rafmacalaba/data-use-mentions-extended huggingface dataset:
may allow us to cleanly identify treatment effects for the majority of interested firms.<sup>9</sup> One can go further and restrict attention to a smaller number of firms from a specific industry, as Bloom et al. (2011) do for textile firms in India. Homogenous firms operating in the same industry are likely to face many of the same seasonal effects and industry-level shocks, which can then be differenced out of the data, making it easier to distinguish the impact of an intervention from all these other factors that change firm outcomes from one period to the next. Such an approach is also helpful for structural modeling, since the production function and the nature of potential market failures facing the industry can likely be better understood and modeled than is the case with a mix of firms from different industries.

Table 6 illustrates using the World Bank Enterprise Surveys the reduction in the coefficient of variation (C.V.) of sales that one obtains by focusing on different sets of more homogeneous firms. I take the set of surveys where there are at least 200 firms with 10 to 100 workers, and then examine the C.V. for all firms in this group, then for the firms in specific industries, for the firms with sales below a certain threshold, and for firms with 10 to 30 workers. We see that focusing on a specific industry often, but not always, reduces the C.V. For example, in Ghana, the C.V. reduces from 3.0 for all firms to 1.2 for garment firms and 2.5 for firms in the food sector. However, in South Africa, the C.V. actually increases from 1.9 for all firms to 2.0 for garment firms and 2.1 for food firms, with the industry-level samples only about 12 percent of the size of the full sample. Focusing on more homogeneous firms in terms of employee size likewise has a general tendency to reduce the C.V
|**Acronym**<br>|**s**<br>|
|---|---|
|AIDS|Acquired Immune Deficiency Syndrome|
|ASAP|AIDS Strategy and Action Plan|
|CDC|Centers for Disease Control and Prevention|
|DHS|Demographic and Health Surveys|
|GHAP|Global HIV and AIDS Program, World Bank|
|HIV|Human Immunodeficiency Virus|
|KAP|Knowledge, Attitude and Practice|
|MSM|Men who have Sex with Men|
|PAHO|Pan American Health Organization|
|PPT|PowerPoint|
|RDS|Respondent Driven Sampling|
|STI|Sexually Transmitted Infections|
|TLS|Time Location Sampling|
|TSS|Time Space Sampling|
|UNAIDS|United Nations Joint Program on AIDS|
|UNGASS|United Nations General Assembly|

6
0.00471***|-0.0161***|-0.00622**|-1.078***|
||(0.00126)|(0.00586)|(0.00242)|(0.241)|
|Observations|3,566|3,550|3,649|3,649|
|Dependent mean|0.400|2.254|0.835|61.220|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|

_Notes:_ The table presents the results of estimating equation (1) for outcomes related to agriculture. Panel A shows results using as dependent variable each crop’s production in 1,000 tons. Panel B uses as dependent variable the share of harvested land of each crop from the total area of each census tract. Panel C uses the actual yield of each crop, which is measured as the total production over the total of cultivated land for each crop. The unit of observation in all columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

# _Inequality and cooperatives_

As shown in the previous section, guerrilla-controlled areas were more likely to experience land fragmentation and remain divided today. Yet, even though these areas are poorer, there may be less income inequality. We explore this notion by evaluating reported income from household surveys and the wealth index from the
the ‘Community Dialogue Council for Conflict Resolution,’ to address local conflict related to property disputes, IDP return, tribal conflicts, and conflicts over resources and services, affording it a wider mandate than other Committees. The indicator concerning daily public life slightly deteriorated after the LPA, possibly due to the COVID-19 pandemic.<sup>79</sup>

# **<u><mark>Tal Afar Covenant (2020)</mark></u>**

The Tal Afar Covenant had a relatively strong focus on security. IDPs accused of ISIL affiliation were represented by their tribal leaders, and while civil society actors did not participate in negotiations, female activists did. There was a strong focus on cooperation with security authorities to identify ISIL suspects, and on the role of security actors to vet potential returnees and permit or refuse their return. The pre-existing Local Peace Committee in Tal Afar was mandated to monitor and implement the Covenant. Overall, the context appeared to improve after signature, including improvements to blocked returns, social cohesion, and movement restrictions.<sup>80</sup> reported before the LPA was signed and absent 3 months post-signature, and no concerns over revenge attacks or tribal tension reported, according to IOM’s Integrated Location Assessment 4.

