March 2021 Harnessing Forests as Pathways to Prosperity in Liberia: Policy Note Report No: AUS0002098 . Liberia Liberia: Forest Sector Analysis Harnessing Forests as Pathways to Prosperity in Liberia: Policy Note . March 2021 Environment, Natural Resources and the Blue Economy Global Practice . . © 2021 The World Bank 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved This work is a product of the staff of The World Bank. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. 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Harnessing Forests as Pathways to Prosperity in Liberia: Policy Note. © World Bank.� All queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org. i POLICY NOTE Harnessing Forests as Pathways to Prosperity in Liberia Abstract Liberia is the most forested country in West Africa, with more than two thirds of its land surface covered by forest. In 2019, 47.5 percent of the Liberian households (HHs) lived in proximity to and were significantly dependent on the country’s forests. Results from the recent sample-based National Household Forest Survey (NHFS 2019) conducted in these forest-proximate areas reveal a high dependence on forest products both for direct consumption and as a source of income. These forest products, ranging from fuelwood to medicinal plants, also provide HHs with an important social safety- net during natural and economic shocks and crises, such as the COVID-19 pandemic. At the same time, Liberia is one of the world’s poorest countries. The NHFS found that the average income for these forest- proximate HHs is US$780, which is substantially below the country’s average annual HH income of US$2,440. This points to the need to maximize the potential of forests for poverty reduction in a sustainable manner. Using the data collected from the recent NHFS, this policy note unpacks the HH and forest interactions, for forest-proximate HHs. The note: (1) identifies the sources within forestry and other sectors from which HHs derive their subsistence and income needs; (2) looks at the income generating potential of various activities that a HH participates in and its labor time allocation; and (3) highlights the gender aspects of poverty, particularly as they relate to the forestry sector. Extensive investments and complementary policy reforms relevant to rural landscapes are needed if the welfare of these poorest of the poor forest-dependent HHs is to improve. Based on our analysis in the above three dimensions, this note suggests several recommendations for the Government of Liberia. These recommendations include: (1) targeting forest-proximate HHs as part of its poverty reduction strategy; (2) investing further in enhancing more diversified income generation from forestry and crop production; (3) creating opportunities for more women and female-headed HHs to participate in the high- return forest activities; (4) ensuring that value-addition is increased for the currently low-return forest activities (which employ a greater proportion of women); (5) supporting forest-proximate HHs to develop more forest-related non-farm enterprises, preferably with greater ownership and management by women; and (6) undertaking more regular surveys such as the Liberia NHFS or integrating forest-based activities into existing national survey operations. ii Acknowledgements This policy note was prepared by a team led by Neeta Hooda. The core team was composed of Valentina Costa, Sydney Gourlay, Nalin Kishor, and Lesya Verheijen. The team received expert advice from peer reviewers: Sofia Ahlroth, Senior. Environmental Economist, Urvashi Narain, Lead Economist, Esther Rojas-Garcia, Senior Operations Officer, Alberto Zezza, Program Manager – all from World Bank, and Arild Skedsmo, Senior Advisor, Norwegian Ministry of Climate and Environment. This policy note was produced under the overall guidance of Khwima Nthara (Country Manager, Liberia) and Sanjay Srivastava (Practice Manager, Environment, Natural Resources and Blue Economy, West and Central Africa). This policy note draws on the data that was collected through the NHFS. The NHFS was coordinated by the Liberia Forestry Development Authority and undertaken by the Liberia Institute of Statistics and Geo- Information Services (LISGIS). The team would also like to acknowledge the generous support provided for preparation of the note by the Norwegian Ministry of Climate and Environment under the Liberia Forest Landscape Single Donor Trust Fund. iii Abbreviations CAPI Computer-Assisted Personal Interviews CSM Chainsaw milling EA Enumeration areas FAO United Nations Food and Agriculture Organization HIES Household Income and Expenditure Survey HH Household LISGIS Liberia Institute of Statistics and Geo-Information Services NFE Non-farm enterprise NHFS National Household Forest Survey PAPD Pro-poor Agenda for Prosperity and Development RuLIS Rural Livelihoods Information System iv Table of Contents Abstract ..................................................................................................................................................................... ii Acknowledgements .................................................................................................................................................. iii Abbreviations ........................................................................................................................................................... iv Table of Contents .......................................................................................................................................................v Figures ........................................................................................................................................................................v Tables .........................................................................................................................................................................v 1. Background and Motivation ..............................................................................................................................1 2. Poverty and Sources of HH Income Through a Gender Lens............................................................................4 2.1 Main Sources of Income for Forest Proximate HHs .................................................................................4 2.2 Female Headed HHs are the Poorest of the Poor ......................................................................................7 2.3 HH Income Sources by Wealth Quintiles .................................................................................................9 3. Unpacking the Major Forest Activities Contributing to HH Incomes .............................................................11 3.1 Diversity in Forest Related Activities .....................................................................................................11 3.2 