Socioeconomic differentiation among African peasants: Evidence...

Journal of International Development: Vol.2 No.1, Jan. 1990 pp.77-109 77

SOCIO-ECONOMIC DIFFERENTIATION AMONG

AFRICAN PEASANTS: EVIDENCE FROM ACHOLI,

SOUTHERN SUDAN

WILLIAM J. HOUSE* ILO Population and Human Resources Adviser,

Ministry of Finance and Economic Planning, Khartoum, Sudan

and

KEVIN D. PHILLIPS-HOWARD University of Jos, Nigeria

Abstract: This paper is concerned with the nature and extent of inequality and poverty in a rural African economy which has yet to experience any form of modern economic growth. Southern Sudan remains one of the least developed areas in Africa, relying largely on subsistence crop cultivation and traditional pastoralism and where the only major modem sector activity is urban-based public administration. The aim of this study is to quantify and explain the nature of differences in household well-being in a fairly typical rural area of this economy set in the context of the Chayanov model ofpeasant behaviour. It is argued that

* The authors gratefully acknowledge the financial and material support provided by the United Nations Population Fund (UNFPA), the International Labour Organisation (ILO), the Norwegian Church Aid/Sudan Programme (NCA/SP), and the University of Juba. The results presented in this paper are derived from one of the activities of the UNFPAALO project 'Population and Human Resources Development and Planning in Southern Sudan' which was operational between 1982 and 1986. The authors would like to thank Richard Anker, Deborah DeGraff, Ghazi Farooq, Henry Rempel, Gerry Rodgers, Guy Standing and RenC WCry for constructive comments made on an earlier draft and Carlos Garcia for computer programming assistance at ILO Geneva. However, the authors remain solely responsible for what follows.

18 Journal of International Development

policy-making requires the exploration of the underlying behaviour of households and how they allocate land and labour in order to help support their survival strategies. Some general policy implications are drawn from the analysis of data collected in a small sample survey which may have a wider application in similarly neglected parts of rural tropical Africa.

INTRODUCTION Research on the determinants of economic differentiation and poverty

has become widely prevalent over the past two decades, culminating in a large body of national-level data on income inequalities for many countries. Two recent surveys hve collated this data and summarised the widely adopted analytical approaches and results (see van Ginneken and Park, 1984, and Lecaillon, Paukert, Morrisson and Germidis, 1984). Few studies, however, have focused on the determinants of economic differentiation and poverty among rural households in strictly Third World settings, particularly in Africa.' Evidently, careful data collection, which can be used to test models of household behaviour in largely susbsistence agricultral economies, remains scant depite the obvious advantages of such an approach for purposes of planning policy intervention.

This paper is concerned with the nature and extent of inequality and poverty in a rural economy which has yet to experience any form of modern economic growth. indeed, Southern Sudan remains one of the least developed areas in Africa, relying largely on subsistence crop cultivation and traditional pastoralism and where the only major modern sector activity is urban-based public administration. The aim of the paper is first to quantify and then to explain differences in well-being in a fairly typical rural area of this economy set in the context of the Chayanov model of peasant behaviour. For policy purposes it is important to explore the underlying behaviour of households and to study how they allocate the principal resources, land and labour, at their disposal in order to maximise household consumption and maintain the reproduction of family labour. With this focus, policies can then be formulated to help support the survival strategies of rural households.

How might rural differentiation be explained and what is its implications for policy? Temporary differences in household income in a single year might be the result of luck and the vagaries of the weather. However, if the observed differences for a single year are indicative of more permanent differences in well-being, then household endowments of both physical and human resources are likely to correlate fairly well with current income. If rural inequalities are small, policies of general assistance to the rural peasantry

Socio-Economic Differentiation Among African Peasants 79

would be most appropriate. If inequalities are large, but do not correlate well with endowments but result from random shocks, then insurance schemes may be the most efficient policy intervention (Collier, Radwan and Wangwe, 1986, p.70). But if inequalities are large and reflect more permanent differences in the distribution of physical and human endowments, then those policy interventions which induce greater egalitarian access to these resources would be optimal.

While the case study is of a part of the Acholi area of Southern Sudan, it is anticipated that some of the implications drawn for policy will have a wider application throughout rural tropical Africa. The following section presents an overview of the local economy and this is followed by a conceptual model of subsistence behaviour. Subsequent sections present some simple bivariate relationships between certain socio-economic and demographic variables and inequality in household cash incomes. This is followed by a multivariate analysis of the determinants of household cash income and the paper concludes by drawing certain implications for policy.

11. A PROFILE O F THE POPULATION AND THE LOCAL ECONOMY The Acholi case study area is located in the south-east comer of Sudan,

covering an areas of 2,700 square kilometres on the east bank of the Nile, and bordering Uganda which lies to the south. In the 1983 census the population was enumerated to be 32,000, although there may have been an underestimate of up to 25 per cent as was the case elsewhere in Southern Sudan (Republic of Sudan, 1984). Population density at roughly 12 persons per square kilometre is very low, particularly in comparison with other neighbouring countries. Therefore, land is readily available to all, although the quantity of good quality fertile land, close to a road and water source, differs across the area. The variation of as much as 20 per cent in any year for the mean annual rainfall adds to the precarious existence of the inhabitants of the area.

The local economy is based on near-subsistence agriculture but includes an increasing amount of production for the market. The basic economic unit remains the independent household or family within which there is a clear-cut divison of labour by sex. Men are primarily responsible for clearing and digging the land in preparation for planting by usingsimple hand hoes. In the dry season they engage in hunting and in house and granary construction. Women are responsible for weeding, harvesting, water collection and most of the food processing, including beer brewing which is an important source of cash income. Emphasis is given to the cultivation of seed or cereal crops. Millet and sorghum (dura) are the staple food crops, although maize has recently grown in importance. Sorghum is a drought-resistant crop which


thrives where rainfall is irregular while cassava provides food security because it does not require immediate harvesting but can be left in the ground over a number of growing seasons. It is also the essential ingredient of aragee, a locally distilled alcoholic drink.

The family labour constraint imposed on the size of the household’s cultivated area can be relaxed by membership in a reciprocal digging company or kampone. Up to forty men in the company each dig a measured plot of member’s land in exchange for an agreed quantity of beer, or beer and food, on a reciprocating basis. Membership of such a group is restricted to those farmers with sufficient accumulated resources which can be used to attract such labour. In this case wealthier farmers may be net hirersof labour through the system and poorer families net suppliers to other households.2 Even then, family labour inputs for weeding, crop protection and harvesting impose severe constraints on farm acreage and output, particularly where the ability to hire wage labour is severely limited.

Transport and marketing problems retard the movement of surplus produce to markets both within and outside of the area. Since 1973 Acholi has been the focus of attention of the Norwegian Church Aid (NCA), an agency charged with promoting agricultural and general development. It has experimented with demonstrating the advantages of ox-ploughing in an area which is not highly conducive to cattle-keeping because of the presence of tse- tse fly. In addition, it has established an extension service to promote improved seeds and technology and attempted to overcome the marketing problems by purchasing surplus produce through the co-operatives it has sponsored and by improving access roads through labour-intensive construction methods. Roving merchants using their own heavy trucks sometimes provide opportunities for Acholi farmers to sell surplus produce, often on very unfavourable terms, although this sales outlet is unreliable. These merchants also bring into the area goods not produced locally. The retailing activities of the co-operatives established by the NCA have attempted to break the monopoly power of these private merchants.

