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close this bookGender and the Expansion of non-traditional Agricultural Exports in Uganda (UNRISD, 2000, 66 p.)
View the document(introduction...)
View the documentSummary
View the documentAbbreviations and Acronyms
View the document1. Introduction1
View the document2. Gender and Macroeconomic Policy in Africa
close this folder3. The National Context
View the document(introduction...)
View the document3.1 Gender and Public Policy in Uganda
close this folder4. The Rural Sector
View the document4.1 Characteristics of the Rural Sector
View the document4.2 Poverty in the Rural Sector
View the document4.3 Gender Roles in Agriculture
close this folder5. Macroeconomic Policy
View the document5.1 The Adjustment Strategy
View the document5.2 Non-Traditional Agricultural Exports Promotion Policies: Potential and Constraints
close this folder6. Gender and NTAE Promotion: Findings from the Field Studies
View the document(introduction...)
View the document6.1 Village Characteristics
View the document6.2 Supply Response
View the document6.3 Labour Constraints
View the document6.4 Other Constraints on Production
View the document6.5 Control and Expenditure of Cash Crop Income
View the document7. Conclusions
View the documentBibliography

6.3 Labour Constraints

The pervasiveness of concern with labour constraints, especially among women, is evident in the data presented above (tables 13, 14 and 15). These data suggest that labour constraints are binding for agricultural production; evidence from the literature indicates that post-harvest processing imposes an additional labour burden, particularly on women. The primary processing technologies at farm-level in Uganda are primitive, and only small quantities of crops can be processed and stored to benefit from the higher prices obtained for off-season sales. Women bear the brunt of processing food crops; they beat large grains with sticks, crush small grains, particularly millet, against stone, and shell groundnuts by hand.

The survey yielded detailed information regarding labour inputs into different crops in the two villages surveyed (tables 20 and 21). Note that these tables provide information whether or not different types of labour were used; they give no information about how much labour was used, the percentage distribution of labour, or the intensity of labour for any crop. Thus, for instance, table 20 shows that, in Gonve, women in 37 per cent of the households surveyed helped clear land for maize production, but it gives no indication of the proportion of the total land clearance performed by women. Not all households were engaged in all tasks (low figures for fertiliser application and transportation to market, for instance, indicate that little fertiliser was used, and that many households sell their produce at farmgate).

These finding suggest that maize is more of a “men’s crop” in Kitanyatta than in Gonve, with male involvement in all aspects of maize production higher in Kitanyatta. The strongest “women’s crop” is cassava in Gonve, while there is a rather unexpectedly high level of male involvement in cassava in Kitanyatta, even in the traditional women’s tasks of weeding and processing. Indeed, men seem to perform these tasks to a significant extent for most crops. Of course, the data do not indicate what proportion of the total weeding, for instance, is done by men, and it is possible that women still perform the majority of this task. But it is interesting to note that the presumed traditional gender division of labour is neither clearly demarcated nor rigidly enforced. It is also interesting to find that the gender division of labour appears to be stronger in Gonve, which still relies heavily on the traditional cash crops. The gender division of labour is more flexible in Kitanyatta, in which food crops are also cash crops. These findings thus corroborate those of Sorensen (1996) in Busoga, discussed above, who argues that the blurring of the distinction between cash crops and food crops has led to a renegotiation of gender relations. These findings also suggest that men’s labour supply may be more elastic than is commonly assumed, and that men will be ready to contribute more labour to traditional women’s tasks if constraints on women’s labour become binding, and if the conditions for production in which men are interested (a ready market and a good price) exist.

The survey respondents were asked who would supply additional labour if it were needed for increased production. Both men and women were likely to contribute to increased labour burdens, although women were more so (table 22). Given women’s existing higher labour burdens, any additional labour requirements are likely, at least initially, to be more onerous for them than for men.

