rafmacalaba/datause-encoder-data huggingface dataset:
22 Organization; 2016 [cited 2022 Dec 12]. 155 p. Available from: https://apps.who.int/iris/handle/10665/246200 9. Reed E, West BS, Frost E, Salazar M, Silverman JG, McIntosh CT, et al. Economic vulnerability, violence, and sexual risk factors for HIV among female sex workers in Tijuana, Mexico. AIDS Behav [Internet]. 2022 Apr 5 [cited 2022 Aug 12]; Available from: https://doi.org/10.1007/s10461-022-03670-0 10. Zullo AR, Caine K, Galárraga O. The dollars and sense of economic incentives to modify HIV-related behaviours. J Int AIDS Soc. 2015 Oct 17;18(1):20724.

11. Galárraga O, Sosa-Rubí SG. Conditional economic incentives to improve HIV prevention and treatment in low-income and middle-income countries. Lancet HIV. 2019;6(10):e705– 14.

12. Thornton R. The Demand for and Impact of Learning HIV Status: Evidence from a Field Experiment. Am Econ Rev. 2008;98(5):1829–63.

13. Björkman Nyqvist M, Corno L, de Walque D, Svensson J. Incentivizing Safer Sexual Behavior: Evidence from a Lottery Experiment on HIV Prevention. Am Econ J Appl Econ. 2018 Jul 1;10(3):287–314.

14. Gorgens M, Ketende S, Longosz AF, Mabuza M, Nkambule M, Dlamini T, et al. The impact of financial incentives on HIV incidence among adolescent girls and young women in Eswatini: Sitakhela Likusasa, a cluster randomised trial. BMJ Glob Health. 2022 Sep;7(9):e007206.

15. Kranzer K, Simms V, Bandason T, Dauya E, McHugh G, Munyati S, et al. Economic incentives for HIV testing by adolescents in Zimbabwe: a randomised controlled trial. Lancet HIV. 2018 Feb;5(2):e79–86.

16. de Walque D, Dow WH, Nathan R, Abdul R, Abilahi F, Gong E, et al. Incentivising safe sex: a randomised trial of conditional cash transfers for HIV and sexually transmitted infection prevention in rural Tanzania. BMJ Open. 2012 Jan;2(1):e000747–e000747.

17. Packel LJ, de Walque D, Feeney KC, Balampama MP, Cooper JE, Kalolella A, et al. A randomized trial of cash incentives for sexual behavior change among female sex workers in Dar es Salaam. Soc Sci Med [Internet]. 2018; Available from: http://www.sciencedirect.com/science/article/pii/S0277953618301205 18. Dutta A, Barker C, Makyao N. Consensus estimates on key population size and HIV prevalence in Tanzania. Natl AIDS Control Programme NACP Minist Health Soc Welf. 2014.

19. Tanzania Ministry of Health and Social Welfare. 2010. HIV Behavioral and Biological Surveillance Survey Among Female Sex Workers in Dares-salaam, 2010. National AIDS Control Programme (NAC), Dar-es-Salaam, Tanzania .
# **Figure 2. The mining project cycle**

_Most mining projects are characterized by several key stages that include exploration, discovery, feasibility assessments and regulatory compliance (including obtaining licenses), project construction, production and eventually closure._

# **A. Time lines for mine development**

<!-- Start of picture text --> Nevada Copper (United States)<br>Exploration<br>Regulatory compliance<br>Construction<br>First operation<br>Oyu Tolgoi (Mongolia)<br>Exploration<br>Reg. comp.<br>Construction<br>First operation<br>2001 2005 2010 2013<br><!-- End of picture text -->

# **B. Duration of mining leases and exploration licenses in selected LICs**

<!-- Start of picture text --> Numbers of years Numbers of years<br>Maximum duration of mining lease<br>Maximum duration of exploration licenses (RHS)<br>40 6<br>30<br>4<br>20<br>2<br>10<br>0 0<br>Botswana Burkina Faso Cameroon C. A. Republic Chad D.R. Congo Ethiopia Guinea Liberia Mali Niger Sierra Leone Uganda<br><!-- End of picture text -->

