rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3: Restrictiveness of GATS (UR) commitments, Doha offers and actual policy by country**

<!-- Start of picture text --> TZA<br>IND<br>PAK TTO<br>GHAKENSEN IND PHL IDNIDNLKALKAMAREGYEGYCOLCOL TUN THA VEN CHLCHL TTO<br>ECU<br>NGA MYS<br>THA<br>IND MNG MAR<br>PER BRA<br>PAKPHLCHNCHN TUN MEX<br>IDNCHN THA SAU KOR<br>EGY BRAZAFZAF MEXMYS SAUKOR<br>TZAGHAKENSEN KHM NGAKHMPAKMNGLKAMARJORJORJORUKRCOLPERPERECU BRAZAF VENARGARGARGMEXCHL LTNLTNLTN TTOHNGHNGHNG CZR CZRKOR JPN JPN _EUN _ _EUNEUN AUSAUSAUSCANCANCAN USAUSAUSA<br>NZL NZL<br>0 10000 20000 30000 40000<br>GDP per capita in 2006 (constant 2005 USD)<br>Restrictiveness of GATS commitments Fitted values<br>Restrictiveness of Doha Offers Fitted values<br>Restrictiveness of actual policies Fitted values<br>100<br>80<br>60<br>40<br>20<br>Restrictiveness of services trade policies 0<br><!-- End of picture text --> Source: Gootiiz and Mattoo (2008)

**Figure 4: Uruguay Round commitments, Doha offer and actual policy by region**

<!-- Start of picture text --> 100.00<br>90.00<br>80.00<br>70.00<br>60.00<br>50.00<br>40.00<br>30.00<br>20.00<br>10.00<br>0.00<br><!-- End of picture text --> <!-- Start of picture text --> Offer improvement<br>(Uruguay Round<br>commitment -Doha<br>Offer)<br>Offer gap (Doha<br>Offer -Actual<br>Policy)<br>Actual Policy<br><!-- End of picture text --> Source: Gootiiz and Mattoo (2008). Note: Where countries did not make a Doha offer, their Uruguay Round commitment is treated as their offer.

9
- 30-

employment to the offer of a financial guarantee makes selection of employer extremely complex, with an uncertain gain.

# Costs

In 1987 total administrative cost for all defined benefit pension plans in the U.S., was 4,514 million dollars and covered 28.432 million workers. Using the same projection and inflation adjustment procedures, the average administrative cost for 1988, expressed in 1992 dollars, was 187.3 dollars per person. For those defined benefit plans with at least 100 members, the average administrative cost was 172.8 in 1992 dollars. The reason for the small difference is that very few employers with less than 100 workers offer defined benefit plans (Trends in Pensions 1992, Tables A-17 and A- 15).

Multiemployer plans have lower administrative costs. Mitchell and Andrews (1981) using 1975 data for defined benefit multiemployer plans and a log linear cost function found a mean cost of $49.03 per participant (1990 dollars) and large economies of scale. The estimated cost falls from $335.6 for 100 participants to $32.63 for 20,000 participants. They find very substantial economies of scale in assets per participant. Turner and Beller (1989) found an average cost for all multiemployer defined benefit plans of $101.11 (1990 dollars) and substantial economies of scale.

The large cost differential with defined contribution and defined benefit plans is one of the explanations, among others, for the growth in the share of defined contribution plans and the fall in the absolute number of defined benefit plans.

The following table shows the differences in cost structure for plans with more than 100 participants:
as before a higher share of banks among respondents is positively correlated with costs. On the other hand, the measures of competition do not enter significantly anymore, a result that appears to be due to the lower number of observations.<sup>19</sup> We also find that broader regulation in the sending country is associated with lower remittance cost of banks.

Table 6 shows that most of our findings are confirmed when restricting the sample to money transfer operators exclusively. A larger number of migrants and greater competition is associated with lower costs, while corridors with higher levels of income and bank participation exhibit larger costs. Unlike the regressions of Table 5 for banks, limiting the sample to MTOs only confirms all the findings of our baseline regressions in Table 3.

