rafmacalaba/datause-encoder-data huggingface dataset:
– Table A.22 Heterogeneous treatment effects on women’s empowerment and well-being

||Had an IGA|at baseline|Married a|t baseline|Consumptio|n at baseline|TCLP par|ticipation|
|---|---|---|---|---|---|---|---|---|
||(1)<br>Treatment|(2)<br>IGA _×_<br>Treatment|(3)<br>Treatment|(4)<br>Married _×_<br>Treatment|(5)<br>Treatment|(6)<br>Conso _×_<br>Treatment|(7)<br>Treatment|(8)<br>TCLP _×_<br>Treatment|
|**Women empowerment**|-0.035|-0.271|-0.234**|0.241**|-0.011|-0.049|-0.108|0.015|
||(0.040)|(0.180)|(0.103)|(0.110)|(0.076)|(0.088)|(0.091)|(0.112)|
|**Women fnancial index**|0.363***|1.024|0.764***|-0.468**|0.232***|0.233*|0.678***|-0.414|
||(0.080)|(0.891)|(0.230)|(0.235)|(0.086)|(0.140)|(0.244)|(0.269)|
|**Women well-being**|||||||||
|Cantrill’s ladder:|||||||||
|Current life satisfaction|0.264***|0.075|0.447***|-0.236|0.145|0.153|0.323*|0.063|
||(0.063)|(0.270)|(0.133)|(0.149)|(0.119)|(0.139)|(0.167)|(0.193)|
|Life satisfaction one year ago|0.218***|0.316|0.341***|-0.142|0.274**|-0.067|0.195|0.181|
||(0.061)|(0.273)|(0.129)|(0.145)|(0.116)|(0.135)|(0.150)|(0.177)|
|Predicted life satisfaction in three years|0.200**|0.415|0.381**|-0.213|0.168|0.059|0.453**|-0.152|
||(0.083)|(0.313)|(0.170)|(0.193)|(0.155)|(0.182)|(0.210)|(0.247)|
|Relative wealth|0.236***|0.213|0.450***|-0.272*|0.233*|0.005|0.384**|-0.097|
||(0.068)|(0.308)|(0.140)|(0.160)|(0.129)|(0.150)|(0.175)|(0.204)|
|MHI-5 score|1.245|4.284|0.973|0.494|0.180|1.528|-1.133|2.749|
||(0.800)|(3.520)|(1.713)|(1.933)|(1.441)|(1.704)|(1.937)|(2.345)|
|**Observations**|1,824|1,824|1,824|1,824|1,824|1,824|949|949|

Notes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

67
# 5. **Conclusions**

There are serious caveats to using DHS data to analyze the relationship between malaria, its treatment, and poverty in Sub-Saharan Africa: there is not a one-to-one correspondence between fever and malaria, even in areas with stable malaria; fever is not always recognized in children, and it may be recognized in a selective way that potentially biases results; the DHS do not collect the data typically used for poverty analysis. Recognizing these caveats, this analysis explores the existing DHS data for patterns between fever and poverty.

The results show a positive, but weak, relationship between reported fever and poverty across geographic space. Some areas have both high levels of fever and poverty and the correlation between the two is positive. However, this correlation, while statistically significant, is small in magnitude. Moreover, it becomes insignificantly different from zero after controlling for the education of mothers in the area. The relationship between fever and wealth across households is also insignificant, although in Eastern and Southern Africa a higher level of wealth in the cluster in which a household lives is associated with a lower incidence of fever. Fever is highly geographically concentrated: even after controlling for a variety of individual, household, and cluster attributes (including wealth), the average level of fever in the cluster in which a child lives is a strong determinant of their own probability of reporting fever.

Treatment or advice from the modern sector is more likely in Eastern and Southern Africa than in Western and Central Africa. In both regions, the percent who seek care in the modern sector is substantially larger for the rich than for the poor, even after controlling for individual, household, and cluster attributes. While the type of treatment sought is related to the wealth of the household, the average level of wealth in the cluster in which the household lives supercedes this relationship. In Western and Central Africa children with fever in wealthier communities are substantially more likely to seek care from government hospitals and from private sources than those from poorer households. Care for children from wealthier _households,_ regardless of the wealth in the cluster, is significantly more likely to be from public lower-level sources in this region. In Eastern and Southern Africa care is significantly more likely to be sought from government hospitals and from private medical facilities in richer than in poorer communities, whereas in this region the use lower level public services falls in wealthier communities. Seeking care from private medical sources increases substantially in wealthier communities, although the use of private commercial sources is unrelated to wealth. The experience that a cluster has with malaria is significantly related to the type of treatment sought in Eastern and Southern Africa, but not in Western and Central Africa.

