space International Development Planning Review, 34 (1) 2012 doi:10.3828/idpr.2012.5

InternatIonal Development plannIng revIew, 34 (1) 2012 doi:10.3828/idpr.2012.5

moises machicado, Claire Heffernan and Kim thomson

4p space the impact of livestock assets among indigenous communities on the Bolivian altiplano:findings from a comparative model

measuring poverty has occupied a lot of space in the development discourse. over the years a number of approaches have been offered to capture the experience of what it means to be poor. However, latterly such approaches often ignore core assets. Indeed, the comparative impact of livestock vs. other core assets such as land and education on poverty has not been well explored. therefore, the authors created an ‘asset impact model’ to examine changes to both tangible and intangible assets at the house-hold level, with a particular focus on gender and ethnicity among communities residing in the Bolivian altiplano. the simple model illustrates that for indigenous women, a 20 per cent increase in the livestock herd has the same impact on household income as increasing the education levels by 20 per cent and household land ownership by 5 per cent. the study illustrates the potential role of a productive, tangible asset, i.e. livestock, on poverty reduction in the short term. the policy implications of supporting asset-focused measures of poverty are discussed.

It has been estimated that for two-thirds of the global poor people living on less than US$2 a day, livestock are a vital part of livelihoods (Heffernan and Misturelli, 2000; Perry and Sones, 2007; Heffernan, 2010). Nevertheless, in the literature on assets, livestock are often overlooked.

Within the development discourse, wider notions of asset acquisition and division have become central to the concept of poverty (Moser, 1998; Narayan et al., 2000; Ziliak, 2003; Misturelli and Heffernan, 2008). For example, Siegel (2005, 7) defined assets as the ‘productive, social, and locational resources used to generate well-being’. And over the years, a variety of authors have offered asset typologies. One of the most accepted approaches classified assets as tangible or intangible (Patrick, 1966; Moser, 1998; Ziliak, 2003). Intangible assets include items such as education, social capital and political rights (the degree of inclusion/exclusion), and the capacity and openness of institutions (Ziliak, 2003). Conversely, tangible assets include infrastructure and capital assets such as land and housing stock. According to Patrick (1966), there are two types of tangible assets: non-reproducible vs. reproducible.

Moises Machicado is a research associate at the Livestock Development Group, Claire Heffernan is the Director of the group and Kim Thomson is a research consultant with the group. The mailing address is Livestock Develop-ment Group, School of Agriculture, Policy and Development, University of Reading, Reading RG6 6AL; email: [email protected]; [email protected]

Paper submitted April 2011; revised paper received and accepted April 2011.

IDPR34_1_05_Machicado.indd 65 09/11/2011 16:00

moises machicado, Claire Heffernan and Kim thomson66

Other authors have focused their typologies on the level of risk. For example, Zimmerman and Carter (2003) described productive assets and buffer assets. As the authors noted (2003, 237),

Productive assets, such as land and livestock, offer reasonably high returns that are highly variable from year to year. Buffer assets, such as cash, stored grain and jewellery offer returns that are low but fairly stable.

Livestock, using the above distinctions, would be considered a reproducible, produc-tive, tangible asset with finite implications in the case of external threats such as drought, disease or civil war. While herd growth rates vary, households will gain from asset growth in the absence of severe environmental disturbances. Equally, in the short-term, benefits can be derived from the selling of off-take, i.e. milk and meat. Nevertheless, contrary to Zimmerman and Carter’s (2003) contention, for many poor people livestock are a critical, if not crucial buffer asset, which can be quickly off-loaded during times of stress (Blench et al., 2003; McPeak, 2006; Siegmund-Schultze et al., 2007). Clearly, livestock contribute to household food security (Foeken and Mwangi, 2000; Owen et al., 2005; Turner, 2009). Keeping animals can further be a source of social recognition (Heffernan and Misturelli, 2000). Thus, livestock cross the boundary into a tangible asset with intangible benefits that further can quickly multiply and act as a resource during times of stress.

