# Decision and Authorization Latency in NASA Programs: A Cliometric Analysis of Program Cost, Schedule, and Mission Cadence, 1958 to 2026

**Candidate:** PHD-08
**Program:** COLLEGIUM 1st Battalion
**Category (NORTH STAR / JPL):** Mission Program Execution Management
**Method:** Quantitative; cliometric time-series and panel regression with program fixed effects
**Date:** 2026-06-15

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## Abstract

This dissertation asks whether the time the National Aeronautics and Space Administration takes to decide and authorize program actions is a measurable variable across its history, and whether longer latency is associated with worse program performance. The motivating observation is simple and widely shared but rarely measured: NASA programs frequently exceed their cost and schedule baselines, and practitioners often attribute part of this to slow internal decision and authorization processes rather than to engineering difficulty alone. The literature on NASA cost and schedule growth is mature on the technical and estimating side, and the public-administration literature has measured administrative burden and its association with organizational performance, but the two have not been joined in a long-run quantitative study of one agency. This study proposes to construct a program-level panel covering NASA programs from 1958 to 2026, to define and measure administrative decision-and-authorization latency from documentary records, and to estimate its association with three outcomes: cost growth relative to baseline, schedule slip relative to baseline, and realized mission cadence. The single falsifiable contribution is stated as a null hypothesis that authorization latency has no association with cost, schedule, or cadence, against the alternative that longer latency is associated with greater cost growth, more schedule slip, and lower cadence. The identification strategy uses program and era fixed effects to remove time-invariant program characteristics and common shocks, and it relies on within-program and within-era variation in latency. The dissertation reports the full research design, the data construction, and a pre-registered analysis plan. Consistent with honest scientific practice, it does not report estimates from the full dataset as if executed; expected signs and an illustrative specification are clearly labeled as design-stage. The methodological discipline follows the quantitative economic history tradition: state the proposition quantitatively, build measures from primary records, and let the data falsify the hypothesis.

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## 1. Introduction and Contribution

### 1.1 The problem

NASA programs have a long record of exceeding the cost and schedule estimates set at their formal commitment points. The technical community has documented this pattern in detail. Studies of NASA science instruments and spacecraft show consistent positive growth in mass, power, cost, and schedule between early formulation and launch, with growth large enough that program reserve guidelines are set explicitly by reference to historical growth distributions [1, 2, 3]. Independent assessments of NASA major projects by the Government Accountability Office have for many years reported that a substantial share of the agency's largest projects exceed their cost and schedule baselines.

Practitioners frequently attribute part of this growth not to the difficulty of the engineering but to the time the institution takes to act. Decisions wait for reviews, authorizations route through multiple layers, and budget actions are paced by an annual appropriations cycle that is itself subject to delay. Recent agency framing makes the proposition concrete. The interval between sensing a condition, understanding its implications, authorizing a response, and executing that response is described as a binding constraint on program execution, and a four-tier model has been proposed in which decisions range from autonomous systems acting in milliseconds to administrative and congressional authorizations that take months [historical framing drawn from internal NASA strategy material]. Concrete examples are cited as evidence. The Constellation program spent roughly nine billion dollars over about five years and flew no operational missions before cancellation. The interval between the first and second crewed flights of the Space Launch System spanned roughly forty-one months, during which development of an upgraded upper stage and a second mobile launcher consumed resources and was subsequently terminated.

These are vivid anecdotes. They are not measurements. The claim that decision-and-authorization latency drives cost growth, schedule slip, and low cadence has not, to the author's knowledge, been tested as a quantitative hypothesis across the full history of the agency. That is the gap this dissertation addresses.

### 1.2 The gap in the literature

Two literatures bear directly on the question, and neither closes it.

The first is the NASA cost-and-schedule literature. It is technically sophisticated and largely produced by the cost-estimating community. It models cost growth as a function of technical parameters such as instrument mass and power, technology readiness, and contract type, and it relates schedule growth to cost growth [1, 2, 3, 4, 5]. The National Research Council produced a consensus study on controlling cost growth of NASA Earth and space science missions that catalogs management, technical, and budgetary causes [6]. This literature treats schedule as an outcome and occasionally as a driver of cost, but it does not isolate administrative decision-and-authorization latency as a measured explanatory variable, and it does not build a single consistent panel spanning the agency's full history.

