The labor share of income around the world: Evidence from a panel dataset

Guerriero, Marta Working Paper The labor share of income around the world: Evidence from a panel dataset ADBI Working Paper Series, No. 920 Provided in Cooperation with: Asian Development Bank Institute (ADBI), Tokyo Suggested Citation: Guerriero, Marta (2019) : The labor share of income around the world: Evidence from a panel dataset, ADBI Working Paper Series, No. 920, Asian Development Bank Institute (ADBI), Tokyo This Version is available at: https://hdl.handle.net/10419/222687 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/3.0/igo/ ADBI Working Paper Series THE LABOR SHARE OF INCOME AROUND THE WORLD: EVIDENCE FROM A PANEL DATASET Marta Guerriero No. 920 February 2019 Asian Development Bank Institute The Working Paper series is a continuation of the formerly named Discussion Paper series; the numbering of the papers continued without interruption or change. ADBI’s working papers reflect initial ideas on a topic and are posted online for discussion. Some working papers may develop into other forms of publication. Suggested citation: Guerriero, M. 2019. The Labor Share of Income Around the World: Evidence from a Panel Dataset. ADBI Working Paper 920. Tokyo: Asian Development Bank Institute. Available: https://www.adb.org/publications/labor-share-income-around-world-evidence-panel-dataset Please contact the authors for information about this paper. Email: [email protected] Marta Guerriero is a senior teaching fellow and deputy h ead of the School for Cross-Faculty Studies (Global Sustainable Development) at the University of Warwick, United Kingdom. The views expressed in this paper are the views of the author and do not necessarily reflect the views or policies of ADBI, ADB, its Board of Directors, or the governments they represent. ADBI does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequences of their use. Terminology used may not necessarily be consistent with ADB official terms. Working papers are subject to formal revision and correction before they are finalized and considered published. Asian Development Bank Institute Kasumigaseki Building, 8th Floor 3-2-5 Kasumigaseki, Chiyoda-ku Tokyo 100-6008, Japan Tel: +81-3-3593-5500 Fax: +81-3-3593-5571 URL: www.adbi.org E-mail: [email protected] © 2019 Asian Development Bank Institute ADBI Working Paper 920 M. Guerriero Abstract There are two fundamental reasons why factor shares have traditionally been overlooked in the economic literature. First, because of their nature, factor shares are conceptually difficult to define and measure. Second, they have for a long time been perceived as constant across time and space. In this study, we provide an evaluation of five different methodologies of estimation commonly used in the labor share literature and propose a new measurement. We then compile a global dataset of the labor income share across 151 economies—both developing and developed—for all or part of the period 1970–2015. Results show that our suggested indicator is correlated to the other five measures but it also retains unique information. Contrary to the traditional assumption of stable factor shares, we document the existence of considerable heterogeneity across economies and variability over time. Specifically, there has been a general decline in the labor share around the world, in particular from the mid-1980s onwards. Keywords: factor shares, income distribution, labor JEL Classification: E25, J30, E01 ADBI Working Paper 920 M. Guerriero Contents 1. INTRODUCTION ......................................................................................................... 1 2. PROBLEMS OF DEFINITION AND ALTERNATIVE APPROACHES ......................... 2 2.1 LS1: The Unadjusted Labor Share .................................................................. 3 2.2 LS2: A Rule of Thumb ..................................................................................... 3 2.3 LS3: The Self-Employed as Workers .............................................................. 4 2.4 LS4: Self-Employment as the Rest of the Economy ........................................ 4 2.5 LS5: Using Data on Workforce Composition ................................................... 4 2.6 LS6: A New Adjustment .................................................................................. 5 2.7 Alternative Methods ......................................................................................... 6 3. THE DATASET ............................................................................................................ 6 4. RESULTS .................................................................................................................... 8 4.1 Global Trends .................................................................................................. 9 4.2 Economy-Level Data ..................................................................................... 11 5. CONCLUDING REMARKS........................................................................................ 28 REFERENCES ..................................................................................................................... 29 APPENDIX ............................................................................................................................ 