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Do SPS and TBT regulations inhibit Guyana's Food and Agriculture Exports to CARICOM markets?

Sanitary and phytosanitary (SPS) and technical regulations (TBT) are non-tariff measures that serve the goals of protecting human, animal, and plant life against health risks associated with imported products. However, they could hinder exports (Murina and Nicita 2014). Several references indicate that SPS and TBT measures act as barriers to Guyana increasing its exports of food and agricultural products to the Caribbean Community (CARICOM) and therefore limiting its capacity to fully exploit the benefits of the CARICOM market (Kaiteur News 2017; CEPAL 1999; Guyana Times International 2011). These are important claims because Guyana's export structure is characterised by a significant share of exports of food and agriculture products, mainly fish, rice, sugar, and rum, which currently account for about 40 percent of its export earnings. In 2015, approximately 39 percent of total exports of commodities from Guyana consisted of food and agricultural products, which fall under HS chapters 01-24. (See Table 1). Agriculture is also a leading economic sector, accounting for approximately 20 percent of GDP (Bank of Guyana 2016). Its economic importance stems from the country's rich resource endowments, including lands suitable for agricultural production (GOINVEST n.d.).

The neighbouring CARICOM market offers the advantages of geographical proximity, cultural similarity, and preferential access under the CARICOM Single Market initiative, to which Guyana is a party (CARICOM n.d.). It is an important destination market for food and agricultural exports, accounting for 27 percent of total exports of food and agriculture products to the world in 2015, the equivalent of US$133,461,061. (See Table 6)

Compared to all other export markets combined, the CARICOM market is a relatively more important one for exports of food and agricultural products. In 2015, while just over 36 percent of total exports to non-CARICOM markets was food and agricultural products, more than 48 percent of total exports to CARICOM market was food and agricultural products. In fact, this figure grew between 2000 and 2015, indicating the market's increasing importance. Between 2000 and 2010 exports of food and agricultural products grew by approximately 60.3%. Between 2010 and 2015 exports more than doubled, growing by 128.6%.

This market will continue to be important because while exports have grown over the years, their value is still small (US$133.5 million in 2015) relative to exports to non-CARICOM markets (US$364 million) (see Table 6). As a consequence of declining local agriculture industries, the CARICOM market also has a high import demand for food and agricultural products, reflected in a high food importation bill of approximately US$4 billion (Guyana Chronicle 2016). Further, Guyana is an important partner in helping to achieve regional food security (CARICOM n.d.).

Regulations carry with them a compliance cost that some exporters may find burdensome (Reyes and Kelleher 2015). In a regional setting, lack of, or limited harmonisation, can further compound compliance costs, due to different regulations across markets. Limited harmonisation also increases the possibility of persistent unilateral protectionist actions by member states geared towards defending their domestic producers.

There have been attempts to harmonise regulations in CARICOM which should have trade-enhancing effects. In fact, harmonising standards and regulations was a goal of the Single Market under Article 67 of the Revised Treaty of Chaguaramas. Subsequently, the CARICOM Regional Organisation for Standards and Quality (CROSQ) was established. Coordinating agricultural health and food safety systems became a later goal through CAHFSA, which was launched on March 18, 2010 (CAHFSA n.d.). However, harmonisation of regulations is still a work in progress.

With the aforementioned factors in mind, the primary goal of this article is to ascertain how pervasive SPS and TBT measures applied on food and agricultural exports from Guyana are, and thereafter, to quantify their impact on exports in order to either substantiate or refute the anecdotal claims that they create obstacles to Guyana increasing exports to CARICOM markets. In addition, this article also seeks to understand how the creation of CAHFSA has impacted exports of food and agricultural products from Guyana.

In order to gauge the incidence of regulations, frequency indexes are calculated for SPS and TBT regulations specifically applied to exports from Guyana at the four-digit level for the first twenty-four chapters of the Harmonized System product nomenclature (see Table 1). This is done for exports to seven CARICOM countries: Barbados, Trinidad & Tobago, Dominica, Grenada, Suriname, Jamaica, and Antigua & Barbuda. While Barbados, T&T and Jamaica are the leading markets for Guyana, the selection was dictated by the availability of data on regulations. The ultimate desire was to capture all CARICOM markets.

The frequency indexes represent a notable empirical contribution as they quantify the incidence of regulations applied to exports from Guyana at both the two- and four-digit levels of the HS classification system. They can therefore be used to support policy analyses and can form the basis of future studies on SPS and TBT regulations.

In order to quantify the actual impact of regulations on exports, the frequency indexes were used in a gravity model specified with fixed effects. The model covers ten years measured at three-year intervals-2005, 2008, 2011, and 2014. There are no other known studies applying panel techniques to quantify the impact of SPS and TBT regulations on bilateral trade between Guyana and its CARICOM trading partners and further extrapolating the impact of efforts at policy harmonisation. Therefore, this is also a notable empirical contribution.

Further, from an empirical perspective, a limited number of studies focus on trade within regional markets of developing countries, which merits examination since the Organisation for Economic Co-operation and Development (OECD) (2005) argues that trade among developing countries is becoming more concentrated within regional markets. This is a relevant point for Guyana whose exports of food and agricultural products to CARICOM increased by 128% between 2010 and 2015, but remains low in absolute terms given its exports to non-CARICOM markets. Therefore, examining Guyana's exports to its trading partners in the CARICOM market presents a unique opportunity to add to this aspect of the literature, particularly in understanding whether SPS and TBT regulations support or hinder increased exports.

The next part of this article summarises the key literature with respect to empirical conclusions on the impact of SPS and TBT regulations and arguments regarding the impact of harmonisation on exports in a regional setting. The methodology is then explained, followed by the findings and a discussion of their implications for Guyana's trade with CARICOM. The conclusion examines policy implications for Guyana.

Related Empirical Literature

Atici (2013) surmises that non-tariff measures such as SPS and TBT regulations can have any of three effects on trade--they can enhance, reduce, or have no trade effect at all. These measures are therefore unlike tariffs, which unequivocally reduce trade. A major argument for the ambiguity of their impact is related to their objective for implementation. Unlike tariffs, SPS and TBT regulations are concerned primarily with quality, packaging, labelling, product and process standards, and conformity assessment procedures. They are therefore mandatory. Henson and Jaffee (2006) argue that because they are mandatory and, in effect, resolve information asymmetry between foreign consumers and exporters regarding the quality of imported products, they can enhance and sustain trade. On the other hand, Murina and Nicita (2014) argue that because they are mandatory, they impose both fixed and variable costs of compliance on exporters that may create distortionary effects on trade. This is adequately summed up in the standards-as-barriers versus standards-as-catalyst dichotomy as put forward by Xiong and Beghin (2013).

A number of factors have been advanced in the empirical literature that dictate the extent to which SPS and TBT regulations barricade or support trade. Both Kareem (2014) and De Frahan and Nimenya (2013) point to the level of development of the exporter, an indication of its scientific, human, and financial capacity for compliance. Their view finds support with Murina and Nicita (2014), who investigated the effects of the European Union's SPS measures for twenty-one broad categories of agricultural goods and found that the burdens associated with SPS measures are higher for lower income countries compared with middle- and high-income countries. Henson and Jaffee (2006) point to the size composition of exporters, with smaller exporters being more likely to experience trade-reducing effects.

DeFrahan and Nimenya (2013) also point to differential impact based on product exported. This is related to arguments by Beghin (2013) and Blind, Mangelsdorf, and Wilson (2013) that due to higher levels of health risks posed, some sectors (such as dairy and seafood) are more regulated. In addition, DeFrahan and Nimenya (2013) make reference to time as a factor in a study that sought to investigate the extent to which private and public European food safety standards affect imports of green beans from Kenya. Using a gravity model with NTMs estimated as ad valorem tariff equivalents for the period 1990-2011, they found different effects on exports associated with different periods. In particular, positive impacts were unearthed during the period 1990-2011, but increasingly negative impacts from 1995 to 2003. With time, which allows for increased knowledge and experience with regulations, capacity for compliance should improve (Shaffaedin 2007).

Those who argue that the regulations are trade reducing, point to the fact that they are applied mainly on food and agricultural products exported by developing countries and mainly in the markets of developed countries. Others such as Peridy and Ghoneim (2013) argue that they are new forms of protectionism to replace the defence mechanism lost due to the successful liberalisation of tariffs over the years, consequent to the work of the World Trade Organization and regional trade agreements. Peridy and Ghoneim (2013) argue that because they address a sensitive matter, health and safety, and are science based and therefore less subject to public scrutiny, they can be become disguised tools to protect local industries against competition from import competing products. Despite the fact that both foreign and local producers may be subject to the same regulations, they could be applied differently (Iacovone 2003).

