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Mapping Emerging and Potential Manufacturing and Agri-Business Clusters in Georgia

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Mapping Emerging and Potential Manufacturing and Agri-Business Clusters in Georgia EU4Business Mapping Emerging and Potential Manufacturing and Agri-Business Clusters in Georgia EU Innovative Action for Private Sector Competitiveness in Georgia (EU IPSC) ENI/2018/401-351 UNIDO project ID: 180316 December 2019 Disclaimer This material has been produced with the assistance of the Euro- pean Union. Its contents are the sole responsibility of UNIDO and do not necessarily reflect the views of the European Union. This material has been produced without formal United Nations editing. The designations employed and the presentation of the material do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations Industrial Development Organization (UNIDO) concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries, or its economic system or degree of development. The opinions, statistical data and estimates contained in signed articles are the responsibility of the author and should not neces- sarily be considered as reflecting the views or bearing the endorsement of UNIDO. Although great care has been taken to maintain the accuracy of information herein, neither UNIDO nor its Member States assume any responsibility for consequences which may arise from the use of the material. Content LIST OF ABBREVIATIONS 5 Executive Summary 6 Introduction 8 Chapter I: Methodology of the report 9 1.1. Limitations 12 Chapter II: Overview of identified emerging and potential clusters in the regions 13 2.1 Overview of Tbilisi and its clusters 13 2.1.1 Overview of Tbilisi and its economy 13 2.1.2 Territorial division and enterprise concentration 17 2.1.3 Past relevant studies 19 2.1.4 Tbilisi statistical analysis 21 2.1.5 Analysis of potential clusters 23 2.1.6 Analysis of potential clusters identified through information gathered from dierent stakeholders 31 2.2 Overview of Kakheti and its clusters 35 2.2.1 Overview of the region and its economy 35 2.2.2 Past relevant studies 41 2.2.3 Regional statistical analysis 42 2.2.4 Analysis potential clusters 44 2.2.5 Analysis of potential clusters identified through information gathered from dierent stakeholders 47 2.3 Overview of Kvemo Kartli and its clusters 50 2.3.1 Overview of the region and its economy 50 2.3.2 Past relevant studies 55 2.3.3 Regional Statistical Analysis 56 2.3.4 Analysis of potential clusters 59 2.3.5 Analysis of potential clusters identified through information gathered from dierent stakeholders 61 2.4 Overview of Mtskheta Mtianeti and its clusters 62 2.4.1 Overview of the region and its economy 62 2.4.2 Past relevant studies 67 2.4.3 Regional statistical analysis 68 2.5 Overview of Shida Kartli and its clusters 72 2.5.1 Overview of the region and its economy 72 2.5.2 Past relevant studies 76 2.5.3 Regional statistical analysis 76 2.5.4 Analysis of potential clusters 79 2.5.5 Analysis of potential clusters identified through information gathered from dierent stakeholders 87 2.6 Overview of Samtskhe-Javakheti and its cluster 89 2.6.1 Overview of the region and its economy 89 2.6.2 Past relevant studies 94 2.6.3 Regional statistical analysis 95 2.6.4 Analysis of potential clusters 97 2.6.5 Analysis of potential clusters identified through information gathered from dierent stakeholders 103 2.7 Overview of Guria and its clusters 106 2.7.1 Overview of the region and its economy 106 2.7.2 Past relevant studies 111 2.7.3 Regional statistical analysis 111 2.7.4 Analysis of potential clusters 114 2.7.5 Analysis of potential clusters identified through information gathered from dierent stakeholders 118 2.8 Overview of Samegrelo – Zemo Svaneti and its clusters 119 2.8.1 Overview of the region and its economy 119 2.8.2 Past relevant studie 125 2.8.3 Regional statistical analysis 126 2.8.4 Analysis of potential clusters 129 2.8.5 Analysis of potential clusters identified through 132 information gathered from dierent stakeholders 136 2.9 Overview of Racha-Lechkhumi and Kvemo Svaneti and its clusters 2.9.1 Overview of the region and its economy 136 2.9.2 Past relevant studies 140 2.9.3 Regional statistical analysis 142 2.9.4 Analysis of potential clusters 145 2.10 Overview of Autonomous Republic of Adjara and its clusters 147 2.10.1 Overview of the region and economy 147 2.10.2 Past relevant studies 153 2.10.3 Regional statistical analysis 154 2.10.4 Analysis of potential clusters 157 2.11 Overview of Imereti and its clusters 161 2.11.1 Overview of the region and its economy 161 2.11.2 Past relevant studies 165 2.11.3 Regional statistical analysis 165 2.11.4 Analysis of potential clusters 168 2.11.5 Analysis of potential clusters identified through information gathered from dierent stakeholders 176 Chapter III: Country level priority cluster selection 178 3.1 Set of criteria and methodology 178 3.2 Raw values of indicators on a cluster level 182 3.3 Cluster selection matrix 192 Annex I – List of