rafmacalaba/data-use-mentions-extended huggingface dataset:
’s has decreased.  The proportion of working adult women ranged from 12 percent (Dominican Republic) to 54 percent (Jamaica) in 1960 and by 2000, the range was between 37 percent (Peru) and 74 percent (Jamaica).  Men’s participation, on the other hand, declined from approximately 92 percent across the region to 85 percent by the end of the century.  Not surprisingly, women’s share of the labor force also increased over this period, from approximately 20 percent of the labor force to onethird of all workers.

**_Women’s participation is driven by education, urbanization and late marriage_** : The diverging trends are due to several factors that differ by gender.  First, educational attainment has increased in the region (World Development Indicators) leading to higher human capital for women, and thus labor force entry among those whose value was previously higher in the home (Blau 1998), but a decrease in male labor force participation as boys spend more time in school rather than entering the labor market.  Over this period, the growth in women’s education attainment exceeded that of men, and in some urban centers in Latin America, women’s stock of education, particularly that of young women, exceeds that of men.  Second, rapid urbanization has lead to a formalization of the work relationship so women, who are often invisible workers in rural family businesses, became visible employees in the cities.  Third, women are marrying later, and thus must work to support themselves (Demographic and Health Surveys).  Finally, the emergence of frequent economic shocks and unemployment in the region has increased the need for female labor to cushion the household from negative income shocks (Geldstein 2000, World Bank 2000, Cunningham 2000).

> 39 See background work by Cunningham (2003)

23
electronics and machinery sectors, while it is lowest in labor-intensive forestry, fishing, agriculture and food production.

To assess the durability of products we calculate the sector’s share of durable consumer products, semidurable consumer products and cars and transport equipment ( _durablek_ ). Durable, semi-durable and transport products at the HS6-digit level are identified based on the UN BEC classification.<sup>18</sup> Durability measures the percentage of HS-6 products that are classified in each of these three non-overlapping categories within an ISIC Rev. 3 4-digit sector. If an ISIC Rev. 3 4-digit does not contain any products classified as durable, semi-durable or transport products, the share takes the value of 0. In an extension, we examine the role of durable consumer products ( _cons_durk_ ), semi-durable consumer products ( _cons_semik_ ) and transport products ( _transpk_ ) separately and also include a measure that combines the share of both durable and semi-durable consumer products only ( _consk_ ).

> 17 Following del Rio-Chanona et al. (2020).

> 18 Durable consumer products correspond to BEC sector 61, semi-durable consumer products correspond to BEC sector 62, and transport products correspond to BEC sectors 51 and 522.

13
some people decide to move while others stay. It also allows understanding the differences in individual and household characteristics that encourage different reasons for internal migration.

**The Census allows for the identification of internal migrants during the period 2015-2020, and it also provides information on the reasons for migration.** The model's dependent variable in the multinomial logistic regression includes four categories based on the reasons for internal migration, and an additional category to account for those who did not migrate, for a total of five categories. The first category corresponds to people who migrated internally for economic reasons, including seeking a job, changing jobs, or having a job offer. The second category corresponds to people who moved internally due to family reunification or because they got married. The third category is people who migrated due to criminal insecurity or violence in the state of origin. Finally, the fourth category corresponds to people who relocated for study, natural disasters, or other non-identified reasons (see Diagram 1).

23
# **Figure 11. Share of Workers within Employment types and welfare level with the job characteristics**

<!-- Start of picture text --> Unpaid workers LE-SE Informal Wage employees<br>1.0 1.0 1.0<br>0.8 0.8 0.8<br>0.6 0.6 0.6<br>0.4 0.4 0.4<br>0.2 0.2 0.2<br>0.0 0.0 0.0<br>Agric Indust Serv Other Low- Med- High- Agric Indust Serv Other Low- Med- High- Agric Indust Serv Other Low- Med- High-<br>skilled skilled skilled skilled skilled skilled skilled skilled skilled<br>Bottom 40% Top 60% Bottom 40% Top 60% Bottom 40% Top 60%<br><!-- End of picture text --> Source: Own elaboration based on GMD. Notes: Cross-country averages using population weights. The number of countries used for each variable is the same as the one indicated in previous figure. Up to secondary incomplete education is classified as "low education" and secondary complete or college education is classified as "high education".