Diversity in Returns from Forest Related Activities ...............................................................................12 3.3 Forest Related Enterprises.......................................................................................................................16 4. Key Recommendations ....................................................................................................................................18 Annex I. Forest Product Participation and Returns ..................................................................................................21 Figures Figure 1. Average HH Income Shares, by Income Source (%) ..................................................................................5 Figure 2. Average HH Income Shares from Various Income Sources, by Gender of HH Head (%).........................9 Figure 3. Average HH Income Shares from Various Income Sources, by Wealth Quintiles (%) ............................10 Figure 4. Diversity in Forest Products Collected or Processed ................................................................................11 Figure 5. Mean HH Level Returns to Highest and Lowest Income Generating Forest Products (US$) ..................13 Figure 6. Participation Rate for Highest and Lowest Income Generating Forest Products (% Forest Participating HHs) .........................................................................................................................................................................14 Figure 7. Share of Participating HHs Whose Primary Laborer for Product is Female.............................................15 Figure 8. Average Labor Hours per Year for Primary Product Laborer...................................................................16 Figure 10. Ownership and Management of Forest-Related Non-Farm Enterprises, by Gender...............................17 Tables Table 1. Participation and Returns, by Income Source (Weighted, US$) ..................................................................5 Table 2. Probit Regression on Participation to Various Income Activities (Marginal Effects) .................................6 Table 3. Mean HH Income, by Head of HH Gender ..................................................................................................8 Table 4. HH Income, by Wealth Quintile (US$) ......................................................................................................10 Table 5. Top Ten Most Commonly Collected or Processed Forest Products ...........................................................12 Table 6. Returns to Forest Related NFEs, by Gender of Enterprise Owner (Unweighted) ......................................17 v 1. Background and Motivation Liberia is the most forested country in West Africa, with more than two thirds of its land surface covered by forest. The National Forest Inventory, conducted by the Liberia Forestry Development Authority in 2018 and 2019, estimates the forest cover in Liberia to be 6.69 million hectares which is approximately 69 percent of the total landmass.1 Forestry is the fourth largest contributor to the Liberian economy, after services, agriculture and fisheries, and mining and panning. The formal (measured) forest contribution to the economy runs between 9-10 percent of gross domestic product. According to the 2020 Global Forest Resources Assessment produced by the Food and Agriculture Organization of the United Nations (FAO), as of 2015, around 39,880 full-time equivalent workers (of which about 35 percent were women) were formally employed by the formal forestry sector.2 However, the formal sector is just a small part of the story. Informal, and largely unmeasured, forest activities provide an important source of jobs and incomes for rural Liberians. The informal chainsaw milling (CSM) sector provides between 19,000 and 24,000 permanent jobs to both urban and rural individuals. The annual revenue generated by chainsaw milling alone is estimated to be US$31-41 million, or about three to four percent of Liberia’s gross domestic product. The charcoal industry is thought to employ up to 28,000 people on a ‘full-time equivalent’ basis.3 The informal collection and use of non- timber forest products are also important for forest communities. They provide a source of livelihood and food for much of Liberia’s rural population.4 The importance of forest contribution to income generating activities in Liberia is particularly pronounced amongst forest-proximate households (HHs). The majority of these HHs (70 percent) collect forest products for either self-consumption only, or for both sale and self-consumption purposes. In addition, 43 percent use forest products to recover from economic and natural shocks, and two thirds of the HHs that experienced food insecurity relied on forest products to meet their needs during these times.5 Box 1 provides key features of the survey.6 The Liberia National Household Forestry Survey (NHFS) conducted in 2018-19 in all the 15 counties in Liberia, showed that the average annual income of forest-proximate HHs is US$780, which is substantially below the average annual HH income of US$2,440. Clearly, these HHs appear to be the ‘poorest of the poor’. Yet, forest-related products and environmental services make significant contributions to the subsistence, incomes, employment and coping needs of these forest-proximate HHs. Therefore, it is important these HHs are a focus for action. 1 Liberia Forest Development Authority. National Forest Inventory 2018-2019. Monrovia, Liberia. 2 FAO. 2020. Global Forest Resources Assessment 2020: Main report. FAO, Rome. https://doi.org/10.4060/ca9825en 3 Hooda, Neeta, Peter E. Aldinger, Matthew G. Owen, and Lesya Verheijen. 2019. Opportunities for Charcoal and Sustainable Forest Management (in English). Washington D.C.: World Bank Group. https://documents.worldbank.org/en/publication/documents- reports/documentdetail/145661549384956090/opportunities-for-charcoal-and-sustainable-forest-management 4 USAID. 