rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10151

# **Abstract**

How the enforcement of competition regulation of information and communications technology affects growth depends on how well firms adapt to competitive pressure. This paper tests this empirically using Peruvian firm-level data matched to a compilation of information and communications technology regulations and competition enforcement cases over 10 years. Based on the theoretical dispersion in markups, the paper shows that by increasing productivity, leaders in a market can avoid the effects of competition while maintaining market share. However, much depends on the regulatory structure, which affects productive firms differently depending on how long they have been in business. Highly productive older firms translate regulations that make processes more complex (such as raising quality standards) into more productivity; productive younger firms benefit more from simplifying rules that facilitate competition through lower entry barriers and improved operating conditions. This feature is consistent across different segments of the information and communications technology sector.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region and the Macroeconomics, Trade and Investment 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/prwp. The authors may be contacted at cjooste@worldbank.org, tarayavechkit@worldbank.org, aurrutiaarrieta@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
# **<mark>The Evolution of COVID-19 Vaccine Hesitancy in Sub-Saharan Africa: Evidence from Panel Survey Data</mark>**

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

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

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

> 1 * World Bank, Development Economics Data Group. ^ UNU-MERIT, United Nations University. 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 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. We are also grateful for support by Alemayehu Ambel, Talip Kilic, and Gero Carletto.
sold brand. The simulated price shock is also calibrated to achieve comparability to a 50 percent price shock. Hence, it is assumed that deciles I and II face a 100 percent price shock, deciles III through VIII are affected by a 36 percent price increase, and deciles IX and X observe a 25 percent price rise in cigarettes. The average price shock across all deciles is 47 percent.

As Figure 7 shows, the implementation of a _specific_ tax increase results in a much more progressive distribution of the benefits of tobacco taxes in Georgia, relative to a similar _ad valorem_ increase. The bottom 20 percent of the population can increase their average income by 3 percent in the medium- and long-term, thanks to the extended benefits of tobacco taxes.

Figure 7. Net income effect under specific excise increase <!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10<br>Decile<br>Medium-bound Elasticity Lower-bound Elasticity<br>Upper-bound Elasticity<br>6<br>4<br>2<br>Inconme Gains (%) 0<br>-2<br><!-- End of picture text --> _Source:_ Author estimations, based on the IHS (2012-2016) and HIES (2017). Data on smoking-attributable death events is taken from the GBD (2017), and tobacco-related medical expenses adapted from Goodchild et al. (2018) and WDI. _Notes:_ Estimation using a price shock of 100% for deciles 1 to 2, 36% for deciles 3 to 8, and 25% for deciles 9 and 10. Deciles are created based on per capita household total consumption. Only out-of-pocket medical expenses are considered.

22
Croke et al. (2016) TMSDG (p.11).<sup>19</sup> Only trials for which a proper intention-to-treat estimate can be obtained are included. Therefore, we require that the study (or trial authors, through personal communication) report outcomes for the population assigned to treatment and comparison groups, independent of whether they received treatment or not.

When estimating the mean effect of MDA on weight, we report results both in the set of trials that take place in settings where the WHO recommends deworming (i.e., those where prevalence of either hookworm, whipworm, or roundworm is over 20%, which is the threshold for annual MDA, or 50%, which is the threshold for multiple annual MDA), and in the full sample (for completeness and comparability to TMSDG).

# **3.2 Search procedure**

We start with the sample of studies included in TMSDG, as a well-known and oft-cited systematic review, for their analysis of the impact of multiple-dose deworming treatment of “all children living in an endemic area” (i.e. mass drug administration, or MDA) at longest follow-up on child weight. We supplement this sample with additional studies we could identify that meet the trial inclusion criteria above. The Campbell Collaboration deworming team generously shared information on additional trials which they identified for use in their own forthcoming systematic review on the impacts of mass deworming. All studies we include were identified by the Campbell Collaboration and any study that they

> hookworm; hookworm infection is the third leading contributor to the global burden of anemia, whereas neither of the other two STH species rank among the top causes of anemia (Kassebaum et al., 2013; Smith and Brooker, 2010). Of the 7 trials in the TMSDG meta-analysis of hemoglobin (which produce 9 treatment estimates, since two are factorial), only one appears to have any significant hookworm prevalence (Dossa and Ategbo, 2001); the other six either do not report
people as poor and deliver the same poverty rate.

