Does market characteristic determine foreign direct investment spillovers?

Fawait, Muhammad et al. Article Does market characteristic determine foreign direct investment spillovers? Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Fawait, Muhammad et al. (2024) : Does market characteristic determine foreign direct investment spillovers?, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-18, https://doi.org/10.1080/23322039.2024.2392199 This Version is available at: https://hdl.handle.net/10419/321575 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Muhammad Fawait, Haura Azzahra Tarbiyah Islamiya, Dyah Wulan Sari, Tri Haryanto, Sanju Kumar Singh & Faiz Masnan To cite this article: Muhammad Fawait, Haura Azzahra Tarbiyah Islamiya, Dyah Wulan Sari, Tri Haryanto, Sanju Kumar Singh & Faiz Masnan (2024) Does market characteristic determine foreign direct investment spillovers?, Cogent Economics & Finance, 12:1, 2392199, DOI: 10.1080/23322039.2024.2392199 To link to this article: https://doi.org/10.1080/23322039.2024.2392199 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 20 Aug 2024. Submit your article to this journal Article views: 643 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Does market characteristic determine foreign direct investment spillovers? Muhammad Fawait a,b , Haura Azzahra Tarbiyah Islamiya a , Dyah Wulan Sari a , Tri Haryanto a , Sanju Kumar Singh a and Faiz Masnan c a Department Economics, Faculty of Economics and Business, Airlangga University, Surabaya, Indonesia; b East Java Regional Representative Council, Surabaya, Indonesia; c Faculty of Business & Communication, Universiti Malaysia Perlis, Perlis, Malaysia ABSTRACT This study examines the significance of foreign direct investment (FDI) and market characteristics both within and across industries in determining the productivity and efficiency of firms. This study also measures the total factor productivity (TFP) growth and its components for both foreign and domestic firms. Using Indonesian annual medium and large manufacturing establishments surveys, wholesale price index, and input-output (I-O) table, the authors calculate the horizontal and vertical spillovers and undertake stochastic frontier analysis to estimate the production and inefficiency function. The results show that the less concentrated market of domestic firms within the industry and suppliers reduces productivity and efficiency, while domestic buyers’less concentrated markets could have the opposite effect. Most domestic and foreign firms still experience deterioration in TFP growth. The policy recommendation is to encourage firms to improve technological progress, such as upgrading machines and investing in human resources, by providing training workers aiming at mastering better managerial expertise. Policymakers should also ensure that the benefits of FDI spillovers outweigh their disadvantages. IMPACT STATEMENT This study contributes to extending recent empirical literature on the possibility of spillovers in the Indonesian automotive industry not only from foreign firms within the industry, but also from potential externalities arising from downstream and upstream markets using stochastic frontier analysis (SFA). Generally, studies on FDI spillovers examine the role of FDI in explaining the efficiency differences measured by the distance to the frontier; however, few studies consider the impact of efficiency improvement and technological progress on productivity gains from FDI. This study attempts to capture the sources of productivity gains through both channels. The other studies have never discussed, based on author knowledge, the impact of spillovers regarding domestic firms’specific market concentration as competitors, buyers, or sellers to foreign firms on efficiency and productivity. This study aims to fill this gap and analyze the importance of market characteristics in determining spillovers, VTI, trade openness, and foreign ownership. Previous studies on market concentration employ the HerfindahlHirschman Index (HHI), while this study utilizes the relative entropy coefficient (RE) to provide another approach to measure market concentration. In this study, the industry-specific characteristic is controlled using the inclusion of firm size and industrial dummy variables. The SFA estimation results are calculated to measure output elasticity with respect to each input and total factor productivity (TFP) growth. The discussion provides TFP decompositions, which are technical efficiency change (TEC), technological progress (TC), and scale efficiency change (SEC). ARTICLE HISTORY Received 7 February 2024 Revised 2 July 2024 Accepted 1 August 2024 