poor people in Ghana.
Hypothesis 1: Small-scale aquaculture has positive direct impacts on poverty and livelihoods of poor households in Ashanti Region, Ghana. The magnitude of these impacts depends on the household and livelihood characteristics and production systems of small-scale pond aquaculture farmers in Ashanti Region, and the institutional and infrastructure context.
87 Challenges of impact assessment
To test Hypothesis 1, an ex-post impact assessment of aquaculture on the livelihoods and poverty status of poor households is required. To measure this impact, the difference between i) impact indicators after adoption of aquaculture; and ii) what these outcomes would have been without aquaculture adoption (the counterfactual scenario), is needed to disentangle the effects of aquaculture from other intervening factors (Baker, 2000) and thus attribute any difference to aquaculture. However it is impossible to measure the impact indicators for adopting households had they not adopted, and in social science research it is extremely difficult to isolate a true ‘control’
group for comparison with a ‘treatment’ group. Thus ‘experimental controls’
are nearly impossible and ‘quasi-experimental controls’ such as the ‘double difference’ approach are often used. Constructing a realistic counterfactual requires both ‘before’ and ‘after’, and ‘with’ and ‘without’ scenarios to be generated for a ‘difference in difference’ approach (Baker, 2000). However as no baseline data exist on impact indicators and poverty levels of the small-scale artisanal fish farming households under analysis before they started fish farming, and on a comparison group of non-fish farmers at the same time, a true impact assessment using a ‘double difference’ approach and constructing a realistic counterfactual to test Hypothesis 1 is very difficult.
In order to overcome this, the following two groups were surveyed: i) a group of small-scale artisanal pond aquaculture farmers; and ii) a comparison group (or counterfactual) of non-fish farmers, constructed using an informal matching method, described in detail below. The limitation of this approach is that the difference in impact indicators between fish farmers and non-fish farmers can only be used to measure impact if it is assumed that both groups were on average at the same poverty level before fish farming was adopted, which may not be the case. However as each comparison non-fish farmer was chosen according to certain criteria to match them on the characteristics of their ‘paired’ fish farmer, it could be assumed that the adoption of fish farming, while not randomly adopted in the wider population as farmers ‘self select’ into adopting and non adopting groups, is randomly adopted within a core group of households with certain similar characteristics (Mendola,
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2007). Thus matching non-fish farmers with fish farmers informally controls for a combination of observable variables. This enables the impact of fish farming on poverty to be measured by the difference in poverty impact indicators between these two groups (or as the coefficient of the binary variable in a linear Ordinary Least Squares (OLS) regression to determine income) (Mendola, 2007). However these issues may potentially lead to selection bias and this was therefore tested for in the analysis presented in Chapter 5.
Quantitative data collection
A household survey was undertaken in early 2011 in the three study districts in Ashanti Region. A sampling frame of 90 small-scale semi-intensive artisanal pond aquaculture farmers who had stocked fingerlings in or harvested fish from their ponds in the past two years, was constructed with the assistance of Regional FC staff in Kumasi and district level agricultural extension agents. The comparison group of non-fish farmers was constructed from the same villages as the selected fish farmers, using informal matching criteria, to represent the counterfactual scenario as described above. The criteria to select the comparison non-fish farmers were as follows: the comparison farmer had to be i) the nearest neighbour of the surveyed fish farmer; ii) within 5 years of age of the fish farmer; and iii) a crop farmer (and not a fish farmer) as all fish farmers interviewed were also crop farmers.
These criteria were chosen as it was thought that farming households located close to each other with similarly aged household heads were likely to have similar household characteristics to their matched fish farmers.
As many as possible of the 90 fish farmers in the sampling frame (and their corresponding 90 non-fish farmers) were surveyed over the course of six weeks, from January to February 2011. In total 158 farmers (79 fish farmers and 79 non-fish farmers) were surveyed in the villages shown in Figure 9 below.
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Figure 9: Villages surveyed in three districts in Ashanti Region
The survey questionnaire collected information on the respondent’s household, the main unit of analysis, and was divided into 7 main sections as follows: i) Human Capital (household characteristics and occupations); ii) Natural Capital (access to land and ponds); iii) Social Capital (information and training on fish farming, access to extension services and association membership); iv) Financial Capital (access to credit); v) Physical Capital (ownership of household assets, access to infrastructure and facilities); vi) Livelihood strategies: aquaculture (goals, production practices), crops and livestock (crop production, livestock holdings); vii) Livelihood outcomes (key impact indicators): income (sources and level of household income for 2010), food security (dietary diversity and food adequacy), vulnerability (crises and coping strategies); impacts of fish farming on households and communities. Comparison non-fish farmers were administered the same
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questionnaire as fish farmers without the questions relating specifically to fish farming. The full questionnaire is presented in Appendix 126.
