ANALYSIS OF THE LEADING SECTOR OF AMBON CITY DURING THE COVID-19 PANDEMIC

Gross Regional Domestic Product (GDP) is a major factor in viewing economic growth in a region. High production will contribute to Regional Original Income (PAD), which can later be used by regions to build infrastructure and other regional needs. However, the COVID-19 pandemic that has hit Indonesia has hampered the economy. This condition also hit Ambon City which is the capital of Maluku Province. The COVID-19 pandemic has had a different impact on every business sector in Ambon City. Through analysis of Location Quotient (LQ), Dynamic Location Quotient (DLQ), and Shift-share, also supported by using overlay identification analysis, conclusions were drawn when entering the COVID-19 pandemic, the period 2016 to 2020, the leading sector in Ambon City was the Financial Services and Insurance as well as Government Administration, Defense, and Compulsory Social Security sectors.


Background
Economic growth is one of the indicators used to measure the welfare of a region. This is because economic growth has a positive impact on the growth of Regional Original Revenue and the welfare of a region (Hairiyah et al., 2021). If the level of Regional Original Revenue is high enough, then the region can meet the internal development needs of the region independently. To see the level of economic growth, it can be seen from production activities in a region. The level of economic growth can be indicated by an increase in the value of the gross regional domestic product (GRDP) and per capita income which can simultaneously reflect the level of community welfare (Sari et al., 2016).
In 2020, global economic conditions are being hit by uncertainty and decreased production. The decline was due to the impact of the COVID-19 pandemic. The impact of the COVID-19 pandemic has caused a decrease in investor interest in the market, which has caused market growth to tend to be negative. (Nasution et al., 2020).
The impact of the pandemic was also felt by the Indonesian government. In March 2020, President Joko Widodo, through Presidential Decree of the Republic of Indonesia Number 12 of 2020 concerning the Determination of the Corona Virus Disease 2019 (COVID-19) Non-Natural Disaster, declared the COVID-19 pandemic a national disaster. The result of this pandemic is a slowdown in the global economy, which in turn affects the Indonesian economy (Nasution et al., 2020). The Central Bureau of Statistics (BPS) noted that in 2020, Indonesia's economic growth was -2.19% (y on y). This condition was caused by the decline in most business sectors in Indonesia. The business sectors that experienced the deepest slowdown were the Transportation and Warehousing sectors by 15.04 percent and the Accommodation and Drinking Food Provision sector by 10.22 percent.
The impact of the COVID-19 pandemic was also felt in Ambon City. As the provincial capital of Maluku Province, Ambon City has a strategic role in influencing the economy in Maluku Province. In 2020, the economy of Maluku Province was recorded to decline by -3.42% (y on y). In the same period, economic growth in Ambon City fell by -1.95%. The realization of this figure is deeper than that of the Indonesian economy. In addition, this figure is also inversely proportional to the Maluku and Papua regional economies, which grew by 1.44 percent. For this reason, the Ambon City government needs strategic measures to overcome this.
In an effort to keep economic growth running positively every year, a region needs to implement policies that can maintain the rate of productivity in the region. One of the efforts that can be made is to encourage production in the basic sector. With the increasing production of the basic sector, it can increase economic growth in a region (Tutupoho, 2019). Based on this, a region needs to know and support production in the basic business sector, to maintain economic growth in the region.
In 2020, as quoted from the Ambon City Government website, the Ambon City government is focusing on restoring the economy of Ambon City through optimizing the re-strengthening of cooperatives and MSMEs. This is different from the statement conveyed in Railen and Jolyne's (2018) research, regarding the economic base sector in Ambon City, that in 2015, the business field sectors classified as base sectors were the water supply sector, garbage processing, waste and recycling and the Information and communication sector. From this incident, does the COVID-19 Pandemic have an impact on the basic and non-basic sectors in Ambon City?
Previous research has analyzed the basic sectors in Maluku Province, including those conducted by Ramly and Matitaputty (2022). In addition, research has also been conducted on the impact of the COVID-19 Pandemic in Indonesia by Akhmad (2022). As well as the impact of the COVID-19 Pandemic conducted by Ramly, Muspida, Loppies (2022). However, none of the three studies have combined the Location Quotient (LQ) and Shift-share analysis methods.
Some studies have used Location Quotient (LQ) analysis and Shift-share Analysis separately to identify leading sectors in other regions. Among them are Pribadi and Nurbiyanto (2021) to examine the leading sector in Central Lampung Regency, and research conducted by Jelira, Sangadji, and Abdulah (2022) which examines the leading sector in Buru Regency. And the latest research by Farras (2023) which examines the leading sectors in North Maluku Province. So the novelty of this research is the use of Location Quotient (LQ) analysis and Shift-share Analysis in Maluku Province during the COVID-19 Pandemic.
Problem formulation: What are the basic sectors that Ambon City has during the COVID-19 pandemic to maintain economic growth when the economy is slowing down?
Objective: To provide recommendations regarding the prioritization of support for increased production in Ambon City in order to provide optimal benefits to economic growth.

