Evidence on the relationship between incentives and exogenous risk

4

Evidence on the Relationship between Incentives and

Exogenous Risk

Luis

B.B.

Braido*t

September 26, 2002

Abstract

Theoretical models on moral hazard provide competing predictions on the incentive-risk relationship. These predictions are derived under the assumptions of homogeneous agents and exogenous risk. However, the existing empirical ev-idence does not account for risk-aversion heterogeneity and risk endogeneity. This paper uses a well-built database on tenancy contracts to address these issues. Detailed information on cropping activities is used to measure the ex-ogenous risk. Risk-aversion heterogeneity and other self-selection problems are addressed through a portfolio schedule and a subsample of farmers who simul-taneously own and sharecrop different farms. This controlled exercise finds a direct relation between incentives and exogenous risk.

Keywords: Tenancy, data, risk, incentives, delegation, principal, agent. JEL Classification: C52, D82, 012, Q15.

"I wish to thank comments from Daniel Ferreira and Marcelo Fernandes. Database provided by

ICRISAT is gratefully acknowledged. .

1

Introduction

There is an intense debate on the relationship between the power of pay-for-performance incentives and the risk inherent to a particular business (e.g., Pren-dergast, 2002). The underlying idea behind pay-for-performance incentives is the existence of hidden actions exerted by an agent (e.g., manager) that stochastically affect an outcome, so that these actions should be induced by a mechanism which attaches the agent's compensation to the final outcome. In this scenario, Holmstrom and Milgrom (1987) establish the existence of a locally negative relation between exogenous risk and incentives. Risk increases the costs of providing incentives to a risk-averse agent through pay-for-performance schemes, reducing the use of such mechanisms.

However, what is treated as hidden actions by the agency theory are, in many cases, costly monitorable actions. Prendergast (2002) suggests that it is easier to monitor agents in low-risk environrnents, and such a negative correlation between risk and the effectiveness of monitoring activities would imply a positive (rather than negative) correlation between exogenous risk and pay-for-performance incentives.

The main hypotheses behind both of these arguments are that agents' prefer-ences are homogeneous and risk is exogenous (i.e., the agent's hidden actions affect the business profitability, but not its risk). This paper argues that the existing empirical literature has not properly taken preference heterogeneity and risk en-dogeneity into account. I address these issues by means of a well-built data base on tenancy contracts, collected by the International Crops Research Institute for Semi-Arid Tropics (ICRISAT) in India.

The ICRISAT database provides very suitable information to identify those the-oretical predictions. First, it contains detailed information on cropping activities (such as quality of land, type of product grOWll, amount of different inputs used during the season, etc), which are useful to construct measures a farm's exogenous risk. Also, it has a complementary schedule on households' portfolios that can be used to control for heterogeneity in risk aversion. Finally, a uni que characteristic of this data set is the presence of a subsample of mixed tenants - i.e., households cultivating multiple plots in the same year and season under contracts with different power: ownership (high power) and sharecropping (low power). This subsample is used to account for selection problems (since tenancy contracts are endogenously chosen based on households' unobserved characteristics). Within this subsample of households who chose to be owners and sharecroppers simultaneously, the choice of each contract must be related to characteristics of the farm, which are

t

siveIy reported in the data. In this well controlled exercise, my findings suggest the existence of a significantly positive correlation between incentives and risk.

The existing empiricaIliterature buiId their conclusions based on three types of data. Many works use data on executive compensation and find distinct reIations between the CEO's incentives and the volatility of the firm's return - see Lam-bert and Larker (1987), Garen (1994), Aggarwal and Samwick (1999), and Conyon and Murphy (2000). A second group of papers use data on franchising and find a positive reIation between the firm's risk and the decision to franchise (high pay-for-performance) - see Martin (1988), Norton (1988), and Lafontaine (1992).1 However, none of these papers control for the possibility of non-random heterogeneity among the sampIed agents. Moreover, their measure of risk might be strongIy affected by agents' endogenous actions - executives usually affect a business' risk through the choice of projects and tasks to be executed.

