rafmacalaba/datause-encoder-data huggingface dataset:
urban and state/region levels. The survey was administered to approximately 23,500 households. Data on child work was gathered on individuals aged 5-17.

In the analysis we use a broad definition of child labor, embracing children engaged in wage work, unpaid family work, farming and self-employment. We do not focus on the distinction between working children and child labor as we are interested in understanding if working affects schooling outcomes regardless of whether or not the work is classified as child labor. We also consider a child as working if he or she is engaged in any one or more than one of the following household chores: cooking, shopping, cleaning, washing clothes, household repairs, childcare, elderly care and other household tasks.

For a robust analysis, this study uses two categories of outcomes: those related to work and those related to education. The first covers participation in wage labor, household chores, and any type of work (either wage labor or household chores), and the time spent doing these activities. The second covers school enrollment and attendance, the latter being a binary variable on attendance during the past week, equal to 1 if the child attended at least three days of school in the past week and 0 otherwise. We use a bivariate probit regression to estimate the joint probability of a child’s participation in wage work, household chores, or any type of work while simultaneously deciding to be enrolled. The bivariate probit model allows us to focus on the trade-off between current income and higher future income that is the return from education. Implicitly, we have a two-period model. In the first period, parents choose either to invest in children’s education or to send them out to work and gain from their earnings. In the second period, parents become inactive and rely on the economic support of their children, whose incomes depend on educational level. Given gender bias in the labor market, this model is often used to explain lower school enrollment for girls. Low expectation of returns to education – due to low quality, poor performance, or job market opportunity – may incline parents to invest little in education. Similarly, preference for present income as opposed to uncertain future income may also lead to scant investment in education.

We use a basic specification for the bivariate probit model in which a structural equation determines the joint model for the two binary outcomes (work and enrollment), which can be correlated with the correlation coefficient even though the decisions are not made at the same time: households decide at the beginning of the school year to enroll or not enroll their children in school and decide later whether to send their children to school or out to work. If the two binary outcomes are not correlated, the two probit models can be estimated separately. However, in the case of a non-zero correlation ( ρ≠0 ), the two models would be better estimated under a joint distribution using a bivariate probit. A significant 7
farmer. This suggests the possibility for youth to be over-optimistic about the odds of succeeding with backway migration, if stigma means that failed migrants do not talk much about their experiences.

# **3. The interventions**

## **3.1 Details of the interventions**

The study randomly assigned males aged 18 to 30 living in rural Gambian settlements (villages) to one of four different groups, with random assignment at the settlement level. The four different groups consist of three different treatment groups (information and deterrence; information and Senegal as a migration alternative; information and vocational training) and a control group, who are delivered the following interventions.

**_Treatment 1 (information and deterrence)_** consists of providing information about the risks involved in migrating irregularly to Europe. Information was conveyed in the form of a video documentary. All videos were dubbed into the three major languages spoken in the regions: Mandinka, Fula, and Wolof. Respondents were shown the videos in their preferred spoken language towards the end of the baseline survey on tablets. This way we could monitor and ensure the video was watched by each study participant in this treatment group. The video participants included members of an NGO, Youth Against Irregular Migration (YAIM) formed by failed irregular migrants to Europe, deportees, and Gambian irregular migrants residing in Italy. They narrated their individual migration experience en route to Europe. The main information conveyed included reasons for migrating, how they obtained information about the trip, duration of the trip, and experience during the trip including witnessing accidents, deaths, kidnapping and general advice to prospective migrants. The video documentary was supplemented with an animation video that conveyed facts about the migration journey. The facts include the average cost of the migration, duration of the trip, probabilities of dying en route, probability of obtaining residence/asylum status, and the chances of being deported back to The Gambia. This information was gathered from reports published by North African Mixed Migration Survey (MHub, 2017) and the Washington Post. The reports document information on deaths of migrants along the route and the detailed reasons for the deaths, abuse and kidnapping incidences experienced by migrants through the Central Mediterranean Route. Information about residence/asylum status was obtained

