Linking global CGE models and sectoral analysis to evaluate the impact of trade openness in service sector towards Indonesia agricultural and agroindustry

Widyastutik, Widyastutik; Meliany, Birka Septy; Amaliah, Syarifah; Hotsawadi; Rifin, Amzul Article Linking global CGE models and sectoral analysis to evaluate the impact of trade openness in service sector towards Indonesia agricultural and agroindustry Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Widyastutik, Widyastutik; Meliany, Birka Septy; Amaliah, Syarifah; Hotsawadi; Rifin, Amzul (2025) : Linking global CGE models and sectoral analysis to evaluate the impact of trade openness in service sector towards Indonesia agricultural and agroindustry, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 7, pp. 1-18, https://doi.org/10.3390/economies13070199 This Version is available at: https://hdl.handle.net/10419/329479 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Academic Editor: Tsutomu Harada Received: 18 May 2025 Revised: 16 June 2025 Accepted: 3 July 2025 Published: 9 July 2025 Citation: Widyastutik, Meliany, B. S., Amaliah, S., Hotsawadi, & Rifin, A. (2025). Linking Global CGE Models and Sectoral Analysis to Evaluate the Impact of Trade Openness in Service Sector Towards Indonesia Agricultural and Agroindustry. Economies,13(7), 199. https://doi.org/10.3390/ economies13070199 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Linking Global CGE Models and Sectoral Analysis to Evaluate the Impact of Trade Openness in Service Sector Towards Indonesia Agricultural and Agroindustry Widyastutik 1,2,* , Birka Septy Meliany 3, Syarifah Amaliah 1, Hotsawadi 4and Amzul Rifin 5 1Department of Economics, Faculty of Economic and Management, IPB University, Bogor 16680, Indonesia; [email protected] 2International Center for Applied Finance and Economics, IRISERDS, IPB University, Bogor 16680, Indonesia 3Program Study of Agricultural Economics, IPB University, Bogor 16680, Indonesia; [email protected] 4Department of Development Economics, Mulawarman University, Samarinda 75119, Indonesia; [email protected] 5 Department of Agribusiness, Faculty of Economic and Management, IPB University, Bogor 16680, Indonesia; [email protected] *Correspondence: [email protected] Abstract Agriculture is the primary sector sustaining the Indonesian economy. However, appropriate policies are also required to support the service sector. Therefore, this study aims to analyze two central policies: the impact of trade openness and the role of the service sector on agriculture and agro-industry in Indonesia. A Computable General Equilibrium (CGE) model with 2016 input–output tables cover 141 regions and 65 sectors based on the Global Trade Analysis Project (GTAP) Version 10 database. The results show that trade openness in the services sector significantly improves the performance and quality of service provision. The improved performance of the services sector will, in turn, encourage increased production in the agricultural and agro-industrial sectors, which rely heavily on service inputs in the production process. This suggests that trade openness in the services sector is important to sustain the performance of the agricultural sector. Keywords: trade on services; computable general equilibrium (CGE) model; international trade; agricultural and agroindustry 1. Introduction The agricultural sector is an important pillar in the Indonesian economy, not only as the primary source of employment for a large proportion of the population but also as a driver of improved social welfare (Sayifullah & Emmalian,2018;Siregar et al.,2024). Despite ongoing industrialization, Indonesia is still classified as an agrarian country, indicating that its overall development depends heavily on agriculture (Kusumaningrum,2019). For Indonesia, which has a large population, the agricultural sector has a strategic and diverse role in ensuring national food security and nutrition (Mukhlis & Gurcam,2022) while contributing to poverty alleviation (Sulistyowati & Yuliyadi,2019). Therefore, agriculture is an inseparable component of economic stability, growth, and development. The agricultural sector plays a strategic role in the Indonesian economy, not only as a major contributor to the Gross Domestic Product (GDP) but also as a provider of employment and a pillar of national food security. By 2023, the agricultural sector will contribute around 13.7% to the total national GDP while absorbing around 30% of the total Economies 2025,13, 199 https://doi.org/10.3390/economies13070199 Economies 2025,13, 199 2 of 18 Indonesian workforce (Statistics Indonesia,2025). This significance is further reinforced by the latest data in the first quarter of 2025, where the agricultural sector’s contribution to GDP by the business sector was recorded at 10.52%, the highest figure in the history of national statistics (Statistics Indonesia,2025). This increase reflects the effectiveness of policies that support domestic production while affirming the role of agriculture as the sector with the highest labor absorption rate, at 28.54% of the national labor force. These findings suggest that the agricultural sector remains an important foundation for Indonesia’s economic and social stability. According to the Ministry of Agriculture (2022), the agricultural sector has comparative