Evaluating Poverty Reduction Strategies in Tanzania and Ethiopia

Poverty is a worldwide problem with many challenges in combating. This dissertation analyzes poverty in two rural African contexts, Tanzania and Ethiopia, to assess aid strategies based on the socio-economic context that causes the poverty to persist. Understanding root cause of poverty is critical in order to combat it. We focus on whether aid programs have spillovers into the environmental realm, which may have impact on effectiveness of aid policies and whether or not a poverty trap, where households become structurally trapped in chronic poverty, exists. In this dissertation, we attempt to enhance our ability to provide optimal aid to populations stuck in poverty based on the underlying characteristics of the poverty. We find a Conditional Cash Transfer program in Tanzania results in unintended spillovers into the fishery sector via increased demand for seafood products as well as an increase in households using fishing as an income source. If unaccounted for, this spillover can lead to additional pressures on the fishery causing a reduction in future wellbeing. Next we provide theoretic model of a multiple equilibria poverty trap, which we use to determine the theoretically optimal level of aid to provide to those facing the poverty trap. We find the cost of aid is the primary factor to consider, as opposed to level of poverty, and find there are significant costs to underproviding aid, which can result in an aid trap, where aid has high cost while poverty is not significantly impacted. Lastly, we introduce a new empirical method of identifying poverty traps and apply it to herd data on Boran Pastoralists in Ethiopia where poverty traps have previously been identified. However, we find no evidence of poverty traps using our methods. Together this dissertation looks at the underlying structure of poverty to determine how aid policy can be better applied.

have impact on effectiveness of aid policies and whether or not a poverty trap, where households become structurally trapped in chronic poverty, exists. In this dissertation, we attempt to enhance our ability to provide optimal aid to populations stuck in poverty based on the underlying characteristics of the poverty. We find a Conditional Cash Transfer program in Tanzania results in unintended spillovers into the fishery sector via increased demand for seafood products as well as an increase in households using fishing as an income source. If unaccounted for, this spillover can lead to additional pressures on the fishery causing a reduction in future wellbeing. Next we provide theoretic model of a multiple equilibria poverty trap, which we use to determine the theoretically optimal level of aid to provide to those facing the poverty trap. We find the cost of aid is the primary factor to consider, as opposed to level of poverty, and find there are significant costs to underproviding aid, which can result in an aid trap, where aid has high cost while poverty is not significantly impacted.
Lastly, we introduce a new empirical method of identifying poverty traps and apply it to herd data on Boran Pastoralists in Ethiopia where poverty traps have previously been identified. However, we find no evidence of poverty traps using our methods.
Together this dissertation looks at the underlying structure of poverty to determine how aid policy can be better applied.
iv ACKNOWLEDGMENTS This dissertation would not have been possible without the help and support from a great number of people. While I cannot name everyone who has played a role in getting me to where I am today, I would like to name a few particular people who were invaluable to me during this process. I would first like to thank my patient advisor, Tom Sproul, for sticking with me and providing many great ideas and continual support throughout the process. This would not have been possible without the countless hours spent on the whiteboard as we discussed ideas as well as the many hours Tom has spent pouring over my work to make sure those ideas sound as good on paper as they did in our heads. I have truly enjoyed working with him on the dissertation and look forward to many more years of future collaboration.
Next I would like to thank my committee who put the time and energy into ensuring my dissertation was thorough and sensible. Emi Uchida was an invaluable aid in obtaining the data used in the second chapter as well as providing me with many insights into the development literature. Liam Malloy was a late addition to the team and filled in admirably, which I cannot thank him enough for. He pushed me to consider the bigger picture and to put my ideas into the broader topic. The last member of the committee, Amelia Moore was a great help in ensuring I understood the context of the study areas and recognized how this impacted the results of my research. Her guidance was a huge help in ensuring the claims I made in the dissertation are understandable to a broader audience and to make sure I did not get too carried away with the theoretical modeling.
v Lastly I would like to thank my friends and family who provided the emotional support, especially during the final stages of the dissertation. My parents have been a huge support my entire life and have always encouraged me to keep striving to improve. They always knew I could succeed in this endeavor and were always there for me when ever I needed help in life. They truly are who sculpted me into who I am today. I would like to thank all my friends who both provided necessary breaks as well as great ideas and discussions. Andrew Boslett, Carrie Gill, Nate Merrill, and Pat Prendergast deserve special recognition. Bouncing ideas off of them and talking about our work together has been a large part of how I succeeded in this program. Writing this dissertation was the hardest task I have ever set out to do and I would not have been able to succeed (while keeping some semblance of sanity) without these people supporting me.

Introduction and Overview
Eliminating extreme poverty has been a world priority for some time, highlighted by its prominence in the Millennium Development Goals. Significant progress has been made on this front, but we still have a long way to go. Combating poverty efficiently requires an understanding of the root causes of poverty in the socio-economic context it occurs in order to tailor poverty alleviation programs to the needs of poor. In this dissertation, we (referring to myself and the significant and essential collaboration with my committee) contribute to this substantial task by analyzing poverty reduction programs in empirical and theoretic contexts.
Poverty reduction programs come in many forms, which can and should be tailored to address the context where poverty is being combatted. There is no overarching solution, as the context of the socio-economic system directly impact how effective various strategies will be. While there are far too many potential aid strategies to detail here, we provide a brief overview of some of the methods available in order to discuss their applicability to combat poverty for the case studies in Tanzania and Ethiopia, which we look at in this dissertation.
Microfinance, providing small-scale financial services to low-inomce populations, has been a popular method of reducing poverty since the 1970s (Marr, 2012). While originally used to provide mostly small scale loans in an attempt to provide the capital needed as well as a means of protecting against negative shocks, more recently these institutions have been moving towards offering micro-savings programs as well (Rooyen, 2012). These programs have been popular as they provide access to capital with which to start up small businesses or invest in productive capital such as fertilizer and farming 2 equipment. This is especially relevant to the poverty trap discussion, which we go into in sections 3 and 4 as one theoretic cause of persistent poverty is the inability to afford productive assets, which would enhance long term outcomes. In fact, much of the theoretic poverty trap literature requires assuming no access to credit markets for this reason. In a review of microfinance in sub-Saharan Africa, Rooyen (2012) finds overall positive results, but also there are cases where it provides significant harm to those receiving micro-loans. Rooyen finds micro-saving to have a more consistent benefit to recipients than the micro-loans, which is a trend that microfinance institutions have been moving towards recently. In addition to income impacts, there also appears to be significant benefits to other areas such as child education and empowerment of women, the latter largely attributed to many microfinance institutions primarily providing services to women in the household (Marr, 2012).
Another popular form of aid has been direct payments to households so long as the households follow some requirements, known as Conditional Cash Transfers (CCT).
These programs provide aid while incentivizing behaviors which can lead to a reduction in long-term poverty such as increased education and better health practices. These programs have been especially prevalent in Latin America and, more recently, have been used in Asia and Africa (Evans et al., 2016). In a review of 13 CCTs in Latin America, Ranganathan and Mylene (2102) show overall these programs have been effective at increasing health behaviors as well as providing short-term aid in the form of payments.
By requiring healthy behavior, these programs enhance the productivity of the recipients, which provides a long-term benefit. This is especially true when trying to combat intergenerational poverty. By incentivizing households to send children to school and 3 attend health facilities at young ages, such as in the CCT we look at in chapter 2, it is hoped that the children of aid recipients will be better able to provide for themselves in the future, thus reducing the need for poverty reduction programs in the future. While CCTs appear effective at reducing poverty and enhancing health and education outcomes, there is some concern of unintended spillovers in terms of how individuals interact with the local environment.
In section 2, we analyze the unintended spillovers of poverty reduction program on local fisheries in rural Tanzania. If unaccounted for, spillovers may have adverse effects on the long term well being of population reducing the effectiveness of aid. This is especially true in rural settings where impoverished populations live on fragile land and have livelihoods that depend heavily on local environment Barbier, 2010). Poverty alleviation should take into account the complex feedbacks between the environment and socio-economic system to avoid negative unintended consequences. In extreme cases, poverty reduction policies with severe negative impacts on local environmental amenities may result in short lived gains to wellbeing if they result in the exasperation or the beginning of a 'poverty-environment trap', where poverty and environmental degradation become self-reinforcing (Barbier (2010).
There is mixed evidence for whether poverty reduction leads to negative impacts on local environmental amenities (e.g. Foster and Rosenzweig, 2003;Zwane, 2007;Alix-Garcia et al., 2011;Hannah and Olivia, 2015) depending on the context being studied and the environmental amenity being studied. While there have been large strides in understanding how poverty reduction policies spillover into some resources, most notably forests and timber products (Koop and Tole, 1999;Martinez et al., 2002;Culas, 2007;Zhou, et al. 2011), little has focused on how poverty reduction programs impact dependence on local fisheries. We fill this gap in the literature by looking at a CCT program in Tanzania and analyze the impact of program participation on demand for marine and freshwater seafood as well as household income from fishing. Our data come from a World Bank pilot study which implemented a conditional cash transfer (CCT) to households in eighty villages within three districts from 2009-2012 (Evans et al., 2014).
In line with much of the world's fisheries, Tanzania's fisheries are experiencing increased pressure and are at risk of overexploitation both inland (Ogutu-Ohwayo, 1990) and in coastal regions (Berachi, 2003;Silva, 2006). Especially in the coastal regions, fisheries management programs have been utilized to try to enhance the sustainability of local fisheries through a combination of marine protected areas in conjunction with ways of promoting alternative livelihoods to reduce pressure on the fisheries (Berachi, 2003).
Since there is currently activity to push households away from fishing in many areas, it may be counter productive if poverty reduction strategies have spillovers which increase demand for seafood and/or increase the number of households who earn income from fishing.
The CCT targeted poor and vulnerable households and was designed to provide aid to these vulnerable populations as well as promote health and education in three rural districts of Tanzania: Bagamoyo (~70 km from Dar es Salaam), Chamwino (~50 km from Dodoma), and Kibaha (~35 km from Dar es Salaam). The populations of these districts depend largely on farming, both subsistence and cash crops, with over 32% of households reporting sale of crops as their primary source of income (Evans et al., 2014).
Only a small percentage of households report fishing income as either their primary or 5 secondary source of income for this sample, so we are not looking at fishing villages in this study, but rather farming communities which may have access to fishing to supplement income. Rural poverty in Tanzania is a long-standing problem with an estimated 39% of its rural population being poor as of 2000, compared to 24% of its urban citizens (Ellis, 2003). Thus, many of the poverty reduction programs in Tanzania have a focus on the rural areas.
The CCT program we look at in chapter 2 targeted the poorest individuals within the communities to be eligible for program participation based on income/expenditure surveys and a final selection process conducted by community leaders. Of the sample, over 90% of households had unimproved floors and under 40% had access to piped water or had an improved roof (Evans et al., 2014). The program required young children (0)(1)(2)(3)(4)(5) to visit health care facilities 6 times a year and elderly to visit at least once a year while children between the ages of 7-15 had to maintain an 80% school attendance to be eligible for payments. The main livelihood for those in the study was farming with 90% of adults performing some agriculture (Evans et al., 2014). The authors report most crops were grown for food, a large portion of households (42%) report the main source of income was from selling food or cash crops. As the study targeted the most vulnerable in the villages, few households own livestock other than chickens or other durable assets and less than 2% reported savings of any kind.
The study conducted a randomized control trial designed to analyze the effectiveness of implementing CCTs in Africa, while much of the previous usage of CCTs had been conducted in Latin America and Asia. We exploit the randomized control trial that was implemented for the first stage of the CCT to assess the impact of the CCT 6 on pressures of local fisheries. We look both at whether increased incomes lead to a higher demand for seafood products as well as if additional income causes a shift away from farming towards fishing. Typically demand for seafood products tend to rise with wealth, which could increase pressures on local fisheries. We find a positive treatment effect on both demand for seafood products and number of households earning income from fishing activities. Additionally, we find heterogeneous wealth effects as poverty reduction policies likely have a systematically different impact on households, which is correlated with household wealth. Lastly, we find a significant lag between program participation and observable spillovers suggesting the need for assessment over longer time frames to ensure spillovers are identified when they exist.
The next two sections look at poverty traps, in essence how households appear stuck in poverty over time. We use data from the Borana Plateau of southern Ethipoia to study this phenomenon. In section 3 we present a general poverty trap model loosely based on this system and section 4 atempts to empirically identify poverty traps using data of herd sizes from Boran pastoralists. We have 17 years of data across four villages in the Borana Plateau of southern Ethiopia, an important rangeland for Ethiopia with livestock from this region supplying various domestic and export markets (Coppock et al., 2014). The arid climate of the region makes crop production relatively unproductive, which is why the dominant livelihood has been pastoralism, especially cattle, which has been prominent in the region for many generations. However, increased pressures due to rising populations in the region and a drier climate have led to increased degradation of rangelands and decreased livestock productivity (Lybbert et al., 2004;Soloman, 2006;Coppock et al., 2014). More recently, there has been growing adoption of non-pastoral 7 livelihoods increasing the diversification of income streams, especially amongst younger households (Berhanu et al., 2007). As the lands are becoming less productive, it is also having to support larger populations, which increases the pressure on the fragile rangelands (Coppock, 2016). The rangeland has been typically unmanaged and has recently experienced bush encroachment and gullying due to the additional pressures and changing environmental conditions, which has led some to propose a more active management of these rangelands to promote sustainable pastoralism (Coppock, 2016).
Along with the changes associated with decreased rainfall and reduced herd sizes amongst pastoralists that has been observed over time for this region, between 1983-85 there was a severe famine largely brought about by civil turmoil in Ethiopia combined with severe drought resulting in substantial livestock loss (Desta and Coppock, 2004).
This famine had widespread effects across the country, but the Boran pastoralists were hit particularly hard with nearly 10% of the population moving to relief shelters during this period (Lindtjørn, 1990). This period certainly represents a major shock to the productive capacity of the Boran pastoralists, which we can clearly see in terms of a reduction in household herd sizes after this period. This process of shocks and rebuilding is not uncommon for pastoralists, there is a tendency for drought and other die offs to reduce herd sizes followed by a rebuilding of herds during good years (Lybbert, 2004).
The pastoralism of the Borana Plateau has been a focal point of the poverty trap literature (e.g. McPeak and Barrett, 2001;Lybbert et al., 2004;Toth, 2014), along with similar setting in other arid and semi-arid dry lands of east and southern Africa where semi-mobile pastoralism is the primary livelihood. This system is an ideal study location due to its reliance on one productive asset (the herd) and lack of access to 8 alternative livlihoods, which we disucss in more detail in section 3. Most importantly there is a more productive livelihood (mobile pastoralism), which is only accessable with a large enough herd size. Thus, there is a clear and visable mechanism, which could explain a poverty trap in this system.
Semi-mobile or transhumant pastoralism occurs when households' move herds between pastures, especially during the dry seasons, while maintaining a permanent home. Typically the herd is broken down into two sub-herds: the warra herd consisting of milk cows and calves under two years old, which remains near the home, and fora herd, which migrates between pastures and water sources consisting of older immature cattle, dry cows and bulls (Lybbert et al., 2004;Soloman, 2006). Accessing pastures away from the immediate village requires a large enough fora herd to support the herders who rely heavily on meat and blood from their animals during the long migrations (Lybbert et al., 2004). Thus, mobile pastoralism requires a minimum herd size. When herds drop below this level, the household must resort to sedentary pastoralism, which is far less productive and associated with significantly higher rates of poverty (Little et al., 2008;Toth 2014).
This represents multiple production technologies (mobile and sedentary pastoralism), where the more productive technology requires a minimum wealth level to access. This is one of the theoretic mechanisms that can cause a multiple equilibrium poverty trap, which we discuss in section 3. Another desirable feature of this system, in the context of identifying poverty traps, is wealth is almost exclusively maintained in the form of the herd with little to no access to financial markets.

9
In chapter 3, we expand upon our theoretic understanding of how to provide optimal aid to populations experiencing a poverty trap. A poverty trap is characterized by self-reinforcing mechanisms causing poverty to persist (Azariadis and Stachurski, 2005).
In this chapter we focus on a specific case of poverty trap where wealth dynamics result in multiple dynamic equilibria each with its own basin of attraction (in essence pulling wealth towards it over time) separated by a critical wealth threshold known as the Micawber Threshold. In this context, once people fall into poverty they are highly likely to remain impoverished in the future due to a lack of productive assets and means to accumulate them. In such cases, individuals are very unlikely to surmount the Micawber Threshold on their own and require external aid in order to escape poverty.
We expand upon this literature by analyzing how the level of aid affects the cost and effectiveness of poverty reduction. Specifically we seek to answer the question of whether aid should target the Micawber Threshold. Barrett (2005) suggests for aid to be effective it must ensure wealth surmounts the Micawber Threshold, however, Barrett only shows this for the deterministic case. On the other side, Plucinski, Ngaonghala, and Bonds (2011) have shown aid set below critical thresholds can lead to escaping poverty trap, but do not attempt to determine if this is optimal.
We present a poverty trap model and use numeric simulation to show optimal aid typically must be set above the Micawber Threshold when combating the multiple equilibrium poverty trap. When aid is set below the Micawber Threshold, typically results in a higher cost of poverty reduction coupled with higher rates of poverty, clearly an undesirable outcome. This arises due to an aid trap forming when aid is set at a level, which households can not maintain over time, as they are still in the basin of attraction of a low wealth equilibria, while not being set high enough to significantly escape poverty in the future. Additionally we show the optimal aid level is largely determined by the cost of providing aid and the level of poverty as well as the weight a society places on poverty, which together can be thought of as the social cost of poverty, plays only a secondary role. This arises due to the coupled relationship between cost and level of poverty, which arises due to the aid trap. This chapter highlights the importance of understanding the dynamic process leading to chronic poverty based on numeric simulation and provides a theoretic starting point with which to make policy decisions when a multiple equilibrium poverty trap exists.
However, the identification of multiple equilibrium poverty traps has numerous challenges (Barrett and Carter, 2013;Nashold, 2013), which limits the applicability of the theoretic results. Identifying the root cause of poverty is a necessary step to provide optimal policy. In section 4, we introduce two novel tests to surmount some of the issues associated with empirically identifying multiple equilibrium poverty traps by testing for implications of poverty traps rather than directly identifying the poverty trap itself. The first issues we circumvent are a lack of observations around the Micawber Threshold, which is a necessary feature of the multiple equilibrium poverty trap as it is an unstable dynamic equilibria with stable equilibria on either side pulling wealth away from the Micawber Threshold over time. The second issue is many empirical test for multiple equilibrium poverty traps do not allow for heterogeneity amongst a population and assume a single underlying asset dynamic (Jalan and Ravallion, 2004;Nashold, 2013), which is unrealistic as individual ability (Ikegami et al., 2016). 11 We first test for mean reversion at the individual level using a variance ratio test, which has been widely used in the finance literature to test whether financial assets follow a random walk (e.g. Lo and MacKinlay, 1988). By testing at the individual level opposed to the aggregate level, we can allow for heterogeinity across individuals.
Additionally we perform a mixture model designed to pick up clustering of data consistent with a multiple equilibrium poverty trap. However, this test does require assuming equilibria are homogeneous across the population. The clustering of data is a necessary feature of equilibria at the aggregate and village levels. The downside to our tests, is we cannot distinguish between whether a multiple equilibrium poverty trap exists or an alternative hypothesis of club convergence where both mean reversion and the equilibria are caused by each individual having access to one of the equilibria. Thus our test is designed as a falsification test to determine when a poverty trap cannot be present.
When we apply our empirical tests to our data on the Boran pastoralists, we find no evidence of a poverty trap. This contradicts previous findings of Lybbert et al., (2004).
In their paper, the authors use a direct test designed to fit a curve to the data to locate the Micawber Threshold and non-convex 1 wealth dynamics associate with a multiple equilibrium poverty trap. We find this results is spurious likely caused by overfitting due to some clustering in the data around the Micawber Threshold they identify. Their analysis does not take into account the likelihood of data being present. If a Micawber Threshold exists, there should be few observations nearby that level of wealth, which 12 they fail to account for. This chapter highlights the importance of multiple and rigorous tests in order to identify the underlying poverty dynamics of a socioeconomic system, as applying poverty alleviation strategies assuming a poverty trap exists when it does not may lead to inefficient policy decisions.
The remainder of this dissertation is organized as follows. Chapter two evaluates the spillovers of a cash transfer program on the dependence of local fisheries. Chapter three introduces a poverty trap model and shows a "guaranteed wealth" aid policy should be set at the Micawber Threshold. Chapter four and five expand on this model by breaking down the cost of providing a guaranteed wealth at the Micawber Threshold into the cost of providing a "cargo net" and the cost of providing a "safety net" and introduce a method for estimating these costs with chapter four focusing on cargo nets and chapter five focused on the safety net. Chapter six concludes this dissertation.

