rafmacalaba/data-use-mentions-extended huggingface dataset:
# School Participation & Progression - (Post-preprimary)

Study countrycode Type: of intervention ES (95% Cl) Outcome_varable High Income - Basic (Felfe et al 2014) ESP Subsidized Childcare & TT & Curriculum 0.11 (-0.23, 0.007, Falling behind a grade in primary* (12 y.0.) (Fetfe et al 2014) ESP Subsidized Childcare & TT & Curriculum -0.08 (-0.25, 0.087, Falling behind a grade at secondary* (15 y.0.) (Drange et al 2076) NOR Subsidized Kindergarten & Play Curriculum ~0.04 (-0.04, 0.027, School performance (test score) (15-16 y.o.) (Brotman et al. 2018) USA TT & Curmiculum & Parental Engagement 0.21 (-0.08, 0.5171, Academie performance (7 y.0.) (Cameiro and Ginja 201454 (H5} - Subsidized Preschool 0.97 (-0.13, 2.0777, In high school (16-17 yo.) Male (Gray-Lobe et al 2027) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.12 (-0.01, 0.2477, Enrollment in college (18 y.0.) (Gray-Lobe et al 2021) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.15 (0.00, 0.30) T. Graduated high school (13 yo.) (Lipsey et al. 2078) USA Subsidized Pre-K, 70.03 (-0.11, 0.057, School attendance (8 yo.) (Lipsey et al. 2078) USA Subsidized Pre-K ~0.04 (-0.08, 0.077, School attendance (6 y.0.) (Lipsey et al. 2078) USA Subsidized Pre-K ~0.04 (-0.12, 0.047, School attendance (7 y.
The adjusted poverty line for each scenario reflects the income levels required for an individual to be able to afford the same food basket despite the disproportionate rise in food prices. The simulated household consumption for the projected year are then compared to the new poverty line to compute poverty measures that take into account the impact of the higher relative price of food.

# **IDP and IS‐affected populations**

As mentioned previously, the twin crises are likely to have impacted households across Iraq through different transmission channels. For instance, households directly affected by the war against the Islamic State– which include the displaced, and those still living in the IS‐affected governorates – will be affected differently, as will households belonging to communities hosting IDPs. In order to include the effect of these shocks in the simulation, additional assumptions are needed in the crisis scenario for 2014.

We first identify two types of populations of interest: a) Internally Displaced Persons (IDP) and b) those left‐behind. The first group is based on information from the IOM Displacement Tracking Matrix (IOM DTM) of families crossing governorate boundaries by origin and source governorate. This is summarized in (Table 6). In the micro‐simulation exercise, we focus on internal displacement from three governorates, which accounts for 90 percent of the total cross‐governorate displacement. Based on the population flows measured in the IOM DTM, a subset of families is randomly selected from three governorates of origin (IS‐affected governorates) ‐ Anbar, Nineveh, and Kirkuk ‐ and allocated in different host governorates in proportion to the movements measured in the IOM DTM. The second group consists of those who continue to live in the three governorates directly affected by IS – Anbar, Nineveh, and Kirkuk.

First, we describe the assumptions made about the income and consumption flows of the IDPs. The consumption of IDPs could have been reduced by a decrease in the quality or flow of services received from housing as well as the loss of immobile durable goods
# 9. New Data for Development Policy

_<mark>Robert Cull, Dean Jolliffe, Vijayendra Rao</mark>_ Until recently, most of development economics research relied on traditional data types such as household and firm surveys and national accounts. Governments (or agents authorized by governments) have typically been central to the data collection efforts and data typically have been collected for specific purposes, often to foster development. For example, the earliest social surveys were undertaken in England by Charles Booth and Seebohm Rowntree in the 1890s to measure poverty, to describe the deplorable living conditions of the poor, and to bring about social policy reform.

Traditional data methods require strong statistical capacity. Trained staff, budgetary autonomy for agencies that collect data, adequate installations, connected databases, and international partnerships are important factors in shaping successful national data systems.<sup>92</sup> These resources are often scarce in low‐income countries,<sup>93</sup> leaving them least equipped to collect the data necessary to assess and understand the scope and nature of development problems and make inroads to solving them. Enhancing the statistical capacity of client countries therefore has, and will continue to be, a point of emphasis for the World Bank Group.

## **Moving beyond traditional data**

At the same time, traditional data collection methods can be relatively costly.<sup>94</sup> Surveys are therefore performed infrequently,<sup>95</sup> and they lack the granularity necessary to make meaningful inferences about sub‐populations of interest. In contrast, so‐called “digital data” from mobile phones, satellites, and other sources can be collected cost‐effectively, with high frequency, and at fine levels of granularity. Digital data therefore may offer new insights for understanding and resolving some development challenges. Recent examples from empirical research include:

- **Measuring poverty, agricultural productivity, and malnutrition.** Influential recent work has used satellite imagery of
The dependent variable is a dummy equal to 1 if the top manager of the firm is a woman and 0 otherwise ( _Woman top manager_ ). The data source is WBES. The mean value of the variable is about 0.18 or 18 percent and the standard deviation is 0.38.

