Cost-Benefit Analysis on Shrimp Aquaculture versus Agriculture and other Natural Resource Management (NRM)

Transcription

1 Cost-Benefit Analysis on Shrimp Aquaculture versus Agriculture and other Natural Resource Management (NRM) Final Report Project: TA (REG): Mainstreaming Environment for Poverty Reduction A study of Community Based Adaptation in Vulnerable Coastal Areas of Bangladesh subproject under the Mainstreaming Environment for Poverty Reduction project of ADB Mohammed Ziaul Haider, Ph.D Associate Professor, Economics Discipline, Khulna University, Khulna 9208, Bangladesh Date: April 08, 2012

2 Contents 1. Introduction Methodology CBA for SW Region of Bangladesh Shrimp and Prawn (SP) Production Shrimp and Prawn (SP) Production Cost Indirect Cost Costs and Benefits Additional Exercises on CBA Secondary Data based CBA Shrimp and Prawn (SP) Cultivated Area Shrimp and Prawn (SP) Production Shrimp and Prawn (SP) Production Cost Indirect Cost Cost of foregone Rice Loss in Livestock Sector Loss in Fruit Sector Loss in Vegetable Loss in Tree Employment Loss Health Cost Indirect Cost Cost-Benefit Analysis (CBA) Primary Data based CBA Primary Data Primary Data based CBA of Shrimp and Prawn (SP) Aquaculture Conclusion and Recommendation References Annex Survey Questionnaire on CBA... 32

3 List of Tables Table 1: Cost Items for CBA on Shrimp and Prawn (SP)... 2 Table 2: Benefit Items for CBA on SP Aquaculture... 3 Table 3: Overview on the SW Region of Bangladesh... 4 Table 4: Shrimp and Prawn (SP) Cultivated Area during Table 5: Shrimp and Prawn (SP) Cultivated Area in Various Years... 7 Table 6: Shrimp and Prawn (SP) Cultivated Area... 8 Table 7: Shrimp and Prawn (SP) Production in SW Region... 9 Table 8: Shrimp and Prawn (SP) Production Cost in SW Region Table 9: Loss in Livestock Sector for Shrimp Farming in SW Region Table 10: Loss in Fruit Sector for Shrimp Farming in SW Region Table 11: Cost-Benefit Analysis of Shrimp and Prawn (SP) Aquaculture in SW Region Table 12: Cost-Benefit Analysis of Shrimp and Prawn (SP) Aquaculture in Bangladesh Table 13: CBA of Shrimp and Prawn (SP) Aquaculture in Four Villages Table 14: Primary Data Table 15: CBA of Shrimp and Prawn (SP) Aquaculture List of Figures Figure 1: Approaches for Quantifying Shrimp and Prawn (SP) Production Figure 2: Approaches for Quantifying Shrimp and Prawn (SP) Production Cost List of Maps Map 1: Map of Study Area Map 2: GIS Social Map of Kalikapur Map 3: GIS Social Map of Ramnagar Map 4: GIS Social Map of Borokupot Map 5: GIS Social Map of Boyersingh... 25

4 Abstract This study addresses a cost-benefit analysis (CBA) of shrimp aquaculture versus other agricultural practices in the south-west (SW) region of Bangladesh. The CBA analysis quantifies the direct as well as indirect costs and benefits. This study is based on the secondary data. A small scale of primary data is also collected from two sample villages randomly selected from four study sites in the SW region and repeats the CBA exercises to cross-check the findings. This study finds a higher cost-benefit (CB) ratio ranging from 0.77 to 0.92 for secondary dataset and from 0.58 to 0.80 for primary dataset due to wider indirect component coverage. The ratio increases to when the family labour is counted as cost component. It further increases for incorporating additional fuel cost, drinking water cost and such other indirect costs originated from shrimp culture. In contrast, the CB ratio remains within when only the direct cost and benefit components are considered and this is what the farmers usually observe and consider in farm-level decision making. Therefore, the farming decisions undermine the actual scenario from society and environment perspectives. This study suggests for taking initiatives to disseminate the actual cost-benefit scenario among the farmers/fishermen. The dilemma of higher visible economic return vs. negative social and environmental consequences of shrimp farming needs to be handled softly through education and information dissemination, establishing a clear set of rules and regulations and finally let the related parties to take the final farming decisions. Such initiatives might help the farmers/fisherman and resource users to understand the pros and cons of farming and resource management, to realize all sorts of related costs and benefits, to take proper decisions, to monitor the shrimp sector, to compensate for the negative consequences generated from shrimp farming and to bring the shrimp sector under systematic guidelines. Finally, all these will facilitate sustainable and good practices for farmers/fisherman and resource users. Key Words: Cost-Benefit Analysis, Shrimp Aquaculture, Agriculture, South-west Region of Bangladesh

