rafmacalaba/datause-encoder-data huggingface dataset:
- 4 -

However, the sponsorship in these cases does not have a profit motive, and therefore neither the MPFs nor the sponsors maintain a sales force designed to attract more members.

**Table 4: Second Pillar Market Structure by Type of Sponsor, 2004**

|**Type of**<br>**Sponsor**||**Assets**<br>**HUF 000**|**Members**<br>**(unit)**|**Contributions**<br>**HUF 000**|
|---|---|---|---|---|
||Min|1,003,353|9,141|347,000|
|**cial**<br>**ps**<br>**)**|Max|224,161,495|681,231|57,267,822|
|**an**<br>**rou**<br>**(10**|Average|76,904,851|214,356|18,605,398|
|**Fin**<br>**G**<br>|Total Financial|769,048,517|2,143,569|186,053,986|
||% of Market|87%|89%|87%|
|**s**|Min|4,410,819|8,195|986,141|
|**yer**<br>|Max|16,081,593|21,622|3,836,397|
|**plo**<br>**(5)**|Average|9,201,794|13,746|2,097,909|
|**Em**|Total Employer|46,008,972|68,733|10,489,545|
||% of Market|5%|3%|5%|
|**t**|Min|1,523,589|2,840|320,274|
|**den**<br>|Max|34,102,743|106,596|8,348,993|
|**pen**<br>**(3)**|Average|22,295,590|63,303|5,593,792|
|**nde**|Total Independent|66,886,770|189,908|16,781,376|
|**I**|% of Market|8%|8%|8%|

Source: HFSA raw data.

13. The MPFs sponsored by financial institutions typically outsource asset management and administration to other specialized companies within the same financial group. The other MPFs usually outsource these services as well, sometimes to the same companies, although there are MPFs sponsored by employers that manage their assets internally. When asset management is outsourced, the asset management company typically charges much lower fees to MPFs outside the financial group. There is a marked segmentation in terms of fees that has not translated into increased switching by members from high fee funds to low fee funds. As noted below, this is due in part to the lack of readily comparable information on returns and fees until recently.

14. Finally, arrangements for revenue collection have remained deficient. Individual MPFs deal with many employers, including thousands of small and medium enterprises. As a result, the industry has been considering the creation of a single company specialized in revenue collection, and owned by all the MPFs.

# **III.   PERFORMANCE OF THE SECOND PILLAR: GROWTH**

15. Membership in the second pillar grew rapidly in the first years of operation, as shown in Figure 1. The initial jump in membership reflected the high rate of voluntary switching of younger workers which had already rights under the old system, especially workers under 40 years of age. In the following years, membership was expected to increase at lower but steady rates, in line with the flow of new entrants to the labor force.
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- Gwilliam, Ken, Vivien Foster, Rodrigo Archondo-Callao, Cecilia Briceño-Garmendia, Alberto Nogales, and Kavita Sethi. 2008. ―The Burden of Maintenance: Roads in Sub-Saharan Africa.‖ AICD Background Paper 14, Africa Region, World Bank, Washington, DC.

- Heinrich C. Bofinger. 2009. ―An Unsteady Course: Growth and Challenges in Africa’s Air Transport Industry.‖ AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

- Kumar, Ajay, and Fanny Barrett. 2008. ―Stuck in Traffic: Urban Transport in Africa.‖ AICD Background Paper 1, Africa Region, World Bank, Washington, DC.

- Ocean Shipping Consultants, Inc. 2009. ―Beyond the Bottlenecks: Ports in Africa.‖ AICD Background Paper 8, Africa Region, World Bank, Washington, DC.

# Water supply and sanitation

Banerjee, Sudeshna, Vivien Foster, Yvonne Ying, Heather Skilling, and Quentin Wodon. 2008.―Cost Recovery, Equity, and Efficiency in Water Tariffs: Evidence from African Utilities.‖ AICD Working Paper 7, World Bank, Washington, DC.

