rafmacalaba/data-use-mentions-extended huggingface dataset:
# **8. Conclusion**

This paper analyzes the reopening process of the countries in Europe and Central Asia after the first wave of the pandemic and provides evidence on the effects of different reopening trajectories, the speed, and sequencing of reopening, on economic recovery. We use high-frequency data on electricity consumption during the reopening period as a proxy indicator for the levels of economic activity.

Our analysis indicates that countries that adopted a gradual and staged reopening experienced stronger economic recovery compared to the countries that rushed into lifting the restrictive measures. The transition from the full to partial to no lockdown appears to be a more effective strategy of lifting the restrictions than a quick lifting of the restrictions from full to no lockdown. Similarly, economic activity seems to react positively to partial school reopening, compared to the full school reopening.

The timing of reopening, i.e., the wait time expressed in the number of days after the first peak of deaths before the reopening process starts, also matters. Opening before the peak of the pandemic reduces electricity consumption relative to predicted, while delaying the opening past the peak leads to faster recovery. However, when this decision is modeled to be based strictly on epidemiological considerations it has no effect on the path of economic recovery. Lastly, countries that reopen slowly by gradually lifting the stringency measures rather than lifting them faster also experience a more robust economic recovery.

We demonstrate that governance, particularly trust in government institutions, is an essential determinant of economic recovery. Our results show that a higher level of trust in government is associated with a faster recovery among countries that carried out a gradual reopening process. Incorporating trust into the analysis even increases the success of a fast reopening process, although there the relationship is more complex. The analysis also shows that fear and anxiety about the spread of the pandemic may hinder the recovery of economic activity as countries reopen. Providing the population with objective information and data about the progress of the pandemic could be an effective policy instrument in promoting faster recovery
elasticities: Note that equations 5 and 6 extend beyond the 1-2-3 model. As mentioned in the literature review, the import equation derived from the CES function is also known as the Armington import demand in the trade literature. Exports are also often expressed as Armington import demand from the rest of the world. However, in our case, the CET function and the estimation of Ω complete the country-specific model.

## _Data_

Data are from the World Bank’s World Development Indicators (WDI)<sup>10</sup> and the United Nations National Accounts database.<sup>11</sup> WDI is used where available. The two sources are combined to extend series or fill in missing observations. We retain the WDI levels and base year for constant prices where the two

> 9 Note that the CES and CET functions are not defined for elasticities that equal 1 (a Cobb-Douglas formulation can be used). However, the export supply and import demand equations are well defined for unitary elasticities and the estimation procedure will work in that case.

> 10 <u>https://databank.worldbank.org/source/world-development-indicators.</u>

> 11 <u>https://unstats.un.org/unsd/snaama/Index.</u>

8
While effects on health outcomes are large for both age groups, we see effects on weight for the younger group and on height for the older group. This difference can be explained by the role of breast-feeding on weight gain and height growth. Micronutrients that are not present in large amounts in breast milk are critical for improvements in height (Dewey and Adu-Afarwuah, 2008). Hence, during the period of exclusive breast-feeding children tend to gain weight rather than height. Reductions in illnesses that impede a child’s absorption of the nutrition in breast milk tend to lead to gains in weight. Once supplemental foods that contain more micronutrients are introduced into the child’s diet, reductions in illnesses that improve a child’s ability to absorb nutrition are manifest in height. According to the 2005 Demographic and Health Survey, 88% of children less than 6 months old are exclusively breastfed. And 31 percent of children age 6 to 9 months did not receive supplementary foods (Institut National de la Statistique du Rwanda and ORC Macro 2006).

# **b. Pathways**

We hypothesize that the observed impacts on child health are due to changes in their utilization and quality of prenatal and postnatal care induced by the introduction of P4P incentives. In this section, we examine the impact of P4P on utilization and quality of care and thereby document that the observed changes in health outcomes are plausibly caused by P4P.<sup>13</sup> In order to validate that the results on the effect of P4P on utilization and quality are plausible, we present and discuss the results in the context of our theoretical conceptual framework.

Our framework predicts that the introduction of P4P will lead to larger increases in services (the _U_ ’s) for which the relative price increase is highest and for those that have the highest relative marginal return to effort; conversely, there may be no effect on services for which payment rates and the marginal return to effort is low. Our theoretical discussion also predicted that the introduction of P4P
new school between the years 1998 and 2009. We then investigate the timing of the introduction of schools across municipalities. For municipalities that receive a school, the outcome is an ordinal variable equal to the number of years from 1997 that a school expansion is realized.<sup>18</sup> These two outcomes are regressed on 1997 municipal characteristics and a set of state dummy variables (Table 2).

