ECON 333: Economic Development Base on Excel answer questions of ECON like growth rates of GDP per capita and you can find it in the Excel tab labeled Question 1.

All the data is from the Penn World Table (version 10). Save this file as “YourLastName YourFirstName Assignment1.xlsx”. All your answers should appear in this file.

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(1) (40 points) The first question concerns growth rates of GDP per capita and you can find it in the Excel tab labeled Question 1.

(2) (60 points) The second question is on growth accounting and the goal is to compute the contribution of factor accumulation and TFP for GDP growth across these countries over time. You can find it in the Excel tab labeled Question 2.

ECON 333: Economic Development, Spring

2

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02

1

College of Business and Economics

Assignment 1

Due March 4th before class starts (upload to Canvas)

Download the Excel file “Assignment1.xlsx” from Canvas. This contains data for 8 countries
(Brazil, China, Germany, India, Japan, Mexico, Spain and the U.S.) from 1970 to 2019.

The variables included in the worksheet are, for the first question: Real GDP per capita at
2017 $US (PPP); and for the second question: Real aggregate GDP at constant national prices (in
mil. 2017 $US), number of employees (in millions), annual hours worked per employee, a human
capital index; capital stock at constant national prices (in mil. 2017 $US), and labor compensation
as a share of GDP. All the data is from the Penn World Table (version 10). Save this file as
“YourLastName YourFirstName Assignment1.xlsx”. All your answers should appear in this file.

(1) (40 points) The first question concerns growth rates of GDP per capita and you can find it
in the Excel tab labeled Question 1.

(1.a) (10 points) For each country and for each year compute the growth rates of GDP per
capita. They should appear along the column labeled 1.a in the cells that show up grey. As an
example, to get the growth rate of per capita GDP in the U.S. in 1971, which should appear in the

row for year 1971 and in the column labeled 1.a, compute
GDP.pcUS1971
GDP.pcUS

1970

− 1 ‘ 2.42%.

(1.b) (10 points) For each country and for each decade compute average growth rates of GDP
per capita. They should appear every 10 years along the column labeled 1.b in the cells that
show up grey. As an example, to get the average growth rate of per capita GDP in the U.S. in
the 1980s, which should appear in the row for year 1990 and in the column labeled 1.b, compute(

GDP.pcUS1990
GDP.pcUS

1980

) 1
10

−1 ‘ 2.41%. Once you’ve done this for all countries and all decades, write the name
and decade of the country that grew the least over a decade in the grey cell just below Answer
in the column labeled 1.b.

(1.c) (10 points) Compute average growth rates of GDP per capita for each country, for the
entire period from 1970 to 2019. They should appear once for every country along the column
labeled 1.c and along the rows for year 2019 in the cells that show up grey. As an example, to get
the average growth rate of per capita GDP in the U.S., which should appear in the row for year

2019 and in the column labeled 1.c, compute

(
GDP.pcUS2019
GDP.pcUS

1970

) 1
49

− 1 ‘ 1.89%. Once you’ve done this
for all countries, write the name of the country that grew at the fastest average rate over the
whole period in the grey cell just below Answer in the column labeled 1.c.

(1.d) (10 points) In the column labeled 1.d compute, from 1970 to 2019, the ratio of the

Brazilian GDP per capita to the US GDP per capita, that is:
GDP BRAt
GDP USt

, for t = 1970 to t = 2019.

Next, plot the series you computed (on the y-axis) against the years (on the x-axis) in the area
labeled PUT CHART FOR 1.d AROUND HERE.

1

(2) (60 points) The second question is on growth accounting and the goal is to compute the
contribution of factor accumulation and TFP for GDP growth across these countries over time.
You can find it in the Excel tab labeled Question 2.

(2.a) (10 points) The first step is to compute, in the Excel sheet, for each year (starting in 1970
and ending in 2019) and for each country, effective labor (in the column labeled Labor). To do this
you will need to multiply Employees by Annual hours worked and by the Human Capital Index.
All the grey cells should be filled. As an example, in the US in 1970, Labor is 490, 185.12.

(2.b) (10 points) Next, compute, for each year (starting in 1971 and ending in 2019) and for
each country, the yearly growth rate of real aggregate GDP (in the column labeled gY ), the yearly
growth rate of the capital stock (in the column labeled gK ), and the yearly growth rate of the labor
aggregate you just computed in part (2.a) (in the column labeled gL). All the grey cells should be

filled. As an example, the growth rate of U.S. GDP in 1971 is
GDP US1971
GDP US

1970
− 1 ‘ 3.29%.

(2.c) (10 points) Next, compute, for each year (starting in 1971 and ending in 2019) and for
each country, the contribution of each factor for growth: WL×gL and WK ×gK . Recall that WL is
the labor compensation share of GDP, that shows up in the column labeled Labor share (WL),
and that WK = 1−WL. Your computations should appear in the columns labeled W K*g K and
W L*g L that are grey. As an example, for the U.S. in 1971, WK ×gK ‘ 1.11%.

(2.d) (10 points) You are now ready to compute, for each country and for each year (starting
in 1971 and ending in 2019), the growth rate of TFP, or Solow residual. This should show up in
the column labeled g A and is given by: gA = gY − WL × gL − WK × gK. As an example, for the
U.S. in 1971, gA ‘ 2.03%.

(2.e) (10 points) Next, compute the share of output growth that is accounted for by growth in
the capital stock sK =

WK×gK
gY

, by growth in labor sL =
WL×gL

gY
, or by total factor productivity

(TFP) growth, sA =
gA
gY

for each country and year from 1971 to 2019. These should appear,
respectively, in the columns labeled s K, s L, and s A. For your reference, in 1971 in the United
States sA = 61.52%. Note that these contributions are not all going to be between zero and 100%
(although most are). They can be negative or greater than 100%. As a check, verify that for every
year and for every country: sA + sL + sK = 100%.

