Foreign investment regulation and firm productivity: Granular evidence from Indonesia

Transcription

1 Foreign investment regulation and firm productivity: Granular evidence from Indonesia Robert Genthner and Krisztina Kis-Katos February 7, 2018 Abstract Based on a yearly census of Indonesian manufacturing firms for , we investigate the effects of a sector-specific investment policy reform on firm productivity. Hereby we exploit a protectionist foreign direct investment reform (the so-called negative investment list), which designated certain sectors at the five digit level to become closed or only conditionally open to foreign investors. The list was first released in 2000 and has been repeatedly revised by the Indonesian authorities since. We use the changes within this regulatory framework to investigate the effects of the policy on firm-level productivity. Controlling for an extensive set of fixed effects, we find robust evidence of declining foreign capital shares in the face of tightening regulation of foreign direct investment, followed by a sizable decrease of firm productivity. Besides these direct effects, we also document backward regulatory spillovers that adversely affect non-regulated parts of the economy through the value chain. JEL Classification: F23, L51, D24, F21, L6 Keywords: FDI, regulation, Indonesia, total factor productivity, spillovers University of Göttingen, Germany University of Göttingen, Germany and IZA, Bonn 1

2 1 Introduction In the course of the last two decades developing and emerging economies liberalized their markets substantially. The process of globalization has not only lead to a successive dismantling of trade barriers but has also facilitated the operation of multinational enterprises by liberalizing the inflows of foreign direct investment (FDI) world-wide. However, this process of trade and market liberalization has not progressed uniformly and also experienced numerous regulatory shifts and reversals, affecting FDI flows (Harding and Javorcik 2011, Bourlès et al. 2013). While a substantial literature has documented links between foreign participation and firm productivity (Aitken and Harrison 1999, Arnold and Javorcik 2009), 1 only few papers have studied the effects of FDI regulation on firm productivity directly (Bourlès et al. 2013, Duggan et al. 2013, Eppinger and Ma 2017). Foreign capital is expected to affect firm productivity through several channels. It can substitute for domestic capital and relieve liquidity constraints if access to domestic capital is limited. Foreign investors have been shown to introduce nontangible productive assets such as technological, managerial and marketing skills, trading contacts and reputation (Aitken and Harrison 1999, Arnold and Javorcik 2009). As a result, firms with foreign participation are typically more productive, more capital intensive and pay higher wages (Harrison and Rodríguez-Clare 2010). Moreover, the effects of FDI can spill over horizontally within industries or vertically along the value chain. Empirically, the evidence is strongest in support of positive backward spillovers from foreign-invested firms to their domestic suppliers, most likely taking place through targeted transfers of technology (Javorcik 2004, Blalock and Gertler 2008, Barrios et al. 2011, Newman et al. 2015). By contrast, studies documenting no or negative horizontal spillovers within the same industry (Djankov and Hoekman 2000, Javorcik 2004, Blalock and Gertler 2008) and no forward spillovers to domestic customers (Javorcik 2004, Newman et al. 2015) suggest that foreign invested firms are successful at preventing technology leakages to their local competitors as well as down the value chain. Since precise data on FDI regulation is frequently unavailable, most studies rely on FDI flows to proxy for reforms in FDI regulation. But as investment flows themselves are influenced by a large number of different factors, this raises the fundamental problem of unobserved heterogeneity (Harrison and Rodríguez-Clare 2010). Alternatively, newer studies rely on aggregated indices of FDI openness (Topalova and Khandelwal 2011, Duggan et al. 2013), which by construction cannot be used to capture differential effects of regulation across more disaggregated sectors. The use of disaggregated regulation data should help us to trace the effects of FDI regulation as well as regulatory spillovers at a much finer sectoral scale. 1 See Görg and Strobl (2001) and Görg and Greenaway (2004) for surveys of the earlier literature. 2

3 Indonesia offers a great case to study the effects of FDI on firms (Blalock and Gertler 2008) as well as the effects of FDI regulation itself. As one of the largest economies in the world, with a wide variety of industries that rely on an abundance of both human and natural resources, Indonesia has emerged as an attractive FDI recipient. At the same time, the Indonesian government has been sending mixed signals to foreign investors, among others by setting up a blacklist of sectors to be closed or only conditionally open to FDI (Lindblad 2015). The first negative investment list (NIL) has been released in 2000 in order to protect selected domestic industries from international competition and foreign acquisitions. It has been repeatedly revised since, with a substantial tightening of FDI policies in 2007 that was followed by partial deregulation in later years. effects of this policy instrument have been hitherto unexplored and offer a particularly interesting opportunity to investigate the effects of FDI regulation on firm performance at a highly disaggregated level. The excellent quality of Indonesian firm data, especially as compared to that from other developing countries (Blalock and Gertler 2008), enables us to investigate the effects of FDI policies on a large sample of middle and large manufacturing enterprises in an emerging economy at an unusually high granularity. This paper exploits the variation of three revisions (in 2007, 2010 and 2014) of the NIL that regulates sectors on the five digit sector code level, listing each sector that will be fully or partially closed to FDI in the future and also specifying whether all firms or only certain types of firms are to be affected. 2 The first revision of 2007 substantially tightened the existing FDI regulation, increasing the restrictiveness towards FDI in a wide range of sectors. By contrast, the revision of 2010 only induced some minor changes. Finally, in 2014 the regulation was substantially relaxed. We link the regulatory changes in the NIL at the fine-grained level of five digit sectors to a firm panel of 15 years (2000 to 2014), derived from the Indonesian yearly census of manufacturing plants. This census intends to include the full universe of manufacturing firms with at least 20 employees and measures a wide range of plant-level outcomes. 3 Our two main outcome variables are the share of foreign ownership of each firm and an estimated measure of firm productivity. The Our regulation indicators are firm-specific and vary by year, linking information from the NIL to the firm s main product (at five digit level) while also utilizing individual firm characteristics (firm size, legal status and prior foreign investment) to identify direct exposure to regulation. In a further step, we construct regulatory spillover variables that measure the indirect penetration of the NIL to other potentially non-regulated sectors through the value chain (Bourlès et al. 2013). 2 The KBLI (Klasifikasi Baku Lapangan Usaha) sector classification is published by BPS (Indonesian Statistical Office, Badan Pusat Statistik). It is equivalent to the United Nation s International Standard Industrial Classification of All Economic Activities (ISIC) on the four digit level, but is adjusted to reflect some country-specific sectors in Indonesia at the five digit level. 3 In what follows, we use the concepts of firm and plant interchangeably as we have no further information on the structure of multiplant firms. 3

