American Economic Review, Vol. 94(Issue 3), pp [11] Sanjaya Jena, Balanced Bank Credits For Balanced Economic Growth in India. International Journal of Management, 8 (4), 2017, pp [12] Caroline, FX Sugiyanto, A.S. Kurnia and Firmansyah, Human Capital Category Interaction Pattern to Economic Growth of ASEAN Member Countries in 2015 by using GeoDa Geo-Information Technology Data. International Journal of Civil Engineering and Technology, 8(11), 2017, pp [13] Wendrila Biswas, Public Private Partnership and Economic Growth with special reference to India An overview. International Journal of Management, 7(3), 2016, pp [14] Settapong Malisuwan, Noppadol Tiamnara and Dithdanai Milindavanij. The Impact of Spectrum Assignment on Economic Growth and Competitiveness in Thailand. International Journal of Management, 6(12), 2015, pp [15] Prof. Keyur N. Brahmbhatt and Dr. Ramji M. Makwana, Comparative Study on Image Fusion Methods in Spatial Domain, International Journal of Advanced Research in Engineering and Technology (IJARET), Volume 4, Issue 2 March April 2013, pp editor@iaeme.com