(2) _membershipic_ = _θ_ 1 _relevantexperienceic_ + _θ_ 2 _PCi_ + _θ_ 3 _CCc_ + _λi_ + _εic_ where I estimate for the _i_ th project implemented in constituency _c_ . _relevantexperienceic_ is a dummy variable indicating whether the congressperson of the constituency in which the project is implemented has relevant qualifications or experience to be eligible as a member of the sectoral committee relevant to the project; other variables are defined as above.

I begin by examining the validity of the assumption that committee membership by experience is unrelated to factors that may affect implementation. I then turn to how membership impacts on projects in the corresponding sector and constituency, using previous qualifications or experience as an instrument for membership. Finally, I look at the consequences of memberships on the political equilibrium of the bureaucracy.

# **4.1 Determinants of Committee Membership**

For the above strategy to be valid, I make the assumption that those representatives who are granted membership of a sectoral committee based on pre-existing qualifications or expertise are members for reasons exogenous to conditions affecting project implementation. Particularly, sector experience should not be systematically correlated with constituency-level features that may significantly influence project implementation.

Both the politician members of the Committee on Selection and their administrative secretaries state that selection decisions are based on the geo-political factors and qualifications I have described in section 2.1.<sup>23</sup> To empirically test these claims, I can assess the factors that determine the sector/s in which a politician has been selected to serve. Table A2 provides motivating evidence that politicians are similar across committees. It reports regressions of characteristics of politicians and their constituencies on dummies for each of the sectoral committees. I find no evidence of sorting across committees on politicians’ observed characteristics, such as their age, sex, years of education, the political competition they face or the extent of poverty in their constituency.

A more formal test of which observable characteristics explain selection into committee membership is reported in Table A3. I estimate a seemingly unrelated regression (SUR) model across indicators of committee membership. For each constituency-level regression in the SUR system, the dependent variable is a binary variable reflecting whether a representative is a member of the committee for the named sector. I regress these indicator variables on the politician and constituency controls described above, and a series of dummy variables that indicate the politician’s geo-political region. I then display the for those variables relevant to all the sectors.

Table A3 presents the results and indicates that there is strong evidence that qualifications play the major role in determining which politicians are allocated to a particular committee. Though the coefficients on relevant qualifications are not 1, they are large and highly significant. The fact that they are large indicates that relevant qualifications are a substantial predictor of committee membership. Conditioning on the fact that more educated individuals serve on the education committee, and more women serve on the women and youth committee, there is little evidence of sorting across committees on observable characteristics of individuals or constituencies. There is also little correlation in error structure, implying

> 23For example, the vice chairman of the Committee on Selection states, as reported in This Day newspaper, “The Selection Committee ... considered cognate experience, areas of specialization and zonal representation in order to ensure that the chairman and vice chairman of a committee do not come from the same geopolitical zone.” (This Day, 2007)

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