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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%. 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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