On completion, each questionnaire was checked for mistakes and inconsistencies and, if necessary, corrected by asking the respondent for clarification (either in person or by phone), to minimise error. The data were then entered into a data base in Statistical Package for Social Scientists (SPSS) Version 16 and cleaned. All outliers and missing data were checked against the questionnaires and corrected. A number of cases were removed based on the presence of outliers (in household size, income level or size of land ownership) leaving a final clean data set containing 143 farmers (69 fish farmers and 74 non-fish farmers) as shown in Table 3 below.
Table 3: Number and percentage of surveyed households by district and fish farming status
District Fish farmer
households
Non-fish farmer households
Total households N % N % N %
Amansie West 19 28 20 27 39 27
Amansie Central 20 29 19 26 39 27
Adansi North* 30 44 35 47 65 46
Total households (Nos.) 69 74 143
Notes: * Including 2 fish farmer and 2 non-fish farmer households from Obuasi Municipality
Qualitative data
Qualitative data were collected to supplement the household survey. FGDs and semi-structured interviews were conducted with fish farmers and FC extension officers and staff before the household survey to refine the questionnaire and ensure questions and impact indicators were relevant and meaningful and the choice of closed answers were comprehensive and
26Responses were pre-coded and all questions were translated into Twi (and back again to English to ensure accurate translation). The questionnaire was tested on ten fish farmers in non survey districts prior to administering the survey and the questionnaire was revised accordingly. Six enumerators (a mixture of staff and National Service volunteers based at the Regional FC office in Kumasi) were trained in interview techniques and on administering the survey questionnaire.
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appropriate for the context. Qualitative research was also undertaken after the survey was completed to help triangulate and interpret survey findings and gain a deeper understanding of the impact of aquaculture on poverty.
Participatory data collection
Participatory wealth rankings (Grandin, 1988) were undertaken in three communities to understand local perspectives on poverty and wealth, and to determine if fish farming was being adopted by those the community considered poor or only by the better off. Each wealth ranking group consisted of 8 to 12 community members of mixed ages and genders and included a community leader. The groups were asked to list all the households in their communities and then separate the households into different groups based on their wealth and/or poverty status, however they defined it. The characteristics of each group of households were then discussed to understand local conceptions of poverty and develop meaningful impact indicators to include in the household survey. Seasonal calendars were also developed in two communities using FGDs with community members (with groups made up of fish farmers, non-fish farmers, men and women) to understand: seasonal variations in activities, food consumption, labour etc.; local production systems; and how aquaculture fits into the general productive system.
Fish farm budgets were estimated with four groups of fish farmers using the method of participatory budgeting (Dorward et al., 1998a). Participatory budgets (PBs) are used to help farmers measure and analyse inputs and outputs, including non-cash resources27. The method is based on a traditional African board game (oware in Ghana) and uses local materials (stones, beans, or anything that can be used as counters in a grid) to develop a budget and does not require farmers to be numerate. The method can be
27 This method was chosen as most fish farmers do not keep good records so it would have been difficult and time consuming to record budget data with each farmer individually during the household survey. It was also thought that the process of developing a PB with farmers would be a learning experience both for farmers and for the FC extension staff who were trained in the method and facilitated the groups, enabling them to use the tool with other farmers in the future.
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used either with individual farmers, with a group of farmers where one is used as a case-study, or an average budget can be developed for a given size of enterprise, if all the farmers in the group have similar production practices (Dorward et al., 1998a). The limitation of this approach for the present analysis is the possibility of non representative farmers being selected as case studies.
Data analysis
The household survey data were analysed using SPSS Version 16 and SAS/STAT software Version 9.3 was used for specific statistical tests. The descriptive analysis compares differences in livelihood characteristics and strategies, and livelihood outcomes (or impact indicators) between the fish farming and non-fish farming households surveyed. Chi square tests for independence are used to test the significance of differences and associations between categorical variables and independent samples t-tests are used to test the significance of differences between the means of continuous variables.
Identification of poor households is necessary to test the hypothesis that fish farming has positive direct impacts on the livelihoods of poor households. As noted in Chapter 2, poverty is a multi-dimensional concept and definitions of
‘the poor’ vary according to who is doing the defining. However, for simplicity,
‘poor’ and ‘non-poor’ households are identified in Chapter 5 by estimating per capita household income and placing households above and below a poverty line, enabling the characteristics of ‘poor’ and ‘non-poor’, fish farming and non-fish farming households to be compared. The results presented in Chapter 5 show that broader poverty measures such as access to assets, household wealth and food security, are positively associated with income measures.
Composite indexes
A number of indexes related to key poverty impact indicators of household wealth and food security are used in Chapter 5 to enable easier comparison of multiple variables between groups, described below.
