Employment and Wage Effects of Export VAT Rebates: Evidence from China

Transcription

1 Employment and Wage Effects of Export VAT Rebates: Evidence from China Bo Gao Durham University First Draft September 2016 This Version September 2017 Abstract This paper studies the employment and wage effects of export VAT rebates (VATRs) with comprehensive firm-level and product-level data of China. To address the potential endogeneity, the paper constructs instruments with predicted VATRs adjustments and demand uncertainty based on the fact that exporters learn from others. The results suggest that a one percentage point increase of firm-specific VATRs raises firm employment by 0.258% while affecting wage insignificantly. Moreover, the paper decomposes firm-specific VATRs adjustment into components of within-product change, reallocation and the net entry due to product entry and exit. It is found that the employment effect of VATRs is attributed to firm-specific VATRs adjustment driven by product entry and exit. Furthermore, this paper studies the mechanisms of employment effect of VATRs. In particular, the paper finds that less than a quarter of employment effect of VATRs is explained by the mechanisms of exports, input substitution and the cost of new products. It appears that the most plausible mechanism for the rest of employment effect is the increase of labor intensity due to product entry and exit. Keywords: VAT Rebates; Employment; Wage; Decomposition; Mechanism JEL: F14, F16, J01 Durham University Business School, Mill Hill Lane, Durham, DH1 3LB, United Kingdom

2 1 Introduction The input value-added tax (VAT) paid by firms for domestic sales is ultimately borne by the consumers. Instead, the paid input VAT for exports is fully or partially refunded by the governments because exports are exempt from VAT to foreign importers. 1 This is the trade policy of VAT rebates (VATRs). 2 There has been few studies on the VATRs effects on exports (Chandra and Long, 2013; Braakmann et al., 2017; and Gourdon et al., 2017). However, there is no research about the VATRs effects on the labor market. Crucially, VATRs effects on the labor market are essential to evaluate the welfare effects of the policy. To address this issue, this paper is the first to study the employment and wage effects of VATRs with comprehensive firm-level and product-level data. This paper makes four contributions. The first contribution is that we construct firmspecific VATRs to measure the firm-specific exposure to product-level VATRs adjustments. The second contribution is that to address the potential endogeneity, we construct the instruments based on the fact that exporters learn from others about the uncertainty of foreign markets, e.g. demand uncertainty and fixed cost uncertainty. The third contribution is that we are able to decompose firm-specific VATRs adjustment into three components: withproduct change, reallocation and the net entry due to product entry and exit. Then we study the effects of each component to shed light on the source(s) of VATRs adjustment that affects the employment and wage. Last but not least, we explore the mechanisms of VATRs effects. Since there has been evidence that VATRs affect exports, it is natural to think that the increase of exports (quantity and quality) may be the main mechanism. Surprisingly, we find that exports only explain a very limited part of VATRs effects. It appears that the plausible mechanism for the most of employment effect is the increase of labor intensity due to product entry and exit. This finding suggests that trade models of multi-product firms might have to incorporate comparative advantage and factor intensity across products to explain the firm s adjustment of product scope in response to trade shocks. In most databases, the employment and wage are collected at firm level. However, the rates of VATRs are set at product level. Given the prevalence of multi-product firms in the market (e.g. Bernard et al., 2010), it is difficult to get the product-level employment and wage with the firm-level data. Instead, we construct firm-specific VATRs and thereby estimate the employment and wage effects of VATRs at firm level. It is intuitive to construct firm-specific VATRs: on the one hand, firms export different products whose VATRs are adjusted differently; on the other hand, exports of each product are different even when firms 1 The importing countries often impose VAT to the imports to ensure an equal competitiveness between the imports and the domestic products. Therefore, to avoid double taxation the exporting countries do not impose VAT for exports. 2 This policy is allowed by the WTO as long as the tax refunded does not exceed the tax levied. 2

3 export the same products. Thus the perceived VATRs adjustments are very different across firms. We take these into consideration and define firm-specific VATRs as the weighted average of VATRs rates of all products exported by the firm, where the weight for each product is its export share in total exports of the firm. 3 Chandra and Long (2013) infer firmlevel VATRs with firm-level data on revenue, paid VAT and overall VAT payable. 4 The fact that we have product-level data on VATRs rates and exports allows us to directly measure firm-specific VATRs. More importantly, our measurement allows us to decompose the firmspecific VATRs adjustment into with-product change, reallocation and the net entry due to product entry and exit, and thereby allows us to find the source of the effects. To empirically study the employment and wage effects of VATRs is hindered by the potential endogeneity. There are four sources. The first source is the firm s selection of products. Employment, wage and selection of products may be jointly determined and affected by some common factors. For example, the fiscal condition of the region that the firm is located in affects the firm employment and wage (e.g. see the implications of government spending for the labor market in Burnside et al. (2004) and Monacelli et al.(2010)). Meanwhile, fiscal condition of the region also affects firm s selection of products because a firm located in a region with high fiscal deficit rate may select products with low VATRs to avoid the risk of no or delayed rebates (Chandra and Long, 2013). This source of endogeneity is largely addressed by the control variables and various fixed effects. The second source is the potential endogeneity of product-level VATRs adjustments. A number of VATRs adjustments are responses to export shocks during economic crisis. The endogeneity issue arises if export shocks affect firm s employment and wage through other channels that are not controlled. However, this source of endogeneity should be fairly minor in our sample because we select the period from January 2005 to December 2006, a period during which VATRs adjustments were aimed at upgrading the economy structure, optimizing resource consumption and reducing environmental pollution. As documented by Braakmann et al. (2017), the adjustments during this period were indeed related to product characteristics, such as whether the product is resource-intensive, high-tech, pollutive and energy-consuming, but are unrelated to export demand shocks. Moreover, we include sector fixed effect in our regression, which acts as an additional safeguard to control for sector-level export shocks. Therefore, VATRs adjustments for our analysis are plausibly exogenous. The third source is allocation of exports across products. The employment or wage at firm level may affect the exports of different products differently. For example, firms may 3 Our firm-specific VATRs are similar to the firm-specific exchange rate shocks in Dai and Xu (2017) who use the weighted average of destination-level exchange rates. 4 In their paper, the actual VAT rebate rate for a company k in year t is (0.17 revenue kt VAT on throughput kt VAT payable kt )/export kt if export kt > 0. 3

