rafmacalaba/data-use-mentions-extended huggingface dataset:
Khatlon (54 percent), and lowest (41 percent) in Regions of Republican Subordination (RSS). Awareness levels among women in Tajikistan also tended to be somewhat lower than among men.

**Figure 3: Awareness of the Different Consequences of Climate Change**

**(a) Tajikistan vs. other countries:**

<!-- Start of picture text --> 100<br>9 10 9<br>90<br>80<br>70 33 37 34<br>60<br>50<br>40<br>48 48<br>30 49<br>20<br>10<br>9 9<br>0 5<br>EU27 countries Kazakhstan Tajikistan<br>Very well informed Fairly well informed Not very well informed Not at all informed<br>Percent of respondents<br><!-- End of picture text -->

- **(b) Across Region: Those that are very well informed / fairly well informed**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40 81<br>30 60<br>54<br>20 41<br>10<br>0<br>Dushanbe Soghd Khatlon RSS<br>Dushanbe Soghd Khatlon RSS<br>Percent of respondents<br><!-- End of picture text -->

**Source:** Authors’ calculations based on P-LITS 2 data. Only 2 sample points (20 interviews) were conducted in GBAO: these have been grouped together with Dushanbe because of the similarity in pattern of response.

17. Respondents in the survey were also asked if they felt they were well-informed about (a) the different causes of climate change, and (b) ways in which we can slow down climate change. Awareness of the different
economic outcomes among different demographic segments, shedding light on the underlying structural inequalities that contribute to poverty and social exclusion. Finally, geographic identifiers (urban/rural and NUTS2 identifiers<sup>35</sup> ) were also added to shed light on subnational disparities.

**To analyze poverty and multiple deprivations among the Roma population, we rely on the latest national SILC from Bulgaria; analysis for other countries is not possible due to a lack of ethnically disaggregated data in the EU-SILC and the unavailability of AROPE indicators in other household surveys** .  In the context of Bulgaria, the examination of multiple deprivations within the Roma population will leverage the comprehensive AROPE framework and the 2021 cross-section and longitudinal SILC <mark>; in Romania, the other country with available ethnic identifiers in the HBS, construction of AROPE was not feasible, due to large differences in survey design.</mark><sup>36</sup> <mark>Construction of AROPE using non-official periodic targeted surveys to include hard-to-reach groups, such as the latest 2021 FRA household-level data, was also not feasible, despite the potential for harmonized comparative analysis of ethnic disparities across several EU countries. These surveys also suffer from some limitations.</mark><sup>37</sup> <mark>.</mark>

**The analysis of chronic deprivations in Bulgaria leverages the longitudinal component of the EU-SILC, allowing for a comprehensive understanding of persistent socio-economic challenges over time.** Using longitudinal data from EU-SILC, it is possible to identify and analyze households and individuals experiencing persistent severe material and social deprivation over several years, similar to assessing persistent poverty risk. <mark>The EU-SILC panel data operates on a rotating panel system, where each year, a sixth of the initial sample is replaced with a new subset, ensuring the complete renewal of the sample every six years.</mark><sup>38</sup> This approach mitigates the issue of attrition commonly observed in full panel datasets
<!-- Start of picture text --> Typical annual expenditure on subsidies in power<br>1.0%<br>0.8%<br>a<br>o 0.6%<br>-<br>2 0.4%<br>0.0%<br>Ethiopia Kosovo Guinea Dominican Argentina<br>Republic<br><!-- End of picture text --> <!-- Start of picture text --> (a) Execution ratio, 2010-18 (b) Sector specific execution ratios, 2010-18<br>14 ry 14<br>e bd e<br>e<br>12 °<br>12 - .e °<br>z1£) ° . ° B- 1 e o, e ©.°e e e<br>B08 Aad e e e oE 06 e e<br>ef ie § vo ts °<br>=<br>0.6 ee e ° ’ z 04 |@ e ® e<br>0.2 e<br>0.4 )<br>0) 2000 4000 6000 8000 10000 12000 14000 0 02 04 06 os 1 12 14<br>@ll @LMI @UMI GDP per capita @L! @LM! @UMI Investments, Power<br><!-- End of picture text -->
Policy Research Working Paper 8696

# **Abstract**

This paper provides empirical evidence on the impact of institutional fragmentation and metropolitan coordination on urban productivity in Latin American cities. The use of night-time lights satellite imagery and high-resolution population data allow the use of a broader definition of metropolitan area. Thus, metropolitan area consists of the urban extent that results from the union between the formally defined metropolitan area and the contiguous patches of urbanized areas with more that 500,000 inhabitants. The initial results suggest that the presence of multiple local governments within metropolitan areas generates opposite effects on urban productivity. On the one hand, smaller governments tend to be more responsive and efficient, which increases productivity. But, on the other hand, multiple local governments face coordination costs that reduce productivity.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice. 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/research. The authors may be contacted at nlozano@worldbank.org and prestrepocadavid@worldbank.org.

