Investigating How Nontariff Measures Impact the Turfgrass Seed Trade

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Scott Petty Department of Applied Economics, University of Minnesota, Twin Cities, 1994 Buford Avenue, St. Paul, MN 55108, USA

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Chengyan Yue Department of Horticultural Science, University of Minnesota, Twin Cities, 1970 Folwell Avenue, St. Paul, MN 55108, USA
Department of Applied Economics, University of Minnesota, Twin Cities, 1970 Folwell Avenue, St. Paul, MN 55108, USA

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Eric Watkins Department of Horticultural Science, University of Minnesota, Twin Cities, 1970 Folwell Avenue, St. Paul, MN 55108, USA

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Abstract

Turfgrass seed, a living organism, is facing more stringent trade regulations compared with nonliving products. We applied multiple empirical approaches to explore the impact of these regulations on trade flows in grass seeds. We constructed a series of novel variables to measure these regulations, such as environment regulation stringency, pre-shipment inspections, market conditions, and product requirements. Our results showed that nontariff trade measures had substantial impacts on the trade of grass seeds. These measures sometimes worked as barriers to trade and at other times worked as catalysts for trade.

The trade of live plants from one country for intended future cultivation in another country offers economists an excellent framework to examine the effects of nontariff trade barriers. There are government policies that restrict international trade through mechanisms other than traditional tariffs and could consist of embargos, sanctions, import quotas, or most commonly through particularly stringent regulations. Because there exists the potential for genuinely catastrophic results in the movement of living organisms into new habitats, the array of nontariff trade barriers within this market is pronounced and a virtually defining attribute of the trade environment. To offer a contextualizing example, up until the early 20th century, the American landscape east of the Mississippi River was practically defined by the American chestnut tree (Castanea dentata). As a fast-growing hardwood tree that tended to grow straight, the American chestnut tree was a staple both for construction lumber and for furniture manufacturing. At the same time, the distinct nuts that this fruiting tree produced in abundance were well represented in early American cuisine, especially around Christmas and during winters. A Chinese chestnut tree (Castanea mollissima) imported and cultivated around the year 1900 carried with it a fungus (Cryphonectria parasitica) that causes a devastating disease now called chestnut tree blight: within 50 years, the American chestnut tree was virtually extinct (Anagnostakis 1987).

As a result of this classic phytopathological horror story, among others, there exist entire classes of trade regulations referred to as “sanitary and phytosanitary” import standards. These elevated standards of inspection and control offer almost unparalleled opportunities to think about how nontariff trade barriers influence trade, both for their stated purposes and as smoke screens for mercantilist impulses. This paper deployed a variety of novel tools, which have not been widely used in trade literature, to better understand this phenomenon. Multiple underused variables have been identified to attempt to capture both the effect of general environmental regulation and to measure commodity-specific inspection standards established by various countries. These variables were then employed in several different classes of statistical models to test their performance in capturing the standards’ impact on trade. This study focused on the international trade in live turfgrass seeds as a representative product and offered general insights into the dynamics of their international trade over the past decade.

Over the decade from 2010 through 2019, ∼$4 billion worth of live turfgrass and turfgrass seeds were traded internationally. Over this studied period, annual trade flows almost doubled (an increase of 79%) from about a quarter of a billion dollars in value to about half a billion dollars in value. Although prices being paid per unit rose slightly over the decade, most of this increase in trade value was driven by increases in trade volume. The United States accounted for ∼30% of this total trade value as the world’s dominant exporter and saw a 106% increase between 2010 and 2017 before plateauing (United Nations 2021). This means that the largest exporter saw its exports grow substantially more than global trade over a short time before plateauing over the end of the period of study; thus, the largest exporter has been contributing disproportionately to the growth in total trade. Northern European countries were also significant exporters. Over this decade, Germany and China were the largest importers, accounting for 20% and 10%, respectively (United Nations 2021). There were a number of species that have been sold as “turfgrass,” and each of these in turn had multiple varieties, which represented distinct and non-substitutable, specialty products.

There has been an oft-repeated expectation that the concurrent effects of improved communications technology, incremental efficiency gains in the transportation industries, and generally increasing global economic integration should yield a marked decline in the negative effects of distance on merchandise trade (Cairncross 1995, 2001). This view was sometimes characterized as the “death of distance.” However, meta-analysis performed by Disdier and Head (2008) examined nearly 1500 gravity model estimations of distance’s impact on trade and found the negative effects on trade flows to be persistent through time.

Allen (2014) studied regional variations in the price of rice across the Philippines, and demonstrated the inadequacy of transportation costs to explain the lagged response to localized price moves and concluded that information frictions played a major role in trade dynamics. Consistent with this idea, Schmeiser (2012) used firm-level longitudinal data to document that distance from a currently served market was significantly more correlated with market entry than distance from the firm’s home market. Chaney (2014) came to a similar view in examining firm-level ability to trade considering a firm’s ability to extend its network into new markets.

As with many other categories of trade, the trade flows of live plants and seeds have increased dramatically over the past few decades. Global trade in seeds increased by more than a factor of four in absolute terms over the past 3 decades of the 20th century (Le Buanec 2002). Liebhold et al. (2012) focused on just US trade in live plants to demonstrate that live plant imports to the United States had more than sextupled over the preceding 4 decades while exports had roughly doubled, and more ominously, the claim was made that as many as 70% of damaging forest insects and pathogens appearing in the United States over the past century had arrived due to imported live plants. It seems obvious that ecologists, government policymakers, and many stakeholders in agricultural economies around the world would be particularly interested in regulating the cross-border trade in live plants. Burgiel et al. (2006) offered a thorough discussion of both the dangers of invasive species generally and the role that trade regulations can play in controlling them.

Sanitary and phytosanitary (SPS) regulations can significantly impact trade volumes of agricultural products (Sumner and Lee 1997). Forsyth and Lynch (1992) investigated the impacts individual SPS regulations had on specific markets. Furthermore, Roberts and Orden (1995) tackled the political economic mechanics of their adoption as well as how specific barriers operated in practice. Because individual regulations were discrete choices as opposed to continuous variables, economic analysis tended to model the effects of such technical trade barriers in a somewhat simplified, “all or nothing” manner (Tyers and Anderson 1992). Sumner and Lee (1997) made the argument that such total effects seemed unlikely to be observed in the real world.

The International Trade Centre (ITC), a joint venture of the United Nations and World Trade Organization, offers a helpful compendium of regulatory requirements related to the bilateral trade of various commodities. Within their presentation of these data, the ITC imposes a taxonomy to categorize regulations for easier conceptualization. We found this system of classification helpful and partially designed our analysis around it. “Product Requirements” are product-specific and include regulations related to both production processes and technical specifications of a given product. For turfgrass, this includes both European Union (EU) rules on genetically modified plants and seeds as well as those aimed at preventing the introduction of certain “harmful organisms,” presumably unrelated to the product being imported, into EU territory. “Market Conditions” are generally focused on market participants. For the EU, these include the licensing, certification, or registration of firms of agents involved in trade. These are phytosanitary certifications of the importer as opposed to inspections of the product itself on a consignment level. “Pre-shipment & Inspection” requirements are the labeling requirements and inspections procedures implementing the other regulations (International Trade Centre 2021). The application of these novel variables allowed us to make an additional contribution related to regulation and standards.

