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Cost-Benefit Analysis on Shrimp Aquaculture versus Agriculture

and other Natural Resource Management (NRM)

Final Report

Project: TA - 6422 (REG): Mainstreaming Environment for Poverty Reduction

A study of ‘Community Based Adaptation in Vulnerable Coastal Areas of Bangladesh’ sub-

project 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

Contents

1. Introduction ................................................................................................................... 1

2. Methodology .................................................................................................................. 2

2.1 CBA for SW Region of Bangladesh ........................................................................ 4

2.1.1 Shrimp and Prawn (SP) Production .................................................................. 4

2.1.2 Shrimp and Prawn (SP) Production Cost .......................................................... 5

2.1.3 Indirect Cost .................................................................................................... 5

2.1.4 Costs and Benefits ........................................................................................... 6

2.2 Additional Exercises on CBA.................................................................................. 6

3. Secondary Data based CBA ........................................................................................... 6

3.1 Shrimp and Prawn (SP) Cultivated Area.................................................................. 7

3.2 Shrimp and Prawn (SP) Production ......................................................................... 8

3.3 Shrimp and Prawn (SP) Production Cost ................................................................. 9

3.4 Indirect Cost ......................................................................................................... 10

3.4.1 Cost of foregone Rice .................................................................................... 10

3.4.2 Loss in Livestock Sector ................................................................................ 10

3.4.3 Loss in Fruit Sector ........................................................................................ 11

3.4.4 Loss in Vegetable........................................................................................... 11

3.4.5 Loss in Tree ................................................................................................... 12

3.4.6 Employment Loss .......................................................................................... 12

3.4.7 Health Cost .................................................................................................... 12

3.4.8 Total Indirect Cost ......................................................................................... 12

3.5 Cost-Benefit Analysis (CBA) ................................................................................ 12

4. Primary Data based CBA ............................................................................................. 14

4.1 Primary Data ......................................................................................................... 14

4.2 Primary Data based CBA of Shrimp and Prawn (SP) Aquaculture ......................... 15

5. Conclusion and Recommendation ................................................................................... 16

References .......................................................................................................................... 18

Annex ................................................................................................................................. 22

Survey Questionnaire on CBA ............................................................................................ 32

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 1999-2009 ..................................... 7

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 ......................................... 10

Table 9: Loss in Livestock Sector for Shrimp Farming in SW Region ................................. 11

Table 10: Loss in Fruit Sector for Shrimp Farming in SW Region ....................................... 11

Table 11: Cost-Benefit Analysis of Shrimp and Prawn (SP) Aquaculture in SW Region...... 13

Table 12: Cost-Benefit Analysis of Shrimp and Prawn (SP) Aquaculture in Bangladesh ..... 13

Table 13: CBA of Shrimp and Prawn (SP) Aquaculture in Four Villages ............................ 14

Table 14: Primary Data ....................................................................................................... 15

Table 15: CBA of Shrimp and Prawn (SP) Aquaculture ...................................................... 15

List of Figures

Figure 1: Approaches for Quantifying Shrimp and Prawn (SP) Production .......................... 25

Figure 2: Approaches for Quantifying Shrimp and Prawn (SP) Production Cost .................. 26

List of Maps

Map 1: Map of Study Area .................................................................................................. 22

Map 2: GIS Social Map of Kalikapur .................................................................................. 24

Map 3: GIS Social Map of Ramnagar .................................................................................. 24

Map 4: GIS Social Map of Borokupot ................................................................................. 25

Map 5: GIS Social Map of Boyersingh ................................................................................ 25

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 0.84-1.12 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 0.30-0.63 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

1

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

2

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 Estimated in

this study

Production cost

Direct production costs associated with SP farming

Calculation based on available secondary data

Yes

Land

degradation

Impact on agriculture

Calculation based on available

secondary data (Net loss for foregone

rice)

Yes

Impact on agriculture (due to

deterioration of soil quality)

Survey / Secondary data, Laboratory

test of soil No

Impact on livestock


secondary data (quantity of lost

livestock * unit price)

Yes

Impact on fruit sector


secondary data (quantity of lost fruits

* unit price)

Yes

Impact on vegetable sector


secondary data (quantity of lost

vegetable * unit price)

Yes

Impact on tree


secondary data (quantity of lost tree * unit price)

Yes

Employment Lost employment for introducing SP

aquaculture


secondary data Yes

Human health

Mortality cost for SP induced water

borne diseases

Incidence of death and Statistical

value of life No

Morbidity cost (Treatment cost) for

SP induced water borne diseases

Incidence of sickness and cost of

treatment Yes

Morbidity cost (Wage lost) for SP

induced water borne diseases Incidence of sickness and lost wages Yes

Fuel wood Cost generated from fuel wood

crisis

Additional cost of fuel wood (in terms

of time and money spent) Partial

Drinking

Water

Cost generated from drinking water

crisis

Additional cost for drinking water (in

terms of time and money spent) Partial

Destruction of

mangrove

Damage of wood and non-wood

products

Value of forest product and area of

mangrove lost No

Biodiversity Loss of flora and fauna; medicinal

plant Survey / Secondary data No

3

Tourism Loss in revenue earning due to loss

in mangrove forests Survey / Secondary data No

Social impact

Family dislocation, violence, school

drop-out, migration, harassment,

changes in land ownership and

management pattern, etc.

Survey / Secondary data No

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

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

Social impact

Women empowerment,

Technology and knowledge

spillover, Network expansion, etc.

Survey / Secondary data No

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.

Equation 1: 𝑪𝑩𝒕 =𝐃𝐢𝐫𝐞𝐜𝐭 𝐂𝐨𝐬𝐭 (𝑫𝑪𝒕) + 𝐈𝐧𝐝𝐢𝐫𝐞𝐜𝐭 𝐂𝐨𝐬𝐭 (𝑰𝑪𝒕)

𝐃𝐢𝐫𝐞𝐜𝐭 𝐁𝐞𝐧𝐞𝐟𝐢𝐭 (𝑫𝑩𝒕) + 𝐈𝐧𝐝𝐢𝐫𝐞𝐜𝐭 𝐁𝐞𝐧𝐢𝐟𝐢𝐭 (𝑰𝑩𝒕)=

∑ 𝑫𝑪𝒊𝒕𝒏𝒊=𝟏 +∑ 𝑰𝑪𝒋𝒕

𝒏𝒋=𝟏

∑ 𝑫𝑩𝒌𝒕𝒏𝒌=𝟏 +∑ 𝑰𝑩𝒍𝒕

𝒏𝒍=𝟏

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.

