WHAT DETERMINES THE DEGREE OF EXPORT DIVERSIFICATION? Aleksandra PARTEKA*, Massimo TAMBERI **

Transcription

1 WHAT DETERMINES THE DEGREE OF EXPORT DIVERSIFICATION? Aleksandra PARTEKA*, Massimo TAMBERI ** ETSG, Warsaw, September 2008 Please do not cite without permission of the authors ABSTRACT Empirical findings confirm that relatively high specialisation of economic structures tends to be associated with low levels of income per capita but countries tend to diversify their export structures along the path of growth. However, usually only per capita income and, eventually, country specific fixed effects are the sole explanatory variables taken into consideration in the estimation of specialisation curves. We extend the analysis of specialisation economic development nexus and search for the determinants of trade diversification process. Using a panel data-set for 60 countries and twenty years ( ) we combine synthetic specialisation measures obtained with manufacturing exports data (SITC Rev.2, 3 digit) with a wide range of country specific variables characterising their size, geographical conditions, endowments, human capital or institutional setting. It turns out that the distance from the major markets and the country size are the most relevant and robust determinants of export diversification process, explaining together around half of between country variability in specialisation patterns. The results are robust to changes in the disaggregation scheme and in the estimation procedure. JEL: F15; O14; O33; C23 Keywords: Structural change; sectoral diversification; trade Contact Details: *Gdansk University of Technology, Faculty of Management and Economics, Gdansk, Poland; aparteka@zie.pg.gda.pl **Università Politecnica delle Marche, Faculty of Economics, Ancona, Italy; m.tamberi@univpm.it 1

2 1. INTRODUCTION The theme of sectoral diversification and its evolution along the development path has been recently scrutinised in the empirical literature. The theme is important, as high degree of overall specialisation - implying concentration of resources in a few sectors (from now on, we use the term overall specialisation as opposite to diversification so that low export specialisation means high export diversification and vice versa) - may be dangerous when we consider risk associated with asymmetric shocks (Kalemli-Ozcan et al. 2003). The consequences may be particularly serious if such shocks hit the core sectors of the economy in which its activity is predominantly intense However, the evidence available so far has some important limits. Firstly, since there are no neat theoretical indications on what the nature of GDP per capita - specialisation relationship should be, most studies propose only restricted empirical estimations of the link between the diversification process and the level of development (from now on, we denote the estimation of such a link a specialisation curve ). Secondly, usually only per capita income and, eventually, country specific fixed effects are the sole explanatory variables taken into consideration in the estimation of specialisation curves. It has been found that that the degree of overall specialisation decreases in the initial phase of economic development, which means that poorer countries are exposed to major risk, while less agreement exists on the trend at higher stages of development - Imbs and Wacziarg (2003), Cadot et al. (2007), as well as Koren and Tenreyro (2007) found a U-shaped industrial specialisation pattern while a decreasing trend has been revealed in trade studies (within frameworks incorporating country fixed effects) by de Benedictis et al. (2006) and Parteka (2007). In Table 1 we present in a synthetic way the overview of existing literature most related to the subject of our interest. 1 To our knowledge no study presents the thorough analysis of not only the empirical assessment of overall specialisation with respect to GDP per capita levels, but also the examination of factors influencing the process of diversification along the development path. Consequently, keeping in mind the findings of existing studies on diversification and growth, we aim at determining in more specific way the forces which lie behind the trend of decreasing overall specialisation in the initial phase of economic growth. 2 In order to do so, 1 Note that U-shaped patterns have been revealed by non parametric lowess estimations which, however, tend to be highly dependant on the value of span determining bias-variance trade off (Hastie and Tibshirani 1990: 30) and do not take into consideration any other determinants of specialisation (not even country fixed effects). 2 This work is a continuation and extension of a comparative specialisation study by Parteka (2007), incorporating country specific fixed effects into non parametric estimations of specialisation GDP per capita relationship. It turns out that when we account for cross country heterogeneity, there is a clear tendency towards despecialisation in the initial levels of economic growth and such finding is not sensitive to the way of specialisation measurement. Moreover in a semi parametric framework with fixed effects, employment and export specialisation 2

