rafmacalaba/datause-encoder-data huggingface dataset:
# **Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2693 Helping People Help Themselves:<br>Toward a Theory of Autonomy-<br>Compatible Help|David Ellerman|October 2001|B. Mekuria<br>82756|
|WPS2694 Financial Development and Financing<br>Constraints: International Evidence<br>from the Structural Investment Model|Inessa Love|October 2001|K. Labrie<br>31001|
|WPS2695 Trade, Credit, Financial Intermediary<br>Development, and Industry Growth|Raymond Fisman<br>Inessa Love|October 2001|K. Labrie<br>31001|
|WPS2696 Firms as Financial Intermediaries:<br>Evidence from Trade Credit Data|Asli Demirgug-Kunt<br>Vojislav Maksimovic|October 2001|K. Labrie<br>31001|
|WPS2697 Regional Integration and Industrial<br>Growth among Developing Countries:<br>The Case of Three ASEAN Members|Dorsati H. Madani<br>|October 2001|L. Tabada<br>36896|
|WPS2698 Foreign Bank Entry: Experience,<br>Implications for Developing Countries<br>and Agenda for Further Research|George Clarke<br>, Robert Cull<br>Maria Soledad Martinez P<br>Susana M. Sanchez|October 2001<br>eria|P. Sintim-Aboagye<br>38526|
|WPS2699 Benefits and Costs of International<br>Financial Integration: Theory and Fac|Pierre-Richard<br>Agenor<br>ts|October 2001|M. Gosiengfiao<br>33363|
|WPS2700 Business Cycles, Economic Crises,<br>and the Poor: Testing for Asymmetric<br>Effects|Pierre-Richard<br>Agenor<br>|October 2001|M. Gosiengfiao<br>33363|
|WPS2701 Trade and Production, 1976-99|Alessandro Nicita<br>Marcelo Olarreaga|November 2001|L. Tabada<br>36896|
|WPS2702 Productivity versus Endowments:<br>A Study of Singapore's Sectoral<br>Growth, 1974-92|Hiau Looi Kee|November 2001|L. Tabada<br>36896|
|WPS2703 Integrating Independent Power<br>Producers into Emerging Wholesale<br>Power Markets|Fiona Woolf<br>Jonathan Halpern|November 2001|Energy Help Desk<br>30652|
|WPS2704 Regulatory Governance and Chile's<br>1998-99 Electricity Shortage|Ronald Fischer<br>Alexander Galetovic|November 2001|G. Chenet-Smith<br>36370|
|WPS2705 Concession Contract Renegotiation<br>Some Efficiency versus Equity<br>Dilemmas|s: Antonio Estache<br>Lucia Quesada|November 2001|G. Chenet-Smith<br>36370|
**Figure 2 Weekly Working Hours and Remittances (Individual Level, 2002)**

<!-- Start of picture text --> 0 20000 40000 60000<br>remesas<br>80<br>60<br>40<br>HOURS WORK PER WEEK 20<br>0<br><!-- End of picture text --> Source: Author's calculation from SLC and LFS.

The panel data analysis at the cluster level also confirms a negative effect of remittances on the labor market participation but not a strong effect on the hours worked of employees at the cluster level. Table 3 reports the result from the fixed effect regression of cluster level labor force participation (LFP) and weekly working hours (WWH) from 1995 to 2002.<sup>21</sup> The coefficient estimate for the current monthly remittance in the Specification I is -0.037 and statistically significant at 5% confidence level. The past remittances at time t-1 and t-2 have negative impact as well, but they are not statistically different from zero. Neither the second moment (standard deviation) and third moment (skewness) of remittances within the cluster has significant impact of current labor force participation decision. Coefficient estimates associated to other control variables are not statistically significant. The regression result from Specification I suggests that the labor force

> 21 The Arellano-Bond linear panel regression will be better to take care of specification errors and endogeneity problem for this type of estimation. However, I have only three or four observations per each cluster without changing the households in the same cluster. For the regression using the short time period, Arellano-Bond estimation performs poorly comparing to the standard fixed effect regression, because the former needs to construct a difference variable using observations for two consecutive time periods.

