rafmacalaba/data-use-mentions-extended huggingface dataset:
6.99e-06**|-5.46e-05***|-2.26e-05**|
||(9.07e-06)|(3.13e-06)|(2.01e-05)|(8.76e-06)|
|Fixed effects|YES|YES|YES|YES|
|Observations|752|755|752|756|
|R-squared|0.085|0.038|0.189|0.071|

_Note:_ Fixed effects include region and sector-specific fixed effects. For categorical variables, base categories are mentioned in parentheses. The low R-squared indicates that results should be interpreted cautiously. Standard errors are clustered on the ward level:<sup>*</sup> p<0.1<sup>**</sup> p<0.05<sup>***</sup> p<0.01

**Table D.2: Regression results for input inventory sizes, using region interactions**

||(1)<br>Input inventories|(2)<br>Input inventories|
|---|---|---|
|VARIABLES|||
|**Risk perception # region:**|**Direct risk**|**Indirect risk**|
|**Dar es Salam**(no risk)|||
|Low risk|-1.268 (9.034)|-0.543 (5.317)|
|Moderate risk|-8.514 (6.518)|-3.392 (4.730)|
|High risk|-11.58 (7.169)|-5.049 (4.866)|
|Very high risk|-16.21** (7.1)|5.992 (13.82)|

40
### **_Province 2: South Kalimantan_**

South Kalimantan, located in southern Borneo, has a population of 4.1 million people. There is a higher population of males than females, with a population sex ratio is of 102.9.<sup>13</sup> Unlike East Java, South Kalimantan is located outside Java and is known as a migrant area; males often predominate in migrant areas as a share of the overall population, since men more commonly migrate for work. Data from the population census in 2010 show that the proportion of male migrants is 58 percent of total migrants. The average GPI for NERs at primary up to senior secondary is 0.99 and the literacy rate for males aged 50 years and above is 7.5 percentage points higher than for females in the same age group. At the primary, junior secondary and senior secondary levels there are more female than male teachers at every level, with about 8,000 more females at primary, 3,000 more at junior secondary and 1,000 more at senior secondary.<sup>14</sup> Yet, only 27 percent of school principals are female. South Kalimantan has a child marriage rate of 33.7 percent (i.e., more than one-third of ever-married females aged 20-24 years), placing it fourth of 33 provinces (UNICEF, 2016).

Unlike Sampang and Bangkalan, which are in rural areas, two urban areas were selected for field visits for this study in South Kalimantan, namely Banjar Baru and Banjarmasin. The most recent data in 2018 show that the average GPI for NERs in Banjar Baru is 0.98.<sup>15</sup> Unlike the national figure where the gender gap in literacy of the younger generation is insignificant, Banjar Baru is found to have a 2-percentage-point gender gap in literacy favoring females. For the older generation, the gender gap is lower than the national average, with a 3.09 percentage point
collectivization, inefficient state enterprises, and excess money creation that led to hyperinflation (600 percent in 1986). According to the CPIA index, Viet Nam was a fragile state throughout this period. These failings were recognized by the Sixth Communist Party congress in December 1986, which launched the strategy of doi moi (“renovation”). During 1987-88, and especially in 198990, a wave of reforms created a Treasury and a two-tier banking system, opened the country to foreign investment, dismantled collective farms, encouraged private ownership in other sectors, unified the multiple exchange rates (effectively devaluing the domestic currency by some 70 percent), liberalized prices, established control over public spending (partly by demobilizing half a million soldiers), and removed trade restrictions. Despite the collapse of trade relations with the CMEA and the termination of Soviet financial assistance in 1989-90, the supply side— especially agriculture—responded strongly to the reforms. This, as well as the gradual coming onstream of oil production starting in the late 1980s, meant that Viet Nam—unlike other former socialist countries—went through a rapid economic transition without experiencing a drop in output: after a slowdown during 1990-91, GDP growth accelerated to 8-9 percent annually. According to the CPIA index, Viet Nam exited state fragility in 1989.

29
likely to understate the true scale of internal displacement in some situations (e.g. Afghanistan, Sudan) (IDMC 2015). On the other hand, in contexts with multiple displacements, there may be people who are counted more than once, e.g. DRC. Additionally, crises that are smaller or less visible tend to be underreported, e.g. displacement due to criminal violence in Central America (IDMC 2015).

