Effects of non-tariff protectionism in Ukraine: sector aspects by Veronika Movchan1, Volodymyr Shportyuk2 Prepared for the Tenth Annual Conference of the European Trade Study Group (ETSG) September 11-13, 2008, Warsaw, Poland Keywords: non-tariff measures, technical barriers to trade, sanitary and phytosanitary measures, Ukraine, protectionism JEL Classification: F15
Draft as of September 2008
Abstract The purpose of the study is to estimate sector impacts of the non-tariff protectionism in Ukraine. Recently the attention to non-tariff protectionism has considerably increased as Ukraine reduced its tariff barriers after becoming the WTO member. Surveys show that the most expensive and burdensome are procedures associated with standards compliance. Though, it remains to be studied what sectors are the most affected by non-tariff protectionism, and whether this policy has a significant trade distortion effects. In this paper we focus on one special aspect of non-tariff protectionism, namely the impact of various mandatory standards on trade flows. The main hypothesis is that the sign of the NTMs variable would reflect the ‘dominant’ nature of the NTMs. In other words, the statistically significant negative sign of the NTM index would mean that trade-distorting effect of the NTM dominates, while the statistically significant positive sign would indicate that information factor is of higher importance.
1 2
Institute for Economic Research and Policy Consulting (Ukraine). E-mail:
[email protected]
National University “Kyiv-Mohyla Academy” (Ukraine), Kyiv School of Economics (Ukraine). E-mail:
[email protected]
Introduction With the reduction of tariffs and quantitative barriers to trade, more and more attention is paid to other types of regulations applied to international trade flows that are broadly defined as non-tariff measures (NTMs). This term replaced the previously widely accepted term ‘nontariff barriers’ (NTBs) (see, e.g., Baldwin, 1970; Walter, 1972; Mayer and Gevel, 1973; Deardorff & Stern, 1997) to emphasize the dual nature of the regulation as discussed in Laird & Yeats (1990). On the one hand, the NTMs could be discriminative against imports being a trade-restrictive measure like defined by Baldwin (1970), Hillman (1991), and others. On the other, the NTMs could be welfare-improving providing consumers with additional information that they cannot find themselves, and thus allowing overcoming imperfect /asymmetric information problem (see, e.g., Bureau et al., 1998, 2001; Movchan, 1999, Disdier et al., 2008). This dual nature of the NTMs is typical for sanitary and phyto-sanitary measures (SPS), and technical barriers to trade (TBT). In Ukraine the compliance with the TBT regulations (so called mandatory third party certification) is rather cumbersome. According to IFC Study of Technical Regulation System in Ukraine (IFC, 2008), almost one third of industrial enterprises state that their products are subject to mandatory certification. The preparation of required documents takes 13 days on average per one certificate, and waiting time is 30 days on average. The IFC estimated foregone profits in manufacturing due to mandatory certification in Ukraine at USD 47.2 bn in 2006. The IER survey of manufacturing firms3 showed that 40% of managers consider mandatory certification as the major or significant obstacle for imports. Ukraine has joined the WTO in May 2008 after more than fourteen years of negotiations. Alongside with the reduction and binding of tariffs, the country committed to adhere the Agreements on SPS and TBT. In particular, Ukraine bound itself to give priority to international standards over regional and other national ones, and to base technical regulations on the relevant international standards by the end of 2011.4 Also, the list of products subject to mandatory certification is to be reduced.
