6.2.1 Introduction and motivation for the Human Development index

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En provenance du cours de University of Geneva

Global Statistics - Composite Indices for International Comparisons

2 notes

University of Geneva

Global Statistics - Composite Indices for International Comparisons

2 notes

The number of composite indices that are constructed and used internationally is growing very fast; but whilst the complexity of quantitative techniques has increased dramatically, the education and training in this area has been dragging and lagging behind. As a consequence, these simple numbers, expected to synthesize quite complex issues, are often presented to the public and used in the political debate without proper emphasis on their intrinsic limitations and correct interpretations. In this course on global statistics, offered by the University of Geneva jointly with the ETH Zürich KOF, you will learn the general approach of constructing composite indices and some of resulting problems. We will discuss the technical properties, the internal structure (like aggregation, weighting, stability of time series), the primary data used and the variable selection methods. These concepts will be illustrated using a sample of the most popular composite indices. We will try to address not only statistical questions but also focus on the distinction between policy-, media- and paradigm-driven indicators.

À partir de la leçon

Liner shipping connectivity indices (UNCTAD) and Human development index (UNDP)

During this week you will be exploring two indices. The first index, the Liner Shipping (Bilateral) Connectivity Index (LSCI/LSBCI) computed each year by UNCTAD since 2004. It provides an overall indicator of a country maritime connectivity related to liner shipping. Throughout this lesson, we give some insights on why the LSCI and LSBCI were developed. We also cover the methodology to build both indices. We then discuss some stylized facts. The second index presented this week is the Human Development Index (HDI) developed by UNDP. During this lesson, you will be slightly introduced with the history of the HDI. We explain the steps of constructing the HDI, i.e. choosing the three dimensions (health, education and living conditions) composing the HDI and their respective indicators, normalizing the indicators and aggregating the indicators and dimensional sub-indices using different methods. Then, we use a practical example to calculate the HDI for one country. At the end, we discuss some limitations of the HDI and give some elements for future improvement.

6.2.1 Introduction and motivation for the Human Development index9:02

6.2.2 Indicators: selection and data issues12:18

6.2.3 Aggregation, methods and reasoning11:43

6.2.4 Interview of Flavio Comim: A response to the common criticisms of the HDI9:56

6.2.6 Interview of Selim Jahan, part I: History of the Human Development index, and its major strengths and weaknesses10:19

6.2.7 Interview of Selim Jahan, part II: Other indices in the Human Development Report (HDR), and future of the HDR13:50

Rencontrer les enseignants

Stefan Andreas Sperlich
Professor
GSEM, Research Center for Statistics
Juan Manuel Rodriguez Poo (in Partnership with UNIGE)
Professor
Departamento de Economía, Universidad de Cantabria

Hello. Today, we are going to look at a particular index,

a multi-dimensional index which is called the Human Development Index.

But before going into the details of the construction of this index,

I would like to briefly recall the key steps that

are involved in the construction of any aggregate index.

This is just refreshing your memory because we've already had

classes on the construction of aggregate composite indices.

But it's good to recall these steps because they're going to be

useful for our particular index that we're considering today.

So the first step,

obviously, is the selection of dimensions.

What are the dimensions that we want to include in our index?

Index of well-being let's see.

Do these dimensions, are these dimensions going to be the

same wherever we are at whatever time period we are?

That is universal, or are they going to be context and time dependent?

These are questions to be answered.

There is no unique answer,

but these are questions that need to be asked before you build the index,

before you go into collecting information to build the index.

So the answer is, that you need to take all the dimensions that are relevant

for describing well-being, in human well-being.

Now, then once you have selected the dimensions,

the next step is to go to the choice of indicators.

What are the appropriate measures that describe,

that represent well-being in each dimension?

So let's say health.

So, how many indicators do we want to choose?

What are the indicators that we want to choose?

Do we want to choose only a few?

That could be one option.

But the risk there is we may not be able to cover

all aspects of well-being that are important for this dimension.

So do we take many?

I think that is a good option.

But in that case, we need to have tools and methods

to combine these information into a summary index.

And then once we've got the indicators

and the dimensions and I think it's important to do this reasoning,

two-level reasoning when you build an index.

