rafmacalaba/datause-encoder-data huggingface dataset:
Carrying out evaluations in conflict affected settings can potentially cause harm to participants of the evaluation team (which under field visits may include implementation staff) and the local population interviewed. For example, in a community driven reconstruction evaluation in the DRC, “the harsh conditions produced great costs to enumerators with high incidence of sickness including malaria and cholera. Although safety regulations were in place in all areas, one of the teams was involved in a tragic accident in which a child died.... Despite the precautions undertaken we did encounter some security issues: 31 villages were not visited due to security risks; one team was ambushed and had to hand over their equipment; and one IRC staff member was abducted (and subsequently released unharmed)” (Humphreys et al., 2012, pgs. 34-35).

Carrying out evaluations in conflict affected settings could furthermore potentially adversely affect intergroup relations and the course of intergroup conflict. While examples were not found of this having happened, an ongoing study in Cote D’Ivoire (not included in the Samii et al. review) made evaluation design adjustments to minimize the risk of exacerbating existing conflict. The evaluation, which looks at the impact of couples discussion groups in addition to a savings intervention to combat intimate partner violence (Gupta and Annan, _ongoing_ ), _included_ women who would not otherwise have been included in the sample. They were interviewing women in savings groups, but were only interested in women who had partners because the outcomes of interest were about partner relations and decision making. Given that the villages were in areas where there had been high ethnic tensions and conflict, the program team felt that if they separated the women and interviewed some and not others, there was the potential to create conflict and suspicion. They therefore decided also to administer a shorter survey to nonpartnered women.

Broadly speaking, there are three reasons for evaluation teams to conduct conflict analyses: 1) to assess the relevance and impact of the program; 2) to assess the risks of negative effects of conflict on the evaluation design and process; and 3) to assess the risks of the evaluation exacerbating conflict (conflict sensitivity) (DFID, 2002). When reviewing the conflict assessments reported on in the summarized studies or commented upon in the survey responses, we found a varied approach. Some teams reported having relied on the assessment of the program implementing agency and partners in the country. In other cases, an assessment of risk to subjects was conducted as part of the institutional review board (IRB) approval process. A number of the studies derived insights from baseline studies that included questions about conflict experience, regular program reviews of the methods and measures with a ‘do no harm’ approach in mind, survey piloting and discussions with people working in similar communities, as well as behavioral monitoring. All of these approaches indicate an adapted use 11
- [50] warren.d.c and l.t.chen (2013) “The Relationship Between Public Service Motivation and Performance,” in E.J.Ringquist (ed.), _Meta-analysis for Public Management and Policy_ , San Francisco, CA: Jossey-Bass.

- [51] williams.m.j (2015) From Institutions to Organizations: Management and Informality in Ghana’s Public Bureaucracies, mimeo, LSE.

- [52] wilson.j (1989) _Bureaucracy: What Government Agencies Do and Why They Do It_ , New York: Basic Books.

- [53] world bank (2018) _World Governance Indicators_ , available at: http://info.worldbank.org/governance/WGI/#reports.

33
loops that are missing in other approaches. Applying this methodology, we find that Kuwaiti females in secondary school who will become civil service workers face close to $40,000 reduction in the present value of their lifetime income; and males who will become civil service workers face close to $70,000 reduction. Furthermore, we demonstrate that the impacts last long – even after almost 30 years.

# **2. Comparing the Two Schooling Disruptions**

Figure 1 illustrates that Kuwait is among the countries with the longest school closures for inperson education caused by the COVID-19 pandemic. Public schools were closed for in-person learning for 62 weeks nationwide between February 26, 2020, and October 2021, when they reopened (Alhouti 2020; Kuwait Times 2021). Further, public schools did not begin fully remote schooling until more than seven months after the last day of in-person schooling (Alhouti 2020; Kuwait News Agency 2020). As in many countries, this remote learning system was hurriedly put together by the government. Unfortunately, the benefits of even well-functioning remote learning are likely below those of in-person schooling (Engzell et al. 2021).

