Economic policy uncertainty and corporate cash policy: International evidence

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Economic policy uncertainty and corporate cash policy: International evidence Xiao Li 1 School of Accountancy, Central University of Finance and Economics, Beijing, China

a r t i c l e

i n f o

Article history: Available online xxxx JEL classification: M41 G30 G15 Keywords: Economic policy uncertainty Cash policy Precautionary saving motives Financial constraints

a b s t r a c t I investigate the influences of economic policy uncertainty on corporate cash policy. The findings show that there is a positive association between economic policy uncertainty and cash holdings, as well as the propensity to save cash out of operating cash flow. Further analyses suggests that economic policy uncertainty affects corporate cash policy by influencing firms’ precautionary saving motives and the effect is larger when firms have difficulty in raising external finance. Using the new index developed by Baker et al. (2016), I extend the literature on economic policy uncertainty and show that it is an important macro-level factor in influencing corporate cash policy. Ó 2019 Elsevier Inc. All rights reserved.

1. Introduction Liquidity management, i.e., corporate cash policy, is key to a firm’s financial flexibility (Graham and Harvey, 2001; Campello et al., 2010; Cheng et al., 2018). The existing literature has emphasized the critical role of corporate cash to other real decisions, such as investments and dividend payouts (Pinkowitz et al., 2006; Duchin et al., 2010; Fresard and Salva, 2010; Pinkowitz et al., 2016). While these studies focus on how firm characteristics affect cash holdings, little attention has been paid to the effect of the macro environment on cash policy. In this study, I examine the effect of economic policy uncertainty (EPU) on cash holdings, using a novel dataset developed by Baker et al. (2016). This new index captures the likelihood that future economic and political policies differ from those in the current period. In particular, economic policy uncertainty captures the portion of the overall uncertainty in economics that is attributable to political and regulatory systems (Drobetz et al., 2018), and reflects information about concurrent and future cash flows, financing and investment opportunities, which are relevant to liquidity management (Chen et al., 2007; Fresard, 2012; Kusnadi, 2015; Xu et al., 2016; Duong et al., 2017). I hypothesize that economic policy uncertainty affects corporate cash holdings through the precautionary savings channel. When policy uncertainty is high, firms are concerned that future cash flows will be insufficient to seize investment opportunities. This increases the propensity to save cash out of cash flows in the current period. Both theoretical and

1 I would like to thank Marco Trombetta (Editor), two anonymous referees, Yunsen Chen, Qingyuan Li, Shangkun Liang, Yunbiao Ma, Yanchao Wang, Xi Wu, Chun Yuan, Bing Zhu and workshop participants from Central University of Finance and Economics. I acknowledge financial support from the National Natural Science Foundation of China (#71802205). E-mail address: [email protected]

https://doi.org/10.1016/j.jaccpubpol.2019.106694 0278-4254/Ó 2019 Elsevier Inc. All rights reserved.

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empirical studies (Minton and Schrand, 1999; Almeida et al., 2004; Han and Qiu, 2007; Bates et al., 2009; Harford et al., 2014; Pinkowitz et al., 2016; Nguyen and Phan, 2017) provide results consistent with the precautionary saving motive. I explore the research question in a cross-country setting. Specifically, the EPU index by Baker et al. (2016) is a countrylevel media-based index, which reflects the frequency of articles in leading newspapers that contain terms describing uncertainty in economics. Based on Baker et al. (2016), the EPU index captures policy uncertainty from various aspects in an economy based on news, tax policies, fiscal policies and monetary policies. From its nature, the index is highly related to other uncertainty measures (e.g., national elections and changes in government policies), but is more comprehensive and more exogenous to firm operations (Chan et al., 2017). An important benefit of the cross-country setting is that it allows me to investigate whether and how both country-level and firm-level variances in financial conditions affect the precautionary savings – cash holdings relationship that policy uncertainty predicts. On average, I find a significant positive relationship between economic policy uncertainty and cash holdings, and a positive relationship between economic policy uncertainty and the propensity to save cash out of cash flow. The evidence is consistent with my prediction that firms tend to hold more cash when precautionary saving demands are higher. The results remain unchanged in various robustness checks, and after controlling other sources of uncertainty. I further explore the roles of cross-country and cross-firm variations in precautionary saving motives, from the perspectives of financial constraints and free cash flow problem. Prior studies find that precautionary saving motives are higher when firms have difficulty in obtaining external finance (Han and Qiu, 2007; Campello et al., 2010). Hence I predict that the effect of the EPU index on cash policy should be more pronounced in firms with difficulty in accessing financing. My findings support the argument. The principal contribution of this paper is to extend the literature on economic policy uncertainty and the use of the new measure developed by Baker et al. (2016). Following prior studies such as Gulen and Ion (2016) and Nagar et al. (2018), which show that economic policy uncertainty affects firm investment and disclosure decisions, this paper introduces the influence of economic policy uncertainty to cash holdings. Second, the paper contributes to the cash management research in international setting. Moreover, I show that the positive relation between economic policy uncertainty and cash holdings is greatly shaped by firms’ ability in accessing external finance, and is more significant in firms with high precautionary saving demand. The remainder of the paper is organized as follows. Section 2 summarizes the related literature and develops the hypothesis. Section 3 discusses the empirical design, sample and descriptive statistics. Section 4 presents the results for the main tests as well as a battery of robustness tests. The results of the cross-sectional variation in the relation between the EPU index and cash policy, and the results of additional analysis are presented in Sections 5 and 6, respectively. Finally, Section 7 concludes.

