agricultural investment and internal cash flow variables

Agricultural & Applied Economics Association

Agricultural Investment and Internal Cash Flow VariablesAuthor(s): Farrell E. Jensen, John S. Lawson and Larry N. LangemeierSource: Review of Agricultural Economics, Vol. 15, No. 2 (May, 1993), pp. 295-306Published by: Oxford University Press on behalf of Agricultural & Applied Economics AssociationStable URL: http://www.jstor.org/stable/1349449 .

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AGRICULTURAL INVESTMENT AND INTERNAL CASH FLOW VARIABLES

Farrell E. Jensen, John S. Lawson, and Lany N. Langemeier

Recent asymmetric information studies have used internal cash flow variables in empirical investment models. A composite model consisting of internal cash flow variables, neoclassical variables, and accelerator variables is developed to explain agricultural investment. Using firm-level data, we show that the addition of internal cash flow variables can increase the explanatory power of agricultural investment models.

Introduction

Recent theoretical developments on asymmetric information and capital markets have sparked an interesting debate in the empirical literature about the impact of financial constraints on investment (Fazzari and Athey; Kopcke; Bernstein and Nadiri). Stimulus for this empirical research with financial constraints, which we call internal cash flow variables,1 was provided by several theoretical studies of information asymmetries in capital markets (Stiglitz and Weiss; Greenwald, Stiglitz, and Weiss). These studies are in contrast to neoclassical theory which assumes that capital markets are perfect and have no asymmetric information problems. In neoclassical models, funds for desired investment can be obtained; and thus, investment is not affected by internal cash flow variables (Jorgenson 1971).

Assuming that no informational asymmetries exist and that financial markets are perfect, may be satisfactory for securities of widely-traded firms, but this assumption certainly does not apply to small firms in agriculture. Several studies in agriculture have

examined lenders' procedures for evaluating a farmer's credit worthiness (Ellinger, Barry and Mazzocco; Barry and Ellinger, Lufburrow, Barry, and Dixon). In essence, these procedures seem directed toward reducing problems of asymmetric information in the debt markets, suggesting that such problems are important. Furthermore, small agricultural firms do not have access to equity capital markets. With firms thus con- strained in their access to equity and debt financing, we hypothesize that internal cash flow variables will be important explanatory variables of agricultural investment.

Several agricultural studies have examined investment dynamics, but the effects of internal cash flow variables were not considered (Stefanou; Vasavada and Chambers; Chavas and Klemme). LeBlanc and Hrubovcak considered the effect of taxes on investment utilizing aggregate time series data without internal cash flow variables. A simulation study showed that some financial statement variables can explain investment (Gustafson, Barry, and Sonka). In a recent study using micro-level data for dairy farms, Weersink and Tauer used a composite model to show that accelerator and neoclassical variables and the internal cash flow variable, real net farm income, can explain investment. They do not, however, consider funds availability within the context of the asymmetric information literature.

In this article, we estimate a composite model of agricultural investment that includes variables suggested by accelerator, neoclassical, and asymmetric information models. We use micro-level data when most other studies have used aggregate data. Our composite investment model was estimated with ordinary least squares and a robust estimation procedure.

Development of the Empirical Model

Fazzari and Athey estimated separate neoclassical and accelerator models with some

Farrell E. Jensen is a Professor of Economics in the Economics Department and John S. Lawson is an Assistant Professor of Statistics in the Statistics Department, Brigham Young University. Larry N. Langemeier is a Professor of Agricultural Economics, Department of Agricultural Economics, Kansas State University.

1Internal cash flow variables include profits, depreciation, interest, and off-farm income.



296 REVIEW OF AGRICULTURAL ECONOMICS, Vol. 15, No. 2, May 1993

internal cash flow variables added. Recently, a composite model that was based on both neoclassical and accelerator theory was used by Weersink and Tauer. Their model recognizes that agricultural investment is a function of the funds available for investment. We expand a composite model using internal cash flow variables suggested by the asymmetric information models as well as neoclassical and accelerator variables to determine their effect on investment. Variables used in our model are selected on the basis of existing theories of investment.

Investment can be expressed as:

Ij,t = Kjt - Kjt_1

" t -K,t (1)

where I = real gross investment in period t; j, J K*t = desired stock of real capital in period t;~,t = actual stock of real capital in period t; and Kj,t1 = actual stock of real capital in the previous period.

