rafmacalaba/datause-encoder-data huggingface dataset:
about 1% of GDP. Illegal trade flourished, while the cartel’s high prices encouraged the entry of Indonesia into the market (see figure 5).<sup>11</sup> Figure 5 Market shares on the world vanilla market, 1965-2003 <!-- Start of picture text --> 1 00.0<br>90.0<br>80.0<br>7 0.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>1 0.0<br>0.0<br>Madagascar<br>Indonesia<br>ROW<br>19651967196919711973197519771979198119831985198719891991199319951997199920012003<br><!-- End of picture text --> Source: Blarel and Dolinsky (2000), COMTRADE (mirror statistics), authors' calculations. Notes: ROW stands for world exports minus Madagascar and Indonesia.

Worst of all, declining revenues meant that the cost of keeping exploding inventories (as the cartel’s high prices discouraged demand) escalated beyond what could be financed out of CAVAGI’s revenue. In the end, three quarters of the stock of inventories, which by 1990 exceeded four years’ worth of export under good times, were ultimately burnt, an extraordinary waste given the high unit value of vanilla and the extreme poverty of the farmers whose output was thus destroyed.<sup>12</sup> 11 Indonesia’s entry into the vanilla sector was essentially driven by market incentives rather than a grand plan of the Government of Indonesia, which had no particular policy in that sector. Indonesia’s vanilla production was kickstarted by the vanilla shortage of the 1970s (de Melo et al., 2000). A key difference between the two major players, is that Indonesia did not tax vanilla exports.

12 Examples of the corruption surrounding the operation of CAVAGI are described in Ecott (chapter 13, especially pp 220-223). This type of confiscatory policy extended beyond vanilla to other sectors (e.g. rice, clove etc.) and contributed to a trend of worsening rural poverty.

18
<!-- Start of picture text --> foe}<br>wo<br>on<br>gs<br>2<br>x<br>S<br>g<br>ot<br>¢<br>fe]Cc<br>6<br>FB<br>©<br>15-19 20-24 25-29 30-39 40-49 50-64<br>age group<br>——— Natives ——— Foreign born<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>2<br>QD<br>2<br>x<br>sé<br>2<br>@<br>zsoc<br>ro)<br>&<br>©<br>~<br>< Upper secondary 1.5 Uppereducation secondarylevel 2.5 Tertiary<br>——— Natives ———— Foreign born<br><!-- End of picture text --> <!-- Start of picture text --> «2<br>QD<br>an<br>x<br>S<br>2<br>zefo}c<br>Re}<br>x<br>wo<br>2<br>~<br>0 2 4income decile6 8 10<br>——— Natives ———— Foreign born<br><!-- End of picture text -->
<!-- Start of picture text --> 0.96 0.95<br>0.90<br>122366 126049 122394 125084 121746 123858 119036 125761<br>0.69<br>0.00 0.00 0.00 0.00<br>Primary Fundamental High School College Primary Fundamental High School College<br>Brazilian Venezuelan Brazilian Venezuelan<br>(a) Mean Monthly Wage Rate (b) Proportion Downgraded<br>1<br>150000<br>.8<br>Mean Wage 100000 .6.4<br>Fraction Downgraded<br>50,000<br>.2<br>0 0<br><!-- End of picture text --> Figure 5: Performances in the Formal Labor Market by Nationality clustered at the province level. Out of the 19,000 Venezuelans in the formal sector, about 5,609 are in Roraima and Amazonas. Summary statistics reveal that an average employed Venezuelan earns about 3.4 percentage points more in monthly wage than an average Brazilian but the di↵erence is statistically insignificant. In Roraima and Amazonas, the raw wage penalty is about 0.5 percentage points but is again statistically insignificant. Figure 5a shows that there is a wage premium for Venezuelans across the di↵erent education level. According to Table 3, Venezuelan formal workers are younger, less likely to be female and white and more likely to have completed high school than their Brazilian peers. They are more likely to worker longer hours a week than the Brazilian formal workers. They are also more likely to be occupationally downgraded than Brazilians. While 72% of the Brazilian report to work in an occupation, where the education requirement is lower than their acquired highest education, 86% of the Venezuelans report so, suggesting that occupation downgrading is more prevalent among Venezuelans. Venezuelans work mainly as industrial workers, especially as machine and vehicle operators and as workers in hotel industry, personal services, hygiene and security services. Figure 5b shows that incidence of being occupationally downgraded at work is higher for high school and college educated Venezuelans than for high school and college educated Brazilians. On average, employed Venezuelans seem to have been in Brazil for about 1.5 years and work in firms that have higher number of non-Venezuelan migrants.

