Guest Blog: World Bank research director critiques the new UN poverty index

July 29, 2010

Videos I liked: animated marxism; leadership and the dancing guy; adapting to climate change

July 29, 2010

The co-creator of the UN's new Multidimensional Poverty Index defends her new baby

July 29, 2010
empty image
empty image

Sabina Alkire responds to the previous posts by Martin Ravallion and me on her new ‘Multidimensional Poverty sabina alkireIndex’. She is director of the Oxford Poverty and Human Development Initiative (OPHI).

“As Martin Ravallion points out, we agree that poverty is multidimensional. The question is whether our efforts to incorporate multiple dimensions into the very definition of who is poor and the measurement of poverty “contributes to better thinking about poverty, and to better policies for fighting poverty.” Let me explain what I think the MPI adds.

The MPI measure has meaning in itself and can also be broken down immediately into its component parts.  Every time you see an MPI figure – for a person, an ethnic group, a state, or a country – you know that it also contains what could be thought of as a drop-down menu in two layers. The first layer shows incidence and intensity. The second breaks the MPI down by indicator and shows what poverty is made of.

If we know someone is income poor, we do not know if they are also illiterate or malnourished. If we know someone is multidimensionally poor, we can unpack the MPI to see how they are poor. That is one added value of our methodology.  That is why we call it a high resolution lens: you can zoom in and see more.

This feature could add value to the MDG indicators too. These show us the percentage of people who are malnourished, and the rate of child mortality and many other things, but not how the deprivations overlap. If 30% of people are malnourished and 30% of children are out of school, it would be useful to know if these deprivations affect the same families or different ones. With the MDG indicators we cannot see that; with the MPI, we can. Of course not for all MDG indicators, but it’s a start.

For example, the Somali have the highest multidimensional poverty of all ethnic groups in Kenya followed by the Masai. Looking at the MPI drop-down menu, we see that 96% of Masai are poor and 88% of the Somali. But poverty among the Somali is more intense: on average they are deprived in 67% of dimensions; the Masai in 62%. Zooming in further we note that the Somali are more deprived in education and child mortality, whereas malnutrition and standard of living indicators are worse among the Masai. So the MPI opens out into a wider field of information. 

The other thing the MPI does is clean data of anomalies and focus on poor people. While indicators drawn from different surveys are tremendously useful for many purposes, they do not identify who is multidimensionally poor, so every MPI poor person experiences multiple deprivations. Consider a self-made millionaire who didn’t go to school. A MDG indicator includes this millionaire in the percentage of people who are uneducated. The MPI does not – if she’s not deprived in anything else, she’s not considered poor. In times of tighter fiscal resources we focus on people who are deprived in several things at the same time.

So, the MPI – and the general methodology it uses that James Foster and I developed – adds value because of how it evaluates poverty. The method first determines the dimensions in which a person is deprived, and then ‘adds up’ that person’s deprivations using weights that reflect the relative importance of each deprivation. A person who is sufficiently ‘multiply deprived’ is considered poor. We measure multidimensional poverty as the incidence (or the percentage of the population that is poor) times the intensity (or the average percentage of deprivations poor people experience). Unlike the HDI, this construction does not add up achievement levels, which requires strong assumptions concerning the variables in question as Martin noted. Instead, we add up deprivations, which does not.

OK, now to the issue of weights. Income poverty aggregates within a country using actual or imputed prices (these are critical for fixing the income poverty standard across countries and time). Setting prices is not unproblematic in practice, particularly in Colombia where I am writing from. Indeed the Presidential address to the 2010 American Economic Association raised concerns such as the prices attributed to housing (Deaton 2010). Chen and Ravallion 2008 carefully review the robustness of their results to different pricing approaches. 

As Martin observed, instead of using prices, the MPI sets weights as value judgements. Amartya Sen among others sees this feature as a strength not an embarrassment: “There is indeed great merit… in having public discussions on the kind of weights that may be used” (1997a).

