rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

In recent years, the number of quantitative studies focused on refugees and host communities has increased dramatically (Verme & Schuettler, 2021). Many of these studies seek to quantify the direct costs of hosting and challenges facing refugees with respect to employment (Alix-Garcia _et al._ , 2018; Fallah, Krafft, and Wahba, 2018), consumption (Maystadt and Verwimp, 2014; Kreibaum, 2016; Mahmud and Nalifa, 2020), health (Baez, 2011; Riley _et al._ , 2017; Chan, Chiu, and Chan, 2018; Jasmine _et al._ , 2020) and local prices (Alix-Garcia & Saah, 2010). More recently, there has been an effort to examine the impacts of the COVID-19 pandemic for refugees and hosts (Bukuluki _et al._ , 2020; Charney, 2020; Islam, Inan, and Islam, 2020; Shammi, Robi, and Tareq, 2020; Vishwanath, Alik-Lagrange, & Aghabarari, 2020).

Empirical analyses of the impacts of refugees on natural capital, however, remain limited. Natural capital is the stock of the planet’s natural assets, including plants, animals, soil, minerals, air, water, and other renewable and non-renewable resources that yield ecosystem services, which are the benefits that people receive from nature. Human activities, including land-use that causes changes in land cover, can lead to the loss of natural capital stocks and reductions in the flow of benefits these stocks generate. Land-use / land cover (LULC) change is increasing due to natural disturbances and growing human activities (Abere, Adgo, & Afework, 2020; Elias, Seifu, Tesfaye, & Girmay, 2019; Lambin, Geist, & Lepers, 2003). However, the impacts of refugee camps on their surrounding natural environments remain understudied.

In this paper, we evaluate whether hosting a large refugee population in refugee camps causes changes in forest cover in Bangladesh, and if so, the specific causes of deforestation. We distinguish between two possible channels of impact. The first is the _direct impact of refugees_ , the consequence of clearing land for the camp expansion, and of camp residents harvesting forest products for shelter and cooking fuel. We differentiate this from the _indirect impacts of hosting refugees,_ which captures the influence refugee camps have on local markets, access to services, humanitarian investment, and consequently, local economic productivity and population distributions.

There are very few precedents for our work in the quantitative literature (Black & Sessay, 1997; Maystadt, Mueller, Van Den Hoek, & Van Weezel, 2020; Rahman, 2017; Salemi, 2021). Many of these past studies do not account for the endogeneity of areas selected for camps, 2
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- **Bryan, Gharad, Shyamal Chowdhury, and Ahmed Mushfiq Mobarak.** 2014. “Underinvestment in a Profitable Technology: The Case of Seasonal Migration in Bangladesh.” _Econometrica_ , 82(5): 1671–1748.

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37
**<u>Table 6.</u>** <u>Effects of gender role attitudes and personality traits on labor productivity (women sample)</u>

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Norms|Norms+<br>Controls|Norms+<br>Controls+<br>Personality|_Norms+_<br>_Controls+_<br>_Impulsive_|_Norms+_<br>_Controls+_<br>_Work_<br>_Central_|_Norms+_<br>_Controls+_<br>_Tenacity_|_Norms+_<br>_Controls+_<br>_Polychroni_<br>_city_|_Norms+_<br>_Controls+_<br>_Locus of_<br>_Control_|_Norms+_<br>_Control+_<br>_Achieveme_<br>_nt_|_Norms+_<br>_Control+_<br>_Power_<br>_Motivation_|_Norms+_<br>_Controls+_<br>_Organized_|_Norms+_<br>_Controls+_<br>_Optimism_|
|Traditional|-0.018***|-0.007|-0.011**|-0.025|0.017|0.015|-0.022|-0.033|0.025|0.011|0.026|-0.019|
||(-3.32)|(-1.44)|(-2.11)|(-1.12)|(0.82)|(0.66)|(-1.37)|(-1.42)|(0.70)|(0.59)|(1.08)|(-0.48)|
|Impulsive|||0.100|-0.272|||||||||
||||(0.89)|(-0.59)|||||||||
|Work centrality|||0.031||0.408||||||||
||||(0.36)||(1.48)||||||||
|Tenacity|||0.035|||0.380|||||||
||||(0.38)|||(1.40)|||||||
|Polychronicity|||-0.166***||||-0.450||||||
||||(-2.70)||||(-1.48)||||||
|Locus of control|||-0.072|||||-0.475|||||
||||(-0.85)|||||(-1.28)|||||
|Achievement|||0.041||||||0.590||||
||||(0.32)||||||(1.23)||||
|Power motivation|||0.139*|||||||0.445|||
||||(1.67)|||||||(1.47)|||
|Organized|||0.150*||||||||0.526*||
||||(1.86)||||||||(1.90)||
|Optimism|||-0.224|||||||||-0.380|
||||(-1.51)|||||||||(-0.71)|
|Traditional*Impulsive||||0.008|||||||||
|||||(0.85)|||||||||
|Traditional*Work centrality|||||-0.006||||||||
||||||(-1.13)||||||||
|Traditional*Tenacity||||||-0.006|||||||
|||||||(-0.96)|||||||
|Traditional*Polychronicity|||||||0.006||||||
||||||||(0.98)||||||
|Traditional*locus control||||||||0.008|||||
|||||||||(1.00)|||||