An important conversation around regulation and the setting of technical standards is whether their effects are purely deleterious. This is particularly relevant within the context of SPS standards. There has been a logic that supported the establishing of clear standards serving as a “catalyst” for international trade. Within the realm of agricultural and food-related imports, Maertens and Swinnen (2006) and Anders and Caswell (2009) both examined the tension between “standards as barriers” and “standards as catalysts” for trade.

International trade flows for nearly any good or category of goods (at various levels of aggregation) have been broadly characterized by zero values. This means that a matrix summarizing bilateral trade values for any good between all countries of the world would be dominated by recorded values of zero, signifying no measurable trade. A common framework to examine multilateral trade was the gravity model. The structural modeling work of Eaton and Kortum (2002) and Anderson and van Wincoop (2003) informed recent gravity model specification. Silva and Tenreyo (2006) built a strong case for the vital importance of being mindful of sources of bias embedded within common methods of working with these models, while recommending the use of pseudo-maximum likelihood techniques to mitigate the problems. Martin and Pham (2015) confirmed these concerns and extended the analysis to other methods for addressing the underlying issues. Shepherd (2016) provided a technical manual for applied economic work using gravity models and offered practical guidance in implementing standard gravity models in line with Anderson and van Wincoop (2003) as well as the methods of Silva and Tenreyo (2006). Kareem and Kareem (2019) offered a useful guide to various methods available to work with trade data dominated by zero values. Yue et al. (2020) offered a roadmap for comparing models when navigating data that are dominated by zero values.

This paper sought to make three distinct contributions to literature. Empirically, we explored the dynamics of trade in grass seeds as an agricultural product and internationally traded commodity. Two types of turfgrass seeds were used to analyze the interplay between tariffs and nontariff trade barriers due to their being “live organisms” subject to more substantive nontariff barriers. Finally, a series of novel tools was proposed and tested to capture these nontariff barriers.

Data

This study required the assemblage of a bespoke dataset to adequately address the central research question of how nontariff trade barriers influenced the dynamics of trade in turfgrass seeds. Various datasets have been located and acquired from a half dozen different governmental and semigovernmental entities.

Trade data

Trade data used in this paper were extracted from the United Nations International Trade Statistics Database, commonly known as COMTRADE, which has been maintained by The United Nations Statistics Division. The trade data used were annually tabulated and covered the period from 2010 to 2019. This period was selected to have a decade-long span while avoiding any potential year-specific volatility related to the COVID-19 pandemic.

At the 6-digit level of commodity classification, trade flows were captured for three different types of cool-season grass seeds: Kentucky bluegrass (Poa pratensis) and ryegrass, a category that includes both perennial ryegrass (Lolium perenne) and annual ryegrass (Lolium multiflorum). For the benefit of readers less familiar with the classification of different grasses, Kentucky bluegrass is a popular lawn grass in many temperate climates around the world, including the northern half of the United States; it is also used for sports fields, parks, and other managed landscapes. US seed production of this species is concentrated in a few states, including Oregon, Washington, Idaho, and Minnesota (Bonos and Huff 2013). Perennial ryegrass is quick to establish and is an important component of many turfgrass seed mixtures sold to consumers and professional turfgrass managers; its wear tolerance and quick recovery are useful traits for high-use areas such as sports fields and golf course fairways (Christians et al. 2016). In the United States, perennial ryegrass seed has been produced mostly in Oregon and Minnesota, with Oregon being the dominant producer (Bonos and Huff 2013). Annual ryegrass is used as a turfgrass when rapid establishment is important and long-term cover is not needed, such as in quick repair mixes and overseeding of existing warm-season turfgrasses during colder periods of the year when those grasses are dormant. In addition, both Kentucky bluegrass and ryegrasses serve as important forage grasses for livestock.

The trade flow data acquired through COMTRADE consists of bilateral gross flows. The raw data were presented on a monthly basis and have been aggregated to the annual level for the purposes of analysis. The data included both trade value and net weight. As prices fluctuated both within and across years, trade value and net weight can and did vary relative to each other.

Kentucky bluegrass

In this paper, Kentucky bluegrass was defined as everything that has been traded under the Harmonized System code 120924 as administered by the World Customs Organization.

Because of differing levels of coverage, roughly half of the bilateral trade relationship pairs (nonzero values) were eventually dropped while conducting analysis. Although the levels of annual mean prices declined slightly, overall trends in relative prices appeared relatively stable. Similarly, most of the actual trade volume was preserved after many outlier prices and small volumes were dropped. To be more concrete, one destination-specific measure of regulation was only available for some destination markets, so only potential trade to those destinations ended up being considered; however, half of all nonzero bilateral trade relationship pairs discarded accounted collectively for less than 5% and less than 10%, respectively, of total global trade in these two categories of turfgrasses.

Table 1 summarizes the largest importers and exporters over the period. The top importers and exporters were the same whether sorted by total trade value or total trade volume; however, inconsistencies in the ordering can be observed in Chinese and Canadian imports because of differing prices. Some of this is driven by growing Chinese imports over the period and prices being higher in the later years. The Netherlands and Germany were both major importers and major exporters.

Table 1.

The top five global importers and exporters of Kentucky bluegrass and the trade values and volumes for the top exporters and importers between 2010 and 2019.

Table 1.

Ryegrass

In this paper, ryegrass was defined as everything that was traded under the Harmonized System code 120925 as administered by the World Customs Organization, which includes both Lolium perenne and Lolium multiflorum.

Table 2 summarizes the largest importers and exporters of ryegrass over the period. The top importers and exporters were the same whether sorted by total trade value or total trade volume; however, inconsistencies in the ordering can be observed in imports because of differing prices over time. The difference in scale of the top exporters relative to the scale of the top importers dominated price fluctuations and timing of trade when ordering the exporters. Note that, as with Kentucky bluegrass, the Netherlands and Germany were both major importers and major exporters of ryegrass seed. It is worth noting the disparity in pricing between countries involved in both importing and exporting turfgrasses. For example, the Netherlands stood out for receiving higher prices for its exports compared with other countries, despite paying similar prices for imports. This difference might be caused by differences in quality standards, production costs, trade regulations, market preferences, or intended market positioning.

Table 2.

The top five global importers and exporters of ryegrass (including both Lolium perenne and Lolium multiflorum) and the trade values and volumes for the top exporters and importers between 2010 and 2019.

Table 2.

