To Err is Human: Inconsistencies in Food Conversion Factors and Inequality in Malawi

Mussa, Richard (2017): To Err is Human: Inconsistencies in Food Conversion Factors and Inequality in Malawi.

Abstract

One of the key deficiencies of household survey data for measuring poverty and inequality is that survey nonresponse depends on the income of the respondent, whereby rich people are less likely to cooperate with household surveys than poor people. This nonrandom nonresponse may hide the true level of poverty and inequality. Another potential problem with household survey data which has received scant attention in the literature is the quality of food conversion factors. Nonrandom errors and inconsistencies in food conversion factors can potentially have a nontrivial impact on measured poverty and inequality.The paper looks at the impact of errors and inconsistencies in food conversion factors on measured consumption inequality. Malawi has been used as case study with data from the Second and the Third Integrated Household Surveys (IHS2 and IHS3). Two consumption aggregates are used; an official aggregate which is contaminated by errors and inconsistencies in food conversion factors and a new aggregate which cleans out these problems. The paper finds that the inconsistencies and errors in the conversion factors were not random in that they affected the richest households more than the poorest households. Consequently, the official aggregate understates the level of inequality as measured by the Gini coefficient. Inequality is underestimated by 4.4 and 2.3 Gini points for 2004/5 and 2010/11 respectively. The disparities are not only sizable but they are also statistically significant. I also find that the official aggregate progressively underestimates the share accruing to higher percentiles. Nonparametric tests for Lorenz dominance confirm that these differences in measured inequality are robust. Using the new aggregate, the paper also finds that inequality is not worsening overtime as the official aggregate suggests. All this implies that the quality of food conversion factors is critical for the accurate measurement of levels of and trends in inequality.

• Item Type: MPRA Paper
• Language: English
• Keywords: Conversion factors; inequality; Malawi
• Subjects: D - Microeconomics > D3 - Distribution; D - Microeconomics > D3 - Distribution > D30 - General

References

Barrett, GF, SG. Donald and Bhattacharya D. 2014. Consistent Nonparametric Tests for Lorenz Dominance, Journal of Business and Economic Statistics, vol. 32:1-13.

Beck, U., Pauw, K. and Mussa, R. 2015. Methods matter: The sensitivity of Malawian poverty estimates to definitions, data, and assumptions. 2015/126. Helsinki: UNU-WIDER.

Chen, S, and Ravallion, M. 2001. How Did the World's Poorest Fare in the 1990s?, Review of Income and Wealth, 47: 283-300.

Chen, S, and Ravallion, M. 2004. How Have the World's Poorest Fared since the Early 1980s?, World Bank Research Observer, 19: 141-169.

Chen, S, and Ravallion, M. 2010. The Developing World Is Poorer Than We Thought, but No Less Successful in the Fight against Poverty, Quarterly Journal of Economics 125: 1577-1625.

Deaton, A. 2005. Measuring poverty in a growing world (or measuring growth in a poor world), Review of Economics and Statistics, 87: 1-19.

Duclos, JY and Araar, A. 2006. Poverty and Equity: Measurement, Policy and Estimation with DAD. Springer, London.

Haughton, JJ and Khandker, SR. 2009. Handbook on Poverty and Inequality, Washington: World Bank Publications.

NSO (National Statistics Office). 2005. Integrated Household Survey 2004-2005. Volume I: Household Socio-economic Characteristics, National Statistics Office, Zomba, Malawi.

NSO (National Statistics Office). 2012. Integrated Household Survey 2004-2005. Household Socio-economic Characteristics Report, National Statistics Office, Zomba, Malawi.

NSO (National Statistics Office). 2015. Statistical Year Book, National Statistics Office, Zomba, Malawi.

Pauw,K, Beck U, and Mussa, R. 2016. Did rapid smallholder-led agricultural growth fail to reduce rural poverty? Making sense of Malawi's poverty puzzle, in: Arndt C, McKay A, and F. Tarp (eds), Growth and Poverty in Sub-Saharan Africa, Oxford University Press: Oxford; p. 89-111.

Piketty, T. 2014. Capital in the Twenty-First Century. Cambridge, MA: The Belknap Press.

Piketty, T., and Saez E. 2014. Inequality in the long run. Science, 344: 838-843.

Pinkovskiy, ML, and Sala-i-Martin, X. 2009. Parametric Estimations of the World Distribution of Income.NBER Working Paper No.15433.

Pinkovskiy, ML, and Sala-i-Martin, X. 2014. Africa is on Time. Journal of Economic Growth 19:311-338.

Pinkovskiy, ML, and Sala-i-Martin, X. 2016. Lights, Camera,....Income! Illuminating the National Accounts-Household Surveys Debate, The Quarterly Journal of Economics, doi: 10.1093/qje/qjw003

Sala-i-Martin, X. 2002. The Disturbing 'Rise'of Global Income Inequality, NBER Working Paper No. 8904.

Sala-i-Martin, X. 2006. The World Distribution of Income: Falling Poverty and ...Convergence, Period. Quarterly Journal of Economics, 121: 351-397.

Verduzco-Gallo, I, Ecker, E. Pauw, K. 2014. Changes in Food and Nutrition Security in Malawi: Analysis of Recent Survey Evidence. International Food Policy Research Institute, MASSP Working Paper No. 6.

Jann, B. 2016. Assessing inequality using percentile shares, The Stata Journal,16: 264-300.