Non-Agricultural Earnings in Periurban Areas of Tanzania : Evidence from Household Survey Data 1

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1 Draft : June 26, 1999 Non-Agricultural Earnings in Periurban Areas of Tanzania : Evidence from Household Survey Data 1 Peter Lanjouw (Free University Amsterdam, and World Bank) Robert Sparrow (Economic and Social Institute, Free University Amsterdam) 1. Introduction Rural households in developing countries have traditionally been viewed as though they were exclusively engaged in agriculture. There is mounting evidence, however, that rural households (including farm households) in such countries diversify their incomes by participating in non-agricultural activities, such as wage and selfemployment in commerce, manufacturing and services. Such nonfarm incomes can contribute significantly to total incomes of farm households in developing countries. Although, initially, much of the empirical and theoretical research on the nonfarm sector was concentrated in the South and East Asian regions, recent years have seen an expansion of research on the sector to Latin America and Africa. Evidence for Africa shows that there exists widespread rural non-farm employment in this region and that it is growing over time. 2 In a review of about 100 farm-household survey studies from the 1970s-1990s, Reardon et al. (1998) find an average share of 42% of nonfarm income in total rural household income in Africa, followed by 40% in Latin America, and 32% in Asia 3. Policymakers are interested to understand better how the non-farm sector contributes to overall economic growth, and in particular, how the sector can help to address rural poverty concerns. There is a growing realization that agricultural growth 1 We are grateful to Hans Hoogeveen, Charles Kenny, Jean Olson Lanjouw, Jaime Quizon, and Shahid Yusuf for helpful comments and suggestions. The views in this paper should not be taken to reflect those of the World Bank or any of its affiliates. All remaining errors are our own. 2 Liedholdm, McPherson and Chuta (1994) and Reardon (1997) provide recent surveys of empirical work in rural Africa. 3 See also Lanjouw and Lanjouw (1997) for a survey of the empirical and theoretical literature on the nonfarm sector.

2 alone cannot be relied on to employ what remains, in many developing countries, a rapidly growing rural population, and that the solution of migration from rural to urban areas carries with it a whole host of attendent social costs. There is thus a growing interest in the design and implementation of policies to promote the rural non-farm sector - especially where it is found to contribute directly to rural poverty alleviation. The non-farm sector is defined essentially in terms of what it is not, and refers, thus, to a far from homogeneous set of activities. This heterogeneity makes it very difficult to offer broad policy prescriptions. Policies for a given country must be founded on detailed analysis of the sector in that specific country, with clear reference to the country s institutions, capacities, and constraints. This paper attempts to fill in some of the empirical details about the non-farm sector for the particular case of Tanzania. The paper analyzes recent household survey data collected in peri-urban areas around six major cities in the country, and tries to highlight findings which are of relevance to policymakers. The dataset does not cover all rural areas in Tanzania, and it is clearly not appropriate to generalize the findings for households in these regions to those located in more dispersed rural areas. However, it is often assumed that non-farm activities in rural areas emerge first, and are most extensive, in peri-urban areas rather than in more outlying regions. If this were true, then one could interpret the findings from this analysis as possibly pointing to the impact of an eventual expansion of non-farm activities to more outlying areas of rural Tanzania. In this paper we will comment further on the stylized fact that peri-urban areas provide the natural setting in which to search for evidence on the scale and significance of non-farm activities in rural areas. The paper is organized as follows. In the next section we briefly review some of the issues surrounding the non-farm sector which have received attention in the literature, and provide a brief summary of previous research on this topic in Tanzania. Section 3 describes the data analyzed in the paper. Section 4 describes the empirical findings from the analysis, and Section 5 concludes. 2. The Non-Farm Sector in Tanzania 2

3 An Overview of the Issues The rural non-farm sector is traditionally seen as a low productivity, residual, sector, producing low quality goods. The sector, in this view, is expected to wither away as a country develops and incomes rise. There is thus no obvious rationale for governments to promote the sector, nor to be concerned about negative repercussions on the rural nonagricultural sector arising from government policies directed at other objectives. In recent years, opinion has been swinging away from this view, and there are a number of arguments which suggest that neglect of the sector is socially costly. For example, it has been argued that the sector has a positive role in absorbing a growing rural labor force, in slowing rural-urban migration, in contributing to national income growth and in promoting a more equitable distribution of income. The perceived contribution of this sector to the economic achievements of the East Asian countries is receiving increased attention and this is likely to have contributed to the rekindled interest in this sector elsewhere as well 4. Are non-agricultural activities more or less efficient in converting resources into output, relative to urban enterprises or agriculture? An important consideration in answering this question is how to assess the opportunity costs of factors of production. While commonly an average agricultural or urban wage is used to value labor and some common interest rate is chosen to value capital, private and social opportunity costs will not typically be reflected in these prices and are likely to vary across localities, households, gender, etc., particularly when markets are far from perfect. If there is widespread employment rationing (due, perhaps, to oligopsonistic markets) it may be preferable to assume that labor has a zero opportunity cost - despite positive market wages. Similarly, where there are large transactions costs in financial markets, the interest rate for someone attempting to borrow may be much higher than the potential returns available to the same individual if he has some small savings. If financial markets are so imperfect that one 4 Aoki, Murdoch and Okuno-Fujiwara (1995) argue that the East Asian success in utilizing cheap labor in rural areas, in sectors outside of traditional farming, was "one of the most important elements of East Asian development" (page 40. See also Hayami, forthcoming.) Otsuka and Reardon (1998) attempt to draw lessons from the East Asian experience of rural industrialization for the Africa region. 3

