rafmacalaba/data-use-mentions-extended huggingface dataset:
interpreted as averages of country averages, intended to represent a global picture of country experiences rather than the story of global population aggregates. The same approach to weighting is used for all regression analysis.

To keep the analysis tractable, we present descriptive statistics for three time periods broadly based on different stages of the pandemic, as shown in Figure 2, and the timing of surveys (Figure A1.3). The first period from April 2020 to June 2020 represents the initial impact of the pandemic, marked by stringent policy measures in many countries including lockdowns and school closures, as captured by the Oxford Covid-19 Government Response Tracker (OxCGRT) stringency index.<sup>10</sup> This corresponds with a substantial decrease in visits to workplaces and transit stations, while people spent more time in residential areas, as reflected in Google Community Mobility data. The second period, July 2020 to December 2020, is characterized by a gradual withdrawal of some containment measures and increased mobility following the initial shock, though there are regional differences. The third period covering 2021 is distinguished by stalled progress in many countries, with little change in policy stringency on aggregate, but some return to normal levels of mobility. More significant differences emerge between regions and countries in this latter period (Figure A1.4).

When countries fielded multiple surveys within one of these time periods, we select a single survey to include in the calculation of summary statistics for each period (Figure A1.3). For the initial period, we choose the survey month corresponding with the highest stringency index to measure peak welfare impacts at the onset of the pandemic. To look at subsequent recovery in welfare and employment outcomes, we use the survey month corresponding to the lowest stringency index in the second half of 2020, and the most recent survey collected in 2021 for the third period.<sup>11</sup> For employment outcomes, the sample is restricted to households in which the respondent was between 18 and 64 years old, since
until case CAN-AD-302 (Cold-rolled steel sheet from Brazil). Furthermore, duties were usually imposed as ad valorem rates. Nevertheless, there were some instances in which the duty was imposed as a tax on any good entering Canada at a price lower than a given selling price reported to customs. Finally, the levels of duty described in the Master spreadsheet are typically the trade-weighted average margin of dumping found across all investigated firms. However, in some cases, the weighted average was not available, so a proxy, such as the un-weighted average, was used instead. We include notes as to how the proxy was constructed.

Next, we were unable to verify the preliminary duty levels on cases appearing before CAN-AD209 and on a few cases afterwards, i.e., CAN-AD-218 through CAN-AD-225. These were gathered from the CBSA Historical Listing, which we found to contain a few minor errors. Second, the dates for CITT cases before CAN-AD-79 were gathered from the _Gazette_ , which means that these dates may be the publication dates for the decision, which usually occurred about 15 days after the actual decision date. The decision dates after CAN-AD-79 have been verified. Third, CITT dates for preliminary rulings before CAN-AD-272 were only available on a select number of cases that went to the final stage. Any cases that were terminated or suspended earlier lacked this information.

There are a few data columns that are redundant in the Canadian data. First, the P_AD_DATE series is largely identical to the P_DUMP_DEC series, as preliminary duties are assessed immediately from the date of the CBSA preliminary decision. Similarly, the F_AD_DATE is largely identical to the F_INJ_DEC, as final duties are assessed as soon as the CITT hands down an affirmative decision. Depending on the specifics of the tribunal’s decision, the final duty may be applied retroactively to goods imported during the provisional duty period. Finally, the INIT_DATE series is identical to the INIT_DATE_CBSA series, as the CBSA always initiates an investigation before the CITT.

# **3.5 Colombia (COL
, 1990-2003<br>Billions of USD<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Total PPI Infrastructure Privatization Difference<br><!-- End of picture text --> Source: PPI and World Bank databases

# _Financial sector_

23. The share of financial sector proceeds has almost doubled since the 1990s, increasing from 12 percent to 19 percent of total proceeds in recent years. Banking and other financial service firms represented 88 percent of financial sector revenues, with insurance firms accounting for 10 percent and real estate the rest. The increase in financial sector revenues came mainly from banking transactions in Eastern and Central Europe, driven by EU accession
1993; McCracken, et al., 1999; Naeher, et al., 2001). In Bangladesh and elsewhere, programs to promote improved stoves have stressed their long-run financial advantages for poor households, as well as their environmental benefits. To date, however, our survey suggests that progress has been quite limited. Of 686 biofuel-using households in our 7-region survey (including Dhaka), only 9 (1.3%) report using an improved stove: 4 in Jessore and 5 in Sylhet. Another 2 households have tried improved stoves, but have stopped using them. Of the 9 current users, 8 are in rural areas.

Our results suggest that limited information may be the greatest deterrent to consideration of improved stoves. Of the 659 biofuel-using households that don’t use improved stoves and offer an explanation, 45% claim to be unaware of them. Another 40% state that improved stoves are not available locally. After accounting for these, our sample includes only 105 biofuel-using households that have considered improved stoves. Of these, 9 (8.5%) decided to use one, 49% didn’t adopt because of the large initial investment, 39% viewed improved stoves as inconvenient, and the remaining 4% had other reasons for non-adoption.

