rafmacalaba/data-use-mentions-extended huggingface dataset:
a few traffic arteries, and air pollution from aggregate fuel consumption data, coupled with air pollutant emission factors for gasoline and diesel vehicles that may have been generated from small samples in other places. As a result, educated guesswork has often guided the placement of subway lines, road corridor improvements and other spatially-targeted interventions for reducing traffic and pollution.

Recent developments in satellite monitoring offer a potential escape from this information trap. High-resolution imagery now permits frequently-updated observations of polluting aerosols and traffic in multiple zones within urban areas. Appropriately mobilized and interpreted, data from these new sources can elevate the state of the art in urban traffic/pollution research, policy analysis and implementation for developing countries.

Satellite coverage is global, so the new approach can enable cities in all regions to benefit from the rigorous analyses that have traditionally been limited to well-endowed cities in developed countries. This paper is a pilot exercise for Dar es Salaam that incorporates spatially and temporally non-uniform distributions of vehicle traffic and air pollution into an investigation of impacts on vulnerable households. We highlight potential health damage in areas of Dar es Salaam with high poverty incidence and large numbers of vulnerable children and elderly adults.

The remainder of the paper is organized as follows. Section 2 reviews prior research on the health impacts of NO2 pollution, the links between traffic and air pollution, and the socioeconomic dimensions of air pollution impact. In Section 3, we introduce the new data sources for our analysis. Section 4 uses econometric modeling to investigate the spatial dynamics of NO2 pollution in the metro region, with a particular focus on critical weather factors that drive the locus 2
. 2019a. _Database on Household Size and Composition 2019._ <u>https://population.un.org/Household/index.html</u>

- ———. 2019b. _Database on Median age by region, subregion and country, 1950-2100 (years)._ <u>https://population.un.org/wpp/Download/Standard/Population/</u>

- US Congressional Budget Office. 2018. “The Distribution of Household Income, 2014.” Washington, DC: Congressional Budget Office. <u>https://www.cbo.gov/publication/53597</u>

- Vyas, Seema, and Lilani Kumaranayake. 2006. “Constructing Socio-Economic Status Indices: How to Use Principal Components Analysis.” _Health Policy and Planning_ 21(6): 459–468. doi:10.1093/heapol/czl029.

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one built by Khandelwal (2010). The problem with this and other indices of vertical differentiation is that they do not include the products defined as homogenous by Rauch. As a consequence, further research is needed in order to establish whether price dispersion across destinations in homogenous goods is led by quality differences or different mark-ups.

Thirdly, Equation (3) takes into account how prices are linked to the characteristics of trade partners, namely: GDP, GDPpc, bilateral distance, and the remoteness index. This is calculated as _remotenessd_ = ∑ _GDPo_ * _distod_ for year 2009. Nevertheless, results do not change drastically if the remoteness index is calculated for every year (the reason being that the index does not change much overtime). The specification includes firm-product and year fixed effects, and the errors are clustered at the firm-product level.

> 24 - <u>http://www.value chains.org/dyn/bds/docs/519/Technoserve%20Tanzania%20Coffee%20Summary%20Case.pdf</u>

14
from the Climate Research Unit (CRU). Second, this work takes advantage of the panel structure of this data set, which includes about half of the households in at least two of the three survey rounds. Regression techniques based on the panel data set can estimate the weather impact on income changes over time while reducing omitted variable bias. Despite the strength of these data and methods, a quantification of future impacts subject to uncertain climatic, environmental and socioeconomic changes is beyond reach.

The remainder of this paper is structured as follows: Section 2 explains the data and methods applied for the analyses. Section 3 shows that rural households remain highly reliant on agriculture and other ecosystem‐based activities. Section 4 demonstrates the extent of weather inter‐ and intra‐annual variation communes are exposed to. Section 5 presents the estimated income effects of weather variation from the various regression analyses. Section 6 concludes that in the face of climate change greater attention needs to be paid to make rural livelihoods more resilient to weather variation.