Table 20
Type of labour used by task and crop: Gonve (n = 197 households)


Maize

Beans

Vanilla

Cassava

Coffee


HM

FM

FF

FC

HM

FM

FF

FC

HM

HF

FM

FF

FC

HM

HF

FM

FF

FC

PM

HM

HF

FM

FF

FC

PM

Land clearance

29

69

73

18

33

76

73

16

32

2

77

41

16

37

1

100

94

28

1

21

1

52

32

11

1

% of hh

15

35

37

9

17

39

37

8

16

1

39

21

8

19

1

51

48

14

1

11

1

26

16

6

1

Land preparation

31

62

104

46

34

70

111

32

35

2

70

66

23

38

2

97

132

36

1

42

3

92

84

27

1

% of hh

16

31

53

23

17

36

56

16

18

1

36

34

12

19

1

49

67

18

1

21

2

47

43

14

1

Planting

4

70

122

36

4

67

132

45

8

3

87

96

31

8

1

94

161

57

1

17

3

107

100

37

1

% of hh

2

36

62

18

2

34

67

23

4

2

44

49

16

4

1

48

82

29

1

9

2

54

51

19

1

Appl. of fertilizer

3

6

6

1

2

4

3

1

3

1

22

10

2

3

1

18

13

4


4

1

32

16

7


% of hh

2

3

3

1

1

2

2

1

2

1

11

5

1

2

1

9

7

2


2

1

16

8

4


Pruning/thinning

1

36

39

8

2

19

29

9

3

1

59

45

8

1

0

32

57

17


9

0

105

65

19

1

% of hh

1

18

20

4

1

10

15

5

2

1

30

23

4

1

0

16

29

9


5

0

53

33

10

1

Weeding

9

59

122

38

7

68

136

47

14

2

84

99

32

10

3

94

163

62


18

2

109

105

37


% of hh

5

30

62

19

4

34

69

24

7

1

42

50

16

5

2

47

83

31


9

1

55

53

19


Harvesting

3

60

119

42

4

62

135

48

4

1

78

90

23

4

3

75

164

57


10

2

112

129

53


% of hh

2

30

60

21

2

31

69

24

2

1

40

46

12

2

2

38

83

29


5

1

57

65

27


Transport home

3

59

116

46

7

56

131

51

3

2

88

103

40

6

0

71

152

53


9

0

109

113

53


% of hh

2

30

59

23

4

28

66

27

2

1

45

52

20

3

0

36

77

27


5

0

55

58

27


Processing

2

33

67

19

5

37

90

26

0

0

38

38

9

2

0

37

76

17


4

0

63

70

22


% of hh

1

17

34

10

3

19

45

13

0

0

19

19

5

1

0

19

39

9


2

0

32

36

11


Transport to mkt

0

3

8

0

0

4

10

1

5

1

81

98

41

0

0

2

7

0


0

0

44

12

0


% of hh

0

2

4

0

0

2

5

1

3

1

41

50

21

0

0

1

4

0


0

0

22

6

0


HM = hired male; HF = hired female; FM = family male; FF = family female; FC = family child; PM = male work party; hh = household

Table 21
Type of labour used by task and crop: Kitanyatta (n = 199 households)


