## 3. Fiscal Impulse, 2009

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---

### I. Introduction and purpose
- Examines the firm- and industry-level effects of the stimulative fiscal response to the 2007–09 global financial crisis (GFC).
- Exploits a cross-country, cross-industry "natural policy experiment": 52 economies (44 with countercyclical fiscal policy, 8 neutral or contractionary).
- Uses firm-level financial statement data to document impact of discretionary fiscal impulse (change in the structural deficit) scaled by sectoral sensitivity to the business cycle on corporate profitability.

### Key empirical findings
- Baseline effect:
  - An increase in the structural deficit of one percent of potential GDP is associated, on average, with a rise in profitability of around 0.14 percentage points of the average firm’s total assets (change in profits in percent of total assets from 2009 to 2010), after controlling for average sectoral business cycle sensitivity.
- Sample-average implied effect:
  - The average fiscal impulse between 2009 and 2010 for the full sample is 1.8 percent of potential GDP.
  - The implied increase in profitability from that average impulse is around 0.3 percentage points of the firm’s total assets.
  - That 0.3 percentage points is described as equivalent to half of the median increase in cross country/cross industry profitability for the period.
- Heterogeneity:
  - Effects are larger on average among advanced economies (AEs).
  - Effects are not statistically significant among the group of emerging economies (EMs).
  - Sectoral concentration: strongest effects in manufacturing, utilities, and retail sectors.
  - Firm pre-crisis financial conditions matter: asset size, debt and leverage ratios influence profitability during the recovery.
- Additional reported coefficients and estimates (preserved verbatim where provided):
  - Baseline regression interaction term coefficients reported across specifications include 1.191*** (Table 5, All signs, Column labelled All) and 0.965** (Table 7, expenditure-based stimulus, All signs).
  - Preferred specification (Column 7) coefficient = 1.2.
  - Implied increase in profitability owing to stimulus ≈ 0.3 percentage points of the average firm’s total assets when considering: average fiscal impulse = 1.8 percent of potential GDP; mean business cycle sensitivity = 0.12 (full sample, Table 1).
  - Figure 4 results: average increase in firm profitability = 0.26 percentage points of total assets with the average fiscal impulse of the full sample; compared to median increase 0,65 percentage points of firm’s total assets.

### Methodology (estimation strategy)
- Core specification (cross-sectional firm-level, with country fixed effects):
  - Dependent variable: change in profits (percent of total assets) from 2009 to 2010 for firm i in industry j in country k.
  - Key interaction: BCS_j × ΔFP_k
    - BCS_j: sector j business cycle sensitivity.
    - ΔFP_k: fiscal impulse (change in structural deficit) of country k.
  - Monetary policy interaction: EFD_j × ΔMP_k
    - EFD_j: sectoral indicator of interest-rate/external-finance sensitivity.
    - ΔMP_k: change (first-difference) in nominal short-term policy rates.
  - Controls (vector of firm characteristics):
    - Size (Size_i,j,k)
    - Indebtedness (Leverage_i,j,k)
    - Change in sales-to-asset ratio
    - Cash-to-assets ratio (Balance sheet liquidity_i,j,k)
    - Short-term debt (portion payable within one year) to total debt
  - Country fixed-effects (α_k) included.
- Rationale:
  - Interacting country policy variables with sectoral sensitivities permits heterogeneous sectoral responses and helps identification in cross-country analysis.

### Measures of sectoral sensitivities and external dependency
- Business Cycle Sensitivity (BCS_j):
  - Constructed as log change in stock prices for US-listed firms over September 10 to September 28, 2001, averaged at three-digit SIC sector level (Tong and Wei, 2008).
  - Assumes sectoral sensitivity is intrinsic and transferable across countries.
- External Financial Dependency indicators (EFD_j):
  - DEF_INVj: capital expenditures less cash flow from operations divided by capital expenditures (Raddatz, 2006).
  - DEF_WKj: 365 times the sum of:
    - (inventories less accounts payable) / cost of goods sold
    - accounts receivable / total sales

### Data
- Firm-level data:
  - Source: Thomson Reuters Worldscope database.
  - Initial sample: 22,333 observations (trimmed to 18,319 when all controls are included).
  - Coverage: non-financial publicly listed firms in corporate calendar year 2010 across 26 advanced and 26 emerging economies.
  - Representativeness: publicly listed firms tend to be larger; on average they represent 26 percent of private domestic investment and profitability comprising about 12 percent of GDP, but only about 8.5 percent of total employment across the sample.
- Fiscal data:
  - Source: IMF World Economic Outlook (WEO) database.
  - Discretionary fiscal impulse measured as change (first-difference) in the government structural balance (in percent of nominal potential GDP) between 2008 and [text truncated in source].
- Consumer price inflation and monetary policy rates: gathered from the IMF’s International Financial Statistics.

