rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4 Online Experiment Methodology and Results**

## **4.1 Methodology**

The design of the online experiment was based on prior surveys about COVID-19 vaccine acceptance conducted by the World Bank in 2021 (Kovacevic et al., 2022; Bidani et al., 2022). It took place via Facebook from March 4 to April 6, 2022. Participants were recruited using Facebook advertisements (see example in Appendix A3) and were taken to a chatbot in Facebook messenger to complete the survey (see example of how the questions appeared in Appendix A3). Advertisements to recruit people to this study reached approximately 825,000 Facebook users and 10,837 people clicked on an advertisement to begin the survey. The sample was drawn from all Facebook accounts in Zambia over the age of 18 and quotas were applied based on demographic characteristics (age, gender, and region) to ensure adequate representation in the sample. At the time of the online survey, it was estimated that almost one-third of adults in Zambia have a Facebook account (Napoleon Cat, 2022), but these individuals were more likely to be younger, male and in urban areas compared to the general population. To address this issue, survey data was weighted using inverse probability weights based on age, gender, and region calculated using the most recent Zambian census from 2010 (see Appendix A3 for details). As a robustness check, the main experimental findings are calculated without survey weights and the results (i.e., the sample average treatment effects) are qualitatively similar to those presented in the body of the paper.

The treatments in the online experiment were informed by the findings of the in-person survey, existing studies on vaccine acceptance, and were also tailored to suit the Zambian context based on consultation with local stakeholders. Two similar, but distinct treatments were included to explore whether any differences existed between messages emphasizing benefits at an individual versus household level from getting a vaccine. Efforts were made to ensure that the treatments
>UGA: plot is equal to the<br>parcel ID in the**Plot-level**<br>**dataset.**Parcel ID is set to<br>missing to prevent confusion|NGA, TZA, MWI w1: absent<br>Can be merged with the following<br>variables in the raw data<br>(concatenated variables are separated<br>by hyphens):<br>ETH: concatenation of_holder_id_, and<br>_parcel_id_(waves 1 to 5)_,_absent for<br>households answering section 12c<br>(wave 5)<br>MWI: concatenation of_y2_hhid_and<br>_garden ID_(wave 2)_,_concatenation of<br>_y3_hhid_,_garden ID_(wave 3)_,_<br>concatenation of_y4_hhid_,_garden ID_<br>(wave 4)<br>MLI: concatenation of_grappe_<br>_menage, bloc_(wave 1 concatenation<br>of_grappe_ _menage, bloc_(wave 2)<br>NER: concatenation of_hid, field_<br>_number_(wave 1)_,_concatenation of<br>_GRAPPE, MENAGE, EXTENSION,_<br>_champ_(wave 2)<br>UGA: concatenation of_Hhid, parcel_<br>_ID_(wave 1)_, HHID , parcel ID_(wave<br>2)_, HHID, parcel ID_(wave 3)_,_<br>_HHID, parcel ID_(wave 4)_, HHID,_<br>_parcel ID_(wave 5)_, hhid, parcel ID_<br>(wave 7)_, hhid, parcel ID_(wave 8)|

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treatment and supply|2124|1680|79.1|
|5|Financial and insurance activities|4380|3624|82.7|
|6|Manufacturing|1992|1668|83.7|
|7|Information and communication|3576|3084|86.2|
|8|Construction|1776|1560|87.8|
|9|Agriculture; forestryand fishing|1464|1296|88.5|
|10|Accommodation and food service activities|1896|1680|88.6|
|11|Miningandquarrying|1488|1356|91.1|
|12|Transportation and storage|1908|1800|94.3|
|13|Human health and social work activities|3180|3012|94.7|
|14|Education|3024|2892|95.6|
|15|Other service activities|2088|2016|96.6|
|16|Public administration and defense|2808|2712|96.6|
|17|Professional, scientific and technical activities|3240|3132|96.7|
|18|Wholesale and retail trade; repair of motor vehicles and<br>motorcycles|1980|1992|100.6|
|19|Arts, entertainment and recreation|2004|2148|107.2|
|20|Administrative and support service activities|2232|2520|112.9|
|21|Real estate activities|4044|n/a|n/a|

Source: ILOSTAT database, International Labor Organization Overall, the participation of women in the Bangladesh economy may have increased, but it is mostly in low-paid and low-skilled jobs. The readymade garment industry is the main employment option for women outside agriculture. However, not only women earn much 13
# **4.3 Control Variables**

