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Dynamics and Determinants of Chronic Poverty in Western Kenya:   Starting Point, Location and Diversification  

Brent M. Swallow, Leah Onyango, Wijaya K. Dassanayake

Draft, 20 June 2010

Abstract:

This paper presents results of a study of the dynamics and determinants of chronic poverty in 14 villages in the Nyando basin of Western Kenya. Data were generated from interviews with village representative groups and analyzed using ordered logistical regression. Casual labour was consistently associated with declines into poverty, while formal sector employment was consistently associated with movements out of poverty. Other livelihood strategies had different effects at different times. For example, households that produced plantation crops (tea and coffee) at the beginning of the 10-year period were more likely to move out of poverty, while those that produced plantation crops at the end of the 10-year period were more likely to have moved into poverty. Households practicing more diverse livelihood strategies were more likely to move out of poverty. Location was a highly significant determinant of poverty dynamics for households that were poor at the beginning of the period, but not significant for households that started non-poor or prosperous.

Key words: chronic poverty, livelihood strategies, Kenya, Africa, poverty dynamics, ordered logistical regression

About the authors: Brent M. Swallow is a Professor and Chair of the Department of Rural Economy at the University of Alberta, Edmonton, Alberta, Canada. He was a Principal Scientist at the World Agroforestry Centre at the time the survey work was done. Leah Onyango is a Lecturer at Maseno University, Maseno, Kenya. She led the fieldwork for this study while a Graduate Fellow at the World Agroforestry Centre, Nairobi, Kenya. Wijaya K. Dassanayake is a PhD student in the Department of Rural Economy, University of Alberta, Edmonton, Alberta, Canada.

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1. Introduction

The establishment of the Millennium Development Goals has helped to raise awareness of the magnitude of the world’s poverty problem. The MDG goal for poverty reduction was to halve, between 1990 and 2015, the number of people whose income is less than the international poverty line of $US 1 per day. National governments across the developing world established Poverty Reduction Strategy Papers (PRSPs) that identify the nature of the poverty problems in their countries and the actions that they would take to address those problems. Between 1990 and 2005 measurable progress was made toward the poverty reduction goal at the global level, with the number of people living on less than $1 per day falling from 1.8 billion to 1.4 billion. However, the rise in world food prices in 2008 and the subsequent turndown in global economic growth have led to increases in the number of people living below the poverty line (United Nations, 2009).

Chronic poverty is a particularly difficult challenge (Hulme and Shepherd, 2003). The Chronic Poverty Research Centre notes “… that the distinguishing feature of chronic poverty is, simply, extended duration. Chronically poor people are those who are always, or usually, living below the poverty line. … concern about chronic poverty leads to a focus on poverty dynamics – the changes in well-being or ill-being that individuals and households experience over time.” (Chronic Poverty Research Centre, 2007, p.1).

The Stages of Progress method takes a participatory, retrospective approach to the study of chronic poverty. Previous studies by Anirudh Krishna (2009) and co-authors have applied the Stages of Progress method to study long-term poverty dynamics in India (Krishna, 2004), Kenya (Krishna et al., 2004), Uganda (Krishna et al., 2006a) and Peru (Krishna et al., 2006b). In this study of poverty dynamics in the Nyando river basin in Western Kenya, we extended the Stages of Progress method to give greater consideration to the links between livelihood strategies and poverty dynamics. Through the estimation of three ordered logistical regression models, we are able to address a number of important questions. 1. How prevalent is chronic poverty and how is it associated with particular livelihood strategies? 2. Is there evidence of geographic poverty traps – do households living in particular villages find it particularly difficult to escape poverty? 3. Why did some fall households fall into poverty, while others were able to climb out of poverty? 4. Are agricultural or non-agricultural activities most important for escaping poverty or remaining out of poverty? 5. Is the diversification of livelihood activities a good strategy to escape poverty, or a necessary response to other pressures that push people into poverty?

The remainder of this paper is organized as follows. The second section describes the context for the case study. The third section describes the methods used in the data collection and the models used in the analysis. The fourth section presents results from the descriptive analysis and ordered logistical regression models. The final section presents conclusions.

2. Background

The case study is set in the East African country of Kenya. National statistics for Kenya indicate that the country experienced a reduction in the percentage of people living below the US$ 1 / day international poverty line from 1992 (38.4 percent) to 1997 (19.5 percent of the population), and a relatively stable percentage of people living in poverty since 1997 (19.7 percent in 2005) (World Development Indicators, 19 April 2010). The strategy of the Government of Kenya to reduce poverty was described in the year 2000 in its Interim Poverty Reduction Strategy Paper, the Economic Recovery Strategy of 2003, the Poverty Reduction Strategy of 2005, and most recently, the Kenya Vision 2030 Document. Swallow (2005) showed that there was a large gap between priorities articulated through decentralized PRSP planning sessions and community development priorities.

The Nyando river basin covers approximately 3,500 km2 of Western Kenya, draining into Lake Victoria to the west. There is a steep elevation gradient running from east to west, with elevation in the headwaters reaching nearly 3,000 meters above sea level and elevation in the lowlands of about 1100 meters above sea level. About 750,000 people live in the basin, with rural population densities ranging from about 50 persons to square kilometer to over 1000 persons per square kilometer. Across the basin there is a variety of land tenure classes, including adjudicated private tenure, government-sponsored

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resettlement schemes on leasehold land, private sub-division schemes on leasehold land, large-scale leasehold, and irregular settlements on public land. The main ethnic groups are the Luo, Kipsigis Kalenjin and Nandi Kalenjin. There also are smaller numbers of Ogiek people, an indigenous group of people who have traditionally lived in mountain forests in central Kenya (Swallow et al, 2005). The Nyando basin spans across three administrative districts, Kericho, Nandi and Nyando.

The Nyando river basin is characterized by high levels of poverty. Figure 1 shows that the average poverty rate in the administrative areas (locations) that comprise the basin vary from about 30 percent to more than 70 percent. Poverty appears to be particularly high in the lower part of the basin, although there are pockets of higher and lower levels of poverty throughout the basin. Before this study, little was known about the transient / chronic nature of poverty in the area, and still less about the determinants of that poverty.

<<Insert Figure 1 about here>>

3. Methods

A study of the dynamics of poverty, livelihood strategies and property rights was undertaken in order to guide future development efforts in the Nyando basin. To begin, a number of spatial data layers were assembled from secondary and primary sources. A digital elevation model was created from 1/50,000 ordinance survey maps and used to map hydrological zones. A new land tenure data layer was created from data availed from the Ministry of Lands and the Ministry of Forestry. These data layers were then used to map the Nyando basin into hydrologic x tenure zones. Fourteen villages of between 50 and 150 households were then purposively selected to represent those zones, with assistance from extension staff from the Ministry of Agriculture. Figure 2 shows the geographic location of the 14 villages in the Nyando basin and Table 1 presents baseline information about the villages

<<Insert Figure 2 about here>>

<<Insert Table 1 about here>>

a. Data collection

The Stages of Progress method was adapted for the collection of household and community-level data in the 14 villages. Village leaders were introduced to the study and asked to prepare a list of all households and suggest a group of village residents who were knowledgeable about the village. Village representative groups usually had 8-12 community members, including men and women and both older and middle-aged persons. Village representative groups spent an average of 4 days with the study team, providing a variety of information about the village and its member households. A variety of participatory techniques were used to collect that information. In addition, a household survey was implemented with about 30 households in each village. This paper presents an analysis of data collected through the village representative groups.