4 cannot invest one's savings except in one's own enterprise, then labor use and capital use are linked. The prevalence of self-employment using exclusively own (or family) capital in rural non-agricultural activities, combined with very rudimentary or non-existent savings institutions in many rural LDC contexts, suggests that this may often be the case. Then the opportunity cost of the use of savings is zero and labor productivity may be the best measure of total productivity (see Vijverberg, 1988). Non-agricultural activities can be broadly divided into two groups of occupations: high labor productivity/high income activities and low labor productivity activities which serve only as a residual source of employment - a "last-resort" source of income (Lanjouw and Lanjouw, 1997). These latter activities are common among the very poor, particularly among women, and earnings are often very low. Such employment may nevertheless be very important from a social welfare perspective for the several reasons: i) off-farm employment income may serve to reduce aggregate income inequality; ii) where there exists seasonal or longer-term unemployment in agriculture, households may benefit even from low non-agricultural earnings; and iii) for certain subgroups of the population who are unable to participate in the agricultural wage labor market, notably women in many parts of the developing world, non-agricultural incomes offer some means to economic security. The distributional impact of rural non-farm incomes on inequality is a topic of considerable interest. Reardon (1997) finds in 18 field studies in Africa that the share (on average) of nonfarm income in total income is twice as great in upper income tercile households as in lower tercile households; in 8 of the 18 studies the nonfarm income share is more than twice as large in upper income households, and in the rest it is between one and two times as high. This is suggestive that in the absence of non-farm incomes, inequality would be lower. It is difficult to draw firm conclusions as to this relationship, however, without a clear indication as to the counterfactual : what would the situation have been in the absence of non-farm occupations? The evidence, as reviewed by Reardon et al (1999), presents a mixed picture. 5 5 Lanjouw (1998) notes that the distributional impact depends on the domain over which inequality trends are viewed : he finds for Ecuador that within rural areas, non-farm incomes contribute to higher income inequality, but that at the national level the effect is reversed. In Ecuador, nonfarm incomes help to reduce the gap between rural and urban areas. 4

5 One important consideration remains that although aggregate income inequality may widen as rural non-agricultural incomes increase, this may occur alongside a decline in absolute poverty (if, for example, all households benefit from off-farm income, but the rich benefit proportionately more). Empirical evidence in many countries supports the notion that agricultural wages are not perfectly flexible, and that rural agricultural labor markets are segmented - with certain subgroups of the population such as women and children unable to obtain employment at the market wage. If indeed agricultural wage employment is not an option for certain family members, then rural non-agricultural employment opportunities, even if they are not highly remunerative can make a real difference - especially for those households which do not possess farm land. The broader relationship between rural non-agricultural activity and agriculture has recieved considerable attention in the literature. During the 1970s, Mellor and Lele (1972), Mellor (1976), and Johnston and Kilby (1975) argued that a virtuous cycle between agricultural intensification and non-agricultural activity could emerge on the basis of production and consumption linkages. Production linkages could emerge whereby for example, demand of agriculturalists for inputs such as plows and machinery repair would stimulate non-agricultural activity via "backward" linkages, or where agricultural goods required processing in spinning, milling or canning factories prior to sale and thereby stimulated non-agricultural activity through "forward" linkages. Consumption linkages could emerge as rising agricultural incomes would feed primarily into increased demand for goods and services produced in nearby villages and towns. In addition, rising agricultural productivity could release labor or raise wages for non-agricultural activities, and agricultural surpluses could provide investment funds for the non-agricultural sector. The expanding non-agricultural sector was then thought likely to act as an impetus to further agricultural intensification, through lower input costs, profits invested back into agriculture, and technological change. The growth in these two sectors could thus be mutually reinforcing. Where this hypothesis has been examined empirically, the different linkages have been found to vary markedly in their influence in different settings. Research in Africa has tended to stress the importance of production linkages. A detailed SAM-based analysis (Social Accounting Matrix) by Lewis and Thorbecke (1992) for the small town of Kutus in Central Province, Kenya, and its surrounding region, finds that 5

