rafmacalaba/data-use-mentions-extended huggingface dataset:
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Fearon, J.D., and D.D. Laitin (2003). ‘Ethnicity, Insurgency, and Civil War’. _American Political Science Review_ , 97(1): 75–90.

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19
of poverty levels.<sup>8</sup> Data has been collected for all of Ethiopia’s woredas,<sup>9</sup> as delineated at the time of the 2007 census. The database includes information on expenditures by sector and year (2008/09-2011/12) from the Ministry of Finance and Economic Development; key service outcomes in education (enrollment, 2007/08-2012/13)

> 7 Indeed, the GTP sets explicit targets for anti-corruption activities and improvements in governance (Federal Democratic Republic of Ethiopia 2013).

> 8 The estimation used data from the 2007 census and the 2010/11 Household Consumption and Expenditure Survey.

> 9 In the Ethiopia government structure, woreda is the administrative unit between zone and kebele. Zone is the 3<sup>rd</sup> level below federal and regional administrations, and kebele is the lowest administrative unit. Woreda-level data is for all regions except Addis Ababa; we have also excluded data from Somali region.

13
sup>18</sup> The food poverty line of monthly R$258 is equivalent to a daily US$3.46 in 2011 PPP, above the international poverty line of US$1.90 per person per day.

The value of the total poverty line found here is also close to the income threshold used to define eligibility to register in the Cadastro Único (R$477, or half a minimum wage in 2018). Thus, our results could be interpreted as providing empirically backed evidence of the relevance of such a value as a way to identify the destitute and vulnerable populations in Brazil.

In the absence of an official poverty line that takes into account the local Brazilian context, this work has estimated a poverty line based on the most recent data available. The results can help inform the policy dialogue around the recent Supreme Court order to set up a basic income scheme for the poor,<sup>19</sup> starting in 2022. The ability to appropriately identify the poor population will be key in this endeavor.

As for the contribution of this work for poverty monitoring in Brazil, more research is needed. In Brazil, poverty monitoring is not based on POF. Instead, another survey – the Pesquisa Nacional por Amostra de Domicílios Contínua (PNADC), which collects information on income instead of consumption – is used. Regular poverty monitoring conducted by IBGE is hence done using an income aggregate. Developing approaches to use the poverty line estimated here for regular poverty monitoring with the income aggregate based on PNADC is beyond the scope of this paper; however, related work has shown that very

> 17 The US$5.50 international poverty line for upper-middle income countries is the median of poverty lines in countries with this level of national income.

> 18 The societal poverty line defines someone as suffering from societal poverty if they live on less than $1 plus half of what the median person in their country consumes (see Jolliffe and Prydz, 2017). With median consumption
**<u>Annex Table 2. Number of people defined as having a disability in each survey</u>**

|_Country and year _|_Type of survey_|_Age range_<br>_considered_|_Number of people_|_Number of people_<br>_with a disability_|
|---|---|---|---|---|
|Burundi 2000|MICS2|0 to 14|9,925|103|
|Cambodia 1999|SES|all|32,348|504|
|Cambodia 2000|DHS|all|66105|1017|
|Indonesia 2000|SES|all|259,237|1,720|
|Jamaica 1998|LSMS|all|26,458|558|
|Jamaica 2000|LSMS|all|6,304|175|
|Mongolia 2000|MICS2|0 to 18|15,025|330|
|Myanmar 2000|MICS2|0 to 14|43,363|66|
|Romania 1995|LSMS|all|72,726|962|
|SierraLeone2000|MICS2|all|24,254|131|

_Note_ : Data are unweighted in order to show the actual number of observations underlying the analysis.

18
# **Table 7: Performance by Development Partner Groups by Indicators A. All Partner Countries** **<u>(including FCS and non-FCS)</u>**

||**Bilateral**|**MDBs**|**Other**<br>**MOs**|**Other**|
|---|---|---|---|---|
|**Ind 3:**Aid flows are aligned with national priorities (aid on<br>budget)|-9.4|26.8|23.4|-9.8|
|**Ind 4:**Strengthen capacity by coordinated support (technical<br>assistance)|-6.4|22.0|28.2|-40.8|
|**Ind 5a: **Use ofcountryPFMsystems|-10.9|25.7|31.8|-8.6|
|**Ind5b: **Use of country procurement systems|-6.4|12.9|19.0|0.3|
|**Ind 6:**Strengthen capacity by avoiding parallel project<br>implementationunits (PIUs)|6.6|-6.4|-38.7|20.7|
|**Ind7: **Aid is morepredictable|-8.1|24.5|19.0|-10.1|
|**Ind 8:**Aidis untied|8.4|14.8|-3.2|-122.5|
|**Ind 9:**Use of common arrangements or procedures (program-<br>based approaches—PBAs)|-9.2|13.2|15.7|37.2|
|**Ind 10a: **Jointmissions to thefield
imported materials. The upstream material sector is perfectly competitive, with suppliers scattered across different locations potentially linked to downstream firms via HSR following a Bernoulli distribution. There are positive iceberg communication costs to connect a downstream firm with its domestic supplier unless the supplier is located in a city that is accessible to the exporting firm via HSR. The number of suppliers, which is also the number of varieties of domestic materials, is endogenously determined in the equilibrium. The HSR expansion boosts connections between exporters and domestic suppliers, leading to lower prices and broader access of domestic inputs. This enables the downstream exporting firms to substitute import with domestic materials, raising DVAR. The model’s predictions — that DVAR increases with greater domestic input variety and firm DVAR increases with the proportion of domestic suppliers that are connected to the exporters via HSR — are consistent with the empirical findings in Kee and Tang (2016) and in this study.

