Economic zones and local income inequality: Evidence from Indonesia

Hornok, Cecília; Raeskyesa, Dewa Gede Sidan Article — Published Version Economic zones and local income inequality: Evidence from Indonesia The Journal of Economic Inequality Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Hornok, Cecília; Raeskyesa, Dewa Gede Sidan (2023) : Economic zones and local income inequality: Evidence from Indonesia, The Journal of Economic Inequality, ISSN 1573-8701, Springer Science and Business Media LLC, Berlin, Vol. 22, Iss. 1, pp. 69-100, https://doi.org/10.1007/s10888-023-09581-x This Version is available at: https://hdl.handle.net/10419/301881 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/ / Published online: 31 May 2023 The Journal of Economic Inequality (2024) 22:69–100 Vol.:(0123456789) https://doi.org/10.1007/s10888-023-09581-x 1 3 Economic zones andlocal income inequality: Evidence fromIndonesia CecíliaHornok1· DewaGedeSidanRaeskyesa2 Received: 14 August 2022 / Accepted: 9 May 2023 © Springer Science+Business Media, LLC, part of Springer Nature 2023 Abstract Economic zones can be powerful drivers of economic growth in developing countries. However, less is known about their distributional impact on the local society. This paper provides empirical evidence from Indonesian provinces on the relationship between economic zones and within-province income inequality. We apply fixed-effects panel estimation to province-level data for the whole of Indonesia, which we then complement with separate studies on the opening of three economic zones in three provinces using the synthetic control method. The results suggest that the above relationship is positive overall. The estimated rise in income inequality after a zone opens is, however, relatively small on average and may be short-lived. Moreover, the average estimate masks large regional differences, which suggests that the inequality implications of economic zone policies depend on local conditions. One possible explanation for the rise in inequality is that the unskilled population benefits disproportionately less from the policy. Keywords Economic zones· Income distribution· Indonesia· Place-based policy· Synthetic control method JEL Classification D31· F63· O15· O25 1 Introduction In his second inaugural speech in December 2019, President of Indonesia Joko Widodo expressed his vision for Indonesia by saying, [...]” our dream, our ambition is that by 2045, after one century of Indonesian independence, Indonesia should, Insya Allah (God willing), have escaped the middle-income trap. Indonesia will have become an advanced * Cecília Hornok [email protected] Dewa Gede Sidan Raeskyesa sidan.raesky[email protected] 1 Kiel Centre forGlobalization, Kiel Institute fortheWorld Economy, Kiel, Germany 2 Department ofSocioeconomics, Institute forInternational Political Economy, Vienna University ofEconomics andBusiness, Vienna, Austria C.Hornok, D.G.S.Raeskyesa 1 3 country [...]” (The Jakarta Post 2019).1 A popular policy tool meant to support the above goal in Indonesia is the establishment of economic zones – two prominent forms of which are industrial estates and, more recently, special economic zones (SEZs). These industrial place-based policies are widely used in developing countries around the world to foster local economic development by attracting foreign investors, creating new employment and helping a country enter global production networks (e.g., Duranton and Venables 2021). This paper focuses on the impact of economic zones on one aspect of socio-economic development – income inequality. Economic zone policies in Indonesia are primarily aimed at fostering growth, mainly through industrialization and foreign direct investment, while virtually no attention is paid by policymakers to their distributional impacts. However, it is not unimportant from a developmental perspective how these policies impact local inequalities. Recent literature has shown that the benefits of industrialization and globalization are often not distributed evenly, which can lead to high inequalities and question the social and political sustainability of the growth process (e.g.,Kniivila 2008; Kanbur 2015; Pavcnik 2017; Dorn etal. 2018). High income inequality has been shown to correlate with slower long-run growth (Barro 2000; Berg etal. 2018) and to cause larger macroeconomic volatility and social insecurity (Stiglitz 2012). Developing countries characterized by high income inequality are also more prone to fall into the middle-income trap (MIT)2 because the inequality hinders the human capital accumulation process and thus hampers economic productivity and innovation (Wang and Lan 2017; Paus 2017; Basri and Putra 2016). In this paper, we look at income inequality within Indonesian provinces, i.e., our focus is on the local impact.3 We ask the question of how income inequality in a province responds to the opening of an economic zone in the same province. The empirical analysis is based on self-collected information on the location and opening date of industrial estates and SEZs in Indonesia, together with province-level panel data on the Gini coefficient to measure income inequality. First, we use standard panel data regression techniques to investigate the above relationship, where we also consider heterogeneities by broad geographical regions. The second part of the paper makes use of a relatively novel technique developed for causal inference– the synthetic control method (SCM). We apply this method to estimate the inequality consequences of three individual zone openings in three Indonesian provinces. These are the opening of an industrial estate in 2008 in Banten, a relatively developed province on Java Island, and two other examples from two less developed, mostly rural provinces, Aceh and West Nusa Tenggara, opening their first industrial estate in 2014 and SEZ in 2017, respectively. Indonesia is an interesting case to study the inequality consequences of economic zone policies. Since the 1970s, the country has opened about 120 industrial estates and—more recently—11 SEZs. Economic zones in Indonesia may represent considerable locational advantages for businesses, as the country is known for its high transaction costs (Indrawati 2 MIT refers to the situation when countries experience growth stagnation at middle-income levels, which Gill and Kharas (2007) termed the middle-income trap. 3 We are aware that economic zones might also influence inequalities between provinces. Indeed, some of them are set up precisely with the aim to promote the development of more disadvantaged regions of a country. 