rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3. Receipts in Developing Countries Under $25/ton CO 2 Levy on International Aviation Fuel** <u>(applied to year 2007)</u>

|Country||Revenue||
|---|---|---|---|
||% of GDP||$million|
|**Fiji**|0.345||13|
|**Bahrain**|0.112||28|
|**Seychelles**|0.090||2|
|**Mauritius**|0.083||12|
|**Senegal**<br>**Hong Kong**|0.080<br>0.075||17<br>221|
|<br>**United Arab Emirates**|0.073||179|
|**Singapore**|0.069||168|
|**Cyprus**|0.061||14|
|**Kenya**|0.058||34|
|**Jamaica**|0.056||12|
|**Turkmenistan**|0.055||16|
|**Jordan**|0.048||14|
|**Qatar**|0.042||38|
|**Iraq**<br>|0.041<br>||39<br>|
|**Maldives**|0.039||1|
|**Thailand**<br>|0.034<br>||177<br>|
|**Azerbaijan**|0.032||22|
|**Panama**|0.032||12|
|**Oman**<br>|0.032<br>||20<br>|
|**Barbados**|0.030||2|
|**Papua New Guinea**|0.030||4|
|**Malaysia**<br>|0.028<br>||100<br>|
|**Kuwait**|0.022||29|
|**Russia**|0.021||513|
|**Saudi Arabia**|0.021||114|
|**Philippines**<br>|0.020<br>||61<br>|
|**Egypt**|0.019||79|
|**South Africa**|0.019||91|
|**Sri Lanka**|0.019||16|
|**Morocco**|0.018||23|
|**Ecuador**<br>|0.017||17|
|**Korea, South**|0.016||209|
|**Ukraine**|0.015||50|
|**Angola**|0.015||13|
|**Tunisia**|0.014||11|
|**Kazakhstan**<br>|0.012<br>||20<br>|
|**Brazil**|0.011||210|
|**Tonga**|0.011||0.05|
|**Comoros**|0.013||0|
|**Argentina**|0.011||58|
|**Belarus**|0.010||11|
|**Mexico**|0.010||147|
|**Pakistan**|0.009||133|
|**Haiti**|0.009||1|
|**Indonesia**|0.008||64|
|**China**<br>**India**|0.007<br>0.007||536<br>222|
|**Algeria**|0.006||17|
|**Peru**|0.006||14|
|**Colombia**|0.006||23|
|**Iran**|0.006||48|
|**Bangladesh**|0.006||11|
|**Nigeria**|0.004||11|
|**Total**|||3,898|

Source: EIA (2011b) and WB (2011).

Note: The tax is $25 per tonne of CO2, applied to all international flights. The above figures do not account for the (modest) reduction in fuel use in response to any charge, and assume that 60 percent of reported jet fuel consumption (domestic plus international) is for international flights for all countries (resulting in an understatement of receipts in small island developing States).

