rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10275

# **Abstract**

COVID-19 vaccination rates in Sub-Saharan Africa lag behind other world regions, with just over 20 percent of the population in Sub-Saharan Africa fully vaccinated. To reach widespread coverage, high willingness to get vaccinated for COVID-19 among the population is considered an important prerequisite. Drawing on two years of panel survey data, this paper studies the dynamics of vaccine acceptance, its correlates, and reasons for hesitancy over time. The data come from multiple rounds of national High-Frequency Phone Surveys in five countries in East and West Africa (Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda), covering the period between 2020 and 2022. The surveys are cross-country comparable and draw their samples from nationally representative sampling frames. The paper finds that COVID-19 vaccine acceptance has been high throughout the study period (68 to 98 percent). However, acceptance levels were lower in 2022 than in 2020 in three countries (Burkina Faso, Malawi, and Nigeria) and higher in one country (Uganda). Moreover, individuals are observed to change their stated vaccine attitudes between survey rounds, to a limited extent in some countries (Ethiopia) and more frequently in others (Burkina Faso, Malawi, Nigeria, and Uganda). Vaccine hesitancy is higher in richer households and among those residing in urban areas, women, and the better educated. Conversely, hesitancy is lower in larger households and among heads of the household. The main reasons for hesitancy are concerns about side effects of the vaccine, its safety and efficacy, as well as assessments of COVID-19 risk, although these reasons fluctuate over time. The findings suggest that vaccine hesitancy is not the primary obstacle to reaching greater vaccine coverage in Sub-Saharan Africa. Instead, low coverage rates may be related to access and delivery barriers as well as supply shortages. Nevertheless, vaccine attitudes appear malleable so that continued efforts are needed to retain high levels of vaccine acceptance.

This paper is a product of the Development Data Group, Development Economics. It is part
% of the compounds and make the compound their principal residence in about half. On average, the rental compounds have 7.7 distinct rental rooms, and generate US$ 115 per month in total rent per compound, or US$ 83 in income after deducting expenses. Approximately three quarters of tenants have written or verbal month-to-month rental agreements and paid a deposit in 47% of the cases. Eighty percent of individual tenancy rents fall between US$ 15 and US$ 35 per month and averages US$ 25. A tenant makes US$ 137 on average in income per month which translates into an average total compound renter income of US$ 1,306.

The primary source of water in the informal settlement is piped water. The utility connects a single piped water and sewer line that serves the entire compound which is considered a single customer. The property owner is responsible for paying the water and

> 14The program target was an 80% collection rate, which is consistent with the Kenyan regulator guidelines. 15We use data from a listing exercise that we conducted in 2014 and combine this with an updated listing exercise conducted by the utility in 2018 which is described in further detail in Section 7.

> 16Based on a survey of property owners and a random sample of their tenants conducted in Kayole Soweto in 2015/2016.

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driving factor is that in rural areas, many households had reached their limits to growth in agricultural incomes owing to productive land shortages, as 58% of Ugandan farms were less than one acre in 2005/6 (World Bank, 2011b). By 2009/10, about 40 percent of rural households operated a non-farm household enterprise (HE) compared to 24 percent in 1992/93.

**Figure 4: Comparison of rural household livelihood portfolios in Uganda, 1992/93 -** **<u>2005/06, shows major increase in sources of income</u>**

<!-- Start of picture text --> HE & Ag wage Ag wage<br>non ag Non ag Other only2% Ag wage Non ag wage Ag wage only family and<br>HE wage wage 5% & Family 3% 2% farm<br>0% 2%<br>2% farm HE & 13%<br>7%<br>Family non ag Other<br>Farm, HE wage 14%<br>& ag 1%<br>wage HE<br>2% Family 3%<br>Farm &<br>HE<br>18%<br>Family<br>farm<br>Family Family<br>30%<br>Farm farm &<br>53% Family HE<br>Family farm, HE 21%<br>Farm & & ag<br>Family<br>non ag wage farm &<br>5%<br>wage<br>non ag<br>9%<br>wage<br>Rural areas, 1992/93 Rural areas, 2005/06 8%<br><!-- End of picture text -->

