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CHAPTER 4. YEAR-END SPENDING 155 the issues we have noted with respect to, among another things, lack of competition and poor management of interagency contracts” (?). Yet despite these accounts, there is no hard evidence on whether year-end spending surges are currently occurring in the U.S. federal government or whether year-end spending is lower-value than spending during the rest of the year. Government Ac- countability Office (GAO) reports in 1980 and 1985 documented that fourth quarter spending was somewhat higher than spending during the rest of year using aggregate agency spending data. In a follow-up report, ? stated that because “substantial reforms in procurement planning and competition requirements have changed the environment . . . year-end spending is unlikely to present the same magnitude of problems and issues as before.” However, this later report was unable to examine quarterly agency spending patterns for 1997 because agency compliance with quarterly reporting requirements was incomplete. Other government professionals cite similar constraints to empirical analysis. In summarizing the response of Department of Defense contracting officers to the interview question, “How would you measure the quality of year-end spending?” ? writes, “Absent flagrant abuse that no one could miss, there is no practical way of weighing year-end spending.” This paper address the empirical shortfall. It begins by documenting the within- year pattern of federal spending on government contracts, the main category of spending where significant discretion about timing exists. The analysis demonstrates that there is a large surge in the 52nd week of the year that is concentrated in procure- ments for construction-related goods and services, furnishings and office equipment, and I.T. services and equipment. It also shows that the date of completion of annual appropriations legislation has a noticeable effect on the timing of federal contracting, consistent with anecdotal claims that part of the difficulties agencies face in effective management of acquisitions comes from tardy enactment of appropriations legislation. The estimates show that a delay of ten weeks, roughly the average over this time period, raises raises the share of spending in the last month by 1 percentage point, from a base of about 15 percent. We then analyze the impact of the end-of-year spending surge on spending quality using a newly available dataset on the status of the federal government’s 686 major
CHAPTER 4. YEAR-END SPENDING 156 information technology projects—a total of $130 billion in spending. Consistent with the model, spending on these I.T. projects spikes in the last week of the fiscal year, increasing to 7.2 times the rest-of-year weekly average. Moreover, the spike is not isolated to a small set of agencies or a subset of years, but rather a persistent feature both across agencies and over time. In tandem with the spending increase, there is a sharp drop-off in investment quality. Based on a categorical index of overall investment performance, which combines assessments from agency information officers with data on cost and timeliness, we find that projects that originate in the last week of the fiscal year have 2.2 to 5.6 times higher odds of having a lower quality score. Ordered logit and OLS regressions show that this effect is robust to agency and year specific factors as well as to a rich set of project characteristic controls. Our findings suggest that the various safeguard measures put into place in response to the 1980 Senate Subcommittee report (?) and to broader concerns about acquisition planning have not been fully successful in eliminating the end-of-year rush-to-spend inefficiency. An alternative solution is to give agencies the ability to roll over some of their unused funding for an additional year. Provisions of this nature have been applied with apparent success in the states of Oklahoma and Washington, as well as in the UK (??). Within the U.S. federal government, the Department of Justice (DOJ) has obtained special authority to roll over unused budget authority into a fund that can be used in the following year. We extend the model to allow for rollover and show that, in the context of the model, the efficiency gains from this ability are unequivocally positive. To test this prediction, we study I.T. contracts at the Department of Justice which has special rollover authority. We show that there is only a small end-of-year I.T. spending spike at DOJ and that the one major I.T. contract DOJ issued in the 52nd week of the year has a quality rating that is well above average. The rest of the paper proceeds as follows. Section 4.2 presents a model of wasteful year-end spending and discusses the mechanisms that could potentially lead to end- of-year spending being of lower quality than spending during the rest of the year. Section 4.3 examines the surge in year-end spending using a comprehensive dataset on federal procurement. Section 4.4 tests for a year-end drop-off in quality using data
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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 8 After treat
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CHAPTER 1. BANKRUPTCY 12 newly edit
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CHAPTER 1. BANKRUPTCY 35 of income
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CHAPTER 1. BANKRUPTCY 40 TaxSim pro
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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 2. HEALTH PLAN CHOICE 66 pl
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Page 134 and 135: Chapter 3 Private Coverage and Publ
Page 136 and 137: CHAPTER 3. MEDIGAP 119 be of intere
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Page 140 and 141: CHAPTER 3. MEDIGAP 123 have benefit
Page 145 and 146: CHAPTER 3. MEDIGAP 128 We use boots
Page 147 and 148: CHAPTER 3. MEDIGAP 130 of costs and
Page 149 and 150: CHAPTER 3. MEDIGAP 132 policy that
Page 151 and 152: CHAPTER 3. MEDIGAP 134 3.6.2 Causal
Page 153 and 154: CHAPTER 3. MEDIGAP 136 3.6.3 Robust
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Page 157 and 158: CHAPTER 3. MEDIGAP 140 (10000,11000
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