Available online at JIME website: https://journal.
id/index.
php/jime/ Response of IndonesiaAos Fisheries GDP to Interest Rates.
Inflation.
Exchange Rates and Fisheries Sector Performance: A Blue Economy Perspective Dyah Ayu Dwi Pitaloka1* .
Deswita Herlina2.
Stannia Cahaya SuciA.
Muhammad Nasim HarahapA Development Economic Study Program.
Sultan Ageng Tirtayasa University.
Serang.
Indonesia A Received: 10 th June 2025.
Revised: 17 th June 2025.
Accepted: 20 th June 2025.
Available online: 30th June 2025.
Published regularly: June 2025.
Abstract This study analyzes how IndonesiaAos fisheries GDP (FISHGDP) responds to real interest rate, inflation, exchange rate, fisheries production, and exports within the Blue Economy framework.
Using VECM and data from 1985Ae2024, it examines short- and long-term relationships through IRF and Variance Decomposition.
Results show fisheriesAo GDP responds positively to fish production, and fish exports, but negatively to inflation and exchange rates, with mixed responses to interest rates.
In the long term.
Meanwhile, the variance decomposition (VD) results reveal that in the long run .
FISHGDP variation is primarily explained by inflation .
35%), followed by its own past values .
32%), fisheries production .
37%), exchange rate .
99%), real interest rate .
49%), and exports .
46%).
These findings emphasize the dominant influence of inflation on fisheries sector growth and the importance of production efficiency, exchange rate stability, and macroeconomic control to support IndonesiaAos Blue Economy development.
Keywords Keyword: fisheries GDP, interest rate, inflation, exchange rate, blue economy.
Abstrak Studi ini menganalisis bagaimana PDB perikanan Indonesia (FISHGDP) merespons suku bunga riil, inflasi, nilai tukar, produksi perikanan, dan ekspor perikanan dalam kerangka Ekonomi Biru.
Dengan menggunakan VECM dan data dari tahun 1985Ae2024, studi ini mengkaji hubungan jangka pendek dan jangka panjang melalui IRF dan Dekomposisi Varians.
Hasil penelitian menunjukkan PDB perikanan merespons positif produksi ikan, dan ekspor ikan, tetapi negatif terhadap inflasi dan nilai tukar, dengan respons beragam terhadap suku bunga.
Sementara itu, dalam jangka panjang .
, hasil dekomposisi varians (VD) mengungkapkan bahwa dalam jangka panjang .
, variasi PDB perikanan terutama dijelaskan oleh inflasi .
,35%), diikuti oleh nilai masa lalunya sendiri .
,32%), produksi perikanan .
,37%), nilai tukar .
,99%), suku bunga riil .
,49%), dan ekspor perikanan .
,46%).
Temuan ini menegaskan dominannya pengaruh inflasi terhadap pertumbuhan sektor perikanan dan pentingnya efisiensi produksi, stabilitas nilai tukar, dan pengendalian makroekonomi untuk mendukung pembangunan Ekonomi Biru Indonesia.
Kata Kunci: GDP perikanan, suku bunga, inflasi, nilai tukar, blue economy *) Corresponding Author Email: deswita@untirta.
id (Deswita Herlin.
Jurnal Ilmu Manajemen dan Ekonomika.
Vol 17.
No.
02 June 2025: 203-218
INTRODUCTION
The fisheries and aquaculture sector plays a vital role globally, contributing over USD 192.
billion in export value and involving more than 61 million people as of 2022 (Food and Agriculture Organization of the United Nations, 2.
In Indonesia, this sector holds enormous potential due to its geographical advantage as the worldAos largest archipelagic state, with more than 17,000 islands and the second-longest coastline globally (Resa et al.
, 2.
Despite abundant marine resources, optimal utilization remains a challenge, calling for integrated and sustainable management strategies to enhance its role in national development (Arianto, 2.
The Indonesian marine area spans over 6.
4 million kmA, including an Exclusive Economic Zone (EEZ) of 3 million kmA (KKP, 2024.
