MSc Development Finance / Course details

The unit provides a unique opportunity for students to have in-depth knowledge of multidimensional poverty and inequality, while meeting the requirements of potential employers in academia, the private sector and international organizations. It introduces students to the pressing issues of growing income inequality and social injustice. Students will analyse these issues using state-of-the-art measurement techniques for poverty and inequality. Hands-on training in statistical software (STATA) will aid the learning process and help to build the quantitative and qualitative knowledge required to address the challenges in measuring welfare. This unit enhances employability skills of the students by providing them with training in data analysis, in particular, application of secondary data to estimate welfare indicators.  

The unit is split into three parts. In the first part, students are introduced to the characteristics of the unidimensional (income) measure of poverty, the estimation of poverty lines, and poverty outcomes at the individual and aggregate levels. Students then learn the tools for poverty measurement in a multidimensional space, and the challenges associated with measuring multidimensional poverty. Students also learn about the new frontiers in the measurement of poverty.

The second part of the unit covers a wide range of inequality measures. Students learn the theoretical foundation of each inequality measure, and how to estimate them using STATA. We introduce students to recent research on global inequality trends, and to another concept, the inequality of opportunity. The second part concludes by having policy discussions on the nexus between economic growth, inequality and structural transformation.

In part 3, we introduce mixed methods. In this part, students are exposed to various methodologies on how to combine the quantitative and qualitative tools to measure poverty and inequality indicators.  

Students must have an understanding of mathematics and statistics equivalent to A-level in order to follow this course. 

The unit aims to:

Endow students with knowledge of the present global challenges and of the requirements of potential employers with regard to poverty measurement in academia, private sector and international organizations.

Enable students to think critically about growing differences in poverty, inequality, and income mobility, and about how to address them.  

Equip students with state-of-the-art quantitative tools to estimate poverty and inequality in both unidimensional and multidimensional spaces using statistical software.  

Prepare students to carry out independent empirical research on welfare issues and assess research reports by leading international organizations.

Empower students by providing them with hands-on training in data analysis and on the estimation of welfare measures to enhance their employability skills.

The unit aims to complement the existing core and elective courses and deliver a set of academic learning objectives. This course improves employability skills for the students by providing them hands-on training in data analysis and preparing them to think critically about growing differences in living standards, social mobility and social injustice. Students learn a range of statistical tools and implement them using a statistical software. In addition, students acquire theoretical and empirical knowledge on quantitative and qualitative methodology to estimate poverty and inequality in both unidimensional and multidimensional space. This unit not only enables students to carry out independent high quality empirical research outputs but also empowers them with in-depth understanding of the present global challenges. 

Syllabus (indicative curriculum content):

Lectures    Tutorials
    
Part 1. Quantitative Methods to Measure Poverty

1.    Why Measure Poverty? Concepts.    Introduction: welfare analysis using STATA.
2.    Consumption Aggregates and Poverty Lines.     Constructing consumption aggregates using STATA. 
3.    Aggregate Poverty Measures.    Estimating poverty line using STATA. 
4.    Multidimensional poverty Measurement.    Estimating multidimensional poverty using STATA.  
5.    New Frontiers in Poverty Measurement.    Local level poverty mapping.

Part 2. Quantitative Methods to Measure Inequality

6.    Measuring Economic Inequality.    Estimating inequality using STATA.  
7.    Inequality, Causes and Consequences.    Understanding the inequality trends.  
8.    Inequality of Opportunity.    Measuring inequality of opportunity. 
9.    Growth, Poverty and Inequality.    Discussion on the Kuznets Process.  
    
Part 3. Mixed Methods (Qualitative + Quantitative)

10.    Mixed Methods to Measure Poverty and Inequality    Introduction: Stanford Center on Poverty and Inequality.

The unit is organized with the help of 10 2-hour lectures and 10 1-hour tutorials.

The first five lectures introduce students to poverty measurement tools in both unidimensional (income) and multidimensional spaces. The next four lectures cover a wide range of inequality measures, and students are exposed to policy discussions on the nexus between economic growth, inequality and structural transformation. The last lecture introduces various methodologies on how to combine the quantitative and qualitative tools to measure poverty and inequality indicators.

The weekly tutorials serve as part of the formative non-graded assessment. Students receive hands-on training on the measurements of poverty and inequality using secondary data. We primarily use household survey data to demonstrate estimation of the welfare indicators using STATA. Students’ understanding of the knowledge and skills in poverty and inequality analysis is further enhanced through weekly exercises drawing upon real-world secondary data. Each tutorial will effectively put into practice what the students have learned in the lecture sessions.  

