rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10191

# **Abstract**

This paper presents new evidence on the levels and trends of vaccine hesitancy in developing countries based on harmonized high-frequency phone surveys from more than 120,000 respondents in 53 low- and middle-income countries. These countries represent a combined 30 percent of the population of low- and middle-income countries. On average across countries, one in five adults is hesitant about the COVID-19 vaccine, with the most cited reasons for hesitancy being concerns about the safety of the vaccine, followed by concerns about its efficacy. Between late 2020 and the first half of 2021, there tended to be little change in levels of hesitancy except in Iraq, Malawi, and Uzbekistan, where hesitancy increased. COVID-19 vaccine hesitancy is higher among female, young, less educated, and rural respondents, after controlling for selected observable characteristics. Country estimates of vaccine hesitancy from the high-frequency phone surveys are correlated with but lower than those from earlier studies, which often relied on less representative survey samples. The results suggest that vaccine hesitancy in developing countries, while less prevalent than previously thought, will be an important and enduring obstacle to recovery from the pandemic.

This paper is a product of the Poverty and Equity Global Practice and the Health, Nutrition and Population 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 jdayton@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table B1: Regression results for alternative models/estimators, Kenya

||Naïve|reghdfe|_π_= 0|_π_= 0_._5|_π_= 1|UBH|UBS|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Child is female|0.10|0.06|0.06|0.06|0.06|0.08|0.07|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Oldest sib|0.06|-0.15|-0.15|-0.15|-0.15|0.04|-0.17|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Never enrolled|-0.52|-0.15|-0.24|-0.22|-0.20|-0.24|-0.19|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Currently enrolled|0.69|0.48|0.47|0.47|0.48|0.49|0.59|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Attends private|0.35|0.13|0.14|0.15|0.16|0.09|0.22|
||(0.01)|(0.01)|(0.00)|(0.00)|(0.00)|(0.01)|(0.01)|
|Never ×s�|||-0.37|-0.21|-0.10|||
||||(0.02)|(0.02)|(0.01)|||
|Private ×s�|||-0.19|-0.16|-0.11|||
||||(0.01)|(0.01)|(0.01)|||
|Obs.|555,919|555,919|555,919|555,919|555,919|555,919|555,919|
|R<sup>2</sup>|0.13|0.72|0.72|0.72|0.72|0.64|0.48|

Note: columns refer to different estimators/models; UBH is the household upper bound (excludes school effects); ‘Naïve’ is simple OLS excluding fixed effects; reghdfe reports results based on the (improved, accelerated) partitioned iterative algorithm due to Correia (2017) in which household effects enter first (using the Stata command of the same name); columns π = (0, 0.5, 1) are taken from the partitioned iterative algorithm set out in the text; UBH is the household upper bound model and UBS is the school upper bound models, in which household effects are proxied by observed characteristics (not shown); all reported coefficients are significantly different from zero; cluster robust standard errors reported in parentheses.

Source: own calculations.

46
# **Selected Readings**

- Anderson, D.R. "All Risks Rating<sup>WithinaCatastropheInsuranceSystem."</sup> _J. of Risk and Insurance_ 43 (1976): 629-651.

- Arrow, K. J. "The Role of Securities in<sup>theOptimalAllocationof RiskBearing."</sup><sup>_Rev.of Econ._</sup> _Studies_ 31 (1964): 91-96.

- Arrow, K. J. "The Theory of Risk-Bearing:<sup>SmallandGreatRisks."</sup><sup>_J. of Riskand Uncertainty_</sup> 12 (1996): 103-111.

- Barnett, B. J. "U.S. Government Natural<sup>DisasterAssistance:HistoricalAnalysis and aProposal</sup> for the Future." _Disasters: The J. of Disaster Studies, Policy_<sup>_&Management._</sup> Forthcoming.

- Buzby, J.C., P.L. Kenkel, J.R. Skees,<sup>J.W.PeaseandF.J.Benson."AComparisonof Subjective</sup> and Historical Yield Distributions with<sup>Implicationsfor MultiplePerilCropInsurance."</sup> _Agr. Fin. Rev._ 54 (1994): 15-23.

- Cutler, D. M. and R. J. Zeckhauser.<sup>"ReinsuranceforCatastrophesandCataclysms."Working</sup> Paper _5913. NBER Working Paper Series,_<sup>NationalBureauofEconomicResearch,</sup> Cambridge, MA. February, 1997.

