Poverty, Forest Dependence and Migration in the Forest Communities of Turkey Evidence and policy impact analysis POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY A B POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY Poverty, Forest Dependence and Migration in the Forest Communities of Turkey Evidence and policy impact analysis JUNE, 2017 Acknowledgements This paper was prepared by a combined team1 of World Bank staff and consultants, working with local Turkish consultants and stakeholders in close collaboration.2 The team would like to acknowledge the efforts of UDA Consulting in Turkey for the survey’s design and implementation. The team would like to acknowledge the support and design contributions of the Program for Forests (PROFOR), who also funded this study. Additionally, the team would like to acknowledge the cooperation of the General Directorate of Forestry (GDF), who provided guidance and the oversight of information that led to the construction of the survey and sample design. The findings from this paper form an integral part of a much broader engagement with the Turkish GDF through a jointly-produced Forest Policy Note. 1 The Team comprised: Craig M. Meisner (World Bank, Task Team Leader and Sr Environmental Economist), Limin Wang (World Bank, Consultant), Raisa Chandrashekhar Behal (World Bank, Consultant), and Priya Shyamsundar (World Bank, Consultant), Andrew Mitchell (World Bank, Sr Forestry Specialist), and Esra Arikan (World Bank, Sr Environmental Specialist). 2 Local Turkish collaborators included: UDA Consulting for survey implementation and the Central Union of Turkish Forestry Cooperatives (OR-KOOP). CONTENTS Executive Summary............................................................................................................................................ 3 1. Introduction................................................................................................................................................... 7 2. Forests and Forestry Institutions in Turkey.......................................................................................................... 9 2.1. Forest Resources.................................................................................................................................... 9 2.1.1. Non-wood Forest Products (NWFPs).............................................................................................. 10 2.1.2. Ecosystem Services..................................................................................................................... 11 2.1.3. Economic Value of Ecosystem Services........................................................................................... 12 2.2. Forest Institutions, Legislative and Policy Framework................................................................................. 12 2.2.1. Institutional Framework................................................................................................................ 12 2.2.2. Forest Villages........................................................................................................................... 13 2.2.3. Historical Support to Forest Villages............................................................................................... 14 2.2.4. Other Key Stakeholders............................................................................................................... 16 2.2.5. Legislation................................................................................................................................. 17 2.2.6. Policy Framework....................................................................................................................... 18 3. Socio-Economic Conditions In Turkey’s Forest Villages........................................................................................ 19 3.1. Socio-economic Household Survey........................................................................................................ 19 3.1.1. Socio-demographic Conditions..................................................................................................... 21 3.1.2. Income Sources......................................................................................................................... 21 3.1.3. Income Diversification and Forest Dependency................................................................................. 22 3.1.4. Poverty in Forest Villages.............................................................................................................. 23 3.1.5. Differences between the Poor and Non-poor................................................................................... 24 4. Forest Resource Use and Management ........................................................................................................... 27 4.1. Income by Product............................................................................................................................... 27 4.2. Forest Resource Dependency: Energy, Health and Housing....................................................................... 28 4.3. Forest and Pasture Management........................................................................................................... 28 5. Analysing Migration Decisions........................................................................................................................ 30 5.1. Descriptive Statistics of Migrant Households in the SEHS.......................................................................... 30 5.2. Factors Influencing Household Migration Decisions.................................................................................. 32 5.3. Simulating Effects on the Migration Decision........................................................................................... 33 6. Pathways Out of Poverty ............................................................................................................................. 34 6.1. Variation of Participation across Income Quintiles ................................................................................... 35 6.2. Determinants of Income........................................................................................................................ 35 7. Interpreting the Results.................................................................................................................................. 37 7.1. Assessing the Poverty Impacts of Policies................................................................................................ 38 8. Conclusions and Policy Recommendations........................................................................................................ 41 References ...................................................................................................................................................... 43 Appendix 1: ORKOY – terms and conditions of support......................................................................................... 45 Appendix 2: Migration analysis......................................................................................................................... 48 Appendix 3: Income regressions, by source......................................................................................................... 49 Appendix 4: Proportion of household asset ownership.......................................................................................... 50 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 1 Figures Figure 2‑1 Turkey’s Forest Cover........................................................................................................................... 9 Figure 2‑2 Active Management of NWFPs in 13 European Regions........................................................................ 11 Figure 2‑3 Ministry of Forestry and Water Affairs................................................................................................. 12 Figure 3‑1 Randomized Sample of Forest Villages Surveyed.................................................................................. 20 Figure 3‑2 Income Diversification........................................................................................................................ 22 Figure 3‑3 Common Combinations of Income Sources (% of households)................................................................. 22 Figure 4‑1 Percentage of Households Collecting Forest Products............................................................................. 27 Tables Table 2‑1 Forest Area and Growing Stock........................................................................................................... 10 Table 2‑2 Support to Forest Villages (FTE = Fulltime Equivalent)............................................................................... 14 Table 2‑3 Historical Development of Forest Legal and Regulatory Framework........................................................... 17 Table 3‑1 Survey Sample................................................................................................................................... 19 Table 3‑2 Household Demographics and Employment Status by Poverty and Migration Area...................................... 20 Table 3‑3 Household Average Income by Source and Participation......................................................................... 21 Table 3‑4 Forest Village Poverty Rates versus Regional Poverty Rates....................................................................... 24 Table 3‑5 Poor and Non-poor Household Comparison: Socio-demographics and Assets............................................ 25 Table 3‑6 Comparison between Poor and Non-poor Households: Income Share and Diversification............................ 25 Table 4‑1 Forest Resource Dependency by Income................................................................................................ 28 Table 4‑2 Forest and Pasture Management........................................................................................................... 29 Table 5‑1 Distribution of Household Migration Status by Stratum (% of total HH)....................................................... 30 Table 5‑2 Household Socio-demographic Profile by Migration Status....................................................................... 31 Table 5‑3 Average Income by Household Migration Status (TL)............................................................................... 31 Table 5‑4 Determination of Migration Probability.................................................................................................. 32 Table 5‑5 Estimated Probability of Migration and Policy Simulation......................................................................... 33 Table 6‑1 The Proportion of Non-participant Households by Income Source and Income Quintile................................ 35 Table 6‑2 Determinants of Income, by Income Source............................................................................................ 36 Table 7‑1 Analysis of Policy Impact on Income and Poverty: simulations (TL)............................................................. 39 Table 7‑2 Poverty Impact across Regions............................................................................................................. 40 Table 8‑1 Comparing Turkey’s Forestry Sector with the EU’s, 1990-2010................................................................. 41 Boxes Box 1. Role of GDF.......................................................................................................................................... 13 Box 2. Forest and Village Relations Department (ORKOY)..................................................................................... 15 Box 3. The Central Union of Forest Villagers Cooperatives (OR-KOOP)................................................................... 16 Box 5. P.R.I.M.E. - Pathways Toward Prosperity.................................................................................................... 34 Box 6. Targeting Development Programs around the World................................................................................... 38 2 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY EXECUTIVE SUMMARY This paper is part of an ongoing collaboration between the and training. Generating higher return forest employment, and World Bank and the General Directorate of Forestry (GDF) in investing in a skilled productive labor force, will have the dual Turkey. In 2013, the GDF requested that the Bank help update effect of improving livelihoods and sustainable forest use and their 5-year Forest Sector Strategy (2017-2021), and together management. To implement the entire FSP 2017-2021, the they developed a Forest Policy Note (FPN) which provided a GDF has notionally allocated over $US 10 billion over this comprehensive overview of the Forestry Sector; an in-depth timeframe to achieve these objectives, with over $283 million analysis identifying areas in which the sector could adopt in forest villager support, including the goal of creating more international best practices in sustainable forestry management. than 5,000 new forest-related employment opportunities. As part of that analysis, a survey was also undertaken to better understand the socioeconomic dimensions of forest villages, Sustainable forest management and poverty alleviation their forest dependency and the constraints to income growth are twin goals embodied in the Constitution and Forest in these rural areas. This paper is a complementary document Law. The government’s forest development policies prioritize to the FPN, and summarizes the findings of the socioeconomic the sustainable management of forests, in conjunction with survey of forest villages and identifies several potential policy anti-poverty measures among forest dwelling communities. directions to improve the livelihoods of forest villagers. These policies are reinforced through the Forest Law and the Constitution. Two articles (169 and 170) in the Constitution Turkey’s forests are an important asset both domestically and are directly related to the overall management and internationally. Turkey’s forests, covering about 28.6% of land development of Turkey’s forest resource. Article 169 focuses area and accounting for 13% of the total forest coverage in the on the protection of state forests, and Article 170 mandates European Union (EU), represent an extremely important asset the necessity for effective co-operation between the state and in both the domestic and international context. State owned inhabitants of forest villages through appropriate measures forests (99.9% of all forests) generate over $225 million in designed to improve their living conditions. The approach is harvesting revenue annually and possess a rich diversity of based on the understanding that if the livelihoods of villagers non-wood forest products (NWFPs), largely unexploited, can be supported and more income opportunities provided, with great export potential to EU countries. Turkey’s forests relations between villagers and the sector would promote more play a critical role in conserving biodiversity, mitigating the efficient protection of forests and better living standards for adverse consequences of climate change, and supporting the forest-dependent communities. Under the Forest Law, forest livelihoods of over 7 million forest villagers (representing about villagers are also given preferential treatment. Under Article 40% of the rural population). Forest villagers also represent a 40, villagers have a right to employment in the harvesting, significant proportion of Turkey’s rural poor. thinning, afforestation, maintenance and transportation activities undertaken by the GDF. The policies and goals of the General Directorate of Forestry (GDF) reflect the Government’s commitment to sustainable forest The livelihoods of forest villagers are also supported through management and poverty alleviation. The General Directorate a specialized grant/loan program administered by the Forest of Forest (GDF), the key forestry governing body, developed and Village Relations Department (ORKOY) within the GDF. a Forest Strategic Plan (FSP) (2017-2021), which determined The purpose of ORKOY is to contribute to the protection, an overall vision with four main objectives. These include: development and attainment of forest production targets by (1) protecting the forests and biodiversity against biotic and supporting the socio-economic development of forest villagers abiotic pests, (2) developing and expanding the existing through the operation of a grant and soft-loan program. It forests and increasing forest harvesting efficiency, (3) meeting is intended to contribute to sustainable forest management the public’s evolving expectations for forest goods and services and reduce the negative pressure on forests. ORKOY’s main and (4) ensuring the institutional development needed to activity is the soft loan/grant program for both individuals provide sustainable forest management. The FSP also contains and cooperatives. In 2017, ORKOY’s total budget was 150 a number of sub-objectives to strengthen the outcomes of the million TL ($US50 million) – with 120 million allocated to 4 main objectives, notably the continued support of raising loans/credits and 30 million to grants. Individuals may apply the standards of living for forest villagers. More specifically, for credits and grants for social (i.e. home energy efficiency they have directed their policies and programs towards: (1) measures) and economic (i.e. income generating) purposes. improving the living standards of forest villagers by creating Loans and grants are also offered to established cooperatives better paid employment opportunities, and (2) modernizing within villages. In 2014, ORKOY provided support to over the forest sector by upgrading forest information systems, 12,500 families and 23 cooperatives. equipment, and also human capital through skills development POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 3 Challenges to achieving GDF’s goals are the result of current Challenge 2: Poorer forest-village households appear forest-use patterns and the changing socio-demographics to be held back by their high forest-dependency and of the forest village population. The government and its lack of income diversification. In certain contexts, forest development partners, including the World Bank and EU, dependency can result in a poverty trap, and this currently have recognized the challenges posed by (1) low productivity appears to be the case in Turkey. Forest dependency is of the forest sector due to inadequate investments in forest reinforcing poverty in forest village households because management technology and the skills of the local labor force, of the low-returns to forest-related activities; a result of and (2) the rapidly declining and ageing population in forest low value-added forest product sales (and prices) and villages due to migration resulting from high poverty and a lack low-skill, forest-related employment. Limited income of employment opportunities. Over the past 35 years, the forest diversification opportunities, as found in the SEHS, further villager population has fallen from 18 million to about 7.1 traps these poor households in a cycle of generating low- million, as of 2014, mostly due to net out-migration to urban value forest income. areas as people seek employment and better opportunities. Since forest villagers constitute GDF’s harvesting labor, Forest income constitutes the largest share of income among managing the forests with a vanishing labor force is becoming the poorest households, with the lowest returns. It is 28% increasingly difficult. Migration will continue to be a growing of a poor household’s income, compared to 8% of total concern as long as the harvesting model relies on mostly low- income for non-poor households. Not only is average gross skilled, low-paid, manual labor with low returns. income the lowest at 2,158 Turkish Lira (TL) ($US 617), but income disparity exists even within participating households The Socio-Economic Household Survey of Forest Villagers as highlighted by the difference in the median gross income (SEHS), conducted by the GDF and the World Bank in 2016, which is only 400 TL ($US 114). Approximately a quarter was launched to provide further insights on the livelihoods of of poor households receive income only from forests, forest-dependent households. This new data source collects compared to 2% of non-poor households. important information on the socio-economic conditions of forest village populations, income generating opportunities, Non-poor households diversify more, and in higher- forest use and management practices, migration and activities return activities. Most often, these households supplement of forest development programs and cooperatives. The forest income with income from livestock, agriculture, analysis highlights the main challenges (summarized below) and pensions. Agricultural income has the highest returns to improving villager livelihoods and forest management and among all sources, averaging 28,700 TL ($US 8,200) provides much needed evidence for informing the design and across households. This is even greater than the average implementation of forest community development programs. household income in the sample, but used more by non- poor households (18%) than poor ones (8%). It is interesting Challenge 1: Poverty rates are high and unequally to note that pensions represent the largest share of distributed. The poverty rate in forest villages, estimated income among non-poor households (constituting 44% of using the national poverty line of 1,115 Turkish Lira (i.e. household income on average) which provides evidence $319 USD) per month, per capita3, is approximately for an ageing demographic and a heavy reliance on cash 80%, which is more than twice the average rural poverty transfers. Moreover, 8% of these households use pensions rate in Turkey according to official statistics. There is also as their only source of income. This is not surprising since significant spatial variation in poverty levels, both across the average pension is approximately 15,500 TL ($US regions and within regions. Comparing SEHS forest- 4,429), almost 60% of average household income. village poverty rates to regional poverty rates from the Income and Living Conditions Survey shows that although Challenge 3: Growing out-migration is most prevalent a region’s overall poverty rate might be low, it might harbor among forest-dependent households, which poses a high-poverty forest villages. The Mediterranean Region is threat to the GDF’s current forest management model a case in point where the regional poverty rate is about owing to its reliance on forest village labor supply. 