rafmacalaba/data-use-mentions-extended huggingface dataset:
below 3,000.<sup>13</sup> We then used data from the 2015/16 integrated household survey to exclude two districts with the lowest shares of households receiving remittances, to ensure we focused on areas with higher likelihoods of migration. We then randomly selected 404 settlements from a pool of 580 eligible settlements to conduct a listing exercise. Figure 2 plots the settlements by treatment assignment (see below).

# _Selection of households and individuals_

Settlements are divided into enumeration areas (EAs), consisting of at most 500 individuals (50 households), and most settlements consist of only one EA. We randomly chose up to 2 EAs per settlement and conducted a door-to-door listing exercise of all households in these 404 settlements to record whether they have at least one male aged 18 to 30. In addition, a quick survey was conducted among village elders in each of these settlements to elicit village level characteristics such as the fraction of households in the village that have a migrant in Europe, in Senegal, and in Banjul; whether any vocational training program had been made available in the village; the cost of a bus to Dakar from that village; and whether any information campaign against irregular migration had already been conducted in the village. Interviewees were also asked to select the 11 males aged 18-30 most likely to migrate from the village in the next 12 months out of a list of 20 males 18-30.<sup>14</sup> Listing took place from March 26, 2019 to April 11, 2019 (see study timeline in Appendix A5) and listed 5,597 households with males aged 18 to 30 in the 404 villages. The baseline survey then took place between April 19, 2019 and May 18, 2019.<sup>15</sup> Data collection was conducted in 391 settlements, after dropping 13 small settlements that had fewer than 4 eligible households. Within the eligible households, the baseline interview took place with a male 18 to 30. We focused on this age
) than the control. Overlaying cadastral with crop map information suggests farmers in treatment and control cultivated 96% and 99% of their registered area, respectively, in the pre-war years (2019-2021); the war triggered a marked decline of cultivated area to 93% or 91%, respectively, in 2022 that was partly but by no means fully reversed in 2023. On average, successful applicants received the equivalent of $1,668 ($962 and $ 6,206 for farms below or above a registered area of 50 ha, respectively) via the PSG program.

To improve comparability between treatment and control, we use entropy matching on first and second moments for all regressions variables to re-weight observations in the control sample, making them more comparable to those in the treatment. Appendix table A6 shows that, after re-weighting, normalized differences between farms that received and that did not receive PSG payment are negligible throughout and well below the rules of thumb considered in the literature. Figure 2 graphically illustrates mean values of our outcome variable (area cultivated with summer crops) for treatment and controls in the pre-and postintervention period, respectively using entropy weights. The null hypothesis of parallel linear trends in the pre-treatment period cannot be rejected at the 5% level although values diverge after PSG became available.

# **4.3 Results and robustness check**

Results, overall (column 1) and with heterogeneous effects by distance to the closest administrative unit affected by conflict (referred to as the ‘front line’), farm size, and formality of organization (columns 2-4)

> 24 For purposes of this analysis, we use the centroid of all parcel centroids as a proxy for the location of the farm.

14
The formation of risk sharing networks", _Journal of Development Economics_ , 83: 326-350.

- [47] Fafchamps, M., S. Lund (2003), “Risk-sharing Networks in Rural Phillipines", _Journal of Development Economics_ , 71(2): 261-287.

- [48] Feigenberg, B., E. Field and R. Pande (2013), “The Economic Returns to Social Interaction: Evidence from a Randomized Experiment with Microentrepeneurs", _The Review of Economic Studies_ , 80(4): 1459-1483.

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- [51] Genicot, G. (2006), “Does Wealth Inequality Help Informal Insurance?", working paper.

30
from 5 studies in Eastern and Southern Africa before highly active antiviral therapy. The probability of survival to year _t_ conditional on getting infected at year _τ_ is estimated as with _ψτ_ reducing with age of infection. (Parameter estimates and median survival time are presented in Table 5.) The mortality hazard increases with both duration and age of infection. The median survival time of an individual infected at ages 15 to 19 is 13.3 years, while it is 8.4 years for an individual who gets infected at ages 40 to 44.

Data from dierent longitudinal studies with repeated assessments of HIV status of adults show higher mortality rates of children born to infected women. In these studies, the HIV status of the children was generally not available. Controlling for dierent background characteristics, mortality rates of children born to HIV-infected mothers were estimated to be about three times higher than those for children born to uninfected mothers, with the eect lasting throughout childhood years (Newell et al., 2004). Crampin et al. (2003) report child mortality rates by status of mother at birth from a retrospective cohort study with more than 10 years of follow-up in Karonga district in Northern Malawi. The rates are reported in Table 6.

