rafmacalaba/data-use-mentions-extended huggingface dataset:
13

## _Changes in survey identifiers_

For a number of data points in the 2019 HEFPI database which we list in Table 2, we abbreviated survey identifiers used in the 2018 version or corrected them where we previously had used an incorrect survey year.

<u>Table 2: Changes in survey identifiers</u>

|2018 survey identifier|2019 survey identifier|
|---|---|
|ARG_2005_NationalRisk FactorSurvey_v01_M|ARG_2005_NRFS _v01_M|
|ARG_2009_National Risk Factor Survey_v01_M|ARG_2009_NRFS _v01_M|
|BEL_2009_EHIS_v01_M|BEL_2008_EHIS_v01_M|
|BGR_2009_EHIS_v01_M|BGR_2008_EHIS_v01_M|
|BRA_2006_National Demographic and Maternal and Child Health<br>Survey_v01_M|BRA_2006_NDMCHS_v01_M|
|BRA_2008_National Household Sampling Survey_v01_M|BRA_2008_NHSS_v01_M|
|CHL_2009_National HealthSurvey_v01_M|CHL_2009_NHS_v01_M|
|CHL_2011_National Socioeconomic Characterization Survey_v01_M|CHL_2011_NSCS_v01_M|
|CRI_1999_Sexual and Reproductive Health Survey_v01_M|CRI_1999_SRHS_v01_M|
|CRI_2006_National HealthSurvey_v01_M|CRI_2006_NHS_v01_M|
|ESP_-1_EHIS2_v01_M|ESP_2014_EHIS2_v01_M|
|EST_2007_EHIS_v01_M|EST_2006_EHIS_v01_M|
|GBR_2014_EHIS2_v01_M|GBR_2013_EHIS2_v01_M|
|LVA_2015_EHIS2_v01_M|LVA_2014_EHIS2_v01_M|
|MEX_2000_National HealthSurvey_v01_M|MEX_2000_NHNS_v01_M|
|MEX_2006_National Health and Nutrition Survey_v01_M|MEX_2006_NHNS_v01_M|
|MEX_2012_National Health and Nutrition Survey_v01_M|MEX_2012_NHNS_v01_M|
|MLT_2015_EHIS2_v01_M|MLT_2014_EHIS2_v01_M|

# 3.2.2. Changes in indicator values due to changes in definitions

For the 2019 HEFPI version, we substantively revised the definition of one of the 46 health equity indicators: pap smears. Following WHO guidelines (World Health Organization 2013), the 2018 version of the indicator was defined as the rate of women – preferably in the 30-49 age-group – who received a pap smear in the five years prior to the survey interview. In formulating its recommendations, WHO considers not only clinical evidence but also “financial, infrastructural, and other resources”, i.e. the recommendations reflect both need and feasibility. HEFPI’s main aim is to facilitate measurement of progress towards UHC, the definition of which – all people having access to the health services they need without suffering financial hardship – considers need only. For the 2019 HEFPI version, we therefore switch to a definition of the pap smear indicator which is based on
# **3. A Nutritional Intervention Aimed at Protecting Young Children**

The goal of supplementary feeding program was to maintain and to improve the nutritional status of children under five, particularly those at risk as a result of the 199798 economic crisis.<sup>8</sup> The program was targeted through a two stage process. First, central and regional governments decided which communities or villages would receive the program, and then determined the timing of implementation within communities. Capacity constraints likely affected the timing of program rollout. Districts differed in their capacity to roll out the PMT program, and communities differed in the availability of staff to administer it. As a result, it is not surprising to find differences in duration of program exposure across communities.

