rafmacalaba/data-use-mentions-extended huggingface dataset:
<br>.03<br>.01<br>.02<br>.005 .01<br>0 0<br><!-- End of picture text --> Source: Own computations using June 2010 (EFSA II) survey. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. Weights are calculated using pre-earthquake assets.

Figure 6 presents the distribution of wealth losses in percentage of pre-earthquake wealth. The percentage of asset loss among households who lost assets is around 25%. This percentage does not seem to vary a lot according to pre-earthquake wealth quintile.

> 15 Note that households with tools/material (for fishery, agricultural production and handicraft) will be classified as poorer households (asset index weight equals -0.951). This is due to the fact that such asset variable is not well correlated with other asset indicators (e.g., private water, electricity for lighting, WC, oven, television, fan and so forth) that would classify them as richer. In this sense, the asset index can be described as the best regressed latent variable on the assets variables (or primary indicators) since no other explained variable is more informative (Asselin, 2009).

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Table 5: Data to be calibrated

|Frequency of lagged shipments in bin|0|1|2|3-5|6-10|11-100|100+|
|---|---|---|---|---|---|---|---|
|Share of total 2016 shipments|0.189|0.051|0.038|0.084|0.089|0.374|0.176|
|Inspections per 100 shipments|14|6.7|6.4|6.0|5.6|5.7|6.0|
|Mean no. of lagged shipments|0|1|2|3.8|7.6|26.9|205|
|Total inspections per 100 shipments||||7.3||||

Table notes: Author calculations using Serbian customs data from 2016.

made up entirely of observations with exactly zero, one, or two lagged shipments, respectively. In the last four bins, we average over _ofk_ triplets that have varying levels of lagged shipment frequencies. The mean for each bin is reported in row 3 of Table 5.

# **Parameterization**

In order to emphasize the role of ambiguity, we shut down by assumption other possible sources of cross-bin variation that might otherwise affect search behavior and/or compliance. We restrict the optimism parameter to be equal across bins, _αl_ = _α_ , _∀l_ . Likewise the firm’s loss parameter, _L_ , is assumed to be common across bins.

The parameter _L_ represents the loss to the importer from having non-compliant goods seized by the border authority. This parameter can be important in determining overall levels of compliance, and our model fits best with generally high levels of compliance. In our exercise we set _L_ = $200.<sup>56</sup> We also choose the Pareto distribution scale parameter to generate
explicitly subject to external monitoring by several organizations, including the Tribunal Contas da União, the Controladoria-Geral da União, the Minsitério Público, and publicly constituted councils at the municipal level. While the first two organizations focus on transparency and accountability more generally, the last two have the attributes of external redress processes in which individual transactions are reviewed. Data on complaints with the program do not appear to be aggregated into easily available and standardized datasets. A survey of 5,604 families in 2006 found that 9% should have been paid but were not (or were paid improper amounts), but it is not clear if these were uncovered on the basis of regular complaints or on the basis of the administrative review and the survey. Overall, the focus of the agencies that monitor irregularities in the PBF appears to be on families improperly included in the program, rather than those improperly excluded.<sup>57</sup>

> 53 The Office of the Independent Adjudicator for Higher Education, homepage, _available at_ : <u>http://www.oiahe.org.uk/.</u>

> 54 ―French Health System – Complaints,‖ _available at_ :  http://www.french-property.com/guides/france/public- <u>services/health/complaints/</u> _<u>.</u>_

> 55 <u>http://www.mediateur-republique.fr/en-20-citoyen-Contact</u>

> 56 ―Tribunal des affaires de securite sociale,‖ _available at_ :  http://www.legislation.cnav.fr/doc/cont/cxgen/tass/BNL- <u>EX_CONT_CXGEN_TASS.htm</u>

> 57 Controle e transparencia na gestao do Programa Bolsa Família, 1 Mostra Nacional de Estudos sobre o Programa Bolsia Familia.

