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«A Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Approved November 2014 by the Graduate ...»

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The dependent variables are the difference in the following variables between 2001 and 2006: the size of farming land (m2), the size of land for rent (m2), the number of poultry (chickens, ducks, and pigeons), the amount of chemical fertilizer applied (kg).

The size of farming land and the size of land for rent are the first two dependent variables measuring agricultural activities, and the units are square meters (m2). These variables were measured in all three survey years, and the differences between 2001 and 2006 would be used as the dependent variables. They were originally measured in bigha and kattha, which are the units of measurement of area of a land. One bigha is equal to about 20 kattha, and 1 kattha is about 338.63 square meters. Based on this conversion, all variables are converted into square meters (m2).

The increase in the size of farming land indicates agricultural intensification, and the opposite direction indicates agricultural de-intensification. On the other hand, the increase in the size of land for rent indicates agricultural de-intensification, and the opposite direction indicates agricultural intensification.

Another dependent variable measuring agricultural activities is the number of poultry a household raises including chickens, ducks and pigeons, and thus, it is a continuous variable. This variable was measured in all three survey years, and the difference between 2001 and 2006 would be used as the dependent variable. The

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intensification, and the decrease would be considered as agricultural de-intensification.

Last, the amount of chemical fertilizer applied is a continuous variable, and the unit is kilograms (kg). The increase in the amount would be considered as agricultural intensification while the opposite would be considered as agricultural de-intensification.

The Changes in the Modes of Production and in the Main Energy Source. To test the effect of migration and household capitals on the changes in the modes of production and in the main energy source, I use data exclusively from the first dataset having monthly migration information for the primary analysis. For the secondary analysis, I use the second dataset having yearly migration information. The method of analysis is binary logistic regression models using discrete-time event history approach controlling for the variation at the neighborhood level. It is the most appropriate technique when 1) the dependent outcome is the dichotomous transition and not all observations experienced the event, and 2) when observations were measured in several clusters so that households from the same neighborhood tend to resemble each other.

These techniques have been used successfully in similar analysis (Yabiku 2004;

Axinn and Yabiku 2001; Barber, Murphy, Axinn, and Maples 2000), and it will be of great use in my analysis as well. These estimation techniques correctly compute the standard errors inherent in clustered data and thus help to avoid incorrect hypothesis tests, which result in wrong conclusions. In the analyses, I will incorporate a random effect for the neighborhood-level variance, because households are grouped by neighborhoods.

Thus each model of the analyses is a two-level multilevel model. PROC GLIMMIX command with RANDOM statement allowing an intercept for each neighborhood in SAS

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technique fails to converge, Laplace’s method is used instead (Kiernan, Tao and Gibbs 2012).

To examine the changes in the modes of production, I use three dependent variables. The first dependent variable is the transition from farming to non-farming between three survey years at the household level. At each survey year, all the households were asked whether or not a household was farming at that time. If a household did farm in 1996, for example, but did not farm in 2001, this is considered as the transition from farming to non-farming. If a household did farm in 1996 and 2001, but did not farm in 2006, this is also considered as the transition. Those households that did not farm in 1996 are excluded from the sample.

To integrate the case of household fission, a household that created a non-farming household that separated from the parental household between 1996 and 2006 is also considered as a household experienced the transition from farming to non-farming. Due to the data structure, the new household, in this case for example, is excluded from the sample.

The events are the transition from farming to non-farming. The duration of the event is one or two since the Household Agricultural Consumption Survey was only conducted in three time points: 1996, 2001, and 2006. The duration is one if a household did farm in 1996 and stopped farming in 2001. If a household did farm in 1996 and 2001, but did not farm in 2006, the duration is two. In the analysis, households become at risk of the transition out of farming in 1996, and they are removed from the risk after they

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and 2006) are censored. The time unit of analysis is the household-time.

An additional consideration is the parameterization of time, which is measured with the time variable that indicates each year. The hazard has been parameterized with a continuous time variable having a value of one or two. Although event history analyses are typically used when there many periods of risk (many years, many months), my analyses examine the transition across only two periods of risk. While this means there will be many ties in the data (many households experiencing the event at the same time), this is not a violation of discrete-time event history, which is amenable to tied events.





While a finer time resolution in the measurement of transitions, would allow for more accuracy, my approach is still valid for examining household variation in the transition from farming to non-farming, given that the household has not experienced the transition before.

The second dependent variable measuring the changes in the modes of production is the transition to the first salary employment at the individual level. The Individual Life History Calendar asked if an individual was employed as a salary worker each year after he or she was born since 1937.

In the analysis, individuals become at risk of the transition to the first salary employment in 1996, and they are removed from the risk after they were employed as a salary worker. The risk ends in 2008, and those individuals who were younger than fifteen years old in 1996 are excluded from the sample. Thus, the duration of the event is between one and twelve years. Individuals who were not employed with a salary job from 1996 until 2008 are censored. And the time unit of analysis is the individual-year.

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with the eight dummy variables indicating the age of a respondent each year since 1996.

The hazard has been parameterized with those eight dummy variables. This decision is based on the comparison between the results of the model with a continuous age variable and a squared term and the results of the model with eight age dummy variables in terms of model fitness.

