rafmacalaba/datause-encoder-data huggingface dataset:
## **A new program for extreme poverty**

In 2013, under a new administration, the _Ingreso Ético Familiar_ (IEF) replaced _Chile Solidario_ . The IEF offers to its beneficiaries a set of cash transfers organized into 3 categories: (i) _Bonos por Dignidad_ (“Transfer for Dignity”), (ii) _Bonos por Deberes_ (“Transfer for Duties”), and (iii) _Bonos por Logros_ (“Transfer for Achievements”). In addition, for the extreme poor population, the _Ingreso Ético Familiar_ also includes a component called _Apoyo Social_ (Social Support) and _Apoyo Laboral_ (Employment Support), both for a maximum of 24 months.

The _Ingreso Ético Familiar_ represents a radical change in thinking - in terms of expanded coverage, lower emphasis on the family support service, and of stronger focus on the employment dimension. Moreover, evaluations have yet to be completed. As such, we do not cover it.

# **4. Design and implementation of** **_Red Unidos_**

In response to the 1999 crisis, Colombia began to build its social assistance system to support the extreme poor. The system consisted of three programs: a cash transfer, a temporary work program, and a youth training scheme; the cash transfer is still in operation as of 2014. Specifically, in 2001, Colombia implemented _Familias en Acción,_ a Conditional Cash Transfer (CCT) program, with the objective of supporting poor families with an income transfer who in return would commit, in the context of co-responsibilities, to invest in their children’s health and education. _Familias en Acción_ rapidly became an important pillar of the country’s poverty reduction strategy and together with other social programs and better economic conditions contributed to a reduction in poverty by 7.8 percentage points between 2002 and 2005. Despite these successes, 22 million Colombians remained poor, and 6.6 million remained in extreme poverty. Poverty was also geographically concentrated: one-fourth of the _departamentos_<sup>_2_</sup> presented poverty incidence rates above 25 percent of the population (CONPES 2006).

> 2 Departamentos are a geographical unit comparable to a state. There are 32 _departamentos_ in Colombia

18
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# **B.2. Pace of Accession to the WTO**

**Vietnam would greatly benefit from fast accession to** WTO. To the extent that WTO sets accepted rules of trade among its members, it de facfo discriminates against non-member countries. As a consequence, Vietnam could gain enormously from WTO accession.<sup>As the</sup> experience of China indicates, accession to WTO generates considerable benefits<sup>in termsof</sup> improved legal framework and enabling environment which result in substantial productivity gains and incremental.capital inflows (Box 2). Also, as a WTO member, Vietnam would be in a better position to defend its interest on the international scene. There is some evidence that, as tariff barriers and quantitative import restrictions are progressively dismantled as part of the on-going global liberalization process, importing countries tend to intensify the use of sanitary and phytosanitary standards (SPS) or allegations of price dumping or subsidies to protect the interests of their domestic producers. Indeed, Vietnamese exporters are increasingly facing these new trade barriers as evidenced in the exports of shrimps to the

10
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# **Table 1: Results with Total Public Expenditures (panel OLS and SURE)**

## **_Dependent variable: Growth rate of GDP per capita_**

||**OLS**|**SURE**|
|---|---|---|
|Private investment<br>(% of GDP)|0.107<br>(1.917)*|0.115<br>(2.698)***|
|Initial human capital|0.007<br>(0.96)|0.006<br>(0.931)|
|Total fiscal revenues<br>(% of GDP)|-0.323<br>(-3.884)***|-0.296<br>(-4.175)***|
|Productive plus unproductive expenditures (-1)<br>(% of GDP)|0.477<br>(3.876)***|0.448<br>(4.263)***|
|Budget surplus<br>(% of GDP)|0.341<br>(3.668)***|0.304<br>(3.942)***|
|Inflation - consumer price index|-0.205<br>(-5.65)***|-0.177<br>(-4.908)***|
|Growth rate of GDP per capita (-1)|0.406<br>(3.928)***|0.378<br>(4.11)***|
|No. of observations<br>Adjusted R2|120<br>0.443|120<br>0.473|

Note: The estimation method is the ordinary least squares for panel data in the first column and the cross‐section seeminly unrelated regression for panel data in the second column. Annual data are used. (-1) indicates variables lagged one period. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients.

