Bureaucratic Performance and Budgetary Reward

Bureaucratic Performance and Budgetary RewardAuthor(s): Ronald S. Warren Jr.Source: Public Choice, Vol. 24 (Winter, 1975), pp. 51-57Published by: SpringerStable URL: http://www.jstor.org/stable/30022844 .

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Articles

BUREAUCRATIC

PERFORMANCE AND

BUDGETARY REWARD

Ronald S. Warren, Jr.*

The relationship between bureaucratic production and subsequent Congres- sional appropriations has been the subject of plausible, yet contradictory, specula- tion by prominent observers of the budgetary process. For example, Aaron

Wildavsky (pp. 93-94) has suggested that fiscal sponsors might respond to successful

agency performance by reducing future appropriations. On the other hand, William Niskanen has argued persuasively that ". ... a bureau that performs better than

expected is likely to be rewarded by higher future budgets" (p. 42). Clearly, sponsor reaction to bureaucratic performance has important implications for the

output strategy of a budget-maximizing bureau chief. Surprisingly, however, empirical investigations of this issue have not surfaced to date. The purpose of this

paper is to develop and test a simple model of budgeting behavior in order to discriminate empirically between these two competing views of the relationship between bureaucratic performance and budgetary reward.

Section I formalizes an estimable model of the budget-output nexus in a bureaucratic environment. Section II describes the data base and estimation

procedure employed in this study. Section III reports the results of the statistical

analysis. Section IV summarizes the findings of this paper, acknowledges several limitations of the present inquiry, and suggests possible future improvements.

*Economist, Bureau of Labor Statistics. This research was undertaken while the author was a Teaching Fellow at the University of North Carolina at Chapel Hill. The author acknowledges helpful comments from Robert P. Strauss, Thomas S. McCaleb, and an anonymous referee, and editorial assistance from Joyce N. Warren. Responsibility for remaining errors rests solely with the author, however.



52 PUBLIC CHOICE

L The Model

To facilitate the analysis of this section, the following symbols are introduced:

Bt = agency appropriation in year t

Qt = agency output in year t

Qt = agency output expected by the sponsor in year t

T = a time trend variable

Agency performance to which current appropriations are related is defined as the difference between actual and expected agency output in the previous period, Qt-1

-Qt-1. Hence, we have the hypothesized relationship

Bt +(QtI - Qt- ) t

or, with a time trend variable added to capture omitted secular influences

Bt = +B(Qt1 + yT t = 1, ..., n. (2)

Operational versions of the models represented in equations (1) and (2) require a specification of output expectations in terms of observable variables. To

accomplish this task, the familiar "adaptive expectations" assumption introduced

by Phillip Cagan (pp. 37-39) is utilized. The sponsor's expectations of next period's agency output are formed adaptively, according to the usual scheme:

Qt-1 - t-2 = (1 - X)(Qt2 -Qt2) 0 < <1 (3)

where X is a fixed reaction coefficient. Equation (3) is equivalent to the geometric distributed lag expression

+* 2D)( +

0 ) 2 t(4) = (1 X) +( -2-3 + 4 * *+ (4)

Substitution for Qt-1

from (4) into (1) and (2) and application of Koyck transformation to each resulting equation yields the following estimable models of the budget-output relationship:

Bt = a(1 - X) + (Qt-1 -Qt-2) + ABt-1+ lt (5)

and

Bt [a + X(y - a)] + B(t - Qt-2

+ XBt-1

(6)

+ (1 - X)yT + p2t



BUREAUCRATIC PERFORMANCE 53

where a, f0, 1, are parameters, and plt and /2t

are stochastic disturbance terms.

Rejection of both the Wildavsky and Niskanen hypotheses requires $ not

significantly different from zero. If > 0 significantly, next year's appropriations are positively relatea to unexpectedly "good" performance and Niskanen's

hypothesis cannot be rejected. On the other hand, if < < 0, unanticipated productivity would be penalized and Wildavsky's dictum, "avoid too good results," would receive empirical support.