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# **Table S8. Estimated total number of households that report income loss due to COVID-19** **<u>pandemic and average household-level incidence of income loss, by source</u>**

||Any type of<br>income loss|Farm<br>income<br>reduced|Business<br>income<br>reduced|Wage<br>income<br>reduced|Remittances<br>reduced|Other income<br>sources<br>reduced|
|---|---|---|---|---|---|---|
|Total|748,720,384|223,353,008|174,818,736|67,815,688|44,469,788|38,765,196|
||(4,869,609)|(4,087,100)|(2,156,618)|(2,204,293)|(1,403,495)|(1,964,309)|
|Mean|0.773|0.637|0.856|0.527|0.707|0.522|
||(0.009)|(0.012)|(0.011)|(0.020)|(0.026)|(0.027)|
|Observations|9,153|5,624|3,849|3,447|1,687|1,574|

_Note_ : Each column reports the estimated total number of households that report loss of income and the average household-level incidence of reporting a loss of income. The estimates are based on the pooled data from 4 countries. Reporting loss of income from a given source is conditional on receiving income from that source prior to the COVID19 pandemic. Data are only from the first phone survey round in each country
# Improving Multi-Topic Household Surveys

# for Better Transport Policy Analysis

## **Mathilde Lebrand, Qiuyan Yin**<sup>*1</sup>

* World Bank

**Keywords:** transport, household survey, LSMS **JEL Classification:** <mark>R20, R41</mark>

> 1 World Bank. Corresponding author: mlebrand@worldbank.org. We thank Vivien Foster, Sudeshna Mitra, Kazuyuki Neki, Nino Pkhikidze, and other participants at internal seminars for their useful comments and contributions. We also thank Gbemisola Oseni, Akiko Sagesaka, and David Newhouse for discussions and suggestions regarding the World Bank LSMS program and harmonized household survey data, and Olivier Dupriez and Tefera Degefu for their help with the World Bank Global Consumption dataset. This paper should not be reported as representing the views of the World Bank. The views expressed in this paper are those of the authors and do not necessarily represent those of the World Bank or the World Bank’s policy.
Co N CD tO O (N<br><!-- End of picture text --> Data source: CSO. Note: Series are log (employees in thousands).

2 The service sector includes public administration, education, health, private domestic, and other.

3 Industry is an aggregation of the following seven sectors: mining and quarrying; manufacturing; electricity and water; construction; distribution, restaurants and hotels; finance; insurance and real estate; and, transport and communications.

6
19 Table 11, col. (1), shows that for households that use natural gas for space heating, the full gas bill accounts for a large portion—more than half—of the total energy and fuel bills. Col. (2) presents the energy and utility costs (after the HUS) as shares of net income for the subset of these households that did not receive HUS assistance, and col. (3) presents the same information for those that do receive the HUS.<sup>20</sup> While the shares were similar across the two groups in 2014, prior to the tariff hike, they were 5 percentage points apart thereafter, although by 2018 the difference seems to have shrunk somewhat.

Shares of energy costs (natural gas, electricity and other fuels) between 6 and 11% of net income are fairly large when compared with other expenditure categories. Between 2014 and 2018, Ukrainian households spent on average 42%-45% of net income (54%-57% of total consumption expenditures) on food, and about 4%-5% of net income (5.7%-7% of consumption expenditures) on their own health. It is noteworthy that in 2016-2018 the average share for nonHUS households is stable at 11%. In other words, the average household that does not receive HUS assistance would still be classified as energy poor by conventional standards.

Table 12 presents the average electricity, gas and fuel shares of net income among households that use natural gas for space heating, separately for households that do and do not receive the HUS, and by income-gas consumption quartile, showing that the shares are considerably lower among HUS recipients, especially for families in the first and second income quartiles. The contrast between panels A and B is especially stark for heavy gas users.