18% (Turkstat), but is higher among forest villages (68% Although migration reduces the pressure on forests, the from the SEHS). This indicates that within-region inequality costs of insufficient forest management will be higher in is being masked when aggregated to the regional level the long run. An improved forest management model and so targeted social programs should be aware of this. that improves the sustainability of forest-use among 3 From TÜRK-IS Survey in July 2016 http://www.turkis.org.tr/TEMMUZ-2016--ACLIK-ve-YOKSULLUK-SINIRI-d1156#sthash.vQEufSOc.dpuf 4 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY villages and directs forest collection and services towards While the first policy (1) generates a much larger overall more profitable opportunities will ensure both a thriving reduction in the poverty rate (30%), the increase is smaller community and forest. across the income distribution; with the poorest seeing a >40% income increase, while the richest see only a 26% Migration rates show no sign of slowing down. Economic increase in income. In contrast, the second policy (2) is migration is a pathway out of poverty among forest village less effective in reducing overall poverty (12% reduction households, and its prevalence is on the rise. In the SEHS, in poverty rate) but it is highly progressive. The poorest 13% of households claimed at least one migrant during saw the largest increase in income (113%) while the the past 5 years, a number 2% higher than the 5-year richest gain only about 1% in income. The implementation period from (2005-2010), indicating an upward trend of these two programs combined is estimated to halve in migration. Moreover, a fifth of households (19%) with the poverty rate among forest villagers (54% reduction), permanent migrants have no prime working-age members which suggests that the two programs are tremendously left at home. complementary. A more in-depth analysis reveals the potential of three Moving forward policy levers that influence migration: membership in forest-related cooperatives and associations, forest- Recommendation 1: To slow out-migration of forest villages, dependency and income diversification to have the create greater income opportunities from the forests most significant effects on the household’s migration and diversify. Migration is an inevitable consequence of decision. Forest cooperative membership and income development. As economies and cities grow, it is natural for diversification reduce the probability to migrate, whereas migration to occur in the search for a better standard of living. forest-dependency increases it. Since previous findings However, in terms of forest resource management, there is an highlighted that the poor are more forest dependent and argument to be made that harvesting labor will largely remain diversify less, we can conclude that poor households are a rural job. So the challenge is in how to incentivize living in more likely to migrate. A policy simulation to estimate the conditions that are less connected to the outside world. In this benefits of cooperatives found that in the hypothetical study, migration was found to increase with forest dependency case of full membership across all households, migration and lower income diversification opportunities. In contrast, would decrease by 19% (i.e. almost 500,000 fewer membership in forest cooperatives was found to reduce the people would migrate). tendency to migrate. Although it would not halt or reverse out- migration, the identification of better income opportunities, Challenge 4: Policy simulations reveal that poor perhaps through ORKOY support to the establishment of forest households benefit more disproportionately from access cooperatives, would help diversify forest income from only the to productive assets and cash transfers (such as pensions low value-added activities being practiced today. But how do and remittances). Results from using SEHS data show we identify these opportunities? that one of major differences between the poor and non- poor households is access to pensions. Based on median Recommendation 2: Investments in value-added activities income by source, pensions are ranked as the second such as NWFP processing, can increase forest villager most important source of income (non-forest wage is employment, productivity, and thereby also the standard of ranked first), and its security and stability indicates that living for forest villagers. Turkey is the 12th largest exporter pension income serves as a safety net to reduce income of NWFPs in the world, but only 20% of NWFPs undergo vulnerability. Access to productive assets was also found domestic value addition (such as processing) before being to be an important factor in generating higher income. exported. The estimated value of this gap is significant. A Three policy simulations were conducted by 1) targeting recent World Bank assessment of non-wood forest ecosystem ORKOY credit programs to households that currently do services estimated the value of NWFPs for Turkey at US$2.30 not have key productive assets, such as trucks and tractors, per hectare per year, compared with an average for Europe 2) providing basic income support to households that of US $20.70 (only 10% of Europe’s average). In another do not receive pensions, and 3) a combination of both study of NWFP management across Europe (EU StarTree interventions. Simulations were conducted by assuming Project) found Turkey to have the smallest share of managed that all villagers were provided these types of support. The or formally-harvested NWFP cultivation (approximately 30%). analysis explored the impact these interventions had on income and the poverty rate. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 5 NWFPs have traditionally been collected by forest villagers at low prices (tariff price) and there is a need to strengthen the value-chain of NWFPs by encouraging more local processing and value adding. Targeted programs that enable investments in Small and Medium Enterprises (SMEs) for processing and packaging of NWFPs would strengthen the value chain and local connections (e.g. via e-commerce). While the potential of NWFPs should not be oversold in terms of their potential for lifting thousands of people out of poverty, they can make a difference to those located near high-value products that can be developed. So where are the opportunities for NWFP strengthening and development? Recommendation 3: A comprehensive and expansive National Forestry Inventory (NFI) is an essential tool for future policy and NWFP sector development. The current system of assessing forest data on a national level relies on the amalgamation of information from forest management plans to provide data on forest resources at a national level for policy, planning and for data to comply with its international reporting commitments. This has a number of shortcomings including the timeliness of data and lack of precision in the main parameters. National forest policy also requires accurate, timely and comprehensive information. An NFI could help identify region-specific issues, such as tourism opportunities, road infrastructure, NWFP location and potential and help the GDF prioritize these issues. Information generated would also be useful to forest villagers for local development and support programs. Work in this area would also include expanding the NFI to cover other important issues such as biomass and soil carbon. This wider accounting stance would allow for estimation of the Total Economic Value (TEV) of forest and ecosystem services, which are not currently valued, which leads to more informed decision making on the development potential of specific forest areas. The NFI could be combined with future socioeconomic surveys of forest villagers to help identify potential new opportunities of support through ORKOY. 6 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 1. INTRODUCTION Steady economic growth over the past few decades has bought development, with its broader range of objectives including Turkey to the threshold of becoming a high-income economy. addressing climate change, conserving environment and According to a World Bank report (2014), Turkey’s achievement biodiversity, reducing poverty and achieving the Millennium is attributed to major policy reforms that have fostered: (1) Development Goals (MDGs). economic integration through trade liberalization and improved connectivity (investment in infrastructure investment and ICT) (2) Four priority areas are identified in FSP, including (1) social inclusion through managed urbanization, job creation improving productivity of wood production and harvesting by and improved public services and solid public finances and (3) forest villager through better technology and equipment, (2) strengthened institution. Recognizing the importance of its forest expanding the collection of NWFPs, and increase the value resources both in the international and domestic context, Turkey added of NWPFs by developing SMEs targeted at processing became a party of the United Nations Framework Convention and packaging (3) improving the efficiency of the timer supply for Climate Change (UNFCCC) in 2004. chain and procurement through investments in forest resource information systems and improving the efficiency of the timber Turkey’s forest covers about 28.6% of the land area, accounting harvesting supply chain by more closely integrating harvesting for 13% of the total EU forest coverage. The sustainable entities (villagers and cooperatives) which are the principle management of forest resources has important implications both suppliers (GDF) and purchasers. for Turkey and the EU region in achieving these development objectives. Forests provide multiple environmental services The two most pressing challenges to achieving the objectives including watershed protection and erosion control, wood set forth in the FSP are: (1) the rapidly declining and ageing panels industry, a rich source of non-wood forest products population in forest villages due to migration (2) low (NWFPs), and support the livelihoods of forest communities. productivity of the forest sector due to inadequate investments They are also home to a population of 7.1 million forest in forest management technology and the local labor force. villagers, accounting for about 9.6% of the national and 40% Over the past 35 years, the forest villager population of the rural population. has fallen from 18 million to some 7.1 million (in 2014), mostly due to net out-migration to urban areas in search of To promote sustainable forest management, several national employment and better opportunities. Other factors that have forest development strategies have been developed, including spurred migration in the past include high levels of poverty the National Forestry Program (2004-2023), and the Forest due to a lack of income sources, poor infrastructure, limited Strategic Plan (FSP) (2017-2021). The FSP was developed access to markets, and inadequate social services (General following the principles of the global policy for sustainable Directorate of Forestry, 2014). POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 7 In response to these challenges, the GDF has launched a range This report has eight chapters. Chapter 1 provides the strategic of initiatives to improve the living standards of forest villagers. context and motivation of this study. Chapter 2 presents key These include increased investments in technology and features of forest resources, forest institutions, the evolution of infrastructure in forest areas, provision of financial assistance forest legislation, and key forest development plans in Turkey. to village populations, skill training programs, and improved Chapter 3 presents a summary of the 2016 SEHS data, collaboration between GDF and forest villages. However, including household demographics, income sources, activity despite the long history of government supported programs participation rates and comparisons between the poor and in forest communities, the impact of external support on a non-poor. Chapter 4 disaggregates income by forest product household’s welfare and migration decisions remains largely and evaluates forest dependence. Chapter 5 explores unknown owing to a lack of data. The few published studies household migration decisions and their influences. Chapter provide non-generalizable conclusions because of their limited 6 discusses findings from an income determination model sampling and geographic scope (Akan and Kilic, 2014; whose results help identify key determinants of household Atmis et al., 2009; Gokce, 2005; Tolunay and Alkan, 2008; income. Chapter 7 provides an interpretation of the results and Yilmaz, 2006). assesses the distributional impact of policy measures proposed to address poverty. The final chapter concludes with policy To fill this information gap, in 2016 the GDF collaborated recommendations to sustain the level of labor force in forest with the World Bank to conduct a large-scale socio-economic communities, primarily by enhancing forest management household survey (SEHS) across forest villages (World Bank, and protections and improving the forest population’s living 2016). This report aims to use the 2016 SEHS to understand standards. the socio-economic condition of forest village populations, in particular, their income generating opportunities, forest dependency, and the linkages between poverty, forest dependence, and migration. This analysis has four main themes: 1) identifying principal income sources and income diversification strategies across forest villages, 2) identifying variations in poverty levels, forest use and forest management practices across villages, 3) examining factors associated with migrations and evaluating linkages between poverty, forest dependence, and migration 4) evaluating ways in which these households can move out of poverty. 8 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 2. FORESTS AND FORESTRY INSTITUTIONS IN TURKEY 2.1. Forest Resources and forest in-growth on abandoned lands. Forest cover is Turkey’s forest area accounts for about 28.6% of land area 4 shown in Figure 2‑1. Approximately 50% of forests are classed and forest resources are almost all (99.9%) owned by the as having an economic function including the production of State, reflecting the nationalization of forests in 1945 (Law roundwood, fire-wood and non-wood forest products, 42% of Nationalization, Law 4785) in an attempt to safeguard has an ecological function including watershed and erosion resources and combat over-exploitation. The forest area has control and the remaining 8% is classed as social and cultural increased by 2.14 million ha since 1973 due to afforestation (General Directorate of Forestry, 2015). Figure 2‑1 Turkey’s Forest Cover 4 The definition of forest in Turkey excludes forest areas less than 3 ha and areas containing species not found in natural forests. Forest areas with a canopy cover of 10% or more are classed as “productive” forest and required to have an allowable cut identified in the forest management plan. The area of forests owned by private persons and public entities with status as a legal entity is approximately 22,000 ha. However due to the definition of forest and the fact that some private land planted with trees remains classed as agricultural land, the area of private forest is significantly understated, and includes an estimated 160,000 - 200,000 ha of high yielding plantations that are mainly poplar. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 9 Table 2‑1 Forest Area and Growing Stock Area (million ha) Growing Stock (million m3) Degraded Degraded Forest Type Productive Forest Forest Total Productive Forest Forest Total High forest 11.92 7.70 19.62 1,506.13 33.69 1,539.82 Coppice forest 0.79 1.94 2.72 60.00 11.95 71.95 Total 12.70 9.64 22.34 1,566.13 45.65 1,611.77 Forests in Turkey are divided into two categories, i.e. high NWFPs receive any form of processing or added value. About forests and coppice5 forests, in terms of the way they are 138 different NWFPs are sourced from Turkey’s forests, but so operated (Table 2‑1). The proportion of coppice forests has far there has been no systematic management planning for these decreased over time due to the policy of converting them to resources, mainly because the necessary institutional capacity high forest. Some 43% of forests are classed as degraded and still needs to be built. The recent World Bank assessment of in need of rehabilitation work. The total growing stock is 1.6 non-wood forest ecosystem services estimated the value of billion m3 with degraded forests accounting for 71.95 million NWFPs for Turkey as US$2.3 per hectare per year, compared m3 or 4.5% of the growing stock. The average growing stock is with a European average of US $20.7, indicating significant 72.14 m3 per ha, which varies from less than 7.46 m3 per ha growth potential in the future (Siikamäki et al., 2015). This is in degraded forest to 121 m3 per ha in productive high forest, reinforced by the findings from the EU StarTree project, which as compared with European and world averages of 105 m3 show that Turkey has not as yet fully exploited the potential per ha and 130 m3 per ha (State of Europe’s Forests, 2011). for cultivated forms of NWFPs or undertaken management of these resources at an intensity as practiced in some countries 2.1.1. Non-wood Forest Products (NWFPs) (see Bursa in Figure 2‑2) (Wong and Prokofieva, 2014). The international trade of selected NWFP commodity Forest villagers have traditionally been the primary collectors of groups reached US$12 billion in 2011 and has shown NWFPs, albeit at low prices (tariff price). GDF is responsible steady growth over previous years (Wong and Prokofieva, for permissions and collection quantities since endangered plant 2014). An increasingly diverse range of products and steady species need protections in order to sustain the biological and demand has typified the sector over the past decade and genetic diversity in Turkey. Despite efforts by the GDF in the ensures continued growth. early 2000s to improve sustainable management of NWFPs and increase their contribution to the rural economy, there is Turkey, rich in NWFPs, is ranked 21st in the world in terms insufficient added value and many NWFPs continue to be of their export. Due to the country’s different climatic and exported in an unprocessed state. However, supply of non-wood geographic conditions, it is home to a wide variety of tree, forest products has continually increased - reaching 429,000 shrub and herbaceous plant species. The majority of the tons as of December 2016, up from 31,000 tons in 2002. NWFPs are found in forests, principally along the coastline. Of the estimated 12,500 plant species in Europe, Turkey has Forest managers believe that the area of NWFPs, while circa 11,707 plant species of which 3,649 are endemic currently of only moderate importance, will become increasingly (Ministry of Forestry and Water Affairs, 2011). Turkey is one important into the future and on a par with biological diversity of the top three worldwide producers of laurel leaves, thyme, (Kuvon et al., 2011). Plants are the natural and biological sage and pine nuts. In 2013, the most recent year for which raw materials for many sectors including the pharmaceutical, data were available, major exports included thyme (US$56.3 cosmetics, medicine, food, dye and chemical industries. It’s million), bay leaves (US$32.26 million), sage (US$6.3 estimated that approximately 500 plants in Turkey are used million) and plant extracts (US$30.82 million) (Secretariat for medical purposes. A NWFP and services department was General of the Central Anatolian Exporters Union, 2014). The established in the GDF headquarters in 2011 and under the principal importer of NWFPs in 2010 was the USA, followed current Strategic Plan there are targets for inventorying NWFPs by Germany, Japan, France, and Hong Kong. by 2021 as well as measures for utilizing them sustainably. Despite the prevalence of NWFPs in Turkey, their export The 2017-2021 Strategic Plan marks inventory work to unveil potential remains largely untapped despite their low collection the actual potential of NWFPs as a priority area, including the costs – given domestic supply and local labor, only 20% of identification of their current state in terms of natural habitats 5 High forest refers to forests which originate from seed and are managed on a long rotation to produce saw logs. Coppice is where the forest is regenerated from shoots arising from the cut stumps of harvested trees. Coppicing usually produces many stems per stump, and is usually managed on shorter rotations for firewood or other lower quality products. 