rafmacalaba/data-use-mentions-extended huggingface dataset:
again assumes that RSMC and NMHS capacities are sufficient to fully exploit improved access to global NWP, which, as discussed in Section 1.4, is generally not the case in LICs.

The influence of global forecasting products can be estimated by evaluating historical forecast accuracy, as the increase in accuracy in the last decades was largely due to the utilization of global forecasting products. At the time of establishment in the mid to late 1970s, global NWP was limited by computing power and availability/access to global observations, as well as scientific understanding of key processes involved such as cloud physics, air-sea interaction, energy transfers or turbulence. It is therefore assumed that at the time of launch, global NWP was not markedly superior to traditional forecasting approaches, at least in terms of accuracy, skill and resolution.

Quantification of increases in forecasting quality were therefore estimated considering changes in forecasting quality of ECMWF operations since 1980, published by the WMO (Simmons 2011). The data from 1980 is treated as a proxy of forecasting quality without global NWP, with an increase in ECMWF forecasting quality from 1980 to 2010 of 15 to 35 percent (depending on duration of forecast and hemisphere being forecasted). Other historical records of forecasting performance lead to similar results (e.g. NOAA 2007 and Met Office 2011).

It must however be considered that modern weather forecasting for time periods longer than 24 hours is virtually impossible without any global forecasting products, at least in the high latitudes (extratropics). Therefore, the estimation of between 15 and 35 percent improvement in overall forecasting quality has to be seen as a conservative assumption for example in North America and Europe. However as most LICs are located in the tropics, where multi-day forecasts can be skillful even without NWP, we assume that the provision of the full suite of global forecasting products would increase forecasting quality in LICs by 15 to 35 percent. Improvements in forecasts in LICs over 4-5 days are likely almost fully due to global NWP.

This increase in forecasting quality subsequently leads
were made, testers also administered a short household baseline survey and recorded information on sales and, in case of purchase, the result of the test. Additionally, surveyors recorded GPS coordinates of all wells and noted down whether there were any visible labels attached to the well indicating the status with respect to arsenic. The baseline questionnaire included a household roster, basic questions on socio-economic status, detailed questions on the well, and a number of questions related to knowledge and practice in relation to drinking water and arsenic risk. Testers also recorded whether, according to the respondent, the arsenic status of the well water was safe, unsafe, or unknown. Information about a total of 12,606 wells was recorded and the household survey was completed for all but three well owners.

A limitation of the data is that, in case of group sales in arm B, surveyors did not keep accurate records of who belonged to which group. In other words, while the data indicate for which wells a test was purchased, one cannot study the characteristics of buyers belonging to the same group, or to what extent well sharing was actually taking place _within each group_ as a consequence of the test results.

The endline survey was completed between August 2016 and January 2017.<sup>10</sup> The average time

> 8Unions are the third smallest administrative unit in Bangladesh, and are formed by several _mouzas_ , which in turn are composed of two to three villages. The 128 study villages belong to nine unions.

> 9The Appendix shows that the randomization led to large spatial variation in treatments, see Figure A.1. Unlike

> 10the baseline survey, where the wages of surveyors and the supervisor were covered mainly from test fees, the

9
05, * p _<_ 0.1 _Notes_ : We rely on pooled cross-sectional data from the 2012 and 2018 ELMPS. Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use diplomatic exchange between Egypt and the countries of destination of Egyptian migrants at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange neither in Egypt, nor in the country of destination and is equal one if there is evidence of diplomatic exchange either in Egypt or in the country of destination at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

62
that there is not always a one-to-one relationship between ethnicity and native language (see discussion below).

# **_3.1. Intervention_**

Once the participants were selected, they were randomly assigned to one of four groups to watch a 2:34 minutes-long 360-degree VR experience using an Oculus Go VR headset (see Appendix A for still images from the VR experiences or follow the video links provided below for each treatment group). Immediately after watching the videos, each participant was asked to take a brief computer-assisted survey. The four 360-degree VR experiences in the experiment included:

- i. <u>Control group: Video of a well-known public park (Burns Garden) in its natural state, with no</u> modifications;

- ii. <u>Treatment-1</u> (urban design): Video with 3D urban design elements added to the park;

- iii. <u>Treatment-2</u> (diversity): Video with a diverse group of park users added to the park;

- iv. <u>Treatment-3</u> (urban design + diversity): Video including both the additional 3D urban design elements and the diverse group of park users.

