## ch2onlineannex

## Source details

**Canonical URL:** [ch2onlineannex](https://www.imf.org/-/media/files/publications/reo/whd/2025/october/english/ch2onlineannex.pdf)

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---

### Extending industry panel through 2018
- The full industry panel was extended through 2018 by:
  - (i) applying 2018 growth rates in value added by industry to 2017 values from SUT, and
  - (ii) recomputing the labor and capital remunerations for 2018 using the sector’s five-year average labor share, ensuring that their sum matched the estimated value added exactly.
- Interior gaps were filled by linear interpolation and endpoints were extrapolated linearly within each country-industry panel. Endpoint projections use the boundary slope, i.e., the average annual change between the two nearest observed years on the relevant edge.
- Annual growth rates were averaged across years using the arithmetic mean. For regions, annual PPP-weighted averages were calculated first, then averaged across years.

### Correspondence between LA KLEMS and EU KLEMS
- Methodology adopts ISIC Rev. 3; EU KLEMS uses NACE Rev. 2. Harmonization applied using a correspondence table (Online Annex Table 2.2).
- Industry mapping (reported):
  - AtB Agriculture, hunting, forestry, and fishing → A
  - C Mining and extraction → B
  - D Manufacturing → C
  - E Electricity, gas, and water → D, E
  - F Construction → F
  - GtH Retail, hotels, and restaurants → G, I
  - I Transportation, storage, and communications → H, J
  - JtK Finance, insurance, real estate, and business services → K, L, M, N
  - LtQ Social community, personal services, and others → O, P, Q, R, S, T, U
  - TOT Total industries

### Harmonizing country aggregates with sectoral composition
- Two adjustments to combined KLEMS data:
  1. Aggregate output, labor, and capital levels from the PWT were distributed across industries based on their respective shares in value added, labor remuneration, and capital remuneration in the KLEMS data.
  2. Labor and capital compensation in the KLEMS were scaled to match the PWT values before calculating industry-level factor shares.
- Quality-adjusted inputs by industry computed as each industry’s share in total factor remuneration:
  - L_it = (C^L_it / Σ_j C^L_jt) ⋅ L_t
  - K_it = (C^K_it / Σ_j C^K_jt) ⋅ K_t
  - Ỹ_it = (Ŷ_it / Σ_j Ŷ_jt) ⋅ Y_t
- Labor and capital remuneration in KLEMS scaled so totals equal implied PWT values, i.e., α_t ⋅ Y_t and (1−α_t)⋅Y_t, before calculating industry-level factor shares:
  - α_it = (C^L_it / Y_it) ⋅ (α_t ⋅ Y_t) / Σ_j C^L_jt
  - β_it = (C^K_it / Y_it) ⋅ ((1−α_t)⋅Y_t) / Σ_j C^K_jt
- Resulting dataset provides consistent measures at national and sector levels for growth accounting.

### Growth accounting (region and sector decomposition)
- Year-on-year output growth decomposition (from year t to t+1):
  - Δ% A_{t+1} = Δ% Y_{t+1} − (α_t ⋅ Δ% L_{t+1} + (1−α_t) ⋅ Δ% K_{t+1})
- Notes:
  - Variable factor shares imply the TFP term reflects both changes in A_t and the direct impact of changing factor shares.
  - Approach consistent with PWT convention: capital services as capital input and quality-adjusted hours as labor input, time-varying factor income shares to weight input growth, and TFP growth as the residual.

### TFP index construction
- Growth rate used:
  - Δln A_{t+1} = Δln Y_{t+1} − (α_t ⋅ Δln L_{t+1} + β_t ⋅ Δln K_{t+1})
- Normalized index starting at t_0:
  - Ã_{t_0+δ} = exp( Σ_{s=1,...,δ} Δln A_{t_0+s} )

### Counterfactual analysis methodology
- Aggregate productivity growth rate equals the value-added weighted average sectoral productivity growth rate:
  - Δ% A_{t+1} = Σ_i ω_{i,t} ⋅ Δ% A_{i,t+1} with ω_{i,t} = Y_{i,t} / Y_t
- Alternative growth rates constructed by changing value-added weights or industry-level growth rates.
- For Chapter 2, Figure 2.1, panel 3, adjustments for resource intensive sectors were considered using the above identity.