32
<!-- Start of picture text --> 0.30<br>2<br>S 025<br>oO<br>2 0.20<br>¥<br>Sg<br>2 0.15<br>=<br>=<br>82 0.10<br>3<br>=53 0.05 ae _ —_—@e—--- —?<br>2 ae<br>S 0.00<br>® —#*— Treatment<br>— — Control<br>2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> <!-- Start of picture text --> 42 1.0 oe Zen<br>s N<br>& SS ~<br>a a ~<br>~ N Wa TMN<br>o 09 XN N<br>o \ 4 nN<br>£ XN a“ »<br>D ‘eo<br>£ 08<br>g°<br>3<br>2906<br>°<br>xOB o5 — © —_— Control<br>2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> <!-- Start of picture text --> 20<br><<br>a 15<br>Q<br>2<br>><br>i oe<br>s oO<br>3 10 ~<br>oO© ~ ~~<br>© ~~<br>iB oe<br>wo5 °<br>E£<br>&<br>—#*— Treatment<br>0 — — Control<br>2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> <!-- Start of picture text --> =2 °04<br>i)<br>=<br>Ss<br>2<br>Ss 03<br>5<br>2Oo<br>=c<br>a<br>2 01<br>a2 gee 4 +<br>5 ool<br>5@ — © —— Control<br>2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> <!-- Start of picture text --> £ 0.10<br>x<br>[=}<br>fe}<br>oO<br>s<br>2<br>x}<br>® 0.05<br>oO<br>=<br>£<br>oO<br>£<br>D<br>£<br>s<br>$ 0004¢-————~—-+¢————}_—e-——— ——-<br>2<br>oO<br>oOo<br>a<br>ce}<br>a<br>3 ——*— Treatment<br>£ — — Control<br>oO<br>2010 2012 2014 2016 2018 2020<br><!-- End of picture text -->
within the sector. During the process of collecting price data, various sources, including _Statistical Yearbook of China,_ Annex A of _China: Socialist Economic Development_ (World Bank, 1983), Anexes B and C of the present report, and World Bank project appraisal reports, were used. For other countries, retail, wholesale and ex-fictory prices were used, though in many cases, Chinese puices had to be compared with world market prices, either by calculating dollar urit values for China's exports and inports, or by direct reference to sources such as _Commod&y Trade and Price Trends_ (World Bankl annual). These world pnces were adjusted for transport costs and other fictors such as taris and subsidies PPPs used here were deived by dividing the official 1981 exchange rate of 1.7050 yuan/doDar by the aveage sectoral price adjustment coefficients in Tayloes study (Taylor, 1986), 15. The confusion should be avoided that is to compare the esfimate for China based on PPP approach with the estimates for other developing economies based on the Atlas approach. The diffirent esfimates of GDP per capita by different methods imply a very different relative standing of China to other counties, as demonstrated in Table 3.

7
Policy Research Working Paper 6081

# **Abstract**

The objective of this paper is to inform Liberia’s mediumterm growth and development strategy for 2012–17 and its National Vision: Liberia Rising 2030, both of which are under preparation. The analysis is based on MAMS (Maquette for MDG [Millennium Development Goal]) Simulations, a computable general equilibrium model. A base scenario (designed to represent a central case for the evolution of Liberia’s economy up to 2030) is compared to a set of non-base scenarios that introduce alternative assumptions for the mining sector, government spending on infrastructure and human development, as well as foreign borrowing. The simulations, which cover the period 2012–2030, indicate that rapid expansion of mining output, front-loaded investment in infrastructure, and improved government efficiency may bring about rapid growth. The findings underscore the importance of allocative and operational efficiency of public spending. It is also important that the government balance spending on infrastructure and human development as they complement each other and face different constraints. Spending on infrastructure tends to have relatively strong and immediate growth and poverty reduction effects, whereas spending on human development has a stronger positive impact on non-poverty MDG indicators at the cost of lowered economic growth in the short to medium terms. It is important to consider that growth driven by rapid mining expansion entails drawbacks and risks, including the persistence of an enclave economy that will not benefit the majority of the population, and increased vulnerability to fluctuations in iron ore world prices.

This paper is a product of the Poverty Reduction and Economic Management, Africa Region; and Development Economics Prospects Group. 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:// econ.worldbank.org. The author may be contacted at hlofgren@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
- 28 -

- 15/ We-have also estimated the case where US auto dealers of Japanese vehicles captured some rents above the premia rate of table 1. The costs of the VERs in this case exceed the estimates of table 2 by about $1.3 billion (see de Melo and Tarr, 1990a).

- 16/ This implies that to obtain the joint costs of VERs in autos and steel one would remove the VERs from a solution where steel is rationed. Joint estimates are not reported here since the values are only very marginally less than the sum of the individual general equilibrium estimates.

- 17/ Sensitivity analysis to the assumed values of demand and supply elasticities with fixed labor supply are reported in de Melo and Tarr (1990a). As explained there, the rent component is sensitive to assumed elasticities only in the steel case. As to the distortionary cost component, when lov (high) elasticities are used, the cost is $1.09 billion ($2.96 billion) for autos and $0.27 billion ($0.10 billion) for steel. The asymmetry with respect to variation in elasticities comes from the fact that quota imposition in steel results in a larger price change the lower the set of elasticities. Quota removal in autos, however, is necessarily treated as the removal of the tariff equivalent of the quota and results in a greater quantity change the greater the elasticities. See de Melo and Tarr (1990b).

