rafmacalaba/data-use-mentions-extended huggingface dataset:
This includes the cost of supporting community leaders, training and mentoring but excludes the time of volunteers and any support provided to the CPBA by community members.

# _Reading instruction intervention_

The reading instruction intervention, entitled “Come Let’s Read and Write” (CLRW), provided teachers in treatment schools with training and materials to implement a new pedagogic approach for reading instruction. The training and materials were coupled with monitoring and coaching, and offered to 1<sup>st</sup> and 2<sup>nd</sup> grade teachers. The approach aligns key learning competencies for basic reading and writing stipulated in the official Tongan curriculum with a greater degree of clarity on the sequence in which these skills should be taught.

Like the school readiness intervention, CLRW was expected to affect student reading outcomes in two stages. First, the intervention provided teachers with training, materials and coaching in order to induce them to adopt the new pedagogic approach. Second, students exposed to the new pedagogic approach were expected to have acquired better reading skills. For this paper, we refer to the former as the CLRW intervention and the latter as the CLRW pedagogic approach.

Evidence that the pedagogic approach can improve students’ reading ability stems from research on what skills children need in order to learn alphabetic languages (Wolf 2007; Linan-Thompson and Vaughn 2007; Sprenger and Charolles 2004; Chiappe & Wade-Woolley 2002; Gove & Cvelich 2011 and National Reading Panel 2000). These include, among others, an understanding of the relationship between printed letters and sounds (Scarborough 2002) and the speed at which a child can read (Fuchs et al. 2001; Abadzi 2006). CLRW is designed around the sequencing of skills and pedagogic methods based on this research (e.g.: August & Shanahan 2006; National Institute for Child Health and Human Development 2000; Pressley 1998; Snow, Burns & Griffin 1998).

In order to induce teachers to implement this structured pedagogic approach, the CLRW intervention relied on highly scripted
Park, Walter. 2001. “Intellectual Property and Patent Regimes.” Chapter 4 in _Economic Freedom of the World: 2001 Annual Report_ . Freedom House: New York.

OECD (Organization for Economic Co-Operation and Development). 1998. _Technology, Productivity and Job Creation_ : Best Policy Practices. Paris: OECD Publications.

OECD. 2001. _Innovative Clusters: Drivers of National Innovation Systems_ . Paris: OECD Publications.

Reinganum, Jennifer F.. 1984. ”Practical Implications of Game Theoretic Models of R&D.” _American Economic Review_ 74(2): 61-66.

Reinganum, Jennifer F.. 1989. “The Timing of Innovation: Research, Development and Diffusion” Chapter 14, Richard Schmalensee and Robert D. Willig eds. _Handbook of Industrial Organization_ , Volume 1. North Holland New York. 850-908.

Romer, Paul M.. 1987. "Growth Based on Increasing Returns Due to Specialization." _American Economic Review_ Papers and Proceedings 77: 56-62.

Romer, Paul M.. 1990. “Endogenous Technological Change.” _Journal of Political Economy_ 98: 71-102.

Segerstrom, Paul S. 1998. “Endogenous Growth without Scale Effects” _American Economic Review_ 88 (5) 1290-1310.

Segerstrom, Paul S., T.C.A. Anant, and Elias Dinpoulos. 1990. “A Schumpeterian Model of the Product Life Cycle.” _American Economic Review_ 80(5): 1077-92.

Trajtenberg, Manuel. 2001. “Innovation in Israel 1968-1997: A Comparative Analysis Using Patent Data.” _Research Policy_ 30(3): 363-389.

UNESCO. 1980. _Statistical Yearbook._ United Nations, Paris.

Windmeijer, Frank. 2000. "A finite sample correction for the variance of linear two-step GMM estimators." IFS Working Papers W00/19, Institute for Fiscal Studies.

Wooldridge, Jeffrey M..1997. "Multiplicative Panel Data Models without the Strict Exogeneity Assumption," _Econometric Theory_ 13, 667-678.