Maize

Beans

Cassava

Coffee


HM

HF

FM

FF

FC

PM

HM

HF

FM

FF

FC

HM

HF

FM

FF

FC

PM

HM

HF

FM

FF

FC

Land clearance

40

7

121

78

16

0

22

2

85

58

17

28

5

114

75

17

0

1

4

0

0

0

% of hh

20

4

61

39

8

0

11

1

43

29

9

14

3

58

38

9

0

1

2

0

0

0

Land preparation

38

12

109

119

22

2

21

4

78

86

22

22

6

107

121

22

2

1

0

5

0

0

% of hh

19

6

55

60

11

1

11

2

38

44

11

11

3

54

62

11

1

1

0

3

0

0

Planting

20

14

124

49

28

11

11

8

81

101

25

14

8

114

138

29


1

1

5

0

0

% of hh

10

7

62

25

14

6

6

4

41

51

13

7

4

58

70

15


1

1

3

0

0

Appl. of fertilizer

0

0

2

1

1


0

0

1

0

0

1

1

2

1

1


0

0

0

0

0

% of hh

0

0

1

1

1


0

0

1

0

0

1

1

1

1

1


0

0

0

0

0

Pruning/thinning

4

4

49

59

10


3

4

24

29

10

0

0

49

52

13

1

0

0

5

0

0

% of hh

2

2

25

30

5


2

2

12

15

5

0

0

25

26

7

1

0

0

0

0

0

Weeding

31

22

125

146

32


17

11

78

99

31

16

8

109

133

32

2

2

1

5

0

0

% of hh

15

11

63

73

16


9

6

39

50

16

8

4

55

67

16

1

1

1

3

0

0

Harvesting

20

19

113

145

28

9

10

62

96

29


8

5

86

139

27


0

0

4

1

0

% of hh

10

10

57

73

14

5

5

31

49

15


4

3

43

70

14


0

0

2

1

0

Transport home

13

8

115

140

31


5

7

66

98

28

5

4

87

136

29


0

0

4

1

0

% of hh

7

4

58

70

16


3

4

33

50

14

3

2

44

69

15


0

0

2

1

0

Processing

14

5

96

123

29


4

5

54

91

29

3

2

49

66

14

1

0

0

3

1

0

% of hh

7

3

48

62

15


2

3

28

46

15

2

1

25

33

7

1

0

0

2

1

0

Transport to mkt.

1

0

81

67

9


0

0

47

49

9

0

0

57

59

8


0

0

4

0

0

% of hh

1

0

41

34

5


0

0

24

25

5

0

0

28

30

4


0

0

2

0

0

HM = hired male; HF = hired female; FM = family male; FF = family female; FC = family child; PM = male work party; hh = household

Table 22
Sources of additional labour requirements


Gonve (n = 197)

Kitanyatta (n = 199)


Number of households

% of households

Number of households

% of households

Male family

129

65

158

79

Female family

155

79

172

86

Child family

65

33

44

22

Hired labour

56

28

43

22

Exchange labour

2

1

1

1

Other labour

0

0

3

2

Table 23, along with tables 20 and 21, above, provide additional information on the use of hired labour in the two villages surveyed. It is interesting to note the relatively high use of hired labour for maize in Kitanyatta, the poorer village, which would be expected to have less cash available for hiring labour. The data may indicate production patterns which depend more heavily on hired labour, fewer household labour resources, a flexible and low-wage labour market, or a combination of these factors. Table 21 indicates that labour was hired not only for the intensive tasks of land clearance and preparation, but also for the traditionally female-dominated tasks of weeding and harvesting. Indeed, as tables 14 and 15, above, show, women are more likely than men to cite lack of hired labour as a constraint on production, and to be more interested than men in increasing their use of hired labour.

Table 23
Hired household labour, by crop


Gonve (n = 197)

Kitanyatta (n = 199)


Households hiring labour

% of households

Households hiring labour

% of households

Maize

16

8

47

24

Beans

16

8

19

10

Vanilla

22

11

0

0

Cassava

28

14

14

7

Coffee

42

21

1

0

Other crops

22

11

24

12

The importance of hired labour for women, combined with the severity of women’s labour constraints, suggests the need for improved labour market functioning in Uganda. Evans’s analysis of the rural labour market in Uganda (1992) indicates that hired labour is important for agricultural production. Although the proportion of the population classified as agricultural labourers is very low (because most agricultural workers also have their own plots), the proportion of households hiring labour is relatively high - around 30 per cent for Uganda as a whole. Younger men, often single, seem to enter the labour market to establish themselves financially or to support a young family, and to exit the labour market when their own household is more securely established. Male agricultural labour market participation is highest at ages 10-25, is quite low at ages 25-49, and turns upward thereafter (Evans, 1992). Women tend to seek employment after they become divorced or widowed, and thus presumably turn to the labour market when they are not supported by an adult male and/or have no access to land for their own production. Women have more constraints on their time, so when they do sell labour it is often a “distress sale”. Thus, although in principle women and men receive the same rates of pay for agricultural labour, in fact women are often in an inferior bargaining position and receive lower rates. Both male and female labourers tend to be employed among neighbouring households, and to work on a piecework basis instead of at a daily rate. Because individuals move in and out of the labour force during different life stages, and because most labourers work in their own community, the labour market operates to some extent as a sort of labour exchange system within communities, in which the labour exchange occurs over different stages of the life cycle.

