rafmacalaba/datause-encoder-data huggingface dataset:
The authors go further by attempting to explain the cross-country variation in efficiency and find that the degree of urbanization and the quality of bureaucracy are the most relevant variables. To capture possible non-linearity, the authors introduce these variables squared. This stage of their work poses several problems. First, it is possible that the (non-linear) quadratic terms reflect heterogeneity across countries and dynamics across time. As shown by Haque, Pesaran, and Sharma (1999), this would produce inconsistent estimates. Second, the authors do not adjust for the fact that the dependent variable (efficiency scores) is censored, given that it can adopt only values between zero and one. And third, the authors do not consider the serial correlation of the efficiency scores (Simar and Wilson, 2004) Greene (2003a) combines the previous two papers in the sense he concentrated on health efficiency only using the WHO panel data and explained inefficiency scores variation across the sample of counties. Greene’s stochastic frontier estimation is much more general and flexible, as it allows for time variation of the coefficients and heterogeneity in the countries’ sensitivity to the explanatory variables. The author first estimates a health production function using expenditure (public and private together) and education as inputs, and then explains inefficiency with a set of explanatory variables of which the only significant ones are the income inequality measure, GDP per capita and a dummy variable for tropical location.

Afonso, Schuknecht and Tanzi (2003) examine the efficiency of public spending using a non-parametric approach. First, they construct composite indicators of public sector performance for 23 OECD countries, using variables that capture quality of administrative functions, educational and health attainment, and the quality of infrastructure. Taking the performance indicator as the output, and total public spending as the input, they perform single-input, single-output FDH to rank the expenditure efficiency of the sample. Their results show that countries with small public sectors exhibit the highest overall performance.

Afonso and St. Aubyn (2004) address the efficiency of expenditure in education and health for a sample of OECD countries applying both DEA and FDH. This paper presents detailed results by comparing input-oriented and output-oriented efficiency measurements. The small overlap of the samples limits the comparability of these results with those presented in the next section. An apparently strange result, reported in earlier drafts of the paper, was the inclusion of Mexico as one of the benchmark countries (on the efficiency frontier). The result is strange given that the sample is the OECD countries, and it counterintuitive. This is the result of Mexico having very low spending and low education attainment results, hence it cam be considered as the “origin” of the efficiency frontier. The next chapter discusses this topic and reports similar counterintuitive results but for other countries.

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recall periods, poor people are ill for large fractions of the year with ailments that they believe to be shortterm and, apparently, easily forgotten. Richer people, who do not forget as easily, do not suffer from acute illnesses nearly as much as the poor. The recall impacts documented here may have implications for our understanding of health reporting across rich and poor households, and measurement and policies regarding access to health care. We consider each in turn.

One long-standing question in the literature on health in developing countries is why the rich appear to be ―sicker,‖ as assessed by self-reports, than the poor. One explanation is that people have different ―thresholds‖ or intolerance to discomfort and that these thresholds decrease with income (Thomas and Frankenberg, 2002 or Paqueo and Gonzalez, 2003, Strauss and Thomas (1998)). Another is that rich people have more accurate information concerning chronic conditions.<sup>13</sup> Richer people may respond ―yes‖ to the question of whether they‘ve been ill if they have been diagnosed with a chronic illness like diabetes. A poorer person will not know to report the underlying chronic condition unless there has been an acute episode of the ailment in the recall period. This correlation between reported health and information extends beyond chronic illnesses—for instance, Dow and others (2008) show in an experimental context that increasing the price of health care leads to a drop in physician visits but an improvement in reported health status. They interpret this result as consistent with a positive correlation between information about health and the probability of reporting sick.<sup>14</sup> This paper suggests that in addition to the problems of reporting thresholds and information about chronic illnesses, survey design may drive some of the results relating self-reported illness and income. Since the effect of the recall period declines with PCE, it influences estimates of the income elasticity of doctor visits, use of medicines, number of illnesses, and amount spent on health care. We cannot decompose the differential reporting of rich and poor into shares contributed by the competing hypotheses, but we do offer evidence of the substantial role played by recall effects. Thus, while differential sensitivity to severity across income groups is an important factor in self-reports, recall bias, awareness of chronic illness and the interaction between the two adds another set of considerations to explain the puzzling empirical regularity of the ―sicker rich.‖ Patterns of health care usage reported here are also problematic for measurements of inequality in access. Standard theories about inequality in health access or the links between health expenditures and per-capita expenditures are consistent with our monthly recall data but harder to reconcile with observed patterns once

> 13 See, for example, the interchange between Murray and Chen (1992,1993) and Riley (1993).