4 hire skilled labor and pay high wage to produce and export more of high-quality products (Verhoogen, 2008). Thus the increase of employment or wage at firm level attributed to the increase of skilled labor, may especially benefit some specific products and shift the export share to those products. Moreover, Ma et al. (2014) find that products within firms have different capital intensity, which suggests that increase of employment may increase the exports of labor-intensive products more than capital-intensive products. The fourth source is the potential endogeneity of product entry and exit. Firm-specific VATRs adjustments are endogenous if the product entry and exit are endogenous. Indeed, a change of firm employment or wage may affect the product entry and exit. For example, an increase of employment and wage due to hiring more high-skilled labor may benefit high-quality products, making firms drop the low-quality products and introduce new high-quality products exploiting the high-skilled labor. Moreover, the employment, wage, product exit and entry may be jointly determined and affected by some common factors. For example, Eckel and Yeaple (2017) show that firms sorting abilities of high-quality workers affect employment, wage and product scope of firms. To address the endogeneity issues, we develop novel instruments of predicted firmspecific VATRs and demand uncertainty based on the fact that exporters learn from others about the uncertainty of foreign markets. The predicted firm-specific VATRs is calculated with predicted export share, which is obtained if the allocation of exports cross the products by the firm is exactly the same as the allocation of exports by the rest of the country when there were only those products. Firm-specific VATRs are affected by the predicted firm-specific VATRs because exporters learn from others about the uncertainty of foreign markets so that they may follow the export pattern of the rest of country. For instance, Segura-Cayuela and Vilarrubia (2008) argue that potential exporters can learn the uncertainty of fixed cost to enter foreign market from existing exporters profitability in foreign markets. Fernandes and Tang (2014) show that exporters learn the demand uncertainty from observing the neighbors exports. Kamal and Sundaram (2016) document that exporters learn from neighbors in generating importer-exporter matches. However, the predicted firmspecific VATRs are not related to single firm s characteristics including employment and wage because the predicted export share is calculated from the nationwide exports subtracting its own exports. Demand uncertainty is a determinant of product entry and exit in the foreign markets (e.g. Iacovone and Javorcik, 2010; and Albornoz et al., 2012). Thus we include a measure of firm-specific demand uncertainty in the instruments to address the potential endogeneity particularly arising from the fourth source. Our measure of demand uncertainty is the weighted average of demand uncertainty of all products exported by the firm. More specifically, in the first step, we calculate the demand uncertainty of every product, which is the 4

5 standard deviation of aggregate export growth in the past several years. In the second step, we calculate the weighted average of demand uncertainty of all products for each firm. To circumvent the endogeneity from the export share, we use the predicted export share as the weight. Thus, our measure of demand uncertainty is related to the product entry and exit, but unrelated to single firm s characteristics. The first set of instruments used to estimate the employment and wage effects of VATRs are the predicted firm-specific VATRs adjustment and demand uncertainty. To shed light on the source(s) of VATRs adjustment that affects the employment and wage, we decompose VATRs adjustment into components of with-product change, reallocation and the net entry. To address the potential endogeneity of each component, we calculate the predicted components with the predicted export share. Then we use the predicted component and demand uncertainty as the instruments for the corresponding component to estimate the employment and wage effects of each component. Moreover, the predicted components and demand uncertainty serve as the second set of instruments for the firm-specific VATRs adjustment. The employment effect of VATRs is not significant with OLS estimation. However, the effect becomes positive and significant when we use instruments to correct for the potential endogeneity. The first stage about instruments of the predicted firm-specific VATRs adjustment and demand uncertainty shows that they are significantly related to firm-specific VATRs adjustments and are thereby valid instruments. The second stage shows that a one percentage point increase of firm-specific VATRs increases firm employment by 0.258%. With the predicted components and demand uncertainty as the instruments, the first stage shows that all components and demand uncertainty are significantly related to firm-specific VATRs adjustments. The second stage reports a similar employment effect: a one percentage point increase of firm-specific VATRs increases firm employment by 0.224%. However, the wage effect VATRs is not significant in the OLS estimation and estimations with instruments. It appears that firms adjust employment rather than wage with VATRs adjustments. The wage effects of all components are not significant as well. However, the employment effects of VATRs adjustment through different components are quite different. Though within-product change of VATRs adjustment accounts for the most of firm-specific VATRs adjustment, it does not affect employment significantly. The employment effect of reallocation is negative and significant while the effect of the net entry is positive and significant. The negative effect of reallocation is surprising at the first glance. However, it is reasonable given that the reallocation is mainly driven by exports reallocation between survival products and new or exiting products. For example, when exports are reallocated from the survival products to new products (export share of survival products is smaller), the reallocation is negative. As a result of the estimation, the employment is increased. On the contrary, when exports are reallocated from the exiting products to the survival products (export share of 5

6 survival products is larger), the reallocation is positive and the employment is decreased. The positive employment effect of the net entry suggests that more new products than existing products or larger export share of new products than existing products increases the employment. If we ignore the employment effect of within-product change due to its insignificance and weakness, we can calculate the employment effects across firms. This exercise shows a significant job reallocation across firms: the firm at the 10th percentile of the distribution of employment effects decreases the employment by 2.29% while the firm at the 90th percentile increases the employment by 4.02%. Another very important implication from the effects of components is that the employment effect of VATRs is mainly attributed to the VATRs adjustment driven by the product entry and exit. The employment and wage effects of VATRs adjustment and its components are very robust to various checks. The first concern of our instruments is that large exporters may lead, rather than follow, the export pattern of the rest of the country. Therefore, we estimate the effects with the sample of small exporters, and the results are very robust. The second concern is that all firms may experience common export shocks. To address this issue, we use lagged exports by one year to calculate the predicted export share and the subsequent instruments. The results of new instruments are again robust. The third concern is that firms may learn from the exporters that are relatively close to them, i.e. the neighbors. Thus, we use regional exports, instead of nationwide exports, to calculate the predicted export share and the instruments. The results are robust as well. The fourth concern is that the results may be affected by the selection bias if the decision to exit the foreign market of firms is affected by firm-specific VATRs. Following Olley and Pakes (1996), we address this issue by modeling the probability to stay in the market as the function of observed variables and including the predicted probability as an additional control variable to estimate the VATRs effects. The results are again robust. We also consider robustness checks on potential impacts of bonded materials and multi-product firms. All results are robust. The estimations on the effects of the components have shown that the employment effect of VATRs is mainly attributed to the VATRs adjustment driven by the product entry and exit. This paper further studies the mechanisms on why the product entry and exit may increase the employment of firms. In particular, we study three mechanisms. The first mechanism is exports: the increase of firm-specific VATRs, especially due to the product entry and exit, increases the export quantity and quality of the firm (e.g. Chandra and Long, 2013; Braakmann et al., 2017; and Gourdon et al., 2017), thereby increasing the firm employment. For example, the new products are exported more and of higher quality than exiting products, thereby requiring more labor to produce. The second mechanism is the input substitution of firms: with higher VATRs, firms may use the labor to substitute the intermediate input. The third mechanism is the cost of new products. For example, the process of product entry, 6

