measures pick up meaningful variation across projects, rather than merely picking up noise that should have led to the multiple reports (either across engineers or over time) being uncorrelated.

These measures of complexity allow me to condition all the specifications on the aggregate complexity of the project, which are likely to be important determinants of project completion. They also allow me to define indices of local and national information needs. Given how important information has been to the study of delegation (Moe, 2005; Mookherjee, 2006), it is important to understand how I am controlling for the informational demands of each project. The literature on delegation emphasizes the possible importance of superior information at a tier of government as a rationale for delegation. Some projects require a lot of local information to be implemented. For example, they may require sourcing of materials from the local area or be characterized by a high degree of uncertainty that requires local information to respond to. Similarly, some projects may require a lot of information more readily available at the national level. For instance, sourcing international expertise is something national organizations are likely to be better at procuring than local organizations.

I asked one of the engineers with whom I had worked to define the complexity data to allocate the complexity variables to one of three indices: (i) indicative of the project requiring local information for successful implementation; (ii) indicative of the project requiring national information for successful implementation; or, (iii) indicative of neither. This process led to three complexity indices being generated using z-scores of the underlying variables. These indices were: (i) localized information index (containing the variables ‘Storage of raw materials’,‘Requires local labor’,‘Access to construction equipment’,‘Design uncertainty’,‘Implementation uncertainty’,‘Design ambiguity’ and ‘Implementation ambiguity’); (ii) national information index (containing the variables ‘Access to raw materials’ and ‘Requires skilled labor’); and (iii) neither information index (containing the variables ‘Project size’, ‘Number of inputs’, ‘Number of methods’, ‘Interdependencies’, ‘Difficulty to manage’ and ‘Number of agencies involved’).

# **References**

AGHION.P, L.BOUSTAN, C.HOXBY, AND J.VANDENBUSSCHE (2005) “Exploiting States’ Mistakes to Identify the Causal Impact of Higher Education on Growth,” Harvard University Working Paper.

AGHION.P, L.BOUSTAN, C.HOXBY, AND J.VANDENBUSSCHE (2009) “The Causal Impact of Education on Economic Growth: Evidence from US,” Harvard University Working Paper.

AGHION.P, M.DEWATRIPONT, C.HOXBY, A.MAS-COLELL, AND A.SAPIR (2010) “The Governance and Performance of Universities: Evidence from Europe and the US,” _Economic Policy_ , 25(61): 7-59.

ALTONJI.J., T.ELDER AND C.TABER (2005) “Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," _Journal of Political Economy_ , 113 (1): 151-184.

ANSOLABEHEREA.S, J.M.HANSEN, S.HIRANO, J.M.SNYDER JR (2007) “The incumbency advantage in U.S. primary elections,” _Electoral Studies_ , 26 (3): 660-668.

BANDIERA.O, A.PRAT, AND T.VALLETTI (2009) “Active and Passive Waste in Government Spending; Evidence from a Policy Experiment,” _American Economic Review_ , 99(4): 1278-1308.

BANERJEE.A, L. IYER AND R. SOMANTHAN (2007) “Public Action for Public Goods,” in Handbook of Development Economics Vol. 4, T.P.Schultz and J.Strauss (eds.), Amsterdam: Elsevier.

BANERJEE.A, S.KUMAR, R.PANDE AND F.SU (2010) “Do Informed Voters Make Better Choices? Experimental Evidence from Urban India,” Massachusetts Institute of Technology Working Paper.

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