Learning-by-exporting in the presence of a quota intervention: evidence from Indonesia

Learning-by-exporting in the presence of a quota intervention: evidence from Indonesia Deasy Damayanti Putri Pane Draft 6 December 2017 Learning-by-exporting refers to the mechanism whereby firms improve their productivity after entering export markets. This study investigates how a quota intervention can affect this mechanism. An example of such an intervention is the Multi-Fibre Arrangement (MFA), a global quota facility for textile and clothing. Using propensity score matching and differencein-difference methods, I analyse how the MFA affected apparel exporter s performance in Indonesia. The results suggest that the impact on productivity was positive and significant during the MFA period, but was not significant after it ended. This suggests that the MFA may have benefited firms, but only in the short run. The benefits may have been due to quota rents. Keywords: learning-by-exporting, total factor productivity, MFA, Indonesia JEL Classification: D22, D24, F13, F14 1. Introduction Learning-by-exporting (LBE) hypothesis argues that exposure from export allows firms to improve their efficiency level, whereas non-exporting firms cannot obtain such of learning benefits. The interactions with buyers and competitors abroad are channels to absorb foreign knowledge, and these opportunities could increase the performance gaps between exporters and firms that only serve the domestic market. Empirically, even though earlier studies showed mixed evidences of LBE, recently, this mechanism has in general been confirmed by various case studies in developing countries (Bigsten & Gebreeyesus 2009; De Loecker 2007; Pane 2016; Van Biesebroeck 2005). The policy implication of LBE is even more important. Exporting is good for firms since it may improve their performances, but do these support the implementation of export promotion policies? Based on their findings in China cases, Du et al. (2012) suggest that the learning effects from exporting could motivate the government to design export promotion policies that allow domestic firms to take benefits from exporting. This suggestion could describe what Girma, Greenaway and Kneller (2004) mention in their paper that almost all governments over the world have a mercantilist instinct to do export promotion activities because they see export as the key to wealth creation. Meanwhile, as several papers suggest, in formulating policies, the evidence of LBE cannot be seen as a stand-alone mechanism, but 1

it needs to be perceived together with the concept of self-selection into export: only productive firms are able to overcome the costs of entering foreign markets. It is true that there are gains from export, but not all firms can export; therefore, export promotion policies may mistarget firms in the economy. This paper is motivated to untangle the confusion above by testing the argument of the importance of policy intervention on the firm s learning-by-exporting ability. 1 As for the intervention of interest, this study employs the implementation (and the abolishment) of Multi-Fibre Arrangement (MFA) as a natural experiment to analyse the LBE hypothesis. The MFA is well known as a global and massive intervention that had governed the world trade of garments 2 for more than three decades. It had helped firms in apparel industry from various developing countries, including Indonesia, especially in the earlier implementation of the policy to access advanced countries such as the US and the EU; even though in the later period, it had limited further expansions of a given exporter as its quota was already fulfilled (Hill 1992). During the quota regimes, it could be argued that those developing countries could enter the specific-quota markets without meaningful global competition. However, starting from the beginning of 2005, after 10 years of preparation 3, the MFA restrictions were gone and the battle for world clothing market was back. Competitions were intensified; cheap products from all over the world could access markets in the previously constrained countries without limitation. This large, measured, fully anticipated and statistically exogenous change in trade policy provides the natural experiment that I can use in this paper to test the learning effects of exporting. This paper tests three fundamental predictions. The first one is that an intervention could benefit exporters by allowing them to improve their productivities. Secondly, once the intervention is abolished, the effect of exporting on productivity vanishes; therefore, the effects of an export intervention are only temporary. Finally, compared to exporters in the other sector that are excluded from the MFA, exporters with such facility performed better during the implementation period but have no effect as the intervention was removed. This may suggest that a more sustained learning effect occurs in a competitive situation, while interventions to help exporters export might only result in pampered and less competent firms. Comparing the learning-by-exporting effects of firms in the apparel industry in Indonesia at the period of MFA (before 2005) versus the period after MFA abolition (after 2005), this study employs 25 years of longitudinal firm-level data of surveys of medium and large establishments from 1990 to 2014. Several empirical strategies are applied. To start the analysis, a simple difference-in-difference (DID) approach is conducted to see the difference 1 In this paper, I use a general term of intervention to narrate a policy that could promote export. 2 The MFA had affected textiles and garments, but this study focus on garments only. This is because garment is an important labour-intensive industry that has become the largest industry in terms of employment. It has been an export-oriented sectors and MFA was an important export-promotion intervention for garment. Meanwhile, textile industry, especially weaving, is relatively old industry that had reached its peak in 1980s (Thee 2009). 3 The announcement of elimination of the MFA was in Uruguay Round meeting in 1995. 2