Concepts of poverty and prosperity were discussed with the village representative group in each village. A Stage of Progress ladder was then constructed for an average household in the village. The groups were asked to consider a very poor household in the village. If that household received a small amount of extra funds, what would they do with it? In every village in Kenya where this work has been done, the consensus answer to this question has been ‘food’ (see Krishna et al., 2004 and results presented in Table 5). What if the same household now received another small amount of extra funds, what would they do with it? This same question is posed again and again until it is acknowledged that the household would be considered very wealthy compared to other households in the village. The result is a consensus list of the stages of progress for households in the village. In the 14 villages included in this study, the minimum number of stages was 8 and the maximum number was 14. Once the village representative group agreed on the stages of progress, they were asked to locate poverty and prosperity lines between stages. That is, households at less than x stage would be considered poor, households higher than y stage would be considered prosperous, and households between x and y stages would be considered to be in a middle tier, neither poor nor prosperous (non-poor). Once these poverty lines were established, there was

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no more discussion of poverty or prosperity in the interviews with the village representative groups. Instead, the focus was on stages of progress and changes in stages. A sample stage of progress ladder is presented in Table 2 for the case of Kiptagen (Village 3).

<<Insert Table 2 about here>>

The village representative group was next asked to identify memorable events that happened 10 years ago and 25 years ago, and to think about major events that occurred around those times. Next, the village representative group was asked to list all of the livelihood activities that are undertaken by households in the village. For each household on the village list, the village representative group was then asked to specify its current stage of progress, the livelihood activities that the household currently engages in (matching with the previous list of activities), its stage of progress 10 years ago, its livelihood activities 10 years ago, its stage of progress 25 years ago, and its livelihood activities 25 years ago. The village representative group was asked to identify reasons for changes or lack of change in the stage of progress over time. All of the data for each household was recorded on an index card.

The study team dubbed this extended version of Stages of Progress, Participatory Assessment of Poverty and Livelihood Dynamics or PAPOLD. Data were collected in 2005 and processed in 2006 and 2007. Swallow et al. (2007) and Onyango et al. (2007) presented selected results from the study, while Onyango (2009) presents comprehensive results. The ordered logit analysis was conducted in 2009 and 2010.

b. Descriptive statistics

The PAPOLD data were used to generate qualitative and quantitative representations of poverty and poverty dynamics in the 14 villages. Stages of progress ladders, poverty lines, and prosperity lines were assembled for each village. For every household in the village, household-level variables were created to represent stages of progress at the time of the study, 10 years ago, and 25 years ago, household well-being now, 10 years ago and 25 years ago (poor, non-poor, prosperous), and livelihood strategies that the household employs now, 10-years ago, and 25 years ago. Following the Stages of Progress method, reasons for movements up or down the stages of progress were listed. Livelihood strategies and reasons were standardized across the 14 villages during the data entry phase and data management phases of the study.

Poverty dynamics are assessed through the construction of transition matrices for the two time periods, 25 years ago to 10 years ago, and 10 years ago to the present time. Descriptive statistics were also used to describe changes in livelihood activities across the three time periods.

c. Ordered logistical regression model of poverty determinants

An econometric model of the determinants of poverty-prosperity changes was constructed, with change in poverty-prosperity status during the period as the dependent variable. The independent variables were binary variables indicating the livelihood activities that were practiced at the beginning and end of the period, the number of agricultural livelihood activities practiced at the beginning and end of the period, and the village in which the household was located.

Ordered logistical regression was used to estimate the equations due to the ordered nature of the dependent variable. Models were estimated for three groups of households based on poverty conditions ten years ago, i.e. poor, non-poor and prosperous as of 1995. Households that were initially poor could remain poor (category 0), become non-poor (category 1), or become prosperous (category 2). Households that were initially non-poor could become poor (category 0), remain non-poor (category 1) or become prosperous (category 2). Households that were initially prosperous could become poor (category 0), become non-poor (category 1) or remain prosperous (category 2). Table 3 shows the poverty transition matrix and the three categories used in each of the three models.

<<Insert Table 3 about here>>

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The standard ordered logistic regression specifications are as follows. The latent variable Yi* is a function of observed vector of explanatory variables Xi and ε is assumed to have a logistic distribution.

Yi* = Xi‘β + εi

Yi = 1 if Yi * is ≤ 0 Yi = 2 if 0 ≤ Yi* ≤ γ Yi = 3 if Yi* > γ γ is unknown parameter to be estimated with β’s using maximum likelihood procedures. The probabilities of each category can be expressed as follows. P(Yi =1| Xi) = P(Yi*≤ 0 |Xi) = L(-Xi

‘β), P(Yi =3| Xi) = P(Yi*> γ|Xi) = 1- L(γ -Xi

‘β) P(Yi =2| Xi) = L(γ -Xi

‘β) - L(-Xi‘β) ;

Where; L (z) = 1______ 1+ e-z z = Xi

‘β X ‘= vil1, vil2, vil3, vil4, vil5, vil6, vil7, vil8, vil9, vil10, vil11, vil12, vil13, cerenow, cere10, ricenow, rice10, plannow, plan10, SCnow, SC10, pulnow, pul10, rootnow, root10, frutnow, frut10, vegnow, veg10, livesnow, lives10, fornow, for10, casnow, cas10, tranow, tra10, fishnow, fish10, nagnow, nag10, nnagnow, nnag10. The explanatory variables used in the ordered logistical regression are defined in Table 4. Explanatory variables represent dummy variables for the villages, livelihood activities practiced now and 10 years ago, and the number of agricultural and non-agricultural livelihood activities. As will be clear in the conclusions, results from these models allow us to address all of the questions raised in the introduction.

<<Insert Table 4 about here>>

4. Results

a. Poverty levels and dynamics

Table 5 summarizes the stages of progress for the 14 communities included in this study. The first column indicates the item in the stage of progress, the second column provides an explanation of that item, while the numbers in columns 3-16 refer to the stage the item was located in the stage of progress ladder constructed for each of the 14 villages. The greek letters indicate whether obtaining the item was associated with being in the poorest group (α), the middle or non-poor group (β), or the relatively prosperous group (γ).

Three types of need can be identified by analyzing the stages of progress for the 14 villages. The first group consists of the basic needs such as food, clothing, improving housing, obtaining primary education and keeping chickens. Table 4 shows that all villages have food and clothing assigned as stages 1 and 2, implying that those basic needs are essentially the same in all communities. The first four to five priority needs must be fulfilled before a particular household could be considered to be “non-poor”.

The second group of needs consist of leasing land, purchasing furniture, keeping small livestock (goats / sheep), purchasing inputs for crop production, keeping cattle, building a semi-permanent house, and obtaining secondary education for household members in general. These needs must be fulfilled before a household is considered to be non-poor. The relative position of these needs on the stages-of-progress ladder varied from village to village.

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The last category of needs is concerned with purchasing land, investing in business, constructing a permanent house, obtaining post secondary education, purchasing a bicycle, cultivating a cash crop, purchasing furniture, constructing rental housing, and purchasing an ox plough. This group of needs is only met by households in the prosperous state.

