rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9837

# **Abstract**

This paper examines the drivers of COVID-19 vaccine hesitancy and tests various means of increasing people’s willingness to receive a COVID-19 vaccine. The study draws on data collected through a broadly representative phone survey with 2,533 respondents and an online randomized survey experiment with 2,392 participants in Papua New Guinea. Both surveys show that less than 20 percent of the respondents who were aware a vaccine existed were willing to be vaccinated. The main reason respondents stated for their hesitancy regarding the vaccine was concern about side effects; however, the majority also said health workers could change their mind, particularly if information was communicated in person. The phone survey illustrated that people’s level of trust in the vaccine and their beliefs about the behavior of others are strongly associated with their intention to get a COVID-19 vaccine. In contrast, people’s concern about COVID-19, most trusted source of information (including social media), and vaccination history were unrelated to their intention to get vaccinated. The online experiment showed that a message that emphasized the relative safety of the vaccine by highlighting that severe side effects are rare, while also emphasizing the dangers of COVID-19, increased intention to get vaccinated by around 50 percent. Collectively, these results suggest that policy makers would be well placed to direct their efforts to boosting the general population’s trust that getting vaccinated substantially reduces the risk of severe illness or death from COVID-19.

This paper is a product of the Poverty and Equity 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 choy@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
to the erosion of useful skills, discouragement, and eventual exits out of the labor force (World Bank 2005b; Cazes and Nesporova 2003). There are two options for measuring long-term unemployment or the share of those unemployed for a year or longer, as a proportion of total unemployed. First, the LSMS survey directly elicits information on the job search period. In particular, survey respondents are asked how long they have been looking for a job or trying to start their own business. The responses may be anywhere from less than a month to over 10 years. Because these are own-assessments of the duration of job search, this may be prone to measurement errors. The alternative is to infer long-term unemployment status data from actual employment status in each of the survey years. Thus, the long-term unemployed in 2004 would be those who were also documented to be unemployed in preceding years.

There is evidence that a substantial core of long-term unemployed workers exists. This is confirmed by our two measures, although both—the second measure in particular—are complicated by the absence of suitable proxies of ILO-consistent unemployment in 2003. Not surprisingly, there is a wide discrepancy between the two estimates. Based on the first measure (Table 2), the data suggests that about 70-80 percent of the unemployed have been unemployed for a year or longer. Based on the second measure, the data suggest that about 37 percent are long-term unemployed. We favor the second measure because it is based on observed, rather than self-reported, longterm unemployment.<sup>10</sup> As a rough benchmark, the average incidence of long term unemployment in the ECA region is about 50 percent.<sup>11</sup> Both measures suggest that there is a larger core of long-term unemployed in RS. The second measure, for example, suggests that the incidence of long term unemployment is about 30 percent in FBH compared to 44 percent in RS.

> 10 We also find it informative that this second measure is lower than self-reported long-term unemployment, possibly for reasons similar to the discrepancy between ILO unemployment and registered unemployment. However, this second measure may still be upwardly biased because the status of survey respondents is not observed continuously between rounds.

> 11 This is based on a sample of 13 transition economies over the 1999 to 2002 period drawn from the ILOKILM database. Data are available from the authors on request.

9
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# **Comparison in ranking games**

Shanghai and Beijing are the two top ranked cities or regions with respect to economic development. Shanghai leads Beijing according to several indicators of performance, such as GDP, GDP per capita, industrial outputs, import-export, FDI, etc.

> It also leads in other respects as well. Table 4 and Table 5 show two different indicator systems compiled by various institutes, all based on the same official published statistics.

**Table 4: Ranks of Beijing and Shanghai in** **_City Development Report of China 2002_**

||Population/<br>Labor force|Economic<br>performance|Social welfare|Infrastructure|General|
|---|---|---|---|---|---|
|Beijing|2|2|2|1|2|
|Shanghai|1|1|1|3|1|

This report was produced by the State Statistic Bureau. Each subsystem in the table was further divided into a series of indexes that can be found in statistic yearbook to calculate the rates of each city. Source: based on _City Development Report of China 2002_

**Table 5: Ranks of Beijing and Shanghai in China Urban Development Report 2001-2002**

|Ranks in subentry|Shanghai|Beijing||Shanghai|Beijing|
|---|---|---|---|---|---|
|_Fundamental Strength_|1|3|_Social welfare_|1|3|
|Resources|1|4|Social justice|1|2|
|Size of economy|1|4|Social security|1|2|
|Industrialization level|1|2|Social advancement|1|3|
|Infrastructure|1|2|_Urban Management_|2|5|
|_Competitive capacity_|1|2|Efficiency|3|13|
|Innovative capacity|1|2|Place promotion|1|6|
|Learning capacity|2|1|Regional spillover|9|5|
|Resource productivity|1|2|_Sustainable development_|9|6|
|Informationalization|1|2|Ecology|47|6|
|Global linkages|1|2|Environmental quality|29|17|
||||Harmony|4|7|
||||Influence|3|13|
|General ranks|Shanghai||Beijing|||
|Growth potential|2||3|||
|General capacity|1||2|||
|Real capacity*|1||2|||

_Note_ : * composition of up two _Source_ : based on _China Urban Development Report 2001-2002_<sup>_4_</sup> .

