rafmacalaba/datause-encoder-data huggingface dataset:
MALDIVES 2. Projections - NRR-1 **by** 2035

|**Tour**|**Pq.AIt1ou**|DW.<br>**ratio**|**Period**|**crauth**<br>rate|t|CD|**TFr**|all<br>**kim**|**Famet**|In|**a1QOO**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990<br>|<br>217<br>|88.6<br>|1990-94<br>|3.31<br>|41.9<br>|8.8<br>|5.97<br>|59.9<br>|63.1<br>|65<br>|89<br>|
|199f<br>|256<br> <br>|93.1<br>|1995-99<br>|3.10<br>|38.7<br>|7.7<br>|5.43<br>|61.8<br>|65.3<br>|54<br>|72<br>|
|**2000**<br>|<br>299<br>|94.1<br>|2000-04<br>|2.88<br>|35.6<br>|6.7<br>|4.90<br>|63.8<br>|67.5<br>|45<br>|57<br>|
|2005<br>|346<br>|66.4<br>|2005-09<br>|2.67<br>|32.9<br>|6.3<br>|4.36<br>|65.2<br>|69.1<br>|39<br>|50<br>|
|2010<br>|39f<br>|81.5<br>|2010-14<br>|2.46<br>|30.3<br>|5.7<br>|3.83<br>|66.6<br>|70.7<br>|34<br>|43<br>|
|2015<br>2020|447<br>499<br>|73.2<br>65.6<br>|2014-19<br>2020-24<br>|2.21<br>1.98<br>|27.3<br>24.5<br>|5.2<br>4.7<br>|3.29<br>2.88<br>|68.1<br>69.7<br>|72.4<br>74.1<br>|28<br>23<br>|35<br>28<br>|
|2025<br>|551<br>|60.0<br>|2025-49<br>|1.37<br>|18.2<br>|4.7<br>|2.19<br>|73.3<br>|78.2<br>|12<br>|i5<br>|
|2050<br>|776<br>|46.3<br>|2050-74<br>|0.72<br>|14.1<br>|6.9<br>|2.07<br>|77.2<br>|82.8<br>|4<br>|6<br>|
|2075<br>|930<br>|58.0<br>|2075-99<br>|0.26<br>|12.5<br>|1.0<br>|2.06<br>|79.6<br>|85.6<br>|3<br>|4<br>|
|2100<br>|992<br>|68.6<br>|2100-24<br>|0.10<br>|12.0<br>|11.0<br>|2.06<br>|81.3<br>|87.7<br>|2<br>|3<br>|
|2125<br>21M0|1016<br> <br>1027|71.7<br>73.2|2125-49|0.04|11.8|11.4|2.06|82.0|88.6|2|3|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75 +<br>70.74<br>e5-69<br>80-64<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br><!-- End of picture text --> <!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. Comparison of Country with<sup>Region and Income Group, 1985-90</sup>

<!-- Start of picture text --> TFR<br><!-- End of picture text --> Cowmtry Region Income group <!-- Start of picture text --> Growth rate<br><!-- End of picture text --> <!-- Start of picture text --> e0<br>.§ Z _~~~~~~~~~~~7<br>Country Region Income group Coiuntry Region Incom group<br><!-- End of picture text -->
# **2.3 Application Scoring**

Applications were scored by the Enterprise Development Center (EDC) of the Pan-African University, a sister institution of the Lagos Business School. Names and other identifying information were removed before scoring to increase the impartiality of this process.<sup>12</sup> Marks were given for the quality and viability of the business idea, the amount of job creation likely, the abilities and market understanding of the applicant, and the passion and commitment displayed in the application, and the likelihood of the proposed business succeeding. This first stage marking was a rapid assessment (taking approximately 10 minutes per plan) given the vast number of applications and the short window for assessment before training was to start. There was a conscious decision to favor existing businesses over new businesses throughout the competition given the assumption that they had at least made the first step of establishing a business. An algorithm was then used to select 6,000 candidates for the business plan training on the basis of location, type of business (new or existing), and score.

# **2.4 Business Plan Training**

Business plan training took place between December 6 and December 20, 2011. Each of the six regions had a local training location. Of the 6,000 individuals selected to attend training, 4,873 attended (81%). The rapid roll-out of the program and short amount of time between notification of selection into the 4-day training session and it taking place is a likely reason for non-attendance. Biometric data (iris scans and fingerprints) were collected on all attendees to enable accurate identification of individuals. Candidates who did not attend training were not eligible to submit a business plan. Appendix 1 provides detail on the content of the course.