7 e.g. invention of products and upgrading the product line, requires the use of labor. As a result, firm employment will be increased with the increase of VATRs driven by product entry and exit. With the predicted (components of) firm-specific VATRs adjustment and demand uncertainty as the instruments, conditional on all these factors the employment effect of a one percentage increase of VATRs is decreased from 0.258% (0.224%) to 0.202% (0.169%). That is to say, in total, 21.7% (24.6%) of employment effect of VATRs is explained by the mechanisms of exports, input substitution and the cost of new products. It appears that the most of the employment effect, i.e. more than three quarters, is used to increase the labor intensity of the firms. For example, the new products may be more labor-intensive than the exiting products. This is consistent with the findings of Ma et al. (2014) that a firm in a labor-abundant country specializes in its core competencies by allocating more resources to produce more labor-intensive products. Using the data including our sample period, they find evidence that Chinese exporters tend to add products that are more labor-intensive than their existing products and drop those that are less labor-intensive. We believe that our strategy to construct instruments based on exporters learning from others is original. A very close strategy comes from Davis and Haltiwanger (2014) and Chodorow-Reich and Wieland (2016). Davis and Haltiwanger (2014) use predicted job reallocation of the state based on the national job reallocation as the instrument of job reallocation of the same state. Chodorow-Reich and Wieland (2016) use predicted labor reallocation based on the employment in the industry of the rest of the country as the instrument for the labor reallocation of the same industry. Both studies rely on the assumption that the reallocation within an industry or a state may follow the pattern of the whole country. We apply the same logic that export pattern of an exporter may follow the pattern of the rest of the country, but ours has strong theoretical and empirical supports from the literature on exporters learning (e.g. Segura-Cayuela and Vilarrubia, 2008; Fernandes and Tang, 2014; and Kamal and Sundaram, 2016). The idea of our strategy is also present in some other literature. For example, a large number of studies on the effects of immigrants uses predicted immigrants based on previous immigrants as the instrument (e.g. Card, 2001; Dustmann et al., 2005; Cortes, 2008; Peri, 2012; and Dustmann et al., 2013). Their intuition is that immigrants location choices follow the settlement patterns of previous immigrants. A series of literature on the impacts of import competition from China uses the contemporaneous composition in a country and the growth of Chinese imports in other similar countries to construct the instrument of import competition from China in the country (e.g. Autor et al., 2013; Autor et al., 2014; Autor et al., 2015; Acemoglu et al., 2016; and Bloom et al., 2016). Their intuition is similar to our paper in the sense that the growth of imports from China in a country may follow the pattern of growth of imports from China in other similar countries. In addition to the work cited above, this paper contributes to the studies on VATRs, in 7

8 particular on VATRs effects on exports. Theoretically, Feldstein and Krugman (1990) show that a partial rebate on VAT makes non-refunded VAT act as an export tax. This export tax is lower as VATRs become higher. As a result, VATRs are positively related to exports. Chandra and Long (2013) use regional fiscal deficit rate as an instrument for firms VATRs to identify the VATRs effect on firm s export value. Braakmann et al. (2017) exploit China s frequent adjustments of product-level VATRs and large-scale data on export transactions to estimate the VATRs effects on exports. They find significant effects on export quantity and export quality. Gourdon et al. (2017) have also studied China s VAT rebates with productlevel data. However, instead of investigating the direct effects of VATRs on exports, they use the non-refunded VAT to measure export tax and explore its effects on exports. There are also some studies on VATRs that do not rely on firm-level or product-level data: Chao et al. (2006) simulate a simple general equilibrium model with a rise of VATRs and find a positive relationship between exports and VATRs; Chen et al. (2006) find VATRs are positively correlated with exports using Chinese country-level data. The literature has also studied the motivations of adoption and adjustments of VATRs, e.g. environmental concerns (Song et al., 2015; Gourdon et al., 2016; and Eisenbarth, 2017) and subsidization of downstream sectors (Garred, 2015; Gourdon et al., 2016). The paper also contributes to the wider literature on the effects of trade policy on the labor market. In particular, the trade liberalization in terms of tariff reduction and exchange rate shocks have been intensively studied. Trade liberalization has been found to be associated with the employment and wage (e.g. Revenga, 1997; Attanasio et al., 2004; Trefler, 2004; Goldberg and Pavcnik, 2005; LaRochelle-Côté, 2007; Artuç et al., 2010; Edmonds et al., 2010; Menezes-Filho and Muendler, 2011; Amiti and Davis, 2012; Krishna et al., 2014). In particular, Amiti and Davis (2012) theoretically and empirically show that the effects of tariff reductions for both input and output on wage are subject to firm-specific engagement into trade. 5 Krishna et al. (2014) emphasize that the effect of tariff reductions on wage is affected by the quality of matching between workers and firms. The effects of exchange rate on labor market have also been investigated by various studies (e.g Goldberg et al., 1999; Campa and Goldberg, 2001; Klein et al., 2003; Verhoogen, 2008; Nucci and Pozzolo, 2010; Ekholm et al., 2012; and Dai and Xu, 2017). In particular, Dai and Xu (2017) construct firm-specific exchange rate shocks and find a significant effect on labor reallocation across firms. Our paper also highlights the firm-specific shocks due to the change of trade policy and studies the effects at the firm level. The rest of this paper is organized as follows. In section 2 we introduce the background 5 They find that a fall in output tariffs decreases wages at import-competing firms but increases wages at exporting firms. Moreover, they find that a fall in input tariffs increases wages at import-using firms relative to those at firms that only use local inputs. 8