2. Literature review and hypothesis development 2.1. Economic policy uncertainty Economic policy uncertainty mainly refers to the likelihood that future policies will differ from current policies, and how these changes could affect macro- and micro-level economic activities (Baker et al., 2016). There are various sources of uncertainty in an economy caused by policy changes. For example, according to Bloomberg, the World Bank cut its Latin America 2019 growth forecast from 1.6% to 0.9% because of the downward economic spiral in Venezuela and the mixed signals in policy making indicated by Mexico’s president.2 Such uncertainty is intensively and continually captured by news agencies and forecast reports. Prior studies also use measures such as elections (Callander, 2008; Lee, 2018; Lee et al., 2018), major political and economic shocks (Bloom, 2007), financial or banking crises (Stock and Watson, 2012), stock price volatility (Kang and Ratti, 2013), macroeconomic variables (e.g., inflation) and interest rates (Baum et al., 2006) as policy uncertainty measures. The EPU index from Baker et al. (2016) is highly related to the established measures of economic and policy uncertainty, yet it is not biased by the political views of any of the newspapers included in the index, and the index is more comprehensive and exogenous to firms due to its news-based nature (Baker et al., 2016; Drobetz et al., 2018). For example, as the major source of uncertainty, the occurrence of a financial crisis in a country can be captured by the EPU index. However, the EPU index also captures changes in government policies during the non-crisis period. Compared with measures using second moments of firm-level outcomes such as volatility of returns, which are likely to be outcomes not proxies for economic policy uncertainty at the aggregate level, the EPU index properly captures economic policy uncertainty rather than more general economic and political effects and consequences. Prior studies find that uncertainty in economics and politics affects market and investor uncertainty, driving different market participants’ behaviors (Verrecchia, 2001; Nagar et al., 2018). For example, Froot et al. (1993) suggest that volatility in market conditions could lead to an increase in interest rates, putting firms refinancing at risk. Pastor and Veronesi (2012, 2013) find that firms take policy uncertainty as exogenous and make decisions based on uncertainty. Political uncertainty commands a risk premium whose magnitude is greater during weak economic condition periods, and makes stocks more volatile and more correlated. Gao and Huang (2016) find that hedge funds earn high returns on policy-sensitivity stocks. Loh and Stulz (2018) find that in a high uncertainty period, analyst revisions have a larger stock-price impact, earnings 2

htps://www.bloomberg.com/news/articles/2019–04-04/world-bank-cuts-latin-america-growth-outlook-on-fresh-headwinds.

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forecast errors per unit of uncertainty fall, and analyst reports are more frequent and longer. Using the international setting, Graham and Leary (2017) find that macro factors could affect cash flow volatility and explain the trend of increasing corporate cash in recent years. In particular, recent studies show that economic policy uncertainty strongly affects firms’ decisions. Gulen and Ion (2016) find that there is a negative relationship between economic policy uncertainty and investment, and that this relation is more pronounced in firms with a greater level of investment irreversibility and in firms relying more on government spending. An et al. (2016) find that political uncertainty negatively affects corporate investment, and that this relation is exacerbated in politically affiliated firms. Bonaime et al. (2018) find that economic policy uncertainty is negatively associated with M&A activities at both the macro- and micro-levels. 2.2. Economic policy uncertainty and corporate cash policy Based on the above discussion, I predict that economic policy uncertainty affects cash holdings through precautionary saving motives. According to the motives, firms hold more cash when they expect difficulty in obtaining future financing (Dudley and Zhang, 2016). During high uncertainty periods, external financing may be difficult because of increased financial frictions and cash flow risks. Anticipating the potential shortfalls in cash flows, firms have incentives to hold and save more cash in order to fund valuable investment opportunities (Almeida et al., 2004; Duchin, 2010; Duchin et al., 2010). Moreover, external financing is costly and difficult to obtain when information asymmetry is high (Myers and Majluf, 1984). With worsened information environment during high uncertainty periods (Xu et al., 2016; Nagar et al., 2018), firms would rather rely on less expensive internal capital to fund investment, increasing firms’ demand for cash. In addition, increase in cash holdings should be more pronounced in firms who have difficulty and high costs in obtaining external finance, i.e. in firms with high precautionary saving demand. In sum, economic policy uncertainty should be positively associated with cash holdings and cash savings. Taken together, the above discussion leads to the following hypothesis: H1. There is a positive association between economic policy uncertainty and corporate cash policy.

3. Sample, variables and model 3.1. Sample I use cross-country setting to explore how economic policy uncertainty affects corporate cash policy. There are three reasons to use international data to investigate the research question. First, cash policy is important not only in the United States but also very important in other countries. In particular, liquidity management is very important in countries where the capital market is undeveloped or unstable. Second, compared with the United States, where the institutional background is homogenous, there are large variations in institutional backgrounds at the cross-country setting, which are important in driving cash policy. In other words, compared with studies that examine how firm-level factors influence cash policy, I am further interested in how fundamental factors at the country-level affect cash management. Third, the EPU index exhibits a large variation across countries. Duong et al. (2017) find that EPU affects cash holdings in the United States. However, this conclusion may not be applied to other countries. Hence, a cross-country sample containing 22 countries is used here to investigate the research question. I start with all of the countries included in WorldScope. First, those countries that are not contained in Baker et al. (2016) for the EPU index are excluded. In addition, financial firms (SIC code 6000-6999) and utility firms (SIC code 4900-4949) are dropped since they are heavily regulated. Firms with missing financial data and those with total assets less than one million US dollars are also deleted. The final sample includes 272,623 observations from 22 countries from 1989 to 2016. Panel A, in Table 1 presents the sample distributions by country. In the sample, the largest three countries are the United States (68,962 firm-years), Japan (61,631 firm-years), and the United Kingdom (16,691 firm-years). The smallest three countries, in terms of the number of observations, are Colombia (442 firm-years), Ireland (1172 firm-years) and the Netherlands (1400 firm-years). Panel B presents the sample distribution by year. There is an increasing trend in number of observations from 1989 to 2016. The distribution is generally comparable to those provided in prior studies. 3.2. Variables Following prior literature (Opler et al., 1999; Dittmar and Mahrt-Smith, 2007; Haushalter et al., 2007; Klasa et al., 2009; Fresard, 2010; Kusnadi, 2015), the dependent variables used in the regression are liquidity management variables. More specifically, in the cash holdings analysis, the dependent variable is cash holdings Cash, which is defined as the natural logarithm of cash and cash equivalents divided by lagged total assets. In the cash savings regressions, the dependent variable Dcash is the annual change in cash and cash equivalents divided by lagged total assets. The main variable of interest is EPU, the economic policy uncertainty index, from Baker et al. (2016). For international countries, EPU is a media-based measure constructed mainly using newspaper articles regarding policy uncertainty in the Please cite this article as: X. Li, Economic policy uncertainty and corporate cash policy: International evidence, J. Account. Public Policy, https://doi.org/10.1016/j.jaccpubpol.2019.106694

