TRADE LIBERALIZATION IN MOROCCO: QUALITY EVOLUTION OF

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1 TRADE LIBERALIZATION IN MOROCCO: QUALITY EVOLUTION OF EXPORTS DKHISSI Kawtar * Abstract This article examines the effect of trade liberalization on the quality of exported goods and the international insertion of Morocco. We use a panel database for Morocco's exports to 81 countries from 1967 to 2014, through a gravity model that includes three dimensions: partner, industry and time. Exports are classified by high-technology industries, medium-hightechnology industries, medium-low-technology industries, low-technology industries and nonmanufactured. The results shed light on a possible positive impact of free trade agreements on technology products. KEYWORDS: Endogenous quality; Trade liberalization; Product quality; technological intensity; international integration; Morocco's exports. JEL CLASSIFICATION: F14, O33, O55, L15. * PhD student in Economics at Paris 13 University, Sorbonne Paris Cité, 99 Avenue Jean Baptiste Clément, Villetaneuse, France, CEPN (Center for Economics of the University of Paris North), UMR CNRS 7234, dkawtar@gmail.com, +33 ( 0)

2 1. INTRODUCTION Theoretical research of technology benefits on economic development activity was initiated by Krugman (1979) and has been enriched since then by several authors (Romer 1986; Romer 1990; Grossman and Helpman 1990; Grossman and Helpman 1994). Additionally, technology has emerged as one of the most important factors in explaining international trade flows (Linder 1961). A large number of studies provide models in which industrialized countries were able to regain their advantage over south countries with lower labor costs due to the scale of quality and sophisticated products (Grossman and Helpman 1991b; 1991c; 1991a). The industrialized countries were able to export their goods at higher prices through relative factor endowments and production techniques. Moreover, there is a positive correlation between export value and exporters' per capita income: countries with higher productivity that produce quality goods display both higher export prices and higher wages generating a relationship between the prices of traded goods and the income per capita of the exporter. Also, export unit values increase systematically with per capita income of the exporting country and the relative endowments of physical and human capital (Hummels and Klenow 2005; Schott 2004; Flam and Helpman 1987). The heterogeneous business models suggest that trade liberalization and low business costs encourage companies to invest in technology and to export (Vannoorenberghe, 2009; Long, Raff, and Stahler 2011; Costantini and Melitz 2008; Ederington and McCalman 2008). It is, however, less clear how trade liberalization affects the quality produced nationally, taking into account that product quality is not an exogenous factor. Countries invest in research and development (R&D) to improve their product quality and this investment may have an impact on their decisions regarding trade openness. Such a relationship between quality and trade liberalization in developing countries is a subject deserves more attention than has so far attracted. Under this framework, the current article focuses on examining the role of trade openness on the quality of exported goods and the international insertion of Morocco. We use a panel database for Morocco's exports to 81 countries from 1967 to 2014, through a gravity model. The dependent variable is the bilateral trade, by technological level. The series are classified by technological level: high-technology industries, medium-high-technology 2

3 industries, medium-low-technology industries, low-technology industries and nonmanufactured as in OECD (2005) and Hatzichronoglou (1997), and are further grouped by level, partner, product and year as in Fontagne, Freudenberg, and Péridy (1997). These industry variables are introduced to control the market structures at the industry level. Each product category will be analyzed by value, in dollars. Then each category is tested separately or jointly to measure the impact of free trade agreements in each case. We incorporate variables related to partner countries (by size and income) and industries (differenced by quality), because the theoretical arguments suggest that these two families of variables play an important role in explaining the evolution of exports. We first present several studies that distinguish the technological products and difficulties to identify the technology content in industry, as well as the choice of thresholds which define the different groups of industries. We then explain the model studied and conclude with the interpretation of the results. 2. THEORETICAL ANALYSIS OF QUALITY The new theories of international trade enabled variety and quality to be used in international trade analysis. It is important to distinguish two major categories of products, because they allow subsequently specify the nature of quality products. On the one hand, horizontally differentiated products are available in different perfectly substitutable varieties and differ only by secondary characteristics (color, design, etc.), but they have the same quality and are sold at the same price. The presence of such products in the market, horizontally differentiated, allows; consumers to satisfy their taste for variety, or move closer to their ideal variety (P. Krugman 1980; Lancaster 1979). On the other hand, products can be differentiated by quality and price. In this case, we deal with vertical differentiation. Quality differences result from spending on R&D, human capital endowments or advertising. Linder (1961), advances explanations of differences in relative prices rejecting the neoclassical model. He distinguishes comparative advantages in trade of manufactured goods between industrialized countries which cannot be explained by the analysis in terms of factor endowments and proportions. To our knowledge, his on the first theory explaining the effects of quality differences on the direction of trade. Hallak (2006), confirms the theoretical prediction found by Linder (1961), in which richer countries tend to import relatively more from countries that produce high-quality goods, by an empirical framework that estimates the 3

