rafmacalaba/data-use-mentions-extended huggingface dataset:
percent of the population lost their home, temporary shelter was needed for no more than 5 percent of the population, less than 1 percent of residential estate was damaged and less than 0.5 percent was declared inhabitable. Analogously, the highest damage level assigned to towns outside of the epicenter corresponds to situations in which more than 10 percent of the population was injured or died, more than 20 percent lost their home, temporary shelter was required for more than 40 percent of the population and more than 30 percent of residential estate was declared damaged or inhabitable.

> 9Since the quake mostly affected two administrative regions, Campania and Basilicata, the government eventually decided to exempt all men in those regions irrespective of the actual damage suffered by their towns of residence, provided they belonged to one of the two regions.

> 10Specifically, for all our analyses we had access to the entire population census and extracted all records pertaining to the units of analysis. This allows us to avoid sampling issues related to the small size of the relevant units (town-cohort) that would arise if we used the 5% public use census sample. Because of privacy legislation, access to the universe of census records was possible only through the National Statistical Institute (Istat) safecenter, located in Rome, Italy.
datasets used in this paper. The inputs are far too numerous to mention but include a range of satellite inputs as well as gauge datasets. There are a wide range of outputs as well, including 2-meter air temperature and rainfall, available at sub-daily intervals and differentiated vertically. ERA5 is coarser spatial resolution than the global and regional merged rainfall datasets, but more detailed than MERRA-2.

The sheer number and complexity of outputs can be a deterrent to the use of weather variables from assimilation models. Uncertainty or lack of understanding about inaccuracies associated with individual output variables of assimilation models, compared to other types of models, is another reason to carefully consider their suitability for particular research (Parker, 2016). Nevertheless, reanalysis datasets are used in a broad range of applications and even outperform other gridded climate datasets in some settings (Zandler et al., 2020).<sup>6</sup> The second reanalysis dataset used in this analysis is MERRA-2, a product of NASA’s Goddard Earth Observing System, version 5 (GEOS-5) assimilation model. Specifically we make use of the variables T2MMEAN from the statD daily statistics collection, and PRECTOTLAND from the Land Surface Diagnostics collection.<sup>7</sup> Last, we consider an EO data product produced primarily from gauge data, using only spatial interpolation techniques to produce a continuous surface from observed measurements. The NOAA Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Daily Precipitation and Temperature datasets were created using all information sources available at CPC and undergoes extensive pre-processing and cleaning, including comparison with contemporaneous data from satellite and other sources (Chen et al., 2008).

NOAA’s Climate Prediction Center (CPC) Unified Gauge-based (CPC-U) datasets for daily temperature and precipitation do not incorporate EO data in the estimation of weather variables. Instead, an optimal interpolation (OI) technique is used on gauge data for precipitation, and Shepard’s algorithm for temperature. CPC-U provides systematic global datasets for validation and climate monitoring. GTS is
But the twin shocks of 2014 more than eroded five years’ worth of progress in poverty reduction, with the poverty rate climbing back to 22.5 percent in 2014 (Krishnan and Olivieri 2016). Three years after the onset of the crises, the living standards are still below the 2012 level, with the poverty rate in 2017-18 estimated at 20.0 percent (World Bank 2019).<sup>6</sup> The crises have especially affected internally displaced persons. The immediate impacts of the crises on IDPs were severe, when per capita _income_ in IDP households dropped by 62 percent, while per capita _consumption_ fell by 22 percent (Krishnan and Olivieri 2016).<sup>7</sup> By 2017-18, the displacement impact had only compounded, with IDPs almost three times as likely to be poor and be vulnerable to poverty as nonIDPs (World Bank 2019).

# 3. Data

This study uses data from the 2017-18 Rapid Welfare Monitoring Survey (SWIFT) conducted by the Central Statistical Organization (CSO) and Kurdistan Regional Statistics Organization (KRSO). The survey was conceived with the express purpose of tracking household well-being at a time when it was infeasible to implement full-scale household surveys in Iraq. Its objective was to provide interim estimates of welfare and well-being until another survey comparable in scope and coverage to the Integrated Household and Socioeconomic Survey (IHSES) could be fielded. Many questions in the 2017 questionnaire were taken from previous rounds of IHSES. A few new modules were added to the 2017 survey to understand how the crises might have left the population more vulnerable, the mechanisms through which people coped with shocks, and how they assessed their own life situation.

> 6 The 2012, 2014, and 2017-18 poverty estimates are not strictly comparable. The 2014 estimates are based on microsimulation. The 2017-18 survey collected consumption information using recall method (compared to diary method for 2012 and 2014), and the survey was not collected over a 12-month period to account for seasonal variation.

