(introduction...)

The first phase of the UNRISD/UNDP research consisted of collecting, reviewing and synthesizing available information relevant to gender and NTAE expansion. The results of this phase were summarized above.

It is clear from these findings that the interaction between gender and NTAE expansion is a complex one, and that, while some dimensions of this interaction are fairly well understood, data are currently insufficient for illuminating other areas, and a number of essential questions are just now beginning to be raised. There is little information available on the agricultural division of labour between women and men in different types of households, or on access to and control over production resources and benefits within the household. Many surveys do not distinguish between male and female-headed households, while some ignore female households altogether. While some of the national data sets reviewed (Balihuta, 1997) had data disaggregated by sex, planners at national and sector levels tend not to use this information. They often use the aggregated data and develop plans in terms of broad categories such as “people”, “communities” or “farmers” - rendering the sex disaggregated data redundant. The concept of gender remains foreign to many planners, who do not seem to be comfortable with programming using gender-disaggregated data.

The second and third phases of the research sought to shed further light on some of the questions raised in the first phase. They involved fieldwork in selected villages in two districts: Kitanyatta, in Masindi District, and Gonve, in Mukono district. First, a participatory rural appraisal (PRA) exercise was carried out in July 1997 in the two villages to explore the local assessment of local conditions and problems. The focus group discussion and preference ranking methods were used to provide insights into men’s and women’s conception of their livelihoods and the constraints that they face as farmers, their explanation for those constraints, and their means of coping with them.

The results of this qualitative part of the study provided both the indicators and the focus of the third phase, which was a questionnaire survey in the same villages, carried out in November-December 1997, in which 396 households participated. The survey was a rather narrow one, focusing on household characteristics, supply response issues, food security and workloads. The sample design endeavoured to include all types of households, which were stratified into low-, medium- and high-income categories. Data collection procedures at the household level were borrowed from Tibaijuka’s (1994) activity profile. A village sampling frame already existed from the chairman of the village council and the PRA village mapping exercise in the villages, which had classified the household types in the villages. The random sampling method was used to select households within each household type as in table 10, observing the proportion of each type. The child-headed households were so few that they were not interviewed. In the male-headed households, husband and wife were interviewed separately. The polygamous households which were sampled were handled like female-headed households, with each wife interviewed separately. However, the questionnaire had a question that required each respondent to state their relationship with the female or male head as well as the husband’s name, where applicable. Each respondent was asked to indicate the type of household they came from, as a separate question. A combination of these two questions made it possible to trace households that shared the same male head.

The use of the case study method - which was dictated by time and funding constraints - limited the generalizability of the findings, and the lack of baseline data was an added disadvantage. Unfortunately, some of the survey information suffers from a high level of missing data, non-response or internal inconsistency. Thus we were not able to use, for instance, data on field size. Acreage, sale and price data are problematic, presumably because respondents were being asked to remember details of the previous year’s harvest. The other data, including labour data, were judged to be more robust.

6.1 Village Characteristics

Kitanyatta is in Masindi District, in the northwest region of the country. Gonve is in Mukono district in the central region, close to Kampala and bordering Lake Victoria. Resources, including fish, are more abundant in Gonve, and soils tend to be better as well. The primary cash crops in Kitanyatta are food crops - maize, beans and cassava - while the primary cash crops in Gonve are vanilla and coffee. According to Agricultural Policy Secretariat data, coffee and vanilla contribute 70 per cent and 25 per cent respectively of household cash income in Gonve, while maize contributes 74 per cent of household cash income in Kitanyatta (Government of Uganda, 1996/97).

Gonve is a relatively prosperous village, with poverty being significantly higher in Kitanyatta, according to national statistics. Local perceptions agree with these data; the PRA exercise indicated that 68 per cent of households were considered well-off in Gonve, while only 25 per cent were well-off in Kintayatta (table 9).

Table 9
Socioeconomic status of households

Household category	Gonve		Kitanyatta
	number	%	number	%
Very poor/destitute	11	5.6	41	20.5
Poor	53	26.9	104	54.4
Fairly well off	128	65.0	46	23.8
Very well off	5	2.5	1	0.5

Female-headed households represent around 12 per cent of the sample of 396 households. This is significantly lower than the proportion found in larger, nationally representative surveys, such as the 1992 Integrated Household Survey. The difference may be due to the regions in which Kitanyatta and Gonve are located, to different ways of assessing what a female-headed household is, or to this survey missing some female-headed households.

