rafmacalaba/data-use-mentions-extended huggingface dataset:
put aggregate direct damages in agriculture at US$ 4.29 billion (Neyter _et al._ 2022b)<sup>9</sup> and estimate indirect war-induced losses in the agricultural sector to amount to US$ 23.3 billion, with logistics disruptions (US$ 11.9 billion) and lower output (US$ 9.6 billion) as main elements (Neyter _et al._ 2022a).<sup>10</sup> Our methodology can provide not only a methodologically sound underpinning for such estimates but also an opportunity to disaggregate them and explore their heterogeneity.

# **3. Data and descriptive statistics**

# **3.1 Incidence and effects of conflict**

<mark>We use two measures of villages’ direct conflict exposure. First, leveraging the high spatial and temporal resolution of Sentinel-2, we rely on comparisons of cloudless Sentinel-2 and Planet satellite imagery in biweekly intervals to identify conflict-related damages to specific fields in the 9 oblasts (districts) directly affected by military action.</mark><sup>11</sup> <mark>Appendix figure 1 provides an example of the front line and damaged fields</mark>

> 7 Zadorozhna (2020) also finds evidence of corruption in land markets, Graubner _et al._ (2021) suggest presence of market power, and Kvartiuk _et al._ (2022) discusses lack of transparency in land auctions used to lease out state land.

> 8 Three laws to govern land relations under martial law (laws 2247-IX; 2254-IX, and 2255-IX were passed on May 12.

> 9 Of this total, about 50% is for land (mining pollution) and unharvested winter crops, 21% for machinery, 14% for stored products, 6% for storage facilities and the remainder for livestock, perennial crops and inputs.

> 10 Output is assumed to fall by 33% for wheat, 32% for sunflower, and 31% for barley; 18% for maize
|_Years independent_|The percentage of years since 1776 that a country has been independent,<br>as reported in Easterly and Levine (1996).|
|---|---|
|_Defense spending_|Defense spending as a share of GDP, as reported in CIA (1994)|

# c) Data on health sector variables

|_Health_<br>_Expenditures_|As part of the World Development Report 1993, Investing in Health,<br>figures on the magnitude of health expenditures were generated<br>(Murray, Govindaraj, and Musgrove, 1995). These have undergone<br>various updates, based on country level reports. We use the latest data<br>available in our analysis.|
|---|---|
|_Percentage of_<br>_national health_<br>_expenditures_<br>_devoted to local_<br>_health services_|As reported in the WHO's Health for All Database. The observation<br>closest to 1990 in the 1986-1993 period is used.|
|_Percentage of the_<br>_population with_<br>_local health_<br>_services, including_<br>_availability of_<br>_essential drugs,_<br>_within one hour's_<br>_walk or travel_|As reported in the WHO's Health for All Database. The observation<br>closest to 1990 in the 1986-1993 period is used.|

37
# 3.2.   Descriptive statistics

Conflict has increased in all regions as measured by the mean number of events a household has experienced. Figure 1 shows us the evolution of conflict events between 2010 and spring 2017. Figure 1, panel a, displays the data of all events as well as property- and violencerelated events. We can see that from 2012 onward, the level of household victimization has greatly increased.

Figure 1, panel b, displays the mean number of events per year by the most common types of perpetrators: insurgents, bandits and criminals, and pastoralists and nomads. We can see that events in which the perpetrator was an insurgent peaked in 2014, which corresponds to the most violent year of the Boko Haram insurgency. The number of events involving bandits and criminals is increasing over time. The number of events involving pastoralists and nomads increased in 2013 and has remained at that level. It is noteworthy to point out that the decrease in 2017 is not representative of the entire year but only until May 2017 (see figure 1, panels a and b). It is meaningful to study the perpetrator and the type of event separately because both violent and property-related events are perpetrated by all types of perpetrators.<sup>11</sup> There are potential limitations in measuring victimization that merit discussion. If past events are not remembered as well as more recent events, our data might suffer from recall bias. To address this issue, we compare the time distribution of victimization to the conflict intensity from the ACLED database at the annual level. Figure B.1 in appendix B displays the time distribution of the events; the vertical axis shows what fraction of all events recorded between 2010 and 2016 for the data set in question happened in each specific year. This way of analyzing the data yields a meaningful comparison between our data set and the ACLED database, as ACLED collects data on the number of conflict events and fatalities reported by newspapers and thus without recall bias. However, the different measures
the municipal level. The improvement due to using AGEB rather than municipal variation is much larger for male LFP, which is harder to predict, than for female LFP.