2015. Liberia: Gap Analysis of Targeted Domestic Natural Resource Markets. USAID, Washington D.C. https://rmportal.net/library/content/gap-analysis-of-targeted-domestic-natural-resource-markets-in-liberia/view 5 The basic survey report, “People and Forests Interface: Contribution of Liberia’s Forests to Household Income, Subsistence and Resilience�, can be accessed at: https://openknowledge.worldbank.org/handle/10986/34438 6 For Liberia NHFS data and documentation, visit the World Bank Microdata Library: https://microdata.worldbank.org/index.php/catalog/3787 1 Analysis of the NHFS survey data will aim to identify potential policy reforms, investments, and other interventions to achieve the objectives of Liberia's Pro-poor Agenda for Prosperity and Development (PAPD).7 To this end this policy note: • Presents a disaggregation of the sources of HH incomes and subsistence, for forest-proximate HHs, to understand the HH dependency on forests, as well as on sources of income from other sectors and occupations (Section 2). • Disaggregates the forest income component to identify the most important income generating forest products and activities, including forest-related non-farm enterprises (NFEs), as well as analyze the allocation of HH labor time across different forest-related activities (Section 3). • Provides guidance on policy directions, as well as the needs and opportunities for (complementary) investments (Section 4). A report focusing on gender disparities in Liberia concluded that, “seventy-four percent of all female workers in Liberia are informal laborers, facing challenges such as a lack of access to credit and banking services, limited financial literacy and business training, and few social protections or childcare options�.8 Since female workers contribute significantly to forest-proximate HH labor, a gender informed analysis exploring opportunities to benefit women and female-headed HHs is given particular attention in this note. In addition, this note focuses on high-level, income-related findings at the country level, though practitioners and researchers are encouraged to explore the vast analytical potential of the NHFS data, including analysis disaggregated by regional clusters (as defined in Box 1). Box 1. Highlights of the NHFS The NHFS focused on forest-proximate HHs. The sampled HHs were located in enumeration areas (EAs) with center points within 2.5 kilometers of the nearest forest. The forest-proximate population (from which the survey sample was drawn) comprises about 47.5 percent of all Liberian HHs. The survey covered 3,000 HHs in 250 EAs through a detailed HH questionnaire. Data were collected across all 15 counties of Liberia (with the exception of urban Montserrado). In addition to the HH level survey, data were also collected at community level from 250 community focus groups, using a community questionnaire. This was designed to complement the HH survey on aspects such as gender, community level decision making, community enterprises, and participation in forest-related programs. The fieldwork for the survey was conducted between August 2018 and January 2019. It was led by the Forestry Development Authority and the Liberia Institute of Statistics and Geo-Information Services (LISGIS), with support from the World Bank. This survey used the Computer-Assisted Personal Interview (CAPI) approach for the first time in a forest sector survey in Liberia.9 The survey instruments (HH and community questionnaires) were based on the publicly available National Socioeconomic Surveys in Forestry guidebook and a set of specialized forestry modules.10 The guidebook modules were adapted to the Liberian context and the Liberia NHFS used these modules to collect information on the full range of forests’ contribution to HH livelihood and incomes. Drawing upon the Liberia Household Income and Expenditure Survey (HIES) 7 Republic of Liberia. 2017. Pro-Poor Agenda for Prosperity and Development (PAPD). http://liberianconsulatega.com/wp- content/uploads/2017/07/PAPD-Pro-Poor-Agenda-for-Prosperity-and-Development.pdf 8 Council on Foreign Relations. Spotlight on Liberia. https://www.cfr.org/womens-participation-in-global-economy/case-studies/liberia/ 9 https://dimewiki.worldbank.org/wiki/Computer-Assisted_Personal_Interviews_(CAPI) 10 FAO, CIFOR, IFRI and World Bank. 2016. National Socioeconomic Surveys in Forestry: Guidance and Survey Modules for Measuring the Multiple Roles of Forests in Household Welfare and Livelihoods, by R.K. Bakkegaard, A. Agrawal, I. Animon, N. Hogarth, D. Miller, L. Persha, E. Rametsteiner, S. Wunder and A. Zezza. FAO Forestry Paper No. 179. 2 instruments, they were then supplemented with several modules on income to allow for computation of total HH income. Given the significant data gap on the issue of gender and forests, the survey team developed a community-level module on gender-related aspects of forest enterprises and female participation in local-level decision making on forest use which was included in addition to the modules from the National Socioeconomic Surveys in Forestry guidebook and the modules aimed at measuring income. Economic, social and environmental conditions, such as density of forest cover, poverty rates, and urbanization vary across Liberia. In order to examine the possible variations in forest dependency across the country, the HH and community survey data were stratified and weighted based on three regional clusters, post-data collection. These clusters comprised the Western Cluster (including Bomi, Gbarpolu, Grand Cape Mount, and Lofa counties), the Central Cluster (including Bong, Grand Bassa, Margibi, Nimba, and rural Montserrado counties), and the Eastern Cluster (Grand Gedeh, Grand Kru, Maryland, River Cess, River Gee, and Sinoe counties). Basic statistics on the forest-proximate population, derived from weighted estimates of NHFS data, are presented below. For more details on the NHFS sample, including analysis disaggregated by cluster, refer to the NHFS survey report.11 Complete NHFS microdata are available on the World Bank’s Microdata Library. 12 Overall Male-Headed HHs Female-Headed HHs % of HHs 100% 76% 24% HH Size 4.0 4.2 3.5 No. of members < 15 years 1.5 1.5 1.3 No. of members ≥ 15 years 2.6 2.7 2.1 Age of HH Head (yrs.) 43.4 43.7 42.1 Education of HH Head (yrs.) 4.3 5.1 1.7 11 The basic survey report, People and Forests Interface: Contribution of Liberia’s Forests to Household Income, Subsistence and Resilience , can be accessed at: https://openknowledge.worldbank.org/handle/10986/34438 12 For Liberia NHFS data and documentation, visit the World Bank Microdata Library: https://microdata.worldbank.org/index.php/catalog/3787 3 2. Poverty and Sources of HH Income Through a Gender Lens 2.1 Main Sources of Income for Forest Proximate HHs Key Findings 1. Forest-proximate HHs, on average, are well below both the food poverty and overall poverty lines for Liberia. 2. Sources of HH income are diverse. Forestry (37%), crops (32%), and non-farm wages (8%) are the most prominent income sources. 3. Participation in forest-based activities is positively associated with asset-based wealth, number of working age HH members and a higher dependency ratio but is negatively associated with female headed HHs. 4. Participation in non-agricultural wage labor, including mining, quarrying and manufacturing labor is positively correlated with higher education and greater asset-based wealth. Estimates derived from NHFS data suggest that forest-proximate HHs had an average annual HH income of US$780, as compared to the average annual HH income of US$2,440 estimated through the Atlas method for Liberia.13 The food poverty line for Liberia is estimated to be US$1,566, while the overall poverty line is US$2,755. This is based on the Liberia HIES survey.14 While caution is needed in comparing poverty lines derived from one survey to the data of a different survey, it is clear that forest- proximate HHs, on average, are well below both the food and overall poverty lines. The NHFS data allows for a reliable breakout of the sources of total HH incomes. The average HH income shares by source is presented in Figure 1.15 Considering the NHFS sample as a whole, which was comprised of forest-proximate HHs, we found that forest income contributed 37 percent of total HH income on average16, followed by crop income (32 percent), non-farm wage employment (eight percent), transfers (eight percent), agriculture and forestry employment (two percent), and mining (one percent). Clearly forest and crop-production related incomes dominate HH earning. 