5 Cost-Benefit Analysis on Shrimp Aquaculture versus Agriculture and other Natural Resource Management (NRM) 1. Introduction Shrimp aquaculture is an important economic activity in Bangladesh (UNEP, 1999). It contributes in poverty alleviation, employment creation, community development and foreign exchange earnings for the country (Huntington, 2003; Masum, 2008; and PRICE, 2010). Bagda (P. monodon or shrimp) and golda (M. rosenbergii or prawn) are the two main varieties of shrimp that are cultured in Bangladesh (DTS, 2006; Huntington, 2003; and Khatun, 2004). Bagda is cultivated in brackish water of the coastal regions, while golda is cultivated in sweat water in any region of the country. Since mid 1980s, the shrimp sector has been expanding rapidly in Bangladesh (Alauddin & Hamid, 1999; DTS, 2006; and Gammage et al., 2005). The introduction of shrimp culture has changed the socio-economic, institutional, ecological and environmental conditions of the coastal regions of Bangladesh (Barmon et al., 2011). A number of studies related to shrimp culture focus on economic returns of shrimp farming (Islam, 2008; and Islam et al., 2005), agrarian change (Ito, 2002 & 2004), impact on soil quality and ecology (Ali, 2004 & 2006) and efficiency (Rahman et al., 2011; Rashid & Chen, 2002; and Barmon et al., 2011). The available literatures demonstrate that the net income from shrimp farming is several times higher than that from rice production (ATDP II, 2005; Bhattacharya & Ninan, 2009; Selvam & Ramaswamy, 2001; and Reddy et al., 2004). However, such calculations don t account for the adverse social and environmental effects of shrimp farming, such as, the destruction of mangroves, conversion of rice fields into shrimp ponds, salinization of agricultural lands, deterioration of groundwater quality, drinking water crisis, health hazards, reduction in production of other agricultural crops and livestock resources in the surrounding areas (Pillay, 1992; Primavera, 1991; and Rajalakshmi, 2002). Some research works have focused on Cost-Benefit Analysis (CBA) and environmental & ecological impacts of shrimp farming (Abedin & Kabir, 1999; Abedin et al., 1997; Alim et al., 1998; Asaduzamman et al., 1988; Bhattacharya, et al., 1999; Habib, 1998; Nijera Kori, 1996; Nabi et al., 1999; Rahman et al., 1995; Datta, 2001; and Sobhan, 1997). Most of the available CBA studies on shrimp farming have captured only the direct costs and benefits (Abedin & Kabir, 1999; Abedin et al., 1997; Ahmed et al., 2008; ATDP II, 2005; DTS, 2006; Haque, 2004; and Hasanuzzaman et al., 2011), while only a few studies have tried to capture some of the indirect costs (Bhattacharya & Ninan, 2009; Bhattacharya et al., 1999; Bundell & Maybin 1996; and UNEP, 1999). However, a complete study covering all of the direct and indirect costs and benefits of shrimp farming is absent in the literatures. Abedin & Kabir (1999); Abedin et al. (1997); Ahmed et al. (2008); Bhattacharya et al. (1999); Haque (2004); and Hasanuzzaman et al. (2011) are the available studies that tried to address CBA on shrimp sector from Bangladesh perspective. Most of these studies consider only direct costs and benefits ignoring indirect counterparts. Bhattacharya et al. (1999) is the only study that attempts to perform CBA on shrimp sector of Bangladesh covering some indirect cost items in addition to direct costs and benefits. However, that study uses data of 1990s. Bangladesh specific CBA studies with recent information is absent in the literature. Therefore, this study attempts to conduct a CBA on shrimp sector of Bangladesh to reflect the latest scenario. It 1

6 attempts to calculate some quantifiable indirect costs in addition to the direct costs and benefits of shrimp farming. Methodology, data collection and analysis, conclusion and recommendation are the chronological steps that are discussed in the sub-sequent sections of this paper. 2. Methodology This study is mainly based on secondary data where both shrimp and prawn are often discussed together. A separate discussion and published dataset on shrimp sector alone are hardly available. However, the CBA requires quantitative data on various indicators/variables. Therefore, this study considers both shrimp and prawn (SP) aquaculture together. The identification and quantification of all costs and benefits arising from SP aquaculture is difficult because of lack of data or comprehensive methodology (Bhattacharya et al., 1999). Keeping this limitation in mind, identifying the cost and benefit items is the prime task of this study. Table 1 and 2 list the cost and benefit items associated with SP aquaculture, respectively. Table 1: Cost Items for CBA on Shrimp and Prawn (SP) Area Observed Costs Method of estimation Production cost Land degradation Employment Human health Fuel wood Drinking Water Destruction of mangrove Biodiversity Direct production costs associated with SP farming Impact on agriculture Impact on agriculture (due to deterioration of soil quality) Impact on livestock Impact on fruit sector Impact on vegetable sector Impact on tree Lost employment for introducing SP aquaculture Mortality cost for SP induced water borne diseases Morbidity cost (Treatment cost) for SP induced water borne diseases Morbidity cost (Wage lost) for SP induced water borne diseases Cost generated from fuel wood crisis Cost generated from drinking water crisis Damage of wood and non-wood products Loss of flora and fauna; medicinal plant Calculation based on available secondary data Calculation based on available secondary data (Net loss for foregone rice) Survey / Secondary data, Laboratory test of soil Calculation based on available secondary data (quantity of lost livestock * unit price) Calculation based on available secondary data (quantity of lost fruits * unit price) Calculation based on available secondary data (quantity of lost vegetable * unit price) Calculation based on available secondary data (quantity of lost tree * unit price) Calculation based on available secondary data Incidence of death and Statistical value of life Incidence of sickness and cost of treatment Incidence of sickness and lost wages Additional cost of fuel wood (in terms of time and money spent) Additional cost for drinking water (in terms of time and money spent) Value of forest product and area of mangrove lost Survey / Secondary data Estimated in this study Yes Yes No Yes Yes Yes Yes Yes No Yes Yes Partial Partial No No 2

7 Tourism Loss in revenue earning due to loss in mangrove forests Survey / Secondary data Family dislocation, violence, school Social impact drop-out, migration, harassment, changes in land ownership and Survey / Secondary data management pattern, etc. Source: Author s Compilation based on Bhattacharya et al. (1999). Table 2: Benefit Items for CBA on SP Aquaculture Area Observed Benefits Method of Estimation Estimated in this study Income Revenue earnings from SP Calculation based on available aquaculture secondary data Yes Employment Employment increase in linkage sectors Survey / Secondary data No Linkage sectors Revenue earnings and employment generation Survey / Secondary data No Human capital Entrepreneurship development Survey / Secondary data No Women empowerment, Social impact Technology and knowledge Survey / Secondary data No spillover, Network expansion, etc. Source: Author s Compilation based on Bhattacharya et al. (1999). All of the items noted in Table 1 and 2 are not easily quantifiable. The items that are considered in this study for doing CBA are marked Yes in column 4 of the tables. The general functional form of CBA is illustrated in Equation 1. = n DC n i=1 it+ j=1 IC jt Direct Benefit (DB t ) + Indirect Benifit (IB t ) n k=1 DB kt + n l=1 IB lt Equation 1: CB t = Direct Cost (DC t) + Indirect Cost (IC t ) Here, CB = Cost Benefit Ratio, DC = Direct Cost, IC = Indirect Cost, DB = Direct Benefit, IB = Indirect Benefit, t = year 2011 n = 1 for DC (Production cost), n = 7 for IC (loss in: rice, livestock, fruit, vegetable, tree, employment; and health cost), n = 1 for DB (Revenue earnings), n = 0 for IB. No No This study primarily focuses on the south-west (SW) region of Bangladesh (Map 1). It uses secondary data for CBA of SP aquaculture in the region. Later, it tries to extend the result and predict for the whole country and also for some selected study sites of SW region. This study also collects some primary data from the SW region for cross-checking the results obtained from secondary sources. The usual CBA studies consider the stream of benefits and costs of an activity/project over its life time/a certain time period and finally coverts these benefits and costs in present value to calculate CB ratio. However, the shrimp farming in Bangladesh is not a time-bound process. Once shrimp culture is started, it continues. For the very reason, it is difficult to confine the CBA study on shrimp sector for a certain time period using present value criteria. On the other hand, the shrimp farming is a regular phenomenon which repeats over the years/seasons. 3

