rafmacalaba/datause-encoder-data huggingface dataset:
Table 6 Comparison of teacher monthly earnings by contract type

|||Teachers on<br>|apermanen<br>|t contract<br>Inter-||Teac|hers on a fixe<br>|d term/temp<br>|orarycontra<br>Inter-|ct||
|---|---|---|---|---|---|---|---|---|---|---|---|
||Median|Standard<br>deviation|Inter-<br>quartile|quartile/<br>mean|N|Median|Standard<br>deviation|Inter-<br>quartile|quartile/<br>mean|N|Ratio of<br>medians|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Burkina Faso|650|733|557|0.88|251|232|464|668|1.4|24|0.36|
|Côte d'Ivoire|1,059|633|593|0.53|47|371|402|593|1.1|32|0.35|
|DR Congo|100|60|18|0.18|544|92|56|41|0.4|373|0.93|
|The Gambia|||N.A.|||||N.A.||||
|Ghana|||N.A.|||||N.A.||||
|Liberia|||N.A.|||||N.A.||||
|Malawi|||N.A.|||||N.A.||||
|Namibia|2,351|894|1,085|0.48|291|1,537|1029|1,772|1.0|58|0.65|
|Niger|542|1902|556|0.57|78|334|179|57|0.2|36|0.62|
|Nigeria|||N.A.|||||N.A.||||
|Senegal|832|633|388|0.43|224|690|1,301|388|0.4|54|0.83|
|Sierra Leone|334|6,007|233|0.20|176|278|7,620|342|0.2|21|0.83|
|Tanzania|805|370|407|0.47|363|713|679|459|0.5|20|0.89|
|Uganda|345|147|94|0.25|95|252|243|222|0.7|66|0.73|
|Zambia|1,691|579|465|0.27|411|1,431|820|1,360|1.1|59|0.85|
|**Average **|**871**|**1196**|**440**|**0.43**|**248**|**593**|**1279**|**590**|**0.7**|**74**|**0.70**|

Notes: Earnings are in PPP ($,2011). Ratio of medians is the ratio of the median earnings for teachers on a fixed term or temporary contract to that for teachers on a permanent contract. Teachers include survey respondents who self-identified as a primary or secondary school teacher and a wage worker. All teachers have secondary or secondary or post-secondary education. The top and bottom 1% earnings are trimmed. Surveys in The Gambia, Ghana, Liberia, Malawi, and Nigeria did not report whether a worker was on a permanent or fixed term/temporary contract. Source: Surveys listed in Table 1.

39
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33
69 Table 10 Structure of Federal Government Finances, 2001-02 (billion pesos and % GDP, cash basis)

||2001||2002||
|---|---|---|---|---|
||bn pesos|% GDP|bn pesos|% GDP|
|Revenue|51.13|19.01|55.73|17.81|
|Taxes|37.16|13.81|40.03|12.79|
|Soc. Sec. contrib.|9.64|3.58|9.71|3.10|
|Other|4.33|1.61|5.99|1.91|
|Expenditure|60.03|22.32|60.37|19.29|
|Salaries|6.54|2.43|6.97|2.23|
|Goods & services|2.24|0.83|2.54|0.81|
|Social security|16.62|6.18|16.54|5.28|
|Transfers to provinces|13.92|5.17|14.77|4.72|
|Transfers to universities|1.63|0.61|1.55|0.50|
|Social transfers|5.82|2.16|8.7|2.78|
|Interest|10.17|3.78|6.81|2.18|
|Capital expenditures|2.64|0.98|2.18|0.70|
|Other|0.45|0.17|0.31|0.10|
|Memoradum:|||||
|overall surplus|-8.72|-3.24|-4.55|-1.45|
|primary surplus|1.46|0.54|2.26|0.72|

Source: MECON (2003b).

Table 11 Federal tax revenue as a percent of GDP

|VA|T<br> Income<br>|Checking|Import<br>|Export|Worker<br>contrib.|Employer<br>contrib.|Other|Total|
|---|---|---|---|---|---|---|---|---|
|1995|6.32<br>2.42|0.00|0.68|0.01|2.17|2.93|1.81|16.35|
|1996|6.64<br>2.50|0.00|0.70|0.01|2.12|2.34|1.51|15.83|
|1997|6.77<br>2.85|0.00|0.83|0.00|2.18|2.46|1.48|16.57|
|1998|6.80<br>3.17|0.00|0.90|0.01|2.09|2.65|1.11|16.74|
|1999|6.42<br>3.26|0.00|0.79|0.01|2.17|2.47|1.70|16.80|
|2000|6.48<br>3.68|0.00|0.68|0.01|2.20|2.30|1.92|17.28|
|2001|5.52<br>3.76|1.09|0.57|0.02|2.14|2.24|1.57|16.90|
|2002|4.48<br>2.85|1.55|0.40|1.60|1.13|2.03|2.07|16.12|