# **2.5 Business Plan Submission and Business Plan Scoring**

Applicants who had attended the training had until January 22, 2012 to submit the business plan. The business plan required much more detail than the first round concept note. It collected information on the business profile including the product, its customer base, pricing, the experience and qualifications of the owner, a detailed description of physical capital, premises, form of business organization, cash flow and projected income statements for the first year,

> 12 There were 25 scorers, led by the Director of EDC, with the scorers consisting of a combination of EDC faculty and successful entrepreneurs within the EDC network.

8
<!-- Start of picture text --> Sons' Primary Completion Estimates by Birth Year<br><!-- End of picture text --> <!-- Start of picture text --> Intercept Linear Coefficient<br>4 04<br>02<br>2<br>><br>? ><br>o> Tr CoC or Oe st ee —. —_— <= ss oes oe<br>><br>ot Lt —*%e ~~<br>-.02<br>><br>><br>-.04<br>-2<br>59 60 61 62 68 #69 #70 71 72 59 60 61 62 68 69 70 71. 72<br>Birth Years<br><!-- End of picture text --> Omitted Birth Year 1957-58 <!-- Start of picture text --> © Comparison Cohort @ Fullly Exposed Cohort<br><!-- End of picture text -->
29

# **Table 4.  Multinomial Logit Model for Ghana**

|Variable|Receive inter|nal remittan|ces (from Ghana)|Receive in<br>(from Afr|ternatio<br>icanoro|nal remittances<br>thercountries)|
|---|---|---|---|---|---|---|
|**Human Capital**<br>Number of members over<br>age 15 with primary school<br>education|**Coefficient**<br>0.567***|**Z**<br>2.59|**Marginal**<br>**effects**<br>1.16E-01|**Coefficient**<br>0.096|**Z**<br>0.18|**Marginal**<br>**effects**<br>-4.07E-06|
|Number of members over<br>age 15 with junior secondary<br>school education|-0.111|-0.86|-2.27E-02|0.008|0.04|2.45E-06|
|Number of members over<br>age 15 with secondary<br>education|0.173|0.65|3.55E-02|0.865**|2.24|4.94E-05|
|Number of members over<br>age 15 with university<br>education|-0.730|-0.71|-1.50E-01|2.597**|2.35|1.70E-04|
|**Household Characteristics**<br>Age of household head|0.004|0.03|-5.06E-02|0.381|1.01|2.30E-05|
|Square of age of household<br>head|0.0002|0.19|5.00E-04|-0.004|-1.04|-2.43E-07|
|Household size|-0.026|-1.10|-5.40E-03|-0.034|-0.64|-1.61E-06|
|Number of males over age<br>15|-0.363***|-6.24|-7.44E-02|-0.342***|-2.90|-1.44E-05|
|Number of children under<br>age 5|0.148|0.33|3.04E-02|-1.047|-0.88|-6.61E-05|
|**Migration Networks**|||||||
|Number of female-headed<br>households receiving internal<br>remittances (from Ghana) in<br>groupin 1998/99|1.581|1.53|3.24E-01|1.016|0.49|3.40E-05|
|Number of female-headed<br>households receiving<br>international remittances<br>(from African or other<br>countries) in group in<br>1998/99|0.539|0.29|1.10E-01|-4.942|-0.57|-3.09E-04|
|Square of internal migrants<br>ingroupin 1998/99|-27.855|-0.42|-5.71E+00|130.126|0.74|8.38E-03|
|**Ethno-religious group**|||||||
|**characteristic**|||||||
|Square of income of ethno-<br>religious group|3.21E-15|0.34|6.57E-10|5.13E-<br>14***|2.57|3.06E-12|
|**Instruments for second stage**<br>International remittances<br>(from African or other<br>countries) as percent of<br>householdincome|27.596**|2.29|5.66E+00|-6.877|-0.32|-8.99E-04|
|International migrants (to<br>Africa or other countries) as<br>percent ofpopulation|-1.846|-0.73|-3.78E-01|-1.817|-0.22|-7.80E-05|
|Constant|-3.807|-1.09||-10.049|-1.13||
|Log likelihood|-2789.04||||||
|Likelihood ratio|676.06||||||
|Pseudo R^2|10.81||||||
|N|3884||||||
Policy Research Working Paper 5501

