Data transformations and analysis and epistemological issues

Chapter 4: Empirical Methods and Fieldwork in Malawi

4.4 Data transformations and analysis and epistemological issues

After each round of household survey, the questionnaires were processed and entered in

SPSS. It is important at this point to stress the restricted character of the empirical fieldwork

research. In order to support the secondary data of this thesis, earlier described, the researcher

conducted a limited fieldwork exercise to gain a more definite feel for critical strengths and

weaknesses of the two programmes under discussion in this thesis. The researcher had no

resources to carry out a full-scale livelihoods and vulnerability analysis of a large sample of

households. For this reason, this thesis is mainly concerned with simple statistical methods for

describing observed food security effects of the two schemes under consideration in this

thesis. To test for differences in means, One-way Analysis of Variance (One way ANOVA)

comparing at least three sample groups or Independent-Samples T-Test comparing two groups

have been employed on the data. To test for differences in proportions between groups,

Mann-Whitney U test (for 2 independent samples) and Kruskal-Wallis H Test (for k

independent samples) have been conducted. In addition, simple regression (y=α+βt) is applied

to the different productivity time series (secondary) data in order to test for the existence of a

positive or negative trend. Simple correlation analysis using Pearson’s product-moment

correlation coefficient is also conducted to test for linear positive or negative relationships

between two variables. These are most appropriate tests to yield the evidence sought in this

research (Colman and Pulford 2006).

A number of transformations have been conducted to the original data in order to permit

comparisons between households. To allow comparisons of labour availability, ages of

household members were converted into adult labour equivalent using conversion factors in

Table 4.18. From the literature all persons above the age of 7 years provide productive labour

in one way or the other, especially on family farms in Africa (Johnson 1982, p.205). In doing

this calculation, there is no implication that ‘child labour’ is desirable. To the extent that the

labour contribution of persons aged below 19 years is considered child labour is a matter of

government policy, which currently lacks a proper framework.

The government has so far

not outlawed or criminalized the participation of young people either on family farms or in

ganyu in neighbouring farms. The attention to date has focussed on child labour in

commercial tobacco and tea estates because of a previous history of using children as cheap

labour (Eldring 2003, Otanez et al. 2006). In fact, the ISP as a main vulnerability reduction

policy initiative in Malawi targets (amongst other categories) households where the head

The Malawi Constitution provides for the protection of children from economic exploitation. The Employment Act of 2000 prohibits the employment of persons below the age of 14 but allows the employment of persons aged 14-18 years as long as it does not harm child development (Eldring, 2003, p.12) labour (Eldring 2003, p.12). Government economic reports (e.g. by NSO) describe economically active age group in Malawi to be 15-64 years.

cannot provide active labour but there are dependents, including children, who can (Mwale

2009). In the Medium Term Plan for the Farm Inputs Subsidy Programme covering the period

2011-16, there is a stated policy provision to reach resource poor households headed by

children and orphans, in the same way as special consideration is to be given to households

headed by the elderly, HIV positive persons, females, disabled persons, or household heads

caring for the elderly, chronically ill or disabled persons (Government of Malawi 2010d,

p.16). During the fieldwork, children as young as five years were reported to have participated

in family farms and even in ganyu but for the purpose of this thesis, labour input of

individuals aged below the age of 7 years is considered to be zero.

Table 4.18: Labour conversion factors (adult units equivalents)

Age (Years)

Female

Male

Average*

Below 7

0.0 7 -14

0.4 15-64

0.8

1.0

0.9 65 and above

0.0

0.5 author

calculations to remove gender bias

Source: Johnson (1982, p.205)

The fieldwork also monitored availability of food (maize) stocks at every survey round. The

stocks were collected in both grain and ufa (maize flour) in units reported by the households

and applicable to most rural Malawi. To permit comparisons to be made between households

in the sample, the quantities were ‘standardized’ into kilogrammes using conversion factors

developed by NSO and widely used as official measures for Malawi (see Table 4.19).

Table 4.19: Maize conversion factors for Malawi

Measure of maize

Kilograms

50 kg bag

46.7 90 kg bag

84.0 Pail (small)

8.7 Pail (large)

20.0 No. 10 plate

0.2 No. 12 plate

0.8 Basket (dengu) shelled

34.6 Basket (dengu) unshelled

13.3 Oxcart (unshelled)

32.3 Oxcart (shelled)

350.0 The ufa was reported in the form of ufa woyela (refined meal), mgaiwa (whole meal) or

mixed. To permit comparative analysis of households, the ufa was converted into maize grain

equivalents using factors derived by leading researchers on food security and nutrition in

eastern and southern Africa (Jayne et al. 1996). These were consistent with conversion factors

earlier provided by nutritionists in the Department of Home Economics and Human Nutrition

at Bunda College of Agriculture of the University of Malawi

. According to Jayne, et al.,

(1996, p.5), the extraction rate for mgaiwa is 96-99 per cent while the rate for ufa woyera is

65 per cent. To account for situations where the ufa in the sample was mixed, an average rate

was derived for this thesis as follows: the average of extraction rate for ufa woyera plus the

average extraction rate for mgaiwa ((60 + (96+99)÷2) ÷2). This gave a working rate of 78.8

per cent with which the ufa was converted back into maize equivalents. In any case, less than

10 per cent of the sample households reported food in ufa form at every survey visit.