15
# **Panel C. Log GDP per capita**

# **Panel D. COVID-19 Policy Stringency Index**

<!-- Start of picture text --> 0 20 40 60 80<br>7 8 9 10 11 Average over the period by country of Oxford Stringency Index<br>Natural logarithm GDP per capita High Inc EAP ECA LAC<br> EAP ECA LAC MENA SA SSA MENA SA SSA<br>100 100<br>80 80<br>60<br>60<br>40<br>40<br>20<br>20 Financial Policy Response Activity Index<br>Financial Policy Response Activity Index<br>0<br>0<br><!-- End of picture text --> Source: World Bank COVID-19 Financial Sector Policy Response Database, Hale et al. (2020); World Health Organization (WHO) and World Bank World Development Indicators; authors’ calculations. Note: For the Policy Stringency Index (Panel D), the chart shows the average between January 30<sup>th</sup> and September 1<sup>st</sup> , 2020.

# **3 Determinants of the Financial Sector Policy Response to COVID-19: Methodological Framework**

# _3.1 Modeling the time until the first policy measure is taken in each category_

The variable of interest is the time elapsed (in days) between the date when a country undertook its first policy measure in a specific category (e.g., _Banking Sector_ ) and January 30, 2020, the date when WHO declared COVID-19 to be a Public Health Emergency of International Concern (PHEIC).<sup>10</sup> An “event” is recorded the first time a country takes a policy measure in one of the four categories described in section 2.1. Thus, we are analyzing four separate events by following each country during the period between January 30, 2020, and September 1, 2020, the end of the study period. Countries that have not (yet) implemented a policy in a specific category as of September 1, 2020 are right-censored.<sup>11</sup> 10 The timing of the spread of the virus was uneven across different regions and, therefore, advanced economies that were affected by the pandemic first might have also been the first to respond. At the same time, many countries had very few or no cases at all, but their financial systems were exposed through cross-border financial and economic linkages with the rest of the world (i.e. spillovers from other economies under lockdowns). Therefore, we chose to use January 30, 2020 as a starting point, the date the WHO declared the COVID-19 outbreak to be a Public Health Emergency of International Concern, and many countries started responding (often preemptively) to the health and economic crisis.

11 In econometric models that model durations, right censoring arises when the specific event under analysis has not transpired during the study period (but it could occur at a later point in time). Other useful concepts for our analyses are “state”, that is whether a country has already taken an action or otherwise; “transition” or moving from no action to implementing a specific policy; and “duration” (also known as “spell length” or “lifetime”) or the time taken to implement a policy.

16
**Table 6. Impacts on total final demand and household demand for goods and services due to the carbon tax (% change from the baseline)**

