the relationship between productivity and leverage

by 

greg filbeck and 

raymond F. Gorman 

 

Greg Filbeck greg@schweser.com is an Adjunct Professor of Finance, University of Wisconsin, LaCrosse and Senior Vice-President, Schweser Study Program. Raymond F. Gorman gormanrf@muohio.edu is a Professor of Finance, Richard T. Farmer School of Business Administration, Miami University (Ohio).

Examined in this article is the relationship between the productivity of a corporation's assets and the level of debt in the firm's capital structure. Overall, a positive relationship between measures of productivity and leverage is revealed. However, the strength of this relationship varies among industries. These differences may be attributed to industry-specific characteristics such as regulation and competition. The relationship between productivity and leverage was stronger during the 1970s and 1980s, but it weakened during the 1990s. Greater diversification and the use of more risk management techniques may account for this trend. The strength of the relationship between productivity and leverage varies across industries and on the ratios used.

Introduction

Examined here is the relationship between the productivity of a corporation's assets and the level of debt in the firm's capital structure. During the post World War II era until 1973, the annual rate of growth of productivity in the United States was about 2.4 percent. However, growth slowed considerably over the next two decades. A recent article entitled "Solving the Paradox" that appeared in The Economist (2000) noted that, while growth in labor productivity slowed to an average of 1.4 percent per year in 1975-95, advances in computer technology has recently reversed the downward trend. Since 1996, growth in labor productivity in Americaís business sector has increased to an annual average of 2.9 percent. When the effects of increases in productivity caused by the computer technology sector are taken out of the mix, productivity numbers remain relatively flat. The primary purpose of this study reported on here is to determine whether the reduction (and recent increase) in productivity in the United States during the past twenty years (See Krugman, 1990.) has occurred because of, or in spite of, the corporate restructuring that has taken place in the past two decades.

Productivity is influenced by changes in technology, capital investment, purchased inputs from outside a sector, capacity utilization, returns to scale, and workforce skill and effort. Eldridge (1999) argues that the existence of a possible upward bias in the Consumer Price Index (CPI) may result in an understatement of productivity growth. However, even accounting for this bias does not account for the lower productivity numbers observed during the period between 1975 and 1995. There is no dearth of possible solutions to improve productivity. Most of those proposed center on the need for more or less government intervention (See Krugman.) However, there is little debate as to the importance of high productivity growth to the U.S. economy.

Literature Review

Capital structure

Any discussion of a relationship between a companyís capital structure and any other value determining aspect of a company must begin with a review of the context for the issue provided by Modigliani and Miller (M&M). In their seminal article, Modigliani and Miller (1958) argue that, with perfect capital markets, capital structure is irrelevant and that the value of the firm is determined by the earning power of its assets, not how the assets are financed. As such, in an M&M world, there should be no relationship between leverage and productivity.

Since M&M, many factors have been identified that may help to explain a firmís capital structure decision. Among the more prominent are corporate taxes (Modigliani and Miller, 1963) and bankruptcy costs (Baxter, 1967). More recently, firm-specific factors such as research and development, advertising, depreciation, growth opportunities, and risk have been identified as determinants of a firmís capital structure.

For example, Balakrishnan and Fox (1993) found that firm specific factors account for over 52 percent of the variance in capital structure. Another firm specific factor that may explain variation in capital structure is the nature of the companyís assets. One form of a hypothesized relationship between leverage and asset type can be found in Williamson's (1988) asset redeployment theory of capital structure. Companies with assets that can easily be redeployed for other uses (e.g., an airline company) and as such have assets that make for good collateral in a loan will likely be able to borrow more easily than companies with assets that are very specific to that firm (e.g., biotechnology companies). These latter companies would likely find debt more expensive due to the low collateral value of their assets.

The interrelationship between assets and leverage is also a component of the free cash flow theory of Jensen who asserts that the controlling aspect of debt should induce companies to manage its assets more efficiently by investing in positive NPV projects. According to Jensen, existing shareholders would prefer that the firm issue new debt rather than equity since the required interest payment on debt induces the managers to act in the interest of stockholders. With a fixed debt payment, an inappropriate use of the investors' money could precipitate a default on debt, bankruptcy proceedings and the possibility that the managers could lose their jobs. To the extent that the pressure to perform in order to meet debt payments increases the productivity of a companyís assets, then we also expect to find a positive relationship between leverage and asset productivity.

