rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

Across the world, women are more likely than men to be children’s primary caregivers and there is increasing awareness that these childcare responsibilities limit women’s economic opportunities (World Bank, 2012; Delecourt and Fitzpatrick, 2021). At the same time, there is broad consensus that investing in early childhood development has the potential to reduce poverty and improve key socio-economic life outcomes (e.g,. Garces, Thomas and Currie, 2002; Gertler et al., 2014; Heckman and Karapakula, 2019; Devercelli and Beaton-Day, 2020). Experts also agree that children need a nurturing environment to thrive (World Health Organization, United Nations Children’s Fund and World Bank Group, 2018). Policymakers and researchers have often considered the objectives of supporting women’s employment and enhancing child development in isolation, instead of fully recognizing their interdependence. In principle, providing affordable childcare could address both of these issues by increasing women’s economic empowerment and stimulating early childhood development. Yet, there are potential trade-offs that emerge. Childcare is often delivered without an explicit focus on early childhood education (ECE), thus positive impacts on child development are not guaranteed. In contrast, ECE interventions are often prohibitively expensive or timeintensive for caregivers and may therefore impede women’s ability to pursue economic activities (Mateo Diaz and Rodriguez-Chamussy, 2016). Understanding how to simultaneously achieve these two objectives remains an understudied issue.

This paper analyzes a community-based model of integrating childcare centers into an urban public works program designed to support youth employment in Burkina Faso. We evaluate the impacts of this accessible childcare provision on women’s employment and other empowerment outcomes as well as on child development. We use a randomized controlled trial to estimate the causal effects of childcare, analyzing data from a sample of 1,990 women participating in the sixmonth long public works program and their eligible children. The program’s implementation team identified 36 urban public works sites with the potential to host a childcare center. We then randomly selected 18 sites to receive the community-based childcare centers and the remaining 18 sites had no additional childcare provision. With this sample size, the study is powered to detect reasonable effect sizes (Section 3 provides detailed discussion). We conducted a baseline survey in the early stages of childcare implementation and a follow up survey 14 months later. The 2
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# **TABLE 15** (continued)

||1989|1990|1991|1992|1993|1994|1995|1996|1997|1998|1999|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Asia**|2,097|2,616|**3,584**|**5,798**|6,419|9,645|7,775|9,061|11,526|13,123|N.A.|
|Korea|247|265|434|617|662|734|974|1,178|1,831|1,951|2,964|
|Taiwan|195|312|429|543|658|1,029|716|891|1,137|1,527|1,557|
|Hong Kong|184|229|309|403|62|250|159|129|189|216|253|
|Israel|10|17|24|43|45|85|47|79|112|137|173|
|Japan|1,309|1,470|1,596|3,041|3,369|4,780|3,952|4,132|4,334|4,537|5,083|
|Singapore|49|46|86|104|158|213|289|383|426|493|540|
|China|0|30|142|425|353|428|521|760|1,247|1,617|1,921|
|Other|104|247|564|622|1,112|2,126|1,117|1,509|2,250|2,645|N.A.|
|Africa|**69**|97|80|98|131|149|129|221|271|368|N.A.|
|**Oceania**|118|256|151|258|268|317|178|261|318|401|N.A.|
|Australia|38|65|80|105|113|167|99|128|166|244|N.A.|
|Other|81|191|72|153|155|150|79|133|151|156|N.A.|
|**Rest**of the World|42|13|1|2|14|103|52|109|191|267|N.A.|

SOURCE: State of the Nation Report from 1989-98, Secofi for 1999, both with Banxico data.

*Exports includes transportation and insurance expenses.