KEYWORDS Foreign direct investment; spillovers; market characteristic; industrialization; productivity; sustainable industrialization JEL CLASSIFICATION CODE L10; L62; L11; F15 SUBJECTS Economics and Development; Economics; Industrial Economics; Industry & Industrial Studies 1. Introduction Indonesia has historically been a preferred place for foreign direct investment (FDI) in Southeast Asia due to its wealth of natural resources, large labor supply, and expanding domestic market (Lindblad, 2015). CONTACT Dyah Wulan Sari [email protected] Department Economics, Faculty of Economics and Business, Airlangga University, Surabaya, Indonesia ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2392199 https://doi.org/10.1080/23322039.2024.2392199 This has given multinational corporations (MNCs) numerous options for internalization when selecting a location for their overseas operations. As reported by Indonesian Bureau of Statistics (2024), FDI inflow to Indonesia increased steadily between 2010 and 2020. By 2022, it accounted for 11% of all FDI inflow to ASEAN, making Indonesia the country with the second-highest FDI inflow after Singapore. This figure amounts to USD 24.7 billion (World Bank, 2024). In addition to being the primary driver of economic development, foreign direct investment (FDI) has aided Indonesia’s industrialization process (Aswicahyono et al., 2011; Narjoko, 2023). Industrial development plays a significant role, especially in developing countries as it aid in reducing poverty and achieving sustainable economic growth (Abdul, 2010). Extensive research has been undertaken on the spillovers of foreign direct investment (FDI). There are conflicting and inconsistent findings in the empirical literature that looks at how multinational corporations (MNCs) affect domestic firms’productivity performance. The majority of the early evidence is negative (Aitken & Harrison, 1999; Bournakis et al., 2022; Djankov & Hoekman, 2000; Huynh et al., 2021). On the contrary, FDI is regarded as a catalyst for economic growth (see, Behera, 2017; Damijan et al., 2003; Fatima, 2016;G € org & Greenaway, 2004; Harris & Robinson, 2003; He et al., 2019; Kayani et al., 2021; Liu, 2002; Nguyen, 2022). Additionally, FDI has admitted to being one of the primary means of disseminating information internationally. Gains from FDI spillovers in domestic enterprises are reported empirically by Blomstr€ om & Sj€ oholm (1999), Li et al. (2001), Haskel et al. (2002), Keller & Yeaple (2003), and Liu & Wang (2022). Nonetheless, the majority of these research concentrate on horizontal spillovers, which quantify how the presence of foreign companies affects native companies in a certain industry. The impact of foreign firms on domestic firms across industries is determined as vertical spillovers. The vertical spillovers can occur in two different linkages, namely backward and forward linkage. The former is the impact of foreign firms on the domestic firms in the upstream market when those domestic firms supply intermediate inputs to foreign firms. The latter is the impact on the downstream industries in using the foreign firm’s output as their intermediate inputs. There are few studies on externalities between industries (Blomstr€ om et al., 2000). A notable exception in the literature on vertical spillovers found mixed results (see, Blalock & Gertler, 2008; Javorcik, 2004; Kugler, 2001; Le & Pomfret, 2011; Marcin, 2008; Schoors & Van Der Tol, 2002). The research conducted by Blalock & Gertler (2008) found that the MNC firms’externalities are beneficial to the local firms as the suppliers in Indonesian manufacturing industries. Schoors & Van Der Tol (2002) examine positive backward spillovers, and the opposite holds for forward spillovers in Hungarian companies. Aitken & Harrison (1991) discover the negative backward spillovers since foreign firms demand imported goods, which prevents domestic suppliers from gaining economic of scale. Nguyen et al. (2020) found that backward spillovers increase local productivity but horizontal and forward linkage spillovers have a detrimental effect on domestic firms’productivity. Research from Bournakis et al. (2022) suggest that only MNCs that are majority or fully owned produce monetarily minor horizontal spillovers. RodriguezClare (1996) suggests that the effect of FDI would be more favorable when the MNC firms demand intensively the intermediate goods produced by the domestic firms in the upstream market, and the quality is at least equal to the home country’s products. When these required conditions do not hold, the host country’s economy could be harmed. The primary finding from the literature on FDI spillovers