# **4. Methods and examples**

The most common method of empirical investigation in this literature has been a regression of the survey responses on subjective welfare on individual and household characteristics, including age, gender, income, education, employment status and household demographics.<sup>17</sup> Typically the researcher has data on subjective welfare ( _SW_ ) across _N_ survey

> 17 Examples include van Praag (1968), van de Stadt et al. (1985), Clark and Oswald (1994, 1996), Kapteyn et al. (1998), Easterlin (1995), Oswald (1997), Winkelmann and Winkelmann (1998), Pradhan and Ravallion (2000), McBride (2001), Ravallion and Lokshin (2001, 2002, 2010), Graham and Pettinato (2002), Senik (2004), Luttmer (2005), Ferrer-i-Carbonell (2005), Herrera et al. (2006), Bishop and Luo (2006), Kingdon and Knight (2006, 2007), Fafchamps and Shilpi (2009), Knight and Gunatilaka (2010) and Castilla (2010).

10
# **References**

- **Barwick, Panle Jia, Shengmao Cao, and Shanjun Li** , “Local Protectionism, Market Structure, and Social Welfare: China’s Automobile Market,” _American Economic Journal: Economic Policy_ , forthcoming.

- **Berry, Steven** , “Estimating Discrete Choice Models of Product Differentiation,” _RAND Journal of Economics_ , 1994, _25_ (2), 242–262.

- **, James Levinsohn, and Ariel Pakes** , “Automobile Prices in Market Equilibrium,” _Econometrica_ , 1995, _63_ (4), 841–890.

- **Busse, Meghan, Jorge Silva-Risso, and Florian Zettelmeyer** , “$1,000 Cash Back: The Pass-Through of Auto Manufacturer Promotions,” _American Economic Review_ , 02 2006, _96_ , 1253–1270.

- **Carley, Sanya, Rachel M. Krause, Bradley W. Lane, and John D. Graham** , “Intent to purchase a plug-in electric vehicle: A survey of early impressions in large US cites,” _Transportation Research Part D: Transport and Environment_ , 2013, _18_ , 39–45.

- **Chetty, Raj, Adam Looney, and Kory Kroft** , “Salience and Taxation: Theory and Evidence,” _American Economic Review_ , September 2009, _99_ (4), 1145–77.

- **Corts, Kenneth** , “Building Out Alternative Fuel Retail Infrastructure: Government Fleet Spillovers in E85,” _Journal of Environmental Economics and Management_ , 2010, _59_ , 219–234.

- **Co ̧sar, A Kerem, Paul LE Grieco, Shengyu Li, and Felix Tintelnot** , “What drives home market advantage?,” _Journal of international economics_ , 2018, _110_ , 135–150.

- **Holland, Stephen, Erin Mansur, Nicholas Muller, and Andrew Yates** , “Are There Environmental Benefits from Driving Electric Vehicles? The Importance of
The selection of households as PROGRESA beneficiaries was accomplished by first identifying the communities to be covered by the program (geographic targeting) and then selecting the beneficiary households within the chosen communities.<sup>6</sup> The household selection within communities covered by the program involves conducting a census of all the households in the communities to be covered by the program and collecting detailed information on household demographic composition, assets and income. Adult per capita income is then compared with the cost of the _Standard Food Basket_ (which is roughly equivalent to two times the minimum wage) of 320 pesos per capita per month in order to generate a new binary variable taking the value of 1 for poor households (if income is less than the cost of the standard food basket) and 0 for nonpoor. Finally, discriminant analysis is applied, separately for each <u>geographical region, in order to identify the variables that discriminate best between poor and</u> nonpoor households and compute an index (discriminant score) that represents parsimoniously the differences between the poor and nonpoor households.<sup>7</sup> In the early stages of the program (i.e. during 1998) the PROGRESA beneficiary selection method led to approximately 52% of the households in the evaluation sample to be classified as eligible for the program benefits. By July 1999, PROGRESA had added new households to the list of beneficiaries since it was felt that the original selection method was biased against the elderly poor who no longer lived with their children.<sup>8</sup> As a result of the revised selection process the fraction of households classified as eligible for program benefits increased from 52%