The 4 function represents the adjustment process between the desired capital stock and actual investment which involves time lags. We now examine separate models that explain K* which will eventually be combined into the composite model.

Accelerator models show the current response and lagged response of investment to changes in the level of real output. Increases in output lead to increases in investment which may be distributed over a period of years (Fazzari and Mott; Eisner). Estimated coefficients on output variables should be positive and become larger as a firm approaches its output capacity. If changes are anticipated to be permanent as opposed to transitory, and if output is increasing, then coefficients will be higher. In accelerator theory, K* can be defined as:

N K 0 m=O ASjt, (2)

m--O

The change in real gross farm income is used as a measure for ASj,tm (Girao, Tomek, and Mount). This variable shows how the firm

adjusts to changes in the level of real output measured in dollars. Increases in this variable should lead to increases in the level of investment.

In neoclassical models, investment adjusts immediately to the desired stock of capital (Jorgenson 1963; Hall and Jorgenson). Invest- ment responds to changes in output prices (Chavas and Klemme), interest rates, technology (Stefanou), relative factor prices, productivity of inputs, the price of capital assets, risk, and taxes (LeBlanc and Hrubovcak; Davenport, Boehlje, and Martin; Lowenberg-DeBoer and Boehlje; Moss, Ford, and Boggess; Hanson and Eidman).

Neoclassical variables included in our model are:

K,= a(RCLC, t-1, Dl,t, (3)

D2,t, MTRjt_1,

RJ)

where RCLCj,-1= relative cost of labor and capital; D1,t= dummy variable for the Tax Equity and Fiscal Act of 1982; D2,t= dummy variable for the Tax Reform Act of 1986; MTRj,t_1= marginal tax rate for each farm by year; and R. -= real variance of net farm income less interest payments.

The recent models of asymmetric information in capital markets suggest that other variables are important in investment decisions. Specifically, these variables are profits, interest, depreciation, and off-farm income. Investment should be positively related to the volume of internal funds generated from depreciation, tax shields, and profits, and negatively related to interest commitments. Off-farm incomeshould have a positive effect on investment. Thus, other things equal, the desired capital stock based on asymmetric information models can be written as:

K:,t= xj,t-1, INTj,t-1, (4)

DEPj,t-I, NFINC),t_I)



AGRICULTURAL INVESTMENT AND INTERNAL CASH FLOW Jensen, Lawson, Langemeier 297

where wj,t-1= real net farm income; INT.j,= interest commitments; DEPj,t-1= real total depreciation; and NFINCjt1= real off-farm income.

In investment studies, demographic type factors are often included to explain investment. In our case, two dummy variables were included to represent type of business (D3,) and operator age(D4,). Our composite model of the desired capital stock includes variables from the accelerator, neoclassical, and asymmetric information models: (2

Kt= mE

ASj RCLCt Lt Y m=o jt-m' j,t-1'( (5)

D1,t, D2,t' MTRj,t-1, Rj,t-1, xj,t-1,

INTj,t, DEP.t_1, NFINCt_1, D3t , D4,j) This equation expresses the desired capital stock as a function of the variables from the different models. By assuming linearity of the y function, equation (5) can be written in functional form as shown in equation (6).

Equation (6) shows the relationship between the variables and the desired capital stock. Our interest is how these variables affect investment. By substituting equation (6) into equation (1), we obtain the empirical equation that is estimated. In equation (7), investment is expressed as a function of the desired stock of capital.

Description of Variables

This section will include additional explanation on the definitions of two variables, I,t, and RCLCj,t1 beyond that provided in Tables 1 and 2. The dependent variable,

Ij,t. includes changes in inventories as part of investment. Adjustments in inventories of inputs are necessary to support changes in output levels. However, in agriculture, changes in inventory may simply reflect price movements with no changes in physical quantities and/or responses to marketing strategies.2 Inventories are an important component of investment that are complementary to investment in other types of capital. The macroeconomic literature has a rich precedent for including inventories in investment.

