Research Article: 2018 Vol: 22 Issue: 4
Toto Gunarto, University of Lampung
Ahmad Sentri, University of Lampung
Muhammad Said, State Islamic University Syarif Hidayatullah
This research aims to find out the pattern of local government expenditures, especially regional expenditures directly related to economic growth, namely regional expenditures in education, health, agriculture, housing, transportation, and social, and linking the respective superior sectors in the New Autonomous Region (NAR) In Sumatera Island. The result of study shows that potential sectors having criteria of the basic sector, and high growth and competitiveness are still dominated by the agricultural sector and services. Meanwhile, local government expenditures for education, health, and social affairs have a positive and significant effect, housing has a negative and significant effect, and government spending on agriculture and transportation has no significant effect on the growth of the new autonomous regions, with the agricultural sector, Manufacturing, electricity, gas and water supply sectors, construction sector, trade, hotel and restaurant sector, as well as transportation and communications sectors being the ones supporting economic development in New Autonomous Regions (NAR).
Government Expenditure, Government Spending, Economic Growth, Potential Sectors.
Government plays important roles in the economic development; allocation, distribution, and stabilization. Government conducts development activity through expenditure to encourage the economic movement, vital service public infrastructure development (Musgrave and Musgrave, 1991). By using fiscal policy instrument, local government serves public and builds economic growth through the Local Income and Expenditure Budget (APBD).
The effect of local government expenditure on a region’s economic growth can be measured using GDP, a total sum of value added resulting from economic activity. GDP can be a parameter for the government and other parties to evaluate the successful economic development, and can be used to find out the local economic development wholly or per sector. Samuelson and Nordhaus (2005) pointed out four factors determines economic development that are human resource, natural resource, capitalization, and technology.
Musgrave and Musgrave (1976) mapped three stage of government expenditure evolution to economic development; beginning, intermediate and advanced stages. At the beginning stage, the ratio of government investment to total investment is relatively large. The government provides investment in the field of education, health, and transportation infrastructures. At the intermediate level, government investment is still remains to improve economic growth to take off by providing larger portion to the private investment. In a development process, the ratio of private investment to GDP is getting larger and the ratio of government investment to GDP will be smaller (Musgrave, 1980).
In advanced stage, the activity in economic development shifts from infrastructure provision to expenditure for social activities such as old age security program and public health service (Akpan, 2005). Folster and Henrekson (1999), Laudau (1983), and Dandan (2011) negated effect of government expenditure on economic growth is negative. Contradictory to them, Korman and Brahmasrene (2007), Donald and Shuaglin (1983) see positive and significant impact of government expenditure to economic growth.
Harshorn (1995), Saad and Kalakech (2009); Loto (2011) used expenditure components defense, education, health, transportation and communication, and agriculture to study the effect of government expenditure on economic growth using. Loto (2011) specifically monitor health and education sectors to improve labor efficiency and it is impact to improve national production growth. In short term, expenditure in agriculture and education affects negatively the economic growth and that in health, transportation and communication as well as security affects positively. While that in national security transportation and communication is not significant statistically.
Lin (1994) points out that government expenditure impact to the improvement economic growth through public commodity and infrastructure, social service and intervention target. In line with Kweka and Morrissey (2000), by adding government transfer, consumption and total output variables find the three components have negative effect, while education expenditure has positive effect, and government investment does not affect the private productivity growth. The result of Zodik (2006) study shows that the government expenditure, both development and routine expenditures, affects the regional economic growth.
Local autonomy generates region expansion in many Indonesian areas since the enactment of Law No. 22 and 25 of 1999, the establishment of New Autonomous Region 217 NARs; 8 provinces, 174 regencies and 34 municipals effect to the economic growth.
Tiebout (1956) area expansion has a power to maintain low tax rate, to provide efficient service, and to allow its individual members of society to express their opinion on any type of services from different government level with vote their feet.
The main rationale of expansion area is the need for distribution local economy, broad geographical condition and easy process of delivering public service. Sumatera Island as one of economic corridor areas in MP3EI program has 17 New Autonomous Regions (Regencies/Municipals). The question is do these 17 New Autonomous Regions have exerted positive effect on development acceleration indicated by economic growth and wellbeing improvement of the people. Thus, this study aims to identify potential sectors in each of NARs to accelerate economic growth, to analyse the effect of government expenditure in education, health, agriculture, housing, communication, and social sectors on economic growth of regencies/municipals in Sumatera.
Economic Growth Theory
Economic growth is the long-term output rise process, measured with indicator of real GDP development over years. Economic growth can originate from Aggregate Demand (AD) and or Aggregate Supply (AS) aspects. From AD aspect, the shift of curve to the right reflects on the increased demand in economy. Meanwhile, from AS aspect, economic growth can be seen from production aspect. Neo-classic group focuses their attention on the positive effect of capital accumulation (physical investment) on economic growth, and the role of technology on output growth does not get explicit attention.
The slow economic growth in developing countries is due to the society’s low demand for product and service (Todaro and Smith, 2003). Low income of the people is due to low labor productivity reflected the low quality of human resource as the most determinant factor (Schultz (1961).