to date is that MNCs’effects on domestic firms’performance are complex, and one should carefully examine the different ways that MNCs affect the host nation’s economic activity (Bournakis et al., 2018; Crespo & Fontoura, 2007; Hayakawa et al., 2012). Studies on FDI spillovers in Indonesia’s manufacturing sector have previously been carried out utilizing the stochastic production frontier such as Suyanto et al. (2021), Suyanto et al. (2014), and Suyanto & Salim (2010). These studies focus on estimating the externalities of FDI on firms’efficiency. However, FDI not only improves domestic firms’efficiency but also generates productivity gains for domestic firms through technological progress. Only studies from Sari et al. (2016) and Sugiharti et al. (2022) use a simultaneous model to examine how FDI spillovers affect productivity and efficiency. Therefore, this study would enhance the literature related to productivity and inefficiency. One of the key factors influencing the benefits of spillover FDI is the industrial market characteristic of the host nation (Blomstr€ om et al., 2000). The efficient-structure (ES) and quiet-life (QL) hypotheses are two competing theories that address degree concentration and efficiency. Advocates of QL, Hicks (1935) contends that significant market concentration lessens competition and lessens the ability of enterprises 2 M. FAWAIT ET AL. to receive incentives for efficiency. The ES hypothesis proposes that the excessive concentration may have been caused by efficient enterprises. Studies on market characteristic have been focused on the role of market characteristic affects firms’efficiency. There only a few study explore the impact of market characteristic’s role on spillover FDI. The exceptions are on the study from Sugiharti et al. (2022) and Suyanto et al. (2009). Sugiharti et al. (2022) concludes that higher market competition boosts industrial productivity within the industry which receives FDI. Study from Suyanto et al. (2009) has also confirmed that greater concentrations are linked to greater spillovers of foreign presence. Nevertheless, no study has ever examined how spillovers affect efficiency and productivity in light of domestic firms’distinct market concentration as foreign firms’customers or sellers. Besides contributing to economic activity and productivity, MNC firms find advantages from sourcing inputs and producing abroad, fragmenting their production process and creating a new trade pattern that is vertical trade of integration (VTI). VTI presents locational advantages and productivity benefits through specialization in different stages of the production process. This vertical specialization exploits efficiency at each stage of the production by linking sequential products and facilities at several regions or countries (Miroudot & Ragoussis, 2009). MNC firms gain benefit from VTI since they serve foreign markets through foreign affiliates. However, another issue arises from VTI, which is additional cost such as distance-related cost. This study contributes to extending recent empirical literature on the possibility of spillovers in the Indonesian automotive industry not only from foreign firms within the industry, but also from potential externalities arising from downstream and upstream markets using stochastic froentier analysis (SFA). Generally, studies on FDI spillovers examine the role of FDI in explaining the efficiency differences measured by the distance to the frontier; however, few studies consider the impact of efficiency improvement and technological progress on productivity gains from FDI (see, Kneller and Pisu, 2007; Ayyagari & Kosov a, 2010; Sari, 2019; and Nguyen et al., 2021). This study attempts to capture the sources of productivity gains through both channels. The other studies have never discussed, based on author knowledge, the impact of spillovers regarding domestic firms’specific market concentration as competitors, buyers, or sellers to foreign firms on efficiency and productivity (see, Sari et al., 2016; Schoors & Van Der Tol, 2002; Suyanto & Salim, 2013). This study aims to fill this gap and analyze the importance of market characteristics in determining spillovers, VTI, trade openness, and foreign ownership. Previous studies on market concentration employ the Herfindahl-Hirschman Index (HHI), while this study utilizes the relative entropy coefficient (RE) to provide another approach to measure market concentration. It is noteworthy that RE shares characteristics with HHI and is an easy index to comprehend (Yi et al., 2018). In this study, the industry-specific characteristic is controlled using the inclusion of firm size and industrial dummy variables. The SFA estimation results are calculated to measure output elasticity with respect to each input and total factor productivity (TFP) growth. The discussion provides TFP decompositions, which are technical efficiency change (TEC), technological progress (TC), and scale efficiency change (SEC). 