rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 4.** Sensitivity of the OLS regression models using different expenditure measures.

_Note_ : Own calculatios based on ProGress and JD-HV database. This table shows, in the vertical axis, the estimated coefficients and confidence intervals associated to the interaction Female× Wave 2017 −18, following the model presented in Table 4 and Equation 1. The x axis shows the values of θ used to compute the dependent variable in each case. The vertical gray line points at the estimate shown in Table 4, where θ= 0.5 Contrary to the findings of the unconditional distributions, we find that the size of the estimates does not vary in the presence of different economies of scale adjustments. If anything, a higher θ (i.e., a measure of spending closer to unadjusted per capita spending), makes the differences between men and women over time appear smaller. However, it is worth noting that the differences are so subtle that they are non-significant across models. This suggests that once the demographic characteristics of the household are controlled for, the choice of expenditure measure used is of little relevance for statistical inference on the average household. This result is consistent with the fact that displacement situations affect all types of households, many of which are in new family arrangements. Once we control for the different type of family arrangements, economies of scale do not capture differences in expenditure better than the headcount measure.

25
# The Mismeasure of Weather: Using Remotely Sensed Earth Observation Data in Economic Contexts<sup>∗</sup>

Anna Josephson<sup>1</sup> , Jeffrey D. Michler<sup>1</sup> , Talip Kilic<sup>2</sup> , and Siobhan Murray<sup>2</sup>

> 1 _Department of Agricultural and Resource Economics, University of Arizona_

> 2 _Development Data Group, World Bank_

## _JEL Classification_ : C38, C81, D83, O13, Q12

_Keywords_ : Remote Sensing Data, Socioeconomic Data, Measurement Error, Weather, Sub-Saharan Africa

> ∗A pre-analysis plan for this research has been filed with Open Science Framework (OSF): https://osf.io/8hnz5/. We gratefully acknowledge funding from the World Bank Living Standards Measurement Study (LSMS) and the Knowledge for Change Program (KCP). This paper has been shaped by conversations with Marc Bellemare, Leah Bevis, as well as seminar participants at the Methods and Measurement Conference 2021, the AAEA annual meetings in Chicago and Atlanta, the 31<sup>_st_</sup> triennial ICAE conference, and participants in presentations at Arizona State University, the University of Minnesota, the World Bank, and Virginia Tech. We are especially grateful to Reece Branham, Alison Conley, Kieran Douglas, Rodrigo Guerra-Su, Emil Kee-Tui, Brian McGreal, and Jacob Taylor for their diligent work as research assistants and to Oscar Barriga Cabanillas and Aleksandr Michuda for early help in developing the Stata `wxsum` package. Thanks also to Laura Josephson for logistical support. We are solely responsible for any errors or misunderstandings.
depend on the fiscal position such that a given level of the fiscal position maps out to a set of model coeffi cients. For presenting the results, we evaluate model coeffi cients at specific values of the fiscal position which are taken to be the percentiles within the sample. Confidence bands are calculated by bootstrapping over 300 iterations. We report median estimates, along with the 16 - 84 percent bands.

## 2.3 Database

Our main database comprises an unbalanced panel that covers 34 countries (19 advanced and 15 developing), at the quarterly frequency over the period 1980:1 — 2014:1.<sup>13</sup> Real government consumption and real GDP are based on the quarterly database in Ilzetzki, Mendoza, and Vegh (2013) which are extended until 2014:1 by splicing from the OECD and Haver Analytics. Real effective exchange rates are from the narrow (wherever available) and broad indices of the BIS, and current account from the IMF’s WEO database. The short-term rate used for discounting the multiplier is drawn mainly from the IMF’s IFS database. For the robustness results, we augment this database to include quarterly real private consumption and private investment series. These are drawn from the OECD, Haver Analytics, and Eurostat. Additional details on the sources and definitions of all of these variables are provided in Table A2 in the Appendix.

The government consumption and GDP series (as well as private consumption and private investment) are converted into logarithmic form, and detrended using a linear quadratic trend as in Ilzetzki, Mendoza, and Vegh (2013). The exchange rate is transformed into quarter-to-quarter growth rates, and the current account series is seasonally-adjusted using the X11 routine. All these series are detrended and demeaned on a country-by-country basis, which effectively controls for country fixed effects in the regressions.