||Scen|ario 1|||Scen|ario 2|||||Scen|ario 3||||Scen|ario 4||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|r|||Cas|e a|Cas|e c|Ca|se e|Ca|se a|Cas|e c|Cas|e e|Ca|se c|Ca|se e|
|Secto|Total final<br>demand|Household<br>demand|Total final<br>demand|Household<br>demand|Total final<br>demand|Household<br>demand|Total final<br>demand|Household<br>demand|Total final<br>demand|Household<br>demand|Total final<br>demand|Household<br>demand|Total final<br>demand|Household<br>demand|Total final<br>demand|Household<br>demand|Total final<br>demand|Household<br>demand|
|C1|1.16|1.14|0.46|0.44|0.09|0.07|0.61|0.46|1.08|1.07|1.05|1.04|1.33|1.25|0.52|0.25|0.59|0.31|
|C2|1.45|0.98|0.63|-0.26|0.14|-0.91|2.04|-0.16|1.29|0.52|1.21|0.33|2.20|0.53|2.20|0.32|2.31|0.13|
|C3|-0.52|-0.85|0.06|-0.23|0.31|0.05|2.41|2.22|0.13|-0.07|0.40|0.26|1.92|2.00|-0.13|-0.59|-0.31|-0.88|
|C4|-6.79|0.00|-4.65|0.00|-3.59|0.0|-6.73|0.0|-5.16|0.00|-4.49|0.0|-6.75|0.0|-5.74|0.00|-7.10|0.0|
|C5|0.70|0.59|0.34|0.27|0.12|0.08|0.92|0.76|0.77|0.72|0.79|0.76|1.32|1.29|1.28|1.25|1.41|1.40|
|C6|0.63|0.32|0.69|0.42|0.64|0.41|2.07|1.32|0.97|0.65|1.07|0.76|2.14|1.55|2.78|2.26|3.11|2.55|
|C7|0.00|-0.16|0.32|0.28|0.45|0.46|1.53|1.68|0.35|0.50|0.48|0.77|1.29|1.90|0.55|0.45|0.58|0.48|
|C8|-7.8|-14.8|-6.9|-13.2|-6.5|-12.5|-7.3|-15.1|-7.0|-13.4|-6.7|-12.8|-7.4|-14.6|-5.7|-11.7|-7.7|-15.3|
|C9|1.16|0.36|0.65|0.26|0.32|0.18|2.25|0.81|1.09|0.52|1.06|0.57|2.16|1.19|2.58|1.72|2.77|1.96|
|C10|0.45|0.16|0.38|0.18|0.32|0.18|0.63|0.03|0.45|0.41|0.44|0.48|0.56|0.54|0.76|0.99|0.81|1.20|
|C11|0.58|0.15|0.28|0.13|0.09|0.10|0.95|0.43|0.53|0.32|0.51|0.38|0.96|0.85|1.17|1.31|1.24|1.53|
|C12|-0.28|-0.05|-0.08|-0.04|0.02|-0.03|-0.27|-0.02|-0.15|0.16|-0.10|0.22|-0.15|0.46|1.05|2.68|1.24|3.14|
|C13|-6.9|-13.3|1.6|1.3|6.3|9.4|-6.6|-14.3|-1.2|-2.1|0.8|2.0|-6.7|-13.7|-7.0|-14.2|-7.1|-14.5|
|C14|-12.9|0.0|-5.5|0.0|-1.3|0.0|-12.8|0.0|-10.1|0.0|-9.1|0.0|-12.7|0.0|-13.1|0.0|-13.5|0.0|
|C15|0.30|0.58|0.19|0.35|0.14|0.21|0.33|0.83|0.25|0.68|0.23|0.72|0.34|1.31|-0.14|0.57|-0.16|0.66|
|C16|-0.65|-1.34|-0.34|-0.69|-0.21|-0.40|1.23|1.46|-0.25|-0.72|-0.08|-0.45|1.23|1.42|0.01|-0.59|-0.17|-0.92|
|C17|0.10|0.78|0.13|0.39|0.13|0.17|0.58|0.74|0.22|0.71|0.24|0.65|0.67|1.15|0.36|0.25|0.38|0.36|
|C18|-0.01|-0.05|0.03|0.14|0.04|0.21|-0.01|-0.03|-0.17|-0.95|-0.27|-1.47|-0.24|-1.31|-0.11|0.32|-0.12|-0.66|

30
# **4. Discussion**

Results from this experiment show that phone reminders can be a cost-effective way to increase survey response rates. That is, 55 out of the 110 people we were able to reach ended up filling out the survey. Given a labor cost of $200 per day – and two days of calling the treatment group – the cost per additional respondent is $7.27.<sup>12</sup> What is more, given the large TOT effect, (very) high response rates are within reach. This opens up the possibility of conducting sample surveys more regularly instead of population surveys. That is, because of low response rates for World Bank surveys, sampling does not guarantee representativeness. Furthermore, since the financial cost of sending out the survey to the whole population is low, such an approach is often taken. But this strategy does not internalize externalities such as survey fatigue and (high) opportunity costs of filling out questionnaires.<sup>13</sup> Knowing that high response rates are attainable, World Bank surveyors may turn to sample surveys more often, therefore reducing survey overload.

While these results have high internal validity, caution is required when generalizing the findings to other situations/surveys. Some important situational variables need to be taken into account. First, the survey topic was about one of the World Bank’s corporate goals. This may have raised additional interest from survey participants. Second, the timing of the survey was specifically chosen as to ensure staff availability. Third, the majority of World Bank staff are proud to work at the WBG and are motivated to contribute more than what is expected (World Bank, 2016). Such personal characteristics – which are

> 12 However, a more effective targeting strategy may further increase the cost-effectiveness of the phone reminders. 13 In an email conversation with an IEG Director, the World Bank’s Chief Economist raised the issue of high opportunity costs.