10 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY Figure 2‑2 Active Management of NWFPs in 13 European Regions and sustainability. The focus is on products having a higher 2.1.2. Ecosystem Services economic value and preparing plans for their sustainable use. The integration of biodiversity and inventory data into forest A growing recognition is that forests can provide many management plans will support planning for the sustainable benefits, identified as ecosystem services. Some of these - such development of NWFPs. In order to identify and diagnose as recreation, relaxation, or shelter - are well appreciated by non-wood forest products and their potential, inventory and the general public while others are less understood, or simply planning studies have been conducted on 1.4 million ha, taken for granted. The Millennium Ecosystem Assessment of covering a total of 210 different species, a result of efforts 2005 defined ecosystem services as provisioning (food, since 2012. The 2017-2021 Strategic Plan envisions that water, wood, genetic resources), regulating (climate, floods, studies will be conducted on an area of 1.9 million ha by disease, water quality), cultural (recreation, spiritual benefits) 2021. In addition, inventory and planning work is designed and supporting (soil formation, primary production). to involve specialization training as well as appropriate employment policies. Under Law No 3234 on the Organization and Tasks of the General Directorate of Forestry, it is tasked with the responsibility The collection of non-wood forest products has potential as a for the provision of recreation areas in forests for public use. An major source of income and employment for those who live in Urban Forests Project launched in 2003 by GDF is ongoing. A rural areas. The diversity of products, potential for in-country total of 145 urban forests have been developed encompassing processing and added value represent a significant opportunity 10,550 ha adjacent to or in the vicinity of cities and towns as for rural communities and the development of an approach of December 2016. Their purpose is to provide for the health, focused on products with high added value as opposed to sport, aesthetic, cultural and social needs of the public while simply harvesting and exporting NWFPs. increasing awareness of flora and fauna. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 11 At the end of 2016, a total of 1,304 forest areas amounting was US$666.3 million for 2013. This figure is seven times the to 16,266 ha have been developed as in-forest recreational current amount accounted for in the national accounts (US$86.4 sites in order to meet the public’s daily recreational and million). The largest portion of the TEV was the indirect use values picnic requirements. In recent years there has been a rapid arising from ecosystem services, including watershed protection, development in nature tours in forest areas of varying duration, carbon sequestration and soil erosion control, which amounted organized by both private sector companies and NGOs for to US$341.4 million or 50.0% of the TEV. In traditional national recreational and training / educational purposes. accounting, these values are largely unaccounted for or partially included in the value-added of other sectors, such as cost 2.1.3. Economic Value of Ecosystem Services reductions in the water supply. The World Bank estimates of Turkey’s total non-wood forest The TEV is a truer reflection of the value and contribution of forests wealth are 2.7 times greater, on average, than those previously to the regional economy, and can help guide development derived. The previous estimates are on average about 39% of programs and policies towards forest protection and a more the revised estimates globally ($26 per hectare per year versus sustainable use of forest resources. The development of forest $67 per hectare per year, in 2013 U.S. dollars). Adding accounts that include estimates of the values of forest services NWFPs and considering the revised measure of accessible would help decision makers to understand the potential forest area increases the revised estimate to $84 per hectare tradeoffs involved with developing certain areas. per year. The estimate for Turkey is $133 per hectare per year - water $98.4, NWFPs $2.4, habitat $1.3 and recreation 2.2. Forest Institutions, Legislative and Policy $31.2 (Siikamäki et al., 2015). Framework A pilot study in the Bolu region on the total economic value 2.2.1. Institutional Framework (TEV) of forestry was completed in 2015 (World Bank, 2015). The direct use, option, indirect use, and non-use values of forest The Ministry of Development (MoD) is responsible for setting products and services were estimated through the use of various the general economic and social development policy in Turkey. valuation methods. The estimated total net economic value (TEV) The National Development Plan for different sectors, including Figure 2‑3 Ministry of Forestry and Water Affairs Minister Head of Inspection and Guidance Deputy Minister Undersecretary Head Internal Audit Turkish Water Institute (SUEN) Deputy Deputy Deputy Deputy Undersecretary Undersecretary Undersecretary Undersecretary Gen Dir Nature Gen Dir Department of Legal Services Protection and Combating Strategy National Parks Desertification and Erosion Dept Press and Department of EU Gen Dir Water Public Relations & Foreign Relations Management Gen Directorate Forestry Dept Training and Department of Gen Dir State Publications Support Services Hydraulic Works Gen Directorate Department of Meteorology Personnel Regional Directorates Department of IT 12 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY Box 1. Role of GDF • Manage forest resources, together with their flora and fauna, in an ecologically integrated fashion by taking into account their ecological (climate change, water, recreational etc.), economic, social and cultural values; • Plan forest resources using a participatory and multi-purpose approach, protecting them against any illegal interventions, natural disasters and fires; combat invasive pests; • Carry out and develop forestry quarantine services; increase forest area and services provided from forests; restore and rehabilitate forests and ensure silvicultural maintenance and regeneration of forests; • Designate recreational areas, urban forests, research forests and arboretums; protective areas for biological diversity; model and protective forests and conserve and sustainably manage these areas; • Carry out activities such as afforestation and erosion control, rehabilitation of rangelands, combat desertification, floods and avalanche control in any area within forests and outside forests; develop and implement integrated watershed projects; • Grow seeds, seedlings, shrubs and forest plants; undertake plant health activities; establish and manage permanent and/or temporary nurseries; • Carry out research and development, inventory, projects related to its services, implement relevant projects and disseminate the outcomes nationally and internationally; • Define technical and administrative principles related to issues within its authority and establish laboratories addressing its field of activities. forestry, is formulated through broad stakeholder consultations, Thirty-five years ago there were approximately 18 million forest including the MoD and line ministries, such as the Ministry of villagers and according to 2014 data, 7,096,483 people Forestry and Water Affairs (MFWA) for the forest sector. live in 22,343 forest villages, constituting approximately 9.6 percent of the national population and 40 percent of the rural The General Directorate of Forestry (GDF) under the MFWA is one. This massive out-migration is largely prompted by the lack established as a corporate body and is responsible for the majority of economic opportunities for prime-aged workers in forest of sustainable forest management activities (see Box 1). These villages. This results in villages largely populated by the very include forest management planning, production and marketing young and old - making the current forest villager harvesting of forest products, the management of forest fires, insects and model less and less sustainable. diseases, forest regeneration and rehabilitation, road construction and maintenance, forest cadaster, urban forests, recreation areas, Responding to the fundamental changes in forestry ecosystem services, reforestation/afforestation, erosion control, approaches, the forestry sector launched assistance programs watershed management, range improvement and support to to forest villages to sustain forest resources and forest-village forest communities and enforcement (see Figure 2‑3). communities. Several initiatives and measures for improvements in rural living conditions have been broadly implemented 2.2.2. Forest Villages under the provisions of the Forest Law 6831 since the late 1950s. This Law provides the legal definition of a forest and Turkey’s rural inhabitant groups can be classified into two: introduced the first set of forest policies and strategies. forest villages and other villages. Forest villages are those containing a forest within their administrative borders (Atmis Two articles (169 and 170) in the Constitution are directly et al., 2010). There are over 21,000 forest villages, with related to the overall management and development of Turkey’s a total population of about 7 million, about 10% of Turkey’s forest resources. Article 169 focuses on the protection of state total population. Forest villages are also divided into villages forests and Article 170 mandates the necessity of effective co- located inside forests or those that are near/adjoining operation between the state and inhabitants of forest villages forests. They are also classified on the basis of whether or not through appropriate measures to be introduced by law for production is performed in forests within village boundaries, the purpose of improving living conditions in these villages. under Articles 31 and 32 of the Forest Law No. 6831. This The approach is based on the understanding that if villagers’ classification also plays a determining role in terms of the livelihoods can be supported and more income opportunities products generated from forests and subsidies provided. Forest provided, then relations between the sector and the villagers villages are given preferential treatment under the Forest Law. would allow for more efficient forest protections and better Under Article 40, villagers there have a right to employment living standards in forest-dependent communities. in harvesting, thinning, afforestation, maintenance and transportation activities undertaken by the GDF. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 13 Table 2‑2 Support to Forest Villages (FTE = Fulltime Equivalent) Average Planned Indicator Unit 2011 - 2014 2015 2016 2017 Employment created through loans to individuals FTE 2,098.0 4,892.0 6,158.0 7,570.0 Employment created through loans to cooperatives FTE 24,285.0 24,695.0 24,875.0 26,060.0 Ratio of people to whom loan support is to be extended to % 30.9 35.1 36.7 38.4 the total forest village population Wood savings provided through social loans ‘000 Stere 658.0 902.0 1,002.0 1,102.0 Source: GDF, 2016. 2.2.3. Historical Support to Forest Villages The aim of these subsidies and support is to: The General Directorate of Forest-Village Relations (GDFVR) A. Promote the sustainability of rural community development was established in 1970 under the Ministry of Forestry (MOF) and enhance rural well-being; with the mandate to contribute to the social and economic B. Improve forest-people relations through increased development of forest areas. Accordingly, over time the participation and involvement in forest management quantity and diversity of assistance for village development practices; and initiatives and measures has increased. The GDFVR developed C. Reduce people’s dependency on forest resources by its activities through alternative employment opportunities introducing alternative income generating activities (i.e. to and income generating facilities for forest villagers and reduce the appeal of unauthorized or illegal harvesting). cooperatives. In 2011, the General Directorate of Forest-Village Relations In 1974, the General Directorate established the “Forest (GDFVR) was closed and its role and responsibilities transferred Village Development Fund” (FVDF) in accordance with the to the GDF as a department, namely the Forest and Village related articles of the Forest Law. Law Nr. 1744 regulates the Relations Department (ORKOY). The principal aim of ORKOY Fund’s implementation structures. It was financed by various is to contribute to the protection, development and attainment sources including a certain portion of forest product sales, the of forest production targets by supporting the socio-economic profits from timber processing facilities and the general budget. development of forest villagers. ORKOY provides employment In addition to the FVDF, through the GDF the State supports opportunities through various channels – including loans forest villages in other ways, such as through employment rights to individuals and cooperatives, and this support has been in forest operations, sales of construction timber and fuelwood increasing over time (Table 2‑2 and Box 2). at highly discounted prices for personal needs, provision of forest planting materials such as seed, etc. 14 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY Box 2. Forest and Village Relations Department (ORKOY) The aim of the Forest and Village Relations Department (ORKOY) within the GDF is to contribute to the protection, development and attainment of forest production targets by supporting the socio-economic development of forest villagers through the operation of a grant and soft-loan program. It is intended to contribute to sustainable forest management and reduce the negative pressure on forests. ORKOY’s main activity is the soft loan/grant program for both individuals and cooperatives. In 2017, ORKOY’s total budget was 150 million TL ($US50 million) – with 120 million allocated to loans/credits and 30 million to grants. Individuals can apply for credits and grants for social and economic purposes. Loans and grants are also offered to established cooperatives within villages. Individual Credit/Grant program Social Purpose Credit Support: These credits target improvements for forest villagers’ quality of life and forest conservation efforts, specifically to reduce the use wood as fuel for heating and preventing the misuse of wood. Some examples include roof covering materials, central heating systems for households and energy efficient stoves with ovens, solar water heating systems, and exterior thermal insulation projects. Social purpose credits may be repaid over a period between 3-7 years, and are interest-free on the first 20%. Between the period 2004-2015, 139,295 solar water heating systems were installed, benefitting over 557,000 forest villagers. In 2016, ORKOY also began supporting electricity production from photovoltaics (PV). Economic Purpose Credit Support: These credits aim to create income-generating opportunities for forest villagers. For example: animal husbandry, beekeeping, mushroom cultivation, medicinal and aromatic plant production, greenhouses, viniculture, fisheries and micro credit programs for housewives. The terms for economic credits are: • For revenue-generating projects, the annual interest rate is 1/7th the T.C. Ziraat Bank’s agricultural loan annual interest rate; currently 1.5% over the maturity date, and loan repayments vary from 4-7 years, depending on the activity. Livestock support is currently interest-free. • There is also grant support for these projects at a rate of 20% of the project amount. *Appendix 1 provides examples and further details on requirements and eligibility. Cooperative Loan/Grant Program Cooperative loans and grants support established forest village cooperatives to improve the cooperation’s capacity, gain greater value-added for forest villager’s products and increase the level of income of cooperative shareholders. Previous successful examples include a trout processing plant, dairy barns and construction equipment. Appendix 1 provides examples and further details on the terms and conditions. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 15 2.2.4. Other Key Stakeholders Other stakeholders include the civil servant unions and the Other key stakeholders in the forest sector include the Central Unions of Forest Workers as affiliated branches in the forestry Union of Forest Villagers Cooperative (OR-KOOP) and a variety sector under related country level unions and confederations. of unions and confederations. OR-KOOP is the second largest The workers’ union represents the rights of permanent and stakeholder, with 2,440 affiliated cooperatives and 294,403 temporary forest workers estimated to comprise 25,000 members throughout the country of which 1,506 and 167,841 people. The Forest Products Exporters, Importers and are forestry based, respectively (OR-KOOP, 2017; see Box 3). Manufacturers Association (TORİD) represents the interests The Chamber of Forest Engineers with 13 regional branches of the forest industry. A number of NGOs are also active in and over 14,000 members is a representative body focusing the sector, including the TEMA Foundation, Foundation for on the problems and issues facing the forestry profession and Protection of Natural Life (DHKV), and Foundation for Turkey’s its members. The Chamber provides facilities for occupational Nature Protection (TTKD), Turkey Foresters Community (TOD), training of foresters and makes recommendations on the Association of Green Turkey Foresters (AGTF) and the Nature forestry practices of the state forestry service. Protection Centre (NPC). Box 3. The Central Union of Forest Villagers Cooperatives (OR-KOOP) The Central Union of Turkish Forest Village Cooperatives, ORKOOP, is a unique example of the Labor Union in Turkey, founded on July 11, 1997 with the support of 7 Regional Unions of Forestry Cooperatives. ORKOOP was created as part of a social security solution to the issues of forest villagers; who are viewed as working under difficult conditions often with inadequate equipment and who receive only modest compensation for their labor. Since its foundation, ORKOOP’s belief is that forests represent Turkey’s largest natural capital wealth and it works to carry out constant communication and cooperation with related institutions as well as defend the economic and social benefits of its partners. ORKOOP advocates on behalf of forest villagers to ensure their equitable share from forest resources. It participates in activities aimed at developing and growing forests to ensure production according to national interests. Main activities focus on providing social rights to forestry villagers, and providing training, auditing, and awareness raising in forest villages and cooperatives. The majority of the cooperative members of OR-KOOP deal mainly with forestry work. Other members also carry out agriculture and animal husbandry work. ORKOOP abides by international cooperative principles. Sources of funding OR-KOOP is funded through established Cooperative Unions in 28 Regional Unions. The State Forest Organizations deduct one percent of the proceeds from the annual allowable cut under the heading “Training and Supervision Deduction” on behalf of the Cooperative Union. These funds are then transferred to the account of the Regional Cooperative Union, with 1% deducted on behalf of OR-KOOP and transferred to the Central Union’s account. This is the main source of income for the OR-KOOP Central Union. OR-KOOP holds an annual financial general assembly meeting and an elections assembly every four years. 