9 and implementation of China s export VATRs. In section 3 we present the identification strategy. In section 4 we describe how to construct the data. In section 5 and 6 we report the empirical results and the discussions on the mechanisms. Section 7 is the conclusion. 2 China s VATRs 2.1 Background China started the policy of VATRs in Exports were exempted from VAT to foreign importers and the paid input VAT was fully refunded. VATRs have experienced a number of adjustments over the years. 7 At the beginning, the adjustments were responses to the heavy fiscal burden of the government and the rebates fraud. 8 However, in the past two decades, VATRs adjustments generally served two practical purposes. The first and foremost one is to promote exports. As an export-promoting tool, it has been frequently adjusted when exports face negative shocks, in particular, during the economic crisis. For example, after 1997 Asian Financial Crisis, China s exports dropped. Instead of depreciation of Chinese currency to promote exports, VATRs were adjusted more than 10 times in 1998 and During this period, a lot of products received higher rates of VATRs. During 2008 and 2009, China s exports were affected by the global financial crisis. Consequently, rates of VATRs were increased for the products whose exports were significantly affected, including textiles, clothing, furniture, toys and electromechanical products. The other purpose is to upgrade the structure of the economy. For example, from 2003 to 2007, VATRs were adjusted more than 10 times. The main aims of theses adjustments were to optimize resource consumption and to reduce environmental pollution. 9 Consequently, these adjustments were mainly reductions of VATRs for high energy-consuming and high polluting (e.g. steel products, pesticide, chlorine and other chemical products), and resource-based products (e.g. rare earth metals, silicon and wooden products) 10 and 6 China s trade policy of rebates for exports was introduced in 1985 based on industrial and commercial standard tax ( Gong Shang Tong Yi Shui in Chinese). In 1994, China s tax system was reformed and VAT became a major tax. Since then, rebates for exports has been based on VAT. 7 See Braakmann et al. (2017) for more details on the adjustments and circulars of the adjustments. 8 In 1994, the rebates from the government increased by 150 percent to 75 billion yuan with 30 billion yuan being deferred to 1995 due to the state s budget constraint (Cui, 2003). To relieve the heavy fiscal burden and solve the fraud problem, VATRs for most products were decreased by 3 percentage points in 1995 and further decreased by 4 percentage points in For example, see Fa Gai Jing Mao[2005] 1482 Hao and 2595 Hao, Circular No 1482 and 2595 jointly issued by National Development and Reform Commission, Ministry of Finance, Ministry of Commerce, Ministry of Land and Resources, General Administration of Customs, State Administration of Taxation and Ministry of Environmental Protection. 10 These products are called Liang Gao Yi Zi in Chinese. 9

10 increasing the VATRs for agricultural products, high-tech equipment and IT products. As this paper is studying the employment and wage effects of VATRs, it is important to avoid adjustments that happened in response to (negative) export shocks. The reason is that export shocks may also affect employment and wage through other channels than affecting firm-specific VATRs. An endogeneity issue arises if we do not control other channels. For example, Dai and Xu (2017) show that firms change employment in response to shocks on firm-specific exchange rate, which might be affected by export shocks. Thus, we select adjustments of VATRs from January 2005 to December 2006, a period when VATRs were (officially) mainly adjusted to upgrade China s economic structure. Braakmann et al. (2017) present evidence that these adjustments were indeed related to product characteristics, such as whether the product is resource-intensive, high-tech, pollutive and energy-consuming, but are unrelated to export demand shocks. 2.2 Implementation For domestic sales, the input VAT is ultimately paid by consumers. For simplicity we disregard the domestic sales. For exports, according to Circular No. 7 cai shui [2002], VAT rebates for eligible firms are: VAT Rebates = Input VAT (Exports BM) (VAT VAT Rs) where Input is the input used for the exports. VAT and VAT Rs are the rate of VAT and VATRs respectively. Exports denotes the value of exports. BM denotes the input which are exempt from VAT, typically the bonded materials, entering China without payments of duty and VAT, to be reshipped out of China after being stored, processed or assembled. If the VAT rebates are positive, firms are refunded by the government; otherwise, firms need to pay VAT to the government. From the above equation, there are three points worth noting on the implementations of VAT rebates in practice. 11 The first point is that VAT rebates are at most equal to the input VAT. If the rate of VATRs of a product is equal to VAT rate, the firms are fully refunded of the input VAT for the exports of the product. In this case, the VATRs are equal to the input VAT and are thereby the largest. However, if the rate of VATRs of a product is less than VAT rate, the firms are partially refunded of the input VAT and the VATRs are smaller than the input VAT. If the firms do not pay any input VAT, e.g. all the inputs are the bonded materials or supplied materials for export processing firms which are exempt from VAT, firms get no VATRs and may pay the VAT to the government. 11 See Braakmann et al. (2017) for the methods on the implementations: no eligibility, exemption-refund and exemption-credit-refund. 10

11 The second point is that for firms using VAT-exempted inputs, the VATRs affect the perceived price of these inputs. The reason is that expenditure on these input is excluded from the export value for the purpose of the calculation of the VAT rebates. From the above equation, the value BM (VAT VAT Rs) is directly attributed to the rebates, thereby making these input less expensive. The larger the VATRs are, the more expensive the input is. The third point is that partial rebates give exporters a tax burden, i.e. VAT VAT Rs in the equation. As shown in Feldstein and Krugman (1990), a partial rebate on VAT makes non-refunded VAT act as an export tax. This export tax is lower as VATR becomes higher. As a result, VATRs are positively related to exports, which motivates the VATRs adjustments during the economic crisis. Braakmann et al. (2017) describe the second and the third point as the cost-tax effect of VATRs. They show how the cost-tax effect affects the exports. In the following, we focus on investigating the employment and wage effects of VATRs. It is natural to think that the increase of exports may be the main mechanism of the effects. Surprisingly, we find that exports only explain a very limited part of VATRs effects. 3 Identification Strategy In this section, we show how to identify the employment and wage effects of VATRs. For the purpose of our analysis, we construct firm-specific VATRs and decompose their adjustments to three components: within-product change, reallocation and the net entry. Then we explain the potential endogeneity and the instruments to correct for the endogeneity problem. 3.1 Firm-specific VATRs The VATRs are at product level in practice. However, the employment and wage are collected at the firm level. Given the prevalence of multi-product firms in the market (Bernard et al., 2010), it is difficult to get the product-level employment and wage with the firm-level data. Thus, we construct the firm-specific VATRs and estimate the employment and wage effects of VATRs at firm level. It is intuitive to construct firm-specific VATRs. On the one hand, firms export different products whose VATRs are adjusted differently; on the other hand, exports of the each product are different even when firms export the same products. Thus the perceived VATRs adjustments are very different across firms. Our firm-specific VATRs take these into consideration and are calculated as the weighted average of VATRs of all products exported by the firm. The weight of each product is the share of eligible exports in total exports of the 11