Table 10
Household type

	Male-headed monogamous %	Male-headed polygamous %	Female- headed %	Child-headed %	N
Gonve	67	18	14	1	197
Kitanyatta	69	20	11	-	199
Both	68	19	12	1	396

6.2 Supply Response

Both of the villages included in the survey seem to have benefited from the price and marketing reforms undertaken as a part of Uganda’s structural adjustment programme: the nominal price of coffee increased from Shs 120/kg in 1990 to Shs 700/kg in 1997, while the nominal price of maize increased from Shs 30/kg to Shs 450/kg over the same period (Government of Uganda, 1996/97). The price data collected in the survey, while not definitive, suggest that farmgate prices in the selected villages were only slightly lower than these national-level data would suggest.

It is very difficult to estimate a national-level supply response to NTAE promotion policies for several reasons. First, there are no reliable recent data on total agricultural output. In addition, while some unofficial cross-border trade has presumably been switched to official trade, no estimate exists on past or current unrecorded exports. Finally, there are no reliable estimates of the proportion of crops marketed domestically. It was not possible to ascertain what proportion of staples is produced for own household consumption or is sold locally.

Unfortunately, the prices for most crops grown in Gonve and Kinyatta changed little over the two survey years (1996 and 1997), and thus very little in the way of a quantitative estimate of a local-level supply response can be derived from the survey data. However, the survey and the PRA exercise did yield a significant amount of information on the conditions necessary for a positive supply response to be obtained. This is discussed below.

The surveys in Gonve and Kitanyatta showed some evidence of limited increased production over the survey years. Forty-nine households in Gonve, or 25 per cent of those surveyed, indicated that they had increased labour demands due to increased production over the last season, and 26 households in Kitanyatta, or 13 per cent, reported the same. The data on acreage were not considered reliable enough to draw firm conclusions about changes in cropping area over the two seasons.

The survey gave some indications of factors entering into smallholders’ production decisions (tables 11 and 12). Pricing was clearly important for cropping decisions, although it was not the only factor. Confidence in markets was also important, because without this smallholders have little faith in pricing projections. In addition, concern for food security was evident; this also limits supply responses to price changes.

Table 11
Percentage of households increasing or decreasing crops: Gonve (survey data)

Crop	% of hh increasing	Reasons	% of hh decreasing	Reasons
Maize	9	previous famine	8	pests
		good price		lack labour
		ready market		other
Beans	13	previous famine	9	lack labour
		good price		other
		ready market
Vanilla	15	good price	9	spoils other crops
		ready market		lack labour
Cassava	22	ready market	12	pests
		previous famine		other
		good price
Coffee	20	good price	4	other
Others*	12	ready market	17	bad season
		easy to grow		other

* Primarily groundnuts and vegetables.

Table 12
Percentage of households increasing or decreasing crops: Kitanyatta (survey data)

Crop	% of hh increasing	Reasons	% of hh decreasing	Reasons
Maize	16	good price	15	lack of seed
		previous famine		bad weather
		ready market		pests
Beans	4	good price	19	bad weather
		previous famine		lack of seed
		ready market		pests
Cassava	18	good price	10	bad weather
		previous famine		lack of seed
		ready market		pests
Coffee	1	no reason given	1	no reason given
Others*	10	good price	13	pests

*Primarily groundnuts and vegetables.

These data suggest that improved pricing, improved markets and increased food security would trigger production increases for most crops. However, it cannot be determined whether an increase in production in any particular crop would be at the expense of another crop - that is, whether total agricultural production would increase, or whether crops would simply be switched. There is some suggestion of a tendency to switch from beans to cassava in Kitanyatta, while in Gonve, the more prosperous village, there is more indication of a slight increase in total production. Constraints on labour and other resources would limit the opportunities for increasing total production; the extent of these constraints in the survey villages is discussed further below.

It was also evident from the PRA data that the factors necessary for production increases - good prices, ready markets and food security - were far from assured in the study villages. In addition, the labour and technological constraints that were identified as reasons for decreasing production were clearly pervasive problems (table 13).

