rafmacalaba/data-use-mentions-extended huggingface dataset:
and Pakistan). In several of the Western and Central African countries where the absolute gap is less than 10 percentage points, the ratio is large (that is, between 1.24 and 1.58 in Burkina Faso, Mali, Niger, and Senegal). There are exceptions however, in Ghana the gap is only 2.5 percentage points and the ratio is 1.03, and in Cameroon and Togo it is close to 9 percentage points and the ratio of about 1.15. Perhaps surprisingly, in Bangladesh in the most recent year (1996-97) there is no female disadvantage (and there is even a small female advantage).

Although the regional patterns are strong, there is still within-region variability.

In most of the other countries covered by the DHS data, there is close to no gender gap in the youngest age group, and in many cases there is a female advantage. There are exceptions however, such as Comoros, Guatemala, Mozambique, and Turkey.

When moving to the slightly older age group, ages 12 to 14, the pattern remains much the same. In most countries where there was a gender disadvantage among 6 to 11 year olds, it is exacerbated both as an absolute and relative measure (although this doesn't hold for Egypt and Morocco). The male-female gap reaches over twenty percentage points in Benin, C.A.R., Chad, India, Nepal, Pakistan and Togo. The male/female ratio was as high as 2.06 in Niger although it has gone down since 1992. In Benin the ratio was 1.92 in 1996 with 56 percent of boys enrolled but only 29 percent of girls. Again, in the rest of the world, Comoros, Guatemala, Mozambique and Turkey stand out as having a large female disadvantage. In two of the countries that did not have a large disadvantage among 6 to 11 year olds, Bolivia and Uganda the male/female ratio is 1.11 forages 12to 14.

The
hours, enter high-paying occupations that demand time and fewer interruptions and find full-time jobs. On the other hand, research by Rege and Solli (2013) finds that, in Norway, four weeks of paid paternity leave during a child’s first year reduces fathers’ future earnings. An analysis of Swedish data by Ekberg, Eriksson and Friebel (2013) finds strong short-term impacts on the incentives for fathers to take leave, but no longterm impacts on wages or employment. Focusing on Norwegian data, Cools, Fiva and Kirkebøen (2015) find that reserving a part of parental leave for use by fathers increases the likelihood that fathers will take time off and may be associated with better school performance for children. However, the authors do not find any evidence that it reduces the gap in working hours and earnings between men and women. Tamm (2019) demonstrates that fathers’ leave-taking significantly reduces their working hours, but these labor market effects seem to be rather short-lived. Analyzing a reform in Quebec, Patnaik (2019) shows that fathers’ employment is not significantly impacted by the introduction of “daddy months.” Interestingly, Johnsen et al. (2020) observe that fathers’ own leave-taking does not affect their labor market trajectory when controlling for their relative eligibility status within the firm. However, fathers have higher earnings
under 15 million people (World Bank, 2021). It is divided into 60 districts and bordered by Zambia, Mozambique, Botswana and South Africa. Its capital, on which this analysis is focused, is Harare, a city with a population of 1.5 million inhabitants (UN, 2012) and 46 wards. Zimbabwe has experienced cholera outbreaks dating back to 1971 with the worst occurring in 2008/9 which resulted in 4,288 reported deaths over 60 of its 62 districts [25]. The focus of this paper is a more localized but severe outbreak which took place between September and December 2018 and was concentrated in the capital, Harare. Of almost 10,000 cases, 93% (9,755) were reported in Harare, resulting in 46 deaths. According to the latest DHS data, 30% of Harare’s population is connected to an existing sewer network, compared to just 12% in neighboring country capital Lusaka, Zambia (DHS, 2015 & 2018).

All the data detailed in the paragraphs below was cleared for use by the Medical Research Council of Zimbabwe (MRC-Z). The approval letter can be found in the supporting information.