93 Durable goods index
A durable goods index is constructed by assigning weights (to represent value) to each of the durable goods owned by each household and summing over all assets. The methodology and weights used to construct the durable goods index (and household asset index below) are adapted from BMGF (2010) which draws on the current literature on asset based approaches to measuring poverty impact. The weights are constructed as follows: radio = 2;
TV = 4; electric fan = 2; refrigerator = 5; phone = 3; bicycle = 6; boat = 10;
motorcycle = 48; vehicle = 160; water pump = 1228. The weight is halved if the asset was owned but not functioning, and weighted values on all items then summed to produce a durable goods index score.
Household asset index
The household asset index represents household wealth. It is composed of the durable goods index, household livestock holdings in Tropical Livestock Units (TLUs)29, and additional variables related to household facilities (ownership of iron roof, latrine and flush toilet). Just as with the durable goods index, weights are assigned (to represent value) to each of the assets, facilities and number of each livestock (in TLUs) owned by each household and summed over all assets. The durable goods index weights are given above, the remaining weights used are as follows: iron roof = 6; latrine = 4;
flush toilet = 8; draught animals = 10; cattle = 10; sheep = 3; goats = 3; pigs = 2; poultry = 1; rabbits = 1; and grasscutter = 1.
Food Consumption Score and Simple Food Count
Food security is a core dimension of poverty and vulnerability. The most common definition defines food security as “access by all people at all times
28 Flush toilet and latrine are excluded from the durable goods index but are included, along with livestock and corrugated iron roof, in a more comprehensive household asset index used as a proxy indicator for wealth described below.
29 The concept of TLU provides a common unit to quantify different livestock types in a standardised way enabling comparison of total livestock holdings between groups. The TLU conversion factors used follow Jahnke (1982) as follows: draught animals (0.80); cattle (0.70); sheep (0.10); goats (0.10);
pigs (0.20); and chickens (0.01). Jahnke (1982) does not estimate conversion factors for rabbits and grasscutters so are assumed here to be equal to chickens (0.01).
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to sufficient food for an active and healthy life” (World Bank, 1986). Food security can be broken down into four components – availability, access, utilisation and vulnerability – each capturing different, but overlapping, dimensions. No single indicator can capture all of these dimensions (Migotto et al., 2006).
The most common food consumption indicator used by the World Food Programme (WFP) in their Comprehensive Food Security and Vulnerability Analyses, is the Food Consumption Score (FCS). The FCS is a proxy indicator representing the dietary diversity and energy, and macro and micro (content) value of the food people eat. It is based on dietary diversity (the number of food groups consumed by a household over a reference period), food frequency (the number of days in a week a particular food group is consumed), and the relative nutritional importance of different food groups30 (WFP, 2009). The FCS used by the WFP is adapted31 and constructed here using the survey data to compare food security between groups.
To construct the FCS, food items are grouped according to the food groups in Table 4 below. The consumption frequencies of all the food items surveyed in each food group are summed (with a maximum consumption frequency of 7 days per week). Each food group is assigned a weight (see Table 4, weight A), reflecting its nutrient density.
30 The FCS has been found to have positive and statistically significant associations with per capita calorie consumption, increasing its validity as a measure of food security per capita (Wiesmann et al., 2009).
31 In the full FCS used by the WFP a wider variety of food groups is used including staples (cereals, tubers and root crops), pulses, sugar and oil. Data on these food groups were not collected due partly to time constraints and partly due to the importance placed on understanding the impact of fish farming on the consumption of fish and protein. However it can be assumed that the households surveyed are food secure in terms of staples and pulses otherwise it is unlikely they would be able to consume fish and meat so regularly (see Chapter 5 for more details).
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Table 4: A completed Food Consumption Score table
Food group Weight (A) Days eaten in average week in dry/rainy season
(B)
Score A x B
Meat and fish (including eggs) 4 7 28
Milk 4 1 4
Vegetables 1 7 7
Composite score 39
For each household, the FCS is calculated by multiplying each food group frequency by each food group weight, then summing these scores into one composite score. Along with the FCS, a Simple Food Count (SFC) index is also constructed for both dry and rainy seasons. This uses the same methodology as for FCS but does not combine food items into groups, giving more variability in the scores.
Income Determination Model
The descriptive analysis described above may not account for all possible differences in household characteristics, other than participation in fish farming, which could cause differences in impact indicators such as income between fish and non-fish farming households. Therefore, a household Income Determination Model (IDM) is used to control for differences in observable characteristics between households and to assess the factors that contribute to differences in income between fish farming and non-fish farming households. The multiple log-linear regression model is estimated using OLS (see Chapter 5, Section 5.2.11 for details).
4.3.2 Data and methods to test Hypothesis 2
This section reviews Objective 2 and Hypothesis 2, and then describes the main data and methods used to test Hypothesis 2.