Data for standard gravity model variables

A variety of general macroeconomic statistics were needed to perform standard analysis using gravity models of international trade. Times series data covering things like gross domestic product (GDP), population, distance between countries and EU membership were obtained from the Centre d’Etudes Prospectives et d’Informations (CEPII 2021). CEPII is the main French center for research into international economics and is housed within the Office of the Prime Minister of France. In addition, CEPII evaluates qualities such as the geographic compactness of countries, the cultural basis of countries’ legal systems, and whether country pairs share a common language. CEPII also collects data on the average amount of time required to formally register a new business as a measure of ease of doing business in both importing and exporting countries.

Tariffs

The tariff data used in this project were acquired through ITC. The most recent available tariff rate was applied for each bilateral pair of importing and exporting countries. Tariffs between the North America Free Trade Agreement countries and the European Union Plus countries that accounted for most of the trade flows were zero throughout the period. Nonzero tariff rates have been fairly stable over the recent past.

Trade regulations

ITC maintains a compendium of information related to “market access” for individual actors seeking to export to particular countries. Among many other pieces of relevant information, lists are provided on the various countries’ regulations covering the import of specific products. We systematically searched across nearly 200 countries for regulations applicable to imports of Kentucky bluegrass and ryegrass as product categories. ITC classified these regulations as “Product Requirements,” “Market Conditions,” and “Pre-shipment Inspections” and provided a count of regulations under each category. We used these count data as proxies of regulatory burden within each market. Because the ITC’s service is meant to aid commercial agents, all information is either for the current year or the most recent on record. As a result, the information of year 2022 has been uniformly applied across the entire period of study.

Environmental stringency

The Organization for Economic Co-operation and Development (2021) developed and maintains a statistical time series that they termed the Environmental Policy Stringency Index. This measure was used in this paper as a proxy for the generalized environmental stringency of the policy regime in importing countries. This variable was calculated largely in terms of regulations regarding air pollution but was a good proxy to examine the general regulatory burden of complying with environmental standards while conducting commercial activities within various countries.

Since the index was first complied in 1990, there have been fluctuations in the values, but the values generally increased over time. The index has been designed for cross-country comparisons within a given year rather than as an intertemporal measure for specific countries. The index was updated until 2012 for most countries and our trade data were for the years 2010 to 2019, so we used the 2012 index in our analysis to maximize the number of countries that can be used in the estimation.

Data summary

Given that COMTRADE covers more than 200 countries, a 10-year period of study started with more than 600,000 observations for each of Kentucky bluegrass and ryegrass. As countries needed to be dropped due to missing data for the various variables of interest, this number eventually declined to 54,653 observations for each type of grass. However, after dropping the observations with missing values, 97.4% of the total trade value for Kentucky bluegrass and 92.6% of the total trade value for ryegrass were preserved. The dropped observations tended to be exceptionally small trade flows and at high outlier prices. Given that most of the total trade taking place was preserved after truncating our dataset, we are confident that the data used in our final analysis represented the trade situation of the two types of grasses.

Estimation strategy

A gravity model takes the standard form:
TVijt = α0Mitβ1Mjtβ2Dijβ3exp(XijtβX)ωijt

The annual value of exports for country i flowing to country j in year t is represented as  TVijt. A vector of characterizing variables, many of which are dummy variables, is recorded as Xijt. The final term, ωijt, is a stochastic error term with E[ωijt|Mit,Mjt,Dij, Xijt]=1. The underlying simplification at the root of the gravity model is to think of two different economies as having specific measures of “mass” that serve to pull themselves toward each other in a manner mitigated by “distance”; this attractive pull is then thought of as a long-term trade relationship. Traditionally, trade economists would plug in the GDP of various country pairs; however, there is reason to suspect that the optimal constructions of “mass” have multiple components that will need to be disentangled. Thus, the measure of “mass” going forward will be specified as Mi,jtβn=GDPi,jtβn = (GDP per capita)i,jtβn1Popi.jtβn2.This choice is being made to disentangle the effects generated by the size of a country as measured by its population from those effects rooted in a country’s relative wealth.

The expression in Eq. [1] is naturally logged, separating the elements into a linear form and then ordinary least squares (OLS) regression analysis can be applied (Anderson and van Wincoop 2003). When ωijt is logged, the mean of the resulting logged variable depends on the variance of ωijt. This is relevant because the error terms generated when working with trade data are likely to be heteroskedastic. The variance of heteroskedastic error terms will then depend on some (or all) of the dependent variables, which in turn will cause the expected value of the logged error term to depend on at least one of the dependent variables. This will then violate one of the necessary assumptions for OLS.

One challenge with using gravity models is the excessive amount of zero values present when working with trade data. Even at the most aggregated level, a bilateral matrix of trade flows will include an abundance of very small values. Because global trade is dominated by a small selection of large, highly developed economies, many smaller, less developed countries do very little trade with each other. Nearly any country-level matrix of global trade flows will contain a significant number of zero values. When most values in a dataset for a dependent variable are zero, the reliability of OLS can be questioned. Violations of the basic assumptions underpinning OLS become likely and the resulting estimators significantly biased. For both reasons, alternative methods must be deployed.

Poisson pseudo-maximum likelihood

Following an alternative approach popularized by Silva and Tenreyo (2006), the gravity model can be estimated using pseudo-maximum likelihood methods. Although these classes of models are often used for count data, they can be productively used in the analysis of gravity models. Under relatively weak assumptions, consistent estimators can be generated. The data do not need to be Poisson nor does the dependent variable need to be limited to integer values.

The two methods used by Silva and Tenreyo (2006) are Poisson pseudo-maximum likelihood (PPML) and gamma pseudo-maximum likelihood (GPML). In addition, a third method called negative binomial pseudo-maximum likelihood (NBPML) is preferred by some economists when working with gravity models of trade data. When using these methods, independent variables are logged, and the dependent variable is not transformed. GPML assumes the dependent variable’s variance is proportional to its conditional mean and, thus, assigns less weight to observations with a larger conditional mean. PPML is similar to GPML but assigned the same weight to all observations. A reasonable argument can be made that PPML is the more natural procedure to follow when there is no information about the pattern of heteroskedasticity. Therefore, we used PPML rather than GPML. Although PPML has become markedly more popular within gravity model literature, NBPML is claimed to have certain advantages as well, the main one being that trade data are likely to be characterized by variances that exceed the associated means and thus demonstrate overdispersion, in which case NBPML could have an efficiency advantage. However, to achieve these efficiency gains one would need to be able to specify the degree of this overdispersion. Alternatively, the fact that NBPML is scale dependent should always be a concern. Whether trade values are measured in dollars or thousands of dollars has the potential to meaningfully impact results when working with NBPML (Shepherd 2016). For these reasons, PPML is being selected over NBPML.

