The Human Development Index (HDI) and its Application for Mountain Areas?
Indicators about quality of life have been introduced to illustrate regional disparities, deficiencies in infrastructure assets and inequalties in the access of socio-economic resources and opportunities. A widely used indicator is the Human Development Index (HDI). This indicator goes back to a United Nations initiative to reduce short-comings of the one-dimensional per capita income, an indicator preferred by the World Bank and globally operating financial institutions. The HDI aims at acknowledging non-monetary transactions as part of domestic economies and at highlighting development effects which cannot be linked in any causal connection to monetary incomes at all. Nevertheless, the first dimension of three is the HDI's appreciation of per capita income in the units of purchasing power parity (PPP), the other two address quality of education and life expectancy.
For our discussion about the standard of development in mountain regions these parameters have to be tested (1). HDI data are mainly available on the basis of nation states, which immediately displays a practical problem (2): national reports are now available for Afghanistan, Bhutan, PR of China, Tajikistan, Kyrgyzstan, Nepal, all of which are used in the discussion here. Reports for a number of Indian states including Assam, Sikkim, Nagaland and Himachal Pradesh are available now, and regional reports for South Asia have been published by the Human Development Centre in Islamabad, Pakistan.). Statistical entities nearly never are congruous with relevant units of investigation. In a number of cases the available data are just the result of rough estimates, an indicator about data quality. In the case of mountain regions in developing countries we find data about nation states in which mountain areas are located. The range goes from some of the poorest countries such as Afghanistan and Ethiopia to the states in the Latin American Cordilleras. In a similar category we find the post-Soviet transformation countries. None is recorded above the middle level (= 0.500 up to 0.800) of the HDI, most African countries are in the lower category (Ethiopia, Uganda, Rwanda) as well as Afghanistan, Bhutan and Nepal. Such statistical data permit the comparison between nation states, but they fail in providing the required information about regional disparities within mountain regions and about highland-lowland differences. The dilemma of data evaluation becomes quite obvious. What knowledge is available for mountain regions and what kind of statements can be made?
Human Development Index (HDI) for nation states and mountain districts in High Asia (Source: data compilation based on AKRSP 1999, Bhutan HDR 2000, China HDR 2002, Lama 2001, Mahbub ul Haq 1997, MHHDC 2002, Nepal South Asia Centre 1998, UNDP 1998-2002). Click on the image to enlarge it.
For a few mountain areas, regionalized data can be discussed. In Tajikistan, the difference between the nation state and the mountain district of Gorno-Badakhshan seems negligible. Similar observations hold true for India and Pakistan when provinces are compared. The Himalayan state of Himachal Pradesh reaches similar HDI values as the Indian Union on average, the newly created union state of Uttarachanal even ranges at a higher level. But deviations from this pattern become obvious when the Karakoram district of Gilgit is compared with the North-West Frontier Province (incorporating most of Pakistan’s share in the Hindukush) and the nation state: Gilgit fares much lower in all components, but especially what concerns the standard of living. In the PR of China the provinces of Qinghai, Tibet (Xizang), and Xinjiang, incorporating major mountain areas, range below the country’s average when life expectancy and educational attainment are considered. The value for standard of living is above average in Xinjiang and Qinghai. Xinjiang’s significant deviation is due to inner-provincial regional disparities. The industrialized northern part of the province excells, while less contribution stems from the mountainous south and west. Taking size and diversity of some provinces into account, no reliable information can be derived for the Tien Shan, Kun Lun Shan and Qilian Shan Mountains. The Tibetan Plateau is represented by Xizang: while PR of China and India differ by quite a margin, Tibet fares at par with Uttarachanal. The interpretation of these data has to be a careful one. Nevertheless, a growing database and a refined regional approach allow for some conclusions which draw closer attention to the problems of poverty measurement in mountain regions.
According to the latest Human Development Report for Nepal (2004) a low HDI value was registered at 0.471 for the national level, while the high mountain region ranges with a value of 0.386 significantly lower than the country's average. Within the high mountain districts, major differences occur as well, there is a wide gap between the low end in Mugu (0.304) and the top level performance of Manang (0.502). Nepal provides such data on a district-wise comparative scale and allows us to test the hypothesis that mountain regions should be always worse off than the rest of the country (data are taken from UNDP 2004a). In Nepal it would be expected accordingly that three zones – Terai, middle mountains, high mountain region – would differ in a manner that we observe decreasing HDI values along a southwest-northeast orographic profile. The results from the quoted investigation differ significantly: districts of supreme centrality such as Kathmandu and Kaski (Pokhara), but as well Rupandehi and Morang in the Terai fare best while Mugu in the mountainous northwest remains at the bottom end. This gap is signified in a life expectancy which is more than a third higher in Kathmandu (69 years) compared to that of Mugu (44 years). The estimates about the level of education differ even more: Only 24% of Mugu's adults are literate while 73% in the capital are capable of reading and writing. The GDP per capita is more than three times higher in Kathmandu than in the poorest districts in the periphery (see UNDP 2004a). It has been argued (RHOADES 2001) that this information is not reflecting the "real" condition of development. Of course, it is not that. Nevertheless, we here find a tool which is widely used in development practice for the diagnosis of shortcomings and scope for improvement. If activities in that field are at stake, then it needs to be discussed what interpretations are possible and what remains to be desired from other indicator systems.
