Economic policy uncertainty and banks’ loan pricingcirforum.org/2019forum_papers/CIRF2019_paper_151.pdfEconomic policy uncertainty and banks’ loan pricing Abstract Using news-based

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Economic policy uncertainty and banks’ loan pricing

Abstract

Using news-based government economic policy uncertainty index (EPU) of Baker et al (2016)

and bank-level data from 17 countries over the period 1998-2012, we find that government

economic policy uncertainty has significant positive association with interest rates on bank

gross loans. Specifically, a one standard deviation increase in EPU leads to 21.84 basis points

increase in average interest rates on bank gross loans. We conjecture the economic policy

uncertainty boosts banks’ loan prices by increasing the borrowers’ default risk. The impact of

EPU on banks’ loan pricing remains persistent after controlling for banks’ own idiosyncratic

default risk and the political risk variables from ICRG database. Results remain robust when

we use general elections as an alternative proxy of government economic policy uncertainty.

Overall, our results suggest that government economic policy uncertainty is an economically

important risk factor for banks’ loan pricing.

Keywords: economic policy uncertainty; EPU; banks’ loan pricing; political risk

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1. Introduction

Government, through her policies, has widespread effects on overall economy of a

country (McGrattan & Prescott 2005). Individuals and firms can make more informed

decisions if government policy making process is smooth. On the contrary, uncertainty about

government policies has severe implications for both real and financial sectors. In this regard,

uncertainty in government regulatory, fiscal and monetary policies, usually referred to as

economic policy uncertainty, is the special focus of recently heated debate. Considering the

impact of policy uncertainty on real economic outcomes, extant literature finds that due to

higher economic policy uncertainty: firms would delay their investments (Bernanke 1983;

Baker et al. 2016; Azzimonti 2018), firms would involve less in new merger and acquisition

deals (Bonaime et al. 2018), industrial production and employment would suffer (Baker et al.

2016), foreign direct investment would decrease (Julio & Yook 2016) and finally the gross

domestic product of a country would drop substantially (Bloom et al. 2018). Focusing on

financial consequences, a parallel literature finds that higher policy uncertainty leads to both

the higher spreads on corporate bonds (Waisman et al. 2015; Bradley et al. 2016) and the

higher cost of equity capital (Pástor & Veronesi 2013; Brogaard & Detzel 2015; Pham 2019)

for firms. Significantly absent from this literature is how economic policy uncertainty would

impact the banks’ loan pricing. Filling this void is the aim of this study.

This question is important because of several reasons: First, bank loans are a major

source of external financing for the real sector around the world. Low cost bank financing can

contribute to real sector growth by capital formation by businesses and promoting

consumption from households. Thus, the performance of real sector, in large part, depends on

the cost of bank financing.

Second, government economic policy uncertainty increases financial constraints for

borrowers by deteriorating the overall external financing environment. For example, as the

degree of economic policy uncertainty increases, the leverage ratios of corporate firms tend to

decrease due to tight financing environment (Zhang et al. 2015). Similarly, the share prices of

firms also tend to decrease and Liu et al. (2017) showed that share prices during political

uncertainty fall due to increase in discount rate rather than the decrease in expected cash flows.

The increase in discount rates, in turn, is an indication that firms’ cost of capital increases

during the episodes of higher economic policy uncertainty. Considering the supply-side, the

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question whether banks charge higher rates on loans during the periods of higher economic

policy uncertainty is largely unexplored.

We postulate that economic policy uncertainty affects banks’ loan pricing by

changing the default risk of borrowers. Underlying economic intuition is that policy

uncertainty risk shock increases the probability of a bad state, and risk-averse banks increase

loan prices to cover the resulting costs (Bloom 2014). This is consistent with Pástor and

Veronesi (2013), to the extent, that policy uncertainty commands a risk premium. More

specifically, banks face asymmetric payoffs because their share in borrowers’ income

remains restricted to the repayment of principal and interest amount, while they may lose all

if borrowers get into financial difficulties. Therefore, banks price the borrowers’ default risk

in loan prices to protect themselves against the higher downside risk due to asymmetric

payoffs. And, the borrowers’ default risk in turn, to the large extent, depends on political

factors. In this context, government economic policy uncertainty increases default risk of

both firms and households. It increases firms’ default risk by increasing the idiosyncratic

dispersion in firms’ productivity (Brand et al. 2019). And this effect is not limited to few firms,

rather an economic policy uncertainty shock increases the variance of firms’ productivity at

individual-, industry- and aggregate-levels (Bloom 2009). Likewise, it will boost household

default risk by increasing not only the volatility of household incomes due to higher

unemployment and less new hiring, but also the volatility of wages of those who remain

employed (Bloom 2014; Li et al. 2018).

For the analysis, we use bank-level data from 17 countries over the period 1998-2012.

Measuring aggregate economic policy uncertainty with the news-based policy uncertainty

index of Baker et al. (2016), we find that banks’ average interest rates include the premium

for economic policy uncertainty; that is, banks charge significantly higher interest rates on

loans during the periods of higher economic uncertainty. We also observe that the impact of

economic policy uncertainty on banks’ loan pricing remains persistent after controlling for

banks’ own idiosyncratic default risk and the political risk variables from ICRG database. We

also confirm the main results using the elections as an alternative proxy of future policy

uncertainty.

Our study is different from a related paper by Francis et al. (2014) in at least two ways.

First, they use individual loan-level data and examine the impact of firm-level political

uncertainty on the cost of individual bank loan facilities. In contrast, we use bank-level data

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and examine the impact of country-level aggregate policy uncertainty on average bank interest

rates. Thus, they only examine the impact of economic policy uncertainty on interest rates

charged on corporate loans, while in our bank-level data, average interest rates not only

include the interest rates on corporate loans but also the interest rates on household loans.

Second, their sample is from the single country e.g., the USA, while we extend the analysis to

a cross-country sample of 17 countries.

We contribute to the existing literature in several ways: First, our paper complements

the recent studies which investigate the impact of government economic policy uncertainty on

the different practices of corporate firms. So far, the major focus of this literature is industrial

firms. We extend this debate to financial firms by analyzing the impact of economic policy

uncertainty on banks’ loan pricing decisions. Second, we contribute to the literature which

explores the determinants of banks’ loan interest rates. We add to this literature by identifying

that government economic policy uncertainty has significant impact on banks’ loan interest

rates. Third, we also add to the studies which explore the impact of politics in banking. In this

regard, we find that the policy uncertainty caused by political process affects banks’ loan

pricing.