World Economic Forum. 2000. _World Competitiveness Report._
Figure 1: Students’ proficiency in locally-managed schools, fifth-grade (2005-2009) <!-- Start of picture text --> Extended winter break Extended winter break<br>Other municipalities Other municipalities<br>Contrafactual Contrafactual<br>220 200<br>210<br>190<br>200<br>190<br>180<br>180<br>170<br>2005 2007 2009 2005 2007 2009<br>(a) Math (b) reading<br>Test score, SAEB scale Test score, SAEB scale<br><!-- End of picture text --> Note: For fifth-graders, the proficiency scale (SAEB scale) ranges from 0 to 350 in reading, and from 0 to 350 in Math. The proficiency for treatment and comparison groups is the average of the proficiency scores of a sample of locally-managed schools in Sao Paulo state, the ones that have the performance of fifth-graders available since 2005. These schools are located in 10 municipalities of the treatment group and 469 municipalities of the comparison group (Table 2). The contrafactual is calculated assuming that the average score of the treatment group would increase at the same pace as that of the comparison group in the absence of the school closures. Because the first year of _Prova Brasil_ assessment was 2005, we cannot test for parallel trends using a longer time frame. Figure A.3 shows the time-trend for grade promotion, repetition, and dropout rates. Source: Data from _Prova Brasil_ /INEP.

Aiming to increase precision and account for potential time-variant confounders, we include a vector of covariates at the school level, as these variables might be correlated with both the decision of the municipality to close its schools and students’ performance in the standardized tests. We use a Lasso ( _Least Absolute Shrinkage and Selection Operator_ ) regression to select the vector of covariates, _Xsmt_<sup>_′_,thatbestpredictthevariationintheschoolaverageproficiency</sup> score (Ahrens et al. (2019))
2 Figure S. 1: Household Total Energy Expenditure (TEE), Rest Energy Expenditure (REE) and Activity Level (AL) by 1-digit National Industrial Classification (NIC) industry <!-- Start of picture text --> Agr. Mining Manuf. natural Manuf. other<br>Utility Construct. Distrib. Transport<br>FIRE/Bus. Oth. service Not working<br>TEE REE<br>AF<br>.05<br>0<br>-.05<br>-.1<br>-.15<br>.05<br>0<br>-.05<br>-.1<br>-.15<br>.05<br>% deviation from sample mean 0<br>-.05<br>-.1<br>-.15<br><!-- End of picture text --> Graphs by Household head 1-digit industry classification _Source_ : Authors’ analysis based on data from 1998-1999 India Time-Use Survey (TUS) and National Family Health Survey (NFHS) rounds 2, 3 and 4. _Note_ : Classification based on 1987 NIC 1-digit industry codes for head of household. Estimates based on TUS sample with REE imputed based on variables common to NFHS, reported as differences relative to the sample mean.
customers (sales from the new customers) increased by 9.7 percent (14 percent) per month more for merchants with contactless technology, and the effect is statistically significant. This result suggests that the positive effect reflects real business growth rather than the substitution effect where consumers simply switch from cash to card payments. Finally, I explore the increase of the contactless payment limit from 30 euros to 50 euros to investigate the substitution between card payments and other means of payment. I find evidence that increasing the digital payment limit accelerates substitution between digital payments and other payment methods including cash. In addition, the increase of the contactless payment limit to 50 euros significantly accelerates the substitution of sales from contact card to contactless card payments.

This paper is, to the best of my knowledge, the first study of how the sales of merchants with the latest digital payment respond to an outbreak and an increase of payment limit relative to the merchants without this technology using firm-level data. It provides a better and more comprehensive understanding of how merchants with the latest payment technology mitigate the impact of epidemic shocks and how the increase in digital payment limit accelerates the substitution between digital payment sales and the sales from the other means of payment. The current paper contributes to the emerging literature on digitization and fintech with the focus on non-cash payment (Fung et al., 2012; Tr ̈utsch, 2014; Bachas et al., 2018; Agarwal et al., 2019; Bounie and Camara, 2020). Fung et al. (2012) and Tr ̈utsch (2014) use survey data on consumer payment behavior to examine the impact of contactless payments on consumer spending. The authors show that contactless payments increase the spending for debit and credit cards payment and reduce on average the total value and volume of cash usage. Using firm-level transaction data, Agarwal et al. (2019) and Bounie and Camara (2020) investigate the causal impact of digital payment technologies on merchant sales. They find that merchants with the latest digital payment
# 4. Data sources

As explained in the introduction, the paper relies on three data sets to explore the link between backyarding and job access: a count of backyard dwellings per parcel drawn from satellite data, job data at the level of transportation zones, and origin-destination trip-time matrices. The sources of these data sets are described below.