34
Columns 3 and 4 present the results. We see that the pro-exports effect of African migrants is particularly important in the case of differentiated goods. This result illustrates that migrant networks not only play an important role as a substitute for institutions, but also in bridging information gaps between trading countries.

# 4.3. African migrants and intra-African trade

Migrants seem to foster exports from their country of origin both by helping to enforce contracts within a weak institutional environment, and by improving information on trading opportunities and on products. National borders in Africa have seldom been drawn, however, to reflect ethnic differences and very often one observes the same ethnic group living on both sides of a border. Under these conditions, migration to join one’s ethnic group in a neighboring country may not contribute as much in fostering exports. Hence, in this section, we will analyze whether the pro-trade effect of migration is enhanced when migrants settle in a country whose ethnic groups are different from their own or when they settle in a more distant country.

Focusing on trade between Niger and Nigeria, Aker et al. (2010) found that common ethnicity across the Niger-Nigeria border promote trade flows between the two countries (through a reduction of the border effect on price dispersion). Belonging to the same ethnic group can promote trade through cultural similarities (Guiso et al. 2009) and trusting relationships (Greif 1993). Since fixed trade costs are higher in culturally distant countries, migrants should produce a larger effect on exports in this case (Peri and Requena-Silvente, 2010). Migrants should thus especially favor trade in countries which are ethnically different because of lower cultural similarity and less established trusting relationships.

To empirically examine whether the diaspora-trade relationship depends on ethnical proximity between the trading partners, we introduce in equation (1) an interaction term between migrants and differences in ethnicity among country pairs. To measure ethnicity differences for each country pair, we use data on ethnic families drawn from Murdock (1959), who distinguishes fifteen distinct ethno-linguistic families in Africa. In some countries, only one large ethnic family is present, whereas several of them are present in other countries. In addition, we use Fearon’s (2003) data on the proportion of the population belonging to each ethnic group. Using information from the CIA’s World Factbook, the Encyclopedia Britannica, the Library of Congress Country Study and from national sources, Fearon (2003) proposes a list of 822 ethnic groups in 160 countries that represent at least one percent of the country population in the early 1990s. We aggregate these ethnic groups into the fifteen large 16
enrollment statuses. Panel B in Table III reports the same information as Panel A, but as reported by the teachers.

The evidence in Panel B reverses the finding on the relative effectiveness of CCT vs. UCT on enrollment. First, we note that the enrollment rates in the control group are lower by approximately 5-6 percentage points (pp). Furthermore, while enrollment rates are still higher in both treatment arms than the control group, the gains in the CCT arm are significantly larger than the UCT arm and the difference between the two treatment arms in terms of total number of terms enrolled during the two-year intervention is significant at the 95% confidence level (p-value=0.011). Furthermore, the impact of the CCT intervention seems to have persisted after the cash payments stopped at the end of 2009, while the enrollment rate in the UCT arm is identical to that in the control group during term 1 of 2010 (column (8)).

