rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 2: Trends in prevalence of food insecurity**

<!-- Start of picture text --> (a) (b)<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>Tigray Amhara Oromia SNNP<br>Aug'19 Apr'20May'20 Jun'20 Aug'20 Sep'20 Oct'20 Nov'20 Dec'20 Jan'21 Feb'21May'21 Aug'19 Apr'20May'20 Jun'20 Aug'20 Sep'20 Oct'20 Nov'20 Dec'20 Jan'21 Feb'21May'21<br>Probabilty of food insecurity<br>Prevalence of moderate or severe food insecurity<br><!-- End of picture text --> Similarly, panel (b) shows that the share of households experiencing food insecurity increased by 25 percentage points (from 55 percent to 80 percent). This is consistent with the recent assessment by the World Food Programme, which reports that 83 percent of people in Tigray are food insecure (WFP, 2022). On the other hand, trends in the other major regions remained relatively stable. To put these trends in context, immediately before the war, the trends in the overall prevalence of food insecurity were broadly comparable across regions (with similar trends in Tigray and Amhara), but by May 2021 the prevalence and probability of food insecurity sharply increased in Tigray while the corresponding trends for the other regions remained relatively stable.

Figure 3 further disaggregates the patterns of food insecurity experiences using some of the questions included in the FIES module. Panel (a) shows that the percentage of households who reported worrying about not having enough food during the previous 30 days. The figure remained reasonably stable between May 2020 and October 2020, after which the share increased from 41 percent to 65 percent in Tigray.<sup>23</sup> For the other regions, the corresponding trend remained stable.

> 23 Compared to May 2020, there are 16 percent more households reporting being worried about having enough food.

16
work and the selection of self employment over wage work were modified to reflect the fact that the respondents are business owners rather than wage workers.

Taken together, the three samples provide survey data on wage workers, microenterprise owners, and larger firm owners. They breadth of information generated by the surveys for each of these three types of workers is, to our knowledge, unique. We use the data to compare the characteristics of the own account workers with wage workers on the one hand and larger firm owners on the other. We examine family background, measures of ability, attitudes toward risk, labor history, and measures from the entrepreneurship psychology literature. The specific measures are discussed in more detail in the following section. We then turn to techniques of species classification, using all or part of this list of characteristics. This allows us categorize each own account worker as having characteristics more like the employers or more like wage workers.

# **Who are the Own Account Workers?**

We begin with simple comparisons of means of variables divided into six categories: ability, parental background, childhood (at age 12) conditions, labor history, attitudes toward risk, and psychological measures. One important difference in the samples is worth noting. Males make up a much larger portion of the SME sample than of either the wage worker or SLMS samples. Seven in eight SME owners (87%) are male, compared with 51% of the SLMS owners and 64 percent of the wage workers.<sup>6</sup> There are also some differences in the average age of the respondents in the three samples. By design, all are between the ages of 20 and 65. On average, the SME owners are the oldest (43.6 years of age), the SLMS owners next oldest (41.9 years) and the wage workers the youngest (37.6 years). As we point out later in discussing the labor history data, surprisingly few of the own account workers say they have ever been wage workers. But previous wage work is more common among the SME owners. So the difference in average age may reflect these transitions from wage work to self employment.

For all of the tables, we will show own account workers in the middle, with SME owners on the left and wage workers on the right. The column between the SME owners and the own account workers shows the level of significance of the difference in means 6 All three samples were stratefied to include a larger share of females than represented by the population.
and Zeger, 1993). Thus, standard errors are corrected using the Huber and White matrix for villagelevel clustering.

# 4. Results

## **4.1     Descriptive Statistics**

Table 1 shows the weighted mean values of the variables in specification (1). About 42 percent of the children in the sample are stunted which is slightly higher than the most recent estimate of the national rate of 36 percent from 2014. About 43 percent of the households use water that is contaminated at the source point, while 62 percent are contaminated at the household point. This is indicative that child, maternal and household characteristics influence contamination between collection at source and consumption at the household points.

When disaggregated by type of water source, E. coli contamination at both source and household points is higher in piped water and other sources compared to tube wells (Annex Figure 1). Among households with piped water, there is little difference in E. coli contamination between collection points. This indicates that E. coli at source is a significant determinant of contamination at the household point when the primary source of drinking water is piped water.

There is a substantial increase in contamination from 40 percent at the source to 61 percent at the household point when tube wells are the main source of drinking water. Again, the difference in contamination between collection points can be attributed to child, maternal, and household characteristics instead of source contamination.