Selection model

The principle behind using selection models in this context is the proposition that trade data can be best understood by analyzing two separate processes, commonly termed the extensive and intensive margins. The extensive margin is what determines whether any trade occurs between two countries, whereas the intensive margin determines how much trade occurs after trade begins. This framework for analysis has been broadly recognized as representing firm-level decision-making and has become accepted as a tool to conceptualize data that are more aggregated at the country-level (Helpman et al. 2008; Kehoe and Ruhl 2013).

As there exists a clear rationale to believe that two distinct nonrandom and intelligible processes may govern the generation of standard trade data, a “two-part model” shall be designed and applied to the relevant trade data. This is accomplished by estimating a logit model on zero vs. nonzero valued observations and then applying an OLS regression to the nonzero observations separately. The full set of dependent variables has been included in each stage, which diverges from common practice with Heckman Selection Models. In addition, because this method requires zero values as opposed to the missing values used in Heckman Selection Models, all undefined trade values after logging are manually substituted with 0. This is a somewhat arbitrary choice, as some researchers choose to universally substitute “x+1” for trade values “x” to accomplish the same ends. The choice being made keeps the zero values as zero values while leaving the nonzero values untransformed. There are no continuity concerns in this case as nonzero trade values are not present close to 0 within this sample. This procedure would be unnecessary when using a Heckman model as it processes truly missing values such as the negative infinity that results from logging 0.

Zero-inflated model

To combine these two broad categories of models, a class of models called zero-inflated models exists. In this case, the underlying assumptions are that an abundance of zero values can be the result of a distinct process, separate from that determining the level of nonzero values and that this separate process can be modeled independently. Zero-inflated models use a logit model in their first stage before running a pseudo-maximum likelihood model in the second stage. These models are then called the zero-inflated Poisson model, the zero-inflated gamma model, and the zero-inflated negative binomial model, respectively. As PPML is chosen above, a zero-inflated Poisson model is being chosen for this analysis. There are two key differences between these models and the selection models discussed in the previous section. The first stage is checking for zero values instead of nonzero values, so the signs on coefficients in the first stage should be reversed. In addition, the dependent variable is not being logged along with the independent variables in accordance with standard practice when using PPML.

Results and analysis

Summary statistics of the dependent and independent variables

When summarizing the variables, we assigned them into different groups: a set of relatively classic variables of gravity models (Table 3), a set of variables selected to target the frictions of internal trade for both countries involved in potential bilateral trade (Tables 4 and 5), a set of variables selected to scrutinize nontariff trade barriers between countries (Table 6), and finally a summary of the dependent variables (Table 7).

Table 3.

Descriptions, means, and standard deviations (SD) of variables used to estimate gravity models. The variables were used as the independent variables in the gravity models. The data between 2010 and 2019 were used in the estimation.

Table 3.
Table 4.

Description, mean, and standard deviation (SD) of variables related to internal frictions used to estimate gravity models. The variables are used as the independent variables in the gravity models. The data between 2010 and 2019 were used in the estimation.

Table 4.
Table 5.

Description, mean, and standard deviation (SD) of variables targeting external frictions and nontariff trade barriers used to estimate gravity models. The variables are used as the independent variables in the gravity models. The data between 2010 and 2019 were used in the estimation.

Table 5.
Table 6.

Description, mean, and standard deviation (SD) of variables related to gross trade values used to estimate gravity models. The variables are used as the dependent variables in the gravity models.

Table 6.
Table 7.

Estimation results of two-part analysis of the gravity model. The dependent variable in the logit model is a dummy variable measuring if there is trade between two countries; the dependent variable of the ordinary least squares (OLS) model is the trade value between countries. The data between 2010 and 2019 were used in the estimation.

Table 7.

GDP and distance are the most important two factors that affect the trade between countries and they were included in all our gravity model analyses (Table 3). The decomposing of GDP into a per capita value and population was a discretionary choice made to disentangle effects related to agent-level wealth from those driven by market size. Except for tariff values, all variables presented above were taken natural logarithm values as was required by the model specifications being used. The tariff values were a single-year cross-section of the bilateral tariff rates faced by exporters across ∼45,000 country-to-country pairs. Current rates were not available for every country, so we used the most recent rate available.

The addition of a dummy variable for countries that span a contiguous land mass is a fairly common practice when using gravity models of trade (Table 4). As shown in Table 4, the mean of the variable was 0.02, indicating 2% of the countries involved in the turfgrass trade during the studied period had contiguous lands. The internal unity of the importing market is viewed as significantly affecting the dynamics of international trade. “Entry time” is an internationally collected and harmonized measure of the time cost of registering a formal firm within the context of a country’s legal and regulatory system (De Soto 1990) and was being added for both the importing and exporting countries. The average number of days required to register a business were 24 d in the exporting counties and 15 d in the importing countries, respectively. Low numbers would be generally thought to represent a measure of the greater “ease of doing business” within a country under its general regulatory regime and legal system.

The set of variables in Table 5 represents an attempt to capture the subtle barriers to trade that exist outside of statutory tariff rates on given commodities. The EU variables were dynamic and had been calculated on an annual basis. The mean for “within EU” was 0.09, indicating 9% of the trade of turfgrasses occurred between countries within the EU’s free-trade area. About 52% of the trade was between EU importers and exporters outside of EU and 6% of the trade was between EU exporters and importers outside of EU. The measure of environmental stringency being used was the 2012 data—this was selected to maximize coverage while using the most recent value available and the mean value was 2.58. The variables counting the number of regulatory requirements that an exporter must comply with were gathered in the spring of 2021 for each type of grass for an exporter wishing to ship into a given destination country and the mean value was 3.03. Although one can reasonably presume that more “Product Requirements” and “Market Conditions” (with a men value of 4.32) represent more stringent barriers, the significance of “Pre-Shipment Inspections” could be somewhat ambiguous. These have been inspections that have been conducted and certified in the country of origin, so a greater number might represent rules that were more clearly articulated and consistently enforced.

Multiple dependent variables were being used in the estimation (Table 6). Each stage of analysis was performed twice on trade flow data for the two types of grasses being examined. Methods based on OLS require the trade values to be naturally logged alongside the relevant gravity model variables. However, methods built around PPML require that the trade values remain unlogged when regressed against the naturally logged values for GDP, population, and distance.

Empirical results

The major focus of this project was the examination of how regulatory regimes drive trade for products that were live organisms and thus findings should be of particular and special concern to regulators. These concerns went beyond more standard frameworks of general environmental concerns to what were broadly labeled as “sanitary and phytosanitary” standards. A series of distinct variables were added to the standard gravity model to focus on these factors.

Although Kentucky bluegrass and ryegrass are similar products that are classified together under many systems of commodity coding, the authors did not have a positive theory that the same factors should drive the trade for both grass types. Although common dynamics would seem more likely, the similarities and differences that can be identified between the drivers of trade for these two products would be of great interest to investigate.

Because the three models being examined are quite different, we evaluate the “goodness of fit” across models by comparing log likelihood values. By this measure, we found that the two-part model had the lowest log likelihood value of −3177.83 and −5666.72, respectively, so the model yielded the best results for each set of data.