Solow and Swan (1956) regard population growth, capital accumulation, technology advance, and output size interact each other. Production function allows for the substitution of Labour (L) for Capital (C). Growth level derives from capital accumulation, increased labour offering, and technology advance. Skill improvement and technique advance improves productivity. Solow-Swan theory sees market mechanism can create equilibrium. To them, it is unnecessary for the government to influence the market mechanism too much except in fiscal and monetary policies. Four important variables in Solow-Swan’s model are output, capital, labour, and knowledge, as formulated below:
Y(t)=F [ K(t), L(t), A(t) ] ……….(1)
Solow’s model growth theory was designed to shows how capital supply growth, labor growth, and technology advance interact in economy and their effect are on a state’s product and service output overall (A/A) (Mankiw, 2003).
Government Expenditure Theory
Adolf Wagner states that in an economy, when per capita income increases, government expenditure will increase relatively. Thus, government should organize the relationship emerging in society, law, education, recreation, culture, and etc. Peacock and Wiseman’s Theory which analyzes government expenditure revenue stated that government tried to increase its expenditure by relying on increasing the tax revenue. Harry W. Richardson (1973) argued that the main determinant of an economic growth is related directly to product and service from outside region (Arsyad, 1999). It underlies location quotient technique thinking, the one helping determine local economic export capacity and self-sufficiency level of a sector.
Glasson in Ghalib (2005) says that the basic concept of economic base divided into two sectors. Firstly, base Sector focuses on the activity of exporting products and services to outside its economic area or marketing products out of its economic area border. Secondly, non-base sector activity that provides products and services for the need of people living in its own economic area without exporting products or service to outside area.
One of important indicators to find out economic condition in an area/province in a certain period is Gross Regional Domestic Product (GRDP) data, both by prevailing price or constant price. GRDP, according to Central Statistical Bureau (2007: 2), is the amount of added value yielded for entire business area in a region or the amount of total final product and service value yielded by all economic units in a region.
Research Hypothesis
The hypotheses proposed in this study are:
H1: Local government expenditure in education sector affects economic growth positively.
H2: Local government expenditure in health sector affects economic growth positively.
H3: Local government expenditure in agricultural sector affects economic growth positively.
H4: Local government expenditure in housing sector affects economic growth positively.
H5: Local government expenditure in transportation/communication sector affects economic growth positively.
H6: Local government expenditure in social sector affects economic growth positively.
H7: Economic development in Sumatera Regions is different between the region with base and the one with non-base economic sectors.
Sample
1. Purposive sampling employed with the certain criteria.
2. Regency/city expanded in 1999 in Sumatera Island.
3. New autonomous regions (Regencies/Municipals) not expanded two times.
4. New autonomous regions (Regencies/Municipals) with reign period of more than 10 years (2 reign periods).
Considering this, the sample of research consisted of 17 regencies/municipals in Sumatera.
Analysis Model
The study applied qualitative and quantitative through economic base approach with Klassen’s typology analysis instruments to prioritize sector, sub sector, business, or commodity of a region and to find out the description a region’s economic growth pattern and structure. Besides, Location Quotient, a method of calculating the ratio of the relative contribution of a sector’s added value in a region (Regency/Municipal) to the contribution of corresponding sector’s added value at province or national scale is also applied in this study.
Considering the result of LQ calculation, the analysis can be drawn if LQ is more than one (LQ>1) is a potentially export base sector, it means that the Regency/municipal’s specialization is higher than that in province level; if LQ is less than one (LQ<1) is a non-base sector, the one with specialization level lower than that at province level; if LQ is equal to one (LQ=1), it means that the specialization of Regency/Municipal is as same as that of province level (Table 1).
| Table 1 POTENTIAL SECTOR RANKING |
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| Potential Sector | LQ | Pr | Dr | Meaning |
| 1 | B | + | + | Base sector has rapider growth and higher comparative advantage than the one does at province level. |
| 2 | B | - | + | Base sector has slower growth but higher comparative advantage than the one does at province level. |
| 3 | B | + | - | Base sector has rapider growth but lower comparative advantage than the one does at province level. |
| 4 | B | - | - | Base sector has slower growth and lower comparative advantage than the one does at province level. |
| 5 | NB | + | + | Non-base sector has rapider growth and higher comparative advantage than the one does at province level. |
| 6 | NB | - | + | Non-base sector has slower growth but higher comparative advantage than the one does at province level. |
| 7 | NB | + | - | Non-base sector has rapider growth but lower comparative advantage than the one does at province level. |
| 8 | NB | - | - | Non-base sector has slower growth and lower comparative advantage than the one does at province level. |
Shift Share analysis was also used to analyze and to find out the shift and the role of local economy. It used to observe economic structure and its shift by means of emphasizing on sector growth in region, compared with the same sector in higher region level or at national level. Local economy dominated by the slowly growing sector will grow below the economic growth level of higher region.
The potential economic sector in this research was analyzed using overlay from Klassen’s typology, Location Quotient (LQ) and Shift Share analyses, by considering typology of New Autonomous Region, base and non-base sectors, and the sector with growth and competitiveness (comparative advantages) in the same sector to the province with the following alternative:
Descriptive Analysis
Descriptive analysis method is the simple one that can be used to describe an observation’s condition by presenting in table, chart and narration aiming to make the readers interpret the result of observation more easily.
Econometric Analysis
This research employed econometric analysis method, panel data (pooled data) regression. Panel data is the combination of time series and cross sectional data. Currently panel data method has been used widely as some weaknesses are found in either cross sectional or time series approach.
The same cross sectional data is observed by time; if each of cross sectional units has the same time series observation, it is called balanced panel, and otherwise when the number of observations is different, it is called unbalance panel.