2. Literature review Compared to local firms, multinational corporations (MNC) receive FDI using sophisticated technology and invest more in research and development (R&D). By exerting technological superiority, better managerial practice, and the ability to exploit economies of scale, MNC firms can establish subsidiary firms even in unexplored countries and compete against domestic firms (Blomstr€ om, & Sj€ oholm, 1999; Belderbos et al., 2021). Possessing knowledge-based intangible assets such as technological superiority, which may not be available in the host country, could create spillovers to domestic firms (Suyanto & Sugiarti, 2019). Spillovers occurred in three channels. The first is the demonstration effect, defined as the process of domestic firms upgrading technology by imitating foreign firms’production processes, products, managerial skills, and organizational innovations. The second factor is labor turnover. Foreign firm workers are mostly trained or given access to intangible assets. As a result, productivity spillovers occur when trained labor eventually resigns and works in domestic firms or establishes their own business (De Mello, 1997; Fosfuri et al., 2001; Keller, 2021). The third factor was competition. Domestic firms are willing to protect their market share; therefore, they are forced to operate more efficiently, leading to productivity gains (Glass & Saggi, 2002; Li & Tanna, 2019). On contrary, because FDI-invested companies typically offer high COGENT ECONOMICS & FINANCE 3 pay and incentives to draw and keep highly trained individuals, the wage disparity between foreign and domestic businesses also affects labor mobility (Huang & Zhang, 2017). Thus, labor churn and the transfer of labor from FDI firms to domestic firms are avoided (Demena, 2015; Gorodnichenko et al., 2014). Additionally, the presence of foreign companies may have a greater competition effect than the demonstration effect and vertical linking effect, which would be detrimental to domestic companies in the host market (Ascani & Gagliardi, 2020; Le & Pomfret, 2011). Furthermore, MNCs frequently boost competitiveness but “steal”market shares from domestic firms, which eventually results in productivity and efficiency losses. Domestic firms may suffer from the heightened level of competition in the short term by seeing a decline in output and market share (Lin et al., 2009). Domestic enterprises may experience crowding-out effect through decreasing productivity when these local firms distribute their fixed expenses over a lower sales volume, as demonstrated by Aitken & Harrison (1999). The entry of MNCs may result in higher labor costs for native companies. This is due to foreign-invested businesses frequently providing higher wages. This statement is strengthened by Aitken et al. (1996) who argue that in labor markets where there is competition, foreign firms may improve wages for all businesses. Bournakis et al. (2022) also explain that the domestic firms charge lower markups due to the greater presence of MNEs in the domestic market indicating that the presence of foreign firms push the domestic ones to compete. Therefore, the net horizontal effect of FDI on domestic enterprises is theoretically uncertain and depends on the relative strengths of the positive technical spillovers and the negative crowding-out effect. Spillovers might not only take place intra-industry but also across the industry, which is revealed as vertical spillovers that occur through the supply chain channel and are divided into backward and forward spillovers. Backward spillovers exhibit a negative impact when foreign firms import intermediate inputs instead of buying intensively from the local upstream markets. Barrios et al. (2011) defines backward spillover as how the actions of foreign firms affect local suppliers’or providers’proactive adaptation to guarantee the standardization and quality of the local inputs supplied. This might be due to quality considerations, as the quality of intermediate inputs produced locally is lower than those purchased from abroad. Higher international requirements for product quality and consistent delivery motivate local suppliers to enhance their offerings and workflows in order to draw consistent business from FDI firms (Huynh et al., 2021). In contrast, positive backward spillovers arise when foreign firms transfer