## Asia: Employment, Policy Responses, and Model-Based Policy Analysis (2020) ## Source details **Canonical URL:** [Asia: Employment, Policy Responses, and Model-Based Policy Analysis (2020)](https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020217-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2020/english/wpiea2020217-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2020/english/wpiea2020217-print-pdf.pdf.json) --- ### Pandemic context and research questions - Research questions: - (i) what is the likely effect of the COVID-19 on inequality in Asia; and - (ii) what policies could protect the most vulnerable while helping the recovery. - Methods and data used: - Documentation of long-term inequality trends. - Use of 2020 high-frequency labor survey data to identify impacts on workers and sectors. - Analysis of policy responses using the IMF Policy Tracker. - Model-based assessment using a novel and extended Susceptible-Infected-Recovered (SIR)-macro model by Engler, Pouokam, Rodriguez, and Yakadina (forthcoming)—with different types of agents and a fiscal policy block—to assess fiscal measures. - Key introductory finding highlights: - The COVID-19 pandemic triggered the worst recession since the Great Depression and poses a high risk of further worsening inequality in Asia. - High-frequency labor market indicators sharply deteriorated and substantially more than during the Global Financial Crisis (GFC). - Aggregate hours worked declined at both the extensive margin (employment rate) and intensive margin (hours worked per employee). - Unemployment surged while labor force participation plunged—an early sign of scarring effects. - The pandemic has particularly adverse effects on vulnerable groups: younger workers and women. - Model-based analysis indicates targeted fiscal support, especially when governments can borrow externally, can both reduce economic costs and diminish the number of infections; targeted measures yield larger favorable effects than untargeted measures. ### Pre-COVID inequality trends in Asia - Growth, poverty, and inequality: - Asia achieved sustained high growth, lifted millions out of poverty and virtually eliminated extreme poverty (per capita household consumption below $1.90/day). - Since the early 1990s many Asian economies have seen rising inequality—Asia’s last 25-plus years of growth have been less inclusive and less pro-poor. - The increase in inequality is especially pronounced on a population-weighted basis due to rises in China, India and Indonesia. - The rise reflects larger income gains for the top 10 percent of the population. - Selected dimensions and measures: - Gender income disparity: Asia’s men take home around 11 percent more as compared to their female counterparts (ILO data, regional comparison). - Urban–rural divide: rural–urban income divide accounts for 45 percent of economy-wide inequality in China (India and Indonesia: 25 percent and 20 percent, respectively). - Education and opportunity gaps: large gap between the wealthiest quintile and poorest quintile in share of population aged 20-24 with less than four years of schooling. - EMDEs in Asia have the largest share of youth not in education, employment or training (NEET). - Healthcare and financial services: large gaps in reproductive, maternal, newborn and child health coverage, and notable gaps in the share of adults with bank accounts between the top 60 percent and bottom 40 percent in some EMDEs (e.g., Lao PDR and the Philippines). ### Labor market impacts during the pandemic (survey evidence) - Aggregate labor market deterioration: - Aggregate hours worked declined at both extensive and intensive margins. - Unemployment rate surged. - Labor force participation plunged—an early sign of scarring effects. - Industry heterogeneity and job flexibility: - Industry groups: - Essential: agriculture, utilities, transport, information/communication, health and public administration—continued operating. - Social: wholesale/retail, hotels/restaurants, arts/entertainment—require face-to-face interactions and were most affected. - Teleworkable: finance, business/professional services and education—more amenable to remote work. - Non-teleworkable: mining, manufacturing and construction—highly affected where remote work is not possible. - Stylized facts: - High contact social industries recorded the largest declines in employment—"over 6 percentage points in June as compared to pre-COVID in December 2019." - Non-teleworkable industries were the second most impacted. - Workers in social and non-teleworkable industries have a larger share of workers with lower average earnings; the average monthly wage in the social sector is less than one-third that of essential and teleworkable industries. - Demographic impacts: - Gender: - Women are heavily affected—represent a larger share of essential frontline workers and the region has a significant gender income gap. - Women account