# **Abstract**

Consumption expenditure has long been the preferred measure of household living standards. However, accurate measurement is a challenge and household expenditure surveys vary widely across many dimensions, including the level of reporting, the length of the reference period, and the degree of commodity detail. These variations occur both across countries and also over time within countries. There is little current understanding of the implications of such changes for spatially and temporally consistent measurement of household consumption and poverty. A field experiment in Tanzania tests eight alternative methods to measure household consumption on a sample of 4,000 households. There are significant differences between consumption reported by the benchmark personal diary and other diary and recall formats. Under-reporting is particularly relevant in illiterate households and for urban respondents completing household diaries; recall modules measure lower consumption than a personal diary, with larger gaps among poorer households and households with more adult members. Variations in reporting accuracy by household characteristics are also discussed and differences in measured poverty as a result of survey design are explored. The study concludes with recommendations for methods of survey based consumption measurement in low-income countries.

This paper is a product of the Poverty and Inequality Team, Development Research 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 kbeegle@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 3: All/Awareness/Management - Self-reported and behavioral measures

||Self-reported|All<br>|Behavior|al|Self-reporte|Awaren<br>d|ess<br>Behavior|al|Self-reported|Manage<br>|ment<br>Behaviora|l|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)<br>(2)|(3)|(4)<br>(5)|(6)|(7)<br>(8)|(9)|(10)<br>(11)|(12)|(13)<br>(14)|(15)|(16)<br>(17)|(18)|
|Women|-0.20***<br>-0.17***|0.05|-0.04<br>-0.05**|-0.02|-0.16***<br>-0.14***|-0.05|-0.03<br>-0.02|-0.10|-0.20***<br>-0.17***|0.09|-0.03<br>-0.05*|0.03|
||(0.03)<br>(0.03)|(0.24)|(0.02)<br>(0.02)|(0.19)|(0.03)<br>(0.03)|(0.24)|(0.02)<br>(0.02)|(0.18)|(0.03)<br>(0.03)|(0.24)|(0.02)<br>(0.02)|(0.21)|
|Years of education|0.03***|0.03***|0.02***|0.02***|0.03***|0.03***|0.01**|0.01|0.03***|0.03***|0.02***|0.02***|
||(0.00)|(0.01)|(0.00)|(0.01)|(0.00)|(0.01)|(0.00)|(0.01)|(0.00)|(0.01)|(0.00)|(0.01)|
|Years of education X Women||0.01||0.00||0.01||0.01||0.01||0.00|
|||(0.01)||(0.01)||(0.01)||(0.01)||(0.01)||(0.01)|
|Cognitive Ability|0.40***|0.29***|0.40***|0.35***|0.34***|0.24***|0.18***|0.16**|0.40***|0.29***|0.41***|0.35***|
||(0.07)|(0.09)|(0.06)|(0.08)|(0.07)|(0.09)|(0.06)|(0.07)|(0.07)|(0.09)|(0.06)|(0.08)|
|Cognitive Ability X Women||0.22**||0.11||0.19*||0.03||0.22**||0.12|
|||(0.11)||(0.10)||(0.11)||(0.09)||(0.11)||(0.10)|
|Social desirability index|0.49***|0.57***|0.19***|0.20***|0.45***|0.52***|0.02|0.01|0.47***|0.55***|0.23***|0.25***|
||(0.04)|(0.05)|(0.03)|(0.04)|(0.04)|(0.05)|(0.03)|(0.04)|(0.04)|(0.05)|(0.03)|(0.04)|
|Social desirability X Women||-0.16**||-0.02||-0.13**||0.02||-0.16**||-0.04|
|||(0.07)||(0.05)||(0.07)||(0.05)||(0.07)||(0.05)|
|Equitable beliefs regarding|-0.03*|-0.05**|0.04***|0.05***|-0.02|-0.05**|0.00|0.02|-0.03*|-0.04**|0.05***|0.06***|
|PSDM abilities|(0.01)|(0.02)|(0.01)|(0.02)|(0.01)|(0.02)|(0.01)|(0.01)|(0.01)|(0.02)|(0.01)|(0.02)|
|Equitable beliefs X Women||0.04*||-0.03||0.07**||-0.03||0.03||-0.02|
|||(0.03)||(0.02)||(0.03)||(0.02)||(0.03)||(0.02)|
|p(Edu. + Edu. X Women = 0)||0.00||0.00||0.00||0.02||0.00||0.00|
|p(CA + CA X Women = 0)||0.00||0.00||0.00||0.01||0.00||0.00|
|p(SD + SD X Women = 0)||0.00||0.00||0.00||0.37||0.00||0.00|
|p(Equit. + Equit. X Women = 0)||0.84||0.10||0.45||0.44||0.58||0.02|
|p(Women + Edu. X Women = 0)||0.65||0.98||0.99||0.77||0.55||0.85|
|p(Women + CA X Women = 0)||0.36||0.79||0.70||0.62||0.29||0.57|
|p(Women + SD X Women = 0)||0.00||0.46||0.00||0.82||0.00||0.42|
|p(Women + Equit. X Women = 0)||0.46||0.61||0.59||0.32||0.44||0.93|
|Observations|4459<br>4459|4459|4459<br>4459|4459|4459<br>4459|4459|4459<br>4459|4459|4459<br>4459|4459|4459<br>4459|4459|
|R-squared|0.12<br>0.20|0.20|0.40<br>0.42|0.42|0.11<br>0.17|0.17|0.46<br>0.46|0.46|0.12<br>0.19|0.19|0.35<br>0.38|0.38|