technology to domestic suppliers to increase the quality of intermediate inputs bought locally. Foreign firms demand high-quality intermediate inputs, forcing domestic suppliers to lessen inefficiency and improve productivity. Foreign firms are eager to transfer technology and provide technical assistance to several suppliers to avoid monopolies when they transfer only to one supplier (Abegaz & Lahiri, 2021; Blalock & Gertler, 2008). However, the inability to absorb some sorts of spillover effects is caused by poor production levels and a lack of worker training (Sarker & Serieux 2022). This is supported by the argument of Krasniqi et al. (2022) who state that domestic firms are unable to take advantage of the opportunity to integrate into global value chains where MNCs operate because they lack the quality standards, scale of production, and connective networks. In addition, one potential obstacle to backward spillovers is the weak absorptive ability of domestic businesses (Gorodnichenko et al., 2014). Newman et al. (2015) and Behera (2017) argued that forward spillovers can create two opposite effects. Negative forward spillovers occur when foreign firms in the (upstream market steal domestic firms’market share in the same industry, resulting in domestic competitors not competing. Foreign firms have the power to increase prices, while domestic firms in the downstream market suffer higher costs because of the increasing prices of intermediate inputs. In contrast, positive forward spillovers occur through foreign products usually accompanied by services or assistance to use the product efficiently, improving the productivity of domestic buyers (Javorcik, 2004; Yuliani et al., 2019). Another possibility is that domestic firms as buyers gain benefit from purchasing less-costly and high-quality intermediate inputs supplied by foreign firms in the upstream market. In contrast, research from Sarker & Serieux (2022) have found that forward spillovers are only experienced by the high-tech domestic firms possessing low-importing and low-exporting foreign investment firms. Unlike previous researches, this study utilizes another measurement as a proxy of degree of concentration, which is the relative entropy coefficient (RE). It is noteworthy that RE is an easy index to be interpreted and having the same traits as HHI (Yi et al., 2018). Theoretically, there are two opposing hypotheses regarding degree concentration and efficiency, which are the quiet-life (QL) and efficient4 M. FAWAIT ET AL. structure (ES) hypothesis. Hicks (1935) promotes QL and argues that the high market concentration reduces the competition and less likely to provide incentives firms to be efficient. On the contrary, the ES hypothesis suggests efficient firms might create the high concentration. Basically, efficient firms could produce at lower cost leading to higher profits and larger market share, thus resulting in efficient firms growing rapidly compared to inefficient firms (Demsetz, 1973). Another explanation is that MNC presence might intensify competitiveness in the market, pushing domestic companies to better leverage their resources to hold onto market share (Esquivias & Harianto, 2020). A higher output share from MNCs suggests a more concentrated market and reduced competition. A few among many that have acknowledged the importance of market size in drawing in foreign direct investment (FDI) are Wheeler & Mody (1992), Schmitz & Bieri (1972), and Pistoresi (2000). Recent studies by Asiedu (2006), Mlambo (2006), and Zhang (2008) examined the critical role that market size plays in drawing in foreign direct investment. According to these authors, one thing that attracts foreign investors is a larger market. Market share is a crucial factor in determining foreign direct investment inflows, according to study by Mughal & Akram (2011). The impact of market concentration on efficiency has been thoroughly studied (see, Esquivias & Harianto, 2020; Sari et al., 2016; Setiawan et al., 2012; Sugiarti, 2019), but little study has been done on how market concentration interacts with the spillovers variable. The two studies by Suyanto et al. (2009). and Sugiharti et al. (2022) that examined the effects of market concentration on efficiency, and both efficiency and productivity respectively, stand out as noteworthy exceptions. However, these researches have never discussed, based on authors knowledge, the impact of spillovers based on domestic firms’specific market concentration as buyers or sellers to foreign firms on efficiency and productivity. 