This paper contributes to several strands of literature. First, it advances research on GVCs and value-added trade by proposing a novel approach to estimating DVAR that streamlines the data required for calculation. Two primary approaches are commonly used to measure GVCs (Antràs and Chor, 2022): macro and micro methods. The macro approach, which relies on aggregate World Input-Output tables to assess a country’s GVC participation (Hummels et al., 2001; Antràs and Chor, 2012; Johnson and Noguera, 2012; Koopman et al., 2014; Johnson, 2014), has been widely applied. However, this approach overlooks the rich heterogeneity in firms’ input sourcing and imported input allocation decisions, leading to significant aggregation bias, particularly for countries heavily involved in processing trade (Johnson, 2018; Koopman et al., 2012; Bems and Kikkawa, 2021; Kee and Tang, 2016; Puzzello, 2012).4 Recent micro approaches to GVC measurement use detailed firm-level transactions. Tang et al. (2016) and Bems and Kikkawa (2021) infuse firm information in IO tables, while Kee
A good example of detailed pay PRP and high-powered performance pay incentives comes from the federal Brazilian tax collection agency. In 1988 the Brazilian government created a bonus program for tax officials that rewarded the identification of tax violations. Base salary was augmented on a monthly basis on an individual and group basis. The group reward was calculated based on the relative performance of one local agency versus others, with relative performance measured based on total fines collected, attainment of pre-defined quotas (total tax collection, number of inspections, collection of overdue taxes) and the size of the agency. Individual rewards were based on monthly evaluations by the direct supervisor, which combined objective performance criteria and managerial discretion and rated employees on a scale from zero to 70. Each employee that scored more than 21 points was entitled to an individual reward with the value being determined by the overall availability of funds (which are proportional to the collected fines) and the performance of co-workers. It was not unusual for total bonus payments to reach 200% of base pay.

(Kahn, De Silva, et al. 2001) found that this incentive scheme resulted in a 75% increase in fines per inspection. At the same time, they also found substantial regional variation, with responses ranging from 19% to 145%. The authors do caution that diverse management techniques resulted in some regions targeting wealthier sources (such as corporations), more aggressively, which points to the potential negative effects of such high powered incentive schemes which may encourage extortion. Unfortunately, limited data prevented the authors from examining these social costs further.

Another study of revenue authorities is by (Bertelli 2006), which explores the potential tradeoff between intrinsic and extrinsic motivation in the Internal Revenue Service in the US. The IRS implemented a paybanding system that imposed high-powered performance incentives on supervisors, but not on nonsupervisory personnel. Using data from the 2002 Federal Human Capital Survey, the author showed that the incentive scheme crowded in intrinsic motivation at the lowest pay levels, and crowded out at the highest
among public doctors reflected a different patient or sickness profile. Furthermore, the DH method, when combined with their inability to use medical personnel in the observation stage, did not provide any information on the accuracy of diagnoses; the researchers suspected that the accuracy of diagnosis was low, but could not validate their suspicions with data.

Therefore, in 2010, Das and others (2012) deployed _standardized patients_ —surveyors recruited from the local community and extensively trained to portray standardized presentations of limited cases—in a sample of health care providers in Delhi and the rural regions of an Indian state. Such standardized patients (SPs) are widely recognized as the “gold standard” in measurements of quality, but the use of SPs in large samples of randomly selected providers, many of them with low medical training, had not been previously attempted.