Jensen also notes that the controlling effects of debt will be less evident in companies that are rapidly growing with large and profitable investment opportunities but no free cash flow. For such companies, their regular visits to the capital markets should afford investors the opportunity for an adequate assessment of the potential investments. It is in the low growth industries with high levels of free cash flows that leverage should provide the greatest benefit. Lang et al. (1996) find that for companies with good investment opportunities, there is no relationship between leverage and growth whereas they find a negative relationship between leverage and growth for companies with poor investment opportunities. They suspect that for companies with poor growth opportunities, leverage has the effect of restraining growth in a way that benefits shareholders. They conclude that this lends support to Jensenís free cash flow theory.

Anderson and Prezas (1999) show how increased debt can lead to an increased managerial effort in operating the firm by additional investment in tangible assets, thus increasing their productivity.

Productivity

A casual glance at the debt levels of U.S. corporations in the past decades reveals that there was indeed an increase in corporate leverage at the time when the productivity growth rate fell. Between 1964 and 1991, the book value debt-equity ratio of nonfinancial U. S. corporations went from 59.9 percent to 113 percent, while the market value debt-equity ratio went from 37.7 percent to 52 percent. [Coffee et al., 1988; Rajan and Zingales, 1995)] The smaller increase in the market value debt-equity ratio is likely due to the rapid rise in stock prices that took place during the 1990s.

These changes can in part be attributed both to the mergers that took place during this period as well as the extra debt that corporations incurred in order to avoid takeover. Between 1974 and 1985 there were about 31,000 mergers and acquisitions. In 1997 alone, there were 10,700 mergers, acquisitions, and spinoffs with a market value of $919 billion in the U.S. [McClenahen, 1998] This study will attempt to show whether this extra debt has affected aspects of corporate performance related to productivity.

Much of the existing literature on relationship between productivity and leverage has centered on firms that have undergone a leveraged buyout. (See e.g., Lichtenberg and Siegel, 1990; Ravencraft and Scherer, 1987.) These studies show an increase in productivity, particularly in the early years, in the wake of a leveraged buyout and its attendant increase in leverage. In addition, Winn (1997) finds that debt reduction did not result in productivity increases. Her sample consisted on those firms whose productivity declined during periods of aggressive growth.

Nickell and Nicolitsas (1999) find a small positive effect on productivity caused by increases in financial pressure. Financial pressure is defined in their research as increases in the ratio of interest payments to cash flow. This too is consistent with Jensenís free cash flow theory. This study will also provide additional evidence as to effect of leverage on the management of an organizationís assets, both tangible and human. Our presumption is that firms that experience disciplinary effects of debt will manage their tangible assets more efficiently and will have more productive workers. Both of these factors should in turn lead to higher performance. In this sense, a positive relationship between leverage and productivity will be viewed as evidence supporting Jensenís free cash flow theory. Demonstrating a relationship between productivity and leverage will have important policy implication for financial managers.

Hypotheses

The principal hypothesis tested in this study is that the productivity of a firm's assets is positively related to the amount of debt used. Four combinations of different productivity measures (sales per employee, total asset turnover, and fixed asset turnover) and leverage measures (debt to book value of equity and debt to market value of equity) are used in this study. Finding a positive relationship between productivity and leverage would be consistent with Jensen's free cash flow hypothesis.

Data and Methodology

The data on productivity and leverage comes from the Standard and Poorís Research Insight database. The firms selected come from industries in which firms are more likely to be operating within a relatively narrow line of business (e.g., the computer and pharmaceutical industries) as opposed to industries where firms tend to have more conglomerate aspects e.g., the beverages and tobacco industries). Our sample period extends from 1978 through 1999. Data availability results in limitations of industry information during certain years.

The contemporaneous relationship between productivity and leverage will be examined cross sectionally by regressing asset utilization ratios as a proxy for productivity (the dependent variable) against leverage (the independent variable). A total of four regressions will be employed: 

  1.  Sales to Fixed Assets against Total Debt to Book Value of Equity, 
  2.  Sales to Total Assets against Total Debt to Market Value of Equity, 
  3.  Sales per Employee to Total Debt against Market Value of Equity, and 
  4.  Sales to Fixed Assets against Total Debt to Market Value of Equity.  

Productivity measures are regressed against pre-filtered, current measures of leverage for each of the industries chosen for the study. A list of the 27 industries chosen for use in the study is shown in Table 1.