28
# **The Impact of COVID-19 on Education in Latin America:**

**Long-Run Implications for Poverty and Inequality**<sup>*</sup>

Jessica Bracco<sup>**</sup> Matías Ciaschi Leonardo Gasparini Mariana Marchionni Guido Neidhöfer

## CEDLAS-UNLP

JEL Codes: O1, I31, I24 Keywords: COVID-19, education, poverty, inequality, incomes, Latin America.

* This paper is based on research commissioned by the Latin American and Caribbean - Poverty and Equity Global Practice (EFI-LCR-POV-Poverty and Equity) under the Poverty and Equity in LAC (P179518) project, which objective is to help form the regional narrative on poverty and shared prosperity while increasing the quality, availability, and use of indicators and micro-data in LAC lead by Sergio Olivieri. In particular, we seek to produce meaningful analytics using this data to inform evidence-based policymaking. We are very grateful to Sergio Olivieri, Hernán Winkler, Hugo Ñopo, Vincenzo Di Maro, Caio Piza and seminar participants at The World Bank and UNLP for helpful discussion and suggestions. We thank Luis Laguinge for excellent research assistance. The usual disclaimer applies.

** All authors are researchers at CEDLAS (IIE, FCE) - Universidad Nacional de La Plata. Gasparini, Marchionni and Ciaschi are also at CONICET. Guido Neidhöfer’s main affiliation is at ZEW Mannheim.
Policy Research Working Paper 9449

# **Abstract**

This paper uses a unique data set with 1.1 million customer transactions from a microfinance institution in the Democratic Republic of Congo from 2017 to 2018. The paper provides evidence of assortative gender matching in agent banking transactions, as clients prefer to transact with agents of their own gender. Female clients show a robust preference for female agents even when they are less available, particularly when making high-value transactions and when they have higher account balances. The underrepresentation of female agents may contribute to the persistent gender gap in financial access and usage.

This paper is a product of the Development Research 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 xgine@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
Policy Research Working Paper 4998

# **Abstract**

There is a strong association between schooling attained and test scores in many settings. If this association is causal, one might expect that programs that increase school enrollment and attainment would also improve test scores. However, if there is self-selection into school based on expected gains, marginal children brought into school by such programs may be drawn disproportionately from the left-hand side of the ability distribution, which could limit the extent to which additional schooling translates into more learning. To test this proposition, this paper uses data from Cambodia. The results show that a program that provides scholarships to poor students had a large effect on school enrollment and attendance, which increased by approximately 25 percentage points. However, there is no evidence that, 18 months after the scholarships were awarded, recipient children did any better on mathematics and vocabulary tests than they would have in the absence of the program. The paper discusses results that suggest that the self-selection of lower-ability students into school in response to the program is an important part of the explanation. The analysis also shows minimal program effects on other outcomes, including knowledge of health practices, expectations about the future, and adolescent mental health.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to study the impact of social programs and their role in promoting human development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dfilmer@worldbank.org and nschady@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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
The shift toward long-term domestic (rupee) debt in government financing after the 1991 crisis has also boosted liquidity. The weighted average maturity of debt issued through dated securities more than doubled from 6.6 years in 1997/98 to 14.3 in 2001/02.<sup>12</sup> Moreover, over 85 percent of central and state government securities are held by nationalized banks, The State Bank of India, the state-owned Life Insurance Corporation and RBI while the remainder is held by the mutual fund, Unit Trust of India, the National Bank for Agriculture and Rural Development (NABARD), employees provident fund and private banks.<sup>13</sup> In short, much of the domesticallyissued debt is in the hands of government-owned or controlled entities.

# 2.2 _Fiscal Trends  1985/86-2001/02_

Table 2 shows that general government revenues fell considerably during the Ninth Plan period relative to the second half of the 1980s.<sup>14</sup> Compared to the second half of the 1980s, capital expenditure fell by over three percentage points of GDP during the Ninth Plan period, while the sum of interest, administration and pensions rose by three percentage points of GDP and a 22 percentage points of revenue. Developmental spending on health and education stagnated, while that on irrigation, power and transport actually declined. Even the fiscal improvement secured during the Eighth Plan period involved a large compression of capital spending, as can be seen from the table. There was a hope that this compression would be offset by rising private investment in infrastructure; but with the exception of telecom, this has not happened on the desired scale. As noted in RBI (2003a), page III-46, a larger private role in infrastructure would require institutional reform and “economically efficient user charges to ensure the reasonable return on investment”.