In 1983, before the outbreak of the on-going civil war in Southern Sudan, which has resulted in mass out-migration, famine and a complete halt to developmental activities, the authors conducted a sample survey of 250 randomly selected households in a fairly typical part of Acholi. It is from this survey that the general characteristics of the population are derived.j Its age structure is fairly typical of tropical Africa, with 46 per cent of the total less than 15 years, indicating high fertility. Indeed, the mean number of live births for women aged 50-59 years is 7.5 and 6.7 for those aged 45-59 years. Female dominance of the 20-29 age group, where the male-female sex ratio is 77, suggests that younger males have out-migrated, often to the regional capital


of Juba, where the sex ratio of this age-group is estimated to be 131 (House, 1985a). Mean household size is 7.8 persons, rising with the age of the household head from 5.4 persons for those aged 20-24 years to 7.7 for those aged 35-39 years, and it peaks at 9.0 for heads aged 50-59 years. This pattern reflects the accumulation of own children and extended family members as the head and household pass through the various stages of the life cycle.

In such a subsistence economy children often contribute to household production at a relatively early age. If persons under 12 years are considered to be dependents then mean dependency, defined to be the ratio of the number of dependents to those aged 12 years and over, is 0.70, suggesting that for every two persons contributing to household production there is slightly more than one dependent. When those aged 60years and over, who constitute only 1.7 per cent of the population, are also considered as dependents, then the ratio rises only very slightly.

One indicator of the poverty status of a population and, in turn, an important determinant of its productivity and overall income and welfare, is the underdeveloped state of its human resources, as reflected in its formal educational attainments. Given that there is only one primary school in the survey area to serve a primary school age population of 2,500, it would be surprising to find educational attainments to bevery high. The lucky few who progress beyond primary school must proceed outside the area to attend junior and senior secondary schools. Overall, educational attainments are appallingly low whereby, for those aged 25-29 years, by which timeschooling will have ended for most people, mean years of education is almost 5.5 for men but only 1.5 for women. Current school enrolment rates are equally depressing. Thirty-seven per cent of boys and 40 per cent of girls in the age- group 10-14 years are not currently attending school. The quality of education leaves much to be desired, given the inadequate facilities, poor quality teachers and intermittent opening of schools (House, 1985b).

The dominance of agriculture is confirmed by thesurvey results. Only 1 1 of the 250 household heads failed to report farming as their principal occupation and these few exceptions are engaged largely by the public sector as teachers, paid labourers, health-workers or sub-chiefs. Ninety per cent of the whole adult population report farming as their main activity and only students make any other significant contribution to the list of main occupations. However, there are many other secondary, traditional income- generating opportunties in the area which include the brewing of alcoholic drinks by women, honey gathering, housebuilding and hunting. But the focal point for all these activities is subsistence farming.

The three crops most widely grown by over threequarters of the population are sorghum, cassava, and sesame (sim-sim). Millet is grown by


more than one-half of the sample households. Other relatively important crops which are grown by at least one-quarter of these farmers include groundnuts, beans, peas, maize and sweet potatoes. The variety of the crops grown is overwhelmingly local, although roughly one-third of the minority who grow maize have planted an improved variety. Of the three most widely grown crops almost one-half of the sampled households sell some of their output. However, while the exchange economy has made deep penetration in the area, almost one-quarter of households claim to eat all of their harvest themselves and to make no sales of any crops. Meanwhile they can still supplement the family income by brewing and other off-farm activities.

What is particularly revealing about the list of crops grown and sold is the lack of any pure cash crops. Those crops with the widest market are all food crops, suggesting that they are sold only when the household has a surplus over and above its own domestic consumption requirements. Of the four major crops the largest mean area is planted with the basic staple of sorghum (2.2 hectares), followed by sim-sim (1.6 hectares), cassava (1.1 hectares) and millet (1 hectare).' From the farmers cultivating each crop a majority of at least two-thirds claim to have grown enough during the previous season for their own domestic subsistence requirements. However, if we count those households who did not grow a particular crop as not having enough for domestic subsistence then the picture is very different. Only in the case of sorghum, cassava and sim-sim do more than 50 per cent of all the households in the survey grow enough of their own produce to be self- sufficient. Those households which are deficient in the basic foodstuffs must necessarily generate surpluses of other crops, sell beer o r other products, or sell family labour, to generate surpluses in cash or kind to be exchanged for these basic staple foods. It is these households whoare likely to enjoy the least food security, and may be close to the absolute poverty line, particularly when wage employment opportunities are so severely restri~ted.~ To identify the socio-economic and demographic characteristics of these potentially poor households remains one of the principal goals of this paper.

111. In order to generate a set of testable hypotheses which can be used to

explain real income inequality in such an area as Acholi, it is worthwhile to summarise a model of peasant behaviour favoured by Hunt (1984) to describe the mechanics of resource allocation by households in a similar low-potential rural area of Kenya. It is derived from the model of early Russian peasant behaviour formulated by Chayanov (1986) where emphasis is given to the household decision-making unit and its collective needs, constrained by climatic, social and household-level factors. The typical household, living in a

A CONCEPTUALISATION OF SUBSISTENCE BEHAVIOUR LN ACHOLI


hostile environment, is both a producing and consuming agent, and most productive activity is undertaken by its members. Since adequate consumption intake is dependent on what the household itself produces and healthy members provide, who are the prime labour inputs in production, the principal objective must be to achieve self-sufficiency in basic food. This can be achieved by production for own use, production for the market and by selling some household labour time.

The household is conceived of as a utility-maximising rather than profit- maximising unit because of its overriding concern to minimise the risk of harvest failure, or to maximise survival chances, arising from the underdeveloped state of markets for basic foodstuffs and the unreliability of rainfall. Such risks are minimised by the household attempting to grow enough drought-resistant crops so as to be self-sufficient in subsistence food. As a result, the composition and quantity of production for domestic consumption will be determined by the identified needs of the household, given its size and structure, and will not be influenced by the market prices of such food crops. In this case the resources available for generating cash income from farm sales, whether from the same crops consumed by the household or from pure cash crops, will be the residue of total household resources, less resources required for subsistence production, less resources applied in off-farm self or wage employment.

It is widely accepted that this kind of behaviour is rational, given the particular social and economic environment. The primary objective must always be to attain some minimally socially acceptable level of consumption for members of the household, which includes adequate supplies of basic foodstuffs, as well as such non-food items of housing, clothing, education and health. Another objective is the maintenance of a set of social relationships which are secured through reciprocal exchange, feasts and ceremonies, all of which will require access to resources, including time. This depends on SUwess in accumulating a surplus of food and cash in order to meet the inherent risks of living in such an environment and to display such forms of wealth as wives and livestock to attain social status (Rempel and Lobdell, 1985). The principal aspect of decision-making in the household concerns the allocation between competing uses of family labour, which is the prime scarce resource at its disposal.