32 ADBI Working Paper 920 M. Guerriero 1 1. INTRODUCTION Recent contributions on income distribution indicate that striking changes have been taking place in recent decades. For example, the decline in the share of labor in national income, which has been witnessed in recent years in several economies, is an interesting phenomenon (Elsby et al 2013; IMF 2017; Karabarbounis and Neiman 2013; Stockhammer 2017). This constitutes a major historical transformation, as the stability of functional income distribution has often been described in the past as a “stylised fact of growth” (Kaldor 1961). Most research on the labor income share provides only a partial picture, focusing mainly on industrialized economies (Elsby et al 2013; Piketty and Zucman 2014), the corporate sector (Karabarbounis and Neiman 2013) and relatively short periods of time (IMF 2017). Authors also question whether this apparent decline is mainly due to problems of measurement. Studies find that, after appropriately adjusting for selfemployment income (Bernanke and Gürkaynak 2001; Gollin 2002), indirect taxation and capital depreciation (Bridgman 2017; Rognlie 2015), factor shares are practically uniform across economies and approximately constant over time. Consequently, there has been little systematic attempt to generate a comprehensive global database of the labor income share. This study intends to address these issues. Firstly, since factor shares are conceptually difficult to define (Gollin 2002) and highly dependent on the way they are constructed (Bridgman 2017; Izyumov and Vahaly 2015; Mućk et al 2018), we examine different methodologies of measurement. Secondly, after comparing five alternative measures used in the existing empirical literature, we propose a sixth indicator, which allows us to compile a new global dataset of the labor income share across 151 economies – both developing and developed – for all or part of the period 1970-2015. Finally, we use descriptive statistics to document the existence of considerable heterogeneity across economies and variability over time. The remainder of this study is organized as follows. Section 2 presents the main problems related to the definition and estimation of factor shares of income, highlights the importance of appropriate measurement and provides an evaluation of the methodologies most commonly used to estimate labor income shares. By building on the empirical work of Gollin (2002) and the theoretical conceptualization of Atkinson (2009), we propose an alternative approach to measuring labor shares. Section 3 provides a brief overview of our dataset, computed using the six methodologies described in Section 2. In Section 4, we use descriptive statistics to present an account of the performance of factor shares over time and across economies, and draw comparisons with the existing empirical literature. Our analysis offers some evidence against the proposition that the labor share is stable over time and that it converges across economies. Concluding remarks are made in Section 5. ADBI Working Paper 920 M. Guerriero 2 2. PROBLEMS OF DEFINITION AND ALTERNATIVE APPROACHES The labor share of income is conventionally computed by dividing the total compensation paid to employees 1 by the national income. Although it may be considered straightforward to determine, several problems of a conceptual and practical nature arise from its measurement. This study builds on the methodologies proposed in the existing academic literature (Krueger 1999; Glyn 2009; Gollin 2002) illustrating measurement issues in both time series and cross-economy data on the labor income share. We use data from the United Nations (UN) National Accounts Statistics2 (UN 2018), which provide yearly national accounts tables for more than 200 economies. Even though the data suffers from some comparability issues (Hartwig 2006), these estimations are useful and have been widely applied in the cross-economy literature on labor shares (Bernanke and Gürkaynak 2001; Gollin 2002; Jayadev 2007). The labor income share is a ratio. Two adjustments are required for the computation of its denominator – the income aggregate – subject to data availability3. First, taxes on production and imports (minus subsidies) are removed from gross value added at market prices, converting the income aggregate to factor cost: indirect taxes (net of subsidies) do not represent any kind of return to capital nor to labor and therefore should not be counted (Glyn 2009; Gollin 2002; Izyumov and Vahaly 2015; Rognlie 2015). Second, capital income needs to be calculated net of capital consumption, by subtracting consumption of fixed capital from the value added to obtain a measure that is net of depreciation (Glyn 2009; Kuznets 1959; Piketty and Zucman 2014). According to Rognlie (2015), the distinction between labor income and net capital income (instead of gross capital income) is indeed more directly relevant to considerations of income distribution and inequality. Turning to the numerator of the ratio, from a conceptual perspective, the total compensation of employees differs from labor income because it disregards the contribution of the self-employed. By counting only payments to corporate workers as labor income, it implicitly classifies all the earnings from the self-employed as capital income. This incorrectly underestimates the measure of labor share, since the income earned by the self-employed often represents a combination of returns to labor and returns to capital. Self-employment may represent emerging entrepreneurship and business start-ups; but it may also be the result of marginal employment and disguised unemployment (Gollin 2002). From a time series perspective, a long-term decline in self-employment income would lead to an increasing trend in the labor share. In terms of international comparisons, since the rate of self-employment varies substantially across economies, the compensation of employees may significantly understate labor income in developing economies, where the self-employed account for a large portion of the workforce. According to OECD (Organisation for Economic Co-operation and Development) data (OECD 2018), self-employment in the United States decreased from 18.0% in 1955 to 6.3% in 2017, and in Japan from 56.5% in 1955 to 10.4% in 2017. Moreover, while the 1 The compensation of employees includes wages and salaries as well as other forms of non-wage compensation which also constitute returns from labor. 