Unlike cross-national results which show a strong association between malaria and poverty, the results of this analysis of DHS data across these 22 countries in Sub_31_
Table 3: First-Stage Enrollment Equation: Multinomial Logit for School Sector Choice

|||Primary|||Secondary||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Government:|||||||
|FPE|-.010|.016|.018|.035|.024|.023|
||(.017)|(.014)|(.014)|(.009)<sup>_∗∗∗_</sup>|(.007)<sup>_∗∗∗_</sup>|(.007)<sup>_∗∗∗_</sup>|
|Head’s Educ||.007|.007||.012|.012|
|||(.002)<sup>_∗∗∗_</sup>|(.002)<sup>_∗∗∗_</sup>||(.001)<sup>_∗∗∗_</sup>|(.001)<sup>_∗∗∗_</sup>|
|Head’s Educ _×_ FPE||-.015|-.012||.003|.004|
|||(.002)<sup>_∗∗∗_</sup>|(.002)<sup>_∗∗∗_</sup>||(.002)<sup>_∗_</sup>|(.002)<sup>_∗∗_</sup>|
|Log Food|||.008|||.017|
||||(.012)|||(.009)<sup>_∗_</sup>|
|Log Food _×_ FPE|||-.044<br>(.017)<sup>_∗∗∗_</sup>|||-.025<br>(.009)<sup>_∗∗∗_</sup>|
|Linear combinations, government:|||||||
|_β_FPE,govt,prim._−β_FPE,govt,sec.|-.045<br>(.019)<sup>_∗∗_</sup>|-.008<br>(.016)|-.005<br>(.016)||||
|_β_educ _×_ FPE,govt,prim._−β_educ_×_ FPE,govt,sec.||-.019<br>(.003)<sup>_∗∗∗_</sup>|-.016<br>(.002)<sup>_∗∗∗_</sup>||||
|_β_food _×_ FPE,govt,prim._−β_food_×_ FPE,govt,sec.|||-.019<br>(.021)||||
|Private:|||||||
|FPE|.070|.057|.054|.025|.018|.017|
||(.015)<sup>_∗∗∗_</sup>|(.010)<sup>_∗∗∗_</sup>|(.010)<sup>_∗∗∗_</sup>|(.007)<sup>_∗∗∗_</sup>|(.004)<sup>_∗∗∗_</sup>|(.004)<sup>_∗∗∗_</sup>|
|Head’s Educ||.005|.004||.003|.003|
|||(.001)<sup>_∗∗∗_</sup>|(.001)<sup>_∗∗∗_</sup>||(.001)<sup>_∗∗∗_</sup>|(.001)<sup>_∗∗∗_</sup>|
|Head’s Educ _×_ FPE||.012|.010||.003|.001|
|||(.002)<sup>_∗∗∗_</sup>|(.002)<sup>_∗∗∗_</sup>||(.001)<sup>_∗∗∗_</sup>|(.001)|
|Log Food|||.016|||-.0004|
||||(.005)<sup>_∗∗∗_</sup>|||(.004)|
|Log Food _×_ FPE|||.034<br>(.010)<sup>_∗∗∗_</sup>|||.018<br>(.008)<sup>_∗∗_</sup>|
|Linear combinations, private:|||||||
|_β_FPE,priv,prim._−β_FPE,priv,sec.|.045<br>(.017)<sup>_∗∗∗_</sup>|.039<br>(.011)<sup>_∗∗∗_</sup>|.036<br>(.011)<sup>_∗∗∗_</sup>|.|||
|_β_educ _×_ FPE,priv,prim._−β_educ _×_ FPE,priv, sec.||.010<br>(.002)<sup>_∗∗∗_</sup>|.008<br>(.002)<sup>_∗∗∗_</sup>||||
|_β_food _×_ FPE,priv,prim._−β_food _×_ FPE,priv,sec.|||.016<br>(.012)||||
|Linear combinations, both sectors:|||||||
|_β_FPEgovt_−β_FPEpriv|-.080|-.041|-.036|.009|.006|.006|
|, ,|(.028)<sup>_∗∗∗_</sup>|(.022)<sup>_∗∗_</sup>|(.022)<sup>_∗_</sup>|(.012)|(.009)|(.009)|
|District-sector-time fxed efects||X|X||X|X|
|Obs.|69513|69513|69513|40556|40556|40556|

Figures in the table are average marginal effects across all observations. Standard errors are shown in parentheses. The three choice options are “not enrolled”, “government” and “private”. “Not enrolled” is the base-category. District-sector-time dummies are included but not shown. The marginal effects of the interaction terms from this discrete choice model are calculated using the procedures discussed in Edward Norton (2004). All standard errors are bootstrapped and account for district level clustering.