8
**Table 4 – Cross-section (between-person) reservation wage gap (%)**

|||**Ma**<br>|**tching opti**<br>|**ons**<br>||
|---|---|---|---|---|---|
||Default|Caliper<br>(0.0001)|Nearest<br>neighbor<br>(10)|Nearest<br>neighbor<br>(50)|Caliper<br>(0.0001),<br>Neighbor<br>(10)|
|**Entire sample**|15.66***|15.64***|13.58***|12.76***|13.41***|
|||**By educatio**|**n**|||
|**Primary**|22.52***|22.53***|20.61***|18.51***|20.95***|
|**Secondary**|14.74***|14.84***|11.70***|9.60***|11.87***|
|**Tertiary**|9.00***|9.09***|7.73***|6.15***|7.83***|
|||**By field**||||
|**General programs**|20.48***|20.38***|17.18***|14.75***|17.17***|
|**Humanities and arts**|9.19*|9.27*|7.81***|5.45***|11.80***|
|**Social sciences**|14.10***|13.93***|10.68***|8.56***|9.00***|
|**STEM**|8.35*|8.25*|9.34***|8.21***|8.65***|
|**Agriculture**|21.81***|22.34***|17.10***|12.50***|17.57***|
|**Health care**|11.46*|10.52*|12.20***|11.37***|10.94***|

_Source: Authors’ calculations. Note: *,** and *** denote statistical significance at the 10, 5 and 1% level,_ _<u>respectively. Population weights accordingly used.</u>_ In the second comparison, we compare the reported reservation wage of unemployed individuals with their potential market wage. The latter is not observed, as they are unemployed. We rely on the approach of Prasad (2003) and use predictions for the market wages, based on observed ones of employed individuals. We first run a Heckman-type (1976; 1979) two-stage Mincerian function to be able to predict market wages based on observable human-capital characteristics, accounting for the selectivity bias. The Mincer-equation results, corrected for selectivity, are provided in Annex 3.

The findings about the declining reservation wage gap are largely corroborated. **Figure 5** plots the reservation wage gap against the potential market wage, which could be considered a conceptual measure of skills endowment. There is a clear negative relationship between the gap and the skill level. For the highly-skilled individuals, the reservation wage gap is prevalently negative. For these individuals, the value of employment may be high enough so that their propensity to accept jobs even below their reservation wage is high. This could be related to the value they assign to their investment in higher education and the (fear of) accelerated depreciation of their human capital, but also to the greater understanding of market potential. By contrast, for large fraction of low-skilled individuals, reservation wages are significantly over the potential market wage, presenting an obstacle for lowskilled individuals into finding and accepting a job.

19
study: transfers between program schools<sup>32</sup> , transfers between program schools and non-program schools, and hidden transfers. A hidden transfer is the enrollment of a baseline child in a school that is not the school to whose baseline she belongs. Transfers between schools are fairly easy to control for with the intention-to-treat estimator, because the data are su¢ ciently informative. We can distinguish between children who were absent because they had transferred and children who were just absent that day. During school participation checks, the ...eld sta¤ read all names of children previously found in the school, and the teacher (or the children who were present in the class) informed whether the child was absent, had dropped out or had transferred to another school. Similarly, the ...eld sta¤ asked where the children who were new to school came from, whether they came from home (i.e. whether they were ...rst-time enrollers), or whether they came from another school.

The ...rst class of transfers are transfers of baseline children between program schools. For those children, the data are complete in the sense that we observe school participation over the whole period. The intention to treat estimator assigns children to the treatment status of the school to whose baseline they belong, regardless of whether the school they actually attended was a treatment school or a comparison school, and regardless of whether the school they attended ...rst was their baseline school or not.

The second class of transfers are transfers of baseline children from a non-program school to a program school or vice versa. Since the NGO did not collect information on school participation in non-program schools, there are no data on a baseline child’s attendance prior to her transfer into the program school, or, if she transferred out of the program school, after her transfer. Baseline children who transferred in or out during a particular year were included in the sample for that year. We assume that their school participation rate prior to the transfer into the program school or after the transfer out of the program school was the same as their school participation rate in the time they spent in the program school.

Overall rates of transfer between schools are quite low. In 2000 and 2001, fewer than 3 percent of baseline children aged 4 to 6 transferred out of the program schools to any school (Table 4). In addition, children who transferred were similar on most dimensions to children who did not transfer (Table 5). Children who transferred were more likely to be enrolled prior to the program, but this is not surprising since a child can only transfer if she was previously enrolled. There are no signi...cant di¤erences between treatment and comparison school baselines in terms of how many children transferred or in terms of the observable characteristics of the baseline children who

> 32 The program schools are the 50 schools that participated in this project, whether they were treatment or comparison schools. All other schools are called non-program schools.