Introduction
The relationship between poverty and environmental quality has been a long-standing question. Especially in rural settings, impoverished populations tend to live on fragile land and have livelihoods that depend heavily on local environment Barbier, 2010). There is a complex relationship between the environment and the livelihoods of the people living in these areas. A central question at this environmentpoverty nexus is whether poverty reduction programs impact environmental quality in the surrounding area. It is unclear whether reducing poverty will put additional strain on local environmental amenities due to increased demand for resource intense products or potentially lead to households investing in environmental quality and reducing environmental damages (Alix-Garcia et al., 2011). The current literature is ambiguous about whether poverty reduction programs have spillovers into the realm of environmental quality as well as the direction of the impact if one exists (e.g. Hannah and Olivia, 2015;Zwane, 2007;Foster and Rosenzweig, 2003).
Understanding how poverty reduction programs impact local environmental quality can have major implications especially when livelihoods depend on the local environment. Reductions in poverty from these programs may be short lived if they degrade the ecosystems which the impoverished populations depend upon and may result in the exasperation or the beginning of an 'poverty-environment trap' as described by Barbier (2010), where poverty and environmental degradation become self-reinforcing.
The environmental and economic conditions are likely to greatly impact how poverty reduction programs may impact local ecosystems. The livelihoods of the impoverished 14 population and how they change in response to poverty reduction programs will greatly influence whether poverty reduction leads to environmental degradation or improvement.
The livelihoods of the target population will also impact which environmental amenities are likely to be affected.
While there have been large strides in understanding the relationship of how poverty reduction policies spillover into some resources, most notably forests and timber products, little has focused on how poverty reduction programs impact dependence on local fisheries. We fill this gap in the literature by looking at a Conditional Cash Transfer (CCT) program in Tanzania and analyze the impact of program participation on demand for seafood 2 and earning income from fishing. We exploit a randomized control trial that was implemented for the first stage of the CCT and find a positive treatment effect on both demand for seafood products and number of households earning income from fishing activities. Additionally, we highlight the importance of looking at heterogeneous wealth effects as poverty reduction policies likely have a systematically different impact on households depending on their wealth. Lastly, we find a significant lag between program participation and observable spillovers indicating the importance of longer period data sets to be sure any effects are picked up on.

Background
Understanding the complex relationship between income and environmental degradation has been a long-standing question with important implications for both poverty reduction and environmental management. The topic largely began with Grossman and Kruger's 15 (1991) adaptation of Kuznets (1955) theory, now known as the Environmental Kuznets Curve (EKC), which predicts an inverted u-shaped relationship between wealth and environmental quality, implying as a poor country gains wealth it will degrade its local resources in the process of development up to a point where it is wealthy enough to afford to invest in environmental quality.
There is mixed evidence of the existence of the EKC and depends largely on the type of pollution being considered. Grossman and Krueger (1995) use data from a panel of many cities in different countries and find evidence in favor of the EKC for air pollution and river water quality. Local air pollution has been one of the more widely studied indicators used to identify the EKC with a large body of supporting evidence (e.g. Seldon and Song, 1992;Cole et al., 1997), however, pollutants with a longer term or dispersed damages appear to monotonically increase with per capita income (Arrow et al., 1995;Cole et al., 1997). There has even been some contention over previous findings; Stern (2004) suggest much of the evidence in favor of the EKC hypothesis may have issues with serial dependence in time series data and/or omitted variable bias and find, once these are correctly accounted for, most pollutants and waste flows are increasing with per capita income. Deforestation is another localized environmental quality indicator with mixed evidence in favor of the EKC hypothesis (e.g. Koop and Tole, 1999;Martinez et al., 2002;Culas, 2007;Zhou, et al. 2011).
While highly debated, both in theory and with contradicting empirical evidence (e.g. Stern, 2004;Li et al.;Choumert et al., 2013), this idea is still prominent in the poverty-environmental quality discussion where there is a concern that increasing wealth amongst poor populations will lead to increased pressure on local natural resources. Thus, 16 there may be unintended negative consequences to providing aid to impoverished populations, which are largely not considered when governments are implementing poverty-reduction policies (Alix-Garcia et al., 2013). Understanding the complex relationship between poverty and natural resource usage may shed light on whether reducing poverty will necessarily reduce environmental quality or if win-win solutions exist in terms of poverty reduction and environmental quality.
A large portion of this literature has focused on deforestation and forest degradation as it is an issue in much of the developing world and, especially in the case of deforestation, highly visible issue. There is mixed evidence concerning the actual effect of poverty reduction on deforestation rates, which may be largely due to differing socio-environmental conditions of the study area. Foster and Rosenzweig (2003) find no evidence increasing income leads to afforestation in open economies where there is a weak link between demand for forest products and forest cover, but in a closed economy, they find a positive relationship between income and forest cover. In contrast, Zwane (2007) find evidence of a positive, but decreasing, relationship between income and deforestation rates in Peru, consistent with the EKC. Similarly, Alix-Garcia et al. (2013) find poverty reduction has negative spillover effects in terms of increasing deforestation in Mexico. Poor households tend to switch to more land-intensive farming activities in response to the increased income.
The economic and environmental contexts are important to consider as they pertain to potential livelihoods. Zwane (2007) and Alix-Garcia et al (2013) focus on deforestation driven largely by households clearing land for agriculture in Peru and Mexico respectively. This is a prominent concern in places where populations live near established forests, which are suitable for farming or pasture lands with access to markets to sell farm products. In other contexts, the demand for forest products come in the form of extractive behaviors such as fuel wood harvesting, which has been a principle concern in places such as India (e.g. Foster and Rosenzweig, 2003), China (e.g. Demurger and Fournier, 2011) and Africa (e.g. Adeoti et al., 2001).
The choice of fuel has numerous health and environmental consequences. Low quality cooking fuels such as dung, wood, and charcoal can have serious negative health effects resulting in lower productivity workers, especially when burned indoors as is typical in many developing countries (Bruce et al., 2000). In addition to health effects, fuel choice impacts demand for local ecosystem services in the form of harvesting wood and charcoal from local forests (Demurger and Fournier, 2011). Thus there may be both an environmental and health benefit associated with households switching away from these fuel sources. The "Energy-Ladder" hypothesis is as a household's wealth increases it shifts towards consuming higher quality fuels (Hanna and Olivia, 2015;Demurger and Fournier, 2011). If fuel wood is an inferior good, that is to say the use decreases as income rises, as the Energy-Hypothesis states, we would expect to see poverty reduction programs result in a lower usage of fuel wood as other, more desirable, fuels become affordable.
There is mixed empirical evidence about how poverty reduction affects demand for fuel wood. Hanna and Olivia (2015) show a wealth effect increases aggregate fuel usage with little substitution towards cleaner fuels in India suggesting an increase in income will likely put increased pressure on local forests. The authors found a relative increase in the use of dirty-burning cow dung, which was likely due to the income increase for households (as part of an aid program) was in the form of cattle. In contrast, Demurger and Fournier (2011) show firewood is an inferior good in rural China. As wealth increases, they find significant switching behavior towards cleaner fuels indicating a reduction in poverty would result in lower harvesting pressure on local forests. The discrepancy may depend on other factors such as relative price and availability of substitutes, which is highly influenced by access to markets.
These studies highlight the importance of understanding the economic and environmental conditions when trying to determine how poverty reduction policies will impact environmental quality. The linkages between poverty and demand for local ecosystem services can come from both livelihoods (e.g. clearing forests for additional farmland) and consumption needs (e.g. increased pressure on forests due to increased demand for forest products). While much of the literature has focused on forest degradation, this is just one potential spillover of poverty reduction programs into the realm of environmental quality. There is still a significant lack of understanding how the spillovers will impact demand for other local ecosystem services, which in turn impacts the overall environmental degradation.
Much of the insights from assessing poverty reduction programs on forest degradation are directly applicable to the impact of poverty reduction on local fisheries as both are, typically, common pool resources poor populations depend upon for livelihoods (e.g. Beck and Nesmith, 2001). These resources can be used to supplement alternative forms of income or as a household's primary source of income and both can be used for direct consumption (e.g. food or fuelwood). One notable difference, a large degree of deforestation is to clear land for other usage, such as farm or pasture land, and there is no 19 analogous process related to fisheries. Thus, studies which found deforestation being caused by transforming forests into agriculture and/or pastureland may not provide much insight into how a fishery may be affected by poverty reduction strategies. Another difference is there usually are different fish species which households can target in a fishery, potentially switching target species depending on socio-economic and environmental conditions, which does not have a good analogy in the forestry sector.
However, most of the linkages between income and natural resource usage appear to have strong similarities between the fishery and forestry sectors allowing us to use literature of poverty reduction programs on forest degradation as motivation and a good starting point to discuss the impact of poverty alleviation on the pressures on local fisheries.
In this paper, we address both the livelihoods and consumption pathways as we investigate the impact of poverty reduction programs on pressures on local fisheries, which, to our knowledge, has yet to be explored. We investigate the change in fishing behavior caused by program participation, which is the direct pressure on the local fisheries, as well as the indirect pressure arising from how household demand for seafood products change due to participation in poverty reduction programs.

Poverty and consumption behavior
At the macro level, total seafood consumption increases with income (Jenson, 2006;Kent, 1997). Developing countries typically get a lager percent of their animal protein from seafood (Kent 1997). Jenson (2006) show as incomes rise consumption shifts from cereals, roots and other staples to consuming more animal products, however, this shift towards animal products is less strong for seafood. As incomes rise the percent 20 of income spent on animal consumption rises, but the share of protein expenditure spent on seafood declines.
Looking at household consumption, Humphries et al (2014) look at food expenditure based on income level in Peru. They show the poorest households spend proportionately more of their income on food, but less of that spending goes towards animal products.
They also show that as food expenditure increases, the proportion devoted to animal products increases for poor households (elasticity > 1). Similarly, Abdulai and Aubert (2004) show budget share of meat fish and eggs increases with wealth in Tanzania and does so to a greater extent than all other foods except cereals. The trend of relatively larger expenditure on seafood, as compared to other animal protein, by the poor is apparent at the household level as well. In a survey of multiple Asian countries, Dey et al. (2005) show poorer households spend a larger percentage of their income on seafood making it an important source of animal protein. The authors point poorer households consume lower value fish, which make it a relatively cheap source of animal protein.
The literature is consistent in suggesting as income rises we would expect increased seafood consumption, but at a slower rate than other sources of animal protein.
However, some forms of less desirable seafood may be inferior goods, which households will switch away from and replace with more expensive seafood. Thus there may be ambiguous effects on the pressure on local fisheries depending on the quality of locally caught seafood. Our study attempts to address this question by looking at different types of seafood which households have access to. Specifically, we separate out dagaa, a small freshwater fish species that is popular amongst East Africa (Bille and Shmkai, 2006).

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While dagaa is consumed in large quantities, it is unclear whether its popularity stems from the fact that it is cheap (i.e. an inferior good) or if it is a choice fish, which people will consume in higher quantities as income rises. By separating out dagaa from other fish species, we can better determine the relationship between poverty and demand for dagaa as well as the demand for all other seafood.

Poverty and fishing behavior
In his meta-analysis, Béné (2003) highlights the lack of empirical investigation into the linkages between poverty and fisheries even though the prevailing view of policymakers and international agencies that fishermen tend to be, as Baily (1988) puts it, " the poorest of the poor." The two schools of thought have been: 'fishermen are poor' the open access nature of fisheries induces too many people to enter the fishery resulting in rent dissipation and eventual impoverishment of those involved (Gordon 1954;Hardin 1968) usually coupled with low incomes outside the fishery sector (Cunningham, 1994) or the 'poor are fishermen' because fishing is a last-resort activity, which is only employed when all other options have been exhausted (Panayotou 1980;Bailey and Jentoft, 1990).
Fishing can provide additional income as well as subsistence for impoverished populations. In Bangladesh, over 70% of rural households report at least some level of fishing (Tofique and Benton, 2014). This relationship is especially important during lean seasons where wild caught food, especially seafood, becomes an important for subsistence consumption between harvests for many parts of Africa (Bille and Shemkai, 2006;de Merode et al., 2004;de Garine and Koppert, 1987).

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Dependence on wild caught foods, including seafood, is a typical of impoverished rural populations, however, there is mixed evidence concerning the relationship between poverty and dependence on wild caught foods. It is widely believed the most impoverished in a community will be most dependent on local common pool resources, including wild caught food using data from India and countries in Western Africa (Beck and Nesmith, 2001). This is expressed in the fisheries literature as the belief that fishers are the 'poorest of the poor' where fishing is typically an activity undertook when households are unable to engage in more profitable activities (e.g. Cunningham, 1994).
However, Merode et al. (2004) find it is the middle-income group, which engage catching wild foods using data from the Democratic Republic of Congo (DRC) and Béné (2009) show income tends to be higher for those engaging in fishing activities in the DRC as well. This is consistent with the idea that the poorest in a community may be unable to access some natural resources due to high fixed cost inputs necessary for extraction (e.g. fishing gear). Thus, the relationship between poverty and dependence on local fisheries is still is still an open question with important implications to how poverty reduction programs will impact pressures on local fisheries. We attempt to answer whether providing aid through participation in the CCT will increase fishing pressures by allowing additional households to overcome the fixed cost associated with fishing or whether fishing pressure decreases as incomes rise from the cash transfers. While the results may generalize to other poverty alleviation mechanisms, we leave this question for future research. To do so it would be useful to distinguish between the wealth effect of program participation (the cash) versus the other effects of program participation (the conditions), which we are unable to separate out here.

Use of CCTs in the literature
A major concern with empirically estimating the relationship between poverty and natural resource use is the endogeneity between income and activities effecting natural resource usage. It is well noted poor populations disproportionately depend on local ecosystem services for their livelihoods (Barret et al., 2011). Even at the village level where access to local ecosystem services is similar, there may be endogeneity between income and dependence on local ecosystem services. For instance, individuals who are better able to perform non-extractive activities (e.g. start a small business) are likely to have higher income and lower dependence on local natural resources independent of any income effect.
One way to overcome the endogeneity issue is to use an instrumental variable that correlates well with income, but does not impact natural resource usage. Zwane (2007) utilize instrumental variables for income (non-farm income and non-labor income) to overcome the endogeneity between income and land clearings as both capture the timeinvariant income that is not impacted by agricultural land usage. The author finds at low incomes, relaxing credit constraint increases deforestation, but at higher incomes this is reversed, which is consistent with the EKC hypothesis. Foster and Rosenzweig (2003) use a crop index and proportions of crops instrumental variable to overcome the endogeneity issue and find increased incomes decrease deforestation through an increased demand for forest products, which increases the marginal value of forest lands and reducing clearing for farmlands. While using instrumental variables is a powerful method, it relies heavily on the assumption the instrumental variables have no causal 24 relationship with natural resource usage. Finding variables that meet this strict requirement are not always possible.
Another way to overcome the endogeneity issue is to exploit an exogenous shift in income for a subset of a population. This exogenous income shift usually comes in the form of a cash transfer from a government aid program. Cash transfer programs are popular poverty reduction policies, however, they are usually implemented without much thought about potential secondary effects (Alix-Garcia et al., 2013). Due to the successful nature of cash transfer programs in reducing poverty, conditional cash transfer (CCT) programs have been adopted in almost every country in Latin America (Fiszbein and Schady, 2009) and combination of CCTs and unconditional cash transfers (UCT) have been implemented in Africa (Evans et al. 2016). With the widespread popularity of cash transfer programs, much of the recent literature has focused on identifying spillover effects of aid policy, largely driven by an increase in income (e.g. Cioda et al., 2015;Handa et al., 2015;Hannah and Olivia, 2015;Asfaw et al., 2014;and Alix-Garcia et al., 2013).
One potential issue with using aid programs is if there relates to potential selection bias due to households having the option to opt out of such programs. Treated households (those enrolled in the program) are potentially systematically different from those not participating even when observable characteristics are accounted for.
Conveniently, many cash transfer programs include a phased rollout where initially only a portion of the households receive cash transfers such as Mexico's Oportunidades (Alix- Garcia et al., 2013), Philippine's Pantawid Pamilya (Crost et al., 2014), Kenya's Cash Transfer Program for Orphans and Vulnerable Children (Asfaw et al., 2014) and the data 25 we use in this paper from Tanzania's pilot community-based CCT (Evans et al., 2016).
Following Evans et al. (2016), we make use of the random assignment to treatment as a randomized control trial removing any potential for selection bias.