The satellite data, provided by GeoTerraImage (Pty) Ltd. is contained in the Building Based Land Use spatial data set, which provides a land-use classification per building. The data are captured from digital ortho-corrected aerial photography and/or high-resolution orthorectified satellite images. It differentiates between 17 classes of residential structures, including formal residential, informal residential and backyard structures. We overlaid the aerial GTI data on a map containing individual parcel contours from the City of Cape Town’s cadastre records, thus generating a count of backyard structures per parcel for 2014.<sup>12</sup> Employment at the transportation-zone level for 2013 is estimated as part of the City of Cape Town’s Land Use Model. The land-use model estimates the number of jobs by applying workplace density assumptions to the internal floor space of various types of non-residential buildings, as measured by the city’s Valuation Department in its non-residential valuation processes. The preliminary results per transport zone are reconciled with citywide job numbers (by occupation) as published in the Statistics South Africa Labour Force Survey.

The origin-destination matrix for commute-trip times is an output of the City of Cape Town’s four-step travel demand model, known as the EMME model. These four steps are (1) trip generation, (2) trip distribution, (3) mode choice and (4) route assignment. EMME was designed by INRO Consultants at the University of Montreal and adopted by the City of Cape Town in 1991. The model implements an equilibrium route assignment based on the distribution of trip origins and destinations in relation to the transport network and modal choice. On this basis, it estimates travel volumes, average trip distances and travel
nomic activity (see Appendix I for a comparison of the municipalities in the manufacturing sample and those not included). They also were strong recipients of IDP during the period of study (see Figure I), had lower levels of conflict-related violence, although higher levels of urban crime (measured through homicide rates) relative to the rest of the country (see panel a of Figure II).

Figure III shows the annual evolution of the mean of all the firm outcomes employed in this study.

# **III.2 Internal forced displacement**

Municipal data on forced displacement caused by violent conflict was obtained from the _Registro ́Unico de V ́ıctimas_ (RUV, Registration of Victims) from the _Unidad para la Atenci ́on y Reparaci ́on Integral de las V ́ıctimas_ of the Colombian government. This is the best source of information on individuals who were displaced by violence because it combines all official sources available where individuals registered upon arrival in the new locations (even if they registered with authorities a long time after their arrival, in which case they are included in the year in which they arrived in the data). Although not all individuals register, these data are an excellent approximation of the total number of individuals received in each municipality because being registered is a condition for accessing any type of governmental support from local authorities. To be registered, individuals have to declare their displacement under oath, attest to the exact dates of the event, their municipality of origin, and some of their socioeconomic conditions, as well as to describe the facts leading to their displacement.

