rafmacalaba/data-use-mentions-extended huggingface dataset:
rate or monetary poverty are heavily exposed to sampling bias due to common and well understood ups and downs in economic activity. Moreover, as in the case of Argentina above, and given that is a function of Var(YtC), the potential bias of interpreting trend-driven gains in social conditions for such indicators would mechanically grow with the volatility of the business cycle WW<sup>YYCC</sup> (see equation (4)).<sup>17</sup> The next section zooms on the role of the business cycle driving movements in cyclical social indicators such as monetary poverty using microdata for a set of LAC countries.

# **4. A Microdata Approach on the Importance of Business Cycle Fluctuations on Monetary Poverty: An Application to LAC**

The previous section showed that while some social indicators (such as the unemployment rate) are cyclical in nature, fluctuating considerably in the short run along with the business cycle, others (such as the HDI, especially its non-income components) follow long-term trends and bear very little relation to the business cycle. Interestingly, monetary poverty lies somewhere in between the two extremes with large heterogeneity across countries. In particular, whether monetary poverty behaves more like the unemployment rate (i.e., affected by cyclical considerations) or the HDI (i.e., determined essentially by the long-run trend) crucially depends on the volatility of the business cycle. In highvolatility countries (such as Argentina), changes in monetary poverty are much more closely related to the business cycle than in low-volatility countries (such as Chile).

> 17 We are forced by virtue of the sample length to rely upon a linear deterministic trend. In light of well-established evidence regarding the different stationary nature of real GDP in emerging markets (see for example seminal paper by Aguiar and Gopinath, 2007), a natural concern is the extent to which the findings of Figure 6 are robust to other detrending more “flexible” techniques. While most data for monetary poverty and
population density based on the distribution of population estimates in similar settlements. We then set population density to zero in locations known to be not settled, and to a low value in locations that could be settled but for which we have no data (around known settlements). Finally, we rescaled the population density map thus obtained using the PESS 2014 regional totals (UNFPA 2014).

# **2.5 Split and merge algorithm for the creation of Enumeration Areas (EAs)**

# **2.5.1 Splitting process**

The aim of the splitting process is to partition the country into regions that are as small as possible so that the subsequent merging process has enough flexibility to combine them into optimal EAs. We used three steps to do this.

_Step 1 - Splitting based on geo-referenced features to create regions with tangible boundaries_ The country was split using road data, rural settlement boundaries, waterway and river data and administrative boundaries from OpenStreetMap (OSM), using the feature to polygons tool in ArcGIS. These data sets where either lines or polygons, whose geometry will be used to create area features, were the input features to the tool. From the merging of these features, each small “closed” area became a feature in the output feature class (here called ‘Primary Units’ (PU). This first step results in a set of fully contiguous units that are much smaller than the target EA size, with no gaps or islands and with all regions delineated by georeferenced features. If the road data are complete, then the process ensures that no building will be cut.

For the areas that do not meet the criteria, Thiessen polygons based on settlement locations were created. However, for sparsely populated areas or vast featureless areas, with only a few geo-referenced settlements or residential areas, areas created from this approach were still larger than 9km<sup>2</sup> .

# _Step 2 - Estimate population and area_

With the PU feature set defined, we
poverty rates, the World Bank usually adjusts or “lines up” survey data from a variety of years to a common year, by scaling up the measured levels of household per capita consumption to account for growth in the intervening years. For example, to “line up” a survey from 2012 to 2015, each household’s per capita consumption, measured in 2012, would be assumed to grow at the same rate. In most countries, this common rate of growth is assumed to be equal to the growth in household final consumption expenditure (HFCE) during the intervening period.<sup>2</sup> Because it is a component of the national accounts, HFCE is published annually and can therefore be used to line up the survey means to any later year.

> 1Estimate is based on the traditional line-up method explained below.

> 2In some countries, growth in per capita GDP is used instead of final household consumption expenditure.

