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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