12 firm. 12 In particular, the rate VATRs of a firm i in year t is: FVAT Rs it = s i jm VAT Rs jm (1) j Ω it m t where s i jm is the export share of product j of firm i in month m. Ω it is the set of exporting products. Let x i jm be the export of product j in month m by firm i, then: 3.2 Identification s i jm = x i jm / x i jm and s i jm = 1 j Ω it m t m t To estimate the employment and wage effects of VATRs, we consider the following equation: lny it = τfvat Rs it + βx it + ς i + ς ct + ς st + ς ot + ε it (2) where lny it is the logarithm of variables on workers in firm i at year t, including employment and wage. FVAT Rs it is the firm-specific VATRs and X it is a set of control variables in logarithmic form. The control variables include domestic sales, non-eligible exports, liquidity, and fixed asset 13. ς i is firm fixed effect, which represents all the firm-level timeinvariant shocks, e.g. productivity shocks. ς ct, ς st and ς ot are city-year, sector-year and ownership-year fixed effects respectively. They control for the city-year, sector-year and ownership-year level shocks respectively. In our sample, we have almost 290 cities. Equation (2) can be estimated directly with the panel data. Alternatively, we consider an equivalent differential equation across years. lny i = τ FVAT Rs i + β X i + η c + η s + η o + ε i (3) means the change of logarithm of variables from year 2005 to FVAT Rs i is the firm-specific VATRs adjustment. Firm-level time-invariant characteristics ς i are canceled out. η c = ς c2006 ς c2005, η s = ς s2006 ς s2005 and η o = ς o2006 ς o2005. ε i = ε i2006 ε i Later in the section of data, we explain what are non-eligible exports. Typically the non-eligible exports are the exports under processing trade with supplied materials, for which firms do not pay any input VAT. Another point worth noting is that according to the equation in the implementation, the weight should be calculated with exports subtracting bonded materials. However, we do not observe how they are allocated across products. Thus we use export directly to calculate the weight and provide the robustness checks on this point later. 13 We assume firms are not able to adjust fixed asset freely. However, we deliberately do not control intermediate input because intermediate input is assumed to be adjusted freely by firms as employment is changed. This is widely used in the studies on estimation of production function, e.g. Levinsohn and Petrin (2003). If we control the intermediate input, we are estimating the employment and wage effects of VATRs conditional on intermediate input. This is how we study the mechanism of intermediate input in section 6. 12

13 The firm-specific VATRs adjustment can be decomposed into three components: withinproduct adjustment, reallocation and the net entry. As a result, we can identify the source(s) of VATRs adjustment that affects the employment and wage. Decomposition Following Collard-Wexler and De Loecker (2015), we consider a dynamic decomposition of firm-specific VATRs adjustment. Let s i jt = m t x i jm / j Ωit m t x i jm be the export share of product j by firm i in year t and VAT Rs i jt = m t s i jm VAT Rs jm /s i jt = m t (x i jm / m t x i jm )VAT Rs jm be the weighted VATRs of a product j by firm i in year t, firm-specific VATRs can be denoted as FVAT Rs it = j Ωit s i jt VAT Rs i jt. Consider three distinct sets of products for the year 2005 and 2006: survival products (S), new products (N), and exiting products (E). Let VAT Rs i j =VAT Rs i j2006 VAT Rs i j2005 be the product-level adjustments of VATRs and s i j = s i j2006 s i j2005 the product-level change of export share. Using these sets, we can write the firm-specific VATRs adjustment, FVAT Rs i, as FVAT Rs i = j Ωit s i j2006 VAT Rs i j2006 j Ωit s i j2005 VAT Rs i j2005 = s i j2005 VAT Rs i j + s i j VAT Rs i j2006 j S } {{ } j S } {{ } Within product Reallocation + s i j2006 VAT Rs i j2006 s i j2005 VAT Rs i j2005 j N } j E {{ } Net entry The first term is the within-product change of VATRs adjustment. It is attributed to the increase of VATRs for survival products provided that their export share were unchanged. The second term is the reallocation attributed to the change of export share. The reallocation is attributed to exports reallocation between the survival products themselves or between the survival products and exiting or new products. Later we will show that it is mainly driven by the exports reallocation between the survival products and exiting or new products. The last two terms construct the net entry, which measures firm-specific VATRs adjustment contributed by product entry offset against product exit. (4) Endogeneity The estimations on the employment and wage effects of VATRs are biased due to the potential endogeneity of firm-specific VATRs adjustment. There are four sources of the potential endogeneity. 13

14 The first source is the potential endogeneity of firm s selection of products Ω it. Employment, wage and the selection of products may be jointly determined and affected by some common factors. For example, the fiscal condition of the region that the firm is located in affects the firm employment and wage (e.g. see the implications of government spending for the labor market in Burnside et al. (2004) and Monacelli et al.(2010)). Meanwhile, fiscal condition of the region also affects the firm selection of products because a firm located in a region with high fiscal deficit rate may select products with low VATRs to avoid the risk of no or delayed rebates (Chandra and Long, 2013). In our model, city fixed effect can control for this factor. Another example is the sector the firm operates in: on the one hand, firms in different sectors may require different intensity of labor skills, thereby hiring different amount of workers and paying different wages; on the other hand, firms in different sectors are constrained to freely select the products. This factor can be controlled for by sector fixed effect in our model. The third example is that a firm with low liquidity may employ less workers, pay lower wage, and select the products that have high VATRs to receive rebates. The control variable, liquidity, can control for this factor. Thus, this source of endogeneity is largely addressed by the control variables and various fixed effects. The second source is the potential endogeneity of product-level VATRs adjustments. In the decomposition, there are terms VAT Rs i j2005 and VAT Rs i j2006. A number of VATRs adjustments are responses to the export shocks during economic crisis. The endogeneity issue arises if export shocks affect firm s employment and wage through other channels that are not controlled. For example, Chodorow-Reich (2014) shows that credit market disruptions due to economic crisis have considerable effects on the decline of firm employment. Dai and Xu (2017) show the exchange rate shocks affect firm employment significantly. Apparently export shocks are related to credit market disruptions during economic crisis and exchange rate shocks, thereby affecting firm s employment and wage through channels other than firm-specific VATRs. However, this source of endogeneity should be fairly minor in our sample because we select the period from January 2005 to December 2006, a period during which VATRs adjustments were aimed at upgrading the economy structure, optimizing resource consumption and reducing environmental pollution. As documented by Braakmann et al. (2017), the adjustments during this period were indeed related to product characteristics, such as whether the product is resource-intensive, high-tech, pollutive and energy-consuming, but are unrelated to export demand shocks. Moreover, we include sector fixed effect in our regression, which acts as an additional safeguard to control for sector-level export shocks. Therefore, VATRs adjustments for our analysis are plausibly exogenous. The third source is potential endogeneity of allocation of exports across products, shown as s i j2005 and s i j2006 ( s i j ) in the decomposition. On the one hand, employment or wage at firm level may affect the exports of different products differently. For example, firms may 14

