## 1. Introduction (Appendix C: Minimum Wages, Efficiency Wages, and Informal Sector Evidence)

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### Empirical motivation and stylized facts
- Minimum wage (MW) laws are important in many LDCs; effects on employment, output, and distribution are controversial.
- Canonical Segmented Labor Markets Model (SLMM) predicts higher MW reduces formal employment and output and increases informal employment and output, depressing informal real wages; empirical evidence challenges these predictions.
- Key empirical summaries:
  - Employment elasticities (meta-analyses/surveys): -.08, -.011, -.20, and -.23 (Nataraj et al. (2014), Bhorat et al. (2017), World Bank (2006), this paper (Appendix C) respectively).
  - Informal-sector employment often decreases more than formal-sector employment in many studies.
  - Informal sector real wages almost always increase (the "lighthouse effect").

### Objective and modeling approach
- Objective: reconcile theoretical predictions with empirical evidence by developing a dynamic general equilibrium (GE) model with:
  - Efficiency wages (EW) operating in both formal and informal sectors.
  - Endogenous capital accumulation in both formal and informal sectors.
  - Interaction of efficiency wages, wage curves, firm-size wage premium, and monitoring costs.
- Stylized empirical inputs used in model design:
  - Wage curves operate in both sectors; informal wages typically more responsive to unemployment.
  - Firm-size wage premiums appear at small establishment sizes (2–5 employees).
  - Large movements into/out of unemployment from salaried informal employment in downturns; job rationing evidence (e.g., South Africa: 80 percent of unemployed reported they could not find any work; only 3 percent cited "unsuitable work").

### Core model mechanisms and efficiency-wage structure
- Formal sector effort function (simplified):
  - e1 = g0 + g1 ln(w1/P) + g3 u − g4 ln L1
  - Effort increases with real wage and unemployment, decreases with firm size; firm-size wage premium arises because larger L1/S reduces effort and firms pay premiums to buy back effort.
- Informal sector effort function (full model):
  - e2 = bo + b1 ln(w2/P) − b2 ln wm + b3 u
  - Lighthouse effect mechanism: increases in wm lower perceived fairness and reduce e2 via b2 term; in equilibrium e2 may be constant with wm ↑ raising informal real wage.
- Three channels that limit formal employment losses after an MW increase:
  1. Effort response to unemployment (wage curve), parameter g3.
  2. Firm-size wage premium / monitoring channel, parameter g4.
  3. General equilibrium demand-side effects (consumption substitutability and tradability of formal output).

### Simplified model insights — short run
- With CES production and EW, partial-equilibrium formal employment elasticity is substantially reduced relative to canonical predictions.
- Overly simple case (g3 = g4 = 0): partial-equilibrium employment elasticity equals −1 (ˆL1/ˆwm = −1).
- Empirically calibrated basecase (θK1 = .40, σ1 = .75, g3 = .80, g4 = .20, L1 = .49): partial-equilibrium employment elasticity equals .332 (formal employment falls by .332 percent per 1 percent MW increase in that calibration).
- Short-run formal real output can increase following an MW increase because higher effort and size-premium effects lower the effective cost of labor (ECL); investment responds to higher MPK as labor productivity rises.

### Full general-equilibrium and long-run insights
- Full GE: P1 depresses when formal output rises; employment falls more in GE than partial equilibrium, magnitude depends on consumption substitution elasticity (ε).
- Long-run (steady state) results:
  - MPK = RCC across steady states; higher MW can increase effective labor supply and lower P1, shifting MPK and RCC.
  - Equilibrium capital stock increases if ε > ε* = σ1 (1 − γ) Q1 / C1.
  - Limiting case ε = ∞ (perfect substitutes): labor demand fully recovers and employment elasticity equals zero across steady states.
  - Employment always decreases less in the long run than in the short run; capital accumulation helps recover labor demand.
- Transition dynamics: Euler and capital accumulation equations imply saddle-point stable steady state; consumption typically jumps at t = 0 and converges smoothly as capital adjusts.

### Welfare (representative-agent) implications
- In the simplified model, raising the MW increases welfare by ameliorating a coordination externality and raising total effective labor supply; equivalent variation (EV) welfare gain given by equation (41) (see source).
- Caveat: representative-agent framework omits distributional concerns; welfare gains assume newly unemployed are compensated or distributional harms are ignored.

### Full model structure and key equations (highlights)
- Two-sector CES production: formal Q1 and informal Q2 with sector-specific substitution elasticities σ1 and σ2 (equations (42)-(43)).
- Investment good assembled with imported machine units and f1, f2 units of formal and informal capital; supply price of capital Pk = 1 + f1 P1 + f2 P2 (equation (44)).
- Preferences: nested CES aggregators with substitution elasticities ε2 and ε3 (equations (45)-(48)).
- Representative-agent Ramsey problem with adjustment costs and dynamics in K1, K2, L1, L2 (equations (49)-(54)).
- Effort functions restated:
  - Formal: e1 = g0 + g1 ln wm + g3 u − g4 ln L1  (equation (59))
  - Informal: e2 = bo + b1 ln(w2/P) − b2 ln wm + b3 u (equation (60))

### Calibration choices and baseline parameter values (selected)
- Pure time preference rate ρ = 10%.
- Elasticities of substitution between capital and labor σ1 = σ2 = .75 (sensitivity checks with .5 and 1).
- Basecase: θK1 = .40, σ1 = .75, g3 = .80, g4 = .20; derived L1 = .49 in basecase calibration.
- Firm-size wage-premium elasticity: g4 = .20 (size premium = .25).
- Informal sector calibration from 19 World Bank Informal Enterprise Surveys:
  - Mean firm employment ≈ 4 (of which 1.75 are unpaid family/other).
  - Labor share of informal sector value added ≈ .50; set θK2 = .25 and θL2 = .50 implying entrepreneurial skill cost share ≈ .25.
- Consumption-share parameters: γm = .14; alternative γ1 values .39, .56, .69 to span formal sector sizes.
- Consumption substitution: ε2 explored in .5–5; ε3 explored at 1, 3, 10.
- Lighthouse parameter b2 baseline = 1.
- Real wage flexibility (formal) [g3/(1 – g4)] = 1 in baseline; informal b3 = 2 baseline.

### Numerical experiment: permanent 10% increase in formal-sector real MW
- Reporting convention: simulated results divided by 10 to compare to empirical elasticities.
- MIC baseline (ε2 = 3, ε3 = 3) main long-run results:
  - Aggregate employment: −2.8%.
  - Formal employment: −1.9%.
  - Informal employment: −3.9%.
  - Total output (GDP): +3.9%.
  - Formal output Q1: +7.2%.
  - Informal output Q2: −2.1%.
  - Informal real wage w2/P: rises by just under 5% (lighthouse effect).
- Sensitivity:
  - Higher consumption substitutability (higher ε2, ε3) increases aggregate gains and shifts gains toward formal sector; formal employment may increase in long run for high substitutability and steep wage curves.
  - Steeper wage curves amplify positive output and employment effects via stronger effort response and capital accumulation.
- EM calibration (formal share ≈ 80% of non-agricultural output) yields qualitatively similar results with larger aggregate gains (e.g., long-run output gains 1.4–1.9 percentage points higher than MIC case for ε2 = 3, ε3 = 10).

