rafmacalaba/datause-encoder-data huggingface dataset:
of the hull and engines; consequently the annual depreciation costs associated with this type of fishing are high, as well. A large part of commercial boats is European. Following the introduction of the new protocol between EU and Mauritania on fishery, at least 60 percent of workers (excluding the officials and highly specialized personnel aboard) must be composed by Mauritanians.

Against this background, it needs to be highlighted that the depletion of fish wealth not figuring in the national accounts of Mauritania is a matter of concern to the eye of an economic analyst, and it should be even more so at the eye of the national policy makers. This is due to a combined effect of weak property rights schemes on fisheries (at the national and international level) and difficulties in obtaining precise and reliable data on market prices, production, costs, job creation related to the sector. The risk is evident, as a fish-rich country could register additional growth year-on-year in the national accounts, derived from a (supposedly) unsustainable overexploitation of the resource, without needing to subtract the corresponding reduction in fish stock capital. Especially in cases like the Mauritanian one, given the economic importance of the sector, omitting natural assets such as fish stocks from the national accounts may entail a substantial oversight in economic perception and surveillance<sup>66</sup> .

|**Assumption - COMMERCIAL**|**Pelagic**|**Cephalopods**|
|---|---|---|
|Number of vessels in year 0|200(**)|230|
|Annual vessel growth|5 percent(*)|- 2 percent|
|Maximum fleet size (over the time of a generation)|600 units(*)|500 units(*)|
|Average catch per day (tons)|60|3|
|Annual number of trips|180|220|
|Average price per ton (USD)|250|5,000|
|Average total costs per vessel (annual, in USD)|> USD 3mln|> USD 3mln|
|of which: Labor costs (USD)|~ USD 800K|~ USD 400K(***)|
|Annual depreciation costs (USD)(****)|~ USD 2 mln|~ USD 2 mln|
|Opportunity cost of capital|15 percent of revenues(*)|15 percent of revenues(*)|
|**Natural wealth per capita estimates ($)**|**1’475**|**1’040**|

# **_Methodology and context for natural gas and crude petroleum wealth estimates_**

Also in the case of natural gas and oil, the methodology of this paper is based on a Net Present Value (NPV) analysis. Resource rents are estimated over the life of the extraction (respectively of gas or oil) and subsequently a social discount rate of 4 percent is applied to the sum of all annual rents<sup>67</sup> . Year t0 is 2013 and the final year, year tn corresponds to the end of the mining operations.

> 66 The Sunken Billions: The Economic Justification for Fisheries Reform.  Joint publication World Bank/FAO, 2008

> 67 Mathematical detailed demonstration is provided in the paragraph on the mining sub-sector.

33
Table 3. Synchronisation of business and financial cycles (concordance index)

||All<br>Countries|Advanced<br>Economies|Emerging<br>Markets|
|---|---|---|---|
|**Output and Credit Cycles**||||
|Mean|0.78|0.82**|0.74|
|Median|0.81|0.83|0.76|
|Standard Deviation|0.10|0.06|1.13|
|**Output and House Price Cycles**||||
|Mean|0.64|0.67**|0.54|
|Median|0.68|0.70|0.50|
|Standard Deviation|0.12|0.15|0.15|
|**Output and Equity Price Cycles**||||
|Mean|0.59|0.57***|0.62|
|Median|0.58|0.57***|0.64|
|e: Each cell represents the concord<br>d i lld h fi<br>Standard Deviation|ance statistic<br>f i h<br>0.06|for the corresp<br>l i<br>0.08|onding two cycles.<br>h h ***<br>0.05|

Note: Each cell represents the concordance statistic for the corresponding two cycles. Concordance is calculated as the fraction of time that two cycles are in the same phase. *** and ** imply significance at the 1% and 5% levels, respectively. Significance refers to the difference between advanced economy and emerging markets (means and medians only).

58
- Dabalen, Andrew and Saumik Paul. 2012. “Estimating the Effects of Conflict on Education in Cˆote d’Ivoire.” <u>Journal</u> of Development Studies 50.

