## wp17169

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### Trends in the labor share of income
- Global labor share declined 5 percentage points to its trough in 2006 and then trended up by about 1.3 percentage points; between 1991 and 2014 the global labor share declined by some 2 percentage points.
- In advanced economies (AEs) labor income shares began trending down in the 1980s, reached their lowest level of the past half century just prior to the global financial crisis of 2008–09, and have not recovered materially since.
- In emerging market and developing economies (EMDEs) labor shares declined in more than half of them since the early 1990s; declines are heterogeneous across countries.
- Between 1991 and 2014, the labor share declined in 29 of the largest 50 economies; those 29 economies accounted for about two-thirds of world GDP in 2014.
- For a sample of 35 advanced economies between 1991 and 2014, the labor share declined in 19 economies, which accounted for 78 percent of 2014 GDP.
- For a sample of 54 emerging market and developing economies, the labor share declined in 32 economies, which accounted for about 70 percent of 2014 emerging market GDP.
- The standard deviation of long-term changes in labor shares was 4.8 across EMDEs and 1.5 across AEs.

### Relationship between wages, productivity, and measurement
- Algebraic identity preserved from source: (wL)/(PY) = (w/P) / (Y/L), where w = money wage (including benefits) per worker; L = employment (hours worked); Y = real output; Y/L = labor productivity; P = GDP deflator; w/P = product wage.
- Product wage differs from consumption wage; consumption wage deflates nominal wage by the CPI and is preferred for purchasing-power assessment.
- Measurement challenges highlighted:
  - Self-employed labor compensation is imputed in national accounts; trends in self-employment affect measured labor shares.
  - Capital depreciation affects factor shares; removing depreciation raises measured labor share.
- Adjustments for self-employment and depreciation materially affect levels and evolution of labor shares, but identified key drivers remain robust to these adjustments (see robustness tables).

### Sectoral, skill-group, and composition findings
- Across industries, labor income shares declined in 7 of the 10 major industries, with sharpest declines in tradable sectors (manufacturing; transportation and communication).
- Shift-share decomposition: more than 90 percent of changes in labor income shares reflect within-industry declines rather than between-industry reallocation (exception: China, where reallocation from agriculture explains ~60 percent of the decline in 1991–2014).
- During 1995–2009, the combined labor income share of low- and middle-skilled labor was reduced by more than 7 percentage points.
- High-skilled labor share increased by more than 5 percentage points.
- Middle-skilled labor: decline in income share driven primarily by a drop in relative wage rate; middle-skilled employment share in total workforce remained stable or rose.
- Sectoral heterogeneity:
  - Sharpest global decline occurred in manufacturing, followed by transportation and communication.
  - Some sectors (food and accommodation; agriculture) saw increases in labor shares globally; in EMDEs agriculture showed the sharpest decline while manufacturing, health services, and construction saw rises (in part reflecting China).
- Skill decomposition results:
  - Technological change and GVC integration exert strong negative impacts on middle-skill labor shares, with little discernible effect on low- or high-skill shares.
  - Countries with higher routinization exposure and greater increases in GVC participation experienced stronger declines in middle-skilled labor share (examples: Austria, Germany, United States).

### Identified drivers and magnitudes (aggregate empirical results)
- Technology and relative price of investment (PI):
  - The relative price of investment declined by about 12 percent in advanced economies and by about 7 percent in EMDEs between 1993 and 2014.
  - About half of the total decline in labor shares in AEs can be traced to the impact of technology (measured via change in PI and interaction with initial routinization).
  - A decline of 15 percent in PI (the average decline in the sample) leads to:
    - a 0.4 percentage point decline in labor share in a country with relatively low initial routinization,
    - and about a 1.5 percentage point decline in a country with high initial exposure to routinization.
  - For a given change in PI, economies with high routinization exposure experienced about four times the decline in labor income shares than those with low exposure (empirical characterization).
- Global integration:
  - Global integration measured by value-added exports/imports, GVC participation (sum of forward and backward linkages), and financial integration (external assets and liabilities excluding reserves as percent of GDP).
  - An increase in intermediate goods imports of 4 percent of GDP (median increase in GVC integration) is associated with a 1.6 percentage point decline in the aggregate labor share on average, with larger impact in emerging markets.
  - GVC participation associated with rising capital intensity; empirical relations linking GVC participation to capital intensity include:
    - Δ log (capital stock/employment) = 0.32*** +1.19 Δ GVC participation
    - Δ log (capital stock/employment) = 0.479*** +2.71* Δ GVC participation
- Financial integration:
  - International financial integration depresses labor shares in advanced economies but raises them in emerging markets (empirical interpretation).
  - Financial integration can lower user cost of capital in EMDEs and partly offset declines in labor shares.
- Policies and institutions:
  - Declines in corporate income tax rates and union density occurred in both AEs and EMDEs.
  - Corporate tax declines show strong bivariate correlation with labor share trends but are not statistically significant once controlling for globalization and technology.
  - Employment protection legislation reforms have a statistically significant negative effect on labor shares within five years in stacked regressions, but are dominated by technology and trade in joint specifications.

### Key regression and robustness statistics (selected preserved coefficients)
- Baseline aggregate (selected):
  - Relative PI * Initial Routinization: 0.267*** (0.0969) ; 0.247*** (0.0779) ; 0.524*** (0.124)
  - Relative PI: 0.0847** (0.0380) ; 0.0444 (0.0336) ; 0.183** (0.0734)
  - Global Value Chain Participation: (5) -0.288*** (0.0717) ; (6) -0.253*** (0.0796) ; (6) -0.574*** (0.0962)
  - Financial Integration: (5) -0.234*** (0.0806) ; (6) -0.205*** (0.0607) ; (6) 1.72* (0.895)
  - Number of Observations reported across columns: 49, 50, 50, 26, 49, 49
  - R^2 reported across columns: 0.196, 0.288, 0.004, 0.377, 0.448, 0.636
- Stacked five-year regressions (selected):
  - Relative PI * Initial Routinization: 0.128** (0.0530) ; 0.273** (0.116)
  - Global Value Chain Participation: (1) -0.152** (0.0655) ; (5) -0.131** (0.0628)
  - Financial Integration: (1) 0.0890*** (0.0219) ; (5) 0.0784 (0.0568)
  - Employment Protection Legislation Reform: (1) -0.00207** (0.000806)
  - Number of Observations: 165, 165, 181, 154, 154, 153
  - R^2: 0.157, 0.197, 0.038, 0.238, 0.501, 0.834
- Robustness — user cost and offshoring measures confirm main results:
  - User cost specifications: Relative PI * Initial Routinization: 0.285*** (0.0743)
  - Alternative offshoring (intermediate goods trade): Intermediate Goods Trade coefficients include -0.499*** (0.161) ; -0.397*** (0.0979)

### Theoretical model highlights (Annex 2)
- Production uses continuum of tasks with CES aggregator: (α K^{1−1/ρ} + (1−α) L^{1−1/ρ})^{ρ/(ρ−1)}; task cost c(r,w;α,ρ) = (α^{ρ} r^{1−ρ} + (1−α)^{ρ} w^{1−ρ})^{1/(1−ρ)}.
- Labor income share for a task: LS = 1 / [1 + α^{ρ} (1−α)^{−ρ} (r/w)^{1−ρ}].
- Key comparative-static:
  - A fall in r/w reduces aggregate labor income share if and only if ρ>1 (elasticity of substitution greater than 1).
  - Proposition 1: A decline in the cost of capital causes more tasks with ρ<1 and fewer tasks with ρ>1 to be offshored from the high-wage country.
  - Combined declines in r and offshoring friction τ make tasks with ρ<1 particularly more likely to be offshored.
- Proposition 2 (special-case illustration): Offshoring together with declines in cost of capital and τ can reduce the labor income share in the high-wage country even if offshorable and non-offshorable tasks have equal average labor shares; global labor share can decline as offshored tasks reduce their labor shares in aggregate.
- Model assumptions noted: partial equilibrium treatment of w, w′, r; capital mobility for offshored projects; general equilibrium corroboration referenced as Lian (forthcoming).