3 we construct a panel data-set (60 countries, ) combining different synthetic indices of specialisation (obtained with disaggregated manufacturing exports data: SITC Rev.2, 3 digit and ISIC Rev.2, 3 digit - as a robustness check) and country specific characteristics potentially influencing diversification patterns. Imbs and Wacziarg (2003) Cadot et al. (2007) Table 1. Specialization along the development path literature overview Method and revealed Data shape of the relationship between specialization and GDP per capita* Measures of specialization No. of countries Production and Employment (ILO 1 digit : 9 sectors; UNIDO 3 digit : 28 sectors ; OECD 2 digit: 20 sectors) Exports (HS6 : 4998 product lines) Time span Lowess: U shaped (~16500 US$) Absolute 99 Prod: Empl: Lowess:U shaped (~ US$) Absolute De Benedictis et al. (2006) Exports (SITC Rev.2,3 digit: 30 sectors and 4 digit: 500 sectors) GAM:Decreasing (-) Relative Koren and Tenreyro (2007) Parteka (2007) Production and Employment (UNIDO 3 digit: 19 aggregated sectors; OECD STAN: 18 aggregated sectors) Employment and Export (ISIC Rev. 2, 3 digit: 27 sectors) Lowess:U shaped (~14000 US$) Absolute 42 UNIDO: OECD: GAM with country fixed effects: Decreasing (-) GAM with country fixed effects: Decreasing (-) Absolute Relative 32 Empl: Export: Note: *turning point of U curve in brackets (expressed in GDP per capita in constant 2000 international/ppp US$); GAM Generalised Additive Models. We apply two step estimation strategy, aiming at determining characteristics which can explain the importance of country fixed effects in the diversification process. We select several variables, along with per capita income, as possible determinants influencing the level and the evolution of trade specialisation. Among those we consider: countries economic size (if the increasing returns are taken into account, small countries should be more specialised than large ones); measures linked to human capital and technological progress (decreasing specialisation, resulting in major diversification of exported products - requires the internal capacity to produce/export a larger set of possibly new goods); measures linked to geographical characteristics of countries, especially those which can influence transport costs and thus the ability to trade intensively; measures of institutional framework quality (focusing on those factors which are able to support structural change and the modifications in the set of supplied goods). patterns show very similar trends. Thus it seems that the differences in the conclusions drawn in various empirical analysis presented in Table 1 result mainly from the choice of a particular way of looking at specialisation (employment or export) or from the use of a particular measure of specialisation (absolute and relative) and vanish after the inclusion of country specific effects into semiparametric GAM (Generalise Additive Models) estimations. 3

4 The rest of the paper is organized as follows: in Section 2 we sketch the theoretical background for our analysis; afterwards in Section 3 we describe the data, the composition of our panel and specialisation measurement issues. The core of the paper is presented in Section 4 which is entirely dedicated to the exploration of factors determining the degree of heterogeneity of export manufacturing structures. In Section 5 we present some robustness tests and, finally, Section 6 concludes. 2. THEORETICAL BACKGROUND This paper focuses on the structural change - that is on the process of a structural transformation of economies along their development paths. Structural transformation is a multifaceted phenomenon, with strong interconnections and mutual dependence among its multiple sides; as an example, Matsuyama (2005:1) recognizes that it is a complex, intertwined phenomenon in which the income growth process and the various aspect of structural change, like sector composition, organization of the industry, financial system, income and wealth distribution, demography, political institutions, and even the society s value system, mutually affect each other. Relevant insights in this direction were proposed in the past by the Nobel laureate Kuznets (1972, 1973). Even if today he is generally known as an advocate of growth patterns, he often made speculations on more subtle and slippery (according to modern economics) subject of a structural change. In Kuznets view, structural change cannot be considered a consequence of economic growth; instead, both phenomena (along with others) are the simultaneous manifestation of what he defined modern economic growth (as in his Nobel Lecture, provided in Stockholm in 1971 and published in AER in 1973). Moreover, structural change is not limited to the sectoral composition of supply and demand, but has many levels and complementary sides. Technological progress, the basis of modern economic growth, manifests itself through the appearance of cost-reducing innovations and the invention of new goods. Structural change depends on the impact of technological advancements differentiated among industries, according to the specificity of the innovation itself and the specificity of the production processes, as well as on the fact that resources that are made available through income growth cannot be fully employed in old goods if they have too low demand elasticities. 4

5 In order to fully understand the complexity of this phenomenon, we should recall that the introduction of innovations necessarily requires several other changes, e.g. regarding size and location of firms, legal and social innovations (so that also the state plays a relevant role), etc. Changes of this kind are linked to: the shifts in labor force status (e.g. from employers to employee), the way labor force is recruited (e.g. on the basis of objective tests more than on personal knowledge), the need of skill upgrading and scholarization, the creation of new needs and goods linked to the new way of living (e.g. the markets for amusement and sports linked to urbanization). All changes of this kind are not a consequence, but a necessary side of economic growth, and they properly define the full notion of a structural change 3. Such a broad view of structural change has not been studied in depth by economists, who have generally left this field to other social scientists. Instead, economists contribution mainly concentrated on the analysis of sectoral composition of economic activity, mainly through very simplified two-sector models. In practise, the identification of a structural change with the sole sectoral transformation is largely accepted (UN-WESS 2006). Moreover, even if the demand side of the question cannot be left aside (and we will recall it shortly), usually only the supply aspect of the phenomena is explored. Also in this paper we concentrate on the sectoral perspective of looking at the structural change process. In particular, we are interested in describing the changes in quantitative distribution of trade activity across manufacturing sector and not in tracing the evolution of trade specialisation patterns from the qualitative point of view (i.e. what kind of sectors a certain country concentrates its export activity). 4 Highly concentrated structure means low diversification of exports and high degree of overall export specialisation. From the theoretical point of view, we can make the first reference to economic growth models. In the recent ones, structural transformation of the economy enters as a fundamental input to the growth process (Barro and Sala-I-Martin 1990; Grossman and Helpman 1991). The usual symbolic representation of the final good production function in this strand of literature is the following: Y i = A L 1 α i N j= 1 ( X ij ) α (1) 3 For sake of brevity, we have not cited many other aspects of a structural change, eg we have not considered the ideological one. To have an idea, it is sufficient to think of the deep debate around the notion of life itself, generated by the introduction of modern bio-technologies (together with many legal problems). 4 For an example of a quantitative assessment of export specialisation at international level see Hausmann et al. (2007). 5

