metadata: authors: 0: author: Zavala,Lucas
1: author: Fernandes,Ana Margarida
2: author: Haygood,Ryan
3: author: Reed,Tristan
4: author: Reyes,Jose Daniel
count: World
docna: 0: docna: Quality Regulation Creates and Reallocates Trade
docty: Policy Research Working Paper
abstracts: cdata!: Quality regulation has become the
            dominant instrument of trade policy. Panel evidence shows
            that regulations classified as sanitary and phytosanitary
            measures and technical barriers to trade both increase trade
            on average. Other non-tariff measures like quotas decrease
            trade. Sanitary and phytosanitary measures reallocate trade
            from lower-income exporting countries to higher-income
            exporting countries, while technical barriers to trade
            measures do the opposite. Sanitary and phytosanitary and
            technical barriers to trade measures increase the sales
            concentration of exporting firms from lower-income
            countries, but do not affect the concentration of exporting
            firms from higher-income countries or importing firms. The
            costs of quality regulation are primarily borne by exporting
            firms, especially in lower-income countries.
display_title: Quality Regulation Creates and
            Reallocates Trade
listing_relative_url: /research/2023/11/34194176/quality-regulation-creates-reallocates-trade
topicv3: Trade Regulation m1326296 1015,Trade Policy and Integration m1327407 775,Trade Policy m1327640 776,Trade in Goods m1504224 2608,Trade Policy and Investment Agreements
            m1504277 2575
publishtoextweb_dt: 2023-11-09T00:00:00Z
dois: 10.1596/1813-9450-10601
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1157185
1: author_key: 658876
2: author_key: NA
3: author_key: 948891
4: author_key: 941773
model_extractions: Policy Research Working Paper 10601 Quality Regulation Creates and Reallocates Trade _Lucas Zavala Ana Fernandes Ryan Haygood Tristan Reed Jose-Daniel Reyes_ Development Research Group Development Impact Group & Macroeconomics, Trade and Investment Global Practice November 2023
Policy Research Working Paper 10601

# **Abstract**

Quality regulations imposing minimum product standards have become a central instrument of trade policy. Using disaggregated panel data from middle-income countries, the authors show that sanitary and phytosanitary (SPS) and technical barriers to trade (TBT) measures increase trade on average, consistent with improved consumer information and contrary to their depiction as “non-tariff barriers”.

However, their distributional effects differ. SPS measures reallocate trade toward higher-income countries, whereas TBT measures favor lower-income countries. Both increase sales concentration among exporters from lower-income countries only. The evidence indicates that the costs of quality regulation are primarily borne by exporting firms, especially those from lower-income countries.

This paper is a product of the Development Impact Group, the Development Impact Group, Development Economics and 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 lzavala@worldbank.org, afernandes@worldbank.org, treed@worldbank.org, reyes@worldbank.org, and ryan.haygood@yale.edu.

_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
# **Quality Regulation Creates and Reallocates Trade**<sup>∗</sup>

Lucas Zavala<sup>b</sup> , Ana Fernandes<sup>a</sup> , Ryan Haygood<sup>c</sup> , Tristan Reed<sup>b</sup> , Jose-Daniel Reyes<sup>b</sup> Originally published in the Policy Research Working Paper Series in _November 2023_ . This version is updated in _February 2026_ .

To obtain the originally published version, please email prwp@worldbank.org.

<u>JEL Codes:</u> F13, F14, L11, O19.

<u>Keywords:</u> Quality regulation, trade policy, non-tariff measures, imports, market concentration, trade reallocation.

> ∗We thank Penny Goldberg, Juan Carlos Hallak, Hiau-Looi Kee, and Ariel Weinberger for comments, as well as seminar participants at the World Bank, UNCTAD, Princeton, and Yale. This paper was supported by the Development Research Group Research Support Budget, the Knowledge for Change Program III for funding, the Umbrella Facility for Trade trust fund (financed by the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom), the Structural Transformation and Economic Growth Small Research Grant, and the Economic Growth Center SYLFF Research Grant. Jan Gabriel Oledan and Paula Suarez provided excellent research assistance. Alejandro Forero assisted with tariff data. Computational reproducibility was verified by DIME Analytics. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank or its affiliated organizations, or those of the Executive Directors of the World Bank, their Managements, or the governments they represent.

a: Corresponding author, World Bank, 1818 H St NW, Washington, DC, 20433, afernandes@worldbank.org; b: World Bank; c: Yale University.
# **1 Introduction**

Over the past three decades, a decline in import tariffs has coincided with a rise in quality regulations (Figure 1). Quality regulations specify minimum standards a product must conform with in order to be sold in the market, and apply to domestic as well as foreign firms.<sup>1</sup> Quality regulations can affect firms’ capabilities, defined by Sutton (2012) as their cost of production, which shifts supply, and their product quality, which shifts demand. For example, conforming with food safety certification could increase firms’ fixed cost, given the investment required to bring the production process into conformity. Yet once consumers are assured by the certification that the food product is safe, demand could increase. How quality regulations affect the allocation of trade has important implications for economic development and inequality between countries. As more importing countries adopt quality regulations, lower-income exporting countries could benefit if such regulations reassure customers that their products are of high quality. At the same time, quality regulation could be a barrier to entry for exporting firms from lower-income countries, if their competitors in higher-income countries have a lower compliance cost due to their prior experience with these regulation in their home markets.

This paper quantifies the heterogeneous effects of quality regulations and other non-tariff measures (NTMs) on trade, focusing on how they shape the allocation of trade between lower- and higher-income economies and the channels through which they achieve these effects. We overcome two key empirical challenges to provide the most comprehensive evidence yet on these important policies. A first empirical challenge concerns measurement: NTMs comprise a highly diverse set of policies that are difficult to measure accurately, especially over time. To overcome this challenge, we assemble a new panel dataset including (1) all NTMs at a disaggregated product level imposed by 16 middle-income Latin American countries from 1996 to 2017; (2) product-level exports to these countries from each of their trading partners; and (3) firm-level exports to these countries from a subset of their trading partners. These middle-income countries in Latin America are a unique laboratory to study the impact of NTMs as they have engaged in substantial adoption of new quality regulations and standards in recent decades, unlike high-income countries, which have already adopted these regulations, and low-income countries, which have yet to adopt them. We leverage rich panel variation in the timing of the adoption of these NTMs to identify their treatment effect on trade.