What are the determinants of a household’s welfare status in such an economy? Such status will be reflected in the extent of food security and the level of its current cash income, as well as being indicated by the kin& of consumer durable goods it has managed to acquire, including bicycles, radios, clothing, housing and items of basic furniture. It is hypothesised that the household’s welfare status will be partly determined by:


(1) the household's endowments of both physical and human productive resources, the most important of which are adequate supplies of fertile agricultural land and of family labour to work the land; managerial ability and a sound knowledge of the local farming environment, including an ability to take risks and innovate. These would be reflected in the efficient use of prevailing technology and successful cropping strategies which ensure food self-sufficiency and crop surpluses. These can then be either directly sold or used within the household as intermediate inputs in food processing activities, the final products of which are sold for cash or exchanged for goods in kind. Alcoholic drink is an important item produced from grain surpluses which can be sold or used to attract and pay members of digging groups whose employment can go a long way to relieve the family labour constraint and increase the cultivated area. It may be that such characteristics ofthe principal decision-maker are acquired by having lived outside of the immediate locality at some time in the past or having made contact with agricultural extension workers; the formal educational attainments of household members, which may be directly related to managerial ability on the farm and improved access to the limited number of off-farm wage employment opportunities; the stock of capital assets e,mployed on the farm. In this context these include only simple hand tools and livestock; the stage of the life-cycle and the dependency burden in the household, as defined by the number of non-labour force members relative to active members available for work. In addition, the absolute number of household producers may influence the level of output per worker and per household member through the influence of task specialisation and economies of scale. The impact of this factor will depend on the technology in use, and it may be small in Southern Sudan where traditional, low-potential farming practices are widespread; the frequency and size of remittances received by the household from former members who have out-migrated, perhaps to an urban area where wage employment chances are greater. Larger sized households may be better able to afford to lose members this way since the labour constraint

(2)

(3)

(4)

( 5 )

(6)


is relaxed. Such a strategy of separating some of the younger, more educated members from the household can be viewed as a risk-averting response, since the credit and insurance markets that are often biased against small farmers are bypassed. This is achieved by migration, via its role in the accumulation of surplus from urban remittances which can be used to improve production technology, and through reducing thelevel of exposure to risk by diversifying the household’s sources of income (Stark, 1978). Better off households may also be in a position to support an out- migrant through resource outflows during the initial period of settling-in and job search in the new place of residence; the quality and fertility of soil, the adequacy of rainfall and population density in the vicinity where the household resides, all of which help to determine accessibility to land.

The underdeveloped state of markets in the Acholi area means that households cannot depend on being able to purchase basic food supplies as and when required. Their rational strategy must be to engage in market sales only after ensuring their own subsistence requirements are satisfied. In this case, a household‘s welfare status would be reflected, first and foremost, in the adequacy of its food security through self-sufficiency. Additional resources can then be used to generate harvest surpluses and cash sales, the proceeds from which can be spent on purchasing more education for children, better housing and on a limited set of durable consumer goods.

It may be suggested, therefore, that a household which is relatively food Secure and which achieves a relatively large cash income per member in a particular year should enjoy a higher standard of living than a household which is deficient in home grown food and which has a smaller cash income, given the premise of our behavioural model that the former has not made cash sales at the expense of sacrificing the household’s food self-sufficiency.6

(7)

IV. How unequally distributed are levels of welfare in Acholi, as reflected in

differences in cash incomes, food security and asset holdings? Having documented the nature and extent of inequality in this section, we can then proceed to attempt to explain such inequality through our model of peasant behaviour. Such an analysis is most relevant for policy formulation in any frontal attack on rural poverty.

Table 1 shows that monthly cash income and cultivated land area are far from equally distributed among Acholi households, as reflected in the size of the Gini coefficients which are in the region of 0.4. These data indicate

T H E NATURE AND EXTENT OF INEQUALITY

00

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Tabl

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1st

2nd

3rd

4th

5th

6th

7th

8th

9th

10th

2.0

3.4

4.7

6.2

7.6

9.1

10.5

12

.7

16.3

27

.5

1.6

3.0

4.5

5.3

6.4

7.7

9.4

11.4

14

.8

35.9

1st

2nd

3rd

4th

5th

6th

7th

8th

9th

10th

1.8

3.7

4.6

5.5

6.7

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11.2

16

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extensive inequality which may not be so apparent to the casual observer of the area.’ However, we should be careful not to exaggerate the differences since even the richest household, with a monthly cash income per adult equivalent of SE26, or $10.6, would hardly enjoy a luxury existence.8 Even so, this represents a twentyfold increase over the mean cash income of the poorest one-tenth of the pop~ la t ion .~

It is of interest to examine how well the other indicators of welfare, such as food security and household asset holdings, relate to cash income per adult equivalent. If they are closely related, this would lend credence to our underlying model of peasant behaviour and suggest that the choice of any one of these indicators would give a fairly consistent picture of a household’s relative welfare status. We have chosen to focus on differences in cash income per adult equivalent as a scalar measure of well-being for obvious reasons. It should be monotonically related to overall food security, to the extent that it is generated from the sale of surplus food crops, and it reflects the purchasing power of the family with which to acquire food in pre-harvest periods and other goods and services which can improve welfare.

In Table 2 we examine the extent of economic differentiation in Acholi by considering indicators of welfare status of three cash income classes of households. Those with a monthly cash income per adult equivalent below SE2 are termed “Poor”; those with cash incomes between SE2 and S€5 are called ‘Middle’; and those with an adult equivalent cash income of more than SE5 are called ‘Better-off. The middle group comprises one-half the sample of households while the remaining one-half are almost equally divided between households who are poor and better-off. It turns out that the poor are not only deficient in cash income but exhibit significantly lower levels of consumption and self-sufficiency in basic food crops and enjoy lower access to the servides of various durable goods than the other two income groups. Their ownership of the commonest animals, which can be exchanged for grain at times of dire need, is also deficient.

From the responses on food self-sufficiency and security in Table 2, we can be fairly sure that, had we been able to undertake the daunting task of imputing the value of home-produced food consumption and adding this to the value of cash income, the resulting ordering of households would not be so much different from that found by cash income alone. In particular, such a more comprehensive indicator of overall welfare would surely identify those Same households as we have determined to be poor in Table 2. Given these revealed patterns, cash income per adult equivalent appears as a fairly consistent proxy measure of the relative Welfare status of peasant households in Acholi, especially in identifying the very poorest group.I0 This result is reassuring because it tends to confirm our underlying behavioural model of


Table 2.

INCOME PER ADULT EQUIVALENT WELFARE INDICATORS BY SIZE-CLASS OF MONTHLY CASH

% of Households

Mean Cash Income p.A.E./annum (SE)

Food Security 9% Self-sufficient in:

Millet Sorghum Cassava Sesame Groundnuts

% Eating ‘Enough’ Food

Asset Ownership: % Owning: Radio Clock/Watch Bicycle Table Bed/Matt ress Cattle Sheep Goats Chicken

Number owned per A.E.:

Sheep Goats Chicken

Total Animals Owned

‘Poor’

24.4

17.9

21 64 44 51 16 51

2 2

15 26 54 0

13 62 82

0.1 0.6 1 .o

10.9

‘Middle’ ‘Better-Off

50.0

41.1

52 91 70 74 42 77

7 3

29 30 65

1 27 74 96

0.2 1 .o 1.9

20.2

25.6

119.2

59 81 80 89 48 70

3 8

22 50 78

5 27 66 92

0.2 0.9 2.0

16.2

F- All statistic

100

55.4 212.6 **

46 82 66 72 37 69

5 4

24 34 66

2 24 69 92

0.2 0.9 1.7

16.9

11.5 ** 11.1 ** 10.4 ** 12.7 ** 8.3 ** 6.8 **

1.7 1.8 2.3 5.2 ** 4.1 * 2.7 2.5 1.7 5.4 **

1.9 3.5 * 9.2 ** 9.0 **

Note: **, indicate significant difference between means at 1% and 5% levels respectively. Source: Authors’ survey.


peasant behaviour. By attempting to account for inequalities in household cash income our explanations may also help to explain differences in more general welfare attainments.

