International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 11, November 2017, pp. 889 900, Article ID: IJCIET_08_11_089 Available online at http://http://www.iaeme.com/ijciet/issues.asp?jtype=ijciet&vtype=8&itype=11 ISSN Print: 0976-6308 and ISSN Online: 0976-6316 IAEME Publication Scopus Indexed HUMAN CAPITAL CATEGORY INTERACTION PATTERN TO ECONOMIC GROWTH OF ASEAN MEMBER COUNTRIES IN 2015 BY USING GEODA GEO-INFORMATION TECHNOLOGY DATA Caroline Ph.D Student in Economics, Diponegoro University Sultan Fatah University FX Sugiyanto Faculty of Economics and Business, Diponegoro University A.S. Kurnia Faculty of Economics and Business, Diponegoro University Firmansyah Faculty of Economics and Business, Diponegoro University ABSTRACT The pattern of spatial interaction of human capital among members of ASEAN is due to economic, political and cooperative cooperation in other fields in ASEAN-10. Economic openness allows physical capital and human capital to move from one country to another to form a spatial pattern of human capital. This research uses spatial autocorrelation method with GeoDa approach. The purpose of this research is to analyze the spatial interaction pattern of human capital in ASEAN-10. This research method uses spatial autocorrelation. The conclusion of this research is LISA GDP per Capita 2015 distribution pattern which is mostly 60 percent of total clustered sample to LH area: Malaysia, Cambodia, Vietnam, Myanmar, Thailand, Philippines. LISA Capital 2015 distribution pattern where part of LISA Capita 50% of all samples clustered to LL: Brunei Darussalam, Cambodia, Lao People's Democratic Republic and Vietnam. LISA MYS 2015 distribution pattern where some of LISA Capital's 50% distribution patterns from all samples cluster to LL areas: Singapore, Cambodia, Malaysia, Myanmar and Vietnam. The distribution pattern of labor 2015 is mostly 70% of the total sample clustered to LL areas: Singapore, Brunei Darussalam, Cambodia, Malaysia, Myanmar and Vietnam, and Lao People's Democratic Republic. Key words: Spatial interaction patterns, human capital, ASEAN, economic growth. http://www.iaeme.com/ijciet/index.asp 889 editor@iaeme.com
PDB per Human Capital Category Interaction Pattern to Economic Growth of ASEAN Member Countries in 2015 by using GeoDa Geo-Information Technology Data Cite this Article: 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. 889 900. http://www.iaeme.com/ijciet/issues.asp?jtype=ijciet&vtype=8&itype=11 1. INTRODUCTION The pattern of spatial interaction of human capital is a phenomenon that occurs as a result of cooperation in economic, political, cultural, and security among ASEAN member countries. ASEAN (Association of South East Asian Nation), established in Bangkok in 1967, is one of the economic cooperation in Southeast Asia Region. Currently, ASEAN members have reached 10 countries, namely Indonesia, Malaysia, Philippines, Singapore, Brunei Darussalam, Vietnam, Laos, Myanmar and Cambodia. ASEAN was formed with the aim of enhancing the economic growth of ASEAN member countries through various agreements. With the agreement that has occurred several phenomena occur in Indonesia that is the policy of free entry visa for 148 countries, Bahasa Indonesia as the official language of ASEAN, based on the Mutual Recognition Agreement of the MEA, there are eight professions that the standards and competencies have been established by participating countries ASEAN Economic Community (MEA), meaning that there are eight of these professions will have access to seeking a cross-country career. Those professions are, Engineer, Architect, Tourism Power, Accountant, Dentist, Surveyee, Medical Practitioner and Nurse. The existence of MEA is believed to increase the economic growth of its 10 member countries. The increase of economic growth among the 10 ASEAN member countries stimulates the labor of a country to migrate to other countries in order to increase income and seek better prosperity in the target country. In general, labor migration has to do with economic growth and population growth. Increasing GDP per capita among ASEAN member countries and an increasing number of working age residents are the reason why migrant workers migrate to other countries. 