# 4 Estimation

The main estimation sample contains data on 1006 women from the MDICP dataset. The information on the _i_ th woman consists of up to 4 years of pregnancy choices _pi_ ( _t_ ) _, t_ = _tpi, ..., tpi_ , � <u>�</u> up to 2 reports of subjective assessments of HIV status � _bi_ ( _t_ ) _, t_ = _tbi, ..., tbi_ <u>�</u> , up to 3 HIV test results (collected in the 2004, 2006 and 2008 rounds), and a vector of xed characteristics: region of residence, schooling level, year of birth, age of marriage, polygamy status of husband, and household's land.

I
- Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47(1), 153161.

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-

- Holtfreter, K., Reisig, M. D., & Pratt, T. C. (2008). Low self control, routine activities, and fraud victimization. Criminology, 46(1), 189-220.

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- Huang, Y., & Clark, W. A. (2002). Housing tenure choice in transitional urban China: a multilevel analysis. Urban studies, 39(1), 7-32.

- Hurst, E., & Lusardi, A. (2004). Liquidity constraints, household wealth, and entrepreneurship. Journal of political Economy, 112(2), 319-347.

- Jappelli, T., Pischke, J. S., & Souleles, N. S. (1998). Testing for liquidity constraints in Euler equations with complementary data sources. Review of Economics and statistics, 80(2), 251-262.

- Jappelli, T., & Pagano, M. (1994). Saving, growth, and liquidity constraints. The Quarterly Journal of Economics, 109(1), 83-109.

- Jorna, P., & Hutchings, A. (2013). Australasian Consumer Fraud Taskforce: Results of the 2012 online consumer fraud survey (p. 13). Canberra, Australia: Australian Institute of Criminology.

- Karlan, D. S. (2007
# **Self-Report Outcomes.** Complementing the evaluation of the intervention’s impact on

detection and sharing of misinformation, we examined differences in attitudes toward the chatbot game.

**_Confidence_ .** Participants were asked to indicate the extent to which they felt more, the same, or less confident in detecting misinformation after completing the game.

**_Perceived Difficulty_ .** Participants were asked to report whether they thought the game was too difficult, the right level of difficulty, or too easy.

**_Recommending the Game_ .** Participants were asked to indicate the extent to which they would recommend the game to others with three possible response categories: yes, maybe, and no.

# **_Participants_**

A total of 2,851 participants completed the study. Of them, 63% identified as male and 33% as female; 49% reported having completed secondary education (3% with no education, 12% with primary, and 3% with tertiary education); 53% reported being between the ages of 18 and 29, 25% in their 30s, 13% in their 40s, and 5% over the age of 50. Finally, 85% reported being vaccinated for COVID-19, 5% unvaccinated but willing to vaccinate, and 5% unvaccinated and unwilling to vaccinate. None of the treatment arms reported significant differences in demographics and vaccination status compared to the control group. See **Appendix** **_E_** for sample composition and randomization balance and **Appendix** **_F_** for age and gender distributions of users who started WhatsApp conversations according to Facebook Ad Manager data.

# **Results**

# **Attrition and Study Completion Time**

16
advanced and 20 emerging. Appendix Table 1 reports the list of economies, the number of debt issuances, and the number of firms per economy. The main results of this paper are robust to the exclusion of the largest advanced and emerging economies (the United States and China).

To study domestic banking crises, we merge our data on corporate debt issuances with data from the Reinhart and Rogoff’s financial crises <u>database, which covers the 1991-2014 period. In their</u> database, domestic banking crisis years are marked by two types of events: (i) bank runs that lead to the closure, merging, or takeover by the public sector of one or more financial institutions; and (ii) no runs, but the closure, merging, takeover, or large-scale government assistance of an important financial institution that marks the start of a string of similar outcomes for other financial institutions.<sup>7</sup> After merging the data sets, we obtain a sample that comprises 170,947 debt issuances conducted by 51,989 firms from 36 economies. We do not split the sample across groups of economies when analyzing domestic banking crises for three main reasons: (i) domestic banking crises consist of relatively similar events across economies; (ii) our sample of economies is smaller when merging the domestic crises data; and (iii) most of the domestic banking crises in emerging economies occurred during the 1990s, when the corporate debt issuance activity by this group of economies was scarcer.