8 Such regularity and time-boundless features facilitate the author to confine the CBA of shrimp aquaculture for any specific year/season. More specifically, this study considers year 2011 while discussing CBA of shrimp aquaculture. The available price and monetary data of various years are converted to present value (PV) of year 2011 using inflation data. Moreover, this study uses 1US$=80Tk exchange rate to convert the PV at Tk in US$. This study considers rice cultivation as the best available alternate of shrimp culture in Bangladesh. It also considers some other agricultural and natural resources, such as, fruit, vegetable, tree, livestock, mangrove, soil, water, etc. while discussing the CBA of shrimp aquaculture. This study follows with and without approach in exercising CBA. After completing the CBA exercises, this study attempts to derive some policy instruments regarding sustainable and good practices for farmers/fisherman and resource users based on CBA results, survey findings, field visits and literatures. 2.1 CBA for SW Region of Bangladesh The Khulna, Satkhira and Bagerhat districts are defined as the SW region of Bangladesh in this study. The basic socio-economic and demographic features of the region are listed in Table 3. Table 3: Overview on the SW Region of Bangladesh Location SW Share of Khulna Bagerhat Satkhira Bangladesh Item Region SW Household (No.) 499, , ,745 1,213,574 25,490, % Population (No.) 2,378,971 1,549,031 1,864,704 5,792, ,355, % Area (sq. km) 4,395 3,959 3,858 12, , % Literacy rate (%) Source: BBS (2011) and Author s Compilation. The methodology of conducting CBA of shrimp and prawn aquaculture for SW region based on secondary data is briefly described in the subsequent sections Shrimp and Prawn (SP) Production To quantify the production data of SP into monetary terms is the main task of CBA for quantifying the direct benefits. Six distinctive approaches are used in this study to quantify SP production data. The first approach uses the total SP production data (in kg) and converts it into million US$ using inflation adjusted price data and conversion factor (1US$=80Tk). The second approach uses total SP cultivated area (in ha) and per unit production (in kg/ha) data and converts it into million US$ using inflation adjusted price data and conversion factor (1US$=80Tk). The third and fourth approaches use total SP cultivated area (in ha) and divide it under bagda and galda categories using available statistics. Then, the third approach uses per unit production (in kg/ha) data and the fourth approach uses per unit production (in Tk/ha) data. Finally, in both the third and fourth approaches, the calculated results are converted into million US$ using inflation adjusted price data and conversion factor (1US$=80Tk). 4

9 The fifth and sixth approaches use total cultivated area (in ha) and divide it under extensive, semi-intensive and intensive cultivation methods of SP farming using available statistics. Then, the fifth approach uses per unit production (in kg/ha) data and the sixth approach uses per unit production (in Tk/ha) data. Finally, in both the fifth and sixth approaches, the calculated results are converted into million US$ using inflation adjusted price data and conversion factor (1US$=80Tk). Figure 1 briefly describes these six approaches. There is no consensus yet in the literatures about which method suits best for calculating monetary value of produced SP. Moreover, the calculated results vary significantly from each other. Therefore, this study considers all of these approaches and finally calculates the simple average of these six approaches to confine into a specific number. This study also reports the corresponding minimum and maximum values to get at least a range value of the SP production, if the calculated average value fails to represent the true scenario Shrimp and Prawn (SP) Production Cost Two approaches are used in this study to quantify the production cost of SP farming in the SW region of Bangladesh. Both the approaches precede production method-wise considering extensive, semi-intensive and intensive farming of SP. The first one considers Tk/kg while the second one considers Tk/ha in estimating production cost data. Figure 2 briefly describes these two approaches. The calculated results vary significantly from each other. Therefore, this study considers both the approaches and finally calculates the simple average of the two approaches to confine into a specific number. This study also reports the corresponding minimum and maximum values to get at least a range value of the SP production cost Indirect Cost This study attempts to calculate indirect cost of SP farming. It considers seven possible sources: (A) Cost of foregone rice, (B) Loss in livestock sector, (C) Loss in fruit sector, (D) Loss in vegetable, (E) Loss in tree, (F) Employment lost and (G) Health cost. SP cultivation either drives out rice or at least reduces rice production. Therefore, the net loss generated from initiating SP farming is treated as indirect cost of SP. Moreover, SP cultivation negatively influences livestock, fruit, vegetable and tree in the area. The differences in rice, livestock, fruit, vegetable and tree productions between non-sp and SP cultivated area are treated as losses. The inflation adjusted price data are used to express these losses in monetary terms. The difference between employment level in non-sp and SP is treated as employment loss for introduction of SP aquaculture. After making some adjustments for employment generated in linkage sectors of SP, wage data is used to quantify the remaining loss in monetary terms. SP farming exerts health cost on the people living in the surrounding area. It spreads water borne diseases (WBD) among the people. The morbidity information, number of people living in the region, frequency of WBD, wage rate, work absenteeism, etc. are used to calculate treatment cost and wage lost, and finally adding these two gives the health cost of SP farming. 5

