Download Is Zimbabwe More Productive Than the United States? Some

ISSN 1178-2293 (Online)
University of Otago
Economics Discussion Papers
No. 1606
JUNE 2016
Is Zimbabwe More Productive Than the
United States? Some Observations
From PWT 8.1
Ayşe Imrohoroğlu, Murat Üngör
Address for correspondence:
Murat Üngör
Department of Economics
University of Otago
PO Box 56
Dunedin
NEW ZEALAND
Email: [email protected]
Telephone: 64 3 479 711
Is Zimbabwe More Productive Than the United States?
Some Observations From PWT 8.1
Ayşe İmrohoroğlu∗
Murat Üngör†
June 15, 2016
Abstract
In Penn World Table (PWT) 8.1, several developing countries stand out as outliers
with high total factor productivity (TFP) levels relative to the United States (U.S.).
For example, in 2011, Zimbabwe and Trinidad and Tobago are reported to have 3 and
1.6 times higher TFP levels than the U.S., respectively. In addition, for several other
countries, such as Turkey and Gabon, the stated levels of TFP are very similar to
that of the U.S. level (1.01 and 1.11 times the U.S. levels, respectively). Estimates
for some of these countries seem rather unlikely when compared with other measures
of productivity (such as output per worker). While in the construction of TFP levels
PWT does use country-specific factor shares we show that their results are very similar
to calculating TFP levels with a Cobb-Douglas production function where capital and
labor shares are assumed to be the same across all countries, i.e., using a constant
labor share of 2/3 for all countries. A simple modification, using a constant labor
share of 2/3 for developed countries and 1/2 for developing countries, generates more
“plausible” estimates for TFP levels.
JEL classification: O11, O40, O47
Keywords: Total factor productivity; labor income shares; Penn Tables
∗
Department of Finance and Business Economics, Marshall School of Business, University of Southern
California, Los Angeles, CA 90089-1427. E-mail address: [email protected]
†
Department of Economics, University of Otago, PO Box 56, Dunedin 9054, New Zealand. E-mail address:
[email protected]
1
Introduction
One of the most important tasks in the study of economic growth and development is understanding the causes and consequences of productivity differences across countries.1 The
Penn World Table (PWT) has been one of the core sources for reliable data for such comparisons. It provides data on gross domestic product (GDP) at purchasing power parity (PPP),
measures of relative levels of income, output, inputs, and productivity with country and
period coverage depending on the release.2 The first PWT, PWT 5.6, includes 152 countries
and territories, for the period 1950-1992. The latest PWT is the PWT 8.1, which covers 167
countries between 1950 and 2011.3 PWT 8.0 and PWT 8.1 include a variable labeled ‘ctfp,’
which reports the measured total factor productivity (TFP) series for each country relative
to the U.S. (TFP level at current PPPs, U.S.=1).
3
2
1
0
ZWE KWT MAC QAT
TTO NOR
IRL
GAB HKG SGP TWN SAU
TUR
Note: ZWE: Zimbabwe, KWT: Kuwait, MAC: China: Macao SAR, QAT: Qatar, TTO: Trinidad and Tobago,
NOR: Norway, IRL: Ireland, GAB: Gabon, HKG: China: Hong Kong SAR, SGP: Singapore, TWN: Taiwan,
SAU: Saudi Arabia, TUR: Turkey.
Figure 1: TFP levels in 2011 (relative to the U.S)
Figure 1 displays TFP levels relative to the U.S. TFP level for a number of countries
using this measure, ‘ctfp’, from PWT 8.1 for 2011. Several countries stand out with TFP
levels higher than the U.S. TFP level. For example, Zimbabwe and Trinidad and Tobago
have 3 and 1.6 times higher TFP levels than that of the U.S., respectively. In addition, TFP
levels of some other countries, such as Turkey and Gabon, are very similar to that of the
1
Many studies provide documentation of TFP levels across countries (see, for example, Islam, 1995, 2001;
Hall and Jones, 1999; Helpman, 2004; Jones and Romer, 2010; Hsieh and Klenow, 2010; Jones, 2015).
2
Although data from the PWT are widely used across the world, there is a literature questioning the
reliability of data in different versions of the PWT from several angles. See, for example, Knowles, 2001;
Dowrick, 2005; Ponomareva and Katayama, 2010; Breton, 2012, 2015; Johnson et al., 2013; Pinkovskiy and
Sala-i-Martin, 2016.
3
All versions of the PWT are available at: http://www.rug.nl/research/ggdc/data/pwt/
1
U.S. Are all the TFP levels reported in PWT 8.1 reasonable? Examining another measure
of productivity, GDP per worker, raises some questions about the reliability of the TFP
measure for some of these countries. For example, in 2011, GDP per worker in Zimbabwe
was only 25.9% of the U.S. GDP per worker, even though Zimbabwe’s TFP level was 3 times
that of the U.S.
In Figure 2, we plot measured TFP levels (ctfp) against output per worker in 2010 and
in 2011 for 111 countries using PWT 8.1.4 The correlation between the two series is 0.81 in
2010 and 0.71 in 2011. If we exclude Zimbabwe, the correlation between two series increases
to 0.89 in both 2010 and 2011. Zimbabwe is clearly an outlier in this sample.
TFP (US=1)
TFP (US=1)
(a): 2010
2
3
(b): 2011
ZWE
ZWE
MAC KWT QAT
2.5
TTO
1.5
2
MAC KWT
QAT
NOR
HKG
SGP
IRL
TWN
TUR SAU USA
BRB
1
CHE
LUX
GBR
SWE
GAB CAN
NLD
IRQ IRN DNK
AUT
BEL
FRA
AUS
PAN ISRDEU
ITA
FIN
EGY POL
NZL
ISL
ESP
CYP
BHR
ZAF
GRC
MUS
SVK
BWA
VEN
MEX
JPN
MLT
KOR
HRV
CHL
PRT
GTM
DOM
LTU
ARG
TUN
MYS
SVN
RUS
KAZ
EST
CZE
URY
HUN
BGR
PER
CRI
LVA
ROU
NAM
COL
ECU
MAR
SRB
0.5 IND
THA
JOR
BRA
TJK
MRT
PHL
CHN
CIV
IDN
FJI
LKA
SWZ
BOL
MNG
ARM
JAM
PRY
CMR
MOZ
UKR
CAF
SEN
MDA
KGZ
HND
TZA
KEN
BEN
LSO
SLE
NER
RWA
BDI
Corr = 0.8127
TGO
0
0
0.5
1
1.5
TTO
1.5
NOR
IRL
GAB
HKG
SGP
TWN
SAU
TUR
USA
1 IRQ BRB GBR
CHE
SWE
LUX
IRN CAN
NLD
AUT
PAN
DNK
FRA
BEL
DEU
AUS
FIN
ITA
POL
ISR
ISL
EGY
NZL
ESP
ZAF
VEN
MUS
SVK
MEX
CYP
JPN
GRC
BWA
HRV
KOR
CHL
MLT
BHR
ARG
LTU
GTM
DOM
PRT
MYS
KAZ
RUS
TUN
EST
URY
LVA
SVN
CZE
PER
BGR
HUN
CRI
ROU
COL
ECU
MAR
SRB
NAM
0.5 IND
TJK
THA
JOR
BRA
IDN
LKA
PHL
MNG
MRT
CHN
FJI
SWZ
BOL
ARM
CIV
PRY
UKR
JAM
CMR
MOZ
CAF
MDA
KGZ
SEN
HND
TZA
KEN
BEN
SLE
LSO
NER
RWA
Corr = 0.7058
BDI
TGO
0
0
2
GDP PER WORKER (US=1)
0.5
1
1.5
2
GDP PER WORKER (US=1)
Figure 2: Reported TFP in PWT 8.1 in 2010 and 2011
It is of course possible for some countries to have higher TFP levels than the U.S. Indeed,
several studies provide explanations for seemingly surprisingly high TFP levels in some
countries. For example, resource-rich countries such as Gabon, Kuwait, Qatar, and Saudi
Arabia are among the top countries in terms of TFP levels. The likely reason for this
observation seems to be their high productivity in oil production. According to data from
the World Bank, oil rent to GDP was 58.6% in Kuwait, 48.8% in Saudi Arabia, 45.3% in
Gabon, and 29.9% in Qatar in 2011. Oil rent to GDP was also more than 10% in Norway and
Trinidad and Tobago.5 Hall and Jones (1999) also report similar observations and subtract
4
We use the variable ctfp for TFP levels. This measure of TFP of the PWT is based on a Tornqvist index
of inputs that incorporates variations in factor shares (see Feenstra et al., 2015). This point is also mentioned
in Jones (2015). We use the variable cgdpo (output-side real GDP at current PPPs (in mil. 2005US$)) for
GDP and the variable emp (number of persons engaged (in millions)) for employment. Using these two
variables, we calculate GDP per worker (labor productivity). We use data for 111 countries, since data for
the variable ctfp are reported for these countries.