> 5 See Skoufias (2001) for a synthesis of all the available results of PROGRESA’s evaluation.

> 6 For a more detailed discussion see Skoufias et al. (2001).

> 7 Discriminant analysis may be considered as analogous to a logit probability model for a binary dependent variable. Its main difference from the
tenure protects against low levels of tenure insecurity but that such protection cannot counter more potent threats; in fact, for severe insecurity the sign of the point estimate reverses, though only attaining marginal significance.

Factors associated with title demand can be analyzed by focusing on parcels without title and estimating where W� is either a zero-one indicator of whether the owner would like to apply for a title or, for cases where this is the case, willingness to pay (in logs). In addition to variables described above, right hand side variables include perceived tenure insecurity (S�) to explore if high levels of perceived tenure insecurity create demand for land documentation and legalization. Table 7 displays results for the regressions with a zero-one indicators of wanting to apply for title (col. 1-3) and willingness to pay for it (col. 4-6). Those suffering from insecurity are more likely to apply for a title overall (col.1) and on customary land (col. 3) but less on state land (col. 2). Comparing the point estimate (0.095) to the dependent variable mean implies that insecurity increases demand for title by some 20%. Transferability is associated with about 30 percentage points higher level of title demand. While having title in process increases the amount willing to pay on state land by some 50%, having an informal certificate is associated with an increment in title demand by 38% and a 27% higher willingness to pay on customary land. Informal and formal documents thus do not seem to be substitutes for each other but rather have a strongly complementary relationship.

# **4.2 Comparing results and methodology to those of the Prindex global poll**

Global polls such as Prindex could complement and put country-level statistics in context if concepts and procedures used are comparable. Comparing descriptive statistics from Prindex to those obtained using the LFS is, however, difficult without a description of the sampling procedure and information on non-response neither of which was available. To nevertheless capitalize on
06|37.85|0.00|
|Own Education|82|1798|7.22|5.22|0.06|137|1434|2.54|2.65|0.61|
|Business Owner (Yes=1)|82|1798|0.63|0.61|0.66|137|1434|0.59|0.58|0.84|
|Digit Span Recall|82|1798|3.87|3.85|0.94|137|1434|2.39|2.71|0.12|
|Risk Tolerance (0=Risk Averse; 10= Risk Lover)|82|1798|4.09|3.80|0.38|137|1434|2.80|3.43|0.02|
|Married (Yes=1)|82|1798|0.77|0.82|0.01|137|1434|0.83|0.85|0.48|
|Index of Optimism|82|1798|-0.23|-0.02|0.21|137|1434|-0.87|-0.29|0.00|
|Business Literacy|82|1798|0.97|0.66|0.07|137|1434|0.14|0.23|0.67|
|<br>Index Female Mobility|82|1798|-|-|-|137|1434|0.08|0.00|0.36|
|Index No Purdah|82|1798|-|-|-|137|1434|-0.30|0.19|0.00|
|Trust in Formal System|82|1798|-0.01|-0.03|0.31|137|1434|-0
women, face particular challenges that are exacerbated by flood risk. Children often miss school during flood events and women are commonly responsible for staying home with children when schools are closed and to clean up the house after floods, forcing them to miss work or not work at all. Thanks to access to rich socioeconomic, gender-disaggregated data, the report also sheds light on the characteristics of affected households and helps us understand the policy relevant link between poverty and flood exposure.