Table 13
Problems limiting productivity identified by farmers, in order of importance (PRA data)

	Gonve	Kitanyatta
Women	1. no market for our crops	1. no money to hire labour
	2. no new seed varieties	2. no tractors for hire
	3. no agricultural officers to consult	3. most time spent looking after families
	4. old unproductive soils	4. no market for our crops
Men	1. low prices for our crops	1. lack of a viable market for our crops
	2. lack of credit facilities	2. bad feeder roads
	3. limited farm implements	3. tractors and oxen too expensive
	4. lack of extension and veterinary services in our village	4. inaccessibility to loans and credit schemes

Both men’s groups considered lack of adequate sales opportunities as the biggest problem limiting their productivity. While women in Gonve are also concerned with marketing, women in Kitanyatta show clear evidence of labour constraints. Of note is the fact that neither women’s group mentioned inaccessiblity of credit as a problem. Women’s lack of interest in credit has been noted in other contexts in which women are primarily responsible for providing staple foods: because they must provide basic needs, they must behave in a very risk-averse manner, which in large part precludes involvement in credit schemes.

Tables 14 and 15 show what women and men believe to be the causes of the problems they identified, and how they cope with these problems.

These tables suggest that the problems confronting men are to a large extent the ones that have been identified and targeted by government programmes for improving the marketing of agricultural products: high costs of transportation, poor storage facilities, little access to credit. It is striking that, in comparison to men, women tend to face problems more in the realm of the process of agricultural production itself: they emphasize poor agricultural technology, poor seeds and soils, and, above all, continual labour constraints.

The qualitative survey and PRA data thus show some potential for a positive supply response to price movements and marketing improvements. This information also makes clear the extent of the problems that smallholders face, and the paucity of resources and coping strategies they have for dealing with these problems. Finally, these data suggest the complexity of the agricultural productivity problem, and the need for policies that deal simultaneously with multiple facets of agricultural production and marketing.

Table 14
Perceived causes of problems, Gonve and Kitanyatta: Men and women (PRA data)

Problem	Perceived causes (men)	Perceived causes (women)
Poor marketing of our crops (low prices and lack of market)	· no competition among buyers · no communication with external market · lack of co-operation among farmers · buyers cheat when weighing produce · government does not care to look for markets for our crops · we have bad feeder roads · no storage facilities, so we sell immediately after harvest when prices are low	· each farmer markets individually · each farmer grows little of many crops · lack of additional labour to produce more · we have no women’s leader who is educated enough to give us new ideas · too many middlemen
Lack of implements	· petrol is very expensive so we cannot hire tractors · our produce is bought at low prices · proceeds from our crops are used for very many things · farm implements are very expensive	· large scale farmers own the tractors and do not want to help the ordinary person · men do not allow us to form groups to hire tractors · we cannot afford implements
Poor extension services	· government workers do not like villages · extension workers do not have transportation · the vets sell animal drugs at high prices · extension workers no longer have demonstration gardens in the villages	· government workers only stop at the sub-county headquarters and select a few people to train · politicians who would help us only come when they are looking for votes
Lack of access to loans and credit schemes	· there are no clear processes for farmers to access loans · the money earmarked for farmers does not get to them · our leaders swindle the money meant for loans · the interest charged on loans is more than the farmers can afford · farmers are looked down upon as illiterate · there are too many things to go through before one gets a loan
Most time is spent looking after families		· some of us are widows · lack of co-operation with husbands (all domestic work is left to women) · husbands are the decision makers, even over their wives’ money · husbands control women’s movement · it is women who care for children
Poor feeder roads	· government does not care about farmers · workers who repair roads are not paid · road maintenance equipment is used for private work · because there are no trustworthy leaders, contracts for maintenance are awarded to incompetent people
No money to hire labour for farming		· most time is spent looking after family, especially children · low prices for crops · we plant late due to lack of tractor · women are not decision makers in farming and marketing · we spend more time on the men’s gardens · we are responsible for producing food for the whole family · there are no alternative ways of making money apart from farming · we are being ruled by husbands so we cannot make a lot of money
No new seed varieties		· we get no visits from agricultural officers · we do not know where to get seeds
Old unproductive soils		· continuous cropping on same piece of land · no labour to open new land · men care for coffee plantations only · no advice on new farming practices to increase yield

