rafmacalaba/data-use-mentions-extended huggingface dataset:
p<0.10, ** p<0.05, *** p<0.01. Standard errors clustered at the ‘grappe’ level in parentheses. Dependent variable is the log of total monetary consumption per capita. Individual-level sampling weights applied. All specifications include controls for the gender, age, and education level of the household head, household size, the dependency ratio, household walls material, household roof material, household floor material, access to electricity, access to improved drinking water, and access to improved sanitation as well as an urban-rural dummy and region fixed effects. Source: EHCVM and World Bank estimates.

# 4.2. Disaggregated differences in consumption

We now disaggregate the results by different parts of the consumption basket to characterize more fully the deprivation implied by the overall changes in consumption and poverty described in Section 4.1. We begin by breaking down the results by food and non-food consumption; that is, we re-estimate Equation 1 with the log of food consumption per capita and the log non-food consumption per capita as the main regressors. Pooling the data from Burkina Faso, Niger, and Senegal, food consumption was 11.4 percent lower in the lean season wave while non-food consumption was 7.4 percent lower (Table 4). The difference between these two effects is even larger at 11.3 percent for food consumption and 6.3 percent for non-food consumption when full controls are included.<sup>23</sup> This in itself suggests that seasonality may have more effect on food consumption. Nevertheless, these results may partly be an artifact of the way in which the EHVCM data were collected; the recall period for the consumption of food items generally covered the previous seven days, while non-food items were recorded for the previous seven days, 30 days, three months, six months, and 12 months, which may be less sensitive to seasonal swings. It is therefore important to dig deeper into the consumption basket.

> 23 We use Stata
is commonly thought to be the case in India.

We sought further empirical support for this intuitively plausible assumption by analyzing the behavior of stamp duty revenues in response to changes in tax rates.<sup>43</sup> This indirect method was chosen because it is difficult to observe underdeclaration directly given that this is an illegal activity. If property undervaluation is common and sensitive to tax rates, we would expect it to affect the relationship between changes in tax rates and revenues over time. It should be emphasized that we are not testing the hypothesis that there is a Laffer curve effect for stamp duties **collections** : if a Laffer curve effect were in evidence for stamp duties, then underdeclaration would decline sufficiently to generate increased revenues from stamp duties themselves. Here we are only looking for evidence that **underdeclaration** declines when stamp duty rates decline from the levels currently in place in India. As the simulations above show, plausible parameter values suggest that, even if there is a reduction in stamp duty revenues when rates decline, additional revenues from other taxes may be generated by staunching the flow of money to the black economy.

> 39 See Indian Public Finance Statistics 2001-2002, Table 1.2

> 40 The weighted average stamp tax rate for the sixteen major states is 9.7 percent as of 2001.

> 41 See Friedrich Schneider (2002), “Size and Measurement of the Informal Economy in 110 Countries Around the World”, paper presented at the Workshop of the Australian Tax Centre, ANU, Canberra, Australia, July 17, 2002.

> 42  See Shankar Acharya et al. (1986), _Aspects of the Black Economy in India_ , National Institute of Public Finance and Policy (New Delhi, India).

> 43 For this purpose, we used a time series of non-judicial stamp tax revenues in selected States kindly provided by Mr. Tapas Sen of the NIPFP.

29
The FEI was originally expected to include the internal rate of return (IRR) and/or economic rate of return (ERR) of projects, depending on data availability. However, given that many of these investments were proposed for projects with no direct monetary benefits and largely indirect economic effects (i.e. mainly projects with a large public good component<sup>18</sup> ), the calculation of economic IRR would have produced unrealistic or incalculable results. The alternative would have been to account for all indirect positive effects and estimate benefits. This would have required data on monetized benefits for such effects, which was unavailable. For these reasons, benefit‐cost ratios (BCRs) were used. The BCRs also allowed for the analysts to control for project size and avoid penalizing projects with higher costs but potentially higher benefits.<sup>19</sup> The results from the transport project SEI and FEI rankings are presented in Figure 7.