Labour data by household type

The survey labour data were disaggregated and analysed by type of household, crop, and the presence or absence of crop sales. The analysis strongly suggests that labour market constraints are binding in terms of the potential for increasing production of cash crops: the marketing of crops largely depends on the presence of hired labour. These findings are consistent with other studies that have documented seasonal labour bottlenecks in household labour limiting agricultural production. Female-headed households tend to have less access to hired labour, and thus have a limited ability to produce crops for the market.

The data provided in tables 20 and 21, above, were disaggregated (with work party labour being excluded), in order to understand the implications of the adoption of certain crops as market opportunities arise. Labour patterns are examined for households in Gonve adopting vanilla, for households in both locations adopting coffee, and for households in both locations marketing beans and maize. The first comparison is between male and female-headed households (tables 24-31).

Table 24
Percentage of male-headed households in Gonve using labour for vanilla (adopters)

Task

Type of labour


Hired male

Hired female

Family male

Family female

Family child

Land clearance

26

2

66

26

7

Land preparation

33

2

59

46

10

Pollinating

4

1

72

67

12

Planting

8

2

80

73

18

Fertilizer app.

3

1

20

11

2

Pruning

3

1

49

28

0

Weeding

14

2

75

75

19

Harvesting

3

2

80

76

26

Transport/field

6

1

75

74

30

Transport/mkt.

0

0

32

14

5

Table 25
Percentage of female-headed households in Gonve using labour for vanilla (adopters)

Task

Type of labour


Hired male

Hired female

Family male

Family female

Family child

Land clearance

16

0

8

75

42

Land preparation

0

0

8

92

50

Pollinating

0

0

8

100

42

Planting

0

0

8

100

50

Fertilizer app.

0

0

0

0

0

Pruning

0

0

8

33

25

Weeding

0

0

8

100

58

Harvesting

0

0

8

100

58

Transport/field

0

0

8

92

58

Transport/mkt.

0

0

0

25

8

Table 26
Percentage of male-headed households using labour for coffee (adopters)

Task

Type of labour


Hired male

Hired female

Family male

Family female

Family child

Land clearance

29

1

68

29

11

Land preparation

32

1

62

45

14

Planting

13

2

77

64

19

Fertilizer app.

4

1

23

12

5

Pruning

5

0

77

35

6

Weeding

12

2

79

69

19

Harvesting

6

2

79

82

33

Transport/field

5

1

78

74

32

Threshing

3

0

45

45

13

Transport/mkt.

0

0

28

6

4

Table 27
Percentage of female-headed households using labour for coffee (adopters)

Task

Type of labour


Hired male

Hired female

Family male

Family female

Family child

Land clearance

18

0

12

70

41

Land preparation

11

0

6

82

47

Planting

6

0

18

82

65

Fertilizer app.

0

0

12

6

6

Pruning

12

0

12

65

47

Weeding

6

0

11

76

59

Harvesting

6

0

12

88

53

Transport/field

6

0

12

82

65

Threshing

0

0

0

47

35

Transport/mkt.

0

0

12

23

12

Table 28
Percentage of male-headed households using labour for maize (all)

Task

Type of labour


Hired male

Hired female

Family male

Family female

Family child

Land clearance

17.3

2.0

52.3

36.3

7.3

Land preparation

18.7

3.5

47.7

55.0

12.0

Planting

6.4

4.4

52.6

67.3

16.1

Fertilizer app.

0.9

0.0

2.4

2.0

0.6

Pruning

1.2

1.2

21.4

24.3

3.2

Weeding

10.9

6.7

51.5

67.0

16.1

Harvesting

5.8

5.6

48.0

65.5

16.4

Transport/field

3.8

2.4

48.5

63.5

17.8

Threshing

3.8

1.5

35.4

46.8

11.4

Transport/mkt.

2.9

0.0

24.0

18.7

1.8

Table 29
Percentage of female-headed households using labour for coffee (all)

Task

Type of labour


Hired male

Hired female

Family male

Family female

Family child

Land clearance

20.4

0.0

12.2

49.0

16.3

Land preparation

10.2

0.0

10.2

63.3

18.4

Planting

4.1

0.0

20.4

73.5

16.3

Fertilizer app.

