rafmacalaba/data-use-mentions-extended huggingface dataset:
Second, we use two indicators of bank efficiency. Overhead cost is our first and primary measure of bank efficiency and is computed as total operating costs divided total costs. Overhead cost varies from less than one percent to 8.3% in our sample, with an average of 3.5%. As alternative efficiency indicator, we use the cost-income ratio, which measures overhead costs relative to gross revenues, with higher ratios indicating lower levels of cost efficiency. This indicator ranges from 33% to 92%, with an average of 62%. In our smaller sample, we find that Islamic banks have significantly higher overhead costs than conventional banks, but only marginally higher cost-income ratios (significant at 10% level).<sup>6</sup> We also note that cost efficiency is significantly higher in our smaller sample than in the large sample.

Third, we use three indicators of asset quality. Specifically, we use (i) loss reserves, (ii) loan loss provisions, and (iii) non-performing loans, all scaled by gross loans. All indicators decrease in asset quality. We note that there might be problems with cross-country comparability, due to different accounting and provisioning standards. Loan loss reserves range from less than one percent to 13.4%, with an average of 4.5%. Loan loss provisions range from less than zero to 4.7%, with a mean of 1.3%. Non-performing loans, finally, range for 0.4% to 20.1%, with an average of 6.2%. In our smaller sample, Islamic banks have significantly lower loan loss reserves and non-performing loans, while there is no significant difference in loan loss provisions.

Fourth, we use several indicators of bank stability. The z-score is a measure of bank stability and indicates the distance from insolvency, combining accounting measures of profitability, leverage and volatility. Specifically, if we define insolvency as a state where losses surmount equity (E<-
outages<br>Phone service|Internet service|Days to install a|Days needed to|
||per year<br>interrupted (per month)|interrupted (per|new telephone|gain the access to|
|||month)||Internet|
|Inland|11.43<br>0.496|1.035|2.863|3.789|
|Coast|11.15<br>0.297|0.828|2.965|3.718|

_Data sources: IP infringement cases are from Peking University legal case database (BeiDaFaBao); The probability of getting legal protection for property rights and legal contracts World Bank Business Environment Survey of 2005;_ the data on road and railway access is from Statistical Yearbook of China; data on the stability of infrastructure access is from WBES-China 2005.

37
**Figure 2: Sector-level concentration of jobs, by gender**

<!-- Start of picture text --> Sector composition (%) by sex<br>Agriculture 38.5<br>61.5<br>Mining 6.4<br>93.6<br>Manufacturing 46.3<br>53.7<br>Public 18.1<br>utilities 81.9<br>Construction 3.5<br>96.5<br>Commerce 30.6<br>69.4<br>Transport & 12.0<br>communications 88.0<br>Finance & 33.2<br>Business 66.8<br>Public 37.3<br>administration 62.7<br>Other 48.7<br>services 51.3<br>0 20 40 60 80 100<br>Percent<br>Female Male<br><!-- End of picture text --> Source: Staff estimates based on Sri Lanka HIES 2016; confidence intervals of estimated workers can be requested from the authors.

**Manufacturing jobs are concentrated in some western and northern provinces** . Manufacturing jobs are concentrated in the Colombo, Gampaha and Kalutara in the Western Province; Kurunegala and Puttalam in the North Western province and Kandy in Central province (Figure 3). The jobs in the textiles and wearing apparel sectors are similarly distributed as the manufacturing sector, which tend to demand similar infrastructure. The level of urbanization is higher in these districts compared to the other districts or provinces in Sri Lanka. Trade and transportation related jobs are rather evenly spread out by districts, except for Colombo and Gampaha districts that employ a disproportionately higher share of jobs in this sector. A similar profile with the social services sector (education, health care and domestic personnel) is observable with jobs evenly distributed across Sri Lanka. However, most jobs in the agriculture sector are
Figure 1. Households are ranked along the horizontal axis by total consumption per adult equivalent. The vertical “drip” lines represent spending on gas, and the per-adult-equivalent poverty line is indicated by the horizontal line at about AMD 25,188. When total household consumption places a household above the poverty line but spending on gas drops them below, it can be argued that impoverishment due to the gas price increase has occurred.

