rafmacalaba/datause-encoder-data huggingface dataset:
|Corruption Obstacles|-0.015|-0.3|2.9%|
|---|---|---|---|
|Overdraft Facility|0.014|0.23|2.7%|
|Informal Competition|-0.012|-0.21|1.6%|
|Web Intensity|0.018|0.17|1.9%|
|Capacityagglomeration<br>|0.074|0.12|5.6%|
|**For LP-Growth**<br>||||
|σY,aaa= 0. 4||||
|<br>Overdraft Facility|0.034|0.23|1.9%|
|<br>Trade Credit|0.036|0.14|1.2%|
|Web Intensity|0.036|0.17|1.5%|
|Capacityagglomeration|0.170|0.12|4.9%|

Note. The coefficients are for the African regions. That is, they are based on Table 4. African Standard Deviations correspond to the whole sample. This table reports the associated change (in percent of one standard deviation of the outcome in Africa) when the key variable changes by one standard deviation (in Africa).

The most important BE element is clearly capacity agglomeration. Increasing it by one African standard deviation is associated with an increase in African employment growth and labor productivity growth standard deviations by 5.6 and 4.9 percent, respectively. This is the largest positive association we observe in all BE variables. Relatedly, improving Informal Competition by one standard deviation is associated with increasing 1.6 percent of labor growth standard deviations.

As a group, improving Basic BE is clearly important. First, improving corruption obstacles by one African standard deviation is associated with higher employment growth by slightly less than 3 percent of one employment growth standard deviation. Second, improving modern infrastructure (i.e., Web Intensity) by one African standard deviation is associated with increasing employment growth and labor productivity growth standard deviations by 1.9% and 1.5%, respectively. The effects are thus also potentially large. Finally, access to finance appears to be an important area to work on. Improving access to bank finance by one African standard deviation is associated with increasing 2.7% of one employment growth standard deviation, and 1.9% of one labor productivity growth standard deviation. Similarly, improving trade credit by one African standard deviation is associated with increasing 1.2% of one labor productivity growth standard deviation.

22
but remittances are. Money transferred by emigrants is directly linked with increases in household income that are hypothesized to play on patterns and timing of family building. In addition, one can reasonably assume that their amount is a good proxy for the intensity of the overall relationship kept by emigrants with their home country, i.e. a remote proxy for ideas, values and models they convey to the community left behind. The contrast between Egypt (Figure 3) and Morocco (Figure 4) is striking. The time correlation between remittances and birth rates is as highly positive in the former (+0.623) as it is negative in the latter (-0.741).

Standardized index defined as: [x – average(x)] / standard deviation (x) <!-- Start of picture text --> Figure 4 : Remittances and Birth Rates in Morocco<br>correl = – 0.741<br>2,5<br>2,0<br>Remittances<br>1,5 (1980 prices)<br>1,0<br>0,5<br>0,0<br>-0,5<br>-1,0<br>-1,5<br>Birth Rates<br>-2,0<br>-2,5<br>1975 1980 1985 1990 1995 2000 2005<br>Source: see Table 7<br>Standardised Index<br><!-- End of picture text --> This paper does not develop the particular role that can be attributed to state policies in the discontinuous history of birth control and fertility decline in Egypt (Fargues 1997) but 19
# **Figure 2. Transition of main infrastructure indicators over time (medians)**

<!-- Start of picture text --> a.) Total road length<br>(sqrt of 1,000 workers x mean arable land)<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>Egypt Developing countries World (60)<br>(40)<br>c.) Main lines per 1,000 workers<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Egypt Developing countries World (60)<br>(40)<br>e.) Power loss (percentage of total output)<br>0<br>-5<br>-10<br>-15<br>-20<br>Egypt Developing countries World (60)<br>(40)<br>1971-80 1981-90 1991-2000 2001-05<br><!-- End of picture text --> <!-- Start of picture text --> b.) Paved roads (the share to total roads)<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Egypt Developing countries World (60)<br>(40)<br>d.) EGC per 1,000 workers<br>(megawatts)<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Egypt Developing countries World (60)<br>(40)<br><!-- End of picture text --> _Note: The balanced data are used._ 19
# **3.4 Empirical Strategy**