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.

4

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

Item Khulna Bagerhat Satkhira

SW

Region Bangladesh

Share of

SW

Household (No.) 499,324 323,505 390,745 1,213,574 25,490,822 4.76%

Population (No.) 2,378,971 1,549,031 1,864,704 5,792,706 124,355,263 4.66%

Area (sq. km) 4,395 3,959 3,858 12,212 147,570 8.28%

Literacy rate (%) 57.81 58.73 45.52 - 46.15 -

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.

2.1.1 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).

5

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.

2.1.2 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.

2.1.3 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.

6

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

2012. 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.

7

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 2008-2009 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 1999-2009 (Table 4).

Table 4: Shrimp and Prawn (SP) Cultivated Area during 1999-2009

Region

Year SP Cultivated Area (ha) Percent

Khulna Bagerhat Satkhira SW region Bangladesh Share of SW

1999-2000 29,551 47,710 29,544 106,805 141,353 75.56%

2008-2009 51,921 59,424 52,357 163,702 217,877 75.13%

Yearly Average 40,736 53,567 40,950 135,253 179,615 75.35%

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 1999-2009. 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 1999-2009. 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) 1983 - 51,000

2 Alauddin & Hamid (1999) 1983 - 51,000

3 Rosenberry (1995) 1994 - 134,000

4 DOF (1994) 1994 - 134,000

5 Barraclough & Finger-Stich (1996) 1996 - 110,000

6 NACA (2002) 1996 - 140,000

7 Raux & Bailly (2002) 1997 - 140,000

8 DTS (2006) 2003 - 203,071

9 Sarwar (2005) 2004 115,900 -

8

Serial

No. Source

SP Cultivated Area (ha)

Reporting Year SW region Bangladesh

10 Khatun (2004) 2004 - 197,687

11 ATDP II (2005) 2005 - 200,000

12 Nupur (2010) 2008 - 217,887

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

1980 35,320 44,505 51,961 44,150 58,105 68,361

1985 42,305 56,197 65,873 56,147 73,893 86,936

1990 53,800 71,259 83,511 71,404 93,971 110,558

1995 68,419 90,364 105,937 90,805 119,506 140,599

2000 87,010 114,695 134,723 115,479 151,978 178,803

2005 110,653 145,640 171,330 146,858 193,265 227,388

2010 140,720 185,090 217,885 186,762 245,721 289,175

2011 147,650 194,186 228,615 195,960 257,810 303,417

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 2011. 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 1980-2011, 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 2011. 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 2011. 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

9

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 2011. This is the calculated

direct benefit of shrimp farming for the SW region of Bangladesh in year 2011.

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 124.37 334.30 634.58

2 Kg/ha/year and Tk/kg 232.11 474.74 914.76

3 Shrimp type-wise kg/ha/year and Tk./kg 210.89 439.36 799.98

4 Shrimp type-wise kg/ha/year and Tk/ha 244.58 321.66 378.70

5 Production method-wise kg/ha/year and Tk/kg 84.99 450.84 936.37

6 Production method-wise kg/ha/year and Tk/ha 35.93 190.69 338.51

Average 155.48 368.60 667.15

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.

10

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 62-411 million US$.

Table 8: Shrimp and Prawn (SP) Production Cost in SW Region


Production Cost in SW Region



1 Method-wise production (kg) and

production cost (Tk/kg) 43.06 236.93 482.06

2 Method-wise production area (ha)

and production cost (Tk/ha) 81.61 226.05 340.63

Average 62.33 231.49 411.34

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

3.4.1 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 23-38 million US$ with an average of 31

million US$ (Table A10).

3.4.2 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

11

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 10-12 million US$.

Table 9: Loss in Livestock Sector for Shrimp Farming in SW Region


Loss in Livestock Sector



1 Net cultivated land, No. of Livestock and price 0.52 0.68 0.80

2 No. of HH, No. of Livestock and price 13.66 13.66 13.66

3 No. of People, No. of Livestock and price 10.48 10.48 10.48

4 Total area, No. of Livestock and price 14.81 19.48 22.94

Average 9.87 11.08 11.97

Source: Author’s compilation based on BBS (2011).

3.4.3 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


1 Banana 0.03

2 Mango 0.14

3 Jackfruit 0.56

4 Pineapple 0.01

5 Litchi 0.003

Total 0.75


3.4.4 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).

12

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).

3.4.6 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 22-35 millions US$ with an average

of 30 millions US$ (Table A18).

3.4.7 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).

3.4.8 Total Indirect Cost

Adding all the indirect costs listed in sections 3.4.1-3.4.7, this study finds that the total

indirect cost of SP farming in the SW region varies from 64-117 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 2011. 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 0.79-0.88

with an average of 0.88.

13

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



B1 SP Production 155.48 368.60 667.15

DB Direct Benefit 155.48 368.60 667.15

B2 Indirect Benefit Not measured Not measured Not measured

B Total Benefit 155.48 368.60 667.15

C1 SP Production Cost 62.33 231.49 411.34

DC Direct Cost 62.33 231.49 411.34

C2 Net Loss of foregone Rice 22.57 30.82 37.61

C3 Loss in Livestock Sector 9.87 11.08 11.97

C4 Loss in Fruit Sector 0.75 0.75 0.75

C5 Loss in Vegetable 1.57 1.57 1.57

C6 Loss in Tree 1.75 1.75 1.75

C7 Employment Loss 22.42 29.49 34.72

C8 Health Cost 4.66 16.73 28.81 IC Indirect Cost 63.58 92.18 117.17

C Total Cost 125.92 323.67 528.51

CB Cost-Benefit Ratio 0.81 0.88 0.79

Source: Author’s compilation.