The fact that SPS and TBT regulations are trade reducing finds large support in the empirical trade policy literature. Studies by Henson, Broader, and Mitullah (2000) and Otsuki, Wilson, and Sewadeh (2001) examined how SPS and TBT regulations impact trade. They use mainly the gravity model framework, and argue that the measures create distortionary effects. For instance, Disdier, Fontagne, and Mimouni (2008) investigated the impact of SPS and TBT measures on exports of 690 food and agricultural products from developing countries to OECD states using the frequency index and AVE approaches, and found that such measures significantly reduced bilateral trade between OECD and developing and least developed countries. Kareem (2014) studied the effects of EU standards on exports of vegetables, coffee, fish, and cocoa from 52 African countries over the period 1995 to 2012. The author using a two-step Helpman, Melitz, and Rubenstein (2008) extensive and intensive trade margins model found that standards for vegetables inhibit trade.

Alaeibakhsh and Ardakani (2012) focused on one country and one product--pistachios from Iran--going to the markets of Germany, the United Arab Emirates, Hong Kong, Russia, Turkey, Italy, Spain, Syria, and India over the period 1996-2008. They also used a gravity model to estimate the impact of SPS and TBT regulations and found them to be trade-reducing. Tran, Wilson, and Anders (2011) found that stricter drug residue standards created distortions in imports of crustaceans from the EU, Japan, Canada, and US.

For regionally integrated markets where tariffs have been liberalised and a common tariff wall erected against non-members, the presence of non-tariff regulations such as SPS and TBT measures can act as barriers to and limit the flow of trade within the market. This is alluded to in studies by Reyes and Kelleher (2015), Trabelsi (2013), Murina and Nicita (2014), and Chen and Mattoo (2008). In fact, Deardorff and Stern (1997) argued that SPS and TBT regulations are often more complicated and challenging than tariffs. Therefore, post integration, trade creation effects may be limited where these persist.

Reyes and Kelleher (2015), in a study of sanitary and phytosanitary measures and technical barriers to trade in Central America, found the persistence of such measures despite the elimination of tariffs under the Dominican Republic-Central America Free Trade Agreement, with substantial heterogeneity among countries. Estimating AVEs they argued that SPS measures inhibit regional integration. The impact of SPS measures on border prices was found to be equivalent to an ad valorem tariff of 11.6% for the entire region and 68.4%, 5.4%, 22.0%, and 5.0% respectively on prices of beef, chicken meat, bread, and dairy products in Guatemala.

Atici (2013) also studied the EU market, but looked at how harmonisation of aflatoxin regulations would affect exports of figs and hazelnuts from Turkey to the EU. They used a gravity model with fixed effects and found that harmonisation of the regulation in 2002 had a positive impact on the volume of hazelnut exports. However, a regulation introduced in 2007 requiring certification had the reverse impact on exports of figs, but no impact on exports of hazelnuts.

Trabelsi (2013) used a gravity model to quantify the impact of non-tariff barriers (NTBs) to agricultural trade in the Euro-Med area for two years -- 1996 and 2008 -- using tariff trade restrictiveness index (TTRI) and the overall trade restriction index (OTRI) to quantify NTBs. The author proved that NTBs are trade restrictive since significant and negative coefficient estimates were found.

The implication of studies such as those by Reyes and Kelleher (2015) and Murina and Nicita (2014) is that countries exporting to markets governed by a regional arrangement can face trade-restrictive SPS and TBT measures, primarily because of the lack of harmonisation of those measures on a regional scale. Most integration arrangements focus on the liberalisation of tariffs, as they are considered the main form of trade barriers. However, evidence suggests that non-tariff measures (NTMs) such as SPS and TBT regulations, and customs and administrative procedures, also raise intra-regional transaction costs and can impede trade as do tariffs (Ferrantino 2003; Carrere and Melo 2011). The lack of harmonisation is the source of the trade-restrictive impact of the measures as heterogeneous regulations increase the cost of complying with regulations. This is particularly argued by Chen and Mattoo (2008) who, using a Heckman two-stage estimator (controlling for fixed effects), found that harmonisation increased the likelihood and volume of intra-regional trade and may also enhance it with extra-regional markets.

The effect of the lack of harmonisation is further compounded by the fact that countries can continue to unilaterally impose regulations that are deliberately trade restrictive, so that protection against import-competing products could be offered to local producers. In fact, Calo-Blanco and Naya (2005) point to a substitution of NTMs for tariffs within preferential trade agreements to protect local firms against foreign competition.

While Chen and Mattoo (2008), Reyes and Kelleher (2015), and Murina and Nicita (2014) argue in support of harmonising regulations to improve trade in a regional setting, Wilson and Otsuki (2003) take the argument further by empirically ascertaining what regulations should be harmonised. They investigated the impact of harmonisation of food standards on global food trade patterns over the period 1995 to 1998. To this end, a fixed effects gravity model was constructed with 31 exporting countries, 21 of which were developing countries and 15 importing countries, four of which were developing countries with sub-group models for cereals, nuts, and dried and preserved fruits. The authors used the direct standard for aflatoxins to test the impact on exports. Like Otsuki, Wilson, and Sewadeh (2001) they found negative impacts for cereals and nuts. They subsequently performed different simulations to compare the impact of the Codex standard, which is the international benchmark, versus the national standards and the EU harmonised standard. They found that exports would rise by $38.8 billion with the adoption of the Codex standard compared with divergent national standards. However, the adoption of the more stringent EU harmonised standard would lead to a reduction of world exports by US$ 3.1 billion when compared to the existing national standard.

Methodology

The structural gravity equation, as specified below by Yotov et al. (2016) is a composite of two components that together explain a country's trade pattern; a size term and a trade cost term.

[mathematical expression not reproducible] (1)

The size term [[Y.sub.i][E.sub.j]/Y] measures the value of production of country i ([[gamma].sub.i]) and value ot expenditure of country j. Country i's value of production ([[gamma].sub.i]) is equivalent to total exports to all regions and country j's expenditure ([E.sub.j]) is the sum of the value of imports across all exporters. The model is arrived at based largely on Armington (1969) and constant elasticity of substitution (CES) assumptions. It is assumed that imported goods are differentiated by country of origin and that countries specialise in the production of one good which is fixed in supply. Further, consumers face constant elasticity of substitutio [[Y.sub.i][E.sub.j]/Y] pe preferences (a) in their demand for goods, as given by equation 2 and maximise consumption of goods subject to their budget constraint as reflected in equation 3 below where [c.sub.ij] is consumption of varieties from country i by consumers in country j and [p.sub.ij] is the price that consumers in country j pay for goods exported by country i.

[mathematical expression not reproducible] (2)

[mathematical expression not reproducible] (3)

The second component of the structural model in equation 1 is the trade cost term. [mathematical expression not reproducible].The exporter's supply price (pi) and price in the import market [p.sub.ji] differ because of the cost to conduct trade between countries i and j, given by the trade cost factor [t.sub.ij], therefore [p.sub.ij] = [p.sub.i][t.sub.ij] but as exporters export they incur [t.sub.ij] - 1 due to iceberg effects where a portion of product is lost during shipment. Trade costs can include any factor such as information, regulatory fees, etc. In the original gravity equation, distance was the cost factor. However, in the structural model, the trade cost term captures the total effects of all factors affecting trade. Ultimately, exporters pass these costs on to consumers in the importing country.

The total value of exports [x.sub.ij] from country i to j is equivalent to its value of production [P.sub.i][C.sub.ij] plus the trade cost term ([t.sub.ij] - 1). The country's total income is based on its total value of exports yielding its total income [mathematical expression not reproducible].