3
Kuziakiv, O. Report of the Results of Survey “Managers Assessment of the Non-Tariff Barriers to Imports and Exports”. IER, 2004 4 http://www.wto.org/english/news_e/pres08_e/pr511_e.htm
2
The purpose of this paper is to study the sector impact of the existing system of SPS and TBT measures in Ukraine, answering two questions: first, in what sectors do these measures significantly influence trade flows? Second, what is the direction of impact of these measures? Answering the questions allows better understanding of the consequences of Ukraine’s WTO membership. Moreover, it will inform government about regulations that should be addressed in the first place in the process of harmonizing the current system with the WTO commitments. The rest of the paper is as follows. In Section 2 we describe the related empirical literature on the topic. Section 3 contains the discussion of data, followed by methodology of estimation (Section 4) and results (Section 5). Section 6 concludes. 2
Related Empirical Literature
The academic debate regarding the NTMs is centred around several important topics: definition, quantification, and determination of the impact. Several researchers as Baldwin (1970), Walter (1972), Mayer & Gevel (1973), and Deardorff & Stern (1997) provided their definitions of this phenomenon. Moreover, international organisations like UNCTAD and GATT/WTO contributed to formulation of the term. Careful analysis of these studies as well as the nature of NTMs themselves allows formulating the following definition: “NTMs are measures, other than tariffs, that are tightly connected with state (administrative) activity and influence prices, quantity, structure and/or direction of international flows of goods and services as well as resources used to produce these goods and services.” (Movchan, 2002). The key aspects here are that the NTMs are the products of state (not private) activity, and that we cannot associate NTMs with welfare losses by definition as it is done by e.g. Baldwin (1970), and Mayer & Gevel (1973). The issue of measurement (quantification) is more complicated. There are several generally accepted methods of non-tariff barriers measurement: frequency (inventory) measures, pricechange measures, quantity measures (Deardorff & Stern, 1997; Laird & Yeats, 1990), and trade restrictiveness indices (Anderson & Neary, 1994, 1999). Among this variety, inventory measures are the most easy to collect and estimate. For instance, the frequency ratio is a share of commodity lines subject to at least one NTM in total number of lines for the respective group of trade nomenclature (Laird & Yeats, 1990). Such indices could be used for the preliminary analysis of the NTMs structure in the economy. Although they cannot be directly
3
plugged in the model to estimate their welfare effects, these measures are widely used in gravity-type studies. Yet other way to quantify NTMs are price-change measures based on the determination of price wedge resulted from the introduction of the NTMs. Unfortunately, significant problems with collecting correct prices and stripping them out of NTM-non-related information usually precludes the use of these measures in macro-level researches (Deardorff & Stern, 1997; Laird & Yeats, 1990; Behgin & Bureau, 2001). The most promising approach seems to be the quantity measures as discussed in Deardorff & Stern (1997) and Laird & Yeats (1990). This approach, in turn, incorporates several empirical methods. First, there could be an estimate of the NTMs as residual in gravity-type trade models. Recently, this approach was used to quantify the NTMs (regulatory costs of trade) in Ukraine by CEPS (2006). The logical extension of this approach is to include variables reflecting NTMs and test their effects (Laird & Yeats, 1990). The most recent and comprehensive study based on this approach is the paper by Kee et al. (2006). Finally, it is the issue of determining the impact of the NTMs. The most straightforward way is to do so within the framework of econometric models (see, for instance, Kee et al., 2006; Harrigan, 1993; Lee & Swagel, 1997; Disdier et al., 2008). Here, key question is the sign of relation. Traditionally, trade regulations are treated as trade distractions as it is reflected even in definitions discussed above, Indeed, studies conform this. For instance, studying developed countries’ trade control measures that Latin America countries faced, Leamer (1990) found that both tariffs and the NTM have a significant import-reducing effect. Harrigan (1993) showed that tariffs and quantity barriers have significant negative impact on bilateral trade in OECD countries. Lee and Swagel (1997) pooled across industries, studying average effects of trade barriers on imports. They conclusion was that trade barriers hinder imports. Kee et al. (2006) also found significant negative impact of core NTMs on trade flows. At the same time, Disdier et al. (2008) showed that TBT and SPS measures could have both trade-distorting and trade-improving effects. Also, Moenius (2004) demonstrated that country-specific standards could promote manufacturing trade resolving asymmetric information problem. Beghin & Bureau (2001) also discussed this issue and stated that regulation brings information and therefore allows overcoming asymmetric information problem.
