rafmacalaba/data-use-mentions-extended huggingface dataset:
(Wetterberg 2002). At the same time, customary _adat_ (customary law) institutions might be indicative of an increased adherence to traditional laws and customs. As such, they may also clash with centrally mandated conflict resolution mechanisms, providing the potential for conflict escalation.

Inadequate policing, ineffective courts and judicial system that allow for high levels of crime tend to also create space for violent conflicts to take place; incidents of crime, when severe, may also constitute conflicts in themselves.<sup>4</sup> Public and communal security arrangements may therefore reduce the likelihood of conflict. However, their existence may also be in response to a worsening security situation.

# **3. Empirical Evidence on Local Conflict**

The Government of Indonesia’s Village Potential Statistics survey (PODES) offers perhaps the first opportunity to look at the incidence and causes of conflict across Indonesia on a nationwide scale. This section summarizes conflict incidence based on the data from PODES, and examines reporting issues. Our comparison to a detailed qualitative study of local conflict (Barron _, et al._ 2004b) suggests that key-respondent (i.e., village heads) based approaches like the PODES are subject to reporting problems. Treated as a complement to more in-depth studies, however, they provide an opportunity to map levels of conflict and to test some of the

> 4 Qualitative fieldwork in over two dozen villages (see Section 3) provided some insight into the linkages between conflict and crime, as well as the underreporting of crime.  In three districts studied, the Bank study followed conflicts which involved rape as a trigger.  In these cases, rapes were public knowledge; indeed, it was the fact that the act had been brought to public attention – rare, given the norm of treating such matters as a family concern, and keeping it within the household as a result – that had led to the triggering of a broader social conflict.  Likewise, whereas none of the thirteen villages in East Java reported having
The second dimension (Censuses and Surveys) reflects the availability and frequency of major censuses and surveys, which are designed to collect information mandated by the National Statistical Acts. This dimension comprises of eight indicators on population and housing census, agricultural census, business census, income and expenditure surveys, and other surveys on agriculture, health, labor force, and establishments.

The third dimension (Availability of Key Indicators) evaluates NSSs by reviewing the availability of the country data for the most recent year in international databases. In addition, the selected indicators produced by NSS should address the development concerns of countries, especially with the SDGs. The 12 indicators included in this category range from the headcount poverty rate to the under-five mortality rate, the primary school completion rate, the manufacturing value added in GDP, and net trade in goods and services. These indicators overlap to large extent with the Tier 1 SDGs, and include other standard macro-economic indicators as well, which are conceptually clear and regularly produced using established methodology and standards.

Providing data to the relevant international agencies demonstrates that an NSS’s practices meet the quality standards and data production timeliness. The last dimension (Dissemination Practices and Openness) is built on the principle that quality statistics should be delivered to the public in a timely, easily accessible manner and free of charge. It includes 10 indicators grouped under two sub-sections: Dissemination Capacity of NSO and Openness of Data.

These four dimensions are closely linked and capture the production cycle of NSSs in collecting, producing, and disseminating high-quality statistics. By following internationally recommended standards and classifications, an NSS can produce data of good quality that are both comparable within the country over time and across different countries. By combining administrative sources and timely censuses and surveys, an NSS can collect, process, and generate 26
model then provides a unique steady-state predictions for the number of links between firms of different sector-location-size types. We first estimate the model to predict the Kenyan firm network as observed. We find that new firms choose 45% of their suppliers through undirected search, conditional on their bias, and the remaining 55% of suppliers are found via existing suppliers.<sup>5</sup> The model-predicted network not only fits the empirical distribution of outlinks (i.e. the number of buyers) well, but also performs well with respect to untargeted moments, such as the share of links accounted for by Nairobi- and Mombasa-based firms.

Using the estimated model, we then predict a revised network that accounts for informal firms by combining the model with granular real-world data on the sectoral and regional composition of the informal sector. Updating the entry probabilities of firms by sector, location, and size requires data on the spatial and sectoral dispersion of economic activity, for which we draw on microdata from the labour force module of the 2019 population census. We initially assume that

> 5In comparison, Chaney (2014) finds that only 40% of all relationships of French exporters with international trade partners are formed via directed search. Our estimate of 55% of links being formed as a result of directed search could suggest that linking frictions are potentially even more binding for firms in Kenya’s domestic firm network.

3
data for the other four countries contain about half of the formal manufacturing sector. This is due to two reasons. First, as explained earlier, small firms are not well-covered in the analysis. In the countries where small business was allowed to operate even prior to 1991 - Hungary and Poland - this means that a relatively larger share of firms is not included. Second, countries that have introduced changes in enterprise codings also show smaller coverage. This is especially the case for Bulgaria, where many firms changed their code number (as reported to the Statistical Office) once they were corporatized in 1994. A similar problem exists in the Polish data. During the 1993-95 period, over 1,000 Polish manufacturing firms were sold through liquidation-privatizations. The process meant that a new company emerged, which was not easily traceable (by the econometrician) to the old state firm. This type of enterprises are hence underrepresented in our data - we have been able to include only 147 such firms in the analysis.