> 1Quality regulations can be set locally by a country or can be based on international standards. An example of international standards are those for food safety developed by the Codex Alimentarius Commission funded by the Food and Agriculture Organization of the United Nations and the World Health Organization.

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We classify NTMs into five clusters according to the date of entry into force: (i) Sanitary and Phytosanitary measures (SPS), (ii) Technical Barriers to Trade (TBT), (iii) simultaneously SPS and TBT (i.e., SBS and TBT measures in the same law), (iv) pre-shipment inspections (PSI), and (v) non-technical measures.<sup>2</sup> The first three clusters are quality regulations often imposed with legitimate rationales, such as protecting the safety, public health, and well-being of consumers. Examples of SPS are a requirement limiting the use of hormones and antibiotics in the production of meat, or a sample test on imported oranges to check for the residue level of pesticides. Examples of TBT are restrictions on toxins in children’s toys, or a requirement that refrigerators need to carry a label indicating their size, weight and electricity consumption level.<sup>3</sup> Unlike the first three clusters, the fourth and fifth clusters of NTMs do not provide consumers with information on quality. An example of a PSI is a requirement that goods must be shipped directly from the country of origin, without stopping at a third country. Non-technical measures include regulations that are explicitly protectionist like import quotas, minimum import prices, and retaliatory tariffs like antidumping and countervailing duties (for discussion of the latter see Bown (2011)).

Our main specification is a gravity model that flexibly identifies heterogeneity in treatment across all five clusters of NTMs and that accounts for a very rich set of fixed effects, including at country pair-product level that allows for _within_ estimation of the treatment effects. The key empirical challenge is that countries may impose NTMs in response to import trends, complicating causal identification of their effects in this model. To overcome potential endogeneity in regulation, we include in the model a control function indicating whether the same NTM is imposed by neighboring countries around the same time. Our sample of Latin American importing countries is ideal for this purpose. These countries share a common history and legal and cultural traditions, which contributed to their adoption of similar quality regulations on similar products concurrently as part of a shared regional reform agenda.

The analysis yields two main reduced-form findings. First, we find that SPS, TBT, and simultaneous SPS-TBT measures increase imports on average, while PSI and non-technical measures reduce imports. These results are consistent with a model in which quality regulation provides consumers with useful information about quality and increases demand, and this effect outweighs the negative trade effect of any increase in cost. Non-technical measures and PSI reduce imports by increasing trade costs without generating any informational

> 2These clusters are based on categories in the standard UNCTAD (2019) nomenclature. Note that while the nomenclature designates a set of measures Technical _Barriers_ to Trade, we let our empirical estimation indicate whether such measures hamper trade or not.

> 3TBT are typically designed to ensure product compatibility, safety, or labeling with an objective of regulatory standardization and risk mitigation.

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benefits for consumers. Since non-technical measures include tariffs and quasi-tariffs, this result was wholly expected.

Second, although quality regulation increases trade on average, it reallocates trade between exporting countries in different ways. SPS measures imposed by importing countries increase imports from higher-income origin countries relative to lower-income origin countries.<sup>4</sup> This finding is consistent with the fact that higher-income countries have historically imposed stringent SPS measures on local producers (Ederington and Ruta, 2016), so their exporting firms’ products already conform when more stringent standards are imposed by new export destinations. Lower-income countries, in contrast, lose market share in the importing countries because their exporting firms are unable to comply. SPS on average make lower-income countries less competitive. This outcome is particularly concerning given the comparative advantage of many lower-income countries in food and agricultural products, which are disproportionately subject to SPS measures. For TBT measures the patterns are reversed. TBT measures increase imports from lower-income countries relative to higherincome countries. This finding suggests that TBT are either relatively easier for exporting firms in lower-income countries to comply with, or that they provide relatively more information about these exporting firms’ products. The reallocation effects cancel each other out when regulations impose both SPS and TBT measures simultaneously on the same product.<sup>5</sup> These findings have important implications for the theory of international trade, which typically assumes perfect information. By contrast, only a model of trade with imperfect information can generate the differential reallocation effects of SPS and TBT measures estimated with our data. We sketch a model with imperfect information building on the work of Sutton (2012), Macedoni and Weinberger (2022), and Disdier, Gaign ́e and Herghelegiu (2023) to illustrate how imperfect information shapes trade in our setting. Firms decide whether to export and, conditional on exporting, whether to signal their quality, which is costly. Quality regulation allows firms to communicate quality at a lower cost: conditional on meeting the minimum quality threshold, they do not need invest in further signaling, because consumers already know that they are above the threshold. However, as the quality threshold introduced by the regulation rises, fewer firms can meet it, leading to exit.

The model has predictions for prices, quantities, and exit, which we test and verify. SPS measures lead to significant exporter exit and increase average export prices, consistent with these regulations imposing high quality thresholds like food safety standards. In contrast, TBT measures have no impact on exit and increase average export quantities, consistent

> 4We employ the World Bank’s income classification and group low- and lower-middle income countries as “lower-income” and upper-middle income and high-income countries as “higher-income” countries.

> 5PSI and non-technical measures deter trade uniformly across exporting countries and therefore do not induce any reallocation of trade.

4
with them imposing more moderate quality thresholds like labeling requirements.

**Related Literature and Policy Implications** Our finding that quality regulation increases trade on average runs counter to the designation of these regulations as ‘non-tariff _barriers_ ” and to a large literature estimating negative impacts of NTMs on trade, especially for agricultural goods.<sup>6</sup> The difference in findings can be explained by several factors.

First, we separately identify the impacts of five different clusters of NTMs and compare them in a common empirical model. In contrast, prior studies focus exclusively on either SPS and TBT (Disdier et al., 2008; Fontagn ́e, Orefice, Piermartini and Rocha, 2015; Atal, Cuesta and Sæthre, 2022) or bundle together quality regulations and protectionist measures (Kee et al., 2009; Kee and Nicita, 2022). For example, Kee and Nicita (2022) estimate a negative trade impact of “border NTMs,” which include both quality regulations and pre-shipment inspections. Our empirical strategy allows us to uncover the trade-enhancing effects of the former together with the trade-deterring effects of the latter.