of learning-by-exporting effects before and after the MFA was abolished. Following De Loecker (2007), this study identifies the learning-by-exporting effect is identified by comparing the productivity of matched exporters with their matched non-exporters after they do export. Some productivity measurements are applied to investigate consistency and robustness as well as to overcome endogeneity issues between export and productivity. Subsequently, I compare the learning benefits of garment exporters with ones in the footwear 4 sector in the two observed periods. A series of robust results explains that the policy intervention has improved the learning premium of exporters in the garment sector, but the effect is just temporary. This article has several contributions to the LBE literature. To the best of my knowledge, this is the first paper that tries to examine learning-by-exporting effects under a policy intervention. Secondly, it also can bring about the channel of learning, something that hasn t been found in the previous literatures. During the MFA period, the main channel of learning was the relation with foreign buyers. The strong linkages with clients allowed the garment exporters to provide a high quality product due to supervision from buyers (Thee 2009). Meanwhile, after the MFA was abolished, the competition effects have been much higher. Results from this study advise that the exporters experience difficulties to switch learning channels; once they are not used to compete in a protected market, they find that competition in an open market is relatively difficult. However, I need to be careful to interpret the results. Due to the data constraints, I realized that this study could not distinguish between revenue productivity and physical productivity. The mark-up effect might be still embedded in the productivity measurement. Therefore, mark-up from quota rents might be one of the explanations of the significantly higher learning-by-exporting effects during the MFA period. The next section discusses the MFA and its implementation in Indonesia that can explain some features of treatment in the model. Section 3 provides the model framework of learning-byexporting. Section 4 explains some econometric strategies to implement the model, while Section 5 explains the data. Section 6 gives results and the final section is for concluding remarks. 2. The Implementation of Multi-Fibre Arrangement in Indonesia The world trade of apparel had been intervened for a long time before the MFA was abolished in 1 January 2005. A massive quota regime of Voluntary Export Restraint (VER) had been established since 1970s, but the other forms of these barriers had been initiated since 1950s (Krishna & Tan 1998). The MFA under The General Agreement on Tariffs and Trade (GATT) was introduced to expand the world trade of textiles and textile products, to reduce the 4 The garment sector and footwear sector are similar and comparable in some aspects. They are both labour intensive industries, so any labour related policies affect those sectors in similar way. 3

barriers of such trade and to ensure the orderly and equity development of this trade. In practice, it had limited the textiles and garments exports to the US and developed countries in the EU from Asian Newly Industrializing Economies (NIEs), such as South Korea, Hong Kong and Taiwan which had spectacular export growth of these products (Hill & Suphachalasai 1992) and instead diverted the export sources to other labour-intensive developing countries, such as Indonesia, Bangladesh, Sri Lanka and Pakistan. These specific country-productsvolumes-timeframes quotas were negotiated bilaterally between the exporting and importing countries every year, but the implementation of these quotas was controlled by the exporting countries (Krishna & Tan 1998). 6 As theory predicts, when a MFA quota was imposed on a specific commodity, the importing country limits the import volume from the exporting country from R to Q (see Figure 1). The price in the importing country rises from P0 to P1, resulting in the quota rent in the shaded area. In a normal situation, the whole rent is potentially received by the exporting country since it controls the quota policy. Some condition, such as monopsony and bilateral monopoly, may prohibit the exporting country to capture all the rents (Krishna & Tan 1998). A research about Indonesia by Krishna and Tan (1998) suggests that on average of 23 product categories, exporters obtained about US$ 0.41 quota rent per product or 9 percent of export price per unit in 1987. The rents are lower in 1988, at about US$ 0.3 or 6 percent per unit. They also show that these figures could be higher if incorporating quality effects, hidden costs associated with the quota distribution system, and market imperfections that were most likely occurred. Even though these quota frameworks had restricted volumes of specific products to enter some developed countries, the MFA had given opportunities for some developing countries, including Indonesia, to begin accessing those markets. These developing countries could enter the specific country-products-volumes-timeframes market with almost no competition pressure from other rival countries. Because of the chances, export growth from developing countries was higher at the beginning until they could fulfil the quotas volumes and could not further expand the export of those specific products. For example, Indonesia s apparel export growth was higher in the beginning years of MFA implementation (see Table 1). 7 In these years, Indonesia had not achieved full quotas volumes. However, in later period, when quota fill rates were high, the export growths or apparel was lower. One of the reasons is because firms faced the more challenging task to increase sales as the constraints become binding; 6 The concept of MFA is unique and quite different from other quota concepts. Similar with quota barriers, it is a measure by which the importing country imposes an upper limit on foreign supply. However, it is distinct in that it is targeted to a very specific commodity category on a certain period, is defined in volume rather than in value terms, and is discriminatory by the country of origin (Hamilton 1984). 7 Other than the MFA factor, there are many reasons why apparel industry in Indonesia was once successful(hill 1992). Firstly, because of the decline in the protection of textiles and garments. Second, reform packages from government, such as a quicker custom procedure, more efficient and flexible financial services, easier licencing requirements, and fewer restriction on foreign investments. The third reason is due to fact that the recession after oil boom provided incentive for textile and garment producers to do export. 4