Table 7 shows results for Western Union, one of the largest MTOs in the world, active in 98 corridors of our sample. Focusing on one specific financial institution allows us to control for any bias that might arise from having different institutions across different corridors (composition bias), even within the group of banks and MTOs. Considering the cost data from Western Union, we verify that a larger number of migrants and lower GDP per capita in the receiving and sending country seem to lead to lower costs. In addition, we find that no exchange rate variability (as a result of a peg or dollarization) is also correlated with lower costs. On the other hand, contrary to previous estimations, none of the competition related indicators enter significantly, which could be due to the fact that Western Union has a dominant position in the remittance business across most corridors.<sup>20</sup>

# **V. Conclusions**

This paper investigates the characteristics of sending and receiving countries that explain the large variation across corridors in the cost of remittance transactions. We find that remittance costs are associated with three main factors. First, the number of migrants is negatively and significantly associated with remittance costs across different samples and different providers. This seems to suggest an important volume effect that works either through scale economies and/or higher competition in a larger market. Second, corridors with higher income per capita in

> 19 We establish this by re-running the regression for the average fee across all providers for the same sample as used in Table 5.

> 20 This could be due to the fact that Western Union may have been in operation in some corridors for longer periods than other firms. Also, in some countries, Western Union could have a better network coverage than other providers.

13
Awareness of the NAIVS program among non-beneficiaries in non-lottery villages is high at 86 percent. In addition, there seems to be no difference in awareness between non-beneficiaries in lottery and nonlottery villages as the coefficient β _L_ on the dummy variable _Lottery_ is small and not statistically significant.

Column 2 of Table 4 reports whether the farmer was asked about his or her ability to afford the top-up when the VVC assessed his or her eligibility for the NAIVS program. This is important because the VVC reported to us anecdotally that they “knew” who could and could not pay and only offered the vouchers to those that could pay. Put differently, the ability to pay the top-up could have been used by the VVC as an excuse to target the program to specific individuals. Indeed, becoming a beneficiary increases the probability of being asked about the ability to pay for the top-up by roughly 8 percentage points (a 54 percent increase from a base of 15 percent among non-beneficiaries in non-lottery villages). This increase, however, is only weakly statistically significant at conventional levels (p-value is 0.093) and there is no difference among non-beneficiaries in lottery and non-lottery villages.

Columns 3 and 5 of Table 4 use as dependent variable whether beneficiaries report receiving the pack of vouchers in the seasons 2010/11 and 2011/12, respectively. Since we use administrative data to determine the beneficiary status of individuals, there are differences between the administrative data and the self-reported voucher allocation. Indeed, according to columns 3 and 5, around 22 percent in 2010/11 and 24 percent in 2011/12 of individuals classified as non-beneficiaries, respectively, claimed to receive vouchers in non-lottery villages. As expected, becoming a beneficiary increases significantly the probability of reporting having received the pack of three vouchers. This increase corresponds to around 34 percentage points in 2010/11 and 17 percentage points in 2011/12. While treatment compliance is far from perfect, beneficiaries in lottery villages are equally likely to receive the vouchers compared to beneficiaries in non-lottery villages. This suggests that the VVC did not interfere with the (random) allocation of vouchers generated by the lottery and distributed vouchers to beneficiaries once they were identified similarly in both lottery and non-lottery villages.

While columns 3 and 5 assess whether beneficiaries according to the administrative data actually received the packs of vouchers, we can restrict the sample to self-reported beneficiaries and examine the number of different inputs they claim to have purchased with vouchers, out of the three possible inputs. We find that beneficiaries purchase on average 2.3 inputs, irrespective of whether the lottery took place 18
# **2. Methodology**

2.1 This section describes the construction of the database containing the descriptions of project components, the derivation from it of the network graph, and the econometric analysis.