1 The unofficial English translation of the speech was retrieved from The Jakarta Post: https:// www. theja karta post. com/ news/ 2019/ 10/ 20/ themainthingisnottheproce ssbuttheresultjokow isfullinaug urati onspeech. html. 70 Economic zones andlocal income inequality 1 3 2020). Indonesian industrial policy also seems to work well in attracting foreign investors, Indonesia being the second-highest FDI recipient amongst ASEAN member states between 2015 and 2019.4 Moreover, recent economic development has successfully pulled many Indonesians out of poverty (Miranti 2010; Kis-Katos and Sparrow 2015). Nevertheless, this process was in part accompanied by growing income inequality. A recent World Bank report notes that the growth of the Gini coefficient in Indonesia was one of the highest among South-East Asian countries in the mid-2010s, raising concerns that the country is at risk of leaving its poor and most vulnerable behind (The World Bank 2016). Although the Gini has declined in the most recent years, it remains at a relatively high level, especially in some parts of Indonesia such as the populous Java Island.5 Economic literature offers no clear guidance on how industrial place-based policies affect local income inequality, as the socio-economic impact of such policies depends on multiple interrelated factors (Duranton and Venables 2021). Even if a policy explicitly targets a disadvantaged region with the aim to help poor locals, success may not be guaranteed (Neumark and Simpson 2015). There are at least two channels through which industrial place-based policies can lead to an increase in local inequalities. One manifests itself through the in-migration of skilled workforce to the area where the policy takes place and the resulting gentrification of the neighborhood. The inflow of more affluent and skilled new residents can worsen the chances of less-skilled locals to benefit from the policy while it also raises the local cost of living (e.g. housing prices). This explanation is proposed by Reynolds and Rohlin (2015) to understand why the Empowerment Zone program in the U.S. (a place-based policy of tax incentives to create new jobs) increased income inequality in the affected regions. Another channel that could be responsible for economic zones having a detrimental impact on some local residents is related to land acquisitions. In agricultural communities, a land acquisition for industrial purposes reduces the land available for agriculture and may deprive some of the local farmers of their livelihood unless adequate compensation is provided. Although industrial development improves the lives of many through creating better infrastructure and employment, there may be groups of people left behind in this process. Economic zone development can as a result increase both poverty and inequality in its neighborhood—at least for a transitional period (e.g., Le etal. 2020, for Vietnam; Aggarwal and Kokko 2021, for India). Useful insights can be gained also from the international economics literature as to how industrialization and globalization-driven economic growth impact a country’s income inequality. The classical Heckscher-Ohlin theory would predict that a country abundant in unskilled labor, like Indonesia, specializes in low-skilled production in the international division of labor. Under certain restrictive assumptions, wages for unskilled labor would then rise relative to wages for skilled labor, leading to a more compressed wage distribution, i.e. a reduction in income inequality. However, this hypothesis does not find much support when contrasted with data. Empirical literature that links globalization and technological progress to income or wage inequality in developing countries tends to find that economic growth is in fact accompanied by growing inequality (e.g., Attanasio etal. 2004; Figini and Görg 4 Source: ASEAN Statistics Data Portal, Inward FDI Flows in million US$. 5 Several factors have been identified as leading causes behind the rise of income inequality in Indonesia. These are unequal access to education, high wealth concentration, low resilience, and regional differences in infrastructure provision (World Bank 2016; Wicaksono etal. 2017). 71 C.Hornok, D.G.S.Raeskyesa 1 3 2011; Jaumotte etal. 2013; Gozgor and Ranjan 2017; Dorn etal. 2018). A major explanation is that economic growth through technological progress is skill-biased, that is, it benefits those who possess higher skills (and capital) disproportionately. The tasks that are outsourced to developing countries within the international production chain may require relatively low skills. Nevertheless, these tasks are often still too complex for the unskilled labor force in these countries. The growing demand for skills raises the relative wages of skilled to unskilled workers (the socalled wage skill premium) and increases wage inequality (e.g., Feenstra and Hanson 1997; Autor etal. 1998). Our study contributes to the literature that empirically assesses the role of economic zones in economic development. These papers typically address the question as to whether these zones are successful in attracting foreign investment, promoting exports, fostering productivity and creating more and better paying jobs. China’s case with special economic zones is an especially well-documented success story (Schminke and Van Biesebroeck 2013; Wang 2013; Lu etal. 2019). Evidence on the experiences of other developing countries are more mixed, suggesting that success is dependent on the institutional context and other initial conditions (e.g., Aggarwal etal. 2008; Steenbergen and Javorcik 2017; Alkon 2018). In Indonesia, the importance of economic zones was studied by Winardi etal. (2019), who found that zones contribute to attract more investment, generate employment and enhance regional economic growth in their neighborhood. Firms located inside the zone are more productive than those outside, which is often attributed to the advantages offered by the zone such as low transportation costs and better infrastructure (Faradila and Kakinaka 2020; Winardi etal. 2017). These beneficial effects however may not accrue in all circumstances (Aritenang and Chandramidi 2020; Rothenberg etal. 2017).6 Research on economic zones in developing countries which also considers local distributional effects is especially scarce. There are two noteworthy exceptions. One is Picarelli (2016), who focuses on the local distributional impact of export processing zones in Nicaragua. Her findings, which are based on household expenditure data, reveal adverse distributional effects. The establishment of export processing zones in Nicaragua benefitted the already well-to-do households disproportionately more than households at the lower parts of the expenditure distribution. In contrast, Brussevich (2020) finds on Cambodian district-level data that SEZs reduced income inequality within the districts where they are located. This is because Cambodian SEZs boosted female employment and provided relatively well-paid jobs to an otherwise disadvantaged social group. She also finds however that SEZs increased the value of land more than wages in their district of location, suggesting that land owners may have benefited more than workers. Our regression-based empirical findings suggest that, for Indonesia as a whole, a zone opening in a province is typically followed by a mild increase of income inequality in that province. This relationship is heterogenous geographically, suggesting that local conditions play a major role in mediating it. Similarly, the synthetic control studies confirm that zone openings can contribute to rising inequality at least in the short run. In two of the three 6 Aritenang and Chandramidi (2020) have found no substantial evidence that SEZs in Batam city in Indonesia would enhance firm productivity. Rothenberg etal. (2017) study the Integrated Economic Development Zone (KAPET) program in the outer islands of Indonesia and find that the program failed to improve development outcomes in their districts of location. 