In short, the results support the use of augmenting survey data with geospatial data when estimating state-level statistics and when estimating municipality-level female and male labor force participation rates. When estimating municipal unemployment rates, however, the estimates are not reliable because the sample contains insufficient information to train a linear model. In particular, the sample contains no unemployed men in 44 percent of municipalities and no employed women in 68 percent of municipalities. Modeling unemployment rates using a linear model is problematic in this setting, highlighting the usefulness of software that can conveniently estimate and apply a wider range of non-linear empirical best predictor models.

> 1 While this is in spirit similar to the sub-area model proposed in Torabi and Rao (2018), the approach outlined in that paper is difficult to implement using publicly available software. 2 Code is available from the authors upon request.

4
lack proper data collection methods for food eaten outside home. In a recent analysis of household surveys globally, 90 percent of the surveys asked about food eaten away from home, but few had any additional follow-up questions (Smith et al., 2014). Only 23 percent of the surveys in the study collected expenditure data on food away from home, and 17 percent collected data of meals eaten outside of home at the individual level. This becomes an increasingly significant issue as countries develop and a larger share of food is consumed outside the home.

# _Non-food, non-durable items_

Most countries include non-food, non-durable items, but the number of items included varies from country to country. Sri Lanka includes just 47 items, while Bhutan, Nepal, and Pakistan include between 50 and 85 items. On the other hand, Bangladesh and India include over 190 items, and Maldives lists 401 items.

# _Consumer durables_

When it comes to consumer durable goods, Pakistan and the Maldives do not include them in the consumption aggregate, but the rest of the countries do. In Afghanistan and Nepal, the monthly value of the consumption of durables is imputed, following recommended practice (Deaton and Zaidi, 2002). In Bangladesh, Bhutan, India, and Sri Lanka, however, only the previous year’s expenditures on consumer durables is included.

# _Housing expenditures_

All countries include actual rent for urban and rural areas, except for India and Maldives. India, along with the Maldives, does not include imputed rent for homeowners. The India NSS asks urban home-owners to estimate rent, but not rural home-owners. Although the NSS collects imputed rent from urban dwellers, this is not included in the consumption aggregate of urban dwellers.

# _Health and education expenditures_

All countries have an estimate of health expenditures included in the national consumption aggregate, except for Nepal and Afghanistan. All countries include education expenditures in their national aggregate.

# 4. **Spatial deflation to account for cost-of-living differences and inter-temporal deflation**

# _<mark>
measurement error. Few empirical papers today would fail to verify the robustness of the results to different specifications (Simonsohn et al., 2020) or different iterations of the data Steegen et al. (2016). Yet economics papers rarely, if ever, verify the robustness of results to different sources of remote sensing data or the use of different weather metrics. As we have shown in this paper, such a blas ́e approach to the use of remote sensing weather data is unjustified.

To investigate the degree to which measurement error is an issue in remote sensing weather data, we tested three hypotheses using a set of heuristics outlined in our pre-analysis plan. Hypothesis 1 considered the degree to which extraction methods used to obfuscate the true location of a household introduced mismeasurement when obfuscated household coordinates are paired with remote sensing data. The preponderance of evidence discussed in section 4.1 leads us to conclude that we cannot reject the null for Hypothesis 1. There is no clear evidence that different obfuscation procedures have different impacts on estimates of agricultural production. Any measurement error which may 43
# **<u>Table 3. Shifts in education level and profession for middle class households in 2005–08 and 2015–18</u>**

||<br>Shares<br>education<br>type of oc<br>|<br>(%) by<br>level and<br>cupation<br>|<br>%|
|---|---|---|---|
||2005–08|2015–18|<br>change|
|_Education of household head_||||
|Primary or less|24|13|–43|
|Lower-secondary|17|15|–14|
|Upper-secondary|35|40|13|
|Post-secondary|2|1|–49|
|Tertiary|22|31|41|
|_Occupation of_ _household head_||||
|Managers, professionals, technicians|48|52|8|
|Support, service, sales workers|23|21|–11|
|Crafts, trades, elementary occupations|18|17|–11|
|Plant/machine operators/assemblers|7|8|15|
|Skilled and unskilled agricultural/etc.|2|2|–10|

_Source_ : Authors’ calculations using data from the EU-SILC UDB-L.