**Source** : Authors‘ calculations based on the IHS 1992/93 and UNHS 2005/06

This diversification was an _addition_ to household livelihoods,
# **5. Analyzing Infrastructure Valuation and Variation in Access**

In this section, we use regression analysis to examine: a) the value placed by households on infrastructure services; and b) correlates of access to a given service. First, we use a hedonic analysis of rental data to gain insights into the implicit valuation of different services. Then, we apply a logistic regression to each of five selected infrastructure services (electricity, private piped water, private toilet, organized garbage collection and sewage disposal) to identify factors that systematically affect the likelihood of a household having access to that service. Further analysis of the housing dimensions of this data set can be found in a paper by Gulyani, Talukdar and Bassett (July 2018), titled “Housing choices in urban Kenya: Shared housing, living conditions, and the rental market in 15 cities.”

# **5.1 Revealed valuations for services:  Insights from housing rents**

To properly guide and inform decisions regarding the financing and prioritization of basic public infrastructure services, it is important to understand how such services are valued by recipients. In section 3.3 we compiled data on household spending on different infrastructure services. Household spending can serve as a crude but generally incomplete proxy for relative demand between services. Another more accurate way to develop insights into implicit valuation of any services is to examine a functioning ‘product market’ where the product’s market value (thus, price) is expected to be driven by product attributes that include the service in question. For infrastructure services, the rental housing market with observations on characteristics of housing units versus rents (“products versus prices”) represents such a functioning product market. Accordingly, we undertook a statistical analysis of the rental housing market in urban Kenya to examine the following questions: To what extent do various infrastructure services explain the observed variations in rents paid across urban Kenya? What do the observed variations in rents paid across rental housing units reveal about the implicit valuations of various
most of the demographics are balanced across the two firm types, including gender, age, ethnicity, educational background, and Ravens (a measure of abstract cognitive ability).

During the panel data collection period, firm owners in one-person firms and in firms with workers both reported blackouts on 31% of days.<sup>14</sup> Among firms with workers, 44% reported at most a single worker reporting to work in a given week, another 22% reported at most two workers in a week, and the remaining 34% reported at least some weeks with at least three workers.

> 12The study defines firms with and without workers using this measure contemporaneous to the main analysis data collection because the labor information in the baseline data (from the February 2014 census) occurs more than a year before the weekly monitoring surveys. Given normal churn in the labor market, defining the firms with workers as those employing non-owner labor in February 2014 adds unnecessary noise to estimates of short term staffing adjustments that the paper argues firms use to cope with blackouts. 20% of firms with workers in March and April of 2015 had no employees as of the February 2014 census and 25% of firms without workers in March and April of 2015 had at least one worker besides the owner in February 2014. Appendix Table 8 estimates the relationship between these annual changes and outages and Section 5 discusses this analysis in more detail.

> 13Appendix Figure 1 shows the location of all sample firms by firm type. The entire sample is within an 11 square kilometer radius.

> 14Firm owners were given the option to report daily power access as blackout, partial blackout, or no blackout. In all specifications partial blackout is coded as a blackout, though the conclusions are robust to classifying partial blackouts as non-blackout days. Unfortunately, the data do not have any more direct measures of blackout duration.

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industrial and trade policy reforms and the general shift towards market-oriented economic policies that occurred in the 1980s and 1990s in India. Theory does not offer clear-cut predictions regarding the effect of industrial de-licensing, trade liberalization, and foreign direct investment reforms on spatial concentration of manufacturing industries, and hence the need for empirical analysis.