4 All our regressions are conditional on firm fixed effects and hence only consider withinfirm variation in the main economic outcomes over time. Additionally, our preferred specifications include two digit sector-year effects that capture all average time variation due to global and national shocks that affect whole industries. By that, we only focus on the differences between regulated and unregulated firms within the same broad economic sector and year. The panel structure of 15 yearly waves allows us to investigate the time profile of regulation in a more flexible way by also including lags and leads of regulatory change. The results document a robust negative relationship between regulation and foreign capital shares: regulated firms shed more foreign capital than their unregulated counterparts. This effect seems to be anticipated by firms already one year before the regulation becomes actually binding, reflecting the importance of anticipation effects. Furthermore, the analysis finds clear indications that total factor productivity of regulated firms decreases relative to that of non-regulated firms operating within the same broad economic sectors in the following year. Finally, we show that regulation also negatively affects productivity of non-regulated firms through spillover mechanisms along the value chain. This effect is driven by negative backward spillovers from regulated firms to their local suppliers. The identification of a causal linkage between FDI regulation and firm-level changes in foreign capital shares and productivity requires two main conditions to be fulfilled: no spuriously correlated further interventions and no endogenous regulation. As to the first condition, by the mid-2000s the tariff liberalization of the Indonesian economy was already mostly over, and although there were some further adjustments in non-tariff barriers to trade over the decade, their sectoral range was much more limited than the sectoral coverage of the NIL. By that, we are fairly confident that two-digit industry-year effects can sufficiently deal with average effects of additional broad regulatory trends. The second requirement is more problematic from our perspective, since firms operating in the least productive sectors may be more willing to lobby for protection against foreign entry or takeovers. Thus, a negative correlation between regulation and productivity may arise as a result of the endogenous lobbying process if firms with a larger labor force or operating in more concentrated sectors are more successful at securing the entry of their main product into the NIL (Grossman and Helpman 1994). The use of firm and two-digit industry year-effects reduces these concerns to a considerable extent as they capture both time invariant differences in firm characteristics and general shifts in the likelihood of regulation at the broad industry level. We assess the remaining scope for endogenous regulation by considering pre-reform differences in firm productivity in the years before regulatory reform. In order to control for the potential drivers of endogenous protection (employment, within-sector concentration of production and other factors) more explicitly, our favorite specifications interact pre-reform sector characteristics at the 5-digit level 4

5 with a set of year effects. These control for the residual correlation between industry characteristics that are most likely to also drive lobbying success and political decisions as well as changes in firm productivity over time. Our results remain robust to the inclusion of these controls which makes us confident that our results are not driven by reverse causality. Our contribution to the literature is twofold. First, we are among the first to exploit fine grained variation in the regulatory framework of FDI. 4 By also taking firm characteristics into account, we identify direct firm exposure to regulation more precisely and document linkages between an investment policy reform and firm productivity in an emerging economy. Second, we add to the literature on FDI spillovers by measuring the horizontal and vertical spillover effects of FDI regulation. Since we use detailed national input-output tables including 175 sectors, we are able to carefully disentangle vertical spillovers within and across two digit industries. We thereby show that our results are mainly driven by regulatory spillovers across but not within two digit industries. 5 This paper proceeds as follows. Section 2 describes the regulatory framework of the negative investment list in Indonesia and introduces the data sources. Section 3 presents the estimation strategy and the identification approach. Section 4 presents our results on the direct effects of the investment reform on foreign capital share and firm productivity as well as the indirect effects through regulatory spillovers. Section 5 concludes. 2 Regulatory background and measurement 2.1 Foreign investment regulation in Indonesia Indonesia started to remove the first barriers to foreign investment already under the New Order regime of President Suharto. The investment coordination board (BKPM, Badan Koordinasi Penanaman Modal) was installed in 1973 in order to deal with foreign investment approvals (Gammeltoft and Tarmidi 2013). However, due to its strong dependence on natural resources, the Indonesian manufacturing sector was only poorly developed until the early 1980s (Lindblad 2015). Starting in 1983, successful efforts towards industrialization increased the importance of the manufacturing sector and made it the driving force behind Indonesia s accelerating growth (Blalock and Gertler 2008). During the 1990s the Indonesian government has changed its previously investment-hostile 4 To our knowledge, there is only one other study (Eppinger and Ma 2017) that looks at FDI regulation at the firm level. Eppinger and Ma (2017) investigate the effect of China s WTO accession and the related massive liberalization of the FDI regime on changes in the ownership structure of firms. Their results show that changing ownership structures also lead to a boost in output and labor productivity. 5 Bourlès et al. (2013) also construct measures of regulatory burden. In their cross-country study they especially focus on the impact of upstream sector regulation on productivity in downstream industries. 5

