## cr1755

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### Summary: background papers for the 2017 Article IV
- Purpose: explore key issues affecting the Indian economy and implications for fiscal, monetary, financial sector and other structural policies.
- Main substantive findings (high-level):
  - Corporate and banking sector vulnerabilities persist amid improved recognition of NPAs; recapitalization needs manageable under baseline but rise sharply with higher provisioning.
  - Safe public debt threshold estimated in the range of 60-65 percent of GDP.
  - Center-state transfers offset about twelve percent of permanent shocks to states’ income but are pro-cyclical; stabilization features need improvement.
  - Universal basic income (UBI) could address subsidy inefficiencies but raises major fiscal affordability and political feasibility concerns.
  - Exports slowdown driven by weak trading-partner demand and real rupee appreciation; high tariffs and trade costs weigh on performance.
  - Recent capital account liberalization measures not fully reflected in de jure indices; financial sector strengthening should accompany liberalization.
  - Labor and product market reforms sequencing should prioritize labor reforms to reinforce long-run gains.
  - Progressive coal taxation found highly effective for reducing air pollution mortality, raising revenue, and meeting mitigation pledges.
  - Relaxing female financial constraints raises female entrepreneurship, output and employment but needs labor market reform to translate into reduced gender gaps.
  - Inequality has risen while enrollment and learning achievements have lagged; policy should raise returns to education and use cash transfers to support liquidity-constrained households.
  - State-level convergence evidence mixed; disparities widened post-1990 reforms.
  - Nutrition and food security require a multi-sectoral approach—agriculture, pulses productivity, sanitation and gender equality.

*Source: cr1755 - References (PDF content provided).*

### Corporate and banking sector vulnerabilities
- AQR and NPA recognition:
  - AQR initiated in December 2015; aimed for full NPA recognition by March 2017.
  - Aggregate PSB gross NPA ratio: 9.3 percent in FY2015/16 (from 5 percent a year earlier).
  - Restructured assets share: 4.1 percent (from 7.1 percent a year earlier).
  - NPA slippage ratio among PSBs: 7.2 percent in FY2015/16 (from 3.4 percent previous year).
- Provisioning, profitability, recovery:
  - PSBs’ aggregate provision coverage ratio: 39 percent as of end-FY2015/16.
  - Provision coverage for previously better-provisioned banks: 44 percent in FY2015/16 (from 50 percent a year earlier).
  - ROAs of PSBs turned negative in FY2015/16 after intensified NPA recognition.
  - Write-offs offset NPA rise by 1.2 percentage points in NPA slippage rates in FY2015/16 vs 0.6 percentage points for loan recoveries.
- Stress simulations and recapitalization needs (bank-by-bank, Tier 1 capital hurdle with 2.5 percent CCB and additional buffers up to 2 percent):
  - Simulation assumption: 25 percent annual transition of restructured advances to NPAs to end-FY2018/19; provisioning minima considered at 40 percent and 70 percent.
  - Under severe continuous deterioration commensurate with recent experience:
    - Recapitalization costs: 1.5 to 2.4 percent of FY2018/19 GDP (cumulative over a 3-year horizon).
    - Government share: 1.0-1.6 percent (cumulative over four years).
  - With provisioning ratio rise to 70 percent:
    - Cumulative recapitalization needs: 3.3-4.2 percent of FY2018/19 GDP.
    - Government share: 2.2-2.8 percent.
  - Credit growth regimes in scenarios: rapid = 1.1xGDP growth; slow = 0.9xGDP growth.
- Corporate sector vulnerabilities and leverage:
  - Debt-at-risk (ICR < 1): 16.6 percent (down from 20.2 percent a year earlier).
  - Median debt-to-equity ratio: 56 percent at end-FY2016 (from 67 percent two years earlier).
  - Highly-levered firms (debt-to-equity > 150 percent) account for about half of outstanding corporate debt.
  - Sectoral pockets of high debt-at-risk (e.g., metals and mining up to 36 percent).
- Policy-relevant implications:
  - Continue timely implementation of debt resolution reforms to strengthen loan recovery and reduce reliance on write-offs.
  - Strengthen provisioning practices and bank balance sheet buffers; higher provisioning materially increases recapitalization needs in stress scenarios.
  - Address sector-specific vulnerabilities (metals and mining, construction and engineering, transportation infrastructure) to reduce future NPA slippage risks.
  - PSBs’ capital needs manageable under baseline but could rise substantially under more conservative provisioning or severe asset deterioration.

### Resilience of the corporate sector: tail-risk balance sheet sensitivity tests
- Methodology and sample:
  - Tail-risk balance sheet approach on samples of 1,830 to 2,057 firms (CapitalIQ data); specific charts use 2,057 firms and 1,874 corporates with full-year data FY2011/12–FY2015/16.
  - Metric: share of aggregate debt of firms with interest coverage ratio (ICR) below one relative to total corporate debt (debt-at-risk).
- Scenarios and shocks (two extremely severe scenarios, applied individually and jointly):
  - Overseas funding rate rise: 200 and 400 basis points.
  - Rupee depreciation: 20 and 29 percent.
  - Domestic policy rate rise: 200 and 250 basis points (impacting non-operating income).
  - Decline in operating profits: 25 percent.
- Key sensitivity test results:
  - Pre-stress debt-at-risk: 16 percent of total corporate debt.
  - Under combined severe domestic and external risks: debt-at-risk rises to up to 35 percent in the more severe scenario.
  - Single-shock impacts:
    - 25-percent decline in profitability → debt-at-risk to 24 percent.
    - 250 bps rise in domestic rates → debt-at-risk to 24 percent.
    - Joint external shocks (FX depreciation + foreign interest rate rise) → debt-at-risk to 19 percent.
  - Relative exposure: firms more sensitive in aggregate to profitability and domestic rate shocks than to FX or LIBOR shocks, though external vulnerabilities remain material.
- External funding dependence and hedging:
  - Aggregate hedging ratio (excluding natural hedges): about 41 percent of corporate borrowings in Q1 FY2015/16 (from about 15 percent in FY2013/14).
  - Uncertainty on persistence and effectiveness of hedges due to maturity mismatches and rising hedging costs.
  - Introduction of rupee-denominated ECBs and Masala bonds in September 2015 should further mitigate FX risks.
- Risk transmission to credit and growth:
  - Slow corporate deleveraging and potential NPA build-up can dampen credit provision and impair growth.
  - Aggregate credit growth across PSBs slowed to 3.7 percent in FY2015/16 (from average 15 percent in preceding three years); contraction concentrated in PSBs with most problem assets.
  - Limited monetary and fiscal space constrains ability to offset further NPA increases.

### Fiscal transfers in federations: center-state transfers in India
- Roles and insurance functions:
  - Transfers close vertical imbalances, achieve redistribution, and insure states against macro shocks (common—stabilization—and idiosyncratic—risk-sharing).
  - In India, transfers simultaneously affect redistribution, stabilization and risk-sharing, complicating disentanglement.
- Empirical specification and interpretation:
  - Fixed effects OLS on 27 states/UTs (1996–2014); transfer variable T_it; regressors include relative real per capita income trend (β = redistribution coefficient) and state output gap (Υ = insurance/stabilization coefficient).
  - Interpretation example: Υ = -0.1 implies 10 percent insurance for a 100-rupee output fall.
- Empirical findings — redistribution:
  - Redistributive function sizable and mainly achieved via central grants.
  - Devolution of center’s revenues supports redistribution but less than grants.
  - Net fiscal flows (transfers minus center’s tax revenue collected in state) show negative redistribution coefficient significant at close to 10 percent.
  - Selected β estimates (relative real per capita income):
    - Without time fixed effects: 0.01, -0.01*, -0.02*, -0.08*, -0.12 [robust s.e. in brackets: [0.02][0.01][0.01][0.05][0.07]]
    - With time fixed effects: 0.00, -0.01*, -0.02*, -0.09*, -0.12 [robust s.e. in brackets: [0.02][0.00][0.01][0.05][0.07]]
- Empirical findings — insurance, risk-sharing, stabilization:
  - Devolution of central taxes: supports interregional risk-sharing but undermines stabilization for common shocks due to positive correlation of central tax revenues with economy-wide output gap (estimated Υ = -0.06 in one specification with fixed effects but positive in others).
  - Central grants: pro-cyclical and not supportive of interregional risk-sharing or stabilization.
  - Total transfers (tax sharing + grants): do not appear to support interregional risk-sharing; pro-cyclicality of grants offsets tax devolution risk-sharing.
  - Fourteenth Finance Commission (2014) increased sharable size to states; may help interregional risk-sharing but at expense of intertemporal risk-sharing.
- States’ own fiscal behavior and constraints:
  - State own tax revenues correlation with state cycles declined slightly; capital expenditure became more counter-cyclical (weak link); revenue expenditure correlation with cycle increased.
  - Budget deficits in many states hover close to borrowing limits; aggregate states’ deficits rising towards about 3 percent of GDP.
  - Panel VAR: states’ gross fiscal deficit to GSDP ratios deteriorate by about 1 percent of GSDP cumulatively over two years following a negative one standard deviation GSDP growth shock.
  - One standard deviation GSDP growth shock ≈ 5 percentage points.
- Policy implications:
  - Tax devolution formula provides interregional risk-sharing but can undermine stabilization.
  - Central grants’ pro-cyclicality reduces overall insurance effectiveness of transfers.
  - Greater devolution strengthens case to reinforce states’ fiscal responsibility and budget frameworks.
  - With near-binding borrowing ceilings, negative shocks may cause states to breach borrowing limits or engage in pro-cyclical fiscal adjustments.

### Universal Basic Income (UBI): proposals, pilots, fiscal affordability, and policy design
- Overview and concern:
  - UBI proposed as alternative to address failures of current social protection; fiscal costs and affordability are central concerns.
- India proposals (select summaries from Table 2):
  - Bardhan (2016): Universal to all Indians; Rs 10,000 per year (10% GDP); financing via removal of existing subsidies and tax holidays; fully-funded claimed.
  - Banerjee (2016): Every adult resident; Rs 13,000 per year (12% GDP) indicated as Rs 250 weekly; verification weekly.
  - Joshi (2016): Universal to households; Rs 17,500 per family or Rs 3,500 per person (3.5% GDP); financing via user charges for services.
  - Ray, Gokarn: variants with opt-out or GDP-share designs; various financing proposals.
- Evidence from pilot (SEWA/UNICEF, Madhya Pradesh):
  - Rural unconditional cash transfer pilot 2012; monthly payments initially Rs 200 adult / Rs 100 child, later Rs 300 / Rs 200; calibrated at 20–30 percent of low-income families’ income (about one third of 2011/12 poverty line); coverage 6,000 individuals for 29 months.
  - Reported impacts: enhanced food sufficiency, improved access to health and education, increased investment in sanitation and drinking water, reduced borrowing for health and education.
- Fiscal space and affordability (Table 4 and narrative):
  - Bardhan’s UBI Rs 10,000 per year (population 1.25 billion) costs about 10 percent of GDP.
  - Total subsidies on food, fertilizers, fuel: 1.5 percent of GDP in FY2016/17.
  - Bardhan’s proposal would require additional financing of about Rs 10 trillion or 8 percent of GDP.
  - IMF estimates: Bardhan-like UBI cost 10% of GDP; IMF alternative estimate: 2% of GDP (Rs 2,000 per year).
  - Financing items in Table 4:
    - Removal of subsidies (fertilizers, power, food): Bardhan 6-7% of GDP 1/; IMF estimates 2% of GDP 2/; IMF alternative 2% of GDP 2/.
    - Termination of tax holidays and exemptions: Bardhan 3% of GDP.
    - Additional financing requirements: Bardhan None; IMF estimates 8% of GDP; IMF alternative None.
  - Notes:
    - 1/ Two-third of total subsidies in which National Institute of Public Finance and Policy estimates total implicit and explicit subsidies ≈ 14 percent of GDP.
    - 2/ From central government budget.
- Other impacts and uncertainties:
  - Small UBI may have negligible poverty reduction; large UBI may create work disincentives.
  - Cash transfers to poor may boost consumption if targeted to those with high marginal propensity to consume, but inflationary pressures possible.
  - Many impacts uncertain without granular design.
- Policy recommendations and design considerations:
  - Undertake thorough ex-ante redistributive and fiscal cost analysis.
  - Administrative and technical viability:
    - Build on JAM (Jan Dhan, Aadhaar, mobile) to reduce leakages; UBI should be cheaper than existing transfers and encourage continued work.
  - Fiscal affordability:
    - UBI should not delay fiscal consolidation nor crowd out health, education or public investment.
  - Political feasibility:
    - Eliminating subsidies to finance UBI could trigger political opposition; sequencing and communication essential.
    - Consider independent monitoring body and clear objectives: reach the vulnerable, reduce inefficiencies, increase sector competitiveness (e.g., agriculture).
    - Detail phasing out of subsidies and a communication plan.

