rafmacalaba/data-use-mentions-extended huggingface dataset:
OLS assumptions and using the Olley-Pakes procedure and report these in Appendix Table A.1. I use data on the firm’s total wage bill as a proxy for the labor input, and on its fixed assets<sup>5</sup> as a proxy for capital. These nominal values are deflated using NIC 2-digit level output and input specific price indices<sup>6</sup> .

# **_Agglomeration Variables_**

The count of other exporters within the district ( exp _jt_ ) and the count of other exporters by industry within the district ( exp _jkt_ ) are weighted by the district population captures the effect of export spillovers. The idea is that proximity to other exporters could result in knowledge spillovers that might help non-exporters to start exporting. In addition more general industrial agglomeration within a location would

> 5 Fixed assets include plant and machinery, computers, electrical installations, transport and communication equipment and infrastructure, fittings and furniture, social amenities and other fixed assets.

6 Since more productive forms are likely to have a lower-than-average firm specific price, the use of industry price indices might systematically underestimate the output of more productive firms and therefore underestimate their productivity. On the other hand, if exporters were more likely to use better quality inputs and materials, then using industryspecific deflators would overestimate productivity. The converse would be true for less productive firms. In the absence of firm-specific prices I am unable to overcome this bias.

7
sensing products. ERA5 is weakly different than both MERRA-2 and CPC at all significance levels, with significant coefficients at _p >_ 0 _._ 95 in 65% of regressions compared to 56% for MERRA2 and CPC. We saw in Figure 28, ERA5 had slightly higher adjusted R<sup>2</sup> values compared to the other two, though these differences did not satisfy our weak criteria.

These results raise the question of whether CHIRPS, CPC, MERRA-2, ARC2, and TAMSAT contain measurement error in precipitation and ERA5 is accurate - or whether the reverse is true. Similarly, the question exists if MERRA-2 and CPC contain measurement error in temperature and ERA5 is accurate - or, again, if the reverse is true. Based on our maintained assumption that weather metrics are significantly correlated with outcomes, we should conclude that ERA5 is more accurate than the other products in terms of both rainfall and temperature, while all the other remote sensing sources produce weather data containing measurement error. However, as CHIRPS, CPC, MERRA-2, ARC2, and TAMSAT, report roughly the same results, it could be that they are the more accurate products and ERA5 is the outlier.

This latter interpretation then begs the question of whether, if ERA5 suffers from measurement error in both rainfall and temperature, is the error classical or non-classical. Based on the descriptive evidence, it is possible that ERA5 suffers from non-classical measurement error, in that it consistently reports higher rainfall levels than most of the other precipitation products, suggesting the measurement error is not random but biased. That ERA5 produces the largest share of significant _p_ -values on rainfall metrics also suggests that if there is measurement error in the product it may be non-classical in nature. This is because the general understanding of the effects of classical measurement error is to introduce noise, not bias, meaning we would expect a product suffering from classical measurement error to produce fewer, not more, significant _p_ -values. For ERA5 temperature measurements, the picture is somewhat
experience of children and adolescents growing up in forcibly displaced households also intersects with gender and multidimensional poverty. Displacement can disrupt cultural and gendered norms to present women with new opportunities for household decision-making and leadership (ODI 2019a). Even if gender norms and attitudes do not change on a structural level within IDP households, there is evidence that the behaviors of men and women in these households do indeed shift in response to the pressures that poverty and displacement status create (Assad et al. 2021, Rubiano-Matulevich 2021). Children and adolescents’ long-term exposure to households with a more equalized division of domestic labor, as a result of violent or political conflict, warrants further investigation.

# **2.3 Country contexts**

The countries with subnational regions covered in this study, using data from 2017 or 2018, are Ethiopia, Nigeria, Somalia, South Sudan, and Sudan. All are located in Sub-Saharan Africa, have undergone or are currently involved in armed conflict, and are affected by environmental issues such as drought, famine or flooding. Despite some commonalities, each faces a unique set of social, political and economic challenges, which cannot be accurately covered in this study. However, to contextualize the findings, a brief introduction of the country context is presented alongside the poverty estimates by the $1.90/day measure and the global Multidimensional Poverty Index (MPI).<sup>3</sup>

> 3 An international measure of acute multidimensional poverty, aligined with the 2030 Agenda, that captures deprivations in health, education, and living standards for more than 100 countries (Alkire and Jahan 2018; Alkire, Kanagaratnam and Suppa 2020).