they needed to penetrate the non-mfa markets or do quality upgrading. 8 Performance of countries like China, Bangladesh and Vietnam remain high. This is probably because they were able to expand their export to other markets as well. Figure 1. The impact of quota Price D S P 1 c P 0 e S P 2 a S D O Q R Quantity Table 1. Apparel export growth rates from several countries Countries 1980-84 1985-89 1990-94 1995-99 2000-04 2005-09 2010-14 Indonesia 0.38 0.32 0.25 0.06 0.04 0.06 0.06 India 0.10 0.23 0.11 0.07 0.06 0.12 0.09 China 0.31 0.24 0.05 0.16 0.13 0.12 Bangladesh 0.50 0.27 0.27 0.12 0.15 0.13 Vietnam 0.09 0.22 0.16 0.19 Cambodia 0.20 0.05 0.17 Source: calculated from UNCOMTRADE Table 2 provides the information about averages fill rates of constrained goods under MFA from several countries to the US. During the decade before MFA expiration date, except in 8 The specification of MFA quota limits is broad and not in terms of values but in physical quantities. This way of specification could cause quality upgrading. Quality upgrading occurs when the quota causes the composition of imports to be titled towards goods that would be relatively more expensive under free trade. There are potentially two distinct types of quality upgrading: change in characteristics of given varieties and a shift in demand toward higher quality varieties. With a physical quantity quota, firms set quota rents as the same dollar amount over marginal cost (Harrigan & Barrows 2009). 5

the last three years, the fill rates of these countries had been very high in almost every specific product. Many product quota lines achieved 100 percent fill rates, except for some small number of categories that have fill rates less than 50 percent. Bangladesh fulfilled the highest quota fill rates among these countries. Meanwhile, Indonesia, China and India cannot fulfil quotas for some products. Table 2. Textiles & apparel export fill rate of quota products from some countries to the US Countries 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Indonesia 83.1 80.8 94.8 96.0 87.7 89.0 83.3 75.0 69.4 62.3 India 89.1 98.6 99.6 99.9 97.8 90.9 79.7 76.7 75.6 68.6 China 80.2 76.8 81.7 80.3 80.3 81.6 78.8 81.8 84.5 - Bangladesh 99.9 99.8 98.5 99.1 99.5 100.0 98.4 90.8 93.3 75.7 Vietnam - - - - - - - - 99.7 70.4 Cambodia - - 97.4-79.9 72.3 72.2 66.3 55.3 58.5 Sources: Office of Textiles and Apparel (OTEXA) The phase-out process of the MFA began after the Uruguay Round in 1995. The trade talks agreed to replace MFA with the Agreement on Textiles and Clothing (ATC) that arranged the schemes of quotas gradual elimination. 11 The ATC organized a series of phasing out stages at the beginning of 1995, 1998, 2002 and 2005, at which time all the remaining quotas were eliminated (Harrigan & Barrows 2009). Indonesia was one of the exporting countries that still had a large quota coverage before the elimination of MFA. For the US quotas categories, Indonesia still hold 64.2 percent quota coverages until the midnight of December 31, 2004, which means that most of Indonesia apparel exports to the US were under MFA frameworks at that time (Harrigan & Barrows 2009). As the new year started, quota restrictions were gone, Indonesia had the opportunity to expand its exports without quota limitation. So do exports from various developing countries; and these make apparel products from Indonesia should compete with other cheap products from all over the world. This exogenous shock was large, but it was anticipated as the exporters were given ten years to adjust. Some countries, such as China and Bangladesh, have intensified their apparel export significantly after the quota regime ended, but many other developing countries exports, such as Mexico, have been shrinking. Prices and qualities of products that enter the US have fell, especially for ones that were constrained before (Harrigan & Barrows 2009). Figure 2 shows the value of apparel exports form several countries over time. I use different axis for China because of its massive export expansion to the global apparel market. Bangladesh, Vietnam and India are also among the top clothing exporters and experience significant export increase after MFA abolition. From the figure, we also find that Mexico and 11 In this paper, to keep acronym profusion in check, I will continue to use MFA term even though it changed name into ATC. 6

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Billion USD Turkey experience export contraction, in which Mexico s performance declines dramatically and Turkey s export seems to increase in the recent years. Indonesia and Thailand export performance are relatively stagnant. Figure 2. Apparel export from various countries 25 200 20 15 10 5 150 100 50 - - Bangladesh Indonesia India Mexico Turkey Vietnam Thailand China Indonesia, the focus of this study, had benefited from MFA. During the implementation of MFA, export had increased, and Indonesia became one of noticeable players in apparel exports with global export share around 2 percent. The majority of apparel exports, about 60 to 80 percent, went to the US and EU countries, most of which were under the quota arrangements. Firms in the garment industry had special treatments and an opportunity to boost their export. Many firms appeared and had been able to access markets in developed countries. Propensity to export and the export intensities were higher compared to those in other sectors. The Ministry of Trade made rules to distribute quotas among firms that could be changing over time. There were some requirements to be a registered exporter and every year the government announced which firms got quota allocations for specific products (Krishna & Tan 1998). Exporters could be divided into four categories: exporters with past performances or experiences, new exporters, economically-weak groups and cooperatives, and export-only producers, which each of them had different volume allocations that could change over time. Firms that obtained this privilege could export garments without any restriction and competition pressure except for the volume limitations. The revocation of MFA has significantly affected Indonesia firms in several ways. Even though the total apparel export has increased, some firms died or stopped export while some others grew bigger. The number of exporters has declined, and ones that survived could learn, adjust 7

