rafmacalaba/data-use-mentions-extended huggingface dataset:
debit (net credit) positions in many cases exceeded their capital levels. Faced with either (a) settling their recalculated payment position by effectively selling or pledging the entire value of their capital or (b) not settling and having their own payments and receipts that day unwound from the settlement, it is clear that the logical choice would be (b).

The possibility of a domino-like series of settlement failures is termed systemic risk. In simulations using CHIPS data, the failure of one randomly chosen participant led to the failure to settle of almost one-half of the over 100 CHIPS (domestic and international bank) participants. It also led to the elimination of around one-third of that day's payments of $885 billion. However, similar simulations for an Italian wire transfer network led to a much smaller number of additional failures and a correspondingly smaller reduction in payment value (Angelini, Maresca, and Russo, 1996). As the value of payments over the Italian network were much smaller, so were the participant net debit/capital ratios. Thus systemic risk is smaller when a country does not have a large and welldeveloped domestic money market (so interbank net debits are not absolutely large) and when a country's banking system is highly concentrated (so that bank capital levels are more likely to cover net debits).

One way to effectively eliminate systemic risk on a net settlement network is to have each participant post collateral sufficient to cover their net debit position. Payment flows would be monitored in real time and payments that would push a participant's net debit above its collateral value would be rejected. If there were a failure to settle, the collateral would be liquidated to obtain a central bank loan to make settlement. CHAPS, established in 1984, plans to have all net debits fully collateralized by 1997. Participants on CHIPS (established in 1971) have since 1990 posted collateral sufficient to cover the single largest net debit permitted to occur on that network. The CHIPS approach eliminates systemic risk if there is
their risk aversion). Using the monthly price series of each crop from 2004 to 2007, we also compute the persistence and volatility measures. After removing linear trends of monthly price series, we include the first-lag autocorrelation ARt−1 as the price persistence measure, the standard deviation SDt−1as the volatility measure, and the price trend TRt−1. We also include the interaction terms between initial crop shares in 2007 and average years of schooling ( _H_ ) to allow the heterogeneity in price expectation formation depending on households’ human capital.

In the first stage, we regress ∆Pt on these variables as follows.

∆Pijt = α1∆Pijt−1 + α2wij,2007 + α3Xij,2007 + α4wij,2007Hij,2007 + α5ARt−1 + α6SDt−1 + α7TRt−1 + Dj + eijt The anticipated component is the projection of the price change based on information available to households in 2007, which is P�<sup>P</sup> . We use the residual of the price shock P�= P− PUP �P to represent the unanticipated component of the price shock.

Using Eq. (7) and Eq. (9), we estimate β ’s in the following regression. This regression also controls for Z= {L0, A0} and X2007. Since the second stage estimator β̂ depends in part on the 13
to the use of household surveys in the migration domain – mainly the problem of small migrant samples.<sup>11</sup> Other issues concern a paucity of questions (consistent over space and time) to accurately identify types of migration (internal/international, permanent/temporary, return/circular) and correlate them with climate shocks, as well as <mark>limited ability in tracking international migrants and cross-border mobility.</mark> <mark>There is increasing agreement among practitioners that no single data source can resolve all migration data gaps; this could not be truer in the case of climate migration, a research field that, by definition, comes at the intersection of many different disciplines (Parrish et al., 2020). Leveraging innovative, non-conventional data can help attenuate methodological issues that come with the use of more traditional sources (Lohr and Raghunathan, 2017). For example, ‘</mark> big data’ or ‘digital traced data’ are emerging as new sources for migration data and currently shaping revolutionary ways to measure migration (Laczko and Rango, 2018).<sup>12</sup> These innovative data sources possess some characteristics that more conventional sources lack and therefore bear great potential for complementing traditional migration data (Kirchberger, 2021; Tjaden, 2021). To illustrate one example, by tracking the approximate location of individuals, digital trace data make it possible to provide objective (rather than self-reported) measures of migration over a large share of the population and on a continuous, almost real-time basis. In principle, these data can allow the identification of different types of migration (both in terms of length of staying and geographic destination) with great accuracy and at a relatively low cost. This inherent flexibility makes this new data source particularly valuable for detecting and analyzing (im)mobility responses that are triggered by rapid-onset climate or extreme nature events that, for example, could evolve into short-term, transitory moves that would be difficult or impossible to capture
more pronounced for exporting firms and firms under foreign ownership, while firms that only operate domestically and are under domestic ownership are more adversely affected by hikes in electricity prices. _Fourth_ , it presents explanatory evidence that is suggestive of a notable change in the relationship between competitiveness indicators and fossil fuel price changes conditional on the price level. We find that the positive correlation between fossil fuel price increases and capital upgrading is more pronounced when fuel prices are low. At higher fuel price levels, this effect becomes smaller and eventually insignificant. Similar non-linear effects cannot be observed for electricity prices.