### Policy implications and recommendations (tailored)
- Advanced economies:
  - Prioritize policies to help workers manage disruptions from technological progress and global integration: skill upgrading, lifelong learning, long-term education investment.
  - Facilitate worker reallocation: reduce costs of job search and transitions.
  - Consider longer-term redistributive measures for workers affected more permanently, tailored to national social contracts.
- Emerging market and developing economies:
  - Given GVCs’ role in raising productivity and living standards, focus on broadening access to gains from growth.
  - Emphasize skill deepening to prepare workers for further structural transformation and potential automation.
- Cross-cutting:
  - Monitor measurement issues (self-employment, depreciation) when designing policy responses and interpreting labor-share trends.
  - Recognize heterogeneity: policies should reflect country-specific exposure to routinization, degree of financial integration, and GVC participation.

*Source: IMF staff content in wp17169 (Annexes and Chapters summarized as provided).*

### Annex 1. Wages and Deflators ...........................................................................................

### Annex 1. Wages and Deflators

### Trends in the labor share of income
- The labor share of income—the share of national income paid in wages, including benefits, to workers—has been on a downward trend in many countries.
- In advanced economies, labor income shares began trending down in the 1980s, reached their lowest level of the past half century just prior to the global financial crisis of 2008–09, and have not recovered materially since.
- In emerging market and developing economies (EMDEs), data are more limited, but in more than half of them—and especially the larger economies in this group—labor shares have also declined since the early 1990s.
- Declines have been diverse across countries, both within the advanced economy and emerging market economy groups.

### Relationship between wages, productivity, and the labor share
- A falling labor share implies that product wages grow more slowly than average labor productivity.
- Formula presented in the source: (wL)/(PY) = (w/P) / (Y/L), where
  - w is the money wage (including benefits) per worker,
  - L is employment (hours worked),
  - Y is real output,
  - Y/L is labor productivity,
  - P is the GDP deflator,
  - w/P is the wage expressed in units of domestic output (the real product wage).
- The product wage may differ from the consumption wage (wages measured in terms of consumption); the consumption wage takes into account the terms of trade and is a preferred measure of the purchasing power of workers’ wage income (Annex 1).

### Heterogeneous causes of declining labor shares
- A decline in the labor share can reflect different underlying dynamics:
  - Rapid productivity growth accompanied by steadily rising labor incomes can produce a declining labor share as a byproduct of favorable development.
  - In several economies, declining labor shares result from the failure of product wage growth to keep up with weak productivity growth.
- The decline in labor shares has coincided with increases in income inequality for two main reasons:
  - Lower-skilled workers have borne the brunt of the fall in labor share amid evidence of persistent declines in middle-skilled occupations and income losses for middle-skilled workers in advanced economies.
  - Capital ownership is typically concentrated among the top of the income distribution, so an increase in the share of returns accruing to capital tends to raise income inequality.

### Evidence on inequality associated with labor share declines
- Lower labor shares are strongly associated with higher income inequality (measured by Gini coefficients) both across countries and over time within countries.
- Regression and fit statistics shown in figures:
  - y = –34.397***x + 62.053, R^2 = 0.1049
  - y = –38.319***x + 50.459, R^2 = 0.1305
  - y = –0.308***x, R^2 = 0.08
  - y = –0.305**x – 0.054, R^2 = 0.06

### Potential global drivers and heterogeneity across countries
- Many advanced and EMDEs have experienced declines through somewhat synchronized evolutions—through domestic business cycles and over a period of profound structural transformation—suggesting key driving forces that are likely global.
- Analysts focusing on the United States and advanced economies have concentrated on two leading explanations for the downward trends in labor shares:
  - The rapid advance of technology.
  - The globalization of trade and capital.
- Both technological progress and globalization have contributed strongly to overall growth and prosperity worldwide and to income convergence in EMDEs, despite adjustment costs for some worker groups.
- In EMDEs, rises in product wages in part reflect integration into the global economy; rising inequality in some EMDEs must also be viewed in the context of rising income levels for many households.

### Related empirical materials and figures (as presented in source)
- Figures referenced in the source include:
  - Figure 1. Evolution of the Labor Share of Income (AEs and EMDEs; weighting and normalization described).
  - Figure 2. Labor Shares and Income Inequality (levels; within-country changes annual and five-year averages).
  - Figure 3. Distribution of Estimated Trends in Labor Shares, 1991–2014.
  - Additional figures examine sectoral trends, skill composition effects, potential drivers, capital costs, global value chain participation, adjusted labor share, shift-share analysis, aggregate and sectoral results, and decomposition analyses (listed in source).
- Appendices and tables in the source provide estimated trends in labor shares across countries, heterogeneity in drivers, country coverage, data sources, baseline and robustness results, sectoral results by skill level, and measurement discussions.

_Implied source: wp17169 - Annex 1. Wages and Deflators (IMF staff content provided in the supplied document)._

### Chapter 2 of the April 2017 WEO documents that stronger capital inflows have tended to come with higher per

### wp17169 - Chapter 2 of the April 2017 WEO documents that stronger capital inflows have tended to come with higher per

### Overview
- The paper documents a broad decline in the labor share of income since the early 1990s, focusing on the period 1991 through 2014.
- The global labor share declined 5 percentage points to its trough in 2006 and then trended up by about 1.3 percentage points; between 1991 and 2014 the global labor share declined by some 2 percentage points.
- The evolution of labor shares is heterogeneous across countries, industries, and skill groups, with more dispersion in emerging market and developing economies (EMDEs) than in advanced economies (AEs).

### Key empirical findings and statistics
- Between 1991 and 2014, the labor share declined in 29 of the largest 50 economies; those 29 economies accounted for about two-thirds of world GDP in 2014.
- Across industries, labor income shares declined in 7 of the 10 major industries, with the sharpest declines in tradable sectors (manufacturing, and transportation and communication).
- A shift-share decomposition shows that more than 90 percent of changes in labor income shares reflect within-industry declines rather than between-industry reallocation. An important exception is China, where reallocation from agriculture accounts for the majority of the decline in the labor share.
- For a sample of 35 advanced economies between 1991 and 2014, the labor share declined in 19 economies, which accounted for 78 percent of 2014 GDP.
- For a sample of 54 emerging market and developing economies, the labor share declined in 32 economies, which accounted for about 70 percent of 2014 emerging market GDP.
- The standard deviation of long-term changes in labor shares was 4.8 across EMDEs and 1.5 across AEs.
- During 1995–2009, the combined labor income share of low- and middle-skilled labor was reduced by more than 7 percentage points.

### Drivers, mechanisms, and measurement
- Technology and the relative price of investment:
  - Technological advancement (measured by long-term change in the relative price of investment goods) and initial exposure to routinization are identified as the largest contributors to the decline in labor shares in advanced economies.
  - The empirical analysis suggests that about half of the total decline in labor shares can be traced to the impact of technology.
  - For a given change in the relative price of investment, economies with high exposure to routinization experienced about four times the decline in labor income shares than those with low exposure.
  - In EMDEs in the aggregate, there is no discernible role of technology in the evolution of labor shares, reflecting a relatively mild decline in the relative price of investment goods and much lower exposure to routinization.
- Global integration:
  - Global integration is measured by three variables: trade in final goods and services (value-added exports and imports relative to GDP), participation in global value chains (sum of forward and backward linkages), and financial integration (sum of external assets and liabilities excluding reserves, in percent of GDP).
  - Global integration, and participation in global value chains in particular, appears to be an important factor behind the decline in labor shares in EMDEs.
  - Financial integration in EMDEs has partly offset the decline in labor shares by raising labor shares, conceivably by lowering the user cost of capital and reflecting limited substitutability between labor and capital.
- Other factors:
  - Trade and financial integration may lower labor’s bargaining power by increasing competitive pressure and relocation threats.
  - Changes in product market structure, regulation of labor and product markets, declining corporate income tax rates, and changes in unionization rates may also affect labor shares; empirical quantification of these policy/institutional effects is limited and may be difficult to separate from trends in global integration and de-unionization.
- Measurement issues:
  - Two measurement challenges are self-employed individuals (whose labor compensation is not recorded separately) and capital depreciation (which arguably should be removed from factor shares).
  - Adjustments for self-employment and depreciation can materially affect levels and evolution of labor shares, but the paper finds that the identified key drivers of the unadjusted labor shares are robust to adjustments for both self-employment and depreciation rates.