10 2.1.4 Costs and Benefits Once all of the considered cost and benefit items are quantified, this study attempts to calculate the cost-benefit (CB) ratio using Equation 1. The higher the ratio, the more costly it is to continue and expand SP farming from society s viewpoint. If the ratio is greater than one, it implies that corresponding cost is higher than benefits! 2.2 Additional Exercises on CBA After calculating the costs and benefits of SP farming for SW region, this study attempts to do the similar exercise for Bangladesh. It considers Chittagong and Cox s bazaar districts in addition to the SW region for discussing from Bangladesh perspective. The considered five districts (Khulna, Satkhira, Bagerhat, Chittagong and Cox s bazaar) cover more than 90% of total SP producing areas in the country. The lost mangrove is considered as an indirect cost item in addition to all other cost items considered for SW region in the country-level CBA exercise. It just plugs-in the country level information to the exercise done for SW region and follows all the procedures noted in section 2.1 of this report. Later, it considers four selected villages located in the SW region of Bangladesh and repeat the similar exercise. These four villages (Ramnagar, Borokupot, Boyersingh and Kalikapur villages of Satkhira district) are the study sites for the Community Based Adaptation in Vulnerable Coastal Areas of Bangladesh sub-project under the Mainstreaming Environment for Poverty Reduction project of ADB. These four villages are selected as study sites of the project for getting information on agriculture and aquaculture adaptation, housing, drinking water supply and clean energy source (PAB, 2011). All of the noted country, region and village level exercises are solely based on secondary data from various sources. Therefore, this study attempts to collect some primary data from the farmers directly. It randomly picks-up two villages (Boyersingh and Kalikapur) from the previously described four study sites. It collects 54 randomly selected farm-level primary data from 1,452 households using a structured questionnaire from these two villages in February Household level socio-economic and demographic information, production and cost of shrimp and rice farming, farm characteristics, wage level, price and such many other issues are covered in that questionnaire. The collected primary data is also plugged-in to the methods described for country, region and village level secondary data based exercises for cross checking. Finally, some policy recommendations are made for sustainable SP farming in the region and country. 3. Secondary Data based CBA The CBA on SP aquaculture for SW region based on secondary data requires accumulating data on SP cultivated area, changes in SP cultivated area over the time period, share of SW region in total SP cultivated area of Bangladesh, per unit production and price. SP type-wise and production method-wise information of some of the above listed indicators is also needed. The production cost; loss in: rice, fruit, vegetable, tree, livestock, employment; and health cost are some other notable issues for which data are needed. The sub-sequent sections briefly describe the raised issues sequentially. 6

11 3.1 Shrimp and Prawn (SP) Cultivated Area Bangladesh Bureau of Statistics (BBS) and Department of Fisheries (DOF) are the available secondary sources for getting data on SP cultivated area in Bangladesh. According to BBS and DOF database, SP cultivated area is 0.16 and 0.22 million ha in for SW region and Bangladesh, respectively with yearly average of 0.14 and 0.18 million ha for the region and country during the period (Table 4). Table 4: Shrimp and Prawn (SP) Cultivated Area during Region SP Cultivated Area (ha) Percent Year Khulna Bagerhat Satkhira SW region Bangladesh Share of SW ,551 47,710 29, , , % ,921 59,424 52, , , % Yearly Average 40,736 53,567 40, , , % Yearly Change 6.46% 2.47% 6.56% 4.86% 4.92% - Source: BBS (2007 and 2011) and DOF (2011). However, a mismatch is observed among the data regarding SP cultivated area reported in some other literatures with this dataset. Most of the available literatures reported country level data and the reporting year varies significantly across the studies (Table 5). Therefore, this study attempts to generate a representative dataset on SP cultivated area using all of the available information. Nupur (2010) states that about 80% of the shrimp farming areas are in the south-west region of Bangladesh, while the rest are in the south-east part of the country. The BBS dataset provides more specific information: the SW region comprises 75.35% of total SP cultivated area of the country during Some available literatures state that the shrimp cultivating area of Bangladesh grows at around 10-20% per annum (Ahmed et al., 2008; Huntington, 2003; Khatun, 2004; and Williams & Khan, 2001). However, Khatun (2004) talks only about bagda, Ahmed et al. (2008) talk only about galda and Huntington (2003) and Williams & Khan (2001) talk about both bagda and golda. All of these growth related information seem to be approximate figures. Moreover, none of them describe the growth for SW region. In contrast, the BBS dataset reports 4.86% and 4.92% annual compound growth rate of SP cultivated area for the SW region and Bangladesh, respectively during Therefore, this study proceeds with this BBS information regarding share of SW region and growth for estimating the SP cultivated area for SW region and Bangladesh over the time period. Table 5: Shrimp and Prawn (SP) Cultivated Area in Various Years Serial No. Source SP Cultivated Area (ha) Reporting Year SW region Bangladesh 1 MPO (1986) ,000 2 Alauddin & Hamid (1999) ,000 3 Rosenberry (1995) ,000 4 DOF (1994) ,000 5 Barraclough & Finger-Stich (1996) ,000 6 NACA (2002) ,000 7 Raux & Bailly (2002) ,000 8 DTS (2006) ,071 9 Sarwar (2005) ,900-7

12 Serial SP Cultivated Area (ha) Source No. Reporting Year SW region Bangladesh 10 Khatun (2004) , ATDP II (2005) , Nupur (2010) ,887 Source: Author s compilation based on Alauddin & Hamid (1999); ATDP II (2005); Barraclough & Finger-Stich (1996); DOF (1994); DTS (2006); Khatun (2004); MPO (1986); Nupur (2010); Raux & Bailly (2002); Rosenberry (1995); Sarwar (2005); and NACA (2002). Table 6: Shrimp and Prawn (SP) Cultivated Area SW Region (ha) Bangladesh (ha) Year Minimum Average Maximum Minimum Average Maximum ,320 44,505 51,961 44,150 58,105 68, ,305 56,197 65,873 56,147 73,893 86, ,800 71,259 83,511 71,404 93, , ,419 90, ,937 90, , , , , , , , , , , , , , , , , , , , , , , , , , ,417 Source: Author s compilation based on Ahmed et al. (2008); Alauddin & Hamid (1999); ATDP II (2005); Barraclough & Finger-Stich (1996); BBS (2007 and 2011); DOF (1994 and 2011); DTS (2006); Huntington (2003); Khatun (2004); MPO (1986); NACA (2002); Nupur (2010); Raux & Bailly (2002); Rosenberry (1995); Sarwar (2005); and Williams & Khan (2001). Based on the BBS dataset regarding SW region s share and expansion of SP cultivated area, this study attempts to extrapolate the information of Table 5 for both SW region and Bangladesh during period 1980 to It assumes that the share and growth information is true for the whole period. After extrapolating data of each of the reported studies in Table 5 for both SW region and Bangladesh separately for the period , it calculates year-wise simple averages to confine into a specific number for each year. Side-by-side, this study reports the year-wise minimum and maximum values to get at least a range value of the SP cultivated area, if the calculated average value fails to represent the true scenario. The calculated results are reported in Table A1 of Annex and Table 6. According to the extrapolation based calculations, the calculated SP cultivation area in Bangladesh is 0.26 million ha with 0.20 million ha minimum and 0.30 million ha maximum values for year Similarly, the calculated SP cultivation area in the SW region of Bangladesh is 0.19 million ha with 0.15 million ha minimum and 0.23 million ha maximum values for year This study performs CBA assuming these calculations valid. 3.2 Shrimp and Prawn (SP) Production This study uses six different approaches to quantify the production of SP. The available information regarding production data is not well organized and mismatches are found for the same variable cited in different sources. For example, some studies report total production in kg per year while some others report production in kg per ha. Some studies consider both bagda and golda together, while some others consider these two separately in reporting production data. Moreover, some studies report production data on the basis of production methods, such as, extensive, semi-intensive and intensive farming approaches. In reporting shrimp type-wise or method-wise production data, again, some sources report production in kg per ha while some others report Tk per ha. Consequently, the logical calculation from the above cited methods 8