_Note_ : This table the composition in terms of education and occupation of the household head for the European households that belong to the middle class under the baseline definition (lower threshold = income associated to a 4 percent probability of falling into poverty; upper threshold = income associated to a 1 percent probability of falling into poverty). Values are pooled for all countries in the sample, population weighted.

22
passthrough (and decreasing with elasticity). For larger elasticities and price shocks (e.g. upper-bound elasticities in Panel C), the positive and progressive price effects illustrate that as tobacco becomes more unaffordable, households cut down on tobacco consumption enough to “free up” budget resources for other uses. Disposable income is higher after the tax increase. Because lower-income households observe the largest shifts away from tobacco, the pattern becomes progressive.

Figure 3. Change in tobacco expenditures after increasing taxes Panel A. 25% Price increase Panel B. 50% Price increase <!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decile Decile<br>Direct Pass-Through Medium-bound Elasticity Direct Pass-Through Medium-bound Elasticity<br>Lower-bound Elasticity Upper-bound Elasticity Lower-bound Elasticity Upper-bound Elasticity<br>Source: Estimation using a price shock of 25% Source: Estimation using a price shock of 50%<br>Panel C. 100% Price increase<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Direct Pass-Through Medium-bound Elasticity<br>Lower-bound Elasticity Upper-bound Elasticity<br>Source: Estimation using a price shock of 100%<br>.5 1<br>0 0<br>-.5 -1<br>Inconme Gains (%) Inconme Gains (%)<br>-1 -2<br>4<br>2<br>0<br>Inconme Gains (%)<br>-2<br>-4<br><!-- End of picture text --> _Source:_ Author estimations, based on the IHS 2012-2016 and HIES 2017. _Notes:_ Deciles are created based on per capita household consumption.

# (B) Reduced Medical Expenses

<u>Figure 4</u> plots the effects of reducing out-of-pocket medical expenses after the tax-induced price increase in cigarettes
**Table 3 compares the vulnerable to the poor population.** The table shows that the poor and vulnerable are similar among several dimensions. Still, there are some systematic differences. First, a lower share of the vulnerable than poor live in urban areas (36.6 percent versus 42.5 percent). Surprisingly, a higher share of the poor are employed (33.1 percent versus 29.1 percent). Also, nearly the full universe of the vulnerable population are informally employed compared to 9 out of 10 poor people. The vulnerable have, on average, more children, more household members, and a higher exposure to criminal activity. When designing targeting mechanisms, these differences should be taken into consideration.

_Table 3: Descriptive Statistics – Vulnerable versus poor (2019)_

|**Variable**|**Vulnerab**|**lepopula**|**tion**<br>**Sdd**|**Poor**|**populati**|**on**<br>**Sdd**|
|---|---|---|---|---|---|---|
||**Observations**|**Mean**|**tanar**<br>**Deviation**|**Observations**|**Mean**|**tanar**<br>**Deviation**|
|Urban|22196|0.366|0.482|18321|0.425|0.494|
|Male|22196|0.469|0.499|18321|0.464|0.499|
|Single|22196|0.373|0.484|18321|0.342|0.474|
|Age|22196|27.905|19.247|18321|27.389|
_Wealth of Nations_ database, the reader is referred to Appendix A of _The Changing Wealth of Nations 2018,_ or the detailed methodology documentation which can be found at <u>https://datacatalog.worldbank.org/dataset/wealthaccounting.</u>

# **Table 1. Data sources**

|**Indicator**|**Source**|
|---|---|
|**Resource rents**: oil, natural gas, coal,<br>minerals, cropland, pastureland, timber|_The Changing Wealth of Nations 2018_|
|**Production**: oil, natural gas, coal, minerals,<br>timber|_The Changing Wealth of Nations 2018_|
|Agriculture Gross Production Index|Food and Agriculture Organization of the<br>United Nations|
|Employed labor compensation|Penn World Table 9.0 (Feenstra et al 2015)|

