rafmacalaba/data-use-mentions-extended huggingface dataset:
We find that while the pattern of investment in socially responsible funds is similar to Islamic funds, the motivation is not. To further test if investors view the two as substitutes, we compare how investments in the Islamic fund change when the alternative is a socially responsible fund. We find investors do not treat socially responsible funds differently from traditional funds. In particular, we find that religious investors continue to invest more in Islamic funds as well as retain more of those investments despite deteriorating financial performance, regardless of the alternative fund choice. This suggests that socially responsible funds are not viewed as substitutes of Islamic funds. This evidence, in addition to being one of our main contributions, has important implications for the communication strategies in the mutual fund industry. Even though a subsection of the Socially Responsible universe, Islamic fund managers should recognize that they cater to unique investor preferences and hence should communicate different salient features to attract and retain investors.

# **5 CONCLUSIONS**

In this paper we investigate what motivates investors in choosing religious investments. We present the results of a lab experiment, specifically designed to identify religious preferences and link them to their investment choices towards different types of investment funds. The finance literature investigates attitudes towards these investments, which are included under the label of socially responsible investments. However, observational data make it difficult to disentangle pecuniary from non-pecuniary motives due to the nature of these investments. Aside from their socially responsible label, these investments offer diversification opportunities and different riskreturn profiles, that may induce investors to prefer them to other traditional investments.

We overcome this issue of identification by exerting a high degree of control on the risk and return metrics using a laboratory study. Taking our cue from the growth in Islamic (Shariah compliant) investments, our experiment utilizes a sample from a large Muslim majority country (Pakistan) of students enrolled in a higher education institution and likely to be investors in the future. We find evidence that individuals displaying strong religious preferences also display a strong inclination towards investing in religiously affiliated
**Natural Monopoly**<br>**Partially Contestable**|
|---|---|
|13|0<br>0|
|100%|0%<br>0%|

Source: Authors’ elaboration

# V. Application: Empirical Exercise to Validate the Sector Taxonomy

An analysis of the average number of firms operating in each disaggregate sector was conducted using data published by Eurostat. In recent years, granular data on the number of firms has increasingly become available at a disaggregated sector level. Data on the number of firms at the level of NACE 4-digit sectors was obtained for 30 high-income countries and 5 upper middle-income countries in Europe using from Eurostat’s Structural Business Statistics (SBS). The data is compiled yearly by national statistical institutes based on information from statistical business registers, administrative sources, and surveys. SBS covers NACE Rev. 2 sections B to N and division 95, corresponding to 505 out of the 615 4-digit sectors in NACE Rev. 2. The analysis used the most recent data for 2019, cleaned to remove country sectors with missing information and country sectors with zero firms. In the latter case the sector has been presumed as nonexistent in a country, hence not relevant for the analysis.

The number of firms operating in disaggregated sectors is used to review and validate the proposed taxonomy. It is important to note that the number of firms proxy is imperfect because the number of firms observed in a sector does not automatically indicate whether a sector is competitive or not. This is due to two reasons. First, the number of firms in a sector may be high but composed of several markets (e.g., local markets) that are not competitive. Secondly, the observed number of firms can be different from 29
- Fiorini, M., Sanfilippo, M., and Sundaram, A. (2021). Trade liberalization, roads and firm productivity. _Journal of Development Economics_ , 153:102712.

- Ghosh, T., Anderson, S. J., Elvidge, C. D., and Sutton, P. C. (2013). Using nighttime satellite imagery as a proxy measure of human well-being. _Sustainability_ , 5(12):4988–5019.

- Henderson, J. V., Storeygard, A., and Weil, D. N. (2012). Measuring economic growth from outer space. _American Economic Review_ , 102(2):994–1028.

- Iimi, A., Mengesha, H. A., Markland, J., Asrat, Y., and Kassahun, K. (2018). Heterogeneous Impacts of Main and Feeder Road Improvements: Evidence from Ethiopia. _World Bank Policy Research Working Paper_ , (8548).