13 generate divergent results and there is no consensus yet about which one best fits to the real scenario. Therefore, this study considers six different approaches (Table A2-A7) and takes a simple average to quantify the production data of SP production. The inflation adjustments are made to convert the results in present value of 2011, if necessary. The corresponding minimum and maximum values are also reported to get at least a range value of the SP production, if the calculated average value fails to represent the true scenario (Table 7). The minimum and maximum values of SP production in the SW region of Bangladesh are 155 and 667 million US$, respectively with an average value of 369 million US$ for year This is the calculated direct benefit of shrimp farming for the SW region of Bangladesh in year Table 7: Shrimp and Prawn (SP) Production in SW Region Approach Information used Production in SW Region (Million US$ in 2011) Minimum Average Maximum 1 Kg/year and Tk/kg Kg/ha/year and Tk/kg Shrimp type-wise kg/ha/year and Tk./kg Shrimp type-wise kg/ha/year and Tk/ha Production method-wise kg/ha/year and Tk/kg Production method-wise kg/ha/year and Tk/ha Average Source: Author s compilation based on Aftabuzzaman (2004); Ahmed et al. (2008); Alauddin & Hamid (1999); ATDP II (2005); Barraclough & Finger-Stich (1996); BBS (2007 & 2011); Bhattacharya et al. (1999); DOF (1994); DTS (2006); Gammage et al. (2005); Haque (2004); Hasanuzzaman et al. (2011); Huntington (2003); Khatun (2004); Mazid (1994); MPO (1986); Muir (2003); NACA (2002); Nupur (2010); Paul & Vogl (2011); Rahman (1999); Raux & Bailly (2002); Rosenberry (1995); Sarwar (2005); and Williams & Khan (2001). 3.3 Shrimp and Prawn (SP) Production Cost Two distinctive ways are used for deriving production cost of SP farming. Both the approaches precede production method-wise considering extensive, semi-intensive and intensive farming methods of SP. The first approach (Table A8) considers Tk/kg while the second one (Table A9) considers Tk/ha in estimating production cost data. For both the approaches, the calculated SP cultivated area (ha) of SW region is divided under extensive, semi-intensive and intensive methods using the corresponding shares reported in Paul & Vogl (2011) and NACA (2002). The production rate (kg/ha/year) for different SP cultivation methods are calculated using SP production related information as reported in Bhattacharya et al. (1999), Gammage et al. (2005), Haque (2004), Mazid (1994), Rosenberry (1995) and DTS (2006). Similarly, the corresponding cost data (Tk/kg) for different SP production methods are calculated using SP production cost related information as reported in Gammage et al. (2005) and DTS (2006). Finally, the calculated figures for three methods are added to get the SP production cost (in million US$/year) of SW region from the first approach. Per unit SP production cost (Tk/ha/year) data for different SP cultivation methods are procured from Gammage et al. (2005), Haque (2004) and DTS (2006) for the second approach. Like the first approach, the calculated figures for three SP cultivation methods are added to get the SP production cost (in million US$/year) of SW region from the second approach. 9

14 The inflation adjustments are made to convert the results in present value of 2011, if necessary. The results obtained from the two approaches are reported in Table 8. As the average as well as upper and lower-bound production cost values differ substantially between the two approaches, this study considers the simple average of these two approaches. The calculated average cost indicates the direct production cost of SP farming. In addition to average, it keeps the minimum and maximum values side-by-side. According to Table 8, the direct cost of SP farming for SW region is 231 million US$ with a range of million US$. Table 8: Shrimp and Prawn (SP) Production Cost in SW Region Approach Information used Production Cost in SW Region (Million US$ in 2011) Minimum Average Maximum 1 Method-wise production (kg) and production cost (Tk/kg) Method-wise production area (ha) and production cost (Tk/ha) Average Source: Author s compilation based on Bhattacharya et al. (1999); DTS (2006); Gammage et al. (2005); Haque (2004); Mazid (1994); NACA (2002); Paul & Vogl (2011) and Rosenberry (1995). 3.4 Indirect Cost Cost of foregone Rice Rice cultivation is the best available alternative of shrimp farming for an agrarian country like Bangladesh. The farmers have to give up rice cultivation if they are intended to shrimp farming. Therefore, the net loss originated from initiating shrimp farming is treated as indirect cost of shrimp. The production difference between non-shrimp and shrimp cultivated area is treated as loss of foregone rice. Rice production (kg/ha) data is available in BBS (2011). The information about production loss for shrimp cultivation is available in Ali (2006) and Sarwar (2005). Moreover, information about price of rice (Tk/kg) is available in BBS (2011). Considering the above cited information and making necessary inflation adjustments, this study calculates the net loss in rice production due to foregone rice originated from shrimp cultivation. This loss is treated as indirect cost of shrimp farming which varies from million US$ with an average of 31 million US$ (Table A10) Loss in Livestock Sector Shrimp cultivation mostly substitutes rice cultivating area which indeed negatively affects the fodder availability for livestock. Moreover, shrimp induced salinity reduces grazing land. This study tries to quantify the loss in livestock sector from shrimp farming. BSS (2011) is the main data source used for this quantification. Net cultivated land, number of households (HH), no of people and total area are the four basic elements considered in this study and corresponding per unit calculations, such as, number of livestock per ha, number of livestock per HH, number of livestock per person and number of livestock per sq. km are made (Table A11- A14). The differences between non-shrimp and shrimp cultivated area in these per unit calculated values are treated as loss in livestock sector for introduction of shrimp. Finally, the 10

