rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3: Diversity of Situations in Fragile States <!-- Start of picture text --> Issue<br>WB Both<br>0.65 BB conflict<br>Comoros CPIA<br>0.60 e Fed. Sts. of Micronesia<br>Haiti<br>e<br>0.55 Central AfR<br>0.50<br>@ Somalia<br>Solomon Islands<br>= os Dem. Rep. of Congo e Rep. of Congo<br>w ® Madagascar Sudan South<br>Myanmar @ @ td]<br>0.40 Cote d'Ivoire Kosovo<br>°<br>4<br>0.35 Liberia Togo Guinea-Bissau Yemen e<br>e ad Nepal bed ° Syri<br>Burundi ee Sudan Palestine yria<br>0.30 Mali @ e<br>Iraq<br>e<br>Afghanistan<br>0.25<br>0.20<br>it 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 5,500 6,000<br>GOP Per Capita<br><!-- End of picture text --> Scatterplot of fragile states, by GDP per capita at purchasing power parity and Gini coefficient, 2010 estimates. Countries omitted from this graph: Bosnia and Herzegovina (Gini: 0.36, GDPPCPPP: $7,290) and Libya (Gini: 0.46, GDPPCPPP: $13,706). Source: International Futures v7.04
- 123 -

# TABLE Al.1: EARNINGS FUNCTION (1nW2 ) WITH PARENTAL EDUCATION VARIABLES

||Al|l|Ma|le|Fem|ale|
|---|---|---|---|---|---|---|
|Intercept|9.952|(80.8)|9.833|(67.7)|9.980|(47.8)|
|Years of Primary Schooling|.039|(1.5)|<br>.059|(2.0)|-.097|(1.4)|
|Years of Secondary Schooling|.130|(5.3)|<br>.121|(4.2)|<br>.182|<br>(3.8)|
|Years of Higher Schooling|.158|(4.2)|<br>.176|(4.2)|<br>.059|<br>(0.6)|
|Years of Current Job Experience<br>|.096|(7.1)|<br>.106|(6.9)|<br>.055|<br>(1.7)|
|(Years of Current Job Experience)<br>2|-.0018|<br>(3.9)|-.0020|<br>(3.9)|-.0008|<br>(0.7)|
|Years of Previous Job Experience<br>|.040|(1.6)|<br>.038|(1.4)|<br>.143|<br>(0.8)|
|(Years of Previous Job Experience)<br>2|-.0011|<br>(0.6)|-.0009|<br>(0.5)|-.0085|<br>(0.6)|
|Years of Apprenticeship|.020|(0.9)|<br>.022|(1.0)|<br>.105|<br>(0.9)|
|Years of VTE|.063|(2.2)|<br>.077|(2.3)|<br>.030|<br>(0.6)|
|CEPE|.125|(0.8)|.118|(0.7)|.548|(1.3)|
|BEPC|.120|(0.5)|<br>.014|(0.1)|<br>.671|<br>(1.3)|
|VTE Diploma|.339|(1.7)|.281|(1.2)|.979|<br>(2.1)|
|Other Diploma|.324|(1.2)|<br>.282|(0.9)|<br>.990|<br>(1.3)|
|Sex (1-female)|-.270|(2.8)|<br>_<br>|-|-_||
|Non-Ivorian|-.292|(2.9)|-.279|(2.4)|<br>.058|<br>(0.2)|
|Seasonal Job|-.640|(3.0)|-.590|(2.5)|-1.593|<br>(2.4)|
|AtUXUJan|.u0o|(0.)|- -.<br>V;Y|(0.1)|.098|(0.I)|
|Rural|-.268|(2.1)|-.282|(2.0)|<br>.340|<br>(0.7)|
|Father's Years of Schooling|.019|(1.4)|<br>.026|(1.5)|<br>.020|<br>(0.8)|
|Mother's Years of Schooling|-.0002|<br>(0.1)|<br>.001|(0.1)|-.026|(0.6)|
|R<sup>2</sup>|.58||.58||.70||
|F|31.9||26.8||8.2||
|n|484||396||88||

