rafmacalaba/data-use-mentions-extended huggingface dataset:
in 15,746 households. Our analyses focus on working age individuals aged 15-64.

# **_3.2 Detection of employment and work_**

# _3.2.1 Individual questions_

The data collection instrument for the LMPSs is made up of household and individual questionnaires. The individual questionnaire uses a short reference period of seven days to detect the current labor force status and a long reference period of three months to detect the usual labor force status. Enumerators are instructed to administer the individual questionnaire to the individual him or herself, and collect data from proxy respondents only after three separate visits are made to attempt to meet the individual him or herself.<sup>6</sup> Besides a direct question on whether the individual was employed during the relevant reference period, those who answer in the negative are asked a standard keyword list made up of 15 different activities to detect work for pay or profit in line with the recommendations regarding the measurement of “employment” of the 19<sup>th</sup> ICLS, with any affirmative answer in either reference period sending the individual to the employment module. We refer to a “yes” in any of the employment or keyword questions as

> 5 Quarterly data reveals that the decline in the FLFP rate in 2020 was likely induced by the pandemic and was sharpest in the second and third quarters (ILO and ERF 2022).

> 6 Parents or guardians of children 6 to 14 can decide whether or not to allow these children to respond for themselves or to respond on their behalf.

6
and co-authors find that child labor is not a major predictor of whether or not child workers will attend school, but family and school factors are. Putnick and Bornstein (2015), analyzing data from almost 200,000 families with children aged between 7 and 14 in 30 low- and middle-income countries, note that many working children continue to be enrolled in school. Their analysis confirms the findings from Ravallion and Wodon (2000), where a school subsidy program increased schooling attainment but did not decrease child labor.

The divergence in findings on the impacts of child labor on enrollment has been linked to the different definitions of child labor in the literature. Some definitions include both household and wage work, whereas others distinguish between the two.

5
_Source_ : Utility data.

Annex 7 shows the available statistics for the 16 countries. Most utilities do not have the systems in place to measure SAIDI and SAIFI accurately and provide some other data related to the duration of blackouts (for example, average outage duration of 40 hours in Côte d’Ivoire), or provide data at the HV and MV levels rather than at the LV level (such as Mali reporting 70 interruptions per year at the HV level and 224 interruptions at the MV level).

The substantial problems with the quality of service suggested by these data are consistent with indicative enterprise data available from the World Bank’s Enterprise Surveys in 41 countries in SSA (World Bank 2016b). The data years for the Enterprise Surveys vary from country to country, nor do they necessarily match the reference years in this paper, but the medians are 6.3 outages per month (equivalent to 76 outages per year) and 5.5 hours per outage for those reporting outages.

27
on transactions, attributes of the property, as well as ancillary data concerning potential income from land and the cost of inputs into land development. These data are managed to provide upto-date and reliable information for professional appraisers as well as the general public.

Institutions that improve the information foundations of the valuation process, including training a cadre of appraisers in property valuation contribute to ensuring transparency in the valuation process, and making information of land values widely accessible. In Korea for example, until the mid-1970s, local government officials assessed the market value and replacement costs of assets for land acquisition purposes. In 1972, the government introduced the Basic Land Prices system, to improve the process of assessments. In this new system, land and buildings had to be assessed by certified private sector appraisers instead of government officials. Two private appraisers had to provide estimated values for the property and the final value was obtained as the mean of the two appraisal values. If the two appraisals differed by more than 10 percent, a third appraiser was selected and the mean recalculated. Since 2003, a third appraiser may be recommended by affected individuals as well (ADB, 2010).

Developing countries often lack the systems to record and manage this information. Transaction data, for instance, may not reflect the true price of land because of black market transactions to save on duties or heavy public subsidies on housing and land use. Land registries are often archaic and lack the dynamic functionality which allows them to be searched or updated quickly. These deficient features in transaction record management translate to a dearth of data on real estate prices, preventing analysis that is critical for land value appraisal. The implications of this bear heavily on local financing mechanisms involving real estate.

