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Economics of Land Reform Models Used in Mashonaland Central Province Of View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by South East Academic Libraries System (SEALS) Economics of Land Reform Models used in Mashonaland Central Province of Zimbabwe. By Lovemore Musemwa Submitted In Fulfillment of the Requirements for the Degree of Doctor of Philosophy In Agricultural Economics Department of Agricultural Economics and Extension Faculty of Science and Agriculture Supervisor: Dr. A Mushunje November 2011 DECLARATION I, Lovemore Musemwa, hereby declare that the work contained in this thesis is my own and that other scholars‟ works referred to here have been duly acknowledged. I also declare that this thesis is original and has not been submitted elsewhere for a degree. Musemwa Lovemore Date i ABSTRACT Economics of Land Reform Models used in Mashonaland Central Province of Zimbabwe. By Lovemore Musemwa The land reform that has unfolded in Zimbabwe since 1980 used different models and had diverse consequences. Since the implementation of the fast tract land reform programme in 2000, Zimbabwe experienced heavy reduction in yield and output at farm level that led to a 70% shortfall in production to meet annual food requirements (Richardson, 2005). The economic crisis in Zimbabwe has been characterized by worsening food insecurity especially in the rural areas where harvests continue to be poor. In the beef sector, Zimbabwe has failed to meet its export quota to the EU. The shortfall in production to meet annual food requirements shows a very grim situation but do not tell us about the performance of resettled farmers who now occupy much of the productive land. The broad objective of the study was to determine and compare the production efficiency of resettled farmers in Zimbabwe across land reform models. In addition, the study determined land use intensity. The study was conducted in the Mashonaland Central Province of Zimbabwe mainly because a wide variety of field crops were grown by resettled farmers. The respondents were stratified into three groups. These were: beneficiaries of land reform before 2000 (resettle scheme), fast track A1 model and fast ii track A2 model. The three models differ on how they were implemented and supported and this might result in different efficiencies of the models. A total of 245 copies structured questionnaire were administered on the resettled farmers from June to September 2010. Descriptive statistics was applied to the basic characteristics of the sampled households. The effect of model of land reform, gender of the household head, marital status, age of the household head, education, household size, religion, dependence ratio, whether the farmer was fulltime or part-time in farming, experience of the farmers in farming at that environment, total land size owned by the farmers and soil type on revenue per hectare and land use rate were determined using the GLM procedure of SAS (2003). Significance differences between least-square group means were compared using the PDIFF test of SAS (2003). The relationship between Revenue and land utilization was examined using the Pearson‟s correlations analysis. Dependance between response variables that had an effect on either revenue per hectare or land utilization with all the other response variables was tested using the Chi-square test for dependance. To find the effect of arable land used and herd size on revenue per hectare and land use the RSREG Procedure of SAS (2003) was used. Input oriented DEA model under the assumption of constant return to scale was used to estimate efficiency in this study. To identify factors that influence efficiency, a Tobit model censored at zero was selected. iii The mean land use rate varied significantly (p<0.05) with the land reform model with A2 having highest land use rate of 67%. The A1 and old resettlement households had land use rates of 53% and 46%, respectively. Sex, marital status, age of the household head, education and household size significantly affected land use (P<0.05). Revenue per hectare was not affected by any the factors that were inputted in the model. Results from the DEA approach showed that A2 farmers (large land owners) had an average technical efficiency score of 0.839, while the lowest ranking model (A1) had an average score of 0.618. Small land holders (A1 and the old resettled farmers) are on average less cost-efficient than large land owners, with a score of 0.29 for the former compared with 0.45 for the latter. From the factors that were entered in the Tobit model, age of household head, excellent production knowledge and farmer status affected technical efficiency whereas allocative efficiency was only affected by good production knowledge, farm size, arable land owned and area under cultivation. Factors which affected economic efficiency of the resettled farmers are secondary education, household size, farm size, cultivated area and arable land owned. None of the included socio-economic variables has significant effects on the allocative and economic efficiency of the resettled farmers. Thus, the allocative and economic inefficiencies of the farmers might be accounted for by other natural and environmental factors which were not captured in the model. Keywords: Data Envelope Analyses, Land Reform, Land Use, Tobit model, Revenue iv DEDICATION To my dear mother, S Chataika and daughter, Nicole v ACKNOWLEDGEMENTS A number of people made important contributions through co-operation, advice and encouragement during the course of this study. My first and foremost thanks go to my supervisor, Dr A Mushunje for his explicit guidance, tireless efforts, patience and constructive sources of ideas from the inception of this project up to its completion. This thesis would not have taken this form without his comments and continuous suggestions. I gratefully thank the Govan Mbeki Research and Development Centre for taking care of my up keep. To the land reform beneficiaries of Zimbabwe specifically Mashonaland Central Province, I thank you for your maximum co-operation in answering the long questionnaire. Your willingness to provide and share your farming experience in field crop production is greatly appreciated. Without your patience, this study would not have been possible. I am also greatly indebted to extension officers of Shamva especially Mr Chitsa, Mr Choto, Mr Banda and Mr Chaparapata for helping me in data collection. I am grateful for and acknowledge the support and materials which include good computers that the Department of Agricultural Economics and Extension provided. I am indebted to my mother, daughter, brothers and sisters and relatives for their love, encouragement and their support. In particular, my mother, elder sister and brother repeatedly were asking me about my return home to Zimbabwe on phone. I further convey sincere thanks and acknowledgements to the following individuals: Mr K Mudita, Mr A Maredza, Prof V Muchenje, Ms T Wapinduka, L Siziba, T Dangarembga and all friends for their constructive ideas, morale and technical support. Last but not least, I thank my heavenly father for giving me spiritual strength, courage, guidance and commitment to do this work. He made it possible for things to happen. vi LIST OF ABBREVIATIONS ADB African Development Bank AE Allocative Efficiency AIDS Acquired Immunodeficiency Syndrome ARDA Agricultural Rural Development Authority BSAC British South African Company CE Cost Efficiency CFU Commercial Farmers Union CRS Constant Returns to Scale DDF District Development Fund DEA Data Envelope Analysis DLA Department of Land Affairs EE Economic Efficiency EIRR Economic Internal Rate of Return FAO Food and Agriculture Organization of the United Nations GDP Gross Domestic Product GLM General Linear Model GNU Government of National Unity GoZ Government of Zimbabwe Ha Hectare HIV Human Immunodeficiency Virus IFRC International Federation of Red Cross vii IMF International Monetary Fund Km Kilometers LSC Large Scale Commercial LU Livestock Unit MDC Movement for Democratic Change MDG Millennium Development Goals mm Millimetres NECF National Economic Consultative Forum NGOs Non-Governmental Organizations NRs Natural Regions OLS Ordinary Least Square RCS Red Crescent Societies RE Revenue Efficiency SACAU Southern African Confederation of Agricultural Unions SAS Statistical Analysis System SPSS Statistical Package for Social Scientists TE Technical Efficiency USDA United States Department of Agriculture US$ United States Dollar VRS Variable Returns to Scale ZANU PF Zimbabwe African National Union – Patriotic Front ZimVac Zimbabwe Vulnerability Assessment Committee viii TABLE OF CONTENTS Page DECLARATION ...................................................................................................... i ABSTRACT ........................................................................................................... ii DEDICATION ........................................................................................................ v ACKNOWLEDGEMENTS .................................................................................... vi LIST OF ABBREVIATIONS ................................................................................. vii LIST OF TABLES ................................................................................................ xv LIST OF FIGURES ............................................................................................. xvi LIST OF APPENDICES ..................................................................................... xvii
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