# **C. Investment risk over a mining project lifecycle**

# **D. Number of years from gold and copper discovery to production**

<!-- Start of picture text --> Value/Risk Project value Number of years Average 1950-70s<br>Investment risk 35 Average 1980-90s<br>Average 2000s<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>EAP ECA HIY LAC MNA SAR SSA<br>Stages of project development and mining<br>Steady State Production<br>Reserve Definition Project Construction<br>Commissioning<br>Feasibility Environmental rehabilitaitn<br>Pre-Feasibility<br>Resource Definition<br>Discovery Closure<br>Exploration<br><!-- End of picture text --> Sources: World Bank, Perott-Humphrey (2011); Gajigo et. al. (2012); http://ot.mn/history, http://pumpkinhollowcopper.com/project-timeline/, both accessed November 4, 2015.

A. Illustrative example of timeline from two copper mines, in the United States and Mongolia. Exploration is not included in lead times discussed in the text.

D. Based on a sample of 46 countries with copper discoveries and 73 countries with gold discoveries. SST denotes Sub-Saharan Africa. EAP = East Asia and Pacific; ECA = Europe and Central Asia; HIY = High-income countries; LAC = Latin America and the Caribbean; MNA = Middle East and Africa; SAR = South Asia; SSA = Sub-Saharan Africa.

6
labor force participation rates are significantly larger than in most European countries.<sup>1</sup> In addition, the country registered increasing gender gaps over the last 20 years, mainly driven by declining female labor force participation rates, while the European Union (EU) on average reported decreasing gender gaps in labor force participation (World Bank 2023). In addition, female labor force participation is among the lowest in the EU. There are also signs of gender norms around childcare usage that could deteriorate female labor market participation. According to data from the World Value Survey (2018) 37.2 percent of Romanians thought that a preschool child with a working mother suffers.

While these trends already make for an interesting country setup, Romania is also an interesting case given its communist past and its long history of centralized public planning. In the pre-transition period, Romania registered one of the highest female labor force participation rates in the world, also driven by a wide range of childcare programs subsidized by the government (Fong and Lokshin 1999). The availability of public childcare services dropped significantly in the post-transition period and in 2020 the share of households with children below six years old that relied on these services was less than 1 percent (EU-SILC 2020).<sup>2</sup> Our setting provides a clean policy setup to study the underlying research question. We find no evidence of confounding policies or trends that affected maternal employment of six-year-olds differently from the one of three- to four-year-olds. While the government introduced several policies that potentially impacted employment decisions, it is unlikely that these policies resulted in different dynamics between mothers of six-year-olds and those of three to four-year-old children.

Our paper contributes to the economic literature studying drivers behind female labor force participation and employment. Next to the literature analyzing the impact of universal childcare, our work is closely related to a number of papers studying childcare subsidies

> 1In 2021, Romania’s female-to-male labor force participation ratio stood at 66 percent, compared to the EU average of 81 percent (World Bank 2023)

> 2We define public childcare service as the share of households with children younger than six years old who spent at least one hour in center-based or day-care centers.

3
The probability of success in these ballots was approximately 10%. Thus, we have a group of migrants and a comparison group who are similar to the migrants, but remain in Tonga only because they were not successful in the lottery. This allows experimental estimates of the mental health effects of migration to be obtained by comparing the mental health of migrants who were successful applicants in the lottery to the mental health of those who applied to migrate under the quota, but whose names were not drawn in the lottery.

The other options available for Tongans to migrate are fairly limited, unless they have close family members abroad. Ninety-four percent of all Tongan migrants are located in New Zealand, the United States and Australia.<sup>10</sup> In the 2004/05 financial year New Zealand admitted 1482 Tongans, of which 58 entered through a business/skilled category, 549 through family sponsored categories and 749 through the Pacific Access Category.<sup>11</sup> Australia admitted 284 Tongans during the same financial year.<sup>12</sup> The United States admitted 324 Tongans in the 2004 calendar year, comprising only 5 under employment-based preferences and 290 under immediate relative or family-sponsored categories.<sup>13</sup> Thus, the PAC accounted for 42% of all migration to these three countries, and over 90% of non-family category migration.