The second is the public-administration literature on red tape and administrative burden. It has developed validated constructs and measures for the rules, clearances, and procedural delays that organizations impose, and it has related these to organizational performance [7, 8, 9, 10, 11]. Decarolis and colleagues, using United States federal procurement data, find that more competent procurement bureaus cause significant reductions in delays and cost overruns, which is direct evidence that the administrative side of program execution affects cost and schedule outcomes [12]. The megaproject literature, in turn, documents systematic cost overrun across large public projects and locates part of the cause in decision processes and incentives rather than in engineering [13, 14, 15]. This literature establishes that administrative process matters for project outcomes in general, but it has not been applied to NASA as a single long-run case with program-level panel data.

The contribution of this dissertation is to join the two: to measure administrative decision-and-authorization latency inside NASA from documentary records, to assemble it into a program panel spanning 1958 to 2026, and to test whether it is associated with cost growth, schedule slip, and mission cadence.

### 1.3 The single falsifiable contribution

The contribution is one testable proposition, stated as competing hypotheses.

**H0 (null).** Administrative decision-and-authorization latency in NASA programs has no association with program cost growth, schedule slip, or mission cadence, after accounting for program and era fixed effects and technical controls.

**H1 (alternative).** Longer administrative decision-and-authorization latency is associated with greater program cost growth, more schedule slip, and lower mission cadence, after accounting for program and era fixed effects and technical controls.

The proposition is falsifiable in the strict sense. If the estimated association between latency and the three outcomes is statistically indistinguishable from zero, or if its sign is opposite to the predicted sign, the contribution fails. The study is designed so that the null is a real possibility, not a straw figure: latency may be endogenous to program difficulty, in which case any raw correlation could reflect difficulty rather than process, and the design must address this directly.

### 1.4 Why it matters for NASA and JPL

If the alternative holds, then a portion of NASA cost growth and schedule slip is attributable to a variable the agency controls directly, namely the time its own decision and authorization processes consume, rather than to the irreducible difficulty of spaceflight. That has practical consequence for program execution management, which is the NORTH STAR and JPL category this dissertation serves. It would mean that compressing authorization latency is a lever on cost and schedule that is independent of technology investment. It would also bear on the management of the Jet Propulsion Laboratory, whose programs are concentrated in deep-space and planetary missions with long development cycles in which authorization latency accumulates over many years. If the null holds, the agency should redirect attention away from process reform and toward the technical and estimating factors the existing literature already emphasizes. Either outcome is informative, which is the mark of a well-posed test.

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## 2. Background and Literature

### 2.1 The cliometric lens

This study is cliometric. It applies quantitative and explicitly hypothesis-testing methods to the historical record of a single institution. Three methodological commitments structure the work, each drawn from a foundational practitioner of quantitative economic history.

The first commitment is to quantification as the precondition of comparison. Angus Maddison's central insight was that no two periods or units can be compared until a transparent, replicable measurement standard exists, expressed in constant units so that one period can be set against another on the same footing [16]. The Maddison Project's long-run national accounts are the canonical example: GDP series constructed in common purchasing-power units so that growth can be compared across centuries and countries. The analogue here is that NASA program cost and schedule growth cannot be compared across 1958 and 2026 until costs are deflated to constant dollars, schedule is measured against a consistently defined baseline, and latency is measured by a single documentary rule applied identically across eras. Maddison's discipline also includes the rule of quantifying proximate sources before reasoning about ultimate causes, and using the long horizon to discipline short-run extrapolation. This dissertation accordingly measures the proximate variable, latency, and its proximate correlates, cost and schedule and cadence, before drawing any conclusion about ultimate institutional causes, and it uses the full 1958-to-2026 span rather than a recent window so that conclusions are not an artifact of one budget era.

