Economic Growth, Rural Assets and Prosperity. Exploring the implications of a twenty year record from Tanzania

Dan Brockington1, Olivia Howland1, Vesa-Mati Loiske2, Moses Mnzava3 and Christine Noe3

1University of Manchester2 Södertörn University

3 University of Dar es Salaamcorresponding author: dan.brockington@manchester.ac.uk

AbstractMeasures of poverty based on consumption suggest that recent economic growth in many African countries has not been inclusive, particularly of rural areas. However we argue that measures poverty using assets may provide a different picture. We present new data based on recent re-surveys of households first visited in the early 1990s. These demonstrate a marked increase in prosperity from high levels of poverty. It does not, however, follow that these improvements are due to GDP growth. We consider the implications of this research for further explorations of the relationship between economic growth and agricultural policy in rural areas.

Key Words:Assets; Consumption; Economic Growth; Rural Livelihood Change; Inclusive Economic Growth; Poverty

Economic Growth and Rural Assets and Prosperity. Exploring the implications of a twenty year record from Tanzania

AbstractMeasures of poverty based on consumption suggest that recent economic growth in many African countries has not been inclusive, particularly of rural areas. However we argue that measures poverty using assets may provide a different picture. We present new data based on recent re-surveys of households first visited in the early 1990s. These demonstrate a marked increase in prosperity from high levels of poverty. It does not, however, follow that these improvements are due to GDP growth. We consider the implications of this research for further explorations of the relationship of economic growth and agricultural policy in rural areas.

Key Words:Assets; Consumption; Economic Growth; Rural Livelihood Change; Inclusive Economic Growth; Poverty

IntroductionRapid economic growth is transforming the landscapes of many African economies (Radelet 2010). Sustained high rates of growth (despite downturns and austerity elsewhere in the world), macro-economic stability, relatively low inflation, and growing investment and infrastructural development are seeing many countries become more prosperous. Some observers are celebrating a rising continent, that will be known for its growth, peace and stability (Chuhan-Pole and Angwafo 2011).

Whether this growth is inclusive and pro-poor is less obvious (Barrett 2011). The highly visible prosperity in urban areas that characterizes current economic growth can conceal persistent poverty in rural areas. Indeed some observers fear that some forms of investment may cause more problems, especially in rural areas, if investment is accompanied by land loss (Benjaminsen and Bryceson 2012; Borras et al. 2011; Fairhead et al. 2012; Gardner 2012). Dercon’s call for more longer term insights into the fortunes of rural households during periods of growth remains as relevant as ever (Dercon 2006).

In this paper we explore the relationship between economic growth and rural poverty in Tanzania. This country presents an apposite case study, in that it has both enjoyed substantial growth in the last twenty years, and is still characterized by high levels of rural poverty. However understanding the dynamics of rural poverty is difficult because of the paucity of data available. Our contention is that by critically examining existing sources, and by exploring new data, we suggest that rural peoples may be more prosperous than critics allow. We also suggest, however, that it may not be possible to connect this improved wellbeing to economic growth as measured in GDP.

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The new data we examine here provide a means of exploring long term trends in asset use and ownership by rural people. Attention to assets is important because current measurement of poverty, and debates about the inclusivity of economic growth, are dominated by measures drawn from indices of consumption. We argue that changes in assets suggest that rural families have prospered more than measures of consumption could adequately portray.

We proceed as follows. First, we outline the debates surrounding Tanzania’s growth and its inclusivity – whether or not it has benefitted the poorer members of Tanzanian society. Second, we critically examine the data which are used to argue that poverty has not declined. We then introduce the new methods and data with which we wish to explore long term dynamics in rural poverty. The sample size we examine here is small, but the findings are provocative, and we use them to suggest further lines of enquiry in the discussion section.

Tanzania - inclusive growth?Many observers are quick to praise Tanzania’ economic success over the last 20 years. Edwards’ history describes a remarkable transformation. In Nyrere’s last years in power, the country was suffering from stagnant agriculture and manufacturing, productivity in ‘free fall’, and a ‘sky-rocketing’ trade deficit (Edwards 2014: 81). Even the broad social benefits that drove his policies (such as free universal primary education) were suffering from the sheer inability of the state adequately to fund these ambitions. Primary school enrollment declined from 94% to 76% between 1979 and 1986 and the quality of education for those in school was low. Since then, with strong donor support, the economy, and government finances, have been transformed. Nord and colleagues summarise the changes as a ‘remarkable turnaround’, compared to the want and scarcity that characterized the country in the 1980s. Now there is low inflation, a ‘buoyant’ economy which has averaged 7% annual growth, real per capita income has risen 50% and poverty is ‘heading downwards’ (Nord et al. 2009: 1). Robinson and colleagues describe a period of accelerated growth since 1996 that has seen macro-economic stability and increased public spending (Robinson et al. 2011).

But there is also much wariness as to whether this growth has been inclusive. In particular, the concern is that the benefits of growth are not being experienced by the rural poor, who make up most of the poor in the country. Robinson and colleagues note that agriculture has not really contributed to this growth, which is a ‘cause of concern’ given that agriculture is the economic mainstay of rural areas where most people live (Robinson et al. 2011: 26-7).

Mashindano and colleagues compare Household Budget Survey (HBS) data on the percentage of people below the food and basic needs poverty lines with GDP growth data (Table 1). They conclude that there has been substantial economic growth, but that this growth has not reached the poor; if anything it has passed them by. The figures here are stark:

‘between 1991/92 and 2000/01, for instance, the economy grew by 59 per cent;

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basic needs and food poverty reduced by under 3 per cent’ (Oswald Mashindano et al. 2013: 126).1

Edwards, using the same data, notes that poverty decline has been far lower in Tanzania than in other countries (Edwards 2014: 251).