Economic Growth
Economic growth is the process of changing the economic conditions of a region in a systematic and sustainable development towards a better situation in a certain period. Regional economic growth can also be interpreted as a process of increasing the production capacity of an economic level which is manifested in the form of an increase in regional income. The existence of economic growth is one indication of successful economic development (Sukirno, 2000).
One way to measure economic growth in a region is to measure the region's GRDP in a certain period (Rahmawati & Hidayah, 2020). The aggregated results of the realization of Gross Regional Domestic Product in each business sector can be used to calculate economic growth. Regional output production is said to be basic if the output results in certain fields can be exported outside the region (Harinta et al., 2018). The level of economic growth of a region can be determined by the level of demand for output from outside the region.

Determination of Economic Growth Base Sector
At the regional scale level, the level of economic growth in a region can be measured through the increase in the output of each sector in the region. The sectors are then classified into two groups, namely the basic sector and the non-basic sector. A sector is classified as a basic sector whether it can produce products that fulfill the internal needs of the region and then distribute the output of the sector to areas outside the producing region. Meanwhile, a sector classified as a non-base sector is a sector that is only able to fulfill output needs within the region (Pribadi & Nurbiyanto, 2021).
Information on the economic sector base at the regional level can be used to measure the level of export movement of a region, so that it can be used to project the economic growth of the region and then calculate the effect of its growth on others (Soepono, 2001). In other words, base sector development can increase the production of non-base sectors. Furthermore, base sector development can also be used to spur economic growth in the region. (Tutupoho, 2019)

Location Quotient (LQ) and Dynamic Location Quotient (DLQ) Analysis
One of the methods that can be used in determining the base and non-base sectors of a region's economy is the Location Quotient (LQ) analysis method. LQ analysis can provide information to determine the role of a region as an exporter or importer of production output in the economic sector in the region (Schaffer, 2010). One of the functions of using LQ analysis is as a tool in measuring indicators of determining the leading sector (Basuki et al., 2017), LQ can compare the magnitude of the role of an economic sector in a region (Ikhwan, 2021). The LQ method has a weakness, namely that this criterion has a static nature because it can only provide an overview at one point in time. This means that the basic sector (superior) this year will not necessarily be superior in the future, on the other hand, sectors that are not yet basic at this time may excel in the future. (Tutupoho, 2019).
To overcome the weakness of the LQ method, the variance analysis of LQ is used, namely the Dynamic Location Quotient (DLQ) method. The DLQ method is an advanced stage of the LQ method by incorporating a factor of the output growth rate of an economic sector over several time periods (Nugroho, 2010). The DLQ calculation has the assumption that each sectoral value increase or GRDP value has a different average growth rate per year in the period from the initial year to the year away (Sambodo, 2002) DLQ can combine the calculation of the economic growth rate of the sector we observe and the economy in the specified period. The results of DLQ analysis can illustrate the potential of whether the economic sector is able to become an economic base in the future. By synchronizing the results of the LQ and DLQ calculations, the government can see the contribution and classify the economic sector as a base or non-base sector, and assess the prospects of the sector in the future (Pribadi & Nurbiyanto, 2021).