13
21 <!-- Start of picture text --> APPAREL GROUP NO. I (DRESSES)<br>Price Per Unit in U S WIluu<br>23~~~~~~~~~~~~~~~~~~~~~2<br>0<br>12, 2 3,0 51<br>o<br>|at / § * I I * I<br>21 1 7 2 X 25XS0<br>13 to0 9 8 3 2644t<br>13 I<br>UNITS STAllS<br>5_.w I_ -_ _<br>Figure 3(1)<br>|. /1 § i 4 I<br>APPAREL GROUP NO. 3 (PLAYSUITS)<br>Pne Per Unit in US Dalian,<br>1W2-I<br>0 424<br>0<br>N 3 -<br>04<br>C 0<br>K (ii<br><!-- End of picture text -->

<!-- Start of picture text --> APPAREL GROUP NO. 2 (SKIRTS)<br>Price Per Unit is Us Drealr,<br>/,<br>0~~~~~~~~~~~~~~~0<br>0<br>! / I I I I~It<br>la1<br>o1<br>UNrICD SYA?O<br>~_ Uu -tl<br><!-- End of picture text -->

# Figure 3(ii)

' //e # #I I'a <!-- Start of picture text --> APPAREL GROUP NO. 4 (SWEATERS)<br>Price Per Unit in U 3 Dollars<br>196219W<br>10 ~ ~ ~~~g<br>0~~~~~ /<br>G<br>0 0 a1<br>K e liue /iue v<br><!-- End of picture text -->
<!-- Start of picture text --> fe Oa\ena ss: “.<br>o*5© oe Awell<br>2<br>A VVau<br>LR o<br>eea<br>Legend ? &—° °o °<br>e oe<br><!-- End of picture text --> <!-- Start of picture text --> ° Participants baseline locations rarer ;<br>@A\ KCBMain branchestowns in study area © . se .<br>States . oe a” e<br>([) ° en: Tojit @<br>Oo NoProject project @ve?° Mahi.% @®©Pajok®<br>*-- Disputed territories 0g? p77 Ni ”<br>0 100 200 km<br>P|<br><!-- End of picture text -->
<!-- Start of picture text --> Age group: ff 0-44 Jj 45-54 9 55-64 | 65-74 i 75-84 9 35+<br><!-- End of picture text -->

<!-- Start of picture text --> Share of deaths by age (%)<br>25% 50 75 100% 25% 50 75 100%<br>Official COVID-19 deaths Excess deaths<br>Bangladesh<br>India<br>Philippines<br>Jordan<br>Indonesia<br>South Africa<br>Paraguay<br>Ukraine<br>Moldova<br>Brazil*<br>Colombia a<br>Mexico<br>Argentina<br>Turkey |<br>Greece<br>Slovakia<br>Hungary<br>Portugal | |<br>Scotland<br>Czechia<br>Slovenia | |<br>Israel<br>tay | a<br>England and Wales<br>Canada |<br>France<br>Belgium<br>Sweden<br>Germany<br>Austria<br>Netherlands<br>USA<br>Switzerland<br>25% 50 75 100% 25% 50 75 100%<br>Share of deaths by age (%)<br><!-- End of picture text -->

*The age-groups available to compute excess deaths for Brazil differ from those used in other countries and correspond to the following: 0-39, 40-49, 50-59, 60-69, 70-79, 80+
**<u>Table 3: Household characteristics by market participation status</u>**

