rafmacalaba/data-use-mentions-extended huggingface dataset:
who may not have received a diagnosis, some questionnaires attempt to find out if respondents have experienced certain symptoms and to figure out if the person has specific health conditions. This would be the case, for instance, with depression for which a questionnaire may ask about a person’s thoughts about death or difficulty sleeping at night. However, this method may require a lot of questions and may not be feasible for all health conditions. Several of the questionnaires noted earlier such as the WG Enhanced and Extended Sets and the Model Disability Survey questionnaire do have questions that try to get at the symptoms of some conditions, particularly depression.

Finally, some questionnaires such as the Model Disability Survey questionnaire and WHO - DAS (WHO 2012) blend several of the disability question types discussed above. WHO-DAS 2.0 has a mix of functional, ADL and broad activity limitations. The Model Disability Survey has questions on functional difficulties, ADL, broad activity limitations, health conditions and environmental factors. This paper reviews the questionnaires in surveys and censuses in low- and middle-income countries and the extent to which they include any disability-related questions and what type of disability question.

8
# **<<B>>** HFA (Primary Data)

Observations of CPIs were done for clients seeking ANC for the first visits (under 24 weeks) and last visits (36 weeks or more); PNC within 48 hours, two weeks, and four to six weeks; STI treatment; and family planning services. Although family planning was not initially one of the services subsidized by the maternal health voucher program, a follow-up program to subsidize the services had already been initiated through additional funding from the United States Agency for International Development (USAID), hence the rationale for conducting CPIs for family planning. Trained nurses conducted the CPIs. They observed both the process (how clients were treated and whether they actively participated) and the content (what clients were told, technical competence of the SP, accuracy of information, and provision of essential information) during consultation. In both contracted and comparison facilities, six clients were selected for each ANC and PNC component, as well as family planning services in each facility. At facilities with more than six clients at the time the research assistants visited, six clients were selected at random. Written informed consent was obtained from the clients before conducting the CPIs. Table A2 in the appendix presents the distribution of CPIs that were conducted using various background characteristics for each of the services.

Interviews with SPs targeted those working in the maternal and child health and family planning units in the facilities included in the 2010 HFA. All SPs in these units were eligible for the interviews. Information was obtained about their knowledge, attitudes, and practices regarding reproductive health issues, including ANC, delivery care, PNC, STIs and HIV, family planning, gender-based violence, and vouchers. Either the nurses or the social scientists conducted the interviews after obtaining written informed consent from the SPs. Table A3 presents the distribution of SPs who were interviewed based on various background characteristics. Similar to the household surveys, the Institutional Review Boards of the Population Council and Mbarara University granted ethical clearance for
2022). By doing so, we can study how state ownership and its different shapes and forms can potentially influence firm decisions even without having full control and measure how this can affect firm performance.

The WB BOS database provides information on entities with state participation across more than 90 countries. It collects firm-level data including characteristics such as age, sector of operation, and size, as well as financial indicators of revenues and profits. It also provides detailed ownership information to differentiate between directly and indirectly owned companies, the level of state participation, and the degree of proximity with the state (i.e., layers of the ownership tree). Following Abramov, Radygin, & Chernova (2017), we differentiate between firms directly owned by the state vis-à-vis those in which the state owns shares through other organizations or chains of firms (i.e., indirectly owned). For the latter, the BOS database informs how many firms or layers connect a firm with the state.<sup>5</sup> Finally, the WB BOS database provides information on the level of state participation to differentiate by minority shareholding (10-24.9%), blocking minority (2549.9%), majority shareholding (50-99.9%), and full state participation (Dall'Olio, et al., 2022).<sup>6</sup>

# 3.2 ORBIS financial module - Bureau van Dijk (BvD)

The second source of information is the raw financial module of ORBIS compiled by the Bureau van Dijk Electronic Publishing (BvD). The raw data was processed and cleaned implementing the protocol in the literature following Kalemli-Ozcan et al. (2015) and Cusolito (2020). This dataset provides a rich set of financial indicators for both privately and government owned entities. However, it does not have an indicator to differentiate whether a firm is a BOSs or a private firm.<sup>7</sup> To identify which firms in the ORBIS financial
# **4. Analysis and Results**

We divide our analysis into two key issues, the _quantification_ of losses, i.e. determining how much was lost, and the _attribution_ of losses to specific disasters. For each of these issues, the paper’s analysis is guided by a set of concrete questions that are empirically assessed in the LSMS-ISA data.