**Figures may not add up due to rounding off.

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Policy Research Working Paper 5048

# **Abstract**

In recent years, the number of surveys of access to and use of financial services has multiplied, but little is known about whether the data generated are comparable across countries, or within the same country over time. This paper reports results from a randomized experiment in Ghana to test whether the identity of the respondent and the inclusion of product-specific cues in questions affect the reported rates of household usage of financial services. The analysis shows that rates of household usage are almost identical when the head reports on behalf of the household and when the rate is tabulated from a full enumeration of household use. Randomly selected informants (i.e., non-heads of the household) provide a less complete summary of household use of financial services than the other two methods. The findings also show that for credit from formal institutions, informal sources of savings, and insurance, usage rates are higher when questions are asked about specific financial products rather than about the respondent’s dealings with types of financial institutions. In short, who is asked the questions and the form in which they are asked both matter.

This paper—a product of the Finance and Private Sector Team, and the Poverty and Inequality Team, Development Research Group—is part of a larger effort in the department to understand household access to financial services and improving survey methods. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at rcull@worldbank.org and kscott1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **TABLE A27:  The World Business Environment Survey (WBS, WDR)**

## **A27. The World Business Environment Survey (WBS)**

http://www.ifc.org/ifcext/economics.nsf/Content/IC-WBESConditions The World Business Environment Survey (WBS) is a survey conducted by the World Bank in collaboration with several other institutions. It is designed to provide information on the business environment facing private enterprises. It was conducted during 1999 and 2000 in 81 countries. The respondents were managers of firms in at least 100 firms per country. This survey asks several questions similar to those in the 1997 World Development Report survey that we use in constructing the 1998 version of the indicators. We therefore treat the WBS as the continuation of this source.

The component of the WBS covering transition economies is referred to as the Business Environment and Enterprise Performance Survey (BPS), described in Table A2. The questionnaire for this region contains more detailed questions about corruption issues, including questions on "state capture" referring to the manipulation of the institutions of the state for private gain on a grand scale. In addition, new rounds of BPS were conducted in 2002 and 2005. For these reasons, we treat the BPS as a separate source.

In the table below we list the variables included in each of the governance indicators. We use data from the WBES survey in 2000 and the WDR Survey in 1998.

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drink water and hence target schools as well households in reducing exposure. However, if the adverse effect of exposure at home is larger, households may be targeted instead of schools. Future studies therefore should aim at collecting additional data on past exposure at all levels of schooling. Furthermore these studies should also involve collection of urine/blood samples from pupils to measure actual levels of arsenic exposure. Ideally these studies should involve randomized evaluations of arsenic decontamination of tube wells (or alternate provision of safe water) and impact on learning outcomes.

15
27 return by 8% or 1.3 percentage points; it also cuts the estimated reduction in stunting from 36% to 28%. The cognition impacts of the program may also be overestimated for the same reasons. Halving the assumed cognition effects of the program reduces the rate-of-return by 9% or 1.6 percentage points. We also explore the effects of changes in the assumed effects of stunting on years of education and adult height. Halving the assumed effects of stunting on years of education and adult height reduces the overall effect on adult income of being stunted as a child from 28% to 14%, and cuts the benefit-cost ratio by 20% and the rate-of-return by 8% or 1.3 percentage points. If we make all these changes simultaneously, we end up with an almost 50% reduction in the rate-of-return, equivalent to a reduction of almost 8 percentage points. Finally, reducing the scale-up period from 10 years to one reduces the rate-of-return by 20% or 3.5 percentage points; a 10-year scale-up is considerably more realistic.

<u>Table 5: Sensitivity of results to assumptions</u>

||Stunting reduction<br>2025 vs. 2010|Benefit-cost ratio|Rate of return|
|---|---|---|---|
|Base estimates|-36.2%|15:1|17.2%|
|Doubling of program cost|-36.2%|8:1|13.7%|
|Halving of program effect on stunting|-28.2%|12:1|15.8%|
|Halving of program effect on cognition|-36.2%|11:1|15.6%|
|Halving of stunting effects on education & height|-36.2%|12:1|15.9%|
|All the above|-28.2%|3:1|9.5%|
|One-year scale-up|-36.2%|7:1|13.7%|