Although international accounting standards (IAS) were introduced in all seven countries (as of January 1995 in Bulgaria and Romania; a year earlier in the other five countries), many

> **1** This procedure mostly affects the Bulgarian dataset. Although we have the complete manufacturing census, only half of all firms report consistently in all years.

8
example of how this difficulty in collecting data affected the 2017 IPC classification was the use of internal displacement, which was one of the statistics mentioned prominently by the official announcement. However, it is very difficult to accurately identify the size of the displaced population in the country. For example, the monthly mobile phone survey conducted by the WFP used in this analysis and the Gallup World Poll both find that the size of the displaced population is up to three times as large as is being identified by the Task Force for Population Movement (TFPM), which is the official source used in the IPC 2017 classification (e.g., World Bank 2017, WFP 2018a).

Despite these difficulties in constructing the 2017 IPC announcement, the classification had a large impact and the messaging around the food emergency in the country gained a new urgency. Even though the there was little change between the 2016 and 2017 IPC announcements, and even though the 2017 IPC announcement did not declare a famine, the humanitarian and development community began describing ”famine-like conditions” in the country and the importance of avoiding a famine (e.g., Nebehay 2017, OCHA 2017). However, there was no reference to famine in the last humanitarian needs assessment completed before the 2017 IPC announcement (e.g., OCHA 2016).

One potential reason for the larger impact was increased global attention on the food emergencies at the time of the 2017 IPC announcement. In March 2017, during a Security Council meeting, the U.N. declared that 20 million people faced starvation amid a funding gap in Nige-

> 4See Appendix 1 for the map identifying the IPC classification of each governorate.

> 5The survey- the mobile Vulnerability and Analysis Mapping Survey- is described in Section 3.

5
from natural conditions by the high levels of diversions and that rates of growth in water diversions increased since 1988. The Independent Audit Groups’s 2000-01 review of cap implementation (MDMBC 2002) indicates that transparency in reporting concerning cap compliance is resulting in pressure upon communities that are not in compliance, as well as their governments.<sup>9</sup> The “Review of the Operation of the Cap” in 2002 concluded that the Cap is an essential first step in achieving a sustainable Basin ecosystem and that it has significantly reduced risk of environmental degradation.

- MDBC Salinity and Drainage Strategy arose from concerns about salinity and waterlogging problems along the Murray River with studies showing that the irrigation areas affected by high water tables could increase from 559,000 hectares in 1985 to 869,000 hectares in 2015 (MDBMC, 1987).  Under the 15year strategy, States agreed to be responsible for actions taken after 1 January 1988 significantly affecting river salinity.  A market in salt credits was set up, a salinity audit was carried out and a model of salt loads and concentrations was

> 9 A region and its government are considered to be in breach of addressing water use overallocattion or to not have commitment to cap compliance when breaching the cap several years running.

24
in the South Asia region, and two in the Middle East and North Africa region.<sup>18</sup> Survey selection for each country depends on which survey contains more relevant employment information. For many countries, the choice of a survey is a tradeoff between the availability of detailed household income and expenditure data in household surveys and extensive employment modules in labor force surveys. Annex table A1 shows the list of available surveys by country-year included in this study. (Table A1 also shows the latest available GDP per capita, poverty headcount ratio, and JQM for wage employees.) The list of developing countries included in this study ranges from low-income and International Development Association–eligible countries to high-income countries across all regions.

The ex post harmonization of data to estimate the quality of jobs across developing countries is one of the main contributions of this paper, but not all survey questions are available across countries. This variation in data coverage and availability presents a challenge in measuring job quality globally. To overcome this constraint and increase coverage, it is common to include more indicators that are substitutes for each other. However, carelessly including more indicators to overcome the lack of survey question availability may reduce comparability across countries and render the JQM meaningless. Furthermore, employment-related surveys are subject to a country’s regulatory framework. For example, if health insurance and pensions are state sponsored, an employment survey may not contain questions regarding social protection coverage at work. To construct a JQM that is both broad in scope and true to the intended use, the process of selecting survey questions to serve as indicators needs to be carefully considered. Indicators must be pertinent to each dimension, but also available and comparable across the

> 18 The labor force and household surveys are not publicly available.

