Are the creative exports inducing economic growth? Evidence from Arab countries.

Mohamed, Nashwa Mostafa Ali

Abstract

Using panel co-integration analysis and the Dynamic Ordinary Least Squares (DOLS) estimator, this paper examines the long-run relationship between creative exports and economic growth, in eight Arab countries (Algeria, Bahrain, Jordan, Lebanon, Morocco, Oman, Saudi Arabia, and Tunisia) during the period (2002-2011). The main finding indicates that there is evidence on the long run relationship between creative goods exports and economic growth in the Arab countries sample. Similarly, the creative goods exports have a significant positive effect on economic growth in these countries.

Keywords: creative economy; creative industries; economic growth; Arab countries; panel cointegration.

JEL classification: F140; Z11, O11, O470

1. INTRODUCTION

Creative economy is a new dynamic sector in the world trade, and increasingly being recognized as a key force driving economic growth. Unlike the traditional economy, which is driven by the availability of natural recourse, creative economy is driven by knowledge and information. This means that creativity is not a given resource, but it is deeply embedded in a country's social and historical context. So it provides new opportunities for many countries to develop new areas of trade and economic growth.

The term "creative economy" has been appeared since 2001, in John Howkins' book, which described it as a new relationship between creativity and economics, creates extraordinary value and wealth (UNCTAD, 2008). From the creative class' point of View, Florida (2002) defined the creative economy, as a set of occupations in which people "add economic value through their creativity".

UNCTAD (2008, 2010) defined creative economy as "the cycles of creation, production, and distribution of goods and services that use creativity and intellectual capital as primary inputs. They constitute a set of knowledge-based activities, focused on but not limited to arts, potentially generating revenues from trade and intellectual property right. They comprise tangible products and intangible intellectual or artistic services with creative content, economic value and market objectives"

The last definition considers the "creative economy" as an evolving concept Based on creative assets potentially generating economic growth and development. It fosters income-generation, job creation and export earning while promoting social inclusion, cultural diversity and human development. It embraces economic, cultural and social aspects interacting with technology, intellectual property and tourism objectives. It is a set of knowledge-based economic activities with a development dimension and cross-cutting linkages at macro and micro levels to the overall economy. It is a feasible development option calling for innovative, multidisciplinary policy responses and interministerial action. At the heart of the creative economy are the creative industries.

According to Peters (2010), the conception of the creative economy refers to "those broadly defined design industries and institutions that draw on the individual and increasingly collective resources of creativity, skill and talent that have strong potential for the generation of wealth and job creation through the development and exploitation of intellectual property".

The first definition of creative industries appeared in the UK creative industries mapping document (DCMS, 1998). And in compatible to Florida (2002), Swenson and Eathington, (2003) considered that creative industries are those that employ large fractions of the creative workforce, invest heavily in research and development, or create and distribute technologically sophisticated or artistic goods and services.

UNCTAD (2004) referred to creative industries as a group of activities in which it is used intensively and with a particularly high degree of professional specificity. These activities lie at the crossroads between the arts, business and technology. In addition, the concept of creative industries is considered as a development of the cultural industries concept (1), including a move from a strong artistic component to "any activity producing symbolic products with a heavy reliance on intellectual property and for as wide a market as possible".

Creative industries have been seen as a part of the innovation system, because of their vital role in the adoption of new ideas, producing and selling creative goods and services, and more importantly, providing them as intermediary inputs to other sectors, leading to process or product innovations. Therefore, creative industries indirectly contribute to economic growth by impacting on the innovation capability of the rest of the economy, through processes of sourcing, adoption, imitation, and buyer-supplier cooperation (HM Treasury, 2005; DCMS, 2007; Tether, 2009; Chapain et al., 2010; Berg and Hassink, 2013).