15 hire skilled labor and pay high wage to produce and export more of high-quality products (Verhoogen, 2008). Thus the increase of employment or wage at firm level attributed to the increase of skilled labor, may especially benefit some specific products and shift the export share to those products. Moreover, Ma et al. (2014) find that products within firms have different capital intensity, which suggests that increase of employment may increase the exports of labor-intensive products more than capital-intensive products in the context of China. On the other hand, employment, wage and export share across products may be jointly determined. The fourth source is the potential endogeneity of product entry and exit. The product entry and exit not only affect the component of the net entry in the firm-specific VATRs adjustment directly, but also affect within-product change and reallocation by changing the se of survival products and export share of survival products. Thus, firm-specific VATRs adjustments are endogenous if the product entry and exit are endogenous. Indeed, a change of firm employment or wage may affect the product entry and exit. For example, an increase of employment and wage due to hiring more high-skilled labor may benefit high-quality products, making firms drop the low-quality products and introduce new high-quality products exploiting the high-skilled labor. Moreover, the employment, wage, product exit and entry may be jointly determined and affected by some common factors. For example, Eckel and Yeaple (2017) show that firms sorting abilities of high-quality workers affect employment, wage and product scope of firms. In particular, a firm with high sorting ability hire more workers, pay high wages and expand product scope. 3.3 Instruments To address the endogeneity issues, we develop novel instruments of predicted firm-specific VATRs and demand uncertainty based on the fact that exporters learn from others about the uncertainty of foreign markets. The Predicted Firm-specific VATRs The predicted firm-specific VATRs is calculated based on the predicted export share. The predicted export share of product i by firm j in year t is defined as: s p i jt = x a jt x i jt j Ωit (x a jt x i jt) (5) where x a jt is aggregate exports of product j in year t at country level. The predicted export share is obtained if the allocation of exports cross the products by the firm is exactly the same 15

16 as the allocation of exports by the rest of the country when there were only those products Ω it. A point worth noting is that the predicted export share is firm-specific. Though the aggregate exports of product x a jt are the same for all firms, the products of firms Ω it and the exports of each product x i jt are different. Thus, the nominator and denominator in right hand side of above equation are both firm-specific. As a result, the predicted export share is firm-specific as well. Based on the predicted export share, we calculate the predicted firm-specific VATRs adjustment: P FVAT Rs i = j Ω it s p i j2006 VAT Rs i j2006 j Ω it s p i j2005 VAT Rs i j2005 Firm-specific VATRs are affected by the predicted firm-specific VATRs because exporters learn from other exporters about the uncertainty of foreign markets so that they may follow the export pattern of the rest of country. 14 For instance, Segura-Cayuela and Vilarrubia (2008) argue that potential exporters can learn the uncertainty of fixed cost to enter foreign market from existing exporters profitability in foreign markets. Fernandes and Tang (2014) show that exporters learn the demand uncertainty from observing the neighbors exports. More specifically, using Chinese data the authors show a firm s probability of entry and initial sales in the market are raised by a positive demand signal learned from neighbors export performance. With Bangladeshi data, Kamal and Sundaram (2016) show that the probability to match with a US importer is higher if neighboring exporters previously transacted with the importer, which suggests exporters learn from neighbors in generating importer-exporter matches. However, the predicted firm-specific VATRs is not related to single firm s characteristics, including firm employment and wage, because the predicted export share is based on the nationwide exports subtracting its own exports. 15 There are two potential threats to our strategy. The first threat is that some firms may not follow the pattern of the rest of the country. For example, if there are very large exporters, they may lead, rather than follow, the export pattern of the rest of the country. This threat is fairly minor, because in our data the market share of the largest exporter is lower than 25% for more than 75% of all products. As a robustness check, we use the small exporters to investigate the effects and the results are very similar. The second threat is that all firms may experience common export shocks. In this situation, export share and the predicted export share are highly correlated due to common shocks. In a robustness check, 14 That exporters learning from themselves has also been analyzed in literature (e.g. Iacovone and Javorcik, 2010; Albornoz et al., 2012; Nguyen, 2012; and Conconi et al., 2016). 15 We also provide the robustness checks with instruments constructed from the regional exports. This is to take into consideration that firms may learn from the exporters close to them, i.e. neighbors. The results are very consistent. 16

17 we use the lagged nationwide exports to calculate the predicted export share, and combine with contemporaneous firm-level exports to calculate the instruments. The results are very consistent. Demand Uncertainty Demand uncertainty is a determinant of product entry and exit in the foreign markets (e.g. Iacovone and Javorcik, 2010; and Albornoz et al., 2012). Thus we include a measure of firm-specific demand uncertainty in the instruments to address the potential endogeneity particularly arising from the fourth source. We measure the firm-specific demand uncertainty as: P uncertainty it = s p k ( lnx a jk t lnx a jk /n)2 i jt j Ω it n 1 where k denotes the past years. Our measure of demand uncertainty is the weighted average of demand uncertainty of all products exported by the firm. More specifically, in the first step, we calculate the demand uncertainty of every product, which is the standard deviation of aggregate export growth (change of log exports) from 2002 to We choose the period after China accession to WTO in order to eliminate the potential peculiar shocks on exports. Thus, k is from 2002 to 2005 and n is 4. In the second step, we calculate the weighted average of demand uncertainty of all products for each firm. To circumvent the endogeneity from the export share, we use the predicted export share as the weight. Thus, our measure of demand uncertainty is related to the product entry and exit, but unrelated to single firm s characteristics. The first set of instruments for the firm-specific VATRs adjustment FVAT Rs are the predicted firm-specific VATRs adjustment P FVAT Rs and demand uncertainty P uncertainty. We expect that the predicted firm-specific VATRs adjustment is positively related to the firmspecific VATRs adjustment. However, the effects of demand uncertainty on the firm-specific VATRs adjustment are not straightforward at the first glance. We will analyze the effects demand uncertainty on each component in the following section. 3.4 Effects of the Components With differential equation (3), we are assuming that the employment and wage effects of the three components are equal. That is to say, the effects of a one percentage point increase of firm-specific VATRs due to within-product change are equivalent to the effects of a one percentage point increase of firm-specific VATRs due to reallocation or the net entry. However, they might be different. To shed light on the source(s) of VATRs adjustment that affects the 17

