Most analyses of tax evasion examine individual behavior, not firm behavior, given obvious and recognized data issues. We use data from the Business Environment and Enterprise Performance Survey to examine tax evasion at the firm level, focusing on a novel determinant of firm tax evasion: the financial constraints (or credit constraints) faced by the firm. Our empirical results indicate across a range of alternative specifications that more financially constrained firms are more likely to be involved in tax evasion activities, largely because evasion helps them deal with financing issues created by financial constraints. We further show that the effects of financial constraints are heterogeneous across firm ownership, firm age, and firm size. Lastly, we present some suggestive evidence on the possible channels through which the impact of financial constraints on firm tax evasion may operate, including a reduction of information disclosure through the banking system, an increase in the use of cash for transactions, and an increase in bribe activities in exchange for tax evasion opportunities.
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In related work, Baumann and Friehe (2010) show theoretically that tax evasion increases a firm’s future expected profits, making more investment feasible. In empirical work, Edwards et al. (2016) provide evidence that firms treat tax planning strategy as a source of financing. Of special relevance, they find evidence that firms facing financial constraints will take actions to increase internally generated funds via tax planning strategies that help reduce the firms’ tax payments.
Recent work on financial markets argues that the development of financial markets leads to improved credit availability and lower transaction costs of credit. For example, see Brown et al. (2009).
Although the World Bank Enterprise Survey (WBES) provides an even larger dataset in terms of country coverage (i.e., 102 countries) and year coverage (i.e., 2002–2010), there were changes in the questionnaires across years, making some of the key variables unavailable for our empirical analysis, particularly the instrumental variables for financial constraints faced by the firms. For this reason, we rely on the BEEPS for our main empirical estimations, using the WBES dataset for robustness checks, but without addressing the endogeneity concern of the financial constraints faced by the firms.
These countries include Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, FYR Macedonia, Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Tajikistan, Turkey, Ukraine, and Uzbekistan.
The strata for BEEPS are firm size, sector, and geographic region within a country.
Firms that operate in sectors subject to government price regulations and prudential supervision, such as banking, electric power, rail transport, and water and wastewater, were excluded.
The full question text is: “Recognizing the difficulties many firms face in fully complying with taxes and regulations, what percentage of total annual sales would you estimate the typical firm in your area of business reports for tax purposes?” The survey instrument does not provide guidance on a firm’s “area of business,” and this can be construed by respondents in a number of ways, such as geographic area, industry area, or perhaps even both. As a result, we have included additional fixed effect controls at the geographic (location), country, and industry levels in our specification in which we clustered standard errors at country level. See Sect. 3.1.
By protecting the privacy of respondents through bundling of sensitive questions about illicit behavior of the firms and about “harmless” topics, Kundt et al. (2017) develop a new survey method (the “crosswise” model) to provide more credible estimates about the extent of tax evasion. However, their approach does not allow them to generate data that could be used to examine the determinants of tax evasion at the firm level.
Just as with Percent Reported Sales, Percent Reported Workforce and Percent Reported Wage Bill represent a firm’s percent workforce and wage bill reported for tax purpose. See Sect. 4.4.1 for a detailed definition.
The cross-country standard deviation is calculated from country average values, whereas the within-country standard deviation is calculated as the mean of the within-country deviations of firm tax evasion across countries.
All responses include “No obstacle = 1,” “Minor obstacle = 2,” “Moderate obstacle = 3,” and “Major obstacle = 4.” The ordinal nature of the response variable allows the variable to be used as an independent variable to measure the severity of obstacles to business operation.
This same measure of financial constraints faced by the firms is also employed by Gorodnichenko and Schnitzer (2013).
Overdue is constructed from answers to the question: “Do you currently have any payments overdue (by more than 90 days) to each of the following (utilities, taxes, employees, material input suppliers)?”
Share of Non-conventional Payments is constructed from answers to the question: “What share of your purchases from suppliers (sales to your customers) was ultimately settled by: Debt swaps or offsets; Exchange of goods for goods?”
The construction of Lost Sales is based on several questions because different questions are asked in different countries and across different waves of the BEEPS. In the 2002 survey, the variable is constructed from responses to the question: “What percent of sales in 2001 was lost due to delivery delays from your material input suppliers?” In the 2005 survey, the variable is constructed from the responses to the following two questions: “What percent of total sales was lost due to the following service interruptions: Power outages or surges from the public grid; Insufficient water supply; Unavailable mainline telephone service?” and “What percent of the value of products your establishment shipped over the last 12 months was lost while in transit due to breakage, spoilage or theft?”
