rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: Conflict Predictions Based on Alternative Classification Algorithms

||**Classification**|**s Based on 2012-20**|**14 data (1=**|**Conflict, 0 oth**|**erwise)**|
|---|---|---|---|---|---|
||**Probit**|**Random Forest**|**Linear**|**Threshold**|**FCS List**|
|Angola|1|1|1|1|..|
|Albania|0|0|0|0|..|
|Argentina|0|0|0|0|..|
|Armenia|0|0|1|0|..|
|Burundi|1|1|1|1|Y|
|Benin|0|0|1|1|N|
|Burkina Faso|1|0|1|1|N|
|Bangladesh|1|1|1|1|N|
|Bulgaria|0|0|0|0|..|
|Bosnia and Herzegovina|0|0|0|0|..|
|Belarus|0|0|0|1|..|
|Belize|0|0|0|0|..|
|Bolivia|0|0|0|0|N|
|Brazil|0|0|1|0|..|
|Botswana|0|0|0|0|..|
|Chile|0|0|0|0|..|
|China|1|1|1|1|..|
|Cameroon|1|0|1|1|N|
|Congo, Rep.|1|1|1|1|N|
|Comoros|0|1|0|1|Y|
|Cape Verde|0|0|0|0|N|
|Costa Rica|0|0|0|0|..|
|Djibouti|1|1|1|1|Y|
|Dominican Republic|0|0|0|0|..|
|Ecuador|1|0|1|1|..|
|Fiji|0|0|0|0|..|
|Gabon|0|0|0|0|..|
|Georgia|0|0|1|1|..|
|Ghana|0|0|1|0|N|
|Guinea|1|0|1|1|N|
|Gambia, The|1|0|1|1|Y|
|Guinea-Bissau|1|1|1|1|Y|
|Equatorial Guinea|1|1|1|1|..|
|Guatemala|0|0|0|0|..|
|Honduras|0|0|0|0|N|
|Croatia|0|0|0|0|..|
|Indonesia|0|1|1|0|..|
|Jamaica|0|0|0|0|..|
|Jordan|1|0|1|1|..|
|Kazakhstan|0|1|0|1|..|
|Kenya|1|0|1|1|N|
|Kyrgyz Republic|1|0|1|0|N|
|Cambodia|1|0|1|1|N|
|Korea, Rep.|0|0|0|0|..|
|Lao PDR|0|0|1|1|N|
|St. Lucia|0|0|0|0|N|
|Sri Lanka|0|1|1|0|N|
|Note: Table continues on next page<br>Lesotho|0|0|0|0|N|

41
skills) can hamper or enhance their ability to disseminate information to an audience that is of a lesser status, lesser wealth and lesser skills. The analysis utilizes data describing changes in knowledge regarding integrated pest management (IPM) among farmers in three provinces of Java, Indonesia, to demonstrate that leadership properties actually help in the diffusion of knowledge, but as the social and wealth disparity between leaders and would-be followers increases, the ability to disseminate knowledge diminishes at the margin, and may in fact decline.

The program of paper is as follows: The next section reviews the relevant literature and derives the conceptual framework underpinning the empirical work in the paper. This is followed by descriptions of the promotion of IPM in Indonesia and the data. A subsequent section outlines the analytical methodology followed by the empirical analysis. The last section provides conclusions and implications.

# **2. LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK**

The seminal work of Rogers (1995) provides a coherent theory, as well as empirical evidence, of many aspects of innovations diffusion. One of the themes elaborated upon is the role and characteristics of “opinion leaders.” Such individuals have the status, expertise, links to external sources of knowledge, or experience that enable them to provide information and advice about innovations to others within their community. Opinion leadership is thus reflected in the ability to influence others’ attitudes and knowledge (Chatman, 1987, Valente and Davis, 1999). Such leadership may be informal rather than formal, but many scholars observed that opinion leaders tend to have higher social status than “followers” (Bandura, 1986, p.151). Researchers also noted that opinion leaders are often more exposed to external sources of information, such as mass media or change agents (e.g., extension workers), have higher formal education, higher levels of literacy, a more cosmopolitan orientation, and higher income and wealth (Rogers, 1995, p. 92; Chatman, 1987; Valente, 1996; Weimann, 1994, p. 217).