4
_These improvements in access to improved water and sanitation services have propelled Uganda toward meeting the MDGs._ Access to improved water increased from 48 percent to 66 percent in of the population between 1995 and 2006, while access to improved sanitation rose from 66 percent to 70 percent over the same period. As a result, Uganda has already met the MDG of 65 percent for improved sanitation, and is close to reaching goal of 72 percent for improved water.

# **Box 1. Understanding the differences between the JMP and government data**

The AICD uses the Joint Monitoring Data (JMP) coverage statistics as the main source of access data on water supply and sanitation, and process under a standardize methodology to allow cross-country comparisons. These data might differ from that reported by governments. The JMP data are based on household surveys and therefore reported by users of the services, the government data is based on utility reports. This implies that there is a time lag between output data (provider) and outcome data (users). Moreover, household surveys capture clandestine use of water services, which are not conveyed in utility data. Other underlying factors explaining potential differences are the definition of what technologies constitute improved access to water supply and sanitation; and, the JMP’s use of several household surveys visà-vis the use of a single data point by several governments. Therefore, the conclusion on progress on MDGs might differ according to the data source used.

_Source:_ Adapted from AMCOW (2010).

_The performance of the urban water sector has been boosted by the remarkable improvements in the performance of the National Water and Sewerage Corporation (NWSC)._ The NWSC provides water supply and sewerage services in the 22 largest urban centers covering only 10 percent of the population in all. The improved performance is attributable to increases in revenue collection and a reduction in distribution losses. Revenue collection doubled from 48 percent of the bills in 1996 to 99 percent of the bills in 2009 (table 9). These rates exceed those
_Figure 4 Guinea-Bissau’s projected electricity demand (MW) and the optimal least-cost energy mix 2020-2035, Least Cost Generation Expansion Plan, 2020._ <!-- Start of picture text --> 100%<br>286 90%<br>273<br>260 80%<br>247<br>234 70%<br>221<br>208 60%<br>195<br>169 182 50%<br>157<br>6390 68103 74118 79130 85142 90 96 101 107 112 118 123 129 134 140 145 40%30%20%10%<br>0%<br>2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035<br>Scénario Bas Scénario Haut Solaire PV Thermique Fioul Hydro Imports ENS<br><!-- End of picture text --> _Source(s) Figure taken from Guinea-Bissau: Power Sector Policy Note 2020, World Bank. Note(s): Solaire PV = Solar PV, Thermique Fioul = heavy-fuel oil, ENS = suppressed demand._ The least-cost approach employed in that study calculates the lowest cost energy mix for a specified level of electricity demand, based on assumptions regarding future technology costs (LCOEs) and a set of constraints, such as decarbonization goals or storage options. However, as discussed above, such approaches overlook the path dependency of technology diffusion and the role that endogenous cost reduction may play.

# **5.3 FTT-FLEX baseline for Guinea-Bissau’s power sector**

Based on historical power sector share data in 2020 and short-term projections up to 2025 that reflect the latest power sector developments mentioned above, FTT-FLEX calculates the power generation mix from 2026 onward to meet the projected electricity demand (growing at around 5% pa) coming from the Guinea-Bissau MFMod macroeconomic model. Due to a lack of data for cost, cost distribution, lifetime, and lead time, the FTT-FLEX analysis reported below excludes electricity imports from neighboring countries as
Ridout, B., & Campbell, A. (2014). Using F acebook to deliver a social norm intervention to reduce problem drinking at university. Drug and alcohol review, 33(6), 667-673.

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Sacarny A, Barnett ML, Le J, Tetkoski F, Yokum D, Agrawal S. Effect of Peer Comparison Letters for High-Volume Primary Care Prescribers of Quetiapine in Older and Disabled Adults: A Randomized Clinical Trial. JAMA Psychiatry. 2018;75: 1003.