- Banerjee, Sudeshna, Heather Skilling, Vivien Foster, Cecilia Briceño-Garmendia, Elvira Morella, and Tarik Chfadi. 2008. ―Ebbing Water, Surging Deficits: Urban Water Supply in Sub-Saharan Africa.‖ AICD Background Paper 12, Africa Region, World Bank, Washington, DC.

- Gulyani, Sumila, Debabrata Talukdar, and Darby Jack. 2009. ―Poverty, Living Conditions, and Infrastructure Access: A Comparison of Slums in Dakar, Johannesburg, and Nairobi.‖ AICD Working Paper 10, World Bank, Washington, DC.

- Keener, Sarah, Manuel Luengo, and Sudeshna Banerjee. 2009. ―Provision of Water to the Poor in Africa: Experience with Water Standposts and the Informal Water Sector.‖ AICD Working Paper 13, World Bank, Washington, DC.

- Morella, Elvira, Vivien Foster, and Sudeshna Ghosh Banerjee. 2008. ―Climbing the Ladder: The State of Sanitation in Sub-Saharan Africa.‖ AICD Background Paper 13, Africa Region, World Bank, Washington, DC.

# Other

- Deloitte. 2009. ―Etude institutionnelle relative à la gestion des services publics de l'Electricité et de l'Eau au Mali.‖ Ministère de l’Energie des Mines et de l’Eau, Direction Nationale de l’Hydraulique. June.

EDM (Energie du Mali). 2009. ―Plan de Redressement EDM-SA, Period 2009–12.‖ Bamako.

- Ministere de l’Energie, des Mines et de l’Eau, Republique du Mali. 2009.  ―Rapport d’activités de la Direction Nationale de l’hydraulique au titre de l’année 2009.‖ Bamako.

- World Bank. 2006. ―Country Economic Memorandum for the Republic of Mali, Report 36571-ML.‖ Washington, DC. September  29.

52
parts and quality, however, and doubts about the stability of the Korean political and labor systems remain deep. The most dynamic and controversial auto producer in the developing world is China. In 2003 China overtook Germany as the third largest market for and passed France as the fourth largest producer of motor vehicles, largely thanks to joint ventures with virtually all of the world’s leading auto assembly and first-tier parts suppliers. Growth in production has been extraordinary, but many observers worry about weak labor and management skills, particularly in the ubiquitous state-owned enterprises; rigid financing arrangements; and the possibility that domestic demand may not keep up with ballooning aggregate capacity. Indonesia, another poor but populous country, while less burdened with the legacy of state-owned enterprises, until recently has been highly protectionist and politicized. Demand and production have largely recovered from the financial crisis of the late 1990s. Liberalization has opened the way for consolidation and streamlining of parts production under increased Japanese control. Given problems in skill formation and technology support, however, there remain significant questions as to local participation in such an expanded, efficient industry.

We presume neither a convergence in national components production nor a one-sizefits-all approach to supplier development. But we do presume that the growth of components production will reflect national policies, institutions and politics. In the conclusion, we draw on the material presented in this chapter to suggest potential national trajectories and the policy/ institutional requirements for their implementation.

# **Performance**

Comparing auto parts industries is complicated by national differences in definition and coverage, reporting, and currencies, as well as the speed of change in developing countries. In this section we provide an overview and standardized data on auto parts trade; more detailed but sometimes less directly comparable statistics appear in the discussion of individual 6
the target areas – in Mexico.<sup>6</sup> Third, it assesses whether the combination of survey and geospatial data generate estimates that improve on small area estimates generated using a census enumerated five years prior as well as a widely available wealth index created by Meta (Chi et al., 2022).<sup>7</sup> Finally, it compares estimates from three different types of small area estimation methods: a household unit-context model that models household per capita income as a function of sub-area and area-level predictors, a sub-area model that utilizes a nested error model at the level of the sub-area, and a Fay-Herriot area-level model (Fay and Herriot, 1979).

