JOBS WORKING PAPER Issue No. 76 Measuring Ex Ante Jobs Outcome of the Bangladesh Livestock and Dair y Development Project Mansur Ahmed, FNU Jonaed, and Nazmul Hoque MEASURING EX ANTE JOBS OUTCOME OF THE BANGLADESH LIVESTOCK AND DAIRY DEVELOPMENT PROJECT Mansur Ahmed, FNU Jonaed, and Nazmul Hoque © 2023 International Bank for Reconstruction and Development / The World Bank. 1818 H Street NW, Washington, DC 20433, USA. Telephone: 202-473-1000; Internet: www.worldbank.org. Some rights reserved This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. 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Further permission required for reuse. 1 Measuring Ex Ante Jobs Outcome of the Bangladesh Livestock and Dairy Development Project Mansur Ahmed1, FNU Jonaed2, and Nazmul Hoque3 1 Mansur Ahmed is a Senior Economist at the Agriculture and Food Practice of the World Bank. 2 FNU Jonaed is a Research Consultant at the Agriculture and Food Practice of the World Bank. 3 Nazmul Hoque is an Assistant Professor of Economics at East West University. 2 Table of Contents LIST OF TABLES ..................................................................................................................................4 LIST OF FIGURES.................................................................................................................................4 ABBREVIATIONS.................................................................................................................................5 ACKNOWLEDGMENT ..........................................................................................................................5 EXECUTIVE SUMMARY .......................................................................................................................6 1. INTRODUCTION ...........................................................................................................................9 2. APPLYING A JOBS LENS TO THE LIVESTOCK SECTOR .................................................................... 12 2.1 METHODS OF FTE JOB MEASUREMENT ............................................................................................. 13 2.2 ANALYTICAL APPROACHES ............................................................................................................. 14 3. AN OVERVIEW OF THE LIVESTOCK SECTOR JOBS IN BANGLADESH ............................................... 17 3.1 RESULTS CHAINS: FROM INTERVENTIONS TO JOBS ............................................................................... 17 3.2 MAPPING KEY LIVESTOCK VALUE CHAINS IN BANGLADESH: UNDERSTANDING JOBS IN CATTLE AND GOAT FATTENING, DAIRY, AND POULTRY SECTORS ............................................................................................... 18 3.2.1 CATTLE AND GOAT FATTENING VALUE CHAIN........................................................................................... 18 3.2.2 DAIRY VALUE CHAIN ............................................................................................................................ 19 3.2.3 POULTRY VALUE CHAIN ........................................................................................................................ 20 3.3 AN OVERVIEW OF LIVESTOCK PRODUCTION AND EMPLOYMENT IN BANGLADESH ........................................ 21 3.3.1 FEMALE EMPLOYMENT IN LIVESTOCK VALUE CHAINS ................................................................................. 23 4. JOB OUTCOMES OF THE LIVESTOCK AND DAIRY DEVELOPMENT PROJECT: AN EX-ANTE ESTIMATION 25 4.1 INSIGHTS INTO JOBS IN LIVESTOCK VALUE CHAINS: EVIDENCE FROM A SAMPLE SURVEY ................................ 25 4.1.1 GENERAL JOBS PROFILE IN LIVESTOCK VALUE CHAINS ................................................................................ 26 4.1.2 INSIGHTS ON FULL-TIME EQUIVALENT (FTE) JOBS IN LIVESTOCK VALUE CHAINS ............................................ 29 4.1.3 JOB CREATION POTENTIAL FOR DIRECT BENEFICIARIES: AN EX ANTE ESTIMATION .......................................... 30 4.1.4 INSIGHTS INTO JOBS IN UPSTREAM AND DOWNSTREAM OF LIVESTOCK VALUE CHAINS .................................... 31 4.1.5 INSIGHTS ON WOMEN AND YOUTH JOBS IN LIVESTOCK VALUE CHAINS ......................................................... 32 4.1.6 INSIGHTS ON QUALITY OF JOBS .............................................................................................................. 34 4.1.7 SEASONALITY IN JOBS ........................................................................................................................... 36 4.2 ESTIMATING JOB OUTCOMES OF THE LDDP USING SAM MULTIPLIER MODEL............................................ 37 4.2.1 IMPACT ON ENDOGENOUS ACCOUNTS .................................................................................................... 37 4.2.2 IMPACT ON EMPLOYMENT .................................................................................................................... 37 4.2.3 IMPACT ON FEMALE EMPLOYMENT ........................................................................................................ 39 5. CONCLUSION AND POLICY RECOMMENDATIONS........................................................................ 41 BIBLIOGRAPHY................................................................................................................................. 43 ANNEX I. SURVEY METHODOLOGY ................................................................................................... 46 ANNEX II. SURVEY QUESTIONNAIRE AND KII CHECKLIST .................................................................... 51 ANNEX III: SAM MULTIPLIER MODEL................................................................................................. 69 3 List of Tables Table 3-1: Availability of Milk, Meat, and Egg ............................................................................................ 22 Table 3-2: Employment Scenario of the Livestock Sector (thousands) ...................................................... 23 Table 3-3: Contribution of Livestock Employment by Sex .......................................................................... 24 Table 4-1: Quartile Distribution of the Current Market Value of Farm (BDT, thousands) ......................... 26 Table 4-2: Assessing the Livestock Farming Practices (%) .......................................................................... 26 Table 4-3: Number of Workers ................................................................................................................... 27 Table 4-4: Age Profile of Workers in the Livestock Sector .......................................................................... 28 Table 4-5: Workers’ Relation with the Main Proprietorship (%) ................................................................ 28 Table 4-6: Number of FTE Jobs in the Livestock Sector ............................................................................. 29 Table 4-7: FTE Job by Employment Type .................................................................................................... 30 Table 4-89: Number of FTE Job ................................................................................................................... 31 Table 4-910: FTE Job by Employment Type ................................................................................................ 32 Table 4-104-11: Employment Types by Worker’s Age ................................................................................ 33 Table 4-12: FTE Jobs by Sex and Age........................................................................................................... 33 Table 4-13: FTE Job by Sex in Upstream and Downstream Firms ............................................................... 34 Table 4-14: Quality of Job ........................................................................................................................... 35 Table 4-15: Statistics Related to Seasonality .............................................................................................. 36 Table 4-164-: Simulation Impact on Endogenous Accounts ....................................................................... 37 Table 4-174: Impact of the LDDP on Employment in the Economy ........................................................... 38 List of Figures Figure 2.1: Summary of livestock job measurement approaches used in the assessment. ....................... 13 Figure 3.1: Theory of Change of the LDDP .................................................................................................. 17 Figure 3.2: Results Chain: From Intervention to Jobs ................................................................................. 18 Figure 3.3: Cattle and Goat Fattening Value Chain ..................................................................................... 19 Figure 3.4: Dairy Value Chain ...................................................................................................................... 20 Figure 3.5: Poultry Value Chain (Meat and Eggs)........................................................................................ 21 Figure 3.6: Production of Milk, Meat, and Eggs .......................................................................................... 22 Figure 3.7: Growth Rates of Milk, Meat, and Eggs ..................................................................................... 22 Figure 4.1: Education Profile of the Workers (%) ....................................................................................... 29 Figure 4.2: Distribution of Workers by Employment Type (%) ................................................................... 32 Figure 4.3: Female Ownership of Livestock ................................................................................................ 34 Figure 4.4: Percentage of Workers Injured ................................................................................................. 35 Figure 4.5: Frequency of Injury (%) ............................................................................................................. 35 Figure 4.6: Percentage of Workers Who Missed Work Due to Injury ........................................................ 36 Figure 4.7: Who Shared the Medical Cost? (%) .......................................................................................... 36 Figure 4.8: Number of Employments Generated by Year ........................................................................... 39 4 Abbreviations BBS Bangladesh Bureau of Statistics BIHS Bangladesh Integrated Household Survey CERC Contingency and Emergency Response Component DLS Department of Livestock Services FTE Full-Time Equivalent FYP Five-Year Plan GDP Gross Domestic Product GoB Government of Bangladesh IFPRI International Food Policy Research Institute KII Key Informant Interview LDDP Livestock and Dairy Development Project LFS Labour Force Survey MFD Maximizing Finance for Development MOFL Ministry of Fisheries and Livestock PO Producer Organization PP Productive Partnership SAM Social Accounting Matrix SANEM South Asian Network on Economic Modeling SDG Sustainable Development Goal SME Small and Medium Enterprises VMCC Village Milk Collection Acknowledgement We would like to express our sincere gratitude to the Supporting Effective Jobs Lending at Scale Initiative, which provided financial support for this assessment under the Jobs Multi-Donor Trust Fund Program. We extend our thanks to Christopher L. Delgado and Harideep Singh for reviewing the paper and providing insightful comments that greatly improved its quality. We are also grateful to the South Asian Network on Economic Modeling (SANEM) for their excellent support in data collection. Additionally, we appreciate the continuous support of Raian Divanbeigi, Christian Berger, Amadou Ba, and the LDDP Project Management Unit throughout the research process. We would also like to acknowledge the guidance provided by Loraine Ronchi and Gayatri Acharya, as SAR AGF PM, during the study period. 5 Executive Summary The livestock sector is a significant contributor to employment in Bangladesh, accounting for 14.5 percent of overall employment and over one-third of total employment in the agricultural sector4. The concentration of women employment in the sector is high, accounting for 88.2% of the sector’s employment and for 41% of total female employment (compared to just 2.4% for men). However, jobs in the livestock sector are heavily concentrated in production activities, with only 2 percent being indirectly employed in livestock manufacturing and service activities and the ratio of indirect to direct jobs is 0.016, implying 1000 new jobs created in livestock production will create 16 indirect jobs in related manufacturing and services. The Livestock and Dairy Development Project (LDDP) is a $579 million World Bank-funded project launched by the Government of Bangladesh, aiming to transform the informal livestock sector into a formal commercial sector, promote climate-smart production systems, and enhance productivity and resilience of smallholder farmers. It also seeks to create job opportunities for vulnerable groups along the value chains and improve the quality of existing jobs. The project is expected to directly create new jobs, as well as contribute to indirect job creation through alleviating intermediate input constraints, reducing seasonality, and increasing incomes of households engaged in the livestock value-chain. This study's key objective is to estimate ex ante the LDDP's job creation potential. It used two approaches for the purpose. The first approach was a survey of 2045 samples that collected quantitative and qualitative information from three livestock value chains i.e., dairy, cattle, and poultry, covering five value chain segments (upstream and downstream). The second approach was a social accounting matrix (SAM) multiplier model, which estimated potential job creation at the national level by linking different sectors and actors in the economy. Measuring jobs in the informal livestock sector of developing countries like Bangladesh is challenging due to underemployment, unpaid family workers, and worker engagement in multi-sectoral activities. Through desk research, the study identified the most common method used to measure job creation from project intervention is the full-time equivalent (FTE) jobs method. This method calculates FTE jobs by dividing the total additional hours worked by all workers by the number of labor hours required for a full- time job, which is identified as 40 hours per week for Bangladesh. Thus, FTE jobs may not accurately reflect the number of people engaged in the activities. The survey revealed that each production farm on average contains 1.87 FTE jobs, with slight variation across subsectors. The involvement of 100 workers in livestock production activities provides about 63 FTE jobs, with unpaid family labor accounting for 46.7%, self-employment for 39.2%, and wage employment for 14.1%. The sector is highly informal and reliant on family labor, but hiring more workers has the highest potential for FTE job creation. Thus, the formalization of the livestock sector could generate more FTE jobs. Again, each upstream and downstream firm on average contains 2.77 FTE jobs, processing firms have the highest average FTE job, while veterinary and other services have the lowest. Among the total FTE jobs in these firms, 36.2 percent are self-employed, 49 percent are wage-employed, 4 Bangladesh Bureau of Statistics (BBS). 2019. Bangladesh Labor Force Survey (LFS) 2016-17. 6 and 14.9 percent are unpaid family members. On average workers in upstream and downstream firms work longer hours than those in livestock farms, 100 workers in these firms create about 90 FTE jobs, which is 29 FTE jobs higher than in livestock farms. There is also observed a significant difference in the number of FTE jobs created by male and female workers in the livestock sector. The employment of 100 male workers generates 65.5 FTE jobs, whereas the number for females is 57.8. This indicates that, on average, male workers work more hours than their female counterparts. This disparity is also reflected in the distribution of FTE jobs, with 64.8 percent of FTE jobs occupied by men and 35.2 percent by women in livestock farms. In non-farm activities, women are significantly underrepresented, holding only 2.5% of FTE jobs in upstream and downstream firms, while males hold 97.5% of these jobs. Livestock farms rely heavily on family labor (89.35%), while upstream/downstream firms have 46.69% non-family wage employees. Most women work as unpaid workers in livestock farms (84%) and upstream/downstream activities (65%). Value-chain development activities could generate more economically beneficial employment opportunities for women, who are more likely to obtain wage- employment in upstream/downstream activities. Youth workers are more likely to benefit from value- chain development activities as they are more engaged in salaried or wage employment. The study found inadequate farming practices in the livestock sector contributed to its slow growth rate. Few farms have authorized registration (19.6%), bank accounts (8.4%), or association/cooperative membership (7.3%). Only 6.05% of salaried workers have signed contracts, but most receive wages on time and festival bonuses. 15.64% of workers reported injuries in the last year. Labor demand is high during festive periods, with 67.08% of employment being season dependent. 16.73% of farms hire seasonal workers, while others induce existing workers to work more hours. Based on the job information collected through the sample survey and following conservative estimations from the relevant experts in the sector, LDDP is expected to create 99,300 additional direct FTE jobs among the beneficiary farms at the end of the project implementation. If the existing distribution of FTE jobs prevails at the end of the project implementation, about 14,000 new salaried and wage-employment are expected to be created through the project support. With similar assumption for women and youth, about 38,000 FTE jobs for women and 30,000 FTE jobs for youth are expected from the project interventions in the livestock value-chains. The SAM multiplier model-based simulation exercise estimated that the investment of BDT 12.3 billion through LDDP, which would directly contribute to enhancing productive capacity, in the livestock sector is expected to increase the economy's gross output, commodity demand, and GDP at the end of implementation in 2025 by 0.13 percent, 0.12 percent, and 0.12 percent, respectively compared to the base case in FY17. The GDP multiplier is calculated to be 1.8, indicating that investing an additional 1 million taka in the livestock sector will boost GDP by BDT1.8 million. Household consumption is also expected to rise by 0.11 percent. The investment is projected to generate 164,000 additional jobs over a period of 14 years including 7 years of implementation period, with livestock farming activities creating the largest number of jobs, around 105 thousand or 64 percent of the total. Indirect and induced jobs in related upstream and downstream activities will contribute to about 59,000 jobs, 36 percent of the new jobs. The project will create 102,000 new jobs for women and approximately 62,000 new jobs for men, 7 with livestock activities contributing to about 91 percent of the new female jobs and nearly 80 percent of the new jobs for men coming from non-livestock activities. Based on the overall findings of the study comes up with the following major policy recommendations: • Focus on enhancing the formalization of the livestock sector by registering farmers and promoting value-chain development activities to improve transparency, access to finance, and promote good practices in animal husbandry. • Develop policies to promote and support youth employment in the livestock sector through targeted training and capacity-building programs. • Implement measures to promote women's participation in upstream and downstream activities in the livestock sector through targeted training and capacity-building programs. • Reduce seasonality in the livestock sector by promoting the establishment of more processing firms and export of livestock products to access markets with different demand patterns. Promote the establishment of livestock product processing plants to create steady increase in demand for products, leading to more and better-quality jobs in livestock farms and processing plants. 