More detailed analyses of the same Household Budget Surveys report that there are signs that agricultural livelihoods are proving particularly unprofitable (Hoogeveen and Ruhinduka 2009). These analyses suggest that Tanzanians were diversifying out of agriculture in order to improve their wealth, and investment in agricultural assets (livestock, ploughs and hoes) declined between 2001 and 2007. Indeed the authors go so far as to state that ‘it is difficult to make a decent living out of agriculture’ (page 29).2

Finally panel survey data in Tanzania (the Living Standards Measurement Survey, LSMS) has also reported that the poverty headcount between the two rounds of the survey (2008/9, and 2010/11) has ‘inched up’ from 15% to 18% (National-Bureau-of-Statistics 2012: 6). Table 2 shows the general decline in consumption across most wealth groups between the two rounds of this survey. If we compare the consumption level by quintile it is plain that consumption has declined generally, and most significantly for the poorest quintile. Although the time period is short, the clear indication is that most households (and particularly most rural households) are not benefitting from the continued economic growth the country was experiencing in this period.

The weight of these analyses is that many Tanzanians can only join vicariously in celebration of their country’s growing prosperity. Economic growth is not reducing poverty sufficiently. Some authors conclude that the important question to consider now is how and why growth has failed to reduce poverty (Oswald Mashindano and Shepherd 2013: 3).

Consumption is Dynamic However, before we address that question, we must take another, closer, look at the data, for when we do so the story becomes more complicated. There is evidence that, in rural Tanzania, this is neither a case of welcome growth, nor a case of the poor being neglected by growth. Rather it is possible that the rural economy is more diverse, and has more potential for growth and prosperity, than it first appears. That proposition animates the present paper.

1 Note that the 2011 HBS for Tanzania found lower levels of poverty of just over 28%. This survey however used a different means of calculating poverty levels than previous surveys, and so findings are not comparable with earlier surveys. Work is underway to re-calculate poverty levels using equivalent methods in previous but they are not currently available. http://www.worldbank.org/en/news/press-release/2013/11/14/new-poverty-figures-from-household-budget-survey viewed 9th September 2014.2 The detailed analyses are not all depressing. There is clear evidence that the growth in education is very pro-poor, with enrollment of the poorest quintile increasing from 47% to 78% (Hoogeveen and Ruhinduka 2009).

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To explore it we need to pay more attention to the problems and challenges of measurement before we can determine what might be going on. There are three issues: the dynamism of consumption, the importance of assets, and the need for more qualitative data to understand the trends observed.

The dynamism of consumption patterns is evident in the LSMS survey. The important difference between this and the HBS is that it is a panel survey. The LSMS revisits the same households for every survey; it is not composed of a new sample constructed for each survey (as with the HBS). Panel surveys are particularly useful when studying poverty dynamics (Baulch 2011; Baulch and Hoddinott 2000; Dercon et al. 2009; Dercon and Shapiro 2007). From the LSMS it is possible to trace how the fortunes of individual households vary over time.

We can do better, therefore, than merely comparing quintiles constructed separately for each survey (as Table 2 does). We can actually follow particular households as they move through different quintiles over time. When we do so it is instantly apparent that the general decline in consumption levels reported between the two surveys conceals considerable dynamism. It is simply not the case that those who were poor in 2008/9 have remained poor (or got poorer). Instead, households are not bound by the general fortunes of their wealth groups. Many families, and most of the poorer families in 2008/9, have increased their consumption in the two to three years between the surveys (Table 3). They have done so more than have the richer families. This is both a rural and an urban phenomenon, although strongest in urban areas, reflecting in part the differential deflation used in the different areas.

The result is that the two sets of consumption quintiles between the two waves are composed of quite different sets of households. Table 4 demonstrates that economic mobility. Only 8% of the total sample remained in the poorest quintile across the surveys, and 12% in the richest. Some movement up and down from quintiles might be expected (shaded in gold on the table), but 26% of the sample is moving two quintiles or more (up or down) between the surveys.

The Kagera Health and Development Survey (KHDS), which was also a panel survey, produced a similar result. There, almost all families increased their consumption. This was strongest where there was migration to urban areas and where villages were better situated and livelihoods diversified (Beegle et al. 2011; De Weerdt 2010). But even families which stayed in rural areas and practiced agriculture enjoyed an 18% increase in their consumption from 1991 to 2004 (Beegle et al. 2011: 1017). Clearly it is difficult to conclude from these data that economic growth is not inclusive, because poorer families were enjoying increased consumption.

The Importance of Assets when Understanding Poverty

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Both the previous findings demonstrate that poverty, as measured by consumption, is not as static as findings from the HBS suggest. But there is a more profound argument that must be made against the HBS findings – which is that consumption may not be the best measure of trends in poverty. It is highly amenable to sophisticated quantitative analysis, but this does not necessarily make it the best measure of change. Other measures of poverty which include assets may provide useful insights which measures of consumption cannot capture.

There are two aspects to the usefulness of assets when measuring poverty. Change in assets are first an important measure of poor households’ behaviour, and second, a better indication of their long term prospects, than consumption. With respect to behaviour one of the classic responses of households which are becoming wealthier is to invest in their assets, rather than in, for example, improving their diet (see for example Scott 2010). As households move out of poverty, their day-to-day consumption may change relatively little. At the same time they might be becoming considerably wealthier by other measures, such as improved houses, education, agricultural equipment, livestock and so on. Assets provide for the long term future of households. They make them more resilient to shocks and problems, and better able to prosper from good fortune.

There also problems with the changing nature, and value, of consumption itself. There is the technical problem of collecting adequate price data in order to calculate the consumer price index (CPI), and so inflation. In Tanzania such CPI data are always collected in towns, meaning that we do not have accurate data on prices changes in rural areas, where most of the poor are found (National-Bureau-of-Statistics 2012). Adjusting for inflation also becomes increasingly sketchy and subject to error over the long term.

Then there are the problems of the changing patterns of consumption itself. Consumption in the early 1990s, for example, did not include the now nearly ubiquitous mobile phones. Consumption poverty now includes the inability to receive, or send, text messages, which was not the case twenty years ago. These changes can affect assets too; to take mobile phones again, these could be said to be assets in some contexts.. However, generally speaking, consumption norms vary over the medium term more than assets do. Today, a milch cow has the same nutritional value, and a span of oxen the same pulling power as they did twenty years ago.