Consistent with our identifying assumption, the correlation between the teenage birthrate and both the introduction of a secondary school or the timing of the expansion is neither significant nor large in magnitude. Rather, we find that supply-demand factors are important in explaining the school expansion. Municipalities with high secondary school density in 1997, which we interpret as a supply factor, are less likely to receive a school in the preceding periods and, if they do receive a school, the expansion takes place later than in municipalities with low initial school density. The municipality’s enrolment rate in the final year of primary school, which we interpret as a demand factor, is positively correlated with how early the municipality receives a new secondary school, but does not determine the receipt of a school. This is consistent with a policy objective to equalize secondary school access across Brazilian municipalities, while giving priority to those municipalities that have a higher immediate demand (Soares 1998).

Identification also depends on the independence between school introductions and trends in birth outcomes. We investigate pre-trends in birth outcomes for municipalities that received secondary schools by performing an event study-style analysis,<sup>19</sup> constructing visual plots reflecting time-demeaned births relative to the periods just before and after a school introduction:

> 18 In the case of municipalities that receive more than one school, this outcome reflects receipt of the first school. 19 The complexity in conducting an event study in this framework is that some municipalities experience multiple ‘events’ by having schools introduced at multiple points in time. We simplify the analysis and focus on only the first ‘event’
# **6.  Global Trends for High-Priority GGUs**

In this section, we extend our sampling and modeling approach to global scale. Among 51,976 Global Geographic Units, we have identified 1,131 GGUs that account for 50% of global methane emissions in the EDGAR database. After limiting our exercise to GGU sampling areas with at least 20 Sentinel-5P observations per year, we estimate trend models (1) and (2) by fixed effects for 775 high-priority GGUs.

# **6.1 Overall Results**

Table 10 provides a summary that aggregates the econometric results into four categories: No Trend (statistically-insignificant results for both models); Mixed (statistically-insignificant results for one model or oppositely-signed but significant results for two models); Decreasing (significant negative results for both models); and Increasing (significant positive results for both). We find No Trend for 56 GGUs because neither model attains classical significance (p=0.05). Results are Mixed for 150 GGUs, Decreasing for 106 and Increasing for 453. In summary, a substantial majority of 775 GGUs have experienced unambiguous increases in CH4 emissions during the past four years, whether judged by the overall trend or change in the most recent year. The same picture emerges if we weight the regression results by EDGAR emissions.

**Table 10:  GGU estimation results (fixed effects)**

||||Emissions-<br>Weighted|
|---|---|---|---|
|Results|Count|%|%|
|No Trend|56|7.2|6.5|
|Mixed|160|20.6|23.3|
|Decreasing|106|13.7|13.1|
|Increasing|453|58.5|57.1|

# **6.2 Sectoral Results**

Table 11 explores the sectoral distribution of results weighted by EDGAR emissions. Soils and Livestock exhibit enormous disparities between Increasing
effect. We have shown relatively precise zero effects across a variety of sensible specifications and ways of measuring exposure to hurricanes. However, it remains possible that particular sub-populations or geographies are in fact experiencing adaptive migration. This is particularly true given that our measure of migration relies on individuals filing tax returns. Very low income or other vulnerable populations may not file returns and so may not appear in our data. The migratory responses of these individuals may therefore be different from what is documented here.

Second, we cannot measure within-county migration. This may be an important dimension of adaptation, as individuals move from more to less-exposed areas within the same county. We cannot capture this form of adaptive migration and so may underestimate the extent of adaptive migration that has occurred between 1992 and 2017. However, recent work utilizing data from U.S. Census tracts finds little to no evidence that migration within U.S. counties has reduced a composite of climate change risk.<sup>26</sup> This result, using aggregate data examining all moves, is contrary to work that focuses on movement after targeted buy-out programs.<sup>25,37</sup> Hence, whether and how migration is a form of adaptation remains an active area of research.

Third, there are many other natural disasters whose frequency will increase as a result of climate change. Our evidence does not indicate that adaptive migration in response to these other disasters is not occurring. Different kinds of disasters may lead to different kinds of migratory responses.

The decision of whether or not to migrate occurs in a specific economic, social, and policy context. It is possible, for example, that our results are driven by existing policy supports and insurance markets that do not fully price-in the effects of climate change, leading people to face lower-than-optimal costs of storm damage.<sup>31,38</sup> Similarly, high-risk areas could be lowering the costs of future hurricanes to individuals by investing in more hurricane-resilient infrastructure and thus
rapid growth in capital formation, as in China, the impact of different rates of depreciation on the estimates of the current growth rate of the capital stock is very limited – though the level of the capital stock does depend on the depreciation rate to a limited extent.

# **_Initial capital stock_**

A number of authors have presented estimates of the capital stock over time. All have made an estimate for the capital stock in 1952 when investment data first became available (Table 1). Several researchers used material published in Chinese sources while others use estimates based on crosscountry comparisons or based on estimates derived from investment flows and depreciation rates. Others preferred to make an assumption based on introspection.