CCT program design and data
We analyze the impact of poverty reduction on households' dependence on local fisheries by making use of a randomized control trial implemented during the pilot phase of a CCT implemented in Tanzania. We use this case study to capture the change in demand for seafood by looking at reported consumption as well as whether households engage in fishing activity as a major source of income. Payments occurred every two months and ranged from US $12 to US $36 depending on household size and composition with a household average reported payment of $14.50. The CCT provided US $3 per month for orphans and vulnerable children up to 15 years old and US $6 per month for elderly, 60 years and older. These payments were 50 percent of the food poverty line for vulnerable children and 100 percent of the food poverty line for Elderly. These payments were conditional on young children attending a healthcare facility at least six times a year, elderly attending health care facilities at least once a year, and children age 7-15 maintaining an 80 percent attendance rate at school. Eligibility criteria and payments were made through a Community Management Committee (CMC) located in each village comprised of elected representatives from that village. The community based structure of the CCT is designed to reduce implementation costs for the program, compared to more centralized CCTs, and allow for CCTs to be implemented effectively in more remote areas.
Each CMC had previous experience managing TASAF projects prior to taking part in the CCT. Additionally, TASAF conducted communication and training programs on the CCT at the regional, district and village levels. The CMCs were then tasked with identifying and prioritizing the poorest and most vulnerable households and survey the poorest half of households. TASAF used these data to rank households within each village, which was finalized by the governing bodies of each village, determining who received benefits. During the pilot CCT, the CMCs in treatment villages were responsible 27 for screening potential beneficiaries, transferring funds, communicating program conditions, and enforcing program conditions. Data were collected from 80 villages, half of which were randomly assigned to a control group after eligible households were identified from all villages. Stratified sampling based on known characteristics (e.g. district and community size) was used to ensure comparability between control and treatment groups. Control villages began receiving transfers in November 2012, following the completion of the endline survey.

Data and summary statistics
We analyze the effect of participation in the CCT on household protein consumption, with a focus on seafood consumption. We use reported household consumption for fish and other protein sources in conjunction with whether households report any income from fishing to assess the impact a cash transfer on household dependence on local fisheries. A large portion of seafood consumption comes in the form of dagaa, a small freshwater fish which makes up over half the seafood consumption in our sample. To capture any difference between dagaa and other seafood and due to potential seasonal differences in availability between fresh and dried seafood, we break down total seafood consumption into four categories: fresh seafood excluding dagaa, dried seafood excluding dagaa, fresh dagaa, and dried dagaa. Our non-seafood protein sources are beef, goat, poultry, and eggs. Consumption variables are measured in grams consumed by household in the previous week. We look at per capita weekly consumption, for the previous week, as well as binary variables for whether the household has any consumption of the variable of interest in the previous week to analyze the intensive and 28 extensive margins respectively. Our data does not include magnitude of fishing income; thus, we only look at households reporting any household income from fishing in the previous year.
In Table 2.1, we compare baseline levels of outcome variables between control and treated groups. To determine if there is a significant difference between treatment and control households, we use ordinary least squares and cluster errors at the village level. As we explain in detail in the next section, our analysis looks at per capita consumption of seafood and other sources of animal protein as well as whether households consume any seafood or other sources of protein. All consumption variables are reported consumption in the previous week. While Evans et al. (2016) find most covariates to be balanced at baseline, many of our variables of interest differ significantly between treatment and control households.
We find a small difference in how many households report fishing income with households in villages assigned to the treatment group being less likely to engage in fishing (10% significance level). Additionally, we find consumption of goat and poultry to be higher amongst villages assigned to treatment (5% significance level). Additionally, we see fresh dagaa consumption per capita, households consuming any fresh seafood (excluding dagaa), and households consuming fresh dagaa are all significantly lower in villages assigned to the treatment villages (1% significance level) during the baseline period. These are all explanatory variables we use to capture the effect of the cash transfer on the local fishery, making the differences at baseline worrisome. Table 2.2 breaks down these seafood consumption variables by district to provide a more detailed view of what may be driving these differences at baseline. We see 29 households assigned to treated villages consume less fresh seafood across all measurements (all at 1% significance level). Bagamoyo is the only district to show differences in fresh dagaa consumption, both per capita and for households consuming any fresh dagaa, and the difference is large enough that we still observe the difference when awe look at all villages together. In Kibaha, we see households assigned to treatment villages consuming less fresh seafood per capita (5% significance level) as well as fewer households assigned to treatment villages consuming any fresh seafood (1% significance level), similar to households in Bagamoyo. In contrast, Chamwino households assigned to treatment villages consume larger amounts of fresh seafood per capita and the number of households consuming any seafood is also larger (both at the 1% significance level). When aggregated, these differences do not show up in fresh seafood per capita, however, we do see it in number of households consuming any fresh seafood. The fact each district shows significant differences between households assigned to control and treated villages is troublesome. It indicates there may be some systematic differences between treatment and control villages. As we discuss in the next section, random assignment to treatment and control groups should be enough to ensure there is little systematic difference between treatment and control households, even though they appear different at baseline. However, randomization is not perfect and if differences these differences in baseline could indicate differences between treatment and control groups, known in the epidemiologic fields as chance bias (Roberts and Torgerson 1999), which would result in biased estimates. While imperfect, the random assignment in conjunction with well balanced covariates on other observable characteristics, as reported in Evans et al. (2016), is evidence there is no systematic difference between groups at baseline. If this holds, we can still determine the causal relationship of the cash transfer on our outcome variables by comparing the trends over time for each group. A good example of this is depicted in figure 2.1 showing the extensive margin of consuming any seafood in the previous week. Even though consumption differs substantially at baseline, period 1, the trends clearly move in opposite directions indicating a treatment effect.

Empirical specification
We exploit the pilot CCTs randomized control trial to identify the effect of poverty reduction programs on dependence on local fisheries. Random assignment removes the worry of selection bias between the treated and control groups and, in expectation, ensures treatment and control groups are similar at baseline. Thus, the difference between groups we see after treatment can be attributed to the causal effect of treatment.
Additionally, we compare observable household characteristics at the baseline (Table 2.1) to ensure treated and control groups are similar characteristics prior to introducing treatment as discussed in the previous section.
We identify the treatment effect utilizing both the midline (1.5 years of receiving cash transfers) and endline (2.5 years) surveys along with the pretreatment baseline to capture how treatment effects very over time, shedding light on the time it takes for changes in income to impact behaviors related to local resources. For the remainder of the paper, we denote baseline, midline, and endline as period 1, period 2 and period 3 respectively. We determine the causal effect of wealth on demand for ecosystem services using the following model specification: subgroups based on a summation of total assets, moderately poor (above median asset level) and extremely poor (below median asset level). Aggregating assets into a single wealth measure has its challenges in order to provide a comparable estimate of wealth for households with a robust set of assets and livelihoods (e.g. Carter and Barrett 2006).
These issues would be more problematic if we were analyzing wealth as a continuous variable and assessing the marginal impact on treatment effect due to a small change in wealth. However, since we use assets solely to break households into two discrete groups, the calculation of the wealth measure has a smaller effect on our results. Regardless of how wealth is calculated, the groups will be largely unchanged with only the households near the median (which is where the cutoff between the groups is defined) are at risk of switching groups, thus impacting results. We test whether participation in the CCT has different effects on moderately poor and severely poor households. Since payments from the CCT depend on household size and composition (e.g. number of children and elderly) and do not take assets or other wealth variables into account, apart from having low enough wealth to be eligible for the program, there is a larger relative wealth effect for poorer households. That is to say, payments represent a larger proportion of income/expenditure for poorer households than wealthier ones. Additionally, households with different wealth levels may have different demands for ecosystem services; hence, may respond differently to an exogenous increase in income. While we cannot separate the magnitude of the wealth effect from whether households of varying wealth have different demand from local ecosystem services, we can assess whether program participation impacted wealthier households differently than poorer ones in aggregate.
Additionally, we test for differing effects for each district using district fixed effects to account for varying local conditions affecting demand for ecosystem services (e.g. fishing income may depend on distance to fishing areas, which varies widely between districts).

Intensive and extensive margins
The majority of our variables of interest (excluding those which are already dummy variables), are truncated at zero and many households report zero in at least one period, which is problematic when running our single difference fixed effect model across the whole sample. We analyze the effects at the intensive and extensive margins respectively to estimate the effect of the cash transfer. The intensive margin estimates the effect of assignment to a treated village for households who report a positive amount of the variable of interest (i.e. consume some amount of seafood in at least one period). All households, which report zero in all three samples are excluded. We then run our fixed effects model described above. We determine the extensive margin by converting the continuous variable into a dummy variable equaling 1 for if the household reports using/consuming any of the variable of interest and zero otherwise. We then run a linear probability model to estimate the change in households who consume/utilize the variable of interest over the sample to determine the treatment effect. Together, the intensive and extensive margins tell us how assignment to treatment village effects both the proportion of houses using/consuming a variable of interest and the relative magnitude for households which do.

Seasonality
In Tanzania, there are four main seasons: January to February is a short dry season followed by a long rain season from March through May. June through October is the long dry season followed by a short rain season from November through December.
There is likely to be seasonal differences in fishing activity resulting in different availability in seafood, especially fresh seafood (Merode et al., 2003).
One concern about the implementation of the CCT is the baseline survey was conducted during either the dry or long rain season while the endline surveys were both conducted during the long dry season. Thus, we may be picking up a change in seasonal behavior rather than the desired treatment effect. Seasonal differences may exist between the short dry season and long dry season (when the endline was conducted), but is a larger concern when comparing the long rainy season to the long dry season where household behavior may be drastically different.
Due to randomization, it is unlikely that treated and control groups are likely to respond in systematically different ways to the change in seasons so long as 36 randomization was successful in (1) geographical location of villages (e.g. treated villages were not systematically located further inland associated with different seasonal conditions) and (2) households surveyed in baseline were balanced across the short dry and long rainy seasons. Random assignment combined with stratification based on district sufficiently ensures (1) holds. However, we find assumption (2) does not hold. Table 2.3 shows a significantly larger number of treated households were surveyed during the long rainy season, as compared to control households, which must be controlled for in order to identify effect of the CCT on household dependence on local ecosystem services. We do so by including fixed effects for the month each survey was conducted. These fixed effects should capture any systematic differences, which arise due to seasonality allowing us to identify an unbiased ITT when comparing treated and untreated households. However, it does not allow us to reliably determine if an explanatory variable changes across the sample. For instance, we would be able to determine if treated households consume relatively more seafood than untreated, but cannot determine if a change in total fish consumption indicates households eating less fish or if there is just less available in the dry season.
An alternative specification could be to include a dummy variable for baseline survey season. However, we would be unable to use this in conjunction with our household fixed effects model as it does not vary for households and, thus, is collinear with household fixed effects.

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In this section, we report our main findings. We break up our results into three subsections: seafood consumption, other protein consumption, and fishing income. We first discuss how treatment affects demand for seafood by looking at both the extensive and intensive margins. We then use the same approach to see how consumption of other animal protein sources change due to treatment to identify if there is any switching behavior away from seafood. Lastly we look at how treatment effects the likelihood of households engaging in fishing as a source of income.

Seafood consumption
To determine the effect of program participation on seafood consumption we look at five consumption variables: total seafood, fresh seafood excluding dagaa, dried seafood excluding dagaa, fresh dagaa, and dried dagaa. We look at how many households have consumed any of the dependent variable (extensive margin) as well as per capita consumption for households consuming the dependent variable in any period (intensive margin). We see from Table 2.4, at the extensive margin, the number of extremely poor households located in treatment villages consuming any seafood, fresh seafood excluding dagaa, and dried dagaa all increase (all at the 5% significance level). We see some of the same effect extremely poor households assigned to treatment villages in period two where the number of households consuming seafood increases, which is driven by an increase in dried dagaa consumption (both at the 10% significance level). This is consistent with poverty reduction policies taking some time to result in observable changes in behavior. We see a slight increase in period two, which continues to larger increase in period three. Additionally, we see a small increase in moderately poor 38 households increasing consumption of fresh dagaa in period two (10% significance level). Table 2.5 shows the intensive margin for seafood consumption. While the results are less statistically significant, we can see some similar trends from the extensive margin. Extremely poor households located in treatment villages increase total seafood consumption per capita, which is largely driven by an increase in fresh seafood excluding dagaa (both at the 10% significance level). We also see moderately poor households in treatment villages increasing per capita consumption of fresh dagaa in period three.
Together the intensive and extensive margins indicate participation in the CCT leads to extremely poor households increasing their seafood consumption. This increase is driven largely by increasing fresh seafood consumption, but the number of extremely poor households consuming any dried dagaa also increases. We see both that any treatment effect from poverty reduction programs are likely to have significant lag before any spillover effects can be observed. Thus, poverty reduction programs appear to increase the demand for seafood, which likely results in increased pressure on local fish resources.
We also see some indication that moderately poor households increase consumption of fresh dagaa. Since we do not see this increase in the extremely poor group, it provides evidence suggesting dagaa is not an inferior good for the people of Tanzania as moderately poor households still seek to increase consumption as income increases through participation in the CCT.
As a robustness check, we use a Tobit model to look at the effect of program participation on seafood consumption. Tobit models are designed to handle truncated 39 data, such as our consumption variables with large numbers of zeros, and do not require us to break the analysis apart into intensive and extensive margins. The downside to this approach, is it is not compatible with fixed effects. For this reason, we prefer the previous models and focus on them in the discussion. Table 6 presents the results from the Tobit model on seafood consumption. We find inconsistent results with our previous estimation strategy, we find the moderately poor households show a significant increase in seafood consumption in period 3 driven largely by an increase in consumption of dried seafood excluding dagaa (both significant at the 1% level). Additionally we see moderately poor households consume more dried dagaa in both periods 2 and 3 (significant at the 5% level). Extreely poor households show a negative treatment effect for fresh dagaa in periods 2 and 3 as well as a decrease in fresh seafood excluding dagaa in period 2 (all at the 1% significance level). We attribute these differences largely to the lack of ability to control for household fixed effects. Failing to account for the heterogeneity between households may result in biased estimation of treatment effects, leading us to prefer analyzing the intensive and extensive margins for our analysis.

Other protein consumption
In addition to seafood consumption, we look at consumption of other animal protein to see if we observe any switching behavior to or away from seafood. Specifically, we look at consumption of beef, goat, poultry, and eggs at both the extensive and intensive margins. Table 2.7 shows the effect of treatment on whether a household consumes any of each type of animal protein over the last week. We include total in order to compare seafood consumption to the other animal protein sources. We find very little evidence of 40 a treatment effecting the likelihood of households consuming other sources of animal protein. The only significant treatment effect is in the amount of extremely poor households in treatment villages consume more goat in period two (10% significance level), but does not carry through into period three. Together, the intensive and extensive margins indicate participation in the CCT does not lead to a change in consumption of animal protein other than seafood. The increased consumption of goat at the extensive margin and eggs at the intensive margin are likely to be spurious rather than indicating a slight change in consumption. The literature is consistent in saying increases in income will lead to increased consumption of animal protein, so a lack of evidence of a positive treatment effect is surprising. We find participation does not lead to households switching away from seafood to other sources of protein and, if anything, treated households appear to consume seafood as a higher portion of their diet. Thus, we find no evidence program participation reducing pressures on local fisheries by shifting demand away from seafood to other animal proteins.

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We estimate the direct effect of treatment on the dependence of local fisheries by looking at the number of households reporting income from fishing. Fishing income is a dummy variable equaling one if the household reports fishing income as the household's primary or secondary source of income. We exclude households from Chamwino as there was only one household reporting income from fishing. Chamwino is furthest from a major water source so the majority of its impact on demand from fisheries will be on the consumption side rather than from directly harvesting seafood. Our data do not include the magnitude of fishing income so we can only analyze how the number of households reporting fishing income changes in response to treatment. Table 2.9 shows three linear probability models all including household and monthly fixed effects with errors clustered at the village level. The first model includes period variables and assignment to treat in periods. We find a slight increase in households reporting fishing income in period three (10% significance level). Model 2 allows for heterogeneous effects for extremely poor and moderately poor households.
Here we see only moderately poor households in treatment villages increase their likelihood of earning income from fishing (10% significance level). We see consistent results when we include village by time fixed effects (Model 3) and see moderately poor households in treatment villages become more likely to earn income from fishing (10% level).
These results suggest program participation leads to more moderately poor households generating fishing income, but it takes time for the effect to come about. This is consistent with the hypothesis there are significant barriers to enter that must be overcome before a household can enter the fishery. The moderately poor households are 42 closet to surmounting the barrier to entry and are the only ones who end up being able to incorporate fishing income into their livelihoods. Additionally, the fishing equipment, such as nets, may be expensive enough households need to save up over time. Thus, we only see an effect in period three across all model specifications.
The increase in number of households relying on fishing as either their primary or secondary source of income due to treatment indicates poverty reduction policies increase pressure on local fisheries. Participation in the CCT increased the number of households depending on a local fishery as an income generator. This increase in fishing pressure may not become problematic until seafood harvest exceeds the ability of the stock to replenish itself. However, it does increase the likelihood of over exploiting the fishery which may lead to a decline of stock and harvests.