72 Economic zones andlocal income inequality 1 3 examples we find growing income inequality in the first few years after the zone opening, but no evidence for an effect in the longer run. In the third study (Mandalika SEZ in West Nusa Tenggara) we find no evidence for an effect on income inequality. These findings shed light on the importance of equity considerations when designing place-based policies. Namely, such policies can contribute to rising inequality even if they help reduce overall poverty. Our results show that such effects are present at least in the short run and call for development policies that are more socially inclusive. The concept of inclusive development may especially resonate in a country like Indonesia, with the political philosophy of Pancasila that emphasizes social justice to all Indonesian citizens (Gibson 2017:11). The paper is structured as follows. Section 2 discusses the history of industrial estates and SEZs in Indonesia. Section3 presents recent trends of province-level income inequality and describes the database that we use in the subsequent empirical analysis. Regression-based evidence is presented in Section4, while Section5 is dedicated to setting out the synthetic control analyses. Finally, Section6 concludes. 2 Industrial estates andSEZs inIndonesia Economic zones – our umbrella term for similar industrial place-based policies7 – are geographically well defined areas that are designated for industrial purposes to promote economic development in a region. For investors and businesses to move into such areas, policymakers offer fiscal and non-fiscal incentives. These may include lower taxes, a more business-friendly regulatory environment or better physical infrastructure than that available outside the economic zone (e.g., Lin 2017). The provision of economic zones can be viewed from the New Structural Economics (NSE) perspective proposed by Lin (2011) of the World Bank, which emphasizes the role of the government in facilitating economic growth by the provision of quality infrastructure and institutions. Indonesia’s policy of economic zones dates back to the beginning of the 1970s, when the country started to set up its first industrial estates (Kawasan Industri) as a form of infrastructure that supports industrial activities.8 According to the official definition, an industrial estate is an estate wherein industrial activities are centralized, complete with supporting facilities and infrastructure developed and managed by an Industrial Estate Company.”9 It serves the objectives i. to accelerate the spread and even distribution of industrial development, ii. to improve industrial development efforts with an environment-based perspective, iii. to enhance investment and industrial competitiveness, and iv. to provide location certainty following the spatial plan. The country’s first industrial estate was set up in 1970 in the capital city Jakarta with the name Jakarta Industrial Estate Pulo Gadung (Octavia 2016), followed by others located in different 7 Over time, different names have been attached to more or less the same concept (industrial estates, free trade zones, export processing zones, special economic zones, etc.), reflecting the objectives and functions these establishments are meant to serve in different countries and times. 8 The legal and institutional framework governing economic zone policy in Indonesia is discussed in detail in Aggarwal (2022). 9 Regulation of Government of The Republic of Indonesia Number 142 of 2015 on Industrial Estate, (unofficial English translation). 73 C.Hornok, D.G.S.Raeskyesa 1 3 parts of Indonesia, such as in Surabaya and Cilacap (in 1974), Medan (1975), Cirebon (1984), and Lampung (1986) (Kwanda 2000). Nevertheless, the growth remained limited until the late 1980s, when a series of regulatory reforms were initiated and the industrial estate business was opened to the private sector (Aggarwal 2022). Afterwards, industrial estates have proliferated in Indonesia. Our list of operating economic zones as of end2020 includes 118 industrial estates. More recently, industrial place-based policies have gained new momentum in Indonesia. Since 2014, President Joko Widodo has directed the government’s priority to accelerate the new establishments of special economic zones (Kawasan Ekonomi Khusus, KEK), supported by a SEZ act (Octavia 2016). SEZs refer to”zones with certain boundaries within the territories of the unitary state of the Republic of Indonesia, and designated to carry out the economic function and are granted certain facilities and incentives.”10 As opposed to industrial estates, SEZs are established in a wider array of economic sectors and may be dedicated to various purposes (e.g., export processing, logistics, manufacturing, technology development, or tourism). The stated objectives of the SEZ policy are to attract foreign investment, enhance economic productivity and reduce inequality between the provinces of Indonesia. To better achieve these goals, investors in SEZs are offered somewhat more favorable direct tax incentives than what is generally applied in the country and Indonesia’s relatively restrictive rules on FDI inflows and foreign equity holdings have also been relaxed for SEZ investors.,11, 12 At the beginning of 2021, the number of approved SEZs was 15, of which 11 were operating and 4 were under construction.13 The Indonesian government also has the intention to transform some of the industrial estates into SEZs, with the purpose to improve their efficiency. Importantly, neither industrial estates nor SEZs aim to reduce local inequalities. Although the SEZ policy mentions the need to reduce inequalities between provinces, it does not address distributional issues between residents within provinces. Of course, we cannot rule out the possibility that differences between industrial estates and SEZs in terms of their policy objectives and scope of activities may lead to differences in terms of impact on inequality. However, we have no a priori expectations as to the direction of these possible differences. Given these considerations as well as the small number of SEZs in operation, we have decided not to distinguish between the two forms of economic zones in the first part of our empirical analysis. In the second part, the synthetic control studies will discuss the examples of two industrial estates and one SEZ. For the purposes of this project we have set up a list of industrial estates and SEZs in Indonesia that includes their names, exact locations and the years when they started 10 Regulation of the Government of The Republic of Indonesia Number 39 of 2009 on Special Economic Zone, (unofficial English translation). 