>
Guatemala City and in Accra, Ghana. The analysis for Guatemala is based on a random sample of mothers with preschool age children from one zone of Guatemala City. The analysis for Accra is based on a household survey.

The urban settings of Guatemala and Ghana are quite different with regard to the type of work that women typically do. In Guatemala City, the landscape is becoming increasingly urbanized and women often engage in formal work, such as factory work. About one-fifth of households are headed by single women, about half of which are poor or indigent. In Accra, 41
highly disruptive because there were few substitutes for Japanese suppliers. For Japanese firms operating in the US, the elasticity of substitution across material inputs was estimated to be only 0.2 in the short run.

Most of the existing literature on natural disasters focuses on how GVCs transmit shocks, domestically (Carvalho et al., 2016) or internationally (Boehm et al., 2019).<sup>3</sup> In this paper, we study instead the longer2 For an analysis of the long-term evolution of global value chains, see Constantinescu et al. (2020) and World Bank (2020).

3 An exception is Zhu et al. (2016) which uses Japanese firm-level data from 2010-2013, to show that the earthquake increased manufacturing offshoring from Japan among firms in the prefectures most affected by the disaster.

2
the region. Third, again due to cultural closeness, the nature of these variations is known across the region.<sup>9</sup>

**Additional measures** : We collected usual socio-economic and demographic information, including: age, gender, marital status and education. In addition, we collected data on selfreported risk preference and a short-form personality survey to ascertain GRIT among participants. Noting that a small number of participants did not answer all survey questions, we undertake a regression-based data interpolation process to complete the dataset.<sup>10</sup>

Table 1: Partner Assignment and Sample Sizes by Treatment and Community Status

|||H|ost|||Re|fugee||
|---|---|---|---|---|---|---|---|---|
||In|group|Ou|tgroup|Ing|roup|Out|group|
||T|C|T|C|T|C|T|C|
|Outreach/Baseline|219|48|203|48|147|72|147|49|
|Endline|179|34|222|37|148|45|133|51|

We present summary statistics of demographic data and other covariates for the baseline (Top) and endline (Bottom) for Jordan in Table 2 and for Lebanon in Table 3.

# [TABLES 2 AND 3 ABOUT HERE]

_Identification:_ The “fuzzy”, treatment intake is not random. As can be seen in Table 1, there are some elements of attrition from the sample. The sample decreases by about 10% from baseline to endline. We, therefore, first examine whether or not there is structure, both, to selection into the treatment group and attrition, which could undermine our econometric approach, where we rely on difference-in-difference estimators. Imbalance between treatment and control groups could undermine the
often economically significant workforce segment. The absence of their representation in the administrative tax data limits our ability to capture this sector's dynamics, challenges, and contributions. Information to identify informal sector workers is available in the EU SILC data; however, the small share of workers earning minimum wage in the household survey limits our analysis with the household survey data.

**The administrative tax data set includes relevant income variables, including gross salary, net salary, social security contributions, taxation base, bonuses, vouchers, and COVID-19 incentives.** Data on gross salaries is crucial for the identification of minimum-wage workers. Our data source also includes other employee characteristics such as region, age, number of days, and hours worked. However, we do not observe information on educational levels or occupations. We also observe the characteristics of the firms: sector of the economy, number of employees, total turnover, total salary fund, and profits. The construction sector is entitled to additional fiscal facilities; therefore, additional variables identify employees in the construction sector and

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# **4. Instrumental Variables**

Even though we concede the difficulty in accounting for endogeneity issues in our estimations given the data at hand, as a robustness we present instrumental variables estimations that may not be compelling, but do provide some insight that may be beneficial for future research. We use two instruments for police strength– a dummy variable indicating whether the party of the chief executive is right-wing or not and the stock of international migrants as a % of the population. The right-wing indicator is obtained from the database of political institutions (DPI) and is defined as conservative, religious or right-wing. On the one hand right, wing governments may prefer less government intervention and are thus more likely to decrease the police force. On the other hand, although right wing governments may be in favor of budget cuts, they may also prefer a larger crackdown of police on criminal activity, consequently increasing the police budget. A priori, it is difficult to see which effect will dominate. Furthermore, it has been shown that conservative voters typically perceive immigrants as illegal, and thus a larger migrant stock may push conservative governments to spend more on increasing the police force (Fennelly and Federico, 2008). Thus we expect the international migrant stock in the country to be positively correlated with the size of the police force.