3
South Asia Sub-Saharan Middle East<br>Eastern Europe Latin America South Asia Sub-Saharan Middle East & Europe & America & Africa & North<br>& Central Asia & Caribbean Africa North Africa Central Asia Caribbean Africa<br><!-- End of picture text --> In the World Bank’s Enterprise Surveys, the MENA region has the lowest percentage of firms with credit lines or loans from financial institutions, at 25.07%, compared to 56.92% for Eastern Europe and Central Asia (ECA), 54.97% for Latin American & the Caribbean (LAC), and 45.02% for South Asia. The percentage of firms in MENA using banks to finance investments was also the lowest. Sixteen (16.45) percent of firms in MENA use bank financing for investment, compared to 34.18% in the OECD, and 49.85% in ECA. Based on the numbers in the same survey, MENA firms also identify access to finance as a major constraint, more than in

> 1 See World Bank Group Enterprise Surveys.

> 2 See ―Private Credit in 129 Countries‖, Simeon Djankov, Caralee McLiesh and Andrei Schleifer, 2006.

> 3 Based on the latest datasets available on www.enterprisesurveys.org on the date of access (February 10, 2010)

3
# **Integrating Survey and Geospatial Data to Identify the Poor and Vulnerable: Evidence from Malawi**<sup>*</sup>

Melany Gualavisi<sup>§</sup> David Newhouse<sup>†</sup>

**Keywords:** _poverty, geographic targeting, small area estimation, poverty mapping, satellite data, machine learning_

**JEL codes:** C51, I32, I38

> * We are indebted to Lina Cardona, German Caruso, Chipo Msowoya, and Boban Paul for their support in providing data, financing, and support for this project. We thank Ifeanyi Edochie for assistance with obtaining geospatial indicators. We are grateful for Richard Akresh, Sarah Jansen, Nobuo Yoshida, and seminar participants at the World Bank and the University of Illinois for constructive comments.

> † Department of Economics, University of Illinois Urbana-Champaign

> § Development Economics Data Group, World Bank, and IZA
are relatively fluid and subject to change.

- Half of the camps in Ukhia and Teknaf were managed by UNHCR and the other half by IOM. Thus, while the NPM block IDs and names were familiar to IOM-governed site management, camp managements governed primarily by the UNHCR or the <mark>Relief and Repatriation Commissioner</mark> (RRRC)/army were not familiar with the information.

In addition, the field team also found it difficult to navigate the camps on the ground using the printed versions of the drone imagery, since they had no GPS support. After a validation exercise on the issues faced on the ground, the field protocols to identify sample blocks were revised.

The blocks successfully recognized by the respective camp management were fully listed. For blocks that could not be located with help from the camp management, the field team was given new instructions. The NPM data for the majhee blocks provided GPS coordinates for the centroid of each block. These coordinates were shared with the field team, who were instructed to locate the points using Google maps. Once they had

> 35 As discussed in the main text, new Rohingya refugee camps (established after the influx of August 2017) are governed by local leaders called “majhees.” A majhee block is defined by IOM as an area governed by one majhee.

18
|T|ABLE1. Workforce<br>1<br>Rural|byEmployment<br>993<br>Urban|Sector(%)<br>1<br>Rural|998<br>Urban|
|---|---|---|---|---|
|Public employees|3.12|20.01|5.90|21.51|
|Private employees|7.60|19.58|18.03|26.82|
|Self-employed|89.26|60.37|76.07|51.67|

_Source:_ Author’s calculations based on VLSS 1993 and VHLSS 2006 data.

Public sector and private sector workers are engaged in very different economic activities. A growing majority of public employees work in government, education, and health services (51 percent in 1993, 62 percent in 2006) whereas only 8 percent of private employees participated in these sectors in 1993. The remainder of public employees work in public firms, which operate in many industries: electricity and water production, mining, food and beverages, textiles, and other fields. Even when they operate in the same industry, however, public and private firms have very different characteristics. In 1998, the average public worker in the paper, metal, and plastics industries had 200 coworkers whereas the average private sector worker in this industry had only 35 coworkers. According to the 2006 Enterprises Survey, public firms are more capitalistic and generate more profits compared with private domestic firms (Vietnam SocioEconomic Development 2008). Within the private sector, foreign-owned enterprises are an exception, with a much larger size and more capital compared with domestic firms, but they represent only a small minority of the employed workers in the period that we consider (up to 5 percent in 2006).