4
suggests that using both poverty as well as rurality might prove useful in identifying the atrisk youth.

_Ethnic identity_ is not a good proxy for youth at-risk, although indigenous youth are over-represented among clusters defined as risk type III (Table 3b). A closer look at the indigenous youth in the Chile sample shows that while indigenous youth have worse risk factors and behaviors, they have better protective factors.<sup>33</sup> The more at risk clusters are about 5 percentage points more indigenous than the average, but the negative relationship between risk factors and positive behaviors is slight and insignificant as seen in Figure 6.<sup>34</sup> We must remember, however, that this is a self-reported survey, with the youth indicating if they identify with a particular indigenous group.<sup>35</sup> Overall levels of risky behavior increase with age, as seen in Tables 3a and 3b. The bubble graphs by age confirm this observed correlation.<sup>36</sup> Older youth have lower levels of positive behaviors across the board. On the other hand the poverty level for the most at risk clusters is higher for the younger cohorts than the older, indicating that with age a wider variety of background characteristics are involved in risky behaviors.

In Mexico, gender does not seem to matter (Table 3a) while in Chile women seem to be less at-risk (Table 3b). A closer look at the data reveals that male risky behavior is related to education and employment and females are more at risk for poor sexual behavior at younger ages. The females participating in risky behaviors at young ages tend to exhibit many risky behaviors, more than the males in the same cohort. Unfortunately, we were not able to look at violent or criminal behavior for this study, which is generally considered to be more common among men. In both Chile and Mexico, the most at-risk groups of females are more rural than males, despite the fact that similar percentages of males and females (across all age ranges) are rural. So being female and living in a rural area is more risky than being male and living in a rural area<sup>37</sup> .

Finally, we see a larger percentage of youth at risk in Mexico than in Chile, perhaps reflecting overall macroeconomic growth or government policy.

> 33 Further details available upon request.

> 34 Youth 15 to 17 years of age have a similar plot which is available upon request.

> 35 See Hall and Patrinos (2006) for documentation of the limitations of using self-reported ethnicity.

> 36 This is seen with both the Chile and the Mexico clusters by comparing the different age cohorts.  We do not present the graphs here but they are available upon request.

> 37 Approximately 13% of females and males live in rural areas in Chile and 24.5% in Mexico. Of those with type III risk, 20% of males and 23.6% of females in Chile and 29.7% of males and 33.7% of females in Mexico live in rural areas.

21
<mark>heterogeneity in response to slow-onset changes, ranging from resource-constrained</mark> immobility to permanent long-distance migration. If you have rich covariate data, consider the use of machine learning algorithms suited for a data-driven search for heterogeneity, such as causal trees and causal forests (Athey & Imbens, 2016; Wager & Athey, 2018), properly adapted for their use in an observational panel data setting, in order to estimate conditional average treatment effects.

If you are interested in fast-onset localized shocks or other specific events/disasters, remember that nationally representative household surveys are usually ill-suited for the purpose due to both timing and sample size issues, and that you should seek access to _ad hoc_ post-disaster surveys that oversample the most affected areas. Regarding identification, stick with difference-in-difference types of estimators based on pre- _vs_ . post-shock comparisons. If there is ‘staggered adoption’, here meaning non-simultaneous exposure to the shock, consider the adoption of modern difference-in-difference estimators for settings with differential treatment timings (Roth et al., 2023).

# **5. Conclusions**

The focus of this paper was to propose a conceptual framework and a methodological agenda and provide empirical guidelines for the investigation of climate-induced migration leveraging the use of multi-topic household surveys such as those implemented under the Living Standards Measurement Study (LSMS) program of the World Bank. The agenda we propose is ambitious but is meant to be the first step in this direction. If pursued, we deem it would deliver large benefits in terms of closing existing data gaps and provide a more accurate and granular measurement of the complex and highly heterogeneous phenomenon that is climate migration. In the meantime, however, we showed that there is a lot that researchers can do – and do in better ways – even with currently existing instruments. Undertaking microlevel climate migration research in a more systematic way and based on improved, more homogeneous methods and tools, will contribute to shedding decisive light on the nexus itself, its underlying mechanisms, and the socioeconomic implications for the main actors involved.