To assess the impacts of the safe spaces and accompanying interventions, we estimate intention-to-treat (ITT) effects, considering the original assignment of adolescent girls to offered interventions, regardless of whether they participated or not. We use an ANCOVA specification for our main analysis whenever the baseline value of the outcome variable is available. Our estimated model is as follows: where _Yijt_ is the outcome for individual i in the locality _j_ at time t, with t = 0 being the baseline and t = 1 being the follow-up. T1 _ij_ , T1Leaders _ij_ , T2 _ij_ , T2Leaders _ij_ , T3 _ij_ , T3Leaders _ij_ , and T4 _ij_ are binary indicators of the assignment status for each individual, following indicator descriptions in section 3.1. _Xij_ 0 is a vector of control variables measured at baseline, including unbalanced variables between the control and the treatment groups, as well as age and the education of the adolescent girl. We also include randomization strata fixed effects ( _S_ ) and the standard errors _εij_ are clustered at the locality level.

To address multiple hypothesis testing concerns and maximize statistical power, we combined multiple variables from the same domains into thirteen indices, following Anderson (2008). These indices, described in table A4, are grouped into three categories: (i) primary outcomes—SRH behavior, economic outcomes, which are the main outcomes targeted by the program at short term; (ii) mechanisms outcomes—SRH knowledge and attitudes, gender attitudes, aspirations, socio-emotional skills (SES), which capture changes expected to mediate effects on primary outcomes; and (iii) secondary outcomes—marriage and pregnancy, violence, decision-making on individual matters, decision-making within the couple, finance, nutrition, and mental health, which are broader girls empowerment and well-being outcomes potentially affected by the interventions and, for some, may require more time to adjust.<sup>11</sup> Each index is weighted following a generalized least squares (GLS) weighting procedure that accounts for the correlation between items included in the index. Each index is also normalized according to the control group mean and standard deviation. Where necessary, we reverse the sign of individual items so that higher scores consistently indicate better outcomes. We also report false discovery rate (FDR)-adjusted q-values for these indices in Appendix Tables A17, A18 and A19.

We conduct two sets of robustness checks. First, we use a double LASSO procedure following Belloni et al. (2014) to select control variables in a data-driven way, reducing the

> 11This study follows to the extent possible the pre-analysis plan for the multi-country randomized controlled trial or RCT evaluating the impacts of safe spaces across five countries in the Sahel, with necessary adaptations due to the specificity of the sample in Côte d’Ivoire (e.g., older adolescents than in other countries of the regional study). The pre-analysis plan is available on the 3ie RIDIE registry page : `https://ridie.3ieimpact.org/index.php?r=search/detailView&id=981.`

16
# <u>Table 3. Factor analysis of self-reported measures</u>

|**Skill**|**Label**|**Factor**|**Uniqueness**|
|---|---|---|---|
|**Grit**||||
|Grit<br>|New ideas and projects sometimes distract me from previous ones<br>|1<br>|0.7149<br>|
|Grit|Setbacks don’t discourage me. I don’t give up easily|1|0.5782|
|Grit|I often set a goal but later choose to pursue a different one|1|0.526|
|Grit|I have difficulty maintaining my focus on project that take more than a few months to complete|1|0.769|
|Grit|I finish whatever I begin|1|0.6359|
|Grit|My interests change from year to year.|1|0.6477|
|Grit|I am diligent. I never give up.|1|0.4094|
|Grit|I have been obsessed with a certain idea or project for a short time but later lost interest|1|0.545|
|Grit|I have overcome setback to conquer an important challenge.|1|0.7781|
|**Big Five**||||
|Openness|is original, comes up with new ideas|4|0.4713|
|Openness|values artistic, aesthetic experiences|4|0.6354|
|Openness|has an active imagination<br>|4|0.377|
|Conscientiousness|does a thorough job|2|0.4286|
|Conscientiousness|tends to be lazy|2|0.703|
|Conscientiousness|does things efficiently|2|0.599|
|Extraversion|is talkative|3|0.5105|
|Extraversion|is outgoing, sociable|3|0.2589|
|Extraversion|is reserved|3|0.2196|
|Agreeableness|is sometimes rude to others|2|0.5878|
|Agreeableness|has a forgiving nature|2|0.4117|
|Agreeableness|is considerate and kind to almost everyone|2|0.3341|
|Emotional stability|worries a lot|5|0.69|
|Emotional stability|gets nervous easily|5|0.2792|
|Emotional stability|remains calm in tense situations|5|0.3463|

_Notes._ Using the data from Pakistan, this table provides the results from an exploratory factor analysis on self-reported grit and big-five items. All items are in a five-point format, which ranges from 1 ("Disagree strongly") to 5 ("Agree strongly"). Following Laajaj and Macours (2021), we applied an acquiescence bias correction (see Appendix D). We perform a principal factor estimation and retain five factors. We rotate the factor loadings so that each item mainly loads on one factor. For each item, we indicate the main factor the item loads on. We also provide the item’s uniqueness, which is the item’s percentage of variance that is not explained by the common factors.