- 18/ Estimates of the benefits of capturing rents differ very slightly due to the fact the estimates depend on the income elasticity of demand for autos which is endogenous.

- 19/ A similar pattern of results occurs when wage distortions are endogenous. A complete set of sensitivity results are in an appendix available from the authors on request.

- 20/ Our CRTS simulations are consistent with those of Dixit (1988). Dixit assumed CRTS and fixed input-output coefficients for labor and capital. He found a subsidy to the production of autos (which is equivalent to a wage subsidy with fixed coefficients) is a superior instrument to a tariff.

- 21/ See Fernandez (1989) for a discussion of other strategic issues that make the achievement of benefits from optimal policies in response to labor market distortions problematical.
These are impressive numbers as absolutes, but in relative terms the fraction comprised of the poorest has fallen from 62 percent in 1997 to 54 percent in the 2013 data. Diversity within the “big tent” of the movement shows in the divergence of trajectories of total borrowers and the “poorest” borrowers from 2010 onward. The Microcredit Summit started to see both a relative and absolute decline in the orientation toward poor customers.

<!-- Start of picture text --> Million<br>250 100%<br>Customers that are among<br>214 90%<br>the poorest (%) 211<br>200 80%<br>73% 205<br>170 195<br>70%<br>62% 61% 62% 155<br>150 60%<br>133<br>113 54% 50%<br>49%<br>92<br>100 40%<br>81<br>68<br>30%<br>55<br>50 20%<br>31<br>24 Total number of<br>21<br>13 borrowers (millions) 10%<br>0 0%<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text -->

# **Figure 1. Microfinance Borrowers, 1997-2013 (Millions)**

Adapted from Figure 1, Microcredit Summit Campaign. _State of the Summit Report 2015_ https://stateofthecampaign.org/data-reported/ 7
<!-- Start of picture text --> +<br>© / aN<br>, / \\<br>y;/ \<br>v4 \<br>7 / \<br>77) /<br>c N / \<br>(a) / / \ \<br>77 ind \\<br>= oO 4 ‘\<br>7 sy<br>/ / ~,<br>Jf<br>oO<br>0 2 4 6 8<br>In(sales per worker ($))<br>—---- FormalMicro §=——— Informal<br><!-- End of picture text -->
||||||||||(0.077)|(0.086)|(0.042)|(0.076)|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|urban enum* high educ resp|||||||||0.010|0.080|0.012|-0.020|||||
||||||||||(0.083)|(0.099)|(0.056)|(0.078)|||||
|experience* high educ resp|||||||||0.048|-0.033|-0.032|0.019|||||
||||||||||(0.080)|(0.088)|(0.048)|(0.080)|||||
|**Respondent poor**|||||||||||||-0.047|-0.031|0.002|0.005|
||||||||||||||(0.068)|(0.087)|(0.052)|(0.084)|
|male enum*poor respo|||||||||||||-0.004|-0.030|-0.031|0.019|
||||||||||||||(0.074)|(0.080)|(0.057)|(0.063)|
|urban enum* poor resp|||||||||||||0.085|0.012|0.022|-0.032|
||||||||||||||(0.064)|(0.079)|(0.061)|(0.074)|
|experience* poor resp|||||||||||||-0.116|-0.103|0.012|-0.090|
||||||||||||||(0.081)|(0.083)|(0.046)|(0.077)|
|Observations|6,245|6,333|6,553|6,320|6,245|6,333|6,553|6,320|6,245|6,333|6,553|6,320|6,245|6,333|6,553|6,320|
|R-squared|0.490|0.469|0.240|0.484|0.490|0.468|0.239|0.484|0.490|0.468|0.239|0.484|0.490|0.468|0.239|0.484|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Notes: This table presents OLS regression results for equation (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC). _Enumerator high education_ is a dummy that takes value 1 if the enumerator has completed university or teaching institute, or has master. _Respondent young_ is a dummy that takes value 1 if the respondent’s age<=32 and 0 otherwise. _Enumerator high education_ is a dummy that takes value 1 if the enumerator has completed university, teaching institute, or master. _Respondent high educated_ is a dummy that takes value 1 if the respondent has completed primary education and 0 otherwise. _Enumerator high education_ is a dummy that takes value 1 if the enumerator has completed university, teaching institute, or master. _Work experience as enumerator_ is the number of months previously worked as an enumerator. _Respondent poor_ is a dummy that takes value 1 if the value of the asset index for the household is lower than the mean value of the asset index for the population and 0 otherwise. Each regression includes as additional control variables: _Want people to know how good can be at work; Strongly motivated by the wage can earn; Enjoy handling new problems; Enjoy trying to solve complex problems; Curiosity is a driving force for your actions_ . Each regression includes community fixed effects and a constant. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10 44
scholarship applicants. Second, we conducted four unannounced visits to the 100 CSP schools (in February, April, and June 2006, and in June 2007). These data allow us to physically verify school attendance of applicants, and are the main source of data we use to estimate the program effects. Finally, we fielded a household survey of almost 3500 randomly selected applicants and their families in five provinces.<sup>7</sup> The household survey was collected between October and December of 2006, approximately 18 months after children filled out the application forms. For the present analysis, we use these data mainly to check the robustness of the findings from the school visits.