12
br>est.,<br>based on<br>U.N. 1980 assessment|U.N. 1988 revision (prelim.)|
|Niue|Sep 76|4 F|UNPVSR<br>4/87 (officiat<br>est.)|||
|Norway|Nov 80|4091 J|UNPVSR<br>4/87 (official<br>est.)|Based on UNPVSR<br>4/87|Based on UNPVSR<br>4/87|
|Oman|--|-.<br>-|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|Based on UNPVSR<br>4/87|
|Pacific Islands,<br>Trust Terr.|Sep 80|133 J|Bank projection from official<br>1983est., UIMPVSR<br>4/87|Based on USBOC<br>1985|Bank est.|
|Pakistan|Mar 81|84254 F|U.N. 1984 assessment|Bank est.|Bank est.|
|Panama|May 80|1825 F|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|1980-85, U.N. 1988 revision<br>(prelim.);<br>1985-90, Bank<br>projection|
|Papua New Guine|a Sep 80|3011 F|UNPVSR<br>4/87 (official<br>est.)|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Paraguay|Jul 82|3030 F|U.N. 1988 revision (prelim.)|Celade printout<br>2/87|Celade printout<br>2/87|
|Peru|Jul 81|17005 F|Bank projection from census,|1980-85,
per hour)|18|12|─|─|─|─|13|
|Crane productivity (tonnes per hour)|16|9|15|─|8|23|14|
|**Charges**||||||||
|Container handling (US$ per TEU)|260|155|180|160|─|220|168|
|General cargo handling (US$ per tonne)|14|8|9|15|8|9|10|

Source: AICD ports database.

ECOWAS needs better transshipment links. Although Abidjan has enjoyed some success as a container transshipment center, it has suffered in recent years from internal strife and problems relating to the ownership of operating rights to the container terminal. It is clear that the West African coast requires another major transshipment center. One testament to this is that the major carriers engaged in West African container trade, Maersk Line and its affiliate Safmarine, now use the port of Malaga, Spain, as their hub for West African container trade. They relay West African cargo moving to or from Europe and Asia there, and handle other key trades there as well. As a general rule, transshipment traffic tends to become a casualty as ports reach capacity constraints, as has recently occurred in several of these hubs. Nigerian ports play an important role in the transshipment of liquid cargo—mainly oil. Liquid cargo transshipment has been concentrated around the oil exporters, with Nigeria being the largest in the continent.

33
period on the current population structure in Cambodia.

4 Information about past international migrations is scant in the Demographic and Health Survey. It is known whether a respondent always resided at the same place or not. If not, the length of the stay in the current residence and the type (urban, rural, abroad) of the previous residence is known. It is therefore possible to identify 130 respondents whose previous place of residence was abroad. The mortality experienced during the 1975-79 period by the siblings of respondents whose previous residence was abroad and later came back to Cambodia differ from the rest of the population: for all ages and genders together, their death probability for 1975-79 was 5.09% (C.I. 3.00-7.18), while for siblings for which there is no indication that the sister migrated abroad, the death probability was 9.60% (C.I. 9.33-9.87). This seems to point to a lower mortality experience for families who migrated abroad. However, the survey gives a very incomplete picture of international migration: there is no information about when the migration took place and the survey only yields information in case it is the residence that immediately preceded the current one that was abroad. In addition, the survey misses families that migrated out of Cambodia and never came back.

5 See however the appendix for a comparison between the reconstruction and the sample approach for selected birth cohorts.

4
7 characteristics of households from the same caste but not having agriculture as their main activity, _UnonAGRc_ . Given the social structure of India, assuming that unobserved characteristics are correlated within castes across occupations seems reasonable. This assumption is discussed in more detail in section 5.<sup>5</sup>

# **3. Data and Descriptive Statistics**

## Data and Variables Definition

The data used to conduct this study are the ARIS-REDS data from the National Council of Applied Economic Research (NCAER). Since 1971, the NCAER has been conducting surveys on a _sample_ of households in 232 villages in the 17 major states of India. Most of the results come from the round of 2006, which has the advantage of providing a _listing_ of every household in the surveyed villages with basic information on households and heads of household such as their demographic characteristics (gender, age, education level, size of the household) and their main occupation. The average level of education of members from the same caste can therefore be computed using all the heads of the households from the same caste in the village and not only the heads of the households that have been sampled, which makes the measure very precise. The information on education from the listing is also useful to deal with measurement error in education of the head of household, as is shown in section 4. The round of 1982 is used for robustness checks.

In the round of 2006, detailed information about agricultural inputs, such as cultivated area or labor use, is not provided in the listing, so the analysis is restricted to households that cultivate land in the _sample_ . The identification strategy also requires at least two castes per village and that at least one caste has households that do not have agriculture as their main activity and households that have agriculture as their main activity. The final sample is composed of 4405 households in 223 villages.