In this paper we ask the following questions. What are the determinants of the high level of spatial concentration in Indian manufacturing? How much of the changes in the spatial distribution of Indian manufacturing industries can be attributed to policy reforms, after conditioning on the traditional determinants on 1 1 - endowments, Mlarshalliantype linkages and knowledge spillovers? Does the spatial distribution of manufacturing plants of different sizes differ in response to policy changes? To answer these questions, we focus on indices of geographic concentration of manufacturing industries in India constructed following Ellison and Glaeser **(1997) -** henceforth **EG** indices **-** based on plant-level data from the Annual Survey of Industries **(ASI)** for the period **1980-99.** Our results show that industrial de-licensing has a significant negative impact on geographic concentration levels (a one standard deviation increase in the proportion of output de-licensed leads to a 9.; V decline in the **EG** index) and the same is true for foreign direct investment (FDI) liberalization (with a one standard deviation increase in the proportion of industry output open to FDI reducing the average **EG** index **by** 10. ). However, our estimates also show that trade policy has no effect on the spatial concentration of Indian manufacturing. Further we find that there is significant size-based variation in the response of concentration to the policy as well as the traditional determinants **of** :w1iii i:f1ij. Delicensing and FDI liberalization cause small plants to disperse. Trade policy has no effect on the concentration of medium-size plants but a one standard deviation decline in the effective rate of protection
<u>Table 1: Secondary Data Indicators</u>

|**IDP Return**|**Social Cohesion**|**Security**|
|---|---|---|
|Number of returnees|Whether returnees feel welcome|Existence of blocked IDP returns|
||Threats between tribalgroups|Revenge attacks|
||Mistrust between returnees and host<br>community|Fear of revenge attacks|
|||Perceptions of ‘daily public life’<sup>71</sup>|

The purpose in presenting these indicators is to understand changes in the context rather than imply that the LPA was directly responsible for changes. This is an important limitation to recognize: due to the myriad of factors affecting IDP return, security, and social relations in each community between 2015 and 2021, it is impossible to isolate the impact of each LPA, and we do not intend to imply there is a causal link between the signing of an LPA and changes in the context. Rather, the secondary data offers a way to understand the context that existed in each community before and after each LPA was signed and therefore make sense of the way each LPA was perceived.

There were several limitations in comparing indicators across the eight LPAs. Each LPA was signed at a different time and there is no consistent (single or consolidated) dataset that can cover all eight agreements. This means that it is impossible to compare identical indicators across all LPAs; or even to compare the same indicator across all LPAs – to give one example, the returnee population prior to LPA signature was available days before signature for some LPAs, months before signature for others, and not available at all for yet others (since in some locations, international actors were not present when the LPA was signed).<sup>72</sup> The ILA and Return Index also utilize different methodologies, varied timing for data collection, different indicators for similar issues, and different questions
|Kazakhstan|6.482|3.550|n.wto|n.wto|
|Tajikistan|4.355|5.861|n.wto|n.wto|
|Kyrgyz Republic|3.237|2.292|3.948|4.335|
|Moldova|4.564|2.396|5.107|3.397|
|Note: n.wto: non-W|TO member.||||

Source: Authors’ calculations using data from UNCTAD accessed through WITS (2012).

Georgia, Kyrgyzstan, and Moldova are WTO members and have lower tariffs than Russia, Belarus, and Kazakhstan. The simple average MFN applied tariff varies between 0.687% for Georgia, the country with many of the lowest trade policy barriers of the region, and 5.107% for Moldova. The highest level of protection in Georgia corresponds to products such as wheat or meslin flour, cigarettes, and cane or beet sugar, which face an MFN applied tariff of 12%. In Kyrgyzstan, import duties are high for goods like cane or beet sugar and tomatoes prepared or preserved, which have tariffs of 25% and 20%, respectively. Moldova’s protection is concentrated on cane or beet sugar and peas, with tariffs of 30% and 25%, respectively. Imports from these countries are mainly concentrated in petroleum oils and

> 3 Technically, the simple average applied tariff including preferences should be the same for Russia, Belarus, and Kazakhstan as these countries form a customs union. Differences are explained by the presence of missing values for some product lines.

4
|(4.228)|
|Father's schooling|1.880|2.144|0.168|
||(7.408)|(7.445)|(7.283)|
|Mother's schooling|4.200|3.842|2.374|
||(8.064)|(8.106)|(8.001)|
|Constant|‐244.5|‐263.3|‐207.3|
||(220.8)|(220.9)|(220.0)|
|Observations|652|650|621|
|R‐squared|0.053|0.049|0.043|
|Standard errors in parentheses||||
|*** p<0.01, ** p<0.05, * p<0.1||||
(-0.73)|
|If phone interview|||-0.0269|(-0.26)|||-0.110|(-0.82)|
|Othervariables and school fixed effects|yes||yes||yes||yes||
|Number of observations|2,962||2,962||2,761||2,761||

Source: TEEP Tracking Survey. Notes: Other variables include house value in 2000, residential land value in 2000, commercial land value in 2000, and agricultural land value in 2000.

t values are in parentheses.

* significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

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Asia. Therefore, in addition to the fact that greenfield investment data is disaggregated at the sector level, it is also a more accurate measure of origin and destination of FDI flows. With this in mind, we turn to how this financing may impact productivity.

# 3.2 Conceptual framework

To guide the empirical analysis, this section introduces the conceptual framework. The first subject of interest is establishing a _direct_ impact of FDI on the labor productivity of the recipient sector. FDI could generate spillovers since workers from multinational firms are mobile and can bring
output tables.<sup>14</sup> Since some industries produce both tangibles and intangibles, the assumption is that inputoutput ratios are similar across both goods.<sup>15</sup> In the following paragraphs, we describe our estimation approach in more detail, starting with our estimation of intangible asset categories, followed by our use of price deflators and the assumptions underlying the calculation of investment from expenditures.

# _(1) Estimation of intangible asset categories_

_A. Computerized information._ We found no surveys in Brazil that present the amount spent by industries in computer software and databases. Therefore, we used the Net Operating Income of computer software and database services industries from the Annual Survey on Services (PAS), and distributed it over industries using sector coefficients from the input-output matrix. Thus, sector expenditures can be obtained by: where is the ―service purchased‖ (computer software or computer databases) and is the coefficient from the input-output matrix that represents the amount of the service/product used as input by sector .

_B. Innovative property._ This category can be divided into three components: scientific R&D, copyright and license costs, and other product development, design and research expenses.

The major category here is expenditures in scientific R&D, which includes science and engineering R&D, R&D in petroleum and natural gas exploration, as well as in mining industries. For R&D in mining industries, we use data provided by the National Department of Mineral Production for the period of 2008-2010. We apply the average expenditure observed in this period as a percentage of GDP in order to estimate a time series of expenditures for the previous years (assuming a constant ratio throughout the entire period).

> 14 We use the same industry classification as that used in the system of national accounts, estimating investment for 55 industry groups.

> 15 This approach cannot capture imports of intangibles.

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/en/860291468129595286/pdf/ 444720WP0Dwall10Box334044B01PUBLIC1.pdf.

- Weiss, D.J., Nelson, A., Gibson, H.S., Temperley, W., Peedell, S., Lieber, A., Hancher, M., Poyart, E., Belchior, S., Fullman, N., Mappin, B., Dalrymple, U., Rozier, J., Lucas, T.C.D., Howes, R.E., Tusting, L.S., Kang, S.Y., Cameron, E., Bisanzio, D., Battle, K.E., Bhatt, S., Gething, P.W., 2018. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336. doi:10.1038/nature25181.

- World Bank, 2016. Strengthening Connectivity in Nepal. Available from https://www.worldbank.org/en/news/feature/2016/02/17/strengthening-rural-connectivity-in-nepal.

- World Bank, 2017. Climbing Higher: Towards a Middle-Income Nepal. Economic Memorandum. The World Bank Group. Washington, DC. Available from http://documents.worldbank.org/curated/en/358501495199225866/pdf/115156-CEM-PUBLIC-SAREC70p-Country-Economic-Memorandum-19-May-2017.pdf.

- World Bank, 2018. Implementation and Completion and Results Report on a Credit in the Amount of SDR 38.7 million to the Government of Nepal for a Bridges Improvement and Maintenance Program. Report 121710-NP. The World Bank Group. Available from http://documents.worldbank.org/curated/en/190621516371769416/pdf/Nepal-BIMP-P125495-ICRMain-Document-Approved-1-15-2018-01162018.pdf.

- World Bank, 2019. NEPAL: Strategic Road Connectivity and Trade Improvement Project. Internal document. World Bank Group. Washington, DC.