4
**Table 12. Continued.**

||Europ|ean Union|J|apan|Unit|ed States|
|---|---|---|---|---|---|---|
||All Products|All Non-Oil Goods|All Products|All Non-Oil Goods|All Products|All Non-Oil Goods|
|NTB Covera2e Ratio (%)|3.4|10.5|0.2|2.0|6.2|11.0<br>|
|Bahrain|0.3|0.3|0.0|0.0|0.0|0.0|
|Cyprus|16.2|16.5|6.4|6.4|0.7|0.7<br>|
|Egypt|13.2|31.1|0.0|0.0|17.6|36.2|
|Iran|0.2|1.2|0.0|0.0|0.0|0.0|
|Israel|5.9|6.0|1.0|1.0|0.4|0.6|
|Jordan|1.2|1.2|14.9|14.9|1.2|1.2|
|Lebanon|1.5|1.5|0.0|0.0|0.2|0.2|
|Libya|0.0|0.0|0.0|0.0|||
|Oman|2.8|7.6|0.1|11.9|0.0|0.0|
|Qatar|0.2|1.0|1.4|76.5|0.0|0.0|
|Saudi Arabia|0.9|7.2|0.0|0.0|5.8|39.0|
|Syria|0.0|0.2|0.0|0.0|0.0|0.0|
|Turkey|13.5|14.2|6.7|7.3|24.4|32.4<br>|
|UAE|0 1|0.6|0.0|0.0|10.4|47.2|

Note: For the EU, Japan and the United States combined the average trade<sup>weighted tariff on all exports is 1.4 percent while the tariff on all non-oil goods is 2 percent. The</sup> average NTM trade coverage ratio for all goods is 2.9 percent while the<sup>ratio for all non-oil goods is 9.3 percent.</sup> Source: World Bank-UNCTAD SMART Database.
# **4.3 How long does the effect last?**

Thus far we have analyzed the spillover impacts over short horizons of one to three days. While in principle we could extend the analysis to beyond three days, but the possibility of omitted variable bias, or that of an endogenous policy response by emerging economies, monetary or otherwise, increases as we lengthen the time period analyzed. Nevertheless, below we ask whether the spillover impact persists over periods beyond the first three days. We estimate regressions similar to those in Equation 1 for 5, 10 and 15 day periods. Results in Table 7 show that the impact of surprise US policy announcements on emerging economies’ exchange rate, equity price and bond yields persists for periods as long as 15 days.

**Table 7: Spillover Estimates over Longer Windows of 5, 10, and 15 days**

||<br>k=5|Exchange rate<br>k=10|s<br>k=15|k=5|Equity prices<br>k=10|k=15|k=5|Bond yields<br>k=10|k=15|
|---|---|---|---|---|---|---|---|---|---|
|2yr Treasury yield|0.043***|0.065***|0.066***|-0.083***|-0.116***|-0.160***|0.098***|0.114***|0.105***|
||[5.77]|[7.14]|[8.11]|[-7.85]|[-7.97]|[-8.84]|[5.83]|[5.99]|[4.43]|
|Constant|-0.006|0.300***|0.304***|0.646***|0.579***|0.800***|-0.178***|-0.328***|-0.625***|
||[-0.58]|[25.04]|[28.49]|[47.70]|[31.66]|[36.41]|[-8.44]|[-13.68]|[-21.14]|
|Observations|1,300|1,300|1,300|1,202|1,193|1,192|1,058|1,051|1,052|
|R-squared|0.041|0.056|0.046|0.035|0.052|0.062|0.039|0.030|0.017|
|# of countries|20|20|20|19|19|19|17|17|17|

_Notes:_ Dependent variables are percent change in exchange rate, equity prices, or bond yields, between the day of FOMC announcements and k days after that, as per Equation 1, where k=5, 10, 15. The independent variable is percent change in 2-year Treasury yield between t and t-1, t being the day of the FOMC announcements. Regressions include country fixed effects. Robust t statistics are in parentheses. *, **, *** indicate that coefficients are significant at 10, 5, and 1 percent levels.