15 average price of livestock is considered to calculate the total loss (Table 9). As the calculated values vary across approaches, this study considers the average value. It also keeps the minimum and maximum values side-by-side. According to Table 9, the loss in livestock sector for introducing SP farming in SW region is 11 million US$ with a range of million US$. Table 9: Loss in Livestock Sector for Shrimp Farming in SW Region Approach Information used Loss in Livestock Sector (Million US$ in 2011) Minimum Average Maximum 1 Net cultivated land, No. of Livestock and price No. of HH, No. of Livestock and price No. of People, No. of Livestock and price area, No. of Livestock and price Average Source: Author s compilation based on BBS (2011) Loss in Fruit Sector The SP cultivation not only influences production and crop type of SP cultivated land, but also influences that of the surrounding land including homestead and fallow areas. The BBS databases report a continuous decline in production of various fruits in SW region which is mostly caused by SP introduction. Therefore, this study tries to quantify the loss in fruit sector due to SP farming. BSS (2011) is the main data source used for this quantification. The production difference between non-shrimp and shrimp cultivated area is treated as loss in fruit sector for introduction of SP. Banana, Mango, Jackfruit, Pineapple and Litchi are the five main fruit types that are considered in this study. The inflation adjusted price data (Tk/quintal) is used to calculate the total loss (Table 10 and A15). According to the estimation results, the loss in fruit sector for introducing SP farming in SW region is 0.75 million US$. Table 10: Loss in Fruit Sector for Shrimp Farming in SW Region Serial No. Name of Fruit Loss in Fruit Sector (Million US$ in 2011) 1 Banana Mango Jackfruit Pineapple Litchi Source: Author s compilation based on BBS (2011) Loss in Vegetable It is assumed that the impact of SP cultivation on vegetable sector is in line with the same direction of fruit sector. It is also assumed that the regional share for fruit in total production of Bangladesh is true for vegetable and the rate of loss in vegetable sector per year for SW region is same as that of fruit sector. Following the similar methodology of fruit sector and using the BBS database, this study finds that the loss in vegetable sector for introducing SP farming in SW region is 1.57 million US$ (Table A16). 11

16 3.4.5 Loss in Tree This study considers the assumptions taken for vegetable sector are also valid for tree and finds that the loss in tree for introducing SP farming in SW region is 1.75 million US$ (Table A17) Employment Loss The literatures claim that SP farming substantially reduces the required number of labour to be employed in shrimp cultivating fields (Bundell & Maybin, 1996; Clay, 1996; Paul & Vogl, 2011; and Shiva, 1995). This study uses the employment information cited in Bundell & Maybin (1996), Haider (2011) and Khan et al. (2010) and assumes two crops in a year to calculate employment level (N/ha/year) in rice production. Bundell & Maybin (1996) and Shiva (1995) states that the employment loss is around 90% due to switch from rice to shrimp. In contrast, author s calculation based on employment related information cited in Clay (1996) finds 53% employment loss. The average of these two figures is 72%, and it is the gross employment loss for switching from rice to SP. However, expansion of SP generates employment in forward and backward linkage segments of SP, such as, hatchery, processing units etc. Assuming half of the lost employment is generated in linkage segments, this study considers 36% as net employment loss for SP introduction. Taking the average agricultural wage data from Haider (2011), this study finds that the calculated employment loss varies from millions US$ with an average of 30 millions US$ (Table A18) Health Cost The introduction of SP farming induces water borne disease (WDB) and it creates negative health impacts on people living in the surrounding area (Masum, 2008; and Sarwar, 2005). However specific data in this regard is hardly available in the literatures. Therefore this study uses the morbidity rate as reported in Begum (1997) to calculate total number of people affected from various WBD, such as, diarrhea, cholera, dysentery etc. in SW region. Assuming 50% of the WBD is caused by shrimp introduction and using population data of BBS (2011), it calculates the total number of people affected by WBD due to shrimp introduction. Then, it uses the inflation adjusted treatment cost of WBD and average duration of remaining bedridden caused by shrimp induced WBD as reported in Begum (1997). The wage data as cited in Haider (2011) is also used to calculate wage lost. Finally, it calculates total health cost of shrimp farming in SW region through adding the treatment cost and wage loss (Table A19). The calculated health cost varies from 5-29 million US$ with an average of 17 million US$ (Table A19) Indirect Cost Adding all the indirect costs listed in sections , this study finds that the total indirect cost of SP farming in the SW region varies from million US$ with an average of 92 million US$ for year 2011 (Table 11). 3.5 Cost-Benefit Analysis (CBA) Table 11 lists all of the calculated benefits and costs associated with SP farming in the SW region for year Direct production cost accounts for only 64% of total estimated cost on average which is visible to the shrimp farmers. The cost-benefit ratio (CB) varies from with an average of