**Table 2. Determinants of Urban Informal Types of Workers**

||Unpaid<br>|workers<br>|Low-educ<br>empl<br>|ated Self-<br>oyed<br>|Inform<br>empl<br>|al Wage<br>oyees<br>|
|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(1)|(2)|(1)|(2)|
|25-54 years old<br>55 years or older|
a PMT model ([62.8%]). Whether similar gains can be achieved in a less densely populated setting is an open question. Similarly, the assumptions of a uniform time per question and the ability to expand survey coverage to nearly the whole area population might prove overoptimistic in a real-world setting. Nevertheless, the urban case study highlights the potential benefit of expanding targeting survey coverage in some settings, even if it entails a moderate accuracy penalty.

A census sweep of the whole population with an extensive questionnaire would provide the most comprehensive data for accurate targeting. But if budgets are constrained, surveying more households with a shorter questionnaire may yield the best results. Beyond maximizing incidence with limited resources, policymakers would also enjoy greater flexibility through the methods presented here. The resources needed to conduct regular reassessment constrain targeting surveys to be conducted with multi-year gaps in the case of survey waves, and potentially also increase the re-assessment intervals in on-demand systems. A reduction in survey cost could contribute to a more dynamically supportive social protection system by raising the frequency of assessment and re-assessment. It is also worth noting that TrESt’s efficient use of survey resources via household-level early stopping, a suitably truncated questionnaire, and a more effective predictive model appears to offer a clear accuracy improvement over the standard PMT for a given average questionnaire length.

To generate transparent results, our simulations consider a single programme with a fixed population coverage rate. Countries with social registries, of which Indonesia’s DTKS is a well-documented example, usually target multiple programmes with differing population coverage rates that result in different eligibility thresholds. Prediction interval-based methods can be adjusted to this common scenario by using an eligibility interval instead of a threshold. The appropriate interval for each household can be identified according to characteristics queried in the core questionnaire. _Ceteris paribus_ , wider eligibility intervals will result in longer questionnaires on average. A related caveat is that the targeting accuracy of new programmes that leverage existing consumption estimates but which have different eligibility thresholds – widely adopted during the recent COVID-19
|Require government<br>agencies to collect<br>data on hate crimes<br>committed against<br>LGBTI individuals or<br>those perceived as<br>such?|Yes|Judiciary System Act<br>(2007), Art. 80 Para. 1,<br>Art. 86 Para. 1, Art.<br>382 Para. 3|<br>N/A|N/A|N/A|N/A|N/A|
|---|---|---|---|---|---|---|---|
|<br>Does the law mandate<br>|trainin|gs of the following profess|ionals on recognizing <br>|and identifyinghate crimes?<br>||||
|Paramedics?|No||N/A|N/A|N/A|N/A|N/A|
|Police officers?|No||N/A|N/A|N/A|N/A|N/A|
|Prosecutors?|No||N/A|N/A|N/A|N/A|N/A|
|Judges?|No||N/A|N/A|N/A|N/A|N/A|
|Social workers?|No||N/A|N/A|N/A|N/A|N/A|
|Other?|No||N/A|N/A|N/A|N/A|N/A|
|Services to Victims of<br>Does the law mandate|Hate C<br>thepr|rimes<br>ovision of anyof the follow|ingservices to victim|s of hate crimes?||||
|Legal assistance (in
maximum wind speed, and location at landfall (NOAA 2009). Estimates of county income and population density are inferred from Census data for the five counties closest to the point of contact. A regression estimates the relationship between storm intensity and damages. We then use international data (EMDAT 2009) to estimate the relationship between storm damages and national income and population density (vulnerability). The limitation of the international data is that it has no information about the intensity of each storm.

> 2 5000 tropical cyclones are seeded into the North Atlantic to get extra accuracy of the changes in this ocean.

3
imports stimulating internal demand by serving as intermediates for production. To test our hypothesis, we present a two-stage methodology in which we first estimate the potential impacts of newly signed trade agreements on trade.