The demand in country j for goods produced by country i given the CES condition that consumers face as outlined in equation 2 yields the following import demand equation:

[mathematical expression not reproducible] (4)

Where [P.sub.j] is the price index in country j. As established previously, exporters pass on the trade cost to consumers in country j such that [P.sub.j] is given by the equation below:

[P.sub.j] = [[[SIGMA].sub.i][([[beta].sub.i][p.sub.i][t.sub.ij]).sup.1-[sigma]]].sup.-/(1-[sigma]) (5)

When we equate this demand equation by country j to total income earned by country i from exporting to country j, yi = [[sigma].sub.j][x.sub.ij] we obtain the market clearance equation below:

[mathematical expression not reproducible]

Anderson and van Wincoop (2003) used the marketing clearing condition to solve for scaled prices, [[beta].sub.i][p.sub.i] which are then substituted into the import demand equation, consequently defining world income as [y.sup.w] [equivalent to] [[SIGMA].sub.j][y.sub.i] income shares by [[theta].sub.j] [equivalent to] [[y.sub.i]/[y.sup.w]] ultimately arriving at the following equation:

[mathematical expression not reproducible] (7)

[[PI].sub.i][P.sub.j] are the price terms for county i and j, respectively. These are what Anderson and van Wincoop (2003) defined as the multilateral resistance terms. [P.sub.i] is the inward multilateral resistance term which captures factors specific to the importer that affect its ease of trading (Yotov et al. 2016). [[PI].sub.i] is the outward multilateral resistance term and measures factors specific to the exporter that affect its trade with other countries (ibid 2016). Because the authors assumed symmetry of trade cost [[PI].sub.i][P.sub.i] the gravity equation then becomes;

[mathematical expression not reproducible] (8)

The structural gravity model can be specified in the following econometric form (ibid 2016);

[mathematical expression not reproducible] (9)

Where:

* [X.sub.ij,t] is nominal trade flows between country i and j at year t

* [[pi].sub.i,t] is the set of time-varying exporter dummies at year t that

* account for outward multilateral resistances

* [X.sub.i,t] is the set of time-varying importer dummies at year t

* that account for inward multilateral resistances

* [[mu].sub.ij] is the set of country-pair fixed effects

* [[eta].sub.1]BT[P.sub.tj,t] is the vector of time-varying bilateral factors

* determining trade

* [[epsilon].sub.ijt] is the error term

Applying this framework, the following empirical structural gravity model is specified to study the impact of SPS and TBT regulations on Guyana's export of food and agricultural products to its selected CARICOM trading partners;

[mathematical expression not reproducible] (10)

Where:

* i refers to Guyana and;

* j is seven of Guyana's leading export markets in CARICOM:Antigua & Barbuda, Dominica, Grenada, Barbados, Jamaica, Suriname, and Trinidad & Tobago.

* t is allowed to vary over ten years at three-year intervals -- 2005, 2008, 2011, 2014 -- to take account the fact that trade flows need time to adjust to trade policy (Olivero and Yotov 2012).

* [[epsilon].sub.jt] is a error term assumed to capture measurement error

* [[pi].sub.it] are exporter-time dummies

* [[chi].sub.jt] are the importer-time dummies

* [[mu].sub.ij] are country-pair dummies

* [[gamma].sub.[kappa]] are sector dummies

Estimates for equation 10 are generated using the within (fixed) effects estimator because it is assumed that there is unobserved country-specific heterogeneity in the independent variables that is correlated with the error term ([[lambda].sub.1]| [X.sub.it]) [not equal to] 0) (Arita, Mitchell, and Beckman 2015). Under these conditions, the fixed effects estimator is argued to perform more efficiently (Kareem 2014). Therefore exporter-time ([[pi].sub.it]) importer-time ([[chi].sub.jt]) and country-pair dummies ([[mu].sub.ij]) are included in the model to capture this heterogeneity as well as possible endogeneity in the trade policy variables, [DCSM.sub.ij] and ln[X.sub.ijt] due to reverse causality (Yotov et al. 2016). From the trade cost perspective, the inclusion of the fixed effects also helps to take account of the multilateral resistance terms (Feenstra 2002) and consequently avoids the 'gold medal mistake', as argued by Baldwin and Taglioni (2006). To this end, the time-varying effects of factors common to all trading partners of Guyana and common to Guyana relative to its trading partners are captured.

The models are estimated with clustered standard errors with country-pair and sector interacted dummies as the cluster variable since the data is disaggregated by sector.

We consider equation 10 to be the baseline model and generate alternative estimates using the Alternative estimates generated using the Poisson Pseudo Maximum Likelihood (PPML) and Heckman sample selection models to take account for the fact that the dataset has a significant proportion of zeroes due to the use of disaggregated trade data than can lead to biased estimates. 81% of the dataset consists of zero observations (4,606 observations). Controlling for zeroes is important since one cannot determine whether these are linked to the punitive impact of regulations (ibid 2012). The Poisson specification of equation 11 is given below;

[mathematical expression not reproducible] (11)

The PPML estimator deals with zero observations by estimating the gravity model in its multiplicative rather than its logarithmic form (Yotov et al. 2016). Therefore, the dependent variable is estimated in levels and zeroes are retained. According to Silva and Tenreyro (2006) the nonlinear PPML estimator provides unbiased and consistent estimates that are robust to the presence of hetereoscedasticity. For the PPML estimator, the food and agriculture model was generated using the ppml command developed by Silva and Tenreyro (2006), which deals better with convergence problems than Stata's built-in xtpoisson command. The model is estimated with clustered standard errors.

Crivelli and Groschl (2012) argue that the Heckman model is better at dealing with zeros because the PPML method assumes that there is nothing special about zeroes. As such we present results using both estimators.

The Heckman model accounts for zero trade values through censoring as such both a selection and an outcome equation are specified (Yotov et al. 2016) as follows. The selection equation is the probit model of export, where [PHI] is the standard normal distribution function. In addition to the standard variables, exchange rate (lnE[R.sub.t][j.sub.t]) is carried because it is assumed to have an impact on the probability of exports.

[mathematical expression not reproducible] (12)

[mathematical expression not reproducible]

The two equations also help to differentiate the effect of SPS and TBT regulations on the extensive margin (the probability of trade) and the intensive margin (the amount of trade conditional on market entry (Crivelli and Groschl 2012). A maximum likelihood procedure is used to simultaneously estimate the outcome and selection equations using Stata's Heckman command with robust clustered standard errors (UNESCAP 2012).

All of the models are estimated using Stata version 13.1.

Description of variables in the model

Table 2 summarises the variables in the model. The dependent variable [X.sub.tjt] is nominal exports of food and agricultural products from Guyana to each export market over time t. It is measured at the four-digit level of the Harmonized System and covers the first 24 chapters which includes 202 products (see Table 3). Exports are measured in US$000. Data was obtained from the United Nations Commodity Trade Statistics Database. The article follows Crivelli and Groschl (2012), Ferraz, Ribeiro, and Monsterio (2015), Grant, Peterson, and Ramniceanu (2015), and Trabelsi (2013) in using unidirectional trade flows; and Genc and Law (2014) in specifying a model with only one exporter.

InGD[P.sub.it] and lnGD[P.sub.jt] capture the income levels of Guyana and trading partners measured at constant US$ million in 2005 prices. All of the variables are indicators of the economic size of Guyana and its trading partners. For Guyana the variables all measure the scale of productive activities while for trading partners the variables indicate purchasing power of the country. In Po[p.sub.it] and In Po[p.sub.jt] measure the population size of Guyana and trading partners in million persons. All of the variables are expected to have a positive impact on exports. Data was obtained from the United Nations National Accounts Main Aggregates Database.

In[DST.sub.ij] is the natural log of the distance between the capital of Guyana and each of its export markets measured in 000km. Data was obtained from the Centre d'Etudes Prospectives et d'Informations Internationales (CEPII) gravity database. Distance is expected to have a negative impact on exports because it is a proxy for factors such as transportation costs that impede trade.

Cultural and historical factors are captured through a dummy that captures whether Guyana and each partner have a common coloniser ([DCol.sub.jf]); speak the same official language ([Dcl.sub.if]); and share a common border ([DCB.sub.if]). The variables take a value of 1 with the presence of any of these factors and 0 otherwise. Positive coefficients are expected for these variables.

[CSM.sub.j] is used to capture the level of commitment each trading partner has made to attempts to achieve a single market in CARICOM. It takes a value of 1 if the trading partner has already signed on to the single market and 0 otherwise. Since the CSM seeks to further remove barriers to intra-CARICOM trade it is expected that this variable will produce a positive coefficient.

[DCAHFSA.sub.jt] is a dummy to capture the signing of the Caribbean Agriculture Health and Food Safety Authority in 2010, which benefits both Guyana and its trading partners. CAHFSA was created to assist CARICOM member states with coordinating and strengthening their infrastructure, institutional, and human resource capacity where agricultural health and food safety are concerned.

