rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3.4: Accessibility of public telephones for rural households by region**

<!-- Start of picture text --> 14 60<br>12 50<br>10<br>40<br>8 Kilometers<br>30<br>6 Minutes<br>20<br>4<br>2 10<br>0 0<br>MetropolitanNortheastSouthwestSoutheastCentralNorthwestNorthPeten<br>Distance to public telephone (kms) Distance to public telephone (mins.)<br><!-- End of picture text --> _Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística - Guatemala As of 2000, 20% of households in Guatemala claimed to have access to either a fixed line and/or a cellular telephone, although coverage rates differ substantially between urban areas (40%) and rural areas (5%). About 74% of Guatemala households continue to obtain their telephone service from TELGUA. With 16% of the household market, COMCEL is the most significant competitor to TELGUA; it has the second largest fixed lines business in the country, as well as a substantial presence in the cellular market. Moreover, it has developed a particular presence in the rural areas where it is the primary service provider for 29% of households.

There is a widespread anecdotal perception, not only in Guatemala, but throughout Latin America, that the advent of cellular telephony has helped to ‘democratize’ the telephone. However, to date, very few household surveys make it possible to distinguish between fixed and cellular telephone ownership. The ENCOVI is unusual in this respect, and hence it is interesting to examine ownership patterns across consumption quintiles (Figure 3.5). The results appear to indicate a high degree of concentration of cellular telephones in higher consumption quintiles, with more than 80% being held by the top two quintiles. Indeed, about half of all cellular telephones in Guatemala are second telephones belonging to households in the highest consumption quintiles.

However, on closer inspection, cellular telephones have become a very important phenomenon for certain other groups. For example, in the second and third quintiles, although cellular telephones are only held by a small minority, there are in fact equal numbers of households with fixed and cellular telephones. The same is true in rural areas, where there are equal numbers of fixed and cellular subscribers, and where two thirds of the households with cellular telephones have no other telephone service and are hence using the device as a substitute for a fixed line service. This is in contrast to urban areas where fixed telephones still outnumber cellular ones by 1.7 to 1.0.

Finally, this data may understate the full importance of cellular telephony in rural areas. On an anecdotal basis, the interview teams for the ENCOVI survey reported that cellular telephones are quite widely used to provide an informal public telephone service in rural areas, with the owner of the telephone allowing his neighbors to make calls on a charged out basis. However, unfortunately, it is not possible to corroborate this phenomenon with the ENCOVI data.

23
**Figure 1 – Tax Revenue (in % of GDP) by Income Groups, 1994-2009**

<!-- Start of picture text --> 35.0<br>29.3<br>30.0 28.4 28.4<br>25.0<br>21.2<br>18.8 19.0 19.3<br>20.0 17.1 High income<br>Middle income<br>15.0 13.6<br>11.3 10.0 10.5 Low income<br>10.0<br>5.0<br>0.0<br>1994 1998 2003 2009<br><!-- End of picture text --> Source: The World Bank classification and WDI. Note: See Table A3 for details.

The highest tax share belongs to the high-income group. They collect almost 2-3 times higher taxes in percentage of GDP when compared to the low-income group and almost 10 percentage points higher taxes when compared to the middle-income group. Tax collection in this group further increased by 1 percentage point between 2003 and 2009, a rise from 28.4 percent to 29.3 percent.

After initial drops in tax collection rates mainly due to the global financial and economic crisis of 2008, the increasing trend in tax collection is expected to continue given that public spending and budget deficit increased enormously in recent years.

# **_Geographical Regions and Taxation_**

Another factor determining tax rates is the geographic region of countries. Figure 2 presents the share of tax revenues in percentage of GDP across regions.<sup>8</sup> The lowest tax rate belongs to the South Asia region (SAR); they collect 10.2 to 10.5 percent taxes as a share of GDP.<sup>9</sup> The East Asia and Pacific (EAP) region has the second lowest tax collection rate in the world. Taxes in

> 8 The countries in each region are listed in Table A4 in the Annex.