V. THE CAUSES OF DIFFERENTIATION We begin our investigation of the causes of inequality by considering the

relationship between household cash income per adult equivalent and particular sources of income. Do the poor have fewer sources of cash income? Do they have restricted marketing opportunities to sell smaller quantities of crop output? We shall then go on to consider the extent to which income differences are due to shortfalls in particular endowments and to a misallocation of resources to lower yielding activities.

Sources of Cash Income The structure of income sources is not very different for our three socio-

economic classes, as revealed in Table 3. All rely overwhelmingly on the head’s cash income from farm sales and the wife’s cash income from making alcoholic drinks. Nor is the distribution of non-farm employment very different since it accounts for less than 5 per cent of employment of the household head in each of the three income classes. Other household members’ income and remittances are relatively unimportant.11 It is the absolute level of earnings of the head and his wife that determine a household‘s relative income status. For the better-off, the head‘s cash income is over nine times greater than in the case of the poor, while the difference is Over four for wife’s income.

Since both these sources of cash income are so dependent on the generation of surplus crops over and above subsistence requirements, we must seek to establish why some households are able to realise such surpluses.

Dvferences in crop sales Is the participation of the poor in the market and the quantity of their

sales less than for the better off? Are the poor in relative poverty because many of them do not grow the major crops and so are unable to generate a surplus for sale? Or is the distribution of land to various crop mixes inefficient? The answers to some of these questions can be inferred from Table 4.12

In Table 4 we find the poor often have a much lower incidence of growing and selling the major crops. When a crop isgrown it appears that, after trying to satisfy their subsistence requirements, the poor have little or no surplus remaining to sell in the market. In the case of cassava, the basic ingredient of alcoholic drinks brewed in the area, a much reduced proportion of poor

Tabl

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S PE

R A

DU

LT

EQ

UIV

AL

EN

T,

BY

CL

ASS

OF

CA

SH I

NC

OM

E

$ % 3

Cro

p

Mill

et %

Gro

win

g %

Sel

ling

‘Poo

r’

b

‘Mid

dle’

‘B

ette

r-O

ff

AN

F- V

alue

3 d 2

36.1

58

.4

65.6

54

.8

6.4

**

5 3 4.

9 10

.4

20.3

11

.6

3.9

* 3

Sorg

hum

%

Gro

win

g %

Sel

ling

Sale

s per

A.E

.

% G

row

ing

Cas

sava

98.4

23

.0

0.1

59.0

- Sa

les p

er A

.E. (

in ti

ns)

0.01

0.

07

0.14

0.

07

3.9

* b 2 s s

3.2

* 2

1.7

6.9

**

R‘

% S

ellin

g Sa

les p

er A

.E.

% G

row

ing

% S

ellin

g Sa

les p

er A

.E.

Sesa

me

18.0

0.

05

99.2

95

.3

98.0

41

.6

54.7

40

.4

0.43

1.

04

0.5

82.4

51

.2

0.21

93.8

79

.6

67.2

47

.2

2 2 B 13

.4 *

* 18

.1 *

* 2

0.26

0.

19

15.4

**

85.1

3 88

.0

93.8

88

.8

1.2

23.0

45

.6

68.8

46

.0

14.6

**

0.09

0.

26

0.97

0.

40

12.7

**

Tabl

e 4

(con

tinue

d)

Gro

undn

uts

% G

row

ing

31.1

52

.8

54.1

48

.0

4.1

**

9% S

ellin

g 6.

6 21

.2

15.6

19

.2

6.2

**

Sale

s per

A.E

. 0.

05

0.25

0.

26

0.21

1.

8 **

Be

ans 9%

Gro

win

g 9%

Sel

ling

Sale

s per

A.E

.

21.3

38

.4

31.3

32

.4

2.8

4.9

21.2

23

.4

20.8

6.

6 **

0.

01

0.11

0.

10

0.08

6.

2 **

M

aize

6 %

Sel

ling

16.4

38

.4

3.1

24.0

17

.8 *

* r: 3 E

AN

Cro

ps

B

% G

row

ing

26.2

43

.2

4. I

29.2

17

.3 *

*

Sale

s pe

r A

.E.

0.09

0.

51

0.05

0.

29

14.5

**

Num

ber

of C

rops

Gro

wn

4.1

5.5

4.6

4.9

12.3

**

g 20

.0

6.3

24.0

20

.9 *

* 2. 2 4- i! 3 B

b

Num

ber

of C

rops

Sol

d 1.

1 2.

8 2.

1 2.

3 18

.1 *

* 9%

With

No

Sale

s 50

.8

3

Nor

e:

Sour

ce:

Aut

hors

’ survey.

All

grai

n crops

sold

are

mea

sure

d in

vol

ume.

The

bas

ic u

nit

of m

easu

rem

ent i

s th

e tin

, whi

ch h

olds

abo

ut 2

0 lit

res.

**,

indi

cate

s sig

nific

ant d

iffer

ence

bet

wee

n m

eans

at

1% a

nd 5

% le

vels

resp

ectiv

ely.

3

-8


households manage to grow the crop. This would help to explain the lower cash income realised by wives of poor households. Almost all the households grow sorghum, the basic staple crop in Southern Sudan, since the poor cannot afford not to grow the essential source of subsistence food.

Why do the poor grow and sell fewer crops than the other socio- economic groups? To what extent are they prevented by deficits in their factors of production, the most important of which is family labour?

Differences in Endowments (i) Land

Land and family labour to work the land are the two most important endowments in this economy. While our data do not allow us to estimate production functions, Table 5 establishes a clear positive relationship between land and labour endowments and the generation of crop surpluses as reflected in differences in household cash income^.'^ In all cases the area of land per adult equivalent in the household devoted to any particular crop is significantly lower in the poorest households. In general, each adult equivalent in these households has about only 50 per cent of the mean cultivated land area for the whole sample on which to subsist and generate a surplus for sale. Despite this, the overall distribution of land devoted to the various crops is not very different across socio-economic classes, other than for a slightly larger share of land being used for sorghum by the poor. Evidently, they attempt to grow almost a full range of crops on a markedly smaller land area, which may account for their inability to raise their level of food self-sufficiency and to generate surpluses to be used for sale.

But why do they cultivate a smaller area of land, particularly when we find in Table 5 that they have an above average number of household members? One reason appears to be that almost one-quarter of the poor claim to suffer from a lack of good land in their residential area, while this problem afflicts only about 10 per cent of the better-off. In addition, only about one- half of the poor realised adequate rainfall in the current growing season, significantly fewer than in the other two income classes. To what extent this p a t year’s distribution of rainfall reflects usual geographic patterns is not possible to determine.

For a more complete picture of the reasons why the poor find themselves in that position we must examine both the stock and the use they make of the labour available to them.

(ii) Labour and human resources As we have already mentioned, the poor cannot besaid to be constrained

by an inadequate family labour supply since they have an above average

Tabl

e 5.