Jessore and Sylhet may have witnessed greater promotional efforts than other areas. Of the 21 Sylhet households (in our sample of 99) that consider improved stoves a feasible option, 5 (24%) actually use one. In Jessore, 4 of 37 households (11%) with a viable option use improved stoves. In all other regions, we have found no adoption among biofuel-using households. Since 85% of all sample households regard improved stoves as unknown or unavailable, our results suggest that an information strategy may offer the best near-term prospect for promoting clean stove use in Bangladesh.

34
rules of society, and in particular the quality<br>of contract enforcement, property rights,<br>the police, and the courts, as well as the<br>likelihood of crime and violence.|‐2.5 to 2.5,<br>higher = better<br>law enforcement|
|Satisfaction with the<br>Government|_SAT_GOV_|European Social Survey<br>(2002-2009), own<br>calculation|Average satisfaction with the national<br>government|0 to 10, higher =<br>more satisfied|
|Government<br>expenditures|_GOV_EXPsq_|Heritage Foundation<br>(2010), own calculation|Government expenditures (consumption<br>and transfers) at all levels as percentage of<br>GDP, squared /100. Recalculated from the<br>Heritage Foundation (2010) Government<br>Spending index: the latter equals<br>100 – 3GOV_EXPsq|0 to 33, higher =<br>higher spending|
|Investment Freedom<sup>b</sup>|_INVEST_FREE_|Heritage Foundation<br>(2010)|Evaluates a variety of restrictions typically<br>imposed on investment (labor market<br>regulations are not included in this index).|0 to 100, higher =<br>less restrictions|
|Trade Freedom|_TRADE_FREE_|Heritage Foundation<br>(2010)|A composite measure of the absence of<br>tariff and non-tariff barriers that affect<br>imports and exports of goods and services|0 to 100, higher =<br>less barriers|
|Business Freedom|_BUS_FREE_|Heritage Foundation<br>(2010)|Measures the ability to start, operate, and<br>close a business|0 to 100, higher =<br>less burdensome<br>regulation|
|Monetary Freedom<sup>a</sup>|_MON_FREE_|Heritage Foundation<br>
obstacles have a negative and significant impact on firm growth when entered individually. When entered together, the effect of the corruption obstacle is subsumed by the other two. They also show that the smallest firms are consistently the most adversely affected by all obstacles. Financial and institutional development weakens the constraining effects of financial, legal and corruption obstacles, and again the small firms benefit the most from developments in financial and legal institutions.<sup>19</sup> Firms in developing countries complain about a laundry list of institutional obstacles that affects their day-to-day operations and growth. So in terms of policy prescriptions to governments on focusing reform priorities, it is not clear whether finance is a first order constraint to growth compared to other reported obstacles, thus deserving the most attention.

Ayyagari, Demirguc-Kunt, and Maksimovic (2008) attempt to answer this question using survey data on the different obstacles to growth that firms report - access to finance, inadequate security and enforcement of property rights, poor provision of infrastructure, inefficient regulation and taxation, corruption and macroeconomic instability. They find that not all of them are equally constraining. Some of the obstacles either affect firm growth only indirectly through their influence on other factors, or have no effect. Using regressions as well as Directed Acyclic Graph (DAG) methodology, they find that access to finance, emerges consistently as the most robust obstacle constraining firm growth. And of the many specific financing obstacles firms identify, only the cost of borrowing is directly associated with firm growth. But the cost of borrowing is itself affected by imperfections in financial markets such as difficulties with posting collateral, limited access to long-term financing, and firms that face high interest rates also perceive that the banks to which they have access are corrupt, underfunded, and require excessive paperwork.

Together, the studies reviewed in this section suggest that access to financing is one of the most constraining obstacles to growth for firms in developing countries.

> 19 Beck, Demirguc-Kunt, Laeven, and Maksimovic (2006) use the same data
Working Paper Series). http://www.nber.org/papers/w20344 Lewis-Faupel, S., Neggers, Y., Olken, B. A., & Pande, R. (2016). Can Electronic Procurement Improve Infrastructure Provision? Evidence from Public Works in India and Indonesia. American Economic Journal: Economic Policy, 8(3), 258–283.

http://www.aeaweb.org/articles?id=10.1257/pol.20140258 Mendes, M., & Fazekas, M. (2017). Towards More Transparent and Efficient Contracting - Public Procurement in the European Union.

https://opentender.eu/blog/assets/downloads/digiwhist_recommendations.pdf Mungiu-Pippidi, A. (2015). The Quest for Good Governance. How Societies Develop Control of Corruption. Cambridge University Press. http://www.cambridge.org/us/academic/subjects/politicsinternational-relations/comparative-politics/quest-good-governance-how-societies-develop-controlcorruption North, D. C., Wallis, J. J., & Weingast, B. R. (2009). Violence and Social Orders. A Conceptual Framework for Interpreting Recorded Human History. Cambridge University Press.