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Table 1 Sample distribution. Panel A. Sample distribution by country Country

No. of firm-years

Percent

Economic policy uncertainty

Australia Brazil Canada Chile China Colombia France Germany Greece Hong Kong India Ireland Italy Japan South Korea The Netherlands Russia Singapore Spain Sweden United Kingdom United States Total

8490 3880 13,362 2507 15,443 442 11,848 9766 2797 9252 14,050 1172 3050 61,631 16,617 1400 2342 3008 1430 4483 16,691 68,962 272,623

3.11 1.42 4.9 0.92 5.66 0.16 4.35 3.58 1.03 3.39 5.15 0.43 1.12 22.61 6.1 0.51 0.86 1.1 0.52 1.64 6.12 25.3 100

101.697 111.701 121.001 98.001 136.559 96.183 121.618 111.219 103.99 123.266 118.275 95.999 107.74 103.927 120.078 96.054 126.585 105.023 106.249 94.584 120.587 104.039

Panel B. Sample distribution by year Year

No. of firm-years

Percent

Economic policy uncertainty

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Total

1985 3059 3468 3903 4389 4738 5772 6253 7716 8301 9656 9829 9719 10,590 11,738 12,397 12,388 13,287 14,277 12,842 13,265 14,320 13,990 15,229 15,249 10,817 11,241 12,205 272,623

0.73 1.12 1.27 1.43 1.61 1.74 2.12 2.29 2.83 3.04 3.54 3.61 3.56 3.88 4.31 4.55 4.54 4.87 5.24 4.71 4.87 5.25 5.13 5.59 5.59 3.97 4.12 4.48 100

88.71 109.4 103.682 106.869 101.422 90.9 89.596 80.912 86.864 113.009 77.846 79.668 116.082 108.068 110.346 86.256 72.343 71.497 77.515 137.66 124.953 138.11 168.394 182.476 128.877 116.099 142.266 195.472

Note: Table 1 presents the sample distribution. Panel A provides a breakdown of sample firms by country. Panel B presents the sample’s composition by firm fiscal year. Non-log format of EPU index from Baker et al. (2016) is provided in the table.

large and influential newspapers in each country.3 The website by Baker, Bloom and Davis provides the monthly EPU index for each country.4 Since the financial data is on a yearly basis, the monthly data is transformed into annual data using the annual mean of the EPU index for each country. The logarithm of the annual mean of the EPU index is then taken to overcome concern 3 Different from for international countries, the EPU index for the United States is based on three components: the media-based component, tax code component and the economic forecast component. 4 www.policyuncertainty.com.

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that the extreme values in certain years could drive the results. The independent variable, EPU, thus captures the uncertainty in each country in each year. Both the firm-level and country-level control variables related to cash are included in the regressions. Size (the natural logarithm of total assets in millions of US dollars), Cash flow from operations (cash flow from operations divided by lagged total assets), Tobin’s Q, Leverage (total leverage divided by total assets), Industry cash flow volatility (the annual standard deviations of cash flow from operations in each industry), Noncash working capital (non-cash working capital divided by lagged total assets), R&D (R&D expenditures divided by net sales), Acquisitions (net assets acquired divided by lagged total assets), Capital expenditure (capital expenditures divided by lagged total assets), and Dividends(cash dividend payouts divided by lagged total assets) are controlled for. GDP, which is the natural logarithm of gross domestic product per capita for each country year, is also included to control for factors related to each country’s economy and wealth. Table 2 presents the descriptive statistics and variable correlations. Panel A presents the summary statistics of both the dependent and independent variables. The mean ratio for Cash is
2.496, with a median ratio of
2.258, indicating that cash is a critical portion of total assets internationally. The mean ratio of Dcash is 0.015, with a median ratio of 0.004. The mean ratio of EPU is 4.649, with a median of 4.657. The mean for Size is 5.694, suggesting that the sample covers both large firms and small firms. The descriptive statistics for both the dependent variables and independent variables are comparable to those provided in prior literature. Panel B provides the variable correlations. Cash is positively related to EPU, Cash flow from operations, Tobin’s Q, Industry cash flow volatility, R&D and GDP, while negatively related to Size, Noncash working capital, Leverage, Capital expenditure, Acquisitions and Dividend. Dcash is positively related to Cash flow from operations, Tobin’s Q, Industry cash flow volatility and R&D, while negatively related to the EPU index, Size, Noncash working capital, Leverage, Capital expenditure, Acquisitions, Dividend and GDP. 3.3. Model specification. I use the following two models to examine how economic policy uncertainty affects corporate cash policy:

Cashit ¼ a0 þ a1 EPU it þ Controls þ Country F:E: þ Industry F:E: þ Year F:E: þ e

ð1Þ

DCashit ¼ b0 þ b1 EPU it þ b2 Cash flow from operationsit þ b3 EPU it  Cash flow from operationsit þ Controls þ Country F:E: þ Industry F:E: þ Year F:E: þ e

ð2Þ

where i and t are the indicators for firm and year, respectively. Eq. (1) examines how economic policy uncertainty affects corporate cash holdings. The dependent variable is Cash, which is the natural logarithm of cash holdings. Therefore, a1 captures the association between economic policy uncertainty and cash holdings. Eq. (2) examines the effect of economic policy uncertainty on the propensity to save cash out of cash flow. The coefficient on the interaction term, b3, reflects how firms’ propensity to save cash changes with economic policy uncertainty. Control variables are defined in Section 3.2. All firm-level variables are winsorized by top and bottom 1%. Country, year and industry fixed effects are included in all of the regressions to control for characteristics that are invariant within the country, year and industry. The standard errors are heteroskedasticity-robust and clustered at the country level.

