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Political connections and voluntary disclosure: Evidence from around the world

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Abstract

Motivated by the international business literature that examines the interactions between political forces and business environments, we investigate whether and how political connections affect managers’ voluntary disclosure choices. We show that compared to non-connected firms, connected firms issue fewer management earnings forecasts. In addition, relative to non-connected firms, connected firms have a greater increase in the frequency of management forecasts subsequent to the elections that damage their political ties. Further analyses suggest that lack of capital market incentives, reduced litigation risk, and lower proprietary costs shape politically connected firms’ unique voluntary disclosure choices.

Résumé

Motivés par la littérature en international business qui examine les interactions entre les forces politiques et les environnements d’affaires, nous étudions si et comment les connexions politiques influencent les choix de divulgation volontaire des dirigeants. Nous montrons qu’en comparaison aux firmes non-connectées, les firmes connectées émettent moins de prévisions concernant la gestion des bénéfices. De plus, par rapport aux firmes non-connectées, les firmes connectées connaissent une plus forte augmentation de la fréquence des prévisions de gestion suite aux élections qui nuisent à leurs liens politiques. D’autres analyses suggèrent que le manque d’incitations du marché du capital réduit le risque de litiges, et que des coûts propres moins élevés façonnent les choix uniques de divulgation volontaire des firmes connectées politiquement.

Resumen

Motivados por la literatura de negocios internacionales que examina las interacciones en las fuerzas políticas y los entornos de negocio, investigamos si y cómo las conexiones políticas afectan las opciones de los gerentes a divulgar información voluntariamente. Mostramos qué comparado con las empresas no conectadas, las empresas conectadas emiten menos pronósticos de ganancias de la gestión. Adicionalmente, en relación con las empresas no conectadas, las empresas conectadas tienen mayor aumento en la frecuencia de los pronósticos de gestión empresarial posterior a las elecciones que dañan sus vínculos políticos. Otros análisis sugieren que la falta de incentivos en el mercado de capitales, reduce el riesgo de litigación, y los menores los costos de propiedad configuran las opciones únicas de divulgación voluntaria de las empresas conectadas políticamente.

Resumo

Motivados pela literatura de negócios internacionais que examina as interações entre forças políticas e ambientes de negócios, investigamos se e como as conexões políticas afetam as escolhas de divulgação voluntárias dos gerentes. Mostramos que, em comparação com empresas não conectadas, as empresas conectadas divulgam menos previsões gerenciais de resultados. Além disso, em relação às empresas não conectadas, as empresas conectadas têm um aumento maior na frequência de previsões gerenciais após eleições que prejudicam seus laços políticos. Análises adicionais sugerem que a falta de incentivos do mercado de capitais, o risco de litígio e custos de propriedade mais baixos definem as distintas escolhas de divulgação voluntárias de empresas politicamente conectadas.

摘要

受研究政治力量与商业环境之间相互作用的国际商务文献的激发,我们调查政治关联是否以及如何影响管理者的自愿披露选择。我们表明,与非关联企业相比,关联企业发布的管理收益预测较少。另外,相对于非关联企业而言,关联企业在有损它们之间政治关系的选举之后的管理预测的频率有较大幅度的增加。进一步的分析表明,资本市场激励的缺乏、诉讼风险的减少和专利成本的降低形成了政治关联企业独特的自愿披露选择。

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Acknowledgements

We thank Garry Biddle (the editor), two anonymous reviewers, and the workshop participants at the Hong Kong University of Science and Technology, Singapore Management University, and York University, for their helpful comments. We are grateful to Mara Faccio for generously providing the data on classifications of political connections. We thank Yi-Chun Chen for her valuable research assistance. The work described in this article was supported by the funding from the RGC Research Grant of The Hong Kong University of Science and Technology. Yongtae Kim acknowledges financial support from Robert and Barbara McCullough Family Chair Professorship. Siqi Li acknowledges financial support from Ernst & Young Faculty Research Fellowship.