Kt = Yo +Y*ASj,t + Y2Sj,t-1 +Y3ASjt-2 + Y4RCLCj,t-1 + Y5DI,t +Y6D2,t + Y7MTR. (6-1 (6)

+ Y8Rj,t- + Y9xj,t- + YoINTj,t + Y11DEPt-1 + Y12NFINCj,t1 +Y13D3j+ Y14D4j

Ij,t " 0y0+ +4IY1 ASj, t + 4Y2ASj,t-1 + 43Y3 ASj,t-2 + 44RCLCj,t-1 + 5Y5l,t +

66D2,t 77MTRj,-1+ 88Rj,-1+ Y9j,t-1+ 10YINTj,t (7)

+ 4u1Y11DEPt-1 + 4~12NFINCj,t-1 .+ 313D3j+ 14Y14D4,j

15Kj,t-1

cprice[1 - itc - mtr x dep] [(1 - L x mtr) x OCC - exinfl] RCLC jt-outinput

agwage (1 - mtr) farmpro

2The model was also estimated with inventories excluded, but the results were inferior.




Table 1. Description of the Variables Included in the Regression Model

Anticipated Sign of

Variable Description Coefficient

Ij,t Real gross investment. Includes purchases of land, breeding Dependent livestock, motor vehicles, machinery, equipment, and buildings. Variable Includes changes in inventories for livestock, crops, supplies, feed, and misc. assets.

ASj,t-m Change in real gross farm income. Calculated as one year Positive differences.

RCLCj,t-1 Relative cost of labor and capital. It is the ratio of cost of Negative capital over cost of labor, both adjusted for productivity. See equation (8).

D1,t Dummy variable to represent the Tax Equity and Fiscal Act Positive of 1982. DI,t=l for 1982, 0 otherwise.

D2,t Dummy variable used to represent the Tax Reform Act of 1986. Negative D2,t=l1 for 1986, 0 otherwise.

MTRj,t_1 Marginal tax rate for each farm by year. See Table 2 for a more Negative detailed explanation of this variable.

Rjt-1 Real variance of net farm income minus interest payments for Negative each farm. A measure of risk. Variance was calculated over three year periods.

Xj,t-1 Real net farm income. Equal to gross farm income minus cash Positive operating costs and depreciation adjusted for inflation. Return to operator's unpaid labor, management, and net worth.

INTj,t Interest commitments. Ratio of total interest payments over Negative gross farm income. It is a flow measure of. leverage and indicates financial risk.

DEPj,t-1 Real total depreciation for each farm by year and by type of Positive asset. See Table 2 for a more detailed explanation of this variable.

NFINCj,t1 Real off-farm income that includes wages, rents, royalties, Positive dividends, and interest.

D3j Dummy variable. If proprietorship, then D3= 1, else 0. Theoretically Undetermined

D4j Dummy variable. If operator age <50 years, then D4J= 1, else Theoretically 0. Undetermined

Kjt-1 Real total capital managed. Includes current assets, Negative intermediate assets, and owned and rented land.

j refers to farm j. t refers to time period t.




Table 2 contains the definition of all variables included in RCLCj,t1. This neoclassical variable represents the ratio of the productivity- adjusted price of capital over the productivity- adjusted price of labor. The price of capital has been adjusted for tax considerations (Fazzari and Athey; Eisner and Nadiri; Hall and Jorgenson). Neoclassical theory would predict that investment depends upon the relative prices of labor and capital. Productivity considerations can also play a role in the relative amounts of labor and capital used in production.

Calculation of the RCLCjt-1 variable is shown in equation (8) on the previous page.

A single equation specification was deemed satisfactory in this case. It is not likely that farm firms included in this study could have affected the supply for capital goods. All appropriate variables in the model are expressed in real terms by dividing nominal variables by the GNP price deflator with 1982= 100.

Description of Data

Information for this study was collected from all farms enrolled in the Kansas Farm Management Association. We use a sample consisting of 552 farms that were members of the association from 1973-1988. The data are arranged in panel format as there are 16 years of data for each farm. Means and standard deviations for all variables, in real terms, are shown in Table 3. The data set consists of 8,836 observations. Farms in this study had an average level of capital managed over the period of $936,889 with gross investment of $41,572 per year. The firms were growing as evidenced by the three ASj,t-m variables. Net farm income is positive with an average value of $30,146. This sample of farms does not have large interest commitments as interest payments are only 10.1 percent of gross farm income (INTjt). The marginal tax rate was slightly higher than 30 percent.