### Trade weakness: drivers, decomposition and reform scenarios
- Drivers of trade weakness:
  - Post-2008 weak trading-partner import demand (post-crisis partner demand growth half of pre-crisis).
  - Real rupee appreciation from historically high domestic inflation vs partners.
  - High MFN tariff rates and trade costs; administrative burdens; labor and product market rigidities.
  - Refined petroleum products ~ one-fifth of goods exports; commodity price collapse affected export values in 2015-2016.
- Empirical decomposition (panel of 16 EMs, 1981–2015):
  - For India during 2013-2015:
    - Decline in import demand of trading partners explained about 39 percent of slowdown in real non-oil export growth.
    - Real rupee appreciation explained about 29 percent.
    - Overall explaining about 81 percent of export slowdown.
- Selected regression coefficients (Table 1 excerpts):
  - Real effective exchange rate change: -0.221, -0.237, -0.213, -0.211, -0.216, -0.095
  - Trading partners’ real non-oil goods import demand growth: 0.853, 0.873, 0.844, 0.812, 0.815, 0.739
  - Change in MFN tariff rates: -0.141, -0.299, -0.290, -0.286, -0.686
  - FDI inflow (% of GDP): 0.200, 0.206, 0.188, 0.217
  - Adjusted R2: 0.35 (models 1–5), 0.51 (model 6)
- Tariff statistics (WTO World Tariff Profile 2016):
  - India MFN simple average: 13.4 percent (all products); 32.7 percent (agricultural); 10.7 percent (non-agricultural).
  - India is second-highest among comparator EMs in these averages.
- Scenario analysis:
  - Lowering import tariffs to EM average projects a boost of nearly 2 percent to India’s export growth over the medium term.
- Structural impediments and FDI:
  - Net FDI inflows: 1.7 percent of GDP in FY2015/16.
  - Services sector accounted for about 18 percent of FDI equity inflows since April 2000.
  - FY2016 sectoral composition (%): Services 17.2; Construction 0.3; Computer 14.8; Telecommunications 3.3; Car industry 6.3; Pharmaceuticals 1.9; Chemicals 3.7; Trade 9.6; Energy 2.2; Hotels 3.3; Other 37.4.
  - FY2016 sources (%): Mauritius 20.9; Singapore 34.2; U.K. 2.2; Japan 6.5; U.S.A. 10.5; Netherlands 6.6; Germany 2.5; Cyprus 1.3; France 1.5; U.A.E. 2.5; Other 11.3.
- Policy recommendations:
  - Lower trade barriers and reduce trade costs; address product and labor market rigidities; continue trade and investment liberalization; improve export quality and complexity.

### Capital controls and capital flows
- Cross-country evidence:
  - De jure capital control indices (Fernandez et al, 2015) show controls have larger effect on outflows than inflows; smaller effect on FDI than on other flows.
  - Earlier literature points to controls influencing composition more than volumes.
- India-specific evidence (scatter-plot relationships, 2014):
  - Inflows vs De Jure Controls, 2014: y = -4.365x + 7.9521; R² = 0.0198
  - Outflows vs De Jure Controls, 2014: y = -6.6657x + 8.4898; R² = 0.0485
  - FDI Inflows vs De Jure Controls, 2014: y = -0.4179x + 2.8269; R² = 0.0023
  - FDI Outflows vs De Jure Controls, 2014: y = -0.6424x + 1.5681; R² = 0.0081
- Changes in FDI policies 2014→2016 (selected entries):
  - Defense industry: 26% in 2014 → 100% in 2016 (approval route changed from Government to Automatic up to 49% then Government route beyond).
  - Insurance companies: 26% in 2014 → 49% in 2016 (Automatic).
  - Multiple other sectors opened up (table enumerates sectoral changes).
- Policy implications:
  - Capital account liberalization should be complemented by structural policies to attract stable, non-debt-creating flows.
  - Pace and direction of liberalization should be calibrated with cost-benefit assessments and accompanied by financial sector and market development.

### Energy and environmental fiscal reforms: coal tax and other instruments
- Coal tax scenarios and outcomes:
  - Modest coal tax: increase by Rs 150 per year from 2017 to reach Rs 2,500 per ton in 2030:
    - Prevents over 270,000 air pollution deaths during phase-in.
    - In 2030 raises approximately 1 percent of GDP in new tax revenue.
    - Reduces CO2 emissions by 12 percent (2030).
    - Generates net economic benefits ≈ 1 percent of GDP in 2030 (domestic environmental benefits about 1.1 percent; economic welfare cost about 0.1 percent).
    - Raises revenues of about 0.3 and 1.0 percent of GDP in 2020 and 2030 respectively (in excess of revenues with no tax change).
  - Aggressive coal tax: increase by Rs 300 per year from 2017 to reach Rs 4,600 per ton in 2030 (about US$69):
    - Reduces CO2 emissions by 7 and 21 percent below BAU in 2020 and 2030 respectively.
    - Would save about 490,000 lives phased in 2017–2030.
    - Equivalent Rs per ton of CO2 provided (e.g., Rs 2,460 per ton CO2 ≈ US$37 for aggressive).
  - Carbon tax (same CO2 charge across fossil fuels) slightly more effective than aggressive coal tax; raises about 40 percent more revenue.
  - Aggressive ETS applied to power and large industry: about 75 percent as effective as carbon tax (same emissions price); auctioned allowances raise about 60 percent of revenues compared with equivalently priced carbon tax.
  - Electricity tax raises revenues ≈ 45 percent of equivalently priced carbon tax.
- Baseline and rationale:
  - BAU CO2 intensity of GDP in 2030 projected 29 percent lower than in 2005.
  - Coal accounts for half of projected primary energy use in 2030 and over 70 percent of fossil fuel CO2 emissions in BAU.
  - Estimated premature deaths from outdoor air pollution from fossil fuel combustion rise from about 200,000 in 2015 to 400,000 in 2030.
- Distribution and industry impacts:
  - Modest coal tax mildly progressive in 2020: burden ~0.14 percent of consumption for poorest quintile and ~0.18 percent for wealthiest.
  - Compensating poorest would use ~6 percent of coal tax revenues (e.g., via PDS and MGNREGA with improved Aadhaar targeting).
  - Modest coal tax raises industry costs on average about 0.2 percent in 2020; for 10 percent most vulnerable industries (e.g., iron and steel) costs rise on average about 1.1 percent.
- Policy implication:
  - Fuel tax reform yields large domestic health and environmental benefits and revenue; India could move unilaterally given domestic gains.

### Gender gaps in finance and labor outcomes: model findings and policy package
- Stylized facts:
  - Female labor force participation (FLFP) about one-third of male participation and declining.
  - Informal sector > 90 percent of total employment; women largely in low-productivity informal jobs.
  - Women entrepreneurs ≈ 10 percent of entrepreneurs, contributing 3 percent to industrial output and employing > 8 million; ~98 percent micro-enterprises; ~90 percent rely on informal finance.
- DSGE model with gender heterogeneity (two sectors: formal and informal):
  - Scenarios: (i) eliminate female access-to-finance gap; (ii) finance gap + labor market deregulation; (iii) finance gap + skills parity.
- Key quantitative and mechanism findings:
  - Eliminating access gap increases female entrepreneurship, FLFP, GDP and lowers unemployment; however, without labor market reform much of new female employment is informal.
  - Combining financial access with deregulation raises formal sector employment and yields larger GDP and employment gains.
  - Combining financial access with skills improvements delivers substantially larger boosts: higher gender parity in LFP, wages and formal employment.
- Selected numeric indicators:
  - More than 240 million previously unbanked gained accounts since PMJDY launch in August 2014; about 47 percent are females.
  - Under PMMY, women’s businesses accounted for about one-half of amount lent and about four-fifths of number of loans.
  - SWIID latest India Gini: 47.9; world average 37.3.
- Policy recommendations (multi-dimensional):
  - Improve financial literacy (rural focus), spread awareness of land inheritance rights, implement targeted skill training for women.
  - Ease labor market regulations to foster formal job creation.
  - Fiscal measures: rural infrastructure (water, sanitation, transport) to reduce domestic time burdens on women.
  - Continue fiscal consolidation to free resources for infrastructure, education and skills.
  - Business climate reforms (including GST implementation) to boost women’s economic participation.
  - Monitor success via rises in female formal entrepreneurship, firm scaling, and FLFP.

### Education, private expenditure, and inequality
- Private expenditure disparities:
  - Top 20 percent spend 13 times as much as bottom 20 percent on primary education; ratio declines to 6 times for tertiary.
- Learning outcomes (IHDS basic skills tests, ages 8–11):
  - Not able to read at all: poorest 20 percent 22.4 percent; richest 20 percent 2.25 percent.
  - Not able to write: poorest 20 percent 40.7 percent; richest 20 percent 10.1 percent.
- Occupational and educational distribution (IHDS 2011/12):
  - Modern Entrepreneur: Share 5.4%, Avg years education 8.5, Avg income normalized 100, Share poor households 6.7%
  - Modern Worker: Share 19.5%, Avg years 9.9, Avg income 99, Share poor 6.3%
  - Traditional Entrepreneur: Share 37.0%, Avg years 5.7, Avg income 45, Share poor 17.1%
  - Traditional Worker: Share 38.1%, Avg years 4.4, Avg income 38, Share poor 26.5%
- Model experiments and results:
  - Targeted cash transfers reduce inequality (Gini) but do not increase average years of schooling unless conditioned on attendance and sufficiently large.
  - Conditioning transfers on attendance can raise schooling and intergenerational mobility but average human capital gains modest unless quality improves.
  - Increasing returns to unskilled education by up to 10 percent can strongly raise schooling, human capital, reduce share with no schooling from 23 percent to 4 percent, bottom 20 percent income share from 1.4 percent to 3.3 percent, and more than double output.
  - Industrial policy shifting demand to unskilled labor lowers skill premium but need not reduce overall inequality; top 10 percent can gain.
- Policy implications:
  - Cash transfers should be well-designed (size, conditionality) and complemented with quality improvements in schooling.
  - Policies that raise returns to education (quality) have potent effects but are challenging to implement.
  - Multifaceted policy packages needed to affect schooling, productivity and inequality.