Shift-Share Analysis
Shift-share analysis can be used to determine changes in the economic structure of a region. Shiftshare analysis is a historical analysis to determine the economic drivers that shape the economic structure of a city (Mourouzi-Sivitanidou, 2021). The Shift-share method can see the economic growth of a region by comparing it with economic growth in a broader scope, for example, the growth of the Regency / City level with the growth of the province level. (Pribadi and Nurbiyanto, 2021). The components in conducting Shiftshare analysis include Provincial Growth (PN), Proportional Growth (PP), and Regional Share Growth (PPW). The growth of a region's economic sector can be identified by measuring the value of the calculation of the net shift of the economic sector in the region. The value of net shift (PB) can be obtained through aggregation between the values of PP and PPW.

Location Quotient (LQ) Analysis
LQ analysis is an analytical tool to show the economic base of the region, especially from the criteria of contribution to national GDP (Wibisono, 2003). In addition, LQ is an index to measure the (relative) degree of specialisation of a sector or subsector of the economy of a particular region (Bendavid, 1991). LQ is formulated with the following formula (Isserman, 1977): With the following information: LQ = Location Quotient coefficient of business sector I in Regency/City J Xij = Realisation of GRDP of the business sector I in district j Xi = Realisation of GRDP of business sector i at the provincial/reference level of district j RVj = total GRDP of regency j RV = total GRDP at the provincial/reference level of district j If the result of the LQ coefficient calculation is LQ > 1, then the industry can be classified as a basic sector in the region. This indicates that the output share of the leading industry at the provincial level is greater than that of the reference province. Conversely, if the LQ value is < 1, then the industry is not a basic sector in the region. This is because the sector has a lower demand than the provincial level.

Dynamic Location Quotient (DLQ) Analysis
DLQ analysis is a modification of LQ analysis by accommodating the magnitude of GRDP (sub-sector production value) over time (Hidayat, 2014). The DLQ coefficient is calculated using a formula as below.
With the following information: DLQ = DLQ coefficient of business sector i in district j gij = average GRDP growth of business sector i in district j gj = average growth of total GRDP in district j gip = average GRDP growth of business sector i at province level p gp = average growth of total GRDP at province p level t = time (year) A DLQ coefficient higher than 1 identifies that the observed economic sector in the region has the potential to be encouraged or is a positive prospect. Conversely, if the DLQ value is lower than 1, then the economic sector is not prospective in becoming the economic base business sector in the area.

3.3
Shift-share Analysis The economic growth and performance of a region can be measured through several dimensions. One of the analyses that can be used to measure the level of regional production is the Shift-share analysis method (Arsyad, 1999). Shift-share analysis can help to divide the economic growth of a region into three components and calculate the contribution of each component (Curtis, 1972). The calculation of the Shiftshare analysis is as follows: With the following information: Ra = Ratio of GRDP in province p A Net Shift (PB) value higher than 0 or positive, identifies that the development of a sector in the region is progressive. Contrary to this, if the PB value is lower than 0 or negative, then the development of the sector in the observed area is classified as slow. Shiftshare analysis results are used to group business sectors that have growth and competitiveness with the following groupings: • Quadrant I. If the PP and PPW values of an economic sector are positive, it means that the sector in this quadrant has rapid growth and strong competitiveness.
• Quadrant II. If the PP value is negative but PPW is positive, it means that the sector in this quadrant has slow growth but strong competitiveness.
• Quadrant III. If the PP value is positive but PPW is negative, it means that the sector in this quadrant has fast growth but has weak competitiveness.
• Quadrant IV. If the PP and PPW values are negative, it means that the sectors in this quadrant have slow growth and weak competitiveness.

Location Quotient (LQ) Analysis Results
The results of the LQ analysis are used to determine which business sectors are classified as basic or non-basic economic sectors. The calculation result of LQ analysis which is more than 1 (LQ>1) means that the sector is classified as a basic sector. Meanwhile, if the result is less than 1 (LQ<1), it is classified as a non-base sector.
To find out the basic sector of Ambon City during the COVID-19 pandemic, the author uses data from 2016 -2020. Since the announcement of the COVID-19 pandemic in April 2020, there has been an economic slowdown in Indonesia, including Ambon City. It can be seen from Table 2, through LQ analysis, that the basic sectors in Ambon city during the COVID-19 period have no difference with the period before COVID-19. The magnitude of the value also varies, there are sectors that increase and there are sectors that decrease, both in the basic sector and in the non-basic sector.