6 where Y is a final product of type i, L is the labor input, j refers to differentiated intermediate inputs and 0<α<1. The key variable X can be interpreted in two different ways which give origin of two perspectives at the structural transformation process. First, we could interpret X as the quality-adjusted quantity of the j th type of intermediate good i.e.: X ij = kj k= 0 k ( q x ) (2) ijk where q k is a quality indicator and aggregate X results as a weighted sum, so that equation 2 defines the quality content of intermediate goods. Since various quality grades of each single intermediate input are perfect substitutes, it means that there is a process of a Schumpeterian creative destruction because new qualities of an intermediate input completely replace the old ones: such an approach is presented in models of quality ladders (Aghion and Howitt 1992). 5 In a second interpretation we could think of X as of the quantity (not qualitatively adjusted as before) of the j th type of intermediate good, i.e. we retain (1) dropping (2). In this case we are in the dominion of so-called models with expanding product variety (Grossman and Helpman 1991: 43-83). There is no substitution among inputs, but new inputs rather add to old ones, so that there is a continuous expansion of the number of inputs in the form of intermediate goods. Economic Dualism literature is another relevant place to look at while analysing structural change. There are plenty contributions here, but we could identify a few common lines: the economy consists of two sectors and the dualism is a consequence of differences in production functions (technology) and/or consumer preferences (elasticities) between goods, together with functional linkages between sectors (Matsuyama 1991, 1992; Temple and Woessman 2006). 6 As said before, this literature pays attention also to the differences in consumer preferences, thus in order to capture the notion of a structural change we should look not only at intermediate goods markets (like in the growth theory) but also at final goods, destined both for production and for consumption purposes. As countries develop, the patterns of consumption preferences adjust to higher income levels (Engel type effects 7 ): increasing output per capita means modifications in the structure of the economy through a shift towards 5 Qualitative aspects of economic structure are also underlined in other supply-side contribution (Lucas, 1988) and in the Keynesian demandside literature (see Thirlwall 1979; McCombie and Thirlwall 2004). 6 Usually, authors consider several other characteristics such as: frictions in the economy (their strength explains dualism persistence) and the possibility that dualism emerges as an endogenous process (eg due to the presence of externalities in the advanced sector). 7 So called Engel s Law states that demanded goods have different income elasticities, thus along the process of economic growth implying growing income per head - structural demand shifts may also cause structural transformation. 6

7 goods with higher demand elasticity. Such mechanism, in turn, influences sectoral productivities which change relative prices and, again, the structural composition of the economy. To sum up, not only the number and quality of intermediate goods change: analogous changes are observable in all markets of supplied and demanded goods. Our main concern is, however, the process of diversification along the development path. As far as the justification of low degree of diversification at initial stage of growth is concerned, Acemoglu and Zilibotti (1997) provide a theoretical framework which emphasises limited diversification opportunities at lower levels of development because of the scarcity of capital and indivisibility of investment projects. Growing GDP per capita is usually linked with dynamic changes regarding the quality of institutions, human capital or widely understood conditions for doing business which all together favour more dynamic and heterogeneous economic structure. Development goes hand in hand with better diversification climate and that is also why more diversified (i.e. less specialised) structures of economic activity can go in parallel with higher levels of per capita output. We present our contribution within the above sketched theoretical background, assessing (some of) the determinants of sectoral transformation process and measuring the number of goods exchanged in the international market, without distinguishing if they are for intermediate or final use. We concentrate on the diversification process and its determinants along the path of growth. In the following parts of the paper we incorporate previously described theoretical arguments into the empirical framework assessing determinants of diversification. 3. DATA AND MEASUREMENT ISSUES 3.1. Panel composition and the data As stated in the introduction, we measure specialisation in terms of internationally exchanged goods using manufacturing export data. Sectoral export statistics come from UNComtrade database (available through WITS 8 ) and are classified following two typologies of disaggregation schemes - SITC Rev.2, 3 digit in the major analysis (list of SITC sectors in Appendix 1) and ISIC Rev.2, 3 digit - used at a later stage as a robustness check (list of ISIC sectors in Appendix 2). 9 In order to be able to draw general conclusions we aimed at 8 World Integrated Trade Solutions, available from www. wits.worldbank.org 9 In the main analysis we use export specialisation measures calculated with SITC Rev.2 3 digit manufacturing data because changes on specialisation patterns are more likely to take place among product groups within the same aggregated sector than between aggregate sectors. Major level of detail is desirable here thus we give priority to SITC database, giving us more information than the data classified into 28 ISIC broad sectors. Concordance tables between the two classifications are available from WITS. We use SITC revision 2, instead of more recent 7