for a large share of informality and lower-paying jobs with little protection. - Additional unpaid care burdens from school and day-care closures fall disproportionately on women. - Labor force participation (December 2019 to June 2020): female participation rate declined by 1.3 percentage points compared to a 1.0 percentage point decline for male. - Age (youth): - Asia’s youth experienced sharper job losses; the average youth unemployment rate rose by 1.4 percentage points by June 2020. - Youth were more likely employed in sectors requiring social contact, making them more vulnerable during the pandemic. - Substantial cross-country variation with the largest increases most notable in Australia and Thailand. - Education-related employment impacts: - Workers with primary education and below experienced the largest impact: average employment rate declined by 1.5 percentage point relative to pre-pandemic levels—surpassing the GFC. - Workers with secondary education and above saw declines in average employment rates largely similar across both recessions. - Observation: lower educational attainment is associated with greater susceptibility to job losses because such jobs generally do not allow telework. ### Observed policy responses - Fiscal packages and delivery capacity: - Governments in Asia implemented policy packages, some sized in double-digit percentage of GDP. - Effectiveness varied with: - Initial coverage of social safety nets. - Fiscal space available (lower outstanding debt at end-2019 associated with greater response ability). - Degree of informality. - Degree of digitalization (countries that introduced targeted cash transfers had, on average, higher digitalization scores). - Advanced economies in Asia more frequently introduced targeted benefits: cash transfers, enhanced unemployment benefits, wage subsidies, and fiscal support to firms; these measures were utilized by fewer emerging markets and low-income countries. - High informality limited reach of measures designed for formal employment. - Targeted measures were less frequent among low-income countries and emerging markets, linked to informality and weaker digital delivery capacity. ### SIR-macro model framework and simulations - Model structure and instruments: - Susceptible-Infected-Recovered (SIR) macro model (Eichenbaum, Rebelo, and Trabandt, 2020) extended to include skilled and unskilled workers, redistributive fiscal policy, and external borrowing in the form of a pandemic bond (Engler et al. 2020). - Lockdown modeled as a time-varying Pigouvian consumption tax with all proceeds rebated to households. - EPRY model features: - Distinguishes between unskilled (low income) and skilled (high income) workers; unskilled constitute 70 percent of the population. - Fiscal instruments include: (i) progressive, time-varying consumption tax rates differing for skilled and unskilled; (ii) progressive (targeted) transfers to each consumer type; and (iv) external debt via a pandemic bond. - Pandemic bond: short-term external borrowing that can be rolled over for 250 weeks but must be fully repaid thereafter through uniform labor income taxes; interest rate exogenous and constant while debt-to-GDP is sustainable, with a fast-rising risk premium when debt ratios become too high. - Simulation scenarios: - Baseline with no policies. - Untargeted policy: same containment and transfers for skilled and unskilled. - Targeted policy: higher earners taxed more, lower earners receive more transfers. ### Model findings: targeted versus untargeted fiscal support - Infection and macro effects: - Fiscal support measures can reduce infections by about one-third relative to the no-intervention baseline by making staying home more affordable and reinforcing social distancing. - Relative performance of targeted policies: - Targeted transfers raise GDP by some 3 percent relative to untargeted transfers. - Targeted transfers lower the debt-to-GDP ratio by 6 percentage points relative to untargeted transfers. - Targeted policies reduce inequality in disposable income and preserve a higher consumption share of GDP for the unskilled while reducing the consumption share of the skilled. - Mechanism highlighted: - Unskilled workers are more exposed via workplaces (transport, retail, production lines, meat processing plants); protecting their livelihoods through transfers reduces workplace exposure and saves lives, which mitigates the depth of the recession and debt accumulation. ### Policy recommendations and implications - Continue targeted support measures to mitigate pandemic and lockdown impacts, given worsening inequality trends exacerbated by the pandemic. - Enhance progressivity of taxes and transfers where appropriate to mitigate anticipated debt buildup that could pose medium-term debt sustainability risks. - Countries with lower informality can extend support through formal channels: wage subsidies for firms and enhanced unemployment