rafmacalaba/datause-encoder-data huggingface dataset:
Figure 1. Historical and projected world population, 1950-2050, and rural-urban populations (from FAOSTAT online, November 2008).

<!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>1950 1970 1990 2010 2030 2050<br>Rural<br>Urban<br>Total<br>Billion people<br><!-- End of picture text --> Table 1. Human populations in five countries of East Africa in 2000 and 2050 (UNPD, 2008)

||**Population, 2000**<br>**(million)**|**Population, 2050**<br>**(million)**|
|---|---|---|
|**Burundi**|6.67|28.31|
|**Kenya**|31.25|84.76|
|**Rwanda**|8.18|22.63|
|**Tanzania**|33.85|85.08|
|**Uganda**|24.69|92.93|
|**Total**|104.64|313.71|

10
**Table 7. Drivers of the skill content of jobs: Demography and Technology. Developing countries sample.**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Non‐routine<br>analytical|Non‐routine<br>interpersonal|Routine cognitive|Routine Manual|Non‐routine<br>manual|
|Working age population (% of population), change|0.0665|0.331|0.161|‐0.112|‐0.123|
||(0.423)|(0.397)|(0.214)|(0.356)|(0.330)|
|Internet users (% of population), change|0.0326|0.0400|‐0.0460|‐0.0701|0.0264|
||(0.0653)|(0.0614)|(0.0331)|(0.0550)|(0.0509)|
|Working age population x Internet, change|0.0393*|0.0433**|‐0.0158|0.00526|0.0161|
||(0.0213)|(0.0200)|(0.0108)|(0.0180)|(0.0166)|
|Observations|355|355|355|355|355|

Note: OLS coefficients from a cross-country regression using the skill content of jobs (in first differences) as the dependent variable (multiplied by 100).

# _Robustness check: The role of the agricultural sector_

One of the limitations of the STEP surveys is that they cover urban areas only, which leads to an under-representation of the agricultural sector. This could introduce a bias to our estimates if most of the changes in the task content of jobs is driven by a transition out of agriculture. To assess to what extent the exclusion of agriculture is driving the results, we re-calculate the O*NET-based indexes including the agricultural sector and occupations in the I2D2 data set. We then estimate the cross-country equations using these indexes. As seen in Table 8 and Table 9, the results are very similar for both samples, as the magnitudes of the main estimated coefficients are very close. Thereby, the main findings do not seem to be driven by the exclusion of the agricultural sector.

26
surveys include socioeconomic and demographic information, but do not include any information on consumption or assets at the household level.

Given that our focus is on college graduate workers, we restrict the sample to individuals aged 23-65 years old (inclusive). For employment patterns, we use the full sample and define ‘work’ as the positive number of weekly hours of work in the week of reference. For the wage sample, we use full-time earnings defined as earnings of those with more than 30 hours of work in the week of reference. When calculating hourly wages, we divide the monthly earnings by hours of work times 4.33. We drop earnings below and above the 1st and 99<sup>th</sup> percentile within education groups from the sample.<sup>6</sup> To calculate the hourly wage statistics for all workers, we use the expansion factor in the survey times hours of work.

Table 1 shows the descriptive statistics for three years in the data: 2000, 2007 and 2014. Panel A includes the full sample and Panel B includes only the working sample (those with positive hours of work in the week of reference). This distinction is important due to the selection into the labor force mainly by women. There are three large changes in the Mexican population and workforce in the 2000-2014 period. First, there is a substantial increase in the female labor force participation. From 40 percent in 2000, it increased to 48 percent by 2014. This increase has been on hold since the macroeconomic crisis in 2008. Second, the proportion of individuals with a university diploma has increased, while at the same time the proportion of individuals with primary or less has declined substantially. This is satisfied both in the full and the working samples. In the case of women, there is positive selection into the workforce by education. Higher educated women are more likely to enter the labor force than less educated women. Third, average incomes declined substantially after the macroeconomic crisis of 2008. In the results that follow, we explore the role of different explanations on the decline of wages of higher-educated workers. We present our results for both men and women in order to analyze whether there are important changes by gender in labor market outcomes.