The scores for the speed of the EBRD reforms are based on different classification systems. For speed of reform in banking sector, we use the following classification: 1: Little progress beyond establishment of a two-tier system. 2: Significant liberalization of interest rates and credit allocation; limited use of directed credit or interest rate ceilings. 3: Substantial progress in establishment of bank solvency and of a framework for prudential supervision and regulation; full interest rate liberalization with little preferential access to cheap refinancing; significant lending to private enterprises and significant presence of private banks. 4: Significant movement of banking laws and regulations toward BIS standards; well-functioning banking competition and effective prudential supervision; significant term lending to private enterprises; substantial financial deepening. 4+: Standards and performance norms of advanced industrial economies; full convergence of banking laws and regulations with BIS standards; provision of full set of competitive banking services. For speed of reform in non-bank financial institutions, we use the following classification: 1: Little progress. 2: Formation of securities exchanges, market-makers, and brokers; some trading in government paper and/or securities; rudimentary legal and regulatory framework for the issuance and trading of securities. 3: Substantial issuance of securities by private enterprises; establishment of independent share registries, secure clearance and settlement procedures, and some protection of minority shareholders; emergence of non-bank financial institutions (for example, investment funds, private insurance and pension funds, leasing companies) and associated regulatory framework. 4: Securities laws and regulations approaching IOSCO standards; substantial market liquidity and capitalization; well-functioning non-bank financial institutions and effective regulation. 4+: Standards and performance norms of advanced industrial economies; full convergence of securities laws and regulations with IOSCO standards; fully developed non-bank intermediation. “+” and “−” ratings are treated by adding 0.33 and subtracting 0.33 from the full value. Averages are obtained by rounding down; for example, a score of 2.6 is treated as 2+, but a score of 2.8 is treated as 3−. For more information, see: http://www.ebrd.com/cs/Satellite?c=Content&cid=1395237866249&d=&pagename=EBRD%2FContent%2FContentLayout.
As before, the possible responses range from “No obstacle = 1,” “Minor obstacle = 2,” “Moderate obstacle = 3,” and “Major obstacle = 4,” and include a “Don’t know” response.
For example, banks may require a larger value of collateral for bank loan applications, or they may increase the interest rates of bank loans for those firms with little information disclosed or few credit records.
Also, as argued by Beck et al. (2014), the major reasons for reforms in the financial sector are to improve credit assessment, to facilitate the access to credit markets, to lower the cost of external credit, and to enhance financial stability. Beck et al. (2014) do not find any evidence that tax evasion is a driving force for these reforms.
For space concerns, the estimated coefficients for other control variables are not reported in Table 3, but their results are briefly summarized here. First, several firm-level variables have positive and statistically significant coefficients across different specifications, including firm age, size (the number of employees and annual sales), and locations. Second, Audited enters significantly in the regressions, and its significant positive coefficient suggests that firms tend to report lower tax evasion after their financial statements are previously audited by external auditors. Third, real GDP per capita and population both consistently enter with positive signs, indicating that higher levels of income and of population are associated with lower firm tax evasion. Finally, the significant and positive effect of Bank Cost-to-Income Ratio on a firm’s Percent Reported Sales is consistent with the results of Beck et al. (2014), suggesting that the increase of efficiency in financial sectors will lower the incidence and extent of firm tax evasion.
With the inclusion of firm-level control variables in columns (2)–(3) and (5)–(6) of Table 3, the sample size for regressions is reduced substantially. In order to ensure that our results are not driven by the changes of sample size, we restrict all regressions to the same sample size. We find the results to be largely the same as the results in Table 3. These results are not reported, but are available upon request.
The questions regarding the firm’s percent workforce and/or actual wage bill reported for tax purposes are collected only in the 2005 wave of the BEEPS, which results in a sharp decline in observations compared with the observations in baseline regressions using Percent Reported Sales as the dependent variable.
A two-step IV probit model is employed in the estimations since maximum likelihood estimation procedures may have difficulty converging (Newey 1987).
