rafmacalaba/data-use-mentions-extended huggingface dataset:
Furthermore, the analysis reveals that farmers' updating of mistaken beliefs in response to information on their true plot size is remarkably incomplete and asymmetric, indicating a greater willingness to adjust beliefs up than down. Updating is stronger only among larger plots, but still asymmetric. These patterns are likewise consistent with inattention as well as confirmation and self-esteem biases. Moreover, the information treatment affects self-reported information on other, non-land inputs, such as fertilizer and labor, consistent with the hypothesis that farmers employ simplifying mental models – e.g., optimal prediction error (Hyslop and Imbens, 2001) – to track these variables. This implies that NCME in one production input likely propagates to other production inputs, generating correlated measurement error, which further complicates econometric correctives, because replacing an erroneous self-reported variable with an accurate measure of the same variable can aggravate rather than reduce bias in regression coefficient estimates if one cannot also correct for the correlated NCME in other variables (Abay et al., 2019). The scale and persistence of the NCME we observe almost surely has distributional and welfare implications, although estimating those effects falls beyond the scope of this paper.

# **2. Experimental Design and Data**

The data come from a randomized experiment that was embedded into the Malawi National Crop Cutting Study (NCCS), which was implemented by the National Statistical Office (NSO) in 2019/20, in collaboration with the World Bank’s Living Standards Measurement Study (LSMS) team. The NCCS was implemented in a national sample of 72 enumeration areas (EAs) selected at random from the sample of EAs that were scheduled to be visited by the Fifth Integrated Household Survey (IHS5) in the months of December 2020 and January - February 2021.<sup>3</sup> In each EA, 24 maize cultivating households were selected at random from the universe of maize cultivating households identified through a full household listing in each EA. Of the sampled households, 16 were selected at random for a separate crop cutting
# **Who Is Employed?**

# **Evidence from Sub-Saharan Africa on Redefining Employment**<sup>**1**</sup>

Isis Gaddis Gbemisola Oseni World Bank and IZA World Bank Amparo Palacios-Lopez Janneke Pieters World Bank Wageningen University and IZA

**_JEL:_** J21, J16, J43, C83

**_Keywords:_** Labor statistics, agricultural labor, gender, own-use production work, survey design, SubSaharan Africa

> 1 The authors gratefully acknowledge funding from the U.K. Department for International Development (DFID) under the “Minding the (Data) Gap: Improving Measurements of Agricultural Productivity through Methodological Validation and Research” project and from the William and Flora Hewlett Foundation under the “Improving the Measurement of Subsistence Agriculture in the Framework of ICLS 19 and Its Implications for Gender Statistics” project. 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. We would like to thank Kathleen Beegle, Bénédicte de la Brière, Antonio Discenza, Louise Fox, Caren Grown, Stephan Klasen, Kieran Walsh and Michael Weber for valuable comments on earlier drafts. Of course, all errors are our own.
Policy Research Working Paper 5497

# **Abstract**

Many developing countries are unable to provide their industrial sector with reliable power and many enterprises have to contend with electricity that is insufficient and of poor quality. Because of these constraints, firms in developing countries opt for self-generation even though it is widely considered a second best solution. This paper develops a theoretical model of investment behavior in remedial infrastructure when physical and credit constraints are present. It then tests econometrically some implications from this model using a large sample of enterprises from 87 countries from the World Bank Enterprise Survey Database. After showing that these constraints interact and have non-linear effects depending on the industrial sector’s degree of reliance on electricity and size of firms, the paper draws differentiated policy recommendations. Credit constraints appear to be the priority in sectors very reliant on electricity to spur entry and convergence to the technological frontier, while in other sectors, firms would benefit more widely from marginal improvements in electrical supply.

This paper—a product of the Research Support Unit, Development Research Department—is part of a larger effort by the World Bank to use enterprise surveys to identify constraints on productivity and growth in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. For information, contact jdethier@ 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
Table 1. Oaxaca-Blinder decomposition of the change in FLFP between 2002 and 2018

||Overall<br>(1)|Explained<br>(2)|Unexplained<br>(3)|
|---|---|---|---|
|Year 2018|0.32<br>[0.0217]***|||
|Year 2005|0.264<br>[0.0256]***|||
|Difference|0.0569<br>[0.00706]***|||
|Total effect||0.153<br>[0.0247]***|-0.0964<br>[0.0252]***|
|Individual characteristics||0.0257|0.401|
|||[0.00235]***|[0.0323]***|
|Household characteristics||0.0139<br>[0.00155]***|-0.118<br>[0.00713]***|
|Economic situation||0.00624<br>[0.00211]***|0.00943<br>[0.00352]***|
|Local labor market variables||0.104<br>[0.0255]***|-0.0757<br>[0.0288]***|
|Geography||0.00341<br>[0.00200]*|0.0199<br>[0.00932]**|
|Constant|||-0.332<br>[0.0306]***|
|Observations|2,909,416|2,909,416|2,909,416|