The second commitment is to the explicit counterfactual and the bounded estimate. Robert Fogel's contribution to economic history was to insist that a claim of the form "outcome Y could not have occurred without X" is an unmeasured counterfactual until the next-best alternative is named and costed [17]. Fogel computed the social saving of railroads by constructing the world in which the railroad was never built and the transport task fell to canals and wagons, and he reported upper and lower bounds rather than a single point. The transfer to this study is direct. The claim that a program would have cost less or flown sooner under faster authorization is a counterfactual. The study cannot run the counterfactual experimentally, but it can approximate it through within-program and within-era comparison, and it reports its central estimate as a bounded range conditioned on stated assumptions rather than as a single causal point. Fogel's review discipline also requires that time saved be valued with a defensible shadow price rather than asserted, and this study treats schedule and cadence as outcomes to be measured, not benefits to be assumed.

The third commitment is institutional, and it comes from Douglass North. North distinguished institutions, the rules of the game, from organizations, the players, and located economic performance in the transaction costs that institutions raise or lower [18]. Moving from personal to impersonal exchange across distance and time multiplies the costs of measuring and enforcing agreements, and institutions exist to lower these costs. Decision-and-authorization latency is, in North's vocabulary, a transaction cost internal to a public organization: the time cost of measuring a proposed action against the rules and of obtaining authorization to proceed. North's further concept of adaptive efficiency and path dependence supplies the historical mechanism. Once a set of review and authorization rules is in place it generates increasing returns; organizations adapt to it, and the cost of changing it rises, so an agency can lock onto a persistent, even inefficient, decision trajectory. This is the theoretical reason latency might be both large and durable across NASA's history, and it is the reason era fixed effects are necessary to separate rule regimes from program-specific variation.

### 2.2 NASA cost and schedule growth

The empirical regularity that NASA programs grow in cost and slip in schedule is well established. Emmons, Bitten, and Freaner used historical NASA cost and schedule growth to set reserve guidelines and, importantly, attempted to separate growth due to external programmatic reasons from growth due to internal technical reasons [1]. That separation is the conceptual ancestor of the present study, because the "external programmatic" category is where authorization latency lives. Subsequent work in the same Aerospace Conference series documented historical mass, power, schedule, and cost growth for NASA science instruments and spacecraft and related instrument schedule growth to mission cost and schedule growth [2, 3]. Bitten and colleagues examined the effect of policy changes on NASA science mission cost and schedule growth, which is direct evidence that administrative regime, not only technology, moves the outcomes [4]. Majerowicz and Shinn examined the relationship between schedule delays and cost overruns specifically [5]. Stahl's survey of cost models for space telescopes shows how parametric cost-estimating relationships are built and where they mislead [19]. The National Research Council's consensus study on controlling cost growth of NASA Earth and space science missions catalogs the causes at the agency level [6]. Work on firm-fixed-price spacecraft contracts examines whether contract structure curbs cost growth [20]. Across this body of work, cost and schedule are the dependent variables and technical and contractual parameters are the regressors; administrative latency is acknowledged in narrative but is not constructed as a measured variable across the full history.

### 2.3 Administrative process and project outcomes

The second literature supplies the missing variable and the evidence that it matters. Bozeman's theory of red tape and the subsequent measurement program, including the validated perceptual scales developed by Rainey, Pandey, and Bozeman and later refinements, established that the procedural rules an organization imposes are measurable and that they vary across organizations [7, 10, 11]. Brewer and Walker provided an empirical analysis of the impact of red tape on governmental performance and found effects that are real but more nuanced than conventional wisdom assumes, which is a caution this study takes seriously [8]. The administrative-burden literature extended the construct to the costs that procedures impose on the participants [9]. Most directly, Decarolis and colleagues used contract-level United States federal procurement data and an instrumental-variable strategy to show that bureaucratic competence causes reductions in delays and cost overruns [12]. The megaproject literature, surveyed in Flyvbjerg's work on cost overrun and in the patterns-causes-cures analysis of infrastructure projects, documents that large public projects overrun systematically and that decision processes and optimism bias are part of the cause [13, 14, 15]. This literature gives the study both a theoretical warrant for expecting a latency effect and an empirical precedent for finding one in government procurement, while leaving the NASA-specific long-run test open.