Figure 7. SEI and FEI, Transport Projects, Panama Pilot 2015 <!-- Start of picture text --> Social and Environmental Indicator (SEI) Financial and Economic Indicator (FEI )<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>Project 3 Project 6 Project 2 Project 8 Project 9 Project 7 Project 5 Project 17 Project 19 Project 10 Project 13 Project 11 Project 18 Project 16 Project 4 Project 14 Project 1 Project 15 Project 12 Project 4 Project 17 Project 5 Project 6 Project 18 Project 1 Project 7 Project 8 Project 2 Project 19 Project 13 Project 9 Project 16 Project 11 Project 3 Project 12 Project 14 Project 10 Project 15<br><!-- End of picture text --> Figure 8 shows the results of plotting each project’s SEI and FEI scores.

Figure 8. SEI and FEI plot for selected transport projects, Panama 2015 <!-- Start of picture text
Table A5: Future employment expectations and outcomes do not differ by treatment

||(1)<br>Day labor|(2)<br>Salaried|(3)<br>Any work|(4)<br>Daily wage|(5)<br>Expects work|(6)<br>Total expected|
|---|---|---|---|---|---|---|
|Work|0.001|-0.004|-0.111|-0.009|-0.064|-0.619|
||(0.015)|(0.009)|(0.043)|(0.341)|(0.143)|(2.713)|
|Cash|0.023|-0.011|-0.070|0.119|0.120|-1.787|
||(0.022)|(0.008)|(0.049)|(0.344)|(0.200)|(3.588)|
|Shrp. q-val Work|1.000|1.000|0.060|1.000|1.000|1.000|
|Test Work = Cash|0.321|0.113|0.356|0.635|0.327|0.681|
|Shrp. q-val Work = Cash|1.000|1.000|1.000|1.000|1.000|1.000|
|Observations|743|743|698|193|698|698|

**Notes:** This table reports the treatment effect of receiving employment or cash relative to the control group on future employment. The columns show OLS estimates of a regression of the outcome on a dummy for assignment
# What Can the Service Delivery Indicator Surveys Tell Us about COVID-19 Preparedness?

Jigyasa Sharma, Kathryn Andrews, Ruben Conner, Roberta Gatti<sup>1</sup> JEL Classification: I1, C83

**Keywords** : COVID-19, Service Delivery Indicators, pandemic preparedness, health facilities

> 1 Jigyasa Sharma (jsharma3@worldbank.org); Kathryn Andrews (kandrews@worldbank.org); Ruben Conner <u>(rconner1@worldbank.org); Roberta Gatti (rgatti@worldbank.org), all in the Human Development Chief</u> Economist’s Office at the World Bank. This paper has benefited from review and feedback from Oliver Fiala, Inaam Ul Haq, Gerard Kambou, Nadir Mohammed, Muhammad Ali Pate and Albert G. Zeufack, and research support from Andres Yi Chang and Sebastian Insfran Moreno.The data used in this paper can be found at <u>https://datacatalog.worldbank.org/.</u> _The views expressed here are the authors’, and do not reflect those of the World Bank, its Executive Directors, or the countries they represent._
Delivery Survey,<sup>7</sup> whose inputs rarely retain the same quality as those used in agronomic field trials.

## _3.1. Yield gains from the use of authentic inputs_

It is often noted that experimental yields from controlled agronomic studies are high but rarely observed on an average farmer’s plot. This can be explained by differences in soil and environmental conditions across farmers (Suri 2011, Harou et al. 2017), variation in crop management practices (Esilaba et al. 2005, Ngome et al. 2011, and Sileshi et al. 2010), and variation in access to high quality inputs (Tjernstrom et al. 2017, Fairbarn et al. 2017, Ashour et al. 2017, Bold et al. 2017). In this section we first consider yield returns observed in experimental studies to be able to provide an upper‐bound estimate on the financial

> 6 We did not find experimental field trials for pesticide use in Uganda or similar countries with detailed data on application rates and costs. Similarly, for coffee and matooke there was no evidence on improved seeds. 7 The NSDS is collected by UBOS and contains information on the differences in services available for the Ugandan population, including agricultural services.