13 World Bank Group. 2018. GNI Atlas Method. https://data.worldbank.org/country/LR 14LISGIS (Liberia Institute of Statistics and Geo-information Services). 2016. Household Income and Expenditure Survey (HIES). Monrovia, Liberia: http://microdata.worldbank.org/index.php/catalog/2986 15 Note that average income shares do not total to 100 percent as a result of the approximately 11 percent of HHs who reported no income, and who were subsequently treated in the analysis as having zero HH income. 16 The incomes generated from charcoaling and CSM are both included in forest income, unless the HH conducts those activities in the form of a HH enterprise in which case the income is considered as forest-related self-employment income. 4 Figure 1. Average HH Income Shares, by Income Source (%) A more detailed look at the returns to the various income sources and their participation rates, that is the share of HHs that engage in a given income earning activity, is available in Table 1. HHs engaged in the collection or processing of forest products earned, on average, US$318. Returns to mining or mineral related collection offered the highest returns to participation (US$2,130 on average), but participation was low with only two percent of HHs engaged in that activity. Returns to engagement in crop production were relatively high, with an average of US$600 earned per year for amongst crop-producing HHs (52 percent of all HHs). Table 1. Participation and Returns, by Income Source (Weighted, US$) Income Source Participation Mean Returns (US$) - Mean Returns (US$) - Rate (%) Participant HHs All HHs Wages 14.04 1,034 145 Agriculture, Forestry, 2.99 809 24 Fishing Mining, Quarrying and 3.96 1,194 47 Manufacturing Services 7.44 986 73 Other Sectors 0.37 114 0 Crop Production 52.03 600 311 Mining/Mineral Collection 2.16 2,130 46 Forest Income (Collection and 69.56 318 220 Processing of Forest Products) 5 Self-Employment/NFEs (by 9.99 212 21 Sector) Agricultural 0.61 381 2 Mining, Manufacturing, 0.71 364 3 Construction Services 5.67 85 5 Forestry 3.30 348 11 Total Transfers 17.70 194 34 Other Income Sources 1.75 158 3 Total HH Income 88.76* 779 780 Notes: Values are reported in US $ using an exchange rate of 1 US $: 158.730 LD (2018). All values reported are annual and net of costs (with the exception of income from transfers and land rent, which are gross receipts). *HH that did not report engagement in any of the activities above are considered to be non -participants. As Table 1 shows, the returns to participation varied widely by income source. For example, HHs that engaged in wage employment enjoyed greater returns than those who engaged in crop production. Therefore, we needed to find out: (1) who participated in the various income-earning activities, and (2) who chose to participate in high-returning versus low-returning activities? Table 2 sheds light on this with a probit analysis of participation in key income-generating activities. Based on the Davis et al. model17, we categorized HH participation in six key categories of income: (1) agriculture and forestry wages; (2) non-agricultural wages; (3) forest product collection and processing; (4) crop agriculture; (5) forest-related NFEs; (6) other NFEs. Participation in each of these income- generating activities was modelled against a set of exogenous variables. These variables represented the human capital and demographic composition of the HH, such as schooling and age of HH head, family labor supply, the gender of the HH head and the dependency ratio. They also included HH access to natural and physical capital, such as HH wealth, distance to forest, travel time to the nearest city and geographical region. Table 2. Probit Regression on Participation to Various Income Activities (Marginal Effects) Forest Agriculture Non- Product Forest- Crop Other and Forestry Agricultural Collection Related Agriculture NFEs Wages Wages and NFEs Processing Age of HH Head (years) -0.000** 0.000 0.001 0.001 0.000 -0.001* Education of Head (years) 0.000 0.011*** -0.003 -0.004* 0.000 0.003*** Female Headed -0.021*** -0.008 -0.045* -0.004 -0.006 0.013 Number of HH members age 0.002 0.004 0.026*** 0.021*** 0.005** -0.004 15-60 Female share of HH 0.005 -0.051** 0.012 -0.031 -0.013 0.017 members 15-60 17Davis, Benjamin, Paul Winters, Gero Carletto, Katia Covarrubias, Esteban J Quiñones, Alberto Zezza, Kostas Stamoulis, Carlo Azzarri, and Stefania Di Giuseppe. "A Cross-Country Comparison of rural Income Generating Activities�. World Development 38, no. 1 (2010): 48- 63. http://www.fao.org/fileadmin/user_upload/riga/pdf/cross_country_comparison_2010.pdf 6 Dependency Ratio 0.006* -0.002 0.023** 0.011 0.000 -0.003 Wealth Index 0.013 0.265*** 0.180** 0.201*** 0.056*** 0.242*** Distance to forest (Km) 0.008*** 0.011** -0.015* -0.013 - -0.012** 0.016*** Travel time to nearest city -0.002 -0.003 -0.004 0.005 -0.001 0.000 (hrs) Cluster: Central -0.012* -0.041*** 0.101*** 0.021 0.010 0.001 South 0.005 0.029** 0.166*** -0.014 0.052*** 0.076*** Psuedo-R2 0.039 0.136 0.028 0.006 0.075 0.104 N 2982 2982 2982 2982 2982 2982 Note : *** = p<0.01; ** = p<0.05, *= p<0.1 Results showed that participation in non-agricultural wage labor, including mining, quarrying and manufacturing labor with relatively high returns, may be positively associated with higher education of the HH head and greater asset-based wealth. This is consistent with the literature which indicates that assets ownership seems to encourage participation in economic activities and education may stimulate the participation in technical and specialized non-agricultural jobs. The same is true for participation in NFEs that are not related to forest products. Participation in crop production, forest product collection, and processing was positively associated with asset-based wealth as well as a larger number of HHs members of working age. However, forestry participation differed from crop production in that it was also positively associated with a higher dependency ratio and negatively associated with female-headed HHs. Overall, female-headed HHs were less likely to participate in forestry activities, while the share of women in total HH labor supply was not significant in predicting participation in any of the income activities. 