Table 15
Problems and coping strategies, Gonve and Kitanyatta: Men and women (PRA data)

Problem	Coping strategies (men)	Coping strategies (women)
Poor marketing of crops	· we just sell to any buyer at very low prices · we have no choice but to accept being exploited	· we sell to whoever comes · we keep telling visitors about our problems hoping they will tell government · we sell our produce in small quantities at the local market
Lack of implements	· we use hand hoes and grow little	· we use hand hoes · we hire casual labour once in a while
Poor extension services	· we just go without them	· we use traditional methods of farming
Lack of access to credit	· we try to argue that credit facilities should be decentralized to communities without much success	· we plant only what we can manage with our own hands
Most time is spent looking after families		· we have no solution but to work hard · we report our husbands to local leaders
Poor feeder roads	· we do not travel much, we wait for buyers to find us at our homes
No money to hire labour for farming		· we only plant what we can manage · sometimes children help
No new seed varieties		· we keep replanting our own seed
Old unproductive soils		· we rest the land from time to time · we make do with our low yields · we reduce the number of meals a day in time of scarcity

Given the suggested potential for a positive supply response in the PRA data, the survey attempted to ascertain more exactly the supply response in the two villages. However, as will become clear, problems with the data - especially price data - limited the extent to which this could be done.

Prices for most of the crops produced in the two villages changed very little over the course of the survey. Aggregate national data for crop prices are available (table 16), but they are not complete. In particular, prices for most crops for the years 1995 and 1996 are not available, since surveys were not carried out for these years. This is unfortunate, as these are the key prices for this study, which looks at supply response between 1996 and 1997.

Table 16
National crop prices, 1993-1998 (Shs/kg)

Commodity	1993	1994	1995	1996	1997	1998
Maize	125	160	-	-	450	-
Beans	250	500	-	-	800	-
Vanilla	-	-	4,000	3,000	2,500	2,500
Cassava	65	100	-	-	300	-
Coffee	250	300	-	-	700	-

Data provided by the following: for vanilla, Sekalala Enterprises; all other crops, Agricultural Policy Secretariat, Ministry of Planning and Economic Development.

Because there are variations in prices paid to farmers at the farm gate, the survey was also intended to provide information about prices received by individual farmers or households. This, as with the cropped area, is a derived figure based on reported sales and volume of crops sold. The resulting price per unit data do not look particularly robust. However, the modes agree quite well with the national data for 1997, although being slightly lower.

Three types of indicators for the supply of crops were derivable from the data set: self-assessed crop increase or decrease from 1996 to 1997; an estimate of changes in the area under crops; and changes in sales of crops. Problems with the quality and consistency of these data mean that quantitative estimates of changes in production cannot be made. However, the data give an overall impression of little shift in production between years. More than 75 per cent of households reported no change in cropping, no change in acreage under production, and no change in crop sales. Among the households reporting changes in these variables, almost equal numbers reported increasing production and decreasing production of each crop.

This stasis of production is not too surprising, given the absence of price changes. In addition, the large proportion of “no change” observations in the supply response variables means that regression equations used to draw out supply relationships are likely to be of limited use, since regression is best used where there is a more even distribution of the dependent variable.

Response by type of household

Despite the data limitations noted above, an attempt was made to test for different patterns of production behaviour in different types of households (male- and female-headed, monogamous and polygamous). These tests yielded few useful results. Taken in relation to minimal average price movements between the two years, the results show, as expected, only marginal changes in planted area on average. It is difficult to identify a pattern, either by crop or by type of household. The strongest positive movements are for maize and cassava are in polygamous households, the strongest negative movement is for beans in female-headed households. For the higher value cash crops of vanilla and coffee, the strongest change in area comes in male-headed monogamous households. Interestingly, for the only crop for which the modal price increases between 1996 and 1997 - beans - the response of all types of households is to decrease area planted.