5
Oil rents are the difference between the value of crude oil production at world prices and total costs of
production. Data are from the World Bank’s World Development Indicators (World Bank, 2016).
2
the value added in the mining industry from GDP in computing their measure of output to
deal with the issue.
It is also the case that several countries stand out as outliers or extreme cases in different
studies that compare productivity levels across countries. For example, Puerto Rico stands
out as the most productive country in 1998 in Hall and Jones (1999), in which Hall and Jones
(1999) use the PWT 5.6. Hall and Jones (1999, footnote 8) note that an overstatement of
real output in Puerto Rico might be responsible for such a finding.6
However, it is not clear why countries such as Turkey or Zimbabwe have higher TFP levels
than the U.S. In this paper, we argue that the choice of the value for the shares of labor and
capital for developing versus developed countries is likely to be the culprit behind some of the
extreme TFP levels reported in PWT. While in the construction of TFP levels, PWT does use
country specific factor shares, we show that their results are very similar to calculating TFP
levels with a Cobb-Douglas production function where capital and labor shares are assumed
to be the same across all countries, i.e., using a constant labor share of 2/3 for all countries.
While Gollin (2002) argues that factor shares adjusted for self-employed income and sectoral
composition are remarkably constant across countries and Bernanke and Gürkaynak (2001)
find no systematic tendency for country labor shares to vary with per capita income, other
studies have argued that labor income shares in developing countries should be less than the
corresponding shares in developed countries (Chen et al., 2010; Izyumov and Vahaly, 2015).
There may be compelling reasons to investigate this issue further, as different factor shares
generate very different conclusions about relative TFP levels for many countries. We show
that a simple modification, using a constant labor share of 2/3 for developed countries and
1/2 for developing countries, generates more plausible estimates for differences in TFP levels
between the U.S and several developing countries, including Zimbabwe and Turkey. Overall,
we show that the level of factor shares used in these calculations makes a big impact on
the comparison of TFP levels across countries and conclude that the relative TFP measure
reported in PWT should be used with caution.
We note that our paper is silent on the trend changes in labor share. There is recent
evidence pointing to declines in the labor share of production in most OECD countries in
the last three to four decades. There has been a new emerging literature documenting and
investigating different explanations for this phenomenon (see, for example, Elsby et al., 2013;
Karabarbounis and Neiman, 2014). Some argue that these negative trends in labor share
call into question the appropriateness of a Cobb-Douglas aggregate production function,
with constant factor shares under competitive factor markets (Jayadev, 2007; Rodriguez
and Jayadev, 2010). We abstract from this issue in this paper.
The paper is organized as follows. Section 2 presents a common framework to measure
TFP levels and provides an alternative method where we treat labor income share to be
6
In addition, differences in methodology matter; according to the results of several approaches, differences
in TFP levels across countries are substantial. This case is well noted in Helpman (2004). Helpman (2004,
p. 29) compares the findings of Islam (1995) and Hall and Jones (1999) and states the following observation:
“Yet the estimated relative productivity levels differ substantially for some countries. An extreme example
is Jordan, for which Islam’s estimate is 25 percent of U.S. productivity while Hall and Jones’s estimate is
about 120 percent of U.S. productivity. These differences notwithstanding, both series of estimates show
large cross-country variations in TFP.” Islam (1995) uses an econometric approach and panel data estimation
while Hall and Jones (1999) use neoclassical assumptions about production to get the parameters.
3
different between developed and developing countries. Section 3 presents the results of
alternative methods and compares the results. Section 4 concludes.
2
A Framework for Measured TFP
2.1
A Common Method
A well-known approach to calculate TFP levels in the development literature can be summarized, á la Caselli (2005), as follows. Labor productivity is apportioned, yti , between
endowments and TFP using the following constant returns to scale Cobb-Douglas technology:
Yti = Ait Kti
αi
(hE)it
1−αi
,
(1)
where A, K, h, and α are, respectively, TFP, the stock of capital, human capital per worker,
and capital factor income share. This form is re-written in an intensive form to arrive at the
following decomposition of labor productivity:
αi 1−αi
yti = Ait kti
hit
,
(2)
where k (≡ K/E) represents capital deepening. Expressing the country i performance
relative to that of the U.S. leads to the following ratio of TFP levels:
Ait
=
AUt S
kti
αi
yti
× hit
1−αi
.
US
ktU S
αU S yt
S
× hU
t
(3)
1−αU S
Given times series for yti , kti , hit , there is a tendency of using a common value of α = 1/3 for
each country in the related literature.7 This way of calculating TFP is widely used in many
studies, such as Jones and Romer (2010), Hsieh and Klenow (2010), and Jones (2015). We
call this approach Method 1.
Method 1:
αi = αU S = 1/3.
(4)
There has been a tradition arguing that the factor shares in national income are roughly
constant over time. The reference for this argument is based on Kaldor’s stylized facts for
the United States (see Kaldor, 1961). Gollin (2002) argues that factor shares adjusted for
self-employed income and sectoral composition are remarkably constant across both time and
countries, and that the capital shares cluster around one-third. In line with Gollin (2002),
Bernanke and Gürkaynak (2001) find no systematic tendency for country labor shares to
7
PWT 8.0 and PWT 8.1 provide data on yti , kti , hit as well as αti . In previous versions, there were no
data for human capital. Data for human capital reported in PWT 8.0 and PWT 8.1 are consistent with
the previous studies, i.e., data for average years of schooling are from Barro and Lee (2013) and data for
returns to education are from Psacharopoulos (1994). Mincerian approach allows for conversion into years of
schooling. See Caselli (2005) on how to construct human capital data using information from Psacharopoulos
(1994) and Barro and Lee (2013).
4
vary with per capita income. Setting a common value of α = 1/3 for each country has been
a widely used practice in cross-country studies since then.
2.2
A Simple Modification
Method 1 rests on unification of factor shares across countries. A comparative perspective
of countries at different levels of income (developing versus developed countries) may reveal
some features of the relationship between the magnitude of the capital/labor income share
and TFP differences that cannot be observed from Method 1.
Recent cross-country studies point to the observation that factor income shares might
differ between developed and developing countries. For example, Izyumov and Vahaly (2015)
contradict the factor income share conversion hypothesis for the 1990-2008 period (using a
group of 55 developed and developing countries) and argue that a country in the middle of
development distribution will have labor share that is 10-15 percentage points below that
of a typical OECD country. Similarly, Chen et al. (2010) use 0.5 as the labor share for
developing economies, because labor is cheap compared with advanced countries, leading to
a lower labor share. Trapp (2015) discusses the challenges of measuring the labor income
share of developing countries studying developing countries from 1990 to 2011 and argues
that the average level of labor share is around 0.47. In addition to cross-country studies,
there are some country-specific studies that argue that the value of labor share parameter
in developing countries such as China and Turkey is around 0.5.8 The idea of using different
labor shares for developing countries is also supported by Young and Lawson (2014) who
study the relationship between the institutions of economic freedom and labor shares in a
panel of up to 93 countries covering 1970 through 2009. They find that countries with higher
economic freedom scores tend to have higher labor shares.9
In accordance with these discussions, we modify Method 1 as follows:


if country i is a developing country
αi = 1/2
Method 2:
(5)
αi = 1/3
if country i is a developed country


αU S = 1/3.
To implement this method, we group countries according to different levels of economic
development. First, the data of all economies are grouped into four categories of status
of economic development according to the World Bank. In the World Bank’s classification
system, economies are ranked by their levels of gross national income (GNI) per capita. These
economies are then classified as low-income countries (L), lower-middle-income countries
(LM), upper-middle-income countries (UM), and high-income countries (H). The income
group thresholds are updated annually at the beginning of the World Bank’s fiscal year with
an adjustment for inflation. This annual adjustment reflects the economies’ development
experiences. For example, China is classified as a low-income economy in 1990, a lowermiddle-income country in 2000, and an upper-middle-income country in 2010. The World
8
See Bai et al., 2006; Brandt et al., 2008; Brandt and Zhu, 2010; Zhu, 2012; Dollar and Jones, 2013 for
China; Altuğ et al., 2008; Ismihan and Metin-Özcan, 2009; and Tiryaki, 2011 for Turkey.