Second, some previous studies measure only self-reported NTMs, which creates a selection bias, as pointed out by Ederington and Ruta (2016). Bao and Qiu (2012) measure NTMs that importing countries notify to the WTO. Yet, notification is not mandatory if NTMs follow internationally-agreed standards, suggesting this sample could exclude the most common NTMs. Fontagn ́e and Orefice (2018) use a dataset of NTMs drawn from Specific Trade Concerns brought to the WTO by exporting countries. Yet, exporting countries are unlikely to bring concerns about NTMs that are trade enhancing. In contrast with these datasets, our dataset measures all NTMs in a country through a complete inventory of domestic law and regulations affecting trade in goods following the UNCTAD (2019) methodology.

Third, we explicitly tackle the potential endogeneity of NTMs by predicting each country’s clusters of NTMs using the clusters of NTMs imposed by neighboring countries. Most of the literature does not explicitly address endogeneity. For example, Bratt (2017) instead aggregates across products and assumes NTMs are exogenous at higher levels of aggregation. Our estimates show that failing to include a control function to address endogeneity tends to generate a downward bias on the trade effect of quality regulations. This downward bias suggests that such regulations are more likely to be implemented in products where trade

> 6See Deardorff and Stern (1997) and Ederington and Ruta (2016) for surveys of the literature. A direct approach uses explicit measures of NTMs as regressors in a gravity model of trade (Kee, Nicita and Olarreaga, 2009; Disdier, Fontagn ́e and Mimouni, 2008; Bao and Qiu, 2012) while an indirect approach estimates the trade-impeding effects of NTMs from smaller than predicted trade flows (comparing actual flows to flows predicted by a micro-founded trade model in a frictionless benchmark scenario in Chen and Novy (2011)) or from price gaps (defined as the difference between import prices affected by NTMs and NTM-free world prices in Cadot and Gourdon (2016)). Chen, Hsieh and Song (2022) infer non-tariff barriers imposed on Chinese imports from the United States from the change in US imports relative to imports from other countries after controlling for the effect of tariffs.

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values are declining, e.g. because of cheaper lower-quality varieties flooding the market.

Some recent work has pointed out that quality regulation can create trade, but brings only data from individual countries. Macedoni and Weinberger (2022) show theoretically that under perfect information quality regulation can create trade, through reallocation toward high-productivity firms, and find evidence consistent with this model among domestic firms in Chile. Disdier et al. (2023) examine theoretically an imperfect information environment, in which quality regulation can create trade even for low-productivity firms by conveying information about product quality, and confirm the models predictions among exporting firms in France. We incorporate these insights into our model, and provide additional evidence using a panel dataset of importing and exporting firms in many countries.

Our results have important policy implications. Standardizing quality regulation across countries has been a significant focus and achievement of the WTO. Under the WTO Agreement on the Application of Sanitary and Phytosanitary Measures and the WTO Agreement on Technical Barriers to Trade, more countries adopted quality regulation similar to that of higher-income countries (Rodrik, 2018). Our results, which show benefits for low-income exporters, can rationalize the poverty-reducing benefits of access to larger markets through international trade agreements including the World Trade Organization (Goldberg and Reed, 2023). Yet, they also suggest that capturing these benefits may require government intervention, especially in lower-income countries.

Consider two recent experiments in emerging markets that provide quality certification to firms. Bai (2025) provides melon vendors in local markets in China with laser cut branding labels, as would be required by a TBT, for premium melons. These labels increase demand and profits. Deutschmann, Bernard and Yameogo (2021) provide groundnut farmers in Senegal with a technology to screen for aflatoxin, which must be below a certain level for exports to conform with an SPS measure in the European Union, and guaranteed price premium for conforming groundnuts. In both experiments, farmers adopt the quality certification technology initially. In the China experiment, farmers continue branding their premium melons when the intervention (subsidy) is withdrawn, consistent with the label conveying valuable information to consumers without much cost, as with a TBT. In contrast, in the SPS experiment, farmers stop screening their groundnuts when the price premium is withdrawn, consistent with the technology increasing costs, as with a SPS.

Like these experiments, our evidence suggests that lower-income country producers will not have trouble conforming with TBT measures. Government support with conformity may be less needed in this area. In contrast, ensuring conformity with SPS measures required for export success is costly, suggesting a potential role for government support. Firm-level subsidies as in the Senegal experiment could be justified for longer periods, allowing firms to 6
achieve scale in export markets. Government support could also extend to so-called “quality infrastructure,” or the system of public and private institutions, legal frameworks, and practices supporting metrology, standardization, accreditation, and conformity assessment. Without such support, the rise in quality regulation as in Latin America could increase global inequality, as smaller firms in lower-income countries are excluded from more lucrative export markets.

The rest of the paper is organized as follows. Section 2 describes our data sources and discusses the rise of quality regulation. Section 3 presents a conceptual framework to guide our empirical strategy. Section 4 presents our empirical strategy and discusses our key findings. Section 5 concludes.

# **2 Data and Context**

## **2.1 Data Sources**

This paper uses a panel of NTMs in 16 Latin America countries collected annually by ALADI from 2012 to 2017.<sup>7</sup> For each country, the data set includes information on the inventory of active multilateral NTMs governing imports each year, classified according to the highest level of disaggregation of the 2019 international classification of NTMs (UNCTAD, 2019).<sup>8</sup> Importantly, we observe the year in which the NTM was introduced and the products (classified using 6-digit HS codes) that it regulates. The initial 2012 data collection allows us to construct a panel of NTMs by product prior to 2012. Annual data collection thereafter allows us to update this panel each year. This annual frequency is particularly important given the acceleration of NTMs after 2010 (Figure 1). Our novel data allow us to observe the implementation of NTMs over time during the two decades ranging from 1996 to 2017 in different countries in Latin America, for different types of NTMs introduced in Section 1 and discussed further in Section 2.2.

To explore the relationship between NTMs and imports, we merge the NTM panel with import flows at the country and product level from 1996 to 2017 obtained from the World Integrated Trade Solution (WITS). This process requires the conversion of HS codes in the NTM database and in WITS to a consolidated version of the HS.<sup>9</sup> We focus the analysis

> 7The information can be accessed through the UNCTAD trade portal (TRAINS): https://trainsonline.unctad.org. The countries included are Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Peru, Paraguay, El Salvador, and Uruguay.