**Figure 1: Impoverishing Effects of Spending on Gas**

<!-- Start of picture text --> 14<br>12<br>Pre- Gas Price Hike Consumption<br>10<br>Total Poverty Line<br>8<br>Post- Gas Price Hike Consumption<br>6<br>4<br>2<br>0<br>HHs ranked in ascending order of consumption expenditures<br>Source: Author’s calculations based on 2008 ILCS<br>HH consumption as multiple of total poverty line<br>1 233 465 697 929 1161 1393 1625 1857 2089 2321 2553 2785 3017 3249 3481 3713 3945 4177 4409 4641 4873 5105 5337 5569 5801 6033 6265 6497 6729 6961 7193 7425 7657<br><!-- End of picture text --> As Figure 1 above illustrates, gas price hike has a significant impact on poverty among Armenian households. Using the concept of impoverishment we can recalculate key poverty indicators in Armenia by “netting out” increase of household spending on gas due to the price hike. As presented in earlier reports, the poverty headcount in Armenia in 2008 was 23.5, a figure based on a consumption aggregate that includes gas spending (World Bank, 2010a; NSS, 2009). If we calculate the poverty headcount without gas expenditures, it rises to 25.4 percent. Thus, an additional 1.9 percent of Armenian households would be classified as poor. Similarly a 16
Our multidimensional assessment of social sustainability in Peru and South Africa shows that on average 67 percent and 65 percent of the Peruvian and South African population, respectively, experience overlapping social gaps—defined as experiencing gaps in least a third of the social dimensions considered. These rates are much higher than their official income poverty rates of 30 and 57 percent, respectively.<sup>15</sup> These people experience an intensity rate of 47 percent in Peru and 53 percent in South Africa, equivalent to 7 and 8 indicators, respectively. Women are especially affected in South Africa, but much less so in Peru. Ethnic populations experience greater levels of social gaps in both countries. Geographically, we find that South African provinces show greater disparities compared to departments in Peru. Our analysis also shows that these findings are robust to changes in the cross-dimensional threshold, confirming patterns of multiple social gaps by gender and ethnicity as well as the drivers found with the 33 percent threshold.

Our measurement approach has several limitations. First, current microdata sources at the individual and household levels fail to provide rich evidence for all of the dimensions of social sustainability. While most household surveys provide a comprehensive diagnostic of access to markets and services, they fail to simultaneously capture issues of trust, satisfaction, participation, and accountability; South Africa and Peru are rare exceptions where comprehensive datasets are collected. Hence, even though the mechanics of constructing the index are replicable across countries, the choice of indicators and the composition of the Social Sustainability Index will inevitably vary based on data availability. Second, some of the most vulnerable population groups remain virtually invisible to household surveys. This is notoriously the case of LGBTI people and irregular migrants. Other disadvantaged population groups in terms of race, ethnicity or disability are more frequently identified in standard household surveys, but their sampling is typically not designed to be statistically representative. Addressing such challenges requires huge efforts in harmonizing national household surveys worldwide; expanding the number and scope of questions typically collected by censuses; and improving
policy experiments make it possible to unpack the contribution of each policy. The experiment relying only on storage policy assumes that India liberalizes its wheat trade, including the use of export subsidies to dispose of excess stocks. Similarly, the trade policy assumption implies no public storage.

