rafmacalaba/data-use-mentions-extended huggingface dataset:
with Australia in terms of power sector emissions share and coal consumption per capita. We hypothesize that the substantial weight on the economies of Israel, Gaza and West Bank may reflect similarities in climate and average income levels. We explore the sensitivity of the results to excluding certain weights in Figure 3.

Table 1: **Covariate balance**

|Variables|Treated|Synthetic|Donor Pool Avg.|
|---|---|---|---|
|Emissions share (power)|0.52|0.47|0.40|
|Emissions share (transportation)|0.21|0.21|0.17|
|Emissions share (other)|0.28|0.33|0.43|
|Log of gdp per capita (USD 000s)|3.68|3.42|2.46|
|Btus of coal per capita (Hundred MMs)|1.04|0.46|0.14|
|Btus of ng per capita (Hundred MMs)|0.50|0.34|0.50|
|Btus of petroleum per capita (Hundred MMs)|0.94|0.97|0.65|
|Emissions per capita in 2001|2.58|2.33|0.23|
|Emissionsper capita in 2007-2008(avg.)|3.00|3.14|1.49|

Notes: Table reports pre-period values of covariates in Australia (‘Treated’ column, _N=1_ ), synthetic Australia (‘Synthetic’ column) and donor pool (Donor Pool Avg. column, _N=33_ ). Each row displays the covariate average over the period 1998-2008 unless otherwise indicated. See Section 3 for details.

Table 2: **Nonzero synthetic control weights**

|Weight|Country (Unit)|
|---|---|
|0.39|Taiwan|
|
weighting assets was introduced by the first Basel capital accord (BCBS, 1988) to distinguish risk of different asset types and require banks to hold more capital against portfolio items with higher risk.

Figure 6 below shows the minimum regulatory capital requirements. On average most of the ECA sub-regions set capital requirements (as % of RWA) well above the average for the rest of the world. There are, however, some important differences. On average EU countries set the lowest capital requirements, but these countries are also more apt to have in place additional capital surcharges, meaning that the minimum regulatory capital set for banks is, in practice, higher than before the financial crisis. Countries in Central Europe and Baltic countries, Turkey and Russia had higher capital requirements than the EU in 2008 but lowered their capital regulatory requisites by the end of 2016.

**Figure 6: Mean regulatory capital requirements (% of RWA, equally weighted)**

<!-- Start of picture text --> 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>EU CE & BC WB<br>SC CA Russia<br>Turkey EE RoW<br>13<br>12<br>11<br>(% of RWA) 10<br>9<br>Mean regulatory capital requirements<br>8<br><!-- End of picture text --> Source: Bank Regulation and Supervision Survey conducted by the World Bank. Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe; **ROW** for the rest of the world **.** Since capital acts as a buffer against liquidity, information and economic shocks, the quality of capital
One reason for the paucity of recent evidence on life cycle wealth accumulation in high-informality settings is the rarity of the repetitive, consistent, and representative microdata with reliable wealth variables needed to disentangle age, year and cohort effects. Most previous examinations of life cycle saving behavior have examined savings rates measured as the residual difference between income and consumption. For example, Deaton and Paxson (1994) examine the case of Taiwan, China, between 1975 and 1990, a country that was further along its demographic and economic transition to high-income status at the time than Pakistan is now. A smaller number of studies measure wealth and its changes with age directly (e.g. Lydall (1953), Shorrocks (1975), Wolf (1981) or more recently Cagetti (2003)) but focus on rich countries. We are not aware of lowincome country descriptions of how the value and mix of assets owned by households evolves over the life cycle. Pakistan’s PLSM survey is uniquely positioned to help empirically pin down the patterns in life cycle wealth accumulation, through the availability of repeated cross-sections spanning more than 18 years and detailed asset data that is homogenous over the survey rounds. This allows us to follow cohorts as they age and analyze most forms of savings (including housing, land, financial assets, business value, durables and liabilities).