> 14 In the Delhi sample, we examined this possibility and also find substantial evidence of a link between information and the reporting of chronic illnesses (Das and Hammer, 2009).

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Inventory (ODIN) index, which assesses the coverage and openness of the systems of official statistics in 187 countries (ODW 2020).

Heterogeneity in the distribution of the four-pillar SPI used here confirms the findings and recommendation of the report on national accounts conducted by the World Bank (World Bank 2008). That study concluded that “the range of developing countries is extensive, some close to economic sophistication and statistical resources to OECD countries. At the other end of the range, however, are countries with relatively small populations, economies that are concentrated in a few areas with a limited number of skilled professionals.” The guide recognized that some recommendations of the report have little effect for countries that lack other features, such as sophisticated financial markets.

The International Monetary Fund (IMF) uses different standards for the collection and provision of macro-statistics for countries that “play the leading role in international capital markets” and the rest of the world (IMF 2020). Hoogeveen and Pape (2020) emphasize that data demands in fragile states differ greatly from demands in countries where granular statistics on income, for example, are essential for maintaining an array of social and employment programs.

Figure 2 plots the coefficient of variation (CV) in the SPI components against the SPI for each country in the sample.<sup>7</sup> The CV shows the degree of dispersion of the SPI components in a country. The maximum CV is reached when only one of the four SPI components is different from zero; the minimum CV corresponds to cases when all four components are equal.

For our sample, the CV of the SPI components decreases with the aggregate SPI. Countries with the highest SPI have the smallest CV of their SPI components (in other words, top performers are good at every dimension of the SPI). Australia, for example, has an aggregate SPI of 0.85. The Data Services pillar is 0.92, the Data Product and the Data Source components are both 0.74 and Data Infrastructure pillar are 1.0 (the highest possible value).

At the other end of the CV spectrum are countries with low SPIs. These countries have very different scores on their SPI components and thus high CVs. Madagascar, for example, has a low

> 7 <u>i</u> C = � , where _μ_ is the mean value of four SPI component for a country and _xi_ is the value ~~�~~<sup><u>∑(xxi−μ)2</u></sup> of a particular component of the SPI. nn−1 μμ

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# **TABLE 1. ENDEI survey**

||Mean<br>5thpercentile|50thpercentile|95thpercentile|
|---|---|---|---|
|Number of firms|3691|||
|**Sales and employment**||||
|Sales (thousands of USD)|7163.8<br>226.3|1508.7|13241.5|
|Number of workers|49.5<br>9|23|108|
|Share managers|0.057<br>0|0.038|0.14|
|Share skilled|0.067<br>0|0.053|0.17|
|Share unskilled|0.876<br>0.67|0.89|1|
|Average annual wage||||
|(USD)|17065.3<br>9236.8|15456.0|25917.0|
|Managers|37193.8<br>12195.1|29268.3|70429.3|
|Skilled workers|22859.5<br>10731.7|19512.2|36707.3|
|Unskilled workers|13984.2<br>7317.1|12829.3|20731.7|
|Gapskilled‐unskilled|0.40<br>0.02|0.35|0.79|
|**Information and communicat**|**ion technologies**|||
|Investment in ICT|0.32<br>0|0|1|
|Investment in ICT/Sales|0.0025<br>0.0002|0.0016|0.0052|
|Workers per computer|6.2<br>1.3|4.4|12.2|
|HHRR management system<br>Production management<br>system|0.68<br>0.43|||

Own calculations based on ENDEI (Encuesta Nacional de Dinámica de Empleo e Innovación), 2010‐2012.

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an additional eight countries.<sup>10</sup> Production data is reported according to the NACE Rev.1 classification. In order to estimate the gravity model, NACE output data need to be concorded to the trade classification for services, which was done on the basis of the “Correspondence between ISIC Categories for Foreign Affiliates (ICFA) and Extended Balance of Payments Services Classification (EBOPS)” as published in Annex IV of the UN’s Manual on Statistics of International Trade in Services, with some modifications. Table 1 displays the 12 sectors that could successfully be concorded. Internal trade and expenditure are calculated from production data in the usual way, ie. a country’s internal trade for any given sector is obtained by subtracting sectoral exports from gross output. A country’s sectoral expenditure data is backed out as the sum of imports from all origin countries including itself or, equivalently, gross output less exports plus imports from abroad.

Standard gravity variables such as distance, common language, contiguity and colonial ties are taken from CEPII’s _Distances_ Database (see Mayer and Zignago, 2006; Head and Mayer, 2000). An important advantage of that source is its provision of population-weighted distances, which can be used to calculate consistently both bilateral distances as well as internal distances. We use the former in the gravity estimations of international services trade and the latter in our study of the determinants of borders.