78 According to IOM’s DTM Master List, 18,984 IDPs had returned by roughly the time the Ayadhiya LPA was signed, and this increased to 25,086 by 12 months post-signature. Context indicators indicated poor return conditions: the rate of physical violence, threats, mistrust and returnees ‘feeling unwelcome’ decreased within 12 months, but still remained, according to IOM’s Integrated Location Assessment 4.

79 According to IOM’s DTM Master List, the rate of IDP return increased only marginally following the signature of the LPA in Zummar (likely influenced by the timing of the LPA and the fact that many IDPs had returned already.) Prior to signature, 95,124 IDPs had returned, and this
12 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

# _4.4 Measuring institutional fragmentation and metropolitan coordination_

Based on the literature desk review and the available data, we construct a database with a series of variables to characterize the functional areas included in this study, in terms of institutional fragmentation and metropolitan coordination. We use the administrative boundaries of local governments that conform the metropolitan areas and distributed population data to calculate institutional fragmentation measures using geoprocessing tools in ArcGIS. Administrative boundaries were obtained from OpenStreetMap<sup>3</sup> (April 25<sup>th</sup> 2017) and the World Bank LAC Spatial Database (Branson et al., 2016). We projected the administrative boundaries and the urban extents to the UTM coordinate system to calculate areas in square kilometers. Population counts at the administrative unit and urban extent levels were estimated using the Global Human Settlement Layer (GHS) distributed population grids produced by the Joint Research Centre (JRC) of the European Union (Freire and Pesaresi, 2015; Pesaresi et al., 2016). These layers show population counts for each pixel at 250 meters of spatial resolution, and were produced for the years 1975, 1990, 2000 and 2015. We used the 2000 layer to account for the population in that same year, and the 2015 layer as proxy for the population in year 2010.

Metropolitan coordination variables were obtained through a number of official information sources to account for the presence of a metropolitan governance body and public services single purpose districts (see Table A1). Tables 3 and 4 present the list of available variables for institutional fragmentation and metropolitan coordination respectively. The descriptive statistics are presented in Table A2.

# _4.5 Control variables_

In order to isolate the predictive power of the variables describing urban form and to reduce omittedvariable bias, we include in the model a number of control variables including city size, locational variables, natural and urban
|8 12,373.2|53-15|
|Total in our sample|59,530.3|<br>38,718.9|132,871.|3 86,420.3|44.80|
|as%of Total Manufacturing||||||
|Output|37|.80|3|3.63||

- a) indirect taxes and subsidies are deducted (see table 2.3). b) includes 3,831.7 million pesos (excl. indirect taxes and subsidies) for petroleum refining which are not shown in the census Resumen General but taken from Sistema de Cuentas Nacionales de Mexico, 1981.

Note: Figures are converted at the exchange rate of 1.5375 pesos to the cruzeiro.

Source: Figures for Brazil from Censo Industrial, and for Mexico from Resumen General (except figures mentioned under footnotes a) and b)).
areas. Fourth, it is unlikely that the effects emerge from forced child recruitment into guerrilla groups. In fact, qualitative evidence suggests the Salvadoran Army extensively recruited children by force but the guerrillas did not.<sup>8</sup> Finally, we demonstrate that the results do not arise from differential patterns of public or private investment in the post-conflict period between treated and control areas. For example, we find no differences in the supply and quality of public education and road connectivity, and no changes in the number of businesses in former FMLN areas relative to nearby areas.