### 2.4 The estimator literature

Because the design is a panel with program fixed effects and because era regimes enter at staggered times, the study draws on the modern panel-estimation literature. The two-way fixed-effects model is the workhorse, but its limitations under heterogeneous and staggered treatment timing are now well understood. Goodman-Bacon showed how the two-way fixed-effects difference-in-differences estimand decomposes into a weighted average of all possible two-group comparisons, some of which can receive negative weights [21]. Callaway and Sant'Anna and de Chaisemartin and D'Haultfoeuille developed estimators robust to multiple time periods and heterogeneous treatment effects [22, 23]. Imai and Kim examined when two-way fixed-effects regression identifies a causal effect with panel data and when it does not [24]. The study uses these results to choose and defend its estimator rather than relying on a naive two-way fixed-effects specification.

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## 3. Data

### 3.1 Named datasets

The study draws on three documented sources, named in the dissertation charge and supplemented by the cost-estimating literature.

**NASA historical budgets and program records.** The agency's budget estimates, congressional justifications, and program commitment documents provide, for each program, the baseline cost and schedule established at the formal commitment point and the actual cost and schedule realized. These records also document the formal decision and authorization events from which latency is constructed: key decision points, program commitment reviews, confirmation reviews, and the budget actions that authorize each phase.

**NASA Technical Reports Server (NTRS).** NTRS holds program documentation, cost-and-schedule estimation reports, and management analyses that supply both technical parameters and the documentary trail of decision events. The cost and schedule estimation study reports and the Technology Cost and Schedule Estimation final report are examples of the kind of record NTRS makes available for variable construction.

**Government Accountability Office cost-and-schedule reports.** The GAO's recurring assessments of NASA major projects provide an independent, consistently formatted measure of cost and schedule performance against baseline for the agency's largest projects, with definitions of baseline and growth that are stable across report years. These reports anchor the modern portion of the panel and provide a cross-check on cost and schedule growth derived from agency records.

### 3.2 Unit of analysis

The unit of analysis is the program-phase observation. Each NASA program contributes one observation per major lifecycle phase for which a baseline and an actual are documented, so that a program with formulation, development, and operations phases contributes multiple observations. This structure follows the lifecycle logic of the cost-estimating literature, in which growth is measured between defined milestones rather than only at program end. The panel dimension is the program; the time dimension is the phase sequence and the calendar year in which each phase milestone occurs.

### 3.3 Variable construction

**Authorization latency (the explanatory variable).** Latency is defined as the elapsed time between a documented trigger event, the point at which a decision or authorization becomes due, and the documented authorization event that resolves it. For each program-phase, the study constructs latency as the median elapsed time across the authorization events in that phase, measured in months from the budget and program records. The measure is built by a single documentary rule applied identically across all eras, in keeping with Maddison's requirement of a consistent standard. Because the documentary detail available in 1962 differs from that available in 2024, the study constructs latency at two resolutions: a coarse measure available for the full 1958-to-2026 span, defined from milestone-to-milestone intervals, and a fine measure available only for the modern subperiod, defined from individual key-decision-point records. Results are reported separately at each resolution so that any finding is not an artifact of changing documentary density.

**Cost growth (outcome 1).** Cost growth is the actual phase cost minus the baseline phase cost, divided by the baseline, with both expressed in constant fiscal-year dollars using the NASA New Start Inflation Index. Deflation to constant dollars is required before any cross-era comparison, per the measurement discipline above.

**Schedule slip (outcome 2).** Schedule slip is the actual phase duration minus the baseline phase duration, divided by the baseline duration, measured in months.

**Mission cadence (outcome 3).** Cadence is measured at the era level as the number of operational mission events per unit time within a defined program family, and at the program level as the interval between successive flight or delivery events. Cadence is the outcome most sensitive to definition, and the study reports it under multiple family definitions.

**Controls.** Technical controls follow the cost-estimating literature: instrument or spacecraft mass and power, technology readiness level at commitment, mission class, and contract type [1, 2, 3, 19, 20]. Programmatic controls include the number of external partners and a funding-instability index constructed from year-over-year deviations between requested and appropriated funds.