## **Financial Resources (Sectoral Grants)**

In Sierra Leone, the grant transfer system provides largely for tied sectoral grants where the vertical envelope is fixed for each year. This implies that the quality of decentralized service delivery is likely to be, in large part, a function of the amount of funding the council receives from the central government for performing its mandated functions. For instance, as early as 2006, one quarter of the national health budget was transferred to local councils (Zhou, 2009). Accordingly, we control for the log of the amount of sectoral transfers to district councils for 2006 and 2007. We include the following itemized transfers: education, birth and death registrations, and health. We use official data on fiscal transfers provided by the Local Government Financial Department, Ministry of Finance and Economic Development, Sierra Leone (Local Government Finance Department (LGFD), Ministry of Finance, 2011).

## **Socio-Demographic Variables**

We control for standard socio-demographic variables that can affect citizens’ evaluations of government including age, gender, education and religion. We include several reasonably good proxies for income, including whether any of member of the household owns the following items: a bicycle, a mobile phone, a generator, a radio or cassette player, and, a television, and a car, truck or motorcycle. We also include an indicator of whether respondents or a member of their household had malaria within the past year. We include an indicator of the type of material of the roof, walls and floors of respondents’ current residence. We also control for the size of household and marriage status, since these are demographic factors that household resources.

Living in a remote and marginalized area might affect citizens’ access to public services, as well as the quality and frequency of their interaction with local authorities, and, consequently, their beliefs toward these authorities. Thus, we also control for the remoteness of respondents
This is also true for settings like Egypt that depend on a mix of resources, remittances, canal fees, and aid rents.

23. Much of the MENA region continues to be characterized by a limited degree of transparency, for example as captured by indices of press freedom and budget transparency presented in Table 3. The region‘s regimes at the end of 2009 were also characterized by a high degree of continuity. In part of buoyant commodity prices, basic rates of growth have been respectable. These indicators have however served to mask a number of the strains and figures that are now increasingly coming to light as part of the ―social media‖ contagions in the region, including around accommodating the aspirations of a new generation driven by relatively high rates of population growth, education, but limited jobs opportunities in the region. As we show, resource rents are likely to continue to condition the political economy trajectories of the region. Greater transparency are likely to be an important ingredient for reforms in the region, but a deeper challenge will be how to balance pressures for greater inclusiveness with the ability to strike new credible political bargains across groups and time.

**Table 3: Institutional Characteristics of MENA**

|||**Leaders**<br>Years in|**hip & Ins**<br>Capacity<br> <br>|**titutional**<br>Policy<br>Coordination|**Quality**<br>Political<br>Inclusiveness|Dev.<br>Orientation|**Opacity**<br>OBI 2008|FH Media|**Vulnerabi**<br>P<br>|**lity**<br>op Ch<br>% p.a<br> <br>|GDP<br>Grow|GDP<br>Grow|GDP<br>Grow|
|---|---|---|---|-
0.044|2126|
|Physical condition of health facility|0.265|-0.042|0.270|0.146|2126|
|Ability to provide high quality of care|0.681|-0.005|0.146|0.182|2126|
|Salary|0.068|0.081|0.124|0.443|2126|
|Overall satisfaction|0.650|0.115|0.088|0.265|2126|

Note: data from interviews of health providers in both rural health centers and health houses. All regressions are the difference-in-differences specification with facility fixed effects. All regressions include controls for facility type, age and gender of providers, position, a binary indicator for experience of over 20 years and an indicator for being born in the district. Standard errors are clustered at the district and survey round level with wild bootstrapping.

For each category, health providers responded whether they are satisfied, unsatisfied or neither. The regressions are run on binary variables indicating the respondent reported satisfaction. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 34
(Bagger et al., 2014).

The previous results refer to February 2020, which was between 2 and 5 months after program completion. Additionally, we also gathered employment information referred to May 2020. This provided us with a unique opportunity to measure whether the program effects make beneficiaries more or less resilient to the onset of the COVID-19 crisis. The evidence suggests so, especially for the sub-sample of the self-employed.