The other measure examined here is crop sales. Again, patterns are hard to identify. Overall, all types of household are selling less maize and beans, and more vanilla. For coffee and cassava there are different responses. Polygamous households seem to be changing their sales of crops the most, especially for coffee. Female-headed households appear to be selling more cassava in 1997.

Supply response for coffee and vanilla by type of household

With coffee and vanilla, sufficient price data exist to allow a more sophisticated analysis. Table 17 shows the results of a simple bivariate correlation of price changes with changes in area planted, while table 18 shows the correlation with volumes marketed. In table 17, none of the Pearson correlation coefficients are significant. The small number of female-headed households growing coffee make correlation in this case impossible.

Table 17
Correlation of price changes with changes in area planted, 1996-97

	Vanilla	Coffee
All households	0.024	-0.131
Female-headed households	-0.322	-

Table 18
Correlation of price changes with changes in crops marketed, 1996-97

	Vanilla	Coffee
All households	-0.193	0.019
Female-headed households	-0.586	-0.007

The correlation results imply first of all a generally low level of relationship between prices and supply, with the exception of female-headed households’ marketed supply of vanilla (the only significant result). In the case of vanilla, all households appear to have a negative supply response; for coffee the correlations are more ambiguous.

A more general analysis of supply response, taking into account non-price factors, was also attempted. To identify the factors determining whether households enter into the cultivation of a particular crop, probit equations were estimated. The explanatory variables were socioeconomic status, the sex of household head, the total area cultivated (as a measure of wealth), a vector of dummies for input use (fertiliser, insecticide, improved seed, and tractor hire), the use of hired labour, and the number of female and male adults in the household. These equations were estimated for both coffee and vanilla. Overall, these equations performed poorly. Both were not significant overall, and although a few of the individual variables were significant, the sex of household head was not.

Finally, for those households growing vanilla or coffee, linear equations for supply response were estimated. The equations included price changes as an explanatory variable.² In order to understand the different supply responses of male-headed and female-headed households, it is necessary to estimate the supply response equations separately for the two groups. However, there are so few cases of female-headed households growing these crops in the sample that separate estimation is impossible. Instead, dummies for sex of household head and polygamy were entered as variables in equations estimated for the sample of all households growing the crops. This is not ideal, but is the best that can be done.

² Note that these equations were estimated only for households already growing the crop, as opposed to the entire sample. This is because the behaviour of households not growing the crop, in response to various factors, is not observed, but rather their response is entered as zero. This would bias parameter estimates downwards. The standard solution to this problem is the use of the tobit model. However, this was not available, making the above approach necessary.

The results are shown in table 19, for both crops and using change in area and change in volume of crop marketed as dependent variables. The supply response analysis includes both the price of the crop and of the other main non-food cash crop. “Normal” supply response would have a positive sign for the parameter on the own-price variable, and a negative sign for the other crop. Use of hired labour and family labour, along with land area and a combination of inputs (including fertiliser and tractor use) are included. Finally, there are dummy variables for whether the household is female-headed or polygamous.

Table 19
Supply response for coffee and vanilla: Regression results

		Dependent variable
		Coffee		Vanilla
Independent variable		Change in area	Change in crops marketed	Change in area	Change in crops marketed
Constant		4.5	1638.12	1.5	350.69
Coffee price change		s	-348.8***	-0.11	-4.7
Vanilla price change		1.7*	-42.9	1.2	95.1
Hired labour (female)		0.410	-672.8	-0.03	-111.5
Hired labour (male)		-0.224	319.5	0.17	70.2
No. females		0.33*	-45.1	0.2	-14.9
No. males		-0.229	-3.2	-0.2	26.1*
Total land area		0.067	-2.4	-0.001	2.9
Inputs		-0.456	-76.3	-0.154	-27.3
Female-headed		-0.165	202.0	0.05	-2.9
Polygamous		0.403	254.9	1.08	134.5*
Adj. R2		.321	.339	-.072	-.117
F		2.135	2.54	0.84	0.77

* significant at the 10 per cent level
** significant at the 5 per cent level
*** significant at the 1 per cent level

Generally the equations for coffee perform rather better than those for vanilla, with adjusted R²s in the low 0.3 range. Only one variable is strongly related to a supply indicator, which is the coffee price for the volume of crops marketed. This is strongly negative. Female-headedness and polygamy are associated positively with supply changes, but not significantly so. Coffee production seems to involve family female labour more than vanilla does, a result not particularly borne out in the labour data below.