# _3.6 Comparisons with other studies of returns to nutrition investments_

Other authors have also reported estimates of the returns to childhood nutrition programs, including the Bhutta et al. program. The studies by Hoddinott et al. (2013) and Hoddinott (2016) are closest to our study. Like us, they estimate the costs and benefits (in terms of higher incomes) of taking the coverage rate of each of the interventions in the Bhutta et al. package from the current rate
# **Table A24:  International Budget Project Open Budget Index (OBI)**

**Data Provider** International Budget Project **Description** Nongovernmental organization based in Washington, United States, devoted to developing civil society capacity to influence government budget processes. **Website** www.internationalbudget.org **Data Source** Open Budget Index **Type** Expert assessment **Respondents** Local experts recruited by the International Budget Project subject to anonymous peer review **Frequency** First release in 2006, intended annual frequency **Coverage** Global country sample **Public Access** Yes **Description** The Open Budget Index is based on a questionnaire with 122 multiple choice questions on various dimensions of the availability, timeliness and quality of central government budget documents.  Note that the 2006 index refers to data reflecting conditions in 2005.

||2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|
|**Voice and Accountability**|||||||||
|Open Budget Initiative|X|X|..|..|..|..|..|..|
|**Political Stability**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Regulatory Quality**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Control of Corruption**<br>NA|..|..|..|..|..|..|..|..|
|**Country Coverage**|**59**|**59**|**..**|**..**|**..**|**..**|**..**|**..**|
|**Year of publication**|**2006**|**2006**|**..**|**..**|**..**|**..**|**..**|**..**|

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**Table 2: Annual Infections, Diarrheal Cases, DALYs, and Deaths Attributable to three bacteria* assuming consumption of fecally-contaminated tap water from an IWS**

|**Region**|**Population**<br>**served by IWS**<br>**(millions)****|**Annual**<br>**infections**<br>**(millions)******|**Annual**<br>**diarrheal cases**<br>**(millions)******|**Annual**<br>**Deaths******|**Annual DALYs**<br>**(thousands)******|
|---|---|---|---|---|---|
|Africa|116|2.16<br>_(0.973-4.06)_|0.5666<br>_(0.256-1.05)_|196<br>_(88-395)_|13.5<br>_(6.12-28.0)_|
|Americas,<br>LMI***|47|0.874<br>_(0.384-1.64)_|0.229<br>_(0.104-0.424)_|79<br>(_36-160)_|5.55<br>(_2.48-11.3)_|
|Eastern<br>Mediterranean,<br>LMI|103|1.91<br>(_0.864-3.60)_|0.503<br>(_0.227-0.930)_|174<br>_(78-351)_|12.2<br>_(5.43-24.8)_|
|Europe, LMI|71|1.32<br>_(0.596-2.48)_|0.346<br>_(0,157-0.641)_|120<br>_(54-242)_|8.38<br>_(3.75-17.1)_|
|South East Asia|409|7.60<br>_(3.43-14.3)_|2.00<br>_(0.902-3.69)_|691<br>_(309-1,390)_|48.3<br>_(21.6-98.6)_|
|Western<br>Pacific,LMI|179|3.33<br>_(1.5-6.26)_|0.874<br>_(0.395-1.62)_|302<br>(_135-609)_|21.1<br>_(9.44-43.2)_|
|Global|925|17.2<br>_(7.76-32.3)_|4.52<br>_(2.04-8.36)_|1,560<br>(_699-3,150)_|109<br>_(48.8-223)_|

_Source: Bivins et al. (2017). *Campylobacter, Cryptosporidium, and Rotavirus ** Dataset assembled by projecting prevalence of IWS found in IBNET onto JMP measures of access to piped-on-premise water supplies. *** LMI = Low-middle income. **** 95% confidence intervals within parenthesis._