Livestock, among the poor, are particularly important to women. Indeed, women are often solely responsible for livestock care and management (World Bank, 2008). Nevertheless, who has rights over assets (McAuslan, 1998; Nogales, 1998; De Soto, 2000; Alston and Mueller, 2003; Merryman and Perez-Perdomo, 2007) has occupied much space in the literature, particularly in relation to gender. Women can formally have rights over an asset, but the control is gender biased (Niamir-Fuller, 1994; Curry, 1996; Banda and Chinkin, 2004; Roy, 2005). The finding is largely true with regard to livestock; while women tend to control small ruminants and micro-species such as poultry, water-fowl, guinea pigs, etc., as livestock get more valuable, women’s control decreases (Bravo-Baumann, 2000). Thus, ownership and control of livestock assets is often the domain of men (Curry, 1996; Huss-Ashmore, 1996; World Bank, 2008).

As Attanasio and Szekely (1999) noted, fundamental to an asset-based approach to poverty lies the question ‘why are some individuals able to accumulate the most productive assets, while others are prevented from doing so?’ To date, the comparative impact of livestock on household income as opposed to other tangible and intangible assets such as land and education has not been explored. Therefore, the aim of the study was twofold: first, to determine how an investment in livestock compared to other forms of capital investment by the poor, and second, to explore how women benefit from this asset. To respond to these aims, the authors created the ‘asset-impact model’, which explored the consequences of change to both tangible and intangible


the impact of livestock assets among indigenous communities on the Bolivian altiplano 67

assets on household income among a sample of livestock keepers residing on the Bolivian Altiplano.

Background to the study setBolivia was chosen as the country of interest for two reasons. First, Bolivia is one of the poorest countries in Latin America. As the Poverty Reduction Strategy Paper noted,

using a poverty line set at US$2 per day, the Headcount Index of poverty is 70 percent of the population (5.6 million poor), and the Headcount Index of extreme poverty is 42 percent (3.3 million). (GOB, 2001, 23)

Second, poverty is a localised phenomenon both ethnically and geographically. The majority of poor people in Bolivia belong to one of the nation’s majority indige-nous groups, i.e. Aymara or Quechua (INE, 2001; Morales et al., 2005). Geographi-cally, Bolivia is divided into three distinct areas: the Andes, the sub-Andes and the Lowlands. The Andean region, known as the Altiplano, is home to both ethnic groups and has the highest levels of both moderate and extreme poverty. Differences between the two ethnic groups have been explored at the socio-economic, welfare and even anthropometric levels (de Meer et al., 1993).

With regard to education levels, over a quarter of the population in rural areas is illiterate (INE, 2001). As such, illiteracy rates in Bolivia are one of the highest in South America. While the census also noted that approximately 80 per cent of the popula-tion between 6 and 19 years of age are in formal education, a higher proportion of boys than girls attend school (81 per cent vs. 78.3 per cent, respectively) (INE, 2001).

Interestingly, studies in Bolivia have found that in comparison to men, women are not particularly economically disadvantaged (Heffernan and Yu, 2007; 2010; Machicado, 2010). However, this is not to say that in relation to specific livelihoods there is income parity between the genders. Rather, in male-dominated livelihoods, women earn up to 20 per cent less than men (Heffernan and Yu, 2007; 2010; Machicado, 2010). Women are also limited in their access to core assets. Indeed, customary law regarding the inheritance of assets, i.e. land and livestock, generally favours men. A study in the Bolivian Andes by Valdivia et al. (1996) found that women tended to inherit small ruminants while men inherited land and cattle.

While the livestock sector contributes only 5 per cent of the Bolivian GDP, livestock is a crucial source of income for the poor (Rushton and Viscarra, 2006). In the high-altitude Altiplano, llamas and alpacas are raised for their meat and wool with sheep another common species (Fairfield, 2004; Kristjanson et al., 2008). For many poor households, guinea pigs are another traditional source of food security. All of these species require little input in the harsh, dry conditions (Dijkman and Sims, 1997).



However, during the 1970s, the government, via donor aid, launched the Programa de Fomento Lechero, a programme to support small-scale dairy production among Altiplano farmers. The large-scale support for dairying jump-started the dairy industry. Today, dairy production is a key livelihood in the peri-urban Altiplano, although donor support has ended recently. Therefore, the ability of farmers to survive without this form of subsidy over the longer term is unclear.

