1. The Dynamics of Resource Allocation in Research Organizations In firms with research units, two interesting problems arise: 1.Managers that allocate resources are often unsure about the quality of the projects being pursued by research-unit heads 2.Managers are often unsure about the quality of the heads themselves Complicating this problem, research units often produce no output for long periods of time; managers have to base their decision of whether to continue a project or keep a unit head on their beliefs A unit head who is poorly suited for the unit’s project can lower expected output dramatically Given this, it may be difficult to determine whether the head should be replaced or the project should simply be abandoned when a unit performs poorly

2. Method This paper develops and tests a simple decision-theoretic model with the above features The unit-level production function has uncertainty, and the head and the project must both be good in order for the unit to have a higher- than-average chance of success As managers resolve their uncertainty over time, they shut down under-performing projects and remove heads believed to be of low quality The results have implications for resource allocation within the firm The paper contributes to the literature by focusing on how selection (shutting down projects and removing heads) and dynamics affect resource allocation within the firm

3. Testable Hypotheses 1.Holding the stream of output constant, if the managers’ initial beliefs about the project or head are more favorable, the unit gets more resources every period. Thus, indicators of quality that can be observed at the time the head is appointed or the unit is formed have lasting effects on resource allocation. 2.Heads who obtain R&D experience before joining their current unit receive more resources than heads who lack such experience 3.Incumbent heads receive more resources than new heads 4.Older units receive more resources than new units 5.Unassigned heads who are believed to be better are assigned to projects that are believed to be better

4. Testable Hypotheses, continued 6. If a new head is assigned to a pre-existing project and at some point thereafter the unit’s performance declines, the head is always replaced before the project is abandoned. Similarly, if an experienced head is assigned to a new project and at some point thereafter the units’ performance declines, he will always be assigned a new project if the current one is abandoned 7. Conditional on survival, older units tend to have more resources (as measured, for example, by the number of workers in the unit) and have more variation in size Survey data on research units in firms is used to test the predictions of the model and to estimate the relative importance of the different effects on the head’s span of control

5. The Model A manager is able to select projects and project heads from a pool and match them up Projects are either good or bad; heads are either good or bad Assume that the manager cannot directly observe project or head type Assume that heads are either incapable of observing project or their own type, or that they are unable to credibly convey this information to the manager Projects exist until they are shut down; heads remain until they are removed Most of the analysis focuses on manager’s choices that pertain to a single representative research unit (a small group working on a single project)

6. Formal Structure

7. New Heads and New Projects

8. Updated Beliefs

9. Implications for Resource Allocation In order to connect the manager’s beliefs to testable predictions about resource allocation, I assume: The Resources Assumption: Assume that the amount of resources that the manager allocates to the unit is increasing in his belief about the likelihood that output occurs

10. Formal Results

11. Lemma 1

13. Results 2 and 4

14. Result 3

15. Result 5

16. Result 6

17. Additional Implications for Cross-Sectional Data The data is a cross-section of research units Given this, it is important to determine the model’s additional implications for cross-sectional data I identify a research unit in the data with a “project” in the model; thus, the empirical counterpart of a “project” is simply whatever activities the unit undertakes The main additional implication is that older units should be larger on average and the variance in size should be larger among older units To see this, suppose a group of new units start today and experience shocks over time; initially all receive the same resources but as shocks occur some are shut down while others grow Conditional on survival, size grows and becomes more variable

18. Data The data is from the International Comparative Study on the Management, Productivity, and Effectiveness of Research Teams and Institutions, an international study of research units conducted by UNESCO during 1971-89 I consider only research units that belong to firms involved in either agriculture, chemistry, physics, or the technical sciences The countries represented are Austria, Belgium, Brazil, Egypt, Finland, Hungary, South Korea, Nigeria, and Spain I control for field-country fixed effects in the econometric analysis Summary statistics suggest that some type of selection process is operating: the distributions of “unit age” and “years as head” are both skewed to the right, and heads are not identified with the unit (they can come and go while the unit lives on)

19. Econometric Model Results 1-4 predict that the amount of resources allocated to the head of a research unit depends on indicators of head quality that can be observed at the time he becomes head, his R&D experience, his length of tenure as head, and the age of the unit Result 5 establishes that unit size is correlated with head quality for another reason – heads who are believed to be better are assigned to units that are believed to be better To evaluate these claims, I need a measure of the resources allocated to the head; I use the number of scientists in the unit: Sci = f(age, education, experience, years as head, field-country dummies)

20. Specification Two key factors determine the econometric specification: 1.The dependent variable is integer-valued. Thus, a count data model is appropriate 2.Since the theoretical model predicts that unit size is more variable in cohorts that have older units and more experienced heads, it is important to allow for cross-section heterogeneity (overdispersion in the number of scientists) The negative binomial count data model is appropriate in this case; it extends the Poisson model to allow for overdispersion

21. The Negative Binomial Model

22. Results The results in all three cases (OLS, Poisson, and negative binomial) are similar, and all three sets of results suggest that there is a positive relationship between the unit size and the four variables of interest, as the model suggests The overdispersion parameter of the negative binomial model  is statistically significant at the 1% level, which suggests that there is substantial cross-sectional heterogeneity (as the model suggests), and that the restriction to Poisson is rejected The results are robust to the exclusion of outliers (a few of the units are quite large relative to the others)

23. Marginal Effects I report the marginal effects (computed at the mean values) from the negative binomial model; the effect of an additional year of education has the largest effect on expected unit size, followed by an additional year as head of the unit Roughly, on average, one extra scientist is added if either the head has four additional years of education, seven additional years as head of the unit, nine additional years of experience before joining the unit, or if the unit is 17 years older