We consider these questions in the context of Mexico. Mexico is a useful case study for this analysis because of the richness of publicly available official data. In the Mexican context, subareas are AGEBs (Area Geoestadística Básica) and areas are municipalities. The official MCSENIGH household survey provides representative statistics at the state level, motivating the use of small area methods to generate municipal estimates. INEGI, the Mexican statistical agency, fielded a large intercensus sample of 5.8 million households in 2015. CONEVAL, the agency that produces poverty statistics in Mexico, used these data to generate official municipal level estimates of monetary poverty, which can be used as a benchmark for evaluation. CONEVAL employed an EBP using a model that included an extensive set of household and municipal characteristics. These type of small area estimates, generated using household-level auxiliary data, are rarely available between census rounds in low- and middle-income countries. They therefore provide a natural benchmark to evaluate whether combining survey and geospatial data produces municipal poverty estimates that are like those produced by a best-practice poverty mapping exercise, based on household data from a large intercensus.

We compare these 2015 benchmark estimates with five sets of municipal poverty estimates. The first is direct sample estimates taken from the 2014 survey. We generate the second, third, and fourth sets of estimates by combining the 2014 sample with geospatial indicators and estimating a household-level “unit-context” model of welfare, a sub-area model of poverty rates, and an arealevel model of poverty rates. The fifth and final set of estimates are the official poverty estimates

> 6 In other words, municipalities are the level at which we wish to estimate poverty. This is below the level of representativeness of the survey.

> 7 https://dataforgood.facebook.com/dfg/tools/relative-wealth-index

5
# : **Deliberative Inequality** A _Text-As-Data_ Study of Tamil Nadu’s Village Assemblies<sup>∗</sup>

Ramya Parthasarathy<sup>†</sup> Vijayendra Rao<sup>‡</sup> Nethra Palaniswamy<sup>§</sup>

**JEL Codes** O12, C49, D02, D70, J16

## **Key Words**

gender, deliberation. village democracy, India, text-as-data, participation

> ∗Tis paper is a product of the World Bank’s Social Observatory. Financial support from the contributions of (1) UK Aid from the UK government, (2) the Australian Department’s of Foreign Affairs and Trade, and (3) the European Commission (EC) through the South Asia Food and Nutrition Security Initiative (SAFANSI), which is administered by the World Bank, is gratefully acknowledged. Te authors are indebted to R.V. Shajeevana, the former Additional Project Director of the Pudhu Vaazhu Project, for her advice and assistance; Kevin Crockford and Samik Sundar Das for their support; as well as Madhulika Khanna, Nishtha Kochhar, Smriti Sakhamuri, G. Manivannan, and GFK-Mode for their help with the fieldwork. Te authors also thank Avidit Acharya, Lisa Blaydes, Nick Eubank, Adriane Fresh, Justin Grimmer, David Laitin, Jeremy Weinstein, and participants of the Indian Political Economy working group in Washington, D.C. for comments and suggestions. Te views expressed here do not necessarily reflect the UK, EC, or Australian government’s official policies or the policies of the World Bank and its Board of Executive Directors.

> †Dept. of Political Science, Stanford University. ramyap1@stanford.edu

> ‡Development Research Group, World Bank. vrao@worldbank.org

> §Poverty Global Practice, World Bank. npalaniswamy@worldbank.org
basic model. We also check to see whether oil resources have any effect in addition to whatever is captured through public and private spending. So we add a dummy variable for oil resources which takes the value 1 if the country in question is in the top 25 countries ranked by level of oil production per capita and 0 otherwise. The MENA and oil dummies are introduced separately as well as in interaction with the public spending variable. The relevant results are reported for the child mortality case in Table 4 and the school enrollment case in Table 5.

