rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1. Introduction

Policy discussions surrounding agricultural water use tend to revolve around water scarcity and variability, and its impact on agricultural productivity. While these are indeed critical areas of focus, particularly in the context of climate change induced rainfall variability, challenges from water quality constitute an equally important threat that is currently underappreciated. Over‐extraction of surface and groundwater, sea‐water intrusion, rainfall variability, and poor irrigation water management all contribute to increased salinization of water supplies and in many regions, with deleterious effects on crop production.

Although the harm that saline soil and water can have on agricultural productivity is well known, and indeed has a long history, to date there has been no attempt to quantify this impact globally. In this paper, we take advantage of several regional and global data sets on agricultural productivity, land use, and water quality and estimate the average impact of high levels of salinity in surface water on crop production. We start with regional analyses in the Mekong River Basin and river basins of India where high‐quality data exist. Using hydrological models and data on surface water irrigation, we link agricultural productivity to upstream water quality monitoring stations which indicate the levels of water salinity that is flowing onto downstream fields for irrigation. By controlling for confounding factors which also impact agricultural productivity (such as rainfall, temperature, and geographic factors), and carefully accounting for the direction of stream flow, we isolate the impact of plausibly exogenous changes in electrical conductivity (EC), the most commonly used measure of salinity in water, on downstream agricultural yields. We then expand the sample and conduct the same analysis using monitoring stations from the GEMStat database, which covers regions in 36 countries around the world.

These estimates are then used to simulate the average annual fall in yields due to saline waters. Impacts on crop production are seen at relatively low levels of salinity and tend to rise at a near linear rate as water salinity rises. A satellite‐based measure of agricultural productivity, combined with estimated elasticities
rate of growth in the economy and not necessarily an absolute decline in economic activity.3 The group of series that form the composite index share a leading relationship with respect to the growth cycle. They are usually selected from a large set of economic variables that include financial, monetary, real sector, and business survey data, taking into account their statistical and economic significance.

The system of leading indicators is widely used by the OECD to predict the growth cycles in the economies of their member countries. These exercises have been very effective in their forecasting ability and accuracy.<sup>4</sup> However, for this technique to work properly it is essential to have an adequate statistical system that provides a large set of economic variables, in a timely and precise manner, preferably on a monthly basis.

# DESCRIPTION OF THE MAIN PROCEDURES

The methodology can be outlined as follows:

1. Selection of the reference series to be forecast (IIP).

2. Determination of the cycles on the reference series, after adjustments for seasonal and trend effects.

3. Selection of the components of the CLI. This selection is based on lead/lag tables, cross correlograms and inspection.

4. Estimation of seasonal adjusted versions of the selected series.

_5._ Detrending the seasonally adjusted component series of the CLI with the HodrickPrescott Filter.

6. Smoothing the cyclical components with Months for Cyclical Dominance (MCD) moving averages, and adjusting their average cyclical amplitude by means of standardization.

7. Aggregation of the components into the CLI.

8. Comparison of the reference series and the CLI in their detrended version, and if desired, restore trend to the series.

- 3 Burns and Mitchell (1946) provide one of the classic definitions of business cycle.

- 4 See for example the study on Mexico by Beziz & Petit (1997).

3
Aga Khan Foundation has set-up a deposit-taking microfinance institution in Syria, which has some characteristics of a microfinance bank microfinance bank, as a Social Financial Banking Institution.  Under Syria‟s General Microfinance Decree such an institution is allowed to provide microlending, deposittaking, and micro insurance.

# _Microcredit in MENA_

**Microcredit**<sup>**3**</sup> **is still limited in scale** in much of the region.  The market coverage ratio for specialized microfinance providers is only half (1.78 percent) the proportion of the adult population as is the case for South Asia or Latin America.<sup>**4**</sup> As indicated in Table 2, the number of active borrowers compared to working age adults is much smaller than all regions except subSaharan Africa, while microcredit loan portfolios represent a significantly smaller proportion of total credit<sup>5</sup> than any other region.

> 3 Defined by the World Bank and CGAP as having an average outstanding loan size less than three times GDP per capita.

> 4 Data for Microfinance Institutions that report to the Microfinance International Exchange. 5 Domestic private credit to the real sector by banks.