4. Economic policy uncertainty and corporate cash policy 4.1. Main analysis Table 3 presents the results for the main analysis as specified in Eqs. (1) and (2). In column (1), the coefficient on EPU is positive and statistically significant at the 1% level (0.030, t-statistic = 3.62), suggesting that during a period of high uncertainty firms tend to increase their cash holdings. The results are consistent with H1, which holds that corporate cash holdings increase in a high uncertainty period. In column (2), the dependent variable is Dcash, and there is a significant positive coefficient on EPU*Cash flow from operations (0.031, t-statistic = 3.35). This result indicates that firms save more cash from operating cash flows during a high uncertainty period, and that the higher the uncertainty, the greater the amount of cash that firms save. The evidence is consistent with the main hypothesis in that the cash savings aspect of liquidity management is influenced by economic policy uncertainty. When uncertainty is high, firms are concerned with volatile cash inflows and they need to save cash from the current period for future investments, so a positive relation between cash savings and EPU is observed. The signs of the coefficients on the control variables are generally consistent with those in prior literature (Opler et al., 1999; Almeida et al., 2004). Cash holdings are positively related to Cash flow from operations, Tobin’s Q, Industry cash flow volatility and R&D, and negatively related to Size, Leverage, Noncash working capital, Acquisitions, Capital expenditure, Dividend and GDP. Cash savings are negatively related to Size, Leverage, Industry cash flow volatility, Noncash working capital, Acquisitions, Capital expenditure, Dividend and GDP, while positively related to Cash flow from operations, Tobin’s Q and R&D. Please cite this article as: X. Li, Economic policy uncertainty and corporate cash policy: International evidence, J. Account. Public Policy, https://doi.org/10.1016/j.jaccpubpol.2019.106694

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Table 2 Descriptive statistics. Panel A. Summary statistics Variable

Mean

Median

Std

P25

P75

Cash Dcash EPU Cash flow from operations Tobin’s Q Size Noncash working capital Leverage Industry cash flow volatility Capital expenditure Acquisitions R&D Dividend GDP


2.496 0.015 4.649 0.091 1.556 5.694 0.043 0.224 0.047 0.054 0.011 0.015 0.664 10.067


2.258 0.004 4.657 0.076 1.204 5.566 0.033 0.202 0.036 0.036 0 0 1 10.452

1.334 0.084 0.348 0.066 1.119 1.908 0.177 0.186 0.034 0.059 0.039 0.037 0.472 1.012


3.217
0.017 4.396 0.045 0.94 4.395
0.063 0.056 0.023 0.014 0 0 0 10.277


1.54 0.037 4.852 0.119 1.722 6.899 0.15 0.349 0.061 0.071 0 0.009 1 10.541

Panel B. Variable correlation (1) (2) (1) (2) (3) (4)

Cash 1 Dcash EPU Cash flow from operations (5) Tobin’s Q (6) Size (7) Noncash working capital (8) Leverage (9) Industry cash flow volatility (10) Capital expenditure (11) Acquisitions (12) R&D (13) Dividend (14) GDP

(3)

(4)

0.312 0.055 0.1 1
0.019 0.171 1
0.041 1

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

0.194 0.192
0.093 0.422


0.105
0.067 0.044
0.128


0.091
0.068
0.039 0.035


0.372
0.108
0.014
0.232

0.1 0.084 0.117 0.239


0.166
0.079
0.042 0.231


0.097
0.071
0.059 0.025

0.24 0.073 0.008 0.08

0.006
0.095
0.003
0.05

0.064
0.006
0.078 0.035

1


0.076
0.026
0.166 1
0.161 0.23 1
0.291 1

0.179
0.156 0.006
0.16 1

0.075 0.034
0.122 0.099 0.037

0.076 0.074 0.002 0.056 0.13

0.237
0.015 0.07
0.189 0.225


0.114 0.311
0.007
0.022
0.303

0.011 0.082
0.001
0.067 0.161

1


0.031
0.069
0.042 1 0.041
0.058 1
0.16 1


0.096 0.108 0.119
0.063 1

Note: Table 2 presents the variable descriptive statistics. Panel A presents the summary statistics. Panel B presents the Pearson correlations for the full sample. See the appendix for variable definitions.

In summary, and in line with my predictions, firms hold and save more cash when economic policy uncertainty is high. This is consistent with the precautionary saving demand mechanism, where volatility in cash flows and increased financing costs push firms to reserve cash for future investments.5 4.2. Robustness tests6 4.2.1. Alternative regression methods I test the robustness of main hypothesis using various tests. The first examination is whether the results are robust to alternative regression methods. Since there is a great variation in the number of observations in each country, as presented in Table 1, following Kusnadi (2015) the weighted least squares (WLS) approach is used, where the coefficients of all of the regression variables are weighted by the number of observations in each country. The results are presented in column (1) of Table 4, Panel A. The results continue to show a positive relation between EPU and cash holdings (0.083, t = 3.33). The FamaMacbeth regression method is then used to adjust cross-sectional variations across years. The results reported in column (2) of Table 4, Panel A remain unchanged. Hence, the previous results are not driven by the variation in countries and years. I then use different fixed effects and clustering methods to re-estimate Eq. (1). To control for omitted variables that are invariant across firms and years, country, industry and year fixed effects are replaced with firm and year fixed effects. I then allow standard errors to be clustered at either the country-year level or the firm level. The results remain unchanged. Taken 5 In untabulated supplementary test, I investigate whether there is a change in precautionary saving demand during high uncertainty period. Precautionary saving demand is defined following Almeida (2011), Duchin (2010) and Cheng et al. (2018), using the standard deviation of industry median operating cash flow, the standard deviation of industry investment opportunities proxied by Tobin’s Q, and the negative of the correlation between industry median operating cash flow and industry median Tobin’s Q. A positive association is observed. The results are consistent with the main evidence. 6 For ease of reporting, in the following robustness tests, additional tests, and cross-sectional analysis, the results for Eq. (1) are tabulated. The results for Eq. (2) remain consistent and will be available upon requests.