11 Indonesia offers massive direct and indirect tax incentives for new investments in 18 targeted industries. Most tax incentives are therefore industry-based and not place-based (Aggarwal 2022). 12 For foreign investors in SEZs, Indonesia’s negative FDI list does not apply, and foreign equity holdings of up to 100% are permitted. However, land ownership by foreigners remains prohibited even within SEZs (Aggarwal 2022). 13 Source of information: Dewan Nasional Kawasan Ekonomi Khusus Republic Indonesia (Republic of Indonesia National Council for Special Economic Zone), https:// kek. go. id/ petasebar ankek, last update on 11 February 2021). 74 Economic zones andlocal income inequality 1 3 to operate.,14, 15 This information was acquired from the Ministry of Industry Indonesia and the Republic of Indonesia National Council for Special Economic Zone. The number of economic zones in Indonesia rose dynamically over time. Table1 shows the number of operating zones by year and main island. From a total of 9 zones in 1990 the number rose to 129 by 2020. Zone establishments proliferated especially in the most recent decade – almost half of the existing economic zones opened between 2010 and 2020. At the same time, the distribution of zones over space remained rather unequal, both between main islands as well as within-island between-province. Most of the zones are located on the islands of Sumatra and Java, as also shown in Fig.6, a map of Indonesia with geocoded locations of all operational zones. The champion province is West Java on Java island with 35 economic zones, followed by the Riau Islands with 26 zones (Sumatra), Banten with 14 zones and East Java with 11 zones (both on Java). Out of the remaining Indonesian provinces, 18 have single-digit economic zones and 12 have no operating zones. This concentration of zones largely mirrors the uneven geographical distribution of population and economic activity within Indonesia. Namely, Java and Sumatra are historically the most populous regions with the highest GDP (Table1). It is no surprise that economic zones are not located randomly, but the choice of location depends on agglomeration forces (e.g., the availability of skilled workers and infrastructure) as well as on political objectives. The targeted establishment of economic zones in more remote and less developed regions has been declared a priority in Indonesia only more recently. Examples of zones, whose recent establishment was also driven by the aim of reducing regional inequalities, are the Mandalika SEZ in West Nusa Tenggara (one of our synthetic control examples) which operates since 2017, the Morotai SEZ in North Maluku, the Sorong SEZ in West Papua, or the Bitung SEZ in North Sulawesi (all three operating since 2019). 3 Data andtrends ofincome inequality This study uses secondary data on the provincial level at the annual frequency. We construct an unbalanced panel database with several economic indicators for the 34 provinces of Indonesia and for years between 2001 and 2020. In the estimation sample the number of provinces reduces to 31 because important variables are missing for two provinces (West Papua, West Sulawesi), while a further province (North Kalimantan) was created only in 2013. Data are collected from three major sources: Statistics Indonesia,16 the Ministry 16 Badan Pusat Statistik Indonesia (https:// www. bps. go. id/). 14 We consider the operation start as the ‘birth’ of the economic zone, even though a zone may have some impact on its neighborhood before that date due to anticipatory effects or boosting construction activities. However, we believe that, when focusing on the impact on inequality, it is more appropriate to focus on the date of operation start because the impact on inequality is likely to unfold only when the effects reach the wider population. 15 It should be noted that our study does not consider the full range of economic zones in Indonesia, which is comprehensively described in Aggarwal (2022). In particular, we do not consider bonded zones separately from industrial estates. Indonesia’s bonded zones are traditional forms of export processing zones and, with some exceptions, must be located within industrial estates. Further, we do not include the thirteen Integrated Economic Development Zones (KAPETs) in the current study. The KAPET program, which was launched in the second half of the 1990s to promote the development of lagging regions in eastern Indonesia, is considered largely unsuccessful in having an impact and was beset with several implementation problems (Rothenberg etal. 2017; Rothenberg and Temenggung 2019). 75 C.Hornok, D.G.S.Raeskyesa 1 3 of Investment Indonesia (formerly known as Investment Coordination Board),17 and the World Bank’s Indonesia Database for Policy and Economic Research (INDO-DAPOER). The Gini coefficient is sourced from Statistics Indonesia, which calculates the Gini based on consumption expenditure information from the National Socio-Economic Survey (Susenas). The Gini is available for 2002, 2005 and every year from 2007 onward. It is assessed once a year up until 2010 and twice a year (in March and September) starting from 2011. For these more recent years we take the simple average of the two observations to form an annual Gini. The other basic indicators include population, the Gross Domestic Regional Product (GDRP), GDRP per capita, government expenditures in GDRP, FDI inflows, employment statistics such as unemployment, underemployment and sectoral employment shares, population density, the level of the minimum wage, the poverty rate, the literacy rate and the net enrollment rate to secondary education. These indicators are mainly used as control variables in the panel regressions as well as predictors of future income inequality in the synthetic control analyses. Income inequality in Indonesia showed an upward trend until the middle of the last decade, as presented on the upper-left chart in Fig.1. Although the trend has been declining since then, the level of inequality remained significantly higher than at the start of our sample period. This hump-shaped trend was characteristic to all major regions of Indonesia and can be attributed to the introduction of several pro-poor policies at the national level to boost domestic demand after President Joko Widodo took office in 2014.18 Nevertheless, country averages hide significant regional variations in the level of inequality, as suggested by the interquartile ranges displayed on the charts. The group of provinces with Gini ratios above the country average is dominated by provinces on Java and Sulawesi. Especially Table 1 Economic zones by main islands of Indonesia Authors’ calculations. Population and GDRP (Gross Domestic Regional Product) figures refer to year 2019 and are sourced from Statistics Indonesia. GDRP is based on expenditure and expressed in current market prices. The number of zones do not include zones under construction. We follow Kis-Katos and Sparrow (2015) in defining the five main islands. Provinces in Sumatra: Bengkulu, Jambi, Kep. Bangka-Belitung, Kep. Riau, Lampung, Aceh, Riau, West Sumatra, South Sumatra, North Sumatra; Java: Banten, DI Yogyakarta, DKI Jakarta, West Java, Central Java, East Java; Kalimantan: West Kalimantan, South Kalimantan, Central Kalimantan, East Kalimantan, North Kalimantan; Sulawesi: Gorontalo, West Sulawesi, South Sulawesi, Central Sulawesi Southeast Sulawesi, North Sulawesi; Outer islands: Bali, North Maluku, Maluku, West Nusa Tenggara, East Nusa Tenggara, West Papua, Papua Island Population million GDRP IRD trillion Number of zones 1990 2000 2010 2020 Sumatra 58.60 3,427 3 16 26 40 Java 151.00 9,487 4 30 39 71 Kalimantan 16.50 1,294 1 1 1 9 Sulawesi 19.70 1,018 1 1 1 5 Outer islands 22.30 852 0 0 0 4 Total 268.10 16,078 9 48 67 129 17 Badan Koordinasi Penanaman Modal. 