4 This research was sponsored by the China Mayor Association, also based on indexes in statistic yearbooks, but with more detailed subentries. The indicate of the general capacity of a city is described from 5 perspectives, i.e. fundamental strength, competitive capacity, social welfare, urban management, and sustainable development, while each perspective is a combination of a set of indexes, for example, the “fundamental strength” was measured by a function with 4 variables, i.e. resources, size of economy, industrialization level, and infrastructure, which can be further calculated with the data in official statistic yearbooks.

11
credit information sharing reforms on firms’ access to bank finance. We are also able to verify the robustness of our results using a firm level panel data set and, separately, instrumenting for the likelihood that a country introduces a credit information sharing scheme. In addition, our analysis contributes to the understanding of how estimates of the impact of credit information schemes differ by firm characteristics (size, experience, and opaqueness). Finally, in our analysis we distinguish between credit bureaus and credit registries. The latter tend to be public institutions that are usually managed by central banks or bank supervision agencies. In general credit registry data are geared towards use by policymakers, regulators, and other officials that supervise the financial system. In contrast, credit bureaus are privately owned commercial enterprises which tend to cater to the information requirements of commercial lenders. Thus, they typically provide additional value-added services, such as credit scores and collection services.

The rest of the paper is organized as follows. Section II describes our data. Section III discusses our empirical methodology. Section IV presents our results and Section V concludes.

# **II- Data**

Our empirical estimations are based on survey data collected at the firm-level paired with country-level data from a variety of sources. We obtain firm-level data on firm’s access to finance across 63 countries over the period 2002-2013 from the World Bank Enterprise Surveys (WBES). In particular, we focus on five main aspects of firms’ access to finance: (a) whether they have loan, line of credit or overdraft, (b) the interest rate they pay on their most recent loan, (c) the maturity of their most recent loan, (d) the fraction of working capital financed by banks, 7
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score. Then, tests of one category were summed into a composite score and standardized again for better interpretability. After these steps, we obtained the following composite scores:

**_Executive function_** (α �.74, ��� �.60�.<sup>8</sup> The final executive function score includes four tests, of which the first two listed here are from the MELQO toolkit. The construct inhibitory control is assessed with the head-knee task (McClelland et al., 2014). Working memory (short-term auditory memory) is assessed with a forward digit span test. The Dimensional Change Card Sort test is used as a measure of cognitive flexibility. We use a selfdeveloped cancellation task to measure sustained attention. In this test, children see a printed matrix with different symbols and are asked to cross-out all symbols that match the given one (e.g. cross out all flowers). When completed, a larger matrix is given and a new symbol has to be crossed-out. The test continues until a child has completed 4 matrices, crossed out more wrong than correct images in a matrix, until the child loses attention, or states that it is done.

**_Language development_** _(_ ��.71, ��� �.65� _._ Language and literacy skills are assessed with a letter name knowledge test in which children have to identify common letters of Khmer script. Expressive language skills are assessed with a self-developed test, similar to the PPVT (Dunn & Dunn, 2007) where children are asked to match a word to one out of four pictures.

Receptive language is assessed with a listening comprehension test. Knowledge of reading concepts is assessed by showing a children’s storybook and asking questions such as where and in which direction one should start reading the story. A drawing test, where children copy shapes, like circles or squares, is used to assess fine-motor skills.

> 8 Cronbach’s � for composite test scores are based on the covariance between the standardized test scores of the individual tests. The rank correlation coefficient for composite test scores gives the correlation between baseline and follow-up composite scores with 11 months in between on average.

12
Policy Research Working Paper 8322

# **Abstract**

This paper revisits the relationship between energy intensity and economic growth, using a flexible piecewise linear regression model. Based on a panel data set of 137 economies during 1990–2014, the analysis identifies a threshold effect of income growth on energy intensity change: although energy intensity is negatively correlated with income growth throughout the entire sample and study period, the declining rate significantly slows by more than 30 percent after the level of per capita income reaches $5,000. Based on index decomposition, the analysis also finds that although structural change is important for intensity levels in all countries, the efficiency effect is more important in higher-income countries. The results suggest that when countries move beyond lower-middle-income levels, energy efficiency policies become far more critical for sustaining the rate of improvement in energy efficiency.