We find that Pakistani households accumulate significant levels of wealth (relative to their consumption levels) and that most of that wealth is relatively illiquid. The average Pakistani household’s net worth grows by 60 months’ worth of consumption (5 years) between ages 25 and 65. The bulk of this increase in the form of residential housing, whereas other forms of wealth such as land, durables, business and farm values and financial assets stagnate over the life cycle. Asset accumulation is slower early in the life cycle and picks up speed between ages 40 and 65. This is corroborated by our findings on the age profiles of consumption, income, and the saving rate. Until
important complications. First, the two properties will be difficult for respondents to disentangle. A mechanism that prevents complications evolving into problems can also be viewed as a way to resolve issues. A mechanism that resolves problems, perhaps with the explicit threat of sanctions, also can prevent them from even arising because of the deterrence effect. Indeed, 'preventing' can be thought of as being produced by the shadow of 'resolving'. Second, respondents might view two very similar questions as repetitive, which reduces the overall quality of a survey.<sup>36</sup> Third, piggybacking on the regular cycle of the WBES meant a strict constraint on the total number of questions that we could ask. Given these conceptual and practical considerations, we chose to ask about both resolution and prevention in one question.

A further issue revolved around the phrasing of the term to connote success when resolving and preventing problems. We chose to use a single, simple term 'effective'.<sup>37</sup> We did not ask respondents to focus upon whether agreements work in an efficient way because this is too demanding a criterion and probably one that is less familiar for business managers than economists. In using this word, we avoided weighted terms such as importance, usefulness, or frequency of either use or occurrence, even as these elements, and perhaps more, enter in determining effectiveness.<sup>38</sup> The terms "important" or "useful" carry the meaning of value and can be prone to more subjective interpretation than the term "effective". Frequency of use or

> 36 Survey methodologists warn against such repetitive design since it contributes to respondents' survey fatigue and affects their responses through the rest of the questionnaire, and even their overall participation. 37 In the papers reviewed by Cao and Lumineau (2015), equivalent notions are satisfaction with the exchange relationship and performance of the partner in the exchange. These notions are entirely analogous to our 'effective',
(1998) and Stan (2005) to assess the accuracy of simulated data on wind speed. To further evaluate the accuracy of the results, model estimations were also compared with simultaneous in‐situ measurements collected during the passage of Hurricanes Jeanne and Frances in 2004 across Florida (Pita el al., 2015). These robustness checks provide important information about the accuracy of information generated by the model.

The use of this wind speed model is a key innovation in this study since it represents a completely exogenous input in the estimation of hurricanes’ potential destructive power at each geographical location, which is completely calibrated and validated for the Central America region. Strobl (2012) and Hsiang and Jina (2014) focus their papers on maximum wind speed using a more simplistic hurricane model not necessarily calibrated for our region of interest.

# 2.2. Hurricane Damage Indexes

Having precise windstorm hazard data is the first step in the study’s characterization of hurricane impact. What makes hurricanes destructive, and hence economically disruptive, is the strong wind speed coming in contact with exposed assets and buildings. In a storm, strong winds run into stationary objects (e.g., trees, buildings, cars, etc.). Some of these elements can safely absorb the energy carried in the moving air, while others cannot. To illustrate this connection, consider two hurricanes of the same magnitude, with the only difference being that one of them strikes a densely populated area, while the other one touches upon an isolated region. These two virtually identical hurricanes will have extremely different and opposite economic impacts. The consideration of exposure of assets and population is a key factor for the estimation of damages. To keep the exogeneity of the hurricane’s damages indexes represents a challenge, as it is difficult to link each economic unit with the level of wind by which it has been hit.<sup>7</sup> In this study we consider three approaches based on 3 different indexes, as outlined in the
Kapitalis 2015).