### Sectoral and skill-group patterns
- Industry patterns:
  - The sharpest global decline in labor share occurred in manufacturing, followed by transportation and communication.
  - Some sectors (food and accommodation, agriculture) witnessed increases in labor shares at the global level; in EMDEs the sharpest decline was observed in agriculture, and labor shares rose in manufacturing, health services, and construction (partly reflecting developments in China).
- Skill patterns:
  - The decline in labor shares driven by technology and global integration has been particularly sharp for middle-skilled labor.
  - The pattern is consistent with routine-biased technological change displacing many tasks performed by middle-skilled workers and contributing to job polarization toward high-skill and low-skill occupations.

### Methodology and research questions
- Two complementary empirical approaches:
  - Shift-share analysis to decompose changes into within-industry declines versus between-industry reallocation.
  - Regression-based quantification using a newly assembled dataset on aggregate and sectoral labor shares for AEs and EMDEs, and labor shares by skill groups.
- Core research questions:
  - How widespread has the decline in the labor share of income been since the early 1990s, and how has it differed across countries, industries, and skill groups?
  - What are the key drivers of the labor share and through which mechanisms do they operate? Do drivers vary between AEs and EMDEs, industries, and skill groups?
  - How have exposures to routinization and participation in global value chains affected labor shares? What roles have regulations of labor and product markets played?

*Source: IMF staff summary of Chapter 2 (April 2017 WEO) as presented in the provided content.*

### 3. Composition of Hours

### 3. Composition of Hours

### Key empirical patterns: AEs and EMDEs
- The decline in the labor share of income for low- and middle-skill workers has been especially pronounced.
- High-skilled labor share increased by more than 5 percentage points.
- The decline in middle-skilled labor’s income share was driven primarily by a drop in their relative wage rate.
- The share of middle-skill employment in the total workforce remained stable or even rose.
- The labor share decline for low-skilled labor and the increase for high-skilled labor were also driven, to a large extent, by the diverging trend in employment composition, reflecting rising levels of education.
- The broad patterns hold for both advanced economies (AEs) and emerging market and developing economies (EMDEs), but they are more pronounced in advanced economies, consistent with evidence of wage and employment polarization in these economies.

### Conceptual driver: elasticity of substitution between capital and labor
- The elasticity of substitution between capital and labor measures how easily one is substituted with the other when their relative cost changes.
- When capital is highly substitutable for labor (the elasticity of substitution is larger than 1), a decline in the relative cost of capital drives firms to substitute capital for labor to such a high degree that, despite the lower cost of capital, the labor share of income declines.
- If, for the tasks offshored from high-wage to low-wage countries, capital cannot easily be replaced by labor (the elasticity of substitution is lower than 1), the labor income share may decline in the receiving country.
- The theoretical model (Annex 2, Proposition 1) suggests that offshoring from advanced economies may indeed involve tasks with lower elasticity of substitution. The key insight is that the capital deepening induced by a decline in the relative price of investment goods renders tasks with a high elasticity of substitution less labor-intensive, which in turn implies that firms benefit less from offshoring these tasks to low-wage destinations.

### Main categories of drivers of labor shares
- Technological advancement
  - Technological progress, embodied in faster productivity growth in the capital goods sector relative to the rest of the economy, lowers the price of investment goods and thus induces firms to substitute capital for labor.
  - The paper emphasizes the rapid advance of information and communications technologies as a relevant technological force.
- Global integration
  - Offshoring and global value chain participation interact with capital–labor substitution elasticities and can influence labor shares in both sending and receiving countries.
- Policies, institutions, and regulation of labor and product markets
  - Country-specific policies, regulations, and reforms (for example, corporate taxation rates and unionization rates) can influence labor shares and may themselves reflect global competitive pressures.
- Measurement issues
  - Measurement challenges can affect the assessment of labor share trends and drivers.

### Empirical methods and data notes (as reported)
- Sources include: World Input-Output Database; Eora Multi-Region Input-Output database; Integrated Public Use Microdata Series International; Integrated Public Use Microdata Series USA; International Labour Organization; Karabarbounis and Neiman (2014); national authorities; Organisation for Economic Co-operation and Development; United Nations database; and IMF staff calculations.
- Panel 2 in the referenced figures shows estimated trends in the labor share. Trend coefficients are reported on the y-axis in units per 10 years.
- Panels 1, 3 and 4 show fixed effects from regressions that also include country fixed effects to account for entry and exit during the sample. The regressions are weighted by nominal GDP in current U.S. dollars. Fixed effects are normalized to reflect the respective variable's level in 1993.
- Notes: AEs = advanced economies; EMDEs = emerging market and developing economies; CIT = corporate income tax rate; UD = union density rate.

*Source: wp17169 - 3. Composition of Hours*

### 4. Corporate Income Tax

### 4. Corporate Income Tax

### Relative price of investment and capital deepening
- The relative price of investment has declined more in advanced economies than in emerging market and developing economies.
- Between 1993 and 2014 the relative price of investment declined by about 12 percent in advanced economies and by about 7 percent in emerging market and developing economies as a whole.  
- Technological advancement accelerates automation of routine tasks and induces firms to substitute capital for labor more where exposure to routine tasks is larger.
- Empirical relations (as presented in sectoral/aggregate regressions):
  - Δ log (capital stock/employment) = 0.26*** – 0.93** Δ log(PI)
  - Δ log (capital stock/employment) = 0.48*** – 0.07 Δ log(PI)
  - Δ log (capital stock/employment) = 0.57*** – 0.40*** Δ log(PI)

### Global value chains (GVCs), trade integration, and capital intensity
- Global value chain participation increased in both advanced economies and emerging market and developing economies.
- Rising global value chain participation is associated with increasing capital intensity in production, particularly in emerging market and developing economies.
- Empirical relations linking GVC participation to capital intensity:
  - Δ log (capital stock/employment) = 0.32*** +1.19 Δ GVC participation
  - Δ log (capital stock/employment) = 0.479*** +2.71* Δ GVC participation
- Mechanism described:
  - Declines in communication and transportation costs enabled unbundling of production into tasks and offshoring to exploit factor cost disparities.
  - In advanced economies, relatively labor-intensive tasks are offshored, making domestic production more capital-intensive and lowering labor shares.
  - In recipient emerging market and developing economies, offshored tasks may have low substitutability between capital and labor or be relatively high-capital-share tasks in local context, which can raise capital shares and lower labor income shares there as well.

### Financial integration
- Financial integration can affect labor shares via two channels:
  - By facilitating relocation of production to countries with cheaper inputs, capital mobility lowers labor’s bargaining position.
  - By increasing access to capital, financial integration lowers the cost of capital in capital-scarce countries, facilitating capital deepening and potentially inducing substitution of capital for labor.
- The second channel may be especially relevant for emerging market and developing economies where financial frictions and credit rationing are more prevalent, and benefits of financial integration accrue largely to high-skilled workers whose skills are more complementary with capital.

### Policies, institutions, and regulations
- Corporate income taxes and union density rates declined in both advanced economies and emerging market and developing economies.
- A decline in corporate income tax rates can raise the relative return to capital, inducing substitution of capital for labor and lowering the labor share of income.
- Declining unionization rates can reflect lower bargaining power of labor, also causing a decline in labor income shares.
- Changes in labor and product market regulations, and changes in market structure (for example, rising concentration and “winner-take-most” dynamics), may affect factor shares through impacts on rents and profit shares.

### Measurement issues affecting labor share estimates
- Two important measurement challenges:
  - The labor income of the self-employed is imputed in national accounts; trends in self-employment can affect measured labor shares.
  - Depreciation of capital can affect the calculation of factor income shares; depreciation arguably should be discarded from factor income shares since it cannot be consumed by workers or capital owners.
- Adjustments:
  - Adjusting for self-employment and depreciation would in general raise the level of the labor share.
  - Falling self-employment rates would, all else equal, make the labor share decline steeper; rising capital depreciation rates would make the decline less pronounced.
- The paper treats measurement issues as a substantive factor and reports robustness of results to different measures of the labor share of income (see evolution plots of unadjusted, self-employment adjusted, and self-employment and depreciation adjusted labor shares).

