Abstract
This study examines the conditions under which local social environments are likely to influence corporate tax behavior. Using a social capital index at the county level, we find that on average, social capital reduces firms’ aggressive tax avoidance behavior. The impact of social capital on corporate tax avoidance is weaker when managers are under excessive pressure to meet earnings targets, during the periods of financial constraints, and when managers are incentivized to undertake risk. We further find that corporate tax avoidance activities engaged by firms headquartered in high-social-capital counties tend to be less value-increasing, indicating that potential social sanctions in these areas may reduce the benefits of tax avoidance activities accrue to firms. However, the negative impact of tax avoidance on firm value in high-social-capital counties tends to be lower for firms with strong corporate governance, which suggests that managers in well-governed firms can better exploit tax avoidance opportunities. Overall, our evidence is consistent with our conjecture that although the local social environments have a significant influence on corporate tax behavior, this influence is fragile in the presence of excessive earnings pressure, financial constraints, and equity risk incentives.
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Notes
Hasan et al. (2017) suggest that “tax-minded” managers tend to view corporate taxes are unfair and inefficient. However, they do not specifically examine what kind of managers are more likely to be “tax-minded.”.
Corporate tax avoidance is not necessarily illegal, for tax laws permit certain tax planning activities (Dyreng et al. 2008). Moreover, all tax systems have loopholes and inconsistencies (Weisbach 2002b), and the tax authorities do not have sufficient resources to audit all transactions (Fox et al. 2014).
We thank the reviewer for bringing this point to our attention.
As discussed in the Empirical results section, in the PSM analysis, we match firms headquartered in high-social-capital counties to firms headquartered low-social-capital counties. In the IV analysis, following Hilary and Huang (2015), we use US county-level violent crime rate and state-level gun ownership as the instruments for social capital.
Our results also hold if we control for firms’ geographical complexity, and county fixed effects.
The costs associated with failing to meet important earnings benchmarks include: severe negative stock market reactions, losing credibility with the capital markets, and hurting managers’ career prospects (Graham et al. 2005).
NRCRD only provides social capital data for the year 1990, 1997, 2005, and 2009. As a robustness check, we extrapolate the social capital data to year 2012. Although we believe this extrapolation makes the social capital measure less accurate, our untabulated results show that the coefficients of social capital are still negative and significant when using the sample period 1990–2012.
All the data required to construct the index can be found at the NERCRD.
We use such a small caliper because important covariate such as firm size can only be balanced when using a very small caliper.
We follow Armstrong et al. (2010) to examine the covariate balance between high-social-capital and low-social-capital observations. Untabulated results show that for all the firm-level covariates, the differences between the treatment (high social capital) and control (low social capital) groups are not significant. Although the differences between treatment and control groups are significant for four demographic variables (EDU, SEX, MINORITY, and POP), the largest absolute percentage bias is only 5.4, indicating the demographic variables are generally well balanced.
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Ziqi Gao declares that he has no conflict of interest. Louise Yi Lu declares that she has no conflict of interest. Yangxin Yu declares that he has no conflict of interest.
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We are grateful for comments from Neil Fargher, Jeong-Bon Kim, Greg Shailer, and seminar participants at Australian National University.
Appendix: Variable Definitions
Appendix: Variable Definitions
Dependent variables | |
TA_CETR | The cash effective tax rate (CETR) is based on the measure of Hoi et al. (2013). CETR is calculated as cash tax paid (TXPD) divided by pre-tax income less special items (PI–SPI). We truncate CETR to the range (0, 1) and set it as missing when PI–SPI < 0. TA_CETR = CETR* − 1 |
SHELTER | The probability of sheltering (SHELTER) is calculated based on the measure of Wilson (2009). It estimates the likelihood of a firm to engage in tax sheltering activities in the current year, as follows: |
SHELTERit = −4.86 + 5.2 × BTDit + 4.08 × DAit − 1.41 × LEVit + 0.76 × ATit + 3.51 × ROAit + 1.72 × FOREIGN_INCOMEit + 2.43 × R&Dit | |