and compete in the new arena. It seems that the surviving exporters changed their product mixes for export or found other potential export markets (Athukorala & Pane 2017) to be able to face the more intense competition. The MFA was not the only policy that might affect exports of Indonesia garments. The increasing support from the government since the mid-1980s by introducing a series of trade reforms to reduce the anti-export bias as well as to maintain the effective real exchange rate (Thee 2009) had positively impacted Indonesia firms. The deregulation package in 1986, which introduced a duty exemption and drawback scheme that enables exporters to purchase their input at international prices, had also benefited export-orientated firms. Furthermore, the 2003 Labour Law might impact the labour-intensive industry, including garments (Manning, Aswicahyono & Dewi 2016). The law has increased protection for labours that increased the costs of permanent employees. Some companies respond to this regulation by hiring more workers on short fixed-term contracts that reduce the incentives to invest in training and skill upgrading. This might have potential implication on measuring the productivity, which is one of the main variables of interest in this study. Later, in the next section, some techniques to minimize this potential bias are introduced. Another factor that also simultaneously happened during the period of observations was the commodity boom. Prices of commodities had significantly increased during the first decade in the new millennium that might impact performances of manufacturing industries due to Dutch diseases effects. The real exchange rate is likely to appreciate during the boom and lower the incentives of non-commodity tradable sectors expansion, including manufacturing. Again, this factor might impact the performance of garment sectors, the focus of this study. I will explain some strategies to lessen this bias later. James, Ray and Minor (2003) argue that China accession to the WTO has become the biggest threat of Indonesia garment export. China has a significant competitive advantage in various sectors since it has a highly mobile and cheap labour force as well as economies of scale in the domestic market. The massive expansion of China s export is not only affecting Indonesia but also all countries in the world. It clearly increases the degree of competition in the global market including garments, and it also has impacts on Indonesia exporters. This factor may also bias the analysis and some techniques are applied to reduce the impact. 3. The model of learning-by-exporting Learning-by-exporting addresses a concept in which a firm improves its productivity once it enters foreign markets and gets exposed to knowledge and experience from abroad. Empirically, this mechanism has been shown in various case studies mainly in developing 8

countries, but is not clear in advanced countries. 12 There are two channels of LBE: the firms learn from their clients or they learn from their competitors. The first one denotes some implicit and explicit assistance from foreign buyers since they have incentives to share knowledge in order to obtain good quality products and precise specs. The latter refers to a fiercer competition situation that pushes firms to improve their performance. Adopting the best-practice technology, investing in marketing, upgrading product quality, doing innovation are some activities that firms might do during the learning process to be able to survive in the market (Athukorala & Rajapatirana 2000; De Loecker 2013). The hypothesis of LBE cannot be separated with the idea of self-selection into export. The self-selection mechanism argues that the distinction between exporting firms and nonexporting firms are already present before they start exporting, but only the more productive ones are able to overcome the cost of entering export markets (Bernard, Andrew B. & Jensen 1999; Bernard, Andrew B & Jensen 2004). Starting export is expensive since firms need to pay sunk costs, such as the costs of making connection with buyers, finding out about the foreign regulatory and assuring that the products can conform to foreign standards, such as testing, packaging, and labelling. In some instance, this may include the costs to set up new distribution channels in the foreign country and to adapt to the shipping regulation in that country (Roberts & Tybout 1997). Evidence from many countries has been consistent with the self-selection hypothesis. 13 Theoretically, Melitz (2003) has shown that attitude towards sunk costs determine firms decisions to export: only the most efficient firms can break into foreign market and make stable profits from exporting, whereas the less productive firms can only serve domestic market and the least productive ones exit. Both of these two hypotheses show the two-way relation between exporting and productivity; therefore, one cannot ignore either one when analysing how exporters are different from non-exporters. 14 This unique relation makes a challenge for researchers to analyse the causality between both as well as to interpret the results of empirical estimation. Since this paper s interest is to see the one-way impact of exporting on productivity, as suggested by previous studies, some methods are applied to minimize biases. 12 See some studies for developing countries, such as Alvarez and Lopez (2005) for Chile, Fernandes and Isgut (2015) for Columbia, De Loecker (2007) for Slovenia,Van Biesebroeck (2005) and for African countries, Du et al. (2012) for China, and also Blalock and Gertler (2004) and Pane (2016) for Indonesia, that find positive learning effects from exporting. On the other hand, results from developed countries are mixed. Bernard, Andrew B. and Jensen (1999) for US, Delgado, Farinas and Ruano (2002) for Spain, Greenaway, Gullstrand and Kneller (2005) for Swedish find no effects from exporting, while Baldwin and Gu (2003) for Canada, Girma, Greenaway and Kneller (2004) for UK suggest the presence of LBE. 13 See studies from Bernard, Andrew B. and Jensen (1999), Clerides, Lach and Tybout (1998), Aw, Chung and Roberts (2000), and Farole et al. (2013). 14 Exporting are systematically different from non-exporting firms in various ways. The former are larger, more productive, and more skill- and capital- incentive, use more varied input mix and pay higher wages than the later (Bernard, Andrew B. et al. 2012). A lot of studies from various countries have shown the evidence. A simple and well-known model by Bernard, Andrew B. and Jensen (1999) has been replicated in many articles and case studies. 9