21
Table 13: Regional agricultural inputs (survey round 2)

|Dependent variable:|(1)|(2)|(3)|
|---|---|---|---|
||Indicator: HH<br>used any<br>fertilizer|ln (kg<br>fertilizer, 1st<br>application)|ln (kg<br>fertilizer, 2nd<br>application)|
|Panel A: North||||
|PWP|0.236**<br>|0.964*<br>|1.083**<br>|
||(0.111)<br>|(0.490)<br>|(0.470)<br>|
|PWP * Top-up|-0.064|-0.294|-0.274|
||(0.058)|(0.244)|(0.329)|
|R-squared|0.16|0.22|0.16|
|Total efect on top-ups|0.171|0.670|0.809|
|P-value for non-zero efect on top-ups|0.13|0.18|0.04|
|Upper bound (total efect)|0.385|1.616|1.521|
|Lower bound (total efect)|-0.042|-0.276|0.097|
|Observations|278|278|278|
|Mean of dep. var.|0.42|1.90|1.44|
|in control group||||
|Panel B: Center||||
|PWP|-0.005|0.009|0.175|
||(0.035)<br>|(0.195)<br>|(0.227)<br>|
|PWP * Top-up|0.042|0.202|0.282|
||(0.033)|(0.159)|(0.222)|
|R-squared|0.11|0.15|0.12|
|Total efect on top-ups|0.037|0.211|0.458|
|P-value for non-zero efect on top-ups|0.25|0.27|0.03|
|Upper bound (total efect)|0.100|0.583|0.869|
|Lower bound (total efect)|-0.026|-0.161|0.046|
|Observations|779|779|779|
|Mean of dep. var.|0.78|3.67|2.00|
|in control group||||
|Panel C: South||||
|PWP|0.009|0.103|-0.238|
||(0.060)|(0.268)|(0.289)|
|PWP * Top-up|0.001|0.017|0.138|
||(0.026)|(0.120)|(0.163)|
|R-squared|0.37|0.36|0.17|
|Total efect on top-ups|0.010|0.120|-0.100|
|P-value for non-zero efect on top-ups|0.87|0.64|0.70|
|Upper bound (total efect)|0.123|0.622|0.416|
|Lower bound (total efect)|-0.103|-0.381|-0.616|
|Observations|1006|1007|1007|
|Mean of dep. var.|0.61<br>|2.68|1.84|
|in control group|45|||

OLS estimates. All specifications include pre-treatment outcomes from the IHS3, residualized to allow for week-of-survey effects in the IHS3 data. Standard errors clustered at the EA level. _∗p <_ 0 _._ 10 _, ∗∗ p <_ 0 _._ 05 _, ∗∗∗p <_ 0 _._ 001 Estimates include district and week-of-interview
# **Appendix D. Additional Estimates**