**Table 1: Descriptive Statistics**

|||2000|||2007|||2014||
|---|---|---|---|---|---|---|---|---|---|
||All|Male|Female|All|Male|Female|All|Male|Female|
|A. Full Sample||||||||||
|N|853,078|400,306|452,772|806,406|372,734|433,672|409,152|191,897|217,255|
|Age|39.1|39.2|39.1|40.3|40.4|40.1|40.9|40.9|41.0|
|% Work|62%|87%|40%|64%|86%|46%|65%|84%|48%|
|% Married|75%|78%|71%|73%|77%|70%|71%|75%|69%|
|% Rural|35%|35%|34%|34%|34%|34%|34%|35%|34%|
|% Primary or less|54%|51%|56%|45%|43%|48%|36%|34%|37%|
|% Junior High||||||||||
|School|19%|21%|18%|23%|25%|22%|28%|29%|27%|
|% High School|16%|14%|17%|17%|16%|18%|21%|20%|21%|

6 This restriction does not affect the trend of the results. Without this restrictions the results are noisier each year. The literature on wage inequality generally follows this convention, see Katz and Autor (1999).

4
by 85.8 percent for the period after policy as compared to the period before policy (Table 2). For the sectors liberalized fully at 100 percent, realized FDI increased by 22.8 percent for the period after the policy as compared to the period before the policy.

We also show results for the sectors in between, the two extremes, keeping in mind the identification issues previously discussed. We see that sectors for which sub-sectors were liberalized at _>_ =25 percent _<_ 50 percent, realized FDI reduced by 12 percent for the period after policy as compared for the period before policy, while for the sectors liberalized at _>_ =50 percent _<_ 75 percent, realized FDI reduced by 20.7 percent. For the sectors liberalized at _>_ =75 percent _<_ 100 percent, realized FDI increased by 14.2 percent for the period after policy as compared to the period before policy. While these are illustrative, the results suggest that FDi was higher in the sectors for which more sub-sectors were liberalized.

Figure 6 a and b shows the period-by-period coefficients of the response to the policy for the non-liberalized and the fully liberalized sectors. We see that the changes in FDI for the nonliberalized sectors are mostly below zero percent.

Table 2: Average Effect on the Treated for Realized FDI by Percentage of Liberalization

||ATET|ATET|ATET|ATET|ATET|
|---|---|---|---|---|---|
|0% (1vs0) Liberalized|-0.858***<br>(0.0482)|||||
|_>_=25%_<_50% (1vs0) Liberalized||-0.120***<br>(0.0199)||||
|_>_=50%_<_75% (1vs0) Liberalized|||-0.207***<br>(0.0750)|||
|_>_=75%_<_100% (1vs0) Liberalized||||0.142**<br>(0.109)||
|100 % (1vs0) Liberalized|||||0.228**<br>(0.0221)|
|Observations|915|915|915|915|915|

Note: Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 . The treatment corresponds to 1 while the control group is 0. ATET denotes the weighted average treatment effect on the treated. ATET estimates are adjusted for sector/panel effects and time effects. The sample period is January 2019 to September 2022. The pre-policy period is defined as the period from the first quarter of 2020 to the first quarter of 2021, and the post-policy period is defined as the period from the third quarter of 2021 to the third quarter of 2022. Source: Author estimations 14
**Table 4**

**The Energy/Edible Oil Price Link**

||**_μ_**|**_1_**|**_2_**|**_100*_****_3_**|**_Adj‐R_**<sup>**_2_**</sup>|**_ADF_**|
|---|---|---|---|---|---|---|
|**1960‐2005**|2.75**|0.23***|0.34*|‐1.37**|0.77|‐2.86*|
||(4.83)|(3.55)|(1.66)|(2.39)|||
|**1960‐2009**|3.52**|0.31***|0.05|‐0.57|0.77|‐2.79*|
||(6.92)|(5.00)|(0.27)|(1.14)|||

**Source:** Author’s estimates based on World Bank price data.

**Notes** _:_ The model structure is the same as in equation (3), where _Pt_<sup>_i_</sup> denotes the non‐energy price index and _Pt_<sup>_j_</sup> denotes the energy price index. For other notes see Tables 1 and 3.