8 1. Introduction Since the early 1990s, agriculture in Bangladesh has made remarkable progress. The average annual growth rate of agriculture accelerated from less than 3 percent in the 1990s to around 4.6 percent in the 2000s, and then to 3.5 percent in the following decade (World Bank 2022). As a result, Bangladesh has made notable progress in domestic food production, which almost doubled in the last two decades, and has achieved near self-sufficiency in rice production. Much of the growth in the agriculture sector was driven by policy reforms that were initiated in the 1980s and followed by strategic investments in research and infrastructure in the following decades (World Bank 2016). However, these policy reforms focused significantly on rice production, with the exception of poultry. The livestock value chain subsector of the agriculture sector has experienced relatively slow growth in the last two decades, and the import of dairy products is increasing over time. Compared to its neighboring economies such as India and Pakistan, the contribution of the livestock sector to the economy in Bangladesh is relatively low. The livestock sector accounted for only 1.6 percent of gross domestic product (GDP) in 2016–2017 in Bangladesh, while it accounted for about 11.63 percent of the GDP in 2019–2020 in Pakistan and 4.67 percent in 2016–2017 in India. However, the livestock sector5 plays a vital role in the economy of Bangladesh, particularly in generating employment. In fact, it accounted for 14.5 percent of total employment in the country as of 2016–2017 (BBS, 2018). Nearly 80 percent of rural households are engaged in livestock or poultry production, although mostly for their own consumption. Additionally, a growing number of rural households without access to arable land are now involved in livestock rearing activities. Evidence shows that the rural poor and landless, especially women, receive a greater share of their income from livestock (Karim et al. 2010; Raney et al. 2011). The livestock sector has the potential to create jobs and livelihood opportunities for women, youth, and vulnerable groups, especially in rural areas. The demand for animal-sourced food such as dairy, meat, and poultry products is increasing in the country due to fast urbanization and income growth in recent years (World Bank 2019). The demand for eggs, meat, and milk is expected to increase by 71 percent, 61 percent, and 20 percent, respectively (World Bank 2021). Therefore, the growing demand for livestock and dairy products presents an opportunity for women, youth, and landless individuals to obtain more and better-paying jobs. The World Bank's 2016 Dynamics of Rural Growth study in Bangladesh highlighted the livestock and fisheries sectors as crucial but underexploited drivers of rural job creation and economic growth. Creating inclusive job opportunities in the agriculture and livestock sectors is also in line with the current priorities of the Government of Bangladesh (GoB). The 8th Five-Year Plan (8FYP) aims to generate good jobs for the significant number of underemployed and new labor force entrants in rural areas through an accelerated transformation of agriculture from a mostly semi-subsistence to a diversified commercial sector. The 8FYP 5 As the focus of this assessment is livestock value chains within the agriculture sector, the paper will use ‘sector’ to refer to the livestock value chains throughout the paper. 9 places emphasis on promoting the livestock sector to boost livestock production and increase income and job opportunities in this field. The livestock sector plays a crucial role in achieving the Sustainable Development Goals (SDGs) in Bangladesh, such as ending poverty and hunger, reducing undernutrition, ensuring decent jobs, and creating employment opportunities for all, regardless of gender and social status. To stimulate the sector, the GoB has launched the Livestock and Dairy Development Project (LDDP), a transformative project financed by the World Bank that aims to enhance productivity, market access, and resilience of smallholder farmers and agro-entrepreneurs involved in livestock value chains across the country. Since 2019, the project, with a total investment of US$579 million, has been promoting climate-smart livestock production systems while improving the ecosystem for value chain development by financing key infrastructures, building capacity, and sharing knowledge. The project also focuses on animal health, food safety, and antimicrobial resistance (AMR) while adhering to the One Health principles. Though the initial objective of the LDDP was to provide business development and financial services to livestock producers, the project also provided emergency cash support to vulnerable livestock producers under the Contingency and Emergency Response Component (CERC) to help them cope with the adverse effect of COVID-19 pandemic. The implementation of the LDDP will continue till July 2025. The project aims to support farmers, women, and youth by enhancing both the quantity and quality of jobs available in the livestock value chains. The project aims to transform the informal livestock sector in Bangladesh into a formal commercial sector by providing training, marketing support, financial services, and inputs to 500,000 livestock producers, 50 percent of whom are women. By enhancing production practices, supporting producer organizations (POs), and creating market links through the ‘productive partnership’ approach, the project is expected to create more job opportunities along the value chains and improve the job quality at farm level. The project has adopted World Bank’s integrated maximizing finance for development (MFD) approach to create higher-skilled jobs along the value chains for dairy and livestock production, processing, logistics, traceability, and food safety. The primary objective of this assessment is to estimate ex ante the project's job creation potential. The specific objectives of this assessment is threefold: 1. To develop a concise framework for measuring jobs in livestock operations. 2. To provide a thorough understanding of the current employment situation in the livestock sector. 3. To estimate the number of jobs that the LDDP is expected to create. Measuring jobs in the livestock sector in developing countries presents a challenge because livestock activities are largely informal. People working in this sector are often underemployed and engaged in other activities, making it difficult to measure the creation of new jobs resulting from intervention. In general, gross job creation due to intervention can be measured by summing up the employment gains at expanding and new establishments within the sector (Davis and Haltiwanger 1992). However, measuring employment gains is difficult when project interventions, like the LDDP, support jobs for those who are often underemployed and/or unpaid family workers as well as those already occupied with multiple low- productivity tasks. The most common approach to measuring job creation from project interventions is to use the full-time equivalent (FTE) jobs method (Leao, Ahmed, and Kar 2018). This approach converts the net additional jobs 10 created by a project into FTE jobs by dividing the extra labor hours generated by the project by the number of labor hours required for full-time work. According to the United Nations System of National Accounts 2008 (United Nations 2008), FTE is calculated by dividing the total hours worked by all employed individuals by the number of hours required for full-time jobs. Therefore, the total number of FTE jobs created by a project can be determined by dividing the net additional hours of work per week due to the project intervention by 40 hours per week.6 However, the FTE number does not accurately reflect the actual number of people engaged in the activities, which remains a challenge when measuring jobs attributable to the intervention. This study utilizes two methods to assess the impact of the project. First, a survey is conducted among the project beneficiaries to establish baseline information about the livestock entities in the value chain and to estimate the potential job creation due to the project interventions. Second, a social accounting matrix (SAM) multiplier model is used to estimate the project's impact on jobs at the national level (The detailed methodology is discussed in the following section). The findings of the study can help policymakers design effective strategies to improve the livestock sector's performance, generate employment, and contribute to the country's economic growth. The rest of the report is organized as follows. Section 2 presents a brief framework for measuring jobs in the livestock sector. Section 3 provides an overview of the livestock value chain and employment in this sector in Bangladesh, highlighting its contribution to the economy. Section 4 presents an ex ante estimate of the job outcomes of the LDDP based on the survey and the SAM multiplier model. Finally, Section 5 concludes the study by summarizing the main findings and providing policy recommendations. 6The number of hours worked per week may vary from one country to another and one sector to another. Existing literature does not provide any standard definition of full-time work hours in a week in the livestock value chains. However, a full-time employed worker in the formal sector usually works 40 hours per week, which has been used as reference number here. Another important point to note is that people sometimes work for more than 40 hours a week in formal and informal sectors. 11 2. Applying a Jobs Lens to the Livestock Sector Measuring jobs in the livestock sector in a country like Bangladesh poses a challenge due to factors such as informality, unpaid family labor, and underemployment, which can involve engagement in multiple activities. To gain a better understanding of the jobs in the livestock sector and the potential impact of an intervention in this sector, it is necessary to provide an overview of the job scenario in this sector. In this regard, relevant policy documents and reports were reviewed and available data from different sources were analyzed. The analysis of secondary data included the Labor Force Surveys (LFS) and the Agriculture Census 2018 of the Bangladesh Bureau of Statistics (BBS) as well as the Bangladesh Integrated Households Surveys (BIHS) conducted by the International Food Policy Research Institute (IFPRI). To estimate the job outcomes of the LDDP, this study used two approaches. First, a value chain survey approach was employed to collect quantitative and qualitative information through a survey of relevant stakeholders along the value chain, providing a baseline and necessary multipliers for measuring potential job outcomes. The survey was conducted by the World Bank through a local partner, the South Asian Network on Economic Modeling (SANEM), and involved the use of a structured questionnaire (see Annex II). Key informant interviews (KIIs) were also conducted with the top management of large formal processing plants to better understand their job creation potential and identify prospects and challenges in the sector. Second, a SAM multiplier model was utilized to derive aggregate figures of estimated job outcomes at the national level due to the project interventions and to complement the findings from the sample survey. Figure 2.1 summarizes the key approaches used in the assessment for jobs measurement in the livestock sector. In addition to measuring FTE jobs, this study aims to assess the quality of jobs in the livestock sector. To achieve this goal, the survey collected the necessary information to measure job quality in the sector. Livestock jobs have several dimensions, such as temporary/seasonal, formal, and informal, and can be categorized as wage employment, self-employment, and unpaid family work. Various project interventions can create additional jobs along livestock value chains, which can be direct, indirect, or induced. To capture these multiple dimensions of jobs in livestock value chains, required questions were included in the structured questionnaire for the survey. 12 Figure 2.1: Summary of livestock job measurement approaches used in the assessment. • Review of secondary data and relevant policy documents • Livestock value chains & measurement of FTE jobs Overview of Livestock Sector • Number of jobs; number of FTE jobs; quality of jobs; types farms/firms Survey & KIIs • Identification short and long term challenges; Ex ante job estimate. • Changes in output/GDP SAM multiplier • Changes in direct and indirect model jobs (using sectoral employment coeff.) 2.1 Methods of FTE Job Measurement To measure FTE jobs accurately in the livestock sector, it is necessary to collect information on the number of hours worked by the people engaged in the sector per week. However, in Bangladesh, livestock activities are highly seasonal, which requires collecting weekly working hours data multiple times over the year to capture the effect of seasonality and obtain a more precise measure of FTE jobs in the sector. For instance, some primary production level jobs may only occur for a short period, such as before a religious festival like Eid-ul-Adha, during which a large number of animals are sacrificed. In this study, an individual's engagement in livestock activities over the past seven days was used to estimate FTE jobs. Full-time work is assumed to be 40 hours or more per week7. 7 Any engagement over 40 hours per week is considered as one FTE job. 13 Calculation of FTE jobs number ∗ Farm-level FTE jobs: = ∑ =1 40 ∗ 40 ≥ 40 where is the number of FTE jobs in farm j, ={ , where is the hours spent on < 40 the job by individual i in farm j and n is the number of individuals in farm j. Total FTE jobs: = ∑ =1 where FTEJ is the total number of full-time equivalent jobs in firm j and N is the number of farms. ∗ Total female FTE jobs: = ∑ =1 ∑=1 40 , where FFTEJ is the total number of female full-time equivalent jobs and is the number of females in farm j. ∗ Total youth (15 to 29 years) FTE jobs: = ∑ =1 ∑=1 40 , where YFTEJ is the total number of full-time equivalent jobs for the young people and is the number of young people in farm j. 2.2 Analytical Approaches To estimate the job outcomes of the project interventions, even before complete implementation, two approaches are employed: a representative field sample survey of potential beneficiaries, including KIIs, and a SAM-based multiplier model. The survey mainly serves as the project's baseline since its implementation at the field level was started in 2022. The data collected from the survey provides a preliminary understanding of the jobs held by the entities involved in livestock activities at different stages of the value chains, along with key multiplier effects of on-farm investments along the value-chain. The survey covered three livestock value chains: cattle and other ruminant animals, dairy, and poultry. It collected information from five segments of the value chain: (a) inputs and other services, (b) production (farmers), (c) aggregation (traders), (d) processing, and (e) retail services. A multistage stratified random sampling was followed to ensure proper representation of small, medium, and large farms. The data were collected from 2,0458 farms (firms9). Of the surveyed entities, 1,613 or 79 percent were involved in livestock production and the remaining 432 or 21 percent were upstream or downstream firms in the livestock value chains. Note that all the surveyed livestock rearing farms are beneficiaries (for the dairy and poultry sample) or potential beneficiaries (for the cattle sample) of the LDDP. The project does not 8 Following the sampling framework, 2,000 farms/firms were surveyed. However, additional 45 farms/firms come from the pilot survey. 9 In this report, the term ‘farm’ represents livestock rearing establishment and ‘firm’ represents the upstream and downstream establishment. 14 provide any direct support to the upstream and downstream firms. To understand the prospects and challenges in the livestock and dairy sector, 10 in-depth interviews were conducted with the relevant officials of formal sector processing plants. The KIIs facilitated an in-depth understanding of the policies and job creation prospects and helped identify the challenges and gaps. The concerns and recommendations of people in the top management of those firms are reflected in the KIIs. Annexes I and II present the details of the survey methodology and the questionnaire. The sample survey provides a baseline scenario of the jobs supported under the project and an approximation of potential jobs outcomes from the project intervention in the livestock sector. . On the other hand, the SAM multiplier model is employed as a relevant tool for ex ante job estimation from the project interventions at the economy level with sectoral breakdowns, which will complement the findings from the sample survey. The SAM model comprehensively incorporates intersectoral linkages and the distribution of institutional incomes, offering a holistic understanding of the economy and enabling an accurate representation of income distribution and its impact. However, the SAM model's main limitation lies in its assumption of excess capacity in the economy, assuming that any increase in demand is immediately met by available unemployed resources. The assumption aligns with the situation in Bangladesh, where a significant labor force remains unemployed and underemployed10. Therefore, the use of the SAM model to estimate the ex ante job outcomes of the project interventions is well suited. By utilizing the SAM multiplier model, the study aims to provide a robust and comprehensive estimation of the potential job creation resulting from the LDDP's transformative impact on the livestock and dairy sector. It is noteworthy that the SAM model primarily provides estimates of output (production) rather than the creation of new jobs. Therefore, to determine the job outcomes, the sectoral employment coefficient11 is integrated with the output estimates generated by the model (See Annex II for details of the methodology). In this study, SAM 2017, the latest available SAM for Bangladesh is employed, which includes 219 accounts comprising 100 activities, 100 commodities, 3 factors of production, and 16 institutions (including eight types of households) (GED, 2019b). However, using LFS 2016-17, the study uses the base employment data for available 82 sectors in line with sectors in SAM. Hence, the 100 activities of the SAM are consolidated to 82 accounts by grouping certain sectors. The sectoral employment coefficient matrix is derived by utilizing sectoral output data, and sectoral employment data extracted from LFS 2016-17 and using the following formula: 10 According to the LFS 2016-17, approximately 2.7 million individuals in the labor force are unemployed, with around 70% of them residing in rural areas. Additionally, an estimated 1.5 million employed individuals are underemployed and actively seeking new or additional employment opportunities. Notably, around 62% of underemployed individuals come from agriculture. Furthermore, it is anticipated that through the development of the sector, LDDP will contribute to an increase in the labor force participation rate, particularly among women, which is only 36.3%. 