### Descriptive statistics and sample descriptors (preserved verbatim)
- Median profitability (EBITDA as percent of total assets):
  - 2010: approximately 9 percent (preferred estimation sample).
  - 2009 (crisis): 8.1 percent.
  - 2007 (pre-crisis): 10.5 percent.
- By economy type (median profitability):
  - Advanced economies: 2007 close to 10 percent; 2009 = 7.4 percent; 2010 = 8.6 percent.
  - Emerging markets: 2007 = 11.4 percent; 2009 = 9.2 percent; 2010 = 9.7 percent.
- Short term to total debt ratios (median, 2007):
  - Advanced economies: 53.3 percent.
  - Emerging markets: 51.2 percent.
- Cash-to-assets ratios (median, 2007):
  - Advanced economies: 10.1 percent.
  - Emerging markets: 8.2 percent.
- Fiscal impulse (change in structural fiscal deficit between 2008 and 2009):
  - Range: expansionary change of 10 percentage points of potential GDP in Russia to contractionary change of -2.7 percent in Hungary.
  - Median change in the deficit: roughly 1.6 percent of GDP.
- Selected full-sample summary stats (Table 1, Final Sample, preserved verbatim):
  - Dependent variable: 2007 Profits, percent of assets — Number of observations = 17,960; Mean = 4.82; Standard deviation = 10.51; Minimum = -208.69; 25th percentile = 5.82; 75th percentile = 15.64.
  - Change in profits 2009-10, percent of assets — Number of observations = 18,319; Mean = 1.66; Standard deviation = 0.65; Minimum = -12.59; 25th percentile = -2.29; 75th percentile = 4.43.
  - 2008-09 Fiscal impulse, percent of potential GDP — Number of countries = 52; Mean = 1.83; Median = 1.59; Standard deviation = 2.09; 25th percentile = 0.73; 75th percentile = 2.59.
  - 2009 IMF expenditure forecast error, percent of GDP — Number of countries = 52; Mean = 1.23; Median = 1.59; Standard deviation = 2.64; 25th percentile = 0.02; 75th percentile = 2.82.
  - Industry business cycle sensitivity — Number of observations = 192; Mean = 0.12; Median = 0.12; Standard deviation = 0.10; 25th percentile = 0.07; 75th percentile = 0.17.
- Advanced economies (Table 2):
  - 2008-09 Fiscal impulse, percent of potential GDP — Number of countries = 26; Mean = 2.00; Median = 1.87; Standard deviation = 1.83; 25th percentile = 0.86; 75th percentile = 3.09.
- Emerging market economies (Table 3):
  - 2008-09 Fiscal impulse, percent of potential GDP — Number of countries = 26; Mean = 1.67; Median = 1.52; Standard deviation = 2.35; 25th percentile = 0.60; 75th percentile = 2.37.

### Sectoral analysis and quantitative sectoral findings
- Sectoral responsiveness: manufacturing, retail, and utilities show statistically significant positive sensitivity of profitability to fiscal stimulus.
- Reported sectoral interaction estimates (preserved verbatim):
  - Manufacturing (All): Fiscal Impulse * business cycle sensitivity = 1.742** (Table 6a).
  - Utilities (All): Fiscal Impulse * business cycle sensitivity = 4.272* (Table 6b).
  - Retail (All): Fiscal Impulse * business cycle sensitivity = 6.109* (Table 6b).
- Quantitative sectoral example:
  - "A fiscal impulse of 1 percentage point of potential GDP directed to [the manufacturing] sector after the crisis is on average associated with an increase in profitability of 0.21 percentage points of the firm total assets for the average business cycle sensitivity" (Table 6a narrative).
- Change in sales scaled by total assets is positive and significant in several sectors (example: Manufacturing: 0.082***).

### Expenditure versus revenue composition of stimulus
- Government expenditure increases are, on average, larger than total fiscal impulse increases in response to the downturn; measures are highly correlated.
- Substituting total fiscal impulse by the change in government spending yields a positive interaction-term coefficient that is significant but slightly smaller than the total-impulse estimate (Table 7: Fiscal Impulse * business cycle sensitivity = 0.965** in All signs).
- Evidence points to most of the impulse effect on firms’ profitability coming from increases in government spending rather than tax measures.

### Robustness and alternative measures
- Results persist across robustness checks including:
  - Replacing baseline fiscal impulse with ILO narrative discretionary fiscal stimulus (Table 8: Fiscal stimulus (ILO) * business cycle sensitivity = 1.738*** in Column (1)).
  - Replacing baseline fiscal impulse with IMF WEO unexpected government expenditure (Table 9: Fiscal stimulus * business cycle sensitivity = 2.535*** in Column (1)); median unexpected government spending constructed as post-crisis outturn minus pre-crisis forecast ≈ 1.6 percent of GDP.
  - Robust standard errors or clustering at country-level both produce similar results.
- Asymmetric-sample checks:
  - Restricting to economies with a positive fiscal impulse yields Fiscal Impulse * business cycle sensitivity = 1.176*** (Table 5, positive-impulse All sample).
- Monetary policy interactions:
  - Change in policy rate * investment capital dependency ≈ -0.053** (Table 5, All signs, All); interpreted in text as a decrease in policy rate of 1 percentage point associated with an increase in profitability of 0.06 percent.
  - Reduction in policy rate interacted with sectoral dependence on external finance for working capital: not significant and excluded from preferred model (Column 7).

### Caveats, limitations, and interpretation
- Sample restriction:
  - Analysis limited to publicly listed non-financial firms; results may not be representative of the entire non-financial corporate sector.
- Period uniqueness:
  - The sample period is immediately following the GFC, notable for the synchronicity of slowing growth and simultaneous countercyclical fiscal and monetary policies across AEs and EMs; external validity to other periods may be limited.
- Measurement caveats:
  - BCS index is derived from the 2001 episode and may capture sector sensitivity specific to that crisis; construction attempts to mitigate this concern.
- Interpretation:
  - Cross-country/sector specific analysis complicates interpretation of aggregate magnitude of profitability gain/loss induced by fiscal stimulus.