Land valuation is integral to local revenue generation since land values form the basis for activities such as property tax collection and land sales or leases. A credible system that helps “discover” and “disseminate” the value of land makes it difficult for buyers to defraud existing landowners. In the absence of
>squared|0.566||0.584||0.589||
|Observations|4,385||4,161||4,161||

Notes: _(1)_ = Estimations controlling by the young adult’s age and gender variables; provider’s age, gender and schooling. _(2)_ = in addition to the variables of (1), state of residence from the ENCEL, _marginalization_ (poverty) index in 1995 of the villages in the sample, and identification of the control or treatment village of the experimental evaluation of 1997-2000. (3)= in addition to the variables in (2), speaking and indigenous language and migrant. OLS= Ordinary least squares. Clustered standard errors in parentheses. Level of significance *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1. Source: estimations performed based on data from the Evaluation of Rural Households Survey (ENCEL) 2017, complemented with data from the ENCEL panel 1997-2000.

The persistence coefficients (in provider’s height) are consistent, regardless of whether OLS is used or the Tobit models, corrected for censorship, are used. However, we observe that controlling for migration and indigenous background improves precision and thus statistical significance (model 2). The estimation that controls for state of residence and migration / indigenous background modifies the persistence coefficient. That is, controlling for the effects of the context increases the mobility potential, which we estimate as 58%. This coefficient is similar to the magnitude order observed for the case of the United States in a similar study (AkbulutYuksel and Kugler, 2016).

Indigenous background and migration are associated with lower height, although this does not significantly affect the estimate of persistence (Table 1, Column 3, Tobit estimations). Thus, it is expected that the mobility potential in height for these groups is of similar magnitude to the rest of the
population, and labor and capital by sector of activity from Brazil’s national accounts data disseminated by Brazil’s Institute for Applied Economic Research ( _Instituto de Pesquisa Econˆomica Aplicada, IPEA_ ).<sup>5</sup> Unfortunately, the data for labor and capital is available only for a subset of municipalities and years.

We use georeferenced data on roads for each decade from 1970 to 2000 obtained from Brazil’s National Department of Transport Infrastructure ( _Departamento Nacional de Infraestrutura de Transportes_ , DNIT). From the road data, we subset the road segments that are paved, duplicated, or in the progress of duplication.<sup>6</sup> For the purpose of identification (see next section), we separate out radial roads extending from Brazilia towards the 25 state capitals<sup>7</sup> along with two coastal highways based on DNIT classification.<sup>8</sup> For each time period, we construct the start and end points of the DNIT radial and coastal highway road segments that existed at the time. Our road access measure is a dummy variable equal to 1 for municipalities that have a radial road within 50 km of its centroid and 0 otherwise.

Electricity access data over the period 1970 to 2000 come from various sources including the Geographic Information System of the Electric Sector ( _Sistema de Informa ̧c ̃oes Geogr ́aficas do Setor El ́etrico, SIGEL_ ) of the Brazil National Electricity Regulatory Authority ( _Agˆencia Nacional de Energia El ́etrica, ANEEL_ ), the Business Information System for the Electricity Sector ( _Sistema de Informa ̧c ̃oes Empresariais do Setor de Energia El ́etrica, SIESE_ ) of a major Brazilian electric utility, _Eletrobras_ , and Brazil’s and Cadastral Geographic Information System of the National Interconnected System ( _Sistema De Informa ̧c ̃oes Geogr ́aficas Cadastrais Do Sistema Interligado Nacional, SINDAT_ ) of the Brazil’s Operator of the National Electricity System ( _Operador Nacional do Sistema El ́etrico, ONS_
Table 7: 2SLS Estimation of Labor Supply by Age