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X. Li / J. Account. Public Policy xxx (xxxx) xxx Table 3 Main results.

EPU Cash flow from operations

Cash (1)

Dcash (2)

0.030*** (3.62) 1.076*** (8.01)


0.011*** (
4.09) 0.020 (0.19) 0.031*** (3.35) 0.010*** (8.20)
0.000*** (
3.23)
0.031*** (
11.38)
0.051** (
2.30)
0.057*** (
13.67) 0.022* (1.78)
0.195*** (
9.93)
0.205*** (
9.66)
0.013*** (
5.36)
0.011 (
1.06) 0.200* (1.66) Yes Yes 272,623 0.09343

EPU* Cash flow from operations Tobin’s Q Size Leverage Industry cash flow volatility Noncash working capital R&D Acquisitions Capital expenditure Dividend GDP Intercept Country, industry, and year FE Cluster at country N Adj R2

0.135*** (10.02)
0.022*** (
2.64)
2.160*** (
12.71) 3.254*** (5.45)
1.365*** (
11.02) 4.872*** (10.36)
1.583*** (
7.58)
1.975*** (
5.86)
0.013 (
0.21)
0.134 (
1.10)
1.262 (
0.87) Yes Yes 272,623 0.3644

Note: Table 3 presents the regression results of the impact of economic policy uncertainty on cash policy. Column (1) represents the estimated coefficients from Eq. (1): Cash = a0 + a1EPU + Controls + Country F.E. + Industry F.E. + Year F.E. + e; Column (2) presents the estimated coefficients from Eq. (2): DCash = b0 + b1EPU + b2Cash flow from operations + b3EPU*Cash flow from operations + Controls + Country F.E. + Industry F.E. + Year F.E.+e. *, ** and *** indicate statistical significance at the 0.1, 0.05 and 0.01 levels, respectively. Country, industry and year fixed effects (FE) are included in the model. The coefficient estimates and t-statistics are reported based on robust standard errors clustered by country. See the appendix for variable definitions.

together, the main results hold when different regression methods other than the OLS method are used to estimate the equations.

4.2.2. Alternative samples and the EPU measure I next check whether the main findings are sensitive to alternative sample compositions. First, the sample period includes the financial crisis period, during which uncertainty is extremely high. To mitigate the concern that uncertainty from the financial crisis drives the main results, data from the years 2007–2008 are dropped and the equation is re-estimated. The results are reported in the first column of Table 4, Panel B. The results show a significantly positive relation between EPU and cash holdings (0.036, t = 3.71). Second, the three sample countries with the largest number of observations (the United States, Japan and the United Kingdom) are dropped. These results, which remain unchanged, are reported in column (2) in Table 4, Panel B. Third, Baker et al. (2016) developed a global EPU index, which is a GDP-weighted average of national EPU indices for 20 countries.7 Global EPU is used as an alternative measure for the country-level EPU index and the results are provided in the third column of Table 4, Panel B. The documented evidence holds using the Global EPU index. Lastly, the logarithm of the EPU index is 7 Based on the BBD website, the 20 countries include: Australia, Brazil, Canada, Chile, China, France, Germany, Greece, India, Ireland, Italy, Japan, Mexico, the Netherlands, Russia, South Korea, Spain, Sweden, the United Kingdom and the United States. For Colombia,

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Table 4 Robustness tests. Panel A. Alternative regression methods

EPU Controls Intercept Country, industry, and year FE Cluster at country N Adj R2

WLS (1)

Fama-Macbeth (2)

Firm and year fixed effect (3)

Cluster at country and year (4)

Cluster at firm (5)

0.083*** (3.33) Yes
3.771 (
1.02) Yes Yes 272,623 0.4214

0.233*** (3.14) Yes
0.692 (
0.07) Yes Yes 272,623 0.3502

0.064** (2.00) Yes 0.018 (0.58) No Yes 272,623 0.4021

0.030*** (2.92) Yes
1.262 (
1.59) Yes No 272,623 0.3644

0.030*** (3.20) Yes
1.262*** (
4.59) Yes No 272,623 0.3644

Panel B. Alternative sample compositions and alternative EPU index

EPU Controls Intercept Country, industry, and year FE Cluster at country N Adj R2

Drop financial crisis period (1)

Drop three largest countries (2)

Global EPU (3)

Non-log EPU index (4)

0.036*** (3.71) Yes
0.998 (
0.72) Yes Yes 245,504 0.3672

0.021*** (3.40) Yes 0.895 (0.62) Yes Yes 125,339 0.3150

0.027*** (3.44) Yes
1.045 (
0.87) Yes Yes 272,623 0.3643

0.001*** (3.80) Yes
1.167 (
0.86) Yes Yes 272,623 0.3644

Note: Table 4 reports the regression results on robustness tests. The dependent variable is Cash. Eq. (1): Cash = a0 + a1EPU + Controls + Country F.E. + Industry F.E. + Year F.E. + e is used to estimate coefficients. Panel A presents the results using alternative regression methods. Panel B presents the results using alternative sample compositions and alternative EPU measure. *, ** and *** indicate statistical significance at the 0.1, 0.05 and 0.01 levels, respectively. Country, industry and year fixed effects (FE) are included in the model. Coefficient estimates and t-statistics are reported based on robust standard errors clustered by country. See the appendix for variable definitions.

removed. The coefficient in the last column of Panel B, Table 4 shows that the main results remain unchanged using the original value in Baker et al. (2016). Overall, the results in Table 4 show that the main findings are robust using alternative regression methods, different sample compositions and non-log form of the EPU measure.

