rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4511

# **Abstract**

There has been a proliferation of non-state providers of education services in the developing world. In Bangladesh, for instance, Bangladesh Rural Advancement Committee runs more than 40,000 non-formal schools that cater to school-drop outs from poor families or operate in villages where there’s little provision for formal schools. This paper presents a rationale for supporting these schools on the basis of their spillover effects on female enrollment in secondary (registered) madrasa schools (Islamic faith schools). Most madrasa high schools in Bangladesh are financed by the sate and include a modern curriculum alongside traditional religious subjects. Using an establishment-level dataset on student enrollment in secondary schools and madrasas, the authors demonstrate that the presence of madrasas is positively associated with secondary female enrollment growth. Such feminization of madrasas is therefore unique and merits careful analysis. The authors test the effects of the Bangladesh Rural Advancement Committee primary schools on growth in female enrollment in madrasas. The analysis deals with potential endoegeneity by using data on number of the number of school branches and female members in the sub-district. The findings show that madrasas that are located in regions with a greater number of Bangladesh Rural Advancement Committee schools have higher growth in female enrollment. This relationship is further strengthened by the finding that there is, however, no effect of these schools on female enrollment growth in secular schools.

This paper—a product of the South Asia Human Development Unit—is part of a larger effort in the unit to promote research and impact evaluations in the education sector of the South Asia Region. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at nchaudhury@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
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|||Page|
|---|---|---|
|7. CASE|STUDY REVIEW<br>.<br>..................-...|71|
|7.1.|INTRODUCTION:<br>TEN AREAS OF ACTION|71|
|7.2.|PHYSICAL PROTECTION.............<br>*......<br>.o..o.o.|72|
|7.3.|PUBLIC EDUCATION.<br>...<br>o........<br>o<br>...|73|
|7.4.|IMPACT MANAGEMENT<br>..<br>.......<br>......<br>.....|74|
|7.5.|COMPATIBLE RESOURCE UTILIZATION<br>.75||
|7.6.|<br>RECREATIONAL<br>DEVELOPMENT|76|
|7.7|.<br>ECONOMICS/FINANCES.<br>. ..<br>o...<br>..<br>.<br>.<br>...<br>. .|76|
|7.8.|ORGANIZATION..<br>......<br>o.<br>.<br>.<br>....o.<br>.<br>..|76|
|7.9.|AREA RESEARCHoooo....-.<br>oo...-..<br>...<br>-......|77|
|7.10.|TRAINING<br>o...o.ooo<br>.<br>oo|78|
|7o11.|MANAGEMENT AND PLANNING. ....|80|
|8. AFTE|PART THREE:<br>RWORD.. o<br>.......-........<br>.<br>......|81|
|8.1.|WORLDBANKSEMINAR.....<br>82.............<br>o..|82|
|8.2.|RESEARCH TOPICS.......................<br>oo|82|
||8.2.1. Conservation Area Protection<br>8.2.2. Indigenous Environmental<br>Research and|83|
||Management<br>........<br>.............<br>..|83|
||8.2.3. The Economics of Protected Areas........|84|
||8.2.4. Vernacular Economy.......o ............|85|
|LIST OF|REFERENCES.....................................|87|
stabilized earth block (CSEB), random rubble with GI wire containment, and bamboo and stone hybrid Structure (DUDBC, 2017). Non-fired bricks are an environmentally friendly option as they do not require the burning of coal or other fossil fuels. Among these, two non-fired bricks (CSEB and HCB) have the most potential for growth in the country (Fordham, 2021).

**Table 8: Cost of building one square meter wall with non-fired alternative bricks and clayfired brick and cost savings in 2018 (NRs)**

|||Cost for 1 m<sup>2</sup>|wall(NRs)|Cost s|avings(%)|
|---|---|---|---|---|---|
||CSEB|HCB|Brick|CSEB|HCB|
|Material (bricks)|1,600|640|2,100|24|70|
|Labor|188|188|270|31|31|
|Raw materials|276|276|890|69|69|
|Total|2,064|1,104|3,260|37|66|

_Notes_ : CSEB is from Build Better Nepal (2018). For HCB, the material cost is from (MinErgy, 2020) and the other costs are the same as those of CSEB.