- Diwakar, Vidya. 2015. “The Effect of Armed Conflict on Education: Evidence from Iraq.” The <u>Journal</u> of Development Studies 51(12):1702–1718.

- Efendic, Adnan, Dejan Kovac and Jacob N. Shapiro. 2022. “Exposure to Conflict, Migrations and Long-run Education and Income Inequality: Evidence from Bosnia and Herzegovina.” Defence and Peace Economics 0(0):1–15.

- Espi ́e, Emmanuelle, Val ́erie Gaboulaud, Thierry Baubet, German Casas, Yoram Mouchenik, Oliver Yun, Rebecca F Grais and Marie Rose Moro. 2012. “Trauma-related psychological disorders among Palestinian children and adults in Gaza and West Bank, 2005-2008.” Int <u>J</u> Ment Health Syst 3.

- Falck, Oliver, Stephan Heblich and Susanne Link. 2011. “The Evils of Forced Migration: Do Integration Policies Alleviate Migrants’ Economic Situations?” Discussion Paper, IZA (5829).

- Favara, Marta, Annina Hittmeyer, Catherine Porter, Saurabh Singhal and Tassew Woldehanna. 2022. “Young people, mental health, and civil conflict: Preliminary findings from Ethiopia’s Tigray region.” Psychiatry Research Communications 2(1):100025. **URL:** _https://www.sciencedirect.com/science/article/pii/S277259872200006X_

- Gilligan, Michael J., Benjamin J. Pasquale and Cyrus Samii. 2014. “Civil War and Social Cohesion: Lab-in-the-Field Evidence from Nepal.” American <u>Journal</u> of Political Science 58(3):604–619.

- Giustozzi, Antonio. 2010. “Nation-Building Is Not for All: The Politics of Education in Afghanistan.” Afghanistan Analysts Network Report .

- Giustozzi, Antonio and Claudio Franco. 2011. “The Battle for the Schools The Taleban and State Education.” Afghanistan Analysts Network Report .

29
Equation (2) and to assess the probability that those not employed are actively seeking work. The associated marginal effects are shown in Table 7.

**-** **<u>Table 7. Job seeking during the COVID 19 pandemic</u>**

||<br>2019-2020<br> <br>|<br>2017-20<br>|20<br>|
|---|---|---|---|
||[1]<br>[2]|[3]|[4]|
|Reasons for stop working||||
|Laid-off<br>Contract termination|-.128***<br>(.034)<br>-.112***<br>(.035)<br>-.143***<br>-.144***|-.156***<br>(.031)<br>-.163***|-.139***<br>(.031)<br>-.158***|
|Other|(.023)<br>(.023)<br>-.047<br>-.046|(.022)<br>.018|(.022)<br>.022|
||(.045)<br>(.045)|(.043)|(.043)|
|Quarters since stop working||||
|0 (current quarter)|-<br>-.383***|-|-.467***|
||(.079)||(.069)|
|1|-<br>-.135***|-|-.130***|
||(.031)||(.031)|
|2|-<br>-.022|-|-.065*|
||(.043)||(.039)|
|3|-<br>-.222***|-|-.210***|
||(.062)||(.060)|
|4|-<br>-.070|-|-.134**|
||(.065)||(.056)|
|5|-<br>-.148***|-|-.164***|
||(.056)||(.054)|
|6|-<br>-.049|-|-.027|
||(.057)||(.056)|
|7|-<br>-.054|-|.028|
||(.056)||(.055)|
|Observations|3416|7637||

Source: EL.STAT, Quarterly Labor Force Survey-LFS (2017 – 2020, 1<sup>st</sup> quarter).

Notes: Reported estimates are average marginal effects drawn from a probit model (interaction effects model) and correspond to the post 2020Q1 period (additive effect). Based on the ILO definition of unemployment, the dependent variable takes the value of 1 if the individual is considered to be unemployed and 0 otherwise. Sample includes all individuals (aged 25-54) who have stopped working during the last 8 quarters (2 years). All models include controls for gender, age, country of birth, education, region and sector of economic activity of the last job. In parentheses, white heteroskedasticity corrected standard errors are reported. All estimates are weighted using the sampling weights provided by the EL.STAT. *** at 1%, ** at 5% and * at 1%.