**Table D1A. Difference-in-Difference Intent to Treat Impacts for Domains of Cognitive, Social, Motor and Language Development by Age in Years (at Round 3) and Duration Class** **<u>(without matching), Philippines ECD Study</u>**

|**Age/ECD Indicator**|**Durat**|**ion of Exposure (m**|**onths)**|
|---|---|---|---|
|**_Gross Motor_**|**4 to 12**|**13 to 16**|**17+**|
|2|1.08 (.284)*|1.44 (.224)*|1.41 (.247)*|
|3|.050 (.206)|.420 (.189)+|.482 (.168)*|
|4|-.005 (.172)|.111 (.206)|.243 (.145)~|
|5|.061 (.168)|.467 (.185)+|.405 (.165)+|
|6+|-.122 (.124)|-.019 (.132)|-.056 (.120)|
|**_Fine Motor_**||||
|2|.248 (.303)|.421 (.249)~|.500 (.262)~|
|3|-.057 (.254)|.158 (.246)|.259 (.188)|
|4|.040 (.207)|.090 (.212)|-.156 (.193)|
|5|.429 (.179)+|.472 (.235)+|.323 (.176)~|
|6+|.115 (.155)|.162 (.182)|.090 (.170)|
|**_Expressive Language_**||||
|2|1.11 (.289)*|1.36 (.273)*|1.00 (.282)*|
|3|.613 (.215)*|.674 (.239)*|1.01 (.172)*|
|4|-.111 (.162)|-.046 (.193)|.086 (.157)|
|5|.139 (.159)|.134 (.167)|.140 (.152)|
|6+|.069 (.119)|.082 (.110)|-.143 (.102)|
|**_Receptive Language_**||||
|2|1.25 (.313)*|1.52 (.284)*|1.45 (.266)*|
|3|1.00 (.271)*|1.20 (.278)*|1.17 (.261)*|
|4|.064 (.175)|.149 (.245)|.122 (.176)|
|5|.074 (.188)|.170 (.199)|.083 (.184)|
|6+|-.109 (.154)|-.326 (.180)~|-.279 (.176)|
|**_Cognitive_**||||
|<br>2|.911 (.200)*|1.12 (.182)*|.931 (.185)*|
|3|.254 (.183)|.675 (.197)*|.552 (.166)*|
|4|.083 (.165)|-.169 (.181)|-.169 (.157)|
|5|<br>.458 (.165)*|<br>.147 (.201)|<br>.265 (.154)~|
|6+|.349 (.150)+|.375 (.154)+|.417 (.132)*|
|**_Self Help_**||||
|2|.150 (.203)|.363 (.178)+|.323 (.187)~|
|3|.136 (.167)|.120 (.167)|.223 (.154)|
|4|.353 (.163)+|-.087 (.162)|-.041 (.159)|
|5|.047 (.183)|.095 (.208)|-.208 (.180)|
|6+|<br>-.282 (.151)|<br>-.130 (.156)|<br>-.172 (.140)|
|**_Social-Emotional_**||||
|2|.468 (.268)~|.702 (.220)*|.691 (.223)*|
|3|<br>.054 (.200)|<br>.526 (.206)+|<br>.522 (.166)*|
|4|-.357 (.151)+|.109 (.174)|.176 (.127)|
|5|.099 (.175)|.559 (.192)*|.365 (.171)+|
|6+|-.033(.162)|.144(.167)|.249(.161)|

_Note_ : Standard errors in parentheses. The table contains the difference-in-difference defined as (Yp3-Yp1)(Ynp3-Ynp1), where p refers to program, np to non-program areas, and the subscripts 3 and 1 index survey round. ~p<.10 +p<.05 *p<.01.

30
10 absolute number of people living in households below the $1-a-day poverty line, as can be seen from panel (b) in Table 2. In fact, the number of poor people rose not only in Africa and Eastern Europe and Central Asia, but also in Latin America, where economic stagnation and persistent inequality in the last decades prevented substantial progress against poverty. These regional trends in poverty reduction are summarized in Figure 3 below, which is also taken from Chen and Ravallion (2007). The dominant role of poverty reduction in East Asia is immediately apparent.