To permit comparisons of the duration (days) the stocks would take to deplete, the stocks

were ‘standardized’ into household maize calorie equivalents by applying a conversion factor

(see Table 4.20) derived from two separate studies conducted on Malawi. In 1992, FAO

estimated that 468.8 grams of maize in Malawi provided 1,422 calories equal to 3,033.3

calories for each kilogram of maize (FAO 1992, Table 24). In 2010, Ecker and Qaim (2010,

p.5) estimated that 381.7 grams of maize provided 1,332 calories equal to 3,489.7 calories for

each kilogram of maize.

These two sources provide an average figure of 3,261.5 calories for

each kilogram of maize, and this is the figure utilised for nutritional conversions in the rest of

the thesis.

The household maize calorie requirements were computed by applying conversion factors that

were provided by the government in the 1998 Profile of Poverty in Malawi that drew on IHS-

1 (see Government of Malawi 2000b, p.109). The factors are adjusted for age, sex and scale

of activity a person performs but the author derived age-based averages to remove ‘gender

biases’. On the basis of these conversion factors, the average per capita maize requirement is

0.43 kg per day or 158.4 kgs per year. To estimate number of days the food stocks would take

Mr Kingsley Masamba, Lecturer in Food Science at Bunda College, provided an extraction rate of 90 per cent for Mgaiwa and 60 per cent for ufa woyera as conversion factors used by Department of Home Economics and Human Nutrition at the College.

Mr Neil Orchardson (Technical Advisor for Food Security and Nutrition) of the Ministry of Agriculture and Food Security informed the researcher in October 2010 that the Ministry has since adopted this as the ‘official’ conversion factor.

to deplete, total household maize calorie requirements per day were derived by summing up

daily maize calorie requirements of different individuals in the household. This was then used

as a denominator with which to divide the total maize calorie equivalents:

Days food would last = Available food stocks (maize calorie equivalents)

Daily household maize calorie requirement

Table 4.20: Per capita calorie and equivalent maize requirements (kgs)

Age

(Years

Mchinji sample Equiv group (yrs)

Daily calorie requirement Daily maize calorie Daily maize requirements (kgs) Annual maize requirements (kgs) 46 <1 0-0.9 820 597.0 0.18 65.7 1-2 1-1.9 1,150 837.2 0.26 94.9 2-3 2-2.9 1,350 982.8 0.30 109.5 3-5 3-4.9 1,550 1,128.4 0.35 127.8 5-7 5-6.9 1,800 1,310.4 0.40 146.0 7-10 7-9.9 1,950 1,419.6 0.44 160.6 10-12 10-11.9 2,075 1,510.6 0.46 167.9 12-14 12-13.9 2,250 1,638.0 0.50 182.5 14-16 14-15.9 2,400 1,747.2 0.54 197.1 16-18 16-17.9 2,500 1,820.0 0.56 204.4 18-30 18-29.9 2,600 1,892.8 0.58 211.7 30-60 30-59.9 2,567 1,868.5 0.57 208.1 60+ 60+ 2,225 1,619.8 0.50 182.5 Average 1,941 1,413.3 0.43 158.4

Source: as explained in the main text above.

4.4.2 Epistemological position in this thesis

Research in the field of social sciences entails purposive and rigorous investigation that aims

to generate new knowledge (Sarantakos 2005, p.4) based on facts that are not just given but

also produced (Mukherjee and Wuyts 1998, p.243). Different research works adopt different

epistemological stances regarding views on ‘generation of new knowledge’ but this research

adopts view that allows for the comparison of different ideas on a relative basis (Proctor

1998b). This epistemological position can be referred to as critical realist strands of thought

which approaches issues of knowledge as constituting both realism and practical activity; that

reality exists but it has to be interpreted within a given context by interpreting the observed

interactions between powers, institutions, actors or forces. In the field of social sciences, the

As a rule of thumb, the government encourages Malawians to keep two and a half 50-kg bag of maize per person per year. But working figures vary considerably. In the BNB surveys described in earlier sections of this Chapter, the methodology has adopted a working maize requirement of two 50-kg bags per month for a family of six persons, translating into 200 kgs per person per year. The average of the two sources yields 162.5 kgs per person per year, which is very close to 158.4 kgs derived for this study – a difference of 4.1 kgs may be acceptable for all practical purposes.

purpose is to uncover deeper issues around causes, patterns, outcomes and effects around

themes of research interest; the interaction between ‘powers’ in issues around poverty and

vulnerability and their most recent policy responses emphasizing social transfers/agriculture

in the case of this thesis. This interpretive process involves aspects of subjectivity as set of

empirical methods are explained and elaborated. The results of such analytical process in this

thesis can provide the basis for ‘policy’ change. In this research, examples that have received

critical analysis include power relations (e.g. role of private sector in ISP), ethical (e.g.

