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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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