Results

Summary results for the 27 industries and the four regressions pairings between productivity and leverage are shown below in Table 1. Overall, the relationship between productivity and leverage varies depending on the industry. In addition, the type of ratio selected to measure productivity and leverage seems to impact the strength of the relationship. All four productivity/leverage regression combinations indicate that positive relationships exist. The relationships, overall, seem stronger during the 1970s and the 1980s, and seem to weaken during the 1990s. In some industries for some measures, the relationships even turn negative.


Table 1

Regressions and Industry Descriptions. Summary results appear in cells with the number of statistical positive relationships and statistically negative relationships. Numbers in parenthesis indicates the numbers of years of complete data available.

General model:  Productivity measuret =

+

( Leverage  measure)

+

 

Industry

dbsf*

dmsa**

dmse***

dmsf****

ap = Aircraft Parts

6+,2- (20)

0+,0- (21)

0+,0- (18)

6+,0- (20)

at = Air Transportation

3+,4- (20)

2+,0- (21)

4+,0- (18)

1+,0- (20)

co = Computer

5+,0- (20)

6+,0- (21)

3+,0- (18)

5+,0- (20)

cs = Clothing Stores

2+,0- (20)

1+,0- (15)

1+,0- (18)

1+,0- (20)

cp = Chemical Products

2+,5- (21)

0+,0- (21)

4+,0- (18)

0+,0- (20)

dc = Drug Store

4+,3- (20)

0+,0- (21)

0+,1- (18)

2+,1- (20)

dr = Drug Company

1+,2- (20)

 

 

 

ds = Department Store

2+,3- (17)

 

 

 

ed = Eat and Drink

6+,0- (20)

4+,0- (21)

1+,0- (18)

3+,0- (20)

es = Electric Services

9+,0- (20)

11+, 0- (21)

2+,0- (18)

10+,0- (20)

fb = Food & Beverage

5+,0- (20)

7+,3- (21)

3+,1- (18)

4+,0- (20)

gr = Grocery

5+,0- (20)

4+,0- (15)

3+,0- (15)

1+,0- (15)

gs = Gas Production

10+,1- (20)

11+,0- (21)

0+,0- (18)

3+,0- (20)

hc = Healthcare

6+,1- (20)

 

3+,0- (18)

1+,0- (20)

hm = Hotel / Motel

3+,1- (20)

4+,0- (15)

5+,0- (18)

8+,0- (20)

ic = Industrial Chemicals

0+,0- (20)

0+,0- (21)

0+,0- (18)

0+,0- (20)

mm = Minerals & Mining

4+,0- (20)

12+,0- (21)

9+,0- (18)

10+,0- (20)

mv = Motor Vehicle

3+,0- (20)

4+,0- (21)

4+,0- (18)

10+,0- (20)

np = Newspaper

2+,2- (20)

4+,0- (21)

2+,0- (18)

3+,1- (20)

og = Oil & Gas

4+,9- (20)

11+,0- (21)

6+,0- (18)

8+,0- (20)

ox = Oil Exploration

7+,0- (20)

4+,0- (21)

4+,0- (18)

2+,0- (20)

pm = Paper & Pulp Mills

8+,0- (20)

5+,0- (21)

1+,1- (18)

4+,0- (20)

rr = Railroad

 

1+,1- (21)

0+,1- (18)

1+,1- (20)

pp = Petroleum Products

9+,2- (20)

11+,0- (21)

9+,0- (18)

9+,0- (20)

ut = Combination Utilities

16+,0- (20)

15+,0- (21)

8+,0- (18)

14+,0- (20)

pr = Printers

7+,4- (20)

3+,1- (21)

2+,0- (18)

4+,0- (20)

si = Steel Industry

5+,0- (20)

2+,0- (21)

0+,0- (18)

1+,0- (20)

             

* dbsf: Debt/Book Value of Equity and Sales/Fixed Assets

** dmsa: Debt/Market Value of Equity and Sales/Total Assets

*** dmse: Debt/Market Value of Equity and Sales/Employee

**** dmsf: Debt/Market Value of Equity and Sales/Fixed Assets

 


The first column in Table 1 summarizes the results shown in Table 2 concerning the relationships between debt/book value of equity and sales/fixed assets. For most industries, this relationship is positive. The strongest positive relationship is observed in the combination utilities industry where in 16 of the 20 years tested, a statistically significant (at the five percent level), positive relationship exists. Other industries exhibiting strong relationships between these two measures of productivity and leverage include the gas production, electric services, paper and pulp mills, and petroleum products. However, in other industries, such as oil and gas, air transportation, and chemical products, the net effect of the relationship is negative. The positive relationship in the utilities may be due to regulatory factors. Itís conceivable that those companies operating in states with a more lenient regulatory climate, companies are allowed to take on more debt and managers respond with greater rates of asset productivity. (For more on the relationship between regulatory climate and financial factors see, for example, Filbeck et al, 1997.)