A review of the Kansas Farm Management Association's annual report for 1987 indicates that this sample of farms has characteristics very similar to the average farm for all associations in the State of Kansas. For example, all farms in the associations had an average level of

capital employed of $832,133, average net farm income was $40,776, and average depreciation allowances of $17,562. Thus, this sample is probably representative of farms in the association. The sample is subject to those criticisms from the use of farm management data in that the sample is probably not representative of all farms because better managed farms join associations.

Empirical Results

Results of the regression analysis are shown in Table 4. After examining residuals from an OLS regression model, we found that the distribution of errors was non-normal (long tailed).3 Because of this problem, the model was estimated with two alternative robust regression procedures that are insensitive to normal distribution assumptions. Particularly, we were interested in whether coefficient estimates changed to a substantial degree with robust procedures. Robust procedures are less efficient than OLS procedures when errors are normally distributed, but are considerably more efficient when errors are nonnormal (Judge et al.). The first of these alternatives was DFFITS which is a procedure for identifying influential data points (Belsley, Kuh, and Welsch). Influen- tial data points are essentially outliers that exert a large influence in OLS analysis. Bisquare, the second robust alternative, is preferable to OLS when there are a few deviant observations or if the assumption of a normal distribution of errors is inappropriate. Bisquare reduces the weight of observations with large residuals (Becker and Chambers).4

In our model, gross farm income is used to measure changes in the level of output. Vari- ations in gross farm income could be caused by changes in prices for specific farm commodi- ties or by changes in the farm enterprise mix

3We tested for heteroskedasticity in the residuals with the Goldfeld-Quandt test. The hypothesis of homoskedasticity was rejected.

4The model was estimated with OLS regression using the heteroskedastic consistent covariance matrix (White), and also a LAD regression. The results were nearly identical to the two models presented in Table 4 in that the coefficients were generally of the same magnitude and sign as the models reported in the paper.




Table 2. Description of the Variables Included in the RCLC Variable

Variable Description Source

cprice Producer Price Index for capital equipment U.S. Council of Economic advisors.

itc Investment tax credit rate. It is the weighted Calculated for each farm. It is a weighted average for each farm by year. Based on the average based on proportion of investment proportion that each investment category was to levels in livestock, vehicles, and machinery. the total investment.

MTR Marginal tax rate. It is the marginal rate for each Based on tax laws in effect, type of business farm by year. It is the rate that each farm would organization, personal exemptions, standard have had to pay on one additional dollar of income deductions, and taxable income. each year. All information was taken from the Federal and State of Kansas income tax codes and applied specifically to each farm by year. For corporations, appropriated tax rates were used. This variable represents the combined Federal and State Income Tax.

OCC Opportunity cost of capital. Prime rate. U.S. Council of Economic Advisors.

dep Present value of tax depreciation allowances per Calculated for each farm based on tax laws. $1 of investment. A weighted average based on Separate values for motor vehicles, machinery, proportion of investment in each category by farm buildings, and livestock. OCC was used as and by year. the discount factor.

L Measure of financial leverage. Ratio of debt to assets.

Exinfl Expected inflation. Calculated on basis ofa three- GNP implicit price deflator. year distributed lag of inflation.

Outinput Farm output per unit of input in real terms. Agricultural Statistics. U.S. Department of Agriculture.

Agwage Index of agricultural wages. Agricultural Statistics. U.S. Department of Agriculture.

Farmpro Farm production per hour of labor from the Economic indicators of the Farm Sector. U.S. northern plains. Department of Agriculture.

from adjustments in relative profitability all of which would provide incentives for new investment.

All three of the output variables, (ASj,tm), are highly significant with positive signs in both models as hypothesized. The significance of the lags indicates that some time is required for capital stock levels to adjust to changes in sales. Sizes of coefficients decrease in the model as lags are further removed from the change in sales. It might be argued that there are correla- tions between these lagged variables. But, since

ASj,t-m is based on changes in real gross farm income, the relationships between successive periods should not be as pronounced as would be the case if just the absolute levels were used. Furthermore, the model was tested for multicollinearity and it was not found to be a problem. Weersink and Tauer used the same variable to measure real output and also found it to be positive and highly significant.