43
mothers’ education on children’s nutrition status and mortality and own education on access to health care.

For a more complete picture, we looked at a combination of approaches that are often used in inequality studies because each approach has its own limitations and can lead to different conclusions (e.g., Moser et al., 2007 and Mackinnon et al., 2014). We computed absolute and relative inequalities from rate differences and rate ratios. However, differences and ratios between different groups do not take into account inequalities by the whole population. We, therefore, examined inequalities using concentration indexes (CI) (Kakwani et al., 2002; O’Donnell et al., 2008). We used concentration curves to illustrate the movement of socioeconomic inequalities in health between the earlier and latest surveys.<sup>7</sup> Following the procedure in O’Donnell (2006) and O’Donnell et al. (2008), we conducted tests of dominance between concentration curves of the outcomes for the earlier and latest surveys. The welfare measure we used for CI analysis is the DHS wealth index. Our analysis also includes decomposition of wealth-related inequalities in health.<sup>8</sup>

> 7 Earlier survey year refers to the 2000 DHS. The latest survey is the 2011 DHS for immunization indicators (full immunization and measles vaccination) and the 2014 DHS is considered as the latest survey for the rest of the indicators ( undernutrition and mortality indicators, contraception,  ANC4+, and SBA). The concentration curve shows the plot of cumulative proportion of a health outcome against the proportion of the population ranked by a welfare measure.

> 8 We used STATA version 13.0 for most of the analyses, including trend analysis, rate difference and rate ratio analysis, concentration indexes, concentration curves and dominance tests. We used ADEPT Health to decompose the inequalities.

9
Table 3 Average Treatment Effects of Participatory Institutions on Tax Outcomes (20002012)

|**Variable**|**Tax Share of**<br>**Revenue**<br>**Treatment**<br>**Effect (SE)**|**Per Capita**<br>**Taxes**<br>**(logged)**<br>**Treatment**<br>**Effect (SE)**|**Per Capita**<br>**IPTU**<br>**(property tax,**<br>**logged)**<br>**Treatment**<br>**Effect (SE)**|**Per Capita**<br>**ISS (Sales**<br>**tax, logged)**<br>**Treatment**<br>**Effect (SE)**|
|---|---|---|---|---|
|PB Spending on<br>Capital Projects and<br>Social Programs|0.006*<br>(0.003)|2.04**<br>(0.09)|1.92**<br>(0.17)|1.85**<br>(0.23)|
|N: 3,256<br>treated/3,744 control|||||
||* indicate<br>** indicat|s P> IZI at less t<br>es P> IZI at less t|han 0.05.<br>han 0.01||

We find that municipalities with PB that pursue a mix of capital and social projects collect more tax than those with PB that pursue more social projects and programs and more than municipalities without PB. This suggests that broad, visible public spending is associated with democratic accountability and perceived legitimacy in government- particularly among wealthy property owners and firms paying the bulk of local tax who might not support a government that spends on health care for the poor to the exclusion of infrastructure projects that could benefit a broader swath of residents.

# **Robustness Checks**

We use matching to address the prospect of endogeneity driving relationships between participatory institutions and tax outcomes above. However, we can go farther to model the presence of participatory institutions and any subsequent relationships they might have with tax outcomes. There is some anecdotal evidence that municipalities with stronger economic conditions adopt and maintain 25
**Table 1: Effects of Reform on World-Wholesale  Margin: Before-After Estimates**