Household water treatment is one way to reduce the level of contamination between collection points. However, among users of piped and tubewells that reported treating water, contamination did not significantly decrease from source to consumption point. It should be noted that the portion of households that treat water is too small (4 percent) to make a conclusive statement on its impact on E. coli contamination and thus, stunting too.

Piped water is more prevalent in urban areas (20%) compared to rural areas (2%). In rural areas, the majority (93 percent) of the households use tubewells. When disaggregated by risk levels, urban households are twice as likely to have very high levels of E. coli (>100 cfu in 100 ml) compared to 8
possesses the institutions and mechanisms needed to convert this raw material into useable wealth. This is still lacking in Latvia. The recommendations outlined below are designed to overcome this weakness.

**Table 4 National Innovation Capacity Index and Sub-Indexes**

||_Inno_<br>_Capac_|_vative_<br>_ity Index_|_Prop_<br>_Sc_<br>_E_<br>_S_|_ortion of_<br>_ientists &_<br>_ngineers_<br>_ub-Index_|_Inno_<br>_Poli_|_vation_<br>_cy Sub-_<br>_Index_|_Cl_<br>_In_<br>_E_|_uster_<br>_novation_<br>_nvironme_<br>_nt Sub-_<br>_Index_|_Link_<br>_Sub-_|_ages_<br>_Index_|
|---|---|---|---|---|---|---|---|---|---|---|
||Rank|Index|Rank|Index|Rank|Index|Rank|Index|Rank|Index|
|United States|1|30.3|6|4.3|1|8.1|1|10.9|1|7.1|
|Finland|2|29.1|7|4.2|4|7.3|2|10.9|3|6.7|
|Israel|11|26.5|19|3.9|14|6.8|15|9.1|2|6.7|
|Singapore|13|26.0|17|3.9|2|7.4|17|8.9|15|5.8|
|Ireland|16|25.4|12|4.0|16|6.6|16|9.1|16|5.7|
|Denmark|19|25.2|10|4.1|19|6.4|20|8.8|13|5.9|
|Iceland|20|24.8|4|4.3|20|6.2|18|8.8|20|5.5|
|Czech Republic|26|21.3|36|3.2|26|5.5|29|7.9|29|4.7|
|Estonia|27|21.2|25|3.8|36|5.0|36|7.4|27|5.0|
|Hungary|28|21.1|34|3.3|25|5.6|38|7.2|25|5.0|
|Russia|30|20.6|3|4.4|52|4.1|30|7.8|42|4.3|
|Slovenia|31|20.4|20|3.9|32|5.2|50|6.8|33|4.5|
|Ukraine|32|20.3|21|3.9|56|4.1|28|7.9|35|4.4|
|Slovakia|34|20.0|26|3.7|49|4.5|35|7.6|44|4.2|
|Poland|36|19.6|32|3.5|50|4.5|37|7.2|36|4.4|
|Lithuania|37|19.2|24|3.8|55|4.1|45|6.9|34|4.4|
|**Latvia**|**41**|**18.5**|**37**|**3.1**|**51**|**4.2**|**43**|**7.0**|**47**|**4.1**|
|Bulgaria|50|16.9|27|3.7|64|3.6|67|5.8|56|3.8|
|Romania|55|16.3|33|3.4|65|3.6|53|6.6|73|2.7|

_Source_ : Porter, M. and Scott Stern, 1999 34
passengers carried on any given flight; passenger weight including luggage comprises in most cases only between 1/5 and 1/8 of total aircraft weight. To the extent that passengerrelated weight is important, this weight is also likely higher in premium classes as seats in these classes are far heavier than economy-class seats. In consequence, we demonstrate, using our analytical model developed in Section 3 below, that the carbon footprint ascribed to a business/first class passenger ought to be greater than for an economy class passenger.

- 2) For organizations whose travel mix differs significantly from the industry average, differences in footprint across travel classes matter for calculating the overall footprint.<sup>9</sup> For air travel involving institutions where the average mix of economy-class and business-class travel is involved is close to the global average for all civil air passenger transport, the differences in footprint according to travel class are not crucial for calculating the institution’s overall carbon footprint. For the staff of the WBG, on the other hand, the share of overall air travel in business or first class is much higher than the global average.