### Firm-level data: sources, definitions, and preparation
- Data sources:
  - Orbis Enterprise Survey (Orbis), prepared as in Diez and others (2021).
  - World Bank Enterprise Survey (WBES).
- Variable definitions (Orbis):
  - Revenue Y_it: “OPER_TURN_ppi” (PPI deflated operational turnover)
  - Cost of goods sold V_it: “COSTGOOD_ppi” (PPI deflated COGS)
    - If COSTGOOD_ppi missing, proxy = MATERIAL_ppi + wagebill_ppi
  - Capital K_it: “FIXEDASSETS_piwdi” (PIWDI deflated tangible fixed assets after depreciation)
- WBES variables:
  - Revenue: “d2” (total annual sales)
  - Sample weights: “wt” (survey sampling weights)
- Deflation conventions:
  - PPI: industry or country-level price indices, 2015 constant USD.
  - PIWDI: World Bank WDI country-level investment deflators, 2015 constant USD.
- Sample selection excludes mining, utilities, transportation and storage, finance and real estate, public, and education and healthcare sectors.

### Observation weights (Orbis aligned to WBES)
- Objective: construct observation weights for Orbis to align firm-size distribution (sales) with WBES to improve cross-country comparability.
- Approach:
  - Firms categorized into size classes c ∈ C by sales. Frequency per size class computed for WBES and Orbis.
  - Relative sampling frequency:
    - ω̂_{c,m,t} = ω_{c,m,t,Orbis} / ω_{c,m,t,WBES}
    - Interpretation: value of 3 means size class is three times as likely to be sampled in Orbis as in WBES; inverse relative sampling frequency applied to Orbis to recover WBES-like distribution.
- For countries without WBES sample but with high Orbis coverage, observation weight set to 1 (applies exclusively to samples from advanced economies).

### Production function estimation
- Cobb-Douglas production function with sector-specific elasticities for variable costs and capital, β^V_s and β^K_s:
  - Y_it = A_it ⋅ V_it^{β^V_{s(i)}} ⋅ K_it^{β^K_{s(i)}}
- Two estimation approaches:
  1. Hsieh and Klenow (2009) cost-share approach:
     - β^V_s = [Σ_{i∈O_s,t} Σ_t V_it] / [Σ_{i∈O_s,t} Σ_t (V_it + R_t ⋅ K_it)]
     - β^K_s = 1 − β^V_s
     - Capital costs estimated using an interest rate of 10 percent as in Hsieh and Klenow (2009).
  2. Diez and others (2021) ACL (Ackerberg, Caves, Frazer 2015) GMM estimator:
     - Elasticities estimated at 2-digit NACE by region using replication code from Diez and others (2022021).
     - Residual productivity:
       - ln Â_it = ln Q_it − β^V_{s(i)} ⋅ ln V_it − β^K_{s(i)} ⋅ ln K_it
     - Output assumed equal to deflated revenue Q_it = Y_it.
- Elasticities estimated separately for each 2-digit industry and region.

### Measuring resource misallocation (Hsieh and Klenow framework)
- Firm-level TFPR summary statistic:
  - TFPR_R_it = Y_it / ((W_it ⋅ L_it)^{β_{s(i)}^L} ⋅ K_it^{β_{s(i)}^K})
- Recover physical productivity A_it:
  - A_it = κ_{s(i),t} ⋅ Y_it^{σ/(σ−1)} / ((W_it ⋅ L_it)^{β_{s(i)}^L} ⋅ K_it^{β_{s(i)}^K})
- Aggregate impact of frictions Λ_t computed as region/country aggregation of sectoral Λ_st using the provided formulas.
- Implementation specifics:
  - Costs of goods sold and capital used as production factors.
  - Elasticity of substitution set at σ=3 (following Hsieh and Klenow 2009).
  - Observation weights applied as constructed above.
  - Estimated frictions and productivity values winsorized at the 1 percent and 99 percent levels.
  - Misallocation measures estimated for each country-year and aggregated to country level using geometric averages; regional values via GDP-weighted geometric average.
- Note:
  - Framework uses revenue rather than value added, following literature noting lower bias when using revenue.