A third line of the empiricaIliterature relies on tenancy data to test the incentive-risk reIationship. Rao (1971) argues that, in lndia, share contracts are extensive for products (such as rice) and areas (such as the northern regions) where risk and entrepreneurial profit are low. On the other hand, fixed-rent contracts are common in situations of high risk and significant scope for decision making (e.g., tabacco farms). The conclusions are based on aggregate statistics, without controlling for endogenous inputs and agents' risk aversion. In another important work, Allen and Lueck (1992) study a database on tenancy contracts from Nebraska and South Dakota in the US and report that sharecropping contracts are more likely to occur in lands where the costs of dividing the output is low. They find that sharecropping contracts are associated with crops such as corn (high volatility) and wheat (low volatility), suggesting that there is no clear relation between risk and contract power (which does not support the risk-sharing explanation of sharecropping). However, data on endogenous inputs are not available and there is no explicit control for pref-erences heterogeneity (although they argue that tenants' characteristics are very similar). In a recent work, Ackerberg and Botticini (2002) stress that the endoge-nous matching between Iandlords and tenants could potentially compromises the findings of the previous empirical works. After addressing the bias caused by the endogenous matching of Iandlords and tenants, their evidence supports risk sharing as an important determinant of sharecropping in early Renaissance Thscany.

As mentioned before, the data set used here allows one to preciseIy control for preferences heterogeneity and other potential sources of selection bias. It also allows one to address the problem caused by the fact that the risk of a farm may be affected

by agents' actions (endogenous volatility). Contrary to Ackerberg and Botticini (2002) and consistently with the evidence on franchising, my findings suggest a direct relation between exogenous risk and incentives, which does not support the prediction in Holmstrom and Milgrom (1987) and does not contradict the theory by Prendergast (2002).

The remainder of this paper is organized as follows. Section 2 presents the competing theoretical predictions, describes the database, and shows some prelimi-nary results. Section 3 proposes an econometric test for the theoretical predictions. Section 4 performs the empirical analysis using the entire database as well as the subsample of mixed tenants. Section 5 serves as a ,conclusion.

2

Preliminaries

2.1

Theoretical Results

There are two competing theories on the incentive-risk relationship. Here, I outline the arguments and present the main assumptions of these theories.

Agency Theory

Based on the classical work by Holmstrom and Milgrom (1987), the agency the-ory predicts a negative correlation between incentives and exogenous risk. Holm-strom and Milgrom (1987) study the problem of a risk-neutral principal (e.g., land-lord) writing a contract to a risk-averse agent (e.g., tenant) with constant absolute risk aversion (p) and quadratic effort disutility, ｾ･ＲＮ＠ The output of a farm depends linearly on the agent's hidden effort (e) and on a normally distributed shock with zero mean and constant variance (cr). An important assumption is that cr2 is con-stant (thus, independent of the agent's effort). They show that the solution for the continuous time version of this model, where output follows a Brownian motion and the agent controIs the drift of such a process, is as if the problem were the static one and the principal were constrained to use linear contracts. Moreover, the power of the optimallinear contract in the static setup would be given by

(1

+

kpcr2) -1. Namely, for fixed values of k and p, there is an inverse relation between the power of the optimal contract and the farm's risk (cr2).

The intuition behind this result is very appealing. Pay-for-performance mecha-nisms increase effort incentives at the cost of imposing risk on the agent. AIso, a risk-averse agent would need a higher compensation leveI in order to accept a riskier contract. As a consequence, riskier environments would be associated with incentive mechanisms with lower power (i.e., those that impose less risk on the agent).

Testable Prediction 1. Controlling for heterogeneity in effort disutility (k) and risk aversion (p), the fraction of the output received by the tenant will be inversely related to_the exogenous risk of the farm

(0-

2 ).

Delegation Theory

Prendergast (2002) considers a risk-neutral principal (e.g., landlord) hiring a risk-neutral agent (e.g., tenant) to exert a multi-task effort that affects a farrn's expected output. The ideal effort-task to be exerted depends on the realization of a random variable (state of the world), which is privately observed by the tenant during the cropping season. The principal chooses a tenancy contract and how much to expend monitoring the tenant's activities. In one extreme, the principal can choose the ex ante optimal leveI of effort (without knowing the state of the world) and implement such a leveI through monitoring the agent. Alternatively, the principal may choose to delegated decision-making power to the agent, who chooses the effort-task after knowing the realization of the random variable. Since agents may have private benefits for exerting some particular tasks, their multi-task effort choice must be induced through a pay-for-performance mechanism.