6 forward linkages from agriculture to the non-farm sector are particularly high : a 1 Ksh increase in coffee output generates 1.12 to 1.42 Ksh in regional value added. They also find that farm-based non-farm activities have stronger linkages to agriculture than does town-based manufacturing. 6 Haggblade et al (1989) estimate that in Kenya and Sierra Leone agricultural income is the source of between 15 and 40 percent of nonfarm investment funds. In sum, the existing literature points to a potentially strong relationship between the rural non-agricultural sector and rural poverty. Because of market imperfections and distortions, non-farm activities are likely to employ labor beyond the point where the marginal product of labor is equal to the prevailing average agricultural or urban wage. The wide range of non-agricultural activities in terms of labor productivity suggests that for some these activities provide a last resort safety-net function, while for others they offer a genuine opportunity for sustained upward mobility. The myriad possible linkages between the non-agricultural sector and agriculture suggest that a vibrant non-agricultural sector can also contribute in an indirect way to poverty reduction via improved agricultural productivity, higher prices for agricultural products and higher agricultural wages. The Non-Farm sector in Tanzania Ellis (1999) provides a recent review of the large-scale sample survey evidence on the significance of the non-farm sector in rural Tanzania (Table 1). 6 Proximity to cities appears to influence the type of non-farm activities households engage in. In a detailed study of employment in the city of Bouake, Cote d Ivoire (population 110,000 in 1970) and surrounding region, Uribe-Echevarria (1991) finds that traditional activities diminshed rapidly in importance close to the city. For example, basket making, weaving and pottery comprised 6.2 percent of total employment at a distance of 25+ km from the city but only 1.9% within 10km. 6

7 Table 1 Composition of Rural Household Incomes from Large Size Household Surveys / % % % % % Subsistence Farm Cash Crops Livestock Nonfarm Cash Wages Business Remittances n.a Total Income Source : Ellis (1999) Based on Tanzania (1969 ; 1977), Collier et al (1986), Bevan et al (1988), Sarris & van den Brink (1993), World Bank (1993), Ferreira (1993), Sarris and Tinios (1995) Although there are serious problems of comparability and interpretability in the figures reported in Table 1, several points are worth noting. 7 First, in rural Tanzania nonmonetized incomes remain quite important, suggesting that the transition out of subsistence agriculture is far from complete. Second, non-farm income shares are fairly low and there is no clear evidence of a marked expansion of these shares over time. Even in 1983, the year for which the non-farm income share is highest (37.5%), this remains below the average of 42% reported in Reardon et al (1998) for a selection of farm household-based studies in Africa. Third, non-farm wage income appears not to have been a very large source of rural incomes in Tanzania during the past thirty years, suggesting the absence and little development of rural labour markets (both agricultural and non-agricultural). The bulk of non-farm incomes tend to come from business activities. Collier et al (1986) provide evidence that in the 20 villages covered by their survey, the share of income from non-farm sources is considerably higher among the non-poor than among the poor. In their study 27% of households received a positive income from non-farm sources in For the poor, the income share from non-farm 7 Ellis (1999) argues that the figures for 1991 reported in the last column are particularly problematic, running contrary to the bulk of qualitative and quantitative evidence on Tanzanian rural households in the 7

8 sources (excluding remittances) amounted to 9% while for the non-poor the comparable figure was 26% (Collier et al, 1986, page 76). We have already seen that the non-farm sector can comprise last-resort, residual, activities of particular importance to the poor, but also activities which generate considerable incomes. The Collier et al (1986) evidence suggests that the latter type of activity is more typical in rural Tanzania than the former The Data The empirical analysis in this paper is based on the 1998 Tanzania Peri-Urban Survey, fielded in the peri-urban areas around Dar es Salaam, Lindi, Mbeya, Mwanza, Arusha and Moshi. Data was collected from 592 households in the two months between February 27 and April 10, A multi-stage sampling procedure was followed. First, strata were defined in terms of distance from the urban center (generally in four strata corresponding to 5km-wide bands around the cities). Then villages (clusters) were randomly selected within these strata. Within each village, around 12 households were selected based on an informal stratification corresponding to the main year-long activity, so as to ensure that 2-6 households receiving non-farm incomes were drawn from each cluster. Household weights were calculated and attached to each household in the dataset, so as to ensure that statistical representativity can be preserved. Throughout this paper we have attempted to explicitly incorporate information on the sample design in our statistical analysis. Failure to do so can seriously undermine statistical inferences (Lanjouw and Howes, 1998, Jolliffe, 1998). The survey collected information on household incomes and their sources, employment in farm and non-farm activities, socio-economic and other constraints to these and other activities, availability and use of basic services (e.g., education, health, agricultural extension), food consumption expenditures, social capital, and private asset ownership and structure. Most of the data collected were based on recall at the time of the survey. For food consumption expenditures this period refers to the past month. 1990s, in which non-farm incomes tend to be considerably more important than the figure of 10.8% suggests (see also below). 8

