rafmacalaba/datause-encoder-data huggingface dataset:
23 it is very important to make a credible commitment to maittain a high (i.e., depreciated) real effective exchange rate for exports. 8 On the other hand, in his synthesis volume, Bhagwati (1978) analyzed in detail the static and dynamic gains from trade liberalization. He argued that the pre-reform data on ERPs and DRCs clearly suggest that liberalization in these countries generated important static gains ln the form of efficiency improvements. He also confirms Krueger's finding that biased regimes negatively affect exports growth and that these are generally responsive to price incentives. However, regarding the dynamic gains of a change in trade orientation, however, he found no evidence linking a smaller bias to either technological superiority or to higher savings ratios.

Balassa (1978, 1982) summarizes a large World Bank project that focused on 11 countries -- Argentina, Brazil, Chile, Colombia, Mexico, Israel, Yugoslavia, India, South Korea, Singapore and Taiwan -- for the period 1960-73. He establishes a fourfold classification according to the timing and extent of the export promotion policies adopted by each country. He then evaluates the effectiveness of these policies using the two-step methodology described above. First Balassa analyzed whether export incentives fostered exports growth and, in the second stage, he investigated the effects of an expansion of exports on output growth. In the first stage of the analysis Balassa makes use of two proxies for quantifying export incentives: the rate of growth of manufacturing exports and the change in the export output ratio in manufacturing. He found that these variables were consistently higher in those countries that followed sustained export promotion policies. In the 18She then argues that the best way to make sure that high RER will be maintained is by supplementing liberalization with increased foreign borrowing and a crawling peg.
# **COVID-19 Vaccine Hesitancy in 53 Developing Countries: Levels, Trends, and Reasons for Hesitancy**

Julia Dayton Eberwein, Ifeanyi Edochie, David Newhouse, Alexandru Cojocaru, Gildas Deudibe, Jakub Kakietek, Yeon Soo Kim, and Jose Montes<sup>1</sup> JEL Codes: I12, I15, Keywords: vaccine hesitancy, COVID-19, high frequency phone surveys Topics: Coronavirus (COVID-19), vaccines, household surveys, health promotion and disease prevention

> 1 Affiliations: Dayton Eberwein, Kakietek: Health, Nutrition and Population Global Practice, The World Bank; Edochie, Cojocaru, Deudibe, Kim, Montes: Poverty and Equity Global Practice; Newhouse: Data, Analytics and Tools, Development Economics. Dayton Eberwein is the corresponding author: <u><mark>jdayton@worldbank.org.</mark></u> The authors are grateful to Renos Vakis and Sven Neelsen for useful comments on an earlier draft of the paper. All views and any remaining errors are those of the authors.
than non‐exporting establishments as foreign markets not only expose them to greater competition, but also to greater access to information and practices that can be assimilated into their operations.

Since size, exporting status, and foreign ownership are all likely to be correlated, it is interesting to ask: which establishment characteristics are significant predictors of better management? Columns (1) to (6) of Table 5 below display the relationship between several individual establishment characteristics and average management score, accounting for sector and location of operation. The results in column (7) show which individual characteristics remain significant after taking the other characteristics into account.

_Table 5: Determinants of management practices_

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|---|---|---|---|---|---|---|---|
|**Y = Managemen**<br>|**t score**<br>|||||||
|**Log(size)**|0.0424***||||||0.0345***|
||(0.0031)||||||(0.0035)|
|**Exporter**||0.0008***|||||0.0004***|
|||(0.0002)|||||(0.0002)|
|**Foreign**<br>**owned**|||0.0003**||||‐0.0001|
||||(0.0002)||||(0.0002)|
|**Sole**<br>**proprietorship**||||‐0.0006***|||‐0.0002|
|||||(0.0002)|||(0.0001)|
|**Offers formal**<br>**training**|||||0.0008***||0.0004***|
||||||(0.0001)||(0.0001)|
|**Externally‐**<br>**recruited**<br>**manager**||||||0.0321***|0.0228**|
|||||||(0.0101)|(0.0099)|
|**Constant**|0.3931***|0.5188***|0.5284***|0.5347***|0.4860***|0.5096***|0.3705***|
||(0.0267)|(0.0230)|(0.0230)|(0.0227)|(0.0230)|(0.0241)|(0.0275)|
|**Observations**|4,398|4,419|4,418|4,422|4,391|4,387|4,337|
|**R‐squared**|0.1768|0.1215|0.1078|0.1159|0.1462|0.1127|0.2068|
|**All results includ**|**e two‐digit in**|**dustry dummie**|**s, country an**|**d region(withi**|**n country) fixed**|**effects.**||

Notes: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Survey weights are used in all regressions.