9
Young and Lawson (2014) report that the average labor share between 1970 and 2009 is 0.599 in Switzerland and 0.163 in Niger.
5
Bank reports the following thresholds in 2010:10 Low-income countries are defined as having
a per capita GNI in 2010 of $1,005 or less; lower-middle-income countries have incomes
between $1,006 and $3,975; upper-middle-income economies have incomes between $3,976
and $12,275; and high-income economies have incomes $12,276 or more. Second, equipped
with the World Bank classification at hand, we group the countries under the categories of
(i) low-income economies, (ii) lower-middle-income economies, and (iii) upper-middle-income
economies as developing countries. Therefore, we set α = 1/2 for these countries. We treat
high-income economies as developed countries and set α = 1/3 for these countries.11
PWT 8.1 provides data on yti , kti , and hit . We use the variable cgdpo (output-side real
GDP at current PPPs (in mil. 2005US$)) for GDP and the variable emp (number of persons
engaged (in millions)) for employment. Using these two variables, we calculate GDP per
worker (labor productivity) for each country. We use the variable ck (capital stock at
current PPPs (in mil. 2005US$)) for kti ; and we use the variable hc (index of human capital
per person, based on years of schooling and returns to education) for hit . To provide the
results for Method 1, we set α = 1/3 for all the countries in the sample while for Method 2
we set α = 1/3 for each developed country and α = 1/2 for each developing country in the
sample.
3
Results and Comparison of Methods
It is difficult to assess whether or not a particular measure results in a “reasonable” comparison of TFP levels across countries. Here, we provide some suggestive evidence. In Figure 3,
we display TFP levels obtained from the two different methods we employ as well as the PWT
measure, ctfp, for a select number of countries. First, we compare the relative TFP levels
obtained using Method 1 with the data provided in PWT. As mentioned before, PWT uses
country-and time-specific factor shares to generate the country-specific TFP levels. Their
measure, however, is very similar to the TFP measures we obtain by using the same factor
shares for both developed and developing countries (where α = 1/3). For Zimbabwe, for
example, PWT reports a TFP level that is 3 times that of the U.S. Using Method 1, we
obtain Zimbabwe’s TFP level to be twice as high as that of the U.S. On the other hand,
Method 2 generates fairly different TFP levels. For example, using Method 2, we obtain a
TFP level for Zimbabwe that is 60% of the U.S. level.12 Similar observations can be made
for Turkey, Trinidad and Tobago, and Gabon. In all these cases, Method 1 delivers results
very similar to what is reported in PWT. Method 2, on the other hand, yields significantly
10
Historical classifications by income are available at: siteresources.worldbank.org/DATASTATISTICS/
Resources/OGHIST.xls
11
According to our classification, there are 43 developed countries and 68 developing countries in our
111-country sample.
12
It is also possible that due to some of the macroeconomic changes in Zimbabwe in recent years, reporting
of economic data may not have been reliable. This might have been especially the case after inflation peaked
at an astounding monthly rate of 79.6 billion percent in mid-November 2008 (Hanke and Kwok, 2009; Hanke
and Krus, 2012). In 2009, Zimbabwe started to adopt the U.S. dollar and became fully dollarized by law.
Between 2009 and 2011, Zimbabwe’s GDP growth averaged close to 10%, making it one of the world’s
fastest-growing countries, after recording a growth rate that was around minus 18% in 2008 (World Bank,
2016).
6
lower TFP levels relative to the U.S.
(a): Zimbabwe
(b): Turkey
3.00
2.50
2.00
1.25
Penn
Method 1
Method 2
1.00
0.75
1.50
0.50
1.00
0.25
0.50
0
0
2011 2010 2000 1990 1980 1970 1960
2011 2010 2000 1990 1980 1970 1960
(c): Trinidad and Tobago
(d): Gabon
2.00
1.75
1.75
1.50
1.50
1.25
1.25
1.00
1.00
0.75
0.75
0.50
0.50
0.25
0.25
0
0
2011 2010 2000 1990 1980 1970 1960
2011
2010
2000
1990
1980
Figure 3: TFP levels in selected countries (relative to the U.S)
(a): Brazil
(b): Russia
0.6
0.7
0.5
0.6
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0
0.1
0
2011 2010 2000 1990 1980 1970 1960
2011
2010
2000
1990
Penn
Method 1
Method 2
(c): China
(d): India
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
2011 2010 2000 1990 1980 1970 1960
2011 2010 2000 1990 1980 1970 1960
Figure 4: TFP levels in BRIC countries (relative to the U.S)
7
We have similar observations for the BRIC countries (Brazil, Russia, India, and China),
the group of emerging markets that encompass significant shares of the world’s land coverage,
population, and GDP (see Figure 4). For China, for 2011, PWT reports a TFP level that is
41% of the U.S. Using Method 1, we obtain China’s TFP level that is 34% of the U.S. On
the other hand, using Method 2, we obtain a TFP level for China that is 6% of the U.S. level
only. It is worth noting that a careful examination of the data leads Bai et al., (2006) to
estimate the labor share of income to be α = 1/2 for China. That is the value of the labor
share used in Method 2. Similar observations are made for Brazil, Russia, and India. In
all these cases, Method 1 delivers results very similar to what is reported in PWT, whereas
Method 2 generates significantly lower TFP levels relative to the U.S.
Next, we examine the time series for the TFP levels for these countries for the different
measures. Figure 5 shows the three different TFP levels as well as the GDP per worker for
these countries. All the series reported are relative to the U.S. All three TFP series are highly
correlated with each other. For example, the correlation between ctfp and TFP (Method 1 )
is 0.96 for Turkey. The corresponding correlation between ctfp and TFP (Method 2 ) is 0.94.
However, differences in TFP levels are striking. Reported TFP series in PWT 8.1 are higher
than those of our calculations for each method. For Turkey, in 2011, the value of ctfp is 1.01;
the value of TFP (Method 1 ) is 0.93; and the value of TFP (Method 2 ) is 0.15. Turkey’s
GDP per worker is 51% of the U.S. in 2011. Depending on the measure used, Turkey is
either more productive than the U.S. or significantly less productive than the U.S.
(a): Zimbabwe (1960-2011)
3.0
2.5
2.0
1.5
1.0
0.5
0
1950
(b): Turkey (1950-2011)
1.2
TFP (PENN)
TFP (α=1/3)
TFP (α=1/2)
GDP per worker
0.9
0.6
0.3
1971
1992
0
1950
2013
(c): Trinidad and Tobago (1960-2011)
1992
2013
(d): Gabon (1980-2011)
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0
1950
1971
1971
1992
0
1950
2013
1971
1992
2013
Figure 5: Productivity levels in selected countries
In Figure 6, we repeat the same exercise for the BRIC countries. Similar to the findings
before, TFP levels obtained with Method 1 yield results very similar to the data provided
by PWTs. Method 2, on the other hand, generates substantially lower levels of TFP and is
much closer to the GDP per worker measure provided for these countries.
8
(a): Brazil (1950-2011)
(b): Russia (1990-2011)
0.9
0.9
0.6
0.6
0.3
0.3
0
1950
1971
1992
0
1950
2013
(c): China (1952-2011)
0.9
0.6
0.6
0.3
0.3
1971
1992
1992
2013
(d): India (1960-2011)
0.9
0
1950
1971
TFP (PENN)
TFP (α=1/3)
TFP (α=1/2)
GDP per worker
0
1950
2013
1971
1992
2013
Figure 6: Productivity levels in BRIC countries
It may also be informative to look at the correlations between different TFP measures
and output per worker for all the countries in PWT. In Figure 7, we provide three scatter
plots of GDP per capita versus a particular TFP measure for the year 2010. Panels (a), (b)
and (c) use the TFP measures obtained from PWT, Method 1, and Method 2 respectively.
