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Spatial Decomposition of Poverty in India
... In the context of interface between growth and poverty, it is useful to decompose the impact of income growth and income distribution on poverty. One policy concern in recent years has been whether wide differences in the poverty across regions in India are due to the differences in the mean income or the differences in the distribution of income (Dhongde, 2003). Several attempts have been made in the past to decompose the total change in poverty over a period of time (Kakwani and Subbarao, 1990;Datt and Ravallion, 1992;Shorrocks and Kollenikov, 2001;and Dongde, 2002). ...
... In a recent Study, Dhongde (2003) analysed how much of the total differences in the state and national level poverty can be explained by differences between state and national mean income, and differences in their income distribution. Based on NSS data, decomposition of poverty is done for the year 1999-00. ...
... The study draws important policy implications, arguing for higher rates of growth of income at state level where the poverty levels are very high. The main results of Dhongde's (2003) study are summarised in Tables 2.7 and 2.8, and Appendix Tables 2.2 and 2.3. The poverty ratios are adjusted for differences in state prices. ...
... documents define ''extreme poverty'' as an individual subsisting on less than $1 US per day. A study specific to India (Dhongde 2003) cites a poverty line figure of 454 rupees per month below which an individual would be considered to live in poverty. Based on either of 4.2 6 10 4 (3.2 6 10 4 ) 2.0 6 10 7 (5.5 6 10 7 ) Percentage values represent number of people afflicted with a given disease during the previous 12 months out of total living family members. ...
... United Nations/World Bank documents generally define anyone subsisting on less than $1 US per day as living in extreme poverty. A study by Dhongde (2003) cites a poverty line of 454 rupees per month for individuals living in urban areas of India such as Varanasi, a figure that is about one-third the United Nations value. By either of these definitions, families with two or more members with total income of less than 1000 rupees per month are considered to be living in poverty. ...
In Varanasi, India, an estimated 200 million liters daily or more of untreated human sewage is discharged into the Ganges River. River water monitoring over the past 12 years has demonstrated faecal coliform counts up to 10(8) MPN (most probable number) per 100 ml and biological oxygen demand levels averaging over 40 mg/l in the most polluted part of the river in Varanasi. A questionnaire-based survey was used to estimate water-borne and enteric disease incidence and study river use among resident users of the Ganges River in Varanasi. The overall rate of water-borne/enteric disease incidence, including acute gastrointestinal disease, cholera, dysentery, hepatitis-A, and typhoid, was estimated to be about 66% during the one-year period prior to the survey. Logistic regression analysis revealed significant associations between water-borne/enteric disease occurrence and the use of the river for bathing, laundry, washing eating utensils, and brushing teeth. Thirty-three cases of cholera were identified among families exposed to washing clothing or bathing in the Ganges while no cholera cases occurred in unexposed families. Other exposure factors such as lack of sewerage and toilets at residence, children defecating outdoors, poor sanitation, low income and low education levels also showed significant associations with enteric disease outcome. This study provides an estimate of water-borne/enteric disease incidence and identifies possible risk factors for residents who live by and use the Ganges River in Varanasi.
... Bourguignon and Morrison (2002) for a study heavily relying on such grouped data as the basis for historical comparisons. 9 For example, Ackland et al (2004), Dhongde (2005), and Fuentes (2005). Minoiu and Reddy (2006) provide an assessment of the kernel density estimation approach. ...
The estimation of poverty and inequality often requires the use of grouped data as complete household surveys are neither always available to researchers nor easy to analyze. In this study we assess the performance of two functional forms for the Lorenz curve proposed by Kakwani (1980) and Villasenor and Arnold (1989). The methods are implemented using the computational tool POVCAL, developed and distributed by the World Bank. To identify biases associated with this method of estimating the two Lorenz curve functional forms, we analyze unit data from several household surveys and a wide range of theoretical distributions. We find that poverty and inequality is better estimated when the data is generated from unimodal distributions than when it is drawn from multimodal distributions. For unimodal distributions, the biases in the estimation of poverty measures are rarely larger than one percentage point. Inequality (measured by the Gini coefficient) is well estimated in most cases considered. Neither of the two Lorenz curve estimation methods provides consistently superior performance, and performance does not always improve with the number of data points analyzed.
... A somewhat different approach to decomposition is taken by authors that address the static decomposition of differences between various groups in the society such as spatial groups, e.g. states in a nation (Dhongde 2003;Kolenikov and Shorrocks 2005), or different castes (Borooah 2005) rather than changes between time periods. Formally this could be seen as the same problem as temporal decomposition, and the same methods could be applied. ...
I discuss how poverty decomposition methods relate to integral approximation, which ultimately is the foundation of every decomposition of the temporal change of a quantity into key drivers. This offers a common framework for the different decomposition methods used in the literature, clarifies their often somewhat unclear theoretical underpinning and identifies the methods� shortcomings. In light of integral approximation, many methods actually lack a sound theoretical basis and they usually have an ad-hoc character in assigning the residual terms to the different key effects. I illustrate these claims for the Shapley-value decomposition and methods related to the Datt-Ravaillon approach and point out difficulties in axiomatic approaches to poverty decomposition. Recent developments in energy and pollutant decomposition offer some improved methods, but ultimately, a further development of poverty decomposition should account for the basis in integral approximation. --
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