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
**Table la: Complete Sample Summary Statistics**

||_Mean|S.D.|Max|Min|
|---|---|---|---|---|
|Pressure Index|-0.000|0.025|0.444|-0.319|
|'T1|0.016|0.022|0.212|0.000|
|T2|0.019|0.021|0.169|0.001|
|R/M1|0.706|0.710|3.562|0.054|
|Ca<sup>2</sup>1|0.003|0.005|0.042|0.000|
|III|-0.004|0.056|0.369|-0.249|
|_(e2_|0.007|0.015|0.180|0.000|
|p2<br>|-0.002|0.023|0.070|-0.190|
|fn|0.684|0.688|2.010|0.069|

Notes: ti =tbreshold, ai =variance of innovations, .i= drift using GARCH. ¶Z 2, 2 using moving average. RIMl = resrves in domestic Currency divided by narrow money. Tj = semi-elasticity of money demand.

**Table lb: Thresholds and Reserve Ratios for Selected Speculative Attacks**

|Country|Crisis -1|¶1|T2|R/Mi|
|---|---|---|---|---|
|Argentina|1994:11|0.036|0.036|0.885|
|BraZil|1994:11|0.072|0.024|1.658|
|Mexico|1994:11|0.010|0.007|0.322|
|Finland|1992:08|0.009|0.001|0.165|
|Italy|1992:08|0.031|0.012|0.054|
|Spain|1992:08|0.023|0.014|0.382|
|Portugal|1992:08|0.011|0.010|1.146|
|Spain|1992:10|0.034|0.015|0.356|
|Portugal|1992:10|0.014|0.017|0.947|
|Spain|1993:04|0.025|0.031|0.299|
|Portugal|1993:04|0.018|0.015|0.828|
|Spain|1993:06|0.004|0.032|0.323|
|Portugal|1993:06|0.010|0.015|0.826|
|France|1993:06|0.001|0.002|0.119|

Notes: Ti = threshold using GARCH, I using moving average. Crisis-1l month before crisis.
iment and show how FDI flows to the country were affected by the crisis.<sup>55</sup> Specifically, I focus on greenfield FDI in the non-energy-and-construction sectors (hereafter referred to as FDI [excl. energy & construction] projects) as: (i) the crisis could have induced significant investment into the energy sector as part of efforts to resolve the crisis. Hence,FDI to the sector may rise in response to the crisis; and (ii) construction related FDIs, such as investment in real estate, are unlikely to respond to energy crisis as these sectors are relatively low energy intensive.

To causally estimate the effect of the crisis on FDI in Ghana, I use the synthetic control approach (SCM) (Abadie and Gardeazabal, 2003; Abadie et al., 2010, 2011, 2015; Peri and Yasenov, 2019; Andersson, 2019). The basic idea behind the SCM is to construct a counterfactual, otherwise referred to as “synthetic control” using a weighted combination of the outcome variable in the control (donor) units that matches the outcome variable of the treated unit in the pre-treatment period. The weights are constructed via a data-driven algorithm by minimizing the differences in the outcome variable as well as key predictors of the outcome variable between the treated and control units. These weights are then used to construct a weighted combination of the outcome variable in the post-treatment period, which serves as a valid counterfactual for the treated unit during the post-treatment period. Therefore, the difference in the observed outcome between the treated unit and the synthetic control (counterfactual) measures the effect of the treatment.<sup>56</sup> A unique feature of the SCM is that, unlike the DiD, the so-called “parallel trends” assumption is not required for identification.

The analysis is undertaken using a panel data on the greenfield FDI projects (excl. energy and construction sectors) in 23 emerging markets in Africa, Latin American and

> 55There are at least two factors that motivate the choice of the Ghanaian crisis as a unique experiment: First, the crisis was a large scale shock to the economy as almost every economic sector and geographic administration in the country was affected. Second, the prolonged nature of the crisis (2013-2016) provides a unique opportunity to assess the effects of a relatively “long-term” shock as opposed to an instantaneous shock.