In interpreting the results that follow, a word of caution is in order regarding the limitations of rational expectations storage models with respect to what they aim to represent and how well they achieve that aim. First, the only motivation for private storage assumed here is intertemporal arbitrage. While that is an important motive per se, it excludes many other important transactional or precautionary motives that could explain much actual stock-holding behavior (Williams 1986). Given that trade is instantaneous in the model, there is also no reason to accumulate stocks to account for time to ship (Coleman 2009). These motivations are quantitatively important, as evidenced by historical stock data. A purely speculative motive would allow for regular stockouts, while at the world level the stock-to-use ratio never decreases much below 20%. Because of this focus on one motive for storage, the simulated statistics on private stock levels are likely to underestimate real storage. Second, stabilization policies are represented as free of any flaws (except potentially in their design): The policies perfectly follow the decided rules without any delay or discretionary political interventions. Such a perfect representation of India’s wheat policies is likely to overestimate the achievable price stabilization. Third, for the sake of tractability, the model focuses on two main sources of price volatility: production shocks in India and in the RoW. This focus excludes other sources of volatility such as shocks to exchange rates, and prices of oil and other agricultural commodities. Overall, the model aims to capture the most important types of spatial and intertemporal arbitrage, but the inherent complexity of this issue implies that the model should not be considered a forecasting tool but rather a means of quantitatively illustrating the consequences of each policy option.

Table 3 presents the effects of the various
sup>_∗∗∗_</sup>|0.036<sup>_∗∗_</sup>|
||(0.015)|(0.014)|(0.103)|(0.018)|(0.171)|(0.015)|(0.018)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|<br>Observations|30,967|30,978|2,521|30,978|4,666|30,934|30,968|
|_R_<sup>2</sup>|0.075|0.027|0.275|0.042|0.179|0.078|0.084|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

could the choice of used the households. as seen shape coping strategies by Firstly, in columns (6) and (7), those households, which have received transfers from social networks prior to the COVID-19 crisis, are more likely to rely on transfers and assistance from family and friends to cope. In addition, when partialling out the effect of the pre-COVID dependency on social network, female-headed households are still 3.7 percentage points more likely to cope using transfers from family members. Secondly, we could observe that poorer households are less likely to take a loan to cope (columns (4)-(5)).

# **4.3 Robustness Checks**

We conduct two sensitivity analyses and check the robustness of the results in
# **4.4.2 Net gains at the regional level**

We then subtract the investment costs from the GDP gains to obtain the net benefits from the corridor. A first cost estimate is provided by the PIDA website, which assess the CAPEX cost of a 2x3 1052km-long highway from Abidjan to Lagos through Accra, Lomé and Cotonou to be 3,000 million USD, hence US$ 3M per kilometer.<sup>6</sup> Given that not all segments of the corridors will be upgraded into 6L roads, we choose a lower estimate as a lower bound, US$ 2M per kilometer. We compare this low bound estimate with other estimates provided from international comparisons. We use the 2018-updated Road Costs Knowledge System (ROCKS) database which was designed to obtain average and range of unit costs based on historical data to improve the reliability of new cost estimates. It relies on all WBG-financed projects with a road component that were completed between the years 2000 and 2017, as well as some roadrelated projects financed by the African and Asian Development Banks. Based on the ROCKS database, the average unit costs per km for a 4L expressway are US$ 5.26M. Unit costs differ across geographic regions and whether we consider estimates or actual numbers which include observed cost overruns. Actual unit costs for a km of 4L expressway range from US$ 3.55 in South Asia to US$8 in Middle-East and North America. There is no estimate for a km of 4L or 6L expressway available for Sub-Saharian Africa. We therefore compare the results using two unit cost estimates: a low-bound of US$ 2M per km and a high-bound of US$ 5.26M per km. Our estimates do not include maintenance costs.

In order to use the investments’ cost estimates in the context of our static model, we cannot simply use the total costs computed above and compare those to a single year of annual gain. Following de Soyres et al. (2020), we use an annualized
Supplemental Table 2. Correlation matrix of baseline age-standardized child development measures