- 3) The footprint per mile for mission air travel is likely to vary systematically also with other flight-specific factors such as average flight length, types of airplanes used, etc. A more precisely calculated footprint related to a given flight activity (involving particular air routes, airlines and equipment) may raise the need for such more specific effects to be calculated separately. WBG mission travel consists predominantly of longer-haul flights. There are also significant differences in per-passenger fuel consumption between plane types and models (for given load factors; where the load factor expresses the percent of available seats that are filled on any given flight).<sup>10</sup> Institutions such as the WBG concentrate their activity to particular airlines that rely on particular plane types and model (and thus not a random selection from the global vehicle fleet), where also average load factors may differ from overall global averages. A more accurate calculation of each institution’s footprint then requires that these numbers be calculated separately, for the actual air travel by the institution in a given year.

- 4) As we have already stressed, the climate forcing effect of emissions from airplanes is likely to differ from that resulting from ground-released carbon emissions due to the altitude at which they are released and to the fact that several types of emissions in addition to carbon are involved. Including such factors is still not standard in carbon footprint accounting, and the factor is at the moment very uncertain. We do not further pursue this issue in this paper, however.

> 9 Keen and Strand (2007), table 12, presents data on global averages by travel class in 2004, the last year for which we have good data.

> 10 The differences in fuel consumption by plane type and vintage can be substantial, and vary by a factor of 3 or more.

5
Table E1: Thresholds to belong to each top quantile (USD 1,000s)

||Lowest|income|by g-perce|ntile and year|
|---|---|---|---|---|
||2014|2016|2018|2019|
|Top 1%|283|341|369|349|
||[31]|[34]|[36]|[33]|
|Top 0.5%|441|511|550|486|
||[48]|[51]|[53]|[46]|
|Top 0.1%|998|1,166|1,248|1,009|
||[108]|[115]|[121]|[96]|
|Top 0.05%|1,379|1,641|1,741|1,509|
||[150]|[162]|[169]|[143]|
|Top 0.01%|3,448|4,243|4,477|4,346|
||[374]|[420]|[434]|[412]|
|Top 0.005%|5,554|6,676|7,256|7,203|
||[603]|[661]|[703]|[683]|
|Top 0.004%|6,797|7,602|8,794|8,203|
||[737]|[753]|[852]|[777]|
|Top 0.003%|8,655|10,118|10,866|9,585|
||[939]|[1,002]|[1,053]|[908]|
|Top 0.002%|12,224|12,973|13,766|12,744|
||[1,326]|[1,284]|[1,335]|[1,208]|
|Top 0.001%|17,731|19,167|19,764|19,951|
||[1,924]|[1,898]|[1,916]|[1,891]|

_Note:_ This table reports the thresholds to belong to each quantile in the tax records, for selected years, both in Lempiras and USD PPP (1,000s).

# **F Adjustment of survey agricultural income**

In this section we provide more details on the adjustments we make on the survey data related to self-employed agricultural income. As we document in Table A.1, in the period 2004 - 2013 the household survey we use included an additional module on agricultural income from self-employed activities. What we observe in the data is that reported income from those activities is extremely high in that period. Furthermore, the microdata available only includes total income from that module, and not a breakdown of all variables included in the survey, which limits our ability to further 64
**Figure 17: Reform Indicators on Utility Governance and Decision-making and Full-Cost Recovery**

<!-- Start of picture text --> a) Cost Recovery and<br>b) Cost Recovery and Utility Governance Index<br>Private Sector Participation Index<br>70% 100%<br>R2 = 0.4107 R2 = 0.1772<br>60% Philippines 90% Kenya<br>80%<br>50% Peru<br>Philippines<br>Uganda 70% Colombia<br>40% Uganda<br>India -Odisha 60% Pakistan Peru Ukraine<br>Colombia<br>30%<br>India -Andhra Ukraine 50% Egypt, Arab Tanzania<br>20% Pradesh India - Pakistan Rep.<br>Rajasthan Kenya 40% Senegal Morocco<br>India -Odisha<br>10% Senegal Vietnam 30% Dom. Rep.<br>0% Egypt, Arab Rep. TanzaniaTajikistan Morocco 20% Tajikistan Vietnam<br>30% 50% 70% 90% 110% 130% 50% 70% 90% 110%<br>Dominican<br>Republic<br>Full-Cost Recovery Full-Cost Recovery<br>c) Change in Cost Recovery and d) Change in Cost Recovery and<br>Private Sector Participation Index Utility Governance Index<br>70% 100%<br>R2 = 0.0809 R2 = 0.0084<br>60% Philippines* 90% Kenya<br>80%<br>50% Peru<br>Philippines*<br>70% Peru Uganda<br>40% Uganda Colombia<br>Pakistan<br>India -Odisha 60%<br>Colombia India - Ukraine<br>30% India – AP Rajasthan<br>50% Egypt, Arab<br>20% KenyaPakistanRajasthanIndia - Ukraine 40% TanzaniaSenegal TajikistanRep.<br>10% Senegal India –AP Vietnam 30% Morocco India - Odisha<br>Dominican Tajikistan Dominican Vietnam<br>0% Republic Tanzania Egypt, Arab Rep. 20% Republic<br>-50% Morocco 0% 50% 100% -50% 0% 50% 100%<br>Change in Full-Cost Recovery Change in Full-Cost Recovery<br>Utility Governance Index<br>Private Sector Participation<br>Private Sector Participation Utility Governance Index<br><!-- End of picture text --> _Notes:_ *The Philippines saw a decline in cost recovery, but post-reform cost recovery was still close to 100 percent on average. _Source:_ World Bank staff.