There are also recently published analyses suggesting that assets are much more valuable and important than has been previously realized. The IGAD initiative has re-valued the contribution of livestock to rural economies in Kenya, Uganda, Ethiopia and the Sudan, increasing their worth by 37% (over $23 billion). Official figures typically dramatically underestimated the value of milk production and manure, nor do they capture the value of livestock as draught animals, or as a form of savings and financial services (Behnke 2010; Behnke and Metaferia 2011; Behnke and Muthami 2011; Behnke and Nakirya 2012; Behnke and Osman 2012). This work is yet to be published in peer-reviewed form, but in some ways it merely repeats, in formal economic terms, long recognized problems with the modern state’s understandings of the economics of pastoral societies (Behnke 1985; Behnke and Scoones 1993; Homewood 2008).

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More daringly Alwyn Young has examined the records of change in assets in the Demographic and Health Surveys (135 surveys across 56 countries over 20 years) to suggest that there has in fact been an ‘African Growth Miracle’ which is unrecognized by current data based on consumption (Young 2012). Young’s work covered only unproductive assets, but he concluded that material consumption had been rising at 3.5-4 times the rates recognized in other sources (see also Sahn and Stifel 2000). Harttgen and colleagues are doubtful of these claims, concluding that ‘the analysis of asset changes that existing GDP and poverty statistics substantially underestimate trueeconomic performance in Africa’ (Harttgen et al. 2013: S60). However their re-analysis is not the end of the matter. Their argument is based partly on the fact that the countries in Young’s work enjoyed growth rates that were unrepresentative of the continent as a whole. This implies that they might be typical of individual countries. They also note a problem of ‘asset drift’, meaning that ‘assets accumulate at the household level even in the absence of income growth’ (S42). But this is still compatible with improved well-being derived from improved assets. Finally they are quite right to complain that assets are poorly correlated with consumption – but that does not mean that consumption is the ‘true’ measure of economic performance. It is just the measure which is most easily enumerated. What matters more is how well both measures correlate with prosperity and well-being. That is harder to determine.

Poverty is a complex and multi-facetted condition (Alkire and Foster 2011; Green and Hulme 2005). Households which have experienced no change in their daily consumption patterns as their country gets richer will want to see more change. Moreover, in the longer term, investment in assets should yield returns which will effect daily consumption patterns. Our point therefore is not that assets matter more than consumption, merely that it will be important to explore asset change in addition to patterns of consumption. This is widely recognized in the literature (Carter and Barrett 2006; Dercon et al. 2012). Growth which is accompanied by loss of assets, inability to use assets well, or a failure to acquire new assets (such as education) among the poor will not be inclusive (McKay and Lawson 2002; Naschold 2012).

But if we need to look at assets then it is important to recognize the restrictions of the HBS poverty lines in this respect. The measurement of poverty used by the HBS deliberately excludes assets, and concentrates only on consumption:

‘The basis for assessing income poverty is a measure of households’ consumption expenditure . . . This is compared with a poverty line, which represents the cost of a basic basket of consumption. Households that fall below the poverty line are poor; individuals are classed as poor if they live in a poor household.’ (United-Republic-of-Tanzania 2007: 47)

Not only is asset ownership not part of these calculations, but even investment in assets (which might be picked up from the month long consumption surveys used in the HBS) are specifically excluded:

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the measure used in the poverty analysis excludes large durable items, which are rare purchases and are not typical of the household’s usual consumption level. Expenditure on medical care, education, water, telecommunications and postage are also excluded . . . Rent and imputed rent were also excluded because of the poor reporting of the latter. (United-Republic-of-Tanzania 2007: 47)

If, therefore, assets are an important part of poverty then it may be premature to conclude, as Mashindano and others have done, that economic growth in Tanzania has excluded the poor. The measure of poverty used did not look at change in assets. It could not pick up poor households that were growing wealthier in terms of their assets.

Taking a critical look at data on economic growth in Tanzania therefore presents a problem. On the one hand the vibrant economic growth of the last twenty years appears to be a restricted urban phenomenon which is simply not enjoyed by the bulk of the population, the rural poor, who need most to see some change. On the other hand the measures which raise this alarm are in themselves incomplete.

How then are we to determine what sorts of changes and dynamics have characterized the last twenty years in rural Tanzania? It is well known that there is a paucity of good data, and, in particular, there are few panel data available, to explore these trends. However, new sources are becoming available, and it is possible to use these to gain some insights into livelihood and prosperity dynamics in the rural economy. In the rest of this paper we present findings from a study which undertakes that task.

MethodsThe method developed here is relatively simple. We have taken a one-off survey conducted in the early 1990s and turned it into a longitudinal survey by re-surveying the same households. This technique has been used before in the KHDS. It is superior to attempts to turn survey data ‘upside down’ (as Dercon and Shapira put it, 2007: 30) by using participants’ memories to explore change over time. That method can suffer from rose-tints and inaccuracy, a riskfor all its insights in the ‘stages of progress’ method (Krishna 2006; 2010; Krishna et al. 2004). We rely instead on actual observations recorded some twenty years ago.

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We have used survey data undertaken in Gitting, in Hanang District, Manyara Region, in north-central Tanzania. 3 This village was surveyed by Loiske between 1991 and 1994 as part of his PhD (Loiske 1995). Loiske’s first step was to explore the distribution of wealth in his study area. His unit of analysis was the household, which was defined as any homestead registered on the village lists.4 The categorization system of household wealth that he used, and its accuracy, is fundamentally important to the argument of this paper and it is important to dwell upon it. Loiske divided households into 7 groups – two poor, two average and three varieties of wealthy farmer. The criteria he used are those his informants thought important when distinguishing between households and are shown in Table 5. From this it should be instantly apparent that the classification system is fundamentally a measure of assets. Households are categorized according to their ability to provide for themselves in a rural economy.5

These wealth categories, and the distribution of households within them, were established with some rigour. Loiske began by taking a list of all households in the village which had been allocated land in the villagisation operation. He then arranged for 18 separate key informants to rank these households in order of wealth. All the key informants were men, and were aged between 25 and 45, and were mostly themselves middle ranking peasants. 609 households were ranked in this way. From this ranking exercise, and discussion, emerged the seven categories of wealth which are shown in Table 5. Loiske then randomly selected 20% of the households of each wealth group (122), of which he was able to interview and/or visit 94 for his research.

