rafmacalaba/datause-encoder-data huggingface dataset:
and Counsel 2010; Gregorsen _et al._ 2011). By conducting in-person surveys with local communities we get at the heart of this question of what are the opportunity costs of carbon sequestration faced by households by directly calculating the costs faced by potential participants in a REDD+ program.

The potential for REDD+ to contribute to climate change mitigation appears significant, but to-date preferences of potential participants and related costs have not been fully explored. Costs associated with community negotiations, meetings, monitoring, risk aversion and high discount rates (Yesuf and Bluffstone, 2009; 2013) could turn out to be potentially significant and make communities unwilling to participate in REDD+ at prices carbon buyers would be willing to pay. A key overarching question therefore is how REDD+ benefits can effectively be transferred to the local level without disrupting the successful adoption of REDD+, especially at the community level (Bluffstone 2013). Further, understanding the preferences for the various attributes of REDD+ contracts allows planners to tailor the REDD+ contracts to better suit the preferences and local conditions. By using a choice experiment survey we are able to analyze the preferences for the various attributes of REDD+ contracts and the overall opportunity cost faced by the participants.

# **3. Study Site and Data**

Ethiopia is a low-income REDD+ partner country of over 90 million people. With closed canopy forest cover of only about 4%, increasing forest cover is a critical policy goal. As a result, in 2000 forest sector reform was initiated and a variety of community-level devolution pilots, experiments and initiatives are underway around the country. CFM is a focus of the Ethiopia REDD Readiness Proposal, which was funded in August 2009 by the World Bank 6
# 4. Multi-criteria assessment

Following the simulations of energy and economic impacts, step 7 (as shown in Figure 1) involved the multi-criteria assessment of recovery measures. This section presents the methods used and their results.

## 4.1. Overview of the assessment framework

Multi-criteria decision analysis (MCDA) has been developed to support DMs, especially when facing decisions involving multiple and potentially competing objectives (Jordan and Turnpenny, 2015). In the last decades, several methods have been developed for many types of decision problems. MCDA techniques have been widely applied in a variety of fields, including energy and environment (Ahmed et al., 2020; Ali et al., 2019; Baumann et al., 2019; Kumar et al., 2017; Liu and Du, 2020). Two of the most common methods used in MCDA problems are the Analytical Hierarchy Process (AHP) method and the Preference Ranking Organisation Method for Enriching Evaluation (PROMETHEE) (Greco et al., 2016; Papathanasiou and Ploskas, 2018). There are several examples of applications of AHP and PROMETHEE in the fields of energy planning, selection of energy projects, and sustainable supply chains (Abdullah et al., 2019; Mastrocinque et al., 2020; Zelt et al., 2019). PROMETHEE has also been used in the development and evaluation of scenarios for energy planning (Simoes et al., 2019; Witt et al., 2020) and for evaluating market opportunities for renewables (Andreopoulou et al., 2018).

AHP and PROMETHEE can be combined, as shown by several studies so far (Abdel-Basset et al., 2021; Neofytou et al., 2020; Seddiki and Bennadji, 2019). AHP can be used to produce the weights of each criterion for each DM, which would be used as an input for applying PROMETHEE to produce the ranking of the actions. A similar framework has been developed in Matlab® for this paper, using a PROMETHEE Group Decision Support System (GDSS) approach. Figure 3 presents a flowchart for the application of AHP/PROMETHEE in this study.

19
60 Like other social insurance systems in China, the emerging mandatory _pension system_ mainly covers urban citizens (about 163 million participants in 2004).<sup>121</sup> It accounts for about 80 percent of total social insurance spending, and it is responsible for 28 out of the 40 percentage points of aggregate social insurance fees. It is a two-layer system.<sup>122</sup> The first layer consists of a strongly redistributive paygo system, amended by a (so far modest) collective buffer fund – the National Social Security Fund, established in 2000. Although the role of the collective buffer fund remains unclear, one purpose seems to be to help poor regions finance their pensions, hence compensating them for the limited geographical pooling.

