rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Table 2: Inclusion of collective action clauses in dollar-denominated emerging market central government bonds, by governing law (2008-2015)**

||WithCAC|Without CAC|
|---|---|---|
|Under U.S. Law|114|22|
|Under U.K. Law|56|12|

Source: Authors’ calculations based on Dealogic data.

The impact of first-time issuances on spreads is also relevant. Governments without a previous history of dollar issuances could incur a relatively higher cost of debt when issuing for the first time. Because of the regional linkages already described, we know that first-time issuers from the same region gravitate to a certain jurisdiction. Specifically, the data show that Eastern European and Sub-Saharan African governments have issued for the first time mostly under U.K. law, while Latin American countries did so under U.S. law (Table 3).

# **Table 3: Countries issuing for the first time central government dollar-denominated debt -** **<u>under either U.K. or U.S. law (1990 2015)</u>**

|Region|1990-2007|of which:<br>under U.K. law|2008-2015|of which: under<br>U.K. law|
|---|---|---|---|---|
|East Asia & Pacific|4|50%|2|<br>50%|
|Europe & Central Asia|13|92%|4|<br>100%|
|Latin America & Caribbean|10|0%|2|<br>-|
|Middle East & North Africa|4|50%|1|<br>100%|
|South Asia|2|100%|0|<br>-|
|Sub-Saharan Africa|4|50%|7|
Africa. _European Journal of Political Economy 27_ , S50–S63. Special Issue:Terrorism.

- Bonhomme, S. and E. Manresa (2015). Grouped patterns of heterogeneity in panel data. _Econometrica 83_ (3), 1147–1184.

- Carment, D., S. Prest, and Y. Samy (2011, 09). 43The Causes and Measurement of State Fragility. In _Fragile States: Causes, Costs, and Responses_ . Oxford University Press.

- Carment, D., Y. Samy, and S. Prest (2008). State fragility and implications for aid allocation: An empirical analysis. _Conflict Management and Peace Science 25_ (4), 349–373.

- Chami, R., M. Coppo, R. Espinoza, and P. Montiel (2021). Macroeconomic policy issues in fragile states: A framework. In R. Chami, R. Espinoza, and P. Montiel (Eds.), _Macroeconomic policy in fragile states_ , pp. 209. Oxford University Press.

- Custodio Jo ̃ao, I., J. Schaumburg, A. Lucas, and B. Schwaab (2022). Dynamic Nonparametric Clustering of Multivariate Panel Data. _Journal of Financial Econometrics_ .

- De Amorim, R. C. and C. Hennig (2015). Recovering the number of clusters in data sets with noise features using feature rescaling factors. _Information sciences 324_ , 126–145.

- Gonzalez, C. Y. and T. Nishiuchi (2018). Revisiting the Relevance of the World Bank’s Country Policy and Institutional Assessment on Economic Growth. World Bank Publications - Reports 31060, The World Bank Group.

- Grimm, S., N. Lemay-H ́ebert, and O. Nay (2014). ‘Fragile states’: introducing a political concept. _Third World Quarterly 35_ (2), S197–S209.

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systems are in place for all major hazards, with outreach to communities” with a score between 0 and 5. We attribute a score equal to zero to countries that did not contribute to the HFA monitoring system.

We assume that individuals with access to early warning can reduce capital losses by ��20% , and that the fraction of the population with access depends linearly on the HFA indicator: 0% for a score of 0, 20% for a score of 1, up to 100% for a score equal to 5. So actual vulnerability is equal to: Where HFA_P2C3 is the HFA indicator related to “Early warning systems are in place for all major hazards, with outreach to communities.”

# 3.3. Exposure

## 3.3.1. Computing population and asset exposure by hazard and return period

The exposure of the population and assets to natural disasters is derived from UN estimates of expected asset losses and our estimates of asset vulnerability. The Global Assessment Report (UN‐ISDR 2015), referred to as the GAR report, provides for each country and hazard (riverine floods, windstorms, storm surge, earthquakes and tsunamis) asset losses per return periods (from 20 to 1,500 years) as well as average annual losses (AAL). We first estimate frequent asset losses per country and hazard (for the one year return period event) such that averaging events over return period matches average annual losses provided by the GAR.

Then, we derive the exposure per country, hazard and return period that is consistent with these asset losses and the vulnerability derived before, that is: Where �� is asset vulnerability as computed in the previous section, Δ� is the value reported in the GAR, and �� is the fraction of assets affected (that is the exposure) we solve for.