The export specialisation index, In[EXSM.sub.tjt] shows specialisation in a market at the product level (Bernatonyte 2011). It is calculated as

ES = ([X.sub.ij]|[X.sub.it])/([M.sub.kj]|[M.sub.kt])

where [X.sub.ij] are exports of product j by country i to the world market; [X.sub.it] are total exports of country j and [M.sub.kj] and [M.sub.kt] are imports of product j by country k and total imports of country k, respectively. Values below one indicate a comparative disadvantage and values above 1 indicate a specialisation in the market. Therefore the variable is expected to have a positive impact on exports.

[FSPS.sub.ijt] and [FTBTi.sub.tjt] are frequency indexes measuring the SPS and TBT regulations in place by each country on trade with Guyana. These are calculated as (UNCTAD 2012)

[mathematical expression not reproducible]

where D is a dummy variable reflecting the presence of NTMs and [M.sub.i] is a dummy indicating whether there are imports of good i. t is the year of measurement. Values range from 0 to 100, with zero indicating no regulation in place on the traded sector and 100 indicating all products in the traded sector that face NTMs. Data is collected at the most disaggregated level (six-digit) and aggregated at the desired level (four-digit).

The frequency index forms part of the broader inventory approach where a catalogue (by count) is taken of non-tariff regulations and simple statistics produced of the frequency of measures in place (Beghin and Bureau 2001). The indexes could be weighted against imports or assessed as unweighted proxies. Other than the frequency index, other inventory-based measures include the import coverage ratio and stringency indexes. The approaches are quite popular in the empirical trade literature (see Cadot and Gourdon 2012; Xiong and Beghin 2013). Data was obtained from the Market Access Map maintained by the International Trade Centre.

ln[TID.sub.jt] is used to estimate the impact of customs and administrative procedures on exports (Liu and Yue 2009) measured as border crossing times vis-a-vis time for each trading partner to import (days). Data was obtained from the World Bank's Doing Business reports (Doing Business n.d.). This variable is expected to have a positive impact on exports as countries make improvements in administrative procedures to reduce the time it takes to import and export.

Given the theoretical discussion regarding the possible impacts of SPS and TBT regulations, we make the following hypotheses regarding the SPS and TBT variables:

* Both SPS and TBT regulations, consequent to the costs exporters must incur to demonstrate compliance, would have a negative impact on exports.

* The creation of the Caribbean Agriculture Health and Food Safety Authority would have had a positive impact on exports.

* Conditional on CAHFSA, SPS regulations would have had trade-enhancing effects.

Empirical Results

Model Summary and Diagnostics

The panel for the food and agriculture sector model is strongly balanced with 5,656 observations (see Table 3). The mean of the export variable is US$64.6 (000) and ranges from 0 to US$28,142.68 (000).

The SPS and TBT index values have very low overall mean values of 11.9 and 14.3, respectively (see Figure 2). The index which produces values that range from 0 to 100 only produces positive values in the presence of both exports and regulations. Therefore 0 and low index score values can indicate either the absence of regulations on a sector or zero exports. Since trade did not take place for many of the 202 product groups there were many 0 values associated with the indexes. For the four years considered, Barbados and Trinidad and Tobago have the highest overall average index scores for both SPS and TBT regulations compared with the other countries; while Suriname had the lowest average index scores for both SPS and TBT regulations. Antigua & Barbuda had the lowest average scores for SPS regulations

When the number of product groups under each of the 24 chapters captured, with index scores above 50 are summarised (see Figure 3), Barbados and Trinidad & Tobago have the largest number of product lines with high index scores indicating that they have more regulations in place on exports from Guyana. For instance, for Barbados, 62 of the product lines captured in the study had TBT scores above 50 while 57 had SPS index scores above 50.

For Barbados, seven product groups under chapter 08 (edible fruit and nuts; peel of citrus fruit or melons) have TBT and SPS index scores above 50. This is the highest concentration of products under a chapter across all of the countries and indicate that this chapter is more highly regulated in Barbados compared with the other countries considered in the article. In addition, six product groups fall under chapter 07 (edible vegetables) and six under chapter 22 (Beverages, spirits and vinegar).

Like Barbados, Trinidad & Tobago has a higher concentration of products under a specific chapter attracting SPS and TBT regulations consequent to the fact that a wider variety of products are exported to T&T compared with the other countries. T&T had SPS and TBT index scores above 50 on three or more products for the following chapters: chapter 08 (edible fruit and nuts); 07 (edible vegetables); 21 (miscellaneous edible preparations); and 12 (oil seeds and oleaginous fruits).

Antigua & Barbuda and Suriname have more TBT regulations in place than SPS regulations. Chapter 22 (beverages, spirits and vinegar) appears to be the most regulated chapter for exports from Guyana with six product groups having TBT index scores above 50. When a product has a similar SPS or TBT index that is usually an indication that they are governed by the same legislation. This is the case for most of the countries. For example, in Trinidad & Tobago the Food and Drugs Act (Chapter 30:01) provides a range of technical measures that cover both SPS and TBT regulations on food products exported to the country.

Chapter 21 (miscellaneous edible preparations) is the most regulated sector for exports to Grenada, with four product groups under this chapter having SPS and TBT scores above 50.

Suriname, Jamaica, and Dominica have a comparatively lower number of product groups under each chapter with SPS and TBT frequency index scores above 50. Noteworthy sectors with index scores above 50 are 08 (edible fruit and nuts...) for Suriname; 19 (preparations of cereals...); 03 (fish and crustaceans); 22 (beverages, spirits and vinegar) and 21 (miscellaneous edible preparations) for SPS for Jamaica; and 22 (beverages, spirits and vinegar); 17 (sugars and sugar confectionery) and 20 (preparations of vegetables, fruit, nuts or other parts of plants) for Antigua & Barbuda.

The export specialization index had a mean value of 39.31 with a minimum value of 0 and a maximum value of 101,234. Figure 3 summarises the export specialization index values by product for 2014. Guyana's highest export specialization index score in 2014 was for exports of wheat and meslin (1001) to Jamaica. However, the highest concentration of products with export specialization indexes above 1 were for chapter 03 (fish and crustaceans). This was mainly for the markets of Trinidad & Tobago, Jamaica, Antigua & Barbuda, Barbados, and Suriname. This indicates that Guyana has a comparative advantage in exporting fish to these countries (see figure 4).

The modified Wald test for GroupWise hetereoscedasticity confirmed hetereoscedasticity in the data given p = 0.000. The kernel density, normal probability, and quantile normal plots also indicate that non-normality in the data (see Figures 5 and 6). Further, for the Ramsey RESET test, both the null hypothesis that the model does not have omitted-variables and that there is no specification error was not rejected given p-value of 0.0636 and 0.356, respectively. This indicates additional variables are not needed and that the model is correctly specified.

The diagnostic tests reinforce the necessity of a non-OLS (ordinary least squares) estimator. The Hausman and Breusch-Pagan tests were therefore conducted to confirm the nature of correlations between the errors in the model. The null hypothesis ([h.sub.0]:E([lambda]1|[X.sub.it]) = 0) of the Hausman test that the differences between the coefficients for the fixed effects and random effects model are not systematic was not rejected at the 5% level of significance given p = 0.5022. This suggests that the fixed effects estimator is appropriate for analyzing the data and will yield more consistent and efficient estimates (see figure 4).

The Breusch-Pagan Lagrangian multiplier test to choose between the random effects and OLS estimators however, led to a rejection of the null hypothesis that correlations across the countries in the panel is zero given p = 0.000. This suggests that the random effects estimator is more appropriate than the pooled OLS model.

Baseline gravity estimates

Equation 10 was estimated using the fixed-effects within effects estimator which is efficient to disturbances associated with both heteroscedasticity and autocorrelation (Kareem 2014). The model was specified with fixed effects dummies (exporter-time; importer-time and exporter-importer and sector effects) and was generated using clustered standard errors to control for hetereoscedasticity and autocorrelation with importer-exporter-sector interaction pair effects as the cluster variable (Yotov et al. 2016). The results are summarised in Table 4. The presence of zeroes in the dataset resulted in a truncation of the sample size from 5,656 observations to 465. The model explains 19% of the variation in exports of food and agriculture products to CARICOM countries.

The fixed effects estimator omitted all of the time invariant variables including; dummies for colonial ties, common language, common border, formation of the Caribbean Agricultural Health and Food Safety Agency. The frequency index measuring TBT regulations and distance were also omitted.