The asset-impact modelWithin the literature, a number of authors have explored asset-based models/approaches to poverty (Moser, 1998; Attanasio and Székely, 1999; Carter and Barrett, 2006). The models/approaches generally explore how assets support poverty alleviation within deterministic or stochastic scenarios. Indeed, Carter and Barrett (2006) offered an ‘asset poverty line’ and detailed how below a certain level of assets, households fell into ‘poverty traps’. As such, the authors make a distinction between ‘stochastic’ vs. ‘structural’ transitions into poverty, where a household experiencing an income shock, but whose core assets were intact would experience stochastic or temporary poverty. Conversely, a household whose asset level fell below the line due to the loss of produc-tive assets would experience structural poverty and was likely to fall into a ‘poverty trap’. Livestock, however, crosses both of these boundaries as it is both a productive asset and, further, the loss of livestock-related income can comprise an income shock. While the lack of land is clearly a ‘structural’ issue, it may be argued that so is the lack of educational attainment. Therefore, the aim of the ‘asset-impact’ model offered by the authors is to explore differing scenarios regarding the role of livestock, land and education on stochastic vs. structural poverty. The simple, deterministic model based on direct evidence from 514 poor households living on the Bolivian Altiplano. The model also details an ‘Asset Index’ which compares a range of tangible vs. intangible assets on poverty.

In recent decades, it has been argued that income-based poverty lines do not reflect the multi-dimensional nature of poverty (Misturelli and Heffernan, 2008; 2009). The use of income as a proxy for poverty in the model is justified in two ways. First, assets, income and poverty are inexorably linked, despite wider conceptions of the multi-dimensional nature of poverty. Second, as noted above, the role of particular assets such as livestock on household income has been under-explored. Recent attempts to measure multi-dimensional poverty ignore key assets such as land and livestock (see Alkire and Santos, 2010). Indeed, the Multi-Dimensional Poverty Index (MDPI) explores 10 indicators of poverty: child mortality (if a child has died in the family); nutrition (if an adult is malnourished); years of education; child enrolment in school; electricity (lack of at the household level); drinking water; flooring; cooking fuel (i.e. wood, charcoal or dung); sanitation (shared toilets); assets (does not own more than



one bicycle, motorbike, radio, TV, telephone) (Alkire and Santos, 2010). Thus, the new index, which has been adopted by the UN, limits the assessment of assets to education and income to a small range of purchased goods.

Therefore, revisiting the relationship between assets, income and poverty among a subset of the poor may prove of value to decision-makers. The analytical framework for the model can be seen in Figure 1.

MethodsThe model measures household access to three tangible assets (land, livestock and labour) and two intangible assets (education and services). The tangible assets represent a mix of both reproductive (land and livestock) and non-reproductive assets (labour). The assets selected were derived from a series of community-level workshops in which community members ranked the assets most important to household well-being.

Household level: tangible assets: land, livestock, labour

Intangible assets:education and services

Baseline assets ofparticipating house-holds

Changes to asset index:land, livestock, labour, education and services

Impact on household income

u

Subsequently, an ‘asset index’ was created to rank the actual levels of assets among study households from a +3 to a –3. A seven-point ordinal scale was constructed to rank categories of variables. A seven-point scale was utilised in order to capture a greater level of nuance between the categories.

Scenarios were then introduced that evaluated changes to both tangible and intan-gible assets and the impact on household income. The model was underpinned by primary data from 514 poor livestock-keeping households, as detailed below.

The sample frame

The authors utilised multi-stage sampling to target poor livestock-keeping communities and households residing in rural, peri-urban and urban communities surrounding El Alto, the principal city of the Bolivian Altiplano. In the first stage of sampling, communi-ties were identified via key informants working in local universities, non-governmental organisations (NGOs) and government offices. Therefore, informants were asked to identify communities that fit the following criteria: mainly indigenous population, large-reliance on livestock-keeping and/or other traditional livelihoods and located within a 60-mile radius of El Alto. Informants were also requested to categorise/map the communities mentioned as rural, peri-urban or urban. At the end of this process, 25 communities were targeted for participation in the study.