## 3.3.2. Summing over return periods

To aggregate asset and welfare losses, taking into account that exposure depends on the return period, we
energy efficiency efforts on industries with high energy intensity, high total energy use, and high economic growth. Industries undergoing growth in production are less capital restricted and may also be more receptive to efforts to improve their competitive edge through increased management of energy costs.

Second, despite recent progress in energy price reforms, subsidies still persist in many countries in ECA. The effectiveness of continued energy price reforms in the region will largely determine the potential to save energy.

Finally, the lack of energy-use data is one of the stumbling blocks to developing rational energy efficiency strategies and policies. Important data gaps remain, especially in Central Asian countries. For example, more than 80 percent of the industrial energy use is reported as “nonspecified” in Kazakhstan, Kyrgyz, Turkmenistan and Uzbekistan. Collection of good quality, timely and detailed data should be a priority in these countries in order to provide the basis for rational policy making.

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# **Are Vaccination Campaigns Misinformed? Experimental Evidence from COVID-19 in Low- and Middle-Income Countries**

Yannick Markhof<sup>1* † ‡</sup> , Philip Wollburg<sup>‡</sup> , Alberto Zezza<sup>‡</sup>

**JEL codes:** I14, I18, C81, C82, C83

**Keywords:** COVID-19, vaccination, survey data, administrative data

1 * Corresponding author (markhof@merit.unu.edu) † United Nations University, UNU-MERIT ‡World Bank, Development Economics Data Group This paper received funding support from the World Bank Research Support Budget grant “Understanding and estimating COVID-19 vaccination attitudes, uptake, and barriers in Sub-Saharan Africa” and the Global Financing Facility. The authors are grateful for support in collecting the data in five Sub-Saharan African countries by the country teams: Marco Tiberti for Burkina Faso; Wilbert Drazi Vundru for Malawi; Asmelash Haile Tsegay and Manex Bule Yonis for Ethiopia; Akiko Sagesaka, Amparo Palacios-Lopez, Ivette Contreras Gonzalez, and Gbemisola Oseni for Nigeria; and Giulia Ponzini and Frederic Cochinard for Uganda. For the remaining countries, we thank the Data for Goals (D4G) team and the Regional HFPS focal points in the EFI Poverty and Equity Global Practice (POV GP) for compiling the data as part of the COVID-19 High-Frequency Monitoring Dashboard. We are also grateful for support and comments by Alemayehu Ambel and Talip Kilic. We would like to thank Graeme Blair, Maximilian Bruder, Antonia Delius, Robert Garlick, Matt Hulse, Eleonora Nillesen and conference participants at the IPA/GPRL Researcher Gathering at Northwestern University, UNU-MERIT, and the World Bank for helpful comments and discussions.
uncertainty. Figure 2 thus implies that for an importer operating in a particular sector and in a specific region, for example Latin America and the Caribbean, the effective clearance time might be significantly longer than suggested by a simple average. Cross-country disparities are further investigated in Figure A1 in the Appendix. We find evidence of significant differences in the distribution of import clearance times, with countries such as Albania or Romania displaying close-to-zero interquartile ranges whereas 50 percent of Ivorian importing firms reported experiencing 13 to 60 days of delay in clearing imported inputs through Customs. Consistent with Volpe Martincus (2016) who argues that using distributions rather than point estimates allows improvements to the measurement of border clearance times given the heterogeneity of border times across observations, these stylized facts clearly suggest that relying on a single, average-type measure of time to import is not appropriate to fully grasp the uncertainty that firms face when importing their inputs.