The first-stage estimates are presented in column 2 of table 3, with a negative coefficient for right wing governments and a positive coefficient for migration stocks, both with a significance of at least 5%. This shows some support of a negative association of right wing governments with police size for the sample of countries in this study. We report the second-stage results using instrumental variables in table 3, column 1. The coefficient of police per 100,000 of population using instrumental variables retains the sign and significance of the base estimations. The magnitude increases in absolute terms from -0.0011 in the base estimations to -0.0025 in the estimations using the instrumental variables. As indicated in the
population in the study EAs. The husband quality super index is a standardized index of all other outcomes in this table (except HIV as it is defined on a smaller sample). All variables are constructed so that higher values are better, except for HIV. The cognitive test score is based on Raven’s Colored Progressive Matrices. The husband's sexual activity and marital fidelity index is constructed from three variables: number of sexual partners ever, number of sexual partners in the past 12 months and an indicator for concurrent multiple partners. Psychological distress is equal to one if the summed General Health Questionaire-12 score is equal to three or higher, and is zero otherwise. Additional details on the variables can be found in Appendix A and Appendix B. Baseline values of the following variables for the beneficiaries are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to husbands of respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). The husband quality super index regression also includes an indicator for whether any of the sub-components of the indicator are missing. Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
Policy Research Working Paper 8958

# **Abstract**

In recent years, there has been renewed interest in access to jobs in relation to transport connectivity. In Sub-Saharan Africa, about 14 million working age people are added to the labor market every year. Ensuring sustained access to jobs seems to be a prerequisite for inclusive and robust economic growth. The paper examines the impact of public transit connectivity on access to jobs, especially focusing on wages. Using data from Antananarivo, Madagascar, it is shown that the wages earned by commuters are systematically higher than the wages earned by those who decided not to commute and are self-employed or engaged with family businesses around their neighborhood. Proximity to public transport, especially taxi-be, is important to promote people’s access to jobs. It is also found that there is a substantial gender inequality in wages in the country: Women are more likely to use buses to commute, and yet, they earn less than men. In addition, the poor tend to benefit less from public transportation. Public bus services are affordable, however, the quality of the services may remain low.

This paper is a product of the Transport and Digital Development 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 author may be contacted at aiimi@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
and D, presents the results of a time-varying model, estimated using a Kalman filter, that captures the time variation in the sensitivity of changes in long-term inflation expectations to inflation shocks. The model is a version of model (1), but it includes time-varying coefficients: where the measures of expected and realized inflation are the same as those in model (1). The model is estimated for each of the 24 advanced economies and 23 EMDEs in the sample, using semiannual data for 1990H1-2018H1 and 1995H1-2018H1, respectively. The

> 1 The model follows Beechey, Johannsen, and Levin (2011); Gürkaynak, Levin, and Swanson (2010); and De Pooter et al. (2014).

> 2 Because there are no available data for most EMDEs in the early 1990s, the panel data set is unbalanced. The sample was split at 2004 to produce two samples of roughly equal length.

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- UNHCR. (2020c). ROHINGYA REFUGEE RESPONSE - BANGLADESH LPG DISTRIBUTION TRACKING DASHBOARD 2020, 2021.

- UNHCR. (2021). Joint Government of Bangladesh - UNHCR Population Factsheet, (June), 2020.

- Verme, P., & Schuettler, K. (2021). The impact of forced displacement on host communities: A review of the empirical literature in economics. _Journal of Development Economics_ , _150_ (January), 102606. https://doi.org/10.1016/j.jdeveco.2020.102606

- Vishwanath, T., Alik-lagrange, A., & Aghabarari, L. (2020). Highly vulnerable yet largely invisible Forcibly displaced in the COVID-19-induced recession, (1).

- Weier, J., & Herring, D. (2020). Measuring Vegetation (NDVI & EVI). Retrieved December 29, 2020, from https://earthobservatory.nasa.gov/features/MeasuringVegetation

- Wooldridge, J. M. (2010). _Econometric Analysis of Cross Section and Panel Data_ (2nd Editio). MIT Press.

- Yuen, J. Q., Fung, T., & Ziegler, A. D. (2017). Carbon stocks in bamboo ecosystems worldwide: Estimates and uncertainties. _Forest Ecology and Management_ , _393_ , 113–138. https://doi.org/10.1016/j.foreco.2017.01.017

