rafmacalaba/datause-encoder-data huggingface dataset:
17 Changes in Educational Levels The PLSS data confirm that schooling levels<sup>have increased steadily</sup> for men and women. But since the rise has been<sup>more rapid for women than for</sup> men, the gender gap in schooling has<sup>narrowed. Women born during 1945-59</sup> showed the largest percentage increase<sup>in number of years of schooling</sup> completed. Trends for men followed<sup>a slightly different pattern; their years</sup> of schooling increased earlier, but<sup>their gains tapered off with the cohort</sup> born in 1955-59.

This trend was the result of increasing enrollment<sup>rates and higher</sup> probability of continuing on to secondary<sup>and higher education. The</sup> proportion of women who never attended school<sup>fell dramatically from over</sup> 40 percent in the two oldest cohorts to<sup>only 8 percent in the youngest cohort.</sup> For men, the proportion never enrolled in<sup>school fell from 15 percent of the</sup> oldest to 3 percent of the youngest cohort.<sup>The proportion of adults with</sup> some secondary schooling more than tripled.<sup>The effect of the expansion of</sup> educational opportunities was particularly evident for individuals born between 1945 and 1959, that is, those who would<sup>have been of secondary school</sup> age during the late 50s and 60s. Similarly,<sup>women born between 1950 and 1954</sup> and who would have been of tertiary school<sup>age during the 60s also showed a</sup> significant increase in postsecondary schooling.
mangrove species. Field observations considering minimum distance between mature trees conclude that planting of _Sonneratia apetala_ at 5m spacing, _Avicennia officinalis_ at 4m spacing and _Heritiera fomes, Excoecaria agallocha, Ceriops decandra_ at 5m spacing in the study regions are feasible and will yield the best resistance to water flow through the mangroves.

# **4. Simulations of Cyclones and Findings**

Mangrove creates resistance and reduces the surge height and flow velocity of water by providing drag force against the incoming tidal energy. The passage of surge was modeled numerically with the upgraded Bay of Bengal model for estimation of the surge height and water flow velocity in our analysis. To start, cyclone Sidr<sup>16</sup> with a maximum wind speed of 248 kph, maximum wind radius of 64km, central pressure of 928 hPa, normal pressure of 1009 hPa was simulated <u>without mangroves with only the already existing polders to capture the current</u> condition. For investigation of the impacts of mangroves on surge height and water flow velocity, potential mangrove afforestation areas were then defined and overlaid along the river/ seafacing shoreline of the study areas. The model was calibrated at Hiron Point where there is an automated water level gauge maintained by Bangladesh Inland Water Transport Authority. Figure 5 presents the calibration plot. Cyclone Sidr was then simulated again with the mangrove <u>forest. For each location, the storm surge model for cyclone Sidr was carried out for a range of</u> width and density of afforestation of different short-listed mangrove species, recording the resulting surge height, and water flow velocity. <u>Finally, the optimum set of afforestation</u> parameters: species, width and density based on the simulation results were reported.

> 16 During Sidr (2007), radius of maximum wind 64km, maximum wind speed 248 kph, central pressure 928 hPa, normal pressure 1009 hPa were recorded.

15
WPS3945

# 4.3 Comparing Siblings Before and After Migration

We classify sample children into two groups by their attained age before the fi rst migration episode for the household. We group children by whether they were 9 or older before the fi rst migrant left the household (group I) or younger than 9 (group II) for all schooling and labor market outcomes, except accumulated grades for age, where we group children by the average reported age in grade 1. This accounts for both delayed entry into schooling as well as any interrupted schooling, since we cannot disaggregate these in the data. Speci fi cally, we group children by whether they had turned 7 or older before the fi rst migration episode of the household.

For each outcome, we also report results from a village fi xed effects speci fi cation. While this is more restrictive, it is likely to be more efficient given our sample size once we con fi ne attention to migrant households. In the case of school dropout rates, we report only this speci fi cation since we are unable to compare siblings since we now need to further restrict the sample to children who have enrolled in school at some point For enrollment and accumulated schooling, the sample includes all children in migrant households age 11 or older at the time of the survey. For school retention we enlarge the sample to include children age 10, since conditional on enrollment, we no longer need to worry about late entry into schooling. We do the same for both labor market outcomes, but constrain the sample to focus only on children in the age range 10-16 since by age 17, a large fraction of boys are working and girls are getting married.<sup>20</sup> There are 891 children in 388 households who were age 11 or older at the time of the survey and 36% of them fall into group I. Every age cohort has at least 24% of such children though there is some increase in the proportion of children in group II in the older age cohorts as one might expect. There is no difference, however, in the proportion of children ever enrolled in school by age.