The paper is organized as follows. In Section 2, we briefly review the related literature.

Section 3 presents our data collection procedures. Section 4 introduces empirical methodology

and variables. In Section 5, we report empirical results. Final section concludes the study.

2. Literature review

Our paper builds on two strands of the recent literature: First are the studies which

examine the impact of economic policy uncertainty on external financing costs of borrowers.

Second, the studies which examine the determinants of banks’ loan pricing.

In the first type, existing studies largely have examined the impact of policy uncertainty

on the cost of equity, corporate bond spreads and the cost of bank loans. For example, Pástor

and Veronesi (2013) model that stock prices respond to the news about the government policy

choice. They show that investors require a risk premium for policy uncertainty and the

magnitude of risk premium is larger in weaker economic conditions. Brogaard and Detzel

(2015) used the news-based index to measure the economic policy uncertainty in the U.S. and

found that economic policy uncertainty positively forecasts log excess market returns. They

show that a one standard deviation change in economic policy uncertainty is associated with a

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1.5% change in forecasted three-month abnormal returns. Similarly, Pham (2019) also

confirms the positive relationship between economic policy uncertainty and the cost of equity.

Focusing on corporate bond spreads, Qi et al. (2010) explored that political rights have

negative association with corporate bond spreads. Using the bond issuance data of firms

incorporated in 39 countries, they found that a one standard deviation increase in political

rights is related with an 18.6% decline in bond spreads. Waisman et al. (2015) showed that

political uncertainty affects the value of corporate bonds. Using the U.S. corporate

bonds issuance information over the period 1980 to 2012, they found that the aggregate

uncertainty linked with the results of U.S. presidential elections leads to a 34 basis point

increase in corporate bond spreads. In another similar study, Bradley et al. (2016) found, using

the data of U.S. corporate bonds issued from 1984 to 2012, that higher uncertainty associated

with the outcome of state-level presidential elections leads to higher borrowing costs for the

firms located in those particular states.

We found only one study by Francis et al. (2014) which specifically examines the

impact of economic policy uncertainty on bank loan contracting. For analysis, they used the

data of loan deals negotiated between the U.S. firms and banks over the period 1990 -2010.

Measuring firm-level exposure to political uncertainty, they found that the uncertainty in the

political environment imposes additional costs on the loan contract. Specifically, they found

that a one standard deviation increase in firm's exposure to political uncertainty is associated

with 11.90 basis points of additional spreads.

We complement these studies by analyzing the impact of country-level aggregate

economic policy uncertainty on individual banks’ loan pricing decisions using a bank-level

dataset from 17 countries over the period 1998-2012.

In the second type, existing studies analyze the determinants of banks loan pricing at

individual loan-level (Asquith et al. 2005; Qian & Strahan 2007; Valta 2012; Waisman 2013;

Ge et al. 2017; Huang et al. 2018). Using data of individual loans, this literature finds that

banks set interest rates on loans keeping in view different characteristics of borrowing firms.

For example, Asquith et al. (2005) found that banks price borrowers’ credit quality, which can

be measured by credit ratings or financial ratios, into loan interest rates. Qian and Strahan

(2007) examined the impact of cross-country differences in legal institutions on the terms of

bank loans and found that loans have lower interest rates and longer maturities in the countries

with strong creditor protection. Both Valta (2012) and Waisman (2013) found that banks

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charge higher loan spreads to firms operating in more competitive industries because of the

reason that higher industry competition results in higher cash flow risk for firms. Similarly,

Francis et al. (2014) explored that the policy uncertainty positively affects spreads on bank

loans. Ge et al. (2017) investigated the relationship between firms’ investment behavior and

loans’ pricing and found that overinvesting firms obtain loans with higher loan spreads. Huang

et al. (2018) discovered that banks price the attention of independent directors into loan

contracts and showed that firms with higher proportion of independent directors paid lower

spreads on bank loans. We add to these studies by identifying that government economic policy

uncertainty has significant impact on banks’ loan interest rates.

3. Data collection

We compiled data from different sources for this study: Data for the main explanatory

variable, government economic policy uncertainty index, was collected from the website

http://www.policyuncertainty.com. Bank-level balance sheet and income statement data was

downloaded from Bankscope database. Data for country-level variables was collected from

World Development Indicators (WDI) and Financial Development databases of World Bank.

We started our sample construction by collecting the data of economic policy

uncertainty index from the website http://www.policyuncertainty.com. This website hosts the

data of news-based economic policy uncertainty index developed by (Baker et al. 2016). The

website also hosts the news-based economic policy uncertainty index for some countries

which was developed by other authors using the same methodology of Baker et al. (2016). We

downloaded the index data for all available 23 countries. Next we obtained bank-level financial

statements data of banks operating in these countries from Bankscope database1 over the

period 1998-2012. Bankscope had the data of active, as well as, the inactive banks. We

included both active and inactive commercial, cooperative and saving banks in sample. The

data of inactive banks was included to avoid the bias in data due to the survival of prudent and

low risk banks.

We collected country-level data for deposit interest rates, lending interest rates and

banking industry concentration from Financial Development database, whereas the data of

macroeconomic variables from World Development Indicators (WDI) database of World Bank

1 A new Bankfocus database was launched in 2017 replacing the original Bankscope. However, we use data

which we downloaded from Bankscope in 2015.

http://www.policyuncertainty.com/

http://www.policyuncertainty.com/

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over the same period. The data for financial crises in sample countries was collected from

Laeven and Valencia (2013)’s financial crisis database. As a final step, we linked bank-level

financial statements data with the data of country-level variables.

Next we refined the sample. To do so, we dropped all observations with missing

necessary data. We also dropped banks with less than three valid yearly observations. This

resulted in a final dataset of 21,747 yearly observations of 3518 banks from 17 countries over

the period 1998-2012. To eliminate outliers, we winsorized bank-level variables at 1% level in

both lower and upper tails.

Table 1 reports the list of sample countries, together with the number of banks and

annual observations per country.