In extensive writings, Sen presents several pertinent observations in favor of setting weights: first, prices may not exist for some aspects of poverty (morbidity, mortality, illiteracy) but giving zero weight to these does not seem right either. Second, setting precise weights may not be necessary: comparisons may be robust to a range of weights. Third, the weights trigger public debates which may be constructive as policy makers weight tradeoffs in practice anyway.

The key, Sen suggests, is to make the weights explicit: “It is not so much a question of holding a referendum on the values to be used, but the need to make sure that the weights – or ranges of weights – used remain open to criticism and chastisement, and nevertheless enjoy reasonable public acceptance” (1997b; see also Decancq and Lugo 2008).

Given this situation, Maria Emma Santos and I proceeded in a very practical way in the MPI. First, the weights are not buried; they are totally transparent (1/3 per dimension, and each indicator within a dimension equally weighted). If people disagree with these weights, they can propose improvements and also recalculate with different weights to check robustness. Second, the weights give some non-zero value to each dimension, which is a starting point. Third, the MPI fixes weights between countries to enable cross-national comparisons; alongside this we strongly encourage countries to develop national measures having richer dimensions, and indicators and weights that reflect their context as Mexico did and Colombia is doing. Fourth, we weight the three dimensions equally, this was corroborated by expert opinion (Chowdhury and Squire 2006), helps make it easy to understand (Atkinson et al. 2002) and at least for the HDI is quite robust (Foster McGillivray and Seth 2009). We do need to create robustness tests for MPI weights, and new methodologies of analysis to guide policy, and OPHI plan to work with other researchers on these. But the key thing is that the present MPI weights are transparent, and critical scrutiny of them is welcomed.

Finally, both previous blogs mentioned data contraints. Duncan criticised the MPI for including only three dimensions, “partly because it still relies on existing data sets.” Well, actually data constraints are the only reason only these three dimensions appear. We and the HDRO wish to include others without losing focus: indeed OPHI’s other research theme highlights the need to gather internationally comparable data on ‘Missing Dimensions’ of poverty – violence, informal work, disempowerment, and isolation/humiliation — given the importance these have in poor people’s lives. Our methodology is flexible enough to accommodate additional dimensions as they become available and we are eager to do so.

Finally, as Martin observed, our data must come from the same survey or from matched surveys. Yet multi-topic surveys have expanded rapidly, especially since the MDGs. The MPI is not perfect, but it uses these surveys to explore joint distribution – the deprivations that batter poor people’s lives at the same time. Such a multidimensional poverty measure complements existing tools. So though no measure is enough, we hope this work will enable others fight poverty and empower poor people more effectively.”

Phew. Thanks to Martin and Sabina for raising the intellectual tone with these top notch contributions. Something altogether more superficial tomorrow, promise.

Update: check out the comments for ongoing discussions between Martin Ravallion and Sabina, and lots of other top contributions


  1. I think Alkire’s absolutely right that exploring the joint distribution of poverty outcomes is valuable. Ravallion doesn’t contest (or address) this benefit of the MPI.

    But I think his main criticism holds. Even given a survey with much better outcome variables, at the household level, with total information on the joint distribution of poverty outcomes (including the ‘missing dimensions’ of poverty), creating a composite index across poverty outcomes for a given household inevitably requires arbitrary weights.

    Alkire responds that zero weights don’t seem right either. Fair enough (though notice Ravallion’s not advocating for zero weights either, he’s arguing for a more nuanced use of multiple poverty outcomes than you can capture in a composite index). But it doesn’t follow that arbitrary weights are better than zero weights — an index that over-emphasizes certain outcomes could be as bad as one that places no emphasis on those outcomes. Ravallion’s examples are good on this point.

    I think the key here is what purpose the MPI is intended to serve. Ravallion seems to be asking whether governments should use the MPI as a measure of their progress, and argues that there’s no reason to think it’s the right variable to be maximizing, and that it could be very wrong. I think that’s right. It strikes me that the most theoretically valid measure of living standards we have is household consumption, and yet the MPI does not include it at all. (I’m not referring to the dollar-a-day line, but simply increase in consumption as increase in living standards.)