# **3. Changes in Debt Issuance Composition during Crises**

The primary debt markets we analyze in this paper have rapidly expanded worldwide since the early 1990s (Figure 1). Between 1991 and 2014, the total amount of corporate bonds and syndicated loans issued increased more than 7-fold in advanced economies and almost 27-fold in emerging economies, reaching $4.6 trillion and $0.81 trillion in 2014, respectively. The growth in debt issuance activity

> 7 The authors
Policy Research Working Paper 7638

# **Abstract**

This paper uses a unique firm-level data set for Mexico, with information never used for research before, to assess how use of information technology (IT henceforth) influences firm performance. Further, the paper explores if, in the context of increasing competition from China, this effect is different for firms more strongly affected by competition where incentives for upgrading and innovation may be more intense. In this perspective, the paper analyzes the complementarity between IT and other changes spurred by competition, taking advantage of the exogenous shock generated by Chinese competition. The results indicate that IT use has higher effects over productivity in the case of firms facing higher competition from China, in the domestic market and in the U.S. market. Furthermore, the paper shows how these changes appear to be driven by complementary investments in innovation and organizational changes.

This paper is a product of the Trade and Competitiveness Global Practice Group. The paper has been prepared as a background research paper for the World Development Report 2016 “Digital Dividends.” 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 authors may be contacted at liacovone@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._
# **Acknowledgements**

The authors would like to thank to the Coordinating Ministry for Economic Affairs and the Ministry of Finance of Indonesia for their partnership in providing access to the KUR program data and providing helpful inputs throughout the study. The authors are also grateful to Alexis Diamond, Tatiana Didier, Aufa Doarest, Michael Fuchs, Calvin Koenig, Clarita Kusharto, Aaditya Mattoo, Lalita Moorty, Vina Noor, Akaravuit Pancharoen, Habib Rab, and Iqbal Wibisono, who provided guidance and support throughout the design and implementation of this study. The authors also would like to thank Ana Maria Aviles, Pietro Calice, Ana Carvajal, Francesca de Nicola, Ivor Istuk, Harish Natarajan, Kiyotaka Tanaka, and Timothy Ogden for useful comments on the draft and the data collection team at REDI for their ability to deliver quality data under difficult circumstances. The team gratefully acknowledges funding from the Swiss State Secretariat for Economic Affairs (SECO) under the Indonesia Financial Sector Strengthening Program and from the East Asia Pacific Gender Innovation Lab (EAPGIL). EAPGIL is supported by the Umbrella Facility for Gender Equality (UFGE) in partnership with the Australian Department of Foreign Affairs and Trade. UFGE has received generous contributions from Australia, Canada, Denmark, Finland, Germany, Iceland, Ireland, the Netherlands, Norway, Spain, Sweden, Switzerland, the United Kingdom, the United States, the Bill and Melinda Gates Foundation (BMGF), and the Wellspring Philanthropic Fund (WPF). Finally, the authors would like to thank the entrepreneurs who volunteered their time to share their experiences with us.

**2**
using the Cholesky decomposition of the variance-covariance matrix, as in Christiano et al. (1999) and many others. The monetary policy instrument (i.e., policy interest rates) is placed after the output and price variables, in line with the Taylor-rule type monetary policy reaction function, and before financial variables—interest rates, exchange rates, and stock price—to reflect the transmission channel of monetary policy shocks through financial markets. Despite using monthly data, endogeneity issues may still persist. To address this, our model includes several exogenous variables (SVAR-X) that account for global demand and supply shocks, capturing the simultaneous movements of variables in small open economies. In addition, the main model is augmented with economic and financial forecasts by consumers, businesses, and professional forecasters.

We first estimate the SVAR models for each country using a Bayesian approach, where the routine aims to find 1,000 successful draws from at least 2,000 iterations with 1,000 burn-ins. The impulse response functions for each individual country are derived as the medians of these

> 9 These are the endogenous variables typically included in VAR models of monetary policy transmission (Rey 2015; 2016, Passari and Rey 2015, Bjørnland 2009). Summary statistics are shown in Table A1.

> 10 While our main interest in this paper is in the transmission of domestic monetary policy shocks into EMEs, we also summarize the results on the transmission of U.S. monetary policy shocks in Appendix III.

> 11 See Table A2 in appendix I for further details.

> 12 For instance, Consensus surveys report annual growth rates or expected inflation for the current and the next year on a monthly basis. We obtain one-year-ahead inflation expectation as a linear combination of the inflation expectations for the current (Eπ�) and next years (Eπ���) . Specifically, we calculate it as Eπ����� = (12-m)/12* Eπ� +m/12* Eπ��� , where m
examine the extensive margin decision of deciding to come to work.