### Policy implications and suggested further research
- Main policy implication:
  - Counter-cyclical fiscal policy during large recessions increased firms’ profitability in the first year after enactment, particularly in advanced economies, supporting the relevance of fiscal policy to credit-constrained firms.
- Channels:
  - Stimulating economic activity to raise sales.
  - Reducing firms’ costs (taxes paid).
- Suggested further research (as reported):
  - Investigate the impact of fiscal policy on firms’ sales, capital expenditure, the wage bill, and firm employment.
  - Explore which revenue and expenditure instruments (types of taxes and spending: public investment, transfers, purchase of goods and services) are most significant in affecting profitability.
  - Delve deeper into sectoral heterogeneity and the mechanisms linking fiscal policy, profits, and investment.

*Source: IMF working paper wp18251 — "3. Fiscal Impulse, 2009" (excerpt).*

### References .............................................................................................................

### References

### Tables
- 1. Summary Statistics, Full Sample .........................................................................................25
- 2. Summary Statistics, Advanced Economies..........................................................................26
- 3. Summary Statistics, Emerging Market Economies .............................................................27
- 4. Results for All Countries for Profits ....................................................................................28
- 5. Asymmetric effects of Fiscal Stimulus on Firms’ Profitability ...........................................29
- 6a. Horse Race Results Per Sector for Profits..........................................................................30
- 6b. Horse Race Results Per Sector for Profits .........................................................................31
- 7. Expenditure-Based Fiscal Stimulus .....................................................................................32

### Figures
- 1. Box-Whisker Plots: Profit (EBITDA) to Asset Ratios ........................................................22
- 2. Box-Whisker Plots: Growth Rate of Total Assets ...............................................................22

*Source: wp18251 - References*

### 3. Fiscal Impulse, 2009 ................................................................................................

### 3. Fiscal Impulse, 2009 ............................................................................................23

### I. Introduction and purpose
- Examines the firm- and industry-level effects of the stimulative fiscal response to the 2007–09 global financial crisis (GFC).
- Exploits a cross-country, cross-industry "natural policy experiment": 52 economies (44 with countercyclical fiscal policy, 8 neutral or contractionary).
- Uses firm-level financial statement data to document impact of discretionary fiscal impulse (change in the structural deficit) scaled by sectoral sensitivity to the business cycle on corporate profitability.

### Key empirical findings
- Baseline effect:
  - An increase in the structural deficit of one percent of potential GDP is associated, on average, with a rise in profitability of around 0.14 percentage points of the average firm’s total assets (change in profits in percent of total assets from 2009 to 2010), after controlling for average sectoral business cycle sensitivity.
- Sample-average implied effect:
  - The average fiscal impulse between 2009 and 2010 for the full sample is 1.8 percent of potential GDP.
  - The implied increase in profitability from that average impulse is around 0.3 percentage points of the firm’s total assets.
  - That 0.3 percentage points is described as equivalent to half of the median increase in cross country/cross industry profitability for the period.
- Heterogeneity:
  - Effects are larger on average among advanced economies (AEs).
  - Effects are not statistically significant among the group of emerging economies (EMs).
  - Sectoral concentration: strongest effects in manufacturing, utilities, and retail sectors.
  - Firm pre-crisis financial conditions matter: asset size, debt and leverage ratios influence profitability during the recovery.

### Robustness and alternative measures
- Results persist across robustness checks including:
  - Replacing baseline fiscal impulse with a measure of discretionary fiscal stimulus identified by the International Labor Organization (ILO) via the narrative approach.
  - Replacing the baseline fiscal impulse with an IMF dataset of unanticipated government expenditure calculated using forecast errors.
- Estimation approaches:
  - Robust standard errors or clustering at country-level both produce similar results.

### Theoretical context and competing channels
- Neo‑Keynesian channel:
  - Under sticky prices/wages, higher government discretionary spending (on goods, services, capital projects) or tax reductions can increase demand, output, and firm profitability.
- Alternative/labor-market channel (Alesina et al., 2002; Hassett and Hubbard, 2002):
  - Expansionary fiscal policy that raises government employment or wages can raise reservation wages and private wages, potentially depressing private profits and investment.
- RBC perspective:
  - Increased government purchases can reduce representative agent wealth (via taxation or crowding out), raising labor supply, lowering real wages, and increasing firms' output and profitability in some cases.
- Tax-reduction channel in models with unions:
  - Decreases in labor taxes or social security contributions can raise worker net wages but reduce pretax real wages faced by employers, potentially raising profits if part of tax burden is borne by firms.
- Ambiguity exists across theories; empirical investigation is required.