Sacarny A, Yokum D, Finkelstein A, Agrawal S. Medicare Letters To Curb Overprescribing Of Controlled Substances Had No Detectable Effect On Providers. Health Affairs. 2016;35: 471–479.

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Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making
displaced due to armed conflict, situations of generalized violence and violations of human rights.<sup>28</sup> Data on IDPs monitored by IDMC are disaggregated and currently published separately for conflict-induced displacement and disaster-induced displacement.<sup>29</sup> At the country level the IOM’s Displacement Tracking Matrix (DTM)<sup>30</sup> provides data on IDPs in both conflict and natural disaster settings (activated in all major natural disaster contexts in recent years).

Global data on conflict-induced internal displacement reflect variations in how IDPs are defined across situations. There is no consensus on how far a person must flee in order to be considered internally displaced. The definition of internal displacement for nomadic populations, which account for a significant share of IDPs in the Horn of Africa and increasingly in the Sahel, is open to controversy.<sup>31</sup> Moreover, while some countries register IDP children born in displacement (e.g. Azerbaijan, Cyprus and Georgia), other countries do not (IDMC 2015). The crafting of a definition for IDPs and its application in a particular context may be heavily influenced by local and national politics in conflict and post-conflict countries, as well as the direct link between estimates of displaced populations and humanitarian assistance, which can lead to both over- and under-reporting. Consequently, comparing or aggregating data across displacement situations may be misleading.<sup>32</sup> Additionally, there is no consensus on when internal displacement ends. There are likely to be IDPs in protracted displacement who have no interest in returning to their original locations, are well integrated and no longer have specific vulnerabilities linked to their displacement. In particular, large numbers of IDPs have moved from rural to urban settings and their displacement is part of an urbanization process that in many parts of the world is a mega-trend that is rarely reversed. The definition of an end to internal displacement is hence complex, as it typically requires return (which may
(Refugee Studies Center, 2012). The final phase of displacement marks the Daesh insurgency period spanning from 2014 to 2018. The post-2014 period represents one of the most important episodes of internal displacement in Iraq (IOM, 2016). However, the current paper focuses on internal displacement before 2012 due to the use of household data that were collected between 2012 and 2013.

Unlike refugees, IDPs are not covered by any protection within an internationally recognized legal framework. The IDPs in Iraq benefit from little support from the Iraqi government through targeted policies unless they volunteer to return to their original location. The Ministry of Displacement and Migration (MoDM), formed in 2003, has implemented new policies towards displacement in July 2008, mostly aiming at promoting the return of IDPs. However, most IDPs 2
**Table 4. Consumption Poverty Matrix under No Shocks**

|||**National Expected Poverty in 2013: Expected Shoc**|**ks=0**|
|---|---|---|---|
|||_Non‐Vulnerable_<br>_Vulnerable_|_Totals_|
|**Observed 2013**|_Non‐poor_|57.1<br>9.4|66.4|
||_Poor_|20.6<br>12.9|33.6|
||_Totals_|77.7<br>22.3|100|
|**Table 4a. Consumpti**|**on Poverty Matrix u**|**nder Expected Shocks**<br>**National Expected Poverty in 2013: Regional Mean S**|**hocks**|
|||_Non‐Vulnerable_<br>_Vulnerable_|_Totals_|
|**Observed 2013**|_Non‐poor_|45.8<br>20.6|66.4|
||_Poor_|10.5<br>23.0|33.6|
|**Table 4b. Northern R**|_Totals_<br>**egion Consumption**|56.4<br>43.6<br>**Poverty Matrix under Expected Shocks**|100|
|||**Northern Region Expected Poverty in 2013: Regional Me**|**an Shocks**|
|||_Non‐Vulnerable_<br>_Vulnerable_|_Totals_|
|**Observed 2013**|_Non‐poor_|35.1<br>28.3|63.4|
||_Poor_|6.4<br>30.2|36.6|
||_Totals_|41.5<br>58.5|100|

**Table 4c. Central Region Consumption
risk or default. Panel B of Table 5 presents the results of this exercise with Argentine data where a default rate is targeted and then the number of borrowers who could be approved for that level of default is calculated. If a large bank was targeting a 5% default rate, then without information sharing it could extend credit to approximately 75% of the applicant pool compared with 84% if they had access to the PCR data – an increase of approximately 12%. For the small bank, the increase is even greater: approximately 18%.