where BTD is the total book-tax difference, which is calculated according to Kim et al. (2011), that is, BTD = book income (PI) less taxable income, scaled by lagged assets (AT). Taxable income is measured as the sum of current federal tax expenses (TXFED) and current foreign tax expenses (TXFO), divided by the statutory tax rate and then subtracting the change in net operating loss carryforwards (TLCF). When current federal tax expenses (TXFED) are not available, total current tax expenses (TXFED + TXFO) are replaced by total income taxes minus deferred taxes, state income taxes, and other income taxes (TXT–TXDI–TXS–TXO). The variable DA is the absolute value of discretionary accruals estimated from the performance-adjusted modified cross-sectional Jones model; LEV is the firm’s leverage ratio; AT is the logarithm of total assets; ROA is the firm’s return on assets; FOREIGN_INCOME is an indicator variable that equals to one if a firm reports foreign income (PIFO) in a particular year and zero otherwise; and R&D is the firm’s R&D expenses (XRD) divided by its total assets (AT) | |
DDBT | The discretionary book-tax difference captures the part of the book-tax difference that cannot be explained by variations of total accruals, as based on the work of Desai and Dharmapala (2006): |
BTit = β1TAit + μi + εit | |
where BT is the total book-tax difference estimated based on the model of Manzon and Plesko (2002). It is calculated as financial income minus estimated taxable income (PIDOM–TXFED/STR–TXS–TXO–ESUB), scaled by lagged assets (AT). The variable TA is total accruals scaled by lagged assets, calculated according to Dechow et al. (1995), μ is the average value of residuals, and ε is the deviation from the average value of residuals. The value of DDBT is the sum of two residual terms of (μ + ε), which is the part of the book-tax difference that cannot be explained by variations of total accruals | |
DTAX | The discretionary permanent book-tax difference (DTAX) is calculated based on the work of Frank et al. (2009) and is calculated as follows: |
PERMDIFFit = β0 + β1INTANGit + β2UNCONit + β3MIit + β4CSTEit + β5dNOLit+ β6LAGPERMit + εit | |
where PERMDIFF is the total book-tax difference, which equals BI–[(CFTE + CFOR)/STR]–(DTE/STR); BI is pre-tax book income (PI); CFTE is current federal tax expenses (TXFED); CFOR is current foreign tax expenses (TXFO); DTE is deferred tax expenses (TXDI); STR is the statutory tax rate in year t; INTANG is goodwill and other intangibles (INTAN); UNCON is income reported under the equity method (ESUB); MI is income attributable to minority interests (MII); CSTE is current state income tax expenses (TXS); dNOL is change in net operating loss carryforwards (TLCF); and LAGPERM is the one-year lagged PERMDIFF value. The residual term ε is the value of DTAX. Consistent with Frank et al. (2009), we handle missing data as follows: When minority interest (MII), foreign income tax expense (TXFO), income from unconsolidated subsidiaries (ESUB), or state income tax expense (TXS) is missing, we set MI, CFOR, UNCON, or CSTE, respectively, to zero. When federal income tax expenses (TXFED) are missing, we set CFTE equal to total tax expenses (TXT) minus foreign income tax expenses (TXFO) minus state income tax expenses (TXS) minus deferred tax expenses (TXDI). If the value of intangible assets (INTANG) is missing, we set INTANG to zero | |
Tobin’s q | Tobin’s q = (AT + PRCC_F*CSHO–CEQ)/AT. where AT is total assets, PRCC_F is closing stock price. CSHO is common shares outstanding. CEQ is book value of equity. Consistent with Desai and Dharmapala (2009), we do not include deferred tax expense in the calculation of the Tobin’s q |
Test variables | |
SK | A social capital index, developed by Rupasingha et al. (2006), specifically calculated by extracting the first principal component from Pvote (voter turnout in the presidential elections), Respn (the response rate to the U.S. Census Bureau’s survey), Assn (the number of social associations), and Nccs (the number of NGOs). The variable Assn is calculated as the sum of bowling centers, civic and social associations, physical fitness facilities, public golf courses, religious organizations, sports clubs, membership sports and recreation clubs, political organizations, professional organizations, business associations, and labor organizations in each county, divided by 12, divided by the county population and then multiplied by 10,000. The variable Nccs is the number of NGOs in a county, excluding those with an international approach, divided by the county population; Pvote is the percentage of voters voted in the most recent presidential election in a county; and Respn is the response rate to the U.S. Census Bureau’s survey. The SK values are only available for 1990, 1990, 1997, 2005, and 2009. Consistent with Hilary and Hui (2009), we linearly interpolate the data to obtain the SK values for the missing years (1991–1996, 1998–2004, and 2006–2008) |
IOTRA | IOTRA is an indicator variable based on firms’ transitory institutional ownership. To calculate IOTRA, we divide the sample into terciles based on rankings of the raw measure of transitory institutional ownership, the highest tercile is coded as one, the lowest tercile is coded as zero, while the middle tercile is discarded. We obtain the institutional ownership data from the 13-F filings by Thomson Financial. The raw measure of transitory institutional ownership is calculated as the percentage of stock held by transitory institutional investors |
ANACOV | ANACOV is an indicator variable based on firms’ transitory institutional ownership. To calculate ANACOV, we divide the sample into terciles based on rankings of the raw measure of analyst coverage, the highest tercile is coded as one, the lowest tercile is coded as zero, while the middle tercile is discarded. Following He and Tian (2013), the raw measure of analyst coverage is calculated as the average of 12 monthly numbers of earnings forecasts in I/B/E/S |