2.2 Female Headed HHs are the Poorest of the Poor Key Findings 1. Within the overall poverty profile, female-headed HHs were poorer than average. Their average HH income was US$604 as compared to US$780 for all forest-proximate HHs. 2. Forestry (32%) and crop production (30%) were still the dominant sources of HH incomes. The role of women within the HH follow a traditional and conservative pattern. As a part of a gendered hierarchy, women and girls aged 15 plus spend 6.3 percent of their time on unpaid care and domestic work compared to 2.6 percent spend by men. Men are understood to have the right and control over HH resources and women are prevented from obtaining land, credit, productive inputs and information.18 Almost a quarter of the forest-proximate HHs in Liberia are female headed. The majority of female-headed HHs (62 percent) are single-person families, including female heads that are widowed (31 percent), at least two adults (38 percent), never married (15 percent), and separated or divorced women (16 percent). 18 UN WOMEN COUNT. Country Fact Sheet, Liberia, Africa. https://data.unwomen.org/country/liberia 7 In contrast, 89 percent of male-headed HHs were composed of at least two adults. As illustrated in Box 1, female-headed HHs were smaller than male-headed HHs on average, with 3.5 HH members compared to the average of 4.2 members for male-headed HHs. The educational attainment of male and female HH heads also distinguished male- and female-headed HHs, with female HH heads possessing a significantly lower education than their male counterparts. We present the income of male- and female-headed HHs below (while acknowledging that headship is not the optimal variable with which to analyze gender). In subsequent sections, gender analysis is based on individuals rather than headship, where feasible. Disaggregation of data by female- versus male-headed HHs indicates that female-headed HHs are poorer and earn a smaller share of income from forests relative to other sectors than do male-headed HHs. Female-headed HHs earned a total of US$604 per year versus US$844 for male-headed HHs on average (see Table 3 below). If we only look at HHs participating in one or more forest-related activities, a rate that is smaller for female-headed HHs than male-headed HHs, the average income for female headed HHs rose to US$713 per annum versus US$956 for male-headed HHs. It is also evident that even amongst those HHs that participated in forest activities, male-headed HHs drew a slightly greater share of their HH income from forests (53 percent) than did female-headed HHs (51 percent). Total HH income was higher for both male- and female-headed HHs that engaged in the collection or processing of forest products, relative to those that do not. The average share of HH income earned from various income sources for male- and female-headed HHs is illustrated in Notes: * This definition excludes income from forest -related businesses. It also excludes gold and other mineral collection or processing. † The share of income column may be less than 0% or greater than 100% due to losses in forestry or other income activities (as is the case for 44 HHs). Two observations identified as outliers are excluded from the computation of the mean share of income from forests. Means are presented as the mean of HH-level share of income from forests. Figure 2.19 For both male- and female-headed HHs, the three dominant sources of income were forestry, crop and non-agricultural wages. Table 3. Mean HH Income, by Head of HH Gender For All HHs For Forest Participating HHs Total Income Mean % of % of HHs Total Income Mean % of Income from HH Income Participating in Income from HH Income Forests* from Forest Forests* from Forests*† Activities Forests*† Male-headed 844 246 38% 72% 956 342 53% HHs Female-headed 604 146 32% 63% 713 234 51% HHs Notes: * This definition excludes income from forest -related businesses. It also excludes gold and other mineral collection or processing. † The share of income column may be less than 0% or greater than 100% due to losses in forestry or other income activities (as is the case for 44 HHs). Two observations identified as outliers are excluded from the computation of the mean share of income from forests. Means are presented as the mean of HH-level share of income from forests. 19 Note that average income shares may not total to 100 percent as a result of some HHs reporting zero income. 8 Figure 2. Average HH Income Shares from Various Income Sources, by Gender of HH Head (%) 2.3 HH Income Sources by Wealth Quintiles Key Findings 1. Mean HH incomes ranged from US$428 for the lowest wealth quintile to US$1,523 for the highest wealth quintile. 2. For all wealth quintiles, forestry, crop production and non-farm wages were the main HH income sources. HH wealth is considered to be an important indicator of HH well-being, of its ability to participate in economic activities and of a HHs future development prospects. The NHFS survey data analyzed HHs by asset-based wealth quintiles.20 Total HH income varied from a low of US$428 per annum in the lowest quintile to a high of US$1523 in the highest quintile (Table 4). The contribution from forestry was US$147 for the lowest quintile and US$405 for the highest. For HHs that participated in forest activities, forest income made up 58 percent of total income for the lowest quintile and 42 percent of total income for the highest quintile. The average income shares for by source, irrespective of forest participation, is presented in Figure 3. The average share of forestry income as a percentage of total HH income was 37 percent for the lowest quintile, 34 percent for the second quintile, 38 percent for the third, 41 percent of the fourth, 20 HH wealth quintiles are derived from an asset-based HH wealth index, estimated using principle component analysis. 9 and 34 percent for the highest quintile (Figure 3).21 When we analyzed all sources of HH income, we found that forestry, crop production, and non-farm wages are consistently the dominant sources of income for all quintiles. Table 4. HH Income, by Wealth Quintile (US$) Wealth Total Forestry % of Income Quintile Income- Income- from Forests All HHs All HHs – Participant HHs 1 428 147 58 2 546 160 48 3 668 189 52 4 805 218 51 5 1523 405 42 Figure 3. Average HH Income Shares from Various Income Sources, by Wealth Quintiles (%) 21 Note that average income shares may not total to 100 percent as a result of some HHs reporting zero income. 10 3. Unpacking the Major Forest Activities Contributing to HH Incomes 3.1 Diversity in Forest Related Activities Key Findings 1. HHs were actively engaged in collecting and processing forest products with over 27% of forest HHs collecting or processing at least five different forest products. 