rafmacalaba/data-use-mentions-extended huggingface dataset:
mini-mills at the expense of a number of US vertically-integrated firms.

This paper also relates to other studies of more general questions regarding import protection and the US steel industry. Blonigen et al. (2013), for example, use plant-level data from the US Census Bureau that covered 1967-2002 and provide evidence that quota-based protection has market power effects, with respect to integrated and mini-mill steel plants, whereas tariff-based import protection does not. Furthermore, Blonigen and Wilson (2010) use product- and foreign country-level data to document evidence of the impact of both cyclical and structural excess capacity (which is associated with foreign subsidies) on steel exports to the US that covered 1979-2002.

Finally, research that improves the understanding of the role and implications of safeguards use is increasingly important given that application of this particular TTB has spread to so many countries. Miranda et al. (1998) and Prusa (2001) were among the first to document the proliferation in the adoption and use of _antidumping_ across high-income and emerging economies worldwide. More recently, Bown

> 6 Bown and Crowley (2011, in press) provide evidence of this not only for the United States, but also in a sample of data at the quarterly frequency that includes four other high-income economies over the period 1988-2010.

5
measures for some common limited dependent variable models. Journal of Economic Surveys 10, 241— 259.

Winkelmann, L. and R. Winkelmann (1998). Why are the unemployed so unhappy? Evidence from panel data. Economica 65, 1-15. World Bank (2001). World Development Report 2000/2001. Washington, DC: World Bank.

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a New Nation. WashWorld Bank (2002b). Empowerment and Poverty Reduction - A Sourcebook. Washington, DC: World Bank.

World Bank (2003). Timor-Leste - Poverty in a New Nation: Analysis for Action. Washington, DC: World Bank.
estimate the growth and size. regressions using the World Bank data in constant dollars.

In particular, we focus on the measure of government size and the relative price of govemrnment consumption. Finally, we address the inclusion of regional dummy variables in the regression.

Individual data points with large residuals or high leverage - outliers - may or may **43** not exert disproportionate influence on our estimates . To test for outliers, we reestimated our three sets of regressions - OLS and IV government size, growth and wellbeing regressions - after removing data points identified as having a large residual and/or high leverage in each regression. Anywhere from five to eleven data points were identified as outliers in each regression. The results after removing the outliers in all our - regression sets were not significantly different from our estimations using the full sample.

We delve fuirther in our outlier analysis to check for the robustness of the government size and growth relationship. In the growth regressions, there was a negative and significant relationship between government size and growth. To test whether this result is due to a few observations with either 1) a large government size and low or negative growth or 2) a small government size and high growth rates we re-estimated the two sets of growth regressions after removing outliers in government size and per capita GDP growth rates 4. In all cases, the relationship remained negative and significant and the other parameters in the regression were not significantly affected.

> 3 See Belsley, Kuh and Welsch (1980).

> 44 Four new sets of regressions were re-estimated. First, observations with large government size - one or one and a half standard deviations greater than the sample mean of government size - were removed. Second, observations with small government size - one or one and a half standard deviations less than the sample mean of government size - were removed. Third, observations with high per capita GDP

48
for Bangladesh;<sup>15</sup> ii) 2018 Consumer Pyramids Household Survey (CMIE) for India;<sup>16</sup> iii) 2010-2011 Living Standards Survey (LSS) for Nepal;<sup>17</sup> iv) 2018-2019 Household Integrated Economic Survey (HIES) for Pakistan;<sup>18</sup> and v) 2016 Household Income and Expenditure Survey (HIES) for Sri Lanka.<sup>19</sup> After the data is aggregated into eight (8) energy and fourteen (14) non-energy, CPAT-compatible good/service categories,<sup>20</sup> households are grouped into population-weighted, per-capita consumption deciles, and budget shares are computed by dividing total consumption expenditure on each CPAT good/service category by each household’s total consumption expenditure across all goods/services.