A second layer is supposed to consist of a fully funded, actuarial system with individual accounts, based on earlier paid contributions, hence a system without intended (ex ante) redistribution.<sup>123</sup> One rationale for such a tier might be that a combination of paygo and funded elements provides better risk pooling than only one of these elements in isolation, since the risk characteristics between them differ. Another rationale might be to boost the development of a broad, domestic capital market. While these two rationales for funded elements in the mandatory pension system are highly relevant for China, two other usual arguments – to raise the aggregate national saving rate and to provide a higher return on mandatory pension saving – may seem less relevant. After all, the aggregate saving rate is already exceptionally high (about 50 percent of GDP), and the fast growth rate of aggregate earnings makes the return on mandatory saving in a paygo system quite high as compared to the return on a funded system.<sup>124</sup> However, since a fully funded system takes decades to build up, and both the saving rate and the growth rate are likely to fall

> 121 According to a survey by the Chinese Research Center on Aging (2002), more than 70 percent of the urban elderly were covered by the new pension arrangements, while the corresponding figure in rural areas was 3 percent.

> 122 See, for instance, OECD (2005a, pp. 187-190); and Wang, (2006, p. 10).

> 123 While the first layer promises a pension annuity amounting to 20 percent of the _average_ monthly wage for employees in the individual’s geographic area (provided a minimum of 15 years of contributions have been paid), the pension level in the second layer can only be guessed at (Ministry of Labor and Social Security, 1998, p. 51). For a general discussion of China’s emerging pension system in urban areas, see, for instance, Song (2002); and Zhao and Xu (2002).

> 124 Broadly speaking, saving by households and enterprises in recent years each accounted for 2/5 of aggregate saving (amounting to close to 40-45 percent of GDP), and the government for the remaining 1/5 (Kuijs, 2005).
Table 2: Earnings abroad and self-employment after return, reduced-form estimation

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||Full sample||Wor|king individuals|only|
||OLS|2SLS|First Stage|OLS|2SLS|First Stage|
|Dependent Variable|Self-employed|Self-employed|ln(Cum.Earn.)|Self-employed|Self-employed|ln(Cum.Earn.)|
|ln(Cum. Earning abroad)|0.031***|0.112***||0.040***|0.110**||
||(0.008)|(0.032)||(0.009)|(0.047)||
|Oil price growth _×_ Oil rents/GDP|||0.855***|||0.824***|
||||(0.119)|||(0.112)|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Year of return FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Origin division FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Destination country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|F-statistics of excluded instruments||51.9|||54.3||
|Observations|3354|3354|3354|2775|2775|2775|

_Note:_ * p _<_ 0.10, ** p _<_ 0.05, * p _<_ 0.01. Standard errors clustered at the destination-origin level are reported in parentheses. The sample is restricted to males between the ages 18 to 59, and excludes individuals who have been back in Bangladesh for less than a year. Control variables include age and squared age at the time of survey, educational attainment dummies, and a dummy for self-employment prior to migration. Oil-price growth is the ratio of the oil price at the time of return to Bangladesh over the one at the time of departure from Bangladesh.
Table A.2: Descriptive relationship between self-employment and migration, baselines and second follow-ups

||**Any**<br>**migration**|**Internal**<br>**migration**|**International**<br>**migration**|**Any**<br>**migration**|**Internal**<br>**migration**|**International**<br>**migration**|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Self-employed|-0.032***|-0.030***|-0.001|-0.033***|-0.031***|-0.001|
||(0.006)|(0.006)|(0.002)|(0.006)|(0.006)|(0.002)|
|Aged 25 to 34|-0.069***|-0.066***|-0.003|-0.069***|-0.067***|-0.003|
||(0.009)|(0.009)|(0.003)|(0.009)|(0.009)|(0.003)|
|Aged 35 to 44|-0.115***|-0.107***|-0.007***|-0.115***|-0.108***|-0.007***|
||(0.009)|(0.009)|(0.003)|(0.009)|(0.009)|(0.003)|
|Aged 45 to 54|-0.110***|-0.103***|-0.007**|-0.110***|-0.104***|-0.007**|
||(0.010)|(0.009)|(0.003)|(0.010)|(0.009)|(0.003)|
|Aged 55 to 65|-0.123***|-0.119***|-0.004|-0.123***|-0.120***|-0.004|
||(0.011)|(0.011)|(0.004)|(0.011)|(0.011)|(0.004)|
|Years of education|0.006***|0.006***|-0.000|0.006***|0.006***|-0.000|
||(0.001)|(0.001)|(0.000)|(0.001)|(0.001)|(0.000)|
|Female|-0.002|-0.000|-0.002|-0.002|0.000|-0.002|
||(0.005)|(0.005)|(0.001)|(0.005)|(0.005)|(0.001)|
|arcsinh(income)||||-0.001*|-0.001|-0.000***|
|||||(0.001)|(0.001)|(0.000)|
|Wage worker mean|0.171|0.159|0.014|0.171|0.159|0.014|
|Sample size|22,830|22,802|19,930|22,727|22,699|19,827|

