rafmacalaba/data-use-mentions-extended huggingface dataset:
at the provincial level, to build a correction factor to consider both the share of employment in the population and the share of employment in agriculture in total employment at the provincial level. Rural shares and employment shares in agriculture are positively but imperfectly correlated (Annex D). In China, manufacturing is the sector with comparative advantage. We use employment data collected at the firm level with information on the postal code of the firm and its sector of production. Employees in manufacturing firms are summed at the district level, and adjusted with the share of employees in total population at the national level to obtain the share of employment in manufacturing per district.

## **3.5.2 Transport costs**

Following the economic geography literature, we use changes in transport times to measure changes in iceberg transport costs.

> 12Raions are subnational levels and belong to oblasts, translated as provinces in English

16
vast majority of households (96 percent) did not have any electricity source.

**Table 2: The data**

||**Burkina Faso**|**Rwanda**|**Senegal**|
|---|---|---|---|
|**Year**<br>**of**<br>**Data**<br>**Collection**|2010|2011|2011|
|**Type of Sample**|Non‐electrified HHs in<br>villages selected for<br>electrification through<br>Solar Home Systems<sup>1</sup><br>before intervention|A random sample of<br>HHs in villages<br>selected for<br>electrification through<br>grid extension before<br>intervention|The random sample<br>of HHs in villages<br>selected for<br>electrification<br>through mini‐grids|
|**Location**|Rural area with slightly|National|Remote rural area,|
||above‐average income|representative|partly|
||opportunities due to||above‐average soil|
||cotton farming||fertility|
|**Sample Size**|553|1008|361|
|**The share of HHs with**|0|0.03|0.25<sup>2</sup>|
|**electricity access**||||

_Notes:_<sup>1</sup> Households within these villages that already have a pre‐electrification source (mainly solar panels) have been excluded.<sup>2</sup> Most common access technology in Senegal is a solar panel; there are also very few car batteries and generators.

The Senegal data are random samples of households in villages that the Senegalo‐ German Energy Access
20 statistical measure, and kerosene had the lowest pass-through, signifying the reluctance of many governments to raise the price of kerosene for household use.

Table 3: Summary of pass-through statistics for January 2009–January 2012, all countries

|**Parameter**|**Gasoline**|**Diesel**|**Kerosene**|**LPG**|
|---|---|---|---|---|
|Number of countries|73|73|45|52|
|Average pass-through (%)|83|71|56|78|
|Median pass-through (%)|95|84|61|75|
|||**Percentage**|**of countries**||
|Pass-through greater than 50%|71|70|56|58|
|Pass-through greater than 75%|60|55|38|50|
|Pass-through greater than 100%|42|34|20|38|
|Pass-throughgreater than 150%|10|1|0|13|

_Sources_ : Tables A2.1 and A2.3, and author’s calculations.

Statistics by income, oil trade status, and region for each fuel can be found in tables 4–7. As expected, net oil importers were much more likely to pass through price increases on the world market. For gasoline, diesel, and kerosene, the percentage of countries with pass-through coefficients higher than 75 percent and the median pass-through increased with rising income. This income trend is somewhat surprising for gasoline, which would generally be a fuel of the rich in low-income countries. For LPG, the degree of pass-through was highest for low-income countries, although this needs to be interpreted with caution because there were only seven countries in that income group. Because LPG use occurs most frequently among better-off urban households in low-income countries, this pattern of pass-through as a function of income would be progressive. These relationships
ownership and M&As**

Studies of domestic bank governance generally focus on the performance effects of bank scale or domestic M&As, and typically do not account for static differences in performance between domesticallyowned banks and their foreign-owned or state-owned rivals, if any. Much of the research uses U.S. data. Research on bank cost scale efficiency using data on U.S. banks from the 1980s generally finds very little scale economies or diseconomies (e.g., Berger, Hanweck, and Humphrey 1987), although data from the 1990s suggests that there may be more cost scale economies than in the 1980s due to technological progress (e.g., Berger and Mester 1997). Some also find possible revenue benefits for large banks in the 1990s (e.g., Demsetz and Strahan 1997). Other research finds that large and small banks may serve 3
), technology adoption (Suri, 2011; Jagnani et al., 2021; Arag ́on et al., 2021). Our finding suggests that economists need to be much more careful about the remote sensing data source and the metric they use to measure weather. While our analysis provides practical guidance about what sources and metrics are generally reliable, researchers may need to demonstrate the robustness of their results to different sources and metrics when these are key to their identification strategy.

