rafmacalaba/data-use-mentions-extended huggingface dataset:
of students at once, or if they target brighter students, as was “widely believed” according to Hogg (2006, p.18)). On the other hand, abductions may be more likely where abductees are more likely to join the Maoists forces, and so the People’s Army may have disproportionately abducted children from areas which were lagging behind. There are also some surprising figures in the abductions data provided by INSEC, such as a total of only 284 abductions by Maoists in Rolpa during the whole conflict.

Taken together, the results presented in this section give no support to the hypothesis that the Nepalese civil conflict had a negative effect on schooling overall. There is a robust but small positive effect of the intensity of the insurgency on female educational attainment. This effect is not accompanied by an increase in the probability of enrollment in education, suggesting that the positive effect on attainment is driven by a faster progression from one grade to the next rather than an increase in enrollment. There is also suggestive evidence of a small decrease in female primary schooling completion where insurgents were more prone to abductions. Finally, districts controlled by Maoists have tended to experience larger primary schooling gains over the period, for both genders.

# 5.3. Marriage

Table 8 presents results from regressions of the probability of being married by the age of 15, for women interviewed individually in the 2006 DHS, restricting (Columns (2) and (4)) or not (Columns (1) and (3)) the sample to women who were already living in the place where they were interviewed at the start of the conflict. The regressions corresponding to the first two columns consider the impact of overall conflict intensity during 1996-2006 as measured by conflict-related casualties. The regressions in the two last columns further include an interaction term between the treatment cohort indicator and the total number of abductions by Maoists (per 1000 inhabitants).

# Table 8 goes about here

When women are assigned the conflict exposure corresponding to the district in
be reducing demand for COVID-19 vaccines and the degree to which it is possible to increase demand through communication campaigns.

Secondly, by closely aligning the design and implementation of broadly representative phone surveys with an online randomized survey experiment, we provide additional insights about the generalizability of the findings. Prior work on COVID-19 vaccine hesitancy has tended to involve either observational data from phone surveys (Solís Arce et al., 2021) or online experiments (Palm et al., 2021) in isolation. In contrast, we asked the same questions to the more broadly representative phone survey samples of respondents as were asked to participants in the online randomized survey experiment. By comparing the results of these different survey techniques we were able to better estimate the effects of the treatments on a broader share of the population. Furthermore, the similarities in descriptive results across the distinct approaches illustrate the robustness of our general findings.

This paper is structured as follows. In the next section we review the related literature about vaccine hesitancy. This is followed by an overview of the setting and methodology used. We then present the results of the representative phone survey and online randomized survey experiment separately, as well as analysis that draws on both. We conclude with a discussion of the implications of our findings.

# **2. Related Literature**

Existing work on vaccine hesitancy comes in two forms. In the first, observational studies are used either to simply quantify levels of vaccine hesitancy or to identify beliefs and traits associated with negative perceptions of vaccination. The second body of literature involves either inference from observational studies or experimental evidence used for the sake of understanding the causes of vaccine hesitancy and learning about means of reducing it. Almost all the published studies in both sets of literature come from prior to the COVID-19 pandemic. The vast majority of available work is from developed countries (Dubé et al., 2015; Solís Arce et al., 2021).

4
to identify each household’s welfare status before the pandemic. For the other five countries that employed random digit dialing methods or did not have prepandemic consumption information, welfare was imputed using a basic set of welfare predictors collected in the HFPS or from recent surveys. As shown in Table 1, various methods employing survey-to-survey multiple imputation such as proxy means testing or Survey of Well-being via Instant and Frequent Tracking (Yoshida, et al., 2015) were used, relying on recent household surveys with household expenditure data.