_Notes:_ Results presented are OLS estimates that include controls for enumerator, age, father’s education, mother’s education and city. Outcome measures are standardized naive scores. Robust standard errors in parentheses. PSDM = Problem-solving and decision-making. Edu. = Years of education. CA = Cognitive Ability. SD = Social Desirability. Equit. = Equitable beliefs. *** p _≤_ 0.01, ** p _≤_ 0.05, * p _≤_ 0.1.
sources, such as year, sample size, stratification, etc. For more detail on the data, please refer to the studies listed in the “References” row in Table 1.

# **III. Transfer Schemes and Simulation Procedures**

# _The transfer schemes_

As described in Section I, we compare a variety of targeting schemes against a benchmark scheme which assumes absolutely no knowledge of the geographic distribution of poverty. One of our main objectives is to see whether, and to what extent the availability of poverty estimates for different geographic locations can help to improve the poverty impact of distributing a given budget. In our baseline, benchmark, case we postulate that the government has a budget, _S_ , available for distribution and wishes to transfer this budget in such a way as to reduce poverty. However, because the government is assumed to have no knowledge of who the poor are or where they are located, it is unable to distribute its budget in any manner other than a lump-sum transfer to the entire population of size _N_ . In our benchmark case, thus, we calculate the impact of transferring _S/N_ to the entire population.<sup>10</sup> Optimal use of geographically disaggregated information on poverty has been investigated by Kanbur (1987), Ravallion and Chao (1988), Glewwe (1991), Ravallion (1993), and Baker & Grosh (1994). Kanbur (1987) formalized the theoretical problem of policy design under imperfect information, while Ravallion & Chao (1998) demonstrated how this general targeting problem can be solved in a computationally feasible way.<sup>11</sup> For our purposes, the

> 10 It could be argued that our benchmark scenario is not terribly realistic.  Perhaps more likely would be a situation where absence of detailed information on the extent and distribution of poverty, and absent any specific effort to target the poor, would result in a default situation of resources being appropriated by the non-poor (see the discussion in Campante and Ferreira, 2003).  To the extent that this is true our estimates of the gains from targeting, once we assume some information on the distribution of poverty, might be seen as conservative estimates of the true benefits.

> 11 As we use predicted expenditures from census data unlike Ravallion and Chao (1988), who use observed income data from household surveys, we utilize a different algorithm to solve the optimization problem.  Applying their algorithm to our setting would yield the same results.