## **Construction of the component database**

2.2 World Bank projects are described in an appraisal document, which is presented to the Bank’s Board for approval. Phasing in around 1997, the Project Appraisal Document (PAD) has followed a standard format, with evolutionary change over time. These documents are publicly available in pdf or text versions. The format includes an annex entitled “Detailed Project Description” (or a variant).

2.3 To extract the components descriptions, the IEG applied text-analytic methods to all PADs for the period 1997-2012. The project-description annex was extracted from each PAD. The contents of the annex were scanned for headers describing the components. Where these could be identified, the components were parsed out and numbered sequentially. For some of the cases, components could not be distinguished, and the project was considered to have a single component. The extracted components were stored in an Access database, which included descriptors of the the associated project such as beginning and end date, sector, and country.

## **Construction of the network**

2.4 The text data was then preprocessed in four steps. First, the text data was corrected for misspellings and other errors in the OCR process. (Table 2.) Second, words were stemmed –that is, different verb and noun forms (‘visit’, ‘visits’, ‘visited’) were mapped to a common root. Algorithms differ in their aggressiveness in distinguishing roots, and the choice is consequential for determining similarity between components. We chose the Porter (1980) algorithm. (Table 3.) Third, country, city and currency names were automatically identified and excluded, so that network proximity would be based on components’ conceptual content rather than location. Also excluded were conjunctions, cardinal numbers, determiners, prepositions and pronouns. Finally, the entire corpus of words was manually inspected to filter out uninformative words that would not be helpful in assessing whether two components were similar in concept. Excluded words included names of currencies; most adverbs and rhetorical words (‘therefore’, ‘however’), numbers, and generic terminology. The filtering process was subjective and carried the risk of both false negatives and false positives. (See example in Table 4.) The classification file is available on request.

2.5 Finally, components with the titles “project management”, “project monitoring”, “project implementation”, and “project administration” were excluded from consideration. Most projects have such a component, devoted to general administration, and so their inclusion would create a confusing and meaningless cluster.

4
Table 15. Statistical Idices Relating to the Level,<sup>Distribution and Range in Regional African Countries' Exports to the United States in 1993.</sup>

|||N<br>|ominal Freight R<br>Quartile Values|ate Level Distrib|ution<br>|Africa<br>(percent<br>|n Margin<br>age points)|
|---|---|---|---|---|---|---|---|
|||Transport|||~~~~~~~~~|~Range<br>||
|African Region|Transport|First|Median|Third||Median|Range|
|All Sub-Saharan Africa|Air|_5.3_|14.1|26.5|0.5 to 87.4<br>|3.5<br>|-13.9 to 71.2<br>|
||Vessel|4.6|7.5|13.8|0.2 to 56.1|1.1|-5.8 to 39.9|
|Low Income East and|Air|3.7|9.2|23.6|0.7 to 56.9|4.4|-11.9 to 44.9<br>|
|Southern Africa|Vessel|4.2|7.1|13.8|0.2 to 55.9|1.2|-5.8 to 22.8|
|Low Income West Africa|Air|3.7|20.5|35.6|0.4 to 92.6|7.4|-0.9 to 41.5<br>|
||Vessel|3.5|9.3|19.4|0.2 to 89.7|1.1|-6.1 to 84.5|
|Middle Income East|Air|2.5|8.0|16.4|0.9 to 29.7<br>|0.9<br>|-19.8 to 14.1<br>|
|and Southern Africa|Vessel|3.8|6.2|8.9|0.7 to 17.5|0.8|-4.3 to 7.0|
|Middle Income West Africa|Air|7.3|13.3|24.2|0.4 to 43.1<br>|3.0<br>|-18.8 to 20.4<br>|
||Vessel|4.9|10.0|12.8|2.3 to 50.6|1.9|-4.0 to 17.3|
|Oil Exporting Countries|Air|na|na|na|na<br>|na<br>|na<br>|
||Vessel|9.2|11.3|17.2|1.0 to 55.7|1.9|-7.2 to 29.6|

Note: Trade Flows or less than $50,000 have been excluded<sup>from the above comparisons. Insufficient data existed to make meaningful air freight comparisons for the African</sup> oil exporting countries.