### Coherence with empirical evidence
- Simulated employment elasticities (MIC and EM calibrations) match empirical distribution:
  - Simulated mean −0.24, sd 0.18; empirical mean −.22, sd 0.20.
- Simulated informal-wage elasticities positive and range from .41 to .71, consistent with empirical estimates (.15 to .90).
- Simulations consistent with empirical findings that MW increases often raise labor productivity and investment.

### Transition dynamics and adjustment timing
- Consumption and output typically jump up at t = 0 due to immediate effort response; K1 typically attains half its long-run increase after ≈ 25 periods in calibrated runs.
- Short-run employment elasticities can be more negative than long-run values (overshooting); studies focusing on short-to-medium term may overstate persistent employment losses.
- Higher employment adjustment costs dampen overshooting and yield smoother transition paths.

### Low-Income Country (LIC) calibration results and risks
- LIC calibration: informal/non-compliant sector ≈ 65% of non-agricultural economy; lower substitutability between formal and informal goods.
- Compared to MIC/EM, responses attenuated and risk of adverse outcomes increases:
  - For ε2 = ε3 = 3 and central wage-curve parameters:
    - Aggregate output growth falls from 3.9% (MIC) to 0.7% (LIC) in long run.
    - Aggregate employment contraction increases from 2.8% to 3.6%.
  - With flatter wage curves, aggregate employment losses can increase further (e.g., 5.9% aggregate, 5.0% formal in one run).
- Critical adverse outcomes in some LIC calibrations:
  - Aggregate output and investment stagnate or contract slightly.
  - Consumption may initially jump then fall below baseline within a few years, producing EV welfare losses.
  - EV welfare loss ranges reported:
    - .10 − .15% of consumption when private discount rate 10%.
    - .43 − .50% when social discount rate 5% in certain LIC runs.

### Policy implications and conditional guidance
- A dynamic GE model with EW and endogenous capital accumulation can explain:
  - Small formal-sector employment elasticities common in many parameterizations (.1 − .3 in many configurations).
  - Lighthouse effect (informal wages rise) and cases where informal employment declines more than formal employment.
  - Positive aggregate effects (GDP, investment, productivity) are common in MICs/EMs but not assured in LICs.
- Key contextual factors conditioning MW effectiveness:
  1. Relative size of formal sector.
  2. Degree of substitutability between formal and informal outputs (ε2).
  3. Absolute and relative wage flexibility in sectoral wage curves (g3, b3).
  4. Tradability of formal-sector output (ε3).
- Hierarchy of MW effectiveness: moving from LICs → MICs → EMEs increases likelihood of GDP and informal real wage gains and reduces employment losses.
- Policy caution: MW increases are not universally welfare-improving in all LDCs; in LICs they may reduce output and welfare.
- Suggested future research priorities:
  - Explicit treatment of MW coverage and compliance.
  - Incorporation of heterogeneous households for distributional welfare analysis.
  - Analysis of efficiency wage effects in the public sector and habit formation/adjustment costs in effort.
  - Macro effects of better enforcement of MW (empirical and theoretical work).

*Source: Appendix C of IMF working paper wpiea2020023-print-pdf (Appendix C: Minimum Wage Studies and Episodes and related model appendices).*

### 1. Introduction ........................................................................................................

### wpiea2020023-print-pdf - 1. Introduction ................................................................................................

### 1. Introduction
- 1. Introduction ........................................................................................................................... 3

### 2. The Case for Efficiency Wages
- 2. The Case for Efficiency Wages ............................................................................................. 6
- 2.1.   Efficiency Wages in the Informal Sector? ................................................................... 6

### 3. Insights from a Simplified Model
- 3. Insights from a Simplified Model ......................................................................................... 8
- 3.1.   The Short Run .............................................................................................................11
  - 3.1.1  The Impact on Real Output ...............................................................................13
  - 3.1.2  The Full General Equilibrium Solution .............................................................14
- 3.2.   The Long Run ..............................................................................................................14
- 3.3.   The Transition Path  ....................................................................................................16
  - 3.3.1  Welfare ..............................................................................................................17

### 4. The Full Model
- 4. The Full Model .....................................................................................................................18
- 4.1.   Model Calibration ........................................................................................................22
  - 4.1.1  The Problem Child: Cost Shares in the Informal Sector ...................................25
  - 4.1.2  Solution Technique ............................................................................................26

### 5. Numerical Results
- 5. Numerical Results ................................................................................................................26
- 5.1.   Middle Income and Emerging Market Calibrations ....................................................27
  - 5.1.1  The Long-Run ...................................................................................................27
  - 5.1.2  Coherence with Empirical Estimates ................................................................29
  - 5.1.3  Transition Paths and Welfare ............................................................................30
  - 5.1.4  Coherence with Empirical Estimates Revisited ................................................31

### 6. The Low-Income Country Case
- 6. The Low-Income Country Case ...........................................................................................32

### 7. Concluding Remarks
- 7. Concluding Remarks ............................................................................................................33

### References
- References ..............................................................................................................................34

### Appendices
- Appendix A ............................................................................................................................42
- Appendix B ............................................................................................................................44

*Source: wpiea2020023-print-pdf - 1. Introduction ........................................................................................................ (IMF PDF table of contents).*

### Appendix C .............................................................................................................

### Appendix C

### Introduction and empirical context
- Minimum wage (MW) laws are now an important policy for combatting poverty in many LDCs, yet their effects on employment, output, and distribution are controversial.
- Canonical Segmented Labor Markets Model (SLMM) predicts higher MW reduces formal employment and output and increases informal employment and output, depressing real wage in informal sector; for standard production functions and plausible parameter values, job losses in the formal sector and redistributive effects are large.
- Empirical evidence accumulated over the past twenty years challenges SLMM predictions:
  - Employment elasticities in meta-analyses and surveys: -.08, -.011, -.20, and -.23 (Natarajet al.(2014), Bhoratetal.(2017), World Bank(2006), this paper (Appendix C) respectively).
  - Informal sector employment often decreases more than formal sector employment in many studies.
  - Informal sector real wages do not decline; they almost always increase (the "lighthouse effect").
- The puzzle: Why are formal-sector employment losses often very small, and why does the lighthouse effect arise? Existing explanations relying on norms of fairness are incomplete.

### Objective and approach
- Objective: bridge the divide between theoretical and empirical research by developing a dynamic general equilibrium model with efficiency wages (EW) and endogenous capital accumulation in both formal and (non-agricultural) informal sectors.
- Key modelling choices:
  - Efficiency wages operate in both formal and informal sectors.
  - Endogenous capital accumulation allows long-run adjustment and recovery of labor demand.
  - Model captures interaction of efficiency wages, wage curves, firm-size wage premium, and monitoring costs.