_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 of the World Bank or the governments they represent._ Produced by the Research Support Team
the importance of this constraint to firm growth using objective measures and controlling for firm characteristics. The sample comes from World Bank Enterprise Surveys conducted in 2006–2010 in mostly emerging and developing countries. The surveys provide both subjective data on perceived obstacles and objective measures of many constraints.

Second, we investigate the effect of financial access variables on firm growth by using firm-level regressions across countries controlling for the effects of different firm sizes, firm ages, sectors, and regions. Our results show that having access to finance in the form of a loan, sales credit, or external finance helps micro firms the most. This finding holds not only for the full sample but also for different regions. Sales credit is important only to micro and small firms, probably because it substitutes for bank loans. Having a loan or overdraft facility and receiving external finance for investment help growth for firms of all sizes across regions.

Third, we find clear evidence that a low level of financial sector development affects the firm size distribution and therefore contributes to the phenomenon of the “missing middle” in developing countries. Firm size distribution is skewed toward small and medium-size firms—and more so in 2
br>0.305<br>0.198*<br>0.482**<br>0.335**<br>(0.187)<br>(0.110)<br>(0.210)<br>(0.133)<br>Observations<br>9,887<br>34,005<br>6,864<br>24,013<br>Outcome mean<br>0.486<br>0.502<br>0.483<br>0.499<br>State fxed efects<br>x<br>x<br>x<br>x<br>District characteristics<br>x<br>x<br>x<br>x<br>Individual characteristics<br>x<br>x<br>x<br>x<br>_Notes_: Data are from the 2015-16 National Family Health Survey (NFHS) of India. All columns present 2SLS estim<br>defne close elections as those in which the victory margin is less that 3 percent of votes. The dependent variable is an<br>modern contraception. Panel A shows heterogeneity by husband’s desire to have more sons than what the couple curr<br>by the discordance in husband’s and wife’s desired number of sons. All regressions include state fxed efects and contr<br>had close elections between women and men. Individual controls include age, years of schooling, and indicator variab<br>caste. District-level controls include the share of female population, the share of urban population, and the share of <br>female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote signif|

38
MENA countries must undertake what might be called a ‘moonshot’, an all-out effort to achieve a goal named after the massive US effort in the 1960s to land a man on the moon. President John F. Kennedy’s 1961 decision to pursue this objective unleashed an extraordinary collective national effort that achieved its seemingly impossible goal in mid-1969.

A MENA moonshot would involve a collective commitment to achieve parity with advanced economies in information and communications technology by 2022. MENA countries would seek to equal or better OECD countries in terms of their level of access to the internet, capacity to transmit data (bandwidth) and the number of financial transactions carried out electronically.

The effort to create modern telecommunications and payments systems will require specific and bold goals, firm deadlines and cooperation among all elements of society, including the private sector. Some of the needed elements are in place already. Young people in MENA are digitally savvy in many ways. They are active on social media and they are heavy users of mobile phones. Mobile phone penetration in the region is among the highest in the world.<sup>12</sup> But that is about as far as it goes. The quality of internet service in most MENA countries is poor and few people have access to it. Only in Sub-Saharan Africa and South Asia is there a lower per capita level of broadband subscriptions, and MENA users have a limited amount of bandwidth per subscriber.

> 12 This is welcome news in view of the fact that the current financial development indicators are uniformly low for MENA relative to peer developing countries. The penetration can help bridge the gap if the youth are appropriately incentivized to be financially entrepreneurial. Fintech entrepreneurs are a case in point.

17
care at public facilities but have to pay a 20 percent coinsurance rate for care at private facilities (HalasaRappel _et al._ 2019). In addition to the insured, the subsidised MOH services can also be accessed by uninsured individuals with a 20 percent co-payment.

The public pension system in Jordan is a mandatory contributory pension system managed by the Social Security Corporation. It covers private sector workers, government employees, as well as army officers.<sup>15</sup> The contribution rate is 17.5 percent of the employee’s salary, of which 6.5 percent is paid by the employee and the rest by the employer. Self-employed workers pay the entirety of the contribution. Pensionable age is 60 years old for men and 55 for women, with at least 180 months of contributions (paid or purchased). However, around 60 percent of the people retire before the legal age.