**Table 4: Determinants of changes in child mortality: MENA region and oil resource effects**

|Variable|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Ln Initial child mortality|0.308**|0.374***|0.319***|0.379***|
||(0.118)|(0.111)|(0.114)|(0.110)|
|Health spending to GDP ratio [1]|0.114*|0.139**|0.108|0.129*|
||(0.065)|(0.069)|(0.065)|(0.069)|
|GDP PC growth|0.078***|0.072**|0.083***|0.077***|
||(0.024)|(0.027)|(0.024)|(0.027)|
|Undernourishment|-0.010*|-0.013**|-0.010*|-0.013**|
||(5.60e-03)|(5.40e-03)|(5.40e-03)|(5.40e-03)|
|Population density|6.00e-04*|5.00e-04*|6.00e-04*|5.00e-04*|
||(3.00e-04)|(3.00e-04)|(3.00e-04)|(3.00e-04)|
|Control of corruption|-0.063|-0.054|-0.066|-0.043|
||(0.097)|(0.110)|(0.096)|(0.107)|
|MENA dummy [2]|0.370***<br>(0.123)||||
|Oil-rich dummy [3]||0.048<br>(0.188)|||
|Interaction [1] and [2]|||0.074***<br>(0.024)||
|Interaction [1] and [3]||||0.025<br>(0.034)|
|Constant|-0.675|-0.955|-0.702|-0.951|
||(0.719)|(0.691)|(0.709)|(0.685)|
|Observations|79|79|79|79|
|Adjusted R-squared|0.277|0.216|0.279|0.222|

Notes: Regressions are run on global samples including developing and high-income countries. The dependent variables are the absolute log changes in child mortality (reduction) over 1980-2014.

Including a dummy for the MENA region has an effect in the child mortality regression but not in the school enrollment regression. In the child mortality regression, the dummy variable for MENA is positive and significant, both by itself and when interacted with the public spending on health variable. This suggests that the child mortality results for MENA are also affected by some unspecified variables 15
2015 escaped poverty in 2019. About 3 percent of smallholders transitioned from being poor in 2012 and escaped poverty in 2015 and 2019. Nearly one-tenth of smallholder farmers were poor in 2012 and 2019 but not in 2015, and about 4 percent were poor in 2015 but not in 2012 and 2019. These transitions in and out of poverty define chronic and transitory poverty in Figure 3. A household that was poor in all three survey waves is chronically poor, while one that was not poor in all waves was never poor. A household is transient poor if it was poor in one or two survey waves.

Figure 2: Poverty transitions among rural households over three surveys: 2012, 2015, 2019 <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20% 44.6 40.5 55.7 43.8 53.1 39.4 45.2 49.1 41.7 46.1<br>26.6<br>10%<br>0%<br>PPP NNN PPN PNN PNP NPP NPN NNP<br><!-- End of picture text --> Notes: Own calculations from RALS (2012-2019). PPP and NNN signal poor and not poor in all three waves, respectively, whereas alternating P and N letters signals whether a household was poor (P) or not poor (N) in a given survey year. For example, PPN means poor in 2012 and 2015 but not in 2019. Figure 3: Proportion of chronic, transient, and never poor rural households between 2012 and 2019 in Zambia <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Chronic poor Transient poor Never poor<br><!-- End of picture text --> Notes: Own calculations from RALS (2012-2019).

16
# Gender Differences in Economics Course-Taking and Majoring: Findings from an RCT

Daniel Halim<sup>a</sup> , Elizabeth T. Powers<sup>b1</sup> , Rebecca Thornton<sup>c</sup> a World Bank, Washington, D.C.

b University of Illinois at Urbana-Champaign, Urbana, Illinois c University of Illinois at Urbana-Champaign, Urbana, Illinois _JEL Classification:_ C93, J16, J7, A22.

_Keywords:_ Gender; Economics; RCT; College Major.

> * Correspondence to: Department of Economics, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801. _Email address_ : epowers@illinois.edu (E. Powers).