17 Table 11: Cost-Benefit Analysis of Shrimp and Prawn (SP) Aquaculture in SW Region Symbol Source of Benefit/Cost Cost and Benefit of SP Aquaculture in SW Region (Million US$ in 2011) Minimum Average Maximum B 1 SP Production DB Direct Benefit B 2 Indirect Benefit Not measured Not measured Not measured B Benefit C 1 SP Production Cost DC Direct Cost C 2 Net Loss of foregone Rice C 3 Loss in Livestock Sector C 4 Loss in Fruit Sector C 5 Loss in Vegetable C 6 Loss in Tree C 7 Employment Loss C 8 Health Cost IC Indirect Cost C Cost CB Cost-Benefit Ratio Source: Author s compilation. Table 12: Cost-Benefit Analysis of Shrimp and Prawn (SP) Aquaculture in Bangladesh Symbol Source of Benefit/Cost Cost and Benefit of SP Aquaculture in Bangladesh (Million US$ in 2011) Minimum Average Maximum B 1 SP Production DB Direct Benefit B 2 Indirect Benefit Not measured Not measured Not measured B Benefit C 1 SP Production Cost DC Direct Cost C 2 Net Loss of foregone Rice C 3 Loss in Livestock Sector C 4 Loss in Fruit Sector C 5 Loss in Vegetable C 6 Loss in Tree C 7 Employment Loss C 8 Health Cost C 9 Mangrove Loss IC Indirect Cost C Cost CB Cost-Benefit Ratio Source: Author s compilation. Table 12 lists all of the measured benefits and costs associated with SP farming in Bangladesh for year This study adds Chittagong and Cox s bazaar districts to SW region to execute the CBA exercise for Bangladesh. These five districts cover more than 90% of total SP producing areas of the country. This study tries to estimate the loss of mangrove in addition to all other cost items considered for SW region while doing country-level CBA exercise. Direct production cost accounts for about 67% of total estimated cost on average which is visible to the shrimp farmers. The cost-benefit ratio (CB) varies from with an average of

18 Table 13 lists all of the measured benefits and costs associated with SP farming in four selected study villages (Ramnagar, Borokupot, Boyersingh and Kalikapur of Satkhira district) of SW region for year These four villages are the study sites for the Community Based Adaptation in Vulnerable Coastal Areas of Bangladesh sub-project under the Mainstreaming Environment for Poverty Reduction project of ADB. This study uses the GIS maps of PAB (2011) to calculate the shrimp cultivated area in the selected villages (Map 2-5). Direct production cost accounts for about 50% of total estimated cost on average which is visible to the shrimp farmers. The cost-benefit ratio (CB) varies from with an average of Table 13: CBA of Shrimp and Prawn (SP) Aquaculture in Four Villages Symbol Source of Benefit/Cost Cost and Benefit of SP Aquaculture in Four Villages (US$ in 2011) Minimum Average Maximum B 1 SP Production 15,979 24,851 40,673 DB Direct Benefit 15,979 24,851 40,673 B 2 Indirect Benefit Not measured Not measured Not measured B Benefit 15,979 24,851 40,673 C 1 SP Production Cost 4,386 9,532 16,146 DC Direct Cost 4,386 9,532 16,146 C 2 Net Loss of foregone Rice 1,554 1,613 1,672 C 3 Loss in Livestock Sector C 4 Loss in Fruit Sector C 5 Loss in Vegetable C 6 Loss in Tree C 7 Employment Loss 1,544 1,544 1,544 C 8 Health Cost 1,572 5,650 9,728 IC Indirect Cost 5,372 9,509 13,646 C Cost 9,758 19,042 29,792 CB Cost-Benefit Ratio Source: Author s compilation. 4. Primary Data based CBA 4.1 Primary Data Secondary data based CBA of SP farming is discussed in the previous section. This section attempts to use some field-level primary data for cross checking the results obtained from secondary data-set with that of primary data-set. Like the secondary data-based discussion of CBA in the previous section for SW region, Bangladesh and selected four villages, this section repeats the same exercises with some primary data. It collects 54 randomly selected farm-level primary data from 1452 households using a structured questionnaire from two (Boyersingh and Kalikapur) villages in February These two villages are randomly selected from four study sites of Satkhira district. Household level socio-economic and demographic information, production and cost of shrimp and rice farming, farm characteristics, wage level, price and such many other issues are covered in that questionnaire. Some important field-level primary information that is used in CBA exercise is reported in Table

19 Table 14: Primary Data Item Unit Amount Item Unit Amount Shrimp production kg/ha/year 383 Wage Tk/day 182 Price of shrimp Tk/kg 442 Price of rice Tk/kg Cost of shrimp production Tk/ha/year 74,344 Rice production kg/ha/year 2,264 Cost of shrimp production Tk/kg/year 249 Price of livestock Tk/no 6,056 Source: Field survey (2012). 4.2 Primary Data based CBA of Shrimp and Prawn (SP) Aquaculture Table 15 lists the primary data-based CBA results for SW region, Bangladesh and selected four study villages. The production, price and cost of shrimp; wage; production and price of rice; price of livestock; cost of fuel wood and drinking water; etc. are the main indicators that are considered in primary data collection stage. These primary-level data collected from the selected sample farmers on the noted indicators are plugged-in the CBA exercises for SW region, Bangladesh and selected four study villages. Table 15 also reports the average findings of CBA with secondary data for the same. Table 15: CBA of Shrimp and Prawn (SP) Aquaculture Cost and Benefit of SP Aquaculture in 2011 Symbol Source of Benefit/Cost SW (m US$) BD (m US$) Four Villages (US$) S P S P S P B 1 SP Production ,851 21,496 DB Direct Benefit ,851 21,496 B 2 Indirect Benefit Not measured B Benefit ,851 21,496 C 1 SP Production Cost ,532 8,486 DC Direct Cost ,532 8,486 C 2 Net Loss of foregone Rice ,613 1,438 C 3 Loss in Livestock Sector C 4 Loss in Fruit Sector C 5 Loss in Vegetable C 6 Loss in Tree C 7 Employment Loss ,544 1,241 C 8 Health Cost ,650 5,223 C 9 Cost of mangrove loss IC Indirect Cost ,509 8,717 C Cost ,042 17,203 CB Cost-Benefit Ratio N.B.: S refers to Secondary data ; P refers to Primary data ; and m refers to Million. Source: Author s compilation. According to Bhattacharya et al. (1999), the cost-benefit ratio of shrimp farming in Bangladesh is As this study tries to cover more indirect cost items, such as, loss in fruit, vegetable, tree, employment etc. the calculated CB ratio increases. The CB ratio ranges from 0.77 to 0.92 for secondary dataset and from 0.58 to 0.80 for primary dataset. The higher production volume and value data partly explain the lower CB ratio calculated from primary dataset. The inclusion of more indirect cost items increases the CB ratio. For example, the CB ratio increases to the range of for SW region and Bangladesh if the family labour used 15