# **5. Robustness Analysis**

We conduct three kinds of robustness tests below. First, we ask if the spillover effects are stronger during, or perhaps even limited, to a few specific events, such as the FOMC announcements in 2008 when the global financial crisis had just erupted; or to the tapering event, which triggered a sharp sell-off in emerging economies. Second, we ask if the estimates are sensitive to including any potential omitted variables such as the VIX or oil prices that may affect Treasury yields as well as emerging economies’ asset prices. Third, we carry out additional tests to ensure that our results are not dependent on the identification strategy that we have chosen to deploy.

20
**Table B.1:** Shares of regions and years

|**Variable**|**% of Total**|
|---|---|
|**Region**||
|North Coast|31.88|
|Central Coast|14.24|
|Lisbon|44.28|
|Inland|7.18|
|Algarve|2.42|
|**Year**||
|1986|5.06|
|1987|5.5|
|1988|5.47|
|1989|5.19|
|1991|6.42|
|1992|6.72|
|1993|6.74|
|1994|6.86|
|1995|7.76|
|1996|8.21|
|1997|8.8|
|1998|8.74|
|1999|9.3|
|2000|9.23|
|Observations|15,613,149|

# **APPENDIX C: Regression results on the effect of international competition on skill acquisition and industry relocation**

The estimated results of the two-equation system (3) presented in Section 4.3 are marginal effects. Appendix Table C.1 displays the regression results instead.

23
**Annex 7. Sharing of Revenue between Pertamina and Government of Indonesia**

<!-- Start of picture text --> Pertamina Net Revenue<br>100%<br>60% Non Tax Oil and Gas 50% is paid to the state account as part<br>Government (PP 41/1982) of government profit (PP 18/1978)<br>20% Government (as<br>Dividend)<br>80% Government 20% Pertamina<br>Annex 8. Domestic fuel consumption (BBM)<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Auto Diesel Gasoline (Premium) Kerosene<br>Ind.Diesel Fuel Oil Avgas<br>avtur<br><!-- End of picture text -->

# **Annex 8. Domestic fuel consumption (BBM)**

**Annex 9. Consumption of fuel derivatives**

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>-<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Kerosene Gasoline Diesel<br>million liters<br><!-- End of picture text --> 32
- 25 -

# **Table 7:  Fixed Effects Estimation, with Robust Standard Errors**

**Dependent Variable:  AR(EARLY) Sample:  1993Q1 – 2003Q3; Cross-Sections Included:  24 Total Panel Observations (Unbalanced):  725** **<u>R</u>**<sup>**2**</sup> **= 0.7995; Adj. R**<sup>**2**</sup> **= 0.7890; F-Statistic = 76.2162 ; P-Value(F-Statistic) = 0.0000**

|**Variable**|**Coefficient**|**Std. Error**|**t-Statistic**|**p-value**|
|---|---|---|---|---|
|C|3.0760|0.2187|14.0668|0.0000<sup>***</sup>|
|RF|0.3639|0.0209|17.4077|0.0000<sup>***</sup>|
|SOFI|0.0029|0.0009|3.1772|0.0016<sup>***</sup>|
|SOA|0.0086|0.0038|2.2509|0.0247<sup>**</sup>|
|LEV|0.0110|0.0039|2.7967|0.0053<sup>***</sup>|
|MSHARE|-3.4900|0.5727|-6.0937|0.0000<sup>***</sup>|
|AP(EARLY)|0.0002|2.57 *<br>5<br>−<br>_e_|6.8931|0.0000<sup>***</sup>|
|CR|-0.0441|0.0180|-2.4431|0.0148<sup>**</sup>|
|HER(EARLY)|-5.1390|0.7219|-7.1191|0.0000<sup>***</sup>|
|TREND|-0.0005|0.0020|-0.2244|0.8225|
|D|-1.2547|0.2437|-5.1479|0.0000<sup>***</sup>|
|RF*D|0.4118|0.0457|9.0149|0.0000<sup>***</sup>|
|AP(EARLY)*D|-0.0001|3.95 *<br>5<br>−<br>_e_|-3.0191|0.0026<sup>***</sup>|
|CR*D|-0.0808|0.0351|-2.3035|0.0215<sup>**</sup>|