2
.689***|3.171***|2.647**|
||(0.070)|(0.153)|(0.479)|(1.029)|(0.037)|(0.188)|(0.223)|(1.212)|
|Centuries since Colonization||0.068||0.609*||-0.108||0.207|
|||(0.051)||(0.341)||(0.073)||(0.469)|
|Constant|-0.217***|-0.305***|8.424***|7.635***|-0.671***|-0.547***|4.890***|4.653***|
||(0.048)|(0.081)|(0.329)|(0.544)|(0.024)|(0.086)|(0.143)|(0.556)|
|Observations|31|31|31|31|19|19|19|19|
|R-squared|0.116|0.171|0.180|0.266|0.890|0.904|0.928|0.929|
|Pvalue Incipient=Superior|0.560|0.536|0.422|0.434|0.00260|0.0191|0.000633|0.362|

_Source_ : REDATAM INDEC Census Argentina 2010. _Notes_ : Weighted Least Squares. Standard errors in parentheses. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01

# **6 Conclusion**

We
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 7 people are more than an hour distant from cities. These estimates contrast sharply with the official estimate of an urban proportion of 75.8% for Latin America and the Caribbean<sup>6</sup> .

It is interesting to compare these results with early tabulations of GRUMP v1 (Balk, pers. comm.; see also Balk et al 2004). Those tabulations find about 36% of LAC population living in settlements of less than 5000 people (including rural areas). Another 6% live in settlements of 5000-20000 population (with a mean population density of 332 – below some thresholds for urbanness). About 48% live in settlements of greater than 100,000 population.

Figure 5 graphs the ‘remote’ (>1 hour) columns of table 2, with countries arranged in ascending order according to the density-150 threshold. It shows that density-100, density150, and density-200 thresholds yield roughly the same ranking of countries, and roughly similar estimates of rurality. However, the density-20 threshold is very poorly correlated with the others. For instance, Costa Rica, Cuba, and Peru have roughly similar rural proportions according to the density-150 threshold but Peru has proportionately far more people living at densities below 20.

Figure 6 compares the density-150/>1 hour rurality criterion to census measures of rurality. There are some striking disparities. Census measures ascribe much lower rural populations to Argentina, Brazil, and Uruguay, and much lower populations to El Salvador and Guatemala, compared to the density/distance based measure.

Finally, tables 7 and 8 cross tabulate population and land area by population density, suitability for rainfed crops, and forest cover. About one fifth of LAC population lives at densities below 150 on non-forested areas, suitable for rainfed crops. They occupy 29% of LAC’s land area. Almost the same number of people live at densities under 150 and occupy land that is rated marginal or unsuitable for rainfed crops; these lands constitute 45
Chong et al.

14

**Table 6.** Friendship Networks Summary Statistics

|||**Cases**|**Percent**|
|---|---|---|---|
|**Treatment students with:**|No friends treated|366|21.2%|
||1 friend treated|277|16.0%|
||2 friends treated|266|15.4%|
||3 friends treated|227|13.1%|
||4 friends treated|286|16.6%|
||5 friends treated|183|10.6%|
||6 friends treated|123|7.1%|
|**Spillover students with:**|No friends treated|1482|88.8%|
||1 friend treated|133|8.0%|
||2 friends treated|11|0.7%|
||3 friends treated|7|0.4%|
||4 friends treated|7|0.4%|
||5 friends treated|13|0.8%|
||6 friends treated|15|0.9%|

_Source_ : Authors’ analysis from student surveys. _Note_ : Friendship link treatment status is established by matching self-reported list of friends with list of names of students answering the survey at (either) follow-up survey. The number of friends treated for students in control schools is equal to zero.

students, a 55 percent increase in redemption ( _p_ = 0.07). The study puts substantial weight on this result as it provides objective evidence of an increase in condom demand. In unreported results, the estimated coefficient is basically unchanged when controlling for distance to the health clinic.