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# **Table 5 –** Pairwise correlations

<!-- Start of picture text --> SME credit/GDP 1.000<br>GNI per capita (2007) 0.4477* 1.000<br>SME credit/GDP GNI per capital (2007) GDP growth (2007) Inflation (2007) Interest spread (2007) Lending rate (2007) real interest rate (2007) Share of rural population (2007) Expected real growth (2009) GDP growth (average 2002‐ 2007) Domestic Credit/GDP growth (avg 2002‐2007) Domestic credit/ GDP 92007) Legal rights index (2007) Credit information index (2007) Closing business ‐ days (2007) Contract enforcement ‐ procedures (2007) Contract enforcement ‐ time (2007) Starting a business ‐ time (2007) Market cpaitalization/ GDP (2007) Self‐employed % (2007) SME definition max number of employees Fuels and minim/ GDP (2007) foreign owned banks, share of assets Government owned banks , share of assets Banking concentration Branches per 100,000 adults Overhead costs/total assets (avg, 2002‐2007) Net interest margin (avg, 2002‐2007) Bank Cost/Income (avg 2002‐2007)<br><!-- End of picture text --> SME credit/GDP GNI per capita (2007) GDP growth (2007) Inflation (2007) Interest spread (2007) Lending rate (2007) real interest rate (2007) Share of rural population (2007) Expected real growth (2009) GDP growth (average 2002‐2007) Dom.Credit/GDP, growth (avg 2002‐2007) Domestic credit/ GDP (2007) Legal rights index (2007) Credit information index (2007) Closing business ‐ days (2007) Contract enforcement ‐ procedures (2007) Contract enforcement ‐ time (2007) Starting a business ‐ time (2007) Market cpaitalization/ GDP (2007) Self‐employed
’s cost of a secondary school student-year or a primary/preparatory school student-year. We assumed the former was twice as expensive (for the government) as the latter two.<sup>39</sup> With that assumption, and with the overall shares of publicly enrolled primary/preparatory and secondary students as taken from CAPMAS (2017), the actual government cost can be calibrated. That is, we let actual numbers of publicly enrolled students (by level) and our assumption that a public secondary school student costs the government twice as much as a primary school student determine the imputed government cost for a student at both the primary and secondary levels.

36 For households’ consumption of tea and sugar not distributed by ration cards, we apply the lowest GST rate: _gomhoria_ tea and unsubsidized crystallized sugar.

> 37 The gasoline is labeled “super” in the law. Furthermore, we use a weighted excise tax rate where 1/3 of the weight is based on the rate for imported products and 2/3 for the local products. This, using as guidance the energy balance table by the International Energy Agency, noting that Egypt’s net imports of oil products account for 32.6 percent of the country’s total consumption in 2015 (including industry own use during the refinery process and use for electricity production).

38 Anecdotally, the Ministry of Health’s budget is perhaps only 50 percent of total public expenditure on the delivery of health care services. Therefore, we are allocating a total, as well as a per‐patient cost and a per‐patient benefit, that is too low. Our baseline values assume the missing special funds are allocated to services and regions in exactly the same proportions as the on‐budget expenditures.

39 We recognize the arbitrariness of this assumption. Other CEQ exercises have shown that the ratio of the cost of a secondary level student to a primary level one can fall in the range of 1.5 and 10.