and competitive advantages in producing high-value commodities in high demand in international markets and as raw materials for domestic industries. In international trade, Indonesia’s agricultural exports have a significant global performance, especially in plantation commodities (e.g., palm oil, cocoa, coffee, tea, etc.). In 2022, Indonesia’s agricultural exports reached USD 51,842.11 million, an increase of 5.39% compared to the previous year (UN Comtrade,2024). In the same year, Indonesia exported USD 9801.76 million worth of agricultural products to China, making it the top export destination with a share of 18.91%, followed by India (11.35%) and the United States (11.33%) (UN Comtrade,2024). This underscores the strategic role of agriculture in the Indonesian economy, which must be aligned with national development priorities. One of the eight main missions of Nawacita focuses on advancing downstream industries and industrialization to drive domestic valueadded creation (Bappenas,2024). The agricultural sector needs to boost production and productivity to fulfill the food supply and supply raw materials for industry. Increasing the production and productivity of the agricultural sector cannot be separated from the role of the service sector. Servicification has emerged as a key strategy to improve agricultural productivity and global competitiveness (Widodo,2020). The services sector can function as a direct lever through its contribution to national output or as an indirect catalyst through second-order productivity effects in the agricultural sector (Khanna,2016). Due to the inter-sectoral causality effect, the agricultural sector can be optimized by leveraging the services sector (Tekilu et al.,2018;Degu,2019). Prasetyo et al. (2023) highlighted countries such as Japan, the United States, and Australia, calling for increased service provision in logistics, certification, and technical support to fulfill country-specific market regulations. The services sector not only facilitates more efficient trade relations such as transport services (sea, air, and land), logistics and supply chain, distribution services, financial services, and telecommunication services but also ensures that Indonesian products can compete in highly regulated markets, which will ultimately benefit exporters and consumers. Research and Development (R&D) services that provide services for certification of agricultural products have an important role to play. Several previous studies suggest that global certification requirements for sustainability, sanitary and phytosanitary (SPS) requirements, and Technical Barriers to Trade (TBT), including packaging and labeling of agricultural products, are significant barriers for agricultural producers, increasing trade costs (Makita,2016;Lee et al.,2020;Y. Chen et al., 2024). The use of digital technologies generated by the R&D services sector drives efficiency and productivity in the agricultural sector, such as using soil and weather sensors. Soil and weather sensors allow farmers and agricultural practitioners to monitor and collect real-time data on environmental conditions at the farming site. The data obtained from these sensors can provide deep insights into soil moisture levels, pH, nutrients, air temperature, air humidity, rainfall, and wind speed. With this accurate and continuous monitoring, farmers can make more intelligent and timely decisions in crop growth management. Given the role of the service sector in improving the performance of the agricultural sector, the availability of service sector services at competitive prices is essential. Economies 2025,13, 199 3 of 18 Referring to the term servicification by Elms and Low (2013), ‘The large proportion of services inputs used in manufacturing production is described as ‘servicification of manufacturing,’ servicification in this study will be used in the context of services used as inputs to the agricultural and agroindustry sector. The increasing role of services in the agricultural sector is inseparable from the availability of services at competitive prices. Based on the latest Input–output Indonesia, most service sectors have limited availability (excess demand), such as telecommunication services, computer and information consulting services, financial and bank services, insurance services, other financial institution services, scientific and technical professional services, rental and business support services. One strategy to improve services is to increase the degree of openness to foreign service providers. Based on the OECD (2022), the degree of openness of Indonesia’s service sector is very restrictive compared to neighboring countries. The average restriction of Indonesia’s service sector is 0.40, Thailand 0.38, Malaysia and Vietnam 0.31. Indonesia’s service sector trade restriction index is higher than the ASEAN-5 group average of 0.323. Meanwhile, this condition is very ironic considering that Indonesia has 20 trade cooperation agreements in the services sector, including the WTO, ASEAN, ASEAN + 1 , Japan, Chile (ratification process), EFTA, AANZFTA, RCEP, Korea, and UAE. At the same time, those in the negotiation process are 10 (ASEAN India FTA, European Union, ASEAN Canada FTA, ASEAN China FTA, Eurasian Economic Union (EAEU), Turkiye, Canada, Mercosur, Peru, GCC) and 