- Jedwab, R. and Storeygard, A. (2016). The Heterogeneous Effects of Transportation Investments: Evidence from sub-Saharan Africa 1960-2010. https://urbanisation.econ.ox.ac.uk/papers/the-heterogeneous-effectsof-transportation-investments-evidence-from-sub-saharan-africa-1960-2010.

- Kebede, H. A. (2021). The gains from market integration: The welfare effects of new rural roads in Ethiopia. Available at SSRN: https://ssrn.com/abstract=3971749.

- Li, X. and Zhou, Y. (2017). A Stepwise Calibration of Global DMSP/OLS Stable Nighttime Light Data (1992-2013). _Remote Sensing_ , 9(6).

- Li, X., Zhou, Y., Zhao, M., and Zhao, X. (2020). A harmonized global nighttime light dataset 1992–2018. _Sci Data_ , 7(168).

- Michaels, G. (2008). The Effect of Trade on the Demand for Skill: Evidence from the Interstate Highway
for some of the variables of interest, in particular self-assessed health. As a result, nearly 20% of observations were dropped. In addition, approximately 10% of the observations had to be dropped because data were only available for one year. A small number of observations were also dropped as outliers in the data.

> 8 The use of self-assessed health to study the impact of health shocks also has important limitations. These are discussed in more detail in the method section.

> 9 The question about self-reported status in the CHNS is asked relative to ‘other people in your age’. As a consequence, changes in SAH should not reflect secular, age-related, changes in health status.
# **Annex 1 Data available for c the assessment indirect impacts**

# _Available data on the economic cost of disasters_

The emergency Events Database (EM‐DAT) maintained by the Center for Research on the Epidemiology of Disasters (CRED) at the Catholic University of Louvain, Belgium (http://www.emdat.be) is an important source of publicly available data on natural disasters. This database is compiled from diverse sources such as UN agencies, NGOs, insurance companies, research institutions and press agencies.

EMDAT defines a disaster as a natural situation or event which overwhelms local capacity and/or necessi‐ tates a request for external assistance. For a disaster to be listed in the EMDAT database, at least one of the following criteria should be met :

- 10 or more people are reported killed

- 100 people are reported affected

- a state of emergency is declared

- a call for international assistance is issued

Disasters can be hydro‐meteorological, including floods, wave surges, storms, droughts, landslides and avalanches; geophysical, including earthquakes, tsunamis and volcanic eruptions; and biological, covering epidemics and insect infestations (these are much more infrequent in this database).

The amount of damage reported in the database consists only of direct damages (e.g., damage to infra‐ structure, crops, and housing). The data report the number of people killed, the number of people af‐ fected, and the dollar amount of direct damages in each disaster.

Reinsurance companies surveys are also an extensive source of data but two main issues are met :

- the data are not publicly available, or only in a really aggregated fashion

- reinsurance companies are collecting data based on the losses insured, so biased towards coun‐

- tries in which insurance is well developed.

# _Data needs for econometric analysis_

For econometric analysis, long time series with sufficient numbers of events are required to have an ade‐ quate sample. Three main problems are met. First collection of data on
managers and visits to the plant in order to get further information for an assessment of the strengths and weaknesses of the firms following Peiex’s standard methodology.

Based on this information, the extensionists propose a plan for introducing some improvements in fields related to strategic management, human resources, finance and cost, marketing, production, design and trade. In cases where the extensionists have no knowledge to give the necessary support for

> 22This includes industrial associations and universities in Brazil.

> 23Due to the fact that we are using annual data we considered the year of implementation based on the semester the NO was implemented in the state. It takes time for these agencies implement the program. Those NOs installed in the first semester of the year were considered as being over the year. S ̃ao Paulo was installed in December 2010. Therefore, it is considered as implemented in 2011.