### Stylized summary findings
- Technological advances and the steep fall in the relative price of investment in advanced economies have triggered greater substitution of capital for labor in advanced economies than in emerging market and developing economies.
- Countries with higher initial routine exposure experienced larger subsequent declines in labor shares.
- Global value chain participation and financial integration are associated with rising capital intensity and can contribute to declining labor shares through multiple channels.
- Policy and institutional changes—corporate income tax declines, falling union density, and regulatory shifts—can reinforce substitution toward capital and lower labor shares.

*Source: IMF Working Paper — Chapter 4, “Corporate Income Tax and Union Density” (wp17169 - 4. Corporate Income Tax).*

### 1. Advanced Economies2. Emerging Market and

### 1. Advanced Economies2. Emerging Market and

### Shift-Share Analysis
- Sample: 27 advanced economies and 13 emerging market and developing economies across 10 one-digit industries (ISIC).
- Method: Decomposes trend changes in labor shares into within-industry and between-industry components (shift-share).
- Main findings:
  - More than 90 percent of the total trend change in labor shares is accounted for by the within-industry component.
  - Over 70 percent of variation is explained by within two-digit sector variation.
  - Most countries cluster around the 45-degree line in the within-versus-total plot, indicating within-industry changes dominate.
  - Exception: China — reallocation from industries with relatively high labor shares (notably agriculture) to expanding industries with lower labor shares (such as wholesale trade and transportation and communication) accounts for some 60 percent of the total decline in the labor share during 1991–2014.
- Robustness: Similar findings when repeated for 22 OECD economies using two-digit (31-sector) data, though countries deviate slightly more from the 45-degree line; between-sector reallocation often tended to increase labor shares in advanced economies in that exercise.
- Qualifications: Shift-share decomposition limitations noted (e.g., structural changes within sectors like internet commerce; aggregation at two-digit level may still be coarse).

### Analysis of Long-Term Changes in the Aggregate Labor Share of Income
- Approach:
  - Focus on long-term annualized changes during 1991–2014 to relate labor share trends to long-term changes in potential drivers (technology, global integration, policies/institutions).
  - Baseline aggregate regression estimated on a sample of 49 countries (31 advanced economies and 18 emerging market economies) with at least 10 years of data in 1991–2014.
  - Key regressors include: change in the relative price of investment goods (PI), initial exposure to routinization (RTI0), measures of globalization (total goods trade, trade in intermediate goods, global value chain participation, imported intermediate inputs as percent of gross value added), financial globalization (external assets and liabilities excluding reserves in percent of GDP), and policy/institutional factors (changes in union density, corporate taxation, employment protection legislation, product market reforms).
  - Policy reform indicators constructed from Fraser Institute Economic Freedom of the World data (hiring and firing regulations; business regulations), using country-specific thresholds to identify major deregulations (value 1), major regulations (value –1), or otherwise zero.
- Key parameterized findings (preserve numeric magnitudes as reported):
  - A decline of 15 percent in the relative price of investment goods (the average decline in the sample) leads to:
    - a 0.4 percentage point decline in the labor share in a country with relatively low initial exposure to routinization,
    - and about a 1.5 percentage point decline in a country with high exposure to routinization.
  - An increase in intermediate goods imports of 4 percent of GDP (corresponding to the median increase in GVC integration in the sample) is associated with a 1.6 percentage point decline in the aggregate labor share, on average, with a significantly larger impact in emerging markets.
- Interpretation of financial integration:
  - International financial integration depresses labor shares in advanced economies but raises them in emerging markets.
  - Rationale: For advanced economies (typically sources of cross-border capital), rising capital mobility increases capital’s bargaining power and lowers labor shares. For emerging markets, capital inflows lower the cost of capital and, with limited substitutability of capital for labor, can raise the labor share—particularly by raising the labor income share of high-skilled workers.

### Empirical Decomposition and Model Fit
- Overall model performance:
  - The empirical model explains about two-thirds of the evolution of aggregate labor share trends across countries during 1991–2014.
  - Notable outliers: China (significant between-sector/industrial composition effects) and South Africa (predicted rise in labor share driven by financial integration, but actual outcomes influenced by extractive-industry-driven cross-border flows).
- Relative contributions (aggregate decompositions, advanced economies vs emerging markets):
  - Advanced economies (AEs):
    - Technology (declining relative price of investment goods and initial exposure to routinization) is the largest contributor, accounting for almost half of the overall decline in labor shares.
    - Global integration (in particular, GVC participation and financial integration) accounts for about half as much as technology (approximately one quarter of the decline).
  - Emerging markets (EMs):
    - GVC participation is the dominant factor for labor share declines.
    - Financial integration tends to offset declines (positive effect on labor share).
    - Technology plays a much smaller role relative to AEs.
- Additional notes:
  - Declines in corporate income taxation show a strong bivariate correlation with labor share trends but are not statistically significant once controlling for contemporaneous globalization and technological trends.
  - The model underscores difficulty separating impacts of technology from global integration and policies, but nonetheless attributes substantial shares of declines to these channels.

*Source: IMF staff calculations and analysis in wp17169 (excerpts provided).*

### 1. Actual and Predicted Average Annual

### 1. Actual and Predicted Average Annual Changes in Labor Shares

### Aggregate findings for advanced economies and emerging market and developing economies
- Advanced economies:
  - Many countries with relatively high exposure to routinization experienced large declines in labor shares; the United States and Germany cited as examples because of high routinization exposure and rising integration into global value chains.
  - The United Kingdom exhibited a modest increase in labor share that does not conform to the general pattern of decline.
  - Finland and Norway had low exposure to routinization and, as predicted, experienced a trend increase in labor shares.
- Emerging market and developing economies (EMDEs):
  - Global integration has had large but partially offsetting effects: participation in global value chains (GVCs) lowered labor shares, while financial integration raised them.
  - Technology's aggregate effect on labor shares is very small, but heterogeneous across countries.
  - There is greater cross-country variation in the relative contribution of drivers than in advanced economies. Examples:
    - Brazil: the increase in the relative price of investment goods together with financial integration explain about half of the trend rise in labor share; participation in GVCs plays a negligible role.
    - Turkey: the decline in labor share is explained almost exclusively by rapid rise in participation in GVCs; technology plays a limited role reflecting very low exposure to routinization.

### Robustness of aggregate-level regression results
- Stacked regressions (panel of nonoverlapping five-year periods) and specification:
  - Five-year periods used (t = 1992–96, 1997–2001, 2002–06, 2007–11, depending on country), stacked for each country c.
  - Stacked regression equation as specified in the source (variables defined as in baseline but with t denoting five-year periods).
  - Advantages: increased number of observations; allows control for country-specific trends and period-specific unobservables.
  - Drawbacks: loses some trend changes discernible only over horizons longer than five years; cyclical and temporary factors not completely purged.
- Key robustness findings from stacked-differences regressions (Table 4):
  - Results strongly confirm baseline findings.
  - Impact of technology similar in magnitude but less precisely estimated (adjustments to technological change materialize over longer horizons).
  - Effect of GVC participation very similar to trend results (implying faster adjustment to globalization forces than to technology).
  - Employment protection legislation reforms have a statistically significant negative effect on labor shares within five years of reform, but are swamped by technology and trade in joint specification.
- Alternative measures of cost of capital (Table 5):
  - Baseline rerun on smaller sample with user cost of capital data.
  - Comprehensive measure of user cost of capital (UCC) derived as:
    - UCC = PI*(real IR + depreciation rate)
    - real interest rate (IR) computed using long-term (10-year) government bond yields deflated by long-term inflation expectations (available for a subsample of 40 countries).
  - Findings:
    - UCC affects labor shares similarly to the price of investment (PI), though less significant (possible measurement error from additional variables, especially depreciation rates).
    - Accounting for general financial deepening (trends in private credit as share of GDP) raises the labor share, driven mostly by the EMDE sample; consistent with average elasticity of substitution < 1 in this group.
    - Effect of participation in GVCs remains significantly negative and of similar magnitude to baseline.
- Alternative measures of offshoring exposure (Table 6):
  - Column 1: intermediate imported input share (percent of GDP) used instead of GVC participation.
  - Column 2: share of imported intermediate goods in total intermediate goods used (controls for production complexity).
  - Column 3: intermediate import shares excluding commodities for subsample with detailed product categories (rules out commodity price swings driving result).
  - Column 4: intrinsic/de jure trends in offshoring measured by interacting initial offshorability index (microlevel occupation data) with trend in import price index.
  - All specifications confirm that globalization in intermediate trade negatively affected labor shares.
- Other robustness checks (Table 7 and Table 8):
  - Robust regression (Huber iteration) dropping gross outliers; weighting countries by average GDP (PPP); excluding transition economies; adding trends in demographics (old-age dependency ratio), trend change in migrant stocks and human capital (relative high-skill supply), initial GDP per capita; ending sample in 2007 to exclude global financial crisis.
  - Labor share data adjusted for self-employment and capital depreciation (see IMF, 2017b for construction) preserve the impact of main drivers in sign and magnitude.
  - Overall: main drivers of labor share trends in cross-section largely preserved across robustness checks.