4 empirical relevance of this prediction. He identifies the effect of quality operating on the demand side through the relationship between per capita income and aggregate demand for quality. Vernon (1966), developed the vertical differentiation model while taking into account technological differentiation, and posits that product differentiation plays a fundamental role as a source of trade. Falvey (1981) and Falvey and Kierzkowski (1987) explain that the differentiated quality products require more capital, so that countries with more equity will produce superior products. Research on trade of high technology products are often used to analyze competitiveness. They seek to identify products and industries that are more technologically intensive. The definition of high-tech products has been empirically tested using indicators such as research and development spending as share of value added, the number of, the number of scientific and skilled personnel in employees of each branch (Boretsky 1974; Boretsky 1982). Kelly (1974; 1977) analyzes the effect of the share spending for R&D on exports by product to distinguish the disaggregated level of intensity products. Based on these results, Aho and Rosen (1980) establish a correspondence between SIC (Standard Industrial Classification) and ISIC (International Standard Industrial Classification). Davis (1982), clarifies the definition of research and development expenditure by distinguishing those concerning the actual development of product and those concerning manufacture. Other studies include a wider range of products such as arming or automobile industry (CEE 1983). Kremp and Larroumets, (1985) provided a methodological definition of high-tech products, they called classifications "majority" where they have included all of the studies mentioned previously. Aiginger (1997), made the first empirical attempt at considering both dimensions (price and quality) jointly, based on a cross analysis of unit values (proxies of relative prices) and trade balances expressed in quantity. A year later (1998), the same author compared "unit value" concepts of product and quality in order to show the strong correlation between such denominations and gross national product". In order to distinguish the relationship unit value and partial productivity, he defines the unit value as an output price multiplied by a Cobb-Douglas production function. By integrating two types of material (material used in the final product, and not embodied material or waste). The indicator is more "quality-oriented" as a traditional indicator of 4

5 productivity because the numerator includes all elements of quality, such as better assessment of consumer, sophistication in production and in service. Like any other measure of partial productivity, the unit value increases, if "other inputs" increase per unit of weight, with more added value in production process, better added labor or capital. A country with a higher unit value will somehow provide more quality, perhaps because of its ability to sell the same product at a higher price (marketing, advertising, quality), or by specializing in a more expensive product segment. By adding additional steps in the production process, unit values also increase. The potential to disaggregate unit values for the regional markets product helps to create exceptions. Differences in quality may be represented by differences in price (unit values), at level of product similarity. Similar products (or horizontally differentiated) represent products whose unit values are close (in a year) or if exports' and imports' unit values differ by less than 15%, otherwise products are considered as vertically differentiated. For instance, Abd- El-Rahman (1991), compared unit values for each NIMEXE 2, which are 6 digits element (about six thousand for manufactured products), ordered by origin and destination. He assumes that unit values are comparable for a given product is synonymous with a comparable quality of exported and imported products. He keeps a maximum difference of 15 percent between the unit value for trade flows, that he considers "bilateral trade" of similar products. Greenaway, Hine and Milner (1994) used the same threshold of 15% in order to distinguish between similar products and vertically differentiated products, and to analyze intra-industry trade in its horizontal and vertical components in the UK. Fontagne and Freudenberg (1997), were based on products' distinction to conclude that various countries will engage in intra-industry trade in vertical differentiation and similar countries in intraindustry trade in horizontal differentiation. Based on this method, a new trade classification brings up products traded by a segment specialization, compared to a European Community standard. Thus, by comparing unit value of flows studied to the EU average, Freudenberg and Muller (1992), Fontagne Freudenberg and Unal-Kesenci (1995), then Fontagne and Freudenberg (1997) distinguish out 2 Nomenclature of Goods for the External Trade Statistics of the Community and Statistics of Trade between Member States (NIMEX), is a description system and goods coding at the European Union as a reference for determining the customs rights rate, applicable to a commodity. The CCT nomenclature (customs) and NIMEXE (statistics) were merged by the EEC, to create the Combined Nomenclature (CN) applicable from 1 January The CN is subject to an annual revision to January of each year 5