### Contributors to growth: decomposition methodology and implementation
- TFP growth decomposition combines Melitz and Polanec (2015) and Griliches and Regev (1995):
  - Aggregate TFP A_t expressed as weighted average of firm-level productivity A_it with weights ω_it over currently operating firms F_t.
  - TFP growth between t and t+Δ decomposed into surviving firms (O_{t,t+Δ}), entering firms (W_{t,t+Δ}), and exiting firms (X_{t,t+Δ}):
    - (A_{t+Δ} − A_t) / A_t = ΔA^{O}_{t,t+Δ} + ΔA^{W}_{t,t+Δ} + ΔA^{X}_{t,t+Δ}
  - Entry and exit components compare productivity of entering/exiting firms to surviving firms at relevant periods (formulas provided in source).
  - Surviving firms’ margin decomposed into across-firm reallocation component and within-firm productivity improvement term (formulas provided in source).
- Implementation notes:
  - Firm-level productivity scaled to match aggregate TFP from PWT.
  - Productivity constructed using residual approach with elasticities per Diez and others (2021).
  - Firm weights constructed from resource inputs (V_it^{β^V_{s(i)}} ⋅ K_it^{β^K_{s(i)}}) then adjusted by observation weights.
  - Entry defined as firm appearing after initial observation; exit defined as firm not observed for at least three consecutive years and stopping activity after initial observation. Firms with missing observations in either year are dropped.

### Size-dependent policies and simplified tax regimes (STRs)
- Several countries in the region have at least two types of STRs with significant differences relative to corporate income tax (CIT) general regimes.
- Eligibility for lower CIT typically determined by firm-level revenue thresholds and/or number of employees.
- Firms just below thresholds benefit from low flat rates; crossing thresholds triggers large, one-time increases in statutory tax rates (especially CIT) and/or more rigorous labor regulations.
- Multiple and unaligned revenue thresholds across tax instruments (PIT, CIT, SSCs, VAT) can create sudden, non-linear increases in tax liability when crossing specific thresholds, potentially distorting firm behavior by discouraging formalization, investment, and growth.
- VAT threshold literature notes slowdown in firm turnover when approaching threshold but no compensating acceleration after crossing (Liu and others, 2021, 2024).

### Simplified Tax Regimes (STRs) and Revenue Thresholds — examples
- STRs included:
  - BRA = Microempreendedor Individual (MEI), Simples Nacional (SIMPLES)
  - CHL = Régimen Tributario enfocado a pequeños y medianos contribuyentes (Pro-Pyme)
  - COL = Régimen Simple de Tributación (RST)
  - MEX = Régimen simplificado de confianza (RESICO), Régimen de Incorporación Fiscal (RIF)
  - PER = Régimen Especial de Renta (RER), Régimen MYPE Tributario (RMT)
- Methodological notes:
  - For BRA - SIMPLES, COL - RST, and MEX - RESICO, the median statutory CIT within each STR schedule by revenue bracket and/or sector is used to illustrate incentive gaps.
  - BRA - SIMPLES rates bundle multiple taxes, including CIT and social security contributions. For comparability with the general regime’s CIT of 25 percent, after surtax, the SIMPLES CIT is proxied by subtracting the 9 percent social contribution from the median SIMPLES nominal rate across revenue brackets and sector.
  - For MEX - RIF, the year-1 schedule (100 percent CIT discount from the general regime rate) is used to reflect entry incentives; the discount decreases by ten percent each year over ten years.
  - For PER - RER, there is no annual CIT, but a statutory monthly revenue-based quota of 1.5 percent.
- Definitions:
  - CIT = corporate income tax
  - STR = simplified tax regime

### Countries without Size-Based Tax Regimes (selected values)
- Countries and Corporate Income Tax (Percent) with notes on size-based SME rates:
  - Czech Republic 19.0 — No size-based SME rates.
  - Denmark 22.0 — No size-based SME rate
  - Estonia 20.0 — No size-based rates - 0 % on retained earnings and 20% on distribution
  - Finland 20.0 — No size-based SME rates
  - Greece 22.0 — No size-based SME rates
  - Hungary 9.0 — Lowest flat rate -no size-based SME rates
  - Latvia 20.0 — No size-based rate – CIT applied on distribution
  - Lithuania 15.0 — No-size based SME rate
  - New Zealand 28.0 — Single rate for all companies.
  - Norway 22.0 — Uniform statutory rate
  - Portugal 21.0 — Single corporate rate – Municipal surtaxes apply equally
  - Sweden 20.6 — One flat rate
  - United States 21.0 — One flat federal CIT – Pass-throughs taxed under personal rates
- Note: In the US C-corp are standard corporations. Most small businesses in the U.S. operate as sole proprietorships, partnerships, LLCs, or S-corps. These are not special regimes but standard entity choices—profits “pass through” to owners and are taxed under individual rates. Tax filing for small businesses is simplified but not the rates. Data as of end 2024.