# **2. Data and methods**

Based on data from the Vietnam Household Living Standard Survey (VHLSS) collected in 2010, 2012, and 2014 with gridded weather data from the Climate Research Unit (CRU), this study fits a number of regression models to explain income differences.

## **2.1 Household and commune data**

Information on incomes and socioeconomic conditions is derived from the household and commune data from the VHLSS 2010, 2012, and 2014. These surveys are conducted by the General Statistics Office (GSO) with technical support from the World Bank in Vietnam. They are nationally representative and contain detailed information on individuals, households and communes. In total ca 9,400 households nationwide are included in each round with about half of the households in each round also being surveyed in the previous round so that the data set includes a short‐term panel.

These analyses focus on rural households and communes leaving a data set of about 20,000 household observations
Since some of our sectors are more aggregated than the original ISIC Rev. 3.1 classification, we aggregate tariffs up using import shares as weights. Tariff data are aligned with the survey year of a country’s enterprise survey. In a few cases where tariff data are unavailable for the specific survey year, we choose the observation of the nearest available year before the survey year.

# **3.3 Measures of Foreign Spillover Potential**

We now turn to the mediating factors MF. In a first step we present measures of FDI spillover potential at the sectoral level based on available data in the Enterprise Surveys Indicator Database. All of the following variables are averages across all foreign firms within a specific sector of a country.

- _own_ = a sector’s average percentage of foreign ownership in a country;

- _market_ = a sector’s average percentage of FDI sales to the domestic market in a country. This measure serves as a proxy for a sector’s average FDI motive in a country, whereby a higher share is associated with market-oriented FDI;

- _inp_ = a sector’s average percentage of domestic input purchases of FDI firms in a country. This measure captures a sector’s average sourcing strategy of foreign firms in a country, whereby a higher share is associated with more local sourcing;

- _tech_ = _iso_ + _tech_for_ + _website_ + _email_ with 0 <= _tech_ <= 4, where _iso_ = 1 if firm owns internationally-recognized quality certification and 0 otherwise, _tech_for_ = 1 if firm uses technology licensed from foreign firms and 0 otherwise, _website_ = 1 if firm uses own website to communicate with clients or suppliers, _email_ = 1 if firm uses email to communicate with clients or suppliers. The technology indicator serves as a proxy for a sector’s average FDI technology intensity in a country.

# **3.4 Measures of Absorptive Capacity**

In a second step, we include the following measures of absorptive capacity that were available in the Enterprise Surveys Indicator Database
While revealed preference suggests there is value in blog posts to both the scholars who maintain them and to the large number of individuals who read them, there is, to date, no quantitative evidence of their impacts.<sup>5</sup> This paper aims to answer three questions regarding the impacts of economics blogs. First, do blogs improve dissemination of working papers or journal articles? Second, do they raise the profile of their creators? Third, do they cause changes in attitudes among their readers or lead to increased knowledge? We conduct event study analysis using download data from RePEc to answer the first question, and combine RePEc data with recent survey data on most admired economists to answer the second. To answer the third set of questions, we use evidence from a recent survey we conducted on the role of blogs in economics, and take advantage of a randomized experiment in which a random sample of the respondents of this survey were encouraged to read a new economics blog. The novel survey and the experiment are also used to shed light on the question of whether blogs improve the public awareness and the reputation of their creators, and of their affiliated institutions.

We find that links to scholarly articles (either publications or working papers) in blogs lead to substantial jumps in their likelihood of being downloaded, with the impact increasing with the popularity of the blog providing the link. We also find evidence that blogs enhance the recognition of their creators, so that bloggers are more likely to be admired or respected among their peers than other scholars of similar publication records. Using the experiment that provided random encouragement to read a new blog produced by four researchers from the World Bank, we find an improvement in the perceived quality of research produced at the World Bank and an increased interest by the survey respondents

> 5 For a nice theoretical exposition of the economics of blogging, particularly the costs and benefits from the perspectives of both the individual blogger and the society, see Ribstein (2006).