16 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 2.2.5. Legislation amnesty for Forest Offences cannot be arranged, (d) forest borders cannot be reduced except under special circumstances The 1982 Constitution of Turkey is a significant source of and (e) the State, in order to protect and improve the forests, substantive forestry law. Article 169 of the Constitution states that takes necessary precautions and creates legislation. (a) irrespective of ownership, all forests are under control of the State, (b) ownership of the State Forests cannot be transferred, Table 2‑3 shows the chronology (starting with the present) of and these forests are run by the State, (c) general and/or special various passed acts and legal arrangements. Table 2‑3 Historical Development of Forest Legal and Regulatory Framework Laws and legal arrangements (national, Year regional, global) Topics and issues addressed 1995- present UNCED, IPF/IFF, CBD, CCD, Takes part in regional and global processes related to forestry dialogue Pan-European Process, for sustainable development of society; Near East process, C&I for SFM etc. Seeking ways to incite public interest in forestry, forest management and Law No 4122 nature protection; Law No 3800 Amendments and/or additions to existing legislation through incorporating increased public needs and the multi-functional benefits of forest resources. 1983-1988 Amendments/additions and changes of Increased forest-based subsidies as in-kind and credit based; forest and forest related legislation mainly New arrangements for encouragement of village co-operatives in private on Forest Law No 6831 afforestation and private forest establishments; Cooperative programs established with agencies other than forestry and village co-operatives for development efforts in forest villages. 1983 National Parks Law No 2873 Considered the environmental and landscape dimensions of forests; Established more natural parks and protected forest areas, particularly in mountain ecosystems. 1969-1973 Forest Village Development Fund The first Ministry of Forestry established; Law No 1744 The Forest Village Affairs General Directorate established; Special fund for village development developed; District-level development plans provided for forest villages; Mechanisms for additional credits and grants to forest villages and village co-operatives. 1956 Forest Law 6831 Established the foundation for today’s forestry concept; Efficient protection and production mechanisms; Multiple management of forest resources; Concessions for forest-dependent villages and village co-operatives. 1937 Forest Law 3116 First comprehensive forestry regulation; Recognition of the importance/influence of forest dependent people on good forest management; Timber-based forest production and oriented forest practices; Setting up scientific and technical based forestry approaches. 1921-1924 Wood cutting Law Only fuelwood production considered; Usufruct Law Forestry organization began to grow and develop; Regulation on fuelwood utilization. 1862-1869 Forest Status Primitive forest regulation; Decisions and commands mainly on fuelwood utilization from forests; Sultanates’ wood-based needs; Foundation of the first Directorate of Forestry. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 17 2.2.6. Policy Framework any type of risks, develop them under an environmentally friendly The main forestry policy documents are the Tenth National understanding and manage them as part of the ecosystem Development Plan (2014-2018), the National Forestry integrity and in such a manner which will provide the public Program (2004-2023), the Strategic Plan of the Ministry of with multi-directional sustainable benefits.” The four strategic Forestry and Water Affairs (2013-2017) and the General objectives are to: (1) Protect the forests and areas qualifying as Directorate of Forestry’s Strategic Plan (2017-2021). These forests as well as their biodiversity against any kind of biotic documents address numerous issues ranging from forest and abiotic pests; (2) Develop the existing forests, increase protections to sustainable production of industrial wood their efficiency and expand their area; (3) Meet developing and fuelwood to meet domestic demand, non-wood forest and changing expectations from the public optimally for products, rehabilitation and reclamation of degraded forest goods and services produced by the forests; and (4) Ensure areas, national parks and protected areas, the protection of the institutional development for providing sustainable forest wildlife, supply of ecosystem and social services, and rural management, offering faster and higher quality services and development. attaining the designated strategic objectives. The National Forestry Program’s (NFP) objectives are to Broadly speaking, the objectives laid out by the NFP and FSP contribute to: (1) Establishment of appropriate institutional are similar including: (1) institutional capacity development, capacities and mechanisms to address forestry subjects with (2) forest protections, (3) generating community awareness of a broader perspective through sustainable development; (2) ecosystem services and potential gains, and (4) supporting Improvement of adaptation and linkages between forestry livelihoods. Recommendations from the World Bank Forest and other sectors; (3) Improvement of awareness, interest, Policy Note (2017) indicate several priority areas for strategies participation, support and contributions of community and to achieve their shared goals of sustainable forest management stakeholders regarding the importance of stable and sustainable and address poverty in forest communities. First, improving the development in the country; (4) Strengthening the support for forest resource information system, in particular the National the rehabilitation of multiple-use forests by improving the multi- Forest Inventory (NFI) would help provide the benchmark for functional and participative forest resources management, identifying the income generating potential of forest resources and improving the living standards in forest villages or in the and assessing policy impacts. The second area is in updating vicinity of forests where poverty and dependency on forests forest legislation, in particular covering NWFPs and their are commonplace; and (5) Strengthening financial support sustainable exploitation. The third area would be to improve (National and International) for forestry activities. the productivity of timber harvesting, the wood processing sector and wood supply chain management. Finally, success The GDF’s Forest Strategic Plan (FSP) (2017-2021) sets out an in achieving the targets set by FSP requires institutional reforms overall mission to: “Protect forests and forest resources against in the forest sector, including the role of GDF. 18 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 3. SOCIO-ECONOMIC CONDITIONS IN TURKEY’S FOREST VILLAGES Turkey’s rural inhabitants can be classified in two groups, 3.1. Socio-economic Household Survey namely forest and non-forest villages. Forest villages are also divided into ones located inside forests or those near Several case studies from the recent past have analyzed or adjoining forests. Thirty-five years ago approximately 18 the specific socio-economic conditions of the forest village million forest villagers resided in some 22,000 forest villages, population, however they are often limited in scope and scale, however by 2014 there were only 7.1 million forest villagers in rendering their results non-generalizable (Alkan and Kilic, 2013; these villages, representing an approximately 60% reduction. Atmis et al., 2009). The 2016 SEHS data analyzed in this paper are more comprehensive in their geographical coverage The rapid population decline in forest villages is a and include a greater amount of information than previous consequence of out-migration to urban areas as people search surveys. This gives us an opportunity to explore the socio- for employment and better opportunities (Gokce, 2005). economic conditions of the forest population in greater detail. Specifically, the high rates of out-migration are due to a lack of income sources and social services, poor infrastructure, The survey, conducted from February-August 2016, attempted and limited access to markets (General Directorate of Forestry, to document the links between poverty, forest dependence, 2014). For example, nearly 10% of all villages do not have and out-migration in forest villages. The Turkish consulting access to water, 80% have no sewerage system and 53% have group UDA managed the survey. no internet access (Turkstat, 2012). Forestry work is seasonal, lasting for approximately five months, mostly in winter, and Table 3‑1 Survey Sample done under very harsh working conditions with low monetary POVERTY remuneration. The combination of the seasonality of the work, LOW HIGH and low pay, means that forests are not meeting the current OUT-MIGRATION Low-Low Low-High needs of forest villagers. With prime-aged members (>16 and LOW Strata 1 Strata 2 <65) leaving villages, the ageing demographic profile limits 62 villages 6 villages the ability of forest villages to undertake hard physical forest High-Low High-High HIGH work (Yilmaz, 2006). In terms of alternative income sources, Strata 3 Strata 4 the average agricultural holding of 2.4 ha is mostly used 77 villages 57 villages for subsistence farming, and affords limited opportunities for additional income. One of the main limitations of using forests for income is that forests are 99.9% State-owned and highly The sample design followed a two-stage stratification method regulated, so they cannot be used in the same manner as they (UDA, 2016). In the first stage, 203 villages were selected are in other countries (see Box 4). based on poverty and net migration rates and then grouped into four areas (or strata): low migration and low poverty (LM- According to a recent socio-economic survey of forest villagers LP), low-migration and high poverty (LP-HP), high migration (described below) approximately 37% of village households and low poverty (HM-LP) and high migration and high poverty have at least one member who permanently left the village (HM-HP). Table 3‑1 presents the village sample distribution by (World Bank, 2016). The rate of out-migration among forest stratum. In the second stage, 2000 households were randomly villages in the past 10 years is over 10%, which is more than selected from 203 representative forest villages across Turkey. 4 times the annual average migration rate from rural to urban The sample distribution is shown in Figure 3‑1. areas (3.5%) between the years 1995-2000 (TUIK, 2016). POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 19 Figure 3‑1 Randomized Sample of Forest Villages Surveyed Source: World Bank, 2016. Table 3‑2 Household Demographics and Employment Status by Poverty and Migration Area Low Migration- Low Migration High Migration High Migration   All villages Low Poverty -High Poverty -Low Poverty -High Poverty Socio-demographics           Age of HH head 56.65 55.63 60.05 57.3 55.68 Male head (%) 95.00 97.00 93.00 94.00 93.00 Household size 4.09 3.79 3.27 4.09 5.12 Dependency ratio 0.54 0.46 0.65 0.52 0.62 % No school or dropout 5.65 3.78 3.27 5.49 10.33 % Primary school 61.98 66.91 60.13 61.68 55.75 % Middle and high school 23.05 23.19 20.26 21.71 21.45 % Tertiary education 3.22 3.58 4.58 3.31 1.81 Employment           Labor force participation rate %   52.07 40.09 48.51 51.69 75.1 % of women in labor force  23.50 15.72 28.57 25.59 35.71 No. of households 1828 431 60 725 545 Note: The dependency ratio is the ratio of total household members aged <16 and aged >65 to total working age members (16-65). 20 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY Household level information, including socio-demographic The labor force participation rate is lower in low migration information, income generating activities - in particular forest regions and higher in regions with high migration (highest in related income, access to forest resources, and support high migration, high poverty regions) In particular, participation from cooperatives, was collected using household modules. of the female labor force is highest among villages with high A village module was also administered to collect village poverty. One possible explanation is that many women engage level information, such as access to infrastructure and in economic activities with low returns. forest resources, and forest village development programs implemented by ORKOY. A detailed discussion on the survey 3.1.2. Income Sources of forest village households was presented in a report by UDA Consulting (UDA Consulting, 2016). The data and analysis Table 3‑3 summarizes the income sources, in terms of income from this survey contributes to better evidence-based policy received from engaging in a variety of economic activities recommendations that can lead to the sustainable development (measured by both mean and median) and household of forest communities in Turkey participation rates for these activities. Income for each activity . is estimated using only participant households, which allows 3.1.1. Socio-demographic Conditions comparisons between forest village households and other non- forest rural communities. Moreover, both mean and median are Table 3‑2 summarizes some key household demographic presented because when the mean is highly skewed by several indicators by the aforementioned areas. High migration large values (as is the case in the survey), the median is a more villages are younger and have more female household heads. appropriate measure of the common household income. The Not only do they have more household members but their wide disparity between these two statistics is also evidence of dependency ratio is also larger i.e. they have more non- further inequality among forest village households; although a prime age members depending on prime-age members. The few households reap high returns for certain activities, such as education level of household heads also varies by stratum. forest collection, most households receive much lower returns. The education attainment is lowest among high migration high poverty villages, with the share of those without any schooling Table 3‑3 also reports gross income totals for the majority being the largest and those with tertiary education the lowest. of activities since the data structure restricted our ability to generate net totals. Agriculture income and livestock sales, Table 3‑3 Household Average Income by Source and Participation Income source Level (TL, annual per HH) Participation   Mean Median No. of  HH Percent 1 (a) Forest collection gross income 2,158 400 1,246 61.2          - forest sale value 6,491 600 269 13.2          - own consumption value 840 360 1,123 55.1 1 (b) Forest collection net income (net of cost) 1940 300 1,242 61.2 2 Forest wage income 13,762 3,900 79 3.9 3 Non-forest wage income 16,709 15,600 377 18.5 4 Pension income 15,446 14,400 868 42.6 5 Capital/interest income 18,259 9,000 39 1.9 6 Agricultural income 28,798 10,000 511 25.1 7 Livestock income 11,959 8,000 571 28 8 Other income 8,656 5,000 229 11.2 Total income (including only participants) 26,250 16,200 1,818 89.2 Total income (including all HH) 23,187 15,000 2,037 100 Per capita income (including only participants) 9,259 5,758 1,818 89.2 Per capita income (including all HH) 8,206 4,906 2,037 100 Note: Gross forest income (1a) consists of sales of forest product collection and subsistence value (the imputed income from household consumption of collected forest products). Forest collection net income is estimated by subtracting cost of transportation and input from collection from gross income.  Agriculture and livestock sales are gross income because the cost for agriculture and livestock production is not collected in the survey. Total income is the summation of the 8 income items from 1.a to 8, but not including 1b. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 21 which lacked cost information, are presented as gross totals Figure 3‑2 Income Diversification because of the unavailability of cost data. They are directly 30% comparable with forest collection gross income. We found that 5% of households (101 of 1,242 households who reported 25% collecting forest products for sale) reported negative net forest Share of total households collection income i.e. losses, over the past 12 months. Although cost information was available for forest collection income, 20% the inability to distinguish between fixed/variable costs might have led to an overestimation of costs, encouraging us to rely 15% on gross forest collection incomes for our analyses. Moreover, gross totals facilitate comparisons across activities. 10% The importance of various sources of income can be 5% evaluated using either participation rates (i.e. accessibility), or income amounts (i.e. profitability). Based on the household 0% participation rate, forest collection is the most prevalent income 1 2 3 4 5 6 7 8 source with roughly 61% of households deriving value from Number of activties household forest products (from market sales or subsistence consumption). engaged in past 12 months Forest collection participation was followed by pensions (42%), Note: Households can receive income from 7 potential activities, including: forest livestock and livestock products (28%), agriculture (25%), and product collection, forest employment, non-forest employment, livestock sales, capital interest, pension, and other non-forest related sources. non-forest wage income (18%). However, non-forest related employment and pensions are the Figure 3‑3 Common Combinations of Income most profitable income sources, in terms of median returns. Sources (% of households) They were followed by agriculture or livestock, and capital/ interest income (covering income from real estate and interest 4% 9% earnings).6 3.1.3. Income Diversification and Forest Dependency 11% Income diversification captures an important aspect of household welfare and poverty, because it reveals household’s resilience to shocks as well as their capability to expand 48% opportunities to improve their livelihood beyond the forest. Less 11% diversification could be interpreted as being more specialized, and this may the case in specific circumstances, however more generally in Turkey, it appears that the poor are more limited in their opportunities to diversify among income sources. 12% Such information is valuable for guiding policies that aim to effectively target poor households and support their movements out of poverty by enhancing productivity and income 17% diversification. The analysis of income source diversification is based on the seven principal income sources listed above Non-forest related employment only in Table 3‑3. Figure 3‑2 below presents the distribution of the Pension only number of income sources that captures the degree of income Forest related activities, Pension, diversification in forest villages. The majority of households, and Agriculture and/or Livestock 30%, participated in 3 activities. Forest related activities only Forest related activities and Agriculture and/or Livestock Rosehip Other combinations 6 Note that receipt of capital or interest income was only reported by about 2% of households in the survey. 22 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY What are the household income-earning strategies? Figure (4.7%), Romania (4.1%) and Georgia (9.8%). Despite these 3‑3 below presents the most common combinations of positive country-level results, rural poverty not only remains income sources,7 capturing the vulnerability of single activity higher than urban poverty in Turkey, but is also decreasing at households, and forest dependency among participants in a slower rate (Azevedo and Atamanov, 2014). Moreover, the forest collection or forest employment. level of poverty among forest communities is most widespread in rural Turkey. Although Turkey has routinely administered The distribution presented in Figure 3‑3 above shows a national household surveys (such as the integrated budget concentration among agriculture, livestock production, and forest- surveys, and Income and Living Conditions Surveys), no official related activities. About 15% of households engaged solely in estimates have been available due to a lack of data from forest agriculture or livestock for income generation, compared to 10% village households. only on forest income. However, households engaging in forest- related activities tend to supplement their forest income with The 2016 SEHS presents an opportunity to analyze the extent other activities; roughly 11% of households added agriculture/ of poverty among forest village households, and its relationship livestock activities, and 10% added both agriculture/livestock with forest dependence and migration. Due to a lack of data, sales and pensions. A surprising 8% of households rely only the measure of welfare used in the following analysis has been on pension income, signaling the aging demographic of forest restricted to per capita household income, despite better known villages. The majority of households in forest villages (43%) alternatives in poverty literature.9 The poverty rate among forest depend on a highly diversified portfolio of income sources, but village households was found to be about 79.6%,10 which these results were not provided since individual combinations is significantly higher than the average rural poverty rate of represented less than 3% of the population. 