The structure of this paper is as follows: section 2, provides an overview of the empirical firm-level literature on the energy prices-productivity nexus while section 3 sets a background of the institutional changes pertaining to energy prices for Chilean manufacturing firms. We then introduce the _Estructura de la industria manufacturera_ data (INE, 2015) in section 4, after which we turn to our empirical strategy and the results in sections 5 and 6, respectively, before concluding in section 7.

# **2 Literature**

The literature identifies four main transmission channels for how firms may respond to energy price interventions (Rentschler et al., 2017; Coste et al., 2018): One strategy, where firms _innovate_ by adopting more efficient technologies and reap economic benefit, is often referred to as the Porter Hypothesis.<sup>6</sup> Additionally, firms may pursue other coping strategies, such as cost _pass-on_ to customers and other firms, _absorption_ or adjusting production inputs, i.e., _substitution_ .

Evaluating these transmission channels, the empirical firm-level literature finds broad support for the Porter-type _innovation_ hypothesis, where policy-led price interventions lead to innovation and notable business upgrading. Brucal and Dechezleprˆetre (2021) show that large and energy-intensive sectors in Indonesian manufacturing tend to reduce energy consumption most significantly in response to an energy price hike. Plants react to higher energy prices by updating their capital stock and investing in new and presumably more energy-efficient technology. Using firm-level
age, education level, income level, marital status, and urban/rural location. Demographic information at the individual level is taken directly from the household survey. Lastly, the number of close friends using an online social network like Facebook or Twitter is used as a proxy for social network effects.

Though three waves of data were collected in years 2008, 2012, and 2017, the survey samples were different in each wave, so no panel is available. Given the rapid pace of change in the ICT sector, analysis is confined to the most recently available data for the year 2017, based on cross-sectional estimation.

**<u>Table 1. Data description</u>**

|**Variable**|**Definition, Year**|**Source**|
|---|---|---|
|_Dependent variable_|||
|Mobile internet|Accessing the internet through a smartphone<br>or feature phone|After Access data, 2017|
|_Infrastructure variable_|||
|3G network coverage|<sup>The individual’s household is covered by a</sup><br>3G mobile network signal|After Access data, 2017; Collins<br>Bartholomew data, 2017|

12 Not all countries have geolocation data at the individual/household level in the ICT household survey. For countries that do not have geolocation data at the individual/household level, a household is considered as not having a 3G mobile network coverage if anyone from the same survey enumerator area identified a lack of mobile coverage as an obstacle to accessing the internet.

10
|48.5|40.4|43.8|
|Capital|5.5|5.1|5.6|1.6|28.1|-0.2|280.8|-20.9|-5.7|-65.9|-1.5|16.0|-3.2|11.7|-0.5|-1.1|
|Pension||||-3.4||32.7|-336.5|24.1|4.4|129.3|-4.2|91.3|4.8|11.8|10.7|-1.0|
|Transfers|17.4|12.0|24.0|13.3|253.4|70.1|-14.5|43.0|48.0|348.1|39.1|79.6|41.9|-31.7|38.7|13.1|
|Other nonlabor income|-29.6|||2.4|34.3|4.3|-86.7|24.9|11.4|-253.2|9.6|-98.5|7.5|11.7|-3.5|4.4|
|Total change|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|

_Source:_ Authors' calculations with data from RIGA for Ghana and Nepal, from household surveys for Bangladesh, Moldova, Romania, Peru, and Thailand, and from
||
|**below 25**|44.1|5.7|77.4|14.7|
|**25 to 45**|27.2|2.6|62.4|19.0|
|**above 45**|23.8|0.6|58.5|23.6|

Source: GHFPS.