18 employment and wage, we also estimate following equation: lny i = τ 1 Within i + τ 2 Reallocation i + τ 3 Net entry i + β X i + η c + η s + η o + ε i (6) To address the potential endogeneity of each component, we calculate the predicted components with the predicted export share. Then we use the predicted component and demand uncertainty as the instruments for the corresponding component. In particular, the predicted within-product change of VATRs adjustment P within is: P within i = s p i j2005 VAT Rs i j j S The instruments for the within-product change are the predicted within-product change P within and demand uncertainty P uncertainty. The predicted reallocation P reallocation is: P reallocation i = s p i j VAT Rs i j2006 j S where s p i j = sp i j2006 sp i j2005 is the predicted change of export share from year 2005 to year 2006 for product j of firm i. The predicted change of export share is obtained if export of each product by the firm grew at exactly the same rate as the export of the product of the rest of the country. The instruments for reallocation are then the predicted reallocation P reallocation and demand uncertainty P uncertainty. The predicted net entry is: P net i = s p i j2006 VAT Rs i j2006 s p i j2005 VAT Rs i j2005 j N j E The instruments for the net entry are then the predicted net entry P net and demand uncertainty P uncertainty. Apparently, the predicted component is positively related to the corresponding component. However, the demand uncertainty may have different effects on different components. If a firm faces a higher demand uncertainty, we may expect the firm is less likely to add products but more likely to drop products. If this is the story, the demand uncertainty will have negative effects on the within-product change as well as the net entry. The higher likelihood to drop products and the lower likelihood to add products associated with larger demand uncertainty mean the net entry is likely smaller. At the same time, the survival products are less, making within-product change smaller. On the contrary, the demand uncertainty may have positive effects on the reallocation. The reason is that the reallocation is mainly driven by the exports reallocation between survival products and exiting or new products (we will 18

19 show evidence in section 5.1). The higher likelihood to drop products and the lower likelihood to add products associated with larger demand uncertainty mean that more exports are reallocated from the dropped products to survival products, thereby making the reallocation larger. We will verify these expectations in the empirical section. Moreover, the construction of instruments for each component also provides us the second set of instruments for the firm-specific VATRs adjustment FVAT Rs, which includes the predicted components P within, P reallocation, P net and the demand uncertainty P uncertainty. 4 Data Our study draws on three main sources of disaggregated data: product-level VATRs data, firm-level production data and transaction-level trade data. 4.1 VATRs We collect the VATRs of all products after the last adjustment (included in our sample) in September 2006 from the website of Minister of Commerce 16. Then we collect all the circulars on the adjustments of VATRs between January 2005 and December 2006 in SAT Taxation Law Database. These circulars state the change of VATRs for the adjusted products. Thus, we are able to recover the VATRs of all products from January 2005 to December For very few 8-digit HS products, the adjustments of the categories of product at the 10-digit or 11-digit HS level are different. Since our data on exports is at 8-digit HS level, we drop the products that have different VATRs or VATRs adjustments at 10-digit or 11-digit HS level. With this exercise, we have monthly VATRs for 7,308 8-digit HS products January 2005 to December During this period, VATRs were adjusted 7 times. See appendix A1.1 and table A15 in appendix A3 for details of the adjustments. The adjustments in May 2005, January 2006 and September 2006 involve 94 products, 137 products and 1,497 products respectively. 1,692 products (23% of all products) were adjusted at least once. The scale of adjustments varied from 2% to 13%. Taking into account that the maximum of VATRs is 17%, the scale of these adjustments was fairly substantial. Moreover, during our observation period, the time between announcement of VATRs and them coming into effect are very close, ranging from one day to ten days, which makes potential anticipation effects highly unlikely accessible on 15 August

20 4.2 Firm-level Production The firm-level production data comes from Chinese annual survey of manufacturing firms (CASMF) collected by the National Bureau of Statistics of China. The survey covers all state-owned manufacturing firms and firms of other ownership with sales above RMB 5 million (above-scale firms). There are around 100 thousand firms in 1999 to 410 thousand firms in The survey records information on firm s production, including employment, total wage, capital, intermediate input, sales, export and etc. The survey also records financial variables of firms, e.g. asset, debt and cash flow. Moreover, the survey reports the information about firm location, ownership and the sector that the firm operates win as well. We clean the data with the following criteria. Firstly, we drop the observations with missing values on any of the key variables, including firm identification number, firm name, sales, export, total asset, intermediate input, employment and total wage. Secondly, observations with duplicated firm identification number or firm name are dropped. Thirdly, observations with non positive values of sales, intermediate input, total wage and asset are dropped. Fourthly, we drop the observations with employment less than 8. Fifthly, we drop the observations that export value is larger than sales. Lastly, we drop the observations that fixed asset or variable asset is larger than total asset. Applying these criteria to the data in year 2005 and 2006, around 1.45% observations are dropped in total in each year. 4.3 Transaction-level Trade Our transactional export quantities and prices are from Chinese Customs Trade Database (CCTD) collected by the General Administration of Customs of China. This database reports export (and import) transactional values and quantities by product-firm-destination (source country for imports) at a monthly frequency. The database also reports registry information of firms, including identifier, name, ownership and the region the firm is situated in. For every transaction, this database also reports the shipment mode and trade mode. Shipment mode includes by air, highway, railway, sea and post. There are 18 possible trade modes, but more than 90 percent of exports are under ordinary trade (OT), processing trade with purchased materials (PTPM) or processing trade with supplied materials (PTSM). See appendix A1.2 for more information on the trade mode. Regarding VATRs, there is a substantial difference between PTPM and PTSM. Under PTSM, operating enterprises only get assembly fees and do not pay any input VAT, thus products exported under PTSM are not eligible for VATRs. By contrast, under PTPM, operating enterprises purchase materials from abroad and/or from the domestic market, and have to pay input VAT, thus the products under PTPM are eligible for VATRs. We clean the data by drop observations with duplicated firm identifier and observations 20