Baltagi (2005) suggests some advantages of panel data collection in econometric analysis, one of which is controlling individual heterogeneity. Panel data states that individuals, companies, places or states are heterogeneous. In panel data, there are size and time, so that there are other variables likely becoming state-invariant or time-invariant that can affect dependent variable. Panel data gives treatment opportunity to each of heterogeneous individual units analyzed.
Research Model Formulation
To analyze the effect of government expenditure on economic growth in New Autonomous Regions (Regencies/Municipals) using regression analysis. The advantage of local autonomy in the sense of public service improvement is reflected on government expenditure in education, health, and infrastructure areas with proxy that government expenditure in public work, in housing, in transportation/telecommunication, and in social areas, added with dummy variable of Regency/Municipal and dense basic economic sector encourage the economic growth. This model estimated in this research is:
Y=f (PEND, KES, PERT, PERUM, TRANS, SOSIAL, DLQ>1(sectori)........................(1)
From equation 1, the following is obtained:
EG=f (PEND α1 KES α2 PERT α3 PERUM α4 TRASN α5 SOSIAL α6 DLQ>1(sektori) β...............(2)
Using an empirical linear model, equation 1 is derived using ln (natural logarithm) so that the regression equation is written as follows:
LnEGit=α0+α1 ln PENDit+α2 ln KESit+α3 ln PERTit+α4 ln PERUMit+α5 ln TRANSit+α6 ln SOSIALit+β2D2i+β3D3i+β4D4i+β5D4i+β6D6i+β7D7i+β8D8i +β9D9i+ εt.......................(3)
Where:
LnEGit=Economic growth
PEN=Education Sector Expenditure
KES=Health Sector Expenditure
PERT=Agricultural Sector Expenditure
PERUM=Housing Sector Expenditure
TRANS=Transportation/Telecommunication Sector Expenditure
SOSIAL=Social Sector Expenditure
D1=LQ Agricultural Sector; 1 when LQ>1 and 0 others
D2=LQ Mining and Exploration Sector; 1 when LQ>1 and 0 others
D3=LQ Processing Industry Sector; 1 when LQ>1 and 0 others
D4=LQ Electricity, Gas andClean Water; 1 when LQ>1 and 0 others
D5=LQ Building Sector; 1 when LQ>1 and 0 others
D6=LQ Trading, Hotel and Restaurant Sector; 1 when LQ>1 and 0 others
D7=LQ Transportation and Communication Sector; 1 when LQ>1 and 0 others
D8=LQ Financial, Lease& Company Service Sector; 1 when LQ>1 and 0 others
D9=LQ Services Sector; 1 when LQ>1 and 0 others; α0=Intercept (constant); εt=error term
see economic growth pattern and structure in New Autonomous Region, local typology analysis can be used. This analysis builds on two main indicators: local economic growth and income or per capita local GDP, by determining economic growth as vertical axis and per capita GDP as horizontal axis. In this research, the items becoming reference are economic growth and per capita income of Sumatera region. The New Autonomous Regions observed are classified into four groups: 1st quadrant includes New Autonomous Region with high growth and high income, the one with economic growth and per capita income higher than the average for Sumatera area; 2nd quadrant is New Autonomous Region with high income but low growth, the one with high per capita income but lower economic growth compared with the average for Sumatera area; 3rd quadrant is New Autonomous Region with high growth but low income, the one with high economic growth but per capita income lower than the average for Sumatera area; 4th quadrant is relatively retarded New Autonomous Region, the one with low economic growth and lower per capita income compared with the average for Sumatera area (Kuncoro, 2012).
In 2001, 9 New Autonomous Regions belong to the 1st quadrant: Kuantan Singingi, Rokan Hulu, Rokan Hilir, Siak, Pelalawan, Dumai, Karimun, Natuna, and Batam regencies, 1 to the 2nd quadrant: Mentawai Island, 1 to the 3rd quadrant: Soralangon, and 6 to the 4th quadrant: Muaro Jambi, Tanjung Jabung Timur, Tebo, Kota Metro, Way Kanan, and Lampung Timur Regencies.
The subsequent mapping is made considering the development of New Autonomous Region in the end of observation year, so that there is a shift since the enactment of local autonomyin 10 yr period. New Autonomous Regions belonging to the 1st quadrant are: Kuantan Singingi, Rokan Hulu, Rokan Hilir, Siak, Pelalawan, Dumai, and Batam (7 New Autonomous Regions), and none belongs to the 2nd quadrant, while those belonging to the 3rd quadrant are: Sarolangon, Muaro Jambi, Tanjung Jabung Timur, Tebo, Natuna, Kota Metro, and Lampung Timur regencies (8 New Autonomous Region), and those belonging to the 4th quadrant are: Mentawai Islands and Way Kanan Regency (2 New Autonomous Regions).