> This research was approved by the Institutional Research Board at the University of Illinois at Urbana-Champaign. We acknowledge funding from the NBER WiE (Women in Economics) program. We thank Melissa Newell for key help implementing early waves of the RCT, and the instructors of the introductory economics courses for their help enrolling students in the study.
**Table B5: Comparison with representative household survey**

|||G-HFLS<br>(1)|GLSS 6<br>(2)|Difference<br>(3)|_p_-value<br>(4)|
|---|---|---|---|---|---|
|Individual Characteristics||||||
|Male||0.412|0.454|-0.042|0.904|
|Age||33.857|35.465|-1.731|0.831|
|Education|Female|9.415|10.022|-0.607|0.931|
||Male|11.233|12.886|-1.653|0.839|
|Employed|Female|0.646|0.768|-0.122|0.749|
||Male|0.731|0.834|-0.102|0.800|
|of which self-employed|Female|0.692|0.732|-0.040|0.935|
||Male|0.503|0.414|0.090|0.887|
|of which in<br>manufacturing|Female|0.075|0.132|-0.057|0.882|
||Male|0.105|0.113|-0.008|0.984|
|of which in trade|Female|0.589|0.437|0.152|0.785|
||Male|0.255|0.177|0.078|0.873|
|of which in services|Female|0.294|0.299|-0.005|0.993|
||Male|0.584|0.399|0.185|0.769|
|of which in public sector|Female|0.042|0.074|-0.032|0.913|
||Male|0.045|0.129|-0.083|0.846|
|Unemployed|Female|0.068|0.030|0.038|0.804|
||Male|0.046|0.028|0.018|0.918|
|Not in the labor force|Female|0.285|0.196|0.090|0.802|
||Male|0.223|0.135|0.087|0.814|
|Sample size||(N = 948)|(N=13204)|||
|Household||||||
|Characteristics||||||
|Household size||4.410|3.649|0.760|0.782|
|Dependency ratio||0.438|0.310|0.128|0.693|
|Maximum education level<br>household|in the|11.537|14.630|-3.093|0.881|
|Female headed||||||
|households||0.376|0.341|0.034|0.950|
|Labor force participation r<br>year olds)|ate (20 -60|0.747|0.585|0.162|0.660|
|<br>Asset index||0.000|0.102|-0.102|0.939|
|Sample size<br>Note: For household charact<br>characteristics both samples|eristics, the GLSS<br>are confined to|N = 354<br>sample is restrict<br>urban respondent|N = 7440<br>ed to urban hou<br>s aged 20 - 60.|seholds. For indiv|idual|

34
# **4.4  Entrepreneurship, Decision-making, and Leadership Training in Peru**

Using a randomized control trial, Karlan and Valdivia (2006) analyze the effect of providing business training as an add-on to a Peruvian group lending program targeted at female microentrepreneurs. The study was conducted with the Foundation for International Community Assistance in Peru (FINCA), a microfinance institution that supports poor, female entrepreneurs in Lima and Ayacucho through village banks. Both the treatment and control groups received loans under the program. In addition to the loans, the treatment groups received 30 to 60 minutes of training during their normal weekly or monthly meetings with the bank over a period of one to two years.<sup>18</sup> The program provided general business skills and strategy training but not clientspecific advice. For example, women learned how to target customers, identify competitors, and position their product as well as promotional and planning strategies. The study finds that the business skills training program had significant benefits for both the clients and for the microfinance institution. The clients improved their knowledge of business processes and increased sales, revenues and profits. The largest positive impacts were observed among the subgroup that expressed the least interest in receiving the business training. The study does not find effects on measures of female empowerment, possibly because the women entrepreneurs participating in the study were relatively empowered already. Also, the study finds that children from mothers in the treatment group spent significantly more time on studying and less time on leisure-related activities. The microfinance institution benefitted from increased client retention and from better repayment of loans. The study suggests that poor entrepreneurs may be helped both by alleviating credit constraints as well as by improving their business-related skills.

# **4.5  Summary**

Evaluation studies of microlending programs in different country contexts yield mixed evidence on the effectiveness of the programs as well as on the benefits of gender-specific targeting.

Pitt and Khandker‟s (1998) study finds substantial benefits of targeting microcredit to women relative to men in the context of the Bangladeshi group-lending program. Program impacts have been larger when targeted to women, as measured by effects on consumption, labor supply and children‟s school-going. Unfortunately, the recent replication analysis of Roodman and Morduch

> 18 The average loan is $203 and the average level of savings of the individuals in the program is $233. The recovery rate is 99 percent. For further discussion of the program, see Karlan and Valdivia (2006).