4.2.3. Additional control variables I then examine whether the main findings remain unchanged after considering other sources of uncertainty. Many studies use changes in macro-economic conditions, especially changes related to monetary policy and labor policy, to capture uncertainty. Nevertheless, although highly correlated with EPU index, these macro-economic uncertainty measures more likely are outcomes of policy uncertainty, while the EPU index captures both the reasons and consequences of uncertainty. Another commonly used uncertainty measure is the national election, especially if the election is called earlier than scheduled. Uncertainty caused by elections affects capital expenditure investment, equity financing, M&A activities, etc. (Çolak et al., 2017; Jens, 2017). While elections contribute to a great portion of uncertainty in politics and economy, its discrete nature determines that only a short interval of uncertainty, mainly that in election years, is captured (Gulen and Ion, 2016). On the contrary, the EPU index is based on continuous news coverage, which not only captures the relatively short-term uncertainty caused by elections in certain years but also summarizes uncertainty related to other policy and economic changes during election intervals (Jiang et al., 2019). To eliminate the concern that the main findings are driven by other types of uncertainty correlated with EPU index, I examine whether the main results are sensitive to the inclusion of other policy uncertainty measures. Five uncertainty measures related to the economy, the capital market, and politics are used in this analysis. The first one is Volatility of exchange rate, which is the annual standard deviation of the exchange rate to US dollars and captures uncertainty related to monetary policy.8 The second measure is Volatility of unemployment rate and captures uncertainty caused by labor policy. The two proxies address the issue of uncertainty related to an economy. The third measure is Volatility of market index, and is based on the annual standard deviation of the market return index of listed companies in each country. The volatility reflects each country’s uncertainty in the capital market. The fourth is Election, which indicates whether national elections take place in certain years in a country. The last measure is Called election, which is an indicator of whether the election was called earlier than expected. The two election measures capture uncertainty in politics. 8

Observations from the United States are dropped when the alternative policy uncertainty measure is Volatility of exchange rate.

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X. Li / J. Account. Public Policy xxx (xxxx) xxx Table 5 Additional control variables.

EPU Volatility of exchange rate

Volatility of exchange rate (1)

Volatility of unemployment rate (2)

Volatility of market index (3)

All volatility variables included (4)

Election

0.057*** (3.85) 0.096***

0.058*** (3.87)

0.049*** (3.81)

0.037*** (2.78) 0.117***

(8.69)

0.030*** (3.62)

0.028*** (3.58)

0.026*** (2.77) 0.116*** (7.46) 0.013**

0.033**

(3.48) 0.036**

(2.02) 0.031**

(2.40)

(2.42)

(5.08) Volatility of market index

All variables included (7)

(7.28) 0.015***

0.002***

Volatility of unemployment rate

(5)

Called election (6)

Election

0.001** (2.08)

Called election Controls Intercept Country, industry, and year FE Cluster at country N Adj R2

Yes
0.371 (
0.34) Yes

Yes
0.373 (
0.34) Yes

Yes
0.229 (
0.22) Yes

Yes
0.267 (
0.27) Yes

Yes
1.262 (
0.87) Yes

0.009** (2.11) Yes
1.245 (
0.85) Yes

Yes 203,661 0.3616

Yes 272,623 0.3615

Yes 272,623 0.3617

Yes 203,661 0.3618

Yes 272,623 0.3644

Yes 272,623 0.3644

(2.08) 0.001 (1.34) 0.007* (1.75) Yes
0.266 (
0.27) Yes Yes 203,661 0.3618

Note: Table 5 reports the regression results controlling for other policy uncertainty measures. Eq. (1): Cash = a0 + a1EPU + Additional controls + Controls + Country F.E. + Industry F.E. + Year F.E. + e is used to estimate coefficients. Additional control variables include Volatility of exchange rate, Volatility of unemployment rate, Volatility of market index, Election, and Called election. *, ** and *** indicate statistical significance at the 0.1, 0.05 and 0.01 levels, respectively. Country, industry and year fixed effects (FE) are included in the model. Coefficient estimates and t-statistics are reported based on robust standard errors clustered by country. See the appendix for variable definitions.

The results are presented in Table 5. I find that there is a positive relationship between cash holdings and economic policy uncertainty after controlling for the additional sources of uncertainty. For example, in column (1), after controlling for uncertainty from monetary policy, the coefficient on EPU is 0.057, with a t-value of 3.85. The results also suggest that firms tend to hold more cash when there is high exchange rate volatility. Collectively, the results show that the documented positive relation between policy uncertainty and corporate cash holdings is not sensitive to the inclusion of other uncertainty measures.