Trends are somewhat more muted for the $2-a-day poverty line. Global incidence in the developing world fell from 67% to 48% (59% to 52% if China is excluded). Poverty also fell markedly in the Middle-East and North Africa (MENA), and South Asia, but doubled in ECA. Because of population growth, the absolute number of poor people (under $2-a-day) rose in every region other than East Asia. Given a very substantial decline in East Asia, the world total grew only slightly, from 2.45 billion to 2.55 billion. This is in contrast to a decline in the absolute number of poor (under $1-a-day), from 1.47 billion to 0.97 billion in the same period. See tables 2 and 3.<sup>16</sup> The 1993 PPP exchange rates on which these calculations were based are known to have a number of problems. In particular, the two most populous countries, China and India, did not participate in the 1993 price surveys, so their PPPs are subject to larger margins of error. This will be corrected in the 2005 PPPs, in which both countries participated. The preliminary release of the new estimates at the time of writing indicate higher price levels in both China and India than implied by the 1993 PPPs, so the poverty rates in these two countries will rise relative to the rest of the developing world. Aggregate poverty counts will then rise, although the rates of aggregate progress over time will actually be higher than implied by Tables 2 and 3, given that India and (especially) China had high rates of poverty reduction over time. (Note that, while the new PPPs change the level comparisons, the real growth rates in a given country are unaffected.)

# **2. Global inequality**

If _constructing_ internationally comparable poverty measures is harder than computing comparable inequality measures (because the latter are scale-, and thus exchange-rate-invariant), _aggregation_ into a single global measure is more difficult for inequality than for poverty.

> 16 For a more detailed discussion, including their recent estimates when accounting for cost-of-living differences between rural and urban areas, see Chen and Ravallion (2007).
survival probability.<sup>10</sup> The log of _h_ ( _t_ ) _S_ ( _t_ ) is the individual contribution to the likelihood function that we maximize.

While the CR model uses information on cause of death explicitly, it involves estimating a separate vector of coefficients for each cause: birth-related, diarrhea, ARI, tetanus, other illness, and accidental.<sup>11</sup> Our PWW model is more parsimonious, but also, to an extent, captures differences in the cause of death by age of death. A large body of clinical evidence shows that the determinants of mortality differ considerably for neonatal (first month of life) and postnatal mortality. Neonatal deaths are typically related to factors associated with maternal care during pregnancy and delivery (Fikree, Azam and Berendes, 2002). Socio-environmental variables are more important determinants of child survival after the first month, as infectious diseases and poor nutrition become more prominent risk factors. Diarrheal disease, in particular, becomes much more prevalent after weaning, which usually occurs by the second year of life (Black, Brown, Becker, Abdul Alim and Merson, 1982). To capture these age effects, we choose three cut-off points for the survival duration in the PWW model: _c_ 1 = 1month; _c_ 2 = 12 months; _c_ 3 = 60 months.

Unobserved heterogeneity is an issue that arises in the estimation of hazard models of any form, but one that we do not address in this paper. It is difficult to distinguish duration dependence from unobserved heterogeneity in single-spell duration (survival time) data (Heckman and Singer, 1994), and the Weibull specifications already allow for duration dependence. Thus, the results from the policy simulations are unlikely to be much affected by, in addition, accounting for unobserved heterogeneity.<sup>12</sup> We do address the issue of the potential endogeneity of environmental risk factors. Household-level variables, such as access to piped water and sanitary housing conditions, are arguably correlated with parental preferences for and knowledge of child

> 10 In the CR model, the survival probability depends not on the integral of a single cause-specific hazard,

> but on the sum of the integrated hazards from all causes (i.e., _S_ ( _t_ ) = exp(−∑θ _j t_ α _j_ ) ).  This is because _j_

> in order to survive to age _t_ a child must avoid dying from _all_ causes.

> 11 In the case of accidents, we do not include any covariates, so that the hazard rate for accidental death is assumed not to depend on any household, community, or child characteristics, except the child’s age. 12 We do account for heterogeneity in our standard error calculations.  All the variance-covariance matrices are adjusted for clustering at the PSU level using the ‘sandwich’ estimator.