### Case studies: reform pathways and sequencing (New Zealand, Estonia, Peru)
- New Zealand (mid-1980s onward)
  - Pillars and timing:
    - Trade liberalization and deregulation (1983–85)
    - Financial market deregulation (1984–85)
    - Tax system overhaul (1984–93)
    - Labor market reforms (1991)
  - Impact summary:
    - Reforms substantially boosted firm efficiency, broadened financial access, and encouraged entrepreneurship and innovation, producing a sustained rise in productivity and business dynamism.
- Estonia (1990s–2014)
  - Key reforms and timing:
    - Trade liberalization and privatization (1990–95)
    - Financial sector reforms (1991–95)
    - Labor market reforms (1991)
    - Tax system reform (1994)
    - Digital governance reforms (1996–2014)
  - Impact and measurable outcomes:
    - Business regulatory quality score improved fivefold between 1990 and 2010.
    - “Freedom to enter markets and compete” indicator doubled between 1990 and 2010.
    - New firm registrations exceeded the peer average by more than threefold.
    - Reforms spurred steady gains in labor productivity and higher firm entry rates relative to high-income peers.
- Peru (1990s)
  - Key reforms and timing:
    - Trade liberalization (1990–97): tariffs cut from over 60 percent to under 15 percent; import licensing eliminated; exchange rate unified.
    - Financial market reforms (1990–93)
    - Tax system overhaul (1991–98)
    - Labor market reforms (1990–93)
  - Impact and measurable outcomes:
    - Labor Market Regulation Index rose from 3.4 in 1990 to 7.3 by 2005.
    - Foreign direct investment increased from under 1 percent of GDP (1985–89) to over 3 percent during 1990–97 and remained elevated thereafter.
    - Credit to the private sector expanded from 8 percent of GDP before the reforms to over 25 percent post-reform.

### Outcomes, indicators, and figures referenced
- Estonia (Online Annex Figure 2.4):
  - Improvements shown in indices of credit market regulations, business regulations, and freedom to enter markets and compete for 1990, 2000, 2010.
  - Labor productivity defined as GDP per person employed in constant 2021 PPP USD; comparison years include 2006, 2016, 2022 for Estonia and high income countries.
  - New business density measured as new registrations per 1,000 people aged 15-64.
- Peru (Online Annex Figure 2.5):
  - Labor Market Regulation index is the simple average of seven subindices (labor regulations and minimum wage; hiring and firing regulations; flexible wage determination; hours regulations; costs of worker dismissal; conscription; foreign labor restrictions).
  - Foreign direct investment shows net inflows; credit is provided by domestic banks to all other sectors of the economy and non-residents.
  - Time series presented for Labor Market Regulations (1990, 1995, 2000, 2005) and for FDI and credit across periods 1985-89, 1990-97, 1998-02.

### Policy-relevant lessons (implied)
- Sequencing reforms across trade, financial markets, taxation, and labor markets can minimize disruption and maximize private sector responsiveness.
- Removing size-based distortions and simplifying tax systems can improve formality, investment incentives, and firm growth.
- Deepening financial intermediation and deregulating credit markets expands access to finance for new and growing firms.
- Digital governance reforms reduce transaction costs, increase transparency, and boost investor confidence.
- Labor market flexibility supports firm expansion and better alignment of wages with firm-level productivity.

*Source: ch2onlineannex - 2018.*

### 2018. The full industry panel was extended through 2018 by: (i) applying 2018 growth rates in value added by

### ch2onlineannex - 2018. The full industry panel was extended through 2018 by: (i) applying 2018 growth rates in value added by

### Extending industry panel through 2018
- The full industry panel was extended through 2018 by:
  - (i) applying 2018 growth rates in value added by industry to 2017 values from SUT, and
  - (ii) recomputing the labor and capital remunerations for 2018 using the sector’s five-year average labor share, ensuring that their sum matched the estimated value added exactly.
- Interior gaps were filled by linear interpolation and endpoints were extrapolated linearly within each country-industry panel. Endpoint projections use the boundary slope, i.e., the average annual change between the two nearest observed years on the relevant edge.
- Annual growth rates were averaged across years using the arithmetic mean. For regions, annual PPP-weighted averages were calculated first, then averaged across years.