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this instance) would lead to an overestimation of the performance of the control group, and we would thus tend to underestimate the difference between the treated and the control group. Ex-ante, it is difficult to know whether and how survivorship bias might affect our results. However, our results suggest that the potential for an upward bias in our estimates due to survivorship bias is limited. First, our results suggest that the more established firms are the ones that raise capital and that the ones that are more likely to de-list or die belong to our control group. Second, we obtained information on de-listings during our sample period from Bloomberg. Only a small fraction of the de-listed firms (about 10 percent) actually raised capital through bonds or equity, and 90 percent of those firms that raised capital are covered in our SDC-Orbis matched data set. The de-listed firms that raised funds in capital markets but that are not included in our data set represent in fact around 3 percent of the firms that raised capital and are included in our data set. Moreover, our SDC-Orbis matched data set covers only 23 percent of the de-listed firms that did not raise capital. The omission of these firms suggests that we might be overestimating the performance of our control group and therefore adding a downward bias to our results. Third, we obtained similar results using the Worldscope data, as further described in Section 4.3. Noticeably the post-issuance growth rates are positive and statistically significant, which suggests that our findings of a significant difference between the treated and the control group represent a lower bound of the effects of capital raising activity on firm performance.

10
each land cover type was, a global Moran’s I statistic was generated for Industrial/Commercial, Residential Regular, and Residential Irregular. Moran’s I measures the spatial autocorrelation of features based on their location, in other words, the degree to which spatial features are similar to their neighbors. Cities where features are highly spatially correlated would have a Moran’s I statistic closer to 1. A value closer to zero indicates little or no clustering.

<mark>Figure X</mark> shows the Moran’s I value for each built‐up land cover class in Nairobi, Dar es Salaam, Kigali, and Addis Ababa. Generally speaking from a land planning perspective, a high Moran’s I value is seen as advantageous, as it suggests there are significant concentrations of land cover. A low Moran’s I, in contrast, is often observed in decentralized, sprawling urban areas, as they experience strip and more discontinuous development.<sup>60</sup>

> 59 Galster, George, Hanson, Royce, Ratcliffe, Michael, Wolman, Harold, Coleman, Stephen, and Freihage, Jason. 2001. Wrestling Sprawl to the Ground: Defining and measuring an elusive concept. Housing Policy Debate, 12 (4), 681‐717.

> 60 Tsai, Yu‐Hsin. 2005. Quantifying Urban Form: Compactness versus ‘Sprawl’. Urban Studies, 42 (1): 141‐161.

33
city level to the subregion level using information provided by the Ministry of Labor on all the cities that belong to each subdelegacia. Finally, we also have information on the number of regional offices in each subregion.

Data for population in 2002 and for the total number of plants in 2002 is collected by the National Statistics Institute (IBGE) at the city level. City level data for GDP (1985), total population (1991) average years of schooling in the population with more than 25

> 48This is the case of the inspections related with formal labor contracts. However, for some speci fi c tasks, like anti slavery inspections, inspectors from different subdelegacias can gather to work in a team. 49At the end of 2002, there was a total of 2341 labor inspectors in Brazil which were distributed as follows for the federal regions that we cover in the Investment Climate Survey: 35 Amazonas, 103 Bahia, 129 Ceara, 71 Goias, 45 Maranhao, 205 Minas Gerais, 34 Mato Grosso, 99 Parana, 42 Paraiba 294 Rio de Janeiro, 137 Rio Grande do Sul , 73 Santa Catarina and 489 Sao Paulo.

28
The key objective of this paper is to systematically explore the robustness of the shared prosperity indicator to different methodological choices in the estimation process such as: grouped versus microdata, nominal welfare aggregate versus adjustments for spatial price variation, and different treatment of income with negative and zero values. The analysis is conducted using household survey data used for estimating extreme poverty and shared prosperity indices. For most tests, we randomly selected at least two countries from each of the six regions to reflect global representation. In addition to robustness tests, we briefly present the most recent results and findings from the Global Database of Shared Prosperity (GDSP) circa 2007‐2012 to provide context and familiarize the readers with the most recent trends in shared prosperity in the world.

The paper continues as follows. Section 2 provides a brief overview of the GDSP. In section 3, we focus on special technical issues in the construction of the shared prosperity index that may affect the obtained results and empirically test them. Section 4 concludes.

# **2. AN OVERVIEW OF THE GLOBAL DATABASE OF SHARED PROSPERITY**

This section provides a brief overview of data and trends in the Global Database of Shared Prosperity (GDSP). This is merely a complement to recent publications on shared prosperity such as Cruz et al. (2015), the World Bank Global Monitoring Report (2015b), and the World Bank Policy Research Report (2015a), which provide a more exhaustive analysis and results on shared prosperity. The GDSP is a collection of the shared prosperity index across the world, where the index is defined as the average annualized growth rate of consumption or income among the bottom 40 percent of the population. The latest version of the GDSP, published in October 2015,<sup>2</sup> has the shared prosperity index for 94 countries, up from 72 in the version published in September 2014.<sup>3</sup> In the latest GDSP, the shared prosperity index is calculated using household surveys circa 2007–