### Sectoral analysis: long-term changes in sectoral labor shares
- Data and scope:
  - Sample restricted to 27 advanced economies with country-sector data for at least 10 years.
  - Sectoral analysis complements aggregate analysis by revealing heterogeneity across industries and cross-country differences within industries (e.g., manufacturing declines in only about two-thirds of countries).
- Empirical strategy at sectoral level:
  - Cross-sectional regressions at country-sector level:
    - LŜ_cs = β1′ Ĝ_cs + β2 PÎ_cs + [β3 RTI0_cs + β4 RTI0_cs PÎ_cs] + γ0′ FE_c + γ1′ FE_s + ε_cs
    - Long-term changes (hats) in sectoral labor shares related to long-term changes in globalization (G, including total, intermediate trade and financial integration), long-term changes in sectoral relative prices of investment (PI), and interactions with sectoral routinization scores (RTI0). Country and sector fixed effects included.
  - Table 9 provides regression results underlying Figure 13, highlighting differences between tradables and nontradables.
- Main sectoral findings:
  - A model incorporating trade and technology explains observed changes in labor shares reasonably well (Figure 13, panel 1).
  - Technology plays a large role in advanced economies (Figure 13, panel 2, and Table 9).
  - Declines in the relative price of investment associated with declines in labor shares, especially for sectors with higher initial routinization.
    - Predicted relatively large declines in manufacturing, mining and quarrying, and transportation (high initial routinization).
    - Predicted increases in agriculture and wholesale and retail trade (low initial exposure to routinization).
  - Quantitative example:
    - The median decline in the price of investment was about 15 percent over 25 years.
    - This median decline would predict a 1.8 percentage point decline in the labor share of a country-sector at the 25th percentile of routinization and an approximately 3.8 percentage point decline in the labor share of a country-sector at the 75th percentile of routinization.
    - The effect of a decline in the price of investment on a highly routinized country-sector is roughly double that for a low-exposure sector.
    - Example matches observed patterns: restaurants and hotels in the United States (low exposure) and manufacturing in Italy (high exposure).
  - Increasing participation in global value chains is associated with declines in labor shares only in tradables sectors.
- Interpretation and caveats:
  - Aggregate results can mask offsetting sectoral contributions (e.g., negative impact of GVCs in tradables offset by positive impact in nontradables).
  - Sectoral analysis is potentially more robust to unobserved country- or sector-specific factors due to inclusion of country and sector fixed effects but has limitations: smaller country coverage and shorter time series.
  - Sectoral results should be seen as complementing aggregate findings.

*Source: IMF staff calculations (excerpt from "1. Actual and Predicted Average Annual Changes in Labor Shares").*

### 1. Actual and Predicted Average Annual

### 1. Actual and Predicted Average Annual Changes in Labor Shares

### Overview
- The paper documents a downward trend in the labor share of income at the global level since the early 1990s, with heterogeneity across countries, sectors, and skill groups.
- In the vast majority of economies, within-sector declines, rather than reallocation toward low-labor-share sectors, have driven the overall decline in labor’s share of income.
- The empirical analysis identifies technology and global integration as dominant drivers, with differing roles across advanced and emerging market economies.

### Sectoral drivers: tradables versus nontradables
- The model is estimated separately for tradables and nontradables to assess differential roles of trade and technology.
- Increasing participation in global value chains is associated with declines in labor shares only in the tradables sectors; global value chain participation does not have a statistically significant effect on nontradables sectors.
- The model predicts a 6 percentage point larger decline in labor shares in manufacturing (around the 75th percentile of the distribution of routinization) than in restaurants and hotels (around the 25th percentile of the distribution of routinization); this is very similar to observed differences.
- In advanced economies, technological progress—reflected in the steep decline in the relative price of investment goods—has been the key driver, along with high exposure to routine occupations that could be automated; global integration also plays a role but to a smaller extent.
- In emerging markets as a group, the evolution of labor shares is explained predominantly by global integration, with a more limited role for technology; this reflects a much less pronounced decline in the relative price of investment goods and lower exposure to routinization.

### Analysis of labor shares by skill
- Sample coverage:
  - Aggregate analysis by skill focuses on a sample of 27 advanced economies and 10 emerging market economies.
  - Sectoral analysis by skill is based on a sample of 27 advanced economies and 5 emerging market economies.
- Skill definitions (World Input-Output database, ISCED 1997):
  - Low skilled = workers with primary and lower secondary education.
  - Middle skilled = workers with upper secondary or postsecondary, nontertiary education.
  - High skilled = workers with first-stage tertiary education or higher.
- Empirical construction:
  - Labor compensation by skill = World Input-Output database’s skill level labor compensation as a percent of total labor compensation, multiplied by labor compensation data at country and sector levels.
  - Labor share by skill = labor compensation by skill divided by value added, at both country and sector levels.
- Key findings:
  - The labor income share of high-skilled workers has been increasing while that of middle- and low-skilled workers has been declining.
  - Decompositions (Figure 14) indicate:
    - Increases in high-skill and decreases in low-skill labor shares are driven predominantly by common shifts to skill supply across countries (through higher educational attainment, for example).
    - Technological change and global value chain integration exert strong negative impacts on middle-skill labor shares, consistent with the hollowing-out hypothesis.
  - Both technological advancement and participation in global value chains have lowered the income share of middle-skilled workers but have had little discernible effect on those of low- or high-skilled workers.
  - Countries with higher exposure to routinization and greater increases in participation in global value chains have experienced stronger declines in the middle-skilled labor income share; this has been especially pronounced in Austria, Germany, and the United States.
  - The stronger negative effect of global value chain participation over technology for the middle-skilled labor share is based on a sample that includes emerging market and developing economies; for a sample consisting only of advanced economies, technology plays a much larger role relative to global value chain participation.

### Sectoral and within-country cross-sector analysis
- Because exposure to routine-biased technological progress differs across sectors, sector-level analysis shows that industries with higher exposure experience stronger declines in their middle-skilled labor income shares.
- Results are consistent and robust across cross-country and within-country cross-sector exercises, although coefficients are not strictly comparable due to differences in sample composition, measurement error, and factor mobility across sectors versus countries.
- The impact of technological advancement on the middle-skilled labor income share is similar after controlling for changes in employment composition (share of each skill group in total hours), suggesting the decline occurs mostly through wage adjustment or relocation within broadly defined sectors.
- The results also exhibit capital-skill complementarity: the coefficient on the relative price of investment suggests that low-skilled workers are more likely to be replaced by capital than middle- and high-skilled workers.