In Delhi, the study sought informed consent from all providers (where the providers knew that a standardized patient would be sent, but did not know the date) and follow-up surveys showed (A) a single case of adverse interaction between the provider and the patient (out of 230 interactions) and (B) less than a 1 percent detection rate for the SPs. On the basis of this pilot, the study was rolled out in the rural district _without_ informed consent from providers, who were all part of a larger study (with informed consent) similar to that in the DH Delhi studies. The reasons for the lack of informed consent stemmed primarily from the remoteness of the sampled populations. In rural villages, most patients were known to the doctor and although unknown patients often presented to the providers, the combination of announcing the SP study _and_ a remote rural setting would have raised suspicions, invalidating the study design. The study was cleared through Institutional Review Boards at Harvard University and Innovations for Poverty Action. For the purposes of the discussion, we note that the study demonstrated, for the first time, the very poor diagnoses rates in Indian health care (the correct diagnosis
# I. SETTING AND DATA

# _Program Features_

Initiated in 1997 and still in effect, _Progresa-Oportunidades_ is a large-scale social program that aims to foster the accumulation of human capital in the poorest communities of Mexico by providing both cash and in-kind benefits, which are conditional on specific behaviors, in the key areas of health and education. The program grants scholarships and school supplies to children aged under 17 conditional on regular attendance at one of the four last grades of primary schooling (grades three to six) or one of the three grades of junior secondary schooling (grades seven to nine). The scholarships increase in amount with school grade level achieved, and in grades seven to nine the scholarships are larger for girls than boys. The program also distributes cash transfers for the purchase of food, provides food supplements, and promotes healthcare through free preventive education intervention on hygiene and nutrition. The distribution of the food stipends and nutritional supplements are conditional on healthcare visits at public clinics. The benefits are delivered to the female head of the household (usually the mother) on a bimonthly basis after verification of each family member ’ s attendance at the relevant facility.<sup>3</sup> The _Progresa_ program is targeted both at the village and household levels. During the first years of the program, poor rural households were selected through a centralized process which encompassed three main steps. First, villages were ranked by a composite index of marginality computed using information on socioeconomic characteristics and access to the program infrastructures from the censuses of 1990 and 1995.<sup>4</sup> Second, 3. Overall cash transfer amounts can be substantial: the median benefits are 176 pesos per month (roughly 18 USD in 1998), equivalent to about 28 percent of the monthly income of beneficiary families.

4. Localities with fewer than 50 or more than 2,500 inhabitants were excluded during the first years of the program. We use the words “locality” and “village” interchangeably when referring to distinct census-designated rural
the change in population urbanization from the 1991 Census to the 2001 Census as a control. Thus, these regressions will be considering the movement of industry over-and-above that of the underlying population base. Section 2 noted that some sub-areas of districts change their urbanization status in 2001 and that this could affect our urbanization measures even if a plant’s location remained fixed. Including this change provides a quantitative check on whether this is important as the redefinition would apply to the sub-area’s population, too.

# **_Basic District Analyses_**

Our base estimation for district d and industry i is specified as: ΔUrban Shared,i = γ∙Initial Urban Shared,i + β∙Xd + ηi + ηs + εd,i.

The dependent variable, ΔUrban Shared,i, is the change in urban share in employment across a time period. Initial Urban Shared,i is the initial urban share in employment at the start of the period. This initial condition is important as we consider shares that are bounded. Thus, highly urbanized areas like Delhi have limited potential to further urbanize.

12
Rwanda and Nigeria have high rates of IPV compared to the global average. According to data gathered using the direct face-to-face method, 45 percent of Rwandan women and 31 percent of Nigerian women report having experienced physical or sexual IPV in the past 12 months (Global Burden of Disease, 2017). This compares to 13 percent of women in the United States and United Kingdom, 42 percent in Kenya, 39 percent in India, and 31 percent in South Africa. In terms of IPV predictors, poorer, less educated women report IPV at higher rates in Rwanda, while richer, more educated women report more IPV in Nigeria (DHS, 2016, 2014).

Both experiments described below were conducted in accordance with WHO guidelines on the safe and ethical collection of data on violence against women (WHO, 2001, 2016).

### **3.1.1 Rwanda**

In Rwanda, the measurement experiment was conducted on a sample of 2,728 heterosexual couples who were participating in the baseline survey of an IPV prevention study. To be eligible for the intervention, couples had to be aged 18 years or over and married or cohabiting for at least 12 months. The study took place in Eastern Province, in eight sectors selected by the Government of Rwanda for high rates of IPV and a high concentration of Village Savings and Loan Associations (VSLAs) (which the intervention was

> 13It also demonstrates that experiments that compare reporting rates across methods can put a sign and bounds on the likely misreporting bias of standard survey methods.

6
prices or shocks. The CPI weights are updated every two years, which is less frequent than the scanner index. Hence, the CPI is more subject to substitution bias and the basket is less relevant over time. The price index _Pj,t,y_<sup>_L_attime</sup><sup>_t_andyear</sup><sup>_y_forproductgroup</sup><sup>_j_foreachstoreisshownbelowin</sup> equation (22): Each unique item is defined by its UPC code. Prices and quantities are observed for each store and UPC pair, which is denoted as product _i_ . By construction, changes in the price index only reflect relative changes in prices for a given bundle and are unaffected by price levels. Therefore, product switching among consumers to more expensive bundles does not change the price index for given price levels.