As a robustness check, bounding exercises were performed with differing assumptions on attrition, as in Karlan and Valdivia (2011), and found that the positive
protection|No|2|6%|
|Public administration|No|2|6%|
|Illicit drugs|No|1|3%|
|Information society|No|1|3%|
|Money laundering|No|1|3%|
|Political dialogue|No|1|3%|
|Innovation policies|No|0|0%|
|Human rights|No|0|0%|

# b. Trade relations

To illustrate the UK‐EU trade relations, we use the information available in the World Input‐Output Database (WIOD). There are two important advantages in using this data set: i) it covers trade in services at the bilateral level, and ii) it allows the decomposition of gross exports in value added terms. The main limitation of the data set is that it provides data for a restricted number of countries, 40 plus an aggregate for the rest of the world, limited to the 1995‐2011 period.

8
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 15. Annual gain in access each year to various modes of water supply and sanitation, 1998–2006**

<!-- Start of picture text --> a. Water b. Sanitation<br>6<br>6<br>5<br>5<br>4 4.0 4 4.0<br>3<br>3<br>2.3<br>2 2.3<br>2<br>1 1<br>0.5 0.5<br>0 0<br>-1 National Urban Rural -1 National Urban Rural<br>-2 -2<br>-3 -3<br>-4 -4<br>-5 -5<br>Flush Toilets Improved Latrines<br>Piped Water Standposts<br>Wells/Boreholes Surface Water Traditional Latrines Open Defecation<br>Annualized Growth Annualized Growth<br>Population gaining access per year (%) Population gaining access per year (%)<br><!-- End of picture text --> Source: WHO 2010a and 2010b, from the 1998 Demographic and Health Survey and 2006 Multiple Indicators Cluster Survey.

Cameroon has made significant institutional reforms affecting urban water supply. By the end of the 1990s the operational and financial performance of the national urban water utility, SNEC,<sup>8</sup> had become problematic (World Bank 2007a). Facing unreliable services, commercial and industrial users started to develop their own alternatives. In October 2005, the government created a public holding company (CAMWATER<sup>9</sup> ) to plan sector development and investments (such as construction, rehabilitation, and management of potable water infrastructures). In addition, the government signed a 10-year _affermage_ contract with CDE<sup>10</sup> commencing in May 2008 for the production, transport,
Figure A1 plots the sector composition and the respective sales and purchase channels of firms covered in these administrative records. Manufacturing and wholesale and retail firms together account for almost half of the sales in the tax records. The graph on the left in Figure 2 shows the geographical dispersion of formal firm headquarters, which shows that many firms concentrate in Nairobi and Mombasa.

Figure A2 plots the share of total observed firm-to-firm links attributed to different groups of firms, distinguishing by size, sector, and location. Panel A shows that aside from retail, wholesale, and manufacturing, business services account for a substantive proportion of supplier linkages. Mining firms account for the least number of both supplier and buyer links. Panel B shows that firms in Nairobi and Mombasa account for more than three quarters of all buyer and supplier links. Panel C shows that firms in the top sales quartile account for a disproportionately large share of total links, especially as suppliers. Aside from the statistics plotted in Figure A2, 61% of total links are formed among firms located in the same county, 17% of links with firms within the same sector, and 45% among firms in the same sales quartile.

# **2.2 Data on Informal Activity**

Informality in firm-to-firm transaction data can either arise because firms themselves are not registered (extensive margin) or because transactions of registered firms are not recorded (intensive margin). Not being registered as a firm or not recording a transaction in turn can either be the result of non-compliance or because of exemptions (e.g. for small firms).<sup>12</sup> To obtain an updated distribution of overall (formal and informal) economic activity that we can compare to the administrative data, we draw on a series of data sources listed in Table 1. Importantly, we not only measure informality as the gap between overall economic activity and what is captured in the administrative data, but additionally rely on measures generated independently of the administrative records. This helps us
above. Lacking a census of businesses and a representative survey of households, we asked Experts SARL to sort municipalities into one of three categories for these variables – high, medium, and low. Our hope was to derive coarse,

> 22 Unfortunately, we were only able to categorize agents’ businesses as providing either services or manufactured goods. A finer delineation of the types of goods and services that agents sell might have produced better insights into the types of businesses that are best suited to accommodate and foster agent transactions.

> 23 Population figures are taken from the most recent census, which occurred in 2004.