> 8The information also includes NTMs applied to exports and NTMs applied in a bilateral fashion. As these represent a negligible share of measures, we do not consider them in our analysis.

> 9We follow the consolidation procedure described in Fernandes, Freund and Pierola (2016).

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on final goods, as defined by the United Nations’ Broad Economic Categories classification system.<sup>10</sup> We also use data on tariff rates by importing country-partner country-HS6 product-year level from WITS-TRAINS. For any given product, we use preferential tariff rates when country pairs are engaged in preferential trade agreements and most-favored nation tariff rates otherwise.<sup>11</sup> For each importing/destination country in Latin America, denoted _d_ , the resulting database includes information about the import value _yodpt_ from each exporting/origin country _o_ of each final good _p_ in each year _t_ , along with the different _NTMdpt_ measures and tariff rates in force in the importing country. Appendix Table A1 provides summary statistics on all key variables based on our final data set.

To understand the role of firm exit and market concentration, we use firm-level customs transaction data sets obtained as part of the expansion of the Exporter Dynamics Database described in Fernandes et al. (2016) covering the universe of firm-level export transactions in each country. We construct four indicators capturing competitive dynamics in each distinct _export market_ that we define to be an exporting country-destination country-HS 6-digit product-year cell: the number of exporters, an indicator for an export monopolist, and the concentration of sales across exporters within that market. The indicator for an export monopolist is equal to one if the market is controlled by a single exporter. Concentration is measured by the Herfindahl–Hirschman Index (HHI) which equals the sum of squared exporter market shares for firms within a market multiplied by 10,000.<sup>12</sup> The export marketbased indicators are constructed for all countries covered by the export customs transaction data in the period 1996-2017 for their exports to the 16 Latin American countries for which we have NTM data.<sup>13</sup> As an example, for Bangladesh, one of the countries with export customs data, we measure the concentration across firms of exports of cotton t-shirts (HS 610910) to Chile and, distinctly, the concentration across firms of exports of cotton t-shirts

> 10We exclude intermediate inputs for two reasons: first, the tradeoff between compliance costs and information benefits—the key mechanism governing the impact of quality regulation—is difficult to model for intermediate inputs; and second, although intermediate inputs account for a larger share of global trade than final goods, they constitute a much smaller share of non-tariff measures.

> 11The assumption used to handle missing WITS-TRAINS tariff data for some importing countries in certain years is to apply the rates from the closest earlier year with available data.

> 12An HHI of 10,000 indicates the product is exported by a monopolist with market share equal to 100 percent of bilateral exports from the exporting country to a given destination country in a year.

> 13The exporting countries are: Albania, Burkina Faso, Bangladesh, Bulgaria, Bolivia, Botswana, Chile, China, Cˆote d’Ivoire, Colombia, Costa Rica, Dominican Republic, Ecuador, Egypt, Ethiopia, Gabon, Georgia, Guinea, Guatemala, Croatia, India, Iran, Jordan, Kenya, Cambodia, Lao, Lebanon, Sri Lanka, Morocco, Madagascar, Mexico, North Macedonia, Myanmar, Mauritius, Malawi, Nicaragua, Nepal, Pakistan, Peru, Paraguay, Senegal, El Salvador, Thailand, Tanzania, Uruguay, South Africa, and Zambia. The time periods of the customs data sets vary across countries. The export customs data is such that for each of the 16 Latin American importing countries, we capture exports by firms from only a _subset_ of exporting countries (those with customs data).

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to Colombia.

# **2.2 The Rise of Quality Regulation**

The prevalence of quality regulation has increased over the last 30 years for our sample, as shown in Figure 1. The average number of quality regulations per importing country-HS 6-digit product increased from almost zero in 1996 to almost 4 in 2020.

We identify quality regulations by first separating NTMs into five clusters according to the date of entry into force. As introduced in Section 1, the clusters are: (i) Sanitary and Phytosanitary measures (SPS), (ii) Technical Barriers to Trade (TBT), (iii) simultaneously SPS and TBT (i.e., SBS and TBT measures in the same law), (iv) pre-shipment inspections (PSI), and (v) non-technical measures.<sup>14</sup> Quality regulations are the first three clusters. The fourth and fifth clusters of NTMs do not provide consumers with information on quality.

Figure 2 displays the number of products affected by new regulations over time in the five clusters for Brazil and Mexico, two of the sample countries. We classify each law in our sample based on the NTMs it imposes and the products that are affected. Quality regulations—SPS and TBT—together with other technical measures such as PSI, regulate the appearance of imported products (such as a nutritional label), the production process (such as a pesticide restriction), or the logistics of importing (such as inspections at specific ports). In contrast, non-technical measures are typically used as instruments of commercial policy, such as import quotas and price controls. The key policy clusters of interest in our paper are SPS, TBT, and joint SPS-TBT, but all analyses control for the other NTM clusters.

To get a better sense of what these NTM clusters represent, consider three laws from our sample. In the SPS cluster, a 2016 law in Colombia established requirements for safe handling of animal products, including certification and inspection of shipments. In the TBT cluster, a 2006 law in Colombia mandated a list of ingredients on manufactured food products. While an initial certificate of compliance was also required, subsequent inspections were not. Compared to the 2016 SPS measure, the 2006 TBT measure was both more salient to consumers (because of the label) and less costly to firms (because of no inspections). In the joint SPS-TBT cluster, a 1997 law in Chile imposed a series of requirements for all raw and manufactured food products, including safe handling, contaminant limits, and nutritional labeling. This measure was likely more costly to firms than the 2006 TBT measure (because of the contaminant limits), yet more salient to consumers than the 2016

> 14Non-technical measures include MAST categories D (contingent measures such as anti-dumping or countervailing duties), E (quantity measures such as quotas or import licenses), and F (price measures such as administrative fixing of minimum import prices).

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SPS measure (because of the label). These laws are representative of our sample. In the SPS cluster, the most common requirements are costly certifications, inspections and import licenses. In the TBT cluster, the most common requirement is labeling. Laws in the joint SPS-TBT cluster typically require both labeling and licensing.