This paper has multiple policy implications. First, it offers evidence that the creation of alterna-

> 7We rule out other dynamics associated with a border, such as a higher incidence of landmines along the boundary, because El Salvador ran a very successful program that cleared all mines by 1994.

> 8It is estimated that of 60,000 Salvadoran Army combatants, about 48,000 (or 80 percent) were under 18 years of age, while only 2,000 of the 9,000 FMLN members were under 18 (or 20 percent) (Courtney, 2010). Moreover, a survey of child soldiers by UNICEF at the end of the war showed that while 91.7 percent of FMLN recruits had joined voluntarily, close to 53 percent of underage Salvadoran Army soldiers were forcibly recruited (Courtney, 2010).

6
The literature on SWB in Brazil has mostly examined the correlates associated with the high levels of Brazilian SWB (e.g. Islam et al., 2009; Gori-Mora, 2013; Golgher & Coutinho, 2020) in comparison with those of many other countries. Some Brazilian studies have focused on the SWB of specific populations such as the elderly (e.g., Pinto et al., 2016; Simões et al., 2021) and adolescents (e.g., Viñas et al., 2019) or are specific quasi-experiments focusing on rural household displacement (Randell, 2016) or land ownership programs (De Moura & De Silveira Bueno, 2013).

Our paper contributes to this literature in several ways. First, using data from the Gallup World Poll for the decade starting in 2010, we identify the factors associated with Brazil’s SWB and its change during this period, paying special attention to differences across groups within the country. The results suggest that having sufficient income, financial security, economic optimism, satisfaction with living standards and health services, social capital, tertiary education, and digital access are the main factors associated with SWB in Brazil. In line with the high SWB inequality, there are substantial differences in SWB along the income distribution, across generations and space, and across groups with different employment and education status. There are also differences in the extent to which the factors underpinning SWB matter for different population groups. Youth derive greater life satisfaction from economic optimism, digital access, and acceptance of diversity than older generations. Satisfaction with living standards, economic optimism, and tertiary education matter more for the poor, while opportunities for social mobility are more important to the happiness of the middle and upper classes. Episodes of financial struggle, confidence in national institutions, and the quality of education services have a stronger influence on the SWB of urban residents compared to rural populations. In the higher-income South and Southeast, people care more about their health, ability to obtain tertiary education, and
compared with modern kilns (Table A2.4). Higher energy requirements for traditional kilns contribute to higher energy costs. Almost three-fourths of all brick kilns are concentrated in three provinces (Madesh, Lumbini, and Bagmati) (Figure 4). The brick sector statistics, such as kiln technology types are not well documented, however, half of all operational kilns are large in size by the number of people engaged, and FC BTK and ZZK are commonly used technologies by these large kilns.

14
rate drives the entrepreneur to choose nonofficial channels. Full unification of the exchange rate therefore becomes the inescapable policy implication in order to stem the flow to unofficial activities (and capital flight) and to attract unofficial activities back to officialdom.

These benefits of unification of the exchange rate regime are not a mere theoretical construct. Data from Ukraine's official auction for foreign exchange, already operating for almost three years, provides compelling evidence. During periods when the official exchange rate was less than 70 percent of the (street) market rate, the (nongovernment) foreign exchange supply to the auction was about $50 million per quarter. Conversely, during times when the official rate has exceeded 80 percent of the street rate, supply to the official auction has averaged about $550 million per quarter, as shown in Figure A3 in the Annex. In other words, the annualized difference in supply between a quasi-unified and nonunified exchange rate auction is about $2.2 billion.

# 6. **Privatization: On the Fallacy of Equity Versus Speed**

From early on in the FSU transition there has been a debate as to the optimal speed, scope, and type of privatization of the enterprise sector. Among other points debated, many have argued for gradualism (and partiality) on the basis of equity and implementation efficiency, so as
throughout the entirety of the bootstrap procedure, only back transforming it at the end, in step 9.<sup>14</sup>

# **2.8 Evaluating Performance**

We have access to unit-level census data for seven countries. This allows us to calculate true sampling distributions with the unit-level census data by simulating separate surveys and saving the results from each iteration. In all countries, we treat subareas as enumeration areas. For each country, we design a sampling scheme that mimics sampling of an actual household survey from that country. For example, we mimic the sampling scheme used in the fifth Integrated Household Survey (IHS5) for Malawi. In all countries, we design the sampling process in a two-stage manner, first stratifying, then drawing an “enumeration area” (which in our case is always the lowest level of aggregation available in the census), before finally drawing households.

