metadata: authors: 0: author: Cali,Massimiliano
1: author: Le Moglie,Marco
2: author: Presidente,Giorgio
count: Indonesia
docna: 0: docna: Gain without Pain ? Non-Tariff Measures,
            Plants’ Productivity and Markups
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Business Cycles and Stabilization Policies,Construction Industry,Plastics & Rubber Industry,Pulp & Paper Industry,Textiles, Apparel & Leather Industry,General Manufacturing,Food & Beverage Industry,Common Carriers Industry,Trade and Multilateral Issues,Trade Policy,Rules of Origin
teratopic: Macroeconomics and Economic Growth,Industry,International Economics and Trade
abstracts: cdata!: This paper studies how productivity and
            markups respond to non-tariff measures. The analysis uses a
            novel time-varying data set on all non-tariff measures
            applied to imported products by Indonesia. Price and
            quantity information is used to disentangle the impact of
            non-tariff measures on plants’ technical efficiency and
            markups. The findings show that on average, non-tariff
            measures generate fewer distortions than import tariffs do.
            However, while specific non-tariff measures increase the
            quality of the products on which they are applied, others
            act as barriers to trade similar to import tariffs. These
            results suggest that to gauge their impacts and guide policy
            making, non-tariff measures should not be bundled together
            in empirical analyses.
geo_reg_key: 517198,517191
repnme: repnme: Gain without Pain ? Non-Tariff Measures,
            Plants’ Productivity and Markups
display_title: Gain without Pain Non-Tariff
            Measures, Plants’ Productivity and Markups
listing_relative_url: /research/2021/05/33063406/gain-without-pain-non-tariff-measures-plants’-productivity-markups
topicv3: Manufacturing Sectors m1326521 1281,Trade m1326924 698,Non-Tariff Measures m1326926 778,Trade Policy and Integration m1327407 775,Trade Policy m1327640 776
publishtoextweb_dt: 2021-05-10T00:00:00Z
dois: 10.1596/1813-9450-9654
historic_topic: Macroeconomics and Economic Growth,Industry,International Economics and Trade
origu: EFI-EAP-MTI-MacroFiscal-2 (EEAM2)
no_of_pages: 59
author_keys: 0: author_key: 1228418
1: author_key: 1743575
2: author_key: 1591863
model_extractions: Policy Research Working Paper 9654 Gain without Pain? Non-Tariff Measures, Plants’ Productivity and Markups _Massimiliano Calì Marco Le Moglie Giorgio Presidente_ Macroeconomics, Trade and Investment Global Practice May 2021
Policy Research Working Paper 9654

# **Abstract**

This paper studies how productivity and markups respond to non-tariff measures. The analysis uses a novel time-varying data set on all non-tariff measures applied to imported products by Indonesia. Price and quantity information is used to disentangle the impact of non-tariff measures on plants’ technical efficiency and markups. The findings show that on average, non-tariff measures generate fewer distortions than import tariffs do. However, while specific non-tariff measures increase the quality of the products on which they are applied, others act as barriers to trade similar to import tariffs. These results suggest that to gauge their impacts and guide policy making, non-tariff measures should not be bundled together in empirical analyses.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mcali@worldbank.org, marco.lemoglie@unicatt.it, and giopresidente@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Gain without Pain? Non-Tariff Measures, Plants’ Productivity and Markups**<sup>$</sup>

Massimiliano Calì<sup>a</sup> , Marco Le Moglie<sup>b</sup> , Giorgio Presidente<sup>c,</sup><sup>_∗_</sup>

> _aWorld Bank_

> _bCatholic University of the Sacred Heart_

> _cOxford Martin School, Oxford University and World Bank_

**JEL Classification:** F10; F13; F14; L10

**Keywords:** Non-tariff measures; tariffs; trade reforms; productivity; markup; Indonesia.

> $We thank Aufa Doarest, Devaki Ghose, Angella Lapukeni, Laura Puzzello and Shafaat Yar Khan for useful comments; Ahmed Mushfiq Mobarak and Denni Purbasari for sharing their data on political connections with us; and Agnesia Adhissa and Nabil Rizky Ryandiansyah for excellent research assistance. Giorgio Presidente gratefully acknowledges funding from Citi. The usual disclaimers apply.

> _∗_ Corresponding author.

> _Email addresses:_ `mcali@worldbank.org` (Massimiliano Calì), `marco.lemoglie@unicatt.it` (Marco Le Moglie), `giorgio.presidente@oxfordmartin.ox.ac.uk` (Giorgio Presidente)
# **1. Introduction**

The economic impact of trade reforms has been one of the most studied topics in economics in the last decades (e.g. Pavcnik (2002); Amiti and Konings (2007); Caliendo and Parro (2015); De Loecker et al. (2016)). The empirical literature has mainly focused on the changes in import tariffs across the 1980s and 1990s, as tariffs were the main trade policy instrument for most of the 20th century.

Modern trade policy, however, has become more complex. Greater product sophistication and increasing public concern around health and safety have resulted in the proliferation of non-tariff measures (NTMs).<sup>1</sup> By 2017, NTMs were applied to goods covering around 60 percent of import values in developing countries and over 80 percent in high-income countries (UNCTAD and World Bank, 2018). In the same year, data from the _Global Trade Alert_ suggest that import tariffs accounted for just 20 percent of all trade policy measures adopted globally.

Despite their extensive use, little systematic evidence exists on the economic impact of NTMs. The few existing studies on the topic use aggregate measures of NTMs, which lump highly heterogeneous regulations together (Kee et al., 2009; Webb et al., 2020), or focus on one-off liberalization of a specific NTM, usually quotas (Khandelwal et al., 2013; Harrigan and Barrows, 2009), which however represent only around 2 percent of all applied NTMs (UNCTAD and World Bank, 2018). An important reason behind the paucity of evidence is the difficulty in collecting sufficiently comprehensive and detailed data on NTMs (Goldberg and Pavcnik, 2016).

To help fill this gap, we build the first available time-varying dataset on all NTMs applied to the universe of goods produced in Indonesia from 2008 to 2015. We exploit the experience of Indonesia because it fits remarkably well the general pattern of trade policy over the past three decades: import tariffs liberalization since the 1990s and a contemporaneous increase the application of NTMs (Patunru and Rahardja, 2015).

Our novel dataset improves on existing NTM data in three crucial aspects. First, instead of looking at industry aggregates or categories of goods (e.g. food _vs_ durable), we focus on ten-digit _Harmonized System_ (HS-10 digits) products. Second, we exploit the most disaggregate available classification of NTMs to assign narrowly defined measures

> 1The United Nations Conference on Trade and Development (UNCTAD) defines NTMs as _"policy measures other than ordinary customs tariffs that can potentially have an economic effect on international trade in goods, changing quantities traded, or prices or both"_ .

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to each product.<sup>2</sup> Third, we analyze changes in the application of NTMs year by year, which allows us to build a time-varying dataset and apply panel data methods.<sup>3</sup> A further advantage of using Indonesian data is the availability of price and quantity information, which allows us to disentangle the impact of NTMs on plants’ physical productivity and price markups.