# **3 Conceptual Framework**

Quality regulation has an ambiguous impact on trade, _a priori_ . On the one hand, certifications introduce new costs for firms, reducing supply. On the other hand, they may convey information about product quality to consumers, increasing demand. To guide our empirical analysis, we propose a stylized model building on the work of Sutton (2012), Macedoni and Weinberger (2022), and Disdier et al. (2023) whose key results are presented here.<sup>15</sup> The model considers firms in a given exporting country that are monopolistically competitive and heterogeneous with respect to productivity, _φi_ (which is exogenous) and quality, _θi_ (which is endogenous). The marginal cost of exporting to a destination market is increasing in quality and decreasing in productivity. However, consumers only perceive the true quality of a product if the firm invests in signaling its quality, such as by labeling the product or obtaining a certification. Consumers perceive the same average level of quality for all non-signaling firms. The firm’s demand in a destination market is decreasing with respect to its price—which is a constant markup over marginal cost—and increasing with respect to _perceived_ quality.

Firms decide whether to export and whether to signal quality, each of which involves a fixed cost. Firms who signal quality choose their level of quality, trading off the increase in demand with the increase in marginal cost it entails.

If no quality regulation is imposed, all exporting firms signal quality. The quality threshˆ old for exporting is given by _θ_<sup>ˆ</sup> , and the corresponding productivity threshold is given by _φ_ . Firms with _φi > φ_ ˆ export and signal their quality.

If a quality regulation is imposed, that specifies a minimum quality for entering a market, firms decide whether to export _and_ whether to signal their quality. This introduces additional quality and productivity thresholds. Let _<u>θ</u>_ and _<u>φ</u>_ indicate the quality and productivity ˆ thresholds for exporting under a quality regulation, and let _θ_<sup>ˆ</sup> and _φ_ indicate the quality and productivity thresholds for signaling quality under such regulation. The optimal level of quality _θ_ provided by signaling firms is a function of the firm’s productivity, _φi_ , the exporting thresholds, _<u>θ</u>_ and _<u>φ</u>_ , and model parameters.

> 15We borrow the notation of Disdier et al. (2023). The model details and derivations are presented in Appendix A.

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Firms with _φi ∈_ [ _<u>φ,</u> φ_ ˆ] export under a quality regulation but do not signal their quality. Firms with _φi > φ_ ˆ export and signal their quality. Furthermore, there exists a threshold _φ_ ̃ _∈_ [ _<u>φ,</u> φ_ ˆ] such that all firms with _φi ∈_ [ _<u>φ,</u> φ_ ̃] provide higher quality under a quality regulation and all firms with _φi ∈_ [ ̃ _φ, φ_ ˆ] provide lower quality under a quality regulation. For exposition, we assume that signaling firms with _φi > φ_ ˆ provide the same quality whether or not a quality regulation is imposed.

We assume there are three types of firm—low-, medium-, and high-productivity—and two types of exporting countries—lower- and higher-income. Lower-income countries contain only low and medium productivity firms, while higher-income countries contain only medium and high productivity. In the baseline equilibrium with no quality regulation imposed, all except the low-productivity firms export and signal their quality. The darker (blue) lines in Figure 3 summarize this equilibrium.

Suppose a quality regulation is introduced in the form of a TBT measure that establishes ˆ a quality threshold, _<u>θT</u> > θ_ , such that _<u>φT</u>_ = _φ_ ˆ. There are _three_ groups of firms in this equilibrium, summarized by the lighter (orange) lines in Panel a) of Figure 3:

- _T_  ̃ _T_

- • Low-productivity firms with _φi ∈_ <u>[</u> _<u>φ</u> , φ_ ] upgrade quality in order to stay in the market. Their market share increases because they are upgrading quality.

- ˆ

- • Medium-productivity firms with _φi ∈_ [  ̃ _φ_<sup>_T_</sup> _, φ_<sup>_T_</sup> ] downgrade quality because it is no longer profitable to signal it. Their market share falls because they are downgrading quality.

- ˆ

- • Medium- and high-productivity firms with _φi > φ_<sup>_T_</sup> continue signaling their quality. Their market share may increase even though their quality does not change, because they face less competition from medium-productivity firms.

The TBT measure reallocates trade toward lower-income countries, since they have relatively more low-productivity exporters, who are gaining market share.<sup>16</sup> The impact on average export prices is ambiguous—prices rise for firms that upgrade and fall for firms that downgrade. Average export quantities increase because demand rises for both low- and high-productivity firms.<sup>17</sup> Alternatively, suppose a quality regulation is introduced in the form of an SPS measure that establishes a higher quality threshold, _<u>θS</u> > θT_ , such that _<u>φS</u>_ = _φ_ ̃ _T_ and _φ_ ̃ _S_ = _φ_ ˆ _T_ . There

> 16This occurs as long as the competition effect of high-productivity firms is not too strong.

> 17Under perfect information, low-productivity firms with _φi ∈_ <u>[</u> _<u>φT ,</u> φ_  ̃ _T_ ] exit the market, while mediumand high-productivity firms with _φi > φ_  ̃<sup>_T_</sup> continue signaling their quality. Such a model cannot explain the reallocation of trade toward lower-income countries.

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are _four_ groups of firms in this equilibrium, summarized by the orange lines in Panel b) of Figure 3:

- _T S_

- • Low-productivity firms with _φi ∈_ [ _<u>φ</u> , φ_ ] exit the market because it is no longer profitable for them to meet the quality threshold.

- _S_ ˆ _T_

- • Medium-productivity firms with _φi ∈_ [ _<u>φ</u> , φ_ ] upgrade quality in order to stay in the market. Their market share rises because they are upgrading quality.

- ˆ

- • Medium-productivity firms with _φi ∈_ [ ˆ _φ_<sup>_T_</sup> _, φ_<sup>_S_</sup> ] downgrade quality because it is no longer profitable to signal it. Their market share falls because they are downgrading quality.

- ˆ

- • High-productivity firms with _φi > φ_<sup>_S_</sup> continue signaling their quality. Their market share may increase even though their quality does not change, because they face less competition from medium-productivity firms.

The SPS measure reallocates trade toward higher-income countries, since they have relatively more high-productivity exporters, and the low-productivity exporters exit the market. Average export prices increase due to the exit of low-productivity exporters. The impact on average export quantities is ambiguous—demand rises for high-productivity firms, but low-productivity firms exit.