Table 13, panel A, shows the percentages of the households in each income-gas consumption quartile cell that are classified as “fuel poor” (with electricity and domestic fuels
using a comparable data set of firms from France, Germany, Spain, and the United Kingdom. The authors find that product innovation has a positive impact on employment, but that process innovation has a displacing effect on employment. However, the positive impact of product innovation generating employment is larger than the displacement effect of process innovation, and, therefore, the net effect of innovation on employment tends to be positive. Using a similar methodology, Hall, Lotti, and Mairesse (2008) find a low but positive effect of product innovation on employment in Italy, and no displacement effect from process innovation. Similar studies are Dachs and Peters (2014) for European countries 5
Review_ 70(11):35–36.

- Doyle, Aoife M., David A. Ross, Kaballa Maganja, Kathy Baisley, Clemens Masesa, Aura Andreasen, Mary L. Plummer, Angela I. Obasi, Helen A. Weiss, Saidi Kapiga, Deborah Watson-Jones, John Changalucha and for the MEMA kwa Vijana Trial Study Group Richard J. Hayes. 2010. “Long-Term Biological and Behavioural Impact of an Adolescent Sexual Health Intervention in Tanzania: Followup Survey of the Community-Based MEMA kwa Vijana Trial.” _PLOS Medicine_ 7(6):1–14. **URL:** _https://doi.org/10.1371/journal.pmed.1000287_

37
- (b) **Geolocate reports.** We extract all landmarks and roads that have an exact match between the gazetteer and the tweet. In Figure 1, “kianda” and “abc way” match several entries in the gazetteer. We extract misspelled matches based on Levenshtein distance varied by length of the n-gram, matches based on the word following a preposition, and matches based on intersections between multiple roads.

- Existing geoparsers extract all possible location references without identifying the unique location that makes the data useful. We resolve two technical challenges to find the location of the crash:

- i. When multiple locations are mentioned in the tweets, we use prepositions to sort locations into tiers, based on the probability of a location being correct after a particular preposition. For example, in Figure 1, “next to” is ranked as tier 1 while “toward” is ranked as tier 6, resulting in the correct geolocation for the crash at “kianda” and not “abc place”.

- ii. When a name refers to multiple landmarks, we adopt a toponym resolution approach. In Figure 1, more than 6 landmarks across Nairobi have “kianda” in the name. We resolve the toponym in three steps: (1) we search for landmarks that are within 500 m of a road if it is mentioned, (2) we use the centroid of the clustered location if 90% or more of the landmarks are in a 500 m radius, or (3) we rank the landmarks by the probability of being correct using the landmark type in the truth data (see SI for statistics on location type). In the example, we use “Waiyaki Way” to narrow down the landmarks “kianda” in a 500 m radius (from 6 to 3) and then use the centroid as the crash location.

We define a correct geoparse as one located within 500 m of the coordinates in the truth dataset. As a benchmark, we compare our algorithm to the Location
have a spatial resolution of 30 arc-seconds (about 1 km at the Equator).

Another known issue of the DMSP-OLS products is the “overglow” effect: dim lighting detected from light in surrounding areas of cities because the scattering of lights in the atmosphere (Wu, Ma, Li, Peng, & Liu, 2014). A novel deblurring process was applied to address the issue of over glow in the radiance-calibrated products. This process involves the use of two sequential filters, a standard deconvolution and the frequency of illumination maxima, to withdraw the light from the surroundings back and restacking it vertically on its source pixels at city centers (Abrahams, Lozano-Garcia and Oram, 2016).

# _4.2.2 Calibration and interannual correction of DMSP-OLS RC NTL images_

We use the deblurred DSMP-OLS RC yearly composites for the years 1996, 2000 and 2010 to delineate urban extents in Latin America. DMSP-OLS RC NTL from different years
migrants have similar education levels. Low-skilled workers tend to have lower language fluency, which tends to limit them to a greater degree to jobs that involve manual labor, rather than interactive tasks, as Peri and Sparber (2009) show. As a result, migrants concentrate in manual labor, while natives have a comparative advantage in finding occupations that involve more complex communication tasks (D’Amuri and Peri, 2014; Foged and Peri, 2016). Similarly, Peri and Sparber (2011) find occupational differences between migrants and native-born workers even if they are highly educated. Among workers with tertiary education, immigrants are concentrated in occupations that involve more quantitative and analytical tasks; by contrast, native-born workers tend to be in jobs that require more intense communication skills.