The results on vanilla in the correlation analysis (i.e. a negative supply response) are not borne out here. The only significant variables are for the change in crops marketed, where the number of adult men in the household and polygamy are positively related to a larger supply response.

In conclusion, the quantitative supply response analysis is hampered by the fact that there was little price movement between the two years of the study. The available data show no clear patterns distinguishing female-headed from male-headed households in their supply response behaviour, nor monogamous from polygamous households. This does not necessarily mean that no such patterns exist: the absence of price changes and non-robust production data make it impossible to generalize from these results. It is interesting, however, that there is no suggestion of a positive supply response, and several suggestions that supply response to increased prices might in fact be negative. This possibility will bear a closer analysis, especially in light of survey data from Zambia with similar findings (Wold, 1997). Given the clear interest shown by smallholders in improved prices and markets, the reason for a negative supply response is likely to lie in constraints on production, and labour appears to be the most binding constraint facing smallholders.

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.

6.4 Other Constraints on Production

Marketing

Problems with the market for agricultural products in Uganda - low farmgate prices resulting from high transportation costs, high spoilage rates and limited competition among buyers - were discussed above (section 4.1, table 4). The survey and PRA findings confirmed a generally low level of confidence in market functioning in the survey villages and a perception that farmgate prices were too low (tables 13 and 14). Tables 20 and 21 show that many households are unable to transport their crops to market, rather they wait for buyers to reach them and are thus forced to accept the price offered in the monopsonistic market. Poor feeder roads have been identified as one reason for these market imperfections, and the government has developed programmes meant to improve them. Increasing household access to means of transportation, including bicycles and carts, would be an important complement to this initiative. Household access to transportation would improve access to markets and thus increase competition among buyers; it would at the same time ease women’s labour burdens by making fuelwood and water more accessible. It is interesting to note that in a survey in Arua district, the tractors meant to assist in ploughing were in fact used for transportation (Uganda Women’s Network, 1995) - an indication of the extent of the transportation deficit in the rural sector.

Lack of credit

Lack of credit has been identified as a problem to be addressed by government programmes, and there are several credit schemes currently in place to channel more credit to smallholders. Improved access to credit is meant to enable households to improve productivity through the purchase of additional inputs and labour, and to smooth out the peaks and troughs in food prices. The survey results confirm that male smallholders would welcome the opportunity to access credit. However, women show much less interest in obtaining credit, and in fact obtain much less. As discussed above, women’s role in staple food provisioning requires them to behave in a more risk-averse manner than do men. Men’s focus on cash cropping and their reliance on women for daily food needs allow them to accept the higher degree of risk associated with credit. At the same time, women will need more access to cash if they are to purchase the labour inputs they clearly need. Improved food security through better storage and higher productivity will make credit an option for more women; in the meantime, enhanced savings schemes might be an alternative to credit for women.

Limited inputs

The survey results indicated that the use of better agricultural inputs was extremely low in the survey villages (table 36). The participants cited lack of extension services, lack of knowledge, lack of access, and lack of cash as reasons for the low level of agricultural technology employed. It will obviously take a concerted effort on many fronts to break out of this low input-low output equilibrium. A failure to do so will mean that any positive supply response to NTAE incentives will be unlikely to represent an increase in total productivity, but will rather be likely to result from crop switching.

Table 36
Use of agricultural inputs

	Gonve (n = 197)		Kitanyatta (n = 199)
	Number of households	% of households	Number of households	% of households
Fertilizer	19	10	1	1
Manure	55	28	16	8
Insecticide	23	12	7	4
Improved seed	13	7	20	10
Hired tractor	4	2	7	4
Other	23	12	3	15

6.5 Control and Expenditure of Cash Crop Income

The study findings suggest that, as elsewhere, women in the villages surveyed tend to emphasize food crops and food security more than do men (table 37). Thus, if crop switching occurs in response to NTAE incentives, this might negatively affect both food security and women within the household. Of course, income generation and food security objectives are linked, and if markets are functioning well, switching to the most productive crops in accordance with comparative advantage should be an effective production strategy (Whitehead, 1991). However, given the recent history of prolonged turmoil in the country, the wide seasonal fluctuations in food prices and the wide marketing margins, confidence in markets is low - and probably rightly so.