<<Insert Table 5 about here>>

Table 6 shows the frequencies and percentages of households in each state i.e. poor, non-poor and prosperous during the 25 years. At the initial stage, 17.8 percent of the total sample was in the poor category, while 51.4 percent and 30.9 percent were in the non-poor and prosperous categories respectively. After 15 years, 54.4 percent of the initially poor remained poor, while 41.4 percent and 4.1 percent had moved into the non-poor and prosperous states respectively. Similar interpretations can be made for the initially non-poor and initially prosperous groups. Of the initially prosperous group, 18.7 percent had fallen into poverty, while only 27.2 percent managed to stay in the prosperous state after 25 years. Further, after 25 years, 28.7 percent of the total sample was in the poorest state, 57.3 percent were non-poor, and 14.1 percent were prosperous.

<<Insert Table 6 about here>>

Table 7 shows village-level trends in poverty status over the 25-year period. Several insights can be obtained from these figures. 11 of the 14 villages reported increases in the proportion of poor households, with a 30% increase reported in village 9. Village 4 was self-described as the most prosperous village at the initial stage i.e. 81.2 percent of people were in the prosperous state, 18.8 percent were in the non-poor state, and none were in the poorest state, but after 25 years 16.0 percent were poor, 60.0 percent were non-poor, and only 24.0 percent were prosperous. Only one village (Village 13) reported a decrease in the percentage of poor households and an increase in the percentage of prosperous households.

<<Insert Table 7 about here>>

Results on the reasons for major changes in Stage of Progress are reported in Tables 8 and 9. The main reasons why households became poor from 10 years ago to the present time were death of a major income earner, poor health, and high health expenses (Table 8). The main reasons that households were able to stay out of poverty over the same time period were income from farming, hard work, receiving salary from a family member, and investment in education (Table 9).

<<Insert Table 8 about here>>

<<Insert Table 9 about here>>

Table 10 shows the summary statistics for the variables included in the ordered logistic regression for each group, based on the initial poverty status. Interpretation of the category depends on the initial poverty status as specified in table 2. For example, as far as the initially poor group is concerned, the 69.6 percent for category 1 means that 69.6 percent of the initially poor group remained poor. Similarly 27.9 percent and 2.5 percent have moved up to the current status of non-poor and prosperous respectively. At the opposite end 42.1 percent of initially prosperous group remained prosperous while 46.3 percent and 11.6 percent have moved down to the current status of non-poor and poor respectively. The last vulnerable group for changes in poverty status is initially non-poor group in which 68.5 percent of them remained non-poor after 10 years.

As far as the agricultural livelihood strategies are concerned, cultivation of cereal crops has been the most common strategy for each group at present as well as 10 years ago. It is also important to note that none of the initially poor households is involved in plantation crops or rice cultivation now, or 10 years ago. Livestock production is more commonly practiced by the initially prosperous and non-poor groups than the initially poor group. As far as non- agricultural current livelihood strategies are concerned,

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providing casual labour is more prevalent among the poor group than the prosperous group. The most common non-agricultural livelihood activity for the prosperous group is now trade, while the non-poor group is about equally involved in both trade and casual labour.

<<Insert Table 10 about here>>

Table 11 shows the summary statistics with respect to diversification of livelihood strategies. From a comparison of mean values it is clear that both agricultural and non-agricultural diversification is more prevalent among the initially prosperous and non- poor groups than the poor group, currently as well as 10 years ago. On the other hand, agricultural diversification is more common than non-agricultural diversification for the both periods among all the groups.

<<Insert Table 11 about here>>

b. Ordered logsiical regression model of poverty determinants

Tables 12, 13 and 14 show the results obtained from the ordered logistic regression for each group defined by the initial poverty state. The standard errors of the variables are adjusted for the possible heteroscedasticity problem using robust standard errors. Estimation of the model was done using Stata 10.1 econometric software package. We interpret our results primarily based on the marginal effect of each category in the explanatory viable but we also report the model estimation results for the each group. The statistical significance and the signs of the coefficients from the model estimation are found to be consistent with those of the marginal effects.

Table 12 shows the results of the ordered logistic regression for the group that was initially poor. The poverty dynamics in this group includes moving from the poor to non-poor states (category 1), poor to prosperous state (category 2) and remaining in the poor state (category 0). Several livelihood activities were found to have significant effects on poverty dynamics of the initially poor people1. Growing crops currently i.e. cereals, rice, pulses, sugarcane or cotton, root crops increases the probability of people moving from poor state to non-poor state and having grown them in past decreases the probability of this transition. Having been involved in animal husbandry in past also has a positive influence on movement out of poverty. People who are currently involved in formal employment have a higher probability of moving from the poor to non-poor state. Besides the different livelihood activities, it is important to note that the number of agricultural livelihood activities done in past has a positively influence on transition toward prosperity.

<<Insert Table 12 about here>>

Table 13 shows the ordered logistic regression for the group that was initially non-poor. There are three possible transitions for these households: moving down from non-poor to poor (category 0), remaining non-poor (category 1) and moving up from non-poor to prosperous (category 2).

Having grown plantation crops and vegetables in past and doing animal husbandry currently increase the probability of moving up from the non-poor to prosperous state. In addition, the number of agricultural activities and number of non-agricultural activities are associated with increased probability of transitions toward prosperity. On the other hand growing cereals, plantation crops, root crops, vegetables currently, being involved in animal husbandry in the past, and doing a causal job currently had negative effects on upward transitions.

Growing cereals, plantation crops, root crops and vegetables and doing a casual job currently increase the probability of moving down from the non-poor state to the poor state, while growing rice, plantation crops, vegetables in past, rearing livestock currently, being employed in the formal sector currently, and

1 Interpretations of marginal effects or coefficients are concerned with at least 10 percent level of probability.

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the number of agricultural and non-agricultural industries decrease the probability of this downward movement.

<<Insert Table 13 about here>>

Table 14 reports results for the group that was initially prosperous. This group also has three situations in terms of poverty dynamics i.e. moving down from the prosperous state to the poor state (category 0), moving down from the prosperous state to the non-poor state (category 1) and staying in the prosperous state (category 2).

Different livelihood strategies have different affects on these three transitions. Growing rice, sugar cane or cotton and fruit crops currently are associated with increasing the probability of staying in the prosperous state. Having formal employment currently, growing plantation crops in the past, and the number of non-agricultural agricultural activities done in the past also have positive effects, while growing plantation crops currently and having grown fruit crops in past, being employed in the formal sector in the past, doing trade activities in the past and doing a casual job currently decrease the probability of staying in prosperous state.

Growing rice, fruit crops and sugar cane or cotton currently and plantation crops in the past decreases the probability of moving down from the prosperous state to the non-poor state. In addition, the number of non-agricultural activities done in the past and currently having formal employment are also associated with decreased probability of this downward transition. However doing casual employment currently, being employed in the formal sector in past, doing trade activities in the past and being involving in fish farming and growing plantation crops currently increase the probability of people moving form prosperous state to non-poor state .

Growing rice, fruit crops, sugarcane or cotton, vegetables currently and plantation crops in past, being employed in the formal sector currently and the number of non-agricultural agricultural activities done in past decreases the probability associated with moving down from prosperous state to poor state.