The results with household fi xed effects are presented in Table 6. The main variables of interest are the group indicator which takes the value 1 if the child is in group I, and its interaction with child gender. Columns (1) and (2) present the results for enrollment using village and household fi xed effects, respectively. As before migration has a strong positive effect on enrollment, and this effect more than doubles when we move from a comparison of migrant children within a village to a comparison of siblings. Among siblings, enrollment

> 20 Including the 17 years olds, has little effect on the odds of working, but the gender difference in days worked disppapears for children in group I. Using the more generous de fi nition of work, increases the size of the effect but the results are qualitatively unchanged.

19
deciles. Across regions, average share of food expenditure is highest for North West at 39 percent and Centre West at 37 percent.

**Figure 6: Share of food expenditure**

<!-- Start of picture text --> a. By decile b. By region<br>p<br>39 38 36 36 39 37<br>35 34 34 32 35 33 35 34<br>30 31<br>27<br>poorest 2 3 4 5 6 7 8 9 richest Grand Tunis Nord Est Nord Ouest Centre Est Centre Ouest Sud Est Sud Ouest<br>mean of foodshare mean of foodshare<br>40 40<br>30 30<br>20 20<br>% total expenditure pc % total expenditure pc<br>10 10<br>0 0<br><!-- End of picture text --> Source: Author’s calculations based on EBCNV 2015 Several demographic or living characteristics of the Tunisian population also make the poor more vulnerable to get affected by COVID-19. First, overcrowded living conditions and low access to health insurance put the Tunisian poor at a higher susceptibility to get infected or to not be able to seek health care in the event that they contract COVID-19. Not surprisingly, poor households tend to live in denser environments, ranging from 7 to 28 percent in the three poorest deciles, in contrast to 1 percent or less in the three richest deciles.

Second, living in an inter-generational household is linked to higher susceptibility to COVID-19, as elder members are more likely to be in closer contact with the younger household members. Inter-generational households are defined as those that include at least one member below the age of 18 years and one member above the age of 64 years. Overall, 10 percent of the population lives in an inter-generational household. Nevertheless, this type of household is disproportionately represented among the poor, ranging from 14 to 17 percent in the three poorest deciles. This type of household is also more prevalent in the South-East region, followed by the North-West and South-West regions.

# **3. Methodology**

Using a methodology similar to that employed by Ajwad, Aran, Azam and Hentschel (2013), this paper employs a hybrid approach that combines the results of macroeconomic projections of a sectoral slowdown over the whole year of 2020 of the Tunisian economy with micro-simulation techniques. Combining information on GDP growth projections by sector and employment elasticity of growth of each sector (to capture how elastic employment is to changes in sectoral GDP), we estimate the loss of employment in each sector. Using household survey data, we then identify individuals who are likely to lose their jobs based on regression analysis and simulate the impact on consumption under various assumptions. With this, we can identify a new distribution of post-pandemic consumption and assess 10
increases in the statutory tax rate. For the years of our sample, the statutory tax rate increased from 15 percent to 17 percent. After 2007, the ENIA’s panel structure is eliminated, so that firms cannot be identified across years. For this reason, we do not use data after 2007, as doing so would have limited some of our quantitative exercises.

Firm and plant level data are only available for 1996 (we do not use this year since it cannot be linked as a panel with the rest of our data). In that year, more than 95% of firms were operating in single-plant establishments. We will use the terms plant and firm interchangeably. The ENIA collects data on revenue, net accounting profit, profit tax, employment, wage bill, fixed assets, and industry among other variables useful for our quantitative analysis. Previous versions of this census have been used in many studies, given its rich plant-level data. In Chile, the manufacturing sector accounted for roughly 17 percent of value added and 14 percent of employment for the period 1998-2007. Further details on the construction and representativeness of our sample can be found in Section A of the appendix.

# **2.2 Tax Rate Facts in Chile**

In this section, we document relevant tax facts about Chile. In Chile, all firms are subject to the same statutory tax rate, regardless of their level of profits. The ENIA collects plant-level data on net accounting profits and profit tax expenses. Using these two variables, we calculate the effective tax rate that each firm faces in a given year, as the ratio between profit tax expenses and gross accounting profits.<sup>4</sup> Figure 1 shows the distribution of effective profit tax rates for 2003, which we have used as an example year, as the distributions for all the years in our sample portray similar characteristics. In particular, four important features characterize the distributions of effective profit tax rates for each year in our time-window of analysis. First, a large number of firms face a 0 percent tax rate (close to 34 percent of firms in 2003). This feature is mainly driven by 4. Gross accounting profits is the sum of profit tax expenses and net accounting profits.

7
for males (left-hand side columns) and females (right-hand side columns). The share of NEET young women in 2010 is already four times bigger than that of young men. Therefore, since the labor market is particularly immobile, after 10 years these women have an 80 to 90 percent probability of remaining NEET, with little chance of improvement. The men’s results confirm that for them is less of a problem: most of them are non-NEET in 2010 and remain that way after 10 years.