benefits. - To minimize longer-term damage from skill mismatches and automation: - Revamp education curriculums to achieve more flexible skill sets and lifelong learning. - Provide new training for adversely affected workers to address challenges from automation and the shift toward digitalization and green energy. ### Conclusions (summary) - High-frequency labor surveys indicate inequality increased during the COVID-19 pandemic because job losses were concentrated among low-income workers, women, and youth. - Policy impact depended on initial coverage of safety nets, fiscal space, degree of informality, and digitalization. - Model-based analysis supports that targeted support to the unskilled is both economically and socially beneficial. - Recovery likely driven by new sectors with greater digitalization and green energy focus, requiring policy attention to skills and training. *Source: Excerpt from IMF working paper text (I. INTRODUCTION; II. KEY INEQUALITY TRENDS IN ASIA BEFORE COVID-19; III. LABOR MARKET SURVEYS INDICATE RISING INEQUALITY).* ### References ............................................................................................................. ### I. INTRODUCTION ### Pandemic context and research questions - Rising income inequality in Asia preceded the COVID-19 pandemic, with the change in inequality (delta) in Asia higher than in other regions and the level of inequality surpassing the world average. - The paper addresses two questions: - (i) what is the likely effect of the COVID-19 on inequality in Asia; and - (ii) what policies could protect the most vulnerable while helping the recovery. - Methods and data used: - Documentation of long-term inequality trends. - Use of 2020 high-frequency labor survey data to identify impacts on workers and sectors. - Analysis of policy responses using the IMF Policy Tracker. - Model-based assessment using a novel and extended Susceptible-Infected-Recovered (SIR)-macro model by Engler, Pouokam, Rodriguez, and Yakadina (forthcoming)—with different types of agents and a fiscal policy block—to assess fiscal measures. ### Key introduction findings (summary) - The COVID-19 pandemic triggered the worst recession since the Great Depression and poses a high risk of further worsening inequality in Asia. - High-frequency labor market indicators sharply deteriorated and substantially more than during the Global Financial Crisis (GFC). - Aggregate hours worked declined at both the extensive margin (employment rate) and intensive margin (hours worked per employee). - Unemployment surged while labor force participation plunged—an early sign of scarring effects. - The pandemic has particularly adverse effects on vulnerable groups: younger workers and women. - Model-based analysis indicates targeted fiscal support, especially when governments can borrow externally, can both reduce economic costs and diminish the number of infections; targeted measures yield larger favorable effects than untargeted measures. --- ### III. KEY INEQUALITY TRENDS IN ASIA BEFORE COVID-19 ### Growth, poverty reduction, and rising inequality - Asia achieved sustained high growth, lifted millions out of poverty and virtually eliminated extreme poverty (per capita household consumption below $1.90/day). - Since the early 1990s many Asian economies have seen rising inequality—Asia’s last 25-plus years of growth have been less inclusive and less pro-poor. - The increase in inequality is especially pronounced on a population-weighted basis due to rises in China, India and Indonesia. - The rise reflects larger income gains for the top 10 percent of the population. ### Dimensions of inequality (selected measures and patterns) - Gender income disparity: Asia’s men take home around 11 percent more as compared to their female counterparts (ILO data, regional comparison). - Urban–rural divide: rural–urban income divide accounts for 45 percent of economy-wide inequality in China (India and Indonesia: 25 percent and 20 percent, respectively). - Inequality of opportunities: - Education gap: large gap between the wealthiest quintile and poorest quintile in share of population aged 20-24 with less than four years of schooling. - Asia’s EMDEs have the largest share of youth not in education, employment or training (NEET). - Healthcare: large gaps in reproductive, maternal, newborn and child health coverage, notably in South Asia. - Financial services: notable gaps in the share of adults with bank accounts between the top 60 percent and bottom 40 percent in some EMDEs (e.g., Lao PDR and the Philippines). --- ### IV. LABOR MARKET SURVEYS: PANDEMIC EFFECTS AND RISING INEQUALITY ### Aggregate labor market deterioration - Labor market indicators deteriorated markedly and more than during the GFC: - Aggregate hours worked declined at both extensive and intensive margins. - Unemployment rate surged. - Labor force participation plunged—an early sign of scarring effects. ### Heterogeneity