Recognized internationally through UNCLOS 1982 and ratified by Law No.
17/1985.
IndonesiaAos maritime status is also reinforced in the 1945 Constitution (Article 25A) and Law No.
27/2007 on coastal and small island management.
However, the fisheries sector still faces complex challenges such as climate change, unsustainable fishing practices, commodity price volatility, and macroeconomic instability.
Exchange rate fluctuations and inflation directly impact production costs, input prices, and fisher income (Patoni et al.
, 2.
For instance, the weakening of the rupiah increases the cost of imported goods like feed, fishing gear, and fuel, thus reducing profit margins.
On the flip side, it also enhances the competitiveness of fishery exports.
Figure 1 illustrates the growth of IndonesiaAos fishery sector GDP over the past 11 years .
, showing a consistent upward trend.
Starting at IDR 189 trillion in 2014, it rose steadily to nearly IDR 293 trillion in 2024.
This increase highlights the sectorAos strong potential in both production and trade.
In 2023, the fishery sector contributed around 2.
66% to IndonesiaAos total GDP (KKP, 2024.
However, challenges remain, particularly in infrastructure, aquaculture technology, and market access (Ditjen Penguatan Daya Saing Produk Kelautan dan Perikanan, 2.
As a strategic sector, fisheries contribute not only to national GDP but also to food security and coastal community welfare.
Nevertheless, macroeconomic factors particularly interest rates, inflation, and exchange rates affect its performance.
In 2023, for example.
IndonesiaAos interest rate reached Figure 1.
Development of Fishery GDP at Constant 2010 Prices .
illion rupia.
26796A 27545A
25227A 2541A
22683A 2386A
18908A 20401A
29057A 29255A
Source: IndonesiaAos Central Bureau of Statistics .
Pitaloka.
, et al.
Respons of IndonesiaAos fish .
ISSN: 2089-4309 .
2579-4841 .
28%, significantly influencing capital costs and financing access in productive sectors including High borrowing costs can slow economic growth and reduce investment, weakening production capacity and purchasing power.
Additionally, the 2024 exchange rate of Rp15,500/USD presents both opportunities and challenges boosting exports but increasing production costs due to imported inputs.
Therefore, macroeconomic stability becomes crucial for sustainable and efficient growth of the fisheries industry.
The concept of the Blue Economy offers a sustainable development strategy that integrates economic, environmental, and social dimensions by utilizing marine resources responsibly.
It spans various sectors fisheries, aquaculture, marine tourism, renewable energy, and marine transport that together drive economic growth (Caporin et al.
, 2024.
Ovchynnykova et al.
, 2024.
Phang et al.
, 2.
In Indonesia, the fisheries sectorAos role in food security has been increasing, reflected in the rising national fish consumption per capita from 1,788 grams in 2019 to 1,896 grams in 2023 (KKP, 2.
As one of IndonesiaAos most vital economic sectors, fisheries not only contribute significantly to national income and employment but also serve as a foundation for coastal community livelihoods (Raeskyesa et al.
, 2.
This trend indicates a growing awareness of fish as a protein source and reflects the sectorAos rising importance in domestic supply.
Despite these advancements, research on the fisheries sector has primarily focused on production or environmental aspects, often overlooking the influence of macroeconomic variables such as interest rates, inflation, and exchange rates.
Understanding how these variables interact with sectoral performance, such as production and exports, is crucial in formulating effective policies that support sustainable growth.
Furthermore, while the Blue Economy framework is gaining traction in government strategies, it is rarely integrated into empirical macroeconomic studies on fisheries.
This study aims to fill this gap by analyzing how IndonesiaAos fishery GDP responds to macroeconomic variables namely interest rate, inflation, and exchange rate as well as sectoral indicators like fish production, aquaculture production, and fishery exports.