Funding was distributed to local public health clinics, the _Pusat Kesehatan Masyarakat (Puskesmas_ ), and a list of children eligible to receive supplementary food was then prepared by the village midwife in consultation with other village leaders. As with other community health programs in Indonesia, program supervision was typically the responsibility of the village midwife if one was present. If a midwife was not available in the community, responsibility was assumed by the _Puskesmas_ .<sup>9</sup> Descriptive information from the IFLS show that in 62 percent of communities, the village midwife administered the program, and in majority of the rest (35 percent of sampled communities), the program was supervised by a member of the Puskesmas staff. Others may have been involved in preparing program’s recipient list, including the

> 8A monthly supplementary feeding program took place prior to the crisis as part of a public health program. In contrast to post-crisis supplementary feeding program, the pre-crisis program was much less intensive and distributed through the Village Integrated Health Post ( _Pos Pelayanan Terpadu_ , _Posyandu)_ . Under this program, food supplements were delivered along with some basic health services to preschool age children and pregnant women residing in the community.

> 9Village midwives are trained to provide basic health services
Policy Research Working Paper 0

# **Abstract**

Using firm-level data from more than 39,000 firms in 111 economies, this paper tests the hypothesis that corruption impedes productivity more at higher levels of regulation. The analysis finds that there is a significant negative relationship between corruption and firm productivity when regulation is high and an insignificant relationship when it is low. These findings are robust to different controls and specifications.

This paper is a product of the Global Indicators 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 hulku@worldbank.org and mamin@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
King, L. P. (2016). An Objective Corruption Risk Index Using Public Procurement Data. European Journal of Criminal Policy and Research, 22(3), 369–397. https://doi.org/10.1007/s10610-016-9308-z Fisman, R., & Golden, M. (2017). How to fight corruption. Science, 356(6340), 803 LP – 804. http://science.sciencemag.org/content/356/6340/803.abstract Funk, M. J., Westreich, D., Wiesen, C., Stürmer, T., Brookhart, M. A., & Davidian, M. (2011). Doubly Robust Estimation of Causal Effects. American Journal of Epidemiology, 173(7), 761–767. https://doi.org/10.1093/aje/kwq439 Good, P.I. (2006) Resampling Methods. A Practical Guide to Data Analysis. 3rd edition, Birkhauser, Boston.

Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking : Coarsened Exact Matching. Political Analysis, 20(1), 1–24. https://doi.org/10.1093/pan/mpr013 Klasnja, M. (2016). Corruption and the Incumbency Disadvantage: Theory and Evidence. Journal of Politics, 77(4), 928–942.

Lewis-Faupel, S., Neggers, Y., Olken, B. A., & Pande, R. (2014). Can Electronic Procurement Improve Infrastructure Provision? Evidence From Public Works in India and Indonesia (No. 20344; NBER 39
# **5 Summary and Conclusions**

With a long history of political instability and conflict, as well as weak infrastructure and mountainous terrain, Afghanistan is particularly vulnerable to economic and natural shocks. The 2007-2008 global food crisis was no exception. Afghanistan experienced rapid increases in the prices of staple foods and other commodities due to both international and domestic factors. Our work makes several contributions in helping to understand how the people of Afghanistan were affected by, and how they coped with, these price shocks. While there has been an abundance of research on the causes of the food crisis, less has been done on household responses to the crisis. The household-level research that exists often uses pre-crisis survey data and simulation methods. We use the nationally-representative NRVA household survey data to assess the impact of rising food prices on household welfare. The data give us the opportunity to capture actual household behavior during the peak of the global crisis, providing rare insight into short-term coping mechanisms in a poor, conflict country.

Over the course of the fieldwork (August 2007 – September 2008), the food price index increased by 40%, with some of the largest price increases (100%) seen in wheat – the Afghan staple. Our analysis examines the food-based coping mechanisms (i.e., reductions in calorie and nutrient intake and dietary diversity) that households employ in the face of such price shocks. During this time, food insecurity (households with per capita daily calorie intake less than 2,100 calories) increased by 10 percentage points to a striking 34% in the fourth quarter; that is, in the summer of 2008 over one-third of Afghan households were unable to meet the minimum daily energy requirements.