Lest this ranking scheme should now appear dated we have included, by way of comparison, the criteria used in the work of Higgins and da Corta, for research in the same country (Higgins and da Corta 2013). With some differences they match reasonably well. Note how poor the two lowest categories in Loiske’s classification are – they correspond to the ‘very poor’ and ‘destitute’ of more recent classifications.

Brockington revisited the original households in 2013 to explore how livelihoods had changed, and what might explain these changes. Of the 94 households Loiske surveyed we were able to identify 86, of which Brockington visited 77. This was part of a year of sabbatical research during which he was based in the neighbouring village of Miaskron. He conducted his work in part with one of Loiske’s former research assistants, and with the assistance of a former village executive officer, who was identified by the village leadership as a useful assistant, and who was old enough to remember the condition of families when Loiske was conducting the research. Interviews were conducted in a mixture of Swahili (in which Brockington is fluent) and Iraqw, which sometimes required translation.

3 Gitting was subsequently split, for administrative purposes, into two contiguous villages named Gitting and Gocho. For simplicity’s sake we shall simply refer to ‘Gitting’ in the text. 4 This household list included all families in the village but omitted itinerant agricultural labourers from the neighbouring region of Singida who were given temporary accommodation on farms. The omission is important because it means that this survey and its long term findings are no guide to the changing fortune of itinerant labourers. It is a reasonable guide to the fortunes of land-owning farmers. 23 households on the household list proved difficult to rank, mostly because they were too new, or their whereabouts were uncertain.5 This is another reason for considering poverty in terms of assets – for the rural poor themselves consider their poverty in this way (Schaffer 2013).

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When meeting with villagers we used a mixture of quantitative and qualitative methods building on our own experience, and others (Adato et al. 2007; De Weerdt 2010; Howe and McKay 2007; Lawson et al. 2003; Shaffer 2012). The quantitative element re-surveyed households visited earlier, and individuals who have left original households to set up their own homes. The qualitative included a discussion of any changes with household members that become apparent in the re-survey as soon as that survey was completed. In addition we took more detailed oral histories from a representative sub-sample of households to explore important events and changes that have taken place in the intervening years. We also took general village, crop and economic histories (including crop prices) from key informants and district and regional records to build up a picture of the general changes in the area. There were two community level methods that complemented these tasks. First we undertook a participatory wealth ranking of all households in the village (conducted with the village executive officers, chairmen and sub-village chairmen) in order to compare how wealth distributions now compare with the past, and to see whether the households we have re-surveyed are still representative of their broader communities.6 Finally we shared summaries of findings and changes in public village meetings so that community members can discuss our findings and offer improvements or correction to them.

ResultsGitting should be a good place to be a farmer. The area is well-endowed for agriculture. The village is close to Mt Hanang, an extinct volcano some 3,400m high. Soils are generally fertile, and Gitting sits on the wetter side of the mountain, with streams providing good water sources for most of the year. Gitting is predominantly composed of the Iraqw ethnic group whose proclivities for agriculture were commended by British colonists and who in part for this reason facilitated the Iraqw expansion out of their Mbulu homeland, northeast from Gitting (Snyder 2005). Gitting is part of this expansion. It has a slightly unusual history, in that the British, in an attempt to create an agricultural yeomanry, supported a select few families in the village to purchase tractors and other implements (Raikes 1978). This lead to some fabulously wealthy families in the village farming hundreds of hectares annually (Loiske 1995). All this land was redistributed in the villagisation operation of the early 1970s, with every household receiving four acres each.

The most significant finding from Loiske’s research, however, despite these endowments, were the very high levels of poverty that he reported. These are shown in the first columns of Table 6. This table makes for depressing reading: it shows that over 56% of households were poor in some way (shaded in red), meaning that they were either destitute, or dependent on uncertain and variable day labour for their livelihood. The largest single category was comprised of the extremely poor and destitute. Indeed it is possible that these levels of poverty are higher than those found in the Household Budget Survey nationally in 1991/2, for this found less than 40% of people were below the basic needs poverty line.

6 Note that this wealth ranking method was almost identical to Loiske’s. He used wealth rankings produced by different people sorting through household lists and merged them. Brockington conducted the same exercise with everyone in a group, which has the added advantage of the group being able to discuss their different perspectives on particular households.

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The most important difference our re-survey found between past conditions and the present is that most people seem to be much richer. Most people in the village are now of average wealth, and in much better condition than twenty years ago. Over 85% belong to the middle categories (shaded in green in Table 6) and the largest category of all comprises relatively wealthy peasants. The destitute are now as rare as the rich, and the poor as a whole constitute just 10% of people.

There are three possible scenarios that could explain this change. First, it is possible that the poor people of twenty years ago have simply left the village, and the village has got richer because there are fewer poor people living there. Alternatively the poor families could have got richer, or there may be a mixture of both factors.

When Brockington revisited surveyed households, he found that few people had left the village. Those that had left tended to belong to once richer families (such as those wealthy in livestock) who moved to areas where there was more space for grazing. Instead, the reason why the villages are now wealthier places is because people who were poor have now become richer. This can be seen in Table 7, this shows the same general movement of households out of the poorest categories (in red), and into the middle categories (in green).

But this is not a simple story of greater prosperity for all. The actual dynamics are more complicated, and these are shown in Table 8. Here the two columns on the left show where the families were in the early 1990s, and the columns on the right show where they were at the time of resurvey. Notice two things about this table. First it shows that most families from the poorer families category have become richer. Those who started off in the poorest categories (5, 6 or 7) have tended to move up to richer groups. But notice also that the families which started off in richer categories 2, 3 and 4, have tended to get poorer, or stay the same. No families have risen to, or remained, in the wealthiest two categories. There are therefore two sets of changes to explain – why have the richer families got poorer, and why the poorer families richer? Both issues were discussed in interviews with families and two village discussion groups.