Table 5 confirms that larger establishments have more structured management practices in place (column (1)), a relationship that holds even after accounting for all other characteristics (column (7)). The table also shows that exporting establishments have higher average management scores (columns (2) and (7)). On average, foreign‐owned establishments also have higher scores (column (3)), but this relationship is not significant once other variables are accounted for (column (7)), which suggests that it is the other characteristics associated with being foreign owned (such as exporting status, or size), and not the effect of foreign ownership itself, which results in higher management practices among these establishments.

15
|**Data Provider**<br>**Description**|**Table A19: IFAD Rural Sector Performa**<br>International Fund for Agricultural Development<br>Multilateral development institution headquart<br>investments in developing countries.|**nce A**<br> <br>ered i|**ssess**<br>n Ro|**ments**<br>me, Ita|**(IFD)**<br>ly, financing ag|ricultu|ral|
|---|---|---|---|---|---|---|---|
|**Website**|www.ifad.org|||||||
|**Data Source**|Rural Sector Performance Assessments|||||||
|**Type**|Expert assessment|||||||
|**Respondents**|IFAD country economists, subject to centralized|review||||||
|**Frequency**|Annual since 2004|||||||
|**Coverage**|IFAD client countries|||||||
|**Public Access**|Yes|||||||
|**Description**|This source assesses 12 dimensions of the rural<br>assessments are used in IFAD's performanc<br>resources across countries.|policy<br>e-base|envi<br>d all|ronmen<br>ocation|t on a 6-point sc<br>system for dis|ale. T<br>tributi|he<br>ng|
||2007|2006|2005|2004|2003<br>2002<br>2000|1998|1996|
|**Voice and Accountab**|**ility**|||||||
|Policy and legal frame|work for rural organizations<br>X|X|X|X|..<br>..<br>..|..|..|
|Dialogue between gov|ernment and rural organizations<br>X|X|X|X|..<br>..<br>..|..|..|
|**Political Stability and**|**Absence of Violence**|||||||
|NA|..|..|..|..|..<br>..<br>..|..|..|
|**Government Effective**|**ness**|||||||
|Allocation & managem|ent of public resources for rural development<br>X|X|X|X|..<br>..<br>..|..|..|
|**Regulatory Quality**||||||||
|Enabling conditions for|rural financial services development<br>X|X|X|X|..<br>..<br>..|..|..|
|Investment climate for|rural businesses<br>X|X|X|X|..<br>..<br>..|..|..|
|Access to agricultural i|nput and produce markets<br>X|X|X|X|..<br>..<br>..|..|..|
|**Rule of Law**||||||||
|Access to land|X|X|X|X|..<br>..<br>..|..|..|
|Access to water for agr|iculture<br>X|X|X|X|..<br>..<br>..|..|..|
|**Control of Corruption**||||||||
|<br>Accountability, transpa|<br>rency and corruption in rural areas<br>X|X|X|X|..<br>..<br>..|..|..|
|**Country Coverage**|**87**|**100**|**121**|**124**|**..**<br>**..**<br>**..**|**..**|**..**|
|**Year of publication**|**2007**|**2006**|**2005**|**2004**|**..**<br>**..**<br>**..**|**..**|**..**|