### Stylized empirical facts motivating the model
- Wage curves (real wage vs unemployment) operate in both sectors; informal sector wages are generally more responsive to unemployment but not frictionless.
- Firm-size wage premiums appear at very small establishment sizes (2–5 employees).
- Labor force dynamics: large movements into/out of unemployment from salaried informal employment in downturns (Bosch and Maloney (2007)); transitions from formal to informal decline during downturns.
- Evidence of job rationing in informal sector in South Africa: 80 percent of unemployed reported they could not find any work; only 3 percent cited "unsuitable work" (Heinz and Posel, 2008).
- Labor force participation rates implausibly low in much of SSA, consistent with discouraged workers misclassified as out of labor force.

### Key mechanisms in the model
- Effort function (formal sector): e1 = g0 + g1 ln(w1/P) + g3 u − g4 ln L1
  - Effort increases with real wage and unemployment and decreases with firm size (monitoring/managerial intensity).
  - Firm-size wage premium arises because larger L1/S reduces effort and firms pay premiums to buy back effort.
- Modified Solow/effort condition yields linking of effort elasticity and wage setting; efficiency-wage effects reduce employment elasticity to wage.
- Three effects that limit formal employment losses after an MW increase:
  1. Effort response to unemployment (wage curve), g3.
  2. Firm-size wage premium / monitoring channel, g4.
  3. General equilibrium demand-side effects (substitutability in consumption and tradability of formal output).

### Insights from the simplified model — Short run
- With CES production, labor demand and effort responses, the short-run partial-equilibrium employment elasticity is substantially reduced by EW effects.
- In an overly simple case (effort depends only on real wage, g3 = g4 = 0), partial-equilibrium employment elasticity equals −1 (ˆL1/ˆwm = −1).
- With empirically plausible parameter values (basecase: θK1 = .40, σ1 = .75, g3 = .80, g4 = .20, L1 = .49), the partial-equilibrium employment elasticity equals .332 (i.e., formal employment falls by .332 percent per 1 percent MW increase in that calibration).
- Short-run real output in the formal sector increases following an MW increase due to downward shift in the effective cost of labor (ECL) from higher effort and firm-size premium effects; investment responds to higher MPK as labor productivity rises.

### Insights — Full general-equilibrium and long run
- General equilibrium depresses P1 when formal output rises; employment falls more in full GE than in partial equilibrium, with degree depending on consumption substitution elasticity (ε).
- Long run (steady state):
  - Across steady states, MPK = RCC; higher MW can lead to increased labor supply (via effort) and lower P1, shifting both MPK and RCC.
  - Equilibrium capital stock increases if ε > ε* = σ1 (1 − γ) Q1 / C1.
  - In limiting case where formal and informal goods are perfect substitutes in consumption (ε = ∞), labor demand fully recovers and employment elasticity equals zero across steady states.
  - Employment always decreases less in the long run than in the short run; capital accumulation helps recover labor demand.
- Transition dynamics governed by Euler and capital accumulation equations; the stationary equilibrium is saddle-point stable. Consumption typically jumps up at t = 0 and converges smoothly to the new steady state.

### Welfare implications (representative agent)
- In the simplified model, raising the MW increases welfare because it ameliorates coordination externality and raises total effective labor supply; the percentage equivalent variation (EV) welfare gain is given by equation (41) (see source).
- Caveat: representative-agent framework ignores distributional concerns; welfare gains assume that the newly unemployed are compensated or do not cause distributional harm.

### Full model (open economy) structure and key equations
- Two-sector CES production: formal Q1 and informal Q2 with sector-specific substitution elasticities σ1 and σ2 (equations (42)-(43)).
- Factories assembled combining imported machine units with f1 and f2 units of formal and informal capital; supply price of capital Pk = 1 + f1 P1 + f2 P2 (equation (44)).
- Preferences: nested CES aggregators with substitution elasticities ε2 and ε3 (equations (45)-(48)).
- Representative-agent Ramsey problem with adjustment costs and dynamics in K1, K2, L1, L2 (equations (49)-(54)).
- Effort functions in full model:
  - Formal: e1 = g0 + g1 ln wm + g3 u − g4 ln L1  (equation (59))
  - Informal: e2 = bo + b1 ln(w2/P) − b2 ln wm + b3 u (equation (60))
  - In equilibrium, informal sector effort may be constant; lighthouse effect arises because increases in wm lower perceived fairness and reduce e2 (b2 term).

### Calibration choices and parameter values (highlights)
- Pure time preference rate ρ set at 10%.
- Elasticities of substitution between capital and labor services fixed at σ1 = σ2 = .75 (sensitivity checks with .5 and 1 show limited sensitivity).
- Basecase parameter values: θK1 = .40, σ1 = .75, g3 = .80, g4 = .20; L1 is back-out equal to .49 in basecase calibration.
- Firm-size wage-premium elasticity chosen so that g4 = .20 (size premium = .25).
- Informal sector labor-share calibrated from 19 World Bank Informal Enterprise Surveys: mean firm employment ≈ 4 (of which 1.75 are unpaid family/other) and labor share of informal sector value added ≈ .50; set θK2 = .25 and θL2 = .50 implying entrepreneurial skill cost share ≈ .25.
- Consumption-share parameter γm = .14 and alternative γ1 values .39, .56, .69 to span formal sector sizes.
- Substitution in consumption between formal and informal goods ε2 explored in runs with ε2 = .5 − 5; ε3 (formal vs imports) explored with ε3 = 1, 3, 10.
- Lighthouse parameter b2 set to 1 in baseline (e2 depends on ratio of informal real wage to MW).

### Numerical results — Middle-Income (MIC) and Emerging Market (EM) calibrations
- Policy experiment: permanent 10% increase in formal-sector real minimum wage; to compare to empirical elasticities, divide simulated results by 10.
- Main long-run results (MIC baseline, ε2 = 3, ε3 = 3):
  - Aggregate employment contracts by 2.8% (long-run).
  - Formal employment contracts by 1.9%; informal employment contracts by 3.9%.
  - Total output expands by 3.9%: formal output expands by 7.2%; informal output contracts by 2.1%.
  - Informal real wage rises by just under 5% (lighthouse effect).
- Higher substitutability in consumption (higher ε2 and ε3) increases aggregate gains and shifts gains toward formal sector; formal employment may even increase in long run for high substitutability and steep wage curves.
- Steeper wage curves amplify positive output and employment effects in both sectors: larger unemployment semi-elasticity leverages effort response and capital accumulation.
- EM calibration (formal share ≈ 80% of non-agricultural output) yields qualitatively similar results but larger aggregate gains (e.g., long-run output gains 1.4–1.9 percentage points higher than MIC case for ε2 = 3, ε3 = 10).

### Coherence with empirical estimates
- Distribution of simulated employment elasticities (MIC and EM calibrations) matches empirical distribution: simulated mean −0.24, sd 0.18; empirical mean −.22, sd 0.20 (see Figure7 in source).
- Simulated informal-wage elasticities positive and range from .41 to .71, consistent with empirical findings (.15 to .90 in country studies cited).
- Simulations consistent with empirical findings that MW increases often raise labor productivity and investment in studied cases.