Discussion
We estimate the effect of participating in the CCT on dependence on local fisheries by looking at both the demand for seafood by looking at household consumption as well as direct pressure on local fisheries by looking at households earning income from fishing.
We find there is an increase in both direct fishing pressure from additional households earning money from fishing due to program participation as well as an increase in demand for seafood. Both put increased pressures on local fisheries. Hence, we find evidence supporting the claim poverty reduction policies can potentially have negative spillovers in environmental quality. This arises both from consumption demand as well as altering livelihoods towards those more dependent on the local fishery resources.
In this chapter we show participation in the CCT leads to increased pressure on local fisheries, but do not attempt to determine if this increased pressure leads to overexploitation of the local fisheries. We do not have data concerning the fish stocks and harvest rates to make an assessment of the fisheries being utilized by our study population to shed light on this question. Rather, we identify pathways, which may lead to overexploitation and potential degradation of the fishery. It is plausible the increased demand on local fisheries can be performed sustainably and that increased pressure due to program participation only arises because there are sufficient fish stocks, which we cannot determine in this study. However, when sustainable management of local fisheries is either not present or not functioning properly, these results suggest it is important to consider the spillover into the fisheries sector as increased demand may lead to increased degradation of the local fisheries.
It is also possible the demand from seafood can be satisfied both from distant fisheries as well as investment in aquaculture, both of which would not result in increased pressure on the local fishery. While we do not attempt to identify where the seafood comes from, it is unlikely either of these contribute to a large portion of the seafood consumed. These villages are located in poor rural areas. Thus, the likelihood of having access to aquaculture raised seafood or seafood from distant fisheries is low. Most of the increased demand from seafood due to program participation is likely to be filled by putting additional pressure on local fish populations.
We find significant differences in the treatment effect based on initial wealth level. The response to treatment appears to depend, at least in part, on the relative wealth of the households. While extremely poor households increase pressures on local fisheries 44 through an increase in demand, the increased pressure on fisheries from the moderately poor comes from an increasing number of households earning income from fishing. We find allowing for heterogeneous wealth effects is useful for understanding the full impact of poverty reduction programs. Not only does understanding how responses may differ, failing to include heterogeneous wealth effects can disguise true effects of program participation.
We also find program participation may take significant time before spillover effects become observable. Both for fish consumption and fishing income, we see stronger treatment effects in period three than period two. This is consistent with households requiring a certain period to adjust to the changes brought about by participating in the poverty reduction program. This is especially important when looking at livelihoods, such as fishing in this study, which may take a longer period of time to adjust to changes than consumption or expenditure variables.
We have a number of potential weaknesses, which we were not able to surmount in this paper. First, we do not have a balance on some explanatory variables in the baseline survey. This could be evidence that treatment and control groups were systematically different making an identification of a treatment effect problematic.
However, Evens et al. 2014 show baseline characteristics on other key variables such as education, health, assets, house characteristics, and water sources are highly balanced.
This in conjunction with the randomized assignment to control and treatment villages is evidence the differences observed are due to chance rather than signifying a systematic difference between treatment and control. So long as the differences at baseline are not due to systematic difference, our estimation strategy should pick up the intention to treat effect even without a balanced baseline. However, we cannot rule out the possibility treated and control households behave systematically differently towards seafood in this study.
A second issue concerns the sampling variability of consumption variables, which are reported as the amount consumed in the previous week. Since we only observe three data points for each household (baseline, midline and endline surveys), we only see snapshots of weekly consumption at three points. It is possible protein consumption is correlated with special events such as weddings. Observing only one week of consumption may be heavily influenced by unobservable factors and may not represent typical consumption patterns. However, this would only result in increased variation within the sample causing increased standard errors, but would not bias our estimates.
Thus it makes identifying treatment statistically significant effect more difficult, but does not hinder the interpretation of results. Additionally, consumption and other explanatory variables may depend on seasonal differences.
The baseline of our study was conducted at a different time of year than the midline and endline. The baseline was conducted in either the short dry or long rain season, depending on the village, while midline and endline were conducted during the long dry season. There may be different access to protein sources and fish species during the different seasons making a direct comparison of consumption problematic (e.g. consuming less seafood in the endline does not imply less seafood is consumed yearly by the household). Additionally, we find significantly more treatment villages were sampled in the long rainy season than control villages during baseline exasperating the seasonality issue. We include monthly fixed effects for baseline to account for this. While we are still 46 unable to identify whether a reduction in consumption between baseline and endline indicates a reduction in consumption for the household, monthly fixed effects do allow us to pick up any differences between treatment and control groups, even with the unbalanced assignment. Thus we can identify a treatment effect to shed light on whether program participation impacts the demand for seafood, but fall short of being able to determine if the aggregate demand changes.
Another potential weakness of the paper is our estimates do not separate out the income effect from other effects of CCT participation, namely schooling and required healthcare visits for children and elderly. Knowing the income effect is potentially more useful, as it can be applied to other situations than understanding the spillovers of poverty reduction programs on local ecosystem services. We discuss whether it is possible to use our estimate of CCT participation a close approximation for an income effect. For this discussion, we break up the effect of the CCT into two categories: income effect and the additional effects of program participation. We see three primary mechanisms in which the additional effects of program participation come about: (1) a reduction in child and elderly labor (from additional time at school and healthcare visits), (2) a reduction in adult labor from time spent taking children and elderly to school and healthcare visits, and (3) an increase in household health resulting in increased productivity.
The main effect of (1) and (2) will be to decrease total household earnings through a reduction in earnings from child activities and decreased labor hours from adults. This effect would partially offset the cash transfer. However, Evans et al. (2016) show CCT participating households are significantly more likely to invest in health insurance, allowing them to seek treatment when they need it, for example seeking 47 treatment at the onset of a disease rather than letting financial liquidity determine when the household will visit a clinic. Thus, CCT participating households are likely to be healthier, which leads to higher productivity and household income. These effects move in different directions and the magnitude of the effects cannot be determined with the information obtained from the survey. While it is likely the case our estimates closely resemble the income effect, if one of these three effects is significantly greater than the others, our estimates would be a biased estimate of income effect without knowing which direction the bias is in. Thus, our paper has focused on estimating the impact of CCT participation rather than attempting to estimate the income effect.
As a robustness check, we test whether there is an effect on time spent by children collecting firewood and fetching water and find no significant treatment effect using a similar fixed effect framework described in the methodology section. This is an indication that program participation does not significantly reduce income generated from children, which we would expect to be a large component of (1).
Our findings shed light on a previously unexplored potential spillover of poverty reduction programs. While many papers have sought to identify how poverty reduction impacts forest cover and demand for forest products, the impact on local fisheries has been left unexplored. We find participation in the CCT leads to increased pressures on local fisheries. Our paper also highlights the importance of having a long enough time frame in order to observe the effects of program participation as there may be a large lag between treatment and the ability to observe environmental spillovers. We also find including heterogeneous wealth effects in our estimation allows us to better identify the true effects of the program.  Results are for when households were surveyed at the baseline. All variables are dummy variables corresponding to the month and Season is a dummy variable equal to zero if the dry season (December, January, and February) and 1 for the short rainy season (March, April, and May). All months apart from December are in 2009. Standard errors are reported in parentheses *** p<0.01, ** p<0.05, * p<0.1. Notes: Results are from a linear probability model for households reporting any consumption of seafood in the prior week in at least one period. Dependent variables are dummy variables equal to 1 if the household reports any consumption in the previous week and zero otherwise. All models include household and month fixed effects. Standard errors are clustered at the village level and presented in parentheses *** p<0.01, ** p<0.05, * p<0.1. Results are for households reporting consumption in at least one period. Consumption variables are in grams consumed by household in the previous week. All models include household and month fixed effects. Standard errors are clustered at the village level and reported in parentheses *** p<0.01, ** p<0.05, * p<0.1.  Results are from a linear probability model for households reporting any consumption of animal protein in the prior week in at least one period. Dependent variables are dummy variables equal to 1 if the household reports any consumption in the previous week and zero otherwise. All models include household and month fixed effects. Standard errors are clustered at the village level and presented in parentheses *** p<0.01, ** p<0.05, * p<0.1. Consumption variables are in grams consumed by household in the previous week. All models include household and month fixed effects. Standard errors are clustered at the village level and presented in parentheses *** p<0.01, ** p<0.05, * p<0.1. Results are from a linear probability model for households reporting fishing income as either their primary or secondary source of income. All models include household and monthly fixed effects. Standard errors are clustered at the village level and presented in parentheses *** p<0.01, ** p<0.05, * p<0.1.

Introduction and Motivation
'Why do the poor stay poor?' has been a long-standing question in the development literature with important implications about how to best combat poverty. Poverty traps occur when populations experience long-term chronic poverty with limited or nonexistent opportunities to accumulate wealth. These systems are characterized by self-reinforcing mechanisms causing poverty to persist (Azariadis and Stachurski, 2005). The most useful application of poverty trap models occurs when there exist multiple long term equilibria separated by a critical asset threshold, called a "Micawber Threshold" (MT), which is an unstable dynamic equilibrium separating the basins of attraction of the high and low wealth equilibria (Zimmerman and Carter, 2003;Carter and Barrett, 2006). This specific type of poverty trap is known as a multiple equilibrium poverty trap where households with initial wealth above the MT tend to experience a growth in wealth and approach the high-wealth equilibrium, while households just below the MT get mired in poverty.
Poverty trap models are useful in explaining why some groups appear to be stuck in chronic poverty while apparently similar groups prosper by highlighting how small initial differences in wealth can dramatically affect long term wellbeing, which has important policy implications.
It is possible short-term aid can change long term wealth trajectories by setting households on a path of wealth accumulation towards a high wealth equilibrium rather than the household falling further into poverty. In the theoretically deterministic setting where random shocks to wealth are assumed away, providing aid just above the MT ensures a household will progress towards the high-wealth equilibrium and would be the 59 ideal location to target aid policies (Barrett 2005). However, if aid falls short of the MT, the impact of aid will be short-lived with household wealth diminishing overtime, approaching a low wealth equilibrium. In this paper, we addresses whether targeting aid at the MT is optimal when wealth generation has a stochastic component.
While poverty traps have recently received a large amount of attention, too little work has been done to analyze how to optimally provide aid to those stuck in poverty traps. There has been some work analyzing how well subsidized insurance programs perform at reducing poverty for populations experiencing or vulnerable to multiple equilibrium poverty traps. Janzen et al. (2012) look at a pastoralist system in Northern Kenya 3 and find those closest to the MT would benefit most from insurance, as even a small downward shock to their productive assets (in this case the loss of an animal) may put the household on a trajectory of further herd loss and potentially resulting in exiting from pastoralism altogether. However, it is also this group who are least likely to purchase insurance due to the high opportunity cost for households around this critical wealth level. For these vulnerable households, selling an animal to pay for insurance may put them below the MT and would place them on the unfavorable negative trajectory. be in the form of direct transfers (in the form of cash or productive input) or alterations to the socio-economic system such as land reform, school feeding programs, subsidized farming inputs, and targeted microfinance (Barrett, 2005). In practice, the form of the cargo and safety net may impact the effectiveness and care must be placed to determine the appropriate form of aid depending on the context. For our purposes, we assume cargo nets and safety nets having the same effect regardless of the specific form they take and for the remainder of the paper, we will be treating a cargo net as a direct transfer and a safety net as free insurance at a specified wealth level. high wealth equilibrium and, hence, no longer require aid. While this work provides substantial insight into optimal aid, the authors only test two policies and do not attempt to identify where the targeted aid policy would be most effective, specifically would a safety net placed slightly above or below the MT provide perform better than a safety net at the MT. Additionally, as the authors point out, it is difficult to imagine an aid program that provides aid to those at risk of falling into poverty while excluding those currently suffering.
We focus on a policy of providing a minimum level of wealth, similar to the needs based aid strategy in Ikegami et al. (2016). However, we focus on identifying the optimal level of aid to provide in the face of poverty traps. Currently there is no consensus of where an optimal safety net would lie in relation to a Micawber Threshold. Barrett (2005) suggests for safety nets to be effective, they must be set above the Micawber Threshold, which is certainly true for the deterministic case. However, Plucinski, Ngonghala, and Bonds (2011) show safety nets set below critical thresholds can lead to long term escape from disease-driven poverty traps, a specific cause of the poverty trap we discuss in the next section, using numerical simulation. It is important to note, Plucinski, Ngonghala, and Bonds (2011) define 'safety net' as a policy bringing individuals up to a minimum level and ensuring they do not fall below it in the future, similar to the policy we consider. This is analogous to a combination of a 'cargo net' and a 'safety net' in Barrett's (2005) terminology and is analogous to the needs based strategy used by Ikegami et al. (2016). Plucinski, Ngonghala, and Bonds (2011) show high level of wellbeing is possible even when aid is provided below the MT, but do not attempt to prove this is optimal, leaving the question of the optimal level of aid unanswered. We seek to fill this gap using numeric simulation to test the effectiveness of aid across a wide range of aid levels to determine where the optimal aid level is located in relation to the MT. We find the MT for a basic wealth policy with the optimal aid level usually falling about 2-3% above the MT. We find the optimal aid is predominately determined by the cost of poverty reduction and the level of poverty reduction is a secondary concern. This arises due to the formation of an 'aid trap', where there is a region below the Mcawber Threshold where both the cost of reducing poverty and the amount of poverty can both be reduced. We show this occurs because the cost of providing aid actually decreases as the level of aid increases for some range of aid levels below the Micawber Threshold. This is 63 the point where aid begins to lift people out of poverty allowing them to no longer be dependent on the safety net.
An aid trap is used to describe the situation where some populations appear to become dependent on external aid to maintain wealth levels. Typically, aid dependency is used to describe when an individual, household, or community cannot meet its basic needs without external aid (Lentz et al., 2005). This dependence may come about by households relying on safety nets rather than taking the necessary steps to self-protect themselves from downside risk, known as the moral hazard problem, potentially even resulting in reducing work hours or exiting the labor force entirely. For instance, Seleka and Lekobane (2016) show government food rations reduce the likelihood of subsistence farming for poor households in Botswana. Even without the moral hazard issue, dependence makes providing aid less desirable, as there are no long-lasting benefits to justify the cost of providing aid. However, if dependence is caused by aid failing to surmount a MT, then there can be significant gains to aid so long as it is placed at the correct level. This is similar to Rosenstein-Rodan (1943)'s big-push theory to deal with poverty traps at the macro level, which is there is a minimum level of resources that must be devoted to an aid program if it is to have any chance of being successful. Barrett and Carter (2013) motivate the importance of understanding the underlying structure of the poverty to determine the appropriate aid to combat it. If a multiple equilibrium poverty trap exists, identifying the existence and location of MT is critical to achieve long-term lasting gains in welfare from aid policy. We show how an aid trap can occur when providing aid to those stuck in a multiple equilibrium poverty trap and show costs can be reduced by increasing the level of aid above the MT to put 64 households on a path of wealth growth and eventual exit from poverty. This reduces the dependency on external aid, resulting in a lower cost of providing aid. We use this observation to introduce a new twist on the aid trap and define the inefficient aid trap, which occurs when both the cost of aid and the levels of poverty can both be reduced.
Thus, the aid policy is inefficient as there is a clearly superior aid policy available. The inefficient aid trap is particularly useful when discussing the location of optimal aid and we show the upper bound of the inefficient aid trap is the lower bound of where optimal aid can occur.
This paper seeks to enhance our understanding of how to combat poverty traps by identifying the aid trap caused by providing too little aid and determining the relationship between the optimal aid level and the MT. We show the cost of providing aid largely determines the location of optimal aid. We use this to find the local minimum for the cost of providing aid and find it occurs slightly above the MT. Thus, optimal aid must either be close to non-existent or set above the MT, consistent with Barrett (2005). We use this minimum aid level to identify the inefficient poverty trap caused, in part, by the increasing costs as level of aid decreases from this point, due to increased number of impoverished people being supported by the safety net. Additionally, we use the shape of the cost curve to show when there is uncertainty concerning the location of the Micawber Threshold and the location of optimal aid, it is better to err on setting aid too high. This occurs because the slope of the cost of providing aid is extremely steep just below the MT, which results in a large penalty for providing aid too low, even if its only by a relatively small amount. Together the results from this chapter provide a theoretic foundation, which can be built upon to inform aid policy decisions when a multiple equilibrium poverty trap is identified.

Poverty Trap Background
To begin the discussion of poverty traps, it is useful to distinguish between different types of poverty as well as measures of poverty. Not all poverty is created equal, as the duration of poverty may be just as important, if not more so, than the severity of it. Some people who are currently poor may not be expected to be poor in future periods, or viceversa. While it is usually more convenient to take a snapshot of wealth levels in a given period to define who is poor and who is not, such as the commonly used Foster for distinguishing between stochastic and structural poverty. Carter and May (2001) introduce the asset poverty line to distinguish between structural/chronic and transitory/stochastic poverty. Stochastic poverty is when a household has a low level of consumption/income in a period, and is hence poor as defined by traditional poverty measures, but is unlikely to remain so in the future due to a 66 large asset base. Structural poverty is when a household has a low-level of productive assets, which would predict a low level of wellbeing in future periods. The asset poverty line is the level of assets at which expected income in the future is equal to the income poverty line. This largely began the transition into focusing on productive assets opposed to income/expenditure measures, which are far more stochastic and less predictive of future wellbeing. The asset poverty line still has the disadvantage of being rather arbitrary. Individuals' with wealth on either sign of this assigned threshold have nearly identical levels of wellbeing, however one is defined as poor while the other is not.
Another downside to using assets, as a wealth measure is it can be difficult to aggregate assets into a usable measure (Carter and May, 2001;Carter and Barrett, 2006), especially since each livelihood will depend on different assets to produce wealth. Even when just considering one livelihood, it may be difficult to determine the relative value of a given productive asset, which may be complicated if certain assets become more productive when owned together, for instance while a plow and an ox each provide some level of productivity to a farmer individually, a plow becomes more productive when paired with an ox to pull it. The more complicated the socio-economic system, the more difficult asset aggregation becomes, which adds to the empirical complications of using asset-based measures (Barrett and Carter, 2013). However, the advantages of productive assets deterministic relationship with future wellbeing still make asset-based poverty measures desirable even with the additional empirical challenge of creating an asset measure.
Carter and May (2001)'s asset measure is still a static way of measuring wellbeing and suffers from not being able to consider potentially predictable changes in 67 assets over time and focuses on some arbitrary asset poverty line. This is improved upon by Carter and Barrett (2006) who introduce the idea of the asset-based MT, which is a forward-looking measure focused on how assets are likely to change over time and whether this will result in poverty, in terms of income/expenditure, in the future. The MT separates where productive assets are expected to grow over time or diminish to a lower level making it a dynamic measure of poverty. Those with assets above the MT are said to be structurally non-poor, regardless of income/expenditure in any current period.
Understanding the structure of poverty is essential to determine the amount and type of aid that can effectively allow households to escape poverty. There are a number ways in which a household can experience poverty and, depending on the form, may require different forms of aid to overcome it (Barrett, 2005). Transitory/stochastic, or short-term, poverty occurs due to the randomness inherent in wealth dynamics. A household, which is typically not impoverished, may fall below a poverty line, however defined, in a period due to a negative shock, but if the household's underlying circumstances have not changed, e.g. there is no change in their productive assets, the household will likely exit poverty in later periods (Carter and Barrett 2006). While not desirable, transitory poverty does not require external aid to fix, as households will recover from shocks over time and progress out of poverty. Providing short-term aid (e.g. food aid in response to drought) will be effective in reducing short-term reduction in wellbeing following negative shocks and should be focused on speeding the recovery process (Nashold, 2013).
Structural, or chronic, poverty is characterized by long spells of low wellbeing where once a household becomes poor it is likely to persist, perhaps even generations.
Structural poverty occurs when a household has a low level of productive assets that are not growing, or expected to grow, over time and this level of assets cannot produce the income/expenditure required to surmount an income/expenditure poverty line. The poverty trap literature breaks chronic poverty down into two categories: single equilibrium and multiple equilibrium poverty traps. Single equilibrium poverty traps describe a situation where the socio-economic circumstances will always push households towards some low-level of well being below a poverty line. Regardless of initial asset endowments, overtime households will be pulled into poverty absent external aid. Providing aid can yield short-term improvements, but will have no long-term impact on wellbeing unless the socio-economic conditions underpinning the poverty trap are changed. In this system, meaningful aid must be long-term without any expectation of eliminating the poverty (Hubbard and Duggan, 2009;Ikegami et al., 2016). wellbeing of the household. Where the household was once able to sustain or grow wealth, if a shock drops wealth below the MT, the household will be on a wealth trajectory where long-term poverty may be inevitable without external assistance. This provides strong motivation for aid policies to prevent households from falling below the MT, in Barrett (2005) terminology, providing a safety net around the MT to keep households on high-wealth trajectory.
In general, multiple equilibrium poverty traps are caused by non-convexities in wealth accumulation or an 'S' shaped relationship of income/asset dynamics. These can occur at any scale from countries down to individuals with the potential for poverty traps at different scales to occur simultaneously and reinforce each other (Barrett and Swallow, 2006). Barrett and Carter (2013) provide a detailed description of what can cause the non-convexities in asset accumulation, which underpin the multiple equilibrium poverty trap at the different scales. We focus on the micro-scale and summarize some of the mechanisms, which, in theory, cause multiple equilibrium poverty traps at the individual or household level.
One largely studied cause of the multiple equilibrium poverty trap centers on physical work capacity, which declines more rapidly when wealth drops below a critical level (e.g. Dasgupta and Ray, 1986). In this example, malnutrition leads to lower productivity resulting in lower incomes, which in turn exacerbate the malnutrition. This With slightly larger initial asset endowments, farmers could invest in fertilizer. This maintains or increases farm productivity eventually resulting in further investment in assets and a higher standard of living.
Multiple equilibrium poverty traps can also be caused by increasing returns to scale over some wealth range caused by multiple production technologies with varying levels of fixed costs. If a more productive livelihood requires an expensive input, a household must have an initial endowment of wealth to purchase it before it has access to the increased level of production (Carter and Barrett, 2006;Ikegami et al., 2016). One largely studied case of multiple production technologies is the pastoralist system of Eastern and Southern Africa, where a minimum herd size is required to engage in the 71 more productive mobile pastoralism (e.g. McPeak and Barrett, 2001;Lybbert et al., 2004;Toth, 2014), which we describe in more detail later in this section.
A necessary feature in all of these is some inability to borrow against the future in order to surmount thresholds. This could be due to the nature of the asset, such as health or, more typically, due to a lack of functioning credit markets. When credit is freely available, individuals could surmount the MT and pay off the debt with the increased returns from the high production technology. However, many impoverished populations especially in remote rural areas do not have access to formal credit markets (Besley, 1995;Jacoby and Skoufias, 1997). Barrett and Carter (2013) suggest the most likely cause of multiple equilibrium poverty traps would be some increasing returns to scale in production over some level, such as different production technologies with varying levels of fixed costs, combined with lack of financial markets preventing households from borrowing to switch to a more profitable production technology. Most of the underlying causes of the multiple equilibrium poverty traps share the same fundamental behaviors.
Thus, while the theory we present in the next section is presented as a poverty trap caused by multiple production technologies, the results can be applied to other causes as well.
The main exception occurs poverty traps are caused by the interaction of wealth and other asset stocks, such health or local natural resources, where the poverty trap must be defined in multiple asset space. This complexity is left for future work where a more complex model allowing for the interaction of multiple assets is allowed for. This model can then be compared to our basic model to determine how this complexity impacts results.

