Research and Development/Tech Change/Emerging Technologies,

Ellingson, Dennis

Peterson, Willis L.

English

Research Papers in Economics

Staff Papers Series
Staff Paper P81-29 November 1981
The Allocation of Research Personnel:
Administrators’ Response to
Expected Rates to Return
Dennis Eliingson and Willis Peterson
Department of Agriculturaland AppliedEconomics
LJnivct-sity d’ Minnesota
Institute ot’Agriculture, Forestry and Home Economics
S1.Paul,Minnesota 551(N+The Allocation of Research Personnel:
Administrators’Response to Expected Rates of Return
by
Dennis EllingsonandWillis Peterson
Staff Papers are published without formal review within the
Department of Agriculturaland Applied EconomicsIntroduction
Bredahl and Peterson (1976)provided estimates of marginal products by
states for the commodity groups cash grains, dairy, poultry and other live-
stock research in the United States using 1969 Census of Agriculture data.
Their results showed national rates of return to crop and livestock research
to be in the 36 to 46% range. Agricultural researchersand administrators
have used these estimates in support of budget requests. It is the intent
of this paper to find whether or not these economic indicatorshave also
resulted in any adjustmentswith regards to the number of personnel employed
at respectiveresearch institutions. The hypothesis being that states which
have experiencedhigh marginal products to research for a specific commodity
group will allocate research funds to expand the size of departmentswhere
the returns to research are the greatest. Similarly, states which have
experiencedlow marginal products to research on specific commoditieswill
reallocate research funds away from those research departments.
The Data
The data to determine the adjustmentsmade by states on departments
involved in research activitiesregarding the four broad commodity groups
mentioned earlier was taken from the annual U.S.D.A. publication, “Professional
Workers in State Agricultural Experiment Stations and Other Cooperating State
Institutions”(69-70,80-81). Data from the same publicationwas used by
Peterson (1969) to inventory the allocation of research, teaching, and exten-
sion personnel by departments in U.S. colleges of agriculture and state
agriculturalexperimentstations. However, tabulation of this data was
approached somewhat differentlydue to the increased difficulty in lacer
publicationsof differentiatingindividualsby their field of specialization-2-
and also by their respectiveduties within that field. Therefore, there was
no attempt made to separate extension activities from teaching and research
activities. Although in cases where departmentshave merged, former publica-
tions were consulted to provide consistencywith regards to individuals’
fields of specialization. A simple head count was taken of department per-
sonnel and included only faculty of the rank instructor or research associate
and higher. Supportingstaff, graduate and undergraduatestudents were
excluded from the tally. In cases where disciplineswere merged within a
single department and there was no other way to discriminatean individual’s
field of specialization,each disciplinewithin that departmentwould be
given a fraction to account for him/her. For example, in cases where poultry,
dairy and livestockwere all included within a single department called
Animal Science, a professor of geneticswould be accounted for as one third
of a person to each of the three disciplines. Departments of Agronomy were
used to measure adjustmentsmade to cash grain research. Although in cases
where departmentsof Soil Science had merged with Agronomy, an effort was
made to differentiatespecialtieson an individualbasis, thereby providing
consistencythroughout. Differences in department sizes were calculated in
both percentage and absolute terms over the eleven year, 1969-1980 period.
These figures provided the dependentvariables. Independentvariables, the
marginal products to research by state, by commodities,came directly from
Bredahl aridPeterson’s article, “The Productivityand Allocation of Research:
U.S. Agricultural Experiment
used as proxies for rates of
respectivemarginal products
Stations” (1976)where marginal products are
return. The numbers of personnel and their
of research are presented in Table 4.-3-
Regression Results:
Simple regressionswere performed to determine if allocative adjustments
were made during the eleven year period following 1969 which was the year in
which Census of Agriculture data was used
research by Bredahl and Peterson (1976).
provided in Table 1. Both percentage and
to calculatemarginal products to
The results of these regressionsare
absolute changes in numbers of
experimentstation personnel are presented as dependent variables, although I
feel the absolute change variable provides a more accurate account of adjust-
ments because in many cases departmentsare small and the loss or gain of a
single employee results in a huge percentage change and is better explained
