This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Some of the contents of this chapter appeared in Wagle (2005). .
- 2.
A study showed, for example, that the per capita income in the Kathmandu Valley was US$ 446 as opposed to US$ 142 in the country as a whole. This was double that in other urban areas and quadruple that in rural areas. See UNDP/Nepal (1998) for more details.
- 3.
To take water, for example, different survey studies have reported that over 30-40 percent of the total population does not have access to piped water supply and that those who have access do not obtain reliable services (Kathmandu Water Supply Program 2000; UMP-UNDP/UNCHS 1998).
- 4.
To take housing, for example, it is estimated that there are at least 33 squatter or slum communities in the Kathmandu Valley with the average population of 15,000. These squatter or slum communities are temporary settlements haphazardly created by people themselves in illegal, public spaces. While there is no empirically justified figure, the majority of these communities are located in the city of Kathmandu. See MPE/IUCN (1999) and UMP-UNDP/UNCHS (1998) for details.
- 5.
This is specifically the case when income data are used. When consumption data are used, on the ether hand, the estimates appear to be close the official estimates given. I know that part of the reason is the inflation in that the 2003/2004 poverty line was used on the 2002/2003 data. This also brings up the issue concerning the reliability of the sample survey. Notwithstanding these considerations, however, the problem also lies in the degradingly low official poverty line itself.
- 6.
A massive influx of people caused by the ongoing political violence in most parts of the country effectively overcrowded the city.
- 7.
See Wagle (2004) for a detailed description of the survey procedure, outcomes, and comparative statistics from the from the 2001 population census.
- 8.
This final model is statistically identified using the t- and two-step rules (Bollen 1989). While I could manually establish identification using these rules, the use of standard software automatically does so in an attempt to estimate SEM models and reports any identification problem. The MPlus software used here indicated that the final version of the model was in fact identified.
- 9.
The addition of two latent concepts forming the economic well-being dimension increases additional layer to the model, thus increasing the number of parameters to be estimated and contributing to its complexity.
- 10.
The exploratory factor analysis helps identify principal components based on their factor loadings on the hypothesized latent factor. This is an additional tool I applied to test whether the appropriateness of the indicators suggested by the literature holds with a single factor analysis model estimated for each dimension. Results are not reported here.
- 11.
Income and consumption were used in their natural log to accommodate their nonlinear loadings.
- 12.
Self-respect and occupational prestige were not used due to data unavailability where as the use of caste discrimination was not supported by the data.
- 13.
These estimates were based on the questions on the respondent's opinion on the degree of caste and ethnic discrimination in their neighborhood, a question which may have been understood differently across respondents with different backgrounds.
- 14.
Albeit seemingly arbitrary, these four occupational categories have much in common with regard to their economic incentives and social recognition. Households with householders in these occupations tend to make substantially lower incomes-on average NRS32,000 annual per capita compared to NRS57,000 for all other households. While armed forces and especially labor occupations have much lower payoffs-on average associated households having annual per capita income of NRS28,000 and NRS14,000 respectively-households associated with all four occupations included in this combined category had annual per capita income of less than NRS50,000. These occupations also indicate low prestige jobs in this urban center, where, unlike in much of the country, people are engaged in a wide variety of occupations. Conversely, the executive and professional occupation, another category included in the analysis, carries considerably higher prestige and higher economic payoff-on average the associated households have annual per capita income of NRS60,000 compared to NRS50,000 for all other households.
- 15.
The use of occupational prestige was not possible due to the unavailability of data.
- 16.
These effects are computed directly from η = Bη + ς by using [I - B]-1 where I is the identity matrix and excluding the ς vector which cannot be estimated precisely after all. Also, note that the effect of one dimension on itself is not necessarily unitary in Table 4.4 as some of the effect systems become dynamic, rather than static, involving multiple iterations of effect determination.
- 17.
The model yielded normalized factor scores with a mean of zero, which were then transformed to ensure comparability with zero starting values. To keep the overall distribution intact, this transformation was performed, using \(\eta _{di}^{'} = \eta _{di} - \eta _{di}^{\min } \) , where \(\eta _{di}^{'}\) is the transformed score, \(\eta _{di}^{'}\) was the estimated score, and \(\eta _{di}^{\min }\) was the lowest score in the distribution.
- 18.
Partly, however, these distributions depend on the distributions of the indicators whose scales were used to measure the respective factor scores. In case of the economic well-being dimension, for example, the values are expressed in Nepali currency, where as the capability scores are measured in years of schooling.
- 19.
Assumed for illustrative purposes, this is not to imply that the inequality of capability and social inclusion will be identical to that of economic well-being. Societies may be economically, capabilistically, or social inclusively more or less equal compared to poverty dimensions other than the one under consideration. Again, this begs a difficult question of how to define poverty using each of the dimensions, which, albeit partly indicated by the distribution of dimension scores, is linked with the absolute versus relative concepts of poverty.
- 20.
The national poverty rate suggested by the CBS (1997) using consumption-income poverty standard was slightly over 40 percent in 1996, the revised estimate for which was 30 percent in 2003/2004 (CBS 2005; World Bank 2006). The poverty incidence for Kathmandu and similar urban areas, however, was estimated to be 10 percent in 2003 (CBS 2005; World Bank 2006). Using the official consumption poverty line, the survey data used here also show a consistent result with a poverty headcount ratio of over nine percent. Application of the international poverty line ($1/day) shows a different picture with a poverty rate of 39 percent in 1995 and that of 24 percent in 2003/2004 for the entire country (World Bank 2006). Other studies using income, consumption, and relative poverty standards have suggested different estimates ranging from 19 to 42 percent specifically for the city (Wagle 2006c). This broad range of poverty estimates suggested for Kathmandu testifies to the fact that poverty measurement can be highly controversial especially given a paucity of reliable data and given the difficulty to derive objectively determined poverty thresholds. Although the suggested range falls between 10 and 40 percent, I see using 10 and 30 percent estimates to comprise two reasonable alternatives for Kathmandu.
- 21.
Again, controversy can arise regarding the application of this criterion for all poverty dimensions as they have different distribution patterns. Given its widespread use, however, it is safe to assume that the resourcefulness of those at the 50 percent of the median value would be minimally needed to avoid poverty.
- 22.
This might be closer to the concept of chronic poverty defined as being consistently poor for over five years, which is in vogue among some poverty researchers (Hulme and Shepherd 2003; Hulme et al. 2001; Metha and Shah 2003). Although the focus of these researchers is basically on the time dimension, the concept of abject poverty goes beyond, incorporating its multiple dimensions.
- 23.
Acharya et al. (1999), for example, find similar empirical results and admit that ‘feminization of poverty’ which is real in Nepal is hard to prove with household level data. The argument relates to intra-household disparities between men and women, leading to similar resources and yet dissimilar levels of individual welfare.
- 24.
This is primarily the reason for no poverty among them following the official poverty line (Table 4.10).
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Wagle, U. (2008). Application I: Nepal. In: Multidimensional Poverty Measurement. Economic Studies in Inequality, Social Exclusion and Well-Being, vol 4. Springer, New York, NY. https://doi.org/10.1007/978-0-387-75875-6_4
Download citation
DOI: https://doi.org/10.1007/978-0-387-75875-6_4
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-75874-9
Online ISBN: 978-0-387-75875-6
eBook Packages: Business and EconomicsEconomics and Finance (R0)