6 three different ranges: Up-market products (which exceed the community average by 15%), and Down-market products (which have a lower unit price than 15%). Middle-market match products whose unit value does not diverge more than 15% average unit value reference. Freudenberg and Muller (1992), highlighted the independence "type of trade" and "exchanged segments" concepts and reserved the use of "segment" term only with European exchanged segments. They used "cross-trade products vertically differentiated" to examine France's and Germany's specialization. The OECD has also classified sectors according to their technological intensity into three categories (high, medium and low technology). After using the classification used in the United States applied since 1989, a first classification based on the direct intensity criterion in R&D, weighted by sector and country was applied. With more disaggregated sectoral data and technology diffusion aspects (indirect intensity), the OECD has developed two new lists. A list by sector (SWAP) and a list by products (product approach). Also, four manufacturing groups: high, medium-high, medium-low and low technology. Thus, direct intensities indicator R&D (production technology), indirect intensity (technology used) and overall intensity (sum of direct and indirect intensity) were used. In order to maintain the consistency of embedded R&D spending, the coefficients of the input-output matrices multiplied by the direct intensities R&D were added. These measures allow us to take into account goods used as inputs technology (R&D spending) embodied in intermediate goods, as well as goods used as inputs in other industries. The thresholds adopted are based on R&D intensities calculated with respect to production and not in relation to added value 3. They can provide stability over time and median stability across countries (Hatzichronoglou 1997). Fontagné and others (1999), combined for the first time three dimensions: technological intensity, quality and the level of processing in traded goods. According to Fontagné and others (1998; 1999), trade quality (ie the cross exchange of products in the same branch of industry), but with different prices, have become dominant within the European Union (EU) since the early nineties. 3 There is a strong correlation between ranks direct intensity (production technology) and indirect intensity (use of technology). The thresholds adopted to consolidate industries in four categories, taking into account the indirect intensity. Thus industries classified in a higher category have higher median intensity to owned industries to a lower category. The industries in a group have R&D intensities higher than the lower group. 6

7 In this article we use the latter classification by technological intensity, integrated in CEPII 4 database, in order to analyze the evolution of the quality of Moroccan products. 3. GRAVITY MODEL 3.1 Theoretical foundations From a theoretical standpoint, Linnemann (1966), and Leamer levinshon (1995), reject the traditional explanations based on differences in endowments. Linnemann (1966), considers that the infamous endogenous variables depend on technology and supersede factors of production, so that trade is determined by increasing returns to scale. He adds "population" variable, or alternatively GNP per capita to capture the size of partner economies and their levels of development, hence the model is named "gravity model increased." By means of a general equilibrium model and two fundamental assumptions (Heckscher-Ohlin theory and Cobb-Douglas preferences), Anderson (1979), allows the gravity model not to be limited to intra-industry trade. He concluded that the volume of trade depends on economies sizes. This approach also includes population size as an exogenous variable. Helpman and Krugman (1985) propose a formalization of the gravity equation compatible with the Chamberlain-Heckscher-Ohlin approach and comparative costs theory. This model does not only reconcile its scope to intra-branch and inter-sectoral trade but also allows to understand why trade depend on the size of the economies concerned, in addition to factors endowments. This model was criticized because of its lack of population variables, for it seems to interpret it as a measure of factor endowments. From this theory and monopolistic competition model, Helpman (1987), confirms that trade increases with economies' size and introduces GDP per capita as a measure of factor endowments. The gravity equation measures trade barriers 5 and transport costs through the "distance" variable. The explanatory power of this variable and its geographic scope are mentioned in Learner and Levinsohn (1995) and Hummels and Levinsohn (1993). They 4 Centre for Prospective Studies and International Information 5 Artificial barriers that are political in nature and refer to: tariffs, quotas, exchange controls put in place by governments 7