In Table 7, showing the results of the two-part model, the standard variables of gravity models were found to be significant drivers of trade. For example, for Kentucky bluegrass, the coefficients for per capita GDP of exporters (2.53) and per capita GDP of importers (0.30) were positive and significant at the 1% significance level for the logit model and they were also positive and significant for the OLS models (2.50 and 0.55). The main conclusion was that the effects were the same both in terms of entry decisions and the ultimate scale of trade flows across both commodities. The positive and significant coefficients indicated that countries with higher per capita GDP traded more both as exporters and as importers, with the effects for exporters being generally larger than for importers. Similarly, countries with larger populations also traded more across the board. For instance, for Kentucky bluegrass, the coefficients of population were 1.05 for exporters and 0.37 for importers in the logit model and 0.36 and 0.30 for the OLS model. Furthermore, countries that were farther away from each other were less likely to trade with each other, which was indicated by the negative and significant coefficients of distance for both logit model and OLS model (−0.90 and −0.33, respectively, for Kentucky bluegrass). Higher tariffs seemed to reduce both entry and post-entry trade volumes as indicated by the negative and significant coefficients of distance. These results were consistent with expectations.

The coefficients of firm entry time were significant and negative for the logit models [−0.07 (exporter) and −0.02 (importer) for Kentucky bluegrasses and −0.03 (exporter) and −0.02 (importer) for ryegrass]. It indicated that the normal regulatory burden of operating commercially as measured in firm entry time was to consistently suppress entry rates whether experienced in the exporting country or the importing country. The effects of firm entry time on trade volume once trade occurred appear to be inconsistent and possibly zero with regard to the exporting countries (as shown by the inconsistent signs of the entry time in the OLS models for the two grass types). Potential exporters were more likely to enter markets when the country was physically contiguous (as indicated by the positive and significant coefficient of variable contiguous in the logit models), and at least in the case of ryegrass, they exported more to those markets after entering (its coefficient was 0.51 and significant at 1% significance level).

The impact of EU boundaries appeared a bit inconsistent between commodities. EU member countries were more likely in general to enter markets outside the EU (coefficients were 1.33 and 1.61 and significant in the logit models) but less likely to trade at volume after entering a market (coefficients were −1.21 and −0.77 and significant in the OLS models). A significant, positive coefficient in the first stage implied a greater probability to enter a market, whereas a significant negative second stage coefficient implied lower trade flows in markets after entry. The results for non-EU countries exporting into the EU were less consistent and predictable. For Kentucky bluegrass, non-EU countries that have entered a market within the EU exported into the EU market in lower volumes (its coefficient was −1.65 and significant) than they did after entering non-EU markets. For ryegrass, non-EU exporters were more likely to enter markets within the EU (its coefficient was 1.61 and significant) but exported in lower volumes than might be otherwise expected to those markets post-entry (its coefficient was −0.77 and significant).

More stringent environmental regulations overall were to be associated with higher probability of entry for ryegrass exporters (with a significant coefficient of 0.17) but not for Kentucky bluegrass exporters. Countries actively exporting to countries with higher measures of environmental stringency exported more Kentucky bluegrass (with a significant coefficient of 0.30) but less ryegrass (with a significant coefficient of −0.27). This latter effect may be related to higher rates of market entry. More product requirements were consistently associated with lower trade volume for exporters (with significant coefficients of −0.18 and −0.28) but with greater entry by ryegrass exporters (with a significant coefficient of 0.09). The probability of entry by exporters was higher for countries with more regulations governing market conditions with ambiguous effects on trade volumes post-entry. This should seem logical if we understood what these measures represented: the former were regulations related to products and were evaluated relative to each consignment, whereas the latter were focused on firms operating within a particular market and generally consisted of what could be thought of as licenses to operate. So, the former was a regulatory equivalent of a variable cost that escalated with scale and the former might be thought of as a fixed cost directly related to entry. More pre-shipment inspections were correlated with lower probability of entry by exporters (with significant coefficients of −0.08 and −0.04) but with increased scale of operation for ryegrass exporters that did enter the market (with a significant coefficient of 0.10). One can think of this as the practical implementation of two previous categories: while more regulations related to inspection should be correlated with more regulations of the previous types, it can also mean that the enforcement of preexisting rules were more thoroughly and clearly articulated.

Although there were significant differences among the three models deployed in terms of “goodness of fit” as measured by log likelihood, we did not find wild variation in the coefficients estimated through the different models. The underlying story told by the collective drivers of trade that we have selected was reassuringly consistent. Even as two of the three models were estimating a separate market entry decision and one was estimating cumulative effects, they were not contradicting one another.

The results yielded under PPML were broadly consistent with those of the two-part model (Table 8). The two aspects most worth pointing out were that i) this model was cumulatively estimating the entire process while no longer capturing a separate market entry decision, and ii) the more straightforward nature of this model gave us the clarity to test the impact of our variables of interest on the overall estimation process. The latter exercise would be difficult with the other two models as they each consisted of two separate processes both theoretically and empirically with their examinations of entry before analyzing trade volumes.

Table 8.

Estimation results of Poisson pseudo-maximum likelihood analysis of the gravity model. The dependent variable is the trade value between countries. The data between 2010 and 2019 were used in the estimation. Ryegrass includes both Lolium perenne and Lolium multiflorum.

Table 8.

The standard variables of gravity models as summarized in Table 9 were found to be significant drivers of trade following patterns similar to those seen in the two-part model. In addition, when our variables of interest were added to the most basic elements of gravity model analysis, they were found to refine results and improve “goodness of fit” without radically altering the estimation results. The largest effects can be seen in the constant term, which we interpreted to mean that the innovative variables we were deploying in this project were mainly capturing drivers of trade missed by more parsimonious models while yielding more accurate estimates for coefficients associated with more standard variables. The results for our selected novel variables were broadly consistent with the results yielded by the two-part model.

Table 9.

Estimation results of zero-inflated Poisson analysis of the gravity model. The dependent variable is the trade value between countries. The data between 2010 and 2019 were used in the estimation. Ryegrass includes both Lolium perenne and Lolium multiflorum.

Table 9.

A key point to keep in mind when reviewing the results in Table 9 is that with zero-inflated models, the first stage was a logit model targeted to the probability of zero values as opposed to nonzero values. The logit-based first stage in the previous model was doing the reverse with the first stage focusing on the probability of nonzero values representing market entry. Thus, the sign on first stage coefficients had the opposite interpretation as the sign on first stage coefficients in Table 6 and represented probabilities of not entering a given market. The second stage here is in essence the model summarized in Table 9 conditional on a separately analyzed market entry decision.

Therefore, the results yielded by this model were broadly consistent with the other two models. Because this model was estimating an entry decision before analyzing trade volumes, the results were easily comparable to the two-part model. When results aligned with the PPML model more than with the two-part model, it may be an artifact of PPML.