Sector-specific percent price increases from the simulated carbon tax are obtained directly from CPAT for each fossil fuel (Table A.2). The fossil fuel-specific price changes and budget shares can be used to estimate the loss in consumption from price increases of fossil fuels (e.g., electricity, gasoline, diesel, natural gas, etc.) following the introduction of a carbon tax (that is, the ‘direct’ incidence effect). Price increases for other consumer goods (due to higher energy and fossil fuel input prices for each sector) are calculated assuming full passthrough of producer cost increases onto consumer prices domestically (that is, perfectly elastic supply curves).<sup>21</sup> Non-fuel sector price increases are obtained as the sum-product of: i) each sector’s fossil fuel intensity; and ii) the price increase of each fossil fuel induced by the climate mitigation policy. Sectoral fossil fuel intensities are obtained from direct requirements matrices of input-output tables. These tables are sourced from the GTAP-10 database,<sup>22</sup> which includes 2014 data for 65 sectors<sup
In the empirical section of this paper, we take these predictions to the data and additionally study the effect of performance incentives on loan officers perception of credit risk. We find that volume incentives lead loan officers to inflate internal ratings, even when these ratings are not tied to the loan officer’s evaluation or incentive scheme, while high-powered incentive contracts lead to a more accurate assessment of credit risk and more profitable lending decisions.

# **3 Experimental Incentive Schemes**

We test the predictions of the model (see appendix) using a framed field experiment, in which commercial bank loan officers evaluate loan applications from the client database of a large Indian bank. To test the impact of performance contracts on loan officer behavior, we exogenously vary the incentive power, the time horizon over which performance incentives are paid, and the degree of limited liability enjoyed by a loan officer. We vary the power of the incentive contract by assigning loan officers to contracts that specify three conditional payments: a payment _wP_ made when a loan is approved and performs, a payment _wD_ , made when a loan is approved and defaults and a payment _<u>w</u>_ that is made when a loan is declined.

Because the outcome of a loan is only observed with some delay, performance incentives, in practice, must be paid with a lag. In our setting, under the non-deferred payment scheme, incentives were paid immediately following an experimental session. In the deferred compensation scheme, incentive payments were delayed three months, with the loan officers given a choice of returning to collect the payment, or receiving a check in the mail. This allows for two contrasts: from the policy perspective, comparing deferred-compensation, performance based pay to immediate compensation based on origination.

Finally, we experimentally relax loan officers’ limited liability constraint, by providing an initial endowment that the participant can lose if he approves a number of non-performing 9
- **Linden, Leigh and Jonah E. Rockoff** , “Estimates of the Impact of Crime Risk on Property Values from Megan’s Laws,” _American Economic Review_ , June 2008, _98_ (3), 1103–27.

- **Livingston, Beth** , “Cornell International Survey on Street Harassment,” Technical Report, Cornell Univserity 2015.

- **Moovit** , “Facts and usage statistics about public transit in Rio de Janeiro Region, Brazil,” Technical Report, Moovit 2018.

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- and informal jobs?,” _Journal of Transport Geography_ , 2016, _51_ , 59 69.

- **Muralidharan, Karthik and Nishith Prakash** , “Cycling to school: Increasing secondary school enrollment for girls in India,” _American Economic Journal: Applied Economics_ , 2017, _9_ (3), 321–350.

- **Nosek, Brian A., Frederick L. Smyth, N. Sriram, Nicole M. Lindner, Thierry Devos, Alfonso Ayala, Yoav Bar-Anan, Robin Bergh, Huajian Cai, Karen Gonsalkorale, Selin Kesebir, Norbert Maliszewski, F ́elix Neto, Eero Olli, Jaihyun Park, Konrad Schnabel, Kimihiro Shiomura, Bogdan Tudor Tulbure, Reinout W. Wiers, M ́onika Somogyi, Nazar Akrami, Bo Ekehammar, Michelangelo Vianello, Mahzarin R. Banaji, and Anthony G. Greenwald** , “National differences in gender–science stereotypes predict national sex differences in science and math achievement,” _Proceedings of the National Academy of Sciences_ , 2009, _106_ (26), 10593–10597.