### Correspondence between LA KLEMS and EU KLEMS
- Methodology adopts ISIC Rev. 3; EU KLEMS uses NACE Rev. 2. Harmonization applied using a correspondence table (Online Annex Table 2.2).
- Industry mapping (as reported):
  - AtB Agriculture, hunting, forestry, and fishing → A
  - C Mining and extraction → B
  - D Manufacturing → C
  - E Electricity, gas, and water → D, E
  - F Construction → F
  - GtH Retail, hotels, and restaurants → G, I
  - I Transportation, storage, and communications → H, J
  - JtK Finance, insurance, real estate, and business services → K, L, M, N
  - LtQ Social community, personal services, and others → O, P, Q, R, S, T, U
  - TOT Total industries

### Harmonizing country aggregates with sectoral composition
- Two adjustments to combined KLEMS data:
  1. Aggregate output, labor, and capital levels from the PWT were distributed across industries based on their respective shares in value added, labor remuneration, and capital remuneration in the KLEMS data.
  2. Labor and capital compensation in the KLEMS were scaled to match the PWT values before calculating industry-level factor shares.
- Quality-adjusted inputs by industry computed as each industry’s share in total factor remuneration:
  - L_it = (C^L_it / Σ_j C^L_jt) ⋅ L_t
  - K_it = (C^K_it / Σ_j C^K_jt) ⋅ K_t
  - Ỹ_it = (Ŷ_it / Σ_j Ŷ_jt) ⋅ Y_t
- Labor and capital remuneration in KLEMS scaled so totals equal implied PWT values, i.e., α_t ⋅ Y_t and (1−α_t)⋅Y_t, before calculating industry-level factor shares:
  - α_it = (C^L_it / Y_it) ⋅ (α_t ⋅ Y_t) / Σ_j C^L_jt
  - β_it = (C^K_it / Y_it) ⋅ ((1−α_t)⋅Y_t) / Σ_j C^K_jt
- Resulting dataset provides consistent measures at national and sector levels for growth accounting.

### Growth accounting (region and sector decomposition)
- Year-on-year output growth decomposition (from year t to t+1):
  - Δ% A_{t+1} = Δ% Y_{t+1} − (α_t ⋅ Δ% L_{t+1} + (1−α_t) ⋅ Δ% K_{t+1})
- Notes:
  - Variable factor shares imply the TFP term reflects both changes in A_t and the direct impact of changing factor shares.
  - Approach consistent with PWT convention: capital services as capital input and quality-adjusted hours as labor input, time-varying factor income shares to weight input growth, and TFP growth as the residual.

### TFP index construction
- Growth rate used:
  - Δln A_{t+1} = Δln Y_{t+1} − (α_t ⋅ Δln L_{t+1} + β_t ⋅ Δln K_{t+1})
- Normalized index starting at t_0:
  - Ã_{t_0+δ} = exp( Σ_{s=1,...,δ} Δln A_{t_0+s} )

### Counterfactual analysis methodology
- Aggregate productivity growth rate equals the value-added weighted average sectoral productivity growth rate:
  - Δ% A_{t+1} = Σ_i ω_{i,t} ⋅ Δ% A_{i,t+1} with ω_{i,t} = Y_{i,t} / Y_t
- Alternative growth rates constructed by changing value-added weights or industry-level growth rates.
- For Chapter 2, Figure 2.1, panel 3, adjustments for resource intensive sectors were considered using the above identity.

### Firm-level data: sources, definitions, and preparation
- Data sources:
  - Orbis Enterprise Survey (Orbis), prepared as in Diez and others (2021).
  - World Bank Enterprise Survey (WBES).
- Variable definitions (Orbis):
  - Revenue Y_it: “OPER_TURN_ppi” (PPI deflated operational turnover)
  - Cost of goods sold V_it: “COSTGOOD_ppi” (PPI deflated COGS)
    - If COSTGOOD_ppi missing, proxy = MATERIAL_ppi + wagebill_ppi
  - Capital K_it: “FIXEDASSETS_piwdi” (PIWDI deflated tangible fixed assets after depreciation)
- WBES variables:
  - Revenue: “d2” (total annual sales)
  - Sample weights: “wt” (survey sampling weights)
- Deflation conventions noted:
  - PPI: industry or country-level price indices, 2015 constant USD.
  - PIWDI: World Bank WDI country-level investment deflators, 2015 constant USD.
- Sample selection excludes mining, utilities, transportation and storage, finance and real estate, public, and education and healthcare sectors.

