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CHAPTER 4. YEAR-END SPENDING 177 There is also a third possible mechanism. 21 Some program managers or contracting officers may be inclined toward procrastination and these same employees may be ones who do a worse job of planning for, writing, or managing contracts. Thus, we may see a surge of low quality contracts at the end of the year because that is when the least effective acquisition professionals get their contracts out the door. This third mechanism could have different policy implications from the first two because allowing agencies to roll over funds would not necessarily improve outcomes—the least effective acquisition professionals would simply issue their contracts at a different time of year. One way to evaluate this procrastination hypothesis would be to examine the within- year calendar distribution of other contracts issued by the contracting officers who issued last week contracts in our I.T. Dashboard sample to see if they appear to be procrastinators who consistently complete their contracts late in the year. While we can identify the contracting officers responsible for a contract in both the dashboard sample and the FPDS sample, we cannot currently link the two samples, though we believe we ultimately will be able to do so. Another way to explore the possible mechanisms behind the poor outcomes for end-of-year contracts is to examine the subcomponents of the overall rating to see which subindices are responsible for the result. Appendix Table 4.10 repeats our main ordered logit analysis with each subindex as the dependent variable. The results show clearly that it is the evaluation by the agency CIO that is responsible for the main finding. Neither the cost rating nor the schedule rating has an odds ratio that is significantly different from 1. The CIO evaluation shows that the odds of having a higher rating are one-sixth as high for last-week-of-the-year contracts. The coefficient in the CIO regression is insensitive to adding the cost rating and scheduling rating into the regression, suggesting that it is information in the CIO rating that is not incorporated in the other rating that is responsible for the result. This finding is not all that surprising. As we mentioned above, the I.T. dashboard explicitly places more faith in the CIO’s assessment than in the other components by allowing the CIO assessment to override the other components if it is lower than the other components. Moreover, the ability to reset milestone targets makes it difficult to assess the cost 21 We thank Steve Kelman for suggesting this third mechanism.
CHAPTER 4. YEAR-END SPENDING 178 and schedule ratings. But while not surprising, the fact that it is the CIO evaluation that is driving the result means that we cannot learn much about the mechanism from the subindices, since the CIO evaluation in a comprehensive measure of the I.T. project’s performance. Another way to explore possible mechanisms is to examine whether other ob- servable features of end-of-year contracts are different from those earlier in the year. Specifically, we examine whether features that policymakers often define as high risk— such as lack of competitive bidding or use of cost-reimbursement rather than fixed cost pricing—are more prevalent in end of year contracts. For this analysis we return to the FPDS sample of all contracts from 2004 to 2009. To facilitate the analysis, we aggregate the 14.6 million observations up to the level of the covariates. We then estimate linear probability models with indicators for contract characteristics (e.g., a non-competitively sourced indicator) as the dependent variable on an indicator for last week of the fiscal year and controls. The regressions are weighted by total spending in each cell. The first three columns of Appendix Table 4.11 examine shifts in the degree of competitive sourcing at the end of the year. The use of non-competitive contracts shows little change. However, contracts that are competitively sourced are significantly more likely to receive only one bid perhaps because the end of year rush leaves less time to allow bidding to take place. The estimates indicate that there is almost a 10 percent increase in the percent of contracts receiving only a single bid—a 1.7 percentage point increase one a base of 20 percent. On net, then, there is a modest increase in “risky” non-competitive and one bid contracts at the end of the year. Column (3) shows that spending on contracts that are either non-competitive or one bid contract increases by 1 percentage points on a base of 49 percent in the last week of the year. The second three columns investigate the type of contract used. Contracts that provide for cost reimbursement rather than specifying a fixed price are often seen as high risk because they have significant potential for cost overruns. Time and material or labor hours (T&M/LH) contracts raise similar concerns because they involve open ended commitments to pay for whatever quantity of labor and materials are used
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ESSAYS IN PUBLIC FINANCE AND INDUST
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I certify that I have read this dis
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insurance choice. Among other thing
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Acknowledgements I am deeply thankf
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Contents Preface iv Acknowledgement
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2.5.3 The Value of Plan Choice ....
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List of Tables 1.1 Asset Exemption
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4.9 First Week Contract Spending fo
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4.3 I.T. Contracting by Week ......
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CHAPTER 1. BANKRUPTCY 2 of the dead
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CHAPTER 1. BANKRUPTCY 4 relevant le
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CHAPTER 1. BANKRUPTCY 35 of income
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Payments CHAPTER 1. BANKRUPTCY 42 $
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OOP payments CHAPTER 1. BANKRUPTCY
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ln(seizable sssets) CHAPTER 1. BANK
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50 60 70 80 90 100 Insurance covera
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Pr(WTP > premium) CHAPTER 1. BANKRU
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Chapter 3 Private Coverage and Publ
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CHAPTER 3. MEDIGAP 119 be of intere
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CHAPTER 3. MEDIGAP 121 hazard for i
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CHAPTER 3. MEDIGAP 123 have benefit
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