## Changes in Credit Standards and the Macroeconomy: Evidence from Mongolia

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---

### I. Introduction and research objective
- Purpose:
  - Identify credit supply shocks in Mongolia by isolating bank lending-standard changes that are not driven by bank-specific or macroeconomic factors that also affect credit demand.
- Method:
  - Construct adjusted credit supply shocks using a confidential quarterly Bank Lending Survey (BLS) submitted to the Bank of Mongolia (BOM); use these shocks and unadjusted lending standards in Vector Autoregression (VAR) analysis to estimate impacts on lending, output, and inflation.
- Rationale:
  - VARs (including Bayesian VARs) capture dynamic interrelationships among credit supply, output, and inflation, handle limited data and endogeneity, and allow impulse response functions (IRFs) and forecast error variance decomposition.

### II. Data and identification of supply shocks
- Data source and sample:
  - Quarterly Bank Lending Survey (BLS) conducted by the Bank of Mongolia since 2017:Q4.
  - Sample period: 2017:Q4-2025:Q1.
  - No. of banks participating: 12 (3 big, 3 medium, 6 small).
  - Observations used in panel regressions: Obs.=330.
- BLS coding and indices:
  - Responses mapped to: -1, -0.5, 0, 0.5, 1 for considerable decrease, some decrease, no change, some increase, considerable increase.
  - Composite indices are bank-level weighted averages using prior-quarter loan shares; diffusion indices take continuous values between -1 and 1.
- Identification strategy:
  - Panel regression (equation (3)) purges demand and other influences from reported credit standards.
  - Dependent variable: DI of bank credit standard (푆푆푖푖푖푖).
  - Controls include lagged credit standard, current-period DI of credit demand (퐷퐷푖푖푖푖), GDP outlook (푚푚푖푖+4 − 푚푚푖푖), contemporaneous real GDP growth, policy rate, inflation, quarterly change in risk tolerance, and bank fixed effects.
- Adjusted credit supply shocks:
  - Residuals from these regressions are interpreted as adjusted credit supply shocks (purged of demand influences); these residuals co-move with DI but are less volatile in ways consistent with demand purging.

### III. Key empirical estimates (panel regressions and diagnostics)
- Total loans (Table 2, preserved exact coefficients):
  - Lag of credit standard coefficient: .329*** (columns (1)–(4)) and .373*** (column (5)).
    - Interpretation: a one-unit tightening leads to a 0.32–0.37 unit tightening in the following quarter.
  - DI of credit demand coefficient: -.381*** (columns (1)–(4)) and -.38** (column (5)).
    - Interpretation: a one-unit increase in credit demand leads to a 0.38 unit loosening of credit standards.
- Bank-specific and macro controls (Table 2, column (5)):
  - Risk tolerance: -.435***.
  - Share of core loans: .195 (not statistically significant).
  - Share of deposits: -.176*.
- Enterprise loans (Table 3):
  - Lag of credit standard: .285*** to .311*** across specifications.
  - DI of credit demand: -.245*** to -.252*.
  - Share of deposits: -.222*** (column (5)).
  - Risk tolerance (enterprise): -.217** (column (5)).
- Household loans (Table 4):
  - Lag of credit standard: .328*** to .358***.
  - DI of credit demand: -.43*** to -.424***.
  - Risk tolerance (household): -.506***.
  - Inflation shows a positive (but not always significant) association with tighter household credit standards in some specifications.

### IV. VAR specification and main impulse responses
- VAR setup:
  - Four-variable VAR with 1 lag (sample 2017:Q4–2025:Q1).
  - Baseline endogenous variables:
    - Version A (adjusted shocks ordered first): [Adjusted supply shocks, Lending growth, Output growth, Inflation].
    - Version B (unadjusted DI ordered last): [Lending growth, Output growth, Inflation, Credit standards].
  - Lending growth: year-on-year growth of outstanding loans.
  - Output growth: year-on-year growth of 4-quarter rolling sum of real GDP.
  - Inflation: year-on-year growth of quarterly average CPI of Ulaanbaatar city, base = 2020.
- Main IRF results (Figure 5; exact peak impacts preserved):
  - A one-standard-deviation tightening in the adjusted credit supply indicator reduces:
    - Lending growth by 5 percentage points (peak).
    - Output growth by 1 percentage point (peak).
    - Inflation by 0.8 percentage points (peak).
  - IRFs from the unadjusted credit standards DI show somewhat smaller but still substantial contractionary effects.
- Disaggregated IRFs (Figures 6 and 7):
  - Enterprise credit supply shocks:
    - Stronger effect on inflation relative to output.
    - Enterprise loan growth responds significantly to enterprise supply shocks.
  - Household credit supply shocks:
    - Stronger and more significant impact on output growth compared with enterprise shocks.
    - Household loan growth responds strongly to household supply shocks.
  - Interpretation:
    - Household credit-driven consumption may be absorbed largely by imports (muting inflation), while enterprise credit affects input costs and prices.