**Table 2: General Government Fiscal Trends 1985/86-2001/02**

|% of GDP|85/86-89/90|8<sup>th</sup>Plan|9<sup>th</sup>Plan|02/03|
|---|---|---|---|---|
|(unless otherwise defined)|Avg.|Avg.|Avg.|Est. <sup>1/</sup>|
|Revenue|19.4|17.9|17.0|18.4|
|Current expenditure <sup>2/</sup>|22.1|21.5|23.1|25.3|
|Capital expenditure<sup>3/</sup>|6.4|3.6|3.2|3.5|
|Gross fiscal deficit|9.0|7.2|9.3|10.3|
|**Memo**|||||
|Interest|3.8|5.1|5.8|6.5|
|Interest/Revenue|19.4|28.5|34.0|35.1|
|Irrigation+ power+ transport|4.7|3.5|3.5|3.8|
|Interest+ admin.+ pensions|6.3|8.1|9.2|10.1|
|(Interest+admin.+pensions)/Revenue|32.6|45.1|54.3|54.8|
|Notes:|||||

1/ Revised estimates for the center and budget estimates for the states 2/ Refers to Revenue expenditure in the budget 3/ Refers to Capital outlay and net loans and advances from the center to the states Sources: GOI budget documents(2003/04 and various issues), RBI bulletins, CSO, Handbook of Statistics on Indian Economy 2001/02 (RBI), Staff estimates

> 12 RBI (2001a), Chapter XI Public Debt Management.

> 13 RBI (2001b), Table 111.

> 14 While 2002/03 shows a large revenue increase, it is based on the budget estimate for states, which tends to be optimistic.

6
(Anderson and Reeb, 2003), and in the case of women inheriting family firms but without effective control, it does not make sense to view the firm as a female entrepreneur firm. Furthermore, the previous literature using the firm sample of several African countries in the WBES data has documented carefully that using the definition of women managers tends to make more intuitive sense than that of women’s participation in firm ownership (Aterido and Hallward-Driemeier, 2011). We thus try to be conservative in defining female-entrepreneur firms and rely on two variables in the WBES data set to capture aspects of female entrepreneurship: women’s participation in ownership (“whether women have some ownership of the firm”), and whether the top manager is a woman. We define female entrepreneurship as the firm’s top manager is a woman _and_ women have ownership share in the firm. We impose the requirement of women being top managers because nonprincipal ownership by itself does not imply female entrepreneurship.

The extent of female entrepreneurship varies greatly in the world. Before presenting the statistics, we must acknowledge that the country-specific samples are not large, some industries are not covered, as mentioned earlier, and as a consequence, we must view the statistics about the extent of female entrepreneurship within a country with caution. This being said, within the sample, on average 10.9 percent of firms are headed by female entrepreneurs, 14% of firms are female-top-managed, and 31% of firms have female ownership participation (Table 3). The sample share of female-entrepreneur firms differs greatly by country (see Table 1), ranging from below 1 percent in Morocco, the Arab Republic of Egypt, Jordan, and Iraq; to 1 to 5 percent in Israel, Pakistan, India, Mauritania, and Bangladesh; to 5 to 10 percent in Sweden, Argentina, Turkey, Chile, Nepal, Ethiopia, Mexico, Tanzania, several large central Asian countries (such as Uzbekistan and Tajikstan), Senegal, Nigeria, Sri Lanka, and Peru; to between 10 and 15 percent in China, Brazil, Czech Republic, Malawi, and Colombia; to 15 to 25 percent in Ghana, Slovak Republic, Honduras, Romania, Hungary, Indonesia, Vietnam, Poland, Kyrgyz Republic, and Ukraine; to more than 25 percent in Namibia, the Philippines, the Lao People’s Democratic Republic, and Mongolia (36%, the highest in our sample). We should point out that the WBES data are not a random sample of all firms (especially informal firms) of a country, and the country average is informative but should not be taken at face value.

