5.1.3 Motivation and data treatment

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Global Statistics - Composite Indices for International Comparisons

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University of Geneva

Global Statistics - Composite Indices for International Comparisons

5 ratings

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.

From the lesson

Export Potential Assessment (ITC)

This module focuses on trade indices developed by the International Trade Centre, the Export Potential Index (EDI) and the Product Diversification index (PDI). Frictions often create a gap between what a country could export and what it does export to markets around the world. Trade advisers could better address these frictions and help firms realize greater exports if they knew exactly which products and markets offer best chances. During this week, you will learn about the Export Potential Assessment (EPI and PDI), which indicates products, sectors and markets for trade development activities for over 200 countries and 4,000 products. Based upon an assessment of the exporting country’s supply capacity, the target market’s demand and tariff conditions as well as the bilateral links between the exporting country and the target market, it provides a ranking of untapped opportunities.

5.1.1 Introduction1:12

5.1.2 Global overview of the export potential assessment3:58

5.1.3 Motivation and data treatment8:40

5.1.4 Economic framework4:06

5.1.5 First dimension: the supply side7:59

5.1.6 Second dimension: the demand side5:41

5.1.7 Third dimension: the bilateral trade relations, and results4:29

5.1.8 Aggregation and final indicator construction5:50

Meet the Instructors

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

The first of our two indicators is the Export Potential Indicator.

Actually, this indicator comprises two sub-indicators.

First, we have the Potential Export Value.

This value is a standard value of how much a country

can expect to export to a particular market for a given product in the coming years.

This value will depend,

of course, of the capacity of this country to export the good.

But it will also depend on the fact that this market is actually importing the good,

and would not be an important role if there is

no import in that particular market for the good.

And a third component would be

the market access conditions for this particular exporter for this good.

Once we have computed the potential export value,

we can compute the second sub-indicator,

which is the unrealized potential.

And this unrealized potential just results of

the comparison between what is currently exported and

what we have computed could be exported by this country to that market.

To do this exercise, we need a number of data.

So for instance, we will need data about trade,

trade flows, export, and import values.

These data come from a database that is produced at ITC.

Then, we also use tariff data,

ad valorem tariffs that come from another ITC database called Market Access Map.

The third set of data that is needed in the calculation is price elasticities.

These price elasticities come from a database that

is from the consortium GTAF for the GTAF database.

Then we need also distances between

countries and these distances have been computed by SEPI.

So, this is a SEPI distance database.

And eventually, we will also need projections of GDP growth.

And these data come from the IMF and they are released in

the World Economic Outlook twice a year.

The problem when we use trade data,

is that the quality of trade data is not always great.

The data reported by exporters and importers may differ.

There may be a number of issues with these data.

To solve that problem,

to tackle it, we apply a number of treatments on these data.

First, we will compute averages over five years instead of just taking one year of data.

And we will filter out some product.

We only consider products that have been exported during

the last three years by a country if we want to compute the EPI for that product.

In terms of markets,

we will only consider markets that regularly,

every year of the five years,

has imported the good.

To address the fact that the quality is not always very good,

we will combine direct and mirror data,

that is data reported by the exporter and data reported by the importer.

We also want to assess the reliability of

the data imported by one exporter in particular for a given year.

Because sometimes, some exporters can really provide very low quality data,

and using them in

our calculation would create a problem even for other countries through mirror data.

So to do that,

we will compare their data reported by

country to the same data reported by all its partners,

all its reliable partners.

And when these data are very different,

we would consider that our country is not reliable.

So since we have to do these comparison with reliable partners only,

it means that there is an iteration needed to eventually obtain

a list of reliable countries for a given year.

Now, when this work has been done,

we will combine data

coming from reliable exporters with data coming from all reliable mirrors.

And if a country is not considered as reliable,

we will only use mirror flows.

Now, for some specific projects,

we also validate ex-post all the little that we have

obtained with some country experts that can check if our results make sense.

Now, a second step in the data treatment is about products that we consider.

So, we start at the finest level that

is of the Harmonized System that is a six-digit level,

the six-digit level classification of products.

However, this classification has been revised several times since its creation.

And countries do not necessarily apply the revisions at the same time.

Also, we need to compute some Dynamic Indicators.

So, it means that we need to be able to compare data in the past and data now.

So, it could create problems with the fact that we have several revisions.

To solve this issue,

we have decided to group products to obtain groups that are

insensitive of these data revision, these classification revisions.

So for instance, if we have a product like salmon that was

corresponding only to one code called 030212 until 2007,

and that has been split into two set products in 2012,

then we will create a group of

products that will collect the data corresponding to the three codes,

the earlier code that was used until 2007 and

the more recent code that has been used in the latest revision.

So at the end,

we end up with product groups.

We will not, for our analysis,

consider all the products group possible.

There are some products that are never really used in any export promotion activities.

So, for those products,

we don't need to show them in our results.

So for instance, that will be waste and pollutants,

because there are very rarely export promotion about waste and pollutants.

We don't want to encourage countries to export those goods.

The same thing for arms and ammunition, tobacco products,

and also some products from the extractive industries,

because there is little to say in terms of export promotion activities for those goods.

So we have eliminated a small list of

products that we considered were not relevant for export promotion.

At the end of the process,

we have a little more than 4,000 product groups that we can

merge into 15 broad sectors or 70 sub-sectors.

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