Table 1. Overview of HFPS data used

|Country|Surveytiming|Samplingmethod|Construction of<br>welfare<br>indicator|Household<br>sample size<br>in Round 1|2020<br>Population<br>size<br>(million)|2020 GDP<br>per capita<br>(2010 USD)|
|---|---|---|---|---|---|---|
|Indonesia|R1: May 2020<br>R2: May-Jun 2020<br>R3: Jul 2020<br>R4: Nov 2020<br>R5: Mar 2021|Subsample of the 2018<br>Urban Perception, 2018<br>Rural poverty, and the<br>2020 Digital economy<br>household surveys|Proxy means<br>test|4,338|273.5|4,312|
|Lao PDR|R1: Jun-Jul 2020<br>R2: Feb-Mar 2021|Random digit dialing|Proxy means<br>test|2,500|7.3|1,836|
|Mongolia|R1: May 2020<br>R2: Sep 2020<br>R3: Dec 2020<br>R4: Apr 2021|Subsample of 2018<br>Household Socio-<br>Economic Survey|Consumption<br>aggregate from<br>previous survey|1,334|3.
Excluding the loss of tariff revenue from the model nearly doubles the estimated average income gains from trade to 3.7% on average across countries (relative to 1.9% in the main model). Moreover, income gains are now positive in 53 out of the 54 countries. Yet, equality gains continue to be negatively correlated with income gains and trade-offs remain widespread.

If instead of assuming away tariff redistribution we assume that the government makes up for the loss in tariff revenue by imposing progressive taxes (calibrated using the World Tax Indicators (WTI) database (Andrew Young School of Policy Studies, 2010)), the inequality costs of trade liberalization show some minor difference, but the overall pattern of results is not impacted. Income gains are negatively correlated with equality gains. Yet, the latter typically are dominated by the former such that countries facing trade-offs are typically better of with freer trade.<sup>15</sup>

# **5.7 Protectionist Scenarios**

Thus far, we have analyzed the inequality-adjusted gains from liberalization, but our framework also lends itself to evaluating trade-offs that might arise if countries become more protectionist. To explore this, we evaluate the welfare impacts associated with three protectionist scenarios that move the economy closer to autarky: (i) a uniform increase in tariffs of 10 percentage points (i.e. adding 10% to all existing tariffs); (ii) a relative increase in tariffs of 10 percent (i.e. multiplying all pre-existing tariffs by 1.1); and (iii) increasing all tariffs to 62.4%.<sup>16</sup> The aim of this exercise is to establish whether the trade-offs that may arise under protectionist scenarios are in general consistent with those derived when countries liberalize. Accordingly, we summarize the results in Table 10 which presents estimates of the (inequality adjusted) gains from trade, the number of countries that exhibit trade-offs, and the trade policy reversals. Country-specific estimates of the gains
# The 2019 Update of the Health Equity and Financial Protection Indicators Database: An Overview

Adam Wagstaff<sup>a</sup> *, Patrick Eozenou<sup>b</sup> , Sven Neelsen<sup>b</sup> , and Marc Smitz<sup>b</sup> a Development Research Group, The World Bank, 1818 H Street, NW, Washington DC 20433, USA b Health, Nutrition and Population Global Practice, The World Bank, 1818 H Street, NW, Washington DC 20433, USA

**Keywords:** Health indicators; health equity; health and inequality; out-of-pocket health expenditures; financial protection; health and poverty; millennium development goals; sustainable development goals; universal health coverage; non-communicable diseases

**JEL codes:** I1, I3, J13
mark>This line was calculated in the following way: (i) the national poverty lines of 15 poor countries was selected, (ii) those poverty lines were inflated to 2011 levels using the CPI of those countries, (iii) the inflated poverty lines were then converted to US dollars using the 2011 PPPs, and (iv) finally, those PPP dollar-denominated poverty lines were averaged to come up with a new poverty line, which was close to $1.90 per person per day (Ferreira et al., 2016). The countries in the reference category are Malawi, Mali, Ethiopia, Sierra Leone, Niger, Uganda, The Gambia, Rwanda, Guinea-Bissau, Tanzania, Tajikistan, Mozambique, Chad, Nepal and Ghana (Ferreira et al., 2016).</mark>

> 3 For more information, consult the World Development Report of 1990. https://openknowledge.worldbank.org/bitstream/handle/10986/5973/ WDR%201990%20-%20English.pdf?sequence=5.