Focus groups and oral histories suggested several driving forces behind the demise of the wealthy. Rich families have become poorer because of illness, because of family troubles (divorce, or the expense of seeking or maintaining a second wife), through taking to drink or simply because they are older and have lost their powers. Rich families may also appear poorer simply because they are moving through the later stages of their life cycles. Some once richer families are headed by elderly couples who are simply less able to manage large farms than they were before.

In other cases the demise merely reflects the inadequacy of the categorization system. Investments in education did not appear in Loiske’s original scheme. Yet some of the wealthy families in the village have done just that, investing returns from agriculture, and selling agricultural assets (livestock, tractors) in order to fund their children’s training. This means that they appear to be less wealthy than before, but they are compensated by their children being employed as teachers or government officers.

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With respect to the move out of poverty, four explanations were offered. The most frequently voiced was that people have got richer because they have worked hard at their farming. They have been able to invest in cattle, modern seeds and farm implements. There has been little, if any, livelihood change. One of the drivers of prosperity could be the improved terms of trade for farm products experienced locally. Table 9 shows that crops have generally increased their farm gate prices by between 160% and 300% in the last twenty years. Moreover, as Table 10 shows, some food cash crops are now yielding considerably greater returns, relative to maize, than they were in previous years. Thus whereas two sacks of beans used to be able to purchase three of maize, now they can purchase almost five.7

Another way of explaining this point is that households have become wealthier because they have retained their assets (their farms), and are earning more from them. The villagers we surveyed demonstrated substantial improvements in prosperity, founded upon retention of assets, and improved returns to them (due to crop price increases), as well as growth of assets (herds) and investment in homes and education.

A second reason that many families reported as causing poverty in the past was alcoholism. Some have been able to stop drinking, or, in other cases children have taken over the farm from alcoholic land managers who merely rented it out each year for enough money to keep them in drink. Loiske’s work shows that this was a serious problem in the 1990s. Conversely those poor families who stayed poor during the years of our survey were often unreformed alcoholics.

We cannot tell whether alcoholism was the cause or consequence of poverty. It was probably a mixture of both. We should also note that alcohol sales are often a means by which women (who make and sell alcohol) gain access to money which is controlled by men (who are the main consumers of the drink). Our data do not allow us to comment on the social dynamics of the relative demise of alcoholism. The point is simply that fewer families in this survey now suffer as a result of it than was the case before.

A third possibility that could explain greater prosperity is that local exploitation of poor families by rich families in the village has decreased. Loiske’s work and local history makes clear that some of the wealthy farmers in the 1990s (the yeomanry families the British supported) were able to rent much land while paying poor families very little money for it. They controlled most of the ploughs and all of the tractors required to plough up large areas of land, and particularly some of the heavy soils allocated to families during villagisation. Now, however, as more people have ploughs, oxen and access to tractors, it is harder for the richer families to dictate terms. Investment in assets has broadened the productive base of the village as a whole. Moreover it is possible that whereas in the early 1990s there was a large labour pool compared to the jobs available, currently there is a larger group of people demanding labour.

7 These prices and terms of trade have not been altered by reduced transportation costs due to road improvements. Road links to the main towns of Babati and Arusha have been generally unchanged for the period under study, half being paved, and the other half unmetalled. The tarmac road between Arusha and Singida (which passes near Gitting) was only completed in 2012.

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Fourthly, in a number of ways, some of the tasks that women have undertaken have become, relatively speaking, easier. There are now readily accessible diesel powered mills to grind corn (as opposed to grinding by hand using stones) and water is more easily available at village stand pipes. There are more clinics, which is reported in the focus groups to have improved maternal health.

DiscussionOur data do not allow us to determine which of the causes of change described here is most important. Our sample size is not large enough, nor the measurement of assets precise enough, for that sort of modeling and correlation. However the value of this sort of research is not in providing such answers. Rather its purpose is to suggest hypotheses for testing in larger studies, and useful avenues of enquiry that may be pursued further, as well as suggesting methodological insights. In that spirit there are several surprises, the broader relevance of which we must consider.

In the first instance there is the proposition that people can have become more prosperous as a result of greater agricultural activity and higher crop prices. This is surprising for two reasons. Most other studies suggest that prosperity increases as a result of access to off-farm income, rather than dependence on agriculture (Bryceson 1999). It is also surprising because many investigations suggest that rural households are net consumers, not producers, of food and any increase in crop prices should make most rural people poorer (Jayne et al. 2010).

With respect to the importance of off-farm income, our data do not present such a conundrum. Many of the wealthier families in the village were wealthy because of their other businesses or because members of their family had employment in government positions (as teachers, nurses or in the district council). Access to other livelihoods in addition to agriculture is clearly advantageous. Our point is simply that even merely agricultural livelihoods have enhanced welfare over the last twenty years. This is concordant with other findings (such as the KDHS survey, cited on page 5 above) which found that well-being in rural agricultural livelihoods can increase in the long term.

The second surprise – that higher crop prices should not benefit the rural poor – requires more discussion. Jayne and colleagues have shown that for numerous countries in the region, most agricultural surplus is produced by a small minority of relatively large farms and prosperous farmers (Jayne et al. 2010). We have reproduced their findings in Table 11 and supplemented it with Tanzanian data from the LSMS. The Tanzanian data show the same levels of inequality as other countries. Bryngelsson and colleagues have expanded Jayne et al’s analysis using the 2004 KHDS and by including all foods, and not just the main staples (Bryngelsson et al. 2012). They found that 87% of the rural population are net buyers of food. Smaller farmers both produce less, and are often required to sell any surplus when the price is particularly low. Higher food prices would not therefore make the rural poor richer; quite the opposite, it would add to their troubles.