1 in the exploration process, namely ASEAN-EU. Indonesia’s various trade partnerships in the services sector can be utilized to increase the availability of Indonesia’s services sector, which experiences excess demand. The utilization of Indonesia’s service sector trade openness is predicted to increase the production and productivity of the service sector, which will impact the agricultural sector. So far, previous studies have focused more on the impact of service sector openness on increasing service sector productivity (Fu et al.,2023), economic growth (Matto et al.,2007;Papaioannou,2018) and the manufacturing sector (servicification) (Peng et al.,2022;Taguchi & Lar,2024; Defever et al.,2022;Hing & Thangavelu,2023). In the case of Indonesia, research on the impact of service sector openness on the manufacturing sector (Hing & Thangavelu,2023). Thangavelu et al. (2018), using a gravity model, found that liberalization of Indonesia’s services sector would reduce trade costs by 26% (percentage of import value). The novelty of this study is that first, it assesses the impact of services trade openness on Indonesia’s agricultural sector and macroeconomy. Sheperd and Marel (2010) highlight the difficulty of quantifying the impact of policies on trade in services due to the rarity of transparent ad valorem measures, such as tariffs. They used the trade cost approach to calculate the restriction index in the services sector, reflecting the significant regulatory barriers present. Widyastutik (2016) analyzed Indonesia’s maritime transport services, focusing on regulatory challenges and infrastructure bottlenecks. Widyastutik (2020) applied this methodology to air transport services, identifying key barriers like air service agreements and air traffic regulations, and offering recommendations to improve Indonesia’s global competitiveness. This study measures trade openness by reducing barriers using the OECD STRI Simulator, a tool also used by Aboushady (2022) and Khachaturian and Riker (2017) to assess non-tariff barriers in services. Additionally, it introduces a general equilibrium model by linking the Global Trade Analysis Project (GTAP) model with the Computable General Equilibrium (CGE) model, providing a more comprehensive analysis than previous partial equilibrium models and allowing a detailed evaluation of how trade liberalization and servicification impact Indonesia’s economy. Using a general equilibrium model aligns with the idea that the interaction between economic actors is complex and challenging to understand with a partial equilibrium Economies 2025,13, 199 4 of 18 model, so using CGE is considered more appropriate. This general equilibrium approach can analyze the market and interact with each other, involving macroeconomic variables and sectors that must be analyzed together. The CGE model includes the possibility of substitution between factors of production. Thus, if there is a change in the relative price of a factor of production, producers will change the composition of the use of factors of production towards factors of production that are relatively cheaper. The GTAP model includes global transport and investment mobility to explain the impact of policies between countries. The impact of trade openness captured in the GTAP model will affect the relative price of exports and imports. The change in the relative price of exports and imports will be used as a variable to shock in the CGE model. 2. Methodology 2.1. Data This study applies the linking model between GTAP and CGE to measure the impacts of liberalization and the increasing role of servicification on the agricultural sector in Indonesia. The GTAP model version 10 used in this study consists of 141 regions and 65 sectors (Licensed to the Department of Economics, FEM IPB, Multiple Academic User License No. 9.0-2043). The data in GTAP version 10 are derived from input–output tables with reference years 2004, 2007, 2011, and 2014. Meanwhile, the CGE model in this study uses the 2016 input–output table published by Statistics Indonesia in 2020, which includes 185 sectors. For regional aggregation, this study will use data aggregating Indonesia and partner countries involved in the services trade cooperation, including countries in the Australia-Indonesia Comprehensive Economic Partnership Agreement (IA-CEPA) and the Regional Comprehensive Economic Partnership (RCEP). These countries are (1) Japan, (2) South Korea, (3) New Zealand, (4) the People’s Republic of China, (5) Malaysia, and (6) Thailand. This study analyzes the liberalization and the impact of the increased role of services in the agricultural sector, which is expected to be achieved through diplomacy and services trade negotiations. One of the primary data sources used is the Services Trade Restrictiveness Index (STRI). The STRI is a measure that refers to discriminatory policies and regulations in the services trade. Quantitative restrictions refer to limitations on the number of providers, particularly those related to minimum requirements in licensing processes for professionals, as well as the absence of regulation in line with equitable access to domestic and foreign services sectors. These regulations may be applied to specific sectors or the entire sector, such as policies on the minimum percentage of domestic workers in