The paper is organized as follows: in Section 2 we discuss the sources and characteristics of the weather data and the household data. We also provide details on how data was integrated, including specifics on how the blinded data was combined. The section concludes by presenting some descriptive evidence of mismeasurement in the weather data. Section 3 provides details of the pre-analysis plan, specifically our estimation strategy and approach to inference. Section 4 discusses results, first covering differences by obfuscation method, then by weather metric, and finally by remote sensing product. Section 5 provides a summary of the results and recommends six best practices for researchers looking to use remote sensing data in combination with socioeconomic data. We also outline future work and then, in Section 6, conclude.

# **2 Data**

We use existing, publicly available satellite-based weather data products combined with publicly available unit-record survey data that have been generated as part of the World Bank LSMS-ISA initiative and that are made available through the World Bank Microdata Library. In this section, 7
to the same location-scale family (see, e.g., Casella and Berger (2002, pp. 104)). In addition, Dang _et al._ (2017) also offer a formula to standardize the variables over multiple periods given further assumptions.

To make the notation simpler, we do not show the equations above with sampling weights. However, sampling weights can be straightforwardly incorporated in the formulas. For example, consider a commonly used stratified two-stage sample design where the population is divided into R strata, nr clusters are sampled out of a population total of Nr clusters from stratum _r_ in the first stage, and mrc households are sampled out of a population total of Mrc households from cluster _c_ in stratum _r_ in the second stage. Suppose that all individuals within a household share the same poverty status (i.e., poverty is measured at the household level), and household _h_ has mrch members. The formula for estimating the poverty rate at the _s_<sup>_th_</sup> random draw given in Equation (4) can be modified accordingly as P(y�2,s1 ≤z1) = ∑ ∑ ∑h=1r P(y�2rchi,s1 ≤z1) (C.9) RR nnrr mm rr=1 cc=1 r rrcch with the sampling weight = ww , and the subscript _i_ indexing individual _i_ in ~~�~~ �� r NNrr MM household _h_ . We provide a Stata user-written routine that automates the estimation procedures (Dang wwrrcch nnrr mm �mmrrcch and Nguyen, 2014).

Dang and Lanjouw (2023) discusses several scenarios where survey-to-survey imputation are most useful in the absence of consumption data. These include i) filling in missing data gaps (most 75
159 countries for the full GEGI. But this does not include the interpolation used to produce measures of hours worked and the sectoral mix (the parts not included in the basic GEGI). Only about half of countries in their sample (53/95) have data on hours worked, and so they interpolate these data for all the other 42 countries. They only use data on sub-sectorial labor productivity and employment by gender for around a quarter of countries in their sample (25/95), and so they interpolate this for other countries. As such, there are only 14 developing countries in their sample with complete data for all three types of gaps.<sup>37</sup> The basic GEGI is the most comparable with the GDP gains from closing labor force participation gaps in MGI (2015). While MGI does not report gaps for all countries, for the regions and selected large countries reported in their exhibits 6 and 7, the effects of closing gender gaps in participation rates on GDP line up quite closely with estimates from the basic GEGI (Figure 10) — and for MENA and SA (ex. India), the basic GEGI is slightly larger. When including the gains from closing gaps in hours and sectoral allocation, the GDP gains are unsurprisingly slightly larger in MGI on average (see Appendix Figure 5). These other two mechanisms are less important for the regions/countries with the largest gaps (such as India and MENA), but more important for the regions with lower participation-based gaps. However, as the hours and sectoral gaps are interpolated for many developing countries, those estimates may be less reliable – especially at the individual country level.