The observations we have summarized using a select number of countries, remains to be
valid when we examine the entire set of countries. The scatter plots using PWT data and
Method 1 yield similar results. Correlation between TFP and output per capita is higher
under Method 2. This method also generates a larger number of low TFP observations.13
(a): PENN
(b): Method 1
2
2
TTO
1.5
MAC
NOR
HKG
SGP
IRL
TWN
TUR GBR
BRB
USA
SAU
CHE
LUX
SWE
GAB
CAN
NLD
IRQ
AUT
IRN
BEL
DNK
FRA
PAN
AUS
ITA
DEU
FIN
ISR
EGY
NZL
POL
ISL
ESP
CYP
BHR
ZAF
GRC
MUS
SVK
BWA
VEN
MEX
JPN
MLT
KOR
HRV
CHL
PRT
GTM
DOM
LTU
ARG
TUN
MYS
SVN
RUS
KAZ
EST
CZE
URY
HUN
BGR
PER
CRI
LVA
ROU
NAM
COL
ECU
MAR
SRB
THA
IND
0.5CIV
JOR
BRA
TJK
MRT
PHL
CHN
IDN
Corr = 0.8127
FJI
LKA
SWZ
BOL
MNG
ARM
JAM
PRY
CMR
MOZ
UKR
CAF
SEN
MDA
KGZ
HND
TZA
BEN
KEN
LSO
SLE
NER
RWA
BDI
TGO
1
0
0
0.5
KWT QAT
MAC KWT QAT
TFP (US=1)
TFP (US=1)
ZWE
1
1.5
1.5
NOR
SGP
HKG
TTO
IRL
BRB SAU
TWN
LUX
USA
CHE
GBR
TURCAN
SWE
NLD
AUT
BEL
DNK
ITA
FRA
FIN
AUS
DEU
ISL
GAB
ISR
ESP
CYP
POL
NZL
GRC
IRN
JPN
BHR
SVK
MLT
HRV
KOR
PRT
PAN
ZAF
VEN
MUS
LTU
SVN
IRQ
MEX
ARG
MYS
CHL
EST
RUS
CZE
EGY
TUN
BWA
HUN
DOM
LVA
URY
KAZ
CRI
ROU
BGR
0.5 GTM
SRB
NAM
PER
COL
JOR
MAR
BRA
Corr = 0.9236
THA
LKA
ECU
SWZ
IND
ARM
MRT
CHN
IDN
MNG
FJI
PHL
UKR
JAM
PRY
CIV
TJK
BOL
MDA
CMR
HND
KEN
KGZ
BEN
LSO
SEN
SLE
RWA
MOZ
NER
TZA
TGO
BDI
CAF
ZWE
1
0
0
2
GDP per worker (US=1)
0.5
1
1.5
GDP per worker (US=1)
(c): Method 2
2
KWT QAT
TFP (US=1)
MAC
1.5
NOR
SGP
HKG
IRL
BRB SAU
TWN
LUX
USA
CHE
GBR
SWE
NLD
AUT
BEL
CAN
DNK
ITA
FRA
FIN
AUS
DEU
ISL
ISR
ESP
CYP
POL
NZL
GRC
JPN
BHR
SVK
MLT
HRV
KOR
PRT
SVN
EST
CZE
HUN
1
0.5
ZWE
TTO
TUR
GAB
PAN
IRQ
ZAF
IRN
EGY
MUS
VEN
MEX
CHL
LTU
GTM
MYS
RUS
TUN
BWA
CRI
DOM
ARG
KAZ
URY
NAM
BGR
LVA
ROU
MAR
LKA
IND
C
PER
OL
SRB
BRA
CIV
JOR
THA
ECU
ARM
FJI
SWZ
MRT
IDN
JAM
PHL
PRY
CHN
BOL
CMR
TJK
KEN
MNG
UKR
RWA
MOZ
HND
KGZ
MDA
SLE
BEN
LSO
SEN
TZA
TGO
BDI
NER
CAF
0
0
0.5
Corr = 0.9398
1
1.5
2
GDP per worker (US=1)
Figure 7: TFP and GDP per worker levels in 2010
13
See Appendix A for a summary of this data for all decades since 1960.
9
2
We summarize the frequency distribution of productivity levels (relative to the U.S.)
of 111 countries in 2011 in Figure 8. Panel (a) in Figure 8 displays that there are 17
countries that have output per worker levels less than or equal to 10% of the U.S. output per
worker level in 2011. There are no countries in this region based on PWT data or Method
1 as summarized in panels (b) and (c). However, with Method 2 (panel (d)), there are 48
countries that have TFP levels less than or equal to 10% of the U.S. TFP level in 2011. In
panel (d), with Method 2, there are 66 countries that have TFP levels less than (or equal
to) 20% of the U.S. TFP level in 2011. On the other hand, according to the reported series
in PWT, there are only two countries (Burundi and Togo) with TFP levels less than 20% of
the U.S. TFP level in 2011. The corresponding number of countries is 8 if Method 1 is used
(Central Africa, Burundi, Togo, Tazmania, Niger, Mozambique, Senegal, and Lesotho).
(a): Output per worker
(b): TFP (Penn data)
40
Number of countries
Number of countries
40
30
20
10
0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
30
20
10
0
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3
2
Relative to the U.S.
Relative to the U.S.
(c): TFP (Method 1)
(d): TFP (Method 2)
70
Number of countries
Number of countries
35
30
25
20
15
10
5
0
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6 1.8
2
60
50
40
30
20
10
0
0
2.2
Relative to the U.S.
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6 1.8
2
2.2
Relative to the U.S.
Figure 8: Frequency distribution of productivity levels in 2011
Method 2, in most of the cases, results in lower estimates of TFP levels. It is, however,
difficult to know what kind of a frequency distribution is “reasonable”. Perhaps Method
2 results in too many countries with very low TFP levels compared to the U.S. What we
present simply provides evidence that TFP level comparisons are highly influenced by the
factor shares used. Among the countries examined in this paper, we argue that the detailed
studies conducted for Turkey and China do provide a more accurate calculation for their
factor shares. The resulting TFP levels for these countries are significantly different from
the ones in PWT and more similar to the ones obtained with Method 2.
Lastly, the simple average of TFP levels for 111 countries in 2011 is 0.41 if Method 2 is
employed. The corresponding average is 0.69 in PWT data and 0.64 if Method 1 is employed.
Figure 9 shows the unweighted averages for 68 developing countries in 2011. For example,
10
unweighted average of TFP levels for 68 developing countries in 2011 is 0.09 if Method 2 is
employed. The corresponding average is 0.55 in PWT data and 0.46 if Method 1 is employed.
0.6
0.5
0.4
0.3
0.2
0.1
0
GDP per worker Penn
Method 1
Method 2
Figure 9: Unweighted averages for 68 developing countries in 2011
Overall, we conclude that the relative TFP measure reported in PWT should be used
with caution. Differences in TFP measures obtained in the methods we report highlight the
importance of the factor shares used in these calculations. For country-specific studies, we
recommend a detailed analysis of the factor shares.
4
Concluding Remarks
One of the most important findings of the development accounting literature is that differences in measured TFP explain more than half of the cross-country differences in output per
worker (Jones and Romer, 2010). The Penn World Table is the most widely used source for
cross-country comparisons for these development and growth accounting procedures. Therefore, studying the data reported in the PWT is important. In PWT 8.1, which is the latest
version of the Penn World Table, several developing countries stand out as outliers, with
high TFP levels, relative to the U.S. Estimates for some of these countries seem rather unlikely when compared with other measures of productivity (such as output per worker). In
this paper, we argue that the measure used for the share of labor and capital for developing
versus developed countries is likely to be the culprit behind some of unexpected TFP calculations in PWT. While in the construction of TFP levels, PWT does use country specific
factor shares, we show that their results are very similar to calculating TFP levels with a
Cobb-Douglas production function where capital and labor shares are assumed to be the
same across all countries.
11
A simple modification, using constant labor share of 2/3 for developed countries and 1/2
for developing countries, generates more plausible estimates for differences in TFP levels
between the U.S and several developing countries. Measurement of factor income shares at
the country-specific level is a demanding task considering the data problems that are wellcited, especially in developing countries. For example, as discussed widely in Gollin (2002),
additional imputations of the labor income of self-employed and family workers should be
made to adjust for the underestimation of the labor income share in the National Accounts
Statistics. However, the number of careful studies at the country level has been increasing
(see, for example, Bai et al., 2006; Bai and Qian, 2010 for China) that will make it possible
to generate more accurate TFP comparisons across countries.
