rafmacalaba/data-use-mentions-extended huggingface dataset:
_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _18_ Using global growth expectations published in the World Economic Outlook (WEO) of April 2022, the results in figure 1 put the 2021-2023 severely food insecure population at above 1 billion (a prevalence rate of 15%) in the World Bank’s 144 IDA and IBRD countries, an increase of over 172 million people (a relative increase of 21%, or a 2 point increase in the prevalence rate) over 2017-2019 pre-pandemic estimates. This projection concerns unmitigated impacts extrapolated from 2019 data that do not reflect the large amounts of aid and supportive policies that characterized the pandemic years, but have also not yet factored in the effects of sanctions and export restrictions imposed after March 31, 2022. Nevertheless, the projection is clear in direction and paints a protracted picture in which the previously anticipated rebound in headline growth occurs unequally, leaving many behind in food insecure conditions. The projected improvement of about 0.5 point in the prevalence rate after the 2022 peak is not sufficient to offset continued population growth. Without large-scale targeted interventions, the figure is projected to remain above 1 billion through 2027.

The projected prevalence of severe food insecurity also clearly exceeds the estimates for the 2008 World Food Price Crisis period. When comparing the current projection to the modeled estimates for this historical period, it is clear that the current rise in food insecurity is much more pronounced in the modeled estimates. To this regard, it is important to highlight that the current rise in food insecurity occurs in a vastly different global economic environment. First, from 2007 through 2012, global poverty rates at 1.90 $ fell each year including by almost a percentage point from 2008 to 2009, while the preliminary consensus is that the pandemic year has triggered a reversal in poverty for the first time in decades. Second, while the 2008 and 2011 global food price spikes led to several severe crises, the global prevalence of undernourishment continued to fall, albeit more slowly in
inflation variation of global demand and oil price shocks have increased to 22 and 17 percent, respectively.

Finally, the contribution of global shocks to domestic inflation variation was larger in advanced economies and countries with higher trade and financial openness, fixed exchange rate regimes, and greater reliance on commodity imports. Domestic shocks contributed more to domestic inflation variation in countries that were less open to global trade and finance and had inflation targeting monetary policy regimes with flexible exchange rates.

# PART B. Expectations and Pass-Through

Part B delves deeper into two key challenges that confront EMDE central banks. Burdened by a history of high inflation, many EMDE central banks struggle to build credibility, leaving inflation sensitive to shocks and inflation expectations unanchored. Additionally, EMDE exchange rates can be subject to severe swings, amplifying the impact of exchange rate movements on inflation.

# Chapter 4. Inflation Expectations: Review and Evidence

In Chapter 4, Kose, Matsuoka, Panizza, and Vorisek argue that, since EMDEs tend to experience more pronounced business and financial cycles than advanced economies, they face greater challenges in anchoring expectations. This makes understanding how inflation expectations are affected by different types of shocks especially critical for policy makers in these economies.<sup>7</sup> Since robust measurement is key to evaluating inflation expectations, they first examine the pros and cons of survey-based and market-based measures.<sup>8</sup> Due to the breadth of country coverage and the availability of long time series, they employ survey-based inflation expectations in their empirical work. They then present a survey of the literature on inflation expectations. Theoretical studies have examined how public and private information is used by economic agents in formulating inflation expectations.<sup>9</sup> A large body of empirical work has tested the predictions of theoretical models and assessed how firmly inflation expectations are anchored, by measuring the sensitivity of expectations to various shocks. This literature, while extensive, has mainly focused on advanced economies.

