Hello,
Any idea how to interpret this variable RER (i.e. number of researchers per 1,000 people employed) in relation to R&D expenditure (expressed as % of GDP)
As per literature, having highly educated people is believed to have beneficial effect on R&D expenditure.
I wanted to transform RER into percentage rather keeping it as whole number (to easily interpret it) but I don't have the total number of people employed. This was not given by OECD database source.
This is my attempt to interpret it, please let me know if it's correct.
An addition of 1 researcher (i.e., RER) in a country, is met by an increase of 0.0001451285 (0.1451285 times 1,000 employed) percentage point (pp.) in a one-year lagged R&D
Any idea how to interpret this variable RER (i.e. number of researchers per 1,000 people employed) in relation to R&D expenditure (expressed as % of GDP)
As per literature, having highly educated people is believed to have beneficial effect on R&D expenditure.
I wanted to transform RER into percentage rather keeping it as whole number (to easily interpret it) but I don't have the total number of people employed. This was not given by OECD database source.
This is my attempt to interpret it, please let me know if it's correct.
An addition of 1 researcher (i.e., RER) in a country, is met by an increase of 0.0001451285 (0.1451285 times 1,000 employed) percentage point (pp.) in a one-year lagged R&D
Code:
Fixed-effects (within) regression Number of obs = 1,007
Group variable: countryid Number of groups = 41
R-squared: Obs per group:
Within = 0.6006 min = 2
Between = 0.8282 avg = 24.6
Overall = 0.7711 max = 36
F(37, 40) = 68.90
corr(u_i, Xb) = 0.4748 Prob > F = 0.0000
(Std. err. adjusted for 41 clusters in countryid)
------------------------------------------------------------------------------
| Robust
RND_L1 | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
PT | .000016 6.43e-06 2.48 0.017 2.96e-06 .000029
RER | .1451285 .0225073 6.45 0.000 .0996396 .1906174
|
year |
1986 | -1.240908 .6413845 -1.93 0.060 -2.537195 .0553785
1987 | -1.22445 .6433891 -1.90 0.064 -2.524788 .0758877
1988 | -1.27232 .6415019 -1.98 0.054 -2.568843 .024204
1989 | -1.298551 .643198 -2.02 0.050 -2.598503 .0014004
1990 | -1.28524 .6408538 -2.01 0.052 -2.580454 .0099741
1991 | -1.262964 .6408489 -1.97 0.056 -2.558168 .0322396
1992 | -1.314941 .6465767 -2.03 0.049 -2.621722 -.0081611
1993 | -1.36549 .6492434 -2.10 0.042 -2.677659 -.0533196
1994 | -1.333842 .6567046 -2.03 0.049 -2.661092 -.0065925
1995 | -1.443588 .6543044 -2.21 0.033 -2.765986 -.1211891
1996 | -1.478862 .6553497 -2.26 0.030 -2.803373 -.1543504
1997 | -1.478968 .652973 -2.26 0.029 -2.798676 -.1592606
1998 | -1.511229 .6517109 -2.32 0.026 -2.828386 -.1940723
1999 | -1.515496 .6541553 -2.32 0.026 -2.837593 -.1933986
2000 | -1.54087 .655177 -2.35 0.024 -2.865032 -.2167075
2001 | -1.519098 .6561638 -2.32 0.026 -2.845255 -.1929415
2002 | -1.489616 .6564095 -2.27 0.029 -2.816269 -.1629625
2003 | -1.485831 .6565628 -2.26 0.029 -2.812794 -.1588682
2004 | -1.511731 .6600905 -2.29 0.027 -2.845823 -.1776383
2005 | -1.546316 .6612633 -2.34 0.024 -2.882779 -.2098531
2006 | -1.527389 .6628624 -2.30 0.026 -2.867084 -.187694
2007 | -1.476641 .6634117 -2.23 0.032 -2.817446 -.1358355
2008 | -1.504534 .6627888 -2.27 0.029 -2.844081 -.1649882
2009 | -1.474459 .667391 -2.21 0.033 -2.823307 -.1256118
2010 | -1.43961 .6698877 -2.15 0.038 -2.793504 -.0857169
2011 | -1.475702 .6679814 -2.21 0.033 -2.825743 -.1256611
2012 | -1.440366 .6681374 -2.16 0.037 -2.790722 -.0900096
2013 | -1.448928 .6674887 -2.17 0.036 -2.797973 -.0998829
2014 | -1.452239 .6669947 -2.18 0.035 -2.800285 -.1041919
2015 | -1.440664 .6661976 -2.16 0.037 -2.7871 -.0942285
2016 | -1.432821 .6674393 -2.15 0.038 -2.781767 -.0838764
2017 | -1.497495 .6688477 -2.24 0.031 -2.849286 -.1457029
2018 | -1.503185 .6722164 -2.24 0.031 -2.861785 -.1445852
2019 | -1.501804 .6740847 -2.23 0.032 -2.86418 -.1394283
2020 | -1.512565 .6752263 -2.24 0.031 -2.877248 -.1478815
|
_cons | 1.992216 .6276246 3.17 0.003 .7237395 3.260693
-------------+----------------------------------------------------------------
sigma_u | .41925763
sigma_e | .23394991
rho | .76255835 (fraction of variance due to u_i)
------------------------------------------------------------------------------
Code:
sum RER
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
RER | 1,133 6.317485 3.470873 .5159566 17.21029