With lack of governmental support, households must absorb the costs of flood damage and try to protect themselves from future exposure. Households in informal settlements take various measures to prevent flood damage (Sakijege, Lupala and Sheuya, 2012). The most common of these are the use of sandbags and tree logs; raised pit latrines and doorsteps; the provision of water outlet pipes above plinth level; the construction of embankments and protection walls; the elevation of house foundations; seasonal displacement; and the boiling and chemical treatment of water.

In April 2018, Dar es Salaam and other parts of Tanzania experienced heavy rains that caused floods in large parts of the city. Information on the impacts of the flood on households and businesses is scarce, an emergency plan of action issued by the Red Cross<sup>4</sup> indicates that the event affected close to 15,900 people across Tanzania –12,000 of them in Dar es Salaam. This flood followed several episodes of heavy rainfall that occurred in Tanzania mainland and Zanzibar between April 12 and April 18. Among the 15 regions affected by floods, Arusha, Dar es Salaam, and Zanzibar were impacted worst. Fifteen people died and 11 were severely injured in Dar es Salaam and one person died in Arusha. The Dar es Salaam Multi-Agency Emergency Response Team and the Tanzania Red Cross estimate that 2,151 households in the city were displaced, 42 houses and 21 latrines completely collapsed and 342 houses severely damaged. The current report provides results suggesting the number of affected households is much larger than official
in income levels between countries can be explained by education (Acemoglu & Dell 2010, Mankiw, Romer & Weil 1992).

The skills that workers bring to the firm influence productivity and performance. Gennioli et al. (2013) use Enterprise Survey data of 20 countries – including Mozambique – to estimate that an additional year of education of an employee leads to a 6-7 percent return in productivity. Moretti (2004) finds that American firms in cities with more college-educated inhabitants are more 5
strands of the literatures in law and economics. An important body of research has sought to identify and quantify the effects of protectionism in procurement, both in terms of trade flows and efficiency.

Theoretical papers in economics have investigated the impact of protectionism in government procurement on trade patterns, production, and welfare. The first general equilibrium model of trade with ‘distorted’ public expenditure, proposed by Baldwin (1970), suggested that protectionism in government procurement would have no effect on trade flows and specialization, as consumers offset the government's shift towards domestic goods by importing more. However, subsequent research has shown that the impact of protectionism in government procurement is more nuanced in settings with differentiated goods (Miyagiwa 1991) and incomplete information (McAfee and McMillan 1989). Furthermore, when increasing returns to scale are allowed, trade barriers in government procurement can change the patterns of specialization and alter the agglomeration of economic activity (Brülhart and Trionfetti 2001; 2004). Home-biased government procurement, which decreases demand for foreign varieties, can lower the price of imported goods and increase the size of government spending. However, this comes at the cost of welfare, as cheap foreign varieties are replaced by more expensive domestic ones (Larch and Lechthaler 2013).

While the theoretical literature has largely assumed protectionism in government procurement, empirical work has attempted to identify the extent to which governments favor domestic firms over foreign ones in procurement contracts and how this behavior affects trade flows. We contribute to the literature that uses aggregated industry- and country-level data. Previous studies, such as those by Rickard and Kono (2014) and Gourdon and Messent (2019), have used aggregate trade and public spending data, without separating out cross-border procurement flows. These studies find that countries with higher government spending import less, suggesting the presence of protectionism in government procurement. Shingal (2011) finds evidence for home bias in procurement of services by Japan and Switzerland. We follow this strand of the literature by investigating the effects of variation in protectionist rules in
the annuity system started and there were few annuities in force. The table purports to represent the period experience of annuitant mortality at the time it was developed, but was partly constructed by making adjustments to mortality data from other countries. The RV-85 was developed for regulatory purposes, and served until recently as the basis for the determination of phased withdrawal (PW) payments and the calculation of technical reserves for annuity providers.