The explanation of the model starts with a general LBE framework that has been developed in the previous studies (Bigsten & Gebreeyesus 2009; De Loecker 2013). In the next subsection, the identification strategy is further explained for model of learning-by-exporting under a policy intervention, the focus of this paper. Productivity estimation Consider a Cobb-Douglas production function (in logs) for firms i at time t where y it is output, l it is labour, k it is capital and m it is material inputs as follows, y it = β l l it + β k k it + β m m it + ω it +v it where ω it captures productivity and v it is the standard i. i. d error term capturing unanticipated shocks to production and measurement error. We can then derive the total factor productivity (TFP) ω it as residual ω it = y it β ll it β kk it β mm it. We hypothesise that TFP depends, amongst other things, on whether or not the firm was exporting in the previous year, ω it = δexport i,t 1 + φcontrols + θ it + ε it where controls denotes industry dummy and year dummy and θ it is firm-level specific aspects that impact productivity and ε it is pure random error. (1) (2) Bias in production function estimation The usual practice is a two-step approach, with TFP is first derived from equation (1) and then estimated on prior exporting status and other controls with equation (2). If the ω it is uncorrelated with the regressor, the productivity function can be estimated using OLS. However, the correlation between the factors and possible unobserved effects that includes productivity may affect the coefficients of the factors, thus biasing the TFP derived. If the unobserved effects are time-invariant firm characteristics, fixed effects estimation could reduce the bias. However, there is another source of endogeneity that might not be solved. If export status is correlated with inputs, then omitting the export dummy from the production function regression could yield inconsistent input coefficients and productivity estimates. In that case, incorporating export status in the function might reduce the bias. Substituting the export decision in the equation (1) and following Van Biesebroeck (2005), assuming that productivity evolves according to an autoregressive process yields the dynamic model, 10

y it = γy it 1 + β k k it + β l l it + β m m it + δexport it 1 + φcontrols + ω it + v it In the equation (3), we treat the export propensity as an endogenous variable since it may have reverse causality with the dependent variable. Applying GMM technique to solve the problem we can obtain input coefficients β k, β l, β m and productivity estimates ω it that are free from simultaneity bias. Another issue that may appear in estimating production function parameters is selection bias. This bias is because of the relationship between productivity shocks and the probability of exit from the market. If a firm s profitability is positively related to its capital stock, then a firm with more capital can be expected to produce greater future profits. The negative correlation between capital stock and probability of exit for a given productivity shock will cause the coefficient on the capital variable to be biased downward unless we control for this effect. We can solve this problem by following a method suggested by Olley and Pakes (1996) in which it is assumed that productivity shocks ω it follows the first order Markov process and capital is accumulated by firms through a deterministic dynamic investment process. Profit maximization yields an investment demand function that depends on state variables capital and productivity, as well as export participation, an additional state variable, as suggested by De Loecker (2007) and Amiti and Konings (2007), I it = i(k it, ω it, export it ). Inverting the investment function gives an expression of productivity as a function of state variables: capital, decision to export and investment, ω it = h(k it, I it, export it ). It is assumed that the adjusted investment function is still increasing in productivity (Van Biesebroeck 2005). By substituting the productivity expression in (1), we can express the production function as: y it = β l l it + β m m it + φ(k it, I it, export it ) + v it The equation (4) can be estimated using the procedures discussed in Yasar, Raciborski and Poi (2008). In the first step we obtain consistent estimates of β l and β m. In the second step of the estimation procedure, the probability that a firm exits from the sample is determined by the probability that the end-of-period productivity falls below an exit threshold. And in the third step, the coefficients of the state variables are estimated using nonlinear least squares. The preferable model is that based on the Olley-Pakes methodology because this procedure takes account of the simultaneity between input choices and productivity shocks, as well as sample selection bias of surviving firms. The model also incorporate the firms decision to enter international market via exporting. This is based on the assumption that there are sunk costs for entering the export market (Melitz 2003). This may be due to the search costs of finding the buyer and setting up the distribution network. In additions, this paper also uses the TFP estimates from GMM method as comparison. Similar to Olley-Pakes method, this approach has also tried to reduce the simultaneity between input choices and productivity shocks. Finally, this method already includes export decision in the production function. (3) (4) 11

4. Identification strategy: learning-by-exporting under a quota intervention Consider two periods that differ on whether or not a policy intervention is implemented. These periods are denoted MFAabol, equal to 0 if the MFA intervention is in effect and 1 if it is not implemented. p it is the term of productivity performance in the firm level that can be represented by TFP or by labour productivity. 16 p it = β 0 + β 1 export it 1 + β 2 MFAabol + β 3 (export it 1 MFAabol) + δz it 1 + ε it (5) Equation (5) suggests that the productivity of firm i in time t depends on last year exporting status as well as the implementation (and elimination) of MFA. I include the interaction term of the two variables to indicate the learning effect of a certain period. The interaction variable is our main interest. Z it 1 represents a series of observable firm level characteristics in the last year (foreign ownership, import share, size). The error term ε it can be divided into some unobservable firm characteristics that may affect the firm performance, time effects and a pure random error. Several combinations of estimation are applied to compare the results, given potential error bias. In equation (5), we only observe firms in the garment sector, the focus of this study, so it is arguably free from any industrial effect that might bias the estimation. However, later on, a dummy control is included to specify some firms that produce multiple products, both garment and textile products. 17 The quota regime was abolished starting in the early 2005, so we denote 2005 and years after as MFAabol equal to 1. However, we have to be careful to identify MFAabol equal to 0. As explained in the previous section, the plan to eliminate the MFA was announced during the Uruguay Round in 1994 and the step-by-step phase out process started in the beginning of 1995. Even though until 31 December of 2004 the quota coverage of Indonesia exports was still high (64.2 percent in the US), it is possible that firms have undergone adjustments before the complete elimination of the MFA. From Table 2 we find that the fill rate of quota products from Indonesia to the US decreased gradually after 2000, implying that exporters might adjust their constrained-unconstrained product mix combination some years before the MFA really ended. To tackle this situation, besides comparing before and after 2005 learning effects, this paper also contrasts the periods before 1995 (MFAabol =0) and 2005 onward (MFAabol =1). The former setting allows us to observe more data, whereas the latter gives us a clearer picture of the effect of MFA. 18 16 In this paper, both TFP estimates derived from the GMM method and from the Olley-Pakes procedures are utilized as dependent variables. In addition, since estimating TFP leads to data truncation, I also applied labour productivity as one of dependent variables. By using labour productivity, we can have more data for analysis. 17 Some garment firms in Indonesia also produce textiles. We include the textile dummy because this kind of firms might have a systematic different performance with firms that only produce apparels. The former might also control inputs (textile products) to produce better clothing in term of quality, cheaper price and so on. 18 A series of robustness checks is examined to see how results differ if some combination of MFA period definitions are used. 12