across industries and job flexibility - Industries classification and impact: - Essential industries: agriculture, utilities, transport, information/communication, health and public administration—continued operating. - Social industries: wholesale/retail, hotels/restaurants, arts/entertainment—require face-to-face interactions and were most affected. - Teleworkable industries: finance, business/professional services and education—more amenable to remote work. - Non-teleworkable industries: mining, manufacturing and construction—highly affected where remote work is not possible. - Stylized facts: - First, high contact social industries recorded the largest declines in employment—"over 6 percentage points in June as compared to pre-COVID in December 2019." These sectors were closely associated with travel and tourism and showed positive job creation during the GFC in contrast to the pandemic. - Second, non-teleworkable industries were the second most impacted. - Third, workers in both social and non-teleworkable industries have a larger share of workers with lower average earnings. Example: the average monthly wage in the social sector is less than one-third that of essential and teleworkable industries. The pandemic has widened already large income disparities, exacerbating income inequality. ### Demographic impacts: gender and age - Gender: - Women are heavily affected—represent a larger share of essential frontline workers and the region has a significant gender income gap. - Women account for a large share of informality and lower-paying jobs with little protection. - Additional unpaid care burdens from school and day-care closures fall disproportionately on women. - Labor force participation (December 2019 to June 2020): female participation rate declined by 1.3 percentage points compared to a 1.0 percentage point decline for male. - Age (youth): - Asia’s youth experienced sharper job losses; the average youth unemployment rate rose by 1.4 percentage points by June 2020. - Youth were more likely employed in sectors requiring social contact, making them more vulnerable during the pandemic. - Substantial cross-country variation: increases in youth unemployment were most notable in Australia and Thailand. --- ### V. POLICY RESPONSES AND MODEL-BASED ANALYSIS ### Observed policy responses - According to the IMF Policy Tracker, many Asian governments implemented significant fiscal packages to mitigate effects on the most vulnerable. - The impact of fiscal packages varied by: - Initial coverage of social safety nets. - Fiscal space available. - Degree of informality and digitalization. ### Model-based findings (SIR-macro model) - Fiscal support measures, when governments have access to external financing, help diminish the economic cost of the pandemic and can significantly reduce the number of infections—thus saving lives. - Targeted fiscal measures produce larger favorable effects on both distributional outcomes and virus containment than untargeted measures. - Allowing governments to borrow externally helps support the economy throughout the pandemic recession but may require more progressive fiscal measures to avoid excessive pandemic debt accumulation and preserve medium-term debt sustainability. --- *Source: Excerpt from IMF working paper text (I. INTRODUCTION; II. KEY INEQUALITY TRENDS IN ASIA BEFORE COVID-19; III. LABOR MARKET SURVEYS INDICATE RISING INEQUALITY).* ### 2020. Data are seasonally adjusted. The horizontal line inside ### Asia: Employment, Policy Responses, and Model-Based Policy Analysis (2020) ### Education and Differential Employment Impacts - Workers with primary education and below experienced the largest impact from the pandemic: average employment rate declined by 1.5 percentage point relative to pre-pandemic levels—surpassing the GFC. - Workers with secondary education and above saw declines in average employment rates largely similar across both recessions. - Observation: lower educational attainment is associated with greater susceptibility to job losses because such jobs generally do not allow telework, which typically requires at least a college degree (Shibata, 2020). - Asia definition (in one note): Bangladesh, Brunei, Cambodia, Lao PDR, Mongolia, Myanmar, Nepal, Tonga, Korea and Vietnam. - Asia definition (in another note for a separate figure): Hong Kong, Korea, Taiwan Province of China and Thailand. ### Policies to Break the Vicious Cycle: Country Characteristics Associated with Effective Responses - Governments in Asia implemented policy packages, some sized in double-digit percentage of GDP. - Effective crisis responses are associated with: - Benefiting from a larger fiscal space (lower outstanding debt at end-2019). - Having broader social safety nets. - Exhibiting lower levels of informality. - Having a higher degree of digitalization. - Countries with lower outstanding debt at the end of 2019 had more ability to respond effectively and protect