We independently draw 100 separate surveys, predict our outcomes of interest with each method, and then evaluate the performance of the methods against the ground truth derived from the full unit-level census data. We calculate the following statistics, where _i_ indexes areas, _y_ ˆ refers to the predicted outcome for area _i_ , and _yi_<sup>_truth_</sup> : refers to the true value for area _i_ :

- Correlation: We calculate both the Pearson correlation coefficient, _r_ , and the Spearman (rank) correlation coefficient, _ρ_ . We present the means across all 100 independent samples:

- Absolute deviation: This is defined for each area as �� _y_ ˆ _si − ysitruth_ ��. We present the average across all areas and all simulations:

- ˆ 2

- • Squared deviation: This is defined for each area as � _ysi − ysi_<sup>_truth_</sup> � . Similarly, we present the average across all areas and simulations:

- Width of confidence interval: For the EBP estimates, this is derived from the estimated MSE _E_<sup>ˆ</
Despite the potential benefits of using locational data with near-universal coverage to inform urban and transportation policies, there are challenges inherent to the big data revolution. One important challenge is the technical expertise required to extract millions of pieces of information from heterogeneous sources and process unstructured data sets into operational decision-making inputs. Another crucial challenge for generating and manipulating big data for transportation analyses relates to the preservation of privacy and confidentiality. Given their geo-referenced nature and the possibility to extract them without users’ formal consent, urban and social sensing data as well as other user-generated content (UGC) can significantly compromise individual privacy. A combination of technical and regulatory approaches has been explored to address these issues without compromising the social and economic benefits. For example, Thakuriah et al. (2017) describe a number of technological solutions, including Privacy Enhancing Technologies (PET), synthetic data, and Trusted Third Party (TTP) mechanisms, that are relevant for privacy preservation. Many organizations and countries have already adapted their personal data protection framework and guidelines to the necessity to reap the benefits of big data while protecting privacy (International Transport Forum, 2015).

In most developing countries, the prospect of big data technology adoption in transportation remains slow to materialize. Possible reasons for the low take-up may stem from the lack of in-house data analytics expertise, low storage capacity for increasingly large data sets, as well as limited collaboration between state agencies, research centers, and the private sector. There are also political economy considerations arising from the coordination failures among the relevant actors and institutions, both in the data and the of the and environment. these mining process design legal regulatory Despite challenges, the penetration of mobile technology at a high pace across different demoin countries new for sustainable graphic groups developing may open opportunities transportation policies.

28
# _3.2.1.1 Available data on quantities and prices_

Energy related data are sourced from the IEA database “World energy balances”, which is available from the OECD (https://www.oecd-ilibrary.org/).<sup>12</sup> In the model, we aggregate energy into four categories: coal (15.2% of the energy mix in 2017), oil (40.5%), natural gas (36.9%), and “renewables” (7.5%). The “renewables” category includes nuclear power, hydro, solar and wind. While nuclear power is not renewable, all these energy sources have in common that their net carbon emissions are zero or very low. A carbon price would thus not directly affect this energy category. In addition to the 55,561 kilotons of oil equivalent (ktoe) used from the energy sector, Pakistani households also used 30,187 ktoe from biofuels in 2011. Biofuels data are not included in the model due to a lack of macroeconomic data. From a carbon accounting point of view the omission is not serious as biomass is a “carbon neutral” energy source when managed well (Favero, Daigneault, and Sohngen 2020), although it can make a significant contribution to local air pollution. Biomass first absorbs carbon from the atmosphere (when a tree grows, for example) and then releases it again (when wood is burned).