  2. A good debate. Thanks to Duncan for hosting and contributing.

    The key question remains: why should we add up these multiple, non-comparable, dimensions of poverty? Sabina points out that her composite measure can be “broken down into its component parts.” Fine, but then I am still left wondering why we bothered adding them up in the first place.

    The weights in any MPI are bound to be contentious (as I noted in my guest blog)—trying to say, for example, what the material conditions are that can compensate for a child’s death, and doing so in one stroke for all countries in the world. The fact that the MPI converts “achievements” into “deprivations” does not make adding these things up any less problematic. Governments do sometimes face very difficult trade-offs in specific settings. But that is when and where we should decide the weights, not sitting in an office in Oxford or anywhere else.

    Sabina also argues that we may be interested in seeing the joint distribution—to what extent the multiple dimensions are shared by the same people. Fine; but you don’t need the MPI for that; a good old fashioned “cross-tab” will suffice (as pointed out by Gabriel Demombynes in his comment on this debate).

    We should not kid ourselves that forming a composite index has somehow taught us something we did not know—adding explanation, understanding or insight where there was none before. That is not what happened when the MPI was formed. Rather, it took things we already knew and re-packaged them, in a contentious fashion. And great, we can also reverse the process, to un-package them.

    Is that really progress?

  3. At UNICEF, we have constructed multi-dimensional poverty indicator for Mongolia as part of our effort to develop multi-dimensional poverty-based targeting tools, using Alkire and Foster (2007) methodology. We first selected poverty dimensions, cutoffs, and weights etc according to various national standards and development goals expressed in PRSP, NDP, and participatory poverty assessment.

    Let me try answer your question of “what is the point of repackaging and unpackaging them”. The way we see it is that there is a potentially ground breaking value in being able to quantify the contribution of each subcomponent to the higher level overall poverty indicator. The ability for a measure to be packaged and unpackaged helps government start thinking about in which geographic units and in what dimensions, interventions are likely to result in the greatest reduction in overall multi-dimensional poverty headcount. This feature may help government prioritize and sequence location and sector specific interventions, as opposed to segmented, ad hoc interventions. Perhpas the most illuminating finding in our Mongolia study is that consumption poverty makes a small contribution to the overall poverty headcount at various subnational levels (and urban/rural subdivisions), raising the question of how effective government’s extensive cash assistance, apparently at the expense of capital investments, has been in reducing multi-dimensional poverty.

    Similar to the comment on Mexico, national development objectives view all dimensions as equally important, hence the equal weighting. Still, similar to Sabina’s finding, we find that a range of weights gives similar ranking across geographic units in Mongolia.

    Any comments are welcome.

  4. Interesting discussion! This raises an interesting question. Can we now use the “Standard of Living” subset of indicators as a good proxy for HH income / consumption? The Pearson correlation is relatively “good” at 0.8. And we all know how difficult it is to collect income data. And it’s full of error despite our best efforts! So do the 5 living standard indicators (electricity, sanitation, clean water, cooking fuel, owning of certain assests) provide a solution to this enduring problem? Should we stop killing ourselves trying to collect income data and collect this much simpler index?

  5. I can see that it would be a bad idea to advise countries to target improvement in MPI scores when they’re designing their anti-poverty policies. There’s no reason to think the MPI weights (or the weights in any similar composite) correctly reflect the policy preferences of a given country.

    I even have mixed feelings about the idea that local policymakers might first go through some exercise of defining the weights, and then target the MPI. I understand that local policymakers are already putting weights on these things implicitly and probably inconsistently; if you make them explicit, you enable democratic debate. Fine. But I doubt the outcome variables in the MPI are the right ones for this, and I don’t think you should rely exclusively on binary deprivation indicators in that process anyway. You should put together a richer household survey, and make your policy decisions based on that.

    But the comments here are trying to convince me that the MPI or any similar measure is meaningless in some deep way. What’s going on?