### Observation weights (Orbis aligned to WBES)
- Objective: construct observation weights for Orbis to align firm-size distribution (sales) with WBES to improve cross-country comparability.
- Approach:
  - Firms categorized into size classes c ∈ C by sales. Frequency per size class computed for WBES and Orbis.
  - Relative sampling frequency:
    - ω̂_{c,m,t} = ω_{c,m,t,Orbis} / ω_{c,m,t,WBES}
    - Example interpretation: value of 3 means size class is three times as likely to be sampled in Orbis as in WBES; inverse relative sampling frequency applied to Orbis to recover WBES-like distribution.
- For countries without WBES sample but with high Orbis coverage, observation weight set to 1 (applies exclusively to samples from advanced economies).

### Production function estimation
- Cobb-Douglas production function assumed with sector-specific elasticities for variable costs and capital, β^V_s and β^K_s:
  - Y_it = A_it ⋅ V_it^{β^V_{s(i)}} ⋅ K_it^{β^K_{s(i)}}
- Two estimation approaches:
  1. Hsieh and Klenow (2009) cost-share approach:
     - β^V_s = [Σ_{i∈O_s,t} Σ_t V_it] / [Σ_{i∈O_s,t} Σ_t (V_it + R_t ⋅ K_it)]
     - β^K_s = 1 − β^V_s
     - Capital costs estimated using an interest rate of 10 percent as in Hsieh and Klenow (2009).
  2. Diez and others (2021) ACL (Ackerberg, Caves, Frazer 2015) GMM estimator:
     - Elasticities estimated at 2-digit NACE by region using replication code from Diez and others (2021).
     - Residual productivity:
       - ln Â_it = ln Q_it − β^V_{s(i)} ⋅ ln V_it − β^K_{s(i)} ⋅ ln K_it
     - Output assumed equal to deflated revenue Q_it = Y_it.
- Elasticities estimated separately for each 2-digit industry and region.

### Measuring resource misallocation (Hsieh and Klenow framework)
- Firm-level TFPR summary statistic:
  - TFPR_R_it = Y_it / ((W_it ⋅ L_it)^{β_{s(i)}^L} ⋅ K_it^{β_{s(i)}^K})
- Recover physical productivity A_it:
  - A_it = κ_{s(i),t} ⋅ Y_it^{σ/(σ−1)} / ((W_it ⋅ L_it)^{β_{s(i)}^L} ⋅ K_it^{β_{s(i)}^K})
- Aggregate impact of frictions Λ_t computed as region/country aggregation of sectoral Λ_st using the provided formulas.
- Implementation specifics:
  - Costs of goods sold and capital used as production factors.
  - Elasticity of substitution set at σ=3 (following Hsieh and Klenow 2009).
  - Observation weights applied as constructed above.
  - Estimated frictions and productivity values winsorized at the 1 percent and 99 percent levels.
  - Misallocation measures estimated for each country-year and aggregated to country level using geometric averages; regional values via GDP-weighted geometric average.
- Notes on choices:
  - Framework uses revenue rather than value added, following literature noting lower bias when using revenue.

### Contributors to growth: decomposition methodology and implementation
- TFP growth decomposition combines Melitz and Polanec (2015) and Griliches and Regev (1995):
  - Aggregate TFP A_t expressed as weighted average of firm-level productivity A_it with weights ω_it over currently operating firms F_t.
  - TFP growth between t and t+Δ decomposed into surviving firms (O_{t,t+Δ}), entering firms (W_{t,t+Δ}), and exiting firms (X_{t,t+Δ}):
    - (A_{t+Δ} − A_t) / A_t = ΔA^{O}_{t,t+Δ} + ΔA^{W}_{t,t+Δ} + ΔA^{X}_{t,t+Δ}
  - Entry and exit components compare productivity of entering/exiting firms to surviving firms at relevant periods (formulas provided in source).
  - Surviving firms’ margin decomposed into across-firm reallocation component and within-firm productivity improvement term (formulas provided in source).
- Implementation notes:
  - Firm-level productivity scaled to match aggregate TFP from PWT.
  - Productivity constructed using residual approach with elasticities per Diez and others (2021).
  - Firm weights constructed from resource inputs (V_it^{β^V_{s(i)}} ⋅ K_it^{β^K_{s(i)}}) then adjusted by observation weights.
  - Entry defined as firm appearing after initial observation; exit defined as firm not observed for at least three consecutive years and stopping activity after initial observation. Firms with missing observations in either year are dropped.