.2 Dynamic Location Quotient (DLQ) Analysis Results
The results of DLQ analysis are used to determine which business sectors are classified as prospective and non-prospective economic sectors. The calculation result of DLQ analysis which is more than 1 (DLQ>1) means that the sector is classified as a prospective sector. Meanwhile, if the result is less than 1 (DLQ<1), it is classified as a non-prospective sector.
For the calculation during the COVID-19 period, the author uses the period 2016 to 2020. From Table 4, it can be seen that during the COVID-19 pandemic, there were 10 business sectors that fell into the prospective category, while the other 7 sectors were classified as non-prospective sectors. The business sector that has the highest growth rate is still the Mining and Quarrying sector. Meanwhile, sectors that experienced an increase in status were the Agriculture, Forestry, and Fisheries sector, Government Administration, Defense and Compulsory Social Security, Education Services, and Health Services and Social Activities. Meanwhile, the sector that experienced a decline in status was the Electricity and Gas Procurement sector.

.3 Shift-share Analysis Result
Through Shift-share analysis, it is found that there are different business sectors that are classified in quadrant I, namely those with rapid growth and strong competitiveness, during the COVID-19 pandemic. There are 5 sectors that still have the potential to grow so that they can be classified in quadrant I. Construction, Financial and Insurance Services, Government Administration, Defence and Compulsory Social Security, Education Services, and Health Services and Social Activities. A clearer picture of the results of the Shift-share analysis can be seen in table 3 below.

.4 Discussion
From the results that have been obtained, to determine the basic sector during COVID-19 Pandemic, the author will use an overlay of the results of the LQ, DLQ, and Shift-share analyses. The coefficients of the three components will be identified as positive (+), if the LQ and DLQ analysis has a base result, and if the Shift-share analysis has a quadrant I result, and identification as negative (-) if it does not meet the two criteria. The identification of the overlay is if the results of the three analyses have positive results (+++), then the sector is the leading sector in Ambon City in that period. The following are the results of overlay identification during the COVID-19 pandemic.
Based on Table 4, it can be seen that the leading sectors in Ambon City are the Financial and Insurance Services sector and the Government Administration, Defense, and Compulsory Social Security sector. This is in accordance with the statement from Hadiwardoyo (2020), that during the COVID-19 pandemic, the insurance sector has the potential to grow. As for the Government Administration, Defense, and Compulsory Social Security sector, it is different from the research conducted by Gani (2021), which shows that the Government Administration, Defense, and Compulsory Social Security sector experienced a decrease in production.

CONCLUSIONS AND SUGGESTIONS
From some of the research results and findings that have been carried out using LQ, DLQ, and Shiftshare analysis and supported by overlay analysis, it can be concluded that there are leading sectors in Ambon City during the COVID-19 pandemic. These leading sectors are the Insurance and Financial Services Sector and the Government Administration, Defense and Compulsory Social Security Sector. During the COVID-19 pandemic, these two sectors have the highest potential to grow and optimize regional production.
However, the government needs to provide an economic cushion to save businesses in sectors that have experienced a decline, in order to protect the sustainability of community businesses and avoid an increase in community unemployment. In addition, it should also be noted that during the COVID-19 pandemic, there were several policies to limit community social activities that might affect the production of each business sector, this can be a problem formulation for further research.

IMPLICATIONS AND LIMITATIONS
In the preparation of this journal, the author experienced limitations in the use of data in 2020, because the data released by the Central Statistics Agency at the time this journal was written did not contain data in 2021, when the impact of the COVID-19 pandemic was still being felt. Because the Indonesian government only eliminated the COVID-19 pandemic status at the end of 2022. Suggestions for future research are to include data in 2021 and 2022 to further clarify the impact of the COVID-19 pandemic in Ambon City.
In addition, additional literacy regarding economic conditions per business sector in Ambon City can be used as material for sharpening future papers. This is useful for linking the research results with the actual situation.

ACKNOWLEDGEMENT
Appreciation is given to all parties who have contributed to the preparation of this paper. Especially the lecturers at the STAN State Finance Polytechnic who have provided support and guidance in the preparation of this paper.