foreign-owned companies. The level of restriction varies significantly between developed and developing countries. STRI simulations allow policymakers and experts to explore the impacts of changes in trade policy on the services sector in detail for each action and to compare a specific country with selected other countries in particular sectors. The National CGE Model will be employed to capture more specific impacts on various sectors of the Indonesian economy. The National CGE model used in this study is based on the ORANI-F model (Horridge,2003) and INDOF (Oktaviani,2000). The input–output (IO) table utilized in the national CGE model is Indonesia’s 2016 IO table. The results of trade openness simulations from the GTAP model, which yield changes in export and import prices, will serve as inputs for simulations in the National CGE model (linking the GTAP model with the National CGE model). 2.2. Research Methods To analyze the impact of liberalization and the increasing role of services (servicification) on the agricultural sector in Indonesia, the General Equilibrium model of the Global Economies 2025,13, 199 5 of 18 Trade Analysis Project (GTAP) is employed. The Global Trade Analysis Project (GTAP) is a multi-country and multi-sector computable general equilibrium (CGE) model used to evaluate the impact of trade policy changes (Hertel & Tsigas,1997). Hertel (2012) asserts that the general equilibrium model integrates microeconomic and macroeconomic factors. The structural Computable General Equilibrium (CGE) model is built upon the foundations of economic theory (microeconomics), where the behavior of economic agents is specified and detailed in the form of behavioral equations. The CGE model also facilitates the depiction of interactions between agents within a country/region and between countries/regions, allowing for a comprehensive analysis of the economic impacts across sectors and regions. The Global Trade Analysis Project (GTAP) model provides a robust multi-regional, multi-sectoral CGE framework for assessing the economic consequences of trade policy changes, especially within the agricultural sector (Hertel & Tsigas,1997). Each GTAP “region” represents a national economy comprising a nested production structure, a single representative household, government, and investment behavior. Production structures rely on primary factors (land, labor, capital, and natural resources) and follow multistage processes. Trade linkages across regions are formalized for goods and services, including crossborder financial intermediation and associated global transport demands. The model assumes constant elasticity of substitution (CES) in aggregating primary factors, which are combined with intermediate goods using a fixed-coefficient Leontief structure. While substitution is restricted among intermediate goods and between intermediates and value-added, the model permits differentiation between domestic and foreign sources of intermediates based on the Armington assumption of imperfect substitutability (Armington,1969). Intra-regional mobility is allowed for capital and labor, facilitating reallocation across sectors, whereas inter-regional mobility is constrained in the short run. Employment levels are held constant within regions, and labor market equilibrium is maintained via wage rate adjustments. Meanwhile, final demand allocation is governed by a regional representative who distributes income from factor earnings and net taxes across private consumption, savings, and public expenditure. The model uses a Cobb–Douglas specification for government demand and a constant difference in elasticity (CDE) functional form for private household preferences. While physical capital does not move internationally in the short run, long-term inter-regional investment flows are possible through savings reallocations, enabling global capital market integration over time. In the GTAP model, the Armington elasticity captures how easily consumers or producers substitute domestic goods for imported (or inter-origin) goods when relative prices change. One of the GTAP model’s assumptions is that imported and domestic goods are not perfect substitutes (different even if they are of the same type, e.g., local vs. imported rice). Consumers/economic actors differentiate goods based on their country of origin. Therefore, the Armington elasticity can be used to measure sensitivity, specifically the sensitivity of demand to changes in the relative price between domestic and imported goods. In detail, Armington elasticity serves to (a) measure how responsive import demand is to changes in relative prices, (b) determine the magnitude of changes in consumption patterns or production inputs due to changes in tariffs, subsidies, or trade barriers. (3) In simulations, the higher the Armington elasticity value, the more sensitive consumers are to prices, meaning that they are more likely to switch to imported goods if domestic prices rise. A previous study employing the agricultural sector of the GTAP was conducted by Rifin et al. (2020) and CGE about impact of biodiesel policy was conducted by Sahara et al. (2022). On the other hand, Nugroho et al. (2021) using GTAP Recursive Dynamic and the Indonesia family life survey (IFLS) accessing impact trade war on poverty in Indonesia periode 2018–2020. Economies 