### Interstate disparities, convergence and growth drivers
- Conditional convergence evidence:
  - States’ long-run income potential differs widely due to policies and structure.
  - Positive growth drivers: larger shares of services/industry, greater investment and credit access, higher spending on health and education.
- Product and labor market regulation correlations:
  - Rank correlation product market regulation vs state growth: -0.46.
  - Rank correlation labor regulation vs state growth: -0.03 (indicative; labor index may not fully capture core Indian labor market issues).
- Panel regression (32 states/territories, 1961–2015; selected coefficients):
  - Initial income per capita coefficients (columns 1–7): -3.617 [4.16]***; -3.62 [4.00]***; -3.493 [2.35]**; -3.761 [2.47]**; -3.173 [2.10]**; -2.933 [2.14]**; -3.814 [1.80]*
  - Share of services sector (selected): 6.751 [2.82]***; 7.841 [2.85]***; 8.593 [3.22]***; others significant.
  - Credit growth (one specification): 0.067 [2.86]***.
  - Adjusted R-squared across specs: 0.66–0.77.
- Panel unit root and convergence tests mixed by subperiod; some evidence of convergence post-1990s in subsets.
- Policy implications:
  - Sound macro policy and comprehensive structural reforms required to boost long-term growth and reduce inter-state inequality.
  - Priorities: investment, better credit allocation, state-level spending on health and education, product and labor market reforms.

### Undernutrition, pulses policy and food security
- Economic costs of undernutrition:
  - Direct productivity losses > 10 percent of lifetime individual earnings; translating to about 2-3 percent loss in GDP.
- NFSA and food safety nets:
  - NFSA (September 2013) provides subsidized food grains to 67 percent of population (75 percent rural; up to 50 percent urban).
  - Only Chandigarh and Pondicherry implementing NFSA in DBT mode among 32 implementing states/UTs.
  - Need comprehensive nutritional data collection and periodic monitoring.
- Pulses importance and constraints:
  - Pulses major protein source for low-income/vegetarian households.
  - India: ~33 percent of global acreage, ~25 percent of world production, ~27 percent of world consumption of pulses.
  - Excess demand for pulses ~3–5 million tons per year.
  - Less than 15 percent of pulses area has assured irrigation.
  - Farmers shift away from pulses due to high production risk, leading to supply shortfalls and price volatility.
- Policy priorities:
  - National strategy on agriculture and pulses involving Union and states (states account for ~90 percent of public agri investment).
  - Invest in logistics, marketing infrastructure, roads; e-NAM launched April 2016 to integrate 585 wholesale markets.
  - Target insurance and irrigation (PMKSY, PMFBY) to pulse-producing areas.
  - Reduce reliance on MSP and public procurement; encourage market-driven diversification.
  - Complement food policies with sanitation, health and gender-equality reforms to enable balanced diets.
  - Target: meet Sustainable Development Goal of ending malnutrition by 2030 and achieve by 2025 internationally-agreed targets on stunting and wasting.

*International Monetary Fund. Chapter and background paper content from cr1755 (PDF content provided).*

### References             _____________________________________________________________________________             12

### cr1755 - References

### Summary: background papers for the 2017 Article IV
- The background papers explore key issues affecting the Indian economy and implications for fiscal, monetary, financial sector and other structural policies.
- Main substantive findings across chapters (as presented in the source):
  - Corporate and banking sector vulnerabilities: corporate sector risks have subsided, but debt repayment capacity remains strained and high leverage levels continue to weigh on corporate resilience and bank asset quality. Public sector banks (PSBs) have stepped up recognition of non-performing assets (NPAs), but debt recovery capacity remains weak. Simulations suggest potential recapitalization needs, at current provisioning levels, should have a modest fiscal impact.
  - Safe public debt level: regression analysis finds the threshold for the safe level of debt for India falls in the range of 60-65 percent of GDP, taking into account buffers needed to accommodate existing contingent liabilities.
  - Center-state transfers: transfers offset about twelve percent of permanent shocks to states’ income (redistribution effect), but transfers are pro-cyclical with respect to idiosyncratic and common shocks, indicating a need to improve stabilization features of central government finances.
  - Universal basic income (UBI): existing subsidy-based social protection has flaws (high administrative costs and leakages). UBI could address some failures, but fiscal affordability and political feasibility are major concerns.
  - Exports slowdown: driven by weak trading partner demand and real appreciation of the rupee; high tariffs and trade costs also weigh on export performance and integration into global value chains.
  - Capital flows and capital account openness: recent liberalization measures have not been reflected in de jure indices; strengthening financial sector and institutions should complement liberalization.
  - Labor and product market reforms: with limited policy space, sequencing should prioritize labor market reforms to reinforce long-run gains, minimize short-term costs, and increase acceptability.
  - Energy/fossil fuel tax analysis: progressively increasing the coal tax would substantially reduce air pollution mortality, raise significant revenue, and help meet mitigation pledges; coal tax is highly effective relative to other mitigation instruments.
  - Gender gaps in finance and labor outcomes: relaxing financial constraints for females increases female entrepreneurship, output and employment; simultaneous labor market reform is needed to translate financial inclusion into reduced gender gaps in outcomes.
  - Education and inequality: income inequality has increased while enrollment and learning achievements have lagged; policies should increase returns to education and use cash transfers to support liquidity-constrained households and reduce inequality.
  - State-level income convergence: some evidence of convergence pre-1990s, but disparities have widened in the post-1990 reform period.
  - Nutrition and food security: multi-sectoral approach needed—address long-term structural bottlenecks in agriculture, improve pulses productivity, sanitation and gender equality to reduce undernourishment and hunger.

*Source: cr1755 - References (PDF content provided).*

### Corporate and Banking Sector Vulnerabilities in India (selected findings and metrics)
- AQR impact and NPA recognition:
  - The Asset Quality Review (AQR), initiated by the Reserve Bank of India (RBI) in December 2015, aimed to lead to full recognition of NPAs by March 2017.
  - Aggregate PSB gross NPA ratio increased to 9.3 percent in FY2015/16, from 5 percent a year earlier.
  - Share of restructured assets in total advances receded to 4.1 percent from 7.1 percent a year earlier.
  - NPA slippages among PSBs accelerated to 7.2 percent in FY2015/16 from 3.4 percent in the previous year (NPA slippage ratio defined as ratio of gross new NPAs during the year to standard assets at the beginning of the year).
  - Most AQR-related recognition of NPAs appears to have already materialized, with potential for further rise from remaining vulnerable accounts.

- Provisioning, profitability, and recovery:
  - PSBs’ aggregate provision coverage ratio was 39 percent as of end-FY2015/16.
  - Provision coverage slipped for previously better-provisioned banks to 44 percent in FY2015/16 from 50 percent a year earlier.
  - ROAs of PSBs turned negative in FY2015/16 following intensified NPA recognition.
  - The rise in NPAs in FY2015/16 was offset primarily via write-offs, which accounted for a 1.2 percentage-point offset in NPA slippage rates in FY2015/16, compared to only 0.6 percentage points for loan recoveries.

- Stress simulations and recapitalization needs:
  - Simulation assumption: 25 percent transition of restructured advances to NPAs in each year to end-FY2018/19, with minimum provisioning scenarios of 40 percent and 70 percent against NPAs.
  - Simulations are carried out bank-by-bank, with slippage, recovery and write-off rates calibrated to banks’ performance in FY2015/16, and using the Tier 1 capital ratio as a hurdle rate (including the 2.5 percent capital conservation buffer (CCB) and additional buffers of up to 2 percent).
  - Under severe continuous deterioration commensurate with recent experience:
    - Recapitalization costs should be manageable at 1.5 to 2.4 percent of FY2018/19 GDP (cumulative over a 3-year horizon).
    - Government share of recapitalization: 1.0-1.6 percent (cumulatively over four years).
  - With a rise in required provisioning ratio to 70 percent:
    - Cumulative recapitalization needs would increase to 3.3-4.2 percent of FY2018/19 GDP.
    - Government share would be 2.2-2.8 percent.
  - Simulation scenarios consider credit growth regimes: rapid credit growth assumed at 1.1xGDP growth; slow at 0.9xGDP growth.

- Corporate sector vulnerabilities and leverage:
  - Corporate sector here defined as incorporated entities in the non-priority sectors.
  - Debt-at-risk (share of debt held by firms with interest coverage ratio below one) declined to 16.6 percent from 20.2 percent a year earlier, indicating improved debt-repayment capacity.
  - However, sectoral debt-at-risk remains elevated in certain sectors; NPA risks remain where debt-at-risk and NPAs are high (e.g., metals and mining as high as 36 percent).
  - Median debt-to-equity ratio fell to 56 percent at end-FY2016, from 67 percent two years earlier.
  - The debt of highly-levered firms (debt-to-equity ratios above 150 percent) accounts for about half of outstanding corporate debt.
  - Leverage is uneven across sectors and firm size; largest firms (top one to three percent of corporate sector assets) have been persistently more levered than other firms.

- Policy-relevant implications (as presented):
  - Need for continued and timely implementation of debt resolution reforms to strengthen loan recovery and reduce reliance on write-offs.
  - Strengthening provisioning practices and bank balance sheet buffers would increase resilience; higher provisioning requirements materially increase recapitalization needs in stress scenarios.
  - Corporate deleveraging remains incomplete and uneven; addressing sector-specific vulnerabilities (notably metals and mining, construction and engineering, transportation infrastructure) is critical for reducing future NPA slippage risks.
  - PSBs’ capital augmentation needs, though potentially manageable in aggregate under baseline simulation assumptions, could increase substantially under more conservative provisioning or more severe asset quality deterioration.

*Source: cr1755 - References (PDF content provided).*

### 6. The resilience of the corporate sector to potential domestic and external shocks is

### 6. The resilience of the corporate sector to potential domestic and external shocks is assessed via sensitivity tests based on a tail-risk balance sheet approach

### Methodology and scenario design
- Approach: tail-risk balance sheet approach—strength of corporates’ debt-repayment capacity under extreme stress used as a gauge of financial soundness.
- Sample: corporate sector risk analysis based on a sample of 1,830 to 2,057 firms with data from CapitalIQ. Specific charts based on a sample of 2,057 firms and on 1,874 corporates with available data for each year in FY2011/12 - FY2015/16.
- Shocks applied to each corporate’s balance sheet; metric: share of aggregate debt of firms with an interest coverage ratio (ICR) below one relative to total corporate sector debt (debt-at-risk).
- Scenarios: two extremely severe scenarios with shocks applied both individually and jointly:
  - Sharp rise in overseas funding rates: 200 and 400 basis points (bps), respectively.
  - Depreciation of the rupee due to capital outflows: 20 and 29 percent.
  - Rise in the domestic policy rate to defend the currency: 200 and 250 bps (all assumed to impact non-operating income).
  - Decline in operating profits: 25 percent under both scenarios.
- Calibration: with the exception of the profitability shock, scenario calibrated based on extreme past movements in the risk factors (the 90+ percentile of joint bilateral distributions of annual changes in risk factors in the first scenario and at unprecedented levels in the second scenario).
- Data limitation: due to lack of firm-by-firm FX liabilities and expenditures, estimates for the aggregate corporate sector were applied.

### Key findings from sensitivity tests
- Aggregate debt-at-risk:
  - Pre-stress debt-at-risk: 16 percent of total corporate debt.
  - Under extreme stress (combined severe domestic and external risks): debt-at-risk expected to rise from 16 percent to up to 35 percent in the more severe scenario.
- Impact of single major shocks:
  - A 25-percent decline in profitability is associated with an increase in debt-at-risk to 24 percent of overall debt.
  - A 250 bps rise in domestic rates is associated with a marked uptick in debt-at-risk to 24 percent of overall debt.
  - Joint materialization of both external-risk factors (FX depreciation and foreign interest rate rise) accounts for a rise in debt-at-risk to 19 percent.
- Relative exposures:
  - Indian firms are less exposed in aggregate to a sharp FX depreciation or a rise in LIBOR rates than to profitability and domestic rate shocks, though vulnerabilities to external risks remain reasonably high.
- Time-series context:
  - The 19 percent debt-at-risk under joint external shocks is a considerably higher level relative to years prior to the most recent rise in corporate vulnerabilities (for example, FY2011/12).