### 3.4 Coverage

The intended coverage is all NASA programs with documented baselines from agency inception in 1958 through 2026. Coverage is dense and high-resolution for the period covered by GAO major-project assessments and by the Aerospace Conference cost-growth datasets, and it is sparser and coarser for the earliest decades, where baselines were defined less formally. The panel is therefore unbalanced by construction.

### 3.5 Limitations

Four data limitations are acknowledged at the outset. First, the documentary definition of a baseline changed over time, so the earliest observations carry more construction uncertainty; the two-resolution latency measure is the response. Second, decision and authorization events are not always recorded with dates, so latency for some program-phases must be bounded rather than point-identified, which is consistent with the bounded-estimate discipline but reduces precision. Third, cost and schedule baselines are themselves chosen by the agency and may be set optimistically, a selection problem the megaproject literature emphasizes [13, 14]. Fourth, cadence depends on program-family definition, and no single definition is uniquely correct, which is why cadence results are reported under alternatives.

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## 4. Research Design and Identification

### 4.1 Estimator

The primary estimator is a panel regression with program fixed effects and era fixed effects. Program fixed effects absorb all time-invariant program characteristics, including unobserved program difficulty that does not change across a program's phases. Era fixed effects absorb common shocks and rule regimes that affect all programs in a given period, including agency-wide reorganizations, appropriations-cycle conditions, and administrative-rule changes. Identification of the latency coefficient therefore comes from variation in latency within a program across its phases and within an era across programs, net of the fixed effects.

### 4.2 Specification

For program-phase observation indexed by program i, phase p, and era t, the baseline specification is:

Outcome(i,p,t) = beta * Latency(i,p,t) + gamma * X(i,p,t) + alpha_i + delta_t + epsilon(i,p,t)

where Outcome is in turn cost growth, schedule slip, and cadence; Latency is authorization latency in months; X is the vector of technical and programmatic controls; alpha_i are program fixed effects; delta_t are era fixed effects; and epsilon is the error. The coefficient of interest is beta. Under H1, beta is positive for cost growth and schedule slip and negative for cadence. Standard errors are clustered by program to allow arbitrary within-program correlation across phases.

### 4.3 Identification strategy and the staggered-regime concern

Because administrative-rule regimes (for example, a reorganization that compresses reporting layers, or a procurement-reform regime) switch on at staggered calendar times across programs, a naive two-way fixed-effects estimate of a regime effect can be biased by the negative-weighting problem [21]. The study addresses this in two ways. First, latency is treated as a continuous within-program variable, not as a single binary treatment, which sidesteps the worst of the staggered-binary problem. Second, where the study estimates the effect of a discrete administrative-regime change on latency and outcomes, it uses the heterogeneity-robust estimators of Callaway and Sant'Anna and of de Chaisemartin and D'Haultfoeuille rather than the two-way fixed-effects difference-in-differences estimator, and it reports the Goodman-Bacon decomposition as a diagnostic [21, 22, 23]. The Imai and Kim results guide the interpretation of the fixed-effects specification and its assumptions [24].

### 4.4 Endogeneity

The central threat is that latency is endogenous to program difficulty: harder programs may both take longer to authorize and overrun more, producing a positive correlation that reflects difficulty rather than process. Program fixed effects remove the part of difficulty that is fixed across a program's life, but not difficulty that varies by phase. The study therefore pursues an instrumental-variable strategy in the spirit of Decarolis and colleagues [12]. Candidate instruments are sources of variation in latency that are plausibly unrelated to the technical difficulty of the specific phase: the contemporaneous workload of the authorizing office, measured as the number of other programs awaiting authorization at the same office in the same period, and the timing of the action relative to the appropriations calendar, which paces authorization for budget-dependent reasons independent of program difficulty. The validity of each instrument is argued and tested, not assumed, and the study reports both the fixed-effects and the instrumented estimates so that the reader can see how much the conclusion depends on the instrument.

### 4.5 Threats to validity

**Internal validity.** The endogeneity of latency to phase-specific difficulty is the principal internal threat, addressed by fixed effects and instruments as above. A second internal threat is baseline gaming: if agencies set optimistic baselines when they anticipate slow authorization, baseline choice and latency are jointly determined. The study tests for this by examining whether baseline conservatism, proxied by the ratio of held reserves to baseline, correlates with latency.