- Dacy, D.C. and H. Kunreuther. _The Economics_<sup>_of Natural Disasters: Implications for Federal_</sup> _Policy._ New York: The Free Press,<sup>1969.</sup>

- Doherty, N. A. _Financial Innovation in_<sup>_The Management of Catastrophic Risk._Presentedpaper</sup> at ASTIN/AFIR Conference, Australia,<sup>August1997.</sup>

- Epstein, R. A. "Catastrophic Responses<sup>toCatastrophicRisks."</sup><sup>_J.of Riskand Uncertainty 12_</sup> (1996): 287-308.

Federal Emergency Management Agency.<sup>Unpublisheddata.</sup>

- Freeman, P. K. and H. Kunreuther. _Managing_<sup>_Environmental RiskThrough Insurance._Boston:</sup> Kluwer Academic Press, 1997.

- Griffin, P. "Investigating the Conflict in<sup>Agricultural Policy Betweenthe FederalCrop Insurance</sup> and Disaster Assistance Programs, and<sup>ConservationReserve Program."Unpublished</sup> Ph.D. dissertation, Department of Agricultural<sup>Economics, Universityof Kentucky,1996.</sup>

- Hazell, P. and J. R. Skees. "Report to<sup>the WorldBankonPotential for RainfallInsurancein</sup> Nicaragua." January, 1998.

- Hoffman, W., C. Campbell and K. Cook.<sup>"SowingDisaster: TheImplicationsofFarmDisaster</sup> Programs for Taxpayers and the<sup>Environment."</sup> Washington, DC: Environmental Working Group, 1994.

- Hogarth, R. M. and H. Kunreuther. "Pricing Insurance and Warranties: Ambiguity<sup>and</sup> Correlated Risks." _The Geneva Papers on_<sup>_Risk and Insurance Theory_17(1992): 35-60.</sup>

- Hogarth, R. M. and H. Kunreuther. "Risk, Ambiguity, and Insurance." _J. of Risk and Uncertainty_ 2 (1989): _5-35._

- Jaffee, D. M. and T. Russell. "Catastrophe<sup>Insurance, Capital Markets,andUninsurableRisks."</sup> _J. of Risk and Insurance_ 64 (1997): 205-230.

- Kaplow, L. "Incentives and Government<sup>ReliefforRisk."</sup><sup>_J.of RiskandUncertainty_</sup> 4 (1991): 167-175.
# I. **Introduction**

Over the years, household surveys have proved to be a popular and valuable data source for empirical research in micro-economics. In developing countries, household survey data have become more available in the past decade, partly as a result of several international programs such as the UN National Household Survey Capability Program, the World Fertility Survey, the World Bank's Living Standards Measurement Study and the Social Dimensions of Adjustment Program, the Demographic and Health Surveys and others. This has spurred a growing interest in the economics of the household in the context of development economics, covering a wide array of topics such as poverty, employment, health, education and fertility.

Many analysts - economists and other social scientists - devote only a limited amount of attention to the sampling design of the surveys they use, taking the data produced by statisticians and survey practitioners "as is". At best, sampling weights are applied to ensure representativeness of the results.

The purpose of this paper is to illustrate the necessity of paying close attention to the sampling aspects of a household survey used in applied micro-economic analysis. This is particularly the case when over-time comparisons are undertaken. The case study in this paper will document that changes in household welfare and in the incidence of poverty observed in Cdte d'Ivoire over the period 1985-88 vanish when corrections are applied for changes in sampling procedures and that even the direction of the trend is reversed. Likewise, the cross-sectional patterns of welfare and poverty observed in earlier analyses for 1985-86 prove to be incorrect.

The data source for the case study in this paper is the Cote d'Ivoire Living Standards Survey (CILSS) conducted between 1985 and 1988. This survey, especially the early years, 1
The Mexican ENAMIN data also includes information on whether the business is located in the owner’s home or not. As shown in Table 8, the difference in the percentage of firms operating inside the owner’s home is even greater in the Mexican data than in the Bolivian data. In the Mexican data, 11.1 percent of male-owned firms operate inside the home, while 30.3 percent of female-owned firms operate inside the home. The difference in the Mexican data remains unchanged even after controlling for whether children under the age of 12 are present in the household. This suggests that household chores other than caring for children influence the location of female-owned businesses. This result is in line with Cunningham and Gomez (2004) who show that their finding that female home-based workers (most of whom are self-employed) have lower earnings and work fewer hours than male home-based workers is largely related to marital status, not to the presence of children. It thus appears that household obligations constrain women to set up businesses that can be operated from within the home. This restricts their choice of industry and potentially also the decision to formalize the firm, which can in turn have consequences for the performance of the business.