# Note: Absolute t - values in parentheses.
Table 1: Character'tics of Sample Farm Households

|Item|County<br>|Gongzhullng<br>(N-200)|Tal<br>(N-200)|Jurong<br>(N1-199)|Xlajlang<br>(N-200)|
|---|---|---|---|---|---|
|1|Mean farm size<br>(Mu) a/|20.75|4.63|6.90|11.31|
|2.|Share of Income<br>from non-farm<br>sources|18|47|47|15|
|3.|Per capita Income<br>In 1987/88 Yuan|952|737|832|472 b/|
|4.|Mean No. of land<br>parcels per HH|3.7|7.05|4.90|16|
|5.|Main crop<br>(s|corn<br>ingle season)|wheat (winter)<br>Rice (Summer)|wheat<br>Irvter)<br>Rice (o.<br>ier)|Rice (double<br>cropping)|

a/ 15 Mu - 1 hectare _/ One season **hi** 1988 **only**
in nominal terms the value of exports is 130 percent higher in 2013 than in 2000. The past decade has witnessed a remarkable increase in DRC’s exports to Sub-Saharan Africa (SSA), rising from a mere 1.5 percent of total exports in 2000 to about 28 percent in 2013. However, a considerable share of DRC’s exports to SSA goes to Zambia and are re-exported. In 2013, Zambia accounted for 95 percent of DRC’s exports to SSA, against 54 percent in 2000. Interestingly, in 2013, the share of exports to SSA (and Zambia) seemed to increase while that of exports to China declined. This suggests that this decline did not occur in fact, with an increasing share of exports reaching China as third party re-exports.

7. **In addition to the increased reliance on China as a main destination of exports, product diversification**<sup>1</sup> **of DRC’s exports has decreased over the last decade.** The relatively high reliance on exports to few countries, particularly China, makes DRC a country with one of the highest market concentration levels in SSA. In terms of product concentration, DRC’s ratio is among the top ten most product concentrated countries in SSA.

8. **DRC’s export concentration indices have increased in recent years due to the concentration on a small number of trading countries and small number of products** (see Figures 4 and 5). The market concentration index declined continuously between 2000 and 2007 from 0.60 to 0.32. In the past few years, the index has been increasing and reached 0.57 in 2013, much higher than the average of just 0.32 for SSA. During 2005-2011, DRC appeared to have improved, with a market concentration index of 0.36, but in more recent years (2012-2013), concentration has increased again. This seems to coincide with the rise of mineral exports, which account for three-quarters of DRC’s exports, since the mid-2000s. However, a longer historical perspective shows that DRC’s market concentration index has not changed when compared to the 1990s. In fact, the average index of the 1990s was 0.57, which is exactly the same as in 2013. This shows DRC’s long-term reliance on few market destinations. In addition, DRC heavily relies on few export commodities, as reflected by its long-standing product concentration index, which

> 1 Herfindahl-Hirschman market (product) concentration index is a measure of the dispersion of trade value across an exporter’s partners (products). The value of the index ranges between zero and one. A country with a preponderance of trade value concentrated in a very few markets (products) will have an index value close to one.

5
Policy Research Working Paper 10395

# **Abstract**

This paper takes stock of the insights and learnings from a COVID-19 emergency cash transfer program that was administered to vulnerable informal sector workers in Sierra Leone. It starts by reviewing relevant examples of cash transfer programs that were instituted in response to the COVID-19 crisis. It then describes the context, intervention, and data of the emergency cash transfer program, before presenting a quasi-experimental analysis of the emergency cash transfer’s potential impacts on various measures of economic security and subjective well-being of households with urban informal sector workers. The analysis is conducted by matching administrative data to survey data and using program eligibility criteria and inverse probability weights to identify the short- and medium-term relationship between a one-off US$135 cash transfer and various labor market, food security, human capital, and subjective well-being outcomes for recipient and nonrecipient households of the emergency cash transfer. The analysis finds a positive potential impact of the transfer and the number of hours worked as well as employment in the medium term. It also finds that program beneficiaries report higher chances of their main income increasing or staying the same compared to nonbeneficiaries. The positive correlation between the transfer and income disappears over the medium term, perhaps suggesting that one-off transfers work best to cushion vulnerable self-employed households and informal wage workers in the short term but do not impact medium-term employment or income.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sadhikari2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> 4.0<br>3.5 m1983 m1987 =1993 m1999 m2004<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Male Female All Male Female All<br>wi903aa7 [owe [awe |<br>e9e7) 2697s aa ||eta | oat |<br>say 260 aa] nota<br>Ee<br><!-- End of picture text -->

<!-- Start of picture text --> 6.0<br>5.0 m1983 1987 m1993 m1999 m2004<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>Hindu Muslim | Christian | Others All Hindu Muslim | Christian | Others All<br>soe} a6 | 090 [298 [as [awe ast | 037 | an | om | ow<br>1967 178088 | 322) 182-1706 | 0a | ast | 068 | ot<br><!-- End of picture text -->
From that point on, growth must depend on the steady accumulation of fundamentals emphasized by neoclassical growth theory. Long-term successes, such as Britain, Germany, and the United States, have all gone through these phases, as have more recent examples, such as Japan, South Korea, and Taiwan, China. If doubts remain about China’s economic future, it is because so much of the country’s institutional transformation, particularly with respect to political institutions, still remains ahead of it.