15
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 26. Mali’s pattern of capital investment in infrastructure differs from that of comparator countries**

Investment in infrastructure sectors as percentage of GDP, by source <!-- Start of picture text --> 3.0 Mali nonfragile low-<br>income<br>2.5<br>countries<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>ICT power transport WSS ICT power transport WSS<br>public ODA non-OECD PPI<br>% of GDP<br><!-- End of picture text --> _Note:_ Private investment includes self-financing by households. _Source_ : Derived from Briceño-Garmendia, Smits, and Foster 2009.

# How much more can be done within the existing resource envelope?

About $200 million of additional resources could be recovered each year by improving efficiency (table 20). By far the largest area of inefficiency is underrecovery of costs, which accounts for 89 percent of the total. Reducing distribution losses to a reasonable benchmark in power and water could save up to $37 million each year. Optimizing staffing levels and increasing collection efficiency respectively could reduce costs by $9 million and $6 million per year, respectively. Budget underexecution (that is, the share of budgeted funds that is actually spent) does not seem to be an issue for Mali. The two sectors that present the largest potential efficiency gains are power and water.

**Table 20. Potential gains from greater operational efficiency**

|US$millionsperyear||||||
|---|---|---|---|---|---|
|Source of inefficiency|ICT|Power|Transport|WSS|Total|
|Underrecovery of costs|n.a.|110|—|38|148|
|Overstaffing|—|9|n.a.|0|9|
|Distribution losses|n.a.|33|n.a.|4|37|
|Undercollection|n.a.|5|—|1|6|
|Low budget execution|0|0|0|0|0|
|Total|0|157|0|43|200|

Source: Derived from Foster and Briceño-Garmendia 2009. Note: Estimations based on operating performance data up to 2007. — = not available; n.a. = not applicable Underpricing of power and water services costs Mali about US$148 million each year. In the power sector, it is estimated that the average total cost of producing electricity has been $0.33 per kilowatt-hour historically (as of 2007), but the average effective residential tariff is only about $0.20. This is sufficient to cover operating and maintenance costs (barely), but not investments. Overall, the power utility covers 50 percent of costs, leaving the capital investment unfunded. The financial burden associated with 44
firms based there. Yet enough telecom firms from outside the United States are listed in the United States for them to be categorized separately. All the infrastructure industries have higher leverage than the median industry and all but “foreign” telecommunications are in the top quartile. Table 1 and Table 2 present data on the average initial leverage of a sample of project-financed infrastructure firms. For all sectors, leverage is high. For infrastructure as a whole, it reached a peak of 73 percent in 2000 and then fell to 66 percent in 2001.

**Figure 2: Leverage by industry among companies listed in the United States, July 2002**

<!-- Start of picture text -->
1.00<br>0.90<br>0.80 0.73 0.73<br>0.70 0.67<br>0.60 0.55<br>0.50 0.42 0.46 0.48 0.49<br>0.40 0.36 0.38<br>0.30 0.22<br>0.20 0.13<br>0.10<br>0.00<br>0.00<br>Minimum1st quartile MedianForeign Telecom.3rd quartileWater UtilityNatural Gas (Distrib.)Electric Utility (East)Telecom. ServicesAir TransportElectric Utility (West) PowerMaximum<br>Leverage<br><!-- End of picture text -->

Source: www.damodaran.com. Note: the “power” industry includes firms that specialize in generation, possibly selling under power-purchase agreements, possibly selling into competitive wholesale markets. Utilities include mostly vertically integrated firms.

**Table 1: Initial leverage of infrastructure project-financed firms by industry, 1997–2001**

||Average debt to<br>total capital<br>(percent)|
|---|---|
|Power|70|
|Transportation|72|
|Telecom|66|
|Oil & Gas|65|
|Water & Sewage|76|

Source: Esty and Christov 2002: 18.