According to UNCTAD (2010), creative industries are divided into four broad groups: heritage, arts, media and functional creations. Cultural heritage is identified as the origin of all forms of arts and the soul of cultural and creative industries. It includes art crafts, festivals, museums, libraries, exhibitions, etc.). Arts are divided into visual arts (like painting, photography and antiques) and performing arts (like live music, theatre, opera, circus, etc.).While media covers publishing and printed media (e.g. books, press)and audiovisuals (e.g. film). Functional creations comprises more demand-driven and services-oriented industries creating goods and services with functional purposes and divided into: design (interior, graphic, fashion, jewellery, toys); new media (software, video games and digitalized creative content); creative services (architectural, advertising, cultural, recreational, creative research and development (R&D), digital and other related creative services.

Figures have reflected the worldwide economic importance of creative industries. In 2010, World exports of creative goods totaled $559.5 billion; it more than doubled in only eight years, with an annual growth of 10.7 per cent in the period 20022010. (UNCTAD, 2012).

The developing countries suffer from many problems, such as high unemployment rate, lack of internal investment, weak export capability, low economic growth and per capita income; thus there is a bad need to a dynamic sector driving their economic growth.

Characteristics of creative industries introduce the reasons, justifying the importance of these industries for accelerating the economic growth of developing countries, in general, and Arab countries, specifically. In this context, Hendrickson et al. (2011) showed that creative industries may be less dependent on the size of the economy and less vulnerable to external shocks, compared to other sectors. They can help to alleviate the unemployment problem, as they are labor intensive. They depend on using domestic capital and materials that produce differentiated products with high value added. So, they are increasing the export productivity. They include a mix of traditional and modern activities, where the last, especially ICT such as multimedia, can serve the former in delivering their contents to the consumer all over the world.

In spite of the Sagnia's point of view (2005), that there are major challenges facing developing countries, include the inadequacy of relevant creative capacity to produce and circulate cultural goods and services in forms that can be readily consumed by developed countries; weak cultural infrastructure and institutional capability; and lack of access to finance and technology. The creative economy was booming in developing countries and currently had a market share of nearly 50 per cent in the global market for creative goods. The creative economy had become a priority sector in national development plans, and it did not require large investments and brings diversification in rural areas. According to the International Trade Center (ITC), the main target sectors in developing countries were crafts, visual arts and music (UNCTAD, 2012).

Until recently, there had been no clear vision of the economic role of Creative industries in the Arab countries. This due to the failure of policymakers to recognize the potential of that role, and partly, because of the lack of data that hindered the previous studies from examining it empirically. Indeed, there is a shortage in the economic literature, regarding the whole economic role of these industries in Arab countries generally, but actually, there is a gap in economic literature about the relationship between the creative industries exports and the economic growth.

The current study will contribute to the economic literature by trying to fill this gap, in contrast to the previous studied, which focused on the determinants of the creative industries, or their impacts in other groups of countries. The countries' sample is expanded than that one included in Harabi (2009) to include eight Arab countries, which are Algeria, Bahrain, Jordan, Lebanon, Morocco, Oman, Saudi Arabia, and Tunisia. These countries were chosen because of the published data availability taken from UNCTAD database, during the period (2002-2011). As well as this study depends on the data of the exports value of creative goods not the ratios of creative industries' value added to GDP that used in the other studies, or the value of these goods themselves. The Methodology of this study adopted an econometric model, while the most previous studies depended basically on a descriptive method, in investigating the long run relationship between creative goods exports (excluding services)and economic growth, using the panel cointegration approach and estimating the effect of creative goods exports on economic growth, depending on the panel DOLS estimator.

The main finding of this study indicates that there is evidence on the long run relationship between creative goods exports and economic growth in the sample of Arab countries. Moreover, the creative goods exports have a significant positive effect on economic growth in these countries. The study is organized as follows. The next section presents the pervious literature, Section 3 discusses the importance of creative goods exports, particularly in Arab countries. Section 4 provides the econometric methodology and data sources for the relevant variables. Section 5 shows the empirical results. Last, Section 6 demonstrates conclusion and recommendations for the policy purpose.