# **4.d. Differences in Risk Aversion**

Another reason why female-owned firms are smaller on average than male-owned firms could be that female firm owners may be more risk averse than male firms owners. This could lead female firm owners to forgo profitable investments and to be reluctant to expand their business. Evidence based on US data suggests that women are less likely to take financial risks than men (Jianakoplos and Bernasek, 1998, and Sunden and Surette, 1998), although this has been called into question by Schubert et al (1999). However, these studies are not limited to entrepreneurs and since owning a business is per se a risky undertaking, the women who select into business ownership may not be more risk averse than men.

Unfortunately, none of the surveys used in the other sections of this paper measure the level of risk aversion of the firm owner. I will thus rely on an Innovations for Poverty Action survey of micro, small, and medium size businesses in Puebla, Mexico, to investigate differences in risk aversion. In this survey, the firm’s principal decision maker was asked to rate their willingness to take risks on a scale from 0 to 10, with 10 being the highest willingness to take risks. 28 percent of principal decision makers in the data are women. Table 9 displays the average willingness to take general risks, health risks, as well as financial risks. Women score slightly higher on all variables than men, although the differences are small and not statistically significant. There is thus no evidence that women are more risk averse than men.

**Table 9: Willingness to Take Risks**

||Women|Men|Difference(Women - Men)|
|---|---|---|---|
|General risk taking|8.04|7.87|0.17|
|Health risk taking|6.00|5.61|0.39|
|Financial risk taking|6.23|6.01|0.22|

_Source_ : Innovations for Poverty Action SME Survey for Puebla, Mexico, 2007.

20
the travel time from farmer’s village to that market. The model also shows that the wealth level of the farmer in fl uences her/his response to the market access index with the ultimate e ff ect of wealth being determined by the relative strength of its impact on the shadow price of leisure and on the ease of accessing market facilities.

The econometric estimation using data from the Indian state of Tamil Nadu con fi rms the theoretical prediction that the probability of selling at the market increases with an increase in the market access index which in turn improves due to an improvement of market facilities or due to a decrease in distance to markets. The interaction of the market access index and wealth is also highly statistically signi fi cant. The negative sign of the interaction term implies that wealth confers bene fi ts in accessing market facilities and these bene fi ts outweigh the negative e ff ect of wealth on opportunity costs of labor. The regression results, which are robust to alternative speci fi cations and estimation techniques and alternative de fi nitions of the market facility index, indicate that wealthy farmers are able to capture proportionately more bene fi ts from the market facilities in a congested market place. The observed interaction e ff ect also implies that poorer farmers may also be facing stringent credit constraint in accessing cheaper but bulkier modes of transportation.

We combine farmers’ choice of place of sales to their value of sales over a year and then simulate a policy change consisting of a 20 percent improvement in the market access index. The results show that such policy change bene fi ts all farmers with land up to 20 acres. Moreover, the policy intervention bene fi ts disproportionately the poorer farmers especially those with less than 2.5 acres of land. Our results thus suggest that while wealthy farmers are able to capture disproportionately the bene fi ts of existing, and in the context of India, heavily congested market facilities, the marginal bene fi t from an improvement in market facilities in particular, and market access index in general will be much higher for the poorer small28
Policy Research Working Paper 8077

# **Abstract**

This paper examines the causal relationship between energy efficiency and economic growth based on panel data for 56 high- and middle-income countries from 1978 to 2012. Using a panel vector autoregression approach, the study finds evidence of a long-run Granger causality from economic growth to lower energy intensity for all countries. The study also finds evidence of long-run bidirectional causality between lower energy intensity and higher economic growth for middle-income countries. This finding suggests that beyond climate benefits, middle-income countries may also earn an extra growth dividend from energy efficiency measures.

This paper is a product of the Office of the Chief Economist, South Asia Region. 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://econ.worldbank.org. The authors may be contacted at fzhang1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
A final practical is whether respondents should be provided with monetary incentives for their answers.<sup>13</sup> The fear is that respondents will not provide accurate enough answers if not rewarded properly. Even if one believes that they should be rewarded, it is not clear how, since the scoring rule may affect the elicitation process itself. One of the first studies to reward respondents is Grisley and Kellogg (1983) who surveyed farmers in northern Thailand about their price, yield and net income expectations at harvest time. Farmers were rewarded based on their accuracy yet critics thought that the rule followed could induce farmers to misrepresent their beliefs since risk aversion could influence their answers. Nelson and Bessler (1989) settled the issue by showing that for the untrained subject, that is, the one that was unfamiliar with the questions and methods as most respondents would be, the scoring rule does not matter. In any event, the question of whether rewarding elicitations leads to more accuracy has not been yet settled, since Grisley and Kellogg (1983) did not randomize rewards. All of the recent studies reviewed here do not provide monetary rewards during the elicitation process. As the next section shows, even without payment, the answers received from such questions appear reasonable.