0.0

0.0

0.0

0.0

0.0

Pruning

2.0

0.0

2.0

28.6

14.3

Weeding

6.1

0.0

8.2

69.4

26.5

Harvesting

4.1

0.0

12.2

71.4

26.5

Transport/field

6.1

0.0

10.2

69.4

30.6

Threshing

6.1

0.0

10.2

51.0

16.3

Transport/mkt.

0.0

0.0

2.0

18.4

6.1

Table 30
Percentage of male-headed households using labour for beans (all)

Task

Type of labour


Hired male

Hired female

Family male

Family female

Family child

Land clearance

12.8

0.6

45.5

30.4

6.4

Land preparation

14.2

1.2

41.7

47.8

12.2

Planting

3.8

2.9

41.2

57.1

15.9

Fertilizer app.

5.8

0.0

1.2

0.9

0.3

Pruning

1.2

1.2

11.9

13.0

3.8

Weeding

6.7

3.5

41.2

58.0

18.3

Harvesting

3.2

3.2

34.5

57.1

17.7

Transport/field

2.6

2.0

34.2

56.5

18.3

Threshing

2.0

1.5

25.2

44.9

12.5

Transport/mkt.

0.0

0.0

14.5

15.4

1.8

Table 31
Percentage of female-headed households using labour for beans (all)

Task

Type of labour


Hired male

Hired female

Family male

Family female

Family child

Land clearance

22.5

0.0

6.1

51.0

20.4

Land preparation

12.4

0.0

6.1

63.3

22.5

Planting

4.1

0.0

10.2

71.4

28.6

Fertilizer app.

0.0

0.0

2.0

0.0

0.0

Pruning

14.3

0.0

2.0

24.5

12.2

Weeding

2.0

0.0

6.1

69.4

28.6

Harvesting

4.1

0.0

8.2

67.4

30.6

Transport/field

4.1

0.0

6.1

67.4

30.6

Threshing

4.1

0.0

6.1

51.0

22.5

Transport/mkt.

0.0

0.0

2.0

12.2

6.1

The first obvious pattern is that female-headed households rely much more heavily on family female and child labour than do male-headed households. A second striking result, as is also evident in tables 20 and 21, is the involvement of male family labour. In male-headed households, the input of male and female family labour into a range of activities (with the exception of land preparation and perhaps marketing) is comparable.

Looking at male-headed households, and comparing different crops, it becomes clear that the relative labour input of women into maize and beans is higher than that of men into those crops. There is a higher percentage of households growing vanilla and coffee with men putting in labour to more processes than women. However, there are one or two areas where women are heavily involved in vanilla and coffee (more heavily than their input to maize or beans), which are weeding, harvesting and transporting vanilla, and picking and transporting coffee. Women’s labour inputs are mirrored by those of children, but the latter work far less. Wage labour is concentrated in the preliminary heavy tasks of land clearance and preparation, and in weeding, and is largely a male phenomenon. Few households used female wage labour, and female-headed households not at all.

The marketing of maize and beans grew rapidly over the early 1990s in Uganda, partly in response to the market created by the crisis in Rwanda and consequent food aid shipments. As maize and beans on average have a higher female labour input than male, it is important to examine whether expanded production for sale is adding significantly to the labour burden of women. A methodological problem here is that the marketing of maize and beans could signal the sale of a surplus grown specifically for that purpose, but could also signal distress sales. However, the results shown in tables 32-35 suggest that this is usually not the case, because marketed crops are associated with higher labour inputs, implying that these crops were grown specifically for the market.3

3 The numbers in tables 32-35 are ratios, so, for instance, in table 32 the first entry, 3.0, means that male-headed households marketing maize use three times as much hired male labour as male-headed households not marketing maize. A 0.0 means that the numerator is zero - i.e. that marketing households use no labour, while a “-” means that the denominator is zero - i.e. that non-marketing households use no labour.

Table 32
Relative proportion of male-headed households using labour for maize, marketers versus non-marketers

Task

Type of labour


Hired male

Hired female

Family male

Family female

Family child

Land clearance

3.0

3.6

1.5

0.9

1.3

Land preparation

2.7

13.6

1.3

1.0

0.7

Planting

9.2

17.7

1.6

1.3

0.6

Fertilizer app.