All of the variables not omitted from the model are statistically significant at either the 1, 5, or 10% levels of significance and have the expected impact on exports; including GDP of Guyana and trading partners the SPS frequency index, time to import, CARICOM Single Market variable and the export specialization index. Guyana's GDP and the CARICOM single market variable in particular, have a large impact on its exports of food and agriculture products. The SPS frequency index show that exports are negatively affected by SPS regulations.

Alternative gravity estimates

Alternative estimators were used to gauge the impact of the significant number of zeroes in the dataset (81%). Equation 11 was estimated using the Psuedo Poisson Maximum Likelihood estimator and equations 12 and 13 were estimated, simultaneously, using the Heckman estimator. The Poisson model was estimated with importer, exporter, importer-exporter pair and sector dummies with clustered standard errors; and the Heckman model was estimated with importer, exporter, and importer-exporter pair dummies. A separate Poisson model was estimated with interaction variables for CAHFSA and the SPS and TBT frequency indexes. The results are summarised in Table 5.

The size of the coefficients is similar for the Poisson and Heckman selection equations. However, they are different from the fixed-effects within-effects estimator, indicating that the presence of zeros affects the estimates of the coefficients.

The Poisson model has a larger sample size (4,116 observations) compared with the model estimated using the fixed effects within effects (465) estimator. This is because the dependent variable is not estimated in its logarithmic form and therefore zeroes are retained. However, the export similarity index variable had to be rescaled to facilitate estimation of the model. Also, the SPS and TBT variables were estimated in levels since a significant proportion of the coefficient values are zeros which would have been lost had the variables being log linearised.

The Poisson model accounts for 95% of the variation in exports of food and agricultural exports. However, the only variables that are statistically significant at either the 1, 5, or 10% levels of significance are distance, the frequency indexes for TBT and SPS regulations, time to import, the export specialization index, CAHFSA dummy, and trading partners' GDP. (See Table 5)

Distance has the expected negative impact on exports in line with other gravity estimates but is statistically insignificant. Exports will increase marginally by 4.0% with a 1% reduction in time to import in days. The export specialization index shows that exports to the markets will reduce by 0.03% with a 1% increase in product specialisation. The SPS frequency index variable has a positive coefficient value, unlike the model estimated using the within effects fixed effects estimator. However, the frequency index measuring TBT regulations show trade-reducing effects.

The Heckman selection model retained the full dataset with 5,656 observations. The results suggest that sample selection is not a problem in the model since the error terms of the selection and outcome equations are not correlated given low rho value of -0.0472 (UNESCAP 2012). Further, the Wald test of independence of the equations where the null hypothesis is that the two error terms are uncorrelated is not rejected, given p value of 0.4396.

The selection equation suggests that the probability to enter the markets of either Trinidad & Tobago, Jamaica, Suriname, Barbados, Dominica, Grenada, or Antigua & Barbuda (extensive margin) is higher in the presence of both SPS and TBT regulations by 1.6% and 3.14%, respectively. However, the outcome equation (intensive margin) suggests that both SPS and TBT measures have no statistically significant impact on exports once a market has been entered.

Other variables affecting the probability of exporting to any of the aforementioned markets include sharing a common coloniser and export specialisation of the product. However, the export specialization index does not have the expected sign. Once a country has entered any of the markets, the GDP of trading partners and the export specialisation of the product are the only two factors that impact the amount of exports, both positively.

Impact of SPS and TBT regulations on Exports

Since sample selection was not found to be a problem the interpretation of results will focus on the Poisson model which deals better with heteroscedasticity (UNESCAP 2012). The Poisson model also has a higher reset test p value of 0.61 (ratio between exports and predicted exports) relative to the fixed-effects within-effects model (0.39).

SPS regulations have a positive impact on exports of food and agriculture products to the markets of Barbados, Trinidad, Jamaica, Suriname, Grenada, Dominica, and Antigua. The regulations increase exports by a margin of US$1.93 for every US$1,000 worth of exports. For 2014, this would have been equivalent to US$180,645.

The positive impacts may have arisen from the fact that complying with regulations improve product quality and buyer confidence, so the gains associated with complying outweigh the costs incurred. This allows for trade to be sustained.

It was hypothesised that SPS regulations would have had a negative impact on exports from Guyana, particularly since sectors such as fresh fish are among the sectors that are more regulated because they pose higher levels of health risks (Beghin 2013; Blind, Mangelsdorf, and Wilson 2013). Fish and crustaceans (03) is the third largest export sector to CARICOM markets, accounting for 18% of total exports of food and agricultural products. Based on export data for 2000 and 2015, Trinidad, Jamaica, Antigua, and Barbados are leading markets for exports of fish and crustaceans (03). In 2015, the value of fish and crustaceans exported to Jamaica grew by over US$7.4 million. (See Figure 5)

Perhaps SPS regulations are laxer in the selected markets. Evidence of this reside in the fact that while it is mandatory to be HACCP (hazard analysis critical control points) certified to export fish to markets in the US and Europe, this is not the case for markets within the CARICOM region. Further CARICOM (2015) pointed to the fact that "[t]here are also either no legally binding protocols managing food safety throughout the region or where they are practiced they are disorganized and informal..." Therefore in relative terms, it is not surprising that in 2015 export of fish was marginally higher (18%) to CARICOM markets compared with exports to non-CARICOM markets (17%). In fact, based on the export specialization index, as previously established, Guyana appears to have a comparative advantage for exporting fish to the countries that form the study, particularly for Trinidad, Jamaica, Antigua, Barbados, and Suriname.

Apart from fish and crustaceans (03), other leading products exp [mathematical expression not reproducible] ARICOM markets are cereals (10) 42%; sugars (17) 27%; beverages and spirits (22) 5%; and preparations of cereals (19) 4%. (See Table 6). Trinidad & Tobago, Jamaica, and Barbados accounted for approximately 79% of total exports to all CARICOM countries in 2015 (see Figure 5). Trinidad, Jamaica, and Haiti are important markets for rice (cereals). For sugar and sugar confectionery (17) the leading export markets are Suriname, Trinidad, St. Lucia, and St. Vincent & the Grenadines. Also important are Dominica and Grenada, while for preparations of flour (19), Jamaica was the number one destination market in 2015. For wine and spirits (22) Trinidad and Barbados were the leading markets in 2015. (See Figure 5). The products are exported in both bulk and processed form and so attract both SPS and TBT regulations. Bulk products, mainly fish, are exported for onward processing and retailing in those markets.

Exports of fruits and vegetables, which also attract a lot of SPS regulations (Beghin 2013; Blind, Mangelsdorf, and Wilson 2013) are low between Guyana and the countries studied, compared with exports to other markets. In 2015, exports of fruits and nuts (08) accounted for 0.56% of total exports of food and agricultural products while edible vegetables (07) accounted for 0.236% of exports. Nevertheless, for markets such as Trinidad & Tobago, Barbados, and Antigua, trade is covered specifically by bilateral trade protocols with compulsory requirements for exporters, including the fact that post-harvest processing must be undertaken by a certified packaging house. This is done by the New GMC packaging house where exporters may also receive assistance from New GMC staff to ensure that product quality requirements are met.

TBT regulations, on the other hand, have a trade-reducing effect, in line with expectations, reducing exports by a margin of US$0.59 for every US$1,000 worth of exports. For 2014, this would have been equivalent to US$55,223.

Technical barriers to trade include: (1) product standards that define characteristics such as size, shape, design, labelling/marking/packaging, functionality, or performance; and (2) conformity assessment procedures, including product testing, inspection, and certification activities (European Commission 2005).

TBT measures are generally applied on processed products. For Guyana, given the value of total exports of food and agricultural products to CARICOM markets, processed products are associated mainly with the following sectors: cereals (10) (18%), sugars and sugar confectionary (17) (27%); beverages, spirits and vinegar (22) (5.35%); preparations of cereals... (19) (3.7%); miscellaneous edible preparations (21) (0.86%); and animal or vegetable fats and oils (15) (0.66%). (See Table 6). For instance, rice is packaged in various sizes and exported for specific buyers in Barbados and Trinidad under the buyer's brands. For instance, Nand Persaud and Company packages and exports the Supa and Eve brands of parboiled rice, among others, for buyers in Trinidad and Barbados, while its own brand, Karibee, is sold mainly in the local market (Persaud 2017).