(Insert Table 1 here)

4. Empirical methodology

We specify following pooled OLS model to estimate the impact of economic policy

uncertainty on banks’ loan pricing.

Where i, j and t subscripts represent bank, country and year, respectively.

Dependent variable, Y, stands for banks’ loan pricing. Annual interest income to gross

loans ratio is used to proxy banks’ loan pricing. This ratio measures the average interest rate

which banks charge on their loan portfolio in a year. αi is constant-term. is a set of

bank-level annual control variables including Return on equity ratio, Equity to total assets

ratio, Interest expense to total liabilities ratio, Non-interest expenses to total assets ratio,

Operating profit to total assets ratio, Loan loss provisions to total assets ratio, Banks size and

Loans to deposits ratio. is a set of country-level variables including Deposits interest rate,

Lending interest rate, Banking industry concentration, Inflation, GDP growth rate, Developing

countries dummy and Financial crisis dummy. Dt is a set of year dummy variables. Ɛi,j,t is an

idiosyncratic error term.

EPU is the main variable of interest and is proxies with the news-based economic

policy uncertainty index (henceforth EPU) of Baker et al. (2016). Baker et al. (2016) calculate

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EPU index based on the frequency of news articles in major newspapers which specifically

focus on the uncertainty in government future economic policy. Currently, this index is

available for 23 countries. They constructed this index by searching the articles in each major

newspaper of a country (the most influential newspapers were selected from each country) by

conducting a computer-automated search for the keywords in three categories: uncertainty,

economy and policy. They searched the words “uncertain” or “uncertainty” for uncertainty,

“economic” or “economy” for the economy, and “central bank”, “regulation”, “tax”,

“government spending” or certain other policy related words for the policy. They chose

specific words for each country by consulting the professionals who were well versed in local

economy and language. The searched articles containing above words were scaled by the

total articles in that newspaper in that month. Next the scaled policy related articles were

standardized to a unit standard deviation. Then the standardized scaled policy related articles

of all major newspapers of a country were averaged to get the monthly EPU index for that

country. Finally the resulting monthly series were normalized over the coverage period of

each country to achieve a mean of 100.

They observed that this index represents government economic policy uncertainty

rather than the more general kind of political or economic factors. Index is available at monthly

frequency. To get annual values, we averaged 12 monthly values for each year for each

country. Following Hu and Gong (2018), we take the natural log of annual values of the index.

For brevity, we refer to logged index as EPU throughout the rest of the paper. In Eq. (1), EPU

variable represents the cross-country differences in borrowers’ average risk due to the

government economic policy uncertainty.

A simple cost plus loan pricing model suggests that bank loan price includes bank

funding costs, bank expenses to provide service, profit margin and the premium for

borrowers’ risk (Diette 2000).

Bank funding consists of debt and equity. Bank debt funding can include customers’

deposits, short-term debt or long-term wholesale debt. Usually customers’ deposits are a key

debt funding source for the majority of commercial banks. To control for the bank debt

funding costs, we include interest expense on interest bearing bank liabilities to total interest

bearing bank liabilities ratio. This ratio measures the average interest rate paid on bank

deposits and short- and long-term debt, and thus represents the average bank debt funding

cost. Bank equity funding includes total outstanding equity of common shareholders. To

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control for bank equity funding costs, we include return on equity ratio. It is measured as the

net after tax income to total common shareholders’ equity. Higher return on equity ratio

represents higher equity costs for a bank. Both the interest expense on interest bearing bank

liabilities to total interest bearing bank liabilities ratio and return on equity ratio are measured

annually at bank-level.

To control for bank expenses to provide service, we include total non-interest

expenses to total assets ratio of each bank, to be measured annually. Similarly, we include

pre-impairment operating profit to total assets ratio to control for bank profit margins.

We use annual loan loss provisions to total assets ratio to control for borrowers’ risk.

Banks which have taken higher risk in their loans’ portfolio would need to recognize higher

provisions for loan losses.

Since our study design is multi-country, we include country-level Deposit interest rate

and Lending interest rate variables to control for cross-country differences in bank funding

costs and borrowers’ average risk, respectively. Deposit interest rate is defined as the average

annual rate paid by commercial or similar banks for demand, time, or savings deposits in a

country. This variable represents the annual average funding costs for all banks operating in a

country. Lending interest rate is defined as the average rate which lenders usually charge on

short- and medium-term loans to the private sector. This rate normally varies based on

borrowers’ creditworthiness and objectives of financing. When overall borrowers’ risk is high,

the lending interest rate in the economy would increase. Lending interest rate may contain

average premium for cross-country and time-series differences in policy uncertainty. After

adding it as a control variable, we can find if EPU index contains marginal information about

bank loan rates. Data for both variables was collected from the Financial Development

database of World Bank.

Apart from Cost-plus model, the price leadership model urges that banks also take

into account the competition from other lenders while setting loan rates. This model implies

that a bank’s position in its industry, aggressiveness in financial intermediation and overall

industry competition affects loan prices. Thus we also include variables, such as Log (total

assets), Loans to deposits ratio and Banking industry concentration, to control for bank size,

aggressiveness in financial intermediation and banking industry competition, respectively.

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Another concern is that policy uncertainty varies countercyclical to domestic business

cycles; that is, it rises in recessions and decreases in booms (Bloom 2014). To eliminate this

concern that our EPU variable is not just representing the macroeconomic conditions or the

domestic business cycles, we include Inflation and GDP Growth rate variables in our model.

Policy uncertainty also depends on countries’ income level and is systematically

higher in developing countries (Bloom 2014). Therefore, we add a dummy variable which

equals 1 if a sample country is developing and zero otherwise.

We also include financial crisis dummy variable to control for the effect of financial

crises in sample countries.

We further include year dummies in our model to control for the effect of global

business cycles.

Table (2) summarizes the definitions of main variables used in this study.


5. Empirical Results

5.1 Summary statistics

Table 3 reports summary statistics of main variables. The 7.53 mean value of Interest

income to gross loans ratio indicates that sample banks, on average, have charged 7.53

percent interest rate on their gross loans portfolios. Further there is remarkable heterogeneity

in the interest rates charged by sample banks; Interest income to gross loans ratio has a

standard deviation of 6.98, minimum value of 1.4 percent and the maximum value of 44.75

percent. The main explanatory variable, EPU, has a mean value of 103.66 with a standard

deviation of 26.59. Similarly, other control variables also demonstrate considerable variation

around sample mean values.