We observe (column 2) that, for those who had been laid-off, the probability of searching for work during the first quarter of 2020 was 11.2 percentage points lower than in the first quarter of 2019. For those who were not working because their last employment contract had been terminated, the drop in the probability of searching employment was even stronger, i.e. 14.4 percentage points lower. The largest drop was experienced by those who had lost their job within the current quarter (38.3 percentage points). This suggests that the slackness of the labor market in the first quarter of 2020 mostly affected the newly jobless individuals. These results are confirmed even when additional years of first quarters are added in the model (column 4).

We now turn to the estimation results of the probability of finding employment during the 1<sup>st</sup> quarter of 2020. Table 8 presents the estimated marginal effects of the probit model described in equation (3). We observe (column 2) that the probability of finding employment in the first quarter of 2020 was 4.6 percentage points lower than during the first quarter of 2019. When additional years are added (columns 3 and 4), we 29
<!-- Start of picture text --> 60<br>50<br>40<br>30<br>Round 4<br>20<br>Round 5<br>10<br>0<br>0 1 2 3 4 5 6 7<br>Entry age<br>Appendix Figure 6. Distribution of age of entry into preschool<br>Notes: The graph plots the distribution of first age of entry into preschool obtained from child’s<br>education module in IFLS 4 (2007/08) and 5 (2014/15). The same information is not collected in<br>previous IFLS rounds. Blue solid bar and patterned orange bar indicate the percent distribution for<br>round 4 and 5, respectively.<br>Public Private<br>.08<br>.06<br>.04<br>.02<br>0<br>0 10 20 30 40 50<br>Travel time (mins)<br>percent<br>Kernel density<br><!-- End of picture text -->

# **Appendix Figure 6. Distribution of age of entry into preschool**

Notes: The graph plots the distribution of first age of entry into preschool obtained from child’s education module in IFLS 4 (2007/08) and 5 (2014/15). The same information is not collected in previous IFLS rounds. Blue solid bar and patterned orange bar indicate the percent distribution for round 4 and 5, respectively.

# **Appendix Figure 7. Distribution of one-way travel time to preschool**

Notes: The graph plots kernel density of one-way travel time to public/private preschools conditional on preschool enrollment. Dashed vertical line indicates median travel time, 10 minutes, in both public and private preschools. Solid blue line and short-dashed red line indicates the distribution of travel time to public and private preschools, respectively.

66
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This increase is solely driven by jobs that can be done at home _and_ do not require internet whose share increases from 3.1 to 3.6 percent in high-income countries, and from 1.7 to 2.0 percent in low-income ones (Figure 2a).

Next, we use PIAAC (Programme for the International Assessment of Adult Competencies) surveys rather than O*NET data to identify the types and extent of occupations requiring internet access. The main shortcoming of O*NET data is that they are based on the task content of occupations as performed in the United States. The PIAAC surveys, in contrast, include rich information on jobs’ characteristics for 35 countries. We restrict the sample to 29 high-income countries where internet coverage is near universal to avoid our measures of ICT usage being downward biased by limited internet availability. Following Hatayama, Viollaz and Winkler (2020), we use several questions related to internet use at work such as frequency of computer and email use, frequency of ICT usage, programming, and participating in video calls. We construct a continuous index of ICT usage and we calculate the share of jobs within each ISCO 2-digit occupation that are above the 50<sup>th</sup> percentile of this index. Occupations above the median in ICT usage are determined to require internet access. We then combine this occupation-level measure of ICT requirements with the DN2020 index to identify the share of jobs that are telecommutable and do not require the internet versus the shares of jobs that are telecommutable conditional on internet access. We identify the share of jobs that are telecommutable and require the internet as the minimum of the share of jobs that can be performed from home according to Dingel and Neiman and the share of jobs requiring internet in each ISCO 2-digit occupation. Telecommutable jobs that do not require internet are obtained by subtracting telecommutable jobs that require internet from all telecommutable jobs. This alternative index does not change the total share of jobs that can be done from home with respect to our baseline results significantly. It increases, marginally, the share of jobs that can be done at home and require internet access but lack connectivity, particularly for lower income countries. The results of using PIAAC to identify internet dependence are presented in Figure 2b. In this case, the share of jobs that can be performed from home without internet declines, from 3.1 to 2.6 percent in high income countries and from 1.7 to 1.0 percent in low income countries.