Thus, we want to examine whether language ability shapes the sorting of migrants into occupations with different skill requirements in the EU – and, as a result, the exposure to the different risks associated with the COVID-19 pandemic. We begin by using the O*NET survey to identify the intensity of the host-country language requirement per occupation. In particular, the knowledge questionnaire includes the question “What level of English language knowledge is needed to perform your current job?” with answers ranging from one to seven. We transform this measure available for each occupation in the US to ISCO three-digit occupations and create a standardized index of intensity of the host-country language requirement with mean of zero and standard deviation of one.

Our analysis shows that immigrant workers in Western European countries are disproportionately concentrated in occupations that require lower levels of host-country language fluency (Figure 4, Panel A). Across the EU labor markets, immigrants work in occupations in which language skill requirements are 0.2 standard deviations below the mean (Figure 4, Panel B). This difference is statistically significant at the 1 percent level. However, the difference in occupational choices between migrants and natives varies with migrants’ fluency in the host-country language. Migrants with advanced or above-average levels of fluency have occupations with
<!-- Start of picture text --> Figure 6: Changes in retuns to education since 1980, women<br>1.20<br>1.00<br>0.80<br>0.60<br>1980 1990 2000 2014<br>Primary Secondary Higher<br><!-- End of picture text --> Source: Montenegro and Patrinos 2014 Standardized data across countries and over a long period of time reveal a compelling picture of a steep decline in the wage returns to primary schooling across the developing world over the past half-century. The evidence also shows an increasing rate of return to higher education, especially for women. While the returns to primary schooling have decreased less for females than for men, the returns to schooling for secondary have decreased much more for females. Since secondary is required for access to higher education, this could be a deterrent to enrolling in secondary schooling or might become one (Kingdon 1998). This is important since investment in schooling by women tends to decrease the proportion of earnings differential with men that is attributable to factors such as discrimination (Dougherty 2005). It is also possible that academically able women are accessing higher education. Nevertheless, this justifies investment in girls’ schooling and compels us to ensure high levels of schooling through to higher education to reap the significant wage returns.

Over time comparisons using the same method and the same definitions of education levels across the countries show that, in general, the returns to education increase as the level of education rises in 41 of 49, or 84 percent, cases in developing economies with over time data. In all cases latest year data show higher returns to higher education than to secondary schooling.

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|

_Source:_ World Bank calculations using UIS and IMF online databases. _Note:_ The figures in the tables show the percentage of the public budget spent on each education level. Pri. = Primary, Sec. = Secondary, and Tert. = Tertiary. Income groups are defined by World Bank country income group classifications as of 2017. LIC = low-income country, LMIC = lower-middle-income country, UMIC = uppermiddle-income country, and HIC = high-income country. AFR = Africa, ECA = Europe and Central Asia, LCR = Latin America and Caribbean, SAR = South Asia, EAP = East Asia and the Pacific, and MNA = Middle East and North Africa.

How regions spend their public education funds often mirrors the spending patterns of the income group into which most of a region’s countries fall. For example, trends in public education spending in Africa and Europe and Central Asia tend to resemble spending patterns for low-income countries, while trends for Europe resemble those for high-income countries. Patterns and trends in South Asia appear to mirror those for lower-middle-income countries. Both Latin America and the Caribbean and East Asia and the Pacific have shifted spending from primary to post-primary education over the last 20 years, reflecting the regions’ strong economic growth and the resulting expansion of their education systems. The Middle East and North Africa has experienced strong shifts in public education spending from secondary to tertiary education even though public education spending in the region has seen limited growth in absolute terms.