# **4 Gravity Estimation and Border Effects**

This section offers partial equilibrium estimates and a discussion of the effects of standard trade cost variables (e.g. distance, common language, contiguity, etc.) on services trade for each of the sectors in our sample. An important contribution of our work is the treatment and analysis of international borders in services trade. Initially, we estimate the effects of international borders on services trade with an indicator variable that takes a value of

> 10These countries are Chile, Hong Kong SAR, China, Israel, Mexico, Norway, New Zealand, the Russian Federation and Turkey.

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# **<u>APPENDIX B. Survey questions about vaccine hesitancy</u>**

# <u>Phone Survey</u>

_1. Do you know if a vaccine for COVID-19 is available in this country?_ Yes; No.

_2. Have you been vaccinated for COVID-19?_ Yes; No.

_3. Are you planning to be vaccinated?_ Yes; No; Unsure.

_4. Why are you not sure or not planning to be vaccinated? [MULTI SELECT]_ I don't think it will work; I am worried about the side effects; I already had COVID-19; I am not enough at risk of contracting COVID-19; In general, I don’t trust vaccines; It is against my religion; I am worried to get infected with COVID-19 at the health facility; Health facility too far or too hard to get to; It will take too long to get vaccinated/ I don’t have time to get vaccinated; Other (specify).

5. _Would you be more likely to receive the COVID-19 vaccine if any of the following individuals/authorities receive or recommend the vaccine?_ Family and friends; Religious leaders; Doctors/nurses/pharmacist/health workers; Community leaders; Equivalent of a traditional healer; Scientists and epidemiologists; Celebrities and social media influencers; Other.

Videos of high-profile people talking about the vaccine; Videos with people who have received the vaccine sharing their experience; Face-to-Face communication with prominent leaders in my community; Church leaders providing correct information during church services; Direct information from health workers; Use people in the community to disseminate information so that their own people can understand; Face-to-Face communication from health care workers; I don't think any of these strategies would work; Other.

Videos of high-profile people talking about the vaccine; Videos with people who have received the vaccine sharing their experience; Face-to-Face communication with prominent leaders in my community; Church leaders providing correct information during church services; Direct information from health workers; Use people in the community to disseminate information so that their own people can understand; Face-to-Face communication from health care workers; I don't think any of these strategies would work; Other.

_8. Do you trust the COVID-19 vaccine?_ Yes; No; Unsure.

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**Table 7   Unemployment rates are particularly high among young workers**

||Youth unemployment<br>as % of labor force 15-<br>24|Youth unemployment<br>rate/overall<br>unemployment rate|
|---|---|---|
|**CEE**|||
|Bulgaria|35.5|2.0|
|Czech R.|16.9|2.3|
|Estonia|17.7|1.9|
|Hungary|11.9|2.1|
|Latvia|24.6|1.9|
|Lithuania|21.4|1.6|
|Poland|41.7|2.1|
|Romania|18.5|2.6|
|Slovakia|37.3|2.0|
|Slovenia|15.3|2.6|
|Turkey|17.8|1.6|
|**SEE**|||
|Croatia|34.5|2.4|
|Serbia and Montenegro|||
|a)|58.0|3.1|
|**Memorandum**|||
|EU15|14.7|1.9|
|OECD Europe|17.6|2.0|
|Total OECD|13.1|1.9|

Note: Labor Force Survey data a) The unemployed include persons who in the reference period did a temporary job Source: CEE and EU: Eurostat Turkey and OECD: Employment Outlook, OECD Croatia: Central Bureau of Statistics, Labour Force Survey 2002 (second half), Bank staff calculations. Serbia and Montenegro: Republic of Serbia, Republican Statistical Office, Labor Force Survey 2002 (October), Bank staff calculations For example, in Poland youth unemployment rate is above 40 percent. In Bulgaria and Slovakia it is between 35 and 37 percent. On average, the youth unemployment rate in CEE is close to 25 percent. This is almost twice as high as the average youth unemployment rate for the OECD countries. Unemployment rates of such magnitude are likely lead to social tension.