This typology helps clarify one of the puzzling aspects of cross-national data: institutional quality and human capital are both highly correlated with income levels, yet improvements in institutions and human capital are not a reliable predictor of economic growth. It suggests that this empirical finding is not a contradiction. Only countries that steadily enhance their fundamental capabilities eventually become rich. But investment in fundamentals is not the quickest or easiest way of getting there, at least during the early stages of development. Early on, it is rapid industrialization that fuels growth, and this requires policies that may differ considerably from conventional fundamentals. Countries that rely exclusively on building up broad-based capabilities are rewarded with modest growth, and may in fact be diverted from those policies as a result (Rodrik 2013a).

We will use this typology to interpret the experiences of our country examples. None of them can be said to have made it definitively to the nirvana of quadrant (4). Botswana has high fundamentals but limited structural change, while Vietnam has relatively rapid structural change but relatively low fundamentals. Our other African examples (Ghana, Nigeria, and Zambia) typically have had episodic growth-promoting structural change at best, moving back and forth between quadrants (1) and (2), although Ghana has recently moved into quadrant (3). Brazil has moved from quadrant (2) to quadrant (3), with greatly improved fundamentals but much weaker growth underpinned by slow structural change. India meanwhile has not experienced the kind of structural change that import-substituting countries (such as Brazil in the 1950s–1970s) or the East Asian exporters (such as Vietnam) have gone through, so its growth prospects remain brittle.

# **Country Studies: Methodology**

In an effort to retain consistency across country studies, all of the country chapters use the same methodology as McMillan and Rodrik (2011). This approach is not intended to resolve questions about causality; rather, it is intended to lay out a set of facts that we hope will help policy makers better 9
|**Table 4**|**– Transmission and Distributi**<br>Hurricane|**on, Resilient Desig**<br>Earthquake|**n Guidelines**<br>Wildfires|
|---|---|---|---|
|Transmission<br>and<br>Distribution|* Poles and towers should be<br>of design and material to<br>survive 240 kph sustained<br>winds (U.S. Department of<br>Energy 2018b)<br>* Enable integration of<br>microgrids (New York Power<br>Authority et al. 2017)<br>* Build flood barrier around<br>site based on observed worst<br>case flooding plus a foot or<br>more for design safety<br>margin (New York Power<br>Authority et al. 2017)<br>* All critical electricity<br>system assets should be<br>located at BFE +1 meter or<br>0.2% flood elevation or sited<br>outside of the floodplain<br>entirely (U.S. Department of<br>Energy 2018b)<br>* Build substations at higher<br>elevations<sup>[66]</sup><br>* Poles should have deeper<br>subgrade support to<br>withstand min. 1 meter of<br>water (New York Power<br>Authority et al. 2017)<br>* Towers in Philippines only<br>designed to withstand winds<br>of 185 kph while Yolanda<br>had winds greater than 220<br>kph (Rappler.com 2013)|* Build to<br>withstand events<br>greater than<br>events in the past<br>and update aging<br>infrastructure (N.<br>Watson 2013)<br>* Determine<br>threshold of<br>acceptable risk<br>and cost<br>increment.<br>(Stevenson et al.<br>2011)<br>*Upgrade major<br>cables,<br>switchyards, and<br>substations to<br>seismic standards<br>(Fenwick and<br>Hoskin 2011)|* Harden grid by<br>replacing wooden<br>poles with steel<br>ones, better<br>insulating power<br>lines, and burying<br>power lines<br>(Rogers 2017)<br>* Be aware of<br>max winds towers<br>and poles can<br>withstand<br>(NDRRMC 2014)<br>* Turn off power<br>lines in time of<br>high fire risk to<br>prevent wildfires<br>(Daniels 2017)|

# **3.2 – Generation Plant Vulnerabilities by Fuel type**

Generation plant vulnerabilities were investigated using case study and curtailment data. By comparing impacts across geographies and hazards, specific guidelines about resilience (or lack the of) of specific power generation systems (fuel types) can be made. The following sections are organized by fuel type (petroleum, coal, natural gas, nuclear, solar and others (including Wind, Hydroelectric, Backup Diesel Generators, Geothermal). For each fuel type, vulnerabilities are detailed specific to the facility and the supply chain or interdependent infrastructure. Additionally, where information was available, the costs of damages and impacts are detailed from specific case studies along with recommendations for more resilient design. A comprehensive case study of component level damages and corresponding baseline and build back better costs are provided in Section 4 specific to Puerto Rico.