> 10 Source: GTAP database of Parsons et al. (2005).

> 11 Source: Residence Decisions by Financial Year datasheet provided by New Zealand Department of Labour. Note that the high number of PAC approvals in the 2004/05 financial year reflects backlog from prior PAC ballots which were not approved until this time.

> 12 Source: Settler Arrivals 2004-2005, Australian Government Department of Immigration and Multicultural Affairs. 13 Source: 2004 Yearbook of Immigration Statistics, U.S. Department of Homeland Security Office of Immigration Statistics.

9
# Figure 10. Governance

# A. Change in governance indicators

<!-- Start of picture text --> Number of EMDEs Deteriorated significantly<br>40 Improved significantly<br>Difference<br>20<br>0<br>-20<br>-40<br>Gov't eff. Reg. quality Rule of law Control of<br>corruption<br>C. 22 largest EMDEs<br>Index<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>Gov't eff. Reg. quality Rule of law Control of<br>corruption<br>1998 2007 2018 1998 2007 2018 1998 2007 2018 1998 2007 2018<br><!-- End of picture text -->

# C. 22 largest EMDEs

# B. Seven largest EMDEs

<!-- Start of picture text --> Index<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>Gov't eff. Reg. quality Rule of law Control of<br>corruption<br>D. Low-income countries<br>Index<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>-1.2<br>-1.4<br>-1.6<br>Gov't eff. Reg. quality Rule of law Control of<br>corruption<br>2018 2018 2018<br>1998 1998<br>1998 2007 2018 1998 2007 1998 2007 1998 2007<br>1998 2007 2018 2007 2018 1998 2007 2018 2007 2018<br><!-- End of picture text -->

# D. Low-income countries

Source: World Bank Worldwide Governance Indicators.

Note: Based on indicators from the Worldwide Governance Indicators measuring aspects of governance. WGI defines governance as “the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.” The four indicators are government effectiveness, regulatory quality, rule of law, and control of corruption.

A. A country significantly improved its rating if it saw a two standard deviation improvement in one of four indicators between 1996 and 2018. The standard errors are the average between two observations.

B.-D. Annual observations are unweighted averages. EM7 are the seven largest EMDEs including Brazil, China, Mexico, India, Indonesia, Turkey, and Russia. EM22 are the 22 largest EMDEs. LICs are Low-income Countries and includes data for 26 economies. Error bands are 1 standard deviation.
Figure 8. Cross-country correlations: interest rates and output (correlation coefficient) <!-- Start of picture text --> 0.6<br>***<br>***<br>0.4<br>***<br>***<br>***<br>0.2<br>0.0<br>Nominal Nominal long- Real short- Real long- Output<br>short-term term interest term interest term interest<br>interest rate rate rate rate<br>1971:1-2016:4 1971:1-1984:4 1985:1-2016:4<br><!-- End of picture text --> Note: The average of cross-country correlations for each variable in the respective periods is presented. The sample consists of 18 advanced economies. *** indicates that average correlations for the period Q1 1971 to Q4 1984 are statistically different from those for the period Q1 1985 to Q4 2016 at the 1% level.

Figure 9. Variance due to the global factor: interest rates and output (in percent) <!-- Start of picture text --> 60<br>50<br>40<br>***<br>30<br>20<br>10<br>0<br>Nominal Nominal long- Real short- Real long- Output<br>short-term term interest term interest term interest<br>interest rate rate rate rate<br>1971:1–2011:3 1971:1–1984:4 1985:1–2011:3<br><!-- End of picture text --> Note: The average percent of the variance explained by the global factor is presented. The sample consists of 18 advanced economies. *** indicates that variance explained by the global factors for the period Q1 1971 to Q4 1984 is significantly different from that for the period Q1 1985 to Q3 2011 at the 1% level.

60
Behrens, K., & Brown, W. M. (2018). Transport costs, trade, and geographic concentration: Evidence from Canada. In Handbook of international trade and transportation. Edward Elgar Publishing.

- Berg, C. N. Uwe Deichmann, Yishen Liu & Harris Selod (2016): Transport Policies and Development. The Journal of Development Studies.