2025,13, 199 6 of 18 In this study, the potential impact of trade liberalization and the increased role of the services sector in agriculture servicification will be analyzed via a steps approach, covering (i) estimating the magnitude of services trade barrier reductions and (ii) linking of the GTAP model and national CGE model. A previous study employing the linking of the GTAP and CGE models was conducted by Zhang and Diao (2020) to measure and analyze the impacts of economic structural changes on the agricultural sector. Similarly, Delzeit et al. (2020) used the linking model to analyze and measure economic policies, particularly in addressing complex global issues affecting economic growth. Based on prior studies, the linking model between CGE and sectoral models can capture the complex interactions between sectors and countries, offering more detailed insights into the analysis of specific sectors. Therefore, applying the linking model between GTAP and CGE is highly relevant in this study to measure and identify the impacts of trade liberalization and the increasing role of servicification on Indonesia’s agricultural sector. Accurately measuring the restrictiveness of non-tariff measures (NTMs) in service sectors remains a significant methodological challenge, primarily due to the lack of comprehensive and reliable data on bilateral services trade flow and regulatory measures. While most of the studies estimating ad valorem equivalents (AVEs) of regulatory trade barriers used a quantity-based approach with augmented gravity models, this study adopted Fontagne et al. (2016) and Benz and Jaax (2022) by utilizing OECD Services Trade Restrictiveness Index (STRI) data. Further improvements are made to capture the existing schedule of commitments of Indonesia and its CEPA partners. Therefore, the trade liberalization scenario in this study is reflected by reducing trade barriers based on a specific schedule of commitments (SoC) using the OECD STRI Simulator. Several researchers have utilized the STRI simulator approach to measure trade barriers in the services sector, including studies conducted by Aboushady (2022) and Khachaturian and Riker (2017). In this context, the OECD STRI simulator addresses the challenges of quantifying regulatory measures in the services trade. This study phase will assess Indonesia’s level of commitment in various trade negotiation fora, explicitly referring to the RCEP and IACEPA scenarios. The underlying rationale of the CEPA selection is that RCEP and IACEPA modalities are considered the most progressive regarding liberalization commitments. Through this analysis, the study aims to gauge Indonesia’s progress and engagement in liberalizing services trade under these agreements. To measure the impact of services trade liberalization on the increased role of services in the agricultural services sector, the next step is to estimate the magnitude of the shock, which is the change in the value of Indonesia’s services trade, both exports and imports. The model specification used is ln(SI)ir =α+β1STRIir +∑ k γkGirk +ε(1) This equation indicates that services imports are influenced by regulations represented by the STRI and other indicators as determinants of trade, which are represented as control variables. The trade determinants in this model are based on gravity model indicators such as the size of the economy, population, and other indicators like exchange rates. The subscript i represents the services trade sector, the subscript r represents countries, and k is the index for trade determinants. The coefficient on the STRI is interpreted as the elasticity of trade. In other words, it represents the percentage change in trade value due to changes in trade barriers as represented by the STRI (binding overhang). The magnitude of the changes in Indonesia’s services imports and exports will be used as shocks in the national CGE model. Economies 2025,13, 199 7 of 18 A linking model between GTAP and CGE will be employed to analyze the potential impacts of trade liberalization and the increasing role of services in agriculture. In the global computable general equilibrium (CGE) framework, trade liberalization is modeled by implementing service barrier reductions in respective countries. This exercise yields a counterfactual global equilibrium reflected by changing world prices and adjusted service barrier structures in Indonesia and partner countries. The changes in world prices are then treated as exogenous shocks within the national CGE model. Using a “small country” assumption, producers and consumers will adjust to changes in import and export prices. Import demand will increase as the import prices become more competitive and export supply contracts due to rising export prices, and vice versa. This approach of linking global and national models to analyze trade liberalization effects is also established in the literature, with applications by Huff et al. (1995) and Oktaviani (2000). The reduction in trade barriers represents trade liberalization. The tool used to calculate the reduction in trade barriers is the OECD STRI Simulator. Using the OECD STRI Simulator helps address the challenge of measuring tariff levels in services trade. At this stage, the extent of Indonesia’s commitment to various services trade negotiation forums (referring to RCEP and IACEPA scenarios) will be assessed by reducing services trade barriers. This study will apply three scenarios to analyze trade openness and the servicification of the services sector within Indonesia’s agricultural sector. The simulation scenarios are as follows: 1. Simulation 1: Indonesia’s trade openness in services. 