CRISIS | We use the period of the recent financial crisis as a proxy for periods of financial constraints. The indicator variable CRISIS equals one for all observations during the period from 2007 to 2009, and zero otherwise |
CONS | CONS is an indicator variable based on rankings of firms’ cash balance and leverage. It is a proxy for firm-level financial constraints. Following Biddle et al. (2009), we first rank firms based on their cash balance into deciles. Next, we multiply firms’ leverage ratios by negative one and rank firms into deciles based on the negative leverage ratios. We then sum the decile rakings of cash balance and the decile rankings of the negative leverage ratios. To calculate CONS, we divide the sample into terciles based on the summed rankings of cash balance and leverage, the highest tercile is coded as one, the lowest tercile is coded as zero, and the middle tercile is discarded |
VEGA | VEGA is an indicator variable based on managers’ stock option vega. To calculate VEGA, we divide the sample into terciles based on rankings of the raw measure of stock option vega, the highest tercile is coded as one, the lowest tercile is coded as zero, while the middle tercile is discarded. We obtain managers’ stock option vega data from the Standard and Poor’s Execucomp database. The raw measure of stock option vega is calculated as the change in managers’ stock option value for a 1% change in stock return volatility |
Control variables | |
ROA | Return on assets, which is pre-tax income (PI) divided by lagged total assets (AT) |
LEV | The financial leverage ratio, which is long-term debt (DLTT) scaled by lagged assets (AT) |
NOL | An indicator variable that equals one if a firm has loss carryforward (TLCF) at the beginning of the year and zero otherwise |
dNOL | Change in loss carryforwards (TLCF) scaled by lagged assets (AT) |
FI | Foreign income (PIFO) scaled by lagged assets (AT) |
PPE | Net PPE (PPENT) divided by lagged total assets (AT) |
INTAN | Intangible assets (INTAN) divided by lagged total assets (AT) |
EQINC | Equity income in earnings (ESUB) divided by lagged total assets (AT) |
SIZE | The logarithm of market equity value (PRCC_F*CSHO) |
MB | The beginning-of-year market value of equity (PRCC_F*CSHO) divided by the beginning-of-year book value of equity (CEQ) |
RD | R&D expenditures (XRD) scaled by lagged assets (AT) |
EDU | The percentage of the population 25 years and older with at least a bachelor’s degree at the county level |
SEX | The male-to-female ratio at the county level |
INCOME | Median household income at the county level |
MINORITY | The percentage of minorities of a county |
AGE | The median age of the population of a county |
MARRIED | The percentage of the population older than 15 that is currently married in a county |
POP | Population size of a county |
RELIG | The percentage of religious adherents in a county. Following Hilary and Hui (2009), we obtain the religiosity data from the ARDA. Specifically, we use the file Churches and Church Membership for 1990, Religious Congregations and Membership Study for 2000, and U.S. Religion Census: Religious Congregations and Membership Study for 2010. Since the data in 1990 include only Judeo-Christian denominations, we exclude non-Judeo-Christian denominations in 2000 and 2010 for internal consistency. Consistent with Hilary and Hui, we linearly interpolate the data to obtain the RELIG values for the missing years (1991–1999 and 2001–2009) |
SCTR | We collect the state corporate income tax rates (SCTR) of each year from the Tax Foundation website. Following Chirinko and Wilson (2008), we calculate SCTR as the marginal state corporate income tax rate for the highest income bracket |
HSK | HSK is an indicator variable which equals one if a county’s social capital is above the median level of social capital, and zero otherwise |
INS | INS is an indicator variable which equals one when the institutional ownership is above the median level of institutional ownership, and zero otherwise |
WELLG | WELLG is an indicator variable which equals one when the G-index developed by Gompers et al. (2003) is below the median level of G-index, and zero otherwise |
STKMIX | STKMIX = BLKVAL/(BLKVAL + SALARY + BONUS). where BLKVAL is the average of Black–Scholes stock options value for top five executives of a firm, SALARY is the average salary of top five executives, and BONUS is the bonus of top five executives. All data required to calculate STKMIX are obtained from Standard and Poor’s Execucomp |
SALE | SALE is firm sales from the Execucomp |
TA | TA is total accruals scaled by lagged assets, calculated according to Dechow et al. (1995) |
VOL | VOL is the volatility of a firm’s stock price volatility, which is the BS_VOLATILITY variable from the Execucomp |
LT_DEBT | LT_DEBT is long-term debt scaled by the book value of assets (LT_DEBT = DLTT/AT) |
ST_DEBT | ST_DEBT is current debt scaled by the book value of assets (ST_DEBT = DLC/AT) |
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Gao, Z., Lu, L.Y. & Yu, Y. Local Social Environment, Firm Tax Policy, and Firm Characteristics. J Bus Ethics 158, 487–506 (2019). https://doi.org/10.1007/s10551-017-3765-2
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DOI: https://doi.org/10.1007/s10551-017-3765-2