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Authors and Affiliations

  1. The Hong Kong University of Science and Technology Business School, Clear Water Bay, Hong Kong

    Mingyi Hung

  2. Leavey School of Business, Santa Clara University, Santa Clara, CA, USA

    Yongtae Kim & Siqi Li

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  1. Mingyi Hung
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  2. Yongtae Kim
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Correspondence to Mingyi Hung.

Additional information

Accepted by Gary Biddle, Area Editor, 3 December 2017. This article has been with the authors for two revisions.

Appendix: Variable Definition

Appendix: Variable Definition

Management Earnings Forecasts Variables

NForecast: The number of management earnings forecasts issued over the fiscal year.

Freq: The natural logarithm of one plus the number of management earnings forecasts over the fiscal year.

Conference Call Variables

Freq_call: The natural logarithm of one plus the number of conference calls over the fiscal year.

Variables of Interest

PC: A dummy variable indicating whether a firm is politically connected based on Faccio (2006). The variable is equal to one if at least one of a firm’s large shareholders (anyone directly or indirectly controlling at least 10 percent of votes) or top directors (CEO, chairman of the board, president, vice-president, or secretary) is a minister or a head of state, a member of parliament, or is closely related to a top official, and zero otherwise.

GovParli: A dummy variable equal to one if a firm is connected to a minister or a head of state, or to a member of parliament, based on Faccio (2006).

Relation: A dummy variable equal to one if a firm is indirectly connected to a top government official through close friendship or other relations, based on Faccio (2006).

PCplus: A dummy variable equal to one if a firm is politically connected based on Faccio (2006) or a firm’s largest shareholder is a government controlling at least 20 percent of the votes, and zero otherwise.

Country-Level Variables

XborderRestrict: A dummy variable that equals one if there is any restriction on the purchase of securities or outward direct investment in a specific country, and zero otherwise, based on “Exchange Arrangements and Exchange Restrictions” from the IMF.

RegScore: An index indicating country-level regulations that prohibit or set limits on the business activities of public officials developed by Faccio (2006). It is formed by adding (1) restrictions on ownership by members of parliament (MPs), (2) restrictions on directorships by MPs, (3) restrictions on MPs in constitution, (4) restrictions on ownership by ministers, (5) restrictions on directorships by ministers, (6) restrictions on ministers in constitution. The index ranges from zero to six, with higher scores indicating tighter regulations.

LnGDP: The natural logarithm of GDP per capita in US$ in 2001, based on the World Development Indicators Database from the World Bank.

Legal Origin: A country’s legal origin based on La Porta et al. (1998).

German: A dummy variable equal to one if a country’s legal origin is German, and zero otherwise.

French: A dummy variable equal to one if a country’s legal origin is French, and zero otherwise.

Scandinavian: A dummy variable equal to one if a country’s legal origin is Scandinavian, and zero otherwise.

Stockmktefficiency: A country-level stock market efficiency index averaged over 2002–2004, which indicates whether stock markets provide adequate financing to companies (El Ghoul et al., 2017).

ICRGCorrupt39: The International Country Risk Guide’s assessment of the corruption in government based on La Porta et al. (1998). Higher scores indicate “high government officials are likely to demand special payments” and “illegal payments are generally expected throughout lower levels of government” in the form of “bribes connected with import and export licenses, exchange controls, tax assessment, policy protection, or loans.”

GermanCorrupt: The German exporters’ corruption index developed by Neumann (1994). The index ranges from zero to five, with higher scores indicating higher levels of corruption.

Other Firm-Level Variables

Capital: A dummy variable equal to one if a firm’s headquarters is located in the nation’s capital city in year t, and zero otherwise.

IndustryPC: The percentage of politically connected firms in a two-digit SIC industry in year t.

TA: Total assets in US dollars in year t.

Size: The natural logarithm of total assets in US dollars in year t.

Age: A company’s age in year t, measured as the number of years since the IPO date.

LnAge: The natural logarithm of a company’s age in year t.

FreeCash: Free cash flows, measured as operating income before depreciation and amortization minus income taxes less changes in deferred taxes, interest expense, preferred dividends, and common dividends, deflated by total assets in year t.

