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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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Chemical Fertilizer. The fourth analysis is to test the effects of migration and household capitals on the changes in the amount of chemical fertilizer. This is a household-level analysis. The most appropriate model for this analysis is the first difference model. In this model, the changes in the amount of chemical fertilizer between 2001 and 2006 are the results of the changes in migration and the changes in household capitals between 1996 and 2001. In case a household does not farm, the amount of chemical fertilizer is considered to be zero. As a result, all the households regardless of farming status are included in the sample.

The descriptive statistics of the amount of chemical fertilizer used by migration status is presented in Table 6. In both years, 2001 and 2006, the amount of chemical fertilizer is smaller for the households without migrants compared to the households with migrants. In both years, the difference is more than 20 kilograms.

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models test the main effects of the changes in migration and household capitals on the changes in the amount of chemical fertilizer used. Model 1 includes only migration, and household capitals are added in Model 2. The result of Model 1 shows that the duration of migration does not have any impact on the amount of chemical fertilizer. This result persists after controlling for household capitals in Model 2. Among household capitals in Model 2, human capital shows significant impacts on the amount of chemical fertilizer.

In detail, one additional household member of working age is associated with a 5.5 kg increase in the amount of chemical fertilizer. In the analysis of the size of farming land, additional labor in a household was associated with more farming land. It seems like the analysis results of chemical fertilizer show a similar result. To generalize, there is additional labor in a household, so the household does farming more. Then naturally, the household tends to use more chemical fertilizer for the increased size of farming land.

The education level of the youngest household member also has positive impact on the amount of chemical fertilizer. One additional year in the education of the youngest household member is associated with a 1.5 kg increase in the amount of chemical fertilizer. This result could reflect the fact that more educated people knows better about new and reliable agricultural technologies available out on the market. This pattern could be observed during my qualitative field work in Chitwan in 2012. One of the interviewees, a farmer, insisted that more rich and educated farmers were using chemical fertilizer and poor and uneducated farmers, like him, just did not know what to use or how to use chemical fertilizer to improve the productivity so that the gap between poor

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education.

The interaction effects are summarized in Table 10B. Model 3 tests the interactions between migration and human capital, Model 4 tests the interactions between migration and natural capital, Model 5 tests the interactions between migration and physical capital, Model 6 tests the interactions between migration and financial capital, and Model 7 tests the interactions between migration and social capital. The interaction results show significant interaction effects of migration with human and financial capitals.

The interaction between migration and number of elderlies in a household is visualized in Figure 16. It shows that less labor over time increases the use of chemical fertilizer as the duration of migration increases while additional labor decreases the use of chemical fertilizer. This indicates that chemical fertilizer is likely to be used to compensate for the loss of labor for agricultural activities. The interaction between migration and number of poultry is visualized in Figure 17. This result is statistically significant at the.05 level of significance, but substantially, there is little difference by number of poultry as the duration increases.

The result of the interaction between migration and proportion of households using chemical fertilizer in the same neighborhood is visualized in Figure 18. When a household lives in a neighborhood where there is no change or decreasing numbers of households using chemical fertilizer over time, migration is positively associated with the amount of chemical fertilizer a household uses. When a household lives in a neighborhood where an increasing number of households uses chemical fertilizer, on the other hand, migration shows no association with the amount of chemical fertilizer a

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neighborhood with a decreasing number of households using chemical fertilizer than the counterpart. In my opinion, in the setting of decreasing use of chemical fertilizer, 1) the setting could indicate that the general socioeconomic status of a given neighborhood is low, and 2) in that setting, using more chemical fertilizer, as an advantage, would maximize their farming productivity.

As the last step of the analysis on the amount of chemical fertilizer, additional analysis was done to test the effects of domestic and international migration on the amount of chemical fertilizer by using the secondary dataset including the information from the Life History Calendar. The results indicate that both domestic and international migration show no effects on the amount of chemical fertilizer.





In summary, the analysis results show significant associations between household capitals and agricultural activities. Further, the results show how household capitals moderate the association between migration and agricultural activities. Two results are consistent across the complex results: more available labor in a household, household capital, and a larger amount of prior investment in agriculture, physical and financial capitals, seem to play important roles for rural farming households to stay in agriculture.

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Note: Only coefficients of migration, number of household members of young age, and the interaction between those two are used for the computation.

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Note: Only coefficients of migration, number of household members of working age, and the interaction between those two are used for the computation.

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Note: Only coefficients of migration, number of household members of working age, and the interaction between those two are used for the computation.

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Note: Only coefficients of migration, proportion of households raising poultry in the same neighborhood, and the interaction between those two are used for the computation.

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Note: Only coefficients of migration, proportion of households using chemical fertilizer in the same neighborhood, and the interaction between those two are used for the computation.

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Transition from Farming to Non-farming. The first analysis is to test the effects of migration and household capitals on the transition from farming to non-farming.