## **<u>Quantification</u>**

- Question 1: Can we separately measure planted area and plant productivity losses in survey data and form a robust, compound loss estimate?

- Question 2: How do farmer reports compare to crop losses based on expert assessments and can we establish an objective, reliable benchmark (‘gold standard’) for crop losses?

- Question 3: Can data from past harvests serve as a suitable benchmark for attainable harvest?

- Question 4: Can farmers’ post-planting harvest expectations be used to construct an estimate of harvest losses?

## **<u>Attribution</u>**

- Question 5: How uniform is farmers’ self-reported shock identification of covariate shocks?

- Question 6: How localized are drought losses and how confidently can we attribute losses to adverse rainfall based on self-reports?

11
literature on quality and availability suggests that the picture is more complicated. Studies that count the number of actual patients and medical staff at health care facilities show that health care providers, on average, are not overloaded and in fact have substantial excess capacity.<sup>20, 21</sup> Consistent with these summary measures, three recent studies that estimated the causal impact of additional patient load on the quality of medical care all found near-zero relationships between caseload and quality outcomes.<sup>10–12</sup> Our study represents a fundamental reassessment of the human resources situation in SSA. First, across a nationally representative sample of health facilities in 10 countries, we confirm that most health care providers operate under conditions of substantial excess capacity; the median _provider_ likely spends at most one hour and forty minutes each day seeing patients. Using facility-level survey data, we provide a new perspective on this fact. In these data, aggregate excess capacity across the provider population masks the fact that a minority of facilities and providers provide the majority of care in each system. The number of patients reported at busy facilities was such that the median _patient_ experience was indeed consistent with long wait times and queues, especially if most patients arrive at specific times of the day.

**12/30**
# **2. Data Sources**

Five data sources containing information on gender outcomes and attitudes have been examined:

- Demographic and Health Survey (DHS)

- World Values Survey (WVS)

- Multiple Indicator Cluster Surveys (MICS).

- Gallup Poll

- Gender and Adolescence: Global Evidence (GAGE)

Table A1 below summarizes the availability of data from these surveys across countries in the region and through different time periods.

Table A1: Data Availability by Survey

||Years available (by su|rvey)|||||
|---|---|---|---|---|---|---|
|Country|DHS<br>(Cross-sectional)|MICS<br>(Cross-sectional)|WVS<br>(Cross-sectional)|GAGE<br>(Cross-sectional)|Gallup<br>(Panel)||
|Afghanistan|2010<sup>+</sup>,2015(2)|2011(1)|(0)|None|||
|Bangladesh|1993-94, 1996-97,<br>1999-00,<br>2001<sup>*</sup>,<br>2004, 2007, 2011,<br>2014,2017(9)|2006, 2012-2013,<br>2019 (3)|1994-1998, 1999-<br>2004,<br>2017-2019<br>(3)|Baseline<br>survey<br>(2017/2018)<br>Only conducted in<br>Dhaka(1)|Every<br>between<br>2020 (15)|year<br>2005-|
|India|1992-93, 1998-99,<br>2005-06, 2015-16<br>(4)|(0)|1989-1993, 1994-<br>1998, 1999-2004,<br>2005-2009, 2010-<br>2014(5)|(0)
# Table 2: **The Effect of Blackouts on Weekly Sales, Profits, and Expenses**

All specifications include time fixed effects, firm fixed effects, and a vector of dummies indicating the number of days (of 5) for which a response about blackout status was reported by the firm owner. Standard errors are clustered at the neighborhood level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

||(1)<br>Revenues<br>(GhC)|(2)<br>Profits<br>(GhC)|(3)<br>Expenses<br>(GhC)|
|---|---|---|---|
|**Panel A: One-Person Firms**||||
|# Blackout days reported (out of 5 days)|-4.40***<br>(1.24)|-4.15***<br>(1.27)|-0.25<br>(0.39)|
|Outcome Variable Average|44.74|31.42|13.32|
|Observations|1,265|1,265|1,265|
|**Panel B: Firms With Workers**||||
|# Blackout days reported (out of 5 days)|0.76<br>(2.33)|2.07<br>(2.09)|-1.31<br>(1.35)|
|Outcome Variable Average|77.75|48.71|29.04|
|Observations|1,097|1,097|1,097|

Source: Author calculations from The Hohoe Garment Makers Project Data 17
years, they are not equal to the real capital stock and are not comparable across time and across firms. Since we do not have all past investments of a firm to construct the real capital stock, we follow Brandt, Van Biesebroeck and Zhang (2012) and make several assumptions to convert the value of their capital stock at the original purchase prices into the real values using the following procedures.

