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Acknowledgements
The writer is grateful to Professors Nurul Islam and Keith Griffin and Dr Rizwanul Islam who read the draft of the paper and gave useful suggestions.
About the Author
Azizur Rahman Khan is Emeritus Professor of Economics, University of California, Riverside, USA.
Notes
1. Diphtheria, pertussis (whooping cough), and tetanus.
2. A rank of one indicates the best performance and 16 the worst. The indicators are for 2011 or the nearest year prior to that. For data source, see Dreze and Sen, 2013, note to Table A.2, page 295.
3. See Porter, Stern, and Green (Citation2014, 15). The index is prepared under the supervision of Professor Michael Porter of The Harvard Business School. See http://www.socialprogressimperative.org/data/spi/methodology.
4. Note, however, that the primary school completion rate for girls was still considerably lower in Bangladesh than in India although the ratio of completion for girls to that for boys is higher in Bangladesh (see World Bank Citation2014).
5. These figures for 2012 are from the online version of the WDI (World Bank Citation2014). Note that the Drèze–Sen data shown in the statistical appendix for the year 2011 from an earlier WDI show a two-year advantage in female life expectancy over male for Bangladesh (Drèze and Sen Citation2013, Table A.1).
6. The Bangladesh figure is from the 2010 Labor Force Survey (BBS Citation2011, 2) and the Indian figures are from the National Sample Survey Office Report on Employment and Unemployment Situation in India 2009/10 (NSSO Citation2011, 65).
7. One further argument for not using the ILO estimates is that they contradict the authors' suggestion that female labor-force participation has been increasing. For Bangladesh the estimated ILO rates for 1990 (62%) and 1995 (58%) were higher than for 2010. Such an anomaly is also present for India in these ILO estimates (see ILO Citation2013; World Bank Citation2010).
8. I am merely echoing the authors' statement already quoted above that Bangladesh's social achievements deserve much greater scrutiny than they have so far received.
9. The only possible exception is Cuba (Drèze and Sen Citation2013, 71–72).
10. For an almost randomly chosen example, see Swaminathan and Rawal's (Citation2011) study, which shows a Gini coefficient for personal income of 0.6 that, for rural areas, would appear to be intolerably high.
11. For India the WDI shows distributional data for only a few education-related indicators. Note, however, that the difference in these indicators between income groups is usually lower for India than for Bangladesh. For example, the primary school completion rate in India is 81% (of the relevant age group) for the poorest quintile and 103% for the richest quintile as compared with the figures shown in for Bangladesh. An anonymous referee has pointed out that the National Family Health Survey for 2005/06 provides health-related data for different quintile groups. Even so it would appear that these distributional data for recent years are unavailable for India from the usual source that one looks at. I am not sufficiently familiar with the national statistical sources in India to know whether the absence of distributional data for health, nutrition and demographic indicators in the WDI for recent years indeed means that these data do not exist for India, although the presumption is that, if available, the World Bank would have reported it. This may be the place for me to admit that perusing such a profound book over a few weeks of part-time reading may easily have resulted in missing some facts and arguments. I would like to apologize to the authors of the book and the readers of this paper if this has affected my reasoning.
12. The WDI shows these data for one year, which does not allow us to document whether or to what extent the inequality between the rich and the poor in indicators of health and educational development has changed over time. It is possible that these data exist for some other years although they are not readily available (e.g. collected by surveys but unprocessed, unpublished or otherwise difficult to access).
13. The inequality measures are based on the research, some of it ongoing, by the writer, mostly in association with Binayak Sen, using data from five household surveys covering the period from 1991/92 to 2010. During this period the Gini ratio of income distribution for the country increased from about 0.30 to about 0.45. This research also showed a steady increase in the ratio of income Gini to consumption Gini over time. See, for example, Khan (Citation2005).
14. Other obstacles—for example, numerous policy distortions promoting the choice of undesirably capital-intensive techniques as documented by the ILO (Citation2009)—would need to be overcome.