57
|Eskom Investment Support Project|Franz Gerner|
|---|---|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Eskom Investment Support Project|Franz Gerner|
|Angola Mineral Cadastre Institution Building (IDF)|Daniele La Porta Arrobas|
|Angola Mineral Cadastre Institution Building (IDF)|Daniele La Porta Arrobas|
|Angola Mineral Cadastre Institution Building (IDF)|Daniele La Porta Arrobas|
|Morocco CapBldgand Mgmt to Improve Schoolingfor Poor JSDF|Nadine T. Poupart|
|Morocco CapBldgand Mgmt to Improve Schoolingfor Poor JSDF|Nadine T. Poupart|
|Morocco CapBldgand Mgmt to Improve Schoolingfor Poor JSDF|Nadine T. Poupart|
|Morocco CapBldgand Mgmt to Improve Schoolingfor Poor JSDF|Nadine T. Poupart|
|EFA Fast Track Initiative Catalytic Fund Grant for Lesotho|Cornelia Jesse|
|EFA Fast Track Initiative Catalytic Fund Grant for Lesotho|Cornelia Jesse|
|EFA Fast Track Initiative Catalytic Fund Grant for Lesotho|Cornelia Jesse|
|EFA Fast Track Initiative Catalytic Fund Grant for Lesotho|Cornelia Jesse|
|EFA Fast Track Initiative Catalytic Fund Grant for Lesotho|Cornelia Jesse|
|EFA Fast Track Initiative Catalytic Fund Grant for Lesotho|Cornelia Jesse|
|EFA Fast Track Initiative Catalytic Fund Grant for Lesotho|Cornelia Jesse|
|EFA Fast Track Initiative Catalytic Fund Grant for Lesotho|Cornelia Jesse|
|EFA Fast Track Initiative Catalytic Fund Grant for Lesotho|Cornelia Jesse|
|EFA Fast Track Initiative Catalytic Fund Grant for Lesotho|Cornelia Jesse|
|EFA Fast Track Initiative Catalytic Fund Grant for Lesotho|Cornelia Jesse|
|Education for All-Fast Track Initiative|Shobhana Sosale|
|Education for All-Fast Track Initiative|Shobhana Sosale|
|Ghana Education For All Fast Track Initiative|Eunice Yaa Brimfah Ackwerh|
|Ghana Education For All Fast Track Initiative|Eunice Yaa Brimfah Ackwerh|
|Ghana Education For All Fast Track Initiative|Eunice Yaa Brimfah Ackwerh|
|Ghana Education For All Fast Track Initiative|Eunice Yaa Brimfah Ackwerh|
|Second Development PolicyOperation|Pedro L. Rodriguez|
|Ethiopia: Extractive Industries TransparencyInitiative Implementa|ti Kirsten Lori Hund|
|Cote d'Ivoire Mineral Sector Institutional CapacityBuildingIDF|Brigitte Marie Khadidja Bocoum|
|Cote d'Ivoire Mineral Sector Institutional CapacityBuildingIDF|Brigitte Marie Khadidja Bocoum|
|Strenghteninginstitutional capacityof the Ministryof Economics a|nBegnadehi Claude Bationo|
|Strenghteninginstitutional capacityof the Ministryof Economics a|nBegnadehi Claude Bationo|
|Strenghteninginstitutional capacityof the Ministryof Economics a|nBegnadehi Claude Bationo|
savings-investment balance; total factor productivity; and domestic and foreign debt stocks;

- the sectoral structure of production, employment, incomes, and trade;

- the labor market: unemployment; and the educational composition of the labor force; and

- poverty and selected other MDG indicators.

This section provides a non-technical summary of MAMS, split into two modules – core, and MDG and education – and its Pakistani database, developed for this application.<sup>5</sup>

# _Core module_

Figure 3.1 summarizes the payment flows that are captured in MAMS, including its application to Pakistan, in an individual simulation year. Drawing on this figure, we will explain the main features of the model. Most of the features of the figure – boxes, markets and payment flows -- are further disaggregated; we present the current disaggregation later in this section.

<u>Activities produce, selling their output at home or abroad, and use their revenues to cover their</u> costs (of intermediate inputs, factors, and taxes). Their decisions to pursue particular activities and employ factors are driven by profit maximization. The shares exported and sold domestically depend on the relative prices of their outputs in world and domestic markets.

MAMS includes three core institutions: households, government, and the rest of the world. <u>Households earn incomes from factors, transfers and interest from the government (with the</u> interest due to loans from the households to the government), and transfers from the rest of the world, net of interest on household foreign debt.<sup>6</sup> These are used for direct taxes, savings, and consumption. The savings share depends on per-capita incomes. Their consumption decisions change in response to income and price changes. By construction (and as required by the household budget constraints), the consumption value of the households equals their income net of direct taxes and savings.

The government gets its receipts from taxes, transfers from abroad, and domestic and foreign borrowing; it uses these for consumption, transfers to households, and investments (providing the capital stocks required for activities producing government services). To remain within its budget constraint, it either adjusts some part(s) of its spending on the basis of available receipts or mobilizes additional receipts of one or more types in order to finance its spending plans.

The <u>rest of the world</u> (which appears in the balance of payments) sends foreign currency to Pakistan in the form of transfers to government and households (net of interest payments on

> 5 For a detailed presentation of MAMS, see Lofgren et al. (2012).

> 6 The household may lend to the government and borrow from the rest of the world; if it does, it receives interest payments from the government and makes interest payments to the rest of the world.

