Social Coordination, Rural Livelihoods and Natural Capital Investments: Evidence from Ethiopia

Social Coordination, Rural Livelihoods and Natural Capital Investments: Evidence from Ethiopia

By

Randall Bluffstone*

Mahmud Yesuf

Takuro Uehara

Bilisuma Bushie

Demessie Damite

*Affiliations: Bluffstone and Uehara, Portland State University. Yesuf, American University, Bushie, Institute of Social Studies, The Haag and Damite, Tilburg University

Acknowledgements

The authors would like to thank Sida and Gothenburg University for financial support and the Department of Economics at Addis Ababa University for making the data available. We would also like to thank colleagues Teki Alemu, Mulugeta Tadesse, Haileselassie Medhin and Alemu Mekonnen for important inputs to the paper. We also acknowledge participants in EDRI seminars for their especially helpful insights and Bluffstone thanks Ecotrust Northwest where he was on sabbatical during the revision.

Abstract

Key words: Millennium Development Goals, Natural Capital, Social Capital, Forests, Rural Poverty, Africa, Ethiopia

This paper uses detailed household panel data spanning the period 2000 – 2007 to analyze incomes and explain accumulation of natural capital assets in highland Ethiopia. The two assets analyzed in the paper, livestock and trees planted on households’ farm plots, make up virtually 100% of privately held disposable assets. The paper looks at what determines investment in these key types of household natural capital with special reference to the role of social coordination. We find that incomes are extremely low and constant, but also that investments increase dramatically over time. On-farm trees are at least as important investments as livestock and investment in this form of capital is critically dependent on forest social coordination. Livestock holdings also are positively related to whether villages are able to coordinate around forest management.

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1. Introduction

If we want to improve our understanding of how poor people live in low income countries, what challenges they face and the nature of their investments as they try to improve their livelihoods, we need to examine the issues at the household or even individual levels. This paper uses detailed household panel data spanning the period 2000 – 2007 – a period of great change and economic reform in Ethiopia - to characterise incomes and assets and also explain accumulation of natural capital[i] in highland Ethiopia. The two key assets analysed in the paper, livestock and trees planted on households’ farm plots, make up virtually 100% of the privately held disposable assets of households. The paper looks at what determines investment in these key types of household natural capital with special reference to the role of social capital and social coordination.

Ethiopia is synonymous with poverty and hunger in the worldwide parlance, with low incomes and major famines engraining this impression in the popular psyche. With gross national income per capita a mere $160 per year, average income is much less than one dollar per person per day and places Ethiopia 202nd out of 208 countries (World Bank, 2006). Adjusting for purchasing power parity (PPP) increases the GNI per person to approximately $1000, but this is still less than $3.00 per day and places Ethiopia 193rd out of 208 countries (World Bank, 2006). In 2006 Ethiopia ranked 170th out of 177 countries in terms of the Human Development Index (UNDP, 2006).

Furthermore, there is very high variability in output, particularly in the agricultural sector, which employs over 85% of Ethiopians. For example, after increasing by 11.5% during 2000/2001, in 2001/2002 year-on-year agricultural output fell by 2.3% and then an additional 12.6% in 2003/2004 before increasing by 18.9% the following year. Contractions in the agricultural sector and indeed the economy as a whole are due to lack of rain on which Ethiopian agriculture almost wholly depends (MOFED, 2004).

Before presenting the data, the next section discusses the key literature. After discussion of data and descriptive statistics in section 3, we present our empirical strategy. Section 5 discusses results and section 6 concludes. The paper uses the Sustainable Livelihoods Framework developed by the UK Department for International Development as an organizing framework (DFID, 1999), because it highlights features critical to livelihood outcomes in developing countries and focuses attention on household capital and institutions as drivers of future incomes. Key findings of the analysis are that incomes are extremely low and did not change appreciably during the period of study. Perhaps even more disturbing, though, is that household physical, natural and financial capital average only about $650. We find reason for hope, however, because investments in livestock and especially on-farm trees increase dramatically during the period of study.