8 consider the poor results of Helpman's model (1987), is due to due to its lack of the distance variable. In addition to transportation costs, Bergstrand (1989), provides a summary of all the variables, taking into account the assumptions of differences in labor costs, in factor endowments, and increasing returns to scale in an estimated generalized gravity equation at a disaggregated level. More recent developments of the gravity model, measure the impact of "border effect" in the volume of trade between the border provinces in the US and Canada. They are interested in the consequences of the countries' size as a controls variables because they are helpful in interpreting (Anderson and Wincoop 2003; McCallum 1995). This ability of the gravity equation to be derived from standard trade theory and new theories of international trade, also characterize other models that were much criticized. Deardorff (1998) considers the empirical estimates cannot account for tests tests validating these assumptions and these specifics theories. Critics by Hummels and Levinsohn (1993; 1995), suggest that the samples are homogeneous enough so as not to compromise the significance of the coefficients. Fontagné (2002), solves this problem and provides an estimate of a sample comprising both developing countries and industrialized countries using the exchange rate in PPP to assess developing countries' GNP and GNP per head to replace population in the equation. He concluded to take the interpretation of the residue with delicacy and correct if necessary. In a nutshell, the model has strong foundations and is more solid when the variable "distance" is included (Leamer and Levinsohn 1995). So the use of gravity models is possible. It remains only to consider the estimated value of the parameters and elasticities used in simulation. The model is generally expressed econometrically in the deterministic log-linear component following the standard formula: lnx ij = α 0 + α 1 lny i + α 2 lny j + α 3 lnn i + α 4 lnn j + α 5 lnd ij + u ij ; (1) Where X ijt is the volume of exports from country i to country j. N i and N j, the populations of countries i and j respectively; α 0 constant; α 1 ; α 2 ; α 3 ; α 4 ; α 5 ; the coefficients associated with variables Y i, Y j, N i, N j and D ij. u ij is the error term. 8

9 3.2 Methodology The gravity model we estimated measures the volume of bilateral trade between two countries i and j, measured here by X ij exports. As in the traditional conception of the gravity model, it will be explained by the size of economies considered through, real gross domestic product, Y jt (in current dollars) in both countries by level and per head in order to avoid heterogeneity and distance problems between i and j. Geographical distance is used as a proxy of transport cost. We use, the geodesic distance between capitals, and we use its log D ij. Dummies may help or restrict trade added namely the bilateral agreements and language diversity (if the trading countries have the same first and second official languages) to capture the effect of trade policies and linguistic diversity. The proposed equation is a log linear panel data, so as to maximize the number of observations and reduce the risk of multicollinearity, using the following specification: lnx ijt = α 0 + α 1 ln Y it + α 2 ln Y jt + α 3 lnpop it + α 4 lnpop jt + α 5 lnd ij + αz Z zij + U ij; (2) With: X ijt : represents the flow of trade from country i to country j; in year t; Y it : is the gross domestic product of country i (current dollars); Y jt : is the gross domestic product of country j (current dollars); Pop it : is the total population of country i; Pop jt : is the total population of country j; D ij : is the geodesic distance between country i and country j. Z ij dummies; Uij error term, according to a log-normal distribution. α 0 : Constant; α 1, α 2, α 3, α 4 and α 5, the coefficients associated with the explanatory variables i and j. In the empirical literature the model is not limited to estimating the corresponding parameters. Other dummies are introduced to capture specific effects of economic policies. The dummies commonly tested are: common border, common language "French", "Arab" or both, the common history through colonial ties and preferential arrangements to capture the influence of these factors on the observed trade flows. In our model the "border" variable is not significant, which is why we have withdrawn it from specifications. Colonial ties are represented by "France" and "Spain" who put Morocco in protectorate during the period of We introduce dummies products by technological intensity: high-technology industries, medium-high-technology industries, medium-low-technology industries, lowtechnology industries and non-manufactured. 9

10 Our specification of the gravity model is: lnx ijt = α 0 + α 1 ln Y it + α 2 ln Y jt + α 3 lnpop it + α 4 lnpop jt + α 5 lnd ij + α 6 lncolonial jt + α 7 ln ArabFrench t + α 8 ln hightech jt + α 9 mediumhigh jt + α 10 mediumlow jt + α 11 lowtech jt + α 12 nonmanuf jt + U ij; (3) In order to measure the creation of additional trade flows, we study the 27 countries of the European Union. This allows us to measure the Morocco Association Agreement with the European Union. Significant preferential agreements are treated: the Agadir Agreement "Agadir99"(signed in 1999), which includes Egypt and Tunisia, the bilateral agreement with the United States "USA03" (signed in 2003) and Turkey "Turquie06" (signed in 2006) (for more details see appendix A.2). Free trade agreements allow us to analyze the following equation: lnx ijt = α 0 + α 1 ln Y it + α 2 ln Y jt + α 3 lnpop it + α 4 lnpop jt + α 5 lnd ij + α 6 lncolonial jt + α 7 ln ArabFrench t + α 8 ln hightech jt + α 9 mediumhigh jt + α 10 mediumlow jt + α 11 lowtech jt + α 12 nonmanuf jt + α 13 EU27 jt + α 14 Turkey06 jt + α 15 USA06 jt + α 8 ln Agadir99 jt + U ij; (4) Avec : U ij : Les résidus 3.3 Data sample The choice of sample is restricted to data availability and reliability criteria. The external trade statistics, data on GDP (Y it, Y jt ) and population (Pop it, Pop jt ), were obtained from CEPII's database. We used the total of exports from Morocco (X ijt ) according to the OECD (2005) classification. The equation is based on a reference sample of 81 developed and developing countries (list of countries in appendix A.1.2). The panel estimate for 48 years from 1967 to The GeoDist data base from the Centre for Prospective Studies and International Information CEPII (Mayer and Zignago 2011), provides a set of distance variables and dummies common to identify the special ties between the countries, such as the colonial past (Colonial) the common languages (ArabFrench). We obtained information on countries partner to free trade agreements and regional groupings, from WTO sites and Ministry of Foreign Trade 6 (EU27, Turkey06, USA06, Agadir99). 6 Rubrique : Les accords commerciaux 10