18 The policies include direct cash transfers to the poor and raising the tax-free income threshold to a level that essentially covers most unskilled workers (Yusuf and Sumner 2015). 76 Economic zones andlocal income inequality 1 3 see, e.g., Abadie etal. (2010). Let us take the treated province as the first province indexed by 1 and denote the year in which the economic zone was opened by T0 . Our aim is to measure the change in the Gini coefficient of the treated province in the post-treatment years, t≥T0 , that can be attributed to the zone opening. This treatment effect for year t, denoted by 𝛽1t , is that is, the difference between the actual value of the Gini coefficient for the treated province in year t ( Gini1t ), and the counterfactual Gini ( GiniN 1t ), that would have been the level of income inequality in the treated province in year t had the economic zone not been opened. Clearly, the counterfactual Gini is not observed. The SCM overcomes this problem by approximating the counterfactual with the synthetic control, where the synthetic control is created from available comparison provinces. How this is done lies at the heart of the methodology behind the SCM. Intuitively, the synthetic control is the linear combination of comparison provinces that best approximates the characteristics of the treated province before the opening of the economic zone. More formally, the synthetic Gini in year t ( Gini SC, t ) is a linear combination of the Ginis of R potential comparison provinces (also called the control pool) in year t with weights w∗ . The weights are restricted to sum to one and to fall between 0 and 1, and they are chosen such that the synthetic control most closely resembles the treated province in the pretreatment years both in terms of income inequality as well as in terms of other characteristics that may predict inequality. More formally, for the optimally set weights w* it must be approximately true that and The matrix Z contains variables that are supposed to influence the future development of inequality in a province but they themselves are not affected by the treatment. The typical SCM application takes the average values of these variables over the entire pre-treatment period but researchers can deviate from this practice by taking averages over subsets of this period only. Once the optimal SC weights are found and the Gini of the synthetic control is calculated, the SCM estimate for the treatment effect is simply obtained as the difference in Gini in the post-treatment years between the treated province and the synthetic control, One of the advantages of the SCM over traditional regression methods lies in the way the SC weights are determined. Since the weights are all restricted to be greater or equal to zero, the method minimizes the possibility of an extrapolation bias (Abadie etal. 2015). A further attractive feature of the SCM is its transparency, as the weights make it explicit (3) 𝛽it =Gini 1t −Gini N 1t (4) Gini SC,t= R ∑ r=1 w∗ rGini rt (5) ∑R r=1 w∗ r Gini rt =Gini 1t for every t<T 0 (6) ∑R r=1 w∗ r Zr=Z 1 for the average of the pre-treatment years . (7)  𝛽 it =Gini1t−GiniSC, t 83 C.Hornok, D.G.S.Raeskyesa 1 3 how individual control units contribute to the counterfactual. A disadvantage though is that the SCM is more susceptible to interpolation biases relative to more traditional estimation methods. 5.2 Three zone openings Recall that economic zones in Indonesia are highly concentrated geographically (Table1). Provinces on Java and Sumatra had several industrial estates already back in the 1990s, and most new zones that appeared during the sample period of this study have been opened in the very same provinces. The rest of the provinces typically started to establish economic zones only in the most recent years. These features drive (and limit) the choice of examples for our comparative studies. An SCM application needs to have sufficiently long pre-treatment and post-treatment periods. This means that, to be able to isolate the effect of one opening event, we need zone openings which were neither preceded nor followed by other zone openings in a reasonably wide time window. Provinces that frequently open zones or provinces that opened their first zone only in the most recent years therefore do not qualify as valid candidates. These considerations limit the number of potential candidates to five. Of these five, only three resulted in synthetic controls that resembled the treated province in its pre-treatment characteristics reasonably well. In the remaining two, no combination of the available donor provinces produced a well-functioning synthetic control, which obviously prevents the application of the method. Fortunately, the three selected examples represent the diversity of Indonesian provinces relatively well. They include the opening of an industrial estate in Banten, a relatively industrialized province on Java Island, and two other, mostly rural provinces, Aceh and West Nusa Tenggara, opening their first industrial estate and SEZ, respectively. The location of the three zones are shown on Fig.8 as large green dots. Taman Tekno BSD Industrial Estate in Banten (2008) Banten province on the Java Island opened the Taman Tekno BSD industrial estate as its ninth industrial estate in 2008. It is a multipurpose estate of relatively small size (ca. 200 hectars) that, in addition to industrial purposes, also serves as a residential and commercial hub. It is located close to the province borders with two other provinces, West Java and the capital city of Jakarta. We find this example especially interesting because it represents one of the economic zones on the Java Island. While being the most developed region of Indonesia with low poverty rates, Java is also characterized by the highest level of income inequality. A further specificity of Banten province is that its population has a low average level of education, despite the fact that the province’s economy has a significant industrial base. This suggests that the shortage of human capital can be a major barrier to a broader population benefiting from economic growth and reducing inequalities.22 During the period 2005–2014, no economic zone was opened in Banten other than the Taman Tekno BSD. This leaves us with a relatively short pre-treatment period (3years, 2005–2007) and a comparatively long post-treatment period (7years, 2008–2014). Unfortunately, the shortness of the pre-treatment period can have negative consequences for the goodness of fit the SCM can achieve, which one needs to keep in mind when interpreting the results. 