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Our response here is simply that we must be careful with such generalisations of rural economies. Analyses of their dynamics must allow for more complexity. Kagera, from where Bryngelsson and colleagues’ data came, is a major coffee growing area, which has a particular dynamic reflecting changes in the coffee economy. Hanang, cultivating wheat, potatoes, maize and beans, will have a different history. We also have to factor in livestock whose economic contributions have been consistently under-estimated (Behnke and Metaferia 2011; Behnke and Muthami 2011; Behnke and Nakirya 2012; Behnke and Osman 2012). In Tanzania, livestock sales are more important than crop sales for households with smaller farms (Table 11, bottom row). This reflects the fact that many pastoral families may not in fact farm any land. Their poor agricultural sales are not a good indication of the returns they experience from participating in the rural economy.

In addition to the complex geography of rural economies, the dynamics of economies over time will have variable impacts upon poorer households. Bryngelsson and colleagues argue that while food price rises in the short term may be harmful to households who spend most of their money on food, and who are net purchasers of food, in the longer term the consequences are more complicated. Higher food prices can lead to higher rural wages and to increased agricultural productivity, such that more families become net producers not net consumers.

Finally note that it is also important to consider what variety of foods households produce and the extent of their dependence on purchased produce. As Bryngelsson and colleagues emphasise, households which produce a significant proportion of their own food can benefit from both increasing and decreasing food prices. They explain this as follows:

‘The reason for this paradoxical result is that rising food prices will raise the relative value of the food they produce and by altering their consumption basket they can . . . increase their welfare . . . If food prices go down, they can shift towards better quality food or more food consumption at the expense of a relatively small decrease in non-food consumption. If food prices go up, on the other hand, they can similarly shift towards more non-food consumption at the cost of a relatively small decrease in food quality or quantity. These new options should increase their total welfare.’ (Bryngelsson et al. 2012: 131)

The LSMS survey shows that, with respect to staples, it appears that most families, most of the time, are net producers, not net buyers of their staple food. This is shown in Table12.8 It is possible therefore that some of these producers, particularly the 66% who own more land and enjoy surpluses all year round, could belong to the categories that benefit from the food price changes.

8 The consumption data in the LSMS survey is collected for only one week. On that basis we can tell in which months households are likely to be dependent upon purchased foods, and when they can rely on their own production.

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The prospect of rural families getting richer from smallholder agriculture remains surprising. It is not typical of other research. But the point of our argument in these paragraphs is that data are thin here. This conclusion does not fly in the face of hundreds of studies – there simply are not adequate survey data in place for it to do so. We suggest therefore that the changes that have occurred at Gitting are both compatible with the responses of poor rural households to crop price increases, and could be replicated in other parts of the country.

The second major issue to consider is the relationship between rural economies and national level GDP. What are we to make of the fact that rural livelihoods in Gitting have risen alongside national GDP growth? Does this suggest that rural economies are well tied to national economic growth? That conclusion would be premature. Indeed it would be misleading to expect a good relationship between the two. We know that GDP has risen as a result mainly of growth in the manufacturing, mining and service sectors. Agriculture contributes only 25% of GDP (Robinson et al. 2011). On the other we know that the statistics used to estimate agricultural contributions may well be weak and unreliable (Jerven 2011). They simply do not capture much of the activity in the informal sector which dominates life in rural Tanzania. 9 Thus it might be possible for the agricultural sector to thrive, and for the broader economy not to and vice versa. If, in fact, most farmers contribute relatively little to the crop sales measured in GDP calculations it is likely that their own livelihood dynamics could be quite separate from the changes suggested by GDP.

Viewed thus we should not be surprised that GDP seems poorly reflected in the consumption patterns of the rural poor as Mashindano and others complain. Nor in fact should we read too much into the fortunes of Gitting appearing to match those of the nation. Rather than trying to explore the connections between the two scales of activity (village and nation), we would require separate sets of explanation for change in each.

Finally what are the methodological implications of this study? Measuring poverty through consumption and through assets measures different things, and can reach starkly different conclusions when determining who is poor (see Schaffer 2013 for a review of the evidence). However it is surprising that the direction of change over time should be so different between our re-survey and the HBS. The nature of change in the asset-based measures in this village from our surveys is different from the consumption based measures in the nation as a whole (Table 1).

These data suggest therefore that it is important to explore change in assets, as well as change in consumption, if we are to understand how inclusive economic growth is for the rural poor. The households described in this village have experienced a remarkable change of fortunes in the last twenty years, as measured by their assets. Measures of household prosperity which ignore assets will be unable to capture the sorts of dynamics described in this survey.

9 Edwards reports the well known case of the drought of late 2005 in Tanzania. This was the worst for 20 years, and it is estimated that food crop production declined by between 10 and 15% in 2006 because of it. Nevertheless government statistics show that agricultural GDP grew by 4.1% in that year (Edwards 2014: 244-5).

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The data presented here suggest that determining the inclusivity, or otherwise, of economic growth in the absence of understanding trends in assets is premature. Current poverty lines, as determined by both the HBS and the LSMS, rely solely on consumption indices. But understanding poverty dynamics during times of economic growth requires incorporating a notion of the dynamics of asset use and ownership.

Rather than asking why growth is not inclusive (as Mashindano and Shepherd recommended), it is important first to ask how change in assets is related to change in consumption. Unfortunately the historical data presented here do not allow us to explore how change in assets is related to change in consumption. That is a task for ongoing analyses of panel data.

ConclusionIt is premature enthusiastically to welcome Tanzania’s recent decades of economic growth if that growth has not included the poor. It is difficult to see what purpose growth serves if it is not inclusive. Until we know how strongly that growth has been felt by rural Tanzanians, it can elicit only a cautious welcome.

Equally, however, before condemning its lack of connection to the rural poor we have to know how to measure the benefits (or otherwise) that economic growth might have for the rural poor. Current misgivings about Tanzanian economic growth excluding the poor have been based primarily on measures of consumption. This is an important aspect of poverty, as it shows that, in a very material sense, the economic growth the rest of the country enjoys is not well shared. But nevertheless this measure overlooks change in assets, as well as demonstrating considerable dynamism. We cannot conclude from the stasis in measures of consumption that economic growth is not assisting households to move out of poverty.