# 3.4 _Main explanatory variable_

Our main explanatory variable is total domestic credit provided by banks to the private sector as a ratio of GDP ( _Bank credit_ ). The variable is lagged by 1 year from the date of WBES in the countryyear.<sup>2</sup> We focus on bank credit because it is one of the most important sources of credit for private SMEs in the developing world (see Berger and Udell 1998, Muravyev et al. 2009). In our sample, the mean value of _Bank credit_ equals 0.41 and the standard deviation is 0.18. The data source is WDI, World Bank.

For robustness, we show that our main result holds for alternative measures of credit availability and financial development (all lagged by 1 year). These include total credit by banks and non-bank financial intermediaries provided to the private sector as a ratio of GDP ( _Total credit to private sector_ ), log of number of ATMs per 100,000 adults, and log of number of bank branches per 100,000 adults. The data source for all these variables is WDI, World Bank. We also use total bank credit to the private sector as a ratio of total bank deposits ( _Bank credit to bank deposit ratio_ ) taken from GFDD, World Bank. This variable captures the quality of financial intermediation and therefore the availability of credit (see Beck et al. 2007, Dutta and Sobel 2018).

# 3.5 _Controls_

> 2 Average values of the variable are used where the average is computed over one year lagged values of the variable for the years the WBES round under consideration was administered in the country and for which data are available. Unless specified otherwise
the weighted averages over the post-transference relative treatment period coefficients (0 _≤ l ≤_ 10), where the weights for each relative period coefficient are based on the proportion of the treated group (areas managed by AP) in the overall population during that specific relative period [49].

Our control group comprises protected areas that AP has determined meet their established criteria for potential future management, which AP refers to as “anchor areas.” Polygons demarcating the boundaries of AP’s anchor areas are publicly available [51]. We identify individual protected areas in our control group in three steps. First, we used Quantum GIS to manually select all polygons from each of the World Database of Protected Areas’ (WDPA) shapefiles that overlapped with the boundaries of AP’s anchor areas. Second, we eliminated duplicate entries from the selected WDPA boundaries. Finally, we manually validated the remaining WDPA boundaries to confirm that protected areas in our control group match the set of polygons displayed on AP’s map of anchor areas. In three cases, we used QGIS and georeferenced polygons from either UNESCO or the literature [95, 96] to manually create shapefiles for AP anchor areas that did not have shapefiles in the WDPA.

Several protected areas in our control group are partly managed by governments and partly managed by an NGO other than AP. We retain these protected areas in our control group because they do not employ a “delegated” management model like AP, where the NGO has full control over management decisions [29]. Several of AP’s 15
interviewed due to lack of guaranteed privacy.

> 18 The sample size for sexual violence is smaller, because in five countries only one of the questions on sexual abuse is included, so that the measure of sexual violence in these countries is not comparable to the rest of the sample. As a result, these countries are excluded from the sexual violence sample (see Table S1.1).

> 19 There is significant coincidence of physical and sexual violence: 19.81 percent of women reported experiencing some form of violence over the last twelve months; of these women, 24.23 percent report both physical and sexual violence. Of the sample of women who reported suffering some form of violence in the last twelve months, 65.81 (9.96) percent reported physical (sexual) violence only.

> 20 The ILO estimates are based on either household labour force surveys or population census data, with the restriction that they must be representative of the whole country, with no geographic limitation. More information on the construction of the estimates is given in Bourmpoula et al. (2015). These rates are positively correlated to within-survey employment rates, with a correlation coefficient of 0.299 with female unemployment rates in the DHS survey. Men’s employment is less correlated (0.030) but this is not surprising because the quality of data on male unemployment in the DHS is low: it is collected in only 18 of our 31 countries, and where it is collected, it is missing in around 67% of cases.

8
self-help groups to exchange ideas for improving their business, organize savings and credit, and cooperate on other aspects of their economic activity.

The program was implemented in two phases. In Phase 1, the researchers randomized 60 of the 120 to receive the intervention. The 60 treated were villages villages further randomized into two sub-groups of 30 villages, with one of them additionally receiving the group dynamics training. The remaining 60 villages were assigned to a waitlist group to be treated in Phase 2, 18 months later.

The baseline survey on the 1,800 program participants was administered between April and June 2009. The endline survey was administered between June and August 2012, 16 months after the first grant.

## <u>Variable details:</u>

- to one if the answers

- Self-employment: equal respondent positively question Q110 of Section 4: Economic Activities (”Are you currently doing any business?”).