### Size-dependent policies and simplified tax regimes (STRs)
- Several countries in the region have at least two types of STRs with significant differences relative to corporate income tax (CIT) general regimes.
- Eligibility for lower CIT typically determined by firm-level revenue thresholds and/or number of employees.
- Firms just below thresholds benefit from low flat rates; crossing thresholds triggers large, one-time increases in statutory tax rates (especially CIT) and/or more rigorous labor regulations.
- Multiple and unaligned revenue thresholds across tax instruments (PIT, CIT, SSCs, VAT) can create sudden, non-linear increases in tax liability when crossing specific thresholds, potentially distorting firm behavior by discouraging formalization, investment, and growth.
- VAT threshold literature notes slowdown in firm turnover when approaching threshold but no compensating acceleration after crossing (Liu and others, 2021, 2024).
- Tables and panels providing taxonomy of STRs and country samples referenced (Online Annex Table 2.4; Online Annex Figure 2.3).

*Source: ch2onlineannex - 2018.*

### 1.  Active STRs and Revenues Thresholds

### ch2onlineannex - 1.  Active STRs and Revenues Thresholds

### Simplified Tax Regimes (STRs) and Revenue Thresholds
- Revenue thresholds refer to the different brackets within simplified tax regimes that assign multiple tax codes according to revenue levels and/or sectors.
- Simplified regimes included here are as follows:
  - BRA = Microempreendedor Individual (MEI), Simples Nacional (SIMPLES)
  - CHL = Régimen Tributario enfocado a pequeños y medianos contribuyentes (Pro-Pyme)
  - COL = Régimen Simple de Tributación (RST)
  - MEX = Régimen simplificado de confianza (RESICO), Régimen de Incorporación Fiscal (RIF)
  - PER = Régimen Especial de Renta (RER), Régimen MYPE Tributario (RMT)
- Methodological notes on STR comparisons:
  - For BRA - SIMPLES, COL - RST, and MEX - RESICO, the median statutory CIT within each STR schedule by revenue bracket and/or sector is used to illustrate the presence of incentive gaps under these regimes.
  - BRA - SIMPLES rates bundle multiple taxes, including CIT and social security contributions. For comparability with the general regime’s CIT of 25 percent, after surtax, the SIMPLES CIT is proxied by subtracting the 9 percent social contribution from the median SIMPLES nominal rate across revenue brackets and sector.
  - For MEX - RIF, the year-1 schedule (100 percent CIT discount from the general regime rate) is used to reflect entry incentives; the discount decreases by ten percent each year over ten years.
  - For PER - RER, there is no annual CIT, but a statutory monthly revenue-based quota of 1.5 percent.
- Definitions:
  - CIT = corporate income tax
  - STR = simplified tax regime

### Countries without Size-Based Tax Regimes (Online Annex Table 2.4)
- Countries and Corporate Income Tax (Percent) with notes on size-based SME rates:
  - Czech Republic 19.0 — No size-based SME rates.
  - Denmark 22.0 — No size-based SME rate
  - Estonia 20.0 — No size-based rates - 0 % on retained earnings and 20% on distribution
  - Finland 20.0 — No size-based SME rates
  - Greece 22.0 — No size-based SME rates
  - Hungary 9.0 — Lowest flat rate -no size-based SME rates
  - Latvia 20.0 — No size-based rate – CIT applied on distribution
  - Lithuania 15.0 — No-size based SME rate
  - New Zealand 28.0 — Single rate for all companies.
  - Norway 22.0 — Uniform statutory rate
  - Portugal 21.0 — Single corporate rate – Municipal surtaxes apply equally
  - Sweden 20.6 — One flat rate
  - United States 21.0 — One flat federal CIT – Pass-throughs taxed under personal rates
- Note: In the US C-corp are standard corporations. Most small businesses in the U.S. operate as sole proprietorships, partnerships, LLCs, or S-corps. These are not special regimes but standard entity choices—profits “pass through” to owners and are taxed under individual rates. Tax filing for small businesses is simplified but not the rates. Data as of end 2024.

### Case Studies: Reform Pathways and Sequencing
- Overview:
  - The annex provides detailed discussion of successful reforms in New Zealand, Estonia, and Peru, focusing on major reforms, sequencing, implementation strategies, and impacts on business dynamism (productivity, firm growth, resource reallocation).
  - Common features across reform pathways: removing distortions, deepening market institutions, enhancing environment for private sector development.