8 including into our panel as many observations on countries export patterns as possible, thus data availability was the only criteria of choice and in the end our analysis covers manufacturing exports from 60 countries (Table 2) and the time horizon of 20 years ( ). 10 Table 2. List of countries and adopted abbreviations Algeria DZA El Salvador SLV Kenya KEN Philippines PHL Argentina ARG Finland FIN Korea, Rep. KOR Poland POL Australia AUS France FRA Macao MAC Portugal PRT Austria AUT Germany GER Madagascar MDG Saudi Arabia SAU Barbados BRB Greece GRC Malawi MWI Singapore SGP Bolivia BOL Hong Kong, China HKG Malaysia MYS Spain ESP Brazil BRA Iceland ISL Mauritius MUS St. Lucia LCA Canada CAN India IND Mexico MEX Sweden SWE Chile CHL Indonesia IDN Morocco MAR Switzerland CHE China CHN Ireland IRL Netherlands NLD Thailand THA Colombia COL Israel ISR New Zealand NZL Trinidad &Tobago TTO Cyprus CYP Italy ITA Norway NOR Tunisia TUN Denmark DNK Jamaica JAM Oman OMN Turkey TUR Ecuador ECU Japan JPN Pakistan PAK United Kingdom GBR Egypt, Arab Rep. EGY Jordan JOR Peru PER United States USA We concentrate on manufacturing data only because it covers large part of countries exports and is less dependant on natural and very specific conditions than, for example, primary sectors. Manufacturing is defined as sectors grouped under headings 5 to 8 (so defined manufacturing sector in 2004 accounted for 82% of total exports reported by 60 countries present in our sample and is particularly high in industrialised countries - detailed country statistics in Appendix 3); after the elimination of items with very pronounced presence of missing values 11 we have kept 149 SITC industries which gave us sectoral observations in SITC dataset. 12 With these disaggregated data we calculate country and time specific synthetic measures of overall export specialisation (see next section 3.2) needed as a basic input in the empirical analysis of diversification process. revision 3 or 4, because older revision gives us the possibility to extend the time span of our analysis back to the 1980 (while for example SITC revision 3 has been used from 1988 onwards) and many countries have never switched their statistics into newer revisions. 10 Countries with too pronounced presence of missing cells have been excluded from the analysis. We preferred to work with a (quasi) balanced dataset because in Comtrade set of export statistics missing cells are not casually distributed but rather concentrated in older periods and poorer countries, thus the inclusion of more countries with considerable number ob sectoral missing observations would have biased our results. We could have included exports data up to 2006 but our main sources of additional variables report complete cross country statistics up to year It would have been possible to include years prior to 1985, but it would have meant the exclusion of China from our analysis (complete Chinese export statistics are available only since mid-1980s.). Given China s importance in the world s economy and trade, we have decided not to do so. 11 Namely: 688 (Uranium depleted in u235&thorium) and 675 (Hoop & strip of iron/steel, hot-roll). 12 Randomly distributed missing values (6% of total) have been filled in through interpolation/extrapolation techniques. In order to have balanced panel we had to replace exports with 0 in 0.4% of cells. 8

9 As far as additional statistics are concerned, GDP per capita (in 2000 int. US$), population size and the degree of openness come from PWT 6.2 (Heston et al. 2006). Human capital and technological variables come mainly from UNESCO and are drawn through UNdata retrieval system. The Fraser Institute and the World Bank are our primary sources of institutional variables: references can be found in Gwartney and Lawson (2007) for the former source and in Kaufmann et al. (2008) for the latter. Geographical characteristics are for the most part based on Gallup et al. (1999), we also use distances from CEPII. In addition, we employ micro data from European Values Study Group and Word Values Survey (2006) for the construction of one of our institutional indicators. Detailed description and the sources of all country specific variables can be found in Appendix Measurement of the degree of overall export specialisation Let s consider n industries (sectors) present in m countries and denote X ij as a value of exports from sector i of country j and X j as medium sectoral value of exports from country j (i.e. X = X n ). Consequently, we can measure the share of exports of products from j i ij sector i =1, 2, n in total exports of country j=1,2, m as: X / X (3) s ij = ij i ij Analogically, we define the typical share of industry i in total world 13 exports as: i = j ij w X / X (4) i j ij Our preference is to measure how different (how diversified) is the export structure of a given country from the rest of the group, thus we use relative specialisation indices. 14 First, we calculate relative Theil entrophy index defined as (Cowell 1995:49): n sij Re ltheil j = sij ln (5) i= 1 wi The lower bound of Theil indices is 0 while the upper limit is equal to ln(n), where n is the number of sectors (industries). Secondly, we compute relative Gini index defined as in Amiti (1999): first step involves the construction of a Lorenz curve by ranking sectoral Balassa 13 Note that world here is treated conventionally because it consists of those m=60 countries which are included in our analysis and not all world economies. As a result, as the benchmark value w i we use not the real industry share in total world exports but rather the share referring to its part consisting of m economies. However, we cover very large part of total world exports: the countries included into our sample in 2004 amounted for 84% of the total world trade value and 90% of world manufacturing trade (total values refer to 160 countries for which the disaggregated data are available from UN Comtrade database). 14 Measures of specialisation are adopted from commonly used indices of inequality and concentration (see Iapadre 2001). The class of most popular relative indices includes: relative Gini index, relative Theil index, dissimilarity index, Krugman specialisation index. We have chosen Theil measure given its desirable properties of independence of scale and population size, while Gini index has the advantage of allowing for a convenient graphical representation of the degree of relative specialisation through graphs similar to Lorenz curves. 9