9 4. Empirical Results Non-Farm Incomes in Peri-Urban Tanzania Table 2 provides a breakdown of income by source in the whole survey as well as separately in the six peri-urban areas of the cities covered by the survey. Table 2 : Income Shares by Source and City Per capita Income Per capita Food Consumption Non farm Income % Farm Income % Net remittance % Survey % 81% -6% Dar es Salaam % 62% 4% Mwanza % 91% -7% Moshi % 77% 15% Arusha % 87% -11% Mbeya % 102% -26% Lindi % 82% 7% Business Non-Ag. Crops Livestock & Hunting & Farm Other Labour Livest. Products Gathering Labour Survey 18% 4% 55% 15% 8% 3% 1% Dar es Salaam 28% 3% 32% 9% 20% 1% 1% Mwanza 8% 6% 59% 22% 3% 5% 2% Moshi 5% 3% 54% 18% 1% 3% 1% Arusha 8% 15% 34% 49% 0% 3% 1% Mbeya 20% 4% 87% 10% 1% 3% 2% Lindi 9% 2% 69% 3% 5% 6% 1% In the six peri-urban regions combined, non-farm incomes represent 24% of total incomes. This percentage can be broken down into 18% deriving from business activities and around 5% form non-agricultural wage labour activities. Crop income, combining subsistence with marketed agricultural output, represents 54% of incomes in these periurban areas. The remainder is made up by livestock and livestock products income 8 Dercon and Krishnan (1996) and Dercon (1998) find, however, that in one village in Mwanza region in western Tanzania, the relationship between non-farm income shares and consumption terciles was the opposite : here the poor and middle terciles received the largest share of income from non-farm sources. 9

10 (16%), hunting and gathering (8%) which includes fishing -, Farm labour and other sources (3% and 1%, respectively). An important finding, and one which contrasts with that reported in Table 1 for all rural areas, is that net remittance incomes are on balance negative from the peri-urban areas ; on average 6% of income is sent out of these periurban areas, possibly to further, outlying rural areas. Breaking the survey down into the respective peri-urban areas we observe considerable variation in the importance of non-farm incomes across regions. In the surroundings of Dar es Salaam, non-farm incomes represented as much as 32% of total incomes. In this region, crop and livestock incomes are relatively unimportant but hunting and gathering (mainly fishing) is more important than average. Of the non-farm incomes in Dar es Salaam, the largest proportion (88%) comes from business activities, while only 3% of total incomes come from non-farm wage labour. Interestingly, households in the per-iurban areas of Dar es Salaam appear on average to remain net recipients of remmittance incomes. The other peri-urban areas in which non-farm incomes represent an important share of total income are Arusha and Mbeya. While the dominance of business activities is once again observed in Mbeya (83% of all non-farm incomes come from business activities), wage labour in the non-farm sector is particularly important in Arusha (accounting for 63% of non-farm incomes). The city of Arusha is located in the vicinity of Tanzania s major safari parks. It is possible that employment in the tourist industry which is centered in this region provides a significant boost to household incomes in the surroundings of this city. Scrutinizing average per capita incomes and food consumption levels in the six peri-urban areas reveals that the richest two regions are Dar es Salaam and Arusha. In per capita income terms Arusha appears to enjoy the highest average incomes, followed by Dar es Salaam. This ranking is reversed when we examine per capita food consumption levels. 9 It is of some interest to note that the richest regions are also those in which non-farm income shares are highest. 9 It is important to note that we have not adjusted incomes or consumption levels for spatial price variation. This could compromise regional comparisons of living standards, as proxied by incomes or consumption levels. 10

11 We examine the relationship between non-farm income shares and living standards directly in Table 3. Table 3 : Income Shares by Food Consumption Quintile Quintile Per capita Per capita Non farm Farm Net remittance (1=poorest) Income consumption Income Income % 99% -5% % 97% -5% % 74% -1% % 70% 0% % 70% -7% Quintile Business Non farm Crops Livestock & Hunting & Farm Other (1=poorest) labour livest. products Gathering labour 1 2% 3% 81% 10% 1% 7% 0% 2 5% 2% 79% 14% 2% 2% 1% 3 15% 6% 46% 15% 11% 2% 3% 4 22% 7% 40% 17% 11% 2% 2% 5 32% 4% 34% 19% 16% 2% 0% Non-farm income shares rise sharply with per capita food consumption quintiles. While the poorest quintile receive some 6% of household income from non-farm sources, the percentage is as high as 37% for the top quintile. Breaking down non-farm activities into business and wage labour, it can be seen that the strongest correlation between income shares and consumption quintiles occurs for business activities. For wage labour activities the relationship is non-monotonic. This observation is consistent with the notion that at least some of the wage labour non-farm activities are poorly remunerated and serve as a residual activity for poor households which would otherwise be even poorer. Such activities, while terribly important in preventing even deeper destititution, cannot be viewed as offering a source of genuine upward mobility. 10 The opposite 10 Unfortunately the data do not allow us to examine in detail the precise type of the activity from which non-farm wage and business incomes are derived. Evidence for other countries tends to show that nonfarm wage activities comprise both casual labour in menial activities, as well as regular, salaried employment. Business activities can also range widely between small-scale, residual or seasonal activities and sophisticated, capital intensive, high-productivity, enterprises. 11