29
# **Findings**

We apply our model and assumptions to the world’s country groups and to the world economy overall. The estimated present value loss in earnings at the individual level is US$2,833 in lowincome countries, US$6,777 in middle-income countries, and US$21,158 in high-income countries. At the global level, this loss is US$11,117 at the individual level.

While this may not sound like too much of an individual price for young people to pay in the fight — — against COVID-19, a look at the impact for all affected students more than 1.5 billion is much more sobering. The total estimated losses range from US$360 billion in low-income countries, to US$6.8 trillion in middle-income countries, and US$4.9 trillion in high-income countries. The global impact is US$15.1 trillion. Even if we adjust to the lower bound estimate of labor force participation, these losses are still large. They range from US$252 billion for low-income countries, to US$4.8 trillion in middle-income countries, to US$3.4 in high-income countries, and a global impact of US$10.6 trillion.

In terms of _current year_ GDP, by country income group, the losses range from 61 percent in lowincome countries, 22 percent in middle-income countries, and 9 percent in high-income countries. At the global level future earnings losses represent about 18 percent of current global GDP. Even when we adjust for labor force participation, the losses range from 43 percent in low-income countries, 15 percent in middle-income countries, and 6 percent in high-income countries. Globally, these losses represent at least 12 percent of current global GDP (see Table 2).

Table 2: Cost of school closures due to earnings losses as a <u>percent of GDP, by country income group and for the world</u>

||Losses as % of GDP|
|---|---|
|Income Group|(1)<br>(2)|
|Low|61<br>43|
|Middle|22<br>15|
|High|9<br>6|
|World|18<br>12|

_Notes_ : (1) assuming full labor force participation; (2) assuming 70% labor force participation. _Source_ : Authors’ calculations The estimates clearly differ by country income group because there are many more students in middle-income countries and much higher earnings in high-income countries. The estimated losses for low-income countries are devastating given already low earnings, depressed growth forecasts, and higher levels of poverty. In middle-income countries, the estimated losses are also substantial, since more than 1 billion of the 1.5 billion students affected by school closures live in middleincome countries.

The last few decades have seen a tremendous growth in tertiary education enrollments; now more than triple what they were in the 1970s. The wage premium associated with obtaining a university 8
# _Increasing contribution of remittances_

Remittances increasingly contribute to the Tajik economy: from 5 percent in 2002, the GDP share of net remittance inflows skyrocketed to 45 percent in 2008, with an estimated value of nearly USD2.5 billion, before declining during the global crisis to reach 25 percent in 2010 (Figure 1). In 2010, the remittance inflows were coming come from an estimated 791 thousands migrants working in Russia, Ukraine, Uzbekistan, Kazakhstan and the Kyrgyz Republic (Migration Factbook, 2011).

**Figure 1. Tajikistan: GDP Expenditure Decomposition, 2002-2010**

(share of GDP) <!-- Start of picture text --> 2.5 0.50<br>0.45<br>2.0 0.40<br>Investment<br>0.35<br>Government<br>1.5 0.30<br>consumption<br>0.25 Net import<br>1.0 0.20<br>Household<br>0.15 consumption<br>0.5 0.10 Net remittance<br>inflows (RHS)<br>0.05<br>0.0 -<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: WDI 2010, Migration Factbook 2011 and authors’ calculation.

By stimulating domestic consumption through soaring imports, remittances have significantly contributed to Tajikistan’s growth since 2004 (Figure 1). However, there isn’t much evidence that remittances have fueled private investment. Indeed, Tajikistan statistics agency data indicate that the share of private investment in agriculture, trade and light industry (sectors reportedly receiving investment from migrants) has decreased from 2 to less than 1 percent between 2007 and 2008, when remittance inflows were reaching record levels. This situation makes an export-led growth strategy more difficult to achieve, particularly given that the country cannot rely on domestic demand from its seven million people to grow sustainably.