Attention to assets matters a good deal if we are to understand poverty dynamics. In this paper we have presented evidence to suggest that use of, and benefits from assets has much improved levels of prosperity in the last twenty years in a previously poor, rural Tanzanian society. High levels of poverty, as indicated by the lack of control and use of productive assets, has been largely reversed. Most families in these villages are now average or above average farmers, and most families whose history we traced had themselves experienced this enrichment.

The case is taken from one Tanzanian village, which is well endowed with agricultural resources and well-placed to benefit from improvements to the agricultural economy. Great care is required when extrapolating from this case study. We are not asserting from this case that most rural Tanzanians have in fact benefitted from twenty years of economic growth. That conclusion could not be supported by our data. We can, however, suggest that it is possible for many (if not most) rural Tanzanians. The structure and variety of the Tanzanian rural economy makes it so.

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The challenge now is to identify how to explore long term trends in rural asset dynamics over the last 20 years in rural Tanzania. We have shown that reconstructed longitudinal surveys like this can provide considerable insights into long term dynamics of livelihood and prosperity. The surveys of doctoral students and post-doctoral researchers that were conducted in Tanzania in the early 1990s are, therefore, potentially an important resource that could be exploited to learn more about the dynamics of the recent past. Fortunately these people rarely throw away their data and the survey schedules are usually recoverable. Moreover in Tanzania there are good records of which foreign researchers have worked in the country (through the research permit granting body COSTECH and the strong networks these researchers have tended to form), and domestic researchers’ theses are kept in the East Africana library at the University of Dar es Salaam. The task of locating suitable surveys, from which longitudinal panel data could be reconstructed, has already begun on a project funded by the ESRC and DfID.10 By extending this technique to other areas of Tanzania, with different agro-ecological conditions, different histories, and different affinities to commercial agriculture we could build up a richer picture of change across the rural economy during a period of GDP growth.

10 For more information see http://longtermlivelihoodchangeintanzania.wordpress.com/.

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Table 1: GDP and the proportion of the population below basic needs and food poverty lines in mainland Tanzania.

Measure 1991/2 2000/01 2007 Change1991/2-2000/01

Change2000/01- 2007

GDP $7.7 bn $10.3bn $16.2 bn 33% 54%Basic Needs Poverty

38.6% 35.7% 33.3% -2.9% -2.4%

Food Poverty 21.6% 18.7% 16.5% -2.9% -2.2%

Reproduced from Mashindano et al 2013: 126 and World Bank DataNote that absolute GDP increases do not reflect increased population. The most relevant figure therefore is GDP per capita – but this is still grew by 29.9% between 2001 and 2007 (Atkinson and Lugo 2010).

Table 2: Change in Consumption per adult equivalent by Quintile for Waves 1 and 2 of the LSMS Survey

ConsumptionQuintile

Mean Consumptionin 2008/9

Mean Consumption in 2010/11 Change

Poorest Quintile 230,006 215,097 -6.5%Second Quintile 365,208 347,149 -4.9%Third Quintile 497,729 486,056 -2.3%Fourth Quintile 719,448 715,884 -0.5%Fifth Quintile 1,683,926 1,646,094 -2.2%

Source: LSMS Survey Data.Prices were deflated according to food price data calculated from the LSMS survey itself. The alternative means of deflation was to use CPI data. However these are calculated from urban areas only, and so are unrepresentative of the rural areas where most of the population is found (National-Bureau-of-Statistics 2012).

Table 3: Average Change in Consumption per adult equivalent

Consumption Quintile2008/9

Consumption change for households in these quintiles in 2010/11

RuralHouseholds

UrbanHouseholds

Poorest Quintile 84% 146%Second Quintile 25% 70%Third Quintile 7% 59%Fourth Quintile -4% 31%Fifth Quintile -20% 14%

Source: LSMS Survey DataPrices were deflated according to food price data calculated from the LSMS survey itself. Different deflators are provided for urban and rural settings which in part explains the differences between the two.

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Table 4: The distribution of the panel sample across the quintiles for the two survey years.

Consumption Quintile2008/9

Consumption Quintile 2010/11Poorest Quintile

Second Quintile

Third Quintile

Fourth Quintile

Fifth Quintile

Poorest Quintile 8% 4% 3% 2% 1%Second Quintile 5% 6% 5% 3% 1%Third Quintile 4% 5% 4% 4% 2%Fourth Quintile 2% 3% 4% 6% 4%Fifth Quintile 1% 1% 3% 5% 12%

Source: LSMS Survey DataThe cells shaded in gold show households remaining in the same quintile, or moving up or down one quintile. 12% of the sample improves by more than two quintiles; 14% declines by more than two quintile.

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Table 5: Wealth Stratification Systems for Tanzania.

Wealth Group

Wealth Group CharacteristicsLoiske

Wealth Group CharacteristicsHiggins and Da Corta

1 Immensely Rich.Knows no barriers has cars, lorries etc

Rich (tajiri)Significant assets and local powerInvolved in large-scale or employment of labourOwns large-scale non-farm assetsMay lend money

2 Very Rich.Many cattle and much land; owns a tractor but not a lorry. Has businesses and land in towns

3 Rich.Employs many vibarua; has many cattle. Has businesses.

4 Above Average farmer.Some cattle; farms their own land and uses vibarua work occasionally.

Resilient (tajiri kiasi, mwenye uwezo)Sufficient capacity (eg. Assets, social networks) to prevent significant downward mobility relative to overall productive wealthMay employ small amounts of labour on the farm or be involved in small-scale trade.