# _Previous estimates_

The coverage and methodology used in these studies is very variable. About half of the studies focus on the economy-wide capital stock and so include both the housing stock and government investment in assets such as offices, schools, hospitals and roads. Overall, the variation of the estimates is too large to be a guide (Table 2). Moreover, many of the estimates are based on introspection rather than original sources. In addition, only four studies provide a sectoral breakdown of capital stock in 1952 (Table 3). One book provides a compendium of economic statistics (Chen) with data on investment and capital stocks for the period 1948 to 1958. This book provides the details of the original Chinese sources which are either in the form of articles by the statistical bureau or academics or reports in official newspapers. None of the reported sources provides an estimate for the capital stock in the government sector.

3
The simple illustrations above demonstrate the relationship between key elements of the framework used in this paper to measure empowerment. In the following sections, we unpack these elements into measurable indicators.

# 1.1 Agency

How can agency—the capacity to make meaningful choice—be measured? In the fivecountry study, and increasingly in other project monitoring systems, _asset endowments_ are used as indicators of agency. These assets can be psychological, informational, organizational, material, social, financial, or human.

Some assets are easier to measure than others. For example, it is easier to quantify human assets (such as skills or literacy) than psychological assets (such as the capacity to envision) or social assets (such as social capital).<sup>9</sup> The indicators drawn from country experience and discussed later in this paper demonstrate that quantifying all types of assets is manageable; however, collecting certain types of data requires a mixed-methods approach.

Understanding the complex interaction among assets also presents challenges. The endowment of a single asset, such as ownership of land, can affect a person’s ability to make meaningful choices. In addition, as box 1 shows, an actor’s or group’s command over one asset can affect the endowment of another asset. For example, education (a human asset) often gives an actor greater access to information (itself an asset) and at times improves his/her capacity to envision alternative options (a psychological asset). In this case, all three assets contribute to an actor’s capacity to make meaningful choices. Therefore, data have to be gathered on a range of assets, and analysis can, if relevant, test for the effects of one asset on another as well as for their effects on empowerment outcomes.

## **Box 1.  The Effect of Education on Other Assets**

The Institute for Adult Education (INEA) in Mexico provides literacy training and basic education to young disadvantaged adults who have not attended
# **Appendix G: Documentation of Input-Output table for Armenia**

# **Jesper Jensen and David G. Tarr**

# **_Summary_**

The core of the model data consists of an input-output table. There exists no recent input-output table for Armenia, so we produced the table based on data provided by the National Statistical Office of the Republic of Armenia. Lacking resources for a full survey, these data were obtained by the National Statistical Office by a pilot sample survey. Our data sources include an unbalanced supply-use table with 16 sectors for the year 2006 and detailed data on GDP for 2007 by types of income, expenditure, and production. The supply-use table contains all the elements we need for the input-output table, but supply deviates significantly from use in most of the sectors. Accounting identities require that supply must equal use for a balanced input-output table and for a table that we can use in our model. The sheer size of the deviations in the supply-use table calls for funding a full survey by the National Statistical Office to obtain additional and more accurate detailed data to reduce the deviations.

Without access to more accurate and detailed data, we develop a balancing procedure to arrive at a balanced input-output table. The procedure involves an optimization problem in which the elements of the table are adjusted such that the sum of the squared deviations from the initial values are minimized and subject to a number of side constraints, including supply-use balance. As part of the procedure, we also use detailed GDP data to update the dataset to the year 2007. Finally, we disaggregate two services sectors to get more details on transport, communication and financials sectors. The final table contains 21 sectors.

# **Introduction**

We begin with a dataset compiled by Light (2010), whose data sources include an unbalanced supply-use table with 16 sectors for the year 2006, and detailed data on GDP for 2007. All data is provided by the National Statistical Office of the Republic of Armenia.

Light (2010)
**Table 2: Summary Statistics of Child Characteristics**

|Child Characteristics|All|Coresident|Non-coresident|P-values|
|---|---|---|---|---|
|Age|41.95|39.12|42.54|<0.001|
|Oldest son (%)|15.90|22.37|14.53|<0.001|
|Youngest son (%)|27.30|61.29|20.12|<0.001|
|Daughter (%)|46.04|12.04|53.22|<0.001|
|Married (%)|91.38|78.49|94.10|<0.001|
|# of children (age<16)|0.34|0.47|0.31|0.001|
|_Education (%)_|||||
|Illiterate|16.91|12.26|17.89|0.001|
|Primary|34.91|33.12|35.29|0.369|
|Middle School|28.65|38.06|26.66|<0.001|
|High School|13.65|13.55|13.67|0.945|
|College and above|5.89|3.01|6.49|<0.001|
|Coresident(%)|17.44|100.00|0.00||
|Observations|2667|465|2202||

Note: 1) Sample are adult children no younger than 25 with at least one parent who is older than 60. 2) P-values are from t-test of the coresident and non-coresident groups.