_62._ However, privatization as a stand-alone measure is not always a successful strategy for improving utility performance, as was the case in Odisha and the Dominican Republic, where all privatized utilities had their licenses revoked for nonperformance.<sup>13</sup> In Odisha, the Regulator revoked WESCO’s distribution

> 13 It also has to be noted that some level of operational or limited capital cost recovery may be necessary to attract private sector participation. In many developing countries the performance of the vertically integrated state-owned enterprise is so poor that it would be hard to attract bids for the entire utility, so unbundling or improving performance would be a prerequisite

34
# **Figure 3a: Value of Crop Output per ha        Figure 3b: Agriculture value added per worker**

<!-- Start of picture text --> 1200 1900<br>1000 1800<br>800<br>1700<br>600<br>1600<br>400<br>1500<br>200<br>1400<br>0<br>1300<br>Timor-Leste<br>Small Island Developing States 1200<br>Low Income Food Deficit Countries 2000 2002 2004 2006 2008 2010 2012 2014 2016<br>Constant (2004-2006) int $<br>1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013<br>Value added per worker (constant 2010 US$)<br><!-- End of picture text --> _Source:_ FAOSTAT This paper uses data from the 2007 and 2014 waves of the Timor-Leste Survey of Living Standards to address the following questions: To what extent does agricultural productivity affect poverty? What are the drivers of agricultural productivity? What are the constraints to increasing agricultural productivity? What can be done to increase agricultural productivity? These questions are in line with the country’s Strategic Development Plan 2011–2030 (SDP), which highlights the relevance of agriculture for poverty alleviation, economic diversification, employment, and economic growth. In committing to deliver this SDP, the Ministry of Agriculture and Fisheries developed the Strategic Plan 2014–2020, which seeks to support the transition from subsistence to commercial agriculture, improve rural incomes and livelihoods, and reduce poverty. Thus, answers to these aforementioned questions will provide guidance on how agricultural policy can help reduce poverty in the country.

Agricultural productivity affects poverty levels both directly through the _“food and income” pathway_<sup>3</sup> and indirectly through the _“wage” pathway_ .<sup>4</sup> Since hired farm labor is usually supplied by poor households, an increase in wages is likely to increase the incomes of poor households and,

> 3 De Janvry and Sadoulet (1996), Acharya and Sophal (2002), and Hazell et al. (1991) provide evidence of the “food and income” pathway to increase the availability of food and household incomes. De Janvry and Sadoulet (1996) observe that a 1 percent increase in total factor productivity (a measure of agricultural productivity) produces a 0.5 percent increase in the income levels of smallholder farmers in Asia.

> 4 Agricultural expansion usually increases the amount of land under cultivation, the intensity of cultivation, and/or the frequency of cropping, which in turn increases the demand for hired farm labor (Hayami and Ruttan 1985; Lipton and Longhurst 1989; Irz et al. 2001) that helps drive up wages.

4
**a~~**

-

1 **Policy, Research, and External Affairs**

**Trade Policy** WPS 791

This paper-- a product of'thc Trade Polic) Division, Country Economics Department -- is part of a larger ef'fort in PRE to cxaniic Itlc implicationis ofexchange rate policy for economic performance. Copies are availabic free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Karla Cabana, room N 10-037, extension 37947 (26 pages). October 1991.

