An empirical analysis of poverty, inequality and the labour market in Malawi by Anderson Sawira Gondwe Dissertation presented for the degree of Doctor of Philosophy (Economics) in the Faculty of Economic and Management Sciences at Stellenbosch University Supervisor: Prof. Servaas van der Berg December 2016 Stellenbosch University https://scholar.sun.ac.za Declaration By submitting this dissertation electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch University will not infringe any third party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualification. December 2016 _____________________________ (Anderson Gondwe) Copyright © 2016 Stellenbosch University All rights reserved i Stellenbosch University https://scholar.sun.ac.za Abstract This thesis is a consolidation of three related studies on Malawi. The first study contains spatial and temporal comparisons of poverty and inequality in Malawi using two non-monetary dimensions, namely an asset index and child nutritional status. Through stochastic dominance tests, the study establishes that poverty and inequality are unambiguously higher in rural areas, which contain 85% of the population, in the Southern region and among households headed by females. Results indicate that poverty has significantly declined over time and that the gains from growth have been pro-poor. We show that welfare does not vary much across regions and areas with respect to child nutritional status but there are large differences in asset poverty. Stunting is a bigger problem among children under the age of five than body wasting and being underweight. Econometric analysis shows that asset ownership is positively associated with household size, the age of household head and education attainment. Age dependency ratio and incidence of sickness are negatively associated with asset ownership. Multivariate analysis of child nutrition reveals that malnutrition first worsens before improving at some critical age. This is consistent with possible recovery found in some of the studies that track children over time. Also in accordance with some literature, we find that boys have weaker nutritional status than girls. The second study looks at the role of education in poverty reduction identified through the labour market. This study contributes to research on returns to education by including self-employment activities and non-farm business enterprises. Unlike previous studies, this study uses panel data which has many advantages, as acknowledged in the literature. We find large and positive returns to education in Malawi suggesting that education is a good investment. The returns increase with the levels of education. Interestingly, females have higher returns to education than males with similar skills. Since the Malawian labour market is not homogeneous, our analysis distinguishes between the formal and informal employment sectors. Furthermore, studying Malawi’s informal sector is important as it accounts for 78% of total employment. Our results show that education externalities exist and play an important role in non-farm enterprises. The findings are robust to sample selection and treatment of outliers. We further show that dealing with inconsistencies in the data helps improves the quality and reliability of the results. The third study applies spatial panel data econometric techniques to the study of migration and employment in Malawi. The study shows that the magnitudes of coefficients drop after taking into account spatial dependencies. This confirms that studies that fail to take into account the spatial effects tend to overstate the results. By matching geographical codes that are consistent over time, it is now feasible to integrate census data with other data for similar spatial analysis. The study further evaluates the impact of land reform policy on spatial migration and employment using a difference-in-difference ii Stellenbosch University https://scholar.sun.ac.za estimation strategy. Results show that the policy has had significant effects on migration and employment patterns in Malawi. iii Stellenbosch University https://scholar.sun.ac.za Opsomming Hierdie tesis is a konsolidasie van drie verwante studies oor Malawi. Die eerste studie ondersoek armoede en ongelykheid in Malawi oor tyd en ruimte heen deur twee nie-monetêre dimensies, naamlik 'n bate-indeks en die voedingstatus van kinders, te gebruik. Deur middel van stogastiese dominansie- toetse word ondubbelsinnig getoon dat armoede en ongelykheid hoër is in landelike gebiede, wat 85% van die bevolking huisves, in die Suidelike streek en onder huishoudings met vroue as hoof van die huis. Resultate toon dat armoede beduidend afgeneem het en dat groei tot voordeel van die armes strek. Ons resultate toon weinig verskille in welsyn tussen streke en gebiede met betrekking tot die voeding status van kinders, maar groot verskille in bate-armoede . Vertraagde groei is 'n groter probleem by kinders onder die ouderdom van vyf jaar as kwyning en ondergewig. Ekonometriese ontleding toon dat bate- besit positief verband hou met die grootte van die huishouding en die ouderdom en opvoedingsvlak van die hoof van die huishouding . Die ouderdom-afhanklikheidslas en die voorkoms van siekte hou negatief verband met bate-besit. Regressie-analise wys dat wanvoeding onder kinders eers met ouderdom toeneem voordat dit by hoër ouderdomme afneem, wat konsekwent is met die moontlikheid van herstel soos party studies wat kinders oor 'n tydperk volg bevind. Ook, in ooreenstemming met party studies, word bevind dat die voedingstatus van dogters beter is as dié van seuns. Die tweede studie bestudeer die rol van onderwys in die vermindering van armoede in die arbeidsmark. Deur die insluiting van selfwerksaamheidsaktiwiteite en nie-landbou sakeondernemings dra die studie by tot navorsing oor die voordele van opvoeding in Malawi. Anders as in vorige studies, gebruik hierdie studie paneeldata, wat baie voordele inhou, soos in die literatuur bevestig. Ons vind groot en positiewe opbrengste op onderwys, wat daarop dui dat dit 'n goeie belegging is. Opbrengste neem toe met vlakke van onderwys. Interessant genoeg, ervaar vroue hoër opbrengste op belegging in onderwys as mans met dieselfde vaardighede. Aangesien die arbeidsmark in Malawi nie homogeen is nie, tref ons analise ‘n onderskeid tussen die formele en informele indiensnemingsektore. Dit belangrik om Malawi