Using a quantitative timeseries approach from 1985 to 2024, this research seeks to provide a comprehensive understanding of the interlinkages between macroeconomic conditions and the fisheries sectorAos economic contribution, while offering valuable insights for policy development aligned with the Blue Economy THEORETICAL FRAMEWORK AND HYPOTHESIS DEVELOPMENT Fishery GDP Fishery GDP serves as the primary indicator of the fisheries sectorAos contribution to the Indonesian economy, encompassing value-added from capture fisheries, aquaculture, processing, and trade (KKP, 2.
In macroeconomic theory.
GDP is explained through multiple lenses: classical Jurnal Ilmu Manajemen dan Ekonomika.
Vol 17.
No.
02 June 2025: 203-218 economics emphasizes full utilization of resources.
neoclassical theory focuses on capital, labor, and technological innovation.
and Keynesian theory highlights the importance of aggregate demand and government intervention (Alfaro et al.
, 2024.
Hasan & Muhammad, 2.
Real Interest Rate Real Interest Rate defined as the nominal lending rate adjusted for inflation is a key monetary variable affecting capital accessibility and investment behavior.
According to KeynesAo liquidity preference theory, interest rates influence the trade-off between holding money and investing in productive assets (Frederic S.
Mishkin, 2.
A high real interest rate raises borrowing costs, discouraging investment in the fisheries sector, especially in capital-intensive areas such as Empirical studies (Ankrah Twumasi et al.
, 2022.
Jayadi & Firmansyah, 2021.
Paul, 2.
show that rising real interest rates negatively impact fisheries output, investment, and technical Inflation Inflation affects both input costs and consumer demand.
Keynesian and monetarist perspectives explain inflation as either demand-driven or influenced by money supply dynamics (Tasya et al.
UssaAodiyah & Nofrian, 2.
In fisheries, cost-push inflation such as rising feed and fuel prices erodes profit margins, while demand-pull inflation may increase prices and income in the short term.
Some studies (Amanda.
Resti.
Lutfi, 2022.
Rizal et al.
, 2.
suggest that moderate inflation may support sectoral GDP, but excessive inflation can undermine sector stability.
Official Exchange Rate Official Exchange Rate, which reflects the government-reported rate for currency conversion, directly impacts trade competitiveness and input costs (Wicaksana & Pracoyo, 2.
Theories such as Purchasing Power Parity .
Capital Flow Theory, and the J-Curve explain exchange rate dynamics (Cassel, 1918.
Darwanto, 2014.
Martoatmodjo, 2.
A depreciating exchange rate may boost export competitiveness but simultaneously raise the cost of imported inputs such as fish feed and fishing gear.
Empirical findings (Elisha Omotunde et al.
, 2023.
Ikpesu & Okpe, 2019.
Nugroho & Nasrudin, 2.
generally show a complex, often negative, short-term relationship between exchange rate movements and fisheries GDP in developing countries.
Fish Production Fish Production, as defined in this study, combines both capture fisheries and aquaculture output.
This aggregated measure captures the total biomass generated by the sector and is analyzed using the Pitaloka.
, et al.
Respons of IndonesiaAos fish .
ISSN: 2089-4309 .
2579-4841 .
Gordon-Schaefer model (Gordon, 1954.
Schaefer, 1.
This bioeconomic model explains that fishery production is maximized at an optimal level of effort referred to as Maximum Sustainable Yield (MSY) beyond which overfishing reduces long-term profitability and resource sustainability.
Empirical studies affirm that fish production positively contributes to GDP (Ahammed et al.
, 2024.
Elzaki, 2024.
Sulistijowati et al.
, 2.
, though the impact may be mediated by technological efficiency, infrastructure, and market access.
Fishery Export Fishery Exports directly increase GDP via the net exportAos component in national income accounting (Y = C I G (X Ae M)).
According to RicardoAos theory of comparative advantage, countries like Indonesia benefit from exporting marine products due to lower production costs and abundant natural resources (Ricardo, 1.
Numerous studies confirm that fishery exports significantly support sectoral GDP growth and foreign exchange earnings (Emam et al.
, 2021.
Jayadi & Firmansyah, 2021.
Kusmaputri & Chinta, 2.