We argue that reported TFP levels in Penn World Tables should be used with caution,
considering the possible measurement issues. We have dealt with a particular aspect of measurement problem here in a very stylized manner. We are aware of the fact there are more
significant and bigger measurement problems for TFP levels, since cross-country differences
in GDP per worker, physical capital, and human capital contribute to cross-sectional gaps
in TFP levels. First, overstating of output in national income accounts is a source of measurement issues (see the discussion on Puerto Rico in Baumol and Wolff, 1996; Hall and
Jones, 1999).14,15 Second, it is well known in the literature that some countries, because
of government inefficiency and corruption, might be systematically overestimating the increase in physical capital taking place each period (see, for example, Prichett, 2000; Hsieh,
2002). Third, recent literature has incorporated the quality-adjusted measures of human
capital into the growth accounting analyses, i.e., the knowledge and skills of the population
as proxied by the consistent international test scores such as the Programme for International Student Assessment (PISA) (see Caselli, 2015 and Cubas et al., 2016 for a recent
cross-country quantitative analysis concerning such issues).16 Cubas et al. (2016) argue that
the quality of labor in rich countries is about twice as large as the quality in poor countries,
and this results smaller disparities in TFP levels compared to those obtained from growth
models using a Mincerian measure of labor quality. These are some of the possible avenues
for future empirical work on productivity measurement to understand TFP differences across
countries.
14
Another important measurement issue is the relationship between intangible investment/capital and
measured productivity since the inclusion/exclusion of intangibles (such as research and development, software, brands, etc.) changes the total output and the related calculations (see Corrado et al., 2009; McGrattan
and Prescott, 2010).
15
GDP revisions, especially in poor countries, have been a constant topic of discussion in recent years. It
is reported in The Economist (2016) that “Nigeria’s GDP was revised up by 89% in 2014. Later that year,
Kenya’s GDP was revised up by 25%. Ghana’s GDP had been upgraded by 60% in 2010.”
16
Providing a coherent criticism of measuring a nation’s human capital by school attainment, Hanushek
and Woessmann (2015) argue that internationally comparable measures of cognitive skills correlate highly
with economic growth, and cognitive skills can explain away large differences in growth rates between world
regions. Jones (2014) argues that ignoring complementarities and scarcity among human capital types would
understate human variation; and implementing an accounting with generalized human capital (i.e., allowing
workers to provide differentiated services) may account for the large income variations between rich and poor
countries.
12
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Appendix A
Countries (and the three-letter country codes) for 1990, 2000, 2010, and 2011 are: (1)
Argentina (ARG), (2) Armenia (ARM), (3) Australia (AUS), (4) Austria (AUT), (5) Burundi
(BDI), (6) Belgium (BEL), (7) Benin (BEN), (8) Bulgaria (BGR), (9) Bahrain (BHR),
(10) Bolivia (BOL), (11) Brazil (BRA), (12) Barbados (BRB), (13) Botswana (BWA), (14)
Central African Republic (CAF), (15) Canada (CAN), (16) Switzerland (CHE), (17) Chile
(CHL), (18) China, People’s Republic of (CHN), (19) Côte d’Ivoire (CIV), (20) Cameroon
(CMR), (21) Colombia (COL), (22) Costa Rica (CRI), (23) Cyprus (CYP), (24) Czech
Republic (CZE), (25) Germany (DEU), (26) Denmark (DNK), (27) Dominican Republic
(DOM), (28) Ecuador (ECU), (29) Egypt (EGY), (30) Spain (ESP), (31) Estonia (EST), (32)
Finland (FIN), (33) Fiji (FJI), (34) France (FRA), (35) Gabon (GAB), (36) United Kingdom
(GBR), (37) Greece (GRC), (38) Guatemala (GTM), (39) China: Hong Kong SAR (HKG),
(40) Honduras (HND), (41) Crotia (HRV), (42) Hungary (HUN), (43) Indonesia (IDN), (44)
India (IND), (45) Ireland (IRL), (46) Iran (Islamic Republic of) (IRN), (47) Iraq (IRQ),
(48) Iceland (ISL), (49) Israel (ISR), (50) Italy (ITA), (51) Jamaica (JAM), (52) Jordan
(JOR), (53) Japan (JPN), (54) Kazakhstan (KAZ), (55) Kenya (KEN), (56) Kyrgyzstan, (57)
Republic of Korea (KOR), (58) Kuwait (KWT), (59) Sri Lanka (LKA), (60) Lesotho (LSO),
(61) Lithuania (LTU), (62) Luxembourg (LUX), (63) Latvia (LVA), (64) China: Macao SAR
(MAC), (65) Morocco (MAR), (66) Republic of Moldova (MDA), (67) Mexico (MEX), (68)
Malta (MLT), (69) Mongolia (MNG), (70) Mozambique (MOZ), (71) Mauritania (MRT),
(72) Mauritius (MUS), (73) Malaysia (MYS), (74) Namibia (NAM), (75) Niger (NER), (76)
Netherlands (NLD), (77) Norway (NOR), (78) New Zealand (NZL), (79) Panama (PAN),
(80) Peru (PER), (81) Philippines (PHL), (82) Poland (POL), (83) Portugal (PRT), (84)
Paraguay (PRY), (85) Qatar (QAT), (86) Romania (ROM), (87) Russian Federation (RUS),
(88) Rwanda (RWA), (89) Saudi Arabia (SAU), (90) Senegal (SEN), (91) Singapore (SGP),
(92) Sierra Leone (SLE), (93) Serbia (SRB), (94) Slovakia (SVK), (95) Slovenia (SVN),
(96) Sweden (SWE), (97) Swaziland (SWZ), (98) Togo (TGO), (99) Thailand (THA), (100)
16
Tajikistan (TJK), (101) Trinidad and Tobago (TTO), (102) Tunisia (TUN), (103) Turkey
(TUR), (104) Taiwan (TWN), (105) United Republic of Tanzania: Mainland (TZA), (106)
Ukraine (UKR), (107) Uruguay (URY), (108) United States (USA), (109) Venezuela (VEN),
(110) South Africa (ZAF), (111) Zimbabwe (ZWE).
Countries (and the three-letter country codes) for 1980 are: (1) Argentina (ARG), (2)
Australia (AUS), (3) Austria (AUT), (4) Burundi (BDI), (5) Belgium (BEL), (6) Benin
(BEN), (7) Bulgaria (BGR), (8) Bahrain (BHR), (9) Bolivia (BOL), (10) Brazil (BRA), (11)
Barbados (BRB), (12) Botswana (BWA), (13) Central African Republic (CAF), (14) Canada
(CAN), (15) Switzerland (CHE), (16) Chile (CHL), (17) China (CHN), (18) Côte d’Ivoire
(CIV), (19) Cameroon (CMR), (20) Colombia (COL), (21) Costa Rica (CRI), (22) Cyprus
(CYP), (23) Germany (DEU), (24) Denmark (DNK), (25) Dominican Republic (DOM), (26)
Ecuador (ECU), (27) Egypt (EGY), (28) Spain (ESP), (29) Finland (FIN), (30) Fiji (FJI),
(31) France (FRA), (32) Gabon (GAB), (33) United Kingdom (GBR), (34) Greece (GRC),
(35) Guatemala (GTM), (36) China: Hong Kong SAR (HKG), (37) Honduras (HND), (38)
Hungary (HUN), (39) Indonesia (IDN), (40) India (IND), (41) Ireland (IRL), (42) Iran (Islamic Republic of), (43) Iraq (IRQ), (44) Iceland (ISL), (45) Israel (ISR), (46) Italy (ITA),
(47) Jamaica (JAM), (48) Jordan (JOR), (49) Japan (JPN), (50) Kenya (KEN), (51) Republic of Korea (KOR), (52) Kuwait (KWT), (53) Sri Lanka (LKA), (54) Lesotho (LSO), (55)
Luxembourg (LUX), (56) China: Macao SAR (MAC), (57) Morocco (MAR), (58) Mexico
(MEX), (59) Malta (MLT), (60) Mongolia (MNG), (61) Mozambique (MOZ), (62) Mauritania (MRT), (63) Mauritius (MUS), (64) Malaysia (MYS), (65) Namibia (NAM), (66)
Niger (NER), (67) Netherlands (NLD), (68) Norway (NOR), (69) New Zealand (NZL), (70)
Panama (PAN), (71) Peru (PER), (72) Philippines (PHL), (73) Poland (POL), (74) Portugal (PRT), (75) Paraguay (PRY), (76) Qatar (QAT), (77) Romania (ROM), (78) Rwanda
(RWA), (79) Saudi Arabia (SAU), (80) Senegal (SEN), (81) Singapore (SGP), (82) Sierra
Leone (SLE), (83) Sweden (SWE), (84) Swaziland (SWZ), (85) Togo (TGO), (86) Thailand
(THA), (87) Trinidad and Tobago (TTO), (88) Tunisia (TUN), (89) Turkey (TUR), (90)
Taiwan (TWN), (91) United Republic of Tanzania: Mainland (TZA), (92) Uruguay (URY),
(93) United States (USA), (94) Venezuela (VEN), (95) South Africa (ZAF), (96) Zimbabwe
(ZWE).