Notice that the gain of delegating decision-making power to the agent increases with the variability of the state of the world (the higher the uncertainty, the higher the gain of decisions made after the realization of the state of the world). Conse-quently, one would observe a positive relation between exogenous risk and the power of tenancy contracts.

Testable Prediction 2. Controlling for preferences heterogeneity, one will observe a direct relation between the contract power and the farm's exogenous risk.

2.2 The Database

another in the same category in case they moved out of the village.2

The main schedule used here, PS files, contains farm-level data (per season of each year) on the following variables.

• Tenancy Contract: ownership or fixed-rent tenancy (high power) and share-cropping tenancy (low power).

• Output: value of farm's main output and byproducts in a certain season (mea-sured in Rupees, R$).

• Area Cropped: number of acres actually cultivated.

• Labor Input: value of hired and family labor used on the farm during the season - the values are measured in R$ by multiplying the number of hours worked by the village wages for males, females and children.

• Non-Labor Input: Value of seeds, fertilizers, pesticides, organic and inorganic manure, plus rental value of owned and hired bullock and machinery (in R$).

• Plot Value: estimated per acre value of the plot (in 100R$).

• Irrigation: total irrigated area in the farm divided by the area cropped.

• Soil Type: Type of the farm's soil according to the following classification - deep black, medium black, shallow black, shallow red, gravelly, saline soil, sandy soil, other soils, and undefined.

• Main Crop: variable constructed from the first letter of the cropping pattern code, which describes a general category for the dominant crop grown -namely: oilseeds, cereals, fiber crops, garden crops, pulses, sugar cane, veg-etables and spices, and fodder crops.

• Village (Aurepalle, Dokur, Shirapur, Kalman, Kanzara, Kinkheda, Boriya, and Rampura); Season (June-October, November-February, March-May, and perennial crops); and Year (1975-1984) in which the field was planted.

A second schedule (P cards in the NP files) is used to account for risk-aversion heterogeneity. This schedule contains annual information on households' portfolios, collected in early July of each year. The variables used are as follows.

2Details on the data collection can be found in Jodha, Aslkan, and Ryan (1977) and Singh, Binswarger, and Jodha (1985).

• Savings (code S): valued invested in savings accounts at the time of the inter-view, measured in Rupees (RS).

• Deposits (code D): Amount of Rupees (RS) invested in deposits.

• Credit Outstanding and other FinanciaI Assets (codes L and Z): value of out-standing credit and other financiaI assets (in RS).

• Liabilities (codes B and Y): value of outstanding loans taken from cooperatives and moneylenders and other liabilities, including interest accrued (in RS).

• Arrears (codes R and A): Accumulated arrears of land revenue and other dues (in RS).

• Life Insurance (code C): value of aillife insurance policies (in RS).

Tables 1 and 2 shows the summary statistics for this data set.

[Table 1]

[Table 2]

As mentioned before, part of the analysis wiil focus on a particular subsample of this data set, which is composed of mixed tenants (households cultivating owned and sharecropped lands in the same year and season). Tables 3 and 4 describes this subsample.

[Table 3]

[Table 4]

2.3 A Preliminary Test

In order to have some feeling about the results in this paper, let us consider a very preliminary test for the theoretical predictions. Let us test if the output variance differs across plots under different tenancy contracts, assuming that such variance is not affected by farmers actions. Namely, let us estimate the foilowing model:

r

•

r

where Yi is the value of the output produced by farm i; Yi is the mean output of the farms cropped under the same contract of farm i; di is a vector with a constant term and the relevant contract dummies; and Ei is a zero-mean error.