> 9 We acknowledge that there may be an overstatement of the case for sharing information, as individual bank is not limited to the information supplied to the credit registry.  However, the variables used and supplied to the credit registry are normally used for credit scoring purposes and typically have a positive marginal effect in terms of the predictive power of scoring models and hence we believe that this remains a useful demonstration of the potential gains from sharing information.

> 10 We report results for arbitrarily selected large and small bank.  Similar results hold for other financial institutions.

11
/sup> It is easy to check that bias in the school against girls found earlier i.e., � _q_<sup>_d_</sup> _< q_<sup>_s_�</sup> _,_ is necessary and sufficient for inequality (21) to hold.

# **(5.I.2) Rural India**

The estimates of intergenerational persistence in schooling in rural India are reported in Table T2.IR. The pattern of estimates from the linear CEF is similar to that in urban India; the IGRC estimate is larger for the daughters implying lower relative mobility, while the intercept is smaller. A comparison with the estimates for urban India shows that the IGRC estimates do not vary significantly across rural vs. urban areas, but the intercept for girls in rural areas is substantially lower. Also, the difference in the intercepts between sons 23
Entity or other authority depending on the size of the contract. The average monthly remuneration of PE officials was assumed to be Tk. 70,000 (US$ 830) based on National Pay Scale, 2015. This corresponds to an hourly remuneration of Tk. 500 (US$ 6). The savings for each OTM tender was thus estimated to be Tk. 4,700 (US$ 55), while it was Tk. 2,300 (US$ 27). The total staff savings was estimated to be Tk. 30 crore (US$ 3.5 million) for about 69,000 tenders.

TABLE 43: SUMMARY OF ADMINISTRATIVE STAFF TIME SAVINGS

|Procedure|Savings per<br>tender (taka)|Savings per tender<br>(US dollar)|Number of<br>tenders|Total Savings<br>(taka)|Total Savings<br>(US dollar)|
|---|---|---|---|---|---|
|OTM|4,700|55|57,308|26.9 crore|3.2 million|
|LTM|2,300|27|11,932|2.7 crore|0.3 million|

3. Bidding Firm Transaction Cost savings

- a. Staff-time savings: The transition to e-procurement has enabled participating bidders to prepare and submit the bid in a shorter time. The staff-time savings for the bidders was estimated by multiplying the mean salary by the difference of the product of the number of bids if the tender had been paper based by the average tender preparation time for paper-based tenders and the product of the actual number of bids and the average tender preparation time for electronic tenders. The number of bids had the tender been manually administered is predicted using the same two models as used for the main results and have been given in the Table 3, Models 1a and 1b.

Based on the survey of bidders
J. Birkmann, T.R. Carter, K.L. Ebi, M. Garschagen, B. Jones, and R. Jones. 2014. “Enhancing the Relevance of Shared Socioeconomic Pathways for Climate Change Impacts, Adaptation and Vulnerability Research.” _Climate Change_ 122 (3): 481–94.

Varangis, Panos, Akiyama Takamasa, and Donald Mitchell. 1996. _Managing Commodity Booms and Busts._ Venables, A. J. 2016. “Using Natural Resources for Development: Why Has It Proven so Difficult?” _Journal of Economic Perspectives_ 30 (1): 161–84.

Warner, A. 2015. “Natural Resource Booms in the Modern Era.” _IMF Working Paper_ . https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Natural-Resource-Boomsin-the-Modern-Era-Is-the-curse-still-alive-43393.

World Bank. 2019a. “Commodity Prices.” https://www.worldbank.org/en/research/commodity-markets.

———. 2019b. “PovcalNet.” http://iresearch.worldbank.org/PovcalNet/povOnDemand.aspx.

54
experienced much higher than average growth in consumption. But as many of them rose up the distribution scale, others who did relatively worse took their place, with the net result that the difference in consumption between the bottom 40% in 2005 and the bottom 40% in 2012 was just about the same as the difference in average consumption for the whole population.