Source: Nominal transport costs computed from United<sup>States Department of the Census trade tapes.</sup> _,'
**Figure 15. Most Advantaged and Least Advantaged Simulations for Social, Emotional, and Cognitive Development by Year (Percentages)**

<!-- Start of picture text --> 2006/7 2011 2012<br>100 99<br>95<br>100<br>90 83<br>79<br>80 74<br>70<br>60<br>50 45<br>36<br>40<br>30<br>18 16<br>20<br>8<br>10 4<br>0<br>ECCE Development Violent Discipline Work & Domestic<br>Activities Work<br>Percentage<br>Least Advantaged Most Advantaged Least Advantaged Most Advantaged Least Advantaged Most Advantaged Least Advantaged Most Advantaged<br><!-- End of picture text --> _Source:_ Authors’ calculations based on MICS 2006/07, ENPSF 2011, and ONDH panel 2012.

_4.4 Circumstances Contributing to Inequality in ECD over Time_ The D-indices indicate the extent of inequality of opportunity in ECD in Morocco due to children’s early circumstances. An important element of rectifying that inequality is understanding the different risk and protective factors driving it. The Shapley decompositions (Table 5) show the relative contributions of different circumstances (wealth, mother’s education, father’s education, region, urban versus rural, and gender) to this inequality, as a percentage. While the contributions of different background characteristics vary over time and across the measures of ECD, a number of consistent patterns stand out. First, wealth contributes substantially and the role of wealth in inequality appears to have been rising slightly over time. Mother’s education also plays an important role in inequality, although less so in nutrition inequality than other dimensions of inequality. Father’s education contributes to inequality but less so than mother’s education. Regional and urban/rural differences contribute to inequality, often substantially, but to varying degrees across indicators. Notably, the child’s gender contributes very little to inequality of opportunity in ECD.

Focusing particularly on health, wealth and mother’s education play an increasing role in the largely decreasing inequalities in health. In 2012, 36–37 percent of the inequality in prenatal and delivery care was due to wealth and 21–25 percent due to mother’s education. Father’s education plays a decreasing role in health inequalities. In nutrition, wealth has increasingly played a role in children being stunted, underweight, and wasted over time, with shares as high as 45 percent for stunting in 2011. Little of the inequalities in nutrition are due to the mother’s or father’s education. There are declining contributions to disparities from different regions but 21
of workers out of agricultural activities<sup>22</sup> continues at a slow rate and informality in nonfarm employment stabilizes at around 40 percent.

**Figure 3 Growth Incidence Curves (GIC) for the Business as Usual scenario**

<!-- Start of picture text --> GIC, 2003 - 2014<br>GIC, 2014 - 2020<br>0 20 40 60 80 100<br>Percentiles<br>Vertical lines represent the % of the population under extreme and moderate poverty, respectively<br>40<br>30<br>20<br>10<br>0<br>-10<br><!-- End of picture text --> The distributional effects of the different pattern of growth of the two periods are graphically summarized by the growth incidence curves (GIC) shown in Figure 3.<sup>23</sup> In the BaU scenario, between 2003 and 2014 real average incomes for the median household in Panama will cumulatively increase by 27.5 percent. This gain is not evenly distributed: incomes of the bottom 10 percent of the distribution will rise only 4.3 percent on average compared to an increase of 32 percent experienced by households located at the top 10 percent of the distribution.

An even more regressive effect is found in the BaU scenario for the second period. The median income increases 9.4 percent with respect to 2014, but incomes at the bottom 10 percent of the distribution decrease 3.7 percent whilst incomes of the richest 10 percent of the population rise almost 20 percent. The regressive income effect shown by the GICs in Figure 3 is explained by an increase in the wage gap between skilled and unskilled workers. In both sub-periods, real wages of unskilled workers in nonagricultural sectors – the largest labor segment group – experience the slowest growth rate. As a result of the increase in the wage differentials, household income distribution deteriorates as it is indicated by the increase in the Gini coefficient reported in Table 5.