In each community, a workshop was held to raise awareness of the project and to explain the aims and objectives and targeting criteria. Community members were asked to rank key assets, at the household level. From the workshops, those individ-uals, who were interested in participating and met the sampling criteria (owning or keeping livestock and living at or below the national poverty line) were identified. In this manner, sampling at the community level was both purposive and opportunistic, i.e. the majority of study participants attended the initial workshops.

Table 1 Gender, age and ethnicity

Gender (n = 514) Age group Aymara (n = 351) Quechua (n = 163)

women (n = 363) >50 15 18

40–49 62 33

30–39 90 42

20–29 52 37

<20 8 6

men (n = 151)

40–49 34 7

30–39 38 7

20–29 20 4

>50 28 8

<20 4 1

The study set was intentionally biased towards women, with an average age of 38 years. The majority of women, who participated in the study, were de-facto or de-jure female-headed households. De-facto female-headed households were those who lost their husbands via divorce or death. A small number of women were former ‘concu-bines’ or common-law wives and when the relationship broke down were left without wider family support. The de-jure female-headed households were the primary breadwinners due to ill-health, migration or alcoholism/drug abuse issues of their spouses. In focus groups attended by more women than men, no attempts were made to balance the genders of participants. Given the predominance of Aymara commu-nities on the Altiplano, not surprisingly more self-identified Aymara than Quechua individuals participated in the research (see Table 1).

The model parameters

The first step in developing the model was to create the Asset Index. The index explores two types of assets: tangible (land, labour and livestock) and intangible (services and



education). As mentioned above, the index ranked the availability of the assets on the individual involved on a seven-point scale of +3 to –3. Therefore, assets were divided into seven categories in order of amount/importance. For example, with regard to land, the categories were simply a reflection of the number of square metres owned from greater than 10,000 to less than 200. Similarly, labour was calculated by the number of adult household members with greater than seven adults the maximum and one person, the minimum. Conversely, given the range of species owned by households, calculating the livestock packages was more detailed:

• Package 1: < 50 micro-species, i.e. poultry, guinea pigs. • Package 2: ± poultry, ± guinea pigs, < 50 small ruminants, < 10 pigs.• Package 3: ± poultry, < 30 guinea pigs, < 50 small ruminants, 1 cow. • Package 4: ± poultry, ± guinea pigs, < 50 small ruminants, < 10 pigs, < 2–3

cattle. • Package 5: micro-species, < 50 small ruminants, < 25 pigs, 4–7 cattle. • Package 6: ± poultry, < 30 guinea pigs, < 25 pigs, < 50 small ruminants, 7–12

cattle. • Package 7: ± poultry, ± guinea pigs, < 25 pigs, < 100 small ruminants, > 12

cattle.

Package 1 is comprised of a limited amount of micro-species, which are generally considered ‘low-value’ livestock assets. The next level up includes small ruminants such as sheep or goats and a limited number of ‘high-value’ livestock assets (i.e. pigs). Package 3 and 4 households also own a small number of the ‘maximum value’ livestock assets or ‘cattle’. Finally, Package 7 households owned the highest number of cattle.

With regard to the intangible assets the categories for education were determined by the number of years of reported school attendance. Therefore, individuals in the lowest category had between 0 and 2 years of school while those in the highest category reported attendance in tertiary education. ‘Access to services’ was categorized into seven ‘tiers’ based upon the following criteria: miles to urban centre; gas connection; infrastructure; medical services; police offices; sewage and transportation links:

• Tier 1: > 50 miles to urban centre, dirt roads, lack of essential services and transport links.

• Tier 2: > 40 miles to urban centre, dirt roads, limited access to transportation links.

• Tier 3: > 40 miles to urban centre, macadam and dirt roads and limited access to transportation links.

• Tier 4: > 30 miles to urban centre, macadam roads, police offices and good transportation links.

• Tier 5: > 30 miles to urban centre, macadam roads, police outpost, sewage and medical services, good transportation links.