(2.f) (10 points) In this final part you will compute, for each country, the average (over all
years from 1971 to 2019) contribution of capital, labor, and TFP for growth in the bounded cells
in the row labeled 2.f Average all years. Start by computing the averages for gY , gK , and gL,

by using the formula
(
x2019
x1970

) 1
49 − 1. For your reference, the average gY in the US was 2.79% and

should appear in the column labeled g Y and along the row labeled 2.f Average all years. Then,
use the average gK and average gL, together with the average WL and average WK , to compute
the averages WL ×gL and WK ×gK (note that the average WL and average WK are computed as
simple arithmetic averages since they are not growth rates). For your reference, for the US, the
average WL × gL was 0.96%. Next compute the average gA by subtracting the averages WL × gL
and WK × gK from the average gY . Finally, compute the average sK , sL and sA, by dividing the
averages WK×gK , WL×gL and gA, respectively, by the average gY . In particular, do not, compute
the averages for sK , sL and sA by averaging over the whole years. For your reference the average
sA for the US was 32.20%.

2

Question 1

$US PPP)

Answer

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil 2017

Brazil

Brazil

1970

China 1971

China 1972

China 1973

China 1974

China 1975

China 1976

China 1977

China 1978

China 1979

China 1980

China 1981

China 1982

China 1983

China 1984

China 1985

China 1986

China 1987

China 1988

China 1989

China 1990

China 1991

China 1992

China 1993

China 1994

China 1995

China 1996

China 1997

China 1998

China 1999

China 2000

China 2001

China 2002

China 2003

China 2004

China 2005

China 2006

China 2007

China 2008

China 2009

China 2010

China 2011

China 2012

China 2013

China 2014

China 2015

China 2016

China 2017

China 2018

China 2019

1970

Germany 1971

Germany 1972

Germany 1973

Germany 1974

Germany 1975

Germany 1976

Germany 1977

Germany 1978

Germany 1979

Germany 1980

Germany 1981

Germany 1982

Germany 1983

Germany 1984

Germany 1985

Germany 1986

Germany 1987

Germany 1988

Germany 1989

Germany 1990

Germany 1991

Germany 1992

Germany 1993

Germany 1994

Germany 1995

Germany 1996

Germany 1997

Germany 1998

Germany 1999

Germany 2000

Germany 2001

Germany 2002

Germany 2003

Germany 2004

Germany 2005

Germany 2006

Germany 2007

Germany 2008

Germany 2009

Germany 2010

Germany 2011

Germany 2012

Germany 2013

Germany 2014

Germany 2015

Germany 2016

Germany 2017

Germany 2018

Germany 2019

1970

India 1971

India 1972

India 1973

India 1974

India 1975

India 1976 1,527
India 1977

India 1978

India 1979

India 1980

India 1981 1,240
India 1982

India 1983

India 1984

India 1985

India 1986

India 1987

India 1988

India 1989 1,360
India 1990

India 1991 1,435
India 1992

India 1993

India 1994

India 1995

India 1996

India 1997

India 1998

India 1999

India 2000

India 2001

India 2002

India 2003

India 2004

India 2005

India 2006

India 2007

India 2008

India 2009

India 2010

India 2011

India 2012

India 2013

India 2014

India 2015

India 2016

India 2017 6,029
India 2018

India 2019

1970

Japan 1971

Japan 1972

Japan 1973

Japan 1974

Japan 1975

Japan 1976

Japan 1977

Japan 1978

Japan 1979

Japan 1980

Japan 1981

Japan 1982

Japan 1983

Japan 1984

Japan 1985

Japan 1986

Japan 1987

Japan 1988

Japan 1989

Japan 1990

Japan 1991

Japan 1992

Japan 1993

Japan 1994

Japan 1995

Japan 1996

Japan 1997

Japan 1998

Japan 1999

Japan 2000

Japan 2001

Japan 2002

Japan 2003

Japan 2004

Japan 2005

Japan 2006

Japan 2007

Japan 2008

Japan 2009

Japan 2010

Japan 2011

Japan 2012

Japan 2013

Japan 2014

Japan 2015

Japan 2016

Japan 2017

Japan 2018

Japan 2019

1970

Mexico 1971 9,041
Mexico 1972

Mexico 1973

Mexico 1974

Mexico 1975

Mexico 1976

Mexico 1977

Mexico 1978

Mexico 1979

Mexico 1980

Mexico 1981

Mexico 1982

Mexico 1983

Mexico 1984

Mexico 1985

Mexico 1986

Mexico 1987

Mexico 1988

Mexico 1989

Mexico 1990

Mexico 1991

Mexico 1992

Mexico 1993

Mexico 1994

Mexico 1995

Mexico 1996

Mexico 1997

Mexico 1998

Mexico 1999

Mexico 2000

Mexico 2001

Mexico 2002

Mexico 2003

Mexico 2004

Mexico 2005

Mexico 2006

Mexico 2007

Mexico 2008