# 3. Data and Methodology

## Data

The work follows the ‘CEQ ‘methodology, an internationally recognised fiscal incidence diagnostic method developed by the Commitment to Equity Institute. This approach uses standard incidence analysis for each tax and transfer. The taxes and transfers from the fiscal accounts are allocated out to households based on the information in national representative household surveys, which include information on household employment and income (determining PIT and receipt of social assistance benefits) and expenditures (indirect taxes and subsidies) as well as use of social services such as health and education. The innovation of the CEQ approach is to combine the sectoral incidence analysis to model the net impact of Jordan’s taxes and transfers on households and determine their welfare and distributional impacts.

The primary data source for households is the 2017-18 HEIS (Household Expenditure and Income Survey) conducted by Jordan’s Department of Statistics. It contains detailed data on household expenditure and income, as well as on direct transfers and household use of education services. There data are also the base for the country’s most recent official poverty estimates. The HEIS
**[9]** Furthermore, SubSaharan Africa is a data-scarce environment with typically little robust information on issues such as vaccine acceptance or barriers of access that can inform the rollout of vaccination campaigns. Yet, SubSaharan Africa is home to over 1.16 billion people, about 15% of the world’s population. This makes it a systemically important region in the global effort to contain COVID-19 and end the pandemic.

The special status of COVID-19 vaccination campaigns in Sub-Saharan Africa has created the need for research to fill knowledge gaps around the effective mass delivery of COVID-19 vaccinations in the region. **[8–12]** In an effort to fill this gap, the World Bank supported a series of cross-country comparable high-frequency phone surveys (HFPS) in early 2020 that collect recurring information on vaccine hesitancy, uptake, barriers of access and information transmission in the context of COVID-19 vaccines in SubSaharan Africa.

In this paper, we bring together two years of data and findings from the HFPS across five Sub-Saharan African countries. We focus on COVID-19 vaccine acceptance as a prerequisite for widespread vaccine coverage in the region and a matter of particular concern given the special circumstances of COVID-19 vaccine development. COVID-19 vaccine hesitancy has been the subject of a number of studies across lowand middle-income countries, including in Sub-Saharan Africa, however, there is a lack of longitudinal evidence. **[9–11, 13–17]** Such longitudinal evidence is needed to understand the dynamics of vaccine acceptance in the region with some of the world’s lowest COVID-19 vaccine coverage figures. By virtue of the recurring nature of the HFPS, we are able to study vaccine attitudes and their correlates at the countrylevel and among a panel of individuals over a 2-year time horizon.

2
# **6. Conclusion**

In this paper, we have defined alternative means of measuring access that go beyond traditional measures of physical access to include functionality, use and service quality, adopting a more holistic understanding of services that mirrors the global transition from MDGs to SDGs. By drawing on a large data set of urban households in Kenya, we have demonstrated how these measures vary across sectors, and how to analyze and interpret these results.

The findings from our analysis have real implications for sectoral policies and infrastructure investments in Kenyan cities. Our data show that an infrastructure gap exists between formal and informal communities and non-poor and poor households. Poor households, and those who live in informal settlements suffer from lower levels of access, and fewer quality services. Interestingly, we find that the size of this gap is significantly larger by settlement type than poverty level. Or in other words, those who live in informal settlements, irrespective of poverty level, are more likely to have lower access to services – at nominal, effective or quality-adjusted levels. This suggests that policy makers in urban Kenya need to seriously examine the drivers of spatial inequality and find mechanisms for closing this gap. The existence of both coverage and quality deficiencies also implies that investment strategies should address both expansion as well as quality improvement in each sector.

Our data on service access by sector suggest that investment in water supply and sanitation is a high priority. Overall, these sectors perform poorly on a relative basis, and also have the largest service gap, which is particularly evident between formal and informal settlements. Delivery mechanisms will need to take into account the high rates of tenancy in informal settlements that can hinder service expansion. Finally, our data reveal that mean spending on infrastructure services is high. Cumulatively these expenses represent a significant part of the household budget. In relative terms, phone and electricity expenditures are high, while expenditures on water and garbage collection services are low. Non-poor households and those who live in formal settlements generally spend more for all
Another drawback is that the combination of GDI per capita with some distributional statistics (to reflect national income distribution) introduces a mixing of two aggregates, calculated from different sources, and this “mixing” is not distributionneutral. On the one hand, we are using as the country GDI per capita, and on the other, we are applying to this mean, distributional parameters obtained from surveys of household disposable income.<sup>13</sup> It was already explained that the two instruments are different by definition. But in addition, such an adjustment is not distribution-neutral. We know that surveys tend to underrepresent capital income or undersurvey rich people (see Mistiaen and Ravallion, 2003).<sup>14</sup> Thus, a simple upscaling of all survey incomes by a given parameter will reduce poverty below what it really is and underestimate inequality.<sup>15</sup> When a different mean (GDI per capita) is applied to a survey-based distribution, we implicitly allocate the difference between GDI per capita and disposable household per capita income across all households (more exactly, in proportion to reported household income). Poor people’s income is increased in the same proportion as rich people’s income. But if most of the difference between the two concepts is due to unreported income of the rich then this approach artificially inflates incomes of the poor.

