rafmacalaba/datause-encoder-data huggingface dataset:
33

**Table 2.2. Objectives and strategies used to address GBV in the health sector**

|**Level**|**Objectives:**|**Examples of Specific Initiatives:**|
|---|---|---|
|**Laws and**<br>**policies**|**To improve laws and policies**<br>•<br>Clarify providers’ legal<br>responsibilities<br>•<br>Encourage a better health sector<br>response to GBV through<br>national, regional, and<br>municipal policies regarding<br>screening, referral,<br>documentation and counseling<br>for victims of violence<br>•<br>Ensure survivors’ rights to<br>services (e.g. emergency<br>contraception, STI prophylaxis,<br>etc.)|•<br>Reforms of laws and policies regulating<br>the medico-legal system (e.g.<br>introduction of forensic nurses)<br>•<br>Reform of laws and policies regulating<br>health care providers’ obligations vis-à-<br>vis victims of GBV<br>•<br>National health policies and protocols<br>•<br>Laws/policies governing forensic<br>medicine; provider obligations, abortion,<br>EC and patient confidentiality|
|**Institutional**<br>**reform**|**To strengthen the response of**<br>**health care and public health**<br>**institutions to gender based**<br>**violence**<br>•<br>Raise awareness of the links<br>between violence and health<br>among service providers,<br>managers, and public health<br>policy makers.<br>•<br>Improve the quality of care for<br>survivors of violence, including<br>identification, treatment,<br>documentation, information,<br>referrals and follow-up.<br>•<br>Increase coordination with other<br>sectors that provide services or<br>workonviolence prevention.|•<br>Policies, procedures and protocols to<br>improve the health care response<br>•<br>Sensitization and training of health<br>professionals<br>•<br>Routine screening and referral systems<br>•<br>Development of information systems<br>such as epidemiological surveillance and<br>morbidity statistics on violence<br>•<br>Specialized survivor services<br>(counseling, support groups)<br>•<br>Improved coordination and referrals to<br>NGOs and other sectors<br>•<br>Curricular changes in training of nurses<br>and medical personnel|
|**Community**<br>**Mobilization**|**To increase community**<br>**mobilization to address GBV as a**<br>**public health problem**<br>•<br>Strengthen community support<br>for survivor services<br>•<br>Strengthen coalitions and<br>networks<br>•<br>Improve attitudes, norms,<br>practices and resources at the<br>communitylevel|•<br>Coalitions for public health research and<br>advocacy<br>•<br>Community level prevention and<br>mobilization initiatives<br>•<br>Community-based awareness campaigns<br>aimed at mobilizing journalists, policy<br>makers and opinion leaders|
**Table 3:** _Descriptive Statistics of Main Variables_

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Variable|Obs.|Mean|Std. Dev.|Max|Min|
|Infant Mortality||||||
|All|1,770,705|0.0701|0.2553|0|1|
|Girl Infant|868,108|0.0648|0.2462|0|1|
|Boy Infant|902,597|0.0752|0.2637|0|1|
|T test (p value)||(0.0000)||||
|Poor Mother|378,183|0.0836|0.2767|0|1|
|Non-Poor Mother|1,314,517|0.0646|0.2458|0|1|
|T test (p value)||(0.0000)||||
|Mother with No Formal Schooling|684,226|0.0894|0.2853|0|1|
|Mother with Primary School or More|1,028,914|0.0579|0.2336|0|1|
|T test (p value)||(0.0000)||||
|Rural Mother|1,206,894|0.0774|0.2672|0|1|
|Urban Mother|563,811|0.0545|0.2270|0|1|
|T test (p value)||(0.0000)||||
|Neonatal Mortality||||||
|All|1,770,705|0.0350|0.1837|0|1|
|Girl Infant|868,108|0.0308|0.1729|0|1|
|Boy Infant|902,597|0.0389|0.1935|0|1|
|<br>T test (p value)||(0.0000)||||
|Poor Mother|378,183|0.0396|0.1950|0|1|
|Non-Poor Mother|1,314,517|0.0328|0.1781|0|1|
|T test (p value)||(0.0000)||||
|Mother with No Formal Schooling|684,226|0.0440|0.2052|0|1|
|Mother with Primary School or More|1,028,914|0.0291|0.1682|0|1|
|T test (p value)||(0.0000)||||
|Rural Mother|1,206,894|0.0379|0.1910|0|1|
|Urban Mother|563,811|0.0287|0.1667|0|1|
|T test (p value)||(0.0000)||||
|Decision Point|1,770,705|0.2983|0.4575|0|1|
|Completion Point|1,770,705|0.1741|0.3792|0|1|
|<br>Girl Infant|1,770,705|0.4903|0.4999|0|1|
|Multiple Birth|1,770,705|0.0271|0.1624|0|1|
|Birth Order|1,770,705|3.0994|2.1431|1|20|
|Quarter of Birth|1,770,705|2.4277|1.1013|1|4|