16
DIALOGIC READING AND STORYBOOK EVALUATION IN KENYA 19 more advanced dialogic reading (e.g., asking children questions, expanding on their statements, asking them to elaborate or expand their statements, or answering their questions); or (iii) were distracted or off-task. The number of 10-second intervals during which the caregiver engaged in each type of behavior was summed, creating ordinal indices of basic reading activities, interactive reading activities, and disengagement. Assessor training for the observations consisted of a discussion of the definitions of each code and refinement until group consensus was achieved. Assessors practiced coding videotaped observations of local parent-child dyads from non-study locations until inter-rater reliability was consistently greater than 90%. Live coding in tight increments could have potentially affected reliability in the field, but it was not feasible to videotape caregivers and children during data collection.

To address the second study objective, we assessed receptive vocabulary in Luo, English, and Swahili using adapted versions of the British Picture Vocabulary Scale (BPVS) - III (Dunn & Dunn, 2009), which includes 168 items for use with children aged 3 to 17. In the standard BPVS, knowledge of receptive vocabulary is measured by asking the respondent to point to one of four pictures that corresponds to a word (object, person, or action) spoken by the assessor. The BPVS has previously been adapted to create receptive vocabulary tests appropriate for coastal Kenya (Holding et al., 2004) and Indonesia (Prado, Alcock, Muadz, Ullman, & Shankar, 2012). As discussed above, one of the key considerations in our setting was the need to develop assessments that captured the development of vocabulary skills in a multilingual environment. There are trade-offs for assessing vocabulary in multilingual contexts, as children may have a larger vocabulary in their first language, or in the language of instruction. For our study we were interested in capturing children’s development in both, so we developed Luo, English, and Swahili subscales and adapted the BPVS to the local context and ages of the children in our
other central European countries. At the bottom end, Hungary, Poland and the Baltic countries (Latvia, Lithuania, and Estonia) have fairly high income inequality (Gini coefficient ~ 0.33–0.38), at least when compared to other EU countries. However, income inequality in these countries is still much lower than in most other countries in the world. The 2006 Gini coefficient of income inequality in Latvia (0.38) was the same as that in the United States (Gini coefficient of 0.38 in 2000), but lower than that in Thailand (Gini coefficient 0.40 in 2002), Chile (Gini coefficient 0.51 in 2000), and Brazil (Gini coefficient 0.59 in 2001).<sup>11</sup>

**Chart 2. Most EU8 countries have average incomes much lower than in the EU15 countries**

|Luxembourg|
|---|
|Denmark|
|Ireland|
|United Kingdom<br>|
|Finland|
|Austria|
|Netherlands|
|Belgium|
|Sweden|
|France|
|Italy|
|Spain|
|Greece|
|Slovenia|
|Portugal|
|Czech Republic|
|Hungary|
|Estonia|
|Slovakia|
|Poland|
|Latvia|
|Lithuania|
|0<br>5,000<br>10,000<br>15,000<br>20,000<br>25,000<br>30,000<br>35,000|
|Equivalized disposable income (euros)|
|**...but have similar levels of income inequality**|
|Slovenia|
|Sweden<br>|
|Denmark|
|Czech Republic|
|Austria|
|Netherlands|
|Finland|
|France|
|Luxemberg|
|Slovakia|
|Belgium|
|Spain|
|Italy<br>|
|Ireland<br>|
|United Kingdom|
|Hungary|
|Poland|
|Estonia<br>|
|Greece<br>|
|Lithuania<br>|
|Portugal|
|Latvia<br>|
|0.000<br>0.050<br>0.100<br>0.150<br>0.200<br>0.250<br>0.300<br>0.350<br>0.400|
|Gini index of income inequality|

_Source:_ World Bank staff calculations based on data from the 2006 EU-SILC

> 11 See World Development Report (2006) for details.

9
Figure 4 Madagascar’s unit values and market share, 1965-2003 <!-- Start of picture text --> 1 00.0 350<br>90.0<br>300<br>80.0<br>250<br>7 0.0<br>60.0<br>200<br>50.0<br>1 50<br>40.0<br>30.0 1 00<br>20.0<br>50<br>1 0.0<br>0.0 0<br>Market share<br>Unit v alues<br>19651967196919711973197519771979198119831985198719891991199319951997199920012003<br>Market share (%) Unit value (US$)<br><!-- End of picture text --> Source: Blarel and Dolinsky (2000), COMTRADE (mirror statistics), authors' calculations.

During this phase, a cartel was also formed in 1962 with the Comoros and the Réunion, the “Vanilla Alliance”. Its objective was to take advantage of the region’s huge market power over the world’s second most expensive spice, but also to correct externalities and bring the collective action necessary to develop the market by generic market promotion in the major consuming countries, and to insure quality. In the mid-seventies, the cartel’s worldwide market share was above 80%.