- Any employment: equal to one if the respondent reports non-zero employment hours in the past month. The activities considered are listed in Section 4 of the survey, before Q99.

- Migration: based on question Q15 of Section 1: Household Characteristics (”Are still in the same location as were when we last interviewed

- you living you you?”).

- Regression controls: the regressions reported in panel B of tables 3 and 4 control for all baseline covariates reported in online Appendix B of Blattman et al. (2016).

# **C.3 Nairobi microfranchising**

The Nairobi microfranchising experiment involved two labor market interventions targeting women aged 18 to 19. The first intervention (”franchise treatment”) combined several elements: business skills training, franchise-specific vocational training, start-up capital (in form of physical capital specific to each franchise), and ongoing business mentoring. The second intervention (”grant treatment”) offered unconditional cash grants of $239. Outcomes were measured at midline, 7 to 10 months after the end of the intervention, and at endline,
status (World Bank 2019).

# **3. During the 2009 global recession: Resilience**

The 2009 global recession affected EMDEs through trade and financial channels. In many EMDEs, trade collapsed. The plunge in commodity prices weighed on growth in commodityexporting countries, while reversals of financial flows, especially portfolio investment and cross-border bank lending, led to severe credit crunches. That said, EMDEs weathered the recession better than advanced economies. First, their links with the financial institutions and markets at the center of the crisis in advanced economies was limited. Second, services trade and flows of FDI, remittances, and foreign aid was resilient. Third, swift policy actions were taken to stabilize financial systems and stimulate aggregate demand.

The global financial crisis in 2008 led the global recession in 2009. Triggered by defaults in the U.S. sub-prime mortgage market, the U.S. financial system came under increasingly severe stress in the second half of 2007 and early 2008, culminating in a collapse in housing prices in late 2008 (Figure 2). The bankruptcy of Lehman Brothers in September 2008 triggered a run on key funding markets. This exposed the fragility of banks that were dependent on short-term wholesale funding, which had been essential to the rapid growth of securitization, and also reflected inadequate regulatory oversight (Duffie 2019).

> 7 Based on 135 EMDEs with available data.

> 8Based on 132 EMDEs with available data. An investment surge is defined as an episode during which the real gross fixed investment-to-GDP ratio is at least one standard deviation above the Hodrick-Prescott-filtered trend (Ohnsorge and Yu 2017; World Bank 2016a).

6
individual household members cannot be directly calculated from most household surveys. One common approach is to compare female- and male-headed households to infer effects of an individual‘s gender on welfare. However, female-headed households (FHH) can be extremely heterogeneous. Some have remitting adult male migrants, others are headed by poor widows who have no means of livelihood and yet others, by wealthy divorcees and other single women who have careers or other independent means of support. On balance, a majority of studies, though by no means all, find that FHH are indeed poorer in many parts of the world; see Buvinic

> 9   A comprehensive review of safety net policies in Mali is given in World Bank, 2010.

> 10  One exception is given by the small schemes targeted to orphans and their often widowed mothers by the NGO Islamic Relief.

9
**Figure A17:** Downward mobility by gender

<!-- Start of picture text --> ARG BOL BRA CHL<br>COL CRI ECU GTM<br>HND MEX NIC PAN<br>PRY 780 URY VEN<br>1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990<br>Birth decade<br>Male Female<br>Graphs by Country<br>.5<br>0<br>.5<br>0<br>.5<br>Downward mobility<br>0<br>.5<br>0<br><!-- End of picture text -->

**Figure A18:** Intergenerational Mobility and Literacy of the Old Generation

<!-- Start of picture text --> IGM UP = .262 + -.3085 lit_par<br> (.0019) (.0042)<br>IGM UP = .217 + .7873 lit_par<br> (.003) (.0065)<br>0 .5 1 0 .5 1<br>Share literate old residual Share literate old residual<br>(a) Upward mobility (b) Downward mobility<br>1.5 .8<br>.6<br>1<br>.4<br>.5<br>Upward IGM residual .2<br>downward IGM residual<br>0<br>0<br><!-- End of picture text --> Notes: This graph uses data at the district-level netting out country fixed effects.

66
is important to appreciate that the coresident sample bias common in the household surveys in developing countries is best modeled as a truncation, not censoring. The most common problem in the context of household surveys in developing countries is that there is no information (on both dependent and independent variables) for the the non-resident children resulting in truncation of the sample. The evidence presented above suggests that the non-resident children are not randomly distributed, both in Bangladesh and India: they come mostly from the tails of the schooling distribution.

21
research in developing countries—accounts for a substantial share of the association between socioeconomic status and cognitive development.