26
and leave camps freely?” Housing assistance corresponds to “Do you think Syrian refugees should be given housing assistance through shelter programs that subsidize their rent?” Intl refugee assistance corresponds to “ Do you think the international community should spend more money to support refugees?” Support for integration index is an index composed of the the following attitude questions: relocation, work, citizenship, work permits, integrated schooling, freedom of movement, and housing assistance. Primary identity corresponds to the following question: “Which of the following best describes you?” (Read the options aloud.); Above all I am a Jordanian; Above all I am a Muslim; Above all I am an Arab; Above all I am a Christian. Days of media consumption corresponds to “In the past 7 days, how many days did you read or listen to the news from any source, including newspapers, online, WhatsApp, etc.?” Refugees a top challenge is an indicator equalling 1 if the respondent volunteered that refugees were one of the most important challenges facing Jordan (options not read aloud). Perception of jordanian economy corresponds to “How would you evaluate the current economic situation in your country?’ Share of Syrians receiving aid and average aid amount correspond to the following questions: “What percent of refugee households in your neighborhood do you think receive any assistance from any organizations in a typical month, in cash or in kind? Enumerator: Do not include assistance received from other households.” and “Of the refugee households in your neighborhood who receive assistance, what do you think is the average value in Dinar of the assistance (in cash or in kind) that they receive from organizations in a typical month?” The negative treatment effect stems from the control group being more likely than the treatment group to report outlandishly large numbers.

- The regressions also have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children
(CCT) available for pregnant women and children under age 2. The welfare transfers are small and could be better targeted, as Figure 18 shows. Non-poor households also possess the IDPoor equity card to access CCT benefits if they have small children, although at a lower incidence than poor households. The CCT for pregnant women and children provided total benefits US$ 190 during pregnancy and a child’s first 2 years, or approximately US$ 63 each year. This translates to just 6 percent of the national poverty line.

Figure 18: Percent of households with an Equity Card and CCT recipients by decile <!-- Start of picture text --> 40%<br>30%<br>20%<br>10%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>Market income decile<br>Equity Cardholders (IDPoor) CCT<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

Because in-kind tertiary education benefits increase with income, they are regressive and not equalizing. Enrollment in tertiary public education is not favorable for poorer segments of the population; about 60 percent of enrollment in tertiary education is among the top 20 percent income households in comparison to 4 percent among the bottom 40 percent. Poor children clearly benefit from having basic education and appear to access primary and secondary public education slightly more than non-poor children. However, due to direct costs and related foregone incomes of remaining in school, these households do not benefit much from tertiary education.

In-kind health benefits are progressive but not equalizing. While poorer households use public health more than richer households, they capture relatively smaller shares of in-kind public health benefits.

26
on the synthetic census, constructed as described in the data section.

7. Benchmark the estimates by multiplying the estimated headcount rates and the estimated root mean squared error by a scaling factor calculated for each state. The scaling factor is calculated as a ratio, where the numerator is the official state headcount estimate taken from the survey data. The denominator, meanwhile, is the average, across each state, of the small area municipalities that are contained in the MCS-ENIGH survey, weighted by the sum of the original sample weights for each municipality. These scaling factors are calculated based on the small area estimates from municipalities included in the survey 17
total number of emigrants is under-estimated, and in some cases one is (mis)led to conclude that 100% their immigrants to the OECD area immigrated to the US; as acknowledged by Carrington and Detragiache, this may approximate the reality for Latin America, but is clearly erroneous, for example, in the case of Africa. Third, the CD data set excludes South-South migration, which may be signi...cant in some cases (e.g., migration to the Gulf States from Arab and Islamic countries, to South Africa from neighboring countries, etc.). Finally, the de...nition of a migrant is simply a foreign-born individual residing in the receiving country; it is therefore impossible to distinguish between immigrants who were educated at the time of their arrival and those who acquired education after they settled in the receiving country; for example, Mexican-born individuals who arrived in the US at age 5 or 10 and graduated from US high-education institutions later on are counted as highly-skilled immigrants. Despite these various shortcomings, the CD estimates constitute a ...rst and very useful step toward building a fully-harmonized data set on migration rates by education levels.

In an attempt to extend Carrington and Detragiache’s work, Docquier and Marfouk (2004) collected data on the immigration structure by education levels and country of birth from most OECD countries in 1990 and 2000. They use the same methodology and de...nitions as Carrington and Detragiache (1998),<sup>3</sup> but extend their work in a number of ways. They use Census data or speci...c and detailed surveys for nearly all OECD countries: Census data reporting educational levels and countries of birth were used for 11 countries in 2000 and 8 countries in 1990. Survey data were used for 13 European countries. These data give the immigrants’ origin for all source countries, not only for the top 5 or 10 countries, thus preventing an under-representation of small countries, as was the