**External validity.** The findings pertain to NASA and may not generalize to other agencies or to commercial programs with different authorization structures. The study frames its claims as NASA-specific and uses the Decarolis federal-procurement evidence only as corroboration, not as a basis for generalization [12].

**Construct validity.** The central construct, authorization latency, is operationalized from documentary records and may not capture the full administrative-time concept; the two-resolution measure and sensitivity analyses test whether the operationalization drives results. Cadence is the most construct-fragile outcome, which is why it is reported under multiple definitions.

**Statistical-conclusion validity.** The unbalanced panel and clustered errors require care; the study reports estimates under alternative clustering, under wild-cluster bootstrap given the modest number of program clusters, and under alternative fixed-effects structures so that conclusions are not specification-dependent.

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## 5. Analysis Plan and Findings

This section is a design-stage analysis plan. It states the procedure that will be executed once the full panel is assembled. It does not report estimates from the full dataset, and the expected signs and the single illustrative specification below are labeled as such and must not be read as results.

### 5.1 Estimation procedure

The procedure proceeds in five steps.

1. **Assemble and validate the panel.** Construct the program-phase panel from NASA budget and program records, NTRS documentation, and GAO assessments. Validate cost and schedule growth against the published Aerospace Conference and GAO figures for overlapping programs, so that the constructed measures reproduce known values where known values exist.

2. **Describe before estimating.** Following the rule of quantifying proximate sources before reasoning about cause, report the univariate distributions of latency, cost growth, schedule slip, and cadence by era, with constant-dollar deflation applied. This descriptive step is itself a contribution: a consistent long-run series of NASA authorization latency does not currently exist.

3. **Estimate the fixed-effects baseline.** Estimate the specification of Section 4.2 for each of the three outcomes, with program and era fixed effects and program-clustered standard errors.

4. **Address endogeneity.** Re-estimate with the instrumental-variable strategy of Section 4.4, report first-stage strength, and compare instrumented and fixed-effects estimates.

5. **Test robustness and heterogeneity.** Re-estimate at both latency resolutions, under alternative cadence definitions, with the heterogeneity-robust estimators for the discrete-regime analyses, and report the Goodman-Bacon decomposition as a diagnostic. Report central estimates as bounded ranges conditioned on stated assumptions, in the Fogel manner, rather than as single points.

### 5.2 Decision rule on the hypothesis

H0 is rejected only if the latency coefficient is statistically distinguishable from zero at conventional levels and carries the predicted sign in the same direction across the fixed-effects and instrumented specifications and survives the robustness battery. If the coefficient is indistinguishable from zero, or changes sign between specifications, or vanishes under the heterogeneity-robust estimators, H0 is retained and the contribution fails. This rule is fixed in advance to prevent specification search.

### 5.3 Expected signs (illustrative, not executed)

The following are the directional expectations under H1, stated so the test is interpretable. They are not findings.

- Cost growth: latency coefficient expected positive. Longer authorization latency is expected to be associated with larger cost growth, because time-dependent standing costs accrue during waiting periods and because delay invites requirements change.
- Schedule slip: latency coefficient expected positive, and expected to be the largest and most robust of the three, because authorization latency is itself a component of elapsed schedule and the mechanism is the most direct.
- Cadence: latency coefficient expected negative. Higher latency per decision is expected to be associated with lower realized mission cadence within a program family.

### 5.4 Illustrative specification (synthetic, for exposition only)

To make the output format concrete, the following table shows the shape of the result the procedure will produce. The numbers are synthetic placeholders chosen only to illustrate the table; they are not estimates and carry no empirical content. They will be replaced by estimated values once the panel is assembled and the procedure of Section 5.1 is executed.

| Outcome | Latency coefficient (per +1 month) | Cluster-robust interval | Specification |
|---|---|---|---|
| Cost growth (fraction of baseline) | [sign expected +] | [to be estimated] | Program + era FE, IV |
| Schedule slip (fraction of baseline) | [sign expected +] | [to be estimated] | Program + era FE, IV |
| Mission cadence (events per period) | [sign expected -] | [to be estimated] | Era FE, family-defined |

The deliberate refusal to populate this table with invented numbers is a methodological choice, not an omission. Reporting fabricated coefficients as if real would violate the falsifiability standard the dissertation is built on.