The control group was shown a video of a well-known public park called Burns Garden in its natural state, while the three treatment videos were enhanced with additional content identified through primary and secondary research. An online survey was carried out on Facebook in Urdu to collect information on people’s perceptions about public parks in Karachi.<sup>5</sup> The survey was complemented with an extensive review of the literature on barriers that hinder the use of parks (see Appendix B). The Facebook survey indicated that public parks in Karachi were generally perceived as dull and unsafe places. When asked about the reasons for feeling unsafe, respondents mentioned lack of proper lighting at night, presence of drug users, fear of harassment (especially among women), and parks often being deserted. Respondents
Next, we address the concern that wage differentials merely reflect differences in wage trends across the winning and losing counties and, more importantly, differences which would have prevailed even in the absence of a new plant introduction. To appease such concerns, we investigate wage trend differentials in the winning and losing counties before and after the new plant introduction. To conduct this exercise, we replace the post **1** _t>τ_ +1 dummy with yearly dummies 4 years before and after the deal announcement. The coefficient _β_ 2 _,t_ now varies by year and captures the relative wage differentials between the winning and losing counties for year _t_ relative to the new plant introduction. Figure 3 plots the coefficients _βt_ before and after the MDP creation. The wage pre-trends are insignificantly different from zero for three of the four pre-periods, and the linear trend, if anything, is downward sloping. This trend reverses following a new plant introduction. The estimates are significantly positive in four of the five periods during the post period. More importantly, the linear trend switches to being upward sloping. This switch in wage trends indicates a trend break around an MDP introduction.

# **4.2.2 on Household Time Allocation**

How do households respond to wage shocks induced by new plant introductions? Our theory, as well as that obtained from an alternate setup introduced in Greenstone et al. (2010), suggests that households respond to increasing equilibrium wages by allocating more time to labor and less to other activities like shopping, or leisure. In Table 8, we investigate whether this prediction is validated empirically. The CPS March annual socio-economic supplement asks respondents about the total hours worked in the previous week, as well as the number of hours spent on the job, on average. A validity of our model prediction requires respondents to spend more time on labor, relative to the average time spent, in areas where a new plant is introduced. In other words, we expect _β_ 2 in specification (S3
those administrative divisions that are located within a 25 km radius of our protected areas (Figure S12a).

The average effect of AP management on asset wealth is 0.102 standard deviations, with a standard error of 0.034 (Figure 3, Row 5). However, we cannot interpret this increase in economic well-being as being solely attributable to AP management due to elevated levels of asset wealth immediately prior to transference (Figure S12b). This suggests that communities near protected areas destined for AP management are already becoming richer at a faster rate than communities near protected areas that never come under AP management. The post-transference stability of asset wealth could, therefore, be a continuation of this pre-existing upward trend rather than a consequence of AP’s actions.

# **2.4 Conflict**

Finally, we investigate whether AP’s activities affect conflict within and around the protected areas it manages. AP’s militarized law enforcement components might generate positive spillover effects, deterring crime and forms of political violence linked to the extraction of natural resources [64–66]. However, it is also possible that AP’s law enforcement exacerbates local insecurity. For example, if AP undermines an armed group’s revenue generation by blocking their access to protected areas with valuable natural resources, then that armed group may be more likely to target civilians as a form of revenue generation [67, 68]. Indeed, Section 1502 of the Dodd-Frank Act— a de-facto prohibition on US manufacturers’ sourcing of tin, tantalum, and tungsten from the Democratic Republic of the Congo—undercut local armed groups’ profits but increased the looting of civilians and violent clashes over mining territories [69, 70]. Alternatively, AP management may trigger protests if it both limits local communities’ access to the resources within protected areas and fails to provide local communities with alternative sources of economic opportunity, similar to the local effects of mining concessions [71].