, although the effect is stronger when accompanied by domestic value addition.
Given these theoretical and empirical foundations, this study hypothesizes that IndonesiaAos Fishery GDP responds significantly to variations in real interest rate, inflation, official exchange rate, fish production .
ombined capture and aquacultur.
, and fishery exports during the period 1985Ae2024.
Furthermore, it posits that each of these variables contributes differently to GDP dynamics, which will be evaluated through variance decomposition analysis.
RESEARCH HYPOTHESES
This study proposes the following hypotheses:
H1: There is a response short-term and long-term influence of interest rates, inflation, exchange rates, fisheries production and fishery exports on the GDP of the fisheries sector in Indonesia during the period 1985 - 2024.
H2: There is a contribution of shocks from interest rates, inflation, exchange rates, fisheries production, and fishery exports to the variation of fisheries GDP in Indonesia, as indicated by the results of the variance decomposition in the VECM model.
RESEARCH METHOD
Scope of Research This study utilizes annual time series data from Indonesia covering the period 1985Ae2024.
Jurnal Ilmu Manajemen dan Ekonomika.
Vol 17.
No.
02 June 2025: 203-218 involves seven endogenous variables: Fishery GDP (FISHGDP), real interest rate (RIR), inflation (INF), official exchange rate (OER), fish production (FISHPROD), and fishery exports (FISHEXP).
The primary focus is on the response of Fishery GDP to the dynamic changes of the other macroeconomic and sectoral variables.
Data Sources The data used in this study are obtained from reliable and internationally recognized institutions.
Fishery GDP and inflation data are sourced from Statistics Indonesia (Badan Pusat Statistik.
BPS).
Data on real interest rates and official exchange rates are retrieved from the World Bank.
Meanwhile, fish production and fishery export figures are collected from the Food and Agriculture Organization of the United Nations (FAO).
These secondary time series data cover the period from 1985 to 2024 and serve as the foundation for the empirical analysis.
Econometric Model and Estimation Technique This study utilizes the Vector Autoregression (VAR) model to capture dynamic interdependencies among the selected variables.
VAR models are well-suited for analyzing time series data without requiring strong theoretical assumptions regarding causality (Widarjono, 2.
When the variables are found to be non-stationary but cointegrated, the Vector Error Correction Model (VECM) is VECM enables the investigation of both short-run dynamics and long-run equilibrium relationships, making it particularly appropriate for analyzing macroeconomic interactions in the fisheries sector.
Before estimation, standard pre-testing procedures are conducted.
These include the Augmented Dickey-Fuller (ADF) test to assess stationarity, the Johansen cointegration test to detect long-run relationships, and optimal lag selection based on the Akaike Information Criterion (AIC) or similar statistical criteria.
The general form of the VECM equation applied in this study is as follows:
OIyayaycIyayayaycEyc = yca 1.
0 yca 1.
1 yayaycIyayayaycEycOe1 yca 1.
2 ycIyaycIycOe1 yca 1.
3 yaycAyaycOe1 yca 1.
4 ycCyaycI ycOe1 yca 1.
5 yayaycIyaycEycIycCyaycOe1 yca 1.
6 yayaycIyayaycUycEycOe1 1yuIyayaycNycOe1 Description:
yca = Intercept yuyco ycn = 1 = The number of lags used in the model, ranging from 1 to k yayaycIyayayaycEycOe1 = First-difference vector of Fishery GDP in year t .
with lag 1 ycIyaycIycOe1 = First-difference vector of the Real Interest Rate in year t .
with lag 1 yaycAyaycOe1 = First-difference vector of the Inflation in year t .
with lag 1 Pitaloka.
, et al.
Respons of IndonesiaAos fish .
ISSN: 2089-4309 .