in absolute terms. The results show that only the commoditiespoultry and cash
grain have a direct causal relationshipbetween the marginal products to research
in these fields and the adjustmentsmade by experiment stations involved in
research on them. In these two fields outliers were omitted to determine
the best fit of the regression line. In the case of poultry the best fit
occurs with the exclusion of Mississippi’sdata which showed a high marginal
product to poultry research but a large decrease in the number of personnel
involved in poultry research. Outliers in the regressionswith cash grain were
Texas and Illinois. Texas had a high marginal product to research but it’s
doubtful that the huge increase in the number of personnel involved in cash
grain research is due solely to this high marginal product. I
of this type in Texas are due to the oil revenue and influx of
state allowing them to expand their research stations. On the
suspect increases
business to that
other hand,
Mississippi is a revenue deficient state and probably cannot afford to even
maintain its present facilities even though they offer high returns. Illinois’
marginal product to cash grain research was the highest by far of any state
but their increase in personnel involved in cash grain research was quite modest;-4-
therefore, exclusion of the Illinois data resulted in the best fit of the
regression line. Regressionsperformed on Dairy and Livestock individually
showed no causal relationshipbetween their marginal products to research and
any changes in their personnel numbers. Dairy and livestock were often merged
under the same department title and it was often difficult to discern the
activitiesof individuals. In order to alleviate this problem the data for
dairy and livestockwere combined and regressed against a weighted value for
their combined marginal products to research (see Table 2), The results of
this regressionwere also insignificantleading one to conclude that adminis-
trators of experimentstations involved in dairy and livestock research do not
take marginal products or rates of return to research into account when
adjusting the size of their departments. Poultry was then combined in similar
fashion to dairy and livestock but again the regression results were insignifi-
cant. Finally, adjustmentsmade in all four commodity groups were combined
and regressed against a combined,
four commodities. The results of
and Minnesota outliers. The best
.
weighted marginal product to research on all
this regressionwere significantwith Texas
fit of the regression line occurred when
Minnesota, with its extremely high combined marginal product to research and
modest personnel growth, was omitted.
The regression results presented in Tables 1 and 2 represent adjustments
made between states to their respective commodities’marginal products of
research. To measure the adjustmentsmade within states, individualregressions
were performed on all 48 states using the absolute changes within each of the
four commodity groups in each state as the dependent variables. The results
of these regressionsare presented in Table 3. Although the number of observa-
tions is small (4 in most states), each observationrepresentsan aggregate
adjustmentmade within a specializedfield of research. Therefore, the small-5-
sample size and,few degrees of freedom are no cause for alarm except in the
cases of Vermont, Massachusettsand Rhode Island. In Vermont’s case there
were no adjustmentsmade during the entire eleven year, 1969-80 period. ThiS
resulted in a perfectly fitted, horizontal regression line. In the cases of
Massachusettsand Rhode Island there were observationsavailable for only
poultry and dairy research. As a result there were zero degrees of freedom
and again perfectly fitted regression lines connected the two points. Results
from the remaining states are insignificantexcept for New Hampshire with a
positive coefficientof .29 reliable at the .025 significancelevel and New
Mexico with a positive coefficientof .49 reliable at the .05 significancelevel.
Conclusions:
The results of this analysis would lead one to conclude that, generally
speaking,marginal products or rates of return to research, are not taken into
considerationby administratorsof experiment stationswhen deciding on how and
whether or not to adjust the numbers of personnel involved in specific research
areas at these stations. Although New Hampshire and New Mexico do appear to
make positive adjustmentswith higher rates of return to their research activi-
ties, coincidencecannot be ruled out in these two instances. Aggregated
results pointed to a direct causal relationshipbetween the expected economic
payoff and the respective adjustmentsmade to the areas of poultry and cash
grain research;whereas there were no adjustmentsmade in the areas of livestock
and dairy research. These are somewhatmysterious findings in that one would
expect
due to
to say
similar behavior between the disciplines of poultry and dairy research