The first set of variables includes the agriculture-related variables, used to estimate the production function. The dependent variable is total agricultural
615 randomly selected poor households that did not receive a cash transfer in the 15 control villages where no households received cash transfers.<sup>9</sup>

> 6 Due to the low primary school enrollment rates in Burkina Faso, the program intervention focused exclusively on primary schooling as opposed to also covering secondary schools.

> 7 Immediately prior to the baseline survey, we conducted an extended household census in every village to collect information from each household about household living structure (flooring, access to latrine), household asset ownership (plow, cart, draft animals, motorcycle, radio), whether the head of household ever attended school, whether the household grows cotton, and whether there was a weekly market in the village. This information was combined with a Burkina Faso nationally representative household survey to calculate a predicted poverty level for each household and compare that with the national poverty line to determine whether a household is considered poor or non-poor, and subsequently is eligible to receive the cash transfer. Compared to non-poor households, these poor households were less likely to have a head who ever attended school (63 versus 12%), less likely to grow cotton (45 versus 10%), less likely to live in a village with a weekly market (20 versus 11%), less likely to own draft animals (64 versus 35%), less likely to own a motorcycle (55 versus 7%), less likely to own a radio (47 versus 33%), less likely to own a plow or cart (48 versus 35%), and less likely to have access to a latrine (10 versus 1%).

> 8 Our research protocol received IRB clearance from the Institutional Review Board at the University of Illinois at Urbana-Champaign (case #08334) and from the Burkina Faso National IRB (“Comité d’Ethique”).

> 9 To minimize child fostering in response to the program introduction and reduce any potential sample attrition (see Akresh 2009 for evidence on the relationship
, *=0.05 level, +=0.1 level.

Using more detailed data from the SPI makes it possible to assess how the availability of specific data sources relates to academic output. Figure 6 shows the linear regression coefficient for the availability of each of the ten data sources considered by the SPI, conditional on log GDP per capita, log population, and log of qualitative papers. The ten data sources include: population census, agriculture census, business/establishment census, household consumption/income survey, agriculture survey, labor force survey, health survey, business/establishment survey, civil registration and vital statistics system (CRVS), and geospatial data at the Admin 1 (usually province/state) level.

14
Nobles, Jenna and Alison Buttenheim (2006). Marriage in Periods of Crisis: Evidence from Indonesia, California Center for Population Research On-Line Working Paper Series. University of California, Los Angeles.

Ojha, D. P. (2004). Inventory of Poverty and Impact Monitoring (PIM) Approaches in Nepal _Poverty Monitoring Unit, GTZ, Nepal_ Palloni, A., Hill, K., and Aguirre, G. P. (1996). Economic Swings and Demographic Changes in the History of Latin America, _Population Studies_ , 50, 105-32.

Rodriguez, C., & Sanchez, F. (2009). Armed Conflict Exposure, Human Capital Investments and Child Labor: Evidence from Colombia _HiCN Working Paper No. 68_ .

Shemyakina, O. (forthcoming). The Effect of Armed Conflict on Accumulation of Education: Results from Tajikistan. _Journal of Development Economics_ .

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In this section, we increase spatial resolution to assess the distribution of endemic percentages using a global grid with 0.25 degree (25 km) resolution. We perform cellwise addition across all rasters in apparent. First, all three groups exhibit huge global variation, from 0% to 60% endemic for terrestrial variation within their borders (e.g., Brazil, United States, South Africa, India, China) while others do not very high endemic intensities over their entire areas. In Brazil, by contrast, all three groups exhibit progressively-declining endemic intensities from the Atlantic coast to the western frontier region. Similarly-broad patterns of variation across regions are visible in the United States, Mexico, Colombia, Argentina, South Africa, Morocco, Ethiopia, India, China, the Russian Federation and Indonesia. Countries have added protection responsibilities for endemic species, so large spatial variations in endemic intensity have significant implications for protected-area policies. Scale economies are important divert fewer hectares from economic use. For both reasons, spatial clusters with high endemic intensity offer the potential for cost-effective area protection. Figures 6 and 7 identify candidate clusters in many 5. The Global Distribution of Small-Occurrence Region Species Jenkins et al. (2015) note that “small range size is the best predictor of extinction risk and, thus, the first metric for conservation priority”. This factor has been studied extensively in the empirical literature (Kraus Brooks and Pimm 1999). It has particular significance because it is a widely-recognized indicator of extinction risk that is computable for any species that can be mapped. In contrast, expert assessments of extinction risks for individual species are limited by their resource intensity. The result is a gap between In this section, we explore the size and global distribution of small-occurrence region species in our GBIF maps database. Then we consider the implications for conservation policymakers, who must balance the of that territory. At the outset we should note that small-range status is not determinate; no single critical minimum habitat size exists, given the myriad interactions