Sources: 2002 and 2018 National Census of Guatemala (INE).
[4.10]|[4.21]|[4.24]|[4.00]|(1.00)|(0.82)|(0.83)|
|Mean annual rainfall 2017<br>(mm)|1108.84|1140.53|1140.11|1083.12|0.42|57.40|56.98|
||[396.23]|[381.88]|[380.26]|[406.66]|(90.84)|(77.59)|(77.13)|
|Distance to Woreda capital<br>(km)|14.62|13.66|15.52|14.65|-1.86|-0.99|0.87|
||[15.42]|[16.31]|[13.69]|[15.69]|(3.67)|(3.27)|(2.85)|
|N|2,382|539|532|1,311|1,071|1,850|1,843|

_Source:_ Authors’ analyses based on baseline survey data.

_Note:_ HH stands for household. Basic asset score comprises the following: HH has modern roof, improved stove, modern lighting, toilet facility. TLU stands for tropical livestock unit. Calculation of food insecurity score based on self-experienced events of food insecurity, based on Household Food Insecurity Access Scale (HFIAS). FTC stands for farmer training center. Main crops are maize, wheat, teff, barley, sorghum. Temperature and rainfall assessed at endline. For means, standard deviations in brackets; for mean comparisons, robust standard errors in parentheses, clustered at the mws level. Significance levels: *** p<0.01, ** p<0.05, * p
# **5. POST-EARTHQUAKE CHARACTERIZATION OF ASSET-WEALTH**<sup>**29**</sup>

# **5.1.Data Sources and Methodology**

A post-earthquake food security-oriented survey was conducted in June 2010 by the CNSA in collaboration with its main partners (ACF, FEWS-Net, Oxfam GB, FAO, UNICEF and WFP). The sampling used for the household survey is a probabilistic cluster method, using two stages: (i) enumeration sections (geographical areas) and camps and (ii) households. 2003 census data is used to select the enumeration sections, with a probability proportional to population size. Eight households are then selected randomly in each section. Camps are selected using the International Organization for Migration (IOM) data; the number of camps selected was proportional to the size of the communes. The sampling method yielded 1901 interviewed households, located in the disaster areas (camp and noncamp sites) as well as in some non-directly affected areas. Geographic strata covered by the EFSA II survey are presented in Figure 2.

To randomly select households, different methods were used for the urban households, the rural households and the camps. For urban households, survey investigators observe and mark the location of households on a street map that does not contain socioeconomic infrastructure, and the households are randomly selected. For rural households, previously mapped buildings are randomly selected using enumeration section maps, and households living in those buildings are interviewed; if there are no households inside, then the closest household is selected. For camps, survey investigators start from the centre of the camp and walk towards the outside in a different randomly selected paths. They number each household encountered in the way, and randomly select two households to interview. For all three types of sampling, when multiple households are found living in the same building or tent, a single household is randomly selected.

# **5.2. Assets**

Based on the June survey, an asset index is calculated using a
# The Effects of Childcare on Women and Children: Evidence from a Randomized Evaluation in Burkina Faso<sup>_∗_</sup>

Kehinde F. Ajayi<sup>_†_</sup> Aziz Dao<sup>_‡_</sup> Estelle Koussoub ́e<sup>_§_</sup> _Keywords_ : gender, labor, welfare, childcare, early childhood development _JEL codes_ : J16, J13, I38, O15

> _∗_ This paper is a product of the World Bank Africa Gender Innovation Lab, Office of the Chief Economist, Africa Region. We thank David Evans, Markus Goldstein, Rachael Pierotti, Christophe Rockmore, colleagues from the Africa Gender Innovation Lab, and seminar participants from the Center for Global Development and the Center for the Study of African Economies 2022 Conference for thoughtful comments. We are grateful to Rebekka Grun, Gilberte Kedote, Florence Kantiono, Roland Berehoudougou, Marie Bernadette Kabre, L’Institut de Formation et de Sp ́ecialisation des Personnels de la Petite Enfance (IFSPPE), and the PTR-HIMO team for their collaboration on this project. This work was funded by the Early Learning Partnership and the Umbrella Facility for Gender Equality multi-donor trust funds. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent. This study was registered on the AEA RCT Registry (AEARCTR-0006811). The Institutional Review Boards at the Health Media Lab, Washington DC and Comit ́e d’Ethique Institutionnel pour la Recherche en Sciences de la Sant ́e (CEIRES), Burkina Faso approved this project.

> _†_ Center for Global Development, 2055 L Street NW, Washington, DC 20036. Email: `kajayi@cgdev.org` .