### Wages, deflators, and measurement implications
- Real wages can be constructed using:
  - Consumption wage: nominal wage deflated by the consumer price index (CPI) — relevant for workers’ purchasing power and welfare.
  - Product wage: nominal wage deflated by the GDP deflator — relevant for firms’ hiring incentives and comparisons with productivity.
- For open economies, differences between CPI and GDP deflator matter because changes in import prices (for example, oil) can raise the CPI relative to output prices; consumption wages can therefore appear to fall relative to productivity even when driven by deflator differences.
- Empirical patterns:
  - Wage growth has been lagging productivity growth, indicating labor has been receiving an ever-smaller share of national income.
  - On average, consumption wages have increased less than product wages, and both have lagged productivity (Figures 15–16).

### Summary implications and policy recommendations
- Main conclusions:
  - The decline in the aggregate labor income share has been borne disproportionately by middle-skilled workers, particularly in advanced economies.
  - Technology (automation and the falling relative price of investment goods) is the dominant driver in advanced economies; global integration is the dominant driver in emerging markets.
  - In emerging markets, the decline linked to global integration has coincided with capital deepening, productivity gains, and strong growth in wages and employment.
- Policy guidance (tailored to country circumstances):
  - Advanced economies:
    - Policies should help workers cope with disruptions from technological progress and global integration, including through skill upgrading.
    - Long-term investment in education and opportunities for lifelong learning and skill upgrading could reduce disruptions from technological change.
    - Policies facilitating reallocation of displaced workers—reducing costs of job search and transitions—should be a priority.
    - For workers affected more permanently, longer-term redistributive measures may be required and should be tailored to national social contracts.
  - Emerging market and developing economies:
    - Given the role of global integration in raising productivity and living standards, policy focus should be on making access to opportunities and gains from growth broadly shared.
    - Skill deepening policies are important to prepare workers for further structural transformation and potential future automation.

*Source: IMF staff calculations and analysis as presented in "1. Actual and Predicted Average Annual Changes in Labor Shares" (wp17169).*

### Annex 2. A Theoretical Model of Relative Cost of Capital, Offshoring, and Labor

### Annex 2. A Theoretical Model of Relative Cost of Capital, Offshoring, and Labor

### Model setup and primitives
- Production of a continuum of tasks indexed by parameters {α, ρ} uses capital K and labor L with a CES production function: (α K^{1−1/ρ} + (1−α) L^{1−1/ρ})^{ρ/(ρ−1)}, where α and ρ govern capital intensity and elasticity of substitution, and both can differ across tasks.
- Cost of producing one unit of task {α, ρ}: c(r,w;α,ρ) = (α^{ρ} r^{1−ρ} + (1−α)^{ρ} w^{1−ρ})^{1/(1−ρ)}, with r the cost of capital and w the wage.
- Labor income share of task {α, ρ}: LS = 1 / [1 + α^{ρ} (1−α)^{−ρ} (r/w)^{1−ρ}].
- Offshoring setup: two countries (high-wage w and low-wage w′ with w′<w). Cost in low-wage country includes frictions τ: (1+τ) c(r,w′;α,ρ).
- Set of offshored tasks A defined by tasks for which c(r,w;α,ρ) > (1+τ) c(r,w′;α,ρ).
- Equivalent characterization (log form): A ≜ { (α,ρ,τ) : ∫_{w′}^{w} (∂ ln c(r,z;α,ρ)/∂z) dz > ln(1+τ) }.

### Key mechanism linking relative cost of capital, offshoring, and labor shares
- Three important drivers of the cost of capital—price of investment goods, interest rate, and corporate income tax—have declined substantially since the early 1980s (motivating the analysis).
- The elasticity of substitution ρ is critical:
  - A fall in the relative cost of capital r/w leads to a decline in the labor income share if and only if ρ>1.
- Capital mobility is assumed (cost of capital r treated as equal across high- and low-wage countries for offshored projects), implying offshoring contributes to capital deepening in recipient locations.

### Comparative-static result on offshoring patterns (Proposition 1)
- Proposition 1: A decline in the cost of capital causes more tasks with ρ<1 and fewer tasks with ρ>1 to be offshored from the high-wage country to the low-wage country.
- Mathematical sign: ∂^2 ln c(r,w;α,ρ) / ∂w ∂r = (ρ−1) r^{ρ−2} w^{−ρ} ( (1−α)/α )^{ρ} [1 + ((1−α)/α)^{ρ} (w/r)^{1−ρ}]^{−2}, so the cross-derivative is
  - >0 if ρ>1
  - <0 if ρ<1
- If r falls from r1 to r2<r1, then for any ρ>1 the integral ∫(...) dz over w′→w decreases, and for any ρ<1 it increases; hence the set of offshored tasks expands among tasks with ρ<1 and contracts among tasks with ρ>1.

### Combined effects of declining costs of capital and offshoring
- Declines in offshoring costs τ make all tasks more likely to be offshored (regardless of ρ).
- Combined effect: when both r and τ decline, tasks with ρ<1 become particularly more likely to be offshored than tasks with ρ>1 (illustrated in Figure 17 in the source).
- Intuition: falling r encourages automation of tasks with high substitutability (ρ>1) in the high-wage country and makes tasks with low substitutability (ρ<1) relatively more attractive to offshore; further declines in τ then expand offshoring across tasks, reinforcing the relative increase in offshoring of ρ<1 tasks.

### Impact on labor income shares (Proposition 2 and implications)
- Special-case example for clarity:
  - Offshorable tasks: Leontief production F(K,L) = min{K/a, L} → zero elasticity of substitution (ρ=0).
  - Non-offshorable tasks: Cobb–Douglas → ρ=1.
  - Consumer preferences: log utility over tasks (so expenditure share on each task is constant).
- Proposition 2: If the average labor income share of offshorable tasks equals that of non-offshorable tasks, offshoring driven by declines in costs of capital and offshoring can reduce the labor income share in the high-wage country.
  - For a Leontief task a, labor income share = wL / F(K,L) = 1 / (1 + a r/w).
  - Offshored tasks satisfy a < a* where a* = (w − (1+τ) w′) / (τ r); remaining tasks have higher a, are more capital intensive, and thus the aggregate labor income share in the high-wage country declines.
- Global effect: log preference and constant task expenditure shares imply a decline in labor income share within offshored tasks reduces global labor income share.
- Effect in the low-wage country: offshoring raises the share of tasks with low elasticity of substitution (ρ<1) in the low-wage economy. If the average labor income share of ρ<1 tasks is substantially lower than that of ρ≥1 tasks, offshoring can reduce the aggregate labor income share in the low-wage country as well—especially when capital scarcity and credit rationing limit firms’ ability to substitute labor for capital.

### Assumptions, scope, and robustness notes
- Partial equilibrium analysis: w, w′, and r are taken exogenously. Lian (forthcoming) provides a general equilibrium analysis that corroborates main conclusions given abundant labor supply in emerging market and developing economies.
- Assumption that r is effectively equal across countries for offshored projects is motivated by foreign direct investment enabling relatively low project-level capital costs.
- The mechanism emphasized does not claim the decline in r is the main cause of offshoring; it holds with other drivers (e.g., declining offshoring costs) because those drivers make all tasks more likely to be offshored and do not offset the compositional mechanism.
- Special-case and comparative-static results illustrate direction and mechanism; full quantitative effects require calibration/simulation (see Lian, forthcoming).

### Empirical coverage and data (as used elsewhere in the paper)
- Country sample for aggregate analysis: 31 advanced economies and 18 emerging market economies (countries with at least 10 years of labor share data over the 1991–2014 period).
- Sectoral sample: 27 advanced economies for sectoral analysis.
- Skill-based sample: 27 advanced economies and 10 emerging market economies at aggregate level; 27 advanced economies and 5 emerging market economies at sectoral level.
- Data sources assembled include national authorities, OECD, Karabarbounis and Neiman (2014), IMF World Economic Outlook, CEIC, Penn World Tables 9.0, World Bank WDI, World Input-Output database, Eora MRIO, UNIDO, UN Comtrade, Autor and Dorn (2013) for routine/manual/abstract measures, and Blinder and Krueger (2013) for offshorability; employment by industry and occupation data from ILO, IPUMS International, IPUMS USA, and National Bureau of Statistics of China.