### Methodology (estimation strategy)
- Core specification (cross-sectional firm-level, with country fixed effects):
  - Dependent variable: change in profits (percent of total assets) from 2009 to 2010 for firm i in industry j in country k.
  - Key interaction: BCS_j × ΔFP_k
    - BCS_j: sector j business cycle sensitivity.
    - ΔFP_k: fiscal impulse (change in structural deficit) of country k.
  - Monetary policy interaction: EFD_j × ΔMP_k
    - EFD_j: sectoral indicator of interest-rate/external-finance sensitivity.
    - ΔMP_k: change (first-difference) in nominal short-term policy rates.
  - Controls (vector of firm characteristics):
    - Size (Size_i,j,k)
    - Indebtedness (Leverage_i,j,k)
    - Change in sales-to-asset ratio
    - Cash-to-assets ratio (Balance sheet liquidity_i,j,k)
    - Short-term debt (portion payable within one year) to total debt
  - Country fixed-effects (α_k) included.
- Rationale for interaction terms:
  - Interacting country policy variables with sectoral sensitivities permits heterogeneous sectoral responses to fiscal/monetary policy.
  - Helps address identification concerns in cross-country analysis (cyclicality collinear with fixed effects; direction of causality; robustness to additional controls).

### Measures of sectoral sensitivities and external dependency
- Business Cycle Sensitivity (BCS_j):
  - Constructed as log change in stock prices for US-listed firms over September 10 to September 28, 2001, averaged at three-digit SIC sector level (Tong and Wei, 2008).
  - Assumes sectoral sensitivity is intrinsic and transferable across countries.
- External Financial Dependency indicators (EFD_j):
  - DEF_INVj: intrinsic dependence on external finance for investment.
    - Constructed as capital expenditures less cash flow from operations divided by capital expenditures (Raddatz, 2006).
  - DEF_WKj: intrinsic dependence on external finance for working capital (cash conversion cycle).
    - Constructed as 365 times the sum of:
      - (inventories less accounts payable) / cost of goods sold
      - accounts receivable / total sales

### Data
- Firm-level data:
  - Source: Thomson Reuters Worldscope database.
  - Initial sample: 22,333 observations (trimmed to 18,319 when all controls are included).
  - Coverage: non-financial publicly listed firms in corporate calendar year 2010 across 26 advanced and 26 emerging economies.
  - Representativeness: publicly listed firms tend to be larger; on average they represent 26 percent of private domestic investment and profitability comprising about 12 percent of GDP, but only about 8.5 percent of total employment across the sample.
- Fiscal data:
  - Source: IMF World Economic Outlook (WEO) database.
  - Discretionary fiscal impulse measured as change (first-difference) in the government structural balance (in percent of nominal potential GDP) between 2008 and [text truncated in source].

### Caveats and limitations
- Sample restriction:
  - Analysis limited to publicly listed non-financial firms; results may not be representative of the entire non-financial corporate sector.
- Period uniqueness:
  - The sample period is immediately following the GFC, notable for the synchronicity of slowing growth and simultaneous countercyclical fiscal and monetary policies across AEs and EMs; external validity to other periods may be limited.
- Measurement caveats:
  - BCS index is derived from the 2001 episode and may capture sector sensitivity specific to that crisis; construction attempts to mitigate this concern.
- Interpretation:
  - Cross-country/sector specific analysis complicates interpretation of aggregate magnitude of profitability gain/loss induced by fiscal stimulus.

### Organization of the paper (as reported)
- Section II: literature review.
- Section III: econometric model and methodology.
- Section IV: results.
- Section V: additional tests and robustness checks.
- Section VI: conclusion.

*Source: IMF staff paper — "3. Fiscal Impulse, 2009" (chapter/section text extracted from the supplied PDF).*

### 2009. Government structural balances are estimated by the IMF country team staff and are

### wp18251 - 2009. Government structural balances are estimated by the IMF country team staff and are

### Methodology and data
- Government structural balances: difference between government revenues and expenditures from which automatic measures tied to the business cycle have been removed, leaving only discretionary changes implemented by the government.
- Main fiscal measure: ex-post estimates of structural deficits, published in the spring 2014 WEO.
- Robustness checks used:
  - Change in government structural spending.
  - WEO fiscal forecast errors.
  - Narrative constructions of discretionary revenue and expenditure policy after the crisis compiled by the International Labor Organization (ILO).
- Consumer price inflation and monetary policy rates: gathered from the IMF’s International Financial Statistics.
- Sample: restricted to publicly traded firms; heterogeneity across firms in responses of profitability before and after the crisis.

### Descriptive statistics and firm-level indicators
- Median profitability (EBITDA as percent of total assets):
  - 2010: approximately 9 percent (preferred estimation sample).
  - 2009 (crisis): 8.1 percent.
  - 2007 (pre-crisis): 10.5 percent.
- By economy type (median profitability):
  - Advanced economies: 2007 close to 10 percent; 2009 = 7.4 percent; 2010 = 8.6 percent.
  - Emerging markets: 2007 = 11.4 percent; 2009 = 9.2 percent; 2010 = 9.7 percent.
- Short term to total debt ratios (median, 2007):
  - Advanced economies: 53.3 percent.
  - Emerging markets: 51.2 percent.
- Cash-to-assets ratios (median, 2007):
  - Advanced economies: 10.1 percent.
  - Emerging markets: 8.2 percent.
- Fiscal impulse (change in structural fiscal deficit between 2008 and 2009):
  - Range: expansionary change of 10 percentage points of potential GDP in Russia to contractionary change of -2.7 percent in Hungary.
  - Median change in the deficit: roughly 1.6 percent of GDP.
- Additional robustness variables:
  - Change in government expenditure, ILO discretionary measures, and IMF fiscal forecast errors in 2009 are all positive for economies in the sample.