An audit of energy firms presented to the council of ministers in August 2014 found a complex web of cross‐subsidies. Among them is a large subsidy historically provided through an artificially low price of crude oil sold by the national oil company to the state‐owned refinery, fixed at TD 50 (US$34–40 depending on the exchange rate) per barrel irrespective of the world price of crude. The government was to eliminate cross‐subsidies for the two companies by January 2015 (World Bank 2013b, IMF 2014m). The estimation of the subsidy reimbursable to the refinery is further complicated by the fact that half of its production is exported (fuel oil and naphtha). In 2014, the refinery did not produce gasoline and imported it to meet domestic demand. While the refinery produced enough fuel oil to meet domestic demand, almost all of it was exported while imported fuel oil was used to meet domestic demand (presumably to take advantage of sulfur content or other quality differences). The net result is that, while the refinery produced half of the total domestic demand in volume in 2014, the country imported more than three times the amount of locally refined products sold on the domestic market (http://www.stir.com.tn).

The government is taking concrete steps to strengthen social safety nets as part of the overall reform program across the economy. The government in mid‐2014 introduced a support program for households that increased transfers to the vulnerable by 10 percent; aimed to broaden the number of beneficiary families by 30,000 between 2013 and the end of 2014; provided a temporary transfer of TD 80 (US$55) to 285,000 families when fuel prices were increased and during the Ramadan; and provided an additional transfer of TD 10 (US$7) per month per child, up to three children per family, to vulnerable families with schoolchildren. In addition, similar to the scheme in India, the authorities are setting up
# **Disability indicators**

In order to determine prevalence or identify a specific ‘functional difficulty status’ group, a threshold needs to be set on the answer scale of functional difficulties. The WG recommends “a lot of difficulty” as the threshold: persons who report “a lot of difficulty” or “unable to do” for at least one domain are considered to have a disability, and persons with ‘no difficulty’ or ‘some difficulty’ to all six questions are deemed as not having a disability.

Different thresholds for the WGSS produce vastly different prevalence estimates (e.g. Bourke et al 2020; Mitra 2018). In addition, if persons with some difficulty are potentially more disadvantaged than persons with no difficulty, this categorization will underestimate the extent of inequalities between persons with and without disability. There is mounting evidence that having ‘some difficulty’ is significantly associated with economic and social deprivations with a gradient from no difficulty to some difficulty to at least a lot of difficulty (Clausen and Barrantes 2020; Mitra 2018). Besides, several censuses under study use yes/no answers to the functional difficulty questions, making the breakdown recommended by the WG impossible. Hence, this study categorizes individuals in three ways. First, for all data sets, it groups individuals into two categories:

- (1) No functional difficulty in all domains;

- (2) Any functional difficulty in at least one domain (answer Yes for data sets with yes/no answers, or reports at least ‘some’ difficulty for graded scales).

Second, following the recommendation of the WG for data sets that have a graded answer scale, we group individuals as follows:

- (i) A lot of difficulty or unable to do in at least one domain;

- (ii) No difficulty or ‘some’ difficulty for all domains

Third, for data sets with graded scales, we partition individuals into three categories:

- (a) No difficulty for all domains;

