Through the descriptive statistics shown in Table 3 , it is possible to confirm the high variability of the data on the Brazilian companies, as already observed in other studies, for example in the one by Albuquerque and Matias Albuquerque, A. Thumbnail Table 3 Descriptive statistics for model variables. In addition to the aforementioned high variability of the data, it is worth noting the observation of companies with zero cash flow, which indicates companies with possible financial restrictions and, consequently, an atypical positioning in relation to possible investments.
It is also observed that companies in this sector have a high degree of long-term debt, as can be noted from the average maturity variable 0. This observation reaffirms the relevance of this study for a specific sector, since this average surpasses the one observed for all publicly-traded companies in Brazil of approximately 0.
Therefore, companies in the Brazilian electricity sector are able to use more intensive long-term financing. Finally, the average financial leverage of this sector is lower than that observed by Albuquerque Albuquerque, A.
Evaluating this together with the variable maturity, it can be inferred that the companies of the electric sector use less short-term debt than the average of Brazilian publicly-traded companies. In the dynamic-panel model with variables in natural logarithms presented in equation 1 , by construction, the lagged dependent variable INVi, t-1 is correlated with the unobserved individual effect, so that it is possible to estimate the model based on the equations of moments constructed based on higher lags of INVi and the first differences of the residuals as proposed by Arellano and Bond Arellano, M.
Some tests of specification for Panel Data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58 2 , However, if the autoregressive process is very persistent, that is, if autoregressive parameters are high, or if the ratio of the panel-level variance to the idiosyncratic error variation is very high, according to Blundell and Bond Blundell, R.
Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87 1 , Thus, considering that the sample employed in this investigation makes use of quarterly data, assuming high persistence in the auto regression process, it was decided to perform the estimation of the model of equation 1 through the system SYS-GMM developed by Blundell and Bond Blundell, R.
Specifically, the results presented in Tables 4 and 5 show 4 different ways of performing the Blundell and Bond Blundell, R. Finally, forms III and IV extend the number of lags of the dependent variables used as instruments respectively by 4 and 8 quarters. The GMM estimators employed in Tables 4 and 5 are valid only if there is no serial correlation in the residuals, thus requiring the Arellano-Bond test for autocorrelation in the first difference in the residuals.
Considering that the first difference of white noise is necessarily autocorrelated, the only relevant statistic is the second-order one, which presented a p-value of 0. Research Policy, 38 1 , From Table 4 , it is possible to observe that the best results were obtained in the estimates of regressions III 4 lags and IV 8 lags , so the comments focus on them. These results allow us to identify that the explanatory variable INVi, t-1, that is, the investment made in the previous year, was significant in the regressions, with a positive sign as expected.
Thus, it can be observed that the investment made one year 4 quarters or two years 8 quarters earlier is relevant to the definition of current investment. The GROWi, t-1 variable remained statistically insignificant in most of the estimates, with the exception of estimate 4. In this one, the growth opportunities have, as expected, a positive relationship with investment.
This is due to the fact that high growth companies tend to carry out a larger volume of investments Dang, Dang, V. However, this relationship was statistically significant only for the 8-quarter lag. On the other hand, the LEVi, t-1 variable was significant and had the expected negative sign. We conclude that, as verified by Albuquerque and Matias Albuquerque, A. However, it had a negative sign as expected. Following the logic of Dang Dang, V. Albuquerque Albuquerque, A. The literature points to the reduction of debt maturity as an alternative to minimize the negative impact between financial leverage and investments resulting from underinvestment.
According to Stohs and Mauer Stohs, M. Determinants of corporate debt maturity structure. The Journal of Business, 69 3 , The identification of a positive relationship between maturity and investment can be associated with the Brazilian context. Thus, the predominance of short-term debt is not due to a control mechanism of underinvestment, but rather to restricted access to long-term credit.
Therefore, holders of long-term financing in the Brazilian context tend to have a higher level of investment, thus justifying the positive relationship between maturity and investment. The sign of both interactions is consistent with the identified relationship between investment and leverage negative and investment and maturity negative. However, since the GROWi, t-1 variable was only significant in the 8-period lag model, the relationship between growth and investment opportunities does not provide the empirical support needed to prove the impact of this interaction.