Variable RCLCj,t- had consistent results because the signs were negative and highly significant for all models. From this result, we




Table 3. Description of Data

Standard Variable Unit Mean Deviation

Ij Real $ 41,572 93,710 A St Real $ 7,068 69,456 A Sj,t- Real $ 6,916 70,089

ASj,t-2 Real $ 5,933 70,893

RCLCj,t-1 Real $ .084 .062

MTRj,t-1 % .302 .223

Rjt-1 Real $ 9.86E8 2.75E9

Xj,t-1 Real $ 30,146 53,250 INTjt % .101 .141

DEPt-1 Real $ 20,253 16,028

NFINCj,t-1 Real $ 11,404 17,233

Kt-1 Real $ 936,889 567,143

Data set has 8,836 observations.

can conclude that investment decreases as the productivity-adjusted price of capital increases relative to the productivity-adjusted price of labor. Similarly, as the productivity-adjusted price of labor increases relative to the price of capital, then investment increases.

Higher marginal tax rates have a deleterious impact on investment even after removing some effects with tax law dummy variables. Increases in marginal tax rates have two impacts on the net present value of projects and investments. First is the impact on the after- tax cash flows that are reduced by increased marginal tax rates if all other taxes do not change. Second, the cost of capital used as the discount rate is also reduced. The net effect depends upon the magnitude of the changes on the after-tax cash flows and the cost of capital. This study indicates that the total investment level on these farms was inversely related to the level of marginal tax rates. Apparently, the tax effect of cash flows exceeds the effect on the cost of capital.

Changes in the tax laws were measured with dummy variables. D1,t shows the impact

of the Fiscal Responsibility Act of 1982. This dummy variable also likely measures the effect of the Economic Recovery Tax Act of 1981 which allowed faster depreciation write offs providing a stimulus for investment. The positive and highly significant coefficients for this variable show that investment did increase after these acts became effective. The variable,

D2,t, shows the negative impact that the Tax Reform Act of 1986 had on investment. Recall that this act repealed the investment tax credit and extended the depreciable life of assets. The level of investment was reduced in the year this act became effective. The fact that MTRt-1 and the two dummy variables are highly significant with the expected signs shows the strong impact that tax policies can have on investment. These results are consistent with LeBlanc and Hrubovcak who found that tax policies can have a major impact on agricultural investment.

The risk variable, Rj,t1, was not significant for either model. Generally, if all other factors are equal, as the level of business risk increases, then investment should decrease. This could be caused by lenders' lack of willingness to lend




Table 4. Estimates of Alternative Regression Models to Explain Real Investment (I)

Variable OLS DFFITS Bisquare

Intercept 8,007 4,222

ASjt .65 .60 (58.63) (81.91)

ASj,t-1 .22 .22 (16.68) (24.53)

ASj,t-2 .08 .06 (7.86) (9.22)

RCLCj,t-1 -88,854 -46,622 (-9.60) (-7.78)

D1,t 7,177 4,074 (3.31) (2.94)

D2,t -9,679 -6,081 (-5.10) (-5.07)

MTRj,t-1 -15,699 -12,753 (-3.64) (-4.61)

Rjt-1 .00000002 -.00000008 (.06) (-.22)

It-1 .35 .29 (13.40) (16.87)

INTjt -31,426 -26,471 (-6.87) (-9.22)

DEPt-1 .59 .51 (12.52) (17.10)

NFINCj,t-1 .15 .08 (4.15) (3.86)

D 5,971 4,617 (4.09) (4.79)

D4 5,943 3,821 (5.50) (5.43)

Kt1 .002 .002 (1.59) (2.34)

Adj R2 .46 .60

Z values are listed in parentheses.




to firms that areperceived to have high levels of business risk.

The internal cash flow variables, xj,t-1,

INT',t, DEPt-1, and

NFINC.,t-1 are highly

significant with expected signs. The profit variable, (wj,t1), has a positive coefficient showing that more profits lead to higher levels of investment in subsequent periods. Higher values for this variable indicate a greater capacity to internally finance investment, and provide evidence to a lender that the farm is profitable and can service the additional debt. Although investment is based on expected profits rather than past profits, this variable indicates that a lag is required to reach the desired level of capital. Weersink and Tauer found real net farm income to have a significant negative sign. A positive sign on this variable is predicted by asymmetric information models. They explained that this inverse relationship might occur during periods of adverse market conditions that cause farms to conserve internal funds rather than to invest. However, our results are more consistent with recent theoretical developments in the asymmetric information literature. The variable, INTj was included to measure effects of interest commitments on investment. The coefficient sign is negative, as expected, and highly significant indicating that investment decreases as the ratio of interest payments to gross farm income increases. As this variable increases, then farms will have less flexibility to undertake new investments. If a farm is highly leveraged, then it must commit a large fraction of its cash flows to service the existing debt load. Lending agencies will also be less willing to lend because of the higher level of risk involved in lending to highly- leveraged firms. Risk for the lender is created because the firm has less ability to withstand difficult financial times.