- (
macroprudential tool especially in developing and emerging markets (Federico _et al._ , 2014), (ii) collecting time series data on RR is, in principle, “easier” than collecting data on other prudential tools such as capital requirements (especially for long time spans), (iii) as is the case when using cyclically-adjusted revenue measures to assess changes in tax policy (e.g., Romer and Romer, 2010; Vegh and Vuletin, 2015; and Riera-Crichton _et al._ , 2016), total banks’ reserves (calculated as the ratio of banks deposits at the central bank to bank deposits) are not valid proxies for changes in policy instruments such as RR (Federico _et al._ , 2014).<sup>7</sup> In particular, we push the empirical frontier on several crucial dimensions. First, in terms of measuring RR, while building upon existing data from Federico _et al._ (2014) that accounts for the different types of RR in terms of maturity and currency denomination, we now construct a novel metric of effective RR which also takes into account the structure of deposits. Second, in terms of the identification of exogenous RR shocks, we develop a Romer and Romer (2010) type of narrative for the nature of RR changes. When shocks to RR are properly identified as exogenous to the business cycle by using a narrative approach, we show that results change dramatically. In other words, we use changes in RR exogenous to the business cycle to analyze their macroeconomic effects, as opposed to those RR changes associated with the policy reaction function of the monetary authority to current or prospective output fluctuations. This is the quintessential problem of reverse causality. Third, since it proves impractical to analyze macroprudential policy in isolation, we also jointly analyze the macroeconomic effects of the central bank policy interest rate (IR, hereafter) on the macroeconomy. To properly identify exogenous IR shocks, we follow Romer and Romer’s (2004) approach and
TASAF worked with an elected community management committee (CMC) in each village to select beneficiary households.<sup>7</sup> The CMC surveyed the poorest half of households, collecting data on eight household characteristics: roof material, light supply, water supply, type of toilet, ownership of four different assets (vehicle/motorcycle, radio, iron, poultry), number of windows on the house, household size, and number of meals eaten per day. TASAF then carried out a proxy means test to propose a ranking of households by poverty level, for CMC and village leader approval. On average, 23 percent of households became beneficiaries.

# **3 Evaluation Design and Data**

## **3.1 Evaluation design**

We evaluate the impacts of the CCT program using three waves of data on beneficiaries and would-be beneficiaries (no data were collected from those not selected to be beneficiaries). Table 1 presents the chronology of the program and impact evaluation. A baseline survey was carried out during January–May 2009 and payments began in January 2010. A midline survey was conducted during July–September 2011 (18-21 months after transfers began) and an endline survey was conducted during August–October 2012 (31–34 months after transfers began). The baseline survey included 1,764 households (a subset of beneficiary households) comprised of 6,918 individuals. The quantitative data collection was supplemented by two rounds of qualitative data collection (following the midline and endline surveys) employing focus group discussions and in-depth interviews.

## **3.2 Data and outcomes**

In each of the three survey rounds, we collected individual-level data on total health clinic visits in the last year,<sup>8</sup> ownership of protective footwear (shoes and slippers) by children,

> 7CMC elections occurred at village meetings; 10–14 members were elected, with secret ballots. To run, a candidate had to have received financial training and successfully
. Pollution data comes from the Van Donkelaar group. Inversion data comes from the MERRA re-analysis data and measures the incidents when temperature at atmospheric levels above the surface is higher than at the surface by district for each fiscal year. Inversions are measured as the share of days with an inversion. Inversions are calculated comparing surface temperature to temperature in the two levels immediately above the surface. If either of these levels has a higher temperature than the surface we define it as an inversion. Winter refers to inversions between October and March. Summer refers to inversions between April and September. All inversion data and weather controls are population weighted averages across districts. The unit of observation is the district year. In all columns we control for the first difference of the number of days in one of fifteen precipitation bins, the number of days the mean temperature is in one of twenty-five bins, a second order polynomial of relative humidity and surface pressure, four bins of wind speed, ten wind direction bins, and interactions of relative humidity and the square of relative humidity with all of our temperature bins. All columns include fiscal year fixed effects. In all columns we cluster standard errors at the district level. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

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stronger effects for banks with higher exposure to local public debt, consistent with lowering crowding out. More concretely, a one-standard-deviation increase in a bank’s pre-Law exposure to local public debt is associated with a 28 percent reduction in the total volume lent to local governments and a 31 percent increase in the total lending volume to firms. Along with the reduction in loan volume, there is a relative increase in interest rates of loans to highly indebted local governments.