# Source:  MECON (2003b)

On the revenue side, it would seem that the single biggest thing the government could do to improve fiscal performance is to increase enforcement of the value added tax. Table 12 shows international revenue experience with the VAT.<sup>68</sup> The entry for Argentina refers, apparently, to 2000, and is considerably higher than last year’s 4.5 percent of GDP. The main point, however, is that strong performances in VAT revenue

> 68 The data in table 12 refer to 2000 or the most recent year available.
country through content that focuses on building knowledge of banking services and covering basic household budgeting and savings topics (LIRS, undated), or content focused on encouraging remittance receivers to better use the money they receive, as is the focus in the content of the Microfinance Opportunities/Freedom from Hunger Global Financial Education Program.<sup>2</sup> However, a number of countries have also started focusing on teaching the migrants themselves more about the costs and details of different methods of remitting; including migrant sending countries like the Philippines and Indonesia, and pilot programs for seasonal migrants from the Pacific Islands working in New Zealand and Australia. However, to date there is no rigorous evidence on the effectiveness of such programs.

This paper presents the results of a randomized experiment designed to measure the impact of providing financial literacy training to migrants in New Zealand and Australia – countries which had recently launched a remittance cost comparison website ( _www.sendmoneypacific.org_ ) for sending money to the Pacific Islands, and, in the case of New Zealand, where regulatory reform had led to the introduction of new remitting methods. The training taught migrants the different elements which make up the cost of sending remittances and how to compare costs across methods, explained how different methods of remitting work including alerting them to the presence of new methods, and also covered content on comparing costs of different methods of short-term credit financing for immigrants. The experiment was carried out on three different groups which had differing levels of existing education and financial knowledge, and differing intensities of remitting. The first group was Pacific Island migrants in New Zealand, who remitted relatively frequently and had relatively low education and financial literacy at baseline. The second group was East Asian migrants in New Zealand, who had low frequencies of remitting but relatively high education and financial literacy, and the final group was Sri Lankan migrants in Melbourne, Australia, who remitted relatively frequently and had relatively high education and financial literacy levels.

We find the training led to increases in financial knowledge of the Pacific Island and East Asian migrants, but not of the Sri Lankans, which is consistent with such training being most important for those with either low knowledge or low experience. This increased knowledge was

> 2 http://www.globalfinancialeducation.org/future.html#remittance.

3
# **c) Grade 10 Students, 2004, Nepal: School Leaving Certificate (SLC) examination**

<!-- Start of picture text --> Nepali English Mathematics Science<br>0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100<br>Total SLC Score out of 100 Total SLC Score out of 100 Total SLC Score out of 100 Total SLC Score out of 100<br>Government Private<br>.04<br>.03<br>.02<br>Frequency distribution<br>.01<br>0<br><!-- End of picture text -->

# **d) Grade 4 Students, 2006, Rural Punjab, Pakistan: Learning and Educational Achievement in Pakistan Schools (LEAPS) survey**

<!-- Start of picture text --> English Urdu Mathematics<br>0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100<br>English score Urdu score Math score<br>Government Private<br>.03<br>.02<br>Frequency distribution<br>.01<br>0<br><!-- End of picture text --> _Source:_ Authors, based on data from student assessments.

17
**Figure 13 – Land Cover Classification and Distance to City Center ‐ Kigali**

<!-- Start of picture text --> Kigali Land cover c. 2012 (Percent) Kigali Land cover c. 2012 (Total)<br>100% 120<br>80% 100<br>80<br>60%<br>60<br>40%<br>40<br>20%<br>20<br>0% 0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Distance from CBD (kilometers) Distance from CBD (kilometers)<br> Residential (Regular) Residential (Irregular) Residential (Regular) Residential (Irregular)<br>Commercial/Industrial Commercial/Industrial<br>Total Land cover<br>Percent Land cover<br><!-- End of picture text -->