38.7% (TUIK, 2016). The analysis of a household’s choices of income sources Poverty can also be measured using a relative poverty line to provides strong evidence that non-forest wage jobs are very assess how poverty varies within forest communities. Using the limited across forest villages, since less than 4% of households conventionally defined relative poverty line, i.e. 60% of median depend on their livelihoods solely from non-forest wage income. per capita household income among forest village households Non-forest wage income is 3-4 times higher than forest wage (480 TL/ per capita per month, or $130), the relative poverty income (median in Table 3‑3), which is an indication that line is found to be 288 TL/ per capita per month or $78). The expanding non-forest employment opportunities can be an results show that about 40% of households in forest villages effective policy instrument to increase household income. lived below this poverty threshold. The estimated (relative) poverty rates among 11 regions (as shown in col -1 in Table 3.1.4. Poverty in Forest Villages 3‑4 below) reveal a large spatial variation in poverty across forest villages. Turkey has made significant progress in poverty reduction. Using the annual Household Budget Survey (HBS) data, the Table 3‑4 below presents two sets of regional poverty rankings poverty headcount ratio decreased from 44% in 2002 to 18% using the income data from the 2016 SEHS, and the 2016 in 2014 (using the international poverty line of 5 dollars/day Income and Living Conditions Survey for two purposes. The (in 2005 purchasing power parity, PPP).8 Extreme poverty, comparisons allow us to check the consistency of poverty measured by the threshold of 2.5 dollars/day (in 2005 PPP), rankings, and help us to place the poverty of forest village also experienced consistent decline, and at an even higher households in a regional context. The comparison shows that proportional rate, decreased from 13% to 3% (Cuevas and the regional poverty ranking is broadly consistent using the Rodriguez-Chamussy, 2016). Turkey made larger gains in two data sources, except in two regions. While Central East reducing national poverty than several other upper-middle Anatolia is ranked as the fourth poorest region among forest income countries in the ECA region. For example, the poverty villages, it was the fourth richest region using the poverty rate rate (using the international poverty line at $3.10 a day) was from the Income and Living Conditions Survey, which covers 2.6% in Turkey in 2013, which is lower than that in Bulgaria all households (both rural and urban areas in the region).11 7 These include income source only from (1) forest-related activities; (2) non-forest wage; (3) agriculture or livestock (4) pensions (5) combination of forest income with either agriculture/livestock, (6) combination of forest income with agriculture/livestock and pension or (7) the rest of permutation excluded in (1)-(6)). The combinations of income sources are numerous, but include such combinations as: forest income + non forest wage; non-forest wage + agriculture / livestock; non forest wage + pension; and agriculture / livestock + pension + non forest wage. 8 The poverty measure used here is monetary poverty. 9 There is a long-standing debate about which is the better measure of standards of living. For developing countries, a strong case can be made for preferring consumption, based on both conceptual and practical considerations (Deaton and Grosh, 2000). The poverty analysis uses income as a welfare measure because of unavailability of consumption data. The seasonality issue associated with income data, to some extent, is lessened as the income covers the past 12 months. 10 Based on the national poverty line of 1,115 TL/ per capita per month. Official statistics are from the results of the TÜRK-IS Survey in July 2016. The official national poverty line defines monthly food expenditures (hunger limits) for a healthy, balanced and adequate nutrition for a "four-person family" and is TL 1,370. The total amount of other monthly expenditures required for clothing, housing (rent, electricity, water, fuel), transportation, education, health and similar needs together with food expenditures (poverty limit) amounted to TL 4,461. This is about 1,115 TL /per capita per month (without taking into account equivalent adult scales). The monthly cost of living for a single employee was TL 1,704.70 (http://www.turkis.org.tr). 11 Similarly, Central East Anatolia is ranked the fifth poorest region among forest villages, but has the highest poverty rate using the national survey data. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 23 Table 3‑4 Forest Village Poverty Rates versus Regional Poverty Rates SEHS Forest Villages Turkstat Country Regions Region Rank Poverty Rate* Poverty Rate** Rank Mediterranean 1 67.9 18.1 6 South East Anatolia 2 60.4 17.3 7 West Marmara 3 53.9 21.2 2 Central East Anatolia 4 52.2 16.3 9 North East Anatolia 5 51.2 20.1 4 West Anatolia 6 50.2 21.2 2 Central Anatolia 7 43.6 18.1 6 West Black Sea 8 43.4 16.7 8 East Black Sea 9 25.8 20.9 3 Aegean 10 22 19.1 5 East Marmara 11 19.3 21.5 1 Total   41.0 21.9   Note: Poverty rate * is estimated using the 2016 SEHS, with the poverty threshold set at 60% of the median per capita income. Poverty rate ** is from Turkstat, using the 2016 Standard Income and Living Conditions Survey. The poverty threshold is set at 60% of the median per capita income. For details see http://www.turkstat.gov. This indicates that richer regions such as Central East Anatolia Poor households have many significant differences from their may harbor high poverty incidences among forest communities wealthier counterparts, and are less well connected socially - and thus greater inequality overall. In formulating national and physically. Poor households are less likely to belong to development policies, policy-makers must recognize in-region cooperatives and associations, and this difference is statistically income inequality to improve the targeting of poverty. significant. Non-poor households, on the other hand, are more likely to have family members who permanently migrated to 3.1.5. Differences between the Poor and Non-poor urban areas within the past 10 years (36%, compared with 26% of poor households). Non-poor households are defined as in the top 30% of the income distribution, while the poor are those below the relative Poor households live in disadvantaged locations: further away poverty line as defined above. The analyses below focus on from the forest (14 km on average, compared with 4 km two areas with the most differences between the groups: (1) among the non-poor) and less often in villages with a water Household Composition - socio-demographic characteristics network - 47% of poor households lived with water networks, and asset ownership, and (2) Household Strategy- diversification compared with 60% of non-poor households. of income sources and forest dependency. Poor households also own fewer assets. Just 5% of the poor Differences in Household Composition: have internet access (while 8% of non-poor do), 34% owned a car or truck (53% for the non-poor), 34% own tractors (46% Table 3‑5 below shows the first set of comparisons by socio- for non-poor) and 41% owned chainsaws (55% for non-poor). demographic status and asset ownership. Many similarities exist between poor households and villages with overall high Differences in Household Strategy: migration. On average, the head of the household among the poor is younger (48), compared with the non-poor (53), but no The second part of the analysis focuses on the difference in significant differences exist in gender and education attainment household income strategies, i.e. income composition and of the household head. Poor households have a much larger diversification between the poor and non-poor households. household size of 4, and high dependency ratio at 0.5 (the As shown in Table 3‑6 below, the poor have a much higher ratio of total number of members under age 16 and above dependence on low return forest-based activities, such as age 65 to total working-age members 16-65), compared with forest-related activities (28% use forests). Non-poor households non-poor at 2.6 and 0.3, respectively. depend more on high return income sources (such as agriculture 24 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY Table 3‑5 Poor and Non-poor Household Comparison: Socio-demographics and Assets Group Mean Significant difference Socio-demographics Poor Non-Poor between groups? Age of household head 48.33 53.80  Share of households with male heads 0.86 0.83   Head with education above mid school 0.21 0.18 Household size 3.99 2.59  Dependency ratio 0.54 0.29  Member of a cooperative 0.20 0.26  HH with migrants within 10-year 0.26 0.36  Distance to forest (km) 14.78 3.65  Share of households in villages with water networks 0.47 0.60  Asset ownership Access to internet 0.05 0.08  Solar panel 0.45 0.49   Livestock owner 0.58 0.61 Own car or truck 0.33 0.53  Own motor bike 0.15 0.15   Own tractor 0.35 0.45  Own chain saw 0.41 0.56  Note: Poor households were classified using a relative poverty line, which was defined as 60% of the median per capita income (total HH income/HH size) of all forest villager households. Households with per capita income in the top 3 deciles were classified as non-poor households. Household weights were used to make the sample representative. Significance is calculated using T-tests. Table 3‑6 Comparison between Poor and Non-poor Households: Income Share and Diversification Group Mean Significant difference   Poor Non-Poor between groups? Income Share   Forest 0.28 0.08  Non-forest wages 0.09 0.13 Agriculture 0.08 0.18s  Livestock 0.14 0.12 Pensions 0.07 0.44  Income portfolio  Forest income only 0.24 0.02  Non-forest wage only 0.03 0.02  Agriculture and/or livestock 0.19 0.08  Pension income only 0.03 0.08  Forest + (ag and/or livestock) 0.13 0.06  Forest + (ag and/or livestock) + pension 0.01 0.18  All other combinations * 0.48 0.63  Note: Poor households were classified using a relative poverty line, which was defined as 60% of the median per capita income (total HH income/HH size) of all forest villager households. Households with per capita income in the top 3 deciles, were classified as Non-Poor households. Household weights were used to make the sample representative. Significance is calculated using T-tests. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 25 and pensions) and less on forests (8%). The high forest In summary, the poverty analysis reveals high poverty among dependence among poor households in SEHS is very much forest communities and significant spatial variations in the aligned with global evidence from 24 developing countries, incidence of poverty across forest villages. In comparison to which shows higher forest dependence among the two bottom- non-poor households, poor ones are younger, have a larger quintile income groups (Angelsen et al., 2014). household size, a higher dependency ratio, and are less likely to have permanent migrants in the family. They also have Poor households also diversify less. Roughly 50% of the poor fewer assets, income sources, and poor infrastructure. They engaged in single income generating activity compared are engaged in low return activities, and in particular they to 20% of the non-poor households. A quarter of the poor are more dependent on forest-related income. In contrast, non- earn income only from forest sources (compared to 2% of the poor households have access to high return and stable income non-poor) and about a fifth derive income only in agricultural sources, including pensions, agriculture, and non-forest wages. and/or livestock activities (in ontrast with 8% of the non-poor). Pensions, which provide the highest returns after non-forest The above evidence indicates that pensions, the degree of forest employment, are scarce among poor households (1%) despite dependence, ability to diversify income sources (including through being prevalent in richer ones (18%). It is important to note that migration), and ownership of productive assets, are all important pensions are a steady source of income, providing a safety net factors related to poverty. The following two chapters focus on or liquidity to constrained households. forest use, and forest resource management and migration. 26 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 4. FOREST RESOURCE USE AND MANAGEMENT Increasing the productivity of forest resources represents Figure 4‑1 Percentage of Households one of the key components of GDF’s Strategic Plan (2017- Collecting Forest Products 2021) for promoting sustainable development and better forest management. The degree of forest dependency of 5% 1% poor households in forest villages further validates the pursuit 6% of better forest management. This section focuses on income generation among forest product categories: Wood Forest 7% Products (WFP), Non-wood forest products (NWFPs), and Agricultural and Horticultural Products (AHPs)). 7% 4.1. Income by Product 54% Figure 4‑1 below presents the frequency of collection of forest products reported by at least 10 households. About 54% of households collected firewood, but only 1% collected wood 10% for industrial wood operations, even though the average sale value (i.e. gross market sales) of industrial wood is almost 30 11% times as much as firewood. Despite the higher profitability of industrial wood, poorer households collected more firewood and less industrial wood than non-poor households, and fewer households sold industrial wood in the market. Firewood Mushroom Other The top five NWFP collected were: mushrooms, herbs, thyme, Herbs Thyme Rosehip rosehip, and pinecones. For all 5, non-poor households Pine cone Industrial wood engaged more in collection and market sales of these NWFPs. As discussed previously, Turkey is one of the top three worldwide Note: Households collected 70 different types of products. For this chart, only producers of laurel leaves, thyme, sage and pine nuts and is products collected in the forest, and by at least 10 households, were used. ‘Other’ ranked 21st in the world in terms of exports of NWFPs. In products include: Sage, Hazelnut, Linden, Stingnettle, Walnut, Chestnut, Blackberry, Trefoil, and Opium. 2013 the major exports were thyme (US$56.3 million), bay leaves (US$32.26 million), sage (US$6.3 million) and plant extracts (US$30.82 million) (Secretariat General of the Central The preceding discussion highlights the mismatch between the Anatolian Exporters Union, 2014). However, the collection rate of return and the collection rate of forest products among and sales rates in the surveyed villages are meager. poor households in forest villages, especially the sparse market sales of NWFPs. Since Turkey is considered well-endowed Details on a few agricultural and horticultural products were in NWFPs, enormous potential exists to improve the income also included in the survey with olives/olive oil as the most sources from NWFPs. About 16% of households surveyed collected and sold product (collected by roughly 10% of obtained income from selling NWFPs even though the median households).12 As with NWFPs, non-poor households collected gross sale of NWFPs was 600 TL (200 TL higher than forest and sold more olive oil (2 times more than poorer households) collection sales). and reaped gross sales that were 17 times higher than those of poorer households. About a quarter of the households surveyed sold agricultural and horticultural products, with sales from tobacco being the most profitable (despite being collected by only 1% of households), followed by tea, olives and apricots. 12 Respondents were asked to list the non-wood forest products they collected - and in some cases they listed agricultural or horticultural products. This could have been a misunderstanding of the question in classifying products. Therefore, we cannot presume that these are the only agricultural and horticultural products collected by households. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 27 Table 4‑1 Forest Resource Dependency by Income Group Mean Significant difference   Poor Non-Poor between groups? Fuelwood       Used for energy in past 12 months % 95.0 95.0   Purchased in past 12 months % 33.0 44.6   Dependent on fuelwood for:       Cooking % 21.6 13.0   Heating % 69.6 75.0  Boiling water % 22.0 10.6   Coal       Used for energy in past 12 months % 46.9 37.6  Purchased in past 12 months % 33.6 34.9  Used forest plants for health % 21.0 33.6  Used forest timber for construction % 4.3 9.5  Note: Poor households were classified using a relative poverty line, which was defined as 60% of the median per capita income (total HH income/HH size) of all forest villager households. Households with per capita income in the top 3 deciles were classified as non-poor households. Household weights were used to make the sample representative. Significance is calculated using T-tests. The potential of NWFPs is currently constrained by slow 4.3. Forest and Pasture Management growth and limited processing. The volume of NWFPs sold grew just 12% over 14 years,13 with only a fifth processed Forest villagers participate in several activities to diversify their before export. Returns from NWFPs could be increased by income, depending on their circumstances –motivated by improving the productivity of harvesting, and increasing the the belief that it will generate greater income. While the true value-added through developing small-scale local processing reasons for participation are quite varied (i.e. access to credit industries. We believe that targeting ORKOY programs to help or other constraints), it pays to look at what villagers chose to forest villages develop their local processing capacity may do. Table 4‑2 summarizes some perceptual and behavioral boost both income and employment. aspects of the forest villager’s choices in land management – mainly how they manage forests and pasture land. 4.2. Forest Resource Dependency: Energy, Health and Housing While a fifth of households recognized a decrease in forest changes in the last five years, non-poor households abandoned The survey data show that the large majority of households more land for natural re-vegetation and planted more woodlots across the income spectrum depend on forests for their energy than average in the last 10 years. The primary reason for needs; approximately 95% of SEHS sampled use fuelwood planting woodlots across households was for food. Secondary and 43% use charcoal (as seen in Table 4‑1 below). However, concerns included soil fertility and carbon sequestration. differences exist in their method of procurement (with non-poor more likely to purchase) as well as their use. Poorer households A fifth of households engaged in pasture management depend on fuelwood for cooking and heating water almost with no significant differences between poor and non-poor twice as much as non-poor households. This possibly reflects households, apart from the average land size managed by their lack of access to modern energy sources, such as households. Pasture land was used mainly for grazing, and electricity and gas. marginally for cropping. While the poor are dependent on fuelwood due to a lack The top two adaptation strategies that households practiced of alternative energy sources, the non-poor benefit more from with regard to climate variability were either planting trees forest plants for health benefits, and access timber to for (54%) or protecting them (24%). Reducing forest clearance housing construction. This finding indicates the poor may use was the only strategy that poorer households practiced more. low-return forest resources while the non-poor are able to take Non-poor households were more likely to implement all the advantage of high-value forest resources. rest of the strategies. In light of previous discussions about 13 National NWFP supply grew from 31,000 tons in 2002 to 429,000 tons in 2016. 