- (e) Difficulties obtaining figures on IDP flows, i.e. new displacement, returns, integration, settlement elsewhere, numbers who have fled across an international border (becoming refugees) and those who have been born or who have died in displacement (IDMC 2015). Data on flows may not be collected or sufficiently disaggregated. Often, less data on flows are available once the initial emergency phase of a displacement crisis is over, especially if there are no humanitarian agencies present (IDMC 2015). IDMC reports that of the 52 countries it monitored in 2015, it was only able to obtain data on new displacements in 20 countries,<sup>92</sup> on returns in 20 countries, on integration in one country, on resettlement in two countries, on children born in displacement in two countries and on deaths in one country; no data was obtained for any county on cross-border flight in 2015 (IDMC 2016). Moreover, existing systems for collecting data on refugees and asylum-seekers make it difficult to know how many were formerly IDPs, and it is possible for some people to be simultaneously counted in both categories, e.g. in the case of the Syrian displacement crisis (IDMC 2016). If no data on returns are available, IDMC risks overstating the number of IDPs (IDMC 2015). On the other hand, in many countries returns are subtracted from population estimates without knowing if sustainable solutions have been achieved.

- (f) The potential to manipulate data for political purposes, for example to show that a humanitarian
## **3.3 Macroeconomic aggregates from National Accounts**

Finally, we use macroeconomic aggregates from the World Inequality Database (WID) to match our aggregates from micro-data to National Accounts (NA) – such that, in every year, the total amount of income observed in our dataset used to compute inequality measures matches the totals in the national accounts.<sup>8</sup> We use data from WID instead of Honduras’ national accounts estimates since the latter are not disaggregated enough to match the components used by the DINA approach. We provide more de. tail about these aggregates in Appendix C

# **4 Building the income distribution**

## **4.1 Unit of observation**

We define the unit of observation in all the analysis as individual adults (20+ years). Therefore, our definition of individual income departs from the one commonly used in household where the sum of all household income is divided all surveys among members, either equally or using some kind of weighting (the OECD guidelines, for example, weight every child as 0.3 adult). Since Honduras is an individual taxation country and there is no joint-filing by couples or households, computation of income at the top of the distribution is only possible at the individual level (“individualistic adult” approach per DINA guidelines) .

## **4.2 Income definition**

Our main set of results refers to estimates of the full distribution of _pre-tax income_ , along the lines of the DINA guidelines (Alvaredo _et al._ , 2020): income accruing to the owners of factors (labor and capital), before taxes but after distribution of social security benefits. We now provide a brief overview of the construction of pre-tax income distributions in the survey and tax administrative records, and include more details in the appendices.

### **4.2.1 Income in survey data**

We use survey micro-data to compute individual income as the sum of four
that are identified are the main sources of electricity generation. Third, the locations of the supply sources are exogenous to economic geographic development. Finally, the locations between the generation sources and the main demand centers are more likely to be electrified.

We identified two sources of energy generation that can be used for the IV strategy: dams for hydroelectricity and wind farms. The main sources of energy supply are hydropower in Ethiopia and hydropower and wind in Kenya (Table A9). Similar to Moneke (2020), we developed an IV that yields a hypothetical electrification status based on a location’s proximity to a straightline corridor from electricity generators to the main cities. We identified the locations of the electricity generators using two databases, one on the opening year of dams and another on the locations of power plants (Platts database). For Ethiopia, we used a database including all dams in Africa, their location, and their year of opening. For Kenya, we used the global power plant database, which includes the capacity and year of commissioning of all power plants by type of energy (hydro, wind, gas, and geothermal). From the year of the dam opening or power plant commissioning onward, all districts lying along the straight lines connecting the dams or power plants to the main demand centers are considered as having access to electricity. We then identified the main sources of demand in each country (Addis Ababa in Ethiopia and Nairobi and Mombasa in Kenya). Map A8 in the appendix shows the results of the construction of the Our IV satisfies the main assumptions of an IV strategy. The choice of location of hydro and wind generators can be assumed to be driven by geographic and climatic characteristics of the locations and not by economic activity in the area. The timing of opening can be considered exogenous, as years of delay are common for such projects. The random assignment assumption of the IV would imply that a district’s inclusion along a straight-line corridor is spatially and temporally as good as random assignment
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
1994; Schlenker _et al._ , 2005). Yet, a key assumption when estimating the coefficients of the climate-related variable from cross-sectional models is that climate change is not correlated with other unobservable factors. Violation of this assumption could result in an omitted variables problem,

> 5 Auffhammer _et al._ (2013) find high correlations between ERA5 reanalysis data and weather station data for temperature, which further supports our study focus on temperature. However, we appear not to have similar supportive evidence for rainfall, particularly in poorer countries with limited ground station data.