2 Prepared by the Statistics Division of the United Nations in collaboration with national and international statistical agencies. 3 Please see the appendix for complete information on data availability. ADBI Working Paper 920 M. Guerriero 3 self-employment rate is currently 6.3% in the United States and 15.5% in the European Union, it is 31.5% in Mexico, 32.9% in Brazil and 51.9% in Colombia. One solution to this problem is to analyze the corporate sector only (Bridgman 2017; Karabarbounis and Neiman 2013), or the manufacturing sector only, where the self-employed are less numerous (Azmat et al 2011; Daudey and Garcia-Peñalosa 2007), however this approach does not resolve the issue entirely. It provides only a partial picture of the economy and it makes international comparisons difficult, since not all economies publish sector-specific data. Alternatively, in order to consider the whole economy we need to derive the labor income component of self-employment income and then add it to the compensation of employees (Johnson 1954; Kravis 1959; Kuznets 1959). Six different measures of labor share will be presented and compared below: the unadjusted measure and five different indicators imputing a wage component to self-employment income – four of which have been proposed in the existing empirical literature. 2.1 LS1: The Unadjusted Labor Share The unadjusted labor share, here called LS1 (see Equation 2.1), is the ratio of the compensation of employees to the value added (net of indirect taxes and consumption of fixed capital): 𝐿𝐿𝐿𝐿 (𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢) 𝑜𝑜𝑜𝑜 𝐿𝐿𝐿𝐿1 = 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑜𝑜 𝑐𝑐𝑐𝑐𝑐𝑐𝑒𝑒𝑐𝑐𝑒𝑒𝑐𝑐𝑐𝑐𝑐𝑐 𝑣𝑣𝑐𝑐𝑒𝑒𝑣𝑣𝑐𝑐 𝑐𝑐𝑎𝑎𝑎𝑎𝑐𝑐𝑎𝑎(−𝑐𝑐𝑐𝑐𝑎𝑎𝑐𝑐𝑖𝑖𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐𝑡𝑡𝑐𝑐𝑐𝑐−𝑜𝑜𝑐𝑐𝑡𝑡𝑐𝑐𝑎𝑎 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑒𝑒) (2.1) As previously argued, although this measure has been widely used in the literature (Daudey and Garcia-Peñalosa 2007; Jayedev 2007; Rodrik 1999), it results in an underestimation of the labor share. 2.2 LS2: A Rule of Thumb The System of National Accounts (SNA) method breaks down value added into: compensation of employees, operating surplus (from rent and capital) and mixed income (or operating surplus of private unincorporated enterprises). Mixed income from selfemployment “implicitly contains an element of remuneration for work done by the owner, or other members of the household, that cannot be separately identified from the return to the owner as entrepreneur” (OECD 1993). The UN National Accounts Statistics provide information on mixed income for a large number of economies4. A common rule, proposed by Johnson (1954), is to impute two-thirds of self-employment income to labor income and the rest to capital income (see Equation 2.2). The choice of the value ‘2/3’ derives from the common belief that labor income represents around twothirds of the overall economy’s income. Self-employment income is then expected to be composed of a similar combination of labor and capital. This rule of thumb has been extensively used in the literature (Guscina 2006; Izyumov and Vahaly 2015). 𝐿𝐿𝐿𝐿2 = 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑜𝑜 𝑐𝑐𝑐𝑐𝑐𝑐𝑒𝑒𝑐𝑐𝑒𝑒𝑐𝑐𝑐𝑐𝑐𝑐+2 3𝑐𝑐𝑐𝑐𝑡𝑡𝑐𝑐𝑎𝑎 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑣𝑣𝑐𝑐𝑒𝑒𝑣𝑣𝑐𝑐 𝑐𝑐𝑎𝑎𝑎𝑎𝑐𝑐𝑎𝑎(− 𝑐𝑐𝑐𝑐𝑎𝑎𝑐𝑐𝑖𝑖𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐𝑡𝑡𝑐𝑐𝑐𝑐−𝑜𝑜𝑐𝑐𝑡𝑡𝑐𝑐𝑎𝑎 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑒𝑒) (2.2) 4 Following Gollin (2002), we collect data on gross mixed income. Please see the appendix for complete information on data availability. ADBI Working Paper 920 M. Guerriero 4 The main problem with this adjustment is that the value ‘2/3’ is arbitrary – some studies, in fact, use a ratio of ‘1/2’ instead of ‘2/3’ – and it treats all economies in the same way (Izyumov and Vahaly 2015). Moreover, given that the division of income between labor and capital remains constant, this measure may ignore the effect of external forces that shift the balance over time. 2.3 LS3: The Self-Employed as Workers A second adjustment (Kravis 1959) involves attributing all self-employment income to labor earnings (see Equation 2.3). The rationale for this is that most of the self-employed in developing economies provide pure labor services. 𝐿𝐿𝐿𝐿3 = 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑜𝑜 𝑐𝑐𝑐𝑐𝑐𝑐𝑒𝑒𝑐𝑐𝑒𝑒𝑐𝑐𝑐𝑐𝑐𝑐+𝑐𝑐𝑐𝑐𝑡𝑡𝑐𝑐𝑎𝑎 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑣𝑣𝑐𝑐𝑒𝑒𝑣𝑣𝑐𝑐 𝑐𝑐𝑎𝑎𝑎𝑎𝑐𝑐𝑎𝑎(−𝑐𝑐𝑐𝑐𝑎𝑎𝑐𝑐𝑖𝑖𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐𝑡𝑡𝑐𝑐𝑐𝑐−𝑜𝑜𝑐𝑐𝑡𝑡𝑐𝑐𝑎𝑎 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑒𝑒) (2.3) By using this approach, however, the labor share is unavoidably overstated, as in reality some self-employed businesses generate and use considerable amounts of capital and land, even in developing economies (Gollin 2002). 