From a short-term perspective, our results are encouraging. We interpret them as proof that the program is effective at making its target population break a barrier of entry into employment in _tech_ in relatively little time. It does so by, first, designing a strong intervention that effectively tackles barriers that could prevent program participation. Then, we show that it imparts a valuable set of skills that is sought by employers in the sector, which ultimately translates into an increase in the probability of finding a job in technol26
# **1. Introduction**

Crises have profound, uneven and long-term effects on aggregate productivity and employment recovery (Reinhart and Rogoff, 2009; Dell’Ariccia et al., 2008). The COVID-19 pandemic, in particular, had an extensive and heterogeneous effect on firms’ operations and performance based on their size, sector, age, and other firm attributes (Apedo-Amah et al., 2020; Cirera et al., 2021). The degree of informality in the context of South Asia has also been a key factor contributing to such differences in effects (Grover and Pereira-López, forthcoming), given its salience in the region (Bussolo et al., 2020). Leveraging three rounds of the World Bank’s Business Pulse Surveys (BPS) in South Asia conducted during the pandemic from May 2020 to August 2022, this paper quantifies the relationship between informality and firms’ investment decisions that may have implications for post-crisis job recovery. Our work suggests that lower investments and employment response among informal firms are potentially attributable to their acute credit constraints and heightened perceived uncertainty.

Evidence from the Great Recession shows that shocks to the financial system can explain the trade collapse (Cetorelli and Goldberg, 2011; Chor and Manova, 2012) and output declines, especially in industries that rely more on banking finance. Such effects are stronger among firms with fewer assets that can serve as collateral or those with limited access to inter-firm finance (Bricongne et al., 2012; Görg and Spaliara, 2014; Iacovone et al., 2019; Paravisini et al., 2015). Although the COVID-19 crisis did not originate in the financial sector, the pandemic-induced mobility restrictions led to supply and demand shocks, and the conditions slowly morphed into a financial recession (Reinhart, 2022). With the increase in risks and uncertainty during the crisis, creditors become more risk-averse and are reluctant to lend in the face of imperfect information regarding default likelihood (“flight to quality”) (Dell’Ariccia et al.,
of the state of origin at the time of migration (2015) are also included as regressors.** Among these variables are labor market conditions (unemployment rate, employment rate, and informality rate), deprivations in non-monetary dimensions (deprivation index), the incidence of crimes, perceived levels of corruption, preschool education supply, and the percentage

> 13 According to the 2020 Census, only 1.1 percent of the population has a master's degree, and 0.2 percent have a Ph.D.

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# 3. Who Benefits from Fertilizer Subsidies in Indonesia?

The incidence of benefits from fertilizer subsidies is analyzed using two different rural household surveys by BPS: the Agriculture Census 2003 and the BPS Rice Household Survey 2008. The first survey comprises a random sample of 46,144 farmers for the rice subsector in 29 provinces. It was collected in 2004 and asked farmers about their usage of inputs in 2003. The latter survey entails data from a smaller sample of 11,297 rice farmers from 15 provinces regarding the harvest in 2007 (covering the 15 largest rice producers in Indonesia and collected in 2008).

## _Methodology_

The incidence analysis looks at two types of fertilizers, urea and SP36, and estimates the spending share captured by farmers according to their land size. The subsidy is defined as the difference between the market price for fertilizer and the actual prices paid by farmers as reported in these two BPS surveys. In addition, the GoI established a ceiling price for fertilizer (HET) that is below the market price and market prices are reported by the State Audit Agency (BPK), the ‘cost of goods sold’ or CoGS.<sup>2</sup> Thus, one can estimate if farmers received subsidy benefits by looking at their paying prices. Those farmers reporting paying at the HET captured fully subsidized fertilizer, while those farmers paying below the market and above the HET, received partial subsidy assistance. The analysis looks at the volume of fertilizer used and at what price and divides the farmers in 5 equal quintiles by the size of their total agricultural land (where quintile 1 groups the smaller-plot farmers and quintile 5 the largest).<sup>3</sup> By aggregating the amount of subsidy spending captured by each farmer in a quintile, one can see how much subsidy assistance benefited each group. (See Annex I for a more detailed discussion on the methodology.) Small farmers on average use more fertilizer per hectare. In 2007, the smaller rice producers used twice as
# **Defining the lag structure of impacts**

Exporting involves lags between the product departure from the exporting country and arrival to the destination country. Since we rely on import data by major destination countries to study the impacts of COVID-19 incidence in the exporting country, it is crucial to carefully define the lag structure for the impacts. For example, identification critically relies on understanding if India’s exports recorded by the US (as imports) in October 2020 were expedited from India in June, July, August, or September 2020. Previous studies argue that shipment times average 2-3 months (Brincogne et al., 2012). But these times depend on the transportation mode which varies with the type of product. Continuing with the example, we may mistakenly attribute India’s September 2020 COVID-19 incidence to products already expedited in July 2020. This is supported by evidence from Flaaen et al. (2021) who use daily data from bill of lading information for US imports and Indian exports to show that India’s national lockdown announced on March 24, 2020, led to a decline in India’s exports to the US recorded in US import data only 5-10 weeks later due to shipping lags.