    It’s a pretty simple measure, basically just counting deprivations. If the count goes down over time, things are probably better. It’s harder to compare two countries in a meaningful way, but it’s one way to start that comparison. At least you have a consistent (even if inevitably contentious) set of deprivation indicators to compare them on, at the household-level.

    Putting a poverty line on consumption doesn’t tell us anything we don’t already know from looking at consumption alone, but it focuses our attention on the low end of the distribution.

    The MPI’s just one statistic on the joint distribution of poverty outcomes. It focuses our attention on a particular aspect of poverty. If we want to look at the full cross-tabs, now there’s an internationally consistent household database that we can use. That seems alright.

  6. I found the MPI very interesting.However the are still a couple of question which unanswered.It was mentioned that the MPI incorporated multidimensional approaches in measuring poverty but the result showes aggregate to me the poor HH in lowland Africa is different from the one in the Highland.Because the asset they own and the standard for poor household among this community varies.The other question what are the methodology that is used to quantify house hold income for these subsistance househols ?
    As their income is seasonal (more income during harvest ) and sometimes some their income are difficult to quntify as some rural household use variour income generating means.

  7. I’m grateful too for this blog Duncan.

    What I wished to clarify, in response to Martin’s comment, is that when we take the MPI apart into what I called component parts, what we have are not the percentages obtained directly from the survey data. Instead, they are ‘multiple deprivation’ headcounts – reflecting only the deprivations of people we have identified as MPI poor (Sen 1976).

    What Martin and Gabriel both suggest is that a cross-tab can be used instead of the MPI to show how many people are deprived in two indicators rather than only one at a time, and that is true and helpful. What I am unsure about is how to proceed with three, four, or ten indicators? Without weights, you can identify as poor the people who are deprived in any one indicator or else in all indicators. But the union approach (any) overlooks joint distribution, and the intersection approach (deprived in all – Atkinson 2003) is too strict if you have multiple indicators (the MPI headcount would be 0% in both India and Gabon).

    So let’s say take something in the middle. If we have 3 indicators maybe we report the percentage of people who were deprived in any 2 of them (equal weights, cutoff of 67%)? Or maybe they have to be deprived in one indicator plus either of the others (weights of 50-25-25 and cutoff of 75%). The identification method that James Foster and I propose is one way of addressing these issues; there are others of course. But the point is that what I called (alas unclearly) ‘component parts’ could not be generated without identifying who is poor and censoring the deprivations of nonpoor people, and for the MPI, weights come in here.

    Well, does it make any difference – reporting these ‘multiply deprived’ headcounts? Two examples: First what percentage of people in Gabon live in a house where no one has five years of schooling? It’s 12%. Of these, 76% are also MPI poor (recall the MPI is for acute poverty only); the other 24% are the folk who are home-schooled or self-taught and self-made (or whose data could be incorrect). So we report 9% (0.76 x 12). Sometimes the difference is larger. In Gabon, 62% of people don’t have ‘adequate’ sanitation. But only just over half of these people are also deprived in enough other things to be MPI poor. So our ‘censored headcount’ for Gabon’s sanitation is 32%. Overall in Gabon, the MPI’s ‘multiply deprived’ headcounts ranged from 53% to 80% of total headcounts; in India, they range from 73% to 96%.

    These differences add up. Overall, in India, 85% of people were deprived in at least one indicator as opposed to 55% being MPI poor (deprived in 30%); in Gabon it was 74% vs 35%. Focusing on people who are multiply deprived helps to focus resources on the poorest (indeed you could focus on those deprived in 40% or 50% of indicators if you wished).

    So to clarify, our ‘multiple deprivation’ headcounts would be missing from the raw survey data. We generated them by identifying who is multidimensionally poor. But for the MPI, that requires weights.

    If we have used weights to generate multiply deprived headcounts (there may be other approaches), then the question of whether to report a summary index is pragmatic. It simply may be more effective to package the information into one headline number that gives a bird’s eye intuition of societal poverty, and let people unpack it as relevant. Of course the headline number has to be credible and well-constructed, hence this welcome debate.