We also employ another database that is an unbalanced panel with the same cross sectional and time series coverage as before but at the annual frequency. This includes the conditioning variables that
# _2.4. Institutional Investors Data_

To analyze what types of investors are behind the purchases of East Asian corporate bonds, we use data on foreign and domestic investors for 2008-16. The data come from different sources and with different breakdowns. Regarding foreign investors, we have data on East Asian corporate bond holdings by all types of investors from the United States (U.S. investors) as well as mutual funds domiciled outside East Asia (including mutual funds in the United States). The former come from the U.S. Treasury International Capital (TIC) System, whereas the latter come from information collected by Calomiris et al. (2019). East Asian corporate bonds are those issued by a firm located in East Asia either in an East Asian market or an international market.

Regarding domestic investors, we collect data on security holdings for pension and insurance as well as mutual funds. Data on investments by pension and insurance funds cover Hong Kong SAR, Korea, Malaysia, and Taiwan and come from the financial supervisory authorities, insurance associations, and pension fund websites. For these investors, we typically have data on their total security holdings, which include public bonds, corporate bonds, and equity from issuers located in East Asia and abroad. These data distinguish whether a pension/insurance fund holds securities issued in their own local capital market or abroad. For Hong Kong SAR, we have data on pension funds only. For pension funds in Korea, we have additional data on corporate bond holdings. For East Asian mutual funds, we have data on their East Asian corporate bond holdings (issued in East Asian and international markets).

# **3. Corporate Bond Financing: Domestic vs. International Markets**

We document the role of domestic and international bonds markets in East Asia in terms of issuance activity and types of firms using each market, focusing on three periods, 1990-98, 1999-2007, and 2008-16. This allows us to compare trends before and after two important milestones: (i
This analysis is not without limitations. The list of data sets under consideration is not exhaustive and might have missed data sets due to questionnaire unavailability or unavailable translations. A similar review is needed for data sets focused on children and should be extended to high income countries.

Overall, the questions on disability found in surveys/census in the past decade are insufficient to ensure the global and systematic monitoring of progress called for by the 2030 Agenda for Sustainable Development to increase the availability of disaggregated data by disability (target 17.18). Although most LMICs have ratified the CRPD, there is much work to be done to fulfill Article 31 of the CRPD that States Parties undertake to collect appropriate information <mark>, including statistical and research data</mark> ” (United Nations 2006).

There is, however, progress compared to the situation up to the 1990s or early 2000s, when there was no internationally comparable disability data at all, let alone any internationally tested and comparable instruments. There are now some data that can be analyzed to inform disability policies at both national and international levels, notably with the surveys and censuses with functional difficulty questions listed in Tables 1 and 2. More research is needed to analyze such data sets.

At the same time, data collection needs on disability remain staggering: the results above show that there is still more work to do in fulfilling the data revolution that is needed to monitor the implementation of the 2030 Agenda for Sustainable Development as well as the CRPD.

National statistics agencies should use internationally tested functional difficulty questions in their population surveys and censuses (United Nations 2015). International agencies and donors have an important role to play in encouraging the collection of internationally comparable disability data. Functional difficulty questions should be included as part of core questionnaires, not as optional and changeable modules as currently in the DHS (Casebolt 2020). Two widely available data sources in LMICs, the global FINDEX and the LSMS of the World Bank, do not generally have questions on disability and
Policy Research Working Paper 11071

# **Abstract**

This paper explores the effect of road rehabilitation on violent conflict using a novel, rich dataset of road rehabilitation projects in the Democratic Republic of Congo. The country received massive external investments in transport infrastructure rehabilitation under conditions of endemic conflict, often with the explicit objective of supporting peacebuilding objectives. The paper finds that investments in road rehabilitation deter violence, which decreases significantly by around 5 to 10 percentage points after the completion of road rehabilitation. However, another significant finding, based on large-scale machine learning analysis of remote sensing data of road quality over time, is that the peace dividend of infrastructure investments is perishable: violence increases again as roads progressively deteriorate. Improved durability and systematic maintenance of roads are thus necessary to extend the “peace dividend” of road investments.

