Modelling Regional Economic Growth in East Java Province 2009-2014 Using Spatial Panel Regression Model Akmad Thoifur and Erni Tri Astuti*) Sekolah Tinggi Ilmu Statistik, Jakarta, Indonesia *) [email protected] Introduction Economic growth is an increase in the capacity of an economy to produce goods and services, compared from one period of time to another at a certain country. Traditionally, aggregate economic growth is measured in terms of gross national product (GNP) or gross domestic product (GDP), although alternative metrics are sometimes used. Economic growth can be measured either at national level or regional level, especially after enactment of regional autonomy. Regional economic growth is very important indicator for evaluating the performance of local government. Government, both central and local will seek to encourage the acceleration of economic growth, so that public welfare is expected to increase. East Java Province is one of the biggest provinces in Indonesia consist of 29 regencies and 9 cities. During 2009 to 2014, although it had achieved the 2nd biggest regional GDP in 2014, which contributed almost 14.40 percent of the national GDP, the average economic growth of East Java province is still 5.95 percent per year, below the average national economic growth 6.18 per year. Morover, if we look up at regency level, there are unbalanced growth among regencies. For example, Kota Batu and Surabaya can achieved economic growth up to 7.18 percent and 6.90 percent per year, while others can only achieved 5.31 percent per year (Sampang Regency) or 4.98 percent per year (Kota Kediri). Due to various characteristics, regions will grow and develop variously, some are fast growing and some are depressed. In facts, region that have high economic growth tend to cluster, as well as regions with low economic growth. The tendency of regional clustering indicates spatial dependency of economic growth in East Java Province. Perroux in Pasaribu (2015) said that the process of economic development in a region are related to the geographical position of the region. Tobler’s law of Geography said that anything are interrelated, but something close has bigger relation than far ones. Directly or indirectly, economic development in a region will affect and be affected the surrounding area. It is proven by the regional input output tables, migration and urbanization flow, and cross-regional flow of commodities. There are some concept about impact of growth center area to surrounding area as follows (Pasaribu 2015): a. Spread effect (trickle-down-effect or positive spillover) is the impact of growth center that pull up surrounding area because it will expand the distribution of resources in the surrounding area b. Backwash effect (negative spillover) is the impact of the growth center that harm the surrounding area because it will absorb resources from the surrounding area. c. Net spillover effects, the impact of growth center initially absorb other local resources, but in the long run will pull up surrounding area. Theoretically, there are many factors that can affect economic growth in certain region, such as natural resources, human resources, capital stocks, investments, technological developments, and labor efficiency (Todaro and Smith, 2012). From statistical modelling point of view, economic growth plays as response variable and others are explanatory variables. Regression model is a tool that can used to reveal the relationship between response variable with some explanatory variables. However classical regression modeling based on ordinary least square estimation (OLS) required some restricted assumptions. One of these assumptions is non autocorrelation, meaning that each observations are independent with others. However, this assumption is often violated, especially when the observational unit concerning area or spatial data. Campbell (2012) said that the main principle of spatial data are autocorrelation and spatial heterogeneity. Autocorrelation or spatial interactions will lead to spatial linkages. This will violating non autocorrelation assumption, and OLS will not efficient anymore. Addressing the conditions, it is necessary to apply regression model that can accommodate spatial autocorrelation, namely spatial regression model. The purpose of this study is to determine factors that influenced economic growth in East Java Province and also incorporating the spatial aspects. Material and Methods This study cover East Java Province which consists of 29 regencies and 9 cities during 2009 and 2014 period of time. The data is in form of panel data. The term panel data refers to multi- dimensional data frequently involving measurements over time. Panel data contain observations of multiple phenomena obtained over multiple time periods for the same region. Economic growth as a response variable is measured by real regional gross domestic product (RGDP) taken from annual report published by BPS Statistics Indonesia. Because of the limitation of the data, we only used three explanatory