12 appears to be the case with other wage-labour activities, and with business activities in general. Although comparability of definitions is unlikely to be perfect, the evidence in Table 2 suggests that, for the survey as a whole, non-farm income shares in peri-urban areas are not significantly higher than in rural areas more generally (see Table 1). We remarked above that it is sometimes argued that one should expect to see more evidence of non-farm activities in areas around major conurbations given the, presumably, better access to infrastructure and markets in such areas. At least two reasons could be put forward as to why this need not be the case. First, it is possible that proximity to a large market for food products provides an incentive to households in peri-urban areas to specialize in the production of food especially perishable items. Second, and relatedly, it is possible that given the proximity to urban centers of production, rural households consume urban-produced non-agricultural goods and services rather than produce them locally. This pattern of relatively low non-farm sector involvement in peri-urban areas has also been observed by Lanjouw (1998) in the case of Ecuador. 11 The data on agricultural production in the Tanzania Peri-Urban Survey suggest that the above arguments - pointing to a focus on foodcrop cultivation alongside, or even instead of, non-farm activities in peri-urban areas - may well be of some relevance. First of all, the data indicate that crop sales account for about 40% of the value of gross agricultural output in these peri-urban areas. This is higher than the figures in Table 1 generally suggest for rural areas as a whole, where subsistence agriculture is most common (the exception is in the final year column of Table 1). Second, the bulk of crop sales are food items (86% of crop sales are food). Third, there is a clear evidence in the data that those households located closest to the cities not only sell more food as a fraction of all crop sales (92%), but also produce a considerably larger proportion of perishable goods such as fruit, than households located further away Interestingly, Lanjouw (1998) also observes a relatively higher incidence of rural poverty in peri-urban areas as compared to small rural towns in Ecuador (although rural poverty remains highest in dispersed rural areas). We are unable to compare peri-urban income or consumption levels with other rural areas as we lack data on non peri-urban rural households. 12 The proportion of fruit sales out of total crop sales is 22% in the case of households located 0-5 kms away from the cities, declining to 16% for those at a 5-10 km distance, 9% for those at kms distance and 1% for those kms away. 12

13 Table 4 focusses specifically on the relationship between income shares and distance from conurbations. Table 4 : Incomes Shares by Distance Strata Distance Per capita Per capita Non farm Farm Net remittance Strata Income Consumption Income income 0-5 km % 82% 2% 5-10 km % 82% -7% km % 82% -18% km % 94% -12% Distance Business Non farm Crops Livestock & Hunting & Farm Other Strata Labour livest. Gathering labour products 0-5 km 9% 6% 57% 17% 5% 3% 1% 5-10 km 19% 4% 49% 24% 6% 2% 2% km 32% 3% 60% 15% 1% 6% 2% km 5% 11% 82% 7% 4% 1% 1% Consistent with the finding in Table 2 that non-farm income shares are not noticeably higher in peri-urban areas than in rural areas as a whole, and with the discussion in the previous paragraph, we find in Table 4 that non-farm shares do not rise with greater proximity to conurbations. The share of non-farm incomes in the lowest distance strata is 15% while it is as high as 36% in the 10-15km strata around the cities. Another interesting finding is that net remittances are positive in the stratum closest to the cities, while they are negative in all other strata. We provide finally, in Table 5, figures on employment rates in the non-farm sector. These are expressed as the percentage of the working population (aged 15-60) engaged in off-farm activities These figures for business activities are likely to be underestimates in that they count only as employed in business activities those who reported the details of these activities to the survey investigators. The figures thus do not include information on the involvement of family members in these activities. 13

14 Table 5 : Employment Shares in Off-Farm Activities by City (individuals, % of working population) Business Non farm Farm labour Labour Survey 10% 5% 6% Dar es Salaam 17% 5% 4% Mwanza 5% 4% 11% Moshi 8% 3% 6% Arusha 2% 12% 2% Mbeya 7% 3% 6% Lindi 5% 3% 14% In total, some 15% of the working population is engaged in non-farm activities in the peri-urban areas of Tanzania. Another 6% are employed as wage labourers in agriculture. The figures are highest, once again, in the surroundings of Dar es Salaam, where as much as 22% of the working population is employed in non-farm activities. And again, as suggested by the income share data, wage labour employment is particularly high in Arusha. The Determinants of Non-Farm Employment Probabilities and Incomes The correlations in Tables 2-4 between non-farm income shares and indicators such as region, consumption quintile and distance, are suggestive. However, they remain only suggestive for at least two reasons. First, each bivariate cross tabulation depicts a correlation between two variables, but one cannot rule out the possibility that the correlation is actually driven by some third variable not included in the tabulation but which happens to be strongly associated with the two variables under scrutiny. Second, a correlation is not the same as a causal association, and so while it may be tempting to claim that, say, a higher non-farm income share results in a higher per capita consumption level, we cannot exclude the possibility that the higher consumption level is in some sense the factor which determines whether a higher non-farm income share is achieved Research on non-farm sector employment in India suggests that a certain degree of wealth is an important pre-requisite to access to certain non-agricultural employment opportunities (such as salaried 14