### External funding dependence and FX hedging
- Corporates face rollover risks, potential rises in LIBOR (affecting ECB funding), and INR depreciation risk if FX funding is insufficiently hedged.
- FX hedging developments:
  - Aggregate hedging ratio (excluding natural hedges) rose to about 41 percent of corporate borrowings in the first quarter of FY2015/16 from about 15 percent in FY2013/14 (RBI data on intentions to hedge ECBs and FCCBs).
  - Uncertainty remains about the level of corporate hedging over time and the ability of hedges to fully mitigate risks due to possible maturity mismatches and potential rises in hedging costs—particularly in case of a large depreciation.
- Policy and market developments:
  - The introduction of rupee-denominated ECBs and overseas bonds (Masala bonds) in September 2015 should further mitigate FX currency risks.

### Slow deleveraging, NPAs, and credit implications
- Slow repair of corporate balance sheets and potential further build-up of NPAs can negatively affect the real economy.
- Credit dynamics:
  - Aggregate credit growth supported by government capital injections in PSBs and a shift of credit demand toward alternatives such as commercial paper (CP) funding.
  - Credit growth across PSBs slowed to 3.7 percent in FY2015/16 from an average of 15 percent in the preceding three years, and contracted particularly for PSBs with the most problem assets.
- Risks to growth and credit provision:
  - A larger-than-anticipated rise in new NPA formation—e.g., due to weaker demand in certain sectors, exchange rate or interest rate shocks—or PSBs’ inability to raise adequate capital could further dampen provision of credit to the real economy and impair growth.
  - High corporate leverage magnifies banks’ losses in the event of shocks and has already been a drag on domestic investment.
  - Limited monetary and fiscal space in India constrains policymakers’ capacity to counteract any additional increase in NPAs.

### Summary assessment
- Downward profitability pressures and a potential upward shift in domestic interest rates continue to be key risks for Indian corporates.
- Overall exposure of Indian corporates to all types of financial risks remains elevated, with notable sensitivity to domestic profitability and interest rate shocks and material vulnerability if multiple shocks materialize jointly.

*Source: IMF staff analysis (chapter 6 of the cited IMF country report).*

### 2. Fiscal transfers in federations play several roles, such as closing vertical imbalances,

### cr1755 - 2. Fiscal transfers in federations play several roles, such as closing vertical imbalances,

### Roles of fiscal transfers
- Fiscal transfers serve to: close vertical imbalances, achieve redistribution goals, and insure states or union territories against macroeconomic shocks.
- Insurance function subdivides into:
  - Insurance against common shocks simultaneously hitting all states (stabilization or intertemporal insurance).
  - Insurance against idiosyncratic macroeconomic shocks hitting individual states (risk-sharing or interregional insurance).
- Transfers also enhance redistribution from richer to poor states and help ensure convergence of cross-state disposable incomes.
- In India, center-state fiscal transfers affect redistribution, stabilization, and risk-sharing simultaneously, complicating disentanglement of these roles.

### Empirical specification (as in Poghosyan and others, 2016)
- Estimated on state-level data (27 states and union territories with available data for 1996-2014) using a fixed effects OLS estimator with robust standard errors.
- Transfer variable: T_it (net transfers, tax sharing, grants, etc.) of state i in year t.
- Key regressors:
  - Nominal potential gross state domestic product (HP filter).
  - Relative real per capita income trend: trend component of ratio of each state’s per capita GDP relative to national average per capita GDP (quadratic trend). Redistribution coefficient = β.
    - Interpretation: Redistribution coefficient of -0.1 implies a region with a 100 rupees permanently lower output relative to the national average would have disposable income 90 rupees below the national average (10 rupees covered by permanent transfer).
  - State output gap (HP filter) = Υ, measuring the insurance role of transfers (how much they offset temporary shocks to regional output).
    - Interpretation: Υ = -0.1 implies 10 percent insurance: disposable income falls by 90 rupees when output falls by 100 rupees (either relative to national average or simultaneous across regions).

### Empirical findings — redistribution
- Redistributive function of transfers is sizable and mostly achieved through center’s grants to states.
- Evidence from Table 1 (columns correspond to different transfer measures):
  - Devolution of center’s revenues to states supports redistribution but magnitude smaller than grants (column 3 vs column 4).
  - State-wise variation in collection of central government tax revenues: coefficients positive but not statistically significant (direct taxes as share of GDP small; Maharashtra, Delhi, and Karnataka account for over 60 percent of center’s direct tax receipts).
  - Net fiscal flows (transfers to a state minus center’s tax revenue collected in that state): redistribution coefficient negative and statistically significant at close to the 10 percent level.

- Selected coefficient estimates (Table 1, without time fixed effects; with time fixed effects in second block):
  - Relative real per capita income (β) across columns (1)–(5):
    - Without time fixed effects: 0.01, -0.01*, -0.02*, -0.08*, -0.12 [robust s.e. in brackets: [0.02][0.01][0.01][0.05][0.07]]
    - With time fixed effects: 0.00, -0.01*, -0.02*, -0.09*, -0.12 [robust s.e. in brackets: [0.02][0.00][0.01][0.05][0.07]]
  - State output gap (Υ) across columns (1)–(5):
    - Without time fixed effects: -0.02, 0.01, 0.03*, 0.08, 0.11*** [robust s.e. in brackets: [0.03][0.01][0.02][0.05][0.03]]
    - With time fixed effects: 0.07*, 0.02**, -0.06**, 0.16***, 0.01 [robust s.e. in brackets: [0.04][0.01][0.03][0.05][0.05]]

### Empirical findings — insurance, risk-sharing, and stabilization
- Devolution of central taxes:
  - Translates into robust interregional risk-sharing but goes against stabilization for common macro shocks.
  - Estimated Υ = -0.06 (column 3 with fixed effects) — indicates interregional risk-sharing; but Υ positive in specification without time fixed effects implies pro-cyclicality rather than counter-cyclicality for macro stabilization.
  - Mechanism: total central tax revenues are positively correlated with economy-wide output gap; devolution co-moves with economy-wide cycle, offsetting risk-sharing gains.
- Central grants to states:
  - Not supportive of interregional risk-sharing or intertemporal stabilization (column 4).
  - Υ coefficient in risk-sharing specification is positive, indicating pro-cyclicality of grants with respect to state-specific economic conditions; also positive without fixed effects (pro-cyclical with respect to economy-wide and state-specific output gaps).
- Total transfers to states (tax sharing + grants):
  - Do not appear to support interregional risk-sharing (column 2).
  - Pro-cyclicality of grants appears to offset interregional risk-sharing built into tax devolution.
  - Note: Implementation of Fourteenth Finance Commission (2014) recommendations increased the sharable size to states; this may help interregional risk-sharing but at the expense of less intertemporal risk-sharing.
- Center’s revenue collection across states:
  - Reveals presence of interregional risk-sharing (positive Υ with fixed effects in column 1).
  - No statistically significant link of central taxes to common shocks (insignificant Υ without fixed effects).
  - Net fiscal flows from center to states (column 5) are clearly pro-cyclical with respect to common shocks and lack interregional risk-sharing.

### Comparison with other federations
- Redistribution impact in India is broadly similar to federations such as the United States, Canada, and Australia (results reported in Poghosyan and others, 2016).
  - Canada has the strongest redistribution channel (constitutional mandate).
  - Data coverage differs: transfers to individuals from the central budget are not available by Indian states (potential understatement of fiscal policy effects in India).
  - Inter-regional risk-sharing is present in the United States, not statistically significant in Australia or Canada.
  - Stabilization (central budget counter-cyclicality) appears more a feature of advanced economies’ central budgets.

- Selected comparative coefficients (Table 2):
  - Relative real per capita income (β): India -0.12; USA -0.13**; Canada -0.15***; Australia -0.14** [India robust s.e. [0.07]]
  - State output gap (Υ):
    - Without time fixed effects: India 0.11*** [0.03]; USA -0.28***; Canada -0.14*; Australia -0.23***.
    - With time fixed effects: India 0.01 [0.05]; USA -0.12**; Canada -0.05; Australia -0.04.

### States’ own fiscal behavior and constraints
- Co-movement with state cycles:
  - States’ own tax revenues correlation with state economic cycles declined slightly in last decade.
  - States’ capital expenditures have become counter-cyclical with respect to their economic cycle, though link is weak.
  - Correlation of states’ revenue expenditures with state economic cycle has increased—concern for fiscal health because room to compress revenue expenditures in a downturn may be constrained.
    - Non-development revenue expenditures (interest payments, administrative services, pensions) are largely precommitted.
    - Recent civil servants’ pay review could raise wage and pension commitments, further limiting downward flexibility.
- Greater devolution of central taxes increases state autonomy, strengthening the case to reinforce states’ fiscal responsibility and budget frameworks.
- Fiscal space and borrowing limits:
  - Budget deficits in many states hover close to borrowing limits; aggregate deficits of states rising towards about 3 percent of GDP.
  - Panel VAR staff analysis suggests states’ gross fiscal deficit to GSDP ratios deteriorate by about 1 percent of GSDP cumulatively over the two years following a negative one standard deviation shock to GSDP growth.
  - One standard deviation GSDP growth shock is about 5 percentage points (note: points to high volatility of states' growth).
  - With near-binding borrowing ceilings, negative shocks may cause states to breach borrowing ceilings or engage in pro-cyclical fiscal adjustment.

- Table 3 — correlations of cyclical components of state fiscal variables with GSDP (27 States and Union Territories; 16 Major States, post-2004 shown):
  - Own tax revenues: All sample 0.27; Post 2004 0.22; Major states All sample 0.29; Post 2004 0.20.
  - Non-tax revenues: All sample 0.14; Post 2004 0.09; Major states All sample 0.05; Post 2004 0.00.
  - Revenue expenditure: All sample 0.22; Post 2004 0.43; Major states All sample 0.17; Post 2004 0.42.
  - Capital expenditure: All sample 0.10; Post 2004 -0.13; Major states All sample 0.11; Post 2004 -0.18.

### Implications and policy-relevant observations (as presented in the chapter)
- Design of transfers shows deficiencies for insurance motives:
  - Tax devolution formula provides interregional risk-sharing but undermines stabilization because of pro-cyclicality tied to aggregate tax revenue fluctuations.
  - Central grants are pro-cyclical and do not provide effective risk-sharing or stabilization.
  - Combined transfers’ effectiveness for interregional risk-sharing is weakened by pro-cyclicality of grants.
- Policy implications discussed in text:
  - The Fourteenth Finance Commission’s increase in sharable size to states may alter risk-sharing vs. stabilization trade-offs (may help interregional risk-sharing but reduce intertemporal risk-sharing).
  - Strengthening states’ fiscal responsibility and budget frameworks is more compelling given greater devolution and constrained fiscal space at the state level.
  - Given limited fiscal space and near-binding borrowing ceilings, there is risk that states either breach borrowing limits or resort to pro-cyclical adjustments following negative shocks.

### Additional relevant numeric facts from the chapter
- Subsidy expenditure declined to about 1.5 percent of GDP in FY2016/17 from 2.5 percent in FY2012/13.
- Aggregate deficits of the states rising towards about 3 percent of GDP.
- Panel VAR result: gross fiscal deficits deteriorate by about 1 percent of GSDP cumulatively over two years after a negative one standard deviation GSDP growth shock.
- One standard deviation GSDP growth shock ≈ 5 percentage points.

*Source: IMF staff estimates.*

### 3. UBI is therefore considered as an alternative to help overcome the failures of the

### cr1755 - 3. UBI is therefore considered as an alternative to help overcome the failures of the

### Overview
- UBI is considered as an alternative to address failures of the current social protection system.
- If carefully designed as part of a strategy to reform an inefficient social protection system, UBI may help reduce waste and inequality.
- Concerns: fiscal costs and affordability weigh heavily on public discussions.