140000 120000 100000 80000 60000 40000 20000 0 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Keterangan : Tahun Myanmar Kamboja Laos Vietnam Philipina Indonesia Thailand Malaysia Brunei Darusaalam Singapura Sumber : ASEAN Statistical Yearbook 2015 Figure 1 GDP Per Capita of 10 ASEAN Countries on Current Prices (Thousands US $) http://www.iaeme.com/ijciet/index.asp 890 editor@iaeme.com
Caroline, FX Sugiyanto, A.S. Kurnia and Firmansyah Figure 1. shows that out of ten ASEAN member countries, Indonesia's GDP per capita is ranked fifth above Thailand. The highest GDP per capita is achieved by Singapore, followed by Brunei Darussalam and Malaysia, while countries with GDP per capita below Indonesia are the Philippines, Vietnam, Laos, Cambodia and Myanmar. In addition to the economic growth of labor migration has to do with the population of a country. Given the large number of people it will encourage workers to migrate to other countries in ASEAN. As for foreign workers entering Indonesia in 2011 and in 2012 as many as 77.3 thousand workers and 72.4 thousand workers, then the number of foreign labors into Indonesia has decreased (62 thousand workers in 2013), and foreign workers entry into Indonesia again increased in 2014, and 2015 with the number of 69 thousand workers, and 74.2 thousand workers, with the number of foreign workers coming from ASEAN member countries entering Indonesia in 2014, among others those from Malaysia 3, 8 thousand workers, Philippines 2.5 thousand workers, and Thailand 941 thousand workers (http://databoks.katadata.co.id/tags/tenaga-kerja). 1.1. The Importance of Geo-Information Technology for Spatial Patterns of Human Capital Visualization The spatial pattern of human capital is generated from Geo-Information technologies of GeoDa (Geo-Data) by providing a visual representation of the spatial patterns of human capital in 10 ASEAN member countries. Geo-Information technologies of GeoDa is important to show visually the spatial patterns of human capital in ASEAN. 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 Scatter Plot. Moran's Scatter Plot is one way to interpret Moran s Index Statistics. Moran's Scatter Plot is to know the spatial relationships. Spatial relationships can be interpreted that 10 countries are in accordance with the value of indicators of certain variables. Moran s Scatter Plot displays a splash graph lag Wz _0 against standardized z_0. Moran s Scatter Plot produces four different quadrants scattered according to the four types of local Indonesian spatial relations with other ASEAN member countries. Moran s Scatter Plot 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. If statistical tests indicate significant, then there are four possible spatial patterns occurring in the region, namely: 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 highhigh (HH) association. 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 lowlow (LL) association. 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. 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 http://www.iaeme.com/ijciet/index.asp 891 editor@iaeme.com
Human Capital Category Interaction Pattern to Economic Growth of ASEAN Member Countries in 2015 by using GeoDa Geo-Information Technology Data (2011) and final year of study (2015) to determine the pattern of spatial interaction of each variable. 