10 indexes (BI ij ), calculated for each country and sector as a ratio of s ij (3) to w i (4), in ascending order. Next, for each country j we represent the cumulative of the denominator of BI i ( pi wk, i = 1,..., n = k = 1 i ( qij skj, i = 1,..., n = k = 1 ) on the horizontal axis and the cumulative of the numerator of BI ) on the vertical axis. Gini index can be calculated as twice the area between the Lorenz curve and the 45 degree line which is associated with a case when country j has the same pattern of revealed comparative advantage as the benchmark group of countries. 15 In order to compute relative Gini index we use the approximate statistical formula (Piccolo 1998: 140): n 1 i= 0 n 1 ( piqi+ 1, j pi+ 1qij ) RelGini = 1 ( q + q (6) j ij 1 + 1, j )( pi+ 1 pi ) = i= 1 where, as previously, i refers to sectors and j to countries. Relative Gini index may vary from 0 to 1. Both indices are positively related to the degree of overall specialisation the bigger its value the higher the specialisation, thus as we use specialisation and diversification terms as antonyms - high values of RelTheil and RelGini are associated with less diversified export structures than the overall benchmark trend. 3.3 Export specialisation measures - results As a basic input into the specialisation curve estimation we have two series of overall relative specialisation measures obtained with SITC dataset (RelTheil_SITC, RelGini_SITC) - each of 1200 pooled observations (n=60 and t=20). Summary statistics of these two measures (along with alternative ISIC ones which will be used at the later stage in the robustness checks section) are presented in Table 3. Table 3. Summary statistics for manufacturing specialization indices Basic measures Mean Std. Dev. Min Max Obs RelTheil_SITCj overall N =1200 SITC Rev.2, 3 digit , 60 countries, 149 manuf. industries between n = 60 within T = 20 RelGini_SITCj overall N =1200 between n = 60 within T = 20 Alternative measures (robustness) Mean Std. Dev. Min Max Obs RelTheil_ISICj overall N =1200 ISIC Rev.2, 3 digit , 60 countries, 28 manuf sectors between n = 60 within T = 20 RelGini_ISICj overall N =1200 between n = 60 within T = Country specific Lorenz curves obtained with export data available on request. 10

11 Variability between is much higher than within which means that we can observe a considerable dispersion of the degree of overall export specialisation between single countries in our sample but not so big variability of sample observations about their separate means (i.e. dispersion around a country s medium degree of export specialisation registered between 1985 and 2004 is lower than cross country variability). In order to give a sample of between country dispersion of specialisation patterns, in Table 4 we present five countries with the highest and the lowest overall export specialisation in 1985 and in Table 4. Countries with lowest and highest overall specialization in manufacturing exports (SITC measures of specialisation) SITC Rev.2, 3 digit , 60 countries, 149 industries RelGini_SITC* RelTheil_SITC** RelGini_SITC* RelTheil_SITC** 5 lowest (lowest overall manufacturing specialization) 5 highest (highest overall manufacturing specialization) GER (0.24) FRA (0.25) GBR (0.34) SWE (0.41) USA (0.42) CHL (0.97) MUS (0.96) EGY (0.94) ISL (0.94) BOL (0.93) GER (0.10) FRA (0.12) GBR (0.19) MEX (0.21) USA (0.29) CHL (3.96) BOL (3.37) MUS (2.92) PER (2.81) ISL (2.77) GER (0.28) USA (0.29) FRA (0.31) GBR (0.34) AUT (0.38) TTO (0.95) DZA (0.94) JOR (0.94) KEN (0.94) ISL (0.93) GER (0.12) USA (0.14) FRA (0.16) GBR (0.22) AUT (0.25) TTO (3.43) CHL (3.35) DZA (3.04) BOL (2.89) ISL (2.87) Note: value of a particular index in brackets *theoretical min=0, theoretical max=1.00 **theoretical min=0, theoretical max=5.0; theoretical max is calculated as ln(n) where n is the number of sectors (industries) and represents the situation when all exports are concentrated in 1 sector (1 industry). It is evident that the differences across countries are very big (for example, taking into account relative Theil measure, in 2004 the most specialised country Trinidad and Tobago - was characterized by more that 28 times more concentrated export structure than the country with the most diversified export structure - Germany). At the same time, many countries which were classified as those with the most diversified (thus the less specialised) export structures in 1985, after 20 years are still more or less on the same place in the ranking; the same is true for the most specialised economies. This was in part expected, since we are dealing with structural characteristics of economies, needing time to adjust, but it may also be a signal that manufacturing export specialisation is a persistent phenomenon, largely dependant on country specific characteristics slowly changing through time or virtually time invariant (like geographical conditions). Note also that among less specialised countries with diversified exports we find rather highly developed economies, while those with very concentrated export structures are either poor countries or those largely depending on natural resources (mainly petrol), as well as very small countries. 11