2. Own consumption was the primary use of the most commonly collected or processed forest products, with the exception of charcoal which was sold by 88% of HHs that produced it. HHs across the wealth distribution derive income from both forest and non-forest activities, which vary in terms of their returns as seen in Section 2 above. Within the forest sector, there is significant variation in the returns to various forest activities, as well as the motivation behind engagement in those activities. HHs in the NHFS reported collecting or processing over thirty different types of forest products. Figure 4 illustrates the variation in HH level forest engagement by presenting the distribution of the total number of forest products collected or processed by forest-dependent HHs. These were HHs which engaged in the collection or processing of any forest products, on average 3.65 different product types. Over 27 percent of forest HHs collected or processed five or more different forest products. The ten most commonly collected or processed products are presented in Table 5, along with the share of HHs that collected or processed those specific products, referred to as participating HHs. Figure 4. Diversity in Forest Products Collected or Processed 11 Table 5. Top Ten Most Commonly Collected or Processed Forest Products HH HH % of % of HH HH Participation Participation Participating Participating Participation Participation Rate Rate HHs HHs Selling Rate Rate (amongst all (amongst Consuming Product (amongst (amongst HHs) forest HHs) Product male-headed female- forest HHs) headed forest HHs) Fuelwood/firewood 46.75% 67.23% 97% 2% 65% 74% Poles/fence posts 18.26% 26.26% 96% 4% 28% 19% Rattan 13.07% 18.80% 88% 14% 20% 13% Bush meat, mammals, reptiles, 12.30% 17.69% 95% 62% 20% 9% birds Fish 12.24% 17.60% 98% 25% 17% 19% Mushroom 12.08% 17.38% 97% 12% 17% 20% Frond/thatch 15% 11.72% 16.85% 95% 4% 17% Bamboo 11.62% 16.71% 93% 9% 18% 13% Snails and locusts 9.23% 13.28% 95% 36% 13% 13% Charcoal 8.70% 12.51% 45% 88% 13% 11% More than 46 percent of all HHs collected fuelwood, primarily for their own use (only two percent of HHs that collected fuelwood sold it). As also evident in Table 5, own consumption was the primary use of the other nine most commonly collected or processed forest products, with the exception of charcoal which was sold by 88 percent of HHs that produced it. Bushmeat, while consumed by 95 percent of HHs who collected it, was also sold by a sizable share of HHs (62 percent). Comparison of products collected or processed by male and female-headed HHs reveals two main distinctions: (1) female-headed HHs had a higher participation rate in collecting fuelwood; and (2) male-headed HHs had a higher participation rate in the collection of bushmeat. The returns to these products are discussed below. 3.2 Diversity in Returns from Forest Related Activities Key Findings 1. Charcoal production resulted in the highest returns, with HHs participating in charcoal production earning returns of US$256 per year, followed by bush meat (US$207 per year), bricks (US$183 per year), timber (US$140 per year), and rattan (US$107 per year). 2. The lowest returning products were bush cherry (US$5 per year), palm cabbage (US$7 per year), mushrooms (US$7 per year), African walnut (US$11 per year), and tree bark, roots, leaves (US$14 per year). 3. Women’s labor was disproportionately more concentrated on the low-return forest products and, when they were involved, women invested less time than their male counterparts in the high-return products. 12 According to data from the NHFS, 70 percent of HHs participated in the collection or processing of forest products, with a mean annual income of approximately US$318 for those HHs.22 However, there was significant variation in the returns to forest activities, specifically by type of forest product. The top and bottom five forest products, in terms of returns to participating HHs, are presented in Error! Reference source not found..23 The returns and participation rates for the full list of products is available in Annex I. Charcoal production resulted in the highest returns, with HHs participating in charcoal production earning returns of US$256 per year, followed by bush meat (US$207 per year), bricks (US$183 per year), timber and logs (US$140 per year)24, and rattan (US$107 per year). Figure 5. Mean HH Level Returns to Highest and Lowest Income Generating Forest Products (US$) Bush cherry 5 Lowest Palm cabbage 7 returning products Mushroom 7 African walnut 11 Tree bark, leaves, roots 14 Rattan 107 Highest Timber/logs 140 returning products Bricks 183 Bush meat, etc. 207 Charcoal 256 0 50 100 150 200 250 300 Error! Reference source not found. presents the participation rates for the highest and lowest returning forest products. In general, the participation rates for the highest returning products were higher than the rate of participation observed for the lowest returning products. Of all HHs collecting or processing at least one forest product, over 12 percent process charcoal (the highest returning product). There were two exceptions. Mushrooms, which yielded a relatively small return of only US$7 per year, were collected by 17 percent of all forest HHs. Similarly, nearly 12 percent of all forest HHs collected tree bark, leaves, and roots with an average return of US$14 per year. 22 The Rural Livelihoods Information System (RuLIS) approach was used to calculate product returns. For more information on the RuLIS methodology, see: http://www.fao.org/in-action/rural-livelihoods-dataset-rulis/en/. 23 Products that were collected or processed by less than two percent of HHs were excluded to minimize the impact of noise in income estimates for those products. Products related to mining, such as gold, are also excluded as those are classified under mining income rather than forestry income. 24 This includes plank production through CSM. 13 Figure 6. Participation Rate for Highest and Lowest Income Generating Forest Products (% Forest Participating HHs) Bush cherry 2.39% Palm cabbage 4.62% Lowest returning Mushroom 17.38% products African walnut 2.86% Tree bark, leaves, roots 11.86% Rattan 18.80% Highest Timber/logs 9.23% returning products Bricks 2.43% Bush meat, etc. 17.69% Charcoal 12.51% 0.00% 5.00% 10.00% 15.00% 20.00% Error! Reference source not found. presents the share of participating HHs in which the primary laborer is female for each of the highest and lowest returning products. It shows female labor was more dominant in the lower income generating activities. The disproportionate application of female labor to lower income-generating products could potentially contribute to across-HH income inequality and could be a reason for the observed lower forest incomes for female-headed HHs–an area worth considering for further analysis. 