2579-4841 .
ycCyaycI ycOe1 = First-difference vector of the Official Exchange Rate in year t .
with lag 1 yayaycIyaycEycIycCyaycOe1 = First-difference vector of the Fish Production in year t .
with lag 1 yayaycIyayaycUycEycOe1 = First-difference vector of the Fishery Export in year t .
with lag 1 yuI = ECT coefficient or Speed of Adjustment parameter, with a negative sign yayaycNycOe1 = Error Correction Term or residual from the long-run equation = Year 1985-2024 Impulse Response Function (IRF) IRF analysis is used to trace the time path of Fishery GDP in response to one-time shocks in each of the independent variables.
This technique provides insight into the dynamic causal relationships and lagged effects within the system, enhancing understanding of macroeconomic shocks on sectoral Variance Decomposition (VD) Variance Decomposition quantifies the proportion of forecast error variance in Fishery GDP attributable to innovations in each explanatory variable.
This allows for evaluating the relative contribution of macroeconomic and sectoral shocks to Fishery GDP variability, supporting more targeted and informed policy recommendations.
RESULT.
DISCUSSION.
AND MANAGERIAL IMPLICATION
The analysis begins with the stationarity test using the Augmented Dickey-Fuller (ADF) method.
As shown in Table 1, the results confirm that all variables real interest rate (RIR), inflation (INF), official exchange rate (OER), fisheries production (FISHPROD), fisheries exports (FISHEXP), and fisheries GDP (FISHGDP) are non-stationary at level but become stationary at first difference.
Specifically, the ADF test statistics for the first differences are all significant at the 1% level, with pvalues of 0.
0000 for most variables, indicating they are integrated into order one.
I .
For example, the first difference of LNFISHGDP yields a t-statistic of -5.
738797 with a p-value of 0.
These results justify the use of the Vector Error Correction Model (VECM), which is suitable for analyzing both short-run adjustments and long-run relationships among non-stationary but cointegrated Following this, the stability of the VAR model was tested using the characteristic root approach.
As presented in Table 2, all the roots of the companion matrix lie inside the unit circle, with the largest modulus being 0.
This confirms that the model is dynamically stable and thus reliable for forecasting and interpreting the results of Impulse Response Functions (IRF) and Variance Decomposition (VD) analyses.
Jurnal Ilmu Manajemen dan Ekonomika.
Vol 17.
No.
02 June 2025: 203-218 Table 1.
Stationarity Test
Variabel
LNFISHGDP
Stage Level Prob t-statistic 1st Difference 0.
Level 1st Difference Level INF 1st Difference Level LNOER 1st Difference Level LNFISHPROD 1st Difference Level LNFISHEXP 1st Difference RIR Source: Output E-views 10 .
Table 2.
Stability Test Root 278876 - 0.
499599 - 0.
038774 - 0.
350699 - 0.
Modulus Source: Output E-views 10 .
In selecting the optimal lag length for the VECM, as shown in Table 3, the Schwarz Information Criterion (SC) identified lag 3 as the most appropriate, as indicated by the asterisk in Table 3.
This lag length strikes a critical balance between adequately capturing the dynamic interactions among variables and avoiding model overfitting.
The choice of lag 3 is further supported by other criteria such as the Final Prediction Error (FPE).
Likelihood Ratio (LR), and Akaike Information Criterion (AIC), which also reach their minimum values at this lag.
Hence, adopting a three-lag structure ensures that the model captures sufficient past information without compromising parsimony and forecasting reliability.
The Johansen cointegration test, as reported in Table 4, identifies at least four cointegrating vectors, confirming the existence of a long-run equilibrium relationship among real interest rate, inflation, exchange rates, fisheries production, fisheries exports, and fisheries GDP.
This long-term linkage supports the theory that macroeconomic fundamentals influence fisheries sector output over Pitaloka.
, et al.
Respons of IndonesiaAos fish .
ISSN: 2089-4309 .
2579-4841 .
Table 3.
Optimal Lag Selection Lag
LogL
FPE
47231* 23711637*
AIC
Source: Output E-views 10 .
Table 4.
Cointegration Test Hypothesized Trace No.
of CE.
Eigenvalue Statistic Critical Value Prob.