As shown in figure 4, government intervention initially brought about positive results, with the world market expanding rapidly and Madagascar exports up by a factor of five in the fifteen years following independence. It is also the phase when Madagascar established its lead in the market for vanilla (see figure 5). Blarel and Dolinsky (p. 295) conclude that this first phase was successful both on equity grounds, as the FOB price was about equally divided between growers, packers, and the GOM/VSF, and on efficiency grounds as the farmers’ supply response was strong and quality maintained.

16
regression. So if we ...nd a negative e¤ect of land restrictions in OLS regressions that omit land productivity controls but include human capital controls, this can be taken as particularly strong evidence.

The OLS regressions reported later provide strong evidence of a signi...cant negative e¤ect of land restrictions on equilibrium wage and suggest that the magnitude of this negative e¤ect decreases with distance. While the OLS estimates may be acceptable as credible evidence of a negative e¤ect of land restrictions on equilibrium wage, they cannot give us an estimate of the magnitude of this e¤ect because of the possible downward bias from omitted productivity heterogeneity. We provide estimates of the causal e¤ect of land restrictions on the wage using an instrumental variables approach that corrects for the possible downward bias.

While the unusual positive land productivity correlation in Sri Lanka is useful for our empirical analysis, there is a second and equally important advantage of Sri Lanka as a case study to understand the e¤ects of land restrictions. The history of malaria infestation from 16th to early 20th centuries, and the unique role it played in the land policy provides a credible source of exogenous variations in the LDO restrictions. The variations in the historical malaria prevalence across DSDs are useful especially because a nationwide malaria eradication program was implemented as early as 1947 (Brown, 1986, Lucas, 2010), long before the data used for this study were collected (we use HIES 2002). We develop an instrumental variables approach that exploits this exogenous source of variations to identify and estimate the e¤ect of LDO restrictions on wages. To ensure the validity of the exclusion restrictions, we control for a set of covariates including current malaria parasite infection rate, individual and DSD level education, age (to capture cohort e¤ects), and district ...xed e¤ects. In a recent paper, Lucas (2010) shows that the cohorts that were exposed to severe malaria in the late 1930s had lower schooling, and malaria eradication had had signi...cant positive e¤ects on the schooling of post-eradication cohorts in Sri Lanka.<sup>8</sup> To check whether the potential long term negative e¤ects of early-age malaria exposure invalidate our results, we perform robustness checks with di¤erent subsamples, excluding cohorts born before 1947 (the start of a nationwide eradication campaign), 1950 and 1960 respectively.<sup>9</sup> Also, note that the long-term

> 8 Note that our identi...cation strategy does not use malaria variations across cohorts.

> 9 Lucas (2010) de...nes the cohorts born before 1947 as exposed to historical malaria prevalence over the period 1937-1941. We also use historical malaria data for the period 1937-1941 (average).

5
<!-- Start of picture text --> GFDL Model<br>10000<br>w= Current<br>eee Future<br>1000<br>“% 100<br>=><br>tJ<br>2<br>2 ,<br>ct)<br>om<br>=<br>s 1<br>0.1<br>0.01<br>1 10 100 1000<br>Return Period (Years)<br><!-- End of picture text -->
was assumed to be associated with the same steepening of the relationship between sanitation and health. However, with such a large dataset, we can model this relationship more flexibly to show the shape of the sanitation-population density interaction.

In this section, we allow the interaction between population density and the healthsanitation gradient to be a fifth-order polynomial. We use an odd-ordered polynomial to capture flexibility in the increasing relationship between population density and the sanitation-infant mortality gradient. We use a 5th order polynomial because of the statistical significance of these terms (F = 5.6; p ¡ 0.01) and the failure of the extra 6th and 7th terms to be jointly significant additions to the model (F = 0.4; p = 0.79).

For both infant mortality and height-for-age, we estimate: As before, we are estimating health outcomes for child _i_ , in region _p_ , in DHS survey _s_ , and in country _c_ . As described above, we introduce fixed effects _α_ at country, survey, and region levels, in stages. We also include the same vector of extended controls _X_ , as well as the household’s own open defecation, as described above.

This functional form implies that the change in health associated with a change from 0% to 100% local open defecation is: Panel A of figure 1 plots the dependence of the infant mortality-open defecation gradient on population density; panel B plots the height-open defecation gradient as a function of population density. In both cases, the same six specifications that were used in section 3.3’s table 3 are plotted: fixed effects at the country, survey, and region level, with and without an 16