Containing other possible non-mfa interventions Equation (5) might still have some problems due to the effects from other possible interventions. As explained before, there are some other possible factors that both affect the export participation as well as productivity. As explained in the previous section, a series of trade reform in the 1980s had benefited exporting firms and it might still have impact on firms performance in the 1990s. Commodity boom in the 2000s might also affect manufacturing performance, both productivity as well as decision to export. Moreover, we cannot ignore that China expansion in the global market has influenced firms all over the world, including Indonesia. The fiercer competition could affect firm s performance and export. In addition, the 2003 Labour Law as well as other labour-related regulations 19 might also affect productivity and export participation of labour intensive industries like garment. Furthermore, if we directly compare firm performance before 1994 and after 2005, there might be some other potential biases because the situations in both periods are much different. Asian financial crisis in 1998, followed by reformation and decentralization could induce structural changes and influence firm performances. However, all those factors also effect firms in every manufacturing sector, not only garment. Therefore, to reduce biases due to those external interventions, we can compare garments with another sector that also experienced all the other external interventions except MFA. Footwear industry might be comparable with garments. Both are footloose labour-intensive industries that are mainly located in Java. They are both export-oriented industries and exporters in these two sectors have strong connection with their foreign buyers. Their buyers have supervised those exporting firms in both sectors in design, fabric, quality, as well as delivery schedules (Manning, Aswicahyono & Dewi 2016; Thee 2009). More importantly, both have experienced similar external interventions mentioned earlier. The 1980s trade reforms, commodity boom, China effects, labour regulation, crisis have affected both sectors in arguably similar way. Since both the MFA implementation and its abolition are in the same period with those other external interventions, and MFA only affects garment and doesn t affect footwear, comparing those two sectors in the model can reduce the bias from other interventions. To examine this, we estimate the following: p it = β 0 + β 1 export it 1 + β 2 MFAabol + β 3 garment i + β 4 (export it 1 MFAabol) + β 5 (export it 1 garment i )+) + β 6 (garment i MFAabol) + β 7 (export it 1 MFAabol garment i ) + δz it 1 + ε it In equation (6), garment i refers to a dummy variable equal to 1 if firm i is in apparel industry and 0 if it is in footwear industry. We have some forms of interaction variables, but the main focus is β 7, the coefficient for interaction of three variables. This coefficient indicates the (6) 19 Such as minimum wages regulations. 13

difference of learning-by-exporting effects of garment- relative to footwear exporters after the MFA abolition compared to when it was still implemented. Reducing selection bias Some earlier studies of LBE propose that comparing the treated (exporting firms) with all (non-exporting) groups might lead to bias, since selection into the treated group is not random and both groups have different characteristics. One solution is to compare those two with similar characteristics through matching procedures. In so doing, some firms in the control group are selected to match with similar treated firms and some firm level variables are used to determine how similar the firms in both groups are (Bigsten & Gebreeyesus 2009; Girma, Greenaway & Kneller 2004). First of all, variables that make a firm more likely to export are identified. Literature suggests foreign ownership, size, capital intensity, import share, firm age, and productivity determine the propensity to export (Bigsten & Gebreeyesus 2009; Roberts & Tybout 1997). The location of firms as well as the type of industry and time effects also define the probability of exporting. In this paper, the probability to export is estimated using the following export participation equation EX it = FDI it 1 + Size it 1 + K/L it 1 + Age it 1 + Java + garment + Year + u it where EX it is an export-dummy, equal to 1 if the firm does export in year t and 0 otherwise. FDI it 1 is the lag foreign ownership dummy, equal to 1 if the firm has foreign ownership last year and 0 otherwise. Size it 1 is the lag number of employees and K/L it 1 is the ratio of capital to the number of workers in last year. Age it 1 is the firm age or the number of years since the firms exist in the data. 20 We include the location dummy (Java and non-java) of the firms, industry dummy (garment and footwear) and year dummy in the matching procedures. The propensity score is estimated with a probit model with nearest-neighbours matching applied. The common support condition is imposed and the outputs are labour productivity and TFP. Only matched observations are then included in the main equations (5) and (6). (7) 5. Data Description The main source of data is Industrial Statistic (Statistik Industri, SI) that surveys all medium and large manufacturing establishment in Indonesia, firms that have 20 or more workers. The data is collected by Central Bureau of Statistics (Badan Pusat Statistik, BPS) and captures 20 The survey doesn t identify the year of firm establishment. To proxy firm age, this paper calculates the number of years that the firms exist in the dataset. 14