se informele sektor in ag te neem, aangesien dit 78% van die totale indiensneming uitmaak. Ons resultate wys dat daar eksternaliteite van onderwys bestaan wat 'n belangrike rol speel in nie-landbou ondernemings. Ons resultate is robuust virsteekproefseleksie en die hantering van uitskieters. Die uitstryk van data-onreëlmatighede dra tot 'n verbetering in die kwaliteit en betroubaarheid van die resultate by. Die derde studie pas ruimtelike paneeldata ekonometriese tegnieke toe op migrasie en indiensneming in Malawi. Die grootte van koëffisiënte neem af as ruimtelike afhanklikhede in ag geneem word. Dit bevestig dat studies wat nalaat om ruimtelike aspekte in berekening te bring geneig is om effekte te oorskat. Deur konsekwente geografiese kodes oor tyd te verbind is dit nou moontlik om sensusdata met ander data te integreer vir verdere ruimtelike analise. Die studie evalueer ook die uitwerking van die iv Stellenbosch University https://scholar.sun.ac.za grondhervormingsbeleid op ruimtelike migrasie en indiensneming deur die gebruik van 'n verskil-in- verskille metodeevalueer. Die resultate dui daarop dat hierdie beleid 'n beduidende uitwerking op migrasie en werkloosheid in Malawi het. v Stellenbosch University https://scholar.sun.ac.za Acknowledgements Firstly, I would like to thank Professor Servaas van der Berg from whom I have received tremendous feedback and guidance throughout my PhD study. I will forever remember him for his positive contribution to my life and for opening my mind to the big picture of research. I know Servaas as a very humble, patient and wise man with outstanding fatherly care. Considering my non-academic background, the fact that I am completing my studies within three years speaks volumes about how Servaas is able to identify and develop talent in people. Secondly, I acknowledge the contribution from Research on Social Economic Policy (RESEP) which is led by Servaas. RESEP has been important to my progress and timely completion of the studies. Apart from the networking, the organisation has been the source of the much needed additional funding for my studies and academic conferences. The RESEP team also boasts an excellent pool of researchers from which I have greatly benefited. It has provided a platform for the transfer of new skills and refining my work through the weekly departmental seminars and training workshops. I particularly thank Dr Dieter von Fintel for his insights that have also helped shape my study. Thirdly, I also say my heart-felt thanks to The Stellenbosch Institute for Advanced Study (STIAS) for the scholarship funding provided through The Graduate School of Economic and Management Sciences (GEMS). The GEMS cohort system, which recruits PhD researchers from different parts of the world, has enabled me to develop important networks with colleagues from three different continents, namely Africa, Asia and Europe. In this regard, special mention goes to Dr Jaco Franken, the manager of the graduate school programme and fellow PhD students in the programme. Lastly, I thank my family and friends who have provided encouragement to me during my physical absence from them. I have made it to the glory of the LORD and Jesus Christ. vi Stellenbosch University https://scholar.sun.ac.za Dedication I dedicate this work to my family and particularly Mary Gondwe. My wish is that there may never cease to be people who attain PhD education throughout our family generations. vii Stellenbosch University https://scholar.sun.ac.za Table of Contents Declaration ............................................................................................................................................... i Abstract ................................................................................................................................................... ii Opsomming ............................................................................................................................................ iv Acknowledgements ................................................................................................................................ vi Dedication ............................................................................................................................................. vii Table of Contents ................................................................................................................................. viii List of Figures ........................................................................................................................................ xi List of Tables ......................................................................................................................................... xii Chapter 1 ................................................................................................................................................. 1 1.1 Introduction ............................................................................................................................. 1 1.2 Geography and history ............................................................................................................ 1 1.3 Economy .................................................................................................................................. 2 1.4 Problem statement ................................................................................................................... 5 1.5 National data sources ............................................................................................................... 6 1.6 Thesis structure........................................................................................................................ 7 Chapter 2 ............................................................................................................................................... 10 2.1 Introduction ........................................................................................................................... 10 2.2 Theoretical considerations in poverty measurement ............................................................. 12 2.3 Inequality measurement ........................................................................................................ 14 2.4 Stochastic dominance analysis .............................................................................................. 