10
the images to the atmospheric radiance values; thus, merging the radiometrically-calibrated images should not produce any anomalous results in classification. However, errors were observed when a merged image was classified for entire Nepal. Therefore, we classified all images separately according to a guided classification scheme using Erdas Imagine 9.3, using hybrid methods combining unsupervised and supervised classification techniques. Initially, an unsupervised Iterative Self-Organizing Data Analysis Technique (ISODATA) routine was run on Landsat bands 1, 2, 3 and 4 of MSS and 15 and 7 of TM and ETM+ to cluster individual images into 25 classes. These classes were visually analyzed using flicker to assign specific class names. Spectral profile curves were used to examine the objects reflection by electromagnetic radiations. Pixels that correspond to clouds, land surfaces under cloud shadows, and shadows caused by the terrain (about 5-7% of the image area) were then removed from the images. The remaining portions of the image areas were then clustered into 20 classes by a second ISODATA routine. Clusters were labeled to represent specific land-cover classes, and signatures of the labeled clusters of each image were used as the basis for a supervised maximum likelihood classification of the individual images. Class names were assigned to various land use and cover classes.

Identification of roads on the classified images was reconfirmed by overlaying the vector layers available from the national Department of Survey. After the proper identification of roads and rivers, they were extracted from the images as separate raster files.<sup>11</sup> We separated these roads and rivers into layers, and merged these classes with the bare-land as bare-land and roads showed overlapping values (1550 - 1680, 1700) in the Transformed Divergence Index (TDI). A TDI calculates standard divergences between pairs of classes, and it is used to measure the separability between land use classes, such as mature forest, secondary growth, and farmlands. In satellite image classification, TDI is needed to assess the quality of the statistics prior to image classification. After careful analysis
enterprise’s performance (table 4.2). Having a bank account is not a common phenomenon in rural Yemen; however, money transfer is more often used in rural business. Therefore, a moneytransfer variable is used as a proxy for a financial service. Facing payment disputes is highly correlated with performance because disputes for payments happened more frequently with more business transactions. Our pooled data also show that transport and electricity are key factors that affect enterprises’ performance.

**Table 4.2 Household Farm and Nonfarm Business Revenue Model**

|Model<br>Enterprise Type<br>Independent Variables|(1)<br>Nonfarm<br>Pooled data|(2)<br>Nonfarm<br>Pooled data|(3)<br>Nonfarm<br>Male|(4)<br>Nonfarm<br>Female|(5)<br>Farm<br>Pooled data|(6)<br>Farm<br>Pooled data|
|---|---|---|---|---|---|---|
|**Enterprise Characteristics**<br>|||||||
|ln (enterprise age)|-0.044|-0.045|-0.085|0.442|-0.378|**-1.153****|
||[0.107]|[0.105]|[0.107]|[0.433]|[0.390]|**[0.472]**|
|dummy: female manager|**1.124*****<br>**[0.281]**|**0.822*****<br>**[0.292]**|||0.894<br>[
|Cambodia|
|---|
|Lao PDR|
|Lesotho|
|Ethiopia|
|Sri Lanka|
|Tanzania|
|Southeast Asia|
|Rest of Africa|
|Rest of the world|

# **_Baseline_**

The GTAP database is benchmarked to 2011. We run the model to 2018, replicating the key macroeconomic aggregates from the World Bank’s _Global Economic Prospects_ (GEP 2016)<sup>[3]</sup> report. Population growth is based on the medium fertility variant of the 2012 UN’s population projections. Labor force growth follows the growth of the working age population – defined here as the demographic cohort between 15 and 64 years of age. The evolution of supply of skilled and unskilled workers is consistent with the IIASA constant educational trends (CET) scenario, where growth rates of the supply of skilled workers exceed that of unskilled. Capital accumulation is equated to the previous period’s (depreciated) capital stock plus investment. Productivity growth in the baseline is “calibrated” to achieve the growth rates for the baseline scenario (as in the GEP (2016)) up to 2018, then we fix the productivity growth for 2018-2030 to be equal to its 2017 value. These productivity growth rates remain fixed in the counterfactual scenarios. The baseline scenario also incorporates tariff and NTM reductions in existing FTAs. These are based on the data set provided by the International Trade Center, including all TPP members’ FTA commitments up to 2046 (MacMaps, 2016).