> 9 It should be noted that one reason for why we observe such a low tax ratio in SAR is that these numbers are for central government only and the tax collection in SAR may be more decentralized than the ones in any other regions.

5
**Table 3 Government Spending and Oil Exports – Oil-Rich Countries, levels in const $**

||Total|Fixed effects||Fixed Effects<br>Total|with Overborro|wing Dummy|
|---|---|---|---|---|---|---|
||Government<br>Expenditure|Current<br>Expenditure|Capital<br>Expenditure|Government<br>Expenditure|Current<br>Expenditure|Capital<br>Expenditure|
||0.52*|0.21*|0.39*|0.51*|0.19*|0.32*|
|Oil Exports|(5.28)|(3.95)|(3.15)|(5.12)|(3.33)|(2.70)|
||24.12*|12.18*|6.28*|23.26*|12.11*|3.19***|
|Constant|(16.31)|(13.43)|(3.25)|(15.22)|(13.33)|(1.65)|
|Overhang||||0.22**|0.06|0.77*|
|variable||||(2.11)|(1.07)|(6.17)|
|No. of Obs.|||||||
|(No. Of|524|546|513|524|546|513|
|Groups)|(21)|(22)|(22)|(21)|(22)|(22)|
|R-sq. overall|0.26|0.26|0.11|0.28|0.26|0.16|
|F Statistic|F=27.89|F=15.59|F= 9.90|F=16.27|F=8.37|F=24.36|
|(Prob > F)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|

Note: 1. Data source: IMF World Economic Outlook database.

2. Sample: 1980-2005; Real Oil exports = Oil exports in current US$ divided by US GDP deflator 3. t-statistics in parentheses; * indicates significance at 1%, ** at 5%, and *** at 10%.

Another interesting difference is that in most ORCs there is a much tighter link between capital expenditure and oil revenues than in Nigeria. In Nigeria oil income goes mostly into consumption, in ORCs capital expenditure dominates. That is good in that assets are not just eaten up, there is an apparent attempt to use the drawing down of assets that oil extraction implies to build up other assets, in particular physical capital. The dark side of this medal is that the adjustment on bad days falls correspondingly heavier on investment; the debt overhang dummy is positive and significantly so for capital expenditure and for total expenditure, but not for government consumption. This is different in Nigeria where the adjustment in bad days has to fall much more on consumption: most likely because there simply is not enough public investment out of oil to implement the entire downward adjustment in bad days through cutting back capital expenditure.

In comparison with other oil rich countries), a number of facts stand out. Nigeria has tended to spend more out of windfalls, allocate more of that already high expenditure to consumption rather than to 22
To assess the consequences for poor households, we overlay our results with an administrative map of Bangladesh and compute mean percent changes in fish species for 110 _upazila_ s that lie within the region. We construct an impact indicator that weights these results by _upazila_ poverty populations identified using two bounds: an upper poverty line, for households whose food expenditures are at or below the food poverty line established by the Bangladesh Bureau of Statistics; and the lower poverty line, for extremely poor households whose total expenditures are at or below the food poverty line. Our calculations encompass 54 cases (27 scenarios, 2 poverty definitions). We find that potential impact rankings are highly correlated, so we use the mean rank across 54 cases as a robust general impact indicator. This enables us to produce rank-orderings for 76 _upazila_ s that lose fish species and 11 _upazila_ s that gain species (23 _upazila_ s exhibit no change). Among the 20 _upazila_ s with top-ten loss and gain indices, 19 are in Khulna and one (a species loss case) is in Barisal.

Our summary results provide striking evidence that projected aquatic salinization may have a strongly regressive impact on poor households in the Sundarbans region. For both poverty definitions, we find that poverty populations in _upazila_ s that lose and gain species have a ratio of approximately 6:1. Given that fish is the main source of protein in the diet of 43.2 million poor people, and the chronic as well as acute malnutrition levels, as indicated by statistics on wasting and stunting of children in Bangladesh, are higher than the WHO’s thresholds for public health emergencies,<sup>18</sup> our finding is serious and emphasizes the importance of mainstreaming climate change in relevant policies, action plans and programs in the country.