    If only I knew how to package all of these points into one summary headline I bet all would be relieved… Sorry!

    ps – my hyperlinks don’t work here –

    Sen 1976 is

    Atkinson 2003 is

    and Raphael may want to look at Filmer and Pritchett

  8. In the decade after Erik Thorbecke, Joel Greer, and I proposed a class of poverty measures (including the headcount ratio, the poverty gap, and the squared poverty gap), Martin Ravallion was one of the earliest adopters of this technology and helped make it a standard way of evaluating income poverty.

    Recently, many authors have considered the question of measuring poverty when the information basis is broadened to include more than one dimension. Sabina Alkire and I have presented a new methodology that builds upon the Foster-Greer-Thorbecke (FGT) class in a natural way.

    A practical aim of our research was to obtain a poverty measure that would work with the qualitative data that can arise in a multidimensional context. A theoretical aim was to reexamine the identification step (addressing the question ‘who is poor?’), which poses a much greater challenge when there are many dimensions.

    The Alkire-Foster methodology is perhaps best seen as a general framework for measuring multidimensional poverty since most of the hard decisions are left to the user. These include the selection of dimensional weights (to indicate the relative importance of the different deprivations), dimensional cutoffs (to determine when a person is deprived in a dimension), and a poverty cutoff (to determine when a person has enough deprivations to be considered to be poor).

    Placing all weight on one dimension converts the Alkire-Foster measures back into the traditional FGT measures. Alternative weights yield the Multidimensional Poverty Index (MPI) devised by the Oxford Poverty and Human Development Initiative (OPHI). It was designed with cross country comparisons, and the ensuing data limitations, in mind.

    Ideally, weights and cutoffs should not be determined in the abstract, or according to a set formula, but rather in consultation with a range of relevant parties. For the MPI, OPHI included a number of qualitative ‘field checks’ to help calibrate the index. However, cross-country comparisons are more challenging precisely because they are not grounded in any one country’s experience. When the methodology is used to construct a measure for a country, the choices can be made locally in order to reflect local values.

    A key property of the FGT measures is that they are ‘decomposable’ so that the overall level of poverty in a country is the average of poverty levels in regions (or other partitions of the population). Researchers and practitioners employ this property to obtain a deeper understanding of overall poverty. It helps answer questions like: To what extent is poverty a rural problem? The Alkire-Foster measures, like the FGT measures, are decomposable by population subgroup.

    In multidimensional poverty measurement there is a second margin for analysis – the multiplicity of dimensions. We might ask: What is the contribution of a particular deprivation to overall poverty? The aggregation method employed by Alkire-Foster allows an analysis of a dimension’s contribution in a way not dissimilar to the above decomposition. Our measure can thus focus in on relevant subgroups and deprivations in ways that facilitate empirical and policy analysis. For a related discussion of decomposability, see Foster and Sen (1997).

    The relevance of all of this requires a belief that how we measure something can importantly influence how we come to understand it, how we discuss it, and how we create policies to influence it. Most of us regard the validity of this to be self-evident. But it may be of some use to examine it further.

    Can a simple repackaging of data make a difference? Of course it can. For example, the Bank’s useful ADePT software allows the user to construct poverty measures (like the FGT), inequality measures (like the Gini), and income standards (like the mean income) – all from the same underlying income data. Each captures an entirely different aspect of the income distribution; selecting the appropriate measure is important. Measuring poverty with, say, the Gini makes no sense. But the squared gap index can reveal a good deal about income poverty that would not be readily apparent from the raw data. Indeed, this is the essence of measurement: transforming objects or data that are difficult to visualize into numbers that embody the desired concept and are much easier to comprehend and compare.

    However, multidimensional poverty measurement is not just an exercise in repackaging existing data. It alters the very definition of who is poor, as well as the metric with which their poverty is being measured. Other variables now become salient because of their constitutive link with poverty. Other policies become part of the discussion because of their direct impact on these variables.