Mexico 2009

Mexico 2010

Mexico 2011

Mexico 2012

Mexico 2013

Mexico 2014

Mexico 2015

Mexico 2016

Mexico 2017

Mexico 2018

Mexico 2019

1970

Spain 1971

Spain 1972

Spain 1973

Spain 1974

Spain 1975

Spain 1976

Spain 1977

Spain 1978

Spain 1979 15,882
Spain 1980

Spain 1981

Spain 1982

Spain 1983

Spain 1984

Spain 1985

Spain 1986

Spain 1987

Spain 1988

Spain 1989

Spain 1990

Spain 1991

Spain 1992

Spain 1993

Spain 1994

Spain 1995

Spain 1996

Spain 1997

Spain 1998

Spain 1999

Spain 2000

Spain 2001

Spain 2002

Spain 2003

Spain 2004

Spain 2005

Spain 2006

Spain 2007

Spain 2008

Spain 2009

Spain 2010

Spain 2011

Spain 2012

Spain 2013

Spain 2014

Spain 2015

Spain 2016

Spain 2017

Spain 2018

Spain 2019

1970

USA 1971

USA 1972

USA 1973

USA 1974

USA 1975

USA 1976

USA 1977

USA 1978

USA 1979

USA 1980

USA 1981

USA 1982

USA 1983

USA 1984

USA 1985

USA 1986

USA 1987

USA 1988

USA 1989

USA 1990

USA 1991

USA 1992

USA 1993

USA 1994

USA 1995

USA 1996

USA 1997

USA 1998

USA 1999

USA 2000

USA 2001

USA 2002

USA 2003

USA 2004

USA 2005

USA 2006

USA 2007

USA 2008

USA 2009

USA 2010

USA 2011

USA 2012

USA 2013

USA 2014

USA 2015

USA 2016

USA 2017

USA 2018

USA 2019

Country Year GDP per capita (

2017 1.a 1.b 1.c 1.d
Answer
Brazil 1970 3,817
1971 4,152
1972 4,549
1973 5,084 PUT CHART FOR 1.d AROUND HERE
1974 5,256
1975 5,382
1976 5,767
1977 5,823
1978 5,862
1979 6,063
1980 5,971
1981 5,908
1982 5,924
1983 5,683
1984 5,868
1985 6,047
1986 6,687
1987 6,783
1988 6,767
1989 6,691
1990 6,357
1991 6,819
1992 6,935
1993 7,340
1994 8,106
1995 9,298
1996 10,963
1997 10,490
1998 9,937
1999 9,388
2000 9,382
2001 9,199
2002 9,224
2003 9,108
2004 9,505
2005 9,609
2006 10,380
2007 11,424
2008 12,535
2009 12,974
2010 14,904
2011 16,448
2012 16,257
2013 16,388
2014 16,186
2015 15,077
2016 14,258
14,292
2018 14,597
2019 14,637
China 1,300
1,396
1,324
1,408
1,395
1,435
1,358
1,433
1,622
1,678
1,680
1,676
1,786
1,884
2,096
2,283
2,333
2,450
2,537
2,496
2,478
2,577
2,744
2,915
3,175
3,487
3,723
3,880
3,858
4,014
4,273
4,606
5,017
5,421
6,029
6,681
7,362
8,064
8,412
9,041
10,126
10,769
1

1,360
11,819
12,231
12,699
13,162
13,724
13,665
13,989
Germany 16,182
16,739
17,550
18,437
18,473
18,594
19,582
20,343
21,241
22,130
22,179
21,460
21,095
2

1,240
21,395
21,529
22,991
23,794
24,977
26,126
27,884
29,455
30,438
30,440
3

1,527
32,459
32,790
34,062
35,604
37,018
37,224
37,609
37,251
37,362
38,118
38,473
40,209
42,276
43,174
41,458
43,694
45,704
46,006
45,709
46,806
47,869
49,138
51,263
51,428
51,590
India 1,430
1,456
1,479
1,524
1,464
1,572
1,489
1,419
1,286
1,273
1,195
1,205
1,176
1,166
1,191
1,228
1,298
1,409
1,475
1,534
1,591
1,683
1,785
1,834
1,951
2,043
2,113
2,199
2,279
2,431
2,604
2,872
3,150
3,490
3,541
3,864
4,374
4,662
4,916
5,037
5,166
5,465
5,772
6,368
6,548
Japan 14,033
14,750
15,882
16,876
16,178
16,555
17,406
18,376
19,733
20,626
20,999
20,820
20,315
19,786
19,704
20,044
21,310
22,578
24,741
26,600
28,531
30,169
31,101
31,833
32,887
34,700
36,717
36,842
36,405
36,079
36,826
36,886
36,761
37,263
37,994
38,463
38,979
39,755
39,035
37,375
38,648
38,200
38,684
39,302
38,721
39,801
38,996
39,238
39,172
39,633
Mexico 8,905
9,514
10,002
10,342
10,636
10,945
11,061
11,796
12,756
13,942
14,754
14,356
13,305
13,226
13,059
11,948
11,976
11,486
11,691
12,091
12,282
12,483
12,468
12,910
12,172
12,557
13,344
13,926
14,469
14,874
14,665
14,388
14,393
15,047
15,230
15,916
16,189
16,188
15,699
16,830
17,985
18,832
18,449
18,629
18,906
19,311
19,277
19,580
19,308
Spain 11,813
12,293
13,205
14,039
14,221
14,255
14,784
15,254
15,733
15,982
14,882
14,376
13,922
13,694
13,555
14,468
15,531
16,672
17,829
18,955
19,866
20,408
20,333
21,151
22,277
23,008
24,662
26,583
27,502
28,636
29,269
29,977
30,207
31,220
32,767
35,580
37,640
37,703
36,058
35,382
35,012
34,572
34,233
34,411
36,532
38,160
39,729
40,666
41,351
USA 25,356
25,970
27,133
28,449
27,679
27,296
28,640
29,664
31,067
31,641
30,792
31,360
30,494
31,760
34,007
35,126
35,929
36,709
37,865
38,862
39,063
38,630
39,653
40,483
41,823
42,504
43,724
45,313
47,057
48,721
50,094
50,105
50,490
51,439
52,969
54,216
55,247
55,714
54,426
52,809
53,671
54,009
55,018
55,912
57,123
58,923
59,710
60,774
62,280
63,400

Question 2

Country Year

share (W_L)