2. Simulation 2: A combination of trade openness in services and increased productivity within the services sector. 3. Simulation 3: A combination of trade openness in services, increased productivity in the services sector, and enhanced services sector involvement in agriculture. 3. Results 3.1. The Impact of Trade Openness and Increased Role of Servification Towards Macroeconomic Indicators The impact of trade openness and the role of services on the agricultural sector is related to the transmission shown through direct and indirect service sector linkages and forward and backward linkages with the agricultural and agroindustry sectors. The openness variable and the increasing role of services further affect the output sector (agriculture and agroindustry). Further, it affects macroeconomic variables such as Gross Domestic Product (GDP), Consumption, Investment, Export, Inflation, and Balance of Trade/GDP. Based on the latest input–output (IO) data published by BPS in 2016, the following is an overview of the direct and indirect forward and backward linkages of the services sector to the agricultural sector (six groups, namely food crops, horticulture, plantations, forestry, fisheries, and livestock) and agroindustry (namely the agroindustry of livestock, horticulture, food crops, plantations, forestry, animal and vegetable oils, and other agroindustries). Backward Direct and Indirect Linkages (KLTLB) show how an increase in final demand in the services sector will affect its upstream sectors, especially those in the agriculture sector. Based on its aggregate value, food and beverage services have the most considerable KLTB value in the agricultural sector. The KLTB value is 0.3171, which means that if there is an increase in final demand of Rp 1,000,000 in the food and beverage services sector, the service sector requires additional inputs from the agricultural sector of Rp 317,100. The total value of input requirements is Rp 92,400 from the food crop sector, Rp 51,900 from the horticulture sector, Rp 38,900 from the plantation sector, Rp 62,500 from the livestock sector, Rp 900 from the forestry sector, and Rp 70,300 from the fisheries sector. The sectors Economies 2025,13, 199 8 of 18 with the largest KTLB in agriculture are sea transport, agriculture, forestry, aquaculture, air, river, lake, and ferry. Based on its aggregate value, food and beverage services again have the most considerable KLTB value in the agricultural sector. The KLTB value is 0.2554, which means that if there is an increase in final demand of Rp 1,000,000 in the food and beverage services sector, the service sector requires additional inputs from the agricultural industry sector of Rp 255,400. The total value of input requirements is Rp 52,300 from the livestock agroindustry sector, Rp 7600 from the horticulture agroindustry sector, Rp 98,400 from the crop agroindustry sector, Rp 26,500 from the plantation agroindustry sector, Rp 6200 from the forestry agroindustry sector, Rp 44,000 from the animal and vegetable oil agroindustry sector, and Rp 20,400 from other agroindustry sectors. The sectors with the largest KTLB in agriculture are river, lake, and ferry transport services; professional, scientific, and technical services; agriculture, forestry, and fisheries services; and sea transport services and computer and information technology consulting services. Direct and Indirect Linkages Forward (KLTD) shows how an increase in final demand in the services sector will affect its downstream sectors, in this case, the agricultural sector sectors. Based on its aggregate value, trade services other than cars and motorcycles have the most considerable KLTD value in the agricultural sector. Other than cars and motorcycles, the trade services sector is the primary contributor because the service products incorporated include wholesale and retail trade services for agricultural raw materials, live animals, processed agricultural products, and food and beverage products. In terms of marketing and selling food crop products, business actors in the food crop sector will need trade services. The KLTD value of the trade sector other than cars and motorbikes is 1.0130. Therefore, if there is an increase in final demand of Rp 1,000,000 in this service sector, the total increase in output from this sector allocated as input to the agricultural sector is Rp 1,013,000. Total increase in output value will be allocated Rp 192,800 to the food crop sector, Rp 152,500 to the horticulture sector, Rp 307,200 to the plantation sector, Rp 251,900 to the livestock sector, Rp 26,600 to the forestry sector, and Rp 82,100 to the livestock sector. The sectors with the largest KLTLD in the agriculture sector are the banking financial services sector, the agriculture, forestry, and fisheries services sector, the rental and business support services sector, and the land transport services sector other than rail transport. The rail transport services sector has the lowest KLTD. Based on its