Livestock has long been known to be critical natural capital in low-income countries (Dercon and Krishnan, 1996; Dessalegn, 1984; Jahnke, 1982; McCann, 1987), but the piece we add is that we find that this form of asset accumulation in all models is dependent on social coordination. We also find that on-farm tree assets are at least as important as livestock and private tree planting is positively related to whether villages are able to coordinate around forest management.

The analysis is of interest, because increasing incomes for the poor is at the top of the international policy agenda. This is perhaps best illustrated by the Millennium Development Goals (MDGs), which were adopted unanimously by United Nations member nations in September 2000 and focus on improving human well-being. There are a total of eight goals, but perhaps the first among equals is Goal 1 to reduce extreme poverty and hunger, where extreme poverty is defined as income of less than $1.00 per person per day (United Nations, 2003). The Sustainable Livelihoods Framework suggests that asset accumulation will be critical to achieving that goal.

As is true for many low income countries, the official definition of poverty in Ethiopia is much less stringent than the MDG definition. In Ethiopia, the poverty line is based on consumption estimates conducted in 1995/96. In 2005 39% of people were poor, with poverty defined as consumption of $0.63 per adult equivalent per day and extreme poverty $0.47 per day (MOFED, 2006). Achieving Target 1 in Ethiopia and most low-income countries is therefore very ambitious (Chen and Ravallion, 2004).

2. Key Literature

Figure 1 Here

The Sustainable Livelihoods Framework is often used by policy makers to analyse livelihood drivers (DFID, 1999). At the heart of the framework presented in Figure 1 are five types of capital that determine household livelihood strategies and outcomes. Human capital is the set of skills possessed by people that are useful for improving livelihoods. Literacy is an example. Physical capital are the machines and other physical tools, with key examples being tractors and agricultural tools. Natural capital are livelihood tools that are provided by nature. Cattle, trees and soil fertility are examples. Financial capital are financial assets held by households. Social capital is defined as the “social resources upon which people draw in pursuit of their livelihood objectives.” Examples of social capital include social networks, formal membership in groups and relationships of trust and reciprocity (DFID, 1999).

Vulnerability to shocks is a key feature of the low incomes addressed by MDG 1, of rural life in developing countries and the Sustainable Livelihoods Framework. Ownership of assets such as land, private on-farm trees and livestock tend to reduce vulnerability of households and allow them to exploit opportunities. Christiansen and Boisvert (2000) find that due to higher social capital exclusively female headed households are less likely to be at risk during droughts. Indeed, a diverse literature provides evidence that because insurance, credit and savings markets are typically poorly developed, people in rural areas of developing countries must rely on their own resources (e.g. savings, natural capital assets, labour and land) combined with social capital to make investments and manage risks (e.g. Yesuf and Bluffstone, 2009; Nyangena, 2011; Fafchamps and Gubert, 2007; Dercon and Krishna, 2000; Chetty and Looney, 2006; Grootaert and Narayan, 2004; Fafchamps and Lund, 2003). The development literature has long linked social coordination with asset accumulation.

Recent developments in the social capital and social coordination literatures are also of substantial interest. Though disciplines other than economics have long examined issues like trust, social norms and other-regarding preferences (e.g. affection and altruism) that are critical to social coordination, the economic literature on the effects of such behaviors is relatively new (Folmer and Johansson-Stenman, 2011). Nevertheless, an increasingly well-developed literature suggests good things come from social coordination (Bouma et al, 2008). Knack and Keefer (1997) and Zak and Knack (2001) both find trust yields macroeconomic payoffs like higher incomes and investment. In developing country settings, Narayan and Pritchett (1999) find that higher levels of social capital are associated with more household expenditures (a key proxy for income). Bluffstone et al (2012) extend the analysis to community level investments in water supply systems and find a strong positive relationship between social coordination and use of piped water rather than uncontrolled sources.