3. Methodological approach 3.1. Data and construction of variables The data employed were annual medium and large manufacturing establishments, which were taken from the Indonesian Central Board of Statistics. The medium establishment survey includes the manufacturing firms employing from 20 to 99 workers while the large establishments employ more than 99 workers at a given year. The complementary data are the wholesale price index (WPI) at a constant price in 2010 and an input-output (I-O) table. The former is implemented to deflate the variables in the production function consisting of monetary data. The latter is utilized to generate vertical spillovers variables that measure the externalities in the upstream and downstream market. The I-O table captures the transaction of goods and services between 175 economic sectors. The unbalanced panel data consists of 3,105 firms in the automotive industry from 2010 to 2014. The production and inefficiency function are estimated using these data. To calculate the Total Factor Productivity (TFP) growth, firm data should be available for all periods of observation to measure growth. For the purpose of computing TFP, this study employs balanced panel data with 469 observations per year, or 2,345 observations altogether. This study will estimate through stochastic production function (SPF) consisting of output and input variables. Output is defined as the total output produced by a firm in a given year, measured in Rupiah, while capital is the monetary value of fixed assets: lands, buildings, machinery, vehicles, and other capital goods. Labor is measured through the number of employees worked in each firm, and material is a proxy for total expenditure on raw materials. Energy is the total cost of energy used in production processes, such as gasoline, diesel fuel, kerosene, public gas, lubricants, and electricity. The cleaning data procedure follows Sari et al. (2016) who control the ratio of material input and output is controlled. The ratio less than 5% and more than 95% will be excluded since using small or huge amounts of materials to produce a certain quantity of outputs seems implausible. However, the data passes the criteria. Therefore, there is no observation excluded following these criteria. The data are cleaned from misreporting and key-punch errors, such as in foreign shares. For instance, the foreign share for a whole selected period is 100% except for one period typed as 0% then this will be corrected to 100%. During the observation period, firms corrected from domestic to foreign were 0.10% while the firms changed to domestic were 0.16%. COGENT ECONOMICS & FINANCE 5 The exogenous variables consist of the key and supporting variables. The key variables are horizontal, forward, and backward spillovers; relative entropy (RE); and the interaction between each spillover variable and RE. The supporting variables are vertical trade integration, import and export intensity, firm ownership, firm size, and an industrial dummy. The summary statistics are presented in Table 1. Horizontal spillover captures the impact of foreign firms’existence on domestic firms in the same industry, and is calculated as follows: Hspilljt ¼Pi2jFShit Qit Pi2jQit (1) Where j denotes industry, PiejFShit is total foreign share in the same industry. Q it measures the output produced by firm i in t given year, whereas Pi2jQit captures the total output of all firms in the same industry. The construction of vertical spillover variables requires data across industry linkages obtained from the input-output framework based on the Leontief inverse matrix, following Kohpaiboon (2009) and Sari et al. (2016). This study captures both direct and indirect (inter-sectoral) linkages. Indirect linkage is constructed based on the Leontief inter-industry accounting framework, considering the input-output framework excluding import transactions: X¼AdXþYdþE,Ad¼akl ½ ,akl ¼Xkl=Xt(2) Where Xis the column vector of the total gross output, Addenotes the domestic input coefficient matrix. Yddefines the column vector of domestic demand for domestically produced goods, whereas E represents the column vector of international demand for domestically produced goods. The element of domestic input-output coefficients matrix is akl ½ :By solving Equation (2), the Xbecomes. X¼½I−Ad−1YdþE ½ ,½I−Ad−1¼½bkl(3) Table 1. Summary statistics. Variables Unit Year 2010 2011 2012 2013 2014 Output and Input variables Output (Q) Billion Rupiah Mean 416.65 484.20 495.96 398.27 391.45 Std. Dev. 2613.74 2462.17 2524.58 2043.21 1975.56 Capital (K) Billion Rupiah Mean 9.98 11.67 9.09 6.96 72.23 Std. Dev. 72.08 64.29 61.87 40.98 564.91 Labor (L) Person Mean 312.68 342.95 349.86 333.74 323.63 Std. Dev. 905.97 952.49 982.06 904.84 859.91 Material (M) Billion Rupiah Mean 