## **Figure 9**

**Price Transmission Elasticities from Energy and Non‐Energy Commodities (estimates based on model (3))**

<!-- Start of picture text --> 1960‐2005 1960‐2009<br><!-- End of picture text -->

# **4. The Cotton By­Products Industry in Sub­Saharan Africa**

To gain a better understanding of the structure and issues of the cotton by‐ products industries in SSA, the experience of four countries was analyzed: Uganda and Tanzania in Eastern and Southern Africa (ESA); and Benin and Bur‐

**_Page 17_**
----- (2005a); World Drug Report, Volumes 1 (Analysis) and 2 (Statistics), United Nations Publication.

Available at: <u>http://www.unodc.org/unodc/en/world_drug_report.html</u>

----- (2005b); Coca cultivation surveys for Bolivia, Peru and Colombia, United Nations Publications.

Available at: <u>http://www.unodc.org/unodc/en/crop_monitoring.html</u>

----- (2006); World Drug Report, Volumes 1 (Analysis) and 2 (Statistics), United Nations Publication.

Available at: <u>http://www.unodc.org/unodc/en/world_drug_report.html</u> University of Michigan (2004); Monitoring the Future, The University of Michigan, Institute for Social Research.

Available at: http://www.monitoringthefuture.org/new.html Walters, J. (2002); The Other Drug War, The Oregonian, Op-Ed., April 22. Available at: <u>http://www.state.gov/p/inl/rls/op/2001/9637.htm</u> 50
**Table 2b Plan to Return (Marginal Effects reported)**

|VARIABLES|Model (4)|Model (5)|Model (6)|Model (7)|Model (8)|Model (9)|Model (10)|
|---|---|---|---|---|---|---|---|
|lnearngap6|-0.098***|-0.096***|-0.094***|-0.093***|-0.093***|-0.091***|-0.081***|
|Assistance|-0.019|-0.024|-0.023|-0.023|-0.023|-0.019|-0.011|
|Many|0.056*|0.053*|0.053*|0.052*|0.052*|0.052*|0.055*|
|Romanians<br>nearby||||||||
|Some<br>Romanians<br>nearby|0.056*|0.052*|0.053*|0.052*|0.052*|0.050*|0.055*|
|YrAgoMigr|-0.007**|-0.005*|-0.005*|-0.004|-0.004|-0.004|-0.003|
|Proficient<br>in<br>host language||-0.174***||||||
|Fluent<br>and|||-0.212***|-0.199***|-0.199***|-0.191***|-0.162***|
|Language<br>Training abroad||||||||
|Fluent and no<br>Language|||-0.168***|-0.165***|-0.165***|-0.161***|-0.138***|
|Training abroad||||||||
|Not Fluent and|||-0.158|-0.158|-0.157|-0.152|-0.163|
|Language<br>Training abroad||||||||
|Other training<br>while abroad||||-0.038|-0.038|-0.036|-0.033|
|Skills<br>and<br>experienced<br>effected<br>investment<br>interest|||||-0.002|-0.002|0.005|
|Invested in host<br>country||||||-0.056|-0.052|
|Legal Migrant|||||||-0.145***|
|Agriculture|-0.081|-0.107**|-0.105**|-0.105**|-0.105**|-0.108**|-0.114**|
|Hotel|-0.073*|-0.071|-0.067|-0.061|-0.061|-0.062|-0.056|
|Manufactured|-0.032|-0.030|-0.029|-0.025|-0.025|-0.028|-0.023|
|Transport|-0.081|-0.069|-0.062|-0.059|-0.059|-0.057|-0.048|

20
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 (Dubeck and Gove, 2015). Moreover, our two measures, comprehension and fluency, show high correlations with one another in 4
Table 12. Average Pre-Uruguay Round Tariffs and Nontariff Measures Facing Middle East Exports to the European Union, Japan and the United States.