### Baseline estimation and main results
- Specification (Table 4, Column 1): includes (i) stimulus interacted with business cycle sensitivity; and (ii) changes in monetary policy rates interacted with dependency on external finance for working capital and investment dependence.
- Main finding for full country sample:
  - Fiscal stimulus (measured as change to the structural deficit, i.e., fiscal impulse) significantly increased corporate profitability during the crisis.
  - Coefficient on fiscal impulse oscillates between 2.9 and 1.2 across specifications; preferred specification (Column 7) coefficient = 1.2.
- Heterogeneity by country group:
  - Advanced economies (AEs): coefficient nearly twice as large as for full sample (Column 8).
  - Emerging markets (EMs): coefficient statistically non-significant (Column 9).
- Interaction term implications (Column 7, preferred specification):
  - Implied increase in profitability owing to stimulus ≈ 0.3 percentage points of the average firm’s total assets when considering:
    - Average fiscal impulse = 1.8 percent of potential GDP.
    - Mean business cycle sensitivity = 0.12 (full sample, Table 1).
  - Figure 4 results: average increase in firm profitability = 0.26 percentage points of total assets with the average fiscal impulse of the full sample; this is close to half of the median increase in cross country/cross industry profitability for the period, 0,65 percentage points of firm’s total assets.
- Sectoral and mechanism findings:
  - Positive and significant coefficient on change in sales scaled by total assets: where fiscal and monetary policy stimulated domestic demand, profits increased.
  - Monetary policy interactions:
    - Reduction in policy rate interacted with sectoral dependence on external finance for working capital: does not affect profitability significantly; variable excluded from preferred model (Column 7).
    - Monetary policy rate interacted with sensitivity to financing for capital investment: significant in all columns; low but significant negative value of around 0.06 percent, suggesting that a decrease in the monetary policy rate of 1 percentage point is associated with an increase in profitability of 0.06 percent (note: sign described as negative coefficient but interpreted as an increase in profitability associated with a decrease in policy rate in text).

### Interpretation and policy implications
- Fiscal stimulus during the financial crisis, implemented as larger structural deficits (either through discretionary spending increases or revenue reductions), supported higher corporate profitability in the first year after enactment.
- Plausible channels:
  - Stimulating economic activity to raise sales (firm revenue).
  - Reducing firms’ costs (taxes paid).
- Results corroborate macro-level findings of large fiscal multipliers during recessions (cited studies: Auerbach and Gorodnichenko, 2012; Baum, Poplawski-Ribeiro, and Weber, 2012; Dell’Erba, Koloskova, and Poplawski-Ribeiro, 2018).
- Heterogeneity suggests stronger effects in advanced economies and limited or insignificant effects in emerging markets, consistent with evidence on fiscal multipliers for EMs.

*Source: IMF working paper wp18251 (excerpt).*

### 0.05 percentage points, possibly by reducing the cost of new financing. The persistence of

### wp18251 - 0.05 percentage points, possibly by reducing the cost of new financing. The persistence of

### Main findings on fiscal stimulus and firm profitability
- In the full sample of economies, given industry business cycle sensitivity, an increase in the structural deficit (i.e. fiscal impulse) of one percent of potential GDP is associated with a rise in corporate profits to assets ratio of around 0.3 percentage points, equivalent to half of the median increase in cross country/cross industry profitability in the sample.
- The baseline regression interaction term (Fiscal Impulse * business cycle sensitivity) is positive and statistically significant for the full sample; specific estimated coefficients reported across specifications include 1.191*** (Table 5, All signs, Column labelled All) and 0.965** (Table 7, expenditure-based stimulus, All signs).
- The positive association is stronger when including both fiscal stimulus and fiscal consolidation episodes, consistent with fiscal expansion increasing profitability and consolidation depressing profits.
- The fiscal impulse effect is especially significant for advanced economies in the sample; effects for Emerging Markets (EMs) are generally weaker and often statistically non-significant.

### Asymmetric effects and firm-level controls
- Restricting the sample to economies with a positive fiscal impulse (excluding Egypt, Germany, India, Ireland, Switzerland, Turkey, the Ukraine and Hungary) yields a slightly weaker but still positive and significant interaction coefficient for the total sample (Table 5: Fiscal Impulse * business cycle sensitivity = 1.176*** in the positive-impulse All sample).
- Firm-size (log of total assets in U.S. dollars in 2007) is significant and positively correlated with increases in corporate profitability after interventions; reported coefficients include 0.291*** (Table 5, All signs, All).
- Leverage prior to the crisis is associated with lower profitability during the crisis for the full sample and for EMs that implemented de facto fiscal stimulus; Table 5 reports leverage: total debt 2007 (log, thousand USD) = -0.214** (All signs, All).
- Changes in policy rate interacted with investment capital dependency negatively affect firm-level profitability (e.g., Change in policy rate * investment capital dependency = -0.053** in Table 5, All signs, All).

### Industry / sectoral analysis
- Sectoral responsiveness: manufacturing, retail, and utilities are the only industries with statistically significant positive sensitivity of profitability to the fiscal stimulus.
- Quantitative sectoral example: "A fiscal impulse of 1 percentage point of potential GDP directed to [the manufacturing] sector after the crisis is on average associated with an increase in profitability of 0.21 percentage points of the firm total assets for the average business cycle sensitivity" (Table 6a narrative).
- Reported sectoral interaction estimates include:
  - Manufacturing (All): Fiscal Impulse * business cycle sensitivity = 1.742** (Table 6a).
  - Utilities (All): Fiscal Impulse * business cycle sensitivity = 4.272* (Table 6b).
  - Retail (All): Fiscal Impulse * business cycle sensitivity = 6.109* (Table 6b).
- Other sectoral covariates: change in sales/asset ratio (2009-10) is often positive and significant in several sectors (e.g., Manufacturing: 0.082***).