Countries (and the three-letter country codes) for 1970 are: (1) Argentina (ARG), (2)
Australia (AUS), (3) Austria (AUT), (4) Belgium (BEL), (5) Bulgaria (BGR), (6) Bahrain
(BHR), (7) Bolivia (BOL), (8) Brazil (BRA), (9) Barbados (BRB), (10) Canada (CAN), (11)
Switzerland (CHE), (12) Chile (CHL), (13) China (CHN), (14) Côte d’Ivoire (CIV), (15)
Cameroon (CMR), (16) Colombia (COL), (17) Costa Rica (CRI), (18) Cyprus (CYP), (19)
Germany (DEU), (20) Denmark (DNK), (21) Dominican Republic (DOM), (22) Ecuador
(ECU), (23) Egypt (EGY), (24) Spain (ESP), (25) Finland (FIN), (26) France (FRA), (27)
United Kingdom (GBR), (28) Greece (GRC), (29) Guatemala (GTM), (30) China: Hong
Kong SAR (HKG), (31) Honduras (HND), (32) Hungary (HUN), (33) Indonesia (IDN), (34)
India (IND), (35) Ireland (IRL), (36) Iran (Islamic Republic of) (IRN), (37) Iraq (IRQ), (38)
Iceland (ISL), (39) Israel (ISR), (40) Italy (ITA), (41) Jamaica (JAM), (42) Jordan (JOR),
(43) Japan (JPN), (44) Kenya (KEN), (45) Republic of Korea (KOR), (46) Kuwait (KWT),
(47) Sri Lanka (LKA), (48) Luxembourg (LUX), (49) Morocco (MAR), (50) Mexico (MEX),
(51) Malta (MLT), (52) Mozambique (MOZ), (53) Malaysia (MYS), (54) Niger (NER), (55)
Netherlands (NLD), (56) Norway (NOR), (57) New Zealand (NZL), (58) Panama (PAN),
17
(59) Peru (PER), (60) Philippines (PHL), (61) Poland (POL), (62) Portugal (PRT), (63)
Paraguay (PRY), (64) Qatar (QAT), (65) Romania (ROM), (66) Saudi Arabia (SAU), (67)
Senegal (SEN), (68) Singapore (SGP), (69) Sweden (SWE), (70) Thailand (THA), (71)
Trinidad and Tobago (TTO), (72) Tunisia (TUN), (73) Turkey (TUR), (74) Taiwan (TWN),
(75) United Republic of Tanzania: Mainland (TZA), (76) Uruguay (URY), (77) United States
(USA), (78) Venezuela (VEN), (79) South Africa (ZAF), (80) Zimbabwe (ZWE).
Countries (and the three-letter country codes) for 1960 are as follows: (1) Argentina
(ARG), (2) Australia (AUS), (3) Austria (AUT), (4) Belgium (BEL), (5) Bolivia (BOL),
(6) Brazil (BRA), (7) Barbados (BRB), (8) Canada (CAN), (9) Switzerland (CHE), (10)
Chile (CHL), (11) China (CHN), (12) Côte d’Ivoire (CIV), (13) Cameroon (CMR), (14)
Colombia (COL), (15) Costa Rica (CRI), (16) Cyprus (CYP), (17) Germany (DEU), (18)
Denmark (DNK), (19) Dominican Republic (DOM), (20) Ecuador (ECU), (21) Egypt (EGY),
(22) Spain (ESP), (23) Finland (FIN), (24) France (FRA), (25) United Kingdom (GBR),
(26) Greece (GRC), (27) Guatemala (GTM), (28) China: Hong Kong SAR (HKG), (29)
Indonesia (IDN), (30) India (IND), (31) Ireland (IRL), (32) Iran (Islamic Republic of) (IRN),
(33) Iceland (ISL), (34) Israel (ISR), (35) Italy (ITA), (36) Jamaica (JAM), (37) Jordan
(JOR), (38) Japan (JPN), (39) Kenya (KEN), (40) Republic of Korea (KOR), (41) Sri Lanka
(LKA), (42) Luxembourg (LUX), (43) Morocco (MAR), (44) Mexico (MEX), (45) Malta
(MLT), (46) Mozambique (MOZ), (47) Malaysia (MYS), (48) Niger (NER), (49) Netherlands
(NLD), (50) Norway (NOR), (51) New Zealand (NZL), (52) Peru (PER), (53) Philippines
(PHL), (54) Portugal (PRT), (55) Romania (ROM), (56) Senegal (SEN), (57) Singapore
(SGP), (58) Sweden (SWE), (59) Thailand (THA), (60) Trinidad and Tobago (TTO), (61)
Tunisia (TUN), (62) Turkey (TUR), (63) Taiwan (TWN), (64) United Republic of Tanzania:
Mainland (TZA), (65) Uruguay (URY), (66) United States (USA), (67) Venezuela (VEN),
(68) South Africa (ZAF), (69) Zimbabwe (ZWE).
18
(a): 2010
ZWE
1.5
MAC KWT QAT
TTO
NOR
HKG
SGP
IRL
TWN
TURGBR
BRB
USA
SAU
CHE
LUX
SWE
GAB
CAN
NLD
IRQPAN
AUT
IRN
BEL
DNK
FRA
AUS
ITA
DEU
FIN
ISR
EGY
POL
NZL
ISL
ESP
CYP
BHR
ZAF
MUS
GRC
SVK
BWA
VEN
MEX
JPN
MLT
KOR
HRV
CHL
PRT
GTM
DOM
LTU
ARG
TUN
MYS
SVN
RUS
KAZ
EST
CZE
URY
HUN
BGR
PER
CRI
LVA
ROU
NAM
COL
ECU
MAR
SRB
THA
IND
JOR
0.5MOZ
BRA
TJK
MRT
PHL
CHN
CIV
IDN
FJI
LKA
Corr = 0.8127
SWZ
BOL
MNG
ARM
JAM
PRY
CMR
UKR
CAF
SEN
MDA
KGZ
HND
TZA
BEN
KEN
LSO
SLE
NER
RWA
BDI
TGO
1
0
0
0.5
1
1.5
1.5
KWT
IRLNOR
LUX
BRBGBR
TWN
SGP
HKG
MAC
SAU
BEL
FRA
SWE
ITA
USA
CAN
AUT
NLD
1 EGY
DNK
FIN
CHE
TURBHR
TTO