||Wage Emp|loyment|Self Empl|oyment|
|---|---|---|---|---|
||(1)<br>Young Adults|(2)<br>Old Adult|(3)<br>Young Adults|(4)<br>Old Adults|
|Panel A: Labor market par|ticipation||||
|Household with migrant|-0.292***|-0.260**|0.252**|0.236***|
||(0.113)|(0.122)|(0.103)|(0.085)|
|Observations|10861|6018|10861|6018|
|Wald _χ_<sup>2</sup>|2028.588|1552.717|15778.586|3651.974|
|_F_-test 1stage|58.207|50.061|58.207|50.061|
|Coecient 1stage|1.836***|1.937***|1.836***|1.937***|
|Panel B: Hours supply|||||
|Household with migrant|-13.546*|-13.516**|-8.632*|-14.457**|
||(7.380)|(5.618)|(5.037)|(5.925)|
|Observations|3913|1932|6901|4617|
|Wald _χ_<sup>2</sup>|2379.303|1482.415|2401.545|545.837|
|_F_-test 1stage|31.766|24.765|52.713|43.511|
|Coecient 1stage|1.645***|
not effectively receive benefits from paid annual leave (or compensation instead of it) or paid sick leave (ILO, 2018b). Despite a clear definition of informality, its measurement varies substantially across developing countries and depends on the availability of informality-related indicators in labor force surveys. To ensure the comparability of informality measurement across countries, this study considers workers informal if they are self-employed or are non-paid family workers. Similar measures of informal employment have been applied in previous studies: Maloney (2001), and Loayza et al. (2011) used selfemployment as a proxy for informal employment when studying the relationship between informality and labor productivity. Burgi et al. (forthcoming) further improve informal employment measurement by including non-paid workers and claim that non-paid workers can account for more than 60 percent of total employment in low -income countries; including them hence substantially improves the informality measurement. For example, a 2017 labor force survey for Bolivia shows that self-employed and non-paid workers accounted for 88 percent of informal employment.<sup>10</sup> The sample of estimation includes 7 highincome countries, 9 low-income countries, 27 lower-middle income countries, and 26 upper-middle income countries.

It is important to note that the I2D2 data set includes one or more surveys per country, but each country might have data for different years. In many developing countries the labor force surveys are conducted only once every few years because they are costly, while in others the data are collected on a quarterly basis. The study uses data for the most recent available year for individual countries in I2D2 where the database might have data available for multiple years.<sup>11</sup> This is because the data analysis for countries that have more than one year’s data available in I2D2 shows that informality measured as a share of informal workers in total does not change much over short periods of time, with any changes visible over the long term. Furthermore, according to the World Bank (2019) informality has remained remarkably stable despite economic
**Figure 4: Frequency (Left) and Mode of Communication (Right) with Current Migrants**

<!-- Start of picture text --> Frequency of Communication Mode of Communication<br>5.31 3.09 0.9 0.78 0.8<br>33.77<br>17.09 30.31<br>68.15<br>39.8<br>Daily Several a week<br>Once a week Every two weeks Phone call App (text/video)<br>Once a month Less Text message SMS Other<br><!-- End of picture text --> _Notes: Listening to the Citizens of Uzbekistan Baseline Survey, Author’s calculations_ In terms of the money they send home, labor migrants from Uzbekistan constitute the largest value of transfers in the region of Central Asia. Data available from the Central Bank of the Russian Federation (which as noted above represents about three-quarters of the total migrant population from Uzbekistan), was equivalent to about $2.6 billion in 2017. In any given month, about 48 percent of migrant-sending households report receiving remittance income. Almost all (about 98 percent) of these transfers are denominated in US dollars, of an average value of about $312 per transfer. Depending on the month, between 20 and 30 percent of transfers go through foreign banks, between 43 and 50 percent through banks in Uzbekistan, and about 20 percent go through an official transfer service such as Western Union. Less than 5 percent of transfers are made by physically bringing money back or sending money with a private person. Administrative records suggest that in 2017, 45.2% of all migrants were employed in the construction industry, 12.2% - industrial production, 9.8% - in the service sector, and 7.4% - in agriculture.

14
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Vietnamese dong equals one US dollar in this period (World Bank, 2018a). 4 There is a large difference in income and expenditure values between the 2008 and the 2010 VHLSS (Table 1). This difference is mainly because of a change in the recall period in the questionnaires. From 2002 to 2008, the survey asked for household expenditure or income in the past 12 months. However, from 2010 onward, these values were asked for the past month and then multiplied by 12 to estimate annual values. As a result, there is a break in values in per capita expenditure between values before 2010 and those since 2010. We have not made any adjustment to our data following a change in the recall period; for more related discussion on this topic, see Deaton and Kozel (2005).