15 In an effort to address these concerns we turn now to a multivariate analysis of the correlates of non-farm employment in the peri-urban areas of Tanzania. This approach also allows us to examine the contribution of a significantly wider range of explanatory variables. We start by estimating a probit model of the probability that an individual is engaged in a business activity. We then estimate a reduced-form income regression, to try to draw out the influence of the explanatory variables on earnings from this sub-sector. These two steps are then repeated for wage-labour employment and earnings. We consider four sets of explanatory variables, each corresponding to different level of aggregation. The first set occurs at the level of the individual. These variables describe the gender (a dummy variable if the individual is female, female) age (age and its squared term, agesq), and education level of the individual (two dummies, primary if the individual has had schooling up through the primary level, and secondary if the individual is educated at the secondary or higher level). We feel that these variables are plausibly exogenous (with perhaps the exception of education). We then consider then a set of household-level variables. Migrant indicates that the household head migrated to this area. Pclandhh and pclandsq represent the per capita land holding of the household (and its squared term). These latter variables might not usually be perceived as exogenous, but in rural Africa land markets are often poorly functioning, and generally quite thin, so that an assumption of exogneity may not be so unacceptable in this case. The variable depend captures the dependency ratio of the household and is constructed as the ratio of dependents to working family members. A third set of variables occurs at the village level. Tel and asphalt indicate whether the village has ready access to a telephone and an asphalted road, respectively. Yldcrop represents the total gross agricultural output of the sampled households divided by total land cultivated by these households. This village-level yield variable is intended to capture the suitability of the village environs to agriculture (such as land quality, availability of irrigation, etc.). The linkages literature would suggest a positive coefficient on this variable in a model of the probability of non-farm employment. government jobs). This is because of the important role played by contacts and bribes in the allocation of such jobs (Lanjouw and Shariff, 1999). 15

16 However, it is also possible that in locations which are particularly suited to agriculture, households tend to concentrate on farming. 15 In an effort to purge this variable of endogeneity the variable is calculated in such a way that, in turn for each respective household, its specific contribution to gross agricultural output and cultivated land is not included in the calculation. The variable pcvpopnf is a village-level calculation of the percentage of the village working age population (proxied by the sampled households from the village) employed in either business or wage labour non-farm activities. This variable is calculated in an analagous manner as yldcrop, in order to purge it of endogeneity, and is intended to proxy the extent of clustering of non-farm activities within the village. There is a sizeable literature, particularly for South and East Asia, documenting the oftobserved phenomenon of clustering of rural non-farm activities (Banerjee, 1997, and Sandee, 1995). Finally, pcvland, represents per capita landholdings in the village and is also calculated in a similar manner as yldcrop and pcvpopnf. This variable captures the extent of population pressure in the village, and can be hypothesized to act as a factor which pushes people into non-agricultural activities. The final set of variables are dummies representing the particular peri-urban region and another set of dummies indicating the distance stratum to which the household belongs. Appendix tables A.1 and A.2 provide summary statistics on the variables used in the econometric analysis which follows below. Table 6 presents the probit model of the the probability of involvement in business activities. Rather than reporting the parameter estimates, which are difficult to interpret on their own, we present in Table 6 the marginal effects associated with each explanatory variable. These can be interpreted as indicating the effect of a percentage change in the explanatory variable on the probability of involvement in non-farm business activities, taking all other variables in the specification at their means. 16 Standard errors have been adjusted to account for clustering in the sample. 15 For a discussion in the context of India, see Jayaraman and Lanjouw (1999). 16 For dummy variables, the marginal effect is calculated as the change in the dependent variable associated with a move from a value of 0 for the dummy, to 1, holding all other variables constant at mean values. 16

17 Table 6 : Probability of Involvement in Non-farm Business Activities (Probit Model) Variable Estimate Probability Value Female (female) -0,09 0,0000 Age (age) 0,04 0,0000 Age Squared (agesq) -0,0005 0,0000 Primary Education (primary) 0,05 0,0030 Secondary & Higher (secondary) 0,02 0,3260 Migrant (migrant) -0,001 0,9720 Dependency Ratio (depend) -0,003 0,7210 Land Owned Per Capita (pclandhh) -0,02 0,0130 Land Owned Squared (pcldhhsq) 0,001 0,0210 Telephone in Village (tel) -0,02 0,1820 Asphalt Road (asphalt) 0,09 0,0000 Village Crop Yield (yldcrop) -0, ,1920 % In Non Farm Activity (pcvpopnf) 0,19 0,0040 Village Per Capita Land (pcvland) 0,01 0,3050 Dar es Salaam 0,09 0,0000 Mwanza -0,02 0,2940 Moshi -0,03 0,0800 Arusha -0,001 0,9400 Lindi 0,07 0,0020 Distance 1 (0-5kms) 0,01 0,5220 Distance 2 (5-10kms) 0,01 0,8000 Distance 3 (10-15kms) 0,01 0,8140 Nr. of Observations 1950 χ 2 (22) 443,90 Prob > χ 2 0,0000 Pseudo R 2 0,2152 Log Likelihood -611 Examining first the individual-level explanatory variables, Table 6 indicates that women are significantly less likely to be employed in business activities then are men : a women has a 9 percentage point lower probability of involvement in business activities at mean values of all other variables. An additional year of age increases the probability of non-farm business activity, but this relationship is not monotonic. Beyond an age of around 41, the probability declines again. Relative to someone with no education, an individual with primary schooling is significantly more likely to be employed in this sub- 17