Table 1 summarizes the characteristics of applicants who were offered a $45 scholarship compared with those who were offered no scholarship (left-hand panel), and applicants who were offered a $45 scholarship compared with those who were offered a $60 scholarship—among all applicants (first three columns of each panel) and among applicants who are within 10 ranks of the school-specific cut-off (next three columns of each panel). In each case and for each characteristic, we report three values: the relevant means, and the coefficient on the indicator of a particular “treatment” (a $45 scholarship or a $60 scholarship) from a “dummy RD” regression of the characteristic on the treatment indicator, schoolspecific intercepts, and a school-specific quartic in the dropout-risk score. This corresponds to our basic estimation specification, discussed in more detail below, and is a standard check on the validity of the regression discontinuity (RD) specification (Imbens and Lemieux 2008).8 A statistically significant coefficient in these regressions indicates a potential violation of the identification assumption that observations “just above” and “just below” the cutoff are equivalent except for their program participation status.

> 7 The provinces are Battambang, Kampong Thom, Kratie, Prey Veng, and Takeo, and the sample was based on randomly selected schools in these five provinces.  The survey was limited to applicants ranked no more than 35 places above the cutoff in these schools.  This restriction was imposed to maximize the number of schools, while maintaining the density of observations “around” the cut-off—an important consideration when estimating program effects based on regression-discontinuity, as discussed below.

> 8 As we discuss below, the regressions that compare $60 and $45 scholarship recipients do not include children who were offered no scholarship at all, while those that compare children who were offered a $45 scholarship with those who were turned down for scholarships exclude children who were offered a $60 scholarship.

6
changed for the 2013 round of UNAIDS estimates (which will be released later this year) to account for the increasing fraction of persons on ART who are over 50 (Jeff Eaton, personal communication).

Epidemic models allowing for ART effects on survival and infectiousness do so with the current incomplete understanding of such effects and can be affected by inaccuracies in reported numbers of individuals on ART. As ART is scaled-up and ART eligibility broadened, more error in the reporting of patient numbers may occur in the stretched ART programs. Additionally, self-reported adherence to therapy may be higher than actual adherence, thus potentially overestimating the effect of ART use on patients’ infectiousness. Spectrum currently sets ART’s transmission reduction effect at 92% (based on HPTN052 clinical trial), whereas real-world programmatic data suggest a lower effect, with 15-30% of patients on ART maintaining viral loads at infectious levels (Kranzer et al. 2013; National AIDS and STI Control Programme 2013; Justman et al. 2013). In the Spectrum model, assumptions around ART are most likely to affect the HIV incidence estimates of the most recent years, since HIV prevalence data are not sufficient to robustly infer HIV incidence in these recent years.

This brief account of how HIV incidence can be estimated through different direct and indirect methods, compared and tracked over time, illustrates that there is no perfect method, but that there are increasingly reliable ways to estimate this key metric of the HIV response.