We estimate effects for Phase 1 (comparing the Private treatment with Control) and for Phase 2 (comparing the Private treatment with the Non-Private treatment). To measure treatment effects, we use the administrative panel data from the factories to estimate difference-in-differences regressions at the worker-day level: where _yit_ is the outcome of interest for worker _i_ on date _t_ . The _PrivateAccti_ indicator equals one if worker _i_ was assigned to receive the Private account treatment. _Postt_ is an indicator that equals one on dates when the blocked savings account is active, and _Announcementt_ is an indicator that equals one during the announcement period (after workers are told their 22
# BUILDING RESILIENT HEALTH SYSTEMS:

EXPERIMENTAL EVIDENCE FROM SIERRA LEONE AND THE 2014 EBOLA OUTBREAK<sup>_∗_</sup> Darin Christensen<sup>_†_</sup> Oeindrila Dube<sup>_‡_</sup> Johannes Haushofer<sup>§</sup> Bilal Siddiqi<sup>_¶_</sup> Maarten Voors<sup>_∥_</sup>

> _∗_ This study utilizes a field experiment implemented in collaboration with Sierra Leone’s Decentralization Secretariat, Ministry of Health and Sanitation, World Bank, International Rescue Committee, Concern Worldwide, and Plan International. We thank the Njala University Museum and Archive for sharing the de-identified data on Ebola cases. We also thank Innovations for Poverty Action for collecting the original survey data, and the respondents for donating their time. Gieltje Adriaans, Ali Ahmed, Carolina Bernal, Alix Bonargent, Fatu Conteh, Afke de Jager, Sarah Dykstra, Caroline Fry, Kevin Grieco, Anne Karing, Anthony Mansaray, Josh McCann, Niccolo Meriggi, Nick Otis, Moritz Poll, Mirella Schrijvers, and Samantha Zaldivar Chimal who provided excellent research assistance. For comments, we thank Rachel Glennerster, Dan Posner, Manisha Shah, and workshop participants at Berkeley, Columbia, LSE, UC San Diego, Zurich, Yale, Northwestern, Norwich, Amsterdam, Rotterdam, EGAP Nairobi, UCLA IDSS, the World Bank’s ABCA, and APSA. We gratefully acknowledge funding from USAID-DIV, the International Growth Centre, AFOSR grant #FA9550-09-1-0314, NWO grant #451-14-001, ESRC grant #ES/J017620/1, the Royal Netherlands Embassy in Ghana, and UCLA’s California Center for Population Research. All errors are our own. The analysis of the survey based outcomes was pre-registered on the AEA registry: `https://www.socialscienceregistry.org/trials/2085`

> _†_ UCLA, Luskin School of Public Affairs ( `darinc@luskin.ucla.edu` )
**Table 6:  The Role of Taxes and Social Benefits in Inequality Trends, 1994-2002**

||**1994**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|
|---|---|---|---|---|---|---|---|---|---|
|**Concentration Coeff**|**icients*:**|||||||||
|Taxes|0.311|0.307|0.318|0.328|0.322|0.367|0.386|0.400|0.413|
|Benefits|-0.270|-0.316|-0.318|-0.334|-0.374|-0.367|-0.371|-0.365|-0.362|
|**Progressivity**:**||||||||||
|Taxes|-0.070|-0.072|-0.071|-0.069|-0.045|-0.012|-0.010|0.002|0.004|
|Benefits|-0.650|-0.695|-0.707|-0.71|-0.72|-0.746|-0.767|-0.762|-0.771|

Method: * Household ranked by original income. ** Kakwani index.

_Source:_ Authors’ calculations based on HBS.

An alternative way of looking at the effect of fiscal policy on poverty is to consider its redistributive effect.
3 provides an overview of the early childhood education policy landscape in Indonesia and the research questions in this study. Then, section 4 introduces the data and methods of the study. In section 5, we outline the empirical strategy – including approaches to correcting for measurement error. Results are presented in section 6. Section 7 introduces an alternative measure of classroom quality that is more contextspecific to Indonesia and presents the association between this alternative measure of preschool quality and children’s development. It also puts the quality of Indonesian ECED services in context – both internationally and in relation to local standards. We discuss our findings and conclude in section 8.

# **2. Literature review**

The following review of the literature discusses empirical research on the link between preschool quality and children’s early childhood developmental outcomes. We focus on three aspects of preschool quality: observed preschool quality, teacher characteristics, and other structural characteristics of early childhood programs.

# _Observed preschool quality_

Decades of research on child development have established the benefits of providing highquality early childhood programs to young children (Campbell and Ramey, 1994; NICHD, 2005). One of the most widely used observational measures of quality of early childhood classroom environments is the revised version of the Early Childhood Environment Rating Scale (ECERS-

> 6 In this paper, the terms “preschool” and “early childhood education” are used interchangeably, and refer broadly to organized, center-based early learning environments for children from the ages of 3 until they enter primary school – usually playgroups and kindergartens.

2