rafmacalaba/datause-encoder-data huggingface dataset:
from those that do not. A natural way to identify their effects is to find good instruments that are correlated with the two facilitation variables but are otherwise not determinants of firm efficiency, but unfortunately we were unable to find good candidates. Instead, we address the potential endogeneity of the two facilitation variables in several alternative ways.<sup>10</sup> First, we do not directly use firm-level measures. Instead, we rely on city-industry averages of firms’ answers to the relevant questions to gauge the local level of government facilitation. This city-industry mean based measure is less subject to the reverse causality issue associated with firm-level answers, and its effect on firm productivity should be viewed as resulting from the level of government activism within a particular industry in a locality, not necessarily from the direct government information or loan help received by the firm.

Second, to check for possible omitted variables, we control for additional determinants of firm efficiency, including CEO characteristics, local-level institutions, access to finance, and firm-level assessment of the efficiency of state machinery, and show that our key results remain robust.

Third, we conduct sub-sample analysis, and examine whether the effects of government facilitation are stronger in certain subsamples in which market failures are supposed to be more debilitating. Confirmation of that conjecture would render support for our basic hypothesis that active government facilitation may help firms in overcoming relevant market failures.

Finally, we rely on the Rajan-Zingales methodology to further ensure that the government facilitation effects are not due to omitted variables (Rajan and Zingales, 1998). Specifically, by controlling for city and industry dummy variables, we investigate whether the effects of government facilitation are more pronounced in industries that would be expected to benefit more from government facilitation. Those city and industry dummies are included to control for all time-invariant factors specific to the local and the industry level, such as human capital, culture, and governance. Our identification therefore comes from comparing firms in the same industry, but in different cities that feature distinct levels of government facilitation.

However, with observational data, endogeneity concerns can never be completely dismissed. This can be especially problematic when potential instruments are correlated through other channels in the residual of the performance equation (Bazzi and Clemens 2010; Morck and

> 10 See also Dollar et al. 2006, Aterido et al. forthcoming; Xu 2011.

11
# <u>Table 3: Simultaneous Estimates; “Active”</u> _<u>versus</u>_ “Inactive”

|**Dependant variable: Welfare Ratio**||**Dependant variable: "Active" HH-head**||
|---|---|---|---|
|Size|-0.053|Size|-0.003|
||(0.007)**||(0.001)*|
|Number above age 60|-0.084|Number above age 60|0.003|
||(0.082)||(0.013)|
|HH-head Couple without children (D)|0.382<br>(0.092)**|HH-head Couple without children (D)<br>|-0.031<br>(0.013)*|
|HH-head single parent with children (D)|0.086|HH-head single parent with children (D)|-0.040|
||(0.095)||(0.014)**|
|HH-head single people (D)|0.280|HH-head single people (D)|-0.086|
||(0.142)*||(0.015)**|
|Average level of education|0.125|||
|in the household|(0.019)**|Time to the nearest tarred road|0.023|
|"Active" HH-head (D)|2.730|in hours|(0.009)*|
||(0.961)**|Male-headed household (D)|-0.044|
|Time to the nearest tarred road|-1.860||(0.012)**|
|in hours|(1.180)|Age|-0.001|
|Time to the nearest primary school|0.389||(0.000)*|
|in hours|(0.276)|Primary instruction (D)|0.013|
|Time to the nearest health center|0.665||(0.010)|
|in hours|(0.442)|Secondary (1st cycle) instruction (D)|-0.004|
|Time to the nearest food market|0.701||(0.012)|
|in hours|(0.467)|Secondary (2nd cycle) instruction (D)|0.035|
|Part of people affected by malaria|-0.488||(0.013)**|
|in the cluster|(0.242)*|Tertiary education (D)|0.063|
|Average level of education|-0.100||(0.014)**|
|in the cluster|(0.182)|||
|Rural Forest|0.943|Rural Forest|0.102|
||(0.701)||(0.040)*|
|Rural Savannah|0.494|Rural Savannah|0.023|
||(0.554)||(0.025)|
|Rural High Plateaux|0.474|Rural High Plateaux|0.055|
||(0.487)||(0.022)*|
|Constant|-2.592|Constant|0.959|
||(1.089)*||(0.054)**|
|District Fixed Effects|yes|District Fixed Effects|yes|
|Instrumental Variables|yes|Instrumental Variables|yes|
|Observations|5938|Observations|5938|
|R Squared|0.3460|R Squared|0.0661|
|RMSE|0 .551|RMSE|0 .271|
|(robust se);*:p<5%;**:p<1%||||