We find that increases in the price of wheat flour lead to large reductions in real monthly per capita food consumption, but smaller declines in per capita daily calorie intake. Households are able to buffer the shock to energy intake by changing the composition of their diets. In particular, households move away from nutrient-rich foods
Policy Research Working Paper 9974

# **Abstract**

Issues of data availability and incomparability in the measurement of household consumption arise frequently when measuring poverty trends over time. Yet, understanding these trends is key to guide national and international policy makers in their poverty reduction efforts. This paper aims to estimate a long-run poverty trend for Nigeria, a country whose poverty trends are crucial for regional and global estimates. In 2020, the Nigerian National Bureau of Statistics released the first official poverty estimates for Nigeria in almost a decade, calculated using the 2018/19 Nigerian Living Standards Survey. Yet the official poverty estimates from the 2018/19 Nigerian Living Standards Survey cannot technically be compared with those from the 2009/10 Harmonized Nigerian Living Standards Survey—the previous official household consumption survey—given key differences in the way household consumption was measured and concerns around data quality in the 2009/10 survey.

To address this challenge, this paper uses two distinct methodologies to construct a poverty trend for Nigeria in the decade before the COVID-19 crisis. First, it uses sector-level gross domestic product growth rates combined with microdata from the 2018/19 Nigerian Living Standards Survey to “backcast” poverty rates back to 2009. Second, it uses survey-to-survey imputation methods and data collected throughout the decade through the General Household Survey panel. Despite their very different foundations, these two approaches produce very similar results, suggesting that there was a small reduction in poverty at the beginning of the decade, followed by a period of stagnation or even a slight uptick in poverty following the 2016 economic recession. The paper estimates a poverty rate of between 42.2 and 46.3 percent in 2009, translating into a reduction in the poverty headcount rate of between 3 and 7 percentage points between 2009 and 2018/19.

This paper is a product of the Poverty and Equity 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
# WPS3598

## **Quantitative Analysis of Crisis: Crisis Identification and Causality**

### Yoichiro ISHIHARA<sup>i</sup> World Bank

Email: yishihara@worldbank.org

### **Abstract**

Studies use different conceptual and operational definitions of crises. The different crisis identifications can lead to inconsistent conclusions and policy formulation even if the same analytical framework is applied. Also, most studies focus on only a few types of crises. This narrow focus on crises may not capture the multidimensionality of crises. Seven crisis types are analyzed; namely, (i) liquidity type banking crises, (ii) solvency type banking crises, (iii) balance of payments crises, (iv) currency crises, (v) debt crises, (vi) growth rate crises and (vii) financial crises. Crisis data are collected from 15 emerging economies in 1980-2002 on a quarterly basis. The crisis identification exercise finds that multidimensionality in which different crisis types occur in short-periods is one of the most important characteristics of recent crises. Further, Granger causality tests in five Asian economies (Indonesia, the Republic of Korea, Malaysia, the Philippines and Thailand) find that currency crises tend to trigger other types of crises, and therefore exchange rate management is essential.

### World Bank Policy Research Working Paper 3598, May 2005

_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 quick ly, 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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> i Ishihara, World
Because women in low socioeconomic neighborhoods consume FAFH more frequently, figure 6 shows the share of overweight and obese women (ages 15–49) in the four socioeconomic groups. The higher the socioeconomic status of the neighborhood, the smaller the share of overweight women, though the standard deviations in the high socioeconomic status group are quite high. While 42 percent of women in neighborhoods with low socioeconomic status are overweight, the proportion decreases monotonically to 32 percent in neighborhoods with high socioeconomic status.

**Figure 6: Percentage of overweight and obese women (ages 15–49), by socioeconomic neighborhoods**

<!-- Start of picture text --> Overweight Obesity<br>Socioeconomic groups Socioeconomic groups<br>low mid-low<br>mid high<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>Percentage of obese individuals<br>Percentage of overweight individuals<br>10 10<br>0 0<br><!-- End of picture text --> _Source:_ Estimates based on data of the continuous demographic and health survey 2013. _Note:_ Overweight is defined as 25 ≤ BMI < 30, and obesity as BMI ≥ 30. Data are weighted.