4
labor allocation, pension participation and household and family characteristics, all of which are crucial for study of retirement and labor supply decisions of the elderly. The original survey was representative of 83% of the Indonesian population and 13 of 27 provinces. As it tracks the movers and split-offs from the original sample, the IFLS was a pioneer among household surveys in the developing world. Tracking is important when working with the 2007/8 round of the IFLS, because it allows us to avoid using a selected sample of

> 21The CHARLS project has funding for nationally representative surveys in 2011 and 2013, and fieldwork on the first national wave will begin in July 2011. Public use data should be available for the research community by June 2012.

13
—such as fees, transportation, costs for delivery, and (3) how the water is allocated and used in the household. It also included four questions that are typically asked in the Demographic and Health Surveys (DHS) regarding who normally makes the decisions within the household on (1) large household purchases, (2) health care for oneself, (3) visits to one’s family or relatives, and (4) how the money the respondent earns is used.

After asking who makes the decision, the respondents then were asked if they desired to be involved in the decision, and if they answered no, they were asked why they did not desire to be involved. They could select all that applied to them and add more reasons if needed. If they were not involved in the decision, they were asked if they would face consequences for participating in the decision and the types of consequences. The respondents were also asked whether they ever had a difference of opinion with others regarding the decision made over the activity (regardless of whether they voiced they opinion regarding this difference), and if they did, whether they tried to influence or persuade others in the decision-making process, and how happy they were with the outcome.

The survey was administered in January and February 2023 to 630 households in 30 villages. These households were randomly selected from a household listing conducted in August 2022. The team oversampled households with co-wives and extended family households. The final sample includes 613 households with a total of 1,059 women and 808 men.<sup>ii</sup> Table 1 describes the women and men in these rural households. Nineteen percent of women and 16 percent of men are married and live with the wife’s in-laws. Sixteen percent of women and 36 percent of men are unmarried and live with their parents. A large share of these young women and men are currently attending school.

4
23 from employee status to self-employment at older ages. Naturally, this is easier for citizens than noncitizens, due to the time limits on foreign work permits. A similar pattern of work status is observed in Kuwait, where there is a tendency for Kuwaiti public sector employees to retire upon eligibility, prior to retirement age, and become self-employed, or in fact shift from part-time informal activity to devoting full-time to their on-going ventures in the private sector. Blau (1986) also finds that earnings of urban self-employed males are higher than their counterparts with similar characteristics in wage employment.<sup>12</sup> Neither Milanovic (1998) nor Blau (1986) distinguish between citizens and non-citizens, however; more detailed analysis of the determinants of earnings is necessary to test the relationship between employment status and income, and will be undertaken using the 1995 household survey data in the following section.

**Figure 7 Distribution by Employment Status**

<!-- Start of picture text --> Non-Citizens 1% 4%<br>28%<br>g nTployer_<br>* Erployee<br>o Self-enployed<br>68%<br>90%<br><!-- End of picture text --> Are there other trends in the composition of labor in Malaysia that reflect foreign labor management policies? Consider the characteristics of individual households with respect to family structure, size and age. The marital status of citizens differs markedly from that of non-citizens; the restrictions on the reunion of families for workers holding visit passes is borne out by the lower incidence of marriage among non-Malaysians, as well as the smaller household size. Whereas 79.8 percent of Malaysians in the sample are married, only 68.1 percent of non-Malaysians are married (see Table 3).

- 12 There is a growing body of evidence contradicting the widely accepted dual labor market assertion that informal self-employment earnings are lower than forrnal wages; for example, Maloney (1998) finds evidence
Our second key question is whether government-owned banks help in relaxing credit constraints during recessions. While there is evidence that lending by government-owned banks is less procyclical than lending by private banks (see, among others, Micco and Panizza, 2006, and Panizza, 2022), previous research could not establish whether countercyclical lending by government-owned banks has an effect on firm outcomes and whether lending countercyclicality particularly benefits credit constrained firms. This is important because credit constrained firms are likely to suffer the most from credit contractions during recessions. Our bank-firm matched dataset allows us to study this specific question. Note that access to credit in good and bad times will change for firms that are always completely excluded from the financial markets. However, all the firms in our sample do have a banking relationship and have borrowed at some point in time.