### V. Variance decomposition results
- Forecast error variance decomposition (Figures 8–10):
  - Adjusted credit supply shocks explain a larger share of variability across lending growth, output growth, and inflation than unadjusted credit standard shocks.
  - For total loans, adjusted credit supply shocks explain over half of long-term variability in loan growth.
  - For enterprise and household decompositions, adjusted credit supply shocks have lower explanatory power than for total loans but still account for a larger share than credit standard shocks in most cases.

### VI. Robustness checks and alternative specifications
- Additional panel controls:
  - Exchange rate depreciation (year-on-year) and coal prices growth included; exchange rate depreciation shows a positive but statistically insignificant association with tightening of credit standards.
  - Dummy variables for large government programs:
    - (i) 10 trillion MNT Comprehensive plan (2021Q1): loosening effect but statistically insignificant.
    - (ii) General Agreement on Loans under Food Supply and Security (2022Q3): positive association at the 10 percent level (suggesting tightening).
    - (iii) “New Cooperative” campaign (2024Q2): statistically insignificant.
- Alternative VAR orderings and exogenous variables:
  - Ordering adjusted credit supply shocks last produces qualitatively similar results: tightening causes notable decreases in lending growth, output growth, and inflation for roughly six quarters followed by recovery.
  - Including year-on-year growth in the exchange rate and coal prices as exogenous variables yields impulse responses similar to baseline.
- Sign-restricted SVAR robustness (exact reported results preserved):
  - Tighter credit standards reduce:
    - Output growth by approximately 1.4 percentage points (first quarter).
    - Inflation by 1.2 percentage points (first quarter).
    - Total loan growth by 5.2 percentage points (first quarter).
  - Enterprise credit tightening reduces:
    - Output growth by 1.4 percentage points, inflation by 1.2 percentage points, and enterprise loan growth by 5.3 percentage points; effects persist about 10 quarters.
  - Household credit tightening reduces:
    - Output growth and inflation by similar margins (1.4 and 1.2 percentage points), and household loan growth by 9.4 percentage points.

### VII. Policy implications and recommendations
- Monitoring and early warning:
  - BOM should prioritize continuous monitoring of credit standards derived from the BLS because tighter lending conditions have significant short- and medium-term consequences for loan growth and macro activity.
- Countercyclical response:
  - Given lags in effects on real GDP and inflation, BOM should remain attentive to delayed impacts and consider counter-cyclical measures (policy rate adjustments and macroprudential instruments) to mitigate downturns.
- Macroprudential tools:
  - Strengthen macroprudential frameworks—e.g., modifying risk weights and debt-service-to-income (DSTI) thresholds—to mitigate procyclicality of credit supply and enhance resilience to external shocks.
- Sectoral focus:
  - Differentiate policy responses by loan type: household credit supply shocks have stronger output effects while enterprise shocks more strongly influence inflation; targeted macroprudential measures may be warranted.

### VIII. Key statistics and exact quantitative findings
- Sample period: 2017:Q4-2025:Q1.
- No. of banks in BLS: 12.
- Observations in panel regressions: 330.
- Panel regression (total loans) key coefficients:
  - Lag of credit standard: .329*** to .373***.
  - DI of credit demand: -.381*** to -.38**.
  - Risk tolerance (total): -.435*** (column (5)).
- VAR peak IRF impacts (adjusted credit supply shock, total loans):
  - Lending growth: -5 percentage points (peak).
  - Output growth: -1 percentage point (peak).
  - Inflation: -0.8 percentage points (peak).
- SVAR sign-restriction peak impacts (first quarter, robustness):
  - Output growth: approx. -1.4 percentage points.
  - Inflation: approx. -1.2 percentage points.
  - Total loan growth: -5.2 percentage points.
  - Enterprise loan growth (enterprise shock): -5.3 percentage points (effects persist ~10 quarters).
  - Household loan growth (household shock): -9.4 percentage points.
- External evidence cited for Mongolia’s lending-channel sensitivity:
  - Kabundi (2025): a 1 percentage point increase in the policy rate leads to a reduction in lending volume to private sector by 1.8 percentage points and loans to individuals by 1.2 percentage points within a year.