Table 12: Cost-Benefit Analysis of Shrimp and Prawn (SP) Aquaculture in Bangladesh


Cost and Benefit of SP Aquaculture in Bangladesh



B1 SP Production 206.62 497.42 892.82

DB Direct Benefit 206.62 497.42 892.82


B Total Benefit 206.62 497.42 892.82

C1 SP Production Cost 82.73 307.34 545.93

DC Direct Cost 82.73 307.34 545.93

C2 Net Loss of foregone Rice 29.96 40.91 49.91

C3 Loss in Livestock Sector 19.24 20.85 22.03

C4 Loss in Fruit Sector 1.63 1.63 1.63

C5 Loss in Vegetable 2.46 2.46 2.46 C6 Loss in Tree 2.74 2.74 2.74

C7 Employment Loss 29.76 39.15 46.08

C8 Health Cost 11.40 40.96 70.52

C9 Mangrove Loss 2.45 2.45 2.45

IC Indirect Cost 99.64 151.15 197.82

C Total Cost 182.37 458.49 743.75



Table 12 lists all of the measured benefits and costs associated with SP farming in

Bangladesh for year 2011. 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 0.83-0.92 with an average of 0.92.

14

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 2011. 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 0.61-0.77 with an average of 0.77.

Table 13: CBA of Shrimp and Prawn (SP) Aquaculture in Four Villages


Cost and Benefit of SP Aquaculture in Four Villages

(US$ in 2011)


B1 SP Production 15,979 24,851 40,673

DB Direct Benefit 15,979 24,851 40,673


B Total Benefit 15,979 24,851 40,673

C1 SP Production Cost 4,386 9,532 16,146

DC Direct Cost 4,386 9,532 16,146 C2 Net Loss of foregone Rice 1,554 1,613 1,672

C3 Loss in Livestock Sector 528 528 528

C4 Loss in Fruit Sector 32 32 32

C5 Loss in Vegetable 68 68 68

C6 Loss in Tree 75 75 75

C7 Employment Loss 1,544 1,544 1,544

C8 Health Cost 1,572 5,650 9,728

IC Indirect Cost 5,372 9,509 13,646

C Total Cost 9,758 19,042 29,792



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 2012. 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 14.

15

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 17.44

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

SW (m US$) BD (m US$) Four Villages (US$)

S P S P S P

B1 SP Production 369 302 497 402 24,851 21,496

DB Direct Benefit 369 302 497 402 24,851 21,496

B2 Indirect Benefit Not measured B Total Benefit 369 302 497 402 24,851 21,496

C1 SP Production Cost 231 91 307 172 9,532 8,486

DC Direct Cost 231 91 307 172 9,532 8,486

C2 Net Loss of foregone Rice 31 27 41 36 1,613 1,438

C3 Loss in Livestock Sector 11 13 21 25 528 639

C4 Loss in Fruit Sector 1 1 2 2 32 32

C5 Loss in Vegetable 2 2 2 2 68 68

C6 Loss in Tree 2 2 3 3 75 75

C7 Employment Loss 29 24 39 31 1,544 1,241

C8 Health Cost 17 16 41 38 5,650 5,223

C9 Cost of mangrove loss - - 2 2 - -

IC Indirect Cost 92 85 151 140 9,509 8,717 C Total Cost 324 175 458 313 19,042 17,203

CB Cost-Benefit Ratio 0.88 0.58 0.92 0.78 0.77 0.80

N.B.: ‘S’ refers to ‘Secondary data’; ‘P’ refers to ‘Primary data’; and ‘m’ refers to ‘Million’.


According to Bhattacharya et al. (1999), the cost-benefit ratio of shrimp farming in

Bangladesh is 0.30. 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 0.84-1.12 for SW region and Bangladesh if the family labour used

16

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 0.94-1.29 for SW region and

Bangladesh. Furthermore, the CB ratio increases to the range of 1.01-1.42 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 0.30-0.63 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 0.84-1.12 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 0.94-1.29 for SW region and Bangladesh.

Furthermore, the CB ratio increases to the range of 1.01-1.42 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 0.30-0.63 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.

17

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.

18

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20

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22

Annex

Map 1: Map of Study Area

23

Source: Author’s compilation based on ASB (2008).

Study Area

Study Area

24

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

25

Source: PAB (2011).

Figure 1: Approaches for Quantifying Shrimp and Prawn (SP) Production

Map 4: GIS Social Map of Borokupot Map 5: GIS Social Map of Boyersingh

Total SP

Cultivate

Area (ha)

Total Production

(kg)

Total Production

(Tk)

Total Production (m US$)

kg/ha Tk/kg Approach 3

Bagda (ha)

Golda (ha)

Total SP Cultivate

Area (ha)

Total Production

(Tk)


Tk/ha Approach 6

Extensive (ha)

Semi-intensive (ha)

Intensive (ha)

Total SP

Cultivate

Area (ha)

Total Production

(Tk)


Tk/ha Approach 4

Bagda (ha)

Golda (ha)

Total SP

Cultivate

Area (ha)

Total Production

(kg)

Total Production

(Tk)



Total Production

(kg)

Total Production

(Tk)


Tk/kg Approach 1

Total SP Cultivate

Area (ha)

Total Production

(kg)

Total Production

(Tk)



Extensive (ha)

Semi-intensive (ha)

Intensive (ha)

26

Source: Author’s Compilation.

Figure 2: Approaches for Quantifying Shrimp and Prawn (SP) Production Cost

Source: Author’s Compilation.