This paper makes three main contributions. First, it examines the net impact of NTMs on plants’ performance and compares it with the impact of import tariffs. Our findings suggest that NTMs across all categories have on average a neutral impact on manufacturing plants. Hence NTMs in Indonesia appear to create less distortion than import tariffs, which consistently with the literature we find to reduce competition and productivity (e.g. Amiti and Konings (2007); De Loecker et al. (2016)). We interpret the neutral impact of NTMs as the result of two countervailing effects. One the one hand, some of the measures boost plants’ performance by increasing the quality of the goods on which NTMs are applied. On the other hand, NTMs can have a negative impact on performance due to an increase in trade compliance costs. The extent to which these two effects are at play differs significantly across NTM types.

To explore the heterogeneity of impact of NTMs, we go beyond their average net effects and study specific measures, which is the paper’s second main contribution. This is important as different types of NTMs are likely to have highly heterogeneous effects according to the objective (explicit or otherwise) they fulfill. On the one hand, NTMs’ _raison d’etre_ should be to increase domestic welfare by addressing market failures, such as negative externalities or information asymmetries. For instance, a country might impose sanitary standards on imported products, or require certain foreign goods to satisfy fabrication and performance requirements in order to be commercialized domestically. To the extent that the main outcome of such measures is the increase in products’ safety and/or quality, this may benefit the productivity of producers using such goods as intermediate inputs. On the other hand, NTMs may also generate compliance costs for foreign producers thereby increasing the costs of sourcing imported goods. This may be particularly evident for those measures which violate one of the key principles of WTO

> 2 The UNCTAD classification can be found at UNCTAD’s web page.

> 3We observe large variation in the application of NTMs across products and over time. For instance, the average HS-10 digit product in Indonesia is subject to 2.7 changes in the application of NTMs over the period 2008-15.

3
rules, i.e. not restricting trade unnecessarily.

We focus on two types of NTMs to tease out such heterogeneity. The first is _antipest treatment requirements_ (APT), which we use as an example of “virtuous NTM”. Most countries apply this type of measure to eliminate a potential negative externality in agricultural products, i.e. the presence of plant and animal pests or disease-causing organisms. The second type of NTM is _pre-shipment inspections_ (PSI), which we deem an example of “protectionist NTM”. This measure requires the goods to be inspected at the port of departure. Only a handful of mostly low-income countries applies PSI, typically to ensure that the import declaration lists the correct classification of the goods to be imported as a way of detecting improper importing activities. Unlike APT, PSI appear redundant in a country like Indonesia, where the customs agency applies a risk management system aiming to detect suspicious shipment at the border.<sup>4</sup> In the absence of clear benefits, the bulk of the effect of this measure is likely to be the increased compliance costs for imports.

The analysis leverages the unusual richness of Indonesia’s manufacturing data to decompose firms’ revenue-total factor productivity (TFPR) into quantity-total factor productivity (TFPQ) and markup. The results of the analysis are in line with our priors linked to the different features of these NTMs. The introduction of APT on a plant’s inputs results in an increase in both the plant’s technical efficiency and its markup, consistently with an increase in the quality of imported inputs. On the other hand, the application of PSI on inputs reduces the markup and the TFPR of the plant. When applied on the same product produced by the plant, the PSI increases the markup, in line with the increased protection for domestic producers.

We are able to explore in detail the mechanism behind the negative effects of input PSI on plants’ performance. This enables the paper to provide a third contribution by unveiling the difference in impacts between PSI and import tariffs, and adding to the literature on fixed trade cost, trade lumpiness, and the behavior of inventories and markups (e.g. Alessandria et al. (2010)). We find that by increasing the cost of imported goods, tariffs trigger a switch from imported to domestically-produced inputs, which translates in lower technical efficiency. Unlike tariffs, however, PSI generate non-monetary fixed costs per-shipment. These costs are related to the uncertainty around the timing of the

> 4Consistently with this view, Indonesian Customs do not take into account the outcome of the PSI in their decision whether to inspect the shipment at the border.

4
inspection outcome. The evidence suggests that instead of switching to less suitable domestic inputs, producers impacted by PSI prefer to smooth the risk of shipping delays by importing larger quantities less frequently. As this strategy increases inventory costs, producers lower markups to increase sales and reduce inventories. As a result, applying PSI on inputs lowers TFPR through lower markups, rather than technical efficiency (TFPQ).

This paper relates to the literature on the economic impact of trade reforms. The bulk of this literature focuses on changes in import tariffs as a result of unilateral trade liberalization (Amiti and Konings, 2007; De Loecker et al., 2016; Pavcnik, 2002), WTO accession (Handley and Limao, 2015; Brandt et al., 2017) or trade agreements (Caliendo and Parro, 2015; McCaig and Pavcnik, 2018). The paper contributes to this literature by providing novel evidence on the impact of NTMs on plants, both in aggregate and for narrowly-defined measures, and comparing their effect to import tariffs.

The paper also adds to the literature by disentangling the impact of trade instruments on TFPR between the impact on TFPQ and on markups. Our results suggest that this decomposition is important. Looking at TFPR risks to underestimate the distortions created by tariffs, as they tend to have countervailing impacts on markups and technical efficiency.

This paper is related as well to the literature studying the interaction between price and inventory decisions in retailing firms. In the framework developed by Aguirregabiria (1999), the existence of stockout probabilities and per shipment costs generate an inverse relationship between inventories and markups. Hornok and Koren (2015) document that fixed costs per-shipment are associated with less frequent and larger shipments. Alessandria et al. (2010) present evidence that per-shipment costs induce shipment delays, leading importers to import infrequently and hold additional inventory, which generates costs.

The rest of the paper is organized as follows. Section 2 introduces the data and the institutional framework; Section 3 describes the empirical methodology; Section 4 presents the main results comparing the impact of NTMs to import tariffs. Section 5 provides examples of “virtuous” and “protectionist” NTMs, and Section 6 concludes.

5
# **2. Data**

We gather information from several sources. This section presents the essential features of the data, focusing on our novel NTM dataset. Summary statistics of all variables used in the analysis are presented in Table 1. Details on all sources and variables’ construction are presented in Appendix B.

# _2.1. NTMs in Indonesia: Institutional Background_

The overall responsibility to determine rules over products sold in Indonesia lies with the Ministry of Trade (MoT). However, different government institutions are also responsible to determine NTMs for specific products. For instance, agricultural products are usually subject to NTMs applied by the Ministry of Agriculture and the Ministry of Health, including the food and drug agency (BPOM). These NTMs typically aim to protect consumer safety from risks such as plant and animal diseases through sanitary and phyto-sanitary (SPS) measures. Similarly, the Ministry of Industry regulates NTMs aiming to ensure consumer protection from risks linked to the use of manufacturing goods through technical barriers to trade (TBT).