aggregate value, trade services other than cars and motorcycles also have the most considerable KLTD value in the agricultural sector. The KLTD value is 3.5851, where if there is an increase in final demand of Rp 1,000,000 in the service sector, the increase in output of the trade services sector other than cars and motorcycles allocated as input to the agricultural industry sector is Rp 3,585,100. The increase in output will be allocated by IDR 429,200 to the livestock agroindustry sector, IDR 116,500 to the horticulture agroindustry sector, IDR 139,700 to the food crop agroindustry sector, IDR 1,169,900 to the plantation agroindustry sector, IDR 713,700 to the forestry agroindustry sector, IDR 26,800 to the animal and vegetable oil agroindustry sector, and IDR 989,400 to other agroindustry sectors. The sectors with the largest KTLD in the agriculture sector are the banking financial services sector, the land transport services sector other than rail transport, the rental and business support services sector, and the telecommunication services sector. Meanwhile, the rail transport service sector has the lowest KLTD value. Increased trade openness is expected to increase the availability of services, improving services in sectors that use services as inputs, such as agriculture and agroindustry, as well as final demand. This study assesses trade openness as an implication of Indonesia’s trade cooperation in services. For this reason, a simulation of reducing trade barriers in services is carried out under business-as-usual conditions. Simulating the reduction in trade barriers is Economies 2025,13, 199 15 of 18 agro-industry sectors, which rely heavily on services inputs during their production process. Several services subsectors show strong forward and backward linkages with the agriculture and agro-industry sectors, namely agriculture, forestry, and fisheries services; wholesale and retail trade (excluding motor vehicles and motorcycles); land transport (excluding railways); banking and financial services; rental and business support services; telecommunications; sea transport; ferry and ferry transport services; air transport; and food and beverage services. Output expansion in the agricultural and agro-industrial sectors driven by increased services inputs contributed positively to Indonesia’s macroeconomy, as indicated by real GDP growth, household consumption, investment, exports, and trade-to-GDP ratio. Simulation 1 represents trade openness in the services sector; there is a potential increase in GDP, consumption, and investment. Simulation 2: There is a significant jump in consumption and GDP by initiating an additional element of productivity improvement in the services sector. This situation indicates that service sector trade openness combined with productivity improvement is the primary driver of growth in the servitisation process. The most significant impact is reflected in Simulation 3, which combines trade openness, increased services productivity, and increased services sector involvement in agriculture. This scenario generates the highest GDP and consumption growth with higher investment and exports than the other scenarios. Increased openness, value added in the services sector, and increased agricultural labor productivity will strengthen the role of services, including adopting various technologies in the agricultural sector, thereby increasing production and contributing to real GDP growth. In terms of sectors, openness to trade in services increased value-added, and labor productivity in the services sector significantly impacted the output of both agriculture and agro-industry, with the impact increasing with the complexity of the policy intervention. In simulation 1, the impact is still limited to the agriculture and agro-industry sectors. The analysis results also show the same phenomenon as the macroeconomy, where the potential impact on Indonesia’s sectors reaches its highest position in Simulation 3. Commodities such as coconut, fruits, and vegetables experience the highest jump in output. The high contribution of the services sector to specific agricultural co-commodities suggests that the integration of the services sector plays a crucial role in driving agricultural sector productivity and growth. Thus, this result underscores the importance of policies that promote the role of the services sector by utilizing Indonesia’s various service sector trade cooperation schemes to boost Indonesia’s agricultural sector and economic performance. Author Contributions: Conceptualization, W. and S.A.; methodology, W. and S.A.; software, H.; validation, S.A. and H.; formal analysis, W.; investigation, W. and S.A.; resources, S.A., and H.; data curation, W. and A.R.; writing—original draft preparation, W. and B.S.M.; writing—review and editing, W. and B.S.M.; visualization, W., B.S.M. and S.A.; supervision, W. and A.R.; project administration, B.S.M.; funding acquisition, W. All authors have read and agreed to the published version of the manuscript. Funding: Corresponding author (W.) pay full of this journal. Informed Consent Statement: Not applicable. Data Availability Statement: The data of the present study is unavailable as participants did not provide their permission to share raw data. 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