### India’s UBI proposals (summary of proposals in Table 2)
- Bardhan (2016)
  - Universal coverage: Universal to all Indians.
  - Unconditional cash transfer: Yes.
  - Uniform cash transfer: Yes.
  - Basic income in rupees/year: Rs 10,000 1/ (10% GDP).
  - Financing: Removal of existing subsidies both implicit and explicit for central and state governments: Yes; Removal of tax holidays and exemptions: Yes.
  - Fully-funded scheme: Yes.
  - Note: Rs 10,000 equals "Three-quarters of the official poverty line at 2014/15 prices."
- Banerjee (2016)
  - Universal coverage: Every adult resident to verify their identity every week.
  - Unconditional cash transfer: Yes.
  - Uniform cash transfer: Yes.
  - Basic income in rupees: 13,000 2/ (12% GDP) [Table indicates Rs 250 weekly income].
  - Fully-funded scheme: ...
  - Financing: ... (table entries show ellipses for some items).
  - Note: 2/ Rs 250 weekly income.
- Ray (2016)
  - Universal coverage: Universal to all Indians.
  - Unconditional cash transfer: Yes.
  - Uniform cash transfer: Yes.
  - Basic share of GDP: Yes (UBI as a fixed share of GDP, level varied each year).
  - Fully-funded scheme: Yes.
  - Financing: Removal of existing subsidies and termination of tax holidays and exemptions are indicated in table as "Yes" for some proposals.
- Joshi (2016)
  - Universal coverage: Universal to all Indian households.
  - Unconditional cash transfer: Yes.
  - Uniform cash transfer: Yes.
  - Basic income in rupees/year: 17,500 per family or 3,500 per person (3.5% GDP).
  - Fully-funded scheme: Yes.
  - Financing: User charges for services paid out: Yes (table entry).
- Gokarn (2016)
  - Universal coverage: Universal to all Indians with opt-out option.
  - Unconditional cash transfer: Yes.
  - Uniform cash transfer: ... (table shows ellipses).
  - Fully-funded scheme: ...
  - Financing: User charges for services paid out: Yes (table entry).

### Cross-country experiences and comparisons (Table 3 highlights)
- No clear evidence of large-scale cross-country UBI implementations; several small pilots/experiments ongoing or planned.
- India’s UBI proposals are closest to Switzerland and Brazil in being truly universal and unconditional.
- Finland and the Netherlands: more limited schemes targeting persons currently receiving government benefits.
- Specific country scheme highlights:
  - Switzerland: A basic monthly income of 2,500 Swiss francs (about US$2,560) to each adult, and 625 francs for each child under 18. Population: 8.3 million. GNI per capita (PPP US$ 2015): 61,930. Status: UBI referendum was rejected on June 5, 2016.
  - Finland: €560 per month, tax free benefit, or about US$7,372 per year. Population: 5.5 million. GNI per capita (PPP US$ 2015): 40,840. Financing: About €20 million from 2017 budget. Status: Basic income experiment will be carried out in Finland in 2017-2018; results to be assessed in 2019.
  - The Netherlands: €960 per month, or about US$12,638. Population: 16.9 million. GNI per capita (PPP US$ 2015): 48,400. Status: A two-year pilot project will begin in January 2017.
  - Canada (Ontario): CAD$ 30,000 guaranteed annual income (not official). Population: 35.9 million. GNI per capita (PPP US$ 2015): 43,970. Financing: CAD$ 25 million from budget. Status: A pilot project will be established by April 2017.
  - Brazil (Quatinga Velho pilot): The project pays a monthly amount of 30 Brazilian reals (about US$15) to 27 members for one year; after 15 months pays unconditional income to 67 residents. Population: 207.8 million. GNI per capita (PPP US$ 2015): 15,020. Financing: Privately financed by a consortium of Natural Persons. Status: Ongoing study.

### Evidence from an Indian pilot (SEWA/UNICEF, Madhya Pradesh)
- Pilot details:
  - Unconditional cash transfer experiment in rural Madhya Pradesh in 2012.
  - Payment frequency: monthly.
  - Initial amounts: Rs 200 per month for each adult and Rs 100 per month for each child; later raised to Rs 300 and Rs 200 per month, respectively.
  - Amounts calibrated at between 20 and 30 percent of the income of families in the lower-income scales, or about one third of the 2011/12 poverty line.
  - Coverage: 6,000 individuals for a period of 29 months.
  - Disbursement channels: bank or cooperative accounts.
- Reported impacts (SEWA, 2014):
  - Enhanced food sufficiency and a qualitative shift in the food basket.
  - Improved affordability of health services and education.
  - Increased investment in better sanitation and access to drinking water.
  - Reduced the burden on households to fund health and education through borrowing.

### Fiscal space and affordability (figures and IMF estimates; Table 4)
- General point: To pay a meaningful basic income, significant fiscal space is needed.
- UBI calculation approach: amount a government could pay if it scraps its non-health transfer payments and spreads them evenly across the population in an annual single payment.
- Observations:
  - Finland and Nordic countries rank high in basic income payouts given relatively large fiscal space.
  - For Mexico and some emerging economies, budget-neutral calculations yield only a small fraction of living standards, or would require significant tax increases or borrowing.
  - UBI for India, proposed at about Rs 10,000 per year, is the lowest among emerging economies in the budget-neutral comparison; feasibility and affordability remain to be determined.
- Fiscal affordability (Table 4, exact entries preserved):
  - Costs of UBI:
    - Bardhan (2016): 10% of GDP.
    - IMF estimates: 10% of GDP.
    - IMF estimates (alternative): 2% of GDP.
  - Annual basic income in rupees (for all Indians of 1.25 billion persons):
    - Bardhan (2016): Rs 10,000 (US$150).
    - IMF estimates (matching Bardhan): Rs 10,000 (US$150).
    - IMF alternative estimate: Rs 2,000 (US$30).
  - Financing of UBI:
    - (i) Removal of subsidies (fertilizers, power, food):
      - Bardhan: 6-7% of GDP 1/
      - IMF estimates: 2% of GDP 2/
      - IMF alternative: 2% of GDP 2/
    - (ii) Termination of tax holidays and exemptions:
      - Bardhan: 3% of GDP
      - IMF estimates: ...
      - IMF alternative: ...
  - Additional financing requirements for UBI:
    - Bardhan: None.
    - IMF estimates: 8% of GDP.
    - IMF alternative: None.
  - Notes:
    - 1/ Two-third of total subsidies in which the National Institute of Public Finance and Policy estimates total implicit and explicit subsidies of about 14 percent of GDP.
    - 2/ From central government budget.
- Specific fiscal calculation example:
  - Bardhan’s UBI proposal at Rs 10,000 per year (population 1.25 billion in 2014/15) costs about 10 percent of GDP.
  - Total subsidies on food, fertilizers, and fuel account for only 1.5 percent of GDP in FY2016/17.
  - Therefore, Bardhan’s proposal would require additional financing needs of about Rs 10 trillion or 8 percent of GDP.
  - Conclusion drawn in text: This UBI scheme appears unaffordable without significant tax increases or additional government borrowing.

### Other potential impacts and uncertainties
- Impact depends on the level of basic income:
  - If too small: poverty reduction may be negligible.
  - If too large: potential disincentives to work and impact on labor force participation.
- Cash transfers to the poor (with higher marginal propensity to consume) may boost consumption and growth, but efficacy depends on household spending and investment choices.
- Increased spending could put additional pressure on India’s already-high rate of inflation.
- Many impacts remain uncertain without more granular UBI design details.

### Policy recommendations (key considerations)
- Undertake thorough ex-ante analysis of redistributive impacts and fiscal costs before introduction.
- Core design considerations:
  - Administrative and technical viability:
    - UBI should provide a cheaper means of providing money to poor people than existing means.
    - It should encourage continued work or participation in the formal labor market and improve living conditions.
    - UBI should build on current improved and better-targeted direct benefit transfers using the JAM (Jan Dhan Yojana bank accounts, Aadhaar identification and mobile connectivity) trinity to minimize leakages.
  - Fiscal affordability:
    - UBI should not delay planned fiscal consolidation nor add excessively to public debt.
    - Any budget re-allocation for UBI should not affect government expenditures on health and education or public investment.
  - Political feasibility:
    - Financing UBI by eliminating existing subsidies could trigger political tension because subsidies benefit powerful interest groups.
    - To gain political support, objectives of any proposed UBI scheme should be clearly stated: reach the most vulnerable and reduce poverty; reduce administrative and inefficiency costs; increase competitiveness of sectors (e.g., agriculture) by reducing inefficient food and fertilizer subsidies.
    - Authorities should detail sequencing of phasing out current subsidies.
    - Set up an independent body to monitor and assess whether UBI objectives are being met.
    - Design a communication plan to inform the public about pros and cons of any prospective UBI scheme.

*Source: IMF staff chapter content from cr1755 PDF.*

### 4. India’s trade weakness can be attributed to both external and domestic factors.

### 4. India’s trade weakness can be attributed to both external and domestic factors.

### Drivers of India’s trade weakness (summary)
- Despite a diversified export base, weak trade is a common phenomenon after the global financial crisis of 2008.
- Growth of India’s trading-partners’ import demands in the post-crisis period was only half that of the pre-crisis period.
- Refined petroleum products made up about one-fifth of the value of India’s goods exports; export values fell in line with the collapse in commodity prices during 2015-2016.
- Historically high inflation vis-à-vis trading partners contributed to the real appreciation of the Indian rupee, affecting India’s price competitiveness.
- India exports high-quality products, but there is room to converge further with other EMs in manufacturing exports of greater quality and complexity.
- High trade barriers—including tariffs and trade costs—and administrative burdens could have affected Indian export performance.
- Rigidities in Indian labor and product markets could also have played a role.

### Empirical findings and decomposition of export slowdown
- Estimation approach: panel regressions of sixteen emerging economies using annual data 1981-2015; dependent variable: growth of real non-oil goods exports. Explanatory variables include trading partners’ real non-oil goods import demand (weighted), CPI-based trade-weighted REER, changes in MFN tariff rates, FDI inflows as share of GDP, plus structural and institutional indicators.
- Key decomposition for India during 2013-2015:
  - Decline in import demand from India’s export partners explained about 39 percent of the slowdown in real non-oil export growth.
  - Real rupee appreciation explained about 29 percent of the slowdown.
  - Overall, the estimated results explain about 81 percent of India’s export slowdown during 2013-2015.
- Excluding oil: oil exports excluded to focus on core (non-commodity) Indian exports and avoid commodity-price volatility distortion.

### Regression results (selected coefficients from Table 1)
- Real effective exchange rate change: -0.221, -0.237, -0.213, -0.211, -0.216, -0.095 (t-statistics in Table 1)
- Growth of real non-oil goods import demand of trading partners: 0.853, 0.873, 0.844, 0.812, 0.815, 0.739
- Change in MFN tariff rates: -0.141, -0.299, -0.290, -0.286, -0.686
- FDI inflow in percent of GDP: 0.200, 0.206, 0.188, 0.217
- Change in export diversification: 0.134, 0.134
- Change in manufacturing export quality: 0.002
- Number of documents to export: -2.973
- Change in good market efficiency score: 5.916
- Lagged real non-oil export growth: -0.002, -0.046, -0.033, -0.026, -0.029, -0.178
- Adjusted R2 reported: 0.35 (models 1–5), 0.51 (model 6)
- Sample sizes (N): 328, 277, 269, 269, 269, 117 (varied by specification)

### Trade policy, tariffs, and scenario analysis
- India’s tariff statistics (WTO World Tariff Profile 2016):
  - India’s tariff rate (simple average of MFN tariff rates) is 13.4 percent for all products.
  - 32.7 percent for agricultural products.
  - 10.7 percent for non-agricultural products.
  - In all three cases India has the second-highest rate among comparator EMs.
- Empirical emphasis on the role of trade policy—particularly tariffs—on international trade.
- Scenario analysis: assuming India’s import tariffs are lowered to the level of the EM average suggests a significant boost of nearly 2 percent to India’s export growth over the medium term.