ESP
ISR
IRQGAB
DEU
MLT
AUS
ISL
NZL
GRC
CYP
MUS
JPN
PRT
KOR
MEX
PAN
ZWE
GTM
ZAF
TUN
LTU
BWA
HRV
CHL
CRI
SVN
POL
URY
HUN
CZE
SVK
IRN
DOM
MYS
EST
ARG
VEN
LVA
NAM
COL
SWZ
BGR
BRA
FJI
0.5CMR
MAR
PHL
ECU
PER
Corr = 0.9154
IDN
JAM
THA
SRB
JOR
CIV
MRT
IND
ROU
HND
LKA
PRY
BOL
CHN
SEN
KAZ
RUS
MNG
CAF
BEN
KEN
MOZ
ARM
LSO
KGZ
UKR
SLE
TJK
NER
MDA
RWA
TZA
TGO
BDI
0
0
2
1.5
(c): 1990
(d): 1980
4
0
0
0.5
1
TFP (US=1)
2
TTO
BHR
GAB
IRQ
MEX
ZAF
FRA
ITA
HKG
TWN
SGP
AUT
MAC
CHE
ESP
BEL
DEU
CAN
USA
ISL
GBR
NLD
TUN
GTM
ISR
1EGY
JOR
LUX
CHL
NOR
AUS
VEN
CRI
URY
MUS
NAM
COL
PAN
PRT
JPN
MAR
TUR
DNK
IRL
ECU
GRC
SWE
FIN
PRY
BRB
MYS
IRN
BRA
CYP
MLT
NZL
DOM
FJI
PHL
PER
BWA
HND
SLE
CIV
ZWE
IDN
HUN
MRT
THA
KOR
RWA
BGR
SWZ
BEN
JAM
BOL
ARG
POL
SEN
CMR
MNG
ROU
NER
KEN
TZA
CHN
MOZ
LKA
CAF
IND
TGO
LSO
BDI
GDP per worker (US=1)
1.5
KWT
6
4
QAT
Corr = 0.9509
2
1
2
3
4
(f): 1960
8
BHR
TTO
SAU
BRB
ZAF
MEX
VEN
CHE
LUX
FRA
IRN
CRI
AUT
CAN
HKG
EGY
URYUSA
E
SP
ISR
GTM
JOR
CHL
TWN
SGP
ITA
TUR
PAN
MAR
IRQISL
PRT
SWE
BEL
TUN
DEU
DNK
AUS
NLD
GBR
IRL
GRC
DOM
JAM
JPN
FIN
NZL
CIV
PRY
PER
COL
BRA
HND
NOR
ECU
CYP
ZWE
MLT
PHL
MYS
ARG
POL
SEN
THA
NER
BOL
LKA
IDN
BGR
KOR
HUN
TZA
CHN
CMR
IND
MOZ
KEN
ROU
Corr = 0.9050
GDP per worker (US=1)
(e): 1970
2
2
KWT
SAU QAT
3
0
0
1.5
TFP (US=1)
TFP (US=1)
1
GDP per worker (US=1)
QAT
MAC
TWN
HKG
SAU
GABBHR
BRB KWT
FRA
ITALUX
LTU
TURTTO
USA
1 EGY MUS
SGP
ESP
GBR
CHE
BEL
ISR
DEU
ZAFHRV
AUT
ISL
JPN
NLD
NCAN
OR
ZWE
IRQ
BWA
IRL
MEX
AUS
PAN
DNK
KAZ
TUN
SWE
GTM
FIN
CZE
NZL
CYP
CRI
KOR
PRT
GRC
TJK
COL
MLT
LVA
RUS
CHL
SVK
SVN
VEN
SWZ
MAR
KGZ
JOR
SLE
NAM
URY
MYS
FJI
DOM
EST
HND
IDN
HUN
THA
ECU
PRY
MNG
PHL
BGR
IRN
BRA
UKR
0.5 SEN
JAM
CMR
SRB
CIV
ARG
POL
MDA
MRT
PER
RWA
BOL
CAF
KEN
CHN
BEN
LKA
IND
ARM
ROU
LSO
MOZ
NER
BDI
TZA
TGO
Corr = 0.8003
TFP (US=1)
0.5
GDP per worker (US=1)
1.5
0
0
QAT
2
TFP (US=1)
TFP (US=1)
2
(b): 2000
4
HKG
BRB
JOR URY
MEX
CHE
ZAF
CHL
IRN
LUX
CANUSA
AUT
VEN
CRI
DOM FRA
GTM
ISL
ISR
TUR
ESP
DNK
GBR
NLD
AUS
SWE
NZL
EGYTWN
DEU
BEL
JAM
IRL
ITA
TUNPRT
FIN NOR
COL
MAR
ECU
PER
JPN
CIV
PHL
IDN
ARG
0.5CHN
ZWE
BRA
LKA
SEN
SGP
MYS
GRC
BOL
CYP
NER
CMR
KOR
IND
KEN
TZA
THA
MOZ
Corr = 0.7355
ROU
MLT
1
0
0
6
GDP per worker (US=1)
TTO
0.5
1
GDP per worker (US=1)
Figure A.1: TFP and GDP per worker: Data Reported in PWT 8.1
19
1.5
(b): 2000
(a): 2010
MAC
1.5
NOR
ZWE TTO SGP
HKG
IRL
BRB GBR
TWN
LUX
USA
SAU
CHE
TURCAN
SWE
NLD
AUT
BEL
ITA
DNK
FRA
FIN
AUS
DEU
ISL
GAB
ISR
ESP
CYP
POL
NZL
IRN
GRC
JPN
BHR
SVK
MLT
HRV
KOR
PRT
PAN
ZAF
VEN
MUS
LTU
SVN
MEX
IRQ
ARG
MYS
CHL
EST
RUS
CZE
EGY
TUN
BWA
HUN
DOM
LVA
URY
KAZ
CRI
ROU
GTM
BGR
SRB
0.5MRT
NAM
PER
COL
JOR
MAR
BRA
Corr = 0.9236
THA
LKA
ECU
SWZ
IND
ARM
CHN
MNG
IDN
FJI
PHL
UKR
JAM
PRY
CIV
TJK
BOL
MDA
CMR
HND
KEN
KGZ
LSO
BEN
SEN
SLE
RWA
MOZ
NER
TZA
TGO
BDI
CAF
1
0
0
0.5
1
1.5
KWT
BRB IRL LUX
NOR
SGP
SAU
HKG
TWN
GBR
MAC
BEL
ITA
FRA
SWE
USA
AUT
CAN
NLD
1 TURBHR
FIN
DNK
CHE
ESP
ISR
DEU
MLT
AUS
ISL
CYP
GRC
NZL
JPN
PRT
TTO
MUS
KOR
GAB
MEX
HRV
SVN
PAN
ZAF
TUN
EGY
POL
LTU
CRI
ZWE
URY
CHL
IRQ
HUN
SVK
CZE
GTM
MYS
ARG
IRN
EST
LVA
VEN
NAM
SWZ
DOM
BWA
0.5MRT
BRA
COL
BGR
FJI
MAR
Corr = 0.9454
SRB
JAM
THA
PER
LKA
JOR
PHL
ECU
IDN
ROU
HND
IND
CIV
PRY
RUS
BOL
CMR
KAZ
CHN
BEN
KEN
SEN
MNG
ARM
LSO
KGZ
UKR
SLE
TGO
RWA
NER
MDA
CAF
MOZ
BDI
TJK
TZA
1.5
0
0
2
1.5
(c): 1990
(d): 1980
0
0
0.5
1
TFP (US=1)
3
2
TTO
BHR
GAB
IRQ
MEX
ZAF
FRA
ITA
AUT
BEL
HKG
ESP
CHE
TWN
DEU
CAN
ISL
USA
SGP
NLD
GBR
MAC
1MAR
LUX
ISR
NOR
AUS
VEN
TUN
JOR
CRI
PRT
URY
JPN
DNK
NAM
GTM
CHL
GRC
SWE
COL
BRB
TUR
IRL
FIN
MUS
PAN
MYS
CYP
ECU
BRA
FJI
NZL
IRN
MLT
PRY
ZWE
DOM
HND
CIV
PER
HUN
SLE
EGY
MRT
SWZ
KOR
PHL
RWA
BEN
IDN
POL
THA
JAM
BGR
BWA
ROU
BOL
ARG
CMR
NER
KEN
LKA
TGO
SEN
IND
CHN
MNG
LSO
TZA
BDI
MOZ
CAF
0
0
1.5
GDP per worker (US=1)
(e): 1970
2
Corr = 0.9724
2
4
2
3
4
1.5
QAT
BHR
SAU
TTO
BRB
ZAF
VEN
MEX
CHE
LUX
AUT
FRA
CAN
CRI
IRN
USA
ESP
ISR
HKG
URY
ITA
PRT
SWE
CHL
BEL
ISL
GTM
TWN
AUS
DEU
DNK
JOR
NLD
TUR
SGP
GBR
MAR
PAN
TUN
FIN
JAM
GRC
IRQ
JPN
IRL
NZL
DOM
CIV
COL
PER
NOR
ZWE
CYP
EGY
HND
BRA
PRY
MLT
MYS
ECU
LKA
POL
ARG
NER