_What type of firms borrow from state-owned banks?_ To assess whether government-owned banks target credit constrained firms or firms that generate positive externalities, we use the three indicators described above to estimate the following model: The dependent variable is a firm characteristic based on one of the indexes described in the previous section (presence of credit constraints, innovation, green activities) for firm i, located in country c, in year t; GOBb(i)t is a dummy variable that takes value one if bank b used by firm i in year t is government-owned, Xb(i)t is a matrix of bank-level controls (size, proxied by the log of total assets, and profitability, proxied by return on assets), Wi(c)t is a matrix of firm-level controls (log size, log age, a dummy for state-owned firms, and a dummy for female-owned firms), and αct is a set of country-year fixed effects that control for all possible country-year specific factors. We also estimate models in which the dependent variable takes value 1 if the firm invested in year t. The idea is that there could be positive externalities associated with increasing investment. We estimate Equation 1 (and
# **1. Introduction and Literature Review**

COVID-19 upended daily life and exacerbated global inequalities, resulting in about 6.88 million reported deaths globally by March 2023 (Johns Hopkins University, 2023) and 14.83 million excess deaths (i.e., the number of deaths during a crisis that exceeded the expected number under normal conditions, including those directly caused by the crisis and those resulting from indirect impacts like healthcare disruptions) (Msemburi et al., 2023). The development and regulatory approval of COVID-19 vaccines changed the trajectory of the pandemic. However, limited access to and acceptance of vaccination resulted in global vaccine coverage inequalities that continued to persist throughout the pandemic. Although 72.3% of the world’s population had received at least one dose of the COVID-19 vaccine as of March 2023 when many countries stopped regularly reporting vaccination data, there remained stark gaps in vaccination rates across regions (Holder, 2023). For example, 37% of the population received at least one dose of vaccination in Africa, 58% in the Middle East, 70% in Europe, 81% in the U.S. and Canada, and 82% in Asia-Pacific and Latin America (Holder, 2023). Among LMICs, surveys found that Europe and Central Asia showed the highest vaccine hesitancy (58%), followed by the Middle East and North Africa (47%), East Asia and Pacific (26%), Sub-Saharan Africa (15%), and Latin America and the Caribbean (8%) (Dayton Eberwein et al., 2023). In particular in LMICs, COVID-19 vaccination can reduce transmission and mortality, prevent healthcare system overload, support economic recovery, and address global vaccine inequity (Ali et al., 2022; WHO, 2021).

The journey to vaccination is a complex process involving structural factors, individual decisionmaking, and last-mile actions. The success of vaccination programs is determined by promoting vaccination intentions and translating these intentions into vaccine uptake.
businesses tend to interact more with micro businesses (25.31%) than larger businesses (19.46%). Unfortunately, there is no information on whether these businesses are formal or informal. Furthermore, the data shows that informal businesses and their primary suppliers are loosely linked. Most businesses have been working with the current primary supplier for solely one year (71%).

**Table 10: Value-Chain Linkages by Survey Type (in %)**

|||Formal Micro|Informal|
|---|---|---|---|
|Main Customer|Individuals|92.53|94.11|
||Business (<5)|3.29|2.98|
||Business (>5)|2.00|2.15|
|Source of Inputs, Goods, and|Individuals|40.47|49.73|
|Supplies|Business (<5)|15.03|25.31|
||Business (>5)|37.69|19.46|
||Self-produce|1.47|3.69|

_Notes_ : All variables are in % of all firms. Sampling weights applied. The number of firms may vary for some variables. “Formal Micro” covers formal micro-businesses included in the World Bank Micro Enterprise Survey (WBMES), and “Informal” covers businesses included in the World Bank Informal Sector Enterprise Survey (WBISES).

Results are summarized in Figure 5. While the effect of distance is negative and statistically significant for informal businesses that do not receive inputs from other businesses, the effect turns insignificant for informal businesses that do receive their main inputs from other businesses. This suggest that even informal businesses can overcome the disadvantage of being far away from more technologically advanced formal firms and learn from their peers by establishing value-chain linkages with them.

