rafmacalaba/data-use-mentions-extended huggingface dataset:
In addition to the size of the forests, the diversity of tree species is also worth mentioning, since five of the ten countries with the most tree species are in the B-rated category (FAO and UNEP, 2020). The FAO (2014) discusses the importance of forest genetic resources in developing economies and economies in transition in the context of their “actual or potential economic, environmental, Given the direct productive capabilities of forests and their indirect socioeconomic benefits, investing into this renewable resource is an economically worthwhile decision. We see support for this in the underlying data. Figure 7a shows the average annual percentage increase in planted forest areas between 2009 and 2020. A planted forest is a "[f]orest predominantly composed of trees established through planting and/or deliberate seeding" (FAO, 2020) and is to be distinguished from natural regrowth. The fact that countries, where agriculture, forestry and fishing constitute a larger share of GDP value-added largely coincide with the countries with the most planted forest expansion, renders luxury investment explanation unlikely. This is further supported by Table 5, Appendix, which decomposes the negative coefficient by rating category. The same investment into forests lowers yields significantly more in B-rated countries.

Table 6, Appendix, shows disaggregated results where forest capital is divided into timber and non-timber forests. Interestingly, the latter type mainly drives the yield lowering effect of forests. Identifying whether this reflects tourism revenues based on non-timber forest capital or in fact captures the hard-to-quantify benefits of its external effects is left for future studies.

# **4.5 Agriculture**

The agricultural sector in the broad sense is the largest employer in the world and faces a monumental task: Feeding 10 billion people in 2050 (FAO, 2018). Ensuring that agricultural land does not become a non-renewable resource due to land degradation is of paramount importance to the economy of a nation and the world.

This wealth category is divided into cropland, which represents the value of primary crops of cereals, fruits, vegetables for human
significantly for the comparable places on the Uttarakhand side of the border. While we cannot fully rule out the displacement effect completely, we take these results as evidence that the significant employment growth due to the policy is not fully driven by the relocation of firms from Uttar Pradesh to Uttarakhand at the border.

Overall, we verify the local channels by which a large place-based policy in India impacts net job creation and firm location. We show the impact takes place through both direct and indirect channels, highlighting the role of agglomeration economies at play. However, owing to data limitation, our analysis sheds light only on the local development impacts of the policy and cannot address the nation-wide net costs and benefits of the New Industrial Policy generated for India as a whole.

This paper extends a strand of relatively new literature evaluating the impacts of place-based policies in developing countries. Most of these existing studies focus on China. In the case of India, Chaurey (2017) uses the Annual Survey of Industries to study the central tax incentives targeting Himachal Pradesh and Uttarakhand, and Shenoy (2018) takes nighttime light data as a proxy of economic activities to investigate a broader set of interventions targeting Uttarakhand. Our analysis complements these studies by exploring firm-level data that have both a more comprehensive coverage (manufacturing and services sectors, formal and informal) and a finer spatial granularity (town and village level instead of district level).

Our research design requires weaker assumptions than much of the prior literature based on differencein-differences estimation—which is the case of Chaurey (2017) — and explores sectoral heterogeneity to assess the impact of fiscal incentives. In doing so, this paper also builds on a large literature studying the impact of taxes on business location decisions (Duranton, Gobillon and Overman 2011; Holmes 1998; Rohlin, Rosenthal and Ross 2014). Another innovative aspect of the design is that we verify the impact of agglomeration economies by looking at spillovers between industries. We provide another piece of 5
are _informal non-farm enterprises_ that are unincorporated and owned by households. Specifically, Household and Micro enterprises are defined as follows:

- _Household Enterprises_ (HEs) are own-account (self-employed) enterprises working in non-agricultural sectors that may employ contributing family workers.

- _Micro Enterprises_ (MEs) are own-account (self-employed) enterprises working in non-agricultural sectors that employ at least one non-family worker on a continuous basis.

- he whole group of unincorporated enterprises identified in household surveys we refer to as _Non-farm Enterprises_ (NFEs). We use this term because in some employment data we are unable to distinguish between HEs and MEs, so we count all those who report their employment status as “self-employed/own account” as NFE.

- When analyzing employment, all participating family workers in HEs or MEs are assigned to the NFE sector as well. Employees in MEs outside the family are not included and likely show up as wage workers.

> 3 Our approach does not deny that many, if not most, people who own or work in HEs in SSA have a precarious livelihood.  In the terminology of the ILO, this activity is usually “vulnerable employment” (ILO, 2011). What our approach considers is whether this activity is a viable employment strategy which enhances the income and welfare of the household, and if so, for whom?

5
reported in Table 1. Since these studies use different methodologies, and time periods, their estimates are not comparable and neither are the estimates presented here comparable with these studies. It is the relative ranking of the countries within each study that is important. In Sarel's study Malaysia's performance is better than Indonesia and Philippines, comparable with Thailand, and a little lower than Singapore. In Bosworth and Collins' work, while Malaysia still does better than Philippines and Indonesia, its performance falls short of the other countries.