# **Addis Ababa, Ethiopia**

Addis Ababa is one of the oldest and largest cities in Africa, making it a central urban hub for many Ethiopians in search of better living and employment conditions. The city accounts for approximately 20% of the country’s GDP, although it is home to less than 5% of the country’s residents.<sup>46</sup> According to the 2007 Ethiopian census, roughly 2.7 million people live in Addis, a number that has doubled every decade since 1984. This rapid growth is expected to continue, with the UN Habitat estimating a population of 12 million by 2024.<sup>4748</sup> Triggered by this population growth, Addis is undergoing a construction boom. With new condominiums, office buildings and infrastructure projects breaking ground almost simultaneously throughout the city. Unfortunately this development has not always followed government regulations, resulting in roads cut off due to building construction, or major road intersections being built with no means for pedestrian crossings.<sup>49</sup> While this new development has created jobs and raised tax revenues, it often lacks coordination and the support of a strong transport system. As a result, neighborhoods are mostly medium density and possess an ‘urban village’ type environment, where residents are in walking distance to all of their amenities. While satellite imagery cannot detect these pockets of mixed use land, the classification in Figure 12 clearly depicts small pockets of various land cover throughout the city,

> 46 Cour, Jean‐Marie. 2004. “Assessing the ‘benefits’ and ‘costs’ of urbanization in Vietnam”. Annex to “Urbanization and Sustainable Development: A DemoEconomic Conceptual Framework and its Application to Vietnam”. Report to Fifth Franco‐Vietnamese Economic and Financial Forum, Ha Long, November 2004.

> 47 http://www.csa.gov.et/newcsaweb/images/documents/surveys/Population%20and%20Housing%20census/ETH‐ pop‐2007/survey0/data/Doc/Reports/Addis_Ababa_Statistical.pdf.

> 48 Un Habitat. 2008. Ethiopia: Addis Ababa Urban Profile.

> 49 Young, Adrian. 2014. “No pain, no gain? Rapid urban growth and urbanism in Addis Ababa. <u>UrbanAfrica.net.</u>

22
**Table ‎2.3:  Percent of Households Engaged in HE, by Area, 2006**

|1.3|1.4||
|---|---|---|
||HE as primary activity*|All HE*|
|Dar|58.4|67.9|
|Other urban|49.1|74.2|
|Rural|13.0|63.5|
|Total|24.4|65.9|

_* Households where HE is the primary activity of at least one member.** Households with HE as a primary or secondary activity of at least one member. Source: Calculations based on the ILFS 2006 data._

**Table ‎2.4:  Percent of Households Engaged in HE, by Asset Quintile, 2006**

||HE non-agriculture*|HE non<br>-agriculture**|
|---|---|---|
|Quintile 1|9.4|66.1|
|Quintile 2|15.3|59.3|
|Quintile 3|20.4|69.2|
|Quintile 4|30.6|73.1|
|Quintile 5|51.1|61.9|
|Total|14.5|65.9|

_Note: * refers to HE as a primary activity ** refers to HE as a primary or a secondary activity Source: Calculations based on the ILFS 2006 data._ 16. Does running an HE reduce poverty? The evidence in Tanzania is not clear. The ILFS 2006 data showed that both poor and non-poor households participate in HEs. The one panel study for Tanzania which analyzed this question found that in rural Kagera, adding an HE to a farm-based activity is indeed a successful route out of poverty, but only for those with access to towns and markets (De Weerdt, 2008). What we can see from our cross-sectional evidence is that when the HE is the primary activity of a household member, ownership of HEs is strongly correlated with higher income -- half of the households in the top quintile in 2006 had a household enterprise as a full-time activity, compared with only 10 percent in the lowest quintile (Table 2.4). A separate study on non-farm rural household enterprises also came up with the finding that richer households own HEs (Jin and Deininger, 2008). What is not known is if households had a successful enterprise because they were already richer (i.e., had the needed start-up and working capital) and could devote full-time attention to their HE, or if a successful enterprise allowed them to work full-time and move up the income ladder.

# **2.3 Motivation for Having HEs**

17. If it is not clear that an HE is a route out of poverty (or a way to stay out of poverty), why do households start and maintain one? The reasons provided by survey respondents vary widely, but can be grouped into “pull” factors -- they were attracted into the business, or “push” factors – 9
limitations, phone surveys proved to be an important substitute for in-person surveys in contexts like Haiti, where in-person surveys are not suitable. Under these considerations, this paper contributes to the existing literature in at least three different ways. First, it documents households’ vulnerability to different types of natural disasters. Then access to and availability of community-level disaster preparedness mechanisms were analyzed. Lastly, the socioeconomic and demographic factors that are correlated with households’ preparedness for disasters in Haiti were investigated. Understanding these correlates can help inform public policies to support vulnerable households to become more resilient to natural disasters.