Our next robustness check uses a different method to allocate internet connectivity along the income distribution. We should recall that the Gallup data contain the share of people with internet 11
150 countries in the world with such data- and calculate the Gini coefficient.<sup>2</sup> China and Luxembourg have the same importance, because we do not take population sizes into account. Every country counts the same, somewhat like in the UN General Assembly.

Consider now the second row of the figure which would help us define Concept 2 inequality or _Inequality 2_ . There, individuals from poor countries are all equally short as before and those from rich countries all equally tall, but the difference lies in the fact that countries’ population sizes are now taken into account. We do exactly the same as we did in Inequality 1, but now China and Luxemburg (or any other country) enter the calculation with their populations. In Figure 1, the poor country is the most populous (5 individuals out of total of 10 displayed there), and the middleincome country, the least populous (2 individuals). Introducing population is very important. As we shall see in the next section, during the past 25 years, the movements in Concept 1 and Concept 2 inequalities were very different. Recall, however, that in both cases the calculation takes into account not actual incomes of individuals, but country averages.

_Inequality 3_ is the global inequality, which is the most important concept for those interested in the world as composed of individuals, not nations. Unlike the first two concepts, this one is individual-based: each person, regardless of her country, enters in the calculation with her actual income. In Figure 1, this is represented by the different heights of individuals who belong to the same country. Not all Americans have the average income of the United States, nor do all Chinese have the average income of China. And indeed in Figure 1, the poorest person is from the middle-income country, while her compatriot is the second richest (the second tallest) in our group of ten individuals.

But moving from Concept 2 to Concept 3 inequality is not easy. The chief difficulty comes from the fact that to calculate the latter we need access to household surveys with data on individual

> 2 Gini coefficient is a statistical measure of inequality  which takes  its  name from the Italian statistician and economist Corrado Gini. The Gini index is the most frequently used measure of inequality, ranging from 0 – when everybody has the same income - to 1, or 100 (expressed as a percentage or an index), when one person gets the entire income of a city (province, nation, world)—whatever is the relevant population over which we calculate inequality.

4
|Number of observations<br>12,408<br>29,645<br>2,221<br>5,926|
|---|
|Note: .01 - ***; .05 - **; .1 - *. Marginal effects from the estimation of a probit model are reported. Standard<br>errors are clustered at the level of the strata; rescaled weights are applied in the regressions. The models also|
|include controls for the region in which the firm is located, the sector in which it operates and the year in which it<br>began operations.|

While the global results provide an overview of the aggregate relationship between gendered laws and informality, in reality, there is significant heterogeneity between regions in our sample in terms of informality and legal equality between men and women. The informal economy accounts for 15-20 percent of GDP in advanced economies and 30-35 percent of GDP in emerging economies (Kelmanson et al., 2019); in Latin America and the Caribbean, it has been estimated to account for approximately 40 percent of GDP (OECD, 2018). As noted in Ohnsorge and Yu (2021), the variation in employment share in the informal sector between regions is even larger. Likewise, the prevalence of gendered laws varies significantly from one region to another. Data from the most recent _Women, Business and the Law_ report (World Bank, 2021), shows that the average global score on the WBL index was 76.1 points, but the regional average ranges from 95.1 points in OECD high-income economies to 51.5 in the Middle East and North Africa region. These regional differences highlight a need to understand the relationship between gendered laws and informality at a more disaggregated level.

Several interesting patterns emerge from the regionally disaggregated results presented in Table 3. The most prominent result is that, for firms with female participation in ownership, greater equality is only associated with a lower probability of beginning informally in Sub-Saharan Africa, the Middle East and North Africa and in South Asia. In other regions, the relationship is not statistically significant. What is also notable is that it is only in Sub-Saharan Africa that we see a positive relationship between gender equality and informality for firms without female participation in ownership, in most other regions, the association is not significant; the exception is the Middle East and North Africa region where gender equality is associated with lower degrees of informality among all firms, that is, both those with and without female owners.