    Many believe that this can help improve our understanding of poverty. But at the same time, it is clear that the traditional income poverty methods have provided, and will continue to provide, a strong foundation for our understanding of economic poverty. The complementary view presented by the new multidimensional methods will need further evaluation and perhaps refinement. We look forward to fruitful discussions with Martin Ravallion and other experts on this important topic.

    James E. Foster

    The George Washington University and OPHI

  9. Congratulations to Alkire and Foster for focusing much needed attention on the issue of multidimensional poverty and, moreover, for advancing the case for a single, multidimensional index to measure deprivation in the developing world. As seemingly most development economists recognize, poverty is more than a lack of income or inadequate consumption, but is composed a host of factors that simultaneously act to constrain capabilities and increase deprivation. Part of the debate around unidimensional or multidimensional metrics plays out something like this: from a policy perspective, if poverty is equated with lack of income, then policies that promote economic growth would appear to be all that are needed to reduce poverty. If, instead, poverty is a multidimensional phenomenon, then, as Kanbur and Grusky have put it, “The task (of remediating multidimensional poverty) …. requires targeting those aspects of inequality and poverty (e.g. residential segregation) that are causal with respect to many outcomes and hence likely to bring about cascades of change (my emphasis).” Our task as researchers and policymakers is to determine which aspects of poverty are causal with respect to many outcomes, and to make those aspects the targets of policy interventions.

    I do have a few questions, though, for the HDRO, particularly if, as noted in the press release back in June, the MPI will replace the Amartya Sen and Sudhir Anand-developed Human Poverty Index.

    In some of the literature that came out in June around the launch of the MPI, it was noted that “the MPI fixes weights between countries to enable cross-national comparisons; alongside this we strongly encourage countries to develop national measures having richer dimensions, and indicators and weights that reflect their context as Mexico did and Colombia is doing.”

    Does this mean that the HDRO will calculate its MPI for country X, while country X may calculate its own in any given year? If they differ, will the HDRO’s calculation be the MPI of record, or will the country’s be? What if country X takes advantage of the MPI’s flexibility in the choice of dimensions and indicators and, due to changes in political leadership, for example, chooses to calculate that country’s MPI with a different array of indicators the following year? What does this do to the ability of researchers to calculate change over time? In this case, would researchers simply resort to the UNDP’s calculation of country X’s MPI?

    And what about the data sources used to calculate the MPI? Ravallion notes that:

    “Rather (the indicators) were chosen because the methodology used by the MPI requires that the analyst has all the indicators for exactly the same sampled household. So they must all come from one survey. There is much better data available on virtually all of the components of the MPI, but these better data can’t be used in the MPI since they are only available from different surveys. This aspect of their methodology greatly constrains the exercise.”

    An advantage that the HPI has over the MPI is that it can be used in the absence of disaggregated data. As I understood it, HDRO statisticians collect new or projected data for each of the HPI’s 4 indicators from one year to the next—from the UN Department of Economic and Social Affairs Population Division’s analysis of national vital registration systems, from UNESCO’s Institute for Statistics, from WHO, etc. From a quick review of OPHI’s country profiles, it appears as though the data for individual country MPIs are drawn either from Demographic and Health Surveys (conducted every 5 years), or from the World Health Surveys (conducted irregularly), or from Multiple Indicator Cluster Surveys (conducted every 5 years or so), or… How will the MPI for any given country be calculated next year, given the infrequency of the surveys on which it depends? For those interested in longitudinal studies of changes in deprivation, how should the MPI be used? It would seem that an argument could be made that the loss in precision is made up for in the HPI’s ability to measure annual changes in deprivation, albeit at an aggregated level.