7
Other parameters are closely related to intertemporal choices and economic growth. Capital growth from period _t_ to _t_ +1 is calculated as the investment of period _t_ net of depreciation during period _t_ . We apply an annual depreciation rate of 5 percent according to Herd (2020). The initial capital stock for the base year (2020) is derived from Holz & Sun (2018).

Technological progress takes two forms: autonomous energy efficiency improvement (AEEI) and Hicks-neutral technological change. Regarding AEEI: for sectors excluding the fossil-based electricity sector, we follow Chen _et al._ (2017), applying a 1 percent annual AEEI rate. For the fossil-based electricity subsectors, we again follow Chen _et al._ (2017), applying an annual AEEI rate of 0.4 percent.

Hicks-neutral technological change applies to all sectors but at different rates across sectors. The rates of Hicks-neutral technological change are set in a way that aligns the model’s reference path with the projections provided by the State Information Center (2020) and the International Renewable Energy Agency (IRENA) (2019a, 2019b). Specifically, the projections indicate that the contributions of agriculture, industry, and service sectors to GDP are expected to change from 7%, 37%, and 56% to 6%, 30%, and 64%, respectively, over the period of 20202035.

The growth rate of effective labor is calibrated so that the resulting GDP is consistent with the government projection, averaging 5.5% during 2020-2025, 4.5% during 2026-2030, and 3.5% during 2031-2035.

# **C. Additional Numerical Results**

## **1. Results of DEPS in Scenarios of Various Overlaps**

In Figure 2 in the main text, we compared the cost per ton of abatement associated with DEPS with various overlapping policy cases. Table C1 includes more details of the results, from which we can further understand why the stated policy overlaps – R2 – could help improve the cost-effectiveness of emissions reductions under a DEPS.

58
# _Refugees from Sudan_

The number of Sudanese refugees in Ethiopia was around 42,000 in 2020, residing in five camps in the ethnically mixed region of Benishangul-Gumuz (UNHCR, 2020b). There are similar shares of adult women and men, while children are 60 percent of the total.

Sudanese refugees arrived in successive waves since the 1980s, with the first group escaping from the Blue Nile areas of Sudan and arriving in 1987-89 (Vemuru et al., 2020). Most of the current refugees arrived in 2011. This includes double refugees, individuals who arrived as refugees in the late 1980s and returned to Sudan in the 2000s only to return as refugees to Ethiopia in 2011 (Vemuru et al., 2020).

Sudanese refugees, like South Sudanese, have limited livelihood opportunities, and primarily engage in activities like artisanal gold mining, wood cutting, and daily farm labor (Vemuru et al., 2020). Access to the local market helped refugees to generate income by running businesses including small shops, restaurants, and cafes. Cross-border trade is also important in the region, which benefits both refugees and locals by providing access to more dynamic markets in Sudan.

# 3 Data

The Skills Profile Survey 2017 (SPS 2017) is an excellent source of data to examine and compare refugee and host community profiles and economic activities – although the data predates the most recent reforms which liberalized economic opportunities for refugees and strengthened their economic and social rights on various fronts, as outlined above. While the situation of refugees may well have improved since the survey, the results are still important and instructive, especially in casting light on constraints associated with camp-based locations in poor developing country settings.

SPS 2017 is a household survey carried out by the World Bank that covered refugees in camps and members of surrounding host communities. The sample consisted of four strata based on the main refugee hosting regions: Tigray-Afar (Eritrean), Gambella (South Sudanese), Benishangul-Gumuz (Sudanese and South Sudanese), and Somali (Somali). Using a multistage stratified sampling, refugee households were randomly selected from 82 enumeration areas (EAs) in each stratum.<sup>7</sup> Since only refugees living in camps were surveyed, the sample is only representative of refugees living in camps in Ethiopia (Pape et al., 2018). The samples for the host communities were selected by classifying Ethiopian non-displaced households residing within a 5 km radius of a camp (in the four stratum) as host community. Then, host community households were randomly selected from 42 EAs in each stratum (World Bank Group, 2019). Figure 2 shows the sample composition across refugee and host communities by gender of the