11 4. MODEL ESTIMATION AND ANALYSIS OF RESULTS 4.1 Expected signs of the variables According to the literature, the predictive signs of the variables show the effect on the direction of trade, and the coefficients measure the proportions in which the flows are advanced. These coefficients can be interpreted as elasticities, as long as logarithms are applied to the variables under analysis. We measure the country's imports demand via its GDP. For it indicates a higher purchasing power and implies a positive effect on dependent variable (exports). So the expected sign of GDP is positive. Country size, measured by population, influences the level of trade. The link is negative, given that for larger countries exports tend to be lower. The absorption effect leads to a reduction in exports when the countries are larger (Linnemann 1966). However, we can have economies of scale where a large country exports more than a small country (Brada and Mendez 1983). Thus, the sign cannot be anticipated for this variable, it can be negative or positive for the reasons just exposed. It is to be expected that size will have a positive impact on the intensity of the division of labour, leading to a reinforcement of the "technological" nature of Moroccan bilateral trade. A greater variety of goods exists in "large" countries. The distance variable measures the trade cost. A large distance between the exporting and importing countries could lead to an increase in transportation costs and transaction costs, therefore to a reduction in trade. Since it is an exchange barrier, its expected sign is negative. In contrast, a country may have closer ties with other countries with which it shares borders. Likewise for sharing a common language, improves trade level. There are reasons to anticipate a positive correlation between the predominant direction of trade in past and adherence to preferential treatment in the present. Historical factors have a significant impact on trade and play a role in international trade direction. Therefore, a standard gravity model, which neglects the role of historical factors, suffers from omitted variable bias (Eichengreen and Irwin 1998). Proximity, linguistic affinities and colonial ties suggest a positive sign for their coefficients. A regional integration agreement is likely to brink closer economies signatories and should boost trade by reducing tariff barriers and transaction costs. 11

12 4.2 Method and estimation results The most common used techniques to estimate gravity models, are panel data techniques and Tobit. The latter is a non-linear estimation method that is used if the proportion of null (or censored) observations is high, but the coefficients from these models are presented as odds explaining the occurrence of the event. This model is purely qualitative, it is not possible to capture quantitative elements such as trade flows. Thus the use of ordinary least squares (OLS) is preferred, the interpretation of coefficients is simpler because they are considered as elasticities. However, the heterogeneity parameters' problem occurs every time that a sample of observations is more than one dimension. The most common case is when the sample is made of two dimensions (individual and temporal). In this case, the OLS model may be biased since the value of the estimated parameters may vary among individuals and/or over time. We have integrated the years such as a time fixed effect and products such as fixed effect individuals. First of all, we apply the OLS on the stacked data, regardless of their nature or that of the hazard (Appendix A.1.1). Then we studied fixed effects and random effects models. Secondly, we analyze the impact of each product separately. Econometric tests were therefore carried out in order to detect potential problems of heterogeneity parameters, of multicollinearity between variable and observations that strongly affect the results. We realized Breusch-Pagan and Cook-Weisberg test for heteroskedasticity. Noticing that residuals are explained by the postulated variables, we used "robust" option in our equation to correct for heteroscedasticity. The Haussmann test indicates that the random effects model is preferred to a fixed effects model. The panel regressions in give satisfactory overall results from the perspective of the expected signs and significance. With determination coefficient R 2 =0.43 in the base model and R 2 =0.55 in the model with free trade agreements dummies. Thus, fluctuations in exports are explained for 43% and 55% respectively by explanatory variables. The coefficients of quantitative variables are expressed in natural logarithm, they are directly interpreted as elasticities, while the coefficients of dummies will be interpreted as semi-elasticities. We use the following formula to calculate the trade variation rate from the dummy studied x=e^(y-1), with y the coefficient (lnx = y). The results for the estimation by random effects are presented in Table 1. The significance level of current GDP of Morocco and its partners and their respective populations 12