4
3
Data
This paper relies on database assembled for paper Movchan (2004). Import flow are taken from the UN Commodity Trade (COMTRADE) database, while the industrial output is based on the OECD Industrial Survey. In the study we consider eight aggregated manufacturing sectors, namely food industry, light industry, production of wood and paper and products thereof, chemical and petrochemical industry, production of other non-metallic mineral products, production of metal and products thereof, machine building, and production of other manufacturing goods. The compound measure of transportation costs that includes both the distance and the weight is applied as discussed in Movchan (2004). It is constructed by multiplying the distance between capitals of trading partner on the weight-to-value ratio: J
∑W
ijk
τ ijk =
j J
∑m
ijk
j
× Dist jk
,
where
τ ijk
=
a proxy for transportation costs;
Wijk
=
a weight of imports of countries j from k ;
mijk
=
a value of respective imports;
Dist jk
=
a distance between capitals of countries j and k .
The hypothesis is that heavier the goods and longer the distance, the less trade will occur. Import tariff rates are taken from the Law on Customs Tariff in Ukraine. All tariffs are transformed into ad valorem equivalents. To match industrial structure, tariffs were aggregated to the four-digit level of the ISIC classification. As already discussed in Movchan (2004), ‘third party’ weighting scheme was chosen for aggregation5. The weight is constructed on the basis of export structure of the country of origin assuming that this structure is stable for all destinations and determined by competitive advantages of the
5
Further discussion of weighting scheme see, for example, Daly & Kuwahara, 1999; Lee & Swagel, 1997; Nogues, Olechowski, & Winters, 1986; Leamer, 1990
5
country. Technically, tariff structure of importing country is matched with export structure of the country of origin, allowing assigning weights in line with potential importance of trade barriers for each partner country. The list of TBT and SPS measures covers mandatory certification, including the metrological and energy-saving controls, ecological control, sanitary, phyto-sanitary, and veterinary controls, as well as registration of medicaments and medical equipment. The inventory approach is taken to construct NTM index. In particular, it was constructed an NTM intensity (NTMI) index that shows the percentage of cases when the pre-selected NTM are actually applied to the given number of tariff lines: ⎛ N L ⎜ ∑∑ NTM nl NTMI = ⎜ n =1 l =1 L⋅N ⎜ ⎜ ⎝
NTM nl
=
⎞ ⎟ ⎟ ⋅100 , ⎟ ⎟ ⎠
where
a dummy variable that takes a value of unity if the n type of the NTM
is applied to the tariff line l and zero otherwise; N
=
a total number of considered tariff lines, n = 1,..., N ;
L
=
a total numbers of considered types of the NTMs, l = 1,..., L .
Thus, the NTMI shows the share of the entire non-tariff protection capacity used by the country.6 As in the case with traditional frequency index, it is possible to add weights to the
NTMI. Here, it is used the ‘third-party’ export-structure weighting scheme, which was already discussed for tariff weighting. 4
Econometric specification
We choose the traditional gravity equation as a basic framework for our analysis. The theoretical foundation of the model are rooted in monopolistic competition model of trade developed by Helpman & Krugman (1985). Therefore, contrary to usual approach using GDP as a key component of gravity equation, we follow Lee & Swagel (1997) and Harrigan (1993), and specify the equation with sector outputs to ensure better match between exporting country production structure and import flows received in destination country. Adopting log6
By construction, the NTMI is very sensitive to the number of the NTM involved in the study. Therefore, for meaningful international comparisons it is necessary to ensure that similar sets of the NTM are involved in the comparisons.