Standards as a barrier or as a catalyst

Given both the stringency and importance of SPS regulations, the sometimes-neglected role that standards can serve in promoting trade through a variety of channels becomes even more crucial to understanding the true cost of regulatory burdens.

The analysis detailed above demonstrates that our two-part model provided by far the best “goodness of fit” as measured by log likelihood when set against a PPML model and a zero-inflated Poisson model. For this section, we focused on the results from the two-part model for an in-depth examination of a selected subset of variables. A key contribution of this paper was the novel application of some measures of regulatory burden compiled by the ITC to the statistical analysis of gravity models of international trade. These variables were numerical counts of the commodity-specific regulations importing countries had in place that the ITC classified as “product requirements,” “market conditions,” and “pre-shipment inspections.”

The model measured factors correlated with one country exporting to another specific country and then with the scale of that trade conditional on trade occurring. We labeled these as an “extensive margin” and an “intensive margin” as though a single firm were making a market entry decision and then optimizing its scale of operation after entering a given market. Although the results were more ambiguous on the intensive margin, a seemingly coherent story can be told on the side of the extensive margin (Table 10). Regulations related to “market conditions” seemed to increase the likelihood of market entry. The results were intuitive as these were the specified rules for an establishment to ship into a market, and the more thorough these rules were spelled out the more confidence a commercial actor might have in choosing to enter a given market. “Product requirements” were the rules governing a specific product entering a given market on a consignment basis. Again, better understanding the standards that one’s goods must meet, in a given market, can be reasonably thought to encourage market entry; for Kentucky bluegrass, these effects appeared ambiguous, while for ryegrass, they were positive. “Pre-shipment inspections” can be thought of as the practical application of the rules from the other two categories. The more involved and complicated application of rules can more purely be interpreted to represent a cost of compliance in time, in effort and in money. “Product requirements” were rules that must be complied with in each consignment shipped into a particular market and so their seeming to discourage operating at scale made sense.

Table 10.

Analysis of regulatory standards as barriers vs catalysts using a two-part model. Two-part model was used for this analysis because of the model’s better goodness of fit compared with other models. The data between 2010 and 2019 were used to estimate the gravity models. Ryegrass includes both Lolium perenne and Lolium multiflorum.

Table 10.

Conclusion

This study applied multiple empirical approaches to statistically model global trade flows for seed of two perennial grasses. In terms of effective execution, this process was complicated by the large volume of zero values representing the absence of trade in the bilateral trade matrix. Efforts have been made to address these issues by using pseudo-maximum likelihood models and selection models that attempted to disentangle the phenomenon of market entry embodied in the extensive margin from the volume-focused decisions of the intensive margin. Ultimately, a true zero-inflated model was constructed that combined attributes from the other two approaches.

A relatively simple, two-part selection model using OLS seemed to deliver the best results as measured by the log likelihood. Although this addressed the issue presented by the preponderance of zero values, the concerns associated with biased estimation introduced by heteroskedasticity in the error terms of a log-linearized structure may remain unresolved. However, when taken along with the other models, the consistency of results across models showed the validity of the results.

Our results showed that nontariff trade measures had substantial impacts on trade, at least within the context of two specialized goods that were expected to face greater regulatory barriers than the average good. The novel variables constructed by us to capture nontariff trade barriers generated interesting results when applied to standard methods for performing gravity model analysis. These measures sometimes worked as barriers to trade and at other times worked as catalysts for trade. For example, more stringent environmental regulations, more product requirements, and more market conditions increased the probabilities for grass seeds to enter a country. More pre-shipment inspections increased the trade volume for ryegrass seeds once they entered the market. However, some measures can be barriers to trade: both increased pre-shipment inspections and increased product requirements decreased the probability of ryegrass seeds entering a country. These results were consistent with findings in previous literature, wherein it has been shown that strict regulations increased the production or trade costs of imports, distorted international trade, and caused mercantilist losses in exporting countries due to reduced exports, as well as welfare losses for importing countries (Yue et al. 2006). However, these standards can beneficially resolve imperfect information problems, and may stimulate consumers’ demand by increasing their confidence in product safety and quality (Beghin and Bureau 2001).

Potential extensions of this work would be to repeat much of this analysis with other “live organism” goods that should exhibit similar effects to determine whether these results and methods are generalizable. Furthermore, other agricultural goods that do not as cleanly qualify as “live organisms” intended for cultivation could be examined. Of particular interest could be an examination of goods that are exported in a “live” form for cultivation or breeding and in a different form for consumption. Seeing whether the novel tools used in this exercise prove relevant when looking at nonagricultural goods also seems worthwhile. Finally, most existing studies are on how policies affect trade. In fact, these policies were created in response to issues that arose from trade. A possible future study could be on how trade increases the risks of introducing invasive species and other unwanted organisms. The study could also explore how such risks influence the formulation and implementation of policies and standards, as well as evaluate the effectiveness of these measures in mitigating associated risks.

References cited

  • Anagnostakis S. 1987. Chestnut blight: The classic problem of an introduced pathogen. Mycologia. 79(1):2337. https://doi.org/10.1080/00275514.1987.12025367.

    • Search Google Scholar
    • Export Citation
  • Allen T. 2014. Information frictions in trade. Econometrica. 82(6):20412083. https://www.jstor.org/stable/43616907.

  • Anders SM, Caswell JA. 2009. Standards as barriers versus standards as catalysts: assessing the impact of HACCP implementation on U.S. seafood imports. Am J Agric Econ. 91(2):310321. https://doi.org/10.1111/j.1467-8276.2008.01239.x.

    • Search Google Scholar
    • Export Citation
  • Anderson JE, van Wincoop E. 2003. Gravity with gravitas: A solution to the border puzzle. Am Econ Rev. 93(1):170192. https://doi.org/10.1257/000282803321455214.

    • Search Google Scholar
    • Export Citation
  • Beghin JC, Bureau JC. 2001. Quantitative policy analysis of sanitary, phytosanitary and technical barriers to trade. Economie Internationale. 87:107130. https://doi.org/10.1142/9789813144415_0003.

    • Search Google Scholar
    • Export Citation
  • Bonos SA, Huff DR. 2013. Cool-season grasses: Biology and breeding. In: Stier JC, Horgan BP, Bonos SA (eds). Turfgrass: Biology, use, and management. Wiley, Hoboken, NJ, USA. https://doi.org/10.2134/agronmonogr56.c17.

  • Burgiel S, Foote G, Orellana M, Perrault A. 2006. Invasive alien species and trade: Integrating prevention measures and international trade rules. Report of the Center for International Environmental Law. https://www.researchgate.net/publication/228384604. [accessed 12 Apr 2024].

  • Cairncross F. 1995. The death of distance: A survey of telecommunications. The Economist. 30(9):5–6. https://doi.org/10.1068/b230387.