> _‡_ The World Bank, 1818 H Street NW, Washington, DC 20433. Email: `adao1@worldbank.
## 2004 census (see Figure A.1(c) for a map of section boundaries).<sup>11</sup>

The 254 clinics in the full sample fall into 205 sections. Figure 1(b) maps all sections included in the study with those include in the Ebola sub-sample in dark grey. Of the 205 sections, 45 sections include multiple clinics. In our primary analysis we restrict attention to the 160 sections that contain a single clinic and, thus, a unique treatment assignment. Of the 160, we retain 54 control sections, 46 CM, and 60 NFA. As a robustness check, we also analyze the data using a dose-response model, which uses the proportion of clinics in each section that receive either treatment (see Table E.11).

# **3.2 Data Collection**

## **3.2.1 Survey Data on Health Clinics, Health Services and Health Outcomes**

Baseline data collection took place in September 2011; endline surveys, in May and June of 2013 (see Figure 2 for a timeline). We rely on three survey instruments: first, surveys at each clinic, in which enumerators audited the staffing, cleanliness, drug stocks, and registers of clinics; second, surveys of leaders of each village regarding amenities, relations with the clinic, and community development; and third, household surveys which captured health and economic outcomes and health-seeking behavior among other outcomes.

We filed an analysis plan for the survey data at the AEA RCT registry on March 2017.<sup>12</sup> The analysis plan defines ten outcome families: (1) general utilization, (2) maternal utilization, (3) health outcomes, (4) satisfaction, (5) health service delivery, (6) clinic quality, (7) community development and political engagement, (8) contributions to clinics, (9) water and sanitation, and (10) economic outcomes. The subcomponents comprising
Nevertheless, no simple generalizations can be made about the relationship between a country’s income level and the vulnerability of its electricity system to natural shocks. In fact, a wide range of factors determine the share of outages that are caused by natural shocks – several of which are analyzed in this study. For instance, hazard intensity and exposure are crucial. In the US, states in the South-east experience a high rate of outages due to natural shocks, as they lie in the path of Atlantic tropical storms. However, even within the US, storms of the same intensity can cause different levels of disruptions in different locations. The study shows that the rate of forest cover, i.e. vegetation, can explain why storms cause more damage in certain places. As trees and branches damage power lines, storms are more likely to be disruptive in forested regions. This also emphasizes the role of maintenance – in regions with high forest cover, maintenance (in the form of tree pruning) is all the more important.

Still, the quality and resilience of the power system itself is key to prevent frequent outages and thus ensure reliable service quality for end-users. As the evidence in this study shows, there are significant differences in network fragility across regions and countries. Using high-frequency climate data, this study shows that power systems in the US and Bangladesh have very different abilities to withstand storms. While the baseline level of outages is already significantly high in Bangladesh, the analysis also shows that the same storm intensity is far more likely to cause outages in Bangladesh than in the US. For instance, wind speeds above 35 km/h are found to be 12 times more likely to cause an outage in Bangladesh than in the US. Overall, these findings suggest that higher network resilience against natural shocks is also likely to result in a reduction of baseline fragility of the network, and thus higher service quality for users. Moreover, we find robust empirical evidence of adaptation effects. This implies that at given levels of
# **Annex D: China firm‐level data**

Table D1: Selected variables from the Thomson Reuters Data base

|**Variable**|**Description**|
|---|---|
|R&D|Represents expenses for research and development of new products and services by a<br>company in order to obtain a competitive advantage. In unit USD|
|Intg.|Represents Intangibles, which are the gross intangibles reduced by accumulated<br>intangible amortization and is reported in USD Intangibles consists of patents,<br>copyrights, franchises, goodwill, trademarks, trade names, secret processes, and<br>organization costs.|

39
and Further Rise in SNAP Enrollment: Disentangling Business Cycle Fluctuations and Policy Changes. _American Economic Journal: Economic Policy 10_ (4), 153–176.

- Goldin, J., T. Homonoff, and K. Meckel (2022, February). Issuance and Incidence: SNAP Benefit Cycles and Grocery Prices. _American Economic Journal: Economic Policy 14_ (1), 152–178.

- Goldsmith-Pinkham, P., I. Sorkin, and H. Swift (2020, August). Bartik Instruments: What, When, Why, and How. _American Economic Review 110_ (8), 2586–2624.

- Handbury, J. and D. E. Weinstein (2015, January). Goods Prices and Availability in Cities. _The Review of Economic Studies 82_ (1), 258–296.

- Hastings, J. and J. M. Shapiro (2018, December). How Are SNAP Benefits Spent? Evidence from a Retail Panel. _American Economic Review 108_ (12), 3493–3540.

35
October 2008, as shown in Table 2.<sup>26</sup> Tests in mathematics and Indonesian, designed by MoNE, were administered to all students in grade four at baseline and six at endline.<sup>27</sup> Survey questions center around hypothesized

> 20 MoNE supplies some support to private schools, but has direct oversight over public schools.