The second column in Table 1 summarizes results from Table 3 for the relationship between debt/market value of equity and total asset turnover. Again, the strongest positive relationship between these measures is observed for the combined utilities industry. Other industries exhibiting a strong relationship with these measures include industrial chemicals, eating and drinking, grocery, newspaper, and petroleum products. Unlike the measures used in Table 2, there are no net negative relationships between these measures of productivity and leverage in any of the industries studied. Compared to the industries that showed a significant relationship when the fixed asset turnover ratio was used, when the total asset ratio is used, industries that are somewhat less capital intensive such as grocery and newspapers show instances of a significant positive relationship. It may be that for companies in these industries that the management of other assets such as inventory and receivables are also affected by the presence of debt in the capital structure.

As noted earlier, Jensen predicts that his free cash flow would hold best in industries with lower positive growth opportunities. The results from Tables 1 and 2 would seem to support this aspect of Jensenís theory. The positive relationship between productivity and leverage appears to hold in industries with fewer growth opportunities, such as the electric utility industry, and less well in industries with more growth opportunities, such as drug (pharmaceutical) and health care companies. Jose et al. (1996) report that the value of Tobinís q, which Lang et al. (1996) note as a measure of growth, opportunity is highest in the professional services, natural resource and retail/wholesale industries, and lowest in the construction and manufacturing industries.

The third column in Table 1 summarizes the results from Table 4 for the relationship between debt/market value of equity and sales/employee. The strongest relationships are observed in the minerals and mining, petroleum products, and combination utilities industries. The fourth column in Table 1 summarizes the results from Table 5 for the relationship between debt/market value of equity and fixed asset turnover ratio. The combination utility, electric services, motor vehicles, and petroleum products industries tend to exhibit the strongest positive relationship between these measures of productivity and leverage. Not surprisingly, we see the strong positive relationship in the more capital-intensive industries with lower growth opportunities where sales per employee are likely to have large positive values. In addition to having low growth opportunities, these more productive companiesí willingness to take on larger amounts of debt may be due to other factors, such as market share, vertical integration, or less volatility in sales.

It is interesting to note that when using debt/book value of equity, rather than debt/market value of equity as a measure of productivity, the results are more mixed. It is reasonable to assume that the more uniform results obtained using a market value of equity may be clouding the issue of the relationship between productivity and leverage, as soaring stock prices over the past couple of decades cloud the issue of the relationship between debt ratios (where debt levels could actually be rising, but the overall ratio could be falling due to the even higher flying equity markets) compared to declining levels of productivity.

Conclusions and Extensions for Future Research

Several conclusions can be drawn from this study. First, we find a positive relationship between measures of productivity and measures of leverage. This pattern is more pronounced in the earlier years of our sample period and less pronounced during later years in our sample period. Finding a positive relationship between productivity and leverage is consistent with our hypothesis and with Jensen's free cash flow hypothesis. Jenson argues that existing shareholders prefer additional debt issues rather than additional equity issues because interest payment on debt induces the managers to act in the interest of stockholders. Although the positive relationship was not found for all industries in all years, there still seems to be a generally positive relationship between measures of leverage and measures of productivity. This relationship appears strongest in industries with fewer growth opportunities, which is consistent with Jensenís observation that the controlling effect of debt would be strongest in industries with lower growth opportunities.

Second, we find that not all industries show a uniform relationship between measures of productivity and measures of leverage. Several factors may account for this. One could be difference in the level of competition in the different industries. As Aggarwal and Samwick (1999) have shown in the context of executive compensation schemes, managers in more competitive industries have less incentive to maximize shareholder wealth. This tendency not to maximize shareholder wealth could manifest itself in not managing the assets as efficiently as possible. It may be the case that the competitive effects outlined by Aggarwal and Samwick mitigate against the bonding effects resulting from additional debt. Another possibility is that the level of a companyís diversification also affects the bonding mechanism of debt. If companies are well diversified, managers of individual divisions may not feel that the presence of debt necessitates any additional reason to manage assets more effectively. They may feel that diversification shields them from the harmful effects of debt. A third, related possibility is that some companies have engaged in risk management practices such as hedging with derivatives that similarly leave the managers with less incentive to manage the assets as efficiently as possible.