**Role of Labor Productivity:** Declining TFP growth can be associated with lower TFP within sectors and/or a slower process of reallocating resources from less to more efficient sectors in the economy. Because data on capital stock and investment by sector are not available, TFP growth within each sector cannot be measured. Labor productivity unadjusted for educational attainment _(Yi/Lj)_ by sector i, can, however, be computed from available data. This information can be utilized to decompose economywide labor productivity change into labor productivity change in each sector and the productivity gains achieved through the reallocation of labor to more productive sectors.

<!-- Start of picture text --> L = y= EsitYi, (5)<br><!-- End of picture text --> 6 Years in which output per worker was negative, namely, 1975, 1985, and 1986.

6
by 8.9 percent if increased labor force participation and productivity are modeled (World Bank, 2019). Pennings (2022) constructs the Employment Gap Index (GEGI) that translates the inequalities in employment between men and women into the potential monetary gains (in GPD-percapita). On average across the countries studied,<sup>4</sup> the GEGIs estimate that long-run GDP per capita would be 19 percent larger if gender gaps could be closed by increasing female employment to that of men. With an application of a Lucas span-of-control model of occupational choice with heterogeneous entrepreneurial endowments, Cuberes and Teignier (2016) examine the impact of inequality between men and women in entrepreneurship and labor force participation on productivity and per-capita income, noting average income losses of 15 percent due to gender gaps.

Devadas and Kim (2020) use the World Bank Long-Term Growth Model (LTGM), which is built on a Solow-Swan growth model, to estimate the effect of increasing labor force participation and education for women on GDP growth. For the indirect impact of closing gender inequality, the study uses an extension of the LTGM-TFP model in which the total factor productivity (TFP) growth rate is a function of a TFP determinant index and initial TFP level. The authors find that the GDP-per-capita loss from gender gaps amounts to 7.9 percent in low-income countries, 21.3 percent in lower-middle income countries, 12.3 percent in upper-middle income countries and 6.4 percent in high-income countries. In a similar exercise, Wodon et al. (2020) employ a comprehensive and domain-specific framework to assess the economic opportunity associated with closing the gender gap by estimating the impact of inequalities using regression analyses or statistical differences. The authors highlight the importance of closing these gaps by separately estimating the impacts and economic costs of gender inequality across five domains:

> 2 See van der Mensbrugghe (2011) for a complete description.

> 3 In the case of Niger, costs calculated are related to earnings, population growth, under-five mortality and standing
to invest more flexibly. For example, an affiliate able to invest in bank CDs at the average rates reported below, without paying the typical AFP fees, would have obtained average returns of around 15-17% p.a., compared to 4.5% p.a. earned in pension fimds.

Table 7: Performance of Financial Instruments in Peru

||180-365 CD|IFCG Total<br>Local Currency|Gross<br>Pension|Net<br>Pension|
|---|---|---|---|---|
|Year|Rate|Index|Fund IRR|Fund IRR|
|(a)|(b)|(c)|(d)|(e)|
|1994|16.9%|80.4%|27.8%|-0.3%|
|1995|17.2%|55.7%|18.0%|-0.9%|
|1996|_15.0%_|17.3%|20.3%|4.5%|

- (a) Months covered for 1994 are Aug 93- Sep 94; Oct-Sep for years 1995 and 1996.

- (b) Interest rates for 180-360 day deposits for September of indicated year. 1996 figure is for August.

- (c) IFC Global Index in local currency with dividends reinvested. Annual change in index December to December figure for the preceding year.

- (d) Annual internal rate of return through the corresponding date without considering fees and commissions.

- (e) Annual internal rate of return through the corresponding date considering fees and commissions.

Sources: IFC Emerging Markets Database; Peruvian Superintendencia de Admninistradoras Privadas de Fondos de Pensiones.

Marketing Expenses. Analysis of the financial results of the system and individual AFPs reveal the same pronounced pattern of marketing expenses. The direct
# **Turning COVID-19 Vaccines into Vaccinations: New Evidence from SubSaharan Africa**

Philip Wollburg<sup>1</sup> Yannick Markhof<sup>1,2</sup> Shelton Kanyanda<sup>1</sup> Alberto Zezza<sup>1</sup>

## **JEL codes: I14, I18, I100, O55**

## **Keywords: COVID-19, vaccination, Africa, vaccine access**

1Development Data Group, World Bank. 2UNU-MERIT, United Nations University. The authors are grateful for support in designing the survey instrument and collecting the data by the country teams: Marco Tiberti for Burkina Faso; Wilbert Drazi Vundru for Malawi; Akiko Sagesaka, Amparo Palacios-Lopez, Ivette Contreras Gonzalez, and Gbemisola Oseni for Nigeria; and Akuffo Amankwah for Tanzania; Antonia Delius and Caleb Leseine Gitau for Kenya. We are furthermore grateful for comments and support by Gero Carletto and Kevin McGee and comments and feedback in the development of the survey instruments by the members of the World Bank Development Data Group’s HFPS Questionnaire Working 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.
0 on a five point scale). The survey instrument was found useful especially in that it asked both importance and satisfaction levels of knowledge and skills. This structure was incorporated in our survey instrument.