variables suspected to affect economic growth, including human resources involved in the process of production, investment, and technological developments. Human resources variable is approached by number of labor (L), investment is approached by capital expenditure spending of local governments (K), while technological developments is approached by human development index (HDI). In spatial analysis, position of any region to others is very important because it will included in the model in terms of spatial weight. Spatial weight 푤푖푗 typically reflects the “spatial influence” of region j on region i .There are many criteria to determine the value of spatial weight 푤푖푗, and in this study we measured the weight by queen contiguity criteria. These weights simply indicate whether spatial units share a boundary or not. Queen Contiguity defines a neighbor when at least one point on the boundary of one region is shared with at least one point of its neighbor (common border or corner). Before doing the analysis, the existence of spatial autocorrelation was checked by calculating Moran’s Index (I). Positive value of I means that region with high value of economics growth tend to be surrounding by others region that have high economic growth, vice versa. Zero I means that thera is no spatial autocorrelation (Anselin, 1988). Sometimes Moran Scatter plot can help to determine this spatial relationship. Moran scatterplot divide the observations or regions into 4 distinct quadrant. Quadrant 1 is called hotspot, consists of fast growing regions surrounded by fast growing regions or high-high clustering. Quadrant 2 consist of slow growing regions surrounded by fast growing regions. Quadrant 3 is called cold spot or low-low clustering, consists of slow growing regions surrounded by slow growing regions. Quadrant 4 consist of fast growing regions surrounded by slow growing regions. Quadrant 2 and 4 are called spatial outlier. Furthermore, a statistical test for the existences of spatial autocorrelation is conducted. The next step is doing model selection in panel data analysis, between spatial error model (SEM) and spatial lag or autoregressive model (SAR). There is also a selection process fixed effect and random effect model. Fixed effects is used when we want to control for omitted variables that differ between regions but are constant over time. Meanwhile, random effects is the model to use when we want to control for omitted variables that change over time but are constant between regions. Hausman test by Mutl dan Pfaffermayr (2008) is applied to choose which effect is appropriate. After selection process, in this study we used spatial auto regression model with fixed effect defined as 38 38 ln RGDPit =λ ∑ wij ln RGDPjt +β1ln Kit+β2 ln Lit+β3 ln HDIit + μi+ρ ∑ wijujt +εit j=1 j=1 Where: 휆 : autoregressive coefficient th βk : slope of k variable (푘 = 1,2,3) ρ : Spatial error coefficient th th wij : Spatial weight between i and j regions th th uit : autocorrelation error of i region t year (푖 = 1,2, … ,38), (푡 = 2009, 2010, … ,2014) th μi : Spatial specific effect of i region εit : error regions i year t Result Figure 1 reflect the economic growth of East Java Province during 2009 to 2014. After four years of outstanding increment and reached the highest value up to 6.95 percent in 2012, the economic growth become decline caused by global financial crisis. Region with the highest economic growth is Kota Batu which can reached 7.18 percent in average per yaar. In the other hands, Kota Kediri has a lowest scored by only 4.98 percent in average. There are 18 regions that have economic growth rate higher than the average growth of East Java Province, while 20 regions were below it. Figure 1. Economic growth of East Java 2009-2014 For examining the spatial relationship between these regions in terms of economic growth, we used Moran scatter plot as shown in Figure 2. There are 11 regions in quadrant I, 6 regions in quadrant II, 15 regions in quadrant III, and 6 regions in quadrant IV. Regarding to the location, regions in central and northern coast of East Java Province clustered in the hotspot areas (quadrant 1). In these regencies or cities, there are many economic activity such as center of industrial, trade and education activity. Not only strategic, but these regions also supported by good infrastructure. Meanwhile, almost all regions at the eastern coast and Madura island clustered in cold spot area (quadrant 3). They have low growth and be surrounded by low growth area as well. These condition may be caused less strategic region, lack of infrastructure, and economic activity that relies on primary sector. On the other hands, regions in south- west of East Java are more heterogenous. Most of them have low economic growth because of unfavorable geographic condition, lack of infrastructure, and economic activity that relies on primary sector. Further investigation by Moran test, where the results is shown in Table 1, in total and in every year as well, Moran Index are significant at α = 5 percent.