*** = significant at the 1% level;** = significant at the 5% level;* = significant at the 10% level

**Table 8:  Fixed Effects Estimation**

**Dependent Variable:  AR(OLD) Sample:  1993Q1 – 2003Q3; Cross-Sections Included: 24; Total Panel Observations (Unbalanced):  675** **<u>R</u>**<sup>**2**</sup> **= 0.7909; Adj. R**<sup>**2**</sup> **= 0.7787; F-Statistic = 65.1110; P-Value (F-Statistic) = 0.0000**

|**Variable**|**Coefficient**|**Std. Error**|**t-Statistic**|**p-value**|
|---|---|---|---|---|
|Constant|2.7371|0.1741|15.7198|0.0000<sup>***</sup>|
|RF|0.3961|0.0182|21.8170|0.0000<sup>***</sup>|
|SOFI|0.0030|0.0011|2.7053|0.0070<sup>***</sup>|
|SOA|0.0070|0.0035|1.9727|0.0490<sup>**</sup>|
|LEV|0.0121|0.0051|2.3558|0.0188<sup>**</sup>|
|MIS|0.0247|0.0589|0.4187|0.6756|
|MSHARE|-2.0611|0.8026|-2.5680|0.0105<sup>**</sup>|
|AP(OLD)|0.0001|1.87 *<br>5<br>−<br>_e_|7.8900|0.0000<sup>***</sup>|
|CR|-0.0280|0.0087|-3.2029|0.0014<sup>***</sup>|
|HER(OLD)|-3.4204|0.6212|-5.5066|0.0000<sup>***</sup>|
|TREND|0.0055|0.0018|3.1318|0.0018<sup>***</sup>|
|D|-1.8350|0.2148|-8.5421|0.0000<sup>***</sup>|
|RF*D|0.5536|0.0406|13.6334|0.0000<sup>***</sup>|
|AP(OLD)*D|-0.0001|3.80 *<br>5<br>−<br>_e_|-3.4935|0.0005<sup>***</sup>|
|CR*D|-0.1340|0.0310|-4.3171|0.0000<sup>***</sup>|

*** = significant at the 1% level;** = significant at the 5% level;* = significant at the 10% level
||level. There are 7 such industries in our<br>baseline sample (used in Table 4).<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|---|---|
|Year fixed effects|A set of dummy variables (one for each year)<br>indicating the year the ES was conducted in<br>the country.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Region fixed effects|A set of dummy variables indicating the<br>region of the country. There are 6 dummy<br>variables one for each world region. The<br>regions are: Sub-Saharan Africa (SSA), Latin<br>America and Caribbean (LAC), Eastern<br>Europe and Central Asia (ECA), Middle East<br>and North Africa (MENA), East Asia and the<br>Pacific (EAP), and South Asia (SAR). These<br>regional groupings are based on World<br>Bank’s classification.|
|City size fixed effects|Dummy variables indicating the size of the<br>city (sampling region) where the firm is<br>located. Based on total population, cities are<br>grouped into the following size categories:<br>more than 1 million population, between 1<br>million and 250,000 population, between<br>50,000 and 250,000 population, and less than<br>50,000 population.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Firm Spent On R&D Y:0 N:0|Dummy variable equal to 1 if the firm spent<br>on R&D activity during the last fiscal year<br>(prior the year ES was conducted) and 0<br>otherwise.<br>Source: Enterprise Surveys.<br>|
||www.enterprisesurveys.org|
|Firm Purchased Fixed Assets Y:1 N:0|Dummy variable equal to 1 if the firm<br>purchased fixed assets during the last fiscal<br>year (prior the year ES was conducted) and 0<br>otherwise.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Foreign Ownership (proportion)|Proportion of the firm that is owned by<br>foreign individuals, companies or<br>organizations.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|

40
hundreds of dollars instead of tens of millions for full travel survey. The type of data we used here is also much more versatile and can thus be at narrower issues or areas targeted without fear of losing statistical power. It can also be collected at much higher frequency than the typical 5 to 8 year gap between consecutive traditional travel surveys.

This type of data is also particularly interesting to evaluate the effects of policy changes in the short-run. For instance, Kreindler (2016) uses a data collection comparable to ours for Delhi to examine the short-term congestion benefits of a new driving restriction based on vehicle plate numbers. Hanna _et al._ (2017) use a similar strategy to assess the effects of the relaxation of a high-occupancy vehicle constraint on certain major arteries in Jakarta. We believe these studies and future studies of this type will shed useful light on many aspects of transportation policy in cities. Many other possible applications are possible. They include, for instance, the monitoring of city recovery after major natural disasters.