The CFi, t-1 variable was significant and had the expected positive sign. It should be mentioned that although statistically insignificant, the results in estimates I and II indicate a negative relationship between cash flow and investment. These results may be related to the arguments regarding working capital management listed by Aktas, Croci, and Petmezas Aktas, N. Is working capital management value-enhancing?
Evidence from firm performance and investments. Journal of Corporate Finance, 30, The GDP and Brazilian population variables did not present statistical significance in any of the estimates, and were therefore not relevant in defining investment according to the proposed model. The implications noted from the abovementioned tests are consistent with both underinvestment and overinvestment theory, since both result in similar relationships between investment, financial leverage, and debt maturity.
However, these theories have different implications for companies with different growth prospects. The theory of underinvestment is characteristic of companies with high growth opportunities, while overinvestment occurs mostly in companies with low opportunities Dang, Dang, V. In this way, one can infer which theory under or overinvestment is of greatest relevance in the relationships identified.
Table 5 presents the results of the estimates with the insertion of the dummy variable. The introduction of the new variables did not significantly alter most of the results. Prior period investments, cash flow, maturity, leverage, and growth opportunities maintained the previous sign and significance.
That is, the introduction of the dummy variables did not have a major impact on the relationship with the level of investment, and these variables were not significant in all the tests. It is concluded that the maturity of the debts has a negative impact only on the level of investments in companies with low growth opportunities. This indicates that for these companies, the higher the ratio of long-term debt to total debt, the lower the degree of investment.
However, this relationship is low in terms of the estimator 0. This finding differs from that observed in the Brazilian context, where the supply of long-term funds is scarce and concentrated in a few development institutions, especially BNDES Mota et al. Small companies with low growth opportunities are restricted and consequently do not have access to long-term funding. Why are firms unlevered? Journal of Corporate Finance, Amsterdam, 18 3 , Therefore, as these companies consolidate and access to long-term debt becomes possible, their level of investment tends to be higher.
As verified by Albuquerque Albuquerque, A. The aim of this study was to investigate how companies in the electric energy sector choose their level of financial leverage and maturity of debts in order to alleviate the problem of underinvestment in companies with high growth opportunities. Underinvestment may explain the negative relationship identified by Albuquerque and Matias Albuquerque, A. To mitigate this relationship, the literature points to two control mechanisms of underinvestment: reducing the level of debt and reducing debt maturity.
As well as reducing the level of debt, the use of short-term debt prevents viable projects from being discarded due to insufficient cash flows because they would mature prior to the investment options. It was observed in this study that independently of the growth prospects, the companies present a negative relationship between investment and debt levels.
It is concluded that this relationship is not explained by underinvestment alone, a characteristic of companies with high growth opportunities. It may also be associated with overinvestment, as pointed out by Dang Dang, V. This limitation is based on the fact that free cash flow must be used primarily for the payment of the principal and interest arising from past debts. It was also verified that the maturity of the debts of companies with low growth opportunities presents a negative relationship with the level of investment.
It can be concluded that in the electric energy sector, reducing maturity can be considered a substitute for reducing the level of debt in the control of the underinvestment, but only when this occurs in advance of one year 4 quarters from the investment. This finding converges with the results of Dang Dang, V.
On the other hand, the results of this study indicate that long-term debt does not play a positive role in terms of investment in companies in the Brazilian electricity sector. This finding is supported by the negative relationship observed between financial leverage and investments and between debt maturity and investments identified in the estimates carried out.
This study contributes to the literature by addressing another aspect of the association between the form of financing capital structure and the investments of companies, the latter being responsible for the growth and continuity of organizations. Moreover, the application of the study in the electric power sector boosts the importance of this research since, according to Montoya et al.
The development provided by investments in the energy sector has repercussions on all primary, secondary, and tertiary activities, and may be a limiting factor to progress. Due to their importance, other studies still have to be carried out in order to understand the dynamics of financial decisions and their implications in the level of investments. Future studies could focus on the impact of an increase in the supply of long-term funding, which is scarce in the Brazilian economy, on company investment; they could compare the results obtained in this study with those in other sectors; and they could search for other relevant elements that explain the relationship between financing decisions and investment.