  • Cairncross F. 2001. The death of distance: How the communications revolution will change our lives. Harvard Business School Press. Brighton, MA, USA.

  • Centre d’Etudes Prospectives et d’Informations. 2021. The Gravity Database. http://www.cepii.fr/cepii/en/bdd_modele/bdd.asp. [accessed 12 Apr 2024].

  • Chaney T. 2014. The network structure of international trade. Am Econ Rev. 104(11):36003634. https://doi.org/10.1257/aer.104.11.3600.

    • Search Google Scholar
    • Export Citation
  • Christians N, Patton A, Law Q. 2016. Fundamentals of turfgrass management. Wiley Publishing Company, Hoboken, NJ, USA.

  • De Soto H. 1990. The Other Path. Harper and Row, New York, NY, USA.

  • Disdier AC, Head K. 2008. The puzzling persistence of the distance effect on bilateral trade. Rev Econ Stat. 90(1):3748. https://www.jstor.org/stable/40043123. [accessed 12 Apr 2024].

    • Search Google Scholar
    • Export Citation
  • Eaton J, Kortum S. 2002. Technology, geography, and trade. Econometrica. 70(5):17411779. https://www.jstor.org/stable/3082019. [accessed 12 Apr 2024].

    • Search Google Scholar
    • Export Citation
  • Forsyth K, Lynch L. 1992. Effects of a free trade agreement on US and Mexican sanitary and phytosanitary regulations. Agricultural Information Bulletin. 649. US Department of Agriculture, Economic Research Service. https://10.22004/ag.econ.309661. [accessed 12 Apr 2024].

  • Helpman E, Melitz M, Rubenstein Y. 2008. Estimating trade flows: Trading partners and trading volumes. Q J Econ. 123(2):441487. https://doi.org/10.1162/qjec.2008.123.2.441.

    • Search Google Scholar
    • Export Citation
  • International Trade Centre. 2021. Global trade helpdesk. https://globaltradehelpdesk.org/en. [accessed 12 Apr 2024].

  • Kareem F, Kareem O. 2019. The issues of zero values in trade data and modelling. Macro Management & Public Policies. 1(1):3650. https://doi.org/10.30564/mmpp.v1i1.749.

    • Search Google Scholar
    • Export Citation
  • Kehoe T, Ruhl K. 2013. How important is the new goods margin in international trade? J Polit Econ. 121(2):358392. https://doi.org/10.1086/670272.

    • Search Google Scholar
    • Export Citation
  • Le Buanec B. 2002. The rules for international seed trade. J New Seeds. 4(1-2):143153. https://doi.org/10.1300/J153v04n01_11.

  • Liebhold A, Brockerhoff E, Garrett L, Parke J, Britton K. 2012. Live plant imports: The major pathway for forest insect and pathogen invasion of the US. Front Ecol Environ. 10(3):135143. https://doi.org/10.1890/110198.

    • Search Google Scholar
    • Export Citation
  • Maertens M, Swinnen JFM. 2006. Standards as barriers and catalysts for trade and poverty reduction. International Association of Agricultural Economists 2006 Annual Meeting, Queensland, Australia. https://doi.org/10.22004/ag.econ.25772.

  • Martin WJ, Pham CS. 2015. Estimating the gravity model when zero trade flows are frequent and economically determined. World Bank Policy Research Working Paper No. 7308. https://doi.org/10.1596/1813-9450-7308.

  • Organization for Economic Co-Operation and Development. 2021. Environmental policy stringency index. https://stats.oecd.org/Index.aspx?DataSetCode=EPS. [accessed 12 Apr 2024].

  • Roberts D, Orden D. 1995. Determinants of technical barriers to trade: The case of US phytosanitary restrictions on Mexican Avocados, 1972–1995. In: Understanding administered barriers to trade. IATRC Proceedings Issue. Tucson, AZ, USA. http://ageconsearch.umn.edu/bitstream/50709/2/RobertsDonna.pdf. [accessed 5 May 2024].

  • Schmeiser K. 2012. Learning to export: Export growth and the location decision of firms. J Int Econ. 87(1):8997. https://doi.org/10.1016/j.jinteco.2011.11.006.

    • Search Google Scholar
    • Export Citation
  • Shepherd B. 2016. The gravity model of international trade: A user guide. United Nations, ESCAP. https://www.unescap.org/resources/gravity-model-international-trade-user-guide-updated-version. [accessed 12 Apr 2024].

  • Silva JMCS, Tenreyo S. 2006. The log of gravity. Rev Econ Stat. 88(4):641658. https://doi.org/10.1162/rest.88.4.641.

  • Sumner D, Lee H. 1997. Sanitary and phytosanitary trade barriers and empirical trade modeling. Understanding Technical Barriers to Agricultural Trade Conference Proceedings, 273–285. https://doi.org/10.22004/ag.econ.50720.

  • Tyers R, Anderson K. 1992. Disarray in world food markets: A quantitative assessment. Cambridge University Press, Cambridge, United Kingdom.

  • United Nations. 2021. UN COMTRADE. http://comtrade.un.org. [accessed 12 Apr 2024].

  • Yue C, Beghin J, Jensen HH. 2006. Tariff equivalent of technical barriers to trade with imperfect substitution and trade costs. Am J Agric Econ. 88:947960. https://doi.org/10.22004/ag.econ.19253.

    • Search Google Scholar
    • Export Citation
  • Yue C, Lai Y, Khachatryan H, Hodges A. 2020. Effect of geographic distance on domestic trade: A case of the green industry. Agribusiness: An International Journal. 38:154174. https://doi.org/10.1002/agr.21715.

    • Search Google Scholar
    • Export Citation
  • Anagnostakis S. 1987. Chestnut blight: The classic problem of an introduced pathogen. Mycologia. 79(1):2337. https://doi.org/10.1080/00275514.1987.12025367.

    • Search Google Scholar
    • Export Citation
  • Allen T. 2014. Information frictions in trade. Econometrica. 82(6):20412083. https://www.jstor.org/stable/43616907.

  • Anders SM, Caswell JA. 2009. Standards as barriers versus standards as catalysts: assessing the impact of HACCP implementation on U.S. seafood imports. Am J Agric Econ. 91(2):310321. https://doi.org/10.1111/j.1467-8276.2008.01239.x.

    • Search Google Scholar
    • Export Citation
  • Anderson JE, van Wincoop E. 2003. Gravity with gravitas: A solution to the border puzzle. Am Econ Rev. 93(1):170192. https://doi.org/10.1257/000282803321455214.

    • Search Google Scholar
    • Export Citation
  • Beghin JC, Bureau JC. 2001. Quantitative policy analysis of sanitary, phytosanitary and technical barriers to trade. Economie Internationale. 87:107130. https://doi.org/10.1142/9789813144415_0003.

    • Search Google Scholar
    • Export Citation
  • Bonos SA, Huff DR. 2013. Cool-season grasses: Biology and breeding. In: Stier JC, Horgan BP, Bonos SA (eds). Turfgrass: Biology, use, and management. Wiley, Hoboken, NJ, USA. https://doi.org/10.2134/agronmonogr56.c17.