Table 4 reports pair-wise correlations between variables. As shown, the most of

variables don’t have high correlation with banks’ loan pricing variable (e.g., Interest income

to gross loans ratio) suggesting that the chances of multicollinearity in multivariate analysis

are lower.


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5.2 Economic policy uncertainty and banks’ loan pricing

Table (5) reports the empirical results when we estimate Eq. (1) with pooled OLS

estimator. Model 1 reports results of baseline model, while Model 2 includes EPU variable.

Results of baseline Model are consistent with the expectation and verify that our

model is well specified. For example, the variables measuring bank funding costs (i.e., Return

on equity ratio, Equity to total assets ratio, Interest expense to total liabilities ratio and

Deposit interest rate), bank expenses to provide service (i.e., Non-interest expenses to total

assets ratio), bank profit margin (i.e., Operating profit to total assets ratio) and the average

borrowers’ risk (i.e., Loan loss provisions to total assets ratio and Lending interest rate) all

enter positive and significant confirming that banks price these factors into loan rates.

Bank size and Banking industry concentration variables enter negatively significant

showing that large banks and banks operating in a concentrated banking industry charge

lower loan rates to borrowers. These results together suggest that large banks benefit from the

economies of scale and size advantages, and consequently can charge lower loan prices.

These results are consistent with the findings of Berger et al. (2005), Fungáčová et al. (2017)

and Grechyna (2018). Similarly, negative and significant results of Loans to deposits ratio

confirms that banks having aggressive behavior in financial intermediation charge lower rates

on loans.

Both Inflation and GDP growth rate enter positive and significant showing banks

price boom and bust business cycles into loan prices. Banks increase loan rates during

inflationary episodes or when the speculative demand of loans is high due to higher GPD

growth rate.

Positive and significant results of Developing countries dummy variable show that

banks charge higher interest rates in developing countries. This is a reflection of the risks

related with developing countries which are priced in loan interest rates.

Negative and significant results of Financial crises dummy variable show the negative

realized outcomes due to higher uncertainty during the crisis period. Since crisis driven

uncertainty spikes lead to a wave of defaults and inefficient liquidations, banks’ realized

income on loans would decrease.

Next we add the main EPU variable in the model. As shown in Model 2, EPU enters

positive and significant at one percent level. This result suggests that government economic

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policy uncertainty has strong positive impact on average loan prices. This result is consistent

with the findings of Francis et al. (2014) who concluded a positive association between

firm-level exposure to political uncertainty and loan-level interest rates.

Economically, a one standard deviation change in EPU (0.26) is associated with a

change in banks’ loan rates of 0.2184 (0.84 *0.26) where the mean value of banks’ loan rates is

7.53 percent. This suggests that in response to a one standard deviation increase in aggregate

EPU, banks would increase average interest rates on loans by 21.84 basis points. This

economic significance is also comparable with Francis et al. (2014) who found a 11.9 basis

points increase in loan-level interest rates in response to a one standard deviation increase in

firm-level exposure to political uncertainty. One possible reason behind our relatively higher

estimate is that the data of Francis et al. (2014) included only the loan facilitates for corporate

firms. While in our case, bank total interest income also includes interest earned from risky

products such as personal loans, auto loans and credit cards, in addition to interest earned on

corporate loans. Results are also comparable with Waisman et al. (2015) who found that the

aggregate uncertainty linked with the results of U.S. presidential elections leads to a 34 basis

point increase in corporate bond spreads.

In sum, our above findings show that higher government economic policy uncertainty

increases banks’ loan prices and is a significant risk factor for the cost of bank loans.


5.3 Robustness tests: controlling for banks’ own idiosyncratic risk

Our main hypothesis is that policy uncertainty affects banks’ loan rates by changing

the default risk of borrowers (i.e., businesses and households). To test this hypothesis, EPU

variable in Eq. (1) captures overall borrowers’ risk due to government economic policy

uncertainty. Borrowers’ risk is a demand-side factor for bank loans. However, economic

policy uncertainty can also affect banks, the supply-side of bank loans, in the same way as it

affects businesses and households. More specifically, policy uncertainty can also affect

banks’ default risk and consequently banks can increase loan rates because of their own risk

(Francis et al. 2014), over and above the risk of borrowers. Then concern arises whether our

EPU variable captures the effect of borrowers’ default risk or the effect of banks’ own risk,

on loan prices. To eliminate this concern, we add z-score in Eq. (1) to control for banks’

idiosyncratic default risk.

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Bank-level annual z-score is measured as z-score = -log (ROA+CAR)/σ(ROA)),

where ROA is annual return on assets before loan loss provisions and taxes, CAR is annual

equity to total assets ratio, and σ(ROA) is standard deviation of annual values of return on

assets before loan loss provisions and taxes calculated over 3-year overlapping periods (i.e.,

1998–2001, 1999–2002 and so on). Z-score shows the number of standard deviations from

mean value by which return has to fall to deplete all shareholders’ equity capital. Higher values

of z-score represent the higher probability of bank default and vice versa. Lepetit and Strobel

(2015) finds that z-score defines bank default risk on the domain of all real numbers and is an

attractive and unproblematic measure of bank default risk. Various recent studies, such as

Laeven and Levine (2009), Houston et al. (2010) and Ashraf et al. (2016), have used z-score to

measure bank default risk. We also use σ(ROA) as an alternative measure of banks risk.

σ(ROA) represents the volatility in overall bank operating income.

Because of three year rolling window, effective annual observations decrease to

14,483 in the models with z-score. For comparison, we first estimate main model and then

add z-score and σ(ROA) one-by-one. We observed that EPU variable has positive correlation

(significant at 1% level) with both bank z-score and σ(ROA), which, to some extent, suggests

that policy uncertainty increases bank risk.

As shown in Table (6), result of EPU remains consistent after controlling for bank

default and the overall risks. This confirms that our results are not driven by banks’ default

risk. As expected, the banks having higher default risk charge higher rates on loans.


5.4 Robustness tests: Controlling for banks’ credit growth

As argued above that economic policy uncertainty can affect businesses, households

and banks alike. In this context, some recent studies specifically focus on banks and found

that economic policy uncertainty lowers bank credit growth (Bordo et al. 2016; He & Niu

2018; Hu & Gong 2018). We control our model with bank credit growth variable to further

confirm that our results are not driven by EPU’s direct impact on banks.