In Haiti, exposure to multi-hazards is high across the territory, and most individuals live in households exposed to three or more hazards. Results shows that 20 percent of respondents resided in households exposed to one to two hazards in 2023, while 76 percent resided in households exposed to three or more hazards, 7 percentage points more than in 2022 (Figure 1). Rural households reported higher exposure to three or more hazards than urban households in all survey years. The percentage of households reporting feeling threatened by earthquakes increased by nearly 50 percentage points by the end of 2022 compared to the end of 2021. Cyclones, heat waves, droughts and extreme rainfall are the hazards most reported by households in 2021, 2022 and 2023, with around 50 percent or more of respondents living in households threatened by each. Both urban and rural households face the same hazards in almost the same proportion, for example, earthquakes are the hazard that threatens in the highest proportion, but slightly higher in rural areas. Rural households are in higher risk of natural hazards than urban households (Figure 2).

**Figure 1: Number of hazards threatening households in urban and rural areas (% of respondents) – (20212023)**

<!-- Start of picture text --> a) By area – 2021 b) By area – 2022 c) By area – 2023<br><!-- End of picture text --> <!-- Start of picture text --> 64.8 73.6 68.2 66.8 72.1 68.6 74.6 78.5 75.9<br>25.2 19 22.8 26.8 22.2 25.3 20.8 19.6 20.4<br>10 7.4 9 6.3 5.7 6.1 4.6 1.9 3.7<br>Urban Rural Total Urban Rural Total Urban Rural Total<br>0 hazards 1-2 hazards 3 or more hazards 0 hazards 1-2 hazards 3 or more hazards 0 hazards 1-2 hazards 3 or more hazards<br><!-- End of picture text --> Note: Results are from entire sample of wave 2, wave 3 and wave 4 households.

3
<!-- Start of picture text --> 2Sua<br>Seee<br>ss<br>20S20<br>C=<br>i==o=ess<br>2fee<br>So<br>a3<br>ag& rey<br>o<br>Kenya Zambia<br>UNI Rigtts/obigations offarmer (RENE Rights/obigations of contractor<br>HE ethod of arbitration (I Post Harvest Services<br>Source: Authors’ calculations based on survey responses<br><!-- End of picture text --> <!-- Start of picture text --> wEY@3BLO<br>25 &<br>oS<br>coBes<br>fc<br>5aoov23 sz<br>BENSeo<br>s2<br>gs<br>Be2x =)S<br>° Ghana Kenya Zambia..<br>PI Rights/obiigations of farmer I Rights /obligations of contractor<br>BER etos of artitration MEDI ost Hanest Senices<br>Source: Authors’ calculations based on survey responses<br><!-- End of picture text -->
|iki|PPR Working Paper Series<br>_AuIXhor_|<br>Afor|Contact<br>aper|
|---|---|---|---|
|WPS320 Protection Facing Exports from<br>Sub-Saharan Africa in the EEC,<br>Japan, and the U.S.|Refik Erzan<br>Peter Svedberg|||
|WPS321 Economic and Policy Determinants<br>of Public Sector Deficits|Jorge Marshall<br>Klaus Schmidt-Hebbel|||
|WPS322 The Earmarking of Govemment<br>Revenues: A Review of Some<br>World Bank Experience|William McCleary|||
|WPS323 The Old and the New is Heterodox<br>Stabilization Programs: Lessons<br>from the Sixties and the Eighties|Miguel A. Kiguel<br>Nissan Liviatan|||
|WPS 324 Ethics and Human Values in Family<br>Planning: The African Perspective|F. Sai<br>K. Newman|||
|WPS325 Manufacturers' Responses to Infra-<br>structure Deficiencies in Nigeria|Kyu Sik Lee<br>Alex Anas|||
|WPS326 Do Exporters Gain from VERs?|Jaime de Melo<br>L. Alan Winters|||
|WPS327 Making Noisy Data Sing: Estimating<br>Production Technologies in<br>Developing Countries|James R. Tybout|||
|WPS328 Europe, Middle East, ano North<br>Africa (EMN) Region Population<br>Projections, 1989-90 Edition|Rodolfo A. Bulatao<br>Eduard Bos<br>Patience W. Stephens<br>My T. Vu|November 1989|S. Ainsworth<br>31091|
|WPS329 Latin America and the Caribbean<br>(LAC) Region Population<br>Projections, 1989-90 Edition|Rodolfo A. Bulatao<br>Eduard Bos<br>Patience W. Stephens<br>My T. Vu|November 1989|S. Ainsworth<br>31091|
|WPS330 Africa Region Population<br>Projections, 1989-90 Edition|Rodolfo A. Bulatao<br>Eduard Bos<br>Patience W. Stephens<br>My T. Vu|November 1989|S. Ainsworth<br>31091|
|WPS331 Asia Region Population Projections,<br>1989-90 Edition|Rodolfo A. Bulatao<br>Eduard Bos<br>Patience W. Stephens<br>My T. Vu|November 1989|S. Ainsworth<br>31091|
underreporting bias. According to information from the Kinshasa port authority (ONATRA) as well, the tonnage shipped from Kinshasa to Brazzaville exceeds that going from Brazzaville to Kinshasa, although according to their statistics by only around 40 percent. Hence, port data in Brazzaville and Kinshasa support our finding of the current dominance of the Matadi-Kinshasa-Brazzaville supply route relative to the Pointe Noire-Brazzaville-Kinshasa route.