Lattuca, Terenzini, and Volkwein, 2006, assessed the impact of accreditation based upon student learning outcomes as introduced by the Accreditation Board for Engineering and Technology (ABET) in the mid-1990s. The expected student learning outcomes of both ABET and the National Board of Accreditation (NBA) in India are similar due to common membership of the so-called Washington Accord, which mutually recognizes engineering degrees across 12 member-countries, (Washington Accord, 2007). Since many questions in our employer survey were drawn from the NBA‘s learning outcomes, our survey results are to some extent comparable with the above study‘s outcomes. Lattuca, Terenzini and Volkwein surveyed 1,622 employers. The employers were asked information on their characteristics and three basic issues: (i) the preparation of recent engineering graduates, (ii) whether or not the skills of the graduates had increased, and (iii) the importance employers attach to each of the 11 EC2000 learning outcomes. The study found that the majority of employers agreed that, overall, graduates were adequately or well prepared for

> 3 In this paper, we use the term ―skills‖ in the broadest interpretation. Hence, it is not limited to skills of a routine nature related to a specific trade, but covers broadly and includes what some studies labels competences, personal attributes, personal characteristics and abilities of both cognitive and affective/interpersonal nature.

4
30 experience with distance delivery and school reopening following the 2014-15 Ebola outbreak. During this last lengthy period of school closures, the government and development partners developed ECE radio content as well as some limited strategies for improving access. This experience equipped Liberia with ready-made resources which Ethiopia, another low-income country, did not have. Relative to the other focus countries, Jamaica has a more developed ECD/ECE system generally, and the presence of a strong guiding institution with coordinating authority was an important enabling factor. The presence of the Early Childhood Commission seems to have protected ECE from forces that might favor deprioritization, such as Ministries’ focus on technology-driven interventions and high-stakes examinations. Although even in Jamaica the response included more autonomous than coordinated action by development partners and civil society, the ECC was an important channel connecting parents and schools to various services and initiatives.

These findings help explain why, according to both our data and the global survey of Ministries of Education, countries were less likely to provide distance learning options for ECE and also less likely to have a plan for nationwide reopening of preschools. The impact of the pandemic on COVID-19 could be seen as another example of the “Matthew effect” (Merton, 1968). In conditions of crisis the political position of ECE, already weak relative to other levels of education and other government priorities, has deteriorated further. The COVID-19 shock could have opened a “policy window” for dramatic change in favor of ECE in our focus countries, if only it was positioned well to take advantage of this opportunity. Our analysis calls attention to the persistent neglect of early learning which undermines earlier investments by governments and development partners in expanding ECE access and quality.
PPP (both growth and level), ICT users (as a share of total population), population by age, exports and imports (both as a share of GDP).

# **4. Methodology**

To estimate our skill indexes, we first construct a conceptual link between tasks and skill categories following the same approach used by Autor, Levy, and Murnane (2003), Acemoglu and Autor (2011), Handel (2012), Spitz‐Oener (2006) and several other studies.

Within this approach, two main methods can be distinguished in the literature. The first one relies on occupational level task indexes estimated by experts, who rank occupations based on worker interviews. The O*NET data set is the outcome of such analysis for the US economy, with 44 different scores being assigned to each detailed level occupation. The second approach, instead, relies on direct worker-level information on the specific tasks performed on the job. It was pioneered by Handel (2008), who developed and used the STAMP survey (that later became the PDII), for the US. This approach allows observation of the tasks at a more disaggregated level, making within-occupation analyses possible. Our methodology falls into this second category, as we employ task information at the worker-level, exploiting the STEP surveys for several developing countries. As our objective is to compare our findings with the counterfactual results that one would obtain using the US classifications, we employ two different specifications, each of which as close as possible to the O*NET and to the PDII specifications, respectively. Since the results using the PDII specification lead to similar conclusions to the ones we obtain using the O*NET one, we only discuss the latter because it provides a greater disaggregation of skill groups (5 vs. 3 categories).

The O*NET specification refers back to the study of Autor, Levy, and Murnane (2003). This specification uses 5 different skill categories: Non-Routine Analytical, Non-Routine Interpersonal, Routine Cognitive
indicated to have|||||||||
|been collected across|||||||||
|all economies||||81/140|||||

Note: Evidence that a survey was collected can be explicit like in a “survey section” of the website or “implicit” like in a report, summary table and/or any mention or reference to the data on the website.