2
# 2.1. Characteristics of Selected Districts

Table 2: Gender Parity Index for Net Enrollment Rates and Literacy Rates

|Gender<br>Parity<br>Index (GPI)<br>for:|Education<br>Level|National|East Java<br>Province|Bangkalan<br>District|Sampang<br>District|South<br>Kalimantan<br>Province|Banjar Baru<br>District|Banjarmasin<br>District|
|---|---|---|---|---|---|---|---|---|
|Net<br>Enrollment<br>Rates (NER)|Primary<br>Jr. Secondary|0.99<br>1.01|0.99<br>1.00|1.00<br>0.79|0.98<br>0.89|0.98<br>1.01|0.97<br>1.14|0.96<br>1.10|
||Sr. Secondary|1.03|1.00|0.97|1.05|1.02|0.82|0.80|
|Literacy<br>rates (ages<br>50+)||0.88|0.81|0.75|0.76|0.93|0.96|0.97|

_Notes: GPI is a_ _<mark>ratio of female to male values of a given indicator. GPI for NER is a ratio of female NER to male NER. GPI for literacy is a ratio of female literacy rates to male literacy rates.</mark>_ <mark>Source: Susenas 2018.</mark>

## **_Province 1: East Java_**

The province of East Java is located on the eastern
# **3 Description of the Survey and Other Data**

## **3.1 Survey Instrument**

The goal of the survey is to gain understanding of aspects of a business connected to spatial aspects. In particular, we are interested in learning in what dimensions the ICT sector differs from the other sectors of the economy. The survey captures measures of firm performance, capital accumulation, trade, and workforce, as well as the perception of growth and the challenges encountered.

The sample frame used is the universe of private sector establishments operating in the West Bank based on the Palestinian Central Bureau of Statistics (PCBS) Establishment Census 2017. The sample was selected using stratified cluster sampling. Strata are defined according to the following variables: North and South of the West Bank, large and small villages, large and small establishments (below and above 10 employees), and industry subgroups (ICT, manufacturing, wholesale and retail trade, and other industries).<sup>1718</sup> Within a stratum, clusters were identified as establishments belonging to the same stratum and located in the same town. A sample of 10% of the establishments was selected in each cluster. Firms within the cluster have been identified using a random walk approach. The sample size has been chosen as to obtain unbiased estimates with 90% confidence intervals at 10% precision for the whole universe, as well as for the following industry sub-groups: IT, manufacturing, and wholesale and retail trade. The survey was conducted via face-to-face interviews and followed by call-back interviews to guarantee high quality of the data, in two phases, from 28/03/2019-05/06/2019 and from 22/12/2019-07/01/2020.<sup>19</sup> More information on the methodology is provided in the appendix.

A total of 533 interviews were completed. For comparison, the World Bank 2019 Enterprise Survey for West Bank and Gaza consisted of 365 interviews, 205 of which were in the
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

In contrast to Bacon and Beasnt-Jones’ ahistorical approach, taking into account the fact that reforms must be designed in the context of previous policy commitments, we can test the effect of energy endowment, dependency on fuel imports or environmental commitment. Drillisch and Riechmann (1998) test the correlation between energy dependency and the choice of liberalisation model, as well as between commitments concerning the environment and the choice of liberalisation model. The paper also incorporates the date in which reforms started – for each year of delay (relative to the 1990 benchmark) half a point is deducted from the liberalisation index. The study is limited to a cross-section analysis, and a time effect is not explicitly incorporated into the model. A temporal dimension is, however, incorporated into the model in the form of the impact of past decisions on energy and electricity dependency, or on environmental commitment.