8
# **Table 2.  Change in Food Assistance Following the 2017 IPC Announcement**

||(1)<br>Share<br>Receiving<br>Food|(2)<br>Share<br>Receiving<br>Food|(3)<br>Share<br>Receiving<br>Food|(4)<br>Share<br>Receiving<br>Food|(5)<br>Share<br>Receiving<br>Food|
|---|---|---|---|---|---|
|VARIABLES|Assistance|Assistance|Assistance|Assistance|Assistance|
|Post Announcement Indicator|0.193***|0.177***|-|0.139***|0.138***|
||[0.023]|[0.025]||[0.021]|[0.021]|
|Quarter Indicators||||||
|____________________<br>Q2 2017 Indicator|-||-0.001|-|-|
||||[0.026]|||
|Q3 2017 Indicator|-||0.200***<br>[0.031]|-|-|
|Q42017 Indicator|-||0.220***|-|-|
||||[0.027]|||
|Q1 2018 Indicator|-||0.202***|-|-|
||||[0.026]|||
|Q2 2018 Indicator|-||0.252***<br>|-|-|
||||[0.036]|||
|Q3 2018 Indicator|-||0.227***|-|-|
||
share of the country’s total capital goods imports. A simple illustrative exercise indicates that a 10 percent nominal devaluation of the birr would increase the cost of government capital goods import by 6.12 billion birr, i.e. about 0.58 percent of GDP.

# **<mark>6.3    Will a devaluation increase the debt burden?</mark>**

<mark>A birr devaluation would also have balance sheet effects by raising the cost of debt servicing and public debt stock as denominated in local currency. Foreign currency-denominated public debt constituted about half of the gross public debt in 2014. External public debt stock as a percentage of GDP has been rising in recent years, which is mainly on account of the l</mark> arge debt financed imports of capital goods for public investmen <mark>t. But although public debt has been recently expanding at an unprecedented pace, it remains at a fairly acceptable level as a share of GDP. The 2013 Joint Bank-Fund Debt Sustainability Analysis indicated that Ethiopia’s risk of external debt stress was ‘low’. However, it increased to ‘moderate’ in 2014/15 due to unexpectedly high non-concessional borrowing and weak export performance, which seems to raise concerns over debt sustainability in the years ahead (IMF, 2015). If the NBE is to devalue the birr, the impact on external debt-service commitments and the overall debt stock should be assessed carefully in view of the country’s rapid debt-financed public investment expansion. A 10 percent devaluation would lead to an increase in the local currency value of annual debt service payments by roughly 1.3 billion birr (i.e. 0.13 percent of GDP) and public sector external debt stock by roughly 26 billion birr (i.e. 2.5 percent of GDP).</mark>

# **<mark>6.4    Would a devaluation engender investor uncertainty?</mark>**

There are claims that exchange rate devaluation
educational distributions of the landless and the landed, we note that very few of the landless have more than ten years of education. We notice a similar pattern using the national ARIS-REDS panel survey of rural households from 2005. Using the sample of all individuals who engaged in migration that year (that is, temporary migrants), we find that

> 15This cut-off follows the structure of education in India: At the end of lower secondary education, in year 10, an exam has to be taken and passed in order to progress, and further education often requires travelling further afield.

14
International, 2009). The assessment is a short oral assessment conducted with a child one-on-one. EGRA is designed to be flexible and adapted across countries and contexts, while maintaining core modules and similarities. EGRA is a timed test, enabling uniformity in how it is conducted. The tests often represent the most common features of the local language and align with the expectations of the grade level.

EGRA includes a series of sub-tasks that align with five phases of reading skills development: pre-alphabetic, partial alphabetic, alphabetic, consolidated-alphabetic and automatic. The subtasks in EGRA include:

- Orientation to print

- Letter name identification

- Letter-sound identification*

- Initial-sound identification

- Segmentation (phoneme or syllables)

- Familiar word reading

- Non-word reading*

- Oral reading fluency*

- Reading comprehension*

- Cloze

- Listening comprehension*

- Vocabulary

- Diction

Of these there is a sub-set of ‘core’ subtasks encouraged to be delivered across all countries and contexts marked with an asterisk above (Dubeck and Gove, 2015).

In our analysis, we analyze two specific EGRA sub-tasks: oral reading fluency (ORF) and reading comprehension. Both are core sub-tasks and therefore found across nearly all EGRA assessments. Moreover, both are conceptually linkable indicators supported by the theoretical and empirical literature.

The use of EGRA data hinges on using measures of comparable learning metrics, even if not perfectly equivalent. This approach is consistent with the approach taken by Dubeck and Gove (2015). Comparable measures must have a conceptual link to other reading assessments used in ISATs and RSATs. Moreover, indicators should be relatively invariant and robust to a myriad of changes that might occur across contexts such as language, timing, and details of implementation. For example, phonics is likely to differ across languages and is not conceptually linkable to other assessments. For this reason, we do not use the ‘letter-sound identification’ sub-task even though it is a core EGRA sub-task.