52 In both Jordan and Georgia, further research is required to fully understand gender disparities in the effects of the pandemic on labor markets. First, females are affected by intrahousehold resource allocation, meaning that they may suffer greater food insecurity, or non-monetary poverty even if their employment income is not affected. Second, the analysis would capture relatively short-term effects of the pandemic as we rely on the data collected during 2020-21. Finally, the analysis does not reveal situations of under-employment and low-paying jobs among female workers. 53 Available data from the 2018 Household Incomes and Expenditure Survey (HIES) show that prior to the pandemic the Azeri population was more likely to be poor than other nationalities in Georgia (World Bank, 2021) due to the types of firms, sectors, and occupations they were employed in.

39
_Social Capital, Household Welfare and Poverty in Indonesia_ 34 capital can help escaping from poverty. In this context, it is important to note that the ownership of social capital (as measured by the interactive social capital index) is fairly equally distributed: the social capital index for the richest quintile is only about 30% higher than for the poorest quintile-about the same degree of inequality as for years of education. Physical assets are distributed much more unequally, especially land and household durables (animal ownership is only weakly related to income, and ownership of farm equipment, which is very low overall, declines with income level).

**Table 6: Ownership of Assets, by Quintile of Household Expenditure per Capita**

||||**Quintiles|**||||
|---|---|---|---|---|---|---|
|I<br>|~~~~~~~~~~~|**(Poorest)**|**2**<br>|<br>**2IQ(R**|<br> <br>**3**<br>|<br>**ichest)**|<br> <br>**4**<br>||<br>**_5_**<br>**l**|**All**<br>Al|
|Social Capital Index|14.99|16.65|16.65|18.00|19.89|17.23|
|Years of Education|4.32|4.64|4.59|5.04|5.65|4.85|
|Land Ownership (hectares)|1.45|1.28|1.96|3.90|2.52|2.17|
|Animal
all but five of the 24 countries studied used wood, charcoal, kerosene, and other solid fuels as primary cooking fuels, with wood and charcoal most frequently cited. A recent analysis of the Multiple Indicator Cluster Surveys found that one form of energy dominated clean cooking energy and there was no even split among two or more options, except in Kazakhstan (Kojima 2021c). This study on Sub-Saharan Africa similarly found that either LPG or electricity overwhelmingly dominated clean cooking energy selection in every country except Tanzania. Unlike other regions of the world with natural gas for urban households, where rural households may cook with LPG in the absence of natural gas pipelines or reliable electricity and urban households cook with natural gas or electricity, the clean cooking fuel use patterns of urban and rural households were similar in Sub-Saharan Africa. In particular, if urban households did not cook with LPG and used electricity instead, rural households did not cook with LPG either.

Averaged across all households, spending on electricity far exceeded that on LPG with the exception of Kenya, Lesotho, and Sudan. This pattern would be consistent with electricity being prioritized before LPG. Total spending on household fuels excluding gasoline and diesel exceeded 8 percent in three countries: The Gambia, Malawi, and Uganda, and all on account of expenditures on charcoal and firewood. To the extent that firewood may not have been purchased and spending instead represents the imputed values of collected firewood, these expenditures would not represent cash layouts, thereby making comparison with the 5 percent threshold for affordability more ambiguous. Imputed values are expected to be more common in rural areas, such as rural Uganda where spending on firewood alone amounted to 6.6 percent, consisting of 5.8 percent by the non-poor but as much as 9.0 percent by the rural poor.

One interpretation of the findings in this study is that once a critical mass of LPG users is reached— Figure 8 may suggest half the population—adoption of LPG as the primary source of energy for cooking by those using LPG is
∗_</sup>|13.3<sup>_∗∗∗_</sup>|
||(0.57)|(0.46)|(0.41)|(0.47)|
|Observations|330|330|330|330|
|_R_<sup>2</sup>|0.457|0.649|0.715|0.720|

Note: Standard errors in parentheses. _<u>∗</u> p <_ 0 _._ 05 _,_ _<u>∗∗</u> p <_ 0 _._ 01 _,_ _<u>∗∗∗</u> p <_ 0 _._ 001 _._ Data: survey on Bamako, Mali, 2009-2012. The table reports the OLS regression of log of land prices at transaction date on controls (including year dummies). All plots are residential and statutory at the time of the survey. A share of plots are Non-Statutory at the time of the purchase (NS purchase) 26
# **Table 5C: T and Rank Tests for Listed vs. Non-Listed Banks in Non-GCC Region**