Overall, 13 government institutions (ministries and agencies) have issued regulations on NTMs in Indonesia during our period of analysis (2008-15). As a result many products are subject to multiple NTMs. In our data, an average of 2.6 NTMs are applied to the universe of HS-10 digit products in 2008-15; and 5.4 for products with at least 1 NTM applied. These span a wide range of NTMs. Using the most refined NTM classification of UNCTAD at the 3-digit level, Indonesia applied 56 individual NTM types to the universe of its products in the 2008-15 period.<sup>5</sup> The Indonesian government has been increasing the use of NTMs over the period of analysis through relatively frequent changes in their application across HS-10 digit products. To the extent that NTMs mainly fulfill the objective of addressing market failures in trade, such changes should be less subject to the kind of regulatory capture typical of import tariffs (Grossman and Helpman, 1992). This would allow to use this

> 5UNCTAD classifies NTMs by assigning codes according to different levels of aggregation. The first level corresponds to six macro-categories: i) SPS; ii) TBT, iii) pre-shipment inspections and other formalities (INSP); iv) non-automatic licensing, quotas and other quantitative controls (QC); v) price controls (PC), and vi) other NTMs (OTH). The next categories are defined according to digits, with 2- and 3-digits.

6
variation across time and products identify the effects of NTMs on the performance of firms more cleanly than in the case of tariffs. However, it is also plausible to conjecture that the government may indeed use some NTMs to protect domestic producers from import competition, which would threaten the validity of the analysis. We cannot rule out this hypothesis, as we do not observe the real motives of the Indonesian government in setting NTMs. Hence, in the Section 3.1 we discuss several ways in which we address potential threats to the identification.

# _2.2. “Virtuous”_ vs. _“Protectionist” NTMs_

NTMs are highly heterogeneous measures with different aims and likely different effects. To guide our analysis, we make one central distinction between NTMs. We ask whether they are likely to breach a key principle of WTO rules, i.e. not restricting trade unnecessarily (Cadot et al., 2012). While NTMs’ _raison d’etre_ is to increase domestic welfare by addressing market failures, they can also distort trade unnecessarily in the process of fulfilling a legitimate policy objective, or even without fulfilling one.

Establishing whether a measure is strictly necessary to achieve a “legitimate policy objectives” is challenging, as policy-makers may have different definitions - even within the same country - of what “legitimate” means. One hint on its “legitimacy” is given by how widespread the application of the measure is across countries. An NTM that is used by most countries is more likely to be necessary than one that is used only by a handful of countries.

We focus on OECD countries, which tend to have more transparent trade policy, and compute the share of OECD countries that apply each of the 2-digit NTMs applied by Indonesia during the period of our analysis. We take data from UNCTAD’s _TRAINS_ dataset on NTMs applied by 34 OECD countries in 2015.

We restrict the analysis to the most common groups of applied NTMs in Indonesia, i.e. SPS, TBT and INSP. We focus in particular on SPS to identify possible “virtuous” NTMs, for two reasons. First, SPS measures address threats to the health and safety of consumers stemming from high-risk products, including food and medical products. The legitimacy of such first-order policy objectives may be more difficult to challenge than that of other NTM categories. Second, the application of SPS on agricultural products and drugs makes them a less suitable instrument to protect the manufacturing sector than 7
the other categories of NTMs.<sup>6</sup> Within the SPS group, we take the 2-digit NTMs which are applied by at least 95 percent of OECD countries. This returns a list of 7 measures, among which we select APT (coded as A5 in the data) as an example of “virtuous” NTM.<sup>7</sup> This measure aims to eliminate a particularly important potential externality in agricultural products, i.e. the presence of plant and animal pests or disease-causing organisms in the final product. As a result, the measure is applied widely across countries and requires different types of treatment which can be applied during production or as a post-production process. For instance, citrus fruits must undergo disinfection processes as treatment to kill bacteria and viruses in order to be commercialized.

At the other end of the spectrum, PSI (coded as C1 in the data) is the NTM applied by the least number of OECD countries among the NTMs applied by Indonesia. Only one OECD country (New Zealand) applies it _erga omnes_ and only on protected animal species, i.e. Red Pandas and African Hunting Dogs. PSI requires the goods to be inspected at the port of departure typically to ensure that the import declaration lists the correct classification of the goods to be imported as a way of detecting improper importing activities. Unlike APT, this type of measure appears redundant in a country like Indonesia, where the customs agency applies a risk management system aiming to detect suspicious shipment at the border. Indeed, Indonesian Customs do not take into account the outcome of the PSI in their assessment of the risk of the shipment at the border.

In the absence of clear benefits, the bulk of the effect of this measure is likely to be the increased compliance costs for imports. Such costs are particularly relevant in the case of Indonesia, as only two State-Owned Enterprises have the authority (granted by the MoT) to perform PSI for the Indonesian government. Anecdotal evidence suggests that such limited supply of inspection agencies increases the uncertainty around the time it takes to comply with such procedure. Thus, PSI can increase the cost of trade not only through the monetary costs of compliance for the importer, but also by increasing

> 6The results in Calì et al. (2021) for Indonesia and in Wood et al. (2019) for China confirm that TBT represent a greater barrier to imports than SPS.

> 7The other measures are: Prohibitions/restrictions of imports for sanitary and phytosanitary reasons; Tolerance limits for residues and restricted use of substances; Labelling, marking and packaging requirements; Hygienic requirements related to sanitary and phyto-sanitary conditions; Other requirements relating to production or post-production processes; Conformity assessment related to sanitary and phytosanitary conditions.

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the uncertainty of the import process. We take PSI as an example of “protectionist” NTMs.

# _2.3. NTM Data Construction_

The original information on NTMs comes from the _TRAINS_ database collected by the _Economic Research Institute for ASEAN and East Asia_ (ERIA) and UNCTAD. This data is based on official regulations that were in effect by January 2015 with an in-force date by January 2016. The building block of our explanatory variables are dummies taking value 1 if in a given year, the Indonesian government applied an NTM on an HS10 digit product. Since we consider the universe of HS-10 digit products, if a particular NTM is not in our data, then it means that the Indonesian government never applied it.<sup>8</sup> The resulting yearly panel at HS-10 digits code starts in January 2008 and ends in December 2015. We adjust the data in a number of ways. First, when the same NTM appeared multiple times for an individual product, we verified whether the corresponding regulations effectively introduced a new measure. Forty percent of repeated NTM codes corresponded to regulation updating an older one, in which case we do not consider it as a separate measure (as the original ERIA dataset does). Second, we adjust the date of entry into force of certain measures when their associated regulation (as recorded in January 2015) was in fact an update of earlier regulations.<sup>9</sup> In such cases, we also record whether there are changes in the list of products the NTM regulation applies to. Finally, as the dataset is based on the coding of data collected in January 2015, there may be concerns about measures that were in force during part of the period of analysis but eventually eliminated before January 2015. To address these concerns, we check for such instances through the _Global Trade Alert_ data, which records both trade restrictive and trade liberalizing measures for all countries since 2009.<sup>10</sup> To link the NTM dataset to our panel of manufacturing plants, we follow the literature on trade reforms.<sup>11</sup> We build two types of variables. The first is an NTM output measure,

> 8Given that our focus is on the effect on imports of NTMs imposed by the Indonesian government, we exclude from our analysis bilateral contingent measures and measures imposed by the exporting country.

> 9In those cases we attribute the date of entry into force of the measure as that of the first regulation. 10 The dataset can be accessed at its own web page.

> 11Our methodology closely follows Amiti and Konings (2007) and De Loecker et al. (2016).

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_NTMit_<sup>_out_, corresponding to the share of HS-10 digit products subject to at least one NTM</sup> within the corresponding five digit industry _i_ in the manufacturing plant-level data, in year _t_ (see sections 2.4 and Appendix B.1 for details on the manufacturing data).