22 Partly in response to this, the Banten government has significantly increased its spending on education, health and social activities from 2010 (Najmuddin 2020). 84 Economic zones andlocal income inequality 1 3 Perikanan Lampulo Industrial Estate in Aceh (2014) The province of Aceh lies on the westernmost part of the Sumatra Island. Its history after World War II was marked by repeated military conflicts with the Indonesian central government, which ended with the 2005 peace agreement that gave the region special autonomous status. Aceh is a predominantly agricultural province in Indonesia, which is also relatively rich in natural oil and gas. The first industrial estate of Aceh, the Perikanan Lampulo Industrial Estate, was opened in 2014 at the fisheries port next to the province’s capital city Banda Aceh and far from other parts of Sumatra. The activities of the industrial estate are primarily centered around the fisheries industry. No further zones were opened in Aceh until 2018, which provides us with a 4-year long post-treatment period (2014–2018). Since it is the first zone of the province, the pre-treatment period is comfortably long. Nevertheless, we decide to shorten it to 7years (2007–2013) mainly because Aceh suffered a devastating earthquake and tsunami in December 2004 (Sumatra–Andaman earthquake) with severe socio-economic consequences. In the first years of reconstruction, Aceh’s economy was dominated by massive reconstruction programs and foreign aid inflows (Liew etal. 2010), a period we decided to exclude from our synthetic control sample. Mandalika special economic zone in West Nusa Tenggara (2017) Our third example is a SEZ designated for tourism, the Mandalika SEZ, which came into operation in 2017 in West Nusa Tenggara. The province of West Nusa Tenggara comprises of the western portion of the Lesser Sunda Islands and lies between the provinces of Bali and East Nusa Tenggara. (In our classification it belongs to the group of “Outer islands”.) West Nusa Tenggara is considered to be one of the least developed provinces of Indonesia, with most of its population still living from agriculture. In recent years, tourism has become an emerging sector in the region. Beside obvious economic advantages, the expansion of tourism can also pose a threat to the agricultural communities, as touristic complexes sometimes require the acquisition of land previously used by farmers.23 The area occupied by the Mandalika SEZ is indeed significant, with 1036 hectares of land. This is a prime reason why we consider this zone opening interesting to study from an inequality point of view. Nevertheless, we have no a priori expectations regarding the distributional impact of the Mandalika SEZ, as preliminary evidence suggests that the project could successfully integrate the local population into its activities through subcontracting to local small and medium enterprises and by employing locals in the construction and service sectors (Adriadi etal. 2022). The Mandalika SEZ was the first economic zone opened in West Nusa Tenggara and remains the only one to date. We start the pre-treatment period from 2010 because starting it from earlier results in a too small donor pool. As a result, the pre-treatment period is 7-years (2010–2016) and the post-treatment period is 4-years long (2017–2020). 5.3 Selecting control pools andpredictors As a next step, one needs to determine the donor pools and the predictor sets for each of the three examples. Several assumptions have to be met about the nature of the donor pool and 23 Hasudungan etal. (2021) conclude that there is a negative relationship between the expansion of the agricultural and tourism sectors in Indonesia. This trade-off could be due to the fact that the two sectors compete for land, a scarce production input that both sectors use intensively. 85 C.Hornok, D.G.S.Raeskyesa 1 3 the predictor variables for the SCM to accurately estimate a policy’s effect (McClelland and Gault 2017). Listed below are three crucial assumption that motivate our selection. 1. No province in the control pool can have a similar zone opening. 2. The zone opening in the treated province cannot affect the Gini coefficient in the provinces of the control pool. 3. The values of the predictor variables for the treated province cannot be outside any linear combination of the values for the provinces in the control pool. In line with assumption 1, we make sure that only those provinces get into the donor pool which do not have a zone opening during the respective sample period. Recall that the sample periods are 2005–2014 for Banten, 2007–2017 for Aceh, and 2010–2020 for West Nusa Tenggara. Also, we exclude West Java and DKI Jakarta from the donor pool of the Banten study because the industrial estate in question is only a few kilometers away from the border between Banten and these two provinces, and therefore they are considered too close to meet Assumption 2. Finally, we exclude North Kalimantan and East Kalimantan from all the three donor pools because these provinces split in 2013. Next, we consider a large set of potential predictor variables and check whether assumption 3 holds over the respective pre-treatment sample years. The list of potential predictors include the following province-level variables: pre-treatment values of the Gini coefficient, log GDRP value, log GDRP per capita, poverty gap, literacy rate, net enrollment rate to secondary education, population density, government expenditure as share of GDRP, share of agricultural employment in total employment, log minimum wage, cumulated FDI inflows per capita, as well as the unemployment and underemployment rates. We follow the literature and average the predictor variables, with the exception of the lagged Ginis, over the entire pre-treatment period.24, 25 As for lagged Ginis, we take the Gini of the immediate pre-treatment year (2007 for Banten, 2013 for Aceh, and 2016 for West Nusa Tenggara) and a few more—but not all—pre-treatment years. Kaul etal. (2018) argue that including all outcome lags as separate predictors renders all other predictors irrelevant and threatens the estimator’s unbiasedness. As a result of this selection process, we end up with donor pools of 22 provinces for Banten, 21 provinces for Aceh and 14 provinces for West Nusa Tenggara (Table5) and three lists of potential predictors specific to the three examples. 5.4 Results Having determined the donor pool and the list of predictor variables, the next step is to run the SCM procedure to create the synthetic control via choosing the optimal SC weights.26 24 See Jordan etal. (2021). 