IL Data and Estimation Procedure

In order to estimate the models developed in the previous section, time-series data on appropriations and output rates for one or more government agencies were

required. Although budget figures were readily available, well-known problems of

defining and measuring public sector output had to be resolved. Recently, the

Advisory Council on Executive Organization (the so-called "Ash Council") developed crude, yet intuitively plausible, productivity measures for various regulatory agencies which it investigated. After careful scrutiny, only the data on the Securities and Exchange Commission (SEC) were deemed appropriate for

present purposes, primarily because of the relatively greater number of observations available. 1

Budget and output data for the SEC were obtained from Roger Noll (Table 1, p. 86) for the period 1945-1969. The budget series was deflated to thousands of 1958 dollars by the implicit price deflator reported by the Council of Economic Advisers (Table C-3, p. 196) for the appropriate years. "Output" was defined as the total number of registration statements (proposals to offer securities to the public) and proxy statements (solicitations of proxies from stockholders) filed each year with the SEC.2 Although obviously imperfect, this measure of output constituted an important source of information for the deliberations and policy recommenda- tions of the Ash Council, and was conceptually consistent with the productivity indices developed recently for the Joint Economic Committee and documented by Jerome Mark.

Under the usual, classical assumptions that Alt and p2t in (5) and (6) are

IN(0,o2),3 the parameters of these models can be consistently estimated by the

application of ordinary least-squares regression to the unrestricted forms:

Model I

Bt = 60 1(+ (Qt- Qt-2 2Bt-1 + Ilt where 60 = a(1 - A), 61 = B, and 62 =

, and

1See Noll (pp. 83-86, Table 1). 2A conversation with an SEC official revealed a very short (2-3 week) average processing

time for registration and proxy statements, so that statements filed in a given year closely approximates statements reviewed.

3A defense of these assumptions concerning the error terms in autoregressive models like (7) and (8) can be found in Klein (pp. 560-561).



54 PUBLIC CHOICE

Model II

Bt 0 + rl1(Qt-I -Qt-2) + 2BBt-1 + r3

2t (8) where

O = a+X( - a), = 2 = A, and

3 = (1 - A) Incidentally, it could be argued that a complete specification of the

budget-output relationship would incorporate a recursive relationship between

appropriations and output, thus casting suspicion on OLS estimates of (7) and (8) alone. Indeed, the dependence of output on fiscal resources was cited by Noll (pp. 81-82) as a possible explanation for the disappointing performance of many regulatory agencies in the late 1950's and early 1960's. However, under the reasonable assumption that the relationship between budgetary outlays and agency performance is diagonally recursive, OLS estimates of the coefficients of (7) and (8) retain most of the desirable properties.4

III. Empirical Results

Using the data sources and statistical technique described in section II, the

following estimated equations were obtained:

Bt = -434.639 - 0.636297(Qt_1 - Qt-2) + 1.10859Bt_1 (456.612) (0.342346) (0.05689)

R2 = 0.9638 h = 2.142 n = 23

Bt = -262.964 - 0.735055(Qt_1 - Qt-2) + 0.978312Bt-1 + 83.2698T

(393.463) (0.293646) (0.065843) (28.4803)

(1.8454 n0) R2= 0.9737 h = .8454 n = 23

where K2 is the coefficient of determination adjusted for degrees of freedom, h is Durbin's one-tailed test statistic for serial correlation in autoregressive models, and n is the number of observations. Standard errors of the estimated regression coefficients appear in parentheses under the coefficient estimates. Estimates of the

parameters 0, X, and 'y associated with the estimated coefficients in (9) and (10) are

presented in Table 1.

In terms of the usual test statistics,5 there is little basis for choosing between the two models. The explanatory power is high and roughly comparable in both models. Values of EDurbin's h statistic reveal significant positive serial correlation of the estimated residuals in Model I but not in Model II, which suggests that caution

4Kmenta (pp. 585-586). Because of the presence of a lagged dependent variable in both specifications, however, unbiasedness of the OLS estimators would be crucially dependent on the assumptions on A/lt and /2t, respectively.

5All tests of significance were conducted at the .05 level.




TABLE

1:

PARAMETER

ESTIMATES

0

X

y

Model

I

- .6363

1.1086

(.3423)

(0.0569)

Model

II

- .7351

.9783

3837.3

(.2936)

(.0658)

(10806.5)

Note:

Estimated

standard

errors

of

the

parameters

are

in

parentheses.