We also hope that more data underlying the production of real-time travel information will be made available for research. The data that we use allow us to learn about mobility, and the price (time cost) of travel for all possible trips at all times. The analogous quantities (i.e., number of travelers) are potentially knowable from the same underlying data. With both prices and quantities, the detailed study of congestion, both on particular road segments and in larger areas, will be possible. Repeated observations of the same travelers would also enable a much better analysis of individual travel behavior. For instance, Kreindler (2018) uses a panel of trip-level data for 2,000 commuters from a smartphone app to learn about individual response to peak travel congestion, and to measure the welfare impact of various pricing policies to alleviate congestion in Bangalore. With appropriate regard for privacy, the availability of larger trip-level samples across cities would allow for a comprehensive analysis of the welfare consequences of better urban mobility and accessibility.

38
# **Abbreviations and acronyms**

|APAFA|Parents’ Association (_Asociación de Padres de Familia_)|
|---|---|
|CBM|Curriculum-Based Measurment|
|CNE|National Education Council (_Consejo Nacional de Educación)_|
|CONEI|Institutional Education Council (_Consejo Educativo Institucional)_|
|CRM|Criterion-Referenced Measure|
|CVR|Truth and Reconciliation Commission (_Comisión de la Verdad y Reconciliación_)|
|DIBELS|Dynamic Indicators of Basic Early Literacy Skills|
|DRE|Regional Education Office (_Dirección Regional de Educación)_|
|EBR|Basic Regular Education (_Educación Básica Regular)_|
|ECE|School Census Evaluation_(Evaluación Censal Escolar)_|
|EGRA|Early Grade Reading Assessment|
|ENAHO|National Household Survey (_Encuesta Nacional de Hogares)_|
|ENCO|National Continuous Survey (_Encuesta Nacional Continua)_|
|ERDA|Early Reading Diagnostic Assessment|
|GORT|<br>Gray Oral Reading Test|
|IE|Education Institution (_Institución Educativa)_|
|INEI|National Institute of Statistics and Information Technology (_Instituto Nacional de_|
||_Estadística e Informática)_|
|INFES|National Institute for Education and Health Infrastructure (_Instituto Nacional de_<br>_Infraestructura Educativa y de Salud)_|
|ITBS|Iowa Test of Basic Skills|
|LLECE|Laboratorio Latinoamericano de Evaluación de la Calidad Educativa (_Latin_<br>_American Laboratory for Evaluation of Quality of Education)_|
|MINEDU|Ministry of Education (_Ministerio de Educación)_|
|MINEDU -UEE|<br>Ministry of Education- Education Statistics Unit_(Ministerio de Educación - Unidad_<br>|
||_de Estadísticas Educativas)_|
|MINEDU -UMC|<br>Ministry of Education- Education Quality Measurement Unit (_Ministerio de_<br>_Educación - Unidad de Medición de la Calidad Educativa)_|
|MEF|Ministry of Economy and Finances (_Ministerio de Economía y Finanzas)_|
|MRTA|Tupac Amaru Revolutionary Movement (_Movimiento Revolucionario Túpac Amaru_)|
|NAEP|National Assessment of National Center for Education|
|NBI|Basic Unsatisfied Need (_Necesidad Básica Insatisfecha)_|
|NRM|Norm-Referenced Measure|
|NRP|National Reading Panel|
|OECD|Organization for Economic Co-operation and Development|
|OREALC/UNESCO|UNESCO’s Regional Office for Latin America and the Caribbean_(Oficina Regional_<br>|
||_para America Latina y el Caribe, UNESCO)_|
|PBI|<br>Gross Domestic Product (_Producto Bruto Interno)_|
|PCP-SL|Communist Party of Peru - Shining Path (_Partido Comunista del Perú - Sendero_<br>|
||_Luminoso)_|
|PEI|Institutional Education Project (_Proyecto Educativo Institucional)_|
|PISA|<br>Program for International Student Assessment|
|SAT|<br>Stanford Achievement Test|
|SBA|Standard-Based Assessment|
|SDRT|Stanford Diagnostic Reading Test|
|TPRI|<br>Texas Primary Reading Inventory|
|UGEL|Local Education Management Unit (_Unidad de Gestión Educativa Local)_|
|UNESCO|United Nations Education Science and Culture Organization|
2 KENYA AND CHINA’S TRADE RELATIONSHIP <!-- Start of picture text --> 10<br>300<br>World<br>8 China<br>200 Kenya<br>Kenya<br>6 SSA<br>100<br>4<br>2000 2005 2010 2014 2000 2005 2010 2014<br>Year Year<br>(a) China’s apparel exports strong even with AGOA (b) Kenya’s apparel exports hit hard in 2004, but<br>rebounded from 2010<br>Exports to US (USD Millions)<br>Log Exports to US (USD Millions)<br><!-- End of picture text --> Figure 12: Kenya’s apparel exports to the US (2000-2014) Source: UN Comtrade 2015