None* At most 1* At most 2* At most 3* At most 4 At most 5 Source: Output E-views 10 .
The impulse response analysis in Figure 2 reveals how fisheries GDP dynamically responds to shocks from key macroeconomic and sectoral variables, namely real interest rate, inflation, exchange rate, fisheries production, and fish exports.
First, real interest rate shocks to the GDP of the fisheries sector show a negative response at the beginning of the period, reflecting direct pressure on fisheries business actors due to increasing borrowing costs.
After the initial period, the response improved and showed mild fluctuations but only began to stabilize in the low positive range after the 30th to 100th This indicates that in the long term, the impact of interest rates on this sector is more controlled, although it still has a structural influence.
Within the framework of Blue Economy development, this condition emphasizes the importance of designing an inclusive and adaptive interest rate policy to the needs of the labor-intensive fisheries sector, as well as the importance of expanding access to cheap and sustainable financing to encourage green transformation and growth in this sector.
Second, inflation shocks show a negative and fluctuating pattern in the short term, with the lowest response values recorded in the 6th and 10th periods.
Although there was a slight improvement in several periods, the negative effects tended to persist until around the 30th period, before finally stabilizing in the range of -0.
005 to -0.
Economically, this inflationary pressure reflects the increase in production input costs and the decline in purchasing power of coastal communities, which ultimately suppresses the output of the fisheries sector.
This finding confirms that stable and sustainable inflation control is an important prerequisite in supporting inclusive and resilient fisheries sector growth within the framework of Blue Economy development.
Third, exchange rate shocks produce a relatively stable negative response in the long term.
After an initial decline in the 2nd to Jurnal Ilmu Manajemen dan Ekonomika.
Vol 17.
No.
02 June 2025: 203-218 sponse to Cholesky One S.
Response .
Innovations to Cholesky One S.
Innovatio Response of D(LNFISHGDP) to D(RIR) Response of D(LNFISHGDP) to D(INF) sponse to Cholesky One S.
Response .
Innovations to -.
Cholesky One S.
Innovatio Response of D(LNFISHGDP) to D(LNOER) Response of D(LNFISHGDP) to D_LNPROD Response to Cholesky One S.
Innovations Response of D(LNFISHGDP) to D(LNFISHEXP) Figure 2.
Impulse Response Function Results Source: Output E-views 10 4th periods, the response continued to move in a low negative range (-0.
0025 to -0.
until the end of the observation period, without showing significant recovery.
Exchange rate depreciation increases the cost of import-oriented production inputs, such as fishing gear and fuel, thereby suppressing business margins and output of the fisheries sector.
In the context of the Blue Economy, stabilizing the exchange rate and strengthening import substitution industries are important strategies to increase the resilience of the fisheries sector to external volatility and encourage growth based on domestic added value.
Fourth, shocks from fisheries production show a strong positive response, especially in the early period, with the highest response value in the 2nd period, and tended to be stable in the range of Pitaloka.
, et al.
Respons of IndonesiaAos fish .
ISSN: 2089-4309 .
2579-4841 .
0026 to 0.
0035 from the 30th to the 100th period.
This pattern indicates that the increase in fisheries production directly drives the growth of this sector and is sustainable in the long term.
Within the Blue Economy framework, increased production accompanied by sustainable practices such as environmentally friendly marine cultivation and efficient fish stock management will strengthen the foundation of inclusive and sustainable Indonesian marine development.
Fifth, shocks from fisheries exports show a fluctuating but low-intensity response, with an initial increase in the 4th period followed by a negative reversal in the 5th and 9th periods, then stabilizing in a very low range between -0.
0002 to 0.
0002 since the 30th period.
This reflects that the export sector has not provided a significant boost to the growth of the fisheries sector consistently.
In the context of the Blue Economy, this low contribution is an important signal of the need to increase the added value of export products, strengthen logistics infrastructure, and implement fisheries downstreaming and industrialization strategies so that exports can become the main pillar in the development of a resilient, competitive, and sustainable marine sector.