**Cuberes and Teignier (2016)** estimate the effects of gender gaps in both entrepreneurship and labor force participation on GDP, using a Lucas span-of-control type model of occupational choice.<sup>38</sup> Their estimates are available for 139 countries (33 OCED, 106 non-OECD), which is about 75
Or the estimate of the quantity consumed each day or the number of days per month a home-produced good is consumed may reflect the pattern during the month that household is interviewed rather than the average over the year.<sup>23</sup> Thus any seasonal variation evident in the surveys could reflect genuine seasonal variation in expenditure as well as systematic seasonal recall biases.

To assess the extent of seasonal variation, we regressed the log of household expenditure on dummy variables for the season during which the household was interviewed as well as on a set of variables included to control for possible non-randomness in the distribution of households across months of the year, including household size, the square of household size, and dummy variables for education levels. To determine whether there were region-specific seasonal interactions, a set of regional dummies was then interacted with dummy variables for three time periods during year: April/May, correspondingly to the 1995 survey; June-November, corresponding to the 1993 survey, and December-March, the remaining months. Imposing the restriction that the sum of the three time period 23 To examine whether there was any seasonality present in the annual estimate of consumption of home production, we regressed the annual estimate of consumption of home production (for those households reporting positive home consumption) on the region and crop dummy variables interacted with the time period variables. The June-November time period was split into two sub-periods: June-August, and September -November, corresponding more closely to the pre and post-harvest periods. In the regression over the sample of households with expenditures less than or equal to 100,000 CFAF per capita, only the East Forest*June-August dummy is significant at the 5 percent level, indicating that expenditures among East Forest non export crop farmers are significantly lower than the December-March period. Over the much larger sample of households whose expenditur **e** xceeds 100,000 CFAF per capita, the Other Cities, East Forest, West Forest and Savannah JuneAugust dummies are significant and negative. This suggests that the time of the
# **2. Method**

## **2.1. Study Sample**

To identify whether different behaviorally informed vaccine messages can increase intent to vaccinate, we conducted 28 randomized survey experiments using social media in 23 countries and territories between January 2021 (when vaccines were not widely available) and June 2022 (when vaccine supply was widely available). See Appendix A for a list of sampled countries and when studies were implemented in each country. Multiple studies were conducted in Lebanon, Iraq, and Jordan throughout the course of the pandemic. Each study followed similar protocols for recruitment, experimental design, and survey measurement, which are described below. The results from individual studies have been reported in other publications for Belize, Haiti, Jamaica (Margolies et al., 2023), Papua New Guinea (Hoy et al., 2023), Saudi Arabia (Lim et al., 2024), and Zambia (Hoy et al., 2022). Also, a separate survey with family doctors in Romania (Badiani-Magnusson et al., 2022) was conducted as part of the same global research program, yet was not included in this IPD meta-analysis. Our experiments were approved by the Health Media Lab Institutional Review Board (HML IRB), approval number # 1017TWBG21.

## **2.2. Participant Recruitment and Sampling**

Participants were recruited through Facebook using quota sampling by displaying ads inviting participants to interact with a chatbot in Facebook Messenger to complete the survey (using languages of preference) for a chance to win a lottery reward.

The sample in each country was drawn from all Facebook accounts 18 years and older in the country. We used quota sampling by exposing the recruitment ads to pre-defined demographic groups based on age, gender, and region to mirror the country’s populations. To get a quota sample and optimize recruitment, we updated the ad placement in real-time based on participating Facebook users’ characteristics to ensure proper representation of each demographic group in the final sample
<br>50% Q4 50% Q4<br>Q3 Q3<br>40% 40%<br>Q2 Q2<br>30% 30%<br>Q1 (Poorest 20%) Q1 (Poorest 20%)<br>20% 20%<br>10% 21% 2% 10% 22%<br>7% 7% 1%<br>0% 0%<br>Basic High Tertiary Basic High Tertiary<br>Scenario 1 Scenario 2<br>Source: LCMS VI<br>Note: Poorest quintile represents the lowest consumption level; richest quintile captures the part of the distribution<br>with the largest consumption per capita.<br>Total and Basic Education (thousands)<br>High School and Tertiary Education (thousands) High School and Tertiary Education (thousands)<br>Quintile participation<br>Total and Basic Education (thousands)<br>Quintile participation<br><!-- End of picture text -->

# **5.1.2. Healthcare Services**

Those individuals who reported being ill or injured during the 15 days before the survey follow a uniform distribution across socioeconomic groups. (See ―Injured or Ill‖ in Figure 4.) The same distribution is observed both for those who did not report visiting any public facility and among those visiting a public 23
capita) and the share of people living in households citing clean energy as their primary sources for cooking.