6 regime by opening up the economy to investments from abroad. It quickly became one of the most promising host countries [for investment], combining liberal legislation with a massive endowment of natural resources and a huge and rapidly growing domestic market for manufactured goods (Lindblad 2015, p. 225). The Asian financial crisis of 1997 marks a break in Indonesia s economic development. Despite immediate intervention by the International Monetary Fund (IMF), the consequences of the rapidly depreciating Rupiah spread to the real economy. This was accompanied by social and political instability, which destroyed much of the confidence in Indonesia as host for investment (WTO 1998). In order to regain the confidence of foreign investors, steps towards democratization, administrative reform and further trade liberalization were taken (Duggan et al. 2013). However, Indonesia did not immediately return to economic growth and foreign investors remained cautious since the business and legal environment remained rather precarious. Major reforms after 2004 induced fiscal incentives to FDI, streamlined bureaucratic procedures (WTO 2013) and prescribed nondiscriminatory treatment for foreign and domestic investors. In the aftermath of these reforms, FDI inflows have massively increased again and economic growth has recovered strongly. Despite the ongoing liberalization, trade and investment policy in Indonesia remains blurred by contradictory signals (Lindblad 2015, p. 229). In the close aftermath of the Asian financial crisis in 2000, the president released the Presidential Decree 96/2000, at the core of which lies the so-called negative investment list, naming sectors that are closed or only conditionally open to FDI. 6 Conditions included the need to form joint ventures between domestic and foreign entities, authorization in certain regions and licensing requirements. Before 2000, no explicitly formulated version of the NIL was available. There was a blacklist of sectors closed to foreign investment, but approval procedures lacked transparency and were completely in the hands of the BKPM. The NIL 2000 was the first to publish regulatory information in a transparent way but still listed sectors verbally, without recurring to detailed sector codes. The NIL has been revised for the first time in 2007 and the new list was released with the Presidential Decree 77/2007. The new version replaced the old vague register from 2000 (Article 7) and listed sectors by detailed KBLI coding on five digit level for the first time. In its trade policy review on Indonesia the WTO highlights that a detailed NIL brings greater transparency with respect to investment and therefore may be beneficial (WTO 2013). However, closing or conditionally opening certain sectors to foreign investment is likely to be associated with wasted gains from FDI. In this sense, the revised version can be considered as a protectionist measure as it adds substantially more sectors and involves more conditions compared to the NIL The NIL 2007 comprises manufacturing as 6 In the following, this version of the list will be referred to as NIL

7 well as agriculture and services and introduces standardized categories of conditions for the first time. According to these conditions, some sectors were fully closed to foreign investment, alternatively, FDI was only allowed in small and medium sized firms, in form of partnerships, up to a certain limit of foreign capital ownership, in certain locations or required licensing by the ministry in charge. The next revision of the NIL came with the Presidential Decree 36/2010, leaving regulation in some sectors unchanged but also removing some sectors from and adding other sectors to the NIL. Finally, the latest revisions in 2014 and 2016 removed many sectors from the list of closed sectors, converted bans into licensing requirements, and clearly decreased the overall extent of regulation. A comprehensive overview of all revisions of the NIL is provided in Table A1 in the Appendix, including its representation in the sample and the shares of regulated firms in total manufacturing output Firm data The source of firm data is the annual manufacturing census of Indonesia (Survei Industri, SI) that surveys the universe of all registered Indonesian manufacturing firms with at least 20 employees. The census has been conducted by BPS yearly since 1975 and contains a rich set of information at the level of manufacturing plants, including the values of inputs and output, foreign ownership, the value of imports and exports as well as employment and capital stocks. We follow the literature by using the share of foreign capital as a proxy of FDI. 8 In the SI data, sector codes always refer to the main product of a firm, which introduces some imprecision as multi-product firms may switch between sectors every year. We assess the potential extent of this issue by recording all switches in the main product from year to year. One concern is that regulation creates incentives to switch the sector to avoid limitations to FDI. We test for the relevance of this channel in the robustness section. The data is cleaned for missing values and extreme outliers. As common in the literature, data points are interpolated between the previous and the next year to avoid the loss of too many observations, while further missing observations are dropped from the sample (Amiti and Konings 2007). 9 Our final dataset consists of an unbalanced panel of 31,025 firms with a total of 209,048 observations. The sample size decreases further in some regressions because of missing values in some of the control variables as well as due to the 7 An important characteristic of all versions of the NIL is that regulation is forward looking and does not apply to previously approved investments. Thus, the regulation refers to new investment plans and interferes with possible future FDI inflows. See Article 8, Presidential Decree 36/2010, article 5 of Presidential Decree 77/2007 and article 9 of Presidential Decree 39/ See for instance Amiti and Konings (2007), Blalock and Gertler (2008) or Arnold and Javorcik (2009), all based on the same SI data. 9 The appendix provides more detailed information on data cleaning procedures. 7