**Table 6: Transition Matrix Based on the Synthetic Panel 2010/2018 by Gender**

|**Status in 2010,**|**_Ma_**<br>|**_le_**<br>|**_Fem_**<br>|**_ale_**<br>|
|---|---|---|---|---|
|**2018**|**Lower-bound**<br>**estimate (%)**|**Upper-bound**<br>**estimate (%)**|**Lower-bound**<br>**estimate (%)**|**Upper-bound**<br>**estimate (%)**|
|_NEET, NEET_|18.31|3.20|74.23|67.75|
|_NEET, non-NEET_|0.14|14.69|8.45|20.90|
|_Non-NEET,_<br>_NEET_|0.00|15.11|0.00|6.48|
|_Non-NEET, non-_<br>_NEET_|81.55|67.01|17.32|4.87|

_Notes:_ results are restricted to the sample of individuals aged between 15 and 24 in 2010 and between 23 and 32 in 2018. “Lower” and “Upper” refer to the two bounds on mobility; for immobility, “Lower” is the upper bound and “Upper” is the lower bound. Upper-bound estimates are based on 50 replications. Individual-level sampling weights are applied.

We also divided the sample into two groups: those in the bottom 80 percent and those in the top 20 percent of the asset index distribution (Table 7). Here also, we noticed some differences between the two groups. First, as expected, there is a higher prevalence of NEETs among households in the bottom 80 percent than in the top 20. Second, among the top 20 percent, there is less persistence in the NEET condition, and there are fewer chances to become a NEET in 2018 if the person was non-NEET in 2010.

**Table 7: Transition Matrix Based on the Synthetic Panel 2010/2018 for Top 20 Percent and Bottom 80 Percent Asset Index**

|**Status in 2010**|**_Asset ind_**<br>|**_ex< 80%_**<br>|**_Asset ind_**<br>|**_ex> 80%_**<br>|
|---|---|---|---|---|
|**,**<br>**2018**|**Lower-bound**<br>**estimate (%)**|**Upper-bound**<br>**estimate (%)**|**Lower-bound**<br>**estimate (%)**|**Upper-bound**<br>**estimate (%)**|
|_NEET, NEET_|32.52|16.50|16.37|7.94|
|_NEET, non-_<br>_NEET_|0.00|11.42|0.00|15.75|
|_Non-NEET,_<br>_NEET_|0.00|16.02|0.00|8.44|
|_Non-NEET, non-_<br>_NEET_|67.48|56.06|83.63|67.87|

23
shows that during bubble periods, mutual funds that hold fewer stocks underpinning the bubble exhibit a lower performance (Dass et al., 2008). Furthermore, herding can also arise from institutional investors using similar risk models (Kremer and Nautz, 2013).

Institutional investors can also trigger contagion effects, spreading crises across countries. Fund managers and investors can produce contagion because they are prone to selling assets in one country when prices fall in another (Kaminsky et al., 2004; Boyer et al., 2006; Chiang and Zheng, 2010; Puy 2013). Likewise, political decisions in one country, such as a decision to increase capital controls, might induce institutional investors to decrease exposure to comparable countries that might be expected to implement similar policies (Forbes et al., 2016). Furthermore, institutional investors can help transmit shocks originated in their home countries. There is evidence that, regardless of their macroeconomic stability, developing countries more exposed to international funds experience larger decreases in external funding when conditions in developed countries worsen (Cerutti et al., 2015). One possible explanation could be that a negative shock in the home country where a fund is domiciled might induce investors to withdraw their money from it. This behavior will lead to a fire sale in which funds need to liquidate their positions, including foreign investments, expanding the crisis toward other countries (Jotikasthira et al., 2012). This pro-cyclical behavior of institutional investors could be one of the reasons why financial globalization is associated with crises.

Although we have highlighted many of the negative effects of institutional investors, they do help channel financing across countries and can produce some positive effects. For example, they help complete financial markets, offering new forms of financing and managing household savings. Moreover, there is some evidence that international mutual funds might help extend the maturity structure of corporate bonds. Empirical evidence shows that these funds invest longer term in corporate bonds than the domestic funds of the countries in which they invest. This behavior can be explained by the ability of international mutual funds to take higher risk given their larger size and their possibility to hedge risk investing in different countries (World Bank, 29
**Pollcy Research**

# **Education and Employment**

**WPS 946**

This paper- a product of the Education **aid** Employment Division, Population and Human Resources - Department is part of a larger effort in the departnent to improve labor policies for managing the social cost of economic adjustment. Copies of the paper are available tree from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact PHREE, room S6-035, extension 33680 (August 1992, 52 pages).