Table A1: Shrimp and Prawn (SP) Cultivated Area SW Region (ha) Bangladesh (ha)

Year Minimum Average Maximum Minimum Average Maximum

1980 35,320 44,505 51,961 44,150 58,105 68,361

1981 37,060 46,670 54,486 46,325 60,966 71,727

1982 38,885 48,940 57,133 48,606 63,969 75,260

1983 38,427 51,105 59,909 51,000 67,119 78,966

1984 40,319 53,590 62,821 53,512 70,425 82,855

1985 42,305 56,197 65,873 56,147 73,893 86,936

1986 44,389 58,930 69,074 58,912 77,532 91,217

1987 46,575 61,796 72,431 61,814 81,351 95,710

1988 48,868 64,802 75,950 64,858 85,357 100,423

1989 51,275 67,954 79,641 68,052 89,561 105,369

1990 53,800 71,259 83,511 71,404 93,971 110,558

1991 56,450 74,725 87,569 74,920 98,599 116,003

1992 59,230 78,359 91,824 78,610 103,455 121,716

1993 62,147 82,171 96,286 82,481 108,550 127,710

1994 65,208 86,167 100,965 86,543 113,896 134,000

1995 68,419 90,364 105,937 90,805 119,506 140,599

1996 71,789 94,766 111,155 95,278 125,391 147,524

1997 75,324 99,393 116,629 99,970 131,566 154,789

1998 79,034 104,252 122,373 104,893 138,046 162,412

1999 82,926 109,349 128,400 110,059 144,845 170,411

2000 87,010 114,695 134,723 115,479 151,978 178,803

2001 91,295 120,303 141,358 121,167 159,463 187,609

2002 95,792 126,184 148,320 127,134 167,316 196,849

2003 100,509 132,354 155,624 133,395 175,556 206,543

2004 105,459 138,833 163,289 139,965 184,202 216,715

2005 110,653 145,640 171,330 146,858 193,265 227,388

2006 116,103 152,788 179,768 154,090 202,773 238,587

2007 121,820 160,288 188,622 161,679 212,749 250,337

2008 127,820 168,156 197,911 169,642 223,217 262,666

2009 134,115 176,420 207,658 177,996 234,199 275,602

2010 140,720 185,090 217,885 186,762 245,721 289,175

2011 147,650 194,186 228,615 195,960 257,810 303,417

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).

Tk/kg kg/ha

Total SP Cultivate

Area (ha)


kg/ha Tk/kg

Approach 1

Extensive (ha)

Semi-intensive (ha)

Intensive (ha)

Extensive (kg)

Semi-intensive (kg)

Intensive (kg)

Extensive (Tk)

Semi-intensive (Tk)

Intensive (Tk)

Tk/ha

Tk/ha Total SP

Cultivate

Area (ha)


Tk/ha

Approach 2

Extensive (ha)

Semi-intensive (ha)

Intensive (ha)

Extensive (Tk)

Semi-intensive (Tk)

Intensive (Tk)

27

Table A2: Shrimp and Prawn (SP) Production: Approach 1

Item Unit Symbol Minimum Average Maximum

Production (Bangladesh) kg/year A 21,000,000 46,686,487 70,722,406

Production (SW region) kg/year B=A*0.75 15,822,875 35,176,878 53,287,228

Price Tk/kg C 629 760 953

Production (SW region) Million Tk/year D=B*C/1 million 9,949 26,744 50,766

Production (SW region) Million US$/year E=D/80 124.37 334.30 634.58

N.B.: The minimum, average and maximum production (kg/year) of SP in Bangladesh are reported in ‘A’. This study uses the information of

BBS (2011), Huntington (2003), Raux & Bailly (2002), DTS (2006) and NACA (2002) to find production data of Bangladesh. Taking

the BBS information regarding share of SW region in Bangladesh, it calculates the production (kg/year) of SP in SW region (‘B’).

Then, it uses the information of DTS (2006) and Hasanuzzaman et al. (2011) for price of shrimp and corresponding minimum,

average and maximum values are reported in ‘C’. Finally, it assumes 1US$=Tk 80 to calculate SP production in the SW region of

Bangladesh for year 2011 (‘E’).

Source: Author’s compilation based on BBS (2007 & 2011), Hasanuzzaman et al. (2011), Huntington (2003), Raux

& Bailly (2002), DTS (2006) and NACA (2002).



Area (SW region) ha A 147,650 194,186 228,615

Production kg/ha/year B 200 257 336

Production (SW region) kg/year C=A*B 29,530,047 49,954,251 76,814,745

Price Tk/kg D 629 760 953

Production (SW region) Million Tk/year E=C*D/1 million 18,568 37,979 73,181

Production (SW region) Million US$/year F=E/80 232.11 474.74 914.76

N.B.: The minimum, average and maximum SP cultivated area (ha) in the SW region of Bangladesh are reported in ‘A’. This study uses the

information of Muir (2003), Rahman (1999) and Raux & Bailly (2002) for getting per unit production (kg/ha/year) data (‘B’). Then,

it calculates the production (kg/year) of SP in SW region (‘C’). It uses the information of DTS (2006) and Hasanuzzaman et al .

(2011) for price of shrimp and corresponding minimum, average and maximum values are reported in ‘D’. Finally, it assumes

1US$=Tk 80 to calculate SP production in the SW region of Bangladesh for year 2011 (‘F’).

Source: Author’s compilation based on Hasanuzzaman et al. (2011), Muir (2003), Rahman (1999) and Raux &

Bailly (2002).




Bagda Area (SW region) ha B 112,214 147,581 173,748

Galda Area (SW region) ha C 35,436 46,605 54,868

Bagda Production kg/ha/year D 133 179 230

Galda Production kg/ha/year E 336 426 496

Bagda Production (SW region) kg/year F=B*D 14,924,486 26,380,117 39,961,957

Galda Production (SW region) kg/year G=C*E 11,906,515 19,851,091 27,214,367

Price Tk/kg H 629 760 953

Bagda Production (SW region) Million US$/year I=F*H/80/1 million 117 251 476

Galda Production (SW region) Million US$/year J=G*H/80/1 million 94 189 324

Production (SW region) Million US$/year K=I+J 210.89 439.36 799.98


information of Huntington (2003) to divide the cultivated area under bagda and galda (‘B & C’). Then, it uses Aftabuzzaman (2004),

DTS (2006), Huntington (2003) and Khatun (2004) to get per unit bagda and galda production (kg/ha/year) (‘D & E’). It uses the

information of DTS (2006) and Hasanuzzaman et al. (2011) for price of shrimp and corresponding minimum, average and maximum

values are reported in ‘H’. Finally, it assumes 1US$=Tk 80 to calculate SP production in the SW region of Bangladesh for year

2011 (‘K’).