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usually persists even after controlling for other socioeconomic factors), health status of household members and access to basic health services, presence of basic infrastructure (roads, electricity, schools, health centers, etc.) and exposure to conflicts and violence.<sup>21</sup> Indicators proxying good governance have also been shown to support income growth and poverty reduction (Doumbia, 2018). Based on the macro- and micro growth literature, we group the potential mediators of the passthrough between GDP and household consumption growth into four categories<sup>22</sup> :

- i. **Human capital:** This category includes indicators of school enrollment and completion at different levels, literacy rates, child and maternal mortality rates, fertility, and life expectancy. We also include an indicator of intergenerational mobility (the probability that a child exceeds the education level of her/his parents for the cohort born in the 1980s). This last variable only varies by country (time series for this variable is available only for a limited set of countries<sup>23</sup> ).

- ii. **Economic and demographic structure** : Share of different sectors in GDP, employment share of different sectors, rents from natural resources as a share of GDP, inflation (average in the five years preceding the spell), gross fixed capital formation (with different lags), share of

> 19 The Statistical Performance Indicators measure the capacity and maturity of national statistical systems by assessing the use of data, the quality of services, the coverage of topics, the sources of information, and the infrastructure and availability of resources (https://www.worldbank.org/en/programs/statistical-performance-indicators). We used a simple average for each country for the period 2014-2019.

> 20 See, for instance, Barro (1991), Levine and Renelt (1992), and Durlauf (2009) for an overview.

> 21 See, for instance, Glewwe
evaluations have been conducted (Renton et al., 2000; Shaw, 2000; Shaw, 2002a; Shaw, 2002b; Paine et al., 2002; White, Greene and Murphy, 2003; Interagency working Group, 2003). For example, the Medical Research Council (from the United Kingdom) conducted an external evaluation of program effectiveness in the Gambia using key informant interviews, focus groups, and a KAP (knowledge, attitudes and practices) survey in four villages, including two program sites and two controls. That study found that the Gambia program improved self-reported attitudes and behaviors related to violence against women. Specifically, the program reduced the social acceptability of wife-beating at the community level and appeared to produce a corresponding drop in that behavior. Qualitative findings from other Stepping Stones sites suggest similar benefits.

# _Program H_

( _Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru_ ) is being carried out by four NGOs. It aims to change gender norms and sexual behaviors in Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru (Barker, 2003; White, Green and Murphy, 2003; Guedes, 2004). The initiative includes four components, namely: a) training professionals to work with young men in the area of health and gender-equity using a set of manuals and videos; b) social marketing of condoms; c) promoting health services; and d) evaluating changes in gender norms. In 2002, PROMUNDO and Horizons began a 2-year evaluation to measure the effectiveness of two different approaches, compared to a control site. Researchers have developed a "Gender-Equitable Men" (Leichert) scale with 24 items for measuring attitudes. Methods include pre and post-tests as well as a six-month follow-up community-based survey. In addition, they are gathering qualitative information among men and their female partners. Preliminary results suggest that the program has been successful at increasing gender equitable norms and reducing behavior that puts men at increased risk of HIV/AIDS.

# _ReproSalud_

_(Peru):_ Manuela Ramos launched ReproSalud
**_a_** , age groups 4-11 and 12-17

**_g_** _c,w_ , income shock projection of country _c_ , if and when available, the simulation can accommodate this parameter by welfare quintile (w)

# **_c_** , country

For simplicity, in each scenario, the Global simulation assumes the same sc for all countries within a particular scenario, and mc and pc vary only by country income level. We assume a uniform income shock across welfare quintile at the global level, since there is no better number available.

# _Simulation 2: Effect on mean (score)_

This analysis builds on scenarios used to estimate the learning losses from simulation 1 and provides an estimate of how much learning will be lost during school closures necessitated by COVID-19 in terms of PISA scores. Estimates are based on (i) typical test score gains from one grade to the next, (ii) data on availability of alternative schooling modalities, (iii) assumptions on the effectiveness of these alternative modalities, and (iv) assumptions on duration of school closures. Results are provided by country and are disaggregated by socio-economic status. The PISA and PISA-D databases are used for these calculations, 2018 or the latest available PISA year.

where,

**sc** , school closure (as a share of the school year) for country _c_

**mw** , mitigation effectiveness by welfare quintile (w)

**pw** , school productivity or learning gains by welfare quintile (w)

**w** , welfare quintile proxied by Social Economic Status (SES)

**c** , country

Note: For simplicity, the basic simulation assumes that, within a country, children have the same school productivity regardless of socio-economic status.

# _Simulation 3: Effect on share of students below a minimum proficiency threshold_

This analysis builds on scenarios used to estimate the learning losses from simulation 2, and provides an estimate of how the share of children performing below minimum proficiency (
the technology in their sectors. In this section, we examine spillovers of sanctions to other (non-sanctioned) firms within the same technological field.