**Table 3: Transfer Patterns**

||All|Zhejiang|Gansu|Urban|Rural|
|
they talked to classmates about investment strategies during the course). The survey was sent electronically in March 2018 to 4,600 students with emails reported in the CSE data set.<sup>19</sup> To encourage participation, students who completed the questionnaire before May 30, 2018 were automatically registered for a lottery with a total payoff of 5,000,000 COP (around 1,600 USD). The response rate was 18% (842 students). According to the survey, 44% of respondents had graduate education, 17% had some type of foreign portfolio investment, and many (86%) reported having conversations about stock trading with classmates.

> 18 See Pedraza, et al., (2019) and Pedraza (2020) for additional description of the Colombian stock exchange, the transactional and portfolio data.

> 19 We tested a pilot of the survey with an ongoing class in February 2018. Follow-up interviews were carried out to confirm the interpretation of the questionnaire.

18
1 in the top 77,290 positions. In the model-predicted threat probability index, the scale composition of the top 77,290 positions is quite different. Among these, for example, only 3.2.2 Spatial Implications To compare spatial distributions before and after the change, we divide the original (Figure 4) after stacking, we construct variables in which species are assigned values equal to their maximum group percentiles [5, 10, ..., 95, 100]. Then we overlay all GBIF species maps for vertebrates and plants on our 0.25 degree grid, compute grid cell sums for the before and after northwest South America. At this scale, small- species occurrence region prioritization via stacking clearly makes a difference; areas with relatively high threat ratings expand eastward and grow markedly in size. By implication, species with very small habitats are more widely In contrast, modest relative increases are observable in many of the areas with highest threat status in Figure 7 (Mexico, the western Andean region, Atlantic Brazil, coastal West and Central
explore the heterogeneity in the impact of hurricanes by interacting our damage index with the predetermined width of mangrove on the path to the coast.

Our results fall into two categories. The first set of results show that, consistent with previous literature, hurricanes have negative short-run effects on economic activity. The estimated effect size suggests that a category three hurricane in the Saffir-Simpson scale can reduce our proxy of economic activity by 16%. We additionally document a 17% reduction in economic activity for coastal lowlands, which are at risk from both wind and storm surge damage.

The second set of results show that the impact of hurricanes, in storm surge prone areas, decreases as the width of mangrove increases. To ensure that the effect is driven by the mangrove vegetation itself, and not by other characteristics of the location of mangrove habitats, we further restrict our sample to cells that have been historically protected by 3
‘Household Sample Surveys in Developing and Transition Countries,’ vol. ST/ESA/STAT/SER.F/96 of _Series F_ (Department of Economic and Social Affairs, United Nations Statistics Division) studies in methods 13, pp. 267–277

- Rao, J., and R. Sitter (1995) ‘Variance estimation under two-phase sampling with application to imputation for missing data.’ _Biometrika_ 82(2), 453–460

34
explaining demand for redistribution. In concrete, expectations of upward mobility are a key element in a few models (Piketty, 1995; Bénabou and Ok, 2001). By adding the subjective views people hold of their future position in the income distribution, these models allow to incorporate the fact that people base their voting on redistribution on their expected permanent income, not just on the current level of income. Expectations of social mobility, or “Prospects of Upward Mobility (POUM)” as Benabou and Ok (2001) call them, are therefore an important determinant of their demand for redistribution. In contrast with the basic Meltzer Richard model, the POUM hypothesis has found quite a bit of empirical support (citations here Cojocaru, 2014, Checchi Filippin 2004, Rainer Siedler 2008). These papers use subjective expectations, as reported by opinion surveys, rather than using the objective mobility in each country.<sup>9</sup> In this paper we use subjective perceptions of inequality, and by explicitly modeling the mechanism through which people form these perceptions, we go one step further and try to combine the relevance of perceptions for demand for redistribution with the heterogeneous views of inequality at the level of the individuals.<sup>10</sup>

# **2.2 Determinants of demand for redistribution**

Political scientists have shown that public opinion has a major influence on many public policy decisions<sup>11</sup> and, in particular, public views of the economic situation tend to have a ‘pivotal rote’ in determining the outcome of elections.<sup>12</sup> Addressing the issue of the _formation_ of public opinion is thus a natural research focus for political scientists. A key contribution in this area is due to Zaller’s 1992 monograph “The Nature and Origins of Mass Opinion”. Challenging what at the time was the consensus, Zaller rejected the idea that survey responses are manifestation of fixed attitudes, and that deviations are simply due to measurement