OECD. (2010). Collusion and Corruption in Public Procurement. OECD.

OECD. (2015). Government at a glance. 2015. OECD.

OECD. (2016). Survey on Public Procurement.

https://qdd.oecd.org/subject.aspx?Subject=GOV_PUBPRO_2016 Olken, B. A. (2007). Monitoring Corruption: Evidence from a Field Experiment in Indonesia. Journal of Political Economy, 115(2), 200–249. http://www.nber.org/papers/w11753 Rothstein, B., & Teorell, J. (2008). What Is Quality of Government? A Theory of Impartial Government Institutions. Governance, 21(2), 165–190.

Strand, I., Ramada, P.
6%|||
|Husband age<sup>18</sup>|||54.43|12.32|
|Wife age|||49.84|12.89|
|Age difference (husband’s age – wife’s age)|||4.34|6.41|
|Couples in formal marriages||91.7%|||
|Couples in common-law marriages||8.3%|||
|Agrarian reform beneficiary’s<sup>19</sup>education level|||7.03|3.42|
|(years)|||||
|Per capita monthly household income (PHP)|||4762.02|6319.78|
|Household below national food poverty|347|80.7%|||
|income threshold|||||
|Women with non-farm income|222|52.6%|||
|Men with non-farm income|244|57.8%|||
|Female agrarian reform beneficiary|107|25.36%|||
|Parcels tilled by household|||1.52|0.77|
|Parcels formally owned by household<sup>20</sup>|||1.17|0.43|
|Household size|||4.64|2.26|

# **Appendix Table A2: Key Decision-making Questions and Answer Options**

|**Question:**<br>**Answer options:**|
|---|
|**Household Activities:**|

16 All statistics are limited to matched couples in our data set.

17 Province is in the island of Luzon.

18 Data on age and income were gathered in the initial baseline survey carried out in 2016 and 2017.

19 Agrarian reform beneficiaries are the spouse who was the primary recipient of the agricultural parcel awarded through CARP.

20
A practical problem with this approach is that cross‐country data on adult height are relatively scarce. Moreover, the interpretation of the cross‐country variation is clouded by genetic differences in populations of different countries. To address this problem, Weil (2007) suggests using a more widely‐ available summary indicator of health, adult survival rates (ASR). The basic insight is that ASR also improves within countries over time with improvements in latent health, in the same way that adult height does. As a result, the within‐country over‐time relationship between improvements in adult height and ASR can be used to transform the “return” to height into a “return” to ASR, and furthermore, ASR can be more meaningfully compared across countries .<sup>17</sup> Specifically, this means that γ�z in the expression for human capital can be replaced with γ��� �ASR , where γ��� �γ������ �β������,��� and β������,��� is the slope coefficient from a regression of adult height on ASR. Intuitively, this slope coefficient captures how both adult height and adult survival rates improve when latent health improves, and this relationship can be used to convert the “return” to height into a “return” to ASR.<sup>18</sup> Again, however, the correct interpretation is not that there is a labour market “return” to adult survival rates. Rather, the correct interpretation is that when latent health improves to the extent that ASR increases by one percentage point, then worker productivity increases by γ��� percentage points.

A complementary strategy to solve the problem of data scarcity for adult height is to instead use data on stunting in childhood, which has become increasingly available, particularly in low‐income countries where stunting is common and recognized as an important marker of poor early childhood
# _4.3. Impacts on Education for Children Ages 7-15_

The analysis of the educational and child labor outcomes is presented in Table 3. In particular, columns one to three analyze the effect on enrollment while columns four to seven examine the impact on unconditional attendance, grade progression, learning and total hours of child labor. The standard errors in this table, as well as in all subsequent tables, are clustered at the unit of randomization, which is the village.

To analyze the impact of cash transfers on school enrollment, we use three measures of enrollment as dependent variables. The first comes from school administrative ledgers we collected at each school (column 1). The second comes from the household survey where we asked parents to provide evidence of school registration by either showing the student ID card/number or other evidence such as official school receipt of registration documents (column 2). This additional evidence of school registration was only requested at our last survey round, round 3. The third measure comes from parental self-reports in the household survey (column 3) and is potentially prone to self-reporting bias as highlighted by Baird and Özler (2012). However, by using three measures, two collected at the household-level – including one asking for documents confirming actual registration - and one collected at the school-level, we reinforce the robustness of our analysis. At round 3, the correlation between the parental self-reports (column 3) and the school-based measure (column 1) is 0.80, it is 0.84 between the parental selfreports and the registration evidence provided by the households (columns 2 and 3) and 0.68 between columns 1 and 2.