• Tier 6: Within 30 miles of an urban centre, macadam roads, police offices, sewage and transportation.

• Tier 7: Within 20 miles of an urban centre, gas connection, infrastructure, police offices, sewage and transportation.

Finally, with regard to income, households were categorised across a range of incomes with greater than 1200 Bolivianos/person/month in the highest category and up to 200 bolivianos/person/month in the lowest category.1

The criteria for the scoring system are summarised in Table 2. For each of the scores, the zero level was set to represent the ‘average’ or median asset level. Using the above index, the proportion of individuals/communities involved in the study, which fell into the above categories was calculated is offered in Table 3. The greatest numbers of the study set fell into the –3 category of the Asset Index, particularly in terms of land, labour, livestock and income. Interestingly, only a few households in the –3 category resided in the most marginalised areas in terms of service delivery.

1 The figures range from US$160 to US$26/person/month using an exchange rate of 6.96 Bolivianos/US dollar. Included in the calculation are all school-age children.

Table 2 Criteria for the scoring system

Index Land size (m) Labour (adults/household)

Livestock package

Education (years)

Services parameter

Income (Bs/individual/month)

+3 > 10,001 7 package 7 > 13 tier 7 > 1201

+2 1001–10,000 6 package 6 9–12 tier 6 1001–1200

+1 801–1000 5 package 5 6–8 tier 5 801–1000

0 601–800 4 package 4 5 tier 4 601–800

–1 401–600 3 package 3 4 tier 3 401–600

Table 3 Asset Index distribution

Asset Index Land (n = 514) (%)

Labour (n = 514) (%)

Livestock packages (%)

Education (n = 514) (%)

Services (n = 514) (%)

Income (n = 514) (%)

+3 18 2 3 3 5 1

+2 9 2 12 21 40 3

+1 2 3 14 17 22 4

0 2 6 11 16 19 6

–1 6 10 15 9 5 5

–2 22 16 19 10 2 13

–3 42 61 26 24 7 68



Next, the model was created in Excel (version 2007). The component parameters informing the model are explored in turn: land and education, income, labour and services.

Land and education

Not surprisingly, across the study set with regard to land ownership/access, women were largely disadvantaged (see Table 4). However, in terms of ethnicity and land, a higher percentage of Quechua households occupied the top (>10,000 m2 of land) and bottom scales (between 0 and 200m2) of the index.

With regard to education, not surprisingly, women were disadvantaged. As the table illustrates, women dominated the lower rankings of the index. Approximately 30 per cent more women than men were in the lowest educational category (–3 index). While both genders were under-represented at the tertiary level of education (+3 index), men were more than twice as likely as women to have attended secondary school (+2 index). Ethnicity had little impact on differences across the Education index.

The income index

The income index was based on the amount of income earned by study participants on a monthly basis (Table 5). As the table illustrates, there was no significant difference between the genders, the majority of both genders can be categorised within the –3 index, although a slightly higher percentage of men were in the bottom category. With regard to ethnicity, a higher percentage of the Aymara occupied the lower tiers of the index. Not surprisingly, the majority of both ethnic groups fell into the –3 category.

Table 4 Land and education index: gender vs. ethnicity

Land Education

Index Women (n = 358)

(%)

Men (n = 156)

(%)

Aymara (n = 351)

(%)

Quechua (n = 163)

(%)

Women (n = 358)

(%)

Men (n = 156)

(%)

Aymara (n = 351)

(%)

Quechua (n = 163)

(%)

3 12 21 17 21 4 2 3 4

2 10 7 10 8 8 20 22 19

1 1 2 2 1 11 18 16 19

0 2 8 2 3 12 12 15 18

–1 5 15 7 3 8 10 10 7

–2 26 28 22 20 15 9 11 7

–3 44 19 40 45 42 29 23 26



Table 5 Income index: gender and ethnicity

Index income Women (n = 358) (%) Men (n = 156) (%) Aymara (n = 351) (%) Quechua (n = 163) (%)