leakages of coupons or social transfers), economic (e.g. cost effectiveness of social transfers)

or political (e.g. politics of social transfer targeting and reporting of the outcomes that have

created unexplained gaps with reality on the ground (Bhaskar 1989, Proctor 1998a, Carter and

New 2004). This study combines social sciences including economics (e.g. optimal use of

resources such as inputs) and politics (how political factors can override evidence-based

policy making) with agriculture (e.g. maize production), geography and environment. These

disciplines fall into either or both of the two main paradigms of positivist or post-positivist

perspectives on knowledge generation. Importantly for this study, a number of different

fieldwork methods were found appropriate, and a combined qualitative and quantitative

approach was followed (Booth et al. 1998, Ellis 2000, Kanbur 2003).

The field work was conducted with the assistances of six research assistants (RAs) who

helped with field data collection variably at different periods but three worked on more or less

‘permanent basis’ while the other three were brought in periodically to confirm the data

collected. The original plan was to recruit research assistants resident in each of the three

study VDCs. It was apparent from initial consultations that the CSCTCs did not favour the

idea because it would kill the spirit of volunteerism since members of the CSCTCs were not

receiving any formal remuneration for their time, resources and services.

On the basis of

these reservations, two assistants were recruited from communities neighbouring VDCs not

participating in the study. Agricultural staff at district and Mikundi EPA helped in the

identification and recruitment process. One RA was recruited from the district headquarters

(Mchinji Boma) and worked with the researcher throughout the study from initial sample and

community selection to data entry. All the three RAs commuted to the study sites on daily

basis using bicycles.

It is discussed in Chapter 6 that members of the CSCTCs are not volunteers in strictest sense because they draw a monthly allowance, now at MK1500 per month in addition to daily allowances every time they participate in project activities, mostly outside their communities.

A central place known as Matutu, a trading centre which forms a boundary between TAs

Kapondo, Nyoka and Mduwa, was identified as a meeting place for the research team. Matutu

also has some houses for agricultural extension workers from Mikundi EPA, one of whom

volunteered venue for training and meetings throughout the fieldwork. Prior to the fieldwork

data collection, one week training was provided to the RAs to orient them to the research

objectives and design, data collection methodologies to be employed and ethical issues in

research (sensitivity, confidentiality etc). Part of the training included a pre-test of the

baseline questionnaire in nearby villages. The pre-test experiences were used to refine the

questionnaires and to map out field logistics before fully-fledged baseline survey.

The three RAs implemented the baseline survey (90 households), first tracking survey (30

households) and final survey (90 households) through interviews with heads of households or

their proxies. Each household survey phase took two weeks maximum and each household

tracking phase was completed within one week. In addition to community level consultations,

the researcher supervised and monitored the data collection by the RAs. But to ensure quality

work, the payment of the RAs was tagged to each properly completed, checked and approved

questionnaire. In the second tracking survey, however, the RAs responsible for Mduwa and

Chiti were replaced with two new RAs (one from Lilongwe and another from Mchinji Boma;

and a third was recruited from Bunda to specifically help with focus group discussions) in

order to independently verify some issues. The new RAs were oriented to the study and

questionnaire. The approach also changed - the research team (3 RAs & the researcher)

moved together, completing the survey in one community before moving to the next

community. Each study site took one full day to administer 10 questionnaires. All the

household level interviews were conducted at respondent’s house/home using a structured

questionnaire. Immediately after completion of each round of household survey, the

questionnaires were coded and entered in SPSS. One RA did the data entry with regular help

from the researcher.

4.5Summary

This chapter has reviewed methodologies that are relevant to the construction and analysis of

this thesis. These methodologies divide into two main categories: those associated with

Malawi government statistics, and those associated with the author’s own fieldwork in

Mchinji district. It is considered important to set out the basis of various government

statistical series or surveys, since the provenance of these affects both questions of data

accuracy and the information that is available for food insecurity policy decision making. In

particular, the targeting methods used in the Mchinji social cash transfer scheme have

depended on a particular interpretation of data contained in the IHS2 conducted in 2004-05.

The chief measure of success of the ISP are the output gains estimated according to the

methodology set out in section 4.2.2 above, which, as discussed in that section, allows

discretionary discussion of data to occur at particular points in the methodology. All the

methods discussed in Section 4.2 above in one way or another have a bearing on data and its

policy interpretation in this thesis.

The second half of the chapter describes the methodology that applies to the fieldwork study

conducted in Mchinji district. The section describes how major data transformations that have

been conducted to the original data to allow comparisons between households. The restricted

scale of the fieldwork is also discussed; because the purpose was to support secondary data

sources described in Section 4.2. The importance of taking epistemological issues into great

depth when conducting empirical research is recognised but not a binding factor in this thesis;

although ‘fair’ consideration has been taken in that direction.

Chapter 5: Agricultural Input Subsidy Programme

In document Comparison of Agricultural Input Subsidies and Social Cash Transfers as Policies for Reducing Vulnerability to Hunger in Malawi (Page 121-129)