13
authors report a VSL of $2.8 million (2000 USD) (S.D. = $1.3 million) based on 18 CV studies and a VSL of 9.6 million (2000 USD) (S.D. = $4.9 million) based on 42 HW studies. Their final estimate, which weights study estimates in inverse proportion to their variance, yields a VSL of $5.4 million with a standard deviation of $2.4 million (2000 USD). The Kochi et al. study is the only meta-analysis that weights VSL estimates in inverse proportion to their variance and that uses stringent criteria for the inclusion of studies in the analysis. We therefore focus on this estimate as representing the state of the literature in high-income countries.

- D. Empirical Estimates of the VSL in Developing Countries and Benefits Transfer

There are published estimates of the VSL for all of the countries listed in Table 7. However, the number of studies per country—and their quality—varies widely. Viscusi and Aldy (2003) cite only 5 HW studies conducted in non-OECD countries—India, Korea and Taiwan. Miller (2000) includes 29 studies from 12 countries outside of the US in his 2000 meta-analysis; however, only two of these (Taiwan and South Korea) are not high-income OECD countries. Robinson (2008) summarizes results from 11 CV studies in developing countries.

**<u>Table 7 Countries with Published Estimates of the VSL</u>**<sup>**30**</sup>

|**High Income OECD**<br>**Countries**|**Other Countries**|
|---|---|
|Austria (2)|Chile (2)|
|Australia (2)|China (3)|
|Canada (8)|Hong Kong (1)|
|Denmark (1)|India (5)|
|France (1)|Malaysia (1)|
|New Zealand (3)|Mexico (1)|
|Sweden (4)|Poland (1)|
|Switzerland (2)|South Korea (2)|
|United Kingdom (10)|Taiwan (5)|
|United States (40+)|Thailand (3)|

What is clear is that the developing country literature at this point is not sufficiently mature to provide estimates for individual countries. This suggests transferring estimates from countries where better studies exist to countries for which there are no empirical estimates of the VSL.

> 30 As cited by either Miller (2000), Viscusi and Aldy (2003) or Robinson (2008). This list is not intended to be exhaustive.

18
and private credit side remain strongly correlated across countries, suggesting that there is a link, even if it is not a rigid one. (Data for 138 countries are shown in Figure 4a: the correlation is +0.71).<sup>18</sup> If the poor have limited access to credit, might this imply that financial development is pro-rich, disproportionately benefiting the wealthy? Or is it for outsiders and newcomers that a well-developed financial system most provides opportunities in terms of smoothing consumption, diversifying risk and overcoming indivisibilities in investment. An interesting but rather rarified theoretical literature has explored a variety of channels through which financial development could affect the evolution of income distribution . Generally speaking, these models pivot on credit constraints and on such aspects as collateral as a prerequisite for credit, and by such considerations as the possibility that improvements in financial intermediation will be seized first by the already wealthy. There has been no professional consensus on which of the various models, with their mutually contradictory conclusions, is most likely to fit reality.

Empirical evidence is accumulating, though, that financial development appears to be correlated not only with aggregate income growth, but also with lower poverty, even conditional on average income levels. Somewhat ironically, this seems to be the case more for financial depth indicators than for financial access indicators (see below).

Li et al. (1998) were the first to look at this kind of issue in a cross-country econometric framework. They found that income inequality (Gini coefficient) was lower in crosssectional regressions with deeper financial systems. Honohan (2004a) showed that absolute poverty (proportion of the population below the $1 a day or $2 a day poverty line) was lower in deeper financial systems, even conditional on the mean income level achieved by the non-rich, and these findings are confirmed on an updated and expanded data set in Table 2. Beck et al. (2004) looked at income growth rates of the poor and

> 18 The outliers are (a) countries with a large nonbank sector (like the US or Norway) funded with liabilities that are not included in the liquid liabilities definition used for the deposit side; (b) offshore financial centers (like Malta) holding sizable foreign assets; (c) Countries with relatively large lending by the banking system to the government (like Japan).