14 Figure 7. Share of Participating HHs Whose Primary Laborer for Product is Female Bush cherry 62% Palm cabbage 12% Lowest returning Mushroom 68% products African walnut 31% Tree bark, leaves, roots 41% Rattan 9% Timber/logs 7% Highest Bricks 7% returning products Bush meat, etc. 5% Charcoal 19% 0% 10% 20% 30% 40% 50% 60% 70% 80% Analyzing the time spent on the highest returning and lowest returning forest products showed some noticeable gender differences. Figure 8 presents the average amount of time spent by gender of the primary laborer for these different products. What it shows is that men were more often the primary laborers for the highest returning forest products. It also shows that men spent a greater amount of time collecting and processing these high-returning products than women did. For example, in HHs where a man was the primary laborer responsible for charcoal production, he spent on average 634 hours per year. In HHs where a woman was responsible for charcoal production, she spent an average of 372 hours per year. When looking at the lowest return products, the time investment was much lower than that of the high- returning products. This may not be surprising given the greater incentive to collect and process the product as well as the more labor-intensive requirements for processing or collecting some of the higher return products, like charcoal and bushmeat. However, more time was invested by women than men in the collection of tree bark and African walnuts. 15 Figure 8. Average Labor Hours per Year for Primary Product Laborer All Female Male 634 700 583 600 500 372 311 308 400 240 300 170 168 151 140 134 200 90 81 76 70 70 58 53 52 49 48 45 41 35 35 31 30 26 100 25 24 0 Highest returning products Lowest returning products 3.3 Forest Related Enterprises Key Findings 1. Slightly more than 3% of HHs reportedly owned or operated a forest–related enterprise, with those HHs earning, on average, US$348. 2. About half of all forest–related enterprises were focused on the trade of forest products, nearly 9% were related to handicraft manufacturing, and another 9% related to carpentry. Only 4% of enterprises were related to timber processing. 3. Gender disparities were significant in the ownership and management of forest-related NFEs. For example, 42% of all forest-related NFEs reported in the NHFS were owned by men and only 8% by women. 37% were jointly owned. Forests contributed to HH income not only through the collection and processing of forest products, but also through the operation of forest-related NFEs. Slightly more than three percent of HHs reportedly owned or operated a forest-related NFE in the NHFS, with those HHs earning, on average, US$348 (as seen in Table 1). About half of all forest-related NFEs were focused on the trade of forest products, nearly nine percent were related to handicraft manufacturing, and another nine percent related to carpentry. Only four percent of enterprises were related to timber processing. The remaining forest-related NFEs were distributed across other activities. Gender disparities were characteristic of the NFE sector as well. As illustrated in Figure 9, only nine of the 119 forest-related NFEs, or eight percent, were owned exclusively by women. In contrast, 41 percent 16 of forest-related NFEs were owned exclusively by men. Joint ownership, with both male and female HH members reported as owners, accounted for 37 percent of all forest-related NFEs. When it came to management rather than ownership, the incidence of female participation increased. Twenty-one percent of all forest-related NFEs were managed exclusively by women, while exclusive management by men accounted for 33 percent of these enterprises. The relatively higher share of female management rather than ownership suggests a discrepancy in the income control stemming from these enterprises, as the owners presumably exercised control over the enterprise’s income. Figure 9. Ownership and Management of Forest-Related NFEs, by Gender Female Only Male Only Joint Outside of HH Not Reported 10% 8% 2% 2% 35% 37% 33% 42% 21% 8% OWNERSHIP MANAGEMENT Returns to forest-related NFEs also varied by the gender of the owner, with enterprises owned exclusively by men earning US$375 on average, while women-owned enterprises earning an average of US$173 (Table 6). Given the small number of female-owned forest-related NFEs, it cannot be said conclusively whether this income gap is, for example, a function of the type of enterprise owned by women rather than men, or a function of their resource endowments. Table 6. Returns to Forest Related NFEs, by Gender of Enterprise Owner (Unweighted) Number of Average Returns Enterprises (US$) All NFEs Female Owned 82 34 Male Owned 112 412 Joint Male and Female 166 -107 Owned* Forestry-Related NFEs Female Owned 9 173 Male Owned 51 375 Joint Male and Female Owned 45 156 Notes: * Negative average returns due to losses from one or more enterprises 17 4. Key Recommendations Recommendation 1 Target forest-proximate HHs in the poverty reduction strategy as they form a significant proportion of poor HHs. This will also help achieve the objectives of Liberia's PAPD. Discussion: Forest-proximate HHs are estimated as making up approximately 47 percent of all Liberian HHs. An estimate derived from NHFS data suggest that forest-proximate HHs had an average annual HH income of US$780, as compared to the average annual HH income of US$2,440 estimated through the Atlas method, for Liberia. The food poverty line for Liberia is estimated to be US$1,566, while the overall poverty line is US$2,755, based on the Liberia HIES survey. Though caution is needed in comparing poverty lines derived from one survey to the data of a different survey, it is clear that forest-proximate HHs, on average, are well below both the food and overall poverty lines. Recommendation 2 Continue to invest in enhancing diversified income generation from forestry and crop production, as well as from the other sources mentioned below, to mitigate risks and reduce poverty for the forest-proximate HHs. Discussion: For forest-proximate HHs, the three main sources of income were: (1) forestry (37 percent); (2) crops (32 percent); and (3) non-farm wages (eight percent). Transfers, mining, self-employment, and agriculture and forestry wages made up the rest. Clearly, on average, HH incomes came from diverse sources and this is desirable as a risk-mitigation strategy for a HH. For example, if crop income should fail because of drought the HH can depend upon incomes such as from forestry, or non-farm wages. In the absence of comprehensive risk insurance schemes (such as for crop failures, job losses, and severe health episodes), these risks to incomes are a real threat. Thus, going forward, it would be desirable to have HHs stay engaged in a portfolio of activities for their incomes. Recommendation 3 Target female-headed HHs to raise them above poverty through gender-tailored interventions and a careful consideration of the socio-economic characteristics of female-headed HHs. Discussion: Within the overall poverty profile of forest proximate HHs, female-headed HHs are poorer