Based on the output NTM variable, we compute the input NTM measure, _NTMit_<sup>_in_.</sup> To that end we construct input-output (IO) tables from the plant-level data. For each five digit industry _i_ , input NTM is defined as the weighted sum of output NTM in each five digit supplying industry _k_ : where _wik_ is the share of _k_ in total input value in industry _i_ . Our approach results in much more granular IO tables to construct upstream linkages, compared with the trade reforms literature (Javorcik, 2004; Bourlès et al., 2013). In particular, the weights _wik_ are obtained from detailed base year information on nine digits raw inputs used by each plant (see sections 2.4 and Appendix B.1). While this data would allow us to compute plant level input NTM variable, we prefer to aggregate the plant level information at the five digit level to minimize measurement error.<sup>12</sup> The results of the analysis below are qualitatively similar when using plant- instead of industry-specific input NTMs although the estimates are less precise, consistently with the larger noise in the data (results available from the authors upon request).

The evolution of output NTM in our sample is depicted by the solid line in Figure 1. The figure suggests a widespread and increasing incidence of NTMs for Indonesian manufacturing plants. Over the years of the sample, the share of products subject to NTM went from 60 to almost 80 percent. The increasing incidence of NTMs for Indonesian plants is also reflected in our measure of input NTM. In Figure 1, the dashed line represents the average share of intermediate inputs subject to at least one NTM. The share of inputs used by plants subject to NTMs went from less than 30 percent in 2008 to 60 percent in 2015. The higher incidence of output NTM presented in Figure 1 suggests that final consumption goods are more likely to be subject to NTMs than intermediate goods, although the gap shrinks considerably over time.

> 12The input data at the plant level appears to be quite noisy with several examples of plants changing their input mix frequently over the years. The aggregation across plants at the five digit industry allows to smooth this variability.

10
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Our data include information on quantities and values of the products produced, and raw inputs (domestic and imported) used by each plant. These are both defined at a highly granular level, namely nine digits _Klasifikasi Komoditi Indonesia_ (KKI), a more detailed classification based on KBLI. In our sample, each plant produces on average 2 products and 25% of the plants produce more than one product. On average, each plant uses four different varieties of raw inputs. The drawback of the high level of detail in materials’ data is that sometimes there are coding mistakes by the reporters. Appendix B.2 describes in detail the methodology we use to clean the data. After computing unit prices by dividing value with quantities, we use them to construct plant-level price deflators (see Appendix B.5).

One challenge of the _Statistik Industri_ data is the lack of complete series of capital stock. Earlier studies tried to re-construct capital stock series applying the _Perpetual Inventory Method_ (PIM) to the first year of capital stock data reported by the plant (Amiti and Konings, 2007; Javorcik and Poelhekke, 2017). However this imputation method crucially relies on the capital value self-reported by the plant the first year this data is available, which is not necessarily accurate.<sup>13</sup> One potential advantage of using PIM is that purchase and sales data might be more accurate relative to selfreported value of the stock, requiring an appropriate calculation of market values and depreciation. However, PIM needs to rely on measures of capital depreciation, which are difficult to accurately estimate. To mitigate such tradeoff, we have adopted a hybrid strategy, which is described in Appendix Appendix B.3.

# _2.5. Estimating Productivity and Markup Measures_

Our main dependent variables are estimates of plant-level productivity and markup. Unlike most existing studies using industry-level price indexes to deflate nominal variables, the granularity of our data allows us to calculate plant-specific output and input price indexes. This allows us to mitigate the bias deriving by inputs’ price heterogeneity across plants and to disentangle technical efficiency (TFPQ) and markups.<sup>14</sup> To estimate plant-level production function parameters, we follow the control function approach and timing assumptions of Ackerberg et al. (2015), which are useful to mitigate

> 13In particular, there is no a priori reason to believe that the quality of the self-reported capital stock the first year is necessarily better than the value in other years.

> 14Foster et al. (2008) discuss the bias arising when using plant revenue deflated by industry deflators. De Loecker et al. (2016) extend the analysis in the contest of unobserved variation in input prices.

12
the simultaneity bias affecting simple OLS coefficients.<sup>15</sup> We slightly modify the standard estimator to allow for the presence of adjustment costs in materials.<sup>16</sup> Then, using the estimated production functions, we calculate plant-level markups based on a model of profit maximization by firms, as in De Loecker and Warzynski (2012). Finally, we combine our estimates of TFPQ and markup to obtain a measure of revenue-total factor productivity (TFPR), which in many existing studies is considered the main productivity variable. The details of our estimator are provided in Appendix C.

# **3. Empirical Identification**

Our main empirical strategy consists in regressing plant-level outcomes on output and input NTMs, by using the following linear model: where _Yjit_ is one of the outcome variables described in Section 2.5, of plant _j_ operating in a 5-digit industry _i_ , at time _t_ . The coefficients of interest are _α_ 1 and _α_ 2 which measure, respectively, the impact of NTMs imposed on the products produced ( _NTMit_<sup>_out_) and the</sup> intermediate inputs ( _NTMit_<sup>_in_) used by a plant.The vector</sup><sup>**X**</sup><sup>_it_includes output and input</sup> tariffs. We add controls for tariffs because of the potential correlation between NTMs and MFN import tariffs. On the one hand, import duties can have a significant impact on plants’ performance (Amiti and Konings, 2007). On the other hand, NTMs might be used as an alternative tool to restrict trade and so they might be correlated with tariffs. Appendix Figure A1 summarizes graphically the correlation between tariffs and NTMs between 2008 and 2015. The figure depicts tariffs and the percentage of products covered by at least one NTM, both of them averaged at the 5-digit industry-level. The figure suggests the two variables are positively correlated, at least in our sample (correlation = coefficient 0.38).<sup>17</sup> In (1), we include 2-digit industry-year fixed effects ( _ust_ ) to control for demand and

> 15Control function approaches to production functions’ estimation have been introduced by Olley and Pakes (1996) and Levinsohn and Petrin (2003).

> 16Allowing for materials’ adjustment costs turns out to be important in our case. This is discussed in Appendix Appendix C.

> 17For the sake of a clearer exposition, only products with tariffs smaller that 50% are considered, since they represent the 99% of the entire sample.