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- Duranton, G., Morrow, P. M., & Turner, M. A. (2014). Roads and Trade: Evidence from the US. Review of Economic Studies, 81(2), 681-724.

- Ghani, E., Goswami, A. G., & Kerr, W. R. (2016). Highway to Success: The impact of the Golden Quadrilateral Project for the location and performance of Indian manufacturing. The Economic Journal, 126(591), 317-357.

- Grover, Arti, Somik V. Lall, and William F. Mal1y. 2022. Place, Productivity, and Prosperity: Revisiting Spatially Targeted Policies for Regional Development. Washington, DC: World Bank. doi:10.1596/978-1-4648-1670-3.

- Head, Keith, and Thierry Mayer. (2014). Gravity Equations: Workhorse, Toolkit, and Cookbook. Handbook of International Economics, 4: 131.

- Hummels, David, Volodymyr Lugovskyy, and Alexandre Skiba. (2009). The trade reducing effects of market power in international shipping. Journal of Development Economics, 89(1): 84 97.

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- Wong, W. F. (2018). The round trip effect: Endogenous transport costs and international trade. University of Oregon.

25
**Table 8. Education and Training (percent)**

||||Educati|onal level|complete|d|||
|---|---|---|---|---|---|---|---|---|
|Training|None|Primary<br>school|Middle<br>school|Second<br>ary<br>school|Vocati<br>onal<br>school|Professio<br>nal and<br>technical<br>schools|Universit<br>y|Full<br>sample|
|In-house|30.9|23.5|24.8|37.9|13.4|14.7|0|28.0|
|By outside<br>providers|35.1|41.2|30.7|32.7|40.3|44.9|76.9|36.2|
|None|24.0|35.3|44.5|29.4|46.3|40.4|23.1|35.8|
|Total<br>Percent|100|100|100|100|100|100|100|100|

_Note:_ The values show the percentage of each category of workers with each level of education. For example, 30.9 percent of the manufacturing workers in the sample with no completed level of education received in-house training.

_Source:_ Round Three of the 1994 Regional Program on Enterprise Development for Ghana.

**_In-house training._** The hypothesis that training has no impact on wages is not

rejected for training within the enterprise. This result indicates that in-house training does not directly contribute to measurable wages. In addition, formal training obtained within the firm is not significantly more productive than the training workers obtain from colleagues in an informal way. The results hint that in-house training is not productive; however, it seems more likely that the impact is not instantaneously revealed in wages and productivity. Initially, there is no measurable effect of in-house training in the data, but effects may be measurable at a later date.

**_Training by outside providers._** The analysis rejects the hypothesis that training supplied by outside providers has no impact on wages. Workers who have undergone formal, structured training outside the firm earn 16 percent more than workers who have not been trained (see appendix table B-i). However, the regression results indicate that training is not associated with higher firm-level productivity. In terms of the level of

26
|**Country**|**Hazard**|**Year**|**Vp**|**Vr**|**Source**|
|---|---|---|---|---|---|
|Bangladesh|Flood|2005|42%|17%|Brouwer et al.(2007)|
|Bangladesh|Flood/cyclone|2009|74%|45%|Rabbani et al.(2013)|
|Bangladesh|Flood/cyclone|2009|10%|6%|Akter & Mallick(2013)|
|Honduras|Hurricane|2001|31%|11%|Carter et al.(2007)|

**Table 2: Estimates of the poverty vulnerability bias from prior studies.**

Moreover, these studies only look at the capital owned by households, not at the capital they depend upon to generate their income. This includes also infrastructure (e.g., roads, electricity network) and productive capital (e.g., factories, supply chains). Here, we assume that the vulnerability of housing capital is comparable to the vulnerability of the rest of the capital – for instance, if poor people’s buildings are 20 percent more vulnerable, then we assume that the capital they use to generate an income is also 20 percent more vulnerable.

# 3.2.1. Vulnerability estimates based on housing data

In this national‐level analysis, and consistent with our focus on the socioeconomic drivers of resilience, we use a very simple methodology. We use the Global Building Inventory database from PAGER, by USGS, which provides a distribution of building types (buildings only, not contents) within countries across the world (USGS 2015). This typology has been developed to assess vulnerability to earthquakes but here we use it for all hazards.