# **_Spending Trends Can Vary Widely across Countries in the Same Region or Income Group_**

These regional and income group averages mask large differences among individual countries. For example, Figure 4 (above) highlighted the large increases in public education spending’s share in GDP in Africa over the last 20 years, but government education spending as a share of GDP actually declined in some countries, such as Sierra Leone and Cabo Verde. There has also been significant variation in the magnitude of the increases. In Latin America and the Caribbean, the
**Decoupling and recoupling**

TIC data collect cross-border holdings of securities measured at market value through security-level surveys (that is, information is reported separately for each security) that conflate data from custodians, issuers, and investors. As the data is collected by the US Treasury, it comprises transactions between U.S. residents and counterparties located outside the United States involving six types of securities, including four domestic (U.S. Treasury bonds and notes, bonds of U.S. government corporations and federally-sponsored agencies, U.S. corporate and other bonds, and U.S. corporate and other stocks) and two foreign (foreign bonds and foreign stocks). We compute liability flows coming from the US as the difference between gross purchases and gross sales of foreign securities by US residents. In turn, EPFR data report the fund net flow to individual emerging economies, as well as the assets under management (AUM) by country.

There are differences between TIC and EPFR data. On the one hand, unlike annual TIC data, EPFR data is available at monthly (and, for a subset of funds, weekly) frequency. More importantly, the simple within-country correlation between both flow measures is rather low ( **Table 8** ), reflecting the distinct behavioral pattern between both sources: as we stated above, global funds tend to keep close to their benchmark, inducing correlation (as a result of global swings in risk appetite and exposure) and possibly exacerbating financial contagion, while the average TIC respondent would tend to trace less and to exhibit a more selective sensitivity to relative price changes.

A degree of complexity in the measurement of de facto FG is introduced by the normalization. On the one hand, normalization by the (US dollar) GDP looks natural for issues related with the country ́s wealth diversification away from domestic shocks (and exposure to external shocks). On the other hand, normalization by local market capitalization seems to be more appropriate when assessing cross-border flows
other country in its peer groups and is becoming a challenge for the country. Fewer than half the wells and boreholes in Africa provide safe water (table 10).

Sanitation access in Sudan is bimodal. On the one hand, 40 percent of the population has access to improved latrines; on the other, 40 percent continues to practice open defecation. Both ratios are substantially higher than for the relevant resource-rich and low-income African peer groups. In contrast to these peers, reliance on flush toilets and traditional latrines is much more limited.

A very small share of the population—15 percent—has access to utility water, well below all other African benchmarks. Available information suggests that there is almost no access to piped water, and stand posts are the only form of utility water that is accessed. Census data for all parts of Sudan indicate that in 1993 about 60 percent of the population had access to utility water but there has been a steep decline in access rates—down to 35 percent—in the 2000s. Since access to utility water is negligible in South Sudan, the access data for all parts of Sudan reported in the census relates primarily to Sudan itself.

> <sup>22</sup>
have a large village, a Class III statutory town (between 20,000 and 50,000) and a census town. Applying the criteria above, three districts, Madhubani, Samastipur, and Paschim Champaran, emerged as likely candidates.

In Samastipur, all the four CTs were in the agglomeration of Samastipur municipality while in Paschim Champaran; all three Census Towns were in the agglomeration of Bettiah. In Madhubani, there are two CTs; of which Madhubani (CT) is part of the Madhubani municipality built-up agglomeration. The other is Satghara. All the three statutory towns, Jhanjharpur, Jainagar and Ghoghardiha, are part of a mixed SA – a collection of settlements whose built up areas are connected and which has both rural and urban settlements. In the last two towns, the town is smaller than the largest village in the agglomeration. Given this, the field sites chosen were Satghara (CT) and the statutory town of Jhanjharpur (Nagar Panchayat). A large village, Simri (pop: 10,846) was the third site.

As in the case of Bihar mentioned above, proximity to a large town was another criterion considered in the selection of the study sites. One of the common perceptions about CTs is that they are in the peri-urban areas of larger towns. However, this is not supported by data. As shown in Table 3, based on the Census data and the methodology devised by Pradhan (2013), Jharkhand and Bihar have a configuration similar to the national average, i.e., around one-third of the new census towns are close to a large city, defined as a class I town in India with a population of more 6