5.4

1.0

1.6

2.0

2.7

Pruning

-

-

1.5

1.3

1.6

Weeding

5.0

9.8

1.8

1.3

0.6

Harvesting

15.4

23.1

1.9

1.3

0.9

Transport/field

9.1

-

2.0

1.3

0.8

Threshing

-

-

2.5

1.8

1.9

Transport/mkt.

0.0

0.0

3.7

2.9

2.7

Table 33
Relative proportion of female-headed households using labour for maize, marketers versus non-marketers

Task

Type of labour


Hired male

Hired female

Family male

Family female

Family child

Land clearance

1.7

0.0

3.1

1.4

2.2

Land preparation

0.0

0.0

0.0

1.6

1.9

Planting

0.0

0.0

0.0

1.4

5.1

Fertilizer app.

-

-

-

-

-

Pruning

0.0

0.0

0.0

1.2

2.6

Weeding

0.0

0.0

0.0

1.5

2.8

Harvesting

0.0

0.0

3.1

1.4

2.8

Transport/field

0.0

0.0

3.8

1.5

2.4

Threshing

0.0

0.0

3.8

2.1

5.1

Transport/mkt.

0.0

0.0

0.0

7.7

30.7

Table 34
Relative proportion of male-headed households using labour for beans, marketers versus non-marketers

Task

Type of labour


Hired male

Hired female

Family male

Family female

Family child

Land clearance

5.7

-

1.3

1.2

1.7

Land preparation

3.9

17.2

1.4

0.7

0.4

Planting

14.7

25.7

1.6

1.0

0.7

Fertilizer app.

-

-

-

-

-

Pruning

17.2

51.5

1.4

0.4

0.0

Weeding

11.0

17.2

1.6

0.9

0.6

Harvesting

14.3

20.6

1.9

1.1

1.2

Transport/field

8.6

12.9

1.9

1.2

1.2

Threshing

2.9

4.3

3.0

1.4

1.8

Transport/mkt.

0.0

0.0

4.3

2.2

0.0

Table 35
Relative proportion of female-headed households using labour for beans, marketers versus non-marketers

Task

Type of labour


Hired male

Hired female

Family male

Family female

Family child

Land clearance

4.8

0.0

0.0

0.0

0.0

Land preparation

9.6

0.0

0.0

1.6

0.0

Planting

48.0

0.0

0.0

1.4

0.0

Fertilizer app.

-

-

-

-

-

Pruning

0.0

0.0

0.0

0.0

0.0

Weeding

0.0

0.0

0.0

1.5

0.0

Harvesting

48.0

0.0

0.0

1.5

0.0

Transport/field

48.0

0.0

0.0

1.5

0.0

Threshing

48.0

0.0

0.0

0.0

0.0

Transport/mkt.

0.0

0.0

0.0

0.0

0.0

In interpreting these tables, several points should be borne in mind. First, the number of female-headed households is much lower than that of male-headed households, and the number marketing maize and beans is lower still. Second, some of the very large proportional increases, such as for hired female labour, are from a very low base.

A striking finding across crops and household type is the higher labour inputs associated with the marketing of crops. Generally, maize production in male-headed households where the maize is marketed is associated with more use of hired male and female labour, and by an increase in family labour, but by a greater increase in male family labour than female labour. Similar results obtain for beans. In female-headed households marketing of maize is associated with higher labour inputs by women and children, but not more hired labour. In the case of beans, the numbers are so small that the data are not that useful.

In sum, analysis of the labour data shows that family labour inputs from both men and women into all crops is ubiquitous, and there is perhaps less of a boundary between male and female tasks than is commonly assumed - although women are covering more of the labour tasks for most crops, as expected. Female-headed households rely more heavily on child labour, and use less hired labour. The marketing of beans and maize is based on the use of more hired labour, and a generally higher level of self-exploitation, but relatively slightly more by men than by women. It is also apparent that the work parties that used to be a source of quick communal labour, especially for labour-intensive tasks, are no longer functioning to a significant extent in the community, and households have become more reliant on the labour market.4

4 The importance of improved labour market functioning for the expansion of marketed agricultural production means that the effect of the AIDS pandemic cannot be ignored (Evans, 1992). AIDS-related deaths and disability affect the most economically active sectors of the population, and are expected to significantly reduce available household labour as well as marketed labour.