9
In addition to high rates of monetary poverty, the country has consistently reported high measures of non-monetary poverty; the incidence of energy poverty and food insecurity is also high for EU standards.** On all the measures of economic strain, Bulgaria consistently ranks higher than the EU-27 average. Consensual indicators of energy poverty show high levels for EU standards, with about 22.5 percent of households in Bulgaria being unable to keep their homes warm in 2022, the highest rate in the EU. The proportion of individuals reporting economic strain is most pronounced among households in the lowest income quintile, with reports as much as 11 times higher than households in the wealthiest income quintile (World Bank, 2021). Even before COVID-19 and the Ukraine crisis, food insecurity was persistently high. Bulgaria ranks 29th of 113 on the 2022 Global Food Security Index.<sup>2</sup> Though there have been some improvements in food insecurity since 2012, the country ranks behind other countries in the EU.

**Figure 1. Poverty decreased during the pre-covid-19 period, but levels are still high for EU standards**

> (a) Income-based poverty rate, upper-middle income (UMI) poverty line ($6.85 per day, 2017 PPP), Percent, 2006-2021

b. Income-based poverty rate, upper-middle income (UMI) poverty line ($6.85 per day, 2017 PPP), Percent, Selected EU countries, 2021

> 1 These estimates were produced by the World Bank using 2017-2022 EU Surveys on Income and Living Conditions. (EU-SILC). No more recent estimates of absolute poverty are available.

> 2 It is only among the moderate-performing countries compared to the rest of the countries in the region. It performs significantly better than Romania, which ranks 45, but worse than Poland, which ranks 21.

2
|||
|Part 1|37.7%|40.3%|44.2%|
|IBRD|54.7%|57.9%|62.8%|
|IDA/Blend|36.0%|37.7%|40.4%|
|Source: Authors’ calculations using the UNESC<br>learning data (LP, LAYS, or PISA) but no schoo<br>regional average byincome level. The estimates a|O School Closures database coverin<br>l closure data, we impute missing v<br>re countryaverages.|g February 2020-February 2<br>alues for share of school sys|022. For countries with<br>tem closed by using the|

7
When the actual diets were compared between 2000 to 2016, the trends for fruits and vegetables and starchy foods intake are in line with the modeled diets. The consumption of milk and milk products has gone down between 2000 and 2016, yet both modeled diets show an increase in consumption of dairy. In terms of protein intake, the FBDG diet shows meat and fish intake comparable to the actual diet in 2016; intake for both these groups has increased since 2000 and if this trend continues, i.e., a further increase, it will exceed the dietary recommendation as well as the GHGe contribution.

The EAT Lancet diet suggests almost a doubling of dairy intake, pulses and halving fish and starchy foods compared to the FBDG diet. The implications for such recommendation in a predominantly rice based diet are enormous; perhaps replacing white rice with whole grains/brown rice would assist the shift towards a healthier diet as per the EAT Lancet guidance. Also, the ongoing introduction of fortified rice in social assistance programs increases nutritional value of the diet among a part of the population for whom rice makes up a large component of their diet. The difference between the FBDG and EAT Lancet diets is noteworthy. Even though the direction of change from current diet is similar in both diets, the change is even more pronounced for the EAT Lancet diet, which requires major changes from current food consumption patterns, even more so than the FBDG diet.

# **4.3 Policy considerations**

There are two broad implications to consider from the results of the Bangladesh diet assessment. First, a recent analysis that assessed country specific dietary shifts to mitigate the climate and water crises, while also addressing healthy diets, concluded that achieving healthy and energy and protein adequate diets in many low- and middle-income countries would require an increase in GHGe (Semba 2020). The results from Bangladesh are in line with this conclusion and policy reform and action will require decision making that takes into account that the relationship and interplay between healthy diets, global
<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>.1 .2 .3 .4 .5 .6 .1 .2 .3 .4 .5 .6<br>share of underweight (WAZ<-2) children under age 3 share of underweight (WAZ<-2) children under age 3<br>bandwidth = .99 bandwidth = .99<br>share of villages with ICDS<br>share of villages with ICDS<br>share of villages with ICDS<br><!-- End of picture text --> _Source_ : NFHS I and II, State Domestic Product data from the Government of India, _Economic Survey_ 2003-04.