The mean value of the TBT frequency index (0.14) suggests that exports face more TBT regulations compared with the SPS frequency index (0.12). This gives the impression that processed products exported from Guyana are more highly regulated than primary products. This is a similar experience to tariff escalation (WTO), where finished and semi-processed products attract higher duties than raw materials, usually as a subterfuge to protect domestic industries.

Given that more than 70% of exporting firms from Guyana export primary products (DaSilva-Glasgow 2018) it suggests that the negative impact of TBT regulations may be concentrated on a few exporting firms.

TBT measures may prohibit trade by raising the cost of exporting through compliance costs (Carrere and Melo 2011), especially where there are discrepancies across markets. Discrimination can also emerge based on how regulations are administered on local firms versus foreign firms or on foreign firms from different countries (Iacovone 2003).

In support of expectations, the CAHFSA variable show a positive impact on exports from the creation of the agency, with exports increasing by a margin of approximately 2.9% (expl.37)-l). Noteworthy, however, is that the DCAHFSAFSPS and DCAHFSAFTBT interaction variables are both statistically insignificant, signalling that the effects of SPS and TBT regulations were not in any way impacted by the formation of CAHFSA, which is not what we had hypothesised.

CONCLUSION AND POLICY RECOMMENDATIONS

This article investigated the impact of SPS and TBT regulations on exports from Guyana to seven of its export markets in CARICOM. The results confirm that TBT regulations have a negative impact on exports while SPS regulations have a positive impact.

Proximity, growing exports, contributing to the attainment of food security goals in the region, and low food production capacity, coupled with resulting high import dependence, remain strong arguments for Guyana to continue targeting markets within CARICOM for exports of food and agricultural products, particularly Barbados, Jamaica, and Trinidad & Tobago, where it already has a strong presence through exports of products such as rice and fish. It is therefore important to understand more fully what factors may account for the trade-reducing effect of TBT regulations. Meanwhile, the following recommendations are put forward for Guyana to continue to strive to improve the quality of its exported products and the capacity of exporters to comply with the compulsory requirements of export markets:

* Firstly, effective enforcement of standards along every aspect of the export supply chain is important. Exporters and producers are not always the same people. Therefore, it is crucial that farmers and manufacturers are educated about regulations when producing so that they are proactive in complying with regulations. This is a task for both private bodies such as the chambers of commerce that may have agro-food producers as members; as well as relevant government agencies that deal directly with farmers.

* Secondly, government agencies such as the New Guyana Marketing Corporation and GOINVEST are important providers of information to exporters on regulations in export markets and in providing linkages to the relevant agencies in importing countries. Therefore, those agencies should be thoroughly equipped with the human and technical resources required to effectively respond to the information needs of exporters.

* Thirdly, the key trade policy body in Guyana, the Department of Foreign Trade, needs to take an active role in pushing for transparency of all regulations being used in the import markets to avoid these being used as disguised measures to protect their producers by discriminating against exporting firms from Guyana, particularly those exporting more processed food products.

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1.1 Appendices
Table 5: Gravity Model Estimated using Poisson and Heckman Estimators

PPML                                         PPML model with interaction
                      Equation 11            variables

                      Dependent              Dependent
                      variable: [X.sub.ijt]  variable: Exports

ln[GDP.sub.it]          -0.4718                -0.5323
                        (0.5151)               (0.5670)
ln[GDP.sub.jt]          -0.2248                -0.2313
                        (0.1507) (*)           (0.1564) (*)
ln [Pop.sub.jt]          1.4788                 3.2434
                        (8.3929)               (8.5086)
ln [Pop.sub.it]        -12.6147               -13.8295
                       (23.3179)              (23.2430)
ln[DST.sub.ij]          -0.1504                -1.6578
                        (7.2763)               (7.3668)
ln[FSPS.sub.ijt]         1.9224                 1.7725
                        (0.3489) (*,**,***)    (0.4908) (*,**,***)
ln[FTBT.sub.ijt]        -0.5896                 0.5143
                        (0.2349) (*,**)        (0.2727) (*)
ln[TID.sub.ijt]          4.0811                 4.1415
                        (0.4875) (*,**,***)    (0.5315) (*,**,***)
[DCol.sub.ij]         (omitted)              (omitted)

[DCL.sub.ij]          (omitted)              (omitted)
[DCB.sub.ij]          (omitted)              (omitted)
[DCSM.sub.j]             0.1293                 0.0894
                        (0.3600)               (3.3833)
ln[EXSM.sub.ijt]        -0.0310                -0.0299
                        (0.0152) (**)          (0.0154) (**)
ln[ER.sub.ijt]

[DCAHFSA.sub.jt]         1.3668                 1.2234
                        (0.3585) (*,**,***)    (0.5385) (*,**)
[DCAHFSAFSPS.sub.jt]                            0.3567
                                               (0.5903)
DCAHFSAFTBT             -0.1943
                                               (0.3275)
_cons                  -16.0723                -4.5064
                        (7.9047) (**)          (7.6088)
[R.sup.2]                0.95                   0.95
N                     4116                   4116
Censored obs
Prob>chi2
Estimated
correlation
(rho)
Wald test
P>Chi (rho)
Reset test               0.8535
p value
Importer-             yes
exporter
dummies
Importer              yes
dummies
Exporter              yes
dummies
Sector                yes
dummies

PPML                  Heckman selection model
                      Equation 12                       Equation 13
                      Selection                         Outcome
                      Dependent variable:ln[X.sub.ijt]


ln[GDP.sub.it]        -0.5490                             -6.1506
                      (0.2939) (*)                        (5.6591)
ln[GDP.sub.jt]         0.0043                              0.9248
                      (0.04371)                           (0.6151) (*)
ln [Pop.sub.jt]        0.4951                            -43.6087 (++)
                      (3.04898)                          (50.9429)
ln [Pop.sub.it]       -4.1741                            -81.7793
                      (8.5013)                           (70.4207)
ln[DST.sub.ij]        -1.0742                             25.4460
                      (1.9235)                           (27.3615)
ln[FSPS.sub.ijt]       1.5736                             -0.5353
                      (0.2692) (*,**,***)                 (0.6323)
ln[FTBT.sub.ijt]       3.1424                             -0.6049
                      (0.1581) (*,**,***)                 (0.6272)
ln[TID.sub.ijt]        0.4943                             13.7634
                      (0.3795)                           (10.34858)
[DCol.sub.ij]          4.2319                            -26.9324
                      (0.5288) (*,**,***)                (32.6742)
[DCL.sub.ij]           3.0022 (2.0602) (*)              (omitted)
[DCB.sub.ij]          (omitted)                         (omitted)
[DCSM.sub.j]          -0.0135                              2.5115
                      (0.1178)                            (1.7131) (**)
ln[EXSM.sub.ijt]      -0.0241                              0.0853
                      (0.0038) (*,**,***)                 (0.0575) (*)
ln[ER.sub.ijt]         0.4332
                      (0.3310)
[DCAHFSA.sub.jt]       0.1988                              3.4886
                      (0.1734)                            (3.2117)
[DCAHFSAFSPS.sub.jt]

DCAHFSAFTBT

_cons                 -5.4697                             -2.3873
                      (2.8260) (*,**)                    (18.7877)
[R.sup.2]
N                                                       5656
Censored obs                                            4605
Prob>chi2                                                  0.000
Estimated                                                 -0.0472
correlation
(rho)
Wald test
P>Chi (rho)                                                0.4396
Reset test
p value
Importer-                                               yes
exporter
dummies
Importer                                                yes
dummies
Exporter                                                yes
dummies
Sector                                                  No
dummies

Notes: Robust standard errors in parentheses; (*), (**), (***) denote
statistical significance at the 10%, 5% and 1% levelSource: STATA
Source: STATA

Table 6: Exports of Food and Agricultural Products (HS 0-24) from
Guyana to World and CARICOM Markets (2015)


                                                            Percent
                                                            (of total
                                            Exports to the  (all
      HS Chapter                             World Market   exports)