The chapter, therefore, represents the first comprehensive
control villages surveyed over the years before and after the road projects were implemented. Poverty in this paper is measured by the daily consumption expenditure needed for an individual to be above the poverty line, where such per capita consumption expenditure is a function of food calorie and other nutritional requirements set by Food and Agriculture Organization (FAO) standards. We define moderate poverty as per capita consumption expenditure required to meet the FAO guidelines of a daily dietary requirement of 2,112 calories, as well as non-food expenditure that is approximately 30 percent of this food expenditure.<sup>28</sup> Similarly, we establish an extreme poverty line using consumption expenditure required to meet a lower calorie requirement of 1,739 calories, with non-food expenditure again about 30 percent of food expenditure. The extreme poverty line is usually 80 percent below the moderate poverty line; the estimated moderate poverty line was Tk.4698 in RRMIMP villages in 2001 and Tk.3984 in RDP villages in 2000.

Table 4 presents the distribution of households across project and control villages over the two survey periods based on the poverty line. In RDP areas, both moderate and extreme poverty declined in project villages, surprisingly without any change in either measure of poverty in control villages. In RDP project villages, about 57 percent of households were moderately poor before the road was paved, while some 48 percent of households were poor after the road was paved, representing a 9 percent reduction of moderate poverty. About a 6 percent reduction of extreme poverty (from 35 percent to 29 percent) was registered in RDP project villages after road was paved. Interestingly, for control villages there was no significant change in poverty over time. In the RRMIMP survey, poverty fell in both project and control villages. Moderate poverty dropped by about 11 percent in project villages, compared to a 12 percent reduction in control villages. On the other hand, an approximate 17 percent reduction of extreme poverty registered in project road villages, against 13 percent reduction in control villages.

Since poverty
set 3 strategies, 383 females (48.5%) set 2 strategies, and 293 females (37.1%) set only one strategy. The strategies identified by participants mostly focused on the adoption of safe sexual behaviors. Figure 2 highlights that the most commonly identified strategy was to use a condom, followed by abstinence and being faithful. Females also wrote about why this goal is important for their future and what obstacles they might face in following through with their specific strategies. We returned four months later in December 2017 to check-in and remind participants of the goal and their strategies. We collected additional information at that time on the female’s perception of whether she was successfully adhering to her strategies.

In Table A3 we use characteristics from the baseline survey and information that was collected during the initial goal setting activity visit in August 2017 to predict the total number of strategies developed and the number of strategies ultimately reported as achieved at endline (both of which range from 0-3). Interestingly, females who likely suffer from depression set and achieved fewer strategies.<sup>8</sup> Consistent with the psychological concept of self-efficacy, females with higher general self-efficacy scores set and achieved more strategies.<sup>9</sup> Females from households with earthen floors (reflecting a relatively poor household) set and achieved fewer strategies.

# **3 Data Collection and Outcomes**

# **3.1 Data Collection**

**Sample.** We conducted a baseline census of members of all 149 ELA clubs in operation in Dodoma, Iringa, and Mbeya from August to October 2016. The census was taken by meeting with the club leader of each club and obtaining a complete list of active members. Members were considered active if they were attending ELA clubs at least two times per week if they were enrolled in school or were attending at least three times per week if they

> time of the intervention or had moved away from the study area.

> 8Likely depression is
19 This paper provided a preliminary assessment of the experience with rural biogas in China, which is by far the country with the strongest record of biogas promotion. Using purposively collected data with a sample size of 2,700 households from 225 villages in 5 provinces, this paper found tentative ground for optimism. Rural biogas in the sampled areas of China appears to deliver on many if not all of its benefits. There was widespread awareness and acceptance of the technology with clear indications of benefits on the ground. These were: time savings from reduced fuelwood collection and easier cooking that disproportionately benefit women; indications of productive use of biogas’ by-products; signs of partial displacement of fuelwood and crop residues in response to biogas adoption, with mixed indications regarding displacement of coal; and some suggestions of benefits to respiratory health.

Moreover, problems with suspension, whether due to technical or human factors, were not evident to a major degree, with suspension of short duration and most equipment breakdown getting repaired with modest delay and expense. This was refreshing and reassuring, given the uneven record of implementing household energy interventions in developing countries (for a recent overview, see Bruce, Rehfuess and Smith, 2011).