145.55 211.29 200.82 148.09 132.53 Std. Dev. 1152.94 1280.18 1215.35 718.03 598.58 Energy (E) Billion Rupiah Mean 15.39 26.13 21.50 17.19 15.73 Std. Dev. 98.92 172.03 149.55 97.21 88.64 Exogenous variables Hspill Ratio Mean 0.05 0.39 0.38 0.55 0.39 Std. Dev. 0.04 0.22 0.22 0.00 0.22 Bspill Ratio Mean 1.12 1.12 1.06 0.11 0.10 Std. Dev. 0.26 0.27 0.24 0.08 0.07 Fspill Ratio Mean 1.17 1.17 1.01 0.94 0.10 Std. Dev. 0.32 0.32 0.34 0.46 0.07 VTI Interval [o,2) Mean 0.05 0.05 0.05 0.04 0.05 Std. Dev. 0.18 0.19 0.17 0.15 0.17 For Binary dummy Mean 0.09 0.23 0.23 0.21 0.23 Std. Dev. 0.28 0.42 0.42 0.41 0.42 RE Ratio Mean 0.66 0.72 0.72 0.69 0.73 Std. Dev. 0.11 0.08 0.08 0.07 0.08 XI Ratio Mean 0.09 0.09 0.09 0.08 0.09 Std. Dev. 0.25 0.24 0.24 0.22 0.24 MI Ratio Mean 0.18 0.19 0.17 0.19 0.19 Std. Dev. 0.29 0.30 0.29 0.28 0.28 Fsize Ratio Mean 0.02 0.02 0.02 0.02 0.02 Std. Dev. 0.08 0.08 0.08 0.08 0.08 Number of observation 558 567 583 686 711 Note. Output and input variables are transformed into natural logarithms. The mean is calculated using the arithmetic mean. The Std. Dev. where represents the standard deviation. Source: Authors. 6 M. FAWAIT ET AL. The ½bklrepresents the total linkages (direct and indirect) captured in the Leontief domestic inverse matrix. This also elucidates the total output required for inter-sectoral linkages from all other sectors, for instance, industry k’s outputs to be used as an intermediate input for industry l when one product of industry l’s demand increases. Forward spillovers are measured by calculating the outputs produced by foreign firms in the upstream market and used by domestic firms as intermediate inputs in the downstream market. Foreign firms’exported outputs were excluded from the measurement. The inputs supplied by firms within the industry are excluded from the calculation, because they are already captured by horizontal spillovers. The measurement is defined as Fspilljt ¼X n bkl PiejFShit ðYit −XitÞ Pi2jðYit −XitÞ(4) where bkl measures industry k’s output demanded by industry l as an intermediate input to produce one unit of industry l’s output, Yit is the output produced by a foreign firm in the upstream market. X it indicates the output of the foreign firm to be exported. Backward spillover captures the impact of foreign firms in the downstream market on the industry that supplies the input. Inputs supplied by firms within the industry were excluded from the calculations. The calculation is as follows: Bspilljt ¼X m bkl Hspilljt (5) where bkl is the number of industry k’s output demanded by an additional unit of industry l’s output. Vertical trade integration (VTI) values range between 0 and 2. The lower bound implies that the firm uses domestic inputs or supplies only to the domestic market. The upper bound indicates that the firm purchases imported inputs, whereas all outputs produced are exported. The formula for VTI is as follows: VTIit ¼2min ðXit,MinpitÞ Qit (6) where min Xit,Minpit ðÞ denote the lowest value between Xit and Minpit,Xit,describes exported outputs, respectively. Minpit is the intermediate input imported by firm i in period t; The foreign firm is a dummy variable valued at one when at least 10% of the capital is owned by foreign firms. The value 0 is when foreign invests capital with a value less than 10% or no foreign capital at all. This baseline is based on the International Monetary Fund. The foreign ownership variable is formulated as follows: Forit ¼1if the share of foreign capital i at time t is greater than or equal to 10% 0if otherwise (7) The relative entropy coefficient, measures the degree of market competition. When the value is zero, only a few firms operate in the market. Therefore, the degree of monopoly is high, whereas the degree of competition is low. The closer the value is to 1, the more competitive the market is, and comprises N equally sized firms. RE is measured as follows: REjt ¼PN i¼1sitlogeð1=sitÞ logeðNÞ(8) where siis the market share of firm i in period t and e is the natural number. where N is the number of firms in the industry. The RE variable interacting with each spillover variable is included in the model. REjt:Hspilljt measures how market characteristics affect the spillovers of foreign firms within an industry. REjt:Bspilljt, and REjt:Fspilljt measures how the upstream and downstream market characteristics affect the spillovers of foreign firms. The trade openness variables were export intensity (XI) and import intensity (MI). Each of these is formulated as follows. XIit ¼Xit Qit (9) COGENT ECONOMICS & FINANCE 7 supply, and the potential forcing firms out of business (Afrianto, 2016; Agustinus & Fitriyani, 2020; Krisnamurthi, 2013, p. 201). The next component is TEC, which, on average, makes the greatest contribution to TFP. The TEC growth of the majority of domestic and foreign firms’TEC growth were negative, indicating that the production processes were inefficient. The lack of adequate managerial skills could affect the negative growth of TEC. Another reason is that firms may not be efficient in using inputs or technology. The last TFP component is SEC, which does not seem to have high volatility. Although the growth was relatively small, with an average of 0.18% and 0.39% for foreign and domestic industries, respectively, these positive SEC values imply that most firms operate on an optimal scale. 