> 21 This restriction was imposed because in the initial design, facilitated meetings with sub-district government education officials were envisioned, and too few villages per meeting would make this intervention ineffective. However, this idea was never implemented, making the restriction unnecessary.

> 22 That meant those with the grade six average student scores below four or above eight. School-level score data obtained from MoNE.

> 23 Parallel classes are grades with more than one section or teacher. This restriction was imposed because the evaluation was not planning on assigning student IDs or ensuring that the student population was identical over time. With only one class per grade, and low dropout and repetition rates, the evaluation team was confident that the same children interviewed in grade four would be in grade six two years later. However, this actually became an issue, since several schools merged, but the team was able to match student names, see below.

> 24 The sampling probability was increased accordingly for schools that were located in villages with more than one school to keep the probability of being sampled equal across schools.

> 25 Random allocation conducted by authors.

26 The survey was conducted by a Moores Roland, a survey firm by MoNE. Five out of 520 schools were not included in the endline survey. Two schools merged, and one school refused interview. In two other schools, the implementer implemented the treatment in schools different than those surveyed in the baseline, so the team felt it unnecessary to survey non-randomly selected schools for which there was no baseline information.. In addition, there were some difficulties with non-response in some of the schools, so we have complete information
policy choices facing governments.

The main goal of this paper is descriptive. Many of the basic facts about disability, poverty and schooling in developing countries are unknown, or not systematically addressed. In order to contribute to the foundations of policy development, this paper analyzes available data to investigate the interactions between physical impairment and participation in schooling, and the intermediary relationship with poverty. The analysis finds that disability among youth is sometimes, but not always, associated with household poverty, but that it is systematically and significantly related to lower school participation.

The paper proceeds as follows. Section 2 compares definitions and the prevalence of disability across the household surveys covered. Section 3 investigates the association

> 1 An early exception to this is Afzal (1992) who analyzes disability and its correlates in Pakistan. Yeo and Moore (2003) review some of the literature on poverty and disability but the literature they refer to is typically not based on large-scale surveys.

> 2 An online guide to the ICF is available at http://www3.who.int/icf/.

1
is particularly relevant for homogeneous products and for the trade between developed and developing countries. This is consistent with the theoretical model that it is easier for ...rms to mis-declare product codes or countries of origin to evade restrictive NTMs when products are homogeneous, and when customs administrations are less developed.

# 8 Concluding Remarks

This paper examines whether some of the discrepancies in the o¢ cial statistics of importing and exporting countries result from trade fraud in which traders deliberately mis-declare the country of origin or product code in order to avoid costly or burdensome NTMs at the borders. Our approach utilizes the detailed product-level trade statistics of a wide range of importing and exporting countries. We ...rst provide a theoretical model to link the restrictiveness of NTMs to trade fraud and to rationalize the presence of origin and product classi...cation fraud. We then estimate the ad valorem equivalents of the NTMs of the importing countries, with variations at the product and exporting country levels and relate the estimated ad valorem equivalents to the existing trade discrepancies, in terms of the quantity and value discrepancies. Our ...ndings indicate that trade ‡ows where larger discrepancies are observed are those that face more restrictive NTMs, suggesting the misclassi...cation of product codes or the misrepresentation of the country of origin in order to reduce or evade the compliance costs associated with Border NTMs. We ...nd that NTM-induced trade fraud is more pronounced for homogeneous products and for trade between developed and developing countries.

One additional contribution of this paper is the quanti...cation of the costs associated with Border NTMs, ...lling an important void in the trade policy literature which thus far has focused primarily on tari¤s. Given that tari¤s have been steadily declining for the 33
</sup> . Second, I compare my results for the capital controls dummy against random dummies of similar structure. For this purpose, I generate 500 random dummies of the same length and the same proportion of -1s and 1s in simulation. In each iteration, a random dummy is placed in exactly the same place as the capital control dummy and used alongside the same macroeconomic data to generate a GDP-nowcast. The capital control dummy outperforms 81% of random dummies in terms of MSFE reduction.<sup>11</sup> These exercises show that

> 10Table 9 in the appendix presents these results in detail.

> 11Figure 10 in the appendix plots the distribution of MSFE reduction of random dummies alongside that of capital controls, shown with the solid red line. It is not surprising that some randomly generated dummies does better than the capital control dummy for this particular sample period.

36
of redistribution at higher levels of inequality may still mean that the redistribution systems play a larger role. As Kanbur (2018) highlights, in this case of higher levels of inequality, the volume of redistribution is higher.

> 6 2007 was chosen as a reference year as it was the earliest year available in EUROMOD. This year also has the advantage that it pre-dates the financial crisis. 2014 was the most recent year in which survey data were available when running the simulations in the first quarter of 2018. Later years’ simulations were also based on 2014 survey data.

7