* indicates instances where collection of recent microdata was not indicated on NSOs website, but the research team discovered it on an external website. These include Iraq: Rapid welfare monitoring survey SWIFT 2017/2018 downloadable from <u>https://microdata.worldbank.org/,</u> Egypt (2014) downloadable from http://www.dhsprogram.com/ and Iraq MICS 2018, Oman MICS 2014, Tunisia: MICS 2018, West Bank and Gaza (Palestine) MICS 2019/20 downloadable from <u>https://mics.unicef.org/surveys.</u>

All MENA NSOs except the Republic of Yemen collect price data for their CPI and or PPI and about half are up to date with respect to their labor force, consumption, and census data. Eleven NSOs report recent surveys in the Labor Force microdata category and 14 recent surveys are found in the consumption data category. For establishment data, only a quarter of NSOs (5) collected such data recently: the 2018 Kuwait’s Annual Survey of Establishments, 2016 Malta’s Labor Cost Survey, 2019 Morocco’s National Business Survey, 2019 Saudi Arabia’s Economic Indicator Survey and the 2018 Palestinian Economic Survey Series.

The NSOs of Saudi Arabia and West Bank and Gaza are up to date with their micro data collection across all data categories- 7 out of 7 recent microdata sets expected. They are closely followed by the Arab Republic of Egypt and Jordan which collected data for 6 out of the 7 recent microdata 10
# **References:**

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Bardhan, P. and Mookherjee, D. (2005), _Decentralization of local governments in developing countries: A comparative perspective_ . Cambridge, MA: MIT Press.

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Estache, A., Foster, V. and Wodon, Q. (2000), Infrastructure reform and the poor. Learning from Latin America’s experience. LAC regional studies program, WBI studies in
## stimulus.

Adverse effects through multiple channels. Recessions affected potential growth through multiple channels. Four to five years after an average recession, the annual growth of investment, employment, and productivity remained significantly lower than in “normal” years (by 3 percentage points, 0.7 percentage point and 0.7 percentage point, respectively). This contrasts with banking crises, which tended to be associated mostly with lasting losses of productivity growth, and epidemics, which were mainly associated with lasting employment losses, possibly reflecting economic shifts caused by behavioral responses to epidemics.

Different features of potential growth estimates. The comprehensive database also allows a comparisons across potential growth measures. Forecast-based estimates tend to be systematically higher than other estimates, and estimates based on univariate filtering techniques systematically lower. Estimates based on filtering techniques tend to be the most volatile and to track actual growth most closely, as expected. Estimates based on the production function approach tend to be the most stable and the least correlated with actual growth as they capture slow-moving drivers of potential growth.

The study proceeds as follows. Section II presents the database. Section III describes movements in potential growth around the world in recent decades. Section IV estimates the effects on potential growth of recessions. Section V documents the channels through which these operates. Section VI concludes.

# II. Database

Three main methods of estimating potential growth estimates have been used in the literature, and several different measures can be derived using variants of them. The comprehensive database developed here allows a comparison of the behaviors of such measures.

The database includes nine measures of potential growth for up to 173 countries over periods as long as 1981-2021. The baseline measure of annual potential growth, estimated using the production function approach, is available for up to 30 advanced economies and 64 EMDEs for 1998-2021 (table 1, annex A). Six univariate and multivariate filter-based estimates of potential growth, which require quarterly data, are available for up to 37 advanced economies and 52 EMDEs for 1980Q1-2022Q1 (annexes B
4-6|1.00|1.12|1.00|580,115|
|Number of children 7-12|1.00|1.47|2.00|976,611|
|Number of people > 12|2.00|3.28|4.00|1,696,260|
|Presence of grandmother|0.00|0.05|0.00|1,696,260|
|Household head|0.00|0.22|0.00|1,696,260|
|Spouse works|1.00|0.89|1.00|1,395,027|
|Income stability|0.00|0.56|1.00|1,696,260|
|Someone in the family receives remittances|0.00|0.08|0.00|1,696,260|
|Household Home & WASH index|-0.21|0.04|0.51|1,696,260|
|House|1.00|0.96|1.00|1,696,260|
|Ownership of the dwelling|1.00|0.79|1.00|1,696,260|
|Resilience of Walls (bricks/concrete)|0.00|0.66|1.00|1,696,260|
|Resilience of Roof (concrete/cement)|1.00|0.98|1.00|1,696,260|
|Access to water (pipeline)|0.00|0.73|1.00|1,696,260|
|Toilet Safety|0.00|0.54|1.00|1,696,260|
|Urban|0.00|0.57|1.00|1,696,260|
|Panel C: Technolo|gy Access