**Figure 6: Impact of Intermittent Water Supply on health.**

<!-- Start of picture text --> 35<br>Annual infections (millions)<br>30 Annual diarrheal cases (millions)<br>25<br>20<br>15<br>10<br>5<br>0<br>Source : Bivins et al. (2017)<br>Africa nean Europe Pacific Global<br>Americas Eastern Mediterra South & East Asia Western<br><!-- End of picture text --> 13
**<u>Table 9: Estimates of program effect on agricultural productivity using implementation data</u>**

||**All s**|**tates**||**8 st**|**ates with imp**|**lementation**|**data**||
|---|---|---|---|---|---|---|---|---|
||**MNREG**|**S dummy**|**Labor **|**days spent on **|**all MNREG**|**S works**|**MNREGs **|**water only**|
|MNREGS|0.021||0.071***||0.103**||0.127|0.242*|
|* post|(0.043)||(0.025)||(0.044)||(0.179)|(0.142)|
|<br>MNREGS *||0.121*||0.071***||0.104**|||
|post* small||(0.071)||(0.024)||(0.042)|||
|<br>MNREGS *||-0.017||0.066**||0.088*|||
|post* medium||(0.070)||(0.026)||(0.051)|||
|<br>MNREGS *||<br>-0.025||<br>0.079***||<br>0.124**|||
|post* large||(0.078)||(0.026)||(0.051)|||
|<br>Log of land|0.602***|<br>0.566***|0.606***|<br>0.585***|0.631***|<br>0.621***|0.610***|0.663***|
||(0.043)|(0.047)|(0.061)|(0.063)|(0.056)|(0.057)|(0.061)|(0.061)|
|Seed|<br>0.110***|<br>0.114***|<br>0.075***|<br>0.076***|<br>0.026|<br>0.026|<br>0.070***|<br>0.019|
||(0.020)|(0.020)|(0.021)|(0.021)|(0.022)|(0.022)|(0.022)|(0.022)|
|Fertilizer|<br>0.159***|<br>0.151***|<br>0.211***|<br>0.206***|<br>0.311***|<br>0.309***|<br>0.216***|<br>0.291***|
||(0.026)|(0.026)|(0.043)|(0.044)|(0.059)|(0.059)|(0.043)|(0.064)|
|Bullock|<br>0.021***|<br>0.020***|<br>0.033***|<br>0.032***|<br>0.007|<br>0.006|<br>0.035***|<br>0.011|
||(0.007)|(0.007)|(0.010)|(0.011)|(0.009)|(0.009)|(0.010)|(0.009)|
|Machinery|<br>0.020***|<br>0.018***|<br>0.011|<br>0.010|<br>0.023**|<br>0.022**|<br>0.009|<br>0.019*|
||(0.006)|(0.006)|(0.010)|(0.010)|(0.011)|(0.010)|(0.010)|(0.011)|
|Labor|<br>0.017**|<br>0.016**|<br>0.016*|<br>0.015|<br>0.033***|<br>0.032***|<br>0.014|<br>0.032***|
||(0.007)|(0.007)|(0.009)|(0.009)|(0.009)|(0.009)|(0.010)|(0.010)|
|Other|<br>0.009|<br>0.011*|<br>0.022**|<br>0.023**|<br>-0.007|<br>-0.008|<br>0.022**|<br>-0.001|
||(0.006)|(0.006)|(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|
|Observations|<br>6,468|<br>6,468|<br>2,484|<br>2,484|<br>1,165|<br>1,165|<br>2,484|<br>1,165|
|R-squared|0.937|0.938|0.943|0.943|0.856|0.857|0.944|0.852|
|<br>Fixed effect|HH|HH|HH|HH|State|State|HH|State|
|Year FE|Yes|Yes|Yes|Yes|No|No|Yes|No|

**Note:** Standard errors are clustered at household or state level. *** p<0.01, ** p<0.05, * p<0.1. The eight states for which employment data are available are Andhra Pradesh, Bihar, Chattisgarh, Haryana, Madhya Pradesh, Maharashtra, Rajasthan, and Tamil Nadu. As explained in the text, the relevant MNREGS variable in cols 1 and 2 is a MNREGS dummy while in cols. 3-6, it is the number of days of work provided under MNREGS in the village since the program was put in place. Outcome variable is log of agricultural output in all colomns.