The first column in Table 5 presents the regression for the entire data base and the second column focuses on the subsample of mixed tenants. Basically, owned farms are riskier than sharecropped lands in both regressions (which contradicts the agency theory and does not contradict the delegation theory). In the first column, the estimated coefficient for the fixed-rent dummy is positive and very large, but it is not significant at a 10% leveI. This result also contradicts the agency theory, but does not fully support the delegation theory. However, one must note that there is a small number of rented farms (229 observations out of 11,517 farms sampled), which might be the reason for the fixed-rent dummy being non-significant.

[Table 5]

3

Testing Strategy

The testing strategy is composed of two parts. First, one needs to construct a measure for a farm's exogenous risk. Next, one must test whether the exogenous risk of sharecropped farms differs from that of owned or rented lands.

3.1

First Part

A crude measure for the exogenous risk would be the variance of the output (as presented in Section 2.3). However, such a measure might be strongly affected by many endogenously chosen variables (such as labor and non-labor inputs) and, therefore, it does not represent the type of risk assumed in the theoretical models. Accounting for this, let us use a Cobb-Douglas production function to model the output value (Yi) as follows:

K

Yi

=

Ai

rr

ｸｾｴ＠ exp (E:i) ,

(2)

k=l

where Yi is the value of plot i's output; Xik is the cost of input k used in plot i; Ai

is a price-adjusted technological term; and E:i is an unobserved error term (which accounts for possible hidden actions and exogenous shocks).

It is assumed that the farm's manager chooses a privately observed effort, which affects the mean of Ei· Theoretically, such an effort choice should be correlated to the power of the contract. Therefore, I model the error term as follows:

(3)

where E: is a constant term; di is the sharecropping dummy; 8 is a constant param-eter; and Ui is a zero-mean error.3

From (2)-(3), one has:

K

In (y;)

=

8di

+

L

Q:k In (Xik)

+

In (Ai)

+

E:

+

Ui.

k=l

(4)

The error Ui is intended to capture exogenous shocks such as climatic changes, infestations, blights, etc. In this paper, the estimated error

(Ui)

is used as a proxy for the exogenous shock of farm i.

3.1.1 Estimation Problems

When estimating (4), one must be careful with the fact that contracts as well as labor and non-Iabor inputs are endogenously chosen. Typically, farmers first choose the tenancy contract. Next, they choose labor and non-Iabor inputs taking as given the contract form as well as the plot's characteristics (such as size, quality of land, irrigation, etc).

Accounting for the tenancy-contract endogeneity, I use a subsample of house-holds who endogenously chose to cultivate owned and sharecropped farms in the same year and season. Within this subsample, the contract choice for each plot must depend on the farm's features rather than the farmer's characteristics. Since the database contains detailed information on the farms, it is likely that self-selection problems are minimized in this subsample.

Addressing the input endogeneity problem, I follow the econometric literature on production-function estimation - see Zellner, Kmenta, and Dreze (1966) and

Hodges (1969). By assuming that farmers maximize expected profits, I derive the input optimality conditions and incorporate them into the regression model.

Input Choices

After observing the contract form and land characteristics, farmers are assumed to choose the amount of non-labor and labor inputs by maxirnizing personal profits. Define Xli and X2i as the values of non-labor and labor inputs, Syi as the fraction of output received by the sharecropping tenant, and Sji the fraction of input j's cost that is paid by the tenant (i.e., Sji

=

Syi

=

1 whenever plot i is owned or rented).4 The farmer's problem then becomes:

(5)

where ri stands for the rent (in the case of fixed-rent contracts) or the opportunity cost of the farm (in the case of ownership); Ti is the information set available to whom is making the input decisions in farm i; and t!Jt.. _py corrects for the fact that Yi is measured according to the price observed by the researcher

(Py),

which is not necessarily the price predicted by the farmer (Py).

Since Yi is given by a Cobb-Douglas function, the necessary and sufficient first-order conditions are:

E

ｻＩＮＮｪｾ＠

I Ti} = Sji, for j E {I, 2} ,

Xji Syi

(6)

where Sji

=

Syi = 1 whenever plot i is owned or rented, and )..j = ｾ｡ｪ＠ is an unobserved term that accounts for the uncertainty about future prices.