Potential Sector in New Autonomous Region
Discussion on the determination of superior/potential sectors in this research is conducted using several analysis instruments: Location Quotient (LQ) and Shift Share Analyses (SSA). To obtain potential sectors in New Autonomous Regions of Sumatera, the combination of Klassen’s New Autonomous Region, Location Quotient (LQ) and Shift Share is used, by considering New Autonomous Region typology, base and non-base sectors. The sectors having growth and competitiveness (comparative advantage) over the same sectors in province can be seen in table below:
Table 2 indicates the New Autonomous Region group with the following criteria: quickly advancing and quickly growing areas and the result of Overlay of LQ and Shift share analysis. Agricultural and services sectors still become the base ones, with rapid sector growth and high competitiveness in the same sector against the province or the first potential sector criterion of agricultural sector owned by 5 New Autonomous Regions (Kuantan Singingi, Pelalawan, Rokan Hulu, Siak and Rokan Hilir regencies), and services owned by 5 New Autonomous Regions (Kuantan Singingi, Rokan Hulu, Siak, Rokan Hilir regencies, and Dumai cities). The second alternative potential sector criterion-the base sector with slow growth but high competitiveness - includes processing industry sector belonging to 5 NARs (Pelalawan, Rokan Hulu, and Siak regencies, and Dumai and Batam cities), financial, lease, and company service sector belonging to 4 NARs (Kuantan Singingi, Pelalawan, Rokan Hulu and Rokan Hilir regencies), and mining and energy sector belonging to 2 NARs (Siak and Rokan Hilir regencies). The third alternative potential criterion the base sector with rapid growth and low competitiveness (comparative advantage) compared with the Province belongs only to Batam City including trading, hotel and restaurant, and financial, lease, andcompany service sectors. NARs belonging to the third quadrant or quickly developing (high growth but low income) NARs are the one with high economic growth rate but per capita income lower than individual provinces do in Sumatera, as shown in the Table 3 below:
| Table 2 COMPARATIVE ADVANTAGE |
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| NAR | Sector | B/NB | Proportional growth (Pr) | Regional Growth (Dr) |
Net Shift (Pr+Dr) |
Potential sector |
| K. Singingi | Agriculture | B | Rapid | High | Advanced | 1 |
| Mining and Energy | NB | slow | High | Advanced | 6 | |
| Processing Industry | NB | slow | High | Advanced | 6 | |
| Electricity, Gas, Clean Water | NB | Rapid | High | Advanced | 5 | |
| Construction | B | Rapid | High | Advanced | 1 | |
| Trading, Hotel and Restaurant | B | Rapid | High | Advanced | 1 | |
| Transportation and Communication | NB | Rapid | High | Advanced | 5 | |
| Financial, Lease & Company Service | B | slow | High | Advanced | 2 | |
| Services | B | Rapid | High | Advanced | 1 | |
| Pelalawan | Agriculture | B | Rapid | High | Advanced | 1 |
| Mining and Energy | NB | slow | Low | Slow | 8 | |
| Processing Industry | B | slow | High | Advanced | 2 | |
| Electricity, Gas, Clean Water | NB | Rapid | High | Advanced | 5 | |
| Construction | NB | Rapid | High | Advanced | 5 | |
| Trading, Hotel and Restaurant | NB | Rapid | High | Advanced | 5 | |
| Transportation and Communication | NB | Rapid | High | Advanced | 5 | |
| Financial, Lease & Company Service | B | slow | High | Advanced | 2 | |
| Services | NB | Rapid | High | Advanced | 5 | |
| R. Hulu | Agriculture | B | Rapid | High | Advanced | 1 |
| Mining and Energy | NB | slow | Low | Slow | 8 | |
| Processing Industry | B | slow | High | Advanced | 2 | |
| Electricity, Gas, Clean Water | NB | Rapid | High | Advanced | 5 | |
| Construction | B | Rapid | High | Advanced | 1 | |
| Trading, Hotel and Restaurant | NB | Rapid | High | Advanced | 5 | |
| Transportation and Communication | B | Rapid | High | Advanced | 1 | |
| Financial, Lease & Company Service | B | slow | High | Advanced | 2 | |
| Services | B | Rapid | High | Advanced | 1 | |
| Siak | Agriculture | B | Rapid | High | Advanced | 1 |
| Mining and Energy | B | slow | High | Advanced | 2 | |
| Processing Industry | B | slow | High | Advanced | 2 | |
| Electricity, Gas, Clean Water | NB | Rapid | High | Advanced | 5 | |
| Construction | NB | Rapid | High | Advanced | 5 | |
| Trading, Hotel and Restaurant | NB | Rapid | High | Advanced | 5 | |
| Transportation and Communication | NB | Rapid | High | Advanced | 5 | |
| Financial, Lease & Company Service | NB | slow | High | Advanced | 6 | |
| Services | B | Rapid | High | Advanced | 1 | |
| R. Hilir | Agriculture | B | Rapid | High | Advanced | 1 |
| Mining and Energy | B | slow | High | Advanced | 2 | |
| Processing Industry | NB | slow | High | Advanced | 6 | |
| Electricity, Gas, Clean Water | B | Rapid | High | Advanced | 1 | |