Also, Drillisch and Riechmann (1998) do not see reform as a sequence of steps, but use two types of model: one for wholesale and one for retail market. There is no control for institutional conditions, macroeconomic policy, and level of development or market size. The only other factors included in the model are the level of prices (i.e. high prices induce liberalisation) and a cultural link to the United Kingdom. These are limitations that refer to the formulation of the model. Another drawback of the study is the very restricted dimensions of reform included in the liberalisation index. For instance, it does not take into account corporatisation, privatisation, restructuring, regulatory change and the introduction of an independent regulator. Further, Drillisch and Riechmann (1998) finds a positive link between import independence and liberalisation but, interestingly, the estimated overall energy independence index is more significant than the independency index of the electricity sector alone. The study fails to detect significant interdependence between environmental commitment and the choice of liberalisation model.

Ruffin (2003) reports
# value.

Further findings include significant effects of the NHIS on non-food consumption and remittances, which suggests that in the absence of the insurance, households first resorted to risk coping strategies that arguably have less detrimental long term consequences. Yet, in monetary terms, these effects are only a fraction of the estimated savings in OOP. In particular, non-food consumption increased by a fifth and remittances fell by another fifth of the estimated drop in medical expenditures. Thus, households were not able to fully cope with the expenditure shock on the basis of these measures alone in the absence of insurance and had to use additional measures, which is consistent with our findings of child labor effects of the NHIS. We also find a significant negative effect of the NHIS on employment participation and hours worked of healthy adult household members.

This paper adds to a small evidence base on the deployment of informal and self-insurance strategies to cope with health shocks and the extent to which formal health insurance substitutes for welfare-decreasing coping mechanisms such as child labor. Chakrabarty (2012) uses data from Bangladesh and finds that micro health insurance in combination with credit can reduce child labor for poor households. Landmann & Fr ̈olich (2015) use a randomized control trial that paired micro-credit with a micro health and accident insurance in Pakistan. Their findings suggest a lower incidence of child labor and reduced child labor earnings. To the best of our knowledge, Liu (2016) is the sole other study that examines a wider range of coping mechanisms in the context of a rural health insurance program in China. The author reports that households insure income and consumption against health shocks through increased levels of labor supply in the absence of access to health insurance. The health insurance reduced the use of these smoothing mechanisms. Also, rural health insurance increased investments in school attendance and reduced the employment of children aged between 7 and 18 years. A limitation of the study is that it does not show to what extent the employment of children constitutes child labor according to the international definition of child
affect financing patterns (see Harris and Raviv 1991 for a review). We next report regression results using firm-specific control variables.'<sup>3</sup> We use nine variables as control variables at the firm level. Those have been used in other studies trying to explain firm financing patterns (Titrnan and Wessels, 1988, Demirguc-Kunt and Maksimovic, 1998 and forthcoming, Rajan and Zingales, 1995). We divide these variables into an expanded control set, used only for the leverage regressions, and a smaller control set, used for all other regressions. The smaller control set consists of four variables. The first variable is firm market value (in log-terms and expressed in US dollars to allow comparability across countries), to control for the effects of size on financing patterns. The second variable is the growth of total assets, deflated using a GDP-price index, to control for the firm-specific growth opportunities which can influence financing patterns. The third variable is the industry classification, as financing patterns can be expected to depend on the type of activity financed including the volatility of the underlying income stream, the degree of informational asymmetries in the management of the particular type of business, etc. We have the two-digit SITC groups for each firm, but this classification is too detailed for our purposes. Instead, we use Campbell (1996) to re-classify the two-digit SIC groups to 12 industry categories.'<sup>4</sup> The fourth variable is the level of GNP per capita (in log termns and expressed in dollars), to control for cross-country differences in the level of development. The latter could affect the amount of risk that the corporate sector is willing to assume.<sup>15</sup> The expanded set of controls includes five additional firm-level characteristics. The first variable is the availability of collateral which can influence the degree to which a firm can obtain long-term financing. It is defined as the sum of inventory and gross plant 24
# **3. The Formal Sector**

# **3.1 The Pre-ERP period**

# **3.1.1 Employment**

Ghana's employment in the formal sector has been dominated by the public sector for a long time, and it seems to be more true in the post ERP era. The employment series are summarized in Table 3.1 reproduced from ISSER 1994 (Table 8.4).

