Advanced Econometrics for Livelihoods Research

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Course TypeCourse CodeNo. Of Credits
Foundation CoreSHE3ED1084

Semester and Year Offered: Monsoon/Winter Semester, as required

Course Coordinator and Team: Prof. AsmitaKabra and Dr KanikaMahajan(SLS)

Email of course coordinator:

Pre-requisites: None

Course Objectives/Description:

This course will familiarize research scholars with the evolution of livelihoods research and expose them to different approaches to the study of livelihoods. They will be taught various measures and indicators of livelihood and economic well-being, including income, food security, poverty, capabilities, vulnerability and resilience, with special reference to rural households and communities. Research scholars will learn about questionnaire design and analysis of secondary quantitative data using small and large datasets. They will learn to work with specialized data analysis software packages in lab mode and will apply these techniques on an independent research project using a primary or secondary dataset.

Learning Outcomes:

  • To understand various measures of income, livelihoods and economic well-being
  • To learn how to select measures that are appropriate for specific livelihood contexts
  • To understand how to design and administer robust household surveys
  • To learn to analyze primary or secondary livelihood survey data using advanced quantitative methods
  • To learn to apply specialized software packages (like R or Stata) on real datasets

Main modules:

S. No.


Unit 1

Framing Livelihoods Research


Evolution of different approaches to livelihoods research


Measures of poverty and well-being: income, capabilities, food security


Risk, vulnerability and adaptation in livelihoods research


Perception surveys on livelihoods and well-being: Issues and concerns


Household survey design using mixed methods

Unit 2

Data Analysis Techniques for Livelihoods Research


Review of Statistics: random variables; Jointly distributed RV’s; conditional expectation; Matrix Algebra


Multivariate Regression Analysis with cross-section data: Matrix Formulation, Partialling out interpretation, Goodness of Fit, OLS as BLUE


Hypothesis testing: Linear combination of parameters, Multiple Linear restrictions


Specification issues: Omitted Variable Bias, Non spherical disturbance, Measurement Error; Dummy variables: Intercept and slope effects


Application of the above concepts using National Sample Survey, Employment and Unemployment data

Unit 3



Independent project


Assessment Details:

Assessments will consist of assignments with 30% weight for modules 1 to 5, 40% weight for modules 6 to 9, and 30% weight for the independent project. The research scholar will be expected to use an existing dataset and conduct data analysis using one of the specialized software packages.

Indicative Reading List:

  • Angelsen A, Jagger P, Babigumira R, et al. 2014. Environmental Income and Rural Livelihoods: A Global-Comparative Analysis. World Development xx.
  • Buchel S &Frantzeskaki N. 2015. Citizens ’ voice : A case study about perceived ecosystem services by urban park users in Rotterdam , the Netherlands. Ecosystem Services 12: 169–177..
  • Cameron, A.C. and Trivedi, P.K. Microeconometrics using Stata, 2nd ed., Stata Press, 2010
  • Castella JC, Lestrelin G, Hett C, et al. 2012. Effects of Landscape Segregation on Livelihood Vulnerability: Moving From Extensive Shifting Cultivation to Rotational Agriculture and Natural Forests in Northern Laos. Human Ecology 41: 63–76.
  • Deaton A. 2004. Measuring poverty. Princeton Research Program in Development Studies
  • deHaan L &Zoomers A. 2005. Exploring the Frontier of Livelihoods Research. Development and Change 36: 27–47.
  • Goebel A. 1998. Process, Perception and Power: Notes from ‘Participatory’ Research in a Zimbabwean Resettlement Area. Development and Change 29: 277–305.
  • Kanbur R & Shaffer P. 2007. Epistemology, Normative Theory and Poverty Analysis: Implications for Q-Squared in Practice. World Development 35: 183–196.
  • Kebede B. 2009. Community Wealth Ranking and Household Surveys: An Integrative Approach. Journal of Development Studies 45: 1731–1746.
  • Lei Y, Liu C, Zhang L, et al. 2016. How smallholder farmers adapt to agricultural drought in a changing climate: A case study in southern China. Land Use Policy 55: 300–308.
  • Liao C, Barrett C &Kassam KA. 2015. Does Diversification Improve Livelihoods? Pastoral Households in Xinjiang, China. Development and Change 46: 1302–1330. Mortimore, Michael (1998), “Roots in the African Dust”, UK, Cambridge University Press
  • Mukherji A. 2013. Evidence on Community-Driven Development from an Indian Village Evidence on Community-Driven Development from an Indian Village. : 37–41.
  • Rawal V. 2006. The Labour Process in Rural Haryana (India): A Field-Report from Two Villages. Journal of Agrarian Change 6: 538–583.
  • Rogers S &Xue T. 2015. Resettlement and climate change vulnerability: Evidence from rural China. Global Environmental Change 35: 62–69.
  • Scoones, Ian et al. Hazards and Opportunities: Farming livelihoods in dryland Africa - Lessons from Zimbabwe. London and New Jersey: Zed Books Ltd., 1996.
  • Scoones I. 2009. Livelihoods perspectives and rural development. Journal of Peasant Studies 36: 171–196.
  • Sen A &Himanshu. 2004. Poverty and Inequality in India: I. Economic and Political Weekly: 4247–4263.
  • Wilmsen B. 2016. After the Deluge: A longitudinal study of resettlement at the Three Gorges Dam, China. World Development 84: 41–54.
  • Wooldridge, Jeffrey M., Econometrics. Cengage Learning, 2010.
  • Wooldridge, J. Econometric Analysis of Cross Section and Panel Data, 2nd ed., MIT Press, 2010.
  • Wunder S, Angelsen A & Belcher B. 2014. Forests, Livelihoods, and Conservation: Broadening the Empirical Base. World Development 64.
  • Xu J, Lebel L & Sturgeon J. 2009. Functional links between biodiversity, livelihoods, and culture in a haniswidden landscape in southwest china. Ecology and Society 14
  • Zhang Y & Wan G. 2005. Why Do Poverty Rates Differ From Region to Region ? The Case of Urban China. 7.
  • Zhao H &Rokpelnis K. 2016. Local perceptions of grassland degradation in China: a socio-anthropological reading of endogenous knowledge and institutional credibility. The Journal of Peasant Studies 6150: 1–18.