The RUV data, consequently, offers information on the municipalities from which migrants were expelled and into which they were received. It is available annually by municipality between 1984 and 2016. In this paper, however, we focus on the period between 1995 and 2010 because it is the period for which firm data is also available. The time evolution of total displacement is presented in Figure I and shows strong variation between 1995 and 2010.
and inequality, we use both types of weather anomalies simultaneously. Following Sedova and Kalkuhl (2020), our measures of weather shocks are, therefore, variables that capture positive and negative temperature and precipitation anomalies during a time period of 90 days before a household’s EPH survey date. First, we construct a Standard Temperature Index (STI) and Standard Precipitation Index (SPI) that measure temperature and rainfall variability as the difference between short-term average temperature or precipitation during the time bin _t_ and the historical average (or long-term climate pattern) during this time of the year between 1981 and 2003, divided by the historical standard deviation (Patel, Chopra, & Dadhwal, 2007), where _Pdt_ is average precipitation during 90 days prior to the interview, _Pdh_ is the historical average precipitation during this time bin, and _σPdh_ is the historical standard deviation of precipitation during the same time bin, which equally applies to the STI. Historical values as reference period of long-term climate conditions cover the 23 years between 1981-2003, corresponding to the start of the CHIRPS and leading up to the start of the EPH data collection. In line with previous literature, we use the STI and SPI to capture weather shocks, as levels of temperature or precipitation matter less in an absolute sense but rather in proportion to a district’s historical variation (Heim, 2002; Hirvonen, 2016; Letta et al., 2018; Randell & Gray, 2016). Based on these indices, we construct four measures of positive and negative weather anomalies as: where a positive (negative) anomaly is defined as the absolute z-score of temperature or precipitation, if the index is larger (smaller) than zero, and zero otherwise. Therefore, in this study, the four weather shock measures are _T_ (+) _dt_ , a positive temperature anomaly (also referred to as heat shock), _T_ ( _−_ ) _dt_ , a negative temperature anomaly (cold shock), _P_ (+) _dt_ , a positive precipitation anomaly (
|Country|Year|Source|Stunting<br>headcount|Stunting<br>gap|Stunting<br>gap squared|<br>t-stat<br>for gap|
|---|---|---|---|---|---|---|
|Peru|2008|DHS|27%|10%|6%||
|Syrian Arab Republic|2006|MICS|27%|17%|18%|14.6|
|**Years: 2011‐2013**|||||||
|Benin|2011|DHS|45%|37%|45%||
|Ethiopia|2011|DHS|44%|25%|23%|17.9|
|Lao PDR|2011|MICS|44%|23%|20%|21.6|
|Congo, Democratic Republic|2013|DHS|42%|26%|25%||
|Bangladesh|2011|DHS|41%|19%|16%|11.3|

_Source: Authors’ calculations using DHS and MICS._

# _4.3: Regional Analysis_

Until now we have focused on headcount and gap measures only at the country level. Extending this analysis to the regional level may provide further insight on malnutrition across the world. Therefore, we examine the regional averages of malnutrition. As survey data are not available every year for most countries, only a few countries in a particular region have a survey in a given year, with some regions having no survey conducted in certain years.

There are two common practices for calculating regional averages in such cases: (1) modeling methods and (2) aggregating over a range of years. An example of modeling methods closely related to this study is the UNICEF-WHO-World Bank joint child malnutrition estimates (JME) (UNICEF, WHO, and the World Bank 2018). The JME adopts
FARM-NONFARM LINKAGES ;N RURAL SUB-SAHARAN AFRICA l/

# STEVE HAGGBLADE _Syracuse University_

# and

# PETER HAZELL AND JAMES **BROWN** _The World Bank, Washington, DC_

# I. INTRODUCTION

Based largely on evidence from Asia, Johnaton and Kilby (1975), Mellor (1976), and Mellor and Johnston (1984) argue that a development strategy focused on small ferms will generate rapid, equitable and geographically dispersed growth because of labor initensive linkages with the rural nonfarm economy. Using examples from India, Pakistan, and Taiwan, Jchnston and Kilby (1975) highlight the potential importance of production linkages. They point to small farmer demand for fertilizer, construction inputs, and equipment and repair services provided by rural blacksmiths. But farmers also purchase consumer goods. Mellor (1976), dellor and Lele (1973) and Hazell and Roell (1983), in particular, have called attention to the potential power of agricultural consumption linkages. They conclude that middle-sized peasant farmers--to a much greater extent than their large scale and urban counterparts--spend incremental income on labor-intensive rurally produced goods, thereby generating important second-round demand multipliers. Hirschman's (1958) early indictment of agriculture as a low-linkage, underpowered engine of growth erred, according to Mellor (1976), because it ignored these important agricultural consumptiun linkages.

