Show simple item record

dc.contributor.authorMupeta, Mavis .C.
dc.date.accessioned2020-10-15T12:01:12Z
dc.date.available2020-10-15T12:01:12Z
dc.date.issued2020
dc.identifier.urihttp://dspace.unza.zm/handle/123456789/6515
dc.descriptionThesisen
dc.description.abstractUrban agriculture has grown tremendously in most Sub-Saharan African (SSA) countries. Increasing poverty levels and increasing food prices with stagnant incomes have resulted in household food insecurity. Urban households are resorting to urban agriculture as an alternative source of food for household consumption and for income generation. Urban agriculture has emerged as an informal entrepreneurial activity with the potential to increase household income and enhance food security. However, literature reveals that there is relatively low empirical evidence of the economic benefits of urban agriculture especially in Zambia. Thus, this study sought to empirically investigate the economic contribution of Urban Agriculture to the livelihood of households in Zambia by identifying factors that influence a household’s decision to participate in urban agriculture and to determine the effect of urban agriculture on household income. The analysis was based on the 2007/2008 Urban Consumption Survey data obtained from Indaba Agricultural Policy Research Institute (IAPRI). The study covered Lusaka and Kitwe towns. The total sample size was 2682 urban households. Both Logistic regression and propensity score matching models were employed for data analyses. Logistic regression model was used to determine factors that influence a household’s participation in urban agriculture because it is the appropriate regression analysis to use when the dependent variable is dichotomous. It is analogous to linear regression except that independent variable should be binary. The propensity score matching methods was used to estimate the effect of urban agriculture on household income. Propensity score matching method takes into account systematic differences in socio-economic characteristics between the treated and untreated units by matching only units from both groups with similar characteristics. This helps eliminate the problem of selection bias. In this case, the observed outcome discrepancy between the two groups can confidently be attributed to the treatment. Results indicate that urban agriculture has a positive significant effect on household income. Household income of households that practiced urban agriculture increased by 13.7% to 19.1%. This implies that urban agriculture has the potential to improve household livelihood through enhanced income. Results also show that the age of the household head, the area of residence of the household, the marital status of the household head, the highest level of education attained by the household head, the gender of the household head, the main source of livelihood of the household head and the quantity of crops harvested in the previous season significantly influence a household’s decision to participate in urban agriculture. Further results show that households in Kitwe town are more likely to participate in urban agriculture than Lusaka residents. This was used as a measure of years of experience considering that literature reveals that in Zambia, urban agriculture was first started on the Copperbelt. From a policy point of view, these results suggest the need for the Zambian Government to recognize urban agriculture and its potential economic benefits to the livelihood of households. The Government should consider integrating urban agriculture in agricultural development policies of the country. Appropriate institutional and technical support services to urban agriculture should complement these policies. This study will contribute to the body of literature on urban agriculture in that the propensity score matching model was used to estimate the average treatment effect to measure the effect of household participation in urban agriculture on household income. In Zambia, none of the previous impact studies on urban agriculture has applied the Propensity Score Matching methods. Key Words: urban agriculture, income, poverty, Propensity Score Matching, treatment effecten
dc.language.isoenen
dc.publisherThe University of Zambiaen
dc.subjectUrban agricultureen
dc.titleThe economic contribution of urban agriculture to the livelihood of households in Zambiaen
dc.typeThesisen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record