Tests are conducted on annual bank-level data for the period 2001-08. Column 5 shows the p-value of a t-test which tests for the equality of means of the groups (allowing for inequality in variance). Column 6 shows the p-value of the Wilcoxon ranksum test which tests whether the groups are samples from populations with the same distribution. Non-GCC countries are Egypt, Jordan, Lebanon, Morocco, Tunisia, and Yemen. State-led countries are Algeria, Libya, and Syria.

|**Variable**|**Listed**|**Non-listed**|**Pvalue for t**|**PValue for Rank**|
|---|---|---|---|---|
|Total Assets|2.9E+06|3.3E+06|0.20|0.01|
|ROA|1.03|0.64|0.00|0.00|
|ROE|11.55|9.32|0.02|0.00|
|Net Interest Margin(Total Assets)|2.32|2.21|0.14|0.03|
|Total Interest Income to Assets|5.57|5.93|0.01|0.00|
|Total Interest Expenses to Assets|3.23|3.77|0.00|0.00|
|Non-Interest Income to Assets|1.57|1.03|0.00|0.00|
|Securities to Assets(All Countries)|40.01|31.70|0.00|0.00|
|Securities to Assets(ExcludingLebanon)|15.86|23.94|0.00|0.00|
|OHCosts to Assets|2.09|1.76|0.
We estimate economic activity in the geographical extent of each hexagon using nighttime lights, as they are measured by the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) series of satellites. We use the _DMSP OLS: Nighttime Lights Time Series Version 4_ product of the DMSPOLS. Version 4 of the DMSP-OLS Nighttime Lights Time Series consists of cloud-free composites made using all the available archived DMSP-OLS smooth resolution data for calendar years. We analyze the _stable_lights_ band, which contains the lights from cities, towns, and other sites with persistent lighting, including gas flares. Ephemeral events, such as fires, have been discarded. The background noise was identified and replaced with values of zero.

One of the limitations of DMSP-OLS is the lack of on-board calibration between satellites. The preflight calibration for the individual instruments is of little value, because the gain commands made to the instrument are not recorded in the data stream (Elvidge et al. 2014). We adopt a methodology developed by Elvidge et al. (2009) to intercalibrate the values of the satellites and allow a temporal analysis. This procedure relies on areas (known as reference areas) where little or no change in lighting has occurred over time. In their analysis, the authors find that the area of Sicily, Italy, has the most favorable characteristics to serve as a reference area: it consists of an even spread of data across the full dynamic range (0-63) and a clearly defined diagonal cluster of points. A second-order regression model is performed for each satellite, using F121999 satellite as the reference (this satellite had the highest digital values) to adjust all other satellite years to its data range. Because in some years multiple satellites have collected data (see table A2.1), for each year we select one satellite that we use for the analysis. The satellites we use are: F101992, F101993, F121995, F121996, F121997, F121998, F121999, F152000, F162008, F162009, F182010, F182011, and F182012. An intercalibration is applied based
kWh|US cents/kWh|0.09||10.27||
|Commercial at 100 kWh|US cents/kWh|||11.73|5.0–10.0|
|Industrial at 50,000 kWh|US cents/kWh|||11.39||
|Average effective tariff|US cents/kWh|9|8|15|12.6|

_Source:_ All data unless specified are for 2005 and based on AICD calculations; data for access to electricity for Sudan are from 2009 and were obtained from the US Energy Information Administration (EIA); data for South Sudan access to electricity are from 2010 and were obtained from World Bank (2011); data for installed capacity for Sudan are from 2009 and were obtained from Platts (2009); data for power outages ere derived from Vennemo and Rosnes (2009); data for Emergency Generation from Eberhard and others (2008); data for firms that find power to be a constraint and firms with own generators are from 2007 and were obtained from World Bank (2009b); data on Sudan collection rate, revenue per unit, and system losses are for 2004 and were taken from World Bank (2007); data for average effective tariff for Sudan are for 2004 and taken from World Bank (2007); data for average effective tariff for South Sudan are taken from World Bank (2011). For the thermal benchmark, data represent primarily residential users.

_Note:_ Access to electricity data for Sudan are estimates based on calculations from the AICD economic model for power investment needs. Installed capacity per million population was calculated based on Platts (2009) estimates for installed capacity. * The aggregate is based on manufacturing firms.