2. LITERATURE REVIEW

The relationship between exports and economic growth was the core interest of the macroeconomics theories that paid more attention to the role of innovation in inducing economic growth (Solow, 1957; lucas, 1988; Romer, 1986, 1990). Furthermore, the economic literature shed a light on the conception, determinants and characteristics of the creativity (Schulze, 1999; Marcus, 2005; Kim and kim, 2011; Murovecand Kavas, 2012) and their indicators (Bobirca and Draghici, 2011). Otherwise, there were few empirical studies that considered the determinants of bilateral trade in cultural goods, and they found that piracy has negative influence (Lionetti and Patuelli, 2009) while common language and past colonial relationships has strong positive influence (Disdier et al., 2010). Snieska and Normantiene (2011) tried to analyze trade structure in cultural and creative goods and their export performance, showed that music, publishing/printed media, visual arts, and design prevail in the structure of exports of Lithuanian creative industries during the period (2002-2008), while the main imported goods of creative industries is attributed to design, music and audiovisuals, in the same period.

The relationship between the creative industries and economic growth, or development, has been investigated in Arab countries, (Harabi, 2009),Sweden (Strom and Nelson, 2010), China (Zhang, 2010; Kloudova and Zhang, 2011), Caribbean (Hendrickson et al., 2011), and Romania and other EU member states (Bobirca and Draghici, 2011), they asserted on the importance of creative industries as a vital element in achieving economic growth and development and a pillar for economic diversification and export growth.

It is clear from the previous review that there is a gap in the economic literature, regarding to empirical evidence on the relationship between creativity and economic growth, through enhancing the export capability. So, the current study will try to fill this gap.

3. THE IMPORTANCE OF CREATIVE EXPORTS

The importance of creative industries exports in the Arab Countries requires-giving a look to their share of total exports and growth rates worldwide (figure 1), where they developed noticeably during the period (2002-2011) and exceeded the double at the end of the period in comparable to the beginning year, since the value of world creative goods exports reached 454,018.9 million US$ in 2011, and 177,226.2 million US$ for creative services exports. New Media exports recorded the highest growth rate amongst the world creative goods exports (13.8%), during the same period, then Design, Visual Arts, Art Crafts, and publishing, respectively (figure 2).

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

Although the absolute value of creative exports of the Arab countries was extremely lower than those of developed countries (UNCTAD), they had the highest annual growth rate (17.9%), in comparable to the other groups of countries, which exceeded both of developing countries (2) (12.13%) and developed countries (6.23%) (Figure 3). This is indicating to the promising opportunity for Arab countries to achieve a superiority comparative economic performance in these exports and accelerate their economic growth.

Regarding to the contribution of creative and related goods exports to economy of the Arab countries, the ratio of creative and related goods exports to total exports was higher in Lebanon than in the others, where it recorded about 6.6% in 2011, while Jordan had the highest share in gross domestic product (GDP), at current price, with 3.11% in 2007, but Algeria had the lowest share in both (UNCTAD, Database online).

[FIGURE 3 OMITTED]

Amongst the Arab countries, Oman recorded the highest growth rate of creative goods exports (17.3%), then Bahrain (16.8%), next Tunisia (11.7%), however Algeria recorded a negative growth rate for the same period (figure 4) the status is better incase of the growth rates creative related goods (3), as shown in since the negative growth rate of Algeria has changed to positive one, Lebanon had the highest growth rate (34.15%), followed by Saudi Arabia (29.75%), Morocco (25.1%), Bahrain (24.1%), Oman and Algeria about (20%), lastly Jordan and Tunisia (9.6%) and (5.8%) respectively (figure 5).

[FIGURE 4 OMITTED]

[FIGURE 5 OMITTED]

4. THE ECONOMETRIC METHODOLOGY

Panel models present more information about the sample, because the time series information is enhanced by that contained in the cross-section data, hence more degrees of freedom and more efficiency. Also, they allow controlling for individual heterogeneity, and identifying effects that cannot be detected in simple time series or cross-section data (Osbat, 2004).