5 Average farmer.A few cattle, farms their own land without using vibarua work.

Vulnerable but not poor (tete ila siyo maskini)More productive assets which take the family through the yearDuring good times can saveDuring bad times will reduce family consumptionVulnerable to downward mobility with a significant shockPoor (maskini)Access to limited productive assets (land and livestock)Cannot earn enough from farming or trade to take family provisioning through the whole year so will reduce family food consumption.Cannot save much in good years.Must sell assets in order to cope in a crisis.Vulnerable to downward mobility to ‘very poor’ category but not to ‘destitute’ category

6 Poorrents land out to others; depends on casual vibarua work for daily needs; few if any livestock.

Very Poor (maskini sana)No clear livelihood source; No significant productive assets; dependent on selling labour and/or scavenging; erratic income and food access; very vulnerable to becoming destitute with shock

7 Extremely poorUnable to get work easily; hard to rent their land out to others; suffering from alcoholism and / or illness.

Destitute (maskini hohehahe)Depends on others for basic needs; Cannot work; tends to be socially excluded.

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Vibarua work refers to casual labour.Source Loiske; Higgins and Da Corta: page 17. The differences in these indexes are first, vulnerability, resilience and shock, are more important in the study of poverty now than in Loiske’s time. Second, Loiske’s village used to have some extremely wealthy families, which was the historical product of a colonial attempt to create a wealthy yeomanry. Third, all the households in Loiske’s study technically had access to assets in the form of lands as they had all received lands in the villagisation operation of the mid-1970s (less than 20 years earlier). However some families appear destitute because they did not have the means to farm them, and some could not even rent them out because it was the custom to hire the labour of the farm owner, and some farm owners were considered too unreliable to rent land from.

Table 6: Social Stratification in Gitting in the 1990s.

Wealth Group1990s 2013

No of H’hlds

% of H’hlds

No of H’hlds

% of H’hlds

1: Immensely Rich 5 1.0 4 0.42: Very Rich 6 1.0 15 1.33: Rich 18 3.0 20 1.84: Above Average 106 17.4 678 59.45: Average 116 19.0 297 26.06: Poor 163 26.8 101 8.87: Very Poor 195 32.0 27 2.4Total 609 1,142

Source: (Loiske 1995) and Participatory ranking exercise with village leaders and executive officers 2013.This difference is highly significant: 2 = 529.21; df = 6.

Table 7: Change to Visited Households 1990s-2013 Part 1

Wealth Group

1990 2013

2 2 03 5 124 18 295 12 246 21 77 19 5

Total 77 77Source: Loiske 1995 and Brockington’s fieldwork 2013

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Table 8: Change to Visited Households 1990s-2013 Part 2

Households in 1990s Wealth Distribution in 2013Original

Wealth GroupNumber of households

in each group3 4 5 6 7

2 2 23 5 1 2 1 14 18 5 4 6 1 25 12 2 5 4 16 21 2 12 6 17 19 6 7 3 3

Total 77 12 29 24 7 5Source: Loiske 1995 and Brockington’s fieldwork 2013

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Table 9. Average farm gate price in Hanang

Deflation byYears

Averaged Maize Beans Wheat Potatoes Sunflowers Pigeon Peas

Purchasing Power Parity

1990-93 34.70 56.21 35.18 40.86 28.23 29.35

2009-12 70.74 169.69 114.62 136.84 58.46 105.10

Increase 204% 302% 326% 335% 207% 358%

Consumer Price Index

1990-93 14,135 22,958 14,340 16,683 11,538 11,997

2009-12 22,605 54,300 36,039 42,228 19,139 35026

Increase 160% 237% 251% 253% 166% 292%Source: Hanang District Council Records

Table 10: Relative price of 100kg of maize to 100kg of other crops in Hanang

Years Averaged Beans Wheat Potatoes Sunflower Pigeon peas

1990-93 162% 101% 118% 82% 85%

2009-12 240% 159% 187% 85% 155%Source: Hanang District Council Records. Prices have been deflated by the Consumer Price Index

Table 11: Distribution of Farming Activity in Selected African Countries

Country &Year of Survey

Attribute:Mean

Quartiles of Land Ownership per capitaLowest quartile

Second Quartile

Third Quartile

Highest quartile

Kenya2003/04

Farm SizeCrop Sales ($)

0.58261

1.25672

2.12979

5.911500

Ethiopia1996

Farm SizeCrop Sales ($)

0.248.3

0.67118

1.15173

2.58380

Rwanda2001

Farm SizeCrop Sales ($)

0.3256.3

0.6374.5

1120

1.82280

Mozambique2002

Farm SizeCrop Sales ($) 0.53

11.81.2

26.11.7634.1

3.1461.4

Zambia2000

Farm SizeCrop Sales ($)

0.7142.7

1.677.3

2.75109

5.81148

Tanzania2010/11

Farm SizeCrop Sales ($)Livestock Sales ($)

1.052464

2.396458

3.96113

42

10.46239

76

Source Jayne et al (Table 2) and LSMS 2011 data. Tanzanian data includes all households, urban or rural, who farmed land. All sales figures have been converted to 2011 US$, using the CPI deflator available on at http://www.measuringworth.com

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Table 12: Net production and purchase of staple through the year

LandQuartile J F M A M J J A S O N D

% ofRural Ppn

No land -6.8 -9.8 -7.7 -8.3 -8.7 -8.8 -6.8 -5.6 -2.5 -8.8 -7.2 -6.5 16

Lowest -2.4 -4.2 -5.2 -3.0 7.9 2.0 2.5 1.9 11.0 -1.5 -3.8 4.1 17

Second -0.1 4.0 6.3 21.5 13.1 16.5 12.6 3.5 12.3 6.0 2.9 6.0 18

Third 1.4 2.6 8.0 20.7 16.3 12.4 9.5 9.2 12.8 13.1 7.3 8.2 22

Richest 5.3 9.6 16.0 17.6 17.4 15.9 9.1 10.0 9.2 7.6 7.8 4.2 26Source: LSMS 2011. Table includes all rural households and shows net weight of staple produced (+) or purchased (-) in the week sampled. Red cells indicate net deficit of staple food. Unfortunately the LSMS datasets do not record the value of home produced food, only its weight. It is thus only meaningfully possible to compare production and consumption of staples. We cannot replicate Bryngelsson and colleagues methods here and examine total expenditure on all foods.

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