(Source: H. KREUTZMANN, calculation based on data from UNDP 2004
For a comparative study, there do not seem to be many alternatives available at present.
For a discussion of the suggestion that mountain regions are always worse off than lowland regions – a statement which was often repeated during several meetings and conferences in the course of the IYM 2002 (see PAPOLA 2004) – a closer look on regionalized data might offer some insight. When all district-wise data are aggregated in the three orographic categories structuring Nepal, then the middle mountains fare best with an HDI value of 0.512, i.e. above Nepal's average, closely followed by the HDI value for the Terai ranging at 0.478 while the evaluation for the high mountain districts produced a significantly lower one of 0.386 (see UNDP 2004a).
The interpretation of regionalized data in Nepal shows a difference between the western parts of the country – irrespective of orography they fare lower than average – and the urban (and tourism) centres and the southeast. The urban-rural bias as well as the East-West disparity seem to be more prominent than the dominance of orography. Similar disparities are reproduced when the gender-related development index (GDI) is applied for Nepali districts. It has to be mentioned again that data quality and the appropriateness of indicators might be questioned. The exercise presented here is meant to stimulate a discussion about possibilities to illustrate development gaps, regional disparities and consequently the eventual uniqueness of regions in the context of mountain geography.
Orography, administrative structure and regional disparities in
human and gender-related development for Nepal in 1996.
(Source: H. KREUTZMANN, calculations based on data provided
by UNDP 2004
.) Click on the image to enlarge it.
Explanations for development gaps need to be sought in the overall economic and socio-political context of a country like Nepal. The neighbouring Himalayan districts of Himachal Pradesh in India fare significantly better than Western Nepal.
The Hindukush and especially Afghanistan seem to be white spots on the development map. Yet, data are provided in the first ever Human Development Report (UNDP 2004b) for a country which has never experienced a population census to date. Therefore, the available numbers need to be analysed with great care as they reflect guesstimates. Tajikistan and Pakistan as its neighbours sharing common mountain ranges with Afghanistan both differ quite a bit in HDI values. The Pamirian administrative unit (oblast) of Gorno-Badakhshan complies with the rest of Tajikistan and significantly above Pakistan's average. In Tajikistan, the Soviet model of modernisation which brought basic infrastructure, sufficient supplies and overall education even to the remotest corners can be interpreted from the high values of life expectancy and level of education. The significant difference in the category standard of living shows the socio-economic pauperisation of the majority of people since the collapse of the Soviet Union. The introduction of economic transformation by the newly independent state did not change the situation significantly.
To date Tajikistan remains at the bottom end in the ranking list of post-Soviet transformation societies (data are taken from UNDP 2003) The supply situation is extremely bad at present, quite differently to the Gilgit District in Pakistan’s Northern Areas although the standard of living index is even lower there. The share of subsistence production compensates for overall supply deficits.
Development activities have reached the high mountain valleys of Nepal and present themselves. (Source: H. KREUTZMANN).
The gaps in the values for Pakistan and Gilgit are most significant in the dimensions of life expectancy and standard of living.
Both reflect the overall deprivation of the Northern Areas in respect to adequate social infrastructure and business opportunities. The mountain people of the Karakoram feature as marginal groups when entrepreneurship and market participation are highlighted. Only the level of education has improved and came close to Pakistan's average which is due to communal, national and international literacy and education programmes (see KREUTZMANN 1996). The brief discussion of a set of available data shows the scope and limitation for data interpretation. Nevertheless, certain conclusions can be drawn. Regional disparities are much more important than reductionist orographic properties. Region in this context means location within nation states and its set of rules and regulations, provision of subsidies and welfare. At the same time region refers to a position within a given mountain area which might be modified by accessibility, incorporation in market relations, exchange patterns and political processes (for some cases in point BLAIKIE & MULDAVIN 2004, BOHLE & ADIKHARI 1998, BREU, MASELLI & HURNI 2005, BYERS 2005, KREUTZMANN 2005b, 2005, 2005d. Therefore indicators might help in identifying a more differentiated picture of development patterns in mountain regions.
(1): Here I omit a necessary and most probably enlightening discussion about the theoretical and methodological justification and interpretational implications of quality of life indicators, for controversial appreciations see KREUTZMANN 2001, PAPOLA (2004) and RHOADES 2001. Practical information about the definition, configuration and mathematical base of the HDI can be found in the UNDP 2004b).
(2):The availability of Human Development Reports gained an amazing momentum in recent years.