    On the question of decomposability, Alkire and Foster tout this feature of the MPI as one of its more significant advantages over other multidimensional indices that rely on aggregated data. However, Sen seems less convinced, noting that when decomposability is insisted on for all possible subgroupings, a basic conceptual problem emerges:

    ” The mathematical form of decomposability has had the odd result of ruling out any comparative perspective (and the corresponding sociological insights), which is, in fact, fatal for both inequality and poverty measurement… It is easy to see why decomposability has such a strong appeal. It is ‘nice’ to be able to ‘break down’ the overall poverty of a total population into poverty in different subgroups of people that make up the total population. It gives, I suppose, some forensic satisfaction in solving a ‘whodunit’ (and by how much respectively)… (However), mathematically the demand that the breakdown works for every logically possible classification has the effect that the only measures of inequality or poverty that survive treat every individual as an island ….” (Sen, in Grusky and Kanbur, eds, Poverty and Inequality, 2006)

    Again, I applaud Alkire and Foster for bringing attention to the measurement of multidimensional poverty. However, readers should be reminded that theirs is only the latest in a long line of similar efforts, each with strengths and weaknesses. The UNDP may want to consider including the MPI to supplement its array of metrics, rather than to supplant the HPI. They measure different, but still important, things.

    Heath Prince
    The Heller School for Social Policy and Management, Brandeis University

  10. The poverty in South Asia is most shocking. The reason for south Asian poverty stems from caste system, which is worse than Apartheid.
    Caste is a south Asian phenomenon. Caste is present not only in India but in all South Asian countries like Nepal Pakistan, Bengladesh SRi Lnka. Every religion in this region is infested with caste system, which is exactly nothing but Hindu pollution.Theologically Islam, christianity, and Buddhism are anti caste; but its physical proximity of cancerous Hinduism,or Brahmanism, also its dominant possition in the region Every religion is now under its attack. Brahmanism is the ideology of caste system. So Brahmanism generates 50% of gobal poverty( South Asia produces 51 percent of global poverty as per OPHI estimate).Seventy percent Indian people live Rupees tweny only( Indian ruppe) Per day. Most castiestic or Brahmanic country is Nepal . So Nepal is most povertied.Eradicationof even the last traces Of Brahmanism is the only available option before these countris to get rid off thier poverty.Brahmanism or Hinduism is enemy No: one of Science and technology.Though Buddhism is basically anti caste : Sri lankan society is caste infested due to Indian Brahmanical influence.

  11. Congratulations to Prof Sabina who formulated a new technique for quantifying poverty both qualitatively and quantitatively. This gives a new insight on poverty and deprivations and it will help to marshal the projects on targeted groups.
    In India the state of kerala is having least poverty. There is some confusion regarding it.
    Regarding much trumpeted kerala model , the advancement in social sectors is consequent of social revolutionary movements By Dalit OBC masses led by mainly Sree narayana Guru (Ezhava OBC) Ayaynkali( Pulaya SC) , Pandit Karuppan ( Fishermen community-Dalit like OBC) Poykayail
    Appachan( Paraya SC) , K P Vallon( Pulaya) Dr Palpu( Ezhava) Vakil P Kumaran Ezhuthachan ( Ezhuthachan OBC) and others in ninenteenth and twentieth centuries.

    Early Europinisation is another reason, which started By 14 98, with the arrival of Portuguese in Calicut led by Vasco De Gama.

    Another most significant , rather monumental work is the missionary work done by London Missionary Society( by European protestant missionaries) and Church Missionary society . L MS work in northern Travoncore is historic; it led to Shanar Revolt ( Nadar community OBC) demanding the right to cover upper portion of the body for their women. This paved the way for Sree Narayana- Ayyankali movement. Sree Narayana Guru and Ayyankali are the makers of modern Kerala.

    Prior to all this there held first anti caste struggle in 1599 , the Synod of Diamper. Then Portuguese Governor and Goan Archbishop Alexi- De Manezis is the architect of this synod.
    He exhorted all christians to abandon all Hindu customs, including the practice of untouchability, then practised by so called native Christians( Nasranis, then Syrian Christians) Archbishop Menezis was the product of council of Trent of european counter reformation.