Third, although we find a greater representation of positive relationships than negative relationships between measures of productivity and measures of leverage, we find that most of the positive relationships were in the earlier years of our study and trailed off by the late 1980s. In fact, for some industries the 1990s is a period where a number of relationships between productivity and leverage are negative. This change may be attributed to companies becoming more diversified and employing more risk management techniques as was noted above. Related to this is the possibility that companies that increased their leverage due to diversification, takeovers, or the threat of takeovers during the 1980s also increased their efficiency closer to the levels of those firms that were already sufficiently leveraged and efficiently managing their resources. If the increase in the average amount of debt was accompanied by a decrease in its dispersion, then the relationship between debt and asset productivity may become less evident. A negative relationship may exist between leverage and productivity with low levels of productivity seeming to "cause" high levels of leverage. Managers who experience a drop in productivity may use additional leverage to enhance the productivity of employees and other assets of the firm.

Finally, the strength of the relationship between productivity and leverage seems to vary based on the measures used. Overall, the greatest number of statistically significant relationships between productivity and leverage occurred with the use of the sales to fixed asset ratio and the debt to book value of equity ratio, respectively. For both the sales to fixed asset ratio and debt to book value of equity ratios, there should be more stability in these ratios and less likelihood that these ratios are fluctuating due to factors beyond management's control. For example, in comparing the fixed asset turnover to the total asset turnover, the total assets may be more affected by the business cycle and price changes of raw materials compared to just fixed assets. Likewise, the market value of equity and, hence, the debt to market value of equity ratio will fluctuate due to market-wide stock price movements which may obscure the relationship between leverage and productivity.

This research embodied in this study contributes to existing literature by determining whether the reduction in the growth in productivity in this country is a result of corporate restructuring. However, this research also raises several other questions that need to be answered by future research. Should a relationship be established between leverage and productivity, the next question to consider is the causality of the relationship. Do firms with varying levels of asset productivity choose their debt levels based on anticipated productivity? Firms that have highly productive assets and employees take on more debt because they are able to support the extra debt because of their increased efficiency. This type of positive relationship between leverage and productivity would be more consistent with Williamson's (1988) asset redeployment theory of capital structure, which bases the firm's capital structure decision on the type of assets that the firm employs. Future research must not only consider the contemporaneity of the relationship between leverage and productivity, but also the direction of causation.

Tables 2 - 5

Table 2

The following table contains statistical results of regressions between the Debt to Book Value of Equity ratio and the Sales to Fixed Assets ratio.

Industry

99

98

97

96

95

94

93

92

91

90

89

88

87

86

85

84

83

82

81

80

79

Ap

^

 

^

                 

**

**

 

**

**

**

   

**

At

                       

^^

 

**

^^

**

 

^^

**

^^

Co

                         

**

**

 

**

   

**

**

Cs

                       

**

     

**

       

Cp

   

^^

     

^^

 

^^

^^

**

**

^^

               

Dc

   

^^

^^

^^

             

**

**

**

**

         

Dr

                         

^^

**

     

**

   

Ds

   

^

 

^

^

             

*

 

**

         

Ed

                             

**

**

**

**

**

**

Es

                       

**

*

**

**

**

**

**

**

**

Fb

                             

**

**

**

 

**

**

Gr

                               

*

**

**

**

**

Gs

           

*

^

     

**

*

**

**

*

**

**

*

 

**

Hc

                         

^^

 

**

**

**

**

*

**

Hm

                       

^

 

**

       

**

**

Ic

                                         

Mm

                               

*

*

**

 

**

Mv

                             

*

**

*

     

Np

 

^

       

**

   

^^

**

                   

Og

^

^

^

^^

^^

^

^

 

^^

 

^^

         

**

**

**

**

 

Ox

               

*

   

**

 

**

**

**

   

*

**

 

Pm

               

**

   

**

   

**

 

*

**

**

**

**

Pp

*

       

*

 

^^

*

**

 

**

 

**

^^

     

**

**

**

Ut

     

*

*

*

*

 

**

**

 

**

**

**

**

**

**

**

**

**

**

Pr

 

^

^

     

^

             

*

*

**

**

**

**

**

Si

                               

*

**

**

**

**

* (**) - Indicates a positive statistical significant at the five (one) percent level.

^ (^^) - Indicates a negative statistical significance at the five (one) percent level.


Table 3

The following table contains statistical results of regressions between the Debt to Market Value of Equity ratio and the Sales to Total Assets ratio.