> 56The SCM is somewhat similar to the DiD method, except that unlike the latter, the former does not impose equal weights on the control units

41
# **Appendix D1: Changes in Averages and Decomposition of Effects, Lewbel Estimator**

||Change in<br>the obtained<br>coefficient<br>(2009-10)<br>DEV MENA|Change in<br>the obtained<br>coefficient<br>(2009-10)<br>ArabSpring|Change in<br>the averages<br>(2009-10)<br>DEV MENA|Change in<br>the averages<br>(2009-10)<br>ArabSpring|Developing<br>MENA<br>first-order<br>effect|Developing<br>MENA<br>second-order<br>effect|Arab Spring<br>countries<br>first-order<br>effect|Arab Spring<br>counties<br>second -order<br>effect|
|---|---|---|---|---|---|---|---|---|
|Dissatisfaction with|0.101|0.015|0.028|0.091|-0.031|0.037|-0.084|0.005|
|Standards of living|||||||||
|People cannot get ahead by<br>working hard (Yes)|0.213|0.120|-0.012|0.004|0.005|0.038|-0.001|0.012|
|Dissatisfaction with efforts<br>of the government to|0.033|-0.098|0.033|0.070|-0.012|0.021|-0.030|-0.060|
|increase high quality jobs|||||||||
|Dissatisfaction with|-0.199|-0.058|0.038|0.052|-0.014|-0.074|-0.015|-0.018|
|freedom to choose life|||||||||
|Dissatisfaction with<br>educational system/schools|1.000|0.910|-0.015|0.076|-0.007|0.382|0.029|0.331|
|Corruption widespread<br>within government/|-0.113|-0.387|0.016|0.111|-0.008|-0.088|-0.040|-0.262|
|business (Yes)<br>Unemployed|0.393|0.179|0.001|0.030|0.000|0.025|-0.012|0.004|
|Working for the|0.339|0.481|0.038|0.034|0.013|0.022|0.020|0.043|
|government|||||||||
|Income (1,000’s)|0.001|-0.009|-0.600|-1.306|-0.015|0.010|-0.025|-0.077|

39
measuring the quality of management practices across 18 dimensions related to operations, planning, monitoring, and human resources, covering thousands of firms in many countries.

Similar to FAT, data on management practices is collected via firm surveys. Experts rank practices based on their quality, and an establishment-level score is constructed to study the drivers of management practices and their association with productivity. We perform a similar analysis for technology sophistication. Beyond the similarities in measurement methods, Bloom, Sadun and Reenen (2012) have hypothesized that technology sophistication and management practices are complementary. We explore the complementarity of technology and management in the context of productivity regression across establishments, finding supporting evidence.

The rest of this paper is organized as follows. Section 2 introduces the FAT survey, and describes various validation exercises of the sophistication rankings, and the data collected. Section 3 presents the technology sophistication measures and illustrates key insights with examples from specific establishments, and sectors in FAT. Section 4 explores the use of technology at the business function level. Section 5 studies technology sophistication across establishments. Section 6 investigates the relationship between technology sophistication and productivity across establishments. Section 7 concludes.

# **2 The Survey**

The FAT survey (henceforth, “the survey") collects detailed information for nationally representative samples of establishments in agriculture, manufacturing, and services about the technologies each establishment uses to perform key business functions necessary to operate in its respective sector. In the following sub-sections, we describe the survey design and implementation, relegating further details to section A in the Appendix.

## **2.1 Structure**

The survey is composed of five modules. Module A collects information on the general characteristics of the establishment.<sup>3</sup> Modules B and C cover the technologies used. Module D focuses on barriers to, and drivers of, technology adoption, while Module E gathers information about the establishment’s financial statements and employment.

> 3The survey is designed, implemented, and weighted at the establishment level. For multi-establishment firms, the survey targets the establishment randomly selected in the sample. The survey can be downloaded at the following address ( `https://dcomin.host.dartmouth.edu/files/FAT_Survey_complete.pdf` ). The implementation manual which includes all instructions for interviewers, training materials and a full description of the technologies in the grid can be downloaded at ( `https://dcomin.host.dartmouth.edu/files/ Implementation_Manual_TAS_29112023.pdf` ).

5
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|---|---|---|---|---|---|---|---|---|
|Country-Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|28.864***|-137.286***|-142.785***|-159.907***|-116.971**|356.014**|318.759*|314.341*|
||(5.496)|(41.975)|(43.934)|(43.977)|(45.647)|(174.004)|(176.062)|(178.129)|
|Number of observations|43,198|43,198|42,299|40,237|36,944|36,944|36,944|36,944|
|R-squared|0.481|0.483|0.488|0.506|0.512|0.515|0.516|0.517|
|Standard errors in brackets. Al|l standard errors a|re Huber-White|robust and clust|ered on country|-times-industry|. Sample size|varies due to m|issing data.|

89