To assess the cost of different building materials, we calculated the costs of building a 1 square meter wall, using alternative bricks (CSEB and HCB) and clay-fired bricks. A striking contrast between alternative bricks and conventional clay-fired brick is the overall cost of building construction (Table 8). We find that the associated total building cost savings are about 37% with CSEB and much higher about 66% with HCB when compared with clay-fired bricks. It is noteworthy that most of the cost savings in absolute value come from the associated costs with material (bricks). This is important because the costs of building materials are the major cost component of the overall building construction of houses in the country. Despite these cost-saving benefits, the current use of CSEB and HCB relative to clay-fired brick for building materials for houses remains limited. The combination of better aesthetic value and longer life of red clay-fired bricks over CSEB and HCB is limiting the use of these alternative brick materials. Other important factors for the low adoption of alternative bricks in the country include a lack of marketing initiatives and national standards, inadequate consumer knowledge and competent labor, the attitude of stakeholders toward alternative bricks, and low-profit margins (Mishra and Aithal, 2021; Poudel, 2023).

There is also a striking contrast between alternative bricks and conventional bricks in terms of the energy consumed during the production process and the associated carbon and PM2.5 26
# **Figure 18 Public investment management index (PIMI), SSA vs. Other Developing Areas**

<!-- Start of picture text --> Appraisal Score<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50 SSA<br>Evaluation Score 0.00 Selection Score LAC<br>EAP<br>Managing Score<br><!-- End of picture text --> _Source: Dabla-Norris, Brumby, Kyobe, Mills, and Papageorgiou (2012)_ Figure 19 depicts the performance of public investment management within the region by income level.<sup>28</sup> The highest average PIMI index is achieved by UMCs (2.07) as opposed to LMCs (1.33) and LICs (1.45). LICs in the region are outperformed by the two other country groups in the phases of project appraisal and evaluation whereas LMCs are outperformed in project managing (monitoring). Note that UMCs and LICs have comparable scores in project management.

# **E.2. Public procurement practices**

Delivering infrastructure may require that governments acquire goods, services or works from the private sector. This process of acquisition is called _public procurement_ and it involves a wide array of economic sectors. For instance, governments may rely on the private sector to supply goods and services to construct schools and hospitals, build a dam, or expand the road network (Djankov 2016). Governments in developing countries are important players in the markets of goods and services. South Asia has the highest share of public procurement in GDP (19.3 percent), followed by Sub-Saharan Africa (14.9 percent of GDP). For some countries in the region (e.g. Eritrea and Angola), a considerable amount of development assistance goes through public procurement. Hence, the shares of public procurement in Eritrea and Angola are 33 and 26 percent of GDP (World Bank 2017).

The efficiency of investment depends not only on the strength of institutions and government public management systems, but also on sound procurement practices and sound institutions governing public and PPP project cycles. Broadly speaking, the enhancement of public procurement practices (say, transparency, equal treatment, open competition and sound procedural management) may boost competition in the markets for government goods and services, and it may render benefits for consumers in the form of greater quality and

> 28 Classifying countries in the region by income renders: 21 LICs (Benin, Burkina Faso, Burundi, Chad, Rep. Congo, Ethiopia, Gambia, Ghana, Guinea, Kenya, Madagascar, Malawi, Mali, Mozambique, Rwanda, Senegal, Sierra Leone, Tanzania, Togo, Uganda, Zambia), 6 LMCs (Cote d’Ivoire, Lesotho, Nigeria, Sao Tome and Principe, Sudan, Swaziland) and 5 UMCs (Botswana, Gabon, Mauritania, Namibia, South Africa).

_Infrastructure in Sub-Saharan Africa - 35_
- 19 -

By regional comparison, the ECA countries in general have had relatively good quality of infrastructure services, similar to the East Asia and Pacific region. The simple average frequency of electricity outages in ECA was less than 10 days per year in 2004. However, this does not mean that all the countries in the ECA region would have overcome the quality problems in utility services. First, some enterprises in the region may continue suffering from long-lasting service interruptions. The average duration required for electricity service recovery is estimated at 5.3 hours in ECA.

Second, there are wide variations in infrastructure quality among ECA countries (Figure 13). Albania has the poorest quality of electricity infrastructure services; the number of days without sufficient electricity exceeded 200 days.<sup>17</sup> It is followed by Bulgaria, Romania, and Serbia and Montenegro in the SEE region.