Panel datatend to exhibit a time trend and are therefore non-stationary (4). Engle and Granger [1987] argue that the direct application of OLS or GLS to non-stationary data produces regressions that are misspecified orspurious in nature (Ramirez, 2006).

Cointegration tests are a very flexible and general statistical method that reveals the possible existence of a significant long-term relationship between a set of variables, whatever the actual mechanisms linking these variables together. It indicates that the series share a common stochastic trend, e.g. they co-evolve together along a long-term path. In the absence of cointegration, the estimated relationship will have absolutely no economic meaning and the regression will be totally spurious (Laurin, 2007).

For this study, with annual data and a small sample, the lag structure might not be very efficient in detecting the short-term dynamics. Besides, cointegration between two or more variables is sufficient for the presence of short term causality in at least one direction, while the existence of short-term effects is neither necessary nor sufficient for the existence of a long term relationship (Engle and Granger, 1987).

Hence, this study deals exclusively with the long-term dynamics between creative goods exports and economic growth by implementing panel cointegration tests, and panel unit root tests for investigating the stationary of variables.

The data source of variables (value of GDP per capita, as an indicator of economic growth, and the sum of exports' value of creative and their related goods) is UNCTAD statistics database online. Values are in US dollars at current prices and current exchange rates, because data of creative goods exports are available only in current prices.

4.1. Panel Unit Root Tests

Levin, Lin and Chu (2002) and Im, Pesaran an Shin (2003) have developed panel-based unit root tests which lead to statistics with a normal distribution in the limit, unlike individual unit root tests that have complicated limiting distributions (Baltagi, 2001).

Levin, Lin and Chu test assumes that there is a common unit root process across the cross-sections, referred to pooling the residuals along the within-dimension. This test employed a null hypothesis of a unit root using the following basic Augmented Dickey Fuller (ADF) specification:

[DELTA][y.sub.it] = [DELTA][y.sub.it-1] + [SIGMA] [[beta].sub.ij] + [X.sub.ij] [delta] + [v.sub.it] i = 1, ..., Nt = 1, ..., T (1)

where [y.sub.it] refers to the pooled variable, [X.sub.it] represents exogenous variables in the model such as country fixed effects and individual time trends, and [v.sub.it]. refers to residuals which are assumed to be mutually independent disturbances. The index i denotes the individual cross-section unit (N=8 Arab countries) and index t denotes the sample period (T=10) As mentioned above, it is assumed that [alpha] = [rho]-1 is homogeneous across the cross-sections, while ImPesaran and Shintest assumes that there is an individual unit root process across the cross-sections allowing for a heterogeneous coefficient, where may vary across cross sections, referred to as pooling the residuals along the between-dimension. Maddala and Wu (1999) and Choi (1999a) proposed a Fisher test, which has the advantage over the Im, Pesaran and Shin test in that it does not require a balanced panel and can use different lag lengths in the individual ADF regressions.

4.2. Pedroni Test for Panel Cointegration

Pedroni (1999, 2000) suggests two types of residual-based tests for the test of the null of no cointegration in heterogeneous panels. For the first type, four tests are based on pooling the residuals of the regression along the within-dimension of the panel (panel tests); for the second type, three tests are based on pooling the residuals of the regression along the between-dimension of the panel (group tests).

In both cases, the hypothesized cointegrating relationship is estimated separately for each panel member and the resulting residuals are then pooled in order to conduct the panel tests. In the case of panel tests, the first-order autoregressive term is assumed to be the same across all the cross sections, while in the case of group tests the parameter is allowed to vary over the cross sections. The seven statistics test the null hypothesis of no cointegration against the alternative of cointegration. Rejection of the null hypothesis means that the variables are cointegrated. (Breitung and Pesaran, 2005; Ramirez, 2006; Costantini and Martini, 2009).