The findings in this paper contribute in important ways to discussions of rural energy and sustainable development. First of all, the findings identify (tentatively) a case of a successful sustainable household energy technology and suggest that China has been able to address some of the technical and managerial problems in its earlier biogas programs. Second, unlike much of the evaluation literature concerned with NGO-supported pilot programs, the findings reported here pertain to a program of massive, national scale—findings apply (potentially) to millions of households. Third, our sample is much larger than the sample used in most of the household energy studies that have conducted purposeful surveys, enabling us to control for placement and supply side effects. Fourth, our findings are rooted in a comprehensive analysis of fuel demand unlike some of the energy and development literature that has taken a
*|Urban|3.1|2.9|1.9|.9|.2|2.5|.6|11.5|11.6|.4|**4.9**|
||**Total**|**3.0**|**.8**|**.4**|**.1**|**.1**|**1.0**|**.3**|**11.4**|**11.3**|**.4**|**2.2**|

Sources: Author’s calculation using the VHLSS 2004 and the _Enterprise Survey_ data 2004.

Notes: *<sup>1</sup> The region excludes Hanoi; *<sup>2</sup> the region excludes HCMC; *<sup>3</sup> the per capita average household monthly expenditure; *<sup>4</sup> land holding is defined as the average per capita area owned by the household members including farming, forestry land and water surface area for aquaculture, residence land and garden, and pond next to housing land; *<sup>5</sup> the minority is defined to represent all ethnic groups other than the Kinh majority and Hoa (Chinese); *<sup>6</sup> the average education years attained by those who are 15 and over; *<sup>7</sup> the proportion of workers employed by state sector relative to total workers **;**<sup>8</sup> the proportion of workers employed by foreign sector relative to total workers.
list is included in Table A1 in the Appendix.<sup>18</sup> Then, for each death we assume that the number of lost-years is equal to the residual life-expectancy at the age of death, as computed from the country's pre-pandemic age-specic mortality rates, obtained from the Global Burden of Disease Database (Dicker et al., 2018). As noted earlier, we consider this assumption conservative, since individuals who die from Covid-19 are given the same residual lifeexpectancy as that of other individuals with the same age.

One particularity of Covid-19 is that its mortality is concentrated among the old. This concentration is illustrated in Figure 1, which shows a histogram by age categories of the Covid-19 deaths observed in Sweden. This concentration implies that evaluations of mortality costs based on lost-lives, which disregard the age distribution of deaths, would tend to overestimate the welfare consequences of mortality, compared to our evaluation

> 16It is not possible to nd age-specic mortality information for all countries.

> 17Belgium has the highest number, the United Kingdom has the second highest and Sweden has the fth highest, beyond Italy and Spain, but Sweden has particularly detailed age-specic mortality information.

> 18All of our data sources are described in greater detail in the Appendix.

8
<!-- Start of picture text --> Sichuan* (province) 2000‐2008<br>1200<br>1000<br>800<br>600<br>400 2008<br>200<br>2000<br>0<br>2000 20000<br>Prefecture GDP per capita (RMB, log scale)<br>Education spending per capita (2008)<br>Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance<br>441.18 441.18 261.91 1.68 197.68 0.45<br>Education spending per capita (2000)<br>Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance<br>74.43 186.79 40.06 4.66 35.59 0.48<br>per capita (RMB)<br>Prefecture education spending<br><!-- End of picture text -->

- The two points within the circled area contain prefectures, Aba 阿坝藏族羌族自治州 (population 894,077)and Ganzi 甘孜藏族自 治州 (population 975,207).  Each prefecture attributes to 1% of the total population of 81,331,778. c

