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CHAPTER 4. YEAR-END SPENDING 167 have different intercepts but are constrained to have the same slope. The estimates show that a delay of ten weeks, roughly the average over this time period, raises the share of spending in the last quarter by 2 percentage points from a base of about 27 percent. A ten-week delay raises the share of spending in the last month by 1 percentage point, from a base of about 15 percent. Both coefficients are statistically significant at the 1 percent level. As we mentioned above, we do not have reliable within-month data on timing for the years before 2004, so we exclude years before 2004 for the analysis of spending during the last week of the year. The estimates indicate that a 10 week delay raises the share of spending by 1 percentage point on a base of 9 percent. Due to the smaller sample, the estimate is less precise, with a p-value of .07. Overall, the analysis in this section shows that the end-of-year spending surge is alive and well, thirty years after Congress and GAO focused significant attention on the problem and despite reforms designed to limit it. Moreover, claims that late appropriations increase the end-of-year volume of contracting activity are accurate, suggesting that late appropriations may be exacerbating the already adverse effects of having an acquisition workforce operating beyond capacity at the end of the year. A surge in end-of-year spending does not necessarily imply bad outcomes. Agency acquisition staff can plan ahead for the possibility that extra funds will be available. Indeed, for large contracts weeks and even months of lead time are generally necessary. The next section of the paper therefore analyzes the relative quality of end-of-year contract spending to explore whether there are any adverse effects of the end-of-year spending surge. 4.4 Is End of Year Spending of Lower Quality? Our model predicts that end-of-year spending will be of lower quality both because agencies will spend money at the end of the year on low value projects and because the increased volume of contracting at the end of the year will lead to less effective management of those acquisitions. As the discussion in the introduction indicated, it has been challenging historically to study contract quality because of the limited
CHAPTER 4. YEAR-END SPENDING 168 availability of data. In this section of the paper, we use a new dataset that includes quality information on 686 of the most important federal I.T. procurements to study whether end-of-the year procurements are of lower quality. 4.4.1 I.T. Dashboard Our data come from the federal I.T. Dashboard (www.itdashboard.gov) which tracks the performance of the most important federal I.T. projects. The I.T. Dashboard came online in beta form in June, 2009 and provides the public with measures of the overall performance of major I.T. projects. Like the USAspending.gov data dis- cussed earlier, the I.T. Dashboard is part of the trend toward “open government” and part of a shift in federal management philosophy toward monitoring performance trends rather than taking static snapshots of performance and of making the trends public both for the sake of transparency and to motivate agencies to achieve high performance (?). 9 Along with providing credible performance data for a portion of contract spending, studying federal I.T. projects has two other advantages. The first is the ubiquity of I.T. spending. Major information technology projects are carried out by nearly all components of the U.S. federal government. Compared to an analysis of, say, the purchase of military or medical equipment, an analysis of I.T. spending shines a much broader light on the workings of government, allowing us to test our hypotheses across agencies with a wide range of missions and organizational cultures. The second advantage is that federal I.T. spending is an important and growing federal activity. Federal I.T. expenditure was $81.9 billion in 2010, and has been growing at an inflation-adjusted rate of 3.8 percent over the past 5 years. 10 . Moreover, these expenditure levels do not account for the social surplus from these projects. 9 The legislative foundation for the I.T. Dashboard was laid by the Clinger-Cohen Act of 1996, which established Chief Information Officers at 27 major federal agencies and called on them to “monitor the performance of the information technology programs of the agency, [and] evaluate the performance of those programs on the basis of applicable performance measurements.” The E-Government Act of 2002 built upon this by requiring the public display of these data. 10 Analytical Perspectives: Budget of the U.S. Government, 2010
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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 6 superficial
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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 15 bound of t
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CHAPTER 1. BANKRUPTCY 20 or coverag
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CHAPTER 1. BANKRUPTCY 22 homestead
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CHAPTER 1. BANKRUPTCY 24 log seizab
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CHAPTER 1. BANKRUPTCY 28 to seizabl
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CHAPTER 1. BANKRUPTCY 31 CARA utili
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CHAPTER 1. BANKRUPTCY 33 Puzzle 3:
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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 1. BANKRUPTCY 52 Table 1.1:
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CHAPTER 1. BANKRUPTCY 64 Table 1.14
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CHAPTER 2. HEALTH PLAN CHOICE 66 pl
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CHAPTER 2. HEALTH PLAN CHOICE 113 6
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Page 136 and 137: CHAPTER 3. MEDIGAP 119 be of intere
Page 138 and 139: CHAPTER 3. MEDIGAP 121 hazard for i
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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