# _Shifting agricultural production_

Another structural transformation Tajikistan is going through occurs in the agriculture sector. Since 1999, the growth patterns of cotton, non-cotton crops and livestock have been markedly different. With the share of cotton in GDP decreasing while the share of non-cotton agriculture has been steadily increasing (Figure 2). This was due to the decreasing price of cotton on world markets, the decreasing productivity in the cotton sector, as well as the increasing demand for horticulture products in regional 3
TABLE 8: regressions on multiple outcome and binary borrower

||2|SLS (1)|2|SLS (2)|2|SLS (3)|
|---|---|---|---|---|---|---|
||Outcome|Borrower|Outcome|Borrower|Outcome|Borrower|
|Borrower|2.750***<br>(0.077)||2.755***<br>(0.082)||2.766***<br>(0.083)||
|Size<br>GDP p.c.<br>Volatility<br>Growth|-0.009<br>(0.009)<br>-0.00001<br>(0.00002)<br>-0.00001<br>(0.00003)<br>0.119<br>(0.124)|0.028<br>(0.029)<br>0.00005<br>(0.00007)<br>0.00005<br>(0.0001)<br>-0.221<br>(0.265)|-0.009<br>(0.009)<br>-0.00001<br>(0.00002)<br>-0.00001<br>(0.00003)<br>0.015<br>(0.196)|0.028<br>(0.029)<br>0.00005<br>(0.00007)<br>0.00005<br>(0.0001)<br>-0.221<br>(0.265)|-0.008<br>(0.009)<br>-0.00001<br>(0.0002)<br>-0.00003<br>(0.00003)<br>0.116<br>(0.095)|0.028<br>(0.029)<br>0.00001<br>(0.00007)<br>0.00007<br>(0.00001)<br>-0.145<br>(0.285)|
|Infrastructure|-0.550<br>(0.630)|0.863<br>(1.942)|-0.550<br>(0.630)|0.863<br>(1.942)|-0.836<br>(0.654)|1.113<br>(1.964)|
|Bureaucracy<br>Corruption|-0.014<br>(0.035)<br>-0.020<br>(0.018)|0.004<br>(0.098)<br>0.011<br>(0.075)|-0.122**<br>(0.071)<br>-0.020<br>(0.018)|0.004<br>(0.098)<br>0.011<br>(0.075)|-0.116<br>(0.074)<br>-0.028<br>(0.026)|0.003<br>(0.099)<br>0.024<br>(0.077)|
|Account.|0.003<br>(0.017)|-0.001<br>(0.006)|0.137***<br>(0.039)|-0.023*<br>(0.014)|0.148***<br>(0.041)|-0.026*<br>(0.015)|
|Stability<br>Ethnic|<br>-0.007<br>(0.009)<br>0.010<br>(0.024)|<br>0.002<br>(0.031)<br>0.012<br>(0.071)|<br>-0.007<br>(0.009)<br>0.010<br>(0.024)|<br>0.002<br>(0.031)<br>0.012<br>(0.071)|<br>-0.007<br>(0.010)<br>0.004<br>(0.024)|<br>0.015<br>(0.031)<br>-0.003<br>(0.072)|
|Ext. conflict|-0.021<br>(0.017)|0.057*<br>(0.029)|-0.005<br>(0.027)|0.021***<br>(0.009)|-0.004<br>(0.030)|0.018*<br>(0.011)|
|Instruments|||||||
|Bank||0.667***(0.02)||0.661***(0.02)||0.661***(0.02)|
|Bank1||3.48***(0.60)||2.53***(0.77)||2.58***(0.79)|
|Bank2||<br>16.3***(2.48)||<br>12.3***(3.30)||<br>12.1***(3.36)|
|Borr1||-4.49***(0.74)||-3.74***(1.0)||-3.84***(1.0)|
|Borr2||<br>-21.9***(3.1)||<br>-19.0***(4.78)||<br>-18.9***(4.96)|
|Country FE|||Yes|Yes|Yes|Yes|
|Year FE|||||Yes|Yes|