Bordo et al. (2016) and Hu and Gong (2018) measure bank credit growth with the

growth in bank loans. While the He and Niu (2018) measure bank credit growth with the

bank loans to total assets ratio. Following these studies, we control our model for

year-on-year growth in bank gross loans and gross loans to total assets ratio one-by-one. We

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observe that our EPU index exhibits strong negative correlation with both the year-on-year

growth in bank gross loans and gross loans to total assets ratio, confirming the findings of

previous studies that EPU has adverse effect on bank credit growth.

As shown in Table (7), the results of EPU variable remain consistent even after

controlling for bank credit growth variables. This confirms that EPU variable in our analysis

is capturing the marginal effect of policy uncertainty on borrowers’ risk.


5.5 Robustness tests: Election and non-election years

To further confirm that our results are driven by the economic policy uncertainty but

not by some factors causing uncertainty, we use general elections dummy variable to

represent policy uncertainty. General elections are one of the significant political events

entailing the greatest uncertainty towards government future economic policy. Baker et al.

(2016) also show that policy uncertainty rises in elections. If our results are driven by policy

uncertainty, we expect that the sample banks would respond to higher uncertainty of election

years by charging higher prices on loans. We create two more dummy variables, Year after

election and Year before election, to compare the effect of election years to that of the years

following and preceding the election year.

As shown in Table (8), Election variable enters positive and significant indicating that

the uncertainty related with the results of election and government future economic policy

significantly increases the banks’ loan prices in election year. In Model 2, Year after election

variable also enters positively significant, but with lower coefficient then Election variable in

Model 1, showing that uncertainty has decreased after the new government resumed the

office. Usually some policy uncertainty exists in the first year after elections because the new

government still has to take position at various policy issues. In Model 3, Year before

election enters insignificant suggesting that it is the last year and the incumbent government

has already devised all major policies. Results remain same when we add three dummy

variables together in Model 4: Election enters with the highest coefficient and Year before

election with the lowest coefficient and the coefficient of Year after election variable is

between. These results confirm our above results that policy uncertainty boosts banks’ loan

interest rates.

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To get more conservative estimates, we also include EPU variable with election

dummy variables. Now the election dummy variables would capture the marginal effect, if

any, of election related uncertainty. As shown, the results again largely confirm that banks

price election related uncertainty into loan rates.


5.6 Robustness tests: controlling for ICRG political risk

Arguably government economic policy uncertainty can be distinguished from general

political risk. Government economic policy uncertainty is defined as the risk which arises

when the future path of government policy is uncertain. On the other hand, more general

political risk can be due to the factors such as the nature of political institutions (i.e., democracy

vs. autocracy), government instability or internal/external conflicts. To confirm that our EPU

index is capturing the effect of economic policy uncertainty but not the more general form of

political risk, we include government stability, democratic accountability, internal conflict,

external conflict and aggregate political risk variables from International Country Risk Guide

(ICRG) database as control variables on-by-one.

As shown in Table (9), the results of EPU index persist even after controlling for

general political risk factors.


6. Conclusion

In this paper, we examine the impact of government economic policy uncertainty on

banks’ loan pricing. We hypothesis that economic policy uncertainty can boost banks’ loan

prices by increasing the borrowers’ default risk.

Using bank-level data from 17 countries over the period 1998-2012 and measuring

policy uncertainty with the news-based government economic policy uncertainty index (EPU)

of Baker et al (2016), our results show that government economic policy uncertainty has

significant positive association with interest rates on bank gross loans. Specifically, a one

standard deviation increase in EPU leads to 21.84 basis points increase in average interest

rates on bank gross loans.

The main results hold for several robustness tests: Specifically, we observe that the

impact of EPU on banks’ loan pricing remains consistent when we control for the impact of

banks’ own idiosyncratic default risk on loan prices. Further, we also observe a positive impact

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on banks’ loan pricing when we proxy policy uncertainty with general elections, which is a

significant political event reflecting future government policy uncertainty. We also confirm

that our results are driven by the economic policy uncertainty, but not by the more general

political risk by adding control variables such as government stability, democratic

accountability, internal conflict, external conflict and aggregate political risk variables from

International Country Risk Guide (ICRG) database.

Overall our results suggest that government economic policy uncertainty is an

economically important risk factor for bank loans.

Page 17 of 27

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Page 19 of 27

Table 1 Sample distribution

Sr. No. Country Banks Annual observations

1 Australia 21 119

2 Brazil 125 1056

3 Canada 27 162

4 Chile 23 109

5 China 98 577

6 Colombia 21 122

7 France 140 716

8 Germany 1576 7185

9 Hong Kong 28 182

10 Italy 90 385

11 Japan 723 8229

12 Korea, Rep. 17 64

13 Mexico 44 251

14 Netherlands 24 126

15 Russia 475 2093

16 Singapore 9 58

17 Sweden 77 313

Total 3518 21747

Page 20 of 27

Table 2 Variable definitions

Variable Definition Data Source

Dependent variables

Interest income to gross loans ratio Equals annual interest income to gross loans ratio of each bank. Bankscope

database

Main independent variable EPU News-based government economic policy uncertainty index

constructed by Baker et al (2016).

Baker et al

(2016)

Independent control variables

(1) Bank-level

Bank size Bank size is measured as the natural logarithm of annual total assets

of each bank.

Bankscope

database

Return on equity ratio This variable for each bank is measured as the annual net income to

total owners’ equity ratio.

Equity to total assets ratio This variable for each bank is measured as the annual total owners’

equity to bank total assets ratio.

Interest expense to total liabilities

ratio

This variable for each bank is measured as the annual interest

expense on interest bearing bank liabilities to total interest bearing

liabilities ratio.

Loan loss provisions to total assets

ratio

This variable for each bank is measured as the annual loan loss

provisions to bank total assets ratio.

Non-interest expenses to total

assets ratio

This variable for each bank is measured as the annual non-interest

expenses to bank total assets ratio

Operating profit to total assets ratio This variable for each bank is measured as the annual

pre-impairment operating profit to bank total assets ratio.