18 sector. Primary schooling increases this probabilty by 5 percentage points (other variables at their means). Secondary schooling, on the other hand, does not appear to significantly contribute to the probability of involvement in non-farm business activities. Considering next the household level variables we observe that larger household per capita landholdings reduce the probability that an individual is involved in the business sub-sector. This suggests that business activities are perceived as an alternative to cultivation large landowners are more likely to engage in agricultural activities (farming or livestock) than are small landowners. This relationship is also not monotonic however. Beyond per capita landholdings of around 8.5 hectares, the negative relationship disappears and larger landholdings are associated with a higher probability of business activities (at mean values of other variables). The first village-level variable of statistical significance is asphalt road access. Access to such a road in the village increases the probabilty of business sector involvement by 9 percentage points. The strong correlation between non-farm activities and infrastructure, especially roads, is a consistent finding across numerous empirical studies (for a review see Lanjouw and Lanjouw, 1997). The village-level yield variable is negative in this model (although not significant). The sign suggests that on balance, villages more suited to agriculture tend to reduce the probability of non-agricultural activities. This suggests that the effect of specialization outweights the impact of linkages between agriculture and non-farm business activities. Increased population pressure in the village does not appear to influence the probability of business activity, but the clustering variable is significant. A ten percentage increase in the village working-age population employed in the non-farm sector increases the probability that an individual is employed in this subsector by 2 percentage points. Business activities thus tend to be concentrated within certain villages, as opposed to widely dispersed. Finally, the regional level variables indicate that the probability of involvement in business sector activities is significantly higher in the peri-urban areas of Dar es Salaam and Lindi, relative to the dropped regional dummy of Mbeya (marginal effects of 8 and 7 %, respectively). Thus, controlling for all other variables, business sector activities appear to be concentrated in the coastal areas of the country. The distance variables do not provide any additional explanatory power to the probability of non-farm 18

19 business sector employment, suggesting, once again, that locational proximity to urban conurbations (as opposed to easier access because of proximity to a road) does not necessarily add to the likelihood of non-farm activities. We turn now to an examination of business sector earnings as opposed to probability of involvement. Our explanatory variables remain unchanged. A brief comment about our econometric approach is in order. It is well known that a regression of non-agricultural incomes on a range of explanatory variables, using simple OLS techniques, yields biased estimates on the explanatory variables 17. This is because the OLS regression does not properly take account of the censoring of the dependent variable at zero (corresponding to all households which do not have any non-agricultural sources of income). A standard approach in this case is to estimate the tobit model instead. Recently however, concerns have been raised regarding the use of the tobit model in contexts, such as ours, where heteroskedasticity is likely to be present (see Deaton, 1997). In the presence of heteroskedasticity, parameter estimates on the tobit model are not consistent. 18 To overcome these difficulties we proceed, like Joliffe (1998), by estimating a censored least absolute deviation model (CLAD) of non-agricultural incomes on a set of explanatory variables (see also Deaton, 1997). The approach here consists of estimating a quantile regression on the full sample of households (both with zero and non-zero nonagricultural incomes), predicting non-agricultural income on the basis of the parameter estimates, dropping those households for which predicted non-agricultural income is negative, re-estimating the quantile regression and then repeating the exercise with multiple iterations, until no more negative predicted values are obtained. We then calculate bootstrapped standard errors on the parameter estimates. 17 Deaton (1997) provides a recent exposition, as well as a useful overview of the approach adopted here. 18 It should be noted that testing for heteroskedasticity in these models is not straightforward as such tests require an assumption of normality, and this is routinely rejected empirically (see Deaton, 1997). 19

20 Table 7 : CLAD Estimates of Log Non-Farm Business Incomes Per Person Estimate Probability Value Male Age Age Squared Primary Education Secondary Education & Higher Migrant Dependency Ratio Land Owned Per Capita (Household) Land Owned Squared Asphalt Road Telephone in Village Crop Yield per Acre (Village) % of Village Pop. In Non Farm Activity Land Owned Per Capita (Village) Dar es Salaam Mwanza Moshi Arusha Lindi Distance Distance Distance Intercept Nr. of Observations 1603 Pseudo R Table 7 reveals that earnings from non-farm business activities are significantly higher for men than for women. Controlling for education and all other explanatory variables, a man would expect to earn more than 100 times as much as a woman. 19 The dramatically lower expected non-farm earnings for a woman than for a man with otherwise the same characteristics, are the result of the combination of three factors : i) the lower probability of becoming involved in non-farm activities in the first place (the percentage of working-age women involved in non-agricultural business is less than half 20