I model equation (6) as follows:

(7)

where

e

=

E { exp (

TJJi)

I

L; }, and

TJJi

is an error with zero mean.

·In many cases, landlords and sharecropping tenants share the costs of some inputs (usually non-Iabor inputs).

10

BIBLIOTECA

MARli] t1tlihilQUE

SUV,ONSEN

The log-linear version of (7) is:

(8)

Now, let us introduce a sharecropping dummy to account for In

(2).

AIso,

Sy'

notice that the coefIicient Àj

=

ｾ｡ｪ＠ is unknown and the uncertainty about future prices might differ across villages, years, seasons, and type of crop grown. Hence,let

Di be a matrix with a constant column and dummy variables for the village, year, season, and type of crop grown, so that:

(9)

where Clj is a constant parameter, Ij is a vector of parameters, and "lji is a zero-mean error that accounts for "lii as well as for some residual from the term -In

(B

Àj)

that is not captured by Dilj.

Estimation Methodology

In short, condition (9) is an econometric model for farmer i's first-order condi-tions for the optimal choice of non-Iabor and labor inputs. When estimating the production function (4), one must take these endogenous input choices into account. Namely, since "lli' "l2i' and Ui are not independent, one must use Zellner's method of estimating Seemingly Unrelated Regressions in order to jointly estimate the equa-tions in (4) and (9). This procedure is used to obtain estimates ofthe first-equation error (Ui).

3.2

Second Part

The second step of the testing strategy will perform heteroskedasticity tests on the error term estimated in the first-step regressions. Controlling for risk-aversion heterogeneity, one must test whether the volatility of Ui is affected by the contract formo

Glesjer's heteroskedasticity tests are used since they allow the inclusion of control variables. Namely, consider the following class of models:

r

r

I

r

,

where 9 (.) is a generic function; di is a vector with a constant terrn and the relevant contract durnmies; Zi is a vector with the portfolio variables used to control for risk-aversion heterogeneitYi and Vi is an error termo

There are three usual specifications for 9 (-). If 9 (.) is linear, the rnodel becomes:

(Úi)2 = di

f3

+

Zi'P

+

Vi. (11) The second usual specification is 9 (-)

=

(.)2,

so that:

(12)

Finally, for 9 (.) = exp (.), one has:

(13)

For each of these specifications, the vector

f3

rneasures whether var( Ui) differs acrass tenancy contracts. It is irnportant to rnention that these specifications are not classical regressions. The error Vi is generated frarn a particular estirnate of the production function, and so it does not have zero rnean and is autocorrelated by construction. In spite of this, OLS estirnates would still be asyrnptotically consistent (see Arnerniya, 1977). A third problern is that Vi is heteroskedastic, so that one rnust use White corrections to cornpute the t-statistics for the estirnated pararneters (see White, 1980).

4

Empirical Results

Here, I present the regressions for each of the two steps defined in the last section. Table 6 and 7 use (respectively) the full database and the rnixed-tenants subsarnple to estirnate the first-step production function. Curiously, the sharecropping durnrny is not significant in any regression. This result was pointed out by Braido (2002) and it suggests that there is not rnissing incentive associated to the sharecropping contract. I do not explore this feature since it does not play any role in the rnain point of this papeI.

[TABLE

6]

[TABLE 7]

Using detailed information on cropping activities, I estimate a stochastic Cobb-Douglas production function and use the residual of this regression as a measure of exogenous risk. Next, I test whether such a residual is heteroskedastic across tenancy contracts. In that part, I use households' portfolios to control for risk-aversion heterogeneity. The results are presented for the entire data set as well as for a subsample of mixed tenants (households who cultivate owned and sharecropped lands in the same year and season). Within this subsample, the contract choice must be related to farmers' characteristics rather than agents' preferences. Since there is detailed information on the farms, one expect to have no selection bias in the estimates of this subsample.

In this well controlled exercise, I find a dírect relation between exogenous risk and incentives.

References

[1] Ackerberg, Daniel A. and Botticini, Maristella. "Endogenous Match-ing and the Empírical Determinants of Contract Form." Joumal of Political Economy, June 2002, llO (3), 564-591.