Labor

Brazil 1970

Brazil 1971

5504

0.5146055818

Brazil 1972

0.5146055818

Brazil 1973

0.5146055818

Brazil 1974

0.5146055818

Brazil 1975

0.5146055818

Brazil 1976

0.5146055818

Brazil 1977

0.5146055818

Brazil 1978

0.5146055818

Brazil 1979

0.5146055818

Brazil 1980

1985

0.5146055818

Brazil 1981

0.5146055818

Brazil 1982

0.5146055818

Brazil 1983

0.5146055818

Brazil 1984

0.5146055818

Brazil 1985

0.5146055818

Brazil 1986

0.5146055818

Brazil 1987

0.5146055818

Brazil 1988

0.5146055818

Brazil 1989

0.5146055818

Brazil 1990

0.5146055818

Brazil 1991

0.5146055818

Brazil 1992

0.5146055818

Brazil 1993

Brazil 1994

Brazil 1995

Brazil 1996

3.5

Brazil 1997

Brazil 1998

Brazil 1999

Brazil 2000

Brazil 2001

Brazil 2002

Brazil 2003

Brazil 2004

Brazil 2005

Brazil 2006

Brazil 2007

Brazil 2008

Brazil 2009

Brazil 2010

Brazil 2011

86197

Brazil 2012

Brazil 2013

Brazil 2014

Brazil 2015

Brazil 2016

Brazil 2017

Brazil 2018

0.5779953599

Brazil 2019

0.5779953599

China 1970

5696

China 1971

0.5921685696

China 1972

898

0.5921685696

China 1973

0.5921685696

China 1974

0.5921685696

China 1975

0.5921685696

China 1976

0.5921685696

China 1977

0.5921685696

China 1978

0.5921685696

China 1979

0.5921685696

China 1980

0.5921685696

China 1981

0.5921685696

China 1982

0.5921685696

China 1983

0.5921685696

China 1984

0.5921685696

China 1985

0.5921685696

China 1986

0.5921685696

China 1987

0.5921685696

China 1988

0.5921685696

China 1989

0.5921685696

China 1990

0.5921685696

China 1991

0.5921685696

China 1992

0.5921685696

China 1993

China 1994

China 1995

39

China 1996

China 1997

China 1998

China 1999

China 2000

China 2001

94

China 2002

China 2003

China 2004

China 2005

361328

.3545872708

China 2006

China 2007

China 2008

China 2009

China 2010

China 2011

China 2012

China 2013

China 2014

China 2015

China 2016

China 2017

0.5862529278

China 2018

2168.9188484802

0.5862529278

China 2019

2168.9188484802

0.5862529278

2.f Average all years

Germany 1970

Germany 1971

0.6681209207

Germany 1972

0.6681209207

Germany 1973

0.6681209207

Germany 1974

0.6681209207

Germany 1975

0.6681209207

Germany 1976

0.6681209207

Germany 1977

0.6681209207

Germany 1978

0.6681209207

Germany 1979

0.6681209207

Germany 1980

0.6681209207

Germany 1981

0.6681209207

Germany 1982

0.6681209207

Germany 1983

0.6681209207

Germany 1984

0.6681209207

Germany 1985

0.6681209207

Germany 1986

0.6681209207

Germany 1987

0.6681209207

Germany 1988

0.6681209207

Germany 1989

0.6681209207

Germany 1990

0.6681209207

Germany 1991

0.6681209207

Germany 1992

Germany 1993

Germany 1994

Germany 1995

Germany 1996

Germany 1997

Germany 1998

Germany 1999

Germany 2000

Germany 2001

Germany 2002

Germany 2003

Germany 2004

Germany 2005

Germany 2006

Germany 2007

Germany 2008

Germany 2009

Germany 2010

Germany 2011

Germany 2012

Germany 2013

Germany 2014

Germany 2015

Germany 2016

Germany 2017

Germany 2018

4

Germany 2019

0.6419044137

2.f Average all years

India 1970

India 1971

0.7537503839

India 1972

0.7537503839

India 1973

0.7537503839

India 1974

555

0.7537503839

India 1975

0.7537503839

India 1976

India 1977

0.7537503839

India 1978

India 1979

India 1980

India 1981

India 1982

India 1983

India 1984

India 1985

India 1986

India 1987

India 1988

India 1989

India 1990

India 1991

India 1992

India 1993

India 1994

India 1995

India 1996

India 1997

India 1998

India 1999

India 2000

India 2001

India 2002

India 2003

India 2004

748

India 2005

India 2006

India 2007

India 2008

India 2009

India 2010

India 2011

India 2012

India 2013

India 2014

India 2015

48315

India 2016

.2430710755

India 2017

India 2018

2122.9406824846

0.5218037367

India 2019

2122.9406824846

0.5218037367

2.f Average all years

Japan 1970

Japan 1971

0.6205279231

Japan 1972

0.6205279231

Japan 1973

0.6205279231

Japan 1974

0.6205279231

Japan 1975

0.6205279231

Japan 1976

0.6205279231

Japan 1977

0.6205279231

Japan 1978

0.6205279231

Japan 1979

0.6205279231

Japan 1980

0.6205279231

Japan 1981

Japan 1982

Japan 1983

Japan 1984

Japan 1985

Japan 1986

Japan 1987

Japan 1988

Japan 1989

Japan 1990

Japan 1991

Japan 1992

Japan 1993

Japan 1994

Japan 1995

Japan 1996

Japan 1997

Japan 1998

Japan 1999

Japan 2000

1858.5

Japan 2001

Japan 2002

Japan 2003

Japan 2004

Japan 2005

Japan 2006

Japan 2007

Japan 2008

Japan 2009

Japan 2010

Japan 2011

Japan 2012

Japan 2013

Japan 2014

Japan 2015