||Europ|ean Union||Japan|Unit|ed States|
|---|---|---|---|---|---|---|
||All Products|All Non-Oil Goods|All Products|All Non-Oil Goods|All Products|All Non-Oil Goods|
|1992 Value of Exports ($million)|39,604.9|12,015.5|27,986.5|1,698.2|18,167.7|5,975.0|
|Bahrain|172.9|148.1|304.1|63.2|71.2|68.9|
|Cyprus|433.2|431.7|2.1|2.1|11.7|11.7|
|Egypt|2,893.4|923.9|91.0|33.9|465.6|223.2|
|Iran|6,649.1|537.9|2,604.5|82.6|0.8|0.8|
|Israel|3,731.3|3,671.8|694.6|694.6|3,902.3|3,880.6|
|Jordan|88.2|88.2|24.1|24.1|18.6|18.6|
|Lebanon|96.8|96.5|2.2|2.2|28.5|28.5|
|Libya|7,021.0|152.2|0.7|0.7|||
|Oman|165.7|165.7|1,962.4|5.1|207.3|97.2|
|Qatar|55.8|24.2|2,175.5|9.6|75.9|71.5|
|<br>Saudi Arabia|11,150.4|1,063.5|<br>10,181.1|303.5|11,285.7|183.2|
|Syria|1,225.1|144.8|8.5|8.5|45.8|12.4|
|Turkey|4,208.4|4,018.1|202.2|202.2|1,183.4|1,155.4|
|UAE|1,713.7|549.0|9,733.3|265.9|871.0|222.9|
|Average Tariff (%)|0.4|1.2|3.0|0.5|1.1|2.7|
|Bahrain|0.8|0.8|0.7|0.0|6.9|4.5|
|Cyprus|3.9|4.0|9.9|9.9|7.9|7.9|
|Egypt|0.6|1.3|2.0|0.7|4.6|8.9|
|Iran|0.1|0.4|3.3|0.4|2.0|2.0|
|Israel|2.0|2.0|0.3|0.3|0.1|0.1|
|Jordan|0.2|0.2|1.1|1.1|3.7|3.7|
|Lebanon|3.0|3.0|1.9|1.9|2.1|2.1|
|Libya|0.0|0.0|0.6|0.6|||
|<br>Oman|0.9|2.4|3.6|0.6|1.5|7.5|
|Qatar|0.1|0.3|3.5|0.0|2.0|3.4|
|<br>Saudi Arabia|0.0|0.2|3.1|0.0|0.6|2.8|
|Syria|0.0|0.1|0.2|0.0|0.5|4.0|
|Turkey|_0.5_|0.4|2.5|2.6|6.3|8.3|
|UAE|0.4|2.6|3.0|0.0|2.5|9.6|

0o
among treatment. Thus, program beneficiaries were 3-4 times more likely to be in school at the end of the 2008 school year than the control group.<sup>24</sup> Since those enrolled in school actually report attending school over 90% of the time, these treatment effects on enrollment do translate into improvements in actual days of school attended.

For the stratum containing baseline schoolgirls, i.e. those who were still in school at baseline, while the absolute numbers are smaller (due to high rates of continued schooling among this group), the relative impact is still impressive (column 3). Among the control group, 89.1% of initial schoolgirls were still enrolled in school at the end of the 2008 school year, compared with 93% in the treatment group. Thinking of these as dropout rates, the CCT program reduced the dropout rate among this group by 35% -- from 10.9% among controls to 7% among treatments.

# **5.3. Marriage and Fertility**

We now turn to early marriage and teen pregnancy as indicators of sexual activity.<sup>25</sup> Table 4 presents the impact of the program on having **never** been married. Early marriage increases coital frequency, significantly decreases condom use, and virtually eliminates the ability to abstain from sex (Clark, 2004). As described earlier, the study sample was selected to be never-married at baseline, so levels of marriage are equal to the incidence during 2008. We see that 27.7% of initial dropouts in the control group have gotten married during the past year, compared with only 16.4% of the same group in

> 24 The school enrollment and attainment data are self-reported by the study respondents. However, the school enrollment and attendance of program beneficiaries, i.e. the treatment group, was monitored as part of the program and can be confirmed. Full enrollment, attendance, school grades, and performance at national examinations will become available for the entire study sample after we complete conducting a school census in Zomba between February and May, 2009.