**Figure 1. Imports and exports of goods and services, constant 2010 $ US Panel B.**

<!-- Start of picture text --> $120<br>Imports<br>$100<br>Exports<br>$80<br>$60<br>$40<br>$20<br>$0<br>Billions<br>1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 2019<br><!-- End of picture text --> Source: Authors’ elaboration using UN COMTRADE data

**Figure 2. Average weighted applied tariff rates for all products, 1995-2016**

<mark>Source: World Bank</mark>

**Figure 3. Egypt Imports, Industry share.** (WITS, 2022)

**Figure 4. Employment share of top importing industries, 2009-2017, the secondary axis for agricultural products, manufacturing, and services industries.**<sup>**4**</sup>

<!-- Start of picture text --> 25 2.5% 60%<br>50%<br>2.0%<br>20<br>40%<br>1.5%<br>15<br>30%<br>1.0%<br>10 20%<br>0.5%<br>10%<br>5<br>Vegetable Products Mineral<br>Chemical Textiles 0.0% 0%<br>Metals Machines<br>0 Transportation Textiles and Apparel Chemicals Metals<br>Machinery Transportation Minerals<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Vegetables Manufactures Services
multiple rounds of Nepal’s population census, only about 3 percent of the population was abroad in 2001, an increase of 0.7 percentage points since 1981. By 2011, over 7 percent of the population was abroad. In terms of households, one in four households had a migrant abroad and almost a fifth (18 percent) of the households had migrants outside India.

The majority of the migrant population is young males. In 2011, about 90 percent of the migrants outside India is male. The fraction of males is only slightly lower (85 percent) for India migrants. The median age of migrants is only 25 years old and almost 90 percent of migrants are aged between 15 and 45 years. Absentee males are 13 percent of the resident male population. Since migrants are mostly of working age, more than a fifth (22 percent) of the national male population aged 15-45 are outside the country. The educated migrants, especially outside India, earn and remit more money to their families in Nepal. Most of the migrants went for work. In particular, over 97 percent of the migrants to Malaysia and the Gulf countries migrated for work. The share of work-related migration is a bit lower at about 85 percent, for India and other destinations.

The pace at which remittances from abroad have increased in Nepal is unprecedented. Until the late 1990s, personal remittances received were less than 1 percent of GDP, lower than that for Bangladesh or India. The first half of the 2000s saw a drastic increase in this share, from 2 percent in 2000 to 15 percent in 2005, 22 percent in 2010 and as much as 29 percent in 2014, while the neighboring countries experienced an increase at a much more modest rate (Figure 7, panel b).<sup>19</sup> Expressed in per capita terms in current US dollars, Nepalis on average received less than $5 of remittances from abroad in 2000 but it reached $205 by 2014, by far the highest in
included an embrace of markets and an unusually high degree of trade openness (Subramanian, 2011). The problem, however, is the strong political perception that China‘s export success has been achieved, and continues to be sustained, in part by an undervalued exchange rate.

It seems unlikely and politically unrealistic to expect China‘s trading partners to open further their markets to China when China is perceived as de facto (via the undervalued exchange rate) imposing an import tariff and export subsidy not just in selected manufacturing sectors but across the board. The evidence on the existence and extent of undervaluation continues to be debated. On the one hand, in a survey of studies on renminbi misalignment conducted by Cline and Williamson (2008), 17 of the 18 studies concluded that the renminbi is undervalued; the average

> 4 While highlighting the difficulties for Doha posed by Chinese dominance, we do not exclude the possibility that there are other reasons for the Doha stalemate.  For instance, the United States is perceived to have progressively raised the bar – perhaps to politically impossible levels - in terms of what it seeks by way of trade concessions from other countries.

6
# <u>2. Context, interventions and research design</u>

# _Country context_

Both interventions were implemented in the Kingdom of Tonga. The country is a small island state with a population of just over 100,000 inhabitants. While its population is small, the country consists of 176 islands of which 40 are inhabited and spans 718 square kilometers. The kingdom is divided into five main island groups: Tongatapu, Vava’u, ‘Eua, Ha’apai and the Niuas. The capital of Tonga, Nuku’alofa, is over 2,000 kilometers from its nearest large market, New Zealand, and over 3,000 kilometers from Australia.