<<Insert Table 14 about here>>

5. Conclusions

The conclusions from this paper are organized around a series of questions that are quite similar to the list of questions in the introduction. While the questions are expressed in terms specific to the Nyando basin case, they have analogs in many parts of the developing world.

a. Did we find evidence of chronic poverty in this area of western Kenya?

The results on poverty dynamics presented in Table 6 show that chronic poverty is a large challenge in the Nyando basin of Western Kenya. Households that were poor 25 years before the survey were most likely to remain poor 25 years later. While a good proportion of households did move from the poor to non-poor categories during the first 15 years, there was almost no movement from the poor to non-poor categories over the last 10 years. The results also make it clear that being non-poor in the early time period was no guarantee of remaining non-poor. During the first 15-year period there were equal numbers of non-poor households becoming poor and becoming prosperous, while during the more recent period many more non-poor households became poor than became prosperous. It appears to be difficult for households to remain prosperous in this area: of households that were prosperous 25 years ago, less than 30 percent were still prosperous at the time of the survey. Overall, the conclusion is that chronic poverty is a major problem in the area, but perhaps more important is the systematic and growing nature of poverty during the last 10-year period.

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b. Are there geographic poverty traps in the Nyando basin?

In several recent articles and books, Jeffrey Sachs has popularized the idea that much of Africa is stuck in a geographic poverty trap of low food productivity, degraded land, heavy burden of disease, malnutrition and physical isolation (eg Sachs, 2007). Barrett and Swallow (2006) show that poverty traps may hold at different levels of aggregation. As a whole, the Nyando basin epitomizes many of these problems: indeed Professor Sachs himself often uses examples from a nearby area of western Kenya to exemplify these problems. Within the Nyando basin, however, there also appears to be large geographic differences in the prevalence of poverty and the associated problems noted by Sachs. The results presented in this study confirm that the upper part of the basin has lower levels of poverty than the lower parts of the basin.

The results also show worrying trends of increasing poverty and declining prosperity in some upper villages, particularly in villages that were re-settled after independence through land-buying companies that purchased former white-owned farms, sub-divided them, and sold small plots. When the study team stayed in those villages we became aware of strong tensions between ethnic groups living nearby each other; those tensions exploded in the post-election violence that rocked Kenya in late 2007 and early 2008. Some of the villages in the upper part of the Nyando basin experienced some of the worst violence and internal displacement during the post-election violence. Kericho district – where almost all of our upper basin villages are located – had over 50,000 internally displaced persons as of January 25th, 2008 (UNHRC, 2008). Our numbers on poverty trends shows warning signs, with some villages reporting almost no poverty 25 years ago, to 20 to 25 percent of households were described as living in poverty at the time of the survey.

The results of the ordered logistical regression analysis suggest that location is a particularly important determinant of poverty dynamics among households that started poor 25 years ago. Everything else equal, among households that were non-poor or wealthy 25 years ago, location was not a significant determinant of poverty dynamics.

c. Why did some households become poor, while others were able to escape poverty?

The results on the reasons for transitions in well-being presented in Tables 7 and 8 help to explain why prosperity is such a difficult state for households to sustain in the study area. Ill health and death were by far the most important reasons for households falling into poverty and for households remaining poor, particularly in the second period. This is consistent with secondary data showing that the second period was the time of very high HIV / AIDS incidence, and before the wide availability of anti-retro-viral drugs. Between 1990 and 2000, the average HIV prevalence at sentinel sites in the western Kenya ranged from 12 to 35% (Moore and Hogg, 2004). Malaria and tuberculosis rates were also very high in western Kenya over this same time period. Another study applying the Stages of Progress approach in a nearby area of Western Kenya also found that ill-health and premature death were major reasons for poverty (Krishna et al., 2004). All households in this study area, previously wealthy or poor, were vulnerable to impoverishment from ill-health and premature death.

d. Are there livelihood strategies that have particularly strong associations with chronic poverty?

The results of the ordered logistical regression models show that casual labour is always associated with households that become poor and stay poor. It is likely that this is not a casual relationship: non-poor households don’t choose to do casual labour and become poor as a result; rather they do casual labour because they have are poor. In some ways, casual labour may serve as a local safety net for the poorest households. Households that hire their neighbours as casual labourers may themselves be fairly close to the poverty line. On the other hand, formal sector employment is the main strategy for getting out of poverty and remaining out of poverty. No wonder most village groups listed investment in primary, secondary and post-secondary education as key steps in the stages of progress from poverty to prosperity.

Other livelihood activities had different effects at different times. For example, the production of perennial crops (esp. tea and coffee) had positive effects on well-being in the first period and negative effects in the second period. This is consistent with macro-level evidence on the decline of markets for

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coffee and other cash crops in the region. It is also consistent with data that Swallow (2005) presents on community priorities in the study region.

e. Does diversification pay?

This study finds that diversification of livelihood activities is an important determinant of poverty and poverty dynamics. We measured diversity simply by the number of agricultural and non-agricultural practices reported for the household, with one variable for the diversity of agricultural practices and another variable for the diversity of non-agricultural practices. Diversity of agricultural activities was particularly important for households that started poor, diversity of both agricultural and non-agricultural activities were important for households that started non-poor, while diversity of non-agricultural sources was important for households that started relatively prosperous. Development strategies that emphasize non-agricultural development may have an implicit bias against less wealthy households.

f. What special insights emerged from this study?

The Stages of Progress method has been established as a useful addition to longitudinal studies of household income or consumption (eg Denninger and Okidi, 2002). In this study we have extended the Stages of Progress method to give more attention to livelihood strategies and the links between poverty dynamics and livelihood dynamics. The results reported in this paper suggest that this was a very useful addition. The Stages of Progress ladder, the transition matrices, the analysis of reasons for stages of progress changes, the summary information on livelihood strategies, and the ordered logistical regression models produce qualitatively different, yet complementary, insights into poverty in the study villages. The results can be of use for planning programs for poverty reduction, agricultural development, health care, and regional development.

Since the time that this survey was undertaken, the Kenya Ministry of Agriculture and the Central Bureau of Statistics have both taken up the technique for different purposes. Staff members from the Kenya Ministry of Agriculture particularly appreciate the Stages of Progress ladders and the insight that the analysis provides of the links between the stages and local livelihood activities.

Acknowledgements:

We express appreciation to the agencies and programs that provided financial support to various aspects of this research: the Comprehensive Assessment of Water and Food in Agriculture, the Challenge Program on Water and Food, the World Agroforestry Centre, the European Union and the University of Alberta. Views expressed do not reflect those of the funding agencies. We thank Anirudh Krishna, Ruth Meinzen-Dick, Patti Kristjanson, Ric Coe, Marian Radeny and David Nyantika who provided helpful advice on the survey design and analysis. We thank Peter Muraya for the work he put into the design and construction of the database. We especially thank the Kenya Ministry of Agriculture staff who helped to identify the villages, the village leaders who facilitated our stay in their villages, and the village residents who provided the data during long sessions.