This paper is a product of the Chief Economist Office, Infrastructure Vertical. 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 jsteinbuks@worldbank.org and mlebrand@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._ Produced by the Research Support Team
# **4.1 Group Selection on Social Media**

To examine refugee displacement and return on these platforms, we created parallel processes to identify, collect data from, and analyze accounts, channels, and groups posting public messages about the Syrian conflict on Twitter, Telegram, and Facebook. While this process aimed to limit selection effects to the extent possible, the different natures of the platforms ultimately mean that our samples comprise different populations, and the differences between search functionalities meant there was no single starting point. Underscoring the replicability of our process — and the process’s resemblance to other selection processes common in the use of social media data — we present source specific models in Appendix 9 to show the selection effects inherent in the study of social media users on any singular platform.

We limited our analysis to Arabic language, the predominant language of use among all actors in the conflict.<sup>5</sup> Our final dataset contains messages in Arabic from 657 public channels and groups on Telegram, 2,106 public Twitter accounts, and 2,124 public Facebook groups and pages.

# **4.2 Identifying Location Specific Messages**

The first step of our process was attributing message sets to certain locations. Twitter, Telegram and Facebook’s functionalities make it difficult to verify the location that users post from in all but a few cases. To understand location specific discussion we searched all of the collected messages for 2004 Syrian census locations using string matching on the names in Arabic. We augmented this dataset with locations from the GeoNames API<sup>6</sup> , which includes common misspellings or dialectic spellings of locations.<sup>7</sup> The `Location Mentions` dataset, comprised of the location mentioning messages associated with the REACH data, includes messages from 770 unique locations, 54 of which are mentioned more than 10,000 times, 229 of which have returnees, and 129 of which have IDPs. A heat map showing the distribution
# **4.1. Data and Estimation**

There are several empirical studies that estimate the causes of financial crises using macro and financial variables. Kaminsky and Reinhart (1998) investigate a data set of 20 countries between 1970 and mid 1995 and show that the increase in growth domestic credit two years ago, which they argue is a proxy for financial liberalization, can help explain banking crises. Caprio (1999) argues that "premature liberalization could be cited in virtually all cases" of financial crises. Demirguc Kunt and Deteragiache (1 998b) estimate the probability of a banking crisis using a data set of 53 countries between 1980 and 1995. They identify several macro, financial, and institutional variables that explain the probability of a crisis. They argue that the banking crises are more likely to occur in liberalized financial system. While these findings are consistent with our model, the model's predictions are more settled. The model's two econometric predictions are as following. First, banking crises are more likely to occur in the period following financial liberalization, and not in any liberalized financial system as argued by Demirguc Kunt and Deteragiache (controlling for the fact that by definition banking crisis cannot occur in countries without financial system). The reason is that in established financial system the priors are very certain and hence the adjustment is very slow while in recently liberalized financial system the priors are very uncertain and hence the adjustment is very fast. The second prediction is that the probability of a financial crisis is higher in the period following liberalization in countries with poor transparency. The reason is that poor transparency increases the probability of confusing firms' quality on the one hand with aggregate shock and non-transparent policy on the other hand.

14
_country_ _~~c~~ ode_ `) [` _output_ _~~g~~ vc_ `] [` _origin_ _~~d~~ estination_ `] [` _standard_ _~~o~~ ptions_ `]`

The `output()` option, i.e. `[` _output_ _~~g~~ vc_ `]` in the reported syntax, allows to get different measures of GVC-related trade by specifying `gvc` , `gvcb` and `gvcf` as arguments for total, backward and forward GVC indicators, respectively. As can be noted from the `icio` results reported in Section 4.1.3, GVC-related indicators are routinely reported as part of the detailed output, when an export flow-at any level of aggregation-is specified.

Also for GVC indicators it is possible to single out the country/sector where the goods/services were originally produced by specifying the `origin(` _country_ _~~-~~ code_ `[,` _sector_ _~~c~~ ode_ `])` option, as well as the market/sector where the goods/services are absorbed in final demand by specifying the `destination(` _country_ _~~c~~ ode_ `[,` _sector_ _~~-~~ code_ `])` option.