We identify the IPC fields affected by the Entity List sanctions through the following procedure. First, we classify firms into technology fields based on their patent applications’ main IPC code at the 4-digit level. We then compute each sanctioned firm’s number of patent applications in each IPC code before being added to the Entity List. A firm’s technology field is defined as the IPC field hosting most of its patents. Second, we match each sanctioned firm in the entity list to PATSTAT and identify its technology field, using the method in the first step. We then define these IPC codes as “sanctioned IPC field” if (1) the sanctioned firms are among the top 10% of Chinese innovating firms in their primary IPC field, or (2) the sanctioned firms patent more than 10% of total patents in their primary IPC field.<sup>17</sup> We have 40 sanctioned IPC fields in our sample, with 22 added after 2018.<sup>18</sup> Most of the sanctioned IPC fields are under the electric communication technique (H04) and measurementtesting technique (G01). Firms patenting in sanctioned IPC fields file significantly more patents overall, and in high-tech fields than other firms in our sample.

We define our treatment group as the set of firms whose primary IPC field is being sanctioned. In our sample, firms can switch among different IPC fields over the years. To eliminate potential bias caused by firms that switch their treatment status, we drop firms that switch in/out of the sanctioned IPC field after the IPC field was sanctioned. Figure 5 shows the total number of firms patenting under the sanctioned IPC field each year. The majority of firms are in the technology field of Computing and Information, Electronics, and Electric Technology.

> 17For example, consider firm ABC: it was added to the Entity
# _4.2 The economic crisis and migration_<sup>_xviii_</sup>

We argue the economic crisis caused by the Great Recession was transmitted to rural Guatemala primarily through its effects on migration and remittances, and consequently likely to affect migrant households more than non-migrant households. Undocumented immigration flows may be particularly sensitive to business cycles (Papademetriou and Terrazas 2009) and immigrants to the United States from Mexico and Central America were concentrated in industries (construction, landscaping and low-skill manufacturing) that experienced high unemployment during the crisis (Kochhar 2009). The mid-2009 qualitative and quantitative findings provide a snapshot taken near the height of the crisis and between the two main household survey rounds, and substantiate these channels—migration and remittances both decreased due to the crisis, with apparent local consequences in the study communities (Saenz de Tejada 2009).

Focus group discussions all spontaneously highlighted increased return migration and it was widely understood that the economy in the United States was in a downturn, with many migrants having become under- or unemployed. Correspondingly, most of the migrants who had returned did so because they could not find work, though a small fraction had been deported. The perceived cost-benefit calculations of migration were changing significantly due to decreased economic opportunity as well as increased travel cost and risk associated with the Mexican leg of the migration journey and stricter immigration law enforcement in the United States. One implication of the muted incentives for new migration to the United States was that shorter-term migration to Mexico became relatively more attractive for some, although devaluation of the Mexican peso and concerns about a “new disease” (influenza AH1N1) somewhat offset the relative advantages. Respondents to the short quantitative survey administered in the four communities overwhelmingly indicated they believed obtaining work in the United States had become much more difficult and that laws and attitudes toward immigrants had worsened in the previous year.
detailed in Rohde & Hausfather (2020).

The BERKEARTH dataset offers data in three distinct grid formats: 1 × 1, 2 × 2, and 3 × 3 degrees. Each ‘1 degree’ in this context refers to a geographical measure, where 1<sup>◦</sup> of latitude corresponds approximately to 111 kilometers (69 miles) on the Earth’s surface. Similarly, 1<sup>◦</sup> of longitude varies in distance, being wider near the equator and converging towards the poles due to the Earth’s spherical shape. For our analysis, we specifically employ the 1 × 1-degree grid format, which provides the most granular weather information. The dataset encompasses three primary variables: mean, maximum, and minimum daily temperatures. In our research, we focus on utilizing the maximum temperature readings.

Income-level data. For the country-level income data, we use the World Bank’s income data.<sup>9</sup> This data is used to investigate whether there are distinct dynamics in the impact of temperature uncertainty on firm behavior across different income levels.<sup>10</sup>

> 6However, Orbis does not provide comprehensive coverage for firms in North America, so the two major countries, the US and Canada, are not fully represented in our sample.

> 7See Lepore & Fernando (2023) for an analysis of physical risks from climate changes at the firm level using Orbis data. 8Similar studies using the grid-level weather data include Nordhaus (2006) and those studies using data from the Climate Research Unit of the University of East Anglia (Harris et al. 2014), such as Auffhammer et al. (2013), Kalkuhl & Wenz (2020).