### Transition dynamics and short-run vs long-run
- Transition paths: consumption and output typically jump upward at t = 0 (due to immediate effort response) then converge as K adjusts; K1 typically achieves half its long-run value after ≈ 25 periods in calibrated runs.
- Short-run employment elasticities can be larger (more negative) than long-run values because of overshooting; empirical studies focusing on short-to-medium term may overstate persistent employment losses.
- Adjustment costs and sectoral adjustment speeds matter: higher employment adjustment costs dampen overshooting and produce smoother, more realistic paths.

### Low-Income Country (LIC) calibration findings and risks
- LIC calibration: informal/non-compliant sector accounts for 65% of non-agricultural economy; lower substitutability in consumption between formal and informal goods.
- Compared to MIC/EM, aggregate responses and formal-sector responses are substantially attenuated:
  - For ε2 = ε3 = 3 and central wage curve parameters, aggregate output growth falls from 3.9% (MIC) to 0.7% (LIC) in long run; aggregate employment contraction increases from 2.8% to 3.6%.
  - With flatter wage curves, aggregate employment losses increase further (e.g., 5.9% aggregate, 5.0% formal sector in one run).
- Critical adverse outcome: in several LIC calibrations, aggregate output and investment stagnate or contract slightly; consumption may initially jump but then fall below baseline within a few years, producing equivalent variation welfare losses:
  - EV welfare loss ranges: .10 − .15% of consumption when private discount rate 10%; .43 − .50% when social discount rate 5% in certain LIC runs (consumption falls below initial level after year four in some cases).
- Conclusion: MW increases that are slightly positive relative to equilibrium wages in MIC/EM often raise welfare and GDP, but in LICs with small formal sector, low substitutability, and flat wage curves there is a tangible risk of harming output and welfare.

### Overall conclusions and policy implications
- A dynamic GE model with EW and endogenous capital accumulation can explain the full range of empirical evidence on MW effects in developing countries, including:
  - Small formal-sector employment elasticities predominant across relevant parameter space (.1 − .3 in many configurations).
  - Lighthouse effect (informal wages rise) and larger informal employment losses in some cases.
  - Positive aggregate effects (GDP, investment, productivity) are common in MICs/EMs but not assured in LICs.
- Context-specific factors condition MW effectiveness: (i) relative size of formal sector; (ii) degree of substitutability between formal and informal outputs; (iii) absolute and relative degree of wage flexibility in sectoral wage curves; (iv) tradability of formal-sector output.
- Hierarchy of MW effectiveness: moving from LICs to MICs to EMEs increases likelihood of GDP and informal real wage gains and reduces employment losses.
- Policy caution: MW increases are not a universal good in all LDCs; in LICs they may reduce output and welfare.
- Future research priorities:
  - Explicit treatment of coverage and compliance with MW laws.
  - Incorporation of heterogeneous households for distributional welfare analysis.
  - Analysis of efficiency wage effects in the public sector and habit formation/adjustment costs in effort.
  - Macro effects of better enforcement of MW (empirical and theoretical work).

*Source: Appendix C of the IMF working paper (wpiea2020023-print-pdf).*

### References

### References (wpiea2020023-print-pdf - References)

### Literature coverage and themes
- Extensive bibliographic coverage of minimum wage, informality, labor markets, production functions, and adjustment costs across developing and developed countries.
- Geographic focus includes: Nigeria, Nicaragua, Morocco, Chile, Kenya, South Africa, Mexico, Colombia, Brazil, Indonesia, Vietnam, China, Honduras, Turkey, Peru, Uruguay, Argentina, Cote d’Ivoire, Tunisia, India, Japan, Korea, Singapore, Thailand, Honduras, Trinidad and Tobago, Costa Rica, Indonesia, Philippines, and others.
- Methodological approaches represented: difference-in-differences, quasi-experiments, structural modeling, panel evidence, Markov processes, Bayesian estimation, CES and Translog production functions, firm-level panel data, and meta-analysis.
- Key recurring topics in the citations: impacts of minimum wages on employment, wages, poverty, informality, compliance/enforcement, wage distribution, youth employment, sectoral effects, returns to formality, elasticity of substitution between capital and labor, factor adjustment costs, and wage curves.

### Notable numeric and bibliographic signals in the references (selected)
- AERC Research Paper 222 (Aigbokhan, 2011).
- Labour Economics 18, S45-S59 (Alaniz, Gindling, and Terrell, 2011).
- World Bank Employment Policy Primer (Almeida and K. Terrell, 2008).
- Journal of Development Economics 91, 37-47 (Badaoui, Strobl, and Walsh, 2010).
- American Journal of Agricultural Economics 96(5), 1402-1419 (Bhorat, Kanbur, and Stanwix, 2014).
- Journal of African Economies 25, AERC Supplement 1, i61-i102 (Bhorat et al., 2016).
- IZA Discussion Paper No.3038 and No.1665 (Bosch & Maloney; Blanchflower & Oswald).
- NBER Working Paper No.19136 (Karabarbounis and Neiman, 2013).
- IZA Discussion Paper No.970 (Kertesi and Kollo, 2003).
- IZA Journal of Labor and Development 4:17, 1-30 (DelCarpio et al., 2015).
- Journal of Development Economics 100, 48-62 (Magruder, 2013).
- Journal of Economic Dynamics and Control 51, 341-355 (Mumtaz and Zanetti, 2015).
- Industrial and Labor Relations Review 54(4), 864-886 (Rama, 2001).
- Journal of Development Economics 99(1), 27-45 (Dinkelman and Ranchhod, 2012).
- NBER Working Paper No.20452 (Oberfeld and Raval, 2014).

*References list as provided in the source PDF.*

### Appendix A — Welfare, private agent budget linearization, and Equivalent Variation (EV)
- For small changes, the welfare change expression:
  - U−Uo C−1/τo = ∞∫0 (C−Co) e−ρt dt. (A1)
- Linearized private agent's budget constraint:
  - C−Co = ρPk(K1−K1,o) − Pk ̇K1 + Q1 θL1 [1−g3L1 − g4 1−g4 (m1 ˆK1 + m2 ˆP1 + m5 θL1 ˆwm ]. (A2)
- Parameter definitions used:
  - m1 = θK1/mo,
  - m2 = σ1(1−γ)/mo,
  - m5 = σ1 θL1(g3L1 + g4) / mo (1−g4),
  - units chosen so that P1 = P = 1 at the initial steady state.
- Envelope theorem implication:
  - No first-order impact on welfare of changes in the capital stock holding labor supply constant; sum of the first two terms in (A2) equals zero.
- Substitution for K1 dynamics:
  - K1 − K1,o = (K∗1 − K1,o)(1 − eλt). (A3)
  - ̇K1 = −λ(K∗1 − K1,o)eλt. (A4)
- Welfare gain/loss depends only on how the minimum wage affects the present value of the total supply of labor services (e1 L1):
  - U−Uo C−1/τo = Q1 θL1 ∞∫0 [1−g3L1 − g4 1−g4 (m1 ˆK1 + m2 ˆP1 + m5 θL1 ˆwm ] e−ρt dt. (A6)
- Solution for ˆP1 in equation (21):
  - ˆP1 = − m3 V ˆK1 − m5 V ˆwm, (21')
  - m3 ≡ θK1 [1 + θL1(1−g3L1 − g4) / mo (1−g4)].
- Collected expression (A8) for the welfare change per unit ˆwm:
  - (U−Uo) C−1/τo ˆwm = Q1 θL1 (1−g3L1 − g4) (1−g4) V m1 K1 [C1 / Q1 − σ1(1−γ)] ∞∫0 (K∗1 − K1,o)(1 − eλt) e−ρt dt + Q1 m5 V C1 / Q1 ∞∫0 e−ρt dt. (A8)
- Equivalent variation (EV) as a percentage of consumption (after multiplying by ρ/C and substitutions):
  - EV = (1−g3L1 − g4) θK1 γ / mo (1−g4) V (−∗) K∗1 − K1,o / K1,o ︸︷︷︸ Sign of −∗ λρ / (λ−ρ) +  γ σ1 θL1 (g3L1 + g4) / mo (1−g4) V. (equation labeled (69) / (41) in text)
- Interpretation:
  - First term: indirect effect tied to capital adjustment and sign of (−∗), scaled by λρ/(λ−ρ).
  - Second term: direct effect of w m ↑. (Direct effect of w m ↑ (69))