### Structural impediments, supply-side constraints, and FDI
- International rankings indicate structural impediments to effective product competition and efficient labor markets remain for India.
- Supply-side constraints and rigidities in product and labor markets weigh on India’s export performance.
- Estimated results show that FDI inflows and structural improvements could potentially have positive impacts on exports, though results are not statistically significant in some specifications (possible data/sample limitations).
- Recent FDI developments:
  - Net FDI inflows to India strengthened to 1.7 percent of GDP in fiscal year (FY) 2015/16, and expected to remain strong in FY2016/17.
  - Since April 2000, the services sector accounted for about 18 percent of all FDI equity inflows.
  - In FY2016, sectoral composition (percent): Services 17.2; Construction 0.3; Computer 14.8; Telecommunications 3.3; Car industry 6.3; Pharmaceuticals 1.9; Chemicals 3.7; Trade 9.6; Energy 2.2; Hotels 3.3; Other 37.4.
  - In FY2016, sources of FDI equity flows (percent): Mauritius 20.9; Singapore 34.2; U.K. 2.2; Japan 6.5; U.S.A. 10.5; Netherlands 6.6; Germany 2.5; Cyprus 1.3; France 1.5; U.A.E. 2.5; Other 11.3.

### Policy recommendations and reform scenarios
- Continued efforts to lower trade barriers and reduce trade costs could help boost exports and revive the virtuous cycle of trade and growth.
- Removal of structural impediments—supply-side bottlenecks, labor and product market rigidities, and difficulties of doing business—could reinforce productivity gains and support future export expansion.
- Continued trade and investment liberalization, and improving the quality and complexity of export products, are vital given weak global trade.
- Reform scenario illustrations (derived from panel regression of non-oil export growth equation):
  - Improved manufacturing export quality.
  - Increase in FDI flow.
  - Lower tariff rates (to EM average) — scenario impact: nearly 2 percent boost to export growth over the medium term.
  - Baseline vs Reform scenario projections shown for 2021 (chart in source).

### Export structure and potential
- India has made progress in export market and product diversification and in integrating into global value chains.
- Export Diversification Index (Theil Index, data as of 2010): India positioned relative to peers (chart in source).
- Export Quality Index in manufacturing products (data as of 2010): India exports high-quality manufacturing products but can still catch up with peers (chart in source).
- India’s Foreign Trade Policy (FTP) 2015–2020 target: increase manufacturing and services exports to US$900 billion, or a doubling of the current level, by 2020.
- Given the tepid and volatile global outlook, weak global trade is likely to persist, increasing the urgency of reforms to improve competitiveness and export complexity.

_International Monetary Fund, Chapter: “India’s trade weakness can be attributed to both external and domestic factors.”_

### 6. The impact of capital controls on capital flows appears to differ between inflows and

### 6. The impact of capital controls on capital flows appears to differ between inflows and

### Effect of capital controls on different types of flows
- Using 2014 capital flow data on more than 80 countries, capital controls (as measured by the de jure indices of Fernandez et al (2015) for 2013) have a larger effect on capital outflows than capital inflows.
- The impact of capital controls on FDI flows seem to be smaller than on some other types of capital flows.
- Earlier empirical research (Montiel and Reinhart, 1999) found that capital controls influence the composition of flows but not their volumes.
- The impact of capital controls was found to change over time and across regions (Asiedu and Lien, 2003).

### Evidence and data (India-focused)
- India: Capital Account Openness (Index) is shown using Quinn-Toyoda (2008) and Chinn-Ito (2006) measures.
- Scatter-plot relationships (2014) reported in the chapter:
  - Inflows vs. De Jure Controls Index, 2014: y = -4.365x + 7.9521; R² = 0.0198
  - Outflows vs. De Jure Controls Index, 2014: y = -6.6657x + 8.4898; R² = 0.0485
  - FDI Inflows vs. De Jure Controls Index, 2014: y = -0.4179x + 2.8269; R² = 0.0023
  - FDI Outflows vs. De Jure Controls Index, 2014: y = -0.6424x + 1.5681; R² = 0.0081

### Policy implications for India
- Capital account liberalization should be complemented by structural policies to attract stable, non-debt creating capital flows to India.
- Capital inflows are essential to support external sustainability in India given its deficit on the current account balance.
- Given differences in the impact of capital control measures on various types of capital flows, the pace and direction of Indian capital flow liberalization should be carefully assessed based on a cost-benefit analysis.
- Implementation of structural policies, including those to strengthen the financial sector and institutions, could increase the benefits of capital account liberalization for the recipient economy.
- Measures to improve the business environment, strengthen the financial sector, and develop financial markets, as recently adopted in India, should continue.

### Changes in FDI policies between 2014 and 2016 (selected entries from Table 1)
- Tea plantation: 2014 = 100% ; 2016 = 100% ; Approval Route 2014 = Government ; 2016 = Automatic
- Defense industry subject to industrial license: 2014 = 26% ; 2016 = 100% ; Approval Route 2014 = Government route up to 26%. Automatic route up to 49%.
- Broadcasting carriage services: teleports, direct to home, cable networks, mobile TV, headend-in-the-sky broadcasting service: 2014 = 74% ; 2016 = 100% ; Approval Route 2014 = Automatic up to 49%. Government route between 49% and 74%.
- Cable networks: 2014 = 49% ; 2016 = 100% ; Approval Route 2014 = Automatic ; 2016 = Automatic
- Terrestrial broadcasting FM: 2014 = 26% ; 2016 = 49% ; Approval Route 2014 = Government ; 2016 = Government
- Uplinking of news and current affairs TV channels: 2014 = 26% ; 2016 = 49% ; Approval Route 2014 = Government ; 2016 = Government
- Uplinking of non-news and current affairs TV channels: 2014 = 100% ; 2016 = 100% ; Approval Route 2014 = Government ; 2016 = Automatic
- Airports - existing projects: 2014 = 100% ; 2016 = 100% ; Approval Route 2014 = Automatic up to 74%. Government route beyond 74% ; 2016 = Automatic
- Scheduled air transport service/domestic scheduled passenger airline: 2014 = 49% ; 2016 = 100% ; Approval Route 2014 = Automatic ; 2016 = Automatic up to 49%. Government route beyond 49%
- Non-scheduled air transport service: 2014 = 74% FDI (100% of NRIs) ; 2016 = 100% ; Approval Route 2014 = Automatic up to 49%. Government route between 49% and 74%.
- Ground handling services in civil aviation sector: 2014 = 74% ; 2016 = 100% ; Approval Route 2014 = Automatic up to 49%. Government route between 49% and 74%.
- Satellites-establishment and operations: 2014 = 74% ; 2016 = 100% ; Approval Route 2014 = Government ; 2016 = Government
- Duty-free shops: 2014 = 100% ; 2016 = 100% ; Approval Route 2014 = Automatic
- Railway infrastructure: 2014 = 100% ; 2016 = 100% ; Approval Route 2014 = Automatic
- Asset reconstruction companies: 2014 = 100% of paid-up capital of the company (FDI+FII/FPI) ; 2016 = 100% ; Approval Route 2014 = Automatic up to 49%. Government route beyond 49%.
- Credit information companies: 2014 = 74% ; 2016 = 100% ; Approval Route 2014 = Automatic ; 2016 = Automatic
- Insurance companies: 2014 = 26% ; 2016 = 49% ; Approval Route 2014 = Automatic ; 2016 = Automatic
- White label ATM operations: 2014 = 100% ; 2016 = 100% ; Approval Route 2014 = Automatic
- Private security agencies: 2014 = 49% ; 2016 = 74% ; Approval Route 2014 = Government ; 2016 = Automatic up to 49%. Government route beyond 49% and up to 74%.
- Pharmaceuticals - brownfield: 2014 = 100% ; 2016 = 100% ; Approval Route 2014 = Government ; 2016 = Automatic route up to 74%. Government route beyond 74%.

### Key takeaway
- Capital controls affect outflows more than inflows and have a smaller effect on FDI than on other capital flow categories; therefore, India’s approach to capital account liberalization should be calibrated and accompanied by structural reforms to strengthen the financial sector, improve the business environment, and develop financial markets to attract stable, non-debt creating capital.

*Source: IMF staff estimates.*

### References

### References

### Energy policy reform in India: assessing the options — key findings and projections
- A progressive increase in the recently introduced coal tax (raising it by Rs 150 per year from 2017 to reach Rs 2,500 per ton of coal in 2030):
  - prevents over 270,000 air pollution deaths during its phase-in;
  - in 2030 raises approximately 1 percent of GDP in new tax revenue;
  - reduces carbon dioxide (CO2) emissions by 12 percent;
  - generates net economic benefits (domestic environmental benefits less economic costs) of approximately 1 percent of GDP.
- A more aggressive coal tax (with twice the annual tax increases) has about 75 percent greater environmental and fiscal effectiveness.
- A carbon tax (applied to all fossil fuels) generates only modest extra health and CO2 benefits compared with a coal tax; an emissions trading system (ETS) is less effective and adds administrative complications.
- Other measures (e.g., road fuel taxes, renewable and energy efficiency incentives) are considerably less effective than coal/fossil-fuel taxes for the same emissions price.
- Recent reforms already undertaken:
  - Gasoline prices liberalized in 2010;
  - Diesel and natural gas prices liberalized in 2014;
  - Excise tax on coal production and imports currently Rs 400 per ton of coal.
- Rationale for further reform:
  - Complements efforts to address rising mortality from urban air pollution;
  - Provides an easily collected source of revenue;
  - Helps meet India’s NDC for the 2015 Paris Agreement of reducing the greenhouse gas intensity of Indian GDP 33-35 percent by 2030 relative to 2005 levels.
- Modeling framework and approach:
  - Practical spreadsheet model parametrized for India projects fuel use forward in a ‘business as usual’ (BAU) scenario and estimates policy effects relative to BAU using evidence on price responsiveness of fuel use and health impacts of air pollution;
  - Incidence analysis links policy-induced energy price impacts to an input-output model and survey data on household spending.
- BAU projections and baseline outcomes:
  - CO2 intensity of GDP in 2030 is projected to be 29 percent lower than in 2005, implying modest policy intervention is needed to meet the NDC (Figure 1a);
  - Coal still accounts for half of projected primary energy use in 2030;
  - Estimated premature deaths from outdoor air pollution from fossil fuel combustion rise from about 200,000 in 2015 to 400,000 in 2030.
- Policy scenario outcomes (preserving all numeric values as reported):
  - Aggressive coal tax: increase by Rs 300 per ton annually from 2017 to 2030, total tax in 2030 Rs 4,600 per ton of coal (about US$69), equivalent to Rs 2,460 per ton of CO2 (about US$37):
    - reduces CO2 emissions by 7 and 21 percent below BAU levels in 2020 and 2030 respectively;
    - would save about 490,000 lives as phased in over 2017–2030.
  - Modest coal tax: increase by Rs 150 per ton annually from 2017 to 2030, total tax in 2030 Rs 2,500 per ton of coal (about US$38):
    - cuts emissions by 4 and 12 percent in 2020 and 2030 respectively (sufficient to meet current NDC, which requires a reduction in projected emissions of 4–6 percent in 2030);
    - would save about 270,000 lives as phased in over 2017–2030;
    - raises revenues of about 0.3 and 1.0 percent of GDP in 2020 and 2030 respectively (in excess of revenues collected with no change in the tax rate);
    - generates a net economic benefit of 1 percent of GDP in 2030 (economic welfare cost about 0.1 percent of GDP; domestic environmental benefits about 1.1 percent of GDP).
  - Carbon tax with same CO2 charge as aggressive coal tax applied across fossil fuels:
    - only slightly more effective than an aggressive coal tax (coal accounts for over 70 percent of fossil fuel CO2 emissions in the BAU);
    - raises about 40 percent more revenue than the equivalently scaled coal tax (due to larger tax base).
  - Aggressive ETS applied to power generation and large industrial sources:
    - about 75 percent as effective as the carbon tax (for the same emissions price);
    - if allowances are auctioned, ETS raises revenues about 60 percent compared with the equivalently priced carbon tax.
  - Electricity tax raises revenues of about 45 percent compared with the equivalently priced carbon tax.
- Distributional and industry impacts:
  - The modest coal tax is mildly progressive in 2020, imposing a burden relative to total consumption of about 0.14 and 0.18 percent for the poorest and wealthiest quintiles respectively;
  - Compensating the poorest would use around 6 percent of coal tax revenues (e.g., via PDS and MGNREGA with improved Aadhaar-based targeting);
  - The modest coal tax raises costs across all industries on average about 0.2 percent in 2020;
  - For the 10 percent of most vulnerable industries (e.g., iron and steel), costs rise on average about 1.1 percent.
- Policy implication:
  - Continued fuel tax reform promotes sustainable economic growth by addressing environmental challenges and providing revenues for socially productive investments; given large domestic benefits, India could move ahead unilaterally.