2.4 LS4: Self-Employment as the Rest of the Economy It is also possible to consider self-employment income as composed of the same combination of labor and capital income as the rest of the economy (Atkinson, 1983; Kravis 1959). The labor share is scaled up by a factor that takes into account the proportion of self-employed, who are attributed a wage equal to the average wage of employees. Mathematically, this is done by deducting mixed income from the income aggregate at the denominator (see Equation 2.4): 𝐿𝐿𝐿𝐿4 = 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑜𝑜 𝑐𝑐𝑐𝑐𝑐𝑐𝑒𝑒𝑐𝑐𝑒𝑒𝑐𝑐𝑐𝑐𝑐𝑐 𝑣𝑣𝑐𝑐𝑒𝑒𝑣𝑣𝑐𝑐 𝑐𝑐𝑎𝑎𝑎𝑎𝑐𝑐𝑎𝑎(−𝑐𝑐𝑐𝑐𝑎𝑎𝑐𝑐𝑖𝑖𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐𝑡𝑡𝑐𝑐𝑐𝑐−𝑜𝑜𝑐𝑐𝑡𝑡𝑐𝑐𝑎𝑎 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑒𝑒)−𝑐𝑐𝑐𝑐𝑡𝑡𝑐𝑐𝑎𝑎 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 (2.4) This adjustment assumes that the split between capital and labor is approximately the same in private unincorporated enterprises and in large corporations (or in the government sector). In reality, these are very different in terms of size of the workforce, structure and degree of labor-intensiveness, and vary greatly from one economy to another. Studies also show that this adjustment leads to unrealistic values of labor shares greater than 1 for some economies (Bernanke and Gürkaynak 2001). Despite being problematic, this approach is more reasonable than the previous one, since it allows for the possibility that the self-employed generate capital income. Being quite straightforward, it has been widely used in the academic literature (Izyumov and Vahaly 2015; Bernanke and Gürkaynak 2001; Rognlie 2015; Ryan 1996). 2.5 LS5: Using Data on Workforce Composition The fundamental problem related to the three adjustments presented above is that they require data on self-employment income. Unfortunately, data on mixed income is not so widely available: the majority of economies report only operating surplus, recording income from self-employment together with capital income. For this reason, an alternative method is required. Gollin (2002) suggests a fourth adjustment, based on data on the composition of the workforce. Not only is it easier to collect data on the number of self-employed than on their actual earnings, but studies have also shown that the self-employed tend to underreport their income (Hurst et al 2010). This approach has been widely used in the ADBI Working Paper 920 M. Guerriero 11 Source: Author’s calculations. 4.2 Economy-Level Data In addition to considering the world as a whole, we can evaluate the data on the labor share of income for each individual economy in the dataset. Table 4 provides a summary of alternative measures of labor share, as calculated in this study and in the existing empirical literature (Bentolila and Saint-Paul 2003; Bernanke and Gürkaynak 2001; EC 2007; Gollin 2002; Izyumov and Vahaly 2015). Most of the estimated labor income shares lie between 0.60 and 0.70, as expected. Compared to previous measurements, our computations seem to generate broadly consistent but relatively higher values, however a comparison among the different studies appears very difficult. Firstly, the measures have not been constructed in the same way. Bernanke and Gürkaynak (2001), Gollin (2002) and Izyumov and Vahaly (2015) use the UN National Accounts Statistics, generating samples that, although smaller than ours, include both developed and developing economies. Conversely, Bentolila and Saint-Paul (2003) draw on the OECD International Sectoral Data Base (ISDB) 1996, concentrating their attention on 15 developed economies only. The European Commission employs the Commission’s AMECO database (EC 2007) and examines only the EU-27, the United States of America and Japan. Secondly, not all studies consider a panel dataset. Bernanke and Gürkaynak (2001), the EC (2007) and Izyumov and Vahaly (2015) construct an unbalanced panel dataset and then compute averages of the measures over the entire period of time. Gollin (2002) and Bentolila and Saint-Paul (2003), instead, consider only the cross-economy dimension, analyzing the labor share data at a particular point in time. ADBI Working Paper 920 M. Guerriero 12 Table 4: Alternative Measures of Labor Share: A Comparison with the Existing Empirical Literature Economy Gollin1 (Cross-economy) Bernanke and Gürkaynak2 (1980–1995) (1) (2) (3) (4) (5) (6) (7) (8) Algeria 0.47 0.61 0.63 Angola Argentina Armenia Aruba Australia 0.50 0.72 0.67 0.68 0.57 0.68 0.66 0.68 Austria 0.61 0.70 0.71 Azerbaijan Bahamas Bahrain Barbados Belarus 0.42 0.55 0.51 Belgium 0.55 0.79 0.74 0.74 0.60 0.74 0.71 0.73 Benin Bermuda Bolivia 0.26 0.83 0.63 0.48 0.37 0.67 Bosnia and Herzegovina Botswana 0.30 0.37 0.34 0.48 0.39 0.45 Brazil British Virgin Islands Brunei Darussalam Bulgaria Burkina Faso Burundi 0.20 0.91 0.73 0.22 0.75 Cabo Verde Cameroon Canada 0.62 0.68 0.69 Cayman Islands Central African Republic Chad Chile 0.42 0.59 0.62 PRC Hong Kong, China 0.51 0.57 Macau, China Colombia 0.45 0.65 Comoros Cook Islands Congo 0.37 0.69 0.58 0.38 0.47 Costa Rica 0.54 0.73 0.74 Cote d’Ivoire 0.29 0.81 0.69 0.43 0.68 Croatia Cuba Curaçao Cyprus Czech Republic Denmark 0.64 0.71 0.72 Djibouti Dominican Republic Ecuador 0.21 0.82 0.57 0.50 0.25 0.45 Egypt 0.43 0.77 El Salvador 0.35 0.58 Estonia 0.47 0.61 0.57 Eswatini (Swaziland) continued on next page ADBI Working Paper 920 M. Guerriero 13 Table 4 continued Economy Gollin1 (Cross-economy) Bernanke and Gürkaynak2 (1980–1995) (1) (2) (3) (4) (5) (6) (7) (8) Faeroe Islands Fiji Finland 0.57 0.76 0.73 0.68 0.62 0.71 0.71 0.73 France 0.52 0.76 0.72 0.68 0.61 0.74 0.71 0.73 Gabon Georgia Germany (before 1991, Fed. Rep. of Germany) 0.63 0.69 0.71 Greece 0.45 0.79 0.86 Greenland Guatemala Guinea Honduras Hungary 0.58 0.80 0.77 0.67 Iceland India 0.69 0.84 0.83 Iran Iraq