21
**Table 2:** Selected women’s characteristics, by the sex of the first-born and age group

|a) All women||||||
|---|---|---|---|---|---|
||frst-born girl|frst-born boy|dif|st. error dif|n|
|# children ever born|4.344|4.369|-0.025|(0.038)|23751|
|wants another child|0.637|0.629|0.008|(0.007)|23598|
|use contraceptives|0.157|0.157|-0.000|(0.005)|23751|
|age at frst birth|19.423|19.295|0.128**|(0.064)<br>|23751|
|polygynous husband|0.333|0.336|-0.003|(0.007)|21690|
|female headed hh|0.144|0.133|0.011**|(0.005)|23751|
|living with husband|0.891|0.903|-0.012**|(0.005)|21652|
|frst union|0.862|0.863|-0.001<br>|(0.005)<br>|23050|
|woman’s eduyrs|5.026|4.811|0.215***|(0.076)|23735|
|husband’s eduyrs|6.329|5.999|0.330***|(0.083)|22657|
|weight-for-height|-0.536|-0.561|0.025|(0.018)|22986|
|body mass index|23.116|23.031|0.085|(0.068)|23115|
|b) Women aged 15–29||||||
||frst-born girl|frst-born boy|dif|st. error dif|n|
|# children ever born|2.541|2.545|-0.004|(0.032)|10048|
|wants another child|0.867|0.852|0.015**|(0.007)|9988|
|use contraceptives|0.132|0.132|-0.000|(0.008)|10048|
|age at frst birth|18.345|18.236|0.109|(0.074)|10048|
|polygynous husband|0.267|0.275|-0.008|(0.010)|9173|
|female headed hh|0.107|0.109|-0.002|(0.007)|10048|
|living with husband|0.900|0.899|0.001|(0.007)|9157|
|frst union|0.915|0.913|0.002|(0.006)|9492|
|woman’s eduyrs|4.889|4.861|0.028|(0.111)|10042|
|husband’s eduyrs|6.359|6.203|0.156|(0.125)|9308|
|weight-for-height|-0.571|-0.567|-0.004|(0.023)<br>|9711|
|body mass index|22.114|22.136|-0.022|(0.089)|9775|
|c) Women aged 30–49||||||
||frst-born girl|frst-born boy|dif|st. error dif|n|
|# children ever born|5.649|5.639|0.010|(0.050)|13703|
|wants another child|0.471|0.475|-0.004|(0.009)|13610|
|use contraceptives|0.174|0.174|0.000|(0.007)|13703|
|age at frst birth|20.202|20.032|0.170*|(0.094)|13703|
|polygynous husband|0.381|0.379|0.002|(0.009)|12517|
|female headed hh|0.170|0.149|0.021***|(0.007)|13703|
|living with husband|0.884|0.905|-0.021***|(0.006)|12495|
|frst union|0.826|0.83|-0.004|(0.007)|13558|
|woman’s eduyrs|5.125|4.776|0.349***|(0.103)|13693|
|husband’s eduyrs|6.309|5.864|0.445***|(0.111)|13349|
|weight-for-height|-0.511|-0.557|0.046*|(0.025)|13275|
|body mass index|23.839|23.654|0.185*|(0.096)|13340|

_Notes._ Robust standard errors adjusted for clustering at the household level in parentheses. Using survey weights.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at 1%, 5% and 10% levels, respectively.