The following proposition summarizes the predictions of our theoretical framework:

**Proposition:** _SPS_ measures reallocate trade toward higher-income countries, cause exporter exit in lower-income countries, and increase average export prices. _TBT_ measures reallocate trade toward lower-income countries, have no impact on exporter exit, and increase average export quantities.

# **4 Empirical Model and Results**

## **4.1 Impact of NTMs on Trade**

Before testing the predictions derived in Section 3, it is useful to estimate the average effect of different clusters of NTMs on bilateral trade. Our main specification is a gravity equation with binary treatment indicators: 12
where _o_ indicates the exporting (origin) country, _d_ indicates the importing (destination) country, _p_ indicates the HS6-digit product, and _t_ indicates the year. The primary outcome variable is the import value, _yodpt_ . The treatment indicator, _NTMdpt_<sup>_m_,equalsoneifanNTM</sup> of type _m_ is in place in destination _d_ on product _p_ during year _t_ . The five types of NTMs correspond to the clusters in Figure 2:<sup>18</sup>

# _m_ = _{_ SPS _,_ TBT _,_ SPS & TBT _,_ Pre-Ship _,_ Non-Tech _}_

We include the average bilateral tariff rate, _τodpt_ , to control for a key trade policy tool that may constrain bilateral trade. In doing so, we also account for the correlation between NTMs and tariffs (Figure 1). Fixed effects at the exporting country-importing countryproduct level, _αodp_ , control for market characteristics, and their inclusion implies that the coefficients are estimated based on variation _within_ markets over time. Fixed effects at the exporting country-year level, _αot_ , and the importing country-year level, _αdt_ , control for broader economic trends and can be interpreted as the multilateral resistance terms in a structural gravity equation.<sup>19</sup> The specification also controls for the Inverse Mills Ratios of each NTM type, _λ_<sup>_m_</sup> _dpt_<sup>,discussedindetailbelow.Thecoefficientsofinterest,</sup><sup>_βm_,measure</sup> the percent change in import value following the implementation of a type- _m_ NTM measure within a “treated” market (exporting country-importing country-HS6-digit product) relative to within an “untreated” market, controlling for all other treatments.<sup>20</sup> Two key challenges arise when estimating Equation (1). First, not all exporting countryimporting country pairs trade a given product in every year of our sample. A logarithmic outcome variable would exclude zero trade flows, creating a selection bias in favor of larger markets. This would be particularly problematic in our setting, as NTMs can cause trade in certain markets to disappear. This concern motivates the exponential specification in Equation (1). The equation is estimated using a PPML estimator after adding zeros to the trade data along the time dimension (Santos Silva and Tenreyro, 2022).<sup>21</sup> Second, NTMs may be chosen and implemented by governments in a way that is motivated by recent trends in a country’s imports or correlated with the unobserved component

> 18If a destination-product market is treated by the same type of NTM multiple times through different laws, we consider the market treated after the first treatment.

> 19Notice that it is not possible to include fixed effects at the exporting country-HS6-digit product-year or importing country-HS6-digit product-year level as these are collinear with the variation in quality regulation.

> 20We exclude from the estimating sample always-treated observations, i.e. exporting country-importing country-product cells subject to any type of NTM throughout the entire period. This results in the exclusion of about 100,000 observations out of a sample of more 7.1 million observations.

> 21Specifically, for every exporting country-importing country-product for which the trade value is ever positive, we fill in zero for all years of that exporting country-importing country-product where trade is missing.

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of a country’s imports, _εodpt_ , potentially generating reverse causality and omitted variable biases. These concerns are less severe for quality regulations relative to other NTMs, as such policies are often implemented in the context of multilateral trade agreements that are plausibly exogenous to product import trends (conditional on origin and destination trends, _αot_ and _αdt_ ). For example, concerns about pesticide safety may lead multiple countries in Latin America to adopt SPS measures simultaneously, independent of their individual food import trends. These biases are a more severe concern for NTMs that are explicitly protectionist, like non-technical measures. For instance, a country may impose a quota in response to strong import growth, which creates competition for domestic producers. In this case, the estimated effect of the non-technical measure on trade may be upward biased.

To account for potential reverse causality and omitted variable biases, we follow Wooldridge (2015) and estimate a control function for each NTM of type _m_ , as in Kee and Nicita (2022): _~~m~~_<sup>_′_</sup> where _NTM dpt_<sup>is the average treatment indicator for NTM of type</sup><sup>_m′_for three neighboring</sup> countries of importing country _d_ .<sup>22</sup> The trade regulations of these neighbors are unlikely to be driven by the political economy concerns of country _d_ , and therefore capture the exogenous component of its trade regulations. Since the outcome variable is an indicator variable, we use a Probit estimator: Φ( _·_ ) indicates the Standard Normal distribution function. A positive estimate for _γ_<sup>_m,m′_</sup> implies that a country is more likely to implement an NTM of type _m_ if its neighbors implement an NTM of type _m_<sup>_′_</sup> . The estimates of Equation (2) in Appendix Table A2 show, as expected, that the adoption of SPS and TBT measures is positively predicted by the neighbors’ adoption of SPS and TBT measures.

Given the estimates _γ_ ˆ<sup>_m,m′_</sup> , we compute the Inverse Mills Ratio (IMR) for each type of NTM as:

> 22The specification for the control function does not include the export country _o_ subscript given that bilateral NTMs are few and excluded from our analysis. Rather the specification takes the perspective of an importing country’s government selecting NTMs of different types to impose on each HS 6-digit product in each year.

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where _φ_ ( _·_ ) is the Standard Normal density function. The IMR measures the hazard of nonselection: the higher the IMR for an NTM, the less likely is an importing country to have imposed the NTM, based on the NTMs imposed by its neighbors. Intuitively, if the IMR is high but the importing country imposes an NTM anyway, endogeneity is more of a concern, and the regression should adjust for this correlation. Formally, we include the IMRs as controls in Equation (1).<sup>23</sup> The specification effectively controls for each market’s _likelihood_ of being treated, based on the policy behavior of neighboring countries.