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12

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NANDI

KERICHO

NYANDO

KISUMU

Kusa

Ahero

Sondu

Arwos

Kibos

Sosiot

Kaigat

Mtetei

Kedowa

Kisumu

MiwaniKiboswa Chemase

Kobujoi Kaptumo

Songhor

Kericho

Ainamoi

Kabeneti Kabianga

Muhoroni

Chemilil

Kapsabet

Chepsoen

Londiani

Paponditi

Chepterit

Kipkelion

Kapkangani

Fort Ternan

Nandi Hills

<2020 - 3030 - 4040 - 5050 - 6060 - 70>70lake

Location BoundaryDivision BoundaryDistricts Boundary

RoadsTowns

Nyando River Basin

N

0 10 20 Kilometers

Scale: 1:430,000

Produced by: Safeguard ProjectCompiled by: ICRAF GIS UnitData Source: ICRAF & ILRI GIS Units

Figure 1: Percent of Population Below the National Poverty Line in the Nyando River Basin

Source

13

Figure 2: Sample sub locations Source Compiled by this study using the ICRAF GIS Lab data base: 2003.

14

Table 1: General information on the 14 sample villages in the Nyando river basin Village number and name

1997 Poverty rate in location

Land tenure Water control in irrigation area

Altitude zone (meters above

sea level) V1 Kaminjeiwa 42.5 Subdivided lease –

resettlement scheme Not applicable 1750-2250

V2 Nyaribari 41.2 Subdivided lease – land buying company

Not applicable 1750-2250

V3 Kiptagen 48.9 Adjudicated Not applicable 1750-2250 V4 Chepkemel 49.2 Adjudicated Not applicable 1250-1750 V5 Ngendui 60.4 Relocated to public land Not applicable 1750-2250 V6 Ongalo 47.0 Sub-divided lease – land

buying company Not applicable 1250-1750

V7 Kimira Aora 48.1 Subdivided lease – resettlement scheme

Not applicable 1100-1250

V8 Poto Poto 47.6 Adjudicated Not applicable 1250-1750 V9 Nakuru 63.3 National Irrigation Board National Irrigation

Board 1100-1250

V10 Kasirindwa 37.2 Adjudicated Individual farms 1100-1250 V11 Karabok 55.5 Adjudicated Individual farms 1100-1250 V12 Miolo 65.3 Adjudicated Not applicable 1250-1750 V13 Kasinwindhi 68.3 Adjudicated Provincial Irrigation

Unit 1100-1250

V14 Awach scheme 72.1 Adjudicated Provincial Irrigation Unit

1100-1250

Sources: Central Bureau of Statistics for small-area poverty estimates; Onyango 2009 for land tenure information; Surveys of Kenya for altitude information; Swallow et al., 2005 for water control information.

15

Table 2: Stages of progress in Kiptegan village (Village 3)

Stage The needs 1. Food 2. Clothing 3. Repairing house (re- thatching) 1 roomed house. 4. Primary education. Poverty line 5. Purchase chicken (Local). 6. Purchase sheep/ goat 7. Purchase cattle/cow. 8. Build a house (iron sheets, walls are of mud) 2/3 roomed. 9. Rent land for farming (at most an acre) 10. Build a granary (store) 11. Secondary education. Prosperity line 12. Buy land. 13. Plant tea. 14. Build a permanent house(stone/ brick, iron sheet)

Source: Compiled using field data from this study Table 3. Specification of the dependent variable (Yi)

Initial condition Category Poor (1) non-poor (2) Prosperous (3) 0 remain poor non-poor to poor prosperous to poor 1 poor to non-poor remain non-poor prosperous to non-poor 2 poor to prosperous non-poor to prosperous remain prosperous

16

Table 4. Description of explanatory variables

Abbreviation Variable name Variable type Vil1 Village 1 (KAMENJEIWA) dummy Vil2 Village 2 (NYARIBARI) dummy Vil3 Village 3 (KIPTEGAN) dummy Vil4 Village4 (CHEPKEMEL) dummy Vil5 Village5 (NG'ENDUI) dummy Vil6 Village6 (ONGALO) dummy Vil7 Village7 (KIMIRA AORA) dummy Vil8 Village8 (POTOPOTO) dummy Vil9 Village9 (NAKURU) dummy vil10 Village10 (KASIWIDHI) dummy Vil11 Village11 (KARABOK) dummy Vil12 Vil1lage2 (MIOLO) dummy Vil13 Village13 (KASIRIDWA) dummy Vil14 Village14 (AWACH) dummy cere Cereal farming dummy rice Rice farming dummy plan Plantation ( tea , coffee or both) crops cultivation dummy SC Sugarcane farming, cotton cultivation or both dummy pul Pulses cultivation dummy root Root crops cultivation dummy frut Fruit crops cultivation dummy veg Vegetables farming dummy lives Livestock production dummy for Formal employments dummy cas Casual employments dummy tra Trade activities dummy fish Fish farming dummy nag Number of agricultural industries continuous nnag Number of non- agricultural industries continuous

17

18

Table 5. Stages of progress in 14 sample villages in the Nyando basin

Villages 1-14 item label V[1] V[2] V[3] V[4] V[5] V[6] V[7] V[8] V[9] V[10] V[11] V[12] V[13] V[14] food food 1α 1 α 1 α 1 α 1 α 1 α 1 α 1 α 1 α 1 α 1 α 1 α 1 α 1 α clothing clothing 2α 2 α 2 α 2 α 2 α 2 α 2 α 2 α 2 α 2 α 3 α 3 α 3 α 2 α house1 house - thatching, repair mud house 3α 3 α 3 α 3 α 3 α 4 β 4 α 3 α 6 α 2 α 4 α 2 α 3 α Ed_prim primary education 4α 4 α 4 α 5 α 4 α 3 α 7 β 3 β 4 α 5 α 5 α 6 α 4 α 4 α chicken purchase chicken 5α 5 α 5 β 4 α 5 α 3 α 5 α 3 α 4 α 2 α 5 α land_lease lease land 6β 9 β 7 β 9 β 9 β 6 β 4 β 9 β land_input crop inputs - fertilizer, hire plough 5 β 7 β goat purchase goat or sheep 6β 7β 6 β 6 β 6 β 8 β 6 α 4 α 7 β 5 α 5 α 6 α cow purchase cow 7β 8β 7 β 8 β 7 β 6 β 10 γ 7 β 7 β 9 β 8 β 7 β 7 β house2 house - iron sheet 8β 10β 8 β 9 β 11 β 9 β 9 β 8 β 10 β 9 β 9 β Ed_sec secondary education 9β 9β 11 β 10 β 8 β 5 β 10 β 8 β 10 γ 9 β 10 β 11 β land_buy buy land 10γ 12γ 12 γ 11 γ 10 β 10 γ 13 γ 7 γ 8 β 10 γ 12 γ 12 γ 11 γ 13 γ inv_busi invest - posho mill 11γ house_t house for teenagers 11 γ Ed_tert post-secondary education 13 γ house3 house - brick or stone 14 γ 14 γ 13 γ 11 γ 11 γ 13 γ 14 γ 12 γ 14 γ store build granary or store 10 β cashcrop plant cash crops - tea, sugar, coffee 13 γ 12 γ 8 β 12 γ 6 β 11 γ 8 β bicycle purchase bicycle 7 β furniture1 purchase furniture 5 α 6 β 6 β 10 β furniture2 purchase better furniture 11 β house_rent build house to rent 11 γ 13 γ Ox purchase ox and plough 11 γ 12 γ hort horticulture for cash 8 β

α Indicates a stage associated with poverty, β indicates a stage associated with non-poverty / non prosperity, γ indicates a stage associated with relative prosperity.