Ireland 0.58 0.73 0.75 Israel 0.59 0.70 0.73 Italy 0.45 0.80 0.72 0.71 0.49 0.71 0.65 0.69 Jamaica 0.43 0.62 0.57 0.53 0.60 Japan 0.56 0.73 0.69 0.72 0.59 0.68 0.73 0.77 Jordan 0.45 0.64 0.67 Kazakhstan Kenya Kuwait Kyrgyz Republic Latvia 0.37 0.55 0.47 Lesotho Libya Liechtenstein Lithuania Luxembourg Malaysia 0.43 0.66 Mali Malta 0.43 0.71 0.63 Marshall Islands Mauritania Mauritius 0.39 0.77 0.67 0.49 0.48 0.57 Mexico 0.34 0.55 0.59 Federated States of Micronesia Monaco Mongolia Morocco 0.36 0.58 Mozambique Namibia Netherlands 0.53 0.72 0.68 0.64 0.59 0.67 0.66 0.67 Netherlands Antilles New Zealand 0.55 0.67 0.69 Nicaragua Niger Nigeria Norway 0.52 0.68 0.64 0.57 0.55 0.61 0.63 continued on next page ADBI Working Paper 920 M. Guerriero 14 Table 4 continued Economy Gollin1 (Cross-economy) Bernanke and Gürkaynak2 (1980–1995) (1) (2) (3) (4) (5) (6) (7) (8) Oman Palau Panama 0.50 0.73 0.76 Papua New Guinea Paraguay 0.32 0.49 0.52 Peru 0.31 0.56 0.59 Philippines 0.35 0.80 0.66 0.87 0.27 0.59 Poland Portugal 0.45 0.82 0.75 0.60 0.52 0.72 0.71 0.73 Qatar Republic of Korea 0.47 0.77 0.70 0.80 0.48 0.65 Republic of Moldova Reunion 0.59 0.83 0.80 Romania Russian Federation Rwanda San Marino Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore 0.47 0.53 0.55 Sint Maarten Slovakia Slovenia Solomon Islands South Africa 0.59 0.62 0.63 Spain 0.52 0.67 0.70 Sri Lanka 0.50 0.78 0.81 Sudan Suriname Sweden 0.61 0.80 0.77 0.72 0.68 0.77 0.74 0.75 Switzerland 0.66 0.76 0.78 Tajikistan Thailand Trinidad and Tobago 0.55 0.69 0.71 Tunisia 0.41 0.62 Turkey Ukraine 0.77 0.78 0.76 United Arab Emirates United Kingdom 0.57 0.81 0.78 0.72 0.65 0.75 0.72 0.74 United Republic of Tanzania United States 0.60 0.77 0.74 0.66 0.65 0.74 0.71 0.71 Uruguay 0.43 0.58 0.59 Vanuatu Venezuela (Bolivarian Republic of) 0.38 0.53 0.55 Viet Nam 0.59 0.83 0.80 Yemen Zambia 0.48 0.72 0.78 Zimbabwe continued on next page ADBI Working Paper 920 M. Guerriero 15 Table 4 continued Economy EC3 (2007) Bentolila and Saint-Paul4 (Cross-economy) Izyumov and Vahaly5 (1990-228) (9) (10) (11) (12) (13) (14) (15) Algeria Angola Argentina 0.46 0.49 0.43 Armenia 0.47 0.73 0.47 Aruba Australia 0.65 0.66 0.63 0.62 0.65 0.62 Austria 0.66 0.64 0.65 0.64 Azerbaijan 0.40 0.38 0.31 Bahamas Bahrain Barbados Belarus 0.57 0.56 Belgium 0.61 0.62 0.72 0.64 0.64 0.66 0.64 Benin Bermuda Bolivia 0.41 1.17 0.40 Bosnia and Herzegovina Botswana 0.29 0.37 0.28 Brazil 0.48 0.62 0.47 British Virgin Islands Brunei Darussalam Bulgaria 0.51 0.48 0.47 0.45 Burkina Faso Burundi Cabo Verde Cameroon Canada 0.67 0.62 0.65 0.60 0.67 0.60 Cayman Islands Central African Republic Chad Chile 0.48 0.61 0.46 PRC Hong Kong, China Macau, China Colombia 0.53 0.64 0.49 Comoros Cook Islands Congo Costa Rica Cote d’Ivoire Croatia 0.61 0.70 0.61 Cuba Curaçao Cyprus 0.57 0.55 0.56 0.54 Czech Republic 0.52 0.56 0.57 0.55 Denmark 0.59 Djibouti Dominican Republic Ecuador Egypt 0.51 0.52 0.45 El Salvador Estonia 0.51 0.58 0.58 0.57 Eswatini (Swaziland) continued on next page ADBI Working Paper 920 M. Guerriero 16 Table 4 continued Economy EC3 (2007) Bentolila and Saint-Paul4 (Cross-economy) Izyumov and Vahaly5 (1990-228) (9) (10) (11) (12) (13) (14) (15) Faeroe Islands Fiji Finland 0.62 0.69 0.70 0.72 0.62 0.68 0.62 France 0.61 0.68 0.72 0.62 0.63 0.65 0.63 Gabon Georgia 0.46 0.64 0.37 Germany (before 1991, Fed. Rep. of Germany) 0.62 0.64 0.69 0.62 0.66 0.65 0.66 Greece 0.66 0.47 0.61 0.45 Greenland Guatemala 0.50 1.22 0.45 Guinea Honduras 0.61 1.03 0.59 Hungary 0.63 0.62 0.62 Iceland India Iran 0.44 0.45 0.34 Iraq Ireland 0.52 0.55 0.50 Israel Italy 0.67 0.64 0.63 0.58 0.63 0.56 Jamaica Japan 0.68 0.57 0.69 0.68 0.57 0.65 0.57 Jordan Kazakhstan 0.54 0.57 0.51 Kenya Kuwait Kyrgyz Republic 0.66 0.61 0.65 Latvia 0.50 0.58 0.56 0.56 Lesotho Libya Liechtenstein Lithuania 0.49 0.54 0.56 0.52 Luxembourg 0.52 0.61 0.60 Malaysia Mali Malta 0.51* Marshall Islands Mauritania Mauritius Mexico 0.47 0.53 0.42 Federated States of Micronesia Monaco Mongolia 0.52 0.63 0.43 Morocco Mozambique Namibia Netherlands 0.63 0.68 0.69 0.59 0.62 0.64 0.61 Netherlands Antilles New Zealand Nicaragua Niger 0.61 0.47 Nigeria Norway 0.68 0.66 0.64 0.59 0.55 0.58 continued on next page ADBI Working Paper 920 M. Guerriero 17 Table 4 continued Economy EC3 (2007) Bentolila and Saint-Paul4 (Cross-economy) Izyumov and Vahaly5 (1990-228) (9) (10) (11) (12) (13) (14) (15) Oman Oman Panama 0.47 0.57 0.43 Papua New Guinea Paraguay Peru Philippines 0.48 0.57 Poland 0.55 0.61 0.61 0.59 Portugal 0.67 0.66 0.74 0.66 Qatar Republic of Korea Republic of Moldova 0.58 0.71 0.57 Reunion Romania 0.68 0.70 Russian Federation 0.58 0.54 0.57 Rwanda San Marino Saudi Arabia Senegal Serbia 0.66 0.78 0.66 Seychelles Sierra Leone Singapore Sint Maarten Slovakia 0.44 0.60 0.48 0.57 Slovenia 0.64 0.67 0.71 0.67 Solomon Islands South Africa 0.58 Spain 0.62 0.65 0.67 0.65 Sri Lanka Sudan Suriname Sweden 0.62 0.70 0.74 0.73 0.67 0.65 0.67 Switzerland 0.70 0.78 0.70 Tajikistan 0.49 0.34 Thailand Trinidad and Tobago Tunisia Turkey Ukraine 0.58 0.62 0.57 United Arab Emirates United Kingdom 0.65 0.65 0.69 0.65 United Republic of Tanzania United States 0.64 0.70 0.68 0.66 0.71 0.67 0.71 Uruguay 0.54 0.47 0.51 Vanuatu Venezuela (Bolivarian Republic of) 0.40 0.38 Viet Nam Yemen Zambia Zimbabwe continued on next page ADBI Working Paper 920 M. Guerriero 18 Table 4 continued Economy Author’s Calculations6 LS1 LS2 LS3 LS4 LS5 LS6 Algeria 0.37 0.59 0.55 Angola 0.22 0.68 0.65 Argentina 0.39 0.48 0.52 0.45 0.53 0.51 Armenia 0.51 0.88 0.88 Aruba 0.64 0.70 0.72 0.70 0.67 0.64 Australia 0.67 0.77 0.82 0.80 0.81 0.75 Austria 0.72 