6
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.g. dry goods such as maize flour, and 28 as perishable, e.g. various types of fresh vegetables. There are some apparent differences in errors between the two characteristics. The incidence error is always greater in absolute magnitude for perishable goods: thus, the usual month overstates the incidence of perishable goods, while the other four understate the incidence relative to the benchmark. With respect to consumption value, perishable goods also tend to demonstrate reporting errors of greater magnitude, that is, the overreporting in the 7-day module is greater for perishables, as is the magnitude of underreporting for the usual month and the diaries. The 14-day recall reveals directionally different errors for the two types of goods: overreporting for storable goods and a slight underreporting error for perishables – this is the one module where the absolute value error is greater for storable goods.

# **VI. Concluding lessons for food consumption survey design**

This paper relies on data from a consumption experiment in Tanzania to describe the nature of reporting errors in food consumption surveys. The goal is not merely to document error patterns, but to generate hypotheses for improved consumption survey design. Of course, any such suggestions must be regarded as preliminary as the conclusions are derived from one setting (albeit national in scope) that is largely rural and in which diets are based on a particular staple crop (maize, by caloric intake). More work is needed to establish the generalizability of any findings reported here. However, we hope that our focus on the underlying mechanisms of misreporting error will increase the external validity of our findings.

We use a simple analytic framework of error decomposition that compares consumption incidence and consumption value to a benchmark of intensively supervised individual diaries. This basic decomposition immediately points to clear patterns in module divergence from the benchmark.

15
# **A Data and spatial methods**

## **A.1 City points**

City locations (latitude and longitude) and census populations were collected from the City Population website and spot-checked with official sources where available.<sup>49</sup> Using city-specific growth rates based on multiple censuses where available, or national urban growth rates from United Nations (2008) otherwise, I generated a population for all years for each city.

For 14 of the 15 countries in the ultimate sample, City Population claims to list all cities above a given (country-specific) population, typically 5,000, 10,000, or 20,000.<sup>50</sup> However, it does not explicitly cite the year for which this claim is made. Of the 738 cities with location and population information in these 15 countries, 9 city points fall below this cutoff for all years 1990 to 2008. These are included in the sample until explicit population cuts are made.

## **A.2 Lights**

The lights data are described in Henderson, Storeygard and Weil (2012). For the present study the 30 satellite-years of lights data were first combined into one binary grid encoding whether a pixel was lit in at least one satellite-year. The resulting contiguous ever-lit areas were converted into polygons, and split by national borders. Only lights within 3 kilometers of one of the city points described above with a known census population were kept in the sample. The 3-km buffer is used because of georeferencing error in both the points and the lights (Balk et al., 2004; Elvidge et al., 2004). While some of the other lights are likely to be small settlements, some are noise from the sensor or from fires lasting for too long to be excluded by the data cleaning algorithm, mines or other facilities. In general, they are smaller and weaker lights as well.

The resulting sample is 485 lights in 15 countries. Populations for
# **_3.2 Explanatory Variables_**

Among the individual-level characteristics in z1ij , z2ij, z3ij, and z4ij, we include a number of socioeconomic variables that we speculate might affect the use of bank accounts. All these variables come from the Gallup World Poll (2012).<sup>23</sup> _Female_ indicates whether the respondent is female. To the extent that it is harder for women to have bank accounts, we expect this variable to have a negative relationship. _Age_ and _Age Squared_ are both in years. We expect the use of bank accounts to first increase and then decline with age, so in order to capture this we also include age squared.

_Rural_ takes the value 1 if the respondent lives in a rural area and 0 otherwise, where a rural area is defined as a town or village with less than 50,000 inhabitants. If this information is not available, a rural classification is based on the interviewer’s perception of whether a respondent lives in a rural area, on a farm, in a small town, or in a village. In general, the presence of financial institutions is more limited in rural areas, so we expect this variable to have a negative impact.

The _Income Quintile_ variables are indicators of within-country relative income, based on the income of the respondents in a country. There are five such dummies (the top quintile is the excluded category in the regressions), which range from the poorest 20 percent to the richest 20 percent.<sup>24</sup> Overall, we expect bank account use to increase with income.

Each respondent falls into one of three education categories, represented by three variables: _0-8 Years of Education_ corresponds to completion of elementary education or less, _915 Years of Education_ corresponds to completion of secondary education and some education beyond that, and _> 15 Years of Education_ corresponds to four years of completed education after

> 23 Table 2 provides a list of all individual and country-level variables with definitions.