# _Earnings in the public and private sectors_

On average, public employees earn more compared with private sector workers. In nominal terms, public
link between an establishment’s _average_ management score and its sales per worker. It also illustrates which other establishment characteristics are, _ceteris paribus_ , associated with higher labor productivity; namely size (bigger establishments have higher labor productivity), foreign ownership, exporting status, and less concentrated ownership structures (relative to sole proprietorships, all other legal forms have higher average labor productivity) _._ Table 6 also presents the results of the relationship between each of the individual practices and labor productivity. Column (2) of table 6 shows that there is a positive and significant relationship between the component of management practices that describes how establishments respond to problems and labor productivity. On the other hand, the survey data reveal no significant relationship between the number of performance metrics monitored (column (3)), nor the difficulty of targets (column (4)) and labor 16
Developing Countries_ , Oxford: Oxford University Press.

- Melitz, Marc J. (2003): “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity,” _Econometrica_ , Vol. 71(6), 1695–1725

- Melitz. Marc J. and Stephen J. Redding (2015), “New Trade Models, New Welfare Implications,” _American Economic Review_ , Vol. 105(3), 1105-1146.

- Melo, Jaime de, and David G. Tarr (1990), "Welfare Costs of US Quotas in Textiles, Steel and Autos," _Review of Economics and Statistics_ , August, 489-497.

- Minor, Peter (2013), “Time as a Barrier to Trade: A GTAP Database of ad valorem Trade Time Costs,” ImpactEcon, Second Edition, October. Available at: http://mygtap.org/resources/.

- Petri, Peter (1997), “Foreign Direct Investment in a Computable General Equilibrium Framework,” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1549616.

- Petri, Peter A., Michael G. Plummer, and Fan Zhai (2012), “Trans-Pacific Partnership and Asia-Pacific Integration: A Quantitative Assessment,” Washington, DC: Peterson Institute of International Economics.

- Rutherford, Thomas F. (2002), “Lecture Notes on Constant Elasticity Functions.” Avaiable at: http://www.gamsworld.org/mpsge/debreu/ces.pdf

45
season<br>2017/18 rainy season<br>2018/19 rainy season|
|Ethiopia|Corner point geolocation<br>method,<br>No area threshold,<br>Optical features only|2018 meher season|2016 meher season<br>2017 meher season<br>2018 meher season<br>2019 meher season|

# **5. Conclusions**

Satellite data sources have tremendous potential for amplifying the insights available from household and farm surveys. The research presented here advances our understanding of how to collect optimal plot-level survey data that can train and validate remote sensing models for highresolution crop type mapping. Specifically, we quantify the interactive effects of (i) plot size, (ii) approach to georeferencing plot locations, and (iii) size of the training data set on the performance of a machine learning-based maize classification model.

The results show that collecting a complete plot boundary is preferable to competing approaches to georeferencing plot locations in large-scale household surveys and that seemingly-small erosion in maize classification accuracy under less preferable approaches to georeferencing plot locations consistently results in total area under maize cultivation to be overestimated - in the range of 0.16 33
Table 4: Number of destinations per firm

||Me|an|Med|ian|Maxi|mum|
|---|---|---|---|---|---|---|
||LAC|All|LAC|All|LAC|All|
|2007|6.4|10.1|6.0|8.0|16|45|
|2008|6.5|10.3|6.0|8.0|16|48|
|2009|6.6|10.7|6.0|9.0|16|48|
|2010|6.7|11.2|7.0|9.0|17|51|
|2011|6.8|11.5|7.0|9.0|17|51|
|2012|6.9|11.6|7.0|9.0|17|49|
|2013|7.0|12.0|7.0|9.0|17|51|
|2014|7.0|12.3|7.0|9.0|17|61|
|2015|7.0|11.9|7.0|9.0|17|61|
|2016|7.0|11.9|7.0|9.0|17|58|
|2017|6.9|11.5|7.0|9.0|16|60|

Source: Author’s calculation based on Colombian customs data. Note: This table reports the number of destination markets by Colombian exporters over time.