8 choice of lag structure: regressions estimating the effects of regulation on firm productivity rely on 169,788 observations in the most stringent setting. We transform all input and output variables to their natural logarithms, using a Box-Cox-transformation to deal with zeros. This not only allows for a more intuitive interpretation of coefficients as elasticities but also makes estimations less vulnerable to remaining outliers. We deflate all monetary values to the base year 2008 by using the yearly regional wholesale price index from BPS. There are some concerns regarding the data quality of the SI. First, doubts arise with respect of its completeness since it claims to include all medium sized and large manufacturing firms in Indonesia. Due to the large number of firms, it is at least possible that SI misses firms in some years or cannot investigate cases of non-respondents, leading to non-random selection and potential bias. Blalock and Gertler (2008) as well as Arnold and Javorcik (2009) argue that there are some financial incentives for the field agents to register new firms and verify firms that do not reply immediately since budgets are linked to the number of reported establishments. However, this may also create wrong incentives by tempting field agents to fill in values of non-reporting firms themselves. A second issue arises because of potential misreporting by firms. Government law guarantees the exclusive and anonymized use of information for statistical purposes. Firms may still be concerned, however, that reported information is leaked to tax authorities or competitors. Therefore, some firms may intentionally report wrong data (Blalock and Gertler 2008). Furthermore, if firms do not put much effort into the correct completion of the questionnaires, numbers may be falsely reported by accident. The above arguments suggest that noise within the data is likely to be a considerable issue. However, as long as firm selection and response behavior is not directly linked to FDI regulation, firm and two digit sector-year fixed effects are likely to lead to unbiased within-estimates. We investigate one specific potential channel of misreporting more explicitly by investigating whether regulation makes firms to switch their reported main product and whether switching firms show different productivity responses to regulation. 2.3 Merging firm and regulation data We combine the firm data with self-collected information from five revisions of the NIL by five digit KBLI sector. 10 While total closure to any investment within a sector unambiguously affects all firms, other rules depend on firm characteristics such as firm size, legal status or location. We thus use the survey information on sales, net assets, legal status and location to determine firm-specific exposure to regulation. Moreover, since the preambles of the Presidential Decrees exclude already existing foreign investments from a new regulation, we offset regulation for those firms that exceed the later legal limits 10 Our regulatory data is based on the Presidential Decrees 96/2000, 77/2007, 36/2010, 39/2014 and 44/2016. See the appendix for a more detailed description of the merging procedures. 8

9 on the share of foreign capital already before the revision of the NIL. Finally, although some of the NIL stipulations verbally narrow regulation to selected subcategories (e.g., to product features) within a five-digit KBLI sector, we always assume regulation for the whole five-digit sector. 11 We combine the detailed conditions of the NIL with firm level information to generate a single measure of exposure to regulation. The dummy Regulated turns to one if a firm is subject to any kind of regulation in a certain year, taking firm characteristics relevant for the applicability of the regulation into account. For example, Regulated takes zero if a medium sized firm operates in a sector that is open to small and medium sized firms but requires licensing from large firms. For a large firm operating in the same sector, however, the dummy Regulated turns to one since FDI is conditional on a successful licensing procedure. 12 We further investigate which types of rules affect foreign share and productivity by explicitly differentiating between different types of regulation (sector-wide and firm-specific bans, licensing) in section 4.3. Table 1 presents summary statistics for the main variables in the years 2000, 2007 and Despite the foreign investment regulation, the overall share of foreign capital increases over time, although most domestic firms receive zero FDI throughout the whole time period. Figure 1 shows the more detailed time patterns of regulation and total factor productivity. The black dashed line shows the average share of regulated firms over time and marks a strong tightening of regulation in 2007 as the share of regulated firms increased from less than 5% to more than 20%. In 2014, the scope of regulation declined somewhat, with a marked shift from firm specific bans towards licensing requirements (cf. Table 1). The solid lines plot the average development of total factor productivity over time. They distinguish between regulated and non-regulated firms in the respective years and show that on average, firms that operated in sectors subject to FDI restrictions were more productive than other firms before In 2007, both regulated and non-regulated firms faced a negative productivity shock on average, but the drop in productivity was larger among the regulated firms. In the aftermath, both lines recover again. This evidence is clearly descriptive but already foreshadows our identified results. Most importantly, this observed negative productivity shock clearly precedes any potential effects of the global financial crisis, the macro-economic effects of which did not reach the emerging markets for other two years. It was only in 2009 that Indonesian GDP experienced a short stagnating period with a subsequent quick recovery and hence the 2007 productivity drop is unlikely to be driven by global market shocks. 11 The resulting measurement error is most likely to cause attenuation bias and thus our results will be underestimated. For a detailed description of the coding procedures, see the Appendix. 12 We define firm size according to the Presidential Decree No. 36/2010 that refers to law 20/2008 on small and medium sized enterprises. A firm is defined as large by this law if its annual sales are higher than 50 billion IDR or its net assets (excluding land and buildings) surpass 10 billion IDR. The Presidential Decree No. 77/2007 refers to an earlier law 9/1995 on small enterprises with very similar definitions once the thresholds are inflation adjusted. We adjust this rule yearly, factoring out inflation. 13 Summary statistics for the full sample can be found in Table A2 in the Appendix. 9