- New Zealand: Comprehensive and well-sequenced reforms (mid-1980s onward)
  - Pillars and timing:
    - Trade liberalization and deregulation (1983–85): elimination of import licensing and tariffs.
    - Financial market deregulation (1984–85): removal of interest rate and foreign exchange controls; adoption of a floating exchange rate.
    - Tax system overhaul (1984–93): replacement of distortionary tax provisions with a broad-based goods and services tax (GST) and lower CIT/PIT rates.
    - Labor market reforms (1991): decentralization of wage bargaining and increased employment flexibility.
  - Impact summary:
    - Reforms substantially boosted firm efficiency, broadened financial access, and encouraged entrepreneurship and innovation, producing a sustained rise in productivity and business dynamism.

- Estonia: Transition economy to digital leadership (1990s–2014)
  - Key reforms and timing:
    - Trade liberalization and privatization (1990–95)
    - Financial sector reforms (1991–95): banking sector restructuring and new bankruptcy legislation
    - Labor market reforms (1991): abolishing size-based policies and increasing employment contract flexibility
    - Tax system reform (1994): flat-rate income tax and VAT
    - Digital governance reforms (1996–2014): X-Road, E-Tax, and E-Residency platforms
  - Impact and measurable outcomes:
    - Business regulatory quality score improved fivefold between 1990 and 2010.
    - “Freedom to enter markets and compete” indicator doubled between 1990 and 2010.
    - New firm registrations exceeded the peer average by more than threefold.
    - Reforms spurred steady gains in labor productivity and higher firm entry rates relative to high-income peers.

- Peru: Liberalization and stabilization in the 1990s
  - Key reforms and timing:
    - Trade liberalization (1990–97): tariffs cut from over 60 percent to under 15 percent; import licensing eliminated; exchange rate unified.
    - Financial market reforms (1990–93): interest rate liberalization; removal of credit controls; privatization of state-owned banks.
    - Tax system overhaul (1991–98): broadened tax base, reduced rates, simplified compliance; elimination of size-based distortions.
    - Labor market reforms (1990–93): reduced dismissal costs, allowed fixed-term contracts, relaxed wage/working-hour regulations.
  - Impact and measurable outcomes:
    - Labor Market Regulation Index rose from 3.4 in 1990 to 7.3 by 2005.
    - Foreign direct investment increased from under 1 percent of GDP (1985–89) to over 3 percent during 1990–97 and remained elevated thereafter.
    - Credit to the private sector expanded from 8 percent of GDP before the reforms to over 25 percent post-reform.

### Outcomes, Indicators, and Figures
- Estonia (Online Annex Figure 2.4):
  - Improvements shown in indices of credit market regulations, business regulations, and freedom to enter markets and compete for 1990, 2000, 2010.
  - Labor productivity defined as GDP per person employed in constant 2021 PPP USD; comparison years include 2006, 2016, 2022 for Estonia and high income countries.
  - New business density measured as new registrations per 1,000 people aged 15-64.

- Peru (Online Annex Figure 2.5):
  - Labor Market Regulation index is the simple average of seven subindices (labor regulations and minimum wage; hiring and firing regulations; flexible wage determination; hours regulations; costs of worker dismissal; conscription; foreign labor restrictions).
  - Foreign direct investment shows net inflows; credit is provided by domestic banks to all other sectors of the economy and non-residents.
  - Time series presented for Labor Market Regulations (1990, 1995, 2000, 2005) and for FDI and credit across periods 1985-89, 1990-97, 1998-02 with pre/post reform comparisons.

### Policy-relevant Lessons (implied by case studies and analysis)
- Sequencing reforms across trade, financial markets, taxation, and labor markets can minimize disruption and maximize private sector responsiveness.
- Removing size-based distortions and simplifying tax systems can improve formality, investment incentives, and firm growth.
- Deepening financial intermediation and deregulating credit markets expands access to finance for new and growing firms.
- Digital governance reforms reduce transaction costs, increase transparency, and boost investor confidence.
- Labor market flexibility supports firm expansion and better alignment of wages with firm-level productivity.

*Source: IMF staff; content unit "ch2onlineannex - 1.  Active STRs and Revenues Thresholds" (PDF).*

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_Source: https://www.imf.org/-/media/files/publications/reo/whd/2025/october/english/ch2onlineannex.pdf_