Exchange rate policy has receivcd renewed attention becausc of' its prominent role in adjustmcnt programs. Several analysts have cxamined the impact of real exchange rate uncertainty on the performance of such economic variables as GDP growth, exports, and investiment, Pritchett uses data on **tile** real exchiange rate for 56 developing countries with managed exchange rates to make three points:

- The distribution of annual changes in rcal cxchange rates is highly non-normal -boLh

skewness andi excess kurtosis (increased probability of tail events).

* This asymmetric non-normality implies that the common pti c ice of using the standard deviation (or coefficient of variation) to compare real exchange rate uncertainty across countries is not justified.

Empirically, the higher order moments (skewness and kurtosis) are at least as important as the standard deviation in explaining crosscountry performanct The IRE Working Paper Series disseniinaies the finldings of wo,k under way in the Bank's Policy, Rcscarch, and Extemal Affairs Compex. An objective (,fthc series i to get tliese findings out quickly, even **if** presentations arc less than fully polkihed. The Findings, interpretations, and conclusion's in these papers do not necessarily represent olficial Bank policy.

Produced **by** the PRE Disscmination Ccnter
Table 3: Public and Private Certification Effects on Beliefs, Search, and Labor Market Outcomes

||(1)|(2)|(3)|||
|---|---|---|---|---|---|
||Skill belief<br>accurate|_>_ median<br>self-esteem|Targeted<br>search|||
|Public certifcation|0.158|0.001|0.052|||
||(0.008)|(0.013)|(0.010)|||
|Private certifcation|0.123|-0.002|0.047|||
||(0.008)|(0.014)|(0.010)|||
|p: public = private|0.000|0.806|0.698|||
|Mean outcome|0.389|0.553|0.155|||
|# observations|6607|6609|6609|||
|# clusters|84|84|84|||
||(4)|(5)|(6)|(7)|(8)|
||Used<br>report<sup>b</sup>|Applications<br>with report<sup>b,c</sup>|Interviews<br>with report<sup>b</sup>|Ofers<br>with report<sup>b</sup>|Expected<br>ofers<sup>a,c</sup>|
|Public certifcation|0.699|1.682|0.432|0.112|0.106|
||(0.013)|(0.040)|(0.023)|(0.011)|(0.019)|
|Private certifcation|0.289|0.572|0.144|0.036|0.053|
||(0.012)|(0.033)|(0.017)|(0.008)|(0.023)|
|p: public = private|0.000|0.000|0.000|0.000|0.025|
|Mean outcome|0.000|0.000|0.000|0.000|4.198|
|# observations|6609|6598|6597|6597|6531|
|# clusters|84|84|84|84|84|
||(9)|(10)|(11)|(12)|(13)|
||Worked|Hours<sup>c</sup>|Earnings<sup>c</sup>|Hourly<br>wage<sup>c</sup>|Written<br>contract|
|Public certifcation|0.052|0.201|0.338|0.197|0.020|
||(0.012)|(0.052)|(0.074)|(0.040)|(0.010)|
|Private certifcation|0.011|0.066|0.162|0.095|0.017|
||(0.012)|(0.048)|(0.078)|(0.046)|(0.009)|
|p: public = private|0.002|0.011|0.028|0.030|0.769|
|Mean outcome|0.309|8.848|159.291|9.840|0.120|
|# observations|6607|6598|6589|6574|6575|
|# clusters|84|84|84|84|84|

Coefficients are from regressing each outcome on a vector of treatment assignments, randomization block fixed effects, and prespecified baseline covariates (measured skills, self-reported skills, education, age, gender, employment, discount rate, risk aversion). Heteroskedasticity-robust standard errors shown in parentheses, clustering by treatment date. Mean outcome is for the control group. Skill belief accurate is the share of the six assessments where the candidate’s perceived tercile matches their actual tercile. Targeted search is an indicator equal to one if the candidate reports mainly applying for jobs that most value the skill in which the candidate scored highest. Above-median self-esteem is an indicator equal to one if the candidate’s response on a shortened version of the Rosenberg (1965) self-esteem scale is above the sample median. All outcomes use a 7-day recall/forecast period unless marked with<sup>a</sup> (30-day recall/forecast period) or<sup>b</sup> (since treatment). Outcomes marked with<sup>c</sup> use the inverse hyperbolic sine transformation for the treatment effects but the control group means are reported in levels. All monetary figures are reported in South Africa Rands. 1 Rand _≈_ USD 0.167 in purchasing power parity terms. The sample sizes differ across columns due to item non-response, mostly from respondents reporting that they don’t know the answer.

19