| Construction | NB | Rapid | High | Advanced | 5 | |
| Trading, Hotel and Restaurant | B | Rapid | High | Advanced | 1 | |
| Transportation and Communication | B | Rapid | High | Advanced | 1 | |
| Financial, Lease & Company Service | B | slow | High | Advanced | 2 | |
| Services | B | Rapid | High | Advanced | 1 | |
| Dumai | Agriculture | NB | Rapid | High | Advanced | 5 |
| Mining and Energy | NB | slow | High | Advanced | 6 | |
| Processing Industry | B | slow | High | Advanced | 2 | |
| Electricity, Gas, Clean Water | B | Rapid | High | Advanced | 1 | |
| Construction | B | Rapid | High | Advanced | 1 | |
| Trading, Hotel and Restaurant | B | Rapid | High | Advanced | 1 | |
| Transportation and Communication | B | Rapid | High | Advanced | 1 | |
| Financial, Lease & Company Service | NB | slow | High | Advanced | 6 | |
| Services | B | Rapid | High | Advanced | 1 | |
| Batam | Agriculture | NB | slow | Low | Slow | 8 |
| Mining and Energy | NB | slow | Low | Slow | 8 | |
| Processing Industry | B | slow | High | Advanced | 2 | |
| Electricity, Gas, Clean Water | B | Rapid | High | Advanced | 1 | |
| Construction | NB | Rapid | Low | Advanced | 7 | |
| Trading, Hotel and Restaurant | B | Rapid | Low | Advanced | 3 | |
| Transportation and Communication | NB | Rapid | Low | Advanced | 7 | |
| Financial, Lease & Company Service | B | Rapid | Low | Lagged | 3 | |
| Services | NB | Rapid | Low | Lagged | 7 | |
Notes: 1=B, Dr+, Pr+; 2=B, Dr+, Pr-; 3=B, Dr-, Pr+; 4=B, Dr-, Pr-; 5=NB, Dr+, Pr+; 6 = NB, Dr+, Pr-; 7 = NB, Dr-, Pr+; 8 = NB, Dr-, Pr-
Source: processed data; BPS, 2011
| Table 3 NARS WITH POTENTIAL SECTOR IN SUMATERA IN THE 3rd QUADRANT DURING 2001-2011 |
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| NAR | SECTOR | B/NB | Proportional growth (Pr) | Regional Growth (Dr) | Net Shift (Prij+Drij) |
Potential sector |
| Karimun | Agriculture | B | slow | Low | Lagged | 4 |
| Mining and Energy | NB | slow | High | Advanced | 6 | |
| Processing Industry | NB | slow | High | Advanced | 6 | |
| Electricity, Gas, Clean Water | B | Rapid | High | Advanced | 1 | |
| Construction | B | Rapid | High | Advanced | 1 | |
| Trading, Hotel and Restaurant | B | Rapid | High | Advanced | 1 | |
| Transportation and Communication | B | Rapid | High | Advanced | 1 | |
| Financial, Lease & Company Service | NB | Rapid | High | Advanced | 5 | |
| Services | B | Rapid | High | Advanced | 1 | |
| Natuna | Agriculture | B | slow | Low | Lagged | 4 |
| Mining and Energy | NB | slow | High | Lagged | 6 | |
| Processing Industry | NB | slow | Low | Lagged | 8 | |
| Electricity, Gas, Clean Water | NB | Rapid | Low | Lagged | 7 | |
| Construction | B | Rapid | Low | Advanced | 3 | |
| Trading, Hotel and Restaurant | B | Rapid | Low | Lagged | 3 | |
| Transportation and Communication | B | Rapid | Low | Lagged | 3 | |
| Financial, Lease & Company Service | NB | Rapid | Low | Lagged | 7 | |
| Services | B | Rapid | Low | Lagged | 3 | |
| Sarolangun | Agriculture | B | slow | Low | Lagged | 4 |
| Mining and Energy | NB | slow | High | Advanced | 6 | |
| Processing Industry | NB | slow | High | Advanced | 6 | |
| Electricity, Gas, Clean Water | NB | Rapid | High | Advanced | 5 | |
| Construction | B | Rapid | Low | Advanced | 3 | |
| Trading, Hotel and Restaurant | NB | Rapid | High | Advanced | 5 | |
| Transportation and Communication | NB | slow | High | Lagged | 6 | |
| Financial, Lease & Company Service | B | Rapid | High | Advanced | 1 | |
| Services | NB | slow | High | Advanced | 6 | |
| M. Jambi | Agriculture | B | slow | Low | Lagged | 4 |
| Mining and Energy | B | slow | Low | Lagged | 4 | |
| Processing Industry | B | slow | Low | Lagged | 4 | |
| Electricity, Gas, Clean Water | NB | Rapid | High | Advanced | 5 | |
| Construction | NB | Rapid | Low | Advanced | 7 | |
| Trading, Hotel and Restaurant | NB | Rapid | High | Advanced | 5 | |
| Transportation and Communication | NB | slow | Low | Lagged | 8 | |
| Financial, Lease & Company Service | NB | Rapid | Low | Lagged | 7 | |
| Services | NB | slow | High | Lagged | 6 | |
| Tj. Timur | Agriculture | B | slow | High | Advanced | 2 |
| Mining and Energy | B | slow | High | Advanced | 2 | |
| Processing Industry | B | slow | High | Advanced | 2 | |
| Electricity, Gas, Clean Water | NB | Rapid | Low | Advanced | 7 | |
| Construction | NB | Rapid | High | Advanced | 5 | |
| Trading, Hotel and Restaurant | B | Rapid | High | Advanced | 1 | |
| Transportation and Communication | NB | slow | High | Advanced | 6 | |
| Financial, Lease & Company Service | NB | Rapid | High | Advanced | 5 | |
| Services | NB | slow | High | Advanced | 6 | |
| Tebo | Agriculture | B | slow | High | Advanced | 2 |
| Mining and Energy | NB | slow | High | Advanced | 6 | |
| Processing Industry | NB | slow | High | Advanced | 6 | |
| Electricity, Gas, Clean Water | NB | Rapid | High | Advanced | 5 | |