Japan 2016

Japan 2017

Japan 2018

0.5636439919

Japan 2019

0.5636439919

2.f Average all years

Mexico 1970

Mexico 1971

0.4280898571

Mexico 1972

0.4280898571

Mexico 1973

0.4280898571

Mexico 1974

0.4280898571

Mexico 1975

0.4280898571

Mexico 1976

0.4280898571

Mexico 1977

0.4280898571

Mexico 1978

0.4280898571

Mexico 1979

0.4280898571

Mexico 1980

0.4280898571

Mexico 1981

0.4280898571

Mexico 1982

0.4280898571

Mexico 1983

0.4280898571

Mexico 1984

0.4280898571

Mexico 1985

0.4280898571

Mexico 1986

0.4280898571

Mexico 1987

0.4280898571

Mexico 1988

0.4280898571

Mexico 1989

0.4280898571

Mexico 1990

0.4280898571

Mexico 1991

0.4280898571

Mexico 1992

0.4280898571

Mexico 1993

0.4280898571

Mexico 1994

2146

Mexico 1995

Mexico 1996

2192

Mexico 1997

Mexico 1998

2168

Mexico 1999

Mexico 2000

2174

Mexico 2001

2146

Mexico 2002

2196

Mexico 2003

2143

Mexico 2004

2123

Mexico 2005

2105

Mexico 2006

2067

Mexico 2007

2045

Mexico 2008

2105

Mexico 2009

1973

Mexico 2010

Mexico 2011

2121

Mexico 2012

2120

Mexico 2013

Mexico 2014

2134

Mexico 2015

Mexico 2016

2146

Mexico 2017

Mexico 2018

2149

Mexico 2019

2137

0.3602636755

2.f Average all years

Spain 1970

Spain 1971

0.6271819472

Spain 1972

0.6271819472

Spain 1973

0.6271819472

Spain 1974

0.6271819472

Spain 1975

0.6271819472

Spain 1976

0.6271819472

Spain 1977

0.6271819472

Spain 1978

0.6271819472

Spain 1979

0.6271819472

Spain 1980

0.6271819472

Spain 1981

0.6271819472

Spain 1982

0.6271819472

Spain 1983

0.6271819472

Spain 1984

0.6271819472

Spain 1985

0.6271819472

Spain 1986

0.6271819472

Spain 1987

0.6271819472

Spain 1988

0.6271819472

Spain 1989

0.6271819472

Spain 1990

0.6271819472

Spain 1991

0.6271819472

Spain 1992

0.6271819472

Spain 1993

0.6271819472

Spain 1994

0.6271819472

Spain 1995

0.6271819472

Spain 1996

Spain 1997

Spain 1998

Spain 1999

Spain 2000

Spain 2001

Spain 2002

Spain 2003

Spain 2004

Spain 2005

Spain 2006

Spain 2007

Spain 2008

Spain 2009

Spain 2010

Spain 2011

Spain 2012

Spain 2013

Spain 2014

Spain 2015

Spain 2016

Spain 2017

Spain 2018

Spain 2019

0.5554321408

2.f Average all years

USA 1970

USA 1971

USA 1972

USA 1973

USA 1974

USA 1975

USA 1976

USA 1977

USA 1978

USA 1979

USA 1980

USA 1981

USA 1982

USA 1983

USA 1984

USA 1985

USA 1986

USA 1987

USA 1988

USA 1989

USA 1990

USA 1991

USA 1992

USA 1993

USA 1994

USA 1995

USA 1996

USA 1997

USA 1998

USA 1999

USA 2000

USA 2001

USA 2002

USA 2003

USA 2004

USA 2005

USA 2006

USA 2007

USA 2008

USA 2009

USA 2010

USA 2011

USA 2012

USA 2013

USA 2014

USA 2015

USA 2016

USA 2017

USA 2018

USA 2019

2.f Average all years

2.a 2.b 2.c 2.d 2.e
GDP (million 2017 $US) Employees (million) Annual hours worked Human capital index Capital stock (million 2017 $US) Labor g_Y g_K g_L W_K*g_K W_L*g_L g_A s_K s_L s_A
586396.5625 29.1173305511 2145 1.513204813 1990086.75 0.5146055818
652893.9375 30.8243865967 2128.5406918888 1.50

2196 2165647
730849.4375 32.5314445496 2112.2076815974 1.4912683964 2374560
832949.125 36.5047187805 2096 1.4804198742 2636271
900834.5 37.4015083313 2079.7706946907 1.4696501493 2931737.75
947407.625 38.3709068298 2063.6670527166 1.458958745 3249594.25
1044611.625 39.4182319641 2047.6881010681 1.4634468555 3579852.25
1096111 43.2518959045 2031.8328742692 1.4679487944 3883709.75
1150587.625 44.6515159607 2016.1004143197 1.4724645615 4192358.25
1228367.5 43.8311233521 2000.4897706373 1.476994276 4501577.5
1341745.75 43.3106002808 1.4815378189 4864920
1284721.5 45.1223487854 1974.0968232429 1.5079419613 5134428
1295384.75 47.5642356873 1963.2535352836 1.5348166227 5358612
1257429.875 48.1008033752 1952.4698071656 1.5621701479 5497325.5
1325331.25 49.8299293518 1941.7453117397 1.5900112391 5636437.5
1429356.375 53.5249023438 1931.0797236536 1.6183485985 5812272.5
1536415.25 55.0177192688 1920.4727193417 1.6380264759 6086334
1590650.75 56.9767951965 1909.9239770163 1.6579438448 6344586.5
1589696.375 58.9441986084 1899.4331766566 1.6781032085 6568493.5
1639930.625 60.1645240784 1889 1.6985077858 6792520
1568593.625 61.6319313049 1879 1.7166913748 6967858.5
1584750.25 61.4488830566 1868.4704974925 1.7446271181 7175984.5
1569911.75 61.2696151733 1858 1.7730175257 7342351
1645835.75 61.9821777344 1858.9997310382 1.80186975 7532847 0.4915674627
1742155.125 62.4584236145 1860 1.8311915398 7787763 0.5116842389
1815736.625 62.9320602417 1855.2316952448 1.8609905243 8064328.5 0.573397398
1854784.5 61.7838897705 1850.4756145381 1.8964483738 83