17
match across datasets, although it is recommended it follows a hierarchical structure similar to the one proposed by Zhao (2006). Note that in this step, the default is to use the defined areas from the simulation step. In this option the user is given the opportunity to change this grouping.

- The hierarchical id should be of the same length for all observations for example: AAMMEEE.

- `weight()` : The `weight` option indicates the new variable which corresponds to the expansion factors to be used in the target/ydump dataset. The default option is to use the weight variable saved in the ydump file, if a variable is specified here all results will be obtained with this new weighing. The user must have added the variable to the target data imported `(sae data import)` .

## **6.5.2 Profiling**

This sub-routine aids researchers in creating a profile from group classifications that are the outcome from the simulated census vectors. For example in the context of poverty it allows researchers to break down characteristics of the poor and non poor. In the context of anthropometric Z-scores it would allow researchers to obtain characteristics for the individuals who fall below the indicator’s threshold. The command syntax is as follows: ``` saeprocessstats,matasource(string)aggids(numlistsort)[contvar(string)catvar(string) plinevar(string)plines(numlistsort)area(string)weight(string)] ```

- `contvar()` : The `contvar` option indicates the continuous variables for which the user wants to estimate the mean/distribution based on poor and non-poor groups defined from the defined poverty lines in either `plines` or `plinevar` . Those statistics will be aggregated at the aggregation levels indicated in the `aggids` option. The user must have added the variable in the `sae data import` command when preparing the target dataset.

- `catvar()` : The `catvar` option indicates the categorical variables for which the user wants to estimate the two-way frequencies/distributions based on poor and non-poor groups defined from the defined poverty lines in either `plines` or `plinevar` . Those statistics will be aggregated at the aggregation levels indicated in the `aggids` option. The user must have added the variable in the `sae data import` command when preparing the target dataset.

All other options resemble those detailed for other subroutines.

# **7 Conclusions**

As we approach the 2020 round of the population census, governments and their respective national systems of statistics have renewed their international commitments towards the Sustainable Development Goals (SDGs). The SDGs will need to be operationalized at a sub-national level or reported for specific subgroups of the population. Moreover, the expansion of the availability and use of administrative, geospatial and Big Data sources for evidence based policy making is on the rise, creating a number of important opportunities for potentially better and more up-to-date measures of social well-being. Against this backdrop, to ensure 26
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7 urbanization can affect the degree of decentralization in the long run indirectly, via its contribution to income growth and democratization.<sup>7</sup> Finally, several studies (Inter-American Development Bank, 1997, Cerniglia, 2003, Arzaghi and Henderson, 2005) confirmed that a formal federal structure contributes to higher fiscal decentralization; however such constitutional considerations do not apply to the analysis at the regional level.

**2.2. Fiscal decentralization in Russian regions.** Russia is a federal state, in which the governments of 89 regions have broad powers with respect to their fiscal policies, particularly the level and structure of budget spending. Regional governments are free to decide on such important elements of expenditure policies as the levels of salaries of civil servants in sectors financed from subnational budgets, subsidies to state-owned enterprises and to households, or the level of social benefits.

The organization of regional fiscal systems and in particular the assignment of expenditure responsibilities to municipal and regional governments, until very recently was largely determined by regional governments themselves. There was little federal regulation on this issue and regions had the opportunity to develop different decentralization models. Moreover, in the 1990s, the Russian central government was quite weak and regional elites had _de facto_ even more freedom of decision making than a broad federalist legal framework would suggest. Most importantly, regional governments were empowered to make frequent changes to the “rules of the game” (Freinkman et al., 1999).

Starting from 2000 much effort has been made by Putin’s administration to strengthen central government control over the regions and a number of laws regulating subnational finance came into force, aimed at improving uniformity in subnational fiscal arrangements (see Institute

> 7 In spite of the strong theoretical support for the urbanization hypothesis, unlike the first four determinants of fiscal decentralization, its analysis is ignored by many empirical studies. Also, Letelier (2005) reports a negative effect of urbanization in a sample of 64 countries.