|**Full S**|**ample**|**Restricte**|**d Sample**|**Matched **|**Sample**|
|---|---|---|---|---|---|
|**(1)**|**(2)**|**(1)**|**(2)**|**(1)**|**(2)**|
|**Announcement Effect**<br>|**Panel**|**A: Same P**|**eriod Crud**|**e Price**||
|**Intercept**<br>**41.63*****|**36.68*****|<br>**48.65*****|**42.95*****|**35.61****|**14.25**|
|(2.990)|(2.742)|<br>(3.441)|(2.834)|(2.485)|(1.281)|
|**Slope**<br>**-**<br>**0.486*****|**-**<br>**0.457*****|<br>**-**<br>**0.564*****|**-**<br>**0.533*****|**-**<br>**0.464*****|**-0.189**|
|(-2.804)|(-2.749)|<br>(-3.208)|(-2.821)|(-2.602)|(-1.356)|
|**Implementation Effect**||||||
|**Intercept**<br>**11.68*****|**6.231**|<br>**22.84*****|**17.72*****|**9.580****|**23.74*****|
|(2.677)|(1.080)|<br>(4.268)|(2.745)|(2.151)|(4.009)|
|**Slope**<br>**-0.0968***|**-0.0649**|<br>**-**<br>**0.220*****|**-0.164****|**-0.101***|**-**<br>**0.309*****|
|(-1.864)|(-0.939)|(-3.527)|(-2.009)|(-1.893)|(-4.360)|
|Observations<br>966|966|<br>687|687|656|656|
|**Announcement Effect**<br>|**Panel B:**|**Two Weeks**|**Lagged C**|**rude Price**||
|**Intercept**<br>**28.96****|**26.06****|<br>**40.01*****|**34.12****|**14.11**|**6.435**|
|(2.483)|(2.362)|<br>(3.041)|(2.250)|(1.289)|(0.483)|
|**Slope**<br>**-0.309****|**-0.288****|<br>**-**<br>**0.431*****|**-0.363***|**-0.166**|**-0.0765**|
|(-2.148)|(-2.175)|<br>(-2.643)|(-1.935)|(-1.251)|(-0.470)|
|<br>**Implementation Effect**<br>||<br>||||
|**Intercept**<br>**12.77****|**4.604**|<br>**17.79*****|**14.15****|**19.52*****|**23.82*****|
|(2.550)|(0.720)|<br>(3.073)|(2.181)|(3.955)|(3.381)|
|**Slope**<br>**-0.0907**|**0.00826**|<br>**-0.146****|**-0.0639**|**-**<br>**0.174*****|**-**<br>**0.258*****|
|(-1.512)|(0.112)|(-2.111)|(-0.799)|(-2.985)|(-3.120)|
|Observations<br>956|956|<br>687|687|646|646|

NOTES: (1) ‘t’ statistics in parenthesis. *** denotes significant at the 1 percent, ** at the 5 percent, and * at the 10 percent level. (2) Slope denotes the slope of wholesale margin with respect to world crude oil price. (3) Regressions in even numbered columns include relative prices, a dummy for upswing in the world market, Ramadan dummy and fuel price (diesel).
Mexico. _Sustainable Cities and Society_ , 38, 795-805. <u>https://doi.org/https://doi.org/10.1016/j.scs.2018.01.031</u> Tarei, P. K., Chand, P., & Gupta, H. (2021). Barriers to the adoption of electric vehicles: Evidence from India. _Journal of Cleaner Production_ , 291, 125847. <u>https://doi.org/10.1016/j.jclepro.2021.125847</u> Teoh, L. E., Khoo, H. L., Goh, S. Y., & Chong, L. M. (2018). Scenario-based electric bus operation: A case study of Putrajaya, Malaysia. _International Journal of Transportation Science and Technology_ , 7(1), 10-25. <u>https://doi.org/10.1016/j.ijtst.2017.09.002</u> Thompson, S. T., James, B. D., Huya-Kouadio, J. M., Houchins, C., DeSantis, D. A., Ahluwalia, R., Wilson, A. R., Kleen, G., & Papageorgopoulos, D. (2018). Direct hydrogen fuel cell electric vehicle cost analysis: System and high-volume manufacturing description, validation, and outlook. _Journal of Power Sources_ , 399, 304-313. <u>https://doi.org/10.1016/j.jpowsour.2018.07.100</u> Thorne, R. J., Hovi, I. B., Figenbaum, E., Pinchasik, D. R., Amundsen, A. H., & Hagman, R. (2021). Facilitating adoption of electric buses through policy: Learnings from a trial in Norway. _Energy Policy_ , 155, 112310. https://doi.org/10.1016/j.enpol.2021.112310 Tiefenbeck, V., Staake, T., Roth, K., & Sachs, O. (2013). For better or for worse? Empirical evidence of moral licensing in a behavioral energy conservation campaign. _Energy Policy_ , 57, 160-171. https://doi.org/https://doi.org/10.1016/j.enpol.2013.01.021 Timilsina, G.R. (2020). _Demystifying the Costs of Electricity Generation Technologies_ . World Bank Policy Research Working Paper, WPS9303. Washington, DC: World Bank.