# **5.1.2 Examples: GVC-related exports**

Figure 1.13 of the World Bank WDR 2020, here reported as Figure 2, is based on EORA MRIO 1990 and 2015 data and shows the GVC-related trade in agriculture (sectors 1 and 2 in EORA) and agri-food sectors (sector 4 in EORA). For instance, in the case of Tanzania, one of the Sub-Saharan African countries that experienced 29
# 1. Introduction

Civil war broke out across The Republic of South Sudan in December 2013 only two years after gaining independence on the 9<sup>th</sup> of July 2011. The South Sudanese conflict has since continued to escalate, resulting in a large‐scale humanitarian crisis where more than a third of the population has been forcibly displaced (World Bank, 2018).<sup>2</sup> Given the extremely difficult context, very little was known about welfare and livelihoods during the early years of the country’s independence in 2011.<sup>3</sup> The last nationally representative household survey measuring consumption and poverty was conducted as far back as 2009. To fill this data gap, the High Frequency South Sudan Survey (HFS), implemented by the National Bureau of Statistics (NBS) in collaboration with the World Bank and funded by the U.K. Department for International Development, conducted several waves of representative surveys across seven of the ten former states between 2015 and 2017 (Appendix A). In the period prior to and during the first wave of the HFS in 2015, conflict had primarily been concentrated in the Greater Upper Nile region (Figure 12in Appendix D).<sup>4</sup> This period of relative stability across the remaining Greater Equatoria and Greater Bah El‐Ghazal regions allowed the preparation and relatively calm implementation of Waves 1 and 2 of the country in 2015 and early 2016.

In summer 2016, clashes broke out in Juba. The escalation of the conflict coincided with the beginning of the implementation of Wave 3 of the HFS, a second urban‐rural representative wave measuring consumption and poverty. The third wave of the HFS provides a relatively rare and extremely valuable glimpse of trends in welfare, consumption, and poverty in a country going through a period of upheaval. Indeed, the South Sudanese economy has since displayed all the characteristics of a war economy, including severe output contraction, rapid currency devaluation, and
indicator _T_ 2). Next, we turn to the labor-market indicators for the female population, which the literature has often shown to be adversely affected by inflows of refugees (e.g. Ceritoglu et al. (2017), Tumen (2016), DelCarpio and Wagner (2015), Ruiz and Vargas-Silva (2018)). The second panel in Table 4 presents estimates based on the indicators for the female working-age population. In this case the estimated coefficients are very small and none is statistically significant at conventional levels. Thus, it does not seem to be the case that the labor market conditions of Ecuadoran women are being affected by the arrival of Venezuelan migrants.

The third panel in Table 4 focuses on the labor market indicators referring to young workers (ages 15-40) with low educational attainment, defined as having completed at most primary education. Once again, we do not find evidence of effects on the participation or employment rates. However, a clear pattern emerges suggesting substantial worsening of the quality of employment and earnings for this group of workers in the 15
price model, Kim assesses the price differences between Hanoi and Ho Chi Minh City to investigate the impact of social norms on property prices. Over time and with the increased penetration of property listing websites, private property price collection efforts have transitioned to online listings where data collection can be automated. Anenberg & Laufer (2017), for instance, use listing information to construct a new house price index to monitor house price developments in the US. Using property listings, the authors construct a 7
researchers in using any of the temperature products since there is no apparent pattern of which product works best in what situation. In linear specifications, mean and median daily temperature tend not to be significant, but in quadratic specifications without fixed effects, mean and median tend to be negatively correlated with outcomes, and in quadratic specifications with fixed effects, mean and median tend to be positively correlated with outcomes. For variance, though, even this pattern falls apart, with a single specification producing both positive and negative coefficients. Based on all of this evidence, we conclude that any of the three temperature products are as likely to produce results substantially similar to each other as they are to produce results substantially different from each other. Researchers may need to verify the robustness of their results to the choice of remote sensing temperature data source. Overall, however, temperature seems to be a poor predictor of agricultural outcomes in these Sub-Saharan African countries.

# **5 Discussion**

Having examined results from 129,600 regressions on panel data from six countries with 33,738 total household observations, it is useful to recapitulate our results and provide direction towards a set of best practices when looking to combine remote sensing weather data with household survey data. We also outline future work, both items from our pre-analysis plan that remain unaddressed and exploratory analysis that was not prespecified.

## **5.1 Towards a Set of Best Practices**

Remotely sensed weather data has become a common component of economic analysis (Dell et al., 2014; Donaldson and Storeygard, 2016). Yet there has been little recognition in the economics literature that these data can suffer from measurement error. Nor has there been a convergence on a set of best practices for dealing with measurement error. Few empirical papers today would fail to verify the robustness of the results to different specifications (Simonsohn et al., 2020) or different iterations of the data Steegen et al. (2016). Yet economics papers rarely, if ever, verify