That is, first look at the dimensions and then see what are

the indicators that are appropriate for each dimension.

We need to put them in the same unit of

measurement because otherwise we won't be able to combine them.

If they are in different units,

they're not comparable and we cannot combine them.

So, convert them. The next step would be to

convert them in the same units so that we can combine,

aggregate, take a mean for example.

And then the next step is,

how do we want to combine them?

So, the question arises,

how do we want to add these indicators,

to aggregate these indicators into a single number?

First, within a dimension.

So, how do we combine indicators that are within a dimension?

Then, how do we aggregate across dimensions.

Here the important, there is an important question that needs to be answered,

that needs to be addressed in this step.

It's that of the degree of

substitutability between indicators and between dimensions, to what extend?

So, the question is,

to what extent we want to substitute or compensate for

a low-level of an indicator via a higher level or a high-level of another indicator?

So, how much substitution do we allow for?

And that is even more important when we look at substitutability across dimensions.

Because, then there is a value judgement here,

saying that an extra year of life for example,

can be compensated by or can

compensate a certain number of years of education for example.

To what extent do we say that we are able to compensate or we allow for substitution

for an extra year of life by a certain number of

years of education or by a certain amount of income?

So, here there are two major approaches in selecting the weights or in choosing weights.

One is called the normative approach.

Which says that the weights are basically decided by the researcher,

or the analyst, or the policymaker.

So, it's coming from normative angle saying that we need,

we give the weights because these are desirable from a certain point of view,

from a theoretical point of view.

They satisfied some desirable properties.

These are called exogenous weights.

The other method is to say that we let

the data speak and we get the weights from the data.

So, these are getting weights using data-driven techniques.

These are basically statistical methods,

but they're also a reply or respond to some criteria.

Some optimality criteria.

The Optimality criteria often is that,

how to combine the information,

combine indices in such a way,

that the information loss is minimal.

So, that we try to reproduce the same information out to the maximum extent possible,

the information contained in the original data.

So, once we have done all this, chosen the dimensions,

chosen the indicators within each dimension,

decided on the way to combine,

made them comparable, decided on

the way to aggregate them into an index within each dimension,

and across dimensions combine across dimensions.

Then, we got a number which is the overall index, the aggregate index.

And then we can go on to interpret this index in

terms of what it says in terms of well-being.

That is, what does the value signify or represent in terms of the well-being situation?

Not only that, we can also say compare for example,

one country or one individual with another country or another individual.

And say that this country is better off than

this other country in terms of well-being or development.

Or we can compare the same country over time,

saying that this country is improving over time or not so improving over time.

So, these are very useful information that we can derive from

looking at the overall index of the development or well-being.

And in addition, we can do many other things like for example,

you can also look at how different is one group from another.

If we can derive or construct these indices for subgroups for example,

men and women, we can say,

are men better off than women? Et cetera.

Or are rural people better off than urban people?

So, they will also use it to analyze disparities among different groups,

different regions and other things.

So, these are many things that can be interpreted using an index.

And then we should also normally look at

the sensitivity of this index with respect to the choices and the decisions you

have made and the different steps that we talked about saying that if we

chose a few more indicators or if we took away some indicators,

how does the aggregate index change?

Or if we alter the degree of substitutability between dimensions,

how does the indicator respond to say,

what is the robustness in this sense of

these indicators to the different choices that you've made in the different steps?

And then once we know what this index is and how it can be interpreted,

then we can go on to use it for, for example,

looking at what is the effect of a policy on overall well-being.

And this could be used as a measure for looking at

the policy impact and we can also see for example,

what are the different dimensions that drive the value of the index, if there are any.

That is, is one dimension contributing in a large proportion to the value of the index?

Or are they contributing,

the different dimensions contributing equally.

And then we can also see what are the factors that enhance the well-being in

different components and what are the factors which on the other hand, reduce well-being.

So, these aggregate measures can be used for many different purposes.

So, now let's come to the particular index that we are going to look at today,

which is called the Human Development Index.

This Human Development Index was launched for

the first time by the United Nations Development Programme,

the UNDP in 1990 as a possible alternative,

as a credible alternative to

the commonly used income per capita or GDP per capita as an indicator of welfare.

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