72
The theory of poverty traps has been applied to a diverse set of applications and used to motivate development policy at scales from the micro to macro level. However, there has been long standing difficulty in empirically identifying multiple equilibrium poverty traps, even in contexts where theory would predict them to occur. Specifically, it is difficult to identify the existence and location of Micawber Thresholds. While many empirical studies have failed to identify poverty traps, this does not prove poverty traps do not exist, but merely testing for non-convexities in asset accumulation has many issues including significant measurement error, lack of observations occurring near nonconvexity, and non-random attrition to name a few, which makes identifying poverty traps extremely difficult (Barrett and Carter, 2013;McKay and Perge, 2013;Kraay and McKenzie, 2014). Additionally, thresholds may be heterogeneous based on ability level The existence of a multiple equilibrium poverty trap for any specific case is inherently an empirical question, which we do not seek to answer here, but rather extend our knowledge of how to provide aid in situations where they are found to arise. We abstract away from many of the empirical challenges and consider the case where a poverty trap exists and, importantly, know the location of the MT to determine whether aid should be targeted at this dynamic wealth threshold to provide the foundation for future aid policy decisions. While the model we present in section 3.3 is intended to be as general enough to be applied to many contexts where multiple equilibrium poverty traps exist, it is helpful to couch some of the discussion in terms of a specific context. We briefly discuss one notable economic system where multiple equilibrium poverty traps have been identified and much of the poverty trap literature has been focused; the pastoralist system eastern and southern Africa.
We follow a large portion of the poverty trap literature and focus on the mobile pastoral system of the arid and semi-arid lands (ASAL) of east and southern Africa. The ASAL is characterized by bi-annual dry seasons with frequent occurrences of severe drought making traditional farming largely unproductive making pastoralism the main livelihood for this region. This system provides an ideal context for studying poverty traps, in part, due to access very limited economic outside of pastoralism and a single productive asset, the herd, is the main form of wealth. With only one main productive asset, we can circumvent the many issues associated with asset aggregation (Carter and Barrett, 2006). Additionally, this economic system is comprised largely of two groups of households, those with large herds engage in mobile pastoralism, moving to different water sources and grazing lands, and households with small or non-existent herds who are forced into sedentarism.
Several studies have empirically identified poverty traps in this system, which arises due to requiring a large enough herd size to engage in mobile pastoralism (McPeak and Barrett, 2001;Lybbert et al., 2004;Toth, 2014). These papers identify a Micawber Threshold in herd size, below which herd size tends to decrease, in other words productive assets decrease, eventually resulting in the far less productive sedentary pastoralism and, sometimes, exiting from pastoralism altogether (Little et al., 2008).
Mobile pastoralism represents a more productive production technology by providing access to additional grazing and watering areas. In this context, mobile pastoralism is 74 only possible when the herd is large enough to support the pastoralists, consuming meat, milk and blood from the herd itself while moving between watering holes and grazing areas. When a herd becomes too small, households are forced to stay near the village which limits access to water (McPeak and Barrett 2001) and grazing lands resulting in further loss of animals largely due to overexploited grazing lands near these villages due to overstocking (Santos and . This is an ideal example of the multiple production technologies causing a non-convexity in asset accumulation. There is strong evidence people perceive and respond to the existence of Micawber Thresholds. Hoddinott (2006) surveyed farmers in rural Zimbabwe, who report requiring at least two cows/heifers to maintain a stable herd size, which is consistent with Hoddinott and Kinsey's (2003) finding farm incomes in the area rise when farmers own at least two cows. In the ASAL context, Santos and  surveyed of the Ethiopian pastoralists who report a critical herd size consistent with empirical findings of Lybbert et al. (2004) of minimum herd size of about 4 TLU per person to engage in mobile pastoralism. There is also mounting evidence of behavioral responses of households, which would only be optimal if the household had assets around a perceived MT. One behavior is 'asset smoothing' where household's around a MT tend to forgo consumption rather than reduce productive assets (either through selling or direct consumption of livestock) when faced with negative shocks (Zimmerman and Carter, 2003;Carter and Lybbert, 2012). This behavior is in opposition to the typical economic prediction of 'consumption smoothing' proposed by Deaton (1991), which predicts households maximize their inter-temporal utility by maintaining relatively consistent consumption, even when faced with negative income shocks. Observing asset smoothing for some range of the population with consumption smoothing for the remainder of the population is a strong indicator of non-convex asset accumulation with a MT in the vicinity of where households' are asset smoothing. Relatedly, we can also observed risk taking behavior just below a MT. While typically households are risk-averse, that is they prefer lower levels of risk, households just below the MT may gamble on riskier production technologies/behaviors in the hopes of a positive stochastic outcome allowing them to surmount the critical asset threshold (Lybbert and Barrett, 2011). Together these studies make a strong case households are aware of the existence and location of MT when a multiple equilibrium poverty trap exists.
This begs the question, if households know the location of MT, why can't they engage in alternative behaviors to surmount this threshold? A clear example in the mobile pastoralism context is if mobile pastoralism is more productive, but requires a minimum herd size; why don't household's with small herds pool their herds, or lend their herd to a household with a large herd, in order to surmount this threshold? The practice of herd aggregation, or professional herders, is more common in western Africa (Swift, 1986), but in the ASAL of eastern and southern Africa the shorter more frequent migrations decrease supervision cost, which increases the cost of reciprocal herd sharing (Santos and Toth, 2014). However, we do see some communal coping mechanisms.

Informal insurance exists in many locations of rural Africa where neighbors and relatives
give productive assets or food items to households who experience large negative shocks, such as livestock death. In eastern Ethiopia, a system of informal insurance based partially on the charitable obligation inherent in the Islamic religion as well as a form of self-insurance due to expectations of reciprocity in the future (Devereux, 2006; Beyene, 76 2013). However, this is not always the case. For instance in rural Tanzania, informal insurance networks are uncommon present even though these socio-economic environments appear very similar (Amani et al., 1987;Dercon, 1998). Even where informal insurance is present, systematic shocks that are frequent and/or severe reduce the likelihood of the success of this type of informal insurance (Beyene, 2013). There does appear to be a lack of mechanisms to deal with the multiple equilibrium poverty traps within the community, motivating the need for external aid. In the following section we present our poverty trap model, which we use to analyze how to best provide this aid.

Poverty Trap Model
Poverty traps are an inherently dynamic method of looking at poverty, which focuses on how wealth, measured in some aggregation of productive assets, changes over time.
While identifying an appropriate wealth measure has its own empirical challenges, which depends heavily on the socio-economic context of the system being studied, our model assumes household wealth is knowable and incorporates all meaningful productive assets making it the sole predictor of future wealth, apart from a stochastic component. We model wealth growth using a transition function with two production technologies, which implicitly incorporates consumption/saving decisions and optimal behavioral responses.
This simplification allows us to circumvent specifying and including an intertemporal utility function to govern wealth dynamics through consumption and savings decisions, which would obscure results due to added complexity. A more complete model should include a utility function to explicitly allow for optimal behavioral responses when facing a poverty trap, most notably asset and consumption smoothing (see Zimmerman and 77 Carter, 2003;Hoddinott, 2006;Carter and Lybbert, 2012), but we save this for future research. Additionally, we assume there is no access to financial markets so households are not able to borrow to overcome the Micawber Threshold, which is consistent with many of the locations where poverty traps have been identified, most notably the pastoralist system of the ASAL of East Africa (e.g. McPeak and Barrett, 2001;Lybbert et al., 2004;Toth, 2014).
Multiple equilibrium poverty traps have two or more dynamic wealth equilibria, with at least one of those falling below a poverty line, however defined. For simplicity, we consider a dual equilibrium poverty trap where there exist two stable dynamic equilibria for assets separated by an unstable dynamic equilibrium, the Micawber Threshold, which we label M. Households choose the production technology, which maximizes wealth in the next period based on the households' current wealth level. For a given wealth, , at time, , we model the transition function for a household as: where is the production technology, which takes values L and H, representing low and high level production technology respectively. and are scaling and curvature parameters for their respective and represents the fixed cost of production technology where the high production technology has some positive fixed cost and the low production technology as has no fixed cost, ensuring wealth growth is strictly possitive, and . Additional constraints on the parameters: and with no restriction on the relationship between and . Alternatively, this can be written as: where is the switching point between technologies, which occurs when . 5 When assets are above , the high production technology will generate more assets than the low production technology and vice versa. For a multiple equilibrium poverty trap to exist, assets must have a negative expected growth rate at the technology switching point, . This yields one unstable dynamic equilibrium at and two stable dynamic equilibria, and , corresponding to where the low and high dynamic equilibria for the low and high technology respectively. Over time, households with assets starting above will approach and those with initial assets below will approach . If , the transition function would exhibit positive growth until is reached, with being a unique dynamic equilibrium. Anywhere the transition function intersectst the 45-degree line represents a dynamic equilibrium with stable equilibria being those that cross from above. Figure 3.1 depicts the two stable equilibria for the high and low production technologies respectively.
Anywhere where the transistion function lies above the 45-degree line will result in asset growth and when the transition function lies below the 45-degree line, assets will decline 5 Note the time subscript is removed here, as the technology switching point is time invariant.  We also introduce an asset poverty line, z, which represents the level of productive assets that produce the amount of income/consumption equal to the more typeical income/consumption poverty line, on average. Any individual with wealth below z in a period can be said to be impoverished acording to this static measure. To distibguish between structural and transitory poverty we also need to look at the Micawber Threshold. As we described in the previous section, the Micawber Threshold represents a dynamic asset poverty line. Everyone with wealth below M are said to be structurally poor regardless of whether current assets are below an asset poverty line.
When houshold wealth is below z and M, the household is structurally and chronically poor and when household wealth is above both z and M the household is non-poor.
However, when wealth falls between the two poveryt measures, we see households which may be strucutrally poor, but currently non-poor or just in transitory poverty and will eventually emerge from it. Figure 3.3 depicts a scenario where z > M , which depicts the case where there exists some transitory poverty. When M < w < z, the household is 80 currently poor as assets are below the asset poverty line, but structurally non-poor as assets will increase over time resulting in an exit from poverty. Figure 3.4 shows the case where M > z . When z < w < M , the household is structurally poor, even though current assets are temporarilly above the asset poverty line, in other words the household is transitorilly non-poor. This highlights the importance of using the benefit of using the Micawber Threshold opposed to static poverty lines, however, in our analysis we utilize more typical poverty measures, some of which depend on the asset poverty line.
We introduce randomness into the model as lognormal error, independent of production technology. A more realistic model would allow each production technology to have systematically different error components; for instance, McPeak and Barrett (2001) show pastoralists in the ASAL of eastern Kenya experience greater risk of animal loss due to raiding 6 the further away from towns they graze, which would be systematically higher variance for the high production technology. We do not allow for technology dependent stochasticity, as it would complicate the decision of when to switch between technologies without substantially altering the fundamentals of the system. Thus, our stochastic model can be written: , where is the process variance. The lognormal error ensures the stochastic component is tied to wealth level ensuring shocks have a proportional impact on households, for example a drought would be better modeled as killing off a percentage of a herd rather 6 Raiding amongst pastoralists and herders of the ASAL is not an uncommon practice to restock herd size after drought or other herd loss and is potentially becoming more prominent and destructive as automatic weapons become more prevalent in the ASAL (McPeak and Barrett 2001;Schilling et al. 2012).
than a given number of animals, which is also consistent with much of the financial literature models how financial assets (stocks, derivatives etc.) evolve over time. Other poverty trap models have modeled randomness as some negative shock, rather than random error that can be positive or negative (e.g. Ikegami et al. 2016), which is intuitive to describe situations where drought kills off a large proportion of a herd with no real reciprocal for a positive benefit. While our model does not lend as easily to this thought process; functionally, it works the same. The main difference is our transition function is centered at the median 7 of the distribution, rather than the maximum of the distribution.
We feel this provides a more meaningful interpretation of the high and low equilibria and the MT while still capturing the dynamics of the poverty trap. With these underlying wealth dynamics, we introduce a social planner with the objective of reducing poverty while still considering the cost of reducing poverty.

The Social Planner's Problem
Rather than discuss optimal aid in terms of a as part of a government or aid organization, which may have multiple, potentially conflicting objectives; it is useful to introduce the abstract social planner with the sole purpose of determining the optimal level of aid to provide to those stuck in poverty. We use the social planner to compare the costs and benefits of providing aid. We can simplify this into two components: the cost of providing aid and the social cost of poverty. Absent aid, there is no cost of providing aid, however, the level of poverty is largest. Thus there exists a tradeoff between providing 7 It is the median, rather than the mean, of the distribution as we are dealing with lognormal errors. The transition function in any given time period can be interpreted as the m in the 82 aid, with a monetary cost, versus the cost to society of having poverty. In this section, we discuss how we model the social cost of poverty in terms of a dollar value, which we can directly compare to the cost of providing aid, and use this to determine the total cost of providing aid at varying levels. We show the optimal aid level is largely determined by the cost of providing aid, which is decreasing just below the Micawber Threshold resulting in a local minimum of the total cost of aid. Due to the shape of the cost of providing aid, the cost of poverty becomes only a secondary consideration, suggesting aid policy decisions can focus on costs of providing aid when combating the multiple equilibrium poverty trap.
We model the social planner's problem as minimizing the sum of two costs: the cost of providing aid and the social cost of poverty. The social planner chooses aid level, , that guarantees a minimum wealth level for all households, which we call the basic wealth policy. This policy is similar to the more familiar basic income policy, except the guarantee is based on household assets rather than income. The present, or discounted, cost of providing aid, , is the sum of payouts to each household, , in population, , with assets below the aid level to bring assets up to at the end of each time period, , summed over the length of the time horizon, , with discount rate, . It is useful to break cost of aid into two components: the initial cost of providing aid to bring households' assets up to , which Barrett (2005) refers to as a 'cargo net', and the cost of preventing households' assets from falling below in all future periods, called the 'safety net'. Breaking it down in this way, we can write the cost of providing aid as: where the + superscript is the shorthand notation, x ( ) + = max x,0 ( ) . Due to the aid policy, wealth is bounded at , which is captured in the second equation. The first term is the cost of the cargo net, paid in the initial period, , and the second term is the discounted cost of the safety net for the duration of the policy. Breaking it down this way allows us to estimate the cost of providing aid, which we describe in detail in chapters four and five.
The social cost of poverty, , is a function of some poverty measure, , which is partially determined by the level of aid, .
where is some weighting function capturing the negative impact of poverty, however measured, and w t is the vector of wealth levels at time t. We do not explicitly include any discounting function for poverty. Any discounting can be included in the function, , or we can interpret the social planner as being indifferent between poverty today or in the future.
In practice, the weighting function, g , will depend on a number of social factors and captures how much a society dislikes poverty. We do not attempt to prescribe a functional form of g , but we can make some standard assumptions about its form, which intuitively hold across societies. The main feature we will rely on in our analysis is g is a strictly positive function, as poverty is non-negative and has some social cost, which is increasing with respect to poverty, . Simply stated, more poverty is worse for society. For our analysis, we do not need to make any assumptions on the second derivative of , although, intuitively, the social cost of poverty is likely to be increasing weakly faster as poverty becomes more extreme, ¶ 2 g(×) ¶P 2 ³ 0. While our results hold for this extremely general form, for the remainder of the paper we will assume a functional form of g to aid in the discussion and make the interpretations more accessible.
Specifically, we assume the social cost of poverty is a multiplicative transform of the poverty measure: Carter and  show these measures can just as easily be applied to asset-based wealth measures, consistent with our modeling framework.
The Gini coefficient is a measure of inequality rather than poverty, per se. It is a measure of how wealth is dispersed across a population. It is calculated: The coefficient can take on values between zero and one, assuming there is no negative wealth. The larger the Gini coefficient, the higher the inequality of the system is. A Gini coefficient of one means perfect inequality, where one individual has all the wealth, and zero indicates perfect equality. A benefit of this measure is it does not rely upon an arbitrary definition of poverty, such as the poverty line, like the members of the FGT family. The major downside with this measure is it says nothing about the wellbeing of households. A population where all households are mired in extreme poverty will score well (a low value) using the Gini coefficient, while a population with high levels of wealth with significant variability between households will score worse (a high value).
While the latter case is clearly preferable, it also has a more inequality, which highlights why using the Gini coefficient by itself can be misleading. However, looking at the Gini coefficient in conjunction with other measures that include some level of wealth or wellbeing is useful.
The Foster-Greer-Thorbecke indices are a family of poverty measures sharing a common functional form: where indicates the poverty measure, is the asset poverty line, and is the number of households below the poverty line. The poverty headcount, , is the first measure in the FGT family. It doesn't put a weight on anything other than the number of households falling below the poverty line and can be written: .
According to this metric, a starving household is just as undesirable as a household just below the poverty line, which is not realistic. However, it can be used to look at the cost of removing households from poverty, especially if one wants to consider the case of eliminating poverty entirely or to bring percentage of households' in poverty to some level, which may be the objective of some government or aid program. A less severe problem with all of the FGT measures is it does not give any weight to how households above the poverty line. An extremely wealthy household with little to no risk of falling into poverty is viewed as the same as a household just above the poverty line. Because the poverty measures (and therefore the social cost) are weakly decreasing in aid, the solution to minimum total cost ends up depending primarily on the cost function for providing aid. We will demonstrate this feature of the problem more extensively 8 We do not include the Micawber Threshold in the figure as it clutters up the image and is not the focus of the discussion here. Intuitively, it may appear the location of optimal aid will depend largely on the social cost of poverty. However, due to the underlying mechanics of the multiple equilibrium poverty traps, we show the cost of providing aid is the primary concern for our basic wealth policy and the social cost of poverty is a secondary concern, in terms of where the optimal solution lies. Clearly, policy makers can choose no aid, which will come at zero cost. Initially, for very low levels of aid, the cost of providing aid is strictly increasing. Barring some edge cases (to be discussed below), if society values poverty reduction sufficiently to take action, the optimal solution will be characterized by a local minimum in the cost function for providing aid. We will show that this local minimum will be at an aid level very close to the Micawber Threshold in nearly all cases where a dual equilibrium poverty trap is the appropriate model. We will also show that this phenomenon is driven by the structure of the poverty trap model. When the asset poverty line, z, is below M, aid only up to z will result in (definitional 9 ) poverty eradication but at very high cost, so policy makers can keep poverty at zero but lower costs (and raise wellbeing of those affected) by raising the aid level. On the other hand, when z is above M, aid is only needed up to (a small interval above) the Micawber Threshold to propel households to the high equilibrium. The supporting details for these claims are covered in the next section.
The social planner's problem is difficult to solve analytically so we turn to numeric analysis to determine the optimal level of aid to combat the multiple-equilibrium poverty trap. We input our model into MATLAB and simulate wealth paths for households under varying levels of S to calculate the cost of providing aid as well as the level of poverty. We start by generating wealth levels for 10,000 households using a uniform distribution across relevant wealth ranges, 1 to 100, and simulated wealth trajectories without aid for twenty periods. The 20 th period we use as the starting wealth levels, which approximates the steady state distribution resulting from the underlying transition function. We then introduce varying aid levels of S and simulate wealth trajectories for these households, which comprises our data. We vary high technology parameters as well as discount rates and the maximum time period to generate data used to determine what factors impact the cost of providing aid and the likelihood of poverty.
We do not vary the low-technology parameters for simplicity and ease of interpretation.
In previous iterations of this simulation we found the low-technology parameters did not significantly impact our results as the high technology determines M and the surrounding region, which largely determines the optimal aid level. For these data we focus on the effect of the high technology parameters and include only a few levels of T and d to show they do not play a significant role in determining the level of optimal aid and aid levels and the inefficient aid trap.