11 The sectoral employment coefficient, also known as the employment-output ratio, quantifies the relationship between employment and output in a sector or industry. It is derived by dividing the number of sector employees by the sector's output level. This coefficient offers valuable insights into the labor intensity and job creation potential of the industry, reflecting the amount of employment generated per unit of output produced. 15 ∁ = Output Where ∁ represents the employment coefficient for sector i. The design the simulation has been consulted with key informants and experts from the sector, including the project implementation team. The assessment used the portion of the project investment that supports productivity improvement of the sector and has to create job opportunities Along the value chain. As a result, out of the total investment of US$ 500 million, specifically the allocation to components A1, A2, B1 and C2, totaling US$ 146.4 million, are expected to directly contribute to enhancing the productive capacity of the sector. The remaining investments would support this productive investment indirectly, by improving overall business environment of the sector. Thus, US$ 146.4 million is used as an exogenous shock in the SAM multiplier model. 16 3. An Overview of the Livestock Sector Jobs in Bangladesh This section delves into the current structures of the livestock sector, with recent trends in employment in the livestock sector. The LDDP can have three types of job outcomes: direct, indirect, or induced. Direct jobs are net additional employment created among the project beneficiaries, including service providers and producers. Indirect jobs are created by other actors within the value chains due to additional purchases of inputs and extra supply of outputs. Induced job creation refers to additional jobs created in other sectors from the improved demand for output and services due to the increased income of direct/indirect beneficiaries. However, measuring induced jobs, such as those in transportation, logistics, and leather sector, can be complex, and thus the assessment did not measure the induced job outcomes of the project from the sample survey. However, the job outcomes form the SAM model provides some insights on the potential of induced job outcomes of the project through various linkages across various sectors in the economy. To measure direct and indirect job outcomes for a livestock sector intervention, a clear understanding of the relevant value chain mappings in the sector and the results chain of the project interventions is crucial. This section presents mappings for three important value chains in the livestock sector and a discussion on the job scenarios in the sector in Bangladesh, following the schematic presentation of the project's results chain toward job creation. 3.1 Results Chains: From Interventions to Jobs The LDDP offers a broad range of support to the livestock producers, including improvement in production practices, market links, critical infrastructure development, and institutional capacity development. These activities are expected to increase overall livestock and dairy productivity, resilience, and market access, which will increase job opportunities for rural people and improve rural incomes and nutrition status in the country. This actual theory of change of the project is summarized in Figure 3.1. Figure 3.1: Theory of Change of the LDDP Activities Outputs A1. Support to producer organizations Outcomes Development Goals Strentgthened prodcuer A2. Improvement of production orgnaizations. Well functioning markets. Improved overall nutrtion and rural practices Greater,safer, and cheaper Increased overall livestock and incomes. B1. Market linkages supply of animal products. dairy productivity. B2. Infrastructure development Basic post-harvest Increased market access. Increased job opportunities for the C1.Institutional capacty and knowledge infrastructures. Improved resillience to selected rural people, especilaly for women development Organized value-chians and risks. and youths. C2. Food safety and food quality direct marketing initiatives. The underlying logic of the theory of change for job measurement is demonstrated in a results chain, as shown in Figure 3.2. 17 Figure 3.2: Results Chain: From Intervention to Jobs Improved performance of More rural jobs; existing farms more jobs for women; more jobs for youth; Increased demand Increased LDDP Increased more formal for input, veterinary activities in the Intervention demand for labor jobs; less and other services processing plants seasonality; increased New farms income; less created poverty. The diagram shows that LDDP interventions, such as improved production practices, enhanced skills through training, market links, and infrastructure development, are expected to improve the productivity of existing farms and attract new ones in the value chain. This increase in productivity and new farms will lead to a rise in labor demand, resulting in direct job creation and reduced underemployment. Additionally, the project will contribute to indirect job creation through the increased demand for inputs and veterinary and other services. The enhanced productivity due to the project intervention will also relieve intermediate input constraints on downstream firms like processing plants, logistics, distribution, and tanneries, thereby promoting indirect job creation. These factors will significantly contribute to job creation, reduce underemployment, create better-quality jobs, and increase incomes, thus reducing poverty in rural areas. It is important to note that these jobs do not require high-level skills and can remain in rural areas without the need for urban migration. 3.2 Mapping Key Livestock Value Chains in Bangladesh: Understanding Jobs in Cattle and Goat Fattening, Dairy, and Poultry Sectors Livestock value chains are critical to the Bangladeshi economy and mapping out the standard value chains is essential to understanding job creation in the sector. This section focuses on three dominant value chains12 in Bangladesh: cattle and goat fattening, dairy, and poultry. The simplified visualization of complex business operations involving multiple actors and their relationships is presented to understand the core value chain, which includes inputs to the livestock sector, production, aggregation, processing, and distribution. Key value chain actors such as livestock farmers, traders, processing entities, and input providers are also discussed. 3.2.1 Cattle and Goat Fattening Value Chain Figure 3.3 presents the core value chain for cattle and goat fattening in Bangladesh. The major sources of cattle for fattening include imports (mostly from neighboring countries), local markets, and artificial insemination (AI) services provided by the DLS and other private sector entities. Farmers buy calves and adult cattle for fattening from traders and other farmers. The necessary cattle feed, which is mostly domestically made, is purchased from the local market. Sometimes, farmers grow their own feed. 12 Leather, feed, and rendering are outside the scope of this assessment. 18 Vaccines and other medicines are obtained from the public and private sectors. The DLS is the key organizing body that coordinates public sector products and private sector vaccine and medicine providers include ACME, Renata, ACI, and other manufacturers. The DLS provides vaccination and medicine through its upazila and district offices. Private sector vaccines and medicines are sold through local veterinary physicians who have their pharmacies in large local markets (Imran et al. 2018). The primary farmers typically sell their cattle to traders or local slaughterhouses, and the traders then sell the collected animals to slaughterhouses, consumers, and formal sector processing plants such as ‘deshi meat’. There are few vertically integrated firms that both rear their own animals and have processing plants, such as Bengal Meat. The beef and mutton industry is also a significant input supplier to tanneries, leather manufacturers, and pharmaceutical companies, with hides and skins collected primarily by traders and supplied to tanneries. After tanning, the leather is sold to manufacturers, who then sell the leather products to retailers and for export. Animal bones are another by-product of the meat industry, collected by traders and sometimes sold by processing units to bone grinding and chopping mills. These mills produce products such as gelatin for pharmaceutical companies, which are sold domestically and internationally. Figure 3.3: Cattle and Goat Fattening Value Chain Inputs & other Primary Aggregation and Processing Distribution services Product Veterinary, AI and Traders Restaurants other services (multiple level) Farmers and Prossesing units formal (Slaughter-house, formal Retailers Conssumers commercial processing plant - Bengle farms Meat, Deshi Meat, etc.) Inputs (medicine, By-product, such as feed, equipment, hides and skins, bones, local and cross- Exports and organic fertilizers border animal supply) Tanneries & Poultry Feed Compost/organic Pharmaceutical Leather product Manufacturers fertilizer companies manufacturers 3.2.2 Dairy Value Chain Figure 3.4 presents an overview of the dairy value chain in Bangladesh. Farmers acquire cattle from cross- border sources, local markets, and through artificial insemination provided by the DLS. Cattle feed is obtained from the local market or grown by farmers themselves. Similar to the cattle and goat fattening process, vaccines and medicine are provided by the DLS through its district and upazila (sub-district) offices, as well as by private sector providers such as ACME, Reneta, Intervet, and Incepta, who distribute through local veterinary practitioners (Imran et al. 2018). 19 Once the milk is produced, dairy farmers sell it to a variety of clients, including consumers, local sweet vendors, traders, and milk collectors for further processing. At this stage, the by-product is organic fertilizer. Milk collectors sell a significant portion of the collected milk to sweet vendors, some directly to processing plants such as Aarong, Milk Vita, Pran, and Fresh, and the remainder to chilling centers maintained by small local organizations, who then sell to the processing plants. The processing plants pasteurize milk and produce milk products such as curd, ghee, and butter, which are sold to consumers through retailers (Oman and Liang 2019). Figure 3.4: Dairy Value Chain Inputs & other Primary Product Aggregation and Processing Distribution services Veterinary, AI and Milk Restaurants other services Collectors Milk Prossesing plants Farmers (pasteurized Retailers Consumers Collection point/chilling milk, chana center (ছানা), curd, ghee, butter, By-products, such Sweet cheese, etc.) as organic shops Inputs (medicine, fertilizers feed, equipment, local and cross- border animal supply) Compost/organic fertilizer 3.2.3 Poultry Value Chain Figure 3.5 outlines the poultry value chain in Bangladesh, with chicken production being the dominant subsector. Poultry breeders in Bangladesh generally use imported eggs, with grandparent stock (GPS) eggs being imported from neighboring countries, especially India. These highly valued eggs have a controlled pedigree, and only a few firms own the GPS. The hatcheries use eggs from the parent line to produce day- old chicks (DOCs) for chicken and egg production for consumers. Poultry farmers receive veterinarian services and buy feed and equipment from feed manufacturers and equipment manufacturers and importers. There are two types of vaccine and medicine providers: the DLS and private sector providers. DLS provides these vaccines and medicines through its local offices, while the private sector products are sold through private veterinary practitioners. At the primary production stages, there are local farmers and formal sector large firms such as CP, Kazi Farm, Bengal Meat, and Paragon. Small farmers sell chicken and eggs to local traders, processing plants, retailers, and directly to consumers. Large formal sector farms are vertically integrated and mostly sell processed meat. There are also contract sellers who take contracts from farms like CP and Kazi Farms, and only sell to them. Traders sell directly to restaurants, consumers, and processing plants (LightCastle Analytics Wing 2020). 20 Figure 3.5: Poultry Value Chain (Meat and Eggs) Inputs & other Primary Aggregation and Distribution services Product Processing Importer of grandparent Traders Restaurants stock (GPS) eggs (multiple level) Grandparent stock Retailers Consumers Parent line Prossesing plants (infomral Farmers processors, formal Hatcheries and formal processing plant - sector Bengle Meat, Deshi farms Meat, CP, Kazi farm, etc.) Inputs (medicine, veterinary services, poultry feed, and equipment) 3.3 An Overview of Livestock Production and Employment in Bangladesh The demand for animal protein in the form of milk, meat, and eggs is increasing in Bangladesh due to the growing urbanization and sustained economic growth. However, although the production of these products is increasing gradually, the growth rate is slowing down in recent years, posing a threat to the country's self-sufficiency and export opportunities. The majority of livestock farming in Bangladesh is done in informal settings at the homestead, utilizing family labor. While almost half of rural households rear ruminant animals and more than two-thirds rear poultry, they operate on a small scale. For example, around 65 percent of households engaged in livestock activities rear at most two large ruminants, 56.2 percent at most two small ruminants, and 68.7 percent at most 10 poultry. However, over the years, commercial livestock production has increased while production for consumption has decreased. In 2019, 37.3 percent of large ruminant rearing households, 49.0 percent of small ruminant rearing households, and 3.3 percent of poultry rearing households were engaged in livestock activities only for commercial purposes. In contrast, in 2019, only 2.0 percent of large ruminant rearing households, 2.2 percent of small ruminant rearing households, and 5.2 percent of poultry rearing households reared livestock solely for consumption. 21 Figure 3.6: Production of Milk, Meat, and Eggs Figure 3.7: Growth Rates of Milk, Meat, and Eggs 140 2500 120 2000 60.0 100 80 1500 40.0 60 1000 40 20.0 500 20 0 0 0.0 Egg (Crore Number) (Secondary Axix) Milk (Lakh Metric Ton) Milk (Lakh Metric Ton) Meat (Lakh Metric Ton) Meat (Lakh Metric Ton) Egg (Crore Number) Source: DLS 2021. Source: DLS 2021. Bangladesh has achieved self-sufficiency in meat and egg production, which are considered essential sources of high-quality protein and micronutrients. However, the country is lagging in milk production. According to Table 3-1, in FY2020–21, the country produced 8.44 million metric tons of meat and 20,576.4 million eggs, while the domestic demand was 7.44 million metric tons of meat (at 120 g/day/head) and 17,659.2 million eggs (at 104 egg/year/head). On the other hand, only 1198.5 million metric tons of milk were produced during the same period, while the demand for milk was 1,549.4 million metric tons (at 250 ml/day/head). The insufficient milk production has led to the import of milk. Furthermore, exporting livestock products such as omasum, abomasum, intestine, cattle horn, and bone has significantly contributed to the country's export. The export earnings from livestock products have risen to BDT 7,425.5 million in FY2020–21 from BDT 2,125.5 million in FY2018–19. This rise in export earnings indicates that Bangladesh's livestock sector has the potential to increase the export of livestock products at a larger scale for specific markets. Table 3-1: Availability of Milk, Meat, and Egg Name of the Products Demand Production Availability 15.49 million metric ton Milk 11.99 million metric ton 193.38 (ml/day/head) (250 ml/day/head) 7.44 million metric ton Meat 8.44 million metric ton 136.18 (g/day/head) (120 g/day/head) 17,659.2 million Egg 20,576.4 million 121.18 (number/year/head) (104 number/year/head) Source: DLS 2021. Note: The estimated population of the country on July 1, 2020: 169.8 million. Table 3-2 presents the key findings of livestock employment from the Bangladesh LFS 2016–2017. According to the LFS data, the livestock sector employs approximately 8.8 million individuals, which 22 accounts for 14.5 percent of the overall employment in the country and more than one-third of total employment in the agricultural sector. However, livestock jobs are heavily concentrated in production activities. Of the people engaged in the livestock sector, 98 percent are directly involved in livestock production activities and the remaining 2 percent are indirectly employed by livestock manufacturing and service activities. This implies that the ratio of direct to indirect jobs in the livestock sector is 64:1 (direct jobs/indirect jobs). Therefore, one indirect job is created against 64 direct jobs in the livestock sector. Alternatively, if 1,000 new jobs are created in livestock production activities, approximately 15.5 indirect jobs will be generated in livestock manufacturing and service activities. Table 3-2: Employment Scenario of the Livestock Sector (thousands) Sector and Subsector LFS 2016–2017 Employment in livestock and poultry production 8,641.0 i) Raising of cattle and buffaloes/dairy farming 69,62.3 ii) Raising of sheep, goats, and pigs 538.2 iii) Raising of poultry/poultry farming 1,140.5 Employment in livestock and poultry related manufacturing 104.2 i) Processing and preserving of meat 1.1 ii) Manufacture of dairy products 82.8 iii) Manufacture of prepared animal feeds 20.3 Employment in livestock and poultry related service 29.8 i) Veterinary activities 29.4 ii) Support activities for animal production 0.4 Total employment related to livestock and poultry 8,775.0 Total employment in Bangladesh 60,600.0 Share of livestock and poultry related employment in total employment (%) 14.5 Total agricultural employment 24,700.0 Share of livestock and poultry production in agricultural employment (%) 35.0 Source: Based on LFS 2016–2017. 