25 One could argue that every zone opening is preceded by a construction phase, during which some of our economic indicators—particularly FDI inflows—may already be affected. To account for this possibility, we double-check our results by modifying the pre-treatment period for the FDI variable to end two years before the treatment year. The main results are robust to this change. 26 We use STATA’s synth function and set the nested optimization option for better performance at the expense of longer computing time. 86 Economic zones andlocal income inequality 1 3 To find the model that produces the smallest prediction error (Root Mean Squared Prediction Error, RMSPE), we try several combinations of the variables in the predictor set (considering all combinations that include at least 5 predictors other than the lagged Ginis) and choose the model with the smallest RMSPE. The resulting SC weights are presented for each example in Table 5. The synthetic Banten is a linear combination of five provinces, Lampung and South Kalimantan with 43% and 31% weights, respectively, and Riau, Papua and Bali with single-digit weights. Synthetic Aceh is 60% Bangka-Belitung Island, 26% Central Kalimantan and single-digit percentages of South Sulawesi, South Sumatra and Jambi. Synthetic West Nusa Tenggara is a composition of three provinces, East Nusa Tenggara, Southeast Sulawesi and West Sulawesi, with roughly equal weights. Let us evaluate the results separately for each example. 5.4.1 Banten 2008 Figure3 displays the path of the Gini coefficients for Banten and its synthetic control in the preand post-treatment years. The two lines are very close before the 2008 treatment year. In the first years after treatment, the true Gini rises well above the synthetic Gini and remains higher than that throughout the entire post-treatment period, even if it decreases in later years. When evaluating this result the question arises as to how well the synthetic Banten represents what the situation would have been in Banten in the absence of the treatment. We can examine how closely the synthetic Banten approximates the true Banten in the pre-treatment period. The pre-treatment Ginis are almost identical but this can be misleading given the shortness of the pre-treatment period in this example (also, the Gini is not available for year 2006). In addition, we can check the goodness of fit in terms of all the predictors in the model. The three columns of Table6 present the pre-treatment values of the predictors for the true Banten, the synthetic Banten, and the population-weighted average of the donor pool provinces. We can conclude that the SCM is effective in the sense that the synthetic Banten is closer to the true Banten than the average of the donors considering all the predictors. Nevertheless, the approximation is not perfect. Synthetic Banten differs from the true Banten in that it has a smaller economic size, higher poverty, and lower literacy. This is not surprising since Banten is one of the more developed provinces in Indonesia and the provinces most similar to it did not qualify for the donor pool either because they also opened economic zones in the same time window or because they are close neighbors (DKI Jakarta, West Java). As a robustness check, we re-ran the SCM while allowing DKI Jakarta and West Java in the donor pool. The synthetic Banten in this case includes the two above-mentioned provinces (with a combined weight of 32%) and approximates the true pre-treatment Banten better than before. At the same time, the positive treatment effect remains, albeit only for the first four posttreatment years (Fig.9). 5.4.2 Aceh 2014 In the Aceh example the pre-treatment fit of the true and synthetic Ginis looks reasonably good (Fig.4), especially when taking into consideration that only three years of the Gini (2007, 2010 and 2013) out of the seven pre-treatment years were included in 87 C.Hornok, D.G.S.Raeskyesa 1 3 the optimization process. In terms of the other predictors, the synthetic control provides only a moderately good approximation of the true Aceh (Table7). The fit is good for the literacy rate and the FDI stock but less so for other characteristics. In particular, the synthetic Aceh is more developed (less agricultural with a higher GDRP per capita) than the true Aceh was before 2014. This reflects the difficulty of finding good comparison provinces for Aceh – a relatively outlying province that was hit by a natural disaster not long ago. Figure4 shows that income inequality in Aceh stayed stable after the treatment year of 2014, while income inequality in synthetic Aceh dropped, resulting in a positive treatment effect of a magnitude of 0.025 Gini points. One may suspect that an idiosyncratic drop in the Gini of one control province is responsible for this result, but it is not the case. As shown in Fig. 1, 2014 was a turning point after which the Gini ratio began to decline in most parts of Indonesia. In fact, the Gini fell in four out of the five donor provinces (Bangka-Belitung, Central Kalimantan, North Sulawesi, South Sumatra) during this period. In other words, the provinces most similar to Aceh experienced declines in their income inequality in the post-treatment period, while Aceh did not. Having weighed the good fit of the pre-treatment Ginis against the above considerations, we interpret this result as a tentative evidence for a positive treatment effect. 5.4.3 West Nusa Tenggara 2017 The third example shows a situation when the SCM is of limited use. The pre-treatment income inequality in West Nusa Tenggara is only weakly matched by the synthetic control (Fig.5). As it turns out, it is not possible to find a combination of control provinces that is a good approximation of pre-treatment West Nusa Tenggara. Nevertheless, the performance with respect to the other predictors is relatively good (Table8). The synthetic control approximates the pre-treatment GDP per capita and minimum wage especially well. All in all, no treatment effect of any sign is visible on Fig.5. The course of the true Gini after the treatment is quite stable, showing only a moderate increase. And without a wellperforming synthetic control, it is almost impossible to identify an effect that is small at Fig. 3 Inequality—Banten vs Synthetic Control 88 Economic zones andlocal income inequality 1 3 best. Hence, we conclude that there is no evidence that the Mandalika SEZ affected income inequality in West Nusa Tenggara. 