*Appendix A equations and expressions as provided in the source PDF.*

### Appendix B — Wage Curves and Unemployment: reported elasticities
- Appendix B tabulates numerous empirical estimates of the elasticity of the real wage with respect to the unemployment rate. Selected findings (elasticities preserved exactly as reported):
  - Berg and Contreras, 2004 (Chile, Greater Santiago Area, 1974-1996):
    - Aggregate = -.076
    - University education = -.08, No university education = -.11
    - Formal Sector = -.08, Informal Sector = -.03 (insignificant)
  - Baltagi et al., 2012 (Turkey, 2005-2009):
    - Aggregate = -.105
    - Formal Sector = -.069, Informal Sector = -.264
    - High Education: Aggregate = -.089, Formal = -.073, Informal = -.139
    - Low Education: Aggregate = -.098, Formal = -.037, Informal = -.288
  - Ramos et al., 2009 (Colombia, 2002-2006):
    - Aggregate = -.071
    - Formal Sector = -.060 (insignificant), Informal Sector = -.179
  - Bucheli and Gonzalez, 2007 (Uruguay, 1991-2005):
    - Aggregate (private sector) = -.132
    - Formal Sector = -.058, Informal Sector = -.241
    - Education: 0-8 years = -.158; 9-11 = -.144; 12+ = -.04 (insignificant)
  - Lugo, 2006 (Chile, 1992-2002):
    - Aggregate (private sector): Male = -.039, Female = -.048
    - Formal: Male = -.031, Female = -.040; Informal: Male = -.040, Female = -.057
    - High Education: Male = 0, -.045, Female = insignificant
  - Alcaraz, 2009 (Mexico, 1995-2001):
    - Formal Sector = 0, Informal Sector = -.138
  - Arango et al., 2010 (Colombia, 2001-2009):
    - Local (city) unemployment rate: -.009 to -.167; cell mean = 0 to -.103
    - Group unemployment rate: -.086 to - .108; cell mean = -.044 to -.097
  - Garcia and Granados, 2005 (Chile?):
    - Regional unemployment rate: -.04
    - Sectoral unemployment rate: -.13
  - Hoddinott, 1996 (Cote d’Ivoire, 1985-1987, males in urban areas):
    - Aggregate = -.119
    - Education: Primary or less = -.215, More than primary = -.111
    - Professional = 0, Non-professional = -.127
  - Ilkaracan et al., 2003 (Turkey, firms with 10+ employees):
    - Aggregate private sector = -.135
    - Primary education or less = -.23
    - Primary-high school education = -.172
    - College graduates = -.029 (insignificant)
    - Non-agricultural labor = -.15-.43
  - Kingdon and Knight, 1999 (S. Africa, 1993):
    - All workers = -.108
    - Private sector = -.141 at mean unemployment rate
    - Urban = -.135 (evaluated at mean unemployment rate)
    - Rural = -.122 (evaluated at mean unemployment rate)
  - Mudiriza and Edwards, 2018 (S. Africa, 2011):
    - Aggregate = -.091
    - Private sector = -.052
  - Von Fintel (2015) (S. Africa, 2000-2004):
    - Close to zero in the short run and approximately -.1 in the long run
  - Baltagi et al., 2017 (Brazil, 2002-2009):
    - Aggregate = -.107
    - Formal = -.039 (insig), Informal = -.246
    - Age 15-29 = -.154, Age 30-44 = -.030 (insig)
    - Tenure < 1 = -.180, 1 < Tenure < 5 = -.137, Tenure > 5 = insig
    - Low skill = -.048 (insig), Medium skill = -.110
  - Castro (2006) (Mexico, urban workers, 1993-2002):
    - Aggregate = -.037, -.039 (MaleFemale)
    - Private Sector -.039-.048
    - Formal Sector -.031-.040
    - Informal Sector -.058-.057
    - High education -.045 insig
    - Low education -.044, -.065  -.041, -.057
    - Age < 26 -.063, -.068  -.063, -.070
    - Age 26-45 -.017, -.067  -.023, -.031
  - Lopez and Mendoza (2017) (Mexico, manufacturing, 2007-2015):
    - Aggregate = -.023, -.045
    - Period of greater stability (07/2009 – 12/2015) = -.092, -.131
  - Galiani (1999) (Argentina, urban, 1990-1997):
    - Aggregate: short run = -.04, long run = -.082
    - Males: short run = -.057, long run = -.119
  - Galindo and Catalan (2010) (Mexico, 1989-2008):
    - Aggregate = -.032
  - Baltagi et al. (2012) (Turkey, 2005-2008):
    - Aggregate = -.099
    - Young (< 34) = -.108 (-.243), Old = -.081 (-.177)
    - Male = -.069, Female = -.237
    - Urban = -.101, Rural = -.105 (insig)
    - Tenure: Low (< 6.9 years) = -.175 (-.309), High = -.011 (insig)
    - Education: Low (< 8 years) = -.086 (-.632), High = -.067 (-.088, insig)
    - Note: Figures in parentheses are for women.
  - Wu (2004) (China, urban youth, 1989-1999):
    - Aggregate = -.224
  - Park and Shin (2008) (Korea, 1998-2002):
    - Aggregate = -.075, -.098

- Overall pattern in Appendix B:
  - Elasticities are frequently negative, often in the range approximately between -0.02 and -0.25, with variation by country, sector (formal vs informal), education, age, tenure, and time horizon (short run vs long run).
  - Several estimates report informal sector elasticities much larger in magnitude (more negative) than formal sector elasticities (e.g., Baltagi et al., 2012; Baltagi et al., 2017; Bucheli and Gonzalez, 2007).