27
female entrepreneurship. The government and/or international organizations could use the more nuanced findings to target firms of specific sizes if they aim at enabling women entrepreneurs. To shed light on this question, in Table 8, we separate the sample firms into two groups: those hiring 50 or fewer permanent employees three years ago (i.e., three years before the firm survey); and the other larger firms.

The general FDI effects on _Female Entrep_ are much larger for small firms than for medium-and-large firms. The coefficient of is more than twice larger for small firms than for medium-and-large firms. Interestingly, the within-industry FDI crowding out cc FF effect on _Female Entrep_ does not differ significantly between these two types of firms. These results support the _FDI-size-bias hypothesis._

# **Firm sector affiliation and FDI effects on female entrepreneurship**

Given women’s general lack of comparative advantage (relative to men) in brawn, and women’s advantage in handling interpersonal relationships, it is generally believed that women tend to do better in service-sector jobs than manufacturing jobs (Buera et al., 2013; Ngai and Petrongolo, 2017). Indeed, women’s job growth in service sectors tends to outpace that in other sectors (Lan and Shepherd, 2018). Given the higher share of women being employed in services, and the consistency between gender norms and job requirements in services than in other sectors, we now examine whether the FDI effects on female entrepreneurship differ by sectors. To this end, in Table 9 we allow the FDI effects to differ by sectors.

The FDI spillover effects are 70 percent larger for firms in services than in manufacturing (i.e., 0.70 vs 0.41). Furthermore, once controlling for country fixed effects, the within-industry FDI premium has further positive spillover effects only in service sectors. In contrast, though statistically insignificant, the negative sign of c in manufacturing is consistent with the existence of within-industry FDI crowding out effects FF on female entrepreneurship in manufacturing sectors. The results support the FDI-sectoralbias hypothesis.

# **Entry barriers for women and FDI effects on female entrepreneurship**

How do barriers to female entrepreneurship shape the overall FDI spillover effect and the intra-industry crowding out effect? If there is an intra-industry FDI crowding out effect, 14
Policy Research Working Paper 8357

# **Abstract**

The paper presents the development and implementation of a geo-spatial model for mapping populations’ access to specified types of water and sanitation services in Nigeria. The analysis uses geo-located, population-representative data from the National Water and Sanitation Survey 2015, along with relevant geo-spatial covariates. The model generates predictions for levels of access to seven indicators of water and sanitation services across Nigeria at a resolution of 1×1 square kilometers. The predictions promise to hone the targeting of policies meant to improve access to basic services in various regions of the country.

This paper is a product of the Water Global Practice. 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 authors may be contacted at Landres@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
Policy Research Working Paper 10702

# **Abstract**

Job quality can impact workers’ productivity and contribute to societal well-being. To analyze the evolution of job quality in Bolivia, this paper employs Bolivian household survey data spanning 2007 to 2021 to construct a synthetic job quality index. The index incorporates a broad definition of a good job, encompassing six dimensions: adherence to - regulations, working conditions, establishment of an appro priate wage-job linkage, productive usage and adaptability of skills, availability of career opportunities, and employment resilience. The findings indicate that job quality in Bolivia has mostly remained incessant, exhibiting limited change even during periods of high growth in economic output. However, this result masks heterogeneities, with significant variation in job quality associated with workers’ demographic and job-specific characteristics and across regions.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at gcanavire@worldbank.org, mdavalos@worldbank.org, and rcuevac1@illinois.edu. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
are perhaps most effective when given to children in the first 1,000 days of their lives covering pregnancy and the child’s first two years.