12 This is the first insight into the factors which may determine the degree of trade structures diversification. In the next section we present the evolution of export diversification along the development process and perform the analysis which aims at revealing the determinants of such a trend. 4. DETERMINANTS OF EXPORT DIVERSIFICATION PROCESS 4.1. Despecialisation along the development path in a framework with country fixed effects The starting point of our analysis is the fact that, generally speaking, low levels of GDP per capita tend to be associated with low degree of economic structures heterogeneity (thus high overall specialisation). Moreover, there is a tendency towards despecialisation as GDP per capita grows - such a trend is econometrically confirmed also in our sample (see Appendix 5 where we present semi parametric GAM 16 plots which, thanks to the inclusion of country specific effects, can be interpreted as typical specialisation curves along the path of economic development). However, we want to specify precisely what kinds of variables are captured by undefined country fixed effects which we incorporated previously into our basic analysis. In other words, we aim at widening the basic model 17 : SPEC = f EconDEV ) (7) it ( it where SPEC={RelTheil_SITC, RelGini_SITC} is one of our overall export manufacturing specialisation measures presented in Section 3.2, EconDEV is the level of development (per capita income), f(.) is a link function, i refers to countries and t to time. As already underlined, previous literature on (overall) specialisation - per capita income nexus presented semi-parametric and parametric regressions with per capita income as the sole explanatory variable or, in some cases, adding country dummies. Parteka (2007) and Cadot et al. (2007) find that only with country specific effects taken into consideration the relation is monotonically decreasing. Even if these specific effects seem to play a non marginal role, we are not aware of any published analysis providing information about their statistical significance, specific components and impact on the specialisation patterns. In order to fill this gap, we perform a two stage procedure to explain the diversification process. 16 Generalised Additive Model. We do not describe here semi parametric methodology (see Hastie and Tibshirani 1990) as it serves only as a supporting tool and its adoption for the needs of specialisation studies has been presented in other specialisation studies (see Parteka 2007 or de Benedictis et al. 2006). 17 Such a model was used for example by Imbs and Wacziarg (2003) which adopted fixed effects formulation or Koren and Tenreyro (2007) which employed non parametric lowess estimation. 12

13 4.2 First stage analysis Estimation strategy At the first stage, we estimate the most general linear equation with GDP per capita (as EconDEV proxy) and country specific fixed effects (FE) introduced as country dummies (D i ) into LSDV 18 model - as the sole explanatory variables, i.e.: n SPEC it = α GDPpc + δ i Di + uit (8) i=1 where α and δ are the coefficients to be estimated and u is the usual error term. Drawing on aforementioned semi-parametric results (Appendix 4) we expect α coefficient to be negative and all δ i to be jointly significant. We will compare the results of estimation (8) with those obtainable with pooled OLS estimations. Since our aim is to understand also the nature of the country-specific effects, we opt for a LSDV estimation procedure that provides single coefficients for every country we are interested in, while this would not be possible through a within estimation (because of the transformation in differences from average values). Results first step In Table 5 we present the results of first step pooled OLS and LSDV estimations. First of all, in all cases model F tests are very good and per capita income is always a strongly significant negative determinant of the degree of overall specialisation (independently on the measure of specialisation used); coefficients are pretty stable in the estimations with and without country fixed effects (OLS vs. LSDV in higher and lower panel of Table 5, respectively), even though their magnitude is different in the estimations obtained with two alternative measures of specialisation. Keeping other things constant, on average an increase in output per capita level by 100% may be associated with as much as 20% to almost 50% decrease (depending on the measure used) in export specialisation (equivalent to the increase in the degree of export diversification by the same proportion) Least Squares Dummy Variable. We already know what is the shape of the relationship between the two main variables of interest (overall manufacturing specialisation decreases as the level of economic development), so now we approximate function s linearly and turn to standard parametric estimation - more immediate and easier in interpretation than GAM, especially in the post-estimation stage 19 In order to illustrate such a phenomena we can look at rapidly developing country like China which in 20 years between 1985 and 2004 has passed from the level of GDP per capita of 1134 US$ in 1985 to 5333US$ in the year 2004 (both values in constant prices, 2000). At the same time China s relative diversification of manufacturing exports (measured with RelGini_SITC) increased by 70%. 13