_Notes:_ The sample is the set of individuals aged 18-65 surveyed in the KHDS 1991/94 and 2010, PSID 2001 and 2017, ELMPS 1998 and 2012, IFLS 2000 and 2014/15, MxFLS 2002 and 2009/12, and LSMS-ISA 2010/11 and 2015/16. We drop respondents who are not self-employed or paid workers in the baseline wave of each survey. Refer to online appendix section B for more details on variable construction in the different surveys. Each column reports marginal effects from a probit regression of an indicator for migration on an indicator for self-employment at baseline. Each regression controls for country fixed effects. Columns (4)(6) additionally control for the inverse hyperbolic sine (arcsinh) of the respondent’s income in the baseline wave.

41
-24 -

**Table S.** Mean Earnings, **Years** of Schooling and Mincerlan Returns to Education: by Sector of Employment, **circa** 1980

|Country|Year|Earnings|(local curr.)|Years of|Schooling|Rate of R|eturn (%)|
|---|---|---|---|---|---|---|---|
|||Private|Public|Private|Public|Private|Public|
|Argentina|1980|657,000|1,319,000|7.7|9.5|6.3|9.7|
|Colombia|1980|10,374|3,442|7.7|_5.0_|16.7|14.0|
|Costa Rica|1981|1,990|1,744|6.3|7.7|15.6|20.6|
|Panama|1979|250|198|7.8|9.6|12.3|15.7|
|Paraguay|1983|40,880|51,151|7.6|11.9|12.0|9.2|
|Uruguay|1981|5,331|2,748|8.3|8.7|9.1|11.9|
|Venezuela|1981|2,895|1,898|7.1|7.8|11.2|14.0|

Source: Annex 3.
||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Produc**<br>**ts**||||||||||||
|**Metal**<br>**Produc**<br>|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**ts**||||||||||||
|**Machi**<br>**nery**<br>**and**<br>**Equip**|0.00|0.00|0.00|0.08|0.16|0.16|0.16|0.16|0.41|0.41|0.41|
|**ment**||||||||||||
|**Energy**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|6.77|6.80|6.81|
|**Constr**<br>**uction**|0.00|0.00|0.00|6.25|12.50|12.50|12.50|12.50|1.18|1.18|1.18|
|**Trade**|0.00|0.00|0.00|1.31|2.63|2.63|2.63|2.63|-19.66|-19.74|-19.79|
|**Accom**<br>**modati**|0.00|0.00|0.00|0.13|0.25|0.25|0.25|0.25|2.37|2.38|2.38|
|**on and**<br>**Food**<br>**Service**||||||||||||
|**s**||||||||||||
|**Transp**<br>**ortatio**|0.00|0.00|0.00|2.58|5.15|5.15|5.15|5.15|3.18|3.20|3.21|
|**n**||||||||||||
|**Bankin**|0.00|0.00|0.00|0.50|1.00|1.00|1.00|1.00|0.94|0.94|0.94|
|**g-**<br>**Financi**||||||||||||
|**ng**||||||||||||
|**Real**<br>**Estate**|0.00|0.00|0.00|0.75|1.50|1.50|1.50|1.50|0.24|0.24|0.24|
|**Public**<br>**Admini**<br>**stratio**|0.00|0.00|0.00|0.13|0.25|0.25|0.25|0.25|0.00|0.00|0.00|
|**n**||||||||||||
|**Educat**<br>**ion**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|1.20|1.20|1.20|
|**Health**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|1.37|1.38|1.38|
|**Other**<br>**Service**<br>**s**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|4.23|4.25|4.26|

_Table B. 5. Annual spending associated with investments and households’ consumption under the preNZEB Measure relative to the Reference Scenario by sector of economic activity for the period 2020-2030 (in million euros)_

||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Agricul**<br>**ture**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Forest**<br>**ry**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Mining**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Food**<br>**Manuf**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|

38
# **Figure A1.3. Real household consumption per capita (US$2009)**

<!-- Start of picture text --> 800<br>750<br>700<br>650<br>base<br>600<br>min+<br>550 inf+<br>hd+<br>500<br>geff+<br>450<br>400<br>350<br><!-- End of picture text -->

**Figure A1.4. Growth in real household consumption per capita (Percent)**

<!-- Start of picture text --> 6<br>5<br>5<br>base<br>4<br>min+<br>4 inf+<br>hd+<br>3<br>geff+<br>3<br>2<br><!-- End of picture text --> 30
_search_ , **50** (4).