**Figure 2  Percentage of people living in households citing different primary energy sources for cooking and national annual expenditures per capita**

<!-- Start of picture text --> Charcoal/coal<br><!-- End of picture text --> <!-- Start of picture text --> Kerosene<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data _Note_ : The Botswana survey combines firewood and charcoal under wood. Expenditures per capita are annual. Clean = LPG, electricity, biogas, and solar; others = mostly agricultural residues and dung.
**<mark>Figure 5a: Bangladesh’s exports and imports up sharply starting in 2005</mark>**

_(Bangladesh’s exports and imports as a share of GDP, 1970–2016, %)_ <!-- Start of picture text --> 30<br> 25<br> 20<br> 15<br> 10<br> 5<br> -<br>Exports Imports<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> _Source:_ World Development Indicators database at the World Bank.

A rise in exports could be the result of increasing supply (due to changes in Bangladesh) or a rising demand (due to changes in the global market). One way to differentiate between a change in supply and a change in demand is to follow the change in prices over time. Figure 5b shows the U.S. unit value (in dollars per square-meter-equivalent, or SME) from OTEXA for U.S. imports from Bangladesh both alone and divided by the unit value of SME imports from the rest of the world. Figure 5b clearly shows a sharp increase in the U.S. price for Bangladesh imports that begins when the MFA ends at the end of 2004.

**Figure 5b: Unit Values of U.S. Imports from Bangladesh**

<!-- Start of picture text --> 1990 2000 2010 2020<br>Year<br>US Bangladesh/World Price P Bangladesh<br>1 3<br>.9<br>.8<br>2.5<br>.7<br>P Bangladesh<br>US Bangladesh/World Price .6<br>2<br>.5<br><!-- End of picture
agreement is ratified, its content is not expected to change unless there is an enlargement in the future. The variable takes a value of zero before an agreement is signed and then turns on to the above sum after the signature and remains the same until the end of the sample period. Therefore, the identification of the impact of depth comes from time variation within each pair of countries.

Another way to measure depth is to separate the disciplines between those that fall under the current mandate of the WTO (such as _tariffs_ , _customs_ and _anti-dumping_ ) and those that go beyond this mandate and are not subject to any kind of WTO agreements (examples are agreements on _investment_ , _competition policy_ , _labor market laws_ , or _environmental regulations_ ). Following (Horn, Mavroidis, & Sapir, 2010) we call the former measure of depth WTO-plus and the latter WTO-X and define them as: i = ∑14 i and i = ∑38 i , where i pp xx pp are 14 WTO+ disciplines and WWTTWWT WWDD ii pp=1 TTD DD ii i are 38 WTO-X disciplines included in an WWTTWWTTWWTTWWTT ii xx=1 TTD DD ii TTD DD ii xx agreement between countries and in year ii . TTD DD DD jj TT In the second stage of our analysis, we use dummy variables to identify the commitment towards individual disciplines in PTAs. While including these dummies sequentially, we also make sure that we control for the rest of the PTA content by including a variable of the “remaining” depth as the sum of disciplines except the one singled out in the specification. We sequentially tested all core disciplines mapped in the database with clear economic content, to eventually restrict ourselves only to those having a significant impact on FDI. As shown in Figure 2, the selected disciplines are also the most frequent WTO-X areas included in PTAs.

# b. Investments

Reliable investment data are not as readily available as trade data. When available the data usually come from surveys conducted by Central Banks or Statistical Offices and are used to comply
> where<sup></sup> _i_<sup>is a policy parameter.</sup>

Profits of the transportation industry will be: Notice that prices of products and inputs are taken at their domestic parity.