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Policy Research Working Paper 10085

# **Abstract**

Social sustainability is often poorly understood and vaguely defined, despite growing appreciation of its relevance as a concept. This paper advances the empirical understanding of social sustainability by constructing a global database of 71 indicators across 193 countries and 37 territories between 2016 and 2020. The indicators are flexibly clustered around four dimensions—social inclusion, resilience, social cohesion, and process legitimacy—for which measurement indices are constructed. A simple empirical analysis using the database confirms that social sustainability is positively and strongly associated with per capita income, negatively and strongly associated with poverty, and negatively but weakly associated with income inequality. Much remains to be analyzed to understand the interactions between dimensions, but the results underscore that social sustainability matters not only in itself, but also to reduce poverty. Furthermore, extending access to markets, basic public services, and social assistance needs to be complemented with strengthening process legitimacy and social cohesion if inequality is to be reduced.

This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jcuesta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._
no statistically significant differences in the pre-shock averages and trends of the outcomes of interest. We conduct an additional pre-trends test using the loan-level data for energy sector borrowers prior to August 2014. The results, displayed in Table A4, also corroborate that there are no statistically significant differences in the loan terms and trends of banks with varying degrees of exposure to the energy sector in the months prior to the shock.

Finally, we test for the existence of nonlinear pre-trends across banks with different exposures to the energy sector prior to the shock. The specification, presented in equation 4, restricts the loan-level data to loans from firms in the energy sector.

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Table 3: Household Variables and Definitions

|**_Panel A_**_: Outcome Variab_|_les_|
|---|---|
|Yield<br>Value|Output in kilograms per hectare for the primary cereal crop in<br>each country data set<br>Output in real USD per hectare for all seasonal farm crop produc-<br>tion|
|**_Panel B_**_: Input Variables_||
|Labor use rate|Number of days per hectare (combined or divided by age and<br>gender)|
|Fertilizer application rate|Kilograms per hectare (combined or divided into Urea and DAP)|
|Seed application rate|Value in USD per hectare|
|Pesticide use|Equal to 1 if yes, 0 if no|
|Herbicide use|Equal to 1 if yes, 0 if no|
|Irrigation use|Equal to 1 if yes, 0 if no|

_Note_ : The table presents definitions for included outcome and input variables from LSMS sources defined in Table 2.

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- Income: sum of labor income and profits in past 12 months. Labor income is given by the answers to question tb35a from Section TB of Book 3A. It measures the sum of after-tax income from wage and salary, piecework, commissions and tips, extra hours, meals, housing, transportation, medical benefits, and others. Business income is either net income, or gross income if net income is missing.

- Education: from the Household Roster in Book C, question ls14 and ls15.

# First follow-up <u>(2005/06):</u>

- Migration: defined using several sources. First (i), we consider information from the household member’s roster, Section LS of Book C. For all individuals who left the household between 2001 and 2006, question ls19e (“Where does (...) live now?”) details if they live in the same locality, same municipality, same state, other state, or another country. According to the answer to this question, we define an indicator for migration that is equal to one if any type of migration has occurred. Second (ii), for individuals still living in the household, we verify whether the current residence of the household has changed with respect to baseline. Third (iii), we use information from the Permanent Migration module (Section MG of Book 3A). We know a respondent has migrated if she reports moving outside of the locality/neighborhood where she used to live in 2001 (question mg08a) and still live at the new location (question mg14). Last (iv), we add information on migrants to the US using the ”emigus” module.

# Second follow-up <u>(2009/12):</u>

- Migration: computed using several sources. First, individuals who moved out of their household at first follow-up are assumed to be currently living outside the household (and have positive migration status) unless they report having
differences, though, in the way hours are measured in each data set. The RAIS data reports contractual hours, while the BPS records actual hours worked during the week of reference.