| Construction | B | Rapid | High | Advanced | 1 | |
| Trading, Hotel and Restaurant | B | Rapid | High | Advanced | 1 | |
| Transportation and Communication | B | slow | High | Advanced | 2 | |
| Financial, Lease & Company Service | B | Rapid | High | Advanced | 1 | |
| Services | B | slow | High | Advanced | 2 | |
| L. Timur | Agriculture | B | slow | High | Advanced | 2 |
| Mining and Energy | B | slow | High | Advanced | 2 | |
| Processing Industry | NB | slow | High | Advanced | 6 | |
| Electricity, Gas, Clean Water | NB | Rapid | High | Advanced | 5 | |
| Construction | NB | slow | High | Advanced | 6 | |
| Trading, Hotel and Restaurant | B | Rapid | High | Advanced | 1 | |
| Transportation and Communication | NB | Rapid | High | Advanced | 5 | |
| Financial, Lease & Company Service | NB | Rapid | High | Advanced | 5 | |
| Services | NB | slow | High | Advanced | 6 | |
| Metro | Agriculture | NB | slow | High | Advanced | 6 |
| Mining and Energy | NB | slow | Low | Lagged | 8 | |
| Processing Industry | NB | slow | High | Advanced | 6 | |
| Electricity, Gas, Clean Water | B | Rapid | High | Advanced | 1 | |
| Construction | NB | slow | High | Advanced | 6 | |
| Trading, Hotel and Restaurant | B | Rapid | High | Advanced | 1 | |
| Transportation and Communication | B | Rapid | High | Advanced | 1 | |
| Financial, Lease & Company Service | B | Rapid | High | Advanced | 1 | |
| Services | B | slow | High | Advanced | 2 | |
Notes: 1 = B, Dr+,Pr + ; 2 = B, Dr+, Pr-; 3 = B, Dr-, Pr+; 4=B, Dr-, Pr-; 5=NB, Dr+, Pr+; 6=NB, Dr+, Pr-; 7=NB, Dr-, Pr+; 8=NB, Dr-, Pr-
Source: Processed data; BPS, 2011
From Table 3, it can be seen that trading, hotel and restaurant sector is the first alternative potential sector (the base sector with higher growth and higher competitiveness (comparative advantage) compared with that in province) belonging to 5 NARs (Karimun, Tj. Timur, Tebo, Lampung Timur regencies, and Metro city); the second alternative potential sector (the base sector with slower growth but higher competitiveness (comparative advantage) compared with that in the provinc) includes agricultural sector belonging to 3 NARs Tj. Timur, Tebo and Lampung Timurregencies), mining and energy sector belonging to 2 NARs (Tj. Timur and Lampung Timur regencies), and services sector belonging to 2 NARs (Tebo regency and Metro city). The New Autonomous Regions belonging to the 4th quadrants, the relatively lagged ones (low growth and low income), or those with lower economic growth and per capita income compared with those in individual provinces of Sumatera can be seen in Table 4.
| Table 4 NARS WITH POTENTIAL SECTOR IN SUMATERA IN THE 4th QUADRANT DURING 2001-2011 |
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| NAR | SECTOR | BASE | Proportional Growth (Pr) | Regional Growth (Dr) | Net Shift (Prij+Drij) |
Potential Sector |
| Mentawai Islands | Agriculture | B | slow | Low | Lagged | 4 |
| Mining and Energy | NB | slow | Low | Lagged | 8 | |
| Processing Industry | NB | slow | Low | Lagged | 8 | |
| Electricity, Gas, Clean Water | NB | Rapid | Low | Lagged | 7 | |
| Construction | NB | Rapid | High | Advanced | 5 | |
| Trading, Hotel and Restaurant | B | slow | Low | Lagged | 4 | |
| Transportation and Communication | NB | Rapid | Low | Advanced | 7 | |
| Financial, Lease & Company Service | NB | Rapid | Low | Lagged | 7 | |
| Services | NB | slow | Low | Lagged | 8 | |
| W. Kanan | Agriculture | B | slow | High | Advanced | 2 |
| Mining and Energy | NB | slow | High | Advanced | 6 | |
| Processing Industry | NB | slow | High | Advanced | 6 | |
| Electricity, Gas, Clean Water | NB | Rapid | High | Advanced | 5 | |
| Construction | NB | slow | High | Advanced | 6 | |
| Trading, Hotel and Restaurant | NB | Rapid | High | Advanced | 5 | |
| Transportation and Communication | NB | Rapid | High | Advanced | 5 | |
| Financial, Lease & Company Service | NB | Rapid | Low | Advanced | 7 | |
| Services | NB | slow | High | Advanced | 6 | |
Notes: 1=B, Dr+,Pr+; 2=B, Dr+, Pr-; 3=B, Dr-, Pr+; 4=B, Dr-, Pr-; 5=NB, Dr+, Pr+; 6=NB, Dr+, Pr-; 7=NB, Dr-, Pr+; 8=NB, Dr-, Pr-
Source: Processed Data; BPS, 2011
Considering Klassen’s typology, out of NARs in Sumatera as shown in Tables 2-4 belongs to lagged region: Mentawai Island and Way Kanan regencies; it is these two regencies have no 1st alternative potential sector (the base sector with higher growth and higher competitiveness (comparative advantage) compared with the one in Province). Then, agricultural sector becomes the 2nd alternative potential one (the base sector with slow growth but highercomparative advantage than the one in province) belonging to Way Kanan Regency, while Mentawai Islands Regency has only agricultural and trading, hotel, and restaurant sectors included into the fourth alternative potential sector (the base sector with slow growth and lower comparative advantage than the one in Province).