As we note in the paper, we must attach one strong caveat to our results. Our measure of potential risk is simply the change in species count because we do not have good evidence on other

> 18 Government of Bangladesh: Strategic Plan for Health Population and Nutrition Sector Development Program - (HPNSDP) 2011-2016, <u>http://www.bma.org.bd/pdf/strategic_Plan_HPNSDP_2011 16.pdf</u>

26
# **(2) OCC Liquidity Report**

# **APPENDIX A: FUNDS FLOW ANALYSIS OF THE ABC BANK**

FOR SELECTED ASSETS AND CREDIT SENSITIVE LIABILITIES $ THOUSANDS Sample Format, tailor as appropriate

|||**BANK**|**ASSETS**||||**BANK LIA**|**BILITIES**|||**PARENT**<br>NONBANK<br>ASSETS|NONBANK<br>LIABILITIES|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(10)|(9)|(11)|(12)|
|Quarter|Federal|**Total**||Money|DOA|||||Other|||
||Reserve<br>Balance|**Loans**&<br>Leases|Free<br>Securities|Market<br>Assets|Net of<br>Float|Consumer<br>Deposits|Fed**Funds**<br>Purchased|COs<br>>$100m|Foreign<br>Deposits|Sensitive<br>Funds/Dep|Short‐Term<br>Assets|Short‐Term<br>Liabs|
|1|$5,000|$310,000|$70,000|$7,500|**$96,000**|$389,000|$10,000|**$40,350**|$0|$0|$10,000|**$8,500**|
|2|$5,000|$320,000|**$68,000**|$7,500|**$94,000**|$384,000|$10,000|$42,000|$1,000|$1,000|$10,000|$8,500|
|3|$5,200|$325,000|**$66,500**|$6,800|**$94,000**|$383,000|$12,000|$43,000|$1,000|$2,200|$10,000|$8,500|
|4|$5,100|$330,000|$67,500|$5,500|$92,400|**$384,000**|**$14,500**|$44,000|$1,000|$3,800|$10,000|$8,500|
|5|$5,000|**$345,000**|**$68,000**|$5,000|$90,400|**$383,900**|$13,000|**$47,400**|$1,000|$4,000|$10,000|$8,500|
|6|$4,800|$396,000|$23,200|$5,000|$74,000|$377,000|$10,000|**$50,500**|$1,000|$5,500|$10,000|$8,500|
|7|$5,100|**$455,500**|$19,000|$4,000|$75,300|$370,000|$11,000|$51,000|$2,700|$7,500|**$6,000**|$4,500|
|8|$3,900|$473,000|$12,500|$2,000|$80,000|**$365,000**|$14,000|$51,100|$5,000|$10,400|$4,500|$3,000|
|Change from|||||||||||||
|previous|($1,200)|$17,500|($6,500)|($2,000)|$4,700|($5,000)|$3,000|$100|$2,300|$2,900|($1,500)|($1,500)|

54
Wage structure effects are more relevant than composition effects in explaining the reduction of wage inequality. Figure 18 shows the contribution of each individual factor to the wage structure effect.

**Figure 18. Wage structure effects by explanatory factor (2002-2012)**

<!-- Start of picture text --> 0.5<br>0.4<br>0.3<br>Geographic<br>0.2 Education<br>0.1 Firm Size<br>0.0 Firm Ownership<br>Min. Wage<br>‐0.1<br>Demographic<br>‐0.2<br>Occupation<br>‐0.3<br>‐0.4<br>5 15 25 35 45 55 65 75 85 95<br>Change in log of hourly wages (2002‐2012)<br><!-- End of picture text --> _Source:_ Own elaboration based on RLMS (Rounds XI and XXI). _Notes:_ decomposition results are based on the subsample of currently employed wage workers in the age group 18-60, reporting more than 140 worked hours in the last 30 days and with non-missing data in wages. The distribution of hourly wages was trimmed at the bottom and top 0.25 percent in each round, to remove outliers.

