|Age|0.23|-0.01|-0.02|-0.01|-0.04|0.29|0.45|
|---|---|---|---|---|---|---|---|
||(0.73)|(-0.03)|(-0.04)|(-0.01)|(-0.09)|(0.83)|(1.22)|
|Size|-0.04|0.02|0.04|0.01|0.04|-0.09|-0.07|
||(-0.39)|(0.17)|(0.33)|(0.07)|(0.37)|(-0.93)|(-0.63)|
|Capital|0.04|0.03|0.03|0.03|0.02|0.04|0.05|
||(0.84)|(0.59)|(0.48)|(0.63)|(0.41)|(0.83)|(0.89)|
|Sector FE|YES|YES|YES|YES|YES|YES|YES|
|Governorate FE|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|YES|YES|
|constant|12.46***|12.61***|12.75***|12.63***|12.85***|12.13***|11.15***|
||(8.64)|(7.58)|(6.79)|(7.60)|(6.76)|(8.22)|(7.11)|
|Observations|342|236|236|236|236|384|384|

_Notes: Dependent variable is the log value of procurement contracts. PCF indicates dummy variables for all connected firms. PCF1 captures firms connected to the inner circle of elites that controls the CDR board. PCF2 includes firms of all political elites. Regression model uses robust standard errors; The table shows beta coefficients and t-statistics in parentheses; Significance levels: * p<0.10, ** p<0.05, *** p<0.01._ We can assign an approximate economic value to the effect size of model 6. We follow an approach by Goldman, Rocholl and So (2013) and calculate the marginal increase in contract values after including all control variables. We first take the estimated coefficient for a model in which we calculate the marginal effect of model 6 without including any controls (~1.7, not reported in table 6). We then use model 6 to calculate the marginal impact of observing a pair of PCF1 designer and contractor. We calculate the reduction of the effect size by dividing the coefficients of model 6 by those of the model without controls and find that the increase in contract value goes down to ~60% of its univariate estimated value. This leaves an increase of $3.5 million, or almost 35%, for a contract of a PCF1 designer-contractor pair relative to the average contract.<sup>10</sup> Observing 45 such PCF1 designer-contractor pairs, this amounts to roughly $160 million in overpricing of contracts throughout the period of investigation.

# **Overspending**

We go on to investigate our hypotheses related to the overspending of contracts. Table 7 provides the results of a set of logistic regressions to estimate the likelihood that a project is being overspent given a vector of dummy variables for each hypothesis. Formally, we estimate the following model

# Pr⁡(Overspent= 1) = ⁡F(β0 + β1Xji + β2logvaluei + β3SVdurationi +β4SVforeigni + β6funding + β6Nli) originki

where _overspent_ is a dummy variable that takes the value of 1 when a contract _i_ is overspent, _X_ is a vector of dummy variables to test our hypotheses _j_ , _logvalue_ the natural log of the contract value, _SVduration_ the duration of the supervision period in years, _SVforeign_ a dummy variable for whether the supervisor is a foreign firm, and _funding_origin_ denotes a vector for the origin of the donor _k_ , that is, whether the funding was provided from domestic, Arab or Western sources. By differentiating the origin

> 10 The calculation is as follows. Table 6 shows the mean values of contracts by political connection. We subtract the mean contract value of PCF1 firms ($16.36 million) from the mean value of all contracts ($10.4 million). We multiply the resulte<sup>1.18</sup> ing difference of the univariate results ($16.36 - $10.4 = $5.96 million) with the fraction of the marginal effects ( e<sup>1.7</sup> = 0.59 or 59.45 percent) to obtain the value of $3.5 million.

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