4.3. Kao Residual Cointegration Test

Kao (1999) presents two types of cointegration tests in panel data, the Dickey-Fuller (DF) and augmented Dickey- Fuller (ADF) types. Kao tests follow the same basic approach as the Pedroni tests, but specify cross-section specific intercepts and homogeneous coefficients on the first-stage regressors. (5)

4.4. Panel Dynamic Ordinary LeastSquares (DOLS) Estimates

Estimating the effect of creative goods exports on economic growth requires using econometric methods, like Ordinary Least Squares (OLS) estimator. But because the last may suffer from endogeneity and serial correlation, Kao and Chiang (2000) suggested other methods, such as The Fully Modified OLS (FMOLS) and Dynamic OLS (DOLS) that may be more promising in cointegrated panel regressions. However, Kao and Chiang (2000) showed that both the OLS and FMOLS exhibit small sample bias and that the DOLS estimator seems to outperform both of them. 6So this study depends on DOLS.

5. EMPIRICAL RESULTS

Table 1 below reports the results of panel unit root tests on the relevant variables in question. As can be readily seen, all the tests fail to reject the unit root null for all the variables in level form, but they reject the null of a unit root in difference form. Thus, the evidence suggests that the variables in question do evolve as nonstationary processes and they are integrated of order one. It is therefore possible to turn to panel cointegration techniques in order to determine whether a long-run equilibrium relationship exists among the non-stationary variables in level form.

Table 2 shows the results of Pedroni tests which indicate that four of them reject the null hypothesis of no cointegration, they are Panel PP-Statistic, Panel ADF-Statistic, Group PP-Statistic and Group ADF-Statistic. Results from applying Kao Residual Cointegration test ensure the previous ones, as seen in table 3, where the t-statistics probability of ADF is 0.09 (significant at 10%). So, the results present an evidence on a long run relationship between creative goods exports and economic growth in the sample of Arab countries.

According to the results, shown in table 4,of estimating the effect of creative goods exports on economic growth in the sample of Arab countries, depending on Panel Dynamic Least Squares (DOLS), the estimated coefficient of creative goods exports is 0.05 and statistically significant at 5%. These findings provide evidence that creative goods exports have a significant positive effect on economic growth in the sample of Arab countries.

6. CONCLUSION AND RECOMMENDATIONS

The study aims to examine the relationship between creative goods exports and economic growth, in eight Arab countries (Algeria, Bahrain, Jordan, Lebanon, Morocco, Oman, Saudi Arabia, and Tunisia) during the period (2002- 2011). The study based on description methodology in reviewing the previous literature and clearing the importance of creative exports, and econometric methodology to test for the existence of a long-run relationship using panel cointegration tests. To apply these tests, the panel untie root tests were necessary to investigate the stationary of variables time series, in advance. The findings indicate that there is evidence on the long run relationship between creative goods exports and economic growth in the Arab countries sample. Moreover the creative goods exports have a significant positive effect on economic growth in these countries.

For promoting the vital role of creative exports, the study recommends for the policy purposes, enhancing creative industries as the high value-added sector for economic growth, protecting intellectual property rights (IPR) which is the core issue in the creative industries and the basis of independent innovation, implementing policies to attract FDIs to the creative sector, setting up an integrated national R&D system to promote innovation creation, building and/or supporting entrepreneurs to create innovation, and providing the needed financial support for pre-production stages. Furthermore, the policy and institutional support is important for these industries, through various forms of contributions and facilitations, such as work permit application processes, tax exemptions. As well as, giving opportunities for people to be more creative and creating a knowledgebase in creativity. In addition to adopting a marketing strategy, since it is a major tool to market and boost sales for all types of goods and services, including the creative sector.

Further research may take into account the growth effects of creative goods imports, as they may increase productivity through learning-on-imports or reverse engineering. Besides, the availability of more data about Arab countries in the future may enable further research to overcome data limitation and get more reliable results.