### Expenditure versus revenue composition of stimulus
- Government expenditure increases are, on average, larger than total fiscal impulse increases in response to the downturn, indicating high correlation between the two measures.
- Substituting total fiscal impulse by the change in government spending yields a positive interaction-term coefficient that is significant but slightly smaller than the total-impulse estimate (Table 7: Fiscal Impulse * business cycle sensitivity = 0.965** in All signs).
- The evidence points to most of the impulse effect on firms’ profitability coming from increases in government spending rather than tax measures.

### Robustness tests
- ILO narrative discretionary fiscal stimulus: replacing the baseline impulse with the ILO discretionary fiscal stimulus maintains a significant and positive interaction term, though with smaller coefficients likely due to the reduced sample (Table 8: Fiscal stimulus (ILO) * business cycle sensitivity = 1.738*** in Column (1)).
- IMF WEO spending forecast errors (unexpected government spending): the main findings are robust. Unexpected government spending constructed as post-crisis outturn minus pre-crisis forecast has a median around 1.6 percent of GDP (Tables 1 and related text). Table 9 reports Fiscal stimulus * business cycle sensitivity = 2.535*** in Column (1).
- Robustness checks confirm significance and direction of most other control variables across alternative fiscal measures.

### Policy implications and suggested further research
- Empirical results indicate a key role for counter-cyclical fiscal policy to increase firms’ profitability during large recessions with financial constraints (the GFC), supporting the relevance of fiscal policy to credit-constrained firms.
- The increase in firms’ profitability from fiscal stimulus likely supported private investment and contributed to mitigating protracted recoveries, particularly in advanced economies.
- Suggested avenues for further research (from the source):
  - Investigate the impact of fiscal policy on firms’ sales, capital expenditure, the wage bill, and firm employment.
  - Explore which revenue and expenditure instruments (types of taxes and spending: public investment, transfers, purchase of goods and services) are most significant in affecting profitability.
  - Delve deeper into sectoral heterogeneity and the mechanisms linking fiscal policy, profits, and investment.

### Key reported statistics and sample descriptors (selected, preserved verbatim)
- Full-sample firm observations and summary stats (Table 1, Final Sample):
  - Dependent variable: 2007 Profits, percent of assets — Number of observations = 17,960; Mean = 4.82; Standard deviation = 10.51; Minimum = -208.69; 25th percentile = 5.82; 75th percentile = 15.64.
  - Change in profits 2009-10, percent of assets — Number of observations = 18,319; Mean = 1.66; Standard deviation = 0.65; Minimum = -12.59; 25th percentile = -2.29; 75th percentile = 4.43.
  - 2008-09 Fiscal impulse, percent of potential GDP — Number of countries = 52; Mean = 1.83; Median = 1.59; Standard deviation = 2.09; 25th percentile = 0.73; 75th percentile = 2.59.
  - 2009 IMF expenditure forecast error, percent of GDP — Number of countries = 52; Mean = 1.23; Median = 1.59; Standard deviation = 2.64; 25th percentile = 0.02; 75th percentile = 2.82.
  - Industry business cycle sensitivity — Number of observations = 192; Mean = 0.12; Median = 0.12; Standard deviation = 0.10; 25th percentile = 0.07; 75th percentile = 0.17.
- Advanced economies sample descriptors (Table 2):
  - 2008-09 Fiscal impulse, percent of potential GDP — Number of countries = 26; Mean = 2.00; Median = 1.87; Standard deviation = 1.83; 25th percentile = 0.86; 75th percentile = 3.09.
- Emerging market economies descriptors (Table 3):
  - 2008-09 Fiscal impulse, percent of potential GDP — Number of countries = 26; Mean = 1.67; Median = 1.52; Standard deviation = 2.35; 25th percentile = 0.60; 75th percentile = 2.37.