There are 106 building types in PAGER, which we aggregate into three categories, by vulnerability level (see Appendix). Then, we apply simple depth‐damage functions for each of the three aggregated categories, inspired Hallegatte et al. (2013). The most vulnerable buildings are aggregated into one “fragile” category, and we assume that 70 percent of the building value is lost if a building in this category is affected. In addition, we have a “median” and a “robust” category, for which losses are equal to 30 and 10 percent. The PAGER data do not tell who is living in buildings of different categories. Here, we assume that the poorest live in the most vulnerable buildings; a very reasonable assumption in most cases. These loss ratios, developed for buildings, are also applied to the rest of the capital, including infrastructure and factories. The underlying assumption is that if individuals are living in fragile homes – for instance a mud wall house – they are also serviced by fragile infrastructure and work in fragile factories and buildings.

11
**_Energy demand models for policy_** **_<u>formulation</u>_**

The MESAP data and information system caters to the data needs and data management issues. Finally, additional tools are available for special purposes – RES Editor, Case Manager, etc. to improve user friendliness of the model.

The model is a Windows based software package that starts with a RES based representation of the energy system.

# **A1.3.1.2 Bottom-up, accounting models**

# **LEAP [Long-range Energy Alternatives Planning model]**

The Long-range Energy Alternatives Planning (LEAP) is a flexible modelling environment that allows building specific applications suited to particular problems at various geographical levels (cities, state, country, region or global). As an integrated energy planning model LEAP covers both the demand and supply sides of the energy system. However, we briefly outline the demand forecasting features of the LEAP model here.

The model follows the accounting framework approach to generate a consistent view of energy demand (and supply) based on the physical description of the energy system. It also relies on the scenario approach to develop a consistent storyline of the possible paths of energy system evolution. Thus for the demand forecasting, the model does not optimise or simulate the market shares but analyses the implications of possible alternative market shares on the demand.

The supply-side of the model does not try to find the least cost solution or system configuration as in the optimisation model but uses accounting and simulation approaches to provide answers to “what-if” type of analysis under alternative possible development scenarios. This spreadsheet like tool is flexible enough to consider various data requirements and supports some econometric and simulation features in addition to basic energy accounting framework. The framework is presented in Fig. A1.2.

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# **Figure 3.1: Age Distribution, by Area and Employment Type, 2006**

## **Urban**

<!-- Start of picture text --> 0.04<br>0.03<br>0.02<br>0.01<br>0<br>15 22 31 39 48 57 66 75 84 92<br><!-- End of picture text --> <!-- Start of picture text --> 0.04<br>0.03<br>0.02<br>0.01<br>0<br>15 24 32 41 50 59 68 77 86 95<br>public wage non-agri private wage non-agri HE wage agri family farming<br><!-- End of picture text -->

**Rural**

_Note: Employment type is based on primary employment only. Source: Calculations based on the ILFS 2006 data_ 13
Policy Research Working Paper 6225

# **Abstract**

This paper uses firm-level data to study the pricingbehavior of Tanzanian exporters. The important question of how exporting firms make pricing decisions has not received significant attention in the trade literature, which is more focused on factors that determine export flows. The results of the paper show that the free on board price of Tanzanian exports is differentiated both across exporters—within product-destination pairs—and across markets—within firm-product pairs. Moreover, contrasting with existing evidence, price differentiation across destinations seems to be mainly relevant for homogenous goods. This result could indicate either that goods classified as homogeneous can potentially be differentiated by their intrinsic quality (such as coffee), or that firms charge different mark-ups in different markets. Although further work is needed to confirm what leads to price dispersion, allowing for the possibility that food products can be vertically differentiable amplifies the spectrum of existing opportunities for developing countries to exploit product differentiation and market niches. The study also discusses the implications of the empirical findings in light of the predictions of price and quality competition models, but finds that the results cannot be explained by a single trade model of quality or price competition.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at valentina.rollo@graduateinstitute.ch.

_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