H

OU

SEH

OL

D E

ND

OW

ME

NT

S O

F L

AN

D, L

ABO

UR

AN

D H

UM

AN

RE

SOU

RC

ES

Land

(in

Hec

tare

s)

Mea

n A

rea

Plan

ted

per

Adu

lt Eq

uiva

lent

to:

Mill

et

Sorghum

Cas

sava

Se

sam

e G

roun

dnut

s B

eans

M

aize

O

ther

A

ll C

rops

% F

eelin

g C

onst

rain

t on

‘Good’ La

nd a

%

With

‘Ade

quat

e’ R

ainf

all

Labo

ur a

nd H

uman

Res

ourc

es

Hou

seho

ld Si

ze

Adu

lt E

quiv

alen

ts (A

.E.)

Num

ber

in L

abou

r Fo

rce

Dep

ende

ncy

Rat

io

per

A.E

.

‘Poo

r’

‘Mid

dle’

‘B

ette

r-O

ff

All

Are

a %

Dis

trib

utio

n A

rea

% D

istr

ibut

ion

Area

%

Dis

trib

utio

n A

rea

YO Di

strib

utio

n

0.03

6.

1 0.

25

44.4

0.

06

9.7

0.14

24

.9

0.0 I

3.4

0.0 1

1.

5 0.

02

4.0

0.04

6.0

0.56

10

0.0

23.0

52

.5

8.0

6.5

0.5

1.52

0.10

8.

7 0.

38

35.6

0.

16

13.5

0.

23

20.7

0.

04

3.8

0.03

3.

1 0.

08

6.3

0.10

8.

3 1.

12

100.

0

17.6

69

.6

8.2

6.8

0.5 1.54

0.10

0.

48

0.14

0.

30

0.03

0.

02

0.0 I

0.01

1.

05

9.4

79.7

6.7

5.4

0.6

1.37

11.7

0.

08

(6.8

**)

8.9

(4.P

’)

39.3

0.

36

(3.9

) 38

.7

(2.8

) 14

.3

0.13

(7

.1**

) 12

.8

(2.6

) 26

.6

0.22

(5

.6**

) 23

.3

(3.P

) 3.

0 0.

03

(7.9

.) 3.

4 (0

.3)

1.7

0.02

(4

.8**

) 2.

4 (2

.6)

2.6

0.07

(3

.5**

) 6.

1 (8

.5**

) 3 6

100.

0 0.

97 (

12.6

**)

100.

0 5

B

68.0

(5

.6**

) 3 E;

6‘

7.8

(6.5

**)

3 k

b

1.49

(0

.7)

kl 9

0.8

0.05

(12

.0.’)

4.

4 (1

0.4*

*)

16.8

(2

.1)

g

6.4

(8.4

**)

0.5

(2.1

) m -e

3 :

Num

ber

in N

on-A

gric

ultu

re

% W

ith Lab

our

Shor

tage

82

.0

Age

of

Hea

d 41

.1

Hea

d's

Yea

rs o

f Sc

hool

ing

2.4

% H

eads

7

Yea

rs S

choo

ling

0.0

Yea

rs o

f Sc

hool

ing

of L

abou

r Fo

rce

per

A.E

. 0.

29

per

A.E

.~

0.01

Tabl

e 5 (

cont

inue

d)

0.01

85

.0

41.7

2.

9 3.

2

0.34

0.02

73

.0

37.9

3.

3 10

.9

0.46

0.01

(1

.0)

81.0

(1

.8)

40.6

(2

.6)

2.9

(2.0

) 4.

4 (5

.0**

)

0.36

(4

.9f*

)

5 9'

%

J k

Not

es:

a.

b.

c.

d.

Res

pond

ents

wer

e as

ked

whe

ther

all

the

good

land

in th

eir

area

was

alre

ady

bein

g cu

ltiva

ted.

8 G s So

urce

: A

utho

rs' s

urve

y.

g

Res

pond

ents

wer

e as

ked

whe

ther

rain

fall

'this

yea

r' ha

d be

en a

dequ

ate.

D

efin

ed as

the

num

ber

of n

on-la

bour

for

ce m

embe

rs a

nd c

hild

ren

per

adul

t eq

uiva

lent

. Number o

f mem

bers

in n

on-a

gric

ultu

ral

occu

patio

ns p

er a

dult

equi

vale

nt.

F- v

alue

s ar

e in

par

enth

eses

whe

re **

, in

dica

te s

igni

fican

t di

ffer

ence

bet

wee

n m

eans

at

1% a

nd 5

% l

evel

s re

spec

tivel

y.

3


number of household members. Yet, Table 5 shows that the number actually in the labour force per adult equivalent household member is slightly higher in the richest households, SO that each economically active member in poor households has to support more dependents. The result is that the poor are much more aware of suffering from a labour shortage as a constraint on cultivated land area and the number of crops that they are able to grow.

The quality of the human resources available to the poor is also deficient. The heads of richer households are younger and perhaps more able to undertake hard physical labour. They also have significantly more schooling which may enhance their decision-making ability. Indeed, the mean years of schooling per household adult equivalent of those members in the labour force is sharply higher in the better-off households.

(iii) Other factors Other factors which could be expected to add to household income

inequalities and give rise to differences in overall living standards are portrayed in Table 6. As expected, the poor score significantly less than the other socio-economic groups on all the indicators. Fewer of them gain access to local markets and to the more profitable markets outside of their immediate area. A smaller number of them are members of a local co- operative, which should guarantee a more reliable outlet for their market sales of surplus crops. And extension officers, largely working under the auspices of the Norwegian Church Aid, appear to avoid almost all of the poor. l4

Table 6. OTHER HOUSEHOLD DIFFERENCES BY CLASS OF CASH INCOME

‘Better- F- ‘Poor’ ‘Middle’ OIY All Value

9% With Local Sales of Crops 49.0 58.8 89.0 63.9 13.3 **

9% With Outside Sales of Crops 19.7 57.6 81.3 54.5 29.9 **

9% Cooperative Membership 11.5 27.2 17.2 20.8 3.5 *

% Head Lived Away 26.2 35.2 51.6 37.2 4.6** % Visited by

Extension Officer 19.7 34.4 29.7 29.6 2.1

Note: **, indicate significant difference between means at I % and 5% levels respectively. Source: Authors’ survey.


Finally, it may be that the experience of having lived outside the Acholi area for an extended period in the past may have exposed such farmers to more modern attitudes and improved cultivation practices which are reflected in greater production, cash sales and higher living standards. Over one-half of the better-off respondents had lived outside of Acholi for at least three months in the past decade compared with only a quarter of the poor. The manner in which this experience has induced the observed differences in cash incomes and living standards requires much more research before we could attribute the patterns of causation.15

Our earlier hypotheses identifying the determinants of household cash income and relative welfare status are generally confirmed by these results. All the socio-economic groups are heavily dependent on producing crop surpluses to generate cash income since opportunities for non-farm employment are negligible. The poor grow and sell fewer crops, a result which is partly explained by their smaller cultivated land area which derives, in turn, from a relative scarcity of good, fertile land and inadequate rainfall. Potential labour supply in poor households is no smaller than for other socio-economic classes, but their labour force participation rate is lower which gives rise to a larger dependency burden on their economically active members. Indeed, the poor are much more aware of the labour constraint they face. The poor’s endowments of fertile land and higher-quality human resources are markedly inferior. If managerial ability is improved by having experienced life outside of the local environment, then the middle-group and the better-off have a distinct advantage. Similarly, efficiency in farm management practices may be raised by contacts with extension workers and the poor appear to be relatively deprived of the advice that they offer.

In order to try and untangle some of theseparate and independent effects of these various economic and demographic influences on the level of cash incomes generated, and by implication, on the relative welfare status of households in Acholi, we must resort to multivariate techniques.