<!-- Start of picture text --> Sichuan** (province) 2000‐2008<br>700<br>600<br>500<br>2008<br>400<br>300<br>200<br>2000<br>100<br>0<br>2000 20000<br>Prefecture GDP per capita (RMB, log scale)<br>Education spending per capita (2008)<br>Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance<br>387.74 387.74 261.91 1.48 96.91 0.25<br>Education spending per capita (2000)<br>
which we refer to as a province for the rest of this paper.<sup>14</sup> Adverse weather events are typically spatially correlated, and even expected long-run changes in weather patterns, in Southern Africa and elsewhere, are expected to occur at broad spatial scales (e.g. Shongwe et al. (2009)). This choice is consistent with interpreting our shocks as being those that affect the entire local economy. Second we use Normalized Difference Vegetation Indices (NDVI) — averaged at the province x month level — to determine harvest cycles for each province. Third, we use rainfall estimates, averaged for each province, to estimate sequences of dry spells. In the rest of this section we describe our methodology and results in more detail.

# A. _Estimation of Harvest Cycles_

The NDVI data were collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the NASA polar orbiting satellite Terra from February 2000 to April 2019. MODIS provided an NDVI estimate every eight days at an approximately 250 meter ground sample (pixel) resolution across the entire study area. To isolate the NDVI to those areas where are the time series was ‘masked’ signal only crops expected by the Croplands and Cropland/Natural Vegetation Mosaic categories found within the IGBP land cover classification (Loveland et al. (2000)). Next, to reduce potential pixel-level noise (both in time and space) and simplify the modeling efforts, the measurements were aggregated to monthly values at a 5 kilometer grid cell size. And finally, the values were spatially and temporally averaged further to each province x month.

We identify harvest cycles — more specifically growing months, the month that a harvest starts, and the lean season — in the following algorithmic manner.<sup>15</sup> First, we calculate the percentage change in monthly NDVI. A month is labeled as a growing month if the percent increase is greater than 20 percent. Second, the start of the harvest season is defined as the
|
|Used hired labor|0.84|0.86|0.83|
|Hired labor/ha (days)|14.22|7.53|18.17|
|**Plot characteristics:**||||
|Years of possession|20.48|23.62|18.63|
|Good soil quality|0.51|0.56|0.47|
|Medium soil quality|0.34|0.32|0.35|
|Flat land|0.73|0.74|0.73|
|Gently sloped land|0.22|0.21|0.23|
|Irrigated land|0.08|0.06|0.08|
|**Crop choice:**||||
|Teff|0.29|0.24|0.32|
|Wheat/barely|0.20|0.19|0.21|
|Sorghum/maize|0.42|0.51|0.37|
|Pulses|0.12|0.12|0.13|
|Oilseeds|0.05|0.04|0.06|
|Number of observations|2213|822|1391|

_Source:_ Own computation from AU/WB/UG land tenure survey.

20
baseline level during the second lockdown.<sup>11</sup> The higher mobility during the second lockdown explains the increase in economic activity suggested by our electricity model. When we juxtapose the third and fourth lockdowns with the first one, mobility was also substantially higher during the former. The higher mobility and higher economic activity can be explained by differences in the containment measures and much better adaptation to such measures during later lockdowns.

**Figure 8:** Average Workplace Mobility Trend in Bangladesh

<!-- Start of picture text --> Percent change from baseline<br>0<br>-10<br>-20<br>-30<br>-40<br>-50<br>-60<br>1st Lockdown 2nd Lockdown 3rd Lockdown 4th Lockdown<br><!-- End of picture text -->

# **8. Discussion and Summary**

This paper established a meaningful relationship between electricity consumption and other economic indicators in Bangladesh. The real-time availability of daily electricity consumption data allows for assessment of the impacts of different shocks on overall economic activity when no other data are available yet. Moreover, in contrast to many other economic measures, electricity consumption data are available at subnational levels. Due to its high frequency and spatial granularity, as well as its strong correlation with GDP and other economic variables, electricity consumption is a valuable high-frequency indicator of economic activity.