21 with missing values on any of the following variables: product HS code, firm name, destination country, quantity, and value. Around 15.7% firms in 2005 are dropped while 6.97% firms in 2006 are dropped. 4.4 Merged Data To construct the firm-specific VATRs, firstly we merge CASMF and CCTD to get detailed information on the exports for exporters, including the products exported and exports for each product. See appendix A1.3 for more details on how to merge two databases and the quality of the merged data. In the merged data, we have 44,512 (51,860) firms in 2005 (2006), which account for 38.65% (32.56%) of all exporters in cleaned CCTD in 2005 (2006) and 59.10% (65.62%) of all exporters in cleaned CASMF in 2005 (2006) 17. Moreover, the merged data contains more than 60% of sales, employment and asset of exporters in CASMF. It contains around 70% of exports in CASMF and more than 50% of exports in CCTD. 18. Secondly we link the merged data with product level VATRs and calculate the firm-specific VATRs with eligible exports that are not under PTSM. 5 Results 5.1 Description of Firm-Specific VATRs Adjustment The descriptive statistics of firm-specific VATRs adjustment and its decomposition are presented in table 1. As shown in the table, 36,469 firms survive from 2005 to The average firm-specific VATRs adjustment is negative at 0.44 percentage point, indicating that firm-specific VATRs are decreased by 0.44 percentage point in average. This is consistent with the policy during this period that most of the VATRs adjustments are to decrease the VATRs. The standard deviation of firm-specific VATRs adjustments are 2.47, which means that the variations of adjustments across firms are quite large. The average value of withinproduct change is negative at 0.43 percentage point, suggesting that most of the firm-specific VATRs adjustments come from within-product change. The value of reallocation is negative at 0.26 percentage point and the value of the net entry is positive at 0.25 percentage point. The variations of all components are quite large as well. 17 In the Chinese annual survey of manufacturing firms (CASMF), all firms are manufacturing firms and can be divided into above-scale non exporters, above-scale exporters trading directly and above-scale exporters trading with agents. In Chinese Customs Trade Database (CCTD), firms can be divided into above-scale exporters trading directly, trading agents, small scale manufacturing exporters and non manufacturing exporters. Thus only the above-scale exporters trading directly are merged. 18 Note that the exporters in CASMF that export through trade agents are not appeared in CCTD and the exports by trading agents and non-manufacturing exports are not shown in CASMF. 21

22 Table 1: The firm-specific VATRs adjustment and its decomposition Variable Mean Std. Dev. Min 5th Median 95th Max # firms FVAT Rs ,469 Decomposition: Within ,469 Reallocation ,469 Net entry ,469 It is interesting that reallocation is negative. The negative value of reallocation does not necessarily mean that exports are reallocated to the products with low VATRs. It also happens if exports are reallocated from survival products to new products. A extreme example is that the export share are less for all survival products, i.e. s i jt < 0 for any j S. As a result, the reallocation term j S s i j VAT Rs i j2006 for firm i must be negative. Next we show that reallocation in the firm-specific VATRs adjustment is mainly driven by the reallocation of exports between survival products and exiting or new products. This is important for the interpretation of the effect of reallocation and the study of mechanisms in later sections. Firstly, we compare the reallocation between different kinds of firms, especially between the firms with exiting and new products and firms without exiting and new products. As shown in column (1) of table 2, the values of reallocation of firms with survival, exiting and new products are very similar to the results of all firms shown in table 1. In column (2) and (3), we report the reallocation of firms with new products and firms without new products. The average reallocation is for firms with new products while it is 0.65 for firms without new products. Moreover, the distribution of reallocation for firms with new products has a large share of negative values than the distribution for firms without new products. These statistics suggest that reallocation is strongly associated with whether there are new products and tends to be negative if there are. In column (4), we report the reallocation for firms without exiting and new products, for which there are no reallocation between survival products and exiting or new products by definition. The distribution shows a large share of small values (e.g. the value of 5th percentile is and the vale of the 95th percentile is 0.04). The average of reallocation, i.e , is positive, which suggests that in general the exports are reallocated from the products with low VATRs to the products with high VATRs if there are no product entry and exit. The average of reallocation is very small, indicating that the reallocation of exports between survival products is very limited. Therefore, the reallocation is mainly driven by the reallocation of exports between survival products and exiting or new products in our analysis. Secondly, we construct a new reallocation term based on the survival products only: Reallocation s = j S s s i j VAT Rs i j2006. In the equation, s s i j = ss i j2006 ss i j2005 and ss i jt = 22

23 m t x i jm / j S m t x i jm is the share of exports of a survival product in the exports all survival products. Thus we have j S s s i jt = 1 and j S s s i j = 0. The new reallocation measures the reallocation of exports between the survival products if there were only survival products. The results of new reallocation are reported in column (5) and (6) of table 2. The average of new reallocation is positive at for all firms and positive at for firms with survival, exiting and new products. Both are similar to the reallocation for firms without exiting and new products. Again, it suggests that the reallocation of exports between survival products is very limited and the reallocation mainly happens between survival products and exiting or new products. Table 2: Reallocation of firm-specific VATRs adjustment (1) (2) (3) (4) (5) (6) Reallocation Reallocation s Mean Std. Dev Min th Median th Max With: Survival products YES - - YES - YES Exiting products YES - - NO - YES New products YES YES NO NO - YES # firms 17,806 24,533 11,936 6,928 35,905 17,806 Thirdly, as shown in table 1, in average the reallocation almost offsets the net entry. Since the net entry is mainly driven by the product entry and exit, it is natural to think that reallocation is also affected by product entry and exit as well. If it is true, it also means that reallocation is mainly driven by the reallocation of exports between survival products and exiting or new products. To see this, we plot the reallocation and the net entry as well as reallocation and the change of export share of survival products in figure 1. As shown in the figure, when the net entry is positive (negative), reallocation tends to be negative (positive). Moreover, when the change of export share of survival products is positive (negative), i.e. exports are reallocated from exiting products to survival products (from survival products to new products), the reallocation tends to be positive (negative). Thus, reallocation mainly happens between survival products and exiting or new products. In conclusion, the reallocation of firm-specific VATRs adjustment is mainly driven by the reallocation of exports between survival products and exiting or new products, rather 23

24 Figure 1: Reallocation, the net entry and the change of export share of survival products than the reallocation from products with low VATRs to products with high VATRs. Hence, in the components of VATRs adjustments, reallocation and the net entry are both attributed to the product entry and exit. This point has important implications on the interpretation of effects of reallocation and the explanations of the mechanisms of VATRs effects. 5.2 Correlation We report the correlation coefficients between the values of the variables and their predicted values in table 3. The correlation coefficient between export share of products s i jt and the predicted export share s p i jt is , indicating a high correlation between them. The correlation coefficient between firm-specific VATRs and the predicted firm-specific VATRs is quite high at The correlation coefficient between firm-specific VATRs adjustments FVAT Rs and the predicted values is , indicating a high correlation between them. Among the components of firm-specific VATRs adjustments, within-product change has the highest correlation 24

25 Table 3: Correlation coefficients between values and predicted values Correlation coefficient # observations Export share ,256 FVATRs ,942 FVAT Rs ,469 Within ,469 Reallocation ,469 Net entry ,469 Figure 2: Firm-specific VATRs adjustments, the components and their predicted values coefficient with its predicted value at Reallocation has the lowest correlation coefficient with its predicted value at The positive relationships between the values and predicted values of firm-specific VATTRs adjustments and their components are also shown in figure 2. The correlation coefficients and the figure suggest that to use predicted firmspecific VATRs adjustments as an instrument of firm-specific VATRs adjustments, and to use predicted component as an instrument of the corresponding component are reasonable. 25