5. Cross-sectional variation in the relation between economic policy uncertainty and corporate cash policy 5.1. The role of financial constraint In this section, I examine how cross-sectional variations in financial constraints affect the main findings. Financial constraint is important in that it affects the dynamics of cash allocation, i.e., cash inflows and cash spending (Campello et al., 2010; Denis and Sibilkov, 2010). If a firm has no difficulty in raising external capital, it worries less about the threat from economic policy uncertainty. On the contrary, for those firms with unbalanced liquidity, the effect of precautionary saving demand from high economic policy uncertainty on cash policy should be more pronounced. Therefore, I predict that financial constraints enhance the impact of economic policy uncertainty on corporate cash holdings. I construct three proxies for financial constraints following prior literature (Almeida et al., 2004; Han and Qiu, 2007). First, Financial market development from Beck et al. (2000, 2010) is country-level variable, which is defined as private credit by deposit money banks as a fraction of GDP in the country. The variable captures credit supply in the country, and firms in countries with more credit supply have easier access to financing (Beck et al., 2000; Liao, 2014). The second variable is firm-level WW index following Whited and Wu (2006), and the third proxy is firm-level KZ index from Kaplan and Zingales (1997). All of these variables are redefined so that higher values indicate that the firm is more financially constrained. The results are presented in Table 6. In column (1), consistent with my prediction, I find a more significant positive relationship between EPU and cash holdings in countries with less developed financial markets. In column (2), where financial constraint is proxied by the WW index, a more significant impact of economic policy uncertainty on cash holdings in financially constrained firms is observed. The results are similar when using the KZ index as the financial constraint proxy. The coefficients on EPU remain statistically insignificant in the three regressions, suggesting that the association between economic policy uncertainty and cash holdings is greatly shaped by financial constraint level. Collectively, the results provide Please cite this article as: X. Li, Economic policy uncertainty and corporate cash policy: International evidence, J. Account. Public Policy, https://doi.org/10.1016/j.jaccpubpol.2019.106694

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X. Li / J. Account. Public Policy xxx (xxxx) xxx

Table 6 Cross-sectional analysis: financial constraints.

EPU Financial constraints EPU*Financial constraints Controls Intercept Country, industry, and year FE Cluster at country N Adj R2

Financial market development (1)

WW index (2)

KZ index (3)

0.023 (0.69) 0.008** (2.20) 0.001** (2.58) Yes 0.248 (0.21) Yes Yes 255,686 0.3701

0.006 (0.54) 0.002 (1.23) 0.024** (2.28) Yes
1.294 (-0.89) Yes Yes 272,623 0.3646

0.003 (0.50) 0.005 (0.03) 0.029** (2.07) Yes
1.388 (-1.01) Yes Yes 272,623 0.3761

Note: Table 6 reports the results from examining the role of financial constraints in affecting economic policy uncertainty and cash policy. The equation: Cash = a0 + a1EPU + a2Financial constraints + a3EPU*Financial constraints + Controls + Country F.E. + Industry F.E. + Year F.E. + e is used to estimate the coefficients. The country-level financial constraint proxy includes Financial market development. Firm-level agency problem proxies include WW index and KZ index. Financial constraints variables are redefined so that higher values of proxies indicate severe financial constraints. *, ** and *** indicate statistical significance at the 0.1, 0.05 and 0.01 levels, respectively. Country, industry and year fixed effects (FE) are included in the model. The coefficient estimates and t-statistics are reported based on robust standard errors clustered by country. See the appendix for variable definitions.

supporting evidence to the main findings. Economic policy uncertainty affects financially constrained firms more in terms of liquidity management, since the precautionary saving demand from external economic policy uncertainty, together with internal financial constraints, force firms to hold and save more cash.

5.2. The role of free cash flow problem Based on free cash flow theory raised by Jensen (1986), rather than saving or distributing cash to shareholders, undisciplined managers have incentives to waste internal free cash flow to invest in negative NPV projects for their own interests. According to this cost of free cash flow by Jensen and Meckling (1976) and Jensen (1986), the firms may be more vulnerable during high uncertainty period because of short of internal savings. Moreover, financing suppliers are reluctant to provide funds to these firms due to the poor investment decisions (Bao et al., 2012). Therefore, I predict a stronger precautionary saving motives on firms with severe free cash flow problem. The free cash flow problem can be captured by the severity of agency costs (Jensen, 1986; Zhang, 2009; Bao et al., 2012). Following prior literature, four proxies are used at both the country-level and the firm-level. The first proxy is Legal origin, which separates countries into common law countries and code law countries. Legal protection on stakeholders is stronger in common law countries (La Porta et al., 1997). The second proxy is Private benefits of control from Dyck and Zingales (2004), and captures to the extent that firm value is not shared among all shareholders but rather among certain blockholders or top executives. Following Francis and Martin (2010), the third proxy is firm-level Operating volatility, which is the standard deviation of daily stock returns. The last one is firm Information asymmetry, calculated as the average daily bid-ask spread in the fiscal year. Stock market return and bid-ask spread data are from Datastream database. All four of these variables are redefined so that a higher value indicates a more serious free cash flow problem. I then interact the four proxies with EPU respectively and re-estimate Eq. (1). The results are presented in Table 7. Column (1) shows the results using Legal origin as the proxy. The coefficient on EPU*Free cash flow problem is positive, suggesting that the positive relation between economic policy uncertainty and cash holdings is more significant in code law countries (when Legal origin = 1). In column (2), the coefficient on EPU* Free cash flow problem is also significantly positive, suggesting that the firms hold more cash in high uncertainty periods in countries with high private benefits of control. The results presented in the last two columns of Table 7 are similar, indicating that the effect of economic policy uncertainty on cash holdings is more significant in firms with high operating volatility and information asymmetry. Collectively, results on the agency costs of free cash flow analysis from Table 7 support the precautionary saving motives. Firms with free cash flow problem are more likely to be vulnerable to the difficulty in raising external finance, since they are short of internal savings as substitute. Hence, they are more likely to hold cash when uncertainty is high9. 9 In untabulated further analysis, I find that there is a decrease in investment, dividend payout, and external financing. I also find that there is an increase in the cost of equity financing and an increase in cost of debt financing. Moreover, the overall value of cash is higher when uncertainty is high. The value of positive excess cash is positively associated with economic policy uncertainty, while the value of negative excess cash is negatively associated with economic policy uncertainty.

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X. Li / J. Account. Public Policy xxx (xxxx) xxx Table 7 Cross-sectional analysis: Free cash flow problem.