2. MATERIALS & EXPERIMENTAL PROCEDURES 2.1. Materials 2.1.1. Matrix of Spatial Weight with Euclidean Distance Approach Geo-Information Technology "GeoDa" can provide distance information between countries one with other countries in ASEAN. The closer regions will have a greater effect from the more distant areas (Anselin, 1988). The way to know the distance between countries one with other countries in ASEAN is by using spatial weight matrix with Euclidean Distance approach that is to obtain spatial weighted matrix or spatial weigher (W) that is using x coordinate distance information and coordinate point y from neighborhood. The use of Euclidean Distance is based on the reasons: Firstly, the other ASEAN 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 purposes to facilitate calculation. Table 1 Spatial Weight Matrix with Euclidean Distance Approach Country Shape Length Shape Area Latitute Longitude Brunei Darussalam 6337903 0.47 14.60 4.77 Indonesia 1008911746 153881247 122.87 10.99 Cambodia 36087711 1509825 102.89 9.92 Thailand 124978757 43289793 100.89 6.42 Singapore 44144764 0,05 103.74 1.17 Philippines 214364142 24586497 119.47 4.59 Malaysia 135403428 26817860 103.42 1.32 Myanmar 22112556679 58298286 97.80 8.82 Lao People's 478197 19678956 103.77 18.49 Democratic Republic Vietnam 4639 310210 106.14 8.61 2.1.2. Data Used and Analysis of Spatial Interaction Pattern of Human Capital The data used in this study is cross-section data that is there are 10 member countries of ASEAN (Indonesia, Malaysia, Philippines, Singapore, Brunei Darussalam, Vietnam, Laos, Myanmar and Cambodia) 2015. Variables used in this study there are 2 namely the dependent variable, GDP per capita, and independent variables: Capital, Mean Years of Schooling (MYS), and Labor (L). Table 2 Description Variables used Variable Indicator Unit Source GDP per capita GDP per capita US$ World Bank Capital Stock Capital Stock US$ World Bank Means Years of Schooling Means Years of Schooling Year World Bank (MYS) Labour (L) (MYS) People aged 15 years or more who have worked for a week according to the highest level of education having been completed (not/have not studied in university in the country. Person World Bank http://www.iaeme.com/ijciet/index.asp 892 editor@iaeme.com
Caroline, FX Sugiyanto, A.S. Kurnia and Firmansyah 2.2. Spatial Autocorrelation Methods The method used in this research is the spatial autocorrelation of Global Moran's Index Stastistic and Local Moran's Index Stastistic. 2.2.1. Global Moran s Index Stastistic Spatial autocorrelation occurs when the spatial distribution of the observed variables shows a systematic pattern (Cliff and Ord, 1982). Positive / negative spatial autocorrelation occurs when geographically a region 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): ( )( )( ) ( ) (1) Remark: N is the amount of time observed (1 year) S_0 = standardized data x = observed variables i = observed region j = neighboring region x the average of x_i ( ) ( ) = spatial weight connectivity i and j ( ) = value matrix N x N ( ) ( ) where d (i, j) is the distance from point i to point j; m is 2 (x coordinate point and y coordinate point); 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 that the observed region has nothing in common with its neighbors. The Moran s Index is worth between -1 I 1. Decision making H_ (0) is rejected or there is autocorrelation among ASEAN member countries if,. If means there is a positive autocorrelation among ASEAN member countries. means there is no positive autocorrelation among ASEAN member countries. 2.2.2. Local Moran s Index Stastistic 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 Scatter Plot (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 s Index is worth between -1 I 1. This http://www.iaeme.com/ijciet/index.asp 893 editor@iaeme.com