The Effect of Government Expenditures in Education, Health, Agricultural, Housing, Communication, and Social Fields On the Economic Growth
The model employed to analyze the effect of government expenditure on economic growth in regencies/municipals constituting NARs was regression analysis with panel data. The model estimated in this research is:
Y=f (PEND, KES, PERT, PERUM, TRANS, SOSIAL, DLQ>1(sectori) .......................(1)
From equation 1, it can be found:
EG=f (PEND α1 KES α2 PERT α3 PERUM α4 TRASN α5 SOSIAL α6 DLQ>1(sektori) β...............(2)
Using a linear empirical model, the 2nd equation is derived using ln (natural logarithm), so that the following regression equation is obtained
EGit=α0+α1 ln PENDit+α2 ln KESit+α3 ln PERTit+α4 ln PERUMit+α5 ln TRANSit+α6 ln SOSIALit+β2D2i+β3D3i+β4D4i+β5D4i+β6D6i+β7D7i+β8D8i +β9D9i+ εt.......................(3)
Considering this model, the test is conducted on fixed effect or random effect models.
Estimation model selection
In panel data method, there are three methods used: pooled least square, fixed effect and random effect. To find out the model to be selected, fixed effect or random effect, Hausman test is conducted (Tables 5 & 6). From the calculation using Eviews 8 software, the following result is obtained.
| Table 5 REDUNDANT FIXED EFFECTS TESTS |
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| Test cross-section fixed effects | |||
| Effects Test | Statistic | df. | Prob. |
| Cross-section F | 7.794239 | (16.139) | 0.0000 |
| Cross-section Chi-square | 108.862536 | 16 | 0.0000 |
| Table 6 CORRELATED RANDOM EFFECTS-HAUSMAN TEST |
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| Test cross-section random effects | |||
| Test Summary | Chi-Sq. Statistic | Chi-Sq. d.f. | Prob. |
| Cross-section random | 117.694982 | 14 | 0.0000 |
Source: processed data
The result shows that both F-test and chi-square are significant (p-value 0.000 is less than 5%) so that H0 is not supported and H1 is supported, so that the model follows fixed effect. Therefore, the next step is Hausman test. The result of Hausman test with Eviews 8 software help is as follows:
Considering the result of Hausman Test, the method employed in this research is Fixed Effect one.
Result of Regression Calculation
After the regression equation is considered as passing through identification test, the next process is to estimate panel data model using Ordinary Least Square (OLS). Corresponding to the result of Hausman specification test, in this research the regression equation is conducted using Fixed Effect Model (FEM). The output of processing using fixed effect model estimation can be written in the regression equation below:
LN_EG=1.191+0.010*LN_KES+0.063*LN_PEN-0.0118*LN_PERUM+0.035*LN_PERT- 0.007*LN_SOS-0.025*LN_TRANS+0.025*D2-0.185*D3-0.030*D4-0.037*D5-0.004*D6 + 0.044*D7-0.085*D8+0.037*D9
The result of regression estimation using panel data shows that there is autocorrelation, as indicated with D-Wstat value of 1.728. Autocorrelation incidence can be affected by many factors; each economic policy will usually need time period to affect economic system, or in other words, time interval confound variables will be interrelated. Widarjono (2003) says that economic policy or economic activity such as monetary or fiscal policy does not occur instantaneously but it needs time or lag. Thus, the following model will be used.
Table 7 shows that autocorrelation problem is still indicated until lag2, and there is a change of 2.05020 in lag 3 with D-Wstat; therefore this research employs lag (3) as estimated regression result. From the result of estimation, it can be seen that there are two insignificant variables: government expenditures in agricultural and in transportation/communication sector, with R2 of 0.898703 or in other words, 89.87% of economic growth change in NARs throughout Sumatera can be explained by determinant variable in the model, while the rest of 10.13% is explained by other variables excluded from the model. Thus, this study employs the result of calculation with lag model 3.