### Macroeconomic linkages between gender gaps in access to finance and labor market outcomes — key findings and implications
- Core observed patterns:
  - Gender gaps in women’s economic participation—both labor market and entrepreneurship—have remained high in India.
  - Female labor force participation (FLFP) is at one-third of male labor force participation and has been falling over time.
  - Informal sector employment constitutes more than 90 percent of total employment, with females largely employed in low-productivity informal jobs in agriculture and services.
  - Women entrepreneurs comprise about 10 percent of the total number of entrepreneurs in India, collectively contributing 3 percent to India’s industrial output and employing over 8 million people.
  - Approximately 98 percent of women-owned businesses are micro-enterprises and approximately 90 percent operate in the informal sector.
  - Over 90 percent of female entrepreneurs rely on informal sources of finance.
- Determinants of gender disparities (three main categories):
  - Gender differences in time use, primarily from care responsibilities due to social norms (Eswaran et al, 2013).
  - Gender differences in access to productive inputs—land, credit and skills (Gonzales et al, 2015).
  - Market and institutional failures—lack of basic infrastructure and safety; lack of implementation of laws against gender-based discrimination (World Bank, 2011; Ghani, 2013).
- Role of formal–informal dichotomy:
  - A tightly regulated formal sector encourages firms to remain small and informal to avoid regulations and to hire labor informally to avoid high costs of hiring and firing (Besley and Burgess, 2004; Sharma, 2009), which contributes to limited formal employment opportunities for women.
- Constraints to women entrepreneurs’ access to finance:
  - Demand-side: limited awareness, social restrictions around inheritance and land ownership rights, limited financial awareness.
  - Supply-side: banks view women-owned enterprises as high-risk (informal, micro scale); absence of collateral leads banks to avoid this sub-segment.
- Economic significance and potential gains:
  - Closing gender gaps could substantially boost economic output; Cuberes and Teignier (2016) estimate closing the gender gap in India could boost GDP by 27 percent (note: this estimate may be an overestimate because it does not fully account for India’s large informal sector).
- Policy implication:
  - Single-sector interventions have limited impact because women face multiple intertwined constraints;
  - A multi-dimensional approach—using a range of fiscal, legal, and structural measures—could maximize long-run economic gains and promote greater economic participation of women.

*Source: Content from cr1755 - References (IMF).*

### 7. The effects of the following policies are analyzed in this chapter. What is the impact of

### cr1755 - 7. The effects of the following policies are analyzed in this chapter. What is the impact of 

### Model and setup
- Framework: small-open economy dynamic stochastic general equilibrium (DSGE) model extending Khera (2016) with financial micro-foundations (a banking sector).
- Agents: households with male and female members; male and female owned enterprises; capital producers; banks; government; rest of the world.
- Two sectors: regulated formal sector and unregulated informal sector.
  - Formal sector: Regulated; High wage bargaining; High hiring/firing cost; High entry cost; High LTV ratio; Traded; Taxed.
  - Informal sector: Not regulated; Low wage bargaining; Low hiring/firing cost; Low entry cost; Low LTV ratio; Not traded; Not taxed.
- Gender-specific heterogeneity introduced via: access to finance, skills (average years of schooling), safety/mobility, social norms, time use (household care responsibilities), and discrimination (Table 2).
- Financial friction mechanism: collateral constraints (physical capital used both as collateral and production input); size of informal finance relates positively to degree of formal financial frictions and share/size of informal firms.

### Scenarios analyzed
- Financial access: Increase in female entrepreneurs’ access to formal finance (i.e. no gender gaps in access to finance).
- Deregulation: Financial access combined with lower regulations (i.e. higher flexibility) in the formal sector labor market (lower hiring costs).
- Skills: Financial access combined with skill development policies (i.e. no gender gap in skills; increase in females’ average years of schooling).

### Key quantitative findings and mechanics
- Aggregate macro effects of eliminating gender gaps in access to formal finance:
  - Leads to an increase in female entrepreneurship and female labor force participation (FLFP).
  - Raises GDP and lowers unemployment.
  - Gender gaps in entrepreneurship fall as female entrepreneurs replace male entrepreneurs in the formal sector who move to the informal sector.
  - Gender gaps in labor force participation improve: overall entrepreneurship raises employment and labor force participation (LFP); females increase participation more than males due to higher likelihood of employment in high-productivity jobs when female-led formal firms do not discriminate.
- Constraint without labor market reform:
  - Despite higher entrepreneurship and higher share of formal sector output, stringent labor market regulations cause new firms to hire workers informally.
  - Result: increase in informality in the labor market and a large share of new female labor market entrants employed in low-paying informal jobs.
- Combined with deregulation:
  - Closing gender gaps in access to finance plus more flexible labor markets raises share of formal sector employment for both females and males.
  - Generates larger gains in GDP and reductions in unemployment.
- Combined with skills improvements:
  - Simultaneously closing gender gaps in financial access and in skills gives a substantially larger boost to the Indian economy.
  - Leads to higher gender parity in LFP, wages, and formal sector employment.
- Figure 2 (author’s calculations) summarizes direction and relative magnitudes across outcomes (entrepreneurship, labor force participation, overall formality, quality of employment & wages, GDP & unemployment) under the three scenarios: Financial access; Deregulation; Skills.

### Selected numeric and empirical indicators cited
- More than 240 million previously unbanked individuals gained access to bank accounts since the launch of PMJDY in August 2014; about 47 percent of them are females.
- Under PMMY, women’s businesses accounted for about one-half of the total amount lent under the scheme, and about four-fifths of the number of loans.
- India’s GDP per capita (in PPP terms) grew on average by 11 percent per year over the last two decades (as reported).
- SWIID latest estimate: India Gini coefficient is 47.9; simple world average is 37.3.
- Primary/secondary school discontinuation among 15–19 year olds in 2011/12: 5.5 percent left school before completing 5th grade; 20.3 percent left before completing 10th grade.
- Public expenditure on education as a share of GDP increased from 3.1 percent in 2006 to 3.8 percent in 2012.
- India spent on average around 30 percent of its educational budget on primary education between 1999 and 2012 (versus Korea spending between 81 percent (1960) and 54 percent (1979) on primary education).

### Policy recommendations (multi-dimensional approach)
- Combine fiscal and structural measures to raise female entrepreneurship, FLFP, and formal sector employment:
  - Improve financial literacy (especially in rural areas) to boost females’ demand for financial services.
  - Spread awareness of females’ land inheritance to mitigate resource restrictions on women’s access to finance.
  - Implement more effective and targeted skill training programs for women to enhance employability across industries.
  - Ease labor market regulations to promote creation of formal sector jobs.
  - Fiscal measures: rural infrastructure spending on access to clean water, sanitation and transportation to reduce time women spend on domestic tasks and facilitate market access.
  - Continue fiscal consolidation to free resources for higher infrastructure spending and investment in education and skills training.
  - Government business climate reforms (including timely implementation of the GST) to help boost women’s economic participation.
- Measure success of financial and other interventions by rises in females’ formal entrepreneurship, mobility of their firms to medium and large sizes, and improvements in females’ labor market participation.

*Source: Author’s calculations.*

### 5. Large differences in private expenditure on education translate into substantial

### 5. Large differences in private expenditure on education translate into substantial

### Private expenditure and educational outcomes
- Private expenditure on education varies substantially by consumption quintile and schooling level.
- The top 20 percent in the consumption distribution spend 13 times as much as the bottom 20 percent on primary education, which declines to 6 times as much for tertiary education.
- Using IHDS basic skills tests (reading, writing and math) for 8 to 11 year olds as proxies for educational quality:
  - Among the poorest 20 percent of households, 22.4 percent of children were not able to read at all.
  - Among the richest 20 percent, 2.25 percent of children were not able to read at all.
  - Writing inability: 40.7 percent of the bottom 20 percent were not able to write, versus 10.1 percent of the top 20 percent.

### Occupational and educational distribution
- IHDS classification separates households by modern vs. traditional activity and entrepreneurs vs. workers.
- More than 42 percent of households rely mainly on entrepreneurial income, most of them traditional.
- For households relying on employment income, two thirds are traditional workers.
- Educational attainment of household main breadwinner by occupation (IHDS, 2011/2012):
  - Modern Entrepreneur: Share in Total Households 5.4%, Average Years of Education 8.5, Average Income (normalized) 100, Share of Poor Households 6.7%
  - Modern Worker: Share in Total Households 19.5%, Average Years of Education 9.9, Average Income (normalized) 99, Share of Poor Households 6.3%
  - Traditional Entrepreneur: Share in Total Households 37.0%, Average Years of Education 5.7, Average Income (normalized) 45, Share of Poor Households 17.1%
  - Traditional Worker: Share in Total Households 38.1%, Average Years of Education 4.4, Average Income (normalized) 38, Share of Poor Households 26.5%
- Modern workers achieve the highest mean years of schooling with 9.9 years; traditional workers on average do not even complete primary school.

### Model and calibration
- A heterogeneous agent DSGE model (similar to Mestieri et al. (2016)) is developed to simulate effects on inequality, schooling decisions and output.
- Model features:
  - Endogenous occupational choice: unskilled or skilled workers; traditional or modern entrepreneurs.
  - Endogenous schooling decisions and investments under financial constraints.
  - Human capital accumulation determined by time in school and private/public investment; production function allows different returns by schooling level and previous investments affect returns to subsequent investments.
  - Government sector funds education through public expenditure and supports households through subsidies; finances via taxes on entrepreneurial profits and wages.
- Calibration:
  - Calibrated to represent the Indian economy using data from 2011/12.
  - Some parameters taken from standard macro literature; others from IHDS and various data sources and by matching moments to represent India in 2011/12.
- Policy experiments compare steady-state outcomes across scenarios; taxes are adjusted to finance changes in government expenditure linked to household income, transfer generosity and schooling levels.