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all costs and benefits of the guarantee scheme, in order to<br>measure its net social impact.|
|**Identification of SME**<br>**finance gap**|Identification of the characteristics of firms that are credit constrained based<br>on the business survey and bankers’ feedbacks, in order to readjust eligibility<br>criteria|
|**Assessment of operational**<br>**parameters**|Assessment of the key operational parameters on the guarantee scheme, based<br>on discussions with stakeholders and using comparisons with international best<br>practices.|
|**Assessment of internal**<br>**processes**|Review of internal processes and systems of the guarantee funds, including<br>quality management, operational costs, credit risk management, recovery<br>process etc.|
|**Financial projections**|Simulation of the need for funding over the medium term, based on<br>assumptions on outreach and default rate|
|**Awareness and customer**<br>**satisfaction**|Measurement of the awareness and satisfaction of bankers and firms though<br>surveys.|

34
0.0357|0.0746|
|_(% end of eriod)_|(00856)|(00839)|(00986)|(00811)|(00646)|(00837)|(00898)|(00881)|(0104)|(00861|(00590|(00884)|
|_p_<br>Current Account|.<br>0.239**|.<br>0.210**|.<br>0.311**|.<br>0.236***|.<br>0.0446|.<br>0.207**|.<br>0.200**|.<br>0.171*|.<br>0.245*|.<br>0.204**|.<br><br>-0.0261|.<br>0.168*|
|Balance_(% GDP)_|(0.0912)|(0.0904)|(0.122)|(0.0885)|(0.0655)|(0.0899)|(0.0956)|(0.0949)|(0.128)|(0.0940)|(0.0598)|(0.0949)|
|_Policy Response_|||||||||||||
|Government Response (GR)|-0.133**|-0.143***|..|..|..|..|-0.129**|-0.139**|..|..|..|..|
|<br>_(Oxford tracker)_|(00528)|(00520)|||||(00554)|(00546)|||||
|<br
........................................................................................................... 40|
|Annex A. World Business Environment Survey .................................................................. 42|
|Annex B. Investment Climate Assessments (ICAs)- Logistics Module............................... 43|
|Annex C. Infrastructure Indicators ....................................................................................... 49|
|Annex D. Module-Based Project Guidelines for Trade Facilitation..................................... 50|
|Annex E. Country Ranking by WMO Trade
population from larger administrative units to only built-up grid cells, where built-up area is identified based on building footprints.<sup>6</sup> The building footprints, in turn, come from the Digitize Africa project, which applied Artificial Intelligence techniques to 50 cm resolution satellite imagery for _circa_ 2015 to map all buildings in 51 SSA countries. Because of its precision, this built-up area data is far superior to, for example, the corresponding 2015 built-up area data from the GHS-BUILT data set on which Henderson et al. (2022) rely. Hence, relative to the Digitize Africa data for 2015, GHS-BUILT vastly under-detects built-up area.<sup>7</sup> Compared to Henderson et al. (2022) and OECD/SWAC (2022), focusing on a single time-period also allows us to go deeper into describing patterns of urbanization for that period. This includes providing empirical analysis of the internal spatial structures of urban areas, a topic about which little is known empirically, especially for SSA.

The second, related, literature to which our paper contributes is that on methods for the more accurate and consistent delineation of urban areas across countries. While there is a notable strand of the urban economics literature that has long been concerned with the questions of how to rigorously define urban and metropolitan areas (see, for example, Berry 1960; Fox and Kumar 1965; Berry et al. 1969; Kanemoto and Kurima 2005; Duranton 2015) and how to delineate urban areas across countries in a comparable manner (Hall and Hay 1980; Cheshire and Hay 1989), the development and large-scale application of algorithms for the more accurate and consistent delineation of urban areas across countries was, for a long time, constrained by both data and computing costs. However, with greater availability of increasingly high-resolution and low-cost satellite imagery, improvements in remote sensing techniques for extracting useful data from satellite images, and declining computational costs

> 5 OECD/SWAC (2020) identify 7,617 urban agglomerations as of 2015.