The third dependent variable measuring the changes in the modes of production is the transition to the first business outside the home at the individual level. The Individual Life History Calendar asked if an individual started a business outside the home each year after he or she was born since 1937.

In the analysis, individuals become at risk of the transition to the first business outside the home in 1996, and they are removed from the risk after they a business. The risk ends in 2008, and those individuals who were younger than fifteen years old in 1996 are excluded from the sample. Thus, the duration of the event is between one and twelve years. Individuals who did not start any business outside home between 1996 and 2008 are censored. And the time unit of analysis is the individual-year.

Time is parameterized with the seven dummy variables indicating the age of a respondent each year since 1996. The hazard has been parameterized with those seven dummy variables. This decision is based on the comparison between the results of the model with a continuous age variable and a squared term and the results of the model with seven age dummy variables in terms of model fitness.

To examine the changes in the main source of energy at the household level, I use one dependent variable: the transition from traditional energy sources, such as fuel wood

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Agricultural Consumption Survey asked which energy sources, among fuel wood, saw dust, biogas, gas, kerosene, electricity, and others, a household used in 1996, 2001, and 2006.

In the analysis, households become at risk of the transition to the use of modern energy sources in 2001, and they are removed from the risk after they used modern energy sources. The risk ends in 2006, and those households that used modern energy sources in 1996 are excluded from the sample. Thus, the duration of the event is between one and two. Households that did not use any modern energy sources in 2001 and 2006 are censored. And the time unit of analysis is the household-time.

Just like the analysis for the transition out of farming, the parameterization of time is measured with the time variable that indicates each year. The hazard has been parameterized with a continuous time variable having a value of one or two.

Measurements of migration The accumulated duration of monthly migration and accumulated duration of yearly migration variable would be used to test the effects of migration on the changes in the agricultural activities, in the modes of production and in the main source of energy.

For all the migration variables, the sum of the duration of each household member aged over fifteen within a certain time period is considered. Due to the structure of the datasets used for the analyses to test the duration of migration on agricultural and energy transitions, migration variables are measured within two time periods: between 1996 and 2001 and between 2001 and 2006. In the case of monthly migration, any migration after

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migration, any migration in 2001 and 2006 are not counted in the analysis. Thus, for example, we assess how the duration of migration between 1996 and 2001 and the household capitals in 1996 affected the changes in the size of farming land between 1996 and 2001.

For the primary analysis using the first dataset having monthly migration history, migrations for more than 3 consecutive months are only counted. This restriction is to increase the chance of avoiding non-successful migrations (early return) and sporadic travels to other parts of Nepal. Previous studies show that early returnees are mainly due to the failure to adjust to the living and working conditions of the destinations (Dabir et al.

2013). Since the dataset does not have the information about the purpose of each migration, this is the best way to capture relevant migration experience for this dissertation.

For the secondary analysis using the second dataset having yearly migration history, domestic and international migrations are considered. The survey questions used to measure domestic and international migration are asking whether or not a respondent lived in any district or a country outside of Chitwan for longer than six months in each year. Thus, it is most likely to sort unsuccessful migration or travels out as the measurement of monthly migration might do.

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Human capital Human capital is measured with household size and education. Household size is from the Household Registry data, and education is from the Individual Questionnaire.

Household size is divided into three age groups: the number of young children under age 15, the number of adults of working age between 15 and 65, and the number of elderly people over 65. In addition, to measure of the household size in 2001, the average household size between 1996 and 2001 is used for all the analysis. This is to avoid missing any household members due to a temporary leave of a household in the month a household took the survey. On average, there is no big difference between the household size in the month of the survey in 2001 and the average household size calculated. For the household size in 1996, the household size in the first month of the survey is used since the Household Registry began collecting household-level information from February in 1997.

The education variable measures the education level of the youngest household member over 15 years old and the education level of the oldest household member. Since the Individual Questionnaire was collected in 1996 and 2008, those individuals who got additional education after 1996 have different years of education in 2008. To fully reflect the additional years of education in the analysis, the education level in 2008 is used if it is used to predict the dependent variables in 2006 or any transitions between 2001 and 2006.

When education is used to predict the dependent variables in 2001 or any transitions between 1996 and 2001, the education level in 1996 is used.

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Natural capital is measured with land possession and environmental perception, and these variables are from the Household Agriculture and Consumption Survey. Land possession includes the possession of bari (upland, less productive and valued farmland) and khet land (irrigated low land, more productive and valued farmland). For both, it is coded 1 if a household owns bari or khet land, otherwise, it is 0. Environment perceptions are measured with the question about a respondent’s perception of water quality (much dirtier, little dirtier, same, little better, or much better) each year compared to the prior three years. The value is 0 if a household answered that the water quality was the same compared to three years ago. And the value is negative if a household answered that the water quality was little dirtier (-1) or much dirtier (-2) compared to three years ago. The value is positive, on the other hand, if a household answered that the water quality was little better (+1) or much better (+2) compared to three years ago.

The perceptions on crop production and crop damage are not used for the analysis since they are only applicable for farming households and the first and the second datasets do include all the households regardless of farming status.



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