21 that of men) ; ii) shorter employment spells, and iii) lower earnings per day. Our inability to control for the time spent in business and wage labour occupations (and the possibility that at a good fraction of these activities are likely to be part-, rather than fulltime) implies that much of the variation in earnings from non-farm activities may be attributable to variations in time spent in these occupations. The parameter estimates suggest that while the probability of employment is highest for young men, business earnings rise with age up to a maximum of around 45 years. At lower ages these earnings can rise particularly sharply with additional years of age. For example at an average age of around 20 years, an additional year of age would raise earnings by about 149%. By an age of 40 years, the impact of an additional year of age is more muted ; raising earnings by only about 30%. Individuals with primary school education earn business incomes which are about 12.5 times higher than those with no education. Education beyond the primary level, however, does not appear to contribute further to earnings. (Although not statistically significant, the data suggest that individuals with secondary education or higher earn less than those with primary education). Household level variables do not appear to signficantly influence earnings from business activities. Only household per-capita landholdings are weakly significant, and negatively related to non-farm incomes (up to a turning point of about 4.58 hectares per person). At the village level the percent of the village population employed in non-farm activities has a separate and significant bearing on earnings. An additional percent of the village population employed in this sector raises earnings by 10 percentage points. Population density also influences earnings. An additional hectare of land per person in the village is associated with higher earnings from business activities of 127%. This finding need not be inconsistent with the argument that higher population density would tend to push people into the non-farm sector push factors would most likely promote residual, low return activities, and so holding all other factors constant one would expect to see higher non-farm income levels in villages with lower population densities. 19 A coefficient c multiplying a dummy variable can be interpreted as a percent change in the endogenous variable only as long as c is close to zero. For larger values, in absolute terms, the percent change in the 21

22 Of the regional variables, the Dar es Salaam and Mwanza dummies are strongly significant but of opposite sign, suggesting that relative to Mbeya (the omitted regional dummy), non-farm earnings in peri-urban Dar es Salaam are some 20 times higher, and in Mwanza they are 89% lower. Significant at the 10% level, the Lindi dummy suggests that non-farm business earnings there are on average 10 times higher than in Mbeya. None of the distance dummies are significant. activities. We turn now to a probit model for employment in non-farm wage labour Table 8 : Probability of Involvement in Non-farm Wage Labor (Probit Model) Variable Estimate Probability Value Female -0,03 0,0000 Age 0,007 0,0000 Age Squared -0, ,0010 Primary Education 0,02 0,1110 Secondary Education & Higher 0,13 0,0000 Migrant 0,01 0,1570 Dependency Ratio -0,01 0,0430 Land Owned Per Capita (Household) -0,003 0,6120 Land Owned Squared -0,0004 0,6430 Telephone in Village 0,03 0,1240 Asphalt Road 0,02 0,1580 Crop Yield per Acre (Village) -0, ,4000 % of Village Pop. In Non Farm Activity -0,001 0,9860 Land Owned Per Capita (Village) 0,01 0,0440 Dar es Salaam 0,01 0,3730 Mwanza 0,01 0,3050 Moshi 0,004 0,7990 Arusha 0,03 0,0700 Lindi 0,01 0,7190 Distance 1 0,02 0,1890 Distance 2 0,02 0,1670 Distance 3 0,01 0,4010 Nr. of Observations 1950 χ 2 (22) 210,8 Prob > χ 2 0,0000 Pseudo R 2 0,1969 Log Likelihood -288 endogenous variable is given by 100[exp(c) 1]. 22

23 Table 8 indicates a similarly lower probability of employment for women in the non-farm wage labour sector as in business activities. At mean values for the other explanatory variables, women have a 3-percentage point lower probability of employment in wage labour activities. The probability of employment in this subsector rises continously with age, up to a turning point of 46. In this model, however, we observe that households with primary schooling are not significantly more likely to be employed in wage labour activities relative to the uneducated, while those with secondary schooling or higher have a 13 percentage point greater likelihood of employment in this subsector (at mean values for other variables). These findings are consistent with the notion that wage labour activities comprise both low return activities (such as casual, manual labour) as well as high-return activities (such as regular, salaried employment). At the household level we observe that persons from households with higher dependency ratios are less likely to be employed in wage labour activities. This could be related to the higher opportunity cost of working outside the home. At the village level, only village per capita landholdings are strongly significant an additional hectare of village land per person raises the probability of non-farm wage employment by 1 percentage point (at mean values of other explanatory variables). Access to a telephone is weakly significant, and indicates that in such villages the probability of non-agricultural wage labour is some 2 percentage points higher. There is little evidence to suggest non-agricultural wage labour activities occur in clusters. Unlike for business activities, the Dar es Salaam dummy is not significant in this model. Instead, we can see that individuals in peri-urban areas around Arusha are 3 percentage points more likely to be employed in non-farm wage labour activities (at average values of other variables). Finally, we examine in Table 9, results from the CLAD model on earnings from non-farm wage labour activities. 23