Source: Author’s compilation based on Aftabuzzaman (2004), Hasanuzzaman et al. (2011), Huntington (2003),

Khatun (2004) and DTS (2006).

28




Bagda Area (SW region) ha B 112,214 147,581 173,748

Galda Area (SW region) ha C 35,436 46,605 54,868

Bagda Production US$/ha D 1,027 1,027 1,027

Galda Production US$/ha E 3,648 3,648 3,648

Bagda Production (SW region) Million US$/year F=B*D/1 million 115 152 179

Galda Production (SW region) Million US$/year G=C*E/1 million 129 170 200

Production (SW region) Million US$/year H=F+G 244.58 321.66 378.70


information of Huntington (2003) to divide the cultivated area under bagda and galda (‘B & C’). Then, it uses DTS (2006) data to

get per unit bagda and galda production (US$/ha) (‘D & E’). Finally, it calculates SP production in the SW region of Bangladesh

for year 2011 (‘H’).

Source: Author’s compilation based on Huntington (2003) and DTS (2006).




Ext. Area (SW region) ha B 103,355 155,348 205,754

Semi-Int. Area (SW region) ha C 14,765 33,982 5,715

Int. Area (SW region) ha D - 4,855 11,431

Ext. Production kg/ha/year E 100 238 350

Semi-Int. Production kg/ha/year F 250 617 1000

Int. Production kg/ha/year G 1000 1500 2000

Ext. Production (SW region) kg/year H=B*E 10,335,516 36,895,268 72,013,823

Semi-Int. Production (SW) kg/year I=C*F 3,691,256 20,955,865 5,715,383

Int. Production (SW region) kg/year J=D*G - 7,281,961 22,861,531

Price Tk/kg K 629 760 953

Ext. Production (SW region) Million US$/year L=H*K/80/1 million 81 351 858

Semi-Int. Production (SW) Million US$/year M=I*K/80/1 million 4 92 61

Int. Production (SW region) Million US$/year N=J*K/80/1 million - 8 18

Production (SW region) Million US$/year O=L+M+N 84.99 450.84 936.37


information of Paul & Vogl (2011) and NACA (2002) to divide the cultivated area under Extensive, Semi-intensive and Intensive

methods (‘B, C & D’). Then, it uses Bhattacharya et al. (1999), Gammage et al. (2005), Haque (2004), Mazid (1994), Rosenberry

(1995) and DTS (2006) to get per unit production (kg/ha/year) data for different methods (‘E, F & G’). It uses the information of

DTS (2006) and Hasanuzzaman et al. (2011) for price of shrimp and corresponding minimum, average and maximum values are

reported in ‘K’. Finally, it assumes 1US$=Tk 80 to calculate SP production in the SW region of Bangladesh for year 2011 (‘O’).

Source: Author’s compilation based on Bhattacharya et al. (1999), Gammage et al. (2005), Haque (2004), Mazid

(1994), Paul & Vogl (2011), Rosenberry (1995), DTS (2006) and NACA (2002).






Int. Area (SW region) ha D 0 4855 11431

Ext. Production Tk/ha/year E 8,339 49,044 95,904

Semi-Int. Production Tk/ha/year F 136,280 153,515 170,749

Int. Production Tk/ha/year G 439,412 498,451 557,490

Ext. Production (SW region) Tk/year H=B*E 861,928,196 7,618,958,305 19,732,611,865

Semi-Int. Production (SW) Tk/year I=C*F 2,012,178,092 5,216,813,166 975,898,716

Int. Production (SW region) Tk/year J=D*G - 2,419,799,926 6,372,535,206

Ext. Production (SW region) Million US$/year K=H/80/1 million 11 95 247

Semi-Int. Production (SW) Million US$/year L=I/80/1 million 25 65 12

Int. Production (SW region) Million US$/year M=J/80/1 million 0 30 80

Production (SW region) Million US$/year N=K+L+M 35.93 190.69 338.51



methods (‘B, C & D’). Then, it uses Gammage et al. (2005), Haque (2004) and DTS (2006) to get per unit production (Tk/ha/year )

data for different methods (‘E, F & G’). Finally, it assumes 1US$=Tk 80 to calculate SP production in the SW region of Bangladesh

for year 2011 (‘N’).

29

Source: Author’s compilation based on Gammage et al. (2005), Haque (2004), Paul & Vogl (2011), DTS (2006) and

NACA (2002).

Table A8: Shrimp and Prawn (SP) Production Cost: Approach 1





Int. Area (SW region) ha D - 4,855 11,431

Ext. Production kg/ha/year E 100 238 350

Semi-Int. Production kg/ha/year F 250 617 1000

Int. Production kg/ha/year G 1000 1500 2000

Ext. Production (SW region) kg/year H=B*E 10,335,516 36,895,268 72,013,823

Semi-Int. Production (SW) kg/year I=C*F 3,691,256 20,955,865 5,715,383

Int. Production (SW region) kg/year J=D*G - 7,281,961 22,861,531

Ext. Production Cost Tk/kg K 252 334 438

Semi-Int. Production Cost Tk/kg L 226 233 240

Int. Production Cost Tk/kg M 234 241 248

Ext. Production Cost (SW region) Million US$/year N=H*K/80/1 million 33 154 394

Semi-Int. Production Cost (SW) Million US$/year O=I*L/80/1 million 10 61 17

Int. Production Cost (SW region) Million US$/year P=J*M/80/1 million - 22 71

Production Cost (SW region) Million US$/year Q=N+O+P 43.06 236.93 482.06



methods (‘B, C & D’). Then, it uses Bhattacharya et al. (1999), Gammage et al. (2005), Haque (2004), Mazid (1994), Rosenberry

(1995) and DTS (2006) to get per unit production (kg/ha/year) data for different methods (‘E, F & G’). It uses information of

Gammage et al. (2005) and DTS (2006) for getting production cost (Tk/kg) data (‘K, L & M’). Finally, it assumes 1US$=Tk 80 to

calculate SP production in the SW region of Bangladesh for year 2011 (‘Q’).

Source: Author’s compilation based on Bhattacharya et al. (1999), Gammage et al. (2005), Haque (2004), Mazid

(1994), Paul & Vogl (2011), Rosenberry (1995), DTS (2006) and NACA (2002).