Given the divergent results between self-reports and teacher-reports, which set of findings should we believe? Baird and Özler (2010) use administrative records from the CCT program to establish that the school ledgers collected independently in Round 3 provide a reliable source to measure attendance. Using attendance information from these school ledgers here as our benchmark, we can examine the extent of misreporting by the students and the teachers. Table IV presents this evidence. In column (1), we see that 17.0% of the girls in the control group who reported being enrolled in school during Term 2 of 2009 were found to have never attended school during that period according to the school ledgers. This likelihood to over-report enrollment is reduced by more than 50% (9.3 pp) in the CCT arm, but is identical in the UCT arm. Column (2) shows not only that the over-reporting is substantially reduced when the information comes from the teachers (5.2% in the control group), but also that the differential misreporting disappears.

The analysis above explains the divergent findings in Table III: girls in both the control group and the UCT arm are significantly more likely than the CCT arm to report being enrolled in 20
combining training with TA increases productivity in three of the four categories of technology. On average, when training increases technology's productivity the benefit is more than six percent. These results indicate the importance of complementary investments in worker training to realize the productivity potential of technology. Training has the greatest effect when a firm adopts machinery tools, and the least effect when a firm adopts robots.

**Table** 5. **The value-added of training** with **technology**

|**Dependentvariable:**|**1**|**992-199**|**9**|
|---|---|---|---|
|**Log(value-added)**|**Coeff.**||**t-Stat.**|
|**ProductionFunction**||||
|Constant|9.1594|**|27.833|
|Log(capital)|0.0948|**|3.843|
|Log(total hours worked)|0.1378|**|5.224|
|TypesofTAandTraining||||
|Automatic equipment|0.0635|*|1.742|
|Machinerytools|0.0789|**|2.593|
|Computerized machinery|0.0671|*|1.684|
|Robots|-0.0041||0.976|
|TAExperience||||
|**1**|-0.1029|**|-3.864|
|2<br>___<br>|_<br>-0.0081||0.817|
|Numberofobs.<br>||2,733||
|OverallR<sup>2</sup>||0.4143||

' Significant at 10% level; ** Significant at 5% level. _Note:_ Regressions included dummy variables for 9 manufacturing divisions, foreign firms and joint-ventures.

# 6. **Conclusions**

This paper attempts to understand how TA affects demand for highly skilled, skilled and semi-skilled, and unskilled workers. Both in terms of wages and employment, with TA firms tend to hire more high skilled workers and pay them better. We also analyze the relative times of TA and demand shifts for a given firm. We find that demand for highly skilled workers increases after the adoption of technology, but not before. By the second period after TA, demand for highly skilled, skilled, and semi-skilled workers has risen notably, while the share of employees made up of unskilled workers tends to diminish after the adoption of technology. Additionally, training and increases in human capital magnify technology-driven productivity gains.

16
Page 14 BOX: 2l Excess Female Mortality in India India is one of the few countries in the world where males substantially outnumber females: 935 females to 1000 males, according to the 1981 census. The sex ratio at birth favors males (952 females to 1000 males). In most populations this biological advantage is eliminated by the age of 1 because of higher mortality among male infants, but in India, male and female infant mortality rates are equal (104 per 1000 live births in 1984). After age 1, female mortality is clearly excessive: the mortality rate among girls 0-4 years old is 43.0, compared to 39.5 for boys (1984). Between ages 1 and 29, female mortality remains 25 to 40 percent higher than male mortality; for the age group 30-34, it is 10 percent higher. The gender gap in mortality is greatest in early childhood when most deaths actually occur: 48.8 percent of all female deaths and 45.3 percent of all male deaths are of children 5 or younger. The ratio of girls to boys decreases continuously up to age 15. The disadvantage to girls worsens as birth order rises. A recent study in the Punjab recorded a female to male mortality ratio of .75 among first-borr. children 4 and younger (Das Gupta 1987). The ratio jumped to 1.3 for second children and to 1.53 for fourth or later children. Even higher mortality rates were found among second-born girls if the first surviving child was already a girl. States with the highest mortality levels have the greatest male-female differentials.