# **4.5 Impact on household welfare**

We also estimate the impact of the project on household welfare. Out of the total sample, 65 percent received a single benefit of BDT 400 per month while the remaining households received two benefits amounting to BDT 800. On average, beneficiaries received a transfer of BDT 542 per month. In Table 8 we analyze the impact of cash transfers on per capita consumption, total household consumption, food consumption, as well as the consumption of the food with high protein content. The results are presented in Table 8. First, we find that there is a significant increase in the household consumption of beneficiaries of about BDT 379, or about 95 percent of the benefit size of BDT 400 or 70 percent of the average size of transfers received by the sample.

**Table 8: Impact of** **_Shombhob_ on consumption**

||Tl|Fd|Protein consum|ption<br>|
|---|---|---|---|---|
||ota<br>consumption|oo<br>consumption|HH receiving nutrition<br>transfers|HH receiving<br>education<br>transfer|
|Imact of_Shbhb_(δ)|378.8*|337.0***|174.2***|118.0**|
|p_omo_|(215.8)|(107.3)|(46.4)|(54.5)|
|(T-C) at baseline (β)|-477.0***|-205.7***|-65.0*|-83.1**|
||(156.5)|(77.8)|(33.5)|(39.7)|
|Trend for non- recipients|52.6|96.1|150.4***|163.7***|
|(γ)|(166.5)|(82.8)|(35.6)|(45.1)|
|Constant|715.6|1970.6*|764.2*|527.3*|
||(2168.9)|(1078.6)|(400.2)|(294.4)|
|Controls|Yes|Yes|Yes|Yes|
|R2|0.4|0.4|0.3|0.2|
|Df|2,330|2,330|1,636|1,458|
|Number Obs.|2,376|2,376|1,680|1,502|

_Significance levels: * p<0.10, ** p<0.05, *** p<0.01.Controls include: household head gender, age, education, household size, housing characteristics, asset ownership, remittances._ This increase in consumption is mostly passed onto food expenses: 89 percent of the increase in consumption constitutes food consumption. _Shombhob_ recipients spend about BDT 30
**7**

**1990;** King and Levine **1993b].** Similarly, **by** facilitating risk management, improving the liquidity of assets available to savers, and reducing trading costs, banks can encourage investment in higher-return activities [Obstfeld 1994; Bencivenga and Smith **1991;** Greenwood and Smith **1997].** The effect of better banks on savings, however, is theoretically ambiguous. Higher returns ambiguously affect saving rates due to well-known income and substitution effects. Also, greater risk diversification opportunities have an ambiguous impact on saving rates as shown **by** Levhari and Srinivasan **(1969).** Moreover, in a closed economy, a drop in saving rates in the presence of a production function with physical capital externalities induces a negative impact on growth. Indeed, if these saving and externality effects are sufficiently large, an improvement in banking development could lower growth [Bencivenga and Smith **1991].** Thus, we attempt to shed some empirical light on these debates and ambiguities that emerge from the theoretical literature. Specifically, we examine whether economies with better-developed banks (i) grow faster, (ii) enjoy faster rates of productivity growth, (iii) experience more rapid capital accumulation, and (iv) have higher saving rates.

To evaluate the impact of banks on growth and the sources of growth, we seek an indicator of the ability of banks to research and identify profitable ventures, monitor and control managers, ease risk management, and facilitate resource mobilization. We do not have a direct measure of these financial services. We do, however, construct a better measure of banking sector development than past studies and we check these results with existing measures of financial sector development.

The primary measure of banking sector development is PRIVATE CREDIT, which equals the value of credits **by** financial intermediaries to the private sector divided **by GDP.** Unlike many past measures [King and Levine 1993a,b], this measure excludes credits issued **by** the central banks. Furthermore, it excludes credit to the public sector and cross claims of one group of intermediaries on

> 4 For overviews of the literature see Gertler **(1988)** and Levine **(1997).**
Michaels, G, 2008. “The Effect of Trade on the Demand for Skill: Evidence from the Interstate Highway System." _The Review of Economics and Statistics_ 90 (4): 683–701.

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27
# Table .