Table A9: Shrimp and Prawn (SP) Production Cost: Approach 2





Int. Area (SW region) ha D 0 4,855 11,431

Ext. Production Cost Tk/ha/year E 43,782 70,755 100,908

Semi-Int. Production Cost Tk/ha/year F 135,724 139,960 144,196

Int. Production Cost Tk/ha/year G 467,007 481,257 495,507

Ext. Production Cost (SW region) Million US$/year H=B*E/80/1 million 57 137 260

Semi-Int. Production Cost (SW) Million US$/year I=C*F/80/1 million 25 59 10

Int. Production Cost (SW region) Million US$/year J=D*G/80/1 million - 29 71

Production Cost (SW region) Million US$/year K=H+I+J 81.61 226.05 340.63

N.B.: The minimum, average and maximum shrimp and prawn cultivated area (ha) in the SW region of Bangladesh are reported in ‘A’. This

study uses the information of Paul & Vogl (2011) and NACA (2002) to divide the cultivated area under Extensive, Semi-intensive and

Intensive methods (‘B, C & D’). It uses information of Gammage et al. (2005), Haque (2004) and DTS (2006) for getting product ion

cost (Tk/ha/year) data (‘E, F & G’). Finally, it assumes 1US$=Tk 80 to calculate shrimp and prawn production in the SW region of

Bangladesh for year 2011 (‘K’).

Source: Author’s compilation based on Gammage et al. (2005), Haque (2004), Paul & Vogl (2011), DTS (2006) and

NACA (2002).

Table A10: Net Loss due to foregone Rice



Rice Production (SW region) kg/ha B 2,509 2,509 2,509

Loss in Rice Production (SW region) kg/ha C 720 720 720

Price of Rice Tk/kg D 17.00 17.64 18.29

Net Loss of foregone Rice (SW region) Million US$/year E=A*C*D/80/1 million 22.57 30.82 37.61

N.B.: The minimum, average and maximum shrimp and prawn cultivated area (ha) in the SW region of Bangladesh are reported in ‘A’. This

study uses the information of BBS (2011) to calculate rice production (kg/ha) in the SW region (‘B’). Then, it follows Ali (2006) and

Sarwar (2005) to calculate loss in rice production for shrimp cultivation (‘C’). It uses BBS (2011) for getting price of rice (Tk/kg)

(‘D’). Finally it calculates the net loss in rice production due to foregone rice originated from shrimp cultivation (‘E’).

Source: Author’s compilation based on Ali (2006), BBS (2011) and Sarwar (2005).

30

Table A11: Net Loss in Livestock Sector: Approach 1


Net Cultivated Land (Bangladesh) acre A 19,097,544 19,097,544 19,097,544

Net Cultivated Land (SW region) acre B 871,356 871,356 871,356

Number of Livestock (Bangladesh) No. C 49,558,000 49,558,000 49,558,000

Number of Livestock (SW region) No. D 2,140,780 2,140,780 2,140,780

Number of Livestock per ha (Bangladesh) No./ha E=C/A/2.47 1.05 1.05 1.05

Number of Livestock per ha (SW region) No./ha F=D/B/2.47 0.99 0.99 0.99

Difference No./ha G=E-F 0.06 0.06 0.06

Loss in number of Livestock (SW region) No. H=G*Shrimp Area 8,259 10,861 12,787

Price of Livestock Tk/No. I 5,000 5,000 5,000

Loss in Livestock sector (SW region) Million US$/year J=H*I/80/1 million 0.52 0.68 0.80

N.B.: This study uses the information of BBS (2011) for getting net cultivated land (ha) and number of livestock in Bangladesh and SW region

which are reported in (‘A, B, C & D’). Then, it uses the calculated shrimp cultivated area in the SW region for deriving the loss in

number of livestock for shrimp cultivation (‘H’). It assumes the average price per livestock is Tk 5,000.00 and finally calculates the loss

in livestock sector for shrimp cultivation (‘J’).




Number of Livestock per HH (Bangladesh) No./HH A 1.94 1.94 1.94

Number of Livestock per HH (SW region) No./HH B 1.76 1.76 1.76

Difference No./HH C=A-B 0.18 0.18 0.18

Number of HH (SW region) No. D 1,213,574 1,213,574 1,213,574

Loss in number of Livestock (SW region) No. E=C*D 218,590 218,590 218,590

Price of Livestock Tk/No. F 5,000 5,000 5,000

Loss in Livestock sector (SW region) Million US$/year G=E*F/80/1 million 13.66 13.66 13.66

N.B.: This study uses the information of BBS (2011) for getting number of livestock per household (HH) in Bangladesh and SW region which

are reported in (‘A & B’). Then, it uses the number of HH in the SW region ‘D’ for deriving the loss in number of lives tock for shrimp

cultivation (‘E’). It assumes the average price per livestock is Tk 5,000.00 and finally calculates the loss in livestock sec tor for shrimp

cultivation (‘G’).




Number of Livestock per Person (Bangladesh) No./Person A 0.40 0.40 0.40

Number of Livestock per Person (SW region) No./ Person B 0.37 0.37 0.37

Difference No./ Person C=A-B 0.03 0.03 0.03

Number of Person (SW region) No. D 5,792,706 5,792,706 5,792,706

Loss in number of Livestock (SW region) No. E=C*D 167,726 167,726 167,726

Price of Livestock Tk/No. F 5,000 5,000 5,000

Loss in Livestock sector (SW region) Million US$/year G=E*F/80/1 million 10.48 10.48 10.48

N.B.: This study uses the information of BBS (2011) for getting number of livestock per person in Bangladesh and SW region which are

reported in (‘A & B’). Then, it uses the number of persons in the SW region ‘D’ for deriving the loss in number of livestock for shrimp

cultivation (‘E’). It assumes the average price per livestock is Tk 5,000.00 and finally calculates the loss in livestock sec tor for shrimp

cultivation (‘G’).