*Source: Annex 2. A Theoretical Model of Relative Cost of Capital, Offshoring, and Labor (wp17169).*

### Annex 4. Tables

### Annex 4. Tables (wp17169)

### Country coverage
- Aggregate Long-Term Analysis: Australia, Austria, Belgium, Canada, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Korea, Latvia, Lithuania, Malta, Netherlands, New Zealand, Norway, Portugal, Singapore, Slovak Republic, Slovenia, Spain, Sweden, United Kingdom, United States; Brazil, Bulgaria, Chile, China, Costa Rica, Egypt, Hungary, Indonesia, Kyrgyz Republic, Mexico, Morocco, Peru, Philippines, Poland, Romania, South Africa, Thailand, Turkey
- Aggregate Stacked Five-Year Analysis: Australia, Austria, Belgium, Canada, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Korea, Latvia, Lithuania, Malta, Netherlands, New Zealand, Norway, Portugal, Singapore, Slovak Republic, Slovenia, Spain, Sweden, United Kingdom, United States; Bolivia, Brazil, Bulgaria, Chile, China, Croatia, Egypt, Hungary, Indonesia, Jamaica, Kyrgyz Republic, Mexico, Morocco, Namibia, Peru, Philippines, Poland, Romania, South Africa, Tanzania, Thailand, Turkey, Venezuela
- Sectoral Analysis: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Korea, Netherlands, New Zealand, Norway, Portugal, Singapore, Slovak Republic, Slovenia, Spain, Sweden, United Kingdom, United States
- Aggregate Analysis by Skill: Australia, Austria, Belgium, Canada, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Japan, Korea, Latvia, Lithuania, Malta, Netherlands, Portugal, Slovak Republic, Slovenia, Spain, Sweden, United Kingdom, United States; Brazil, Bulgaria, China, Hungary, India, Indonesia, Mexico, Poland, Romania, Turkey
- Sectoral Analysis by Skill: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Korea, Netherlands, New Zealand, Norway, Portugal, Singapore, Slovak Republic, Slovenia, Spain, Sweden, United Kingdom, United States; Brazil, China, Mexico, Romania, Turkey

### Data sources (Table 2)
- Labor Share (Aggregate): Karabarbounis and Neiman (2014); national authorities; Organisation for Economic Co-operation and Development
- Labor Share (Sectoral): CEIC database; EU KLEMS database; Organisation for Economic Co-operation and Development
- Labor Share by Skill: World Input-Output Database, Socio Economic Accounts, Release of July 2014
- Price of Investment: IMF, World Economic Outlook database
- Intermediate Imports: EORA MRIO database; World Input-Output Database
- Global Value Chain Participation: EORA MRIO database; IMF staff calculations
- Domestic Value Added: EORA MRIO database
- Imports and Exports of Goods and Services: IMF, World Economic Outlook database
- Union Density Rate: Database on Institutional Characteristics of Trade Unions, Wage Setting, State Intervention and Social Pacts; Organisation for Economic Co-operation and Development
- Routinization: Autor and Dorn (2014); European Union Labor Force Survey; Eurostat; IPUMS International; IPUMS USA; International Labour Organization; national authorities; United Nations
- Corporate Income Tax: IMF, Fiscal Monitor database
- GDP, Per Capita GDP: IMF, World Economic Outlook database
- External Assets and Liabilities: External Wealth of Nations Mark II database
- Credit to Private Sector: World Bank World Development Indicators database
- Inflation Expectations: Consensus Forecast database; IMF, World Economic Outlook database
- Capital Depreciation Rate: World Bank database
- Old-Age Dependency Ratio: World Bank database
- Migrant Stock: United Nations database
- Relative Skill Supply (Percent of population with primary, secondary, tertiary education): Barro Lee Educational Attainment for Population Aged 15 and over database (2013); World Input-Output Database; IMF staff calculations
- Long-Term Treasury Yield: IMF, International Financial Statistics database; IMF, World Economic Outlook database

### Baseline aggregate results (Table 3 — selected coefficients)
- Initial Routinization: (1) -0.000135 (0.00119); (2) 0.0000178 (0.00110); (3) -0.000119 (0.00137)
- Relative PI * Initial Routinization: (1) 0.267*** (0.0969); (2) 0.247*** (0.0779); (3) 0.524*** (0.124)
- Relative PI: (1) 0.0847** (0.0380); (2) 0.0444 (0.0336); (3) 0.183** (0.0734)
- Financial Integration: (5) -0.234*** (0.0806); (6) -0.205*** (0.0607); (6) 1.72* (0.895)
- Global Value Chain Participation: (5) -0.288*** (0.0717); (6) -0.253*** (0.0796); (6) -0.574*** (0.0962)
- Corporate Taxation: (1) 0.194** (0.0710); (2) 0.0384 (0.0373); (3) 0.0170 (0.0316)
- Relative PI * AE dummy: -0.177* (0.0954)
- Global Value Chain Participation * AE dummy: 0.483*** (0.101)
- Financial Integration * AE dummy: -1.88** (0.897)
- AE dummy: -0.00117 (0.000820)
- Number of Observations: columns report 49, 50, 50, 26, 49, 49
- R^2: 0.196, 0.288, 0.004, 0.377, 0.448, 0.636
- Note: All variables (except initial routinization) are expressed as long-term changes. Robust standard errors in parentheses. Long-term change in financial integration is divided by 100. AEs = advanced economies; PI = price of investment. Significance: *** p < 0.01, ** p < 0.05, * p < 0.1

### Stacked aggregate results (Table 4 — selected coefficients)
- Initial Routinization: column (1) -0.00222* (0.00120); (2) -0.0150* (0.00887); (3) -0.0126 (0.00819); (4) -0.0149** (0.00644); (5) -0.0293*** (0.00459)
- Relative PI: column (1) 0.0339 (0.0279); (2) 0.0535 (0.0434); (3) 0.0112 (0.0457); (4) 0.0615 (0.0489); (5) 0.0223 (0.0350)
- Relative PI * Initial Routinization: (1) 0.128** (0.0530); (2) 0.101 (0.201); (3) 0.233 (0.193); (4) 0.207 (0.172); (5) 0.273** (0.116)
- Global Value Chain Participation: (1) -0.152** (0.0655); (2) -0.207*** (0.0627); (3) -0.253*** (0.0632); (4) -0.174* (0.0911); (5) -0.131** (0.0628)
- Financial Integration: (1) 0.0890*** (0.0219); (2) 0.0726* (0.0369); (3) 0.0744** (0.0338); (4) 0.0312 (0.046); (5) 0.0784 (0.0568)
- Corporate Taxation: (1) 0.0201 (0.0524); (2) 0.0709 (0.0711); (3) 0.0651 (0.0646); (4) 0.0511 (0.0573); (5) 0.127*** (0.0425)
- Employment Protection Legislation Reform: (1) -0.00207** (0.000806); other columns reported
- Fixed effects and sample details: Country Fixed Effects reported in later columns; Period Fixed Effects in final columns
- Number of Observations: 165, 165, 181, 154, 154, 153
- R^2: 0.157, 0.197, 0.038, 0.238, 0.501, 0.834
- Note: Robust standard errors clustered at country level. PI = price of investment.