- Vargas-Silva, C. (2008). Are remittances manna from heaven? A look at the business cycle properties of remittances. _The North American Journal of Economics and Finance_ , **19** (3), 290– 303.

- Wooldridge, J. M. (2010). _Econometric Analysis of Cross Section and Panel Data_ . The MIT Press, 2nd edn.

- Yang, D. (2008). Coping with disaster: the impact of hurricanes on international financial flows, 1970-2002. _The B.E. Journal of Economic Analysis & Policy_ , **8** (1 (Advances)), Article 13.

- and Choi, H. (2007). Are remittances insurance? Evidence from rainfall shocks in the Philip-

- pines. _The World Bank Economic Review_ , **21** (2), 219–248.

20
3% of households in control villages reach the asset stock of a median middle-class household within four years, estimating the probability of catching up to the upper socioeconomic groups close to zero in this setting.

# **8 Costs and Benefits of the Program**

We compare program costs to the present value of non-durable consumption, based on Banerjee et al. (2015), and building on the estimates by Bedoya et al. (2019) for the midline results. It is worth highlighting that this method, using only non-durable consumption, will underestimate the benefits of the TUP program that go beyond consumption but are difficult to monetize, including impacts on the primary woman’s psychological well-being and empowerment, children’s school enrollment, durable consumption (i.e., household assets), and potential impacts on other outcomes and gender inequality reported in Bedoya et al. (2019). We estimate that the program will break even at year 5 only using non-durable consumption estimates as a benchmark.<sup>36</sup> See Online Appendix Text 4 for details on these estimates.

# **9 Conclusion**

In 2016, the TUP program was delivered through a public lottery to ultra-poor households across 80 villages within the Balkh province of Afghanistan. By tracking these households over five years, during a period of significant upheaval including macroeconomic deterioration, escalating violence, severe droughts and the COVID-19 pandemic, we are uniquely positioned to illustrate the potential protective effects of the TUP program against these multiple shocks and unpack some of the mechanisms through which the program may improve resilience in an already fragile setting. While all households in the study area experienced significant reductions in economic activity, food security and consumption between midline and endline when these external shocks occurred, we see that the program impacts relative to the UP control group are sustained, and that UP treatment households reduce gaps with non-UP households across most outcomes measured.

When faced with multiple external shocks, households generally use a variety of approaches to mitigate their harmful impacts. The most common approach to coping with shocks among vulnerable households has been to take out loans, which are typically informal and command high interest rates. Even though control and treatment households are equally likely to be exposed to a shock, treatment households are less likely to rely on loans to mitigate these shocks, with control households facing significantly higher debt burdens

> 36For this, we assume a sell-off of excess livestock value for treatment over the control group by year 5, to be consumed in non-durable goods at that point.

