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Determinants of firm value in Latin America: an analysis of firm attributes and institutional factors

Abstract

This study analyses the impact of firm-level variables as well as country-level institutional factors on firm value in the Latin American region. The theoretical framework used to develop the research hypotheses has followed a corporate governance approach. The sample includes public firms from Argentina, Brazil, Chile, Colombia, Mexico, and Peru for the 1997–2013 period. The main findings indicate that ownership concentration, capital structure, and dividend policy are significant drivers of the market value of the firm. The results from determinants at the country-level show that legal enforcement and regulatory systems positively impact the market value of the firm, whilst the findings show unexpected results concerning the development of the financial system.

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Notes

  1. 1.

    Crisóstomo et al. (2014) claim that nonfinancial firms as blockholders in Brazil bring more active management monitoring; reduce the likelihood of overinvestment; lower the change of managerial discretionary behavior; reduce the agency conflicts between ownership and control; and improve the information with financial markets. In that sense, Dyck and Zingales (2004) analyze the premium paid for control blocks in 37 countries. Their findings suggest that the premium is 27 % for Argentina and Colombia, 65 % for Brazil, 18 % for Chile, 34 % for Mexico, and 14 % for Peru.

  2. 2.

    The free cash flows are those available for the discretional use of managers once the future growth opportunities with positive net present values have been financed.

  3. 3.

    Covenants are particular clauses in debt contracts of firms that restrict business policy, giving creditors the possibility of putting precise actions into force and enhancing their incentives to monitor (Rajan and Winton 1995).

  4. 4.

    Despite of these major limitations of the reduced-form OLS estimations, for robustness purposes to double check our results, the models were also estimated under this method. In general, although the signs of the most important parameters were the same as those reported in this work; the magnitude of the regression coefficients were quite different. For space-saving reasons, outputs under OLS estimations are not tabulated but are available upon request to the authors. The authors appreciate the valuable comments of one of the referees in addressing properly the estimation method through panel data analysis with robust standard errors.

  5. 5.

    Love (2011) argues that neither the fixed-effect nor the instrumental variables techniques fully remove the possibility of time varying omitted variables, on the one hand; and none of these techniques address reverse causality, on the other hand.

  6. 6.

    However, this is not considered a problem because \(\Delta \varepsilon_{it} = \varepsilon_{it} - \varepsilon_{it - 1}\) might be correlated with \(\Delta \varepsilon_{it - 1} = \varepsilon_{it - 1} - \varepsilon_{it - 2}\) given that both share the common term \(\varepsilon_{it - 1}\).

  7. 7.

    We used the Fisher-type test because it does not require strongly balanced data. This test for panel data unit roots follows a meta-analysis perspective. That is, this test conducts unit-root tests for each panel individually, and then combines the p-values from these tests to produce an overall test.

  8. 8.

    Financial firms, for example, have very different financing policies which are determined by regulatory constraints, reserve requirements, and portfolio risk, among other variables, which ensure the financial decisions are differently determined from non-financial firms. Thus, since in our work we use leverage as an explanatory variable, we had to remove all financial firms.

  9. 9.

    The latest update took place in November 2013. Information can be downloaded from the permanent URL http://go.worldbank.org/X23UD9QUX0.

  10. 10.

    The latest update took place in September 2014. Information can be downloaded from www.govindicators.org.

  11. 11.

    We appreciate the thorough recommendation of one of the anonymous referees to measure the dependent variable in this way.

  12. 12.

    A much better way to analyse the ownership structure is based on the relationship between the cash flow rights and voting rights of the major/controlling shareholder. However, since we do not account for this sort of information from our firms´ sample, we had to measure the ownership concertation based only on the direct voting rights. Despite this particular limitation in the construction of these variables, the measure applied in the empirical analysis has also been widely used in the previous empirical literature (Gupta et al. 2009; Jara et al. 2008; López and Crisóstomo 2010).

  13. 13.

    This is a consequence of the gradual adoption of the IFRS of the firms in our sample during the period of analysis. For instance, Brazil and Chile adopted the international accounting standards in 2010, Argentina in 2011, Mexico and Peru in 2012 and Colombia in 2015 (outside of our period of analysis).

  14. 14.

    The computation of the critical value in the first regression of Table 3 is done by calculating the first derivative of this regression with respect to the \(OWN\) variable, and then making it equal to zero as \(\frac{\partial FV}{\partial OWN} = 0\). After that we have to solve for \(OWN\) which represents the point at which the firm value is maximized. Specifically speaking, this solution takes the form: \(\frac{\partial FV}{\partial OWN} = 2.202 - 2 \times \left( {2.937 \times OWN} \right) = 0\). Consequently, when \(OWN = 37.50\,\%\) the firm value is maximized. Idem calculations are done for all the other regressions which include \(OWN^{2} .\)

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Acknowledgments

We thank the valuable comments of Burcin Yurtoglu, Stijn Claessens, Yishay Yafeh, Francisco Urzúa, Mauricio Jara, Alesia Slocum and the seminal participants in the 5th International Conference on Corporate Governance in Emerging Markets at Leipzig, Germany (2015).

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Correspondence to Paolo Saona.