10 Table 2 shows the means of foreign capital share, total factor productivity and employment by sector and year. Regulation across sectors shows a very heterogeneous picture. Some industries are not affected by the NIL at all whereas other sectors like wood and wood products were already strictly regulated in Our analysis will utilize variation in regulation and productivity across the sub-sectors of the presented two digit sectors over time, while controlling for sector-year effects. Thus, we do not try to explain changes in FDI penetration or sector-wide changes in productivity, rather, we focus at the within sector differential relationship between FDI regulation and firm outcomes. 3 Estimation strategy 3.1 Baseline model We investigate the effect of the foreign investment regulation on firm outcomes by estimating the equation y ijt = αreg ijτ + X ijtβ + λ i + γ rt + ψ st + Z j,2005 φ t + ε it τ = t 1, t, (1) where y ijt measures the relevant outcomes of firm i operating in the five-digit sector j in year t. Our two main outcomes measure the percentage of foreign equity in total firm equity (FDI share) and the log of total factor productivity per firm. REG ijτ is the investment restriction in sector j in year τ, conditional on the characteristics of firm i. We test for contemporaneous as well as lagged effects of regulation, τ = t, t 1, and in further tests also include up to three lags and leads of regulation at the same time. All regressions include a vector of controls X ijt to capture time-variant firm characteristics, such as a set of indicators for firm age categories, a public enterprise indicator (if more than half of firm capital is owned by the state) and a Herfindahl sales concentration index on five-digit sector level. We condition our results on firm fixed effects λ i, a set of year effects that vary by macro-region (island) γ rt, 14 and two digit sector-year fixed effects ψ st. The residuals ε it are robustly estimated and clustered at the firm level. Our extensive fixed effects mitigate issues with unobserved heterogeneity and endogenous regulation. Firm fixed effects absorb all time invariant unobservable firm characteristics, including the firms average propensity to enjoy protection or be subject to regulation (Goldberg and Pavcnik 2005). Island-year fixed effects control flexibly for all regional influence factors that may correlate with both regional exposure to regulation and shifts in foreign capital shares. The sector-year fixed effects control for time variant incentives to lobby for protection at two-digit sector level (Blalock and Gertler 2008). Certain 14 We distinguish between Sumatra, Java, Kalimantan, Sulawesi and the rest of smaller islands. 10

11 industries, for instance those that exhibit low foreign market linkages, may share common interests concerning protectionist regulation to prevent future FDI inflows. The time effects also implicitly cancel out common time trends and common macroeconomic or regulatory shocks to FDI and productivity. Adding further lags and leads of regulation helps us to better understand the timing patterns of regulatory effects and to look for anticipation effects or pre-trends. In our preferred specifications, we capture a further set of determinants of the propensity to be subject to regulation at the detailed five-digit sectoral level by interacting average sectoral characteristics from two years before the major regulatory reform with a full set of year effects: Z j,2005 φ t. By doing so, we explicitly control for sectoral characteristics at the five-digit level that may explain the later success of lobbying behavior. In our main specifications we interact the sectoral concentration of sales, the share of blue-collar (production) workers and the share of public enterprises within the sector in 2005 with a full set of year effects. A higher sector concentration provides firms with more power to lobby for protection (Grossman and Helpman 1994). Sectors with a larger share of lowskilled workers are potentially more vulnerable and policymakers tend to protect those sectors more (Topalova and Khandelwal 2011). Finally, sectors with a high share of government-owned enterprises may be more successful in hindering foreign entry (Chari and Gupta 2008). In further specifications, we also test for a more extended set of initial sector traits in 2005, again interacted with a full set of year effects. These additional characteristics include employment growth between 2000 and 2005, capital intensity, average foreign capital share and import penetration. Employment growth controls for declining industries which may be treated with special care by policymakers (Grossman and Helpman 1994). Moreover, high capital intensity, existing FDI presence or strong competitive pressure from foreign imports have also been argued to influence the political decision for regulation (Gawande and Krishna 2003). 3.2 Estimating total factor productivity We estimate total factor productivity (TFP) for each firm, while simultaneously accounting for the correlation of the firm s input choices with the error term (cf. Javorcik 2004, Amiti and Konings 2007, Newman et al. 2015). 15 If a firm adjusts its choice of inputs to unobserved productivity shocks, disregarding this adjustment will induce a severe simultaneity problem and, thus, lead to biased coefficients. 16 The estimation is based on a Cobb-Douglas production function in value added terms on 15 Two standard semi-parametric methodologies of total factor productivity estimation are Olley and Pakes (1996) and Levinsohn and Petrin (2003). 16 See van Beveren (2012) for a detailed discussion of total factor productivity estimation in the literature. 11