We next study the crowding-out mechanism in more detail using loan-level data. This data allows us to study changes in the loan terms of the average firm and identify adjustments in the supply of credit, by saturating our specifications with _firm*month_ fixed effects. We find that in a state with average ex ante public indebtedness, a one-standard-deviation increase in the ex-ante exposure of a bank to local government debt leads to an 8.8 percent increase in the loan volume extended to firms. This effect is substantially larger in more indebted states.

We then evaluate whether the type of ex ante spending of a local government has any impact on the state’s subsequent economic activity. We find stronger positive effects on economic activity and bank lending to the private sector in states with higher ex ante public spending on social services over infrastructure. For a state with average ex ante public indebtedness, a onestandard-deviation increase in the ex-ante share of spending in non-infrastructure projects is associated with a 0.15 p.p. increase in quarterly growth rate of GDP and a 0.1 p.p. increase in the employment ratio after the passage of the FD Law.<sup>8</sup> Credit effects vary depending on how financially constrained firms are (proxied by shorter credit histories). For firms with short credit histories, the relation between state ex ante indebtedness and firm credit access after the FD Law is significantly stronger—especially for firms headquartered in states with ex ante higher share of fiscal spending on non-infrastructure. Overall, we interpret this finding as evidence that private
# 3.1. Business costs of infrastructure disruptions: Self-reported losses due to power outages

This section uses self-reported Enterprise Surveys data to compute the value of sales lost due to electricity disruptions in our sample. It then constructs a regression model to estimate the impact of power outage durations on these sales losses. Aggregating firm data from 138 mostly small- and middle-income countries accounting for 32% of global GDP, it finds sales losses worth 81.6 billion 2018 real USD due to power outages. As a considerable fraction of firms (about 15%) did not respond to the question registering their sales, this number can be interpreted as a lower bound. The total annual blackout duration reported by firms is estimated to increase sales losses by 0.29 percentage points for each 100 hours of blackout.

On average, countries with higher power outage durations experience larger sales losses. A review of the literature described in Section 2 shows that the relationship between number of outages and sales losses is relatively flat for countries where firms, on average, face power outages of up to 10 hours (see Figure 3). For countries with longer outages, however, the sales losses appear to increase in an almost exponential pattern. Investigating this phenomenon at scale, our analysis confirms the non-linear and positive relationship between monthly hours without power and sales lost due to outages. Looking at the spatial distribution of relative losses, Figure 4 shows that Africa and South Asia are most affected.

Figure 3: Sample firms’ average sales losses and power outages at the country-level <!-- Start of picture text --> 35<br>Enterprise Survey estimates<br>30 From literature<br>25<br>20<br>15<br>10<br>5<br>0<br>0.1 1 10 100<br>Monthly hours without power (logarithmic scale)<br>% of sales lost due to power outages<br><!-- End of picture text -
effectiveness of class size reductions is .029 but the cost effectiveness of increasing school physical resources is 3.2's.

R) Fducation production function.estimates Do these few cost effectiveness studies represent the typical case across a variety of conditions? While there are few studies that calculate cost effectiveness for different types of inputs, there are numerous studies which relate student achievement to school characteristics, such as inputs, while controlling for student's background characteristics (e.g. parental income, education) and there have been several prominent reviews of this literature. These reviews face enormous difficulties in summarizing the empirical results of the different studies as the estimated coefficients, which are reported in physical quantity units, (e.g. test scores gain per square foot) are not directly comparable either across inputs or across studies.

Therefore the reviews summarize only whether the effect was estimated to be positive or negative and whether it was statistically significant (although some do attempt to report "effect sizes" which, at least normalizes the test scores to standard deviation, Lockheed and Haunshek 1988).

_Confirmation percentage evidence._ Holding these problems firmly in mind, there are two ways we can bring the enormous educational production function literature to bear on our competing positive models. First, we can compare the "confirmation percentage" of various inputs, that is the frequency with which the variable is found to be statistically significant of 13 Since this study did not report cost data for school resources, we used data from World Bank (1996a) to calculate these ratios.

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