Consequently, we apply a product-specific origin-destination country lag structure for the COVID-19 incidence variable’s impact on trade combining US Census data on HS4 imports by transportation mode in 2015 with searates.com data on shipping days between capital cities as of early 2020 as follows:

- (i) For HS4 products whose share of imports by air transport is above 75 percent, a one-month lag is used;

- (ii) For the remaining HS4 products, the lag length depends on the number of shipping days between the country pair: one-month for fewer than 7 shipping days, two-month for 7 to

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budget of $13.2 million in 2018, rising to $13.6 million in 2019.<sup>60</sup> Estimates for compliance with U.S. privacy law are wide-ranging, but generally significantly lower.

The review below shows that compliance with the GDPR for large firms is quite expensive. Our survey respondents generally ranked the EU privacy regime to be the costliest of the three frameworks. They described the US as less expensive, whether for large or small firms, and compliance with Chinese privacy law as the least expensive, though that may be because of lack of awareness of the law. Among our respondents, cybersecurity costs seemed to be more significant with respect to Chinese and U.S. law compliance, than with EU law compliance. EU compliance costs seem to be heavily skewed towards personnel, both inhouse personnel and outside consultants.

As the wide ranges might suggest, the data is inherently limited. There is no consistent framework for analyzing the costs of compliance with data privacy laws. Every study seems to adopt its own methodology. One study, for example, breaks down costs as consisting of 1) the costs of granting access to data gathered on each consumer, 2) the costs of providing notice of privacy policies, 3) the costs of obtaining individual consent, 4) the costs of creating greater transparency, and 5) the costs of granting customers choice, including that of opting out or opting in to the database.<sup>61</sup> Another study meanwhile identifies the following components of data privacy costs: data protection and enforcement activities, incident response plans, compliance audits and assessments, policy development, communications & training, staff certification, redress activities, investments in specialized technologies to protect data assets such as threat intelligence, managed file transfer, identity and access governance, cyber analytics, data loss prevention, and encryption.<sup>62</sup> The studies are based on surveys of selected participants, which of course reflect who
The main variables in this analysis come from the woman’s questionnaire where a number of anthropometric measures are collected.

We make use of adult height as our main health variable. The micro‐econometric literature often uses adult height as a proxy indicator for overall health and long‐term adult well‐being since it reflects the accumulation of shocks to health through childhood and adolescence. A rough consensus drawn from this literature is that an improvement in health associated with a 1‐centimeter increase in adult height raises productivity by 3.4 percent (Kraay, 2018). Respondent’s height is reported in centimeters in the DHS data.

The DHS also records how long the individual has resided in the current location. We utilize this information to restrict the sample to only those women whose birth‐place coincides with the current location. This allows us to guard against the possibility of mis‐measuring exposure to nitrogen pollution if the woman was born in a different location than the location in which she currently resides. We trace the birth‐year histories of all adult women ranging from 1966 to 1999, a period when the effect of the Green Revolution was already in force yet nitrogen fertilizers were still increasing in use.

# 2.2 Water Quality Data

The Central Water Commission (CWC) within the Ministry of Water Resources monitors data on ambient water quality throughout the country. We compile and harmonize a rich data set of water pollution measurements between the years 1963‐2017 along a network of 375 river monitoring stations throughout India. Many gaps in the data exist since the pollution measures are not consistently recorded over the entire sample time frame and the panel is unbalanced. Over the 1963‐2017 period, 75% of water quality data are missing (60% between 1986‐2017). To circumvent these problems, we build on a new class of spatial statistical network model for stream data to interpolate and fill in missing observations across monitor‐year pairs (ver Hoef and Peterson, 2010). The model takes advantage
_The Global Information and Communications Technology Industry: Where Vietnam Fits in GVCs_

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