The results suggest that returns to schooling decreased, potentially reflecting the expansion in the supply of university graduates and/or the decline in the quality of the university system. The reduction was higher for those at the top of the wage distribution, contributing to the overall decrease in wage inequality (see Table 2).

Regarding geographic location, returns to living in rural areas increased at the middle of the distribution. This contributed to the compression of the wage distribution, and its contribution was higher for those at the top of the distribution (90-50th percentile log wage differential) than those at the bottom (50-10th percentile log wage differential).

Changes in returns driven by firms’ characteristics decrease when moving to the right of the wage distribution, especially in terms of firm ownership type. Both diminish returns to firm size and firm ownership contributed to the overall decrease in inequality, measured by the 90-10th percentile log wage differential, the variance, and the Gini index of the logarithm of hourly 36
**_Energy demand models for policy_** **_<u>formulation</u>_**

used in some sectors. The model has 13 final users who are then grouped into four major sectors, namely industry, transport, services and domestic. Each final user sector is further disaggregated by fuels. The model structure of the model is shown in Fig. A1.3.

Fig. A1.3: DTI energy model overview <!-- Start of picture text --> Other assumptions GDP and activity World Fuel<br>assumptions prices<br>UK Fuel<br>Cost of electricity<br>prices<br>generation Final energy<br>demand<br>Electricity prices<br>Fuel demand by sector<br>Electricity demand<br><!-- End of picture text --> Source: DTI<sup>39</sup> The supply side of the model considers the electricity supply system in detail. It captures the diversity of the capacity mix, technological differences and characteristics, and determines the cost of generation and operation of the system to meet the demand. The supply and conversion of other fossil fuels are taken into consideration as well.

> 39 See (http://www.berr.gov.uk/files/file26611.ppt).

136
|**TABLE 7: REGRESSION RESULTS**|**Mode**<br>|**l 1**<br>|**Mod**<br>|**el 2**<br>|
|---|---|---|---|---|
||**Coef.**|**Std. Err.**|**Coef.**|**Std. Err.**|
|Constant|-10.641<br>***|1.558|-10.771<br>***|1.549|
|Tariff Rates|-1<br>.155***|0.318|-1<br>.163***|0.318|
|PortEfficiency of Importer|0.307*|0.163|0.338*|0.160|
|PortEfficiency of Exporter|<br>0.924***|0.148|0.938***|0.146|
|CustomsEnvironment of Importer|0.472**|0.199|0.486*|0.199|
|RegulatoryEnvironment of Importer|<br>0.281*|0.144|0.264|0.144|
|RegulatoryEnvironment of Exporter|<br>0.620***|0.132|0<br>.580***|0.131|
|Service sector infrastructure of Importer|0<br>.729***|0.224|0<br>.657**|0.224|
|Service sector infrastructure of Exporter|1.943***|0.216|1.943***|0.217|
|GNPof Importer|0.915***|0.014|0.915***|0.014|
|Percapita GNPof Importer|-0.182***|0.037|-0.210***|0.037|
|GNPof Exporter|1.246***|0.014|1.241***|0.014|
|Percapita GNPof Exporter|-0.226***|0.029|-0.251***|0.029|
|Geographical Distance|-1.258***|0.025|-1.225***|0.025|
|Adjacency dummy|0.336***|0.114|0.426***|0.108|
|MembershipDummyforanyFTA|||-0.021|0.078|
|ASEANMembershipDummy|0.509***|0.190|||
|NAFTA MembershipDummy|-0.645|0.501|||
|LAIA MembershipDummy|0.593***|0.154|||
|AUNZ MembershipDummy|1.118|0.858|||
|MERCOSUR MembershipDummy|0.229|0.302|||
|EUMembershipDummy|-0.515***|0.106|||
|Dummyforany Common Language|||0.82<br>**<br>3*|0.061|
|English LanguageDummy|0.808***|0.089|||
|French LanguageDummy|-1.413***|0.500|||
|Spanish LanguageDummy|0.598***|0.098|||
|ArabicLanguageDummy|-1.223|0.992|||
|ChineseLanguageDummy|1.747***|0.406|||
|German LanguageDummy|-0.826|0.505|||
|PortugueseLanguageDummy|0.569|0.986|||
|Russian LanguageDummy|2<br>.026***|0.362|||
|Year 2000 dummy|-0.031|0.039|-0.038|0.039|
|AdjustedR-squared|0.758||0.755||
|Numberofthe observations|7,904||7,904||
|Note: The significance levels at:<br>10%: "*"<br>5% : "**"<br>1% : "***"|||||
|_Source: Wilson et al., 2004._|||||