*Appendix B wage-curve elasticity estimates as provided in the source PDF.*

*Content unit: wpiea2020023-print-pdf - References (source PDF content used verbatim).*

### Appendix C

### Appendix C — Minimum Wage Studies and Episodes

### Employment Effects
- Bhorat et al. (2016), S. Africa
  - F: Small positive effect in retail and taxi sectors.
  - I: Small negative effect in agriculture; no significant effect in the other three sectors.
  - Significant decrease in hours in agriculture (I), but longer hours in the taxi industry (F).
  - Overall: No significant impact.
- Bell (1997), Colombia
  - F: EE = .15-.33 for unskilled labor and .55-1.22 for target group (firms where the wage is ≤ 1.5 MW).
  - MW has a stronger impact the wage in I than F.
- Gindling and Terrell (2009), Honduras, 1990-2004
  - F: EE = .46-.55 in the short run and 1.22 in the long run.
  - I: Positive EE; range = .39-.51.
  - Overall: Elasticity of unemployment = .96 for fixed effects, but insignificant for 2SLS and GMM.
- Lemos (2009), Brazil, 1982-2004
  - No discernable impact on employment in F or I (including self-employment in I).
- Gindling and Terrell (2007), Costa Rica, 1988-2000
  - F: EE = .11-.55.
- Ham (2018), Honduras, 2006-2012
  - F: EE = .8-1. (covered sector).
  - I: Positive EE = .5-.7 (employers, self-employed, unpaid family members).
  - Overall: -.10.
- Andalon and Pages (2008), Kenya, 1998-1999
  - F: .11-.55.
  - I: Positive EE for self-employed ranges from .27 to .59; no significant effect on salaried workers.
- Rama (2001), Indonesia, 1988-1994
  - F: Estimates suggest positive impact, not statistically significant.
  - I (small manufacturing): Average of three EE = -.96; highest EE = -1.30.
  - Overall: Small negative, statistically insignificant effect.
- Kertesi and Kollo (2003), Hungary, 2001-2002
  - F: No evidence of effect at large firms.
  - I: Significant decrease in employment (3% in first year); EE ranges from -.27 to -.32.
- Jones (1998), Ghana, 1970-1991
  - I: No effect of private sector MW.
  - Total: Positive EE = .53 (probably capturing large firm response).
- Gindling and Terrell (2005), Costa Rica, 1988-1999
  - Impact of MW much stronger in I than F.
- Strobl and Walsh (2003), Trinidad and Tobago, 1996-1998
  - Total: Probability of job loss for those directly affected by the MW = 9% for males; statistically insignificant for females.
- Dinkelman and Ranchbod (2012), S. Africa, 2001-2004, domestic workers
  - No statistically significant effect on employment on either the intensive or extensive margins.
- Neumark et al. (2006), Brazil, 1996-2001, six largest metropolitan areas
  - Total: EE = .07 for total employment (not target group most directly affected).
- Suryahadi et al. (2003), Indonesia, 1988-1999
  - Urban F: EE = .112 for all workers, .196 for less-educated workers, and .140 for blue-collar workers.
- Comola and De Mello (2011), Indonesia, 1996-2004
  - F: Employment decreases.
  - I: Employment increases.
  - Total: Employment increases.
- Murray and van Walbeek (2007), S. Africa, 2005, large sugar farms
  - F: No layoffs but total work hours reduced 10.1% in response to the MW introduced in agriculture in 2002.
- Lathinipat and Poggi (2016), Thailand, 2002-2013
  - Employment losses concentrated among micro firms and small and medium enterprises.
  - Six quarters out, EE = -.523 at micro firms and -.472 at small and medium enterprises.
  - EE increases at large firms (EE = .287).
  - Total EE = -.273 for working-age population and -.688 for low-skill youth, six quarters out.
- Magruder (2013), Indonesia, 1993-2000
  - EE positive .10 for full-time wage work.
  - Mean EE positive .206 for large firms and -.437 for small firms.
- Del Carpio et al. (2019), Thailand, 2001-2011
  - EE = .064. Adjusted for the limited bite of the MW, EE= .174. Employment losses concentrated among the less educated.
- Del Carpio et al., 2015, Indonesia, 1993-2006
  - EE = .034-.047 for production workers and .055-.062 for non-production workers.
  - Employment losses concentrated among “small” firms (< 150 employees).
  - Adjusting for limited bite, EE = .23-.31 for production workers.

### Real Wages and Productivity
- Bhorat et al. (2016), S. Africa
  - F: increases 11% (retail). Alternative estimate = 26.3%.
  - I: Increases 11.3-14.6% (agriculture, domestic workers). Alternative estimate = 15.8-58.2%.
- Bell (1997), Colombia
  - MW has a stronger impact the wage in I than F.
- Lemos (2009), Brazil
  - Strong evidence from kernel densities that MW affects wages in both F and I.
- Andalon and Pages (2008), Kenya
  - Strong effect on average real wage in non-agriculture. Elasticity with respect to the MW equals 1.02.
- Rama (2001), Indonesia
  - 95% increase in real MW that increased average real wages 5-15% was associated with an increase in labor productivity of 14.4-22.6%.
  - Increase in labor productivity in small-firm manufacturing was 38.7%.
- Kertesi and Kollo (2003), Hungary
  - 57% increase in the real MW in 2001.
  - Large increase in labor productivity in the small firm sector.
  - Aggregate labor productivity increased ≈ 7%.
- Azam (1997), Morocco, 1978-1991, agricultural MW
  - Strong positive effect on agricultural output.
  - Elasticity on output with respect to the real MW = 1.18 for the largest crop (barley).
- Gindling and Terrell (2005), Costa Rica
  - Elasticities of average wage with respect to MW: Urban F = .108; Urban I = .152; Rural F = .164; Rural I = .398.
- Bosch and Manacorda (2010), Mexico, 1989-2001
  - F: Kernel densities show pronounced spikes at exact multiples and fractions of the MW.
  - I: No discernible impact.
- Neumark et al. (2006), Brazil
  - I: Strong evidence MW serves as reference wage; 11.4% of informal workers paid exactly the MW.
  - Total: One dollar increase in MW increases the average wage 43 cents.
- Dinkelman and Ranchbod (2012), S. Africa
  - Wages increased 19-22% despite no monitoring or active enforcement in this part of the informal sector.
  - Work conditions also improved.
- Mayneris et al. (2018), China, 2003-2005, industrial sector
  - Elasticity of the average real wage with respect to the real MW = .361.
  - Large increases in labor productivity. Elasticity of labor productivity with respect to the MW = .38 for private firms; elasticity was half as large in the state sector.
  - Labor productivity increased 8.4% vs. average real wage increase of 7.9%. No decrease in firm profitability.
- Fajnzylber (2001), Brazil, 1982-1997
  - Strong effects of the MW on entire wage distribution in both F and I.
  - Elasticity of real wage with respect to the real MW in F = 1.43 for workers earning 50-90% of the MW; 1.08 for 90-110% of the MW; .89 for 110-150% of the MW.
  - Corresponding elasticities for I-salaried = 1.18, 1.03, and .82; self-employed = 1.18, 1.32, and .77.
- Hertz (2005), S. Africa, 2001-2004, domestic workers
  - Despite high noncompliance, real wages increase 6.6-12.3% for men and 19-21.5% for women.
- Rani and Ranjbar (2015), Brazil, India, Indonesia, S. Africa, and Mexico, 2005-2010
  - MW strongly affects the wage distribution in all countries.
  - Elasticity of real wage in I with respect to the effective MW (log of Kaitz index) at 20th and 40th quantiles in 2009-2010:
    - Brazil: .79, .64
    - India: .46, .48
    - Indonesia: .82, .92
    - Mexico: .74, .66
    - S. Africa: .82, .82