5. Conclusion The existence of foreign firms within industries spills over both productivity and efficiency of domestic firms. The same is true when domestic companies are suppliers in the upstream market. On the other hand, domestic firms that use multinational corporations’(MNC’_ products as intermediate inputs have a negative effect on productivity and efficiency. Based on these results, policymakers should ensure that the benefits of foreign direct investment (FDI) spillovers outweigh their disadvantages. The government should attract foreign investment when MNC firms produce outputs used as intermediate inputs for export or final goods that are ready to be used. This is because the empirical results show that selling intermediate inputs to local buyer firms may create negative spillovers. The empirical results show that vertical trade integration may negatively affect productivity and efficiency. On the contrary, firm size, exports, and import intensity are associated with improved productivity and reduced inefficiency. The higher the degree of market concentration, the more efficient the firm is. The interaction variable between market concentration and horizontal spillover implies that the spillovers of FDI within the industry are smaller in a less concentrated market. A less concentrated market of domestic suppliers has a negative impact on the firm’s productivity and less impact on reducing inefficiency. By contrast, the less concentrated local buyers’market has a positive impact on the firm’s productivity and contributes to a lower degree of inefficiency. Despite positive spillovers, total factor productivity (TFP) shows negative growth in both domestic and foreign firms. The main concerns are technological change (TC) and technical efficiency changes (TEC), as the growths are mostly negative and relatively high. The policy recommendation for this circumstance is to encourage firms to improve technological progress, such as upgrading their machines and investing in human resources, by providing training workers aiming at mastering better managerial expertise. The government should support research and development (R&D) by facilitating firms, providing incentives for research conducted by universities or national laboratories, or providing tax incentives. Authors’contributions HATI: conception, analysis, interpretation; MF: interpretation, writing draft; DWS: conception, supervision; TH: supervision; SKS: clearing data; FM: writing revision. Disclosure statement No potential conflict of interest was reported by the authors. Funding This research was funded by the Indonesian Ministry of Education, Culture, Research and Technology. Notes on contributors Muhammad Fawait, is a doctoral economics student at Universitas Airlangga. He worked as a politician at the East Java Regional Representative Council. 14 M. FAWAIT ET AL. Haura Azzahra Tarbiyah Islamiya, works as a lecturer and researcher in the Department of Economics, Faculty of Economics and Business, Universitas Airlangga. Dyah Wulan Sari, is a professor in the Department of Economics, Faculty of Economics and Business, Universitas Airlangga. She work as a lecturer, researcher, and consultant. Research has focused on industrial production efficiency, econometrics. Tri Haryanto, is a senior lecturer and researcher at the Department of Economics, Faculty of Economics and Business, Universitas Airlangga. Sanju Kumar Singh, is an active lecturer and researcher in the Department of Management at the Faculty of Economics and Business, Universitas Airlangga. Faiz Masnan, a lecturer at the Universiti Malaysia Perlis, Perlis, Malaysia. ORCID Haura Azzahra Tarbiyah Islamiya http://orcid.org/0000-0002-2015-4344 Dyah Wulan Sari http://orcid.org/0000-0003-3567-6513 Data availability statement The data used in this study includes annual medium and large manufacturing establishments, which are taken from the Indonesian Central Board of Statistics. 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