The education system is quite small with approximately 17,000 students in primary school covering ages 6 to 11 and 14,500 secondary students aged 12 to 17. Enrollment rates in primary and secondary school are approximately 90 and 80 percent, respectively. Preschools are private, operated by either churches or communities, and data on preschool participation are limited, though UNESCO UIS reports approximately 2,000 students enrolled.

Despite this level of enrollment at the primary level, students’ learning outcomes remain a challenge. For example, assessments of early grade literacy in Tonga in 2009 found that only 30 percent of 3<sup>rd</sup> grade students were able to read fluently for comprehension (World Bank 2012a). Similar outcomes were found in other Pacific Island countries. In Vanuatu, approximately a quarter of 3<sup>rd</sup> grade students were able to read fluently for comprehension (World Bank 2012b, 2012c). In Kiribati and Tuvalu, 20 percent of 3rd grade children achieved minimum reading comprehension proficiency (World Bank 2017a, b). In Papua New Guinea, early grade reading assessments conducted in four provinces between 2011 and 2013 found that students lagged two years behind curriculum targets for fundamental pre-reading skills (World Bank 2014a, 2014b, 2014c, 2014d).

This poor performance in reading outcomes in Pacific Island
networks|6.3|3.7|3.9|4.2|3.7|2.9|3.4|4.2|
|_of which: ICT_|_1.2_|_1.4_|_2.0_|_2.4_|_2.1_|_1.6_|_2.2_|_2.8_|
|Otherparts and engineering products|8.9|7.6|8.1|9.2|9.0|8.9|11.6|11.8|
|Memorandum: Share in Bulgaria's EU-<br>oriented exports|32.2|33.5|37.6|41.4|38.6|44.0|44.2|45.5|

Source: Derived from EU trade data as reported to the UN COMTRADE database.

Although exports from ‘buyer-driven’ chains continued to grow faster than total EUoriented exports in both 1996-99 and 2000-03, two significant changes have been occurring. First, textiles and clothing ceased to be the levers of ‘buyer-driven’ exports in 2000-03, although their export growth—driven increasingly by textiles—has remained impressive at an average annual growth rate of 13 percent. Second, furniture was the most dynamic export in 2002-03. The share of T&C products in ‘buyer-driven’ network exports fell from 82 percent in 1998-2001 to 80 percent in 2002-03 due to the surge in exports of furniture network products in 2002 and 2003. The share of furniture exports in total ‘buyer-driven’ networks’ exports grew from 4 percent in 1997-2001 to 6 percent in 2002-03.

Until recently Bulgaria had remained outside the EU-based ‘producer-driven’ networks. But in 2001-03 there were significant changes indicating entry of Bulgarian producers into supply chains of both networks. Bulgaria inherited industrial structure favoring specialization in ICT products rather then automotive ones,<sup>22</sup> yet automotive sectors accounted in 1996 for 85 percent of total exports of
**Figure 8 – Ratio of female to male labor force participation rates**

<!-- Start of picture text --> 110<br>105<br>100<br>95<br>90<br>85<br>80<br>75<br>70<br>65<br>60<br>55<br>50<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>Cambodia China Indonesia Korea, Rep. Lao PDR Malaysia<br>Mongolia Myanmar Philippines Thailand Vietnam<br>Source: World Bank World Development Indicators.<br>Figure 9 – Ratio of female to male nominal wage rates<br>110%<br>105%<br>100%<br>95%<br>90%<br>85%<br>80%<br>75%<br>70%<br>65%<br>60%<br>55%<br>50%<br>2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Cambodia Korea, Rep. Mongolia Myanmar<br>Philippines Taiwan, China Thailand Vietnam<br>rates in the EAP region (%)<br>Ratio of female to male labor force participations<br>EAP region (%)<br>Ratio of female to male nominal wage rates in the<br><!-- End of picture text -->

**Source: ILO Global Wage Report Collection.**

With regard to the gender wage gap, in Vietnam wages of men are on average around 20 percent higher than those earned by women, with little change in recent years. A decomposition of Vietnam’s labor-related gender differentials by Pierre (2012) finds that only a small part the gender wage gap is

- 20 -
(female/male ratio of 0.86) see a wage decline of 58 percent due to GSS construction, compared to essentially no decline for villages at the 75th percentile of the distribution (female/male ratio of almost 1).