13 are 1%. GDP contributes positively to increase the volume of trade. An increase by 10% Moroccan GDP (exporting country i), leads all things being equal, an increase in exports by 11.35% and 10% increase from importing country j causes an increase in exports by 4.66%. So, Morocco exports more when country has a relatively high level of development. As discussed above, the countries' size, measured by population, remains a determinant of trade intensity but the sign of this variable can be negative or positive. The results of our estimation show a positive effect on the partner countries size of Morocco. 10% increase in importing country j size lead an increase of Moroccan exports by 1.34%. This reflects the importance of the effect of economies of scale in trade where a large country with high income, imports more (Brada and Mendez 1983). However, the report is negative, if Morocco's size is reduced, exports are increasing. An increase by 10% of the population of Morocco leads, other things equal, a decrease in exports by 37.4%. That said, there is the possibility of import substitution effects dominate, the market size effects given that for larger countries exports tend to be lower or the absorption effect leads to a reduction in exports when the countries are larger (Linnemann 1966). Other possibilities come out of the literature on endogenous growth. For example, the process of development may be led by the innovation or invention of new products. Closer sizes offering a wide field of product quality in both countries and thus at the crossroads of trade flows. This is a confirmation of a central argument of the new theory of international trade. Thus, an interpretation of the model taking into account the quality of products lead to a positive relationship between the intensity of quality and volume of bilateral trade in quality products. The results show a positive impact on the "technological" intensity of Morocco's bilateral trade to medium high technology products and low technology. High-tech products are not significant. Note that the low average product is omitted in the regression. The distance from the economic centers of the countries partner of Morocco, picks up trade barriers related costs such as customs duties and/or non-tariff barriers, therefore it reduces flows export. Thus, an increase in distance by 10% between the two countries reduced their trade on average of 5.87%. 13

14 Cultural affinity and historical ties included in our study have a significant and positive sign. Morocco is exporting more with France and Spain as well as countries speaking French and Arabic in first and second official language. Table 1: Estimate random effects (1) (2) (3) VARIABLES lnexp lnexp lnexp lnpib_ct 0.506*** 0.489*** 0.466*** (0.0775) (0.0729) (0.0727) lnpib_ct_mar 1.584*** 1.610*** 1.135*** (0.355) (0.352) (0.383) lnpop (0.0979) (0.0885) (0.0855) lnpop_maroc *** *** *** (1.172) (1.171) (1.215) lndist *** *** *** (0.103) (0.0974) (0.101) colonial 1.682*** 1.684*** (0.513) (0.518) Arab-French 0.467** 0.424* (0.209) (0.222) hightech (0.175) (0.175) mediumhigh 0.682*** 0.682*** (0.177) (0.175) lowtech 0.901*** 0.901*** (0.184) (0.183) nonmanuf 1.434*** 1.434*** (0.187) (0.185) EU ** (0.212) Turkey *** (0.403) Agadir *** (0.339) USA *** (0.341) Year fixed effects included 1 Oui Oui Oui Constant (1.333) (1.291) (1.320) Observations 19,680 19,680 19,680 Number of cn_ind R² between Standard errors in parentheses, *** p<0.01, ** p<0.05, * p< Years omitted fixed effects 1979, 1998,