> 25 The reader may object to marriages in this study being described as ‘early’ and pregnancies as ‘teenage’. While it is true that the study sample does include some over the age of 19, this is a small percentage (less than 12% at the end of Year 1) of the sample.

17
# 3. Is the nexus rising in EMDEs? A first look at the data

## 3.1. Deterioration of fiscal conditions across all regions

EMDE central government debt has increased significantly across all regions, particularly in Sub‐ Saharan Africa (AFR) and Middle East and North Africa (MENA) (Figure 2, A and B). EMDE government debt reached US$14 trillion in 2017 (48% of GDP), rising from US$8 trillion in 2010. EMDE medians tell a similar story: In 2007, at the start of the global financial crisis, median general government debt to GDP stood at 35.3% and has risen to 49.3% in 2017 ‐‐ it is as high as 66.8% to GDP for poor performers (75<sup>th</sup> percentile). This pattern is consistent across all regions. In particular, debt levels are high and rising in many low‐income countries, many of them in AFR where the median currently stands at 54%. Further, median debt is in excess of 65% in MENA and South Asia (SAR) and poor performers in MENA even breached 100%.

In tandem, there is a broad‐based and consistent deterioration of the fiscal balance in EMDEs, although conditions have improved slightly since 2016 (Figure 2, C and D). In 2007, the median fiscal position for EMDEs was ‐0.7% of GDP but deteriorated quickly in the wake of the crisis reaching a nadir in 2009 of ‐3.6% before recovering. However, since 2012, the median fiscal position has deteriorated further and stood at ‐2.9% to GDP in 2017, a 0.4 percentage point improvement compared to 2016. Particularly poor performers (25<sup>th</sup> percentile) are currently faced with deficits of 5.4% to GDP. In all regions, except Europe and Central Asia (ECA), the median deficit is at least 2% of GDP – deficits are more than 4% in AFR, SAR, and MENA.

While reflecting a smaller country sample, the evolution of EMBI spreads tell a consistent story (Figure 2, E and F).<sup>4</sup> The median spread reached a decade high in 2016 of 414 basis points but fell by about 100 basis points in 2017.Widening spreads suggest a tightening of external funding conditions over and above an increase in global interest rates. This is both a reflection of weaker international investors’ risk appetite as well as their assessment of external sovereign debt

> 4 The EMBI spreads are a widely followed benchmark of the weighted yield of dollar‐denominated sovereign bonds over US government debt securities of about 36 emerging markets.

18
Policy Research Working Paper 10821

# **Abstract**

This paper builds on recent advances in machine-based pattern recognition to estimate species occurrence maps, using georeferenced open-source data from the Global Biodiversity Information Facility. With currently available data, the estimation algorithm has produced maps for more than 600,000 vertebrates, invertebrates, other animals, vascular plants, and fungi. The algorithm is designed for rapid map updates and estimation of new maps with continued increases in Global Biodiversity Information Facility occurrence reports. The paper compares the algorithm-produced maps with species-matched sets of expert maps for mammals, ants, and vascular plants. Using comparative species density counts in a high-resolution grid, it finds close similarity in global distribution patterns. It also traces regional differences to technical differences in estimation methods or cases where the boundaries of existing expert maps could be revised to reflect species-level patterns in Global Biodiversity Information Facility reports. The paper uses the estimated Global Biodiversity Information Facility maps to explore the global distributions of endemic species and species whose small occurrence regions increase their extinction risks. It finds a high overall incidence of endemism, with significant variations across major species groups. It also identifies about 118,000 species whose small occurrence regions create significant extinction risks. For both endemic and small-occurrence region species, the paper finds patterns of spatial clustering that identify candidate areas for cost-effective protection.

This paper is a product of the Development Research Group, the Development Data Group, Development Economics and the Environment, Natural Resources and Blue Economy 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 bblankespoor@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 is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team