13
_Table 6. Effect of different treatment arms on mathematics scores_

||Depen<br>|dent variable is t<br>|he score in mathem<br>|atics. OLS estima<br>|tion<br>||
|---|---|---|---|---|---|---|
||(1)|(2)<br>All children|(3)<br>Leaving out|(4)|(5)<br>Children aged|(6)<br>8 to 12<br>|
||All sample|All Sample|untreated HH in<br>treated villages|All sample|All Sample|Leaving out untreated<br>HH in treated villages|
|Endline * 0 GNF|-0.425|-0.163||-0.342|-0.0744||
||(0.366)|(0.375)||(0.444)|(0.449)||
|Endline * 70k GNF|-0.0996|0.173|0.332|-0.167|0.128|0.205|
||(0.340)|(0.358)|(0.189)*|(0.380)|(0.400)|(0.298)|
|Endline * 140k GNF|-0.0158|0.252|0.421|0.0244|0.317|0.408|
||(0.355)|(0.369)|(0.234)*|(0.400)|(0.412)|(0.344)|
|Endline|1.653|1.652|1.496|1.692|1.690|1.606|
||(0.256)***|(0.256)***|(0.264)***|(0.272)***|(0.273)***|(0.334)***|
|Girl|-0.572|-0.469|-0.490|-0.602|-0.482|-0.510|
||(0.0671)***|(0.0740)***|(0.103)***|(0.0892)***|(0.101)***|(0.140)***|
|Treated*Girl||-0.549|-0.527||-0.592|-0.562|
|||(0.150)***|(0.163)***||(0.199)***|(0.217)**|
|Constant|-3.990|-4.036|-5.010|-5.253|-5.281|-6.579|
||(0.547)***|(0.547)***|(0.488)***|(2.356)**|(2.344)**|(3.161)**|
|Observations|16,982|16,982|8,817|8,025|8,025|4,213|
|R-squared|0.300|0.301|0.310|0.183|0.183|0.190|
|Individual controls|yes|yes|yes|yes|yes|yes|
|HH controls|yes|yes|yes|yes|yes|yes|
|Fixed-Effects|Villages|Villages|Villages|Villages|Villages|Villages|
|Clustered-SE<br>Robust standard error|Villages<br>s in parentheses.|Villages<br>*** p<0.01, ** p|Villages<br><0.05, * p<0.1|Villages|Villages|Villages|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 <mark>Individual controls include: age, age squared, ethnicity dummies (sousou, malinke, peul, other). Household controls: household size Standard errors are clustered at the village level, for 148 villages.</mark> 30
Policy Research Working Paper 10755

# **Abstract**

This paper conducts a correspondence study to assess demand for soft skills in the context of hiring discrimination in Malaysia. No evidence of gender-based discrimination is found, including in science, technology, engineering, and mathematics occupations. However, in line with previous studies in the same context, there is evidence of ethnic discrimination. The paper then test the relevance of two soft skills: leadership and teamwork. These tests find some evidence that the labor market rewards simple signals of teamwork for the average applicant. Teamwork also plays an important role in the context of labor market discrimination, reducing the discrimination gap by 40 percent. In contrast, signaling leadership skills has no effect. Last, the paper considers the role of labor market competition. Companies facing competition in the labor market, measured by the number of competitors advertising similar positions, are 56 to 66 percent less likely to discriminate. On the supply side, discrimination increases with the relative quality of the pool of applicants. The results provide novel evidence that soft skills and labor market competition both play important roles in understanding hiring discrimination. This underlines potential pathways to overcome labor market discrimination and improve job matching.

This paper is a product of 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/prwp. The authors may be contacted at yghorpade@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
Due to the high risks of a credit boom, several authors set out to examine whether the credit expansion experienced in certain countries entailed excessive credit growth or such growth in equilibrium. In relation to Hungary, Kiss et al. (2006) provided guidance as regards the soundness of the credit growth of the period before 2005. It is important for the purposes of our analysis that the study (for methodology see Box 1) reviews the corporate sector and the household sector separately. The period stretching to 2004 suggests that the ratio of corporate credit to gross domestic product (GDP) did not exceed the level justified by macroeconomic variables. We supplemented the chart presented in the study of Kiss et al. (2006) with the median of country constants. Considering it as an equilibrium path, the chart reveals that the corporate credit/GDP ratio was below the equilibrium level. At the same time, its dynamics deviated from that of the equilibrium path; its level gradually approached the equilibrium level (Chart 3). Nonetheless, based on the model, credit expansion cannot be considered excessive in the period under review.<sup>4</sup>

**Chart 3. Estimation of equilibrium credit/GDP of nonfinancial corporations**

<!-- Start of picture text --> Per cent Per cent<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Estimated equilibriums with the different country constants<br>Equilibrium level based on median of country constants<br>Loan to GDP actual level<br><!-- End of picture text --> Source: Kiss et al. (2006); authors‘ calculations. Note: We have plotted out the out-of-sample equilibrium levels with different country constants and also calculated a level based on the median of country constants.

# **_Retail segment_**

During the transformation of the banking system in the 1990s, lending to households in Hungary was marginal. For the reasons described above, foreign-owned banks were not active in this market segment and household customers were mainly served by a handful of banks that remained under domestic ownership. Consequently, the net flow of credit to retail customers remained at very low levels until the turn of the millennium. The new millennium represented a turning point in the attitude of foreign banks present in Hungary. Fierce competition began to chip away at the profitability of the corporate segment, while positive growth prospects and households‘ optimistic expectations generated burgeoning demand for banking products from the household segment. The newly established institutional conditions for mortgage lending, as well

> 4 The study does not take account direct borrowings from abroad, which distort the results somewhat. This notwithstanding, we believe that the conclusion holds true.

5