Therefore, the response results for all variables show that there is a long-term relationship between exogenous variables and fisheries GDP such as previous research by (Ahammed et al.
, 2024.
Akseptori et al.
, 2022.
Omotunde et al.
, 2023.
Emam et al.
, 2021.
Ikpesu & Okpe, 2.
Overall, these IRF results suggest that internal dynamics, especially production, are more influential for fisheries GDP than external macroeconomic shocks like interest rates, inflation, or exchange rate This has managerial implications for policy focus: strengthening domestic production systems and ensuring sustainable harvest practices may yield more predictable and long-term benefits for the fisheries sector compared to relying on macroeconomic tools.
The results of Variance Decomposition show that in the long term .
th perio.
, inflation is the most dominant variable in explaining the variation of GDP in the fisheries sector, with a contribution 35%, followed by fisheries production .
37%) and exchange rate .
99%).
Real interest rates are in fourth place with a contribution of 7.
49%, while fisheries exports remain the smallest contributor with only 0.
This finding emphasizes the importance of controlling inflation as well as increasing production efficiency and exchange rate stability as the key to strengthening the national fisheries sector.
Managerial Implications These findings provide important managerial implications for fisheries sector development in Indonesia.
The dominance of inflation in explaining fisheries GDP variation suggests that effective price stabilization policies particularly in managing input costs like fuel and feed are essential to ensure sector resilience.
The consistent positive influence of fisheries production highlights the need Jurnal Ilmu Manajemen dan Ekonomika.
Vol 17.
No.
02 June 2025: 203-218 Table 7.
Variance Decomposition (VD) FISH
FISH
RIR
INF
OER
GDP
PROD
FISH
EXP
Period Source: Output E-views 10 .
for investment in sustainable aquaculture and capture fisheries practices, including technological innovation and capacity-building for fishers.
Meanwhile, the adverse effects of exchange rate volatility reinforce the importance of enhancing domestic input substitution and export Policymakers should therefore prioritize macroeconomic stability alongside production efficiency and targeted support mechanisms to strengthen the long-term growth of the fisheries sector under the Blue Economy agenda.
CONCLUSION.
SUGGESTION.
AND LIMITATIONS
This study concludes that IndonesiaAos fisheries sector is strongly influenced by macroeconomic dynamics, especially inflation.
In the short term, impulse response analysis (IRF) shows that fisheries GDP responds positively to shocks in fisheries production and exports, while inflation and exchange rate shocks tend to suppress growth.
In the long run, the results of variance decomposition (VD) reveal that the variation in fisheries GDP (FISHGDP) is primarily explained by inflation .
35%),
followed by its own past values .
32%), fisheries production .
37%), exchange rate .
99%), real interest rate .
49%), and fisheries exports .
46%).
These findings underscore the critical role of inflation control, production efficiency, and exchange rate stability in fostering a resilient and sustainable fisheries sector.
Strengthening these aspects is essential for supporting IndonesiaAos transition toward a Blue Economy that balances economic growth with marine resource sustainability.
Pitaloka.
, et al.
Respons of IndonesiaAos fish .
ISSN: 2089-4309 .
2579-4841 .
The government should focus on aligning policies with Blue Economy principles by enhancing the stability of real interest rates and exchange rates, as these factors significantly impact the fisheries sectorAos performance.
Emphasizing macroeconomic strategies that support long-term investment in fisheries and aquaculture infrastructure is crucial for sustainable growth.
Given that fishery production has the largest long-term influence on fisheries GDP fluctuations, targeted investments and capacity-building in sustainable capture fisheries and aquaculture are essential.
Improving technology, logistics, and access to finance for small-scale fishers and producers will help increase productivity and sector resilience.
This study has some limitations.
It uses aggregated data for fishery production and exports, which may hide specific sector dynamics.
Institutional and environmental factors were not included due to data limits.
The VAR model used does not capture causality or structural relationships, suggesting the need for more advanced models in future research.
Finally, the results are specific to Indonesia and the study period, so they may differ in other contexts or times.
REFERENCES