3.3.1 Female Employment in Livestock Value Chains The female labor force participation rate in Bangladesh is only 36.3 percent, compared to 80.5 percent for males. Despite this, the livestock sector employs a disproportionately large number of women, with 41 percent (7.6 million) of female workers in this sector compared to just 2.4 percent of men. In fact, out of the total 8.6 million people employed in livestock activities, an astounding 88.2 percent are women, while only 11.8 percent are men. This is in stark contrast to the national level, where women comprise just 30.7 percent of total employment and men comprise 69.3 percent. Although women make up the majority of employees in the livestock sector, livestock ownership presents a different reality. The BIHS of 2019 revealed that male ownership of more valuable large ruminants is 23 72.4 percent, while female ownership is only 25.6 percent. As for small ruminants and poultry, male ownership decreases to 53.8 percent and 15.5 percent, respectively, with female ownership at 44.3 percent and 83.8 percent. Table 3-3: Contribution of Livestock Employment by Sex Sector and Subsector Unit Male Female Total Value (millions) 1.0 7.6 8.6 Employment in livestock production % 11.8 88.2 100.0 Value (millions) 13.6 11.2 24.7 Total agricultural employment % 54.9 45.1 100 Value (millions) 42.0 18.6 60.6 Total employment in Bangladesh % 69.3 30.7 100.0 Share of livestock production in total employment % 2.4 41.0 14.3 Source: Based on LFS 2016–17. 24 4. Job Outcomes of the Livestock and Dairy Development Project: An Ex Ante Estimation The LDDP became effective in early 2019. However, due to the COVID-19 pandemic, the project's implementation has been significantly delayed. The pandemic has adversely affected the livestock sector, along with other sectors of the economy. To help vulnerable small-scale dairy and poultry producers cope with this shock, the project offered emergency cash support. Besides the emergency cash relief program of LDDP, the GoB initiated several other campaigns to provide additional support in response to the COVID-19 induced crisis. However, the implementation of emergency support activities under the CERC caused significant delays in the originally planned project activities. To understand the current job situation of the potential LDDP beneficiaries and the prospect of new job creation, a field survey was conducted along with KII. For ex ante job estimates of the project, a field survey was conducted and the relevant information from the survey was then utilized to estimate the project's direct and indirect job outcomes. The survey sample for the jobs measurement primarily consisted of the list of potential LDDP beneficiaries developed to provide cash transfers and other emergency supports to recover from the losses due to the COVID-19 pandemic. A SAM multiplier model was employed to obtain aggregate figures of potential job creation at the national level. 4.1 Insights into Jobs in Livestock Value Chains: Evidence from a Sample Survey The survey included small, medium, and large farms13 from the LDDP’s potential beneficiary list to ensure adequate representation. Table 4-1 displays the quartile distribution of the current market value by farm category. The market value of the surveyed livestock farms ranges from BDT 6,000 to BDT 210 million. However, like the overall sector, the sample is dominated by smallholders and the median market value of the sample farms is BDT 0.7 million. One-quarter of the surveyed farms have a current market value less than or equal to BDT 400,000 (the value of the first quartile), while 25 percent of the farms have a current market value14 exceeding BDT 1.4 million (the value of the third quartile). 13 Farms are commonly classified into small, medium, and large categories based on the number of employees or/and the number of livestock. However, when the primary focus of the study is measuring employment (jobs), it is not appropriate to classify farms solely based on the number of employees. Moreover, due to the complexity of farms operating in multiple sub-sectors, establishing clear criteria for classifying farms based on the number of livestock becomes challenging. Thus, considering the current market value of the farms can offer valuable insights into their size, i.e., the scale of operation, the number of animal stock that the farm has, and even employment. 14 Current market value refers to the estimated monetary worth of the entire farm, including the housing, livestock, and other equipment at the time of the survey. 25 Table 4-1: Quartile Distribution of the Current Market Value of Farm (BDT, thousands) Minimum First Quartile Median Third Quartile Maximum Cattle fattening 50 400 700 1300 210000 Dairy 10 450 750 1500 180000 Poultry 6 350 600 1200 85000 Overall 6 400 700 1400 210000 Source: SANEM-World Bank Survey, 2021. A closer examination of the data reveals that the institutional and business practices among the livestock producers are inadequate. Only one in every five farms possess authorized registration or license for business (Table 4-2). Also, a mere 8.4 percent of the farms have their own bank accounts for farm-related transactions, though 66.6 percent keep some sort of records of their business finances. The cooperative or producer group approach is also limited among the livestock producers in Bangladesh; just 7.3 percent of the surveyed farms are presently linked with any association or cooperative15. Table 4-2: Assessing the Livestock Farming Practices (%) Cattle Dairy Poultry Overall Percentage of farm keeping an accounting of its investment, costs, and revenue, or profits 68.3 57.8 81.9 66.6 and losses Percentage of farms having a bank account 9.28 8.4 7.4 8.4 Percentage of farms having authorized 18.3 16.1 27.5 19.6 registration or license for their business Percentage of farms having membership of 7.2 8.8 4.5 7.3 any association (PO) or cooperative Source: SANEM-World Bank Survey, 2021. 4.1.1 General Jobs Profile in Livestock Value Chains The survey encompasses all paid and unpaid workers who have worked for the farms (firms) for at least one hour weekly. The information of 6,122 workers has been collected, of which 4,809 workers belong to 1,613 livestock farms, and the remaining 1,313 workers belong to 432 upstream and downstream firms. Thus, the average number of workers in both on-farm and off-farm in the livestock value-chains is just around 3. At the farm level, average number of jobs is 2.98, while the average number of jobs in the upstream and downstream firms is 3.04. Furthermore, it has been observed that among the sub-sectors, 15 The formation and support of producer groups are integral to LDDP. However, since the survey was conducted before the establishment of the LDDP producer groups, the percentage of farms with membership in any association or cooperative appears to be exceptionally low. 26 the dairy sub-sector demonstrates a relatively higher labor intensity, with an average of 2workers per 1 million BDT market value. In comparison, the average for cattle is 1.2 workers, for poultry it is 1.9 workers, and for upstream and downstream firms, it is 1.7 workers. Table 4-3 illustrates the data breakdown by different subsectors and entities. The data show that employment in livestock farms is male dominated, with a male-female ratio of 1.62. The upstream and downstream firms also exhibit male domination at a larger scale with one female worker in every 32 male workers in the these segments of the value-chain. The finding of lower female employment contrasts the results from the LFS data, presented in Section 3, which indicate that female employment dominates the livestock sector with a share of 88 percent of the national livestock employment. The survey results suggests a much lower female employment share in the livestock sector-only 38.1 percent. However, both surveys are not cpmpareable as the LFS considers only the primary job to identify the sector of employment if a worker has multiple jobs 16 Table 4-3: Number of Workers Number of Total Male Female Male/Female Mean Worker/ Farms(Firms) Worker Worker Worker Ratio Worker MMV17 Cattle 388 1,155 725 430 1.69 2.97 1.20 Dairy 806 2,405 1,426 978 1.46 2.98 2.03 Poultry 419 1,249 825 424 1.95 2.97 1.88 Total livestock 1,613 4,809 2,976 1,832 1.62 2.98 1.83 farm Upstream and downstream 432 1,313 1,273 40 31.83 3.04 1.71 firms Total 2,045 6,122 4,249 1,872 2.27 2.99 1.80 Source: SANEM-World Bank Survey, 2021. Livestock activities are mainly dominated by adult workers, only one in every three workers in the sector is youth. The concentration of youth workers is relatively higher in the upstream and downstream activities. Specifically, over 80 percent of workers engaged in upstream or downstream activities are ages 15–45, while the corresponding percentage is just 67 percent for workers engaged in livestock farm activities (Table 4-4). Due to high informality in the sector, still children below 14 years of age are engaged in the livestock value-chains, mainly in on-farm activities. 16 The employment figures here are not directly comparable to the LFS data because of how LFS defines the sector of employment for workers. LFS considers only the primary job to identify the sector of employment if a worker has multiple jobs. As evident from the analysis, nearly 50 percent of the male workers have other jobs and only about 16 percent of them consider livestock activities as their primary job. Another major factor contributing to the difference is that the surveyed entities are actively engaged in the livestock sector, where 42 percent (= 0.5 × 0.832) of the male workers report having a non-livestock job as their primary job. Since most livestock activities are homestead-based informal activities, livestock employment reported from a non-livestock household is likely to be almost exclusively for females. In sum, once all these factors are considered, the discrepancy in the male- female employment ratio in the livestock sector based on LFS and the survey data are not so large after all. 17 Denotes the average number of workers per 1 million BDT market value. 27 Table 4-4: Age Profile of Workers in the Livestock Sector Upstream and Livestock Farm Total Stage Age Category Downstream Number Percent Number Percent Number Percent Children 5–14 223 4.64 23 1.75 246 4.02 Youth 15–29 1,429 29.72 457 34.81 1,886 30.81 30–45 1,794 37.31 588 44.78 2,382 38.91 Adult 46–64 1,154 24.00 213 16.22 1,367 22.33 Old 64+ 209 4.35 32 2.44 241 3.94 Total 4,809 100.00 1,313 100.00 6,122 100.00 Source: SANEM-World Bank Survey, 2021. The livestock farms predominantly rely on family labor, with 89.35 percent of workers being family members (33.54 percent proprietor, 1.35 percent partner, and 54.46 percent family members) and only 10 percent of on-farm workers are hired wage employees. On the other hand, the upstream and downstream firms in the value-chains have a higher percentage of non-family hired wage employees, about half of their workforce is hired workers. The share of family workers in the upstream and downstream activities are only 17.44 percent. Table 4-5: Workers’ Relation with the Main Proprietorship (%) Overall Upstream and Cattle Dairy Poultry Livestock Farms Downstream Firms Main proprietor/ 33.51 33.59 33.55 33.54 32.9 owner Partner (sub- 1.25 1.9 1.04 1.35 2.97 proprietor) Family member 58.13 53.16 48.6 54.46 17.44 Employee (non-family 7.11 11.34 16.81 10.65 46.69 member) Total 100.00 100.00 100.00 100.00 100.00 Source: SANEM-World Bank Survey, 2021. 28 The data presented in Figure 4.1 indicate that a significant percentage of livestock Figure 4.1: Education Profile of the Workers (%) 40.41 38.44 workers have received at least a secondary 29.12 27.19 education, with almost 60 percent (57 16.12 percent in livestock farms and 58 percent in 12.79 10.86 9.45 8.78 6.83 related firms) falling into this category. This trend is highly encouraging as having an educated workforce is crucial for effective Never Attend Primary Secondary Higher Tertiary management, use of modern technology, School Secondary skill development through training, and Livestock farm Upstream and downstream firm improvement of overall productivity in the livestock sector. Source: SANEM-World Bank Survey, 2021. 4.1.2 Insights on Full-Time Equivalent (FTE) Jobs in Livestock Value Chains Following the FTE jobs measurement approach presented in Section 2, the survey data have been used to calculate FTE jobs in the livestock sector. The data show that in the 1,613 surveyed farms, the total number of unweighted FTE jobs is 3,010, resulting in an average of 1.9 FTE jobs per farm (Table 4-6). As Table 4-6 illustrates, there is a slight variation across subsectors. Every 100 workers employed in livestock farms create only about 63 FTE jobs, with cattle fattening having the highest FTE job creation and poultry having the lowest. The effect of the LDDP intervention on FTE job creation is yet to be seen as the project implementation is ongoing. A follow-up survey after project implementation would be required to comprehend the FTE job creation in this sector. Table 4-6: Number of FTE Jobs in the Livestock Sector Total FTE Jobs Number of Farm Average FTE FTE Jobs Per 100 (Unweighted) Workers Cattle 773.1 388 2.0 66.9 Dairy 1,536.7 806 1.9 63. 9 Poultry 700.7 419 1.7 56.1 Overall 3,010.4 1,613 1.9 62.6 Source: SANEM-World Bank Survey, 2021. Table 4-7 displays the distribution of FTE jobs in the livestock sector. The findings reveal that unpaid family labor accounts for the largest share of the FTE jobs in the sector (46.7 percent), while self-employment and wage employment constitute 39.2 percent and 14.1 percent, respectively. This implies that the sector is highly informal, with a significant reliance on family labor. Farms with hired labor has the highest potential for FTE job creation, with approximately 86 FTE jobs for every 100 hired workers employed in the sector. The data indicate that the formalization of the livestock sector is likely to generate more FTE jobs. 29 Table 4-7: FTE Job by Employment Type FTE Jobs Per 100 FTE Job Percentage Workers Self-employed 1,179.4 39.2 71.2 Salaried or wage-employed 425.3 14.1 85.7 Unpaid family worker 1,405.7 46.7 52.9 Total 3,010.0 100.0 62.6 Source: SANEM-World Bank Survey, 2021. 4.1.3 Job Creation Potential for Direct Beneficiaries: An Ex Ante Estimation According to the findings of the survey, the average number of workers per farm participating in the LDDP project is 2.98. Among these workers, only 10.65 percent are hired employees, leading to an average of 0.32 employees per farm. The FTE figures in Table 4-7 suggests that significant underemployment rate exists in the livestock sector, on average, a worker in the livestock sector works 25 hours in a week. Thus, there is a significant room for improvement in addressing underemployment in the sector. The project aims to support approximately 331,000 small and medium farms with improved production practices and integrating them into markets. The livestock experts and policy makers in the Ministry of Fisheries and Livestock expects, the support from the LDDP will address the underemployment problem along with supporting job creation for youths entering the rural job market. With the project support for improved production practices and market linkage activities, the livestock workers from the beneficiary households will need to dedicate more time in a week to their livestock activities. With a conservative estimate of an extra 4 hours employment in week per workers, LDDP is expected to create 99,300 additional direct FTE jobs at the end of the project implementation. If the existing distribution of FTE jobs prevails at the end of the project implementation, about 14,000 new salaried and wage-employment are expected to be created through the project support. With similar assumption for women and youth, about 38,000 FTE jobs for women and 30,000 FTE jobs for youth are expected from the project interventions in the livestock value-chains. Table 4-8: Estimated Additional FTE Jobs in Livestock Farms due to LDDP Support Additional FTE Jobs Total Livestock Farms 99,300 Women 38,000 Youth 30,000 Salaried/Hired Workers 14,000 Source: Authors’ Own Calculation based on SANEM-World Bank Survey, 2021. It is important to note that the estimates pertain solely to the beneficiary farms of the LDDP project and thus, does not account the job impacts of the project support to the upstream and downstream segments of the livestock value-chains. However, job creation may extend beyond these farms as a positive spillover effect or positive externality. Additionally, a considerable number of jobs are expected to be generated 30 within the upstream and downstream segments of the livestock production value chain, as well as in other interconnected sectors. Section 4-2 presents the results derived from the SAM multiplier model, which takes into account all of these factors to provide comprehensive estimates. 4.1.4 Insights into Jobs in Upstream and Downstream of Livestock Value Chains In addition to direct job creation, the LDDP is expected to generate employment opportunities in the upstream and downstream segments of the livestock value chains. The survey data indicates that 1,196 unweighted FTE jobs are created in 432 surveyed firms, equivalent to an average of 2.8 FTE jobs per firm (Table 4-9). Aggregation and processing firms generate on average higher number of FTE jobs than the firms engaged in veterinary and other services. On average, workers in upstream and downstream firms work longer hours and experience less seasonality compared to the workers in the livestock farms. The employment of 100 workers in upstream and downstream firms creates about 90 FTE jobs, which is approximately 29 FTE jobs higher than that of livestock farms18. Table 4-89: Number of FTE Job Total FTE Jobs Number of Average Number of FTE Jobs Per 100 (Unweighted) Farm (Firm) FTE Workers Workers Input producer/ sellers 272.100 136 2.00 302 90.08 Veterinary and other 68.725 46 1.49 77 89.25 services Aggregation (traders) 62.700 20 3.13 67 93.54 Processing firm 678.700 181 3.75 741 91.60 Retail services 113.500 49 2.32 126 90.06 Upstream and 1,195.700 432 2.77 1,313 91.06 downstream firms Source: SANEM-World Bank Survey, 2021. Table 4-10 shows that the upstream and downstream firms mainly employ hired workers, unlike the livestock farms. Among the total full-time jobs in these firms, 36.2 percent are self-employed, 49 percent are wage-employed, and 14.9 percent are unpaid family members. On average, 95.7 FTE jobs are created for every 100 salaried workers, while the figure is 76.2 for unpaid family workers. The full-time employment of self-employed workers in the upstream/downstream firms is relatively high compared to the self-employed workers in the livestock farms. Every 100 self-employment in the off-farm segments of the value-chains create 92 FTE jobs; while the corresponding FTE number for on-farm self-employment is just 71. 