Is financial wealth leading to high quality government services?

August 6, 2010

It is natural to assume that financial wealth leads to better government. It is further reasonable to expect that wealthy countries have higher quality of the e-government services compared to countries with less financial wealth. But how much does the finances alone influence quality e-government services? This short study gives a peek of how finances affects e-government services.

UN E-government 2010 report

In this study the data used for quality of e-government services is the E–Government Development Index (E-readiness score) from the United Nations E-Government Survey 2010. Thus, it is directly assumed that a government with high quality e-government services will receive a high score, and visa versa. The remaining data used is from the World Bank Data Catalog.

The following figure presents a box plot of the differences between the E–Government Development Index of Developing and Developed countries. The plot shows that developing countries have in average score of 0.4 while developed countries have an average score of about 0.7. Furthermore, all developing countries have scores less than 0.7, while all the developed countries have a score higher than 0.5. Thus based on the United Nations E–Government Development Index score it is, not surprisingly, significant difference between e-government services in developing and developed countries.

Developing countries have in average of 0.4 while developed countries have an average of about 0.7. All the developing countries have e-readiness score less than 0.7 while all the developed countres have a score higher than 0.5.

E-readiness score versus developing and developed countires.

Thus, the quality is clearly dependant on the finances, but how much of the quality e-Government services are influenced by finances alone?

The development of government services is complex procedure shaped by many factors. There exists no general conclusion of which factors influence the quality of the government service. It is however possible to determine to what extent data from the financial situation in a country can be used to predict the e-readiness score.

The following graph presents the plot between E–Government Development Index and GNI per capita. The graph also includes a regression, which can be used to calculate the E–Government Development Index based on the GNI per capita alone.

A dotplot showing the trends between E-readiness and GNI per capita.

E-readiness versus GNI per capita

The trends in the data are clearly visible. The regression can be seen as the black line, the mean response is shows as a green dashed line while the prediction interval is presented as the blue dashed line.

The regression line (black line) shows the relationship between the E–Government Development Index and GNI per capita. If no correlation existed between the two data sets, the line would be completely horizontall. The regression line can be used to predict the E–Government Development Index using only the GNI per capita. The graphs shows us that the relationship is not linear, but more complex.
The mean response interval (green dashed line) tells the estimated mean of the data.
The prediction interval (blue dashed line) tells where future data is expected be located (similar to confidence interval).

The data shows that the mean response interval and prediction interval changes as the GNI per capita increases. Generally, we are more certain of the prediction when these intervals are small. From this we can draw the following conclusion. It is relatively easy to predict the E-readiness score when a country has a low GNI per capita. In contrast, to predict the E-readiness score based on the GNI Per Capita alone for wealthy countries is a lot less precise. I.e. lack of finances generally means low quality services, while wealth alone is not sufficient to ensure quality in e-government.


Weighing Indices in the UN E-Government Survey

May 14, 2010

The United Nations E-government Survey index is a weighted combination of three indices:

  • Web Measure, which represents the sophistication level of online citizen services.
  • Human Capital, which represents the education level of a country.  This index is again weighted with two-third weight of adult literacy and one-third to weights to enrollment.
  • ICT Infrastructure, which represents the infrastructure in a country. This is again average weighted including  number of computers per person, telephone lines, mobile phones etc.

These three are all weighted equally contributing 1/3 to the score, which means that formally the e-readiness is as following:
E-readiness =

1/3 * Web Measure +

1/3 * Human Development +

1/3 * ICT Infrastructure

An interesting question that follows is what happens if we assign other weights to these indices For example, if we change the weights, can we also change the ranking a country gets?

Using Monaco as an example, it was ranked as member state number 112 in the UN e-readiness survey 2010. However, by adjusting weights of the three indices, we can change the ranking of Monaco from 112 up to 25, or down to 184.

In the following plot, possibile combination from 10% up to 80% of the three indicies are plotted and the corresponding ranking of Monaco.

Ranking of Monaco when weighting the indicies Web Measure, Human Capital and ICT Infrastructure differently

Similarly, the following graphs how the top five member states, according to the E-readiness ranking in the 2010 survey, would rank if different weights would be used.

Ranking of the top five countries with different weights

(Note that for reason of clarity some weightings have deliberatly been removed).

The question which naturally arises is:

Why does the current E-readiness index use equal weights, and is this any more correct than any weights?

Thanks do Deniz Susar for input on this idea.