The governments of most industrial countries provide fmancial support for adult training programs intended to retrain displaced workers. Leigh draws lessons from the experience of six industrial countries (Australia, Britain, Canada, Japan, Sweden, and the United States) on how to design and implement such retraining programs in low-income developing nations and middleincome countries.

**By** retraining, he means both improving job skills and remediating deficiencies in basic education. These are the lessons he emphasizes:

> e T'raining programs should be independent of the educational system, with its rigid ties to degree requirements and academic schedules.

* Training programs should be designed to minimize trainees' forgone earnings; basic education should be relevant to the jobs the trainees might seek.

* Extemal providers of education must be made accountable - but with care; the system of accountability should also ensure that the needs of displaced workers most likely to suffer longterm unemployment are met.

e Not all displaced workers require relatively expensive retraining; some may need only inexpensive job-search assistance services.

> * A permanent, institutionalized training system is preferable to short-term intervention.

* Links to employers must be developed and maintained so that trainees have marketable skills on completing the program.

ThePolicy ResearchWorking PaperSeries disseminates thefindings of work **under** way in theBank. Anobjective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
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wide basis enhances the 'export pay-off' of harmonizing domestic product standards as much as possible with those of the EU. The same applies regarding the use of EU customs procedures and documentation. Care must be taken that the adoption of regulations/directives does not conflict with a country's comparative advantage, however, specifically as regards environmental and social policies.

An important caveat concerns the potential costs of trade diversion that an **FTA** with the EU could entail. If external tariffs on products originating in the rest of world are not lowered concurrently, a FTA may have large opportunity costs (Rutherford et al., 1993). As noted earlier, the best strategy in this connection is if an EU-link is used as part of broader effort to liberalize trade and investment regimes. Insofar as credibility problems exist, a FTA with the EU may do more to convince the private sector that planned reform efforts will be implemented than unilateral efforts. A supporting policy in this connection should also be to critically review the rationale for existing regional agreements within the Arab world. As noted in Section 2, many MENA countries have concluded preferential trade agreements with one another. These agreements should be either abolished or converted into full-fledged FTAs.

# **6. Concluding Remarks**

Despite the progress that has been made in recent years to reform policies, on average the MENA region remains more inward oriented than many other parts of the world. Anti-export biases are strong, reflecting not only tariff policies but also the regulatory burdens and administrative red tape that confronts businesses. State intervention in the economy often remains pervasive. Only by allowing greater competition on domestic markets--through reduction of barriers to imports and exports and by allowing foreign direct investment to occur--will the countries in the region be able to achieve sustainable real growth. So far it has proven difficult for a number of countries in the region to implement far-reaching reform of trade and investment policies. External developments suggest that in the foreseeable future many--if not most--of MENA' comparator/competitor countries will be more open, more private sector-friendly and more dynamic. The CEECs illustrate the challenge--not 38
richer. Private transport expenditures are overall increasing in a non-linear way. Figure 8 shows that for many countries a substantial share of households still consumes mostly public transport services, and have yet to start spending on private transport goods and services, which mostly depend on asset ownership.

Figure 8: Share of transport expenditure in total expenditure for the LSMS sample of countries <!-- Start of picture text --> Afghanistan Burkina Faso Cameroon Iraq<br>20 .8<br>15 .6<br>10 .4<br>5 .2<br>0 0<br>Kyrgyz Republic Malawi Mali Mongolia<br>20 .8<br>15 .6<br>10 .4<br>5 .2<br>0 0<br>Nepal Nicaragua Niger Nigeria<br>20 .8<br>15 .6<br>10 .4<br>5 .2<br>0 0<br>Pakistan Sierra Leone Tanzania Uganda<br>20 .8<br>15 .6<br>10 .4<br>5 .2<br>0 0<br>4 6 8 10 4 6 8 10 4 6 8 10 4 6 8 10<br>Annual expenditure per person (log scale)<br>All transport Private Services Household density<br>Share of total expenditure, in %<br><!-- End of picture text --> Note: Authors’ calculations using the LSMS surveys.

# **3.4 Income and private transport**

Expenditures for private transport include the acquisition, use, and maintenance of vehicles. The extent to which the consumption of private transport will affect carbon emissions depends on the number and types of vehicles and the intensity of use.

## **3.4.1 Income and vehicle ownership**

**Fact 6: As countries get richer, more households acquire cars and each household might acquire more than one car.** Figure 9 uses data from the International Road Federationfor 2017 to plot the number of passenger cars per 1000 inhabitants against country GDP per capita. Except for the poorest countries, there is a log-linear relation between country income levels and car ownership.<sup>7</sup> Using similar data for motorcycles, we show that there is no similar relationship between motorcycle ownership and GDP per capita.

> 7The correlation coefficient between the number of passengers cars and the log of GDP per capita is 81%.

17