# **Refineries of oil**

Refineries use oil to produce gasoline, diesel and other fuels. It is assumed that they are obtained in fixed proportions of total production. All products can be consumed domestically or exported. The main source of domestic demand is the transportation industry. The supply of gasoline, diesel and other fuels are given by: The main sources of demand for gasoline and diesel are exports and domestic use by households, firms and transportation: In the case of the rest of fuels, exports and consumption by manufactures are the main uses: Therefore, profits of the industry can be written as:

> Here  _oil_ is the input requirement of oil per unit of production of the refineries.

# **The Ghg emissions index.**

> This index _Ghgeit_ is computed as:

_t_<sup>_e_</sup> _j_<sup>are emissions of activity</sup><sup>_j_(estimated following UN environmental reports of Argentina) and</sup><sup>_A_</sup> _j_<sup>is the</sup> activity level of period _t_ .

32
market<br>*<br>Accountancy profession and<br>standards<br>*<br>Financial and banking<br>development<br>Literacy<br>S<br>Legal conventions,<br>|
|Accountability|*<br>Corruption<br>*<br>Revenue loss due to<br>administrative lapses<br>*<br>Taxpayer survey<br>|*<br>Human and non-human<br>resource quality and quantity<br>in management inspection,<br>audit, vigilance and<br>anticorruption<br>*<br>Training facilities for<br>accountability staff<br>*<br>Civil society involvement|*<br>Internal and external audit<br>*<br>Ombudsman<br>*<br>Taxpayer survey process<br>*<br>Vigilance<br>*<br>Penalty structure and<br>effectiveness for administrators<br>*<br>Reward structure and<br>effectiveness<br>*<br>Performance evaluation<br>procedure and execution for<br>staff and administrative units<br>|constitutional law<br>*<br>Law and order<br>*<br>Compliance attitudes of<br>taxpayers<br>*<br>Credibility of govermnent<br><sup>*</sup><br>Government stability<br>*<br>Civil service conditions|
|Service<br>delivery:<br>Effectiveness|*<br>Revenue to potential<br>revenue indicators<br>*<br>Activity-wise effectiveness<br>indicators<br>|*<br>Human and non-human<br>resource quality and quantity<br><sup>*</sup><br>In administrative activities<br>*<br>Recruitment Career paths,<br>|0
57

- Partnerships among governmental, nongovernmental, and local organizations that are built on sustained commitment and incorporate each member’s expertise are essential for the delivery of well-integrated and high-quality services.

- The community’s ethnic identity (in this case, of predominately Aymara origins) must be recognized and incorporated in the targeting of interventions and in the promotion of rights and equity.

Source: Gregoria Apaza Center for the Promotion of Women, 2003

# **_______________________________________________________**

campaigns have included international campaigns (such as the 16 Days of Activism Against Gender Violence Campaign), national campaigns (such as the annual campaigns conducted by the Nicaraguan Network of Women against Violence), and local campaigns conducted within individual communities. These campaigns often appear to successfully raise awareness and increase knowledge. For example, national surveys suggest that—at least partly due to the annual campaigns—women in Nicaragua are increasingly aware of their rights. A Demographic and Health Survey (DHS) found that more than two-thirds of women believed that violence against women was never justified and that they should be able to refuse sex with their partner for any reason. While it is difficult to disentangle the contributions of multiple initiatives on changes in awareness and attitudes, the DHS found that one half of women in the country had seen or heard at least one campaign message and one out of four women surveyed were able to repeat one or more of the messages (Ellsberg, Liljestrand et al. 1997; Rosales, Loaiza et al. 1999). Once again, however, researchers have not yet been able to measure the impact of these initiatives on levels of violence against women; moreover, experiences from other fields suggest that while these types of mass media campaigns play an important role in awareness-raising, strategies such as ‘edutainment’ are more likely to change behaviors over time (CDC, 2004).

Other organizations in Nicaragua have targeted specific populations such as men and youth. An example of this was a campaign carried out by the Nicaraguan organization Puntos de Encuentro.