*Source: IMF Working Paper “Changes in Credit Standards and the Macroeconomy: Evidence from Mongolia” (Bank Lending Survey data and authors’ calculations; sample 2017:Q4–2025:Q1).*

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

### Changes in Credit Standards and the Macroeconomy: Evidence from Mongolia

### I. Introduction and research objective
- Purpose: Identify credit supply shocks in Mongolia by isolating bank lending-standard changes that are not driven by bank-specific or macroeconomic factors that also affect credit demand.
- Method: Construct adjusted credit supply shocks using a confidential quarterly Bank Lending Survey (BLS) submitted to the Bank of Mongolia (BOM); use these shocks and unadjusted lending standards in Vector Autoregression (VAR) analysis to estimate impacts on lending, output, and inflation.
- Rationale for methodology: VARs (including Bayesian VARs) are used to capture dynamic interrelationships among credit supply, output, and inflation, handle limited data and endogeneity, and allow impulse response functions (IRFs) and forecast error variance decomposition.

### II. Data and identification of supply shocks
- Data source and sample:
  - Quarterly Bank Lending Survey (BLS) conducted by the Bank of Mongolia since 2017:Q4.
  - Sample period: 2017:Q4-2025:Q1.
  - No. of banks participating: 12 (3 big, 3 medium, 6 small).
  - Observations used in panel regressions: Obs.=330.
- BLS response coding for credit standards and credit demand (categorical scale converted to numeric DI components):
  - Responses mapped to: -1, -0.5, 0, 0.5, 1 for considerable decrease, some decrease, no change, some increase, considerable increase.
- Composite indices construction:
  - Bank-level weighted averages using prior-quarter loan shares (equations (1) and (2) in the source).
  - Diffusion indices take continuous values between -1 and 1 and reflect banks’ relative shares.
- Identification strategy:
  - Panel regression (equation (3)) purges demand and other influences from reported credit standards:
    - Dependent variable: DI of bank credit standard (푆푆푖푖푖푖).
    - Controls include lagged credit standard, current-period DI of credit demand (퐷퐷푖푖푖푖), GDP outlook (푚푚푖푖+4 − 푚푚푖푖), contemporaneous economic conditions (real GDP growth, policy rate, inflation, quarterly change in risk tolerance), and bank fixed effects.

### III. Key empirical estimates (panel regressions and diagnostics)
- Persistence and demand effects (Table 2, total loans; all coefficient magnitudes preserved):
  - Lag of credit standard coefficient: .329*** (columns (1)–(4)) and .373*** (column (5)); interpretation: a one-unit tightening leads to a 0.32–0.37 unit tightening in the following quarter.
  - DI of credit demand coefficient: -.381*** (columns (1)–(4)) and -.38** (column (5)); interpretation: a one-unit increase in credit demand leads to a 0.38 unit loosening of credit standards.
- Bank-specific and macro controls (Table 2, column (5)):
  - Risk tolerance: -.435***.
  - Share of core loans: .195 (not statistically significant).
  - Share of deposits: -.176*.
- Enterprise loans (Table 3):
  - Lag of credit standard: .285*** to .311*** across specifications.
  - DI of credit demand: -.245*** to -.252*.
  - Share of deposits: -.222*** (column (5)).
  - Risk tolerance (enterprise): -.217** (column (5)).
- Household loans (Table 4):
  - Lag of credit standard: .328*** to .358***.
  - DI of credit demand: -.43*** to -.424***.
  - Risk tolerance (household): -.506***.
  - Inflation shows a positive (but not always significant) association with tighter household credit standards in some specifications.
- Residuals from these regressions are interpreted as adjusted credit supply shocks (purged of demand influences); Figure 4 shows these residuals co-move with DI but are less volatile in ways consistent with demand purging.