Area (Bangladesh) sq. km A 147,570 147,570 147,570

Area (SW region) sq. km B 12,212 12,212 12,212

Number of Livestock (Bangladesh) No. C 49,558,000 49,558,000 49,558,000

Number of Livestock (SW region) No. D 2,140,780 2,140,780 2,140,780

Number of Livestock (Bangladesh) No./sq. km E=C/A 335.83 335.83 335.83

Number of Livestock (SW region) No./sq. km F=D/B 175.30 175.30 175.30

Difference No./sq. km G=E-F 160.53 160.53 160.53

Difference No./ha H=G/100 1.61 1.61 1.61

Loss in number of Livestock (SW region) No. I=H*Shrimp Area 237,017 311,718 366,986

Price of Livestock Tk/No. J 5,000 5,000 5,000

Loss in Livestock sector (SW region) Million US$/year K=I*J/80/1 million 14.81 19.48 22.94

31

N.B.: This study uses the information of BBS (2011) for getting area and number of livestock in Bangladesh and SW region which are reported

in (‘A, B, C & D’). Then, it uses the calculated shrimp cultivated area in the SW region for deriving the loss in number of livestock for

shrimp cultivation (‘I’). It assumes the average price per livestock is Tk 5,000.00 and finally calculates the loss in livestock sector for

shrimp cultivation (‘K’).


Table A15: Net Loss in Fruit Sector Item Unit Symbol Banana Mango Jackfruit Pineapple Litchi

Production (SW) MT A 35,144 12,259 12,138 904 1,350

Loss in SW region % B -4% -8% -7% -7% -8%

Loss in SW region MT/year C=-A*B 1,340 970 889 64 114

Price Tk/quintal D 167 1,181 5,068 1,116 244

Loss in SW region Million US$/year E=C*D*10/80/1 million 0.03 0.14 0.56 0.01 0.003


Table A16: Net Loss in Vegetable Item Unit Symbol Minimum Average Maximum

Production (SW) MT A 143,795 143,795 143,795

Loss in SW region % B (Assuming same rate as fruit) -6.91% -6.91% -6.91%

Loss in SW region MT/year C=-A*B 9,939 9,939 9,939

Price Tk/quintal D 1,264 1,264 1,264

Loss in SW region Million US$/year E=C*D*10/80/1 million 1.57 1.57 1.57

N.B.: ‘A’ is calculated after assuming that the regional share for fruit in total production of the country is true for vegetable also. Similarly, ‘B’

is the rate of loss in fruit per year for SW region and it is assumed that the same rate is true for vegetable also.


Table A17: Net Loss in Tree Item Unit Symbol Minimum Average Maximum

Production (SW) Million Tk A 2,024 2,024 2,024

Loss in SW region % B -6.91% -6.91% -6.91%

Loss in SW region Million US$/year C=-A*B/80 1.75 1.75 1.75

N.B.: ‘A’ is calculated after assuming that the regional share for fruit in total production of the country is true for vegetable also. Similarly, ‘B’

is the rate of loss in fruit per year for SW region and it is assumed that the same rate is true for vegetable also.


Table A18: Employment Loss Item Unit Symbol Minimum Average Maximum


Employment in Rice N/ha/year B 151 151 151

Employment loss (gross) for SP % C 72% 72% 72%

Employment loss (net) for SP % D=C/2 36% 36% 36%

Employment Loss for SP (SW) N/ha/year E=B*D 54 54 54

Wage (SW region) Tk/day F 226 226 226

Wage Loss for Shrimp (SW region) Tk/ha/year G=E*F 12,149 12,149 12,149

Wage Loss in SW region for Shrimp Million US$/year H=A*G/80/1 million 22.42 29.49 34.72

N.B.: This study uses the calculated shrimp cultivated area (ha) in SW region ‘A’. It uses Bundell & Maybin (1996), Haider (2011) and Khan et

al. (2010) to calculate employment level (N/ha/year) in rice production ‘B’. Then, it uses Bundell & Maybin (1996), Haider (2011),

Khan et al. (2010) and Shiva (1995) to calculate employment loss (N/ha/year) for shrimp farming in SW region ‘E’. Then, it uses

Haider (2011) to find the wage lost in SW region ‘H’.

Source: Author’s compilation based on Bundell & Maybin (1996), Haider (2011), Khan et al. (2010), Shiva (1995)

and NACA (2002).

Table A19: Health Cost for Shrimp in SW Region Item Unit Symbol Minimum Average Maximum

Population (SW region) No. A 5,792,706 5,792,706 5,792,706

Morbidity rate % B 12.50% 12.50% 12.50%

Total Morbidity (SW region) No. C=A*B 724,088 724,088 724,088

Share of water borne disease (WDB) % D 62% 62% 62%

Total Morbidity for WDB (SW region) No. E=C*D 445,314 445,314 445,314

Share of Shrimp in WBD % F 50% 50% 50%

Total Morbidity for Shrimp (SW region) No. G=E*F 222,657 222,657 222,657

Treatment cost of WBD Tk/person/year H 836 3695 6554

Treatment cost of Shrimp caused WBD (SW) Tk/year I=G*H 186,224,861 822,765,393 1,459,305,925

Treatment cost of Shrimp caused WBD (SW) Million US$/year J=I/80/1 million 2.33 10.28 18.24

Average duration of remaining bedridden Day/person/year K 7 21 34

32

Total duration of remaining bedridden (SW) Day/year L=G*K 1,647,663 4,564,471 7,481,280

Half of total duration of remaining bedridden Day/year M=L/2 823,831 2,282,236 3,740,640

Wage (SW region) Tk/day N 226 226 226

Wage Loss for Shrimp (SW region) Tk/year O=M*N 186,235,328 515,922,192 845,609,056

Wage Loss in SW region for Shrimp Million US$/year P=O/80/1 million 2.33 6.45 10.57

Total Health Cost in SW region for Shrimp Million US$/year Q=J+P 4.66 16.73 28.81

N.B.: This study uses BBS (2011) to get total population of SW region ‘A’. Then, it considers Begum (1997) to get the morbidity rate ‘B’ and

calculates the total number of people affected from various water borne diseases (WBD), such as, diarrhea, cholera, dysentery etc. in SW

region ‘E’. Assuming 50% of the WBD is caused by shrimp introduction, it calculates the total number of people affected by WBD due to

shrimp introduction ‘G’. It also calculates total treatment cost of shrimp caused WBD in SW region ‘J’. Again, it calculates total wage loss

for shrimp caused WBD using Begum (1997) and Haider (2011) in SW region ‘P’.