28 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY Table 4‑2 Forest and Pasture Management Group Mean Significant difference   Poor Non-Poor between groups? Perceptions of forest change % decreased 18.1 22.2  Land abandoned last 10 years (m2) 405.2 490.2   Planted woodlots in last 10 years (%) 22.1 40.7  Access to pasture land       Very easy (%) 15.6 14.5   Distance to pasture land (km) 2.9 2.4   Pasture management       HH managed pasture last 12 months 20.0 18.4   Land area managed by household (m ) 2 750.6 502.1   % used for grazing 96.9 96.0   % used for cropping 0.1 0.1   Adaptation to climate-related disasters       % planted trees 38.8 56.0  % diversified forest income 1.4 2.8   % reduced / stopped forest clearance 5.1 5.0   Received benefits from forest services (% yes) 42.1 64.2  Note: Poor households were classified using a relative poverty line, which was defined as 60% of the median per capita income (total HH income/HH size) of all forest villager households. Households with per capita income in the top 3 deciles were classified as non-poor households. Household weights were used to make the sample representative. Significance is calculated using T-tests. income diversification it is interesting to note that roughly 2% of non-poor). The next most important benefits were shade households diversified income in response to climate change, and aesthetics. This provides evidence of not only a lack of and non-poor households did so more so than poorer ones. awareness about the ecosystem services of forests, but also the significant untapped potential of Turkey’s ecosystems. The Realizing ecosystem benefits from forests was lower than recent World Bank assessment of non-wood forest ecosystem average among poorer households. The most important services estimated the value of NWFPs for Turkey as US$2.30 benefit of ecosystems perceived across households was water per hectare per year, compared with a European average of conservation, although support of this fact was stronger among US$20.70 - i.e. as little as 10% of Europe’s average. non-poor households (20% among the poor, 36% among POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 29 5. ANALYSING MIGRATION DECISIONS The high out-migration rates of prime-aged forest villagers is 5.1. Descriptive Statistics of Migrant Households in a concern, not only because low living standards and low the SEHS employment rates are the primary reasons for leaving, but also because forest villagers constitute the labor force responsible Forest village households can be classified into three groups for forest management. While the benefit of migration is a based on their migration status: households with at least one reduced pressure on forests, the costs associated with a permanent migrant,14 households with potential migrant(s),15 shrinking forest labor force are higher in the long run. and households without migrants. Table 5‑1 below presents a summary of migrant frequency across the sample strata. Over Economic migration is a pathway out of poverty among forest half of households surveyed had either permanent or potential village households, and its prevalence is on the rise. In the migrants. Even in low migration-low poverty areas (presumable SEHS 13% of households had at least one migrant during the the most well-off of all strata), half of the households had either past 5 years, which is 2% higher than the earlier 5-year period the intention to migrate, or a family member who had already (2005-2010), indicating an upward trend in migration. The done so. As stated earlier, it is possible that many of the poorest results from Section 3 (of this report) show that other factors households cannot afford to send a migrant, since it is a costly being the same, households with permanent migrants have investment that requires a certain threshold of liquidity. higher per capita income, which can either be evidence of a credit threshold necessary to support a migrant, or the benefits Table 5‑2 below presents the summary of the age and education of remittances (Adger et al., 2002). However, since the survey attainment of the household head, as well as household was conducted for only one time period, we do not know demographic structure by migration status. The demographic which of these two it may be; in all likelihood it’s probably a structure is captured using the dependency ratio, or the bit of both. A review of global evidence, Hecht et al. (2015) proportion of non-prime members (<15 and >65) supported shows that migration has long been a feature of communities by prime-age members (15-65). About 30% of households living near and using tropical forests, and forest dependent with permanent migrants who left 10 years ago have no households have used migration as an important livelihood prime-aged members living in the household, compared with strategy. The following section presents evidence of (1) the 12% among those who permanently migrated within past 10 types of households that support migration (2) and the factors years. Moreover, a fifth of households (19%) with permanent that most affect household migration decisions in SEHS. migrants have no prime-working age members left at home. Table 5‑1 Distribution of Household Migration Status by Stratum (% of total HH) All villages Low Migration Low Migration High Migration High Migration Household migrant status - Low Poverty - High Poverty - Low Poverty - High Poverty With permanent migrants 37.6 35.0 43.9 39.6 40.6 With members who intend to migrate 14.1 13.3 12.0 14.9 13.5 No migrants 48.4 51.7 44.1 45.5 45.9 All HH 100.0 100.0 100.0 100.0 100.0 14 A member who has migrated permanently is classified separately from a temporary or a seasonal migrant who is expected to return home in the short term. 15 The SEHS asked household heads if anyone in their household hoped to migrate. Any household who responded positively was flagged as having a potential migrant. It is important to note that a household can have both permanent and potential migrants. 30 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY Table 5‑2 Household Socio-demographic Profile by Migration Status   Household head Household   Age Education attainment (%) No prime-age members Age dependency ratio Household migrant status Dropout Never in school Mid and above (%) (%) Have permanent migrants 55 12 75 13 19 35 - within 10 years 55 10 76 14 12 29 - 10 years ago 56 15 72 13 31 46 Intend to migrate 45 8 61 31 2 54 No migrants 49 7 69 24 11 46 All HH 51 9 70 22 13 43 Note: Age dependency ratio is the ratio of the total number of aged below 15 and above 65 to prime working age (15-65). Based on the distribution of migration duration, households with permanent migrants are regrouped into (1) those with long-term migrants (defined as leaving more than 10 years ago) and those with relatively recent migrants (within 10 years). Table 5‑3 Average Income by Household Migration Status (TL) Household migration status       Permanent Potential No All Income source Migrants Migrants Migrants Households Forest collection (Mean) 2,111 2,098 2,229 2,158 Forest collection (Median) 480 435 360 400 Forest wage 13,287 14,158 13,813 13,762 Non-forest wage 16,320 16,005 17,064 16,709 Retirement pension 14,961 14,755 16,049 15,446 Agriculture 37,049 14,411 24,770 28,798 Livestock 9,473 11,510 13,928 11,959 Other income 9,135 9,219 7,932 8,656 Total income 29,635 19,916 25,301 26,250 Per capita income (Total HH income / HH size) 12,028 5,749 8,030 9,259 No of households 757 259 1,021 2,037 Note: All income sources except forest collection are reported as average TL only. Because of the wide disparity between mean and median forest collection income, both statistics were provided. This indicates that migration has posed a serious challenge, Table 5‑3 below presents average income by source across leading to a shortage of prime-age labor in forest communities. households with varied migrant prevalence. Households with The results also indicate that heads of households with potential permanent migrants have a much higher level of per capita migrants are much younger, and more educated, than heads income (12,028 TL) than no-migrant households (8,030 TL) of households with permanent migrants. Given that the majority and potential migrant households (5,749 TL). This is likely a of migrants (97%) are sons or daughters, the age gap between reflection of household size and indeed, the average household household heads suggests there could be a cohort effect. size among migrant households is smaller (2.9) than non- That is, non-migrant households may have a lower number of migrant households (3.6) and those intending to migrate (4.4), migrants since they are younger households (the average age which contributes to higher per capita income. However, we of household head is 45 compared with 55 for households cannot definitively say whether households with higher income with migrants) and as such their children are too young to can now afford to migrate, or whether the migrant is sending work outside villages. Migration is apparently a more realistic money back, increasing the household’s income. possibility when the head’s children reach prime working age. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 31 Although there is little variation among the returns from forest- Table 5‑4 Determination of Migration Probability related activities, households generate varied returns from  Migration probability 2012-2016 2007-2011 non-forest related activities such as livestock and agriculture Indicators for education of     (highlighted in Table 5‑3 above). This may suggest a negative household head association between a household’s non-forest income generation capacity and the propensity to migrate, i.e.     Never in school     households who are more capable of generating non-forest     Primary school     related income (such as from agriculture and livestock), are less     Mid-high school     likely to send members away seeking jobs.16 Age of HH head    5.2. Factors Influencing Household Migration Age of head (squared)    Decisions Male head     A household’s decision to support a member’s migration HH size   is understandably dependent on its income and income log (total income)   sources, which are further dependent on many known and Share of forest income   unknown household and community characteristics (Adger et Share of non-forest wage income     al., 2002). Forest development programs and social groups, such as associations and cooperatives, can also have an HH is member of forest coop    impact on a household’s economic opportunities and their HH is member of other coop     livelihood strategies, including migration decisions. While it HH has internet access     is challenging to identify all of the possible pathways through which these variables may affect the migration decision, SEHS HH is owner of livestock   presents an opportunity to capture the most disruptive factors HH has tractor     by means of an econometric analysis. Living in village with water network     The econometric model used in this report aims to determine Asset index     factors that affect the probability of migration, while accounting Legend: for household and village-level characteristics (e.g. differences Positive in infrastructure, access to basic services, and government Negative supported programs).17 To examine the impact of policy changes on migration over time, the analysis was carried out separately p< 0.01 = ***  for recent migrant households (defined as migrants within 5 years p< 0.05 = **  of the time of survey, i.e. during the period of 2012-2016) and p< 0.10 = *  those with migrants who left between 2007 and 2011.18 Notes: The checks () indicate level of significance of the variable in the regression, not the magnitude of the effect. The empirical results (presented in Table 5‑4 below) provide The asset index includes 8 items: cellphone, computer/tablet, freezer, solar panel, evidence that household income, forest dependence, car/truck, motorcycle/scooter, tractor and chainsaw. For the complete regression table, please refer to Appendix 2. productive assets (livestock) and social assets (measured as membership in cooperatives) all have significant effects on migration, while controlling for differences in household demographic characteristics. The former three factors are was found to be significant within the recent five years from positively associated with the probability of migrating, while 2012-2016 (no effect on migration in the earlier five-year membership in forest cooperatives reduces a household’s period from 2007-2011). This either implies that only recent propensity to migrate. benefits affect household migration decisions, or that the structure of membership has also changed such that it reduces migration. Although the SEHS is a cross-sectional survey, the long cooperative membership periods allowed us to chart the effect It is interesting to note that the membership impact on migration of long-term memberships on recent migrations, resulting in during the period from 2012-2016 coincides with the the discovery of a causal relationship between cooperative institutional restructuring of GDF. The General Directorate of membership and a lack of migration. Cooperative membership Forest Village Relations (GDFVR), established in 1970 under 16 Several households communicated that they had purchased livestock as an incentive to keep the younger generation from leaving home. In some cases, it worked. 17 Probability (migration) = f (Xhh, Wvillage, Policy variables), where Xhh are household variables including age, education of household head, household income; Wvillage are village-level characteristics including village infrastructure; and policy variables including membership in a forest cooperative, membership of other cooperatives, and forest dependency (measured by the share of forest-related income). 18 The table in Appendix 2 contains results for the two time periods reported: 2007-2011 and 2012-2016. 32 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY the Ministry of Forestry, was the principal agency responsible 5.3. Simulating Effects on the Migration Decision for supporting social and economic development in forest communities. However, in 2011 GDFVR was closed, and Based on the results from the migration model, it is useful its role and functions transformed into ORKOY, which was to assess the impact that various policy proposals have on re-mandated to be responsible for meeting forest production migration. Table 5‑5 below illustrates a policy simulation that targets and supporting forest villages. ORKOY’s programs expands membership of forest cooperatives to cover all forest include social credit and loans to household and cooperatives, village households. It is important to note that the survey data employment and income diversification opportunities (such revealed that currently only about 6% of households belong to as animal husbandry and NWFPs cultivation). ORKOY’s either forest cooperatives or associations, and the estimated effectiveness has been reviewed using data from 11 villages probability of households sending migrants abroad over (Alkan and Kilic, 2014), which presents positive feedback a 5-year period is 36%. If all households were to become from village households. However, it is unclear whether the members of cooperatives, the probability of migration falls to impact on migration is due to improvement of the program 29%, representing a 19% reduction.19 Using the official 2014 implementation or other macro-level factors, such as population data, this indicates that about 500,000 people improvements in employment opportunities in the forest village who would have migrated permanently would instead remain communities over time. Further exploring the underlying reasons in forest villages. for the positive impact of membership is important, although it involves a more detailed assessment of programs implemented While the simulation exercise should be regarded only for by forest associates and cooperates, which is beyond the illustrative purposes, the results demonstrate the scale of the scope of this study. potential impact of new initiatives on migration. In next chapter, the analysis focuses on linkages between poverty, forest From what we can ascertain from this survey, households that dependence and migration, with the objective of identifying are more dependent on forests for income (measured as share pathways for forest households to move out of poverty. of forest income) are more likely to have permanent migrants in the family. This result remains consistent over the past 10 Table 5‑5 Estimated Probability of Migration years, as shown in Table 5‑4 above. Since the survey results and Policy Simulation indicated that poorer households are more forest-dependent, this Simulated average finding confirms that forest income alone is insufficient to support (assuming all households livelihoods, and migration may be a pathway out of poverty. Sample become cooperative   average members) Forest cooperatives play several roles, but an important one Share of households 5.8 100 is to create employment opportunities for its members in forest in cooperatives villages. Almost 60 percent of Turkey’s total wood production is carried out through forest cooperatives every year. Annual forest Estimated probability of migration decisions (%) production revenues are about US$225 million – but only a Sending a permanent 36 29 small proportion of that revenue is retained within the villages migrant through wages and income (General Directorate of Forestry,       2017). In the past, this income was very important to support Estimated number of 2,554,734 2,057,980 and maintain living conditions in the forest. However, with a people leaving within declining population and more limited forest-related income 5 years opportunities – the sustainability of this previous forest villager labor model appears to be in question (World Bank, 2017). Note 1: The simulation is carried out using the estimated coefficients during the period 2012-2016 in Table 5‑4, and newly constructed explanatory variables based on the choice of policy proposals. In this case, the new variable is the cooperative membership, which changes all non-member households into members (i.e. setting the cooperative member dummy variable into one) and all other explanatory variables are set to the sample mean. The simulated migration probability is 29%. Note 2: The official data shows that in 2014, there were 7,096,483 people living in forest villages. 19 Calculated as (2,057,980-2,554,734)/ 2,554,734. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 33 6. PATHWAYS OUT OF POVERTY Global experience with forest-poverty dynamics among forest Based on what appears in the literature, together with lessons dependent households shows that the process of moving out of and evidence from recent projects and public investment in poverty is a slow one, and at times takes several generations the forest sector undertaken by the World Bank, a conceptual (Shyamsundar et al., 2017). Households living in remote forest framework of pathways to prosperity in forest landscapes locations have attempted a variety of strategies to improve their has been developed, called P.R.I.M.E, (Shyamsundar et livelihoods, including resource extraction (Angelsen, 2010), al., 2017). The framework includes improvements in the migration (Hecht et al., 2015) and transforming forests for productivity (P) of land in forest landscapes; strengthening food production, timber and other economic benefits (Brack et communities, households and women’s rights to forest access al., 2016). The key question lies in whether those alternative (R) complementary investments in institutions (I) public services income-generating strategies can sustain a reduction in poverty and increased access to markets (M) and strengthened without forest degradation. mechanisms for valuing ecosystem services (E) to ensure that benefits accrue to the poor (see Box 5 below). Box 5. P.R.I.M.E. - Pathways Toward Prosperity Five broad pathways can help launch the forest-dependent poor onto a sustainable path toward prosperity. These pathways, referred to as PRIME, identify economic development strategies and build on the premise that forests themselves remain intact. PRODUCTIVITY: Growth in labor and resource productivity (P) is integral to economic development. In forested landscapes, labor productivity can be improved by enhancing individual and community skills in sustainable forest management. Resource productivity can be improved through the infusion of capital (for instance, portable saw mills), forest fire and pest management or tree plantations. Associated technologies, policies and capacity strengthening activities need to meet the requirements of women, indigenous people and other marginalized households to ensure that the poorest benefit. RIGHTS: Wealth accumulation is an essential pathway out of poverty. One strategy is to increase the wealth of the poor by strengthening their rights (R) over natural capital. A large body of literature and local environmental movements point to the importance of community rights to using and selling forest resources in the reduction of poverty. Within forested communities, it is particularly important to empower women and other marginalized individuals to have tenure rights and decision-making power. INVESTMENTS: Poverty reduction in forested landscapes will not be possible without investments (I) in complementary institutions and public services. Forest-related pathways to prosperity are only likely if the poor also have inclusive and affordable access to complementary public services such as education, health, agricultural extension, transportation and mobile phone access. The role of gender-responsive institutional arrangements in providing information, enabling local level innovation and offering insurance against down-side risks will be important. MARKETS: Income generation and diversification require the strengthening of small and medium timber and non- timber enterprises and increasing their access to markets (M). Markets for a small number of high-value non-timber forest products (e.g. Brazil or Shea nuts) are one example of a pathway that is likely to be more beneficial to women. Timber certification and export markets for timber offer an alternate broader approach. This pathway may need careful designing to be responsive to the preferences of women, indigenous households and youths, as well as conservation requirements. ECOSYSTEMS: Ecosystems and their hidden services (E) are integral to prosperity. Over the last decades, policy instruments such as eco-tourism, payments for eco-system services and carbon markets have proven to be useful mechanisms to regulate ecosystem services and their benefits. It is important to channel this demand for ecosystem services into monetary and non-monetary support for the poor, and, women within poor households. 