Loans to deposits ratio This variable for each bank is measured as the annual gross loans

to total customers’ deposits ratio.

(2) Industry-level

Bank concentration Bank concentration is measured as the market share of three largest

banks in terms of total assets (i.e., =the sum of assets of three

largest banks / the sum of assets of all commercial banks). This is an

industry–level variable and is measured at annual frequency for

each country.

Global

financial

development

database,

World Bank

(3) Country-level

Deposit interest rate Deposit interest rate is defined as the rate paid by commercial or

similar banks for demand, time, or savings deposits in a country.

Global

financial

development

database,

World Bank

Lending interest rate Lending rate is defined as the rate which banks usually charge on

short- and medium-term loans to the private sector.

GDP growth rate Equals year-on-year annual GDP growth rate of each country. World

Development

Indicators

database,

World Bank

Inflation Equals annual percentage change in consumer prices in a country.

Developing countries dummy Dummy variable equals to 1 if a country is classified as developing

by the World Bank and 0 otherwise.

Financial crises dummy Dummy variable equals to 1 if a country is in financial crisis in a

year and 0 otherwise.

Laeven and

Valencia

(2013)

Page 21 of 27

Table 3 Summary statistics of main variables

Variables Observations Mean S.D. Min Max

Interest income to gross loans

ratio 21747 7.53 6.98 1.40 44.75

EPU 21747 4.61 0.26 3.63 5.50

EPU without log 21747 103.66 26.59 37.60 244.40

Return on equity ratio 21747 4.17 11.95 -58.22 35.35

Equity to total assets ratio 21747 7.72 6.45 2.06 40.66

Interest expense to total

liabilities ratio 21747 2.97 3.51 0.00 20.71

Deposit interest rate 21747 0.03 0.04 0.00 0.28

Loan loss provisions to total

assets ratio 21747 0.54 0.78 -0.58 4.84

Lending interest rate 21747 0.09 0.11 0.01 0.86

Non-interest expenses to total

assets ratio 21747 3.08 4.40 0.51 33.94

Operating profit to total assets

ratio 21747 1.27 1.60 -1.33 9.30

Bank size 21747 13.92 2.02 9.29 19.92

Loans to deposits ratio 21747 103.03 95.50 16.27 642.54

Banking industry concentration 21747 52.54 17.05 21.84 100.00

Inflation 21747 2.02 3.95 -3.95 85.74

GDP growth rate 21747 2.02 3.11 -7.80 14.78

Developing countries dummy 21747 0.20 0.40 0.00 1.00

Financial crises dummy 21747 0.13 0.33 0.00 1.00

Page 22 of 27

Table 4 Matrix of pair-wise correlations between variables

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)

(1) Interest income to gross

loans ratio 1.00

(2) EPU -0.02 1.00

(3) Return on equity ratio 0.27 -0.06 1.00

(4) Equity to total assets ratio 0.51 -0.05 0.17 1.00

(5) Interest expense to total

liabilities ratio 0.79 -0.02 0.24 0.42 1.00

(6) Deposit interest rate 0.79 0.01 0.25 0.43 0.86 1.00

(7) Loan loss provisions to

total assets ratio 0.35 0.02 -0.21 0.15 0.29 0.25 1.00

(8) Lending interest rate 0.75 0.04 0.22 0.38 0.83 0.96 0.26 1.00

(9) Non-interest expenses to

total assets ratio 0.54 -0.07 0.07 0.48 0.43 0.46 0.19 0.36 1.00

(10) Operating profit to total

assets ratio 0.64 -0.05 0.46 0.53 0.51 0.50 0.45 0.47 0.35 1.00

(11) Bank size -0.32 0.12 -0.01 -0.34 -0.23 -0.19 -0.08 -0.16 -0.34 -0.18 1.00

(12) Loans to deposits ratio 0.36 -0.06 0.15 0.43 0.37 0.34 0.17 0.27 0.43 0.40 -0.23 1.00

(13) Banking industry

concentration -0.01 0.01 0.07 -0.17 0.12 0.04 -0.03 0.08 -0.16 -0.12 -0.09 -0.17 1.00

(14) Inflation 0.59 -0.04 0.24 0.52 0.55 0.55 0.19 0.45 0.50 0.52 -0.28 0.49 -0.22 1.00

(15) GDP growth rate 0.19 -0.15 0.25 0.19 0.16 0.11 0.07 0.10 -0.13 0.31 -0.05 0.18 -0.02 0.27 1.00

(16) Developing countries

dummy 0.64 0.03 0.29 0.57 0.59 0.63 0.25 0.54 0.49 0.61 -0.12 0.48 -0.35 0.74 0.41 1.00

(17) Financial crises dummy -0.04 0.08 -0.15 0.04 -0.08 -0.03 0.06 -0.07 0.19 -0.04 0.05 0.02 -0.38 0.03 -0.27 0.06 1.00

Note: This table reports pair-wise Pearson correlations between variables.

Page 23 of 27

Table 5 Economic policy uncertainty and banks’ loan pricing: main specification

Variables Interest income to gross loans ratio

Model (1) Model (2)

EPU 0.842***

(0.000)

Return on equity ratio 0.009** 0.010**

(0.017) (0.011)

Equity to total assets ratio 0.033*** 0.036***

(0.006) (0.003)

Interest expense to total liabilities ratio 0.582*** 0.583***

(0.000) (0.000)

Deposit interest rate 13.973** 15.606**

(0.042) (0.024)

Loan loss provisions to total assets ratio 0.491*** 0.498***

(0.000) (0.000)

Lending interest rate 12.928*** 12.081***

(0.000) (0.000)

Non-interest expenses to total assets ratio 0.304*** 0.307***

(0.000) (0.000)

Operating profit to total assets ratio 0.838*** 0.830***

(0.000) (0.000)

Bank size -0.300*** -0.292***

(0.000) (0.000)

Loans to deposits ratio -0.007*** -0.007***

(0.000) (0.000)

Banking industry concentration -0.016*** -0.014***

(0.000) (0.000)

Inflation 0.060** 0.061**

(0.042) (0.039)

GDP growth rate 0.126*** 0.106***

(0.000) (0.000)

Developing countries dummy 0.842*** 0.940***

(0.005) (0.002)

Financial crises dummy -0.792*** -0.853***

(0.000) (0.000)

Year dummies Yes Yes

Constant 7.703*** 3.774***

(0.000) (0.000)

Observations 21,747 21,747

R-squared 0.777 0.778

Note: This Table reports the results for the impact of economic policy uncertainty on banks’ loan pricing. Banks’ loan pricing is dependent variable in all Models and is proxied by annual ‘interest income to gross loans ratio’ of each bank. EPU is news-based economic policy uncertainty index of Baker et al. (2016). Others are control variables. Detailed definitions of variables are given in Table 2. The results are estimated with pooled OLS estimator using heteroskedasticity robust standard errors. P-values are given in parenthesis. ***, **,* represent statistical significance at 1%, 5%, and 10% levels, respectively.