Focusing first on the results at the end of the two year experiment (round 3), we find that the cash transfers increase enrollment. In the comparison between the transfers made to the mothers and the fathers (panel A), the coefficient on “treatment” can be interpreted as the impact of cash transfers given to mothers. For example,
The paper is organized as follows. Section 2 describes the survey and earth observation data. Section 3 presents the empirical methodology. Section 4 discusses the results and section 5 concludes.

# **2. Data**

# **2.1. Survey data**

We use nationally representative, multi-topic household survey data collected in Malawi and Ethiopia by the respective national statistical offices over the period of 2018-2020 with support from the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) initiative. The key variables that drive each survey’s sampling design is household consumption expenditures and poverty. However, the surveys do provide large samples of agricultural households and extensive data on their agricultural activities. Maize is the primary crop grown in Malawi, while in Ethiopia, small grains are more prevalent, but maize still plays an important role as a staple crop. More details regarding the survey data are provided below.

# **2.1.1. Malawi**

The survey data in Malawi stem from the Integrated Household Panel Survey (IHPS) 2019 and the Fifth Integrated Household Survey (IHS5) 2019/20. The surveys were implemented concurrently by the Malawi National Statistical Office.

IHPS 2019 is the fourth follow-up to a national sample of households and individuals that had been interviewed for the first time in 2010, and later in 2013 and 2016. At baseline, the IHPS was designed to be representative at the national-level and separately for rural and urban domains.<sup>6</sup> Starting in 2013, the IHPS attempted to track all household members that were interviewed in the last survey round and that were projected to be at least 12 years of age and were known to be residing in mainland Malawi during the follow-up survey round.<sup>7</sup> Once a split-off individual was located, the new household that he/she may have joined vis-a-vis the prior survey round was brought into the IHPS sample. Based on these protocols, the dynamically expanding IHPS sample included 3
a weak link in the economics of climate change (Weitzman, 2010).

Various methodologies have been employed for the estimation of their parameters, from subjective expert assessment (Nordhaus, 1994) to panel methods (Dell, Jones and Olken, 2014) to meta-analyses of non-economic literature (Tol, 2002). Also, the functions may be built by summing up different effects into a single aggregate, or they may retain some sectoral detail. The first approach is typical of earlier models like RICE (Nordhaus and Yang, 1996, Nordhaus and Boyer, 1999), MERGE (Manne, Mendelsohn and Richels, 1995) and CETA (Peck and Teisberg, 1992), where a relationship is posited between loss of potential income (GDP) and temperature. More recent contributions, based on multisectoral models like DART (Deke et al., 2001), GTEM (Pant, 2002), ICES (Eboli, Parrado and Roson, 2010) and ENVISAGE (Roson and van der Mensbrugghe, 2012) keep the sectoral detail and attribute the various impacts to different variables and parameters in a disaggregated macroeconomic model, which typically has a general equilibrium structure.

The main advantage of holding distinct the different economic effects of climate change, despite the cost of higher computational complexity, is that it is possible to trace the various mechanisms through which the climate can affect the economic system. Furthermore, in a general equilibrium formulation, it is possible to account for second-order effects linked to variations in relative prices, which are often very relevant.

This document illustrates the methodology and presents some results for the estimation of damage functions parameters, for all 140 countries and regions in the GTAP9 dataset, and for six climate impacts: sea level rise, variation in crop yields, heat effects on labor productivity, human health, tourism and household energy demand. Effects from 1°C up to 5°C average temperature increments are separately considered, as most impacts are non-linear.

The GTAP social accounting matrix has become a de-facto standard for the calibration
two other studies that report out-of-sample validation of “bottom-up” population estimations using similar satellite data (Harvey, 2002; Biljecki et al., 2016). Second, our predictions outperform many “top-down” estimates with the exception of Facebook and WorldPop 2015. Although ours is not the most accurate, it crucially does not _directly_ rely on the census data.

The main reason why this method is useful is because surveys are more frequent and less expensive than censuses, and satellite data can be acquired routinely and with complete spatial coverage, and hence can potentially be more efficiently utilized to track population changes in small areas. Frequent up-to-date small area population estimations are important inputs for governments in applications such as tracking disaster management, delivering policy programs, understanding migration patterns, and many more. The Sri Lankan Department of Census and Statistics conducts the HIES every three years; the most recent was fielded in 2016, and the one before that was in 2012-13, and the next one is planned for 2019. Furthermore, Sri Lanka runs a continual Labor Force Survey that could also be used for this purpose. Therefore, the results support the case for the Department of Census and Statistics to keep careful track of the number of buildings identified in the relisting phase, and use that information to generate revised local population estimates.

14