# **3.2 Tariffs in LAC**

Colombian exporters saw declining tariffs in their regional destination markets between 2007 and 2017. The applied tariff decreased steadily from 6.7% in 2007
5 representative sample of refugees or IDPs) or event/transaction datasets whose unit of analysis is not the household or individual. If datasets do not have a representative sample of refugees or IDPs or their unit of analysis is not the household or individual, these datasets are flagged as false positive and thus excluded from our study. We identify 43 false positive datasets from the UNHCR MDL and removing them leaves us with 369 datasets from the UNHCR MDL.

Figure 1: Methodology to identify FDP datasets from UNHCR and WB MDLs <!-- Start of picture text --> Extract the metadata • 412 datasets<br>of all existing datasets from UNHCR<br>MDL; 1,927<br>from UNHCR and WB<br>datasets in<br>MDLs WB MDL*<br>Remove false • 369 datasets<br>positives from from UNHCR<br>UNHCR MDL MDL**<br>Apply nlp4dev • 358<br>and keyword "relevant"<br>search to filter datasets<br>dateasets from from WB<br>WB MDL MDL<br>Remove WB MDL • 62 "relevant"<br>datasets cross- datasets<br>listed in UNHCR from WB<br>MDL MDL<br>Remove false • 6 datasets<br>positives from from WB<br>WB MDL MDL***<br>Combine the • 369 (from UNHCR<br>remaining datasets<br>from UNHCR and MDL) + 6 from WB<br>WB MDL MDL = 375 datasets<br><!-- End of picture text -->

* This refers to all micro-level datasets from the 53 low-income and middle-income countries analyzed for this study.

** Not all datasets in UNHCR MDL are micro-level datasets or sample explicitly from refugees or IDPs and should be manually removed.

**Most of the relevant datasets in WB MDL prove false positive because while having some references to relevant topics such as migration and displacement in
closures and fears around the COVID-19 pandemic further reduced both the ability and desire to migrate (Bah et al, 2021).

The number of recorded Gambian migrants fell from 8,522 in 2017 to only 394 migrants in 2019 and 285 in 2020. The extent to which this dropped was unanticipated by us, and not well-known in the country, even ex-post. In June 2021, we held a dissemination event at the University of The Gambia, and showed 30 local researchers, aid officials, and journalists the Frontex numbers for each year from 2014-2017, and asked them what they thought rates had been in 2018, 2019, and 2020. The median belief was for the migration numbers to have fallen from 8,522 in 2017 to 7,532 in 2018, and down to 6,000 in 2020. That is, most locals believed there had been a much more gradual decline than recorded. Part of this may reflect underreporting in the Frontex data, since anecdotally we heard accounts of Gambians claiming to be from other countries such as Sierra Leone to increase their chances of asylum status. But the decreasing trends seem to be accurate and in line with the results from our own survey.

# **2.2 Sample and randomization**

Since international migration is a rare event, we aimed to focus on regions of higher migration prevalence, and then within these regions, focus on the demographic group most likely to migrate. Our sample was selected as follows.

# _Selection of settlements_

The Gambia is divided into five administrative regions and the capital city area of Banjul. We selected the two Eastern-most regions of the Upper River Region (URR) with a population of 240,000; and the Central River Region (CRR) with a population of 226,000. These regions are remote, largely rural, and are at a driving distance of 300 km or more from the capital city, and 450 km or more from Dakar, Senegal. These regions were chosen due to their high propensities of irregular migration
the advanced countries now have open capital accounts as implied by the de jure measures whereas a number of developing countries still maintain relatively closed capital accounts. In the case of the Chinn-Ito indicators, the average degree of financial integration for developing countries peaked at 0.5 in 2008, compared to 0.96 for advanced economies. For example, the Chinn-Ito indicator suggests that for Denmark, France and Norway, the degree of financial integration increased from 0.4 in the early 1970s to 1 (fully open) starting in the early 1990s. The same indicator suggests that, for Colombia, the degree of financial integration increased from 0 (completely closed) in the late 1980s to 0.4 starting in 2004. For Brazil, the Chinn-Ito indicator suggests that the degree of financial integration was 0 (completely closed) until 1997 but reached 0.53 in 2006.