||**Total**|**Ma**|**rket transact**|**ions**|**Non-marke**|**t transactions**|
|---|---|---|---|---|---|---|
||**sample**|**Sale**|**Autarkic**|**Purchase**|**Autarkic**|**Inherit**|
||||**1**|**982**|||
|**Household characteristics**|||||||
|Household size|6.97|6.56**|6.95|7.32**|6.95|7.00|
|No. of people between 14 & 60|4.15|3.84***|4.15|4.36***|4.15|4.06|
|No. of unmarried sons (5-25 years)|0.80|0.88**|0.73|1.08***|0.80|1.42***|
|No. of unmarried daughters (5-25 years)|0.56|0.68***|0.51|0.78***|0.57|0.86***|
|<br>SC/ST share|17.53|9.18***|19.25|13.41***|17.38|12.24|
|**Assets, income and consumption**|||||||
|Per capita land endowment of the dynasty (ha)|1.47|2.00***|1.45|1.30*|1.36|3.98***|
|Share of households from landless dynasty|20.99|2.41***|24.18|14.87***|23.31|5.43***|
|Value of all assets|15,906|16,408|15,866|15,820|15,196|23,979***|
|Per capita income (Rs.)|1,514|1,607|1,492|1,566|1,496|1,777*|
|Per capita consumption exp. (Rs.)|1,275|1,376|1,255|1,318*|1,262|1,370|
|**Income Sources**|||||||
|Agricultural production|59.50|63.67 **|59.48|57.31 *|57.87|74.74 ***|
|Salary and self-employment|19.59|18.55|19.20|21.99 *|20.37|13.49 ***|
|Wage income|17.90|14.39 **|18.82|15.44 **|18.72|9.00 ***|
|No. of observations(dynastyhouseholds)|3816|329|2885|602|3431|118|
||||**1**|**999**|||
|**Household characteristics**|||||||
|Household size|6.01|5.57***|5.99|6.34***|6.13|5.44***|
|No. of people between 14 & 60|3.74|3.53***|3.69|4.08***|3.85|3.27***|
|**Assets, income and consumption**|||||||
|Per capita land endowment of household (ha)|0.77|0.74|0.71|1.12***|0.72|0.97***|
|Share of households landless|25.29|29.41|29.04|2.07***|27.98|1.03***|
|Value of all assets|61,367|57,644|56,798|86,748***|60,418|60,189|
|Per capita income|2,707|2,438|2,470|4,063***|2,634|3,156**|
|Per capita consumption exp. (Rs.)|1,640|1,724**|1,579|1,908***|1,629|1,653|
|**Income sources:**|||||||
|Agricultural production|51.40|45.73 *|49.92|61.78 ***|48.87|66.44 ***|
|Salary and self-employment|18.78|23.28 **|18.12|19.80|19.55|15.63 **|
|Wage income|26.76|26.21 *|29.18|14.73 ***|28.68|16.38 ***|
|No. of observations(includingsplits)|5932|459|4581|892|5059|593|

<u>a The 1982 figures for this item refers to those at the time when the current household head became head.</u> b All values are in 1982 Rs with 1999 values deflated by state level deflators.

*, **, *** significantly different from the sample mean at 10%, 5% and 1% respectively. _Source:_ Own computation from NCAER ARIS/REDS survey data.

21
# **Figure S5: Empirical Cumulative Distribution Functions (CDF)**

<!-- Start of picture text --> Random Forest Logistic, stepwise BIC<br>Credit<br>Bureau<br>Repaid ECDF<br>Not Repaid ECDF<br>Repaid ECDF<br>Not Repaid ECDF<br>0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8<br>Score Quantiles Score Quantiles<br>CDR<br>Repaid ECDF Repaid ECDF<br>Not Repaid ECDF Not Repaid ECDF<br>0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0<br>Score Quantiles Score Quantiles<br>1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>F(x) F(x)<br>0.4 0.4<br>0.2 0.2<br>0.0 0.0<br>1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>F(x) F(x)<br>0.4 0.4<br>0.2 0.2<br>0.0 0.0<br><!-- End of picture text --> <mark>Random Forest Weekly Ensemble OLS FE, stepwise BIC CDR</mark> <!-- Start of picture text --> Repaid ECDF<br>Not Repaid ECDF<br>Repaid ECDF<br>Not Repaid ECDF<br>0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0<br>Score Quantiles Score Quantiles<br>1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>F(x) F(x)<br>0.4 0.4<br>0.2 0.2<br>0.0 0.0<br><!-- End of picture text --> _<mark>Note:</mark>_ <mark>C</mark> DR represents Call Detail Record, and BIC Bayesian Information Criterion <mark>. Cells computed based on out of sample predictions resulting from 5 fold cross validation. X axis represents quantile in the distribution of predicted repayment scores, and Y axis represents the cumulative distribution function. Source: authors’ analysis from telecom data.</mark> 6
Table 2.3: Poor and Nonpoor Household Size and Average Members below Age 15 <u>Selected Provinces in Dispersed Rural Areas of Argentina, 2003</u>