+3 1 2 2 1

+2 3 2 2 4

+1 5 11 3 5

0 6 6 6 6

–1 16 14 5 6

–2 24 21 14 11

–3 45 44 69 67

Table 6 Labour and services index by gender

Labour Women (n = 358) (%) Men (n = 156) (%)

+3 2 1+2 3 0+1 3 30 6 16

–1 9 14

–2 16 17

–3 62 50

Services+3 4 6+2 38 45+1 20 270 21 15

–1 6 3

–2 3 1

–3 9 2

Labour and services

Women reported low labour resources within the household (Table 6). Overall, the Quechua were slightly more labour-constrained in the lower rankings of the index, but large differences were not noted. Further, gender differences in access to services, infrastructure and utilities were also apparent. As the table illustrates, women tended to occupy the lower levels of the index, while a greater percentage of men were categorised in the higher tiers.

With regard to ethnicity, overall, Aymara communities were better connected than the Quechua in the higher categories (+2, +3) and less-well connected in some of the lower (–1, –3).



Table 7 Services index and ethnicity

Index services Aymara (n = 351) (%) Quechua (n = 163) (%)

+3 4 7

+2 43 34

+1 23 20

0 19 21

–1 3 8

–2 2 2

–3 6 8

Results: exploring alternate scenariosAs noted in the Introduction, the aim of the model was to examine structural vs. stochastic changes in asset levels and thereby to further explicate the role of tangible and intangible assets in well-being. Therefore, the data set was utilised to derive model parameters for two alternate scenarios: one in which women had access to more household land and their education levels were increased and one in which only women’s livestock-related income was raised (Box 1). Therefore, Scenario I examined the impact of tangible vs. intangible assets on household income, while Scenario II explored the impact of a reproducible asset (i.e. livestock). In this manner, while Scenario I examined structural poverty, Scenario II explored the role of livestock in stochastic poverty transitions.

Scenario 1: Women education levels increased by 20 per cent; land owned increased by 5 per cent.

Parameters:• Impact of educations levels on income: 1 year of education = 16 per cent

increase in income/person/month • Impact of land ownership on income: 1 ha increase = 44 per cent increase

in income per month

Scenario 2: Women’s livestock-based income raised 20 per cent; education remains the same; land ownership remains the same.

Parameters:• Livestock-based income: proportion of livestock-based income/total income

raises 20 per cent



The scenario parameters were set utilising the Poverty Assessor software program (see Heffernan and Yu, 2007; 2010). The Poverty Assessor is a decision support tool that links household data to the poverty line in the country of interest. The program illustrated that across the data, a 20 per cent increase in the level of schooling obtained translated into a 16 per cent increase in income. Conversely, increasing household land by 5 per cent more than doubled per-person income per month. The software also illustrated how increases in livestock production were poverty-protective. There-fore, to explore the impact of these changes across the index, two approximately equal parameters were set for comparison: 20 per cent increase in education levels and land size and a 20 per cent increase in livestock keeping (Box 1).

Plugging Scenario 1 parameters into the model, not surprisingly, across all categories, women advanced up the scale (see Table 8). Similar rises occurred across the index when livestock-related income was raised in Scenario 2.

Increasing education and land had approximately the same overall effect as a 20 per cent rise in income derived from livestock-keeping. From the results, it is clear that a small increase in land size had the largest impact on household income, followed by livestock, followed by education levels. While development policy can improve factors such as land tenure, etc., in most countries farm sizes are decreasing, rather than increasing. Therefore, as will be further discussed below, livestock can be a critical means of supporting lives and livelihoods.

ConclusionsPoverty in Bolivia is a localised, ethnic and gender-specific reality. Indeed, the model illustrated that women were disadvantaged in terms of both tangible and intangible assets: land, livestock and education levels. While in the wider development literature this would equate with economic vulnerability and marginalisation, interestingly in

Table 8 Scenarios 1 and 2 vs. original index

Index income Original: Women (n = 358) (%)

Scenario 1:Women (n = 358) (%)

Scenario 2:Women (n = 358) (%)

+3 1 6 8

+2 3 10 9

+1 5 16 13

0 6 14 14

–1 26 13 18

–2 24 17 15

–3 45 24 23



Bolivia, this was not the case. As the index illustrated, women and men had similar income levels. Many women who participated in the study were micro-entrepreneurs, therefore their income parity was largely derived from petty trade. Thus, the study revealed the problems in relying solely on income-based measures of poverty. Without a notion of underlying asset levels, measuring income alone can prevent a deeper understanding of both the structural and stochastic elements of poverty.