ORF scores have been used to make cross-country comparisons in the literature and have shown relatively high validity and reliability (
<!-- Start of picture text --> Figure 6. Employment Kernel Density Functions of Exporters and Non-Exporters in<br>Egypt<br>a. 2020 b. 2016<br>0 2 4 6 8 0 2 4 6 8 10<br>ln Employment ln Employment<br>Exporter Non-exporter<br>Exporter Non-exporter<br>c. 2013<br>0 2 4 6 8 10<br>ln Employment<br>Exporter Non-exporter<br>.6 .5<br>.4<br>.4<br>.3<br>.2<br>Kernel Density .2 Kernel Density<br>.1<br>0 0<br>.5<br>.4<br>.3<br>Kernel Density .2<br>.1<br>0<br><!-- End of picture text --> Source: Author calculations using 2013-2016-2020 panel, Enterprise Survey for Egypt.

38
income, including agricultural finances (Malapit et al. 2020), although men are often given more power over major budgetary decisions such as buying and selling land (Ashraf 2009). In agricultural households, women are expected to be generalists, making decisions over family matters, finances, and some elements of agriculture, while men are expected to be specialists in farming (Parks, Christie & Baganes 2015). A further relevant data point to contextualize our results is the existence of strong social norms in favor of consultative decisionmaking between spouses, although men still dominate agricultural decision-making in most households (Arugay et al. 2021).

# <u>3.2. Data Set</u>

The data set used in this paper comes from a detailed spousal survey carried out with farming couples in the Philippines from February to May of 2018. Part of the baseline survey of a larger impact evaluation on land reform, all couples interviewed were landowners, having received at least one agricultural parcel from CARP. Local enumerators interviewed a sample of 997 respondents in 570 agrarian reform beneficiary households, of which 422 were matched monogamous couples.<sup>3</sup> Only married respondents or respondents in common-law unions<sup>4</sup> were interviewed. Most of our sample was interviewed across six provinces of the southern island of Mindanao, while 72 households were interviewed in the Bicol Region of southeastern Luzon. As our sample was drawn strictly from former tenant farmers who had received land parcels at least 10 years prior to the survey, it skews older and poorer than the Philippines population overall (see Appendix Table 1). Thus, our results should be considered representative only of the population sampled. Most households in our sample (82.2%) lay below the 2018 national per capita food poverty income threshold of 7,337 pesos per month.

Spouses were interviewed simultaneously, with enumerators visiting households in pairs and administering questionnaires to male and female spouses in separate areas where they could not hear or influence
and (4) institutional/market structure variables (size/scope of the social security system, the level of financial development, the share of state and foreign insurers, market concentration., and the quality of the legal/regulatory framework). The second column indicates the expected impact of the variable on the life insurance sector, and the four following columns show the signs of the estimated coefficients in recent studies: positive (+), negative (-), or not significant (N/S). Shaded cells indicate that the variable was not included in the particular study.

Income is a central variable in insurance demand models. Higher income should increase the demand for life insurance for several reasons, including the greater affordability of life 4
yield risks. However, if a farmer’s yields are perfectly correlated with the index, the index-based plan will provide perfect coverage for their yield risks.

However, index-based plans may offer some advantages. A greater availability of data will likely allow insurance premium rates and other parameters of the contract to be measured with greater accuracy. This limits the problems associated with adverse selection. Index-based plans also lessen concerns of moral hazard. If an individual farmer is so small as to be unable to affect the overall index, there is no potential for moral hazard since the agent’s actions cannot influence the likelihood that indemnities will be paid. In addition, reflecting the fact that such index-based plans are much cheaper to administer since individual producer records are not needed and loss adjustment need only be done at the aggregate level, index-based plans can generally be offered at a lower cost.

It may also be the case that there is a lag in measuring the index, generating a delay in settlement of claims. Under the Indian crop insurance program, for example, the delay is close to one year. It is caused by the time taken to carry out crop cutting experiments and, in some cases, delayed flows of funds from the state government. The delays prevent the restoration of credit eligibility of farmers for the following crop season (World Bank 2003). Likewise, indemnity payments under the U.S. area yield plan, are not made until the following year due to the lag associated with the measurement of area yields.

# **_2.2.  Specific Peril versus Multiple Peril versus All Peril Coverage_**

An important distinction in the nature of coverage offered under alternative crop insurance plans pertains to the types of hazards that are insured against. Some forms of insurance are specific peril, meaning that only losses from a pre-specified peril are insurable. A prominent example is hail insurance, which will pay only for those losses that are caused by hail damages. Fire insurance is another example. Alternatively, crop insurance may