39
Appendix 7: Linear probability model controlling for size of the need (primary gap) Table 44

|Dependent variable: F|iscal adju|stment whe|n needed b|ased on Esc|olano et a|l.(2014)|||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|-0.015|-0.023***|-0.014|-0.027***|-0.016**|0.073**|-0.080***|-0.063***|
|(% of GDP)|(0.013)|(0.008)|(0.011)|(0.008)|(0.008)|(0.029)|(0.028)|(0.020)|
|Primary gap (t-1)|-0.004|0.003|0.001|-0.003|-0.004|-0.004|-0.003|-0.004|
||(0.010)|(0.006)|(0.010)|(0.008)|(0.008)|(0.007)|(0.008)|(0.008)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.307||0.736|0.848|0.731|0.611|0.725|
|||(1.360)||(1.246)|(1.224)|(1.252)|(1.227)|(1.279)|
|Inflation (t-1)||2.579***||2.706**|3.066***|2.976***|3.203***|2.552**|
|||(0.931)||(1.042)|(1.057)|(1.032)|(0.965)|(1.113)|
|U.S. Interest rate||-0.124**||-0.159**|-0.178**|-0.206***|-0.194**|-0.174***|
|||(0.051)||(0.064)|(0.074)|(0.060)|(0.073)|(0.052)|
|**Political factors**|||||||||
|Gini|||0.010|0.013|0.016|0.081***|0.015|0.007|
||||(0.011)|(0.012)|(0.012)|(0.023)|(0.012)|(0.011)|
|Rule of law|||-0.218**|0.110|0.167|0.183|-0.106|0.113|
||||(0.086)|(0.142)|(0.162)|(0.144)|(0.139)|(0.143)|
|Margin of Majority|||0.509|0.269|0.259|0.474|0.258|-1.475*|
||||(0.443)|(0.397)|(0.387)|(0.366)|(0.384)|(0.794)|
|Election's year|||-0.111|-0.097|-0.089|-0.078|-0.087|-0.083|
||||(0.080)|(0.078)|(0.075)|(0.075)|(0.079)|(0.081)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||-0.034<br>(0.030)||||
|Gini * Rigidity||||||-0.003***<br>(0.001)|||
|Rule of law * Rigidity|||||||0.011**<br>(0.005)||
|Majority * Rigidity||||||||0.073*<br>(0.042)|
|Constant|0.611|1.191***|0.791|0.314|0.164|-2.660**|1.288|1.447|
||(0.395)|(0.305)|(0.971)|(1.004)|(0.962)|(1.293)|(1.016)|(0.983)|
|Observations|185|183|165|163|163|163|163|163|
|R-squared|0.009|0.115|0.073|0.160|0.169|0.192|0.178|0.189|
|Number of ifscode|60|60|53|53|53|53|53|53|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust standard error|s clustere|d by country|in parent|heses|||||
|*** p<0.01, ** p<0.05|, * p<0.1||||||||

- 73 -
15 llnited States earlier in refugee status. Refugees are eligible to adjust to lawful permanent resident status after one year of continuous residence in the United States, although many refugees do not adjust their status until they have been in the United States for several years. Annual adjustments of refugees and asylees have not fallen below 90,000 per year since FY1980 and they are likely to maintain about this level for the next 5-10 years. New international conflicts could further increase refugee admissions.

Table 6. Refugee Admissions to the United States since Fiscal Year 1980

|Fiscal Year|Authorized<br>Admissions|<br>Actual<br>Admissions|Refugee and Asylee<br>Adjustments to<br>Immigrant Status|
|---|---|---|---|
|1980|231,700|207,116|<br>88,057|
|1981|217,000|159,252|<br>107,573|
|1982|140,000|97,355|156,601|
|1983|90,000|61,681|102,685|
|1984|72,000|71,113|92,127|
|1985|70,000|68,045|95,040|
|1986|67,000|62,440|104,383|
|1987|70,000|64,831|96,474|

Sources: Immigration and Naturalization Service (1988b, 1987); Refugee Reports 10/16/87, 12/18/87); Now York Times (9/14/88).

Although the World Bank normally allocates refugees back to their country of origin, particularly if they are in countries of first asylum, the refugees to the United States that are enumerated above are clearly permanent. residents of the United States. Even if the conflicts that drove them out of their home countries were to be resolved, few of the refugees would return home. Therefore, future projections of immigration to the United States must include a substantial number of refugees. The best guess about the future course of refugee admissions would be the average level of 65,000 annually over the last five years. This is close to the annual level of 62,250 that the General Accounting Office assumes for refugees from 1987 to 1990 (General Accounting Office, 1988). According to the GAO, 54.4 percent of the refugees will be males and 90 percent of the refugees will originate from Indo-China (although the Indo-Chinese component may decrease in the future).

Emigration from the United States. Emigration figures are difficult to come by since the United States has not collected any emigration data since 1958. Indirect estimates of emigration, which need to be interpreted cautiously, indicate that there is a substantial amount of emigration. Warren and Kraly (1985) found that over 9 million non-U.S. citizens are estimated to have emigrated from the United States between 1900 and 1979, compared to an inflow of about 30 million immigrants during the same period. In the most recent period, 1970-1979, they estimated that 1,176,000 non-U.S. citizens emigrated compared to an inflow of 4,334,000 immigrants. Warren and Peck