Excessive reliance on generators to cope with erratic and unreliable power supply increases production costs and reduces competitiveness. A significant share of manufacturing enterprises (from 36 percent in North Kordofan to 96 percent in Red Sea) share or own generators. The share of
005|
||(0.061)|(0.057)|(0.023)|(0.020)|(0.004)|(0.003)|
|_First Stage_|||||||
|Imputed Outfows|0.205***|0.209***|0.205***|0.209***|0.205***|0.209***|
||(0.054)|(0.052)|(0.054)|(0.052)|(0.054)|(0.052)|
|F Statistic (excl. instrument)|664.48|786.67|664.48|786.67|664.48|786.67|
|**Panel C: Reduced Form**|||||||
|Imputed Outfows|_−_0.037***|_−_0.036***|_−_0.015***|_−_0.016***|0.001*|0.001*|
||(0.009)|(0.009)|(0.004)|(0.003)|(0.001)|(0.001)|
|Observations|10,020|10,020|9,974|9,974|10,020|10,020|
|Dep. Var. Mean 1992|3.77|3.77|0.034|0.034|0.27|0.27|
|Municipality & Year FE|✓|✓|✓|✓|✓|✓|
|Controls _×_ Time Trends||✓||✓||✓|

_Notes:_ The table illustrates the estimates of specifications (1), (3),
and Conclusion**

Sub-Saharan Africa has, over the last six decades, been among the world’s fastest urbanizing regions. And, by 2050, the number of people living in its towns and cities is projected to expand by a further 0.95 billion. Despite this, however, remarkably little is known about the detailed anatomy of patterns of urbanization across the region’s countries. In this paper, we have attempted to remedy this by applying the dartboard algorithm to gridded population data derived from very high-resolution satellite data for all building footprints in the region, thereby allowing us to delineate urban areas for 46 SSA countries. Using this data, we have provided a detailed descriptive analysis of patterns of urbanization within the region for _circa_ 2015, including both patterns across and within urban areas.

Our results reveal, _inter alia_ , that SSA countries are generally more urbanized than data on officially defined urban areas would have us believe. Despite this, however, there remain large differences in the share of people who live in urban areas across countries with the urban share of a country’s population being positively correlated with both its level of development and area. Regardless of the type of urban area – i.e., cities, towns, or both – considered, most countries, have built-up area elasticities that are greater than one, implying that a doubling of an urban area’s population is associated with a more than doubling of its built-up area. In this regard, SSA countries appear to differ from high-income countries, which tend to have built-up area elasticities that are less than unity. One explanation for this difference may be higher costs of vertical development in SSA countries related to dysfunctional land and property markets, as well as failures in planning. On other dimensions, however, urbanization patterns within SSA countries appear more like those in developed countries. Hence, SSA countries exhibit levels of urban primacy that, on average, are similar to those in high-income countries, while Zipf’s law generally holds quite well
US$4 million to US$2.4 billion (2011 PPP) across countries. To our knowledge, these are the first crosscountry estimates of the poverty impact of drought in Africa.

There are several ways in which damage functions for rainfall deficits have been estimated in the climate economics literature. Auffhammer (2018) provides a review and examples of the approaches applied to US farm incomes. The one used for estimating the impact of extreme weather events uses observed data to identify the causal impact of weather changes on outcomes of interest (Dell, Jones, and Olken 2014). Data on hazards are matched to household observations in survey data using information on the geographic location of a household, and a fixed effects regression is run to identify the impact of weather in determining economic outcomes.

Although not undertaken with the purpose of estimating vulnerability functions, several studies have used this method to estimate the impact of drought on welfare. For example, in Africa, Hill and Porter (2017) look at the impact of a water requirement satisfaction index (WRSI) on consumption and poverty, and Hill and Mejia-Mantilla (2017) look at the impact of WRSI on household income and consumption in Uganda. In Malawi, Baquie and Fuje (2020) look at the impact of rainfall on consumption and poverty. Wineman, Mason, Ochieng and Kirimi (2019) examine the relationship between rainfall and income, calorie consumption and poverty in Kenya. Baez, Kshirsagar and Skoufias (2019) look at the impact of rainfall defined dry spells on nutrition outcomes. Outside Africa, Kochhar and Knippenberg (2023) look at the impact of NDVI, rainfall and temperature on consumption and poverty in Afghanistan. Studies that use estimated impacts of drought to predict the welfare cost of other weather events include Hill and Porter (2017), Porter and White (2016), Kochhar and Knippenberg (2023).<sup>2</sup> This study uses the same methods as these papers but takes a regional approach (as in Baez,