### Robustness checks — User cost (Table 5)
- Initial Routinization: (1) -0.00103 (0.000809); (2) 0.00228 (0.00280); (3) 0.00214 (0.00188); (4) -0.000356 (0.000755)
- Relative PI * Initial Routinization: (1) 0.285*** (0.0743); (2) 0.220*** (0.0702)
- Relative PI: (1) 0.0556* (0.0327); (2) 0.0450 (0.0296)
- Global Value Chain Participation: (1) -0.166** (0.0653); (2) -0.168** (0.0751); (3) -0.235*** (0.0651)
- Financial Integration: (1) -0.182* (0.0973); (2) -0.220* (0.120); (3) -0.236** (0.106)
- Initial Routinization * User Cost of Capital: (1) 0.121** (0.0613); (2) 0.0889* (0.0541)
- User Cost of Capital: reported 0.00320 (0.0161) and 0.00290 (0.0137)
- Private Credit/GDP: 0.0290* (0.0154)
- Number of Observations: 40, 40, 40, 49
- R^2: 0.492, 0.170, 0.362, 0.478

### Robustness checks — Alternative measure of offshoring (Table 6)
- Intermediate Goods Trade: (1) -0.499*** (0.161); (2) -0.397*** (0.0979); (3) -0.242* (0.135)
- Initial Offshorability: 0.000154 (0.00223)
- Initial Offshorability * Import Price Index: 0.159** (0.0670)
- Import/GDP: various estimates including 0.0161 (0.0166), -0.0000922 (0.0155), -0.00146 (0.0134), -0.0481* (0.0276)
- Financial Integration: -0.160** (0.0604); -0.169*** (0.0593); -0.0764 (0.0720); -0.152** (0.0726)
- Relative PI * Initial Routinization: 0.261*** (0.0879); 0.339*** (0.0829); 0.211** (0.0959); 0.230** (0.0943)
- Relative PI: 0.0539 (0.0335); 0.0740** (0.0303); 0.0431 (0.0357); 0.0697* (0.0366)
- Corporate Taxation: 0.0536 (0.0410); 0.0510 (0.0406); 0.0946** (0.0414); 0.107*** (0.0381)
- Number of Observations: 49, 49, 48, 48
- R^2: 0.417, 0.470, 0.335, 0.400

### Robustness checks — Other (Table 7)
- Relative PI * Initial Routinization: (1) 0.235*** (0.0835); (2) 0.335** (0.132); (3) 0.923** (0.430); (4) 0.282*** (0.0846); (5) 0.292** (0.111)
- Global Value Chain Participation: (1) -0.235*** (0.0809); (2) -0.282** (0.120); (3) -0.0838** (0.0342); (4) -0.384*** (0.0664); (5) -0.145** (0.0600)
- Financial Integration: (1) -0.206 (0.131); (2) -0.105 (0.0901); (3) -0.184** (0.0813); (4) -0.206*** (0.0657); (5) -0.164** (0.0714)
- Additional controls reported in columns include Old-Age Dependency Ratio 0.000312 (0.000995), Migrant Stock 0.0629 (0.139), Initial GDP per Capita 0.000399 (0.000595), Human Capital 0.541 (0.335)
- Number of Observations: 49, 49, 25, 44, 50
- R^2: 0.357, 0.425, 0.584, 0.581, 0.338

### Robustness checks — Measurement issues (Table 8)
- Baseline Labor Share results and alternatives:
  - Initial Routinization: Baseline 0.0000178 (0.00110); Self-Employment-Adjusted 0.00691** (0.00300); Depreciation-Adjusted 0.000655 (0.00173); Self-Employment- and Depreciation-Adjusted 0.00762** (0.00346)
  - Relative PI * Initial Routinization: Baseline 0.247*** (0.0779); Self-Employment-Adjusted 0.460* (0.264); Depreciation-Adjusted 0.322*** (0.0933); Self-Employment- and Depreciation-Adjusted 0.570* (0.305)
  - Global Value Chain Participation: Baseline -0.253*** (0.0796); Self-Employment-Adjusted -0.617** (0.252); Depreciation-Adjusted -0.227* (0.134); Self-Employment- and Depreciation-Adjusted -0.665** (0.291)
- Number of Observations: Baseline 49; Self-Employment-Adjusted 48; Depreciation-Adjusted 49; Both-adjusted 48
- R^2: 0.448; 0.362; 0.339; 0.377

### Baseline sectoral results (Table 9)
- Tradables sectors:
  - Relative PI: 0.000412 (0.000279)
  - Initial Routinization: -0.00598** (0.00256)
  - Relative PI * Initial Routinization: -0.0000989 (0.000488)
  - Trade Integration: -0.000673** (0.000292)
  - Global Value Chain Participation: -0.00220** (0.000857)
  - Number of Observations: 92; R^2: 0.356
- Nontradables sectors:
  - Relative PI: -0.00167*** (0.000491)
  - Initial Routinization: -0.00584 (0.00879)
  - Relative PI * Initial Routinization: 0.00486** (0.00181)
  - Trade Integration: -0.0000691 (0.000122)
  - Global Value Chain Participation: 0.00171 (0.00279)
  - Number of Observations: 37; R^2: 0.173
- Note: Tradables include agriculture, mining and quarrying, manufacturing, wholesale and retail trade, and transportation. Nontradables include construction, finance, real estate, government, and community.

### Aggregate results by skill level (Table 10)
- High Skilled:
  - Relative PI: 0.0317 (0.0338)
  - Initial Routinization: -0.001 (0.00110)
  - Relative PI * Initial Routinization: 0.0460 (0.0616)
  - Global Value Chain Participation: 0.0315 (0.0989)
  - Financial Integration: 0.839*** (0.266)
  - Corporate Taxation: 0.0268 (0.0576)
  - Relative Skill Supply: 0.666** (0.308)
  - Number of Observations: 37; R^2: 0.299
- Middle Skilled:
  - Relative PI: 0.224** (0.104)
  - Initial Routinization: 0.002 (0.00263)
  - Relative PI * Initial Routinization: 0.408** (0.169)
  - Global Value Chain Participation: -0.811** (0.354)
  - Financial Integration: -0.195 (0.301)
  - Corporate Taxation: -0.237 (0.151)
  - Relative Skill Supply: 1.738 (1.545)
  - Number of Observations: 37; R^2: 0.351
- Low Skilled:
  - Relative PI: -0.0293 (0.0686)
  - Initial Routinization: -0.0001 (0.00187)
  - Relative PI * Initial Routinization: -0.104 (0.146)
  - Global Value Chain Participation: -0.100 (0.187)
  - Financial Integration: -0.316 (0.339)
  - Corporate Taxation: -0.0701 (0.0847)
  - Relative Skill Supply: -0.156 (2.152)
  - Number of Observations: 37; R^2: 0.047

### Sectoral results by skill level (Tables 11–13 — selected highlights)
- Sectoral (Table 11) — Relative PI * Initial Routinization significant for middle-skilled sectors in columns (3) and (4):
  - Column (3) Relative PI * Initial Routinization: 0.0755* (0.0405)
  - Column (4) Relative PI * Initial Routinization: 0.0795** (0.0376)
  - Number of Observations range: 275–297; R^2 range: 0.059–0.435
- Controlling for skill composition (Table 12):
  - Skill Share in Total Hours: High Skilled 0.511*** (0.0650); Middle Skilled 0.733*** (0.0846); Low Skilled 0.712*** (0.114)
  - Relative PI * Initial Routinization: Middle Skilled 0.0649* (0.0331)
  - Number of Observations: 289, 297, 275; R^2: 0.506, 0.564, 0.329
- Controlling for policy and institutions (Table 13):
  - Financial Integration: High Skilled 0.805*** (0.182); Middle Skilled 1.52*** (0.334); Low Skilled -0.689* (0.395)
  - Unionization: various coefficients including -0.00635* (0.00363) and -0.0226*** (0.00797)
  - Relative PI * Initial Routinization: Middle Skilled 0.0659* (0.0392)
  - Number of Observations: columns report 373, 382, 357, 357, 365, 342; R^2 range: 0.050–0.237

### Appendix figures (Annex 5)
- Appendix Figure 1: Estimated Trends in Labor Shares across the World (Percentage points per 10 years). Map legend: Less than –2; –2 to 0; 0 to 1; More than 1; No data. Note: countries with at least 10 years of data starting in 1991.
- Appendix Figure 2: Heterogeneity in the Evolution of Key Drivers of the Labor Share (Percentage points; changes shown in units per 10 years). Panels:
  1. Changes in Relative Price of Investment
  2. Changes in Global Value Chain Participation
  3. Changes in Corporate Tax Rates and Union Density Rates
  - Boxplot features: horizontal line = median; top and bottom quartiles shown; red markers denote top and bottom deciles. AEs = advanced economies; EMDEs = emerging market and developing economies.

*Source: IMF staff compilation; tables and figures as in wp17169 Annex 4.*

### REFERENCES

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### Selected Empirical and Working Papers
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*Content adapted from the REFERENCES section of wp17169.*

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_Source: https://www.imf.org/-/media/files/publications/wp/2017/wp17169.pdf_