# Other Statistics

|Observ.<br>|1710|1710|1710|1710|1710|1710|
|---|---|---|---|---|---|---|
|Adj. R<sup>2</sup>|0.445|0.462|0.461|0.470|0.456|0.470|
|Prob(F)|0.00|0.00|0.00|0.00|0.00|0.00|
|Sargan Test||0.182||0.479||0.391|
|Basman Test||0.185||0.516||0.418|

Note: both the project outcome variable and the borrower performance variable are reported on a 0 (unsuccessful) and 1 (successful) scale. For further references, see the text. The number of observations (1709) is lower than the sample because some instruments do not present a total coverage of the sample.

25
Trujillo, L. and Nombela, G. - _Privatization and regulation of the seaport industry_ _Box_ 4. **Finances of port infrastructure across different countries**

||**Maritime Access**<br>**Infrastructure**|**Port Area**<br>Infrastructure|**Port Area**<br>Superstructure|**Land Access**<br>Infrastructure|
|---|---|---|---|---|
|**Argentina**|P.A./Private|P.A.|Private|Most port authorities|
|**Belgium**|State|Public|Private|are responsible for<br>roads and other|
|**Cyprus**|P.A.|P.A.|Concession|transport connections|
|**Denmark**|P.A.|P.A.|Private|within port areas.<br>Connection to the|
|**Finland**|P.A.|P.A.|Private|<br>hinterland is usually|
|France|State/P.A.|Public/A. P.|Concession|competence of|
|**Germany**|State|Public|Private|govermments.<br>Regarding railways|
|Greece|State|Public/A. P.|Concession|,<br>responsibility can be|
|Hong Kong|P.A.|Private|Private|national (Belgium),<br>t thiti|
|**Ireland**|P.A.|P.A.|Concession|por auores<br>(Gennany) or the|
|**Italy**|State/P.A.|Public/A. P.|Concession|railways|
|Malta|State|P .A .|Concession|concessionaire<br>(Argentina). In the|
|Mexico|P.A.|P.A.|Private|<br>case of Hong Kong,|
|**Netherlands**|State|P.A.|Private|private sector is|
|**Portugal**|P.A.|P.A.|Concession|responsible for|
|**Spain**|P.A.|P.A.|Concession|infrastructures within|
|**Sweden**|P.A.|P.A.|Concession|the port area.|
|UK|P.A.|P.A.|Concession||
|Venezuela|P.A.|P.A.|Private||

_Sources:_ European Parliament (1993), ESPO (1996) _Notes: (1)_ P.A. - Port Authority (financed with own resources)

- (2) Public: Financed by central, regional or municipal governments

- (3) Concession: in cases indicated, superstructure is publicly owned but operated by private firms.

- (4) This classification refers to main seaports of each country. In every case, it is possible that within the same country there may exist ports with other alternative modes of financing infrastructure than shown here.

Technological changes introduced in the last decades in the maritime transport sector can be summarized in two points: (1) containerization of cargo; and (2) development of larger and deeper specialized ships. These transformations have led to some new requirements for seaports to modernize their infrastructures and to buy new equipment in order to continue to be able to provide services to shipping companies.

Regarding the first change, it can be observed in the figures on the world traffic of containers. The fast growth of this mode of transport started in Europe, the US and Japan, but it is now spreading elsewhere. _Box 5_ presents figures for different regions in the world, and it is remarkable to observe the spectacular growth rates of South America, Asia and India, and in general, all developing countries. Four of the five largest world ports in terms 14
- **19** -

**Figure 6**

<!-- Start of picture text --> INFLATION RATE<br>Annual average charge in CPI<br>160<br>1SO -- 7^<br>140<br>130<br>120<br>110<br>u 100<br>a, 701. g g<br>30 -<br>* 00_g316 30.32f.0<br>2OG0<br>30<br>20 40. _<br>~~~~~19.1 s=3189<br>Ghana Kenya Malawi Taruanla Ugerwe<br><!-- End of picture text --> Source: IMF and World Bank data, and authors' estimates.