 0.1  Live animals; animal products             1,430,053    0.11
 0.2  Meat and edible meat offal                   44,423    0.00
 0.3  Fish and crustaceans...                  87,211,790    6.78
 0.4  Dairy produce; birds' eggs...               150,557    0.01
 0.5  Products of animal origin...                  3,850    0.00
 0.6  Live trees and other plants...                  669    0.00
 0.7  Edible vegetables and certain roots
      and tubers                                  587,714    0.05
 0.8  Edible fruit and nuts; peel
      of citrus fruit or melons                 5,218,923    0.41
 0.9  Coffee, tea, mate and spices                921,287    0.07
10    Cereals                                 212,685,998   16.54
11    Products of the milling industry...         417,903    0.03
12    Oil seeds and oleaginous fruits...          373,330    0.03
15    Animal or vegetable fats and oils...      1,308,121    0.10
16    Preparations of meat, of fish...             98,546    0.01
17    Sugars and sugar confectionery          135,490,156   10.54
18    Cocoa and cocoa preparations                 10,683    0.00
19    Preparations of cereals, flour,
      starch or milk; pastry
      cooks' products                           6,469,678    0.50
20    Preparations of vegetables,
      fruit, nuts...                            2,653,484    0.21
21    Miscellaneous edible preparations         2,004,387    0.16
22    Beverages, spirits and vinegar           38,552,873    3.00
23    Residues and waste...                     2,095,970    0.16
24    Tobacco and manufactured tobacco
      substitutes                                     646    0.00
      Total food and agriculture (US$)        497,731,041   38.71
      Total all products (US$)              1,285,827,944

                                            Percent of
                                            total (food
                                            and                Total
                                            agriculture   exports to
      HS Chapter                            exports)         CARICOM

 0.1  Live animals; animal products           0.29            13,945
 0.2  Meat and edible meat offal              0.01            43,484
 0.3  Fish and crustaceans...                17.52        24,050,077
 0.4  Dairy produce; birds' eggs...           0.03                 0
 0.5  Products of animal origin...            0.00                 0
 0.6  Live trees and other plants...          0.00           106,958
 0.7  Edible vegetables and certain roots
      and tubers                              0.12           343,923
 0.8  Edible fruit and nuts; peel
      of citrus fruit or melons               1.05           742,431
 0.9  Coffee, tea, mate and spices            0.19           517,426
10    Cereals                                42.73        55,917,180
11    Products of the milling industry...     0.08           408,336
12    Oil seeds and oleaginous fruits...      0.08           312,199
15    Animal or vegetable fats and oils...    0.26           878,315
16    Preparations of meat, of fish...        0.02            88,796
17    Sugars and sugar confectionery         27.22        36,060,000
18    Cocoa and cocoa preparations            0.00             7,236
19    Preparations of cereals, flour,
      starch or milk; pastry
      cooks' products                         1.30         4,955,018
20    Preparations of vegetables,
      fruit, nuts...                          0.53            37,234
21    Miscellaneous edible preparations       0.40         1,142,104
22    Beverages, spirits and vinegar          7.75         7,134,331
23    Residues and waste...                   0.42           702,068
24    Tobacco and manufactured tobacco
      substitutes                             0.00                 0
      Total food and agriculture (US$)      100.00       133,461,061
      Total all products (US$)                           277,675,083

                                            Percent   Percent of total
                                            of total  (food and
                                            (all      agriculture
      HS Chapter                            exports)  exports)

 0.1  Live animals; animal products          0.01       0.01
 0.2  Meat and edible meat offal             0.02       0.03
 0.3  Fish and crustaceans...                8.66      18.02
 0.4  Dairy produce; birds' eggs...          0.00       0.00
 0.5  Products of animal origin...           0.00       0.00
 0.6  Live trees and other plants...         0.04       0.08
 0.7  Edible vegetables and certain roots
      and tubers                             0.12       0.26
 0.8  Edible fruit and nuts; peel
      of citrus fruit or melons              0.27       0.56
 0.9  Coffee, tea, mate and spices           0.19       0.39
10    Cereals                               20.14      41.90
11    Products of the milling industry...    0.15       0.31
12    Oil seeds and oleaginous fruits...     0.11       0.23
15    Animal or vegetable fats and oils...   0.32       0.66
16    Preparations of meat, of fish...       0.03       0.07
17    Sugars and sugar confectionery        12.99      27.02
18    Cocoa and cocoa preparations           0.00       0.01
19    Preparations of cereals, flour,
      starch or milk; pastry
      cooks' products                        1.78       3.71
20    Preparations of vegetables,
      fruit, nuts...                         0.01       0.03
21    Miscellaneous edible preparations      0.41       0.86
22    Beverages, spirits and vinegar         2.57       5.35
23    Residues and waste...                  0.25       0.53
24    Tobacco and manufactured tobacco
      substitutes                            0.00       0.00
      Total food and agriculture (US$)      48.06     100.00
      Total all products (US$)

                                                           Percent
                                               Exports to  of total
                                                     non-  (food and
                                                  CARICOM  agriculture
      HS Chapter                                  markets  exports)

 0.1  Live animals; animal products             1,416,108    0.39
 0.2  Meat and edible meat offal                      939    0.00
 0.3  Fish and crustaceans...                  63,161,713   17.34
 0.4  Dairy produce; birds' eggs...               150,557    0.04
 0.5  Products of animal origin...                  3,850    0.00
 0.6  Live trees and other plants...             -106,289   -0.03
 0.7  Edible vegetables and certain roots
      and tubers                                  243,791    0.07
 0.8  Edible fruit and nuts; peel
      of citrus fruit or melons                 4,476,492    1.23
 0.9  Coffee, tea, mate and spices                403,861    0.11
10    Cereals                                 156,768,818   43.04
11    Products of the milling industry...           9,567    0.00
12    Oil seeds and oleaginous fruits...           61,131    0.02
15    Animal or vegetable fats and oils...        429,806    0.12
16    Preparations of meat, of fish...              9,750    0.00
17    Sugars and sugar confectionery           99,430,156   27.30
18    Cocoa and cocoa preparations                  3,447    0.00
19    Preparations of cereals, flour,
      starch or milk; pastry
      cooks' products                           1,514,660    0.42
20    Preparations of vegetables,
      fruit, nuts...                            2,616,250    0.72
21    Miscellaneous edible preparations           862,283    0.24
22    Beverages, spirits and vinegar           31,418,542    8.63
23    Residues and waste...                     1,393,902    0.38
24    Tobacco and manufactured tobacco
      substitutes                                     646    0.00
      Total food and agriculture (US$)        364,269,980  100.00
      Total all products (US$)              1,008,152,861

Source: Author based on UNCOMTRADE


?Las Regulaciones de MSF y OTC Inhiben las Exportaciones de Alimentos y Agriculture de Guyana a los Mercados de CARICOM?

Dianna Da Silva-Glasgow y Roger Hosein

El marco del modelo de gravedad se utiliza para evaluar el impacto de la incidencia de las reglamentaciones sanitarias y fitosanitarias (MSF) y tecnicas (OTC) en las exportaciones de productos agroalimentarios de Guyana a mercados seleccionados de la Comunidad del Caribe, incluidos Barbados, Trinidad y Tobago, Jamaica, Suriname, Granada, Dominica y Antigua y Barbuda; asi como el intento de coordinar y fortalecer los sistemas de sanidad agropecuaria e inocuidad de los alimentos mediante la creacion de la Agenda de Sanidad Agropecuaria e Inocuidad de los Alimentos del Caribe (CAHFSA). El analisis abarca diez anos, 2005-2014. Los resultados sugieren que las reglamentaciones sobre OTC en general reducen el comercio, mientras que la incidencia de las reglamentaciones sanitarias y fitosanitarias mejora las exportaciones. CAHFSA tambien ha tenido efectos de mejora del comercio.

Palabras clave: Exportaciones, alimentos y agriculture, gravedad, Guyana, MSF

Les Reglementations SPS et OTC fFreinent-elles les Exportations de Produits Alimentaires et agricoles du Guyana vers les Marches de la CARICOM?

Le modele de gravite est utilise pour tester l'impact de l'incidence des reglementations sanitaires et phytosanitaires et des reglements techniques sur les exportations de produits alimentaires et agricoles du Guyana vers certains marches des de la communaute des Caraibes comprenant la Barbade, Trinite-et-Tobago, la Jamaique, le Suriname, la Grenade, la Dominique et Antigua-et-Barbuda; ainsi que la tentative de coordonner et de renforcer les systemes de sante et de securite sanitaire des aliments grace a la creation de l'Agence pour la sante des produits agricoles et la securite alimentaire des Caraibes (CAHFSA). L'analyse couvre dix annees, 2005-2014. Les resultats suggerent que les reglementations OTC reduisent generalement les echanges, tandis que l'incidence des reglementations SPS ameliore les exportations. La CAHFSA a egalement eu des effets d'amelioration des echanges.