The congressional AGOA Extension and Enhancement Act of 2015 has created greater uncertainty for Lesotho’s exporters and their trading partners. The Act provides the United States with greater flexibility in reviewing countries on an ongoing basis, allowing the privileges to be either withdrawn or suspended throughout the calendar year. The possibility of quarterly reviews of Lesotho’s compliance with AGOA requirements could lead to lower orders from the United States depending on investors’ assessment of the likelihood of Lesotho retaining the AGOA privileges.

Potential changes to
and with at least one sibling attending a school similar to the main treated school in the treatment districts.

Imasen, a local survey firm, conducted data collection together with a local institution specializing in nutrition ( _Instituto de Investigation Nutritional_ ). The surveys were performed by more than a dozen teams, each consisting of a field supervisor, anthropometric and biometric technicians, household enumerators, and an observer for structured observations. All field team members received extensive training and followed standardized protocols supervised by the project’s investigators to ensure accurate and consistent data collection.

# **4.1 Variables of Interest and Measurement**

Data were collected on a wide range of variables and from different sources. First, a comprehensive household survey inquired about dwelling characteristics, household assets, education, income, labor outcomes, water sources, sanitation, and toilet and handwashing facilities, among other relevant variables (see Appendix 1 for the complete list). Also included were questions on exposure to handwashing campaigns, determinants for handwashing behavior—handwashing knowledge, beliefs, and access to and placement of soap and water—and caregiver self-reported handwashing behavior. To complement the handwashing behavior module, structured observations, during which the family’s handwashing behavior within the household was observed for four to five hours, were conducted on another day following the survey. During that period, the interviewer noted in detail any handwashing event involving adults and children during her stay.<sup>26</sup> In addition, to measure environmental contamination, drinking water microbiology samples were collected inside the households at the time of the survey and analyzed in the field using a membrane filtration technique. The samples were analyzed to determine the presence of _Escherichia coli_ and other types of coliforms.

To assess the household situation with regard to children, caregivers were given a detailed questionnaire with questions about the child’s environment, maternal depression, the

> 26 Structured observations were only carried out for the treatment and control samples of the school treatment.

14
can also be difficult to distinguish from one another (De Brauw and Carletto, 2012).<sup>12</sup> Finally, specifically concerning climate-related hazards, it is unlikely that migration caused by a fastonset climatic disaster would be captured in an LSMS-ISA survey, given the localized nature of these types of events and unless the survey takes place soon after the shock (De Brauw and Carletto, 2012).

> 12 _Seasonal_ migrants are those who leave for a specified period of time each year and should be identified through questions about repeated, short migration spells. _Return_ migrants migrated at some time in the past and have returned to the country or household somewhat permanently. _Circular_ migrants are those who have returned but plan to leave again for a significant period of time, or repeatedly migrate for long spells.

25
covering four thematic areas: health service facilities, personnel, supplies, and excess mortality (Appendix Table 1).

Evidence to aid such comprehensive planning, however, is scarce. For instance, globally there is a particular concern about health facility capacity for infection control and protection of health workers at the frontline of diagnosing and treating COVID-19. In absence of reliable, real-time data on these indicators, large-scale health facility surveys could be utilized to gauge health system preparedness in these matters. Likewise, health service delivery assessments (such as the Service Availability and Readiness Assessment (SARA),<sup>6</sup> Service Provision Assessment (SPA)<sup>7</sup> and Service Delivery Indicator (SDI) survey<sup>8</sup> ) can also help measure overall system strength for maintaining core functions while absorbing surge cases, and resilience to withstand shocks during pandemics.