The lagged depreciation variable is highly significant with a positive sign showing that increases in lagged depreciation lead to higher levels of investment. This variable represents book depreciation allowances and not economic depreciation. From the standpoint of a financing

variable, we were interested in depreciation allowances since these allowances affect internal cash flows through tax savings. Since depreciation is a non-cash expense, it contributes to the cash flow through a reduction in taxes paid.

The coefficient for off-farm income was significant and positive, indicating that investment is positively affected by the level of off- farm income.

We wanted to determine if type of business organization had an effect on investment. Sole proprietorships are represented by D3.. Since this dummy variable is positive and highly significant, it indicates that sole proprietorships had higher levels of investment than corporations or partnerships. Perhaps partnerships and corporations demand a higher rate of return on investment and would be less likely to invest for a given level of profitability.

We hypothesized that operator age could influence investment. Older operators might be less inclined to undertake investment projects. The dummy variable, D4, was included to measure this effect and represents operators less than 50 years of age. Younger operators had a significantly higher level of investment activity than older operators. Perhaps younger operators have been forced below their desired capital stock because of credit rationing, and they move to an optimum as rationing allows. Lagged real total capital managed was not significant for the OLS DFFITS model and was significant for the Bisquare model.

The adjusted R2 range from .46 for the OLS DFFITS model to .59 for the Bisquare model.6,7

Estimates of Elasticity

Estimates of elasticities for the Bisquare model are shown in Table 5. Since the coefficients for the OLS DFFITS and Bisquare models are approximately equal in magnitude,

5The riskvariable was significant with a negative sign in the OLS model as anticipated. After adjustments for the DFFITS model, the variability was reduced.

6The model was tested with a different specification of the dependent variable, real gross investment, in which inventories for feeder stock, grain, hay and forage, cash field crops, feed, and miscellaneous items were removed from investment. Generally, the results were inferior to the model with inventories included. As expected, the coefficients and adjusted R2 were smaller. However, signs tended to remain the same, and coefficients were still significant.




the elasticity estimates are about the same size. Consequently, only the Bisquare estimates are reported. In general, magnitudes of elasticities will provide an indication of responsiveness of investment to individual variables.

Table 5. Elasticities of Variables

Variable Bisquare Model

,t 10

ASj,t-1 .04

ASj,t-2 .01

RCLCj,t-1 -.09

MTRjt-l -.09

7cj,t-1 .21

INTj -.06

DEPt.1 .25

NFINC,t-1 .02

Kjt-1 .05 Elasticities are calculated at the means.

Elasticity estimates for the ASjt-i variables are generally quite inelasticwith values ranging from .01 to .10. Marginal tax rates are relatively inelastic as well as the productivity-adjusted price of labor and capital. The internal cash flow variables, profits and depreciation, are the most elastic of all variables. The off-farm income variable had the lowest elasticity of the internal cash flow variables.

It is evident that lagged depreciation and lagged profits appear to exert the most influence on investment followed by changes in sales, the

relative cost of labor, and capital and marginal tax rates. Other variables are much less important in terms of their influence. Considering the size of the elasticities, it appears that investment is more responsive to internal cash flow variables than other variables included in the study.

Conclusions

Previous research has provided the theoretical basis for including internal cash flow variables in our empirical model. Results of this study applied to agricultural firms are consistent with previous studies for nonagricultural firms which show that internal cash flow variables are important in explaining investment. We found that accelerator, neoclassical, and internal cash flow variables are all important and that the addition of internal cash flow variables can improve the explanatory power of agricultural investment models. In terms of elasticity, investment was more responsive to internal cash flow variables than either accelerator or neoclassical variables.

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agricultural investment and internal cash flow variables

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