The disproportionately high youth unemployment rates largely reflect higher levels of labor turnover among young workers, who tend to change jobs more often than older workers, presumably looking for the best worker-job match. However, they may also be associated with labor market rigidities and barriers created by stringent employment protection legislation, which favors insiders (workers with secure jobs) at the cost of outsiders (the unemployed, contingent workers). For example, an extremely high fraction of young labor market entrants among the unemployed in FYR Macedonia can be at least partly attributed to high degree of job protection granted to incumbent workers (World Bank, 1998). Another possible source of high youth unemployment is the minimum wage set at above market clearing level. For example, in economically depressed, high unemployment regions of Poland the minimum wage accounted 27
abroad. Since this decision affects the labor supply choices of the entire household, labor supply could even increase in connection with remittance flows. As reported by Amuedo-Dorantes and Pozo (2005): “Remittances, on the one hand, may help lift budget constraints, raise reservation wages and, through an income effect, reduce the employment likelihood and hours of work of remittance-receiving individuals. On the other hand, these monetary inflows are usually preceded by the migration of a working-aged household member, which may induce other remaining family members to increase their labor supply to supplement for lost household income.” Despite these difficulties, a few studies find a negative relationship between remittances and labor supply. Rodriguez and Tiongson (2001), who examine the labor force participation of households in Manila in 1991, estimate that having a migrant abroad reduces the probability of working by 9.4 percentage points for men and 18.1 percentage points for women. When remittances are increased from $0 to $40 per non-migrant family member, men decrease their labor force participation by a third of a percentage point, whereas women respond by reducing their labor force participation by only one-fifth of a percentage point. Funkhouser’s (1992) study of households in Nicaragua also concentrates on the responses of urban households. His probit estimates indicate that an increase in remittances from $0 to $100 decreases male and female labor force participation by 2 and 5 percentage points, respectively. However, Funkhouser also finds that self-employment hours increase by about 1 percent for both men and women. Hence, Funkhouser (1992) finds evidence of the work effort shifting across various types of employment on account of remittance receipt.

While not specifically addressing the impact of remittances on total work effort, Matshe and Young (2004) measure the responsiveness of rural households in Zimbabwe to overall non labor income. In particular, they assess how households’ decision with respect to supplying their labor to the off-farm labor market is affected by non-labor income flows. They find that these income flows reduce off-farm labor activity. Amuedo-Dorantes and Pozo (2005) carry out a preliminary analysis of the impact that remittances sent by Mexican migrants may have on the labor supply patterns of their working-age male and female family members back home. They find that remittance income sometimes reduces hours worked, whereas other times these monetary inflows increase work effort depending on the type of work, the gender of the recipient and the location of the household. For the specific case of Jamaica, Kim (2006) constructs a pseudo-panel data using household surveys covering the period 1995 to 2002 and finds a negative impact of remittances on labor market participation. This finding is used to inform the general equilibrium analysis carried out in this paper; in particular, Kim’s (2006) elasticities have been ‘borrowed’ to calibrate the endogenous labor supply function used in our model.

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number of products, or number of destinations (the extensive margin).<sup>39</sup> Naturally it is important to integrate such data with program data such as from the project monitoring database.

The trade and integration unit of the World Bank Development Research Group is involved in a major data collection exercise that may help the IE of trade-related interventions in the next few years. As described in Freund and Pierola (2011), the exercise consists of the collection and compilation of the first ever database on exporter-level customs transaction data across countries and over time. Data has been obtained for 20 countries in Africa, Asia, Eastern Europe, and Latin America, and negotiations are in progress to obtain data for 25 more countries. The database will include statistics on exporters’ characteristics and behavior by country, industry, and destination market. The purpose of the database is to provide policymakers, development agencies, researchers, and the public with a novel source of information to conduct analysis of export growth at the micro level and allow for the evaluation of programs and policies affecting that growth.

Data on firm characteristics (covariates), used to control for selection bias, is typically hard to obtain. If an industrial survey is available in the country where the trade intervention is taking place, it can provide the required variables (e.g., location, age of the firm, education of its head, number of employees, foreign ownership). However this requires that customs and industrial census data be merged, which raises confidentiality concerns and may require active collaboration by busy officials in local institutions. When data is not available, an alternative is to conduct a ―retrospective survey‖ — although this method may be biased. In its evaluation of Tunisia’s export-promotion agency, Gourdon et al. (2011) use a combination of data from a survey and from national sources (the customs agency and national statistics institute). Yet another alternative is to include questions on program participation and details in ongoing surveys. This also requires close collaboration between the evaluator and the local institution implementing the survey.

# **6. Looking ahead: Challenges facing IE of trade assistance**

In this section, we consider three key challenges that credible IE of trade interventions must address.

## **6.1 External validity and cost**

One concern with impact evaluations is that their external validity is an act of faith. When a program is found to be effective (or ineffective), how do we know that the result would carry over to similar programs run in different environments?

> 39 See Freund and Pierola (2011) and Lederman, Rodriguez-Clare, and Xu (2011) for uses of such data in a non-IE context.

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