14
# **1 Introduction**

Conflict and political violence have become the _new normal_ in many developing and developed economies, with over 1.7 billion people worldwide living in fragile or conflictaffected countries. In 2024 alone, 167 countries reported incidents of political violence, with 50 facing extreme, high, or turbulent levels of conflict—resulting in more than 233,000 fatalities (ACLED, 2024).<sup>1</sup> Despite the severe suffering and disruption caused by political violence and conflict, economic activity continues. Economists and policymakers are increasingly interested in understanding how firms operate under such challenging conditions. A growing number of studies provide rigorous estimates of the effects of conflict on firm performance and offer in-depth analyses of the underlying mechanisms (see, e.g., Amodio and Di Maio, 2018; Couttenier et al., 2022; Korovkin and Makarin, 2023; Bernal et al., 2024). However, research on firm behavior in settings characterized by political violence and conflict remains limited to a small number of countries, and existing findings are largely countryand conflict-specific.

This paper is the first to provide a global perspective on the impact of conflict on a range of firm-level outcomes and the mechanisms underlying these effects. Moreover, by exploiting differences in cross-country characteristics, it shows how economic, institutional, and political factors shape the way in which firms cope with conflict exposure.

Our analysis relies on the combination of two main data sources. First, we use a confidential version of the global World Bank Enterprise Survey (WBES), which, beyond the rich firm-level data in the public version, includes geolocation information for each firm. We focus on its panel component, covering 89 countries from 2006 to 2019. Second, we draw on the Integrated Crisis Early Warning System (ICEWS) dataset to obtain geolocated data on conflict and political violence events in each country. This allows us to construct a firm-specific, time-varying measure of conflict exposure, which we use in a firm-level fixed-effects model to estimate
one (Ravallion 2003, Deaton 2005, Lakner et al. 2022, Prydz et al. 2022). To account for this, we multiply the per capita GDP growth in 2020 by 0.7 for surveys that use a consumption aggregate and 1 for surveys that use an income aggregate, based on the analysis by Mahler et al. (forthcoming).<sup>14</sup> For the roughly 3% of the global population without microdata in PIP, we use the regional average distribution as we did for 2019.

A concern with our counterfactual estimate is that it may be a poor approximation of what would have happened without COVID-19 for several reasons. For one, the growth forecasts may be inaccurate. While we are unable to test this, the growth forecasts are based on all the information available at the beginning of 2020. Second, even if the growth forecasts are accurate, the counterfactual distribution assumes that welfare in household surveys grows in accordance with the growth in real GDP per capita and that inequality remains constant. As shown by Mahler et al. (forthcoming), these two assumptions are on average relatively accurate compared to more sophisticated modeling approaches. Third, other unanticipated events happened in 2020. For example, in countries where environmental disasters hit in 2020, the difference between our 2020 estimates and the counterfactual captures the pandemic as well as the disaster. Given the size of the COVID-19 shock, it is reasonable to assume that most changes between the 2020 growth forecasts and realized growth rates are due to the pandemic.

Our preferred estimates of inequality and poverty are the net impact of the pandemic – that is the difference in 2020 between the counterfactual and the observed distribution (discussed in the next section). However, we also report the change in inequality and poverty from 2019 to 2020, which can be thought of as a gross effect.

# **3.3 Estimating the observed 2020 welfare distribution**

## **(i) Household surveys**

Microdata from household
The SAR is an electronic registry, established in August 2022, with the objective of transferring support to small and medium farmers in a transparent yet expeditious way. Farmers can sign up at the SAR website (https://www.dar.gov.ua/) using their electronic signature and provide a minimum of personal information including a bank account to which any resource transfers can be made, irrespectively of their legal status, i.e., registered legal entity, family-owned business (FOP), or individual. The system gathers information for all land parcels to which the farmer has registered rights from the registry of rights and the cadaster and adds information on the farm from several other official registries.<sup>10</sup> Information in SAR can be used by MAPF or any authorized entity to advertise or implement programs in support of the agriculture sector and to interact electronically with potential participants. Farmers can take any actions required digitally rather than by filling paper forms, including uploading scanned documents, photos, or providing authorization for providers of certain services to access specific types of personal information stored on the system.

