Abstract
The emergence of global value chains (GVCs) has provided some firms the opportunity to internationalize by specializing in the production of specific inputs or tasks along the chain, with a direct impact on a country’s competitiveness. China, for instance, managed to enter low value added phases in GVCs to rapidly become a major player in world trade. Against this background, this paper asks whether North Africa, given its favourable geographic position in the Mediterranean and proximity to Europe, can grab similar opportunities. In particular, it analyzes the GVCs participation of North African firms and its implications for productivity. Since the coordination of vertically fragmented production processes increasingly relies on an adequate level of quality and reliability, especially when the inputs from several stages and locations must come together in a specific way, we identify firms involved in GVCs as traders with internationally recognized quality certification. Using a propensity score matching diff-in-diff method, the paper finds that firms that enter GVCs both perform better ex ante and show additional productivity gains ex post. Results suggest that policies designed to support certifications and compliance with international standards and to increase trust between firms in different countries, represent an important tool for linking developing countries to global production networks, with possible positive consequences on their economic development and growth.
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Source: Authors’ elaboration based on Eora
Source: Authors’ elaboration based on Eora
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Notes
The analysis is performed on the years before the beginning of both the Great Recession and Arab Spring. In particular, the firm-level analysis applies to the 2004 and 2007 panel survey of Egyptian and Moroccan firms (cross-sectional surveys available for the year 2004 include also Algeria). Two main reasons motivate the choice of the period beside data availability. The first concerns macroeconomic conditions: studying the pre-2007 period keeps the possible shocks of the Great Recession out of the analysis. The second reason is idiosyncratic to North Africa, as the more recent years have been characterized by political instability and turmoil following the Arab Spring. While investigating the economic effects of those extreme events is of primary interest, this goes beyond the scope of this paper and is left for future research.
In the following figures, CHN stands for China; IND: India; LAC encompasses Argentina, Guyana, Chile, Suriname, Bolivia, Ecuador, Colombia, Brazil, Uruguay, Paraguay, Peru and Venezuela; NAFTA: Mexico, USA and Canada; EU27: Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Spain, Sweden and UK; ASEAN: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand and Viet Nam; MEAST: Bahrain, Cyprus, Egypt, Gaza Strip, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria, Turkey, UAE and Yemen; OCEANIA: Australia, Fiji, New Zealand, Papua New Guinea, Samoa and Vanuatu; NA: Algeria (DZA), Egypt (EGY), Libya (LBY), Morocco (MAR) and Tunisia (TUN); SSA: Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Congo, Cote d’Ivoire, DR Congo, Djibouti, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Swaziland, Tanzania, Togo, Uganda, Zambia and Zimbabwe.
Note that considering the individual countries separately, we are likely to inflate the extent of GVC participation of the EU27 relative to other large single countries, such as the China and India.
Sectoral analysis results for the other NA countries, i.e. Algeria, Libya and Tunisia, are available upon request.
Using more recent I–O tables, Del Prete et al. (2017) provide a more detailed sectoral analysis and show that this situation has changed in the last few years, as Morocco has increasingly invested in Free Tax zones and Industrial policy.
WBES are representative of an economy's private sector, but tend to oversample large firms. Unfortunately, the panel surveys employed in this paper do not provide sampling weights. Specifically, the WBES provides info on 276 Moroccan and 654 Egyptian firms. Panel data are not available in these years for other NA countries.
This definition is based on the question: “Has your establishment received an internationally recognized quality certification (e.g. ISO 9001, 9002 or 14000, or sectors specific certifications such as HACCP for food, AATCC for textiles, etc.)?”.
These definitions are based respectively on the questions: (i) “What percent of your establishment’s sales were exported directly?” (ii) “What percent of your establishment’s purchases of material inputs and supplies were purchased through direct imports?” Further, since the Moroccan survey in the second question does not explicitly mention whether the imports were direct or indirect, we additionally checked if: “Avez-vous importé une partie de vos intrants/matières premières? (importation directe).”
Numbers refer to the pooled sample.
To improve comparability with the aggregate IO indicators reported in the previous section, different ISIC rev.3 sectors have been aggregated. Manufacturing sectors without GVCs firms and those with a negligible total number of firms are not reported.
As GVC production processes involve many industries, firms can be active in one on more activities even different from the final good sector. This clearly applies to the above cited case studies. For instance, the Moroccan Matis Aerospace assembles metal products, such as complex wire bundles, whose final use is in the aircraft industry.
Details on the TFP estimation are provided in the next section. Similar figures are obtained with the other productivity measures.
Due to missing data, for some firms we are not able to estimate TFP.
Specifically, we perform a Kernel PSM-DiD estimation. The Kernel matching identifies the counterfactual using weighted averages of all control units within the common support, with the major advantage that all the available information is exploited.
Size is measured as the number of employees. Human capital is the ratio of skilled workers to total number of workers. Capital intensity is capital (machinery and land) over total number of workers. Age is measured in quintiles as 2007 minus the year of foundation. The descriptive statistics for the outcome variables and the firm level characteristics are reported in Appendix in Table 6.
TFP estimation assumes a Cobb–Douglas production function in which value added is the output variable. The estimation is performed at the country level as the number of observations is not sufficient to perform separate country-sector estimations and no deflator is available. We estimate production function coefficients according to different methodologies: Levinsohn and Petrin (2003), Ackerberg et al. (2015) and Wooldridge (2009). The latter is our TFP benchmark measure, as not only this methodology addresses in a parsimonious way several issues related to the estimation of TFP, but by now it is also considered a benchmark in the empirical literature (CompNet Network 2014; Borin and Mancini 2016). The correlation matrix between the estimated productivity measures is reported in Appendix in Table 7.
Industries are classified as two-digit ISIC rev 3.1 activities.
In Egypt, starting in 2004, “the institutional and legislative framework for adopting technical standards has improved significantly. The national body in charge of technical standards, the Egyptian Organization for Quality (EOS), has played a major role in raising exporters’ and importers’ awareness of commitments under the Technical Barriers to Trade Agreement. Egypt is currently rolling out its TradeNet electronic trade document system to connect all agencies to a single electronic point of transaction” (African Development Bank 2013).
Results for the alternative measures of productivity are available upon request.
Alternatively, we also run separate PSM-DDD estimations on the subsamples split according to W i . Results available upon request.
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We would like to thank participants to: the OECD workshop “Value chains: global or regional?”, Paris (April, 2015); the ITSG workshop, Rome (December, 2015); the 56th SIE conference, Naples (October, 2015); the CSAE conference, Oxford (March, 2016); the ETSG conference, Helsinki (September, 2016); the 57th SIE conference, Milan (October, 2016); the Vienna Investment Conference (September, 2016); the ITSG workshop, Lucca (October, 2016). We are also grateful to the editor and two anonymous reviewers for their useful comments. Usual disclaimers apply.
Appendix
Appendix
See Tables 6, 7, 8, 9, 10, 11, 12 and 13 and also Figs. 5, 6, and 7
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Del Prete, D., Giovannetti, G. & Marvasi, E. Global value chains participation and productivity gains for North African firms. Rev World Econ 153, 675–701 (2017). https://doi.org/10.1007/s10290-017-0292-2
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DOI: https://doi.org/10.1007/s10290-017-0292-2