2174 0.5642244816
1917388.25 62.7958717346 1845.7317265422 1.9325817823 8609659 0.5411159992
1918066.125 62.611366272 1841 1.969403863 8872491 0.5472792983
1922939.25 65.8896026611 1813.8428151365 2.0069272518 9062699 0.5451173782
2005745 68.1304550171 1837.9270499712 2.0451657772 9271775 0.5378065109
2032082.625 68.4563903809 1828.3263944361 2.0859029293 9471979 0.5451472402
2086097.125 71.2536468506 1818.7257389011 2.1274518967 9629394 0.5330697894
2110016.75 72.3045806885 1811.1825146205 2.1698281765 9753475 0.5295776129
2228804.5 76.1592788696 1803.1260891788 2.2130486965 9915965 0.5252270699
2300173.75 78.4147796631 1783.1312271869 2.257130146 10079144 0.5354456902
2391306.5 80.879119873 1779.1678511675 2.2988085747 10269700 0.5404710174
2536455.5 81.3537826538 1774.2664847889 2.3412563801 10518990 0.5415161252
2665667.75 82.7599563599 1771.2618621636 2.3844883442 10830966 0.5496699214
2662313.75 83.1514282227 1764.5467105248 2.4285185337 11097101 0.562466979
2862739 86.3538284302 1761.3457471387 2.4733617306 11482914 0.5524567366
2976516.25 88.6289291382 1769.6

2137 2.5608959198 11900606 0.5570166707
3033700.25 89.5062713623 1765.102172487 2.6218743324 12295924 0.5658205152
3124857.5 91.1481933594 1751.5537707817 2.6843047142 12723252 0.5703140497
3140605.5 92.8335876465 1738.8688781138 2.7482216358 13080524 0.5681036115
3029247 92.447227478 1722.8758054408 2.8136603832 13283081 0.5811979771
2930011.5 90.6264724731 1714.6750280246 2.8806574345 13382092 0.5801931024
2968771.75 90.5013885498 1709.5943512356 2.9492499828 13467870 0.5779953599
3007877 92.0909423828 1706.0571693863 3.0194754601 13584286
3042064 93.9568252563 1707.7950578668 3.0913732052 13716488
2.f Average all years
1085269.375 360.9563903809 1976.3119856575 1.4501166344 2739706.5 0.59

2168
1182377.5 375.9302978516 1975.7936833508 1.4778516293 2956127.5
1173504.25 386.572

2045 1975.5076960975 1.5061171055 3113516.75
1261241 395.443145752 1975.3647961066 1.5349230766 3358432.75
1299265.25 407.2581176758 1975.1897018204 1.5642801523 3639644.5
1391620 419.7919311523 1974.8974413427 1.5941985846 3904822
1349792.625 431.8581542969 1974.2074791756 1.6236088276 4132089.5
1428672.5 442.6376342773 1973.4352539229 1.6535615921 4347092
1594657.625 454.2028503418 1972.7274368915 1.6840670109 4708544.5
1719669.5 467.5601196289 1972.1038175192 1.7136608362 5084995.5
1804609.25 484.6688537598 1971.4967389257 1.7374356985 5368837.5
1861478.125 504.6518249512 1971.10161985 1.7590692043 5630197
2031570.5 529.5593261719 1970.7062530245 1.7809720039 6061248
2178120.75 543.1723022461 1970.1927309413 1.8031475544 6307498.5
2414811.25 557.9721069336 1969.7229067993 1.8255991936 6595202
2602175.25 573.3280029297 1969.3904644388 1.8483303785 6912905.5
2780417 587.2228393555 1970.4415985476 1.8693943024 7385940.5
3008289.25 599.6497192383 1971.4633120173 1.8906981945 7803220.5
3141822.25 617.7464599609 1972.6763854047 1.9122446775 8099619
3153460.25 641.0508422852 1973.9872776908 1.9340369701 8307742
3165062.75 660.4765014648 1975.3539929574 1.9560774565 8407349
3300265.5 675.5034790039 1976.8880628789 1.9911968708 8543077
3556142.5 682.7409667969 1978.4228544564 2.0269467831 8784302
3920703.5 688.5791625977 1981.6452945905 2.0633385181 9538352 0.5668126941
4256049.5 693.9137573242 1989.2989299088 2.1003837585 10230663 0.5610336661
4656878.5 699.2023925781 2001.833933255 2.1380939484 11

2146 0.5673509836
4990403 705.6328125 2017.2646824365 2.1720845699 12194281 0.5883905888
5274646.5 713.3764038086 2037.3638589516 2.206615448 12852704 0.5885296464
5305648.5 721.1416015625 2062.0523042881 2.2416954041 13491075 0.5924127698
5572817.5 728.7011108398 2082.0665404096 2.2773330212 14562359 0.6062768698
596

1858.5 735.7231445313 2090.4753106194 2.3135373592 15760559 0.5919618011
6370494 742.6759643555 2103.5151761336 2.3296835423 17

2067 0.5880331993
6969362 748.6954345703 2118.201375887 2.3459424973 18975916 0.6032833457
7496418 753.5504150391 2138.0822770289 2.3623151779 20683408 0.5981279612
8225268.5 758.6143188477 2167.8959458149 2.3788018227 22941258 0.5728844404
9019165 763.