**Table 2: Initial leverage of infrastructure project-financed firms, by year, 1997–2001**

|1997|1998|1999|2000|2001|
|---|---|---|---|---|
|Mean leverage (%)<br>68|69|70|73|66|
|Source: Esty and Christov 2002: 18.|||||

11
linkage (Wolf and Hamner 2000; Katz and Fischhendler 2011). All three strategies may be used to further bind the parties to the agreement’s tenants by making it costlier to defect so long as benefits accrue from cooperation. Examples of treaties codifying such strategies include the 1973 Helmand River Agreement between Iran and Afghanistan, 1986 Orange River Agreement between South Africa and Lesotho, and 1998 Framework Agreement among various Central Asian countries.

# <u>4. METHODOLOGY</u>

In this study, we seek to assess the efficacy of various allocation and institutional mechanisms designs under conditions of resource variability by evaluating their impact on instances of transboundary cooperation and conflict over time. Specifically, we seek to discern what types of allocation mechanisms – flexible vs. rigid, specific vs. non-specific – seem to promote cooperation more effectively. Similarly, we evaluate the importance of a series of institutional mechanisms, which, as detailed above, are expected to reduce conflict and promote cooperation. We present a description of the database and variables used to conduct these evaluations, and grounded in the theoretical discussion above include hypotheses regarding their expected impact. In terms of explanatory variables, we have two overall types: those representing type of allocation mechanism and those representing types of institutional mechanisms. A number of control variables are included representing climate variability as well as a variety of other factors that may impact conflict or cooperation between riparians. We explain each below and offer hypotheses as to their expected effects. We then follow with a description of the regression models used in this assessment.

# <u>4.1. Description of Dataset and Variables</u>

We assembled a database of all treaties ratified between 1948 and 2001 and taking riparian signatory country pairs (dyads) as our unit of observation. Our analytical framework considers long-term trends and long range mean values for these variables and their effects on treaty resilience. A similar cross-sectional approach used to investigate environmental treaty formation or examine international water treaties has been adopted by Neumayer (2002a), Dinar et al. (2010), Dinar et al. (2011), and De Stefano et al. (2012).

9
32 , Table 8 National saving rate: Alternative estimators, full sample (Dependent variable: Gross national saving/GND))

||a||ae|a||
|---|---|---|---|---|---|
|Estimator<br>Regression<br>instruments|OLS-CS<br>Levels<br>-|OLS-Pool<br>Levels<br>-|Within<br>Devia.<br>from mean|GMN-Diff<br>Differences<br>Levels|GMM-Syst<br>Levs - Diffs<br>Diffs - Levs|
|Lagged national saving rate|-|0.802<br>(34.2)|0.572<br>(19.054)|0.378<br>(6.143)|0.384<br>(6.650)|
|Real per-capita GNDI*|0.043<br>(5.012)|0.008<br>(3.009)|0.064<br>(5.255)|0.070<br>(1.501)|0.102<br>(2.685)|
|Real growth rate of per-capita GNDI”|0.878<br>(3.216)|0.285<br>(9.459)|0.271<br>(10.07)|0.249<br>(2.573)|' 0.447<br>(4.831)|
|Real interest rate* °|0.023<br>(0.288)|0.0121<br>(0.669)|0.016<br>=<br>(0.649)|-0.333<br>(-2.503)|-0.136<br>(-1.215)|
|M2/GNP|0.089<br>(2.854)|0.021<br>(3.109)|-0.031<br>(-2.236)|-0.154<br>(-2.504)|-0.019<br>(-0.410)|
|Terms of trade*|-0.032<br>(-1.242)|0.015<br>(2.031)|0.045<br>(4.746)|0.084<br>(5.753)|0.057<br>(5.243)|
|Urbanization ratio|-0.044<br>(-1.418)|-0.020<br>(-2.062)|-0.048<br>(-1.107)|0.031<br>(0.383)|-0.500<br>(-3.373)|
|Oid dependency ratio|-0.571<br>{-3.537)|-0.133<br>{-3.409)|-0.480<br>(-4.599)|-0.343<br>(-1.225)|-0.772<br>(-1.687)|
|Young dependency ratio|-0.016<br>{-0.365)|-0.020<br>(-1.985)|0.051<br>(2.100)|-0.066<br>(-.823)|-0.156<br>(-2.236)|
|Domestic credit flow/GNDI|0.061<br>(0.209)|-0.118<br>(-4.508)|-0.107<br>(-4.473)|-0.365<br>(-3.764)|-0.359<br>(-4.136)|
|inflation rate® °|-0.012<br>(-0.131)|0.104<br>(3.977)|0.148<br>(4.232)|-0.016<br>(-0.174)|0.180<br>(2.044)|
|Waldtestofjointsignificance (p-value)|0.000|0.000|0.000|0.000|~—-0,000|
|Sargan test (p-value)|-|-|-|0.070|0.156|
|Test for 1st-order serial correlation (p-value)|-|0.033|0.000|0.000|0.000|
|Test for2nd-order serial correlation (p-value)|-|0.340|0.357|0.248|0.174|
|Number of observations (Number of<br>countries)|102 (102)|1836(98)<br>|1738(98)<br>e|1640 (98)<br>|1640 (98)<br>e|