### Key policy experiment results
- Targeted cash transfers:
  - Baseline targeted transfer is income-dependent, calibrated to volumes reported in IHDS.
  - Increasing subsidies substantially:
    - Has no effect on average years of schooling.
    - Increases average human capital slightly.
    - Decreases inequality measured by the Gini coefficient and increases the share of the bottom 20 percent in consumption and income distributions.
  - Conditioning transfers on children's school attendance:
    - Can have large effects on average years of schooling and on educational intergenerational mobility (probability a child becomes skilled if parent was unskilled).
    - Transfer size must be large enough to offset households’ trade-off between foregone child earnings today and future higher earnings.
    - Despite higher average years of schooling, average human capital increases only slightly, suggesting longer attendance does not necessarily imply higher productivity; higher public spending on quality may be necessary.
  - Output fluctuates marginally as various channels offset each other.
- Increasing returns to unskilled education:
  - Interpreted as increasing returns in the human capital production function for the first 8 years (classified as unskilled education).
  - Increases in returns by up to 10 percent:
    - Lead to significant increases in years of schooling and average human capital.
    - Reduce the share of the population with no schooling from 23 percent to 4 percent.
    - Bottom 20 percent income share grows from 1.4 percent to 3.3 percent.
    - Output more than doubles as entrepreneurs and workers achieve higher productivity and spend more time in school.
  - Caveat: model does not account for potential costs of implementing such quality-improving policies.
- Shifting demand towards unskilled labor (industrial policy/"Make in India" style):
  - Shifting weight from skilled to unskilled labor in modern sector production decreases the skill premium for workers.
  - This does not translate into lower inequality (Gini), because the top 10 percent gain income share as they profit from replacing costlier skilled labor with unskilled labor.
  - The income share of the bottom 20 percent remains constant at 1.45 percent.
  - Considered magnitudes do not affect average years of schooling and only slightly increase average human capital.
  - If such policies also raise productivity or access to finance, wages and profits could increase more strongly, potentially raising returns to education and incentives to invest in it.

### Conclusions and policy implications
- Interactions are complex; policy design must account for direct and indirect channels affecting income and education inequality.
- Simple cash transfers reduce inequality via redistribution but may not increase average schooling levels unless conditioned on attendance and sufficiently large.
- School attendance alone, without improved school quality and greater public spending, may not raise student productivity.
- Increasing returns to education (improving quality) could strongly raise attendance, productivity and output, but poses significant implementation challenges beyond infrastructure investment (curricula, teaching, evaluation).
- Industrial policy increasing demand for unskilled labor can reduce the skill premium but may not reduce overall inequality because of gains to entrepreneurs.

*Source: cr1755 - 5. Large differences in private expenditure on education translate into substantial*

### 6. Differences in states’ policies and economic structure appear to contribute to the

### cr1755 - 6. Differences in states’ policies and economic structure appear to contribute to the disparities in income and growth performance across Indian states

### Key findings on drivers of interstate disparities and convergence
- Conditional income convergence among Indian states, taking into account states’ policies and economic structure, reflects wide gaps in individual states’ steady-state or long-run income potential (Table 2).
- Factors found to have a positive impact on states’ per capita income growth:
  - A larger share of higher value-added economic sectors (services and industry).
  - Greater investment and access to credit.
  - Higher spending on health and education.
- In line with Purfield (2009), the transition of states into higher value-added services or industry is linked to job creation and higher growth.

### Evidence on product and labor market regulation and state growth
- Product market regulation:
  - Based on OECD measures of the degree to which state policies are supportive of market competition, states with relatively less supportive regulatory frameworks for market competition would likely grow slower than those with pro-competitive product market regulations.
  - The rank correlation between product market regulation and state growth is negative and high at -0.46.
- Labor market regulation:
  - More rigid labor market regulation could hinder job creation and therefore be detrimental to state growth.
  - The rank correlation between a measure for the degree of labor market regulation burden and state growth is negative at -0.03.
- Caveat on labor index:
  - The labor market regulation index is from the OECD and indicates reform progress on labor market regulation at the Indian state level and is the only set of indicators available.
  - The index may not fully capture core problems in the Indian labor market, particularly segmented labor markets and informality, and short samples from data limitation preclude a more formal analysis. Therefore, the negative relationship found between labor market regulation and growth is only for indicative purposes.

### Empirical panel results (selected statistics from Table 2)
- Dependent variable: Growth of income per capita (panel of 32 Indian states and territories, 1961-2015; fixed effects and time effects included).
- Coefficients on Initial income per capita (columns 1–7):
  - (1) -3.617 [4.16]***  
  - (2) -3.62 [4.00]***  
  - (3) -3.493 [2.35]**  
  - (4) -3.761 [2.47]**  
  - (5) -3.173 [2.10]**  
  - (6) -2.933 [2.14]**  
  - (7) -3.814 [1.80]*  
- Coefficients on sectoral shares and other covariates (selected):
  - Share of services sector (various columns): 6.751 [2.82]***; 7.841 [2.85]***; 8.593 [3.22]***; 7.853 [2.49]**; 5.649 [1.77]*; 7.259 [1.68]
  - Credit growth (column with significance): 0.067 [2.86]***
  - Net in-migration ratio (reported in one specification): 0.127 [1.26]
- Constants and model fit:
  - Constant terms across columns include 42.322 [4.36]***, 38.113 [3.61]***, 40.827 [2.59]**, 48.374 [2.96]***, 43.259 [2.44]**, 42.704 [2.56]**, 54.63 [2.27]**.
  - Adjusted R-squared values: 0.77, 0.77, 0.7, 0.72, 0.66, 0.67, 0.74 (columns 1–7).
  - Number of observations (N) by specification: 152, 146, 102, 99, 75, 74, 81.

### Additional empirical diagnostics (selected)
- Panel unit root and convergence tests are reported (Levin-Lin, IPS, bootstrapped IPS, bootstrapped Fisher (MW method)) for samples and subgroups (1961-1991, 1991-2015, 1961-2015, 1994-2015, 18 states, 32 states). Selected test outcomes:
  - For 1961-2015 (whole sample): Levin-Lin ADF-stat 3.06 (p-val 1.00) — No convergence.
  - For 1991-2015 (18 states): IPS ADF-stat -2.27 (p-val 0.01) — Yes convergence.
  - Bootstrapped IPS ADF-stat examples: 1.22 (p-val 0.89) — No convergence; -2.52 (p-val 0.01) — Yes convergence.
  - Bootstrapped Fisher stat (MW method) examples: 31.35 (p-val 0.69) — No convergence; 52.67 (p-val 0.04) — Yes convergence.
- Panel unit root tests use an unbalanced panel of 32 Indian states and territories from 1961-2015.

### Policy implications and recommendations
- Continued sound macroeconomic policies combined with comprehensive structural reforms are needed to boost India’s long-term growth and lessen income inequality across states.
- Policy priorities identified:
  - Greater investment.
  - Better credit allocation to the most productive sectors.
  - Policies to facilitate states’ transition into higher value-added services or industry to create more jobs and boost growth.
  - Increased spending on health and education by states to improve the quality of human capital.
  - Wide-ranging product and labor market reforms to boost competitiveness and productivity, thereby improving states’ long-term growth potential.

*Source: IMF staff estimates and text in the supplied content unit.*

### 3. Undernutrition entails substantial economic costs associated with poor learning outcomes

### 3. Undernutrition entails substantial economic costs associated with poor learning outcomes

### Economic costs of undernutrition and learning deficits
- Direct productivity losses from undernutrition are estimated at more than 10 percent of lifetime individual earnings.
- These direct losses entail about a 2-3 percent loss in GDP.
- Indirect losses are associated with deficits in cognitive development and schooling, and increased costs of health care (Spears, 2012).

### Food Safety Net Programs and the National Food Security Act (NFSA)
- The Government of India passed the National Food Security Act (NFSA) in September 2013, described as one of the largest safety net programs in the world.
- The NFSA provides subsidized food grains through the TPDS to 67 percent of the population (75 percent of the rural population and up to 50 percent of the urban population).
- Notes on implementation and design:
  - The NFSA has special provisions for nutritional support to women and children.
  - Overcoming distortions in previous schemes (which could be achieved through direct cash transfer of the food subsidy via direct benefit transfer (DBT) mode or by increasing access to a diverse set of food crops) is essential for creating a balanced diet.
  - Amongst the 32 States and Union Territories implementing the Act at present, only Chandigarh and Pondicherry are implementing the Act in DBT mode.
  - Periodic monitoring by putting in place a comprehensive nutritional data collection system is needed for its success.

### Importance of pulses for protein consumption
- Increasing protein consumption is a policy priority; pulses have been included in the National Food Security Mission (NFSM), along with wheat and rice.
- For a majority of low-income and vegetarian households, pulses are a major source of protein, cheaper than fish and meat (National Sample Survey (NSS) 68th round).
- India's role in the global pulses market:
  - India is the world’s largest producer, consumer and importer of pulses.
  - India accounts for about 33 percent of the global acreage area, 25 percent of total world production, and 27 percent of world consumption of pulses.

### Pulses: production, demand gap, and price volatility
- Production and area:
  - Despite being the world’s largest producer of pulses, India has consistently fallen short of meeting its own domestic demand.
  - The area under cultivation of pulses has not increased much and yields have remained stagnant for a long time.
  - Less than 15 percent of the area under pulses has assured irrigation (DES, Ministry of Agriculture).
- Demand gap and imports:
  - The excess demand for pulses is estimated at about 3–5 million tons per year.
  - Imports fill part of this excess demand, but global supply limitations push up world prices.
- Drivers of farmers shifting away from pulses:
  - High risk and uncertainty in production and prices (pulses are typically grown in rain-fed areas with high incidence of pests) has led farmers to move to crops with better return and lower risk (such as rice, wheat and cash crops).
- Price effects and policy responses:
  - Rising prices of pulses have been one of the key drivers of food—and overall—inflation in recent years, also leading to a nutrient poor diet.
  - Short-term government interventions to keep prices low can hurt long-term production and availability of pulses.
  - Large hikes in the Minimum Support Prices (MSP) for rice and wheat, combined with the government’s massive cereal stockpiling, have resulted in a shortfall in the production of pulses and high food inflation.
  - Hikes in the MSP for pulses have not been effective in stimulating pulses production due to technological and market constraints, and a failure to back up price support policies with sufficient and effective procurement.
  - Bans on exports and forward markets, and stock limits have undermined price discovery and de-incentivized farmers.

### Policy priorities: way forward
- National and state coordination:
  - A national strategy on agriculture and pulses should be undertaken involving both the Union and state governments.
  - Close to 90 percent of public investment in agriculture is at the state level.
  - Pulses production is highly concentrated in a few states: five states—Maharashtra, Rajasthan, Karnataka, Madhya Pradesh, Andhra Pradesh—typically account for more than 70 percent of Indian production.
- Investments to improve supply chains and market function:
  - Investment in logistics, marketing infrastructure, and roads can help create efficient supply chains linking the farmer to the consumer across states.
  - The Prime Minister launched a common electronic trading platform for National Agriculture Market (e-NAM) in April 2016, which aims to integrate 585 wholesale markets across India.
  - This is expected to improve competitiveness in marketing through larger participation of buyers and yield a more transparent system of bidding.
  - Karnataka government launched a unified online agricultural market in 2014; a total of 105 markets spread across 27 districts have been brought under the Unified Market Platform (UMP) as of March 2016.
- Risk reduction and production responsiveness:
  - To reduce weather-dependent production risks, providing affordable and wide coverage of insurance and provision of irrigation will help make supply more responsive to increases in prices.
  - The assured irrigation initiative under the Pradhan Mantri Krishi Sinchayee Yojana (PMKSY) program launched in July, 2015 and the crop insurance scheme, Pradhan Mantri Fasal Bima Yojana (PMFBY), launched in February, 2016 should target pulse-producing areas.
- Market-driven diversification:
  - Ultimately, public policies should ensure market-driven diversification in agriculture and reduce the dependence of farmers on the current system of MSP and public procurement.

### Complementary measures and development goals
- Complementing food policies with improvements in sanitation and reforms to agricultural policies is important for enabling a balanced diet and for disease control.
- A multi-sectoral approach is needed, aimed at better sanitation, health and gender equality, along with addressing long-term structural bottlenecks and improving productivity within the agricultural sector.
- Targets:
  - India should aim to achieve its Sustainable Development Goal of ending all forms of malnutrition by 2030, including achieving by 2025 the internationally-agreed targets on stunting and wasting in children under five years of age.

*International Monetary Fund. Chapter: "3. Undernutrition entails substantial economic costs associated with poor learning outcomes" from accompanying material.*

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_Source: https://www.imf.org/-/media/files/publications/cr/2017/cr1755.pdf_