||AverageHo|usehold Si|ze|POO|Ave<br>R|rage#of House|holdMem|bers<15||
|---|---|---|---|---|---|---|---|---|---|
|Mendoza|Santiago del<br>Estero|Chaco|Santa<br>Fe|Total|Mendoza|Santiago del<br>Estero|Chaco|Santa<br>Fe|Total|
|5.8|6.5|5.7|5.5|5.8|1.8|2.3|2.0|2.0|2.1|
|(2.1)|(2.4)|(2.2)|(2.0)|(2.3)|(1.7)|(1.8)|(1.7)|(1.6)|(1.7)|
|||||NONP|OOR|||||
|3.6|3.1|3.4|3.9|3.6|1.1|.84|1.0|1.1|1.1|
|(1.4)|(2.1)|(1.9)|(1.7)|(1.8)|(1.2)|(1.3)|(1.6)|(1.3)|(1.4)|
||||T|OTALS|AMPLE|||||
|4.6|5.4|4.4|4.2|4.6|1.6|1.9|1.4|1.3|1.5|
|(2.0)|(2.8)|(2.3)|(1.9)|(2.3)|(1.5)|(1.7)|(1.7)|(1.4)|(1.6)|

Note: Standard deviations in parenthesis. _Source_ : Own calculation based on RHS 2003.

Table 2.4: Dependency Ratio in Dispersed Rural Areas in Argentina, 2003

||Total S<br>Mean|ample<br>Std.<br>Dev.|P<br>Hous<br>Mean|oor<br>eholds<br>Std.Dev.|Non<br>Hous<br>Mean|poor<br>eholds<br>Std.<br>Dev.|
|---|---|---|---|---|---|---|
|Dependency|2.4|2.1|2.9|2.4|1.9|1.7|
|Household size|4.7|2.3|5.8|2.3|3.6|1.8|
|# of household members with a job|1.7|1.1|2.0|1.3|1.5|0.8|
|#of householdmembers without a job|3.1|2.2|3.9|2.2|2.3|1.7|

Note: Dependency rate is defined as the total number of household members without a job relative to the total number of household members with a job. Source: Own calculation based on RHS 2003.

Demographic trends have lowered the dependency ratio and may lead to a reduction in the poverty headcount. This trend is likely to deepen further in the future as Argentina’s poorer regions, such as the Northeast and Northwest, experience lower fertility rates. Unfortunately, disaggregated urban-rural fertility data are not available in Argentina.

The typical poor person lives in a larger household with more children than the nonpoor. In Argentina, poor households in dispersed rural areas had an average of 5.8 individuals in 2003 (Table 2.3). Poor households had 2.2 more individuals than nonpoor households. Moreover, the average number of household members below age 15 is also higher in poor households than in nonpoor. Poor households have an average of 2.1 children below age 15, nearly the double the average of the nonpoor. The dependency ratio is also much higher in poor households (Table 2.4). Each worker in a poor household supports 2.9 family members, compared to the nonpoor worker who supports 1.9 family members.