The IEA provides data on energy produced, imported and exported and the shares of fuel type in electricity generation. The data are available in kilotons of oil equivalent (ktoe). While Pakistan exports some oil products, it is a net importer in every energy category, so that we bundle exports and imports as “net imports”. We denote energy produced by Pakistan (in ktoe) as with indicating the category (coal, oil, gas, renewable). Net imports are denoted as , while energy commodities consumed
Figure 27. Average annual investment rates, percent in GDP between 2004-2014 <!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Pre- Boom Post- Pre- Boom Post- Pre- Boom Post-<br>boom boom boom boom boom boom<br>RR nonRR RR RoW<br>Gross fixed capital formation Expenditure on education Expenditure on health<br>% GDP<br><!-- End of picture text --> Note: Average annual investment rates calculated as the group average of gross fixed capital formation, investment on education and health share in GDP over the 2004-2014 period. RR = resource-rich Africa, nonRR = non-resource rich Africa, RR RoW = resource-rich excluding Africa. Source: authors’ calculations using IMF data for the public-sector gross fixed capital formation, World Bank data for investment on health and UNESCO for investment on education.

African countries abundant in fossil fuels were the largest contributors to resource-rich Africa’s gross fixed capital formation. In Equatorial Guinea, Gabon, Angola, Republic of Congo and in the mineral-rich Botswana, cumulative public and private investment per capita exceeded 10 thousand US dollars between 2004 and 2014, while the remaining resource-rich African countries accumulated less than 5 thousand US dollars per person (Figure 28). In the non-resource-rich group, excluding the smaller insular countries, only South Africa, Namibia, had comparable accumulation of public and private fixed capital derived from their abundant mineral production.

38
mobility in education. These variables will conceivably report stronger correlations with measures of income mobility which, unlike education mobility, will be more sensitive to the functioning of the labor market.

# **6. Robustness**

In this section we re-evaluate the trends and patterns in intergenerational mobility using alternative measures for both absolute and relative mobility.

## **6.1. Intergenerational mobility across space and time**

### _Absolute mobility_

As a robustness check we also evaluate absolute mobility (denoted by _YOS_ ) using education data in its most disaggregated form, which is years of schooling completed. _YOS_ measures the share 24
<mark>The second element is measuring energy price shocks. We use the commercial electricity rate shown in Figure 1, compiled quarterly by Global Petrol Prices for each country. For the countries included in the sample, electricity rates, on average, fell during 2020 and 2021, reaching their nadir in the last quarter of 2021. Subsequently, there was a recovery in the rates during 2022. The price fuctuaton ranged from $0.03 to $0.38 per kWh. In the case of oil prices, the range was between $33.7 and $113.</mark> The third aspect refers to fossil fuel subsidies. We measure this aspect using the IMF data (Black et al., 2023), which provide, for 170 countries, the estimates of explicit subsidies for fossil fuels, i.e., undercharging for the supply cost of fossil fuels. All but five countries in the dataset, among them Tajikistan, have no (zero) petroleum subsidies. In the case of explicit subsidies for electricity, only three countries in the dataset do not have subsidies. Considering countries with explicit subsidies, the petroleum subsidies range from 0.00004 to 3.58 percent of GDP, while electricity subsidies range from 0.04 to 9.76 percent of GDP. The distributions of such subsidies overtime are depicted in Figures 3 (panels a and b).

<mark>The fourth data aspect concerns frms' adopton of energy efciency practces. This is a crucial factor because it can signifcantly infuence a frm's resilience to energy price fuctuatons. To account for these practces, we draw on data from the BPS, which features a module focused on frms' energy efciency measures. The survey queries frms on implementng technologies or methods to improve energy usage efciency. Those who have adopted energy efciency solutons are prompted to detail the specifc technologies or practces in use. These practces encompass questons about LEED certfcaton for buildings, adoptng efcient lightng systems, adherence to ISO 14001 or 50001