If women’s reproductive behavior is affected by the gender of their first-born child, differences in characteristics may develop among women over time. I split the sample into subsamples of women aged 15–29, and 30–49. Panel (b) shows that the differences are much smaller for younger women (other than the desire for more children, which is expected to be affected among women of 14
**Table 10 Elasticity of reported import values, quantities and prices with respect to tariffs at firmproduct-source country level**

|**The Elasticity of Import**<br>|**s With Respe**<br>|**ct to Tariffs a**<br>|**t the Firm-P**<br>|**roduct-Sour**<br>|**ce Country le**<br>|**vel**<br>|
|---|---|---|---|---|---|---|
|_Dependent Variable:_|_Log V_|_Log Q_|_Log P_|_Log V_|_Log Q_|_Log P_|
||(1)|(2)|(3)|(4)|(5)|(6)|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Log (Tariff+1)|-0.133*|-0.133|0.001||||
||(0.073)|(0.098)|(0.059)||||
|Ben Ali *Log (Tariff+1)|<br>0.098|<br>0.211***|<br>-0.113**|0.092|0.194***|-0.102*|
||(0.062)|(0.078)|(0.057)|(0.071)|(0.074)|(0.054)|
|Offshore*Log (Tariff+1)|0.251***|0.152*|0.099|0.193*|0.042|0.150**|
||(0.066)|(0.081)|(0.055)|(0.081)|(0.092)|(0.060)|
|Public *Log (Tariff+1)|-0.070|0.223|-0.293**|0.127|0.562***|-0.434***|
||(0.144)|(0.183)|(0.129)|(0.170)|(0.140)|(0.141)|
|Ben Ali firm|-0.279|-0.498*|0.218|-0.185|-0.425*|0.239|
||(0.226)|(0.289)|(0.190)|(0.242)|(0.249)|(0.156)|
|Offshore|0.251***|0.152*|0.099*|-0.372|<br>-0.009|<br>-0.363*|
||(0.213)|(0.300)|(0.214)|(0.277)|(0.358)|(0.205)|
|Public|1.168**|0.177|0.991**|0.633|-0.877*|1.510***|
||(0.515)|(0.651)|(0.460)|(0.664)|(0.499)|(0.495)|
|Log Y|0.136***|0.142***|-0.006|0.157***|0.159***|-0.002|
||(0.027)|(0.029)|(0.016)|(0.022)|(0.024)|(0.016)|
|Log L|0.100***|0.029|0.071**|0.099***|0.055*|0.044|
||(0.024)|(0.033)|(0.029)|(0.021)|(0.029)|(0.027)|
|Log Age|-0.028|-0.009|-0.019|-0.037|-0.023|-0.014|
||(0.027)|(0.042)|(0.030)|(0.028)|(0.040)|(0.026)|
|Log (NTB + 1)|-0.039|-0.091|0.052||||
||(0.057)|(0.072)|(0.045)||||
|N|48032|48032|48032|48032|48032|48032|
|Number of firms|3052|3052|3052|3052|3052|3052|
|Sector FE|Yes|Yes|Yes||||
|Country*Year FE|Yes|Yes|Yes||||
|<br>Product FE|Yes|Yes|Yes||||
|Product*Country*Sector*Year FE||||Yes|Yes|Yes|
|<br>R2|0.363|0.577|0.703|<br>0.471|<br>0.672|<br>0.790|
|Adjusted R2|0.352|0.569|0.698|0.377|0.614|0.753|
|Note: Standard errors are robust and c|lustered byfir|m. *** p=<0.|01, ** p<0.05|, * p=0.10.No|te: sample is|confined to|

(5-digit) sector-product-source combinations in which (i)at least one Ben Ali firm and at least one other firm of a different types are simultaneously importing during the same year, (ii) for which tariff data exist,(iii) in which imports reported in Tunisia and exports reported in partner countries are strictly positive, (iv) from countries which are among the top 15 source countries in terms of cumulative import value (either as declared by partners or as declared by Tunisian customs) over the period 2002-2009, and (v) products which account for at least 0.01 percentage points of import value (either as declared by partners or as declared by Tunisian customs) over this period. In addition, (vi) observations that fall in the top and bottom 1% of normalized prices, quantities and values are excluded. The sample comprises 9 public firms, 1787 onshore firms, 1145 offshore firms, and 113 Ben Ali firms.