# **6.2 Potential Benefits from Trade Facilitation**

In this section we present the results of several simulation exercises conducted by Wilson et al.[2003]. These simulations use a formula that brings the below-average countries in the group 28
15

**Figure 5. Probability of Credit-less Recoveries in ECA, 2009–2010**

<!-- Start of picture text --> LVA 2009Q4<br>UKR 2009Q2<br>RUS 2009Q2<br>EST 2009Q2<br>LTU 2009Q4<br>TUR 2009Q1<br>KAZ 2009Q1<br>GEO 2009Q3<br>ARM 2010Q3<br>SVN 2009Q3<br>MKD 2010Q1<br>HUN 2009Q3<br>SVK 2009Q3<br>ALB 2009Q4<br>POL 2010Q1<br>KGZ 2010Q2<br>MDA 2009Q3<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Probability<br><!-- End of picture text --> Note: The solid line is 0.5 and the dash line is 0.3, which is the share of credit-less events in the sample of emerging market economies. The trough dates are shown next to the corresponding country codes.

Source: Authors’ estimations.

# _Macroeconomic policies_

Table 2 shows probit regression results with macroeconomic policy indicators. The purpose is to ascertain if certain policies might decrease or increase the likelihood of a credit-less recovery. The table looks specifically at two policy indicators: (i) changes in fiscal policy and (ii) changes in monetary policy. Due to data availability, all the policy variables are computed on annual data; the data is taken from the IMF World Economic Outlook database (October 2012). Also, the policy indicators used reflect the stance during the recession, thus reducing the likelihood of endogeneity biases. Interestingly, as shown also, the conclusions drawn are not affected if policies are looked at in their different combinations; i.e., the estimated coefficients are largely unchanged.
Table C.3: Technology Sophistication and Firm Characteristics

||(1)|(2)<br>GAP|(3)|
|---|---|---|---|
|VARIABLES|ABF|GBF|SSBF|
|Vietnam|-0.05***|-0.03|-0.09***|
||(0.02)|(0.02)|(0.03)|
|Senegal|-0.26***|-0.20***|-0.55***|
||(0.02)|(0.02)|(0.03)|
|Manufacturing|0.23***|0.32***|0.05|
||(0.04)|(0.04)|(0.06)|
|Services|0.20***|0.33***|-0.14**|
||(0.04)<br>|(0.04)<br>|(0.06)<br>|
|Medium|0.06***|0.04**|0.15***|
||(0.02)|(0.02)|(0.03)|
|Large|0.12***<br>|0.05<br>|0.46***<br>|
||(0.03)|(0.03)|(0.05)|
|Age 6 to 10|-0.05**|-0.02|-0.09**|
||(0.02)|(0.02)|(0.04)|
|Age 11 to 15|-0.04*|-0.00|-0.11***|
||(0.02)|(0.02)|(0.04)|
|Age 16+|-0.02|0.00|-0.03|
||(0.02)|(0.02)|(0.03)|
|Foreign Owned|-0.01|-0.01|0.01|
||(0.03)|(0.03)|(0.06)|
|Exporter|0.20***|0.18***|0.27***|
||(0.02)|(0.02)|(0.03)|
|Observations|3,893|3,888|3,076|
|R-squared|0.11|0.07|0.19|

Note: Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Reference group for country, sector, size, and age categories is Brazil, Agriculture, Small, and Age 0 to 5, respectively. Regression includes constant and a dummy for whether a firm has SSBF.