### IV. VAR specification and main impulse responses
- VAR setup:
  - Four-variable VAR with 1 lag (sample 2017:Q4–2025:Q1).
  - Endogenous variables (baseline specifications):
    - Version A (adjusted shocks ordered first): [Adjusted supply shocks, Lending growth, Output growth, Inflation].
    - Version B (unadjusted DI ordered last): [Lending growth, Output growth, Inflation, Credit standards].
  - Lending growth: year-on-year growth of outstanding loans.
  - Output growth: year-on-year growth of 4-quarter rolling sum of real GDP.
  - Inflation: year-on-year growth of quarterly average CPI of Ulaanbaatar city, base = 2020.
- Main IRF results (Figure 5; exact peak impacts reported):
  - A one-standard-deviation tightening in the adjusted credit supply indicator reduces:
    - Lending growth by 5 percentage points (peak).
    - Output growth by 1 percentage point (peak).
    - Inflation by 0.8 percentage points (peak).
  - IRFs from the unadjusted credit standards DI show somewhat smaller but still substantial contractionary effects.
- Disaggregated IRFs (Figures 6 and 7):
  - Enterprise credit supply shocks:
    - Stronger effect on inflation relative to output.
    - Enterprise loan growth responds significantly to enterprise supply shocks.
  - Household credit supply shocks:
    - Stronger and more significant impact on output growth compared with enterprise shocks.
    - Household loan growth responds strongly to household supply shocks.
  - Interpretation: household credit-driven consumption may be absorbed largely by imports (muting inflation), while enterprise credit affects input costs and prices.

### V. Variance decomposition results
- Forecast error variance decomposition (Figures 8–10):
  - Adjusted credit supply shocks explain a larger share of variability across lending growth, output growth, and inflation than unadjusted credit standard shocks.
  - For total loans, adjusted credit supply shocks explain over half of long-term variability in loan growth.
  - For enterprise and household decompositions, adjusted credit supply shocks have lower explanatory power than for total loans but still account for a larger share than credit standard shocks in most cases.

### VI. Robustness checks and alternative specifications
- Additional controls in panel regressions:
  - Inclusion of exchange rate depreciation (year-on-year) and coal prices growth: exchange rate depreciation shows a positive but statistically insignificant association with tightening of credit standards in the sample.
  - Dummy variables for large government programs:
    - (i) 10 trillion MNT Comprehensive plan (2021Q1): loosening effect but statistically insignificant.
    - (ii) General Agreement on Loans under Food Supply and Security (2022Q3): positive association at the 10 percent level (suggesting tightening).
    - (iii) “New Cooperative” campaign (2024Q2): statistically insignificant.
- Alternative VAR orderings and exogenous variables:
  - Ordering adjusted credit supply shocks last produces qualitatively similar results: tightening causes notable decreases in lending growth, output growth, and inflation for roughly six quarters followed by recovery.
  - Including year-on-year growth in the exchange rate and coal prices as exogenous variables yields impulse responses similar to baseline.
- Sign-restricted SVAR robustness (results preserved exactly as reported):
  - Tighter credit standards reduce:
    - Output growth by approximately 1.4 percentage points (first quarter).
    - Inflation by 1.2 percentage points (first quarter).
    - Total loan growth by 5.2 percentage points (first quarter).
  - Enterprise credit tightening reduces:
    - Output growth by 1.4 percentage points, inflation by 1.2 percentage points, and enterprise loan growth by 5.3 percentage points; effects persist about 10 quarters.
  - Household credit tightening reduces:
    - Output growth and inflation by similar margins (1.4 and 1.2 percentage points), and household loan growth by 9.4 percentage points (indicating stronger sensitivity of household loans).

### VII. Policy implications and recommendations
- Monitoring and early warning:
  - BOM should prioritize continuous monitoring of credit standards derived from the BLS because tighter lending conditions have significant short- and medium-term consequences for loan growth and macro activity.
- Countercyclical response:
  - Given lags in effects on real GDP and inflation, BOM should remain attentive to delayed impacts and consider counter-cyclical measures (policy rate adjustments and macroprudential instruments) to mitigate downturns.
- Macroprudential tools:
  - Strengthening macroprudential frameworks—e.g., modifying risk weights and debt-service-to-income (DSTI) thresholds—can mitigate procyclicality of credit supply and enhance resilience to external shocks.
- Sectoral focus:
  - Differentiate policy responses by loan type: household credit supply shocks have stronger output effects while enterprise shocks more strongly influence inflation; targeted macroprudential measures may be warranted.