0.78 0.82 0.80 0.83 0.79 Azerbaijan 0.23 0.50 0.46 Bahamas 0.49 0.58 0.55 Bahrain 0.33 0.35 0.34 Barbados 0.71 0.81 0.80 Belarus 0.54 0.61 0.64 0.60 0.57 0.56 Belgium 0.63 0.77 0.81 0.78 0.76 0.74 Benin 0.22 0.67 0.91 0.68 Bermuda 0.69 0.81 0.76 Bolivia 0.37 0.59 0.57 Bosnia and Herzegovina 0.71 0.97 0.75 Botswana 0.40 0.35 0.36 0.34 0.58 0.57 Brazil 0.48 0.55 0.59 0.54 0.75 0.72 British Virgin Islands 0.53 0.62 0.58 Brunei Darussalam 0.22 0.24 0.24 Bulgaria 0.48 0.60 0.66 0.59 0.56 0.54 Burkina Faso 0.26 Burundi 0.22 Cabo Verde 0.38 0.63 0.75 0.61 0.66 0.64 Cameroon 0.27 0.64 0.81 0.61 Canada 0.69 0.77 0.80 0.78 0.81 0.76 Cayman Islands 0.57 0.63 0.59 Central African Republic 0.18 0.25 0.29 0.20 Chad 0.20 Chile 0.47 0.56 0.60 0.55 0.66 0.64 PRC 0.54 0.59 Hong Kong, China 0.51 0.57 0.54 Macau, China 0.36 0.40 0.39 Colombia 0.39 0.54 0.63 0.50 0.65 0.62 Comoros 0.13 0.46 0.44 Cook Islands 0.72 0.85 0.73 Congo Costa Rica 0.54 0.69 0.74 0.70 0.74 0.70 Cote d’Ivoire 0.29 0.58 0.74 0.51 Croatia 0.70 0.80 0.85 0.82 0.91 0.86 Cuba 0.47 0.63 0.53 Curaçao 0.71 Cyprus 0.62 0.70 0.75 0.71 0.80 0.75 Czech Republic 0.60 0.73 0.80 0.75 0.71 0.68 Denmark 0.75 0.83 0.87 0.85 0.85 0.79 Djibouti 0.60 Dominican Republic 0.38 0.68 0.83 0.70 0.70 0.67 continued on next page ADBI Working Paper 920 M. Guerriero 19 Table 4 continued Economy Author’s Calculations6 LS1 LS2 LS3 LS4 LS5 LS6 Ecuador 0.32 0.57 0.53 Egypt 0.29 0.48 0.58 0.41 0.49 0.41 El Salvador Estonia 0.63 0.68 0.70 0.68 0.69 0.67 Eswatini (Swaziland) 0.57 0.74 0.73 Faeroe Islands 0.70 0.71 0.72 0.72 Fiji 0.49 0.82 0.81 Finland 0.71 0.78 0.82 0.80 0.84 0.81 France 0.69 0.77 0.81 0.78 0.80 0.77 Gabon 0.31 0.47 0.46 Georgia 0.31 0.53 0.64 0.47 0.81 0.80 Germany (before 1991, Fed. Rep. of Germany) 0.69 0.78 0.74 Greece 0.43 0.65 0.75 0.64 0.72 0.66 Greenland 0.72 Guatemala 0.37 0.53 0.62 0.49 0.69 0.66 Guinea 0.16 0.53 0.72 0.35 Honduras 0.56 0.66 0.72 0.66 Hungary 0.62 0.76 0.82 0.79 0.74 0.72 Iceland 0.75 0.83 0.85 0.84 0.91 0.85 India 0.39 Iran 0.28 0.51 0.62 0.43 0.54 0.51 Iraq 0.17 0.30 0.29 Ireland 0.58 0.58 0.61 0.57 0.75 0.71 Israel 0.68 0.79 0.76 Italy 0.56 0.71 0.79 0.73 0.79 0.74 Jamaica 0.57 0.94 0.91 Japan 0.65 0.73 0.76 0.74 0.82 0.80 Jordan 0.48 0.59 0.55 Kazakhstan 0.43 0.58 0.66 0.56 0.68 0.67 Kenya 0.43 0.44 Kuwait 0.31 0.32 0.32 Kyrgyz Republic 0.35 0.72 0.90 0.79 0.70 0.69 Latvia 0.63 0.72 0.78 0.73 0.74 0.71 Lesotho 0.49 0.62 0.69 0.61 0.60 0.60 Libya 0.28 0.48 0.46 Liechtenstein 0.61 0.68 0.71 0.68 Lithuania 0.56 0.66 0.72 0.67 0.68 0.66 Luxembourg 0.59 0.69 0.73 0.70 0.66 0.63 Malaysia 0.34 0.47 0.46 Mali 0.14 1.76 0.76 Malta 0.57 0.69 0.72 0.69 0.66 0.63 Marshall Islands 0.75 Mauritania 0.27 0.70 0.69 Mauritius 0.46 0.57 0.49 Mexico 0.35 0.53 0.61 0.48 0.59 0.56 Federated States of Micronesia 0.48 0.68 0.78 0.69 Monaco 0.53 Mongolia 0.30 0.55 0.67 0.49 0.77 0.76 continued on next page ADBI Working Paper 920 M. Guerriero 20 Table 4 continued Economy Author’s Calculations6 LS1 LS2 LS3 LS4 LS5 LS6 Morocco 0.36 0.84 0.82 Mozambique 0.28 0.63 0.83 0.59 Namibia 0.54 0.80 0.76 Netherlands 0.69 0.78 0.83 0.80 0.79 0.76 Netherlands Antilles 0.80 0.89 0.92 0.91 0.96 0.90 New Zealand 0.59 0.73 0.67 Nicaragua 0.42 0.60 0.70 0.58 0.79 0.76 Niger 0.18 0.67 0.91 0.70 Nigeria 0.17 Norway 0.65 0.70 0.72 0.70 0.72 0.70 Oman 0.33 0.40 0.43 0.36 0.38 0.37 Palau 0.55 0.56 0.57 0.56 0.56 Panama 0.38 0.46 0.50 0.44 0.57 0.55 Papua New Guinea 0.36 Paraguay 0.38 0.53 0.61 0.49 0.75 0.71 Peru 0.33 0.41 0.49 0.33 0.67 0.64 Philippines 0.32 0.62 0.59 Poland 0.52 0.74 0.84 0.77 0.73 0.70 Portugal 0.60 0.83 0.90 0.87 0.83 0.79 Qatar 0.25 0.25 0.27 0.24 0.25 0.25 Republic of Korea 0.52 0.95 0.90 Republic of Moldova 0.50 0.63 0.69 0.61 0.72 0.71 Reunion Romania 0.42 0.57 0.64 0.55 0.70 0.69 Russian Federation 0.66 0.75 0.79 0.76 0.71 0.70 Rwanda 0.24 San Marino 0.61 0.79 0.87 0.83 0.69 0.62 Saudi Arabia 0.32 0.31 0.32 0.29 0.35 0.34 Senegal 0.28 0.83 0.83 Serbia Seychelles 0.47 0.54 0.53 Sierra Leone 0.47 Singapore 0.45 0.53 0.50 Sint Maarten 0.79 Slovakia 0.57 0.75 0.84 0.78 0.64 0.62 Slovenia 0.74 0.83 0.88 0.86 0.88 0.85 Solomon Islands 0.53 South Africa 0.65 0.78 0.73 Spain 0.57 0.78 0.85 0.81 0.76 0.72 Sri Lanka 0.52 0.88 0.86 Sudan 0.36 Suriname 0.38 0.47 0.46 Sweden 0.77 0.77 0.79 0.78 0.85 0.82 Switzerland 0.76 0.87 0.93 0.92 0.91 0.85 Tajikistan 0.24 0.45 0.44 Thailand 0.29 0.80 0.78 Trinidad and Tobago 0.51 0.68 0.65 Tunisia 0.48 0.70 0.58 continued on next page ADBI Working Paper 920 M. Guerriero 27 Table 6 continued Economy Labor Share Averages Labor Share Trends* 1970s 1980s 1990s 2000s 2010s 1970s 1980s 1990s 2000s 2010s Norway 0.7581 0.7260 0.7090 0.6140 = = = = Oman 0.3810 0.4206 0.3396 0.3394 – – + = + Panama 0.6117 0.5629 0.4514 = – – – Paraguay 0.7237 0.7468 0.6327 ++ – – Peru 0.8100 0.6712 0.6367 0.4807 0.3884 – = = – – – Philippines 0.6131 0.6105 0.4752 + – – – Poland 0.7400 0.6580 + – Portugal 0.7653 0.7071 0.7548 0.8923 0.8951 – – – ++ = – Qatar 0.3868 0.2099 0.1753 – – – – Republic of Korea 0.9472 0.9197 0.8828 0.8507 = = = = Republic of Moldova 0.7307 0.7376 0.6825 0.7260 – ++ – Romania 0.6601 0.7021 + = Russian Federation 0.7248 0.7074 0.6425 – – + – – San Marino 0.6246 0.5854 0.6699 = = – Saudi Arabia 0.3971 0.3101 + – Senegal 0.7749 0.8192 0.9408 = + + Seychelles 0.4764 0.5534 0.5188 + = = Singapore 0.5032 0.5023 0.5084 0.4996 = = = = Slovakia 0.6461 0.6103 0.6076 = = = Slovenia 0.8973 0.8263 – = South Africa 0.7622 0.7809 0.7586 0.6645 0.6799 = = = – + Spain 0.6898 0.7331 0.7458 – + = Sri Lanka 0.8661 0.8381 0.8526 0.9279 = = + = Suriname 0.4624 0.4665 = – Sweden 0.8396 0.8208 0.7984 