PHL
HUN
BOL
KOR
KEN
SEN
THA
IND
TZA
BGR
CHN
IDN
ROU
MOZ
0CMR
1
(f): 1960
6
4
Corr = 0.9462
GDP per worker (US=1)
KWT
8
2
KWT
QAT
SAU
4
TFP (US=1)
TFP (US=1)
1
GDP per worker (US=1)
QAT
MAC
SAU
KWT
HKG
BRBTWN
FRA
ITA
LUX
BHR
USA
1
ESP
CAN
CHE
GAB
BEL
TTO
SGPGBR
TUR
DEU
ISR
AUT
ISL
N
OR
NLD
JPN
LTU
ZAF
AUS
MUS
HRV
IRL
DNK
SWE
FIN
MEX
CYP
CZE
KAZ
NZL
TUN
PRT
GRC
IRQ
CRI
PAN
ZWE
SVN
KOR
MLT
RUS
GTM
VEN
LVA
SVK
COL
SWZ
KGZ
NAM
MAR
CHL
URY
BWA
JOR
MYS
EST
FJI
SLE
HUN
HND
DOM
EGY
TJK
0.5 CMR
SRB
BRA
UKR
BGR
PRY
IDN
IRN
ECU
THA
POL
JAM
MDA
CIV
PHL
ARG
MRT
MNG
SEN
RWA
LKA
ARM
PER
KEN
ROU
BEN
IND
BOL
LSO
CHN
TGO
NER
BDI
CAF
TZA
MOZ
Corr = 0.8836
TFP (US=1)
0.5
GDP per worker (US=1)
1.5
0
QAT
2
KWT QAT
TFP (US=1)
TFP (US=1)
2
TTO
BRB
HKG CHE
MEX
URY
ZAF
CANUSA
VEN LUX
1 JOR
CRIAUT
IRN
CHL FRA
DOM
ISL
ESP
ISR
DNK
NLD
AUS
GBR
SWE
DEU
NZL
GTM
TUR
BEL
ITA
PRT
JAM
FIN NOR
TWN
COL
IRL
TUN
PER
LKA
MAR
ECU
0.5EGY
JPN
ZWE
CIV
MYS
BRA
ARG
PHL
GRC
CYP
IDN
SGP
BOL
NER
SEN
CHN
KEN
KOR
IND
CMR
TZA
Corr = 0.8156
THA
ROU
MOZ
MLT
0
0
6
GDP per worker (US=1)
0.5
1
GDP per worker (US=1)
Figure A.2: TFP and GDP per worker: Method 1
20
1.5
(a): 2010
(b): 2000
MAC
1.5
NOR
SGP
HKG
IRL
BRB GBR
TWN
LUX
SAU
USA
CHE
SWE
NLD
AUT
BEL
CAN
ITA
FRA
DNK
FIN
AUS
DEU
ISL
ISR
ESP
CYP
POL
NZL
GRC
JPN
BHR
SVK
MLT
HRV
KOR
PRT
SVN
EST
CZE
0.5 ZWE HUN
Corr = 0.9398
TTO
TUR
GAB
PAN
IRN
ZAF
EGY
MUS
VEN
MEX
GTM
CHL
LTU
RUS
MYS
CRI
DOM
TUN
BWA
ARG
KAZ
URY
NAM
BGR
LVA
ROU
IND
MAR
LKA
CIRQ
OL
PER
SRB
CIV
JOR
HA
ECU
MRT
ARM
FJI
SWZ
IDN
PRY
JAM
PHL
CHN
CMR
BOL
TJK
KEN
MNG
UTBRA
KR
RWA
HND
KGZ
MOZ
MDA
SLE
BEN
LSO
SEN
TZA
TGO
BDI
NER
CAF
1
0
0
0.5
1
1.5
1.5
1
0.5
2
0
0.5
1
1.5
GDP per worker (US=1)
(c): 1990
(d): 1980
0
0.5
1
TFP (US=1)
3
2
BHR
FRA
ITA
AUT
BEL
HKG
ESP
CHE
TWN
DEU
CAN
ISL
USA
SGP
GBR
NLD
MAC
1 BRB
LUX
ISR
NOR
AUS
PRT
JPN
DNK
GRC
SWE
IRL
FIN
CYP
NZL
MLT
HUN
KOR
POLIRQ
GAB
MEXTTO
ZAF
TUN
SLE
CRI
GTM
ZWE
MAR
JOR
RWA
MUS
EGY
NAM
CHL
COL
PAN
URY
PRY
VEN
FJI
MYS
TUR
CIV
BRA
ECU
BEN
HND
DOM
PHL
PER
IRN
SWZ
IDN
THA
MRT
BWA
BGR
BOL
ROU
CMR
KEN
ARG
JAM
LKA
TGO
CHN
LSO
BDI
SEN
TZA
NER
IND
MNG
MOZ
CAF
0
0
1.5
GDP per worker (US=1)
(e): 1970
2
Corr = 0.9728
2
4
2
3
4
1.5
QAT
BHR
SAU
BRB
CHE
LUX
AUT
FRA
CAN
USA
ESP
ISR
HKG
ITA
PRT
SWE
BEL
ISL
TWN
AUS
DEU
DNK
NLD
SGP
GBR
FIN
GRC
JPN
IRL
NZL
CYP
MLT
POL
HUN
TTO
KOR
ZAF
CRI
MVEN
EX
EGY
JOR
MAR
UNOR
RY
GTM
CHL
IRN
CIV
TUN
PAN
ZWE
DOM
TUR
PRY
IRQ
PER
JAM
HND
BRA
LKA
COL
ARG
MYS
ECU
KEN
THA
PHL
CHN
TZA
SEN
NER
BGR
BOL
IND
ROU
IDN
MOZ
0CMR
1
(f): 1960
6
4
Corr = 0.9476
GDP per worker (US=1)
KWT
8
2
KWT
QAT
SAU
4
TFP (US=1)
TFP (US=1)
Corr = 0.9521
GDP per worker (US=1)
QAT
MAC
SAU
KWT
HKG
BRBTWN
FRA
ITA
LUX
BHRGBR
USA
1
ESP
CAN
CHE
BEL
SGPDEU
ISR
AUT
ISL
N
OR
NLD
JPN
AUS
HRV
IRL
DNK
SWE
FIN
CYP
CZE
NZL
PRT
GRC
SVN
KOR
MLT
SVK
EST
HUN
0.5 POL
Corr = 0.8992
ZWE
LTU
GAB
SLE
TTO
MUS
TUR
ZAF
CRI
GTM
TUN
PAN
KAZ
KGZ
MEX
EGY
SWZ
BWA
IRQ
LVA
NAM
FJI
COL
MAR
CHL
HND
JOR
VEN RUS
TSRB
JK
RWA
DOM
MYS
URY
IDN
CMR
PRY
CIV
THA
BRA
BGR
PHL
JAM
MNG
MDA
UKR
SEN
ECU
IRN
BEN
KEN
ARM
ARG
MRT
LKA
IND
BOL
CHN
LSO
PER
ROU
BDI
TGO
CAF
NER
TZA
0MOZ
TFP (US=1)
KWT
BRB IRL LUX
NOR
SGP
SAU
HKG
TWN
GBR
MAC
BEL
ITA
FRA
SWE
USA
AUT
CAN
NLD
FIN
DNK
CHE
ESP
ISR
DEU
BHR
MLT
AUS
ISL
CYP
GRC
NZL
JPN
PRT
KOR
HRV
SVN
POL
HUN
SVK
CZE
EST
ZWE
TTO
TUR
EGY
MUS
GAB
PAN
CRI
ZAF
MEX
GTM
IRQ
LTU
TUN
CHL
NAM
URY
BWA
DOM
MYS
LVA
SWZ
IRN
BGR
ARG
BRA
CFJI
VEN
OL
MAR
JAM
LKA
MRT
CIV
IND
IDN
PHL
CMR
HND
SRB
BOL
PER
PRY
ECU
TKAZ
JOR
HA
KEN
ROU
CHN
BEN
RWA
LSO
SEN
ARM
MNG
RUS
TGO
KGZ
MOZ
BDI
SLE
CAF
TZA
MDA
UKR
TJK
0NER
1.5
0
QAT
2
KWT QAT
TFP (US=1)
TFP (US=1)
2
6
BRB
HKG LUX
CHE
CANUSA
AUT
1
FRAISL
ESPDEU
ISR
DNK
NLD
AUS
GBR
SWE
NZL
BEL
ITA
PRTIRL
FIN NOR
TWN
0.5 SGP
JPN
GRC
TTO
KORCYP
JOR
URY
CRIZAF
MEX
CHL
DOM
IRN
VEN
GTM
T
UR
Corr = 0.8063
EGY
TUN
JAM
LKA
MAR
CIV
MLT
PER
ZWE
COL
CHN
ARG
ECU
IDN
BRA
PHL
MYS
BOL
KEN
SEN
IND
CMR
NER
TZA
ROU
THA
0MOZ
0
GDP per worker (US=1)
0.5
1
GDP per worker (US=1)
Figure A.3: TFP and GDP per worker: Method 2
21
1.5