19

Table 6. Poverty status during 25 year period

Initial Stage Stage 15 years later

Frequency Percentage Stage 25 years later

Frequency Percentage

1 92 54.4 1 90 53.3 2 70 41.4 2 71 42.0

Poor (1) 17.8% 3 7 4.1 3 8 4.7

Total 169 100.0 Total 169 100.0

1 46 9.4 1 128 26.2 2 399 81.6 2 315 64.4

Non-poor (2) 51.4% 3 44 9.0 3 46 9.4 Total 489 100.0 Total 489 100.0

1 13 4.4 1 55 18.7 2 132 44.9 2 159 54.1

Prosperous (3) 30.9% 3 149 50.7 3 80 27.2 Total 294 100.0 Total 294 100.0

20

Table 7. Poverty status across the villages and over time (% of households in the three categories in each village)

Initial Stage 15 Yrs Later 25 Yrs Later Village 1 2 3 1 2 3 1 2 3

1

2.9

61.8

35.3

2.9

70.6

26.5

10.1

71.0

18.9 2 0 57.1 42.9 0 71.4 28.6 13.5 70.3 16.2 3 15.6 66.4 18.0 8.6 79.7 11.7 15.7 66.9 17.4 4 0 18.8 81.2 4.3 56.5 39.1 16.0 60.0 24.0 5 20.0 74.0 6.0 14.0 82.0 4.0 38.0 62.0 0.0 6 7.4 64.7 27.9 20.6 61.8 17.6 24.6 59.0 16.4 7 1.2 89.5 9.3 7.0 82.6 10.5 23.2 67.4 9.4 8 3.7 77.8 18.5 3.7 66.7 29.6 10.4 74.7 14.9 9 34.8 55.1 10.1 34.8 60.9 4.3 62.3 37.7 0.0

10 3.3 41.0 55.7 4.9 50.8 44.3 28.5 50.9 20.6 11 9.1 27.3 63.6 11.1 44.4 44.4 22.7 56.6 20.7 12 34.7 52.5 12.9 24.8 69.3 5.9 56.4 37.6 5.9 13 38.5 44.2 17.3 26.9 61.5 11.5 36.5 44.2 19.2 14 47.1 14.9 37.9 35.6 34.5 29.9 37.5 35.2 27.3

Category 1 is poor, category 2 is non-poor, and category 3 is prosperous.

21

Table 8: Reasons given for why households remain in or fall into poverty

Remained poor (25 years ago to 10 years ago)

Became poor (25 years ago to 10 years ago)

Remained poor (10 years ago to current)

Became poor (10 years ago to current)

Overdrinking 2 1 7 5

Death of major income earner

4 4 10 25

Small land size 1 0 2 6

Poor health 0 3 2 14

Advanced age 2 2 7

High health expenses

1 2 3 10

Death and funeral expenses

0 0 0 1

Table 9: Reasons for why households escape from or remain out of poverty

Escaped poverty (25 years ago to 10 years ago)

Stayed out of poverty (25 years ago to 10 years ago)

Escaped poverty (10 years ago to current)

Stayed out of poverty (10 years ago to current)

Hard work 6 17 0 25

Income from farming

4 18 2 30

Salary from household member

0 3 1 18

Investment in education

0 0 1 12

22

Table 10. Summary statistics for the variables used in the ordered logistic regression of poverty dynamics

Initial Poverty Status (1995) Poor Non-poor Prosperous frequency percentage frequency percentage frequency percentage Category 0 110 69.6 151 22.7 25 11.6 category 1 44 27.9 455 68.5 100 46.3 category 2 4 2.5 58 8.7 91 42.1 Cerenow 75 47.5 416 62.6 142 65.7 cere10 63 39.9 342 51.5 104 48.2 Ricenow 23 14.6 39 5.9 23 10.7 rice10 0 0.0 78 11.8 28 13.0 Plannow 0 0.0 59 8.9 24 11.1 plan10 0 0.0 39 5.9 18 8.3 Scnow 7 4.4 109 16.4 48 22.2 sc10 5 3.2 110 16.6 34 15.7 Pulnow 9 5.7 70 10.5 18 8.3 pul10 6 3.8 58 8.7 13 6.0 Rootnow 4 2.5 15 2.3 5 2.3 root10 8 5.1 18 2.7 5 2.3 Frutnow 0 0.0 5 0.8 3 1.4 frut10 0 0.0 7 1.1 3 1.4 Vegnow 8 5.1 69 10.4 41 19.0 veg10 4 2.5 42 6.3 21 9.7 Livesnow 18 11.4 262 39.5 105 48.6 lives10 4 2.5 206 31.0 86 39.8 Fornow 6 3.8 49 7.4 43 19.9 for10 6 3.8 50 7.5 53 24.5 Casnow 76 48.1 213 32.1 42 19.4 cas10 62 39.3 161 24.3 32 14.8 Tranow 31 19.6 205 30.9 73 33.8 tra10 23 14.6 155 23.3 49 22.7 Fishnow 0 0.0 3 0.5 3 1.4 fish10 0 0.0 8 1.2 3 1.4

23

Table 11. Summary statistics for the diversification of agricultural and non-agricultural livelihood activities (year 2005)

Initial Poverty Status (1995) Poor Non-poor Prosperous Mean St.dev Min Max Mean St.dev Min Max Mean St.dev Min Max Nagnow 1.07 1.33 0 5 1.39 1.28 0 6 1.72 1.47 0 7 nag10 0.98 1.33 0 5 1.22 1.33 0 6 1.33 1.74 0 9 Nnagnow 0.73 0.60 0 2 0.76 0.71 0 4 0.82 0.69 0 4 nnag10 0.58 0.64 0 2 0.59 0.69 0 4 0.69 0.68 0 3

24

Table 12: Results for the ordered logistical regression for the initially poor group

Category (0) Category (1) Category(2) Model Estimation

Marginal Effects Marginal Effects Marginal Effects

variable coefficient P value coefficient P value coefficient P value coefficient Std. error P value