[2] Allen, Douglas and Lueck, Jean. "Contract Choice in Modem Agriculture: Cash Rent versus Cropshare." Journal of Law and Economics, October 1992, 35 (2), 397-426.

[3] Aggarwal, Rajesh K. and Sarnwick, Andrew A. "The Other Side of the Trade-Off: The Impact of Risk on Executive Compensation." Joumal of Political Economy, February 1999, 107 (1), 65-105.

[4] Arnerniya, Takeshi. "A Note on a Heteroscedastic Model." Journal of Econo-metrics, November 1977, 6 (3), 365-370.

[5] Braido, Luis H.B. "Essays on Moral Hazard." Ph.D. Thesis, University of Chicago, 2002.

[6] Cheung, Steven N.S. "The Theory of Share Tenancy." University of Chicago Press, Chicago, 1969.

[8] Conyon, Martin J. and Murphy, Kevin J. "The Prince and the Pauper? CEO Pay in the US and UK." Economic Journal, November 2000, 110 (467), 640-671.

[9] Garen, John E. "Executive Compensation and Principal-Agent Theory."

Journal of Political Economy, December 1994, 102 (6), 1175-1199.

[10] Hermner, Thomas. "On the not so Obvious Relation between Risk and In-centives in Principal-Agent Relations." University of Chicago, mimeo, 2002.

[11] Hodges, Dorothy J. "A Note on Estimation of Cobb-Douglas and CES Pro-duction Function Models." Econometrica, October 1969, 37 (4), 721-725.

[12] Holmstrom, Bengt. "Moral Hazard and Observability." Bell Journal of

Eco-nomics, Spring 1979, 10 (1), 74-91.

[13] Holmstrom, Bengt and Milgrom, Paul. "Aggregation and Linearity in the Provision of Intertemporal Incentives." Econometrica, March 1987, 55 (2), 303-328.

[14] Lafontaine, Francine. "Agency Theory and Franchising: Some Empirical Results." RAND Journal of Economics, Summer 1992, 23 (2), 263-283.

[15] Lambert, Richard and Larker, David. "An Analysis of the Use of Ac-counting and Market Measures of Performance in Executive Compensation Contracts." Journal of Accounting Research, Supplement 1987, 25 (Studies on Stewardship Uses of Accounting Information), 85-125.

[16] Martin, Robert E. "Franchising and Risk Management." American Eco-nomic Review, December 1988, 78 (5), 954-968.

[17] Norton, Seth W. "An Empirical Look at Franchising as an Organizational Form." Journal of Business, Apri11988, 61 (2), 197-218.

[18] Prendergast, Canice. "What Trade-off of Risk and Incentives?" American Economics Association Papers and Pmceedings, May 2000, 90 (2), 421-425.

[19] Prendergast, Canice. "The Tenuous Tradeoff between Risk and Incentives."

Journal of Political Economy, October 2002, 110 (5), 1035-1070.

[20] Rao, C.H. Hamunantha. "Uncertainty, Entrepreneurship, and Sharecrop-ping in India." Econometrica, May-June 1971, 79 (3), 578-595.

T ABLE 5. A Preliminary Test Ordinary Least Square

Dependent Variable:

Cv, -

yJ

Ownership Dummy

t-statistic Robust Std. Err.

Fixed-Rent Dummy

t-statistic Robust Std. Err.

Constant

Sample Size

Full Sample Mixed Tenants

497,547'" 701,093**'

(4.04) (3.63)

... (1nI8

_{8J ... Jl.9},3}2).}

515,487

( 1.55) ... ... ＮＮＮＨＳＳＲＬＲｾＺ［ｊ＠

800,119'" 889,320'"

11,517 11,517

.0007 .0031

T ABLE 6. Production Function (Full Sample)

Seemingly Unrelated Regressions

Log Output Log Output / Non- Log Output / Labor Input Labor Input

Sharecropping Dummy -.003 -.001 -.004

z-statistic (-.15) (-.04) (-.21)

Std. Err. (.02) (.02) (.02)