### VIII. Key statistics and exact quantitative findings
- Sample period: 2017:Q4-2025:Q1.
- No. of banks in BLS: 12.
- Observations in panel regressions: 330.
- Panel regression (total loans) key coefficients:
  - Lag of credit standard: .329*** to .373***.
  - DI of credit demand: -.381*** to -.38**.
  - Risk tolerance (total): -.435*** (column (5)).
- VAR peak IRF impacts (adjusted credit supply shock, total loans):
  - Lending growth: -5 percentage points (peak).
  - Output growth: -1 percentage point (peak).
  - Inflation: -0.8 percentage points (peak).
- SVAR sign-restriction peak impacts (first quarter, robustness):
  - Output growth: approx. -1.4 percentage points.
  - Inflation: approx. -1.2 percentage points.
  - Total loan growth: -5.2 percentage points.
  - Enterprise loan growth (enterprise shock): -5.3 percentage points (effects persist ~10 quarters).
  - Household loan growth (household shock): -9.4 percentage points.
- External evidence cited for Mongolia’s lending-channel sensitivity:
  - Kabundi (2025): a 1 percentage point increase in the policy rate leads to a reduction in lending volume to private sector by 1.8 percentage points and loans to individuals by 1.2 percentage points within a year.

*Source: IMF Working Paper “Changes in Credit Standards and the Macroeconomy: Evidence from Mongolia” (Bank Lending Survey data and authors’ calculations; sample 2017:Q4–2025:Q1).*

### References

### References

### Key topics covered
- Monetary policy transmission, financial conditions, and financial stability
  - Adrian, T., & Liang, N. (2018). Monetary Policy, Financial Conditions, and Financial Stability. International Journal of Central Banking, 14(1), 73–131.
  - Gertler, M., & Karadi, P. (2011). A model of unconventional monetary policy. Journal of Monetary Economics, 58(1), 17–34.
  - Bernanke, B. S., & Blinder, A. S. (1988). Credit, Money, and Aggregate Demand. The American Economic Review, 78(2), 435–439.
  - Uhlig, H. (2005). What are the effects of monetary policy on output? Results from an agnostic identification procedure. Journal of Monetary Economics, 52(2), 381–419.

- Bank lending channel, loan supply, and lending standards
  - Adrian, T., & Shin, H. S. (2010). Liquidity and leverage. Journal of Financial Intermediation, 19(3), 418–437.
  - Bassett, W. F., Chosak, M. B., Driscoll, J. C., & Zakrajšek, E. (2014). Changes in bank lending standards and the macroeconomy. Journal of Monetary Economics, 62, 23–40.
  - Jiménez, G., Mian, A., Peydró, J.-L., & Saurina, J. (2020). The real effects of the Bank Lending Channel. Journal of Monetary Economics, 115, 162–179.
  - Broadbent, E., Ennis, H., Pike, T., and Sapriza, H. (2024). Bank Lending Standards and the U.S. Economy. Federal Reserve Bank of Richmond Working Paper, WP 24-07.
  - Giovanni Dell’Ariccia & Deniz Igan & Luc Laeven (2012). "Credit Booms and Lending Standards: Evidence from the Subprime Mortgage Market," Journal of Money, Credit and Banking, vol. 44(2‐3), pages 367-384, March.

- Credit spreads, credit cycles, collateral, and macro-financial shocks
  - Gilchrist, S., & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. American Economic Review, 102(4), 1692–1720.
  - Asea, P. K., & Blomberg, B. (1998). Lending cycles. Journal of Econometrics, 83(1–2), 89–128.
  - Lown, C. S., & Morgan, D. P. (2006). The credit cycle and the business cycle: New findings using the loan officer opinion survey. Journal of Money, Credit, and Banking, 38(6), 1575–1597.
  - Peek, Joe, and Eric S. Rosengren. 2000. "Collateral Damage: Effects of the Japanese Bank Crisis on Real Activity in the United States." American Economic Review 90 (1): 30–45.