5.5 Placebo analysis In two of the three synthetic control studies we find some evidence for a positive treatment effect, meaning that income inequality of the treated province increased relative to the synthetic control following the treatment year. Of course, as in all statistical analyses, these estimates are subject to uncertainty. This section attempts to quantify this uncertainty by using the inferential technique developed by Abadie etal. (2010). We conduct placebo tests on provinces in the donor pool to evaluate the significance of the results for the treated province. If the post-treatment difference between the treated province and its synthetic control is larger than the difference for most of the placebo provinces, then we can conclude that the treatment had an effect. In these placebo exercises we keep every detail unchanged relative to the original study, with the exception that the Fig. 4 Inequality—Aceh vs Synthetic Control Fig. 5 Inequality—West Nusa Tenggara vs Synthetic Control 89 C.Hornok, D.G.S.Raeskyesa 1 3 treated province (Banten, Aceh, or West Nusa Tenggara) is replaced by one of the provinces in the respective donor pools (and thus the number of provinces in the donor pool reduces from R to R − 1). Figures10, 11 and 12 plot the differences between the placebos and their synthetic controls (grey lines) together with the original treatment effect for the truly treated province (thick black line). The figures for Banten and Aceh confirm the existence of a treatment effect for the first few post-treatment years. The treatment effect for Aceh is above most of the placebos during the entire four-year post-treatment period. Similarly, the treatment effect for Banten is larger than most placebos up to the fourth posttreatment year. Beyond this four-year horizon however the evidence for a treatment effect weakens. 6 Conclusion Economic zone policy has demonstrated its potential to promote economic growth. Our study directs attention to the question of how inclusive this growth is. We offer new empirical evidence from Indonesia on the local inequality consequences of economic zone policies. Our results based on panel regressions and synthetic control analysis suggest that economic zones can lead to rising income inequality within their province of location. Although the estimated average effect is relatively small, it may mask large regional differences. Some of the synthetic control examples display a considerably larger treatment effect, suggesting that the inequality implications of economic zone policies may vary with local circumstances. Our findings also suggest that the effect may be short-lived. In the synthetic control studies we find no evidence that the effect would persist longer than a four-year horizon. A systematic exploration of the determinants of the above relationship and the mechanisms involved is out of the scope of this study. Nevertheless, our investigation provides some insights into the relative importance of the channels mentioned in the Introduction. We suspect that the more likely explanation for why economic zone policies increase inequality is skill-biased growth and the resulting increase in the skill premium on wages. This is suggested by the regression-based evidence that lower unemployment is associated with higher income inequality in Indonesia. Low skill levels can reinforce the mechanism above, as only a small segment of the population can benefit from the economic opportunities created by new investments. The studied examples of Banten and Aceh can be seen as tentative evidence here. In Banten, the educational level of the population is particularly low compared to other provinces in its neighborhood. In Aceh, although the school enrolment rate is high, there is evidence that this does not translate into a well-educated population due to the uneven quality of teaching and school facilities (Liew etal. 2010). In contrast, we found no indication that the channel through land acquisitions would play a significant role – at least not at this level of aggregation. On the one hand, our regression estimate is robust to controlling for between-province differences in the initial agricultural share. On the other hand, our third synthetic control study, which features a dominantly agricultural province opening a relatively large SEZ for tourism, did not show signs of rising income inequality. 90 Economic zones andlocal income inequality 1 3 In this context, the obvious policy advice is to create broad opportunities for quality education and vocational training—a point that has been emphasized by many (e.g. Castelló and Doménech 2002). Corporate engagement can accelerate this process and ensure that the skills acquired meet the needs of businesses (see the example of cooperative training programs between schools and enterprises in economic zones discussed in Aggarwal (2007)). Having said that, we emphasize that the evidence we provide on the relative role of possible causal channels is suggestive at best, and therefore any policy recommendation is tentative. Future research is needed to explore the causal linkages behind the observed relationship. Appendix Data availability Information on economic zones in Indonesia has been collected by the authors and is available upon reasonable request. It contains the names, exact locations and years of opening of 118 industrial estates and 15 SEZs in Indonesia as of the end of 2020. Primary sources of data collection were the Ministry of Industry Indonesia (for industrial estates) and the Republic of Indonesia National Council for Special Economic Zone. In many cases, location and year of opening information was collected from webbased sources on individual zones. To produce Figs.6 and 8, we geocoded the postal addresses of the zones using Google services. Then we located the zones on Indonesia’s map using the ArcGIS software and a shapefile from the GADM database (www. gadm. org; version 2.5, July 2015). All other data used in the empirical analysis were obtained from publicly available sources. The Gini coefficient is directly obtained from Statistics Indonesia (https:// www. bps. go. id/ linkT ableD inamis/ view/ id/ 1116, accessed on Jan 12, 2021). Further variables that are sourced from Statistics Indonesia are the Gross Domestic Regional Product (GDRP), Government expenditures, Population, GDRP per capita and the Minimum wage. Another important data source was the World Bank’s Indonesia Database for Policy and Economic Research (INDO-DAPOER; https:// datac atalog. world bank. org/ search/ datas et/ 00410 56, last update May 7, 2019). Variables used from INDO-DAPOER are the Enrollment ratio in secondary education, Employment, Unemployment, Underemployment, Employment by economic sectors, Labor force, Poverty rate, Poverty gap, Literacy rate and Total area of a province (which was used to calculated Population density). Because GDRP and Government expenditures data under the 2008 System of National Accounts is available from 2010 only, we used historical annual growth rates from INDODAPOER to extend these two series back in time. Data on foreign direct investment (FDI) inflows is obtained from the website of the Ministry of Investment Indonesia (https:// www3. bkpm. go. id/ en/ stati stic/ forei gndirectinves tmentfdi, accessed on Dec 3, 2020). Conversion of inflows in US dollars to Indonesian rupiahs was done with the annual average exchange rate reported by the Asian Development Bank in its Key Indicators Database (https:// kidb. adb. org/ econo mies/ indon esia, accessed on Dec 4, 2020). We generated inward FDI stocks by cumulating the inflows over the available years, starting from year 2000. 91 C.Hornok, D.G.S.Raeskyesa 1 3 Fig. 6 Location of economic zones in Indonesia. Note: Map of Indonesia showing provinces as areas bordered by dark grey lines. 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