the vulnerable. - Advanced economies in Asia more frequently introduced targeted benefits: cash transfers, enhanced unemployment benefits, wage subsidies, and fiscal support to firms; these measures were utilized by fewer emerging markets and low-income countries. - Higher informality limited the reach of measures designed for formal employment because a higher share of workers were outside social insurance schemes (self-employed, informal sector, gig economy). - Digitalization facilitated delivery of support: countries that introduced targeted cash transfers had, on average, higher digitalization scores than those that did not. ### Targeted Help to Households and Workers (Policy Patterns) - Fiscal responses varied across AE (advanced economy), EM (emerging market), and LIC (low-income country) groups in Asia. - Targeted measures (e.g., targeted cash transfers) were less frequent among low-income countries and emerging markets, linked to informality and weaker digital delivery capacity. ### Policy Analysis: SIR Macro Model with Inequality and Fiscal Instruments - Model framework: Susceptible-Infected-Recovered (SIR) macro model (Eichenbaum, Rebelo, and Trabandt, 2020) extended to include skilled and unskilled workers, redistributive fiscal policy, and external borrowing in the form of a pandemic bond (Engler et al. 2020). - Lockdown modeled as a time-varying Pigouvian consumption tax with all proceeds rebated to households. - Key EPRY model features: - Distinguishes between unskilled (low income) and skilled (high income) workers; unskilled constitute 70 percent of the population. - Fiscal instruments include: (i) progressive, time-varying consumption tax rates differing for skilled and unskilled; (ii) progressive (targeted) transfers to each consumer type; and (iv) external debt via a pandemic bond. - Pandemic bond: short-term external borrowing that can be rolled over for 250 weeks but must be fully repaid thereafter through uniform labor income taxes; interest rate exogenous and constant while debt-to-GDP is sustainable, with a fast-rising risk premium when debt ratios become too high. - Simulations: baseline with no policies; untargeted policy (same containment and transfers for skilled and unskilled); targeted policy (higher earners taxed more, lower earners receive more transfers). ### Model Findings: Targeted vs. Untargeted Fiscal Support - Fiscal support measures can reduce infections by about one-third relative to the no-intervention baseline by making staying home more affordable and reinforcing social distancing. - Targeted fiscal policies outperform untargeted policies on multiple metrics: - Targeted transfers raise GDP by some 3 percent relative to untargeted transfers. - Targeted transfers lower the debt-to-GDP ratio by 6 percentage points relative to untargeted transfers. - Targeted policies reduce inequality in disposable income and preserve a higher consumption share of GDP for the unskilled while reducing the consumption share of the skilled. - Mechanism: unskilled workers are more exposed via workplaces (transport, retail, production lines, meat processing plants); protecting their livelihoods through transfers reduces workplace exposure and saves lives, which mitigates the depth of the recession and debt accumulation. ### Policy Recommendations from Analysis - Continue targeted support measures to mitigate pandemic and lockdown impacts, given worsening inequality trends exacerbated by the pandemic. - Enhance progressivity of taxes and transfers where appropriate to mitigate anticipated debt buildup that could pose medium-term debt sustainability risks. - Countries with lower informality can extend support through formal channels: wage subsidies for firms and enhanced unemployment benefits. - To minimize longer-term damage from skill mismatches and automation: - Revamp education curriculums to achieve more flexible skill sets and lifelong learning. - Provide new training for adversely affected workers to address challenges from automation and the shift toward digitalization and green energy. ### Conclusions (summary) - High-frequency labor surveys indicate inequality increased during the COVID-19 pandemic because job losses were concentrated among low-income workers, women, and youth. - Policy impact depended on initial coverage of safety nets, fiscal space, degree of informality, and digitalization. - Model-based analysis supports that targeted support to the unskilled is both economically and socially beneficial. - Recovery likely driven by new sectors with greater digitalization and green energy focus, requiring policy attention to skills and training. *Source: IMF staff calculations and analysis based on IMF Policy Tracker, ILO, Haver Analytics, Engler and others (2020), Eichenbaum, Rebelo and Trabandt (2020), and related model documentation as presented in the source content.* --- _Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020217-print-pdf.pdf_