vil32 -0.8542 0.0000 0.5955 0.0010 0.2586 0.1840 6.1604 1.5509 0.0000

vil4 -0.5238 0.1450 0.5133 0.1350 0.0105 0.5930 2.3764 1.7522 0.1750

vil5 -0.7099 0.0000 0.6776 0.0000 0.0323 0.5070 3.5688 1.5781 0.0240

vil6 -0.6047 0.0150 0.5908 0.0130 0.0140 0.4490 2.8553 1.3954 0.0410

vil7 -0.2077 0.5580 0.2057 0.5570 0.0020 0.6670 1.0532 1.5098 0.4850

vil8 -0.7946 0.0000 0.6603 0.0000 0.1343 0.4380 5.0218 1.3724 0.0000

vil10 -0.8119 0.0000 0.5685 0.0050 0.2434 0.2370 5.7615 1.1990 0.0000

vil11 -0.5108 0.0940 0.5019 0.0900 0.0089 0.5150 2.3660 1.4259 0.0970

vil12 -0.5669 0.0040 0.5565 0.0040 0.0104 0.3830 2.7259 1.0383 0.0090

vil13 -0.3533 0.1970 0.3490 0.1950 0.0043 0.5110 1.6862 1.1351 0.1370

cerenow -0.5131 0.0050 0.5072 0.0050 0.0059 0.3830 3.2336 1.3150 0.0140

cere10 0.6244 0.0000 -0.6160 0.0000 -0.0083 0.2850 -5.0385 1.5666 0.0010

ricenow -0.5229 0.0740 0.5142 0.0700 0.0087 0.5010 2.4916 1.4220 0.0800

scnow -0.7067 0.0000 0.6753 0.0000 0.0314 0.4590 3.5406 1.5338 0.0210

sc10 0.2163 0.0000 -0.2149 0.0000 -0.0013 0.3900 -5.4505 1.9595 0.0050

pulnow -0.7191 0.0050 0.6860 0.0010 0.0331 0.6510 3.6418 2.3846 0.1270

pul10 0.2195 0.0000 -0.2181 0.0000 -0.0014 0.3920 -5.1133 2.6017 0.0490

rootnow -0.6841 0.0020 0.6560 0.0000 0.0281 0.5960 3.3614 1.8348 0.0670

root10 0.2258 0.0000 -0.2244 0.0000 -0.0014 0.3920 -4.6729 1.8399 0.0110

vegnow -0.1216 0.7470 0.1206 0.7470 0.0010 0.7920 0.6680 1.8130 0.7130

veg10 0.1193 0.2680 -0.1185 0.2670 -0.0008 0.5270 -1.0931 1.4795 0.4600

livesnow -0.2217 0.2460 0.2196 0.2430 0.0021 0.5650 1.1446 0.8130 0.1590

lives10 -0.7061 0.0010 0.6720 0.0000 0.0340 0.6060 3.5561 1.9784 0.0720

fornow -0.7469 0.0000 0.6986 0.0000 0.0483 0.5940 3.9716 2.1251 0.0620

for10 -0.4226 0.1200 0.4164 0.1150 0.0062 0.5570 1.9365 1.1574 0.0940

casnow 0.0389 0.8520 -0.0386 0.8520 -0.0003 0.8520 -0.2539 1.3630 0.8520

cas10 0.1194 0.2480 -0.1185 0.2470 -0.0009 0.5270 -0.8192 0.7452 0.2720

tranow 0.0261 0.9160 -0.0259 0.9160 -0.0002 0.9140 -0.1755 1.7214 0.9190

tra10 0.0546 0.5350 -0.0542 0.5360 -0.0004 0.5990 -0.3875 0.6888 0.5740

nagnow 0.1490 0.1520 -0.1479 0.1520 -0.0011 0.4220 -0.9699 0.7084 0.1710

nag10 -0.3000 0.0020 0.2977 0.0020 0.0022 0.3700 1.9526 0.6576 0.0030

nnagnow -0.2164 0.3260 0.2148 0.3270 0.0016 0.4830 1.4087 1.4400 0.3280

probability = 0.810 probability = 0.188 probability = 0.001

Pseudo R sq =0.4325 LT test = 69.49 (000) Log likelihood = -62.87311 total observations = 158

2 The dummy variables for village 1, 2, and 9 were not included as controlled variables due to lack of variability. Village 14 is the reference village for all models.

25

Table 13: Results for the ordered logistical regression for the initially non-poor group

Category (0) Category (1) Category(2)

Marginal Effects Marginal Effects Marginal Effects Model Estimation

Variable coefficient P value Coefficient P value coefficient P value coefficient Std. error P value