There is a similar pattern in the prevalence of obesity. The prevalence rate is 25 percent in in the low socioeconomic status neighborhoods and falls to 17 percent in the high socioeconomic status neighborhoods. Thus, there is descriptive evidence of a socioeconomic gradient in overweight and obesity. Especially women in neighborhoods of low socioeconomic status that more frequently consume FAFH (see figure 5) exhibit the highest prevalence of overweight and obesity, in line with the low nutritional quality of diets in the restaurants in these neighborhoods (see figure 2). However, it may be that this pattern is merely an outcome of other underlying factors. The following section examines if this is the case and estimate the strength of these relationships once
collecting qualitative observations from public online spaces in Jordan. First, I analyze hundreds of business marketing posts in Facebook groups and document significant differences in male and female users’ posting behavior. Women are significantly more likely than men to conceal parts of their identity by using full names and real pictures on the profiles from which they market their businesses. Second, I document the existence of dozens of regional and national private women-only Facebook trading groups in Jordan, with thousands of members in each. These gender-segregated groups are often used as ‘safe spaces’ for female entreprenerus to interact and transact without male presence. Their popularity is consistent with existing evidence on female entrepreneurs generally segregating into female-dominated industries with less interactions with men (Ashraf, Delfino, and Glaeser 2019). Together, these observations suggest that cultural constraints on women’s visibility and interactions are often mirrored online, but also that female entrepreneurs use digital platforms’ features to operate _within_ these constraints, potentially at the expense of their businesses’ visibility.

Based on these findings, I set up an experiment aiming to increase businesses’ online exposure without compromising owners’ visibility. This is possible by setting up public Facebook business pages, which allow marketing a business without publicizing its owner’s identity. To understand the impact of online markets on low-mobility women, including those with limited digital literacy, I also designed the intervention to lift informational, logistical, and financial barriers to technology adoption. Particularly, I work with 1,122 female entrepreneurs recruited from a database of microfinance clients, and I randomize their access to logistical support in setting up virtual storefronts for their businesses on Facebook. The support offered creating a public Facebook business page, marketing it for up to 6 months, and covering paid advertisements of up to $20 for active pages. The support package therefore provided a digital marketing outsourcing service for the treatment group. To help treated participants maintain their online presence, the logistical support was additionally bundled with access to a virtual asynchronous training on digital marketing. The training
46 percent of the corporate credit portfolio and 20 percent of the total credit portfolio (Figure 4). In other words, 46 percent of the loans that banks allocate to corporates are subject to potential financial losses due to a disruption of ecosystem services. By analyzing separately this aggregate estimate by the source of resources (earmarked vs. non-earmarked), size of banks, borrowers’ size, and loan quality, we find evidence of heterogeneity in the exposures to physical risks.

Earmarked credit is marginally more exposed to the risk of biodiversity loss. The Brazilian credit market is historically characterized by heavy government interventions for allocative purposes. Earmarked resources are credit granted by financial institutions with implicit or explicit subsidies

> 23 See https://www.reuters.com/article/us-brazil-jbs-nordea/nordea-drops-jbs-shares-over-environment-covid-19- <u>response-idUSKBN24X3VD</u> 24 See https://www.theguardian.com/environment/2020/aug/12/hsbc-sounds-alarm-over-investment-in-meat- <u>giant-jbs-due-to-deforestation-inaction</u>

> 25 We acknowledge that we might be incurring a significant underestimation given that we only account for listed companies. Nonetheless, to the best of our knowledge, the MSCI ESG database has the most comprehensive coverage of firms' environmental controversies in Brazil.

12
that predict large long-run costs of the pandemic in terms of educational dropout and decrease in average years of schooling (see e.g. Azevedo _et al_ ., 2021; Neidhöfer _et al_ ., 2021) In the second part of the paper, we carry out some basic microsimulations to assess the potential effect of changes in human capital due to the COVID-19 crisis on future income distributions. Specifically, we simulate earnings assuming the COVID-19 pandemic affected human capital through two channels: a reduction in school days and an increase in dropouts. We find that the pandemic is likely to have significant long-term consequences in terms of incomes and poverty if strong compensatory measures are not taken soon.