# Background on the SDI surveys

Service Delivery Indicator (SDI) surveys generate nationally representative data on the quality of education and health services. The indicators enable governments and citizens to identify gaps and track progress within and across countries over time. They transform the way quality of social sector services is measured, and in so doing, how countries develop policies and design interventions to improve health and education outcomes around the globe.

The SDI program is a product of a partnership between the World Bank Group, the African Economic Research Consortium (AERC) and the William and Flora Hewlett Foundation. Since the inception of the initiative in 2010, 24 surveys have been completed in 12 countries in Africa, capturing the health and primary education service delivery experience of more than 500 million people. The surveys have been extended not only in Africa but around the globe: data collection for the first SDI surveys in Latin America, East Asia/Pacific, and South Asia is currently underway.

Information available through health SDI surveys is not readily or reliably available through routinely collected administrative data even when they exist. In some countries with weak health
self-reporting. Finally, the general disability question tends to lead to underestimates of disability prevalence (Mont, 2007) as they capture severe impairments and tend to miss disabilities associated with old age as older people may not think of themselves as disabled but simply as older persons. For these reasons, it is not considered good practice to use the general disability question, let alone for the purpose of producing internationally-comparable statistics on disability.

Yet, likely due to its conciseness and a lack of awareness on its drawbacks, this question can still be found in some surveys and censuses and this paper examines in the next section to what extent this has continued to be the case in the past decade.

# **Other disability-related questions**

There are many other types of disability questions beside functional difficulties, ADL, broad activity limitations, and general disability questions. Some surveys such as the Model Disability Survey have questions on barriers people may face in their environment, whether physical, social, or attitudinal. These are particularly useful in understanding the drivers of difficulties or deprivations people may experience.

In some countries, surveys or censuses ask respondents if they are part of an official registry of people with disabilities which would give them access to services or benefits. More often, surveys have questions on impairments and on specific health conditions.

Health conditions may be temporary or chronic, physical, or mental, life threatening or not, infectious or non-communicable. Not everyone has access to health services, which is necessary to have a diagnosis associated with doctor/clinic visit or hospitalization. Such questions, in fact, identify those with a diagnosis as among those with access to health care, which may be a small minority in the global south. In order to capture health conditions among those 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
suggest that a half million Haitians live in the Dominican Republic. Given a total population for Haiti of 9.5 million in 2005 (based on an adjusted figure from the country’s 2003 National Census), this means that roughly 10 percent of the Haitian-born population lives abroad. It is unclear, however, in what direction this biases the figures presented here.

# **4. Determinants of School Attendance**

In this section we switch from examining the long-run historical evolution of school attainment to consider the more recent story of school attendance. Figure 5 summarizes attendance rates by age, based on children observed in the 1994, 2000, and 2005 DHS. Substantial increases in attendance rates between 1994 and 2000 are evident at every age, and overall the primary attendance rate (for children 6-14) increased from 73.6 to 86 percent between those two years. The years 2000-05 registered a slight decline in primary school attendance, to 83.4 percent. Table 2 shows attendance rates disaggregated by area of residence and gender. The rural-urban gap in attendance rates diminished greatly between 1994 and 2000 but was still 11 percentage points in 2005. The gender gap in schooling reversed in the most recent survey, so that there were more girls than boys attending school in 2005. This phenomenon follows the recent trends in the Central American and Caribbean region as whole, where girls have higher attendance rates, lower dropout, and better overall performance.

The large expansion of school supply during the late 1990s appears to have narrowed the gap between rich and poor in school attendance. Figure 6 shows the attendance rates for the years 1994, 2000 and 2005, by quintiles of a wealth index. From 1994 to 2000 the attendance rates for the poorest quintile increased from 51 percent to 71 percent, while the attendance rates increased only slightly for the richest quintile. Gaps, however, persist: in 2005 67 percent of children in the poorest quintile attended school compared to 95 percent of the children in the top quintile.

In Figure 8