|Rece|ptive vocabulary|Expressive|Adapt|ed MDAT|
|---|---|---|---|---|
|English<br>S|wahili<br>Luo|vocabulary|Fine motor|Language|
|Receptive|||||
|vocabulary|||||
|English<br>1|||||
|Swahili<br>0.358958|1||||
|Luo<br>0.364414|0.400669<br>1||||
|Expressive|||||
|vocabulary<br>0.43381|0.434502<br>0.490148|1|||
|adapted MDAT|||||
|Fine motor<br>0.323954|0.430528<br>0.490547|0.447659|1||
|adapted MDAT|||||
|language<br>0.421838|0.319749<br>0.476982|0.465957|0.555477|1|
|Notes. English, Swahili, and Luo rece<br>the British Picture Vocabulary Scale (<br>from the PPVT. The adapted MDAT a<br>Assessment Tool (MDAT). All_z-_scores|ptive vocabulary are_z-_scores, m<br>BPVS). Expressive vocabulary_z-_<br>re the_z-_scores from the Kenya<br>were age adjusted. All correla|easured using s<br>scores were mea<br>adaptation of th<br>tions are signific|eparate assess<br>sured using a<br>e Malawi Deve<br>ant at_p_< 0.00|ments based on<br>tool developed<br>lopmental<br>1.|
) 800<br>700<br>2000<br>600<br>1500 500<br>400<br>1000 300<br>200<br>500<br>100<br>0 0<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br>2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

## **D. Investment by 5,000 largest multinational**

Sources: Haver Analytics; Thomson ONE; UNCTAD (2016); World Integrated Trade Solution, World Bank; World Bank.

A. Denotes levels of real gross fixed investment as well as exports.

B. Weighted averages. Long‐term average for investment starts in 1991 due to data availability.

C. Capital goods trade and gross fixed capital formation expressed in current U.S. dollars. Trend line shows the pre‐crisis (2003‐ 08) trend of the average of capital goods trade.

D. Top 5,000 MNEs capital expenditures and acquisition outlays based on data from Thomson ONE.

35
which the Government of Pakistan is currently undertaking. To be sure that records can be matched, households could be required to report in person to the DISCO office with their national ID card and a recent electricity bill. They would identify themselves using the biometrics stored in the card, and the CNIC number would be used to look up their poverty score and make an instant determination of eligibility for assistance.<sup>15</sup> For renters, a tenant would be entitled to claim a subsidy even if the bill is in the name of a landlord, but the subsidy would be limited to one per family, with families encouraged to re‐register at their new address if they move. NADRA could provide the technical support to implement such a targeting system through DISCO offices or another government post. To limit subsidies to one per family, a central database of beneficiaries would need to be developed.

A simpler alternative, which might work well if the objective is to eliminate the richest households only, would be to apply exclusion filters to determine eligibility. For example, households with registered land, payers of income tax, holders of bank accounts, and so on, could be identified by CNIC and excluded from the benefit. Further analysis would be needed

> 15 NADRA has the capacity to access the NSER database in real time, and the CNIC is equipped with biometric identification capabilities.

18
economy-wide and cross-country surveys. Later, our statistical analysis estimates the broad descriptive strategies that emerge from responses to the question posed on different agents. We regard the agent-based and strategy-based approaches as true alternatives since they could not be combined in a single survey question of the type used in this paper, for reasons we clarify below.

The third element of the economics literature that underpins the conceptual basis of our survey question is the view that the pertinent agents can be identified and arranged on a spectrum, from the narrowest to the broadest. Although the language varies enormously across papers, this view is common, although often implicit and not comprehensive in scope (see, e.g., Dixit 2003, World Bank 2002, Greif 1997). To clarify this point and connect it to the structure of our survey question, we use Greif's (1997, 2005) formulations since the notion of a spectrum of agents is an important feature of his highly influential work.

Greif (1997) differentiates between first-, second-, and third-party enforcement (see also Dixit 2009), any combination of which can be relevant to any specific agreement. First-party enforcement reflects the personal characteristics of agents themselves, characteristics that are often relevant to all of the agent's actions, not only those undertaken in implementing a particular agreement. Problems arise less frequently when that agent is trustworthy, trusting, honest, and has absorbed common social norms. The rest of society can provide background to these purely firstparty motives, since many moral values arise as the result of social interactions (Granovetter, 1985).<sup>14</sup> However, these motives are purely first-party because enforcement occurs without any reference to second or third parties: guilt (as opposed to shame) does not imply direct social interaction (Greif 1994, 1997, Argyres et al. 2020). Problem-solving occurs simply because of "...