6
form and including transportation costs alongside with tariffs and NTM, the equation looks as follows: ⎛m log ⎜ ijk ⎜ Πj ⎝
⎞ ⎛ yij ⎞ ⎟⎟ = β1i + β 2i log ⎜ ⎟ + β 3i log(1 + τ ijk ) + β 4i log(1 + κ ijk ) + β 5i log(1 + NTM ijk ) + ε ijk ⎝ yi ⎠ ⎠ (3)
where
mijk
=
imports of industry i output by country j from country k ,
Πj
=
country j ’s aggregate spending,
yik
=
output of industry i in country k ,
τ ijk
=
average tariff on industry i output by country j from country k ,
κ ijk
=
transportation costs to deliver industry i output by country j to country k ,
NTM ijk =
NTM index on industry i output by country j from country k .
Though, as discussed in Kee et al. (2006), Trefler (1993), Harrigan (1993), and Lee & Swagel (1997), such a specification bears high likelihood of endogeneity problem. Thus, we follow the procedure proposed in Kee et al. (2006), and move the tariff term to right-hand side of the equation. The import demand elasticity for Ukraine is assumed to remain fixed over sectors, and within studied time frame. Thus, the estimation equation is transformed into:
⎛m log ⎜ ijk ⎜ Πj ⎝
⎞ ⎛ yij ⎞ ⎟⎟ − β3 log(1 + τ ijk ) = β1i + β 2i log ⎜ ⎟ + β 4i log(1 + κ ijk ) + β 5i log(1 + NTM ijk ) + ε ijk , ⎝ yi ⎠ ⎠
where β 3 = 1.06 in line with estimations done by Kee et al. (2004) for Ukraine. 5
Results
In Table 2 we present our estimation results. Consistently with the a priori hypothesis, for all sectors imports is positively and significantly correlated with output of the exporting country, and negatively and significantly correlated with transportation costs measured by weight-tovalue ratio multiplied by distance. Also, the significance of the most of period dummies demonstrates that they capture some period-specific variations of Ukraine’s imports. Now let us turn to non-tariff measures impact that is in the centre of the study. As shown in Table 1, the highest level of the TBT and SPS protection is set for food industry, each
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product of which is subject to at least one non-tariff regulation (as seen from non-zero minimum of the NTM index). This sector is followed by chemical products, including pharmaceuticals. The least protected are metal production, and production of other manufacturing goods. Though, the intensity of non-tariff protection doesn’t necessarily correlate with trade-distorting effect of NTMs application.
Food industry
Textile and leather industry
Wood and wood thereof; paper and paper thereof
Chemical and petrochemical industry
Other nonmetallic mineral products
Metal production
Machine building
Other manufactured products
Table 1. Descriptive statistics for NTM index
Mean Median Maximum Minimum Std. Dev.
34.12 33.33 64.97 16.67 8.10
5.67 3.19 26.92 0.00 6.54
8.38 5.24 30.34 0.00 8.37
13.35 11.49 33.33 0.00 10.01
5.01 3.93 16.67 0.00 4.36
1.40 0.97 7.45 0.00 1.49
4.72 3.35 21.76 0.00 4.95
2.15 0.42 11.12 0.00 2.81
Observations Cross sections
1893 320
1320 220
960 160
1434 239
600 100
240 40
2760 460
480 80
Source: NTM database for Ukraine, UN ComTrade, authors’ estimates