various detailed information of firms, such as location, inputs and components of production costs, outputs and value added, ownership, export status and export intensity, import status and volume, employment, capital and new investment. All data in value are deflated using wholesale price index that is also from BPS. We can observe 25 years panel data from 1990 to 2014, but as explained in the previous section, this paper used two different sets of data: 1990-2004 and 2005-2014 as well as 1990-1994 and 2005-2014. Since in the data we still include the period of the Asian financial crisis, 1997-1999, and global financial crisis, 2008-2009, we included the year dummy in the estimation. As explained before, there are still a lot of factors that might distort the data, but these will be dealt with our identification strategy to minimize the bias. Since our main focus is to see the learning effect from exporting, we ignore firms that do export one time only for the whole period. That is, we assume that these firms only do export for trial and error so we do not expect them to learn from exporting. Incorporating them may therefore lead to bias results. As a consequence, the number of observations every year varies with the overall observation of 52,825 for garments and 8,778 for footwear. The capital stock data could be problematic given there are many missing observations in various years. In the raw data, some observations have no information about capital. For 2006, there is no record about the capital stock at all. To deal with these issues, I undertook the following. 21 All firms with no capital data in all years were dropped. As for 2006, I interpolated the capital stock data based on the values in 2005 and 2007. One consequence is that all firms with missing capital stock data for both 2005 and 2007 are gone. Next, firms with missing capital data in three or more continuing years were also removed. For those with missing data up to two consecutive years, I again applied interpolation. And finally, firms with negative capital data were removed. These procedures reduce observations by about 48%. The final number of observations are 28,195 for garments and 3,878 for footwear. I am aware that this data problem could lead to attrition bias. I test for the attrition bias and the result show that there is no significant difference in variable export, foreign ownership, and import share after the attrition, but the test show that removed observations are larger firms. Therefore, this study conducts a separate analysis for these two datasets: the full observation set and the reduced one. For equation (5) and (6), the dependent variable is the firm performance in the form of labour productivity or the TFP. If the left hand side is the labour productivity, the full dataset can be applied. However, the reduced dataset is used if the dependent variable is TFP. It is argued that the better model is when we use TFP as the dependent variable because it has less bias, albeit at the costs of reduced dataset. However, if the results for all estimations, whether by using the full dataset or the reduced one, go in the same direction (and hence interpretation), we could infer that the results are robust. 21 For some of the steps in cleaning the capital data, I follow Blalock and Gertler (2004). 15

Table 3. Descriptive statistics for full dataset Variables Garment MFA = 0 MFA = 1 1990-1994 1990-2004 2005-2014 Mean Sdt. Dev. Mean Sdt. Dev. Mean Sdt. Dev. Number of observations 4,962 25,136 27,689 Log value added per worker 8.52 0.95 8.85 0.98 9.29 0.93 Exporting firms (0-1) 0.23 0.42 0.17 0.38 0.14 0.40 Export intensity (0-100) 18.96 37.03 14.34 33.17 11.98 30.40 FDI (0-1) 0.06 0.24 0.07 0.25 0.07 0.25 Import share (0-100) 10.52 26.04 11.20 27.40 9.27 24.96 Total workers 264.36 653.27 245.29 611.26 205.72 630.87 Multiproduct firm 0.55 0.50 0.52 0.50 0.54 0.50 Footwear Number of observations 978 4,585 4,193 Log value added per worker 8.88 0.95 9.17 0.95 9.79 0.87 Exporting firms (0-1) 0.35 0.47 0.23 0.42 0.14 0.34 Export intensity (0-100) 25.74 39.40 17.05 34.25 9.84 27.09 FDI (0-1) 0.14 0.35 0.13 0.33 0.11 0.32 Import share (0-100) 22.62 34.18 17.12 30.45 9.24 23.90 Total workers 685.31 1,291.88 679.80 1,678.50 481.60 1,847.96 Source: Statistics Industry (1990-2014) Table 3 and Table 4 present the statistics of the full data set and the reduced data set, respectively. Table 3 compares the statistics for garments and non-garments as well as the period with MFA and without MFA. We expect that the statistics of some control variables for garments before and after the removal of MFA remain similar. The average proportions of foreign-owned firms remain similar in both periods. As explained in the model section, some establishments do both garment and textile activities (or multiproduct firms ). The average number of exporting firms decreases and the labour productivity increases. Table 4 also shows similar patterns. In this table, we also present the average figures of capital per labour as well as estimated TFPs. The TFPs are produced using different approaches, namely Olley- Pakes and GMM estimations. 16