34 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY Table 6‑1 The Proportion of Non-participant Households by Income Source and Income Quintile Income Quintile (%) Total household with non Income Source -zero income per source 1 2 3 4 5 Total Forest 790 29.2 17.7 19.2 18.6 15.2 100 Agriculture 1,525 26.6 20.1 18.5 20.2 14.6 100 Livestock 1,465 27.0 17.7 18.1 20.1 17.1 100 Forest Wage 1,957 20.8 19.5 20.2 19.9 19.5 100 Non Forest Wage 1,659 24.5 18.9 18.2 19.7 18.6 100 Pension 1,168 35.0 28.3 17.2 9.1 10.5 100 6.1. Variation of Participation across Income 6.2. Determinants of Income Quintiles Econometric analysis can help reveal relationships that effect The following section uses the SEHS data to identify some household incomes; given the breadth of income-generating of the potential pathways highlighted in the P.R.I.M.E. activities and the differences in participation across different framework, focusing on factors that are important determinants types of households, the analysis was conducted for each of household income. Supporting the analysis of Table 3‑3 individual type of income as well. While income is influenced (Household average income by source and participation), by many factors, the causal links between the results should be Table 6‑1 below further breaks down participation rates by interpreted with some caution since the data were collected income quintile. However, since we are interested the lack of for one-time period only, and thus causality cannot be fully access or use of certain activities this analysis instead quantifies explored. non-participation. Table 6‑2 below summarizes the estimated returns to a range Those in the poorest quintile participate the least across all of factors, both at the household and village level. The analysis income-generating activities except forest wage income, which is carried out for multiple single income sources as well as remains relatively constant across the income distribution. Similar aggregate income (measured as total household and per to previous results, the largest gaps between the poorest and capita household income). First, focusing on forest income (a) richest quintile are in pension, agriculture and livestock incomes. the results show that membership in cooperatives, access to POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 35 infrastructure such as water networks, and owning productive A few other results highlight the key factors or necessary assets assets such as tractors and chainsaws, all have a significant for participating in the respective income-generating activity. impact on income. Households with cooperative membership Results for agricultural income (b) show that only two variables earn a third more forest income, compared with non-member have a significant impact: ownership of trucks and tractors. For households. Households who own trucks, tractors or chain wage income (e), it is interesting to observe that access to the saws have 34%, 21% and 20% higher income, respectively. Internet is positively associated with non-forest wage income On the other hand, households located in villages with a water (but has no effect on forest wage income (d)) and households network generate less forest income (50% lower), compared with Internet access earn a 22% higher wage income than do with their counterparts who lived in villages without a water those without Internet access. It is possible that access to the network. This latter finding may suggest that households with Internet provides households with better-paid job opportunities better water access may seek more profitable activities than outside the forest. forest-related activities. This conjecture is indeed supported by the results from the livestock income regression (c) - on average; households in villages with a water network generate 34% higher livestock income.20 Table 6‑2 Determinants of Income, by Income Source Dependent variable: log (income by source) Forest Forest Income Agricultural Livestock wages Non-forest Pension Total Per-capita (a) income (b) income (c) (d) wage (e) income (f) income (g) income (h) Household Migrants              Decisions Coop             member Village Water               Infrastructure network Household Assets Internet                Car          Tractor            Chainsaw            Livestock               No. of observations   665 291 351 40 256 440 1017 1017 Notes: The checks () indicate level of significance of the variable in the regression, not the magnitude of the effect. For the complete regression table, please refer to Appendix 3. Legend: Positive   Negative   p< 0.01 = ***  p< 0.05 = **  p< 0.10 = *  20 The sample size for forest and non-forest wage regressions is relatively small due to the small number of households participating in wage employment. This makes it difficult to generalize the conclusions to all forest villages. 36 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 7. INTERPRETING THE RESULTS Productive and social capital. While the positive relationship better understanding of the operation of cooperatives and between access to productive assets and income is well associations in forest communities with a special emphasis on known, the significant effect of the social capital (cooperative membership conditions and inequality in membership access membership) deserves further discussion. Addressing poverty between poor and the non-poor households. The proposals to in forest communities has been a central focus of the GDF expand the scale of cooperatives in forest communities (Atmis development plans for past few decades. The implementing et al., 2010) should be assessed based on their impact on agency of GDF, previously the GDFVR established in 1970, multiple indicators, including migration and income distribution. which was replaced by ORKOY in 2011, has a long history of providing development assistance to forestry communities Income security. The results show strong evidence of positive through the promotion of income diversification, creating associations between household income security (measured by employment while reducing dependency of forest resources access to pensions and remittances) and household welfare. (i.e. to reduce uncontrolled harvesting). Pensions and remittances are more stable income sources, thereby providing safety nets that both prevent households from Since 2011, ORKOY has implemented a range of specific falling into poverty in the event of adverse shocks and boost a measures to support forest villagers. These include providing credit household’s confidence to invest in productive assets. to households and cooperatives, supporting business projects such as animal husbandry, beekeeping, and mushroom cultivation. They Global evidence from the past two decades has highlighted also installed energy efficient stoves and solar water systems to that one of the major causes of poverty, in particular among reduce wood use, and provided training and technical assistance rural populations, is vulnerability to adverse shocks (World in forest management. The significant income enhancing effect of Development Report, 2000). Poor people have limited capacity cooperative membership captures these positive benefits. to diversify, and their livelihoods are often more dependent on natural resources, such as forests, land and water, and Unequal membership rates (as shown in Table 3‑5) between consequently their income sources are more volatile. At the same the poor and the non-poor (20% for the poor versus 26% for time, their ability to cope with shocks is limited because they the latter) is a potential indication that policies that increase have limited access to financial assets (credit and insurance) the inclusiveness of cooperatives are likely to have a large to cope with risks. Therefore, without external support from the impact on poverty by reaching the poorest households in public sector, vulnerability can be a poverty trap to the poor. forest communities. Future research should aim to gain a POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 37 Box 6. Targeting Development Programs around the World One of the key components of program development is to concentrate resources on “target groups” of poor or vulnerable households. Based on a review of experiences and lessons learnt from 122 antipoverty programs/projects in 47 transition and developing countries, Coady, Grosh and Hoddinott (2004), conclude that targeting has the potential to increase the effectiveness of these programs. Targeting is particularly important for the transfer of programs that constitute safety nets to address vulnerability, but the choice of targeting methods must be driven by local context. The recognition of the important linkages between poverty and vulnerability has led to a dramatic expansion in the number of developing countries that have established relatively large cash transfer programs focused on society’s low income and excluded groups (Hanlon, Barrientos and Hulme, 2010). Social protection programs cover public transfers, in cash or in kind, to protect and raise the consumption of the poorest households. Fiszbein et al. (2014) estimated that social protection programs are currently preventing 150 million people from falling into poverty. However, development programs that aim to simultaneously improve social as well as ecological protections remain very few. Some examples include public employment programs, such as India’s Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS), South Africa’s Working for Water program, and the Brazilian Bolsa Verde program which links an existing social protection program (Bolsa Família) with a scheme of Payment for Environmental services (PES) (Schwarzer et al., 2016). One of the world’s largest social protection programs, Bolsa Verde, has been implemented in many extractive reserves and forests in Brazil where rural poverty was widespread. In 2011, the Ministry of Environment collaborated with the Ministry of Social Development to create Bolsa Verde as part of the Brazil Without Extreme Poverty Plan, which distributed $34 million to 44,388 households. Bolsa Verde provides households with social security support and guaranteed quarterly income combined with training and technical support, in exchange for a household’s contribution to various activities linked to forest management and conservation. 7.1. Assessing the Poverty Impacts of Policies The FSP (2017-2021) provides direction for promoting sustainable development through better forest management, The central focus of the FSP for 2017-2021 is to achieve the enhancing the productivity of wood and NWFP harvesting, twin goals of sustainable forest management and increasing and improving the wellbeing of the forest village population the standards of living in forest villages. However, achieving through economic diversification and increased development these objectives requires effective local institutions, such as assistance. The priority areas identified in the development village cooperatives, as well as a more informed roll-out of the plan include: (1) improving the productivity of wood production forest development programs identified in the GDF’s strategic and harvesting by forest villagers through better technology plan. The former involves both improving the effectiveness and equipment, (2) expanding the collection of NWFPs, and of operation of cooperatives and expanding membership of increasing the value-added of NWFPs by developing SMEs cooperatives to include the poorest households. The latter targeted at processing and packaging, and (3) improving involves both increasing resources allocated to forest community the efficiency of the timber supply chain and procurement, assistance programs and better targeting resources to ORKOY through investments in forest resource information systems and activities that have a larger impact (see Box 6). Specifically, improving the efficiency of the timber harvesting supply chain this involves channeling resources to upgrade timber harvesting by more closely integrating of harvesting entities (villagers and equipment, increase the value-added to NWFPs, and enable cooperatives), principle suppliers (GDF) and purchasers. better paid forest management jobs through skill training and capacity building in forest management. In the short to medium term, the new forest model proposed by the GDF focuses on harvesting and increasing value-added It is critical to understand the poverty impact of these programs. activities related to NWFPs while engaging villagers in more The above analysis identifies key determinants of household collaborative approaches to forest management. This includes income among forest village households, making it possible fully utilizing their local knowledge as forest caretakers and to simulate the impacts of various policies and development protectors, as well as their labor resources for harvesting and programs aiming to address rural poverty through sustainable reforestation. forest management in forest communities. 38 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY Table 7‑1 Analysis of Policy Impact on Income and Poverty: simulations (TL)     Policy A Policy B Policy C Income quintiles Baseline (BL) A % change B % change C % change       (A- BL)/BL   (B - BL)/BL   (C - BL)/BL 1 588 853 45 1,254 113 2,107 258 2 2,862 4,060 42 3,320 16 7,380 158 3 5,681 7,809 37 6,019 6 13,827 143 4 9,268 12,601 36 9,532 3 22,134 139 5 29,205 36,798 26 29,591 1 66,389 127 All income groups 7,577 9,964 31 8,023 6 17,987 137 Poverty rate (%) Using relative line 41.0 32.3 -30.4 36.0 -12.3 18.7 -54.4 Using national line 82.5 75.0 -24.9 82.1 -0.5 55.4 -32.8 Note: Policy A covers expanding co-op membership and targeting loans to help HHs purchase tractors and trucks. Policy B provides HHs who currently have no pension income with 20% of the median pension income in the form of basic income support. Policy C is a combination of A and B. While it is difficult to fully quantify the impact of the full range of increasing their standard of living. It is also relatively easy to policies developed in GDF’s plan, a policy simulation exercise target households without pensions. can be useful in illustrating the potential impact of some specific policy proposals on income. The income analysis provides the The policy simulation exercise focuses on the distributional marginal impact of several policy variables that can be used impact of the proposed programs across income groups and in the policy simulation.21 Based on findings from the above regions. The simulation is based on a per capita income analysis (per capita income) and a review of global experience regression presented in Appendix 3. Table 7‑1 presents the (Shyamsundar et al., 2017), three highly simplified yet practical summary of impacts, measured by the change in household programs were chosen for the policy simulation. They include: income and poverty rate, from the baseline case. The results (A) targeting ORKOY credit programs to households that show that both proposed programs provide more benefits to currently do not have key productive assets, such as trucks and poor households. tractors (B) providing basic income support to households that do not receive pensions, and (C) a combination of (A) and (B). While policy (A) generates a much larger overall reduction in the poverty rate (32% reduction, using the national poverty The choice of expanding basic income support among forest line), the increase is smaller across income groups; with a village households that have no access to stable income sources 45% income increase in the bottom two quintiles, and a 26% is motivated by two considerations. First, the findings from increase in the top quintile. In contrast, Policy (B) is less effective the previous chapters consistently show that one of the major in reducing overall poverty (12% reduction in poverty rate) but differences between poor and non-poor households is access it is highly progressive. The poorest saw the largest increase in to pensions. Based on median income by source, pensions are income (113%) while the top income quintiles gain about 1%. ranked as the second most important source of income (non- Implementing these two programs combined is estimated to forest wage ranks first), and its security and stability indicates halve the poverty rate among forest villagers (54% reduction). that pension income plays the role of a safety net to reduce vulnerability. Other studies also show pension income (both Table 7‑2 summarizes the policy impacts across regions. coverage and size) as an important factor behind poverty Measured by the reduction in the poverty rate, this simulation reduction during 2002-2014 (Azevedo and Atamanov, shows less of an increase across regions than in income 2014). The share of social spending in GDP in Turkey was groups – but the findings are still quite substantial. The poorest relatively high given its demographic structure with a large and regions did not see the largest reduction in the poverty rate, young working population, and the size of pension benefits indicating that these programs may not be perfectly targeted relative to average earnings (which were ranked second to reach the poorest. However, given that over 80% of forest after New Zealand among OECD countries). This suggests villagers lived below the national poverty line, achieving that targeting social spending to forest village households in geographical targeting may be less of a concern in the design the area of pension coverage can be an important route to of the program’s implementation. 21 The estimated marginal effect is presented in Appendix 2 in the income regression analysis. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 39 Table 7‑2 Poverty Impact across Regions The policy simulation and impact assessment should be Poverty rate (%) Change (%) regarded as an illustration rather than a policy prescription. The development of new forest programs/projects requires Region Baseline (BL) Policy C (BL-C)/BL the collection of more program-base information, including Mediterranean 67.85 34.35 49.38 program cost and implementation feasibility assessments to South East Anatolia 60.43 34.42 43.05 evaluate cost effectiveness. In addition, the program’s design West Marmara 53.87 29.33 45.54 should aim to generate synergies among a range of activities in order to maximize impact on development. Program managers Central East Ana 52.23 21.23 59.36 should assess the impact of complementary programs including North East Anatolia 51.23 21.60 57.84 combining ORKOY programs (skill training of forest villagers to West Anatolia 50.24 35.37 29.60 use modern technology for forest management and protection, Central West Ana 43.60 19.41 55.47 information on marketing, exporting and e-commerce for NWFPs) with other forest and non-forest fiscal policies. West Black Sea 43.38 17.08 60.62 East Black Sea 25.78 10.05 60.99 Given Turkey’s long tradition of government-supported Aegean 21.97 6.59 70.01 programs in forest communities, policy-makers have much to East Marmara 19.27 8.53 55.76 gain from understanding the impact on household welfare. Such knowledge is particularly useful for developing a new Total 41.03 18.71 54.41 forest community model that aims to integrate sustainable forest management with the objectives of alleviating poverty and promoting economic development in forest communities. The simulation results provide important information for informing Unfortunately, information on the impact of these programs/ program design. That is, to achieve the goal of reducing projects is sparse due to lack of data collection for program poverty across forest villages, community-based programs monitoring and evaluation. A few studies in this area include could focus more on how to modify existing programs (e.g. Atmis et al. (2009), and Alkan and Kilic (2013), using small- provision of credit to purchase productive assets) and develop scale surveys that collected limited information. Building the new programs (provision of basic income support to address capacity of GDF for household data collection and program vulnerability) than focusing on program placement in forest impact assessment should be regarded as a key component villages (i.e. the issue of geographical targeting). The analysis of the new forest community development model to improve indicates that there exists an ample scope to improve the cost program design and implementation. effectives of the current forest development programs. 