0.8109 = = = = Switzerland 0.8460 0.8537 0.8481 = = = Tajikistan 0.4372 0.4574 – – ++ Thailand 0.6547 0.8044 0.9044 0.7776 0.7602 + = + – = Trinidad and Tobago 0.6597 0.7664 0.7240 0.4695 – ++ – – Tunisia 0.5334 0.5999 0.6515 + + = Turkey 0.4366 0.5332 0.5024 0.4882 ++ + – – = Ukraine 0.6469 0.6716 0.6504 0.8248 + + + United Arab Emirates 0.3086 0.2539 0.2566 0.2895 + – – + = United Kingdom 0.7644 0.7601 0.7648 0.7605 = = = = United States 0.7457 0.7536 0.7405 0.7319 0.7090 = = = = – Uruguay 0.5859 0.5671 + – Venezuela 0.6590 0.6333 0.5578 0.5691 0.6058 = – + = – Yemen 0.5876 0.7341 ++ = Source: Author’s calculations. * Please note: ++ Average annual variation greater than +3%; + Average annual variation between +1% and +3%; = Average annual variation between –1% and +1%; – Average annual variation between –3% and –1%; – – Average annual variation less than –3%. ADBI Working Paper 920 M. Guerriero 28 5. CONCLUDING REMARKS The study of the labor income share is severely hampered by measurement problems. As summarized by Kravis (1959, p. 918), it “is handicapped by the fact […] that the nature of the components of income for which we have data has not been determined by the requirements of the economists but by legal and institutional arrangements of our society.” This study represents an attempt to construct a global dataset of the labor share of income. By suggesting an adjustment to the most commonly used methodologies of estimation, it offers an argument on the importance of accurate measurement and some useful information for future research. We compile a new measure of the labor share of income across 151 economies – both developing and developed – using the UN National Accounts Statistics and the ILO Yearbooks of Labour Statistics for all or part of the period 1970–2015. Compared to five other measurements previously used in the empirical literature, the estimate suggested here allows us to consider a large sample of economies and it retains unique information. Our analysis of the data offers evidence against the traditional hypothesis of the stability of factor shares (Kaldor 1961). We also reject more recent suggestions that changes in factor shares are mainly due to the lack of appropriate adjustment for self-employment income (Bernanke and Gürkaynak 2001; Gollin 2002), indirect taxation and capital depreciation (Bridgman 2017; Rognlie 2015). Our study finds evidence that the labor income share varies considerably across economies and it has generally declined over time, especially in the last three decades. On a socio-political level, this trend risks creating perceptions that workers are not receiving ‘fair’ shares of the income they produce, and it thus may endanger socio-political stability (Atkinson 2009). On an economic level, it may risk jeopardizing the sustainability of future economic growth by constraining wage-based household consumption (Onaran and Galanis 2013). These issues are even the more significant in light of the negative repercussions on labor markets caused by the global financial crisis and its slow recovery in many parts of the world (Smeeding and Thompson 2011). Our results are relevant for policymakers wishing to pursue adequate pro-poor and pro-labor policies. These are particularly important today, given the recent changes in global labor markets caused by increasing international trade and capital flows and by rapid technological progress. Given that factor shares are found to be relatively persistent over time, policies in both industrialized and developing economies should aim to devise instruments which safeguard labor and should reconsider traditional approaches targeted at protecting capital. ADBI Working Paper 920 M. Guerriero 29 REFERENCES Askenazy, P. 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Guerriero 32 APPENDIX List of Economies and Data Availability Economy Time Series Adjustments to Value Added Adjustments to Self-employment Income Net of Indirect Taxes Net of Consumption of Fixed K Gross Mixed Income Workforce Composition Employees Employers Algeria 1970–1978 and 1989–2015 Yes Yes No Yes Yes Angola 2002–2015 Yes No No Yes Yes Argentina 1993–2013 Yes No Yes Yes Yes Armenia 1994–2009 Yes Yes No Yes Yes Aruba 1994–2002 Yes Yes Yes Yes Yes Australia 1970–2008 Yes Yes Yes Yes Yes Austria 1976–2008 Yes Yes Yes Yes Yes Azerbaijan 1995–2012 Yes Yes No Yes Yes Bahamas 1989–2010 Yes Yes No Yes Yes Bahrain 1994–2015 Yes Yes No Yes Yes Barbados 1974–1975 No Yes No Yes Yes Belarus 1990–2015 Yes No Yes Yes Yes Belgium 1975–2008 Yes Yes Yes Yes Yes Benin 1974–1978, 1982–1986, 1994–2012 Yes Yes Yes No No Bermuda 1996–215 Yes Yes No Yes Yes Bolivia 1970–1986, 1988–2015 Yes No No Yes Yes Bosnia and Herzegovina 2005–2011 Yes Yes No Yes Yes Botswana 1974–2001, 2003–2015 Yes Yes Yes Yes Yes Brazil 1992–2013 Yes No Yes Yes Yes British Virgin Islands 1970–1977, 1984–1987, 1995–2012 Yes Yes No Yes Yes Brunei Darussalam 2010–2015 Yes No No Yes Yes Bulgaria 1994, 1996–2010 Yes Yes Yes Yes Yes Burkina Faso 1979–1984 and 1999–2014 Yes Yes No No No Burundi 1984–1988 and 2005–2014 Yes Yes No No No Cabo Verde 2007–2014 Yes No Yes Yes Yes Cameroon 1974–1988, 1990, 1993–2011, 2013–2014 Yes Yes Yes No No Canada 1970–2010 Yes Yes Yes Yes Yes Cayman Islands 1983–1991, 2006–2015 Yes Yes No Yes Yes Central Afr. Rep. 2005–2006 Yes No Yes No No continued on next page ADBI Working Paper 920 M. Guerriero 33 Appendix table continued Economy Time Series Adjustments to Value Added Adjustments to Self-employment Income Net of Indirect Taxes Net of Consumption of Fixed K Gross Mixed Income Workforce Composition Employees Employers