23
- 13 -

29. To examine which variable has the largest influence on freight costs, all the elasticities are evaluated at sample means ( **Table 3** ). The largest elasticity (in absolute terms) is found for the weight variable. This is robust regardless of specification. The elasticity is estimated at 0.59 to 0.77. This confirms the particular importance of load factors to determine road freight costs. The elasticity associated with driving speed, a proxy for road conditions, is also high at 0.29 to 0.46. It means that holding everything else constant, a 10 percent increase in speed would reduce freight costs by 3 to 5 percent. Then, the elasticity of distance follows, which is relatively small at 0.04 to 0.06.

**<u>Table 2. OLS estimation results: Pooled model</u>**

||Linear<br>||Quadratic<br>|<br>|Logarithm<br> <br>|ic<br>|
|---|---|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|Coef.<br>|Std.Err.|
|_speed_|-0.042|<br>(0.002) ***|-0.338|<br>(0.011) ***|||
|_speed_<sup>2</sup>|||0.0031|(0.0001) ***|||
|ln_speed_|||||-0.395|(0.015) ***|
|_dist_|1.6E-04|(1.3E-04)|-2.3E-03|(3.6E-04) ***|||
|_dist_<sup>2</sup>|||3.4E-06|(6.6E-07) ***|||
|ln_dist_|||||-0.064|(0.005) ***|
|_weight_|-0.194|<br>(0.005) ***|-1.040|<br>(0.031) ***|||
|_weight_<sup>2</sup>|||0.025|<br>(0.001) ***|||
|ln_weight_|||||-0.664|(0.015) ***|
|_elev_|2.1E-03|(7.9E-05) ***|9.0E-04|(1.7E-04) ***|||
|_elev_<sup>2</sup>|||8.3E-07|(3.2E-07) ***|||
|ln_elev_|||||0.049|(0.001) ***|
|_D_elev_|||||-0.076|(0.078)|
|_crossborder_|0.152|<br>(0.025) ***|0.170|<br>(0.025) ***|0.024|(0.004) ***|
|_cloth_|-1.168|<br>(0.269) ***|3.496|<br>(0.340) ***|-0.029|(0.064)|
|_equipment_|-0.241|<br>(0.269)|4.153|<br>(0.339) ***|0.141|(0.064) **|
|_food_|-1.116|<br>(0.270) ***|3.522|<br>(0.341) ***|0.005|(0.064)|
|_mineral_|-0.154|<br>(0.273)|4.333|<br>(0.342) ***|0.107|(0.065) *|
|_goods_|-0.426|<br>(0.269)|3.973|<br>(0.339) ***|0.019|(0.064)|
|constant|9.533|<br>(0.270)***|19.185|<br>(0.393)***|4.650|(0.086)***|
|Obs.|51,137||51,137||51,137||
|R-squared|0.2526||0.3293||0.3337||
|F stat.|129.66||163.15||160.26||
|No. of time dum|my variables||||||
||131||131||131||

Note: The dependent variable is freight rate, _p_ . for the first two models, and the log of freight rate, ln _p_ , for the last column model. Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.
Figure 2. Percentage of Working People Contributing to Social Security per Educational Level.

<!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Did not attend Incomplete Primary Studies<br>Complete Primary Studies Incomplete Secondary Studies<br>Complete Secondary Studies Incomplete University Studies<br>Complete University Studies<br><!-- End of picture text --> Source: author elaboration based on ENEMDU Figure 3 shows the percentage of working people contributing to social security per geographical area (whether urban or rural). Results show a wide difference in scope in the pension contributory system per area. While in year 2016, the percentage of contributing working people in urban areas achieved 36%, in rural areas it only climbed up to 17%. These differences were stable throughout the whole study term.

Figure 3. Percentage of Working People Contributing to Social Security per Urban/Rural Area.

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Rural Urban<br><!-- End of picture text --> Source: author elaboration based on ENEMDU 10