### Lighthouse Effect, Compliance, and Distributional Impacts
- Lighthouse (LE) effect reported as present in many studies:
  - LE: Yes — Bhorat et al. (2016); Bell (1997); Lemos (2009); Gindling and Terrell (2007); Andalon and Pages (2008) (Probably); Dinkelman and Ranchbod (2012); Bosch and Manacorda (2010) (Yes for F, No for I); Neumark et al. (2006); Comola and De Mello (2011); Kristensen and Cunningham (2006); Rani and Ranjbar (2015); Fajnzylber (2001); Lemos (2004); others.
- Kernel density and distributional evidence:
  - Multiple studies show spikes at exact multiples/fractions of the MW, affecting wage distribution in F and I (Bosch and Manacorda (2010); Kristensen and Cunningham (2006); Neumark et al. (2006); Fajnzylber (2001); Lemos (2004); Rani and Ranjbar (2015)).
- Compliance and enforcement:
  - Kertesi and Kollo (2003): MW enforced across all firm sizes; widespread compliance.
  - Gindling et al. (2015): Effective enforcement campaign at small and medium-sized firms showed no reduction in full-time employment.
  - Dinkelman and Ranchbod (2012) and Hertz (2005): Wage increases observed despite limited monitoring/noncompliance in parts of the informal sector.

### Selected Productivity and Output Findings
- Rama (2001), Indonesia
  - 95% increase in real MW associated with labor productivity increases of 14.4-22.6% (average real wages increased 5-15%); small-firm manufacturing productivity increase 38.7%.
- Kertesi and Kollo (2003), Hungary
  - Aggregate labor productivity increased ≈ 7% following a 57% real MW increase in 2001.
- Mayneris et al. (2018), China
  - Labor productivity increased 8.4% vs. average real wage increase of 7.9%; no decrease in firm profitability.
- Bhorat et al., 2014, S. Africa, agricultural sector, 2001-2007
  - MW increased farm workers 30% relative to control group; employment decreased 13-18% with increase in full-time employment and large losses in part-time employment.
  - Large increase in labor productivity; output growth stays on trend despite large decrease in employment.
  - Investment boom following introduction of agricultural MW.

### Meta-analyses and Aggregated Evidence
- Nataraj et al. (2014), survey and meta-regression of nine studies
  - F: Meta-regression concludes EE = .078 (based on only four studies).
  - I: Share of total employment increases (four studies); impact on self-employed uncertain.
- Lemos (2004), Brazil, 1982-2000
  - No significant effect on employment; maximum possible EE = .16 in the short run and .05 in the long run.
  - Adjustment occurs almost entirely through hours worked.
  - Strong evidence MW compresses wage distribution; impact on F-sector wage is 2-3 times larger than on I-sector wage.

*Appendix C — Minimum Wage Studies and Episodes (extracted from wpiea2020023-print-pdf - Appendix C)*

### 2010. Indonesia,

### 2010. Indonesia, manufacturing sector, 1990-1996.

### Empirical evidence on minimum wage (MW) effects and employment elasticities (EE)
- EE = .123 - .179 for production workers (manufacturing sector, 1990-1996).
- Employment losses larger at small firms: EE = .116 at large firms.
- Adjusting for limited bite of the MW, EE = .18-.27.
- LE: Probably; elasticity of real wage for production workers with respect to the real MW in textiles, footwear and apparel = 1 at large firms and .69 at small firms (more likely part of the informal, noncompliant sector).
- Hobberg and Lay, 2015 (Indonesia, 1997-2007): Positive impact of MW on probability of employment in the formal sector and negative impact on probability of employment in the informal sector (self-employed and unpaid family workers).
- LE: Weak evidence of an effect. Kernel density plots suggest an effect while estimates of the elasticity of the informal wage with respect to the MW are positive but not statistically significant.
- Authors caution: due to limitations of the data and a weak identification strategy, the positive EE is not strong evidence of an indirect effect of the MW on employment in the informal sector.
- Note: The 13-18% figure is an estimate based on data in the paper.

### Table 1: Calibration of the Model (Base Case values)
- Depreciation rate (δ) .05
- Intertemporal elasticity of substitution (τ) .5
- Urban unemployment rate (u) .10
- Cost share of capital in the formal sector (θK1) .40
- Cost share of production labor in the formal sector (θL1) 1 .48
- Pure time preference rate (ρ) .10
- Elasticity of substitution between capital and labor (σ1, σ2) .75
- q-elasticity of investment spending (Ω)2
- Adjustment costs to changing employment (ν3, ν4) ν4 = .5ν1 PkK1/P1, ν3 = .1ν4
- Firm-size wage premium [g4/(1- g4)] .25
- Formal sector wage premium (ψ = w1/w2) 1.5
- Consumption shares (γ1, γ2, γm) γ1 = .56, γ2 = .30, γm = .14
- Elasticity of substitution in consumption between the composite formal good and the informal good (ε2) .5-5
- Elasticity of substitution between imported consumer goods and the formal good (ε3) 3-10
- Cost share of the formal/informal good in production of investment goods (α1, α2) α1 = .56, α2 = .30
- Lighthouse effect (b2) 1
- Real wage flexibility in the formal sector [g3/(1 – g4)] 1
- Real wage flexibility in the informal sector (b3) 2
- Cost share of capital in the informal sector (θK2) .25
- Cost share of labor in the informal sector (θL2) .50
- Cost share of entrepreneurial talent in the informal sector (θH) .25
- Footnote: The cost shares for capital and production labor satisfy the adding-up constraint θK1 + θL1/(1 – g4) = 1. This and the value of g4 backed out from the size premium imply θL1 = .48 and a cost share of .12 for managerial/supervisory labor.

### Table 2: Labour Share in value added and employment (cross-sectional means)
- (Reported labour costs) / VA: Mean 0.238, Median 0.244, Std. dev 0.129, Max 0.548, Min 0.081
- (Avg.wage*emp)/VA: Mean 0.426, Median 0.118, Std. dev 0.118, Max 0.702, Min 0.119
- Labour Share in value added – trimmed:
  - (Reported labour costs) / VA: Mean 0.494, Median 0..482, Std. dev 0.093, Max 0.706, Min 0.354
  - (Avg.wage*emp)/VA: Mean 0.488, Median 0.481, Std. dev 0.087, Max 0.702, Min 0.363
- Employment:
  - Paid Employees: Mean 2.8, Median 2.0, Std. dev 2.3, Max 10.5, Min 0.7
  - Unpaid Employees: Mean 0.7, Median 0.7, Std. dev 0.5, Max 1.6, Min 0.0
  - Family: Mean 0.8, Median 0.8, Std. dev 0.4, Max 2.2, Min 0.4
- Source: Cross sectional means from 19 World Bank Informal Firms Survey (see Note 23)
- Notes: [a] Total self-reported labour costs as a share of derived value added; [b] Average reported wage time total employment (including family members); [c] Labour share initially calculated excluding firms where measured labour share in excess of 1; trimmed calculation excludes firms with calculated labour share less than 0.15.