15 2.5 Poverty and inequality decomposition .................................................................................. 17 2.6 Pro-poor growth analysis ....................................................................................................... 17 2.7 Data ....................................................................................................................................... 20 2.8 Poverty lines .......................................................................................................................... 27 2.9 Cumulative density curves .................................................................................................... 27 2.10 FGT poverty estimates .......................................................................................................... 28 2.11 Poverty dominance analysis .................................................................................................. 30 2.12 Gini and GE inequality estimates .......................................................................................... 31 2.13 Inequality dominance analysis .............................................................................................. 33 2.14 Poverty decomposition .......................................................................................................... 34 2.15 Subgroup inequality decomposition ...................................................................................... 34 2.16 Spatial distribution of poverty and inequality ....................................................................... 37 2.17 Factors affecting asset poverty .............................................................................................. 40 viii Stellenbosch University https://scholar.sun.ac.za 2.18 Child nutritional status in Malawi ......................................................................................... 42 2.19 Multivariate analysis of child nutrition ................................................................................. 44 2.20 Asset index and pro-poor growth analysis ............................................................................ 47 2.21 Pro-poor growth in child nutritional status ............................................................................ 52 2.22 Conclusions ........................................................................................................................... 55 2.23 Policy discussion ................................................................................................................... 55 Chapter 3 ............................................................................................................................................... 57 3.1 Introduction ........................................................................................................................... 57 3.2 Methodology ......................................................................................................................... 59 3.2.1 Theoretical framework .................................................................................................. 59 3.2.2 Estimating returns to education ..................................................................................... 62 3.2.3 Sample selection ............................................................................................................ 63 3.2.4 Modelling unobserved heterogeneity ............................................................................ 64 3.3 Description of the data .......................................................................................................... 65 3.3.1 Work and non-work activities of the employed ............................................................ 66 3.3.2 Describing employment structure and hours worked .................................................... 67 3.3.3 Treatment of outliers, missing data and zero earnings .................................................. 69 3.3.4 Dealing with inconsistencies ......................................................................................... 71 3.4 Labour force participation ..................................................................................................... 71 3.4.1 Size of labour force and labour force participation rates ............................................... 72 3.4.2 Changes in the labour force according to background characteristics .......................... 73 3.4.3 Shares in the labour force .............................................................................................. 75 3.4.4 Multivariate analysis of labour force participation........................................................ 76 3.5 Unemployment ...................................................................................................................... 78 3.6 Employment trends and characteristics ................................................................................. 79 3.6.1 Employment shares and growth rates ............................................................................ 79 3.6.2 Multivariate analysis of employment likelihood ........................................................... 80 3.7 Earnings and changes in employment status ......................................................................... 81 3.7.1 Employed in either wave ............................................................................................... 82 3.7.2 Employed in both waves ............................................................................................... 82 3.7.3 Identifying sources of increases in earnings .................................................................. 83 3.8 Econometric analysis of returns to education ........................................................................ 85 3.8.1 Wage employment ......................................................................................................... 85 3.8.2 Household non-farm enterprise earnings ....................................................................... 97 3.9 Measurement error using panel data ...................................................................................... 99 3.10 Comparing income and consumption .................................................................................. 100 ix