> 7 Details on the sampling procedure can be found at - - <u>https://microdata.worldbank.org/index.php/catalog/3445/study description#metadata sampling</u>

8
characteristics of female-headed households that are strongly correlated with poverty such

**as** low education.

**Table** 7: **Poverty and Gender of Household Head**

|_Gender ofHousehold Head_|_Bulgaria_|_Hungary_|_Poland_|_Estonia_|_Kyrgyz_<br>_Republic_|_Russia_|
|---|---|---|---|---|---|---|
||||**Headcou**|**nt**(P0, in pe|rcent)||
|**Male**|24.0|19.1|22.7|27.9|41.6|37.8|
|Female|40.5|25.6|23.7|39.1|50.5|46.0|
|All|26.1|20.6|23.0|30.5|42.5|39.4|
||**Poverty**G|ap (in pe|rcent)**_11_**||||
|**Male**|18.6|13.3|13.0|18.7|24.7|28.5|
|**Female**|24.5|16.0|14.2|23.9|27.5|34.5|
|**All**|19.8|14.1|13.3|20.2|25.0|29.8|

> _Source:_ Household Expenditure and Income Date for Transition Economies Data Set (HEIDE). _Notes:_

I/ he poverty gap is the poor's average shortfall in expenditures from the povertv line, expressed as a percentage of the poverty line (this measure is also known as the expenditure gap ratio).

The age distribution of poverty in Table 8 highlights the extent to which poverty in Eastern Europe is concentrated among the very young and the very old. The average poverty incidence in Poland is 23 percent, but among children under ten it exceeds 30 percent. The numbers for Hungary show a similar pattern. In Bulgaria, the relative concentration of poverty among children is actually least. This is not a contradiction with the earlier finding that in Bulgaria poverty rates among households with three or more children are very high, because such households are quite rare in Bulgaria (much rarer than in the other two countries). Hence, in Bulgaria most children live in households with one or two children where poverty rates are lower.

37
microfinance institutions in 38 developing countries for our largest regressions incorporating bank penetration variables. The Beck et al. (2007) data provide measures of bank penetration that serve as the key explanatory variables in this analysis: the number of bank branches in a country per capita and the number of branches per square kilometer. We also add banking sector ownership and concentration variables, and two more country-characteristic variables: the share of the population residing in rural areas and rural population growth.

We start from the observation that commercial banks initially were deterred from entering the microfinance niche by the small scale of the transactions that define it, but that the commercialization of microfinance has started to change that mindset. A growing number of commercial banks are downscaling their operations, opening up services to poorer segments of the population, and competition is emerging as a result. Increased competition could change the industry in a number of ways, some for the better and others less favorably. We again look at MIX data, in search of evidence on where the balance between these competing effects rests.

We look for a relationship between competition and a profitability-outreach trade-off. There are plausible explanations for both a positive and negative relationship. If microfinance institutions facing greater competition from commercial banks attempt to compensate by shifting their loan portfolios away from segments of the population that are perceived as being more costly to serve – i.e. the relatively poor and women – competition may hinder outreach. However, competition could support the financial self-sufficiency of microbanks if the benefits of agglomeration effects and a stronger regulatory environment outweigh negative spillovers, and could lead to deeper outreach.<sup>6</sup> We find that greater competition, as indicated by greater bank penetration in the overall economy, is associated with deeper outreach by the microfinance institutions, suggesting that competition pushes microbanks toward poorer markets, as reflected by smaller average loans sizes and greater outreach to women. However, in this sample, competition seems to have little effect on the profitability of microbanks. This is a useful finding, as it complements analyses of competition between different microfinance institutions (in contrast to the competition analyzed

> 6 Microfinance experience to date shows competing impacts on costs from serving poorer customers and women. On the one hand, women are relatively more reliable borrowers than men. On the other, women tend to request smaller loans on average, which increases average costs (Armendáriz and Morduch, 2010, chapter 7).

16