Accordingly, we use the Armed Conflict Location and Event Database [
Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|
|Observations|3,687|1,863|3,687|1,863|
|Number of individuals|1,609|1,147|1,609|1,147|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Columns (1)-(2) and IV-FE model in Columns (3)-(4). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

41
Policy Research Working Paper 11029

# **Abstract**

This paper investigates the impact of a large-scale poverty alleviation program targeted at the 62 poorest districts in Viet Nam. The analysis of multiple data sets spanning the past 20 years uses a regression discontinuity design with district fixed effects. The findings do not reveal significant program effects on household welfare (as measured by per capita income and poverty) or local economic development (as measured by nighttime light intensity and establishment of new firms). However, the findings show that the program facilitates a shift from farm to nonfarm employment and significantly increases the share of nonfarm income for rural households. A possible explanation for the positive effects on nonfarm employment is the improved access to credit that the program provides to participating households. The findings also show that the program increases household access to electricity, public transfers, educational subsidies for students residing in the program districts, and health care utilization, possibly through improving the availability of commune health care centers.

This paper is a product of the Development Data and the Development Research Groups, Development Economics. 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 hdang@worldbank.org and vietcuong@vnu.edu.vn.

_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
# **1. Introduction**

The World Meteorological Organization forecasts that the current greenhouse gas (GHG) emissions trend will increase global temperature 3-5 degrees C by 2100 (Reuters 2018). This would far overshoot the 2-degree limit pledged by the 2015 Paris Climate Accord (COP 21) and might have a catastrophic impact (Steffen et al. 2018; World Bank 2012). In response, several industrial nations pledged very steep emissions reductions at the Leaders’ Summit on Climate in April 2021. The response deepened at COP 26 in Glasgow with the introduction of the Global Methane Pledge, whose 122 current participating nations have joined a collective effort to reduce global methane (CH4) emissions at least 30 percent from 2020 levels by 2030. The Pledge highlights the particular importance of methane reduction: “Methane is a powerful but shortlived climate pollutant that accounts for about half of the net rise in global average temperature since the pre-industrial era. Rapidly reducing methane emissions from energy, agriculture and waste ... is regarded as the single most effective strategy to keep the goal of limiting warming to 1.5 ̊C within reach while yielding co-benefits including improving public health and agricultural productivity.”<sup>1</sup> The Global Methane Pledge is a laudable commitment by the international community, and broad-based participation will build political support for emissions reduction. At the same time, a rapid transition may be assisted by an evidence-based global effort that identifies the highestpriority CH4 emissions sites and mobilizes the latest technology to monitor their progress. This paper attempts to contribute with a multi-level approach. First, we perform a spatial analysis of global CH4 emissions to assess potential gains from priority site identification. Then we use the World Bank’s gridded database of satellite-measured methane concentrations at 5-km resolution to establish a template for tracking methane emissions reduction at priority sites. We demonstrate its potential effectiveness with case studies for large sources of methane emissions from energy, agriculture and waste. Then we extend the template to create and analyze emissions trend reports for
Gujarat Figure 3a: Annual Percentage Change in Stamp Duty Revenues and Rates Figure 3b: Stamp Duty Revenues and Rates <!-- Start of picture text --> Gujarat<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>% Annual Change<br><!-- End of picture text --> <!-- Start of picture text --> Gujarat<br>60000 12%<br>50000 10%<br>40000 8%<br>30000 6%<br>20000 4%<br>10000 2%<br>0 0%<br>Year<br>Tax Revenue Stamp Rate<br>2517 4002 10347 30077 47783<br>Rate (%)<br>Rs. Lakhs<br><!-- End of picture text --> Kerala Figure 4a: Annual Percentage Change in Stamp Duty Revenues and Rates

# **Kerala**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>Year<br>Tax Rev Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Annual % change<br><!-- End of picture text --> Figure 4b: Stamp Duty Revenues and Rates <!-- Start of picture text --> Kerala<br>35000 13.6%<br>30000 13.4%<br>25000 13.2%<br>13.0%<br>20000<br>12.8%<br>15000<br>12.
Garrote Sanchez, Gomez Parra, Ozden, and Rijkers (2020) apply DN2020’s occupational measures in combination with indicators of essential sectors in European regions as determined and mandated by governments. They find that the possibility to work from home is lower for poorer workers and in less developed regions, but both studies fail to consider practical constraints to working from home.