**Table: 3.1: Formal Sector Employment, 1960-1991 ('000)**

|||||Annual|Change (|percent)|
|---|---|---|---|---|---|---|
|Year|Private<br>Sector|Public<br>Sector|All Sectors|All<br>Sectors|Public|SOPSE(<br>percent)|
|1960|149|184|333|-|-|44.7|
|1965|118|278|396|+3.8|+10.2|29.8|
|1970|110|288|398|+0.1|+0.7|27.6|
|1975|137|318|_455_|+2.9|+2.1|30.1|
|1980|46|291|337|-5.2|-1.7|13.6|
|1985|67|397|463|+7.5|+7.3|_14.5_|
|1986|66|347|414|-10.8|-12.6|15.9|
|1987|79|315|394|-4.8|-9.2|20.0|
|1988|55|252|307|-22.1|-20.0|17.9|
|1989
Authors’ calculation using LiTS.</u> <u>Note: In the LiTS, informal employment is defined as working without a written contract in the main job. Rosstat’s official measure</u> of informal employment is estimated using the LFS and includes workers that are employed but not at an enterprise in their primary or secondary job. All estimates are weighted. 95% confidence intervals are shown using dotted lines.

33 Informal Employment and Worker’s Wellbeing in Russia / June 2019
# **6. Mobility and Underlying Poverty**

In face of the sizeable income and expenditure shocks, one might wonder how long-lasting economic positions are. Do the rich generally remain rich and the poor remain poor, or do individuals frequently switch positions? The degree to which individuals keep their position in the income distribution can be measured by the correlation between this period's income and the next period's. Table 3 shows these correlations for income and expenditures in Russia and Poland.

**Table 3. Correlations in Income and Expenditure**

||R|ussia|P|oland|
|---|---|---|---|---|
||Income|Expenditure|Income|Expenditure|
|**A. Traditional correlations**|||||
|Current month and 12 months later|0.335|0.479|0.535|0.627|
|Current month and 24 months later|0.254|0.431|0.549|0.626|
|Currentmonth and 36 months later|..|..|0.457|0.569|
|Current month and 48 months later|0.266|0.380|||
|B.**Underlying correlations**<br>Correlation between_underlying_<br>income/expenditure in current<br>month and 12 months later|0.884|0.924|0.934|0.937|
|Memo: "traditional" correlation<br>over the same time period.|0.338|_0.510_|0.575|0.678|

Notes: Equivalent income and equivalent expenditure are measured in logarithmic form. In panel A, the current month is taken from **the** first of the 4 waves of the data. The first wave of the Russian data was collected in November/December of 1994 and the first wave
Policy Research Working Paper 10040 Data Triangulation Strategies to Design a Representative Household Survey of Hosts and Rohingya Displaced in Cox’s Bazar, Bangladesh _Joaquin Endara Maria Eugenia Genoni Afsana I. Khan Walker Kosmidou-Bradley Juan Muñoz Nethra Palaniswamy Tara Vishwanath_ Poverty and Equity Global Practice May 2022
to the real world.

Karachi’s socially diverse setting offered an interesting context for the VR experiment discussed in this paper. Serving as the commercial hub of Pakistan, Karachi is a megacity of 15 million people who belong to different ethnicities. According to the 1998 Census, the latest year for which ethnicity information was available at the city level, 48.5 percent of the city’s residents were Urdu speakers (Muhajirs), 13.9 percent were Punjabi speakers (Punjabis), 11.4 percent were Pashto speakers (Pashtuns), 7.2 percent were Sindhi speakers (Sindhis), 4.3 percent were Balochi speakers (Balochs), and the remaining 14.5 percent were categorized as Others (Pakistan Bureau of Statistics, 1998). While many neighborhoods are segregated based on income, the city also has clusters with ethnically homogenous neighborhoods being affected by urban violence emanating from inter-ethnic and political tensions in the recent decades. The relationship between social groups remains difficult and many residents struggle to find adequate mechanisms for their voice and inclusion (World Bank 2017, Breman 2012). Not only does the city have a shortage of public spaces that could facilitate interaction between the city’s diverse 3