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Table 1: Summary Statistics

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||N|Mean|SD|Min|Max|
|(a): Plant-level sample||||||
|Number of plants|–|15,550|0|15,550|15,550|
|Output tarif|62,212|0.092|0.085|0|2.749|
|Input tarif|62,212|0.051|0.029|8.61e-05|0.523|
|Output NTM|62,212|0.716|0.405|0|1|
|Input NTM|62,212|0.424|0.187|0|1|
|Pre-shipment inspections (output)|62,212|0.456|0.453|0|1|
|Pre-shipment inspections (input)|62,212|0.152|0.114|0|0.716|
|Anti-pest treatment (output)|62,212|0.002|0.031|0|0.771|
|Anti-pest treatment (input)|62,212|0.005|0.014|0|0.252|
|TFPQ (log)|62,212|6.334|3.905|-0.094|19.38|
|Markup (log)|62,212|1.168|1.079|-1.591|4.181|
|TFPR (log)|62,212|7.502|4.289|-1.524|23.29|
|Price defator (index)|42,383|4.679|0.912|4.77e-07|19.89|
|Real inventories of raw inputs (log)|42,383|10.66|3.044|-0.179|28.04|
|Real sales (log)|42,383|14.40|2.119|-1.581|31.80|
|Employment (log)|42,383|4.246|1.210|2.197|10.52|
|Real inventories of fnished products (log)|36,588|11.12|2.925|-0.639|29.94|
|(b): Plant-inputs sampl|e|||||
|Import tarif|68,736|0.057|0.032|0|0.400|
|Pre-shipment inspections|68,736|0.202|0.401|0|1|
|Anti-pest treatment|68,736|0.003|0.050|0|1|
|Average input price (log)|68,736|3.134|2.414|-14.06|19.14|
|Import/domestic switching dummy|68,736|0.009|0.096|0|1|
|Share of imported inputs|68,736|0.065|0.231|0|1|
|(c): Customs data samp|le|||||
|Number of importers|–|3,983|0|3,983|3,983|
|Import exposed to pre-shipment inspections (% imported value)|13,453|0.002|0.061|0|5.686|
|Number of shipments (log)|13,453|1.243|1.027|0|2.485|
|Value per shipment (log)|13,453|8.108|5.316|0|18.68|

**Note** : The table shows summary statistics for the plant-level sample (Panel a), the plant-inputs sample (Panel b) and the custom data sample (Panel c).

14
supply trends that might be correlated with the decision of imposing NTMs. To control for geographic trends related to local development, we include region-year fixed effects ( _urt_ ).

Unlike in previous studies looking at the impact of NTMs, we are able to exploit the time-varying nature of our data and include in (1) plant fixed effects ( _uf_ ). These absorb both observable and unobservable time invariant plant-level heterogeneity. Finally, we cluster standard errors at the five-digit industry level to match the variation in the trade policy variables.

# _3.1. Threats to the empirical identification_

Our identification relies on the assumption that conditional on all the time-varying and time-invariant controls, NTMs (and also tariffs) are exogenous to plants’ performance or outcomes. This assumption is plausible to the extent that introducing or eliminating NTMs is a national government decision, which individual plants cannot There are two potential threats to this assumption. On the one hand, some firms or industries might be able to lobby the government, directly or indirectly. On the other hand, the government could be interested in protecting specific industries for example as they ensure large levels of employment. To the extent that these characteristics are related to the plants’ performance, the issue could result in biased estimates.

We tackle the concern in four different ways. First, in all regressions we include 2- digit industry-year fixed effects which should also capture eventual changes in lobbying activity over time. The 2-digit industry classification is suitable for that objective as it roughly matches the classification around which sectoral business associations are organized (e.g. textile, garments, processed food, etc.).

It could also be the case that lobbying may occur at a finer level of sectoral disaggregation than 2-digit industry. To control for such a possibility, we exploit the empirical finding in Bombardini and Trebbi (2012) that industry concentration is a key determinant of lobbying activity on trade policy. We compute the base year value of the (revenue-based) Herfindahl-Hirschman Index in each 5-digit industry and interact it with year effects. Such a control should purge the estimates from potential bias at the most refined level of industry aggregation allowed by our empirical specification.

Third, we test whether 5-digit industry trends predict subsequent changes in NTMs. To do so, we regress the probability of observing any type of NTMs in a 5-digit industry 15
during the period 2008-2015, on the average rate of change between 2004 and 2007 of four industry-level performance measures, i.e. total output, employment, TFPQ and markups. We use them as separate regressors and all at once.<sup>18</sup> As a further robustness test, we use the NTM probability only in the first year of the NTMs database (i.e., 2008). The results of these tests are presented in the Appendix Table A1. Odd columns show the simple correlation between dependent and independent variables, while even columns add 2-digit industry fixed effects. Virtually, all the coefficients of the performance measures are statistically indistinguishable from zero, thus providing further evidence against the possible violation of our identifying assumption.<sup>19</sup> Finally, after presenting our baseline estimates in the following sections, we check that the results still hold whenever we exclude large plants - defined as those producing more than 10 percent of a 5-digit industry’s output - or politically connected plants, as they might affect government’s decision on the application of NTMs.<sup>20</sup>

# **4. Impact of NTMs** **_vs_ Import Tariffs on Performance**

Table 2 presents the estimated coefficients of Equation 1 for three dependent variables: TFPQ, markup and TFPR. We do not find any evidence of a statistically significant impact of the aggregate NTM variables on productivity and markups.

The muted impact of NTMs contrasts with the effect of import tariffs, which in line with previous studies have a negative effect on firms’ performance. The coefficient of output tariffs on TFPQ (column 1) is negative, although not statistically significant at conventional levels. On the contrary, output tariffs have a positive and significant impact on markups (column 2). This can be interpreted as a competition effect: by lowering competitive pressure from imports, output tariffs allow plants to charge higher markups, while allowing their inefficiencies to increase. The combination of these 2 effects on TFPQ and markup results in a negative but not significant effect of tariffs on TFPR. The lack of significance might be due to the countervailing effect on TFPQ and markup.

> 18All these variables are taken in logarithmic scale.

> 19These results are robust to the use of different time definitions of both the dependent and independent variables, as well as to the use of an intensive measure of NTM introduction in the referring period. Results of such robustness tests are available upon request.

> 20Politically connected plants are those being identified as having connections with the Suharto regime by Mobarak and Purbasari (2006). There are 246 such plants in our dataset.

16
On the other hand, input tariffs are associated to a large reduction in both TFPQ and markup (columns 1-2). As a result, input tariffs have a negative and statistically significant effect on TFPR (column 3). Our evidence is thus similar to Amiti and Konings (2007) in that we find a larger detrimental impact on productivity for input tariffs than for output tariffs. Our decomposition of TFPR shows that tariffs harm technical efficiency through both channels, but in the case of output tariffs the effect is offset by the countervailing impact on markups.

For robustness, Appendix Tables A2 and A3 report the coefficients obtained when excluding the largest and politically connected plants, respectively. In Table A2, the output tariff coefficient of markup is no longer statistically significant. However, all other coefficients are in line with those presented in the main analysis, including the NTMs coefficients that are still not statistically significant.

Based on the estimated coefficients in Table 2, Table 3 displays the average marginal effect of NTMs and import tariffs as opposed to the marginal effects as in Table 2.<sup>21</sup> On average, NTMs do not appear to have any significant cumulative (i.e. the sum of outputand input-NTMs) impact on plants’ performance. To the extent that the imposition of NTMs increases trade-related compliance costs, the zero net effect could reflect some countervailing positive impact of NTMs on performance. Such positive impact would be consistent with the role of NTMs in addressing market failures of trade, which would translate into an increase in the quality of and/or the demand for the products subject to the NTM. However, the zero net effect could also be the result of very low compliance costs associated with negligible externalities. In the next section we will shed further light on these effects for two specific categories of NTMs.

In fact, NTMs might still have non neutral impacts on welfare, which we are unable to observe in our data. For instance, quality and safety standards might have a beneficial impact on consumers’ utility. This is different to the case of import tariffs, which have a negative impact on TFPQ and TFPR, and unlike NTMs, they should not have any positive impact on consumers.