**Kenya’s exports to Uganda and Tanzania are falling** Chinese goods may have also hurt Kenya’s exports to its neighbors. Exports to Tanzania and Uganda are quite similar to China’s, compared to both countries’ exports to the United States or the UK. The greater overlap in East Africa suggests that Chinese goods will likely displace Kenyan exports. Between 2008 and 2014, manufacturing exports to Tanzania fell 36.1 percent; exports to Uganda increased slightly by 4.5 percent, but compared to previous years, the growth was slow.<sup>7</sup>

**Some fear that Chinese imports could lead to deindustrialization** Because Kenya produces and trades few intermediate goods, researchers have concluded that Chinese imports could lead to a de-industrialization. Many suspect a premature decline of industry because manufacturing growth was only 3.4 percent in 2014, down from 5.6 percent in 2013 (Chen _et al_ 2015). The manufacturing sector is ten percent of GDP, but the government wants it to be 20 percent of GDP as part of its Vision 2030 program (KNBS 2015). Kenya will need to promote more FDI into manufacturing, improve labor productivity and infrastructure, lower transport costs, and lighten the regulatory burden of trade it if hopes to boost exports and the share of manufacturing in GDP (Farole 2011).

**But Ethiopia is building a strong manufacturing industry** Ethiopia does a much better job of attracting FDI in manufacturing. Between 2010 and 2013, the FDI to GDP ratio in Ethiopia was 1.39 compared to only 0.67 in Kenya, and the FDI to Export ratio was 0.1 in Ethiopia and 0.03 in Kenya; it also has a lower cost of doing business, offering lower taxes, electricity, and labor costs

> 7We calculate the export similarity of Kenya and China in Tanzania to be .0247 and in Uganda to be .164; in contrast, the export similarity in the United States is only 0.06. Let _Xij_ be the share of product _i_ in country _j_ ’s exports and _Xik_ be the share of product _i_ in country _k_ ’s exports. The export similarity is

13
Based Policing Matrix also has a more narrow focus on one outcome, rather than a set of outcomes.

Thus, there are overlaps between the methodologies of existing evidence products, and evidence gap maps. 3ie evidence gap maps are unique in that they bring together the following key features: (1) visualization of the existing evidence using a framework of interventions and outcomes, using a theory-based approach with a focus on outcomes along the causal chain; (2) critical appraisal of systematic review evidence and a clear visualization of the quality of the evidence in the map; (3) links to a database with summaries of systematic reviews and impact evaluations; and (4) a broad scope covering a range of interventions and outcomes, focusing on a sector or subsector rather than a research question or field of enquiry.

# _What gap maps do not do_

Users should consider gap maps as one of a multitude of tools and sources of information to aid decision making for policy and practice. Evidence gap maps do not answer a specific research question, but focus on providing a broad overview of the existing evidence. Moreover, they are restricted to studies that assess the effectiveness of interventions and therefore do not include evidence on predictive factors, implementation, barriers and facilitators to effectiveness and other types of evidence.

Due to their broad scope the evidence gap maps also do not provide details about the contextual background of the included evidence, nor do they synthesize the findings of included systematic reviews and impact evaluations. Finally, evidence gap maps are not intended to provide recommendations or guidelines for policy and practice in and of themselves, but rather to be one of the sources that inform policy development and guidelines for practice. The next section outlines the key stages in developing an evidence gap map, as summarized in Box 2 below.

8