### Table 3: Long-run outcome, MIC calibration (selected panels and outcomes)
- Panel [a] (ε2 = ε3 = 3)
  - Columns: g3/g1 = .5, b3 = 1 | g3/g1 = 1, b3 = 2 | g3/g1 = 1.5, b3 = 3
  - w2/P 5.6 | 4.6 | 4.1
  - L1 -3.3 -1.9 -1.3
  - L2 -6.0 -3.9 -3.0
  - L1 + L2 -4.5 -2.8 -2.0
  - K1 4.6 6.1 6.8
  - K2 -2.0 -.7 -.1
  - Q1 5.6 7.2 7.9
  - Q2 -3.3 -2.1 -1.5
  - GDP 2.4 3.9 4.6
- Panel [b] (ε2 = 1.5 and ε3 = 3)
  - w2/P 5.7 | 4.6 | 4.1
  - L1 -4.8 -3.4 -2.8
  - L2 -4.1 -2.1 -1.1
  - L1 + L2 -4.5 -2.8 -2.0
  - K1 3.0 4.5 5.2
  - K2 0 1.3 1.9
  - Q1 4.1 5.7 6.4
  - Q2 -2.1 -.7 -.1
  - GDP 2.0 3.5 4.2
- Panel [c] (ε2 = 5 and ε3 = 10)
  - w2/P 6.0 | 5.1 | 4.8
  - L1 -1.0 .4 1.1
  - L2 -7.7 -6.0 -5.2
  - L1 + L2 -4.1 -2.5 -1.8
  - K1 7.0 8.5 9.2
  - K2 -3.6 -2.4 -1.9
  - Q1 7.7 9.3 10.0
  - Q2 -4.9 -3.7 -3.1
  - GDP 3.3 4.8 5.4
- Panels [d]-[f] present further permutations with ε2 and ε3 varying; reported values follow the same format (w2/P, L1, L2, L1+L2, K1, K2, Q1, Q2, GDP).

### Table 4: Long-run outcome, HIC/EME calibration (selected panels)
- Panel [a] (ε2 = ε3 = 3)
  - w2/P 6.6 | 5.7 | 5.3
  - L1 -2.4 -1.3 -.8
  - L2 -6.2 -4.5 -3.8
  - L1 + L2 -3.5 -2.2 -1.6
  - K1 5.5 6.7 7.3
  - K2 -1.6 -.5 0
  - Q1 6.2 7.5 8.1
  - Q2 3.5 -2.4 -1.9
  - GDP 4.3 5.5 6.1
- Panel [b] (ε2 = 3 and ε3 = 10)
  - w2/P 7.1 | 6.4 | 6.1
  - L1 -1.7 - .6 - .1
  - L2 -6.5 -5.2 -4.6
  - L1 + L2 -3.0 -1.9 -1.3
  - K1 6.3 7.4 8.0
  - K2 -1.6 - .7 - .2
  - Q1 6.8 8.0 8.6
  - Q2 -3.7 -2.8 -2.4
  - GDP 4.8 5.9 6.4
- Panel [c] (ε2 = .5 and ε3 = 10)
  - w2/P 6.9 | 6.2 | 5.9
  - L1 -4.2 -3.1 -2.6
  - L2 - .4 1.1 1.8
  - L1 + L2 -3.2 -2.0 -1.4
  - K1 3.5 4.7 5.3
  - K2 4.7 5.8 6.2
  - Q1 4.4 5.6 6.2
  - Q2 1.0 2.0 2.4
  - GDP 3.7 4.9 5.4

### Table 5: Long-run outcome, LIC calibration (selected panels)
- Panel [a] (ε2 = ε3 = 3)
  - w2/P 4.4 | 3.1 | 2.5
  - L1 -5.0 -3.4 -2.7
  - L2 -6.1 -3.7 -2.6
  - L1 + L2 -5.9 -3.6 -2.6
  - K1 2.8 4.5 5.3
  - K2 -3.1 -1.5 - .8
  - Q1 4.2 6.1 6.9
  - Q2 -3.9 -2.2 -1.5
  - GDP -1.1 .7 1.4
- Panel [b] (ε2 = 1, ε3 = 3)
  - w2/P 4.5 | 3.3 | 2.7
  - L1 -8.3 -6.6 -5.8
  - L2 -4.8 -2.5 -1.4
  - L1 + L2 -5.7 -3.5 -2.5
  - K1 - .7 1.1 2.0
  - K2 -1.6 - .1 .5
  - Q1 1.0 3.0 3.8
  - Q2 -2.8 -1.3 - .6
  - GDP -1.5 2.1 1.0
- Panel [c] (ε2 = 1 and ε3 = 1)
  - w2/P 4.4 | 2.9 | 2.3
  - L1 -8.6 -7.3 -6.8
  - L2 -4.8 -2.4 -1.3
  - L1 + L2 -5.8 -3.7 -2.7
  - K1 -1.0 .3 1.0
  - K2 -1.7 - .3 .4
  - Q1 .7 2.3 3.0
  - Q2 -2.8 -1.3 - .6
  - GDP -1.6 0.6

### Figures and transition-path insights (selected highlights)
- Figure 7: Empirical and simulated employment elasticities
  - Mean = -0.23 ( = -0.22 for empirical estimates only)
  - Plot compares empirical estimates [Appendix B] and model-based elasticities for Middle Income Country Calibration [Table 3] and Emerging Market Calibration [Table 4].
  - Title: Elasticity of formal sector employment to change in MW — Empirical versus model-based employment elasticities.
- Figures 8–14: Transition paths under varying calibrations (Table 3, Table 4, Table 5 panels and columns).
  - Figure 8: Transition Path: Table 3, panel [d] [g3/g1 = 1, b3/b1 = 2, ε2 = 3 and ε3 = 10. v3 = .10 v1 and v4 = .5 v1 k1] [Blue = consumption; red = GDP] [Blue = formal; red = informal; green = aggregate]
  - Figure 9: Transition Path: Table 4, panel [c] [g3/g1 = 1, b3/b1 = 2, ε2 = 3 and ε3 = 10. v3 = .10 v1 and v4 = .5 v1 k1] (Notes: see Figure 8.)
  - Figures 10–14: Additional transition path comparisons across parameter permutations (ε2, ε3, g3/g1, b3) and model outcomes for consumption, GDP, capital stocks K1/K2, unemployment rate u, and formal/informal employment L1/L2.
- Figures illustrate dynamic responses (t on horizontal axis) for variables including w2/P, C,GDP, K1,K2, u, and L1,L2 across scenarios; exact plotted values follow the tables cited above.

*Source: wpiea2020023-print-pdf - 2010. Indonesia*

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