Source: Author’s compilation based on BBS (2011), Begum (1997) and Haider (2011).

Survey Questionnaire on CBA

Cost-Benefit Analysis on Shrimp Aquaculture versus Agriculture and other

Natural Resource Management (NRM)

District

Code

Thana

Code

Union

Code

Village

Code

Sample

Code

Survey Personnel

Code

Day Month Year

Name of the Survey

Personnel

33

N.B.: The information collected through this ADB supported study will be used solely for

research purpose. The confidentiality of the provided information will be strictly

maintained. For clarity or query, don't hesitate to contact with Dr. Mohammed Ziaul

Haider, Associate Professor, Economics Discipline, Khulna University, Khulna – 9208,

Bangladesh; Phone: +88-017-3000-4131; e-mail: [email protected]; URL:

http://sites.google.com/site/haidermz.

A1. District Name : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

A2. Union Name : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

A3. Thana Name : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

A4. Village Name : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

A5. House No. : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

A6. Name of the Respondent : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

A7. Contact No. of the Respondent : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

A8. Name of the Household Head (HHH) : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

A9. Respondent’s relation with the HHH : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

(Self, Husband, Wife, Father, Mother, Brother, Sister, Son, Daughter, Others? _ _ _ _)

A10. Name of the Father of the Respondent : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

A11. Name of the Mother of the Respondent : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

Signature of the

Survey Personnel

A. GENERAL INFORMATION

34

B1. Number of Family Members: (Count the people who eat from the same kitchen)

B2. Land ownership of your household (HH) in year 2011:

Item

How

many

plots do

you have?

What is the

total area

of your

land?

(in

decimal)

Your biggest plot

What is

the size of

your

biggest

plot? (in

decimal)

What is the

distance of the

biggest plot

from your

homestead? (in

meter)

A B C D E

B2.1 Homestead

B2.2 Cultivated only rice

B2.3 Cultivated only Bagda

B2.4 Cultivated Bagda + Rice

B2.5 Cultivated Galda + Rice

B2.6 Fallow land

B. HOUSEHOLD INFORMATION

35

C. The biggest ‘only rice cultivated’ land in year 2011:

C1.1. What is the land size? (in decimal) : _ _ _ _ _ _ _ _ _ _ _ _ _ _ (B2.2D)

C1.2. Rice production : (Fill up the following table for the biggest ‘only rice cultivated land’)

Season

What is the

name of rice produced?

What is the amount of rice

produced?

(Mound)

What is the

selling price? (Tk./Mound)

Total

(Tk.)

A B C D = (B*C)

C1.2.1

Jan. –April

C1.2.2

May – Aug.

C1.2.3

Sept. – Dec.

C1.3. Direct production cost (Tk.) : (Fill up the following table for the biggest ‘only rice cultivated land’)

Season Item Seed Fertilizer Water Pesticide Family labor

Hired labor

Plough Harvesting Others

Unit Kg Kg Kg

Man-day

Man-day

Jan. –April

Quantity

Cost per unit

Total

May – Aug.

Quantity

Cost per unit

Total

Sept. –

Dec.

Quantity

Cost per

unit

Total

C1.4. Mention land rent of the plot, if any (Tk./year) : _ _ _ _ _ _ _ _ _ _ _ _ _

C. ONLY RICE CULTIVATED LAND

36

D. The biggest ‘only bagda cultivated’ land in year 2011:

D1.1. What is the land size? (in decimal) : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ (B2.3D)

D1.2. What is the amount of bagda yield? (Mound) : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

D1.3. What is the price of bagda? (Tk./Mound) : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

D1.4. Direct production cost (Tk.) : (Fill up the following table for the biggest ‘only rice cultivated land’)

Item Land

preparation Water charge

Fry Feed Fertilizer Chemical Family labor

Hired labor

Others

Unit Tk. (000) kg kg Man-day Man-day

Quantity

Cost per unit

Total

D1.5. Mention land rent of the plot, if any (Tk./year) : _ _ _ _ _ _ _ _ _ _ _ _ _ _

D1.6. Land elevation : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

For land elevation, High=3 (<2 mm wl/yr), Medium=2 (<2-4 mm wl/yr), Low=1 (<4+ mm wl/yr); Water

logging (wl) refers to the situation when crop cannot be cultivated due to existence of water in the

land; mm refers to month; yr refers to year.

D1.7. What is the year of initiating bagda cultivation in this land? : _ _ _ _ _ _ _ _ _ _ _

D1.8. Write the amount of others (fish, crab, etc.) yield (Taka) : _ _ _ _ _ _ _ _ _ _ _ _

D1.9. List the name of those other items noted in question D1.8. : _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

D. ONLY BAGDA CULTIVATED LAND

37

E1. Describe your livestock resource in year 2011.

Name of the livestock

(Write name: Cow, Goat,

Chicken, Duck, Pigeon, etc.)

Do you nurture it in

homestead? (1=Yes/0=No)

Write total number

What is the average age?

(year)

What is the total cost of

rearing? (Tk.)

Mention the amount of

family labour used for

rearing (Man-day)

What is the price (Tk.) per

unit?

F1.1. Note down the amount of your owned land that was converted from mangrove forest:

_ _ _ _ _ _ _ _ _ decimal

F1.2. If F1.1. is non-zero, when converted? _ _ _ _ _ _ _ _ _ (mention year)

F1.3. If F1.1 is zero, have you heard anything about converting mangrove forest to agricultural

land or in any other form in your area? Yes / No

F1.4. If F1.3. is yes, when? _ _ _ _ _ _ _ _ _ (mention year)

Thanks for your cooperation.

Dr. Mohammed Ziaul Haider

Associate Professor

Economics Discipline, Khulna University

Khulna – 9208, Bangladesh

Phone : +88-017-3000-4131

e-mail : [email protected]

URL : http://sites.google.com/site/haidermz

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