Page 24 of 27

Table 6 Economic policy uncertainty and banks’ loan pricing: controlling for banks’ own idiosyncratic risk


Model (1) Model (2) Model (3)

EPU 0.721*** 0.726*** 0.717***

(0.001) (0.001) (0.001)

Return on equity ratio 0.010** 0.012*** 0.012**

(0.031) (0.009) (0.014)

Equity to total assets ratio 0.023 0.032* 0.019

(0.156) (0.066) (0.248)

Interest expense to total liabilities ratio 0.556*** 0.551*** 0.547***

(0.000) (0.000) (0.000)

Deposit interest rate 28.434*** 29.166*** 30.269***

(0.001) (0.001) (0.000)

Loan loss provisions to total assets ratio 0.614*** 0.615*** 0.613***

(0.000) (0.000) (0.000)

Lending interest rate 9.847*** 9.532*** 9.032***

(0.000) (0.000) (0.000)

Non-interest expenses to total assets ratio 0.274*** 0.271*** 0.262***

(0.000) (0.000) (0.000)

Operating profit to total assets ratio 0.840*** 0.842*** 0.835***

(0.000) (0.000) (0.000)

Bank size -0.263*** -0.252*** -0.258***

(0.000) (0.000) (0.000)

Loans to deposits ratio -0.007*** -0.007*** -0.007***

(0.000) (0.000) (0.000)

Banking industry concentration -0.010*** -0.010*** -0.009***

(0.000) (0.000) (0.002)

Inflation 0.103*** 0.098*** 0.096***

(0.001) (0.002) (0.002)

GDP growth rate 0.093*** 0.088*** 0.089***

(0.004) (0.007) (0.006)

Developing countries dummy 0.567* 0.435 0.505

(0.066) (0.158) (0.102)

Financial crises dummy -0.369*** -0.399*** -0.406***

(0.003) (0.001) (0.001)

Z-score 0.140***

(0.000)

σ(ROA) 0.318**

(0.035)

Year dummies Yes Yes Yes

Constant 3.146*** 3.488*** 3.062***

(0.002) (0.000) (0.002)

Observations 14,483 14,483 14,483

R-squared 0.792 0.792 0.792


Page 25 of 27

Table 7 Economic policy uncertainty and banks’ loan pricing: controlling for EPU’s effect on banks’ credit growth


Model (1) Model (2) Model (3)

EPU 0.889*** 0.859*** 1.049***

(0.000) (0.000) (0.000)

Return on equity ratio 0.009** 0.010** 0.009**

(0.025) (0.014) (0.015)

Equity to total assets ratio 0.037*** 0.039*** 0.013

(0.001) (0.001) (0.241)

Interest expense to total liabilities ratio 0.588*** 0.581*** 0.610***

(0.000) (0.000) (0.000)

Deposit interest rate 16.318** 18.091*** 17.171**

(0.016) (0.008) (0.013)

Loan loss provisions to total assets ratio 0.492*** 0.531*** 0.664***

(0.000) (0.000) (0.000)

Lending interest rate 12.246*** 11.868*** 9.723***

(0.000) (0.000) (0.000)

Non-interest expenses to total assets ratio 0.322*** 0.315*** 0.306***

(0.000) (0.000) (0.000)

Operating profit to total assets ratio 0.809*** 0.798*** 0.882***

(0.000) (0.000) (0.000)

Bank size -0.282*** -0.280*** -0.287***

(0.000) (0.000) (0.000)

Loans to deposits ratio -0.007*** -0.007*** -0.004***

(0.000) (0.000) (0.000)

Banking industry concentration -0.015*** -0.015*** -0.007***

(0.000) (0.000) (0.004)

Inflation 0.046 0.043 0.062**

(0.113) (0.136) (0.029)

GDP growth rate 0.102*** 0.104*** 0.113***

(0.000) (0.000) (0.000)

Developing countries dummy 0.696** 0.585** 0.680**

(0.017) (0.046) (0.022)

Financial crises dummy -0.810*** -0.807*** -0.619***

(0.000) (0.000) (0.000)

Growth in gross loans 0.012***

(0.000)

Growth in total assets 0.015***

(0.000)

Gross loans to total assets ratio -0.042***

(0.000)

Year dummies Yes Yes Yes

Constant 3.377*** 3.426*** 4.579***

(0.000) (0.000) (0.000)

Observations 21,626 21,656 21,747

R-squared 0.785 0.781 0.785


Page 26 of 27

Table 8 Economic policy uncertainty and banks’ loan pricing: elections as alternative proxy of policy uncertainty


Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7) Model (8)

EPU 0.891*** 0.829*** 0.870*** 0.820***

(0.000) (0.000) (0.000) (0.000)

Return on equity ratio 0.008** 0.009** 0.009** 0.008** 0.009** 0.009** 0.010** 0.009** (0.035) (0.025) (0.021) (0.037) (0.025) (0.017) (0.014) (0.026)

Equity to total assets ratio 0.032*** 0.030** 0.030** 0.033*** 0.035*** 0.033*** 0.033*** 0.036***

(0.009) (0.015) (0.015) (0.007) (0.004) (0.008) (0.007) (0.003) Interest expense to total

liabilities ratio

0.565*** 0.574*** 0.572*** 0.564*** 0.567*** 0.575*** 0.574*** 0.566***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Deposit interest rate 14.374** 12.413* 12.046* 15.722** 16.101** 13.909* 13.673* 17.182**