The SAR simplifies program implementation by providing reliable information from official registries and providing an incentive to ensure the currency of these; simplifying the process of program application by eliminating the need for repeated filling of paper forms; and leveling the playing field and increasing competition in factor markets by creating a basis of potential clients to which service providers or banks can market their services. Use of the SAR to implement an EU-supported producer support grant (PSG) as well as other programs,<sup>11</sup> facilitated rapid sign-up, especially by small and medium scale farms, many of which had been in the informal sector before.

As the survey was launched in the second half of October, we set Oct. 15, 2022, as the cut-off date to construct the sample frame by dropping farms without any registered land or a valid phone number. Table 1 provides comparison of the resulting
<!-- Start of picture text --> Figure 7. Change in Household Consumption to Income Ratio<br><!-- End of picture text --> <!-- Start of picture text --> 10.0%<br>7.4%<br>8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>-2.0%<br>-4.0%<br>-3.2%<br>-6.0%<br>-8.0%<br>-10.0%<br>-9.2%<br>-12.0%<br>Bangladesh, Peru, 2004-2010 Thailand, 2000-<br>2000-2010 2009<br><!-- End of picture text --> Source: Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 - 2010.

Figure 8. Contribution to the decline in Poverty <!-- Start of picture text --> A. National Moderate Poverty Lines B. USD 1.25 Poverty Line in PPP<br>140 250<br>120 200<br>100 19<br>150<br>80 13 163<br>107 100 17<br>53 110<br>60 61<br>50 80<br>40 36 42<br>0 -10 -1215<br>20 40 -43<br>2 21<br>12 -50<br>0<br>-12 -62<br>-24 -24<br>-20 -100<br>-5<br>-40 -150<br>Bangladesh, Peru, 2004 to Thailand, 2000 to Bangladesh, Peru, 2004 to
time to haul water as potentially endogenous, and use as identifying instruments the dummy variable indicating whether the household head is a widow(er) or is divorced, whether the household lives in a rural or urban community, and the one-period lagged hauling time. The place of residence (rural or urban community) is assumed exogenous in the sense that water infrastructure was likely not the main factor driving households’ choice in terms of residence location. We also argue that average hauling time in the community in the previous period of observation is a good instrument for current hauling time, since the two should be highly correlated, while past-period hauling time should not explain average school attendance in the current period. These two regressions are estimated using OLS but controlling for unobserved cluster-specific effects (Chamberlain,

> 13 The wealth index is provided in the DHS surveys. It is calculated using principal components analysis based on data concerning the household’s ownership of a number of consumer items such as a television and car; dwelling characteristics such as flooring material; type of drinking water source; toilet facilities; and other characteristics that are related to wealth status. The resulting asset scores are standardized in relation to a standard normal distribution with a mean of zero and a standard deviation of one. These standardized scores are then used to create the break points that define wealth quintiles as: Lowest, Second, Middle, Fourth, and Highest, see http://www.measuredhs.com/.

13
people have the right to obtain or take advantage of telecom services—it does not necessarily mean they use the service. Affordability indicators should give a sense of the extent to which telecom services are provided at a reasonable price. Quality indicators should encompass both qualitative and quantitative measures of quality. The former captures perceived quality or costumer satisfaction, while the latter captures technical quality reported by the operators with a low degree of discretion involved. Finally, productivity indicators should provide a measure of the improvements on the production side of the business.<sup>10</sup> With this in mind, we have selected six indicators from the ITU. The proxy for access is the number of telephone subscribers defined as “total telephone (fixed mainlines plus cellular) subscribers per 1000 inhabitants.” This variable or one of its variations has been used in almost all previous papers. We use telephone subscribers, as opposed to mainlines, in order to have a better representation of the current telephone market in which mainlines and mobiles are substitutes. Even though this may be the best access indicator available, it is far from perfect. As Wallsten (1999) explains, it is not possible to 10 See Estache and Goicoechea (2005b) for more details on performance dimensions.