13
Asia. Similarly, if we did not record forest income (or all environmental incomes, respectively), a much larger share of the PEN sample households (36% for forest income, 41% for all environmental income excluded) would fall below the poverty line. Hence, we can say that for 14% of all households the access to (forest and non-forest) environmental resources makes an income difference that comes to lift them above the international poverty line (for forest incomes alone, the share is 9%). Correspondingly, income surveys such as the Living Standard Measurement Surveys (LSMS) that capture environmental incomes only superficially (if at all) will come to overestimate the degree of local poverty when people have access to rich extractive resources (Davis et al. 2010).

**Table 6: Share of the population below the US$1.25 poverty line, for different income measures (%)**

||**Latin**<br>**America &**<br>**Caribbean**|**South**<br>**Asia**|**East Asia**<br>**& Pacific**|**Sub-**<br>**Saharan**<br>**Africa**|**PEN**<br>**total**|
|---|---|---|---|---|---|
|Total income|2|7|13|43|27|
|Cash income only|8|32|31|69|49|
|Total without forest income|6|14|22|54|36|
|Total without forest and non-forest<br>environmental income|9|17|27|59|41|

Lastly, we also compare inequality within regions, using different income aggregations (Table 7). For recall, the Gini coefficient expresses the share of total income mass that should be moved around in the income distribution to make all participants equal. As compared to the baseline Gini for the full sample, the coefficient rises sharply if we were to consider only cash income (i.e. excluding subsistence incomes), and for most continental regions to a lesser extent if we excluded environmental income (or alternatively only its forest component): the increment in the Gini coefficient is between 0.03 and 0.07, meaning that an additional 3-7% of income would have to be moved around to restore the same level of equality as was found in the full sample. This indicates the egalitarian effects of both subsistence income and forest/ environmental income. Note also that the Gini coefficients here are calculated for the full regional samples. When we consider income inequality at each site, the average Gini is somewhat lower: 0.35 for total income and 0.39 when environmental incomes are excluded (Angelsen et al. 2014).

**Table 7: Income inequality for different income types; Gini coefficients**

||**Latin**<br>**America &**<br>**Caribbean**|**South**<br>**Asia**|**East Asia**<br>**& Pacific**|**Sub-**<br>**Saharan**<br>**Africa**|
|---|---|---|---|---|
|Gini total income|0.50|0.37|0.44|0.57|
|Gini cash income only|0.55|0.46|0.52|0.66|
|Gini without forest income|0.55|0.40|0.48|0.60|
|Gini without environment income|0.57|0.41|0.50|0.60|

20
**Figure 7 The district poverty incidence in 1999 and 2006**

<!-- Start of picture text --> 0 200 400 600<br>Districts<br>0 20 40 60 80 100<br>1999 2006 The poverty incidence in 1999<br>Source: Authors’ estimation from VHLSS 2006 and ARFC 2006<br>Figure 8 presents the relation between poverty reduction in the period 1999-2006<br>and the inequality level in 2006. It shows that provinces and districts with a larger<br>poverty reduction in the period 1999-2006 tend to have a lower level of inequality in<br>2006. It means that poverty reduction can be associated with inequality reduction.<br>Figure 8 Poverty reduction and inequality during 1999-2006<br>0 10 20 30 40 -20 0 20 40<br>Poverty reduction during 1999-2006 Poverty reduction during 1999-2006<br>Source: Authors’ estimation from VHLSS 2006 and ARFC 2006<br>100<br>40<br>80<br>60 20<br>40<br>The poverty incidence 20 0<br>Poverty reduction during 1999-2006<br>0<br>-20<br>.35 .5<br>.4<br>.3<br>.3<br>Gini coefficient Gini coefficient<br>.25<br>.2<br>.2 .1<br><!-- End of picture text --> Figure 8 presents the relation between poverty reduction in the period 1999-2006 and the inequality level in 2006. It shows that provinces and districts with a larger poverty reduction in the period 1999-2006 tend to have a lower level of inequality in 2006. It means that poverty reduction can be associated with inequality reduction.

# **V. Estimates of Income Poverty and Inequality**

Since the government of Vietnam is using the income poverty line, we also estimate income poverty and inequality measures for provinces and districts.

16