However, the evidence in Table 3 concerns the _net_ impact of all NTMs combined. In fact, as discussed in Section 2, NTMs are a highly diverse set of measures, whose

> 21The calculation of the average impact is based on (1) and it is calculated as follows: Average impact of NTMs = _α_ ˆ1 _×NTM_  ̄<sup>_out_</sup> + _α_ ˆ2 _×NTM_  ̄<sup>_in_</sup> , where _NTM_  ̄<sup>_out_</sup> and _NTM_  ̄<sup>_in_</sup> are, respectively, the sample averages of the output and input NTM variable.

17
economic effects are likely to be very heterogeneous. Hence, the next section focuses on specific NTMs trying to tease out some of this heterogeneity. This approach is also more policy relevant than considering the aggregated NTM, as policy-makers consider the and costs of individual NTMs.

# **5. A Tale of Two NTMs**

An analysis of the impact of all individual NTMs applied by Indonesia (47 at 2-digit level) is impractical and well beyond the scope of this paper. Instead, in this section we provide two examples of NTMs with markedly different degrees of policy legitimacy. As explained in section 2.2, we focus on APT as an example of “virtuous NTM” and on PSI as an example of “protectionist NTM”. This allows us to show the different effects of NTMs and as well as to describe in a detailed way the channels of impact.

# _5.1. Anti-Pest Treatments_

Table 4 reports the results of estimating Equation 1 while replacing the aggregate NTM output and input variables with the corresponding variables on APT. To avoid omitted variable bias, we control as well for the aggregate NTM variables excluding APT. None of the coefficients on the output measure is statistically significant. On the other hand the input APT has a positive and statistically significant effects on TFPQ, markup and TFPR. These results are consistent with APT increasing the quality of inputs, which allows plants to produce more efficiently and increase the margins on the sale of the final product.

The lack of significance of the output APT coefficient may simply reflect its limited application on manufacturing products. In fact, only a few plants produce products that require APT. At the same time, such products are used as intermediate inputs by a larger number of plants. Indeed, the mean values of the input APT measure is almost double the size of the output measure (Table 1). This ensures greater variation of the input- relative to the output-APT measure.

Similarly to the general case analyzed in the previous section, we test the robustness of the results with respect to the exclusion of the largest and politically connected plants. Appendix tables A4 and A5 show the results are robust to these tests.

Overall, these results confirm the hypothesis that APT may be considered as an example of a virtuous measure. The compliance costs that it might generate appear to 18
Table 2: Impact of NTMs _vs_ Import Tariffs

||(1)<br>TFPQ|(2)<br>Markup|(3)<br>TFPR|
|---|---|---|---|
|Output NTM|0.008|-0.064|-0.057|
||(0.029)|(0.111)|(0.125)|
|Input NTM|0.044|0.100|0.144|
||(0.080)|(0.134)|(0.190)|
|Output tarif|-0.280|0.262*|-0.0184|
||(0.197)|(0.158)|(0.279)|
|Input tarif|-1.112***|-0.804**|-1.915***|
||(0.402)|(0.389)|(0.600)|
|Observations|59,791|59,791|59,791|
|R-squared|0.981|0.694|0.955|
|Plant FE|yes|yes|yes|
|2-dig ind-year FE|yes|yes|yes|
|Region-year FE|yes|yes|yes|
|5-dig HH-year FE|yes|yes|yes|
|Other NTMs|yes|yes|yes|

**Note:** This table shows the effect of NTMS and import tariffs, applied either to output or inputs, on plant’s TFPQ (col. 1), markup (col. 2) and TFPR (col. 3) in the years between 2008 and 2015. _Output NTM_ is the share of HS-10 digits products subject to at least one NTM within the corresponding five digit industry. For each five digit industry, _Inputs NTM_ is the weighted sum of output NTM in all five digit supplying industries. _Output tariff_ is the average tariff rate in the 5 digit industry of a plant. _Inputs tariff_ is the weighted average of the tariffs rates applied to the inputs used by a plant. The coefficients are obtained by the estimation of Equation 1. All specifications include plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted with year fixed effects. Standard errors are robust to heteroskedasticity and clustered at the industry level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

19
Table 3: Impact of NTMs _vs_ Import Tariffs: Average Marginal

||(1)|(2)|(3)|
|---|---|---|---|
||TFPQ|Markup|TFPR|
|Impact of NTMs|0.028|-0.005|0.022|
||(0.038)|(0.025)|(0.137)|
|Impact of tarifs|-0.083***|-0.017|-0.099***|
||(0.017)|(0.114)|(0.032)|

**Note:** This table shows the average marginal effect of NTMS and import tariffs on plant’s TFPQ (col. 1), markup (col. 2) and TFPR (col. 3) in the years between 2008 and 2015. The average marginal effects are computed from the estimates showed in Table 2. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

be compensated by the higher quality of the products it applies to. When the latter are used as inputs, they increase the productivity and markup of the final products.

# _5.2. Pre-Shipment Inspections_

Table 5 reports the results of estimating Equation 1 with a specific focus on PSI. Output PSI does not have a significant impact on TFPQ (column 1), but it has a positive and significant impact on markups (column 2). This is consistent with the hypothesis that compliance with the inspection is costly for foreign producers. As a result, the marginal cost of commercializing the product increases and so does its sale price. This creates a cost advantage for domestic producers, which allows them to charge a higher markup. Column (3) suggests that when a PSI is imposed on an imported product, the plants producing the same product domestically experience an increase in TFPR. The results in columns (1) and (2) indicate that this is the result of higher margins due to reduced competition, rather than higher technical efficiency.This interpretation is also consistent with the reduction in imports associated with the imposition of PSI documented in in Table 7.

The second row of Table 5 reports the coefficients of input PSI. While input PSI do not have an impact on TFPQ, the coefficients in columns (2) and (3) show that they have a negative impact on markups, which translates into a negative impact on TFPR.

20
Table 4: Impact of Anti-Pest Treatments (APT)

||(1)<br>TFPQ|(2)<br>Markup|(3)<br>TFPR|
|---|---|---|---|
|Anti-pest treatment (output)|-0.126|-0.353|-0.479|
||(0.219)|(0.298)|(0.297)|
|Anti-pest treatment (input)|3.756***|5.478**|9.235***|
||(0.885)|(2.167)|(2.559)|
|Observations|59,791|59,791|59,791|
|R-squared|0.981|0.695|0.955|
|Plant FE|yes|yes|yes|
|2-dig ind-year FE|yes|yes|yes|
|Region-year FE|yes|yes|yes|
|5-dig HH-year FE|yes|yes|yes|
|Other NTMs|yes|yes|yes|

**Note:** This table shows the effect of anti-pest treatments (APT), applied either to output or inputs, on plant’s TFPQ (col. 1), markup (col. 2) and TFPR (col. 3) in the years between 2008 and 2015. _Output APT_ is the share of HS-10 digits products subject to at least one APT within the corresponding five digit industry. For each five digit industry, _Inputs APT_ is the weighted sum of output APT in all five digit supplying industries. The coefficients are obtained by the estimation of Equation 1. All specifications include controls for other NTMs and import tariffs applied on plant’s outputs and inputs, plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted with year fixed effects. Standard errors are robust to heteroskedasticity and clustered at the industry level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