Industry

99

98

97

96

95

94

93#

92

91

90

89

88

87

86

85

84

83

82

81

80

79

78

Ap

                                           

At

                   

*

*

                   

Co

                           

**

*

**

**

**

**

   

Cs

                                 

*

       

Cp

                                           

Dr

                                           

Ed

             

**

*

   

*

               

*

 

Es

                 

**

 

**

**

**

*

 

**

**

**

**

**

**

Fb

     

^

^

^

               

*

 

*

**

**

**

**

**

Gr

                       

**

**

**

**

           

Gs

                                           

Hm

                   

**

**

**

**

**

**

**

**

**

**

**

**

Ic

                                 

**

*

 

**

**

Mm

                     

**

   

**

**

       

**

 

Mv

               

*

 

**

**

**

**

**

**

**

**

**

   

*

Np

       

*

   

**

     

**

 

*

               

Og

               

*

 

**

**

**

**

**

**

**

**

**

   

**

Ox

       

*

 

**

     

**

 

*

                 

Pm

**

 

**

       

*

 

**

                     

**

Rr

                                 

^

   

*

 

Pp

             

*

*

 

**

**

**

**

 

**

**

**

**

**

   

Ut

             

**

**

**

**

**

**

**

**

**

**

**

**

**

**

**

Pr

 

^

                               

**

**

 

*

Si

                           

**

           

*

* (**) - Indicates a positive statistical significant at the five (one) percent level.

^ (^^) - Indicates a negative statistical significance at the five (one) percent level.

# - Indicates that incomplete data is available for this year.


Table 4

The following table contains statistical results of regressions between the Debt to Market Value of Equity ratio and the Sales per employee ratio.

Industry

99

98

97

96#

95#

94#

93#

92

91

90

89

88

87

86

85

84

83

82

81

80

79

78

Ap

                                           

At

                   

**

**

               

*

*

Co

                                 

*

**

**

   

Cs

                     

**

                   

Cp

                     

**

**

**

*

             

Dc

                   

^

                     

Ed

             

*

                           

Es

             

*

**

                         

Fb

^

                               

**

**

**

   

Gr

                       

**

**

*

             

Gs

                                           

Hc

                         

*

     

*

**

     

Hm

                         

**

*

**

**

**

       

Ic

                                           

Mm

                       

**

**

*

*

 

**

**

**

**

**

Mv

                                 

**

**

**

*

 

Np

                             

**

       

**

 

Og

                   

**

     

*

*

*

 

**

   

*

Ox

                     

*

   

**

*

 

**

       

Pm

             

^

                         

**

Rr

^

                                         

Pp

                   

**

**

**

**

 

*

**

**

**

**

   

Ut

               

**

**

**

**

**

**

**

**

           

Pr

                                   

**

*

   

Si

                                           

* (**) - Indicates a positive statistical significant at the five (one) percent level.

^ (^^) - Indicates a negative statistical significance at the five (one) percent level.

# - Indicates that incomplete data is available for this year.


Table 5

The following table contains statistical results of regressions between the Debt to Market Value of Equity ratio and the Sales to Fixed Assets ratio.

Industry

99

98

97

96

95

94

93#

92

91

90

89

88

87

86

85

84

83

82

81

80

79

78

Ap

                       

**

**

**

       

*

*

*

At

     

*

                                   

Co

   

*

                     

*

   

**

**

**

   

Cs

                         

*

               

Cp

                                           

Dc

                   

^

           

*

*

     

Ed

             

**

**

**

                       

Es

                 

**

 

**

**

**

   

**

**

**

**

**

**

Fb

                                   

**

**

**

**

Gr

                     

**

                   

Gs

             

*

**

**

                       

Hc

                           

*

             

Hm

*

   

*

             

**

**

   

**

**

**

     

**

Ic

                                           

Mm

                       

*

*

*

**

**

**

**

**

**

**

Mv

             

**

**

**

   

**

**

     

*

**

*

**

**

Np

 

^

                 

**

 

*

*

             

Og

                     

**

**

**

**

**

*

 

**

**

   

Ox

       

*

           

*

                   

Pm

**

 

*

           

*

                     

*

Rr

                                 

^

   

*

 

Pp

             

*

     

**

**

**

*

**

**

**

**

   

Ut

             

**

**

**

 

**

**

**

**

**

**

**

**

**

**

**

Pr

                                   

**

**

*

**

Si

                           

*

             

* (**) - Indicates a positive statistical significant at the five (one) percent level.

^ (^^) - Indicates a negative statistical significance at the five (one) percent level.

# - Indicates that incomplete data is available for this year.

 


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