VI. MULTIVARIATE ANALYSIS OF THE DETERMINANTS OF CASH INCOME Because our measures of household cash income are correlated to some

extent with our other indicators of relative deprivation, especially food security, it is not unreasonable to suggest that the ranking of households according to cash income will be fairly consistent with most other relative welfare indicators that we might have adopted. This section tests some ofthe basic hypotheses of the Chayanov model described earlier by assessing the independent effects of various economic and demographic influences on household cash income in the knowledge that the dependent variable


provides a fairly reliable and consistent synthetic index of a household’s relative position in any overall measure of welfare.

Our index of household welfare is proxied by the mean monthly level of per adult equivalent cash income (AEMY) realised in the past year, which is used as one of our dependent variables. However in order to test for non- linearities in the functional forms, equations are also estimated with the natural logarithm of cash income per adult equivalent as the dependent variable (LNAEMY). In this case the regression coefficients estimate the percentage change in income for a unit change in the continuous explanatory variable. When the latter is a dummy or binary variable the regression coefficient underestimates the percentage change in income attributable to the included over the excluded category (see Halvorsen and Palmquist, 1980). The following set of explanatory variables were incorporated in the analysis in recognition of socio-economic and demographic differentiation in the sample population. Their choice is partly determined by the results of the preceding bivariate analysis and by the availability of quantified measures of the concepts incorporated in the Chayanov model.

Age of the Household Head It is expected that the welfare of a household depends on the stage of the

life-cycle in which it is found. These stages are represented by a set of dummy variables (DMAGE) according to the age of the household head.

Education of the Household Head Evidence from other countries around the world indicates that formal

education has a positive influence on the efficiency of operations and the output of small farmers, especially in a modernising environment, which may not be too representative of the situation in Southern Sudan (Lockheed, Jamison and Lau (1980)). We would expect that education might help to raise crop production and the size of the surplus generated for sale in Acholi via an improved efficiency in plant husbandry and an expanded awareness of and responsiveness to the market economy. This effect is represented by a set of dummy variables (DED) indicating different stages of completion of the school cycle.

Dependency Household dependency is measured by the number of labour force

members per adult equivalent (LFPADQ). We would expect that the larger the number of inactive members that have to be supported, the lower will be household cash income and welfare.

Socio-Economic DifSerentiation Among African Peasants 99

outside Experience It might be expected that households where the head has livedoutside of

the community for an extended period of three months or more in the past decade would have acquired certain modern perceptions and perhaps greater consumption requirements, more savings and a more market-oriented approach to farming. In this case we would predict that our binary variable (LICAWAY) indicating a period of living away will be positively related to household cash income.

Market Orientation Furthermore, we would expect households whose crops are sold outside

of the immediate community to receive better prices for their farm products and to realise higher cash incomes. The act of selling outside of the community is represented by a binary variable (SELLOUT). l6

Land We would expect that the greater the area of land cultivated per houshold

adult (LANDADQ) the larger would be cash income per adult equivalent. In addition, in order to determine the relative contribution of the various types of crops to household cash income we also experimented with separate measures of the area of land per adult equivalent devoted to the ten major cultivated crops. In the case of land planted, for example with sesame or sim- sim, this was denoted as (LANDSIM).

Land Constraint Respondents were asked whether all of the good soil in their area is

already being used for cultivation. If they confirmed that they were aware of Some land pressure, which was presumed to constrain their operations, our binary variable representing this land constraint (POORLAND) was set equal to one. We would expect the 17 per cent of households experiencing such a constraint to realise smaller cash incomes as the restriction on their operations does not allow for the generation of surplus crops for sale.

Non-Agricultural Employ m en f It is expected that those few households with access to non-agricultural

employment providing a regular cash income would realise greater total cash income per adult equivalent member of the household. This was measured as the number of members with off-farm employment per adult equivalent (NONAG).


Other Influences We also predicted that other factors would influence the level of

household cash incomes. For example, the number of extension officer visits in the past twelve months, binary variables indicating co-operative membership and adequate rainfall, and the size of the working party or kampone to which the household belongs, were included in various formulations of our estimating equations. However, none proved to be significant and they were subsequently dropped from the analysis.''.

The definitions of the variables used in multivariate exercises and their means are reported in Table 7.

Regression Results Ordinary least squares (OLS) estimates of these hypothesized

relationships are reported in Table 8. Given the nature of our cross-sectional and household-level data from a relatively small sample, we are reasonably successful in identifying some significant variables and in explaining the variation in household cash income, with the range of values of the adjusted RZ from 0.19 to 0.26.

Life cycle factors are evidently important determinants of household cash income given that the set of dummy variables representing the age ofthe household head are jointly significant at the 1 per cent level in all three equations. However, only DMAGE24 is significantly different from the excluded age group (DMAGE39). The set of dummy variables representing the education level of the household head are jointly significant at the 1 per cent level, although only DED9 is consistently significant in all three equations. The small number of respondents in our sample who progressed beyond primary school and acquired 7-9 years of formal schooling earn an income premium of 33 per cent more than those who did not go to school.

Our index of the size of the household's labour force (LFADQ) and the number with non-farm employment (NONAG) fail to be significant. There appears to be no direct influence of household size and labour force membership on cash earnings, but the effect is likely to be indirect via the positive relationship between family labour and the area of land cultivated, and between land area and cash income. In equation (3) we find the index of land planted (LANDADQ) to be highly significant indicating that each additional hectare of cultivated land per adult equivalent raises cash income by 16 per cent. In equations (1) and (2) the structure of crop cultivation fails to appear influential except that each per adult equivalent hectare of land planted with sim-sim raises cash income by about one-third.'*

Some of the strongest and most consistent influences on household income come from the variables LIVAWAY and SELLOUT. As predicted,


Table 7. DEFINITIONS, MEANS AND STANDARD DEVIATIONS OF THE

VARIABLES USED IN THE REGRESSION ANALYSIS

Variable Symbol Definition Mean

~

AEMY Household cash income per adult equivalent per month (S€) 4.62

LNAEMY Natural logarithm of household cash income per adult equivalent per month 1.25

DMAGE 24 I if household head aged 20-24; 0 otherwise 0.04 DMAGE 29 1 if household head aged 25-29; 0 otherwise 0.13 DMAGE 34 1 if household head aged 30-34; 0 otherwise 0.18 DMAGE 39 1 if household head aged 35-39; 0 otherwise 0.14 DMAGE 44 0.12 DMAGE 49 1 if household head aged 45-49; 0 otherwise 0.16 DMAGE 59 1 if household head aged 50-59; 0 otherwise 0.18 DMAGE 60 1 if household head aged 60 and over;

0 otherwise 0.05 DEDO 1 if household head never went to school;

0 otherwise 0.25 DED3 1 if household head has 1-3 years of

education; 0 otherwise 0.39 DED6 1 if household head has 4-6 years of

education; 0 otherwise 0.29 DED9 1 if household head has 7-9 years of

education; 0 otherwise 0.07 LFPADQ The number of labour force members per

adult equivalent 0.55 LIVAWAY 1 if household head lived elsewhere for 3+

months since 1972 0.37 SELLOUT 1 if sales of crops are made outside the community 0.54 POORLAND 1 if limited availability of good, fertile

land; 0 otherwise 0.17 NONAG Number of household members with non-farm

LANDADQ Cultivated land area, in hectares, per adult equivalent 0.97

LANDSIM Area of land devoted to sim-sim per adult equivalent 0.22

1 if household head aged 4&44; 0 otherwise

employment per adult equivalent 0.01


Table 8. REGRESSION EQUATIONS EXPLAINING VARIOUS MEASURES OF MONTHLY HOUSEHOLD CASH INCOME PER ADULTEQUIVALENT

Explanatory variables

CONSTANT (DMAGE 39) DMAGE 24 DMAGE 29 DMAGE 34 DMAGE 44 DMAGE 49 DMAGE 59 DMAGE 60 (DEDO) DED3 DED6 DED9 LFPADQ LIVAWAY SELLOUT POORLAND NONAG LANDADQ LANDSIM R* F No. of Observations