# **2.2 Export Dynamics**

We match the WBES with a rich array of variables drawn from the Exporter Dynamics Database (EDD). The latter contains measures of the degree of product and market export diversification, firm dynamics in terms of entry, exit and survival in export markets, as well as the average unit prices of the products they export over the period 1997-2014 (Fernandes, Freund, and Pierola, 2016). Our analysis focuses on the export dynamics of manufacturing firms at the country-destination-sector-year level.<sup>21</sup> More specifically, we investigate whether uncertainty in import clearance times affects firm entry, exit as well as first-, second- and third-year survival rates in export markets. Out of the 72 developing countries originally present in EDD, 48 were successfully matched with the WBES. Since the latter are only conducted every four to six years and data collection takes over a year, we allow matching EDD data with up to three years backward or forward relative to the WBES to enhance the size of the final dataset. Table A2 in the Appendix displays the list of countries
)|(4.178)|(1.276)|(1.509)|(2.402)|(2.317)|
|Real GDP Growth, y-o-y,|0.094|0.447***|0.071|0.087|-0.505***|-0.496***|
|<br>quarterly data lagged 1 quarter|(0.113)|(0.162)|(0.074)|(0.070)|(0.064)|(0.104)|
|<br>_LMP_ACTIVE_: Active labor|||<br>-7.158***|<br>-6.865***|<br>-26.202***|<br><br>-26.165***|
|market measures spending, %<br>GDP per percentage point of|||(2.487)|(2.077)|(5.626)|(6.533)|
|unemployment (lagged 1 yr)|||||||
|_LMP_PASSIVE_: Defined in the|23.538***|25.798***|||18.609***|17.967***|
|same way as_LMP_ACTIVE_|(8.589)|(4.380)|||(5.178)|(5.670)|
|_UBSA_(defined in Table 4),<br>|-0.488***|-0.416***|||||
|lagged 1 year|(0.141)|(0.074)|||||
|Union Density,|0.078**|0.091***|0.032***|0.031
, the economic sanctions increased the probability of dropping out of high school. The

> 60Using HIES this analysis cannot be done for family education spending because in this data the share of total education spending on each child is not observable. Also, I cannot do this analysis for each grade because in the data education levels are reported based on ISCED (less than primary, primary, lower secondary, upper secondary, 2-year tertiary vocational education, Bachelor’s, Master, and Ph.D.), thus the grades are not observable.

> 61Older people are not allowed to enroll or continue their education in high school two. Instead, they can enroll in the adult education system which is not available in all cities due to low demand. The data shows very few persons return to school as adults, and I did not find any significant change in enrollment in the adult education system.

> 62The results in Panel B are less precise because for cohort analysis a single year data is used, thus the sample size is smaller compared to that of Panel A. For example, the population of fully-exposed children (aged 16-20 in 2007) in Panel A is ten times larger than that in Panel B.

31
for adaptation to climate change have been foreclosed.<sup>14</sup>

# **4 Climate Impacts on Prices**

## **4.1 Climate data**

The NSS household data that we describe below were collected using clustered sampling, but the precise location of primary sampling units has not been released. Thus, the district is the lowest level of geographic disaggregation at which we can link household and climate variables. By 2003, India had 576 districts, excluding those in Lakshadweep, Andaman and Nicobar Islands, and in the mountainous northern state of Jammu/Kashmir, with an average land area of around 5000 km<sup>2</sup> .

We use monthly temperature readings from 370 weather stations throughout India with at least 20 years of records during 1951-1980. These data are available from the India Meteorological Department (IMD), which also provides a high resolution daily gridded (on 1<sup>_◦_</sup> latitude by 1<sup>_◦_</sup> longitude cells) rainfall dataset covering 1951-2003 based on daily records from more than 1800 weather stations.<sup>15</sup> Normal precipitation during the 1960-2000 period for a district are interpolated from the 296 cells covering India using the proportion of the district’s area in each cell as weights. District-level temperature normals are an average of records from the three nearest stations to the district’s geographical center, weighted by the inverse of the squared distance to the district centroid.

Figure 1 illustrates the tremendous variation in climatic conditions across India, ranging from the desert-like western Rajasthan to the eastern foothills of the Himalayas, one of the wettest regions on earth. Most precipitation falls during the southwestern monsoon, extending from June through September. The monsoon plays a critical role in determining the success of the kharif (summer season) harvest; rabi (winter) season production depends to a much greater extent on irrigation.

## **4.2 Land values
The ATT estimates for comparison of Jalanidhi and other community managed schemes indicate no significant differences across various indicators related to availability and reliability, household satisfaction and household cost of service (Table 12). Jalanidhi schemes, however, experienced fewer breakdowns compared to other community managed schemes. Jalanidhi schemes also perform significantly better in certain dimensions of institutional sustainability. Jalanidhi schemes have a larger proportion of females in executive committees and significantly higher bank balances compared to other community managed schemes. Similarly, a higher proportion of Jalanidhi schemes have female treasurers and maintain records regularly.