Notes: T-statistics (in brackets) computed with heteroskedasticity-consistent standard errors.

* Expressed in logs (log of {1+x] for the real interest rate and the inflation rate). » Measured by the first difference of the log. ° Both the real interest rate and the inflation rate are bounded between —50 percent and 50 percent.
have had access to the new schools with those that would have graduated by the time of the school construction.<sup>90</sup> A regression discontinuity design was used to estimate the impact of India’s huge road‐ building program targeted to smaller villages by comparing outcomes in villages just below the population cutoff to those just above it.<sup>91</sup> In conclusion, in recent decades development economists have honed several empirical tools that demonstrate both the feasibility and importance of providing rigorous analysis to support evidence‐based policy making.

> 90 Duflo, E. 2001. "Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment." _American Economic Review_ , 91 (4): 795‐813.

> 91 Asher, S., and P. Novosad. Forthcoming. “Rural Roads and Local Economic Development.” _American Economic Review_ .

29
Table 3: Program Impacts on Grades 3 Test Scores by Gender, Ability, and Region

||French|Math|Oral|Index|PPVT|
|---|---|---|---|---|---|
|Panel A: Beginning of G|rade (First|Follow-Up)||||
|Male|0.022<br>|0.024<br>|0.011<br>|0.041<br>||
||(0.017)|(0.014)|(0.017)|(0.073)||
|Female<br>|0.037<sup>_∗∗_</sup>_†_<br>(0.016)<br>|0.031<sup>_∗∗_</sup>_†_<br>(0.014)<br>|0.047<sup>_∗∗∗_</sup>_†_<br>(0.017)<br>|0.217<sup>_∗∗∗_</sup>_†_<br>(0.073)<br>||
|Male Control Mean (SD)|0.54 (0.25)|0.56 (0.24)|0.37 (0.22)|0.03 (0.97)||
|Female Control Mean (SD)|0.53 (0.24)|0.53 (0.24)|0.33 (0.22)|-0.13 (0.99)||
|Low Ability|0.006|-0.007|0.025|-0.019||
||(0.018)|(0.016)|(0.018)|(0.081)||
|High Ability|0.027<br>|0.029<sup>_∗_</sup><br>|0.005<br>|0.136<sup>_∗_</sup>_†_<br>||
||(0.020)|(0.016)|(0.019)|(0.075)||
|Low Control Mean (SD)|0.47 (0.22)|0.43 (0.20)|0.25 (0.17)|-0.46 (0.83)||
|High Control Mean (SD)|0.62 (0.24)|0.68 (0.20)|0.48 (0.20)|0.49 (0.85)||
|North|0.012<br>(0.016)|0.015<br>(0.014)|0.019<br>(0.016)|0.066<br>(0.067)||
|South|0.102<sup>_∗∗∗_</sup>_†_|0.079<sup>_∗∗∗_</sup>_†_|0.074<sup>_∗∗∗_</sup>_†_|0.390<sup>_∗∗∗_</sup>_†_||
||(0.030)|(0.027)|(0.028)|(0.123)||
|North Control Mean (SD)|0.56 (0.24)|0.57 (0.23)|0.38 (0.22)|0.10 (0.95)||
|South Control Mean (SD)|0.41 (0.23)|0.43 (0.22)|0.23 (0.19)|-0.63 (0.88)||
|Panel B: End of Grade (|Second Foll|ow-Up)||||
|Male|0.026|0.016|0.024|0.079|0.207<sup>_∗_</sup>|
||(0.018)|(0.017)|(0.022)|(0.087)|(0.115)|
|Female|0.043<sup>_∗∗_</sup>_†_|0.019|0.054<sup>_∗∗_</sup>_†_|0.245<sup>_∗∗_</sup>_†_|0.096|
||(0.019)|(0.018)|(0.023)|(0.102)|(0.128)|
|Male Control Mean (SD)|0.67 (0.23)|0.69 (0.22)|0.46 (0.22)|-0.01 (0.92)|-0.07 (0.95)|
|Female Control Mean (SD)|0.65 (0.24)|0.67 (0.23)|0.43 (0.23)|-0.17 (1.03)|-0.10 (1.01)|
|Low Ability|0.027|0.001|0.036|0.081||
||(0.022)|(0.022)|(0.026)|(0.116)||
|High Ability|0.029|0.027|0.028|0.174<sup>_∗_</sup>_†_||
||(0.022)|(0.019)|(0.024)|(0.096)||
|Low Control Mean (SD)|0.60 (0.23)|0.60 (0.22)|0.34 (0.20)|-0.45 (0.93)||
|High Control Mean (SD)|0.75 (0.20)|0.78 (0.19)|0.56 (0.20)|0.39 (0.82)||
|North|0.024|0.000|0.023|0.087|0.130|
||(0.018)|(0.016)|(0.020)|(0.084)|(0.097)|
|South|0.079<sup>_∗∗_</sup>_†_|0.084<sup>_∗∗_</sup>_†_|0.105<sup>_∗∗∗_</sup>_†_|0.450<sup>_∗∗∗_</sup>_†_|0.181|
||(0.037)|(0.033)|(0.035)|(0.161)|(0.250)|
|North Control Mean (SD)|0.69 (0.23)|0.71 (0.22)|0.48 (0.22)|0.07 (0.92)|-0.21 (0.88)|
|South Control Mean (SD)|0.58 (0.24)|0.57 (0.23)|0.31 (0.21)|-0.67 (0.96)|0.43 (1.13)|