  • Burgiel S, Foote G, Orellana M, Perrault A. 2006. Invasive alien species and trade: Integrating prevention measures and international trade rules. Report of the Center for International Environmental Law. https://www.researchgate.net/publication/228384604. [accessed 12 Apr 2024].

  • Cairncross F. 1995. The death of distance: A survey of telecommunications. The Economist. 30(9):5–6. https://doi.org/10.1068/b230387.

  • Cairncross F. 2001. The death of distance: How the communications revolution will change our lives. Harvard Business School Press. Brighton, MA, USA.

  • Centre d’Etudes Prospectives et d’Informations. 2021. The Gravity Database. http://www.cepii.fr/cepii/en/bdd_modele/bdd.asp. [accessed 12 Apr 2024].

  • Chaney T. 2014. The network structure of international trade. Am Econ Rev. 104(11):36003634. https://doi.org/10.1257/aer.104.11.3600.

    • Search Google Scholar
    • Export Citation
  • Christians N, Patton A, Law Q. 2016. Fundamentals of turfgrass management. Wiley Publishing Company, Hoboken, NJ, USA.

  • De Soto H. 1990. The Other Path. Harper and Row, New York, NY, USA.

  • Disdier AC, Head K. 2008. The puzzling persistence of the distance effect on bilateral trade. Rev Econ Stat. 90(1):3748. https://www.jstor.org/stable/40043123. [accessed 12 Apr 2024].

    • Search Google Scholar
    • Export Citation
  • Eaton J, Kortum S. 2002. Technology, geography, and trade. Econometrica. 70(5):17411779. https://www.jstor.org/stable/3082019. [accessed 12 Apr 2024].

    • Search Google Scholar
    • Export Citation
  • Forsyth K, Lynch L. 1992. Effects of a free trade agreement on US and Mexican sanitary and phytosanitary regulations. Agricultural Information Bulletin. 649. US Department of Agriculture, Economic Research Service. https://10.22004/ag.econ.309661. [accessed 12 Apr 2024].

  • Helpman E, Melitz M, Rubenstein Y. 2008. Estimating trade flows: Trading partners and trading volumes. Q J Econ. 123(2):441487. https://doi.org/10.1162/qjec.2008.123.2.441.

    • Search Google Scholar
    • Export Citation
  • International Trade Centre. 2021. Global trade helpdesk. https://globaltradehelpdesk.org/en. [accessed 12 Apr 2024].

  • Kareem F, Kareem O. 2019. The issues of zero values in trade data and modelling. Macro Management & Public Policies. 1(1):3650. https://doi.org/10.30564/mmpp.v1i1.749.

    • Search Google Scholar
    • Export Citation
  • Kehoe T, Ruhl K. 2013. How important is the new goods margin in international trade? J Polit Econ. 121(2):358392. https://doi.org/10.1086/670272.

    • Search Google Scholar
    • Export Citation
  • Le Buanec B. 2002. The rules for international seed trade. J New Seeds. 4(1-2):143153. https://doi.org/10.1300/J153v04n01_11.

  • Liebhold A, Brockerhoff E, Garrett L, Parke J, Britton K. 2012. Live plant imports: The major pathway for forest insect and pathogen invasion of the US. Front Ecol Environ. 10(3):135143. https://doi.org/10.1890/110198.

    • Search Google Scholar
    • Export Citation
  • Maertens M, Swinnen JFM. 2006. Standards as barriers and catalysts for trade and poverty reduction. International Association of Agricultural Economists 2006 Annual Meeting, Queensland, Australia. https://doi.org/10.22004/ag.econ.25772.

  • Martin WJ, Pham CS. 2015. Estimating the gravity model when zero trade flows are frequent and economically determined. World Bank Policy Research Working Paper No. 7308. https://doi.org/10.1596/1813-9450-7308.

  • Organization for Economic Co-Operation and Development. 2021. Environmental policy stringency index. https://stats.oecd.org/Index.aspx?DataSetCode=EPS. [accessed 12 Apr 2024].

  • Roberts D, Orden D. 1995. Determinants of technical barriers to trade: The case of US phytosanitary restrictions on Mexican Avocados, 1972–1995. In: Understanding administered barriers to trade. IATRC Proceedings Issue. Tucson, AZ, USA. http://ageconsearch.umn.edu/bitstream/50709/2/RobertsDonna.pdf. [accessed 5 May 2024].

  • Schmeiser K. 2012. Learning to export: Export growth and the location decision of firms. J Int Econ. 87(1):8997. https://doi.org/10.1016/j.jinteco.2011.11.006.

    • Search Google Scholar
    • Export Citation
  • Shepherd B. 2016. The gravity model of international trade: A user guide. United Nations, ESCAP. https://www.unescap.org/resources/gravity-model-international-trade-user-guide-updated-version. [accessed 12 Apr 2024].

  • Silva JMCS, Tenreyo S. 2006. The log of gravity. Rev Econ Stat. 88(4):641658. https://doi.org/10.1162/rest.88.4.641.

  • Sumner D, Lee H. 1997. Sanitary and phytosanitary trade barriers and empirical trade modeling. Understanding Technical Barriers to Agricultural Trade Conference Proceedings, 273–285. https://doi.org/10.22004/ag.econ.50720.

  • Tyers R, Anderson K. 1992. Disarray in world food markets: A quantitative assessment. Cambridge University Press, Cambridge, United Kingdom.

  • United Nations. 2021. UN COMTRADE. http://comtrade.un.org. [accessed 12 Apr 2024].

  • Yue C, Beghin J, Jensen HH. 2006. Tariff equivalent of technical barriers to trade with imperfect substitution and trade costs. Am J Agric Econ. 88:947960. https://doi.org/10.22004/ag.econ.19253.

    • Search Google Scholar
    • Export Citation
  • Yue C, Lai Y, Khachatryan H, Hodges A. 2020. Effect of geographic distance on domestic trade: A case of the green industry. Agribusiness: An International Journal. 38:154174. https://doi.org/10.1002/agr.21715.

    • Search Google Scholar
    • Export Citation
Scott Petty Department of Applied Economics, University of Minnesota, Twin Cities, 1994 Buford Avenue, St. Paul, MN 55108, USA

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Chengyan Yue Department of Horticultural Science, University of Minnesota, Twin Cities, 1970 Folwell Avenue, St. Paul, MN 55108, USA
Department of Applied Economics, University of Minnesota, Twin Cities, 1970 Folwell Avenue, St. Paul, MN 55108, USA

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Eric Watkins Department of Horticultural Science, University of Minnesota, Twin Cities, 1970 Folwell Avenue, St. Paul, MN 55108, USA

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Contributor Notes

C.Y. is the corresponding author. E-mail: yuechy@umn.edu.

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