Hourly wages in each data set are constructed using the compensation and hours worked in that data set. Note that none of our measures of hourly wages is, in principle, exempted from measurement errors. Since individuals’ working time arrangements are not time invariant, even if they stay in the same job, some of the observed monthly wage changes will actually reflect changes in labor input. Computing hourly wages from RAIS will only partially mitigate this problem as long as changes in hours are reflected in the contract. The hourly wage measure in Uruguay might be more accurate, but variations in weekly hours of work throughout the month will result in measurement errors. In conclusion, even if our two data sources are less likely to be subject to measurement error than survey data, we cannot rule out the possibility of error. Hence, the model described in section 3 allows for the presence of measurement error in the estimation.

For both Brazil and Uruguay, we use the same sample selection criteria. First, we restrict our sample to full-time employees working in the private sector. Second, whenever individuals have more than one job, we always keep the observation with the highest paying job. Finally, our benchmark analysis of individual wage growth, which we define as the difference in log wages over a 12-month interval, is limited to job stayers, who are characterized by working in the same establishment in the same month in two adjacent years. These data restrictions leave 7 years of wage change observations in Brazil, containing between 857,589 and 1,200,120 individual cases, and 8 years in Uruguay, including between 68,494 and 97,721 individual wage changes.

# 2.2 Descriptive Evidence

In order to get an initial impression of the different types of rigidities that prevail under different inflation scenarios, we look in this section at
health and those relating to human capital accumulation. We registered a set of primary and secondary outcomes within each of these families of outcomes (Tables 1 and 2, respectively).<sup>15</sup> Table 1 shows the mental health outcomes. The primary mental health outcomes consist of two binary indicators: (i) having a Patient Health Questionnaire 8 (PHQ-8) score _≤_ 4, which is indicative of showing no or minimal depression (Kroenke et al., 2009); and (ii) having a General Health Questionnaire 12 (GHQ-12) score _<_ 3, which indicates one is not

> 14The study design deviates from a classical 2x2 factorial design as there is no ‘cash only’ treatment arm. While a 2x2 factorial design would have allowed estimating the marginal impact of therapy over cash alone, a cash-only arm was not included – given that cash transfers were not something BRAC Uganda was considering to add to their adolescent programming.

> 15The JDE Registered Report can be accessed at `https://afosterri.org/jdepreresults/ sample-page/` .

10
<!-- Start of picture text --> 40 Low-Income Countries 28 High-Income Countries<br>50 50<br>a a<br>nq 40 a 40<br>©) a<br>a A<br>lo)<br>&a<br>30 30<br>3<br>% &$ a4 a, a%q a<br>x coal<br>aa 207 4 afc 4 20<br>cy<br>» =) oO<br>=fo) 10 e A 10<br>G,<br>off<br>0 4 African O Non-African 0<br>0 5 10 15 20 25 0 5 10 15 20 25<br>Trade Tax Revenue (% of GDP) Trade Tax Revenue (% of GDP)<br>Source:Note 1: BaunsgaardEach dot is a andfive-yearly Keen (2010)average between 1982 and 2006<br><!-- End of picture text -->
investigated by checking whether the included projects are also implemented in locations with different precision categories (see appendix). Reassuringly, more than 60 percent of the projects retained in the sample are also assigned to at least one location at the ADM1 (precision 4) or ADM2 (precision 3) level. The projects in the sample are also compared to projects with at least one location at precision 3 or 4 but without any location at precision 1 or 2, in terms of observable characteristics (see appendix). The projects in the two groups are broadly similar, although there are some differences in terms of sectoral composition; the projects in the sample have, on average, smaller shares in agriculture, health, and education, but larger shares in energy, transport, and water sanitation. This suggests that the sample of precise locations has (unsurprisingly) a disproportionate share of projects dedicated to the building of facilities and infrastructure.