To put it differently, India has remained as unequal (or equal) in terms of per capita consumption in 2012 as it was in 2005, but with a lot of “churning” underneath in terms of households moving up and down relative to other households, and in poverty status. As Table 1 shows, nearly one‐third of all households in the IHDS panel changed poverty status from 2005 to 2012, which includes 27 percent who moved out of poverty and 7 percent who moved into poverty.

The IHDS panel also shows that **Table 1: Movements out of poverty exceed shifts into poverty** movements out of poverty dominated movements into poverty: 69 percent of <mark>2012</mark> _% of Households_ households who were poor in 2005 <mark>Poor</mark> Non‐poor moved out of poverty in 2012, and only Poor 12.2 26.8 11 percent of non‐poor households in 2005 2005 fell into poverty in 2012. This is Non‐poor 7.0 54.0 not surprising, given the high rate of _Source:_ Authors’ estimates using IHDS panel (2005, 2012) consumption growth among the (anonymous) bottom 40%, which caused the overall distribution to shift rightwards significantly, lifting all (or most) boats in the process.

A high degree of “churning” amid upward mobility is also observed in synthetic panels constructed from three rounds of NSS data over the period 2005‐2012 (Dang & Lanjouw, 2015a).<sup>7</sup> Their analysis classifies the population into the poor, the vulnerable and the middle‐class
A Tale of Two Surveys: Comparing the Outcomes of an In-Person and Web-Based Survey of Mental Health in the West Bank and Gaza<sup>1</sup> Arden Finn<sup>2</sup> Jessica Anderson Alia Aghajanian

**Keywords:** Survey Methodology, Internet Survey, Selection Bias, Sensitivity Bias, Conflict, Mental Health, West Bank and Gaza

JEL Classification: C81, C83

> 1 **Acknowledgments** : This report was prepared under the overall guidance of Johannes Hoogeveen and Alan Fuchs. Minh Cong Nguyen and the MENA Data Lab provided valuable support in setting up the Facebook survey and in calculating survey weights. We are grateful to Hamza Mighri (IMF) and Jawad al-Saleh (Palestinian Central Bureau of Statistics) for helpful inputs and suggestions in the early stages of planning the Facebook survey. Reviewer comments were received from Ganesh Seshan, Sharad Tandon and Hugo Nepo, and we are very grateful to these colleagues for flagging some analytical gaps, and for greatly improving the report.

> 2 Corresponding author: afinn1@worldbank.org
use US input-output tables to proxy each industry’s dependency on the network and professional services sector. The authors then rely on these dependencies to create a weighted average score for the regulatory stringency each sector is exposed to through its relevant upstream network and professional services sectors. They then show that service regulation has a significant negative effect on the growth rate of value-added, labor productivity, and exports of downstream industries.

With a focus on labor market outcomes, Bassanini (2015) discusses the short-term effect of lowering entry barriers in three network industries (public utilities, transport, and communications) on industry employment. The author documents a reduction in employment prior to and shortly after the reforms are implemented. He conjectures that this finding hinges on the structure of the downstream sectors considered, which generally consist of large incumbent firms which—compared to small firms as are more predominant in the retail sector, for example—have more scope for protecting their standing in the market by engaging in immediate re-organization and reduction of overstaffing after deregulation. He argues that in sectors dominated by small firms, new entrants following deregulation may even help achieve overall employment growth in the short run.

Gal and Hijzen (2016) use cross-country firm-level data from the Amadeus/Orbis database to examine the effects of product market deregulation in three sectors (network industries, retail trade, and professional services). Using local projection techniques, they find positive effects of product market reforms on capital, output, and employment in the respective sector of the reform. To identify downstream spillovers, they construct indirect measures that weigh reforms in upstream industries in a way that reflects their importance for downstream production. In addition to using national industry-level input-output tables, the authors also consider cross-country linkages and firm-level information about the general reliance on intermediate inputs in production. They find positive spillovers on firms in downstream industries both domestically and abroad. Furthermore, their results suggest that the impact of upstream reforms is more positive for downstream sectors that exhibit a high level of competition ex-ante. Compared to services