Poverty Trap and Aid Mechanics
The total cost in the social planners problem is the sum of the cost of providing aid and the social cost of poverty, which is the objective function we seek to minimize. The poverty and, hence, determine the social cost of poverty by assuming some value for the weighting function b , for each aid level. The sum of these components will be the total cost, which the social planner is trying to minimize. In this section, we describe the general form of these cost functions and detail some specific cases to motivate why we can focus almost exclusively on the cost of providing aid for much of our analysis.
While the cargo net is strictly increasing with respect to aid level, the cost of the safety net is decreasing over some range of aid level. This decrease is large enough to cause the cost of aid to be decreasing over much of this range as well.   low, as poverty is not a large concern in either case, resulting in a low level or nonexistent aid. These cases will have aid provided at a level below the start of the inefficient aid trap. While these represent potential situations in the real world, they correspond to situations where poverty is relatively unimportant making much of the discussion of aid policy moot. Thus, we do not focus on this scenario for our analysis and focus on cases where the poverty trap leads to substantial poverty, which society cares about enough to eliminate.

A Poverty Line in the Region of the Aid Trap
A second notable case occurs when the poverty line lies within the inefficient aid trap.
When this occurs, the poverty measures which depend on the poverty line will all go to zero sharply within the inefficient aid trap, making at least some of the range having a zero cost of poverty. This results in optimal aid being exactly the minimum aid level, so long as b is large enough. After the poverty line is reached, the total cost depends solely on the cost of providing aid making the minimum of total cost be equal to the minimum of the cost of providing aid.
First, some notation. In the context of our model, we define the inefficient aid trap as including all levels of aid below Ŝ , for which the cost of aid is at least as high as cŜ ( ) . We denote as S 0 the lower bound of this interval, for which c S 0 ( ) = cŜ ( ) , which is depicted in Figure 3.10. It is immediately clear from the Figure that the case of the low poverty line (Section 3.5.1 above) is one in which z < S 0 . The beginning of the inefficient aid trap occurs when cost first reach this level, which we label S 0 . We do not devote much time to the S 0 as it is not easy to empirically determine, it does not have a useful economic interpretation nor is it particularly relevant for policy decisions.

96
Now, consider the case where z Î S 0 ,Ŝ ( ) . Figure 3.11 shows this case where, once again, the total cost is shown as the solid black line, the dot/dashed line is the cost of providing aid and the dashed line is the social cost of poverty. We see after the cost of poverty goes to zero, which occurs when aid level is higher than the poverty line, which is set at 40 for this example, we see the total cost overlays the cost of providing aid, as these are equal beyond this point. We see the local minimum, which is the minimum aid level also is the global minimum. Thus, when the poverty measure used to value poverty is in the FGT family and the poverty line lies within the inefficient aid level, the optimal aid level lies exactly at the minimum aid level.

A Poverty Line above the Aid Trap
The last case occurs when the b is sufficiently large and either the poverty line lies above the minimum aid level or we are using a poverty measure, which does not depend on a poverty line, such as the Gini coefficient. For this case, the minimum aid level becomes a lower bound for the optimal aid level. The exact location of optimal aid will depend on b , the slope of the poverty measure, and the slope of the cost of providing aid. This is the case where the solution to the social planners problem holds, that is to say the It is the structure of the dual-equilibrium poverty trap model that causes costs to increase relatively faster than the poverty level drops at points above the Micawber Threshold. It is for this reason that the minimum cost aid level is always so close to M, and that the social planner can focus primarily on cost. Once aid is this high, the cost of providing a safety net each period is relatively low. Increasing the level of aid does not have much of an effect on the safety net cost each period, but has a large effect on the initial cost of the cargo net. Thus increasing aid at points significantly above the Micawber Threshold have larger costs due to the increased cost of the cargo net, without a significant change in the likelihood of people falling below the aid level and having to be supported by the safety net. Thus, poverty levels, however defined, do not change to a large degree as safety nets rise beyond this point, but costs do increase significantly.
3.6 Does the Minimum Aid Level Approximate the Micawber Threshold? 98 We identify the upper bound of the inefficient aid trap by finding the minimum aid level, Ŝ , by identifying the local minima of the cost function with respect to the aid level. For the vast majority of aid levels below this point, there will be a higher cost of providing aid with a lower reduction in poverty, which comprises the rest of the inefficient aid trap.
Any aid set below Ŝ with a lower cost will have a non-noticeable impact on poverty levels, which occurs at extremely low aid levels. While we cannot rule out the potential the optimal aid level lies in this region, this would only be optimal if society has a very low social cost of poverty, g , making the discussion of poverty reduction moot as discussed in section 3.5. We focus on the relationship between M and Ŝ to determine whether the Micawber Threshold should be the target level for aid policy. specifications indicates Ŝ has almost a one to one relationship with M , which is significant at the 1% level. We include the high equilibrium and sigma in alternative models, which moderately improves the regression fit; both variables are equal at the 1% level. We see our process variance parameter is has a positive effect on the location of minimum aid, suggesting the more variance in the system, the larger the minimum aid level should be. can be seen in figure 3.14. This occurs because the basin of attraction for the high wealth equilibrium shrinks the closer the Micawber Threshold and the high wealth equilibrium, in other words the equilibrium is less stable. Even if households reach this equilibrium, the stochastic process is likely to drop them below it in the near future. This situation would be better described as a single equilibrium poverty trap with some minor periods of relatively high well being for the initially well endowed, or those who experience repeated good luck, but this high wellbeing is short lived. We look closer at the distribution of minimum aid levels for select parameters later in this section to get a better estimate of how much larger Ŝ is than M . We find the minimum aid level for our basic wealth policy lies slightly above the MT. Rather than looking at the magnitudes of the MT and minimum cost when making this comparison, it is helpful to consider the difference in percentage terms. Positive values are interpreted as how much larger the minimum cost of basic wealth is larger than the MT, in percentage terms, with zero indicating the minimum cost is located at the MT. We find the minimum aid level is, on average, 2-3% larger than the MT. The spread of the distribution is largely determined by the process variance, s 2 , with a larger s 2 increasing the distance between the minimum cost level and the Micawber Threshold.
We also see larger shape parameters, a H and c H reduce the variability and lower the distance. The distribution of minimum aid level lies fully above the MT, for these parameter combinations as these parameters had a sufficiently large gap between the high 100 equilibrium and MT. Table 3 Looking at the cost function around Ŝ , we see costs increase faster below the minimum aid level than above it, ¶c(Ŝ -) ¶S > ¶c(Ŝ + ) ¶S , where the positive and negative sign represent above and below respectively. This is an asymmetric penalty for misspecifying the aid level, where there is a higher penalty, in terms of cost, to set aid too low than too high. Since the level of poverty is decreasing with increasing aid, not only does setting aid too low have a higher dollar cost, it also reduces the effectiveness of reducing poverty. In the empirical setting there may be significant uncertainty in the location of M and it may be impossible to determine Ŝ . Due to the asymmetric penalty, these results would suggest placing an aid level above the estimated MT would be desirable. This has the additional benefit of decreasing poverty to a larger degree while still having a lower expected cost of aid.
Identifying the minimum aid level and the inefficient aid trap is one step short of identifying the optimal aid level for a society, which depends on the social cost of poverty g . However, due to the shape of the cost and likelihood of poverty curves, we can still make some observations about the likely location of the optimal aid level without assuming g . The interaction of the value of g is unlikely to significantly change the aid level and is, at most, a secondary concern.

Discussion
The primary contribution of this chapter is to further our theoretical understanding of how to best provide aid to individuals stuck in a multiple equilibrium poverty trap. Due to the interaction of the multiple equilibrium poverty trap and our basic wealth aid policy, we find the cost of providing aid can be used to locate the optimal level of aid, with the social cost of poverty being, at most, a secondary consideration. This occurs because of the inefficient aid trap, which forms when a safety net is set at a level, which holds individuals at a level without significantly increasing a household's chances of exiting poverty. We find the Micawber Threshold is a good approximation of the upper bound for the inefficient aid trap, which indicates optimal aid should be set slightly above this level. When there is uncertainty in estimating the Micawber Threshold, the asymmetric penalty of misspecifying aid level suggests policy makers should err on the side of setting aid too high so as to avoid the sharp increase in costs caused by setting aid within the inefficient aid trap.
We distinguish between dependency where aid policy attempts to hold individuals up to a level, which they could not maintain without external aid and the inefficient aid 102 trap is when altering level of aid provided can reduce both the cost of providing aid and the level of poverty. Dependency is typically thought of as the aid trap, where households will be dependent on aid to maintain the high-productivity livelihood without being able to accumulate wealth to be able to take advantage of its benefits. In the ASAL example, this would describe the case where you hold household wealth just high enough where they will engage in mobile pastoralism, as it will be more productive than sedentary grazing, but the herd is too small to fully sustain the pastoralists resulting in the herd shrinking until the safety net replaces the livestock.
We find the Micawber Threshold is a sufficient upper bound for the inefficient aid trap, which means optimal aid must be set above the Micawber Threshold. This is consistent with Barrett (2005)  have less ability to protect themselves from disease and sick individuals earn less money, which can cause a downward spiral into inescapable poverty. This system has an added layer of complexity where they consider two sets of assets, wealth and health, and study the interaction. It is unclear whether our policy recommendations hold when poverty traps are caused by the interaction of multiple assets in this way. In the future, we hope to expand this model to answer questions such as these by modeling the resource dependence, in a similar manor to the feedbacks between health and wealth from Plucinski et al. (2011), to assess whether these results are robust to more complex situations, which may cause multiple equilibrium poverty traps.
The distinction between dependency and the inefficient aid trap, which we introduce, may be a useful when determining optimal policy decisions. The independent aid trap focuses on the efficiency of aid policy and should be avoided, as it represents a sub-optimal policy. However, this is not the typical definition of the aid trap. In contrast, dependency is not always sub-optimal. For the case of single equilibrium poverty traps, maintaining a population at a certain wealth level may be desirable even without the chance of reducing the dependency on aid in the future. Aid resulting in dependency may also be desirable in the short term, while larger overhauls of the economic system or other aid is not possible, but may become so in the future. So while both are forms of an aid trap, the IAT should always be avoided while UAT may have some value as part of aid policy.
An important implication of this paper is it does not pay to underinvest in aid, when poverty traps are present. Governments and aid organizations with limited budgets may perceive an incentive to lower the level of aid in an effort to save money. This may be especially true in the empirical setting where identifying thresholds such as the Micawber Threshold have significant uncertainty; an agency may look to select the lower bound of the potential threshold. However, we show this may have drastic implications for cost of aid. Due to the shape of the cost as a function of aid level, there is an asymmetric penalty for setting aid levels incorrectly. Undershooting the aid level has steep costs, resulting in the aid trap and a far less effective aid program, while 104 overshooting aid by the same amount has only a modest increase in cost. This occurs due to the long-term benefits of lifting household wealth to a point where they can accumulate wealth and only require aid when they experience large or repeated negative shocks.
For our analysis we focus on a social cost of poverty modeled as a linear weight on a poverty measure for simplicity. However, results hold for the more general case as well where the cost of poverty is any increasing function of the aid measure. The choice of the weighting function is not as telling as the choice in measure itself. Each poverty measure places different value on aspects of poverty such as inequality or difference from a poverty threshold. A society's preferences for the social cost of poverty will determine which is the appropriate measure to use. The weighting function acts to transform this poverty measure into some dollar amount to be used in comparing the cost and benefits of poverty reduction. Imposing a non-linear weighting function, where costs increase faster as the poverty measure rises, would increase the relative cost of poverty at the lower tail. This would cause the slope of the cost of poverty to decrease (more negative for much of the range), in essence, tilting the social cost of poverty curve to the right.
This would likely shrink the relative size of the inefficient aid trap, as now the relative cost of poverty is lower as we approach the poverty line. However, a weighting function of a poverty measure is more of a theoretic construction and has little practical application.
One shortcoming of our model is it does not allow for behavioral adjustments in response to safety nets, most notably asset smoothing when assets are near the Micawber Threshold. While our model is consistent with asset smoothing in general, we are unable 105 to account for how behavioral responses to shocks and thresholds change in response to policy interventions. Ikegami et al. (2016) provide compelling work highlighting the importance of how ex-ante responses to negative shocks change when individuals risk exposure is reduced due to intervention policies. They show there can be a significant crowding-in of investment due to risk reduction policies, making it optimal for agents to invest into risky productive assets increasing their likelihood of escaping poverty.
Additionally, safety nets may crowd-in asset transfers from informal insurance systems as there is a higher chance of reciprocity in the future when aid is present, due to less chance of being forced to exit pastoralism (Santos and . Since we do not account for responses to aid programs, results of our model may produce a downward bias on the effectiveness of the basic wealth policy in reducing poverty. It is unclear how allowing agents to respond to aid would affect the optimal level of aid and is a direction for future research. An alternative interpretation of our model is it represents myopic agents who do not alter their behavior due to safety nets, which may not be so grand an assumption in development contexts. Little (2008) suggests most Northern Ethiopian farmers are not 'foolhardy' enough to rely on external food aid, a form of safety net, as it is uncertain and usually poorly timed. If this perception is prevalent, behavioral responses to safety nets may take a long period to form or may not come about at all, which adds some validity to our results.
Additionally, we only consider the case where a multiple equilibrium poverty trap is known to exist and exists with a known, unique Micawber Threshold. Barrett and Carter (2013), among others, highlight how this rarely, if ever, occurs in the real world.
For one, individual characteristics, such as innate ability, will heavily influence the varying Micawber Thresholds or even with some individuals all-but-destined to be pulled towards a low equilibrium due to innate characteristics and circumstances.
With these limitations in mind, this chapter is intended to get us another step closer to providing policy recommendations about how to help those stuck in a poverty trap. Incorporating these additional complexities and analyzing how the recommendations for optimal aid changes will hopefully provide important insights, which will translate into more informed policy decisions in the future.           Each column has the same variance parameter and each row has the same shape parameters. The histograms, in blue, show the density at each level of percent difference and the red lines are the Gaussians fitted to the data. Data come from simulating cost of providing basic wealth at various levels, with minimum cost defined as the level of basic wealth resulting in a local minimum of cost. Simulation was performed one thousand times for each set of parameters.