34 The State Domestic Product per capita are for the years 1993-94, and 1998-99, to correspond roughly to the two survey years.

23
# **Introduction**

Are cryptocurrencies, particularly as epitomized by Bitcoin, genuinely a potential competitor for fiat money or gold, or are they mainly a speculative bubble akin to 17<sup>th</sup> century Dutch tulips? A critical question is the extent to which there is an underlying transactions demand, now or in the future, to justify their high if wildly fluctuating prices. The results of this paper provide perhaps the strongest quantitative evidence to date demonstrating that, the use of Bitcoin for both domestic and international payments has been significant worldwide.

The vast bulk of media focus has been on cryptocurrencies as an evolving asset class. And although there has been some previous quantitative research providing some insight into the use of Bitcoin in facilitating illegal activities, this work typically concentrates on the analysis of the users, not the type of use, classifying users into legal and illegal agents, rather than distinguishing between investment purchases and transactions use. Furthermore, these analyses have only been applied to on-chain transactions,<sup>1</sup> that is, those that are immutably registered on the blockchain. On-chain transactions, however, constitute only a small share of the universe of all Bitcoin trades, most of which are “off-chain” utilizing some form of exchange, some heavily regulated, some not so much.<sup>2</sup> This paper thus develops a new methodology for examining off-chain transaction data that allows one to probabilistically tag individual trades as “crypto vehicle transactions,” that are clearly distinguished from the use of Bitcoin as an investment vehicle. We do not know whether results such as ours can potentially explain prices for crypto currencies, but they certainly show that the popular media focus on Bitcoin as speculative asset ignores its very active use in payments and capital flows.

> 1  See Chung, 2019; Foley et al, 2019; Framewala et al, 2020; Ron and Shamir, 2013; Yang et al 2019; Zhao and Guan, 2015.

> 2  Though arriving
growth of the industries of category B is pulled up by that of the industries of category C – the value-added of the industries of category C was 24% of the entire category B in 1988, and 38% in 1997. In contrast, the valueadded of the industries of category D was more than two-thirds of that of all the industries in 1988, but only about a half in 1997.

The relative decline of the labor intensive industries and the rapid growth of the high-tech industries imply an upgrade of the production structure in China. However, this upgrade is not equally distributed across the entire territory. The

> 13 We focus our analysis using the data of 1988-1994 and 1997 for those of 1995 and 1996 are not available, and the industry classification changes after 1998. 14 See more detail in Table 2.

10
of 16 days We have downloaded georeferenced measures of XCO2 (the column-averaged dry air mole fraction of CO2) and computed daily mean values for each 10 km grid cell in our global database.

We use georeferenced facility-level global databases to obtain capacity measures and technology specifications for power plants (Byers et al. 2021), steel mills (GEM 2021), cement plants (McCaffrey et al. 2021), and oil refineries (Auch 2017). We use capacity estimates in the regressions because production estimates are both scanty and assigned low confidence by the database producers. Van der Werf et al. (2017) provide monthly estimates of carbon emissions from agricultural and forest burning at 25 km resolution.

The World Cities Database (2021) provides data on urban centroid locations and populations. Population density data are provided at 5 km resolution by CIESIN (2018). We use two sources to construct our georeferenced measure of income per capita. From the G-Econ database (Nordhaus et al. 2006), we obtain GDP per capita in 2005 purchasing power parity for a global grid with 100 km resolution. Each grid cell is assigned to its geographically-dominant country by G-Econ. For each cell in a country, we compute the ratio of cell GDP per capita to the national mean for all cells. We merge the results with annual World Bank estimates of GDP per capita in constant $US 2015, and use the cell ratios to estimate annual GDP per capita for each cell. We introduce another proxy for economic activity by incorporating monthly observations for VIIRS global nighttime lights (EOG 2021) at 500 m resolution. Mistry (2019) has provided global estimates of monthly heating degree days at 25 km resolution.