*Source: IMF working paper wp18251 (text and tables as provided).*

### REFERENCES

### REFERENCES

### Public investment & fiscal multipliers
- Abiad, Abdul, Davide Furceri, and Petia Topalova. 2016. “The Macroeconomic Effects of Public Investment: Evidence from Advanced Economies.” Journal of Macroeconomics, Vol. 50, pp. 224–40.
- Auerbach, Alan J., and Yuriy Gorodnichenko. 2013. “Fiscal Multipliers in Recession and Expansion.” In Fiscal Policy after the Financial Crisis, edited by Alberto Alesina and Francesco Giavazzi. Chicago: University of Chicago Press.
- Baum, Anja, and Gerrit B. Koester. 2011. “The impact of fiscal policy on economic activity over the business cycle–evidence from a threshold VAR analysis.” Deutsche Bundesbank, Series 1, No. 03/2011, Deutsche Bundesbank, Frankfurt am Main.
- Baum, Anja, Marcos Poplawski-Ribeiro, and Anke Weber. 2012. “Fiscal Multipliers and the State of the Economy.” Working Paper 12/286, International Monetary Fund, Washington, D.C.
- Dell’Erba Salvatore, Ksenia Koloskova, and Marcos Poplawski-Ribeiro. 2018. “Medium-Term Fiscal Multipliers during Protracted Economic Contractions,” Journal of Macroeconomics, Vol. 56, pp. 35–52.
- Ilzetzki, Ethan, Enrique G. Mendoza and Carlos A. Vegh. 2013. “How Big (Small?) are Fiscal Multipliers?” Journal of Monetary Economics Vol. 60, Issue 2, pp. 239-254.
- Mineshima, Aiko, Marcos Poplawski-Ribeiro, and Anke Weber. 2014. “Size of Fiscal Multipliers.” In Post-Crisis Fiscal Policy, edited by Carlo Cottarelli, Phill Gerson, and Abdelhak Senhadji. Cambridge: MIT Press.
- Nekarda, Cristopher and Valerie A. Ramey. 2011. “Industry Evidence on the Effects of Government Spending.” American Economic Journal: Macroeconomics 3 (1): 36–59.
- Poplawski-Ribeiro, Marcos, 2009. “New Evidence on the Effectiveness of Europe's Fiscal Restrictions,” CEPII Working Paper 2009-13, CEPII research center, Paris.

### Fiscal consolidation, policy responses, and automatic stabilizers
- Alesina, Alberto, Silvia Ardagna, Roberto Perotti, and Fabio Schiantarelli. 2002. “Fiscal Policy, Profits, and Investment” American Economic Review 92(3): 571–89.
- Carrière-Swallow, Yan, Antonio David, and Daniel Leigh. 2018. “The Effects of Fiscal Consolidations in Developing Economies: Evidence from Latin America.” IMF Working Paper, No. 18/142, International Monetary Fund, Washington, DC.
- Devries, Pete, Jaime Guajardo, Daniel Leigh, and Andrea Pescatori. 2011. “A New Action-based Dataset of Fiscal Consolidation.” Working Paper 11/128, International Monetary Fund, Washington, DC.
- Darby, Julia, and Jacques Melitz. 2008. “Social spending and automatic stabilizers in the OECD,” Economic Policy 23: 715–756.
- Guajardo, Jaime, Daniel Leigh, and Andrea Pescatori. 2014. “Expansionary Austerity? International Evidence.” Journal of European Economic Association 12(4): 949–68.
- Guerguil, Martine, Marcos Poplawski-Ribeiro, and Anna Shabunina. 2014. “Fiscal Policy Response During the Crisis in Low-Income African Economies.” In “Post-Crisis Fiscal Policy” edited by Carlo Cottarelli, Phill Gerson, and Abdelhak Senhadji, Chapter III.11, pp. 287-314. Cambridge, MA: MIT Press, July.
- Fernández-Villaverde, Jesús, Pablo Guerrón-Quintana, Keith Kuester, Juan Rubio-Ramírez. 2015. “Fiscal Volatility Shocks and Economic Activity.” American Economic Review 105 (11): 3352–3384.
- Perotti, Roberto. 2008. “In Search of the Transmission Mechanism of Fiscal Policy.” In NBER Macroeconomics Annual 2007, edited by Daron Acemoglu, Kenneth Rogoff and Michael Woodford. Chicago: University of Chicago Press.

### Firm-level studies, productivity, and micro evidence
- Andrews, Dan, Chiara Criscuolo, and Peter N. Gal. 2015. “Frontier Firms, Technology Diffusion and Public Policy: Micro Evidence from OECD Countries,” Working Paper 1137, OECD Economics Department, OECD, Paris.
- Blanchard, Olivier, Changyong Rhee, and Lawrence Summers. 1993. “The Stock Market, Profit, and Investment.” Quarterly Journal of Economics 108(1): 115–36.
- Claessens, Stijn, Hui Tong, and Shang-Jin, Wei. 2012. “From the Financial Crisis to the Real Economy: Using Firm-Level Data to Identify Transmission Channels.” Journal of International Economics 88 (2): 375–87.
- Claessens, Stijn, and Luc Laeven. 2003. “Financial Development, Property Rights and Growth”, Journal of Finance 58 (6): 2401–2436.
- Di Giovanni, Julian, Andrei A. Levchenko, and Isabelle Mejean. 2014. “Firms, Destinations, and Aggregate Fluctuations.” Econometrica 82 (4): 1303–40.
- Gal, Peter N., Alexander Hijzen, and Zoltan Wolf. 2013. “The Role of Institutions and Firms Heterogeneity for Labour Market Adjustment: Cross-Country Firm-Level Evidence.” Discussion Papers 7404 (May), Institute for the Study of Labor, Bonn.
- Gal, Peter N., and Gabor Pinter. 2013. “Capital over the Business Cycle: Renting versus Ownership.” Working Paper 478 (August), Bank of England, London.
- Guajardo, Jaime, Daniel Leigh, and Andrea Pescatori. 2014. “Expansionary Austerity? International Evidence.” Journal of European Economic Association 12(4): 949–68.
- Hassett, Kevin A., and R. Glenn Hubbard. 2002. “Tax Policy and Business Investment.” In Auerbach, Alan J., and Martin Feldstein, Handbook of Public Economics, Vol. 3, Chapter 20, pp. 1293–343.
- Kalemli-Ozcan, Sebnem, Bent Sorensen, Sevcan Yesiltas. 2012. “Leverage Across Firms, Banks, and Countries.” Journal of International Economics 88(2): 284–98.
- Kneller, Richard, and Florian Misch. 2014. “The Effects of Public Spending Composition on Firm Productivity.” Economic Inquiry 52(4): 1525–42.
- Lledó, Victor, and Marcos Poplawski-Ribeiro. 2013. “Fiscal Policy Implementation in Sub-Saharan Africa,” World Development, Vol. 46(C), pp. 79–91.
- Medina, Leandro. 2012. “Spring Forward or Fall Back? The Post-Crisis Recovery of Firms.” Working Paper 12/292, International Monetary Fund, Washington, D.C.
- Rahaman, Mohammad, Varouj A. Aivazian, and Ling Sun. 2012. “The Real Effects of Credit Contractions: A Firm-Level Analysis”. Mimeo (July). http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2083301
- Woo, Jaejoon. 2014. “Public Debt and Growth: New Evidence from Industry-Level Data and the Average Treatment Effect.” Mimeo (July).