Human Capital Category Interaction Pattern to Economic Growth of ASEAN Member Countries in 2015 by using GeoDa Geo-Information Technology Data study adopted Local Moran s I stastistic from Anselin (1995). The study time is one year i.e. 2015. where : The Moran's-I-statistic model of spatial autocorrelation is locally written, ( ) (2) ( ) [ ] [ ] E[ ] B = ( ) ( )( ) ( ) ( ( ) ) [ [ ] ] Remark : is Local Moran's-I-statistic N = 10 ASEAN Member States (Indonesia, Malaysia, Philippines, Thailand, Singapore, Brunei Darussalam, Vietnam, Laos, Myanmar and Cambodia); is the average value of ;. is the observed variable; is an element of the spatial weight matrix that links observation of state i (the observed state) with its neighboring state, j using the Ecludean distance approach based on the x coordinate point and the coordinate point of a state. Determination of spatial pattern of human capital used The Moran s Scatter Plot. The Moran s 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 position determination on The Moran s Scatter Plot based on the variable value in the observed state ; and variable values in neighboring countries,. where :,, http://www.iaeme.com/ijciet/index.asp 894 editor@iaeme.com
Caroline, FX Sugiyanto, A.S. Kurnia and Firmansyah Source: Dube and Legros (2014) Figure 2 The Moran Scatter Plot 3. DISCUSSION 3.1. GDP per capita Table 2 identifies the spatial weight matrix with the Euclidean Distance (Wz) and Z approach where Wz is the spatial weighting matrix of GDP per Capita, and Z is GDP per Capita. Tabel 2 Spatial Weight Matrix of GDP per Capita Country Wz Z Brunei Darussalam -0.77-0.03 Indonesia 2.51-0.14 Cambodia -0.78-0.31 Thailand 0.68-0.51 Singapore 0.04-0.43 Philippines -0.14 0.90 Malaysia 0.12-0.44 Myanmar -0.55-0.25 Lao People's Democratic Republic 0.76-0.18 Vietnam -0.35-0.27 Source: data processed with GeoDa version 16.8 The Global Spatial Autocorrelation view of the Moran s Scatter plot is shown in Figure 1. The Moran s scatter plot view shows spatial patterns divided into four quadrants, where the value of the variables based on observations is divided into the observed variables and the variable values of neighboring states. The value of global variables of autocorrelation of neighboring countries is calculated based on spatial weight matrix with Euclidean Distance approach. Euclidean Distance occurs because it has determined the point of coordinates x or latitude, and the coordinate points y or longitude of each country. This research uses GeoDa to know Euclidean Distance. Euclidean Distance Unit is a mills. Figure 1 shows that the value of Moran's I of 0.0032 means that the overall autocorrelation value is low seen in the nearzero Moran value I. http://www.iaeme.com/ijciet/index.asp 895 editor@iaeme.com
Human Capital Category Interaction Pattern to Economic Growth of ASEAN Member Countries in 2015 by using GeoDa Geo-Information Technology Data Figure 3 Moran Scatter Plot GDP per Capita 2015 Table 3 identifies the distribution patterns of LISA GDP per Capita 2015 which is mostly 60 percent of the total clustered sample to the LH areas: Malaysia, Cambodia, Vietnam, Myanmar, Thailand, Philippines. Tabel 3 LISA GDP Distribution Pattern per Capita 2015 HH LH LL HL Brunei Darussalam Malaysia Indonesia Singapore Cambodia Lao People's Democratic Republic Vietnam Myanmar Thailand Philippines 3.2. Capital Table 4 identifies the spatial weight matrix with the 2015 Euclidean Distance Capital 2015 Approach where WZ matrix of spatial capital and z is capital treated with GeoDa version 16.8. Table 4 Spatial Weight Matrix of Capital 2015 Country Wz Z Brunei Darussalam -0.67-0.08 Indonesia 2.63-0.07 Cambodia -0.68-0.31 Thailand 0.25-0.45 Singapore 0.02-0.47 Philippines -0.08 1.01 Malaysia 0.17-0.47 Myanmar -0.69-0.23 Lao People's Democratic Republic -0.69-0.31 Vietnam -0.24-0.29 Source: data processed with GeoDa versi 16.8 Figure 4 identifies Moran's I 0.0243 meaning that the overall autocorrelation value is low seen in the near-zero Moran I value. http://www.iaeme.com/ijciet/index.asp 896 editor@iaeme.com