Our database also includes world and country-specific interest rates. For the world interest rate, we use the dollar London Interbank Offer Rate (LIBOR) rate. The real rate is adjusted for US inflation using the consumption price index from the Penn World Tables (PWT 8.1). For the country-specific real interest rates, we use the LIBOR rate adjusted by the country-specific consumption price indices in PWT 8.1.

# 2.2 Basic Econometric Framework

We follow the large literature that has tested various implications of the consumption Euler equation. We start with the basic consumption-saving decision of a representative agent whether to consume today or invest in a risk-free asset. The representative agent maximizes utility function u(Ct<sup>j), where jindexes the countries, j= 1, . . . , Jand C</sup> t<sup>jdenotes aggregate consumption at time t.</sup> As usual, we also assume that countries have access to risk-free international bond markets, where 5
GSB/KTB|3,058|0.248|0.432|
|*|1 - value of loans not repaid as of end-December 2008/value of loans|disbursed 2009|||

- ** Capital from initial injection + loans in + deposits + shares + donations + other receipts

*** Reported profit - equity*average borrowing cost - value of volunteer labor (price 300 baht/day) **** Poorer half of villagers are those with expenditure per capita below the village median.

Source: Village Fund Survey, 2010 20
In this paper, we study the propensity of digital-oriented technological firms to adopt ethical practices. Our analysis is particularly relevant in today’s digital age, where having an online presence is crucial for firms to increase their competitive advantage. For example, a website enhances a company’s reputation by effectively sharing and communicating its mission and values to the public. Websites also significantly increase sales opportunities by reaching a broader audience and improving customer engagement and experience (Dolan et al., 2015; Chaffey, 2015). Similarly, the use of social media, a digital technology that enables the sharing of ideas and information, allows companies to communicate their Corporate Social Responsibility (CSR) to the public without having to go through the gatekeeping function of the news media (Vogler et al., 2021). We combine the concepts of technology and online presence to define these firms as digital-tech-oriented firms. We then assess the ethical behavior of such firms by examining their monitoring of CO2 emissions, provision of employee training, and employment of female top managers. Our research focuses on the adoption of ethical practices as it signals that companies are concerned about the environment and the social consequences of their activities.

For our analyses, we gather firm-level data from the World Bank Enterprise Surveys (ES). Our sample encompasses up to 192,132 observations across 158 countries from 2006 to 2023. We consider three main dimensions of firms’ ethical behavior capturing environmental, social, and governance (ESG) standards: monitoring of CO2 emissions (environmental dimension, or E), formal training programs for employees (social dimension, or S), and the presence of female top 2
microenterprise participation. In other words, we do not know if the households that adopt microenterprise are better off to begin with, or whether it is the adoption of microenterprise activities that actually leads to growth in income, expenditure, and poverty reduction. In order to understand the causality, we must estimate the net effect of microenterprise participation on these household outcomes after controlling for other factors, including the decision to participate in a microenterprise activity.

Consider the following standard welfare outcome variable _Yijt_ (per capita income, consumption, the indicator of whether or not a household is moderate or extreme poor) measuring the outcome of household _i_ in thana _j_ in year _t_ ) conditional on the participation in a microenterprise activity ( _Mijt):_ where δ measures the estimates of microenterprise adoption, and other variables are as defined in the earlier section. The participation equation is stated earlier in equation (1).

The distinction between the participation equation (1) and an outcome equation (2) is whether or not village-level characteristics are included in the equation (2). If we assume that the program placement of institutional credit including microcredit at the village level only directly affects the adoption of a microenterprise activity of a household, but does not directly affect household income, expenditure and poverty, we could then use the program placement variables as instruments to estimate the impacts of microenterprise adoption on the outcomes of equation (2). That is, we predict the adoption of microenterprise using equation (1), which includes the program placement variables, and get the predicted value of microenterprise adoption, which we use in the 12