These findings are in line with others in the literature that link investments of a variety of types with more effective social coordination. Glaeser et al (2002) finds that social capital is correlated with investments in human capital and housing. The seminal paper by Coleman (1988) also finds links between social and human capital. At the macroeconomic level Zak and Knack (2001) find that countries with higher levels of social capital (measured by associations) also have larger portions of national incomes invested. Ostrom (1990) and several follow-on papers document important cases where social capital is critical to irrigation investments. In East Africa Nyangena (2011) estimates that Kenyan households with higher levels of social capital also have higher levels of private on-farm investments in soil and water conservation.

The work of Nepal et al. (2007), Bluffstone et al. (2008), Hansen et al (2005) and Mekonnen (2009) are most directly related to this paper, because of the focus on links between social capital, market structures and investment in trees on households’ own farms. Nepal et al. (2007) looks at a variety of social networks and finds that forest-related institutions particularly spur on-farm tree investments. Other less forest-related groups have limited effects. Bluffstone et al. (2008) examine whether forest sector social capital has similar effects in Bolivia. They find that social capital at its highest level of aggregation is positively correlated with more and higher quality on-farm trees. Mekonnen (2009) looks at tree investments in Ethiopia and finds that a variety of labour, asset and credit market imperfections affect on-farm tree investments.

A number of specific pathways from social coordination to private investment are identified or more likely posited in the literature. In some cases risk sharing and insurance functions are most important. In others social coordination substitutes for saving and lending markets and in other cases social links provide critical information necessary for investment. Little explicit evidence on the avenues by which social capital affects private investments is available and this is a potentially important area of future research.

There are two broad views of these mechanisms and indeed the nature of social capital itself. Glaeser et al (2002) and others view social capital as an individual asset that is built by individuals and accrues to the individual. For example, individuals invest in building professional networks that then yield dividends in the future. On the other side, Bowles and Gintis (2002) and Putnam (1993) argue social capital is a community asset that cannot be “privatized.” Examples include trust between strangers, neighborhood safety programs and common belief in the rule of law. Depending on specific circumstances both views are appropriate, but as will be shown in section 3 from an empirical modeling standpoint it is important to understand whether we are in group or individual-based social capital worlds.

The Sustainable Livelihoods Framework offers a way to envision the implications for modelling natural capital asset accumulation. We see in figure 1 that in the individual view the question is really one of the relationships between types of private capital – natural and social. Social institutions may inform that relationship, but are not central; the key point is whether private social networks can be brought to bear for investment purposes.

The group view of social capital is quite different. In this case we would be looking directly at the relationships between the performance of social institutions and investments. Positive social norms of the type discussed by Ostrom (1990) and Agrawal (2001) represent this type of social capital. For example, community norms of fairness, participation and accountability could have important implications for private investments.

Social capital has been measured using both survey and experimental methods (e.g. Fehr and Gächter, 2000) and in this paper we take a survey approach. Under the survey method using general notions of social capital, such as “trust,” has been criticized as perhaps unreliable (Folmer and Johansson-Stenman, 2011). We therefore choose social capital around and management details of common forests as a specific and challenging social institution around which Ethiopian communities typically must coordinate.

Forests are critical natural capital that villages must share, are subject to serious deterioration if poorly managed and the literature suggests that how well those assets are managed can have profound effects on household behaviour and investments. Moreover, because of its importance common forest coordination is likely to tell us something about the general level of social coordination in villages.

In most low-income developing countries households depend on forests for a variety of products that are essential to daily life, including fuelwood, fodder for animals and building materials. Common forests provide important “off-site” benefits, including erosion and flood control that create incentives for free riding. These interdependencies between community members are important to the functioning and productivity of forests and poor coordination may result in open access that provides few incentives for stewardship, planting and management (Gordon 1954). Economist (2010) and World Bank (2009) note that about 25% of developing country forests are under some sort of collective control.