18 It should be noted that the survey only captures a fraction of the formal processing and feed-producing firms and did not include very large processing and feed-producing firms. Thus, the actual magnitude of indirect job creation could be even higher. 31 Table 4-910: FTE Job by Employment Type FTE Job Percentage FTE Jobs Per 100 Workers Self-employed 432.5 36.2 92.4 Salaried or wage-employed 585.6 49.0 95.7 Unpaid family worker 177.6 14.9 76.2 Total 1,196.0 100.0 91.1 Source: SANEM-World Bank Survey, 2021. 4.1.5 Insights on Women and Youth Jobs in Livestock Value Chains While a significant proportion of women in rural areas are involved in livestock and related activities, most of them work as unpaid workers: 84 percent in livestock farm activities and 65 percent in upstream/downstream activities in the value chains (Figure 4.2). However, women are more likely to obtain wage employment opportunities in upstream/downstream activities than in livestock farm activities. Over a quarter of women workers in the off-farm livestock activities are hired employees, while the corresponding figure is just below 2 percent in on-farm livestock activities. Therefore, value chain development activities supported by the LDDP could generate more economically beneficial employment opportunities for women in the livestock sector. Figure 4.2: Distribution of Workers by Employment Type (%) 84.0 65.0 55.2 47.2 46.9 46.6 37.6 36.5 35.6 34.5 27.5 17.8 16.3 15.6 14.3 10.3 7.5 1.8 Male Female Total Male Female Total Livestock farm Upstream and downstream firm Self-employed Salaried or wage employed Unpaid family worker Source: SANEM-World Bank Survey, 2021. The age-specific distribution of workers in the livestock sector, as shown in Table 4-10, reveals that a significant proportion of young workers fall into the unpaid family worker category, as demonstrated by the information presented in Table 4-11. However, the data also indicate that a greater percentage of youth workers are engaged in salaried, or wage employment compared to adult workers. Consequently, the implementation of value chain development activities in the livestock sector has the potential to benefit young workers more than their adult counterparts, given the higher proportion of youth in the salaried/wage employment category. 32 Table 4-104-11: Employment Types by Worker’s Age Self-Employed Salaried or wage-employed Unpaid family worker Age % of % of % of % of % of % of Total Stage Categ Work Work Work Column Row Column Row Column Row Worker ory er er er Sum Sum Sum Sum Sum Sum Child 5–14 0 0.0 0.0 25 2.3 10.2 221 7.6 89.8 246 Youth 15–29 296 13.9 15.7 507 45.8 26.9 1,083 37.5 57.4 1,886 30–45 1,070 50.4 44.9 449 40.5 18.8 863 29.9 36.2 2,382 Adult 46–64 672 31.6 49.2 119 10.7 8.7 576 19.9 42.1 1,367 Old 64+ 87 4.1 36.1 8 0.7 3.3 146 5.1 60.6 241 Total 2,125 100.0 34.7 1,108 100.0 18.1 2,889 100.0 47.2 6,122 Source: SANEM-World Bank Survey, 2021. The evidence from the sample survey suggests a significant difference in the number of FTE jobs created by male and female workers in the livestock sector. While every 100 male employment in the livestock sector accounts 65.5 FTE jobs; the corresponding figures for women and youth are 57.8 and 58.7 respectively (Table 4-12). This is mainly because a large share of women are engaged in poultry rearing on a part-time basis and dedicate less than half of their time to the activities. Therefore, despite a large number of women in the rural areas are engaged in the livestock sector, they account only 35 percent of total FTE jobs, while male employment in the sector accounts 65 percent of the FTE jobs in the sector. Table 4-12: FTE Jobs by Sex and Age Male Female Youth FTE Jobs FTE Jobs FTE Jobs FTE Jobs FTE Jobs FTE Jobs Percent Per 100 Percent Per 100 Percent Per 100 (Number) (Number) (Number) Workers Workers Workers Cattle 512.7 66.3 70.7 260.3 33.7 60.5 216.1 28.0 64.7 Dairy 948.5 61.7 66.5 587.5 38.2 60.1 366.7 23.9 56.4 Poultry 489.3 69.8 59.3 211.4 30.2 49.9 256.4 36.6 57.6 Overall 1950.4 64.8 65.5 1059.2 35.2 57.8 839.2 27.9 58.7 Source: SANEM-World Bank Survey, 2021. The data presented in Table 4-13 reveal that women are significantly underrepresented in nonfarm activities such as input production, processing firms, veterinary, and other related services. Specifically, only 2.5 percent of FTE jobs in upstream and downstream firms are occupied by women workers, while male workers hold 97.5 percent of these jobs. This implies a major gender gap in the nonfarm sector of the livestock industry, where women's participation is virtually nonexistent. 33 Table 4-13: FTE Job by Sex in Upstream and Downstream Firms Male Female Youth Total Number Percent Number Percent Number Percent Input producer/ sellers 272.100 100.0 0.000 0.0 100.500 36.9 272.100 Veterinary and other services 66.600 96.9 2.125 3.1 21.600 31.5 68.725 Aggregation (traders) 61.675 98.4 1.000 1.6 13.875 22.1 62.700 Processing firm 651.450 96.0 27.275 4.0 226.625 33.4 678.700 Retail services 113.475 100.0 0.000 0.0 51.375 45.3 113.500 Upstream and downstream firms 1,165.300 97.5 30.400 2.5 414.000 34.6 1,195.700 Source: SANEM-World Bank Survey, 2021. Even though women make up a higher Figure 4.3: Female Ownership of Livestock percentage of livestock employment 88.11 than men, their ownership of the animals 83.43 82.84 81.06 is minimal, particularly for large ruminants. The survey conducted for this study confirms this trend, as female ownership is only 16.37 percent 17.66 16.38 16.37 14.95 compared to the employment share of 38.1 percent. However, the ownership19 score does not reflect the full picture, with an overall effective ownership score CATTLE DAIRY POULTRY OVERALL of 83.43 percent (as shown in Figure 4.3). Percentage of farms having any owners who are women It is important to note that the effective Average effective women ownership score ownership score is based on the respondents’ answers to the question Source: SANEM-World Bank Survey, 2021. “what percentage of this farm is effectively owned by women.” 4.1.6 Insights on Quality of Jobs The livestock sector mostly comprises informal jobs, which poses a significant challenge in terms of formalizing it. The owners and workers are not well versed with their roles and responsibilities, making the process even more difficult. Table 4.13 indicates that only approximately 1 percent of workers sign contracts for their jobs. It is worth noting that since most livestock jobs are family oriented, not having a formal contract is considered normal. Even, for salaried or wage-employed workers, signing a proper employment contract is rarely practiced and only 6.05 percent of salaried or wage-employed workers in surveyed farms have signed a contract for their jobs. Nevertheless, 94.96 percent of these workers receive their wages on time, and 76.41 percent receive festival bonuses. 19 Effective ownership means having the power to make decisions on the overall management of the farm. 34 Table 4-14: Quality of Job Male Female Total Percentage of workers sign a contract for his job (N 1.38 0.33 0.98 = 4,809) Percentage of salaried or wage-employed workers 6.48 0.00 6.05 sign a contract for his job (N =4 96) Percentage of workers get salary regularly (N = 496) 94.60 100.00 94.96 Percentage of workers get a bonus on occasions (N = 76.67 72.73 76.41 496) Source: SANEM-World Bank Survey, 2021. Livestock farming involves several tasks that carry a risk of injury. According to the data presented in Figure 4.4, 15.64 percent of livestock workers have been injured at least once while performing their duties in the last 12 months. Among the injured workers, 43.75 percent were injured once, 30.19 percent were injured twice, 15.16 percent were injured thrice, and 10.9 percent were injured more than three times (Figure 4.5). In some cases, these injuries were severe enough to cause the workers to miss work, as demonstrated in Figure 4.6, where 51.33 percent of the injured workers missed work due to work- related injuries. Regarding medical expenses, the employer did not agree to share the bill in 8.51 percent of cases, partially shared the bill in 9.44 percent of cases, and fully covered the bill in 61.7 percent of cases (Figure 4.7). Figure 4.4: Percentage of Workers Injured Figure 4.5: Frequency of Injury (%) 20.17 18.34 15.64 43.75 43.35 42.86 50 32.62 30.19 29.25 28.21 15.45 15.42 15.16 12.82 12.47 10.9 8.97 8.58 6.24 Cattle Dairy Poultry Total Once Two times Cattle Dairy Poultry Total Three times More than three Source: SANEM-World Bank Survey, 2021. Source: SANEM-World Bank Survey, 2021. 35 Figure 4.6: Percentage of Workers Who Missed Work Figure 4.7: Who Shared the Medical Cost? (%) Due to Injury 60 58.97 61.7 51.5 49.89 51.33 50 40 30 20.35 20 8.51 9.44 10 Did not require Employer/farm Employer/farm Employer/farm 0 any medical bill did not share partially shared fully bear the Cattle Dairy Poultry Total the medical the bill medical co Source: SANEM-World Bank Survey, 2021. Source: SANEM-World Bank Survey, 2021. 4.1.7 Seasonality in Jobs In Bangladesh, livestock farming for meat production is heavily reliant on seasonal patterns, particularly during festive periods. The demand for cattle and goats significantly increases before Eid-ul-Adha, a religious festival of sacrifice for Muslims, which also affects the demand for poultry. According to the survey results, 67.08 percent of farms’ employment is season dependent, and there is a notably high demand for labor (81.19 percent) in the cattle subsector. To cater to the increased demand for labor in peak seasons, approximately 16.73 percent of farms employ seasonal workers, while others induce existing workers to work more hours. Farms that hired seasonal workers in the last year, on average, hired 2.84 workers. These seasonal workers worked for an average of 43.73 hours per week in farm-related activities and for an average of 2.14 months. Table 4-15: Statistics Related to Seasonality Cattle Dairy Poultry Total Percentage of firms employment depends on the season 81.19 58.81 69.93 67.08 Percentage of firms employ a seasonal employee 15.87 19.41 13.31 16.73 Average number of seasonal employees did the farm 3.68 2.69 2.60 2.84 employ in the last year Average hours per week did a seasonal worker work for 41.39 43.15 36.59 43.73 the farm Average months a seasonal worker has worked for the 1.86 2.13 2.83 2.14 farm in last year Source: SANEM-World Bank Survey, 2021. 36 4.2 Estimating Job Outcomes of the LDDP Using SAM Multiplier Model Bangladesh SAM 2017 has been converted into a SAM multiplier model to explore the economy-wide employment effects of LDDP investment. To design the simulation, the study team consulted with the LDDP implementation team, including representatives from the Government and the World Bank. The objective was to identify the portion of LDDP's investment that holds the potential to create job opportunities. As a result, it was determined that out of the total investment of US$ 500 million, specifically the allocation to components A1, A2, B1 and C2, totaling US$ 146.4 million (equivalent to BDT 12.3 billion)20, would directly contribute to enhancing productive capacity. The remaining investments would support this productive investment indirectly, but not directly contribute to productive capacity. Thus, to determine the intervention effects on livestock output and employment in the economy, BDT 12.3 billion is used as an exogenous shock in the SAM multiplier model. 4.2.1 Impact on Endogenous Accounts Table 4-16 shows the expected outcomes of the SAM multiplier model's four endogenous accounts. The investment of BDT 12.3 billion (equivalent to US$146.4 million) in the productive capacity building in the livestock sector through the LDDP is projected to increase the economy's gross output by 0.13 percent, commodity demand by 0.12 percent, and value added or GDP by 0.12 percent compared to the base case in FY2016–17. According to the specific simulation, the GDP multiplier is calculated as 1.8, which indicates that investing an additional BDT 1 million in the livestock sector will boost GDP by BDT 1.8 million. Additionally, household consumption is expected to rise by 0.11 percent in response to the injection in the livestock sector. Table 4-164-: Simulation Impact on Endogenous Accounts Base Value Simulation Percentage Change Multiplier (BDT, millions) (BDT, millions) Over Base Activity output 36,468,044 46,921 0.13 3.8 Commodity demand 41,747,238 51,693 0.12 4.2 Value added 18,699,231 22,424 0.12 1.8 Household consumption 17,394,107 18,876 0.11 1.5 Source: Based on SAM multiplier simulation results. 4.2.2 Impact on Employment The projected impact of the LDDP on employment in the economy is presented in Table 4-17. The simulation results suggest that an investment of BDT 12.3 billion in the livestock sector would generate 164,000 additional jobs, which is nearly 0.27 percent of the base employment. Among the new jobs created due to the intervention, livestock farming activities will create the largest number of jobs, around 105,000 or 64 percent of the total. This expansion of the livestock sector will increase demand for inputs and services and raise the supply of inputs to downstream industries. The increase in livestock and related 20 Component A1- Support to Farmers/producers Organization (US$ 29.45 milllion), A2- Support to Improving Production Practices (US$ 79.14 million), B1- Market Linkage through Productive Partnership (US$ 27.53 million), and C2- Food Safety and Quality Assurance (US$ 10.25 million) 37 activities jobs will boost income, creating more demand for other goods and services, thereby leading to further job creation. As a result, indirect jobs in related upstream and downstream activities, along with induced jobs in other sectors, will contribute to about 59,000 jobs, 36 percent of the new jobs. The agriculture, forestry, and fishing sector will contribute the largest share of indirect and induced jobs, with approximately 25,000 positions. This is due to the mutually beneficial exchange of resources and support between these sectors. For example, agriculture, forestry, and fishing provide essential inputs to the livestock industry, including feed crops, timber, wood products, and fishmeal. In return, livestock farming generates manure that is used as organic fertilizer in crop production. Note that multiplier effects account for the combined dynamic effects of economic links over time through feedback effects. For instance, forward production links suggest that an increase in livestock production will boost the production of processed food by improving the supply of inputs to this sector. This is the first-round effect between livestock production and food processing. In the second round, the increase in processed food production will have additional forward production linkage effects on other sectors such as the transportation sector, distribution, cold chain suppliers, and restaurants that use processed foods as an intermediate input. In the same way, in the third round, the expansion of the restaurant sector will generate even more demand for other sectors. This chain of effects continues over many rounds, creating new jobs in the economy. Table 4-174: Impact of the LDDP on Employment in the Economy New jobs Base Post-Intervention %Δ % of Due to Employment Employment Over Generated Sector LDDP (millions) (millions) Base Employment (millions) Agriculture, forestry, and 16.019 16.044 0.025 0.15 15.09 fishing Livestock rearing 8.636 8.741 0.105 1.22 64.05 Manufacture of food and 0.647 0.648 0.001 0.13 0.50 beverage Leather and footwear 0.133 0.133 0.000 0.07 0.06 Fabric, cloth, and RMG 4.772 4.773 0.002 0.04 1.02 Machinery and equipment 0.150 0.150 0.000 0.01 0.01 Chemical, fertilizer, and 0.253 0.253 0.000 0.12 0.18 pharma Other industry 2.815 2.818 0.002 0.08 1.37 Construction 3.446 3.446 0.000 0.01 0.11 Wholesale and retail 8.643 8.657 0.014 0.16 8.24 Transport 5.226 5.232 0.007 0.13 4.22 Health and education 2.617 2.619 0.003 0.10 1.60 Financial services 0.377 0.378 0.000 0.13 0.30 Hotel and restaurant 1.145 1.146 0.001 0.11 0.79 Other services 5.432 5.436 0.004 0.07 2.47 Total 60.311 60.475 0.164 0.27 100.00 Source: Based on SAM multiplier simulation results. 38 It is important to note that the estimated effects will be realized over a period of time and not in a single year. There is currently no direct estimate available in the existing literature regarding how many years it will take to fully realize the multiplier effect within the SAM framework. However, experts in the field have provided a rough estimate indicating that it will take around seven years for the project effects to spread throughout the economy following the successful completion of the project. As the project commenced in 2019, and based on the current progress, it will take 14 years to realize the complete effect of the project. The breakdown of the new job creation resulting from the LDDP by year is presented in Figure 4.8. The simulation exercise assumes that 2 percent of the budget will be spent in the first year, followed by 8 percent in the second year, 15 percent in the third and fourth years, and 20 percent in the next three years. The results indicate that during the project implementation period, the number of new jobs created will continue to rise, reaching its peak of 30,900 jobs in the seventh year. Although the project will be completed within the first seven years, the economy will still experience the project's impact for the following seven years or so. However, the impact during the subsequent years will be much lower and will gradually become negligible. Figure 4.8: Number of Employments Generated by Year 30.9 30.2 35.0 29.1 30.0 Job (Thousand; %) 21.8 20.5 25.0 20.0 10.6 15.0 8.8 10.0 4.0 2.6 2.2 1.4 5.0 0.9 0.7 0.3 0.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Year Source: Based on SAM multiplier simulation results. 4.2.3 Impact on Female Employment Table 4-18 presents simulation results on the impact of the LDDP intervention on employment by gender. According to the results, the project will create around 102,000 new jobs for women and approximately 62,000 new jobs for men. This suggests that the intervention will have a relatively greater impact on female employment, which is one of the key objectives of the project. As the livestock sector predominantly employs female workers, the majority of new female jobs (about 91 percent) will be in livestock activities. On the other hand, non-livestock activities will contribute to only about 9 percent of the new female jobs in the economy. In contrast, nearly 80 percent of the new jobs for men will come from non-livestock activities. 