Introduction and background
There has been a long-standing difficulty in empirically identifying multiple equilibrium poverty traps. This could be because existence of these wealth dynamics is relatively rare or non-existent, the tools used to identify poverty traps are insufficient, or a combination of the two. As discussed in section 3, multiple equilibrium poverty traps are distinct phenomena under the umbrella of poverty traps. This chapter introduces two novel methods of testing for multiple equilibrium poverty traps, by testing for necessary features of asset transformation at the individual and village levels as well as in the pooled data, which the data must exhibit if a multiple equilibrium poverty trap exists. We perform these tests on the publicly available replication dataset where a poverty trap has previously been found to exist, published under Lybbert et al. (2004). Unfortunately, we find little evidence of a poverty trap in these data. Lybbert et al. find a multiple equilibrium poverty trap consisting of two stable dynamic wealth equilibria, which we refer to as a dual equilibrium poverty trap, by non-parametrically fitting a curve to the data using kernel smoothing. Their identification of a dual equilibrium poverty trap is simply a graphical observations of a non-convexity in wealth accumulation. However, we find these data do not exhibit behavior necessary for a dual equilibria poverty trap, namely mean reversion of wealth at the household level and two clusters of wealth changes in the pooled data and for each village. The non-convexity in asset dynamics found by Lybbert et al. (2004) may occur when fitting a curve to the data, but is not indicative of a poverty trap. Instead the fitted curve may be picking up clustering in the data more consistent with heterogeneous differences in asset levels and shocks over 125 villages in the sample. This chapter emphasizes the importance of using multiple tests to ensure the data are consistent with an empirical model, in this case multiple equilibrium poverty traps, rather than relying too heavily on curve fitting techniques which may cause spurious overfitting.
There are many known issues surrounding the identification of poverty traps, which Barrett and Carter (2013) discuss in detail, making them difficult to empirically identify should they exist. First there will be very few observations around the Micawber Threshold, the unstable dynamic equilibrium separating the basins of attraction between high and low wealth equilibria. When the equilibria have substantial pull, few households exist that switch between wealth equilibria. The nature of the unstable Micawber Threshold rapidly pushes wealth away from it resulting in few observations around this level. This makes it difficult to identify the location of the Micawber Threshold as well as to observe the non-convexity in asset accumulation, which occurs at and below this point.
A second issue is it is difficult to distinguish between state dependence and heterogeneous single equilibria. So even observing two clusters of wealth levels is not sufficient to determine a multiple equilibrium poverty trap exists. In our analysis, we circumvent these issues by answering the question of can a multiple equilibrium poverty trap exist in our data, rather than does it exist. This distinction is important, as we do not attempt to distinguish between these alternative hypotheses in this paper. Thus, we seek to provide evidence against the existence of a multiple equilibrium poverty trap when it is not present.
Another difficulty with identifying the multiple equilibrium poverty trap is heterogeneous factors at the household level may allow for some households to 126 experience a multiple equilibrium poverty trap while others in the same population may have only one dynamic equilibrium (e.g. Ikegami et al., 2016) or none at all (i.e. a random walk). Given data constraints, most notably length of time and sampling frequency, empirical estimation typically requires assuming a common underlying asset dynamic for all households (Nashold, 2013, Jalan andRavallion, 2004) exasperating this issue. We surmount this issue by testing for mean reversion at the household level. This test does not require an assumption of identical underlying asset dynamics.
There are also temporal issues that play a role. Micawber Thresholds may shift over time due to changing market/environmental conditions making the identification of unstable equilibria more difficult. This is problematic because identifying poverty traps usually requires a dataset spanning large time intervals increasing the likelihood of a changing Micawber Threshold. Using cross-sectional data does not provide enough information to identify a multiple equilibrium poverty trap, as it does not capture the wealth dynamics. A multiple peaked cross section of wealth levels is neither necessary nor sufficient to indicate a poverty trap. Thus, we need to look at assets over time. A long sampling period is desirable because many of the tests for multiple equilibrium poverty traps rely on asset based measures, which are slow to change over short samples. The methods we present do not help alleviate these issues and require assuming any dynamic equilibria are stable over time.
These issues have been used to explain why poverty traps have been so difficult to identify in practice even in socio-economic environments where theory suggests they are likely (e.g. Jalan and Ravallion, 2004;Lokshin and Ravallion, 2004;Kraay and Raddatz, 2006;Nashold 2013;McKay and Pergee, 2014). These papers all seek to identify poverty traps in rural areas of Southern Africa with limited access to financial markets, and few potential livelihoods (mostly subsistence farming and pastoralism). Barrett and Carter (2013) make the case that failing to find multiple equilibrium poverty traps should not be interpreted as evidence against the existence of poverty traps. The authors take the stance that many of the empirical difficulties involved likely cause researchers to fail to identify poverty traps in cases where they exist. A major contribution of this chapter is we design falsification test designed to look for necessary conditions of the poverty trap without attempting to identify the Micawber Threshold explicitly, as there are many difficulties in attempting to locate it, which we discuss below.
The empirical literature has seen numerous strategies attempting to directly identify the elusive multiple equilibrium poverty trap by estimating wealth dynamics, especially attempting to identify non-convexities in wealth growth over certain ranges as well as locating stable and unstable dynamic equilibria. The direct measures can be broken down into three categories based on identification methods. Nashold (2013), provides an in depth discussion of these. The first methods were fully parametric methods that fit some polynomial function, which allow for some non-convexities (i.e. cubic and higher order) and identify whether the estimated function results in multiple dynamic equilibria (e.g. Jalan and Ravallion, 2004;Santos and Barrett, 2011). These models are overly restrictive, as they require specifying a cubic or quadratic function to the data.
Alternatively, non-parametric techniques allow for more flexibility by not restricting estimation to fit a specific functional form and have been successful in identifying poverty traps in some contexts (e.g. Adeto et al., 2006;; this is the technique used by Lybbert et al. (2004). These techniques fit kernel, or other smoothing functions, to the data to look for non-convexities in wealth accumulation, providing more flexibility than the parametric techniques. While more flexible, the nonparametric fitting still requires substantial observations around the non-convexity and the Micawber Threshold, which are rarely occurring. In fact, having enough data to fit the non-convexity with any degree of confidence either requires a massive data set or is indicative of a lack of a poverty trap.
The Micawber Threshold necessarily represents an unstable dynamic equilibrium, which, in essence, is constantly pushing wealth away from it. This must result in the distribution of wealth levels will be more disperse around this point making the likelihood of observing wealth in this area should be low. Failing to account for this is problematic and may lead to spurious identification of multiple equilibrium poverty traps when they do not exist. Previous identification strategies have treated the identification of a non-convexity as sufficient evidence of a multiple equilibrium poverty trap, however it is merely a necessary condition of one. A non-convexity found by fitting a curve through a large cluster of data is inconsistent with a multiple equilibrium poverty trap making this form of identification insufficient to determine a multiple equilibrium poverty trap to exist. The curve fitting approaches, both fully parametric and semi-parametric, have typically not accounted for the likelihood of observations and are prone to over identifying poverty traps in this maner, which we show is likely the case for the findings in Lybbert et al. (2004) using their replication data. Nashold (2013) analyzes the effectiveness of the fully parametric and nonparametric methods of directly observing poverty traps and recommends using semiparametric techniques to combine the flexibility of non-parametric techniques with the ability to control for household characteristics and time fixed effects (Nashold, 2013).
Using household data from Pakistan and Ethiopia, Nashold (2013) assess the effectiveness of numerous means of testing for poverty traps and finds one unique dynamic equilibrium around the same level using a parametric, non-parametric and semiparametric methods. While Nashold holds the semi-parametric approach will perform better when there are non-convexities underlying the multiple equilibrium poverty traps, the semi-parametric methods still face many of the challenges of the direct measures most notably a lack of observations around the non-convexity.
More recently, Barrett and Carter 2013, among others, have begun using indirect methods of testing for poverty traps, which circumvent many issues of the direct approaches. These indirect methods seek to identify behaviors, which are consistent with multiple equilibrium poverty traps. These methods seek to identify behaviors, which would only be rational if individuals were indeed facing a multiple equilibrium poverty trap, such as asset smoothing at certain wealth levels (Zimmerman and Carter, 2003;Carter and Lybbert, 2012), exhibiting risk seeking behavior when assets are just below a Micawber Threshold (Lybbert and Barrett 2011), or avoiding providing gifts and loans to households near the Micawber Threshold (Santos and . These techniques bypass many of the empirical challenges presented in Barrett and Carter (2013) and show signs of being able to improve our ability to identify MEPT.
These indirect methods do not rely on identifying the non-convexity in wealth accumulation nor require locating the Micawber Threshold. A benefit of indirect estimation is it typically does not require assuming an underlying wealth dynamic for all households, as is typically required for the direct measures, as many of the implications 130 of poverty traps can be specified in a way to require a smaller sample size and can be done at the individual level. Testing for non-convexities at the individual level is near impossible, while testing for specific savings and consumption decisions should be done at the individual level. Thus these methods should perform better than direct methods when heterogeneity across individuals may result in different wealth equilibria within a sample by being able to distinguish between these competing hypotheses.
In this chapter, we introduce two novel indirect tests, which can be used to rule out the existence of a multiple equilibrium poverty trap. Rather than identify the Micawber Threshold or the non-convexity in wealth dynamics, in-line with previous direct test, we focus on identifying trends in the data that are consistent with the existence of dynamic equilibria. We test for dynamic equilibria in two ways: (1) testing for mean reversion using a variance ratio test at the individual level, and (2) using a mixture model to determine if there is clustering in the pooled data and for each village, which would indicate multiple dynamic equilibria.
If a dynamic wealth equilibrium exists, then we should observe wealth trending towards it over time with some random error, in other words we should observe mean reversion. When there exist multiple dynamic equilibria, we should observer mean reversion so long as wealth remains in the basin of attraction of one equilibrium.
However, once the Micawber Threshold is crossed due to the stochastic nature of wealth dynamics, wealth will tend towards a different equilibrium making it more difficult to identify mean reversion in the sample. The nature of multiple equilibrium poverty traps makes it relatively rare for these thresholds to be crossed, resulting in the few 131 observations around Micawber Thresholds (Barrett and Carter, 2013) ensuring the majority of the sample will not have this issue.
We test for mean reversion of wealth at the household level using a variance ratio test. While the existence of mean reversion for an individual is a necessary condition for the existence of a multiple equilibrium poverty trap, it is also occurs in the case of one dynamic wealth equilibrium. This mean reversion is a necessary, but not sufficient condition of a multiple equilibrium poverty trap. The variance ratio test is used to test the hypothesis that the rate of return of an asset (in our case the change in wealth from one period to the next) is independent over time, indicating a random walk. It rests on the known statistical property that the summation of two independent random variables will have a variance equal to the sum of the two. Thus, an independent time series will exhibit additive variance of the rate of return (change in wealth) as the time-step increases, which is used to construct the null hypothesis of a random walk. The variance ratio test has been widely used in the finance literature to determine whether financial asset prices are mean reverting, trend following or if they follow a purely random walk (e.g. Lo and MacKinlay, 1988). We test this at the household level and do not find statistically significant evidence of mean reversion in our data. In principle, this test can be done with the pooled and at thevillage level, but is unlikely to identify mean reversion unless household equilibria are clustered together. We find slightly over 5% of individuals exhibit mean reversion, which is not significantly different than what we would expect to find due to random chance. This is evidence against the existence of dynamic equilibria for individuals and, therefore, evidence against the existence of the dual equilibria poverty trap found by Lybbert et al. (2004). While this test rules out a multiple 132 equilibrium poverty trap, we also fit a mixture model to the data at the village level and with the pooled data and find further evidence against the existence of multiple equilibrium poverty traps. We find some evidence concerning potential spurious overfitting by Lybbert et al. (2004) by identifying a cluster of data near the Micawber Threshold identified in their paper.
Finite mixture models have been used to empirically classify individuals into different groups or 'clusters'. A finite mixture model is essentially a form of latent class modeling intended to identify different classes of individuals within a population (Harrison and Rutström, 2009). It can be used to both identify the number of clusters within a dataset as well as predict assignment of individuals to each cluster. In economics, finite mixture models have been predominantly used to determine behavioral decision rules when playing games or making complex decisions (Bruhin et al., 2010;Conte et al., 2010;Sproul and Michaud, 2017). In the development field, finite mixture models have been used to look at informal employment (Günther and Launov, 2009) and even wealth dynamics at the macro-level (Alfo et al., 2008;Owen et al., 2009). Alfo et al. (2008 find the Solow growth model performs better when allowing for multiple heterogeneous groups fitted from a finite mixture model. Similarly, Owen et al. (2009) find multiple single equilibria at the macro-scale, which is best predicted by institutional features rather than factors such as region or income. We perform a similar analysis at the micro-scale, while focusing on using the fitting of the mixture model to test for multiple equilibrium poverty traps.
We fit a mixture model to the pooled data and for each village in an attempt to observe clustering consistent with the dual equilibria poverty trap found in Lybbert et al 133 (2004). The larger scales have the benefit of larger sample size, but at the cost of any heterogeneity within the sample potentially obscuring the multiple equilibrium poverty trap, should it exist. However, a mixture model fit at the individual level runs the risk of over fitting due to small sample size. It also should fail to observe clustering around multiple equilibria for those stuck at a single equilibrium and will prefer a single component mixture. We find no evidence to support the existence of the dual equilibria poverty trap using the mixture model. Depending on the criteria used to select the best fit, which we discuss in the methods section, we find either the one cluster or three cluster model is fits the data better for the pooled data as well as at the village level. We perform a numeric simulation to show AICc and BIC perform well at fitting the two cluster model when it exists, indicating we either have more than two equilibria or wealth dynamics follow a random walk, both of which are inconsistent with the Lybbert et al. (2004) findings.
While neither test is sufficient to determine the existence of a multiple equilibrium poverty trap, both test for necessary features. Using the data from Lybbert et al. (2004) we find the data does not exhibit mean reversion at the individual level and do not find evidence of clustering consistent with the dual equilibria poverty trap found by Lybbert et al. (2004) using pooled data or at the village level. These tests can be used to rule out the existence of a multiple equilibrium poverty trap, but do not help us in distinguishing between alternative hypotheses such as heterogeneous dynamic equilibria for individuals, which we save for future work. These methods circumvent the issues surrounding the direct tests for multiple equilibria, as they do not require identifying neither non-convexities nor the location of a Micawber Threshold. Thus, these tests are most useful as a first pass at identifying a multiple equilibrium poverty trap or as a falsification test for previous empirical studies where poverty traps have been reported.

Context and data
Our data come from pastoralists in the Borana Plateau of Southern Ethiopia over 17 years from 1980from -1997from , which Lybbert et al. (2004 have used previously to identify a dual equilibrium poverty trap. The data contain 55 randomly selected households from four villages 10 . From these 55 households, we restrict our sample to households with at least 10 years of data leaving us with 49 households from four villages. The data contain information of household size, herd size, animal births, animal mortality, herd sales, herd gifts, and a measure of rainfall. Lybbert et al. (2004) show sales represent an insignificant portion of herd loss and gifting/loaning animals is relatively limited so we do not separate these factors from herd mortality. For our analysis we focus on herd size per capita, in line with previous poverty trap literature for the region (e.g. Lybbert et al., 2004, Barrett, 2005Toth, 2014).
An important caveat to the data is it only includes information for pastoralists active in 1997 (Lybbert et al. 2004). Any households that exited the sample during the study period are not included. Through discussion with pastoralists in the area, Lybbert et el (2004) state those exiting pastoralism were households with small herds coupled with negative shocks from drought, disease or distress sale with no households 'graduating' 10 Village membership refers to a household living near the geographic village center over the course of the sample. Village membership is stable with the typical household being part of the same village for multiple generations.
135 out of pastoralism due to high wealth levels. Thus, there is an upward attenuation bias present in the sample. Failing to observe the households forced out of pastoralism potentially obscures a low-level Micawber threshold below which pastoralism altogether becomes unsustainable. However, we should still be able to identify a multiple equilibrium poverty trap arising from the switching between sedentary pastoralism and mobile pastoralism even with the attenuation bias.
The data we use may not be identical to the final data used in Lybbert et al. (2004). When we received the data from Travis Lybbert, he explained the code for their analysis as well as their finalized version of the data were lost due to a computer failure.
We have had substantial difficulty in recreating some of their results using this data set, which is also publically available alongside their publication. The inability to replicate results is problematic for conducting a conclusive falsification test of the Lybbert et al. (2004) results, but we proceed with our analysis to test for a multiple equilibrium poverty trap within the data we were able to obtain.
The Borana Plateau is characterized as arid and semi arid lands (ASAL). As discussed in sections 1 and 3, the pastoralist system of the ASAL is an ideal context to study poverty traps. There are apparently similar groups, mobile pastoralists and sedentary households, with very different outcomes. A compelling explanation is there is a critical herd size, which is their primary form of wealth and productive assets, above which households can engage in the more lucrative mobile pastoralism, but below which requires the household to stay near the village resulting in a dwindling herd and possible exit from pastoralism altogether. This phenomenon arises due to less grazing area 136 accessible to sedentary pastoralists, which is potentially exacerbated by the 'tragedy of the commons' in which sedentary herds over graze the pastureland close to the village.