Source: Meera Chatterjee, World Bank, 1989b Background Paper, 1988 BOX: 3 Literacy Among Women in India Most Indian women are illiterate. According to the 1981 census, only 28.5 percent of females could read, compared to 53.5 percent of males (excluding children aged 0-4 years). Of the 340 million illiterates in India, about 200 million are women. The rural female literacy rate in 1981 was 20.7 percent -- less than half the rural male literacy rate and barely more than one-third of the urban female literacy rate. In urban areas, 54 percent of women are literate, compared to 74 percent of men. Younger women are more literate: 36 percent of rural girls 10-14 years old can read, compared to 62 percent of boys. In urban areas, the gap begins to close: 73 percent of girls aged 10-14 can read, compared to 82 percent of boys. Illiteracy is far more prevalent in the poorer districts of five states (Andhra Pradesh, Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh); in Kerala, by contrast, all districts report that 50 to 80 percent of females can read. Among scheduled castes and tribes in rural areas, fewer than one-tenth of females and about one-fourth of males are literate.

Source: Kurrien, World Bank, 1989b Background Paper 1988
and hence may be reducing information asymmetries.

To further understand the mechanisms through which eBay reduces the impact of distance on bilateral trade flows, we interact distance with measures of exporter corruption and information-availability. Our corruption measure is from the World Governance Indicators. Our measure of country information-availability is the number of Google search results for the country name. The idea is that there is more available information about countries that have more Google results. The marginal effects of distance as a function of corruption and country popularity are reported in Figure 7 (drawn from Table 6). The higher the level of corruption in the importing or the exporting country, the larger the distance-effect difference between online and offline flows. Similarly, the lower the degree of country information, the larger the difference in distance coefficients. Furthermore, these differences in distance effects are largest when exporters, rather than importers, are corrupt or unpopular. This confirms that due to eBay’s ’cash-in-advance’ system, it is trust in the exporter that matters most.

Finally, as noted earlier, the difference in the effect of distance could be due to a selection of ’international’ buyers rather than a ’technology’ effect. While the appended model including importer-eBay and exporter-eBay fixed effects partly corrects for these selection effects, buyer and seller characteristics might also affect the impact of distance. For example, online buyers may tend to be richer and rich individuals may prefer purchasing goods from faraway countries. Ideally, we would like to observe individual characteristics of buyers online and offline, but we do not have access to that data. Thus, we check whether distance matters less online at different levels of income inequality and internet penetration in the importing country. The idea is that in highly unequal societies with low internet penetration only a few privileged ’international’ buyers have access to internet and buy on eBay. In these countries buyers on eBay and offline are likely to be most different. As reported in Figure 8 (drawn from Table 7), we find the biggest differences in distance effects in unequal countries and in countries with low internet penetration, suggesting part of the difference may reflect a selection of ’international’ buyers online. Still, we find that even for the most equal or most internet-penetrated countries, where the online and offline buyers are plausibly most similar, the distance effect is still statistically smaller online. This reinforces the idea that 14
# **c. Bank supervisory agency / resolution authority required to prepare resolution plans for banks (% of respondents answering Yes)**

**d. Formal supervisory and regulatory framework to deal with the resolution of international banks (% of respondents answering Yes)**

Source: Wave 5, BRSS.

Note: The questions reported in this Table are new questions introduced in the BRSS round 5. Off-shore financial centers are excluded from the calculations. DEV are developing countries. The number of countries for which we have a response for the specific item/question appears within parentheses.

The availability of accurate and timely information is a prerequisite for effective monitoring. Stringent disclosure rules, independent external audits, and availability of public and private credit ratings all increase transparency and allow for better monitoring by market participants (Djankov et al., 2007). In recent years, developing countries have shown a decrease in some aspects of availability and quality of information.<sup>11</sup> Knowledge of the ultimate owner and controller of a bank facilitates the consolidated assessment of its exposures. However, market participants in developing countries often do not have information on the ultimate (beneficial) owner of a bank Figure 8, Panel A). This is also often true for bank supervisors because the controlling company may sit outside the regulatory perimeter. By the same token, disclosure to the public of the governance and risk management frameworks of a bank enables the assessment of its risk management approach. For high-income OECD countries and the SA and SSA regions this information was disclosed less frequently in BRSS round 5 than in round 4 (Figure 8, Panel B).<sup>12</sup>

> 11 We acknowledge, however, that these declines are not generally statistically significant. 12 The decline was statistically significant for the SSA region only.

16
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