73
change in environmental performance variable is quite revealing. First, with a value of 8.702 and a P-value of 0.003, the chi-squared test points to the lack of independence between “news” and “awareness of news publication”. However, independence seems to be the outcome between “news” and “change in environmental performance” as well as between “awareness of news publication” and “change in environmental performance” as the relevant chi-squared test statistics indicate. In either case, the relevant testable hypotheses are the following. H1: ‘News in media’ does not affect change in environmental performance; H2: ‘Awareness of news publication’ does not affect change in environmental performance; H3: ‘News in media’ and ‘Awareness of news publication’ do not affect change in environmental performance.

**Table 1 Summary statistics of ‘news’ and ‘awareness of news publication’**

||**Class**|**Frequency**|**Mean**|**Std**|
|---|---|---|---|---|
|Newsin media (News)|0|27|||
||1|22|||
||||0.449|0.502|
|Awareness of news publication(Anews)|0|16|||
||1|33|||
||||0.673|0.474|
|2<br>χ**test for independence**|**Value**|**P-value**|||
|News andAnews|8.702|0.003|||
|News and Chenv|1.462|0.232|||
|Anews and Chenv|0.738|0.390|||

A host of other variables may have an impact on change in environmental performance. Following Doonan et al. (2006), different sources of pressure may impact a firm’s environmental performance including legal, economic, social, managerial, and 7
significantly hampered by the lack of cooperation between of the RPF regime in Rwanda and the refugee camp leaders for creating right conditions for repatriation.

Although voluntary repatriation seems to be the preferred solution by governments and international organizations to address the crisis of refugees in most countries in Africa (Mills 2002), this case study highlights a fundamental factor that determines the viability of this solution. Peaceful organized repatriation cannot be achieved without the political will of the main actors involved, while the state of origin has the ultimate obligation to provide just conditions of return. Essentially, the conditions that relate to land and property restoration, financial compensation, trials, security and the respect of basic human rights.

# **Case Study #5: The return of refugees to Burundi**

In Burundi, former exiles from the 1972 and 1993 episodes of large‐scale political violence and genocide began to return to the country by the end of the civil war (from 2000‐2002 onwards). The civil war in Burundi can large be described as a struggle for control over the state, first between the Tutsi army (who were in control of the state) and Hutu rebel groups, and later between fractions of the Hutu rebels once they entered the political process. Burundi has a constitution with a lot of regional and ethnic checks and balances, often referred to as consociationalism, but the system over government is currently under threat as a result of a nationwide victory of one of the former rebel groups (CNDD‐FDD) in the latest presidential and parliamentary elections. Since the end of the civil war several hundred thousand refugees have returned to Burundi, causing land disputes between current and former occupants.

Verwimp and Munoz‐Mora (2013) studied the welfare of returnees to Burundi in comparison with their neighbors who were not expelled. They found (i) that a former refugee household consumes on average 7% less compared to a non‐refugee household and (ii) that it takes 8 to 10 years after return before the welfare of a former refugee household is at the same level of its non‐refugee neighbor. Key data are reported in Table 5, where formerly displaced households are over‐represented among the very poor.

**Table 5: the welfare of returnees (formerly displaced) and non‐displaced households in Burundi**

|Forced<br>|Le<br>|vel of Pov<br>|erty<br>|Totals|
|---|---|---|---|---|
|Displacement|Very poor|Poor|Non- Poor||
|Never|1104|492|583|**2179**|
||50.67|22.58|26.76|100%|
|At least once|2533|983|1005|**4521**|
||56.03|21.74|22.23|100%|
|_Total_|**3637**|**1475**|**1588**|**_6700_**|
||54.28|22.01|23.70|100%|

**Notes** –  Pearson Chi Square (2)=  20.96 [p-value=0.000] Data source: Core Welfare Survey for Burundi (2006).

**5.3 Some Key Findings from Case Studies**

29