_Table 5: Composition of Inbound and Outbound Goods Traffic at the Port of Brazzaville, 2009_

||From Kinshasa to Br|azzaville|From Brazzaville to|Kinshasa|
|---|---|---|---|---|
||_Tons_|_%_|_Tons_|_%_|
|Fuel|131,915|38.4|17,981|61.0|
|Cement|56,675|16.5|530|1.8|
|Flour|30,154|8.8|145|0.5|
|Wood|26,977|7.9|-|-|
|Foodstuffs|26,297|7.7|146|0.5|
|Building materials<br>andgravel|7,860|2.3|2,253|7.6|
|Mineral water|6,282|1.8|472|1.6|
|Vegetable oil|6,114|1.8|-|-|
|Vehicles|4,912|1.4|5,464|18.5|
|Soap|4,340|1.3|-|-|
|Containers|2,991|0.9|76|0.3|
|Empty packaging|899|0.3|181|0.6|
|Sugar|756|0.2|0|0.0|
|Sheet metal|505|0.1|0|0.0|
|Chemicals|487|0.1|10|0.0|
|Ground nuts|483|0.1|-|-|
|Corrugated iron|314|0.1|5|0.0|
|Other|35,372|10.3|2,237|7.6|
|_Total_|_343,333_|_100.0_|_29,500_|_100.0_|

Officially recorded goods only. Source: Port of Brazzaville (PABPS).

# **2.3 Passenger Traffic**

**Passenger traffic on the river is dominated by traffic across Malebo Pool, the river basin between Kinshasa and Brazzaville, with the total number of fare-paying river crossings estimated at around 212,000 annually** .<sup>11</sup> Official data from the port authority in Brazzaville and from ONATRA in Kinshasa are presented in Table 6. Less than 5 percent of passengers arrive from or travel to upriver destinations. Oddly, according to these statistics, the number of passengers who arrive at Brazzaville

> 11 According to estimates given to us by ONATRA, their ferry “Matadi” carries some 600 paying passengers per average workday. This would correspond to around 190,000 passengers per year. As some additional travelers cross on the much smaller CNTF ferry and on private speed boats, a  reasonable estimate of the total number of fare-paying passengers crossing annually between the two capitals may be found by doubling the number of inbound passengers recorded by the port of Brazzaville, i.e. some 212,000 crossings.

14
seen some increase in the proportion of total income earned from wages and salaries. This suggests the increasing importance of large and commercial farms in these regions of Sudan. As seen earlier (Figure 6b), due to the lower rates of poverty for households earning incomes from wages and salaries, their increasing share in the population has contributed to a reduction in rural poverty.

Figure 12: Proportion of Income Earned from Agricultural, Nonagricultural Wages in Rural Sudan, 2009 and 2014/15

# (a) Proportion of Income by Source, 2014/15

<!-- Start of picture text --> 100%<br>Agriculture Non-Agriculture Wages and Salaries<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text --> _Note_ : Agricultural income comes from crops, horticulture products, livestock, livestock products, poultry and poultry products, fish and fish products, forest products, and other agricultural activities. Nonagricultural income comes from wages and salaries, industrial activities, commerce, rented estates, other rents, transport and communication, transfers from abroad, domestic transfers, supports, and other sources.

- (b) Change in Proportional Income by Source, 2009 to 2014/15

<!-- Start of picture text --> 50%<br>Agricultre Non-Agriculture Wages and Salaries<br>40%<br>30%<br>20%<br>10%<br>0%<br>-10%<br>-20%<br>-30%<br>% Δ<br><!-- End of picture text --> _Source:_ Authors’ calculations using NBHS 2009 and NHBPS 2014/15.

The contribution of nonagricultural income to total household income has not increased, either for the poor or the non-poor (Figure 13). In fact, on average, households were slightly less reliant on nonagricultural incomes in 2014 than in 2009. Rural Sudan has therefore become more reliant on agricultural wage incomes (possibly a form of tenant farming) and less reliant on nonagricultural 14