    Caste system was at its worst in Kerala.
    Untouchables are also unseeables and unapproachable.
    Swami Vivekanandan called then kerala “Lunatic asylum”
    Muslim community also played very historic role in modernising Kerala polity.
    They organised seris of bloody revolts against Landlordism, anti Upper caste domination, also against tyrant British rule, that culminated in 1921 Glorious Malabar peasant revolt,wrongly portrayed as Mppila revolt. This was the foundation of kerala’s glorious Muslim political movemt. (In all other states such powerful political Muslim movement is absent)
    Upper caste contribution in modernising kerala is almost nill.
    Theirs is only a storm in tea cup.
    Kerala’s Brahmnical left led by veteran EM S Nambuthiripad hijacked these social revilutionary movement and neutralised it to a mechanical class movemnt losing its entire vitality.
    The left is consolidated in kerala as the offshoot of Sree Narayana Ayyankali movement. This is a monstrous treachery.
    The result , Kerala is at present an intellectual desert , least creative, least innovative, least scientific, least technological, least entrepreneurial.
    Kerala society is under the strangle hold of Brahmanism and Marxism( threaded).

  12. I find this debate very interesting. This is well intentioned and the inventors of the MPI need to be congratulated. I am working on the Regional Poverty Profile of SAARC member countries. Now we have the theme of the Profile “Food Security Challenges for the Poor and Social Inclusion”. I agree with Prof. Vijaya Kumar that in South Asia, the Caste System is a major culprit to obstruct the poor dalits to improve their living conditions. Some of the poors in South Asia particularly in the triangle of Bihar/UP/Nepal/ Bangladesh are poorer than the people of Sub-Saharan Africa.

  13. This is indeed a very interesting discussion. To be honest, I am very sceptical about all these development indexes out there. What are they measuring, for whom and for what end purpose?

    For example this research is claiming that 90% of Ethiopians are “multidimensionally poor” while only 40% are “income poor”. This means Sierra Leoneans are “multidimensionally richer” than Ethiopians. How this will affect donors’ policies and development aid in the future? Will Sierra Leone receive less funding than Ethiopia?

    Years ago, we were using GDP per capita to determine whether a country is rich or not. This metric was since changed to the GDP per capita adjusted for differences in purchasing power or PPP. Why, because people felt that $1 in the USA is worth less $1 in Burkina or Bangladesh but worth more $1 in UK or Japan.

    Now we are saying “one can be rich without money”. Elders in african villages will probably agree with this statement but the younger generation will challenge it. Why? Because the MPI is not considering the fact we live in a globalised and capitalist world where money (at least material possession) translates into power. Thefore a “multidimensionally rich” might be denied lot of opportunities if he or she is “income poor”, this is the sad reality we must all face in this “multidimensional world”.

    Last but not least, more development indexes mean more manipulations especially by developing countries political leaders. There will be an increase temptation to use the most favourable index while dismissing all the others. Why should we expect Meles Zenawi, the Prime Minister of Ethiopia, to quote the MPI during his speeches if the $1-a-day measure is more favourable.

  14. Congratulations Prof Sabina!!!! you have formulated the new technique for quantifying poverty both qualitatively and quantitatively.i think you may not drag away by the comments of pseudo intellectuals from India like prof. T.B Vijayakumar .The main reason of poverty in India is not by the caste system or by the Brahmins but by the lack of education and poor infrastructure development . The most dangerous situation in India especially in Kerala is the caste based organistion like SNDP, NSS, Ezhuthachan Samajam etc.

  15. Ultimately, a composite index, whether HDI, or MPI, has only advocacy value but no practical policy usefulness. In the end, you will be able to say given the index, you have disparity between one area and another, but what use will it be when in the end, you will still have to examine the specific component? The index is just adding apples and oranges, and coming up with a measure that will just not be understood by policy makers. If I can’t understand it, and I consider myself an above average person in intellectual capacity, I am sure most people won’t understand it. So why come up with statistics that will not be understood? If you want to measure something, whether poverty, or any concept, keep it simple. Even if the concept is complicated, keep the measurement simple. This MPI is just not useful at all.

Leave a comment