In Column (7), we look at analogous results using households per capita with access to radios as an indicator of village-level development. While the results for the interaction term are only signicant at the 26 percent level in this case, the signs are in the expected

> 22 Since our dependent variable is log (wage), the coecients of about -0.32 on the GSS existence dummy represent a decrease of approximately 27 percent in average wages. For example, in Column (2), the coecient implies that, in villages with a GSS, wages change by a factor of e<sup>�0:3207</sup> (or 0.7256) , which is equivalent to a 27.44 percent decline.

26
Table A5: Early Life Exposure to Coffee Mills and Test Scores: Effects by Gender

|||All Subjects|||STEM|||Non STEM||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||||||Male|||||
|CatchmentArea X Post|0.0597***|0.0622***|0.0607***|0.0460**|0.0492**|0.0466**|0.0636***|0.0656***|0.0648***|
||(0.0210)|(0.0217)|(0.0215)|(0.0195)|(0.0210)|(0.0208)|(0.0216)|(0.0222)|(0.0220)|
||[0.015]|[0.015]|[0.015]|[0.034]|[0.035]|[0.043]|[0.015]|[0.015]|[0.015]|
|R-squared|0.3792|0.4221|0.4222|0.3698|0.4099|0.4099|0.3574|0.4026|0.4026|
|Observations|668639|668285|668285|668639|668285|668285|668638|668284|668284|
||||||Female|||||
|CatchmentArea X Post|0.0379**|0
: The next section, titled “Governance,” evaluates progress achieved in the area of institutional reform. This section deals with the way governments manage their resources.** In this respect, Latin American countries have generally had two deficiencies, which the current wave of reforms are attempting to remedy. The first is that a large share of government resources was spent on a bloated and ineffective bureaucracy. Public employment was used to reward political loyalties, and in order to accommodate the surplus of employees, new government departments were created, tasks were duplicated, and bureaucratic paper work was made more lengthy and complicated. The recent empirical-growth literature provides substantial evidence that in countries whose government spendsa large share of its revenues to maintain a large bureaucracy, economic-growth performance suffers. Using pooled crosscountry and time-series information, these empirical studies® find that an increase in government consumption, of which wages and salaries to public employees are the largest component, negatively affects the rate of per capita GDP growth. In order to measure the progress achieved by Latin American countries in this respect, the ratio ofwages and salaries of government employees io primary expenditures is considered. The level of government used in the evaluation is the central government, except in the case of Argentina, Brazil and Colombia, countries for which the general government is used. Given the decentralized nature of these countries’ governments, and the fact that fiscal reform in local governments there is not proceeding at the same pace as in the central government, it is essential to consider general government as the unit ofanalysis for those countries.. As Chart 12 shows, a few countries in the region -- namely, the Dominican Republic, El _ Salvador, Peru, and Uruguay -- have shown in the last ten years a clear tendency to spend less for the services of the bureaucracy. Chart 12 also shows that there is considerable variation in the share of government primary expenditures devoted to public employees’ salaries, from nearly 50 percent in the Bahamas, Costa Rica, and Ecuador in the
Table A2: ESS3 <u>weights — Impacts of hosting refugees on household welfare and poverty</u>

|||All househol<br>|ds<br>||
|---|---|---|---|---|
||Consumption|Consumption poverty|Wealth|Wealth poverty|
||(1)|(2)|(3)|(4)|
|Refugee Intensity|-0.175**|0.167***|0.047|-0.007|
||(0.071)|(0.051)|(0.044)|(0.043)|
|Constant|9.599***|-0.814**|0.478|0.623**|
||(0.417)|(0.337)|(0.318)|(0.311)|
|Round FEs|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|
|R-squared|0.068|0.042|0.185|0.112|
|# of EAs|432|432|432|432|
|N|12711|12711|13274|13274|

_Notes:_ This table provides estimates of the impacts of logarithm of refugee intensity on logarithm of total household consumption expenditure per capita (column (1)), a binary measure of household consumption poverty status (column (2)), “started log” of household wealth score (column (3)), and a binary measure of household wealth poverty status (column (4)). Household sample weights from ESS3 applied. All columns control for household (HH) FEs, round FEs, and 12 timevarying characteristics. The coefficient estimates for these 12 characteristics