### Financial sector, crises, credit channels, and international transmission
- Ağca, Şenay, and Oya Celasun, 2012, “Sovereign debt and corporate borrowing costs in emerging markets.” Journal of International Economics 88: 198–208.
- Aghion, Philippe, David Hémous, and Enisse Kharroubi. 2014. “Cyclical Fiscal Policy, Credit Constraints, and Industry Growth.” Journal of Monetary Economics 62 (March): 41–58.
- Claessens, Stijn, Hui Tong, and Shang-Jin, Wei. 2012. “From the Financial Crisis to the Real Economy: Using Firm-Level Data to Identify Transmission Channels.” Journal of International Economics 88 (2): 375–87.
- Forbes, Kristin J. 2004. “The Asian Flu and Russian Virus: The International Transmission of Crisis in Firm-Level Data.” Journal of International Economics 63(1): 59–92.
- Kalemli-Ozcan, Sebnem, Bent Sorensen, Sevcan Yesiltas. 2012. “Leverage Across Firms, Banks, and Countries.” Journal of International Economics 88(2): 284–98.
- Krozsner, Randall S., Luc Laeven, and Daniela Klingebiel. 2007. “Banking Crisis, Financial Dependence, and Growth.” Journal of Financial Economics 84(1): 187–228.
- Laeven, Luc, and Fabian Valencia. 2013. “The Real Effects of Financial Sector Interventions during Crises.” Journal of Money, Credit and Banking 45(1): 147–77.
- Rajan, Raghuram G., and Luigi Zingales. 1998. “Financial Dependence and Growth.” The American Economic Review, 88 (3): 559–86.
- Tong, Hui, and Shang-Jin Wei. 2011. “The Composition Matters: Capital Inflows and Liquidity Crunch During a Global Economic Crisis.” Review of Financial Studies 24 (6): 2023–52.
- Rahaman, Mohammad, Varouj A. Aivazian, and Ling Sun. 2012. “The Real Effects of Credit Contractions: A Firm-Level Analysis”. Mimeo (July). http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2083301

### Tax policy, corporate taxation, and productivity
- Arnold, Jens Matthias, Bert Brys, Christopher Heady, Asa Johansson, Cyrille Schwellnus, Laura Vartia. 2011. “Tax Policy for Economic Recovery and Growth.” The Economic Journal 121 (550): F59–F80.
- Hassett, Kevin A., and R. Glenn Hubbard. 2002. “Tax Policy and Business Investment.” In Auerbach, Alan J., and Martin Feldstein, Handbook of Public Economics, Vol. 3, Chapter 20, pp. 1293–343.
- Schwellnus, Cyrille and Arnold, Jens. 2008. “Do Corporate Taxes Reduce Productivity and Investment at the Firm-Level? Cross-Country Evidence from the Amadeus Dataset.” OECD Economics Department Working Papers 641, OECD, Paris.

### Methodological and theoretical contributions; reviews and meta-analyses
- Gechert, Sebastian. 2015. “What Fiscal Policy is Most Effective? A Meta-Regression Analysis.” Oxford Economic Papers 67(3): 553–80.
- Koo, Richard. 2011. “The world in balance sheet recession: causes, cure, and politics”, Real-world economics review, Issue no. 58, pp.19–37.
- Perotti, Roberto. 2008. “In Search of the Transmission Mechanism of Fiscal Policy.” In NBER Macroeconomics Annual 2007, edited by Daron Acemoglu, Kenneth Rogoff and Michael Woodford. Chicago: University of Chicago Press.
- Mineshima, Aiko, Marcos Poplawski-Ribeiro, and Anke Weber. 2014. “Size of Fiscal Multipliers.” In Post-Crisis Fiscal Policy, edited by Carlo Cottarelli, Phill Gerson, and Abdelhak Senhadji. Cambridge: MIT Press.
- Guerguil, Martine, Marcos Poplawski-Ribeiro, and Anna Shabunina. 2014. “Fiscal Policy Response During the Crisis in Low-Income African Economies.” In “Post-Crisis Fiscal Policy” edited by Carlo Cottarelli, Phill Gerson, and Abdelhak Senhadji, Chapter III.11, pp. 287-314. Cambridge, MA: MIT Press, July.

*wp18251 - REFERENCES*

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_Source: https://www.imf.org/-/media/files/publications/wp/2018/wp18251.pdf_