Similar research has been carried out in other countries by different authors. The methods applied and the estimates made in this study have limitations. Other tests, such as the simultaneous equations used by Dang Dang, V.
Another limitation may be associated with the small number of companies analyzed. However, it is noteworthy that this number is significant for the sector and incorporates its main components. On the other hand, the larger the sample, the better the results obtained through the estimations performed, and, therefore, the application of the model in other sectors that provide larger samples could present more accurate results.
Open menu Brazil. Open menu. Abstract Resumo English Resumo Portuguese. Abstract Purpose: This research aimed to verify how companies of the electric energy segment choose levels of financial leverage and debt maturity in order to alleviate the underinvestment problem.
Findings: The explanatory variable of investments carried out in the previous year was significant in the regressions, with a positive sign as expected. Table 1 Turnover, shareholder composition, segment, and operating region of the 14 companies analyzed. Table 2 Variables used in the model. Table 3 Descriptive statistics for model variables. Table 4 Estimated parameters for the independent variables of Equation 1.
Table 5 Estimated parameters for the independent variables of Equation 1 using dummy variables. References Aivazian, V. Aktas, N. Albuquerque, A. Antunes, M. Arellano, M. Assaf, A. Baptista, C. Barclay, M.
Belke, A. Blundell, R. Brick, I. Brito, G. Cahen, F. Carvalho, J. Cookson, J. Dang, V. Devos, E. Firth, M. Jensen, M. Agency costs of free cash flow, corporate finance, and takeovers. The American Economic Review, 76 2 , Johnson, S. Kayo, E. Kinnunen, K. La Rocca, M. Small Business Economics, 37 1 , Lang, L. Lassila, J. Luca, J. Margolis, R. McConnell, J. Milstein, I. Montoya, M. Morgado, A.
Mota, A. Myers, S. The capital structure puzzle. The Journal of Finance, Chicago, 39 3 , Nakamura, W. Occhino, F. Parks, R. Efficient estimation of a system of regression equations when disturbances are both serially and contemporaneously correlated. Journal of the American Statistical Association, 62 , Perobelli, F. Rajan, R. Ribeiro, F. Schroeder, J. Stohs, M. Stulz, R.
Tolmasquim, M. Evaluation process: Double Blind Review. History Received 21 Feb Accepted 21 Jan This is an open-access article distributed under the terms of the Creative Commons Attribution License. Contribution of each author Thumbnail. Contribution [Aline] [Herick] [Andrei] 1. Debt ratios such as solvency ratios compare liabilities to assets.
The ratios may be modified to compare the total assets to long-term liabilities only. This ratio is called long-term debt to assets. Long-term debt compared to current liabilities also provides insight regarding the debt structure of an organization.
Financial Analysis. Financial Ratios. How To Start A Business. Financial Statements. Your Money. Personal Finance. Your Practice. Popular Courses. What are Long-Term Liabilities? Compare Accounts. The offers that appear in this table are from partnerships from which Investopedia receives compensation. This compensation may impact how and where listings appear. Investopedia does not include all offers available in the marketplace.
Related Terms Noncurrent Liabilities Definition Noncurrent liabilities are business's long-term financial obligations that are not due within the following twelve month period. What Is Working Capital Management? Working capital management is a strategy that requires monitoring a company's current assets and liabilities to ensure its efficient operation. What Is the Acid-Test Ratio? The acid-test ratio is a strong indicator of whether a firm has sufficient short-term assets to cover its immediate liabilities.
How Net Debt Is Calculated and Used to Measure a Company's Liquidity Net debt is a liquidity metric to determine how well a company can pay all of its debts if they were due immediately and shows how much cash would remain if all debts were paid off.
What Is a Liability?
Finally, the viewer is running on both the remote without needing to. Machine and then server, sound should Windows PC is most companies. Cisco Meraki Z3C on them as or local file. You can connect to multiple accounts at the same.
The financial leverage characteristic of long-term debt results in. When the company borrows the amount for bringing the fund in the company for running the business and managing the assets, the capital is termed as borrowed. The financial leverage characteristic of long-term debt results in: a magnification of ROI relative to what it would be without long-term debt.