Third, it is noticeable that not all enterprises in Albania are equally suffering infrastructure difficulties, and that there are also a number of firms operating under harsh infrastructure conditions in other countries. Some firms in Bulgaria experienced continuous power outages every day.<sup>18</sup> Several companies in Macedonia also claimed that electricity interruptions occurred more than 200 days a year, which is at the same frequency as in Albania. Importantly, the quality of utility services is also changing over time. When compared the 2002 and 2005 BEEPS results, most countries, except Albania, succeeded in improving the quality of utility services. For instance, Azerbaijan achieved the most spectacular improvement in this area for recent years; by investing a lot of public resources in power stations, the country succeeded in restoring a nearly 24-hour electricity supply.

> 17 One might consider Albania to be an outlier, possibly creating statistical noise in data, when pooling its observations. The following empirical analysis will account for this fact and show that the main estimation results are robust regardless of whether the country is included or not.

> 18 Note that in the BEEPS, the infrastructure quality data were collected in the way that the interview had been conducted to firm managers. Therefore, they are not subjective views but may reflect some approximations made by managers. However, all the indications are that in each country there is a significant variation from company to company in the level of infrastructure service quality they received.
Table 2: Value Added Content of Final Demand **G**<sup>_′_</sup> , Italy % share in 2007

||||||_s_|_upply sec_|_tors_|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||S1|S2|S3|S4|S5|S6|S7|S8|S9|S10|Total|
|_demand_<br>_sectors_|prim|proc<br>foods|text<br>cloth|chem<br>petro|autos|other<br>mach|other<br>manuf|util<br>consl|mkt<br>servs|pub<br>servs||
|S1 primary<br>production|0.80|0.03|0.00|0.02|0.00|0.01|0.03|0.03|0.10|0.01|1.02|
|S2 processed<br>foods|0.62|2.04|0.01|0.11|0.00|0.06|0.24|0.16|1.10|0.06|4.41|
|S3 textiles<br>clothing|0.11|0.04|1.88|0.10|0.00|0.05|0.16|0.12|0.87|0.04|3.39|
|S4 chemicals<br>petrochems|0.07|0.03|0.02|1.13|0.00|0.05|0.16|0.16|0.52|0.03|2.17|
|S5 autos|0.03|0.02|0.02|0.09|0.65|0.15|0.43|0.09|0.64|0.03|2.16|
|S6 other|0.12|0.08|0.04|0.27|0.02|5.05|1.39|0.34|2.69|0.13|10.13|
|machinery||||||||||||
|S7 other<br>manufacturing|0.17|0.10|0.06|0.22|0.01|0.15|4.28|0.36|1.85|0.10|7.29|
|S8 utilities<br>construction|0.16|0.07|0.03|0.29|0.01|0.28|1.28|7.13|2.59|0.19|12.02|
|S9 market<br>services|0.46|0.51|0.12|0.73|0.04|0.49|1.17|0.92|28.34|0.41|33.21|
|S10 public<br>services|0.10|0.11|0.04|0.23|0.01|0.28|0.42|0.42|3.05|19.54|24.20|
|Total|2.66|3.03|2.23|3.20|0.74|6.56|9.56|9.72|41.76|20.55|100.00|

_source: own calculations, using GTAP8 data._ motor vehicles and machinery account for 28.3 percent (last columns of rows S5 and S6). Like the results for total final demand in Table 2, in terms of exports, 25.5 percent of total value added costs for other machinery exports follow from inputs of market services (column S9 and column total for row S6). In terms of market access, there is a dual message here. Italy exports a substantial amount of services through the manufacturing sector. As such, the machinery sector has an interest in increased competitiveness and efficiency in the upstream services sectors. At the same time, the service sector clearly has an interest in improved market access for machinery, as this is an indirect channel for export. We return to these issues in Section 7.

Returning to Table 1, note that values are influenced by net export positions. For example, the United States runs a net trade surplus in services. As such, while the share of services in total exports on a value added basis is roughly 30 percent, most of this is because of direct exports rather than linkages to (and exports contained in) manufacturing. Thus, 23.9 percent of value added in exports is in services and follows from direct services exports, while just 5.2 percent is linked to indirect exports and downstream linkages. In contrast, for both Korea and Brazil, though commercial services are a relatively low share of exports on a gross value or direct value added basis, they are clearly a major factor in the total cost structure of industry. In both cases, services account for roughly one-quarter of the activity content of exports on a value-added basis.