According to our results, the application of TBT and SPS measures have significant negative impact on food imports, on imports of other non-metallic mineral products (construction materials), and on metal production. Thus, two sectors that face the highest and the lowest protection suffers the most from the application of the TBT and SPS measures. The negative sign obtained for food industry in general corresponds to product-level results by Disdier et al. (2008), and Moenius (2004). In particular, Disdier et al. (2008) showed that SPS and TBT measures have significant negative impact on nine food-industry sectors out of fifteen sectors with significant sign. At the same time, in line with our assumptions regarding the changing sign of the TBT and SPS measures depending on the dominant nature of their impact, our estimations (all specifications) showed significant positive impact of the NTMs on imports of products belonging to chemical and petrochemical industry, and textile and leather industry. Moreover, the second model specification shows that TBT and SPS requirements have trade-stimulating impact on imports of machine building products, and on wood and paper and products thereof. 8
Table 2. Influence of NTMs – Sector Approach Food industry
Textile and leather industry
Wood and wood thereof; paper and paper thereof (A) (B) (C)
Chemical and petrochemical industry (A) (B) (C)
(A)
(B)
(C)
(A)
(B)
(C)
-9.43a (0.89) 0.41a (0.08) -0.23b (0.10) -0.51b (0.20) -0.28 (0.23) -0.49c (0.25) -0.46c (0.26)
-7.51a (1.15) 0.41a (0.08) -0.27a (0.10) -0.40c (0.21) -0.02 (0.23) -0.25 (0.26) -0.14 (0.27) -4.67b (1.94)
-5.02a (0.61) 0.54a (0.04) -0.43a (0.05) -0.23c (0.14) 0.05 (0.14) -0.24c (0.12) -0.08 (0.14) -7.19a (1.16)
-3.52a (1.05) 0.69a (0.06) -1.12a (0.14) 0.04 (0.19) 0.46b (0.21) 0.61a (0.23) 0.65a (0.23)
-3.94a (1.06) 0.71a (0.06) -1.06a (0.14) 0.09 (0.19) 0.53a (0.20) 0.68a (0.22) 0.68a (0.23) 4.50b (2.22)
-1.13b (0.53) 0.80a (0.02) -1.42a (0.07) 0.12 (0.15) 0.45a (0.14) 0.80a (0.15) 0.74a (0.16) 2.75a (0.93)
-5.91a (1.42) 0.45a (0.10) -0.77a (0.18) 0.27 (0.21) 0.66a (0.21) 0.56b (0.26) 0.60b (0.26)
-5.37a (1.56) 0.47a (0.11) -0.84a (0.21) 0.27 (0.22) 0.70a (0.21) 0.62b (0.26) 0.67b (0.27) 1.08 (2.60)
-3.51a (0.60) 0.53a (0.03) -1.08a (0.08) 0.17 (0.13) 0.70a (0.14) 0.81a (0.13) 0.57a (0.14) 3.02a (0.78)
-6.25a (0.86) 0.51a (0.08) -0.52a (0.11) 0.09 (0.17) 0.57a (0.17) 0.60a (0.19) 0.61a (0.19)
-4.41a (0.81) 0.63a (0.70) -0.91a (0.11) 0.03 (0.15) 0.59a (0.16) 0.68a (0.19) 0.58a (0.18) 11.53a (1.73)
-3.24a (0.20) 0.69a (0.02) -1.09a (0.03) 0.07 (0.10) 0.69a (0.10) 0.71a (0.10) 0.52a (0.11) 14.71a (0.64)
No. Obs.
648
647
647
635
635
635
529
529
529
634
630
630
Adjusted R2
0.66
0.63
0.99
0.81
0.81
0.97
0.78
0.78
0.99
0.80
0.81
0.99
Const Log Output Log Weight & Distance D1 D2 D3 D4 Log NTM
Note: Scenarios in columns (A) and (B) are run envisaging correction for period heteroskedasticity and for general correlation of observations within a given cross-section, and and scenario in column (C) is run using specification with GLS cross-section weights that assumes the presence of cross-section heteroskedasticity. Standard errors are in parentheses with a, b, and c denoting significance at 1%, 5%, and 10% levels respectively.