12 plant level: Y it M it = A it L α L it Kα K it, (2) where the value added of firm i in year t is calculated by subtracting the value of the intermediate inputs M it from total firm output Y it. Value added, VA it, is a function of productivity A it, the freely variable input factor labor L it and quasi-fixed capital K it. Taking natural logs results in: ln(va) it = α 0 + α L l it + α K k it + ω it + e it, (3) where small letters denote logs. The error term can be decomposed into two components, an unobserved productivity component ω it and the independently identically distributed error term e it. Simultaneity bias is introduced because a part of the productivity shocks is also correlated with the choice of the variable inputs, namely labor and intermediate goods. In order to consistently estimate total factor productivity, we apply an approach suggested by Wooldridge (2009). Total factor productivity is estimated on two digit sector level, which takes into account the varying importance of input factors across industries. 17 Thus, we estimate log total factor productivity (TFP) for each two-digit sector s separately (i still stands for the firm and t for time): ln(tfp) ist = ln(va) ist ˆα s 0 ˆα s l l ist ˆα s k k ist, (4) where α s l and α s k are the sector-specific input coefficients (see also table A3) The effects of regulatory spillovers As a further step, we investigate the effects of horizontal and vertical regulatory spillovers that are propagated along the value chain: y ijt = α REG ijt + δ 1 Horizontal kt + δ 2 Backward kt + δ 3 Forward kt + X ijtβ + λ i + γ rt + Z j,2005 φ t + ε it τ = t 1, t, (5) where Horizontal kt captures the extent of overall regulation within the three-digit sector k in year t and tests whether a higher presence of FDI regulation also exerts an influence 17 A more disaggregated estimation on three digit sector level is also feasible, even though some sectors have to be omitted because of insufficient observations. All results go through with the more disaggregated total factor productivity estimates. 18 See the Appendix for a more detailed description of the approach and Newman et al. (2015) for a different application. 12

13 on FDI or productivity of non-regulated firms within the same sector. It is measured by the share of protected firms within a sector weighted by each firm s median sales Sales i (Javorcik 2004): 19 Horizontal kt = ( ) / REG it Sales i Sales i. (6) i k i k Horizontal regulatory spillovers may turn out productivity reducing if they reduce the overall competitive pressure or increase the generally perceived insecurity within the more strongly regulated sectors. Alternatively, they may increase firm productivity because of avoidance behavior if foreign capital is induced to switch to non-regulated sub-sectors or to different types of firms within the same sub-sector. Backward kt proxies for regulatory penetration in the customer industries v of the three digit industry k: Backward kt = α kv Horizontal vt, (7) v k where α kv is the proportion of sector k s output supplied to sector v, taken from the nationwide input-output (IO) table of 2005 (BPS 2005). Unlike international IO databases 20 the BPS provides highly disaggregated information that distinguishes 175 sectors in total (and 87 manufacturing sectors), which is broadly comparable to the three-digit sector level. Due to this high level of disaggregation, we are able to differentiate between two different types of backward spillovers separately. We define the variable Intra-sectoral Backward kt to capture spillovers to suppliers that occur within the same two-digit industries, while Inter-sectoral Backward kt to capture those that affect suppliers that are active in other two-digit sectors. Due to the relatively low level of disaggregation, other studies usually refer to inter-sectoral spillovers whenever they identify backward linkages (Javorcik 2004, Blalock and Gertler 2008, Newman et al. 2015). We expect a negative impact of regulation in downstream industries on firm productivity if both industries are sufficiently distinct from each other (inter-sectoral backward linkages). Protection of important customer sectors may reduce transfers of technology and other non-tangible assets to domestic upstream firms. This can be either because of a decrease in foreign presence or because existing foreign-invested firms abstain from knowledge transfers to local suppliers in the face of higher uncertainty. However, the mechanisms are not necessarily the same if the customer and upstream industry operate within the same two digit sector as in this case the intra-sectoral backward effect could also pick up spillovers that are usually labeled 19 We determine the median level of sales for each firm to make sure that results are not driven by timevarying output. Results are robust to the use of time-varying sales. 20 See for example OECD (2005) or the world input-output database (Timmer et al. 2015). 13

14 as horizontal and reflect avoidance behavior by foreign investors towards non-regulated sub-sectors or types of firms. The net effect in this case is not clear ex-ante. Finally, we construct Forward kt that proxies for regulatory penetration in the supplying industries w of three digit industry k (Bourlès et al. 2013): Forward kt = w k σ wk Horizontal wt, (8) where σ wk is the proportion of sector k s intermediate inputs that it purchases from sector w. Like before, we disentangle Intra-sectoral Forward kt and Inter-sectoral Forward kt to account for potentially different linkages across sectors within the same industry and across industries. In all cases, variation within the horizontal and vertical spillover measures stems from changes in regulation over time. Both the input-output coefficients and the firm sales are fixed to make sure that we only measure effects that originate in revisions of the NIL and not in general structural change. 4 Results 4.1 Pre-trends and anticipation effects We first test for the presence of pre-trends and present some evidence that supports our interpretation that regulation affects firm outcomes instead of just reflecting pre-existing trends in the variables. Figure 2 graphs estimated coefficients from fully specified firm level regressions that include time variant controls, initial sector traits in 2005 interacted with a full set of year effects, firm fixed effects as well as island-year and two digit sectoryear fixed effects. Moreover, they include the main regulation indicator together with its three lags and three leads: y ijt = t+3 τ=t 3 α τreg ijτ + X itβ + λ i + γ rt + ψ st + Z j,2005 φ t + ε it, (9) the estimated coefficients α τ from which then are mapped in Figure 2. Figure 2 shows no statistically significant differences between regulated and non-regulated firms in terms of foreign capital shares and productivity in the years before the regulation was implemented. Panel (a) shows no significant differences of FDI shares in firms three-to-two years before the regulatory intervention, but indicates that FDI starts to go down already one year before the regulation is introduced, with a further decrease in the year of regulation. After that, the effect vanishes and potentially is showing even a slight rebound three years after regulation. Panel (b) plots the time effects on total factor productivity and shows no pre-trends in the three years before regulation as reg- 14