vil1 -0.1553 0.0000 -0.0947 0.6680 0.2499 0.3240 2.5572 1.3694 0.0620

vil2 -0.1004 0.2090 0.0319 0.5770 0.0686 0.6120 1.1906 1.5249 0.4350

vil3 -0.1582 0.0180 0.0239 0.7830 0.1344 0.3720 1.9753 1.3101 0.1320

vil4 -0.1310 0.0140 -0.0005 0.9970 0.1314 0.4700 1.8002 1.4212 0.2050

vil5 -0.1029 0.1710 0.0374 0.3060 0.0655 0.5490 1.1802 1.3139 0.3690

vil6 -0.1313 0.0120 0.0025 0.9830 0.1288 0.4370 1.7862 1.3257 0.1780

vil7 -0.0811 0.4230 0.0447 0.1070 0.0364 0.6270 0.8037 1.2675 0.5260

vil8 -0.1426 0.0000 -0.0405 0.8000 0.1831 0.3520 2.1582 1.2625 0.0870

vil9 0.2984 0.2910 -0.2682 0.3210 -0.0302 0.0280 -1.5899 1.1985 0.1850

vil10 0.1144 0.6520 -0.0961 0.6740 -0.0182 0.4670 -0.7309 1.3520 0.5890

vil11 -0.0954 0.2520 0.0406 0.0480 0.0548 0.5840 1.0490 1.3260 0.4290

vil12 -0.0053 0.9720 0.0039 0.9720 0.0014 0.9730 0.0427 1.2361 0.9720

vil13 0.1100 0.5810 -0.0922 0.6070 -0.0178 0.3880 -0.7073 1.0763 0.5110

Cerenow 0.1546 0.0050 -0.0982 0.0020 -0.0564 0.0430 -1.3612 0.5166 0.0080

cere10 -0.0477 0.4530 0.0351 0.4560 0.0126 0.4520 0.3779 0.5025 0.4520

Ricenow -0.0209 0.8450 0.0146 0.8360 0.0063 0.8630 0.1754 0.9503 0.8540

rice10 -0.1395 0.0000 0.0242 0.6060 0.1152 0.1220 1.7485 0.6751 0.0100

Plannow 0.1916 0.0660 -0.1661 0.0860 -0.0255 0.0040 -1.1334 0.4933 0.0220

plan10 -0.1247 0.0000 0.0114 0.7990 0.1133 0.0720 1.6496 0.5374 0.0020

Scnow -0.0599 0.2390 0.0383 0.1560 0.0216 0.3790 0.5418 0.5316 0.3080

sc10 -0.0072 0.9030 0.0052 0.9020 0.0020 0.9060 0.0580 0.4846 0.9050

Pulnow -0.0057 0.9430 0.0041 0.9420 0.0015 0.9450 0.0456 0.6461 0.9440

pul10 0.0014 0.9880 -0.0011 0.9880 -0.0004 0.9880 -0.0114 0.7375 0.9880

Rootnow 0.3855 0.0480 -0.3549 0.0620 -0.0305 0.0000 -1.9087 0.7915 0.0160

root10 0.0461 0.7410 -0.0366 0.7550 -0.0095 0.6730 -0.3295 0.9034 0.7150

Frutnow 0.0848 0.5120 -0.0702 0.5410 -0.0146 0.3240 -0.5608 0.7315 0.4430

frut10 -0.0662 0.2670 0.0358 0.0140 0.0305 0.5180 0.6671 0.7848 0.3950

Vegnow 0.1983 0.0920 -0.1717 0.1170 -0.0265 0.0060 -1.1736 0.5634 0.0370

veg10 -0.1337 0.0000 -0.0064 0.9170 0.1400 0.0760 1.8679 0.5890 0.0020

Livesnow -0.2004 0.0000 0.1241 0.0000 0.0763 0.0000 1.7554 0.3140 0.0000

lives10 0.0726 0.1170 -0.0561 0.1370 -0.0165 0.0720 -0.5371 0.3193 0.0930

Fornow -0.1304 0.0000 0.0103 0.8510 0.1201 0.1170 1.7274 0.6235 0.0060

for10 -0.0106 0.8890 0.0076 0.8870 0.0030 0.8960 0.0865 0.6399 0.8920

Casnow 0.2243 0.0050 -0.1827 0.0090 -0.0416 0.0010 -1.4832 0.4538 0.0010

cas10 -0.0626 0.2030 0.0415 0.1500 0.0211 0.3160 0.5501 0.4785 0.2500

Tranow 0.0289 0.6590 -0.0217 0.6670 -0.0072 0.6360 -0.2233 0.4911 0.6490

tra10 -0.0622 0.2560 0.0411 0.1960 0.0212 0.3700 0.5482 0.5361 0.3060

Fishnow 0.1875 0.6370 -0.1650 0.6610 -0.0226 0.3070 -1.0715 1.7897 0.5490

fish10 -0.0237 0.8510 0.0163 0.8380 0.0074 0.8730 0.2023 1.1601 0.8620

Nagnow -0.1051 0.0140 0.0772 0.0160 0.0279 0.0220 0.8365 0.3343 0.0120

26

nag10 0.0605 0.1450 -0.0445 0.1510 -0.0161 0.1480 -0.4818 0.3321 0.1470

Nnagnow -0.1573 0.0010 0.1155 0.0020 0.0418 0.0020 1.2523 0.3843 0.0010

nnag10 0.0732 0.1690 -0.0538 0.1750 -0.0195 0.1720 -0.5830 0.4264 0.1720

probability =0.147 probability= 0.818 probability= 0.034

Pseudo R sq = 0.2517 Log likelihood = -397.16269 LT test =202.99 (000) total observations= 664

27

Table 14: Results for the ordered logistical regression for the initially prosperous group

Category (0) Category (1) Category(2)

Marginal Effects Marginal Effects Marginal Effects Model Estimation

Variable coefficient P value coefficient P value coefficient P value coefficient Std. error P value

vil1 -0.0333 0.0300 -0.3852 0.1120 0.4185 0.0980 1.8166 1.3826 0.1890

vil2 0.0705 0.7460 0.1560 0.1650 -0.2265 0.4880 -1.1964 2.3807 0.6150

vil3 -0.0322 0.0780 -0.3651 0.2440 0.3973 0.2260 1.7032 1.7112 0.3200

vil4 -0.0356 0.0670 -0.3617 0.1660 0.3973 0.1520 1.6872 1.3637 0.2160

vil5 -0.0262 0.0820 -0.2955 0.2640 0.3218 0.2460 1.3424 1.3063 0.3040

vil6 0.0853 0.4780 0.1677 0.0010 -0.2530 0.1060 -1.3710 1.2079 0.2560

vil7 0.4000 0.3040 -0.0198 0.9540 -0.3802 0.0000 -3.1590 1.6942 0.0620

vil8 -0.0016 0.9730 -0.0095 0.9740 0.0111 0.9730 0.0469 1.4044 0.9730

vil9 -0.0022 0.9620 -0.0133 0.9640 0.0154 0.9640 0.0652 1.4187 0.9630

vil10 0.4458 0.1430 0.0226 0.9260 -0.4684 0.0000 -3.6499 1.4356 0.0110

vil11 0.0359 0.5720 0.1421 0.3690 -0.1780 0.4190 -0.8224 1.1307 0.4670

vil123 0.3828 0.4320 -0.0157 0.9710 -0.3671 0.0000 -3.0474 1.9838 0.1240

cerenow 0.0121 0.6980 0.0760 0.7160 -0.0881 0.7130 -0.3707 1.0003 0.7110

cere10 0.0146 0.6720 0.0839 0.6560 -0.0985 0.6580 -0.4211 0.9568 0.6600

ricenow -0.0403 0.0160 -0.4671 0.0360 0.5074 0.0290 2.3240 1.5339 0.1300

rice10 -0.0023 0.9490 -0.0139 0.9510 0.0162 0.9510 0.0683 1.0992 0.9500

plannow 0.0904 0.3360 0.1867 0.0000 -0.2771 0.0380 -1.4821 0.9793 0.1300

plan10 -0.0425 0.0030 -0.5380 0.0000 0.5805 0.0000 2.9648 1.1517 0.0100

scnow -0.0603 0.0040 -0.5402 0.0000 0.6005 0.0000 2.8241 0.9875 0.0040

sc10 0.0442 0.5320 0.1528 0.2320 -0.1971 0.3160 -0.9385 1.0906 0.3900

pulnow -0.0300 0.1540 -0.3131 0.3220 0.3431 0.3050 1.4336 1.5616 0.3590

pul10 -0.0247 0.2170 -0.2407 0.4040 0.2654 0.3870 1.0876 1.3129 0.4070

rootnow 0.0356 0.6840 0.1188 0.4380 -0.1543 0.5190 -0.7414 1.3486 0.5820

root10 0.0107 0.8760 0.0518 0.8490 -0.0624 0.8540 -0.2759 1.5656 0.8600

frutnow -0.0341 0.0030 -0.4925 0.0010 0.5266 0.0000 2.6835 1.4631 0.0670

frut10 0.1886 0.3970 0.1237 0.3530 -0.3122 0.0020 -2.0850 1.3613 0.1260

vegnow -0.0302 0.1530 -0.2576 0.2150 0.2878 0.2040 1.1896 0.9707 0.2200

veg10 -0.0319 0.0600 -0.3345 0.1470 0.3664 0.1330 1.5425 1.1697 0.1870

livesnow -0.0094 0.6880 -0.0550 0.6800 0.0644 0.6810 0.2741 0.6686 0.6820

lives10 0.0062 0.8180 0.0354 0.8120 -0.0416 0.8130 -0.1780 0.7553 0.8140

fornow -0.0563 0.0010 -0.5365 0.0000 0.5928 0.0000 2.7970 0.7905 0.0000

for10 0.0746 0.3480 0.2224 0.0240 -0.2970 0.0840 -1.4612 1.0526 0.1650

casnow 0.0575 0.2560 0.1831 0.0160 -0.2406 0.0470 -1.1671 0.7039 0.0970

cas10 0.0552 0.4700 0.1687 0.1040 -0.2239 0.2050 -1.0969 1.0583 0.3000

tranow -0.0299 0.2290 -0.2046 0.2410 0.2345 0.2340 0.9834 0.8361 0.2400

tra10 0.2476 0.2400 0.2372 0.0230 -0.4848 0.0000 -2.9608 1.3113 0.0240

fishnow 0.0565 0.5720 0.1431 0.0910 -0.1996 0.2700 -1.0220 1.2116 0.3990

fish10 -0.0148 0.6790 -0.1198 0.7600 0.1346 0.7530 0.5491 1.7110 0.7480

nagnow 0.0042 0.8050 0.0249 0.8020 -0.0291 0.8020 -0.1240 0.4954 0.8020

3 A dummy variable for village 13 was not included as a controlled variable due to lack of variability

28

nag10 -0.0045 0.7590 -0.0267 0.7570 0.0312 0.7570 0.1328 0.4292 0.7570

nnagnow -0.0059 0.6740 -0.0346 0.6720 0.0405 0.6720 0.1725 0.4084 0.6730

nnag10 -0.0946 0.0180 -0.5558 0.0010 0.6503 0.0010 2.7679 0.8506 0.0010

probability = 0.035 probability= 0.587 probability= 0.377

Pseudo R sq = 0.3269 Log likelihood = -141.06296 LT test = 151.63 (000) total observations = 216