15
11 -

-

estimate that poverty rose between 1980 and 1985. The WDR estimates that between 1980 and 1985, "the number of Africans in poverty would have increased by 55 millic.." Social Indicators have shown considerable improvement in all parts of the developing world, particularly in the 1960s and 1970s. As table 2 shows, life expectancy in the South has increased from 46 years in 1960 to 62 years in 1987. Tables 4 and 5, reproduced from HDR, show similar improvements in the in-'ant mortality rate as well as the adult literacy rate. There is, however, some evidence that the improvement slowed down in the 1980s, particularly in Sub-Saharan Africa. For example, the WDR shows that primary enrollment rates decreased in that region during the early part **of** the 1980s.

Despite the remarkable improvements over the past 30 years, the problem of poverty rezmains severe, and there may have been some reversals during the past 1980s. What can policy do to accelerate progress? This question is taken up in the next section.

# 4. Policies and Poverty

- 4.1 Who are the Poor, and how can they be helped?

Before designing policies to help the poor, it is important to understand the characteristics of the poor -- their location, sources of income, etc. The WDR starts with this analysis. The broad conclusion is that while the poor are heterogeneous, certain general conclusions can be drawn from the evidence -- and these are not very surprising. Poverty is primarily a rural phenomenon. In Africa and in Asia, the incidence of poverty is far
56

|Surve of Cos&n of Denos|it insurance to BankR;|
|---|---|
|Yearly Bank<br>Assessment<br>Rate (%)||
|1.20|Chile (Complementary)|
|1.10||
|0.94<br>0.90|Nigeria|
|0.70||
|0.50||
|0.40||
|0.36|Argentina (w/o refund)|
|0.35||
|0.32|Argentina (w/ refund)|
|0.30|Turkey, Colombia|
|0.25||
|0.20||
|0.15|Norway|
|0.12|Spain|
|0.10|Canada, Kenya|
|0.09|Philippines|
|0.08|United States|
|0.05|India|
|0.03|Germany|
|0.02|Belgium|
|0.008|Japan|

NT: Deposit **base may** differ amoung countrLes. **These figures reflect the** latest avallable ,at&. Countrles that levy ex-post assessments **are** excluded.
this framework does not attempt to present a global model for all child schooling and work decisions, but rather to understand the observed shifts in child labor after the onset of _Pantawid_ .

# **III. Background and study context**

# _Education and child labor in the Philippines_

Recent (2011) ILO survey data show that 95 percent of 10-to-14-year old Philippine children are in school and that 13 percent of children in the same age range are engaged in economic activities (Understanding Children’s Work, 2016). About 85 percent are in school only, 11 percent combine school and work, 3 percent are idle (i.e. in neither in school nor in work), and 2 percent are in work only. Boys are more likely to work than girls (15 percent versus 10 percent) and somewhat less likely to be in school (93 percent versus 97 percent).<sup>6</sup> Children in this age range are not legally allowed to engage in economic activities in the Philippines, although the enforcement of such laws has been under-resourced, at least until the establishment, in 2015, of an interagency council to enforce child labor laws (US Department of Labor, 2016).

# _The Program_

_Pantawid_ aims to support poor households in satisfying their consumption needs and to encourage investment in their children's education and health. The program began in 2008 with the first enumeration of potential beneficiary households through a listing exercise that collected a 6 The self-declared reasons for children’s participation in economic activities are varied: a substantial number of children work to help in the household-operated farm or business (53 percent) or to otherwise support family income (20 percent), while a smaller fraction works to gain experience in the labor market (10 percent) or to appreciate the value of work (5 percent). While school attendance rates are comparable between rural and urban areas, rural child employment rates are higher (15 percent versus 8 percent). In rural areas, about 82 percent of children aged 10 to 14 are in school only, 13 percent combine school and work, and 3 percent each are idle and in work only.

7