We presented an electricity consumption model that explains over 90 percent of the daily variation in electricity consumption. Deviations of actual consumption from the model prediction can be used to assess the impacts of economic shocks. We analyzed the impacts of Bangladesh’s lockdowns to contain the spread of COVID-19 on economic activity in Dhaka and showed that the first lockdown came with much higher costs than the subsequent ones. The smaller economic impacts of the later lockdowns can partly be explained by the nature of the lockdowns. For example, while all of them seem to have been similarly stringent according
, accounting for both food bought away from home and consumed at home and food consumed outside of the home. Further, to facilitate food quantity reporting for respondents, a list of non‐standard units, along with their conversion to kilograms, was developed for each item, with inputs from regional experts and experience from the accompanying market price survey (Oseni et al., 2017).<sup>5</sup> The questionnaire was designed to capture purchased food, home production, and gifts.

The nonfood consumption module consisted of 90 items, which were assigned to core and optional modules in the same manner as the food items. The choice of nonfood items followed the COICOP classification system, with all relevant COICOP categories represented in the list of nonfood items.

# 3.2. Fieldwork and monitoring

The fieldwork strategy was designed to facilitate high‐quality data collection and safety of field teams.<sup>6</sup> All enumerators and team leaders attended rigorous training sessions and had to sit a final exam to be hired. Forty‐five teams were assembled for fieldwork, staffed each with one team leader, three regular enumerators, and two reserve enumerators. The large number of teams was essential, on the one hand, for security reasons. It allowed teams to enter and exit an area swiftly before their presence would draw too much suspicion and endanger their safety and that of survey respondents. On the other hand, this arrangement allowed teams to be composed of enumerators native to the areas which they covered.

The survey was piloted in each region before the beginning of fieldwork. Fieldwork was monitored in near real‐time to verify data collection progress, data quality, and enumerator performance. To implement

> 5 The Market Price Survey (MPS) is a component of the SHFS. The MPS collects weekly exchange rates and prices of a broad range of 91 products and services as well as exchange rates from 14 key markets across all Somali regions. 6 The Somali High Frequency Survey was implemented
Figure 5: Intervention Impacts on Payment Behavior <!-- Start of picture text --> A. Impact of Engagement on B. Impact of Targeted Enforcement on<br>Proportion of Paying Customers Proportion of Paying Customers<br>2018.8 2019 2019.2 2019.4 2019.6 2019.8 2018.8 2019 2019.2 2019.4 2019.6 2019.8<br>Date Date<br>Engagement Control Enforcement Control<br>C. Impact of Engagement on D. Impact of Targeted Enforcement on<br>Total Amount Paid (US$) Total Amount Paid (US$)<br>2014 2016 2018 2020 2014 2016 2018 2020<br>Date Date<br>Engagement Control Enforcement Control<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>Proportion of paying customers Proportion of paying customers<br>150<br>80<br>100<br>40<br>50<br>Total amount paid (US$) 0 Total amount paid (US$) 0<br><!-- End of picture text --> _Notes._ All figures present the impacts of the experimental interventions on payment behavior using utility billing data over time. Panel A shows the cumulative distribution function (CDF) of the proportion of people making at least one payment over time (post-intervention), comparing property owners in the control (red) and engagement intervention (blue) group. Panel B shows the CDF comparing property owners receiving the enforcement intervention vs. control. Panel C shows the total cumulative amount paid by property owners over time, comparing the engagement and control groups. Panel D shows the cumulative payments of the enforcement vs. control group. Black dashed vertical line is the date at which the enforcement intervention began. All monetary amounts are in USD using an exchange rate of 100:1, unless otherwise stated
Board published the<br>Media Sustainability Index, which was substantally revised<br>into the Vibrant Informaton Barometer in 2020. We use data<br>from the Media SustainabilityIndex between 2000 and 2019.|
|OBI|Internatonal<br>Budget<br>Partnership|Open Budget<br>Survey|125 countries covering<br>a global sample.|Every two years.<br>Used in WGI<br>since 2005.|Expert/NGO|Expert assessment scored by locally recruited expert<br>respondents with expertse in budget processes. Scoring<br>follows adetailed questonnairesubject to centralized review.|

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