How is the presence of FDI firms related to female ownership of domestic firms?

9
add to the literature on the effects of human capital accumulation and increased age at marriage on marriage market outcomes (Anderson and Bidner 2015; Ashraf et al. 2015; Field and Ambrus 2008). Theory suggests that these two factors affect spousal quality in opposite directions with, _ceteris paribus_ , increased education improving marital outcomes while delaying marriage worsening them. Our findings provide empirical support for these predictions. Third, our study contributes to a large literature on the effects of programs that support pregnant women and young children. Policies for child development often target the first 1,000 days from conception to the second birthday (Barham, Macours and Maluccio 2013). What is novel in our study is that we examine the effects of targeting cash transfers to adolescent females of childbearing age and provide evidence on the important policy question of how to time interventions to protect early childhood development.<sup>8</sup> Our findings suggest that unconditional income support for adolescent girls and young women of childbearing age might cause significant increases in height-for-age z- scores of their children.

The remainder of this paper is structured as follows. Section 2 describes the study setting, study design, and data collection instruments. Section 3 presents our estimation strategy. Sections 4 presents program impacts on the core respondents, followed by an examination of some key characteristics of their husbands and children. Section 5 concludes.

# **2. STUDY SETTING, DESIGN, AND DATA SOURCES**

# **2.1 Study Setting**

The “Schooling, Income, and Health Risks” study (SIHR) tracks the lives of a sample of young women who were enrolled as never-married 13-22 year olds in Zomba, Malawi in 2007. We interviewed them for the fourth time in 2012 – approximately five years after baseline and

> 8 Currie and Almond (2011) state “...one of the more effective ways to improve children's long term outcomes might be to target women of child bearing age in addition to focusing on children after birth”.

8
In Karnataka, every school has a mandatory School development and monitoring committee (SDMC). The SDMC members include the head teacher of the primary school, elected members of the village government and parents of children. One of the parents is the chair of SDMC. The head teacher and SDMC chair jointly operate the school account. The types of funds received in the school account are similar to those in the case of MP. SDMC is supposed to call a meeting of all parents every 3 months to discuss trimester exam results. There are no contract teachers in Karnataka. The SDMC can inspect the schools and complain to the district or block education office.

Previous work suggests community members and members of local government do not often know about the state mandated controls they have over teachers and school accounts. A recent study in UP finds that VEC members are not informed of their roles and responsibilities and that VECs are not functional (Banerjee et. al., 2006). The baseline findings reported below also show that in MP and UP in particular, VEC or PTA members are unaware of their roles, meetings are rarely held and PTAs are involved neither in verification of teachers’ attendance nor in managing school accounts.

# **3. Methods**

This is a cluster randomized controlled trial of 610 _gram panchayats_ across the three states, randomly allocated to receive or not receive the information campaign. Cluster randomized sample size calculations were based on a 5 percent significance level and 80 percent power. In each state four districts were chosen purposefully, matched across states in terms of the literacy rate. Within a district, two blocks were randomly chosen. A block is an administrative unit between a district and a village. Within the two blocks, 25 villages were randomly chosen to be in the treatment group and another 25 randomly chosen to be in the control group. This gives a total of 100 control villages and 100 treatment villages per state in MP and UP. In Karnataka, an additional set of treatment villages were added that received a second treatment- information and advocacy campaign. The number of villages in each of the three cells in Karnataka (Control, Treatment 1 (information), Treatment 2 (information plus advocacy) is 70.

One randomly selected public primary school per _gram panchayat_ was chosen to be in the sample. All teachers teaching grades 1 to 5 are included. In MP and UP, 45 randomly chosen students per school (15 each from grades 2-4) and in Karnataka 30 randomly chosen students per school (15 each from grades 4-5) are in the sample.

**Timeline** A baseline survey was administered between February-April 2006 in MP and UP and between August-September 2006 in Karnataka.

# **Outcome indicators in baseline surveys**

In the baseline, four unannounced visits were made per school to collect information on the following.

5