**Table 12: PSM Comparison of Jalanidhi with Other Community Managed Schemes: Selected**

**<u>Indicators</u>**

|**Variable**|**Jalanidhi**|**Other**<br>**Bottom-up**|**ATT**|**SE**|**P-value**|
|---|---|---|---|---|---|
|**Availability & Reliability Index**||||||
|No Irregular Supply|0.62|0.53|0.10|0.13|0.443|
|No Low Pressure|0.65|0.55|0.10|0.16|0.539|
|No Seasonal Shortage|0.49|0.54|-0.05|0.13|0.723|
|**Household Satisfaction Index**||||||
|Overall Satisfaction|3.5|3.5|0.04|0.15|0.820|
|Regularity of Supply|3.4|3.2|0.19|0.23|0.415|
|**Household Cost of Service Index**||||||
|HH Opinion on Contribution|2.1|
2 KENYA AND CHINA’S TRADE RELATIONSHIP <!-- Start of picture text --> Fish and crustaceans<br>20<br>Coffee, tea, and spices<br>Iron ore and concentrates<br>Titanium ores and concentrates<br>Plastics<br>Tanned or crust hides and skins of bovine<br>Tanned or crust skins of sheep or lambs<br>10<br>Raw hides of other animals<br>Vegetable textile fibres<br>Copper<br>0<br>Commodity<br>Export Value (USD Millions)<br><!-- End of picture text --> Figure 8: Kenya exports titanium ores and copper to China (2012-2014) Source: UN Comtrade database 2015 <!-- Start of picture text --> 10000<br>7500<br>5000<br>2500<br>200<br>150<br>100<br>50<br>1990 1995 2000 2005 2010 2015<br>Year<br>copper<br>USD per metric ton<br>iron.ore<br><!-- End of picture text --> Figure 9: Metals prices have been falling since January 2010 (1990-2015) Source: World Bank Commodity Price Data (The Pink Sheet)

**Finished leather has a window of opportunity in the Chinese market** Because of economies of scale and low input costs, China is a much more competitive producer of leather products. Kenya, however, has room to export finished leather products to China. China has a limited presence in the high end leather market, giving Kenya a chance to supply more value-added leather products. (Hansen, Moon, Mogollon 2015). At the moment, Kenya exports raw hides and semi-processed leather, products that are in high demand in China. Just as Ethiopia attracted Chinese investment in the leather sector to meet demand, so may Kenya work to bring more investment to
than 90% of areas are urban (e.g., Yerevan).

8. Any polygon in output 7 that crosses the output 2 boundary is an urban area. The rural class encompasses the remaining portion of the administrative boundaries.

# **3.2.2 The preEA Tool**

The “preEA” is a powerful and flexible package that has been developed by WorldPop in close collaboration with GeoData and consultation with various governments, UN agencies, and experts around the world (Qader et al., 2021, 2023). Since its development is ongoing, the tool is not yet available to the public. It has been designed as a user-friendly QGIS plugin using the Python programming language. The implementation of the preEA tool is fully automatic; however, some preparation processing steps should be taken before using the tool. These processes will be elucidated below.

**Input Data Preparation:** The data preparation consists of three steps. First, the input boundary datasets are reprojected into the projected UTM WGS 1984 coordinate system. The preEA tool is compatible with such projection to ensure that the output units are in familiar area units such as meters or feet. Second, the digitized boundary (roads, waterways, and railways) is masked by the extent of the administrative boundary. Third, to prevent the creation of sliver polygons in the outputs, double lines (such as motorways) are merged

13
the core land institutions mentioned above remain important, city and neighborhood level zoning, building codes, and FSIs gain importance.

Finally, as demand for urban built space increases with incomes, cities spread out (sprawl), and individual cities become part of a larger metropolitan area. Policies that promote coordinated and consolidated management of metropolitan areas will gain importance in the land policy portfolio for countries at middle incomes and cities at mature stages of urbanization.<sup>6</sup> However, no matter the stage of urbanization or the levels of incomes, urban transformation is buttressed by strong institutional foundations that govern the functioning of land markets. These include (i) clear definition and protection of property rights, (ii) institutions that enable independent valuation and public dissemination of land values across uses, and (iii) a judicial system that can handle disputes that may arise in the process.