15 Overall, the results show that trade agreements have positive effects on exports of Morocco. All agreements are positive and significant at the 1% level. Unless the association with the EU, which is significant at the 5% threshold. The success of such models to explain the value of trade is more or less guaranteed, precisely because of the presence of gravitational variables related to the size and distance of the country. However, the separate estimation of the values of each type of trade is a priori not guaranteed. That is why we estimate the model OLS (in annexes) and random effects. Table 2: Estimate technology products by random effects (1) (2) (3) (4) (5) VARIABLES High tech Medium high Medium low tech Low tech Non manufacture lnpib_ct 0.420*** *** 0.984*** 0.444*** (0.0667) (0.0757) (0.0745) (0.0658) (0.0672) lnpib_ct_mar *** * (0.902) (0.867) (0.886) (0.687) (0.714) lnpop *** *** 0.286*** (0.0787) (0.0959) (0.0924) (0.0896) (0.0900) lnpop_maroc *** * (3.007) (2.884) (2.949) (2.282) (2.372) lndist 0.257* ** *** *** *** (0.145) (0.188) (0.178) (0.187) (0.185) colonial 1.336** 1.800** 2.162*** 1.544* 1.959** (0.665) (0.870) (0.823) (0.866) (0.858) Arab-French * 0.865* (0.341) (0.445) (0.421) (0.443) (0.439) EU ** 0.940*** 0.622*** 0.709*** (0.170) (0.165) (0.169) (0.132) (0.137) Turkey * *** 1.631** 2.292*** (0.842) (0.813) (0.830) (0.645) (0.670) Agadir *** 2.141*** 1.665*** 1.297*** (0.492) (0.478) (0.488) (0.380) (0.395) USA *** 2.400*** 1.966** ** (0.842) (0.813) (0.830) (0.645) (0.670) Effets fixes Année inclus 1 Oui Oui Oui Oui Oui Constant * ** 4.983** (2.505) (2.635) (2.622) (2.295) (2.333) Observations 3,936 3,936 3,936 3,936 3,936 Number of cn_ind R² between Standard errors in parentheses, *** p<0.01, ** p<0.05, * p< Years omitted fixed effects 1979, 1998 and 2012 Regarding other variables products, taken individually, product trade increases with country size (population) and income (current GDP). Except for the high average product. 15

16 Geographical distance reduces the intensity of trade for four technology products except for high-tech products. Free trade agreements also have characteristics in line with expectations. They are positive and significant in the majority of cases. However, the results point out that the exchange of non-manufactured products with the EU, Tunisia and Egypt is not significant, as well as exchanges of low technology products with the USA and the average low-tech products with Turkey. 5. CONCLUSION The development of new international trade theories introduced imperfect substitutes as justification for model, but they have often applied to industrialized countries, omitting developing countries. Because these latter, applies more the theories based on the abundant endowment of unskilled labor or natural resources. However, integration of these countries into market structures, and the importance of the development process through innovation has led to a deep paradigm change. Most gravity tests focus on total trade between countries and the quality studies are based on unit prices. Thus, consideration of total exports at current prices without including unit price's exports may be contested but the measure changes in product exports by technological intensity shows good results. The main interest of this study and which is also the original part is to estimate the nature of bilateral trade in total exports of four product classified by quality. We studied Morocco as developing countries that opened its market to foreign competition and has signed several free trade agreements. The estimates are positive and encouraging results. Free trade agreements play a positive role with partner countries and within industries. APPENDIX A.1 Elements of the regression model A.1.1 Estimation by ordinary least square (OLS) Table A.1.1: Estimation by ordinary least square (OLS) panel 16

17 (1) (2) (3) VARIABLES lnexp lnexp lnexp lnpib_ct 0.548*** 0.506*** 0.483*** (0.0148) (0.0146) (0.0150) lnpib_ct_mar 1.524*** 1.585*** 1.066** (0.432) (0.419) (0.420) lnpop *** *** *** (0.0162) (0.0157) (0.0160) lnpop_maroc *** *** ** (1.450) (1.407) (1.406) lndist *** *** *** (0.0251) (0.0261) (0.0273) colonial 1.660*** 1.696*** (0.121) (0.121) Arab-French 0.461*** 0.468*** (0.0613) (0.0647) hightech (0.0544) (0.0542) mediumhigh 0.682*** 0.682*** (0.0544) (0.0542) lowtech 0.901*** 0.901*** (0.0544) (0.0542) nonmanuf 1.434*** 1.434*** (0.0544) (0.0542) EU *** (0.0732) Turkey *** (0.362) Agadir *** (0.203) USA *** (0.365) Year fixed effects included 1 Yes Yes Yes Constant * (1.081) (1.053) (1.050) Observations 19,680 19,680 19,680 R-squared Standard errors in parentheses, *** p<0.01, ** p<0.05, * p< Years omitted fixed effects 1979, 1998 and