14 Table 5. Basic estimation results diversification along the development path (SITC measures of specialisation) Dependent variable: manufacturing export specialization Pooled OLS annual observations Ln_RelTheil_SITC (1) Ln_GDPpc -0.47*** (-23.43) Ln_RelGini_SITC (2) -0.19*** (-23.83) R_ F p>f N Dependent variable: manufacturing export specialization LSDV annual observations Ln_GDPpc Joint significance of country effects Ln_RelTheil_SITC (1) -0.49*** (-12.12) Prob > F = Ln_RelGini_SITC (2) -0.19*** (-11.81) Prob > F = R_ F p>f N Note: all variables enter in natural logs, t-statistics in parenthesis*, ** and *** denote significance at 10%, 5% and 1% level, respectively., in OLS estimations constant included not reported; country specific coefficients from LSDV estimations available on request. As far as country specific effects are concerned, FE are always highly significant (second line of the second panel of Table 5): they cannot be ignored in the estimation of the relationship between SPEC and GDPpc. Moreover, the contribution of inbuilt country heterogeneity towards the explanation of overall specialisation patterns is relevant and fundamental, as demonstrated by very high adjusted R 2 of LSDV estimates, especially when compared to R 2 values of the pooled OLS estimations. The results hold both for annual observations and for 5-year averages estimations (performed to account for possible business cycle effects). 4.3 Second stage analysis Estimation strategy The previous result is indeed neat, but the use of closer undefined fixed effects is in some way not satisfying, since they collect many country features that remain unknown. Is it possible to discover at least part of those characteristics? Our next step goes exactly in this direction: at the second stage, we aim at determining what kind of additional characteristics 14

15 can explain between countries variability in patterns of export diversification. Thus, we consider a cross section equation with dependent vector variable composed of country specific coefficients obtained at the first stage of analysis and a set of k country specific characteristics as explanatory variables, i.e.: m δ i = α 0 + β k X k + ui (9) k = 1 The aim of second step estimations is to quantify the importance of time invariant (or slowly changing) characteristics, describing countries specific conditions and incorporated previously in country fixed effects, in the diversification process. Variables incorporated in the sum term come from our wide set of country features (presented below and described in detail in Appendix 4), potentially influencing the process of expanding product variety of exported goods. Set of variables possibly explaining country specifics patterns of diversification The first obvious candidate is the country size (SIZE) which we proxy both in geodemographical and economic terms, measuring the former by population size while approximating the latter with total GDP. 20 We would expect that larger countries, having usually also more diversified structure of economic resources, have more heterogeneous economic structures (thus lower overall specialisation). Theoretical explanations on the link between the degree of overall specialisation and country size can be found in New Trade Theory (Dixit and Norman, 1980; Helpman and Krugman, 1985; Krugman, 1981) arguing that market size directly affects the degree of product differentiation. According to the view presented in monopolistic competition models bigger countries can produce wider range of products (thus they are less specialised). Hummels and Klenow (2005) empirically estimate the linkage between economy size, measured by total income, and the overall degree of specialisation. However, level of economic development and country size cannot explain the whole pattern of specialisation observable across the countries. Thus in the second group of variables, we consider characteristics rooted in endogenous growth theory (Aghion and Howitt 1998) which affect the general conditions for product differentiation and are directly linked to the level of economic development. Higher quality of human capital (HC) should facilitate the diversification of production process and penetration of the economy by new 20 We have also tried to use land area as a proxy of country size and the results are very similar, even though it is not a very robust variable and also its economic interpretation is troublesome. 15

16 activities; therefore we expect the coefficients associated with high quality of human capital variables (high school enrolment, low illiteracy rate) to be negative. Then, better opportunities for research and development (R&D) should promote the introduction of new goods, structural change and diversification process. We include several HC and R&D variables in a group called TECH_HC. Then, we incorporate a set of variables which describe the quality of institutions (INST). It seems reasonable to assume that institutional setting is not only an important factor of growth (Rodrik et al. 2004) but also influences diversification opportunities. Such characteristics as the effectiveness and size of government, protection of property rights, access to money and credit, labour market and business regulations quality, freedom to trade internationally, political stability, rule of law, control of corruption, social trustiness etc. would directly influence the capacity of local producers to adjust flexibly their production structures to international surrounding, enhancing heterogeneous economic activity. Thus we expect that widely defined good quality of institutions and governance are among positive determinants of greater exports diversification (thus lower specialisation). New Economic Geography models (Amiti and Venables 2002; Venables and Limao 2002) suggest that among important factors influencing the economic structure of a country we may find the proximity to world markets and other geographical characteristics. Thus we also include into our set of explanatory variables a group of geographical variables (GEO) which describe the geo-political position of a given country and determine the facility to export intensively (and at lower costs) a large variety of products. Such characteristics as the distance from major markets (New York, Rotterdam and Tokyo), climate zone and the presence of tropics or the accessibility of water transport influence trade costs and may affect the ability to operate intensively in the international market (Frankel and Romer 1999). We also recognize that progressing globalisation and integration tendencies can imply spatial interdependence of trade structures, especially between countries located in the geographical proximity. Spatial correlation (Greene 2008: ) can arise, in this context, because of two main reasons: firstly, because several countries may share a common institutional framework, both because of cultural spillovers and because of specific integration agreements; secondly, according to long empirical tradition of trade gravity models (Deardorff 1984), trade is influenced by distances (and masses ), thus we suspect that this influence may also regard export structures and the degree of their diversification. 16