Make critical judgments on the characterization of multidimensional space to measure poverty and inequality. 
Frame problems on social mobility and income disparity in efficient and effective manner.  
Draw reasoned conclusions from the theory and evidence. 

Gain competence in tools used to analyse data on individual and societal wellbeing.   
Use these tools to monitor economic and social development. 
Combine quantitative and qualitative techniques to produce timely and policy-relevant advice about global development.  

Conduct applied independent research using the knowledge of statistical software taught in this course. 
Compile research reports based on empirical evidence from applying the tools introduced in this module.

Individual essays on one of the topics related to poverty and inequality applied to a country (or multiple countries) in the Global South. Research report should include the statistical code for data analysis, a brief literature survey and explanation of the main findings and policy conclusions.

Feedback will be provided as per Faculty of Humanities guidelines. 

Topic 1. Why Measure Poverty? Concepts, and Data
    Ravallion, M. (1996) "Issues in Measuring and Modeling Poverty", Economic Journal, Vol. 106, September, pp. 1328-44.
    Hickel, J. (2017). “Is global inequality getting better or worse? A critique of the World Bank’s convergence narrative”, Third World Quarterly. 
    Sachs, Jeffrey D. (2005), The End of Poverty: Economic Possibilities for Our Time, Penguin Group, USA.

Topic 2. Consumption Aggregates and Poverty Lines
    Deaton, A. and Paxson, C. (1998) Economies of Scale, Household Size and the Demand for Food, Journal of Political Economy, 106(5): 897-930.
    Deaton, Angus; Zaidi, Salman (2002). “Guidelines for Constructing Consumption Aggregates for Welfare Analysis,” LSMS Working Paper No. 135.

Topic 3. Aggregate Poverty Measures
    Deaton, Angus; Zaidi, Salman (2002). “Guidelines for Constructing Consumption Aggregates for Welfare Analysis,” LSMS Working Paper No. 135. 
    Lambert, Peter J., The Distribution and Redistribution of Income: A Mathematical Analysis, Manchester University Press.
    Haughton, Jonathan and Shahidur R. Khandker (2009) "Handbook on Poverty and Inequality," The World Bank Group, number 11985.

Topic 4. Multidimensional poverty Measurement
    Foster, J. and S. Alkire (2011) “Counting and Multidimensional Poverty Measurement” Journal of Public Economics, Volume 95, Issues 7–8, pages 476-487.
    Foster, J. E., Lopez‐Calva, L. F., & Szekely, M. (2005). Measuring the Distribution of Human Development: methodology and an application to Mexico. Journal of Human Development, 6(1), 5–25.
    Sen, A. 1999. Development as Freedom. Oxford: Oxford University Press.

Topic 5. New Frontiers in Poverty Measurement
    Christiaensen, L., Lanjouw, P., Luoto, J. and Stifel, D. (2010) ‘The Reliability of Small Area Estimation Prediction Methods to Track Poverty’, WIDER Working Paper No. 2010/99.
    Lanjouw, J., Lanjouw, P., Milanovic, B., and Paternostro, S. (2004) Economies of Scale and Poverty: the Impact of Relative Price Shifts During Economic Transition, Economics of Transition 12(3) 509-536.

Topic 6. Measuring Inequality
    Haughton, Jonathan and Shahidur R. Khandker (2009) "Handbook on Poverty and Inequality," The World Bank Group, number 11985. 
    Atkinson, A., Piketty, T. and Saez, E. (2011). “Top incomes in the long run of history”, Journal of Economic Literature, 49(1), 3-71.
    Atkinson, A. 2015. Inequality: What Can be Done? Cambridge, MASS: Harvard University press.  
    Piketty, T., E. Saez, and G. Zucman (2022) "Twenty years and Counting: Thoughts about Measuring the Upper Tail" with, Journal of Economic Inequality 20, 255-264.

Topic 7. Inequality, Causes and Consequences
    Bourguignon, F. (2015). The Globalisation of Inequality, Princeton: Princeton University Press.
    Deaton, A. (2013). The great Escape: health, wealth and the origins of inequality, Princeton: Princeton University Press.  
    Milanovic, Branko (2016) Introducing Kuznets waves: How income inequality waxes and wanes over the very long run. VOXEU https://cepr.org/voxeu/columns/introducing-kuznets-waves-how-income-inequality-waxes-and-wanes-over-very-long-run
    Fogel, R., Enid M. Fogel, Mark Guglielmo, and Nathaniel Grotte (2013) Political Arithmetic: Simon Kuznets and the Empirical Tradition in Economics, University of Chicago Press.

Topic 8. Inequality of Opportunity
    Roe