EPU

Legal origin (1)

Private benefits of control (2)

Operating volatility (3)

Information asymmetry (4)

0.011 (0.59)

0.002 (0.79)

0.020*** (3.02) Yes
1.098 (-1.23) Yes Yes 272,623 0.3784

0.030*** (3.34) Yes
4.962 (-0.80) Yes Yes 236,819 0.3679

0.185*** (2.84) 0.272*** (4.37) 0.056*** (4.07) Yes
2.290 (-1.61) Yes Yes 239,582 0.3683

0.025 (0.89) 0.797** (2.34) 0.017** (2.09) Yes 0.505 (0.56) Yes Yes 171,265 0.3417

Free cash flow problem EPU*Free cash flow problem Controls Intercept Country, industry, and year FE Cluster at country N Adj R2

Note: Table 7 reports the results examining the role of free cash flow problem in affecting economic policy uncertainty and cash policy. The equation: Cash = a0 + a1EPU + a2Free cash flow problem + a3EPU*Free cash flow problem + Controls + Country F.E. + Industry F.E. + Year F.E. + e is used to estimate coefficients. Country-level proxies include Legal origin and Private benefits of control. Firm-level agency problem proxies include Operating volatility and Information asymmetry. Free cash flow problem variables are redefined so that higher values of proxies indicate higher agency costs. *, ** and *** indicate statistical significance at the 0.1, 0.05 and 0.01 levels, respectively. Country, industry and year fixed effects (FE) are included in the model. Coefficient estimates and tstatistics are reported based on robust standard errors clustered by country. See the appendix for variable definitions.

6. Conclusion In this paper, I examine one of the real consequences of economic policy uncertainty. More specifically, using the new EPU index developed by Baker et al. (2016), I investigate the association between economic policy uncertainty and cash holdings. The findings are consistent with the hypothesis that economic policy uncertainty affects cash policy through precautionary saving motives. There is also a significant increase in both cash holdings and cash savings. The increases in cash holdings and cash savings are consistent with the precautionary saving demand of firms. Economic policy uncertainty causes cash flow volatility, and firms may not have adequate cash inflows to support future investments and need to rely on internal cash reserves. This concern for cash flow shortfalls motivates firms to increase their cash holdings and save more cash from current cash flow. A battery of robustness checks has been used to examine the sensitivity of the main evidence. I further find that the relation between the EPU index and cash policy is more pronounced in firms who are financially constrained or in firms with severe free cash flow problem. The cross-sectional analysis provide supporting evidence to the precautionary saving channel. The paper contributes to the growing literature of the real effects of economic policy uncertainty. Prior research has mainly studied the capital market effects of uncertainty, such as changes in stock price volatility and risk premiums. By linking corporate internal cash decisions to external uncertainty, and using the novel index developed by Baker et al. (2016), this paper extends this line of research and shows that the macro-level economic policy uncertainty significantly impacts firms’ decisions. These results support the findings of Nagar et al. (2018), which show that firm decisions such as management disclosures are affected by uncertainty. This paper also corresponds with the cash holding literature, such as Almeida et al. (2004), Han and Qiu (2007) and Duchin’s (2010), and suggests that precautionary saving demand is an important determinant of cash policy. In addition, the results from cross-sectional tests show that the impact of economic policy uncertainty on cash holdings changes significantly with the precautionary saving demand, and under certain circumstances only exists in firms with high precautionary saving demand. The findings presented in this paper have important policy implications. Economic policy uncertainty caused by elections, changes in regulations, the enforcement of laws, etc. is increasing in recent years worldwide. The findings on how the uncertainty affects cash policies may thus help policymakers better understand how regulatory decisions can affect firm decisions. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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X. Li / J. Account. Public Policy xxx (xxxx) xxx

Appendix A Variable definitions Variable

Definition

Source

Cash

The natural logarithm of cash and cash equivalents divided by lagged total assets. Annual change in cash and cash equivalents divided by lagged total assets. Natural logarithm of the economic policy uncertainty index from Baker et al. (2016). Operating cash flow, defined as net operating cash flow divided by lagged total assets. Tobin’s Q, defined as the market value of total assets to the book value of total assets, where the market value of total assets is defined as the book value of total assets plus the difference between the market value of equity and the book value of equity. Natural logarithm of total assets in millions of US dollars. Net working capital, defined as non-cash working capital divided by lagged total assets. Leverage, defined as the sum of long-term debt and short-term debt divided by total assets. Industry volatility of operating cash flow, defined as the median value of the standard deviation of the operating cash flow of all firms in the same industry over the past 10 years. Capital expenditures, defined as capital expenditure divided by lagged total assets. Acquisitions, defined as net assets from acquisitions divided by lagged total assets. R&D expenditures divided by net sales. Dividend payouts, defined as cash dividend payouts divided by lagged total assets. GDP per capita, defined as the natural logarithm of GDP per capita for each country year. Natural logarithm of the global economic policy uncertainty index from Baker et al. (2016). Dummy variable that takes a value of 1 for each election year, and 0 otherwise.

WorldScope

Dcash EPU Cash flow from operations Tobin’s Q

Size Noncash working capital Leverage Industry cash flow volatility Capital expenditure Acquisitions R&D Dividends GDP Global EPU Election Called election Volatility of exchange rate Volatility of unemployment rate Volatility of market index

WorldScope Baker et al. (2016) WorldScope WorldScope

WorldScope WorldScope WorldScope WorldScope

WorldScope WorldScope WorldScope WorldScope World Bank

Dummy variable that takes a value of one if the election is called earlier than expected. Annual standard deviation of the exchange rate to US dollars.

Baker et al. (2016) Beck et al. (2001) Government release Capital IQ

Annual standard deviation of the unemployment rate in each country.

Capital IQ

Annual standard deviation of the market return index of listed companies in the country.

Capital IQ

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