Caroline, FX Sugiyanto, A.S. Kurnia and Firmansyah Figure 4 Moran Scatter Plot Capital 2015 Table 6 identifies the LISA Capital 2015 distribution pattern in which some LISA Capita 50% distribution patterns of all samples cluster to LL areas: Brunei Darussalam, Cambodia, Lao People's Democratic Republic and Vietnam. Table 6 LISA Capital Distribution Pattern 2015 HH LH LL HL - Philippines Brunei Darussalam Indonesia Cambodia Thailand Myanmar Singapore Lao People's Democratic Republic Malaysia Vietnam 3.3. Mean of Years (MYS) Table 7 shows that Brunei Darussalam has Wz and Z of -0.67 and -0.08; Indonesia with Wz and Z 2.63 and -0.07; Cambodia with Wz and Z -0.68 and -0.31; Thailand with Wz and Z 0.25 and -0.45; Singapore with Wz and Z 0.02 and -0.47; Philippines with Wz and Z -0.08 and 1.01; Malaysia with Wz and Z 0.17 and -0.47; Myanmar with Wz and Z -069 and -0.23; Laos with Wz and Z 0.69 and -0.31; and Vietnam with Wz and Z -0.24 and -0.29. Tabel 7 Spatial Weight Matrix of MYS 2015 Country Wz Z Brunei Darussalam 0.76-0.20 Indonesia 1.95-0.84 Cambodia 0.76-0.17 Thailand 0.76-0.18 Singapore -0.04 0.18 Philippines -0.84 0.66 Malaysia -0.84 0.13 Myanmar -0.84-0.39 Lao People's Democratic Republic -0.84-0.05 Vietnam -0.84 0.12 Source: data processed with GeoDa versi 16.8 Figure 5 identifies Moran's I -0.2194 meaning that the overall autocorrelation value is low seen in the near-zero Moran I value. http://www.iaeme.com/ijciet/index.asp 897 editor@iaeme.com
Human Capital Category Interaction Pattern to Economic Growth of ASEAN Member Countries in 2015 by using GeoDa Geo-Information Technology Data Figure 5 Moran s Scatter Plot MYS 2015 Table 8 identifies the LISA MYS 2015 distribution pattern in which part of LISA Capital's 50% distribution pattern of the entire sample clustered to the LL areas: Singapore, Cambodia, Malaysia, Myanmar and Vietnam. Table 8 LISA MYS 2015 Distribution Pattern HH LH LL HL Brunei Darussalam Singapore Indonesia Philippines Cambodia Thailand Lao People's Democratic Republic Malaysia Myanmar Vietnam Labor Table 9 identifies the 2015 spatial weight matrix of labor with the Euclidean Distance Approach approach where Wz is the spatial weight matrix of labor and Z is labor. Table 9 Spatial Weight Matrix of Labor 2015 Country Wz Z Brunei Darussalam -0.33 1.04 Indonesia 2.70-0.32 Cambodia -0.42-0.34 Thailand 0.58-0.43 Singapore -0.44-0.27 Philippines -0.33 1.00 Malaysia -0.41-0.28 Myanmar -0.30-0.36 Lao People's Democratic Republic -0.44-0.11 Vietnam -0.43-0.31 Source: data processed with GeoDa version 16.8 Figure 6 identifies Moran's I -0.075 meaning that the overall autocorrelation value is low seen in the near-zero Moran I value. http://www.iaeme.com/ijciet/index.asp 898 editor@iaeme.com
Caroline, FX Sugiyanto, A.S. Kurnia and Firmansyah Figure 6 Moran Scatter Plot Labor 2015 Table 10 identifies the distribution patterns of labor in 2015 for the most of 70% of the total clustered samples to the LL areas: Singapore, Brunei Darussalam, Cambodia, Malaysia, Myanmar and Vietnam, and Lao People's Democratic Republic. Table 10 LISA Labor Distribution Pattern 2015 HH LH LL HL Philippines Singapore Indonesia Brunei Darussalam Thailand Cambodia Malaysia Myanmar Vietnam Lao People's Democratic Republic 4. CONCLUSIONS LISA's GDP per Capita 2015 distribution pattern, which is mostly 60 percent of the total sample clustered to LH areas: Malaysia, Cambodia, Vietnam, Myanmar, Thailand, Philippines. LISA Capital 2015 distribution pattern where part of LISA Capita 50% of all samples clustered to LL: Brunei Darussalam, Cambodia, Lao People's Democratic Republic and Vietnam. LISA MYS 2015 distribution pattern where some of LISA Capital's 50% distribution patterns from all samples cluster to LL areas: Singapore, Cambodia, Malaysia, Myanmar and Vietnam. The distribution pattern of labor 2015 is mostly 70% of the total sample clustered to LL areas: Singapore, Brunei Darussalam, Cambodia, Malaysia, Myanmar and Vietnam, and Lao People's Democratic Republic. REFERENCES [1] Anselin, L. (1995). Local indicators of spatial association LISA. Geographical analysis, 27(2), 93-115. [2] Anselin, L., & Getis, A. (1992). Spatial statistical analysis and Georaphic information systems. The Annals of Regional Science, Vol 26, pp. 19-33. [3] Anselin, L., & Hudak, S. (1992). Spatial econometrics in practice: A review of software options. Regional science and urban economics, 22(3), 509-536. http://www.iaeme.com/ijciet/index.asp 899 editor@iaeme.com
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