Herf: The Herfindahl index, measured at the two-digit SIC level in year t.

ROA: Net income divided by total assets in year t.

MTB: Market capitalization divided by book value of equity in year t.

LEV: Total long-term debts divided by total assets in year t.

EarnVol: Standard deviation of annual earnings deflated by total assets over 5 years ending in year t − 1.

RetVol: Standard deviation of annual stock returns over 5 years ending in year t − 1.

NAnalyst: The number of analyst following in year t.

BadNews: A dummy variable equal to one if a firm has a negative earnings change from year t − 1 to t, and zero otherwise.

EquityIssue: A dummy variable equal to one if the split-adjusted number of shares outstanding increases by 20 percent or more in year t+1, and zero otherwise.

Cross: A dummy variable equal to one if a firm is cross-listed in the US in year t, and zero otherwise.

BigN: A dummy variable equal to one if a firm has a Big N auditor in year t, and zero otherwise.

IAS: A dummy variable equal to one if a firm’s accounting standards is IAS/IFRS in year t, and zero otherwise.

Closeheld: The number of closely held shares divided by total shares outstanding in year t.

Lambda: The inverse Mills Ratio calculated based on the Heckman two-stage model.

EarnQuality: Standard deviation of 5 year performance-matched discretionary current accruals, as in Chaney et al. (2011).

FError: The absolute value of the difference between the last consensus forecast prior to earnings announcement and actual earnings, deflated by stock price at the beginning of the year.

Family: A dummy variable equal to one if a firm’s largest shareholder is a family or individual controlling at least 20 percent of the votes, and zero otherwise.

State: A dummy variable equal to one if a firm’s largest shareholder is a government controlling at least 20 percent of the votes, and zero otherwise.

Post: A dummy variable equal to one if a firm-year falls in the post-election period (i.e., 2 years after the election), and zero if it falls in the pre-election period (i.e., 2 years before the election).

Yr − 1: A dummy variable equal to one if a firm-year falls in the fiscal year prior to the election year, and zero otherwise. The election year is defined as in Julio & Yook (2012).

Yr0: A dummy variable equal to one if a firm-year falls in the election year, and zero otherwise. The election year is defined as in Julio & Yook (2012).

Yr1: A dummy variable equal to one if a firm-year falls in the fiscal year after the election year, and zero otherwise. The election year is defined as in Julio & Yook (2012).

Yr2: A dummy variable equal to one if a firm-year falls in the fiscal year 2 years after the election year, and zero otherwise. The election year is defined as in Julio & Yook (2012).

Additional line items: The number of line items forecasted (operating cash flows, sales, EBITDA, operating income, income before taxes, income before extraordinary items and discontinued operations, net income, and total comprehensive income).

Explanation: A dummy variable equal to one if the earnings forecast is accompanied by an explanation, and zero otherwise.

Loss: A dummy variable equal to one if the forecasted earnings is a loss, and zero otherwise.

Specificity: A categorical variable indicating the form of management earnings forecasts, which equal to one for qualitative forecasts; two for a one-sided forecasts; three for a range forecast; and four for a point forecast.

Litigious: A dummy variable equal to one if a firm operates in an industry facing high litigation risk, namely industries with primary four-digit SIC code 2833–2836, 8731–8734 (bio-tech), 3570–3577 (computer hardware), 3600–3674 (electronics), 7371–7379 (computer software), 5200–5961 (retailing), 4812–4813, 4833, 4841, 4899 (communications), or 4911, 4922–4924, 4931, 4941 (utilities).

AdjR&D: The R&D expense deflated by sales, adjusted by the two-digit SIC industry average.

Others

Year FE: Indicator variables for years.

Industry FE: Variables indicating industry membership based on two-digit SIC codes.

Country FE: Indicator variables for countries.

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Hung, M., Kim, Y. & Li, S. Political connections and voluntary disclosure: Evidence from around the world. J Int Bus Stud 49, 272–302 (2018). https://doi.org/10.1057/s41267-017-0139-z

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