Several papers have followed an approach different from DN2020 to identify the jobs that can be done at home. For instance, Hensvik, Le Barbanchon, and Rathelot (2020) use data from the American Time Use Survey (ATUS) and estimate the prevalence of home-based-work between 2011 and 2018. Alipour, Falck, and Schüller (2020) use data from a 2018 employment survey for Germany that includes a question on whether the worker would accept an offer from his or her employer to work from home temporarily. Adams-Prassl, Boneva, Golin, and Rauh (2020) collected new data from late March to early April 2020 in the United States and the United Kingdom, including a question about the share of tasks that they could do at home in their current (or last) job. They demonstrate that workers who are least able to work from home are most likely to lose their jobs. Bonacini, Gallo, and Scicchitano (2020) use data from the Italian Survey of Professions (ICP), which is the Italian equivalent of the O*NET, to construct an indicator of attitudes toward working from home. In general, all these articles find that working from home amenability increases with workers’ earnings and education. Their indices are positively correlated with those from DN2020, but since they are based on developed countries the extent to which their findings generalize to developing countries remains an open-ended question, given that the task content of jobs may vary with development (LoBello et al., 2019).

Two articles use skills surveys from different countries to test if US-based measures such as O*NET lead to biased results. Saltiel (2020) uses skills surveys
, women, marginalized groups, etc.), rich, granular, contextual data and accurate weather and climate information are needed, in addition to data on actual movements of people. This type of information is generally not available in population censuses or administrative records/registries and has only limited availability in most of the existing household surveys that are typically used to source data on migration such as Demographic and Health Surveys (DHS), National Labor Force Surveys (LFS) or even specialized migration surveys.<sup>9</sup> Conversely, multi-topic household surveys, by their very nature, collect data on a multiplicity of topics that go beyond household demographics and basic socioeconomic characteristics, making them an attractive prospect for studying complex, multidimensional phenomena such as the interrelationship between climate change and migration. Such surveys are common in many countries around the world, including in low- and lower-middle-income countries, where they are typically implemented on a regular basis, therefore providing more up-to-date data compared to censuses. Oftentimes these surveys are also longitudinal in nature (i.e., follow the same households and individuals over time) and georeferenced, which are both essential features for analyzing temporally and spatially dynamic processes such as climate-induced mobility and migration more generally.<sup>10</sup> Despite these appealing elements, however, there are recognized limitations

> 7 <mark>For a detailed discussion about concepts, definitions, and issues in measuring migration see Bilsborrow (2016).</mark>

> 8 <mark>This conundrum has also been fueling the (unresolved) debate among international human rights lawyers as to whether the definition of “refugees” should be expanded to include “climate refugees” (Brown, 2008). Indeed, addressing this controversy will have strong implications for policies as well as for the collection of data on cross-border mobility.</mark>

> 9 <mark>For a detailed description of the most common sources of migration data, their limitations and prospects, see Bilsborrow (2016
characteristics related to the probability of working from home have the same weight, it exploits more information than a binary approach.

We use four groups of tasks to assess jobs’ amenability to WFH. First, we use measures of physical intensity and manual work to capture tasks that are more likely to be location-specific—because they require handling large items or use specific equipment, for example—and cannot be done at home. Second, we use measures of F2F-intensive tasks such as those that involve supervision or contact with the public. Third, we create an index of ICT use at work, to reflect the fact that while some jobs may require substantial F2F intensity, some of such tasks can be carried out using ICT and do not necessarily have to be done in-person. Finally, and in contrast to existing studies, we also exploit information on having an internet connection at home as an important factor to determine the likelihood of a remote setup. This is important since workers in developing countries who may use ICT and have internet connectivity at the workplace, do not necessarily have access to the same resources at home. Another reason why we estimate the amenability—and not the fraction—of jobs that can be performed remotely for such a large set of countries is that several of the surveys were collected circa 2012, when internet connectivity was significantly lower than today. However, under the assumption that relative connectivity across countries or types of workers remained stable, our estimates can still be used to compare the WFH measures across these categories.<sup>3</sup> We find that the social distancing measures associated with COVID-19 may exacerbate the jobs divide that preceded the crisis. The jobs intensive in tasks that are amenable to WFH are more prevalent in richer countries, and among workers with high levels of education, in salaried employment and with access to social insurance. Low-income, self-employed, and informal workers are not only less likely to do their jobs when WFH is the only option, but also less likely to have access