The second table, referred to as RV-98, is a period table based on more extensive Chilean annuitant mortality data collected between 1995 and 1997. The table represents an improvement over the RV-85, by including more information on the mortality of the Chilean annuitant population. However, while the male tables were mostly determined from the data, the female tables largely impute the observed rate of change between the

> 3 The Pension Law approved in 2004 has allowed other types of annuities, but all the MWRs presented in this report refer to annuities fixed in UFs, i.e., annuities indexed to prices. 4 See, e.g., Brown et al (2001), and James, Song and Vittas (2003).
of 2 percent in other regions. These tariffs remained relatively constant in recent years. Moreover, the level of tariff protection on digital goods correlates negatively with income per capita across African countries, with upper-middle-income economies like South Africa imposing tariffs as low as 0.9 percent, while low-income countries such as the Central African Republic and Cameroon applies tariffs as high as 15 percent, with some large economies such as Ethiopia and Nigeria, imposing tariffs around 9 and 10 percent, respectively on average.

We proceed by estimating the quality and quality-adjusted prices of Africa’s imports of digital goods. We apply the methodology proposed by Feenstra and Romalis (2014), using detailed data on international trade flows and tariffs. We find that African nations tend to import relatively low quality, low price digital goods, as would be expected given their relatively low income per capita. Once we adjust prices for quality differences, digital goods in Africa are sourced at broadly similar prices relative to higher-income countries. If import tariffs, along

> 3The ICP item-level prices for machinery and equipment include import duties and other product taxes paid by the purchaser, the costs of transporting the asset to the place where it will be used, and any charges for installing the asset so that it is ready for use in production. Thus, it captures the final prices to customers, which include factors beyond trade tariffs.

2
(Kleibergen-Paap)|8.934|9.239|8.457|
|Cluster by community||||
|Regional Fixed Effects||||
|*** p<0.01, ** p<0.05, * p<0.1||||

Source: CLS-WILL: First wave – household questionnaire, Instituto Geográfico Agustín Codazzi (IGAC) and Fernández (2010).

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# **3.1 Impact of Internet job search on employment**

Relying on panel data from the 2012 and 2018 survey rounds of the ELMPS, I examine the effect of Internet job search on employment and estimate various regression specifications. First, I estimate equation (1) where I examine the impact of Internet job search by the unemployed in 2012 on the probability of finding a job within a 5 year timeframe following their unemployment spell. E E E E E E 1 2 (1) ii,2013−2017<sup>� U</sup> ii,2012 ii,2012 ii,2012 ii PP(EI estimate the impact of Internet job search in 2012 on the probability of finding a job in the 5<sup>E</sup> �= ββ IIUUIIEEIIUUEEII jjEEjj ssEEssIIssh + BB XX + εε years timeframe conditional on being unemployed in 2012 (relying on the market definition of labor force participation). The regression controls for home Internet access in 2012 in addition to the vector , which is a vector of individual characteristics in 2012. The control variables include: individual’s age, a dummy variable for being male, a dummy variable for being married, ii,2012 XX three dummies for the individual’s educational attainment (primary or preparatory education, secondary education, either general or vocational, and above secondary education, either postsecondary institute or university and above, and the reference category is no educational degree, either illiterate individuals or literate individuals without any diploma), a dummy variable for rural areas, as well as dummies for the various geographical regions (Greater Cairo, Alexandria and Suez cities, Urban Lower Egypt, Urban Upper Egypt, Rural Lower Egypt, and Rural Upper Egypt).

I also employ a first difference specification in equation (2) relying on the panel dimension of the data. The dependent variable is a first-difference in dummy variable indicators for being employed between 2018 and 2012, while the independent variable of interest thus becomes the firstdifference in dummy