- World Bank, 2020. Cox’s Bazar Growth Diagnostic. Growth Diagnostic. The World Bank. Washington, DC.

- Xia, J
or decades to complete <mark>(D'Arcy and Nistotskaya 2018).</mark><sup>5</sup> Building footprints are now available freely or can be generated from high-resolution satellite imagery or, at city scale, drones (Gevaert et al. 2020) at minimal cost,<sup>6</sup> eliminating the need for costly and complex aerial photography (Carolini _et al._ 2020).

The use of such imagery to update property tax rolls and improve fairness of taxation by minimizing tax evasion in Italy led to large increases in collection of local property taxes. Beyond providing economic benefits, this also pushed up rates of incumbent re-election. Political returns were higher where public goods were provided efficiently and where tolerance for tax evasion had been lower at the start (Casaburi and Troiano 2016). Since 2004, <mark>Bogotá has been a pioneer in introducing policies of land taxation and land value capture, to very positive effect (</mark> Garza and Gonzalez 2021).

_Automated valuation based on market prices._ Once the universe of taxable properties is identified, a transparent and fair process for determining each property’s tax liability needs to be established, ideally distinguishing land and buildings. Automated computer-assisted mass valuation (CAMA) involves regressing price data on property characteristics and using the coefficients thus obtained to generate property-specific predicted values. CAMA can overcome the disadvantage of flat rates and is thus a preferred option (Hill 2013). Beyond data on property characteristics and coordinates (Diewert and Shimizu 2022), it requires price data that are ideally obtained from registry records or, if these are not available or distorted due to high transaction taxes, sample enumeration that can be checked against other data sources, such as listings of high-end properties.<sup>7</sup> The potential of CAMA is illustrated by the mass valuation of properties in Rwanda, which suggests that a 1 percent ad valorem tax could increase revenue up to tenfold and be more equitable
a population of 29.8 million, this corresponds to a GDP per capita of US$623. Tables 2.2a and 2.2b show a macro version of the 2020 SAM and its accounts, respectively. The SAM, which is expressed as percent of GDP, is highly aggregate – among other things, it has only one production sector (i.e., one activity producing one commodity) – making it easy to learn about the macro situation in 2020 from the SAM. The government current account deficit was 4.5 percent (of GDP) (cell [cap-gov,gov]) while government consumption and investment amounted

> 2 For additional details on the SAM, see the appendix.

> 3 The disaggregation is primarily based on the most recent UN data. Although it would be possible to further disaggregate the sector assuming that the patterns of the 2014 IFPRI SAM still are valid, this does not seem meaningful: it would not add anything significant to the analysis and, furthermore, the extent to which these patterns also were valid in 2020 is unknown.

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# **_<u>________________________________________________________</u>_**

# **<u>Annex I.   Summary of Main Data Sources</u>**

|**Data**|**Coverage**|**Years**|**Variables/Description**|
|---|---|---|---|
||**INV**|**ESTMENT CL**|**IMATE**|
|World Competitiveness<br>Yearbook|59|Since 1989|Competitiveness factors (economic<br>performance, government efficiency,<br>business efficiency, and<br>infrastructure)<sup>iv</sup>.|
|Doing Business<br>Database|155|Annual|Indicators of regulatory costs of<br>business and enforcement|
|Investment Climate<br>Surveys (ICS)|32,000<br>entreprene<br>urs in 62<br>countries.|Expanded<br>since mid<br>1990s|Indicators of firm productivity and<br>performance.|
|EBRD Transition Report|26 Eastern<br>Europe<br>and CIS<br>countries||Quality of business environment|
|World Business<br>Environment Survey<br>(WBES)|80|1999-2000|Corruption, judiciary, quality of<br>business environment, efficiency of<br>customs (including time taken to clear<br>customs), quality and efficiency of<br>public services|
|World Bank :<br>Investment Climate<br>Assessments<br>(ICAs)|18||Indicators on entry regulation,<br>infrastructure indicators, regulatory and<br>administrative delays (including<br>customs clearance)|
|World Economic Forum<br>: Global<br>Competitiveness Report<br>(GCR)|117|annual|