(0.045) (0.081) (0.091) (0.029) (0.025) (0.051) (0.056) (0.017) Loan loss provisions to total

assets ratio

0.482*** 0.478*** 0.491*** 0.480*** 0.490*** 0.488*** 0.499*** 0.489***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Lending interest rate 12.962*** 13.591*** 13.681*** 12.654*** 12.059*** 12.780*** 12.817*** 11.853***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Non-interest expenses to total assets ratio

0.308*** 0.307*** 0.302*** 0.310*** 0.312*** 0.310*** 0.306*** 0.313***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Operating profit to total assets ratio

0.834*** 0.837*** 0.836*** 0.831*** 0.825*** 0.829*** 0.828*** 0.823***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Bank size -0.292*** -0.299*** -0.299*** -0.288*** -0.282*** -0.290*** -0.290*** -0.279*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Loans to deposits ratio -0.007*** -0.007*** -0.007*** -0.007*** -0.007*** -0.007*** -0.007*** -0.007***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Banking industry concentration -0.015*** -0.014*** -0.015*** -0.014*** -0.013*** -0.013*** -0.013*** -0.013***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Inflation 0.067** 0.067** 0.068** 0.066** 0.069** 0.069** 0.070** 0.067** (0.034) (0.031) (0.028) (0.037) (0.028) (0.025) (0.023) (0.031)

GDP growth rate 0.117*** 0.133*** 0.128*** 0.135*** 0.095*** 0.111*** 0.106*** 0.113***

(0.000) (0.000) (0.000) (0.000) (0.001) (0.000) (0.000) (0.000)

Developing countries dummy 1.018*** 0.857*** 0.881*** 1.023*** 1.118*** 0.950*** 0.976*** 1.116***

(0.001) (0.006) (0.005) (0.001) (0.000) (0.002) (0.002) (0.000)

Financial crises dummy -0.878*** -0.722*** -0.768*** -0.848*** -0.943*** -0.785*** -0.829*** -0.912*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Election 0.461*** 0.535*** 0.472*** 0.538***

(0.000) (0.000) (0.000) (0.000) Year after election 0.258*** 0.408*** 0.220*** 0.362***

(0.000) (0.000) (0.002) (0.000)

Year before election 0.053 0.278*** 0.050 0.258*** (0.475) (0.001) (0.500) (0.002)

Year dummies Yes Yes Yes Yes Yes Yes Yes Yes

Constant 7.179*** 7.596*** 7.616*** 6.800*** 2.996*** 3.724*** 3.547*** 2.984*** (0.000) (0.000) (0.000) (0.000) (0.001) (0.000) (0.000) (0.001)

Observations 21,565 21,564 21,564 21,561 21,565 21,564 21,564 21,561 R-squared 0.780 0.779 0.779 0.780 0.780 0.780 0.780 0.780


Page 27 of 27

Table 9 Economic policy uncertainty and banks’ loan pricing: controlling for ICRG political risk variables


Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7)

EPU 0.853*** 0.871*** 0.805*** 0.700*** 0.323* 0.873*** 0.815***

(0.000) (0.000) (0.000) (0.000) (0.078) (0.000) (0.000)

Return on equity ratio 0.010** 0.010** 0.010** 0.011*** 0.010** 0.008** 0.010*** (0.013) (0.010) (0.011) (0.006) (0.012) (0.040) (0.010)

Equity to total assets ratio 0.036*** 0.036*** 0.036*** 0.036*** 0.044*** 0.041*** 0.039***

(0.003) (0.003) (0.003) (0.003) (0.000) (0.001) (0.001) Interest expense to total liabilities

ratio

0.584*** 0.580*** 0.585*** 0.569*** 0.571*** 0.540*** 0.584***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Deposit interest rate 15.349** 17.291** 16.712** 26.882*** 24.300*** 15.200** 16.148**

(0.027) (0.011) (0.020) (0.000) (0.001) (0.026) (0.019) Loan loss provisions to total assets

ratio

0.497*** 0.497*** 0.500*** 0.504*** 0.522*** 0.510*** 0.501***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Lending interest rate 12.226*** 11.606*** 11.730*** 8.243*** 10.749*** 12.539*** 12.181***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Non-interest expenses to total assets ratio

0.306*** 0.309*** 0.307*** 0.316*** 0.300*** 0.298*** 0.307***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Operating profit to total assets ratio 0.831*** 0.827*** 0.831*** 0.816*** 0.807*** 0.820*** 0.829*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Bank size -0.292*** -0.291*** -0.292*** -0.293*** -0.258*** -0.275*** -0.285***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Loans to deposits ratio -0.007*** -0.007*** -0.007*** -0.007*** -0.007*** -0.007*** -0.007***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Banking industry concentration -0.014*** -0.015*** -0.015*** -0.030*** -0.018*** -0.021*** -0.020*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Inflation 0.059** 0.062** 0.063** 0.070** 0.025 0.014 0.069**

(0.045) (0.034) (0.039) (0.021) (0.360) (0.627) (0.018) GDP growth rate 0.100*** 0.121*** 0.108*** 0.088*** 0.158*** 0.098*** 0.115***

(0.001) (0.000) (0.000) (0.001) (0.000) (0.000) (0.000)

Developing countries dummy 0.917*** 0.972*** 0.964*** 1.007*** 2.171*** 0.342 1.415***

(0.002) (0.001) (0.001) (0.001) (0.000) (0.209) (0.000)

Financial crises dummy -0.869*** -0.829*** -0.873*** -0.955*** -1.126*** -0.677*** -0.813***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Government stability 0.029

(0.517)

Democratic Accountability 0.072 (0.220)

Internal conflict 0.037

(0.557) External conflict 0.568***

(0.000)

Military in politics 1.006*** (0.000)

Ethnic tensions -0.653***

(0.000) Political risk 0.043***

(0.000)

Year dummies Yes Yes Yes Yes Yes Yes Yes

Constant 3.452*** 3.280*** 3.524*** -1.239 -0.127 7.209*** 0.435

(0.000) (0.001) (0.000) (0.342) (0.890) (0.000) (0.726)

Observations 21,747 21,747 21,747 21,747 21,747 21,747 21,747

R-squared 0.778 0.778 0.778 0.779 0.780 0.780 0.778


Documents

Economic policy uncertainty and banks’ loan pricingcirforum.org/2019forum_papers/CIRF2019_paper_151.pdfEconomic policy uncertainty and banks’ loan pricing Abstract Using news-based