> 6 World Bank (2008), Dowall (1992) , and Bertaud (2004, 2010)

2
Table 9 (Contd.)

# Wage Premium for Public Sector Jobs

|Study|Estimation<br>technique|Burkina<br>Faso|Cameroon|Cote<br>d'Ivoire|Mali|Senegal|
|---|---|---|---|---|---|---|
|Levy and<br>Newman<br>(1989; Table<br>6, page 106)|OLS<br>with dummy<br>variable<br>for public<br>sector jobs|||- 17.3 %<br>[ONFP, 1984;<br>N = 10,835]|||
|Miller and<br>Vallee<br>(1995; Table<br>6.5, page 163)|OLS<br>with dummy<br>variable<br>for 100%<br>private**firms**||11.1 %<br>[RPED, 1994;<br>N = 517]||||
|Van der Gaag<br>_etal._<br>(1989; Table<br>5, page 28)|FIML<br>with<br>switching<br>regression<br>model|||-26.5 %<br>[CILSS, 1985;<br>N = 513]|||

Note: Depending on the studies, the public sector may or may not include state-owned enterprises. All studies control for individual characteristics, but the set of controls varies across studies. The wage premium is based on the coefficient (c) multiplying the public sector dummy variable, except in the case of Berth6lemy and Bourguignon (1992), where it is based on the difference between coefficients for government and other formal sector jobs. For large values of the c coefficients, in absolute terms, the percent premia is approximated
However, there is some support for an alternate conception that sees the lack of protection as one dimension of an unregulated, yet dynamic, sector of small scale entrepreneurs many of whom enter the sector voluntarily and who choose, and are able, to remain largely outside the formal regulatory structures.<sup>4</sup> Labor markets may therefore be broadly integrated, and the existence of unprotected labor is not in itself evidence of segmentation. Arguably, workers contemplating selfemployment would wait for an auspicious business climate before leaving a protected job to launch their enterprise and would be more likely to fail during a downturn, inverting the counter-cyclical patterns of entry and exit predicted by the dualistic view. As. will be argued, the benefits of being informal may extend even to subcontracting relations which are generally thought to benefit only the employer.

Research on the informnal sector has largely relied on case studies, or static comparisons of earnings differentials which tend to show that formal sector workers earn more given their human capital. But to date, the data have not existed to study sectoral interactions across time. This paper takes advantage of an extraordinary longitudinal data set from Mexico that permits an examination of the dynamics among the various subsectors of the labor markets from 1987-1993. This period encompasses a complete business cycle, a far reaching trade reform, and pursuit of regional integration through NAFTA.

The paper first argues that traditional earnings comparisons are not reliable measures of segmentation, even when viewed across time. It then examines movements in relative sector sizes, the role of the various sectors in generating and absorbing the unemployed, and the changing patterns

> 4See, for example **Hart (1972), de** Soto **(1989), Tokman (1992), Fields (1990), Tunham and Er&cal** (1990), Portes and Shauffier (1992).

2
32% of the population of SSA. Third, the evidence we present is timely and supersedes potentially outdated information on issues such as vaccine hesitancy from before the start of vaccination for the general population in SSA.<sup>14–16</sup> Fourth, our analysis is comprehensive and provides microlevel evidence on all five dimensions of vaccine hesitancy, _confidence, complacency, convenience, communication,_ and _context,_ lastmile delivery barriers, and potential strategies to reach those who remain unvaccinated.<sup>17</sup> These insights come at a crucial moment for vaccination campaigns which have struggled to pick up pace.

Based on our findings, we elaborate policy options to turn vaccines into vaccinations in Sub-Saharan Africa.

# **Methods**

## **Data**

We use data from High Frequency Phone Surveys (HFPS) in five countries in East and West Africa – Burkina Faso, Kenya, Malawi, Nigeria, and Tanzania. The surveys were conducted by countries’ national statistical agencies in collaboration with the World Bank. Since 2020, the HFPS have collected crosscountry comparable longitudinal data on a wide range of topics, including the impacts of COVID-19 on households and individuals.

Some rounds of the HFPS include a module on COVID-19 vaccination. The data we use in this study was collected between November 2021 and May 2022: Burkina Faso (April-May 2022), Kenya (November 2021-March 2022), Malawi (February 2022), Nigeria (December 2021 – January 2022), and Tanzania (December 2021).

## **Sampling and sample representativeness**

The HFPS are re-contact surveys with national scope whose samples are drawn from nationally representative samples of households interviewed in recent in-person surveys. In Kenya, an additional sample of households was drawn via random digit dialing. Phone survey samples may not be fully representative of the general population, because of limited mobile phone coverage, non-response, and attrition, especially in lower-income contexts. To mitigate these concerns, sampling weights were recalibrated using propensity score