| Table 7 PANEL DATA ESTIMATION USING FIXED EFFECT MODEL WITH CROSS SECTION WEIGHT) AND LAG MODEL |
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| Lag Model 1 | Lag Model 2 | Lag Model 3 | ||||||
| Variable | Coefficient | Prob. | Variable | Coefficient | Prob. | Variable | Coefficient | Prob. |
| C | 1.373714 | 0.0000 | C | 1.519562 | 0.0000 | C | 1.388471 | 0.0000 |
| LN_KES (-1) | 0.003928 | 0.7912 | LN_KES (-2) | 0.012254 | 0.1184 | LN_KES (-3) | 0.032560 | 0.0099 |
| LN_PEN | 0.052208 | 0.0033 | LN_PEN | 0.023976 | 0.0448 | LN_PEN | 0.027914 | 0.0333 |
| LN_PERUM | -0.013311 | 0.0481 | LN_PERUM | -0.012667 | 0.0230 | LN_PERUM | -0.018842 | 0.0085 |
| LN_PERT | 0.021743 | 0.1071 | LN_PERT | 0.024516 | 0.0914 | LN_PERT | -0.004242 | 0.5483 |
| LN_SOS | -0.001461 | 0.8527 | LN_SOS | 0.005846 | 0.4521 | LN_SOS | 0.030053 | 0.0001 |
| LN_TRANS | -0.022250 | 0.0887 | LN_TRANS | -0.022773 | 0.0335 | LN_TRANS | -0.009383 | 0.3181 |
| D2 | 0.062196 | 0.2853 | D2 | 0.071704 | 0.2580 | D2 | 0.005277 | 0.9204 |
| D3 | -0.105473 | 0.2437 | D3 | -0.037551 | 0.5773 | D3 | -0.244190 | 0.0018 |
| D4 | -0.003587 | 0.9063 | D4 | -0.022674 | 0.3302 | D4 | -0.055733 | 0.0174 |
| D5 | 0.027874 | 0.2892 | D5 | 0.050828 | 0.0766 | D5 | 0.067813 | 0.0055 |
| D6 | -0.000856 | 0.9734 | D6 | -0.037249 | 0.1007 | D6 | -0.065029 | 0.0000 |
| D7 | 0.035883 | 0.1568 | D7 | 0.052780 | 0.0096 | D7 | 0.066999 | 0.0011 |
| D8 | -0.062647 | 0.0759 | D8 | -0.052902 | 0.1540 | D8 | -0.041806 | 0.4861 |
| D9 | 0.026747 | 0.3499 | D9 | 0.016719 | 0.5380 | D9 | 0.035835 | 0.1297 |
| R2 | 0.769481 | R2 | 0.822780 | R2 | 0.898703 | |||
| F-statistic | 13.57465 | F-statistic | 16.24946 | F-statistic | 26.02452 | |||
| D-W stat. | 1.708539 | D-W stat. | 1.750601 | D-W stat. | 2.045020 | |||
Source: Processed data
Hypothesis Testing on the Effect of Individual Independent Variables on Economic Growth in NARs in Sumatera
The result of calculation shown in Table 8 suggests that local government expenditure in education sector affects the economic growth of NARs in Sumatera positively and significantly at significance level of 95% (?=5%), with Probability value of 0.033, because it is <0.05; therefore this variable is in the area supporting Ha or not supporting H0. The coefficient of government expenditure in education sector is inelastic.
| Table 8 THE RELATIONSHIP OF INDEPENDENT VARIABLE TO ECONOMIC GROWTH IN NARS IN SUMATERA |
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| Independent Variable | Coefficient | Prob. | Correlation Found | Significance |
| C | 1.388 | 0.000 | Positive (+) | Significant |
| Government Expenditure in Health Sector (KES(-3)) | 0.033 | 0.010 | Positive (+) | Significant |
| Government Expenditure in Education Sector (PEN) | 0.028 | 0.033 | Positive (+) | Significant |
| Government Expenditure in Housing Sector (PERUM) | -0.019 | 0.009 | Negative (-) | Significant |
| Government Expenditure in Agricultural Sector (PERT) | -0.004 | 0.548 | Negative (-) | Insignificant |
| Government Expenditure in Social Sector (SOS) | 0.030 | 0.000 | Positive (+) | Significant |
| Government Expenditure in Transportation Sector (TRANS) | -0.009 | 0.318 | Negative (-) | Insignificant |
Source: Processed data
Local government expenditure in health sector affects the economic growth of NARs in Sumatera positively and significantly, at significance level of 95% (?=5%), with Probability value of 0.010, because the value is <0.005 in lag3, with coefficient value of government expenditure in health sector of 0.033, meaning when there was an increase of 1% in government expenditure in health sector three years ago, economic growth will increase by 0.033 percent today.
Local government expenditure in agricultural sector affects economic growth of NARs in Sumatera negatively and insignificantly. Local government expenditure in housing sector affects economic growth of NARs in Sumatera negatively and significantly at significance level of 95% (?=5%) with Probability value of 0.009 or less than<0.005, so that this variable is in the area supporting Ha or not supporting H0. The coefficient of government expenditure in housing sector is -0.019, meaning that a 1% increase in government expenditure in housing sector will decrease economic growth by 0.019%. Local government expenditure in transportation sector affects economic growth negatively and insignificantly. Local government expenditure in social sector affects positively and significantly the economic growth of NARs in Sumatera at significance level of 95% (?=5%), with probability value of 0.000 and inelastic coefficient of government expenditure in social sector.
Economic growth of NARs is different between one region and another having base economic sector. The result of dummy variable estimation at significance level of 95% (?=5%)shows that 6 sectors have probability value<0.05 (significantly), agricultural, processing industry, electricity, gas and clean water, construction, trading, hotel, and restaurant, and transportation and communication sectors meaning that those six sectors are the ones that can support economic growth in NARs.
Potential sector belonging to regions, particularly those with high growth and high income criteria, having with base sector and high growth and high competitiveness against the province is still dominated with agricultural and services sectors. Local government expenditure in education, health, and social sectors affects the growth positively and significantly, while local government expenditure in agricultural and transportations sectors does not affect economic growth significantly, and local government expenditure in housing sector affects economic growth of NARs negatively and significantly.
Economic development of NARs is different between one region and another having base economic sector. The result of dummy variable estimation at significance level of 95% (<=5%) shows that 6 sectors have probability value<0.05 (significantly), agricultural, processing industry, electricity, gas and clean water, construction, trading, hotel, and restaurant, and transportation and communication sectors meaning that those six sectors are the ones that can support economic growth in NARs.