Notes

(1.) Culture products classified to tangible and intangible. The first such as CD, printed paper, film reel, while the later determines their contents of ideas and symbols, which have the characteristics of public goods such as non-rivalry and non-excludability in consumption. For more details see: Anderson et al. (2000).

(2.) Including Arab countries.

(3.) Related industries goods are supporting industries or equipment needed to produce or consume creative content. UNCTAD selected 170 codes in the list of HS 2002 for creative industries related goods. The number of codes included in each sector is: visual arts, 49 codes; design, 35 codes; publishing, 11 codes; Performing arts, 28 codes; and audiovisuals, 42 codes.(see: UNCTAD, CER 2010, Explanatory notes, Statistical Annex, Database online).

(4.) The variables have means, variances, and covariances that are not time invariant.

(5.) Equations and specification presented in Kao (1999).

(6.) Equations and specification presented in Kao and Chiang (2000).

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NASHWA MOSTAFA ALI MOHAMED *

* Assistant Professor in Economics and Foreign Trade Department, Helwan University- Egypt

** Associate Professor in Economics Department- King Saud University-KSA

Table 1
Panel Unit Root Tests Results

                                         Level

                              gdp                     ere

Method              Statistic     Prob.     Statistic     Prob.

                    Null: Unit root (assumes common unit root process)

Levin, Lin & Chu       -5.001     0.3085      -1.6996     0.0446

                Null: Unit root (assumes individual unit root process)

Im, Pesaran and         1.705      0.956      0.78158     0.7828
Shin W-stat
ADF--Fisher            5.8355     0.9898      12.2471     0.7268
Chi-square
PP--Fisher            16.3159     0.4311       6.2467     0.9970
Chi-square

                                    1st difference

                              gdp                     ere

Method              Statistic     Prob.     Statistic      Prob.

                    Null: Unit root (assumes common unit root process)

Levin, Lin & Chu      -5.4182     0.0000     -3.28019       0.000

                Null: Unit root (assumes individual unit root process)

Im, Pesaran and       -2.3752      0.008     -1.35175    0.0882 *
Shin W-stat
ADF--Fisher            35.048      0.003      25.5458    0.0608 *
Chi-square
PP--Fisher            57.4642      0.000      42.7899       0.000
Chi-square

--Intercept included in the test equation.

--Probabilities for Levin, Lin & Chu and Im, Pesaran and Shin tests
assume asymptotic normality, while Fisher tests are computed using an
asymptotic Chi-square distribution.

* Indicating significance at 10%.

Table 2
Results of PedroniTest

                                            Statistic      Prob.

Alternative hypothesis: common AR coefs. (within-dimension)-weighted

Panel v-Statistic                            -0.54094      0.7057
Panel rho-Statistic                           0.86598      0.8067
Panel PP-Statistic                           -2.38325    0.0086 *
Panel ADF-Statistic                          -3.19843    0.0007 *

Alternative hypothesis: individual AR coefs. (between-dimension)

Group rho-Statistic                            1.8326      0.9666
Group PP-Statistic                            -2.1436    0.0160 *
Group ADF-Statistic                           -1.8812    0.0300 *

--Intercept and deterministic trend included in the test equation.

--Automatic lag length selection lag length selection based on SIC
with a maxlag of 2.

* Indicating significance at 5%.

Table 3
Kao Residual Cointegration test

           t- statistics    Prob.

ADF            -1.310487     0.09

--No deterministic trend

--Automatic lag length selection based on AIC with a max lag of 2.

Table 4
Results of Panel Dynamic Least Squares (DOLS)

Variable    Coefficient    Std. Error    t-Statistic     Prob.

CRE           0.050288      0.020242       2.484341     0.0204
             R-squared      0.984355

--Results estimated by using Eviews 8.

--Cointegrating equation deterministics with constant and trend
(lead=1, lag=1).