First, we estimate the nominal value of the capital stock for each year between a firm’s birth year and the first year in which the firm appears in our data set. We assume that it is 1998, the first year of our panel. We assume that the growth rate of the nominal capital stock of each firm equals to the growth rate of the nominal capital stock in the corresponding two-digit industry as reported in the _China Statistical Yearbooks_ .<sup>24</sup> We then calculate the nominal capital stock in 1998 as follows: N 1998<sup>= N</sup> 1998 ) (A2) Where N 1998<sup>is the nominal captial stock in 1998 reported in the ASIF data,</sup> ss<sup>∏</sup> ττ=ss (1 + rrττ<sup>_s_indicates the firm’s</sup> first year of operation, N growth rate of the nominal capital stock in the two-digit industry in year _t_ , as reported by the _China_ ss<sup>is the nominal captial stock of the firm in its birth year, andrr</sup> ττ<sup>is the</sup> _Statistical Yearbooks_ . From equation (A2), we calculate the nominal stock in each year between the firm’s birth year and 1998. Second, the annual nominal investment N is the change in the nominal capital stock between two consecutive years, that is, ii N = N ii ii<sup>−N</sup> ii−1<sup>(A3)</sup
Leventhal, K. S., Gillham, J., DeMaria, L., Andrew, G., Peabody, J., & Leventhal, S. (2015). Building psychosocial assets and wellbeing among adolescent girls: A randomized controlled trial. Journal of adolescence, 45, 284-295.

Lippman, L., Moore, K. A., Guzman, L., Ryberg, R., McIntosh, H., Ramos, M., & Kuhfeld, M. (2014). Flourishing children: Defining and testing indicators of positive development. Springer.

Nehru, V., E. Swanson, et al. (1995). "A new database on human capital stock in developing and industrial countries: Sources, methodology, and results." Journal of Development Economics 46(2): 379-401.

Mello, Z. R. (2008) “Gender Variation in Developmental Trajectories of Educational and Occupational Expectations and Attainment from Adolescence to Adulthood” _Developmental Psychology_ , 44(4), 1069–1080 Miguel, E. and M. Kremer (2004) “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities”, _Econometrica_ , Vol. 72, No. 1, pp. 159–217 Schoon, I. and S. Parsons (2002) “Teenage Aspirations for Future Careers and Occupational Outcomes”, _Journal of Vocational Behavior_ , 60(2), 262–288 Schoon, I., P. Martin, and A. Ross (2007) “Career Transitions in Times of Social Change: His and Her Story”, _Journal of Vocational Behavior_ , 70(1), 78–96 Schoon, I. and E. Polek (2011) “Teenage Career Aspirations and Adult Career Attainment: The Role of Gender, Social Background and General Cognitive Ability”, _International Journal of Behavioral Development_ , 35(3) 210–217 Schwarzer, R., & Jerusalem, M. (1995). Generalized Self-Efficacy scale
<u>reports/digital-technologies-and-learning-outcomes-students-low-socio-economic-</u> - <u>background analysis</u>

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2

# **Acknowledgments**

We are indebted to the task team leaders of the 2000, 2007 and 2012 databases, Davidson Gwatkin and Caryn Bredenkamp, without whose efforts the 2018 database would not have been possible. We are grateful to Leander Buisman who assisted in the processing of the microdata, to Caryn Bredenkamp, Tania Dmytraczenko, Olivier Dupriez, Rose Mungai, Minh Cong Nguyen, Marco Ranzani, Aparnaa Somanathan, Ajay Tandon and Joao Pedro Wagner De Azevedo who provided access to microdata, and to Qinghua Zhao for help with PovcalNet. We are grateful to Rantimi Adetunji, Nastassha Arreza, Amanda Kerr, Lingrui Liu, Jie Ren and Margarida Rodrigues for their tireless research assistance. We acknowledge the contributions of our collaborators on several projects whose ideas helped shape the 2018 database, including Daniel Cotlear, Tania Dmytraczenko and Owen Smith at the World Bank, Gabriela Flores at WHO Geneva, and Gisele Almeida at PAHO/WHO. Finally, we are grateful to Michele Gragnolati, Christoph Kurowski and Magnus Lindelow for their support, and to Tony Fujs, Karthik Ramanathan and Tariq Khokhar for engineering the online products. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.