Wealth Dynamics
How wealth changes over time is the fundamental question of the poverty trap literature.
The multiple equilibrium poverty trap describes the case where similar households have access to multiple long-term stable equilibria depending on initial asset levels, where at least one of these equilibria lies below some poverty line. See chapter 3 for a more detailed discussion of the theoretical mechanisms, which result in multiple equilibrium poverty traps. We seek to distinguish this from alternative hypotheses of heterogeneous single equilibria and that assets follow a random walk. We use this observation to construct two tests, one explicitly testing for mean reversion a second attempting to identify multiple equilibria in our dataset using a mixture model to pick up any clustering in the wealth dynamics.
First we test for mean reversion at the individual level using a variance ratio test to determine the existence and direction of autocorrelation of wealth dynamics. The variance ratio test has been widely used in the finance literature to test whether financial assets follow a random walk (e.g. Lo and MacKinlay, 1988). We apply this technique in a novel way to attempt to identify a multiple equilibrium poverty trap by explicitly testing for mean reversion.
The variance ratio test is based on the statistical property that the variance of a sum of independent random variables is equal to the sum of the individual variances: .
Using this, we can look at the ratio of variances at different time steps to test whether wealth dynamics exhibit autocorrelation. The variance ratio is: .
The null hypothesis of the variance ratio test is that , i.e. assets do not exhibit autocorrelation and follow a random walk. When assets exhibit mean reversion.
We perform the analysis comparing the variance of a two year lag, , to the one year lag. Testing on additional lags can also be done, but requires more intricate multiple hypothesis testing to avoid overfitting or too many false rejections of the null (Chow and Denning 1993), which we save for future work. We follow Lo and MacKinlay (1988) and use overlapping datasets to ensure maximum sample size (n-1 time periods rather than n/2 which would occur if you did not allow for overlapping), which they show enhances the power of the variance ratio test. A downside to the overlapping approach is it becomes more difficult to determine the distribution of the variance ratio test statistic making statistical inference more difficult (Charles and Darne, 2009 T -1 143 independent due to the bootstrapping procedure. We perform this procedure 10,000 times per individual, which becomes our empirical distribution of the variance ratio for each individual.
As we are testing for the presence of mean-reversion, a one-tailed test is appropriate. We determine the 95% confidence interval for the one-tailed variance ratio test by ordering the simulated variance ratios and finding the 500 th test statistic. When the observed variance ratio is below this number, we reject the null hypothesis that assets follow a random walk in favor of mean reversion. Figure 4.3 shows the distribution of simulated variance ratios for a sample village along with the location of the 95% confidence level for the one-tailed test, in red, and the observed variance ratio, in black.
For this household, we observe variance ratio is below the 95% confidence level and reject the one-tailed variance ratio test. We perform this test on the 39 households with all 17 years of data.
Our next test uses a finite mixture model to determine if we observe clustering in the pooled data and village data, consistent with the dual equilibrium poverty trap found in Lybbert et al., (2004). Finite mixture models use the expectation maximization (EM) algorithm composed of two steps: the expectation step (E) and the maximization step (M) (Dempster, et al., 1977). This is an iterative process starting with a starting value of the parameters of Gaussian clusters; the procedure estimates 'membership probability' of observations in other words it determines which component each observation is expected to be drawn from, called the E-step (expectation). With the membership probabilities fixed, the log likelihood is then maximized, the M-step (maximization), by varying the parameters of the clusters. This is followed by another E-step and repeats until convergence is reached. The number of clusters is fixed, but the procedure can be run for different number of clusters.
We determine the appropriate number of clusters by comparing different criterion assessing how well each model fits the data. The typical criterion; Akaike information criterion (AIC), corrected Akaike information criterion (AICc), and Baysian information criterion (BIC) can all be used, however, Biernacki et al. (2000) find these do not sufficiently penalize additional clusters and introduce a modification of the BIC, which they name the Integrated classification likelihood (ICL), which they find performs better for selecting the appropriate number of clusters in a finite mixture model. A caveat with the ICL is in its standard definition the best fit is the one which maximizes ICL, which is opposite of the other criterion where the best fit is determined by the minimum value. For the remainder of the paper, we will use the term ICL to refer to the negative value of ICL for ease of interpretation.
There is no guarantee the criteria will all agree on the model that provides the best fit for the data and we find AICc and BIC reliably prefer a different number of  households where we reject the null hypothesis of a random walk in favor of mean reversion. Here we would expect to find 5% of households exhibit mean reversion due to random chance, which is nearly identical to the 5.13% we find. A lack of mean reversion is evidence against the existence of equilibria where household wealth converges. This applies both to the multiple equilibrium poverty trap hypothesis and heterogeneous single equilibria hypothesis. Put another way, our variance ratio testing results fail to reject the independence null (aka a random walk).
One potential problem with our analysis is the shock caused by the 1983-85 famine in Ethiopia may impair our ability to identify mean reversion even if it exists. As discussed in section 1, this period represents a major downward shock resulting in substantial loss of cattle (Lybbert et al., 2004). The presence of severe downward shocks would make identification more difficult. To account for this, we run the variance ratio test on the data for years after 1985 to determine if mean reversion exists in this subsample. Figure 4.5 show the distribution of the variance ratio tests and the corresponding one-tailed 95% confidence interval for the restricted sample. We see only one observation outside this range, consistent with the random walk hypothesis. Thus, we see consistent results amongst the whole data set and when the pre-famine years are excluded, which enhances our confidence in these results.
We also fail to find evidence of the dual equilibrium poverty trap using pooled data and at the village level using the finite mixture model. We present two new indirect testing necessary conditions of a multiple equilibrium poverty trap to overcome some of the empirical difficulties associated with identifying poverty traps. These are intended to be falsification tests used to identify cases where a poverty trap is not present, rather than attempting to identify the poverty trap itself. We apply these tests to the Lybbert et al. (2004) data, where a dual equilibria poverty trap was previously found, but find contradictory results.
First we attempted to identify mean reversion for individuals using a variance ratio test and find an insignificant portion of the population experience mean reversion over our 17-year sample. Failing to identify mean reversion is evidence against the existence of any equilibrium for household wealth, much less multiple dynamic equilibria consistent across villages and households as found in Lybbert et al. (2004).
Next we try to explicitly identify the two stable dynamic wealth equilibria identified in Lybbert et al. (2004) Lybbert et al. (2004) is likely a spurious result due to curve fitting picking up some minor clustering of the data. Using the pooled data, we find some clustering around the Micawber Threshold identified in Lybbert et al. (2004), around a 149 herd size of 15 cattle, which may explain why they identified a non-convexity in asset accumulation before this point. However, the large mass of observations around this level is actually evidence against the existence of a Micawber Threshold at this location, as the Micawber Threshold is an unstable equilibria and theory suggests it would push wealth away from this level rather than pulling towards it as clustering suggests.
One major caveat that weakens this claim is our tests require the assumption any wealth equilibria are stable over time for individuals. If socio-economic conditions such as infrastructure change, natural resource levels (e.g. health of nearby grazing lands), adoption of new technology, etc. change over time, there is no guarantee dynamic wealth equilibria will be stable. Thus, the mean where wealth keeps reverting towards may be a moving target, which may appear as a random walk. This would be especially true if these changes were gradual over time.
While poverty traps may exist for other populations, failing to find it in a data set which had previously identified a dual equilibria poverty trap in an environment where theory suggests we would be most apt to find a poverty trap [is disheartening]. This highlights the importance of empirically identifying a multiple equilibrium poverty trap using a robust set of tests before attempting to combat poverty using the results we present in section 3. While the theory of poverty traps may still be useful, this paper presents evidence multiple equilibrium poverty traps may not be as prevalent as previously thought. In the future, we intend to apply these tests to other data where poverty traps have previously been identified using direct tests to determine if these tests are systematically finding poverty traps when they do not exist. The solid red line depicts the empirical distribution using kernel smoothing and the dashed line represents the normal distribution fitted to the data. For the second figure, the right half of a normal distribution, as absolute values are all positive. All figures use a log wealth scale on the x-axis and the density on the y-axis. The first graph depicts the change in log wealth. The middle graph captures the absolute distance of an observation from the mean, taking into account both x and y distances. The right graph depicts the distance from the mean with negative values indicating the x-value is less than the mean x-value.

Figure 4.2: Distribution of estimated variance Ratios for an individual
Notes: Figure depicts the density of the simulated variance ratios for an individual. The vertical red line represents the one-tailed 95% confidence interval and the black vertical line is the observed variance ratio for the individual. The observed variance ratio lies below the 95% percent confidence interval indicating we reject the null hypothesis of a random walk in favor of mean reversion for this individual. Variance ratios are calculated for comparing a two period lag, , to the one period lag.

Figure 4.3: Difference between observed variance ratio and one-tailed 95% confidence interval for all individuals
Notes: Figure depicts the difference between the observed variance ratio and the onetailed 95% confidence interval for the 39 individuals with the full 17 periods of wealth observations. The red line depicts a normal distribution fitted to the data. Observations below zero indicate a rejection of the null hypothesis changes in wealth follow a random walk in favor of mean reversion. We reject the null for two individuals out of the 39 total, which is nearly identical to the 5% we would expect to observe by random chance. Variance ratios are calculated for comparing a two period lag, , to the one period lag.

Figure 4.4: Difference between observed variance ratio and one-tailed 95% confidence interval excluding famine years
Notes: Figure depicts the difference between the observed variance ratio and the onetailed 95% confidence interval for the 39 individuals with the full 17 periods of wealth observations. We exclude timer periods prior to 1986 to remove the famine years. The red line depicts a normal distribution fitted to the data. Observations below zero indicate a rejection of the null hypothesis changes in wealth follow a random walk in favor of mean reversion. We reject the null for two individuals out of the 39 total, which is nearly identical to the 5% we would expect to observe by random chance. Variance ratios are calculated for comparing a two period lag, , to the one period lag.  Table shows summary statistics for wealth for each village. We see substantial differences in mean herd sizes across villages, which indicates different conditions across villages. Notes: Table presents the information criterion AICc, BIC and negative ICL for fitting mixture models with one, two and three components respectively. We present the negative ICL for ease of interpretation, as the proffered model for each criterion is the minimum value within the column, which is presented in bold font. We reliably find AICc and BIC to prefer a three-component mixture, while ICL prefers one for the pooled data and for each village.  Table presents the percentage of times AICc, BIC and negative ICL fit mixture models with one, two and three components respectively for simulated data with a twocomponent model. Parameters for the simulated data come from the 2-component mixture fit for the pooled data and villages respectively. We find AICc and BIC perform well at fitting the two component mixture, when it exists, with AICc being more prone to over fitting the number of components. We find ICL consistently fits the one-parameter model, indicating it does not perform well at identifying the two-component mixture. Additionally, we present the number of households in each sample, which we label n.

Conclusion
In this dissertation, we investigate poverty alleviation strategies and their outcomes to shed light on how to optimally provide aid. Optimal aid needs to take into account not only the underlying cause of poverty and associated underlying asset dynamics, but also identify potential spillovers aid may have. Understanding the context of poverty in terms of the socio-economic and environmental contexts is critical in order to ensure poverty alleviation is performing effectively. We focus on rural populations in eastern Africa, specifically Tanzania and Ethiopia.
Using a CCT conducted in Tanzania designed to increase health and education outcomes as well as provide poverty relief, we find there is an increase in both direct fishing pressures from additional households earning money from fishing due to program participation as well as an increase in demand for seafood. Both put increased pressures on local fisheries indicating this poverty reduction policy may have negative spillovers in terms of local fisheries. While we do not go as far as to say this policy is sub-optimal because of these spillovers, we stress the importance of identifying spillovers and planning for them when combating poverty.
The results from chapter two are insufficient in themselves to determine if the spillover into the fishery sector is a negative impact. The other part needs to be to determine if local fisheries are at risk of overfishing. Understanding the complete picture of the nexus between the environment and the socio-economic system is essential to understanding the extent of how spillovers will impact the long-term wellbeing of the people in the system. In a study using data up to the year 2000, Berachi (2003) finds many Tanzanian fisheries have already surpassed their maximum sustainable yield. Thus, increased fishing pressures both from increased consumption and additional households 158 using fishing as an income source, are likely to exasperate the issue. Especially in inland fisheries, even when fish catches are increasing, it is possible to be depleting stocks of specific fish resulting in a loss of biodiversity and reduction in the sustainability of the fishery (Allens et al., 2005). The spillover into the fishery sector are not necessarily a bad thing, especially when fish populations are abundant.
In cases where local fisheries can support additional fishing pressures, the spillover could be a substantial benefit to the populations. We found the moderately poor show increased likelihood of entering the fishing industry due to participation in the CCT. One explanation is these households were able to afford fishing gear and gained access to an additional revenue stream and means of diversifying income. In other words, they are able to surmount a Micawber Threshold, which could put them on a higher wealth trajectory resulting in long-term reduction in poverty. Regardless of the state of fisheries, it is important to consider how poverty reduction policies may impact the fisheries in order to manage the fishery in a sustainable manor. A change in aid policy may need to be accompanied by changes to how nearby fisheries are managed in order to maximize the benefit of the aid program and minimize the negative spillovers to the fishery sector.
Next we present a poverty trap model based loosely on the pastoralist system of the Borana Plateau in southern Ethiopia and use numeric simulation to determine optimal level of aid. We identify a range of inefficient aid levels below the Micawber Threshold, which we define as the inefficient aid trap. This region is characterized by higher rates of poverty and higher costs due to individuals being held at an unsustainable wealth level.
Thus, the Micawber Threshold is a lower bound for optimal aid level and that aid should 159 be set significantly above the estimated Micawber Threshold when there is uncertainty of the location of the Micawber Threshold to avoid the aid trap. Additionally, we find the coupled relationship between level of poverty and cost ensures the cost of poverty alleviation is the primary driver of optimal aid and the social cost of poverty is a secondary concern. These results hold for multiple equilibrium poverty traps where households have homogenous dynamic wealth equilibria. We leave it for future research to determine if this changes when we allow for heterogeneity amongst individuals. Initial steps would be to use a more complex poverty trap model in line with Ikegami et al.
(2016), which allows for heterogeneous individual ability and apply a basic wealth policy to these individuals. Additionally, the model would benefit from explicitly modeling the intertemporal choice problem of individuals to include consumption and savings decisions to determine if the ability to asset and/or consumption smooth impacts the results of our basic model.
Lastly, we introduce a novel test to identify multiple equilibrium poverty traps.
We provide a falsification test by identifying mean reversion at the individual level using a variance ratio test. This test surmounts two major issues associated with identifying multiple equilibrium poverty traps: few observations near the Micawber Threshold and it allows for heterogeneous equilibria across the population. Our second test utilizes a mixture model to identify clustering in the aggregate and village level data. This is a weaker test, as it still requires assuming homogeneous wealth equilibria. We find no evidence using a data set, which was previously used to identify a multiple equilibrium poverty trap. This highlights the importance of rigorous tests to ensure we know the underlying cause of poverty to ensure we combat it appropriately.

160
The empirical difficulty of identifying multiple equilibrium poverty traps has important implications for the theoretic model of combating poverty traps described in chapter 3. First, it is extremely unlikely that poverty traps exist where there is a unique and stable Micawber threshold for all individuals within a population. Even in the relatively simple environment of the Boran pastoralists, it is difficult to identify a unique Micawber threshold within a village, much less across the sample as we show in chapter 4. When more diverse livelihoods are available, which is the case in most contexts, this problem is exasperated. Thus, finding a unique Micawber Threshold to use as a target for aid policy is unlikely to be appropriate. However, our finding of asymmetric costs of incorrectly targeting aid still has important policy implications even when heterogeneous Micawber Thresholds exist.
Rather than looking at our results in terms of an overarching policy, we can think of it in terms of each individual. As there is a much larger cost for underproviding aid to any individual, when Micawber Thresholds are heterogeneous providing a basic wealth policy should be placed towards the upper end of the distribution of individual Micawber Thresholds. For instance, assume two individuals with heterogeneous Micawber Thresholds (similarly you could think of two groups of individuals with each group having a unique Micawber Threshold). Targeting a basic wealth policy at the average of the two Micawber Thresholds would clearly be inefficient as increasing the level would reduce the cost of providing aid as the cost of providing aid is much higher when aid is set below the Micawber Threshold compared to an equal amount above it. This inference is similar to the case of an uncertain Micawber Threshold, where erring on the side of overproviding aid is preferable when combating multiple equilibrium poverty traps.

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An alternative approach to combating multiple equilibrium poverty traps heterogeneous Micawber Thresholds could be using insurance. This would benefit from not having to identify individual Micawber Thresholds, as individuals would be able to determine when and how much insurance to purchase based on their experience and conditions. The issue with insurance is those closest to the Micawber Threshold, i.e.
those who would benefit most, are least likely to purchase insurance as the cost may place them below the Micawber Threshold (Janzen 2012). Insurance would only be effective with substantial subsidies designed to allow those near the Micawber Threshold to invest in it.
Future work concerning exactly how to incentivize this while minimizing the issues of the moral hazard problem would improve the effectiveness of this approach.
The goal would be to allow individuals to insure up to their individual Micawber Threshold or, potentially, slightly above it at a rate far below the fair insurance price.
However, if they desire insurance above this level, the price should rise approaching the fair price. However, this would either require extensive cost to identify individual Micawber Thresholds or accepting some level of providing aid in the form of insuring at a higher level than the Micawber Threshold for individuals with relatively lower Micawber Thresholds. While potentially costly, this additional insurance would be a form of aid, which would effectively provide a safety net and protect individuals from falling into the poverty trap region and when set at reasonable levels near the upper end of the Micawber Threshold distribution, may represent significant improvement in efficiency for society. Determining the appropriate level of insurance and how to discount it should 162 be done as future work through numeric simulation and ideally a randomized control trial implementing a subsidized insurance program to test its effectiveness.
Insurance by itself would only work as the safety net portion of a program. This will need to be combined with some form of cargo net to bring individuals up to their respective Micawber Thresholds. This is especially important for situations where the low production technology draws upon a local public good, such as in the case of the Boran pastoralists. Failing to bring herd sizes up to the point where they can engage in mobile pastoralism may put additional pressures on the local pasturelands exasperating the tragedy of the commons (Lybbert et al., 2004). This potential spillover into the natural resources available is similar to what we explored in chapter 2. If aid is set too low where households are able to maintain their local warra herd, but not large enough for to keep a mobile fora herd may result in additional overgrazing and a reduction of productivity. This environment-poverty trap would put additional pressures on the already fragile ecosystem and cause additional dependence on external aid resulting in an aid trap.
Utilizing conditional cash transfers may help to ensure this does not happen. If aid requires any cattle purchased (similarly the transfer could be in the form of livestock) would have to be grazed away from the village, this would reduce the likelihood of localized overgrazing. This condition may require households to pool herds, which is atypical for these populations. This would run the risk of imposing restrictions that go against culture and tradition and should be discussed at lengths with village leaders before attempting to implement. This is especially important because how linked status is with one's herd. However, changing conditions may necessitate the shifting of cultural 163 norms in order for the pastoralism of the Borana Plateau to remain viable. Changes still need to come from within rather than being forced upon them by outside agencies in order to ensure the important aspects of the local culture remain intact and that aid is received and utilized by the pastoralists.
Another form of aid to consider for the pastoralists is providing access to microfinance, which could act as both a form of cargo net and safety net. Providing loans to allow households to surmount their personal Micawber Thresholds would allow for households to put themselves on a path of wealth accumulation, allowing them to pay back the loans in future periods. Alternatively, during bad periods households would have the option to take out loans rather than draw down the productive assets of the herd allowing for more effective consumption smoothing without the risk of falling below the Micawber Threshold.
A major downside to microfinance is the asymmetric information between borrowers and lenders results in the adverse selection and moral hazard problems (Udry, 1994). A common solution used in rural microfinance has been to use joint-liability contracts placing the liability of individual loans on the whole group (Marr, 2012). This would utilize social pressures to help ensure loans are repaid when able. However, much of the risks associated with the Boran pastoralists tend to be systematic, such as drought affecting much of the plateau, which may reduce the effectiveness of these joint loans during bad years, which would increase the cost of providing loans and, in turn, increasing the price for the pastoralists. It may be desirable to combine this with some kind of index-based insurance program, potentially funded through government aid programs, to reduce the risk of the majority of loans failing for a village, thus reducing 164 the risk of the lenders. This combined policy would remove much of the moral hazard and adverse selection issues, while still providing additional aid during drought years.
Regardless of the form the aid takes, when multiple equilibrium poverty traps are present it is critical to ensure individuals are able to surmount the critical Micawber Threshold and to protect assets from falling below this level in future periods. The goal of aid is to both enhance current wellbeing and, perhaps more importantly, to enable households to maintain a high-level of well being in future periods. Enabling selfsufficiency rather than building a reliance on aid programs resulting in the aid trap is critical to ensuring aid budgets can be used to create the most benefit possible. Short-term thinking of trying to reduce current payments may be contrary to this goal of creating sustainable growth if aid is provided short of the Micawber Threshold.
Together these chapters stress the importance of identifying the cause of poverty, understanding the underlying wealth dynamics leading to poverty, and identifying any spillovers of aid policies in order to design effective aid strategies. Failing to do any of these may potentially lead to undesirable outcomes depending on the context. We hope these results can be built upon and used in the future to provide better aid to impoverished populations to help make the dream of eliminating extreme poverty a reality.