Column 1 of Table 1 presents estimates for Equation (1) _without_ the control function for endogeneity of NTMs while column 2 adds the IMRs resulting from the control function estimation. Comparing these two columns reveals a key finding: failing to account for the endogeneity of NTMs biases their estimated trade impact toward zero. The estimated impact of TBTs and joint SPS-TBTs is biased downward. This suggests that quality regulation is more likely to be implemented in products where trade values are declining, e.g. because of lower-quality varieties flooding the market.<sup>24</sup> Importantly, the estimates show that SPS and TBT measures enhance trade on average, whether implemented independently or simultaneously. This is consistent with the hypothesis that SPS and TBT increase product quality and stimulate demand. In contrast, pre-shipment inspections and non-technical measures deter trade (weakly in the case of preshipment inspections), as expected given their often protectionist motives.<sup>25</sup> The estimated treatment effects are large, ranging from a 23.5% increase in imports following the introduction of an SPS measure, to a 20.7% decrease in imports following the introduction of a

> 23The control function approach is preferred to an instrumental variables approach given (1) the binary nature of the endogenous variables and (2) the use of a PPML estimator with high-dimensional fixed effects. There is currently no estimator that accommodates a Probit first stage and PPML second stage, both with high-dimensional fixed effects. IV regressions with a linear first stage and high-dimensional fixed effects yielded unreasonably large coefficients, indicating the importance of correctly specifying the Probit first stage. The control function approach yields consistent estimates as long as the IMR captures the dependence between the PPML error term and the endogenous NTM variable Wooldridge (2015).

> 24OLS estimates of Equation (1) using the probability of trade at the exporting country-importing countryproduct-year level as an outcome confirm that some markets indeed disappear following new regulations. Estimates are shown in Appendix Table A3.

> 25Reassuringly, the unreported coefficients on tariffs are also negative. While this indicates that higher tariffs are associated with lower imports, we do not interpret this effect as causal since we do not estimate a control function for tariffs.

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non-technical measure, such as a quota.<sup>26</sup> The findings in column 2 of Table 1 are robust to excluding disproportionately small or large markets as seen in Appendix Table A4.

# **4.2 Price and Quantity Decomposition**

Our theoretical framework predicts that quality regulation can create trade through two channels: by providing quality information to consumers about lower-productivity firms and by imposing costs that lead to selection of higher-productivity firms into exporting. If the information channel dominates, the increase in trade should be driven by higher quantities, but if the cost channel dominates, the increase in trade should be driven by higher prices. To test for the importance of these channels, we estimate the impact of NTMs on quantities and unit prices using the following specification: where the outcome _y_ is either the logarithm of quantity, _Q_ , or the logarithm of price captured by the unit value, _P_ = _V/Q_ .<sup>27</sup> Since unit values are defined only when trade is positive, Equation (3) is estimated using OLS for observations with positive trade flows (intensive margin).<sup>28</sup> A control function is also estimated for the different types of NTMs.

The results are shown in columns 3 and 4 of Table 1. SPS measures have a negligible impact on quantities but increase prices by 4.5%. In contrast, TBT measures increase quantities by 24.4% but have a much smaller impact on prices. Consistent with our theoretical framework, these results suggest that TBT measures provide relatively more information to consumers, while SPS measures impose relatively higher costs on firms. In contrast, pre-shipment inspections reduce both quantities and prices in a manner similar to the nontechnical measures studied in Atkin, Blaum, Fajgelbaum and Ospital (2024).

# **4.3 Reallocation of Trade across Exporting Countries**

Quality regulation increases imports on average, but this aggregate effect masks important reallocation patterns. Our theoretical framework predicts that the information channel should benefit exports by lower-income countries, while the cost channel should benefit exports by

> 26Given the PPML estimator, the coefficient _βm_ corresponds to a an impact of 100% _×_ ( _eβ_<sup>_m_</sup> _−_ 1).

> 27Import quantities are sometimes reported in different units over time. For consistency, we restrict the sample for estimation of Equation (3) to markets for which import quantities are reported in the same unit over time.

> 28Given the different samples and estimation methods used, the coefficients on logarithmic quantity and prices in columns (3) and (4) do not add up to the coefficient on logarithmic value in column (2).

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higher-income countries. To test this, we add interaction terms to our main specification: where _higho_ equals one if the exporting country is a higher-income country in 2010, according to the World Bank’s classification and other variables are defined as in Equation (1). The coefficients _β_<sup>_m_</sup> measure the impact of a type- _m_ NTM measure on imports from lower-income exporting countries, while the coefficients _ζ_<sup>_m_</sup> measure the differential impact on imports from higher-income exporting countries, relative to lower-income exporting countries.<sup>29</sup> The sum of _β_<sup>_m_</sup> and _ζ_<sup>_m_</sup> gives the impact of a type- _m_ measure on imports from higher-income exporting countries.

Column 1 of Table 2 shows the results. SPS measures reallocate trade from lower-income exporting countries to higher-income exporting countries, while TBT measures reallocate trade from higher-income exporting countries to lower-income exporting countries. These reallocation effects wash out when SPS and TBT measures are implemented simultaneously, indicating the importance of carefully classifying NTMs. In contrast, pre-shipment inspections and non-technical measures have a consistent negative effect across all exporting countries. This core result is robust to excluding disproportionately small or large markets. 2).

Table 1’s estimates from quantity and price regressions suggest that the impact of SPS measures is driven by the cost channel, while the impact of TBT measures is driven by the information channel. Given this, heterogeneity in information benefits and compliance costs across higher- and lower-income exporting countries could explain the reallocation of trade estimated in Table 2. SPS measures will reallocate trade away from lower-income exporting countries if they are relatively harder to comply with, or if they provide relatively less information about the quality of products from these countries. This can be expected for inspection requirements. Conversely, TBT measures will reallocate trade toward lowerincome exporting countries if they are relatively easier to comply with, or if they provide relatively more information about the quality of products. This can be expected for labeling requirements.

In contrast, pre-shipment inspections and non-technical measures may be equally difficult for firms in higher- and lower income exporting countries to comply with. For example, preshipment inspections may require passage through a particular port, which is costly for firms from any exporting country, and non-technical measures may impose similar import quotas on all exporting countries. For such measures, there is no reallocation of trade across

> 29Notice that the marginal effect of the higher-income country indicator is absorbed by the fixed effects.