Notes: Standard errors are in parentheses and are adjusted for clustering.<sup>_<u>∗</u>_</sup> _p <_ 0 _._ 10 ,<sup>_<u>∗∗</u>_</sup> _p <_ 0 _._ 05 ,<sup>_<u>∗∗∗</u>_</sup> _p <_ 0 _._ 01 correspond to p-values from the usual single-hypothesis tests. _†_ corresponds signicance at the 10% level of Romano Wolf (2005) p-values from joint tests of French, mathematics, and oral (3 tests each, by row) or to the index alone. Conditioning variables: Grade, gender, household size, number of children, education of head, distance to school, wealth index, interview language, baseline scores, missing dummies.

18
sharp contrast to the seven attempted experiments in African contexts. In Puebla, the director of the program wanted to prove that it worked, and had heard of the MIT Poverty Action Lab. Impetus for the randomized evaluation therefore came directly from the head of the program himself, who directed program staff to follow all the suggestions made by the evaluation team in order to ensure that the program could be rigorously evaluated. While there appear to have been some incentives for him to have done this (proving his program worked would help in budget discussions), it also appears to in part reflect the research interests and educational background of this director, making it harder to replicate elsewhere.

Ownership of the evaluation at the level of the implementation team is helpful for a successful impact evaluation. In a couple of our matching grant studies, our initial discussions and workshops were with the management team, top government officials, and Monitoring & Evaluation (M&E) specialists to garner buy-in for the impact evaluation. Conversations with the day-to-day operations team that would implement the project happened only in a second stage - in one of these cases in particular, the project was based on a specific region of the country, which we visited only after the initial interactions. The top officials were interested in learning about their projects and using the evaluations to prove the programs’ effectiveness and obtain further funding. Nonetheless, when we started working with the implementation teams on the ground, we faced significant blocks in implementing the evaluations. The political economy of headquarters versus regional offices came into play and the implementation teams’ feared that we would be auditing their work and assessing their competencies, despite numerous discussions about the objectives of the study. For these two projects, the implementation teams saw the evaluation as risky (these were two existing projects with problems) and although the top levels officials considered the evaluation to be important, it was not important enough to justify potentially damaging already difficult relations with operational teams.

b) _Overly strict eligibility criteria:_ The eligibility criteria in many cases were set in a way to exclude the vast majority of firms in most African countries. They tended to be based on a notion of picking the firms which would grow the fastest, not necessarily the ones which would benefit the most from the grant. Despite most African firms having fewer than 10 workers and not being fully formal (McKenzie, 2011b), most programs required firms to be fully formal. This could be fine if the existence of the program served as an incentive to bring firms into the formal registration and financial systems, but in some cases the programs required the firms to have several years of formal registration, including audited accounts and current tax records. In one case, we sat with the project implementation team to 21