Now, income of the rich which accounts for the bulk of the difference between GDI and disposable income is of two types: first, the definitionally-different part which consists of publicly-financed health and education, corporate profits etc. that are 13 “The difference in coverage and definition between [National accounts] and survey means that, even if everything were perfectly measured, it would be incorrect to apply inequality or distributional measures, which are derived from surveys which measure one thing, to means which are derived from national accounts, which measure another (Deaton., 2003, p.35).

14 Income from property is notoriously
|20_._98|8_._83|36_._39|61_._23|
|Wheat|7_._00|||5_._48|8_._14|28_._24|48_._22|
|Wheat||0_._70||20_._98|8_._83|36_._39|61_._24|
|Wheat|||0_._05|24_._91|7_._81|38_._93|69_._25|
|Wheat||||24_._91|7_._81|38_._96|69_._26|

Notes. In this table we present the results of the SD approach using the groundwater supply or volume measure using the aquifer dataset constructed by World Bank. Each row represents one simulation. Column (1) specifies the crop for which the analysis was run. Columns (2)-(4) specify whether or not constraints were imposed; if yes, then what the thresholds were. Column (5) presents the fraction of all croplands where the constraints were met. Column (6) presents the share of croplands where we see production gains, as compared to the all the croplands where the constraints were met. Columns (7) and (8) present the median and mean of the simulated gains in production.

29
|||||||procedures is availablehere.Full access to the data is<br>availablehere.|
|---|---|---|---|---|---|---|
|WBS|World Bank|World Bank<br>Enterprise<br>Surveys|56 economies from all<br>regions and income<br>groups.|Every three<br>years on a<br>rotatng three-<br>year cycle.|Survey/Firm|These surveys are implemented by the World Bank’s Enterprise<br>Survey program which has been operatng since the early<br>2000s and has covered over 150 countries, although with<br>irregular frequency. Startng in 2023, the Enterprise Surveys<br>will cover global sample of 180 countries on a rotatng three-<br>year cycle. This table entry refers to the frst round of this<br>expanded coverage. A complete descripton of the WBES<br>methodology, including survey questonnaires and sampling<br>methodology, is availablehere.Full public access to the<br>record-level data is availablehere.Note that the Business<br>Environment and Enterprise Performance Surveys are also a<br>product of the Enterprise Survey program and treated as a<br>separate source in the WGI between 2000 and 2015, see entry<br>for BPS above.|
|WJP|World Justce<br>Project|Rule of Law<br>Index|142 economies<br>covering a global<br>sample.|Annual. Used in<br>the WGI since<br>2011.|Expert/NGO<br>and<br>Survey/HH|The Rule of Law Index is a hybrid of expert assessments and<br>household survey data. The expert assessments are scored by<br>locally recruited subject mater experts following a detailed<br
A windstorm hazard model is used to simulate maximum sustained wind speeds,<sup>16</sup> which is a proxy for potential damage from hurricanes, experienced by the affected areas at a resolution of 1 square kilometer. It is capable of producing a fully exogenous measure of hurricane intensity and its potential destructive power at a fine-grained geographical level. The resulting wind speed data are then merged with nightlight data.

Recent studies have focused on improving natural hazard modeling to more explicitly address the impact of adverse natural events on socioeconomic indicators. For example, Hsiang and Jina (2014) and Strobl (2012) evaluate hurricane windstorm hazard data using global hurricane models to generate a gridded data set with different levels of resolution. One of the main innovations in this paper is its use of the fully probabilistic hurricane windstorm model developed by Strobl (2012), which we have validated and calibrated for the Caribbean region to generate hazard information with the temporal and spatial resolution needed for this study. We use windstorm hazard data to calculate the damage indexes, which we then use as inputs in both the macro and micro models. As a result, the paper substantially improves the understanding of how hurricane windstorm hazards could affect socioeconomic outcomes.

# **2.3.Mangrove forest data**

Mangroves are commonly found along sheltered coastlines in the tropics and subtropics, where they fulfill important socioeconomic and environmental functions, from providing a large variety of wood and nonwood forest products to protecting the coast against the effect of wind, waves, and water currents, and conserving coral reefs, sea-grass beds, and endangered animals for biological diversity (FAO 2007). Together, coastal mangroves, coral reefs, and seagrass beds act as a natural barrier from the high wave energy and strong coastal currents that are typical of the Caribbean environment (Menéndez et al. 2020).

Although Caribbean mangroves have sustained human activity since pre-Columbian times (Sanoja 1992), climate-forcing factors, such as extreme winds caused by tropical storms, and anthropogenic factors like deforestation