Nevertheless, as noted in the introduction, current efforts to measure ‘multi-dimensional’ poverty have moved away from actually measuring income and assets (see Alkire and Santos, 2010). While it may be argued that the MDPI focuses on the ‘experience’ of poverty, such experiences require measurement. The 10 indicators of the approach do attempt to measure some goods as a proxy for disposable income. However, the danger in such an approach is clear: ignoring core assets such as land and livestock completely distorts the manner and means by which the majority of the world’s poor can escape poverty. Measuring goods such as telephones, motorbikes and bicycles are less-precise measures of well-being than determining income itself and offer little in the way of understanding a household’s access to core resources. Both rich and poor households may have these items, but as the results clearly illustrated, a household with no access to core assets (i.e. land or livestock) will be less well-off. Thus, the study revealed the importance of understanding worth and permanence in any exploration of assets: not all assets hold the same value in terms of preventing a household from falling into or climbing out of poverty traps. Clearly, owning land and livestock will be better than owning motorbikes and televisions for most poor households.

The notion of value with regard to assets was also important at the individual level. Women particularly suffered in terms of the value of the assets they could acquire. Indeed, women rarely inherited land or the livestock species of greater economic value, such as cattle. Also, as the index displayed, education levels were much lower for women than men. Consequently, despite their income parity, women were both structurally and stochastically more at risk to move deeper into poverty. Nevertheless, the results also illustrated that the impact of the core assets themselves on earning power were not equal. A small increase in the percentage access to land had the largest proportional shift in income followed by an increase in livestock herds and then the number of years in education. Thus, according to the model, for the households involved, education did not appear to be the primary pathway out of poverty.

On a policy level, while the government in Bolivia is attempting to address both land tenure and educational attainment among these communities, livestock are being largely ignored. Presently, livestock development policy in Bolivia is geared towards the commercial sector and the needs of the subsistence farmers are widely ignored. Further, with government decentralisation, decisions regarding service delivery are the domain of local authorities. Hence, issues relating to livestock services relevant to the



poor are often accorded a low priority. Enhancing support for local livestock-related services is likely to strongly aid poverty reduction strategies at the national level.

Nevertheless, such policies also need to recognise these disparities in control over livestock assets by women. The new Bolivian constitution is attempting to address wider biases in customary law regarding gender, particularly in terms of land. The results of the model demonstrate, however, that including livestock in such anti-discrimination legislation could also have a big impact on poverty. Equally, as the Asset Index illustrated, many of the households involved were labour constrained. Livestock are clearly not a labour-neutral asset and as pointed out in the introduc-tion and women in Bolivia are the primary care-takers. As such, the viability of a household’s labour force is a factor when targeting households for livestock aid. Inter-estingly, however, a wide body of research has illustrated that motivation for livestock-keeping is the most critical parameter at the household level when implementing such projects and programs (Heffernan et al., 2001; 2004).

However, the model had several limitations. First, the overall vulnerability or sensi-tivity of these core assets to negative events (e.g. livestock contracting diseases and crop failure) was beyond the purview of the simple, deterministic model. However, it is clear that the number or range of dimensions measured is less relevant than what is actually measured. Indeed, the results demonstrate that measures of poverty, which do not include assessments of core assets, are likely to be less accurate than those that do. Despite these limitations, the model did illustrate how support for livestock-keeping can prevent households from falling further into poverty.

Defining and measuring poverty has occupied development practitioners for decades and it is unlikely that this will change in the coming decades. Nevertheless, it is clear that moves to widen the definitions of poverty and approaches to measurement can be counterproductive. Rather, core assets have enduring value. In this manner, the study illustrated that ignoring the role of livestock on the lives of the poor has the potential to distort the results of both present and future poverty assessments.

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