39 Table 4-18: Impact of the LDDP on Employment by Sex Male Employment (millions) Female Employment (millions) Post- New Jobs %Δ % of Post- New %Δ % of Base Interv due to Over Generat Base Interven Jobs due Over Generat ention LDDP Base ed tion to LDDP Base ed Agriculture, forestry, and 12.531 12.552 0.020 0.16 33.05 3.488 3.493 0.004 0.13 4.34 fishing Livestock 1.044 1.057 0.013 1.22 20.66 7.591 7.684 0.092 1.22 90.64 rearing Manufacture of 0.496 0.496 0.001 0.12 0.99 0.152 0.152 0.000 0.13 0.20 food products Leather and 0.100 0.100 0.000 0.07 0.12 0.033 0.033 0.000 0.08 0.02 Footwear Fabric, cloth, 2.651 2.652 0.001 0.04 1.57 2.121 2.122 0.001 0.03 0.70 and RMG Machinery and 0.129 0.129 0.000 0.01 0.02 0.021 0.021 0.000 0.01 0.00 equipment Chemical, fertilizer, and 0.213 0.213 0.000 0.12 0.41 0.040 0.040 0.000 0.12 0.05 pharma Other industry 2.310 2.312 0.002 0.08 2.95 0.505 0.505 0.000 0.09 0.42 Construction 3.182 3.182 0.000 0.01 0.26 0.264 0.264 0.000 0.01 0.02 Wholesale and 8.013 8.026 0.013 0.16 20.38 0.630 0.631 0.001 0.16 0.96 retail Transport 5.018 5.025 0.007 0.13 10.81 0.208 0.208 0.000 0.13 0.26 Health and 1.521 1.523 0.002 0.10 2.48 0.190 0.190 0.000 0.23 0.44 education Financial 0.303 0.304 0.000 0.13 0.64 0.074 0.074 0.000 0.13 0.10 services Hotel and 0.962 0.963 0.001 0.11 1.77 0.183 0.184 0.000 0.11 0.20 restaurant Other services 3.287 3.289 0.002 0.07 3.87 2.145 2.147 0.002 0.08 1.64 41.761 41.82 0.062 0.15 100.00 17.64 17.746 0.102 0.58 Total 100.00 2 4 Source: Based on SAM multiplier simulation results. 40 5. Conclusion and Policy Recommendations The livestock sector, a subsector of the agricultural sector, makes a significant contribution to national employment, especially female employment, despite its low contribution to GDP. Improvement in livestock operations can increase earnings and enhance the quality of existing jobs in this sector. This sector also has the potential to create more jobs as the economy is experiencing both economic and population growth. As the demand for animal products such as meat, poultry products, milk and milk product is income elastic, economic growth will accompany disproportionately higher demand for these products. If the additional demand is met domestically, this sector can contribute to both job creation as well as food and nutritional security of the country. The data from the secondary sources show that a large number of people are involved in beef fattening, milk production, milk processing, poultry rearing, production and trading of fodder crops and poultry feeds, and so on. Additionally, more employment opportunities could be generated through the upstream and downstream segments of this sector. Processing of animal-sourced products is mainly a labor- intensive industry. The livestock sector also supplies key inputs for dairy products, fertilizer, and leather goods, which have a huge export demand and is associated with substantial employment opportunities. The results from the SAM multiplier model show that the LDDP is expected to create, on average, 21,000 jobs annually over a period of seven years due to the LDDP intervention. Additional disaggregation of the results shows that 62 percent of the newly created jobs will be for females and the remaining 38 percent for males. As found in the survey, female jobs are mainly in livestock rearing, with about 97 percent of the new jobs for females, while about half of the new jobs for males are in non-livestock activities. The LDDP aims to enhance the productivity, profitability, and competitiveness of the livestock sector in rural areas and support economically gainful job creation in the sector. To achieve these objectives, several policy recommendations are emerging from the results and findings: • First, overall labor productivity is low among the livestock workers and LDDP needs to prioritize the implementation of productivity-enhancing activities to improve productivity of the existing livestock workers in the sector, which in turn can improve quality of jobs in the sector. • Second, the project needs to implement efficiently the planned productive partnerships between livestock farms and processing firms and SMEs in the downstream of the value-chain, which is assumed a key factor contributing to the job creation both on farms and off the farms. By partnering with processing firms and SMEs, the LDDP can facilitate the development of value chains, leading to job creation in both upstream and downstream segments. Such partnerships can also help address challenges faced by livestock farmers, such as market access and limited access to finance. • Third, the project needs to promote formality in the livestock sector by supporting farmers registry and market integration through productive partnerships. Formalizing the livestock sector will improve transparency, enable access to finance, and promote good practices in animal husbandry, leading to increased productivity and competitiveness. • Fourth, the results suggest that the potential of salaried and FTE job creation is relatively higher among upstream and downstream firms. Thus, LDDP needs to address constraints faced by those 41 firms by creating favorable business environment, besides linking them with the beneficiary farms, for reaping better results in terms of job creation. • Fifth, the project should develop policies and interventions that focus on promoting and supporting youth employment in the livestock sector, particularly in salaried/wage employment opportunities. The livestock sector has the potential to create many jobs for the youth, and this can be achieved by enhancing their skills through targeted training and capacity-building programs. • Sixth, the LDDP should implement women-specific measures to promote women’s participation in upstream and downstream activities in the livestock sector. Women are often marginalized in the livestock sector and promoting their participation will contribute to gender equality and enhance the project’s impact. 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Liang. 2019. “The Dairy and Beef Value Chain in Bangladesh: Diagnostics, Investment Models and Action Plan for Development and Innovation.” https://www.unido.org/sites/default/files/files/2019- 05/Bangladesh%20dairy%20and%20beef%20vc%20report%20%28Wei%27s%20final%20version %29%20.pdf. 44 Rahman, R. I., and R. Islam. 2013. Female Labor Force Participation in Bangladesh: Trends, Drivers and Barriers. DWT for South Asia and Country Office for India: International Labor Organization. Raihan, S. 2011. “Infrastructure and Growth and Poverty in Bangladesh.” Munich Personal RePEc Archive. Raihan, S., and N. Mahmud. 2008. “Trade and Poverty Linkages: A Case Study of the Poultry Industry in Bangladesh.” www. cuts-citee. org/pdf/TDP-Book. Raney, T., G Anriquez, A. Croppenstedt, S. Gerosa, S. K. Lowder, I. Matuschke, and J. Skoet. 2011. “The Role of Women in Agriculture.” ESA Working Paper 289018, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA). United Nations. 2009. System of National Accounts 2008. New York: United Nations. World Bank. 2016. Dynamics of Rural Growth in Bangladesh World Bank. 2019. Foods for Nutrition in Bangladesh. World Bank. 2021. Promoting Agrifood Sector Transformation in Bangladesh: Policy and Investment Priorities. World Bank. 2022. World Development Indicators. 45 Annex I. Survey Methodology For data collection on the job and quality of job (a baseline scenario) of the LDDP, a survey with a sample size of 2,000 was conducted. The survey covered three livestock value chains, that is, cattle, dairy, and poultry and included samples from five segments of the value chain: (a) inputs and other services, (b) production (farmers), (c) aggregation (traders), (d) processing, and (e) retail services. Steps to the quantitative survey The survey was conducted following a three-step procedure: (a) preparation, (b) implementation and fieldwork, and (c) analysis and validation (Figure AI.1). Tasks performed in the preparation phase include sampling design, questionnaire development, designing of the questionnaire in mobile-based data collection tools (KoBo Toolbox), training of the enumerators, and piloting of the draft questionnaire. In the implementation phase, the field data were collected, followed by data cleaning operations. In the last phase, the information obtained from the quantitative data was visualized along with quantitative analysis. Figure AI.1: Fieldwork Activities Preperation Implementation Analysis and and field work validation •Sampling design •Data collection •Analyze the data •Questionnaire •Quality control •Visualize and development •Data cleaning interpret findings •Designing of data •Cross-validate collection tools with the existing •Training of policy supports enumerators •Piloting Sampling framework A sample of 2,000 was surveyed. The sample was distributed from two aspects, that is, geographical aspect and sectoral aspect. For selecting the geographical locations a Figure AI.2: Geographical Locations Covered in the Survey21 multistage procedure was followed. In the first stage, from all the eight divisions 16 districts (two districts from each division) were selected by stratified random sampling. Since there are different dynamics across the coastal and non- coastal districts, to ensure the representation from the coastal areas, one coastal district and one non-coastal district were selected randomly from the divisions with coastal districts. Through this process, 5 coastal districts (31.25 percent 22) and 11 non-coastal districts were selected (FigureAI.2). In the second stage, from each of the selected districts, two upazilas, one remote (far from the city) upazila and another upazila near a city, were selected using stratified random sampling.23 The selected upazilas were considered as the primary sampling units (PSUs). From each of the PSUs, 62/63 farms/firms (firms for upstream and downstream actors of the value chain) were surveyed. 21 The LDDP is not operational in the hill tract districts of Chattagram. 22 In Bangladesh, 19 districts (29.68 percent) out of 64 districts are regarded as coastal districts 23 Considering the distance of each upazilas from the sadar upazila of each district, the upazilas have been classified as remote and near-city. The median distance value (for each district) has been considered as the threshold. 47 For the sectoral aspect of the sampling, considering Figure AI.3: Sampling Distribution by Subsector and Segments of the Value Chain the relative importance (most of the LDDP beneficiaries are from the dairy sector), 50 percent 800 weight was provided to the dairy subsector, 26 percent weight to the poultry subsector, and 24 416 400 percent weight to the cattle subsector. Again, in the 384 case of segments of the value chain, 80 percent weight was provided to the production segment and the remaining 20 percent weight to the upstream and downstream segments. Note that the large Dairy Cattle Poultry Upstream and formal processing firms and input producers were Downstream not included in the survey. Rather it includes local- Segments level processors, that is, slaughterhouse, sweets and Production yoghurt producers, and feed and fodder producers and sellers. Questionnaire development A detailed structured questionnaire was developed with attention to the objectives of the study (see Annex II. The questionnaire included sections on farm’s/firm’s core information, worker’s demographic information, jobs, quality of job, seasonality, and LDDP intervention. Once the questionnaire was finalized, it was transformed into a mobile-based data collection tool named KoBo Toolbox. Hiring of enumerators A two-layer selection process was followed for the recruitment of the enumerators. At the first stage, based on the curriculum vitae (CV), enumerators with prior experience and a minimum of graduate degree were selected for an interview. From the interviews, 35 enumerators were selected for a two-day training and a field piloting. Based on overall performance in the training sessions, qualifications, mock tests, and field piloting, 32 enumerators were selected for the survey. Training of the enumerators The two-day long training had three sessions: (a) briefing session, (b) mock session, and (c) evaluation and field tests session. In the briefing session, the trainers provided an orientation on the background of the study, study objectives, study design, survey population, location of the survey, and other nitty-gritty. Also, the questionnaire, the survey manual, and the data collection process through the KoBo Toolbox were explained in detail during the briefing session. Enumerators were also introduced to the techniques to ensure data quality. In the mock session, enumerators formed groups of two, where one played the respondent's role and the other was the interviewer. During the role play, the enumerators filled up the survey questionnaire on the KoBo Toolbox. The research team closely monitored their progress and catered to their queries. In the evaluation session, the enumerators were evaluated based on their performance of the tasks assigned to them and the assessment of their progress. Finally, the enumerators were sent to the field tests. Feedback received from the field test was incorporated in the survey 48 questionnaire along with necessary modifications. Lastly, once the questionnaire was finalized, taking all the modifications, the enumerators were briefed again on the final version of the questionnaire. Data collection, cleaning, and compilation After the training, the enumerators were divided into several groups and sent to different divisions. As trained, they collected the data on KoBo Toolbox. Once the data were entered into the KoBo Toolbox, it immediately became available on the KoBo Toolbox server. After the end of the survey, the research team accessed the data from the KoBo Toolbox server and exported it to STATA. The research associate of the team checked and cleaned the data which were later handed to the experts for analysis. Quality control Hiring professional and experienced enumerators and providing in-depth training to enumerators helped ensure data quality. Moreover, as KoBo Toolbox was used for data collection, it facilitated real-time monitoring of data collection and therefore errors were addressed promptly. This also allowed for minimizing data entry errors as the questionnaire becomes responsive to the conditions, constraints, and logic put in place by the designer. Besides, in KoBo there are several techniques to monitor and track the movement/activities of the enumerators, which was utilized in the survey. In addition, the research team constructed a monitoring and supervision team. The monitoring and supervision team regularly checked the data and supervised the enumerators. If the enumerators did any mistakes during data collection, the monitoring and supervision team edited and validated the data in the KoBo Toolbox server. Furthermore, the team performed regular backcheck. The team leader was updated regularly by the monitoring and supervision team about the progress of the survey. All of these protocols ensured the high quality of data. COVID protocol Since this was an in-person survey and data were collected between September 17 and 30, 2021, to minimize the probability of being affected by COVID and to smooth the survey operation, a COVID protocol was developed and strictly followed by every enumerator during the data collection. Actions included in the protocol are presented in Box AI.1. Box AI.1: Health Protocol for the Enumerators During fieldwork, the enumerators must abide by the following guidelines: • All communication with selected respondents should be carried out without any physical contact (for example, shaking hands) • All will wear face masks properly such that the nose and mouth are covered the entire time the enumerator is with the respondent. • All will wear hand gloves during the data collection period. • All will carry hand sanitizer and disinfectant spray and use them when required. • Enumerators will make sure every respondent is wearing a face mask properly. If not, the enumerator will provide a face mask to the respondent. • The data collection through face-to-face interviews must be carried out only with the target respondent by maintaining a minimum distance of 3 feet between the enumerator and respondent. Other farm (firm) members are not expected to be present in the room. 49 • The enumerator will carry his/her necessary items and will not use anything provided by the respondent. • SANEM will provide adequate face masks, hand gloves, hand sanitizers, and disinfectant sprays to the enumerators. Key informants interview (KII) To understand the prospect and challenges in the livestock and dairy sector, 10 in-depth interviews were conducted with the relevant officials of formal sector processing plants. KIIs facilitated an in-depth understanding of the policies and prospects of job creation and helped identify the challenges and gaps. The officials’ concerns and recommendations are also reflected in the KIIs. There are only a small number of formal processing plants in the livestock sector. To identify the potential key informants, at first, a listing exercise of the formal sector processing plants in the livestock sector was carried out and then, they were approached for an interview. Upon getting their schedule, interviews were conducted through ‘Zoom’, ‘Skype’, and telephonic conversations. 50 Annex II. Survey Questionnaire and KII Checklist Jobs Outcome Assessment of the Bangladesh Livestock and Dairy Development Project Survey Details: SD1: Date SD2: Start Time SD3: End Time Auto-generated by KoBo Toolbox Auto-generated by KoBo Toolbox SD4. Division Select one from the drop- SD5. District Select one from the drop-down list. down list. SD6. Upazila Select one from the drop- SD7. Union Select one from the drop-down list. down list. SD8. PSU No. SD9. Farm ID. SD10. Unique Farm ID No. Note: Though in Bangladesh livestock is generally reared in the homestead, and livestock value chains are largely informal, there are also some structured and semi-structured farms. However, to best serve the objectives of the survey, the questionnaire has been designed considering a farm/establishment-level structure. In this case, a household-based livestock rearing platform and/or an informal business setting (for other segments of value chains) will be considered as a farm. Potential Respondent: The main proprietor or the sub-proprietor or any worker who has engaged with the farm and has adequate knowledge about the farms ’ activities. Consent Statement (Convey the following information to the respondent) Greetings! My name is __________________________. I am working as an enumerator for the South Asian Network on Economic Modeling (SANEM). SANEM is a Dhaka-based renowned research organization. SANEM, in association with the World Bank, is collaborating on a project where the overall objective is to understand the scenario of the livestock sector in Bangladesh and an ex ante estimate of the job outcomes from the World Bank’s Livestock and Dairy Development Project (LDDP). We are conducting this survey as part of that project. The principal objective of this survey is to collect data on several indicators related to job creation, quality of the job, and other aspects in the livestock sector. We request your kind participation in this survey. The interview will take around 40 minutes. All information collected for this survey will be considered confidential and will be used only for research. Your participation in the survey is completely voluntary. You can refuse to take part in this survey now or terminate the survey at any time during the interview. You may skip/avoid any question if you do not know the answer or feel uncomfortable answering the question. CS1: Can I start the interview now? 1. Yes 2. No [thank the respondent and end the interview] (refer to Section: Interview details) Section 1: Farm Information (Non-Roster) Setup: Types of Firms? 