Table 4. Descriptive statistics for reduced dataset Variables Garment MFA = 0 MFA = 1 1990-1994 1990-2004 2005-2014 Mean Sdt. Dev. Mean Sdt. Dev. Mean Sdt. Dev. Number of observations 2,426 12,673 15,522 Log value added per worker 8.35 0.93 8.65 0.98 9.08 0.94 TFP - Olley Pakes 3.63 0.47 0.38 0.48 3.95 0.49 TFP - GMM 5.08 0.58 5.17 0.59 5.31 0.56 Exporting firms (0-1) 0.22 0.41 0.18 0.39 0.14 0.35 Export intensity (0-100) 18.46 36.90 15.37 34.25 12.05 30.27 FDI (0-1) 0.04 0.21 0.05 0.22 0.05 0.22 Import share (0-100) 9.47 24.55 10.51 26.55 8.16 24.17 Total workers 234.25 652.01 206.04 596.91 174.17 586.16 Multiproduct firm 0.57 0.49 0.57 0.49 0.58 0.49 Footwear Number of observations 431 1,918 1,960 Log value added per worker 8.86 0.88 9.07 0.91 9.59 0.86 TFP - Olley Pakes 3.36 0.44 3.47 0.43 3.71 0.42 TFP - GMM 5.22 0.57 5.26 0.55 5.34 0.51 Exporting firms (0-1) 0.33 0.47 0.25 0.43 0.13 0.34 Export intensity (0-100) 25.06 39.33 17.57 34.72 9.76 27.14 FDI (0-1) 0.11 0.31 0.09 0.29 0.08 0.27 Import share (0-100) 24.08 35.21 16.55 30.09 6.38 20.96 Total workers 699.59 1,446.57 644.22 1,539.37 312.45 1,181.93 Source: Statistics Industry (1990-2014) and TFP estimations. Comparing garment and footwear might also be problematic because they may have different industry characteristics. To reduce this problem, industry characteristics are included when doing matching procedures as well as estimating how those sectors differ in learning-byexporting in both periods. Figure 3 shows the average performance and average characteristics of garments and footwear industries over years. These two sectors have similar trend in the labour productivity and the total factor productivity. Moreover, in both sectors, the trend of foreign ownership participation, one of the control variables, seems to move in the same direction during the period of observations. This figure may give some indication that these two sectors are comparable. 17

Figure 3. Comparing garment and footwear performance Total Factor Productivity Labour Productivity 3.2 3.4 3.6 3.8 4 4.2 8 8.5 9 9.5 10 1990 1995 2000 2005 2010 2015 (min) opdisic 1990 1995 2000 2005 2010 2015 (min) opdisic garment footwear garment footwear a. Trend of TFP Olley Pakes of two sectors b. Trend of productivity of two sectors.02.04.06.08.1.12 Foreign firms 1990 1995 2000 2005 2010 2015 (min) opdisic 3.5 4 4.5 5 5.5 Ln employment 1990 1995 2000 2005 2010 2015 (min) opdisic garment footwear garment footwear c. Trend of foreign owned participation d. Trend of employment 6. Results TFP estimation Table 5 shows results from production function estimations for garment and footwear sectors based on various model: OLS, fixed effects, GMM and Olley-Pakes. All the results indicate that both industries are labour-intensive sectors. Table 5. Coefficients of the production function Industry Labour Material Capital OLS FE GMM OP OLS FE GMM OP OLS FE GMM OP Garment 0.43 0.39 0.31 0.42 0.57 0.57 0.52 0.56 0.10 0.03 0.05 0.08 Footwear 0.38 0.35 0.25 0.37 0.63 0.61 0.57 0.62 0.05 0.05 0.03 0.06 18

Matching procedures As noted earlier, to reduce the selection bias from export we apply propensity score matching procedures before doing the main equation. We match exporters with their non-exporters counterparts of similar characteristics. The results from the matching show that lag foreign ownership, firm age, capital per labour, and import share are significant in determining export participation. Industry dummy is also significant, but variable location (Java) is not significant. Figure 4 shows results distribution of exporters and non-exporters before and after matching. Figure 4. Results from matching procedures kdensity _pscore 0 2 4 6 8 kdensity _pscore 0 2 4 6 8 kdensity _pscore 0 1 2 3 4 5 kdensity _pscore 0 1 2 3 4 5 0.2.4.6.8 propensity scores BEFORE matching 0.2.4.6.8 propensity scores AFTER matching 0.2.4.6.8 1 propensity scores BEFORE matching 0.2.4.6.8 1 propensity scores AFTER matching treated control treated control a. Matching results from full database b. Matching results from reduced database Test for structural break I construct a time series of 25 years data for performing a test for structural break. Firstly, I test for structural break at predetermined year of 2005. The test rejects the null hypothesis of no structural break. These results support the argument that the MFA abolition at the beginning of 2005 can be associated with a transformation of the garment industry performance in Indonesia. Subsequently, the study performs another test to detect the year of break. 22 The test rejects the null hypothesis of no structural break and detects a break in 2003. I interpret this as the adjustment effect. Since firms had anticipated the MFA removal, the structural break occurred earlier in 2003. The test result also can explain Table 2 in which Indonesia fill rate had declined few years before the MFA elimination. Figure 5 plots the time path of the productivity and export of garments over the period. 22 I test using command sbknown in Stata to test for structural break at predetermined year. I also conduct a test using command sbsingle to detect when the break occurs. 19