The

estimated

standard

error

of

y in

Model

II was

computed

by

the

approximation

formula

given

in

Kmenta

(p.

444).



56 PUBLIC CHOICE

should accompany statistical inferences which use the implied t-ratios in equation (9). The parameter estimates are strikingly similar in the two models. The estimates of the coefficients of the performance and reaction variables are significantly different from zero in both models. The estimated constant term is not statistically different from zero in either model. In addition, the estimated trend parameter (,) is not statistically different from zero - in spite of the significance of [(1 -X) ] in equation (10). Finally, the estimated adjustment parameter (1) displays a

disturbingly high value in equation (9), thus casting further suspicion on the

reliability of Model I. In view of the evidence of serial correlation and the

inadmissably large magnitude of X in the estimates of Model I, the regression analysis reveals a somewhat superior statistical performance by Model II.

More importantly, from the standpoint of the hypotheses under investigation, the estimated coefficients of the performance variables were significantly negative in both equations, thus lending empirical support to Wildavsky's view of the dynamic reward structure facing bureaucratic organizations. The findings of this study suggest, then, that the budget-maximizing bureaucrat may wish to avoid claiming unexpected success in meeting program objectives for fear that his sponsor might reduce future budget allocations.

IV. Concluding Remarks

This paper has developed and tested a simple, but suggestive, model of budgeting behavior in order to choose empirically between alternative views of the relationship between bureaucratic performance and budgetary reward. On the basis of the exploratory analysis conducted herein, Niskanen's hypothesis was rejected in favor of the opinion advanced by Wildavsky that the appropriations process may penalize agencies which perform better than expected.

It would be careless, of course, to regard this test as definitive in any sense, and future research on the budgetary process should attempt to circumvent some of the obvious limitations of the present study. First, productivity measures for the public sector must be developed which are comparable in coverage and quality to those now reported for the private economy. Accordingly, the current interagency project on federal productivity measurement should be expanded. Second, the adaptive mechanism for generating expectations from observed past values was borrowed from the economics literature. This procedure imposes potentially important restrictions on the resulting expectations series and, furthermore, may not be appropriate for modeling political behavior. Finally, the possibility of simultaneous equation bias in the single equation approach adopted here cannot be ignored. However, improvements along this line await the development of a more complete model of budgetary dynamics.6

A recent effort by Wildavsky, Davis, and Demster illustrates the predictive potential of empirical models of the budgetary process.




REFERENCES

Cagan, Phillip. "The Monetary Dynamics of Hyperinflation." Studies in the

Quantity Theory of Money. Edited by Milton Friedman. Chicago: The

University of Chicago Press, 1956. Durbin, James. "Testing for Serial Correlation in Least-Squares Regression When

Some of the Regressors Are Lagged Dependent Variables." Econometrica, 38 (May 1970), 410-421.

Klein, Lawrence R. "The Estimation of Distributed Lags." Econometrica, 26(1958), pp. 553-565.

Kmenta, Jan. Elements of Econometrics. New York: The Macmillan Company, 1971.

Mark, Jerome A. "Progress in Measuring Productivity in Government," Monthly Lab. Rev., 95 (December 1972), 3-6.

Niskanen, William A. Bureaucracy and Representative Government. Chicago: Aldine-Atherton, 1971.

Noll, Roger G. Reforming Regulation. Washington, D.C.: The Brookings Institu- tion, 1971.

Wildavsky, Aaron. The Politics of the Budgetary Process. Boston: Little, Brown and

Company, 1964. , Otto Davis, and Michael Demster. "Towards a Predictive Theory

of Government Expenditure: U.S. Domestic Appropriations." British Jour. Pol. Sci., 4 (October 1974), 419-453.

Advisory Council on Executive Organization A New Regulatory Framework:Report on Selected Independent Regulatory Agencies. Washington, D.C.: Government Printing Office, 1971.

Council of Economic Advisers. Economic Report of the President. Washington, D.C.: Government Printing Office, 1973.

Joint Economic Committee. Measusuring and Enhancing Productivity in the Federal Sector. 92nd U.S. Congress, 2nd Session, Committee Print, 1972.



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Bureaucratic Performance and Budgetary Reward