Log Area Cropped .002

Log Non-Labor Input .09***

Log Labor Input .91***

Log Plot Value (per acre) .006*

Irrigation .003

Soil Dummies Yes No No

Constant and Dummies for Village,

Yes Yes Yes

Main Crop, Year, and Season

Sample Size 10,687 10,687 10,687

R1 _.75

_.11

_.11

TABLE 7. Production Function (Mixed Tenants)

Seemingly Unrelated Regressions

Log Output Log Output I Non-Labor Input

.019

Log Output I

Labor Input

-.025 Sharecropping Dummy

z-statistic

-.020

(-.84)

... (02) -.001

(.62) (-1.0)

Std. Err.

Log Area Cropped Log Non-Labor Input

Log Labor Input

Log Plot Value (per acre)

Irrigation

Soil Dummies

Constant and Dummies for Village, Main Crop, Year, and Season Sample Size

.088'"

.918'"

.011

.003 Yes

Yes

3,602 .77

. ... LQ3) ... . ...(02) ..

No No

Yes Yes

3,602 3,602

.11

.15

-T ABLE 8. Glesjer's Heteroskedasticity -Tests (Full Sample)

Ordinary Least Square

Variable

(ui

(ui

I

Ui

I

I

Ui

I

ln<l Ui

I)

ln<l Ui

b

Ownership Dummy .067** .067** .030** .029** .027 .024

t-statistic (2.46) (2.43) (2.41) (2.30) (.89) (.79)

Ｎｾｯ｢ｵｳｴ＠ Std. Err: ...(Q!) .(.03) . ..(,()I) ... (,01) ... (.03)

_..

_{ＨｯＮｾｌ＠}

Fixed-Rent Dummy .052 .049 -.001 -.002 -.050 -.052

t-statistic (.52) (.50) (-.01) (-.04) (-.58) (-.60)

Robust Std. Err. ... (I()L . (.10) .. (,()4) ... (,04) . .(.04) (09)

Savings .071 .079*** .217***

Deposits .019 .013 -.061

Liabilities .001 -.001 .010

Arrears -.001 -.001 -.002

Life Insurance .179 .047 .078*

Other Financiai Assets -.0004 -.001 .003

Constant .452*** .447*** .500*** .497*** -1.15***

Sample Size 10,687 10,687 10,687 10,687 10,687 10,687

R2 _{.0004 .0016 .0005 .0018 .0001 .0014}

TABLE 9. Glesjer's Heteroskedasticity Tests (Mixed Tenants)

Ordinary Least Square

Variable

(ui

( U;)2

I

_U;

I

I

_U;

I

_In(

I

_Ui

I)

Ind U;

I) .

Ownership Dummy .102*** .102*** .053*** .054*** .072* .075*

t-statistic (2.74) (2.80) (3.34) (3.45) ( 1.80) (1.89)

Robust Std. Err. (.04) (.04) (.02) (.02) ... (04) (.04)

Savings -.085 -.002 .159

Deposits .063 .034 -.011

Liabilities .018 .005 .020

Arrears .006* .004** .007

Life Insurance .254 .067 .087

Other FinanciaI Assets .003 -.0002 -.004

Constant .438*** .426*** .489*** .485*** -1.17*** -1.18***

SampleSize 3,602 3,602 3,602 3,602 3,602 3,602

R2 _{.0019 .0080 .0030 .0060 .0009 .0027}

FUNDAÇÃO GETULIO VARGAS

BIBLIOTECA

ESTE VOLUME DEVE SER DEVOLVIDO A BIBLIOTECA

NA ÚLTIMA DATA MARCADA

N.Cham. P/EPGE SA B814ev

Autor: Braido, Luis H.B.

Título: Evidence on the relationship between incentives

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ｾｾｾｾＶＸＷＸ＠

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ｱｌｊｏｾｾｓＭＭｾ＠

B I li L Ｌｾ［＠ í '- • A

MARIO H 'hIQui: ｾ＠ IMONSEN

ｆｵｴＬｄｾｃￃｏ＠ Gf. ￚｾｉｏ＠ v RàAS

N° Pat.:346878

000346878

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