Timilsina, G.R. (2021). _Economics of Distributed Photovoltaics: An Illustration from Bangladesh_ . World Bank Policy Research Working Paper, WPS9699. Washington, DC: World Bank.

Tugcu, C. T., & Tiwari, A. K. (2016). Does renewable and/or non-renewable energy consumption matter for total factor productivity (TFP) growth? Evidence from the BRICS. _Renewable and Sustainable Energy Reviews_ , _65_ , 610-616. https://doi.org/10.1016/j.rser.2016.07.016 Ugur, M., Trushin, E., Solomon, E., & Guidi, F. (2016). R&D and productivity in OECD firms and industries: A hierarchical meta-regression analysis. _Research Policy_ , 45(10), 20692086. <u>https://doi.org/10.1016/j.respol.2016.08.001</u> 54
Table 5 Determinants of water pollution

||**TSS**|**COD**|
|---|---|---|
|INSP|_-0.0500***_<br>(0.100)|-0.0850**<br>(0.033)|
|LcINSP|0.0092<br>**(0.195)**|0.0176<br>(0.131)|
|Lcw|-0.2293<br>(0.340)|-0.0433<br>(0.780)|
|Le|0.0026<br>(0.419)|-0.1511<br>(0.236)|
|Lagged dependent variable|0.9964**<br>(0.036)|0.6800*<br>(0.000)|
|Emp|-0.0004<br>(0.211)|-0.0008**<br>(0.020)|
|State|0.3800<br>(0.415)|1.3950***<br>(0.056)|
|Coll|0.2137<br>(0.712)|0.7462**<br>(0.032)|
|Textile|7.4556<br>(0.158)|4.7110<br>(0.140)|
|Petrol|-4.0784<br>(0.150)|-0.7307**<br>(0.020)|
|Tobacco|-1.1711 ***<br>(0.062)|-54.0391<br>(0.155)|
|Beverage|-2.0485**<br>(0.038)|0.4573<br>(0.545)|
|Food|-0.8097<br>(0.112)|-0.4784<br>(0.264)|
|Paper|-0.9390<br>(0.252)|0.8722<br>(0.673)|
|Chemical|-0.6761<br>(0.351)|-1.4565*<br>(0.002)|
|Test Overid. Restr.|7.0918|1.9825|
||(0.131)|(0.852)|
|# of observations|649|736|

Note: GMM is the method of estimation. GMM instruments are provided in the text. Variables are defined as in the text. Test Overid. Rest. tests the validity of overidentifying restrictions. Numbers in brackets are p-values. Robust standard errors are used throughout. **('),** (") and ("') mean significant at the 1%, 5% and 10% level, respectively.

19
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the treatment and control group. When the difference is too large, the null hypothesis (that the samples are balanced with respect to the covariates when they are balanced with respect to the propensity score) is rejected.

We also control for selection using a control-function approach that closely resembles the Heckit procedure (Heckman 1979). The approach proceeds, again, in two steps. The first-step regression is as before, i.e. (2). In the second step, inverse Mills ratios retrieved from the first step are added to (1) as additional regressors.

Before we turn to the results, note that, besides selection bias, other issues may complicate the estimation of _γ_ in (1). One is serial correlation. Persistence in the process driving the error term may be aggravated by the extreme form of serial correlation in the treatment variable. Bertrand, Duflo and Mulainathan (2004) show that ignoring this source of serial correlation can lead to an inflated probability of type-I errors (wrongly rejecting the null hypothesis of no effect, i.e. being over-optimistic in the evaluation of the treatment’s impact). This calls for a correction that they suggest. The correction consists of a two-step procedure in which, in Step 1, individual performance for both treated and untreated individuals is regressed on all observables except the treatment. In Step two, residuals from Step 1 for the treated individuals only are retrieved and averaged for (i) the pretreatment period, (ii) the treatment period. The procedure requires a common treatment period, since otherwise the pre- and post-treatment periods would be undefined for the control group. Those average residuals form a two-period panel. They are then regressed on where _τ_ = 1 denotes the treatment period. The estimated ATT is the coefficient on _dikjτ_ in the second step.

# **4 Results**

## **4.1 Baseline Results**

Balancing properties are addressed by testing for equality of means between treated and matched controls for nearest-neighbour matching. Table 4.1 reports results from balancing tests. The table reports, for each covariate included in the probit model determining selection into treatment, the percentage bias after matching, the reduction in the bias, and the t-test statistics for the difference in means between treated and control groups after matching. Variables included in the 16