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## 6. Discussion

### 6.1 Implications if H1 holds

If the analysis rejects H0 in the predicted direction, the practical implication is that a measurable share of NASA cost growth and schedule slip is attributable to the time the agency's own decision and authorization processes consume. Because that time is a variable the agency controls, the finding would identify a lever on program performance that is independent of technology investment and contract structure, the two levers the existing literature emphasizes. For program execution management, including at the Jet Propulsion Laboratory where long development cycles accumulate authorization events over many years, the implication is that investment in faster, lower-layer authorization may return cost and schedule savings comparable to investment in technical risk reduction. The finding would also give the red-tape and administrative-burden constructs a concrete, dollar-and-month-denominated instantiation inside a single agency over a long horizon [7, 8, 12].

### 6.2 Implications if H0 holds

If the analysis retains H0, the implication is equally useful: it would mean that, once program difficulty and era are accounted for, authorization latency does not move cost, schedule, or cadence, and that the agency should concentrate reform on the technical and estimating factors the cost-growth literature documents rather than on process speed [1, 4, 6]. A null result would also discipline the practitioner narrative that attributes overruns to slow decision-making, by showing that the narrative does not survive measurement.

### 6.3 Rival explanations

Three rival explanations must be weighed against H1 even if the predicted association is found. First, reverse causation: programs that are already overrunning may generate more authorization events and longer latency as a consequence of trouble, not a cause of it. The instrumental-variable strategy and the use of latency measured early in each phase are the responses. Second, common cause: an underlying era condition, such as a period of budget instability, may produce both high latency and high cost growth without latency causing cost growth; era fixed effects and the funding-instability control address this. Third, optimistic baselines: if baselines are set optimistically and independently of latency, measured growth reflects baseline choice rather than process, and the baseline-conservatism test of Section 4.5 is the check.

### 6.4 External validity

The study is a single-agency long-run case. Its external validity rests on the consistency of its measures with the broader procurement evidence rather than on statistical generalization. The Decarolis federal-procurement result, which finds that administrative competence causally reduces delays and overruns across United States federal contracting, is the nearest external benchmark, and agreement between this study's NASA-specific finding and that broader result would strengthen the case that the mechanism is general while leaving the magnitude NASA-specific [12].

### 6.5 What would falsify the contribution

The contribution is falsified by any of the following: a latency coefficient indistinguishable from zero in the preferred specification; a coefficient whose sign reverses between the fixed-effects and instrumented specifications; a coefficient that vanishes under the heterogeneity-robust estimators or under the alternative latency resolution; or a demonstration that the apparent association is fully explained by baseline gaming or by reverse causation. The pre-registered decision rule of Section 5.2 makes these falsification conditions binding rather than discretionary.

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## 7. Contribution and Conclusion

This dissertation proposes and designs a single falsifiable test: whether administrative decision-and-authorization latency in NASA programs is a measurable variable across the agency's history, and whether longer latency is associated with greater cost growth, more schedule slip, and lower mission cadence. The contribution is threefold and, importantly, the first part stands even if the hypothesis is not confirmed. First, the study constructs a consistent long-run series of NASA authorization latency from documentary records, a measurement that does not currently exist and that is valuable independent of the regression result, in the Maddison tradition of building the consistent series first. Second, it joins the NASA cost-and-schedule literature to the public-administration literature on administrative process, two bodies of work that have not previously been combined in a long-run quantitative study of one agency. Third, it states and pre-registers a falsifiable hypothesis with a fixed decision rule, an explicit treatment of the counterfactual through within-program and within-era comparison, and a refusal to report invented estimates as real.

The honest posture of the dissertation is that the test has not yet been run on the full dataset. The expected signs are stated so the test is interpretable, and the illustrative table is left unpopulated by design. Whether the data ultimately reject or retain the null, the measurement and the design are constructed so that the answer, when it comes, is defensible. The discipline throughout is the discipline of quantitative economic history: state the proposition quantitatively, build the measures from primary records, bound the estimate, and let the data decide.

---

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