# **3.2 CO2 Diffusion Effects**

Space-based observations detect higher CO2 concentrations over significant emissions sources because atmospheric diffusion is not instantaneous. As emissions diffuse from their sources, deviations from background concentrations will persist for some distance. By implication, the effects of emissions sources in the model should not be constrained by arbitrarily-scaled grid
and 2005. We collect stock names, amount invested in each stock by each fund, and country of origin of these stocks.<sup>17</sup> One difficulty in constructing the holdings database is that mutual funds report their asset allocation separately over time, that is, their holdings are not linked and do not tend to have identifiers across reports. Therefore, each security needs to be identified at each point in time. This is not a simple task as stock identifiers are rarely available and, when they are, they are not always unique. We match these holdings across mutual funds over time based on the country of origin (when available) and the stock name for each security holding. We can then determine whether the same stocks appear in different mutual fund portfolios across and within fund families.

Table 1 describes the data. We collect data on 8,420 fund-year portfolio holdings over the period 1991 to 2005, covering 499 mutual fund families (companies) and a total of 1,904 funds. Each mutual fund family has on average four different mutual funds. Some families sell the same portfolio to investors under different names depending on their fee structure and minimum investment requirements. In this paper, we consider these different funds (with identical portfolios) only once; i.e., we do not treat them as separate funds as Morningstar does.<sup>18</sup> The total number of asset-level observations in our dataset is 1,359,750, counting each stock-level allocation across all funds over time.

The U.S. mutual fund industry is organized by splitting funds according to their investment scope (Figure 1). In particular, funds are classified into five categories: world funds, foreign funds, emerging market funds, regional funds, and country funds. Regional funds are divided into: Asia (and Pacific) funds, Europe funds, and Latin America (and the Caribbean) funds.<sup>19</sup> World funds invest
corresponding change for the 50+ control category. The specification is estimated separately for each of the three synthetic age cohorts and their outcomes are compared with the corresponding plant cohort when the GQ was completed in 2009. For example, the outcomes of 0-5 year-old plant cohort in 2000 are compared with 9-14 year-old cohorts in 2009.

All estimations include as a control the initial level of ( 2000) in the district, although the results are little affected by its inclusion. The vector contains district-level controls: national highway access, state ii YY YY highway access, broad-gauge railroad access and district-level measures from the 2000 Census of log total ii XX population, age profile (measured as demographic dividend),<sup>9</sup> female-male sex ratio, population share in urban areas, population share in scheduled castes or tribes, literacy rates and an index of within-district infrastructure measuring local access to electricity, roads, telecom, and water/sanitation facilities (see Ghani et al., 2012 for details).

Panels A-C in Table 1 present the results from estimation of specification (1) across the three age groups and for each of the seven outcome variables, reported across columns in the three panels. Columns 1-3 report district-level aggregate changes in logged output, employment and plant count; columns 4 and 5 report changes in average plant size measured as employment or output; columns 6 and 7 report average plant performance measured as labor productivity and revenue TFP. These results are robust to splitting the residual category of 50 plus kilometer distance band into finer groupings (Annex Table A.2).

Panel A shows that the arrival of the GQ had a positive and significant impact for the young cohort on output, average plant size, measured by both employment and output levels (columns 4 and 5), and on median plant performance, measured as labor productivity and TFP (columns 6 and 7). By comparison, for mature and older cohorts we observe either no significant positive or adverse effects in Panels B and C.
2)<br>Exit|(3)<br>PCM|(4)<br>PE|(5)<br>HHI|(6)<br>MS4|(7)<br>lnN|(8)<br>lnP|(9)<br>Z|
|---|---|---|---|---|---|---|---|---|---|
|Panel A: Family C|onnections|||||||||
|PC family (MS)|-0.018|-0.097|0.186|-0.515|0.252**|0.197|0.126|-0.020|-4.795**|
||(0.082)|(0.068)|(0.292)|(1.875)|(0.116)|(0.123)|(0.212)|(0.095)|(2.118)|
|Observations|800|800|800|800|800|800|800|800|800|
|Industries|200|200|200|200|200|200|200|200|200|
|R-squared|0.065|0.102|0.072|0.055|0.110|0.082|0.401|0.672|0.173|
|Panel B: Broad co|nnections|||||||||
||(10)|(11)|(12)|(13)|(14)|(15)|(16)|(17)|(18)|
||Entry|Exit|PCM|PE|HHI|MS4|lnN|lnP|Z