19
cannot always be certain of this since sometimes the source was ambiguous about this difference.<sup>23</sup>

# **3.4 Data on the covariates**

The available data limit our ability to test the Relative Prices Hypothesis. The <u>International Comparison Program provides estimates from each ICP round of the prices of</u> different consumption categories, including health services, education services and the aggregate category of food and non-alcoholic beverages. With this information, the relative price of health and education to food and non-alcoholic beverages can be easily calculated.<sup>24</sup> However, the ICP rounds are not annual: they are only available for years 2005, 2011 and 2017.<sup>25</sup> The health and education price relatives are highly correlated (r=0.92 for 2005, r=0.90 for 2011 and r=0.82 for 2017), so we show results with them entering separately as well as jointly.

In testing Governance 1, thanks to the Word Bank’s WGI, we have regular measures of the responsiveness of governments to their citizens, namely their “voice and accountability” (VA) index. As defined on the WGI website, VA “... captures perceptions of the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media.” The index is a composite of indicators on democracy, electoral processes, accountability of public officials, rights, reliability of government budget documents, transparency in policy making, the freedoms allowed for citizens, and citizens’ trust in their government. The index comes normalized to have mean zero and a standard deviation of unity in every year.

<mark>We consider two other measures of accountability. The first is the electoral democracy</mark> index (also known as the polyarchy index) produced by the Varieties of Democracy (V-DEM) project. This measures the extent to which a country has free and fair electoral competition, suffrage is extensive, political and civil society organizations operate freely, there is freedom of expression and the media is independent. The index ranges from 0 to 1, where zero indicates a regime furthest away from electoral democracy and 1 is a full electoral democracy. The second

> 23 <mark>We checked against the IMF estimates on the total additional public spending (whether on SP or something else) implemented during the pandemic (IMF 2021); in no case did our estimate exceed the corresponding IMF number.</mark> 24 The ICP provides information of the price level of each category indexed to the world average, which takes a value of 100. The ratio of these price levels provides an indicator of relative prices.

> 25 The number of countries for which social protection and ICP data is available is 81 in 2005, 99 in 2011 and 102 in 2017.

16
than the actual tariff collection rate of 4 percent.<sup>41</sup> These numbers suggest that in 2000 the leakage in the tariff collection system associated with export-related fraud was not larger than US$63 million or 2.8 percent of total imports. Since by 2007 the simple average tariff will fall to 9.8%, the leakage in the tariff collection system is expected to be negligible.

The costs of administering China’s duty drawback system have been modest. According to China’s General Administration of Custom, there were 40,000 Customs employees in 2001. Approximately 3,000 Customs employees were involved in visiting production facilities, registering and processing export processing contracts. In addition, there were officials who investigated various violations of the export processing trade rules. These numbers suggest that around 10 percent of the total Customs’ labor force monitored processing trade. Assuming that customs officers received the average government wage,<sup>42</sup> the cost of duty exemption administration in 2001 is estimated at 48,568,000 Yuan or US$5.9 million.

Despite the low administrative costs and leakages in the tariff collection system associated with export processing trade, China’s duty drawback system is not without problems. The export processing system has led to a highly concentrated export market for Chinese products. China’s imports for processing trade come mainly from other parts of East Asia, such as Hong Kong, Taiwan, Korea, and Japan, while its exports go predominantly to the United States, the European Union, and Japan.<sup>43</sup> Among all its trading partners the largest US trade deficit is with China ($US113 billion in 2002).<sup>44</sup> It accounts for approximately 20 percent of the total US trade deficit. Naughton (2000)

> 41 Tariff collection rates were lower than 3 percent between 1994 and 1998. In 1999 they jumped to slightly more than 4 percent as a result of the government’s anti-smuggling efforts and the policy of encouraging domestic demand through government spending.

> 42 According to China Statistical Yearbook (2002) the average government wage in 2001 was 12,142 Yuan.

> 43 Lardy (2002) reports China’s trade surplus with the United States to be around US$69 billion. 44 Source: WITS/COMTRADE.

30