Several empirical studies have documented the power of farm-nonfarm linkages in Asia. Based on data from India, Rangarajan (1982) founld that a one percent addition to the agricultural growth rate stimulated a 0.5 percent addition to the growth rate of industrial output, and<sup>a 0.7</sup> percent addition to the growth rate of national income. Bell, Hazell and
ranked all 366 households by their wealth index score and report a cumulative distribution of subsidies by cumulative percentage of sample households.

# **_Calculation of the total average cost of electricity and water services_**

Another critical parameter when calculating the subsidy received by each household _i_ for service _j_ is AC _j_ , or the total average cost of providing service _j_ in Addis Ababa. We calculated AC _j_ using data from the utility companies and the following formula: In this expression, OM is annual value of operation and maintenance (O&M) costs, _K_ is the annual capital cost, _R j_ is the ratio of capital costs to O&M costs, _P_ is the annual production level, and _L_ is the level of annual losses during production and distribution.<sup>20</sup> These data were provided directly by various departments of the utility companies: the EEU’s Budgetary Control Office Finance Department and Planning Department; and the AAWSA’s Finance Department and Project Planning Department. For both water and electricity, we also collected data on capital costs, which were sometimes inconsistent among departments. Even so, the average information on capital costs helped us verify that the data underlying _R_ (the ratio of capital costs to O&M costs) were a viable proxy. For electricity provision, <mark>O&M costs were considered to be 5% of the total cost and the full financial costs (including all investment costs) were considered to be 95% of the total cost; for water provision,</mark> t <mark>he full financial costs of water production (including all</mark> <mark>investment costs) were considered to be 3 times higher than the O&M costs. Because</mark> our estimates are annual costs, we collected data from the most recent years 2012–2015 for the purposes of our

> 20 Energy and water losses play an important role in the calculation of costs because they increase the average unit
Table 7 examines questions with the least degree of personal reference. These questions ask about behaviors of 'typical establishments in this line of business' or 'establishments in your sector of activity'. While the respondent’s company could obviously be included in the term 'in this line of business', he could just as well argue that he does not represent what 'typically' happens in his sector and might therefore have a way of excluding himself from the group he answers about. Thus he might not feel his answers are personally incriminating. Regression results in this category of questions do not lend any support to the hypothesis that reticent respondents tend to deny sensitive acts more strongly than their possibly candid counterparts. In fact in the cases in which we obtain a statistically significant coefficient, we find a (counterintuitive) positive effect, which would imply that reticent respondents give answers that indicate more involvement in sensitive acts than do possibly candid respondents.

Finally, in Table 8 we confirm that reticence effects are _not_ present when looking at responses to _nonsensitive_ questions. For this confirmatory exercise we focused on the set of 35 yes/no questions in the survey that we had not previously identified as sensitive in Tables 4-7. We then randomly selected a subset of five of them as placebo questions (by choosing every 7<sup>th</sup> question), and performed the same analysis as in Tables 4-7. With one exception we find no significant differences in the responses of reticent and possibly candid respondents to these questions. The one exception is the question on ISO certification where we find that reticent respondents are significantly more likely to claim their firms are ISO-certified.

The ISO-certification result could reflect reticence working in the opposite direction—respondents who are not fully candid might claim positive attributes that are in fact exaggerations. This is something that AM found in Romania, where reticent respondents claimed higher moral values when faced with a set of questions taken from the World Values Survey. For example, AM found that reticent respondents were much
# **Estimating Food Price Inflation from Partial Surveys**

_By_ Bo Pieter Johannes Andr ́ee<sup>*</sup> JEL: C01, C14, C25, C53, O10.

Keywords: Inflation, Food Security, Financial Stability, Machine Learning.

> * The World Bank, Development Economics, Data Group, Analytics and Tools Unit. The author may be contacted at bandree(at)worldbank.org. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. 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. This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership”. The program is funded by UK aid, it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of the UK government, the WBG or UNHCR. The author would like to thank Paolo Verme, Aart Kraay, Phoebe Spencer, Olivier Dupriez, Kamwoo Lee, Andres Chamorro, Nadia Piffaretti, Benjamin Stewart, Roberta Gatti, Daniel Lederman, and Talip Kilic for detailed feedback, inputs and rich discussions on early results.

_1_