20 for shrimp farming are added in the cost-side. The inclusion of additional fuel cost originated from shrimp culture increases the CB ratio to the range of for SW region and Bangladesh. Furthermore, the CB ratio increases to the range of while the external cost incurred for drinking water due to introduction of shrimp farming for SW region and Bangladesh are added. However, such indirect cost items are mostly invisible to farmers and they seldom consider such items while taking farming decisions. The CB ratio remains within when only the direct cost and benefit components are considered and this is what the farmers usually observe and consider in farm-level decision making. 5. Conclusion and Recommendation The costs and benefits of shrimp farming are widely reported in the literatures. However, majority of the research studies talk only about the direct benefits and costs. The indirect components related to social and environmental dimensions of shrimp farming are almost absent in those literatures. Only a few studies try to capture some indirect components. However, these studies capture the indirect components partially. Moreover, a complete CBA on shrimp farming from Bangladesh perspective using recent dataset is absent in the literatures. This study exercises both primary and secondary data to cross-check the findings. It also segregates the analysis from SW region, Bangladesh and finally four selected village perspectives using both primary and secondary dataset. Such dimensions help to better understand the shrimp sector of Bangladesh and device policy recommendations. This study finds a higher CB ratio compared to other studies due to its wider indirect component coverage. The calculated CB ratio ranges from 0.77 to 0.92 for secondary dataset and from 0.58 to 0.80 for primary dataset. The inclusion of more indirect cost items increases the ratio. For example, the CB ratio increases to the range of for SW region and Bangladesh if the family labour used for shrimp farming are added in the cost-side. The inclusion of additional fuel cost originated from shrimp culture increases the CB ratio to the range of for SW region and Bangladesh. Furthermore, the CB ratio increases to the range of for SW region and Bangladesh after adding the external cost incurred for drinking water due to introduction of shrimp farming. However, considering only the direct cost and benefit components, the CB ratio remains within and this is what the farmers usually observe and think in farm-level decision making. The indirect components are not visible to them and they often fail to recognize those costs. As a result, the farming decisions made by the farmers based on observing only the direct cost and benefit components undermine the actual scenario from society and environment perspectives. Therefore, this study suggests for taking proper initiatives to disseminate the actual cost-benefit scenario among the farmers/fishermen. The literatures demonstrate that the shrimp sector is contributing in employment generation and export earnings. On the other hand, the CBA exercises find huge social and environmental costs of shrimp farming which are mostly unaccounted. The shrimp sector is constraining rice farming and other natural resources. This dilemma of higher visible economic return vs. negative social and environmental consequences of shrimp farming needs to be addressed carefully. There is no simple answer about sustainable and good practices for farmers/fisherman and resource users. 16

21 This study tries to point out some policy instruments for sustainable and good practices for farmers/fisherman and resource users in Bangladesh. Education and consciousness development among farmers/fishermen and resource users; licensing of shrimp farms; imposition and implementation of tax; clear land zoning for shrimp vs. rice and other agro-products; assigning clear property rights; introducing mixed rice-shrimp farming; imposing effluent charges; and arranging mandatory mangrove management, preservation and development programs are some notable policy instruments that are derived from CBA results, survey findings, field visits and literatures. Education will help the farmers/fisherman and resource users to be conscious about the direct and indirect costs and benefits of farming activities. Licensing will facilitate planned expansion and continuation of farming and resource extraction activities. Land tax imposition will prevent undesirable changes in land use pattern. A clear land zoning system will allow limiting the external effects related to farming and resource extraction. Assigning a clear property right will facilitate property owners to take farming/resource management decisions independently without being influenced by third parties. The introduction of mixed rice-shrimp farming will work for earning more at comparatively lower indirect costs. Imposition of effluent charges will help to internalize external costs and bound the polluters to account that cost. The initiation of mandatory mangrove management, preservation and development initiatives will work for environmental and ecological balance and biodiversity preservation. The said instruments might help the farmers/fisherman and resource users to understand the pros and cons of farming activities, to realize all sorts of related costs and benefits, to take proper decisions, to monitor the shrimp sector, to compensate for the negative consequences generated from shrimp farming and to bring the shrimp sector under systematic guidelines. All the above understanding might be useful to decide the fate of the shrimp aquaculture sector. Any top-down approach of imposing ban on shrimp farming might create another set of negative social consequences like unemployment, crime, dislocation of people and slowing down the foreign exchange earnings. Therefore, it would be better to handle the situation softly through education and information dissemination, establishing a clear set of rules and regulations and finally let the related parties to take the final decision. Such initiatives will guide the shrimp sector towards sustainable position and allow the related parties to adjust through allowing some time. This study conducts a CBA using mostly secondary information. Therefore, the study findings depend on the validity of those information sources. It also makes some crucial assumptions if relevant information is not available. Moreover, it attempts to cross-check the secondary data-based findings with field-level primary data. However, only 54 randomly selected samples from two villages are considered and surveyed due to time, budget and other constraints. Therefore, the results of this study need to be carefully interpreted and used. It would be better to pick-up and interpret the trend and comparative findings instead of placing much importance on the derived numerical figures. 17

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26 Annex Map 1: Map of Study Area 22

27 Study Area Source: Author s compilation based on ASB (2008). 23

28 Map 2: GIS Social Map of Kalikapur Map 3: GIS Social Map of Ramnagar 24

29 Map 4: GIS Social Map of Borokupot Map 5: GIS Social Map of Boyersingh Source: PAB (2011). Figure 1: Approaches for Quantifying Shrimp and Prawn (SP) Production Approach 1 Production (kg) Tk/kg Production (Tk) Production (m US$) Approach 2 SP Cultivate Area (ha) kg/ha Production (kg) Tk/kg Production (Tk) Production (m US$) Approach 3 SP Cultivate Area (ha) Bagda (ha) Golda (ha) kg/ha Production (kg) Tk/kg Production (Tk) Production (m US$) Approach 4 SP Cultivate Area (ha) Bagda (ha) Golda (ha) Tk/ha Production (Tk) Production (m US$) Approach 5 SP Cultivate Area (ha) Extensive (ha) Semi-intensive (ha) Intensive (ha) kg/ha Production (kg) Tk/kg Production (Tk) Production (m US$) Approach 6 SP Cultivate Area (ha) Extensive (ha) Semi-intensive (ha) Intensive (ha) 25 Tk/ha Production (Tk) Production (m US$)