TABLE 5: MAIN EFFECTS: EFFICIENCY

|Dependent variable|Submissi<br>Model (1a):|on Period<br> <br>Model (1b):|Decisio<br> <br>Model (2a):|n Period<br> <br>Model (2b):|Signing<br> <br>Model (3a):|Period<br> <br>Model (3b):|Lead<br> <br>Model (4a):|Time<br> <br>Model (4b):|
|---|---|---|---|---|---|---|---|---|
|Model|CEM<br>within PEs|CEM<br>within|CEM<br>within PEs|CEM<br>within|CEM<br>within PEs|CEM<br>within|CEM<br>within PEs|<br>CEM<br>within|
||across<br>years|years<br>across PEs|across<br>years|years<br>across PEs|across<br>years|years<br>across PEs|across<br>years|years<br>across PEs|
|Regression Type|O|LS|O|LS|O|LS|O|LS|
|Control: Manual|||||||||
|Processing|||||||||
|Treatment: E-|-6.417***|-6.176***|2.210*|2.067+|-6.995***|-4.160***|-19.344***|-15.615***|
|procurement|(0.340)|(0.377)|(1.003)|(1.107)|(0.556)|(0.532)|(1.655)|(1.519)|
|L f Ctt Vl|1.082***|1.163***|2.151***|3.239***|0.572***|1.032***|5.755***|8.066***|
|n o onrac aue|(0.022)|(0.039)|(0.067)|(0.118)|(0.034)|(0.052)|(0.096)|(0.153)|
|Procurement Method:|0.330***|0.841***|4.090***|3.284***|0.304**|0.044|7.567***|9.003***|
|OTM (LTM–base)|(0.057)|(0.105)|(0.175)|(0.308)|(0.104)|(0.186)|(0.264)|(0.447)|
|PE Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bootstrap iterations|500|500|500|500|500|500|500|500|
|R<br>2|0.387|0.338|0.201|0.284|0.155|0.189|0.246|0.296|
|N|62506|22378|58013|21905|76367|33965|76589|33482|

Note 1: + p<0.10 * p<0.05 ** p<0.01 *** p<0.001 Note 2: Models 1b, 2b, 3b and 4b use bi-annual time windows.

# 6.2 MECHANISMS

77. After estimating the main effects and concluding that they are largely in line with our theoretical expectations, we further probe some of the key underlying impact mechanisms for each hypothesis.

78. First, we explore one impact mechanism underlying increased access due to e-procurement (H1). One of our main theoretical arguments was that e-procurement lowers intentional barriers to entry by making it harder to erect barriers for unwanted competitors and making rent-seeking riskier. If this is the dominant mechanism underpinning H1, we would expect e-procurement to have a larger impact on those PEs which had limited access to their tenders prior to the intervention. Hence, PEs in different quantiles of the access distribution at baseline are expected to react differently to e-procurement. We analyze this formally by interacting PE-level measures of the degree of open access at baseline with the e-procurement treatment variable. We add this interaction term to the same regression setup as above.

79. In each of the regressions containing the interaction between e-procurement and PEs’ baseline average dependent variable, the coefficient of the interaction is negative while the coefficient of the PE’s pre-e-procurement average dependent variable is large and positive (Table 6). This suggests that the higher the baseline level of access, the lower the impact of e-procurement on access. In other words, e-procurement is particularly suited to improving access in PEs where access was limited to start with, while its impact is much weaker for PEs which were more open to competition prior to the intervention.

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