In recent decades an enormous literature has emerged that analyses community member cooperation around natural resources (e.g. Olson 1965; Dayton-Johnson 2000; Wade 1988; Ostrom 1990; Bromley 1990; Baland and Platteau 1996; Sethi and Somanathan 1996; Baland and Platteau 1999; Bluffstone et al. 2008). A related literature discusses desirable aspects of common forest management (CFM) and attempts to disaggregate its components. This work suggests that effective CFM systems are incentive compatible at the household level (Shyamsundar, 2008) when they empower communities, have clear access and extraction rules, fair and graduated sanctions, public participation, clear quotas and successful monitoring (Ostrom 1990; Agrawal 2000, 2001).

Recent work has emphasized that CFM in low-income developing countries is often subtle and home grown and may work very well, not at all or anywhere in between (Agrawal et al, 2008). There is therefore a need to analyse CFM as a multi-faceted continuum rather than a binomial variable where households do or do not participate (Jodha, 2008; Shyamsundar, 2008; Agarwal, 2010; Bluffstone et al, 2008). This approach represents an important extension of past literature (e.g. Edmonds 2002; Heltberg 2001; Heltberg et al. 2000) that viewed CFM as dichotomous.

Evidence on the effects of CFM components is limited and the subject of empirical research (Hegan et al. 2003; Amacher et al. 1996, 1999; Cooke 2000, 2004; Edmonds 2002; Heltberg 2001; Heltberg et al. 2000; Linde-Rahr 2003; Nepal et al. 2007; Bluffstone et al. 2008). As Bluffstone et al (2008) have shown in a theoretical model for Bolivia and as Mekonnen and Bluffstone (2008) analyse for Ethiopia, there may be important economic incentives – independent of social capital explanations discussed in the literature - causing households experiencing higher levels of social coordination around common forests to plant more trees on their farms. The basic logic is that better management of common forests implies that individual households must restrict their extractions below open access levels in order to allow forests to regenerate. The authors show that if on-farm trees substitute for common forests, households will move labour and other resources out of forest-dependent activities and into agro-forestry. Because livestock is complemented by common forests, a corollary is that households would be expected to have less livestock compared with when free access exists.

3. Data and Descriptive Statistics

Data come from four household panel survey rounds conducted in the Ethiopian highlands during April to June 2000, March to April, 2002, March to May 2005 and May to June 2007. The same households were therefore visited repeatedly. The sample comes from six counties (woredas) in the South Wollo and East Gojjam Zones of Amhara Regional State, with households within each village selected at random. In 2005 two additional woredas were added. Households surveyed in each round are 1518, 1522, 1752 and 1754. Data include household characteristics, health, social capital, land use, production, consumption, tree stocks, livestock, income sources, credit, water, energy and cooking.

Figure 2 Here

To implement the survey, which was conducted by Addis Ababa University and the Ethiopian Development Research Institute, there was one supervisor for each village under which 10 enumerators were employed for two months. Each enumerator interviewed 100 households. On average, each enumerator took 1.6 days to interview a household. Once a questionnaire was completed it was verified by the field supervisor and the enumerator revisited the household when there were unrealistic responses. After field visits, all questionnaires were verified by a different set of supervisors before the data were finalized.

As in the rest of Ethiopia, in the study area there is very limited irrigation and agricultural production depends on timely rainfall, which occurs during the long rainy period June to September (meher) and again during the short rains March/April (belg). Most households in the study area are subsistence farmers that complement their production with minimal outside incomes, producing from small plots with limited access to markets. Average time to the nearest road is 32 minutes and 70 minutes to the nearest town. The major crops grown include teff (27 – 34% of households), wheat (9-15%), barley (6- 7%), maize (9-10%), beans (5-7%) and sorghum (5-12%), but a variety of vegetables, including garlic, tomatoes, potatoes, cabbage, pumpkins, onions, sugar cane, and tree crops like lemons, oranges, coffee are also grown. In 2002 households grew 50 different crops.