Table 2 (cont.) Other non-metallic mineral products (A) (B) (C)
Metal production (A)
(B)
(C)
(A)
(B)
(C)
(A)
(B)
(C)
-4.93a (1.32) 0.44a (0.09) -0.82a (0.15) 0.30 (0.29) 0.65a (0.24) 0.61b (0.29) 0.48c (0.26)
-5.80a (1.35) 0.39a (0.09) -0.70a (0.15) 0.35 (0.29) 0.97a (0.27) 0.88a (0.31) 0.77a (0.29) -7.50b (3.18)
-5.20a (0.45) 0.42a (0.02) -0.76a (0.06) 0.34a (0.11) 0.93a (0.12) 0.93a (0.11) 0.76a (0.12) -7.31a (1.76)
3.66c (1.90) 0.65a (0.14) -1.96a (0.25) -0.58c (0.29) 0.60 (0.37) 0.35 (0.34) 1.08a (0.40)
6.57a (2.07) 0.65a (0.13) -2.31a (0.27) -0.60c (0.30) 0.70c (0.36) 0.48 (0.36) 1.33a (0.40) -28.69b (13.02)
5.18a (1.63) 0.61a (0.08) -2.11a (0.21) -0.48c (0.24) 0.85a (0.24) 0.59b (0.26) 1.34a (0.27) -41.16a (11.42)
-6.58a (0.55) 0.71a (0.05) -0.69a (0.09) 0.32b (0.15) 0.93a (0.15) 0.81a (0.16) 0.82a (0.16)
-6.56a (0.55) 0.72a (0.05) -0.69a (0.08) 0.33b (0.15) 0.94a (0.15) 0.84a (0.17) 0.83a (0.17) 1.56 (1.90)
-5.81a (0.17) 0.76a (0.02) -0.81a (0.03) 0.36a (0.07) 0.95a (0.07) 0.94a (0.07) 0.85a (0.07) 4.71a (0.68)
-4.97b (2.04) 0.71a (0.08) -0.94a (0.30) -0.10 (0.28) 0.52c (0.29) 0.43 (0.33) 0.29 (0.30)
-4.90b (2.04) 0.72a (0.08) -0.94a (0.29) -0.10 (0.28) 0.52c (0.29) 0.48 (0.33) 0.33 (0.30) 0.63 (6.56)
-3.12a (0.79) 0.73a (0.03) -1.16a (0.11) -0.39b (0.19) 0.23 (0.18) 0.33c (0.18) 0.15 (0.19) 2.63 (2.81)
No. Obs.
285
285
285
94
94
94
1210
1204
1204
151
151
151
Adjusted R2
0.85
0.85
0.99
0.96
0.94
0.98
0.70
0.70
0.99
0.91
0.90
0.99
Const Log Output Log Weight & Distance D1 D2 D3 D4 Log NTM
Machine building
Other manufactured products
Note: Scenarios in columns (A) and (B) are run envisaging correction for period heteroskedasticity and for general correlation of observations within a given cross-section, and and scenario in column (C) is run using specification with GLS cross-section weights that assumes the presence of cross-section heteroskedasticity. Standard errors are in parentheses with a, b, and c denoting significance at 1%, 5%, and 10% levels respectively.
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6
Conclusion
Our short study confirmed that the impact of TBT and SPS measures could be both tradedistorting and
trade-facilitating. As shown, in Ukraine only three out of eight studied
manufacturing sectors (at high level of aggregation) face such severe TBT and SPS measures that they significantly distort trade. The highest protection is set for food products, and here the government intervention should be the most prompt and, at the same time, well-balanced. The harmonisation of the technical requirements and sanitary and phyto-sanitary standards with the international as Ukraine committed to the WTO should have a positive impact on trade flows into the country. References
Anderson J., Neary P. (1994) Measuring the Restrictiveness of Trade Policy. The World Bank Economic Review. Vol. 8, No. 2. Anderson J., Neary P. (1999) The Mercantilist Index of Trade Policy. NBER Working Paper #6870. Andriamananjara, Soamiely, et al. (2004) The Effects of Non-Tariff Measures on Prices, Trade, and Welfare: CGE Implementation of Policy-Based Price Comparisons. United States International Trade Commission, Office of Economics Working Paper 2004-04-A Baldwin R. (1970). Non-tariff Distortions of International Trade. The Brookings Institution Washington, D.C. Behgin, J., Bureau, J.