Our findings are based on a single cross-section of observational data, and interpretation of the associations we report is therefore subject to some uncertainty. To the extent that the relations are causal, they suggest that a three-pronged approach may be useful for improving cognitive development among young children in poor countries. First, programs that directly raise household SES may have high returns in terms of child development. Second, improving children’s health should be given high priority, both for its own sake and because of its association with cognitive outcomes. There is, for example, strong evidence from a randomized experiment in Guatemala that protein supplementation in childhood results in long-run improvements in cognitive outcomes (Brown and Pollitt, 1996), although experimental evidence on the effects of iron supplementation (reviewed in Grantham-McGregor and Ani, 2001) and treatment for helminth infection (reviewed in Dickson et al., 2000) is more mixed. Third, programs and policies that increase the amount of cognitive stimulation children receive and improve the quality of their home environments may also be valuable. Only a small number of randomized control trials have been conducted that examine interventions in developing countries that aim at improving both health and parenting-cognitive stimulation, through homebased or center-based interventions (McKay et al., 1978; Waber et al., 1981; GranthamMcGregor et al., 1991 and 1997; Powell et al., 2004; see also Behrman et al. 2004 for an observational, non-experimental study from Bolivia). Several of these interventions have yielded promising results. For example, the recent study by Powell and colleagues, based on a sample of undernourished pre-school children from Jamaica, concludes that a home visiting intervention that provided children with cognitive stimulation yielded an increase of nearly one standard deviation in a global measure of development. In our sample, only a minority of children were read to, even though the fraction of parents who are illiterate was very small. Controlling for
been as steep as it appears in the survey data.<sup>3</sup> Finally, a related weighting issue has implications for the national poverty figures. Because the 2000/01 survey sampling frame was based on the 1988 census, the sampling weights understate the relative weight of areas like Dar es Salaam that experienced rapid population growth. The overall effect of this phenomenon is small. Poverty calculations done after reweighting the survey data to reflect the regional population distribution in the 2002 census show estimated national poverty incidence to be 35.3%, versus 35.6% using the original weights. This difference can be attributed entirely to the greater weight accorded Dar es Salaam with the revised weights. Using population estimates from the 2002 census, 7.4% of the population lives in Dar es Salaam as opposed to 5.8% when population numbers from the 1988 census are used. The difference between the estimates is not due to changes in poverty incidence within the three strata. Reweighting has no effect on poverty estimates for Dar es Salaam and rural areas as a whole, and in other urban areas it increases the estimate negligibly, from 25.8% to 25.9%. To maintain comparability with the official poverty statistics, in the remainder of the paper we calculate poverty rates using the official (non-reweighted) sampling weights. Only in section 3.2, table 4, where we decompose changes in poverty into changes by sector and population shifts across sectors, do we make use of reweighted survey weights.

# **2.2 National Accounts**

The other main source of economic information for Tanzania is the national accounts information, tabulated in the _Economic Survey 2002_ (United Republic of Tanzania, Office of the President, 2002). Key data drawn from the report is shown in Table 2.

adult equivalent basis used for the poverty figures. It is also possible that a slightly different consumption aggregate than that employed here was used to calculate mean consumption.

> 3 It would
practices (Pandey et al. 2019; Kochar et al. 2019; Datta et al. 2015; Ban et al. 2020). Findings are less conclusive on the impacts on household consumption, expenditures, and assets. In India, an impact evaluation found that the _Andhra Pradesh District Poverty Initiatives Project (APDPIP)_ increased per capita consumption by 11 percentage points; however, studies of the _NRLM_ , _Tamil Nadu PVP_ , and _JEEViKa_ projects did not find these projects to improve households’ total or consumption expenditure (Institute of Rural Management Anand 2017; Khanna et al. 2015; Datta et al. 2015). Relatedly, while there is some evidence that these projects increase assets for beneficiary households, this effect varies depending on the asset type (Institute of Rural Management Anand 2017; Khanna et al. 2015; Datta 2015).

Other studies show more mixed or null results on economic welfare. <mark>In Afghanistan, an RCT of the</mark> _<mark>NSP</mark>_ <mark>found that the project had strong positive effects on perceived economic welfare, especially among women, but no strong effects on household income, consumption, or assets (Beath et al. 2013). Similarly, an evaluation of</mark> _<mark>KALAHI-CIDSS</mark>_ <mark>showed no effects on household poverty status or labor force participation and earnings (Beatty et al. 2018).</mark> Variation in a project’s overall design, the type of subprojects funded, and the per capita allocation of funds likely affect CDD’s impacts on economic welfare outcomes.<sup>29</sup> For example, subprojects chosen by the community that consist primarily of improving clean water and sanitation or health services may not necessarily have direct impacts on per capita income in the short or

> 26 As measured by nighttime light output. Each completed _CSF_ project increased the NTL output by 20 percent. The median area received