Mots-cles: Exportations, alimentation et agriculture, gravite, Guyane, SPS
Table 1: HS sections included in the panel

HS        Description
Chapters
01        Live animals; animal products
02        Meat and edible meat offal
03        Fish and crustaceans etc. and crustaceans, molluscs and other
          aquatic invertebrates
04        Dairy produce; birds' eggs; natural honey; edible products of
          animal origin, not elsewhere specified or included
05        Products of animal origin, not elsewhere specified or included
06        Live trees and other plants; bulbs, roots and the like; cut
          flowers and ornamental foliage
07        Edible vegetables and certain roots and tubers
08        Edible fruit and nuts; peel of citrus fruit or melons
09        Coffee, tea, malt and spices
10        Cereals
11        Products of the milling industry; malt; starches; inulin;
          wheat gluten
12        Oil seeds and oleaginous fruits; miscellaneous grains, seeds
          and fruit; industrial or medicinal plants; straw and fodder
13        Lac; gums, resins and other vegetable saps and extracts
14        Vegetable plaiting materials; vegetable products not
          elsewhere specified or included
15        Animal or vegetable fats and oils and their cleavage
          products; prepared edible fats; animal or vegetable waxes
16        Preparations of meat, of Fish and crustaceans etc. or of
          crustaceans, molluscs or other aquatic invertebrates
17        Sugars and sugar confectionery and Sugars and sugar
          confectionery
18        Cocoa and cocoa preparations
19        Preparations of cereals, Preparations of cereals, flour,
          etc., starch or milk; pastrycooks' products
20        Preparations of vegetables, fruit, nuts or other parts of
          plants
21        Miscellaneous edible preparations
22        Beverages, spirits and vinegar
23        Residues and waste from the food industries; prepared animal
          fodder
24        Tobacco and manufactured tobacco substitutes

Source: Author

Table 2: Summary of variables and data sources

Dependent          Description
Variable

In[X.sub.ijt]      Exports of food and agricultural products from Guyana
                   to each trading partner at time f measured at US$'000
                   covering 24 HS chapters.
Independent
Variables
In[GDP.sub.it]     The natural log of Guyana's GDP at time t measured
                   at US$million in 2005 prices
In[GDP.sub.it]     Trading partners GDP at time t measured at
                   US$million in 2005 prices
In[Pops.sub.it]    Population of Guyana in millions
In[Pops.sub.it]    Population of trading partners in millions
[DCol.sub.ij]      Dummy to capture Guyana and each partner have a
                   common coloniser
[DCL.sub.ij]       Dummy to capture if Guyana and its trading partners
                   share the same official language.
[DCB.sub.ij]       Dummy to capture if Guyana and its trading partners
                   share the same border.
1n[DST.sub.ij]     The natural log of the distance between the capital
                   of Guyana and the capital of each of its export
                   markets
1n[ER.sub.ijt]     The natural log of market exchange rates between
                   trading partner's currency and the US dollar.
[DCSM.sub.j]       Dummy to capture if a trading partner has signed on
                   to the Single Market.
[DCAHFSA.sub.jt]   Dummy to represent the establishment of the
                   Caribbean Agriculture Health and Food Safety
                   Authority. It takes a value of 1
1n[EXSM.sub.ijt]   The natural log of the export specialisation index

1n[TID.sub.ijt]    The natural log time to import measured in days
1n[FSPS.sub.ijt]   Frequency index for SPS regulations

1n[FFTBT.sub.ijt]

Dependent          Data Source
Variable

In[X.sub.ijt]      United Nations Commodity Trade
                   Statistics Database (UNCOMTRADE)

Independent
Variables
In[GDP.sub.it]     United Nations National Accounts Main
                   Aggregate Database
In[GDP.sub.it]

In[Pops.sub.it]    United Nations National Accounts Main
In[Pops.sub.it]    Aggregate Database
[DCol.sub.ij]      CEPII Database

[DCL.sub.ij]

[DCB.sub.ij]

1n[DST.sub.ij]


1n[ER.sub.ijt]     United Nations National Accounts Main
                   Aggregate Database
[DCSM.sub.j]       CARICOM Secretariat

[DCAHFSA.sub.jt]


1n[EXSM.sub.ijt]   United Nations Commodity Trade
                   Statistics Database (UNCOMTRADE)
1n[TID.sub.ijt]    World Bank Doing Business reports
1n[FSPS.sub.ijt]   International Trade Centre Market Access MAP
1n[FFTBT.sub.ijt]  World Bank, World Integrated Trade Statistics
                   database

Source: Authors

Table 3: Descriptive Statistics

Variable          Obs   Mean          Std. Dev.     Min

[X.sub.ijt]       5656    64.55168     826.7124        0
[Pop.sub.it]      5656     0.752592      0.008074      0.742495
[Pop.sub.jt]      5656     0.730663      0.914747      0.070542
[GDP.sub.-jt]     5656  5539.268      6363.233       370.415
[GDP.sub.ijt]     5656  1586.199       219.2363     1315.44
[DST.sub.ij]      5656     0.992147      0.624037      0.339601
[DCol.sub.ij]     5656     0.1426803     0.3497775     0
[DCL.sub.ij]      5656     0.857143      0.349958      0
[DCB.sub.ij]      5656     0.142857      0.349958      0
[DCSM.sub.j]      5656     0.747525      0.434471      0
[DCAHFSA.sub.ja]  5656     0.5           0.500044      0
[EXSM.sub.ijt]    5656    39.31902    1415.826         0
[FSPS.sub.ijt]    5656     0.119068      0.3231        0
[FTBT.sub.ijt]    5656     0.143071      0.349334      0
[TID.sub.ijt]     5656    16.99947       5.161666      8
[ER.sub.ijt]      5656    14.63271      28.7642        2

Variable           Max

[X.sub.ijt]        28,142.68
[Pop.sub.it]            0.763893
[Pop.sub.jt]            2.783301
[GDP.sub.-jt]      19,665.9
[GDP.sub.ijt]        1891.78
[DST.sub.ij]            2.375058
[DCol.sub.ij]           1
[DCL.sub.ij]            1
[DCB.sub.ij]            1
[DCSM.sub.j]            1
[DCAHFSA.sub.ja]        1
[EXSM.sub.ijt]    101,234
[FSPS.sub.ijt]          1
[FTBT.sub.ijt]          1
[TID.sub.ijt]          26
[ER.sub.ijt]          110.9345

Source: STATA

Table 4: Fixed-effects (within) regression estimates Fixed-effects
(within) regression model

Dependent variable:    [lnX.sub.ijt]

[lnGDP.sub.ijt]          45.9104
                         (7.6236) (*,**,***)
[lnGDP.sub.jt]            3.5275
                         (0.6141) (*,**,***)
[lnPop.sub.jt]           (omitted)
[lnPop.sub.it]           (omitted)
[lnDST.sub.ij]           (omitted)
[lnFSPS.sub.ijt]         -4.1151
                         (1.9094) (*)
[lnFTBT.sub.ijt]         (omitted)
[lnTID.sub.ijt]           4.1232
                         (2.2278) (*)
[DCol.sub.ij]            (omitted)
[DCL.sub.ij]             (omitted)
[DCB.sub.ij]             (omitted)
[DCSM.sub.j]             20.5923
                         (3.2025) (*,**,***)
[lnEXSM.sub.ijt]          0.3483
                         (0.1375) (*)
[DCAHFSA.sub.jI]         (omitted)
_cons                  -369.2317
                        (64.6382) (*,**,***)
[R.sup.2]                 0.19
sigma_u                  11.83058
sigma e                   1.8345
N                       465
F                         0.0000
Rho                       0.98
Importer-time dummies   yes
Exporter-time dummies   yes
Exporter- importer      yes
Sector dummies          Yes
1 Reset test p value      0.3883

Notes: Robust standard errors in parentheses; (*), (**), (***) denote
statistical significance at the 10, 5%, and 1% level
Source: STATA
COPYRIGHT 2018 University of the West Indies, Sir Arthur Lewis Institute of Social and Economic Studies
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Article Details
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Title Annotation:sanitary and phytosanitary; technical barriers to trade
Author:Silva-Glasgow, Dianna Da; Hosein, Roger
Publication:Social and Economic Studies
Article Type:Report
Geographic Code:3GUYA
Date:Jun 1, 2018
Words:13215
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