15 ulated and non-regulated firms show comparable levels of productivity once sector-year, island-year and firm fixed effects are factored out. Starting with the year of regulation, productivity drops for the group of regulated firms and the negative effects persist for two further periods. The pre-trends of productivity make it unlikely that policy-makers were implementing protectionist measures in sectors with declining productivity and show that reverse causality is unlikely to drive our results. Nonetheless, the significant FDI reaction in the year before regulation shows that anticipation effects may have played a role. Anecdotal evidence supports our finding that firms already anticipated changes in the regulatory framework. With regard to the changes of the NIL in 2007, the largest Indonesian newspaper, Kompas, has already started to cover the topic in mid of June 2005, reporting that the wheat industry will not enter the list. 21 Coverage got more pronounced at the beginning of In February, Kompas announced that the Ministry of Industry wants sugar refineries to enter the list. 22 When the revision finally took place in July 2007, Kompas addressed concerns of business actors who criticize the list because existing investment is difficult to develop even though the NIL is not retroactive. 23 Similarly, Kompas already started to report on plans to revise the NIL at the beginning of 2013 while the Presidential Decree was only released in April In February 2013, Kompas quoted the head of the Investment coordination board, M. Chatib Basri, saying that the main goal is to improve national competitiveness and to be more investor friendly while there are still sectors that must be protected. 24 Another article reported on plans for relaxation of investment in alcoholic beverage industry in July Finally, by the end of 2013 coverage of the topic increased substantially. For example, Reuters reported on ease of regulation to allow foreign companies [...] to manage and operate airports. 26 One week later, Kompas published a letter to the editor in which a concerned reader names further sectors where access to foreigners is planned, among others also pharmaceuticals. 27 Even though this evidence is only anecdotal, the fact that newspapers openly discussed the revisions of the NIL more than one year ahead shows its relevance for the Indonesian economy. We further think that industries and firms may have been aware of more detailed plans of the revisions even before they entered public media. 21 Kompas, edition from 30 June, Kompas, edition from 8 February, Kompas, edition from 16 July, 2007, page Kompas, edition from 18 February, 2013, page Kompas, edition from 12 July, 2013, page Reuters, Foreign Investors Eye Airports as Indonesia Rolls Out Welcome Mat, 20 November, Kompas, edition from 28 November, 2013, page 7. 15

16 4.2 Baseline results Results in Table 3 show a significant decline in FDI within regulated firms. The first five columns report the baseline effects of regulation on FDI shares within the firms, using equation 1 and linking FDI shares to contemporary regulation (τ = t). As FDI has been responding immediately to regulation (and even showing anticipation effects, see Figure 2), we focus here on the immediate response within the year of regulation. The first column in Table 3 includes only firm fixed effects and island-year effects whereas further columns consecutively extend this specification, without leading to a change in the magnitude or statistical significance of the estimated effect. Column 2 adds categories of firm age, a dummy for state enterprises and the Herfindahl sales concentration index as time variant controls, column 3 extends the model by two-digit sector-year interactions, column 4 and 5 interact five-digit sector characteristics in the pre-reform year 2005 with a full set of year effects. While column 4 uses a more limited set of sector characteristics, including sector concentration, the share of blue-collar workers and the share of public enterprises, column 5 extends this list even further by adding employment growth, capital intensity, the share of foreign capital and import penetration. Throughout all specifications, the estimated impact of the regulation indicator on the foreign capital share is highly significant and implies that becoming protected is associated with a 0.7 percentage points lower foreign equity ownership share. This effect may not seem very substantial but it still amounts to about 10% of the mean ownership within the sample (which is about 7%). Column 6 shows that our result is not driven by the intensive margin of firm ownership only. When we substitute the dependent variable by an indicator for a multinational enterprise (MNE, defined as a firm with at least 10% foreign ownership), the results show that regulation also reduces the likelihood of a firm having considerable foreign capital (extensive margin). Overall, the coefficients stay remarkably stable, indicating that potentially endogenous regulation is unlikely to drive our results. In a next step, Table 4 shows that regulation is also linked to a statistically significant decline in total factor productivity. Unlike before, we use the first lag of the regulatory indicator (τ = t 1) in our main specifications as productivity can be expected to adjust more slowly upon regulation. The first five columns of Table 4 report the baseline effects of regulation on TFP (based on equation 1). As before, the table starts with firm and islandyear effects only and adds further controls step-by-step. Unlike in the case of FDI shares, controls do make a difference for our TFP estimates. Although the productivity effects of regulation have a negative sign even without any controls, they turn out insignificant and only gain significance once two-digit sector-year effects are included. This make sense as sector-year effects are often considered as crucial controls for productivity estimates (Goldberg and Pavcnik 2005). For our case this implies that we are only able to detect negative productivity effects of FDI regulation once we focus on the variation in produc- 16