Table 37
Crops preferred by farmers and why: Gonve (PRA data)

Women’s preferences	Why	Men’s preferences	Why
1. Cassava	· family food · can be kept in the dry season · grows in poor soils · can be eaten over a long period without getting tired of it	1. Coffee	· sold for cash · helps to pay school fees · can be stored · can be used to pay tax · easy to look after
2. Beans	· source of protein · is storable until next season · keeps children healthy · first crop grown after dry season	2. Vanilla	· money from it comes in a lump sum · grows well in our soils · helps to pay school fees
3. Sweet potatoes	· can be sold for money · matures fast	3. Cassava	· we can sell some and eat some
4. Plantain	· appreciated as food for the family · easy to prepare for a quick meal; is tasty and satisfying	4. Sweet potatoes	· we eat them

Table 38
Crops preferred by farmers and why: Kitanyatta (PRA data)

Women’s preferences	Why	Men’s preferences	Why
1. Maize	· it brings in money · it has a market	1. Maize	· we make money from it · market is sometimes available · it grows fast · we brew beer from it · it helps pay school fees
2. Groundnuts	· protein source · it has a market (although prices are too low for the effort)	2. Groundnuts	· we make money from it · we eat it
3. Beans	· we eat it · it can also be sold · it matures early	3. Cassava	· we eat it · sometimes we sell it · it grows well around here
4. Cassava	· it is good in days of famine · it has a market · can be stored in the soil and picked when needed · can be used for brewing “waragi”	4. Coffee	· it brings in money · it has a ready market

Although the income data from the survey are not robust enough to derive quantitative estimates of income changes, table 39 suggests that over the period 1996-97 Gonve showed signs of an increasing specialization in cash crops, and an increased reliance on the market, with households moving away from maize in particular as a cash crop. Kitanyatta, however, showed the opposite pattern (table 40). It had seemingly fewer expenditures from all cash crops, suggesting a reluctance or inability to increase marketed surplus. This evidence, although partial and preliminary, thus suggests that farmers are not compromising food security in response to NTAE incentives.

Table 39
Expenditure of income from cash crops, 1996 and 1997, Gonve (number of households)

	1996							1997
	Food	Education	Clothing	Medical	Building	Business	Other	Food	Education	Clothing	Medical	Building	Business	Other
Maize	3	3	2	3	0	0	5	2	0	0	0	0	0	3
Beans	0	3	3	2	0	2	3	0	1	0	1	1	1	2
Vanilla	27	35	31	17	14	2	28	35	32	32	14	24	4	31
Cassava	3	6	6	5	0	0	3	7	4	9	3	1	0	6
Coffee	25	36	40	18	15	3	33	42	50	50	24	23	7	34
Others	2	4	5	0	0	0	2	5	2	6	4	2	0	3

Table 40
Expenditure of income from cash crops, 1996 and 1997, Kitanyatta (number of households)

	1996							1997
	Food	Education	Clothing	Medical	Building	Business	Other	Food	Education	Clothing	Medical	Building	Business	Other
Maize	16	20	64	19	11	4	40	20	12	66	17	14	3	4
Beans	4	1	18	3	2	0	8	5	1	12	2	1	0	0
Cassava	6	1	21	7	2	0	6	8	1	26	2	2	0	1
Coffee	0	1	1	1	0	0	2	0	0	1	0	0	0	0
Others	14	7	31	3	5	3	27	11	3	27	2	3	5	0

It is interesting to note that cassava is a significant cash crop in both villages, suggesting that it may be useful to take cassava more seriously as an important cash crop in the region. Cassava is an important “famine food” and source of food security. Farmers find it easy to grow and to store, and there is a ready market for it in Kenya, the Sudan, and the Republic of Congo. Increased extension services and the introduction of more pest-resistant varieties may make cassava a readily accepted, efficient and viable cash crop.

The survey also sought an indication of how men and women control productive resources and share the rewards of the cash income from agriculture (table 41).