40 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 8. CONCLUSIONS AND POLICY RECOMMENDATIONS Turkey’s forestry challenges are embodied in the twin objectives Table 8‑1 Comparing Turkey’s Forestry Sector of sustainable forest management and increasing the standard with the EU’s, 1990-2010 of living in forest communities. As the country’s primary source 1990 2010 of harvesting labor, forest villagers’ standard of living is of Value added Turkey 1,804 3,077 particular concern to the General Directorate of Forestry (GDF). ($ mil, 2010 price) The GDF provides support to forest villagers to reduce their Finland 4,301 4,019 dependence on forests (i.e. reduce any illegal harvesting) and Sweden 3,516 5,890 to reduce pressure on the forests itself. However, this support is often too little, of itself, to fully lift villagers out of poverty – leading to further out-migration. Forest sector employment Turkey 0.7 0.6 as % labor force EU 1.6 0.9 Out-migration has resulted in a rapidly declining and ageing   population in forest villages, a trend that is not unique to Turkey. Comparisons of Turkey’s forestry sector to EU countries Forest sector as % of GDP Turkey 1.0 0.8 show that Turkey is beginning to experience a similar trend EU 1.6 0.9 in forest sector employment. In particular, the EU experience   has illustrated that as countries shift from manual labor to more capital-intensive (mechanized) harvesting - this increases Forest sector as % of Turkey 2.3 3.0 the efficiency and management of the resource. While this agriculture and manufacturing GDP EU 5.2 5.0 shift requires less labor - those who do continue to work in the forests generally make higher wages due to their more   advanced skills. But even under the current model, Turkey still Forest exports as % of total Turkey 1.0 2.1 has enormous potential, in particular through its contribution to export EU 4.4 3.1 total exports (Table 8‑1). Source: FAO data sources. The global evidence shows that many high-income countries Note: Turkey (forest area of 216,781 km2) has a similarly-sized forest coverage to Finland (233,320 km2) and Sweden (234,855 km2), so they were chosen for have gone through a similar process of economic development comparisons. and structural changes, accompanied by rural-urban migration as part of the urbanization process. As Turkey is reaching The evidence generated from the forest village household the threshold of becoming a high-income economy, the key survey supports these policies and is consistent with existing issue is how to manage the forest sector transition through research, case studies and analysis in Turkey and more broadly the development of policies and programs that ensure that in line with global evidence. The analysis explored some the goals of sustainability and poverty alleviation in forest aspects of the linkages between poverty, forest dependence, communities are mutually reinforcing rather than in competing income vulnerability and migration. Findings show that the with one another. In this regard, the forestry sector should also poor are more forest dependent because of their lack of work with other institutions responsible for rural livelihoods. alternative income options, a low level of productive assets, This would include other social program assistance, such as social capital (e.g. members of a cooperative) and high pensions, and even the private sector. vulnerability. As a result, they have limited capacity to diversify income sources and move to higher-return economic activities - such as agriculture and owning livestock. To a certain extent, forest dependency represents a poverty trap - since income opportunities are low in the value chain and do not pay that well. However, specific interventions, such as strengthening the value chain through greater local level processing, can improve the situation. Currently, the most forest dependent individuals are in the bottom 20% of the income quintile. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 41 The analysis using the SEHS data shows that ample scope transfer programs) and (4) increasing local NWFP processing exists to improve the existing forest program in order to target capabilities given the untapped potential of value addition at activities that could have a large impact on increasing income the local level. Programs could enable investments in Small as well as addressing income vulnerability. These include: (1) and Medium Enterprises (SMEs) for local processing and focusing credit to support household investment in productive packaging of NWFPs, and strengthen local connections to the assets, such as tractors, chainsaws, and access to the supply value chain (e.g. via e-commerce). Policy simulation internet; (2) increasing social capital, such as membership in results show that these community assistance programs are cooperatives and associations, (3) increasing access to stable highly progressive and can benefit those most in need. income support to reduce vulnerability (e.g. pensions or other 42 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY REFERENCES Adger, W.N., P.M. Kelly, A. Winkels, L.Q. Huy, C. Locke Cuevas, P.F. and L. Rodriguez-Chamussy (2016) Exploring the (2002) Migration, remittances, livelihood trajectories, and determinants of the positive trends in poverty and shared social resilience, Ambio 31(4): 358-66. prosperity in Turkey, Working Paper, Turkey Poverty and Equity Program of Analytical and Advisory Services, World Alkan H. and M. Kilic (2014) Forests and forestry organizations Bank, Washington: DC. from the forest villagers’ perspective: a case study from Turkey. iForest 7:240-247. Available at: URL: http:// Deaton, A. and M. Grosh, (2000) Consumption, in designing www.sisef.it/iforest/contents/?id=ifor0999-007 household survey questionnaires for developing countries: Lessons from 15 years of the Living Standards Measurement Angelsen, A. (2010) Policies for reduced deforestation and Study, M. Grosh and P. Glewwe (Eds), World Bank: their impact on agricultural production. Proceedings of the Washington, DC. National Academy of Sciences 107(46): 19639–19644. Fiszbein, A., R. Kanbur, R. Yemtsov (2014) Social protection Angelsen, A., P. Jagger, R. Babigumira, B. Belcher, and N.J. and poverty reduction: global patterns and some targets, Hogarth (2014) Environmental income and rural livelihoods: World Development 61: 167–177. a global-comparative analysis. World Development 64(S1): S12-S28. General Directorate of Forestry (GDF) (2015) Forest inventory results 2015. Forest Management and Planning Ariel Fiszbein Ravi Kanbur Ruslan Yemtsov (2013) Social Department, General Directorate of Forestry, Ministry of protection, poverty and the post-2015 agenda, World Forestry and Water Affairs, Ankara. Bank. General Directorate of Forestry (GDF) (2014) Forestry Atmiş, E., H. B. Günşen, B. Lise and W. Lise (2009) Factors Directorate General Strategic Plan 2014-2017. Forest affecting forest cooperative’s participation in forestry in Management and Planning Department, General Turkey, Forest Policy and Economics 11: 102-108. Directorate of Forestry, Ministry of Forestry and Water Affairs, Ankara. Atmiş, E., H. B. Günşen, S. Ozden (2010) How can Turkey’s forest cooperatives contribute to reducing rural poverty? General Directorate of Forestry (GDF) (2017) Forestry Directorate Unasylva (English ed.) 61(234/235): 51-53. General Strategic Plan 2017-2021. Forest Management and Planning Department, General Directorate of Forestry, Azevedo, J.P. and A. Atamanov (2014) Pathways to the Ministry of Forestry and Water Affairs, Ankara. middle class in Turkey: How have reducing poverty and boosting shared prosperity helped? Policy Research Gökçe, O. (2005) Dynamics of forest, agriculture and public Working Paper No. 6834. World Bank, Washington: relations. Environment and Forestry, Antalya, Turkey, pp. DC. Available at: https://openknowledge.worldbank. 1567-1574. [in Turkish] org/handle/10986/17722 License: CC BY 3.0 IGO. Hanlon, J., A. Barrientos and D. Hulme (2010) Just give money Brack, D., A. Glover, and L. Wellesley (2016) Agricultural to the poor: the development revolution from the global commodity supply chains: Trade, consumption and south. Sterling, Vancouver, USA: Kumarian Press. deforestation. London: Chatham House. Hecht, S.B., A.L. Yang, B. SS. Basnett, C. Padoch, and N.L. Chambers, R., M. Leach, and C. Conroy (1993) Trees as Peluso (2015) People in motion, forests in transition: Trends savings and security for the rural poor. Gatekeeper Series in migration, urbanization, and remittances and their Number 3. International Institute for Environment and effects on tropical forests. Center for International Forestry Development, London, UK. Available online at: http:// Research (CIFOR), Bogor, Indonesia. Retrieved from http:// www.iied.org/pubs/pdfs/6025IIED.pdf. www.cifor.org/library/5762/people-in-motion-forests-in- transition-trends-in-migration-urbanization-and-remittances- Coady, D., M. Grosh, J. Hoddinott (2004) Targeting of transfers and-their-effects-on-tropical-forests/ in developing countries: review of lessons and experience, World Bank, Washington, DC. Available at: https:// Hosonuma, N., M. Herold, V. de Sy, R.S. de Fries, M. openknowledge.worldbank.org/handle/10986/14902 Brockhaus, L. Verchot, A. Angelsen, and E. Romijn (2012) License: CC BY 3.0 IGO. An assessment of deforestation and forest degradation drivers in developing countries. Environmental Research Letters, 7(4): 44009. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 43 Kuvon, Y., S. Y. Erol, H. T. Yilirim (2011) Forest manager Sunderlin, W. D., S. Dewi, A. Puntodewo, D. Müller, perceptions of the foremost forestry issues and functions in A. Angelsen, and M. Epprecht (2008) Why forests Turkey. Polish Journal of Environmental Studies 20(2): 393- are important for global poverty alleviation: a spatial 403. explanation. Ecology and Society 13(2): 24. Available at: http://www.ecologyandsociety.org/vol13/iss2/art24/ McSweeney, K. (2004) Forest product sale as natural insurance: the effects of household characteristics and the TEEBcase (2013) Economic valuation of forest goods and nature of shock in eastern Honduras. Society and Natural services, Tunisia, by Hamed Daly-Hassen, Available at: Resources 17(1): 39-56. TEEBweb.org Millennium Ecosystem Assessment, 2005 (2005) Ecosystems Tolunay A. and H. Alkan (2008) Intervention to the misuse of and human well-being: synthesis. Island Press, Washington: land by the forest villages: a case study from Turkey. Ekoloji DC. 17(68): 1-10. Ministry of Forestry and Water Affairs (2011) National TUIK (2016) Official statistics downloaded from the Turkish Biodiversity Monitoring Report - 2011. Ankara, Turkey. Statistical Institute. Available at: http://www.turkstat.gov. tr/Start.do. Pattanayak, S.K., and E.O. Sills. (2001) Do tropical forests provide natural insurance? The microeconomics of non- UDA Consulting (2016) Socio-economic analysis of forest timber forest product collection in the Brazilian Amazon. villagers in Turkey. Ankara, Turkey. Land Economics 77(4): 595-612. Viana, J.P. (2015) Leveraging public programmes with Schwarzer, H., C. van Panhuys, and K. Diekmann (2016) socio-economic and development objectives to support Protecting people and the environment: Lessons learnt from conservation and restoration of ecosystems: the price- Brazil’s Bolsa Verde, China, Costa Rica, Ecuador, Mexico, support policy for socio-biodiversity derived products and South Africa and 56 other experiences. Social Protection the green grant programme of Brazil. (Commissioned by Department Working Paper No. 54. ILO. the Secretariat of the Convention on Biological Diversity United Nations Environment Programme). IPEA. Secretariat General of the Central Anatolian Exporters Union (2014) Wood and Forest Products Industry Report Wong, J.L.G. and I. Prokofieva (Eds) (2014) Report presenting 2014. Web 22 May 2017. Available at: http://www. synthesis of regional sectoral reviews to describe the “State turkishwood.org/Eklenti/73,oaibagacmamulleriveormanu of the European NWFP”. StarTree deliverable D1.3. 96 runlerisektorraporu2014ingpdf.pdf?0 &_tag1=66BAAB1 pp, references and 3 Annexes. 28AA1CAF6FD38CCB3DA2CA3C1CFF887CF&crefer =EB91626A4AD6E278DBAC3990FC73902028E1F4 World Bank (2015) Valuing forest products and services in BFEDF3ACA62B56CB9CEBCE1264. Turkey: a pilot study of the Bolu forest area, World Bank, Washington: DC. Shyamsundar, P, S. Ahlroth, P. Kristjanson and S. Onder (2017)  Investing in pathways to prosperity in forest World Bank (2016) Socio-economic analysis of forest villagers landscapes – a P.R.I.M.E. approach, World Bank, in Turkey. Funded by the Program for Forests (PROFOR), Washington: DC. World Bank, Washington: DC. Siikamäki, J., F. Santiago-Ávila, and P. Vail (2015) Global World Bank (2017) Turkey: Forest Policy Note. Report No. assessment of nonwood forest ecosystem services. PROFOR AUS10803. World Bank, Washington: DC. Working Paper, Washington: DC. Yilmaz, E. (2006) Participatory approaches at planning & State of Europe’s Forests 2011 (2011) Status and trends in management of forest resources. Congress on Socio- sustainable forest management in Europe. FOREST EUROPE Economic Problems in Forestry, Çankiri, Turkey, pp. 196- Liaison Unit, Oslo, Norway. 200. [in Turkish] Sunderlin, W. D., A. Angelsen, B. Belcher, P. Burgers, R. Nasi, L. Santoso, and S. Wunder (2005) Livelihoods, forests, and conservation in developing countries: an overview. World Development 33(9): 1383-1402. 44 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY APPENDIX 1: ORKOY – TERMS AND CONDITIONS OF SUPPORT Terms of individual grant/loan program support: A. The forest villager must apply to the Local Unit of the D. The forest villager must use the credit as specified in its pre- Forestry Directorate in written form; set conditions and be committed to paying the credit back; B. Must have lived in that particular village for at least one E. A document as proof of need from the legal head of year from the credit support date; village (Mukhtar); C. The forest villager must have documented proof of being F. The forest villager cannot be a permanent employee, a forest villager; officer, tradesman or retired official. Note: If there is insufficient demand within a village, requirements e) and f) are not necessary. Credit support limits and caps Maturity Grace Payment 2017 Credit Upper Project type Unit Timeframe Period Period Limits (TL) SOCIAL PURPOSE PROJECTS No Grace Period Exterior Thermal Insulation and Solid Fuel Fired Central Heating (100 m²) 7 16,000 Roof Cover 150 m² 5 2,200 Solid Fuel Heating System 5 5,000 Exterior Fencing 100 m² 5 11,000 Solar Energy System 2-3 collectively 3 1,800 ECONOMIC PURPOSE PROJECTS Viticulture 5 decares 7 4 3 14,500 Orcharding 5 decares 7 4 3 12,000 Dairy Cattle 2 head 6 1 5 27,000 5 head 6 1 5 Milk Condensation 30+1 head 6 1 5 29,000 Family-operated Hostel 6 6 29,500 Plastic Greenhouse 500 m² 5 5 21,500 Plastic Greenhouse 1000m² 31,000 Thyme Breeding 5 decares 5 2 3 8,000 Sage Cultivation 5 decares 5 2 3 9,000 Fenni Beekeeping 30 beehives 4 4 14,000 30 hives with enclosure 4 4 17,000 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 45 Examples of a Social Purpose Credit Roof covering materials Exterior thermal insulation Solar water heating Central heating system for households Examples of an Economic Purpose Credit Animal husbandry Beekeeping Greenhouses Microcredit for housewives Mushroom cultivation 46 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY Terms of Cooperative\ Grant/Loan Program support: Examples of Cooperative Credits A. The Cooperative must fulfill the obligations of the Law of Cooperatives, and from the Main Contract must set up quality bookkeeping; B. A general meeting of shareholders should be held within the legal period, and the scope of activities must be clarified in the general meeting of shareholders or main contract; C. 51% of households in the cooperative’s central village must be shareholders of the cooperative; D. 10% of the project amount must be financed from the Trout preparation plant shareholders’ equity; E. The applicable project should be accepted by 51% of the shareholders in a general meeting, and must be approved by the General Directorate; F. A notarized credit contract and bank guarantee or instrument of charge must be provided. ORKOY - Forest villager support in 2014 Support Type Number of families Roof Cover 1,039 Dairy barn Solar Water Heating 4,889 Heat Insulation 518 Central Heating System 98 Heat Insulation and Central Heating System 48 Total Social Support 6,592 Bee Keeping 1,046 Animal Husbandry 4,398 Greenhouse 164 Mushroom Cultivation 16 Eco Tourism 1 Micro Credit 273 Total Economic Support 5,946 Total Individual Support 12,538 Total Cooperative Support 23 Source: General Directorate of Forestry, 2016. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 47 APPENDIX 2: MIGRATION ANALYSIS The econometric analysis for migration is based on a probit Determination of migration probability model to estimate the relationship between migration status  Migration probability 2012-2016 2007-2011 (households with permanent migrants) and a set of household Indicators for education of household head and village variables. Migration status is defined over two time periods: (1) 2007-2011 and (2) 2012-2016. The impact Never in school 0.012 0.113 of household and village covariates is estimated using the Primary school 0.073 0.301 following equation: Mid-high school -0.254 -0.063 Age of HH head 0.156* -0.046 Log (Phh) = β + β1 Xhh + β2 Wvillage + regional fixed effect + u Age of head (squared) -0.001* 0.001 where P is the probability that a HH has permanent migrants Male head -0.072 -0.050 during a specific period, Xhh are household variables including HH size -0.119*** -0.149*** age and education of the household head, log household log (total income) 0.086* 0.095* income, membership of forest cooperative, membership in other cooperatives, living in a village with a water network, Share of forest income 0.491** 0.393* and a household asset index. Share of non-forest wage income -0.078 -0.225 HH is member of forest coop -0.498* -0.188 HH is member of other coop 0.086 0.060 HH has internet access -0.329 0.006 HH is owner of livestock 0.267* 0.285* HH has tractor 0.033 -0.116 Living in village with water network 0.010 0.026 Asset index 0.044 0.005 Note: The asset index is constructed using Principle Component Analysis covering 8 assets: cellphone, computer/tablet, freezer, solar panel, car/truck, motorcycle/ scooter, tractor, and chainsaw. The first principle component (PC1) explains 47% of the total variation of 8 durable/asset variables. 48 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY APPENDIX 3: INCOME REGRESSIONS, BY SOURCE Determinants of income, by income source Dependent variable: log (income by source) Forest Agriculture Livestock Forest Non-forest Pension Total Per-capita Variable income income income wage wage income income income Has migrants 0.17 0.22 -0.42*** -0.34 0.09 -0.02 0.13* 0.19* Coop member 0.31** 0.19 0.31** -0.06 -0.03 0.01 0.32*** 0.24** Household size 0.01 0.03 0.01 -0.02 0.05* -0.01 0.05* - Live in village with water network -0.50*** -0.34 0.34*** -0.22 0.11 0.06 0.03 0.09 Has internet 0.16 -0.11 0.16 1.15 0.22* 0.10 0.18 0.07 Has solar panel -0.06 0.09 0.15 0.09 0.06 0.00 0.07 -0.02 Has car -0.26** 0.31* 0.13 -1.34 0.26** 0.08* 0.34*** 0.34*** Has motorbike 0.22 0.24 0.04 -1.22 0.08 0.06 0.11 0.08 Has tractor 0.30** 0.58*** -0.04 1.65 0.08 -0.12** 0.30*** 0.21* Has chainsaw 0.39*** 0.10 0.14 -1.12 0.18* 0.06 0.25*** 0.21** Own livestock -0.08 -0.27 1.24 1.75 -0.28*** -0.02 0.17* 0.08 The following variables are with respect to household head: Graduated primary school 0.32 0.17 -0.15 (omitted) 0.71 0.14 -0.64 -1.17*** Graduated middle school 0.15 0.67 0.06 -1.02 0.92 0.23 -0.25 -0.63* Graduated high school 0.32 0.68 0.06 0.08 1.01 0.29 -0.23 -0.56 Attended tertiary school -0.49 0.77 0.35 -4.49** 1.03 0.40 0.24 -0.22 Age 0.02 0.03 -0.10*** 0.28 0.12*** -0.01 -0.05* -0.05 Age squared 0.00 0.00 0.00*** 0.00 -0.00*** 0.00 0.00** 0.00** Male 0.21 0.14 0.25 -0.44 -0.03 0.09 0.04 -0.18 Regional fixed effect included sig sig sig sig sig sig sig sig No. of obs 665 291 351 40 256 440 1017 1017 R sq 0.17 0.09 0.15 0.08 0.18 0.04 0.24 0.25 Note: The income model is in semi-log form, therefore, the estimated coefficients (times 100), represent a percent change in income for unit increase in the covariates (in the case of continuous variables) and discrete change (in the case of dummy variables). For example, col (7) for total income, 0.13*, means that, on average, households belonging to cooperatives, earn a 13% higher total income, compared with non-member households, all other factors being the same. POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY 49 APPENDIX 4: PROPORTION OF HOUSEHOLD ASSET OWNERSHIP Item % HH ownership Internet 0.63 Cell phone 8.86 Computer 1.17 Dishwasher 2.47 Fridge 2.54 TV 9.79 Solar panel 4.71 Car/truck 4.24 Horse 0.35 Donkey 0.68 Motorbike 1.36 Tractor 4.16 Generator 0.26 Handheld harvester 0.01 Harvest combine 0.00 Harvest harrow 0.01 Water pump 0.70 Chainsaw 4.88 Motorcycle 0.00 Livestock 6.18 50 POVERTY, FOREST DEPENDENCE AND MIGRATION IN THE FOREST COMMUNITIES OF TURKEY JUNE 2017 Poverty, Forest Dependence and Migration in the Forest Communities of Turkey Evidence and policy impact analysis 1818 H Street NW Washington D.C. 20433 United States of America www.profor.info