37
|P150430|2015|2017|1/4/2017|4 Active|Mauritania|
|---|---|---|---|---|---|
|P150430|2015|2017|6/29/2017|5 Active|Mauritania|
|P150430|2015|2018|1/19/2018|6 Active|Mauritania|
|P150430|2015|2019|7/30/2018|7 Active|Mauritania|
|P150430|2015|2019|3/2/2019|8 Active|Mauritania|
|P150430|2015|2020|9/11/2019|9 Active|Mauritania|
|P150430|2015|2020|3/18/2020|10 Active|Mauritania|
|P150450|2020|2015|12/6/2014|1 Dropped|Albania|
|P150450|2020|2015|6/16/2015|2 Dropped|Albania|
|P150450|2020|2016|12/21/2015|3 Dropped|Albania|
|P150450|2020|2016|3/10/2016|4 Dropped|Albania|
|P150450|2020|2017|8/17/2016|5 Dropped|Albania|
|P150450|2020|2017|1/14/2017|6 Dropped|Albania|
|P150450|2020|2017|5/27/2017|7 Dropped|Albania|
|P150450|2020|2018|1/25/2018|8 Dropped|Albania|
|P150450|2020|2018|6/25/2018|9 Dropped|Albania|
|P150450|2020|2019|1/9/2019|10 Dropped|Albania|
|P150450|2020|2020|7/30/2019|11 Dropped|Albania|
|P150450|2020|2020|12/24/2019|12 Dropped|Albania|
|P150472|2015|2015|6/5/2015|9 Active|Cambodia|
|P150472|2015|2016|1/29/2016|10 Active|Cambodia|
|P150472|2015|2016|6/21/2016|11 Active|Cambodia|
|P150475|2016|2016|5/27/2016|1 Closed|Colombia|
|P150481|2015|2015|3/27/2015|1 Active|West Bank and Gaza|
|P150481|2015|2016|9/10/2015|2 Active|West Bank and Gaza|
|P150481|2015|2016|1/29/2016|3 Active|West Bank and Gaza|
|P150481|2015|2017|7/8/2016|4 Active|West Bank and Gaza|
|P150481|2015|2017|12/5/2016|5 Active|West Bank and Gaza|
|P150481|2015|2017|4/19/2017|6 Active|West Bank and Gaza|
|P150481|2015|2018|10/2/2017|7 Active|West Bank and Gaza|
|P150481|2015|2018|1/26/2018|8 Active|West Bank and Gaza|
|P150481|2015|2018|3/29/2018|9 Active|West Bank and Gaza|
|P150481|2015|2018|6/29/2018|10 Active|West Bank and Gaza|
|P150481|2015|2019|11/1/2018|11 Active|West Bank and Gaza|
|P150481|2015|2019|12/18/2018|12 Active|West Bank and Gaza|
|P150481|2015|2019|3/22/2019|13 Active|West Bank and Gaza|
|P150481|2015|2020|9/11/2019|14 Active|West Bank and Gaza|
|P150481|2015|2020|3/18/2020|15 Active|West Bank and Gaza|
|P150481|2015|2021|7/23/2020|16 Active|West Bank and Gaza|
|P150488|2014|2016|11/2/2015|1 Closed|Tajikistan|
|P150488|2014|2016|3/29/2016|2 Closed|Tajikistan|
|P150488|2014|2017|2/3/2017|3 Closed|Tajikistan|
|P150494|2016|2017|6/27/2017|1 Closed|West Bank and Gaza|
|P150494|2016|2018|6/1/2018|2 Closed|West Bank and Gaza|
|P150496|2017|2018|10/30/2017|1 Active|Tanzania|
|P150496|2017|2018|3/13/2018|2 Active|Tanzania|
|P150496|2017|2019|12/14/2018|3 Active|Tanzania|