9
# **<u>Table 2: Financial Integration and the Sensitivity of Consumption to Income</u>**

|**_All Countries_**|**Chinn-Ito**<br>**[1]**|**Quinn**<br>**[2]**|**Chinn-Ito**<br>**[3]**|**Quinn**<br>**[4]**|**Chinn-Ito**<br>**[5]**|**Quinn**<br>**[6]**|
|---|---|---|---|---|---|---|
|Output|0.899<br>[0.010]***|0.987<br>[0.028]***|0.869<br>[0.018]***|1.063<br>[0.025]***|0.862<br>[0.020]***|0.981<br>[0.028]***|
|Output × _De Jure_|**-0.136**<br>[0.010]***|**-0.285**<br>[0.045]***|**-0.113**<br>[0.026]***|**-0.424**<br>[0.035]***|**-0.087**<br>[0.028]***|**-0.261**<br>[0.044]***|
|World Interest Rate×<br>_De Jure_|||0.018<br>[0.001]***|0.015<br>[0.001]***|||
|Country Specific<br>Interest Rate×_De Jure_|||||0.014<br>[0.001]***|0.010<br>[0.002]***|
|Observations|3,555|4,526|3,467|3,826|3,467|3,826|
|Countries|88|85|88|85|88|85|
|**_Advanced Economi_**<br>Output|**_es_**<br>1.121<br>[0.074]***|1.045<br>[0.180]***|1.099<br>[0.075]***|0.969<br>[0.124]***|1.067<br>[0.065]***|0.939<br>[0.097]***|
|Output × _De Jure_|**-0.434**<br>[0.088]***|**-0.319**<br>[0.168]*|**-0.433**<br>[0.131]***|-0.284<br>[0.195]|**-0.383**<br>[0.124]***|**-0.252**<br>[0.136]*|
|World Interest Rate×<br>_De Jure_|||-0.009<br>[0.015]|0.024<br>[0.021]|||
|Country Specific<br>Interest Rate×_De Jure_|||||0.009<br>[0.022]|0.035<br>[0.021]*|
|Observations|892|1,337|870|1,053|870|1,053|
|Countries|22|22|22|22|22|22|
|**_Developing Econom_**|**_ies_**||||||
|Output|0.887<br>[0.018]***|0.945<br>[0.050]***|0.847<br>[0.037]***|1.004<br>[0.059]***|0.815<br>[0.040]***|0.886<br>[0.095]***|
|Output × _De Jure_|**-0.103**<br>[0.026]***|**-0.217**<br>[0.080]***|-0.086<br>[0.056]|**-0.303**<br>[0.102]***|-0.055<br>[0.061]|-0.100<br>[0.161]|
|World Interest Rate×_De_|||0.055|0.035|||
|_Jure_|||[0.004]***|[0.006]***|||
|Country Specific Interest<br>Rate×_De Jure_|||||0.024<br>[0.003]***|0.014<br>[0.004]***|
|Observations<br>Countries|2,663<br>66|3,189<br>63|2,597<br>66|2,773<br>63|2,597<br>66|2,773<br>63|

Note: Dependent variable is consumption growth. GMM Dynamic Panel Estimates. Lags 2-4 of consumption growth, all right hand side variables and lagged saving are used as instruments. De Jure refers to financial integration measures of Chinn-Ito and Quinn. In columns [3]-[6], interest rate is also included as a control. Sample period 1960-2011 or as dictated by availability of data.

19
**Table 4 Digital Health Interventions and Complexity**

|**TYPE**|**DESCRIPTION**|
|---|---|
|**Intervention**<br>**complexity**|Many DHIs are multi-attribute, with inter-dependent parts and delivery<br>mechanisms, and often adapt over time.|
|**Causal pathway**<br>**complexity**|The causal pathway linking observed outputs to outcome and impact can have<br>substantial complexity, compounded by range of identified outcomes and<br>complex attribution and feedback loops.|
|**Outcome complexity**|DHIs commonly have range of outcomes, including health and health system<br>effects and involve spillovers and externalities, impact on patients, users, and<br>the wider population.|

_Source: Petticrew et al 2013, McNamee 2016_

# STEP 4: Apply analytical principles

The fourth step of the framework is to apply economic evaluation principles as detailed in table 5. This builds on the extensive methods literature in economic evaluation [4][33]. It is beyond the scope of this framework document to replicate existing methodological guidance, but to identify the principles that should ideally be followed when making context and intervention-specific methods choices. An important consideration is that while there may be some variation to the particular approach of any economic evaluation, these principles are expected to apply for both digital and non-digital interventions (with the exception of predictive analytics that is expected to be unique to DHIs with a predictive analytics component).