# V.2.4 Innation Rate

Figure 6 presents data on average annual inflation rates in the two periods for Tanzania and the four comparator countries. Tanzania's record of controlling inflation is positive and compares favorably to that of the other countries. Starting from an initially distorted situation, despite substantial devaluation and price liberalization during the reform period, Tanzania managed to keep inflation at historical levels on average. By comparison, structural adjustment in Malawi and Uganda was initially accompanied by an increase in the rate of inflation.
percent between 2011 and 2030 in the baseline, and 0.08 percentage points lower in the full age structure freeze scenario.

The impacts of demographics on real GDP per capita, capital stock, and capital deepening are similar when the low growth baseline is considered, differing only in magnitude. An examination of the real GDP per capita growth rates by country – and the contributions of the demographic dividends to them -in the low growth and high growth scenarios can illustrate. Columns I and III of Table 4 are the real GDP per capita growth rates for the region in the two baselines, while columns II and IV are the contributions of the demographics to the growth (as determined through the decomposition exercise of the full and partial age structure freeze scenarios discussed in Section 3.2).

**Table 4: Real GDP per capita growth rates and contribution of demographic dividend in high and low growth baselines, 2011-2030**

||**High**|**Growth**|**Low**|**Growth**|
|---|---|---|---|---|
||**Baseline(%)**|**Dividend(% point)**|**Baseline(%)**|**Dividend(% point)**|
||I|II|III|IV|
|Burkina Faso|4.32|1.36|2.20|1.36|
|Botswana*|4.27|0.17|5.12|0.18|
|Côte d’Ivoire|4.62|0.51|1.35|0.45|
|Cameroon|3.85|0.88|0.61|0.79|
|Ethiopia|5.93|2.27|1.11|1.69|
|Ghana|4.50|0.80|2.12|0.51|
|Kenya|3.93|0.81|0.69|0.68|
|Madagascar|3.66|0.77|0.44|0.68|
|Mozambique|5.12|0.57|2.22|0.55|
|Malawi|3.63|0.87|0.41|0.75|
|Nigeria|4.18|0.22|1.29|0.19|
|Rwanda|5.51|1.21|1.08|1.02|
|Senegal|3.64|1.10|0.41|1.05|
|Tanzania|4.35|0.42|1.12|0.36|
|Uganda|3.99|1.11|2.11|1.05|
|Rest of SSA|4.05|0.58|1.48|0.53|
|South Africa|3.77|-0.17|0.47|-0.14|
|Zambia|4.16|0.72|0.94|0.62|
|Zimbabwe|3.45|1.97|0.50|1.90|
|Sub-Saharan Africa|3.67|0.42|0.79|0.37|

Note: * Botswana’s growth was higher in the 1980s and 1990s than in the 2000-09. However, for consistency with the rest of the region, the lower growth of the 2000-09 was applied in the otherwise high growth baseline, and the higher growth of 198099 was applied in the otherwise low growth baseline.

Source: Simulation results By construction, real GDP per capita was lower in the low growth scenario than in the high growth scenario and is not surprising. It should be noted however that the contributions of demographic change to growth in the two baselines were largely the same. The importance of demographics to growth was thus greater in the low growth baseline.

This can be explained by once again considering the growth drivers of the model and direct channels by which demographic change affects them. The two baselines differ primarily in their productivity growth rates, with the productivity growth rates being lower in the low growth case than in the high growth case. Aside from this, both scenarios have the same labor supply growth and the same evolution of dependency ratios. Since the dependency ratios are the same in both sets of scenarios, 17