> 14 Poppo et al. (2016) describe this as follows: "relational trust arises
to the market or world in column 2. Interestingly, however, is the fact that also in this case the R&D variable in column 1 is positive as in the case of Malaysia. When applying a 1‐year lag this result becomes significant at the 10 percent level, which is also the case for the research collaboration variable in column 5.

# **4.2.3 China**

For China, we have different data which are not survey‐based. Data on innovation for China are generally extremely hard to obtain. We are therefore forced to use data from the Thomson Reuters data base that records information of private and public companies whose headquarters are in China. Only two variables are recorded that seem relevant for our research purpose which are the net intangible assets and R&D expenditure (both in USD). Years span a longer time period, which therefore covers our entire duration of the data policy index, namely from 2009‐2019.<sup>11</sup> Figure 11 provides an overview of the developments of our restrictiveness index for China. As one can see, little variation can be detected for the country given that the level of restrictiveness is extremely high throughout the entire period.

Figure 10 shows how the variable of R&D expenditures when divided by the number of employees for each firm is correlated with our ICT‐services input coefficient. (Note again that the Coke & Petroleum sector is excluded.) As one can see, the correlation is positive and tight and shows that sectors intensive in the use of ICT services as part of their overall input structure have higher per capita firm‐level expenditures on R&D. For the regressions, because we perform an LPM we 11 The Thomson Reuters data report data by fiscal year which may not entirely overlap with the calendar year of our restrictiveness index. Moreover, the database reports data calling each year “Fiscal Year 0”, Fiscal Year ‐ 1”, etc. In order to assign
= {1, 2, 3, 4, 5, 6, 7, 8, 9, 10} from higher retail prices following the introduction of a climate mitigation policy is calculated as: where g stands for the main categories of goods/services consumed by households in each decile, d is the share of decile d’s total consumption spent on good/service g at time t, and d is the relative price increase for good/service g caused by the climate mitigation policy. For example, for a tt tt ππ ρρ good with a budget share of 2 percent of total household consumption, expression (A) implies that a 5 percent increase in said good’s price will reduce decile d’s consumption by 0.1 percentage points.

We perform distributional analysis within CPAT for five South Asian economies (Bangladesh, India, Nepal, Pakistan, and Sri Lanka) using data on household budget shares obtained from the following Household Budget Surveys (HBSs): i) 2016-2017 Household Income and Expenditure Survey (HIES)

> 12 See, for example: https://www.imf.org/en/Topics/climate-change/energy-subsidies.

> 13 Detailed documentation on the CPAT methodology is available here: https://cpmodel.github.io/cpat_public/

> 14 Though parameterization is broadly consistent with the modeling literature, which (to varying degrees) incorporates these factors. See further discussion of these issues in IMF (2019b), Parry, Mylonas, and Vernon (2021) as well as Black et al. (forthcoming).

10
**<u>Table 7: Determinants of Learning, PISA 2006 - Extended Model</u>**

||Coef.|S.E.|
|---|---|---|
|School determines pedagogy|25.3|(7.99)*|
|School competes for students<sup>*</sup>|35.8|(6.72)*|
|School can fire teachers|14.1|(6.74)**|
|Achievement data used to evaluated teachers|-4.9|(4.91)|
|Public|40.5|(5.64)*|
|Student-teacher ratio|-0.3|(0.13)*|
|Adequate supply of teachers|11.3|(3.75)*|
|Math hours|12.5|(0.37)*|
|Teachers with certificate|-4.5|(4.11)|
|% of students repeating grade|-1.7|(0.39)*|
|Rural (<3,000)|-10.8|(3.45)**|
|9<sup>th</sup>Grade|15.6|(2.51)*|
|10<sup>th</sup>Grade|65.7|(4.03*|
|11th Grade|65.9|(6.25)*|
|Age|-8.2|(1.81)*|
|Female|-19.3|(2.30)*|
|Mother’s education - Primary|5.6|(2.37)*|
|Mother’s education - Lower secondary|9.1|(2.55)*|
|Mother’s education - Upper secondary|13.8