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countries, they can impose costs irrespective of quality, leading to a net negative effect in Table 2.<sup>30</sup>

# **4.4 Firm Exit and Market Concentration**

Our evidence so far suggests that SPS measures reallocate trade toward higher-income countries via the cost channel, while TBT measures reallocate trade toward lower-income countries via the information channel. Our theoretical framework implies that the cost channel should also increase firm exit from exporting in lower-income countries, while the information channel should have no impact on firm exit. We test this in two ways.

First, we estimate the specification below for the number of exporting firms—imputing zero when there is zero trade in a given market—using PPML: where _y_ is the number of exporting firms.

Second, we estimate the specification below for the two other indicators of competitive dynamics by export market, the indicator for an export monopoly and the HHI using OLS: where _y_ is one of the indicators of competitive dynamics.

The results are shown in columns 2 through 4 of Table 2.<sup>31</sup> In line with the predictions from our theoretical framework, SPS measures lead to more concentrated export markets with fewer firms in lower-income exporting countries. The number of firms in a market falls by 8.8-14.3% on average following the implementation of an SPS or joint SPS-TBT measure (column 2). The share of markets with an export monopoly increases by 4.1-5 percentage points (column 3) for lower-income exporting countries following the implementation of an SPS or joint SPS-TBT measure. Such measures lead to an increase in the HHI of export markets by 234-302 points among lower-income exporting countries (column 4). These effects are large relative to the baseline level of concentration: the average HHI is 7,248, indicating that products are effectively exported from a given origin to a given destination by one

> 30Appendix Table A5 shows that when the impact of quality regulation is estimated on imports of intermediate inputs, imprecise estimates are obtained.

> 31In Appendix Table A6, we estimate the specification in column 1 for the sample with available firm-level export data. The estimates are consistent with our core reallocation result for SPS, although precision is lost in this smaller sample.

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or two firms. The additional increase in concentration is meaningful in the context of the United States Department of Justice’s guidelines for horizontal mergers, which state that HHI increases of more than 100 points in already concentrated markets are likely to enhance market power. In contrast, and also in line with our theoretical framework, there is no detectable change in the number of firms from the introduction of TBT measures.

In Appendix B, we estimate the impact of quality regulation on similar indicators for competitive dynamics from the perspective of the _importing_ country. Although this is outside the scope of our theoretical framework, our rich firm-level data allows us to examine these outcomes. Overall, quality regulation does not meaningfully reduce the number of importing firms or increase their concentration. This suggests that the costs of quality regulation are primarily borne by exporting firms, particularly in lower-income countries.

# **4.5 Heterogeneous Benefits of Information**

As a final test of the importance of the information channel, we examine heterogeneity in the potential benefits of information about product quality embedded in quality regulations by allowing the impacts of such regulations to differ across differentiated and non-differentiated products. The expectation is that differentiated products have a greater scope for quality information (Rauch, 1999). Columns 1 and 2 of Table 3 present estimates of Equation (1), while columns 3 and 4 present estimates of Equation (4). Quality regulation stimulates strongly the imports of differentiated products (column 1), but deters weakly the imports of non-differentiated products (column 2). In contrast, non-technical measures deter trade across both types of products. Furthermore, differentiated products drive the finding of reallocation of trade—toward higher-income countries in the case of SPS measures and toward lower-income countries in the case of TBT measures (column 3). There is no such reallocation of trade for non-differentiated products (column 4).

In some cases, SPS and TBT measures are implemented in the context of a deep preferential trade agreement (PTA). In addition to reducing tariffs between participating countries, PTAs may contain provisions for harmonizing or mutually recognizing quality regulations across partner countries.<sup>32</sup> The information benefits and compliance costs of quality regulations may depend on the degree to which regulations are harmonized across countries due to PTAs.

To examine this possibility, we combine our data with information on deep PTAs and

> 32These provisions can involve mutual recognition, whereby country A implements distinct NTMs but recognizes compliance with the NTMs of country B, or equivalence, whereby country A implements NTMs that are legally identical to those of country B or the adoption by all preferential partners of international standards.

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their SPS and TBT provisions from Fernandes, Lefebvre and Rocha (2021).<sup>33</sup> We construct an indicator equal to one for each exporting country-destination country-year featuring an active PTA with provisions for harmonizing or mutually recognizing SPS or TBT measures. We estimate the following equation using PPML: where _harm_ is the harmonization indicator.

Table 4 presents the results. Column 1 includes only the country pairs for which a PTA is in force at some point in our sample period. Column 2 includes all country pairs, imputing zeros for the harmonization indicator for country pairs that never enter a PTA. The results are similar across both columns. The interaction term is positive for SPS measures, indicating that these measures are more beneficial to trade when they are harmonized across PTA trading partners. In contrast, the interaction term is negative for TBT, indicating that these measures are _less_ beneficial to trade when they are harmonized. This is consistent with harmonization reducing the costs of complying with SPS measures while also reducing the information benefits of TBT measures.

# **5 Conclusions**

Quality regulation measures—either sanitary and phytosanitary standards (SPS), technical barriers to trade (TBT), or laws including both SPS and TBT measures—are sometimes classified with other non-tariff measures (NTMs) as “non-tariff _barriers_ ” and conjectured to be imposed with a protectionist intent. Yet quality regulations are treated distinctly in international trade law, which recognizes such regulations can create trade by improving the quality of products and signaling information about such quality. Our evidence based on a rich panel of NTM and trade data confirms that quality regulations are indeed tradeenhancing, unlike other NTMs such as import quotas or pre-shipment inspections that deter trade.

Importantly, our evidence shows a clear trade reallocation effect across countries of different types of NTMs. TBT measures increase exports from lower-income countries, creating the potential for poverty reduction. In contrast, SPS measures reduce exports from lowerincome countries, as their firms are less able to conform with production process standards from higher-income countries. SPS measures restrict entry of firms, while TBT measures,

> 33Details on the collection of data on deep PTAs and their provisions across different policy areas, including SPS and TBT, are provided in Mattoo, Ruta and Rocha (2020).

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rather than being technical “barriers” to trade, facilitate the entry of exporters from lowerincome countries. Given the substantial heterogeneity in treatment effects of quality regulations, further country- and regulation-specific research is required to design policies to upgrade quality.

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