than average. Their average HH income was US$604 as compared to US$780 for all forest-proximate HHs. This provides a powerful justification for interventions to improve the welfare of female-headed HHs. Forestry (32 percent) and crop (30 percent) were still the dominant sources of incomes for these HHs. However, before appropriate interventions can be designed, data needs to be collected on the composition of HH members and labor availability in such HHs and of their priority responsibilities and time allocations (such as for taking care of children and the elderly, cooking and other HH chores) which often come before income generation. For income generating activities to work, they would need to be flexible and complementary to a woman’s priority responsibilities. This finding on disparity in forest incomes together with some others as elaborated in other recommendations is a reflection of, and relevant 18 to the broader gender disparity context in Liberia. It calls for enhanced action through national gender strategy given that Liberia ranks rather low on the gender inequality index.25 Recommendation 4 Undertake analysis to identify factors that can enhance overall value-addition and greater income retention by the producer HHs, for various forest activities. In addition, consider facilitating women’s participation in some of the high-return forest activities. Discussion: Within forestry, HHs were actively engaged in collecting and processing forest products with over 27 percent of forest-proximate HHs collecting or processing at least five different forest products. Own consumption was the primary use of the most commonly collected or processed forest products, with the exception of charcoal which was sold by 88 percent of HHs that produced it. Charcoal production resulted in the highest returns, with HHs earning returns of US$256 per year, followed by bush meat (US$207 per year), timber (US$140 per year), and rattan (US$107 per year). The lowest return products were bush cherry (US$5 per year), palm cabbage (US$7 per year), mushrooms (US$7 per year) and African walnut (US$11 per year. Women’s labor was disproportionately more concentrated on the low-return forest products and, when they were involved, women invested less time than their male counterparts in the high-return products. There is a clear potential to increase value-addition in all forest related activities via interventions to ensure sustainable harvesting, higher on-site processing, improve market access, reduce transport costs, improve product quality, streamline middlemen margins, and increase the shares of the primary producers. A careful analysis of these factors and estimates of their productivity (in terms of incomes earned per labor unit) and on factors encouraging HH participation (including for women), would allow prioritization of these products for further development. Additionally, Government of Liberia could consider engaging with young women and men, including children and adolescent boys and girls through new approaches that favor a change in traditional and conservative pattern on one hand, and increase their access to inputs and services essential for carrying out more productive functions. Recommendation 5 Support forest-proximate HHs to develop forest-related NFEs with sustainable use of forest inputs through measures such as credit, capacity building, and skills training. In addition, ensure greater ownership and management by women. Discussion: Only about three percent of forest proximate HHs reportedly owned or operated a forest- related NFE, with those HHs earning, on average, US$348. This clearly shows the important contribution forest-related NFEs make to HH income. About half of all forest-related NFEs were focused on the trade of forest products, nearly nine percent were related to handicraft manufacturing, and another nine percent related to carpentry. Only four percent of forest-related NFEs were related to timber processing. This suggests a promising scope to establish new NFEs. Gender disparities were significant in the ownership and management of forest-related NFEs. For example, 42 percent of all forest-related NFEs reported in 25Liberia Gender Inequality Index ranks at 154 out of 159. HDI, GII. UNDP Human Development Report 2017: http://hdr.undp.org/en/composite/GII 19 the NHFS were owned by men, 37 percent were jointly owned, while only eight percent were owned by women alone. Increasing ownership and management oversight by women will put incomes directly into the hands of women making a strong dent on gender differences. Recommendation 6 Undertake surveys such as the Liberia NHFS on a regular basis (once every three years, for example). Alternatively, integrate data collection on forest-based activities into existing national survey operations, potentially with an oversample for forest-proximate HHs. Discussion: Investing in data and knowledge is needed to manage forest resources so that they can sustainably contribute to livelihoods and poverty reduction for all Liberians. A survey, such as the Liberia NHFS, implemented repeatedly, would allow for analysis of income dynamics, with an emphasis on forest-based income, monitoring of forest-based activities over time as well as the implications on sustainability, and monitoring of the economic progress of forest-proximate HHs. 20 Annex I. Forest Product Participation and Returns Product # HHs HH Participation HH Participation Returns to Participating Rate (amongst Rate (amongst all Participation (US$) forest HHs) HHs) - Participant HHs Collected timber/logs 30182 9.23% 6.42% 140 Goods poles/fence posts 85857 26.26% 18.26% 26 fuelwood/firewood 219789 67.23% 46.75% 106 tree barks/leaves/roots 38769 11.86% 8.25% 14 rattan 61460 18.80% 13.07% 107 bamboo 54627 16.71% 11.62% 25 frond/thatch 55090 16.85% 11.72% 15 piassava 17218 5.27% 3.66% 31 bush cherry 7798 2.39% 1.66% 5 monkey apple 17676 5.41% 3.76% 30 african walnut 9349 2.86% 1.99% 11 mushroom 56817 17.38% 12.08% 7 roots and tubers 8349 2.55% 1.78% 85 bitter kola (garcinia kola) 26372 8.07% 5.61% 44 bush kola (nitida) 15402 4.71% 3.28% 20 ganagana 28505 8.72% 6.06% 18 country atayee 11555 3.53% 2.46% 22 bush pepper, country spice (xylopia), etc. 24989 7.64% 5.32% 51 bush yam/wild yam 38259 11.70% 8.14% 26 palm cabbage 15118 4.62% 3.22% 7 makindo palm wild 19468 5.95% 4.14% 86 worlor 7071 2.16% 1.50% 30 bush meat, mammals, etc. 57847 17.69% 12.30% 207 snails and locusts 43409 13.28% 9.23% 38 fish 57551 17.60% 12.24% 69 honey 12249 3.75% 2.61% 18 gold 9858 3.02% 2.10% 2,219 Palm nut 7390 2.26% 1.57% 64 Processed charcoal 40904 12.51% 8.70% 256 Goods Furniture and other articles 21695 6.64% 4.61% 18 Juices and oils from forest products 26743 8.18% 5.69% 75 alcoholic beverages (piassava, palm wine) 16101 4.93% 3.42% 59 bricks 7958 2.43% 1.69% 183 Note: Products which were collected and processed by less than two percent of forestry HHs are excluded from table. 21