# **2.2 Location of Mission Stations**

The analysis relies on two different historical sources for information on the locations of Christian mission stations. The preferred source is _Geography and Atlas of Christian missions_ by Beach (1903), digitized by Cagé and Rueda (2016). It includes the locations of Protestant mission stations in Africa as of 1903, together with information on the investment of each mission (school, dispensary, hospital, etc.). An alternative source is _Ethnographic Survey of Africa: Showing the Tribes and Languages_ by Roome (1924), digitized by Nunn (2010). This source reports locations of both Protestant and Catholic foreign mission stations in Africa as of 1924.

As is apparent from fig. 1, the two sources do not overlap perfectly. The cell-level correlation between Protestant missions from Beach (1903) and Roome (1924) is 0.31. One reason for the low correlation could be that missionaries started penetrating the African inland only after first settling on the coast. The 1924 data indeed show a higher concentration of mission stations farther from the
of income particularly in the regions of Afar, Borena and Somali. At present, the LEAP tool is centered mainly on developing beneficiary numbers for the crop seasons _meher_ and _belg_ . By integrating a pastoral index into the tool, LEAP could additionally generate beneficiary estimates for pastoral areas. GoE and WFP are currently in the process of extending the LEAP tool to integrate pastoral information.

- (iv) **_Flood monitoring:_** Additionally to drought, flood risks represent a major hazard in Ethiopia, with floods leading to average annual losses of US$ 200 million<sup>47</sup> . It is therefore envisaged that the LEAP tool could be enhanced by integrating a flood index, such that it could serve as a flood monitoring tool to generate beneficiary estimates for floods.

# IV.2 The LIAS tool

## _IV.2.1  The LIAS tool methodology_

The Livelihood Impact Analysis Sheet (LIAS) is an early warning tool that estimates beneficiary numbers based on the Household Economy Approach (HEA). LIAS was developed and collected initially through the Livelihood Integration Unit (LIU), which was established in 2006 as a USAID-funded project based in the former DPPA. The LIU was in operation until 2015, and was housed in the EWRD as part of DRMFSS. The objective of the LIU was to improve the accuracy and objectiveness of seasonal needs assessments in Ethiopia, by integrating an understanding of local livelihoods into the needs assessment process<sup>48</sup> .

> 46 At the micro-level, it is possible for LEAP data to be used to inform insurance. This would, however, require a detailed local-level index design process.

> 47 World Bank (2014b).

> 48 FDRE Disaster Risk Management & Food Security Sector, MoARD and USAID: The Livelihoods Integration Unit Uses of the Baseline Information and Analysis.

13
respectively. The mean number of new leases per village concluded over the entire period was close to 170 and 67 for commercial and personal land, respectively.

For the subset of transactions (12% of commercial land) for which we obtained registry information, mean field area and tenant size are, with 95 and 515 hectares, respectively, are more than a third and nearly half above the means for the sample with tenant characteristics reported in column 4. For this sample, the tenant’s share of village land (36%) and mean and median prices (US$ 138 and US$ 116) are higher and the share of unmapped land lower (3.9%) than in the sample with tenant characteristics. Registry data show that, although use rights to some 80% of field area are registered by the field’s largest user, the mean number of users with registered rights in a field is 2.5 with 10% of fields having 5 or more registered users.

Moreover, 81% of fields include land (8.6% of cultivated field area on average) that, albeit mapped in the cadaster, is not registered. For 16% of cases, the area involved is minuscule (<1% of area) whereas for 47% and 18%, it is either non-negligible (1%-10% of area) or significant (>10% of area). Also, 29% of fields, while having use rights to most of their area registered by a legal entity, include land that is registered in the name of the owner only, possibly indicating presence of an informal lease contract. In 6%, 20%, and 3% of cases, the share of owner-registered area is minuscule (<1%), not negligible (1%-10%), and significant (>10%), respectively.

_Sales markets:_ Parcels transacted in sales markets are slightly larger than those in the lease market, with a mean