Table 41
Authorization of expenditure of cash crop income Gonve and Kitanyatta, 1997 (number of households)

	Gonve (n = 197)				Kitanyatta (n = 199)
	Husband	Wife	Child	Other	Husband	Wife	Child	Other
Maize	2	1	0	0	71	35	4	4
Beans	3	3	0	0	12	2	0	0
Vanilla	66	43	1	0	0	1	0	0
Cassava	10	11	0	1	22	11	0	0
Coffee	93	44	2	0	1	0	0	0
Other	7	3	1	0	36	15	1	0

The figures in table 41 suggest that women have a significant degree of control of cash crop income, authorizing its expenditure in 30 per cent or more of households. However, the picture is different if the data are disaggregated by total expenditure instead of numbers of households (because of data constraints, this is only possible for vanilla and coffee in Gonve). Table 42 indicates that over 90 per cent of the income from vanilla and coffee is controlled by men.

Table 42
Authorization of expenditure of cash crop income Gonve (Ugandan shillings)

	Vanilla		Coffee
1996	Income	%	Income	%
Male	12,095,000	90.7	30,086,750	93.0
Female	1,240,500	9.3	2,271,000	7.0
Total	13,335,500		32,357,750
1997
Male	14,079,000	90.1	31,704,250	90.1
Female	1,550,950	9.9	3,479,000	9.9
Total	15,629,950		35,183,250

The survey respondents were asked to state who spent the income earned from the sale of crops and to list the items on which this income was spent. The results, disaggregated by gender, are given in tables 43 and 44.

Table 43
Husbands' and wives' expenditure patterns, number of households (Gonve)

	1996				1997
	Husband		Wife		Husband		Wife
	No.	% (rank)	No.	% (rank)	No.	% (rank)	No.	% (rank)
Food	25	13.2(3)	36	26.3(1a)	63	23.4(1)	42	29(1)
Education	36	18.9(1)	26	19(2)	35	13(3a)	26	17.9(3)
Clothing	30	15.8(2)	36	26.3(1b)	47	17.5(2)	34	23.4(2)
Medical	19	10(5)	9	6.6(3)	18	6.7(4)	11	7.6(4)
Land/building	23	12.1(4)	2	1.5(4)	35	13(3b)	4	2.8(5)
Business	13	6.8(6)	1	0.7(5)	16	5.9(5)	2	1.4(6)
Other/labour/ bicycles	14	23.2	27	19.7	55	20.4	26	17.9
Total	190	100	137	100	269	100	145	100

Table 44
Husbands' and wives' expenditure patterns, number of households (Kitanyatta)

	1996				1997
	Husband		Wife		Husband		Wife
	No.	% (rank)	No.	% (rank)	No.	% (rank)	No.	% (rank)
Food	22	12.2(3)	14	21.2(1)	30	15.1(2)	14	20.3(2)
Education	15	8.3(5)	2	3(4b)	16	8(5)	2	2.9(6)
Clothing	26	14.4(2)	11	16.7(2)	33	16.6(1)	15	21.7(1)
Medical	20	11.1(4)	6	9.1(3a)	21	10.6(4)	9	13(3)
Land	12	6.7(6)	2	3(4a)	15	7.5(6)	3	4.3(5)
Business	35	19.4(1)	6	9.1(3b)	27	13.6(3)	6	8.7(4)
Other/labour/bicycles	50	27.8	25	37.9	57	28.6	20	29
Total	180	100	66	100	199	100	69	100

The category “other” is a significant one, especially in Kitanyatta, and includes both labour and consumer durables such as bicycles. Unfortunately, labour was not separately coded on the questionnaire. Women were more likely than men to spend their income on food, and clothing was a significant expense for both men and women. The commonly held assumption that women are more likely to spend income that they control on household needs, especially food, tends to be supported by these data. Because women are traditionally responsible for providing food for the household, it is not surprising that food purchases are normally ranked higher for women than for men. However, men also spend a significant proportion of their income on food, and there are also indications that men’s expenditure allocations change as the household’s needs change. Both men and women increased their expenditure on food between 1996 and 1997 in both Gonve and Kitanyatta - with the absolute increase being greatest for men in Gonve - suggesting that the need for food was greater in 1997 and that men were willing to change their expenditure patterns to meet this need.