17 There is a growing literature using space correlation variables which aim at catching spillover effects between neighbouring areas 21 (countries, in our analysis) and taking into account the distance between them. 22 Supposing that spatial correlation effects not only the volume of trade between different countries, but also can have an impact on the structure of trade, we introduce spatial correlation variable (SPACE_CORR): each country's specialisation pattern can be substantially dependent on the degree of specialisation of other countries, and, in particular, this influence is likely to be more pronounced in case of geographically closer countries. We employ a spatial weighing scheme using symmetric matrix W (NxN) with elements w ij (i and j refer to single countries) obtained from bilateral distances matrix coming from CEPII. In our case, the matrix W, which serves as a weighting element in the construction of spatial correlation variable, is row-standardized i.e. elements of each row are scaled so that i, j = 1,..., N, i j 0 w ij 1 and the row sums to one. Moreover, as original distance data from CEPII contain internal distances (a proxy of distance existing between producers and the internal market) while we are interested in the distances between internationally trading countries, we replaced all diagonal cells in the distance matrix with 0 thus w ij =0 if i=j. In practice, we introduce a measure through which our dependent variable, composed of a set of country and time specific Gini or Theil measures of export specialisation, enters as an explanatory variable of other countries specialisation patters: N = j= 1 SpatialCorr _ Distit wij SPEC jt, i. j = 1,..., N (10) where i and j refer to countries and t to time period. Subsequently, we consider a group of variables called TRADE which are of a quantitative (overall degree of openness and the relation of manufacturing exports to GDP), as well as of a categorical nature (countries which are members of selected active RTAs). The motivation behind including these variables is the fact that trade liberalisation can act as market extension (Krugman and Venables 1990, Haaland 2002) and potential gains from trade may cause major product diversification (Costas et al. 2008). Finally, patterns of exports can be affected by natural endowments (ENDOW) countries rich in one kind of resources are likely to concentrate their exports in the manufacturing of related products (Harrigan and Zakrajsec 2000) and have highly concentrated export structures. Petrol exporting countries or those largely dependant on agriculture are expected to have more specialised trade structures. 21 See Garrett et al. (2005) for a recent application to growth empirics. 22 Distance can be a geographical or economic concept. Here we limit our attention to the geographical distance expressed in km. 17

18 In the end, our complete set of possible determinants of diversification process denoted as X term in (9) includes (see Appendix 4 for detailed description and data sources): SIZE = {POP, GDP}; TECH_HC= { EnrSec_pop, EnrTer_pop, IllitRate, RDSpending, Researchers } INST={Gov_Size, Leg_PropRights, SoundMoney, FreeTrade, RegBusinessCredit, EconFreedomIndex, VoiceAccountability, PoliticalStability, GovEffectiveness, RegulatoryQuality, RuleOfLaw, ControlOfCorruption, Gov6index, Gov4index, Gov3index, Trust} GEO={CoastRiv, Tropics,, MarketDist} SPACE_CORR = {SpatialCorr_dist} TRADE={Open, ManufExport, RTA) ENDOW = {Petrol, AgricVA, AgricEmpl } Some of these variables are virtually time invariant (mainly GEO group) while others change through time (those changing most significantly are POP an GDP) - in the cross section model (9) we include all variables averages calculated over the whole period of analysis but in the robustness section we will control if the results hold in an alternative period. 23 The choice of country specific variables As the equation to be estimated with all of the above listed country characteristics would be rather complex and there could occur serious collinearity problems 24 we first have to choose the variables to be put in the final model. Since we have so many potential candidates, our choice is to make discrimination on the basis of simple cross section OLS univariate estimations. In order to do so we regress separately country coefficients δ i, obtained through estimation (8), on each of the explanatory variables and then only significant variables are utilized in multivariate estimations. If within variables describing more or less the same thing (thus impossible to appear contemporarily in the final estimation because of collinearity problems) more than one is significant, we choose the one with lower p-value. Negative coefficient associated with a given variable indicates its positive link with exports diversification. The results of univariate estimations are presented in Table Due to limited data availability, while calculating overall means needed for second stage estimations we can use only more recent statistics of some variables. However, such problem refers to those country specific features (R&D spending and the number of researchers, selected governance indicators) which tend to change very slowly through time, thus their means calculated with the data we have can be treated as a fair approximation of the overall average value. 24 In such case OLS estimators are sill BLUE (that is, they are unbiased, consistent and efficient) but the effect of near multicollinearity among explanatory variables is to increase the standard errors and reduce the t-statistics, as well as to make it difficult to interpret the meaning of individual coefficients and to isolate single effects. 18