Fecundity—measured as the number of children per mother—dropped from 2.8 in 1991 to 2.4 in 2001 (University of La Plata 2004). Women’s increased participation in the 10
**Table 1: Individual monthly earnings in PPP 2019 U.S. dollars as a function of business size and other characteristics. Latin America (11 countries).**

||1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|_Business size_|||||||
|1 to 4|72.28|114.05|172.53|175.84|172.86|161.28|
||(0.97)|(0.97)|(0.91)|(1.05)|(0.99)|(1.02)|
|5 to 10|343.96|376.38|287.81|321.42|277.02|260.83|
||(1.35)|(1.35)|(1.26)|(1.34)|(1.27)|(1.30)|
|11 to 50|505.85|538.09|381.18|454.91|363.51|342.85|
||(1.14)|(1.15)|(1.08)|(1.15)|(1.09)|(1.16)|
|51 or more|690.66|717.52|500.58|601.98|470.45|452.09|
||(1.05)|(1.06)|(1.01)|(1.09)|(1.05)|(1.18)|
|Years of schooling|||-118.93||-117.42|-112.83|
||||(0.30)||(0.31)|(0.31)|
|Years of schooling<sup>2</sup>|||11.92||11.65|11.21|
||||(0.01)||(0.02)|(0.02)|
|Sex (woman=1)||-248.61|-349.21|-298.08|-345.80|-342.27|
|||(0.70)|(0.66)|(0.77)|(0.73)|(0.76)|
|Age||43.85|47.79|43.77|46.70|46.16|
|||(0.15)|(0.14)|(0.14)|(0.14)|(0.13)|
|Age<sup>2</sup>||-0.40|-0.40|-0.39|-0.38|-0.38|
|||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Constant|612.58|-361.61|-644.90|-590.70|-644.83|-116.32|
||(0.63)|(3.22)|(3.36)|(3.76)|(3.81)|(16.83)|
|R<sup>2</sup>|0.04|0.06|0.16|0.09|0.17|0.19|
|Obs with factor weights|168,830,240|168,830,240|168,830,240|168,830,240|168,830,240|168,830,240|
|Obs in dataset|758,388|758,388|758,388|758,388|758,388|758,388|
|Sector FE|NO|NO|NO|YES|YES|YES*|

**Note** : The table presents coefficients from regressions of individuals’ earnings on business size categories and other characteristics. Regressions are estimated separately for each country. Reported coefficients correspond to a weighted average of individual country coefficients, where the weight for each country is the total number of country observations. Standard errors (in parentheses) are the square root from the weighted average of the squares of the standard errors. Regressions exclude workers still enrolled in education. Columns 4 and 5 include one-digit sector fixed effects for all countries. Column 6 includes sector-level fixed effects at the most disaggregate level available for each country. **Sources** : Authors’ calculations done using National Household Surveys for 2019 (2017 for Chile and 2018 for Mexico).

To quantify the importance of workforce allocation across different business sizes in overall earnings inequality, we decompose inequality into its extensive (i.e., allocation) and intensive margins. We do so comparatively for LATAM and for one of our reference economies, the US. The extensive margin captures the role played by the differential allocation of workers across business size categories, while the intensive margin captures the role of earnings differentials between those categories, proxying for productivity differentials. In particular, we decompose the gap between the overall median of the earnings distribution and the average earnings of each earnings decile into within -and between- size category components. We index these categories by _j_ , which indicates 15
Sicular and Zhao (2002) explore the question of rural-urban inequality in greater detail for nine different provinces using the China Health and Nutrition Survey (CHNS). The authors conclude that the apparent labor market distortion is about 42% of the rural-urban labor income differential and 48% of the hourly earnings differential. 4 When applied to the average wage differential, this amounts to an _ad valorem_ rate of apparent transactions “tax” on rural wages of 81%.5 We model these transactions costs as real costs that are assumed by the temporary migrants. Of course these migrants are heterogeneous and the extent of the burden varies widely. Those individuals who are single, and live close to the urban area in which they are working are likely to experience minor inconvenience as a result of this temporary migration. We expect them to be the first to migrate ( _ceteris paribus)_ in response to higher urban wages. On the other hand, some migrants have large families and come from a great distance. Their urban living conditions are often very poor and it is not uncommon for them to be robbed on the train when they are returning home after their work. For such individuals, the decision to migrate temporarily is likely to be a marginal one – and one which they may not choose to repeat. With this heterogeneous population in mind, we postulate a transactions cost function that is increasing in the proportion of the rural population engaged in temporary work. This transactions cost function has a simple, constant elasticity functional form, which begins at the origin and reaches the observed wage gap (adjusted for transport and living costs) at the current level of