47
While this paper describes and evaluates a specific program-FONCODES-it adds to an ongoing debate about the relationship between educational inputs and outcomes (for a summary see Hanushek, 1995. and the response by Kremer, 1995). There is a growing body of literature which suggests that there are high rates of return to expenditures on school facilities in many developing countries (for example, Harbison and Hanushek, 1992; Velez, Schiefelbein and Valenzuela, 1994; Glewwe and Jacoby, 1994; Hanushek, 1995; Glewwe, Grosh, Jacoby, **and** Lockheed, 1995; Duflo, 1998). Our results suggest that expenditures on educational infrastructure in Peru had an impact on some educational outcomes-such as the attendan ze rate of young children-but not on others-such as the likelihood that these children would be at an appropriate grade for their age. Because expenditures made by FONCODES on education were well targeted towards poor districts and (less clearly) poor households, improvements in attendance rates were concentrated amongst the most needy.

We believe that this paper also makes a contribution to the evaluation literature. WN'e lase the geographic variation in FONCODES expenditures to estimate the impact of FONCODES investments in school facilities on educational outcomes. Identification strategies based on geographic differences are often used to estimate the impact of social programs and policies in the United States (for example, Card, 1990, 1992, and 1993; Card and Krueger, 1994 and 1998) and, more recently, in a developing country context (for example, Pitt, Rosenzweig and Gibbons, 1993; Angrist and Lavy, 1997; Duflo; Case and Deaton, 1999). Such estimation strategies are themselves not uncontroversial, however (for example, Besley and Case, 1994; Heckman, Farrar and Todd, 1996). In this paper we contrast results based on a number of estimation stratei;ies, including "naive" regressions, "differences--in-differences", and instrumental variables to t ,st the robustness of our findings, and the assumptions on which these and similar results are basc d.

The rest of the paper proceeds as follows. In Section 2, we provide a brief overviev of the Peruvian context, andl FONCODES. Section 3 describes the data set. Section 4 briefly disau.sses previous evaluations of FONCODES. Sections 5 and 6 present our results on the targeting **and** impact of FONCODES investments in education. Section 7 concludes.

surveys. Chronic malnutrition could be a more promising health outcome, since it affected 30% of children under five in 1994, and 23.8% in 1997 (World Bank, 1999, p. 10). However, there appears to be no cle ar relationship between changes in chronic malnutrition and FONCODES expenditures on health, water and sanitation.

3
Table 9: Disaster fatalities, aid and colonial background

||Storms|Floods|Earthquakes|
|---|---|---|---|
|Ln(_AIDj,t-1_)|0.644|-0.262|0.029|
||(0.412)|(0.605)|(0.634)|
|_COLONYj _|0.275|-0.115|2.897**|
||(0.641)|(0.509)|(1.246)|
|_COLONYj*AIDj,t-1_|0.380|-0.299|-1.027|
||(0.501)|(0.487)|(0.657)|
|Ln_(FRENCH AIDj,t-1)_|-1.379|0.152|-2.650|
||(0.501)|(0.312)|(0.866)|
|_FRENCH COLONYj _|1.207***|-0.421|n.a.|
||(0.291)|(0.243)||
|_FRENCH COLONYj*AIDj,t-1_|-1.303|-4.400***|n.a.|
||(0.927)|(0.390)||
|Ln_(UK AIDj,t-1)_|0.266|-0.712*|-1.019|
||(0.469)|(0.387)|(2.019)|
|_UK COLONYj _|0.005|-0.633*|-0.148|
||(0.445)|(0.373)|(1.490)|
|_UK COLONYj*AIDj,t-1_|-2.430**|0.077|4.078|
||(0.800)|(0.402)|(3.303)|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. Covariates from the baseline specification (equation (5)), continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