21
Before turning to explore the mechanisms behind the observed impact of PSI on productivity and markups, we check the robustness of the results to a battery of tests. First, we exclude the largest and politically connected plants (Appendix Tables A6 and A7, respectively) and once again the results remain unchanged. An additional concern for both the results in tables 4 and 5 is that the difference in effects is not intrinsically due to the different categories of NTM, but rather to the characteristics of products to which the NTMs are applied. While industry-year effects should mitigate the concern, we check the robustness of our findings explicitly in Appendix tables A8 and A9. Table A8 assesses the impact of APT exclusively on plants subjects to PSI, while Table A9 assesses the impact of PSI exclusively on plants subject to APT. The results are broadly consistent in such specifications, although the impact of PSI on plants subjects to APT is less precisely estimated due to the much lower number of observations.<sup>22</sup>

# _5.3. Mechanisms behind the impact of PSI on plants_

The reduced-form effects of PSI on plants’ performance suggests that the measure increases the costs of trade. The negative effect should dominate in the absence of a countervailing positive impact of PSI through the resolution of a market failure. In order to better understand what would happen to the dynamics of the plants in that case, we explore in more detail the mechanisms behind the estimates in Table 5.

Our hypothesis goes as follows. When a PSI is applied to an imported product, it increases the uncertainty around the time to complete the import procedure as the timing of the inspection itself is uncertain.<sup>23</sup> Shipment delays might disrupt production chains and generate monetary costs. From the perspective of a producer utilizing the product as an input, it would be safer to purchase the same product from domestic producers. However, the quality or suitability of the domestic input might be inferior to the imported one. To edge against shipping delays, the importer can follow an alternative strategy: increasing inventories to build a buffer of the imported input. However, heavier inventories entail higher storage costs, which induce plants to empty inventories as quickly as possible. To empty out inventories, one common strategy is running “promotions” (Aguirregabiria, 1999), i.e. lowering markups to increase sales.

> 22Recall that APT is applied to a limited number of plants.

> 23This problem is amplified by the artificial limited supply of authorized inspection agencies in Indonesia as explained above.

22
Table 5: Pre-Shipment Inspections (PSI)

||(1)<br>TFPQ|(2)<br>Markup|(3)<br>TFPR|
|---|---|---|---|
|Pre-shipment inspections (output)|0.056|0.330**|0.386**|
||(0.060)|(0.155)|(0.184)|
|Pre-shipment inspections (input)|-0.052|-0.906**|-0.958*|
||(0.206)|(0.398)|(0.534)|
|Observations|59,791|59,791|59,791|
|R-squared|0.981|0.694|0.955|
|Plant FE|yes|yes|yes|
|2-dig ind-year FE|yes|yes|yes|
|Region-year FE|yes|yes|yes|
|5-dig HH-year FE|yes|yes|yes|
|Other NTMs|yes|yes|yes|

**Note:** This table shows the effect of pre-shipment inspections (PSI), applied either to output or inputs, on plant’s TFPQ (col. 1), markup (col. 2) and TFPR (col. 3) in the years between 2008 and 2015. _Output PSI_ is the share of HS-10 digits products subject to at least one PSI within the corresponding five digit industry. For each five digit industry, _Inputs PSI_ is the weighted sum of output PSI in all five digit supplying industries. The coefficients are obtained by the estimation of Equation 1. All specifications include controls for other NTMs and import tariffs applied on plant’s outputs and inputs, plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted with year fixed effects. Standard errors are robust to heteroskedasticity and clustered at the industry level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

23
To test such hypothesis, we first check whether or not the application of PSI on imported inputs induces plants to switch towards domestically produced inputs. To that end we exploit highly disaggregated plant-level data on the use of intermediate inputs at 9 digit-level. Importantly, the data allows to distinguish between domestically sourced and imported inputs. More specifically, we estimate the following linear model: In Equation 2, _Yjmt_ is an outcome variable for input _m_ used by plant _j_ at time _t_ . The variable _Input PSImt_ is a dummy taking value 1 if PSI is applied on an intermediate input _m_ at time _t_ . The vector **X** _mt_ includes import tariffs and dummy variables for whether NTMs other than those under analysis are applied to input _m_ .<sup>24</sup> We also include 5-digit industry-year fixed effects ( _uit_ ) that control for differences in competition and other industry-level confounders, and region-year fixed effects ( _urt_ ). Plant-input fixed effects are denoted by _ujm_ . Standard errors are clustered at the plant-level.

Table 6 presents the estimates for Equation (2).<sup>25</sup> The dependent variable in column (1) is a dummy variable taking value 1 if a plant switches from an imported to a domestic variety of a given 9-digit input. The dummy takes value 1 if the imported quantity of an input decreases and the domestic quantity of the same input increases.

The coefficients in column (1) suggest that while PSI does not induce plants to switch to domestic inputs, import tariffs do. The results in column (1) are also supported by those in column (2), where the dependent variable is the share of imported quantity of an input over the total use of the same input (i.e., imported plus domestically sourced).

This switch to domestic inputs induced by tariffs may help explain the negative impact of tariffs on TFPQ, as domestically produced inputs may be of lower-quality than imported inputs. This is supported by the result in column (3), which suggests that import tariffs are associated with a reduction of the average unit price of the used inputs. Consistent with the view that PSI does not affect the quality of inputs used by the plants, the coefficients of PSI are not significant on the dependent variables presented in Table 6.

Why do import tariffs and PSI applied on imported inputs induce two different

> 24Also in this specification, we use aggregate 1-digit categories of NTMs.

> 25The number of observations is higher in such specification, due to the fact that each plant utilizes multiple inputs.

24
Table 6: Evidence from Intermediate Inputs Data

||(1)<br>Pr(switch)|(2)<br>Share Imp. Inp.|(3)<br>Avg. Inp. Price|
|---|---|---|---|
|Input PSI|-0.000|-0.001|-0.054|
||(0.003)|(0.004)|(0.045)|
|Import tarif|0.153***|-0.170***|-6.835***|
||(0.043)|(0.057)|(0.709)|
|Observations|138,464|138,464|138,464|
|R-squared|0.354|0.830|0.704|
|Plant-input FE|yes|yes|yes|
|5-digit ind-year FE|yes|yes|yes|
|Region-year FE|yes|yes|yes|
|Other NTMs|yes|yes|yes|

**Note:** This table shows the effect of pre-shipment inspections (PSI) applied to inputs, on plant’s probability of switching to domestic inputs (col. 1), share of imported inputs (col. 2) and average input price (col. 3) in the years between 2008 and 2015. _Input PSI_ is a dummy taking value 1 if a NTM is applied on an intermediate input _m_ at time _t_ . The coefficients are obtained by the estimation of Equation 2. All specifications include controls for other NTMs and import tariffs applied on plant’s inputs, plant-input fixed effects, 5-digit industry-year fixed effects and region-year fixed effect. Standard errors are robust to heteroskedasticity and clustered at the plant level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

25
responses by plants? To answer the question, we notice that the two trade policy instruments generate different cost structures. From an importer’s perspective, PSI generates both a monetary and a likely larger non-monetary fixed cost per shipment, due to the increased risk of shipping delay. Rather than switching to domestic inputs, the uncertainty cost can be “smoothed” by an alternative strategy, i.e. importing less frequently larger quantities.