2120 2192 2.3954036236 25472820 0.5682186484
9981658 767.2093505859 2183.0583150257 2.4044585228 28098400 0.5573593378
10976025 771.0383911133 2152.6502141501 2.4135477543 30290010 0.5530818701
11615480 774.4563598633 2131.5926871658 2.4226713181 32863452 0.5520761609
12577085 777.6272583008 2138.8517374716 2.4318294525 36855464 0.5526545048
13946387 781.0355224609 2172.7101831154 2.4410219193 42331708 0.5482550859
14917428 784.7133178711 2178.8091165402 2.4754698277 46622832 0.5499829054
15414981 788.2928466797 2184.292875717 2.5104036331 50743640 0.5603400469
16561333 791.6427612305 2178.3250154072 2.5458304882 55869636 0.571143806
17710510 794.6456298828 2178.6624775996 2.5746221542 60808008 0.5780063868
18378150 797.3352661133 2170.4593170263 2.5990278721 64797304 0.5831686854
19131150 798.5303344727 2167.2276895867 2.6236650944 68728456 0.5862529278
19685860 799.1860961914 2168.9188484802 2.6485359669 72763856
19839984 799.306640625 2.6736423969 76689208
20571246 798.8077392578 2.6989870071 81726344
1681999 38.0957984924 1973.2090254053 2.9142844677 7845500.5 0.6681209207
1734691 38.0671005249 1941.2366648935 2.9396884441 8300380
1809288.625 38.078250885 1916.1227287186 2.965313673 8749618
1895727 38.3314247131 1885.5623967467 2.9911625385 9179683
1912600.375 38.035320282 1852.2788668763 3.0172364712 9520939
1896023.25 37.4351425171 1812.5403493947 3.0435378551 9813579
1989862.375 37.5206718445 1824.9460287101 3.0700683594 10116110
2056467.25 37.6305160522 1806.8871895035 3.0968301296 10434063
2118336 37.921131134 1787.8250814521 3.1238253117 10767020
2206254.5 38.3973503113 1775.7858553143 3.1510558128 11123019
2237337 38.7049713135 1756.7237472629 3.1785235405 11472935
2249178 38.6210479736 1734.6518326771 3.2028431892 11764739
2240297.25 38.3096542358 1723.6158753842 3.2273490429 12006718
2275524 38.0301895142 1710.5733804017 3.2520422935 12257448
2339760.75 38.2259216309 1699.5374231088 3.2769243717 12494805
2394229 38.5728874207 1676.4622396781 3.3019967079 12720738
2448992.75 39.0164794922 1657.4001316267 3.3272612095 12953003
2483331.5 39.3557395935 1634.3249481961 3.3527188301 13184101
2575394.5 39.5983161926 1629.308603972 3.3783714771 13436262
2675746 39.8316688538 1606.2334205414 3.4042201042 13726373
2816356.5 39.5477294922 1583.1582371107 3.4302666187 14059705
2960223.75 38.6425018311 1554.0634406112 3.4436352253 14411917
3017150.75 38.0705223083 1565.0938477581 3.4570555687 14778004 0.6762937903
2987677.75 37.5483283997 1540.1843488366 3.4705281258 15082446 0.6779438853
3059140 37.622341156 1536.5769951688 3.4840533733 15408579 0.6618257761
3106377.75 37.8158302307 1530.5714736344 3.4976315498 15722324 0.6611482501
3131409.25 37.8073997498 1516.751189006 3.5112621784 16019278 0.6581737399
3187529.25 37.7703323364 1507.6761303891 3.5249462128 16305674 0.6465569139
3251724 38.2179298401 1504.5590336407 3.5386836529 16609219 0.6398169398
3313092.5 38.8072547913 1491.5132924335 3.5524744987 16928360 0.645175457
3409586.25 39.6031150818 1465.9378049086 3.5663189888 17241402 0.6542061567
3466917.75 39.4514389038 1458.1650317369 3.5749263763 17504746 0.6412127018
3460054 39.2335510254 1448.9235113195 3.5835545063 17693756 0.6359941363
3435829.75 38.8254508972 1443.4080077478 3.5922033787 17858990 0.6338881254
3476203.5 38.9729156494 1442.5842182816 3.6008732319 18013060 0.6196286082
3501639.25 38.9236679077 1432.4234946961 3.6095640659 18165114 0.6128906608
3635277.25 39.1863441467 1453.1885339058 3.6182756424 18365856 0.5984931588
3743479.75 39.8144607544 1454.0872069925 3.6270084381 18579110 0.5901789069
3779412.5 40.34532547 1447.3284685832 3.6357622147 18787008 0.6046036482
3564218.75 40.4292068481 1405.0558638731 3.644537

2105 18897210 0.6264617443
3713199.25 40.6318817139 1425.7454687195 3.6533331871 19046654 0.6159981489
3858949.25 41.2245788574 1426.8967841325 3.6557762623 19250232 0.616050899
3875099 41.677947998 1407.9821033342 3.6582205296 19440828 0.6284950376
3892056 41.9961509705 1396.4580873672 3.6606667042 19611998 0.630767107
3978053 42.3312606812 1400.4119753751 3.6631145477 19804594 0.6279784441
4037402.75 42.5355796814 1400.7930986503 3.6655640602 20002378 0.6291250587
4127436.75 42.9273605347 1394.7543236743 3.6680150032 20223006 0.6296030879
4234831.5 43.5932197571 1391.3397215693 3.6704676151 20453236 0.6333663464
4288529 44.3060302734 1389.9317786597 3.67292

2134 20701212 0.6419044137
4312350 44.7951965332 1386.1968343797 3.6753778458 20957202
684997.4375 195.3599853516 2077.3308240502 1.1807312965 2374539.5 0.7537503839
696251.375 201.424621582 2077.0851414111 1.1897438765 2514196
692399.0625 207.6302032471 2076.5603952365 1.1988252401 2657787
715217.25 214.0022125244 2074.829158481 1.2079758644 2787159.75
723694.875 220.582