Chad 1975, 1995–2001, 2005–2010 Yes Yes No No No Chile 1974–2014 Yes Yes Yes Yes Yes PRC 1992–2014 Yes No No Yes No Hong Kong, China 1980–2013 Yes No No Yes Yes Macau, China 1992–2015 Yes Yes No Yes Yes Colombia 1970–2015 Yes No Yes Yes Yes Comoros 2007–2014 Yes No No Yes Yes Cook Islands 1995–2007 Yes Yes No Yes Yes Costa Rica 1970–2013 Yes Yes Yes Yes Yes Cote d’Ivoire 1974–1979, 1989–2000, 2005–2013 Yes Yes Yes No No Croatia 1997–2011 Yes Yes Yes Yes Yes Cuba 1996–2009 Yes No No Yes Yes Curacao 2000–2012 Yes Yes No No No Cyprus 1996–2010 Yes Yes Yes Yes Yes Czech Republic 1992–2008 Yes Yes Yes Yes Yes Denmark 1970–2008 Yes Yes Yes Yes Yes Djibouti 1990–1998 Yes No No No No Dom. Republic 1991–2005 Yes Yes Yes Yes Yes Ecuador 1970–1991, 2007–2014 Yes Yes No Yes Yes Egypt 1996–2013 Yes Yes Yes Yes Yes Estonia 1993–2013 Yes Yes Yes Yes Yes Eswatini (Swaziland) 1980–1987 No Yes No Yes Yes Faeroe Islands 1999–2012 Yes No Yes No No Fiji 1977–1989, 1996–2001 No Yes No Yes Yes Finland 1970–2008 Yes Yes Yes Yes Yes France 1970–2009 Yes Yes Yes Yes Yes Gabon 1972–1978, 2001–2005 Yes Yes No Yes Yes Georgia 1998–2015 Yes Yes Yes Yes Yes Germany (pre-1991, Fed. Rep.) 1970–2008 Yes Yes Yes Yes Yes Greece 1995–2008 Yes Yes Yes Yes Yes Greenland 2003–2015 Yes No No No No Guatemala 2001–2012 Yes No Yes Yes Yes Guinea 2006–2013 Yes No Yes No No Honduras 1992–2015 Yes Yes Yes No No Hungary 1980–1989, 1995–2008 Yes Yes Yes Yes Yes Iceland 1973–2005 Yes Yes Yes Yes Yes India 1980–2008 Yes Yes No No No Iran 1994–2014 Yes Yes Yes Yes Yes continued on next page ADBI Working Paper 920 M. Guerriero 34 Appendix table continued Economy Time Series Adjustments to Value Added Adjustments to Self-employment Income Net of Indirect Taxes Net of Consumption of Fixed K Gross Mixed Income Workforce Composition Employees Employers Iraq 1997–2015 No Yes No Yes Yes Ireland 1970–2008 Yes Yes Yes Yes Yes Israel 1995–2011 Yes Yes No Yes Yes Italy 1970–2008 Yes Yes Yes Yes Yes Jamaica 1998–2015 Yes Yes No Yes Yes Japan 1970–2007 Yes Yes Yes Yes Yes Jordan 1970–2012 Yes Yes No Yes Yes Kazakhstan 1998–2013 Yes Yes Yes Yes Yes Kenya 1970–2013 Yes Yes No Yes No Kuwait 1992–2015 Yes Yes No Yes Yes Kyrgyz Republic 2001–2012 Yes Yes Yes Yes Yes Latvia 1994–2010 Yes Yes Yes Yes Yes Lesotho 1997– 20013 Yes Yes Yes Yes Yes Libya 1971–1979 No Yes No Yes Yes Liechtenstein 1998–2014 Yes Yes Yes No No Lithuania 1995–2009 Yes Yes Yes Yes Yes Luxembourg 1970–2008 Yes Yes Yes Yes Yes Malaysia 1970–1971, 1973, 1978, 1983 No Yes No Yes Yes Mali 1999–2013 Yes No No Yes Yes Malta 1973–2011 Yes Yes Yes Yes Yes Marshall Isl. 1997–2015 Yes Yes No No No Mauritania 2001, 2005–2006 Yes No No Yes Yes Mauritius 1970–2010 Yes No No Yes Yes Mexico 1980–2011 Yes Yes Yes Yes Yes Fed. States of Micronesia 1995–2015 Yes No Yes No No Monaco 2005–2009 Yes No No No No Mongolia 1995–2009 No Yes Yes Yes Yes Morocco 1998–2015 Yes No No Yes Yes Mozambique 1996–2012 Yes Yes Yes No No Namibia 1989–2015 Yes Yes No Yes Yes Netherlands 1970–2008 Yes Yes Yes Yes Yes Netherlands Antilles 1992–2008 Yes Yes Yes Yes Yes New Zealand 1971–2006 Yes Yes No Yes Yes Nicaragua 1994–2015 Yes Yes Yes Yes Yes Niger 1975–1977, 1995–2015 Yes Yes Yes No No Nigeria 1981–2013 Yes Yes No No No Norway 1970–2009 Yes Yes Yes Yes Yes Oman 1988–2015 Yes Yes Yes Yes Yes Palau 2000–2015 Yes No Yes Yes No Panama 1996–2012 Yes Yes Yes Yes Yes Papua New Guinea 1970–1974, 1983–1991, 1994–2006 Yes Yes No No No ADBI Working Paper 920 M. Guerriero 35 continued on next page Appendix table continued Economy Time Series Adjustments to Value Added Adjustments to Self-employment Income Net of Indirect Taxes Net of Consumption of Fixed K Gross Mixed Income Workforce Composition Employees Employers Paraguay 1994–2015 Yes Yes Yes Yes Yes Peru 1970–2011 Yes Yes Yes Yes Yes Philippines 1992–2012 Yes Yes No Yes Yes Poland 1991–2008 Yes Yes Yes Yes Yes Portugal 1977–2010 Yes Yes Yes Yes Yes Qatar 1995–2013 Yes Yes Yes Yes Yes Rep. of Korea 1970–2008 Yes Yes No Yes Yes Rep. of Moldova 1989–2014 Yes No Yes Yes Yes Romania 1995–2010 Yes No Yes Yes Yes Russian Federation 1989–2013 Yes Yes Yes Yes Yes Rwanda 1975–1989 Yes Yes No No No San Marino 1997–2014 Yes Yes Yes Yes Yes Saudi Arabia 1995–2009 Yes Yes Yes Yes Yes Senegal 1990–2014 Yes Yes No Yes Yes Seychelles 1976–1996 Yes Yes No Yes Yes Sierra Leone 1984–1990, 2001–2014 Yes Yes No No No Singapore 1980–2012 Yes Yes No Yes Yes Sint Maarten 2008–2014 No Yes No No No Slovakia 1993–2015 No Yes Yes Yes Yes Slovenia 1995–2009 Yes Yes Yes Yes Yes Solomon Islands 1984–1986 No Yes No No No South Africa 1970–2015 Yes Yes No Yes Yes Spain 1980–2008 Yes Yes Yes Yes Yes Sri Lanka 1983–2013 Yes No No Yes Yes Sudan 1972, 1978–1983, 1995–2010 Yes Yes No No No Suriname 2007–2010 Yes No No Yes Yes Sweden 1970–2008 Yes Yes Yes Yes Yes Switzerland 1990–2015 Yes Yes Yes Yes Yes Tajikistan 2000–2015 Yes Yes No Yes Yes Tanzania 1998–2013 No Yes No No No Thailand 1970–2015 Yes No No Yes Yes Trinidad and Tobago 1970–2009 Yes Yes No Yes Yes Tunisia 1992–2011 Yes Yes No Yes Yes Turkey 1987–2006, 2009–2015 Yes Yes Yes Yes Yes Ukraine 1989–2013 Yes Yes Yes Yes Yes United Arab Emirates 1983–1990, 2001–2014 Yes Yes No Yes Yes United Kingdom 1970–2005 Yes Yes Yes Yes Yes United States 1960–2011 Yes Yes Yes Yes Yes Uruguay 1997–2005 Yes Yes Yes Yes Yes Vanuatu 2001–2012 Yes No No No No Venezuela 1970–2015 Yes Yes Yes Yes Yes Yemen 1972–1982 No No No Yes Yes Zimbabwe 1970–1990, 2009–2015 Yes No Yes No No Source: UN National Accounts Statistics (available to download at: http://data.un.org/). ILO Statistics (available to download at: https://www.ilo.org/ilostat).