The second stage is similar to the first stage and aggregates the product group-specific price indices for each store using expenditure shares _sj,y−_ 1 that are lagged one year and fixed within year. To follow the CPI more closely, a Tornqvist price index can also be constructed using the average expenditure shares between two periods as weights. Although we present results using the first method, both methods give almost identical results and are shown in

> 61There are limitations with the publicly available BLS indices such that we do not to use them in this paper. First, there are only about 25 cities for the BLS indices with many cities defined across state borders, making it difficult to apply our IV which utilizes state-level variation. Second, our retail scanner indices are robust to many weighting schemes and correlates strongly with the BLS indices for the cities that are available, as shown in Leung (2021). Third, with our indices, we can segment by store types, focus on grocery stores and chains that price locally, and have finer location to implement triple differences etc.

> 62Prices of goods that did not sell within a given week are not recorded in retail scanner data
# **Tables**

Table 1: Definition of Conflict Variables

|Variable|Definition|
|---|---|
|_cohortt_|Dummy equal to one when the individual belongs to the exposed cohort<br>(definition depends on the variable of interest), and zero otherwise. Appears<br>interacted with one of the five variables below:|
|_*casualtiesj_|the total number of conflict-related casualties in district_j_during 1996-2006, per<br>1000 inhabitants (as of the 1991 Census of Nepal, omitted hereafter)|
|_*schooldestrj_|the total number of schools destroyed in district_j_during 1996-2006, per 1000<br>inhabitants|
|_*abductionsj_|the total number of abductions by Maoists in district_j_during 1996-2006, per 1000<br>inhabitants|
|_*control1j_|Dummy equal to one when the individual is in a district controlled by Maoists in<br>2003 according to both the Maoists’ own classification and that used by the<br>government, and zero otherwise. (Definition 1)|
|_*control2j_|Dummy equal to one when the individual is in a district controlled by Maoists in<br>2003 according to the government’s, and zero otherwise. (Definition 2)|
|_casualties_by_surveyjt_|Total number of conflict-related deaths at the district level, per 1000 inhabitants,<br>from the (Nepali) year of birth to the year of the survey in which most data were<br>collected (2001 DHS: 2057; 2006 DHS: 2062; NLSS: 2060)|
|_casualties_age1age2jt_|Number of conflict-related deaths at the district level, per 1000 inhabitants, during<br>the period when the individual was aged between age1 and age2 (in turn, between<br>6 and 10 and between 11 and 14).|

Table 2: Definition of Outcome Variables

|Variable|Definition|
|---|---|
|Primary|Dummy equal to one if the individual has
heterogeneity in effect size and a significant role of crop varietal traits, climate (temperature and soil moisture) and management (treatment of crop residue, tillage options, and planting dates), such studies suggest that real-world data can add significant insights to what is available from agronomic trials.

A second avenue is to rely on information derived from remotely sensed imagery. For the United States, the cropland data layer (CDL), based on 30 m pixels from Landsat imagery that are available for all 48 states since 2008 (Boryan _et al._ 2011) are a frequently used source. With proper methodology (Lark _et al._ 2017), the CDL can be used for field-level analysis. Wang and Ortiz-Bobea (2019) use 2005-14 CDL data for the US midwest to show that the 2005-09 biofuel boom triggered a significant increase in maize monocropping and GM maize adoption, especially in the vicinity of newly established biofuel plants.<sup>3</sup> Hendricks _et al._ (2014) use CDL data to argute that estimates of supply elasticities based on aggregate data may be biased and to show that use of field-level data that take into account crop rotation benefits results in estimates that are larger in the short than the long run, contrary to standard models.

> 2 For example, comparing a two-year standard maize-soybeans rotation with more diverse a three- or four-year rotations that includes small grains and leguminous fodder crops (clover or alfalfa) led to equal or greater profit, reduced input use, and significantly lower toxicity (Davis _et al._ 2012). 3 Economic factors may also lead producers to forgo the yield advantage of rotating crops for higher profitability from monoculture as has indeed been observed in the United States where major crops are increasingly grown in monoculture patterns (Plourde _et al._ 2013).

5
# Liberia’s Infrastructure: A Continental Perspective

## **_Vivien Foster and Nataliya Pushak_**

### **Acknowledgments**

This paper draws upon a wide range of contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Dick Bullock on railways, Mike Mundy on ports, Heinrich Bofinger on air transport, Maria Shkaratan on power, Elvira Morella on water and sanitation, Michael Minges on information and communication technologies, Nataliya Pushak on public expenditure, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank country teams; notably Ishac Diwan (country director), Ohene Owusu Nyanin (country manager), Muhammad Zulfiqar Ahmed (roads), Fanny Missfeldt-Ringius (power), Errol Graham (macro).