(1) AEMY

3.59

3.18(2.22)** 1.08( 1.12) 0.68(0.78)

-0.63(0.65) -0.32(0.36)

0.40(0.45) 0.79(0.63)

- 1.38(2.13)** - 1.10( 1.56)

2.05( 1.79)*

0.86( 1.65)* 1.72(3.33)**

0.81(0.14)

1.76(1.79)* 0.191 3.177**

-0.6q0.44)

- 1.22( 1.8 I)*

-

250

(2) LNAEMY

0.70

0.47(1.87)* 0.22( 1.26) 0.13(0.83)

-0.1 l(0.62) -0.1 l(0.73)

0.12(0.80) 0.2q1.17)

-O.lO(O.SS) -0.06(0.47)

0.3 3( 1.65)* 0.07(0.28) 0.18(1.90)** 0.47(5.14)**

1 .O 1 (1.03)

0.34( 1.92)* 0.248 4.046**

-0.25(2.08)**

-

250

(3) LNAEMY

0.64

0.50( 2.05)* *

0.13(0.92) 0.20(1.21)

-0.07(0.45) -0.12(0.83)

0.13(0.85) 0.29( 1.33)

-0.07(0.62) -0.03(0.24)

0.34(1.79)* 0.13(0.50) 0.19(2.06)** 0.5 1(5.92)**

1.08( 1.15) 0.16(2.84)**

0.263 6.545**

-0.25(2.24)**

-

250

Note: ( I ) Student’s ‘t’ values are in parentheses while ++ indicates coeffcient issignificant at least at the 5 per cent level and * indicates significance at the 10 per cent level. The excluded class of dummy variables are in parentheses. In equations( l)and(2) nineothervariablessimilar to LANDSIM wereincluded to represent land area per adult equivalent devoted to specific crops. None proved to be significant and are not reported here.

(ii)


where the head of the household has spent some time away from the survey area, monthly adult equivalent cash income is almost 20 per cent greater. When the household sells some of its crops outside of the surrounding area, cash income rises by about 50 per cent. We would interpret the variable LIVAWAY to proxy for certain traits and characteristics acquired by the head of the household during an extended absence away from home. These might include the assimilation of improved farming techniques and a more acquisitive and material approach to life, as reflected in a greater desire to produce and sell surplus crops in order to purchase goods and services for the family. In this case policy-makers might be interested in trying to impart a comparable set of lessons and incentives to the general populace in Acholi through an improved and expanded extension service. An ability to market surplus crops outside the community, as reflected in the variable SELLOUT, is also an important determinant of household income. Policy makers evidently must undertake to expand the extended marketing possibilities of farmers in the area. The effect on cash incomes would be quite dramatic.

The adverse effect of land pressure is present in that POORLAND is negative and significant at the 10 per cent level in equation (1) and at the 5 per cent level in equations (2) and (3) and suggests that the 17 per cent of households who perceive themselves to have a land constraint have monthly a s h incomes of 25 per cent less than those without such a constraint.

VII. CONCLUSIONS The limited data collected from this small sample of subsistence farmers

in Acholi, Southern Sudan, tend to confirm our behavioural model of peasants who are conceived to be risk averters and utility-maximisers rather than straight-forward profit-maximisers. The general policy implications that can be drawn from this result are clear and may be expected to have an extended application to similar parts of rural Africa which remain grossly underdeveloped and over-dependent on subsistence agriculture.

The level of real welfare enjoyed by the people of the Acholi area, as measured by various indicators, remains appallingly low and reveals the precarious nature of existence of the population. Given the paucity of public funds for development projects and the lack of trained manpower, Southern Sudan will remain heavily dependent for many years on foreign development agencies to improve the quality of life of the general population. It is to be hoped that the type of household-level data analysis presented here will suggest certain directions for future technical assistance, particularly since the data were collected prior to the outbreak of the continuing civil war in the region. To this extent the portrait which has been painted here represents normal, peacetime conditions from which development activities will need to


recommence once the hostilities eventually cease and the displaced population is resettled.

Since even the richest in the Acholi area are so poor in terms of cash income, food security and asset holdings, development assistance which is directed to all segments of the community appears warranted. Yet, even within such mass poverty, inequalities are relatively large and appear to be directly related to household endowments of land and human resources. A very large overlap appears between households whom we classified to be poor in terms of their inadequate food security, asset holdings and their low cash incomes. In general, the most deprived 25 per cent of households have lower educational attainments of both the head and family members, a smaller cultivated land area, fewer crops grown and sold, more members, greater insecurity in food supplies, fewer durable assets including animals, and a smaller chance of marketing their surplus crops outside the survey area where higher prices are likely to be realised. They also have a greater probability of facing a land constraint and are less likely to be headed by someone who has lived away from the area in the past decade. Evidently, members of this group are in need of special attention from development agencies. Marked differences between the middle group and the better-off are not so apparent.

More generally, the importance of family labour in achieving relatively high cash incomes and food security suggests that fertility in the survey area, and in Southern Sudan in general, will remain high for a long period to come. Indeed, fertility can be expected to rise in the near future given the observed tendency for female age at first marriage to decline (House, 1985b). High fertility is evidently a rational response on the part of households to attain various forms of security, in spite of the likely negative effects on maternal and child health, mortality and morbidity. Therefore, until a major structural transformation of the underlying subsistence economy is achieved, which involves increased labour productivity, more exposure to the market economy and improved food security, a n y change in the pro-natalist inclinations of parents is most unlikely to emerge. The largest scope for effective comprehensive population policies lies in efforts to improve the survival chances of infants through institutional interventions in the fields of maternal-child health (MCH), and through demonstrating the advantages of personal hygiene and improving water and sanitation facilities. The erection of rural health clinics and dispensaries well stocked with para-medical personnel and drugs is an essential prerequisite to any increase in the population's overall welfare.

Any attempt to raise labour productivity will be enchanced by effforts to improve the quality of human resources in the survey area and throughout Southern Sudan. Primary school enrolment rates must be raised and


opportunities need to be expanded for more children to go to school. Our results indicate that cash incomes from farming rise dramatically if children manage to complete six years of primary school.

Only when productivity is increased and opportunities for the sale of surplus food crops expanded, which are so dependent on improved transport and marketing facilities, can we expect any long-term overall improvement in the general well-being on the surveyed population. The growth of rival non- farm activities, so notable by their absence in Acholi, depends on increased cash incomes from farming. Such interactions between agriculture and the rural non-farm sector is considered to be of crucial importance to the growth of employment, productivity and output (Johnston and Clark, 1982, p.78).