1. Farmer (Production) 2. Upstream and Downstream of Value Chain S1Q1 S1Q1a S1Q2 S1Q3 S1Q5A S1Q5B S1Q6 S1Q7A S1Q7B In which subsector Which one is the In which When did What is the What is the Does the What is the What is does the farm dominating sector segments of the the farm approximate approximate farm amount (in the belong? for the farm? value chain does start its market value (in market value have any BDT) of the amount the farm operation? BDT) of the farm (in BDT) of the burden loan? (in BDT) (Select multiple (This question will belong? right now? farm right of of the from the drop- be visible if selected (Select now? loans? (Select one loan? down list.) more than one (Select one from month and (Select one from from the option in S1Q1) the drop-down year in the the drop-down (Write the drop-down (Write the (This question will list.) calendar) list) amount) list) amount) be visible if selected option 1 in setup) 1. 1. 1. Input 1. Less than The amount 1. Yes 1. Less than The Cattle/Goat/Sheep Cattle/Goat/Sheep producer/sellers, 50,000 should be 2. No 50,000 amount 2. Dairy 2. Dairy 2. Veterinary and 2. 50,000 to within the 2. 50,000 to should be 3. Poultry 3. Poultry other services 100,000 value bracket 100,000 within the 3. Production 3. 100,000 to selected in the 3. 100,000 value (farmers) 200,000 previous to 200,000 bracket 4. Aggregation 4. 200,000 to question. If you 4. 200,000 selected (traders) 400,000 write an to 400,000 in the 5. Processing 5. 400,000 to amount 5. 400,000 previous 6. Retail services 800,000 outside the to 800,000 question. bracket, KoBo 6. More than will restrict you 6. More than 800,000 from moving 800,000 forward S1Q8A S1Q8B S1Q9 S1Q10 S1Q11 S1Q12 S1Q13 S1Q14 S1Q15 How much profit (in How much Does the Does the Does the What is the Does the farm Does this farm What BDT) did the farm profit (in BDT) farm keep farm have farm have legal status of have any have any percentage of make in the last year did the farm the any bank any the farm? membership of owners who this farm is (September 2020 to make in the accounting account? authorized any association are women? effectively August 2021)? last year of its license? (This question (PO) or owned by (September investments, will be visible if cooperative? women? (Select one from the 2020 to costs and . selected option drop-down list) August 2021)? revenues? 1 (yes) in S1Q11. (Write the amount) 1. Less than 50,000 The amount 1. Yes 1. Yes 1. Yes 1. Sole 1. Yes 1. Yes Effective 2. 50,000 to 100,000 should be 2. No 2. No 2. No proprietorship 2. No 2. No ownership 3. 100,000 to 200,000 within the 2. Partnership means having 4. 200,000 to 400,000 investment 3. Family the power to 5. 400,000 to 800,000 bracket association make decisions 6. More than 800,000 selected in the 4. Cooperative on the overall previous 5. Others management question. of the farm 53 S1Q16A S1Q16B S1Q16C S1Q16D S1Q17 S1Q18 How many cattle How many How many How many In general, what problems do the farm During the COVID what problems does the farm sheep/goat does milking cow poultry face? did the farm encounter? currently have? the farm currently does the does the have? farm farm (Select multiple from the drop-down list) (Select multiple from the drop-down (if selected option 1 currently currently list) (Cattle/Goat/Sheep) have? have? in S1Q1 and 3 (if selected option 1 (production) in (Cattle/Goat/Sheep) (if selected (if selected S1Q2) in S1Q1 and 3 option 2 option 3 (production) in (Dairy) in (Poultry) in S1Q2) S1Q1 and 3 S1Q1 and 3 (production) (production) in S1Q2) in S1Q2) 1. Inadequate supply 11. Low demand 1. Decrease in 10. Closure of of input materials for the sales (low processing plants/ (feed, machinery, and product/service demand) slaughterhouses so on) 12. High input 2. Decrease in 11. 2. Inadequate supply price/production production Transportation of veterinary services cost 3. Decrease in problem (drug, vaccines, and 13. Oppression income 12. Shortage of other services) by the 4. Laid-off animal feeds 3. Low-quality input middleman workers 13. Shortage of materials, that is, 14. Profit margin 5. Had to halt drugs and seeds, and fertilizer is low the business vaccines 4. Lack of land 15. Frequent 6. Had to cull 14. Shortage of 5. Shortage of skilled death of animals animals and machinery labor due to diseases downsize 15. Lack of 6. Insufficient 16. Lack of 7. Input cost fund/cash investment government increases 16. Restriction on 7. Inadequate credit support (drugs, 8. Getting export-import support vaccines, breed, lower price activities 8. Inadequate input subsidy, than usual 17. Unable to machinery and stimulus, and so 9. Cannot access technological on) reach the government equipment 17. Lack of access upstream and support, other 9. Not getting fair to extension downstream than the LDDP price of the product services 54 10. Inadequate 18. Others actors (supply 18. Others (please product marketing (please specify) chain break) specify) arrangements Section 1A: Other Services (This subsection will be effective only for the production segment) S1AQ1 S1AQ2 S1AQ3 S1AQ4 S1AQ5 S1AQ6 Did the farm On average, for how On average in a month, On average in a month, On average in a month, On average in a month, use any many hours in a month how much does the farm how much does the farm how much does the how much does the transportation does the farm use spend on transportation? spend on marketing- farm spend on farm spend on feed service? transportation service? (This question will be related services? veterinary and other and fodder? (This question will be visible if selected option 1 services? visible if selected option (Yes) in S1AQ1) 1 (Yes) in S1AQ1) 1. Yes 2. No 55 Section 2: Worker’s Information Roster (All workers of the farm/establishment) S2Q1: Including you, how many paid or unpaid workers/staff have been involved with the farm (work at least one hour in a week) in the last year (September 2020 to August 2021)? (The subsequent questions of this section will be repeated based on the total workers of the farm) S2Q2 S2Q3 S2Q4 S2Q5 S2Q6 S2Q7 S2Q8 S2Q9 S2Q10 Worker ID Name Sex Relation with the Age Marital status Current status of What is the Does (name) (WID) main proprietor education highest class that have any kind of (First input (Select (Write (Select one (name) passed? disability? (Worker ID lead one from (Select one from complete from the drop- will be proprietor’s the the drop-down years. For down list) (Select one from This question will (Select one from auto- information) drop- list) example, if the drop-down be visible if the drop-down generated down age is 18 list) (name) ever list.) by KoBo list) (Constraint: First years and 3 attended school Toolbox) entry for each months, farm should be of write only 18 the main years proprietor (owner) 1. Male 1. Main 1. Married 1. Student For options, For options, 2. proprietor/owner 2. Divorced 2. Completed please see codes please see codes Female 2. Partner (sub- 3. Separated study/Dropout in the following in the following 3. proprietor) 4. Widowed/ 3. Never table table Others 3. Family member Widower attended school 4. Employee 5. Never (non-family married member) 56 Codes for Section 2 Codes for S2Q9: Education Codes for S2Q10: Disability (Select the highest completed class. For 8. Class 8/JSC/JDC/Equivalent 16. Medical/MBBS 1. No known disability example, if currently in class III, select class II 9. Class 9 17. Nursing 2. Difficulty in seeing completed) 10. Class 10/SSC/Alim Candidate 18. Engineering 3. Difficulty in hearing 0. Pre-Primary Education 11. SSC/ Dakhil/ equivalent 19. Diploma 4. Difficulty in walking/physical movement 1. Class 1 12. Class 11/HSC/Alim candidate 20. Vocational 5. Difficulty in communicating/speaking 2. Class 2 13. HSC/ Alim/ equivalent 21. Technical Education 6. Others (specify) 3. Class 3 14. Graduate / Bachelor/ 22. PhD 4. Class 4 BA/BBA/BSC/BSS/Fazil/ equivalent 23. Nurani/Hafezia/Kiratia 5. Class 5/PSC/PEC/Equivalent 15. Postgraduate/ 24. Others 6. Class 6 Masters/MA/MBA/MSC/MSS/ Kamil/ 7. Class 7 equivalent 57 Section 3: Measuring Jobs and Quality of Job S3Q1 S3Q2 S3Q3 S3Q4 S3Q5 S3Q6 S3Q7 S3Q8 S3Q9 WID How many hours did Usually on How does (name-sex- What types of What In what method How much How (name-sex-age) average age) engage with the work (name- percentage of (name-sex-age) wage did much (KoBo spend in the farm how many farm? sex-age) does profit is received (name-sex- (name- Toolbox activities in the last 7 hours for the farm? shared with his/her wage? age) receive sex-age) will repeat days? does (From Section 1, KoBo (name-sex- in each earned the worker (name- Toolbox will show Name- (Select age)? (Select one from period? from ID from (From Section 1, sex-age) Sex-Age so that each multiple from the drop-down this Section 1) KoBo Toolbox will spend in person can be tracked the drop-down (Write the list) This question farm in show Name-Sex-Age the farm properly) list) percent value) will be visible a so that each person activities This question if selected year/last can be tracked in a week? This question will be visible if options 1 to year? properly) will be visible selected option 4 in S3Q7. if selected 2 in S3Q4. option 1 in S3Q4 1. Self-employed (mostly 1. Feeding the (If this is a sole 1. Daily basis for the proprietor and animal proprietorship 2. Weekly basis sub-proprietors. Family 2. Cleaning the farm then the 3. Monthly basis members with whom livestock and percentage 4. Year/ season profit will be shared will sheds value could be end basis be under this category.) 3. Guarding 100) 5. Work for food 2. Salaried or wage- 4. Selling the and/or employed (paid family product and accommodation members will under this by-products category. If anyone 5. works for only food Transportation and/or accommodation, 6. Other will be considered in this supportive category.) work 3. Unpaid family worker 58 S3Q10 S3Q11 S3Q12 S3Q13 S3Q13A S3Q14 S3Q14A S3Q15 S3Q16 S3Q17 S3Q18 S3Q19 Did (name- Does Does Have How Did How Did (name- Other than Which one Usually on On sex-age) (name- (name- (name- many (name- many sex-age)’s the is (name- average average sign a sex-age) sex-age) sex-age) times sex-age) days employer engagement sex-age)’s how many how much contract for get paid get been have miss (name- share with the main hours does (name- his/her job/ regularly? bonuses? injured (name- work sex-age) medical farm, does occupation? (name- sex-age) engagement due to sex-age) because missed bills? (name-sex- sex-age) earned with the This This job- been of job- work age) have (This spend on from the farm? question question related injured related because (Select one any other question the second second will be will be activities in the injuries of job- from the occupations? will be occupation occupation visible if visible if in the last 12 in the related drop-down visible if in a week? per selected selected last 12 months? last 12 injuries list) selected month? option 2 option 2 months? months? in the ‘yes’ in (This in S3Q4. in S3Q4. This last 12 This S3Q16) question (This question This months? question will will be question will be question be visible if visible if will be visible if will be This selected selected visible if selected visible if question option 1 in ‘yes’ in selected option 1 selected will be S3Q13 S3Q16 ‘yes’ in in option 1 visible if S3Q16 S3Q13. in selected S3Q13. option 1 in S3Q13 59 1. Yes 1. Yes 1. Yes 1. Yes 1. Yes 1. Did not 1. Yes 1. [Here, the 2. No 2. No 2. No 2. No 2. No require any 2. No Considered second medical bill livestock- occupation 2. Employer related could be did not activities his primary share the 2. job or medical bill Considered secondary 3. Employer activities job.] partially other than shared the livestock- bill related 4. Employer activities fully bear the medical cost Note: Since we will take all responses from one person, an individual’s satisfaction on the job, harassment status, and other job q uality-related questions cannot be captured. For those, we need to interview every worker of the farm, which is not possible in the current survey setting. 60 Section 4: Seasonality (Non-Roster) S4Q1 S4Q2 S4Q3 S4Q4 S4Q5 S4Q6 S4Q7 S4Q8 S4Q9 Does What is the ‘Peak’ What is the What is the ‘Off- What is the ‘Not Did the farm ▪ How many On On average the period for the ‘Regular’ period peak’ period for operational’ employ any seasonal or average, how many farm’s farm? for the farm? the farm? period for the seasonal or temporary how many months a employ farm? temporary employees did this hours per seasonal ment [When the [When the [When the volume employees farm employ during week did a worker has (volume volume of work is volume of work of work is [When the farm during the the last year? seasonal or worked for of work/ substantially is not so high or substantially remains out of its last year? temporary the farm in activitie higher than the so low, it is lower than the activities, it is employee the last s) regular time it is regarded as the regular time, it is regarded as the (Seasonal or (If ‘yes’ selected in (who was year? depend regarded as the regular period] regarded as the not operational temporary S4Q6) employed on the peak period] off-peak period] period] employees S4Q S4Q7 S4Q in the last season? (This question refer to 7A B 7C year) work (This question will will be visible if (This question will (This question will employees Total Wom Yout for the be visible if selected ‘yes’ in be visible if be visible if who work for en h farm? selected ‘yes’ in S4Q1) selected ‘yes’ in selected ‘yes’ in a limited or (Age S4Q1) S4Q1) S4Q1) certain 18 (Select multiple period in a to (Select multiple from the drop- (Select multiple (Select multiple year) 29) from the drop- down list.) from the drop- from the drop- down list) down list.) down list) 1. Yes 0. Not Applicable 1. Before Eid-ul- 0. Not applicable 0. Not applicable 1. Yes (If yes (If yes 2. No 1. Before Eid-ul- Adha 1. Before Eid-ul- 1. Before Eid-ul- 2. No selected in selected in Adha 2. After Eid-ul- Adha Adha S4Q6) S4Q6) 2. After Eid-ul- Adha 2. After Eid-ul- 2. After Eid-ul- Adha 3. Before Eid-ul- Adha Adha 3. Before Eid-ul- Fitr 3. Before Eid-ul- 3. Before Eid-ul- Fitr 4. After Eid-ul- Fitr Fitr 4. After Eid-ul-Fitr Fitr 4. After Eid-ul-Fitr 4. After Eid-ul-Fitr 5. January (Magh) 5. January 5. January (Magh) 5. January (Magh) 6. February (Magh) 6. February 6. February (Falugun) 6. February (Falugun) (Falugun) 7. March (Choitro) (Falugun) 7. March (Choitro) 7. March (Choitro) 8. April (Boishak) 7. March 8. April (Boishak) 8. April (Boishak) 9. May (Joisto) (Choitro) 9. May (Joisto) 9. May (Joisto) 10. June (Asar) 8. April (Boishak) 10. June (Asar) 10. June (Asar) 61 11. July (Srabon) 9. May (Joisto) 11. July (Srabon) 11. July (Srabon) 12. August 10. June (Asar) 12. August 12. August (Vadro) 11. July (Srabon) (Vadro) (Vadro) 13. September 12. August 13. September 13. September (Asshin) (Vadro) (Asshin) (Asshin) 14. October 13. September 14. October 14. October (Kartik) (Asshin) (Kartik) (Kartik) 15. November 14. October 15. November 15. November (Agrohayon) (Kartik) (Agrohayon) (Agrohayon) 16. December 15. November 16. December 16. December (Pous) (Agrohayon) (Pous) (Pous) 16. December (Pous) Section 5: LDDP Intervention (This section will apply only for the potential beneficiaries [farmers]. Although it is expected that the impact of the LDDP interventions will indirectly extend to other segments of the value chain, we could not capture their expectation regarding the LDDP in this survey setting.) Convey the following information to the respondent (The enumerator will brief overall LDDP interventions to the respondent): Bangladesh Livestock and Dairy Development Project (LDDP) is a World Bank-supported project that is being implemented by the Government of Bangladesh (GoB). The objective of this project is to create employment opportunities in the rural Bangladesh livestock sector, with a special focus on women, youth, and vulnerable groups. To facilitate the objectives, the project will provide support to livestock producers; micro, small, and medium enterprises (MSMEs); and service providers. The project will help establish producer organizations (POs) and will provide training on knowledge and skills development through farmer field schools that will cover not only production management but business planning, negotiation, product marketing, and information and communication technology (ICT) (to support financial management and use of Information Network for Animal Production and Health [INAPH] for production and market data). It will also facilitate inputs and marketing support to smallholder farmers. To improve climate-smart production practices, the project will finance to facilitate feed (production, storage, marketing, and ration balancing), breed (development, dissemination, and producer-based improvement programs), health (surveillance, vaccination, biosecurity, and control of production diseases through vaccination, for example, against foot and mouth disease, and deworming), housing, and manure management. Moreover, to increase producers’ link to profitable markets; improve the volume, quality, and safety of livestock products being marketed; and decrease transaction costs along the value chains, the project will support POs and MSMEs through productive partnerships (improved commercial relationship) between POs or/and MSMEs and large agribusiness buyers. For instance, dairy hubs will be one set of 62 productive partnerships supported under the project. Dairy hubs are investments made by agribusiness farms, which are based on a network of 20–25 village milk collection centers (VMCC) in a radius of about 15–25 km, that is, one center for every 1–2 villages. In partnership with a commercial dairy company, VMCCs feed milk into a hub owned and operated by the company. Supporting POs and entrepreneurs with VMCCs as part of a productive partnership can help tip the balance on greater MFD dairy hub investments by reducing supply risk as well as connecting more smallholders to higher-value commercial markets. Note: The research team will explain in detail the Bangladesh LDDP to the enumerators during training so that they can well explain the project to the respondent. S5Q1 S5Q2 S5Q3 S5Q4 S5Q5 S5Q6 S5Q7 S5Q8 S5Q9 What assistance has When did How What has What kind of assistance If the farm To maximize If the farm Do you the farm received the farm much been done do you (farm) expect receives assistance benefits, do receives expect from the LDDP so far? receive the with the from the LDDP in the from the LDDP in you think the LDDP support LDDP assistance farm cash COVID future? the future, LDDP should in the future, support to (This question will be from the has relief? according to you, provide what impact impact visible only for the LDDP? receive how effectively support only do you labor direct beneficiaries) d as (Select (Select multiple from will it support an to farmers expect in quality (KoBo will COVID multiple the drop-down list) increase of who are part farm labor (tasks show a cash from the production on the of a PO (hour) becoming calendar. relief? drop-down farm? demand? easier to From the list) perform or calendar (This less risky, the questio (This and so on)? enumerato n will question r has to be will be select the visible if visible if date, option 1 option 1 is month, is selected in and year of selected S5Q1) assistance in received) S5Q1) 1. Received cash as 1. Buy new 1. 