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Academic year 2019/2020

L3, IEM

Statistics for Economics and Business

(Moritz M¨uller / Christian Freund)

Coursebook

Task ‘Human capital and economic growth’

Background

King Arthur and the knights of the round table searched for the holy grail. Long after the

mystic island Avalon immersed in the floods, Solow and the neo-classical economists entered

the quest for long-run economic growth. If we only understood the determinants of long-run

economic growth — the story goes — we could implement policies to foster wealth across all

nations. Your task it to contribute to that noble endeavor.

Assume wealth is a social product, i.e. the outcome of the joint effort of all individuals in a

society. How could we increase wealth? Well, probably the simplest way would be to increase

the society — double the number of people and everything else, and you double the output. But

that doesn’t make anybody richer because the doubled output is going to serve twice as many

people.

Thus, in order to grow the wealth of each individual in the society, something needs to grow

that helps the members of that society to achieve their goals. The general term for such a

thing in economics is capital. At the beginning the focus was very much on physical capital,

i.e. installed machinery, and how these improve over time due to technical progress. Only later,

human capital as an important productive factor has been emphasized, with schooling being

one important input to human capital.

Task

If we would increase years of schooling by one year, how would that affect economic

growth?

Investigate this question by doing a statistical analysis using R in groups of 2 students (and

at most one group of three students). Groups will be assigned randomly, no choice. However,

please alarm us if something does not work well in your group (e.g. if you feel exploited).

Your analysis should include descriptive statistics of the most relevant variables and a linear

model estimated with OLS. The output which is going to be evaluated is a report submitted

by each group, as well as the contribution of individual students in discussions prior and post

submission of reports. The report should NOT EXCEED 10 pages (standard formatting, Arial,

11pt, including graphs, tables, and references).

Reports are due until the session 25 of October where you present your findings. The report

should be send in digital form to mueller@unistra.fr and c.freund@unistra.fr (as pdf named

‘econgrowth Surname1 Surname2.pdf’ with subject ‘IEM L3 final report’) and one print out

should be provided at the session 25 of October.

The data set provided for the analysis is panel data compiled from several sources: population

data comes from the United Nations (2015), henceforth UN, the schooling attainment data

is from (Barro and Lee, 2013), henceforth Barro-Lee, and economic data from the Penn World

Tables (Feenstra et al., 2013), henceforth Penn-World-Tables. The data set covers 125 countries

over 17 five-year periods and provides the following variables:

Variable name Description Source

isocode iso country abbreviation –

year year of measurement –

country name of country –

region2 some ‘world-region’ Barro-Lee

workpop population between 20 and 64 years old in 1000 UN

unpop.x population total in 1000 UN

yr sch average years of schooling for 15 to 99 years Barro-Lee

rgdpo output-side real GDP Penn-World-Tables

at current PPPs in Mio 2005 US$

ck physical capital per worker

at current PPPs in Mio 2005 US$ Penn-World-Tables

Evalution of reports takes into account the following points:

1. Any relevant question builds on a theoretical concept, here ‘economic growth’, and any

statistical analysis uses indicators (or variables, or measure), here e.g. ‘GDP’. A good

report provides a short discussion of the theoretical concept, why a certain indicator has

been chosen (and not other reasonable indicators), and what the limits of the indicator are

in terms of measuring the theoretical concept. While you have no choice in the indicators

here, you may well discuss their limitations.

2. There is nothing such as an ideal data set. Available data sets are never complete and

often not all observations potentially offered by the data set can be used due to missing

information. One needs to state clearly which data set has been used for the analysis and

how the final sample for the analysis has been obtained. E.g. we restrict to countries x, y,

and z observed from 19xx to 20xx.

3. Whenever a statistic (i.e. any calculation on the data, e.g. a sum is a statistic) or an analysis

method (e.g. a linear regression) is used, one needs to provide the mathematics of how

it is calculated and what the interpretation of the result is. Consider for example Variance:

The unbiased sample variance Vˆ of a sample X of N observations x1, x2, . . . , xi, . . . , xN is

estimated as Vˆ (X) = 1

i=1(xi − x¯), where x¯ denotes the sample average. The interpretation

is that the variance is a measure of the spread of the distribution; the higher the

variance, the more diverse are observations in the sample.

4. The report should provide a discussion on what has been done to tackle the question and

where the limits of the report are, perhaps mentioning what one could do if there would

be more time or more data.

5. The report should provide a conclusion. E.g. ‘We did x and y. While the results of x

suggest that increasing schooling by one year increases economic growth by xy, yz does

actually not support this idea. In sum, taking into account the limits of the analysis as

discussed in the section Discussion above, our analysis suggests that statement A is true

in that respect but not true in some other respect.’

6. Writing and structure. Does the sequence of paragraphs and sections follow a logic? Does

each sentence and paragraph transmit a clear statement to the reader?

References

United Nations, 2015. World Population Prospects: The 2015 Revision, DVD Edition. United

Nations, Department of Economic and Social Affairs, Population Division.

Barro, R., Lee, J.-W., 2013. A new data set of educational attainment in the world, 1950-2010.

Journal of Development Economics 104, 184-198.

Feenstra, R. C., Inklaar, R., Timmer, M., 2013. The next generation of the penn world table.

Tech. Rep. 19255, NBER.

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