This strategy on the other hand, is not effective in reducing the cost increase of tariffs, as they generate monetary costs proportional to the quantity imported. The absence of an available smoothing strategy may make switching to domestic inputs a more likely choice, even at the expense of inputs’ quality.

To test this hypothesis, we turn to monthly customs’ data (see Appendix B.4 for details on the data). For each importer and year, we regress the (log) number of annual shipments and their (log) value on the share of imports exposed to PSI.<sup>26</sup> Customs data only provide information on the values of traded goods, but not additional information on the importer.<sup>27</sup> Therefore, we can only control for importer fixed effect and year fixed effect. The results - presented in Table 7 - are consistent with our hypothesis. In particular we find strong evidence that within a year, the application of PSI is associated with a lower number of shipments (column 1), but with shipment of higher value on average (column 2). For comparison, we report the impact of import tariffs as well. Unlike PSI, import tariffs on do not appear to significantly affect the frequency and value of shipment, although both coefficients are large and negative.

The strategy of importing higher value shipments less frequently should impact plants’ inventories of raw inputs, a hypothesis supported by the results in Table 8. The coefficient in column (1) suggests that PSI on inputs increases inventories.<sup>28</sup> This is consistent with the notion that higher inventories help buffering against the greater uncertainty of input supply generated by PSI.<sup>29</sup> Inventory costs increase with the length of time the input lies in stock. Therefore, plants can lower markup to increase sales and reduce inventories. Consistent with this view, the coefficient of input PSI on sales in

> 26We express the shares as a fraction of the total value of imports in the first available observation. We then drop the first observation to avoid issues of endogeneity.

> 27Due to such data limitations we cannot link customs and SI data.

> 28The lower number of observations relative to Table 5 is due to missing data on inventories.

> 29Inventories are expressed as (log) averages of beginning- and end-of-the-year values of raw inputs, deflated by the plant-level input price index.

26
Table 7: Evidence from Customs’ Office Data

||(1)<br>N. Ship.|(2)<br>Val. per Ship.|
|---|---|---|
|Input PSI|-0.303***|0.189***|
||(0.016)|(0.071)|
|Input tarif|-0.816|-12.164|
||(1.355)|(11.637)|
|Observations|13,035|13,035|
|R-squared|0.863|0.789|
|Other NTMs|yes|yes|
|Importer FE|yes|yes|
|Year FE|yes|yes|

**Note:** This table shows the effect of inputs exposure to pre-shipment inspections (PSI) on the plant’s average number of shipments (col. 1) and the average value per shipment (col. 2), both in logarithmic scale, in the years between 2014 and 2018. _Input PSI_ is the share of the total value of imported goods covered by PSI. The impact of import tariffs is measured analogously. All specifications include controls for other NTMs applied on plant’s inputs, importer fixed effects and year fixed effects. Standard errors are robust to heteroskedasticity and clustered at the importer level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

27
column (2) of Table 8 is positive, but not significant.<sup>30</sup> As sales may react with a lag to lower prices, column 3 regresses next period’ sales on PSI. The coefficient becomes positive and significant. Having expressed inventories in logs allows us to compare the elasticities. Consistent with the idea that lower markups have the objective to boost sales and empty out inventories, we find that the increase in sales is of similar magnitude as the increase in inventories.

To further corroborate the mechanism just described in Appendix Table A10, we provide two further tests. First, the coefficient of input PSI showed in column (1) shows that the application of this type of NTM on inputs does not have any impact on inventories of finished goods, which should be only partially impacted by restrictions on intermediate materials. Second, in column (2) of Appendix Table A10, we show that input PSI does not have any impact on employment, consistent with the idea that the increase in sales may not be related to a successful growth strategy of the plant.

# **6. Conclusions**

This paper builds a novel NTM dataset on all NTMs applied to the universe of goods produced in Indonesia from 2008 to 2015 and uses the data to explore the impact of NTMs in a large panel of manufacturing plants. The results suggest that on average NTMs do not harm plants’ performance, whether through the input or the output channel. To the extent that some of the benefits of NTMs in addressing market failures may not be fully captured by the plant-level outcomes, NTMs might even have a net positive impact on welfare. This is different to the case of import tariffs, which have a negative impact on productivity and - unlike NTMs - they also do not appear to have any positive impact on consumers.

This average neutral effect of NTMs masks a wide heterogeneity across individual NTMs. To tease out some of this heterogeneity, the paper focuses on two specific measures (APT and PSI), which we argue differ markedly in terms of how necessary they are to achieve legitimate policy objectives. In line with our hypothesis, the application of APT on imported inputs has a positive impact on TFPR through increases in markup, which we interpret as a sign of quality improvement linked to the measure. On the other hand, the results for PSI suggest that this acts mainly as an import barrier,

> 30Sales are the log of total production minus the yearly change in inventories of finished products, deflated by the plant-level output price deflator.

28
Table 8: Pre-Shipment Inspections (PSI): Inventory and Sales Behavior

||(1)<br>Raw invent|(2)<br>Sales|(3)<br>Sales (t+1)|
|---|---|---|---|
|Pre-shipment inspections|(output)<br>0.003|-0.042|-0.075|
||(0.079)|(0.093)|(0.094)|
|Pre-shipment inspections|(input)<br>0.582**<br>(0.285)|0.336<br>(0.291)|0.519**<br>(0.243)|
|Observations|40,102|40,102|38,309|
|R-squared|0.927|0.908|0.906|
|Plant FE|yes|yes|yes|
|2-dig ind-year FE|yes|yes|yes|
|Region-year FE|yes|yes|yes|
|5-dig HH-year FE|yes|yes|yes|
|Other NTMs|yes|yes|yes|

**Note:** This table shows the effect of pre-shipment inspections (PSI), applied either to output or inputs, on plant’s raw materials inventory (col. 1), sales (col. 2) and next period’ sales (col. 3) in the years between 2008 and 2015. _Output PSI_ is the share of HS-10 digits products subject to at least one PSI within the corresponding five digit industry. For each five digit industry, _Inputs PSI_ is the weighted sum of output PSI in all five digit supplying industries. The coefficients are obtained by the estimation of Equation 1. All specifications include controls for other NTMs and import tariffs applied on plant’s outputs and inputs, plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted with year fixed effects. Standard errors are robust to heteroskedasticity and clustered at the industry level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

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reducing competition, raising markup in the output markets, and increasing the cost of sourcing the imported inputs. We provide evidence that PSI induces plants to import larger quantities less frequently, in order to edge against the uncertainty generated by the inspection. At the same time, the larger inventories prompt plants to lower markups to increase sales and absorb inventory costs.

This type of analysis underscores the importance of moving beyond the aggregate NTM variables that much of the literature has focused on. Besides capturing the large heterogeneity in the economic effects of NTMs, focusing on the individual measures allows to inform directly the trade policy debate, which centres around specific rather than aggregate NTMs. We have proposed some methods to start identifying different types of individual measures on the basis of their _ex ante_ legitimacy. We view additional systematic ways of classifying NTMs and further empirical analyses as a fruitful avenue for future research.

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