Appendix

Appendix

Sector-adjusted firm value

$$FV = \frac{{MkCptz_{it} + TD_{it} }}{{TA_{it} }}$$

where MkCptz it corresponds to the market capitalization of the firm i in year t. TD it is the total short- and long-term debt and TA it is the firm´s total assets. Following López and Crisóstomo (2010), the sector-adjusted firm value is then computed by subtracting the median value for the firms in the same industrial sector, year and country.

Corporate ownership concentration

OWN corresponds to the percentage of outstanding shares in the hands of the controlling shareholder.

INSOWN is the percentage of closely held shares which includes the shares in the hands of executives, directors, controlling shareholder, cross holdings (e.g. related parties), government, and employees.

Capital structure

$$LEV = \frac{{TD_{it} }}{{TA_{it} }}$$

Dividend policy

$$DIV1 = \frac{{DPS_{it} }}{{EPS_{it} }}$$

where DPS it is the annual dividend per share and EPS it is the earnings per share.

$$DIV2 = \left\{ {\begin{array}{*{20}l} {1,} \hfill & {if\, the\, country \,requires \,mandatory \,dividends} \hfill \\ 0 \hfill & {otherwise} \hfill \\ \end{array} } \right.$$

Firm size

$$SIZE = Ln\left( {TA_{it} } \right)$$

Profitability

$$ROA = \frac{{EBT_{it} }}{{TA_{it} }}$$

where EBT it is the pretax income.

Insolvency risk

$$Z = 6.56WC_{it} + 3.26RE_{it} + 6.72EBIT_{it} + 1.05BvE_{it} + 3.25$$

where WC it is the working capital over total assets, RE it is the retained earnings over total assets, EBIT it is the earnings before interest and taxes, and BvE it is the book value of equity over total liabilities.

Corporate diversification

DIVERSIF corresponds to the number of industry groups in which a firm operates according to the SIC (Standard Industrial Classification) codes.

IFRS

$$IFRS = \left\{ {\begin{array}{*{20}l} {1,} \hfill & {if\, the\, company \,uses \,IFRS} \hfill \\ {0,} \hfill & {otherwise} \hfill \\ \end{array} } \right.$$

where IFRS is the International Financial Reporting System.

Legal and regulatory systems

All the following legal system variables were obtained from Kaufmann et al. (2011) where the indexes range from approximately −2.5 (weak) to 2.5 (strong) governance performance, although for our sample these variables do not have such extreme values.

  1. 1.

    VA measures the Voice and accountability.

  2. 2.

    PS measures the Political stability and absence of violence/terrorism

  3. 3.

    GE measures the Government effectiveness.

  4. 4.

    RQ measures the Regulatory quality.

  5. 5.

    RL measures the Rule of law.

  6. 6.

    CC measures the Control of corruption.

Bank Concentration

BANKCONC is the market share of the three largest banks by country.

Financial development

All the following financial development variables were obtained from Beck et al. (2000).

DBAGDP is the claims on domestic real nonfinancial sector by deposit money banks as a share of GDP, calculated using the following deflation method: \(\frac{{0.5\left[ {\frac{{F_{t} }}{{P_{et} }} + \frac{{F_{t - 1} }}{{P_{et - 1} }}} \right]}}{{\left[ {\frac{{GDP_{t} }}{{P_{at} }}} \right]}}\); where F is deposit money bank claims, P e is end-of the period Consumer Price Index (CPI), and P a is average annual CPI.

PCBGDP is the Private credit by deposit money banks and other financial institutions as a share of GDP, calculated using the following deflation method: \(\frac{{0.5\left[ {\frac{{F_{t} }}{{P_{et} }} + \frac{{F_{t - 1} }}{{P_{et - 1} }}} \right]}}{{\left[ {\frac{{GDP_{t} }}{{P_{at} }}} \right]}}\); where F is the credit to the private sector, P e is end-of the period Consumer Price Index (CPI), and P a is average annual CPI.

BCBD is the private credit by deposit money banks as a share of demand, time and saving deposits in deposit money bank.

SMKGDP which is the value of listed shares to GDP, calculated using the following deflation method: \(\frac{{0.5\left[ {\frac{{F_{t} }}{{P_{et} }} + \frac{{F_{t - 1} }}{{P_{et - 1} }}} \right]}}{{\left[ {\frac{{GDP_{t} }}{{P_{at} }}} \right]}};\) where F is the stock market capitalization, P e is end-of the period CPI, and P a is average annual CPI.

SMKVTGDP is the total shares traded on the stock market exchange to GDP.

SMKTO is the ratio of the value of total shares traded to average real market capitalization, the denominator is deflated using the following method: \(\frac{{\frac{{T_{t} }}{{P_{at} }}}}{{0.5\left[ {\frac{{M_{t} }}{{P_{et} }} + \frac{{M_{t - 1} }}{{P_{et - 1} }}} \right]}};\) where T is total value traded, M is the stock market capitalization P e is end-of the period CPI, and P a is average annual CPI.

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Saona, P., San Martín, P. Determinants of firm value in Latin America: an analysis of firm attributes and institutional factors. Rev Manag Sci 12, 65–112 (2018). https://doi.org/10.1007/s11846-016-0213-0

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Keywords

  • Corporate governance
  • Firm value
  • Emerging markets
  • LATAM

JEL Classification

  • G32