**Table 5 Analytical Principles**

|**ITEM**|**PRINCIPLE**|
|---|---|
|**Comparator**|The comparator(s) against which costs and benefits are measured should accurately<br>reflect the decision problem|
|**Timeframe**|The time horizon of the analysis should be of sufficient length to capture all<br>significant differences between the intervention and comparator|
|**Costs**|All differences between the intervention and the comparator in expected resource<br>and costs of delivery to the target population(s) should be incorporated into the<br>evaluation|
|**Benefits**|All differences between the intervention and the comparator in expected benefits to<br>the target population(s), should be incorporated into the evaluation|
|**Predictive**<br>**analytics**|The dynamics of predictive analytics on evidence generation, costs and benefits<br>should be reflected in the analysis|
|**Uncertainty**|The uncertainty associated with the evaluation estimates should be appropriately<br>represented|
|ln<br>_it_<br>_ECS_|0.425<sup>***</sup>|0.103<sup>*</sup>|0.595<sup>***</sup>|0.563<sup>***</sup>|0.941<sup>***</sup>|
|---|---|---|---|---|---|
||(9.124)|(1.724)|(6.452)|(5.367)|(6.564)|
|ln<br>_it_<br>_EI_|0.476<sup>***</sup>|0.621<sup>***</sup>|0.566<sup>***</sup>|0.481<sup>***</sup>|0.169<sup>***</sup>|
||(7.541)|(7.067)|(4.850)|(4.895)|(8.787)|
|ln<br>_it_<br>_PCG_|0.341<sup>***</sup>|2.012<sup>***</sup>|0.687<sup>***</sup>|0.379<sup>***</sup>|0.158<sup>**</sup>|
||(5.735)|(9.042)|(4.366)|(4.612)|(2.393)|
|2<br>ln<br>_it_<br>_PCG_|0.014<sup>*</sup>|0.226<sup>***</sup>|-0.065|0.052|0.012|
||(1.733)|(6.861)|(-1.670)|(1.250)|(0.049)|
|ln<br>_it_<sup>_P_</sup>|0.344<sup>***</sup>|0.560<sup>***</sup>|0.804<sup>***</sup>|0.839<sup>***</sup>|0.789<sup>***</sup>|
||(8.454)|(9.200)|(6.429)|(5.479)|(9.364)|
|Constant|0.812<sup>***</sup>|2.026<sup>***</sup>|1.186<sup>***</sup>|0.648<sup>***</sup>|1.168<sup>**</sup>|
||(5.830)|(3.955)|(3.570)|(2.986)|(2.076)|
|Hansen test|0.121|0.390|0.493|0.603|0.115|
|AR(2) test|0.224|0.192|0.178|0.149|0.191|
|Turning point|-|-|-|-|-|

**_Note:_** ***, **, and * denote statistical significance at 1%, 5%, and 10%, respectively; the values in parentheses represent t-statistics; the values of Hansen and AR(2) tests represent p-values; and the unit of turning point is 2010 US$.

# **4.2.1 The 110-country panel**

According to the first column in Table 2, for the 110-country panel, the estimated coefficients of ln _EC_ , ln _ECS_ , ln _EI_ , ln _P C G_ , and l n _P_ between 1980 and 1990 are positive and significant at a 1% significance level. GDP per capita and population were the primary drivers leading to an increase in CO2 emissions between 1980 and 1990, while the factors that slowed the increase of the emissions over the same period were emission coefficient, energy mix, and energy intensity.

The first column in Table 3 indicates that the estimated coefficients of the variables between 2005 and 2015 are positive and significant. Furthermore, Figs. B1 & B2 in Appendix B suggest that emission coefficient, per capita GDP, and population were the primary driving forces of increasing CO2 emissions between 2005 and 2015, while the factors that slowed the increase of the emissions over the same period were energy mix 20
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