the fact that they are both quite specific in nature. Let it suffice
that there are many factors involved in
nel allocation among research fields; and some
consideredmore important than economic payoff
allocation.
decisionswith regards to person-
of these factors
when it comes to
are obviously
research funds-6-
Differential rates of return on alternative investmentsbetween states
could explain at least some of the between state variation rates of return
to research. For example, Mississippihad a high marginal product to
poultry research, but experienceda drastic reduction in the number of
research personnel involved in poultry research during this period. It
is possible that during this period alternative investmentsin such things
as road construction,harbor development,etc. showed a higher return to
investmentand were thus selected for greater amounts of state funding.
Likewise, in Texas, where the returns to research were not particularly
high and the number of research personnel increased dramatically,it is
possible that alternativeuses for this states revenues have a lower
economic payoff than research, resulting in a higher appropriationof
funds to research. Investigationinto the economic returns of alternative
investmentsby the governmentsof these states would be interestingand
possibly quite revealing. Of course even if such interstatedifferences
in rates of returns to research can be rationalizedfor short run periods,
it does not explain or justify the large differences in rates of return
that exist within states between the four commodity groups.-7-
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Table 2. Regressions of Weighted Marginal Products of Research on Combined Adjustments,
Between States.
Change in Number
of Experiment
Station Personnel
69-80
Dairy, Livestock Dairy, Livestock
(Dependent
Dairy & Livestock & Poultry Poultry & Cash Grain
Variable) Constant Coeff. R2 Constant Coeff. R2 Constant Coeff. R2
Absolute Change 2.00 .065 .0295 -.169 .094 .054 -3.57 .322 .1901
(1.18) (1.62) (3.29)
Absolute Change
(ExcludingTexas
-.063 .217 .1889
& Minnesota)’
(3.2)
Absolute Change 2.3 .163 .1442
(ExcludingTexas) (2.75)
Absolute Change
(Excluding
-8.2 .427 .2552
(3.93)
Minnesota)-9-
Table 3. Within State Adjustments to Marginal Products of Research
R’
No. of
Constant Coeff. Observations
Maine
New Hampshire
Vermontl
Massachusetts
Rhode Islandl
Connecticut
New York
New Jersey
Pennsylvania
Ohio
Indiana
Illinois
Michigan
Wisconsin
Minnesota
Iowa
Missouri
North Dakota
South Dakota
Nebraska
Kansas
Delaware
Maryland
Virginia
1.5
-2.94
0
-1.7
-3.01
-1.96
.34
-5.05
5.31
-2.27
11.68
-8.84
-21.88
3.0
3.89
-5.01
4.59
3.02
3.09
1.45
3.6
2.13
2.46
-3.64
-.07
(-.4)
.29
(25.07)
(~.a.)
(~.a.)
.48
(n.a.)
-.18
(-1.18)
-.19
(-1.61)
(1::3)
-.76
(-2.55)
.75
(.44)
-.42
(-.63)
,55
(;:;8)
-.012
(-.29)
-.022
(-.25)
.021
(.23)
.086
(.29)
.075
(.15)
.043
(.15)
-.032
(-.07)
.23
(1.38)
-.059
(-.52)
.005
(.02)
.396
(.74)
.1403
.9984
1.0
1.0
1.0
.5819
.5653
.6272
.7645
.0865
.164
.5492
.6831
.0408
.0312
.025
.0416
.0116
.0106
.0027
.4877
.1205
.0001
.2163
3
3
3
2
2
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4-1o-
Table 3. continued
R’
No. of
Constant Coeff. Observations
West Virginia
North Carolina
South Carolina
Georgia
Florida
Kentucky
Tennessee
Alabama
Mississippi
Arkansas
Louisiana
Oklahoma
Texas
Montana
Idaho
Wyoming
Colorado
New Mexico
Arizona
Utah
Nevada
Washington
Oregon
California
15.1
9.51
3.37
5.68
9.85
3.19
4.09
,98
8.03
11.65
2.57
1.86
57.69
-1.68
10.48
-1.16
10.4
-3.03
4.94
3.59
-1.21
-.608
-10.62
15.24
-1.34
(-4.14)
-.15
(-.42)
.098
(.38)
.22
(1.38)
-.41
(-1.04)
.069
(.22)
-.34
(-.42)
.076
(1.5)
-.31
(-2.37)
-.055
(0.21)
.207
(.19) “
.008
(.02)
-.76
(-.28)
.41
(.98)
-.21
(-.35)
.35
(2.98)
-.19
(-.58)
.49
(4.26)
-.61
(-1.3)
.198
(.61)
.25
(3.46)
.18
(.13)
1.61
(1.52)
-.53
(-2.34)
.8956
.0814
.0686
.4862
.3512
.0228
.082
.5303
.7381
.0208
.0178
.0003
.0369
.2998
.0565
.8159
l 1447
l 9007
.4595
.1553
.9231
.0088
.5359
.7317
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
4
4
4
‘Regressions made from
t-statisticsand show
for Massachusetts and
Vermont.
observationson Vermont, Massachusetts and Rhode Island lacked
dubious results due to the small sample size (2 observations
Rhode Island) and the absence of any response on the part of-11-
u
22
\
.
.-12-
References:
Bredahl, Maury and Willis Peterson, “The Productivityand Allocation of Research:
U.S. AgriculturalExperiment Stations,”American Journal of Agricultural
Economics,Vol. 58, No. 4, (November1976), pp. 684-692.
Peterson, Willis L., “The Allocation of Research, Teaching, and Extension
Personnel in U.S. Colleges of Agriculture,”American Journal of Agricultural
Economics,Vol. 51, No. 1, (February1969), pp. 41-56.
U.S. Department of Agriculture, “ProfessionalWorkers in State Agricultural
Experiment Stations,”U.S.D.A. Handbook No. 305, (69-70,80-81).

THE ALLOCATION OF RESEARCH PERSONNEL: ADMINISTRATORS' RESPONSE TO EXPECTED RATES TO RETURN

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