28
Table 2: Hybrid NKPC

|Method|Instruments|_λ_|_γ f_|_γb_|_JCHNSW_|
|---|---|---|---|---|---|
|_GMM_|_L_=4|0.174|0.403|0.350|13.40|
|||[0.31]|[0.10]|[0.12]||
||_L_=8|0.350|0.813|0.444|17.15*|
|||[0.23]|[0.17]|[0.15]||
||_L_=20|0.380|0.983|0.521|19.32|
|||[0.27]|[0.16]|[0.12]||
|_TJIVE_|_L_=4|0.112|0.494|0.351|12.45|
|||[0.08]|[0.17]|[0.12]||
||_L_=8|0.12|0.674|0.277|10.25*|
|||[0.05]|[0.03]|[0.01]||
||_L_=20|0.151|0.656|0.320|15.2*|
|||[0.04]|[0.002]|[0.001]||

The p-values appear in brackets for the null hypothesis that the estimate is equal to zero. * means the model is correctly specified (at the 5% level).

15
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50
mosquitos were to move to animal hosts, the cycle of malaria transmission would be broken.

For estimating the mortality effect of the use of treated bed-nets elsewhere, it may be worth remembering one or two distinctive characteristics of malaria in The Gambia. One is the highly seasonal nature of transmission; we have no evidence of the results will be the same in populations in which malaria is present all year round. Secondly, the AnoRheles gambiensis does not bite duriing the day whereas other species have different biting habits. Thirdly, the high level of immunization achieved in The Gambia may make the effects of malaria control on mortality clearer than elsewhere since the number of competing risks from other infectious diseases has been limited. Finally the form of living arrangements of the Mandinka, large families living in crowded compounds with wives, co-wives and children sharing a common sleeping room, may be responsible for some of the especially strong effects of the bed-nets on malaria and overall mortality. In rural Gambia, a 1991 survey revealed that 62% of beds in PHC villages and 51% of beds in non-PHC villages had bednets around them. 10

**000**

10 M Aikins and S Bennett, National bednet survey, MRC, The Gambia.

19
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30
work activities, and find that costs are significantly higher on average in countries that score poorly on corruption indicators.

Analyzing a large database on firms’ banking transactions, Mironov and Zhuravskaya (2016) conclude that “tunneling” of cash to fly-by-night firms is likely directed to politicians in exchange for allocation of procurement contracts. They show that where procurement awards depend more on bribes and kickbacks, contracts tend to get awarded to less productive firms.

Several studies provide evidence that external audits reduce corruption in procurement. In a randomized control trial in Indonesian villages, Olken (2007) found that a large increase in the probability of being audited reduced corruption in road construction. Di Tella and Schargrodsky (2003) found prices paid for homogeneous inputs in public hospitals in Buenos Aires fell by 17% when intensive audits were performed as part of a crackdown on corruption. The introduction of random audits of procurement processes in Brazilian municipalities significantly reduced corruption, even when the probability of being audited remained low (Avis et al., 2016; Zamboni and Litschig (2016).

Cross-country evidence on the relationships between procurement systems and outcomes is very sparse. Evenett and Hoekman (2005b: 3) note the dearth of evidence on the relative payoffs to alternative types of procurement reforms, and the need for research “to shed light on the success or otherwise of public procurement policies in a range of developing countries.” This paper aims to begin filling this evidence gap, using data for 88 countries from PEFA assessments and the World Bank’s Enterprise Surveys. We look at two outcomes experienced by business firms: participation in procurement markets, and kickbacks needed to obtain government contracts. According to Ware et al. (2007: 306): “kickback schemes are the primary vehicles through which corruption is perpetrated in public procurement.” Our data do not measure other aspects of procurement processes experienced by firms, such as other forms of corruption, transactions costs, or payment delays, or outcomes valued by public procuring agencies, such as cost, quality, or collusion by firms.

Procurement outcomes are measured in our study by two linked questions in the World Bank’s Enterprise Surveys.<sup>4</sup> The first question asks: “Over the last year, has this establishment secured or attempted to secure a government contract?” Among the 33,385 firms in the 88-country surveys in our

> 4 Surveys are designed to be broadly representative of formally-registered firms in the manufacturing and service sectors. Sampling is stratified by size, sector, and geographic region. Sample sizes typically vary from about 150 to 1,800, increasing with the size of the country. Survey methodology and content are described in more detail at <u>http://www.enterprisesurveys.org/methodology.</u>

6