- Macroprudential policy and regulatory capital
  - Borio, Claudio & Zhu, Haibin (2012). "Capital regulation, risk-taking and monetary policy: A missing link in the transmission mechanism?" Journal of Financial Stability, vol. 8(4), pages 236-251.
  - ESRB (European Systemic Risk Board). (2022). A Review of Macroprudential Policy Measures in the EU.
  - Tsenguunjav, B., & Saruul, Kh. (2018). Effectiveness of Selected Macroprudential Tools in Mongolia. Macroprudential Policies in SEACEN Economies (RP103, pp. 71–87).

### Methods, identification strategies, and empirical tools cited
- Structural vector autoregressions, sign and zero restrictions
  - Arias, J. E., Rubio-Ramirez, J. F., & Waggoner, D. F. (2018). Inference based on structural vector autoregressions identified with sign and zero restrictions: Theory and applications. Econometrica, 86(2), 685–720. https://doi.org/10.3982/ecta14468
- Local projections and impulse response estimation
  - Jordà, Òscar. 2005. "Estimation and Inference of Impulse Responses by Local Projections." American Economic Review 95 (1): 161–182.
- Vector autoregressions and Bayesian multivariate time series
  - Stock, James, H., and Mark W. Watson. 2001. "Vector Autoregressions." Journal of Economic Perspectives 15 (4): 101–115.
  - Koop, Gary and Korobilis, Dimitris, (2009), Bayesian Multivariate Time Series Methods for Empirical Macroeconomics, Working Paper series, Rimini Centre for Economic Analysis.

### Mongolia-specific studies and domestic research cited
- Transmission of monetary policy and lending in Mongolia
  - Bayardavaa, B., & Davaadalai, B. (2014). Loan Quality and Macroeconomic Shocks. The Central Bank of Mongolia, Collection of Research and Theoretical-Practical Works booklet. (in Mongolian)
  - Bayardavaa, B., Undral, B., & Altan-Ulzii, Ch. (2015). Monetary Policy Transmission in Mongolia. Bank of Mongolia, Research Book #10 (pp. 92–133). Ulaanbaatar.
  - Enkhzaya, D. (2011). Lending Channel. Bank of Mongolia, Research Book #7 (pp. 159–174). Ulaanbaatar.
  - Enkhzaya, D. (2018). Fiscal and Monetary Policy: Coordination or Conflict? International Economic Journal, 32(4), 547–571.
  - Gan-Ochir, D., & Munkhbayar, P. (2022). Effects of macro-financial shocks on bank liquidity creation: evidence from Mongolia. Journal of the Asia Pacific Economy, 29(3), 1165–1190.
  - Davaajargal, L., & Khuslen, D. (2019). Transmission Mechanism of Monetary Policy. Money, Finance, and Wealth Journal, 2019 (in Mongolian).
  - Urgamalsuvd, N. (2018). Economic Impact of the Monetary Policy Implemented by the Bank of Mongolia from 2012 to 2016. Money, Finance, and Wealth Journal, 2018. (in Mongolian)
  - Kabundi, A. (2025). Important channels of transmission of monetary policy in Mongolia. IMF Working Paper (forthcoming).

### Other influential empirical and theoretical contributions cited
- Credit channel, trust in banks, and macroeconomic effects of financial shocks
  - Ciccarelli, M., Maddaloni, A., & Peydró, J.-L. (2010). Trusting the bankers: A new look at the credit channel of monetary policy. European Economic Review, 54(8), 935–956.
  - Jermann, Urban, and Vincenzo Quadrini. 2012. "Macroeconomic Effects of Financial Shocks." American Economic Review 102 (1): 238–71.
  - Gilchrist, S., & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. American Economic Review, 102(4), 1692–1720.
  - Kuttner, K. N. (2012). Low Interest Rates and Housing Bubbles: Still No Smoking Gun. In A. G. Haldane (Ed.), The Changing Policy Landscape: 2012 Jackson Hole Symposium (pp. 59–80). Federal Reserve Bank of Kansas City.

*Interactions Between Public and Private Sector Wages and Inflation in Mongolia — Working Paper No.WP/2025/262*

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_Source: https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025262-source-pdf.pdf_