> 22 The CGE model does not explicitly account for rural to urban ( ̳geographic‘) migration of workers, but only for agriculture to non-agriculture ( ̳inter-sectoral‘) labor reallocation. Only the first type of workers‘ movement can be defined as internal migration and precisely linked to urbanization. However, in the main text, due to the high correlation (0.58) between working in non-agricultural sectors and being located in urban areas, the terms sectoral reallocation and urbanization are at times used interchangeably. 23 The GIC is shows the changes in welfare along the entire income distribution, therefore capturing, in a single graph, the growth and distributional components of overall welfare changes. For a detailed description of the properties characterizing the growth incidence curves see Ravallion and Chen (2003)

17
Policy Research Working Paper 8605

# **Abstract**

This paper intends to inform the effort of the Sri Lankan government to reform the targeting efficacy of its social protection programs, in particular, Samurdhi, which currently distributes benefits based on self-reported income. The paper develops a proxy means test for Sri Lanka based on the Household Income and Expenditure Survey 2016 and evaluates its performance for targeting benefits of Samurdhi. The paper considers a range of models and policy parameters that could be applied depending on data availability and country preferences. The results indicate that switching to a proxy means test could considerably improve the targeting performance of Samurdhi and would significantly improve the poverty impact of the program. The analysis finds that the performance of the proposed proxy means test model suffers when the coefficients are estimated from samples smaller than 1,000 households. However, the analysis does not find a similar loss of model performance when the model is estimated from seasonal data, provided the sample size is sufficiently large. The proposed model could be applied to targeting a variety of safety net programs after validating and refining the model by conducting a pilot survey.

This paper is a joint product of the Poverty and Equity Global Practice and 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/research. The authors may be contacted at twalker@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
Table 2: Means and Standard Deviations for All Variables in Panel Analyses by Level of Development

||A<br>Cou<br>(n=|ll<br>ntries<br>63)|Mor<br>Co<br>(n|e-Devel<br>untries<br>=20)|oped Les<br>Co<br>(n|s-Developed<br>untries<br>=43)|
|---|---|---|---|---|---|---|
||bkmS|D|_M=a_|<br>_=_|MM|S|
|Log of per capita RGDP 1985|3.40|.46|3.89|.15|3.18|.37|
|LogofpercapitaRGDP 1975|3.35|.42|3.80|.15|3.13|.34|
|Log of per capita RC DP 1960|3.15|.38|3.56|.20|2.96|.29|
|Mean Yearly Instructional Hours (circa 19|60) in:||||||
|All Elementary School Subjects|870.8|163.6|917.0|154.8|849.3|164.9|
|Language Education|305.6|109.3|350.0|74.2|284.9|117.3|
|Mathematics|144.5|40.4|167.5|44.5|133.8|33.9|
|Sciences|63.3|42.3|62.4|37.1|63.7|44.9|
|History, Geog., Social Studies, Civics|75.6|36.5|71.1|37.6|77.7|36.2|
|Arts, Music, Dance|81.2|36.3|92.9|30.3|75.8|37.9|
|Physical Education|58.7|30.3|66.6|31.6|55.0|29.3|
|Religion, Moral Education|50.5|43.3|47.6|46.4|51.8|42.3|
|Pre-Vocational, Practical subjects|65.4|69.2|46.4|47.1|74.2|76.2|
|Primary Enrollment Ratio 1960|77.4|27.3|96.3|5.9|68.7|29.0|
|Secondary Enrollment Ratio 1960|24.2|19.0|44.8|13.7|14.6|12.5|
|Total Fertility Rate 1965|4.9|1.9|2.5|.44|6.0|1.1|
|Dumny Variable for Mining/Oil Exporters|.13|.34|.00|.00|.19|.39|
|Export Commodity Concentration 1960|46.4|30.2|17.6|15.5|59.8|25.6|