This is a household-level analysis. As discussed, the most appropriate model for this analysis is the multilevel binary logistic model using discrete-time event history approach.

In this model, the transition from farming to non-farming is the result of migration experience and household capitals a household had before the transition. Based on the dataset I used for this analysis, which includes all the households who took the survey in 1996, 2001, and 2006, total 184 households out of 1,157 households experienced the transition out of farming between 1996 and 2006. These households include the households who did have a new household with the non-farming status separated from the parental household between the same periods. Since this analysis examines the transition to non-farming status at the household level, only farming households in 1996 are included in the sample as a starting point.

The main effects of migration and household capitals are summarized in Table 11A. Model 1 includes only migration, and household capitals are added in Model 2.

The results are presented as odds ratios, so a coefficient greater than one represents a positive effect that accelerates the rate of the transition, while a coefficient less than one represents a negative effect that delays the transition.

The result of Model 1 shows that duration of migration does not have any impact on the transition out of farming. Yet, household capitals show some significant impacts on the transition. One additional household member of working age increases the likelihood of the transition by 13% (1.13 – 1.00 =.13) while one additional elderly

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young members in a household would reflect a high tendency of young generations to find a job in the non-farming sector while more elderlies in a household indicate the lack of labor or the retirement from farming. On the other hand, possessing khet land (irrigated low land) decreases the likelihood of the transition by 39%. Also, one unit increase in livestock decreases the likelihood of the transition out of farming by 28%.

These two results indicate that a household possessing a substantial amount of agricultural assets tends to stay in farming rather than move out of farming. Last, one unit increase in housing quality is associated with 8% increase in the likelihood of the transition. In the context of Chitwan, upgrading the housing is highly desired after a household has accumulated financial capital to afford it. Thus, good housing quality might be the result of their efforts in previous years. Accordingly, they are likely to be at the end of the life cycle, close to retirement, and ready to transition out of farming.

Another scenario is that those households with good housing quality might try to invest their accumulated capitals in the non-farming sector as a society develops. For example, they want to start a new business in Narayanghat, the most developed city in the Chitwan valley.

The interaction effects are summarized in Table 11B. Model 3 tests the interactions between migration and human capital, Model 4 tests the interactions between migration and natural capital, Model 5 tests the interactions between migration and physical capital, Model 6 tests the interactions between migration and financial capital, and Model 7 tests the interactions between migration and social capital. The interaction results show significant interaction effects between migration and physical capital only,

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low, migration shows almost no association with the transition out of farming. When a housing quality of a household is high, on the contrary, migration shows a negative association with the transition out of farming. The results indicate that migration could give more livelihood options for those households with middle or higher socioeconomic status to stay in farming than it does for the households with poverty.

In sum, the analysis results support the hypotheses HB1 and HB2. In detail, the results of the number of household members of working or old age and the level of education support the hypothesis HB1 stating that households rich in human capital are associated with a higher chance of the changes in the modes of production. And the result of khet land supports the hypothesis HB2 stating that households rich in natural capital are associated with a lower chance of the changes in the modes of production.

As the last step of the analysis on the transition out of farming, additional analysis was done to test the effects of domestic and international migration on the transition out of farming by using the secondary dataset including the information from the Life History Calendar. The results indicate that both domestic and international migration show no direct effects on the transition out of farming.

Transition to the First Salary Employment. The second analysis is to test the effects of migration, individual characteristics and household capitals on the transition to the first salary employment. This is an individual-level analysis. Due to the limitations in data structure, yearly migration information from the Life History Calendar, not monthly

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international migrations are used as the main variables to test migration effects.

As discussed, the most appropriate model for this analysis is the multilevel binary logistic model using discrete-time event history approach. In this model, the transition to the first salary employment is the result of migration experience at the individual and the household levels, individual characteristics and household capitals a household had before the transition. Since this analysis is using the Life History Calendar exclusively, yearly domestic and international migrations will be tested in each model. And based on the dataset I used for this analysis, which includes all the individuals from the households who took the survey in 1996, 2001, and 2006, a total 149 individuals out of 1,432 individuals who did not have experience in salary employment before 1996 experienced the transition to the first salary employment between 1996 and 2008.

The main effects of migration and household capitals are summarized in Table 12A. Model 1 includes domestic and international migration of a respondent and other household members, and Model 2 includes individual characteristics in addition to migration, and household capitals are added in Model 3. The results are presented as odds ratios, so a coefficient greater than one represents a positive effect that accelerates the rate of the transition, while a coefficient less than one represents a negative effect that delays the transition.

The results of Model 2 and 3 show that the duration of domestic migration of an individual prior to the transition does have an impact on the transition to the first salary employment. One additional year in the duration of domestic migration is associated with 19% decrease in the likelihood of the transition to the first salary employment. This result

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