The variable Manager’s Time Spent with Officials is constructed from answers to the question: “What percent of senior management’s time over the last 12 months was spent in dealing with public officials about the application and interpretation of laws and regulations and to get or to maintain access to public services?”
The Boone Indicator is a measure of the degree of competition, based on profit efficiency in the banking market and calculated as the elasticity of profits to marginal costs. An increase in the Boone Indicator implies a deterioration of the competitive conduct of financial intermediaries. For more information, see Boone (2008). The Lerner Index is a measure of market power in the banking market, and it is defined as the difference between output prices and marginal costs (relative to prices). Higher values of the Lerner Index indicate less bank competition.
More specifically, the WBES does not have questions regarding whether the firm currently has any payments overdue (by more than 90 days) for utilities, taxes, employees, and/or material input suppliers; the WBES also does not have questions regarding the share of non-conventional payments during transactions with customers and suppliers. Furthermore, the EBRD only reports indices of the speed of reforms in banking sector and non-bank financial institutions for 27 transitional countries, which is consistent with the country coverage in the 2002 and 2005 BEEPS.
Firm ownership is defined by the largest shareholder of a firm, as provided directly by the survey.
These are the categories defined by the BEEPS.
Gorodnichenko and Schnitzer (2013) also find similar effects of financial constraints on firm innovation for firms in both manufacturing and service industries.
For example, Kenyon (2008) finds evidence that tax-evading firms are less likely to participate in financial markets because they are concerned about information disclosure that may alert tax authorities.
For example, Denis and Sibilkov (2010) find that financially constrained firms tend to have greater cash holdings than unconstrained firms. Also, Gordon and Li (2009) claim that a typical way for firms to avoid paying taxes is to shift their business into cash transactions and avoid making use of the financial sector, resulting in no paper trails or bank records that the government can use to enforce tax laws.
The full question asked in the BEEPS is: “Does your establishment have a checking (saving) account? Yes/No.” Admittedly, whether a firm has a checking or saving account in the banks cannot exactly capture all the information the firm would like to share with the financial sector. However, this variable reflects to some extent a firm’s willingness to disclose financial statements and cash flow status to the banks and also to the public.
The dependent variables are drawn from the responses to the question: “What share of your purchase from supplier (sales to customers) over the last 12 months was ultimately settled by cash?”
The share of cash used in transaction with suppliers and customers is directly correlated with one of the instruments (i.e., the share of non-conventional payments) that we employed for the financial constraints variables. As a result, we exclude the share of non-conventional payments from the instrument list for the estimations in Panel B of Table 7.
We have also employed the percentage of contract value paid in unofficial payment to public officials as an alternative measure of bribe activities of the firms, with largely unchanged results. These results are not reported, but available upon request.
The percentage of sales paid in unofficial payment to public officials is constructed using the following questions of the BEEPS: “On average, what percent of total annual sales do firms like yours typically pay in unofficial payments/gifts to public officials?” The frequency of bribery is a self-reported measure of frequency of unofficial payments/gifts that a firm makes to deal with taxes and tax collection as evaluated on a scale ranging from 1 (“Never”) to 6 (“Always”).
Note that Alm et al. (2016) also find evidence that corruption may exacerbate firm tax evasion, mainly because corrupt officials seek more income via bribes and loosen tax enforcement.
More generally, Alm (2017) argues that any answers that emerge from empirical research apply only to the specific setting that is being considered; that is, specific circumstances differ so profoundly across individuals, firms, markets, countries, and time that almost any attempt to take conclusions from a single study and apply them in all circumstances will lead to profoundly misleading policy recommendations.
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We would like to thank Janina Enachescu and other participants at the 5th International Conference on “The Shadow Economy, Tax Evasion and Informal Labor,” held in Warsaw, Poland, in July 2017, for many helpful comments. This research was supported by the National Natural Science Foundation of China (Nos. 71773128; 71533006) and the Fok Ying Tung Education Foundation (No. 151085). We are especially grateful to two anonymous referees for many helpful comments and suggestions that have substantially improved the paper.
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Alm, J., Liu, Y. & Zhang, K. Financial constraints and firm tax evasion. Int Tax Public Finance 26, 71–102 (2019). https://doi.org/10.1007/s10797-018-9502-7
- Tax evasion
- Financial constraints
- Firm-level data