> 3 In this model we assume that the elasticity of land with respect to on-farm labor is unitary

> 4 There are likely other, unobserved factors inducing this rural-urban wage differential, in which case estimation of the labor market distortion via subtraction of known factors is biased in the direction of overstating the _hukou_ -related distortion. Therefore, it is useful to also estimate the direct impact of household registration status on the observed wage difference among households. Shi (2002) takes this approach to the problem, using the same CHNS data set. He finds that only 28% of the ruralurban wage difference can be explained directly via the coefficient on the _hukou_ registration variable. This is quite a bit less than the 48% left unexplained via the subtraction approach of Shi, Sicular and Zhao (2002).

> 5 See Hertel and Zhai (2004) for a detailed description of how this _ad valorem_ distortion is obtained.

6
many in the years following 2015 and its effects on different proxies of social cohesion such as trust, perceived fairness, and attitudes towards immigrants. Similarly, they find no evidence of any effect, neither negative nor positive. In contrast, they identify an increased incidence of anti-immigrant violence in the short-term which was larger in areas with higher unemployment and greater support for right-wing parties. Such null effects of refugee migration on native attitudes in host communities are also identified by Zhou et al. (2021) for the case of Sudanese refugee immigration to Uganda.

# **4 Research Design and Data**

## **4.1 Data**

Our analysis relies on data from the following sources: _Encuesta Dirigida a la Población Venezolana que Reside en El País (ENPOVE)_ is a specialized survey of Venezuelans living in Peru conducted by the National Institute of Statistics (INEI) in December 2018. The sample covers five main urban areas in the country where Venezuelan immigrants were most likely to be present. The survey collects data on the immigrant’s origin, migration date, and details on their current employment. Importantly, a full module asks about the immigrant’s experiences with locals, which includes questions about discrimination and hostile attitudes towards them. The respondent’s current location is identified down to the _centro poblado_ level, which roughly corresponds to an urban neighborhood or a rural town.

_Encuesta Nacional de Hogares (ENAHO)_ is the Peruvian version of the Living Standards Measurement Survey, e.g. a nationally representative household survey collected monthly on a continuous basis. For our analysis, we use data from January 2007 to December 2020. The survey covers a wide variety of topics, including basic demographics, educational background, labor market conditions, crime victimization, and a module on respondent’s perceptions about the main problems in the country and trust on different local and national level institutions. Observations are also spatially identified at the municipality level, but here we focus on variation in the Venezuelan share of the population at the province level, of which there are 196, as these are best representative of local labor markets.

_Latin American Public Opinion Project (LAPOP)_ is a opinion survey conducted bi-annually in all countries in Latin America and designed to be representative of urban populations. This was fielded in Peru in 2010, 2012, 2014, 2017 and 2019 and consists of about 2,000 observations from mostly urban areas. The survey questions are centered around politics, 8
Policy Research Working Paper 10722

# **Abstract**

This paper presents distributional national accounts for Honduras over 2003–2019, using survey microdata, administrative tax records, and national account aggregates. It assembles comprehensive data on formal income for high-income individuals, including information on corporate shareholders, which allows corporate profits to be assigned to their owners. The estimates suggest a high and persistent inequality in the country: the top 1 percent highest earners received approximately 30 percent of the total income over the period, placing Honduras among the most unequal countries in the world. Undistributed corporate profits are the overwhelming income source at the very top of the distribution, highlighting its importance in the measurement of income inequality. Finally, using a panel of tax records, the paper also documents that not only is inequality persistent, but the same individuals are often observed at the top, suggesting that the observed inequality has deep roots.

This paper is a product of the Development Impact Group, Development Economics. 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/prwp. The authors may be contacted at tscot@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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