2143 2072.3656910449 1.2171963453 2920558.5
789912.375 227.3964538574 2069.9564415449 1.2264872789 3042384.25
803049.4375 234.4356536865 2067.1599924767 1.2379615307 3186890.5 0.7339187264
861308.8125 241.6928253174 2065.3795315874 1.24954319 3361929.25
910511.3125 249.1635131836 2064.4177857059 1.2612332106 3543345.25 0.7317959666
862817.0625 256.8391113281 2064.2245944513 1.2730326653 3714750.25 0.7090594172
920934.8125 264.7066650391 2064.0279137743 1.2849423885 3886239.5 0.7366718054
976248.125 272.1511230469 2063.6229736382 1.3029783964 4048154 0.7167580724
1010179.875 279.712097168 2064.1530490527 1.3212676048 4221987.5 0.7072074413
1083810.75 287.3619689941 2065.1204736922 1.3398134708 4412323 0.7186240554
1125220.375 295.0686035156 2066.7083048097 1.3586198092 4595774.5 0.7154123187
1184342.75 302.7951660156 2068.6076371972 1.3776899576 4784622.5 0.6987954378
1240913.75 311.1026000977 2070.5778181376 1.3988746405 5000251.5 0.6957070827
1290120.375 319.5054931641 2072.2705445192 1.4203851223 5249094.5 0.6990755796
1414330.375 328.0375976563 2075.0774401227 1.4422262907 5495053 0.6968601942
1498445.5 336.6645507813 2078.1320423342 1.4644033909 5766595.5 0.6821734905
1581361.125 345.3869934082 2078.4794864875 1.4869214296 6105189.5 0.6799628735
1598073.625 354.1060180664 2079.3041861084 1.5091027021 6371149 0.6759575605
1685686.25 363.0844116211 2076.9105199854 1.5316148996 6677011 0.6695857644
1765769.375 372.4365539551 2075.8692795182 1.5544629097 6953852 0.6555981636
1883350.75 377.7109680176 2075.6873226661 1.5776517391 7246440.5 0.6416736841
2026005.125 381.6901245117 2077.0971475462 1.6011863947 7594811.5 0.6244466305
2178958.75 385.8723449707 2079.8303202854 1.6403032541 7950137.5 0.6234526634
2267202.5 390.4251403809 2079.3824646648 1.680375576 8341610 0.6226934791
2407415.75 395.1845703125 2080.7411752891 1.7183970213 8778706 0.6108543873
2620370 400.287322998 2082.693782023 1.7499445677 9349454 0.6022925377
2721018 409.2485961914 2086.628391141 1.782071352 9851579 0.5954799652
2852279.25 419.7620239258 2086.7727865028 1.7967816591 10519205 0.5832549334
2960779 430.7332763672 2088.8935631369 1.8116134405 11129446 0.5583105087
3193507.75 442.3812255859 2091.9958655162 1.8265676498 11775585 0.5503489971
3446527.25 454.5449829102 2095.778

2123 1.8416452408 12533966 0.5142386556
3719610.25 457.6380615234 2096.9160870902 1.8568472862 13437015 0.5009073615
4019438.5 457.9736633301 2097.0965368183 1.8782007694 14486343 0.4818879068
4327360 459.0289001465 2098.121330846 1.8997995853 15729500 0.4806512892
4460933 460.8129272461 2099.6330905813 1.9216468334 16946774 0.5013304949
4811646 463.632598877 2103.5361849097 1.9437453747 18271270 0.5107861161
5220520 467.4125061035 2110.3926759065 1.9660980701 19780460 0.51054883
5494143.5 472.2144775391 2113.6245919229 1.9878915548 21474090 0.511983633
5793925 469.8310852051 2115.7138643416 2.0099265575 23179180 0.5114070773
6163931.5 483.8543395996 2117.1133162646 2.0322058201 24719094 0.5159932375
6620693.5 483.0845947266 2118.5447105505 2.0547320843 26274554 0.5157233477
7150100.5 482.7004699707 2118.8

2149 2.0775079727 27915780 0.5073056221
7740434.5 482.9890136719 2121 2.1005363464 29662402 0.5175037384
8285657 487.1909790039 2122.9406824846 2.1238200665 31536496 0.5218037367
8792705 491.0772094727 2.1473617554 33579996
9164505 497.6157226563 2.1711645126 35423260
1601438 55.0172424316 2137.2173163974 2.7995312214 5270958 0.6205279231
1676689.375 55.4775390625 2129.295431607 2.81945014 5929284
1817758.5 55.7903213501 2131.6647734032 2.8395104408 6636369
1963771.625 57.0627250671 2117.5442456874 2.8597140312 7417648
1939710.75 56.8368873596 2058.716300508 2.8800611496 8060056.5
1999678.375 56.681388855 2030.2618730075 2.9005527496 8662330
2079165.375 57.1728744507 2057.4999942859 2.9211905003 9265911
2170447.75 57.8800506592 2064.0062837923 2.9419751167 9870660
2284872.5 58.4456977844 2067.987690657 2.9629075527 10524471
2410176 59.0479393005 2072.7030528158 2.9839887619 11204698
2478084.75 59.4690170288 2073.9437605575 3.0052201748 11838825
2582395.75 59.9564323425 2068.8276275419 3.0220630169 12456723 0.6184104681
2667936.25 60.475730896 2068.6185422387 3.0390002728 13036598 0.6224491596
2761929 61.3908691406 2074.8601641942 3.0560326576 13589490 0.6260395646
2886270.75 61.5945663452 2089.2797542121 3.0731604099 14149387 0.6148210764
3037320.5 61.9119949341 2076.7673324168 3.0903842449 14728837 0.6059867144
3138357.75 62.1682090759 2078.495231861 3.107704401 15344431 0.5967991948
3286822.75 62.393787384 2083.9681701062 3.1251215935 16036288 0.5888170004
3509834.75 63.1110191345 2080.4377183727 3.142636776 16849182 0.5798812509
3680343.5 64.0283966064 2061.3616914341 3.1602499485 17704650 0.5770709515
3860412.75 65.104019165 2028.41875 3.1779618263 18591442 0.5735070705
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