> 9 `https://blogs.worldbank.org/opendata/new-world-bank-country-classifications-income-level-2022-2023`

> 10Some economies, such as Malta, Réunion, Taiwan, and Hong Kong, do not have their income information recorded in the World Bank income data. As a
# **1 Introduction**

Rural Sub-Saharan Africa has the largest concentration of poor and deprived households in the world (World Bank (2018), Skoufias et al. (2019)). These households are particularly vulnerable to anomalous weather events.<sup>1</sup> These events are projected to increase in frequency and become more severe in a warmer world. However, policy responses to these events are often slow and large funding gaps are the norm. A transparent and data-driven early warning and monitoring framework may engender the requisite budget flexibility and responsiveness.<sup>2</sup> Yet, survey data that would inform such a framework are collected infrequently and at a considerable cost (Beegle et al. (2016), Castañeda et al. (2018)). Consequently, existing surveys need to be complemented with current data and additional analysis. Our objective is to contribute to the design of a transparent and comparable early warning framework that identifies deprived children in areas that are both data-constrained and vulnerable to the impacts of climate change.

In what follows, we develop a drought-contingent framework, for five countries in Southern Africa, that identifies children at risk of being stunted. This paper makes four contributions that may inform policies designed to mitigate the impacts that climate change has on the world’s most vulnerable populations. First, we provide a prototype of a drought-contingent targeting system for data-scarce contexts.<sup>3</sup> While several countries (c.f. Devereux and Nzabamwita (2018)) and economists (e.g. Alderman (2009), Barrett (2010), Clarke and Dercon (2016), and Hill et al. (2019)) have advocated for a standard and data-driven approach, there are few studies that provide a detailed empirical analysis of a drought-contingent targeting strategy. In this paper we build prototype droughtframeworks that are across countries because em- contingent targeting comparable they ploy standard, objective and verifiable measures of child and maternal anthropometrics, in
br>20 EPU Others Actual<br>10<br>0<br>-10<br>-20<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: Bloomberg, Haver Analytics, World Bank estimates.

A.C. Vector autoregressions are used for estimation on a sample of aggregate EMDE variables for 1998Q1‐2016Q2. The model includes the EPU for the Euro Area, emerging market stock price (Euro Area) index, emerging market bond index, aggregate real output and investment growth in 6 ECA countries, with G7 real GDP growth, U.S. 10‐year bond yields, and MSCI World Index as exogenous regressors and estimated with two lags.

B.D. Country‐specific autoregressions are estimated for Brazil for 1998Q1‐2016Q2. The model includes the domestic EPU, domestic stock market index, domestic short‐term interest rates, and real output and investment growth, with G7 real GDP growth and VIX as exogenous controls and estimated with two lags.

A.B. Show cumulative responses of investment to a 10 percent policy uncertainty shock in the Euro Area and Brazil. Solid lines indicate median responses. Dotted lines indicate the 16‐84 percent confidence intervals. Figures C. and D. indicate historical decomposition to investment growths in ECA and Brazil, respectively. Estimates for 2016 are based on the first half in 2016 (annualized).

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rates in developing countries appear to be larger than in OECD countries. This is likely to be explained by the subjective nature of some of the key innovation concepts. Respondents in different countries may have different understanding of what is considered as innovation, particularly when they are asked whether the firm has introduced a “new or significantly improved” product or process. In countries with higher prevalence of very incremental innovation, such as developing countries, it becomes even more subjective to judge how substantial or significant these innovation efforts are.

Third and more importantly, implementing innovation surveys is costly, especially in developing countries where the use of self‐administered or web‐based questionnaires is limited and data collection at the firm level heavily relies on face‐to‐face interviews. Comparability and reliability of the data collected are two key elements to consider when implementing innovation surveys. Measuring innovation is costly and difficult, and identifying the best instrument to collect innovation data is key from a cost‐benefit perspective.

These challenges motivated the Enterprise Analysis Unit of the World Bank Group to conduct an experiment to test two different approaches to measure innovation in developing countries. The first approach, more economical, uses a short questionnaire embedded in a general firm‐level survey, namely the Enterprise Surveys (ES). The second approach uses a standalone, longer questionnaire and it is significantly more expensive, although it provides more information.<sup>2</sup> The hypothesis to test is whether a cheaper approach could provide reliable higher frequency innovation data for key innovation inputs and outputs indicators by embedding a few questions in general firm‐level surveys.

This paper summarizes the results of the experiment. Using a rich data set of 11,150 firms in 15 countries we quantify differences in innovation rates as measured by the short and long questionnaire and, more importantly, based on insights from the subjective measurement literature, we provide some empirical evidence on what determines these differences.

First we compare self‐reported innovation rates in the short and long questionnaire across