17 tivity across firms (and detailed sectors) within any given major industry. Adding time effects interacted with initial five-digit sector characteristics to the regressions in columns 4 and 5 does not change the point estimates though, which shows that the changes in productivity are not driven by characteristics of the finely grained sectors that may make regulation more likely. The point estimate on the regulation coefficient implies that a firm that has become regulated in the previous year experiences a 3.6 percent reduction of its TFP ceteris paribus. Column 6 checks the sensitivity of our results to the specific TFP estimation. It takes the log of value added per worker as an alternative dependent variable which is often used as proxy for labor productivity in the literature (cf. Amiti and Konings 2007). The coefficient of regulation barely changes and remains significant at the five percent level. Our baseline TFP regressions measure the full regulatory effect, without controlling for changes in FDI shares directly. However, alternative specifications that include the foreign capital share as additional control result in virtually the same regulatory coefficient (see Table A4 in the appendix). While we also find that TFP is increasing with foreign ownership shares (in line with the literature, see e.g., Amiti and Konings 2007), the regulatory effects do not seem to be driven by immediate drops in the firms FDI shares and may rather reflect changing patterns of technological upgrading or changing expectations with respect to competitive pressure that lead to adjustments in factor use. 4.3 Effect heterogeneity Our general regulation indicator combines a range of different provisions, not all of them equally restrictive and hence our baseline results estimated an average regulatory effect. In order to better understand which types of regulatory interventions are more likely to affect FDI and productivity, Table 5 differentiates between major types of regulation by contrasting licensing requirements with the more direct bans and limitations on FDI. Licensing requirements leave the affected sectors open to FDI but potentially increase the compliance burden by introducing costly and time-consuming procedures. However, as licensing requirements have been more extensively used in the later years (as part of the easing of regulation in 2010 and later), their introduction may have also been seen as guaranteeing openness and signaling that FDI will still be possible in these sectors. By contrast, the diverse bans and limitations on FDI are less ambiguous in their effect as they are more likely to restrict FDI flows. We further disentangle these bans into three groups: 1. into sector-wide limits that introduce upper limits on FDI shares for whole sectors, 2. sector-wide bans that prohibit FDI in the given sectors irrespectively of firm characteristics, and 3. firm specific bans that restrict FDI if certain firm characteristics 17

18 are not fulfilled (the firm is not small or medium sized or not organized as partnership). 28 Columns 1 to 4 in table 5 show that licensing requirements do not curb foreign investment but even lead to increased foreign capital shares whereas bans and limits to FDI negatively affect foreign investment. Thus, in the case of licensing, the positive effects seem to overweight any potentially FDI reducing effects. Moreover, it turns out that the negative results for bans are driven by the firm-specific restrictions: these seem to be the most directly affecting FDI shares. The second part of table 5 (columns 5 to 8) shows that licensing is also not the driving factor behind the drop in productivity. This conforms with the descriptive evidence that documents an increase in the use of licensing within the later and more liberal revisions of the NIL. The potential costs of licensing requirements seem not to be crucial here. Like before, the negative effect stems from FDI limitations and bans. Next, column 8 shows that only sector-wide bans significantly reduce TFP, while sector-wide limits come out with a positive coefficient. One should note, however, that in a vast majority the upper limit is set to be 95% of total firm capital. It therefore may be that sector-wide limits do not interfere much with investment decisions and even reduce uncertainty among foreign investors. In a next step, we take a closer look at impact variation across firms. First, we check for differential effects of regulation among those firms that have either exported or imported, or had an above ten percent foreign capital participation at least in one year of our sample period. 29 The idea behind is that those firms with linkages to international markets may be more strongly affected by protectionist measures. Second, we split up firms into three groups according to their position within the productivity distribution. The low (high) productivity group includes those firms that have been in the lowest (highest) percentile of two digit sector TFP distribution in at least one year. The residual group of medium productive firms have never been in the upper sectoral distributions or, in some cases, appeared in both the upper and lower percentile due to sector switches across two digit industries. 30 Table 6 shows the results. Column 1 tests the hypothesis that firms involved in foreign trade are differentially affected by regulation as compared to the domestically operating firms. The interaction effect of regulation and trading firm is negative but insignificant. In column 2, the interaction with foreign firms becomes significant and is also much larger in magnitude as compared to the regulatory effect on the firms not classified as foreign. Next, column 3 presents results of the second approach. The effect of regulation for low, medium and high productivity firms is negative and strongly significant in all cases. 28 See Appendix for further details. 29 There are 11,202 firms with import or export activity and 3,207 firms with foreign participation. 2,674 firms have both trade and FDI linkages. 30 There are 8,047 firms in the lowest category, 15,966 firms in mid category and 7,012 firms are identified as highly productive firms according to our definition. 18