18 Table A.1.2: Estimation by ordinary least square (OLS) : Technology products (1) (2) (3) (4) (5) VARIABLES High tech Medium high Medium low tech Low tech Non manufacture lnpib_ct 0.410*** 0.159*** 0.191*** 0.811*** 0.845*** (0.0337) (0.0340) (0.0341) (0.0283) (0.0293) lnpib_ct_mar *** (0.948) (0.953) (0.959) (0.794) (0.823) lnpop *** 0.435*** 0.240*** *** ** (0.0362) (0.0364) (0.0366) (0.0303) (0.0314) lnpop_maroc *** (3.171) (3.190) (3.208) (2.658) (2.754) lndist 0.322*** *** *** *** *** (0.0617) (0.0620) (0.0624) (0.0517) (0.0535) colonial 1.400*** 1.995*** 2.381*** 1.477*** 1.227*** (0.273) (0.274) (0.276) (0.228) (0.237) Arab-French 0.285* 0.599*** 0.838*** 0.826*** * (0.146) (0.147) (0.148) (0.122) (0.127) EU *** 0.967*** 0.592*** 0.475*** 0.346** (0.165) (0.166) (0.167) (0.138) (0.143) Turkey * 3.385*** 3.861*** 1.540** (0.817) (0.822) (0.826) (0.685) (0.709) Agadir *** 1.456*** 2.064*** 1.168*** (0.457) (0.460) (0.462) (0.383) (0.397) USA *** 2.078** 2.541*** (0.822) (0.827) (0.831) (0.689) (0.714) Effets fixes Année inclus 1 Oui Oui Oui Oui Oui Constant ** ** 5.178** (2.365) (2.380) (2.392) (1.983) (2.054) Observations 3,936 3,936 3,936 3,936 3,936 R-squared Standard errors in parentheses, *** p<0.01, ** p<0.05, * p< Years omitted fixed effects 1979, 1998 and 2012 A.1.2 List of countries in the sample used for the regression Albania Germany Algeria Saudi Arabia Argentina Australia Austria Bangladesh Belarus Bolivia Bosnia Brazil Brunei Darussalam Bulgaria Cameroon Canada Chile China Czech Rep. China, Hong Kong SAR Colombia Cote d'ivoire Croatia Cyprus Dem.People's Rep. of Korea Denmark Ecuador Egypt Estonia Finland France Gabon Greece Herzegovina Hungary India Indonesia Ireland Iceland Israel Italy Japan Kazakhstan Kenya Kyrgyzstan Latvia Lithuania Libya TFYR of Macedonia Malaysia Malta Mexico Netherlands New Zealand Nigeria Norway Pakistan Paraguay Peru Philippines Poland Portugal Romania Russian Federation Serbia and Montenegro Singapore Slovakia Slovenia Spain Sri Lanka Sweden Switzerland Thailand Taiwan Tunisia Turkey Ukraine USA 18

19 UEBL * United Kingdom Uruguay Venezuela Viet Nam *UEBL=Belgium-Luxembourg A.2 Trade agreements A.2.1 Association Agreement EU-Morocco Different dummies variables who have given a significant contribution to the model described below. The dummy variable is 1 if the considered countries belong to the preferential agreement, 0 otherwise. Agreement with European Union (EU). Table A.2.1: The Association Agreement EU-Morocco Variable UE27 Partner countries Germany, Austria, Benelux (Belgium and Luxembourg), Bulgaria, Cyprus, Denmark, Estonia, Finland, France, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Malta, the Netherlands, Poland, Portugal, Czech Republic, Romania, United Kingdom, Slovakia, Slovenia and Sweden. Signature / Come into force 26/02/ /03/2000 A.2.2 Bilateral FTAs Morocco's trading partners have signed bilateral agreements, retained in the model are: Turkey, US. Table A.2.2: Free trade bilateral agreements Variable Partner countries Signature date Come into force Turquie06 Turkey 07/04/ /01/2006 USA06 United States 15/06/ /01/2006 Agreement with regional groupings: Agadir agreement Table A.2.3: Agadir agreement Variable Partner countries Signature date Come into force Agadir99 Agadir declaration was crowned the free trade agreements signed with: 25/02/04 27/03/2007 Tunisia 16/03/ /03/1999 Egypt 27/05/ /04/1999 Jordan* 16/06/ /10/1999 * Jordan, is not included in the study REFERENCES Abd-el-Rahman, Kamal Firms Competitive and National Comparative Advantages as Joint Determinants of Trade Composition. Weltwirtschaftliches Archiv 127 (1): Aho, Michael, and Howard Rosen Trends in Technology Intensive Trade. Bureau of International Labor Affairs. Office of Foreign Economic Research, United States. 19

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