# Emolovees and Wages in Manufacturing Turkey, 1963 and 1980

||||M1censuses|||
|---|---|---|---|---|---|
||Emplo|a<br>yees|Aunnual. wa<br>averages,|ges,<br>TL|1980,|
||1961|1980|1961|1980|19631|
|Bigt,>10 persons|69.4|78.3|6,942|321,588|46.3|
|Small|30.6|21.7|2,592|67.428|26.0|
|Total|100|100|5,611|266,529|47.5|
|Weighted averages:||||||
|Laspeyres|||5,611|243,815|_4-.5_|
|Paasche|||5,998|266,529|44.4|
|Fisher|||5,801|254,919|43.9|
||||Daily<br>wa<br>averages,|ges,<br>TL||
|SMI.|||19.4|872|44.9|
|Censuses,<br>total|||18.7|888|47.5|
|SII,all<br>insured|||17.9|427|2'.9|
|W,manufacturing|||16.6|457|27.5|

# Sourcess Statistical Yearbook of Turkey, several issues.

a percent b interpolated.

-58-
# **A Novel Downside Risk Measure and Expected Returns**

Jinjing Liu<sup>_∗_</sup> The World Bank Key words: Downside Risk, Risk-return Relation, Prediction JEL classification number: C15, C31, G17, G12

> _∗_ I would like to thank John Galbraith, Deniz Anginer, Francesca Carrieri, Jean-Marie Dufour, Victoria ZindeWalsh, Patrick Augustin, David Schumacher, Feng Jiao, Amir Akbari, Raymond Kan (NFA discussant), Matthias Pelster (FMA discussant) as well as participants at the 2016 FMA Annual Meeting, 2016 NFA Annual Meeting, the 49th CEA Annual Conference, Brownbag seminar at McGill University, 12th Centre Interuniversitaire de Recherche en Economie Quantitative (CIREQ) PhD Students’ Conference, and CIREQ Seminar at McGill University for their helpful comments. This paper’s findings, interpretations, and conclusions are entirely mine and do not necessarily represent the views of the World Bank, its executive directors, or the countries they represent. Please address correspondence to jliu19@worldbank.org.
expected to rise from the current levels of 6.5 billion gallons to 14 billion gallons, and corn price would remain at about $4 a bushel.

The existing literature not only assesses the impacts of biofuels on the 2007-2008 food crisis but also projects the impacts on food prices in the future. The International Food Policy Research Institute (IFPRI) finds price increases for maize of 23% to 72%, wheat of 8% to 30%, oilseeds of 18% to 76%, and sugar of 11.5% to 66%, in response to countries implementing the plans they have announced for biofuels by 2020 (ODI, 2008). Trostle (2008) projects price rises of 65%, 64%, 33% and 19%, for maize, sorghum, wheat and soy oil, respectively, due to the expansion of biofuels, rising energy costs and demand from emerging economies. Moreover, should biofuel production be frozen at 2007 levels for all countries and for all crops used as feedstock, maize prices can be expected to decline by 6 percent by 2010 and 14 percent by 2015, along with lesser price reductions for oil crops, cassava, wheat, and sugar (Rosegrant et al., 2008). If a global moratorium on crop-based biofuel production is imposed from 2007 onwards, by 2010, prices of key food crops would drop even further: by 20 percent for maize, 14 percent for cassava, 11 percent for sugar, and 8 percent for wheat.

Taheripour et al. (2010) emphasize the importance of including by-products when modeling the impact on non-energy commodity prices of expanded biofuel production in response to US and EU biofuel mandates. They show that the price of coarse grains increases sharply in the US, EU, and Brazil by 22.7%, 23.0%, and 11.9%, respectively, over the period 2006-2015; once byproducts are incorporated into the model, the price of coarse grains exhibits significantly lower growth rates of 14%, 15.9%, and 9.6%, respectively. The inclusion of by-products reduces the price rise of oilseeds in the EU from 62.5% to 56.4% in the same period. The prices of most other agricultural commodities grow at a slightly lower rate when by-products are accounted for.

Fischer et al. (2009) model the prices of food staples in 2020 and 2030 under several different scenarios for biofuel production. Under a scenario based on the International Energy Agency’s World Energy Outlook 2008 projections, price increases for both cereals and other crops in 2020 are about 10 percent higher compared to a reference scenario where biofuel development after 2008 is kept constant at the 2008 level. Since the contribution of second-generation biofuels is 22