-C. (2001) “Quatitative policy analysis of sanitary, phytosanitary, and technical barriers to trade”. Economie Internationale, 2001/3, No. 87. p.107-30 CEPS (2006). The Prospect of Deep Free Trade Between the European Union and Ukraine. Report prepared by Centre for European Policy Studies (CEPS), Brussels; Institut fur Weltwirtschaft (IFW), Kiel; International Centre for Policy Studies (ICPS), Kyiv. Chen, N. (2004) “Intra-National versus International Trade in the European Union: Why Do National Borders Matter?” Journal of International Economics 63, 93-118 Daly, M, Kuwahara, H. (1999) “Tariffs and Non-Tariff Barriers To Trade in Hungary. The impact of the Uruguay Round and the EU accession.” Economics of Transition. Vol. 7, 71739 Deardorff А., Stern R. (1997) Measurement of Non-Tariff Barriers. OЕCD Economic Department Working Paper No. 179, 117 р. Disdier, A.-C., Fontagne, L., Mimouni, M. (2008) “The impact of regulations on agricultural trade: evidence from the SPS and TBT agreements” American Journal of Agrarian Economics, 90(2), p. 336-350
Harrigan, J. (1993) “OECD Imports and Trade Barriers in 1983” Journal of International Economics 35, 91-111 Helpman, E., Krugman, P. (1985) Market Structure and Foreign Trade: Increasing returns, imperfect competition, and the international economy. MIT Press, Cambridge, MA Hillman, J.S. (1991) Technical Barriers to Agricultural Trade, Westview Press, Boulder IFC, 2008. Technical Regulation in Ukraine: how to ensure economic development and consumers’ rights protection. International Financial Corporation (in Ukrainian) Kee, Hiau Looi, Alessandro Nicita and Marcelo Olarreaga (2004), “Import demand elasticities and trade distortions”, World Bank Policy Research Working Paper No. 3452 Kee, Hiau Looi, Alessandro Nicita and Marcelo Olarreaga (2006) “Estimating Trade Restrictiveness Indices” World Bank Policy Research Working Paper, No. 3840 Laird S., Yeats A. (1990) Quantitative Methods for Trade-Barrier Analysis. The Macmillan Press Ltd., 307 р. Leamer E. (1990) “Latin America as a Target of Trade Barriers Erected by the Major Developed Countries in 1983” Journal of Development Economics 32, 337-68 Lee, J.-W., Swagel, P. (1997) “Trade Barriers and Trade Flows Across Countries and Industries” The Review of Economics and Statistics, Vol. 79, No. 3, 372-82 Mayer L., Gevel A. (1973) Non-Tariff Distortions in International Trade: A Methodological Review. //Prospects for Eliminating Non-Tariff Distortions. Edited by A. Scarlanda. A.W. Sjithoff/Leiden, 1973. – 316 p. Moenius, J. (2004) “Information versus product adaptation: the role of standards in trade” Working paper, International Business and Markets Research Centre Movchan V. (1999) Welfare Costs of Certification. EERC conference proceedings Movchan, V. (2002) “Non-Tariff Barriers in Ukraine’s Foreign Trade: Did Liberalization Occur?” Prepared for the EERC Annual Conference “Economic Transition of Ukraine”, Kiev, Ukraine, May 15-16, 2002 Movchan, V. (2004) “Influence of Tariffs and Non-Tariff Barriers on Ukraine’s Imports: Would the WTO Membership Make Change?” Prepared for the Sixth Annual Conference of the European Trade Study Group (ETSG), September 9 - 11, Nottingham, UK Nogues, J., Olechowski, A., Winters, A. “The Extent of Nontariff Barriers to Industrial Countries Imports” The World Bank Economic Review, Vol. 1, No. 1, 181-99 Trefler, D. (1993) Trade liberalization and the theory of endogenous protection: an econometric study of US import policy. Journal of Political Economy 101, pp.138-160 Walter, I. (1972) “Non-Tariff Protection Among Industrial Countries: Some Preliminary Evidence.” Economia Internazionale, Vol. 25, 335-54
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