# **Accessing the database**

A portal version of the 2018 database with visualization functionality can be accessed at <u>http://datatopics.worldbank.org/health-equity-and-financial-protection/. The data set itself can be</u> accessed and downloaded, indicator by indicator, or in its entirety, from <u>https://datacatalog.worldbank.org/node/142861 from which model Stata ‘do’ files can be downloaded</u> to replicate the datapoints in the HEFPI data set.

# **Citing the database**

The reference citation for the data is: Wagstaff, Adam, Eozenou, Patrick, Neelsen, Sven and Smitz
and store of value. Finally, many authors define currency substitution as a situation in which domestic money demand is influenced by foreign economic variables.

Another term that has been subject to different interpretations is _dollarization._ Throughout the 1980s, it was common to treat the terms "currency substitution" and "dollarization" as indistinguishable. Both terms described a situation where local residents demand dollars. This misunderstanding follows perhaps from the fact that most of the studies of currency substitution in the 1980s were focused on Latin America, and it is well documented that the substitution of currencies in Latin American countries favored the U.S. dollar. Currently, the term dollarization is also used to describe the replacement, by monetary authorities, of the national currency by the dollar as legal tender.' While there exists a good theoretical understanding of the implications of having two monies, the empirical consequences are still an open issue. It is commonly believed tlhat allowing a foreign currency to coexist with the domestic one provides the opportunity for greater domestic intermediation, promotes financial sophistication by increasing the number of available assets, and increases credibility by raising the cost of monetary indiscipline. Furthermore, the rapid development of foreign currency-denominated operations in the banking system affects the stability of monetary aggregates, the dynamics of exchange rates, and government revenues from seigniorage.<sup>2</sup> Specifically, the higher the demand elasticity of substitution between monies, the larger the shift from foreign to domestic currency as a result of the fall in expected inflation, and thus the higher the fall in the nominal exchange rate. Currency substitution also reduces monetary independence, which may then endanger the ability of central bankers to implement stabilization programs. Finally, currency substitution lessens the central bank's ability to act effectively as a lender of last resort.

The purpose of this paper is to determine empirically the causes and significance of currency substitution in Latin America, as well as to explore some of its macroeconomic consequences in the last decade. Using panel data for thirteen Latin American
*|**Average number**<br>**per household**|**Region**|**Number of core**<br>**food items**|
|---|---|---|---|
|Mogadishu (Urban)|1.0|Mogadishu (Urban)|5|
|North‐east Urban|0.8|North‐east Urban|6|
|North‐east Rural|0.8|North‐east Rural|20|
|North‐west Urban|0.9|North‐west Urban|7|
|North‐west Rural|0.8|North‐west Rural|17|
|Central regions Urban|1.8|Central regions Urban|1|
|Central regions Rural|1.1|Central regions Rural|13|
|Jubbaland Urban|2.1|Jubbaland Urban|20|
|Jubbaland Rural|2.6|Jubbaland Rural|20|
|South West Urban|1.0|South West Urban|10|
|South West Rural|0.9|South West Rural|8|

Source: Authors' calculations.

Source: Authors' calculations.

While the validity rate of submissions was in line with other regions (Table 4), the consumption data were flagged as outliers more often than in other regions during the review and cleaning process (Table 6). Further, the profile of food consumption for households in urban Jubbaland was different than in other urban areas for 20 of 38 core food items (Table 7). These issues in the consumption modules led to inconsistent poverty rates. Therefore, the information on the consumption modules (food, non‐food and assets) was discarded and poverty estimated based on sociodemographic and other household characteristics in a multiple imputation process routine.

# Rural North‐East

The implementation of the survey also experienced some constraints in the recruitment of field teams in the rural
economic activities into climate scenarios and transition risk classes: the NACE-CPRS-IAM classification, NGFS Technical notes.

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team had a record of contacting the male in our study sample, and, in column 2, the outcome is an indicator that the GRS team recorded enrolling the male in our study sample. The coavariates in the rows are a binary indicator that the male is enrolled in school, a binary indicator that the male’s household owns the house he lives in, a binary indicator that the male never talks to his dad about SRH topics, age of the male, a binary indicator that the male’s house has electricity, number of household members, and indicators for region of residence. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates. .01 ***, .05 **, .10 *. _Source._ Male baseline data, restricted to males in the _Boys_ treatment arm.

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