The rest of the paper is organized as follows. In Section 2 we discuss the key features of the COVID-19 pandemic in Latin America along with the ensuing social distancing measures, stressing the scope of the school closures. In Section 3 we analyze the impact of the pandemic on different educational outcomes (enrollment, public-private schooling) taking advantage of a large set of harmonized national household surveys in 12 Latin American countries, including those collected in 2020. In Section 4 we adopt the framework of Neidhöfer _et al_ . (2021) to approximate months of instructional losses associated to the school closures during the crisis. In particular, we use an updated version of these calculations and compute them at the individual level by exploiting microdata from national household surveys. In Section 5 we carry out microsimulations to provide some rough estimates of the potential impact of the educational losses on future incomes, and on indicators of poverty and inequality in the region. We conclude in Section 6 with a discussion of the results and their implications.

# **2. COVID-19 and school closures**

During the COVID-19 pandemic the well-being of children was challenged by several contemporaneous shocks potentially affecting their human capital persistently. The health crisis was accompanied by an economic crisis, and, on 3
Measured stocks of capital are required to characterize the transformation process and to distinguish growth that is due to the accumulation of input factors from that due to changes in factor productivity. Moreover, in order to compare this transformation process among countries, a compatible measure is needed across countries and through time. In general, this has been lacking, especially for panels that include developing countries.<sup>4</sup> By necessity, empirical studies have therefore looked to proxies for agricultural capital, most often measures related to on-farm machines, livestock, and orchards, or have omitted capital entirely.<sup>5</sup> As we argue below, the composition of capital varies as the prevalent technologies change as part of the structural transformation of economies. Consequently, the roles played by on-farm machines or other proxies for a general measure of capital change. In general, machinery capital becomes less relevant as the stock of capital in agriculture grows; this creates problems for studies of agricultural productivity and growth.

Mundlak, Larson, and Butzer (1999) and Coelli and Rao (2005) list a number of early crosscountry studies of agricultural productivity, including important studies by Bhattacharjee (1955), Hayami and Ruttan (1970, 1971), Evenson and Kislev (1975), Nguyen (1979), Mundlak and Hellinghausen (1982), Kawagoe and Hayami (1983, 1985), Antle (1983), and Lau and Yotopoulos (1989). Varied capital measures, based on combinations of farm machinery, tractors, livestock, orchards, and, less frequently, data on irrigation and farm structures, were used. Recent econometric studies are less uniform in their methodology, but many still include capital proxies (most often in the form of livestock and machinery) in estimated production functions; examples include Fulginiti and Perrin (1993, 1998), Block (1994), Craig, Pardey, and Roseboom (1997), Wiebe et al. (2003), Cermeño, Maddala, and Trueblood (2003
Lag T 30-33C: Non-Major Season|0_._005<br>(0_._012)|||
|Lag T 27-30C: Non-Major Season|_−_0_._013<sup>_∗_</sup><br>(0_._007)|||
|Lag T_>_33C: Non-Growing Season||0_._010<br>(0_._008)||
|Lag T 30-33C: Non-Growing Season||0_._006<br>(0_._009)||
|Lag T 27-30C: Non-Growing Season||0_._003<br>(0_._007)||
|Outcome Mean|0.871|0.871|0.871|
|**Fixed Effects**||||
|Year|X|X|X|
|Enumeration Area|X|X|X|
|Observations|10,207|10,207|10,207|
|R<sup>2</sup>|0.331|0.332|0.328|

Notes. Estimates are from linear regressions using panel fixed effects. The dependent variable is the number of negative food consumption shocks experienced by the household in a given calendar year. The regressors represent the number of days in the temperature bins during the previous calendar year. All regressions control for precipitation bins from the previous calendar year. Errors are clustered at the enumeration area level. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

24