interviewed key informants 194 References 198 LIST OF ABBREVIATIONS AR Autonomous Republic n.e.c. not elsewhere classified ARDA Agricultural and Rural Development NTFP Non-Timber Forest Products Agency PAFAI Promoting Access to Finance and CIS Commonwealth of Independent States Agricultural Insurance DCFTA Deep and Comprehensive Free Trade Area PIN People in Need ENPARD European Neighbourhood Programme for PMC Policy and Management Consulting Agriculture and Rural Development RAS Rural Advisory Service EPI Economic Prosperity Initiative R&D Research and Development EU European Union SITC Standard International Trade Classification EUR Euro SRCA Scientific Research Center of Agriculture FAO Food and Agriculture Organization SSA Shift Share Analysis GCCI Georgian Chamber of Commerce UN United Nations and Industry UNDP United Nations Development Programme GDP Gross Domestic Product UNESCO United Nations Educational, Scientific GEL Georgian Lari and Cultural Organization GHGA Georgian Hazelnut Growing Association UNIDO United Nations Industrial GIZ Deutsche Gesellschaft für Internationale Development Organization Zusammenarbeit UNJP United Nations Joint Programme GMP Good Manufacturing Practices USD United States Dollar HACCP Hazard Analysis and Critical Control Point USAID United States Agency for Horeca Hotel/Restaurant/Café International Development HPP Hydro Power Plant VAT Value Added Tax HS Harmonized System ICT Information and Communications Technology IE Individual Entrepreneur IOM International Organization for Migration IPSC Innovative Action for Private Sector Competitiveness ISET International School of Economics at TSU ISIC International Standard Industrial Classification ISO International Organization for Standardization JSC Joint Stock Company LEPL Legal Entity of Public Law LLC Limited Liability Company LTD Limited Company LQ Location Quotient MSMEs Micro, Small and Medium Enterprises NACE Statistical classification of economic activities in the European Community 5 Executive Summary This nationwide cluster mapping was conducted under EU Innovative Action for Private Sector Competitive- ness in Georgia (EU IPSC), implemented by UNDP, FAO, UNIDO and IOM, and funded by EU. UNIDO’s com- ponent of the UNJP aims at strengthening capacities of policy-makers and other stakeholders to identify and develop clusters. As part of this component, UNIDO mapped 53 emerging and potential clusters with a focus on manufacturing and agribusiness in Georgia using the UNIDO cluster definition which defines a cluster as a sectoral and geographical concentration of enterprises and/or individual producers that produce a similar range of goods or services and face similar threats and opportunities. A cluster encompasses enterprises as well as their support- ing institutions (public and private), and civil society and academia. The report was prepared according to the UNIDO cluster development approach , which argues that a well-designed and executed cluster selection process is a precondition for a successful initiative. The report uses two layers of analysis with both qualitative and quantitative information that has been investi- gated and well-elaborated. These two data sources have a complementary function in this process: quantitative analysis acts as a screening exercise that generates a preliminary, but not an exhaustive, list of existing or poten- tial clusters in the regions. Quantitative information is triangulated and refined by comprehensive qualitative assessment. Throughout the mapping process, a number of in-depth interviews have been conducted with various stakeholders. To identify industry clusters, the report mostly uses Location Quotient (LQ) analysis, which measures how concentrated a particular industry/cluster is in a region as compared to the nation in terms of employment. After calculation of the LQs, the top 20 sectors with highest LQ above 1.25 in the regions and 1.5 in Tbilisi were selected. The report aims to prioritize manufacturing industries as this the main purpose of the study; however, due to limited number1 of agglomerations in manufacturing sectors, primary agriculture and mining has also been included. The exact stages for the selection of sectors in the regions are presented below: • Drop all sectors except agriculture, mining and manufacturing; • Keep sectors in which the number of companies is higher than one; • Extract the top 20 sectors by LQs; • Extract the top 20 sectors by number of companies in the sector; • Keep the sectors that are covered in both categories: the top 20 sectors by LQ and the top 20 sectors by number of companies. In Tbilisi, sectors with an LQ higher than 1.5 and ten enterprises have been discussed. The methodology presents number of limitations, including a large number of enterprises in the main database which did not have an indication of the corresponding industry, employment statistics for LQs are estimated indirectly, and the high number of unregistered companies, especially in the agricultural sector.
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