# **5. Do subjective expectations help predict economic decisions and future outcomes?**

We have shown that there are ways of eliciting subjective expectations in developing countries, that individuals do understand probabilities, and that asking these questions need not be prohibitive in terms of survey time. The key question is then whether there are demonstrated benefits from including such questions.

An important rationale for eliciting subjective expectations is that they include information not captured by the standard list of variables commonly collected, and as such, are useful for predicting economic decisions and future outcomes of interest. Economic theory certainly gives plenty of examples of cases where expectations should matter for predicting behavior. For example, investment decisions should depend on the expectations of the return and riskiness of the project, while classic theories of migration such as Sjaastad (1962) and Harris and Todaro (1970) assume that expectations of incomes and employment probabilities in the migrant destination should be important determinants of the migration decision. However, it is ultimately an empirical question as to whether the expectations themselves yield any additional

> 13 This debate is much more prevalent in measurement of risk aversion.

- 21 -
protected area-year. This approach enables us to understand eBird data in terms of visits to specific protected areas over time.

There are 3,625 observations in the regression because the data span the years 1998 to 2022 and there are 145 protected areas. The mean value of the dependent variable in control protected areas is 0.510.

We also repeat our regression with log(number of eBird visits per year) as the dependent variable (Figure S11b). Because the dependent variable is a natural logarithm, we restrict the data to area-years with positive visits. The number of observations in the regression is therefore 1,767. We obtain an ATT of 0.259 with a standard error of 0.096.

# **A.5 Economic Development (Figure S12)**

We measure economic development using data on “asset wealth”. Atlas AI provided asset wealth data to us at an annual frequency and delineated by second-level administrative divisions, spanning the years from 2003 to 2021 [63, 106]. We filter the data to include only those administrative divisions that are located within a 25 km radius of our treatment and control protected areas. When multiple administrative divisions intersect a protected area, we weight asset wealth across administrative divisions by their area of overlap with the protected area. The resulting panel data set consists of 2,755 observations, because we observe 145 protected areas over 19 years. Given that the asset wealth index is unitless, we standardize asset wealth across all protected area-years. This involves subtracting the mean asset wealth and then dividing by the standard deviation of asset wealth. Figure S12a illustrates the mean standardized asset wealth for each protected area included in our data set, and Figure S12b displays the dynamic difference-in-differences estimates.

# **A.6 Conflict (Figures S13 to S18)**

The Armed Conflict Location and Event Database Project (ACLED) uses reports from local, national, and international sources to generate geocoded event data on conflict around the world. Data from ACLED document numerous features of conflict events, including their timing, location, type, and the involved actors. ACLED prioritizes external validity in its data collection protocol and therefore captures a wider range of conflict types in comparison to other sources of conflict event data [107].

As of May 30, 2023, ACLED has documented over 306,000 conflict events on the African continent alone. This population of events forms the basis of the outcome measures we use to estimate the effect of AP management on conflict. We exclude from these data conflict events that cannot be geolocated to the town-level to minimize measurement error when determining where conflict occurred relative to protected areas’ boundaries and corresponding buffer areas. We also exclude from the ACLED data conflict events classified as strategic developments (“contextually important events which may contribute to a state’s political disorder and/or may trigger future events”). Such 10
# **E Reason for Not Registering in RAMV**

Table E.1. Reason for Not Registering in RAMV

||Non-RAMV|Non-RAMV|
|---|---|---|
||Referrals|Organizations|
||(1)|(2)|
|Did not know about RAMV|51.23%|51.71%|
|Was not in Colombia|19.51%|23.43%|
|No proof of Ven. nationality|11.16%|9.71%|
|Was working|8.23%|5.24%|
|Too expensive or complicated|5.99%|5.71%|
|Fear of deportation|2.59%|2.38%|
|Other|1.29%|1.81%|

Table E.2. Reason for Not Having PEP

||Non-RAMV<br>Referrals<br>(1)|Non-RAMV<br>Organizations<br>(2)|RAMV<br>NO PEP<br>(3)|
|---|---|---|---|
|Lack of information or misinformation|58%|58.71%|29.93%|
|Did not register in the RAMV census|11.04%|11.23%|0.00%|
|Was not in Colombia|10.70%|12.75%|2.19%|
|RAMV registration got lost|6.19%|7.71%|23.36%|
|Did not plan to stay in Colombia|5.18%|3.43%|0.00%|
|Did not have access to Internet|1.80%|1.52%|5.84%|
|Did not need it|0.45%|0.57%|0.73%|
|Other|7.09%|4.38%|39.42%|

61