Agencies such as theNorwegian Church Aid which have been involved in Acholi must ensure that efforts to accelerate agricultural output are broad- based but also reach down to the poorest 25 per cent of households which have been identified here. Substantial investments are required in infrastructure, crop research and other supporting services so that the inherent risks to farmers are reduced. This requires an intensification of attempts to develop improved varieties of high-yielding basic food crops which are disease resistant, unattractive to pests and have good storage qualities. Additional food crops, and perhaps pure cash crops, can increase cash incomes, improve the local diet and add flexibility to the household labour demand profile. Efforts in this area should be expanded but not at the expense of the production of sorghum and cassava which are more drought- resistant and which, therefore, provide a higher degree of food security.

Of immense scope for an expanded and more effective extension service is the need for widespread encouragement of ox-ploughing which would help to Overcome the family labour constraint in all operations involving planting, weeding and harvesting. However, de Coninck et al. (1984) suggest that the introduction of animal power will only be successful as part of a change to the whole farming system. It may be most effective if undertake by innovative entrepreneurs setting up as contractors to provide ox-ploughing, weeding and transport services on a commercial basis. Its expanded use in Acholi, however, depends crucially on efforts to curtail the spread of the tse-tse fly in the area.

NOTES 1. Two recent examples from Africa using household level data are

Radwan and Lee (1986) for Egypt and Collier, Radwan and Wangwe (1986) for Tanzania.


2, Collier and La1 (1986) illustrate various traditional communal labour sharing arrangements designed to meet the peak demands for labour during certain periods of the agricultural cycle amongst the Kikuyu tribe of Kenya. Ngwatio usually covered between three and ten women who moved to each members’ plot until the required tasks of the participants had been completed. Wira involved invitations by a farmer to a number of participants on a specific day to complete a certain task, such as digging (men) or weeding (women), with compensation for the work taking the form of beer and food. Both forms of bartering labour are still widely practised in Southern Sudan.

More details of sample design, coverage and the results of the survey can be found in an earlier working paper (See House and Phillips-Howard (1986)).

Land area was calculated from the plot length and width, measured by the farmers of the area in tal. A tal is a wooden pole, 6 feet long in the survey area, which is used to measure plots to be dug, especially when a working party or kampone is engaged. In this case each kampone member is assigned a specific area of land, so the respondents appear to be very much aware of plot areas.

Only 4 of the 250 respondents employ non-family labour on their land.

It may be that elsewhere in Southern Sudan, particularly among the Nilotic people on the floodplain, that poor households which are food- deficient in grain or root crops can make good the deficit through sales of livestock, particularly in years of poor rainfall. The limited ownership of cattle in the Acholi area, however, partly due to the presence of tse-tse fly, does not allow our households to adopt such a strategy. It is widely recognised that income data generated from sample surveys can be subject to reporting error as respondents are very sensitive about revealing their true incomes to interviewers whom they fear may report the information to the tax authorities. While we hesitate to claim complete accuracy in the data presented here, it should be mentioned that all the interviewers came from the local area and could not be identified in any way with the Government. And since the sources of income are few, it should have been relatively easy for respondents to recall their crop sales and cash incomes realised in the past twelve months. These data have been divided by twelve to generate monthly cash incomes.

3.

4.

5.

6.

7


8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

The official exchange rate at the time of the survey in 1983 was US$=€S2.45. The unofficial, market rate ranged between US$=SE4 and SE5. Cash income here includes gross receipts from sales of farm output and does not account for intermediate costs, which in this case are largely represented by inputs of unpaid family labour.

Differences in the welfare indicators between the two highest cash income groups are not so clear cut.

Given that out-migration from the area is thought to be high, particularly amongst males in the age group 20-29 who are most likely to be in employment, there may be some underreporting of remittance income in our data. It could also be that the poor realise lower prices for their sales because of such market imperfections as monopsony power and ignorance. Unfortunately, we have no data on crop prices.

Where intercropping of two or more crops takes place the individual crop areas were added to obtain total land area cultivated. To this extent the latter figure is exaggerated. An alternative interpretation would reverse the causation and suggest that the better-off enjoy their prosperity precisely because they have had contact with extension workers.

Of course, it may also be the case that pre-existing differences in individuals induced their earlier migration from Acholi and these are what lead to their observed higher cash incomes.

Of the 54 per cent of respondents making sales outside the community almost one-half sell to local buyers who then transport the produce, 38 per cent sell to a co-operative which is responsible for shipping the Crops, usually to an urban centre, and 8 per cent claim to take their own surplus to markets outside the survey area. Multicollinearity could not explain the lack of significance, given the small size of the between-variable zero-order correlation coefficients, In unreported equations we found that each adult member present raises total household cash income by about 10 per cent but also depresses cash income per adult equivalent by an equal amount.

Journal of International Development 108

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Thorner, D., Kerblay, D. B. and Smith, R. Homewood: Irwin.

Collier, P. and Lal, D. (1986) Labour and Poverty in Kenya, 1900-1980. oxford: Clarendon Press.

Collier, p., Radwan, S. and Wangwe, S. (1986) Labour and Poverty in Rural Tanzania. Oxford: Clarendon Press.

de Coninck, J. D., Duncan, A. and Winter, P. E. (1984) ‘Agricultural Equipment and Innovation in Southern Sudan’, in Ahmed, I. and Kinsey, D., Farm Equipment Innovations in Eastern and Central Southern Africa. Aldershot: Gower.

van Ginneken, W. and Park, Jong-goo ( 1984) Generating Internationally Comparable Income Distribution Estimates. Geneva: International Labour Office.

Halvorsen, R. and Palmquist, R. (1980) ‘The Interpretation of Dummy Variables in Semi-logarithmic Equations’, American Economic Review, Vo1.70, N0.3, June.

House, W. J. (1985a) A Socio-Economic and Demographic Profile of the Population of Urban Juba. Working Report No.3, UNFAPALO Project SUD/79)P06, mimeographed.

House, W. J. (1985b) Population, Poverty and Deprivation in Southern Sudan: A Review. Population and Labour Policies Programme Working Paper No. 154, Geneva, International Labour Office.

House, W. J. and Phillips-Howard, K. D. (1986) Population and Poverty in Rural Southern Sudan: A Case Study of the Acholi Area. Population and Labour Policies Programme Working Paper No. 155, Geneva, International Labour Office.

Hunt, D. (1984) The Impending Crisis in Kenya: The Case for Land Reform. Aldershot: Gower.

Johnston, B. F. and Clark, W. C. (1982)Redesigning Rural Development: A Strategic Perspective. Baltimore: Johns Hopkins University Press.

Lacaillon, J., Paukert, F., Morrisson, C. and Germidis, D. (1984) Income Distribution and Economic Development: An Analytical Survey. Geneva: International Labour Office.

So&-Economic Differentiation Among African Peasants 109

Lockheed, M., Jamison, D. and Lau, L. (1980) ‘Farmer Education and Farm Efficiency: A Survey’, Economic Development and Cultural Change, Vo1.29, No. 1, October.

Radwan, S. and Lee, E. (1986) Agrarian Change in Egypt: An Anatomy of Rural Poverty. London: Croom Helm.

Rempel, H. and Lobdell, R. (1985) ‘A Model of Labour Allocation Decision- making in Peasant-Type Households’, Working Paper No.422, Institute for Development Studies, University of Nairobi.

Republic of Sudan (1984) The 1983 Population Census: Administrative Report for the Southern Sudan. Juba: Regional Census Commission.

Stark, 0. (1978) Economic-Demographic Interactions in Agricultural Development: The Case of Rural to Urban Migration. Rome: UN FAO.

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