1. Probably not 1. Yes 1. Labor 1. Yes, COVID relief livestock Training/demonstratio effective 2. No demand will slightly 2. Receive equipment 2. Improve n on livestock rearing 2. Somehow decrease 2. Yes, as COVID relief housing 2. effective 2. Labor moderately 3. Deworming conditions Training/demonstratio 3. Moderately demand will 3. Yes, campaign n on livestock housing effective highly 63 4. FMD vaccine for the and manure 4. Effective be the same 4. No, 5. Benefited livestock management 5. Very effective as before probably training/demonstrati 3. Repay 3. In-kind support for 3. Labor not on activities the improving livestock demand will 6. Other forms of previous housing and manure slightly support loan management increase 7. Did not receive 4. Spend on 4. Training on PO, 4. Labor anything yet (refer to livestock business planning, and demand will S5Q5) food and negotiation moderately veterinary 5. Training on product increase 5. Spend to marketing and the use 5. Labor bear other of on-farm ICT demand will operating 6. Feed support significantly costs (that 7. Breed support increase is, utility 8. bill) Medicine/Vaccination 6. Spend on support household 9. Support to buy new and livestock personal 10. Improved issues technology in 7. Improve production technology 11. Other livestock in services production S5Q10 S5Q11 S5Q12 S5Q13 S5Q14 S5Q15 S5Q16 Do you Do you On creating If you are selected by the LDDP to benefit from a Do you How could it If you have expect that expect that employment conditional cash/input voucher to buy 5 items from think it benefit any the LDDP the LDDP opportunities in the the following positive list, which 5 items would be would you/your suggestions support will support will overall livestock most useful for your farm, provided that you would benefit farm? or improve the lead to an sector, on a scale of 1 have to contribute 20% of the costs to have those? your farm? comments quality of the increase in to 5 (not effective to regarding job for your your/farm’s very effective) the LDDP farm? income? according to you how please let effective the LDDP me know. intervention will be? 64 1. Yes, slightly 1. Yes, slightly 1. Probably not 1. Secure steel milk pails 15. Improved animal 1. Yes, Open ended Open 2. Yes, 2. Yes, effective and cans feeding equipment slightly ended moderately moderately 2. Somehow effective 2. Mobile milking 16. Solar/biogas fueled 2. Yes, Please write 3. Yes, highly 3. Yes, highly 3. Moderately machines animal cooling system moderately the Please 4. No, 4. No, effective 3. Small biogas unit with 17. Larger biogas unit with 3. Yes, comments in write the probably not probably not 4. Effective burner electricity generation highly detail. Here comments 5. Very effective 4. Hay baling machines 18. California Mastitis Test 4. No, you can use in detail. 5. Rubber mats (CMT) kit and solution probably the Bangla Here you 6. Insect protective nets 19. Small-scale forage not language can use the 7. Spraying equipment chopper/silage machine Bangla and cans 20. Artificial insemination language 8. Water dispensers, 21. Pregnancy diagnostic improved watering (ultrasound machine) equipment 22. Servicing of biodigester 9. Teat dipping (kit and and associated equipment solution) to prevent 23. Farm-level mastitis manure/nutrient 10. Improved animal management plan shed (roof) 24. Forage seeds 11. Improved animal 25. Incubator machine for shed (concrete slab) poultry 12. Improved manure 26. Egg holder Kmart collection system 27. Chilling point for milk including pit preservation 13. Improved water 28. Cooling fan tanks (metal) 29. Other (please specify) 14. Food fermentation pot 65 Interview Details ID1 ID2 ID3 ID4 ID5 ID6 ID8 Survey Status Respondent Respondent Affiliation Respondent Enumerator Name Enumerator ID Enumerators Name with the Farm Mobile No. Comment 1. Complete 1. Owner/Proprietor 2. Incomplete 2. Partner 3. Rejected 3. Director 4. CEO/President/VP 5. Manager 6. Others 66 Checklist for KII Details of LDDP: The LDDP is being implemented by the GoB with the support of US$500 million from the World Bank to create employment opportunities in the rural Bangladesh livestock sector, with a special focus on women, youth, and vulnerable groups. The project aims to provide business development skills, training, inputs, marketing support, and financial services to 500,000 livestock producers, among which 50 percent are female. The broad objective of this project is to make a transformation from the informal to formal sector jobs in the livestock sector. The project will help establish POs and will increase producers’ link to profitable markets. This project aims to support POs and MSMEs through productive partnerships between POs or/and MSMEs and large agribusiness buyers. For instance, dairy hubs will be one set of productive partnerships supported under the project. Dairy hubs are investments made by agribusiness farms based on a network of 20–25 VMCCs in a radius of about 15–25 km, that is, one center for every 1– 2 villages. In partnership with a commercial dairy company, VMCCs feed milk into a hub, owned and operated by the company. Supporting POs and entrepreneurs with VMCCs as part of a productive partnership can help tip the balance on greater MFD dairy hub investments by reducing supply risk as well as connecting more smallholders to higher-value commercial markets. Study interests: SANEM, in collaboration with the World Bank, is conducting a study where the overall objective is to understand the scenario of the livestock sector in Bangladesh and an ex ante estimate of the job outcomes from the World Bank’s LDDP. Disclaimer: The identity of the informant will not be disclosed unless prior and proper consent is received from the informant. Information provided by the informant will be used solely for research purposes. [The SANEM team will ask for the informant’s consent to record this interview.] Key Areas of Discussion 1. What are the major products that your organization produces by utilizing inputs (beef/poultry/milk/eggs) from the livestock sector? - Did your organization export such products? If yes on average how much does it export in a year? What are the major destination countries of the exports, and what is the export share by country? 2. From where do you collect the inputs (beef/poultry/milk/eggs) and how are these collected? What criteria are to be fulfilled by the input (beef/poultry/milk/eggs) providers to supply those in this organization? - Which regions/districts do these inputs mostly come from, what is the regional share? 3. In general, what problems do your organization face during the collection of inputs (beef/poultry/milk/eggs) from different points of the country? How does your organization manage to solve those? 4. One of the objectives of the LDDP is to link livestock producers (cattle, dairy, and poultry) directly to the processing farms so that producers can reach the formal processing units without any hustle. What do you think about this initiative? Under the initiative, if your organization gets investment support to establish a productive partnership with a grassroots producer group, do you think it will be beneficial for your organization? If yes, how? Do you think such links will help generate employment in your organization? - In your understanding, how effectively the initiative could contribute to increasing the supply chain? - In your understanding, in which channel/mechanism the initiative will help increase the overall employment of the livestock sector? 5. At present, in your organization, how many workers are working in the department which supports/processes/utilizes livestock-related products, that is, meat, milk, and eggs? Among them, how many are females and how many are youth (ages 18–29)? Do you have any worker who belongs to an ethnic group? If yes, how many? - How many of the workers are temporary (%) - Do the workers (especially temporary) offer formal contracts and medical allowances? Do they use safety gear when they work? 6. What problems did the organization face during the lockdown of the COVID-19 pandemic? Did incidents like workers’ lay-off and closure of any processing plant happen during that time? 7. According to you, what are the major problems in the livestock sector supply chain of Bangladesh? What measures can be taken to improve the chain? What initiative should the government take to create more employment in this sector? 68 Annex III: SAM Multiplier Model Social Accounting Matrix (SAM) A SAM is an extension of the input-output table. The extension is made by clubbing other parts (actors) of the economy, that is, primary and secondary income distribution and institutions of economy. More specifically, in the input-output table, each horizontal row narrates how one industry’s total output is divided among all others production processes and final consumption, while each vertical column shows the combination of input used within one industry. A table of this type (Figure AIII.1) draws the dependence of an industry on the output of other industries. However, SAM is a square matrix that incorporates all the main circular flows (Figure AIII.2) within an economy in an organized and consistent classification system. The systematic database of a SAM explicitly incorporates various crucial transactions from the structure of production and the mapping of the household income distribution to the factorial income distribution, among others (Raihan 2011). Table AIII.1 presents the basic structure of a SAM. Figure AIII.1: Basic Structure of Input-Output Table Activity Final Demand Total Use A1 … … … A100 Cp Cg I Ex C1 .. .. Commodity .. Final Demand Technology matrix (100 x 100) C100 Compensation GDP (Expenditure Value- added Operating Surplus GDP (Income Approach) Approach) Indirect Taxes Import Total Supply Source: Khondker et al. 2020. Figure AIII.2: Circular Flow in an Economy Factor earnings (value-added) Domestic private savings Factor markets Indirect taxes Direct taxes Fiscal surplus Productive Households Government Investment activities Intermediate demand Social transfers Commodity Sales income markets Consumption Recurrent Investment spending (C) spending (G) demand (I) Exports (E) Imports (M) Rest of world Source: Breisinger, Thomas, and Thurlow 2009. 69 The input-output part of SAM captures production links across sectors. The links are determined by sectors’ production technologies and can be segregated into backward and forward links. The stronger the links are, the large the multiplier is. The backward links are backed by the additional inputs demand made by industries to supply additional goods and services. The more input-intensive a sector’s production technology is, the stronger its backward links are. On the other hand, forward links account for the increased input supply to upstream industries. Thus, the more important a sector is for upstream industries, the stronger its forward links will be. Here, it should be mentioned that the livestock sector in Bangladesh has strong backward and forward links. Table AIII.1: Basic Structure of a SAM Expenditure columns Activities Commodity Factors Households Government Investment Rest of world Total C1 C2 C3 C4 C5 C6 C7 Activities Domestic Activity R1 supply Income Commodities Intermediate Consumption Recurrent Investment Export Total R2 demand spending (C) spending (G) demand (I) earnings (E) demand Factors Total factor Value added R3 income Factor Total Income rows Households Social Foreign payments to household R4 transfers remittances households income Government Sales taxes and Direct Foreign grants Government R5 import tariffs taxes and loans income Current Savings Private Fiscal Total account R6 savings surplus savings balance Foreign Rest of world Import exchange R7 payments (M) outflow Total Foreign Total factor Total household Government Total Gross output Total supply investment exchange spending spending expenditure spending inflow SAM multiplier model The move from a SAM data framework to a SAM multiplier model requires decomposing the SAM accounts into ‘exogenous’ and ‘endogenous’. The accounts meant to be used as policy instruments, that is, government expenditure, investment, and exports, are generally considered exogenous, while the accounts considered objectives or targets, that is, output, commodity demand, factor return, and household income, must be made endogenous ( 70 Figure AIII.3). When any shock (injection) is applied to the exogenous accounts of the SAM, the effect is transmitted among the endogenous accounts through the interdependent SAM system. This system implies that the production and the income of factors and households are formed through a multiplier process from exogenous injections into the economy. 71 Figure AIII.3: SAM Multiplier Model Specification Activity Factors Institution Total Use A1 … … … A100 LAB CAP HH GoV SAV RoW C1 E E Commodity .. .. Endogenous .. [Multiplier) Exogenous C100 Institution Factor Labour s Capital Household Government) E E Savings Leakage Other Rest of the world Total Supply The multiplicative impact of any exogenous injection into the economy (or any specific sector) comes through different channels, that is, direct effects, indirect effects, and induced effects (Figure AIII.4). As such, the SAM methodology helps estimate direct, indirect, and induced effects from any intervention. Figure AIII.4: Process of Multiplier Impact Here, the multiplier process is fabricated based on the assumption that an endogenous account spends any new exogenous injection in the same proportions as shown on the matrix of average propensity to spend (APS). By dividing each call by the sum of its corresponding column, the elements of the APS matrix are calculated. To have a clear understanding let us consider the general SAM modular structure as shown in Table AIII.2. Table AIII.2: General SAM Modular Structure 1a-PA 1b-CM 2-FP 3a-HH-OI 3b-Gov 4-KHH-OI 5-ROW TDD 1a PA T1a, 1b 0 Y1a 1b CM T1b, 1a T1b, 3a T1b, 3b T1b, 4 T1b,5 Y1b 2 FP T2, 1a T2, 5 Y2 3a HH-OI T3a, 2 T3a, 3a T3a, 3b T2, 5 Y3 3b Gov T3b, 1a T3b, 1b T3b, 3a T3b, 3b T3a, 5 4 KHH-OI T4, 1a T4, 3 T4, 5 Y4 5 ROW T5, 1b T5, 2 T5, 3a T5, 3b T5, 4 0 Y5 TSS E1a E1b E2 E3a E3b E4 E5 Note: Where, Endogenous: 1a-PA = Production Activities and 1b-CM = Commodities; 2-FP = Factors of Production; 3a-HH-OI = Households and Other Institutions (excluding Government); 72 Where Exogenous: 3b-Gov= Government; 4 KHH-OI = Capital Account of Households and Other Institutions (including government); 5-ROW = Rest of the World (current and capital account). Blank entries indicate that there are no transactions by definition. From the SAM, it is easy to come up with the APS matrix (generally known as the coefficient matrix). SAM coefficients (Aij) are derived from payments flows by endogenous accounts to themselves (Tij) and other endogenous accounts to the corresponding outlay (Ei = Yj); similarly, the leak coefficients (Bij) are derived from flows reflecting payments from endogenous accounts to exogenous accounts (Table AIII.3). Table AIII.3: Coefficient Matrices and Vectors of the SAM Model Account 1a-PA 1b-CM 2-FP 3a-HH&OI 3b … 5 EXO Income A1a,1b 1a-PA X1a Y1a = T1a,1b/ Y1b A1b,1a A1b,3a 1b-CM X1b Y1b = T1b,1a/ Y1a = T1b,3a/ Y3a A2,1a 2-FP X2 Y2 = T2,1a/ Y1a A3a,2 A3a,3a 3a-HH&OI X3a Y3a = T3a,2/ Y2 = T3a,3a/ Y3a B1a B1b B2 B3a 3b … 5 Leaks = L1a / Y1a = L1b / Y1b = L2/ Y2 = L3a / Y3a Expenditure E1a = Y1a E1b = Y1b E2 = Y 2 E3 = Y3a The multiplier analysis using the SAM framework helps us understand the links between the different sectors and the institutional agents at work within the economy. Accounting multipliers have been calculated according to the standard formula for accounting (impact) multipliers, as follows: = + = ( − )−1 = , where: Y is a vector of incomes of endogenous variables X is a vector of expenditures of exogenous variables A is the matrix of average expenditure propensities for endogenous accounts = (I − A)−1 is a matrix of aggregate accounting multipliers (generalized Leontief inverse). Variation in any one of the exogenous accounts (that is, in this case ΔX) will produce total (economy-wide) impacts (ΔY) of endogenous entries through the multipliers (Ma ). Thus, Δ = Ma ∗ Δ. Here, ΔY captures the economy-wide impacts. The impact can be obtained through the four endogenous accounts: (a) gross output, (b) commodity demand, (c) factor returns, and (d) household. 73 Table AIII.4: Description of the Endogenous and Exogenous Accounts and Multiplier Effects Endogenous (y) Exogenous (x) The activity (gross output multipliers) indicates the total effect on the sectoral gross output of a unit income increase in a given account i in the SAM and is obtained through the association with the commodity production activity account i. The consumption commodity multipliers indicate the total effect Intervention through activities (x = i + g + on the sectoral commodity output of a unit income increase in a e), where I = GFC + ST (GFCF) given account i in the SAM and is obtained by adding the Exports (e) associated commodity elements in the matrix along the column for account i. Government Expenditure (g) Investment Demand (i) Inventory Demand (i) The value added or GDP multiplier gives the total increase in GDP resulting from the same unit income injection and is derived by summing up the factor payment elements along account i’s column. The household income multiplier shows the total effect on Intervention through households household and enterprise income and is obtained by adding the (x = r + gt + ct), where elements for the household groups along the account i column. Remittance (r) Government Transfers (gt) Corporation Transfers (ct) 74 Most Recent Jobs Working Papers: 73. Does Agricultural Intensification Pay? (2023) Ghislain Aihounton and Luc Christiaensen. 72. Cost-Effectiveness of Jobs Projects in Conflict and Forced Displacement Contexts—Annexes. (2022) Virginia Barberis, Laura Brouwer, Jan Von Der Goltz, Timothy Hobden, Mira Saidi, Kirsten Schuettler and Karin Seyfert. 72. 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